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# coding=utf-8
# Copyright (c) 2020, VinAI Research and the HuggingFace Inc. team.
# Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Tokenization classes for BERTweet"""
import html
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
import regex
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {
"vocab_file": "vocab.txt",
"merges_file": "bpe.codes",
}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"vinai/bertweet-base": "https://huggingface.co/vinai/bertweet-base/resolve/main/vocab.txt",
},
"merges_file": {
"vinai/bertweet-base": "https://huggingface.co/vinai/bertweet-base/resolve/main/bpe.codes",
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"vinai/bertweet-base": 128,
}
def get_pairs(word):
"""
Return set of symbol pairs in a word.
Word is represented as tuple of symbols (symbols being variable-length strings).
"""
pairs = set()
prev_char = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
prev_char = char
pairs = set(pairs)
return pairs
class BertweetTokenizer(PreTrainedTokenizer):
"""
Constructs a BERTweet tokenizer, using Byte-Pair-Encoding.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
Path to the vocabulary file.
merges_file (`str`):
Path to the merges file.
normalization (`bool`, *optional*, defaults to `False`):
Whether or not to apply a normalization preprocess.
bos_token (`str`, *optional*, defaults to `"<s>"`):
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the `cls_token`.
</Tip>
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the `sep_token`.
</Tip>
sep_token (`str`, *optional*, defaults to `"</s>"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
cls_token (`str`, *optional*, defaults to `"<s>"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
mask_token (`str`, *optional*, defaults to `"<mask>"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__(
self,
vocab_file,
merges_file,
normalization=False,
bos_token="<s>",
eos_token="</s>",
sep_token="</s>",
cls_token="<s>",
unk_token="<unk>",
pad_token="<pad>",
mask_token="<mask>",
**kwargs,
):
try:
from emoji import demojize
self.demojizer = demojize
except ImportError:
logger.warning(
"emoji is not installed, thus not converting emoticons or emojis into text. Install emoji: pip3"
" install emoji==0.6.0"
)
self.demojizer = None
self.vocab_file = vocab_file
self.merges_file = merges_file
self.encoder = {}
self.encoder[str(bos_token)] = 0
self.encoder[str(pad_token)] = 1
self.encoder[str(eos_token)] = 2
self.encoder[str(unk_token)] = 3
self.add_from_file(vocab_file)
self.decoder = {v: k for k, v in self.encoder.items()}
with open(merges_file, encoding="utf-8") as merges_handle:
merges = merges_handle.read().split("\n")[:-1]
merges = [tuple(merge.split()[:-1]) for merge in merges]
self.bpe_ranks = dict(zip(merges, range(len(merges))))
self.cache = {}
self.normalization = normalization
self.tweetPreprocessor = TweetTokenizer()
self.special_puncts = {"’": "'", "…": "..."}
super().__init__(
normalization=normalization,
bos_token=bos_token,
eos_token=eos_token,
sep_token=sep_token,
cls_token=cls_token,
unk_token=unk_token,
pad_token=pad_token,
mask_token=mask_token,
**kwargs,
)
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A BERTweet sequence has the following format:
- single sequence: `<s> X </s>`
- pair of sequences: `<s> A </s></s> B </s>`
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
if token_ids_1 is None:
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
cls = [self.cls_token_id]
sep = [self.sep_token_id]
return cls + token_ids_0 + sep + sep + token_ids_1 + sep
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
if token_ids_1 is None:
return [1] + ([0] * len(token_ids_0)) + [1]
return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. BERTweet does
not make use of token type ids, therefore a list of zeros is returned.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of zeros.
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
@property
def vocab_size(self):
return len(self.encoder)
def get_vocab(self):
return dict(self.encoder, **self.added_tokens_encoder)
def bpe(self, token):
if token in self.cache:
return self.cache[token]
word = tuple(token)
word = tuple(list(word[:-1]) + [word[-1] + "</w>"])
pairs = get_pairs(word)
if not pairs:
return token
while True:
bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
if bigram not in self.bpe_ranks:
break
first, second = bigram
new_word = []
i = 0
while i < len(word):
try:
j = word.index(first, i)
except ValueError:
new_word.extend(word[i:])
break
else:
new_word.extend(word[i:j])
i = j
if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
new_word.append(first + second)
i += 2
else:
new_word.append(word[i])
i += 1
new_word = tuple(new_word)
word = new_word
if len(word) == 1:
break
else:
pairs = get_pairs(word)
word = "@@ ".join(word)
word = word[:-4]
self.cache[token] = word
return word
def _tokenize(self, text):
"""Tokenize a string."""
if self.normalization: # Perform Tweet normalization before performing BPE
text = self.normalizeTweet(text)
split_tokens = []
words = re.findall(r"\S+\n?", text)
for token in words:
split_tokens.extend(list(self.bpe(token).split(" ")))
return split_tokens
def normalizeTweet(self, tweet):
"""
Normalize a raw Tweet
"""
for punct in self.special_puncts:
tweet = tweet.replace(punct, self.special_puncts[punct])
tokens = self.tweetPreprocessor.tokenize(tweet)
normTweet = " ".join([self.normalizeToken(token) for token in tokens])
normTweet = (
normTweet.replace("cannot ", "can not ")
.replace("n't ", " n't ")
.replace("n 't ", " n't ")
.replace("ca n't", "can't")
.replace("ai n't", "ain't")
)
normTweet = (
normTweet.replace("'m ", " 'm ")
.replace("'re ", " 're ")
.replace("'s ", " 's ")
.replace("'ll ", " 'll ")
.replace("'d ", " 'd ")
.replace("'ve ", " 've ")
)
normTweet = (
normTweet.replace(" p . m .", " p.m.")
.replace(" p . m ", " p.m ")
.replace(" a . m .", " a.m.")
.replace(" a . m ", " a.m ")
)
return " ".join(normTweet.split())
def normalizeToken(self, token):
"""
Normalize tokens in a Tweet
"""
lowercased_token = token.lower()
if token.startswith("@"):
return "@USER"
elif lowercased_token.startswith("http") or lowercased_token.startswith("www"):
return "HTTPURL"
elif len(token) == 1:
if token in self.special_puncts:
return self.special_puncts[token]
if self.demojizer is not None:
return self.demojizer(token)
else:
return token
else:
return token
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
return self.encoder.get(token, self.encoder.get(self.unk_token))
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self.decoder.get(index, self.unk_token)
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
out_string = " ".join(tokens).replace("@@ ", "").strip()
return out_string
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
out_vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
out_merge_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
)
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file, out_vocab_file)
elif not os.path.isfile(self.vocab_file):
with open(out_vocab_file, "wb") as fi:
content_spiece_model = self.sp_model.serialized_model_proto()
fi.write(content_spiece_model)
if os.path.abspath(self.merges_file) != os.path.abspath(out_merge_file):
copyfile(self.merges_file, out_merge_file)
return out_vocab_file, out_merge_file
# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
# filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
# tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
# tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
# return ''.join(tokens_generated_so_far)
def add_from_file(self, f):
"""
Loads a pre-existing dictionary from a text file and adds its symbols to this instance.
"""
if isinstance(f, str):
try:
with open(f, "r", encoding="utf-8") as fd:
self.add_from_file(fd)
except FileNotFoundError as fnfe:
raise fnfe
except UnicodeError:
raise Exception(f"Incorrect encoding detected in {f}, please rebuild the dataset")
return
lines = f.readlines()
for lineTmp in lines:
line = lineTmp.strip()
idx = line.rfind(" ")
if idx == -1:
raise ValueError("Incorrect dictionary format, expected '<token> <cnt>'")
word = line[:idx]
self.encoder[word] = len(self.encoder)
# Natural Language Toolkit: Twitter Tokenizer
#
# Copyright (C) 2001-2020 NLTK Project
# Author: Christopher Potts <[email protected]>
# Ewan Klein <[email protected]> (modifications)
# Pierpaolo Pantone <> (modifications)
# URL: http://nltk.org/
# For license information, see LICENSE.TXT
#
"""
Twitter-aware tokenizer, designed to be flexible and easy to adapt to new domains and tasks. The basic logic is this:
1. The tuple regex_strings defines a list of regular expression strings.
2. The regex_strings strings are put, in order, into a compiled regular expression object called word_re.
3. The tokenization is done by word_re.findall(s), where s is the user-supplied string, inside the tokenize() method of
the class Tokenizer.
4. When instantiating Tokenizer objects, there is a single option: preserve_case. By default, it is set to True. If it
is set to False, then the tokenizer will lowercase everything except for emoticons.
"""
######################################################################
#
# import regex # https://github.com/nltk/nltk/issues/2409
# import html
#
######################################################################
# The following strings are components in the regular expression
# that is used for tokenizing. It's important that phone_number
# appears first in the final regex (since it can contain whitespace).
# It also could matter that tags comes after emoticons, due to the
# possibility of having text like
#
# <:| and some text >:)
#
# Most importantly, the final element should always be last, since it
# does a last ditch whitespace-based tokenization of whatever is left.
# ToDo: Update with http://en.wikipedia.org/wiki/List_of_emoticons ?
# This particular element is used in a couple ways, so we define it
# with a name:
# docstyle-ignore
EMOTICONS = r"""
(?:
[<>]?
[:;=8] # eyes
[\-o\*\']? # optional nose
[\)\]\(\[dDpP/\:\}\{@\|\\] # mouth
|
[\)\]\(\[dDpP/\:\}\{@\|\\] # mouth
[\-o\*\']? # optional nose
[:;=8] # eyes
[<>]?
|
<3 # heart
)"""
# URL pattern due to John Gruber, modified by Tom Winzig. See
# https://gist.github.com/winzig/8894715
# docstyle-ignore
URLS = r""" # Capture 1: entire matched URL
(?:
https?: # URL protocol and colon
(?:
/{1,3} # 1-3 slashes
| # or
[a-z0-9%] # Single letter or digit or '%'
# (Trying not to match e.g. "URI::Escape")
)
| # or
# looks like domain name followed by a slash:
[a-z0-9.\-]+[.]
(?:[a-z]{2,13})
/
)
(?: # One or more:
[^\s()<>{}\[\]]+ # Run of non-space, non-()<>{}[]
| # or
\([^\s()]*?\([^\s()]+\)[^\s()]*?\) # balanced parens, one level deep: (...(...)...)
|
\([^\s]+?\) # balanced parens, non-recursive: (...)
)+
(?: # End with:
\([^\s()]*?\([^\s()]+\)[^\s()]*?\) # balanced parens, one level deep: (...(...)...)
|
\([^\s]+?\) # balanced parens, non-recursive: (...)
| # or
[^\s`!()\[\]{};:'".,<>?«»“”‘’] # not a space or one of these punct chars
)
| # OR, the following to match naked domains:
(?:
(?<!@) # not preceded by a @, avoid matching foo@_gmail.com_
[a-z0-9]+
(?:[.\-][a-z0-9]+)*
[.]
(?:[a-z]{2,13})
\b
/?
(?!@) # not succeeded by a @,
# avoid matching "foo.na" in "[email protected]"
)
"""
# docstyle-ignore
# The components of the tokenizer:
REGEXPS = (
URLS,
# Phone numbers:
r"""
(?:
(?: # (international)
\+?[01]
[ *\-.\)]*
)?
(?: # (area code)
[\(]?
\d{3}
[ *\-.\)]*
)?
\d{3} # exchange
[ *\-.\)]*
\d{4} # base
)""",
# ASCII Emoticons
EMOTICONS,
# HTML tags:
r"""<[^>\s]+>""",
# ASCII Arrows
r"""[\-]+>|<[\-]+""",
# Twitter username:
r"""(?:@[\w_]+)""",
# Twitter hashtags:
r"""(?:\#+[\w_]+[\w\'_\-]*[\w_]+)""",
# email addresses
r"""[\w.+-]+@[\w-]+\.(?:[\w-]\.?)+[\w-]""",
# docstyle-ignore
# Remaining word types:
r"""
(?:[^\W\d_](?:[^\W\d_]|['\-_])+[^\W\d_]) # Words with apostrophes or dashes.
|
(?:[+\-]?\d+[,/.:-]\d+[+\-]?) # Numbers, including fractions, decimals.
|
(?:[\w_]+) # Words without apostrophes or dashes.
|
(?:\.(?:\s*\.){1,}) # Ellipsis dots.
|
(?:\S) # Everything else that isn't whitespace.
""",
)
######################################################################
# This is the core tokenizing regex:
WORD_RE = regex.compile(r"""(%s)""" % "|".join(REGEXPS), regex.VERBOSE | regex.I | regex.UNICODE)
# WORD_RE performs poorly on these patterns:
HANG_RE = regex.compile(r"([^a-zA-Z0-9])\1{3,}")
# The emoticon string gets its own regex so that we can preserve case for
# them as needed:
EMOTICON_RE = regex.compile(EMOTICONS, regex.VERBOSE | regex.I | regex.UNICODE)
# These are for regularizing HTML entities to Unicode:
ENT_RE = regex.compile(r"&(#?(x?))([^&;\s]+);")
######################################################################
# Functions for converting html entities
######################################################################
def _str_to_unicode(text, encoding=None, errors="strict"):
if encoding is None:
encoding = "utf-8"
if isinstance(text, bytes):
return text.decode(encoding, errors)
return text
def _replace_html_entities(text, keep=(), remove_illegal=True, encoding="utf-8"):
"""
Remove entities from text by converting them to their corresponding unicode character.
Args:
text:
A unicode string or a byte string encoded in the given *encoding* (which defaults to 'utf-8').
keep (list):
List of entity names which should not be replaced. This supports both numeric entities (`&#nnnn;` and
`&#hhhh;`) and named entities (such as ` ` or `>`).
remove_illegal (bool):
If `True`, entities that can't be converted are removed. Otherwise, entities that can't be converted are
kept "as is".
Returns: A unicode string with the entities removed.
See https://github.com/scrapy/w3lib/blob/master/w3lib/html.py
Examples:
```python
>>> from nltk.tokenize.casual import _replace_html_entities
>>> _replace_html_entities(b"Price: £100")
'Price: \\xa3100'
>>> print(_replace_html_entities(b"Price: £100"))
Price: £100
```"""
def _convert_entity(match):
entity_body = match.group(3)
if match.group(1):
try:
if match.group(2):
number = int(entity_body, 16)
else:
number = int(entity_body, 10)
# Numeric character references in the 80-9F range are typically
# interpreted by browsers as representing the characters mapped
# to bytes 80-9F in the Windows-1252 encoding. For more info
# see: https://en.wikipedia.org/wiki/ISO/IEC_8859-1#Similar_character_sets
if 0x80 <= number <= 0x9F:
return bytes((number,)).decode("cp1252")
except ValueError:
number = None
else:
if entity_body in keep:
return match.group(0)
else:
number = html.entities.name2codepoint.get(entity_body)
if number is not None:
try:
return chr(number)
except (ValueError, OverflowError):
pass
return "" if remove_illegal else match.group(0)
return ENT_RE.sub(_convert_entity, _str_to_unicode(text, encoding))
######################################################################
class TweetTokenizer:
r"""
Examples:
```python
>>> # Tokenizer for tweets.
>>> from nltk.tokenize import TweetTokenizer
>>> tknzr = TweetTokenizer()
>>> s0 = "This is a cooool #dummysmiley: :-) :-P <3 and some arrows < > -> <--"
>>> tknzr.tokenize(s0)
['This', 'is', 'a', 'cooool', '#dummysmiley', ':', ':-)', ':-P', '<3', 'and', 'some', 'arrows', '<', '>', '->', '<--']
>>> # Examples using *strip_handles* and *reduce_len parameters*:
>>> tknzr = TweetTokenizer(strip_handles=True, reduce_len=True)
>>> s1 = "@remy: This is waaaaayyyy too much for you!!!!!!"
>>> tknzr.tokenize(s1)
[':', 'This', 'is', 'waaayyy', 'too', 'much', 'for', 'you', '!', '!', '!']
```"""
def __init__(self, preserve_case=True, reduce_len=False, strip_handles=False):
self.preserve_case = preserve_case
self.reduce_len = reduce_len
self.strip_handles = strip_handles
def tokenize(self, text):
"""
Args:
text: str
Returns: list(str) A tokenized list of strings; concatenating this list returns the original string if
`preserve_case=False`
"""
# Fix HTML character entities:
text = _replace_html_entities(text)
# Remove username handles
if self.strip_handles:
text = remove_handles(text)
# Normalize word lengthening
if self.reduce_len:
text = reduce_lengthening(text)
# Shorten problematic sequences of characters
safe_text = HANG_RE.sub(r"\1\1\1", text)
# Tokenize:
words = WORD_RE.findall(safe_text)
# Possibly alter the case, but avoid changing emoticons like :D into :d:
if not self.preserve_case:
words = [x if EMOTICON_RE.search(x) else x.lower() for x in words]
return words
######################################################################
# Normalization Functions
######################################################################
def reduce_lengthening(text):
"""
Replace repeated character sequences of length 3 or greater with sequences of length 3.
"""
pattern = regex.compile(r"(.)\1{2,}")
return pattern.sub(r"\1\1\1", text)
def remove_handles(text):
"""
Remove Twitter username handles from text.
"""
pattern = regex.compile(
r"(?<![A-Za-z0-9_!@#\$%&*])@(([A-Za-z0-9_]){20}(?!@))|(?<![A-Za-z0-9_!@#\$%&*])@(([A-Za-z0-9_]){1,19})(?![A-Za-z0-9_]*@)"
)
# Substitute handles with ' ' to ensure that text on either side of removed handles are tokenized correctly
return pattern.sub(" ", text)
######################################################################
# Tokenization Function
######################################################################
def casual_tokenize(text, preserve_case=True, reduce_len=False, strip_handles=False):
"""
Convenience function for wrapping the tokenizer.
"""
return TweetTokenizer(preserve_case=preserve_case, reduce_len=reduce_len, strip_handles=strip_handles).tokenize(
text
)
###############################################################################
| transformers/src/transformers/models/bertweet/tokenization_bertweet.py/0 | {
"file_path": "transformers/src/transformers/models/bertweet/tokenization_bertweet.py",
"repo_id": "transformers",
"token_count": 12290
} | 75 |
# coding=utf-8
# Copyright 2022 The HuggingFace Team and Microsoft Research AI4Science. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization classes for BioGPT."""
import json
import os
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json",
},
"merges_file": {"microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt"},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"microsoft/biogpt": 1024,
}
def get_pairs(word):
"""
Return set of symbol pairs in a word. word is represented as tuple of symbols (symbols being variable-length
strings)
"""
pairs = set()
prev_char = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
prev_char = char
return pairs
class BioGptTokenizer(PreTrainedTokenizer):
"""
Construct an FAIRSEQ Transformer tokenizer. Moses tokenization followed by Byte-Pair Encoding.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
Path to the vocabulary file.
merges_file (`str`):
Merges file.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
bos_token (`str`, *optional*, defaults to `"<s>"`):
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the `cls_token`.
</Tip>
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the `sep_token`.
</Tip>
sep_token (`str`, *optional*, defaults to `"</s>"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
vocab_file,
merges_file,
unk_token="<unk>",
bos_token="<s>",
eos_token="</s>",
sep_token="</s>",
pad_token="<pad>",
**kwargs,
):
try:
import sacremoses
except ImportError:
raise ImportError(
"You need to install sacremoses to use BioGptTokenizer. "
"See https://pypi.org/project/sacremoses/ for installation."
)
self.lang = "en"
self.sm = sacremoses
# cache of sm.MosesTokenizer instance
self.cache_moses_tokenizer = {}
self.cache_moses_detokenizer = {}
""" Initialisation"""
with open(vocab_file, encoding="utf-8") as vocab_handle:
self.encoder = json.load(vocab_handle)
self.decoder = {v: k for k, v in self.encoder.items()}
with open(merges_file, encoding="utf-8") as merges_handle:
merges = merges_handle.read().split("\n")[:-1]
merges = [tuple(merge.split()[:2]) for merge in merges]
self.bpe_ranks = dict(zip(merges, range(len(merges))))
self.cache = {}
super().__init__(
bos_token=bos_token,
eos_token=eos_token,
sep_token=sep_token,
unk_token=unk_token,
pad_token=pad_token,
**kwargs,
)
@property
def vocab_size(self):
"""Returns vocab size"""
return len(self.encoder)
def get_vocab(self):
return dict(self.encoder, **self.added_tokens_encoder)
def moses_tokenize(self, text, lang):
if lang not in self.cache_moses_tokenizer:
moses_tokenizer = self.sm.MosesTokenizer(lang=lang)
self.cache_moses_tokenizer[lang] = moses_tokenizer
return self.cache_moses_tokenizer[lang].tokenize(
text, aggressive_dash_splits=True, return_str=False, escape=True
)
def moses_detokenize(self, tokens, lang):
if lang not in self.cache_moses_detokenizer:
moses_detokenizer = self.sm.MosesDetokenizer(lang=lang)
self.cache_moses_detokenizer[lang] = moses_detokenizer
return self.cache_moses_detokenizer[lang].detokenize(tokens)
def bpe(self, token):
word = tuple(token[:-1]) + (token[-1] + "</w>",)
if token in self.cache:
return self.cache[token]
pairs = get_pairs(word)
if not pairs:
return token + "</w>"
while True:
bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
if bigram not in self.bpe_ranks:
break
first, second = bigram
new_word = []
i = 0
while i < len(word):
try:
j = word.index(first, i)
except ValueError:
new_word.extend(word[i:])
break
else:
new_word.extend(word[i:j])
i = j
if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
new_word.append(first + second)
i += 2
else:
new_word.append(word[i])
i += 1
new_word = tuple(new_word)
word = new_word
if len(word) == 1:
break
else:
pairs = get_pairs(word)
word = " ".join(word)
if word == "\n </w>":
word = "\n</w>"
self.cache[token] = word
return word
def _tokenize(self, text, bypass_tokenizer=False):
"""Returns a tokenized string."""
if bypass_tokenizer:
text = text.split()
else:
text = self.moses_tokenize(text, self.lang)
split_tokens = []
for token in text:
if token:
split_tokens.extend(list(self.bpe(token).split(" ")))
return split_tokens
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
return self.encoder.get(token, self.encoder.get(self.unk_token))
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self.decoder.get(index, self.unk_token)
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
# remove BPE
tokens = [t.replace(" ", "").replace("</w>", " ") for t in tokens]
tokens = "".join(tokens).split()
# detokenize
text = self.moses_detokenize(tokens, self.lang)
return text
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A BioGPT sequence has the following format:
- single sequence: `</s> X `
- pair of sequences: `</s> A </s> B `
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
if token_ids_1 is None:
return [self.sep_token_id] + token_ids_0
sep = [self.sep_token_id]
return sep + token_ids_0 + sep + token_ids_1
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
# no bos used in fairseq
if token_ids_1 is not None:
return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1))
return [1] + ([0] * len(token_ids_0))
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A FAIRSEQ
Transformer sequence pair mask has the following format:
```
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence | second sequence |
```
If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
"""
sep = [self.sep_token_id]
# no bos used in fairseq
if token_ids_1 is None:
return len(token_ids_0 + sep) * [0]
return len(token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
merge_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
)
with open(vocab_file, "w", encoding="utf-8") as f:
f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
index = 0
with open(merge_file, "w", encoding="utf-8") as writer:
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning(
f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
" Please check that the tokenizer is not corrupted!"
)
index = token_index
writer.write(" ".join(bpe_tokens) + "\n")
index += 1
return vocab_file, merge_file
def __getstate__(self):
state = self.__dict__.copy()
state["sm"] = None
return state
def __setstate__(self, d):
self.__dict__ = d
try:
import sacremoses
except ImportError:
raise ImportError(
"You need to install sacremoses to use XLMTokenizer. "
"See https://pypi.org/project/sacremoses/ for installation."
)
self.sm = sacremoses
| transformers/src/transformers/models/biogpt/tokenization_biogpt.py/0 | {
"file_path": "transformers/src/transformers/models/biogpt/tokenization_biogpt.py",
"repo_id": "transformers",
"token_count": 6244
} | 76 |
# coding=utf-8
# Copyright 2021 The Facebook, Inc. and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch BlenderbotSmall model."""
import copy
import math
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from ...activations import ACT2FN
from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOutputWithPastAndCrossAttentions,
CausalLMOutputWithCrossAttentions,
Seq2SeqLMOutput,
Seq2SeqModelOutput,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from .configuration_blenderbot_small import BlenderbotSmallConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "BlenderbotSmallConfig"
BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST = [
"facebook/blenderbot_small-90M",
# See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
]
# Copied from transformers.models.bart.modeling_bart.shift_tokens_right
def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
"""
Shift input ids one token to the right.
"""
shifted_input_ids = input_ids.new_zeros(input_ids.shape)
shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
shifted_input_ids[:, 0] = decoder_start_token_id
if pad_token_id is None:
raise ValueError("self.model.config.pad_token_id has to be defined.")
# replace possible -100 values in labels by `pad_token_id`
shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
return shifted_input_ids
# Copied from transformers.models.blenderbot.modeling_blenderbot.BlenderbotLearnedPositionalEmbedding with Blenderbot->BlenderbotSmall
class BlenderbotSmallLearnedPositionalEmbedding(nn.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, num_embeddings: int, embedding_dim: int):
super().__init__(num_embeddings, embedding_dim)
def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0):
"""`input_ids_shape` is expected to be [bsz x seqlen]."""
bsz, seq_len = input_ids_shape[:2]
positions = torch.arange(
past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device
)
return super().forward(positions)
# Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->BlenderbotSmall
class BlenderbotSmallAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
is_causal: bool = False,
config: Optional[BlenderbotSmallConfig] = None,
):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
self.config = config
if (self.head_dim * num_heads) != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
f" and `num_heads`: {num_heads})."
)
self.scaling = self.head_dim**-0.5
self.is_decoder = is_decoder
self.is_causal = is_causal
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, _ = hidden_states.size()
# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# get key, value proj
# `past_key_value[0].shape[2] == key_value_states.shape[1]`
# is checking that the `sequence_length` of the `past_key_value` is the same as
# the provided `key_value_states` to support prefix tuning
if (
is_cross_attention
and past_key_value is not None
and past_key_value[0].shape[2] == key_value_states.shape[1]
):
# reuse k,v, cross_attentions
key_states = past_key_value[0]
value_states = past_key_value[1]
elif is_cross_attention:
# cross_attentions
key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
elif past_key_value is not None:
# reuse k, v, self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
else:
# self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_states, value_states)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.reshape(*proj_shape)
value_states = value_states.reshape(*proj_shape)
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
f" {attn_weights.size()}"
)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
if layer_head_mask is not None:
if layer_head_mask.size() != (self.num_heads,):
raise ValueError(
f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
f" {layer_head_mask.size()}"
)
attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
if output_attentions:
# this operation is a bit awkward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to be reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
else:
attn_weights_reshaped = None
attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
attn_output = torch.bmm(attn_probs, value_states)
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz * self.num_heads, tgt_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
# partitioned across GPUs when using tensor-parallelism.
attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights_reshaped, past_key_value
# Copied from transformers.models.bart.modeling_bart.BartEncoderLayer with Bart->BlenderbotSmall, BART->BLENDERBOT_SMALL
class BlenderbotSmallEncoderLayer(nn.Module):
def __init__(self, config: BlenderbotSmallConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = BLENDERBOT_SMALL_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=config.encoder_attention_heads,
dropout=config.attention_dropout,
config=config,
)
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
def forward(
self,
hidden_states: torch.FloatTensor,
attention_mask: torch.FloatTensor,
layer_head_mask: torch.FloatTensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
`(encoder_attention_heads,)`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states, attn_weights, _ = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
residual = hidden_states
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.final_layer_norm(hidden_states)
if hidden_states.dtype == torch.float16 and (
torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()
):
clamp_value = torch.finfo(hidden_states.dtype).max - 1000
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
# TODO: Implement attention with SDPA for TimeSeriesTransformer.
BLENDERBOT_SMALL_ATTENTION_CLASSES = {
"eager": BlenderbotSmallAttention,
}
# Copied from transformers.models.bart.modeling_bart.BartDecoderLayer with Bart->BlenderbotSmall, BART->BLENDERBOT_SMALL
class BlenderbotSmallDecoderLayer(nn.Module):
def __init__(self, config: BlenderbotSmallConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = BLENDERBOT_SMALL_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
dropout=config.attention_dropout,
is_decoder=True,
is_causal=True,
config=config,
)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.encoder_attn = BLENDERBOT_SMALL_ATTENTION_CLASSES[config._attn_implementation](
self.embed_dim,
config.decoder_attention_heads,
dropout=config.attention_dropout,
is_decoder=True,
config=config,
)
self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
cross_attn_layer_head_mask: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = True,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
encoder_hidden_states (`torch.FloatTensor`):
cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
`(encoder_attention_heads,)`.
cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of
size `(decoder_attention_heads,)`.
past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
# Self Attention
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
# add present self-attn cache to positions 1,2 of present_key_value tuple
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
past_key_value=self_attn_past_key_value,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
# Cross-Attention Block
cross_attn_present_key_value = None
cross_attn_weights = None
if encoder_hidden_states is not None:
residual = hidden_states
# cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
hidden_states=hidden_states,
key_value_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
layer_head_mask=cross_attn_layer_head_mask,
past_key_value=cross_attn_past_key_value,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.encoder_attn_layer_norm(hidden_states)
# add cross-attn to positions 3,4 of present_key_value tuple
present_key_value = present_key_value + cross_attn_present_key_value
# Fully Connected
residual = hidden_states
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.final_layer_norm(hidden_states)
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights, cross_attn_weights)
if use_cache:
outputs += (present_key_value,)
return outputs
class BlenderbotSmallPreTrainedModel(PreTrainedModel):
config_class = BlenderbotSmallConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
@property
def dummy_inputs(self):
pad_token = self.config.pad_token_id
input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device)
dummy_inputs = {
"attention_mask": input_ids.ne(pad_token),
"input_ids": input_ids,
"decoder_input_ids": input_ids,
}
return dummy_inputs
BLENDERBOT_SMALL_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`BlenderbotSmallConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
BLENDERBOT_SMALL_GENERATION_EXAMPLE = r"""
Conversation example:
```python
>>> from transformers import AutoTokenizer, BlenderbotSmallForConditionalGeneration
>>> mname = "facebook/blenderbot_small-90M"
>>> model = BlenderbotSmallForConditionalGeneration.from_pretrained(mname)
>>> tokenizer = AutoTokenizer.from_pretrained(mname)
>>> UTTERANCE = "My friends are cool but they eat too many carbs."
>>> print("Human: ", UTTERANCE)
Human: My friends are cool but they eat too many carbs.
>>> inputs = tokenizer([UTTERANCE], return_tensors="pt")
>>> reply_ids = model.generate(**inputs)
>>> print("Bot: ", tokenizer.batch_decode(reply_ids, skip_special_tokens=True)[0])
Bot: what kind of carbs do they eat? i don't know much about carbs.
>>> REPLY = "I'm not sure"
>>> print("Human: ", REPLY)
Human: I'm not sure
>>> NEXT_UTTERANCE = (
... "My friends are cool but they eat too many carbs.__end__ __start__what kind of carbs do they eat? "
... "i don't know much about carbs__end__ "
... "__start__ I'm not sure."
... )
>>> inputs = tokenizer([NEXT_UTTERANCE], return_tensors="pt")
>>> next_reply_ids = model.generate(**inputs)
>>> print("Bot: ", tokenizer.batch_decode(next_reply_ids, skip_special_tokens=True)[0])
Bot: they eat a lot of carbs. carbs are high in fat, protein, and fats.
```
"""
BLENDERBOT_SMALL_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are decoder input IDs?](../glossary#decoder-input-ids)
BlenderbotSmall uses the `bos_token_id` as the starting token for `decoder_input_ids` generation. If
`past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
`past_key_values`).
decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
be used by default.
head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
decoder_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
`last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be
input (see `past_key_values`). This is useful if you want more control over how to convert
`decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value
of `inputs_embeds`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
class BlenderbotSmallEncoder(BlenderbotSmallPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`BlenderbotSmallEncoderLayer`].
Args:
config: BlenderbotSmallConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: BlenderbotSmallConfig, embed_tokens: Optional[nn.Embedding] = None):
super().__init__(config)
self.dropout = config.dropout
self.layerdrop = config.encoder_layerdrop
embed_dim = config.d_model
self.padding_idx = config.pad_token_id
self.max_source_positions = config.max_position_embeddings
self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
if embed_tokens is not None:
self.embed_tokens = embed_tokens
else:
self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim, self.padding_idx)
self.embed_positions = BlenderbotSmallLearnedPositionalEmbedding(
config.max_position_embeddings,
embed_dim,
)
self.layers = nn.ModuleList([BlenderbotSmallEncoderLayer(config) for _ in range(config.encoder_layers)])
self.layernorm_embedding = nn.LayerNorm(embed_dim)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def forward(
self,
input_ids=None,
attention_mask=None,
head_mask=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
embed_pos = self.embed_positions(input_shape)
hidden_states = inputs_embeds + embed_pos
hidden_states = self.layernorm_embedding(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
# expand attention_mask
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
attention_mask = _prepare_4d_attention_mask(attention_mask, inputs_embeds.dtype)
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
# check if head_mask has a correct number of layers specified if desired
if head_mask is not None:
if head_mask.size()[0] != len(self.layers):
raise ValueError(
f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
f" {head_mask.size()[0]}."
)
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
to_drop = False
if self.training:
dropout_probability = torch.rand([])
if dropout_probability < self.layerdrop: # skip the layer
to_drop = True
if to_drop:
layer_outputs = (None, None)
else:
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
encoder_layer.__call__,
hidden_states,
attention_mask,
(head_mask[idx] if head_mask is not None else None),
output_attentions,
)
else:
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
class BlenderbotSmallDecoder(BlenderbotSmallPreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`BlenderbotSmallDecoderLayer`]
Args:
config: BlenderbotSmallConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: BlenderbotSmallConfig, embed_tokens: Optional[nn.Embedding] = None):
super().__init__(config)
self.dropout = config.dropout
self.layerdrop = config.decoder_layerdrop
self.padding_idx = config.pad_token_id
self.max_target_positions = config.max_position_embeddings
self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
if embed_tokens is not None:
self.embed_tokens = embed_tokens
else:
self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx)
self.embed_positions = BlenderbotSmallLearnedPositionalEmbedding(
config.max_position_embeddings,
config.d_model,
)
self.layers = nn.ModuleList([BlenderbotSmallDecoderLayer(config) for _ in range(config.decoder_layers)])
self.layernorm_embedding = nn.LayerNorm(config.d_model)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, value):
self.embed_tokens = value
def forward(
self,
input_ids=None,
attention_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
head_mask=None,
cross_attn_head_mask=None,
past_key_values=None,
inputs_embeds=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
of the decoder.
encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules in the decoder to avoid performing
cross-attention on hidden heads. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
# past_key_values_length
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
attention_mask = _prepare_4d_causal_attention_mask(
attention_mask, input_shape, inputs_embeds, past_key_values_length
)
# expand encoder attention mask
if encoder_hidden_states is not None and encoder_attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
encoder_attention_mask = _prepare_4d_attention_mask(
encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
)
# embed positions
positions = self.embed_positions(input_shape, past_key_values_length)
# BlenderbotSmall applies layer norm on hidden_states
inputs_embeds = self.layernorm_embedding(inputs_embeds)
hidden_states = inputs_embeds + positions
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
next_decoder_cache = () if use_cache else None
# check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
if attn_mask is not None:
if attn_mask.size()[0] != len(self.layers):
raise ValueError(
f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
f" {head_mask.size()[0]}."
)
for idx, decoder_layer in enumerate(self.layers):
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
if output_hidden_states:
all_hidden_states += (hidden_states,)
if self.training:
dropout_probability = torch.rand([])
if dropout_probability < self.layerdrop:
continue
past_key_value = past_key_values[idx] if past_key_values is not None else None
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
head_mask[idx] if head_mask is not None else None,
cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None,
None,
output_attentions,
use_cache,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
cross_attn_layer_head_mask=(
cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None
),
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[3 if output_attentions else 1],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
if encoder_hidden_states is not None:
all_cross_attentions += (layer_outputs[2],)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if not return_dict:
return tuple(
v
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
cross_attentions=all_cross_attentions,
)
@add_start_docstrings(
"The bare BlenderbotSmall Model outputting raw hidden-states without any specific head on top.",
BLENDERBOT_SMALL_START_DOCSTRING,
)
class BlenderbotSmallModel(BlenderbotSmallPreTrainedModel):
_tied_weights_keys = ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight"]
def __init__(self, config: BlenderbotSmallConfig):
super().__init__(config)
padding_idx, vocab_size = config.pad_token_id, config.vocab_size
self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx)
self.encoder = BlenderbotSmallEncoder(config, self.shared)
self.decoder = BlenderbotSmallDecoder(config, self.shared)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.shared
def set_input_embeddings(self, value):
self.shared = value
self.encoder.embed_tokens = self.shared
self.decoder.embed_tokens = self.shared
def get_encoder(self):
return self.encoder
def get_decoder(self):
return self.decoder
@add_start_docstrings_to_model_forward(BLENDERBOT_SMALL_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[Union[Tuple, BaseModelOutput]] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.Tensor] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.FloatTensor], Seq2SeqModelOutput]:
r"""
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, BlenderbotSmallModel
>>> model = BlenderbotSmallModel.from_pretrained("facebook/blenderbot_small-90M")
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot_small-90M")
>>> inputs = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="pt")
>>> decoder_inputs = tokenizer("Studies show that", return_tensors="pt") # Batch size 1
>>> outputs = model(input_ids=inputs.input_ids, decoder_input_ids=decoder_inputs.input_ids)
>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 3, 512]
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if encoder_outputs is None:
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
# If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
encoder_outputs = BaseModelOutput(
last_hidden_state=encoder_outputs[0],
hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
)
# decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
encoder_hidden_states=encoder_outputs[0],
encoder_attention_mask=attention_mask,
head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if not return_dict:
return decoder_outputs + encoder_outputs
return Seq2SeqModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
past_key_values=decoder_outputs.past_key_values,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
encoder_hidden_states=encoder_outputs.hidden_states,
encoder_attentions=encoder_outputs.attentions,
)
@add_start_docstrings(
"The BlenderbotSmall Model with a language modeling head. Can be used for summarization.",
BLENDERBOT_SMALL_START_DOCSTRING,
)
class BlenderbotSmallForConditionalGeneration(BlenderbotSmallPreTrainedModel):
base_model_prefix = "model"
_keys_to_ignore_on_load_missing = ["final_logits_bias"]
_tied_weights_keys = ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight", "lm_head.weight"]
def __init__(self, config: BlenderbotSmallConfig):
super().__init__(config)
self.model = BlenderbotSmallModel(config)
self.register_buffer("final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings)))
self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_encoder(self):
return self.model.get_encoder()
def get_decoder(self):
return self.model.get_decoder()
def resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None) -> nn.Embedding:
new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
self._resize_final_logits_bias(new_embeddings.weight.shape[0])
return new_embeddings
def _resize_final_logits_bias(self, new_num_tokens: int) -> None:
old_num_tokens = self.final_logits_bias.shape[-1]
if new_num_tokens <= old_num_tokens:
new_bias = self.final_logits_bias[:, :new_num_tokens]
else:
extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device)
new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1)
self.register_buffer("final_logits_bias", new_bias)
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
@add_start_docstrings_to_model_forward(BLENDERBOT_SMALL_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
@add_end_docstrings(BLENDERBOT_SMALL_GENERATION_EXAMPLE)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[Union[Tuple, BaseModelOutput]] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.Tensor] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Returns:
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
if use_cache:
logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.")
use_cache = False
if decoder_input_ids is None and decoder_inputs_embeds is None:
decoder_input_ids = shift_tokens_right(
labels, self.config.pad_token_id, self.config.decoder_start_token_id
)
outputs = self.model(
input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
encoder_outputs=encoder_outputs,
decoder_attention_mask=decoder_attention_mask,
head_mask=head_mask,
decoder_head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
lm_logits = self.lm_head(outputs[0]) + self.final_logits_bias
masked_lm_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (lm_logits,) + outputs[1:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return Seq2SeqLMOutput(
loss=masked_lm_loss,
logits=lm_logits,
past_key_values=outputs.past_key_values,
decoder_hidden_states=outputs.decoder_hidden_states,
decoder_attentions=outputs.decoder_attentions,
cross_attentions=outputs.cross_attentions,
encoder_last_hidden_state=outputs.encoder_last_hidden_state,
encoder_hidden_states=outputs.encoder_hidden_states,
encoder_attentions=outputs.encoder_attentions,
)
def prepare_inputs_for_generation(
self,
decoder_input_ids,
past_key_values=None,
attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
use_cache=None,
encoder_outputs=None,
**kwargs,
):
# cut decoder_input_ids if past is used
if past_key_values is not None:
past_length = past_key_values[0][0].shape[2]
# Some generation methods already pass only the last input ID
if decoder_input_ids.shape[1] > past_length:
remove_prefix_length = past_length
else:
# Default to old behavior: keep only final ID
remove_prefix_length = decoder_input_ids.shape[1] - 1
decoder_input_ids = decoder_input_ids[:, remove_prefix_length:]
return {
"input_ids": None, # encoder_outputs is defined. input_ids not needed
"encoder_outputs": encoder_outputs,
"past_key_values": past_key_values,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
"use_cache": use_cache, # change this to avoid caching (presumably for debugging)
}
@staticmethod
def _reorder_cache(past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
# cached cross_attention states don't have to be reordered -> they are always the same
reordered_past += (
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past[:2])
+ layer_past[2:],
)
return reordered_past
# Copied from transformers.models.bart.modeling_bart.BartDecoderWrapper with Bart->BlenderbotSmall
class BlenderbotSmallDecoderWrapper(BlenderbotSmallPreTrainedModel):
"""
This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
used in combination with the [`EncoderDecoderModel`] framework.
"""
def __init__(self, config):
super().__init__(config)
self.decoder = BlenderbotSmallDecoder(config)
def forward(self, *args, **kwargs):
return self.decoder(*args, **kwargs)
# Copied from transformers.models.bart.modeling_bart.BartForCausalLM with Bart->BlenderbotSmall, facebook/bart-base->facebook/blenderbot_small-90M
class BlenderbotSmallForCausalLM(BlenderbotSmallPreTrainedModel):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
config = copy.deepcopy(config)
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = BlenderbotSmallDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.model.decoder.embed_tokens
def set_input_embeddings(self, value):
self.model.decoder.embed_tokens = value
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def set_decoder(self, decoder):
self.model.decoder = decoder
def get_decoder(self):
return self.model.decoder
@replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
if the model is configured as a decoder.
encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used
in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional
tensors are only required when the model is used as a decoder in a Sequence to Sequence model.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, BlenderbotSmallForCausalLM
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot_small-90M")
>>> model = BlenderbotSmallForCausalLM.from_pretrained("facebook/blenderbot_small-90M", add_cross_attention=False)
>>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
>>> expected_shape = [1, inputs.input_ids.shape[-1], model.config.vocab_size]
>>> list(logits.shape) == expected_shape
True
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model.decoder(
input_ids=input_ids,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
head_mask=head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
logits = self.lm_head(outputs[0])
loss = None
if labels is not None:
labels = labels.to(logits.device)
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
)
def prepare_inputs_for_generation(
self, input_ids, past_key_values=None, attention_mask=None, use_cache=None, **kwargs
):
# if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
if attention_mask is None:
attention_mask = input_ids.new_ones(input_ids.shape)
if past_key_values:
past_length = past_key_values[0][0].shape[2]
# Some generation methods already pass only the last input ID
if input_ids.shape[1] > past_length:
remove_prefix_length = past_length
else:
# Default to old behavior: keep only final ID
remove_prefix_length = input_ids.shape[1] - 1
input_ids = input_ids[:, remove_prefix_length:]
# first step, decoder_cached_states are empty
return {
"input_ids": input_ids, # encoder_outputs is defined. input_ids not needed
"attention_mask": attention_mask,
"past_key_values": past_key_values,
"use_cache": use_cache,
}
@staticmethod
def _reorder_cache(past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
reordered_past += (
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
)
return reordered_past
| transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py/0 | {
"file_path": "transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py",
"repo_id": "transformers",
"token_count": 32333
} | 77 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Convert BLIP-2 checkpoints from the original repository.
URL: https://github.com/salesforce/LAVIS/tree/main/projects/blip2
"""
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install -U git+https://github.com/nielsrogge/LAVIS.git@blip2_float32
# to make sure we can compare both original and HF implementation in float32
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokenizer,
Blip2Config,
Blip2ForConditionalGeneration,
Blip2Processor,
Blip2VisionConfig,
BlipImageProcessor,
OPTConfig,
T5Config,
set_seed,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def load_demo_image():
url = "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"
image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
return image
# here we list all keys to be renamed (original name on the left, our name on the right)
def create_rename_keys(config):
rename_keys = []
# fmt: off
# vision encoder
rename_keys.append(("visual_encoder.cls_token", "vision_model.embeddings.class_embedding"))
rename_keys.append(("visual_encoder.pos_embed", "vision_model.embeddings.position_embedding"))
rename_keys.append(("visual_encoder.patch_embed.proj.weight", "vision_model.embeddings.patch_embedding.weight"))
rename_keys.append(("visual_encoder.patch_embed.proj.bias", "vision_model.embeddings.patch_embedding.bias"))
rename_keys.append(("ln_vision.weight", "vision_model.post_layernorm.weight"))
rename_keys.append(("ln_vision.bias", "vision_model.post_layernorm.bias"))
for i in range(config.vision_config.num_hidden_layers):
rename_keys.append((f"visual_encoder.blocks.{i}.norm1.weight", f"vision_model.encoder.layers.{i}.layer_norm1.weight"))
rename_keys.append((f"visual_encoder.blocks.{i}.norm1.bias", f"vision_model.encoder.layers.{i}.layer_norm1.bias"))
rename_keys.append((f"visual_encoder.blocks.{i}.norm2.weight", f"vision_model.encoder.layers.{i}.layer_norm2.weight"))
rename_keys.append((f"visual_encoder.blocks.{i}.norm2.bias", f"vision_model.encoder.layers.{i}.layer_norm2.bias"))
rename_keys.append((f"visual_encoder.blocks.{i}.attn.qkv.weight", f"vision_model.encoder.layers.{i}.self_attn.qkv.weight"))
rename_keys.append((f"visual_encoder.blocks.{i}.attn.proj.weight", f"vision_model.encoder.layers.{i}.self_attn.projection.weight",))
rename_keys.append((f"visual_encoder.blocks.{i}.attn.proj.bias", f"vision_model.encoder.layers.{i}.self_attn.projection.bias"))
rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc1.weight", f"vision_model.encoder.layers.{i}.mlp.fc1.weight"))
rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc1.bias", f"vision_model.encoder.layers.{i}.mlp.fc1.bias"))
rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc2.weight", f"vision_model.encoder.layers.{i}.mlp.fc2.weight"))
rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc2.bias", f"vision_model.encoder.layers.{i}.mlp.fc2.bias"))
# QFormer
rename_keys.append(("Qformer.bert.embeddings.LayerNorm.weight", "qformer.layernorm.weight"))
rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.layernorm.bias"))
# fmt: on
return rename_keys
def rename_key(dct, old, new):
val = dct.pop(old)
dct[new] = val
def read_in_q_v_bias(state_dict, config):
for i in range(config.vision_config.num_hidden_layers):
# read in original q and v biases
q_bias = state_dict.pop(f"visual_encoder.blocks.{i}.attn.q_bias")
v_bias = state_dict.pop(f"visual_encoder.blocks.{i}.attn.v_bias")
# next, set bias in the state dict
qkv_bias = torch.cat((q_bias, torch.zeros_like(v_bias, requires_grad=False), v_bias))
state_dict[f"vision_model.encoder.layers.{i}.self_attn.qkv.bias"] = qkv_bias
def get_blip2_config(model_name, eos_token_id):
image_size = 364 if "coco" in model_name else 224
vision_config = Blip2VisionConfig(image_size=image_size).to_dict()
# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
# seems like flan-T5 models don't have bos_token_id properly set?
if "opt-2.7b" in model_name:
text_config = OPTConfig.from_pretrained("facebook/opt-2.7b", eos_token_id=eos_token_id).to_dict()
elif "opt-6.7b" in model_name:
text_config = OPTConfig.from_pretrained("facebook/opt-6.7b", eos_token_id=eos_token_id).to_dict()
elif "t5-xl" in model_name:
text_config = T5Config.from_pretrained("google/flan-t5-xl", dense_act_fn="gelu", bos_token_id=1).to_dict()
elif "t5-xxl" in model_name:
text_config = T5Config.from_pretrained("google/flan-t5-xxl", dense_act_fn="gelu", bos_token_id=1).to_dict()
config = Blip2Config(vision_config=vision_config, text_config=text_config)
return config, image_size
@torch.no_grad()
def convert_blip2_checkpoint(model_name, pytorch_dump_folder_path=None, push_to_hub=False):
"""
Copy/paste/tweak model's weights to Transformers design.
"""
tokenizer = (
AutoTokenizer.from_pretrained("facebook/opt-2.7b")
if "opt" in model_name
else AutoTokenizer.from_pretrained("google/flan-t5-xl")
)
eos_token_id = tokenizer("\n", add_special_tokens=False).input_ids[0]
config, image_size = get_blip2_config(model_name, eos_token_id=eos_token_id)
hf_model = Blip2ForConditionalGeneration(config).eval()
model_name_to_original = {
"blip2-opt-2.7b": ("blip2_opt", "pretrain_opt2.7b"),
"blip2-opt-6.7b": ("blip2_opt", "pretrain_opt6.7b"),
"blip2-opt-2.7b-coco": ("blip2_opt", "caption_coco_opt2.7b"),
"blip2-opt-6.7b-coco": ("blip2_opt", "caption_coco_opt6.7b"),
"blip2-flan-t5-xl": ("blip2_t5", "pretrain_flant5xl"),
"blip2-flan-t5-xl-coco": ("blip2_t5", "caption_coco_flant5xl"),
"blip2-flan-t5-xxl": ("blip2_t5", "pretrain_flant5xxl"),
}
name, type = model_name_to_original[model_name]
# note: this script is tested on 2 GPUs, as models are compared in float32,
# which requires quite some memory. Hence loading both on a
# separate device is the easiest to compare
hf_model_device = "cuda:0" if torch.cuda.is_available() else "cpu"
lavis_device = "cuda:1" if torch.cuda.is_available() else "cpu"
# load original model
print("Loading original model...")
original_model, vis_processors, _ = load_model_and_preprocess(
name=name, model_type=type, is_eval=True, device=lavis_device
)
original_model.eval()
print("Done!")
# update state dict keys
state_dict = original_model.state_dict()
rename_keys = create_rename_keys(config)
for src, dest in rename_keys:
rename_key(state_dict, src, dest)
# some keys can be renamed efficiently
for key, val in state_dict.copy().items():
val = state_dict.pop(key)
if key.startswith("Qformer.bert"):
key = key.replace("Qformer.bert", "qformer")
if "attention.self" in key:
key = key.replace("self", "attention")
if "opt_proj" in key:
key = key.replace("opt_proj", "language_projection")
if "t5_proj" in key:
key = key.replace("t5_proj", "language_projection")
if key.startswith("opt"):
key = key.replace("opt", "language")
if key.startswith("t5"):
key = key.replace("t5", "language")
state_dict[key] = val
# read in qv biases
read_in_q_v_bias(state_dict, config)
missing_keys, unexpected_keys = hf_model.load_state_dict(state_dict, strict=False)
assert len(missing_keys) == 0
assert unexpected_keys == ["qformer.embeddings.position_ids"]
image = load_demo_image()
original_pixel_values = vis_processors["eval"](image).unsqueeze(0).to(lavis_device)
input_ids = tokenizer(["\n"], return_tensors="pt").input_ids.to(hf_model_device)
# create processor
image_processor = BlipImageProcessor(
size={"height": image_size, "width": image_size}, image_mean=OPENAI_CLIP_MEAN, image_std=OPENAI_CLIP_STD
)
processor = Blip2Processor(image_processor=image_processor, tokenizer=tokenizer)
pixel_values = processor(images=image, return_tensors="pt").pixel_values.to(hf_model_device)
# make sure processor creates exact same pixel values
assert torch.allclose(pixel_values, original_pixel_values.to(pixel_values.device))
original_model.to(lavis_device)
hf_model.to(hf_model_device)
with torch.no_grad():
if "opt" in model_name:
original_logits = original_model({"image": original_pixel_values, "text_input": [""]}).logits
logits = hf_model(pixel_values, input_ids).logits
else:
original_logits = original_model(
{"image": original_pixel_values, "text_input": ["\n"], "text_output": ["\n"]}
).logits
labels = input_ids.masked_fill(input_ids == tokenizer.pad_token_id, -100)
logits = hf_model(pixel_values, input_ids, labels=labels).logits
assert original_logits.shape == logits.shape
print("First values of original logits:", original_logits[0, :3, :3])
print("First values of HF logits:", logits[0, :3, :3])
# assert values
assert torch.allclose(original_logits.to(logits.device), logits, atol=1e-4)
print("Looks ok!")
print("Generating a caption...")
prompt = "Question: what object is in this image? Answer:"
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(hf_model_device)
set_seed(42)
original_outputs = original_model.generate(
{"image": original_pixel_values, "prompt": prompt}, use_nucleus_sampling=True
)
outputs = hf_model.generate(
pixel_values,
input_ids,
do_sample=True,
num_beams=5,
max_length=30,
min_length=1,
top_p=0.9,
repetition_penalty=1.0,
length_penalty=1.0,
temperature=1,
)
output_text = processor.batch_decode(outputs, skip_special_tokens=True)
output_text = [text.strip() for text in output_text]
print("Original generation:", original_outputs)
print("HF generation:", output_text)
if pytorch_dump_folder_path is not None:
processor.save_pretrained(pytorch_dump_folder_path)
hf_model.save_pretrained(pytorch_dump_folder_path)
if push_to_hub:
processor.push_to_hub(f"nielsr/{model_name}")
hf_model.push_to_hub(f"nielsr/{model_name}")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
choices = [
"blip2-opt-2.7b",
"blip2-opt-6.7b",
"blip2-opt-2.7b-coco",
"blip2-opt-6.7b-coco",
"blip2-flan-t5-xl",
"blip2-flan-t5-xl-coco",
"blip2-flan-t5-xxl",
]
parser.add_argument(
"--model_name",
default="blip2-opt-2.7b",
choices=choices,
type=str,
help="Path to hf config.json of model to convert",
)
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Whether to push the model and processor to the hub after converting",
)
args = parser.parse_args()
convert_blip2_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| transformers/src/transformers/models/blip_2/convert_blip_2_original_to_pytorch.py/0 | {
"file_path": "transformers/src/transformers/models/blip_2/convert_blip_2_original_to_pytorch.py",
"repo_id": "transformers",
"token_count": 5162
} | 78 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert Bros checkpoints."""
import argparse
import bros # original repo
import torch
from transformers import BrosConfig, BrosModel, BrosProcessor
from transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
def get_configs(model_name):
bros_config = BrosConfig.from_pretrained(model_name)
return bros_config
def remove_ignore_keys_(state_dict):
ignore_keys = [
"embeddings.bbox_sinusoid_emb.inv_freq",
]
for k in ignore_keys:
state_dict.pop(k, None)
def rename_key(name):
if name == "embeddings.bbox_projection.weight":
name = "bbox_embeddings.bbox_projection.weight"
if name == "embeddings.bbox_sinusoid_emb.x_pos_emb.inv_freq":
name = "bbox_embeddings.bbox_sinusoid_emb.x_pos_emb.inv_freq"
if name == "embeddings.bbox_sinusoid_emb.y_pos_emb.inv_freq":
name = "bbox_embeddings.bbox_sinusoid_emb.y_pos_emb.inv_freq"
return name
def convert_state_dict(orig_state_dict, model):
# rename keys
for key in orig_state_dict.copy().keys():
val = orig_state_dict.pop(key)
orig_state_dict[rename_key(key)] = val
# remove ignore keys
remove_ignore_keys_(orig_state_dict)
return orig_state_dict
def convert_bros_checkpoint(model_name, pytorch_dump_folder_path=None, push_to_hub=False):
# load original model
original_model = bros.BrosModel.from_pretrained(model_name).eval()
# load HuggingFace Model
bros_config = get_configs(model_name)
model = BrosModel.from_pretrained(model_name, config=bros_config)
model.eval()
state_dict = original_model.state_dict()
new_state_dict = convert_state_dict(state_dict, model)
model.load_state_dict(new_state_dict)
# verify results
# original BROS model require 4 points (8 float values) for each bbox, prepare bbox with [batch_size, seq_len, 8] shape
bbox = torch.tensor(
[
[
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[0.4396, 0.6720, 0.4659, 0.6720, 0.4659, 0.6850, 0.4396, 0.6850],
[0.4698, 0.6720, 0.4843, 0.6720, 0.4843, 0.6850, 0.4698, 0.6850],
[0.4698, 0.6720, 0.4843, 0.6720, 0.4843, 0.6850, 0.4698, 0.6850],
[0.2047, 0.6870, 0.2730, 0.6870, 0.2730, 0.7000, 0.2047, 0.7000],
[0.2047, 0.6870, 0.2730, 0.6870, 0.2730, 0.7000, 0.2047, 0.7000],
[1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000],
]
]
)
processor = BrosProcessor.from_pretrained(model_name)
encoding = processor("His name is Rocco.", return_tensors="pt")
encoding["bbox"] = bbox
original_hidden_states = original_model(**encoding).last_hidden_state
# pixel_values = processor(image, return_tensors="pt").pixel_values
last_hidden_states = model(**encoding).last_hidden_state
assert torch.allclose(original_hidden_states, last_hidden_states, atol=1e-4)
if pytorch_dump_folder_path is not None:
print(f"Saving model and processor to {pytorch_dump_folder_path}")
model.save_pretrained(pytorch_dump_folder_path)
processor.save_pretrained(pytorch_dump_folder_path)
if push_to_hub:
model.push_to_hub("jinho8345/" + model_name.split("/")[-1], commit_message="Update model")
processor.push_to_hub("jinho8345/" + model_name.split("/")[-1], commit_message="Update model")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="jinho8345/bros-base-uncased",
required=False,
type=str,
help="Name of the original model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
required=False,
type=str,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Whether or not to push the converted model and processor to the 🤗 hub.",
)
args = parser.parse_args()
convert_bros_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| transformers/src/transformers/models/bros/convert_bros_to_pytorch.py/0 | {
"file_path": "transformers/src/transformers/models/bros/convert_bros_to_pytorch.py",
"repo_id": "transformers",
"token_count": 2040
} | 79 |
# coding=utf-8
# Copyright Google AI and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization classes for CANINE."""
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
logger = logging.get_logger(__name__)
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"nielsr/canine-s": 2048,
}
# Unicode defines 1,114,112 total “codepoints”
UNICODE_VOCAB_SIZE = 1114112
# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
PAD = 0
CLS = 0xE000
SEP = 0xE001
BOS = 0xE002
MASK = 0xE003
RESERVED = 0xE004
# Maps special codepoints to human-readable names.
SPECIAL_CODEPOINTS: Dict[int, str] = {
# Special symbols are represented using codepoints values that are valid,
# but designated as "Private Use", meaning that they will never be assigned
# characters by the Unicode Consortium, and are thus safe for use here.
#
# NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
# excluded and should fail with a hard error.
CLS: "[CLS]",
SEP: "[SEP]",
BOS: "[BOS]",
MASK: "[MASK]",
PAD: "[PAD]",
RESERVED: "[RESERVED]",
}
# Maps special codepoint human-readable names to their codepoint values.
SPECIAL_CODEPOINTS_BY_NAME: Dict[str, int] = {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}
class CanineTokenizer(PreTrainedTokenizer):
r"""
Construct a CANINE tokenizer (i.e. a character splitter). It turns text into a sequence of characters, and then
converts each character into its Unicode code point.
[`CanineTokenizer`] inherits from [`PreTrainedTokenizer`].
Refer to superclass [`PreTrainedTokenizer`] for usage examples and documentation concerning parameters.
Args:
model_max_length (`int`, *optional*, defaults to 2048):
The maximum sentence length the model accepts.
"""
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__(
self,
bos_token=chr(CLS),
eos_token=chr(SEP),
sep_token=chr(SEP),
cls_token=chr(CLS),
pad_token=chr(PAD),
mask_token=chr(MASK),
add_prefix_space=False,
model_max_length=2048,
**kwargs,
):
bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token
cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token
pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
# Creates a mapping for looking up the IDs of special symbols.
self._special_codepoints: Dict[str, int] = {}
for codepoint, name in SPECIAL_CODEPOINTS.items():
self._special_codepoints[name] = codepoint
# Creates a mapping for looking up the string forms of special symbol IDs.
self._special_codepoint_strings: Dict[int, str] = {
codepoint: name for name, codepoint in self._special_codepoints.items()
}
self._unicode_vocab_size = UNICODE_VOCAB_SIZE
self._num_special_tokens = len(self._special_codepoints)
super().__init__(
bos_token=bos_token,
eos_token=eos_token,
sep_token=sep_token,
cls_token=cls_token,
pad_token=pad_token,
mask_token=mask_token,
add_prefix_space=add_prefix_space,
model_max_length=model_max_length,
**kwargs,
)
@property
def vocab_size(self) -> int:
return self._unicode_vocab_size
def get_vocab(self):
vocab = {chr(i): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def _tokenize(self, text: str) -> List[str]:
"""Tokenize a string (i.e. perform character splitting)."""
return list(text)
def _convert_token_to_id(self, token: str) -> int:
"""Converts a token (i.e. a Unicode character) in an id (i.e. its integer Unicode code point value)."""
try:
return ord(token)
except TypeError:
raise ValueError(f"invalid token: '{token}'")
def _convert_id_to_token(self, index: int) -> str:
"""
Converts a Unicode code point (integer) in a token (str). In case it's a special code point, convert to
human-readable format.
"""
try:
if index in SPECIAL_CODEPOINTS:
return SPECIAL_CODEPOINTS[index]
return chr(index)
except TypeError:
raise ValueError(f"invalid id: {index}")
def convert_tokens_to_string(self, tokens):
return "".join(tokens)
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A CANINE sequence has the following format:
- single sequence: `[CLS] X [SEP]`
- pair of sequences: `[CLS] A [SEP] B [SEP]`
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
result = cls + token_ids_0 + sep
if token_ids_1 is not None:
result += token_ids_1 + sep
return result
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
result = [1] + ([0] * len(token_ids_0)) + [1]
if token_ids_1 is not None:
result += ([0] * len(token_ids_1)) + [1]
return result
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A CANINE
sequence pair mask has the following format:
```
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence | second sequence |
```
If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
result = len(cls + token_ids_0 + sep) * [0]
if token_ids_1 is not None:
result += len(token_ids_1 + sep) * [1]
return result
# CanineTokenizer has no vocab file
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None):
return ()
| transformers/src/transformers/models/canine/tokenization_canine.py/0 | {
"file_path": "transformers/src/transformers/models/canine/tokenization_canine.py",
"repo_id": "transformers",
"token_count": 3965
} | 80 |
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import torch
from clip import load
from transformers import CLIPConfig, CLIPModel
def copy_attn_layer(hf_attn_layer, pt_attn_layer):
q_proj, k_proj, v_proj = pt_attn_layer.in_proj_weight.chunk(3, dim=0)
q_proj_bias, k_proj_bias, v_proj_bias = pt_attn_layer.in_proj_bias.chunk(3, dim=0)
out_proj_weights = pt_attn_layer.out_proj.weight
out_proj_bias = pt_attn_layer.out_proj.bias
hf_attn_layer.q_proj.weight.data = q_proj
hf_attn_layer.q_proj.bias.data = q_proj_bias
hf_attn_layer.k_proj.weight.data = k_proj
hf_attn_layer.k_proj.bias.data = k_proj_bias
hf_attn_layer.v_proj.weight.data = v_proj
hf_attn_layer.v_proj.bias.data = v_proj_bias
hf_attn_layer.out_proj.weight = out_proj_weights
hf_attn_layer.out_proj.bias = out_proj_bias
def copy_mlp(hf_mlp, pt_mlp):
copy_linear(hf_mlp.fc1, pt_mlp.c_fc)
copy_linear(hf_mlp.fc2, pt_mlp.c_proj)
def copy_linear(hf_linear, pt_linear):
hf_linear.weight = pt_linear.weight
hf_linear.bias = pt_linear.bias
def copy_layer(hf_layer, pt_layer):
# copy layer norms
copy_linear(hf_layer.layer_norm1, pt_layer.ln_1)
copy_linear(hf_layer.layer_norm2, pt_layer.ln_2)
# copy MLP
copy_mlp(hf_layer.mlp, pt_layer.mlp)
# copy attn
copy_attn_layer(hf_layer.self_attn, pt_layer.attn)
def copy_layers(hf_layers, pt_layers):
for hf_layer, pt_layer in zip(hf_layers, pt_layers):
copy_layer(hf_layer, pt_layer)
def copy_encoder(hf_encoder, pt_model):
# copy embeds
hf_encoder.embeddings.token_embedding.weight = pt_model.token_embedding.weight
hf_encoder.embeddings.position_embedding.weight.data = pt_model.positional_embedding
# copy layer norm
copy_linear(hf_encoder.final_layer_norm, pt_model.ln_final)
# copy hidden layers
copy_layers(hf_encoder.encoder.layers, pt_model.transformer.resblocks)
def copy_text_model_and_projection(hf_model, pt_model):
# copy projection
hf_model.text_projection.weight.data = pt_model.text_projection.data.T
# copy text encoder
copy_encoder(hf_model.text_model, pt_model)
def copy_vison_model_and_projection(hf_model, pt_model):
# copy projection
hf_model.visual_projection.weight.data = pt_model.visual.proj.data.T
# copy layer norms
copy_linear(hf_model.vision_model.pre_layrnorm, pt_model.visual.ln_pre)
copy_linear(hf_model.vision_model.post_layernorm, pt_model.visual.ln_post)
# copy embeds
hf_model.vision_model.embeddings.patch_embedding.weight.data = pt_model.visual.conv1.weight.data
hf_model.vision_model.embeddings.class_embedding = pt_model.visual.class_embedding
hf_model.vision_model.embeddings.position_embedding.weight.data = pt_model.visual.positional_embedding.data
# copy encoder
copy_layers(hf_model.vision_model.encoder.layers, pt_model.visual.transformer.resblocks)
@torch.no_grad()
def convert_clip_checkpoint(checkpoint_path, pytorch_dump_folder_path, config_path=None):
"""
Copy/paste/tweak model's weights to transformers design.
"""
if config_path is not None:
config = CLIPConfig.from_pretrained(config_path)
else:
config = CLIPConfig(projection_dim=512, text_config={}, vision_config={})
hf_model = CLIPModel(config).eval()
pt_model, _ = load(checkpoint_path, device="cpu", jit=False)
pt_model = pt_model.eval()
copy_text_model_and_projection(hf_model, pt_model)
copy_vison_model_and_projection(hf_model, pt_model)
hf_model.logit_scale = pt_model.logit_scale
input_ids = torch.arange(0, 77).unsqueeze(0)
pixel_values = torch.randn(1, 3, 224, 224)
hf_outputs = hf_model(input_ids=input_ids, pixel_values=pixel_values, return_dict=True)
hf_logits_per_image = hf_outputs.logits_per_image
hf_logits_per_text = hf_outputs.logits_per_text
pt_logits_per_image, pt_logits_per_text = pt_model(pixel_values, input_ids)
assert torch.allclose(hf_logits_per_image, pt_logits_per_image, atol=1e-3)
assert torch.allclose(hf_logits_per_text, pt_logits_per_text, atol=1e-3)
hf_model.save_pretrained(pytorch_dump_folder_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
args = parser.parse_args()
convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| transformers/src/transformers/models/clip/convert_clip_original_pytorch_to_hf.py/0 | {
"file_path": "transformers/src/transformers/models/clip/convert_clip_original_pytorch_to_hf.py",
"repo_id": "transformers",
"token_count": 2176
} | 81 |
# coding=utf-8
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Weights conversion script for CLVP
"""
import argparse
import os
import torch
from huggingface_hub import hf_hub_download
from transformers import ClvpConfig, ClvpModelForConditionalGeneration
_MODELS = {
"clvp": "https://huggingface.co/jbetker/tortoise-tts-v2/blob/main/.models/clvp2.pth",
"decoder": "https://huggingface.co/jbetker/tortoise-tts-v2/blob/main/.models/autoregressive.pth",
}
dim = 1024
sub_dim = dim // 16
CLVP_ENCODERS_MAPPING = {
"text_transformer.transformer.attn_layers": "text_encoder_model",
"speech_transformer.transformer.attn_layers": "speech_encoder_model",
"text_transformer.transformer.norm": "text_encoder_model.final_layer_norm",
"speech_transformer.transformer.norm": "speech_encoder_model.final_layer_norm",
"to_text_latent": "text_encoder_model.projection",
"to_speech_latent": "speech_encoder_model.projection",
"text_emb": "text_encoder_model.token_embedding",
"speech_emb": "speech_encoder_model.token_embedding",
"1.wrap.net.0": "mlp.fc1",
"1.wrap.net.3": "mlp.fc2",
"1.wrap": "self_attn",
"to_out": "out_proj",
"to_q": "q_proj",
"to_k": "k_proj",
"to_v": "v_proj",
"temperature": "logit_scale",
}
CLVP_DECODER_MAPPING = {
"conditioning_encoder.init": "conditioning_encoder.mel_conv",
"conditioning_encoder.attn": "conditioning_encoder.mel_attn_blocks",
"mel_attn_blocks": "group_norms",
".norm.weight": ".weight",
".norm.bias": ".bias",
"text_embedding": "conditioning_encoder.text_token_embedding",
"text_pos_embedding.emb": "conditioning_encoder.text_position_embedding",
"final_norm": "speech_decoder_model.final_norm",
"mel_head": "speech_decoder_model.lm_head",
"gpt.ln_f": "speech_decoder_model.model.decoder.layer_norm",
"mel_embedding": "speech_decoder_model.model.decoder.input_embeds_layer",
"mel_pos_embedding.emb": "speech_decoder_model.model.decoder.position_embeds_layer",
"gpt.h": "speech_decoder_model.model.decoder.layers",
"ln_1": "input_layernorm",
"ln_2": "post_attention_layernorm",
}
def update_index(present_index):
if present_index % 2 == 0:
return int(present_index / 2)
else:
return int((present_index - 1) / 2)
def convert_encoder_weights(original_weights):
converted_weights = {}
original_weights_keys = sorted(original_weights.keys())
for original_key in original_weights_keys:
updated_key = original_key
# for input_rmsnorm.weight and post_attention_rmsnorm.weight
if "0.0.g" in updated_key:
present_index = updated_key.split(".")[4]
if int(present_index) % 2 == 0:
updated_key = updated_key.replace("0.0.g", "input_rmsnorm.weight")
else:
updated_key = updated_key.replace("0.0.g", "post_attention_rmsnorm.weight")
if "transformer.attn_layers.layers" in updated_key:
present_index = updated_key.split(".")[4]
updated_index = update_index(int(present_index))
updated_key = updated_key.replace(
f"transformer.attn_layers.layers.{present_index}", f"transformer.attn_layers.layers.{updated_index}"
)
for k, v in CLVP_ENCODERS_MAPPING.items():
if k in updated_key:
updated_key = updated_key.replace(k, v)
converted_weights[updated_key] = original_weights.pop(original_key)
return converted_weights
def convert_decoder_weights(original_weights):
converted_weights = {}
original_weights_keys = sorted(original_weights.keys())
for original_key in original_weights_keys:
updated_key = original_key
if len(updated_key.split(".")) > 3:
index, attr = updated_key.split(".")[2], updated_key.split(".")[-1]
# for decoder attention
if "attn.c_attn" in updated_key:
if attr == "weight":
slice1, slice2, slice3 = original_weights[updated_key].squeeze(-1).T.split(split_size=dim, dim=0)
else:
slice1, slice2, slice3 = original_weights[updated_key].split(split_size=dim, dim=0)
converted_weights[f"speech_decoder_model.model.decoder.layers.{index}.attn.q_proj.{attr}"] = slice1
converted_weights[f"speech_decoder_model.model.decoder.layers.{index}.attn.k_proj.{attr}"] = slice2
converted_weights[f"speech_decoder_model.model.decoder.layers.{index}.attn.v_proj.{attr}"] = slice3
continue
if "attn.c_proj" in updated_key:
converted_weights[f"speech_decoder_model.model.decoder.layers.{index}.attn.out_proj.{attr}"] = (
original_weights[updated_key].squeeze(-1).T
)
continue
if "attn.bias" in updated_key or "attn.masked_bias" in updated_key or "text_head" in updated_key:
original_weights.pop(updated_key)
continue
# conditional encoder attention
if "qkv" in updated_key:
if attr == "weight":
slice1, slice2, slice3 = original_weights[updated_key].squeeze(-1).split(split_size=dim, dim=0)
else:
slice1, slice2, slice3 = original_weights[updated_key].split(split_size=dim, dim=0)
indices = torch.arange(dim)
index1, index2, index3 = (
indices.unfold(0, sub_dim, sub_dim * 3).flatten(),
indices[sub_dim:].unfold(0, sub_dim, sub_dim * 3).flatten(),
indices[2 * sub_dim :].unfold(0, sub_dim, sub_dim * 3).flatten(),
)
converted_weights[f"conditioning_encoder.mel_attn_blocks.{index}.q_proj.{attr}"] = torch.concatenate(
[slice1[index1], slice2[index3], slice3[index2]],
axis=0,
)
converted_weights[f"conditioning_encoder.mel_attn_blocks.{index}.k_proj.{attr}"] = torch.concatenate(
[slice1[index2], slice2[index1], slice3[index3]],
axis=0,
)
converted_weights[f"conditioning_encoder.mel_attn_blocks.{index}.v_proj.{attr}"] = torch.concatenate(
[slice1[index3], slice2[index2], slice3[index1]],
axis=0,
)
continue
if "proj_out" in updated_key:
converted_weights[f"conditioning_encoder.mel_attn_blocks.{index}.out_proj.{attr}"] = original_weights[
updated_key
].squeeze(-1)
continue
for k, v in CLVP_DECODER_MAPPING.items():
if k in updated_key:
updated_key = updated_key.replace(k, v)
converted_weights[updated_key] = original_weights.pop(original_key)
return converted_weights
def _download(url: str, root: str):
repo_id = f"{url.split('/')[3]}/{url.split('/')[4]}"
filename = f"{url.split('/')[-2]}/{url.split('/')[-1]}"
hf_hub_download(
repo_id=repo_id,
filename=filename,
force_filename=root,
local_dir_use_symlinks=False,
)
def convert_clvp_weights(checkpoint_path, pytorch_dump_folder_path):
converted_checkpoint = {}
for each_model_name, each_model_url in _MODELS.items():
each_model_path = os.path.join(checkpoint_path, each_model_url.split("/")[-1])
if not os.path.exists(each_model_path):
print(f"\n{each_model_name} was not found! Downloading it to {each_model_path}")
_download(url=each_model_url, root=each_model_path)
if each_model_name == "clvp":
clvp_checkpoint = torch.load(each_model_path, map_location="cpu")
else:
decoder_checkpoint = torch.load(each_model_path, map_location="cpu")
# Converting the weights
converted_checkpoint.update(**convert_encoder_weights(clvp_checkpoint))
converted_checkpoint.update(**convert_decoder_weights(decoder_checkpoint))
config = ClvpConfig.from_pretrained("susnato/clvp_dev")
model = ClvpModelForConditionalGeneration(config)
model.load_state_dict(converted_checkpoint, strict=True)
model.save_pretrained(pytorch_dump_folder_path)
print(f"Model saved at {pytorch_dump_folder_path}!")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# # Required parameters
parser.add_argument(
"--checkpoint_path", type=str, help="Path to the folder of downloaded checkpoints. (Please enter full path)"
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=str,
help="Path to the output PyTorch model. (Please enter full path)",
)
args = parser.parse_args()
convert_clvp_weights(args.checkpoint_path, args.pytorch_dump_folder_path)
| transformers/src/transformers/models/clvp/convert_clvp_to_hf.py/0 | {
"file_path": "transformers/src/transformers/models/clvp/convert_clvp_to_hf.py",
"repo_id": "transformers",
"token_count": 4095
} | 82 |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert Conditional DETR checkpoints."""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
rename_keys = []
for i in range(6):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(f"transformer.encoder.layers.{i}.self_attn.out_proj.weight", f"encoder.layers.{i}.self_attn.out_proj.weight")
)
rename_keys.append(
(f"transformer.encoder.layers.{i}.self_attn.out_proj.bias", f"encoder.layers.{i}.self_attn.out_proj.bias")
)
rename_keys.append((f"transformer.encoder.layers.{i}.linear1.weight", f"encoder.layers.{i}.fc1.weight"))
rename_keys.append((f"transformer.encoder.layers.{i}.linear1.bias", f"encoder.layers.{i}.fc1.bias"))
rename_keys.append((f"transformer.encoder.layers.{i}.linear2.weight", f"encoder.layers.{i}.fc2.weight"))
rename_keys.append((f"transformer.encoder.layers.{i}.linear2.bias", f"encoder.layers.{i}.fc2.bias"))
rename_keys.append(
(f"transformer.encoder.layers.{i}.norm1.weight", f"encoder.layers.{i}.self_attn_layer_norm.weight")
)
rename_keys.append((f"transformer.encoder.layers.{i}.norm1.bias", f"encoder.layers.{i}.self_attn_layer_norm.bias"))
rename_keys.append((f"transformer.encoder.layers.{i}.norm2.weight", f"encoder.layers.{i}.final_layer_norm.weight"))
rename_keys.append((f"transformer.encoder.layers.{i}.norm2.bias", f"encoder.layers.{i}.final_layer_norm.bias"))
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(f"transformer.decoder.layers.{i}.self_attn.out_proj.weight", f"decoder.layers.{i}.self_attn.out_proj.weight")
)
rename_keys.append(
(f"transformer.decoder.layers.{i}.self_attn.out_proj.bias", f"decoder.layers.{i}.self_attn.out_proj.bias")
)
rename_keys.append(
(
f"transformer.decoder.layers.{i}.cross_attn.out_proj.weight",
f"decoder.layers.{i}.encoder_attn.out_proj.weight",
)
)
rename_keys.append(
(
f"transformer.decoder.layers.{i}.cross_attn.out_proj.bias",
f"decoder.layers.{i}.encoder_attn.out_proj.bias",
)
)
rename_keys.append((f"transformer.decoder.layers.{i}.linear1.weight", f"decoder.layers.{i}.fc1.weight"))
rename_keys.append((f"transformer.decoder.layers.{i}.linear1.bias", f"decoder.layers.{i}.fc1.bias"))
rename_keys.append((f"transformer.decoder.layers.{i}.linear2.weight", f"decoder.layers.{i}.fc2.weight"))
rename_keys.append((f"transformer.decoder.layers.{i}.linear2.bias", f"decoder.layers.{i}.fc2.bias"))
rename_keys.append(
(f"transformer.decoder.layers.{i}.norm1.weight", f"decoder.layers.{i}.self_attn_layer_norm.weight")
)
rename_keys.append((f"transformer.decoder.layers.{i}.norm1.bias", f"decoder.layers.{i}.self_attn_layer_norm.bias"))
rename_keys.append(
(f"transformer.decoder.layers.{i}.norm2.weight", f"decoder.layers.{i}.encoder_attn_layer_norm.weight")
)
rename_keys.append(
(f"transformer.decoder.layers.{i}.norm2.bias", f"decoder.layers.{i}.encoder_attn_layer_norm.bias")
)
rename_keys.append((f"transformer.decoder.layers.{i}.norm3.weight", f"decoder.layers.{i}.final_layer_norm.weight"))
rename_keys.append((f"transformer.decoder.layers.{i}.norm3.bias", f"decoder.layers.{i}.final_layer_norm.bias"))
# q, k, v projections in self/cross-attention in decoder for conditional DETR
rename_keys.append(
(f"transformer.decoder.layers.{i}.sa_qcontent_proj.weight", f"decoder.layers.{i}.sa_qcontent_proj.weight")
)
rename_keys.append(
(f"transformer.decoder.layers.{i}.sa_kcontent_proj.weight", f"decoder.layers.{i}.sa_kcontent_proj.weight")
)
rename_keys.append(
(f"transformer.decoder.layers.{i}.sa_qpos_proj.weight", f"decoder.layers.{i}.sa_qpos_proj.weight")
)
rename_keys.append(
(f"transformer.decoder.layers.{i}.sa_kpos_proj.weight", f"decoder.layers.{i}.sa_kpos_proj.weight")
)
rename_keys.append((f"transformer.decoder.layers.{i}.sa_v_proj.weight", f"decoder.layers.{i}.sa_v_proj.weight"))
rename_keys.append(
(f"transformer.decoder.layers.{i}.ca_qcontent_proj.weight", f"decoder.layers.{i}.ca_qcontent_proj.weight")
)
# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
rename_keys.append(
(f"transformer.decoder.layers.{i}.ca_kcontent_proj.weight", f"decoder.layers.{i}.ca_kcontent_proj.weight")
)
rename_keys.append(
(f"transformer.decoder.layers.{i}.ca_kpos_proj.weight", f"decoder.layers.{i}.ca_kpos_proj.weight")
)
rename_keys.append((f"transformer.decoder.layers.{i}.ca_v_proj.weight", f"decoder.layers.{i}.ca_v_proj.weight"))
rename_keys.append(
(f"transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight", f"decoder.layers.{i}.ca_qpos_sine_proj.weight")
)
rename_keys.append(
(f"transformer.decoder.layers.{i}.sa_qcontent_proj.bias", f"decoder.layers.{i}.sa_qcontent_proj.bias")
)
rename_keys.append(
(f"transformer.decoder.layers.{i}.sa_kcontent_proj.bias", f"decoder.layers.{i}.sa_kcontent_proj.bias")
)
rename_keys.append((f"transformer.decoder.layers.{i}.sa_qpos_proj.bias", f"decoder.layers.{i}.sa_qpos_proj.bias"))
rename_keys.append((f"transformer.decoder.layers.{i}.sa_kpos_proj.bias", f"decoder.layers.{i}.sa_kpos_proj.bias"))
rename_keys.append((f"transformer.decoder.layers.{i}.sa_v_proj.bias", f"decoder.layers.{i}.sa_v_proj.bias"))
rename_keys.append(
(f"transformer.decoder.layers.{i}.ca_qcontent_proj.bias", f"decoder.layers.{i}.ca_qcontent_proj.bias")
)
# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
rename_keys.append(
(f"transformer.decoder.layers.{i}.ca_kcontent_proj.bias", f"decoder.layers.{i}.ca_kcontent_proj.bias")
)
rename_keys.append((f"transformer.decoder.layers.{i}.ca_kpos_proj.bias", f"decoder.layers.{i}.ca_kpos_proj.bias"))
rename_keys.append((f"transformer.decoder.layers.{i}.ca_v_proj.bias", f"decoder.layers.{i}.ca_v_proj.bias"))
rename_keys.append(
(f"transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias", f"decoder.layers.{i}.ca_qpos_sine_proj.bias")
)
# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
[
("input_proj.weight", "input_projection.weight"),
("input_proj.bias", "input_projection.bias"),
("query_embed.weight", "query_position_embeddings.weight"),
("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
("class_embed.weight", "class_labels_classifier.weight"),
("class_embed.bias", "class_labels_classifier.bias"),
("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
("transformer.decoder.ref_point_head.layers.0.weight", "decoder.ref_point_head.layers.0.weight"),
("transformer.decoder.ref_point_head.layers.0.bias", "decoder.ref_point_head.layers.0.bias"),
("transformer.decoder.ref_point_head.layers.1.weight", "decoder.ref_point_head.layers.1.weight"),
("transformer.decoder.ref_point_head.layers.1.bias", "decoder.ref_point_head.layers.1.bias"),
("transformer.decoder.query_scale.layers.0.weight", "decoder.query_scale.layers.0.weight"),
("transformer.decoder.query_scale.layers.0.bias", "decoder.query_scale.layers.0.bias"),
("transformer.decoder.query_scale.layers.1.weight", "decoder.query_scale.layers.1.weight"),
("transformer.decoder.query_scale.layers.1.bias", "decoder.query_scale.layers.1.bias"),
("transformer.decoder.layers.0.ca_qpos_proj.weight", "decoder.layers.0.ca_qpos_proj.weight"),
("transformer.decoder.layers.0.ca_qpos_proj.bias", "decoder.layers.0.ca_qpos_proj.bias"),
]
)
def rename_key(state_dict, old, new):
val = state_dict.pop(old)
state_dict[new] = val
def rename_backbone_keys(state_dict):
new_state_dict = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
new_key = key.replace("backbone.0.body", "backbone.conv_encoder.model")
new_state_dict[new_key] = value
else:
new_state_dict[key] = value
return new_state_dict
def read_in_q_k_v(state_dict, is_panoptic=False):
prefix = ""
if is_panoptic:
prefix = "conditional_detr."
# first: transformer encoder
for i in range(6):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
in_proj_weight = state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight")
in_proj_bias = state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias")
# next, add query, keys and values (in that order) to the state dict
state_dict[f"encoder.layers.{i}.self_attn.q_proj.weight"] = in_proj_weight[:256, :]
state_dict[f"encoder.layers.{i}.self_attn.q_proj.bias"] = in_proj_bias[:256]
state_dict[f"encoder.layers.{i}.self_attn.k_proj.weight"] = in_proj_weight[256:512, :]
state_dict[f"encoder.layers.{i}.self_attn.k_proj.bias"] = in_proj_bias[256:512]
state_dict[f"encoder.layers.{i}.self_attn.v_proj.weight"] = in_proj_weight[-256:, :]
state_dict[f"encoder.layers.{i}.self_attn.v_proj.bias"] = in_proj_bias[-256:]
# We will verify our results on an image of cute cats
def prepare_img():
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
im = Image.open(requests.get(url, stream=True).raw)
return im
@torch.no_grad()
def convert_conditional_detr_checkpoint(model_name, pytorch_dump_folder_path):
"""
Copy/paste/tweak model's weights to our CONDITIONAL_DETR structure.
"""
# load default config
config = ConditionalDetrConfig()
# set backbone and dilation attributes
if "resnet101" in model_name:
config.backbone = "resnet101"
if "dc5" in model_name:
config.dilation = True
is_panoptic = "panoptic" in model_name
if is_panoptic:
config.num_labels = 250
else:
config.num_labels = 91
repo_id = "huggingface/label-files"
filename = "coco-detection-id2label.json"
id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
id2label = {int(k): v for k, v in id2label.items()}
config.id2label = id2label
config.label2id = {v: k for k, v in id2label.items()}
# load image processor
format = "coco_panoptic" if is_panoptic else "coco_detection"
image_processor = ConditionalDetrImageProcessor(format=format)
# prepare image
img = prepare_img()
encoding = image_processor(images=img, return_tensors="pt")
pixel_values = encoding["pixel_values"]
logger.info(f"Converting model {model_name}...")
# load original model from torch hub
conditional_detr = torch.hub.load("DeppMeng/ConditionalDETR", model_name, pretrained=True).eval()
state_dict = conditional_detr.state_dict()
# rename keys
for src, dest in rename_keys:
if is_panoptic:
src = "conditional_detr." + src
rename_key(state_dict, src, dest)
state_dict = rename_backbone_keys(state_dict)
# query, key and value matrices need special treatment
read_in_q_k_v(state_dict, is_panoptic=is_panoptic)
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
prefix = "conditional_detr.model." if is_panoptic else "model."
for key in state_dict.copy().keys():
if is_panoptic:
if (
key.startswith("conditional_detr")
and not key.startswith("class_labels_classifier")
and not key.startswith("bbox_predictor")
):
val = state_dict.pop(key)
state_dict["conditional_detr.model" + key[4:]] = val
elif "class_labels_classifier" in key or "bbox_predictor" in key:
val = state_dict.pop(key)
state_dict["conditional_detr." + key] = val
elif key.startswith("bbox_attention") or key.startswith("mask_head"):
continue
else:
val = state_dict.pop(key)
state_dict[prefix + key] = val
else:
if not key.startswith("class_labels_classifier") and not key.startswith("bbox_predictor"):
val = state_dict.pop(key)
state_dict[prefix + key] = val
# finally, create HuggingFace model and load state dict
model = ConditionalDetrForSegmentation(config) if is_panoptic else ConditionalDetrForObjectDetection(config)
model.load_state_dict(state_dict)
model.eval()
model.push_to_hub(repo_id=model_name, organization="DepuMeng", commit_message="Add model")
# verify our conversion
original_outputs = conditional_detr(pixel_values)
outputs = model(pixel_values)
assert torch.allclose(outputs.logits, original_outputs["pred_logits"], atol=1e-4)
assert torch.allclose(outputs.pred_boxes, original_outputs["pred_boxes"], atol=1e-4)
if is_panoptic:
assert torch.allclose(outputs.pred_masks, original_outputs["pred_masks"], atol=1e-4)
# Save model and image processor
logger.info(f"Saving PyTorch model and image processor to {pytorch_dump_folder_path}...")
Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
model.save_pretrained(pytorch_dump_folder_path)
image_processor.save_pretrained(pytorch_dump_folder_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--model_name",
default="conditional_detr_resnet50",
type=str,
help="Name of the CONDITIONAL_DETR model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
)
args = parser.parse_args()
convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)
| transformers/src/transformers/models/conditional_detr/convert_conditional_detr_original_pytorch_checkpoint_to_pytorch.py/0 | {
"file_path": "transformers/src/transformers/models/conditional_detr/convert_conditional_detr_original_pytorch_checkpoint_to_pytorch.py",
"repo_id": "transformers",
"token_count": 6952
} | 83 |
# coding=utf-8
# Copyright 2022 Meta Platforms, Inc. and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch ConvNext model."""
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_convnext import ConvNextConfig
logger = logging.get_logger(__name__)
# General docstring
_CONFIG_FOR_DOC = "ConvNextConfig"
# Base docstring
_CHECKPOINT_FOR_DOC = "facebook/convnext-tiny-224"
_EXPECTED_OUTPUT_SHAPE = [1, 768, 7, 7]
# Image classification docstring
_IMAGE_CLASS_CHECKPOINT = "facebook/convnext-tiny-224"
_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"
CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST = [
"facebook/convnext-tiny-224",
# See all ConvNext models at https://huggingface.co/models?filter=convnext
]
# Copied from transformers.models.beit.modeling_beit.drop_path
def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor:
"""
Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks,
however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the
layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the
argument.
"""
if drop_prob == 0.0 or not training:
return input
keep_prob = 1 - drop_prob
shape = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device)
random_tensor.floor_() # binarize
output = input.div(keep_prob) * random_tensor
return output
# Copied from transformers.models.beit.modeling_beit.BeitDropPath with Beit->ConvNext
class ConvNextDropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
def __init__(self, drop_prob: Optional[float] = None) -> None:
super().__init__()
self.drop_prob = drop_prob
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
return drop_path(hidden_states, self.drop_prob, self.training)
def extra_repr(self) -> str:
return "p={}".format(self.drop_prob)
class ConvNextLayerNorm(nn.Module):
r"""LayerNorm that supports two data formats: channels_last (default) or channels_first.
The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch_size, height,
width, channels) while channels_first corresponds to inputs with shape (batch_size, channels, height, width).
"""
def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"):
super().__init__()
self.weight = nn.Parameter(torch.ones(normalized_shape))
self.bias = nn.Parameter(torch.zeros(normalized_shape))
self.eps = eps
self.data_format = data_format
if self.data_format not in ["channels_last", "channels_first"]:
raise NotImplementedError(f"Unsupported data format: {self.data_format}")
self.normalized_shape = (normalized_shape,)
def forward(self, x: torch.Tensor) -> torch.Tensor:
if self.data_format == "channels_last":
x = torch.nn.functional.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
elif self.data_format == "channels_first":
input_dtype = x.dtype
x = x.float()
u = x.mean(1, keepdim=True)
s = (x - u).pow(2).mean(1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.eps)
x = x.to(dtype=input_dtype)
x = self.weight[:, None, None] * x + self.bias[:, None, None]
return x
class ConvNextEmbeddings(nn.Module):
"""This class is comparable to (and inspired by) the SwinEmbeddings class
found in src/transformers/models/swin/modeling_swin.py.
"""
def __init__(self, config):
super().__init__()
self.patch_embeddings = nn.Conv2d(
config.num_channels, config.hidden_sizes[0], kernel_size=config.patch_size, stride=config.patch_size
)
self.layernorm = ConvNextLayerNorm(config.hidden_sizes[0], eps=1e-6, data_format="channels_first")
self.num_channels = config.num_channels
def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
num_channels = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration."
)
embeddings = self.patch_embeddings(pixel_values)
embeddings = self.layernorm(embeddings)
return embeddings
class ConvNextLayer(nn.Module):
"""This corresponds to the `Block` class in the original implementation.
There are two equivalent implementations: [DwConv, LayerNorm (channels_first), Conv, GELU,1x1 Conv]; all in (N, C,
H, W) (2) [DwConv, Permute to (N, H, W, C), LayerNorm (channels_last), Linear, GELU, Linear]; Permute back
The authors used (2) as they find it slightly faster in PyTorch.
Args:
config ([`ConvNextConfig`]): Model configuration class.
dim (`int`): Number of input channels.
drop_path (`float`): Stochastic depth rate. Default: 0.0.
"""
def __init__(self, config, dim, drop_path=0):
super().__init__()
self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim) # depthwise conv
self.layernorm = ConvNextLayerNorm(dim, eps=1e-6)
self.pwconv1 = nn.Linear(dim, 4 * dim) # pointwise/1x1 convs, implemented with linear layers
self.act = ACT2FN[config.hidden_act]
self.pwconv2 = nn.Linear(4 * dim, dim)
self.layer_scale_parameter = (
nn.Parameter(config.layer_scale_init_value * torch.ones((dim)), requires_grad=True)
if config.layer_scale_init_value > 0
else None
)
self.drop_path = ConvNextDropPath(drop_path) if drop_path > 0.0 else nn.Identity()
def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
input = hidden_states
x = self.dwconv(hidden_states)
x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C)
x = self.layernorm(x)
x = self.pwconv1(x)
x = self.act(x)
x = self.pwconv2(x)
if self.layer_scale_parameter is not None:
x = self.layer_scale_parameter * x
x = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W)
x = input + self.drop_path(x)
return x
class ConvNextStage(nn.Module):
"""ConvNeXT stage, consisting of an optional downsampling layer + multiple residual blocks.
Args:
config ([`ConvNextConfig`]): Model configuration class.
in_channels (`int`): Number of input channels.
out_channels (`int`): Number of output channels.
depth (`int`): Number of residual blocks.
drop_path_rates(`List[float]`): Stochastic depth rates for each layer.
"""
def __init__(self, config, in_channels, out_channels, kernel_size=2, stride=2, depth=2, drop_path_rates=None):
super().__init__()
if in_channels != out_channels or stride > 1:
self.downsampling_layer = nn.Sequential(
ConvNextLayerNorm(in_channels, eps=1e-6, data_format="channels_first"),
nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride),
)
else:
self.downsampling_layer = nn.Identity()
drop_path_rates = drop_path_rates or [0.0] * depth
self.layers = nn.Sequential(
*[ConvNextLayer(config, dim=out_channels, drop_path=drop_path_rates[j]) for j in range(depth)]
)
def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
hidden_states = self.downsampling_layer(hidden_states)
hidden_states = self.layers(hidden_states)
return hidden_states
class ConvNextEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.stages = nn.ModuleList()
drop_path_rates = [
x.tolist() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths)).split(config.depths)
]
prev_chs = config.hidden_sizes[0]
for i in range(config.num_stages):
out_chs = config.hidden_sizes[i]
stage = ConvNextStage(
config,
in_channels=prev_chs,
out_channels=out_chs,
stride=2 if i > 0 else 1,
depth=config.depths[i],
drop_path_rates=drop_path_rates[i],
)
self.stages.append(stage)
prev_chs = out_chs
def forward(
self,
hidden_states: torch.FloatTensor,
output_hidden_states: Optional[bool] = False,
return_dict: Optional[bool] = True,
) -> Union[Tuple, BaseModelOutputWithNoAttention]:
all_hidden_states = () if output_hidden_states else None
for i, layer_module in enumerate(self.stages):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
hidden_states = layer_module(hidden_states)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states] if v is not None)
return BaseModelOutputWithNoAttention(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
)
class ConvNextPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ConvNextConfig
base_model_prefix = "convnext"
main_input_name = "pixel_values"
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, (nn.Linear, nn.Conv2d)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
CONVNEXT_START_DOCSTRING = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`ConvNextConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
CONVNEXT_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`ConvNextImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare ConvNext model outputting raw features without any specific head on top.",
CONVNEXT_START_DOCSTRING,
)
class ConvNextModel(ConvNextPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.embeddings = ConvNextEmbeddings(config)
self.encoder = ConvNextEncoder(config)
# final layernorm layer
self.layernorm = nn.LayerNorm(config.hidden_sizes[-1], eps=config.layer_norm_eps)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(CONVNEXT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPoolingAndNoAttention,
config_class=_CONFIG_FOR_DOC,
modality="vision",
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
def forward(
self,
pixel_values: torch.FloatTensor = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPoolingAndNoAttention]:
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
embedding_output = self.embeddings(pixel_values)
encoder_outputs = self.encoder(
embedding_output,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_state = encoder_outputs[0]
# global average pooling, (N, C, H, W) -> (N, C)
pooled_output = self.layernorm(last_hidden_state.mean([-2, -1]))
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
)
@add_start_docstrings(
"""
ConvNext Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
""",
CONVNEXT_START_DOCSTRING,
)
class ConvNextForImageClassification(ConvNextPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.convnext = ConvNextModel(config)
# Classifier head
self.classifier = (
nn.Linear(config.hidden_sizes[-1], config.num_labels) if config.num_labels > 0 else nn.Identity()
)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(CONVNEXT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=ImageClassifierOutputWithNoAttention,
config_class=_CONFIG_FOR_DOC,
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
)
def forward(
self,
pixel_values: torch.FloatTensor = None,
labels: Optional[torch.LongTensor] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, ImageClassifierOutputWithNoAttention]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.convnext(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)
pooled_output = outputs.pooler_output if return_dict else outputs[1]
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
)
@add_start_docstrings(
"""
ConvNeXt backbone, to be used with frameworks like DETR and MaskFormer.
""",
CONVNEXT_START_DOCSTRING,
)
class ConvNextBackbone(ConvNextPreTrainedModel, BackboneMixin):
def __init__(self, config):
super().__init__(config)
super()._init_backbone(config)
self.embeddings = ConvNextEmbeddings(config)
self.encoder = ConvNextEncoder(config)
self.num_features = [config.hidden_sizes[0]] + config.hidden_sizes
# Add layer norms to hidden states of out_features
hidden_states_norms = {}
for stage, num_channels in zip(self._out_features, self.channels):
hidden_states_norms[stage] = ConvNextLayerNorm(num_channels, data_format="channels_first")
self.hidden_states_norms = nn.ModuleDict(hidden_states_norms)
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(CONVNEXT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BackboneOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: torch.Tensor,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> BackboneOutput:
"""
Returns:
Examples:
```python
>>> from transformers import AutoImageProcessor, AutoBackbone
>>> import torch
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> processor = AutoImageProcessor.from_pretrained("facebook/convnext-tiny-224")
>>> model = AutoBackbone.from_pretrained("facebook/convnext-tiny-224")
>>> inputs = processor(image, return_tensors="pt")
>>> outputs = model(**inputs)
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
embedding_output = self.embeddings(pixel_values)
outputs = self.encoder(
embedding_output,
output_hidden_states=True,
return_dict=return_dict,
)
hidden_states = outputs.hidden_states if return_dict else outputs[1]
feature_maps = ()
for stage, hidden_state in zip(self.stage_names, hidden_states):
if stage in self.out_features:
hidden_state = self.hidden_states_norms[stage](hidden_state)
feature_maps += (hidden_state,)
if not return_dict:
output = (feature_maps,)
if output_hidden_states:
output += (hidden_states,)
return output
return BackboneOutput(
feature_maps=feature_maps,
hidden_states=hidden_states if output_hidden_states else None,
attentions=None,
)
| transformers/src/transformers/models/convnext/modeling_convnext.py/0 | {
"file_path": "transformers/src/transformers/models/convnext/modeling_convnext.py",
"repo_id": "transformers",
"token_count": 9129
} | 84 |
# coding=utf-8
# Copyright 2018 Salesforce and HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch CTRL model."""
from typing import Optional, Tuple, Union
import numpy as np
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutput
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import Conv1D, find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
from .configuration_ctrl import CTRLConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "CTRLConfig"
CTRL_PRETRAINED_MODEL_ARCHIVE_LIST = [
"Salesforce/ctrl"
# See all CTRL models at https://huggingface.co/models?filter=ctrl
]
def angle_defn(pos, i, d_model_size):
angle_rates = 1 / torch.pow(10000, (2 * (i // 2)) / d_model_size)
return pos * angle_rates
def positional_encoding(position, d_model_size, dtype):
# create the sinusoidal pattern for the positional encoding
angle_rads = angle_defn(
torch.arange(position, dtype=torch.int64).to(dtype).unsqueeze(1),
torch.arange(d_model_size, dtype=torch.int64).to(dtype).unsqueeze(0),
d_model_size,
)
sines = torch.sin(angle_rads[:, 0::2])
cosines = torch.cos(angle_rads[:, 1::2])
pos_encoding = torch.cat([sines, cosines], dim=-1)
return pos_encoding
def scaled_dot_product_attention(q, k, v, mask, attention_mask=None, head_mask=None):
# calculate attention
matmul_qk = torch.matmul(q, k.permute(0, 1, 3, 2))
dk = k.shape[-1]
scaled_attention_logits = matmul_qk / np.sqrt(dk)
if mask is not None:
nd, ns = scaled_attention_logits.size(-2), scaled_attention_logits.size(-1)
scaled_attention_logits += mask[ns - nd : ns, :ns] * -1e4
if attention_mask is not None:
# Apply the attention mask
scaled_attention_logits = scaled_attention_logits + attention_mask
attention_weights = torch.softmax(scaled_attention_logits, dim=-1)
# Mask heads if we want to
if head_mask is not None:
attention_weights = attention_weights * head_mask
output = torch.matmul(attention_weights, v)
return output, attention_weights
class MultiHeadAttention(nn.Module):
def __init__(self, d_model_size, num_heads):
super().__init__()
self.num_heads = num_heads
self.d_model_size = d_model_size
self.depth = int(d_model_size / self.num_heads)
self.Wq = nn.Linear(d_model_size, d_model_size)
self.Wk = nn.Linear(d_model_size, d_model_size)
self.Wv = nn.Linear(d_model_size, d_model_size)
self.dense = nn.Linear(d_model_size, d_model_size)
self.pruned_heads = set()
def prune_heads(self, heads):
attention_head_size = self.d_model_size // self.num_heads
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(heads, self.num_heads, attention_head_size, self.pruned_heads)
# Prune linear layers
self.Wq = prune_linear_layer(self.Wq, index)
self.Wk = prune_linear_layer(self.Wk, index)
self.Wv = prune_linear_layer(self.Wv, index)
self.dense = prune_linear_layer(self.dense, index, dim=1)
# Update hyper params
self.num_heads = self.num_heads - len(heads)
self.d_model_size = attention_head_size * self.num_heads
self.pruned_heads = self.pruned_heads.union(heads)
def split_into_heads(self, x, batch_size):
x = x.reshape(batch_size, -1, self.num_heads, self.depth)
return x.permute([0, 2, 1, 3])
def forward(
self,
v,
k,
q,
mask,
layer_past=None,
attention_mask=None,
head_mask=None,
use_cache=False,
output_attentions=False,
):
batch_size = q.shape[0]
q = self.Wq(q)
k = self.Wk(k)
v = self.Wv(v)
q = self.split_into_heads(q, batch_size)
k = self.split_into_heads(k, batch_size)
v = self.split_into_heads(v, batch_size)
if layer_past is not None:
past_key, past_value = layer_past[0], layer_past[1]
k = torch.cat((past_key, k), dim=-2)
v = torch.cat((past_value, v), dim=-2)
if use_cache is True:
present = torch.stack((k, v))
else:
present = (None,)
output = scaled_dot_product_attention(q, k, v, mask, attention_mask, head_mask)
scaled_attention = output[0].permute([0, 2, 1, 3])
attn = output[1]
original_size_attention = scaled_attention.reshape(batch_size, -1, self.d_model_size)
output = self.dense(original_size_attention)
outputs = (output, present)
if output_attentions:
outputs = outputs + (attn,)
return outputs
def point_wise_feed_forward_network(d_model_size, dff):
return nn.Sequential(nn.Linear(d_model_size, dff), nn.ReLU(), nn.Linear(dff, d_model_size))
class EncoderLayer(nn.Module):
def __init__(self, d_model_size, num_heads, dff, rate=0.1):
super().__init__()
self.multi_head_attention = MultiHeadAttention(d_model_size, num_heads)
self.ffn = point_wise_feed_forward_network(d_model_size, dff)
self.layernorm1 = nn.LayerNorm(d_model_size, eps=1e-6)
self.layernorm2 = nn.LayerNorm(d_model_size, eps=1e-6)
self.dropout1 = nn.Dropout(rate)
self.dropout2 = nn.Dropout(rate)
def forward(
self, x, mask, layer_past=None, attention_mask=None, head_mask=None, use_cache=False, output_attentions=False
):
normed = self.layernorm1(x)
attn_outputs = self.multi_head_attention(
normed,
normed,
normed,
mask,
layer_past=layer_past,
attention_mask=attention_mask,
head_mask=head_mask,
use_cache=use_cache,
output_attentions=output_attentions,
)
attn_output = attn_outputs[0]
attn_output = self.dropout1(attn_output)
out1 = x + attn_output
out2 = self.layernorm2(out1)
ffn_output = self.ffn(out2)
ffn_output = self.dropout2(ffn_output)
out2 = out1 + ffn_output
outputs = (out2,) + attn_outputs[1:]
return outputs
class CTRLPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = CTRLConfig
base_model_prefix = "transformer"
def _init_weights(self, module):
"""Initialize the weights."""
if isinstance(module, (nn.Linear, Conv1D)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
CTRL_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`CTRLConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
CTRL_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
`input_ids_length` = `sequence_length` if `past_key_values` is `None` else `past_key_values[0].shape[-2]`
(`sequence_length` of input past key value states). Indices of input sequence tokens in the vocabulary.
If `past_key_values` is used, only input IDs that do not have their past calculated should be passed as
`input_ids`.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
[`PreTrainedTokenizer.encode`] for details.
[What are input IDs?](../glossary#input-ids)
past_key_values (`Tuple[Tuple[torch.FloatTensor]]` of length `config.n_layers`):
Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model (see
`past_key_values` output below). Can be used to speed up sequential decoding. The `input_ids` which have
their past given to this model should not be passed as input ids as they have already been computed.
attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
token_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
1]`:
- 0 corresponds to a *sentence A* token,
- 1 corresponds to a *sentence B* token.
[What are token type IDs?](../glossary#token-type-ids)
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare CTRL Model transformer outputting raw hidden-states without any specific head on top.",
CTRL_START_DOCSTRING,
)
class CTRLModel(CTRLPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.d_model_size = config.n_embd
self.num_layers = config.n_layer
self.pos_encoding = positional_encoding(config.n_positions, self.d_model_size, torch.float)
self.w = nn.Embedding(config.vocab_size, config.n_embd)
self.dropout = nn.Dropout(config.embd_pdrop)
self.h = nn.ModuleList(
[EncoderLayer(config.n_embd, config.n_head, config.dff, config.resid_pdrop) for _ in range(config.n_layer)]
)
self.layernorm = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.w
def set_input_embeddings(self, new_embeddings):
self.w = new_embeddings
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
"""
for layer, heads in heads_to_prune.items():
self.h[layer].multi_head_attention.prune_heads(heads)
@add_start_docstrings_to_model_forward(CTRL_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPast]:
r"""
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, CTRLModel
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("Salesforce/ctrl")
>>> model = CTRLModel.from_pretrained("Salesforce/ctrl")
>>> # CTRL was trained with control codes as the first token
>>> inputs = tokenizer("Opinion My dog is cute", return_tensors="pt")
>>> assert inputs["input_ids"][0, 0].item() in tokenizer.control_codes.values()
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 5, 1280]
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
use_cache = use_cache if use_cache is not None else self.config.use_cache
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
batch_size = input_ids.shape[0]
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
batch_size = inputs_embeds.shape[0]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
device = input_ids.device if input_ids is not None else inputs_embeds.device
if past_key_values is None:
past_length = 0
past_key_values = tuple([None] * len(self.h))
else:
past_length = past_key_values[0][0].size(-2)
if position_ids is None:
position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
position_ids = position_ids.unsqueeze(0)
# Attention mask.
if attention_mask is not None:
if batch_size <= 0:
raise ValueError("batch_size has to be defined and > 0")
attention_mask = attention_mask.view(batch_size, -1)
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and the dtype's smallest value for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility
attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min
# Prepare head mask if needed
head_mask = self.get_head_mask(head_mask, self.config.n_layer)
if token_type_ids is not None:
token_type_ids = token_type_ids.view(-1, input_shape[-1])
token_type_embeds = self.w(token_type_ids)
token_type_embeds *= np.sqrt(self.d_model_size)
else:
token_type_embeds = 0
if inputs_embeds is None:
inputs_embeds = self.w(input_ids)
# inputs_embeds = embedded.unsqueeze(0) if len(input_ids.shape)<2 else embedded
seq_len = input_shape[-1]
mask = torch.triu(torch.ones(seq_len + past_length, seq_len + past_length), 1).to(device)
inputs_embeds *= np.sqrt(self.d_model_size)
# `self.pos_encoding` won't be sent to the correct device along the model, so we do it manually.
self.pos_encoding = self.pos_encoding.to(device)
pos_embeds = self.pos_encoding[position_ids, :]
hidden_states = inputs_embeds + pos_embeds + token_type_embeds
hidden_states = self.dropout(hidden_states)
presents = () if use_cache else None
all_hidden_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
for i, (h, layer_past) in enumerate(zip(self.h, past_key_values)):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
outputs = h(
hidden_states,
mask,
layer_past=layer_past,
attention_mask=attention_mask,
head_mask=head_mask[i],
use_cache=use_cache,
output_attentions=output_attentions,
)
hidden_states, present = outputs[:2]
if use_cache is True:
presents = presents + (present,)
if output_attentions:
all_attentions += (outputs[2],)
hidden_states = self.layernorm(hidden_states)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, presents, all_hidden_states, all_attentions] if v is not None)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=presents,
hidden_states=all_hidden_states,
attentions=all_attentions,
)
@add_start_docstrings(
"""
The CTRL Model transformer with a language modeling head on top (linear layer with weights tied to the input
embeddings).
""",
CTRL_START_DOCSTRING,
)
class CTRLLMHeadModel(CTRLPreTrainedModel):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.transformer = CTRLModel(config)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=True)
# Initialize weights and apply final processing
self.post_init()
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, use_cache=None, **kwargs):
# only last tokens for inputs_ids if past is defined in kwargs
if past_key_values is not None:
past_length = past_key_values[0][0].shape[2]
# Some generation methods already pass only the last input ID
if input_ids.shape[1] > past_length:
remove_prefix_length = past_length
else:
# Default to old behavior: keep only final ID
remove_prefix_length = input_ids.shape[1] - 1
input_ids = input_ids[:, remove_prefix_length:]
return {"input_ids": input_ids, "past_key_values": past_key_values, "use_cache": use_cache}
@add_start_docstrings_to_model_forward(CTRL_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], CausalLMOutputWithPast]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
Returns:
Example:
```python
>>> import torch
>>> from transformers import AutoTokenizer, CTRLLMHeadModel
>>> tokenizer = AutoTokenizer.from_pretrained("Salesforce/ctrl")
>>> model = CTRLLMHeadModel.from_pretrained("Salesforce/ctrl")
>>> # CTRL was trained with control codes as the first token
>>> inputs = tokenizer("Wikipedia The llama is", return_tensors="pt")
>>> assert inputs["input_ids"][0, 0].item() in tokenizer.control_codes.values()
>>> sequence_ids = model.generate(inputs["input_ids"])
>>> sequences = tokenizer.batch_decode(sequence_ids)
>>> sequences
['Wikipedia The llama is a member of the family Bovidae. It is native to the Andes of Peru,']
>>> outputs = model(**inputs, labels=inputs["input_ids"])
>>> round(outputs.loss.item(), 2)
9.21
>>> list(outputs.logits.shape)
[1, 5, 246534]
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.transformer(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = transformer_outputs[0]
lm_logits = self.lm_head(hidden_states)
loss = None
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = lm_logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
if not return_dict:
output = (lm_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=lm_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
@staticmethod
def _reorder_cache(
past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor
) -> Tuple[Tuple[torch.Tensor]]:
"""
This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
[`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
beam_idx at every generation step.
"""
return tuple(
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past)
for layer_past in past_key_values
)
@add_start_docstrings(
"""
The CTRL Model transformer with a sequence classification head on top (linear layer).
[`CTRLForSequenceClassification`] uses the last token in order to do the classification, as other causal models
(e.g. GPT-2) do. Since it does classification on the last token, it requires to know the position of the last
token. If a `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in
each row. If no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot
guess the padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last
value in each row of the batch).
""",
CTRL_START_DOCSTRING,
)
class CTRLForSequenceClassification(CTRLPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = CTRLModel(config)
self.classifier = nn.Linear(config.n_embd, self.num_labels, bias=False)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(CTRL_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
Returns:
Example of single-label classification:
```python
>>> import torch
>>> from transformers import AutoTokenizer, CTRLForSequenceClassification
>>> tokenizer = AutoTokenizer.from_pretrained("Salesforce/ctrl")
>>> model = CTRLForSequenceClassification.from_pretrained("Salesforce/ctrl")
>>> # CTRL was trained with control codes as the first token
>>> inputs = tokenizer("Opinion My dog is cute", return_tensors="pt")
>>> assert inputs["input_ids"][0, 0].item() in tokenizer.control_codes.values()
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> predicted_class_id = logits.argmax().item()
>>> model.config.id2label[predicted_class_id]
'LABEL_0'
```
```python
>>> import torch
>>> torch.manual_seed(42) # doctest: +IGNORE_RESULT
>>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
>>> num_labels = len(model.config.id2label)
>>> model = CTRLForSequenceClassification.from_pretrained("Salesforce/ctrl", num_labels=num_labels)
>>> labels = torch.tensor(1)
>>> loss = model(**inputs, labels=labels).loss
>>> round(loss.item(), 2)
0.35
```
Example of multi-label classification:
```python
>>> import torch
>>> from transformers import AutoTokenizer, CTRLForSequenceClassification
>>> tokenizer = AutoTokenizer.from_pretrained("Salesforce/ctrl")
>>> model = CTRLForSequenceClassification.from_pretrained(
... "Salesforce/ctrl", problem_type="multi_label_classification"
... )
>>> # CTRL was trained with control codes as the first token
>>> inputs = tokenizer("Opinion My dog is cute", return_tensors="pt")
>>> assert inputs["input_ids"][0, 0].item() in tokenizer.control_codes.values()
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> predicted_class_id = logits.argmax().item()
>>> model.config.id2label[predicted_class_id]
'LABEL_0'
```
```python
>>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
>>> num_labels = len(model.config.id2label)
>>> model = CTRLForSequenceClassification.from_pretrained("Salesforce/ctrl", num_labels=num_labels)
>>> num_labels = len(model.config.id2label)
>>> labels = torch.nn.functional.one_hot(torch.tensor([predicted_class_id]), num_classes=num_labels).to(
... torch.float
... )
>>> loss = model(**inputs, labels=labels).loss
>>> loss.backward() # doctest: +IGNORE_RESULT
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.transformer(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = transformer_outputs[0]
logits = self.classifier(hidden_states)
if input_ids is not None:
batch_size, sequence_length = input_ids.shape[:2]
else:
batch_size, sequence_length = inputs_embeds.shape[:2]
if self.config.pad_token_id is None and batch_size != 1:
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
if self.config.pad_token_id is None:
sequence_lengths = -1
else:
if input_ids is not None:
# if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
sequence_lengths = sequence_lengths % input_ids.shape[-1]
sequence_lengths = sequence_lengths.to(logits.device)
else:
sequence_lengths = -1
logger.warning(
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
)
pooled_logits = logits[range(batch_size), sequence_lengths]
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(pooled_logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(pooled_logits, labels)
if not return_dict:
output = (pooled_logits,) + transformer_outputs[2:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=pooled_logits,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
| transformers/src/transformers/models/ctrl/modeling_ctrl.py/0 | {
"file_path": "transformers/src/transformers/models/ctrl/modeling_ctrl.py",
"repo_id": "transformers",
"token_count": 15197
} | 85 |
# coding=utf-8
# Copyright 2022 The HuggingFace Team and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Decision Transformer model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"edbeeching/decision-transformer-gym-hopper-medium": (
"https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class DecisionTransformerConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`DecisionTransformerModel`]. It is used to
instantiate a Decision Transformer model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that of the standard
DecisionTransformer architecture. Many of the config options are used to instatiate the GPT2 model that is used as
part of the architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
state_dim (`int`, *optional*, defaults to 17):
The state size for the RL environment
act_dim (`int`, *optional*, defaults to 4):
The size of the output action space
hidden_size (`int`, *optional*, defaults to 128):
The size of the hidden layers
max_ep_len (`int`, *optional*, defaults to 4096):
The maximum length of an episode in the environment
action_tanh (`bool`, *optional*, defaults to True):
Whether to use a tanh activation on action prediction
vocab_size (`int`, *optional*, defaults to 50257):
Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`DecisionTransformerModel`].
n_positions (`int`, *optional*, defaults to 1024):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
n_layer (`int`, *optional*, defaults to 3):
Number of hidden layers in the Transformer encoder.
n_head (`int`, *optional*, defaults to 1):
Number of attention heads for each attention layer in the Transformer encoder.
n_inner (`int`, *optional*):
Dimensionality of the inner feed-forward layers. If unset, will default to 4 times `n_embd`.
activation_function (`str`, *optional*, defaults to `"gelu"`):
Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`.
resid_pdrop (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
embd_pdrop (`int`, *optional*, defaults to 0.1):
The dropout ratio for the embeddings.
attn_pdrop (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention.
layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
The epsilon to use in the layer normalization layers.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
scale_attn_weights (`bool`, *optional*, defaults to `True`):
Scale attention weights by dividing by sqrt(hidden_size)..
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
scale_attn_by_inverse_layer_idx (`bool`, *optional*, defaults to `False`):
Whether to additionally scale attention weights by `1 / layer_idx + 1`.
reorder_and_upcast_attn (`bool`, *optional*, defaults to `False`):
Whether to scale keys (K) prior to computing attention (dot-product) and upcast attention
dot-product/softmax to float() when training with mixed precision.
Example:
```python
>>> from transformers import DecisionTransformerConfig, DecisionTransformerModel
>>> # Initializing a DecisionTransformer configuration
>>> configuration = DecisionTransformerConfig()
>>> # Initializing a model (with random weights) from the configuration
>>> model = DecisionTransformerModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "decision_transformer"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {
"max_position_embeddings": "n_positions",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__(
self,
state_dim=17,
act_dim=4,
hidden_size=128,
max_ep_len=4096,
action_tanh=True,
vocab_size=1,
n_positions=1024,
n_layer=3,
n_head=1,
n_inner=None,
activation_function="relu",
resid_pdrop=0.1,
embd_pdrop=0.1,
attn_pdrop=0.1,
layer_norm_epsilon=1e-5,
initializer_range=0.02,
scale_attn_weights=True,
use_cache=True,
bos_token_id=50256,
eos_token_id=50256,
scale_attn_by_inverse_layer_idx=False,
reorder_and_upcast_attn=False,
**kwargs,
):
self.state_dim = state_dim
self.act_dim = act_dim
self.hidden_size = hidden_size
self.max_ep_len = max_ep_len
self.action_tanh = action_tanh
self.vocab_size = vocab_size
self.n_positions = n_positions
self.n_layer = n_layer
self.n_head = n_head
self.n_inner = n_inner
self.activation_function = activation_function
self.resid_pdrop = resid_pdrop
self.embd_pdrop = embd_pdrop
self.attn_pdrop = attn_pdrop
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_range = initializer_range
self.scale_attn_weights = scale_attn_weights
self.use_cache = use_cache
self.scale_attn_by_inverse_layer_idx = scale_attn_by_inverse_layer_idx
self.reorder_and_upcast_attn = reorder_and_upcast_attn
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
| transformers/src/transformers/models/decision_transformer/configuration_decision_transformer.py/0 | {
"file_path": "transformers/src/transformers/models/decision_transformer/configuration_decision_transformer.py",
"repo_id": "transformers",
"token_count": 2795
} | 86 |
# coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team, The Google AI Language Team and Facebook, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
RetriBERT model
"""
import math
from typing import Optional
import torch
import torch.utils.checkpoint as checkpoint
from torch import nn
from ....modeling_utils import PreTrainedModel
from ....utils import add_start_docstrings, logging
from ...bert.modeling_bert import BertModel
from .configuration_retribert import RetriBertConfig
logger = logging.get_logger(__name__)
RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
"yjernite/retribert-base-uncased",
# See all RetriBert models at https://huggingface.co/models?filter=retribert
]
# INTERFACE FOR ENCODER AND TASK SPECIFIC MODEL #
class RetriBertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = RetriBertConfig
load_tf_weights = None
base_model_prefix = "retribert"
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
RETRIBERT_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`RetriBertConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
@add_start_docstrings(
"""Bert Based model to embed queries or document for document retrieval.""",
RETRIBERT_START_DOCSTRING,
)
class RetriBertModel(RetriBertPreTrainedModel):
def __init__(self, config: RetriBertConfig) -> None:
super().__init__(config)
self.projection_dim = config.projection_dim
self.bert_query = BertModel(config)
self.bert_doc = None if config.share_encoders else BertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.project_query = nn.Linear(config.hidden_size, config.projection_dim, bias=False)
self.project_doc = nn.Linear(config.hidden_size, config.projection_dim, bias=False)
self.ce_loss = nn.CrossEntropyLoss(reduction="mean")
# Initialize weights and apply final processing
self.post_init()
def embed_sentences_checkpointed(
self,
input_ids,
attention_mask,
sent_encoder,
checkpoint_batch_size=-1,
):
# reproduces BERT forward pass with checkpointing
if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
return sent_encoder(input_ids, attention_mask=attention_mask)[1]
else:
# prepare implicit variables
device = input_ids.device
input_shape = input_ids.size()
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
head_mask = [None] * sent_encoder.config.num_hidden_layers
extended_attention_mask: torch.Tensor = sent_encoder.get_extended_attention_mask(
attention_mask, input_shape
)
# define function for checkpointing
def partial_encode(*inputs):
encoder_outputs = sent_encoder.encoder(
inputs[0],
attention_mask=inputs[1],
head_mask=head_mask,
)
sequence_output = encoder_outputs[0]
pooled_output = sent_encoder.pooler(sequence_output)
return pooled_output
# run embedding layer on everything at once
embedding_output = sent_encoder.embeddings(
input_ids=input_ids, position_ids=None, token_type_ids=token_type_ids, inputs_embeds=None
)
# run encoding and pooling on one mini-batch at a time
pooled_output_list = []
for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size)):
b_embedding_output = embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
b_attention_mask = extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
pooled_output = checkpoint.checkpoint(partial_encode, b_embedding_output, b_attention_mask)
pooled_output_list.append(pooled_output)
return torch.cat(pooled_output_list, dim=0)
def embed_questions(
self,
input_ids,
attention_mask=None,
checkpoint_batch_size=-1,
):
q_reps = self.embed_sentences_checkpointed(
input_ids,
attention_mask,
self.bert_query,
checkpoint_batch_size,
)
return self.project_query(q_reps)
def embed_answers(
self,
input_ids,
attention_mask=None,
checkpoint_batch_size=-1,
):
a_reps = self.embed_sentences_checkpointed(
input_ids,
attention_mask,
self.bert_query if self.bert_doc is None else self.bert_doc,
checkpoint_batch_size,
)
return self.project_doc(a_reps)
def forward(
self,
input_ids_query: torch.LongTensor,
attention_mask_query: Optional[torch.FloatTensor],
input_ids_doc: torch.LongTensor,
attention_mask_doc: Optional[torch.FloatTensor],
checkpoint_batch_size: int = -1,
) -> torch.FloatTensor:
r"""
Args:
input_ids_query (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary for the queries in a batch.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask_query (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
input_ids_doc (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary for the documents in a batch.
attention_mask_doc (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on documents padding token indices.
checkpoint_batch_size (`int`, *optional*, defaults to `-1`):
If greater than 0, uses gradient checkpointing to only compute sequence representation on
`checkpoint_batch_size` examples at a time on the GPU. All query representations are still compared to
all document representations in the batch.
Return:
`torch.FloatTensor``: The bidirectional cross-entropy loss obtained while trying to match each query to its
corresponding document and each document to its corresponding query in the batch
"""
device = input_ids_query.device
q_reps = self.embed_questions(input_ids_query, attention_mask_query, checkpoint_batch_size)
a_reps = self.embed_answers(input_ids_doc, attention_mask_doc, checkpoint_batch_size)
compare_scores = torch.mm(q_reps, a_reps.t())
loss_qa = self.ce_loss(compare_scores, torch.arange(compare_scores.shape[1]).to(device))
loss_aq = self.ce_loss(compare_scores.t(), torch.arange(compare_scores.shape[0]).to(device))
loss = (loss_qa + loss_aq) / 2
return loss
| transformers/src/transformers/models/deprecated/retribert/modeling_retribert.py/0 | {
"file_path": "transformers/src/transformers/models/deprecated/retribert/modeling_retribert.py",
"repo_id": "transformers",
"token_count": 3872
} | 87 |
# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Tokenization classes for Transformer XL model. Adapted from https://github.com/kimiyoung/transformer-xl.
"""
import glob
import os
import pickle
import re
from collections import Counter, OrderedDict
from typing import List, Optional, Tuple
import numpy as np
from ....tokenization_utils import PreTrainedTokenizer
from ....utils import (
cached_file,
is_sacremoses_available,
is_torch_available,
logging,
requires_backends,
strtobool,
torch_only_method,
)
if is_sacremoses_available():
import sacremoses as sm
if is_torch_available():
import torch
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {
"pretrained_vocab_file": "vocab.pkl",
"pretrained_vocab_file_torch": "vocab.bin",
"vocab_file": "vocab.txt",
}
PRETRAINED_VOCAB_FILES_MAP = {
"pretrained_vocab_file": {
"transfo-xl/transfo-xl-wt103": "https://huggingface.co/transfo-xl/transfo-xl-wt103/resolve/main/vocab.pkl",
}
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"transfo-xl/transfo-xl-wt103": None,
}
PRETRAINED_CORPUS_ARCHIVE_MAP = {
"transfo-xl/transfo-xl-wt103": "https://huggingface.co/transfo-xl/transfo-xl-wt103/resolve/main/corpus.bin",
}
CORPUS_NAME = "corpus.bin"
MATCH_NUMBERS = r"(?<=\d)[,.](?=\d)", r" @\g<0>@ "
DETOKENIZE_NUMBERS = [(r" @\,@ ", r","), (r" @\.@ ", r".")]
def tokenize_numbers(text_array: List[str]) -> List[str]:
"""
Splits large comma-separated numbers and floating point values. This is done by replacing commas with ' @,@ ' and
dots with ' @.@ '.
Args:
text_array: An already tokenized text as list.
Returns:
A list of strings with tokenized numbers.
Example:
```python
>>> tokenize_numbers(["$", "5,000", "1.73", "m"])
['$', '5', '@,@', '000', '1', '@.@', '73', 'm']
```"""
tokenized = []
for i in range(len(text_array)):
reg, sub = MATCH_NUMBERS
replaced = re.sub(reg, sub, text_array[i]).split()
tokenized.extend(replaced)
return tokenized
def detokenize_numbers(text: str) -> str:
"""
Inverts the operation of *tokenize_numbers*. This is replacing ' @,@ ' and ' @.@' by ',' and '.'.
Args:
text: A string where the number should be detokenized.
Returns:
A detokenized string.
Example:
```python
>>> detokenize_numbers("$ 5 @,@ 000 1 @.@ 73 m")
'$ 5,000 1.73 m'
```"""
for reg, sub in DETOKENIZE_NUMBERS:
text = re.sub(reg, sub, text)
return text
class TransfoXLTokenizer(PreTrainedTokenizer):
"""
Construct a Transformer-XL tokenizer adapted from Vocab class in [the original
code](https://github.com/kimiyoung/transformer-xl). The Transformer-XL tokenizer is a word-level tokenizer (no
sub-word tokenization).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
special (`List[str]`, *optional*):
A list of special tokens (to be treated by the original implementation of this tokenizer).
min_freq (`int`, *optional*, defaults to 0):
The minimum number of times a token has to be present in order to be kept in the vocabulary (otherwise it
will be mapped to `unk_token`).
max_size (`int`, *optional*):
The maximum size of the vocabulary. If left unset, it will default to the size of the vocabulary found
after excluding the tokens according to the `min_freq` rule.
lower_case (`bool`, *optional*, defaults to `False`):
Whether or not to lowercase the input when tokenizing.
delimiter (`str`, *optional*):
The delimiter used between tokens.
vocab_file (`str`, *optional*):
File containing the vocabulary (from the original implementation).
pretrained_vocab_file (`str`, *optional*):
File containing the vocabulary as saved with the `save_pretrained()` method.
never_split (`List[str]`, *optional*):
List of tokens that should never be split. If no list is specified, will simply use the existing special
tokens.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
eos_token (`str`, *optional*, defaults to `"<eos>"`):
The end of sequence token.
additional_special_tokens (`List[str]`, *optional*, defaults to `['<formula>']`):
A list of additional special tokens (for the HuggingFace functionality).
language (`str`, *optional*, defaults to `"en"`):
The language of this tokenizer (used for mose preprocessing).
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ["input_ids"]
def __init__(
self,
special=None,
min_freq=0,
max_size=None,
lower_case=False,
delimiter=None,
vocab_file=None,
pretrained_vocab_file: str = None,
never_split=None,
unk_token="<unk>",
eos_token="<eos>",
additional_special_tokens=["<formula>"],
language="en",
**kwargs,
):
logger.error(
"`TransfoXL` was deprecated due to security issues linked to `pickle.load` in `TransfoXLTokenizer`. "
"See more details on this model's documentation page: "
"`https://github.com/huggingface/transformers/blob/main/docs/source/en/model_doc/transfo-xl.md`."
)
requires_backends(self, "sacremoses")
if special is None:
special = []
self.counter = Counter()
self.special = special
self.min_freq = min_freq
self.max_size = max_size
self.lower_case = lower_case
self.delimiter = delimiter
self.vocab_file = vocab_file
self.punctuation_symbols = '!"#$%&()*+,-./\\:;<=>?@[\\]^_`{|}~'
self.punction_without_space_before_pattern = re.compile(rf"[^\s][{self.punctuation_symbols}]")
self.punctuation_with_space_around_pattern = self._compile_space_around_punctuation_pattern()
self.language = language
self.moses_punct_normalizer = sm.MosesPunctNormalizer(language)
self.moses_tokenizer = sm.MosesTokenizer(language)
self.moses_detokenizer = sm.MosesDetokenizer(language)
self.idx2sym = []
self.sym2idx = OrderedDict()
# This try... catch... is not beautiful but honestly this tokenizer was not made to be used
# in a library like ours, at all.
try:
vocab_dict = None
if pretrained_vocab_file is not None:
# Priority on pickle files (support PyTorch and TF)
if not strtobool(os.environ.get("TRUST_REMOTE_CODE", "False")):
raise ValueError(
"This part uses `pickle.load` which is insecure and will execute arbitrary code that is "
"potentially malicious. It's recommended to never unpickle data that could have come from an "
"untrusted source, or that could have been tampered with. If you already verified the pickle "
"data and decided to use it, you can set the environment variable "
"`TRUST_REMOTE_CODE` to `True` to allow it."
)
with open(pretrained_vocab_file, "rb") as f:
vocab_dict = pickle.load(f)
# Loading a torch-saved transfo-xl vocab dict with pickle results in an integer
# Entering this if statement means that we tried to load a torch-saved file with pickle, and we failed.
# We therefore load it with torch, if it's available.
if isinstance(vocab_dict, int):
if not is_torch_available():
raise ImportError(
"Not trying to load dict with PyTorch as you need to install pytorch to load "
"from a PyTorch pretrained vocabulary, "
"or activate it with environment variables USE_TORCH=1 and USE_TF=0."
)
vocab_dict = torch.load(pretrained_vocab_file)
if vocab_dict is not None:
for key, value in vocab_dict.items():
if key not in self.__dict__ or key in ["sym2idx", "idx2sym"]:
self.__dict__[key] = value
elif vocab_file is not None:
self.build_vocab()
except Exception as e:
raise ValueError(
f"Unable to parse file {pretrained_vocab_file}. Unknown format. "
"If you tried to load a model saved through TransfoXLTokenizerFast, "
"please note they are not compatible."
) from e
if vocab_file is not None:
self.build_vocab()
super().__init__(
special=special,
min_freq=min_freq,
max_size=max_size,
lower_case=lower_case,
delimiter=delimiter,
vocab_file=vocab_file,
pretrained_vocab_file=pretrained_vocab_file,
never_split=never_split,
unk_token=unk_token,
eos_token=eos_token,
additional_special_tokens=additional_special_tokens,
language=language,
**kwargs,
)
# these are not required to initialize the parent class as only used when tokenizing.
if never_split is None:
never_split = self.all_special_tokens
self.never_split = never_split
@property
def do_lower_case(self):
return self.lower_case
def _compile_space_around_punctuation_pattern(self):
look_ahead_for_special_token = f"(?=[{self.punctuation_symbols}])"
look_ahead_to_match_all_except_space = r"(?=[^\s])"
return re.compile(r"" + look_ahead_for_special_token + look_ahead_to_match_all_except_space)
def count_file(self, path, verbose=False, add_eos=False):
if verbose:
logger.info(f"counting file {path} ...")
assert os.path.exists(path), f"Input file {path} not found"
sents = []
with open(path, "r", encoding="utf-8") as f:
for idx, line in enumerate(f):
if verbose and idx > 0 and idx % 500000 == 0:
logger.info(f" line {idx}")
symbols = self.tokenize(line, add_eos=add_eos)
self.counter.update(symbols)
sents.append(symbols)
return sents
def count_sents(self, sents, verbose=False):
"""
sents : a list of sentences, each a list of tokenized symbols
"""
if verbose:
logger.info(f"counting {len(sents)} sents ...")
for idx, symbols in enumerate(sents):
if verbose and idx > 0 and idx % 500000 == 0:
logger.info(f" line {idx}")
self.counter.update(symbols)
def _build_from_file(self, vocab_file):
self.idx2sym = []
self.sym2idx = OrderedDict()
with open(vocab_file, "r", encoding="utf-8") as f:
for line in f:
symb = line.strip().split()[0]
self.add_symbol(symb)
if "<UNK>" in self.sym2idx:
self.unk_idx = self.sym2idx["<UNK>"]
elif "<unk>" in self.sym2idx:
self.unk_idx = self.sym2idx["<unk>"]
else:
raise ValueError("Token not in vocabulary and no <unk> token in vocabulary for replacement.")
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if os.path.isdir(save_directory):
vocab_file = os.path.join(
save_directory,
(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["pretrained_vocab_file"],
)
else:
vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory
with open(vocab_file, "wb") as f:
pickle.dump(self.__dict__, f)
return (vocab_file,)
def build_vocab(self):
if self.vocab_file:
logger.info(f"building vocab from {self.vocab_file}")
self._build_from_file(self.vocab_file)
logger.info(f"Final vocab size {len(self.sym2idx)}")
else:
logger.info(f"building vocab with min_freq={self.min_freq}, max_size={self.max_size}")
self.idx2sym = []
self.sym2idx = OrderedDict()
for sym in self.special:
self.add_special(sym)
for sym, cnt in self.counter.most_common(self.max_size):
if cnt < self.min_freq:
break
self.add_symbol(sym)
logger.info(f"Final vocab size {len(self.sym2idx)} from {len(self.counter)} unique tokens")
@torch_only_method
def encode_file(self, path, ordered=False, verbose=False, add_eos=True, add_double_eos=False):
if verbose:
logger.info(f"encoding file {path} ...")
assert os.path.exists(path), f"Output file {path} not found"
encoded = []
with open(path, "r", encoding="utf-8") as f:
for idx, line in enumerate(f):
if verbose and idx > 0 and idx % 500000 == 0:
logger.info(f" line {idx}")
symbols = self.tokenize(line, add_eos=add_eos, add_double_eos=add_double_eos)
encoded.append(self.convert_to_tensor(symbols))
if ordered:
encoded = torch.cat(encoded)
return encoded
@torch_only_method
def encode_sents(self, sents, ordered=False, verbose=False):
if verbose:
logger.info(f"encoding {len(sents)} sents ...")
encoded = []
for idx, symbols in enumerate(sents):
if verbose and idx > 0 and idx % 500000 == 0:
logger.info(f" line {idx}")
encoded.append(self.convert_to_tensor(symbols))
if ordered:
encoded = torch.cat(encoded)
return encoded
def add_special(self, sym):
if sym not in self.sym2idx:
self.idx2sym.append(sym)
self.sym2idx[sym] = len(self.idx2sym) - 1
setattr(self, f"{sym.strip('<>')}_idx", self.sym2idx[sym])
def add_symbol(self, sym):
if sym not in self.sym2idx:
self.idx2sym.append(sym)
self.sym2idx[sym] = len(self.idx2sym) - 1
def move_added_token(self, token: str, target_idx: int):
"""
Moves an added token to a specific position in the vocab. This method should be used when resizing an embedding
layer other than the last one in the `AdaptiveEmbedding` in order to move the token in the tokenizer from the
default position (at the very end) to the desired one.
Args:
token: The token to move to a specific position in the vocab.
target_idx: The position where the token should be moved to.
"""
assert token in self.added_tokens_encoder, "Token which should be moved has to be an added token"
assert token not in self.idx2sym, "Token which should be moved is already in vocab"
# Insert sym into vocab
self.idx2sym.insert(target_idx, token)
self.sym2idx[token] = target_idx
# Shift following indices in sym2idx
for idx in range(target_idx + 1, len(self.idx2sym)):
current_sym = self.idx2sym[idx]
self.sym2idx[current_sym] = idx
# Delete token from added_tokens
old_index = self._added_tokens_encoder.pop(token)
self._added_tokens_decoder.pop(old_index)
def moses_punct_norm(self, text):
return self.moses_punct_normalizer.normalize(text)
def moses_tokenize(self, text):
return self.moses_tokenizer.tokenize(
text, aggressive_dash_splits=True, return_str=False, escape=False, protected_patterns=self.never_split
)
def moses_pipeline(self, text: str) -> List[str]:
"""
Does basic tokenization using [`sacremoses.MosesPunctNormalizer`] and [`sacremoses.MosesTokenizer`] with
*aggressive_dash_splits=True* (see [`sacremoses.tokenize.MosesTokenizer.tokenize`]). Additionally, large
comma-separated numbers and floating point values are split. E.g. "23,000 people are 1.80m tall" -> "23 @,@ 000
people are 1 @.@ 80m tall"
Args:
text: Text to be tokenize
Returns:
A list of tokenized string
Example:
```python
>>> tokenizer = TransfoXLTokenizer.from_pretrained("transfo-xl/transfo-xl-wt103")
>>> tokenizer.moses_pipeline("23,000 people are 1.80 m tall")
['23', '@,@', '000', 'people', 'are', '1', '@.@', '80', 'm', 'tall']
```"""
text = self.moses_punct_norm(text)
text = self.moses_tokenize(text)
text = tokenize_numbers(text)
return text
def _convert_id_to_token(self, idx):
"""Converts an id in a token (BPE) using the vocab."""
assert 0 <= idx < len(self), f"Index {idx} out of vocabulary range"
return self.idx2sym[idx]
def _convert_token_to_id(self, sym):
"""Converts a token (str) in an id using the vocab."""
if sym in self.sym2idx:
return self.sym2idx[sym]
else:
# logger.info(f'encounter unk {sym}')
# assert '<eos>' not in sym
if hasattr(self, "unk_idx"):
return self.sym2idx.get(sym, self.unk_idx)
# Backward compatibility with pre-trained models
elif "<unk>" in self.sym2idx:
return self.sym2idx["<unk>"]
elif "<UNK>" in self.sym2idx:
return self.sym2idx["<UNK>"]
else:
raise ValueError("Token not in vocabulary and no <unk> token in vocabulary for replacement.")
def convert_tokens_to_string(self, tokens):
"""
Converts a sequence of tokens (string) in a single string. Additionally, the split numbers are converted back
into it's original form.
"""
out_string = self.moses_detokenizer.detokenize(tokens)
return detokenize_numbers(out_string).strip()
@torch_only_method
def convert_to_tensor(self, symbols):
return torch.LongTensor(self.convert_tokens_to_ids(symbols))
@property
def vocab_size(self):
return len(self.idx2sym)
def get_vocab(self):
vocab = self.sym2idx.copy()
vocab.update(self.added_tokens_encoder)
return vocab
def _tokenize(self, line, add_eos=False, add_double_eos=False):
line = line.strip()
# convert to lower case
if self.lower_case:
line = line.lower()
# empty delimiter '' will evaluate False
if self.delimiter == "":
symbols = line
else:
symbols = self.moses_pipeline(line)
if add_double_eos: # lm1b
return ["<S>"] + symbols + ["<S>"]
elif add_eos:
return symbols + ["<eos>"]
else:
return symbols
class LMOrderedIterator(object):
def __init__(self, data, bsz, bptt, device="cpu", ext_len=None):
"""
data -- LongTensor -- the LongTensor is strictly ordered
"""
self.bsz = bsz
self.bptt = bptt
self.ext_len = ext_len if ext_len is not None else 0
self.device = device
# Work out how cleanly we can divide the dataset into bsz parts.
self.n_step = data.size(0) // bsz
# Trim off any extra elements that wouldn't cleanly fit (remainders).
data = data.narrow(0, 0, self.n_step * bsz)
# Evenly divide the data across the bsz batches.
self.data = data.view(bsz, -1).t().contiguous().to(device)
# Number of mini-batches
self.n_batch = (self.n_step + self.bptt - 1) // self.bptt
def get_batch(self, i, bptt=None):
if bptt is None:
bptt = self.bptt
seq_len = min(bptt, self.data.size(0) - 1 - i)
end_idx = i + seq_len
beg_idx = max(0, i - self.ext_len)
data = self.data[beg_idx:end_idx]
target = self.data[i + 1 : i + 1 + seq_len]
data_out = data.transpose(0, 1).contiguous().to(self.device)
target_out = target.transpose(0, 1).contiguous().to(self.device)
return data_out, target_out, seq_len
def get_fixlen_iter(self, start=0):
for i in range(start, self.data.size(0) - 1, self.bptt):
yield self.get_batch(i)
def get_varlen_iter(self, start=0, std=5, min_len=5, max_deviation=3):
max_len = self.bptt + max_deviation * std
i = start
while True:
bptt = self.bptt if np.random.random() < 0.95 else self.bptt / 2.0
bptt = min(max_len, max(min_len, int(np.random.normal(bptt, std))))
data, target, seq_len = self.get_batch(i, bptt)
i += seq_len
yield data, target, seq_len
if i >= self.data.size(0) - 2:
break
def __iter__(self):
return self.get_fixlen_iter()
class LMShuffledIterator(object):
def __init__(self, data, bsz, bptt, device="cpu", ext_len=None, shuffle=False):
"""
data -- list[LongTensor] -- there is no order among the LongTensors
"""
self.data = data
self.bsz = bsz
self.bptt = bptt
self.ext_len = ext_len if ext_len is not None else 0
self.device = device
self.shuffle = shuffle
def get_sent_stream(self):
# index iterator
epoch_indices = np.random.permutation(len(self.data)) if self.shuffle else np.array(range(len(self.data)))
# sentence iterator
for idx in epoch_indices:
yield self.data[idx]
@torch_only_method
def stream_iterator(self, sent_stream):
# streams for each data in the batch
streams = [None] * self.bsz
data = torch.LongTensor(self.bptt, self.bsz)
target = torch.LongTensor(self.bptt, self.bsz)
n_retain = 0
while True:
# data : [n_retain+bptt x bsz]
# target : [bptt x bsz]
data[n_retain:].fill_(-1)
target.fill_(-1)
valid_batch = True
for i in range(self.bsz):
n_filled = 0
try:
while n_filled < self.bptt:
if streams[i] is None or len(streams[i]) <= 1:
streams[i] = next(sent_stream)
# number of new tokens to fill in
n_new = min(len(streams[i]) - 1, self.bptt - n_filled)
# first n_retain tokens are retained from last batch
data[n_retain + n_filled : n_retain + n_filled + n_new, i] = streams[i][:n_new]
target[n_filled : n_filled + n_new, i] = streams[i][1 : n_new + 1]
streams[i] = streams[i][n_new:]
n_filled += n_new
except StopIteration:
valid_batch = False
break
if not valid_batch:
return
data_out = data.transpose(0, 1).contiguous().to(self.device)
target_out = target.transpose(0, 1).contiguous().to(self.device)
yield data_out, target_out, self.bptt
n_retain = min(data.size(0), self.ext_len)
if n_retain > 0:
data[:n_retain] = data[-n_retain:]
data.resize_(n_retain + self.bptt, data.size(1))
def __iter__(self):
# sent_stream is an iterator
sent_stream = self.get_sent_stream()
for batch in self.stream_iterator(sent_stream):
yield batch
class LMMultiFileIterator(LMShuffledIterator):
def __init__(self, paths, vocab, bsz, bptt, device="cpu", ext_len=None, shuffle=False):
self.paths = paths
self.vocab = vocab
self.bsz = bsz
self.bptt = bptt
self.ext_len = ext_len if ext_len is not None else 0
self.device = device
self.shuffle = shuffle
def get_sent_stream(self, path):
sents = self.vocab.encode_file(path, add_double_eos=True)
if self.shuffle:
np.random.shuffle(sents)
sent_stream = iter(sents)
return sent_stream
def __iter__(self):
if self.shuffle:
np.random.shuffle(self.paths)
for path in self.paths:
# sent_stream is an iterator
sent_stream = self.get_sent_stream(path)
for batch in self.stream_iterator(sent_stream):
yield batch
class TransfoXLCorpus(object):
@classmethod
@torch_only_method
def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs):
"""
Instantiate a pre-processed corpus.
"""
vocab = TransfoXLTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
is_local = os.path.isdir(pretrained_model_name_or_path)
# redirect to the cache, if necessary
try:
resolved_corpus_file = cached_file(pretrained_model_name_or_path, CORPUS_NAME, cache_dir=cache_dir)
except EnvironmentError:
logger.error(
f"Corpus '{pretrained_model_name_or_path}' was not found in corpus list"
f" ({', '.join(PRETRAINED_CORPUS_ARCHIVE_MAP.keys())}. We assumed '{pretrained_model_name_or_path}'"
f" was a path or url but couldn't find files {CORPUS_NAME} at this path or url."
)
return None
if is_local:
logger.info(f"loading corpus file {resolved_corpus_file}")
else:
logger.info(f"loading corpus file {CORPUS_NAME} from cache at {resolved_corpus_file}")
# Instantiate tokenizer.
corpus = cls(*inputs, **kwargs)
corpus_dict = torch.load(resolved_corpus_file)
for key, value in corpus_dict.items():
corpus.__dict__[key] = value
corpus.vocab = vocab
if corpus.train is not None:
corpus.train = torch.tensor(corpus.train, dtype=torch.long)
if corpus.valid is not None:
corpus.valid = torch.tensor(corpus.valid, dtype=torch.long)
if corpus.test is not None:
corpus.test = torch.tensor(corpus.test, dtype=torch.long)
return corpus
def __init__(self, *args, **kwargs):
self.vocab = TransfoXLTokenizer(*args, **kwargs)
self.dataset = None
self.train = None
self.valid = None
self.test = None
def build_corpus(self, path, dataset):
self.dataset = dataset
if self.dataset in ["ptb", "wt2", "enwik8", "text8"]:
self.vocab.count_file(os.path.join(path, "train.txt"))
self.vocab.count_file(os.path.join(path, "valid.txt"))
self.vocab.count_file(os.path.join(path, "test.txt"))
elif self.dataset == "wt103":
self.vocab.count_file(os.path.join(path, "train.txt"))
elif self.dataset == "lm1b":
train_path_pattern = os.path.join(
path,
"1-billion-word-language-modeling-benchmark-r13output",
"training-monolingual.tokenized.shuffled",
"news.en-*",
)
train_paths = glob.glob(train_path_pattern)
# the vocab will load from file when build_vocab() is called
self.vocab.build_vocab()
if self.dataset in ["ptb", "wt2", "wt103"]:
self.train = self.vocab.encode_file(os.path.join(path, "train.txt"), ordered=True)
self.valid = self.vocab.encode_file(os.path.join(path, "valid.txt"), ordered=True)
self.test = self.vocab.encode_file(os.path.join(path, "test.txt"), ordered=True)
elif self.dataset in ["enwik8", "text8"]:
self.train = self.vocab.encode_file(os.path.join(path, "train.txt"), ordered=True, add_eos=False)
self.valid = self.vocab.encode_file(os.path.join(path, "valid.txt"), ordered=True, add_eos=False)
self.test = self.vocab.encode_file(os.path.join(path, "test.txt"), ordered=True, add_eos=False)
elif self.dataset == "lm1b":
self.train = train_paths
self.valid = self.vocab.encode_file(os.path.join(path, "valid.txt"), ordered=False, add_double_eos=True)
self.test = self.vocab.encode_file(os.path.join(path, "test.txt"), ordered=False, add_double_eos=True)
def get_iterator(self, split, *args, **kwargs):
if split == "train":
if self.dataset in ["ptb", "wt2", "wt103", "enwik8", "text8"]:
data_iter = LMOrderedIterator(self.train, *args, **kwargs)
elif self.dataset == "lm1b":
kwargs["shuffle"] = True
data_iter = LMMultiFileIterator(self.train, self.vocab, *args, **kwargs)
elif split in ["valid", "test"]:
data = self.valid if split == "valid" else self.test
if self.dataset in ["ptb", "wt2", "wt103", "enwik8", "text8"]:
data_iter = LMOrderedIterator(data, *args, **kwargs)
elif self.dataset == "lm1b":
data_iter = LMShuffledIterator(data, *args, **kwargs)
else:
data_iter = None
raise ValueError(f"Split not recognized: {split}")
return data_iter
@torch_only_method
def get_lm_corpus(datadir, dataset):
fn = os.path.join(datadir, "cache.pt")
fn_pickle = os.path.join(datadir, "cache.pkl")
if os.path.exists(fn):
logger.info("Loading cached dataset...")
corpus = torch.load(fn_pickle)
elif os.path.exists(fn):
logger.info("Loading cached dataset from pickle...")
if not strtobool(os.environ.get("TRUST_REMOTE_CODE", "False")):
raise ValueError(
"This part uses `pickle.load` which is insecure and will execute arbitrary code that is potentially "
"malicious. It's recommended to never unpickle data that could have come from an untrusted source, or "
"that could have been tampered with. If you already verified the pickle data and decided to use it, "
"you can set the environment variable `TRUST_REMOTE_CODE` to `True` to allow it."
)
with open(fn, "rb") as fp:
corpus = pickle.load(fp)
else:
logger.info(f"Producing dataset {dataset}...")
kwargs = {}
if dataset in ["wt103", "wt2"]:
kwargs["special"] = ["<eos>"]
kwargs["lower_case"] = False
elif dataset == "ptb":
kwargs["special"] = ["<eos>"]
kwargs["lower_case"] = True
elif dataset == "lm1b":
kwargs["special"] = []
kwargs["lower_case"] = False
kwargs["vocab_file"] = os.path.join(datadir, "1b_word_vocab.txt")
elif dataset in ["enwik8", "text8"]:
pass
corpus = TransfoXLCorpus(datadir, dataset, **kwargs)
torch.save(corpus, fn)
return corpus
| transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py/0 | {
"file_path": "transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py",
"repo_id": "transformers",
"token_count": 15167
} | 88 |
# coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team, The Google AI Language Team and Facebook, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" DistilBERT model configuration"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
logger = logging.get_logger(__name__)
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/config.json",
"distilbert-base-uncased-distilled-squad": (
"https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"
),
"distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/config.json",
"distilbert-base-cased-distilled-squad": (
"https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"
),
"distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json",
"distilbert-base-multilingual-cased": (
"https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"
),
"distilbert-base-uncased-finetuned-sst-2-english": (
"https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"
),
}
class DistilBertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`DistilBertModel`] or a [`TFDistilBertModel`]. It
is used to instantiate a DistilBERT model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that of the DistilBERT
[distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the DistilBERT model. Defines the number of different tokens that can be represented by
the `inputs_ids` passed when calling [`DistilBertModel`] or [`TFDistilBertModel`].
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
sinusoidal_pos_embds (`boolean`, *optional*, defaults to `False`):
Whether to use sinusoidal positional embeddings.
n_layers (`int`, *optional*, defaults to 6):
Number of hidden layers in the Transformer encoder.
n_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
dim (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
hidden_dim (`int`, *optional*, defaults to 3072):
The size of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
activation (`str` or `Callable`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
qa_dropout (`float`, *optional*, defaults to 0.1):
The dropout probabilities used in the question answering model [`DistilBertForQuestionAnswering`].
seq_classif_dropout (`float`, *optional*, defaults to 0.2):
The dropout probabilities used in the sequence classification and the multiple choice model
[`DistilBertForSequenceClassification`].
Examples:
```python
>>> from transformers import DistilBertConfig, DistilBertModel
>>> # Initializing a DistilBERT configuration
>>> configuration = DistilBertConfig()
>>> # Initializing a model (with random weights) from the configuration
>>> model = DistilBertModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "distilbert"
attribute_map = {
"hidden_size": "dim",
"num_attention_heads": "n_heads",
"num_hidden_layers": "n_layers",
}
def __init__(
self,
vocab_size=30522,
max_position_embeddings=512,
sinusoidal_pos_embds=False,
n_layers=6,
n_heads=12,
dim=768,
hidden_dim=4 * 768,
dropout=0.1,
attention_dropout=0.1,
activation="gelu",
initializer_range=0.02,
qa_dropout=0.1,
seq_classif_dropout=0.2,
pad_token_id=0,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.sinusoidal_pos_embds = sinusoidal_pos_embds
self.n_layers = n_layers
self.n_heads = n_heads
self.dim = dim
self.hidden_dim = hidden_dim
self.dropout = dropout
self.attention_dropout = attention_dropout
self.activation = activation
self.initializer_range = initializer_range
self.qa_dropout = qa_dropout
self.seq_classif_dropout = seq_classif_dropout
super().__init__(**kwargs, pad_token_id=pad_token_id)
class DistilBertOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
else:
dynamic_axis = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
]
)
| transformers/src/transformers/models/distilbert/configuration_distilbert.py/0 | {
"file_path": "transformers/src/transformers/models/distilbert/configuration_distilbert.py",
"repo_id": "transformers",
"token_count": 2693
} | 89 |
# coding=utf-8
# Copyright 2010, DPR authors, The Hugging Face Team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" DPR model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
DPR_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"facebook/dpr-ctx_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"
),
"facebook/dpr-question_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"
),
"facebook/dpr-reader-single-nq-base": (
"https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"
),
"facebook/dpr-ctx_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"
),
"facebook/dpr-question_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"
),
"facebook/dpr-reader-multiset-base": (
"https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"
),
}
class DPRConfig(PretrainedConfig):
r"""
[`DPRConfig`] is the configuration class to store the configuration of a *DPRModel*.
This is the configuration class to store the configuration of a [`DPRContextEncoder`], [`DPRQuestionEncoder`], or a
[`DPRReader`]. It is used to instantiate the components of the DPR model according to the specified arguments,
defining the model component architectures. Instantiating a configuration with the defaults will yield a similar
configuration to that of the DPRContextEncoder
[facebook/dpr-ctx_encoder-single-nq-base](https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base)
architecture.
This class is a subclass of [`BertConfig`]. Please check the superclass for the documentation of all kwargs.
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the DPR model. Defines the different tokens that can be represented by the *inputs_ids*
passed to the forward method of [`BertModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the *token_type_ids* passed into [`BertModel`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
pad_token_id (`int`, *optional*, defaults to 0):
Padding token id.
position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
[Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
projection_dim (`int`, *optional*, defaults to 0):
Dimension of the projection for the context and question encoders. If it is set to zero (default), then no
projection is done.
Example:
```python
>>> from transformers import DPRConfig, DPRContextEncoder
>>> # Initializing a DPR facebook/dpr-ctx_encoder-single-nq-base style configuration
>>> configuration = DPRConfig()
>>> # Initializing a model (with random weights) from the facebook/dpr-ctx_encoder-single-nq-base style configuration
>>> model = DPRContextEncoder(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "dpr"
def __init__(
self,
vocab_size=30522,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
layer_norm_eps=1e-12,
pad_token_id=0,
position_embedding_type="absolute",
projection_dim: int = 0,
**kwargs,
):
super().__init__(pad_token_id=pad_token_id, **kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.projection_dim = projection_dim
self.position_embedding_type = position_embedding_type
| transformers/src/transformers/models/dpr/configuration_dpr.py/0 | {
"file_path": "transformers/src/transformers/models/dpr/configuration_dpr.py",
"repo_id": "transformers",
"token_count": 2766
} | 90 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_import_structure = {
"configuration_efficientformer": [
"EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EfficientFormerConfig",
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["image_processing_efficientformer"] = ["EfficientFormerImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_efficientformer"] = [
"EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"EfficientFormerForImageClassification",
"EfficientFormerForImageClassificationWithTeacher",
"EfficientFormerModel",
"EfficientFormerPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_efficientformer"] = [
"TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFEfficientFormerForImageClassification",
"TFEfficientFormerForImageClassificationWithTeacher",
"TFEfficientFormerModel",
"TFEfficientFormerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_efficientformer import EfficientFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_efficientformer import (
EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
EfficientFormerForImageClassification,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerModel,
EfficientFormerPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_efficientformer import (
TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerModel,
TFEfficientFormerPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| transformers/src/transformers/models/efficientformer/__init__.py/0 | {
"file_path": "transformers/src/transformers/models/efficientformer/__init__.py",
"repo_id": "transformers",
"token_count": 1330
} | 91 |
# coding=utf-8
# Copyright 2019 The Google AI Language Team Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" TF Electra model."""
from __future__ import annotations
import math
import warnings
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import (
TFBaseModelOutputWithPastAndCrossAttentions,
TFMaskedLMOutput,
TFMultipleChoiceModelOutput,
TFQuestionAnsweringModelOutput,
TFSequenceClassifierOutput,
TFTokenClassifierOutput,
)
from ...modeling_tf_utils import (
TFMaskedLanguageModelingLoss,
TFModelInputType,
TFMultipleChoiceLoss,
TFPreTrainedModel,
TFQuestionAnsweringLoss,
TFSequenceClassificationLoss,
TFSequenceSummary,
TFTokenClassificationLoss,
get_initializer,
keras,
keras_serializable,
unpack_inputs,
)
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
from ...utils import (
ModelOutput,
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from .configuration_electra import ElectraConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "google/electra-small-discriminator"
_CONFIG_FOR_DOC = "ElectraConfig"
TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST = [
"google/electra-small-generator",
"google/electra-base-generator",
"google/electra-large-generator",
"google/electra-small-discriminator",
"google/electra-base-discriminator",
"google/electra-large-discriminator",
# See all ELECTRA models at https://huggingface.co/models?filter=electra
]
# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention with Bert->Electra
class TFElectraSelfAttention(keras.layers.Layer):
def __init__(self, config: ElectraConfig, **kwargs):
super().__init__(**kwargs)
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number "
f"of attention heads ({config.num_attention_heads})"
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.sqrt_att_head_size = math.sqrt(self.attention_head_size)
self.query = keras.layers.Dense(
units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query"
)
self.key = keras.layers.Dense(
units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key"
)
self.value = keras.layers.Dense(
units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value"
)
self.dropout = keras.layers.Dropout(rate=config.attention_probs_dropout_prob)
self.is_decoder = config.is_decoder
self.config = config
def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
# Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
tensor = tf.reshape(tensor=tensor, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size))
# Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size]
return tf.transpose(tensor, perm=[0, 2, 1, 3])
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
head_mask: tf.Tensor,
encoder_hidden_states: tf.Tensor,
encoder_attention_mask: tf.Tensor,
past_key_value: Tuple[tf.Tensor],
output_attentions: bool,
training: bool = False,
) -> Tuple[tf.Tensor]:
batch_size = shape_list(hidden_states)[0]
mixed_query_layer = self.query(inputs=hidden_states)
# If this is instantiated as a cross-attention module, the keys
# and values come from an encoder; the attention mask needs to be
# such that the encoder's padding tokens are not attended to.
is_cross_attention = encoder_hidden_states is not None
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_layer = past_key_value[0]
value_layer = past_key_value[1]
attention_mask = encoder_attention_mask
elif is_cross_attention:
key_layer = self.transpose_for_scores(self.key(inputs=encoder_hidden_states), batch_size)
value_layer = self.transpose_for_scores(self.value(inputs=encoder_hidden_states), batch_size)
attention_mask = encoder_attention_mask
elif past_key_value is not None:
key_layer = self.transpose_for_scores(self.key(inputs=hidden_states), batch_size)
value_layer = self.transpose_for_scores(self.value(inputs=hidden_states), batch_size)
key_layer = tf.concat([past_key_value[0], key_layer], axis=2)
value_layer = tf.concat([past_key_value[1], value_layer], axis=2)
else:
key_layer = self.transpose_for_scores(self.key(inputs=hidden_states), batch_size)
value_layer = self.transpose_for_scores(self.value(inputs=hidden_states), batch_size)
query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)
if self.is_decoder:
# if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(tf.Tensor, tf.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_layer, value_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
# (batch size, num_heads, seq_len_q, seq_len_k)
attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype)
attention_scores = tf.divide(attention_scores, dk)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in TFElectraModel call() function)
attention_scores = tf.add(attention_scores, attention_mask)
# Normalize the attention scores to probabilities.
attention_probs = stable_softmax(logits=attention_scores, axis=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(inputs=attention_probs, training=training)
# Mask heads if we want to
if head_mask is not None:
attention_probs = tf.multiply(attention_probs, head_mask)
attention_output = tf.matmul(attention_probs, value_layer)
attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3])
# (batch_size, seq_len_q, all_head_size)
attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.all_head_size))
outputs = (attention_output, attention_probs) if output_attentions else (attention_output,)
if self.is_decoder:
outputs = outputs + (past_key_value,)
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "query", None) is not None:
with tf.name_scope(self.query.name):
self.query.build([None, None, self.config.hidden_size])
if getattr(self, "key", None) is not None:
with tf.name_scope(self.key.name):
self.key.build([None, None, self.config.hidden_size])
if getattr(self, "value", None) is not None:
with tf.name_scope(self.value.name):
self.value.build([None, None, self.config.hidden_size])
# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfOutput with Bert->Electra
class TFElectraSelfOutput(keras.layers.Layer):
def __init__(self, config: ElectraConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob)
self.config = config
def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
hidden_states = self.dense(inputs=hidden_states)
hidden_states = self.dropout(inputs=hidden_states, training=training)
hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor)
return hidden_states
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "dense", None) is not None:
with tf.name_scope(self.dense.name):
self.dense.build([None, None, self.config.hidden_size])
if getattr(self, "LayerNorm", None) is not None:
with tf.name_scope(self.LayerNorm.name):
self.LayerNorm.build([None, None, self.config.hidden_size])
# Copied from transformers.models.bert.modeling_tf_bert.TFBertAttention with Bert->Electra
class TFElectraAttention(keras.layers.Layer):
def __init__(self, config: ElectraConfig, **kwargs):
super().__init__(**kwargs)
self.self_attention = TFElectraSelfAttention(config, name="self")
self.dense_output = TFElectraSelfOutput(config, name="output")
def prune_heads(self, heads):
raise NotImplementedError
def call(
self,
input_tensor: tf.Tensor,
attention_mask: tf.Tensor,
head_mask: tf.Tensor,
encoder_hidden_states: tf.Tensor,
encoder_attention_mask: tf.Tensor,
past_key_value: Tuple[tf.Tensor],
output_attentions: bool,
training: bool = False,
) -> Tuple[tf.Tensor]:
self_outputs = self.self_attention(
hidden_states=input_tensor,
attention_mask=attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_value=past_key_value,
output_attentions=output_attentions,
training=training,
)
attention_output = self.dense_output(
hidden_states=self_outputs[0], input_tensor=input_tensor, training=training
)
# add attentions (possibly with past_key_value) if we output them
outputs = (attention_output,) + self_outputs[1:]
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "self_attention", None) is not None:
with tf.name_scope(self.self_attention.name):
self.self_attention.build(None)
if getattr(self, "dense_output", None) is not None:
with tf.name_scope(self.dense_output.name):
self.dense_output.build(None)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertIntermediate with Bert->Electra
class TFElectraIntermediate(keras.layers.Layer):
def __init__(self, config: ElectraConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = get_tf_activation(config.hidden_act)
else:
self.intermediate_act_fn = config.hidden_act
self.config = config
def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
hidden_states = self.dense(inputs=hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "dense", None) is not None:
with tf.name_scope(self.dense.name):
self.dense.build([None, None, self.config.hidden_size])
# Copied from transformers.models.bert.modeling_tf_bert.TFBertOutput with Bert->Electra
class TFElectraOutput(keras.layers.Layer):
def __init__(self, config: ElectraConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob)
self.config = config
def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
hidden_states = self.dense(inputs=hidden_states)
hidden_states = self.dropout(inputs=hidden_states, training=training)
hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor)
return hidden_states
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "dense", None) is not None:
with tf.name_scope(self.dense.name):
self.dense.build([None, None, self.config.intermediate_size])
if getattr(self, "LayerNorm", None) is not None:
with tf.name_scope(self.LayerNorm.name):
self.LayerNorm.build([None, None, self.config.hidden_size])
# Copied from transformers.models.bert.modeling_tf_bert.TFBertLayer with Bert->Electra
class TFElectraLayer(keras.layers.Layer):
def __init__(self, config: ElectraConfig, **kwargs):
super().__init__(**kwargs)
self.attention = TFElectraAttention(config, name="attention")
self.is_decoder = config.is_decoder
self.add_cross_attention = config.add_cross_attention
if self.add_cross_attention:
if not self.is_decoder:
raise ValueError(f"{self} should be used as a decoder model if cross attention is added")
self.crossattention = TFElectraAttention(config, name="crossattention")
self.intermediate = TFElectraIntermediate(config, name="intermediate")
self.bert_output = TFElectraOutput(config, name="output")
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
head_mask: tf.Tensor,
encoder_hidden_states: tf.Tensor | None,
encoder_attention_mask: tf.Tensor | None,
past_key_value: Tuple[tf.Tensor] | None,
output_attentions: bool,
training: bool = False,
) -> Tuple[tf.Tensor]:
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
self_attention_outputs = self.attention(
input_tensor=hidden_states,
attention_mask=attention_mask,
head_mask=head_mask,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_value=self_attn_past_key_value,
output_attentions=output_attentions,
training=training,
)
attention_output = self_attention_outputs[0]
# if decoder, the last output is tuple of self-attn cache
if self.is_decoder:
outputs = self_attention_outputs[1:-1]
present_key_value = self_attention_outputs[-1]
else:
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
cross_attn_present_key_value = None
if self.is_decoder and encoder_hidden_states is not None:
if not hasattr(self, "crossattention"):
raise ValueError(
f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers"
" by setting `config.add_cross_attention=True`"
)
# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
cross_attention_outputs = self.crossattention(
input_tensor=attention_output,
attention_mask=attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_value=cross_attn_past_key_value,
output_attentions=output_attentions,
training=training,
)
attention_output = cross_attention_outputs[0]
outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights
# add cross-attn cache to positions 3,4 of present_key_value tuple
cross_attn_present_key_value = cross_attention_outputs[-1]
present_key_value = present_key_value + cross_attn_present_key_value
intermediate_output = self.intermediate(hidden_states=attention_output)
layer_output = self.bert_output(
hidden_states=intermediate_output, input_tensor=attention_output, training=training
)
outputs = (layer_output,) + outputs # add attentions if we output them
# if decoder, return the attn key/values as the last output
if self.is_decoder:
outputs = outputs + (present_key_value,)
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "attention", None) is not None:
with tf.name_scope(self.attention.name):
self.attention.build(None)
if getattr(self, "intermediate", None) is not None:
with tf.name_scope(self.intermediate.name):
self.intermediate.build(None)
if getattr(self, "bert_output", None) is not None:
with tf.name_scope(self.bert_output.name):
self.bert_output.build(None)
if getattr(self, "crossattention", None) is not None:
with tf.name_scope(self.crossattention.name):
self.crossattention.build(None)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertEncoder with Bert->Electra
class TFElectraEncoder(keras.layers.Layer):
def __init__(self, config: ElectraConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.layer = [TFElectraLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)]
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
head_mask: tf.Tensor,
encoder_hidden_states: tf.Tensor | None,
encoder_attention_mask: tf.Tensor | None,
past_key_values: Tuple[Tuple[tf.Tensor]] | None,
use_cache: Optional[bool],
output_attentions: bool,
output_hidden_states: bool,
return_dict: bool,
training: bool = False,
) -> Union[TFBaseModelOutputWithPastAndCrossAttentions, Tuple[tf.Tensor]]:
all_hidden_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
next_decoder_cache = () if use_cache else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
past_key_value = past_key_values[i] if past_key_values is not None else None
layer_outputs = layer_module(
hidden_states=hidden_states,
attention_mask=attention_mask,
head_mask=head_mask[i],
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_value=past_key_value,
output_attentions=output_attentions,
training=training,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[-1],)
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if self.config.add_cross_attention and encoder_hidden_states is not None:
all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
# Add last layer
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(
v for v in [hidden_states, all_hidden_states, all_attentions, all_cross_attentions] if v is not None
)
return TFBaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=next_decoder_cache,
hidden_states=all_hidden_states,
attentions=all_attentions,
cross_attentions=all_cross_attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "layer", None) is not None:
for layer in self.layer:
with tf.name_scope(layer.name):
layer.build(None)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertPooler with Bert->Electra
class TFElectraPooler(keras.layers.Layer):
def __init__(self, config: ElectraConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
activation="tanh",
name="dense",
)
self.config = config
def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
first_token_tensor = hidden_states[:, 0]
pooled_output = self.dense(inputs=first_token_tensor)
return pooled_output
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "dense", None) is not None:
with tf.name_scope(self.dense.name):
self.dense.build([None, None, self.config.hidden_size])
# Copied from transformers.models.albert.modeling_tf_albert.TFAlbertEmbeddings with Albert->Electra
class TFElectraEmbeddings(keras.layers.Layer):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config: ElectraConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embedding_size = config.embedding_size
self.max_position_embeddings = config.max_position_embeddings
self.initializer_range = config.initializer_range
self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob)
def build(self, input_shape=None):
with tf.name_scope("word_embeddings"):
self.weight = self.add_weight(
name="weight",
shape=[self.config.vocab_size, self.embedding_size],
initializer=get_initializer(self.initializer_range),
)
with tf.name_scope("token_type_embeddings"):
self.token_type_embeddings = self.add_weight(
name="embeddings",
shape=[self.config.type_vocab_size, self.embedding_size],
initializer=get_initializer(self.initializer_range),
)
with tf.name_scope("position_embeddings"):
self.position_embeddings = self.add_weight(
name="embeddings",
shape=[self.max_position_embeddings, self.embedding_size],
initializer=get_initializer(self.initializer_range),
)
if self.built:
return
self.built = True
if getattr(self, "LayerNorm", None) is not None:
with tf.name_scope(self.LayerNorm.name):
self.LayerNorm.build([None, None, self.config.embedding_size])
# Copied from transformers.models.bert.modeling_tf_bert.TFBertEmbeddings.call
def call(
self,
input_ids: tf.Tensor = None,
position_ids: tf.Tensor = None,
token_type_ids: tf.Tensor = None,
inputs_embeds: tf.Tensor = None,
past_key_values_length=0,
training: bool = False,
) -> tf.Tensor:
"""
Applies embedding based on inputs tensor.
Returns:
final_embeddings (`tf.Tensor`): output embedding tensor.
"""
if input_ids is None and inputs_embeds is None:
raise ValueError("Need to provide either `input_ids` or `input_embeds`.")
if input_ids is not None:
check_embeddings_within_bounds(input_ids, self.config.vocab_size)
inputs_embeds = tf.gather(params=self.weight, indices=input_ids)
input_shape = shape_list(inputs_embeds)[:-1]
if token_type_ids is None:
token_type_ids = tf.fill(dims=input_shape, value=0)
if position_ids is None:
position_ids = tf.expand_dims(
tf.range(start=past_key_values_length, limit=input_shape[1] + past_key_values_length), axis=0
)
position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids)
final_embeddings = inputs_embeds + position_embeds + token_type_embeds
final_embeddings = self.LayerNorm(inputs=final_embeddings)
final_embeddings = self.dropout(inputs=final_embeddings, training=training)
return final_embeddings
class TFElectraDiscriminatorPredictions(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(config.hidden_size, name="dense")
self.dense_prediction = keras.layers.Dense(1, name="dense_prediction")
self.config = config
def call(self, discriminator_hidden_states, training=False):
hidden_states = self.dense(discriminator_hidden_states)
hidden_states = get_tf_activation(self.config.hidden_act)(hidden_states)
logits = tf.squeeze(self.dense_prediction(hidden_states), -1)
return logits
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "dense", None) is not None:
with tf.name_scope(self.dense.name):
self.dense.build([None, None, self.config.hidden_size])
if getattr(self, "dense_prediction", None) is not None:
with tf.name_scope(self.dense_prediction.name):
self.dense_prediction.build([None, None, self.config.hidden_size])
class TFElectraGeneratorPredictions(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
self.dense = keras.layers.Dense(config.embedding_size, name="dense")
self.config = config
def call(self, generator_hidden_states, training=False):
hidden_states = self.dense(generator_hidden_states)
hidden_states = get_tf_activation("gelu")(hidden_states)
hidden_states = self.LayerNorm(hidden_states)
return hidden_states
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "LayerNorm", None) is not None:
with tf.name_scope(self.LayerNorm.name):
self.LayerNorm.build([None, None, self.config.embedding_size])
if getattr(self, "dense", None) is not None:
with tf.name_scope(self.dense.name):
self.dense.build([None, None, self.config.hidden_size])
class TFElectraPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ElectraConfig
base_model_prefix = "electra"
# When the model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [r"generator_lm_head.weight"]
_keys_to_ignore_on_load_missing = [r"dropout"]
@keras_serializable
class TFElectraMainLayer(keras.layers.Layer):
config_class = ElectraConfig
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.is_decoder = config.is_decoder
self.embeddings = TFElectraEmbeddings(config, name="embeddings")
if config.embedding_size != config.hidden_size:
self.embeddings_project = keras.layers.Dense(config.hidden_size, name="embeddings_project")
self.encoder = TFElectraEncoder(config, name="encoder")
def get_input_embeddings(self):
return self.embeddings
def set_input_embeddings(self, value):
self.embeddings.weight = value
self.embeddings.vocab_size = shape_list(value)[0]
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
raise NotImplementedError
def get_extended_attention_mask(self, attention_mask, input_shape, dtype, past_key_values_length=0):
batch_size, seq_length = input_shape
if attention_mask is None:
attention_mask = tf.fill(dims=(batch_size, seq_length + past_key_values_length), value=1)
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
attention_mask_shape = shape_list(attention_mask)
mask_seq_length = seq_length + past_key_values_length
# Copied from `modeling_tf_t5.py`
# Provided a padding mask of dimensions [batch_size, mask_seq_length]
# - if the model is a decoder, apply a causal mask in addition to the padding mask
# - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, mask_seq_length, mask_seq_length]
if self.is_decoder:
seq_ids = tf.range(mask_seq_length)
causal_mask = tf.less_equal(
tf.tile(seq_ids[None, None, :], (batch_size, mask_seq_length, 1)),
seq_ids[None, :, None],
)
causal_mask = tf.cast(causal_mask, dtype=attention_mask.dtype)
extended_attention_mask = causal_mask * attention_mask[:, None, :]
attention_mask_shape = shape_list(extended_attention_mask)
extended_attention_mask = tf.reshape(
extended_attention_mask, (attention_mask_shape[0], 1, attention_mask_shape[1], attention_mask_shape[2])
)
if past_key_values_length > 0:
extended_attention_mask = extended_attention_mask[:, :, -seq_length:, :]
else:
extended_attention_mask = tf.reshape(
attention_mask, (attention_mask_shape[0], 1, 1, attention_mask_shape[1])
)
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
extended_attention_mask = tf.cast(extended_attention_mask, dtype=dtype)
one_cst = tf.constant(1.0, dtype=dtype)
ten_thousand_cst = tf.constant(-10000.0, dtype=dtype)
extended_attention_mask = tf.multiply(tf.subtract(one_cst, extended_attention_mask), ten_thousand_cst)
return extended_attention_mask
def get_head_mask(self, head_mask):
if head_mask is not None:
raise NotImplementedError
else:
head_mask = [None] * self.config.num_hidden_layers
return head_mask
@unpack_inputs
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
encoder_hidden_states: np.ndarray | tf.Tensor | None = None,
encoder_attention_mask: np.ndarray | tf.Tensor | None = None,
past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: Optional[bool] = False,
) -> Union[TFBaseModelOutputWithPastAndCrossAttentions, Tuple[tf.Tensor]]:
if not self.config.is_decoder:
use_cache = False
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = shape_list(input_ids)
elif inputs_embeds is not None:
input_shape = shape_list(inputs_embeds)[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
batch_size, seq_length = input_shape
if past_key_values is None:
past_key_values_length = 0
past_key_values = [None] * len(self.encoder.layer)
else:
past_key_values_length = shape_list(past_key_values[0][0])[-2]
if attention_mask is None:
attention_mask = tf.fill(dims=(batch_size, seq_length + past_key_values_length), value=1)
if token_type_ids is None:
token_type_ids = tf.fill(dims=input_shape, value=0)
hidden_states = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
past_key_values_length=past_key_values_length,
training=training,
)
extended_attention_mask = self.get_extended_attention_mask(
attention_mask, input_shape, hidden_states.dtype, past_key_values_length
)
# Copied from `modeling_tf_t5.py` with -1e9 -> -10000
if self.is_decoder and encoder_attention_mask is not None:
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, mask_seq_length, mask_seq_length]
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
encoder_attention_mask = tf.cast(encoder_attention_mask, dtype=extended_attention_mask.dtype)
num_dims_encoder_attention_mask = len(shape_list(encoder_attention_mask))
if num_dims_encoder_attention_mask == 3:
encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :]
if num_dims_encoder_attention_mask == 2:
encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow/transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = tf.math.equal(encoder_extended_attention_mask,
# tf.transpose(encoder_extended_attention_mask, perm=(-1, -2)))
encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -10000.0
else:
encoder_extended_attention_mask = None
head_mask = self.get_head_mask(head_mask)
if hasattr(self, "embeddings_project"):
hidden_states = self.embeddings_project(hidden_states, training=training)
hidden_states = self.encoder(
hidden_states=hidden_states,
attention_mask=extended_attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_extended_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
return hidden_states
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "embeddings", None) is not None:
with tf.name_scope(self.embeddings.name):
self.embeddings.build(None)
if getattr(self, "encoder", None) is not None:
with tf.name_scope(self.encoder.name):
self.encoder.build(None)
if getattr(self, "embeddings_project", None) is not None:
with tf.name_scope(self.embeddings_project.name):
self.embeddings_project.build([None, None, self.config.embedding_size])
@dataclass
class TFElectraForPreTrainingOutput(ModelOutput):
"""
Output type of [`TFElectraForPreTraining`].
Args:
loss (*optional*, returned when `labels` is provided, `tf.Tensor` of shape `(1,)`):
Total loss of the ELECTRA objective.
logits (`tf.Tensor` of shape `(batch_size, sequence_length)`):
Prediction scores of the head (scores for each token before SoftMax).
hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
logits: tf.Tensor = None
hidden_states: Tuple[tf.Tensor] | None = None
attentions: Tuple[tf.Tensor] | None = None
ELECTRA_START_DOCSTRING = r"""
This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.
<Tip>
TensorFlow models and layers in `transformers` accept two formats as input:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
- a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
`model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
Note that when creating models and layers with
[subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
about any of this, as you can just pass inputs like you would to any other Python function!
</Tip>
Parameters:
config ([`ElectraConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
ELECTRA_INPUTS_DOCSTRING = r"""
Args:
input_ids (`Numpy array` or `tf.Tensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
[`PreTrainedTokenizer.encode`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
position_ids (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
head_mask (`Numpy array` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`tf.Tensor` of shape `({0}, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
training (`bool`, *optional*, defaults to `False`):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
@add_start_docstrings(
"The bare Electra Model transformer outputting raw hidden-states without any specific head on top. Identical to "
"the BERT model except that it uses an additional linear layer between the embedding layer and the encoder if the "
"hidden size and embedding size are different. "
""
"Both the generator and discriminator checkpoints may be loaded into this model.",
ELECTRA_START_DOCSTRING,
)
class TFElectraModel(TFElectraPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.electra = TFElectraMainLayer(config, name="electra")
@unpack_inputs
@add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFBaseModelOutputWithPastAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
encoder_hidden_states: np.ndarray | tf.Tensor | None = None,
encoder_attention_mask: np.ndarray | tf.Tensor | None = None,
past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: Optional[bool] = False,
) -> Union[TFBaseModelOutputWithPastAndCrossAttentions, Tuple[tf.Tensor]]:
r"""
encoder_hidden_states (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`)
contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
use_cache (`bool`, *optional*, defaults to `True`):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`). Set to `False` during training, `True` during generation
"""
outputs = self.electra(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "electra", None) is not None:
with tf.name_scope(self.electra.name):
self.electra.build(None)
@add_start_docstrings(
"""
Electra model with a binary classification head on top as used during pretraining for identifying generated tokens.
Even though both the discriminator and generator may be loaded into this model, the discriminator is the only model
of the two to have the correct classification head to be used for this model.
""",
ELECTRA_START_DOCSTRING,
)
class TFElectraForPreTraining(TFElectraPreTrainedModel):
def __init__(self, config, **kwargs):
super().__init__(config, **kwargs)
self.electra = TFElectraMainLayer(config, name="electra")
self.discriminator_predictions = TFElectraDiscriminatorPredictions(config, name="discriminator_predictions")
@unpack_inputs
@add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TFElectraForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: Optional[bool] = False,
) -> Union[TFElectraForPreTrainingOutput, Tuple[tf.Tensor]]:
r"""
Returns:
Examples:
```python
>>> import tensorflow as tf
>>> from transformers import AutoTokenizer, TFElectraForPreTraining
>>> tokenizer = AutoTokenizer.from_pretrained("google/electra-small-discriminator")
>>> model = TFElectraForPreTraining.from_pretrained("google/electra-small-discriminator")
>>> input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
>>> outputs = model(input_ids)
>>> scores = outputs[0]
```"""
discriminator_hidden_states = self.electra(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
discriminator_sequence_output = discriminator_hidden_states[0]
logits = self.discriminator_predictions(discriminator_sequence_output)
if not return_dict:
return (logits,) + discriminator_hidden_states[1:]
return TFElectraForPreTrainingOutput(
logits=logits,
hidden_states=discriminator_hidden_states.hidden_states,
attentions=discriminator_hidden_states.attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "electra", None) is not None:
with tf.name_scope(self.electra.name):
self.electra.build(None)
if getattr(self, "discriminator_predictions", None) is not None:
with tf.name_scope(self.discriminator_predictions.name):
self.discriminator_predictions.build(None)
class TFElectraMaskedLMHead(keras.layers.Layer):
def __init__(self, config, input_embeddings, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embedding_size = config.embedding_size
self.input_embeddings = input_embeddings
def build(self, input_shape):
self.bias = self.add_weight(shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="bias")
super().build(input_shape)
def get_output_embeddings(self):
return self.input_embeddings
def set_output_embeddings(self, value):
self.input_embeddings.weight = value
self.input_embeddings.vocab_size = shape_list(value)[0]
def get_bias(self):
return {"bias": self.bias}
def set_bias(self, value):
self.bias = value["bias"]
self.config.vocab_size = shape_list(value["bias"])[0]
def call(self, hidden_states):
seq_length = shape_list(tensor=hidden_states)[1]
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.embedding_size])
hidden_states = tf.matmul(a=hidden_states, b=self.input_embeddings.weight, transpose_b=True)
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.config.vocab_size])
hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias)
return hidden_states
@add_start_docstrings(
"""
Electra model with a language modeling head on top.
Even though both the discriminator and generator may be loaded into this model, the generator is the only model of
the two to have been trained for the masked language modeling task.
""",
ELECTRA_START_DOCSTRING,
)
class TFElectraForMaskedLM(TFElectraPreTrainedModel, TFMaskedLanguageModelingLoss):
def __init__(self, config, **kwargs):
super().__init__(config, **kwargs)
self.config = config
self.electra = TFElectraMainLayer(config, name="electra")
self.generator_predictions = TFElectraGeneratorPredictions(config, name="generator_predictions")
if isinstance(config.hidden_act, str):
self.activation = get_tf_activation(config.hidden_act)
else:
self.activation = config.hidden_act
self.generator_lm_head = TFElectraMaskedLMHead(config, self.electra.embeddings, name="generator_lm_head")
def get_lm_head(self):
return self.generator_lm_head
def get_prefix_bias_name(self):
warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning)
return self.name + "/" + self.generator_lm_head.name
@unpack_inputs
@add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint="google/electra-small-generator",
output_type=TFMaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
mask="[MASK]",
expected_output="'paris'",
expected_loss=1.22,
)
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: np.ndarray | tf.Tensor | None = None,
training: Optional[bool] = False,
) -> Union[TFMaskedLMOutput, Tuple[tf.Tensor]]:
r"""
labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
"""
generator_hidden_states = self.electra(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
generator_sequence_output = generator_hidden_states[0]
prediction_scores = self.generator_predictions(generator_sequence_output, training=training)
prediction_scores = self.generator_lm_head(prediction_scores, training=training)
loss = None if labels is None else self.hf_compute_loss(labels, prediction_scores)
if not return_dict:
output = (prediction_scores,) + generator_hidden_states[1:]
return ((loss,) + output) if loss is not None else output
return TFMaskedLMOutput(
loss=loss,
logits=prediction_scores,
hidden_states=generator_hidden_states.hidden_states,
attentions=generator_hidden_states.attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "electra", None) is not None:
with tf.name_scope(self.electra.name):
self.electra.build(None)
if getattr(self, "generator_predictions", None) is not None:
with tf.name_scope(self.generator_predictions.name):
self.generator_predictions.build(None)
if getattr(self, "generator_lm_head", None) is not None:
with tf.name_scope(self.generator_lm_head.name):
self.generator_lm_head.build(None)
class TFElectraClassificationHead(keras.layers.Layer):
"""Head for sentence-level classification tasks."""
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
classifier_dropout = (
config.classifhidden_dropout_probier_dropout
if config.classifier_dropout is not None
else config.hidden_dropout_prob
)
self.dropout = keras.layers.Dropout(classifier_dropout)
self.out_proj = keras.layers.Dense(
config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="out_proj"
)
self.config = config
def call(self, inputs, **kwargs):
x = inputs[:, 0, :] # take <s> token (equiv. to [CLS])
x = self.dropout(x)
x = self.dense(x)
x = get_tf_activation("gelu")(x) # although BERT uses tanh here, it seems Electra authors used gelu here
x = self.dropout(x)
x = self.out_proj(x)
return x
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "dense", None) is not None:
with tf.name_scope(self.dense.name):
self.dense.build([None, None, self.config.hidden_size])
if getattr(self, "out_proj", None) is not None:
with tf.name_scope(self.out_proj.name):
self.out_proj.build([None, None, self.config.hidden_size])
@add_start_docstrings(
"""
ELECTRA Model transformer with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks.
""",
ELECTRA_START_DOCSTRING,
)
class TFElectraForSequenceClassification(TFElectraPreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.electra = TFElectraMainLayer(config, name="electra")
self.classifier = TFElectraClassificationHead(config, name="classifier")
@unpack_inputs
@add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint="bhadresh-savani/electra-base-emotion",
output_type=TFSequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_output="'joy'",
expected_loss=0.06,
)
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: np.ndarray | tf.Tensor | None = None,
training: Optional[bool] = False,
) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
r"""
labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
outputs = self.electra(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
logits = self.classifier(outputs[0])
loss = None if labels is None else self.hf_compute_loss(labels, logits)
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "electra", None) is not None:
with tf.name_scope(self.electra.name):
self.electra.build(None)
if getattr(self, "classifier", None) is not None:
with tf.name_scope(self.classifier.name):
self.classifier.build(None)
@add_start_docstrings(
"""
ELECTRA Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
softmax) e.g. for RocStories/SWAG tasks.
""",
ELECTRA_START_DOCSTRING,
)
class TFElectraForMultipleChoice(TFElectraPreTrainedModel, TFMultipleChoiceLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.electra = TFElectraMainLayer(config, name="electra")
self.sequence_summary = TFSequenceSummary(
config, initializer_range=config.initializer_range, name="sequence_summary"
)
self.classifier = keras.layers.Dense(
1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
)
self.config = config
@unpack_inputs
@add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFMultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: np.ndarray | tf.Tensor | None = None,
training: Optional[bool] = False,
) -> Union[TFMultipleChoiceModelOutput, Tuple[tf.Tensor]]:
r"""
labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):
Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]`
where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above)
"""
if input_ids is not None:
num_choices = shape_list(input_ids)[1]
seq_length = shape_list(input_ids)[2]
else:
num_choices = shape_list(inputs_embeds)[1]
seq_length = shape_list(inputs_embeds)[2]
flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None
flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None
flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) if token_type_ids is not None else None
flat_position_ids = tf.reshape(position_ids, (-1, seq_length)) if position_ids is not None else None
flat_inputs_embeds = (
tf.reshape(inputs_embeds, (-1, seq_length, shape_list(inputs_embeds)[3]))
if inputs_embeds is not None
else None
)
outputs = self.electra(
input_ids=flat_input_ids,
attention_mask=flat_attention_mask,
token_type_ids=flat_token_type_ids,
position_ids=flat_position_ids,
head_mask=head_mask,
inputs_embeds=flat_inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
logits = self.sequence_summary(outputs[0])
logits = self.classifier(logits)
reshaped_logits = tf.reshape(logits, (-1, num_choices))
loss = None if labels is None else self.hf_compute_loss(labels, reshaped_logits)
if not return_dict:
output = (reshaped_logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return TFMultipleChoiceModelOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "electra", None) is not None:
with tf.name_scope(self.electra.name):
self.electra.build(None)
if getattr(self, "sequence_summary", None) is not None:
with tf.name_scope(self.sequence_summary.name):
self.sequence_summary.build(None)
if getattr(self, "classifier", None) is not None:
with tf.name_scope(self.classifier.name):
self.classifier.build([None, None, self.config.hidden_size])
@add_start_docstrings(
"""
Electra model with a token classification head on top.
Both the discriminator and generator may be loaded into this model.
""",
ELECTRA_START_DOCSTRING,
)
class TFElectraForTokenClassification(TFElectraPreTrainedModel, TFTokenClassificationLoss):
def __init__(self, config, **kwargs):
super().__init__(config, **kwargs)
self.electra = TFElectraMainLayer(config, name="electra")
classifier_dropout = (
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
)
self.dropout = keras.layers.Dropout(classifier_dropout)
self.classifier = keras.layers.Dense(
config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
)
self.config = config
@unpack_inputs
@add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint="bhadresh-savani/electra-base-discriminator-finetuned-conll03-english",
output_type=TFTokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_output="['B-LOC', 'B-ORG', 'O', 'O', 'O', 'O', 'O', 'B-LOC', 'O', 'B-LOC', 'I-LOC']",
expected_loss=0.11,
)
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: np.ndarray | tf.Tensor | None = None,
training: Optional[bool] = False,
) -> Union[TFTokenClassifierOutput, Tuple[tf.Tensor]]:
r"""
labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
"""
discriminator_hidden_states = self.electra(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
discriminator_sequence_output = discriminator_hidden_states[0]
discriminator_sequence_output = self.dropout(discriminator_sequence_output)
logits = self.classifier(discriminator_sequence_output)
loss = None if labels is None else self.hf_compute_loss(labels, logits)
if not return_dict:
output = (logits,) + discriminator_hidden_states[1:]
return ((loss,) + output) if loss is not None else output
return TFTokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=discriminator_hidden_states.hidden_states,
attentions=discriminator_hidden_states.attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "electra", None) is not None:
with tf.name_scope(self.electra.name):
self.electra.build(None)
if getattr(self, "classifier", None) is not None:
with tf.name_scope(self.classifier.name):
self.classifier.build([None, None, self.config.hidden_size])
@add_start_docstrings(
"""
Electra Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
""",
ELECTRA_START_DOCSTRING,
)
class TFElectraForQuestionAnswering(TFElectraPreTrainedModel, TFQuestionAnsweringLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.electra = TFElectraMainLayer(config, name="electra")
self.qa_outputs = keras.layers.Dense(
config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
)
self.config = config
@unpack_inputs
@add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint="bhadresh-savani/electra-base-squad2",
output_type=TFQuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
qa_target_start_index=11,
qa_target_end_index=12,
expected_output="'a nice puppet'",
expected_loss=2.64,
)
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
start_positions: np.ndarray | tf.Tensor | None = None,
end_positions: np.ndarray | tf.Tensor | None = None,
training: Optional[bool] = False,
) -> Union[TFQuestionAnsweringModelOutput, Tuple[tf.Tensor]]:
r"""
start_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
"""
discriminator_hidden_states = self.electra(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
discriminator_sequence_output = discriminator_hidden_states[0]
logits = self.qa_outputs(discriminator_sequence_output)
start_logits, end_logits = tf.split(logits, 2, axis=-1)
start_logits = tf.squeeze(start_logits, axis=-1)
end_logits = tf.squeeze(end_logits, axis=-1)
loss = None
if start_positions is not None and end_positions is not None:
labels = {"start_position": start_positions}
labels["end_position"] = end_positions
loss = self.hf_compute_loss(labels, (start_logits, end_logits))
if not return_dict:
output = (
start_logits,
end_logits,
) + discriminator_hidden_states[1:]
return ((loss,) + output) if loss is not None else output
return TFQuestionAnsweringModelOutput(
loss=loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=discriminator_hidden_states.hidden_states,
attentions=discriminator_hidden_states.attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "electra", None) is not None:
with tf.name_scope(self.electra.name):
self.electra.build(None)
if getattr(self, "qa_outputs", None) is not None:
with tf.name_scope(self.qa_outputs.name):
self.qa_outputs.build([None, None, self.config.hidden_size])
| transformers/src/transformers/models/electra/modeling_tf_electra.py/0 | {
"file_path": "transformers/src/transformers/models/electra/modeling_tf_electra.py",
"repo_id": "transformers",
"token_count": 33552
} | 92 |
# Copyright 2023 The HuggingFace and Baidu Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available
_import_structure = {
"configuration_ernie_m": ["ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieMConfig"],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_ernie_m"] = ["ErnieMTokenizer"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_ernie_m"] = [
"ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST",
"ErnieMForMultipleChoice",
"ErnieMForQuestionAnswering",
"ErnieMForSequenceClassification",
"ErnieMForTokenClassification",
"ErnieMModel",
"ErnieMPreTrainedModel",
"ErnieMForInformationExtraction",
]
if TYPE_CHECKING:
from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_ernie_m import ErnieMTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ernie_m import (
ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
ErnieMForInformationExtraction,
ErnieMForMultipleChoice,
ErnieMForQuestionAnswering,
ErnieMForSequenceClassification,
ErnieMForTokenClassification,
ErnieMModel,
ErnieMPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| transformers/src/transformers/models/ernie_m/__init__.py/0 | {
"file_path": "transformers/src/transformers/models/ernie_m/__init__.py",
"repo_id": "transformers",
"token_count": 1020
} | 93 |
# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Constants used in AlphaFold."""
import collections
import copy
import functools
from importlib import resources
from typing import Dict, List, Mapping, Sequence, Tuple
import numpy as np
# Internal import (35fd).
# Distance from one CA to next CA [trans configuration: omega = 180].
ca_ca = 3.80209737096
# Format: The list for each AA type contains chi1, chi2, chi3, chi4 in
# this order (or a relevant subset from chi1 onwards). ALA and GLY don't have
# chi angles so their chi angle lists are empty.
chi_angles_atoms: Dict[str, List[List[str]]] = {
"ALA": [],
# Chi5 in arginine is always 0 +- 5 degrees, so ignore it.
"ARG": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD"], ["CB", "CG", "CD", "NE"], ["CG", "CD", "NE", "CZ"]],
"ASN": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "OD1"]],
"ASP": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "OD1"]],
"CYS": [["N", "CA", "CB", "SG"]],
"GLN": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD"], ["CB", "CG", "CD", "OE1"]],
"GLU": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD"], ["CB", "CG", "CD", "OE1"]],
"GLY": [],
"HIS": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "ND1"]],
"ILE": [["N", "CA", "CB", "CG1"], ["CA", "CB", "CG1", "CD1"]],
"LEU": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD1"]],
"LYS": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD"], ["CB", "CG", "CD", "CE"], ["CG", "CD", "CE", "NZ"]],
"MET": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "SD"], ["CB", "CG", "SD", "CE"]],
"PHE": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD1"]],
"PRO": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD"]],
"SER": [["N", "CA", "CB", "OG"]],
"THR": [["N", "CA", "CB", "OG1"]],
"TRP": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD1"]],
"TYR": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD1"]],
"VAL": [["N", "CA", "CB", "CG1"]],
}
# If chi angles given in fixed-length array, this matrix determines how to mask
# them for each AA type. The order is as per restype_order (see below).
chi_angles_mask: List[List[float]] = [
[0.0, 0.0, 0.0, 0.0], # ALA
[1.0, 1.0, 1.0, 1.0], # ARG
[1.0, 1.0, 0.0, 0.0], # ASN
[1.0, 1.0, 0.0, 0.0], # ASP
[1.0, 0.0, 0.0, 0.0], # CYS
[1.0, 1.0, 1.0, 0.0], # GLN
[1.0, 1.0, 1.0, 0.0], # GLU
[0.0, 0.0, 0.0, 0.0], # GLY
[1.0, 1.0, 0.0, 0.0], # HIS
[1.0, 1.0, 0.0, 0.0], # ILE
[1.0, 1.0, 0.0, 0.0], # LEU
[1.0, 1.0, 1.0, 1.0], # LYS
[1.0, 1.0, 1.0, 0.0], # MET
[1.0, 1.0, 0.0, 0.0], # PHE
[1.0, 1.0, 0.0, 0.0], # PRO
[1.0, 0.0, 0.0, 0.0], # SER
[1.0, 0.0, 0.0, 0.0], # THR
[1.0, 1.0, 0.0, 0.0], # TRP
[1.0, 1.0, 0.0, 0.0], # TYR
[1.0, 0.0, 0.0, 0.0], # VAL
]
# The following chi angles are pi periodic: they can be rotated by a multiple
# of pi without affecting the structure.
chi_pi_periodic: List[List[float]] = [
[0.0, 0.0, 0.0, 0.0], # ALA
[0.0, 0.0, 0.0, 0.0], # ARG
[0.0, 0.0, 0.0, 0.0], # ASN
[0.0, 1.0, 0.0, 0.0], # ASP
[0.0, 0.0, 0.0, 0.0], # CYS
[0.0, 0.0, 0.0, 0.0], # GLN
[0.0, 0.0, 1.0, 0.0], # GLU
[0.0, 0.0, 0.0, 0.0], # GLY
[0.0, 0.0, 0.0, 0.0], # HIS
[0.0, 0.0, 0.0, 0.0], # ILE
[0.0, 0.0, 0.0, 0.0], # LEU
[0.0, 0.0, 0.0, 0.0], # LYS
[0.0, 0.0, 0.0, 0.0], # MET
[0.0, 1.0, 0.0, 0.0], # PHE
[0.0, 0.0, 0.0, 0.0], # PRO
[0.0, 0.0, 0.0, 0.0], # SER
[0.0, 0.0, 0.0, 0.0], # THR
[0.0, 0.0, 0.0, 0.0], # TRP
[0.0, 1.0, 0.0, 0.0], # TYR
[0.0, 0.0, 0.0, 0.0], # VAL
[0.0, 0.0, 0.0, 0.0], # UNK
]
# Atoms positions relative to the 8 rigid groups, defined by the pre-omega, phi,
# psi and chi angles:
# 0: 'backbone group',
# 1: 'pre-omega-group', (empty)
# 2: 'phi-group', (currently empty, because it defines only hydrogens)
# 3: 'psi-group',
# 4,5,6,7: 'chi1,2,3,4-group'
# The atom positions are relative to the axis-end-atom of the corresponding
# rotation axis. The x-axis is in direction of the rotation axis, and the y-axis
# is defined such that the dihedral-angle-definiting atom (the last entry in
# chi_angles_atoms above) is in the xy-plane (with a positive y-coordinate).
# format: [atomname, group_idx, rel_position]
rigid_group_atom_positions: Dict[str, List[Tuple[str, int, Tuple[float, float, float]]]] = {
"ALA": [
("N", 0, (-0.525, 1.363, 0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.526, -0.000, -0.000)),
("CB", 0, (-0.529, -0.774, -1.205)),
("O", 3, (0.627, 1.062, 0.000)),
],
"ARG": [
("N", 0, (-0.524, 1.362, -0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.525, -0.000, -0.000)),
("CB", 0, (-0.524, -0.778, -1.209)),
("O", 3, (0.626, 1.062, 0.000)),
("CG", 4, (0.616, 1.390, -0.000)),
("CD", 5, (0.564, 1.414, 0.000)),
("NE", 6, (0.539, 1.357, -0.000)),
("NH1", 7, (0.206, 2.301, 0.000)),
("NH2", 7, (2.078, 0.978, -0.000)),
("CZ", 7, (0.758, 1.093, -0.000)),
],
"ASN": [
("N", 0, (-0.536, 1.357, 0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.526, -0.000, -0.000)),
("CB", 0, (-0.531, -0.787, -1.200)),
("O", 3, (0.625, 1.062, 0.000)),
("CG", 4, (0.584, 1.399, 0.000)),
("ND2", 5, (0.593, -1.188, 0.001)),
("OD1", 5, (0.633, 1.059, 0.000)),
],
"ASP": [
("N", 0, (-0.525, 1.362, -0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.527, 0.000, -0.000)),
("CB", 0, (-0.526, -0.778, -1.208)),
("O", 3, (0.626, 1.062, -0.000)),
("CG", 4, (0.593, 1.398, -0.000)),
("OD1", 5, (0.610, 1.091, 0.000)),
("OD2", 5, (0.592, -1.101, -0.003)),
],
"CYS": [
("N", 0, (-0.522, 1.362, -0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.524, 0.000, 0.000)),
("CB", 0, (-0.519, -0.773, -1.212)),
("O", 3, (0.625, 1.062, -0.000)),
("SG", 4, (0.728, 1.653, 0.000)),
],
"GLN": [
("N", 0, (-0.526, 1.361, -0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.526, 0.000, 0.000)),
("CB", 0, (-0.525, -0.779, -1.207)),
("O", 3, (0.626, 1.062, -0.000)),
("CG", 4, (0.615, 1.393, 0.000)),
("CD", 5, (0.587, 1.399, -0.000)),
("NE2", 6, (0.593, -1.189, -0.001)),
("OE1", 6, (0.634, 1.060, 0.000)),
],
"GLU": [
("N", 0, (-0.528, 1.361, 0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.526, -0.000, -0.000)),
("CB", 0, (-0.526, -0.781, -1.207)),
("O", 3, (0.626, 1.062, 0.000)),
("CG", 4, (0.615, 1.392, 0.000)),
("CD", 5, (0.600, 1.397, 0.000)),
("OE1", 6, (0.607, 1.095, -0.000)),
("OE2", 6, (0.589, -1.104, -0.001)),
],
"GLY": [
("N", 0, (-0.572, 1.337, 0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.517, -0.000, -0.000)),
("O", 3, (0.626, 1.062, -0.000)),
],
"HIS": [
("N", 0, (-0.527, 1.360, 0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.525, 0.000, 0.000)),
("CB", 0, (-0.525, -0.778, -1.208)),
("O", 3, (0.625, 1.063, 0.000)),
("CG", 4, (0.600, 1.370, -0.000)),
("CD2", 5, (0.889, -1.021, 0.003)),
("ND1", 5, (0.744, 1.160, -0.000)),
("CE1", 5, (2.030, 0.851, 0.002)),
("NE2", 5, (2.145, -0.466, 0.004)),
],
"ILE": [
("N", 0, (-0.493, 1.373, -0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.527, -0.000, -0.000)),
("CB", 0, (-0.536, -0.793, -1.213)),
("O", 3, (0.627, 1.062, -0.000)),
("CG1", 4, (0.534, 1.437, -0.000)),
("CG2", 4, (0.540, -0.785, -1.199)),
("CD1", 5, (0.619, 1.391, 0.000)),
],
"LEU": [
("N", 0, (-0.520, 1.363, 0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.525, -0.000, -0.000)),
("CB", 0, (-0.522, -0.773, -1.214)),
("O", 3, (0.625, 1.063, -0.000)),
("CG", 4, (0.678, 1.371, 0.000)),
("CD1", 5, (0.530, 1.430, -0.000)),
("CD2", 5, (0.535, -0.774, 1.200)),
],
"LYS": [
("N", 0, (-0.526, 1.362, -0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.526, 0.000, 0.000)),
("CB", 0, (-0.524, -0.778, -1.208)),
("O", 3, (0.626, 1.062, -0.000)),
("CG", 4, (0.619, 1.390, 0.000)),
("CD", 5, (0.559, 1.417, 0.000)),
("CE", 6, (0.560, 1.416, 0.000)),
("NZ", 7, (0.554, 1.387, 0.000)),
],
"MET": [
("N", 0, (-0.521, 1.364, -0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.525, 0.000, 0.000)),
("CB", 0, (-0.523, -0.776, -1.210)),
("O", 3, (0.625, 1.062, -0.000)),
("CG", 4, (0.613, 1.391, -0.000)),
("SD", 5, (0.703, 1.695, 0.000)),
("CE", 6, (0.320, 1.786, -0.000)),
],
"PHE": [
("N", 0, (-0.518, 1.363, 0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.524, 0.000, -0.000)),
("CB", 0, (-0.525, -0.776, -1.212)),
("O", 3, (0.626, 1.062, -0.000)),
("CG", 4, (0.607, 1.377, 0.000)),
("CD1", 5, (0.709, 1.195, -0.000)),
("CD2", 5, (0.706, -1.196, 0.000)),
("CE1", 5, (2.102, 1.198, -0.000)),
("CE2", 5, (2.098, -1.201, -0.000)),
("CZ", 5, (2.794, -0.003, -0.001)),
],
"PRO": [
("N", 0, (-0.566, 1.351, -0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.527, -0.000, 0.000)),
("CB", 0, (-0.546, -0.611, -1.293)),
("O", 3, (0.621, 1.066, 0.000)),
("CG", 4, (0.382, 1.445, 0.0)),
# ('CD', 5, (0.427, 1.440, 0.0)),
("CD", 5, (0.477, 1.424, 0.0)), # manually made angle 2 degrees larger
],
"SER": [
("N", 0, (-0.529, 1.360, -0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.525, -0.000, -0.000)),
("CB", 0, (-0.518, -0.777, -1.211)),
("O", 3, (0.626, 1.062, -0.000)),
("OG", 4, (0.503, 1.325, 0.000)),
],
"THR": [
("N", 0, (-0.517, 1.364, 0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.526, 0.000, -0.000)),
("CB", 0, (-0.516, -0.793, -1.215)),
("O", 3, (0.626, 1.062, 0.000)),
("CG2", 4, (0.550, -0.718, -1.228)),
("OG1", 4, (0.472, 1.353, 0.000)),
],
"TRP": [
("N", 0, (-0.521, 1.363, 0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.525, -0.000, 0.000)),
("CB", 0, (-0.523, -0.776, -1.212)),
("O", 3, (0.627, 1.062, 0.000)),
("CG", 4, (0.609, 1.370, -0.000)),
("CD1", 5, (0.824, 1.091, 0.000)),
("CD2", 5, (0.854, -1.148, -0.005)),
("CE2", 5, (2.186, -0.678, -0.007)),
("CE3", 5, (0.622, -2.530, -0.007)),
("NE1", 5, (2.140, 0.690, -0.004)),
("CH2", 5, (3.028, -2.890, -0.013)),
("CZ2", 5, (3.283, -1.543, -0.011)),
("CZ3", 5, (1.715, -3.389, -0.011)),
],
"TYR": [
("N", 0, (-0.522, 1.362, 0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.524, -0.000, -0.000)),
("CB", 0, (-0.522, -0.776, -1.213)),
("O", 3, (0.627, 1.062, -0.000)),
("CG", 4, (0.607, 1.382, -0.000)),
("CD1", 5, (0.716, 1.195, -0.000)),
("CD2", 5, (0.713, -1.194, -0.001)),
("CE1", 5, (2.107, 1.200, -0.002)),
("CE2", 5, (2.104, -1.201, -0.003)),
("OH", 5, (4.168, -0.002, -0.005)),
("CZ", 5, (2.791, -0.001, -0.003)),
],
"VAL": [
("N", 0, (-0.494, 1.373, -0.000)),
("CA", 0, (0.000, 0.000, 0.000)),
("C", 0, (1.527, -0.000, -0.000)),
("CB", 0, (-0.533, -0.795, -1.213)),
("O", 3, (0.627, 1.062, -0.000)),
("CG1", 4, (0.540, 1.429, -0.000)),
("CG2", 4, (0.533, -0.776, 1.203)),
],
}
# A list of atoms (excluding hydrogen) for each AA type. PDB naming convention.
residue_atoms: Dict[str, List[str]] = {
"ALA": ["C", "CA", "CB", "N", "O"],
"ARG": ["C", "CA", "CB", "CG", "CD", "CZ", "N", "NE", "O", "NH1", "NH2"],
"ASP": ["C", "CA", "CB", "CG", "N", "O", "OD1", "OD2"],
"ASN": ["C", "CA", "CB", "CG", "N", "ND2", "O", "OD1"],
"CYS": ["C", "CA", "CB", "N", "O", "SG"],
"GLU": ["C", "CA", "CB", "CG", "CD", "N", "O", "OE1", "OE2"],
"GLN": ["C", "CA", "CB", "CG", "CD", "N", "NE2", "O", "OE1"],
"GLY": ["C", "CA", "N", "O"],
"HIS": ["C", "CA", "CB", "CG", "CD2", "CE1", "N", "ND1", "NE2", "O"],
"ILE": ["C", "CA", "CB", "CG1", "CG2", "CD1", "N", "O"],
"LEU": ["C", "CA", "CB", "CG", "CD1", "CD2", "N", "O"],
"LYS": ["C", "CA", "CB", "CG", "CD", "CE", "N", "NZ", "O"],
"MET": ["C", "CA", "CB", "CG", "CE", "N", "O", "SD"],
"PHE": ["C", "CA", "CB", "CG", "CD1", "CD2", "CE1", "CE2", "CZ", "N", "O"],
"PRO": ["C", "CA", "CB", "CG", "CD", "N", "O"],
"SER": ["C", "CA", "CB", "N", "O", "OG"],
"THR": ["C", "CA", "CB", "CG2", "N", "O", "OG1"],
"TRP": ["C", "CA", "CB", "CG", "CD1", "CD2", "CE2", "CE3", "CZ2", "CZ3", "CH2", "N", "NE1", "O"],
"TYR": ["C", "CA", "CB", "CG", "CD1", "CD2", "CE1", "CE2", "CZ", "N", "O", "OH"],
"VAL": ["C", "CA", "CB", "CG1", "CG2", "N", "O"],
}
# Naming swaps for ambiguous atom names.
# Due to symmetries in the amino acids the naming of atoms is ambiguous in
# 4 of the 20 amino acids.
# (The LDDT paper lists 7 amino acids as ambiguous, but the naming ambiguities
# in LEU, VAL and ARG can be resolved by using the 3d constellations of
# the 'ambiguous' atoms and their neighbours)
# TODO: ^ interpret this
residue_atom_renaming_swaps: Dict[str, Dict[str, str]] = {
"ASP": {"OD1": "OD2"},
"GLU": {"OE1": "OE2"},
"PHE": {"CD1": "CD2", "CE1": "CE2"},
"TYR": {"CD1": "CD2", "CE1": "CE2"},
}
# Van der Waals radii [Angstroem] of the atoms (from Wikipedia)
van_der_waals_radius: Dict[str, float] = {
"C": 1.7,
"N": 1.55,
"O": 1.52,
"S": 1.8,
}
Bond = collections.namedtuple("Bond", ["atom1_name", "atom2_name", "length", "stddev"])
BondAngle = collections.namedtuple(
"BondAngle",
["atom1_name", "atom2_name", "atom3name", "angle_rad", "stddev"],
)
def map_structure_with_atom_order(in_list: list, first_call: bool = True) -> list:
# Maps strings in a nested list structure to their corresponding index in atom_order
if first_call:
in_list = copy.deepcopy(in_list)
for i in range(len(in_list)):
if isinstance(in_list[i], list):
in_list[i] = map_structure_with_atom_order(in_list[i], first_call=False)
elif isinstance(in_list[i], str):
in_list[i] = atom_order[in_list[i]]
else:
raise ValueError("Unexpected type when mapping nested lists!")
return in_list
@functools.lru_cache(maxsize=None)
def load_stereo_chemical_props() -> (
Tuple[
Mapping[str, List[Bond]],
Mapping[str, List[Bond]],
Mapping[str, List[BondAngle]],
]
):
"""Load stereo_chemical_props.txt into a nice structure.
Load literature values for bond lengths and bond angles and translate bond angles into the length of the opposite
edge of the triangle ("residue_virtual_bonds").
Returns:
residue_bonds: dict that maps resname --> list of Bond tuples residue_virtual_bonds: dict that maps resname -->
list of Bond tuples residue_bond_angles: dict that maps resname --> list of BondAngle tuples
"""
# TODO: this file should be downloaded in a setup script
stereo_chemical_props = resources.read_text("openfold.resources", "stereo_chemical_props.txt")
lines_iter = iter(stereo_chemical_props.splitlines())
# Load bond lengths.
residue_bonds: Dict[str, List[Bond]] = {}
next(lines_iter) # Skip header line.
for line in lines_iter:
if line.strip() == "-":
break
bond, resname, bond_length, stddev = line.split()
atom1, atom2 = bond.split("-")
if resname not in residue_bonds:
residue_bonds[resname] = []
residue_bonds[resname].append(Bond(atom1, atom2, float(bond_length), float(stddev)))
residue_bonds["UNK"] = []
# Load bond angles.
residue_bond_angles: Dict[str, List[BondAngle]] = {}
next(lines_iter) # Skip empty line.
next(lines_iter) # Skip header line.
for line in lines_iter:
if line.strip() == "-":
break
bond, resname, angle_degree, stddev_degree = line.split()
atom1, atom2, atom3 = bond.split("-")
if resname not in residue_bond_angles:
residue_bond_angles[resname] = []
residue_bond_angles[resname].append(
BondAngle(
atom1,
atom2,
atom3,
float(angle_degree) / 180.0 * np.pi,
float(stddev_degree) / 180.0 * np.pi,
)
)
residue_bond_angles["UNK"] = []
def make_bond_key(atom1_name: str, atom2_name: str) -> str:
"""Unique key to lookup bonds."""
return "-".join(sorted([atom1_name, atom2_name]))
# Translate bond angles into distances ("virtual bonds").
residue_virtual_bonds: Dict[str, List[Bond]] = {}
for resname, bond_angles in residue_bond_angles.items():
# Create a fast lookup dict for bond lengths.
bond_cache: Dict[str, Bond] = {}
for b in residue_bonds[resname]:
bond_cache[make_bond_key(b.atom1_name, b.atom2_name)] = b
residue_virtual_bonds[resname] = []
for ba in bond_angles:
bond1 = bond_cache[make_bond_key(ba.atom1_name, ba.atom2_name)]
bond2 = bond_cache[make_bond_key(ba.atom2_name, ba.atom3name)]
# Compute distance between atom1 and atom3 using the law of cosines
# c^2 = a^2 + b^2 - 2ab*cos(gamma).
gamma = ba.angle_rad
length = np.sqrt(bond1.length**2 + bond2.length**2 - 2 * bond1.length * bond2.length * np.cos(gamma))
# Propagation of uncertainty assuming uncorrelated errors.
dl_outer = 0.5 / length
dl_dgamma = (2 * bond1.length * bond2.length * np.sin(gamma)) * dl_outer
dl_db1 = (2 * bond1.length - 2 * bond2.length * np.cos(gamma)) * dl_outer
dl_db2 = (2 * bond2.length - 2 * bond1.length * np.cos(gamma)) * dl_outer
stddev = np.sqrt(
(dl_dgamma * ba.stddev) ** 2 + (dl_db1 * bond1.stddev) ** 2 + (dl_db2 * bond2.stddev) ** 2
)
residue_virtual_bonds[resname].append(Bond(ba.atom1_name, ba.atom3name, length, stddev))
return (residue_bonds, residue_virtual_bonds, residue_bond_angles)
# Between-residue bond lengths for general bonds (first element) and for Proline
# (second element).
between_res_bond_length_c_n: Tuple[float, float] = (1.329, 1.341)
between_res_bond_length_stddev_c_n: Tuple[float, float] = (0.014, 0.016)
# Between-residue cos_angles.
between_res_cos_angles_c_n_ca: Tuple[float, float] = (-0.5203, 0.0353) # degrees: 121.352 +- 2.315
between_res_cos_angles_ca_c_n: Tuple[float, float] = (-0.4473, 0.0311) # degrees: 116.568 +- 1.995
# This mapping is used when we need to store atom data in a format that requires
# fixed atom data size for every residue (e.g. a numpy array).
atom_types: List[str] = [
"N",
"CA",
"C",
"CB",
"O",
"CG",
"CG1",
"CG2",
"OG",
"OG1",
"SG",
"CD",
"CD1",
"CD2",
"ND1",
"ND2",
"OD1",
"OD2",
"SD",
"CE",
"CE1",
"CE2",
"CE3",
"NE",
"NE1",
"NE2",
"OE1",
"OE2",
"CH2",
"NH1",
"NH2",
"OH",
"CZ",
"CZ2",
"CZ3",
"NZ",
"OXT",
]
atom_order: Dict[str, int] = {atom_type: i for i, atom_type in enumerate(atom_types)}
atom_type_num = len(atom_types) # := 37.
# A compact atom encoding with 14 columns
# pylint: disable=line-too-long
# pylint: disable=bad-whitespace
restype_name_to_atom14_names: Dict[str, List[str]] = {
"ALA": ["N", "CA", "C", "O", "CB", "", "", "", "", "", "", "", "", ""],
"ARG": ["N", "CA", "C", "O", "CB", "CG", "CD", "NE", "CZ", "NH1", "NH2", "", "", ""],
"ASN": ["N", "CA", "C", "O", "CB", "CG", "OD1", "ND2", "", "", "", "", "", ""],
"ASP": ["N", "CA", "C", "O", "CB", "CG", "OD1", "OD2", "", "", "", "", "", ""],
"CYS": ["N", "CA", "C", "O", "CB", "SG", "", "", "", "", "", "", "", ""],
"GLN": ["N", "CA", "C", "O", "CB", "CG", "CD", "OE1", "NE2", "", "", "", "", ""],
"GLU": ["N", "CA", "C", "O", "CB", "CG", "CD", "OE1", "OE2", "", "", "", "", ""],
"GLY": ["N", "CA", "C", "O", "", "", "", "", "", "", "", "", "", ""],
"HIS": ["N", "CA", "C", "O", "CB", "CG", "ND1", "CD2", "CE1", "NE2", "", "", "", ""],
"ILE": ["N", "CA", "C", "O", "CB", "CG1", "CG2", "CD1", "", "", "", "", "", ""],
"LEU": ["N", "CA", "C", "O", "CB", "CG", "CD1", "CD2", "", "", "", "", "", ""],
"LYS": ["N", "CA", "C", "O", "CB", "CG", "CD", "CE", "NZ", "", "", "", "", ""],
"MET": ["N", "CA", "C", "O", "CB", "CG", "SD", "CE", "", "", "", "", "", ""],
"PHE": ["N", "CA", "C", "O", "CB", "CG", "CD1", "CD2", "CE1", "CE2", "CZ", "", "", ""],
"PRO": ["N", "CA", "C", "O", "CB", "CG", "CD", "", "", "", "", "", "", ""],
"SER": ["N", "CA", "C", "O", "CB", "OG", "", "", "", "", "", "", "", ""],
"THR": ["N", "CA", "C", "O", "CB", "OG1", "CG2", "", "", "", "", "", "", ""],
"TRP": ["N", "CA", "C", "O", "CB", "CG", "CD1", "CD2", "NE1", "CE2", "CE3", "CZ2", "CZ3", "CH2"],
"TYR": ["N", "CA", "C", "O", "CB", "CG", "CD1", "CD2", "CE1", "CE2", "CZ", "OH", "", ""],
"VAL": ["N", "CA", "C", "O", "CB", "CG1", "CG2", "", "", "", "", "", "", ""],
"UNK": ["", "", "", "", "", "", "", "", "", "", "", "", "", ""],
}
# pylint: enable=line-too-long
# pylint: enable=bad-whitespace
# This is the standard residue order when coding AA type as a number.
# Reproduce it by taking 3-letter AA codes and sorting them alphabetically.
restypes: List[str] = [
"A",
"R",
"N",
"D",
"C",
"Q",
"E",
"G",
"H",
"I",
"L",
"K",
"M",
"F",
"P",
"S",
"T",
"W",
"Y",
"V",
]
restype_order: Dict[str, int] = {restype: i for i, restype in enumerate(restypes)}
restype_num = len(restypes) # := 20.
unk_restype_index = restype_num # Catch-all index for unknown restypes.
restypes_with_x: List[str] = restypes + ["X"]
restype_order_with_x: Dict[str, int] = {restype: i for i, restype in enumerate(restypes_with_x)}
def sequence_to_onehot(sequence: str, mapping: Mapping[str, int], map_unknown_to_x: bool = False) -> np.ndarray:
"""Maps the given sequence into a one-hot encoded matrix.
Args:
sequence: An amino acid sequence.
mapping: A dictionary mapping amino acids to integers.
map_unknown_to_x: If True, any amino acid that is not in the mapping will be
mapped to the unknown amino acid 'X'. If the mapping doesn't contain amino acid 'X', an error will be thrown.
If False, any amino acid not in the mapping will throw an error.
Returns:
A numpy array of shape (seq_len, num_unique_aas) with one-hot encoding of the sequence.
Raises:
ValueError: If the mapping doesn't contain values from 0 to
num_unique_aas - 1 without any gaps.
"""
num_entries = max(mapping.values()) + 1
if sorted(set(mapping.values())) != list(range(num_entries)):
raise ValueError(
"The mapping must have values from 0 to num_unique_aas-1 without any gaps. Got: %s"
% sorted(mapping.values())
)
one_hot_arr = np.zeros((len(sequence), num_entries), dtype=np.int32)
for aa_index, aa_type in enumerate(sequence):
if map_unknown_to_x:
if aa_type.isalpha() and aa_type.isupper():
aa_id = mapping.get(aa_type, mapping["X"])
else:
raise ValueError(f"Invalid character in the sequence: {aa_type}")
else:
aa_id = mapping[aa_type]
one_hot_arr[aa_index, aa_id] = 1
return one_hot_arr
restype_1to3: Dict[str, str] = {
"A": "ALA",
"R": "ARG",
"N": "ASN",
"D": "ASP",
"C": "CYS",
"Q": "GLN",
"E": "GLU",
"G": "GLY",
"H": "HIS",
"I": "ILE",
"L": "LEU",
"K": "LYS",
"M": "MET",
"F": "PHE",
"P": "PRO",
"S": "SER",
"T": "THR",
"W": "TRP",
"Y": "TYR",
"V": "VAL",
}
# NB: restype_3to1 differs from Bio.PDB.protein_letters_3to1 by being a simple
# 1-to-1 mapping of 3 letter names to one letter names. The latter contains
# many more, and less common, three letter names as keys and maps many of these
# to the same one letter name (including 'X' and 'U' which we don't use here).
restype_3to1: Dict[str, str] = {v: k for k, v in restype_1to3.items()}
# Define a restype name for all unknown residues.
unk_restype = "UNK"
resnames: List[str] = [restype_1to3[r] for r in restypes] + [unk_restype]
resname_to_idx: Dict[str, int] = {resname: i for i, resname in enumerate(resnames)}
# The mapping here uses hhblits convention, so that B is mapped to D, J and O
# are mapped to X, U is mapped to C, and Z is mapped to E. Other than that the
# remaining 20 amino acids are kept in alphabetical order.
# There are 2 non-amino acid codes, X (representing any amino acid) and
# "-" representing a missing amino acid in an alignment. The id for these
# codes is put at the end (20 and 21) so that they can easily be ignored if
# desired.
HHBLITS_AA_TO_ID: Dict[str, int] = {
"A": 0,
"B": 2,
"C": 1,
"D": 2,
"E": 3,
"F": 4,
"G": 5,
"H": 6,
"I": 7,
"J": 20,
"K": 8,
"L": 9,
"M": 10,
"N": 11,
"O": 20,
"P": 12,
"Q": 13,
"R": 14,
"S": 15,
"T": 16,
"U": 1,
"V": 17,
"W": 18,
"X": 20,
"Y": 19,
"Z": 3,
"-": 21,
}
# Partial inversion of HHBLITS_AA_TO_ID.
ID_TO_HHBLITS_AA: Dict[int, str] = {
0: "A",
1: "C", # Also U.
2: "D", # Also B.
3: "E", # Also Z.
4: "F",
5: "G",
6: "H",
7: "I",
8: "K",
9: "L",
10: "M",
11: "N",
12: "P",
13: "Q",
14: "R",
15: "S",
16: "T",
17: "V",
18: "W",
19: "Y",
20: "X", # Includes J and O.
21: "-",
}
restypes_with_x_and_gap: List[str] = restypes + ["X", "-"]
MAP_HHBLITS_AATYPE_TO_OUR_AATYPE: Tuple[int, ...] = tuple(
restypes_with_x_and_gap.index(ID_TO_HHBLITS_AA[i]) for i in range(len(restypes_with_x_and_gap))
)
def _make_standard_atom_mask() -> np.ndarray:
"""Returns [num_res_types, num_atom_types] mask array."""
# +1 to account for unknown (all 0s).
mask = np.zeros([restype_num + 1, atom_type_num], dtype=np.int32)
for restype, restype_letter in enumerate(restypes):
restype_name = restype_1to3[restype_letter]
atom_names = residue_atoms[restype_name]
for atom_name in atom_names:
atom_type = atom_order[atom_name]
mask[restype, atom_type] = 1
return mask
STANDARD_ATOM_MASK = _make_standard_atom_mask()
# A one hot representation for the first and second atoms defining the axis
# of rotation for each chi-angle in each residue.
def chi_angle_atom(atom_index: int) -> np.ndarray:
"""Define chi-angle rigid groups via one-hot representations."""
chi_angles_index = {}
one_hots = []
for k, v in chi_angles_atoms.items():
indices = [atom_types.index(s[atom_index]) for s in v]
indices.extend([-1] * (4 - len(indices)))
chi_angles_index[k] = indices
for r in restypes:
res3 = restype_1to3[r]
one_hot = np.eye(atom_type_num)[chi_angles_index[res3]]
one_hots.append(one_hot)
one_hots.append(np.zeros([4, atom_type_num])) # Add zeros for residue `X`.
one_hot = np.stack(one_hots, axis=0)
one_hot = np.transpose(one_hot, [0, 2, 1])
return one_hot
chi_atom_1_one_hot = chi_angle_atom(1)
chi_atom_2_one_hot = chi_angle_atom(2)
# An array like chi_angles_atoms but using indices rather than names.
chi_angles_atom_indices_list: List[List[List[str]]] = [chi_angles_atoms[restype_1to3[r]] for r in restypes]
chi_angles_atom_indices_ours: list = map_structure_with_atom_order(chi_angles_atom_indices_list)
chi_angles_atom_indices = np.array(
[chi_atoms + ([[0, 0, 0, 0]] * (4 - len(chi_atoms))) for chi_atoms in chi_angles_atom_indices_list]
)
# Mapping from (res_name, atom_name) pairs to the atom's chi group index
# and atom index within that group.
chi_groups_for_atom: Dict[Tuple[str, str], List[Tuple[int, int]]] = collections.defaultdict(list)
for res_name, chi_angle_atoms_for_res in chi_angles_atoms.items():
for chi_group_i, chi_group in enumerate(chi_angle_atoms_for_res):
for atom_i, atom in enumerate(chi_group):
chi_groups_for_atom[(res_name, atom)].append((chi_group_i, atom_i))
chi_groups_for_atom = dict(chi_groups_for_atom)
def _make_rigid_transformation_4x4(ex: np.ndarray, ey: np.ndarray, translation: np.ndarray) -> np.ndarray:
"""Create a rigid 4x4 transformation matrix from two axes and transl."""
# Normalize ex.
ex_normalized = ex / np.linalg.norm(ex)
# make ey perpendicular to ex
ey_normalized = ey - np.dot(ey, ex_normalized) * ex_normalized
ey_normalized /= np.linalg.norm(ey_normalized)
# compute ez as cross product
eznorm = np.cross(ex_normalized, ey_normalized)
m = np.stack([ex_normalized, ey_normalized, eznorm, translation]).transpose()
m = np.concatenate([m, [[0.0, 0.0, 0.0, 1.0]]], axis=0)
return m
# create an array with (restype, atomtype) --> rigid_group_idx
# and an array with (restype, atomtype, coord) for the atom positions
# and compute affine transformation matrices (4,4) from one rigid group to the
# previous group
restype_atom37_to_rigid_group = np.zeros([21, 37], dtype=int)
restype_atom37_mask = np.zeros([21, 37], dtype=np.float32)
restype_atom37_rigid_group_positions = np.zeros([21, 37, 3], dtype=np.float32)
restype_atom14_to_rigid_group = np.zeros([21, 14], dtype=int)
restype_atom14_mask = np.zeros([21, 14], dtype=np.float32)
restype_atom14_rigid_group_positions = np.zeros([21, 14, 3], dtype=np.float32)
restype_rigid_group_default_frame = np.zeros([21, 8, 4, 4], dtype=np.float32)
def _make_rigid_group_constants() -> None:
"""Fill the arrays above."""
for restype, restype_letter in enumerate(restypes):
resname = restype_1to3[restype_letter]
for atomname, group_idx, atom_position in rigid_group_atom_positions[resname]:
atomtype = atom_order[atomname]
restype_atom37_to_rigid_group[restype, atomtype] = group_idx
restype_atom37_mask[restype, atomtype] = 1
restype_atom37_rigid_group_positions[restype, atomtype, :] = atom_position
atom14idx = restype_name_to_atom14_names[resname].index(atomname)
restype_atom14_to_rigid_group[restype, atom14idx] = group_idx
restype_atom14_mask[restype, atom14idx] = 1
restype_atom14_rigid_group_positions[restype, atom14idx, :] = atom_position
for restype, restype_letter in enumerate(restypes):
resname = restype_1to3[restype_letter]
atom_positions: Dict[str, np.ndarray] = {
name: np.array(pos) for name, _, pos in rigid_group_atom_positions[resname]
}
# backbone to backbone is the identity transform
restype_rigid_group_default_frame[restype, 0, :, :] = np.eye(4)
# pre-omega-frame to backbone (currently dummy identity matrix)
restype_rigid_group_default_frame[restype, 1, :, :] = np.eye(4)
# phi-frame to backbone
mat = _make_rigid_transformation_4x4(
ex=atom_positions["N"] - atom_positions["CA"],
ey=np.array([1.0, 0.0, 0.0]),
translation=atom_positions["N"],
)
restype_rigid_group_default_frame[restype, 2, :, :] = mat
# psi-frame to backbone
mat = _make_rigid_transformation_4x4(
ex=atom_positions["C"] - atom_positions["CA"],
ey=atom_positions["CA"] - atom_positions["N"],
translation=atom_positions["C"],
)
restype_rigid_group_default_frame[restype, 3, :, :] = mat
# chi1-frame to backbone
if chi_angles_mask[restype][0]:
base_atom_names = chi_angles_atoms[resname][0]
base_atom_positions = [atom_positions[name] for name in base_atom_names]
mat = _make_rigid_transformation_4x4(
ex=base_atom_positions[2] - base_atom_positions[1],
ey=base_atom_positions[0] - base_atom_positions[1],
translation=base_atom_positions[2],
)
restype_rigid_group_default_frame[restype, 4, :, :] = mat
# chi2-frame to chi1-frame
# chi3-frame to chi2-frame
# chi4-frame to chi3-frame
# luckily all rotation axes for the next frame start at (0,0,0) of the
# previous frame
for chi_idx in range(1, 4):
if chi_angles_mask[restype][chi_idx]:
axis_end_atom_name = chi_angles_atoms[resname][chi_idx][2]
axis_end_atom_position = atom_positions[axis_end_atom_name]
mat = _make_rigid_transformation_4x4(
ex=axis_end_atom_position,
ey=np.array([-1.0, 0.0, 0.0]),
translation=axis_end_atom_position,
)
restype_rigid_group_default_frame[restype, 4 + chi_idx, :, :] = mat
_make_rigid_group_constants()
def make_atom14_dists_bounds(
overlap_tolerance: float = 1.5,
bond_length_tolerance_factor: int = 15,
) -> Dict[str, np.ndarray]:
"""compute upper and lower bounds for bonds to assess violations."""
restype_atom14_bond_lower_bound = np.zeros([21, 14, 14], np.float32)
restype_atom14_bond_upper_bound = np.zeros([21, 14, 14], np.float32)
restype_atom14_bond_stddev = np.zeros([21, 14, 14], np.float32)
residue_bonds, residue_virtual_bonds, _ = load_stereo_chemical_props()
for restype, restype_letter in enumerate(restypes):
resname = restype_1to3[restype_letter]
atom_list = restype_name_to_atom14_names[resname]
# create lower and upper bounds for clashes
for atom1_idx, atom1_name in enumerate(atom_list):
if not atom1_name:
continue
atom1_radius = van_der_waals_radius[atom1_name[0]]
for atom2_idx, atom2_name in enumerate(atom_list):
if (not atom2_name) or atom1_idx == atom2_idx:
continue
atom2_radius = van_der_waals_radius[atom2_name[0]]
lower = atom1_radius + atom2_radius - overlap_tolerance
upper = 1e10
restype_atom14_bond_lower_bound[restype, atom1_idx, atom2_idx] = lower
restype_atom14_bond_lower_bound[restype, atom2_idx, atom1_idx] = lower
restype_atom14_bond_upper_bound[restype, atom1_idx, atom2_idx] = upper
restype_atom14_bond_upper_bound[restype, atom2_idx, atom1_idx] = upper
# overwrite lower and upper bounds for bonds and angles
for b in residue_bonds[resname] + residue_virtual_bonds[resname]:
atom1_idx = atom_list.index(b.atom1_name)
atom2_idx = atom_list.index(b.atom2_name)
lower = b.length - bond_length_tolerance_factor * b.stddev
upper = b.length + bond_length_tolerance_factor * b.stddev
restype_atom14_bond_lower_bound[restype, atom1_idx, atom2_idx] = lower
restype_atom14_bond_lower_bound[restype, atom2_idx, atom1_idx] = lower
restype_atom14_bond_upper_bound[restype, atom1_idx, atom2_idx] = upper
restype_atom14_bond_upper_bound[restype, atom2_idx, atom1_idx] = upper
restype_atom14_bond_stddev[restype, atom1_idx, atom2_idx] = b.stddev
restype_atom14_bond_stddev[restype, atom2_idx, atom1_idx] = b.stddev
return {
"lower_bound": restype_atom14_bond_lower_bound, # shape (21,14,14)
"upper_bound": restype_atom14_bond_upper_bound, # shape (21,14,14)
"stddev": restype_atom14_bond_stddev, # shape (21,14,14)
}
restype_atom14_ambiguous_atoms = np.zeros((21, 14), dtype=np.float32)
restype_atom14_ambiguous_atoms_swap_idx: np.ndarray = np.tile(np.arange(14, dtype=int), (21, 1))
def _make_atom14_ambiguity_feats() -> None:
for res, pairs in residue_atom_renaming_swaps.items():
res_idx = restype_order[restype_3to1[res]]
for atom1, atom2 in pairs.items():
atom1_idx = restype_name_to_atom14_names[res].index(atom1)
atom2_idx = restype_name_to_atom14_names[res].index(atom2)
restype_atom14_ambiguous_atoms[res_idx, atom1_idx] = 1
restype_atom14_ambiguous_atoms[res_idx, atom2_idx] = 1
restype_atom14_ambiguous_atoms_swap_idx[res_idx, atom1_idx] = atom2_idx
restype_atom14_ambiguous_atoms_swap_idx[res_idx, atom2_idx] = atom1_idx
_make_atom14_ambiguity_feats()
def aatype_to_str_sequence(aatype: Sequence[int]) -> str:
return "".join([restypes_with_x[aatype[i]] for i in range(len(aatype))])
| transformers/src/transformers/models/esm/openfold_utils/residue_constants.py/0 | {
"file_path": "transformers/src/transformers/models/esm/openfold_utils/residue_constants.py",
"repo_id": "transformers",
"token_count": 19142
} | 94 |
# coding=utf-8
# Copyright 2019-present CNRS, Facebook Inc. and the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Flaubert configuration"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
logger = logging.get_logger(__name__)
FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"flaubert/flaubert_small_cased": "https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json",
"flaubert/flaubert_base_uncased": "https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json",
"flaubert/flaubert_base_cased": "https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json",
"flaubert/flaubert_large_cased": "https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json",
}
class FlaubertConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`FlaubertModel`] or a [`TFFlaubertModel`]. It is
used to instantiate a FlauBERT model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that of the FlauBERT
[flaubert/flaubert_base_uncased](https://huggingface.co/flaubert/flaubert_base_uncased) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
pre_norm (`bool`, *optional*, defaults to `False`):
Whether to apply the layer normalization before or after the feed forward layer following the attention in
each layer (Vaswani et al., Tensor2Tensor for Neural Machine Translation. 2018)
layerdrop (`float`, *optional*, defaults to 0.0):
Probability to drop layers during training (Fan et al., Reducing Transformer Depth on Demand with
Structured Dropout. ICLR 2020)
vocab_size (`int`, *optional*, defaults to 30145):
Vocabulary size of the FlauBERT model. Defines the number of different tokens that can be represented by
the `inputs_ids` passed when calling [`FlaubertModel`] or [`TFFlaubertModel`].
emb_dim (`int`, *optional*, defaults to 2048):
Dimensionality of the encoder layers and the pooler layer.
n_layer (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
n_head (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for the attention mechanism
gelu_activation (`bool`, *optional*, defaults to `True`):
Whether or not to use a *gelu* activation instead of *relu*.
sinusoidal_embeddings (`bool`, *optional*, defaults to `False`):
Whether or not to use sinusoidal positional embeddings instead of absolute positional embeddings.
causal (`bool`, *optional*, defaults to `False`):
Whether or not the model should behave in a causal manner. Causal models use a triangular attention mask in
order to only attend to the left-side context instead if a bidirectional context.
asm (`bool`, *optional*, defaults to `False`):
Whether or not to use an adaptive log softmax projection layer instead of a linear layer for the prediction
layer.
n_langs (`int`, *optional*, defaults to 1):
The number of languages the model handles. Set to 1 for monolingual models.
use_lang_emb (`bool`, *optional*, defaults to `True`)
Whether to use language embeddings. Some models use additional language embeddings, see [the multilingual
models page](http://huggingface.co/transformers/multilingual.html#xlm-language-embeddings) for information
on how to use them.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
embed_init_std (`float`, *optional*, defaults to 2048^-0.5):
The standard deviation of the truncated_normal_initializer for initializing the embedding matrices.
init_std (`int`, *optional*, defaults to 50257):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices except the
embedding matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
bos_index (`int`, *optional*, defaults to 0):
The index of the beginning of sentence token in the vocabulary.
eos_index (`int`, *optional*, defaults to 1):
The index of the end of sentence token in the vocabulary.
pad_index (`int`, *optional*, defaults to 2):
The index of the padding token in the vocabulary.
unk_index (`int`, *optional*, defaults to 3):
The index of the unknown token in the vocabulary.
mask_index (`int`, *optional*, defaults to 5):
The index of the masking token in the vocabulary.
is_encoder(`bool`, *optional*, defaults to `True`):
Whether or not the initialized model should be a transformer encoder or decoder as seen in Vaswani et al.
summary_type (`string`, *optional*, defaults to "first"):
Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
Has to be one of the following options:
- `"last"`: Take the last token hidden state (like XLNet).
- `"first"`: Take the first token hidden state (like BERT).
- `"mean"`: Take the mean of all tokens hidden states.
- `"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2).
- `"attn"`: Not implemented now, use multi-head attention.
summary_use_proj (`bool`, *optional*, defaults to `True`):
Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
Whether or not to add a projection after the vector extraction.
summary_activation (`str`, *optional*):
Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
Pass `"tanh"` for a tanh activation to the output, any other value will result in no activation.
summary_proj_to_labels (`bool`, *optional*, defaults to `True`):
Used in the sequence classification and multiple choice models.
Whether the projection outputs should have `config.num_labels` or `config.hidden_size` classes.
summary_first_dropout (`float`, *optional*, defaults to 0.1):
Used in the sequence classification and multiple choice models.
The dropout ratio to be used after the projection and activation.
start_n_top (`int`, *optional*, defaults to 5):
Used in the SQuAD evaluation script.
end_n_top (`int`, *optional*, defaults to 5):
Used in the SQuAD evaluation script.
mask_token_id (`int`, *optional*, defaults to 0):
Model agnostic parameter to identify masked tokens when generating text in an MLM context.
lang_id (`int`, *optional*, defaults to 1):
The ID of the language used by the model. This parameter is used when generating text in a given language.
"""
model_type = "flaubert"
attribute_map = {
"hidden_size": "emb_dim",
"num_attention_heads": "n_heads",
"num_hidden_layers": "n_layers",
"n_words": "vocab_size", # For backward compatibility
}
def __init__(
self,
pre_norm=False,
layerdrop=0.0,
vocab_size=30145,
emb_dim=2048,
n_layers=12,
n_heads=16,
dropout=0.1,
attention_dropout=0.1,
gelu_activation=True,
sinusoidal_embeddings=False,
causal=False,
asm=False,
n_langs=1,
use_lang_emb=True,
max_position_embeddings=512,
embed_init_std=2048**-0.5,
layer_norm_eps=1e-12,
init_std=0.02,
bos_index=0,
eos_index=1,
pad_index=2,
unk_index=3,
mask_index=5,
is_encoder=True,
summary_type="first",
summary_use_proj=True,
summary_activation=None,
summary_proj_to_labels=True,
summary_first_dropout=0.1,
start_n_top=5,
end_n_top=5,
mask_token_id=0,
lang_id=0,
pad_token_id=2,
bos_token_id=0,
**kwargs,
):
"""Constructs FlaubertConfig."""
self.pre_norm = pre_norm
self.layerdrop = layerdrop
self.vocab_size = vocab_size
self.emb_dim = emb_dim
self.n_layers = n_layers
self.n_heads = n_heads
self.dropout = dropout
self.attention_dropout = attention_dropout
self.gelu_activation = gelu_activation
self.sinusoidal_embeddings = sinusoidal_embeddings
self.causal = causal
self.asm = asm
self.n_langs = n_langs
self.use_lang_emb = use_lang_emb
self.layer_norm_eps = layer_norm_eps
self.bos_index = bos_index
self.eos_index = eos_index
self.pad_index = pad_index
self.unk_index = unk_index
self.mask_index = mask_index
self.is_encoder = is_encoder
self.max_position_embeddings = max_position_embeddings
self.embed_init_std = embed_init_std
self.init_std = init_std
self.summary_type = summary_type
self.summary_use_proj = summary_use_proj
self.summary_activation = summary_activation
self.summary_proj_to_labels = summary_proj_to_labels
self.summary_first_dropout = summary_first_dropout
self.start_n_top = start_n_top
self.end_n_top = end_n_top
self.mask_token_id = mask_token_id
self.lang_id = lang_id
if "n_words" in kwargs:
self.n_words = kwargs["n_words"]
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, **kwargs)
class FlaubertOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
else:
dynamic_axis = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
]
)
| transformers/src/transformers/models/flaubert/configuration_flaubert.py/0 | {
"file_path": "transformers/src/transformers/models/flaubert/configuration_flaubert.py",
"repo_id": "transformers",
"token_count": 4615
} | 95 |
# coding=utf-8
# Copyright 2021 Google Research, Google AI, Google Brain and the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Tokenization classes for FNet model."""
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model",
"google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model",
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"google/fnet-base": 512,
"google/fnet-large": 512,
}
SPIECE_UNDERLINE = "▁"
class FNetTokenizer(PreTrainedTokenizer):
"""
Construct an FNet tokenizer. Adapted from [`AlbertTokenizer`]. Based on
[SentencePiece](https://github.com/google/sentencepiece). This tokenizer inherits from [`PreTrainedTokenizer`]
which contains most of the main methods. Users should refer to this superclass for more information regarding those
methods.
Args:
vocab_file (`str`):
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
contains the vocabulary necessary to instantiate a tokenizer.
do_lower_case (`bool`, *optional*, defaults to `False`):
Whether or not to lowercase the input when tokenizing.
remove_space (`bool`, *optional*, defaults to `True`):
Whether or not to strip the text when tokenizing (removing excess spaces before and after the string).
keep_accents (`bool`, *optional*, defaults to `True`):
Whether or not to keep accents when tokenizing.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
sep_token (`str`, *optional*, defaults to `"[SEP]"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
cls_token (`str`, *optional*, defaults to `"[CLS]"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
mask_token (`str`, *optional*, defaults to `"[MASK]"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
sp_model_kwargs (`dict`, *optional*):
Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
to set:
- `enable_sampling`: Enable subword regularization.
- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
- `nbest_size = {0,1}`: No sampling is performed.
- `nbest_size > 1`: samples from the nbest_size results.
- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
using forward-filtering-and-backward-sampling algorithm.
- `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
BPE-dropout.
Attributes:
sp_model (`SentencePieceProcessor`):
The *SentencePiece* processor that is used for every conversion (string, tokens and IDs).
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ["input_ids", "token_type_ids"]
def __init__(
self,
vocab_file,
do_lower_case=False,
remove_space=True,
keep_accents=True,
unk_token="<unk>",
sep_token="[SEP]",
pad_token="<pad>",
cls_token="[CLS]",
mask_token="[MASK]",
sp_model_kwargs: Optional[Dict[str, Any]] = None,
**kwargs,
) -> None:
# Mask token behave like a normal word, i.e. include the space before it and
# is included in the raw text, there should be a match in a non-normalized sentence.
mask_token = AddedToken(mask_token, lstrip=True, special=True) if isinstance(mask_token, str) else mask_token
cls_token = AddedToken(cls_token, special=True) if isinstance(cls_token, str) else cls_token
sep_token = AddedToken(sep_token, special=True) if isinstance(sep_token, str) else sep_token
mask_token = AddedToken(mask_token, special=True) if isinstance(mask_token, str) else mask_token
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
self.do_lower_case = do_lower_case
self.remove_space = remove_space
self.keep_accents = keep_accents
self.vocab_file = vocab_file
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(vocab_file)
super().__init__(
do_lower_case=do_lower_case,
remove_space=remove_space,
keep_accents=keep_accents,
unk_token=unk_token,
sep_token=sep_token,
pad_token=pad_token,
cls_token=cls_token,
mask_token=mask_token,
sp_model_kwargs=self.sp_model_kwargs,
**kwargs,
)
@property
def vocab_size(self):
return len(self.sp_model)
def get_vocab(self):
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def __getstate__(self):
state = self.__dict__.copy()
state["sp_model"] = None
return state
def __setstate__(self, d):
self.__dict__ = d
# for backward compatibility
if not hasattr(self, "sp_model_kwargs"):
self.sp_model_kwargs = {}
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(self.vocab_file)
def preprocess_text(self, inputs):
if self.remove_space:
outputs = " ".join(inputs.strip().split())
else:
outputs = inputs
outputs = outputs.replace("``", '"').replace("''", '"')
if not self.keep_accents:
outputs = unicodedata.normalize("NFKD", outputs)
outputs = "".join([c for c in outputs if not unicodedata.combining(c)])
if self.do_lower_case:
outputs = outputs.lower()
return outputs
def _tokenize(self, text: str) -> List[str]:
"""Tokenize a string."""
text = self.preprocess_text(text)
pieces = self.sp_model.encode(text, out_type=str)
new_pieces = []
for piece in pieces:
if len(piece) > 1 and piece[-1] == str(",") and piece[-2].isdigit():
cur_pieces = self.sp_model.EncodeAsPieces(piece[:-1].replace(SPIECE_UNDERLINE, ""))
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0]) == 1:
cur_pieces = cur_pieces[1:]
else:
cur_pieces[0] = cur_pieces[0][1:]
cur_pieces.append(piece[-1])
new_pieces.extend(cur_pieces)
else:
new_pieces.append(piece)
return new_pieces
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
return self.sp_model.PieceToId(token)
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self.sp_model.IdToPiece(index)
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.convert_tokens_to_string
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
current_sub_tokens = []
out_string = ""
prev_is_special = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(current_sub_tokens) + token
prev_is_special = True
current_sub_tokens = []
else:
current_sub_tokens.append(token)
prev_is_special = False
out_string += self.sp_model.decode(current_sub_tokens)
return out_string.strip()
def _decode(
self,
token_ids: List[int],
skip_special_tokens: bool = False,
clean_up_tokenization_spaces: bool = None,
spaces_between_special_tokens: bool = False,
**kwargs,
) -> str:
text = super()._decode(
token_ids=token_ids,
skip_special_tokens=skip_special_tokens,
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
spaces_between_special_tokens=spaces_between_special_tokens,
**kwargs,
)
# Mimic the behavior of the Rust tokenizer:
# No space after <unk>
if not spaces_between_special_tokens:
text = text.replace("<unk> ", "<unk>")
return text
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. An FNet sequence has the following format:
- single sequence: `[CLS] X [SEP]`
- pair of sequences: `[CLS] A [SEP] B [SEP]`
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return cls + token_ids_0 + sep
return cls + token_ids_0 + sep + token_ids_1 + sep
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
if token_ids_1 is not None:
return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
return [1] + ([0] * len(token_ids_0)) + [1]
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. An FNet sequence
pair mask has the following format: :
```
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second sequence |
```
If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
out_vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file, out_vocab_file)
elif not os.path.isfile(self.vocab_file):
with open(out_vocab_file, "wb") as fi:
content_spiece_model = self.sp_model.serialized_model_proto()
fi.write(content_spiece_model)
return (out_vocab_file,)
| transformers/src/transformers/models/fnet/tokenization_fnet.py/0 | {
"file_path": "transformers/src/transformers/models/fnet/tokenization_fnet.py",
"repo_id": "transformers",
"token_count": 6513
} | 96 |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
GIT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/main/config.json",
}
class GitVisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GitVisionModel`]. It is used to instantiate a GIT
vision encoder according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the vision encoder of the GIT
[microsoft/git-base](https://huggingface.co/microsoft/git-base) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
patch_size (`int`, *optional*, defaults to 16):
The size (resolution) of each patch.
hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported.
layer_norm_eps (`float`, *optional*, defaults to 1e-5):
The epsilon used by the layer normalization layers.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
Example:
```python
>>> from transformers import GitVisionConfig, GitVisionModel
>>> # Initializing a GitVisionConfig with microsoft/git-base style configuration
>>> configuration = GitVisionConfig()
>>> # Initializing a GitVisionModel (with random weights) from the microsoft/git-base style configuration
>>> model = GitVisionModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "git_vision_model"
def __init__(
self,
hidden_size=768,
intermediate_size=3072,
num_hidden_layers=12,
num_attention_heads=12,
num_channels=3,
image_size=224,
patch_size=16,
hidden_act="quick_gelu",
layer_norm_eps=1e-5,
attention_dropout=0.0,
initializer_range=0.02,
**kwargs,
):
super().__init__(**kwargs)
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_channels = num_channels
self.patch_size = patch_size
self.image_size = image_size
self.initializer_range = initializer_range
self.attention_dropout = attention_dropout
self.layer_norm_eps = layer_norm_eps
self.hidden_act = hidden_act
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
cls._set_token_in_kwargs(kwargs)
config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
# get the vision config dict if we are loading from GITConfig
if config_dict.get("model_type") == "git":
config_dict = config_dict["vision_config"]
if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
logger.warning(
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
)
return cls.from_dict(config_dict, **kwargs)
class GitConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GitModel`]. It is used to instantiate a GIT model
according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the GIT
[microsoft/git-base](https://huggingface.co/microsoft/git-base) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vision_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`GitVisionConfig`].
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the GIT model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`GitModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 6):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 1024):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
[Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
num_image_with_embedding (`int`, *optional*):
The number of temporal embeddings to add, in case the model is used for video captioning/VQA.
Examples:
```python
>>> from transformers import GitConfig, GitModel
>>> # Initializing a GIT microsoft/git-base style configuration
>>> configuration = GitConfig()
>>> # Initializing a model (with random weights) from the microsoft/git-base style configuration
>>> model = GitModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "git"
def __init__(
self,
vision_config=None,
vocab_size=30522,
hidden_size=768,
num_hidden_layers=6,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=1024,
initializer_range=0.02,
layer_norm_eps=1e-12,
pad_token_id=0,
position_embedding_type="absolute",
use_cache=True,
tie_word_embeddings=False,
bos_token_id=101,
eos_token_id=102,
num_image_with_embedding=None,
**kwargs,
):
super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, pad_token_id=pad_token_id, **kwargs)
if vision_config is None:
vision_config = {}
logger.info("vision_config is None. initializing the GitVisionConfig with default values.")
self.vision_config = GitVisionConfig(**vision_config)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.position_embedding_type = position_embedding_type
self.use_cache = use_cache
self.tie_word_embeddings = tie_word_embeddings
self.num_image_with_embedding = num_image_with_embedding
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
| transformers/src/transformers/models/git/configuration_git.py/0 | {
"file_path": "transformers/src/transformers/models/git/configuration_git.py",
"repo_id": "transformers",
"token_count": 4236
} | 97 |
# coding=utf-8
# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" TF 2.0 OpenAI GPT-2 model."""
from __future__ import annotations
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import (
TFBaseModelOutputWithPastAndCrossAttentions,
TFCausalLMOutputWithCrossAttentions,
TFSequenceClassifierOutputWithPast,
)
from ...modeling_tf_utils import (
TFCausalLanguageModelingLoss,
TFConv1D,
TFModelInputType,
TFPreTrainedModel,
TFSequenceClassificationLoss,
TFSequenceSummary,
get_initializer,
keras,
keras_serializable,
unpack_inputs,
)
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
from ...utils import (
ModelOutput,
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from .configuration_gpt2 import GPT2Config
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "openai-community/gpt2"
_CONFIG_FOR_DOC = "GPT2Config"
TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST = [
"openai-community/gpt2",
"openai-community/gpt2-medium",
"openai-community/gpt2-large",
"openai-community/gpt2-xl",
"distilbert/distilgpt2",
# See all GPT-2 models at https://huggingface.co/models?filter=openai-community/gpt2
]
class TFAttention(keras.layers.Layer):
def __init__(self, nx, config, scale=False, is_cross_attention=False, **kwargs):
super().__init__(**kwargs)
n_state = nx # in Attention: n_state=768 (nx=n_embd)
# [switch nx => n_state from Block to Attention to keep identical to TF implementation]
assert n_state % config.n_head == 0
self.n_head = config.n_head
self.split_size = n_state
self.scale = scale
self.output_attentions = config.output_attentions
self.is_cross_attention = is_cross_attention
if self.is_cross_attention:
self.c_attn = TFConv1D(n_state * 2, nx, initializer_range=config.initializer_range, name="c_attn")
self.q_attn = TFConv1D(n_state, nx, initializer_range=config.initializer_range, name="q_attn")
else:
self.c_attn = TFConv1D(n_state * 3, nx, initializer_range=config.initializer_range, name="c_attn")
self.c_proj = TFConv1D(n_state, nx, initializer_range=config.initializer_range, name="c_proj")
self.attn_dropout = keras.layers.Dropout(config.attn_pdrop)
self.resid_dropout = keras.layers.Dropout(config.resid_pdrop)
self.pruned_heads = set()
self.embed_dim = n_state
def prune_heads(self, heads):
pass
@staticmethod
def causal_attention_mask(nd, ns, dtype):
"""
1's in the lower triangle, counting from the lower right corner. Same as tf.matrix_band_part(tf.ones([nd, ns]),
-1, ns-nd), but doesn't produce garbage on TPUs.
"""
i = tf.range(nd)[:, None]
j = tf.range(ns)
m = i >= j - ns + nd
return tf.cast(m, dtype)
def _attn(self, q, k, v, attention_mask, head_mask, output_attentions, training=False):
# q, k, v have shape [batch, heads, sequence, features]
w = tf.matmul(q, k, transpose_b=True)
if self.scale:
dk = tf.cast(shape_list(k)[-1], dtype=w.dtype) # scale attention_scores
w = w / tf.math.sqrt(dk)
if not self.is_cross_attention:
# if only "normal" attention layer implements causal mask
# w has shape [batch, heads, dst_sequence, src_sequence], where information flows from src to dst.
_, _, nd, ns = shape_list(w)
b = self.causal_attention_mask(nd, ns, dtype=w.dtype)
b = tf.reshape(b, [1, 1, nd, ns])
w = w * b - 1e4 * (1 - b)
if attention_mask is not None:
# Apply the attention mask
attention_mask = tf.cast(attention_mask, dtype=w.dtype)
w = w + attention_mask
w = stable_softmax(w, axis=-1)
w = self.attn_dropout(w, training=training)
# Mask heads if we want to
if head_mask is not None:
w = w * head_mask
outputs = [tf.matmul(w, v)]
if output_attentions:
outputs.append(w)
return outputs
def merge_heads(self, x):
x = tf.transpose(x, [0, 2, 1, 3])
x_shape = shape_list(x)
new_x_shape = x_shape[:-2] + [x_shape[-2] * x_shape[-1]]
return tf.reshape(x, new_x_shape)
def split_heads(self, x):
x_shape = shape_list(x)
new_x_shape = x_shape[:-1] + [self.n_head, x_shape[-1] // self.n_head]
x = tf.reshape(x, new_x_shape)
return tf.transpose(x, (0, 2, 1, 3)) # (batch, head, seq_length, head_features)
def call(
self,
x,
layer_past,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
use_cache,
output_attentions,
training=False,
):
if encoder_hidden_states is not None:
if not hasattr(self, "q_attn"):
raise ValueError(
"If class is used as cross attention, the weights `q_attn` have to be defined. "
"Please make sure to instantiate class with `GPT2Attention(..., is_cross_attention=True)`."
)
query = self.q_attn(x)
kv_out = self.c_attn(encoder_hidden_states)
key, value = tf.split(kv_out, 2, axis=2)
attention_mask = encoder_attention_mask
else:
x = self.c_attn(x)
query, key, value = tf.split(x, 3, axis=2)
query = self.split_heads(query)
key = self.split_heads(key)
value = self.split_heads(value)
if layer_past is not None:
past_key, past_value = tf.unstack(layer_past, axis=0, num=2)
key = tf.concat([past_key, key], axis=-2)
value = tf.concat([past_value, value], axis=-2)
# to cope with keras serialization
if use_cache:
present = tf.stack([key, value], axis=0)
else:
present = (None,)
attn_outputs = self._attn(query, key, value, attention_mask, head_mask, output_attentions, training=training)
a = attn_outputs[0]
a = self.merge_heads(a)
a = self.c_proj(a)
a = self.resid_dropout(a, training=training)
outputs = [a, present] + attn_outputs[1:]
return outputs # a, present, (attentions)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if self.is_cross_attention:
c_attn_shape = 2 * self.embed_dim
else:
c_attn_shape = 3 * self.embed_dim
if getattr(self, "c_proj", None) is not None:
with tf.name_scope(self.c_proj.name):
self.c_proj.build([None, None, self.embed_dim])
if getattr(self, "c_attn", None) is not None:
with tf.name_scope(self.c_attn.name):
self.c_attn.build([None, None, c_attn_shape])
if getattr(self, "q_attn", None) is not None:
with tf.name_scope(self.q_attn.name):
self.q_attn.build([None, None, self.embed_dim])
class TFMLP(keras.layers.Layer):
def __init__(self, n_state, config, **kwargs):
super().__init__(**kwargs)
nx = config.n_embd
self.c_fc = TFConv1D(n_state, nx, initializer_range=config.initializer_range, name="c_fc")
self.c_proj = TFConv1D(nx, n_state, initializer_range=config.initializer_range, name="c_proj")
self.act = get_tf_activation(config.activation_function)
self.dropout = keras.layers.Dropout(config.resid_pdrop)
self.intermediate_size = n_state
self.embed_dim = nx
def call(self, x, training=False):
h = self.act(self.c_fc(x))
h2 = self.c_proj(h)
h2 = self.dropout(h2, training=training)
return h2
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "c_fc", None) is not None:
with tf.name_scope(self.c_fc.name):
self.c_fc.build([None, None, self.intermediate_size])
if getattr(self, "c_proj", None) is not None:
with tf.name_scope(self.c_proj.name):
self.c_proj.build([None, None, self.embed_dim])
class TFBlock(keras.layers.Layer):
def __init__(self, config, scale=False, **kwargs):
super().__init__(**kwargs)
nx = config.n_embd
inner_dim = config.n_inner if config.n_inner is not None else 4 * nx
self.ln_1 = keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="ln_1")
self.attn = TFAttention(nx, config, scale, name="attn")
self.ln_2 = keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="ln_2")
if config.add_cross_attention:
self.crossattention = TFAttention(nx, config, scale, name="crossattention", is_cross_attention=True)
self.ln_cross_attn = keras.layers.LayerNormalization(
epsilon=config.layer_norm_epsilon, name="ln_cross_attn"
)
self.mlp = TFMLP(inner_dim, config, name="mlp")
self.hidden_size = config.hidden_size
def call(
self,
x,
layer_past,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
use_cache,
output_attentions,
training=False,
):
a = self.ln_1(x)
output_attn = self.attn(
a,
layer_past=layer_past,
attention_mask=attention_mask,
head_mask=head_mask,
encoder_hidden_states=None,
encoder_attention_mask=None,
use_cache=use_cache,
output_attentions=output_attentions,
training=training,
)
a = output_attn[0] # output_attn: a, present, (attentions)
outputs = output_attn[1:]
x = x + a
# Cross-Attention Block
if encoder_hidden_states is not None:
# add one self-attention block for cross-attention
if not hasattr(self, "crossattention"):
raise ValueError(
f"If `encoder_hidden_states` are passed, {self} has to be instantiated with "
"cross-attention layers by setting `config.add_cross_attention=True`"
)
ca = self.ln_cross_attn(x)
output_cross_attn = self.crossattention(
ca,
layer_past=None,
attention_mask=attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=False,
output_attentions=output_attentions,
training=training,
)
ca = output_cross_attn[0] # output_attn: a, present, (cross_attentions)
x = x + ca
outputs = outputs + output_cross_attn[2:] # add cross attentions if we output attention weights
m = self.ln_2(x)
m = self.mlp(m, training=training)
x = x + m
outputs = [x] + outputs
return outputs # x, present, (attentions, cross_attentions)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "ln_1", None) is not None:
with tf.name_scope(self.ln_1.name):
self.ln_1.build([None, None, self.hidden_size])
if getattr(self, "attn", None) is not None:
with tf.name_scope(self.attn.name):
self.attn.build(None)
if getattr(self, "ln_2", None) is not None:
with tf.name_scope(self.ln_2.name):
self.ln_2.build([None, None, self.hidden_size])
if getattr(self, "mlp", None) is not None:
with tf.name_scope(self.mlp.name):
self.mlp.build(None)
if getattr(self, "crossattention", None) is not None:
with tf.name_scope(self.crossattention.name):
self.crossattention.build(None)
if getattr(self, "ln_cross_attn", None) is not None:
with tf.name_scope(self.ln_cross_attn.name):
self.ln_cross_attn.build([None, None, self.hidden_size])
@keras_serializable
class TFGPT2MainLayer(keras.layers.Layer):
config_class = GPT2Config
def __init__(self, config, *inputs, **kwargs):
super().__init__(*inputs, **kwargs)
self.config = config
self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states
self.use_cache = config.use_cache
self.return_dict = config.use_return_dict
self.num_hidden_layers = config.n_layer
self.n_embd = config.n_embd
self.n_positions = config.n_positions
self.initializer_range = config.initializer_range
self.wte = keras.layers.Embedding(
input_dim=config.vocab_size,
output_dim=config.hidden_size,
embeddings_initializer=get_initializer(config.initializer_range),
name="wte",
)
self.wpe = keras.layers.Embedding(
input_dim=config.n_positions,
output_dim=config.n_embd,
embeddings_initializer=get_initializer(config.initializer_range),
name="wpe",
)
self.drop = keras.layers.Dropout(config.embd_pdrop)
self.h = [TFBlock(config, scale=True, name=f"h_._{i}") for i in range(config.n_layer)]
self.ln_f = keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="ln_f")
self.embed_dim = config.hidden_size
def get_input_embeddings(self):
return self.wte
def set_input_embeddings(self, new_embeddings):
self.wte = new_embeddings
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
"""
raise NotImplementedError
@unpack_inputs
def call(
self,
input_ids: TFModelInputType | None = None,
past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
encoder_hidden_states: np.ndarray | tf.Tensor | None = None,
encoder_attention_mask: np.ndarray | tf.Tensor | None = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: Optional[bool] = False,
) -> Union[TFBaseModelOutputWithPastAndCrossAttentions, Tuple[tf.Tensor]]:
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = shape_list(input_ids)
input_ids = tf.reshape(input_ids, [-1, input_shape[-1]])
elif inputs_embeds is not None:
input_shape = shape_list(inputs_embeds)[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if past_key_values is None:
past_length = 0
past_key_values = [None] * len(self.h)
else:
past_length = shape_list(past_key_values[0][0])[-2]
if position_ids is None:
position_ids = tf.expand_dims(tf.range(past_length, input_shape[-1] + past_length), axis=0)
if attention_mask is not None:
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
attention_mask_shape = shape_list(attention_mask)
attention_mask = tf.reshape(attention_mask, (attention_mask_shape[0], 1, 1, attention_mask_shape[1]))
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
one_cst = tf.constant(1.0)
attention_mask = tf.cast(attention_mask, dtype=one_cst.dtype)
attention_mask = tf.multiply(tf.subtract(one_cst, attention_mask), tf.constant(-10000.0))
# Copied from `modeling_tf_t5.py` with -1e9 -> -10000
if self.config.add_cross_attention and encoder_attention_mask is not None:
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, mask_seq_length, mask_seq_length]
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
encoder_attention_mask = tf.cast(encoder_attention_mask, dtype=encoder_hidden_states.dtype)
num_dims_encoder_attention_mask = len(shape_list(encoder_attention_mask))
if num_dims_encoder_attention_mask == 3:
encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :]
if num_dims_encoder_attention_mask == 2:
encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow/transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = tf.math.equal(encoder_extended_attention_mask,
# tf.transpose(encoder_extended_attention_mask, perm=(-1, -2)))
encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -10000.0
else:
encoder_extended_attention_mask = None
encoder_attention_mask = encoder_extended_attention_mask
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
if head_mask is not None:
raise NotImplementedError
else:
head_mask = [None] * self.num_hidden_layers
# head_mask = tf.constant([0] * self.num_hidden_layers)
position_ids = tf.reshape(position_ids, [-1, shape_list(position_ids)[-1]])
if inputs_embeds is None:
check_embeddings_within_bounds(input_ids, self.config.vocab_size)
inputs_embeds = self.wte(input_ids)
position_embeds = self.wpe(position_ids)
if token_type_ids is not None:
token_type_ids = tf.reshape(token_type_ids, [-1, shape_list(token_type_ids)[-1]])
token_type_embeds = self.wte(token_type_ids)
else:
token_type_embeds = tf.constant(0.0)
position_embeds = tf.cast(position_embeds, dtype=inputs_embeds.dtype)
token_type_embeds = tf.cast(token_type_embeds, dtype=inputs_embeds.dtype)
hidden_states = inputs_embeds + position_embeds + token_type_embeds
hidden_states = self.drop(hidden_states, training=training)
output_shape = input_shape + [shape_list(hidden_states)[-1]]
presents = () if use_cache else None
all_attentions = () if output_attentions else None
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
all_hidden_states = () if output_hidden_states else None
for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
if output_hidden_states:
all_hidden_states = all_hidden_states + (tf.reshape(hidden_states, output_shape),)
outputs = block(
hidden_states,
layer_past,
attention_mask,
head_mask[i],
encoder_hidden_states,
encoder_attention_mask,
use_cache,
output_attentions,
training=training,
)
hidden_states, present = outputs[:2]
if use_cache:
presents = presents + (present,)
if output_attentions:
all_attentions = all_attentions + (outputs[2],)
if self.config.add_cross_attention and encoder_hidden_states is not None:
all_cross_attentions = all_cross_attentions + (outputs[3],)
hidden_states = self.ln_f(hidden_states)
hidden_states = tf.reshape(hidden_states, output_shape)
# Add last hidden state
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if output_attentions:
# let the number of heads free (-1) so we can extract attention even after head pruning
attention_output_shape = input_shape[:-1] + [-1] + shape_list(all_attentions[0])[-2:]
all_attentions = tuple(tf.reshape(t, attention_output_shape) for t in all_attentions)
if not return_dict:
return tuple(
v
for v in [hidden_states, presents, all_hidden_states, all_attentions, all_cross_attentions]
if v is not None
)
return TFBaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=presents,
hidden_states=all_hidden_states,
attentions=all_attentions,
cross_attentions=all_cross_attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "wte", None) is not None:
with tf.name_scope(self.wte.name):
self.wte.build(None)
if getattr(self, "wpe", None) is not None:
with tf.name_scope(self.wpe.name):
self.wpe.build(None)
if getattr(self, "ln_f", None) is not None:
with tf.name_scope(self.ln_f.name):
self.ln_f.build([None, None, self.embed_dim])
if getattr(self, "h", None) is not None:
for layer in self.h:
with tf.name_scope(layer.name):
layer.build(None)
class TFGPT2PreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GPT2Config
base_model_prefix = "transformer"
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [r"h.\d+.attn.bias", r"h.\d+.crossattention.bias"]
@property
def input_signature(self):
# Although GPT-2 supports token_type_ids in theory, in practice they are rarely used, and the implementation
# means that passing token_type_ids=0 yields different outputs from token_type_ids=None.
# Therefore, we remove the token_type_ids argument by default, even though it would usually be included.
return {
"input_ids": tf.TensorSpec((None, None), tf.int32, name="input_ids"),
"attention_mask": tf.TensorSpec((None, None), tf.int32, name="attention_mask"),
}
@dataclass
class TFGPT2DoubleHeadsModelOutput(ModelOutput):
"""
Base class for outputs of models predicting if two sentences are consecutive or not.
Args:
logits (`tf.Tensor` of shape `(batch_size, num_choices, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
mc_logits (`tf.Tensor` of shape `(batch_size, num_choices)`):
Prediction scores of the multiple choice classification head (scores for each choice before SoftMax).
past_key_values (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads,
sequence_length, embed_size_per_head)`).
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
`past_key_values` input) to speed up sequential decoding.
hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
logits: tf.Tensor = None
mc_logits: tf.Tensor = None
past_key_values: List[tf.Tensor] | None = None
hidden_states: Tuple[tf.Tensor] | None = None
attentions: Tuple[tf.Tensor] | None = None
GPT2_START_DOCSTRING = r"""
This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.
<Tip>
TensorFlow models and layers in `transformers` accept two formats as input:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
- a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
`model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
Note that when creating models and layers with
[subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
about any of this, as you can just pass inputs like you would to any other Python function!
</Tip>
Parameters:
config ([`GPT2Config`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
GPT2_INPUTS_DOCSTRING = r"""
Args:
input_ids (`Numpy array` or `tf.Tensor` of shape `(batch_size, input_ids_length)`):
`input_ids_length` = `sequence_length` if `past_key_values` is `None` else `past_key_values[0].shape[-2]`
(`sequence_length` of input past key value states). Indices of input sequence tokens in the vocabulary.
If `past_key_values` is used, only input IDs that do not have their past calculated should be passed as
`input_ids`.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
[`PreTrainedTokenizer.encode`] for details.
[What are input IDs?](../glossary#input-ids)
past_key_values (`List[tf.Tensor]` of length `config.n_layers`):
Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model (see
`past_key_values` output below). Can be used to speed up sequential decoding. The token ids which have
their past given to this model should not be passed as input ids as they have already been computed.
attention_mask (`tf.Tensor` or `Numpy array` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
If `past_key_values` is used, `attention_mask` needs to contain the masking strategy that was used for
`past_key_values`. In other words, the `attention_mask` always has to have the length:
`len(past_key_values) + len(input_ids)`
[What are attention masks?](../glossary#attention-mask)
token_type_ids (`tf.Tensor` or `Numpy array` of shape `(batch_size, sequence_length)`, *optional*):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
1]`:
- 0 corresponds to a *sentence A* token,
- 1 corresponds to a *sentence B* token.
[What are token type IDs?](../glossary#token-type-ids)
position_ids (`tf.Tensor` or `Numpy array` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
head_mask (`Numpy array` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
training (`bool`, *optional*, defaults to `False`):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
@add_start_docstrings(
"The bare GPT2 Model transformer outputting raw hidden-states without any specific head on top.",
GPT2_START_DOCSTRING,
)
class TFGPT2Model(TFGPT2PreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.transformer = TFGPT2MainLayer(config, name="transformer")
@unpack_inputs
@add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFBaseModelOutputWithPastAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: TFModelInputType | None = None,
past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
encoder_hidden_states: np.ndarray | tf.Tensor | None = None,
encoder_attention_mask: np.ndarray | tf.Tensor | None = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: Optional[bool] = False,
) -> Union[TFBaseModelOutputWithPastAndCrossAttentions, Tuple[tf.Tensor]]:
r"""
encoder_hidden_states (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`)
contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `past` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have
their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
use_cache (`bool`, *optional*, defaults to `True`):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past`). Set to `False` during training, `True` during generation
"""
outputs = self.transformer(
input_ids=input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "transformer", None) is not None:
with tf.name_scope(self.transformer.name):
self.transformer.build(None)
@add_start_docstrings(
"""
The GPT2 Model transformer with a language modeling head on top (linear layer with weights tied to the input
embeddings).
""",
GPT2_START_DOCSTRING,
)
class TFGPT2LMHeadModel(TFGPT2PreTrainedModel, TFCausalLanguageModelingLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.transformer = TFGPT2MainLayer(config, name="transformer")
def get_output_embeddings(self):
return self.get_input_embeddings()
def set_output_embeddings(self, value):
self.set_input_embeddings(value)
def prepare_inputs_for_generation(self, inputs, past_key_values=None, use_cache=None, **kwargs):
token_type_ids = kwargs.get("token_type_ids", None)
# only last token for inputs_ids if past is defined in kwargs
if past_key_values:
inputs = tf.expand_dims(inputs[:, -1], -1)
if token_type_ids is not None:
token_type_ids = tf.expand_dims(token_type_ids[:, -1], -1)
position_ids = kwargs.get("position_ids", None)
attention_mask = kwargs.get("attention_mask", None)
if attention_mask is not None and position_ids is None:
position_ids = tf.math.cumsum(attention_mask, axis=-1, exclusive=True)
if past_key_values:
position_ids = tf.expand_dims(position_ids[:, -1], -1)
return {
"input_ids": inputs,
"attention_mask": attention_mask,
"position_ids": position_ids,
"past_key_values": past_key_values,
"use_cache": use_cache,
"token_type_ids": token_type_ids,
}
@unpack_inputs
@add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFCausalLMOutputWithCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: TFModelInputType | None = None,
past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
encoder_hidden_states: np.ndarray | tf.Tensor | None = None,
encoder_attention_mask: np.ndarray | tf.Tensor | None = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: np.ndarray | tf.Tensor | None = None,
training: Optional[bool] = False,
) -> Union[TFCausalLMOutputWithCrossAttentions, Tuple[tf.Tensor]]:
r"""
encoder_hidden_states (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`)
contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `past` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have
their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
use_cache (`bool`, *optional*, defaults to `True`):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past`). Set to `False` during training, `True` during generation
labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the cross entropy classification loss. Indices should be in `[0, ...,
config.vocab_size - 1]`.
"""
transformer_outputs = self.transformer(
input_ids=input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
hidden_states = transformer_outputs[0]
logits = tf.matmul(hidden_states, self.transformer.wte.weights, transpose_b=True)
loss = None
if labels is not None:
# shift labels to the left and cut last logit token
shifted_logits = logits[:, :-1]
labels = labels[:, 1:]
loss = self.hf_compute_loss(labels, shifted_logits)
if not return_dict:
output = (logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return TFCausalLMOutputWithCrossAttentions(
loss=loss,
logits=logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
cross_attentions=transformer_outputs.cross_attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "transformer", None) is not None:
with tf.name_scope(self.transformer.name):
self.transformer.build(None)
@add_start_docstrings(
"""
The GPT2 Model transformer with a language modeling and a multiple-choice classification head on top e.g. for
RocStories/SWAG tasks. The two heads are two linear layers. The language modeling head has its weights tied to the
input embeddings, the classification head takes as input the input of a specified classification token index in the
input sequence).
""",
GPT2_START_DOCSTRING,
)
class TFGPT2DoubleHeadsModel(TFGPT2PreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
config.num_labels = 1
self.transformer = TFGPT2MainLayer(config, name="transformer")
self.multiple_choice_head = TFSequenceSummary(
config, initializer_range=config.initializer_range, name="multiple_choice_head"
)
@unpack_inputs
@add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFGPT2DoubleHeadsModelOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids: TFModelInputType | None = None,
past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
mc_token_ids: np.ndarray | tf.Tensor | None = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: Optional[bool] = False,
) -> Union[TFGPT2DoubleHeadsModelOutput, Tuple[tf.Tensor]]:
r"""
mc_token_ids (`tf.Tensor` or `Numpy array` of shape `(batch_size, num_choices)`, *optional*, default to index of the last token of the input):
Index of the classification token in each input sequence. Selected in the range `[0, input_ids.size(-1) -
1]`.
Return:
Examples:
```python
>>> import tensorflow as tf
>>> from transformers import AutoTokenizer, TFGPT2DoubleHeadsModel
>>> tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
>>> model = TFGPT2DoubleHeadsModel.from_pretrained("openai-community/gpt2")
>>> # Add a [CLS] to the vocabulary (we should train it also!)
>>> num_added_tokens = tokenizer.add_special_tokens({"cls_token": "[CLS]"})
>>> embedding_layer = model.resize_token_embeddings(
... len(tokenizer)
... ) # Update the model embeddings with the new vocabulary size
>>> choices = ["Hello, my dog is cute [CLS]", "Hello, my cat is cute [CLS]"]
>>> encoded_choices = [tokenizer.encode(s) for s in choices]
>>> cls_token_location = [tokens.index(tokenizer.cls_token_id) for tokens in encoded_choices]
>>> input_ids = tf.constant(encoded_choices)[None, :] # Batch size: 1, number of choices: 2
>>> mc_token_ids = tf.constant([cls_token_location]) # Batch size: 1
>>> outputs = model(input_ids, mc_token_ids=mc_token_ids)
>>> lm_prediction_scores, mc_prediction_scores = outputs[:2]
```"""
if input_ids is not None:
input_shapes = shape_list(input_ids)
else:
input_shapes = shape_list(inputs_embeds)[:-1]
seq_length = input_shapes[-1]
flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None
flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None
flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) if token_type_ids is not None else None
flat_position_ids = tf.reshape(position_ids, (-1, seq_length)) if position_ids is not None else None
transformer_outputs = self.transformer(
input_ids=flat_input_ids,
past_key_values=past_key_values,
attention_mask=flat_attention_mask,
token_type_ids=flat_token_type_ids,
position_ids=flat_position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=None,
encoder_attention_mask=None,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
hidden_states = transformer_outputs[0]
hidden_states = tf.reshape(hidden_states, input_shapes + shape_list(hidden_states)[-1:])
if return_dict and output_hidden_states:
# We do this to match the slightly odd PT behaviour - the final hidden state is reshaped to rank 4 when the
# input is rank 3, but all other hidden states remain at rank-3 (with the first 2 dims merged)
all_hidden_states = transformer_outputs.hidden_states[:-1] + (hidden_states,)
else:
all_hidden_states = None
lm_logits = tf.matmul(hidden_states, self.transformer.wte.weights, transpose_b=True)
mc_logits = self.multiple_choice_head(hidden_states, mc_token_ids, training=training)
mc_logits = tf.squeeze(mc_logits, axis=-1)
if not return_dict:
return (lm_logits, mc_logits) + transformer_outputs[1:]
return TFGPT2DoubleHeadsModelOutput(
logits=lm_logits,
mc_logits=mc_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=all_hidden_states,
attentions=transformer_outputs.attentions,
)
@property
def input_signature(self):
return {
"input_ids": tf.TensorSpec((None, None, None), tf.int32, name="input_ids"),
"attention_mask": tf.TensorSpec((None, None, None), tf.int32, name="attention_mask"),
"mc_token_ids": tf.TensorSpec((None, None), tf.int32, name="mc_token_ids"),
}
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "transformer", None) is not None:
with tf.name_scope(self.transformer.name):
self.transformer.build(None)
if getattr(self, "multiple_choice_head", None) is not None:
with tf.name_scope(self.multiple_choice_head.name):
self.multiple_choice_head.build(None)
@add_start_docstrings(
"""
The GPT2 Model transformer with a sequence classification head on top (linear layer).
[`TFGPT2ForSequenceClassification`] uses the last token in order to do the classification, as other causal models
(e.g. GPT-1) do.
Since it does classification on the last token, it requires to know the position of the last token. If a
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
each row of the batch).
""",
GPT2_START_DOCSTRING,
)
class TFGPT2ForSequenceClassification(TFGPT2PreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.score = keras.layers.Dense(
config.num_labels,
kernel_initializer=get_initializer(config.initializer_range),
name="score",
use_bias=False,
)
self.transformer = TFGPT2MainLayer(config, name="transformer")
self.config = config
@unpack_inputs
@add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint="microsoft/DialogRPT-updown",
output_type=TFSequenceClassifierOutputWithPast,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: TFModelInputType | None = None,
past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: np.ndarray | tf.Tensor | None = None,
training: Optional[bool] = False,
) -> Union[TFSequenceClassifierOutputWithPast, Tuple[tf.Tensor]]:
r"""
labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the cross entropy classification loss. Indices should be in `[0, ...,
config.vocab_size - 1]`.
"""
transformer_outputs = self.transformer(
input_ids=input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
hidden_states = transformer_outputs[0]
logits = self.score(hidden_states)
logits_shape = shape_list(logits)
in_logits = None
if self.config.pad_token_id is None:
sequence_lengths = -1
else:
if input_ids is not None:
sequence_lengths = (
tf.argmax(tf.cast(tf.math.equal(input_ids, self.config.pad_token_id), input_ids.dtype), axis=-1)
- 1
)
sequence_lengths = tf.where(sequence_lengths >= 0, sequence_lengths, input_ids.shape[-1] - 1)
in_logits = tf.gather(logits, sequence_lengths, batch_dims=1, axis=1)
else:
sequence_lengths = -1
logger.warning(
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
)
loss = None
if labels is not None:
assert (
self.config.pad_token_id is not None or logits_shape[0] == 1
), "Cannot handle batch sizes > 1 if no padding token is defined."
if not tf.is_tensor(sequence_lengths):
in_logits = logits[0 : logits_shape[0], sequence_lengths]
loss = self.hf_compute_loss(tf.reshape(labels, [-1]), tf.reshape(in_logits, [-1, self.num_labels]))
pooled_logits = in_logits if in_logits is not None else logits
if not return_dict:
output = (pooled_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "score", None) is not None:
with tf.name_scope(self.score.name):
self.score.build([None, None, self.config.n_embd])
if getattr(self, "transformer", None) is not None:
with tf.name_scope(self.transformer.name):
self.transformer.build(None)
| transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py/0 | {
"file_path": "transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py",
"repo_id": "transformers",
"token_count": 24717
} | 98 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization classes for GPTSANJapanese."""
import collections
import json
import os
import re
from typing import List, Optional, Tuple, Union
import numpy as np
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import (
BatchEncoding,
PreTokenizedInput,
PreTokenizedInputPair,
TextInput,
TextInputPair,
TruncationStrategy,
)
from ...utils import PaddingStrategy, logging
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt", "emoji_file": "emoji.json"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"Tanrei/GPTSAN-japanese": "https://huggingface.co/Tanrei/GPTSAN-japanese/blob/main/vocab.txt",
},
"emoji_file": {
"Tanrei/GPTSAN-japanese": "https://huggingface.co/Tanrei/GPTSAN-japanese/blob/main/emoji.json",
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"Tanrei/GPTSAN-japanese": 1280,
}
def load_vocab_and_emoji(vocab_file, emoji_file):
"""Loads a vocabulary file and emoji file into a dictionary."""
with open(emoji_file, "r", encoding="utf-8") as f:
emoji = json.loads(f.read())
vocab = collections.OrderedDict()
raw_vocab = collections.OrderedDict()
ids_to_tokens = collections.OrderedDict()
with open(vocab_file, "r", encoding="utf-8") as f:
token = f.readlines()
token = [[t.rstrip("\n")] if (t == ",\n" or "," not in t) else t.rstrip("\n").split(",") for t in token]
for idx, b in enumerate(token):
ids_to_tokens[idx] = b
raw_vocab[",".join(b)] = idx
for wd in b:
vocab[wd] = idx
return vocab, raw_vocab, ids_to_tokens, emoji
class GPTSanJapaneseTokenizer(PreTrainedTokenizer):
"""
This tokenizer is based on GPTNeoXJapaneseTokenizer and has the following modifications
- Decoding byte0~byte255 tokens correctly
- Added bagofword token handling
- Return token_type_ids for Prefix-LM model
The bagofword token represents a repetition of the previous token and is converted to 3 consecutive tokens when
decoding In addition, the original Japanese special Sub-Word-Encoding has been released in this repository
(https://github.com/tanreinama/Japanese-BPEEncoder_V2). The token_type_ids is a mask indicating the prefix input
position of the Prefix-LM model. To specify a prefix position, specify a prefix input for prefix_text, or specify a
sentence of the prefix part and the part after it as a text pair of batch input.
Example:
```python
>>> from transformers import GPTSanJapaneseTokenizer
>>> tokenizer = GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese")
>>> # You can confirm both 慶応 and 慶應 are encoded to 17750
>>> tokenizer("吾輩は猫である🐯。実は慶応(慶應)大学出身")["input_ids"]
[35993, 35998, 34347, 31459, 30647, 31448, 25, 30659, 35729, 35676, 32417, 30647, 17750, 35589, 17750, 35590, 321, 1281]
>>> # Both 慶応 and 慶應 are decoded to 慶応
>>> tokenizer.decode(tokenizer("吾輩は猫である🐯。実は慶応(慶應)大学出身")["input_ids"])
'吾輩は猫である🐯。実は慶応(慶応)大学出身'
```
Example for Prefix-LM:
```python
>>> from transformers import GPTSanJapaneseTokenizer
>>> tokenizer = GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese")
>>> tokenizer("実は慶応(慶應)大学出身", prefix_text="吾輩は猫である🐯。")["input_ids"]
[35993, 34347, 31459, 30647, 31448, 25, 30659, 35729, 35676, 35998, 32417, 30647, 17750, 35589, 17750, 35590, 321, 1281]
>>> # Mask for Prefix-LM inputs
>>> tokenizer("実は慶応(慶應)大学出身", prefix_text="吾輩は猫である🐯。")["token_type_ids"]
[1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]
```
Example for batch encode:
```python
>>> from transformers import GPTSanJapaneseTokenizer
>>> tokenizer = GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese")
>>> tokenizer([["武田信玄", "は、"], ["織田信長", "の配下の、"]], padding=True)["input_ids"]
[[35993, 8640, 25948, 35998, 30647, 35675, 35999, 35999], [35993, 10382, 9868, 35998, 30646, 9459, 30646, 35675]]
>>> # Mask for Prefix-LM inputs
>>> tokenizer([["武田信玄", "は、"], ["織田信長", "の配下の、"]], padding=True)["token_type_ids"]
[[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
>>> # Mask for padding
>>> tokenizer([["武田信玄", "は、"], ["織田信長", "の配下の、"]], padding=True)["attention_mask"]
[[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
```
Args:
vocab_file (`str`):
File containing the vocabulary.
emoji_file (`str`):
File containing the emoji.
unk_token (`str`, *optional*, defaults to `"<|nottoken|>"`):
The token used for unknown charactor
pad_token (`str`, *optional*, defaults to `"<|separator|>"`):
The token used for padding
bos_token (`str`, *optional*, defaults to `"<|startoftext|>"`):
The beginning of sequence token.
eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
The end of sequence token.
sep_token (`str`, *optional*, defaults to `"<|segmenter|>"`):
A special token to separate token to prefix part and general input part.
do_clean_text (`bool`, *optional*, defaults to `False`):
Whether or not to clean text for URL, EMAIL, TEL, Japanese DATE and Japanese PRICE.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ["input_ids", "attention_mask", "token_type_ids"]
def __init__(
self,
vocab_file,
emoji_file,
unk_token="<|nottoken|>",
pad_token="<|separator|>",
bos_token="<|startoftext|>",
eos_token="<|endoftext|>",
sep_token="<|segmenter|>",
do_clean_text=False,
**kwargs,
):
if not os.path.isfile(vocab_file):
raise ValueError(
f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"
" model use `tokenizer = GPTSanJapaneseTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
)
if not os.path.isfile(emoji_file):
raise ValueError(
f"Can't find a emoji file at path '{emoji_file}'. To load the emoji information from a Google"
" pretrained model use `tokenizer = GPTSanJapaneseTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
)
self.do_clean_text = do_clean_text
self.vocab, self.raw_vocab, self.ids_to_tokens, self.emoji = load_vocab_and_emoji(vocab_file, emoji_file)
self.subword_tokenizer = SubWordJapaneseTokenizer(
vocab=self.vocab, ids_to_tokens=self.ids_to_tokens, emoji=self.emoji
)
super().__init__(
unk_token=unk_token,
pad_token=pad_token,
bos_token=bos_token,
eos_token=eos_token,
sep_token=sep_token,
do_clean_text=do_clean_text,
**kwargs,
)
@property
# Copied from tokenization_gpt_neox_japanese.GPTNeoXJapaneseTokenizer.vocab_size
def vocab_size(self):
# self.vocab contains support for character fluctuation unique to Japanese, and has a large number of vocab
return len(self.raw_vocab)
# Copied from tokenization_gpt_neox_japanese.GPTNeoXJapaneseTokenizer.get_vocab
def get_vocab(self):
return dict(self.raw_vocab, **self.added_tokens_encoder)
# Copied from tokenization_gpt_neox_japanese.GPTNeoXJapaneseTokenizer._tokenize
def _tokenize(self, text):
return self.subword_tokenizer.tokenize(text, clean=self.do_clean_text)
# Copied from tokenization_gpt_neox_japanese.GPTNeoXJapaneseTokenizer._convert_token_to_id
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
return self.vocab.get(token, self.vocab.get(self.unk_token))
# Copied from tokenization_gpt_neox_japanese.GPTNeoXJapaneseTokenizer._convert_id_to_token
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self.subword_tokenizer.convert_id_to_token(index)
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
words = []
byte_tokens = []
for word in tokens:
if word[:6] == "<|byte" and word[-2:] == "|>":
byte_tokens.append(int(word[6:-2]))
else:
if len(byte_tokens) > 0:
words.append(bytearray(byte_tokens).decode("utf-8", errors="replace"))
byte_tokens = []
if word[:7] == "<|emoji" and word[-2:] == "|>":
words.append(self.emoji["emoji_inv"][word])
elif word == "<SP>":
words.append(" ")
elif word == "<BR>":
words.append("\n")
elif word == "<TAB>":
words.append("\t")
elif word == "<BLOCK>":
words.append("▀")
elif word == "<KIGOU>":
words.append("ǀ")
elif word == "<U2000U2BFF>":
words.append("‖")
elif word == "<|bagoftoken|>":
if len(words) > 0:
words.append(words[-1])
words.append(words[-1])
words.append(words[-1])
elif word.startswith("<|") and word.endswith("|>"):
words.append("")
else:
words.append(word)
if len(byte_tokens) > 0:
words.append(bytearray(byte_tokens).decode("utf-8", errors="replace"))
text = "".join(words)
return text
@property
def default_chat_template(self):
"""
A simple chat template that adds standard BOS, SEP and EOS tokens between messages while discarding role
information.
"""
logger.warning_once(
"\nNo chat template is defined for this tokenizer - using the default template "
f"for the {self.__class__.__name__} class. If the default is not appropriate for "
"your model, please set `tokenizer.chat_template` to an appropriate template. "
"See https://huggingface.co/docs/transformers/main/chat_templating for more information.\n"
)
return (
"{% for message in messages %}"
"{% if not loop.first %}{{ bos_token}}{% endif %}"
"{{ sep_token }}{{ message.content }} {{ eos_token }}"
"{% endfor %}"
)
# Copied from tokenization_gpt_neox_japanese.GPTNeoXJapaneseTokenizer.save_vocabulary
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
index = 0
if os.path.isdir(save_directory):
vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
emoji_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["emoji_file"]
)
else:
vocab_file = (
(filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["vocab_file"]
)
emoji_file = (
(filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["emoji_file"]
)
with open(vocab_file, "w", encoding="utf-8") as writer:
for token_index, token in self.ids_to_tokens.items():
if index != token_index:
logger.warning(
f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
" Please check that the vocabulary is not corrupted!"
)
index = token_index
writer.write(",".join(token) + "\n")
index += 1
with open(emoji_file, "w", encoding="utf-8") as writer:
json.dump(self.emoji, writer)
return vocab_file, emoji_file
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
# docstyle-ignore
"""
The tokenizer returns token_type_ids as separators between the Prefix part and the rest.
token_type_ids is 1 for the Prefix part and 0 for the rest of the token.
Example:
```python
>>> from transformers import GPTSanJapaneseTokenizer
>>> tokenizer = GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese")
>>> x_token = tokenizer("ｱｲｳｴ")
>>> # input_ids: | SOT | SEG | ｱ | ｲ | ｳ | ｴ |
>>> # token_type_ids: | 1 | 0 | 0 | 0 | 0 | 0 |
>>> x_token = tokenizer("", prefix_text="ｱｲｳｴ")
>>> # input_ids: | SOT | ｱ | ｲ | ｳ | ｴ | SEG |
>>> # token_type_ids: | 1 | 1 | 1 | 1 | 1 | 0 |
>>> x_token = tokenizer("ｳｴ", prefix_text="ｱｲ")
>>> # input_ids: | SOT | ｱ | ｲ | SEG | ｳ | ｴ |
>>> # token_type_ids: | 1 | 1 | 1 | 0 | 0 | 0 |
```"""
prefix_len = 0
if self.sep_token in self.vocab:
segid = self.vocab[self.sep_token]
if segid in token_ids_0:
prefix_len = token_ids_0.index(segid)
if token_ids_1 is None:
total_len = len(token_ids_0)
else:
total_len = len(token_ids_0 + token_ids_1)
return prefix_len * [1] + (total_len - prefix_len) * [0]
def prepare_for_tokenization(self, text, prefix_text=None, add_sep_token=None, **kwargs):
# GPTSAN inserts extra SEP tokens in Prefix-LM in addition to SOT for text generation.
# SOT at the beginning of the text, and SEP at the separator between the Prefix part and the rest.
if add_sep_token is None:
add_sep_token = self.sep_token not in text # If insert un-prefix position explicitly
prepared = self.bos_token if self.bos_token in self.vocab else ""
prepared += prefix_text if prefix_text is not None else ""
if add_sep_token:
prepared += self.sep_token if self.sep_token in self.vocab else ""
prepared += text
return (prepared, kwargs)
def _batch_encode_plus(
self,
batch_text_or_text_pairs: Union[
List[TextInput], List[TextInputPair], List[PreTokenizedInput], List[PreTokenizedInputPair]
],
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
max_length: Optional[int] = None,
stride: int = 0,
is_split_into_words: bool = False,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[str] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
) -> BatchEncoding:
# This tokenizer converts input text pairs into Prefix input and subsequent input
if isinstance(batch_text_or_text_pairs[0], tuple) or isinstance(tuple(batch_text_or_text_pairs[0]), list):
# As a single text with an explicit un-prefix position
batch_prefix_texts = []
for pref, txt in batch_text_or_text_pairs:
batch_prefix_texts.append(pref + self.sep_token + txt)
batch_text_or_text_pairs = batch_prefix_texts
return super()._batch_encode_plus(
batch_text_or_text_pairs,
add_special_tokens,
padding_strategy,
truncation_strategy,
max_length,
stride,
is_split_into_words,
pad_to_multiple_of,
return_tensors,
return_token_type_ids,
return_attention_mask,
return_overflowing_tokens,
return_special_tokens_mask,
return_offsets_mapping,
return_length,
verbose,
)
class SubWordJapaneseTokenizer(object):
"""
This tokenizer is based on GPTNeoXJapaneseTokenizer and has the following modifications
- Decoding byte0~byte255 tokens correctly
- Added bagofword token handling
https://github.com/tanreinama/Japanese-BPEEncoder_V2 This tokenizer class is under MIT Lisence according to the
original repository.
MIT License
Copyright (c) 2020 tanreinama
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to
permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of
the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO
THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
# Copied from tokenization_gpt_neox_japanese.SubWordJapaneseTokenizer.__init__
def __init__(self, vocab, ids_to_tokens, emoji):
self.vocab = vocab # same as swe
self.ids_to_tokens = ids_to_tokens # same as bpe
self.emoji = emoji
self.maxlen = np.max([len(w) for w in self.vocab.keys()])
self.content_repatter1 = re.compile(r"(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)")
self.content_repatter2 = re.compile(r"[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*")
self.content_repatter3 = re.compile(r"[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}")
self.content_repatter4 = re.compile(
r"([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"
)
self.content_repatter5 = re.compile(
r"(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"
)
self.content_repatter6 = re.compile(
r"((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*"
)
keisen = "─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿"
blocks = "▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟"
self.content_trans1 = str.maketrans({k: "<BLOCK>" for k in keisen + blocks})
# Copied from tokenization_gpt_neox_japanese.SubWordJapaneseTokenizer.__len__
def __len__(self):
return len(self.ids_to_tokens)
# Copied from tokenization_gpt_neox_japanese.SubWordJapaneseTokenizer.clean_text
def clean_text(self, content):
content = self.content_repatter1.sub("<URL>", content)
content = self.content_repatter2.sub("<EMAIL>", content)
content = self.content_repatter3.sub("<TEL>", content)
content = self.content_repatter4.sub("<DATE>", content)
content = self.content_repatter5.sub("<DATE>", content)
content = self.content_repatter6.sub("<PRICE>", content)
content = content.translate(self.content_trans1)
while "<BLOCK><BLOCK>" in content:
content = content.replace("<BLOCK><BLOCK>", "<BLOCK>")
return content
# Copied from tokenization_gpt_neox_japanese.SubWordJapaneseTokenizer.tokenize
def tokenize(self, text, clean=False):
text = text.replace(" ", "<SP>")
text = text.replace(" ", "<SP>")
text = text.replace("\r\n", "<BR>")
text = text.replace("\n", "<BR>")
text = text.replace("\r", "<BR>")
text = text.replace("\t", "<TAB>")
text = text.replace("—", "ー")
text = text.replace("−", "ー")
for k, v in self.emoji["emoji"].items():
if k in text:
text = text.replace(k, v)
if clean:
text = self.clean_text(text)
def check_simbol(x):
e = x.encode()
if len(x) == 1 and len(e) == 2:
c = (int(e[0]) << 8) + int(e[1])
if (
(c >= 0xC2A1 and c <= 0xC2BF)
or (c >= 0xC780 and c <= 0xC783)
or (c >= 0xCAB9 and c <= 0xCBBF)
or (c >= 0xCC80 and c <= 0xCDA2)
):
return True
return False
def checku2e(x):
e = x.encode()
if len(x) == 1 and len(e) == 3:
c = (int(e[0]) << 16) + (int(e[1]) << 8) + int(e[2])
if c >= 0xE28080 and c <= 0xE2B07F:
return True
return False
pos = 0
result = []
while pos < len(text):
end = min(len(text), pos + self.maxlen + 1) if text[pos] == "<" else pos + 3
candidates = [] # (token_id, token, pos)
for e in range(end, pos, -1):
wd = text[pos:e]
if wd in self.vocab:
if wd[0] == "<" and len(wd) > 2:
candidates = [(self.vocab[wd], wd, e)]
break
else:
candidates.append((self.vocab[wd], wd, e))
if len(candidates) > 0:
# the smallest token_id is adopted
_, wd, e = sorted(candidates, key=lambda x: x[0])[0]
result.append(wd)
pos = e
else:
end = pos + 1
wd = text[pos:end]
if check_simbol(wd):
result.append("<KIGOU>")
elif checku2e(wd):
result.append("<U2000U2BFF>")
else:
for i in wd.encode("utf-8"):
result.append("<|byte%d|>" % i)
pos = end
return result
def convert_id_to_token(self, index):
return self.ids_to_tokens[index][0]
| transformers/src/transformers/models/gptsan_japanese/tokenization_gptsan_japanese.py/0 | {
"file_path": "transformers/src/transformers/models/gptsan_japanese/tokenization_gptsan_japanese.py",
"repo_id": "transformers",
"token_count": 11883
} | 99 |
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert Hubert checkpoint."""
import argparse
import torch
from s3prl.hub import distilhubert
from transformers import HubertConfig, HubertModel, Wav2Vec2FeatureExtractor, logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
MAPPING = {
"post_extract_proj": "feature_projection.projection",
"encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
"self_attn.k_proj": "encoder.layers.*.attention.k_proj",
"self_attn.v_proj": "encoder.layers.*.attention.v_proj",
"self_attn.q_proj": "encoder.layers.*.attention.q_proj",
"self_attn.out_proj": "encoder.layers.*.attention.out_proj",
"self_attn_layer_norm": "encoder.layers.*.layer_norm",
"fc1": "encoder.layers.*.feed_forward.intermediate_dense",
"fc2": "encoder.layers.*.feed_forward.output_dense",
"final_layer_norm": "encoder.layers.*.final_layer_norm",
"encoder.layer_norm": "encoder.layer_norm",
"mask_emb": "masked_spec_embed",
}
def set_recursively(hf_pointer, key, value, full_name, weight_type):
for attribute in key.split("."):
hf_pointer = getattr(hf_pointer, attribute)
if weight_type is not None:
hf_shape = getattr(hf_pointer, weight_type).shape
else:
hf_shape = hf_pointer.shape
assert hf_shape == value.shape, (
f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
f" {value.shape} for {full_name}"
)
if weight_type == "weight":
hf_pointer.weight.data = value
elif weight_type == "weight_g":
hf_pointer.weight_g.data = value
elif weight_type == "weight_v":
hf_pointer.weight_v.data = value
elif weight_type == "bias":
hf_pointer.bias.data = value
else:
hf_pointer.data = value
logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.")
def recursively_load_weights(fairseq_model, hf_model):
unused_weights = []
fairseq_dict = fairseq_model.state_dict()
feature_extractor = hf_model.feature_extractor
for name, value in fairseq_dict.items():
is_used = False
if "conv_layers" in name:
load_conv_layer(
name,
value,
feature_extractor,
unused_weights,
hf_model.config.feat_extract_norm == "group",
)
is_used = True
else:
for key, mapped_key in MAPPING.items():
mapped_key = mapped_key
if key in name:
is_used = True
if "*" in mapped_key:
layer_index = name.split(key)[0].split(".")[-2]
mapped_key = mapped_key.replace("*", layer_index)
if "weight_g" in name:
weight_type = "weight_g"
elif "weight_v" in name:
weight_type = "weight_v"
elif "weight" in name:
weight_type = "weight"
elif "bias" in name:
weight_type = "bias"
else:
weight_type = None
set_recursively(hf_model, mapped_key, value, name, weight_type)
continue
if not is_used:
unused_weights.append(name)
logger.warning(f"Unused weights: {unused_weights}")
def load_conv_layer(full_name, value, feature_extractor, unused_weights, use_group_norm):
name = full_name.split("conv_layers.")[-1]
items = name.split(".")
layer_id = int(items[0])
type_id = int(items[1])
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f"{full_name} has size {value.shape}, but"
f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
)
feature_extractor.conv_layers[layer_id].conv.bias.data = value
logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}.")
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f"{full_name} has size {value.shape}, but"
f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
)
feature_extractor.conv_layers[layer_id].conv.weight.data = value
logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}.")
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
" found."
)
feature_extractor.conv_layers[layer_id].layer_norm.bias.data = value
logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.")
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f"{full_name} has size {value.shape}, but"
f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
)
feature_extractor.conv_layers[layer_id].layer_norm.weight.data = value
logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.")
else:
unused_weights.append(full_name)
def convert_config(model):
config = HubertConfig()
fs_config = model.config
config.activation_dropout = fs_config.activation_dropout
config.apply_spec_augment = False
config.attention_dropout = fs_config.attention_dropout
config.conv_bias = False
conv_layers = eval(fs_config.extractor_conv_feature_layers)
config.conv_dim = [x[0] for x in conv_layers]
config.conv_kernel = [x[1] for x in conv_layers]
config.conv_stride = [x[2] for x in conv_layers]
config.feat_extract_activation = "gelu"
config.feat_extract_norm = "layer" if fs_config.extractor_mode == "layer_norm" else "group"
config.feat_proj_layer_norm = False
config.feat_proj_dropout = 0.0
config.final_dropout = 0.0
config.hidden_act = fs_config.activation_fn
config.hidden_dropout = fs_config.dropout
config.hidden_size = fs_config.encoder_embed_dim
config.initializer_range = 0.02
config.intermediate_size = fs_config.encoder_ffn_embed_dim
config.layer_norm_eps = 1e-5
config.layerdrop = 0.0
config.num_attention_heads = fs_config.encoder_attention_heads
config.num_conv_pos_embedding_groups = fs_config.conv_pos_groups
config.num_conv_pos_embeddings = fs_config.conv_pos
config.num_feat_extract_layers = len(conv_layers)
config.num_hidden_layers = fs_config.encoder_layers
return config
@torch.no_grad()
def convert_hubert_checkpoint(pytorch_dump_folder_path, config_path=None):
"""
Copy/paste/tweak model's weights to transformers design.
"""
model = distilhubert().model.model
if config_path is not None:
config = HubertConfig.from_pretrained(config_path)
else:
config = convert_config(model)
model = model.eval()
feature_extractor = Wav2Vec2FeatureExtractor(
feature_size=1,
sampling_rate=16000,
padding_value=0,
do_normalize=False,
return_attention_mask=False,
)
hf_model = HubertModel(config)
recursively_load_weights(model, hf_model)
feature_extractor.save_pretrained(pytorch_dump_folder_path)
hf_model.save_pretrained(pytorch_dump_folder_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
args = parser.parse_args()
convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)
| transformers/src/transformers/models/hubert/convert_distilhubert_original_s3prl_checkpoint_to_pytorch.py/0 | {
"file_path": "transformers/src/transformers/models/hubert/convert_distilhubert_original_s3prl_checkpoint_to_pytorch.py",
"repo_id": "transformers",
"token_count": 3907
} | 100 |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_import_structure = {
"configuration_imagegpt": ["IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ImageGPTConfig", "ImageGPTOnnxConfig"]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["feature_extraction_imagegpt"] = ["ImageGPTFeatureExtractor"]
_import_structure["image_processing_imagegpt"] = ["ImageGPTImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_imagegpt"] = [
"IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
"ImageGPTForCausalImageModeling",
"ImageGPTForImageClassification",
"ImageGPTModel",
"ImageGPTPreTrainedModel",
"load_tf_weights_in_imagegpt",
]
if TYPE_CHECKING:
from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
from .image_processing_imagegpt import ImageGPTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_imagegpt import (
IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
ImageGPTForCausalImageModeling,
ImageGPTForImageClassification,
ImageGPTModel,
ImageGPTPreTrainedModel,
load_tf_weights_in_imagegpt,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| transformers/src/transformers/models/imagegpt/__init__.py/0 | {
"file_path": "transformers/src/transformers/models/imagegpt/__init__.py",
"repo_id": "transformers",
"token_count": 1004
} | 101 |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert Jukebox checkpoints"""
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
PREFIX = "https://openaipublic.azureedge.net/jukebox/models/"
MODEL_MAPPING = {
"jukebox-1b-lyrics": [
"5b/vqvae.pth.tar",
"5b/prior_level_0.pth.tar",
"5b/prior_level_1.pth.tar",
"1b_lyrics/prior_level_2.pth.tar",
],
"jukebox-5b-lyrics": [
"5b/vqvae.pth.tar",
"5b/prior_level_0.pth.tar",
"5b/prior_level_1.pth.tar",
"5b_lyrics/prior_level_2.pth.tar",
],
}
def replace_key(key):
if key.endswith(".model.1.bias") and len(key.split(".")) > 10:
key = key.replace(".model.1.bias", ".conv1d_1.bias")
elif key.endswith(".model.1.weight") and len(key.split(".")) > 10:
key = key.replace(".model.1.weight", ".conv1d_1.weight")
elif key.endswith(".model.3.bias") and len(key.split(".")) > 10:
key = key.replace(".model.3.bias", ".conv1d_2.bias")
elif key.endswith(".model.3.weight") and len(key.split(".")) > 10:
key = key.replace(".model.3.weight", ".conv1d_2.weight")
if "conditioner_blocks.0." in key:
key = key.replace("conditioner_blocks.0", "conditioner_blocks")
if "prime_prior" in key:
key = key.replace("prime_prior", "encoder")
if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
key = key.replace(".emb.", ".")
if key.endswith("k"): # replace vqvae.X.k with vqvae.X.codebook
return key.replace(".k", ".codebook")
if "y_emb." in key:
return key.replace("y_emb.", "metadata_embedding.")
if "x_emb.emb." in key:
key = key.replace("0.x_emb.emb", "embed_tokens")
if "prime_state_ln" in key:
return key.replace("prime_state_ln", "encoder.final_layer_norm")
if ".ln" in key:
return key.replace(".ln", ".layer_norm")
if "_ln" in key:
return key.replace("_ln", "_layer_norm")
if "prime_state_proj" in key:
return key.replace("prime_state_proj", "encoder.proj_in")
if "prime_x_out" in key:
return key.replace("prime_x_out", "encoder.lm_head")
if "prior.x_out" in key:
return key.replace("x_out", "fc_proj_out")
if "x_emb" in key:
return key.replace("x_emb", "embed_tokens")
return key
def fix_jukebox_keys(state_dict, model_state_dict, key_prefix, mapping):
new_dict = {}
import re
re_encoder_block_conv_in = re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)")
re_encoder_block_resnet = re.compile(
r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"
)
re_encoder_block_proj_out = re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)")
re_decoder_block_conv_out = re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)")
re_decoder_block_resnet = re.compile(
r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"
)
re_decoder_block_proj_in = re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)")
re_prior_cond_conv_out = re.compile(r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)")
re_prior_cond_resnet = re.compile(
r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"
)
re_prior_cond_proj_in = re.compile(r"conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)")
for original_key, value in state_dict.items():
# rename vqvae.encoder keys
if re_encoder_block_conv_in.fullmatch(original_key):
regex_match = re_encoder_block_conv_in.match(original_key)
groups = regex_match.groups()
block_index = int(groups[2]) * 2 + int(groups[3])
re_new_key = f"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"
key = re_encoder_block_conv_in.sub(re_new_key, original_key)
elif re_encoder_block_resnet.fullmatch(original_key):
regex_match = re_encoder_block_resnet.match(original_key)
groups = regex_match.groups()
block_index = int(groups[2]) * 2 + int(groups[3])
conv_index = {"1": 1, "3": 2}[groups[-2]]
prefix = f"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."
resnet_block = f"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
re_new_key = prefix + resnet_block
key = re_encoder_block_resnet.sub(re_new_key, original_key)
elif re_encoder_block_proj_out.fullmatch(original_key):
regex_match = re_encoder_block_proj_out.match(original_key)
groups = regex_match.groups()
re_new_key = f"encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"
key = re_encoder_block_proj_out.sub(re_new_key, original_key)
# rename vqvae.decoder keys
elif re_decoder_block_conv_out.fullmatch(original_key):
regex_match = re_decoder_block_conv_out.match(original_key)
groups = regex_match.groups()
block_index = int(groups[2]) * 2 + int(groups[3]) - 2
re_new_key = f"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"
key = re_decoder_block_conv_out.sub(re_new_key, original_key)
elif re_decoder_block_resnet.fullmatch(original_key):
regex_match = re_decoder_block_resnet.match(original_key)
groups = regex_match.groups()
block_index = int(groups[2]) * 2 + int(groups[3]) - 2
conv_index = {"1": 1, "3": 2}[groups[-2]]
prefix = f"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."
resnet_block = f"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
re_new_key = prefix + resnet_block
key = re_decoder_block_resnet.sub(re_new_key, original_key)
elif re_decoder_block_proj_in.fullmatch(original_key):
regex_match = re_decoder_block_proj_in.match(original_key)
groups = regex_match.groups()
re_new_key = f"decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"
key = re_decoder_block_proj_in.sub(re_new_key, original_key)
# rename prior cond.model to upsampler.upsample_block and resnet
elif re_prior_cond_conv_out.fullmatch(original_key):
regex_match = re_prior_cond_conv_out.match(original_key)
groups = regex_match.groups()
block_index = int(groups[1]) * 2 + int(groups[2]) - 2
re_new_key = f"conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"
key = re_prior_cond_conv_out.sub(re_new_key, original_key)
elif re_prior_cond_resnet.fullmatch(original_key):
regex_match = re_prior_cond_resnet.match(original_key)
groups = regex_match.groups()
block_index = int(groups[1]) * 2 + int(groups[2]) - 2
conv_index = {"1": 1, "3": 2}[groups[-2]]
prefix = f"conditioner_blocks.upsampler.upsample_block.{block_index}."
resnet_block = f"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
re_new_key = prefix + resnet_block
key = re_prior_cond_resnet.sub(re_new_key, original_key)
elif re_prior_cond_proj_in.fullmatch(original_key):
regex_match = re_prior_cond_proj_in.match(original_key)
groups = regex_match.groups()
re_new_key = f"conditioner_blocks.upsampler.proj_in.{groups[-1]}"
key = re_prior_cond_proj_in.sub(re_new_key, original_key)
# keep original key
else:
key = original_key
key = replace_key(key)
if f"{key_prefix}.{key}" not in model_state_dict or key is None:
print(f"failed converting {original_key} to {key}, does not match")
# handle missmatched shape
elif value.shape != model_state_dict[f"{key_prefix}.{key}"].shape:
val = model_state_dict[f"{key_prefix}.{key}"]
print(f"{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match")
key = original_key
mapping[key] = original_key
new_dict[key] = value
return new_dict
@torch.no_grad()
def convert_openai_checkpoint(model_name=None, pytorch_dump_folder_path=None):
"""
Copy/paste/tweak model's weights to our Jukebox structure.
"""
for file in MODEL_MAPPING[model_name]:
if not os.path.isfile(f"{pytorch_dump_folder_path}/{file.split('/')[-1]}"):
r = requests.get(f"{PREFIX}{file}", allow_redirects=True)
os.makedirs(f"{pytorch_dump_folder_path}/", exist_ok=True)
open(f"{pytorch_dump_folder_path}/{file.split('/')[-1]}", "wb").write(r.content)
model_to_convert = MODEL_MAPPING[model_name.split("/")[-1]]
config = JukeboxConfig.from_pretrained(model_name)
model = JukeboxModel(config)
weight_dict = []
mapping = {}
for i, dict_name in enumerate(model_to_convert):
old_dic = torch.load(f"{pytorch_dump_folder_path}/{dict_name.split('/')[-1]}")["model"]
new_dic = {}
for k in old_dic.keys():
if k.endswith(".b"):
new_dic[k.replace("b", "bias")] = old_dic[k]
elif k.endswith(".w"):
new_dic[k.replace("w", "weight")] = old_dic[k]
elif "level_2" not in dict_name and "cond.model." in k:
new_dic[k.replace(".blocks.", ".model.")] = old_dic[k]
else:
new_dic[k] = old_dic[k]
key_prefix = "vqvae" if i == 0 else f"priors.{3 - i}"
new_dic = fix_jukebox_keys(new_dic, model.state_dict(), key_prefix, mapping)
weight_dict.append(new_dic)
vqvae_state_dict = weight_dict.pop(0)
model.vqvae.load_state_dict(vqvae_state_dict)
for i in range(len(weight_dict)):
model.priors[i].load_state_dict(weight_dict[2 - i])
Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
with open(f"{pytorch_dump_folder_path}/mapping.json", "w") as txtfile:
json.dump(mapping, txtfile)
print(f"Saving model {model_name} to {pytorch_dump_folder_path}")
model.save_pretrained(pytorch_dump_folder_path)
return weight_dict
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="jukebox-5b-lyrics",
type=str,
help="Name of the model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default="jukebox-5b-lyrics-converted",
type=str,
help="Path to the output PyTorch model directory.",
)
args = parser.parse_args()
convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)
| transformers/src/transformers/models/jukebox/convert_jukebox.py/0 | {
"file_path": "transformers/src/transformers/models/jukebox/convert_jukebox.py",
"repo_id": "transformers",
"token_count": 5498
} | 102 |
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Processor class for LayoutXLM.
"""
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class LayoutXLMProcessor(ProcessorMixin):
r"""
Constructs a LayoutXLM processor which combines a LayoutXLM image processor and a LayoutXLM tokenizer into a single
processor.
[`LayoutXLMProcessor`] offers all the functionalities you need to prepare data for the model.
It first uses [`LayoutLMv2ImageProcessor`] to resize document images to a fixed size, and optionally applies OCR to
get words and normalized bounding boxes. These are then provided to [`LayoutXLMTokenizer`] or
[`LayoutXLMTokenizerFast`], which turns the words and bounding boxes into token-level `input_ids`,
`attention_mask`, `token_type_ids`, `bbox`. Optionally, one can provide integer `word_labels`, which are turned
into token-level `labels` for token classification tasks (such as FUNSD, CORD).
Args:
image_processor (`LayoutLMv2ImageProcessor`, *optional*):
An instance of [`LayoutLMv2ImageProcessor`]. The image processor is a required input.
tokenizer (`LayoutXLMTokenizer` or `LayoutXLMTokenizerFast`, *optional*):
An instance of [`LayoutXLMTokenizer`] or [`LayoutXLMTokenizerFast`]. The tokenizer is a required input.
"""
attributes = ["image_processor", "tokenizer"]
image_processor_class = "LayoutLMv2ImageProcessor"
tokenizer_class = ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")
def __init__(self, image_processor=None, tokenizer=None, **kwargs):
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead.",
FutureWarning,
)
feature_extractor = kwargs.pop("feature_extractor")
image_processor = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("You need to specify an `image_processor`.")
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`.")
super().__init__(image_processor, tokenizer)
def __call__(
self,
images,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
boxes: Union[List[List[int]], List[List[List[int]]]] = None,
word_labels: Optional[Union[List[int], List[List[int]]]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = None,
max_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
return_tensors: Optional[Union[str, TensorType]] = None,
**kwargs,
) -> BatchEncoding:
"""
This method first forwards the `images` argument to [`~LayoutLMv2ImagePrpcessor.__call__`]. In case
[`LayoutLMv2ImagePrpcessor`] was initialized with `apply_ocr` set to `True`, it passes the obtained words and
bounding boxes along with the additional arguments to [`~LayoutXLMTokenizer.__call__`] and returns the output,
together with resized `images`. In case [`LayoutLMv2ImagePrpcessor`] was initialized with `apply_ocr` set to
`False`, it passes the words (`text`/``text_pair`) and `boxes` specified by the user along with the additional
arguments to [`~LayoutXLMTokenizer.__call__`] and returns the output, together with resized `images``.
Please refer to the docstring of the above two methods for more information.
"""
# verify input
if self.image_processor.apply_ocr and (boxes is not None):
raise ValueError(
"You cannot provide bounding boxes "
"if you initialized the image processor with apply_ocr set to True."
)
if self.image_processor.apply_ocr and (word_labels is not None):
raise ValueError(
"You cannot provide word labels if you initialized the image processor with apply_ocr set to True."
)
if return_overflowing_tokens is True and return_offsets_mapping is False:
raise ValueError("You cannot return overflowing tokens without returning the offsets mapping.")
# first, apply the image processor
features = self.image_processor(images=images, return_tensors=return_tensors)
# second, apply the tokenizer
if text is not None and self.image_processor.apply_ocr and text_pair is None:
if isinstance(text, str):
text = [text] # add batch dimension (as the image processor always adds a batch dimension)
text_pair = features["words"]
encoded_inputs = self.tokenizer(
text=text if text is not None else features["words"],
text_pair=text_pair if text_pair is not None else None,
boxes=boxes if boxes is not None else features["boxes"],
word_labels=word_labels,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
return_tensors=return_tensors,
**kwargs,
)
# add pixel values
images = features.pop("pixel_values")
if return_overflowing_tokens is True:
images = self.get_overflowing_images(images, encoded_inputs["overflow_to_sample_mapping"])
encoded_inputs["image"] = images
return encoded_inputs
def get_overflowing_images(self, images, overflow_to_sample_mapping):
# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
images_with_overflow = []
for sample_idx in overflow_to_sample_mapping:
images_with_overflow.append(images[sample_idx])
if len(images_with_overflow) != len(overflow_to_sample_mapping):
raise ValueError(
"Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
f" {len(images_with_overflow)} and {len(overflow_to_sample_mapping)}"
)
return images_with_overflow
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer
to the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
@property
def model_input_names(self):
return ["input_ids", "bbox", "attention_mask", "image"]
@property
def feature_extractor_class(self):
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.",
FutureWarning,
)
return self.image_processor_class
@property
def feature_extractor(self):
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.",
FutureWarning,
)
return self.image_processor
| transformers/src/transformers/models/layoutxlm/processing_layoutxlm.py/0 | {
"file_path": "transformers/src/transformers/models/layoutxlm/processing_layoutxlm.py",
"repo_id": "transformers",
"token_count": 3556
} | 103 |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" LiLT configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
LILT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"SCUT-DLVCLab/lilt-roberta-en-base": (
"https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
),
}
class LiltConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`LiltModel`]. It is used to instantiate a LiLT
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the LiLT
[SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the LiLT model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`LiltModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer. Should be a multiple of 24.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the `token_type_ids` passed when calling [`LiltModel`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
[Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
classifier_dropout (`float`, *optional*):
The dropout ratio for the classification head.
channel_shrink_ratio (`int`, *optional*, defaults to 4):
The shrink ratio compared to the `hidden_size` for the channel dimension of the layout embeddings.
max_2d_position_embeddings (`int`, *optional*, defaults to 1024):
The maximum value that the 2D position embedding might ever be used with. Typically set this to something
large just in case (e.g., 1024).
Examples:
```python
>>> from transformers import LiltConfig, LiltModel
>>> # Initializing a LiLT SCUT-DLVCLab/lilt-roberta-en-base style configuration
>>> configuration = LiltConfig()
>>> # Randomly initializing a model from the SCUT-DLVCLab/lilt-roberta-en-base style configuration
>>> model = LiltModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "lilt"
def __init__(
self,
vocab_size=30522,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
layer_norm_eps=1e-12,
pad_token_id=0,
position_embedding_type="absolute",
classifier_dropout=None,
channel_shrink_ratio=4,
max_2d_position_embeddings=1024,
**kwargs,
):
super().__init__(pad_token_id=pad_token_id, **kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.position_embedding_type = position_embedding_type
self.classifier_dropout = classifier_dropout
self.channel_shrink_ratio = channel_shrink_ratio
self.max_2d_position_embeddings = max_2d_position_embeddings
| transformers/src/transformers/models/lilt/configuration_lilt.py/0 | {
"file_path": "transformers/src/transformers/models/lilt/configuration_lilt.py",
"repo_id": "transformers",
"token_count": 2571
} | 104 |
# coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert RoBERTa checkpoint."""
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class LightningModel(pl.LightningModule):
def __init__(self, model):
super().__init__()
self.model = model
self.num_labels = 2
self.qa_outputs = nn.Linear(self.model.config.hidden_size, self.num_labels)
# implement only because lightning requires to do so
def forward(self):
pass
def convert_longformer_qa_checkpoint_to_pytorch(
longformer_model: str, longformer_question_answering_ckpt_path: str, pytorch_dump_folder_path: str
):
# load longformer model from model identifier
longformer = LongformerModel.from_pretrained(longformer_model)
lightning_model = LightningModel(longformer)
ckpt = torch.load(longformer_question_answering_ckpt_path, map_location=torch.device("cpu"))
lightning_model.load_state_dict(ckpt["state_dict"])
# init longformer question answering model
longformer_for_qa = LongformerForQuestionAnswering.from_pretrained(longformer_model)
# transfer weights
longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict())
longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict())
longformer_for_qa.eval()
# save model
longformer_for_qa.save_pretrained(pytorch_dump_folder_path)
print(f"Conversion successful. Model saved under {pytorch_dump_folder_path}")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--longformer_model",
default=None,
type=str,
required=True,
help="model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.",
)
parser.add_argument(
"--longformer_question_answering_ckpt_path",
default=None,
type=str,
required=True,
help="Path the official PyTorch Lightning Checkpoint.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
args = parser.parse_args()
convert_longformer_qa_checkpoint_to_pytorch(
args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
)
| transformers/src/transformers/models/longformer/convert_longformer_original_pytorch_lightning_to_pytorch.py/0 | {
"file_path": "transformers/src/transformers/models/longformer/convert_longformer_original_pytorch_lightning_to_pytorch.py",
"repo_id": "transformers",
"token_count": 1071
} | 105 |
# coding=utf-8
# Copyright 2018, Hao Tan, Mohit Bansal
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" LXMERT model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json",
}
class LxmertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`LxmertModel`] or a [`TFLxmertModel`]. It is used
to instantiate a LXMERT model according to the specified arguments, defining the model architecture. Instantiating
a configuration with the defaults will yield a similar configuration to that of the Lxmert
[unc-nlp/lxmert-base-uncased](https://huggingface.co/unc-nlp/lxmert-base-uncased) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the LXMERT model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`LxmertModel`] or [`TFLxmertModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
num_qa_labels (`int`, *optional*, defaults to 9500):
This represents the total number of different question answering (QA) labels there are. If using more than
one dataset with QA, the user will need to account for the total number of labels that all of the datasets
have in total.
num_object_labels (`int`, *optional*, defaults to 1600):
This represents the total number of semantically unique objects that lxmert will be able to classify a
pooled-object feature as belonging too.
num_attr_labels (`int`, *optional*, defaults to 400):
This represents the total number of semantically unique attributes that lxmert will be able to classify a
pooled-object feature as possessing.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the *token_type_ids* passed into [`BertModel`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
l_layers (`int`, *optional*, defaults to 9):
Number of hidden layers in the Transformer language encoder.
x_layers (`int`, *optional*, defaults to 5):
Number of hidden layers in the Transformer cross modality encoder.
r_layers (`int`, *optional*, defaults to 5):
Number of hidden layers in the Transformer visual encoder.
visual_feat_dim (`int`, *optional*, defaults to 2048):
This represents the last dimension of the pooled-object features used as input for the model, representing
the size of each object feature itself.
visual_pos_dim (`int`, *optional*, defaults to 4):
This represents the number of spacial features that are mixed into the visual features. The default is set
to 4 because most commonly this will represent the location of a bounding box. i.e., (x, y, width, height)
visual_loss_normalizer (`float`, *optional*, defaults to 6.67):
This represents the scaling factor in which each visual loss is multiplied by if during pretraining, one
decided to train with multiple vision-based loss objectives.
task_matched (`bool`, *optional*, defaults to `True`):
This task is used for sentence-image matching. If the sentence correctly describes the image the label will
be 1. If the sentence does not correctly describe the image, the label will be 0.
task_mask_lm (`bool`, *optional*, defaults to `True`):
Whether or not to add masked language modeling (as used in pretraining models such as BERT) to the loss
objective.
task_obj_predict (`bool`, *optional*, defaults to `True`):
Whether or not to add object prediction, attribute prediction and feature regression to the loss objective.
task_qa (`bool`, *optional*, defaults to `True`):
Whether or not to add the question-answering loss to the objective
visual_obj_loss (`bool`, *optional*, defaults to `True`):
Whether or not to calculate the object-prediction loss objective
visual_attr_loss (`bool`, *optional*, defaults to `True`):
Whether or not to calculate the attribute-prediction loss objective
visual_feat_loss (`bool`, *optional*, defaults to `True`):
Whether or not to calculate the feature-regression loss objective
"""
model_type = "lxmert"
attribute_map = {}
def __init__(
self,
vocab_size=30522,
hidden_size=768,
num_attention_heads=12,
num_qa_labels=9500,
num_object_labels=1600,
num_attr_labels=400,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
layer_norm_eps=1e-12,
l_layers=9,
x_layers=5,
r_layers=5,
visual_feat_dim=2048,
visual_pos_dim=4,
visual_loss_normalizer=6.67,
task_matched=True,
task_mask_lm=True,
task_obj_predict=True,
task_qa=True,
visual_obj_loss=True,
visual_attr_loss=True,
visual_feat_loss=True,
**kwargs,
):
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.num_qa_labels = num_qa_labels
self.num_object_labels = num_object_labels
self.num_attr_labels = num_attr_labels
self.l_layers = l_layers
self.x_layers = x_layers
self.r_layers = r_layers
self.visual_feat_dim = visual_feat_dim
self.visual_pos_dim = visual_pos_dim
self.visual_loss_normalizer = visual_loss_normalizer
self.task_matched = task_matched
self.task_mask_lm = task_mask_lm
self.task_obj_predict = task_obj_predict
self.task_qa = task_qa
self.visual_obj_loss = visual_obj_loss
self.visual_attr_loss = visual_attr_loss
self.visual_feat_loss = visual_feat_loss
self.num_hidden_layers = {"vision": r_layers, "cross_encoder": x_layers, "language": l_layers}
super().__init__(**kwargs)
| transformers/src/transformers/models/lxmert/configuration_lxmert.py/0 | {
"file_path": "transformers/src/transformers/models/lxmert/configuration_lxmert.py",
"repo_id": "transformers",
"token_count": 3447
} | 106 |
# coding=utf-8
# Copyright 2022 Meta Platforms, Inc.and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" MaskFormer model configuration"""
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"facebook/maskformer-swin-base-ade": (
"https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"
)
# See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}
logger = logging.get_logger(__name__)
class MaskFormerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MaskFormerModel`]. It is used to instantiate a
MaskFormer model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the MaskFormer
[facebook/maskformer-swin-base-ade](https://huggingface.co/facebook/maskformer-swin-base-ade) architecture trained
on [ADE20k-150](https://huggingface.co/datasets/scene_parse_150).
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Currently, MaskFormer only supports the [Swin Transformer](swin) as backbone.
Args:
mask_feature_size (`int`, *optional*, defaults to 256):
The masks' features size, this value will also be used to specify the Feature Pyramid Network features'
size.
no_object_weight (`float`, *optional*, defaults to 0.1):
Weight to apply to the null (no object) class.
use_auxiliary_loss(`bool`, *optional*, defaults to `False`):
If `True` [`MaskFormerForInstanceSegmentationOutput`] will contain the auxiliary losses computed using the
logits from each decoder's stage.
backbone_config (`Dict`, *optional*):
The configuration passed to the backbone, if unset, the configuration corresponding to
`swin-base-patch4-window12-384` will be used.
backbone (`str`, *optional*):
Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this
will load the corresponding pretrained weights from the timm or transformers library. If `use_pretrained_backbone`
is `False`, this loads the backbone's config and uses that to initialize the backbone with random weights.
use_pretrained_backbone (`bool`, *optional*, `False`):
Whether to use pretrained weights for the backbone.
use_timm_backbone (`bool`, *optional*, `False`):
Whether to load `backbone` from the timm library. If `False`, the backbone is loaded from the transformers
library.
backbone_kwargs (`dict`, *optional*):
Keyword arguments to be passed to AutoBackbone when loading from a checkpoint
e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set.
decoder_config (`Dict`, *optional*):
The configuration passed to the transformer decoder model, if unset the base config for `detr-resnet-50`
will be used.
init_std (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
init_xavier_std (`float`, *optional*, defaults to 1):
The scaling factor used for the Xavier initialization gain in the HM Attention map module.
dice_weight (`float`, *optional*, defaults to 1.0):
The weight for the dice loss.
cross_entropy_weight (`float`, *optional*, defaults to 1.0):
The weight for the cross entropy loss.
mask_weight (`float`, *optional*, defaults to 20.0):
The weight for the mask loss.
output_auxiliary_logits (`bool`, *optional*):
Should the model output its `auxiliary_logits` or not.
Raises:
`ValueError`:
Raised if the backbone model type selected is not in `["swin"]` or the decoder model type selected is not
in `["detr"]`
Examples:
```python
>>> from transformers import MaskFormerConfig, MaskFormerModel
>>> # Initializing a MaskFormer facebook/maskformer-swin-base-ade configuration
>>> configuration = MaskFormerConfig()
>>> # Initializing a model (with random weights) from the facebook/maskformer-swin-base-ade style configuration
>>> model = MaskFormerModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```
"""
model_type = "maskformer"
attribute_map = {"hidden_size": "mask_feature_size"}
backbones_supported = ["resnet", "swin"]
decoders_supported = ["detr"]
def __init__(
self,
fpn_feature_size: int = 256,
mask_feature_size: int = 256,
no_object_weight: float = 0.1,
use_auxiliary_loss: bool = False,
backbone_config: Optional[Dict] = None,
decoder_config: Optional[Dict] = None,
init_std: float = 0.02,
init_xavier_std: float = 1.0,
dice_weight: float = 1.0,
cross_entropy_weight: float = 1.0,
mask_weight: float = 20.0,
output_auxiliary_logits: Optional[bool] = None,
backbone: Optional[str] = None,
use_pretrained_backbone: bool = False,
use_timm_backbone: bool = False,
backbone_kwargs: Optional[Dict] = None,
**kwargs,
):
if use_pretrained_backbone:
raise ValueError("Pretrained backbones are not supported yet.")
if backbone_config is not None and backbone is not None:
raise ValueError("You can't specify both `backbone` and `backbone_config`.")
if backbone_kwargs is not None and backbone_kwargs and backbone_config is not None:
raise ValueError("You can't specify both `backbone_kwargs` and `backbone_config`.")
if backbone_config is None and backbone is None:
# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
backbone_config = SwinConfig(
image_size=384,
in_channels=3,
patch_size=4,
embed_dim=128,
depths=[2, 2, 18, 2],
num_heads=[4, 8, 16, 32],
window_size=12,
drop_path_rate=0.3,
out_features=["stage1", "stage2", "stage3", "stage4"],
)
if isinstance(backbone_config, dict):
backbone_model_type = backbone_config.pop("model_type")
config_class = CONFIG_MAPPING[backbone_model_type]
backbone_config = config_class.from_dict(backbone_config)
# verify that the backbone is supported
if backbone_config is not None and backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
f"Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. "
f"Supported model types: {','.join(self.backbones_supported)}"
)
if decoder_config is None:
# fall back to https://huggingface.co/facebook/detr-resnet-50
decoder_config = DetrConfig()
else:
# verify that the decoder is supported
decoder_type = (
decoder_config.pop("model_type") if isinstance(decoder_config, dict) else decoder_config.model_type
)
if decoder_type not in self.decoders_supported:
raise ValueError(
f"Transformer Decoder {decoder_type} not supported, please use one of"
f" {','.join(self.decoders_supported)}"
)
if isinstance(decoder_config, dict):
config_class = CONFIG_MAPPING[decoder_type]
decoder_config = config_class.from_dict(decoder_config)
self.backbone_config = backbone_config
self.decoder_config = decoder_config
# main feature dimension for the model
self.fpn_feature_size = fpn_feature_size
self.mask_feature_size = mask_feature_size
# initializer
self.init_std = init_std
self.init_xavier_std = init_xavier_std
# Hungarian matcher && loss
self.cross_entropy_weight = cross_entropy_weight
self.dice_weight = dice_weight
self.mask_weight = mask_weight
self.use_auxiliary_loss = use_auxiliary_loss
self.no_object_weight = no_object_weight
self.output_auxiliary_logits = output_auxiliary_logits
self.num_attention_heads = self.decoder_config.encoder_attention_heads
self.num_hidden_layers = self.decoder_config.num_hidden_layers
self.backbone = backbone
self.use_pretrained_backbone = use_pretrained_backbone
self.use_timm_backbone = use_timm_backbone
self.backbone_kwargs = backbone_kwargs
super().__init__(**kwargs)
@classmethod
def from_backbone_and_decoder_configs(
cls, backbone_config: PretrainedConfig, decoder_config: PretrainedConfig, **kwargs
):
"""Instantiate a [`MaskFormerConfig`] (or a derived class) from a pre-trained backbone model configuration and DETR model
configuration.
Args:
backbone_config ([`PretrainedConfig`]):
The backbone configuration.
decoder_config ([`PretrainedConfig`]):
The transformer decoder configuration to use.
Returns:
[`MaskFormerConfig`]: An instance of a configuration object
"""
return cls(
backbone_config=backbone_config,
decoder_config=decoder_config,
**kwargs,
)
| transformers/src/transformers/models/maskformer/configuration_maskformer.py/0 | {
"file_path": "transformers/src/transformers/models/maskformer/configuration_maskformer.py",
"repo_id": "transformers",
"token_count": 4190
} | 107 |
# coding=utf-8
# Copyright 2020 The Facebook AI Research Team Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_mbart import MBartTokenizer
else:
MBartTokenizer = None
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"facebook/mbart-large-en-ro": (
"https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
),
"facebook/mbart-large-cc25": (
"https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
),
},
"tokenizer_file": {
"facebook/mbart-large-en-ro": "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json",
"facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json",
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"facebook/mbart-large-en-ro": 1024,
"facebook/mbart-large-cc25": 1024,
}
FAIRSEQ_LANGUAGE_CODES = ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"] # fmt: skip
class MBartTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" MBART tokenizer (backed by HuggingFace's *tokenizers* library). Based on
[BPE](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=BPE#models).
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.
The tokenization method is `<tokens> <eos> <language code>` for source language documents, and `<language code>
<tokens> <eos>` for target language documents.
Examples:
```python
>>> from transformers import MBartTokenizerFast
>>> tokenizer = MBartTokenizerFast.from_pretrained(
... "facebook/mbart-large-en-ro", src_lang="en_XX", tgt_lang="ro_RO"
... )
>>> example_english_phrase = " UN Chief Says There Is No Military Solution in Syria"
>>> expected_translation_romanian = "Şeful ONU declară că nu există o soluţie militară în Siria"
>>> inputs = tokenizer(example_english_phrase, text_target=expected_translation_romanian, return_tensors="pt")
```"""
vocab_files_names = VOCAB_FILES_NAMES
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
model_input_names = ["input_ids", "attention_mask"]
slow_tokenizer_class = MBartTokenizer
prefix_tokens: List[int] = []
suffix_tokens: List[int] = []
def __init__(
self,
vocab_file=None,
tokenizer_file=None,
bos_token="<s>",
eos_token="</s>",
sep_token="</s>",
cls_token="<s>",
unk_token="<unk>",
pad_token="<pad>",
mask_token="<mask>",
src_lang=None,
tgt_lang=None,
additional_special_tokens=None,
**kwargs,
):
# Mask token behave like a normal word, i.e. include the space before it
mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
_additional_special_tokens = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens]
)
super().__init__(
vocab_file=vocab_file,
tokenizer_file=tokenizer_file,
bos_token=bos_token,
eos_token=eos_token,
sep_token=sep_token,
cls_token=cls_token,
unk_token=unk_token,
pad_token=pad_token,
mask_token=mask_token,
src_lang=src_lang,
tgt_lang=tgt_lang,
additional_special_tokens=_additional_special_tokens,
**kwargs,
)
self.vocab_file = vocab_file
self.lang_code_to_id = {
lang_code: self.convert_tokens_to_ids(lang_code) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
self._src_lang = src_lang if src_lang is not None else "en_XX"
self.cur_lang_code = self.convert_tokens_to_ids(self._src_lang)
self.tgt_lang = tgt_lang
self.set_src_lang_special_tokens(self._src_lang)
@property
def can_save_slow_tokenizer(self) -> bool:
return os.path.isfile(self.vocab_file) if self.vocab_file else False
@property
def src_lang(self) -> str:
return self._src_lang
@src_lang.setter
def src_lang(self, new_src_lang: str) -> None:
self._src_lang = new_src_lang
self.set_src_lang_special_tokens(self._src_lang)
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. The special tokens depend on calling set_lang.
An MBART sequence has the following format, where `X` represents the sequence:
- `input_ids` (for encoder) `X [eos, src_lang_code]`
- `decoder_input_ids`: (for decoder) `X [eos, tgt_lang_code]`
BOS is never used. Pairs of sequences are not the expected use case, but they will be handled without a
separator.
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: list of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
if token_ids_1 is None:
return self.prefix_tokens + token_ids_0 + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_0 + token_ids_1 + self.suffix_tokens
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. mBART does not
make use of token type ids, therefore a list of zeros is returned.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of zeros.
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
def _build_translation_inputs(
self, raw_inputs, return_tensors: str, src_lang: Optional[str], tgt_lang: Optional[str], **extra_kwargs
):
"""Used by translation pipeline, to prepare inputs for the generate function"""
if src_lang is None or tgt_lang is None:
raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
self.src_lang = src_lang
inputs = self(raw_inputs, add_special_tokens=True, return_tensors=return_tensors, **extra_kwargs)
tgt_lang_id = self.convert_tokens_to_ids(tgt_lang)
inputs["forced_bos_token_id"] = tgt_lang_id
return inputs
def prepare_seq2seq_batch(
self,
src_texts: List[str],
src_lang: str = "en_XX",
tgt_texts: Optional[List[str]] = None,
tgt_lang: str = "ro_RO",
**kwargs,
) -> BatchEncoding:
self.src_lang = src_lang
self.tgt_lang = tgt_lang
return super().prepare_seq2seq_batch(src_texts, tgt_texts, **kwargs)
def _switch_to_input_mode(self):
return self.set_src_lang_special_tokens(self.src_lang)
def _switch_to_target_mode(self):
return self.set_tgt_lang_special_tokens(self.tgt_lang)
def set_src_lang_special_tokens(self, src_lang) -> None:
"""Reset the special tokens to the source lang setting. No prefix and suffix=[eos, src_lang_code]."""
self.cur_lang_code = self.convert_tokens_to_ids(src_lang)
self.prefix_tokens = []
self.suffix_tokens = [self.eos_token_id, self.cur_lang_code]
prefix_tokens_str = self.convert_ids_to_tokens(self.prefix_tokens)
suffix_tokens_str = self.convert_ids_to_tokens(self.suffix_tokens)
self._tokenizer.post_processor = processors.TemplateProcessing(
single=prefix_tokens_str + ["$A"] + suffix_tokens_str,
pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str,
special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str, self.prefix_tokens + self.suffix_tokens)),
)
def set_tgt_lang_special_tokens(self, lang: str) -> None:
"""Reset the special tokens to the target language setting. No prefix and suffix=[eos, tgt_lang_code]."""
self.cur_lang_code = self.convert_tokens_to_ids(lang)
self.prefix_tokens = []
self.suffix_tokens = [self.eos_token_id, self.cur_lang_code]
prefix_tokens_str = self.convert_ids_to_tokens(self.prefix_tokens)
suffix_tokens_str = self.convert_ids_to_tokens(self.suffix_tokens)
self._tokenizer.post_processor = processors.TemplateProcessing(
single=prefix_tokens_str + ["$A"] + suffix_tokens_str,
pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str,
special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str, self.prefix_tokens + self.suffix_tokens)),
)
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer."
)
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory.")
return
out_vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
copyfile(self.vocab_file, out_vocab_file)
return (out_vocab_file,)
| transformers/src/transformers/models/mbart/tokenization_mbart_fast.py/0 | {
"file_path": "transformers/src/transformers/models/mbart/tokenization_mbart_fast.py",
"repo_id": "transformers",
"token_count": 5169
} | 108 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" MGP-STR model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}
class MgpstrConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of an [`MgpstrModel`]. It is used to instantiate an
MGP-STR model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the MGP-STR
[alibaba-damo/mgp-str-base](https://huggingface.co/alibaba-damo/mgp-str-base) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
image_size (`List[int]`, *optional*, defaults to `[32, 128]`):
The size (resolution) of each image.
patch_size (`int`, *optional*, defaults to 4):
The size (resolution) of each patch.
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
max_token_length (`int`, *optional*, defaults to 27):
The max number of output tokens.
num_character_labels (`int`, *optional*, defaults to 38):
The number of classes for character head .
num_bpe_labels (`int`, *optional*, defaults to 50257):
The number of classes for bpe head .
num_wordpiece_labels (`int`, *optional*, defaults to 30522):
The number of classes for wordpiece head .
hidden_size (`int`, *optional*, defaults to 768):
The embedding dimension.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
mlp_ratio (`float`, *optional*, defaults to 4.0):
The ratio of mlp hidden dim to embedding dim.
qkv_bias (`bool`, *optional*, defaults to `True`):
Whether to add a bias to the queries, keys and values.
distilled (`bool`, *optional*, defaults to `False`):
Model includes a distillation token and head as in DeiT models.
layer_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the layer normalization layers.
drop_rate (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings, encoder.
attn_drop_rate (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
drop_path_rate (`float`, *optional*, defaults to 0.0):
The stochastic depth rate.
output_a3_attentions (`bool`, *optional*, defaults to `False`):
Whether or not the model should returns A^3 module attentions.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
Example:
```python
>>> from transformers import MgpstrConfig, MgpstrForSceneTextRecognition
>>> # Initializing a Mgpstr mgp-str-base style configuration
>>> configuration = MgpstrConfig()
>>> # Initializing a model (with random weights) from the mgp-str-base style configuration
>>> model = MgpstrForSceneTextRecognition(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "mgp-str"
def __init__(
self,
image_size=[32, 128],
patch_size=4,
num_channels=3,
max_token_length=27,
num_character_labels=38,
num_bpe_labels=50257,
num_wordpiece_labels=30522,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
mlp_ratio=4.0,
qkv_bias=True,
distilled=False,
layer_norm_eps=1e-5,
drop_rate=0.0,
attn_drop_rate=0.0,
drop_path_rate=0.0,
output_a3_attentions=False,
initializer_range=0.02,
**kwargs,
):
super().__init__(**kwargs)
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.max_token_length = max_token_length
self.num_character_labels = num_character_labels
self.num_bpe_labels = num_bpe_labels
self.num_wordpiece_labels = num_wordpiece_labels
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.mlp_ratio = mlp_ratio
self.distilled = distilled
self.layer_norm_eps = layer_norm_eps
self.drop_rate = drop_rate
self.qkv_bias = qkv_bias
self.attn_drop_rate = attn_drop_rate
self.drop_path_rate = drop_path_rate
self.output_a3_attentions = output_a3_attentions
self.initializer_range = initializer_range
| transformers/src/transformers/models/mgp_str/configuration_mgp_str.py/0 | {
"file_path": "transformers/src/transformers/models/mgp_str/configuration_mgp_str.py",
"repo_id": "transformers",
"token_count": 2316
} | 109 |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_import_structure = {
"configuration_mobilebert": [
"MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"MobileBertConfig",
"MobileBertOnnxConfig",
],
"tokenization_mobilebert": ["MobileBertTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_mobilebert_fast"] = ["MobileBertTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_mobilebert"] = [
"MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"MobileBertForMaskedLM",
"MobileBertForMultipleChoice",
"MobileBertForNextSentencePrediction",
"MobileBertForPreTraining",
"MobileBertForQuestionAnswering",
"MobileBertForSequenceClassification",
"MobileBertForTokenClassification",
"MobileBertLayer",
"MobileBertModel",
"MobileBertPreTrainedModel",
"load_tf_weights_in_mobilebert",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_mobilebert"] = [
"TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFMobileBertForMaskedLM",
"TFMobileBertForMultipleChoice",
"TFMobileBertForNextSentencePrediction",
"TFMobileBertForPreTraining",
"TFMobileBertForQuestionAnswering",
"TFMobileBertForSequenceClassification",
"TFMobileBertForTokenClassification",
"TFMobileBertMainLayer",
"TFMobileBertModel",
"TFMobileBertPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_mobilebert import (
MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
MobileBertConfig,
MobileBertOnnxConfig,
)
from .tokenization_mobilebert import MobileBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mobilebert_fast import MobileBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilebert import (
MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertLayer,
MobileBertModel,
MobileBertPreTrainedModel,
load_tf_weights_in_mobilebert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mobilebert import (
TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFMobileBertForMaskedLM,
TFMobileBertForMultipleChoice,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertMainLayer,
TFMobileBertModel,
TFMobileBertPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| transformers/src/transformers/models/mobilebert/__init__.py/0 | {
"file_path": "transformers/src/transformers/models/mobilebert/__init__.py",
"repo_id": "transformers",
"token_count": 1912
} | 110 |
# coding=utf-8
# Copyright 2018 The HuggingFace Inc. team, Microsoft Corporation.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch MPNet model."""
import math
from typing import Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN, gelu
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOutputWithPooling,
MaskedLMOutput,
MultipleChoiceModelOutput,
QuestionAnsweringModelOutput,
SequenceClassifierOutput,
TokenClassifierOutput,
)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mpnet import MPNetConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "microsoft/mpnet-base"
_CONFIG_FOR_DOC = "MPNetConfig"
MPNET_PRETRAINED_MODEL_ARCHIVE_LIST = [
"microsoft/mpnet-base",
]
class MPNetPreTrainedModel(PreTrainedModel):
config_class = MPNetConfig
pretrained_model_archive_map = MPNET_PRETRAINED_MODEL_ARCHIVE_LIST
base_model_prefix = "mpnet"
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
class MPNetEmbeddings(nn.Module):
def __init__(self, config):
super().__init__()
self.padding_idx = 1
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=self.padding_idx)
self.position_embeddings = nn.Embedding(
config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.register_buffer(
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
)
def forward(self, input_ids=None, position_ids=None, inputs_embeds=None, **kwargs):
if position_ids is None:
if input_ids is not None:
position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx)
else:
position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
if input_ids is not None:
input_shape = input_ids.size()
else:
input_shape = inputs_embeds.size()[:-1]
seq_length = input_shape[1]
if position_ids is None:
position_ids = self.position_ids[:, :seq_length]
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
position_embeddings = self.position_embeddings(position_ids)
embeddings = inputs_embeds + position_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
def create_position_ids_from_inputs_embeds(self, inputs_embeds):
"""
We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
Args:
inputs_embeds: torch.Tensor
Returns: torch.Tensor
"""
input_shape = inputs_embeds.size()[:-1]
sequence_length = input_shape[1]
position_ids = torch.arange(
self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
)
return position_ids.unsqueeze(0).expand(input_shape)
class MPNetSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
f"heads ({config.num_attention_heads})"
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.q = nn.Linear(config.hidden_size, self.all_head_size)
self.k = nn.Linear(config.hidden_size, self.all_head_size)
self.v = nn.Linear(config.hidden_size, self.all_head_size)
self.o = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
position_bias=None,
output_attentions=False,
**kwargs,
):
q = self.q(hidden_states)
k = self.k(hidden_states)
v = self.v(hidden_states)
q = self.transpose_for_scores(q)
k = self.transpose_for_scores(k)
v = self.transpose_for_scores(v)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(q, k.transpose(-1, -2))
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
# Apply relative position embedding (precomputed in MPNetEncoder) if provided.
if position_bias is not None:
attention_scores += position_bias
if attention_mask is not None:
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
attention_probs = self.dropout(attention_probs)
if head_mask is not None:
attention_probs = attention_probs * head_mask
c = torch.matmul(attention_probs, v)
c = c.permute(0, 2, 1, 3).contiguous()
new_c_shape = c.size()[:-2] + (self.all_head_size,)
c = c.view(*new_c_shape)
o = self.o(c)
outputs = (o, attention_probs) if output_attentions else (o,)
return outputs
class MPNetAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.attn = MPNetSelfAttention(config)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.attn.num_attention_heads, self.attn.attention_head_size, self.pruned_heads
)
self.attn.q = prune_linear_layer(self.attn.q, index)
self.attn.k = prune_linear_layer(self.attn.k, index)
self.attn.v = prune_linear_layer(self.attn.v, index)
self.attn.o = prune_linear_layer(self.attn.o, index, dim=1)
self.attn.num_attention_heads = self.attn.num_attention_heads - len(heads)
self.attn.all_head_size = self.attn.attention_head_size * self.attn.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
position_bias=None,
output_attentions=False,
**kwargs,
):
self_outputs = self.attn(
hidden_states,
attention_mask,
head_mask,
position_bias,
output_attentions=output_attentions,
)
attention_output = self.LayerNorm(self.dropout(self_outputs[0]) + hidden_states)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
# Copied from transformers.models.bert.modeling_bert.BertIntermediate
class MPNetIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertOutput
class MPNetOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class MPNetLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.attention = MPNetAttention(config)
self.intermediate = MPNetIntermediate(config)
self.output = MPNetOutput(config)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
position_bias=None,
output_attentions=False,
**kwargs,
):
self_attention_outputs = self.attention(
hidden_states,
attention_mask,
head_mask,
position_bias=position_bias,
output_attentions=output_attentions,
)
attention_output = self_attention_outputs[0]
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
outputs = (layer_output,) + outputs
return outputs
class MPNetEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.n_heads = config.num_attention_heads
self.layer = nn.ModuleList([MPNetLayer(config) for _ in range(config.num_hidden_layers)])
self.relative_attention_bias = nn.Embedding(config.relative_attention_num_buckets, self.n_heads)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = False,
**kwargs,
):
position_bias = self.compute_position_bias(hidden_states)
all_hidden_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_outputs = layer_module(
hidden_states,
attention_mask,
head_mask[i],
position_bias,
output_attentions=output_attentions,
**kwargs,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
# Add last layer
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_attentions,
)
def compute_position_bias(self, x, position_ids=None, num_buckets=32):
bsz, qlen, klen = x.size(0), x.size(1), x.size(1)
if position_ids is not None:
context_position = position_ids[:, :, None]
memory_position = position_ids[:, None, :]
else:
context_position = torch.arange(qlen, dtype=torch.long)[:, None]
memory_position = torch.arange(klen, dtype=torch.long)[None, :]
relative_position = memory_position - context_position
rp_bucket = self.relative_position_bucket(relative_position, num_buckets=num_buckets)
rp_bucket = rp_bucket.to(x.device)
values = self.relative_attention_bias(rp_bucket)
values = values.permute([2, 0, 1]).unsqueeze(0)
values = values.expand((bsz, -1, qlen, klen)).contiguous()
return values
@staticmethod
def relative_position_bucket(relative_position, num_buckets=32, max_distance=128):
ret = 0
n = -relative_position
num_buckets //= 2
ret += (n < 0).to(torch.long) * num_buckets
n = torch.abs(n)
max_exact = num_buckets // 2
is_small = n < max_exact
val_if_large = max_exact + (
torch.log(n.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact)
).to(torch.long)
val_if_large = torch.min(val_if_large, torch.full_like(val_if_large, num_buckets - 1))
ret += torch.where(is_small, n, val_if_large)
return ret
# Copied from transformers.models.bert.modeling_bert.BertPooler
class MPNetPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
first_token_tensor = hidden_states[:, 0]
pooled_output = self.dense(first_token_tensor)
pooled_output = self.activation(pooled_output)
return pooled_output
MPNET_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`MPNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
MPNET_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare MPNet Model transformer outputting raw hidden-states without any specific head on top.",
MPNET_START_DOCSTRING,
)
class MPNetModel(MPNetPreTrainedModel):
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.embeddings = MPNetEmbeddings(config)
self.encoder = MPNetEncoder(config)
self.pooler = MPNetPooler(config) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embeddings.word_embeddings
def set_input_embeddings(self, value):
self.embeddings.word_embeddings = value
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
@add_start_docstrings_to_model_forward(MPNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPooling,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs,
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPooling]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
device = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
attention_mask = torch.ones(input_shape, device=device)
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
embedding_output = self.embeddings(input_ids=input_ids, position_ids=position_ids, inputs_embeds=inputs_embeds)
encoder_outputs = self.encoder(
embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPooling(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
class MPNetForMaskedLM(MPNetPreTrainedModel):
_tied_weights_keys = ["lm_head.decoder"]
def __init__(self, config):
super().__init__(config)
self.mpnet = MPNetModel(config, add_pooling_layer=False)
self.lm_head = MPNetLMHead(config)
# Initialize weights and apply final processing
self.post_init()
def get_output_embeddings(self):
return self.lm_head.decoder
def set_output_embeddings(self, new_embeddings):
self.lm_head.decoder = new_embeddings
@add_start_docstrings_to_model_forward(MPNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], MaskedLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.mpnet(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
prediction_scores = self.lm_head(sequence_output)
masked_lm_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return MaskedLMOutput(
loss=masked_lm_loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class MPNetLMHead(nn.Module):
"""MPNet Head for masked and permuted language modeling."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
# Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
self.decoder.bias = self.bias
def forward(self, features, **kwargs):
x = self.dense(features)
x = gelu(x)
x = self.layer_norm(x)
# project back to size of vocabulary with bias
x = self.decoder(x)
return x
@add_start_docstrings(
"""
MPNet Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
output) e.g. for GLUE tasks.
""",
MPNET_START_DOCSTRING,
)
class MPNetForSequenceClassification(MPNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.mpnet = MPNetModel(config, add_pooling_layer=False)
self.classifier = MPNetClassificationHead(config)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(MPNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.mpnet(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.classifier(sequence_output)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
MPNet Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
softmax) e.g. for RocStories/SWAG tasks.
""",
MPNET_START_DOCSTRING,
)
class MPNetForMultipleChoice(MPNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.mpnet = MPNetModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(MPNET_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
`input_ids` above)
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
flat_inputs_embeds = (
inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
if inputs_embeds is not None
else None
)
outputs = self.mpnet(
flat_input_ids,
position_ids=flat_position_ids,
attention_mask=flat_attention_mask,
head_mask=head_mask,
inputs_embeds=flat_inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
reshaped_logits = logits.view(-1, num_choices)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits, labels)
if not return_dict:
output = (reshaped_logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return MultipleChoiceModelOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
MPNet Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.
""",
MPNET_START_DOCSTRING,
)
class MPNetForTokenClassification(MPNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.mpnet = MPNetModel(config, add_pooling_layer=False)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(MPNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.mpnet(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
sequence_output = self.dropout(sequence_output)
logits = self.classifier(sequence_output)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class MPNetClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
def forward(self, features, **kwargs):
x = features[:, 0, :] # take <s> token (equiv. to BERT's [CLS] token)
x = self.dropout(x)
x = self.dense(x)
x = torch.tanh(x)
x = self.dropout(x)
x = self.out_proj(x)
return x
@add_start_docstrings(
"""
MPNet Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
""",
MPNET_START_DOCSTRING,
)
class MPNetForQuestionAnswering(MPNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.mpnet = MPNetModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(MPNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=QuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
start_positions: Optional[torch.LongTensor] = None,
end_positions: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.mpnet(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1).contiguous()
end_logits = end_logits.squeeze(-1).contiguous()
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions = start_positions.clamp(0, ignored_index)
end_positions = end_positions.clamp(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def create_position_ids_from_input_ids(input_ids, padding_idx):
"""
Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
are ignored. This is modified from fairseq's `utils.make_positions`. :param torch.Tensor x: :return torch.Tensor:
"""
# The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
mask = input_ids.ne(padding_idx).int()
incremental_indices = torch.cumsum(mask, dim=1).type_as(mask) * mask
return incremental_indices.long() + padding_idx
| transformers/src/transformers/models/mpnet/modeling_mpnet.py/0 | {
"file_path": "transformers/src/transformers/models/mpnet/modeling_mpnet.py",
"repo_id": "transformers",
"token_count": 18262
} | 111 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_import_structure = {
"configuration_musicgen": [
"MUSICGEN_PRETRAINED_CONFIG_ARCHIVE_MAP",
"MusicgenConfig",
"MusicgenDecoderConfig",
],
"processing_musicgen": ["MusicgenProcessor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_musicgen"] = [
"MUSICGEN_PRETRAINED_MODEL_ARCHIVE_LIST",
"MusicgenForConditionalGeneration",
"MusicgenForCausalLM",
"MusicgenModel",
"MusicgenPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_musicgen import (
MUSICGEN_PRETRAINED_CONFIG_ARCHIVE_MAP,
MusicgenConfig,
MusicgenDecoderConfig,
)
from .processing_musicgen import MusicgenProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_musicgen import (
MUSICGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
MusicgenForCausalLM,
MusicgenForConditionalGeneration,
MusicgenModel,
MusicgenPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| transformers/src/transformers/models/musicgen/__init__.py/0 | {
"file_path": "transformers/src/transformers/models/musicgen/__init__.py",
"repo_id": "transformers",
"token_count": 799
} | 112 |
# coding=utf-8
# Copyright 2022 The Fairseq Authors and The Google Flax Team Authors And The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Flax OPT model."""
from functools import partial
from typing import Optional, Tuple
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.linen import combine_masks, make_causal_mask
from flax.linen.attention import dot_product_attention_weights
from flax.traverse_util import flatten_dict, unflatten_dict
from jax import lax
from jax.random import PRNGKey
from ...modeling_flax_outputs import FlaxBaseModelOutput, FlaxMaskedLMOutput
from ...modeling_flax_utils import ACT2FN, FlaxPreTrainedModel, append_call_sample_docstring
from ...utils import add_start_docstrings, logging
from .configuration_opt import OPTConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "facebook/opt-350m"
_CONFIG_FOR_DOC = "OPTConfig"
OPT_START_DOCSTRING = r"""
This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a Flax Linen
[flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a
regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.
Finally, this model supports inherent JAX features such as:
- [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
- [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
- [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
- [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
Parameters:
config ([`OPTConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights.
dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
`jax.numpy.bfloat16` (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given `dtype`.
**Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.**
If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and
[`~FlaxPreTrainedModel.to_bf16`].
"""
OPT_INPUTS_DOCSTRING = r"""
Args:
input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartAttention with Bart->OPT
class FlaxOPTAttention(nn.Module):
config: OPTConfig
embed_dim: int
num_heads: int
dropout: float = 0.0
causal: bool = False
bias: bool = True
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self) -> None:
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
f" and `num_heads`: {self.num_heads})."
)
dense = partial(
nn.Dense,
self.embed_dim,
use_bias=self.bias,
dtype=self.dtype,
kernel_init=jax.nn.initializers.normal(self.config.init_std),
)
self.q_proj, self.k_proj, self.v_proj = dense(), dense(), dense()
self.out_proj = dense()
self.dropout_layer = nn.Dropout(rate=self.dropout)
if self.causal:
self.causal_mask = make_causal_mask(
jnp.ones((1, self.config.max_position_embeddings), dtype="bool"), dtype="bool"
)
def _split_heads(self, hidden_states):
return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads, self.head_dim))
def _merge_heads(self, hidden_states):
return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,))
@nn.compact
def _concatenate_to_cache(self, key, value, query, attention_mask):
"""
This function takes projected key, value states from a single input token and concatenates the states to cached
states from previous steps. This function is slighly adapted from the official Flax repository:
https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252
"""
# detect if we're initializing by absence of existing cache data.
is_initialized = self.has_variable("cache", "cached_key")
cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype)
cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype)
cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32))
if is_initialized:
*batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape
# update key, value caches with our new 1d spatial slices
cur_index = cache_index.value
indices = (0,) * len(batch_dims) + (cur_index, 0, 0)
key = lax.dynamic_update_slice(cached_key.value, key, indices)
value = lax.dynamic_update_slice(cached_value.value, value, indices)
cached_key.value = key
cached_value.value = value
num_updated_cache_vectors = query.shape[1]
cache_index.value = cache_index.value + num_updated_cache_vectors
# causal mask for cached decoder self-attention: our single query position should only attend to those key positions that have already been generated and cached, not the remaining zero elements.
pad_mask = jnp.broadcast_to(
jnp.arange(max_length) < cur_index + num_updated_cache_vectors,
tuple(batch_dims) + (1, num_updated_cache_vectors, max_length),
)
attention_mask = combine_masks(pad_mask, attention_mask)
return key, value, attention_mask
def __call__(
self,
hidden_states: jnp.ndarray,
key_value_states: Optional[jnp.ndarray] = None,
attention_mask: Optional[jnp.ndarray] = None,
init_cache: bool = False,
deterministic: bool = True,
) -> Tuple[jnp.ndarray]:
"""Input shape: Batch x Time x Channel"""
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
batch_size = hidden_states.shape[0]
# get query proj
query_states = self.q_proj(hidden_states)
# get key, value proj
if is_cross_attention:
# cross_attentions
key_states = self.k_proj(key_value_states)
value_states = self.v_proj(key_value_states)
else:
# self_attention
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = self._split_heads(query_states)
key_states = self._split_heads(key_states)
value_states = self._split_heads(value_states)
# handle cache prepare causal attention mask
if self.causal:
query_length, key_length = query_states.shape[1], key_states.shape[1]
if self.has_variable("cache", "cached_key"):
mask_shift = self.variables["cache"]["cache_index"]
max_decoder_length = self.variables["cache"]["cached_key"].shape[1]
causal_mask = lax.dynamic_slice(
self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length)
)
else:
causal_mask = self.causal_mask[:, :, :query_length, :key_length]
causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:])
# combine masks if needed
if attention_mask is not None and self.causal:
attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape)
attention_mask = combine_masks(attention_mask, causal_mask)
elif self.causal:
attention_mask = causal_mask
elif attention_mask is not None:
attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))
# During fast autoregressive decoding, we feed one position at a time,
# and cache the keys and values step by step.
if self.causal and (self.has_variable("cache", "cached_key") or init_cache):
key_states, value_states, attention_mask = self._concatenate_to_cache(
key_states, value_states, query_states, attention_mask
)
# Convert the boolean attention mask to an attention bias.
if attention_mask is not None:
# attention mask in the form of attention bias
attention_bias = lax.select(
attention_mask > 0,
jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype),
)
else:
attention_bias = None
dropout_rng = None
if not deterministic and self.dropout > 0.0:
dropout_rng = self.make_rng("dropout")
attn_weights = dot_product_attention_weights(
query_states,
key_states,
bias=attention_bias,
dropout_rng=dropout_rng,
dropout_rate=self.dropout,
broadcast_dropout=True,
deterministic=deterministic,
dtype=self.dtype,
precision=None,
)
attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
attn_output = self._merge_heads(attn_output)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights
class FlaxOPTDecoderLayer(nn.Module):
config: OPTConfig
dtype: jnp.dtype = jnp.float32
def setup(self) -> None:
self.embed_dim = self.config.hidden_size
self.self_attn = FlaxOPTAttention(
config=self.config,
embed_dim=self.embed_dim,
num_heads=self.config.num_attention_heads,
dropout=self.config.attention_dropout,
causal=True,
dtype=self.dtype,
)
self.do_layer_norm_before = self.config.do_layer_norm_before
self.dropout_layer = nn.Dropout(rate=self.config.dropout)
self.activation_fn = ACT2FN[self.config.activation_function]
self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
self.fc1 = nn.Dense(
self.config.ffn_dim,
dtype=self.dtype,
kernel_init=jax.nn.initializers.normal(self.config.init_std),
)
self.fc2 = nn.Dense(
self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std)
)
self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
def __call__(
self,
hidden_states: jnp.ndarray,
attention_mask: jnp.ndarray,
init_cache: bool = False,
output_attentions: bool = True,
deterministic: bool = True,
) -> Tuple[jnp.ndarray]:
residual = hidden_states
# 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
if self.do_layer_norm_before:
hidden_states = self.self_attn_layer_norm(hidden_states)
# Self Attention
hidden_states, self_attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
init_cache=init_cache,
deterministic=deterministic,
)
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
hidden_states = residual + hidden_states
# 350m applies layer norm AFTER attention
if not self.do_layer_norm_before:
hidden_states = self.self_attn_layer_norm(hidden_states)
# Fully Connected
hidden_states_shape = hidden_states.shape
hidden_states = hidden_states.reshape(-1, hidden_states.shape[-1])
residual = hidden_states
# 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
if self.do_layer_norm_before:
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.fc1(hidden_states)
hidden_states = self.activation_fn(hidden_states)
hidden_states = self.fc2(hidden_states)
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
hidden_states = (residual + hidden_states).reshape(hidden_states_shape)
# 350m applies layer norm AFTER attention
if not self.do_layer_norm_before:
hidden_states = self.final_layer_norm(hidden_states)
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
return outputs
class FlaxOPTDecoderLayerCollection(nn.Module):
config: OPTConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layers = [
FlaxOPTDecoderLayer(self.config, name=str(i), dtype=self.dtype)
for i in range(self.config.num_hidden_layers)
]
self.layerdrop = self.config.layerdrop
def __call__(
self,
hidden_states,
attention_mask,
deterministic: bool = True,
init_cache: bool = False,
output_attentions: bool = False,
output_hidden_states: bool = False,
):
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
for decoder_layer in self.layers:
if output_hidden_states:
all_hidden_states += (hidden_states,)
layer_outputs = decoder_layer(
hidden_states,
attention_mask=attention_mask,
init_cache=init_cache,
output_attentions=output_attentions,
deterministic=deterministic,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attns += (layer_outputs[1],)
outputs = [hidden_states, all_hidden_states, all_self_attns]
return outputs
class FlaxOPTLearnedPositionalEmbedding(nn.Embed):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def setup(self):
self.offset = 2
self.embedding = self.param(
"embedding", self.embedding_init, (self.num_embeddings + self.offset, self.features), self.param_dtype
)
def __call__(self, positions):
"""`input_ids_shape` is expected to be [bsz x seqlen]."""
return super().__call__(positions + self.offset)
class FlaxOPTDecoder(nn.Module):
config: OPTConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
offset: int = 2
def setup(self):
self.dropout_layer = nn.Dropout(rate=self.config.dropout)
embed_dim = self.config.hidden_size
self.padding_idx = self.config.pad_token_id
self.max_target_positions = self.config.max_position_embeddings
self.embed_tokens = nn.Embed(
self.config.vocab_size,
self.config.word_embed_proj_dim,
embedding_init=jax.nn.initializers.normal(self.config.init_std),
dtype=self.dtype,
)
self.embed_positions = FlaxOPTLearnedPositionalEmbedding(
self.config.max_position_embeddings,
embed_dim,
embedding_init=jax.nn.initializers.normal(self.config.init_std),
dtype=self.dtype,
)
if self.config.word_embed_proj_dim != self.config.hidden_size:
self.project_in = nn.Dense(self.config.hidden_size, use_bias=False)
self.project_out = nn.Dense(self.config.word_embed_proj_dim, use_bias=False)
else:
self.project_in = None
self.project_out = None
# Note that the only purpose of `config._remove_final_layer_norm` is to keep backward compatibility
# with checkpoints that have been fine-tuned before transformers v4.20.1
# see https://github.com/facebookresearch/metaseq/pull/164
if self.config.do_layer_norm_before and not self.config._remove_final_layer_norm:
self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
else:
self.final_layer_norm = None
self.layers = FlaxOPTDecoderLayerCollection(self.config, self.dtype)
def __call__(
self,
input_ids,
attention_mask,
position_ids,
init_cache: bool = False,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
deterministic: bool = True,
):
input_shape = input_ids.shape
input_ids = input_ids.reshape(-1, input_shape[-1])
inputs_embeds = self.embed_tokens(input_ids)
if self.project_in is not None:
inputs_embeds = self.project_in(inputs_embeds)
positions = self.embed_positions(position_ids)
hidden_states = inputs_embeds + positions
hidden_state, all_hidden_states, attentions = self.layers(
hidden_states,
attention_mask,
deterministic=deterministic,
init_cache=init_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
if self.final_layer_norm is not None:
hidden_state = self.final_layer_norm(hidden_state)
if self.project_out is not None:
hidden_state = self.project_out(hidden_state)
if output_hidden_states:
all_hidden_states += (hidden_state,)
outputs = [hidden_state, all_hidden_states, attentions]
if not return_dict:
return tuple(v for v in outputs if v is not None)
return FlaxBaseModelOutput(
last_hidden_state=hidden_state,
hidden_states=all_hidden_states,
attentions=attentions,
)
class FlaxOPTPreTrainedModel(FlaxPreTrainedModel):
config_class = OPTConfig
base_model_prefix: str = "model"
module_class: nn.Module = None
def __init__(
self,
config: OPTConfig,
input_shape: Tuple[int] = (1, 1),
seed: int = 0,
dtype: jnp.dtype = jnp.float32,
_do_init: bool = True,
**kwargs,
):
module = self.module_class(config=config, dtype=dtype, **kwargs)
super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
# init input tensors
input_ids = jnp.zeros(input_shape, dtype="i4")
attention_mask = jnp.ones_like(input_ids)
batch_size, sequence_length = input_ids.shape
position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
params_rng, dropout_rng = jax.random.split(rng)
rngs = {"params": params_rng, "dropout": dropout_rng}
module_init_outputs = self.module.init(
rngs,
input_ids,
attention_mask,
position_ids,
return_dict=False,
)
random_params = module_init_outputs["params"]
if params is not None:
random_params = flatten_dict(unfreeze(random_params))
params = flatten_dict(unfreeze(params))
for missing_key in self._missing_keys:
params[missing_key] = random_params[missing_key]
self._missing_keys = set()
return freeze(unflatten_dict(params))
else:
return random_params
def init_cache(self, batch_size, max_length):
r"""
Args:
batch_size (`int`):
batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache.
max_length (`int`):
maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized
cache.
"""
# init input variables to retrieve cache
input_ids = jnp.ones((batch_size, max_length), dtype="i4")
attention_mask = jnp.ones_like(input_ids, dtype="i4")
position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)
init_variables = self.module.init(
jax.random.PRNGKey(0), input_ids, attention_mask, position_ids, return_dict=False, init_cache=True
)
return unfreeze(init_variables["cache"])
def __call__(
self,
input_ids: jnp.ndarray,
attention_mask: Optional[jnp.ndarray] = None,
position_ids: Optional[jnp.ndarray] = None,
params: dict = None,
past_key_values: dict = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
dropout_rng: PRNGKey = None,
deterministic: bool = True,
):
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.return_dict
if attention_mask is None:
attention_mask = jnp.ones_like(input_ids)
if position_ids is None:
position_ids = (attention_mask.cumsum(axis=1) * attention_mask) - 1
# Handle any PRNG if needed
rngs = {"dropout": dropout_rng} if dropout_rng is not None else {}
inputs = {"params": params or self.params}
# if past_key_values are passed then cache is already initialized a private flag init_cache has to be passed
# down to ensure cache is used. It has to be made sure that cache is marked as mutable so that it can be
# changed by FlaxOPTAttention module
if past_key_values:
inputs["cache"] = past_key_values
mutable = ["cache"]
else:
mutable = False
outputs = self.module.apply(
inputs,
input_ids=jnp.array(input_ids, dtype="i4"),
attention_mask=jnp.array(attention_mask, dtype="i4"),
position_ids=jnp.array(position_ids, dtype="i4"),
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=deterministic,
rngs=rngs,
mutable=mutable,
)
# add updated cache to model output
if past_key_values is not None and return_dict:
outputs, past_key_values = outputs
outputs["past_key_values"] = unfreeze(past_key_values["cache"])
return outputs
elif past_key_values is not None and not return_dict:
outputs, past_key_values = outputs
outputs = outputs[:1] + (unfreeze(past_key_values["cache"]),) + outputs[1:]
return outputs
class FlaxOPTModule(nn.Module):
config: OPTConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.decoder = FlaxOPTDecoder(self.config, dtype=self.dtype)
def _get_decoder_module(self):
return self.decoder
def __call__(
self,
input_ids,
attention_mask,
position_ids,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
deterministic: bool = True,
init_cache=False,
):
decoder_outputs = self.decoder(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=deterministic,
init_cache=init_cache,
)
if not return_dict:
return decoder_outputs
return FlaxBaseModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
hidden_states=decoder_outputs.hidden_states,
attentions=decoder_outputs.attentions,
)
# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartModel with Bart->OPT
class FlaxOPTModel(FlaxOPTPreTrainedModel):
config: OPTConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
module_class = FlaxOPTModule
append_call_sample_docstring(FlaxOPTModel, _CHECKPOINT_FOR_DOC, FlaxBaseModelOutput, _CONFIG_FOR_DOC)
@add_start_docstrings(
"The bare OPT Model transformer outputting raw hidden-states without any specific head on top.",
OPT_START_DOCSTRING,
)
class FlaxOPTForCausalLMModule(nn.Module):
config: OPTConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.model = FlaxOPTModule(config=self.config, dtype=self.dtype)
self.lm_head = nn.Dense(
self.config.vocab_size,
use_bias=False,
dtype=self.dtype,
kernel_init=jax.nn.initializers.normal(self.config.init_std),
)
def __call__(
self,
input_ids,
attention_mask,
position_ids,
init_cache: bool = False,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
deterministic: bool = True,
):
outputs = self.model(
input_ids,
attention_mask,
position_ids,
init_cache=init_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=deterministic,
)
hidden_states = outputs[0]
if self.config.tie_word_embeddings:
shared_embedding = self.model.variables["params"]["decoder"]["embed_tokens"]["embedding"]
lm_logits = self.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
else:
lm_logits = self.lm_head(hidden_states)
if not return_dict:
return (lm_logits,) + outputs[1:]
return FlaxMaskedLMOutput(
logits=lm_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
OPT Model with a language modeling head on top (linear layer with weights tied to the input embeddings) e.g for
autoregressive tasks.
""",
OPT_START_DOCSTRING,
)
class FlaxOPTForCausalLM(FlaxOPTPreTrainedModel):
module_class = FlaxOPTForCausalLMModule
def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jax.Array] = None):
# initializing the cache
batch_size, seq_length = input_ids.shape
past_key_values = self.init_cache(batch_size, max_length)
# Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
# But since the decoder uses a causal mask, those positions are masked anyway.
# Thus, we can create a single static attention_mask here, which is more efficient for compilation
extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4")
if attention_mask is not None:
position_ids = attention_mask.cumsum(axis=1) - 1
extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, attention_mask, (0, 0))
else:
position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length))
return {
"past_key_values": past_key_values,
"attention_mask": extended_attention_mask,
"position_ids": position_ids,
}
def update_inputs_for_generation(self, model_outputs, model_kwargs):
model_kwargs["past_key_values"] = model_outputs.past_key_values
model_kwargs["position_ids"] = model_kwargs["position_ids"][:, -1:] + 1
return model_kwargs
append_call_sample_docstring(
FlaxOPTForCausalLM,
_CHECKPOINT_FOR_DOC,
FlaxBaseModelOutput,
_CONFIG_FOR_DOC,
)
| transformers/src/transformers/models/opt/modeling_flax_opt.py/0 | {
"file_path": "transformers/src/transformers/models/opt/modeling_flax_opt.py",
"repo_id": "transformers",
"token_count": 13784
} | 113 |
# coding=utf-8
# Copyright 2022, Google and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch PEGASUS-X model."""
import dataclasses
import math
from typing import Optional, Tuple, Union
import numpy as np
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from ...activations import ACT2FN
from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOutputWithPastAndCrossAttentions,
Seq2SeqLMOutput,
Seq2SeqModelOutput,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from .configuration_pegasus_x import PegasusXConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "google/pegasus-x-base"
_CONFIG_FOR_DOC = "PegasusXConfig"
PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST = [
"google/pegasus-x-base",
"google/pegasus-x-large",
# See all PEGASUS models at https://huggingface.co/models?filter=pegasus-x
]
@dataclasses.dataclass
class DimensionInfo:
"""Wrapper for dimension info."""
batch_size: int # batch size
seq_len: int # token length
block_size: int # block size
num_heads: int # num heads
hidden_dim: int # hidden dim
dim_per_head: int # dim per head
num_blocks: int # num blocks
global_len: int # global length
padded_seq_len: int # padded token seq length
# Note: Compared to the original Flax implementation, we will pad the token representations to
# a multiple of block size at the start of the encoder layers, so T=P always.
# Copied from transformers.models.bart.modeling_bart.shift_tokens_right
def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
"""
Shift input ids one token to the right.
"""
shifted_input_ids = input_ids.new_zeros(input_ids.shape)
shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
shifted_input_ids[:, 0] = decoder_start_token_id
if pad_token_id is None:
raise ValueError("self.model.config.pad_token_id has to be defined.")
# replace possible -100 values in labels by `pad_token_id`
shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
return shifted_input_ids
class PegasusXSinusoidalPositionalEmbedding(nn.Module):
"""This module produces sinusoidal positional embeddings of any length."""
def __init__(self, embed_dim, max_scale: int = 10000.0):
super().__init__()
self.embed_dim = embed_dim
self.max_scale = max_scale
@torch.no_grad()
def forward(self, input_embeds: torch.Tensor, past_key_values_length: int = 0) -> torch.Tensor:
"""`input_ids_shape` is expected to be [bsz x seqlen]."""
batch_size, seq_len = input_embeds.shape[:2]
positions = torch.arange(
past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=input_embeds.device
)[:, None]
pe = torch.zeros((seq_len, self.embed_dim), device=input_embeds.device, dtype=input_embeds.dtype)
half_d_feature = self.embed_dim // 2
div_term = torch.exp(
torch.arange(half_d_feature, device=input_embeds.device, dtype=torch.int64).type_as(input_embeds)
* -(np.log(float(self.max_scale)) / (half_d_feature - 1))
)
pe[:, :half_d_feature] = torch.sin(positions * div_term)
pe[:, half_d_feature:] = torch.cos(positions * div_term)
return pe[None].expand(batch_size, -1, -1)
# Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->PegasusX
class PegasusXAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
is_causal: bool = False,
config: Optional[PegasusXConfig] = None,
):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
self.config = config
if (self.head_dim * num_heads) != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
f" and `num_heads`: {num_heads})."
)
self.scaling = self.head_dim**-0.5
self.is_decoder = is_decoder
self.is_causal = is_causal
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, _ = hidden_states.size()
# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# get key, value proj
# `past_key_value[0].shape[2] == key_value_states.shape[1]`
# is checking that the `sequence_length` of the `past_key_value` is the same as
# the provided `key_value_states` to support prefix tuning
if (
is_cross_attention
and past_key_value is not None
and past_key_value[0].shape[2] == key_value_states.shape[1]
):
# reuse k,v, cross_attentions
key_states = past_key_value[0]
value_states = past_key_value[1]
elif is_cross_attention:
# cross_attentions
key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
elif past_key_value is not None:
# reuse k, v, self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
else:
# self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_states, value_states)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.reshape(*proj_shape)
value_states = value_states.reshape(*proj_shape)
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
f" {attn_weights.size()}"
)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
if layer_head_mask is not None:
if layer_head_mask.size() != (self.num_heads,):
raise ValueError(
f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
f" {layer_head_mask.size()}"
)
attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
if output_attentions:
# this operation is a bit awkward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to be reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
else:
attn_weights_reshaped = None
attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
attn_output = torch.bmm(attn_probs, value_states)
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz * self.num_heads, tgt_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
# partitioned across GPUs when using tensor-parallelism.
attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights_reshaped, past_key_value
class PegasusXGlobalLocalAttention(nn.Module):
"""Global + Local attention. For use with Encoder only."""
def __init__(
self,
embed_dim: int,
num_heads: int,
block_size: int,
dropout: float = 0.0,
is_decoder: bool = False,
):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.block_size = block_size
self.dropout = dropout
self.head_dim = embed_dim // num_heads
if (self.head_dim * num_heads) != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
f" and `num_heads`: {num_heads})."
)
self.scaling = self.head_dim**-0.5
self.is_decoder = is_decoder
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=False)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=False)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=False)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=False)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
token_hidden_states: torch.Tensor,
global_hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
"""Input shape: Batch x Time x Channel"""
dim = DimensionInfo(
batch_size=token_hidden_states.shape[0],
seq_len=token_hidden_states.shape[1],
block_size=self.block_size,
num_heads=self.num_heads,
hidden_dim=token_hidden_states.shape[2],
dim_per_head=self.head_dim,
num_blocks=token_hidden_states.shape[1] // self.block_size,
global_len=global_hidden_states.shape[1],
padded_seq_len=token_hidden_states.shape[1],
)
# [batch_size, num_heads, padded_seq_len, dim_per_head]
local_q = self._shape(
self.q_proj(token_hidden_states) * self.scaling,
seq_len=dim.padded_seq_len,
bsz=dim.batch_size,
)
local_k = self._shape(
self.k_proj(token_hidden_states),
seq_len=dim.padded_seq_len,
bsz=dim.batch_size,
)
local_v = self._shape(
self.v_proj(token_hidden_states),
seq_len=dim.padded_seq_len,
bsz=dim.batch_size,
)
# [batch_size, num_heads, global_len, dim_per_head]
global_q = self._shape(
self.q_proj(global_hidden_states) * self.scaling,
seq_len=dim.global_len,
bsz=dim.batch_size,
)
global_k = self._shape(
self.k_proj(global_hidden_states),
seq_len=dim.global_len,
bsz=dim.batch_size,
)
global_v = self._shape(
self.v_proj(global_hidden_states),
seq_len=dim.global_len,
bsz=dim.batch_size,
)
global_attn_output, global_attn_probs = self.compute_global_attention_representations(
global_q=global_q,
global_k=global_k,
global_v=global_v,
local_k=local_k,
local_v=local_v,
mask=attention_mask,
dim=dim,
)
local_attn_output, local_attn_probs = self.compute_local_attention_representations(
global_k=global_k,
global_v=global_v,
local_q=local_q,
local_k=local_k,
local_v=local_v,
mask=attention_mask,
dim=dim,
)
# [batch_size, global_len, hidden_dim]
global_attn_output = (
global_attn_output.transpose(1, 2).contiguous().view(dim.batch_size, dim.global_len, dim.hidden_dim)
)
# [batch_size, global_len, hidden_dim]
global_attn_output = self.out_proj(global_attn_output)
# [batch_size, num_heads, block_size, num_heads, dim_per_head]
local_attn_output = local_attn_output.permute(0, 2, 3, 1, 4).contiguous()
# [batch_size, padded_seq_len, hidden_dim]
local_attn_output = local_attn_output.view(dim.batch_size, dim.padded_seq_len, dim.hidden_dim)
# [batch_size, padded_seq_len, hidden_dim]
local_attn_output = self.out_proj(local_attn_output)
if output_attentions:
attn_probs = {"global": global_attn_probs, "local": local_attn_probs}
else:
attn_probs = None
return local_attn_output, global_attn_output, attn_probs
def compute_global_attention_representations(
self, global_q, global_k, global_v, local_k, local_v, mask, dim: DimensionInfo
):
"""Compute attention representations for global tokens.
Global tokens will attend to both global tokens as well as all input sequence tokens. Because the input
sequence tokens are arranged in blocks for local attention, we unblock them and compute attention.
Args:
global_q (`torch.FloatTensor`) of shape [batch_size, num_heads, global_len, dim_per_head]:
query vectors from global tokens
global_k (`torch.FloatTensor`) of shape [batch_size, num_heads, global_len, dim_per_head]:
key vectors from global tokens
global_v (`torch.FloatTensor`) of shape [batch_size, num_heads, global_len, dim_per_head]:
value vectors from global tokens
local_k (`torch.FloatTensor`) of shape [batch_size, num_heads, padded_seq_len, dim_per_head]:
key vectors from local tokens
local_v (`torch.FloatTensor`) of shape [batch_size, num_heads, padded_seq_len, dim_per_head]:
value vectors from local tokens
mask (`torch.FloatTensor`) of shape [batch_size, padded_seq_len]: attention mask
dim (DimensionInfo): DimensionInfo wrapper for dimensions
Returns:
output of shape `[batch_sizes, length, features]`. where length will be padded to a multiple of block_size
"""
# [batch_size, num_heads, global_len+padded_seq_len, dim_per_head]
global_and_local_k = torch.cat([global_k, local_k], dim=2)
# [batch_size, num_heads, global_len+padded_seq_len, dim_per_head]
global_and_local_v = torch.cat([global_v, local_v], dim=2)
# [batch_size, global_len+padded_seq_len]
extended_mask = nn.functional.pad(mask, pad=(dim.global_len, 0), value=0)
# [batch_size, num_heads, global_len, global_len+padded_seq_len]
attn_weights = torch.einsum("BHGF,BHXF->BHGX", global_q, global_and_local_k)
attn_weights = attn_weights + extended_mask[:, None, None, :]
attn_probs = nn.functional.softmax(attn_weights, dim=-1)
attn_probs = nn.functional.dropout(attn_probs, p=self.dropout, training=self.training)
# [batch_size, num_heads, global_len, F]
attn_output = torch.einsum("BHGX,BHXF->BHGF", attn_probs, global_and_local_v)
return attn_output, attn_probs
def compute_local_attention_representations(
self, global_k, global_v, local_q, local_k, local_v, mask, dim: DimensionInfo
):
"""Compute attention representations for local tokens.
Local tokens will attend to both global tokens as well as all other tokens within the same local block. Hence,
we need to tile and concatenate the global tokens to every local block
Args:
global_k (`torch.FloatTensor`) of shape [batch_size, num_heads, global_len, dim_per_head]:
key vectors from global tokens
global_v (`torch.FloatTensor`) of shape [batch_size, num_heads, global_len, dim_per_head]:
value vectors from global tokens
local_q (`torch.FloatTensor`) of shape [batch_size, num_heads, padded_seq_len, dim_per_head]:
query vectors from local tokens
local_k (`torch.FloatTensor`) of shape [batch_size, num_heads, padded_seq_len, dim_per_head]:
key vectors from local tokens
local_v (`torch.FloatTensor`) of shape [batch_size, num_heads, padded_seq_len, dim_per_head]:
value vectors from local tokens
mask (`torch.FloatTensor`) of shape [batch_size, padded_seq_len]: attention mask
dim (DimensionInfo): DimensionInfo wrapper for dimensions
Returns:
output of shape `[batch_sizes, length, features]`. where length will be padded to a multiple of block_size
"""
# [batch_size, num_heads, num_blocks, block_size, dim_per_head]
blocked_local_q = local_q.view(dim.batch_size, dim.num_heads, dim.num_blocks, dim.block_size, dim.dim_per_head)
# [batch_size, num_heads, num_blocks, block_size, dim_per_head]
blocked_local_k = local_k.view(dim.batch_size, dim.num_heads, dim.num_blocks, dim.block_size, dim.dim_per_head)
# [batch_size, num_heads, num_blocks, block_size, dim_per_head]
blocked_local_v = local_v.view(dim.batch_size, dim.num_heads, dim.num_blocks, dim.block_size, dim.dim_per_head)
# [batch_size, num_blocks, global_len+block_size]
extended_mask = nn.functional.pad(
mask.view(dim.batch_size, dim.num_blocks, dim.block_size),
pad=(dim.global_len, 0),
value=0,
)
# [batch_size, num_heads, num_blocks, block_size, global_len]
blocked_local2global = torch.einsum("BHNKF,BHGF->BHNKG", blocked_local_q, global_k)
# [batch_size, num_heads, num_blocks, block_size, block_size]
blocked_local2local = torch.einsum("BHNKF,BHNXF->BHNKX", blocked_local_q, blocked_local_k)
# [batch_size, num_heads, num_blocks, block_size, global_len+block_size]
attn_weights = torch.cat([blocked_local2global, blocked_local2local], dim=-1)
attn_weights = attn_weights + extended_mask[:, None, :, None, :]
attn_probs = nn.functional.softmax(attn_weights, dim=-1)
attn_probs = nn.functional.dropout(attn_probs, p=self.dropout, training=self.training)
# [batch_size, num_heads, num_blocks, block_size, global_len]
local2global_attn_probs = attn_probs[:, :, :, :, : dim.global_len]
# [batch_size, num_heads, num_blocks, block_size, block_size]
local2local_attn_probs = attn_probs[:, :, :, :, dim.global_len :]
# [batch_size, num_heads, num_blocks, block_size, dim_per_head]
local2global_attn_output = torch.einsum("BHNKG,BHGF->BHNKF", local2global_attn_probs, global_v)
# [batch_size, num_heads, num_blocks, block_size, dim_per_head]
local2local_attn_output = torch.einsum("BHNKX,BHNXF->BHNKF", local2local_attn_probs, blocked_local_v)
# [batch_size, num_heads, num_blocks, block_size, dim_per_head]
attn_output = local2global_attn_output + local2local_attn_output
return attn_output, attn_probs
class PegasusXEncoderLayer(nn.Module):
def __init__(self, stagger_blocks_this_layer: bool, config: PegasusXConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = PegasusXGlobalLocalAttention(
embed_dim=self.embed_dim,
num_heads=config.encoder_attention_heads,
block_size=config.block_size,
dropout=config.attention_dropout,
)
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.global_self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
self.stagger_blocks_this_layer = stagger_blocks_this_layer
self.block_size = config.block_size
def forward(
self,
hidden_states: torch.Tensor,
global_hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
output_attentions: bool = False,
) -> torch.Tensor:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape *(seq_len, batch, embed_dim)*
global_hidden_states (`torch.FloatTensor`): global token hidden states
*(seq_len, num_global_tokens, embed_dim)*
attention_mask (`torch.FloatTensor`): attention mask of size
*(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
global_residual = global_hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
global_hidden_states = self.global_self_attn_layer_norm(global_hidden_states)
if self.stagger_blocks_this_layer:
# Pad the blocks to simulate staggering
hidden_states, attention_mask = self.pad_local_tokens(
hidden_states=hidden_states, attention_mask=attention_mask, block_size=self.block_size
)
hidden_states, global_hidden_states, attn_weights = self.self_attn(
token_hidden_states=hidden_states,
global_hidden_states=global_hidden_states,
attention_mask=attention_mask,
output_attentions=output_attentions,
)
if self.stagger_blocks_this_layer:
# Undo the padding
hidden_states = self.unpad_local_tokens(padded_hidden_states=hidden_states, block_size=self.block_size)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
global_hidden_states = nn.functional.dropout(global_hidden_states, p=self.dropout, training=self.training)
global_hidden_states = global_residual + global_hidden_states
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
global_residual = global_hidden_states
global_hidden_states = self.final_layer_norm(global_hidden_states)
global_hidden_states = self.activation_fn(self.fc1(global_hidden_states))
global_hidden_states = nn.functional.dropout(
global_hidden_states, p=self.activation_dropout, training=self.training
)
global_hidden_states = self.fc2(global_hidden_states)
global_hidden_states = nn.functional.dropout(global_hidden_states, p=self.dropout, training=self.training)
global_hidden_states = global_residual + global_hidden_states
outputs = (hidden_states, global_hidden_states)
if output_attentions:
outputs += (attn_weights,)
return outputs
@classmethod
def pad_local_tokens(cls, hidden_states, attention_mask, block_size):
# hidden_states: [batch_size, seq_len, hidden_dim]
pad_size = block_size // 2
mask_min_value = torch.finfo(hidden_states.dtype).min
padded_hidden_states = torch.nn.functional.pad(
hidden_states,
pad=(0, 0, pad_size, pad_size),
)
padded_mask = torch.nn.functional.pad(
attention_mask,
pad=(pad_size, pad_size),
value=mask_min_value,
)
return padded_hidden_states, padded_mask
@classmethod
def unpad_local_tokens(cls, padded_hidden_states, block_size):
# padded_hidden_states: [batch_size, padded seq_len, hidden_dim]
pad_size = block_size // 2
return padded_hidden_states[:, pad_size:-pad_size, :]
class PegasusXDecoderLayer(nn.Module):
def __init__(self, config: PegasusXConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = PegasusXAttention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
dropout=config.attention_dropout,
is_decoder=True,
bias=False,
)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.encoder_attn = PegasusXAttention(
self.embed_dim,
config.decoder_attention_heads,
dropout=config.attention_dropout,
is_decoder=True,
bias=False,
)
self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = True,
) -> torch.Tensor:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape *(seq_len, batch, embed_dim)*
attention_mask (`torch.FloatTensor`): attention mask of size
*(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values.
encoder_hidden_states (`torch.FloatTensor`):
cross attention input to the layer of shape *(seq_len, batch, embed_dim)*
encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
*(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values.
past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache: Whether to us KV cache for decoding
"""
residual = hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
# Self Attention
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
# add present self-attn cache to positions 1,2 of present_key_value tuple
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
past_key_value=self_attn_past_key_value,
attention_mask=attention_mask,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
# Cross-Attention Block
cross_attn_present_key_value = None
cross_attn_weights = None
if encoder_hidden_states is not None:
residual = hidden_states
hidden_states = self.encoder_attn_layer_norm(hidden_states)
# cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
hidden_states=hidden_states,
key_value_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
past_key_value=cross_attn_past_key_value,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
# add cross-attn to positions 3,4 of present_key_value tuple
present_key_value = present_key_value + cross_attn_present_key_value
# Fully Connected
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights, cross_attn_weights)
if use_cache:
outputs += (present_key_value,)
return outputs
class PegasusXPreTrainedModel(PreTrainedModel):
config_class = PegasusXConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = [r"PegasusXEncoderLayer", r"PegasusXDecoderLayer"]
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
PEGASUS_X_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`PegasusXConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
PEGASUS_X_GENERATION_EXAMPLE = r"""
Summarization example:
```python
>>> from transformers import AutoTokenizer, PegasusXForConditionalGeneration
>>> model = PegasusXForConditionalGeneration.from_pretrained("google/pegasus-x-base")
>>> tokenizer = AutoTokenizer.from_pretrained("google/pegasus-x-large")
>>> ARTICLE_TO_SUMMARIZE = (
... "PG&E stated it scheduled the blackouts in response to forecasts for high winds "
... "amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were "
... "scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow."
... )
>>> inputs = tokenizer(ARTICLE_TO_SUMMARIZE, max_length=1024, return_tensors="pt")
>>> # Generate Summary
>>> summary_ids = model.generate(inputs["input_ids"])
>>> tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"California's largest electricity provider has turned off power to hundreds of thousands of customers."
```
"""
PEGASUS_X_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are decoder input IDs?](../glossary#decoder-input-ids)
PEGASUS-X uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If
`past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
`past_key_values`).
decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
be used by default.
encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
`last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be
input (see `past_key_values`). This is useful if you want more control over how to convert
`decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value
of `inputs_embeds`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
class PegasusXEncoder(PegasusXPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`PegasusXEncoderLayer`].
Args:
config: PegasusXConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: PegasusXConfig, embed_tokens: Optional[nn.Embedding] = None):
super().__init__(config)
self.dropout = config.dropout
self.layerdrop = config.encoder_layerdrop
embed_dim = config.d_model
self.max_source_positions = config.max_position_embeddings
self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
if embed_tokens is not None:
self.embed_tokens = embed_tokens
else:
self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim)
self.embed_global = nn.Embedding(config.num_global_tokens, embed_dim)
self.embed_positions = PegasusXSinusoidalPositionalEmbedding(embed_dim)
self.layers = nn.ModuleList(
[
PegasusXEncoderLayer(
stagger_blocks_this_layer=i % 2 == 1 and config.stagger_local_blocks, config=config
)
for i in range(config.encoder_layers)
]
)
self.layer_norm = nn.LayerNorm(config.d_model)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def resize_position_embeddings(self, new_num_position_embeddings: int):
"""
Resizes position embeddings matrix of the model if `new_num_position_embeddings !=
config.max_position_embeddings`.
Arguments:
new_num_position_embeddings (`int`):
The number of new position embeddings. If position embeddings are learned, increasing the size will add
newly initialized vectors at the end, whereas reducing the size will remove vectors from the end. If
position embeddings are not learned (*e.g.* sinusoidal position embeddings), increasing the size will
add correct vectors at the end following the position encoding algorithm, whereas reducing the size
will remove vectors from the end.
"""
logger.info(f"Setting `config.max_position_embeddings={new_num_position_embeddings}`...")
self.config.max_position_embeddings = new_num_position_embeddings
self.embed_positions = PegasusXSinusoidalPositionalEmbedding(self.config.d_model)
self.embed_positions.to(self.device)
def get_position_embeddings(self) -> nn.Embedding:
"""
Returns the position embeddings matrix
"""
return self.embed_positions
def forward(
self,
input_ids=None,
attention_mask=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
embed_pos = self.embed_positions(inputs_embeds)
hidden_states = inputs_embeds + embed_pos
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
batch_size, seq_len, _ = hidden_states.shape
# Setup mask
if attention_mask is None:
attention_mask = torch.ones(*input_shape, dtype=inputs_embeds.dtype, device=inputs_embeds.device)
attention_mask = attention_mask.to(dtype=hidden_states.dtype)
mask_min_value = torch.finfo(hidden_states.dtype).min
inverted_mask = 1.0 - attention_mask
attention_mask = inverted_mask.masked_fill(
inverted_mask.to(torch.bool),
mask_min_value,
)
# padding to block_size
if seq_len % self.config.block_size != 0:
pad_len = self.config.block_size - seq_len % self.config.block_size
hidden_states = nn.functional.pad(hidden_states, pad=(0, 0, 0, pad_len), value=0)
attention_mask = nn.functional.pad(attention_mask, pad=(0, pad_len), value=mask_min_value)
# Global tokens
global_hidden_states = self.embed_global(
torch.arange(self.config.num_global_tokens, device=hidden_states.device)[None].expand(batch_size, -1)
)
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
to_drop = False
if self.training:
dropout_probability = torch.rand([])
if dropout_probability < self.layerdrop: # skip the layer
to_drop = True
if to_drop:
layer_outputs = (None, None)
else:
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
encoder_layer.__call__,
hidden_states,
global_hidden_states,
attention_mask,
output_attentions,
)
else:
layer_outputs = encoder_layer(
hidden_states,
global_hidden_states,
attention_mask,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
global_hidden_states = layer_outputs[1]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[2],)
# Undo padding-to-block-size
hidden_states = hidden_states[:, :seq_len]
hidden_states = self.layer_norm(hidden_states)
if output_hidden_states:
encoder_states = encoder_states + ((hidden_states, global_hidden_states),)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
class PegasusXDecoder(PegasusXPreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`PegasusDecoderLayer`]
Args:
config: PegasusXConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: PegasusXConfig, embed_tokens: Optional[nn.Embedding] = None):
super().__init__(config)
self.dropout = config.dropout
self.layerdrop = config.decoder_layerdrop
self.max_target_positions = config.max_position_embeddings
self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
if embed_tokens is not None:
self.embed_tokens = embed_tokens
else:
self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model)
self.embed_positions = PegasusXSinusoidalPositionalEmbedding(config.d_model)
self.layers = nn.ModuleList([PegasusXDecoderLayer(config) for _ in range(config.decoder_layers)])
self.layer_norm = nn.LayerNorm(config.d_model)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, value):
self.embed_tokens = value
def forward(
self,
input_ids=None,
attention_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_values=None,
inputs_embeds=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
of the decoder.
encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of
shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing
`input_ids` you can choose to directly pass an embedded representation. This is useful if you want more
control over how to convert `input_ids` indices into associated vectors than the model's internal
embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
# past_key_values_length
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
attention_mask = _prepare_4d_causal_attention_mask(
attention_mask, input_shape, inputs_embeds, past_key_values_length
)
# expand encoder attention mask
if encoder_hidden_states is not None and encoder_attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
encoder_attention_mask = _prepare_4d_attention_mask(
encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
)
# embed positions
positions = self.embed_positions(inputs_embeds, past_key_values_length)
positions = positions.to(inputs_embeds.device)
hidden_states = inputs_embeds + positions
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
next_decoder_cache = () if use_cache else None
for idx, decoder_layer in enumerate(self.layers):
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
if output_hidden_states:
all_hidden_states += (hidden_states,)
if self.training:
dropout_probability = torch.rand([])
if dropout_probability < self.layerdrop:
continue
past_key_value = past_key_values[idx] if past_key_values is not None else None
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
None,
output_attentions,
use_cache,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[3 if output_attentions else 1],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
if encoder_hidden_states is not None:
all_cross_attentions += (layer_outputs[2],)
hidden_states = self.layer_norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if not return_dict:
return tuple(
v
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
cross_attentions=all_cross_attentions,
)
@add_start_docstrings(
"The bare PEGASUS-X Model outputting raw hidden-states without any specific head on top.",
PEGASUS_X_START_DOCSTRING,
)
class PegasusXModel(PegasusXPreTrainedModel):
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
def __init__(self, config: PegasusXConfig):
super().__init__(config)
vocab_size = config.vocab_size
self.shared = nn.Embedding(vocab_size, config.d_model)
self.encoder = PegasusXEncoder(config, self.shared)
self.decoder = PegasusXDecoder(config, self.shared)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.shared
def set_input_embeddings(self, value):
self.shared = value
self.encoder.embed_tokens = self.shared
self.decoder.embed_tokens = self.shared
def get_encoder(self):
return self.encoder
def get_decoder(self):
return self.decoder
def resize_position_embeddings(self, new_num_position_embeddings: int):
"""
Resizes position embeddings matrix of the model if `new_num_position_embeddings !=
config.max_position_embeddings`.
Arguments:
new_num_position_embeddings (`int`):
The number of new position embeddings. If position embeddings are learned, increasing the size will add
newly initialized vectors at the end, whereas reducing the size will remove vectors from the end. If
position embeddings are not learned (*e.g.* sinusoidal position embeddings), increasing the size will
add correct vectors at the end following the position encoding algorithm, whereas reducing the size
will remove vectors from the end.
"""
self.config.max_position_embeddings = new_num_position_embeddings
self.encoder.resize_position_embeddings(new_num_position_embeddings)
self.decoder.resize_position_embeddings(new_num_position_embeddings)
def get_position_embeddings(self) -> Tuple[nn.Embedding]:
"""
Returns the position embeddings matrix
"""
return (self.encoder.get_position_embeddings(), self.decoder.get_position_embeddings())
@add_start_docstrings_to_model_forward(PEGASUS_X_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.Tensor] = None,
decoder_attention_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[Tuple[torch.FloatTensor]] = None,
past_key_values: Optional[Tuple[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.Tensor] = None,
decoder_inputs_embeds: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, Seq2SeqModelOutput]:
r"""
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, PegasusModel
>>> tokenizer = AutoTokenizer.from_pretrained("google/pegasus-x-large")
>>> model = PegasusModel.from_pretrained("google/pegasus-x-large")
>>> inputs = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="pt")
>>> decoder_inputs = tokenizer("Studies show that", return_tensors="pt")
>>> outputs = model(input_ids=inputs.input_ids, decoder_input_ids=decoder_inputs.input_ids)
>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 4, 1024]
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if encoder_outputs is None:
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
# If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
encoder_outputs = BaseModelOutput(
last_hidden_state=encoder_outputs[0],
hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
)
# decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
encoder_hidden_states=encoder_outputs[0],
encoder_attention_mask=attention_mask,
past_key_values=past_key_values,
inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if not return_dict:
return decoder_outputs + encoder_outputs
return Seq2SeqModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
past_key_values=decoder_outputs.past_key_values,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
encoder_hidden_states=encoder_outputs.hidden_states,
encoder_attentions=encoder_outputs.attentions,
)
@add_start_docstrings("The PEGASUS-X for conditional generation (e.g. summarization).", PEGASUS_X_START_DOCSTRING)
class PegasusXForConditionalGeneration(PegasusXPreTrainedModel):
base_model_prefix = "model"
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
def __init__(self, config: PegasusXConfig):
super().__init__(config)
self.model = PegasusXModel(config)
self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_encoder(self):
return self.model.get_encoder()
def get_decoder(self):
return self.model.get_decoder()
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def resize_position_embeddings(self, new_num_position_embeddings: int):
"""
Resizes position embeddings matrix of the model if `new_num_position_embeddings !=
config.max_position_embeddings`.
Arguments:
new_num_position_embeddings (`int`):
The number of new position embeddings. If position embeddings are learned, increasing the size will add
newly initialized vectors at the end, whereas reducing the size will remove vectors from the end. If
position embeddings are not learned (*e.g.* sinusoidal position embeddings), increasing the size will
add correct vectors at the end following the position encoding algorithm, whereas reducing the size
will remove vectors from the end.
"""
self.config.max_position_embeddings = new_num_position_embeddings
self.model.encoder.resize_position_embeddings(new_num_position_embeddings)
self.model.decoder.resize_position_embeddings(new_num_position_embeddings)
def get_position_embeddings(self) -> Tuple[nn.Embedding]:
"""
Returns the position embeddings matrix
"""
return (self.model.encoder.get_position_embeddings(), self.model.decoder.get_position_embeddings())
@add_start_docstrings_to_model_forward(PEGASUS_X_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
@add_end_docstrings(PEGASUS_X_GENERATION_EXAMPLE)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.Tensor] = None,
decoder_attention_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[Tuple[torch.FloatTensor]] = None,
past_key_values: Optional[Tuple[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.Tensor] = None,
decoder_inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, Seq2SeqLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Returns:
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
if use_cache:
logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.")
use_cache = False
if decoder_input_ids is None and decoder_inputs_embeds is None:
decoder_input_ids = shift_tokens_right(
labels, self.config.pad_token_id, self.config.decoder_start_token_id
)
outputs = self.model(
input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
encoder_outputs=encoder_outputs,
decoder_attention_mask=decoder_attention_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
lm_logits = self.lm_head(outputs[0])
masked_lm_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (lm_logits,) + outputs[1:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return Seq2SeqLMOutput(
loss=masked_lm_loss,
logits=lm_logits,
past_key_values=outputs.past_key_values,
decoder_hidden_states=outputs.decoder_hidden_states,
decoder_attentions=outputs.decoder_attentions,
cross_attentions=outputs.cross_attentions,
encoder_last_hidden_state=outputs.encoder_last_hidden_state,
encoder_hidden_states=outputs.encoder_hidden_states,
encoder_attentions=outputs.encoder_attentions,
)
def prepare_inputs_for_generation(
self,
decoder_input_ids,
past_key_values=None,
attention_mask=None,
use_cache=None,
encoder_outputs=None,
**kwargs,
):
# cut decoder_input_ids if past is used
if past_key_values is not None:
past_length = past_key_values[0][0].shape[2]
# Some generation methods already pass only the last input ID
if decoder_input_ids.shape[1] > past_length:
remove_prefix_length = past_length
else:
# Default to old behavior: keep only final ID
remove_prefix_length = decoder_input_ids.shape[1] - 1
decoder_input_ids = decoder_input_ids[:, remove_prefix_length:]
return {
"input_ids": None, # encoder_outputs is defined. input_ids not needed
"encoder_outputs": encoder_outputs,
"past_key_values": past_key_values,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"use_cache": use_cache, # change this to avoid caching (presumably for debugging)
}
def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
@staticmethod
def _reorder_cache(past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
# cached cross_attention states don't have to be reordered -> they are always the same
reordered_past += (
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past[:2])
+ layer_past[2:],
)
return reordered_past
# Copied from transformers.models.bart.modeling_bart.BartDecoderWrapper with Bart->PegasusX
class PegasusXDecoderWrapper(PegasusXPreTrainedModel):
"""
This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
used in combination with the [`EncoderDecoderModel`] framework.
"""
def __init__(self, config):
super().__init__(config)
self.decoder = PegasusXDecoder(config)
def forward(self, *args, **kwargs):
return self.decoder(*args, **kwargs)
| transformers/src/transformers/models/pegasus_x/modeling_pegasus_x.py/0 | {
"file_path": "transformers/src/transformers/models/pegasus_x/modeling_pegasus_x.py",
"repo_id": "transformers",
"token_count": 32980
} | 114 |
# coding=utf-8
# Copyright 2023 Authors: Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan,
# Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao and The HuggingFace Inc. team.
# All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Pvt model configuration"""
from collections import OrderedDict
from typing import Callable, List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
logger = logging.get_logger(__name__)
PVT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"pvt-tiny-224": "https://huggingface.co/Zetatech/pvt-tiny-224",
# See all PVT models at https://huggingface.co/models?filter=pvt
}
class PvtConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`PvtModel`]. It is used to instantiate an Pvt
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the Pvt
[Xrenya/pvt-tiny-224](https://huggingface.co/Xrenya/pvt-tiny-224) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
image_size (`int`, *optional*, defaults to 224):
The input image size
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
num_encoder_blocks (`int`, *optional*, defaults to 4):
The number of encoder blocks (i.e. stages in the Mix Transformer encoder).
depths (`List[int]`, *optional*, defaults to `[2, 2, 2, 2]`):
The number of layers in each encoder block.
sequence_reduction_ratios (`List[int]`, *optional*, defaults to `[8, 4, 2, 1]`):
Sequence reduction ratios in each encoder block.
hidden_sizes (`List[int]`, *optional*, defaults to `[64, 128, 320, 512]`):
Dimension of each of the encoder blocks.
patch_sizes (`List[int]`, *optional*, defaults to `[4, 2, 2, 2]`):
Patch size before each encoder block.
strides (`List[int]`, *optional*, defaults to `[4, 2, 2, 2]`):
Stride before each encoder block.
num_attention_heads (`List[int]`, *optional*, defaults to `[1, 2, 5, 8]`):
Number of attention heads for each attention layer in each block of the Transformer encoder.
mlp_ratios (`List[int]`, *optional*, defaults to `[8, 8, 4, 4]`):
Ratio of the size of the hidden layer compared to the size of the input layer of the Mix FFNs in the
encoder blocks.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
drop_path_rate (`float`, *optional*, defaults to 0.0):
The dropout probability for stochastic depth, used in the blocks of the Transformer encoder.
layer_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the layer normalization layers.
qkv_bias (`bool`, *optional*, defaults to `True`):
Whether or not a learnable bias should be added to the queries, keys and values.
num_labels ('int', *optional*, defaults to 1000):
The number of classes.
Example:
```python
>>> from transformers import PvtModel, PvtConfig
>>> # Initializing a PVT Xrenya/pvt-tiny-224 style configuration
>>> configuration = PvtConfig()
>>> # Initializing a model from the Xrenya/pvt-tiny-224 style configuration
>>> model = PvtModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "pvt"
def __init__(
self,
image_size: int = 224,
num_channels: int = 3,
num_encoder_blocks: int = 4,
depths: List[int] = [2, 2, 2, 2],
sequence_reduction_ratios: List[int] = [8, 4, 2, 1],
hidden_sizes: List[int] = [64, 128, 320, 512],
patch_sizes: List[int] = [4, 2, 2, 2],
strides: List[int] = [4, 2, 2, 2],
num_attention_heads: List[int] = [1, 2, 5, 8],
mlp_ratios: List[int] = [8, 8, 4, 4],
hidden_act: Mapping[str, Callable] = "gelu",
hidden_dropout_prob: float = 0.0,
attention_probs_dropout_prob: float = 0.0,
initializer_range: float = 0.02,
drop_path_rate: float = 0.0,
layer_norm_eps: float = 1e-6,
qkv_bias: bool = True,
num_labels: int = 1000,
**kwargs,
):
super().__init__(**kwargs)
self.image_size = image_size
self.num_channels = num_channels
self.num_encoder_blocks = num_encoder_blocks
self.depths = depths
self.sequence_reduction_ratios = sequence_reduction_ratios
self.hidden_sizes = hidden_sizes
self.patch_sizes = patch_sizes
self.strides = strides
self.mlp_ratios = mlp_ratios
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.initializer_range = initializer_range
self.drop_path_rate = drop_path_rate
self.layer_norm_eps = layer_norm_eps
self.num_labels = num_labels
self.qkv_bias = qkv_bias
class PvtOnnxConfig(OnnxConfig):
torch_onnx_minimum_version = version.parse("1.11")
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
]
)
@property
def atol_for_validation(self) -> float:
return 1e-4
@property
def default_onnx_opset(self) -> int:
return 12
| transformers/src/transformers/models/pvt/configuration_pvt.py/0 | {
"file_path": "transformers/src/transformers/models/pvt/configuration_pvt.py",
"repo_id": "transformers",
"token_count": 2810
} | 115 |
# coding=utf-8
# Copyright 2020, The RAG Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""RAG Retriever model implementation."""
import os
import pickle
import time
from typing import Iterable, List, Optional, Tuple
import numpy as np
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import cached_file, is_datasets_available, is_faiss_available, logging, requires_backends, strtobool
from .configuration_rag import RagConfig
from .tokenization_rag import RagTokenizer
if is_datasets_available():
from datasets import Dataset, load_dataset, load_from_disk
if is_faiss_available():
import faiss
logger = logging.get_logger(__name__)
LEGACY_INDEX_PATH = "https://storage.googleapis.com/huggingface-nlp/datasets/wiki_dpr/"
class Index:
"""
A base class for the Indices encapsulated by the [`RagRetriever`].
"""
def get_doc_dicts(self, doc_ids: np.ndarray) -> List[dict]:
"""
Returns a list of dictionaries, containing titles and text of the retrieved documents.
Args:
doc_ids (`np.ndarray` of shape `(batch_size, n_docs)`):
A tensor of document indices.
"""
raise NotImplementedError
def get_top_docs(self, question_hidden_states: np.ndarray, n_docs=5) -> Tuple[np.ndarray, np.ndarray]:
"""
For each query in the batch, retrieves `n_docs` documents.
Args:
question_hidden_states (`np.ndarray` of shape `(batch_size, vector_size)`):
An array of query vectors.
n_docs (`int`):
The number of docs retrieved per query.
Returns:
`np.ndarray` of shape `(batch_size, n_docs)`: A tensor of indices of retrieved documents. `np.ndarray` of
shape `(batch_size, vector_size)`: A tensor of vector representations of retrieved documents.
"""
raise NotImplementedError
def is_initialized(self):
"""
Returns `True` if index is already initialized.
"""
raise NotImplementedError
def init_index(self):
"""
A function responsible for loading the index into memory. Should be called only once per training run of a RAG
model. E.g. if the model is trained on multiple GPUs in a distributed setup, only one of the workers will load
the index.
"""
raise NotImplementedError
class LegacyIndex(Index):
"""
An index which can be deserialized from the files built using https://github.com/facebookresearch/DPR. We use
default faiss index parameters as specified in that repository.
Args:
vector_size (`int`):
The dimension of indexed vectors.
index_path (`str`):
A path to a *directory* containing index files compatible with [`~models.rag.retrieval_rag.LegacyIndex`]
"""
INDEX_FILENAME = "hf_bert_base.hnswSQ8_correct_phi_128.c_index"
PASSAGE_FILENAME = "psgs_w100.tsv.pkl"
def __init__(self, vector_size, index_path):
self.index_id_to_db_id = []
self.index_path = index_path
self.passages = self._load_passages()
self.vector_size = vector_size
self.index = None
self._index_initialized = False
def _resolve_path(self, index_path, filename):
is_local = os.path.isdir(index_path)
try:
# Load from URL or cache if already cached
resolved_archive_file = cached_file(index_path, filename)
except EnvironmentError:
msg = (
f"Can't load '{filename}'. Make sure that:\n\n"
f"- '{index_path}' is a correct remote path to a directory containing a file named {filename}\n\n"
f"- or '{index_path}' is the correct path to a directory containing a file named {filename}.\n\n"
)
raise EnvironmentError(msg)
if is_local:
logger.info(f"loading file {resolved_archive_file}")
else:
logger.info(f"loading file {filename} from cache at {resolved_archive_file}")
return resolved_archive_file
def _load_passages(self):
logger.info(f"Loading passages from {self.index_path}")
passages_path = self._resolve_path(self.index_path, self.PASSAGE_FILENAME)
if not strtobool(os.environ.get("TRUST_REMOTE_CODE", "False")):
raise ValueError(
"This part uses `pickle.load` which is insecure and will execute arbitrary code that is potentially "
"malicious. It's recommended to never unpickle data that could have come from an untrusted source, or "
"that could have been tampered with. If you already verified the pickle data and decided to use it, "
"you can set the environment variable `TRUST_REMOTE_CODE` to `True` to allow it."
)
with open(passages_path, "rb") as passages_file:
passages = pickle.load(passages_file)
return passages
def _deserialize_index(self):
logger.info(f"Loading index from {self.index_path}")
resolved_index_path = self._resolve_path(self.index_path, self.INDEX_FILENAME + ".index.dpr")
self.index = faiss.read_index(resolved_index_path)
resolved_meta_path = self._resolve_path(self.index_path, self.INDEX_FILENAME + ".index_meta.dpr")
if not strtobool(os.environ.get("TRUST_REMOTE_CODE", "False")):
raise ValueError(
"This part uses `pickle.load` which is insecure and will execute arbitrary code that is potentially "
"malicious. It's recommended to never unpickle data that could have come from an untrusted source, or "
"that could have been tampered with. If you already verified the pickle data and decided to use it, "
"you can set the environment variable `TRUST_REMOTE_CODE` to `True` to allow it."
)
with open(resolved_meta_path, "rb") as metadata_file:
self.index_id_to_db_id = pickle.load(metadata_file)
assert (
len(self.index_id_to_db_id) == self.index.ntotal
), "Deserialized index_id_to_db_id should match faiss index size"
def is_initialized(self):
return self._index_initialized
def init_index(self):
index = faiss.IndexHNSWFlat(self.vector_size + 1, 512)
index.hnsw.efSearch = 128
index.hnsw.efConstruction = 200
self.index = index
self._deserialize_index()
self._index_initialized = True
def get_doc_dicts(self, doc_ids: np.array):
doc_list = []
for doc_ids_i in doc_ids:
ids = [str(int(doc_id)) for doc_id in doc_ids_i]
docs = [self.passages[doc_id] for doc_id in ids]
doc_list.append(docs)
doc_dicts = []
for docs in doc_list:
doc_dict = {}
doc_dict["title"] = [doc[1] for doc in docs]
doc_dict["text"] = [doc[0] for doc in docs]
doc_dicts.append(doc_dict)
return doc_dicts
def get_top_docs(self, question_hidden_states: np.ndarray, n_docs=5) -> Tuple[np.ndarray, np.ndarray]:
aux_dim = np.zeros(len(question_hidden_states), dtype="float32").reshape(-1, 1)
query_nhsw_vectors = np.hstack((question_hidden_states, aux_dim))
_, docs_ids = self.index.search(query_nhsw_vectors, n_docs)
vectors = [[self.index.reconstruct(int(doc_id))[:-1] for doc_id in doc_ids] for doc_ids in docs_ids]
ids = [[int(self.index_id_to_db_id[doc_id]) for doc_id in doc_ids] for doc_ids in docs_ids]
return np.array(ids), np.array(vectors)
class HFIndexBase(Index):
def __init__(self, vector_size, dataset, index_initialized=False):
self.vector_size = vector_size
self.dataset = dataset
self._index_initialized = index_initialized
self._check_dataset_format(with_index=index_initialized)
dataset.set_format("numpy", columns=["embeddings"], output_all_columns=True, dtype="float32")
def _check_dataset_format(self, with_index: bool):
if not isinstance(self.dataset, Dataset):
raise ValueError(f"Dataset should be a datasets.Dataset object, but got {type(self.dataset)}")
if len({"title", "text", "embeddings"} - set(self.dataset.column_names)) > 0:
raise ValueError(
"Dataset should be a dataset with the following columns: "
"title (str), text (str) and embeddings (arrays of dimension vector_size), "
f"but got columns {self.dataset.column_names}"
)
if with_index and "embeddings" not in self.dataset.list_indexes():
raise ValueError(
"Missing faiss index in the dataset. Make sure you called `dataset.add_faiss_index` to compute it "
"or `dataset.load_faiss_index` to load one from the disk."
)
def init_index(self):
raise NotImplementedError()
def is_initialized(self):
return self._index_initialized
def get_doc_dicts(self, doc_ids: np.ndarray) -> List[dict]:
return [self.dataset[doc_ids[i].tolist()] for i in range(doc_ids.shape[0])]
def get_top_docs(self, question_hidden_states: np.ndarray, n_docs=5) -> Tuple[np.ndarray, np.ndarray]:
_, ids = self.dataset.search_batch("embeddings", question_hidden_states, n_docs)
docs = [self.dataset[[i for i in indices if i >= 0]] for indices in ids]
vectors = [doc["embeddings"] for doc in docs]
for i in range(len(vectors)):
if len(vectors[i]) < n_docs:
vectors[i] = np.vstack([vectors[i], np.zeros((n_docs - len(vectors[i]), self.vector_size))])
return np.array(ids), np.array(vectors) # shapes (batch_size, n_docs) and (batch_size, n_docs, d)
class CanonicalHFIndex(HFIndexBase):
"""
A wrapper around an instance of [`~datasets.Datasets`]. If `index_path` is set to `None`, we load the pre-computed
index available with the [`~datasets.arrow_dataset.Dataset`], otherwise, we load the index from the indicated path
on disk.
Args:
vector_size (`int`): the dimension of the passages embeddings used by the index
dataset_name (`str`, optional, defaults to `wiki_dpr`):
A dataset identifier of the indexed dataset on HuggingFace AWS bucket (list all available datasets and ids
with `datasets.list_datasets()`).
dataset_split (`str`, optional, defaults to `train`)
Which split of the `dataset` to load.
index_name (`str`, optional, defaults to `train`)
The index_name of the index associated with the `dataset`. The index loaded from `index_path` will be saved
under this name.
index_path (`str`, optional, defaults to `None`)
The path to the serialized faiss index on disk.
use_dummy_dataset (`bool`, optional, defaults to `False`):
If True, use the dummy configuration of the dataset for tests.
"""
def __init__(
self,
vector_size: int,
dataset_name: str = "wiki_dpr",
dataset_split: str = "train",
index_name: Optional[str] = None,
index_path: Optional[str] = None,
use_dummy_dataset=False,
):
if int(index_path is None) + int(index_name is None) != 1:
raise ValueError("Please provide `index_name` or `index_path`.")
self.dataset_name = dataset_name
self.dataset_split = dataset_split
self.index_name = index_name
self.index_path = index_path
self.use_dummy_dataset = use_dummy_dataset
logger.info(f"Loading passages from {self.dataset_name}")
dataset = load_dataset(
self.dataset_name, with_index=False, split=self.dataset_split, dummy=self.use_dummy_dataset
)
super().__init__(vector_size, dataset, index_initialized=False)
def init_index(self):
if self.index_path is not None:
logger.info(f"Loading index from {self.index_path}")
self.dataset.load_faiss_index("embeddings", file=self.index_path)
else:
logger.info(f"Loading index from {self.dataset_name} with index name {self.index_name}")
self.dataset = load_dataset(
self.dataset_name,
with_embeddings=True,
with_index=True,
split=self.dataset_split,
index_name=self.index_name,
dummy=self.use_dummy_dataset,
)
self.dataset.set_format("numpy", columns=["embeddings"], output_all_columns=True)
self._index_initialized = True
class CustomHFIndex(HFIndexBase):
"""
A wrapper around an instance of [`~datasets.Datasets`]. The dataset and the index are both loaded from the
indicated paths on disk.
Args:
vector_size (`int`): the dimension of the passages embeddings used by the index
dataset_path (`str`):
The path to the serialized dataset on disk. The dataset should have 3 columns: title (str), text (str) and
embeddings (arrays of dimension vector_size)
index_path (`str`)
The path to the serialized faiss index on disk.
"""
def __init__(self, vector_size: int, dataset, index_path=None):
super().__init__(vector_size, dataset, index_initialized=index_path is None)
self.index_path = index_path
@classmethod
def load_from_disk(cls, vector_size, dataset_path, index_path):
logger.info(f"Loading passages from {dataset_path}")
if dataset_path is None or index_path is None:
raise ValueError(
"Please provide `dataset_path` and `index_path` after calling `dataset.save_to_disk(dataset_path)` "
"and `dataset.get_index('embeddings').save(index_path)`."
)
dataset = load_from_disk(dataset_path)
return cls(vector_size=vector_size, dataset=dataset, index_path=index_path)
def init_index(self):
if not self.is_initialized():
logger.info(f"Loading index from {self.index_path}")
self.dataset.load_faiss_index("embeddings", file=self.index_path)
self._index_initialized = True
class RagRetriever:
"""
Retriever used to get documents from vector queries. It retrieves the documents embeddings as well as the documents
contents, and it formats them to be used with a RagModel.
Args:
config ([`RagConfig`]):
The configuration of the RAG model this Retriever is used with. Contains parameters indicating which
`Index` to build. You can load your own custom dataset with `config.index_name="custom"` or use a canonical
one (default) from the datasets library with `config.index_name="wiki_dpr"` for example.
question_encoder_tokenizer ([`PreTrainedTokenizer`]):
The tokenizer that was used to tokenize the question. It is used to decode the question and then use the
generator_tokenizer.
generator_tokenizer ([`PreTrainedTokenizer`]):
The tokenizer used for the generator part of the RagModel.
index ([`~models.rag.retrieval_rag.Index`], optional, defaults to the one defined by the configuration):
If specified, use this index instead of the one built using the configuration
Examples:
```python
>>> # To load the default "wiki_dpr" dataset with 21M passages from wikipedia (index name is 'compressed' or 'exact')
>>> from transformers import RagRetriever
>>> retriever = RagRetriever.from_pretrained(
... "facebook/dpr-ctx_encoder-single-nq-base", dataset="wiki_dpr", index_name="compressed"
... )
>>> # To load your own indexed dataset built with the datasets library. More info on how to build the indexed dataset in examples/rag/use_own_knowledge_dataset.py
>>> from transformers import RagRetriever
>>> dataset = (
... ...
... ) # dataset must be a datasets.Datasets object with columns "title", "text" and "embeddings", and it must have a faiss index
>>> retriever = RagRetriever.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base", indexed_dataset=dataset)
>>> # To load your own indexed dataset built with the datasets library that was saved on disk. More info in examples/rag/use_own_knowledge_dataset.py
>>> from transformers import RagRetriever
>>> dataset_path = "path/to/my/dataset" # dataset saved via *dataset.save_to_disk(...)*
>>> index_path = "path/to/my/index.faiss" # faiss index saved via *dataset.get_index("embeddings").save(...)*
>>> retriever = RagRetriever.from_pretrained(
... "facebook/dpr-ctx_encoder-single-nq-base",
... index_name="custom",
... passages_path=dataset_path,
... index_path=index_path,
... )
>>> # To load the legacy index built originally for Rag's paper
>>> from transformers import RagRetriever
>>> retriever = RagRetriever.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base", index_name="legacy")
```"""
def __init__(self, config, question_encoder_tokenizer, generator_tokenizer, index=None, init_retrieval=True):
self._init_retrieval = init_retrieval
requires_backends(self, ["datasets", "faiss"])
super().__init__()
self.index = index or self._build_index(config)
self.generator_tokenizer = generator_tokenizer
self.question_encoder_tokenizer = question_encoder_tokenizer
self.n_docs = config.n_docs
self.batch_size = config.retrieval_batch_size
self.config = config
if self._init_retrieval:
self.init_retrieval()
self.ctx_encoder_tokenizer = None
self.return_tokenized_docs = False
@staticmethod
def _build_index(config):
if config.index_name == "legacy":
return LegacyIndex(
config.retrieval_vector_size,
config.index_path or LEGACY_INDEX_PATH,
)
elif config.index_name == "custom":
return CustomHFIndex.load_from_disk(
vector_size=config.retrieval_vector_size,
dataset_path=config.passages_path,
index_path=config.index_path,
)
else:
return CanonicalHFIndex(
vector_size=config.retrieval_vector_size,
dataset_name=config.dataset,
dataset_split=config.dataset_split,
index_name=config.index_name,
index_path=config.index_path,
use_dummy_dataset=config.use_dummy_dataset,
)
@classmethod
def from_pretrained(cls, retriever_name_or_path, indexed_dataset=None, **kwargs):
requires_backends(cls, ["datasets", "faiss"])
config = kwargs.pop("config", None) or RagConfig.from_pretrained(retriever_name_or_path, **kwargs)
rag_tokenizer = RagTokenizer.from_pretrained(retriever_name_or_path, config=config)
question_encoder_tokenizer = rag_tokenizer.question_encoder
generator_tokenizer = rag_tokenizer.generator
if indexed_dataset is not None:
config.index_name = "custom"
index = CustomHFIndex(config.retrieval_vector_size, indexed_dataset)
else:
index = cls._build_index(config)
return cls(
config,
question_encoder_tokenizer=question_encoder_tokenizer,
generator_tokenizer=generator_tokenizer,
index=index,
)
def save_pretrained(self, save_directory):
if isinstance(self.index, CustomHFIndex):
if self.config.index_path is None:
index_path = os.path.join(save_directory, "hf_dataset_index.faiss")
self.index.dataset.get_index("embeddings").save(index_path)
self.config.index_path = index_path
if self.config.passages_path is None:
passages_path = os.path.join(save_directory, "hf_dataset")
# datasets don't support save_to_disk with indexes right now
faiss_index = self.index.dataset._indexes.pop("embeddings")
self.index.dataset.save_to_disk(passages_path)
self.index.dataset._indexes["embeddings"] = faiss_index
self.config.passages_path = passages_path
self.config.save_pretrained(save_directory)
rag_tokenizer = RagTokenizer(
question_encoder=self.question_encoder_tokenizer,
generator=self.generator_tokenizer,
)
rag_tokenizer.save_pretrained(save_directory)
def init_retrieval(self):
"""
Retriever initialization function. It loads the index into memory.
"""
logger.info("initializing retrieval")
self.index.init_index()
def postprocess_docs(self, docs, input_strings, prefix, n_docs, return_tensors=None):
r"""
Postprocessing retrieved `docs` and combining them with `input_strings`.
Args:
docs (`dict`):
Retrieved documents.
input_strings (`str`):
Input strings decoded by `preprocess_query`.
prefix (`str`):
Prefix added at the beginning of each input, typically used with T5-based models.
Return:
`tuple(tensors)`: a tuple consisting of two elements: contextualized `input_ids` and a compatible
`attention_mask`.
"""
def cat_input_and_doc(doc_title, doc_text, input_string, prefix):
# TODO(Patrick): if we train more RAG models, I want to put the input first to take advantage of effortless truncation
# TODO(piktus): better handling of truncation
if doc_title.startswith('"'):
doc_title = doc_title[1:]
if doc_title.endswith('"'):
doc_title = doc_title[:-1]
if prefix is None:
prefix = ""
out = (prefix + doc_title + self.config.title_sep + doc_text + self.config.doc_sep + input_string).replace(
" ", " "
)
return out
rag_input_strings = [
cat_input_and_doc(
docs[i]["title"][j],
docs[i]["text"][j],
input_strings[i],
prefix,
)
for i in range(len(docs))
for j in range(n_docs)
]
contextualized_inputs = self.generator_tokenizer.batch_encode_plus(
rag_input_strings,
max_length=self.config.max_combined_length,
return_tensors=return_tensors,
padding="max_length",
truncation=True,
)
return contextualized_inputs["input_ids"], contextualized_inputs["attention_mask"]
def _chunk_tensor(self, t: Iterable, chunk_size: int) -> List[Iterable]:
return [t[i : i + chunk_size] for i in range(0, len(t), chunk_size)]
def _main_retrieve(self, question_hidden_states: np.ndarray, n_docs: int) -> Tuple[np.ndarray, np.ndarray]:
question_hidden_states_batched = self._chunk_tensor(question_hidden_states, self.batch_size)
ids_batched = []
vectors_batched = []
for question_hidden_states in question_hidden_states_batched:
start_time = time.time()
ids, vectors = self.index.get_top_docs(question_hidden_states, n_docs)
logger.debug(
f"index search time: {time.time() - start_time} sec, batch size {question_hidden_states.shape}"
)
ids_batched.extend(ids)
vectors_batched.extend(vectors)
return (
np.array(ids_batched),
np.array(vectors_batched),
) # shapes (batch_size, n_docs) and (batch_size, n_docs, d)
def retrieve(self, question_hidden_states: np.ndarray, n_docs: int) -> Tuple[np.ndarray, List[dict]]:
"""
Retrieves documents for specified `question_hidden_states`.
Args:
question_hidden_states (`np.ndarray` of shape `(batch_size, vector_size)`):
A batch of query vectors to retrieve with.
n_docs (`int`):
The number of docs retrieved per query.
Return:
`Tuple[np.ndarray, np.ndarray, List[dict]]`: A tuple with the following objects:
- **retrieved_doc_embeds** (`np.ndarray` of shape `(batch_size, n_docs, dim)`) -- The retrieval embeddings
of the retrieved docs per query.
- **doc_ids** (`np.ndarray` of shape `(batch_size, n_docs)`) -- The ids of the documents in the index
- **doc_dicts** (`List[dict]`): The `retrieved_doc_embeds` examples per query.
"""
doc_ids, retrieved_doc_embeds = self._main_retrieve(question_hidden_states, n_docs)
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(doc_ids)
def set_ctx_encoder_tokenizer(self, ctx_encoder_tokenizer: PreTrainedTokenizer):
# used in end2end retriever training
self.ctx_encoder_tokenizer = ctx_encoder_tokenizer
self.return_tokenized_docs = True
def __call__(
self,
question_input_ids: List[List[int]],
question_hidden_states: np.ndarray,
prefix=None,
n_docs=None,
return_tensors=None,
) -> BatchEncoding:
"""
Retrieves documents for specified `question_hidden_states`.
Args:
question_input_ids (`List[List[int]]`) batch of input ids
question_hidden_states (`np.ndarray` of shape `(batch_size, vector_size)`:
A batch of query vectors to retrieve with.
prefix (`str`, *optional*):
The prefix used by the generator's tokenizer.
n_docs (`int`, *optional*):
The number of docs retrieved per query.
return_tensors (`str` or [`~utils.TensorType`], *optional*, defaults to "pt"):
If set, will return tensors instead of list of python integers. Acceptable values are:
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return Numpy `np.ndarray` objects.
Returns: [`BatchEncoding`]: A [`BatchEncoding`] with the following fields:
- **context_input_ids** -- List of token ids to be fed to a model.
[What are input IDs?](../glossary#input-ids)
- **context_attention_mask** -- List of indices specifying which tokens should be attended to by the model
(when `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names`).
[What are attention masks?](../glossary#attention-mask)
- **retrieved_doc_embeds** -- List of embeddings of the retrieved documents
- **doc_ids** -- List of ids of the retrieved documents
"""
n_docs = n_docs if n_docs is not None else self.n_docs
prefix = prefix if prefix is not None else self.config.generator.prefix
retrieved_doc_embeds, doc_ids, docs = self.retrieve(question_hidden_states, n_docs)
input_strings = self.question_encoder_tokenizer.batch_decode(question_input_ids, skip_special_tokens=True)
context_input_ids, context_attention_mask = self.postprocess_docs(
docs, input_strings, prefix, n_docs, return_tensors=return_tensors
)
if self.return_tokenized_docs:
retrieved_doc_text = []
retrieved_doc_title = []
for b_idx in range(len(docs)):
for doc_idx in range(n_docs):
retrieved_doc_text.append(docs[b_idx]["text"][doc_idx])
retrieved_doc_title.append(docs[b_idx]["title"][doc_idx])
tokenized_docs = self.ctx_encoder_tokenizer(
retrieved_doc_title,
retrieved_doc_text,
truncation=True,
padding="longest",
return_tensors=return_tensors,
)
return BatchEncoding(
{
"context_input_ids": context_input_ids,
"context_attention_mask": context_attention_mask,
"retrieved_doc_embeds": retrieved_doc_embeds,
"doc_ids": doc_ids,
"tokenized_doc_ids": tokenized_docs["input_ids"],
"tokenized_doc_attention_mask": tokenized_docs["attention_mask"],
},
tensor_type=return_tensors,
)
else:
return BatchEncoding(
{
"context_input_ids": context_input_ids,
"context_attention_mask": context_attention_mask,
"retrieved_doc_embeds": retrieved_doc_embeds,
"doc_ids": doc_ids,
},
tensor_type=return_tensors,
)
| transformers/src/transformers/models/rag/retrieval_rag.py/0 | {
"file_path": "transformers/src/transformers/models/rag/retrieval_rag.py",
"repo_id": "transformers",
"token_count": 12868
} | 116 |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert RegNet 10B checkpoints vissl."""
# You need to install a specific version of classy vision
# pip install git+https://github.com/FrancescoSaverioZuppichini/ClassyVision.git@convert_weights
import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs
from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger()
@dataclass
class Tracker:
module: nn.Module
traced: List[nn.Module] = field(default_factory=list)
handles: list = field(default_factory=list)
name2module: Dict[str, nn.Module] = field(default_factory=OrderedDict)
def _forward_hook(self, m, inputs: Tensor, outputs: Tensor, name: str):
has_not_submodules = len(list(m.modules())) == 1 or isinstance(m, nn.Conv2d) or isinstance(m, nn.BatchNorm2d)
if has_not_submodules:
self.traced.append(m)
self.name2module[name] = m
def __call__(self, x: Tensor):
for name, m in self.module.named_modules():
self.handles.append(m.register_forward_hook(partial(self._forward_hook, name=name)))
self.module(x)
[x.remove() for x in self.handles]
return self
@property
def parametrized(self):
# check the len of the state_dict keys to see if we have learnable params
return {k: v for k, v in self.name2module.items() if len(list(v.state_dict().keys())) > 0}
class FakeRegNetVisslWrapper(nn.Module):
"""
Fake wrapper for RegNet that mimics what vissl does without the need to pass a config file.
"""
def __init__(self, model: nn.Module):
super().__init__()
feature_blocks: List[Tuple[str, nn.Module]] = []
# - get the stem
feature_blocks.append(("conv1", model.stem))
# - get all the feature blocks
for k, v in model.trunk_output.named_children():
assert k.startswith("block"), f"Unexpected layer name {k}"
block_index = len(feature_blocks) + 1
feature_blocks.append((f"res{block_index}", v))
self._feature_blocks = nn.ModuleDict(feature_blocks)
def forward(self, x: Tensor):
return get_trunk_forward_outputs(
x,
out_feat_keys=None,
feature_blocks=self._feature_blocks,
)
class FakeRegNetParams(RegNetParams):
"""
Used to instantiace a RegNet model from classy vision with the same depth as the 10B one but with super small
parameters, so we can trace it in memory.
"""
def get_expanded_params(self):
return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]
def get_from_to_our_keys(model_name: str) -> Dict[str, str]:
"""
Returns a dictionary that maps from original model's key -> our implementation's keys
"""
# create our model (with small weights)
our_config = RegNetConfig(depths=[2, 7, 17, 1], hidden_sizes=[8, 8, 8, 8], groups_width=8)
if "in1k" in model_name:
our_model = RegNetForImageClassification(our_config)
else:
our_model = RegNetModel(our_config)
# create from model (with small weights)
from_model = FakeRegNetVisslWrapper(
RegNet(FakeRegNetParams(depth=27, group_width=1010, w_0=1744, w_a=620.83, w_m=2.52))
)
with torch.no_grad():
from_model = from_model.eval()
our_model = our_model.eval()
x = torch.randn((1, 3, 32, 32))
# trace both
dest_tracker = Tracker(our_model)
dest_traced = dest_tracker(x).parametrized
pprint(dest_tracker.name2module)
src_tracker = Tracker(from_model)
src_traced = src_tracker(x).parametrized
# convert the keys -> module dict to keys -> params
def to_params_dict(dict_with_modules):
params_dict = OrderedDict()
for name, module in dict_with_modules.items():
for param_name, param in module.state_dict().items():
params_dict[f"{name}.{param_name}"] = param
return params_dict
from_to_ours_keys = {}
src_state_dict = to_params_dict(src_traced)
dst_state_dict = to_params_dict(dest_traced)
for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items(), dst_state_dict.items()):
from_to_ours_keys[src_key] = dest_key
logger.info(f"{src_key} -> {dest_key}")
# if "in1k" was in the model_name it means it must have a classification head (was finetuned)
if "in1k" in model_name:
from_to_ours_keys["0.clf.0.weight"] = "classifier.1.weight"
from_to_ours_keys["0.clf.0.bias"] = "classifier.1.bias"
return from_to_ours_keys
def convert_weights_and_push(save_directory: Path, model_name: str = None, push_to_hub: bool = True):
filename = "imagenet-1k-id2label.json"
num_labels = 1000
repo_id = "huggingface/label-files"
num_labels = num_labels
id2label = json.load(open(cached_download(hf_hub_url(repo_id, filename, repo_type="dataset")), "r"))
id2label = {int(k): v for k, v in id2label.items()}
id2label = id2label
label2id = {v: k for k, v in id2label.items()}
ImageNetPreTrainedConfig = partial(RegNetConfig, num_labels=num_labels, id2label=id2label, label2id=label2id)
names_to_config = {
"regnet-y-10b-seer": ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1], hidden_sizes=[2020, 4040, 11110, 28280], groups_width=1010
),
# finetuned on imagenet
"regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1], hidden_sizes=[2020, 4040, 11110, 28280], groups_width=1010
),
}
# add seer weights logic
def load_using_classy_vision(checkpoint_url: str) -> Tuple[Dict, Dict]:
files = torch.hub.load_state_dict_from_url(checkpoint_url, model_dir=str(save_directory), map_location="cpu")
# check if we have a head, if yes add it
model_state_dict = files["classy_state_dict"]["base_model"]["model"]
return model_state_dict["trunk"], model_state_dict["heads"]
names_to_from_model = {
"regnet-y-10b-seer": partial(
load_using_classy_vision,
"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch",
),
"regnet-y-10b-seer-in1k": partial(
load_using_classy_vision,
"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch",
),
}
from_to_ours_keys = get_from_to_our_keys(model_name)
if not (save_directory / f"{model_name}.pth").exists():
logger.info("Loading original state_dict.")
from_state_dict_trunk, from_state_dict_head = names_to_from_model[model_name]()
from_state_dict = from_state_dict_trunk
if "in1k" in model_name:
# add the head
from_state_dict = {**from_state_dict_trunk, **from_state_dict_head}
logger.info("Done!")
converted_state_dict = {}
not_used_keys = list(from_state_dict.keys())
regex = r"\.block.-part."
# this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
for key in from_state_dict.keys():
# remove the weird "block[0,1]-part" from the key
src_key = re.sub(regex, "", key)
# now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
dest_key = from_to_ours_keys[src_key]
# store the parameter with our key
converted_state_dict[dest_key] = from_state_dict[key]
not_used_keys.remove(key)
# check that all keys have been updated
assert len(not_used_keys) == 0, f"Some keys where not used {','.join(not_used_keys)}"
logger.info(f"The following keys were not used: {','.join(not_used_keys)}")
# save our state dict to disk
torch.save(converted_state_dict, save_directory / f"{model_name}.pth")
del converted_state_dict
else:
logger.info("The state_dict was already stored on disk.")
if push_to_hub:
logger.info(f"Token is {os.environ['HF_TOKEN']}")
logger.info("Loading our model.")
# create our model
our_config = names_to_config[model_name]
our_model_func = RegNetModel
if "in1k" in model_name:
our_model_func = RegNetForImageClassification
our_model = our_model_func(our_config)
# place our model to the meta device (so remove all the weights)
our_model.to(torch.device("meta"))
logger.info("Loading state_dict in our model.")
# load state dict
state_dict_keys = our_model.state_dict().keys()
PreTrainedModel._load_pretrained_model_low_mem(
our_model, state_dict_keys, [save_directory / f"{model_name}.pth"]
)
logger.info("Finally, pushing!")
# push it to hub
our_model.push_to_hub(
repo_path_or_name=save_directory / model_name,
commit_message="Add model",
output_dir=save_directory / model_name,
)
size = 384
# we can use the convnext one
image_processor = AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k", size=size)
image_processor.push_to_hub(
repo_path_or_name=save_directory / model_name,
commit_message="Add image processor",
output_dir=save_directory / model_name,
)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default=None,
type=str,
help=(
"The name of the model you wish to convert, it must be one of the supported regnet* architecture,"
" currently: regnetx-*, regnety-*. If `None`, all of them will the converted."
),
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=Path,
required=True,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
default=True,
type=bool,
required=False,
help="If True, push model and image processor to the hub.",
)
args = parser.parse_args()
pytorch_dump_folder_path: Path = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| transformers/src/transformers/models/regnet/convert_regnet_seer_10b_to_pytorch.py/0 | {
"file_path": "transformers/src/transformers/models/regnet/convert_regnet_seer_10b_to_pytorch.py",
"repo_id": "transformers",
"token_count": 4917
} | 117 |
# coding=utf-8
# Copyright 2022 Microsoft Research, Inc. and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch ResNet model."""
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig
logger = logging.get_logger(__name__)
# General docstring
_CONFIG_FOR_DOC = "ResNetConfig"
# Base docstring
_CHECKPOINT_FOR_DOC = "microsoft/resnet-50"
_EXPECTED_OUTPUT_SHAPE = [1, 2048, 7, 7]
# Image classification docstring
_IMAGE_CLASS_CHECKPOINT = "microsoft/resnet-50"
_IMAGE_CLASS_EXPECTED_OUTPUT = "tiger cat"
RESNET_PRETRAINED_MODEL_ARCHIVE_LIST = [
"microsoft/resnet-50",
# See all resnet models at https://huggingface.co/models?filter=resnet
]
class ResNetConvLayer(nn.Module):
def __init__(
self, in_channels: int, out_channels: int, kernel_size: int = 3, stride: int = 1, activation: str = "relu"
):
super().__init__()
self.convolution = nn.Conv2d(
in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=kernel_size // 2, bias=False
)
self.normalization = nn.BatchNorm2d(out_channels)
self.activation = ACT2FN[activation] if activation is not None else nn.Identity()
def forward(self, input: Tensor) -> Tensor:
hidden_state = self.convolution(input)
hidden_state = self.normalization(hidden_state)
hidden_state = self.activation(hidden_state)
return hidden_state
class ResNetEmbeddings(nn.Module):
"""
ResNet Embeddings (stem) composed of a single aggressive convolution.
"""
def __init__(self, config: ResNetConfig):
super().__init__()
self.embedder = ResNetConvLayer(
config.num_channels, config.embedding_size, kernel_size=7, stride=2, activation=config.hidden_act
)
self.pooler = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.num_channels = config.num_channels
def forward(self, pixel_values: Tensor) -> Tensor:
num_channels = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration."
)
embedding = self.embedder(pixel_values)
embedding = self.pooler(embedding)
return embedding
class ResNetShortCut(nn.Module):
"""
ResNet shortcut, used to project the residual features to the correct size. If needed, it is also used to
downsample the input using `stride=2`.
"""
def __init__(self, in_channels: int, out_channels: int, stride: int = 2):
super().__init__()
self.convolution = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False)
self.normalization = nn.BatchNorm2d(out_channels)
def forward(self, input: Tensor) -> Tensor:
hidden_state = self.convolution(input)
hidden_state = self.normalization(hidden_state)
return hidden_state
class ResNetBasicLayer(nn.Module):
"""
A classic ResNet's residual layer composed by two `3x3` convolutions.
"""
def __init__(self, in_channels: int, out_channels: int, stride: int = 1, activation: str = "relu"):
super().__init__()
should_apply_shortcut = in_channels != out_channels or stride != 1
self.shortcut = (
ResNetShortCut(in_channels, out_channels, stride=stride) if should_apply_shortcut else nn.Identity()
)
self.layer = nn.Sequential(
ResNetConvLayer(in_channels, out_channels, stride=stride),
ResNetConvLayer(out_channels, out_channels, activation=None),
)
self.activation = ACT2FN[activation]
def forward(self, hidden_state):
residual = hidden_state
hidden_state = self.layer(hidden_state)
residual = self.shortcut(residual)
hidden_state += residual
hidden_state = self.activation(hidden_state)
return hidden_state
class ResNetBottleNeckLayer(nn.Module):
"""
A classic ResNet's bottleneck layer composed by three `3x3` convolutions.
The first `1x1` convolution reduces the input by a factor of `reduction` in order to make the second `3x3`
convolution faster. The last `1x1` convolution remaps the reduced features to `out_channels`. If
`downsample_in_bottleneck` is true, downsample will be in the first layer instead of the second layer.
"""
def __init__(
self,
in_channels: int,
out_channels: int,
stride: int = 1,
activation: str = "relu",
reduction: int = 4,
downsample_in_bottleneck: bool = False,
):
super().__init__()
should_apply_shortcut = in_channels != out_channels or stride != 1
reduces_channels = out_channels // reduction
self.shortcut = (
ResNetShortCut(in_channels, out_channels, stride=stride) if should_apply_shortcut else nn.Identity()
)
self.layer = nn.Sequential(
ResNetConvLayer(
in_channels, reduces_channels, kernel_size=1, stride=stride if downsample_in_bottleneck else 1
),
ResNetConvLayer(reduces_channels, reduces_channels, stride=stride if not downsample_in_bottleneck else 1),
ResNetConvLayer(reduces_channels, out_channels, kernel_size=1, activation=None),
)
self.activation = ACT2FN[activation]
def forward(self, hidden_state):
residual = hidden_state
hidden_state = self.layer(hidden_state)
residual = self.shortcut(residual)
hidden_state += residual
hidden_state = self.activation(hidden_state)
return hidden_state
class ResNetStage(nn.Module):
"""
A ResNet stage composed by stacked layers.
"""
def __init__(
self,
config: ResNetConfig,
in_channels: int,
out_channels: int,
stride: int = 2,
depth: int = 2,
):
super().__init__()
layer = ResNetBottleNeckLayer if config.layer_type == "bottleneck" else ResNetBasicLayer
if config.layer_type == "bottleneck":
first_layer = layer(
in_channels,
out_channels,
stride=stride,
activation=config.hidden_act,
downsample_in_bottleneck=config.downsample_in_bottleneck,
)
else:
first_layer = layer(in_channels, out_channels, stride=stride, activation=config.hidden_act)
self.layers = nn.Sequential(
first_layer, *[layer(out_channels, out_channels, activation=config.hidden_act) for _ in range(depth - 1)]
)
def forward(self, input: Tensor) -> Tensor:
hidden_state = input
for layer in self.layers:
hidden_state = layer(hidden_state)
return hidden_state
class ResNetEncoder(nn.Module):
def __init__(self, config: ResNetConfig):
super().__init__()
self.stages = nn.ModuleList([])
# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
self.stages.append(
ResNetStage(
config,
config.embedding_size,
config.hidden_sizes[0],
stride=2 if config.downsample_in_first_stage else 1,
depth=config.depths[0],
)
)
in_out_channels = zip(config.hidden_sizes, config.hidden_sizes[1:])
for (in_channels, out_channels), depth in zip(in_out_channels, config.depths[1:]):
self.stages.append(ResNetStage(config, in_channels, out_channels, depth=depth))
def forward(
self, hidden_state: Tensor, output_hidden_states: bool = False, return_dict: bool = True
) -> BaseModelOutputWithNoAttention:
hidden_states = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
hidden_states = hidden_states + (hidden_state,)
hidden_state = stage_module(hidden_state)
if output_hidden_states:
hidden_states = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None)
return BaseModelOutputWithNoAttention(
last_hidden_state=hidden_state,
hidden_states=hidden_states,
)
class ResNetPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ResNetConfig
base_model_prefix = "resnet"
main_input_name = "pixel_values"
def _init_weights(self, module):
if isinstance(module, nn.Conv2d):
nn.init.kaiming_normal_(module.weight, mode="fan_out", nonlinearity="relu")
elif isinstance(module, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(module.weight, 1)
nn.init.constant_(module.bias, 0)
RESNET_START_DOCSTRING = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
RESNET_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`ConvNextImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare ResNet model outputting raw features without any specific head on top.",
RESNET_START_DOCSTRING,
)
class ResNetModel(ResNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.embedder = ResNetEmbeddings(config)
self.encoder = ResNetEncoder(config)
self.pooler = nn.AdaptiveAvgPool2d((1, 1))
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(RESNET_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPoolingAndNoAttention,
config_class=_CONFIG_FOR_DOC,
modality="vision",
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
def forward(
self, pixel_values: Tensor, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None
) -> BaseModelOutputWithPoolingAndNoAttention:
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
embedding_output = self.embedder(pixel_values)
encoder_outputs = self.encoder(
embedding_output, output_hidden_states=output_hidden_states, return_dict=return_dict
)
last_hidden_state = encoder_outputs[0]
pooled_output = self.pooler(last_hidden_state)
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
)
@add_start_docstrings(
"""
ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
""",
RESNET_START_DOCSTRING,
)
class ResNetForImageClassification(ResNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.resnet = ResNetModel(config)
# classification head
self.classifier = nn.Sequential(
nn.Flatten(),
nn.Linear(config.hidden_sizes[-1], config.num_labels) if config.num_labels > 0 else nn.Identity(),
)
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(RESNET_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=ImageClassifierOutputWithNoAttention,
config_class=_CONFIG_FOR_DOC,
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
)
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> ImageClassifierOutputWithNoAttention:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.resnet(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)
pooled_output = outputs.pooler_output if return_dict else outputs[1]
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=loss, logits=logits, hidden_states=outputs.hidden_states)
@add_start_docstrings(
"""
ResNet backbone, to be used with frameworks like DETR and MaskFormer.
""",
RESNET_START_DOCSTRING,
)
class ResNetBackbone(ResNetPreTrainedModel, BackboneMixin):
def __init__(self, config):
super().__init__(config)
super()._init_backbone(config)
self.num_features = [config.embedding_size] + config.hidden_sizes
self.embedder = ResNetEmbeddings(config)
self.encoder = ResNetEncoder(config)
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(RESNET_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BackboneOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self, pixel_values: Tensor, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None
) -> BackboneOutput:
"""
Returns:
Examples:
```python
>>> from transformers import AutoImageProcessor, AutoBackbone
>>> import torch
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50")
>>> model = AutoBackbone.from_pretrained(
... "microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]
... )
>>> inputs = processor(image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> feature_maps = outputs.feature_maps
>>> list(feature_maps[-1].shape)
[1, 2048, 7, 7]
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
embedding_output = self.embedder(pixel_values)
outputs = self.encoder(embedding_output, output_hidden_states=True, return_dict=True)
hidden_states = outputs.hidden_states
feature_maps = ()
for idx, stage in enumerate(self.stage_names):
if stage in self.out_features:
feature_maps += (hidden_states[idx],)
if not return_dict:
output = (feature_maps,)
if output_hidden_states:
output += (outputs.hidden_states,)
return output
return BackboneOutput(
feature_maps=feature_maps,
hidden_states=outputs.hidden_states if output_hidden_states else None,
attentions=None,
)
| transformers/src/transformers/models/resnet/modeling_resnet.py/0 | {
"file_path": "transformers/src/transformers/models/resnet/modeling_resnet.py",
"repo_id": "transformers",
"token_count": 8017
} | 118 |
# coding=utf-8
# Copyright 2023 Bo Peng and HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch RWKV model."""
import math
from dataclasses import dataclass
from pathlib import Path
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from ...modeling_utils import PreTrainedModel
from ...utils import (
ModelOutput,
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
is_bitsandbytes_available,
is_ninja_available,
is_torch_cuda_available,
logging,
)
from .configuration_rwkv import RwkvConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "RWKV/rwkv-4-169m-pile"
_CONFIG_FOR_DOC = "RwkvConfig"
RWKV_PRETRAINED_MODEL_ARCHIVE_LIST = [
"RWKV/rwkv-4-169m-pile",
"RWKV/rwkv-4-430m-pile",
"RWKV/rwkv-4-1b5-pile",
"RWKV/rwkv-4-3b-pile",
"RWKV/rwkv-4-7b-pile",
"RWKV/rwkv-4-14b-pile",
"RWKV/rwkv-raven-1b5",
"RWKV/rwkv-raven-3b",
"RWKV/rwkv-raven-7b",
"RWKV/rwkv-raven-14b",
# See all RWKV models at https://huggingface.co/models?filter=rwkv
]
rwkv_cuda_kernel = None
def load_wkv_cuda_kernel(context_length):
from torch.utils.cpp_extension import load as load_kernel
global rwkv_cuda_kernel
kernel_folder = Path(__file__).resolve().parent.parent.parent / "kernels" / "rwkv"
cuda_kernel_files = [kernel_folder / f for f in ["wkv_op.cpp", "wkv_cuda.cu", "wkv_cuda_bf16.cu"]]
# Only load the kernel if it's not been loaded yet or if we changed the context length
if rwkv_cuda_kernel is not None and rwkv_cuda_kernel.max_seq_length == context_length:
return
logger.info(f"Loading CUDA kernel for RWKV at context length of {context_length}.")
flags = [
"-res-usage",
"--maxrregcount 60",
"--use_fast_math",
"-O3",
"-Xptxas -O3",
"--extra-device-vectorization",
f"-DTmax={context_length}",
]
rwkv_cuda_kernel = load_kernel(
name=f"wkv_{context_length}",
sources=cuda_kernel_files,
verbose=(logging.get_verbosity() == logging.DEBUG),
extra_cuda_cflags=flags,
)
rwkv_cuda_kernel.max_seq_length = context_length
class RwkvLinearAttention(torch.autograd.Function):
@staticmethod
def forward(ctx, time_decay, time_first, key, value, state=None, return_state=False):
batch_size, seq_len, hidden_size = key.size()
if seq_len > rwkv_cuda_kernel.max_seq_length:
raise ValueError(
f"Cannot process a batch with {seq_len} tokens at the same time, use a maximum of "
f"{rwkv_cuda_kernel.max_seq_length} with this model."
)
if batch_size * hidden_size % min(hidden_size, 32) != 0:
raise ValueError(
f"The product of batch size ({batch_size}) and hidden size ({hidden_size}) needs to be a round "
f"multiple of {min(hidden_size, 32)}."
)
ctx.input_dtype = key.dtype
if (
time_decay.device.type != "cuda"
or time_first.device.type != "cuda"
or key.device.type != "cuda"
or value.device.type != "cuda"
):
raise ValueError("Calling the CUDA kernel for wkv attention requires all tensors to be on CUDA devices.")
time_decay = -torch.exp(time_decay.float().contiguous())
if key.dtype == torch.float16:
time_first = time_first.float()
key = key.float()
value = value.float()
time_first = time_first.contiguous()
key = key.contiguous()
value = value.contiguous()
# The CUDA kernel will fill this tensor.
output = torch.empty_like(key, memory_format=torch.contiguous_format)
if return_state or state is not None:
if state is None:
state = torch.zeros(
batch_size,
hidden_size,
3,
dtype=torch.float32,
device=key.device,
memory_format=torch.contiguous_format,
)
state[:, :, 2] -= 1e38
else:
state = torch.cat([s.unsqueeze(2) for s in state], dim=2).contiguous()
if key.dtype == torch.bfloat16:
forward_func = rwkv_cuda_kernel.forward_with_state_bf16
else:
forward_func = rwkv_cuda_kernel.forward_with_state
forward_func(time_decay, time_first, key, value, output, state)
else:
forward_func = rwkv_cuda_kernel.forward_bf16 if key.dtype == torch.bfloat16 else rwkv_cuda_kernel.forward
forward_func(time_decay, time_first, key, value, output)
ctx.save_for_backward(time_decay, time_first, key, value, output)
if state is not None:
state = [s.squeeze(2) for s in torch.chunk(state, 3, dim=2)]
return output.to(ctx.input_dtype), state
@staticmethod
# g stands for grad
def backward(ctx, g_output, g_state=None):
input_dtype = ctx.input_dtype
time_decay, time_first, key, value, output = ctx.saved_tensors
# The CUDA kernel will fill those tensors.
g_time_decay = torch.empty_like(
time_decay,
memory_format=torch.contiguous_format,
dtype=torch.bfloat16 if input_dtype == torch.bfloat16 else torch.float32,
)
g_time_first = torch.empty_like(time_first, memory_format=torch.contiguous_format)
g_key = torch.empty_like(key, memory_format=torch.contiguous_format)
g_value = torch.empty_like(value, memory_format=torch.contiguous_format)
if input_dtype == torch.float16:
g_output = g_output.float()
backward_func = rwkv_cuda_kernel.backward_bf16 if input_dtype == torch.bfloat16 else rwkv_cuda_kernel.backward
backward_func(
time_decay,
time_first,
key,
value,
output,
g_output.contiguous(),
g_time_decay,
g_time_first,
g_key,
g_value,
)
return (
g_time_decay.to(input_dtype),
g_time_first.to(input_dtype),
g_key.to(input_dtype),
g_value.to(input_dtype),
None,
None,
)
def rwkv_linear_attention_cpu(time_decay, time_first, key, value, state=None, return_state=False):
# For CPU fallback. Will be slower and probably take more memory than the custom CUDA kernel if not executed
# within a torch.no_grad.
_, seq_length, _ = key.size()
output = torch.zeros_like(key)
if state is None:
num_state = torch.zeros_like(key[:, 0], dtype=torch.float32)
den_state = torch.zeros_like(key[:, 0], dtype=torch.float32)
max_state = torch.zeros_like(key[:, 0], dtype=torch.float32) - 1e38
else:
num_state, den_state, max_state = state
# For numerical stability
# real_numerator_state = num_state * torch.exp(max_state)
# real_denominator_state = den_state * torch.exp(max_state)
time_decay = -torch.exp(time_decay)
for current_index in range(seq_length):
current_key = key[:, current_index].float()
current_value = value[:, current_index]
# wkv computation at time t
max_for_output = torch.maximum(max_state, current_key + time_first)
e1 = torch.exp(max_state - max_for_output)
e2 = torch.exp(current_key + time_first - max_for_output)
numerator = e1 * num_state + e2 * current_value
denominator = e1 * den_state + e2
output[:, current_index] = (numerator / denominator).to(output.dtype)
# Update state for next iteration
max_for_state = torch.maximum(max_state + time_decay, current_key)
e1 = torch.exp(max_state + time_decay - max_for_state)
e2 = torch.exp(current_key - max_for_state)
num_state = e1 * num_state + e2 * current_value
den_state = e1 * den_state + e2
max_state = max_for_state
if return_state or state is not None:
state = [num_state, den_state, max_state]
return output, state
def rwkv_linear_attention(time_decay, time_first, key, value, state=None, return_state=False):
no_cuda = any(t.device.type != "cuda" for t in [time_decay, time_first, key, value])
# Launching the CUDA kernel for just one token will actually be slower (there is no for loop in the CPU version
# in this case).
one_token = key.size(1) == 1
if rwkv_cuda_kernel is None or no_cuda or one_token:
return rwkv_linear_attention_cpu(time_decay, time_first, key, value, state=state, return_state=return_state)
else:
return RwkvLinearAttention.apply(time_decay, time_first, key, value, state, return_state)
class RwkvSelfAttention(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.config = config
kernel_loaded = rwkv_cuda_kernel is not None and rwkv_cuda_kernel.max_seq_length == config.context_length
if is_ninja_available() and is_torch_cuda_available() and not kernel_loaded:
try:
load_wkv_cuda_kernel(config.context_length)
except Exception:
logger.info("Could not load the custom CUDA kernel for RWKV attention.")
self.layer_id = layer_id
hidden_size = config.hidden_size
attention_hidden_size = (
config.attention_hidden_size if config.attention_hidden_size is not None else hidden_size
)
self.attention_hidden_size = attention_hidden_size
self.time_decay = nn.Parameter(torch.empty(attention_hidden_size))
self.time_first = nn.Parameter(torch.empty(attention_hidden_size))
self.time_mix_key = nn.Parameter(torch.empty(1, 1, hidden_size))
self.time_mix_value = nn.Parameter(torch.empty(1, 1, hidden_size))
self.time_mix_receptance = nn.Parameter(torch.empty(1, 1, hidden_size))
self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
self.key = nn.Linear(hidden_size, attention_hidden_size, bias=False)
self.value = nn.Linear(hidden_size, attention_hidden_size, bias=False)
self.receptance = nn.Linear(hidden_size, attention_hidden_size, bias=False)
self.output = nn.Linear(attention_hidden_size, hidden_size, bias=False)
# TODO: maybe jit, otherwise move inside forward
def extract_key_value(self, hidden, state=None):
# Mix hidden with the previous timestep to produce key, value, receptance
if hidden.size(1) == 1 and state is not None:
shifted = state[1][:, :, self.layer_id]
else:
shifted = self.time_shift(hidden)
if state is not None:
shifted[:, 0] = state[1][:, :, self.layer_id]
key = hidden * self.time_mix_key + shifted * (1 - self.time_mix_key)
value = hidden * self.time_mix_value + shifted * (1 - self.time_mix_value)
receptance = hidden * self.time_mix_receptance + shifted * (1 - self.time_mix_receptance)
key = self.key(key)
value = self.value(value)
receptance = torch.sigmoid(self.receptance(receptance))
if state is not None:
state[1][:, :, self.layer_id] = hidden[:, -1]
return receptance, key, value, state
def forward(self, hidden, state=None, use_cache=False):
receptance, key, value, state = self.extract_key_value(hidden, state=state)
layer_state = tuple(s[:, :, self.layer_id] for s in state[2:]) if state is not None else None
rwkv, layer_state = rwkv_linear_attention(
self.time_decay,
self.time_first,
key,
value,
state=layer_state,
return_state=use_cache,
)
if layer_state is not None:
state[2][:, :, self.layer_id] = layer_state[0]
state[3][:, :, self.layer_id] = layer_state[1]
state[4][:, :, self.layer_id] = layer_state[2]
return self.output(receptance * rwkv), state
class RwkvFeedForward(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.config = config
self.layer_id = layer_id
hidden_size = config.hidden_size
intermediate_size = (
config.intermediate_size if config.intermediate_size is not None else 4 * config.hidden_size
)
self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
self.time_mix_key = nn.Parameter(torch.empty(1, 1, hidden_size))
self.time_mix_receptance = nn.Parameter(torch.empty(1, 1, hidden_size))
self.key = nn.Linear(hidden_size, intermediate_size, bias=False)
self.receptance = nn.Linear(hidden_size, hidden_size, bias=False)
self.value = nn.Linear(intermediate_size, hidden_size, bias=False)
def forward(self, hidden, state=None):
if hidden.size(1) == 1 and state is not None:
shifted = state[0][:, :, self.layer_id]
else:
shifted = self.time_shift(hidden)
if state is not None:
shifted[:, 0] = state[0][:, :, self.layer_id]
key = hidden * self.time_mix_key + shifted * (1 - self.time_mix_key)
receptance = hidden * self.time_mix_receptance + shifted * (1 - self.time_mix_receptance)
key = torch.square(torch.relu(self.key(key)))
value = self.value(key)
receptance = torch.sigmoid(self.receptance(receptance))
if state is not None:
state[0][:, :, self.layer_id] = hidden[:, -1]
return receptance * value, state
class RwkvBlock(nn.Module):
def __init__(self, config, layer_id):
super().__init__()
self.config = config
self.layer_id = layer_id
if layer_id == 0:
self.pre_ln = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_epsilon)
self.ln1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_epsilon)
self.ln2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_epsilon)
self.attention = RwkvSelfAttention(config, layer_id)
self.feed_forward = RwkvFeedForward(config, layer_id)
def forward(self, hidden, state=None, use_cache=False, output_attentions=False):
if self.layer_id == 0:
hidden = self.pre_ln(hidden)
attention, state = self.attention(self.ln1(hidden), state=state, use_cache=use_cache)
hidden = hidden + attention
feed_forward, state = self.feed_forward(self.ln2(hidden), state=state)
hidden = hidden + feed_forward
outputs = (hidden, state)
if output_attentions:
outputs += (attention,)
else:
outputs += (None,)
return outputs
class RwkvPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = RwkvConfig
base_model_prefix = "rwkv"
_no_split_modules = ["RwkvBlock"]
_keep_in_fp32_modules = ["time_decay", "time_first"]
supports_gradient_checkpointing = True
def _init_weights(self, module):
"""Initialize the weights."""
if isinstance(module, RwkvSelfAttention):
layer_id = module.layer_id
num_hidden_layers = module.config.num_hidden_layers
hidden_size = module.config.hidden_size
attention_hidden_size = module.attention_hidden_size
ratio_0_to_1 = layer_id / (num_hidden_layers - 1) # 0 to 1
ratio_1_to_almost0 = 1.0 - (layer_id / num_hidden_layers) # 1 to ~0
time_weight = torch.tensor(
[i / hidden_size for i in range(hidden_size)],
dtype=module.time_mix_key.dtype,
device=module.time_mix_key.device,
)
time_weight = time_weight[None, None, :]
decay_speed = [
-5 + 8 * (h / (attention_hidden_size - 1)) ** (0.7 + 1.3 * ratio_0_to_1)
for h in range(attention_hidden_size)
]
decay_speed = torch.tensor(decay_speed, dtype=module.time_decay.dtype, device=module.time_decay.device)
zigzag = (
torch.tensor(
[(i + 1) % 3 - 1 for i in range(attention_hidden_size)],
dtype=module.time_first.dtype,
device=module.time_first.device,
)
* 0.5
)
with torch.no_grad():
module.time_decay.data = decay_speed
module.time_first.data = torch.ones_like(module.time_first * math.log(0.3) + zigzag)
module.time_mix_key.data = torch.pow(time_weight, ratio_1_to_almost0)
module.time_mix_value.data = torch.pow(time_weight, ratio_1_to_almost0) + 0.3 * ratio_0_to_1
module.time_mix_receptance.data = torch.pow(time_weight, 0.5 * ratio_1_to_almost0)
elif isinstance(module, RwkvFeedForward):
layer_id = module.layer_id
num_hidden_layers = module.config.num_hidden_layers
hidden_size = module.config.hidden_size
ratio_1_to_almost0 = 1.0 - (layer_id / num_hidden_layers) # 1 to ~0
time_weight = torch.tensor(
[i / hidden_size for i in range(hidden_size)],
dtype=module.time_mix_key.dtype,
device=module.time_mix_key.device,
)
time_weight = time_weight[None, None, :]
with torch.no_grad():
module.time_mix_key.data = torch.pow(time_weight, ratio_1_to_almost0)
module.time_mix_receptance.data = torch.pow(time_weight, ratio_1_to_almost0)
@dataclass
class RwkvOutput(ModelOutput):
"""
Class for the RWKV model outputs.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
state (list of five `torch.FloatTensor` of shape `(batch_size, hidden_size, num_hidden_layers)`):
The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to
avoid providing the old `input_ids`.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
last_hidden_state: torch.FloatTensor = None
state: Optional[List[torch.FloatTensor]] = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
@dataclass
class RwkvCausalLMOutput(ModelOutput):
"""
Base class for causal language model (or autoregressive) outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss (for next-token prediction).
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
state (list of five `torch.FloatTensor` of shape `(batch_size, hidden_size, num_hidden_layers)`):
The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to
avoid providing the old `input_ids`.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
state: Optional[List[torch.FloatTensor]] = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
RWKV_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`RwkvConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
RWKV_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`):
`input_ids_length` = `sequence_length` if `past_key_values` is `None` else
`past_key_values[0][0].shape[-2]` (`sequence_length` of input past key value states). Indices of input
sequence tokens in the vocabulary.
If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as
`input_ids`.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.LongTensor` of shape `(batch_size, input_ids_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
This is currently not used by `RwkvModel`, but will be supported in the future.
[What are attention masks?](../glossary#attention-mask)
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
state (tuple of five `torch.FloatTensor` of shape `(batch_size, hidden_size, num_hidden_layers)`, *optional*):
If passed along, the model uses the previous state in all the blocks (which will give the output for the
`input_ids` provided as if the model add `state_input_ids + input_ids` as context).
use_cache (`bool`, *optional*):
If set to `True`, the last state is returned and can be used to quickly generate the next logits.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare RWKV Model transformer outputting raw hidden-states without any specific head on top.",
RWKV_START_DOCSTRING,
)
class RwkvModel(RwkvPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
self.blocks = nn.ModuleList([RwkvBlock(config, layer_id=idx) for idx in range(config.num_hidden_layers)])
self.ln_out = nn.LayerNorm(config.hidden_size)
self.layers_are_rescaled = False
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embeddings
def set_input_embeddings(self, new_embeddings):
self.embeddings = new_embeddings
@add_start_docstrings_to_model_forward(RWKV_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=RwkvOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.LongTensor] = None, # noqa
inputs_embeds: Optional[torch.FloatTensor] = None,
state: Optional[List[torch.FloatTensor]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, RwkvOutput]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if self.training == self.layers_are_rescaled:
self._rescale_layers()
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is None and inputs_embeds is None:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if inputs_embeds is None:
inputs_embeds = self.embeddings(input_ids)
if use_cache and state is None:
shape = (inputs_embeds.size(0), self.config.hidden_size, self.config.num_hidden_layers)
state = [
torch.zeros(
*shape, dtype=inputs_embeds.dtype if i <= 1 else torch.float32, device=inputs_embeds.device
)
for i in range(5)
]
state[4] -= 1e30
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
hidden_states = inputs_embeds
all_self_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
for idx, block in enumerate(self.blocks):
if self.gradient_checkpointing and self.training:
hidden_states, state, attentions = self._gradient_checkpointing_func(
block.__call__, hidden_states, state, use_cache, output_attentions
)
else:
hidden_states, state, attentions = block(
hidden_states, state=state, use_cache=use_cache, output_attentions=output_attentions
)
if (
self.layers_are_rescaled
and self.config.rescale_every > 0
and (idx + 1) % self.config.rescale_every == 0
):
hidden_states = hidden_states / 2
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if output_attentions:
all_self_attentions = all_self_attentions + (attentions,)
hidden_states = self.ln_out(hidden_states)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(x for x in [hidden_states, state, all_hidden_states, all_self_attentions] if x is not None)
return RwkvOutput(
last_hidden_state=hidden_states,
state=state,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)
def _rescale_layers(self):
# Layers should be rescaled for inference only.
if self.layers_are_rescaled == (not self.training):
return
if self.config.rescale_every > 0:
with torch.no_grad():
for block_id, block in enumerate(self.blocks):
if self.training:
block.attention.output.weight.mul_(2 ** int(block_id // self.config.rescale_every))
block.feed_forward.value.weight.mul_(2 ** int(block_id // self.config.rescale_every))
else:
# Deal with quantization statistics
if hasattr(block.attention.output.weight, "SCB"):
block.attention.output.weight.SCB.div_(2 ** int(block_id // self.config.rescale_every))
block.feed_forward.value.weight.SCB.div_(2 ** int(block_id // self.config.rescale_every))
elif hasattr(block.attention.output.weight, "quant_state"):
self._bnb_4bit_dequantize_and_rescale(block.attention.output, block_id)
self._bnb_4bit_dequantize_and_rescale(block.feed_forward.value, block_id)
else:
block.attention.output.weight.div_(2 ** int(block_id // self.config.rescale_every))
block.feed_forward.value.weight.div_(2 ** int(block_id // self.config.rescale_every))
self.layers_are_rescaled = not self.training
def _bnb_4bit_dequantize_and_rescale(self, target_layer, block_id):
r"""
Perform the dequantization and rescaling of the weights of a given layer. After that operation the layer will
be quantized again.
"""
if not is_bitsandbytes_available():
raise ImportError("Please install bitsandbytes to use this method.")
import bitsandbytes as bnb
dequant_weights = bnb.functional.dequantize_4bit(target_layer.weight.data, target_layer.weight.quant_state)
dequant_weights.div_(2 ** int(block_id // self.config.rescale_every))
# re-quantize the model:
# we need to put it first on CPU then back to the device
# this will create an overhead :/
# We set requires_grad=False as we cannot compute gradients on top of 4bit parameters anyway and to avoid
# bugs with bnb
quant_weight = bnb.nn.Params4bit(dequant_weights.to("cpu"), requires_grad=False).to(dequant_weights.device)
setattr(target_layer, "weight", quant_weight)
@add_start_docstrings(
"""
The RWKV Model transformer with a language modeling head on top (linear layer with weights tied to the input
embeddings).
""",
RWKV_START_DOCSTRING,
)
class RwkvForCausalLM(RwkvPreTrainedModel):
_tied_weights_keys = ["head.weight"]
def __init__(self, config):
super().__init__(config)
self.rwkv = RwkvModel(config)
self.head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_output_embeddings(self):
return self.head
def set_output_embeddings(self, new_embeddings):
self.head = new_embeddings
def generate(self, *args, **kwargs):
# Thin wrapper to raise exceptions when trying to generate with methods that manipulate `past_key_values`.
# RWKV is one of the few models that don't have it (it has `state` instead, which has different properties and
# usage).
try:
gen_output = super().generate(*args, **kwargs)
except AttributeError as exc:
# Expected exception: "AttributeError: '(object name)' object has no attribute 'past_key_values'"
if "past_key_values" in str(exc):
raise AttributeError(
"You tried to call `generate` with a decoding strategy that manipulates `past_key_values`. RWKV "
"doesn't have that attribute, try another generation strategy instead. For the available "
"generation strategies, check this doc: https://huggingface.co/docs/transformers/en/generation_strategies#decoding-strategies"
)
else:
raise exc
return gen_output
def prepare_inputs_for_generation(self, input_ids, state=None, inputs_embeds=None, **kwargs):
# only last token for inputs_ids if the state is passed along.
if state is not None:
input_ids = input_ids[:, -1].unsqueeze(-1)
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
if inputs_embeds is not None and state is None:
model_inputs = {"inputs_embeds": inputs_embeds}
else:
model_inputs = {"input_ids": input_ids}
model_inputs["state"] = state
return model_inputs
@add_start_docstrings_to_model_forward(RWKV_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=RwkvCausalLMOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.LongTensor] = None, # noqa
inputs_embeds: Optional[torch.FloatTensor] = None,
state: Optional[List[torch.FloatTensor]] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, RwkvCausalLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
rwkv_outputs = self.rwkv(
input_ids,
inputs_embeds=inputs_embeds,
state=state,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = rwkv_outputs[0]
logits = self.head(hidden_states)
loss = None
if labels is not None:
# move labels to correct device to enable model parallelism
labels = labels.to(logits.device)
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
if not return_dict:
output = (logits,) + rwkv_outputs[1:]
return ((loss,) + output) if loss is not None else output
return RwkvCausalLMOutput(
loss=loss,
logits=logits,
state=rwkv_outputs.state,
hidden_states=rwkv_outputs.hidden_states,
attentions=rwkv_outputs.attentions,
)
| transformers/src/transformers/models/rwkv/modeling_rwkv.py/0 | {
"file_path": "transformers/src/transformers/models/rwkv/modeling_rwkv.py",
"repo_id": "transformers",
"token_count": 16622
} | 119 |
# coding=utf-8
# Copyright 2021 ASAPP Inc. and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" SEW-D model configuration"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"asapp/sew-d-tiny-100k": "https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json",
# See all SEW-D models at https://huggingface.co/models?filter=sew-d
}
class SEWDConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`SEWDModel`]. It is used to instantiate a SEW-D
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the SEW-D
[asapp/sew-d-tiny-100k](https://huggingface.co/asapp/sew-d-tiny-100k) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 32):
Vocabulary size of the SEW-D model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`SEWD`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
squeeze_factor (`int`, *optional*, defaults to 2):
Sequence length downsampling factor after the encoder and upsampling factor after the transformer.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
position_buckets (`int`, *optional*, defaults to 256):
The maximum size of relative position embeddings.
share_att_key (`bool`, *optional*, defaults to `True`):
Whether to share attention key with c2p and p2c.
relative_attention (`bool`, *optional*, defaults to `True`):
Whether to use relative position encoding.
pos_att_type (`Tuple[str]`, *optional*, defaults to `("p2c", "c2p")`):
The type of relative position attention, it can be a combination of `("p2c", "c2p")`, e.g. `("p2c")`,
`("p2c", "c2p")`, `("p2c", "c2p")`.
norm_rel_ebd (`str`, *optional*, defaults to `"layer_norm"`):
Whether to use layer norm in relative embedding (`"layer_norm"` if yes)
hidden_act (`str` or `function`, *optional*, defaults to `"gelu_python"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"`, `"gelu_python"` and `"gelu_new"` are supported.
hidden_dropout (`float`, *optional*, defaults to 0.1):
Deprecated. Not used by the model and will be removed in a future version.
activation_dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
final_dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for the final projection layer of [`SEWDForCTC`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-7):
The epsilon used by the layer normalization layers in the transformer encoder.
feature_layer_norm_eps (`float`, *optional*, defaults to 1e-5):
The epsilon used by the layer normalization after the feature encoder.
feat_extract_norm (`str`, *optional*, defaults to `"group"`):
The norm to be applied to 1D convolutional layers in feature encoder. One of `"group"` for group
normalization of only the first 1D convolutional layer or `"layer"` for layer normalization of all 1D
convolutional layers.
feat_proj_dropout (`float`, *optional*, defaults to 0.0):
The dropout probability for output of the feature encoder.
feat_extract_activation (`str, `optional`, defaults to `"gelu"`):
The non-linear activation function (function or string) in the 1D convolutional layers of the feature
extractor. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported.
conv_dim (`Tuple[int]` or `List[int]`, *optional*, defaults to `(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)`):
A tuple of integers defining the number of input and output channels of each 1D convolutional layer in the
feature encoder. The length of *conv_dim* defines the number of 1D convolutional layers.
conv_stride (`Tuple[int]` or `List[int]`, *optional*, defaults to `(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)`):
A tuple of integers defining the stride of each 1D convolutional layer in the feature encoder. The length
of *conv_stride* defines the number of convolutional layers and has to match the length of *conv_dim*.
conv_kernel (`Tuple[int]` or `List[int]`, *optional*, defaults to `(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)`):
A tuple of integers defining the kernel size of each 1D convolutional layer in the feature encoder. The
length of *conv_kernel* defines the number of convolutional layers and has to match the length of
*conv_dim*.
conv_bias (`bool`, *optional*, defaults to `False`):
Whether the 1D convolutional layers have a bias.
num_conv_pos_embeddings (`int`, *optional*, defaults to 128):
Number of convolutional positional embeddings. Defines the kernel size of 1D convolutional positional
embeddings layer.
num_conv_pos_embedding_groups (`int`, *optional*, defaults to 16):
Number of groups of 1D convolutional positional embeddings layer.
apply_spec_augment (`bool`, *optional*, defaults to `True`):
Whether to apply *SpecAugment* data augmentation to the outputs of the feature encoder. For reference see
[SpecAugment: A Simple Data Augmentation Method for Automatic Speech
Recognition](https://arxiv.org/abs/1904.08779).
mask_time_prob (`float`, *optional*, defaults to 0.05):
Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking
procecure generates ''mask_time_prob*len(time_axis)/mask_time_length'' independent masks over the axis. If
reasoning from the propability of each feature vector to be chosen as the start of the vector span to be
masked, *mask_time_prob* should be `prob_vector_start*mask_time_length`. Note that overlap may decrease the
actual percentage of masked vectors. This is only relevant if `apply_spec_augment is True`.
mask_time_length (`int`, *optional*, defaults to 10):
Length of vector span along the time axis.
mask_time_min_masks (`int`, *optional*, defaults to 2),:
The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step,
irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length <
mask_time_min_masks''
mask_feature_prob (`float`, *optional*, defaults to 0.0):
Percentage (between 0 and 1) of all feature vectors along the feature axis which will be masked. The
masking procecure generates ''mask_feature_prob*len(feature_axis)/mask_time_length'' independent masks over
the axis. If reasoning from the propability of each feature vector to be chosen as the start of the vector
span to be masked, *mask_feature_prob* should be `prob_vector_start*mask_feature_length`. Note that overlap
may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is
True`.
mask_feature_length (`int`, *optional*, defaults to 10):
Length of vector span along the feature axis.
mask_feature_min_masks (`int`, *optional*, defaults to 0),:
The minimum number of masks of length `mask_feature_length` generated along the feature axis, each time
step, irrespectively of `mask_feature_prob`. Only relevant if
''mask_feature_prob*len(feature_axis)/mask_feature_length < mask_feature_min_masks''
diversity_loss_weight (`int`, *optional*, defaults to 0.1):
The weight of the codebook diversity loss component.
ctc_loss_reduction (`str`, *optional*, defaults to `"sum"`):
Specifies the reduction to apply to the output of `torch.nn.CTCLoss`. Only relevant when training an
instance of [`SEWDForCTC`].
ctc_zero_infinity (`bool`, *optional*, defaults to `False`):
Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`. Infinite losses mainly
occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance
of [`SEWDForCTC`].
use_weighted_layer_sum (`bool`, *optional*, defaults to `False`):
Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an
instance of [`Wav2Vec2ForSequenceClassification`].
classifier_proj_size (`int`, *optional*, defaults to 256):
Dimensionality of the projection before token mean-pooling for classification.
Example:
```python
>>> from transformers import SEWDConfig, SEWDModel
>>> # Initializing a SEW-D asapp/sew-d-tiny-100k style configuration
>>> configuration = SEWDConfig()
>>> # Initializing a model (with random weights) from the asapp/sew-d-tiny-100k style configuration
>>> model = SEWDModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "sew-d"
def __init__(
self,
vocab_size=32,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
squeeze_factor=2,
max_position_embeddings=512,
position_buckets=256,
share_att_key=True,
relative_attention=True,
pos_att_type=("p2c", "c2p"),
norm_rel_ebd="layer_norm",
hidden_act="gelu_python",
hidden_dropout=0.1,
activation_dropout=0.1,
attention_dropout=0.1,
feat_proj_dropout=0.0,
final_dropout=0.1,
initializer_range=0.02,
layer_norm_eps=1e-7,
feature_layer_norm_eps=1e-5,
feat_extract_norm="group",
feat_extract_activation="gelu",
conv_dim=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512),
conv_stride=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1),
conv_kernel=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1),
conv_bias=False,
num_conv_pos_embeddings=128,
num_conv_pos_embedding_groups=16,
apply_spec_augment=True,
mask_time_prob=0.05,
mask_time_length=10,
mask_time_min_masks=2,
mask_feature_prob=0.0,
mask_feature_length=10,
mask_feature_min_masks=0,
ctc_loss_reduction="mean",
ctc_zero_infinity=False,
use_weighted_layer_sum=False,
classifier_proj_size=256,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
**kwargs,
):
super().__init__(**kwargs, pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id)
self.hidden_size = hidden_size
self.feat_extract_norm = feat_extract_norm
self.feat_extract_activation = feat_extract_activation
self.conv_dim = list(conv_dim)
self.conv_stride = list(conv_stride)
self.conv_kernel = list(conv_kernel)
self.conv_bias = conv_bias
self.num_conv_pos_embeddings = num_conv_pos_embeddings
self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups
self.num_feat_extract_layers = len(self.conv_dim)
self.num_hidden_layers = num_hidden_layers
self.intermediate_size = intermediate_size
self.squeeze_factor = squeeze_factor
self.max_position_embeddings = max_position_embeddings
self.position_buckets = position_buckets
self.share_att_key = share_att_key
self.relative_attention = relative_attention
self.norm_rel_ebd = norm_rel_ebd
self.pos_att_type = list(pos_att_type)
self.hidden_act = hidden_act
self.num_attention_heads = num_attention_heads
self._hidden_dropout = hidden_dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.feat_proj_dropout = feat_proj_dropout
self.final_dropout = final_dropout
self.layer_norm_eps = layer_norm_eps
self.feature_layer_norm_eps = feature_layer_norm_eps
self.initializer_range = initializer_range
self.vocab_size = vocab_size
if (
(len(self.conv_stride) != self.num_feat_extract_layers)
or (len(self.conv_kernel) != self.num_feat_extract_layers)
or (len(self.conv_dim) != self.num_feat_extract_layers)
):
raise ValueError(
"Configuration for convolutional layers is incorrect. "
"It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`, "
f"but is `len(config.conv_dim) = {len(self.conv_dim)}`, `len(config.conv_stride) "
f"= {len(self.conv_stride)}`, `len(config.conv_kernel) = {len(self.conv_kernel)}`."
)
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
self.apply_spec_augment = apply_spec_augment
self.mask_time_prob = mask_time_prob
self.mask_time_length = mask_time_length
self.mask_time_min_masks = mask_time_min_masks
self.mask_feature_prob = mask_feature_prob
self.mask_feature_length = mask_feature_length
self.mask_feature_min_masks = mask_feature_min_masks
# ctc loss
self.ctc_loss_reduction = ctc_loss_reduction
self.ctc_zero_infinity = ctc_zero_infinity
# sequence classification
self.use_weighted_layer_sum = use_weighted_layer_sum
self.classifier_proj_size = classifier_proj_size
@property
def inputs_to_logits_ratio(self):
return functools.reduce(operator.mul, self.conv_stride, 1)
@property
def hidden_dropout(self):
logger.warning_once("hidden_dropout is not used by the model and will be removed as config attribute in v4.35")
return self._hidden_dropout
def to_dict(self):
"""
Serializes this instance to a Python dictionary.
"""
output = super().to_dict()
output["hidden_dropout"] = output.pop("_hidden_dropout")
return output
| transformers/src/transformers/models/sew_d/configuration_sew_d.py/0 | {
"file_path": "transformers/src/transformers/models/sew_d/configuration_sew_d.py",
"repo_id": "transformers",
"token_count": 6565
} | 120 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_torch_available,
)
_import_structure = {
"configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2TextConfig"],
"feature_extraction_speech_to_text": ["Speech2TextFeatureExtractor"],
"processing_speech_to_text": ["Speech2TextProcessor"],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_speech_to_text"] = ["Speech2TextTokenizer"]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_speech_to_text"] = [
"TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFSpeech2TextForConditionalGeneration",
"TFSpeech2TextModel",
"TFSpeech2TextPreTrainedModel",
]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_speech_to_text"] = [
"SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
"Speech2TextForConditionalGeneration",
"Speech2TextModel",
"Speech2TextPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, Speech2TextConfig
from .feature_extraction_speech_to_text import Speech2TextFeatureExtractor
from .processing_speech_to_text import Speech2TextProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speech_to_text import Speech2TextTokenizer
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_speech_to_text import (
TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSpeech2TextForConditionalGeneration,
TFSpeech2TextModel,
TFSpeech2TextPreTrainedModel,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_to_text import (
SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
Speech2TextForConditionalGeneration,
Speech2TextModel,
Speech2TextPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| transformers/src/transformers/models/speech_to_text/__init__.py/0 | {
"file_path": "transformers/src/transformers/models/speech_to_text/__init__.py",
"repo_id": "transformers",
"token_count": 1361
} | 121 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert SpeechT5 checkpoint."""
import argparse
import torch
from transformers import (
SpeechT5Config,
SpeechT5FeatureExtractor,
SpeechT5ForSpeechToSpeech,
SpeechT5ForSpeechToText,
SpeechT5ForTextToSpeech,
SpeechT5Processor,
SpeechT5Tokenizer,
logging,
)
from transformers.tokenization_utils import AddedToken
logging.set_verbosity_info()
logger = logging.get_logger("transformers.models.speecht5")
MAPPING_SPEECH_ENCODER_PRENET = {
"speech_encoder_prenet.layer_norm": "speecht5.encoder.prenet.feature_projection.layer_norm",
"speech_encoder_prenet.post_extract_proj": "speecht5.encoder.prenet.feature_projection.projection",
"speech_encoder_prenet.pos_conv.0": "speecht5.encoder.prenet.pos_conv_embed.conv",
"speech_encoder_prenet.mask_emb": "speecht5.encoder.prenet.masked_spec_embed",
}
MAPPING_TEXT_ENCODER_PRENET = {
"text_encoder_prenet.encoder_prenet.0": "speecht5.encoder.prenet.embed_tokens",
"text_encoder_prenet.encoder_prenet.1.alpha": "speecht5.encoder.prenet.encode_positions.alpha",
}
MAPPING_SPEECH_DECODER_PRENET = {
"speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0": "speecht5.decoder.prenet.layers.0",
"speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0": "speecht5.decoder.prenet.layers.1",
"speech_decoder_prenet.decoder_prenet.0.1": "speecht5.decoder.prenet.final_layer",
"speech_decoder_prenet.decoder_prenet.1.alpha": "speecht5.decoder.prenet.encode_positions.alpha",
"speech_decoder_prenet.spkembs_layer.0": "speecht5.decoder.prenet.speaker_embeds_layer",
}
MAPPING_SPEECH_DECODER_POSTNET = {
"speech_decoder_postnet.feat_out": "speech_decoder_postnet.feat_out",
"speech_decoder_postnet.prob_out": "speech_decoder_postnet.prob_out",
"speech_decoder_postnet.postnet.postnet.0.0": "speech_decoder_postnet.layers.0.conv",
"speech_decoder_postnet.postnet.postnet.0.1": "speech_decoder_postnet.layers.0.batch_norm",
"speech_decoder_postnet.postnet.postnet.1.0": "speech_decoder_postnet.layers.1.conv",
"speech_decoder_postnet.postnet.postnet.1.1": "speech_decoder_postnet.layers.1.batch_norm",
"speech_decoder_postnet.postnet.postnet.2.0": "speech_decoder_postnet.layers.2.conv",
"speech_decoder_postnet.postnet.postnet.2.1": "speech_decoder_postnet.layers.2.batch_norm",
"speech_decoder_postnet.postnet.postnet.3.0": "speech_decoder_postnet.layers.3.conv",
"speech_decoder_postnet.postnet.postnet.3.1": "speech_decoder_postnet.layers.3.batch_norm",
"speech_decoder_postnet.postnet.postnet.4.0": "speech_decoder_postnet.layers.4.conv",
"speech_decoder_postnet.postnet.postnet.4.1": "speech_decoder_postnet.layers.4.batch_norm",
}
MAPPING_TEXT_DECODER_PRENET = {
"text_decoder_prenet.embed_tokens": "speecht5.decoder.prenet.embed_tokens",
}
MAPPING_TEXT_DECODER_POSTNET = {
"text_decoder_postnet.output_projection": "text_decoder_postnet.lm_head",
}
MAPPING_ENCODER = {
"encoder.layers.*.self_attn.k_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj",
"encoder.layers.*.self_attn.v_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj",
"encoder.layers.*.self_attn.q_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj",
"encoder.layers.*.self_attn.out_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj",
"encoder.layers.*.self_attn_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.layer_norm",
"encoder.layers.*.fc1": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense",
"encoder.layers.*.fc2": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense",
"encoder.layers.*.final_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm",
"encoder.layer_norm": "speecht5.encoder.wrapped_encoder.layer_norm",
"encoder.pos_emb.pe_k": "speecht5.encoder.wrapped_encoder.embed_positions.pe_k",
}
MAPPING_DECODER = {
"decoder.layers.*.self_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj",
"decoder.layers.*.self_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj",
"decoder.layers.*.self_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj",
"decoder.layers.*.self_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj",
"decoder.layers.*.self_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm",
"decoder.layers.*.encoder_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj",
"decoder.layers.*.encoder_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj",
"decoder.layers.*.encoder_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj",
"decoder.layers.*.encoder_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj",
"decoder.layers.*.encoder_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm",
"decoder.layers.*.fc1": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense",
"decoder.layers.*.fc2": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense",
"decoder.layers.*.final_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm",
}
MAPPING_S2T = {
**MAPPING_SPEECH_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_TEXT_DECODER_PRENET,
**MAPPING_TEXT_DECODER_POSTNET,
}
MAPPING_T2S = {
**MAPPING_TEXT_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_SPEECH_DECODER_PRENET,
**MAPPING_SPEECH_DECODER_POSTNET,
}
MAPPING_S2S = {
**MAPPING_SPEECH_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_SPEECH_DECODER_PRENET,
**MAPPING_SPEECH_DECODER_POSTNET,
}
TOP_LEVEL_KEYS = []
IGNORE_KEYS = [
"encoder.version",
"encoder.layers.*.norm_k.weight",
"encoder.layers.*.norm_k.bias",
"decoder.version",
"decoder.layers.*.norm_k.weight",
"decoder.layers.*.norm_k.bias",
"decoder.pos_emb.pe_k",
"speech_encoder_prenet.embed_positions._float_tensor",
"text_decoder_prenet.embed_positions._float_tensor",
]
IGNORE_KEYS_S2T = IGNORE_KEYS + [
"encoder.proj",
"text_encoder_prenet.*",
"speech_decoder_prenet.*",
"speech_decoder_postnet.*",
]
IGNORE_KEYS_T2S = IGNORE_KEYS + [
"encoder.proj",
"speech_encoder_prenet.*",
"text_decoder_prenet.*",
"text_decoder_postnet.*",
]
IGNORE_KEYS_S2S = IGNORE_KEYS + [
"encoder.proj",
"text_encoder_prenet.*",
"text_decoder_prenet.*",
"text_decoder_postnet.*",
]
def set_recursively(hf_pointer, key, value, full_name, weight_type):
for attribute in key.split("."):
hf_pointer = getattr(hf_pointer, attribute)
if weight_type is not None:
hf_shape = getattr(hf_pointer, weight_type).shape
else:
hf_shape = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
f" {value.shape} for {full_name}"
)
if weight_type == "weight":
hf_pointer.weight.data = value
elif weight_type == "weight_g":
hf_pointer.weight_g.data = value
elif weight_type == "weight_v":
hf_pointer.weight_v.data = value
elif weight_type == "bias":
hf_pointer.bias.data = value
elif weight_type == "running_mean":
hf_pointer.running_mean.data = value
elif weight_type == "running_var":
hf_pointer.running_var.data = value
elif weight_type == "num_batches_tracked":
hf_pointer.num_batches_tracked.data = value
else:
hf_pointer.data = value
logger.info(f"{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.")
def should_ignore(name, ignore_keys):
for key in ignore_keys:
if key.endswith(".*"):
if name.startswith(key[:-1]):
return True
elif ".*." in key:
prefix, suffix = key.split(".*.")
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def recursively_load_weights(fairseq_dict, hf_model, task):
unused_weights = []
if task == "s2t":
feature_encoder = hf_model.speecht5.encoder.prenet.feature_encoder
MAPPING = MAPPING_S2T
IGNORE_KEYS = IGNORE_KEYS_S2T
elif task == "t2s":
feature_encoder = None
MAPPING = MAPPING_T2S
IGNORE_KEYS = IGNORE_KEYS_T2S
elif task == "s2s":
feature_encoder = hf_model.speecht5.encoder.prenet.feature_encoder
MAPPING = MAPPING_S2S
IGNORE_KEYS = IGNORE_KEYS_S2S
else:
raise ValueError(f"Unsupported task: {task}")
for name, value in fairseq_dict.items():
if should_ignore(name, IGNORE_KEYS):
logger.info(f"{name} was ignored")
continue
is_used = False
if "conv_layers" in name:
load_conv_layer(
name,
value,
feature_encoder,
unused_weights,
hf_model.config.feat_extract_norm == "group",
)
is_used = True
else:
for key, mapped_key in MAPPING.items():
# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if "*" in key:
prefix, suffix = key.split(".*.")
if prefix in name and suffix in name:
key = suffix
# if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
if key in name:
is_used = True
if "*" in mapped_key:
layer_index = name.split(key)[0].split(".")[-2]
mapped_key = mapped_key.replace("*", layer_index)
if "weight_g" in name:
weight_type = "weight_g"
elif "weight_v" in name:
weight_type = "weight_v"
elif "bias" in name:
weight_type = "bias"
elif "weight" in name:
weight_type = "weight"
elif "running_mean" in name:
weight_type = "running_mean"
elif "running_var" in name:
weight_type = "running_var"
elif "num_batches_tracked" in name:
weight_type = "num_batches_tracked"
else:
weight_type = None
set_recursively(hf_model, mapped_key, value, name, weight_type)
continue
if not is_used:
unused_weights.append(name)
logger.warning(f"Unused weights: {unused_weights}")
def load_conv_layer(full_name, value, feature_extractor, unused_weights, use_group_norm):
name = full_name.split("conv_layers.")[-1]
items = name.split(".")
layer_id = int(items[0])
type_id = int(items[1])
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f"{full_name} has size {value.shape}, but"
f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
)
feature_extractor.conv_layers[layer_id].conv.bias.data = value
logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}.")
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f"{full_name} has size {value.shape}, but"
f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
)
feature_extractor.conv_layers[layer_id].conv.weight.data = value
logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}.")
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f"{full_name} has size {value.shape}, but"
f" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."
)
feature_extractor.conv_layers[layer_id].layer_norm.bias.data = value
logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.")
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f"{full_name} has size {value.shape}, but"
f" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."
)
feature_extractor.conv_layers[layer_id].layer_norm.weight.data = value
logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.")
else:
unused_weights.append(full_name)
@torch.no_grad()
def convert_speecht5_checkpoint(
task,
checkpoint_path,
pytorch_dump_folder_path,
config_path=None,
vocab_path=None,
repo_id=None,
):
"""
Copy/paste/tweak model's weights to transformers design.
"""
if config_path is not None:
config = SpeechT5Config.from_pretrained(config_path)
else:
config = SpeechT5Config()
if task == "s2t":
config.max_length = config.max_text_positions
model = SpeechT5ForSpeechToText(config)
elif task == "t2s":
config.max_speech_positions = 1876
config.max_text_positions = 600
config.max_length = config.max_speech_positions
model = SpeechT5ForTextToSpeech(config)
elif task == "s2s":
config.max_speech_positions = 1876
config.max_length = config.max_speech_positions
model = SpeechT5ForSpeechToSpeech(config)
else:
raise ValueError(f"Unknown task name: {task}")
if vocab_path:
tokenizer = SpeechT5Tokenizer(vocab_path, model_max_length=config.max_text_positions)
# Mask token behaves like a normal word, i.e. include the space before it
mask_token = AddedToken("<mask>", lstrip=True, rstrip=False)
tokenizer.mask_token = mask_token
tokenizer.add_special_tokens({"mask_token": mask_token})
tokenizer.add_tokens(["<ctc_blank>"])
feature_extractor = SpeechT5FeatureExtractor()
processor = SpeechT5Processor(tokenizer=tokenizer, feature_extractor=feature_extractor)
processor.save_pretrained(pytorch_dump_folder_path)
fairseq_checkpoint = torch.load(checkpoint_path)
recursively_load_weights(fairseq_checkpoint["model"], model, task)
model.save_pretrained(pytorch_dump_folder_path)
if repo_id:
print("Pushing to the hub...")
processor.push_to_hub(repo_id)
model.push_to_hub(repo_id)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--task",
default="s2t",
type=str,
help="Type of the SpeechT5 model you'd like to convert. Should be one of 's2t', 't2s', 's2s'.",
)
parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--vocab_path", default=None, type=str, help="Path to SentencePiece model")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
)
parser.add_argument(
"--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
)
args = parser.parse_args()
convert_speecht5_checkpoint(
args.task,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.vocab_path,
args.push_to_hub,
)
| transformers/src/transformers/models/speecht5/convert_speecht5_original_pytorch_checkpoint_to_pytorch.py/0 | {
"file_path": "transformers/src/transformers/models/speecht5/convert_speecht5_original_pytorch_checkpoint_to_pytorch.py",
"repo_id": "transformers",
"token_count": 7959
} | 122 |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Image processor class for Swin2SR."""
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
infer_channel_dimension_format,
is_scaled_image,
make_list_of_images,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import TensorType, logging
logger = logging.get_logger(__name__)
class Swin2SRImageProcessor(BaseImageProcessor):
r"""
Constructs a Swin2SR image processor.
Args:
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
parameter in the `preprocess` method.
rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the
`preprocess` method.
"""
model_input_names = ["pixel_values"]
def __init__(
self,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1 / 255,
do_pad: bool = True,
pad_size: int = 8,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.do_rescale = do_rescale
self.rescale_factor = rescale_factor
self.do_pad = do_pad
self.pad_size = pad_size
self._valid_processor_keys = [
"images",
"do_rescale",
"rescale_factor",
"do_pad",
"pad_size",
"return_tensors",
"data_format",
"input_data_format",
]
def pad(
self,
image: np.ndarray,
size: int,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
):
"""
Pad an image to make the height and width divisible by `size`.
Args:
image (`np.ndarray`):
Image to pad.
size (`int`):
The size to make the height and width divisible by.
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format for the output image. If unset, the channel dimension format of the input
image is used. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
input_data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
Returns:
`np.ndarray`: The padded image.
"""
old_height, old_width = get_image_size(image, input_data_format)
pad_height = (old_height // size + 1) * size - old_height
pad_width = (old_width // size + 1) * size - old_width
return pad(
image,
((0, pad_height), (0, pad_width)),
mode="symmetric",
data_format=data_format,
input_data_format=input_data_format,
)
def preprocess(
self,
images: ImageInput,
do_rescale: Optional[bool] = None,
rescale_factor: Optional[float] = None,
do_pad: Optional[bool] = None,
pad_size: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
data_format: Union[str, ChannelDimension] = ChannelDimension.FIRST,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
):
"""
Preprocess an image or batch of images.
Args:
images (`ImageInput`):
Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
passing in images with pixel values between 0 and 1, set `do_rescale=False`.
do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
Whether to rescale the image values between [0 - 1].
rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
Rescale factor to rescale the image by if `do_rescale` is set to `True`.
do_pad (`bool`, *optional*, defaults to `True`):
Whether to pad the image to make the height and width divisible by `window_size`.
pad_size (`int`, *optional*, defaults to 32):
The size of the sliding window for the local attention.
return_tensors (`str` or `TensorType`, *optional*):
The type of tensors to return. Can be one of:
- Unset: Return a list of `np.ndarray`.
- `TensorType.TENSORFLOW` or `'tf'`: Return a batch of typ, input_data_format=input_data_formate
`tf.Tensor`.
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
- `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
- `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
The channel dimension format for the output image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- Unset: Use the channel dimension format of the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
"""
do_rescale = do_rescale if do_rescale is not None else self.do_rescale
rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
do_pad = do_pad if do_pad is not None else self.do_pad
pad_size = pad_size if pad_size is not None else self.pad_size
images = make_list_of_images(images)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
validate_preprocess_arguments(
do_rescale=do_rescale,
rescale_factor=rescale_factor,
do_pad=do_pad,
size_divisibility=pad_size, # Here the pad function simply requires pad_size.
)
# All transformations expect numpy arrays.
images = [to_numpy_array(image) for image in images]
if is_scaled_image(images[0]) and do_rescale:
logger.warning_once(
"It looks like you are trying to rescale already rescaled images. If the input"
" images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
)
if input_data_format is None:
# We assume that all images have the same channel dimension format.
input_data_format = infer_channel_dimension_format(images[0])
if do_rescale:
images = [
self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
for image in images
]
if do_pad:
images = [self.pad(image, size=pad_size, input_data_format=input_data_format) for image in images]
images = [
to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images
]
data = {"pixel_values": images}
return BatchFeature(data=data, tensor_type=return_tensors)
| transformers/src/transformers/models/swin2sr/image_processing_swin2sr.py/0 | {
"file_path": "transformers/src/transformers/models/swin2sr/image_processing_swin2sr.py",
"repo_id": "transformers",
"token_count": 4087
} | 123 |
#!/usr/bin/env bash
# Use this script as follows ./download_from_gcp.sh /path/to/folder/to/store/downloads
folder_to_store_downloads=${1}
# Replace by gcp_path to T5 cloud bucket folder here
# To download the official `t5-small` model of https://github.com/google-research/text-to-text-transfer-transformer#released-model-checkpoints:
gcp_path="gs://t5-data/pretrained_models/small"
# Number of files the checkpoint is split into
num_of_checks=16
# Create dir if not exist
mkdir -p ${folder_to_store_downloads}
# Copy all meta information files
gsutil cp "${gcp_path}/operative_config.gin" ${folder_to_store_downloads}
gsutil cp "${gcp_path}/checkpoint" ${folder_to_store_downloads}
gsutil cp "${gcp_path}/model.ckpt-1000000.index" ${folder_to_store_downloads}
gsutil cp "${gcp_path}/model.ckpt-1000000.meta" ${folder_to_store_downloads}
# Copy all model weights
# single digit num checkpoitns
for ((i = 0 ; i < ${num_of_checks} ; i++)); do
gsutil cp "${gcp_path}/model.ckpt-1000000.data-0000${i}-of-000${num_of_checks}" ${folder_to_store_downloads}
done
# double digit num checkpoints
for ((i = 0 ; i < ${num_of_checks} ; i++)); do
gsutil cp "${gcp_path}/model.ckpt-1000000.data-000${i}-of-000${num_of_checks}" ${folder_to_store_downloads}
done
# Having run this script, you should create a suitable config.json, *e.g.* by
# looking at `https://huggingface.co/t5-small`.
# Then you can run `python convert_t5_original_tf_checkpoint_to_pytorch.py --tf_checkpoint_path "${folder_to_store_downloads}" --config_file "config.json" --pytorch_dump_path "/path/to/store/pytorch/weights"
| transformers/src/transformers/models/t5/download_from_gcp.sh/0 | {
"file_path": "transformers/src/transformers/models/t5/download_from_gcp.sh",
"repo_id": "transformers",
"token_count": 579
} | 124 |
# coding=utf-8
# Copyright 2020 Google Research and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Tokenization class for TAPAS model."""
import collections
import datetime
import enum
import itertools
import math
import os
import re
import unicodedata
from dataclasses import dataclass
from typing import Callable, Dict, Generator, List, Optional, Text, Tuple, Union
import numpy as np
from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...tokenization_utils_base import (
ENCODE_KWARGS_DOCSTRING,
VERY_LARGE_INTEGER,
BatchEncoding,
EncodedInput,
PreTokenizedInput,
TextInput,
)
from ...utils import ExplicitEnum, PaddingStrategy, TensorType, add_end_docstrings, is_pandas_available, logging
if is_pandas_available():
import pandas as pd
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
# large models
"google/tapas-large-finetuned-sqa": (
"https://huggingface.co/google/tapas-large-finetuned-sqa/resolve/main/vocab.txt"
),
"google/tapas-large-finetuned-wtq": (
"https://huggingface.co/google/tapas-large-finetuned-wtq/resolve/main/vocab.txt"
),
"google/tapas-large-finetuned-wikisql-supervised": (
"https://huggingface.co/google/tapas-large-finetuned-wikisql-supervised/resolve/main/vocab.txt"
),
"google/tapas-large-finetuned-tabfact": (
"https://huggingface.co/google/tapas-large-finetuned-tabfact/resolve/main/vocab.txt"
),
# base models
"google/tapas-base-finetuned-sqa": (
"https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/vocab.txt"
),
"google/tapas-base-finetuned-wtq": (
"https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/vocab.txt"
),
"google/tapas-base-finetuned-wikisql-supervised": (
"https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/vocab.txt"
),
"google/tapas-base-finetuned-tabfact": (
"https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/vocab.txt"
),
# medium models
"google/tapas-medium-finetuned-sqa": (
"https://huggingface.co/google/tapas-medium-finetuned-sqa/resolve/main/vocab.txt"
),
"google/tapas-medium-finetuned-wtq": (
"https://huggingface.co/google/tapas-medium-finetuned-wtq/resolve/main/vocab.txt"
),
"google/tapas-medium-finetuned-wikisql-supervised": (
"https://huggingface.co/google/tapas-medium-finetuned-wikisql-supervised/resolve/main/vocab.txt"
),
"google/tapas-medium-finetuned-tabfact": (
"https://huggingface.co/google/tapas-medium-finetuned-tabfact/resolve/main/vocab.txt"
),
# small models
"google/tapas-small-finetuned-sqa": (
"https://huggingface.co/google/tapas-small-finetuned-sqa/resolve/main/vocab.txt"
),
"google/tapas-small-finetuned-wtq": (
"https://huggingface.co/google/tapas-small-finetuned-wtq/resolve/main/vocab.txt"
),
"google/tapas-small-finetuned-wikisql-supervised": (
"https://huggingface.co/google/tapas-small-finetuned-wikisql-supervised/resolve/main/vocab.txt"
),
"google/tapas-small-finetuned-tabfact": (
"https://huggingface.co/google/tapas-small-finetuned-tabfact/resolve/main/vocab.txt"
),
# tiny models
"google/tapas-tiny-finetuned-sqa": (
"https://huggingface.co/google/tapas-tiny-finetuned-sqa/resolve/main/vocab.txt"
),
"google/tapas-tiny-finetuned-wtq": (
"https://huggingface.co/google/tapas-tiny-finetuned-wtq/resolve/main/vocab.txt"
),
"google/tapas-tiny-finetuned-wikisql-supervised": (
"https://huggingface.co/google/tapas-tiny-finetuned-wikisql-supervised/resolve/main/vocab.txt"
),
"google/tapas-tiny-finetuned-tabfact": (
"https://huggingface.co/google/tapas-tiny-finetuned-tabfact/resolve/main/vocab.txt"
),
# mini models
"google/tapas-mini-finetuned-sqa": (
"https://huggingface.co/google/tapas-mini-finetuned-sqa/resolve/main/vocab.txt"
),
"google/tapas-mini-finetuned-wtq": (
"https://huggingface.co/google/tapas-mini-finetuned-wtq/resolve/main/vocab.txt"
),
"google/tapas-mini-finetuned-wikisql-supervised": (
"https://huggingface.co/google/tapas-mini-finetuned-wikisql-supervised/resolve/main/vocab.txt"
),
"google/tapas-mini-finetuned-tabfact": (
"https://huggingface.co/google/tapas-mini-finetuned-tabfact/resolve/main/vocab.txt"
),
}
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {name: 512 for name in PRETRAINED_VOCAB_FILES_MAP.keys()}
PRETRAINED_INIT_CONFIGURATION = {name: {"do_lower_case": True} for name in PRETRAINED_VOCAB_FILES_MAP.keys()}
class TapasTruncationStrategy(ExplicitEnum):
"""
Possible values for the `truncation` argument in [`~TapasTokenizer.__call__`]. Useful for tab-completion in an IDE.
"""
DROP_ROWS_TO_FIT = "drop_rows_to_fit"
DO_NOT_TRUNCATE = "do_not_truncate"
TableValue = collections.namedtuple("TokenValue", ["token", "column_id", "row_id"])
@dataclass(frozen=True)
class TokenCoordinates:
column_index: int
row_index: int
token_index: int
@dataclass
class TokenizedTable:
rows: List[List[List[Text]]]
selected_tokens: List[TokenCoordinates]
@dataclass(frozen=True)
class SerializedExample:
tokens: List[Text]
column_ids: List[int]
row_ids: List[int]
segment_ids: List[int]
def _is_inner_wordpiece(token: Text):
return token.startswith("##")
def load_vocab(vocab_file):
"""Loads a vocabulary file into a dictionary."""
vocab = collections.OrderedDict()
with open(vocab_file, "r", encoding="utf-8") as reader:
tokens = reader.readlines()
for index, token in enumerate(tokens):
token = token.rstrip("\n")
vocab[token] = index
return vocab
def whitespace_tokenize(text):
"""Runs basic whitespace cleaning and splitting on a piece of text."""
text = text.strip()
if not text:
return []
tokens = text.split()
return tokens
TAPAS_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING = r"""
add_special_tokens (`bool`, *optional*, defaults to `True`):
Whether or not to encode the sequences with the special tokens relative to their model.
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
Activates and controls padding. Accepts the following values:
- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).
- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided.
- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
lengths).
truncation (`bool`, `str` or [`TapasTruncationStrategy`], *optional*, defaults to `False`):
Activates and controls truncation. Accepts the following values:
- `True` or `'drop_rows_to_fit'`: Truncate to a maximum length specified with the argument `max_length`
or to the maximum acceptable input length for the model if that argument is not provided. This will
truncate row by row, removing rows from the table.
- `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).
max_length (`int`, *optional*):
Controls the maximum length to use by one of the truncation/padding parameters.
If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
is required by one of the truncation/padding parameters. If the model has no specific maximum input
length (like XLNet) truncation/padding to a maximum length will be deactivated.
is_split_into_words (`bool`, *optional*, defaults to `False`):
Whether or not the input is already pre-tokenized (e.g., split into words). If set to `True`, the
tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace)
which it will tokenize. This is useful for NER or token classification.
pad_to_multiple_of (`int`, *optional*):
If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta).
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors instead of list of python integers. Acceptable values are:
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return Numpy `np.ndarray` objects.
"""
class TapasTokenizer(PreTrainedTokenizer):
r"""
Construct a TAPAS tokenizer. Based on WordPiece. Flattens a table and one or more related sentences to be used by
TAPAS models.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods. [`TapasTokenizer`] creates several token type ids to
encode tabular structure. To be more precise, it adds 7 token type ids, in the following order: `segment_ids`,
`column_ids`, `row_ids`, `prev_labels`, `column_ranks`, `inv_column_ranks` and `numeric_relations`:
- segment_ids: indicate whether a token belongs to the question (0) or the table (1). 0 for special tokens and
padding.
- column_ids: indicate to which column of the table a token belongs (starting from 1). Is 0 for all question
tokens, special tokens and padding.
- row_ids: indicate to which row of the table a token belongs (starting from 1). Is 0 for all question tokens,
special tokens and padding. Tokens of column headers are also 0.
- prev_labels: indicate whether a token was (part of) an answer to the previous question (1) or not (0). Useful in
a conversational setup (such as SQA).
- column_ranks: indicate the rank of a table token relative to a column, if applicable. For example, if you have a
column "number of movies" with values 87, 53 and 69, then the column ranks of these tokens are 3, 1 and 2
respectively. 0 for all question tokens, special tokens and padding.
- inv_column_ranks: indicate the inverse rank of a table token relative to a column, if applicable. For example, if
you have a column "number of movies" with values 87, 53 and 69, then the inverse column ranks of these tokens are
1, 3 and 2 respectively. 0 for all question tokens, special tokens and padding.
- numeric_relations: indicate numeric relations between the question and the tokens of the table. 0 for all
question tokens, special tokens and padding.
[`TapasTokenizer`] runs end-to-end tokenization on a table and associated sentences: punctuation splitting and
wordpiece.
Args:
vocab_file (`str`):
File containing the vocabulary.
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
do_basic_tokenize (`bool`, *optional*, defaults to `True`):
Whether or not to do basic tokenization before WordPiece.
never_split (`Iterable`, *optional*):
Collection of tokens which will never be split during tokenization. Only has an effect when
`do_basic_tokenize=True`
unk_token (`str`, *optional*, defaults to `"[UNK]"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
sep_token (`str`, *optional*, defaults to `"[SEP]"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
pad_token (`str`, *optional*, defaults to `"[PAD]"`):
The token used for padding, for example when batching sequences of different lengths.
cls_token (`str`, *optional*, defaults to `"[CLS]"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
mask_token (`str`, *optional*, defaults to `"[MASK]"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
empty_token (`str`, *optional*, defaults to `"[EMPTY]"`):
The token used for empty cell values in a table. Empty cell values include "", "n/a", "nan" and "?".
tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see this
[issue](https://github.com/huggingface/transformers/issues/328)).
strip_accents (`bool`, *optional*):
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for `lowercase` (as in the original BERT).
cell_trim_length (`int`, *optional*, defaults to -1):
If > 0: Trim cells so that the length is <= this value. Also disables further cell trimming, should thus be
used with `truncation` set to `True`.
max_column_id (`int`, *optional*):
Max column id to extract.
max_row_id (`int`, *optional*):
Max row id to extract.
strip_column_names (`bool`, *optional*, defaults to `False`):
Whether to add empty strings instead of column names.
update_answer_coordinates (`bool`, *optional*, defaults to `False`):
Whether to recompute the answer coordinates from the answer text.
min_question_length (`int`, *optional*):
Minimum length of each question in terms of tokens (will be skipped otherwise).
max_question_length (`int`, *optional*):
Maximum length of each question in terms of tokens (will be skipped otherwise).
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__(
self,
vocab_file,
do_lower_case=True,
do_basic_tokenize=True,
never_split=None,
unk_token="[UNK]",
sep_token="[SEP]",
pad_token="[PAD]",
cls_token="[CLS]",
mask_token="[MASK]",
empty_token="[EMPTY]",
tokenize_chinese_chars=True,
strip_accents=None,
cell_trim_length: int = -1,
max_column_id: int = None,
max_row_id: int = None,
strip_column_names: bool = False,
update_answer_coordinates: bool = False,
min_question_length=None,
max_question_length=None,
model_max_length: int = 512,
additional_special_tokens: Optional[List[str]] = None,
**kwargs,
):
if not is_pandas_available():
raise ImportError("Pandas is required for the TAPAS tokenizer.")
if additional_special_tokens is not None:
if empty_token not in additional_special_tokens:
additional_special_tokens.append(empty_token)
else:
additional_special_tokens = [empty_token]
if not os.path.isfile(vocab_file):
raise ValueError(
f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"
" model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
)
self.vocab = load_vocab(vocab_file)
self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()])
self.do_basic_tokenize = do_basic_tokenize
if do_basic_tokenize:
self.basic_tokenizer = BasicTokenizer(
do_lower_case=do_lower_case,
never_split=never_split,
tokenize_chinese_chars=tokenize_chinese_chars,
strip_accents=strip_accents,
)
self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=str(unk_token))
# Additional properties
self.cell_trim_length = cell_trim_length
self.max_column_id = (
max_column_id
if max_column_id is not None
else model_max_length
if model_max_length is not None
else VERY_LARGE_INTEGER
)
self.max_row_id = (
max_row_id
if max_row_id is not None
else model_max_length
if model_max_length is not None
else VERY_LARGE_INTEGER
)
self.strip_column_names = strip_column_names
self.update_answer_coordinates = update_answer_coordinates
self.min_question_length = min_question_length
self.max_question_length = max_question_length
super().__init__(
do_lower_case=do_lower_case,
do_basic_tokenize=do_basic_tokenize,
never_split=never_split,
unk_token=unk_token,
sep_token=sep_token,
pad_token=pad_token,
cls_token=cls_token,
mask_token=mask_token,
empty_token=empty_token,
tokenize_chinese_chars=tokenize_chinese_chars,
strip_accents=strip_accents,
cell_trim_length=cell_trim_length,
max_column_id=max_column_id,
max_row_id=max_row_id,
strip_column_names=strip_column_names,
update_answer_coordinates=update_answer_coordinates,
min_question_length=min_question_length,
max_question_length=max_question_length,
model_max_length=model_max_length,
additional_special_tokens=additional_special_tokens,
**kwargs,
)
@property
def do_lower_case(self):
return self.basic_tokenizer.do_lower_case
@property
def vocab_size(self):
return len(self.vocab)
def get_vocab(self):
return dict(self.vocab, **self.added_tokens_encoder)
def _tokenize(self, text):
if format_text(text) == EMPTY_TEXT:
return [self.additional_special_tokens[0]]
split_tokens = []
if self.do_basic_tokenize:
for token in self.basic_tokenizer.tokenize(text, never_split=self.all_special_tokens):
# If the token is part of the never_split set
if token in self.basic_tokenizer.never_split:
split_tokens.append(token)
else:
split_tokens += self.wordpiece_tokenizer.tokenize(token)
else:
split_tokens = self.wordpiece_tokenizer.tokenize(text)
return split_tokens
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
return self.vocab.get(token, self.vocab.get(self.unk_token))
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self.ids_to_tokens.get(index, self.unk_token)
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
out_string = " ".join(tokens).replace(" ##", "").strip()
return out_string
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
index = 0
if os.path.isdir(save_directory):
vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
else:
vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory
with open(vocab_file, "w", encoding="utf-8") as writer:
for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning(
f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
" Please check that the vocabulary is not corrupted!"
)
index = token_index
writer.write(token + "\n")
index += 1
return (vocab_file,)
def create_attention_mask_from_sequences(self, query_ids: List[int], table_values: List[TableValue]) -> List[int]:
"""
Creates the attention mask according to the query token IDs and a list of table values.
Args:
query_ids (`List[int]`): list of token IDs corresponding to the ID.
table_values (`List[TableValue]`): lift of table values, which are named tuples containing the
token value, the column ID and the row ID of said token.
Returns:
`List[int]`: List of ints containing the attention mask values.
"""
return [1] * (1 + len(query_ids) + 1 + len(table_values))
def create_segment_token_type_ids_from_sequences(
self, query_ids: List[int], table_values: List[TableValue]
) -> List[int]:
"""
Creates the segment token type IDs according to the query token IDs and a list of table values.
Args:
query_ids (`List[int]`): list of token IDs corresponding to the ID.
table_values (`List[TableValue]`): lift of table values, which are named tuples containing the
token value, the column ID and the row ID of said token.
Returns:
`List[int]`: List of ints containing the segment token type IDs values.
"""
table_ids = list(zip(*table_values))[0] if table_values else []
return [0] * (1 + len(query_ids) + 1) + [1] * len(table_ids)
def create_column_token_type_ids_from_sequences(
self, query_ids: List[int], table_values: List[TableValue]
) -> List[int]:
"""
Creates the column token type IDs according to the query token IDs and a list of table values.
Args:
query_ids (`List[int]`): list of token IDs corresponding to the ID.
table_values (`List[TableValue]`): lift of table values, which are named tuples containing the
token value, the column ID and the row ID of said token.
Returns:
`List[int]`: List of ints containing the column token type IDs values.
"""
table_column_ids = list(zip(*table_values))[1] if table_values else []
return [0] * (1 + len(query_ids) + 1) + list(table_column_ids)
def create_row_token_type_ids_from_sequences(
self, query_ids: List[int], table_values: List[TableValue]
) -> List[int]:
"""
Creates the row token type IDs according to the query token IDs and a list of table values.
Args:
query_ids (`List[int]`): list of token IDs corresponding to the ID.
table_values (`List[TableValue]`): lift of table values, which are named tuples containing the
token value, the column ID and the row ID of said token.
Returns:
`List[int]`: List of ints containing the row token type IDs values.
"""
table_row_ids = list(zip(*table_values))[2] if table_values else []
return [0] * (1 + len(query_ids) + 1) + list(table_row_ids)
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a question and flattened table for question answering or sequence classification tasks
by concatenating and adding special tokens.
Args:
token_ids_0 (`List[int]`): The ids of the question.
token_ids_1 (`List[int]`, *optional*): The ids of the flattened table.
Returns:
`List[int]`: The model input with special tokens.
"""
if token_ids_1 is None:
raise ValueError("With TAPAS, you must provide both question IDs and table IDs.")
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] + token_ids_1
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.
Args:
token_ids_0 (`List[int]`):
List of question IDs.
token_ids_1 (`List[int]`, *optional*):
List of flattened table IDs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
if token_ids_1 is not None:
return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1))
return [1] + ([0] * len(token_ids_0)) + [1]
@add_end_docstrings(TAPAS_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def __call__(
self,
table: "pd.DataFrame",
queries: Optional[
Union[
TextInput,
PreTokenizedInput,
EncodedInput,
List[TextInput],
List[PreTokenizedInput],
List[EncodedInput],
]
] = None,
answer_coordinates: Optional[Union[List[Tuple], List[List[Tuple]]]] = None,
answer_text: Optional[Union[List[TextInput], List[List[TextInput]]]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TapasTruncationStrategy] = False,
max_length: Optional[int] = None,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
"""
Main method to tokenize and prepare for the model one or several sequence(s) related to a table.
Args:
table (`pd.DataFrame`):
Table containing tabular data. Note that all cell values must be text. Use *.astype(str)* on a Pandas
dataframe to convert it to string.
queries (`str` or `List[str]`):
Question or batch of questions related to a table to be encoded. Note that in case of a batch, all
questions must refer to the **same** table.
answer_coordinates (`List[Tuple]` or `List[List[Tuple]]`, *optional*):
Answer coordinates of each table-question pair in the batch. In case only a single table-question pair
is provided, then the answer_coordinates must be a single list of one or more tuples. Each tuple must
be a (row_index, column_index) pair. The first data row (not the column header row) has index 0. The
first column has index 0. In case a batch of table-question pairs is provided, then the
answer_coordinates must be a list of lists of tuples (each list corresponding to a single
table-question pair).
answer_text (`List[str]` or `List[List[str]]`, *optional*):
Answer text of each table-question pair in the batch. In case only a single table-question pair is
provided, then the answer_text must be a single list of one or more strings. Each string must be the
answer text of a corresponding answer coordinate. In case a batch of table-question pairs is provided,
then the answer_coordinates must be a list of lists of strings (each list corresponding to a single
table-question pair).
"""
assert isinstance(table, pd.DataFrame), "Table must be of type pd.DataFrame"
# Input type checking for clearer error
valid_query = False
# Check that query has a valid type
if queries is None or isinstance(queries, str):
valid_query = True
elif isinstance(queries, (list, tuple)):
if len(queries) == 0 or isinstance(queries[0], str):
valid_query = True
if not valid_query:
raise ValueError(
"queries input must of type `str` (single example), `List[str]` (batch or single pretokenized"
" example). "
)
is_batched = isinstance(queries, (list, tuple))
if is_batched:
return self.batch_encode_plus(
table=table,
queries=queries,
answer_coordinates=answer_coordinates,
answer_text=answer_text,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
**kwargs,
)
else:
return self.encode_plus(
table=table,
query=queries,
answer_coordinates=answer_coordinates,
answer_text=answer_text,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
**kwargs,
)
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, TAPAS_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def batch_encode_plus(
self,
table: "pd.DataFrame",
queries: Optional[
Union[
List[TextInput],
List[PreTokenizedInput],
List[EncodedInput],
]
] = None,
answer_coordinates: Optional[List[List[Tuple]]] = None,
answer_text: Optional[List[List[TextInput]]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TapasTruncationStrategy] = False,
max_length: Optional[int] = None,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
"""
Prepare a table and a list of strings for the model.
<Tip warning={true}>
This method is deprecated, `__call__` should be used instead.
</Tip>
Args:
table (`pd.DataFrame`):
Table containing tabular data. Note that all cell values must be text. Use *.astype(str)* on a Pandas
dataframe to convert it to string.
queries (`List[str]`):
Batch of questions related to a table to be encoded. Note that all questions must refer to the **same**
table.
answer_coordinates (`List[Tuple]` or `List[List[Tuple]]`, *optional*):
Answer coordinates of each table-question pair in the batch. Each tuple must be a (row_index,
column_index) pair. The first data row (not the column header row) has index 0. The first column has
index 0. The answer_coordinates must be a list of lists of tuples (each list corresponding to a single
table-question pair).
answer_text (`List[str]` or `List[List[str]]`, *optional*):
Answer text of each table-question pair in the batch. In case a batch of table-question pairs is
provided, then the answer_coordinates must be a list of lists of strings (each list corresponding to a
single table-question pair). Each string must be the answer text of a corresponding answer coordinate.
"""
if return_token_type_ids is not None and not add_special_tokens:
raise ValueError(
"Asking to return token_type_ids while setting add_special_tokens to False "
"results in an undefined behavior. Please set add_special_tokens to True or "
"set return_token_type_ids to None."
)
if (answer_coordinates and not answer_text) or (not answer_coordinates and answer_text):
raise ValueError("In case you provide answers, both answer_coordinates and answer_text should be provided")
elif answer_coordinates is None and answer_text is None:
answer_coordinates = answer_text = [None] * len(queries)
if "is_split_into_words" in kwargs:
raise NotImplementedError("Currently TapasTokenizer only supports questions as strings.")
if return_offsets_mapping:
raise NotImplementedError(
"return_offset_mapping is not available when using Python tokenizers. "
"To use this feature, change your tokenizer to one deriving from "
"transformers.PreTrainedTokenizerFast."
)
return self._batch_encode_plus(
table=table,
queries=queries,
answer_coordinates=answer_coordinates,
answer_text=answer_text,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
**kwargs,
)
def _get_question_tokens(self, query):
"""Tokenizes the query, taking into account the max and min question length."""
query_tokens = self.tokenize(query)
if self.max_question_length is not None and len(query_tokens) > self.max_question_length:
logger.warning("Skipping query as its tokens are longer than the max question length")
return "", []
if self.min_question_length is not None and len(query_tokens) < self.min_question_length:
logger.warning("Skipping query as its tokens are shorter than the min question length")
return "", []
return query, query_tokens
def _batch_encode_plus(
self,
table,
queries: Union[
List[TextInput],
List[PreTokenizedInput],
List[EncodedInput],
],
answer_coordinates: Optional[List[List[Tuple]]] = None,
answer_text: Optional[List[List[TextInput]]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TapasTruncationStrategy] = False,
max_length: Optional[int] = None,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = True,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
table_tokens = self._tokenize_table(table)
queries_tokens = []
for idx, query in enumerate(queries):
query, query_tokens = self._get_question_tokens(query)
queries[idx] = query
queries_tokens.append(query_tokens)
batch_outputs = self._batch_prepare_for_model(
table,
queries,
tokenized_table=table_tokens,
queries_tokens=queries_tokens,
answer_coordinates=answer_coordinates,
padding=padding,
truncation=truncation,
answer_text=answer_text,
add_special_tokens=add_special_tokens,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
prepend_batch_axis=True,
return_attention_mask=return_attention_mask,
return_token_type_ids=return_token_type_ids,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_length=return_length,
verbose=verbose,
)
return BatchEncoding(batch_outputs)
def _batch_prepare_for_model(
self,
raw_table: "pd.DataFrame",
raw_queries: Union[
List[TextInput],
List[PreTokenizedInput],
List[EncodedInput],
],
tokenized_table: Optional[TokenizedTable] = None,
queries_tokens: Optional[List[List[str]]] = None,
answer_coordinates: Optional[List[List[Tuple]]] = None,
answer_text: Optional[List[List[TextInput]]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TapasTruncationStrategy] = False,
max_length: Optional[int] = None,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = True,
return_attention_mask: Optional[bool] = True,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
prepend_batch_axis: bool = False,
**kwargs,
) -> BatchEncoding:
batch_outputs = {}
for index, example in enumerate(zip(raw_queries, queries_tokens, answer_coordinates, answer_text)):
raw_query, query_tokens, answer_coords, answer_txt = example
outputs = self.prepare_for_model(
raw_table,
raw_query,
tokenized_table=tokenized_table,
query_tokens=query_tokens,
answer_coordinates=answer_coords,
answer_text=answer_txt,
add_special_tokens=add_special_tokens,
padding=PaddingStrategy.DO_NOT_PAD.value, # we pad in batch afterwards
truncation=truncation,
max_length=max_length,
pad_to_multiple_of=None, # we pad in batch afterwards
return_attention_mask=False, # we pad in batch afterwards
return_token_type_ids=return_token_type_ids,
return_special_tokens_mask=return_special_tokens_mask,
return_length=return_length,
return_tensors=None, # We convert the whole batch to tensors at the end
prepend_batch_axis=False,
verbose=verbose,
prev_answer_coordinates=answer_coordinates[index - 1] if index != 0 else None,
prev_answer_text=answer_text[index - 1] if index != 0 else None,
)
for key, value in outputs.items():
if key not in batch_outputs:
batch_outputs[key] = []
batch_outputs[key].append(value)
batch_outputs = self.pad(
batch_outputs,
padding=padding,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
return_attention_mask=return_attention_mask,
)
batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors)
return batch_outputs
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING)
def encode(
self,
table: "pd.DataFrame",
query: Optional[
Union[
TextInput,
PreTokenizedInput,
EncodedInput,
]
] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TapasTruncationStrategy] = False,
max_length: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
**kwargs,
) -> List[int]:
"""
Prepare a table and a string for the model. This method does not return token type IDs, attention masks, etc.
which are necessary for the model to work correctly. Use that method if you want to build your processing on
your own, otherwise refer to `__call__`.
Args:
table (`pd.DataFrame`):
Table containing tabular data. Note that all cell values must be text. Use *.astype(str)* on a Pandas
dataframe to convert it to string.
query (`str` or `List[str]`):
Question related to a table to be encoded.
"""
encoded_inputs = self.encode_plus(
table,
query=query,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
return_tensors=return_tensors,
**kwargs,
)
return encoded_inputs["input_ids"]
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, TAPAS_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def encode_plus(
self,
table: "pd.DataFrame",
query: Optional[
Union[
TextInput,
PreTokenizedInput,
EncodedInput,
]
] = None,
answer_coordinates: Optional[List[Tuple]] = None,
answer_text: Optional[List[TextInput]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TapasTruncationStrategy] = False,
max_length: Optional[int] = None,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
"""
Prepare a table and a string for the model.
Args:
table (`pd.DataFrame`):
Table containing tabular data. Note that all cell values must be text. Use *.astype(str)* on a Pandas
dataframe to convert it to string.
query (`str` or `List[str]`):
Question related to a table to be encoded.
answer_coordinates (`List[Tuple]` or `List[List[Tuple]]`, *optional*):
Answer coordinates of each table-question pair in the batch. The answer_coordinates must be a single
list of one or more tuples. Each tuple must be a (row_index, column_index) pair. The first data row
(not the column header row) has index 0. The first column has index 0.
answer_text (`List[str]` or `List[List[str]]`, *optional*):
Answer text of each table-question pair in the batch. The answer_text must be a single list of one or
more strings. Each string must be the answer text of a corresponding answer coordinate.
"""
if return_token_type_ids is not None and not add_special_tokens:
raise ValueError(
"Asking to return token_type_ids while setting add_special_tokens to False "
"results in an undefined behavior. Please set add_special_tokens to True or "
"set return_token_type_ids to None."
)
if (answer_coordinates and not answer_text) or (not answer_coordinates and answer_text):
raise ValueError("In case you provide answers, both answer_coordinates and answer_text should be provided")
if "is_split_into_words" in kwargs:
raise NotImplementedError("Currently TapasTokenizer only supports questions as strings.")
if return_offsets_mapping:
raise NotImplementedError(
"return_offset_mapping is not available when using Python tokenizers. "
"To use this feature, change your tokenizer to one deriving from "
"transformers.PreTrainedTokenizerFast."
)
return self._encode_plus(
table=table,
query=query,
answer_coordinates=answer_coordinates,
answer_text=answer_text,
add_special_tokens=add_special_tokens,
truncation=truncation,
padding=padding,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
**kwargs,
)
def _encode_plus(
self,
table: "pd.DataFrame",
query: Union[
TextInput,
PreTokenizedInput,
EncodedInput,
],
answer_coordinates: Optional[List[Tuple]] = None,
answer_text: Optional[List[TextInput]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TapasTruncationStrategy] = False,
max_length: Optional[int] = None,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = True,
return_attention_mask: Optional[bool] = True,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs,
):
if query is None:
query = ""
logger.warning(
"TAPAS is a question answering model but you have not passed a query. Please be aware that the "
"model will probably not behave correctly."
)
table_tokens = self._tokenize_table(table)
query, query_tokens = self._get_question_tokens(query)
return self.prepare_for_model(
table,
query,
tokenized_table=table_tokens,
query_tokens=query_tokens,
answer_coordinates=answer_coordinates,
answer_text=answer_text,
add_special_tokens=add_special_tokens,
truncation=truncation,
padding=padding,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
prepend_batch_axis=True,
return_attention_mask=return_attention_mask,
return_token_type_ids=return_token_type_ids,
return_special_tokens_mask=return_special_tokens_mask,
return_length=return_length,
verbose=verbose,
)
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, TAPAS_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def prepare_for_model(
self,
raw_table: "pd.DataFrame",
raw_query: Union[
TextInput,
PreTokenizedInput,
EncodedInput,
],
tokenized_table: Optional[TokenizedTable] = None,
query_tokens: Optional[TokenizedTable] = None,
answer_coordinates: Optional[List[Tuple]] = None,
answer_text: Optional[List[TextInput]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TapasTruncationStrategy] = False,
max_length: Optional[int] = None,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = True,
return_attention_mask: Optional[bool] = True,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
prepend_batch_axis: bool = False,
**kwargs,
) -> BatchEncoding:
"""
Prepares a sequence of input id so that it can be used by the model. It adds special tokens, truncates
sequences if overflowing while taking into account the special tokens.
Args:
raw_table (`pd.DataFrame`):
The original table before any transformation (like tokenization) was applied to it.
raw_query (`TextInput` or `PreTokenizedInput` or `EncodedInput`):
The original query before any transformation (like tokenization) was applied to it.
tokenized_table (`TokenizedTable`):
The table after tokenization.
query_tokens (`List[str]`):
The query after tokenization.
answer_coordinates (`List[Tuple]` or `List[List[Tuple]]`, *optional*):
Answer coordinates of each table-question pair in the batch. The answer_coordinates must be a single
list of one or more tuples. Each tuple must be a (row_index, column_index) pair. The first data row
(not the column header row) has index 0. The first column has index 0.
answer_text (`List[str]` or `List[List[str]]`, *optional*):
Answer text of each table-question pair in the batch. The answer_text must be a single list of one or
more strings. Each string must be the answer text of a corresponding answer coordinate.
"""
if isinstance(padding, bool):
if padding and (max_length is not None or pad_to_multiple_of is not None):
padding = PaddingStrategy.MAX_LENGTH
else:
padding = PaddingStrategy.DO_NOT_PAD
elif not isinstance(padding, PaddingStrategy):
padding = PaddingStrategy(padding)
if isinstance(truncation, bool):
if truncation:
truncation = TapasTruncationStrategy.DROP_ROWS_TO_FIT
else:
truncation = TapasTruncationStrategy.DO_NOT_TRUNCATE
elif not isinstance(truncation, TapasTruncationStrategy):
truncation = TapasTruncationStrategy(truncation)
encoded_inputs = {}
is_part_of_batch = False
prev_answer_coordinates, prev_answer_text = None, None
if "prev_answer_coordinates" in kwargs and "prev_answer_text" in kwargs:
is_part_of_batch = True
prev_answer_coordinates = kwargs["prev_answer_coordinates"]
prev_answer_text = kwargs["prev_answer_text"]
num_rows = self._get_num_rows(raw_table, truncation != TapasTruncationStrategy.DO_NOT_TRUNCATE)
num_columns = self._get_num_columns(raw_table)
_, _, num_tokens = self._get_table_boundaries(tokenized_table)
if truncation != TapasTruncationStrategy.DO_NOT_TRUNCATE:
num_rows, num_tokens = self._get_truncated_table_rows(
query_tokens, tokenized_table, num_rows, num_columns, max_length, truncation_strategy=truncation
)
table_data = list(self._get_table_values(tokenized_table, num_columns, num_rows, num_tokens))
query_ids = self.convert_tokens_to_ids(query_tokens)
table_ids = list(zip(*table_data))[0] if len(table_data) > 0 else list(zip(*table_data))
table_ids = self.convert_tokens_to_ids(list(table_ids))
if "return_overflowing_tokens" in kwargs and kwargs["return_overflowing_tokens"]:
raise ValueError("TAPAS does not return overflowing tokens as it works on tables.")
if add_special_tokens:
input_ids = self.build_inputs_with_special_tokens(query_ids, table_ids)
else:
input_ids = query_ids + table_ids
if max_length is not None and len(input_ids) > max_length:
raise ValueError(
"Could not encode the query and table header given the maximum length. Encoding the query and table "
f"header results in a length of {len(input_ids)} which is higher than the max_length of {max_length}"
)
encoded_inputs["input_ids"] = input_ids
segment_ids = self.create_segment_token_type_ids_from_sequences(query_ids, table_data)
column_ids = self.create_column_token_type_ids_from_sequences(query_ids, table_data)
row_ids = self.create_row_token_type_ids_from_sequences(query_ids, table_data)
if not is_part_of_batch or (prev_answer_coordinates is None and prev_answer_text is None):
# simply set the prev_labels to zeros
prev_labels = [0] * len(row_ids)
else:
prev_labels = self.get_answer_ids(
column_ids, row_ids, table_data, prev_answer_text, prev_answer_coordinates
)
# FIRST: parse both the table and question in terms of numeric values
raw_table = add_numeric_table_values(raw_table)
raw_query = add_numeric_values_to_question(raw_query)
# SECOND: add numeric-related features (and not parse them in these functions):
column_ranks, inv_column_ranks = self._get_numeric_column_ranks(column_ids, row_ids, raw_table)
numeric_relations = self._get_numeric_relations(raw_query, column_ids, row_ids, raw_table)
# Load from model defaults
if return_token_type_ids is None:
return_token_type_ids = "token_type_ids" in self.model_input_names
if return_attention_mask is None:
return_attention_mask = "attention_mask" in self.model_input_names
if return_attention_mask:
attention_mask = self.create_attention_mask_from_sequences(query_ids, table_data)
encoded_inputs["attention_mask"] = attention_mask
if answer_coordinates is not None and answer_text is not None:
labels = self.get_answer_ids(column_ids, row_ids, table_data, answer_text, answer_coordinates)
numeric_values = self._get_numeric_values(raw_table, column_ids, row_ids)
numeric_values_scale = self._get_numeric_values_scale(raw_table, column_ids, row_ids)
encoded_inputs["labels"] = labels
encoded_inputs["numeric_values"] = numeric_values
encoded_inputs["numeric_values_scale"] = numeric_values_scale
if return_token_type_ids:
token_type_ids = [
segment_ids,
column_ids,
row_ids,
prev_labels,
column_ranks,
inv_column_ranks,
numeric_relations,
]
token_type_ids = [list(ids) for ids in list(zip(*token_type_ids))]
encoded_inputs["token_type_ids"] = token_type_ids
if return_special_tokens_mask:
if add_special_tokens:
encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(query_ids, table_ids)
else:
encoded_inputs["special_tokens_mask"] = [0] * len(input_ids)
# Check lengths
if max_length is None and len(encoded_inputs["input_ids"]) > self.model_max_length and verbose:
if not self.deprecation_warnings.get("sequence-length-is-longer-than-the-specified-maximum", False):
logger.warning(
"Token indices sequence length is longer than the specified maximum sequence length "
f"for this model ({len(encoded_inputs['input_ids'])} > {self.model_max_length}). Running this "
"sequence through the model will result in indexing errors."
)
self.deprecation_warnings["sequence-length-is-longer-than-the-specified-maximum"] = True
# Padding
if padding != PaddingStrategy.DO_NOT_PAD or return_attention_mask:
encoded_inputs = self.pad(
encoded_inputs,
max_length=max_length,
padding=padding.value,
pad_to_multiple_of=pad_to_multiple_of,
return_attention_mask=return_attention_mask,
)
if return_length:
encoded_inputs["length"] = len(encoded_inputs["input_ids"])
batch_outputs = BatchEncoding(
encoded_inputs, tensor_type=return_tensors, prepend_batch_axis=prepend_batch_axis
)
return batch_outputs
def _get_truncated_table_rows(
self,
query_tokens: List[str],
tokenized_table: TokenizedTable,
num_rows: int,
num_columns: int,
max_length: int,
truncation_strategy: Union[str, TapasTruncationStrategy],
) -> Tuple[int, int]:
"""
Truncates a sequence pair in-place following the strategy.
Args:
query_tokens (`List[str]`):
List of strings corresponding to the tokenized query.
tokenized_table (`TokenizedTable`):
Tokenized table
num_rows (`int`):
Total number of table rows
num_columns (`int`):
Total number of table columns
max_length (`int`):
Total maximum length.
truncation_strategy (`str` or [`TapasTruncationStrategy`]):
Truncation strategy to use. Seeing as this method should only be called when truncating, the only
available strategy is the `"drop_rows_to_fit"` strategy.
Returns:
`Tuple(int, int)`: tuple containing the number of rows after truncation, and the number of tokens available
for each table element.
"""
if not isinstance(truncation_strategy, TapasTruncationStrategy):
truncation_strategy = TapasTruncationStrategy(truncation_strategy)
if max_length is None:
max_length = self.model_max_length
if truncation_strategy == TapasTruncationStrategy.DROP_ROWS_TO_FIT:
while True:
num_tokens = self._get_max_num_tokens(
query_tokens, tokenized_table, num_rows=num_rows, num_columns=num_columns, max_length=max_length
)
if num_tokens is not None:
# We could fit the table.
break
# Try to drop a row to fit the table.
num_rows -= 1
if num_rows < 1:
break
elif truncation_strategy != TapasTruncationStrategy.DO_NOT_TRUNCATE:
raise ValueError(f"Unknown truncation strategy {truncation_strategy}.")
return num_rows, num_tokens or 1
def _tokenize_table(
self,
table=None,
):
"""
Tokenizes column headers and cell texts of a table.
Args:
table (`pd.Dataframe`):
Table. Returns: `TokenizedTable`: TokenizedTable object.
"""
tokenized_rows = []
tokenized_row = []
# tokenize column headers
for column in table:
if self.strip_column_names:
tokenized_row.append(self.tokenize(""))
else:
tokenized_row.append(self.tokenize(column))
tokenized_rows.append(tokenized_row)
# tokenize cell values
for idx, row in table.iterrows():
tokenized_row = []
for cell in row:
tokenized_row.append(self.tokenize(cell))
tokenized_rows.append(tokenized_row)
token_coordinates = []
for row_index, row in enumerate(tokenized_rows):
for column_index, cell in enumerate(row):
for token_index, _ in enumerate(cell):
token_coordinates.append(
TokenCoordinates(
row_index=row_index,
column_index=column_index,
token_index=token_index,
)
)
return TokenizedTable(
rows=tokenized_rows,
selected_tokens=token_coordinates,
)
def _question_encoding_cost(self, question_tokens):
# Two extra spots of SEP and CLS.
return len(question_tokens) + 2
def _get_token_budget(self, question_tokens, max_length=None):
"""
Computes the number of tokens left for the table after tokenizing a question, taking into account the max
sequence length of the model.
Args:
question_tokens (`List[String]`):
List of question tokens. Returns: `int`: the number of tokens left for the table, given the model max
length.
"""
return (max_length if max_length is not None else self.model_max_length) - self._question_encoding_cost(
question_tokens
)
def _get_table_values(self, table, num_columns, num_rows, num_tokens) -> Generator[TableValue, None, None]:
"""Iterates over partial table and returns token, column and row indexes."""
for tc in table.selected_tokens:
# First row is header row.
if tc.row_index >= num_rows + 1:
continue
if tc.column_index >= num_columns:
continue
cell = table.rows[tc.row_index][tc.column_index]
token = cell[tc.token_index]
word_begin_index = tc.token_index
# Don't add partial words. Find the starting word piece and check if it
# fits in the token budget.
while word_begin_index >= 0 and _is_inner_wordpiece(cell[word_begin_index]):
word_begin_index -= 1
if word_begin_index >= num_tokens:
continue
yield TableValue(token, tc.column_index + 1, tc.row_index)
def _get_table_boundaries(self, table):
"""Return maximal number of rows, columns and tokens."""
max_num_tokens = 0
max_num_columns = 0
max_num_rows = 0
for tc in table.selected_tokens:
max_num_columns = max(max_num_columns, tc.column_index + 1)
max_num_rows = max(max_num_rows, tc.row_index + 1)
max_num_tokens = max(max_num_tokens, tc.token_index + 1)
max_num_columns = min(self.max_column_id, max_num_columns)
max_num_rows = min(self.max_row_id, max_num_rows)
return max_num_rows, max_num_columns, max_num_tokens
def _get_table_cost(self, table, num_columns, num_rows, num_tokens):
return sum(1 for _ in self._get_table_values(table, num_columns, num_rows, num_tokens))
def _get_max_num_tokens(self, question_tokens, tokenized_table, num_columns, num_rows, max_length):
"""Computes max number of tokens that can be squeezed into the budget."""
token_budget = self._get_token_budget(question_tokens, max_length)
_, _, max_num_tokens = self._get_table_boundaries(tokenized_table)
if self.cell_trim_length >= 0 and max_num_tokens > self.cell_trim_length:
max_num_tokens = self.cell_trim_length
num_tokens = 0
for num_tokens in range(max_num_tokens + 1):
cost = self._get_table_cost(tokenized_table, num_columns, num_rows, num_tokens + 1)
if cost > token_budget:
break
if num_tokens < max_num_tokens:
if self.cell_trim_length >= 0:
# We don't allow dynamic trimming if a cell_trim_length is set.
return None
if num_tokens == 0:
return None
return num_tokens
def _get_num_columns(self, table):
num_columns = table.shape[1]
if num_columns >= self.max_column_id:
raise ValueError("Too many columns")
return num_columns
def _get_num_rows(self, table, drop_rows_to_fit):
num_rows = table.shape[0]
if num_rows >= self.max_row_id:
if drop_rows_to_fit:
num_rows = self.max_row_id - 1
else:
raise ValueError("Too many rows")
return num_rows
def _serialize_text(self, question_tokens):
"""Serializes texts in index arrays."""
tokens = []
segment_ids = []
column_ids = []
row_ids = []
# add [CLS] token at the beginning
tokens.append(self.cls_token)
segment_ids.append(0)
column_ids.append(0)
row_ids.append(0)
for token in question_tokens:
tokens.append(token)
segment_ids.append(0)
column_ids.append(0)
row_ids.append(0)
return tokens, segment_ids, column_ids, row_ids
def _serialize(
self,
question_tokens,
table,
num_columns,
num_rows,
num_tokens,
):
"""Serializes table and text."""
tokens, segment_ids, column_ids, row_ids = self._serialize_text(question_tokens)
# add [SEP] token between question and table tokens
tokens.append(self.sep_token)
segment_ids.append(0)
column_ids.append(0)
row_ids.append(0)
for token, column_id, row_id in self._get_table_values(table, num_columns, num_rows, num_tokens):
tokens.append(token)
segment_ids.append(1)
column_ids.append(column_id)
row_ids.append(row_id)
return SerializedExample(
tokens=tokens,
segment_ids=segment_ids,
column_ids=column_ids,
row_ids=row_ids,
)
def _get_column_values(self, table, col_index):
table_numeric_values = {}
for row_index, row in table.iterrows():
cell = row[col_index]
if cell.numeric_value is not None:
table_numeric_values[row_index] = cell.numeric_value
return table_numeric_values
def _get_cell_token_indexes(self, column_ids, row_ids, column_id, row_id):
for index in range(len(column_ids)):
if column_ids[index] - 1 == column_id and row_ids[index] - 1 == row_id:
yield index
def _get_numeric_column_ranks(self, column_ids, row_ids, table):
"""Returns column ranks for all numeric columns."""
ranks = [0] * len(column_ids)
inv_ranks = [0] * len(column_ids)
# original code from tf_example_utils.py of the original implementation
if table is not None:
for col_index in range(len(table.columns)):
table_numeric_values = self._get_column_values(table, col_index)
if not table_numeric_values:
continue
try:
key_fn = get_numeric_sort_key_fn(table_numeric_values.values())
except ValueError:
continue
table_numeric_values = {row_index: key_fn(value) for row_index, value in table_numeric_values.items()}
table_numeric_values_inv = collections.defaultdict(list)
for row_index, value in table_numeric_values.items():
table_numeric_values_inv[value].append(row_index)
unique_values = sorted(table_numeric_values_inv.keys())
for rank, value in enumerate(unique_values):
for row_index in table_numeric_values_inv[value]:
for index in self._get_cell_token_indexes(column_ids, row_ids, col_index, row_index):
ranks[index] = rank + 1
inv_ranks[index] = len(unique_values) - rank
return ranks, inv_ranks
def _get_numeric_sort_key_fn(self, table_numeric_values, value):
"""
Returns the sort key function for comparing value to table values. The function returned will be a suitable
input for the key param of the sort(). See number_annotation_utils._get_numeric_sort_key_fn for details
Args:
table_numeric_values: Numeric values of a column
value: Numeric value in the question
Returns:
A function key function to compare column and question values.
"""
if not table_numeric_values:
return None
all_values = list(table_numeric_values.values())
all_values.append(value)
try:
return get_numeric_sort_key_fn(all_values)
except ValueError:
return None
def _get_numeric_relations(self, question, column_ids, row_ids, table):
"""
Returns numeric relations embeddings
Args:
question: Question object.
column_ids: Maps word piece position to column id.
row_ids: Maps word piece position to row id.
table: The table containing the numeric cell values.
"""
numeric_relations = [0] * len(column_ids)
# first, we add any numeric value spans to the question:
# Create a dictionary that maps a table cell to the set of all relations
# this cell has with any value in the question.
cell_indices_to_relations = collections.defaultdict(set)
if question is not None and table is not None:
for numeric_value_span in question.numeric_spans:
for value in numeric_value_span.values:
for column_index in range(len(table.columns)):
table_numeric_values = self._get_column_values(table, column_index)
sort_key_fn = self._get_numeric_sort_key_fn(table_numeric_values, value)
if sort_key_fn is None:
continue
for row_index, cell_value in table_numeric_values.items():
relation = get_numeric_relation(value, cell_value, sort_key_fn)
if relation is not None:
cell_indices_to_relations[column_index, row_index].add(relation)
# For each cell add a special feature for all its word pieces.
for (column_index, row_index), relations in cell_indices_to_relations.items():
relation_set_index = 0
for relation in relations:
assert relation.value >= Relation.EQ.value
relation_set_index += 2 ** (relation.value - Relation.EQ.value)
for cell_token_index in self._get_cell_token_indexes(column_ids, row_ids, column_index, row_index):
numeric_relations[cell_token_index] = relation_set_index
return numeric_relations
def _get_numeric_values(self, table, column_ids, row_ids):
"""Returns numeric values for computation of answer loss."""
numeric_values = [float("nan")] * len(column_ids)
if table is not None:
num_rows = table.shape[0]
num_columns = table.shape[1]
for col_index in range(num_columns):
for row_index in range(num_rows):
numeric_value = table.iloc[row_index, col_index].numeric_value
if numeric_value is not None:
if numeric_value.float_value is None:
continue
float_value = numeric_value.float_value
if float_value == float("inf"):
continue
for index in self._get_cell_token_indexes(column_ids, row_ids, col_index, row_index):
numeric_values[index] = float_value
return numeric_values
def _get_numeric_values_scale(self, table, column_ids, row_ids):
"""Returns a scale to each token to down weigh the value of long words."""
numeric_values_scale = [1.0] * len(column_ids)
if table is None:
return numeric_values_scale
num_rows = table.shape[0]
num_columns = table.shape[1]
for col_index in range(num_columns):
for row_index in range(num_rows):
indices = list(self._get_cell_token_indexes(column_ids, row_ids, col_index, row_index))
num_indices = len(indices)
if num_indices > 1:
for index in indices:
numeric_values_scale[index] = float(num_indices)
return numeric_values_scale
def _pad_to_seq_length(self, inputs):
while len(inputs) > self.model_max_length:
inputs.pop()
while len(inputs) < self.model_max_length:
inputs.append(0)
def _get_all_answer_ids_from_coordinates(
self,
column_ids,
row_ids,
answers_list,
):
"""Maps lists of answer coordinates to token indexes."""
answer_ids = [0] * len(column_ids)
found_answers = set()
all_answers = set()
for answers in answers_list:
column_index, row_index = answers
all_answers.add((column_index, row_index))
for index in self._get_cell_token_indexes(column_ids, row_ids, column_index, row_index):
found_answers.add((column_index, row_index))
answer_ids[index] = 1
missing_count = len(all_answers) - len(found_answers)
return answer_ids, missing_count
def _get_all_answer_ids(self, column_ids, row_ids, answer_coordinates):
"""
Maps answer coordinates of a question to token indexes.
In the SQA format (TSV), the coordinates are given as (row, column) tuples. Here, we first swap them to
(column, row) format before calling _get_all_answer_ids_from_coordinates.
"""
def _to_coordinates(answer_coordinates_question):
return [(coords[1], coords[0]) for coords in answer_coordinates_question]
return self._get_all_answer_ids_from_coordinates(
column_ids, row_ids, answers_list=(_to_coordinates(answer_coordinates))
)
def _find_tokens(self, text, segment):
"""Return start index of segment in text or None."""
logging.info(f"text: {text} {segment}")
for index in range(1 + len(text) - len(segment)):
for seg_index, seg_token in enumerate(segment):
if text[index + seg_index].piece != seg_token.piece:
break
else:
return index
return None
def _find_answer_coordinates_from_answer_text(
self,
tokenized_table,
answer_text,
):
"""Returns all occurrences of answer_text in the table."""
logging.info(f"answer text: {answer_text}")
for row_index, row in enumerate(tokenized_table.rows):
if row_index == 0:
# We don't search for answers in the header.
continue
for col_index, cell in enumerate(row):
token_index = self._find_tokens(cell, answer_text)
if token_index is not None:
yield TokenCoordinates(
row_index=row_index,
column_index=col_index,
token_index=token_index,
)
def _find_answer_ids_from_answer_texts(
self,
column_ids,
row_ids,
tokenized_table,
answer_texts,
):
"""Maps question with answer texts to the first matching token indexes."""
answer_ids = [0] * len(column_ids)
for answer_text in answer_texts:
for coordinates in self._find_answer_coordinates_from_answer_text(
tokenized_table,
answer_text,
):
# Maps answer coordinates to indexes this can fail if tokens / rows have
# been pruned.
indexes = list(
self._get_cell_token_indexes(
column_ids,
row_ids,
column_id=coordinates.column_index,
row_id=coordinates.row_index - 1,
)
)
indexes.sort()
coordinate_answer_ids = []
if indexes:
begin_index = coordinates.token_index + indexes[0]
end_index = begin_index + len(answer_text)
for index in indexes:
if index >= begin_index and index < end_index:
coordinate_answer_ids.append(index)
if len(coordinate_answer_ids) == len(answer_text):
for index in coordinate_answer_ids:
answer_ids[index] = 1
break
return answer_ids
def _get_answer_ids(self, column_ids, row_ids, answer_coordinates):
"""Maps answer coordinates of a question to token indexes."""
answer_ids, missing_count = self._get_all_answer_ids(column_ids, row_ids, answer_coordinates)
if missing_count:
raise ValueError("Couldn't find all answers")
return answer_ids
def get_answer_ids(self, column_ids, row_ids, tokenized_table, answer_texts_question, answer_coordinates_question):
if self.update_answer_coordinates:
return self._find_answer_ids_from_answer_texts(
column_ids,
row_ids,
tokenized_table,
answer_texts=[self.tokenize(at) for at in answer_texts_question],
)
return self._get_answer_ids(column_ids, row_ids, answer_coordinates_question)
def _pad(
self,
encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
max_length: Optional[int] = None,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
pad_to_multiple_of: Optional[int] = None,
return_attention_mask: Optional[bool] = None,
) -> dict:
"""
Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
Args:
encoded_inputs:
Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
max_length: maximum length of the returned list and optionally padding length (see below).
Will truncate by taking into account the special tokens.
padding_strategy: PaddingStrategy to use for padding.
- PaddingStrategy.LONGEST Pad to the longest sequence in the batch
- PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
- PaddingStrategy.DO_NOT_PAD: Do not pad
The tokenizer padding sides are defined in self.padding_side:
- 'left': pads on the left of the sequences
- 'right': pads on the right of the sequences
pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
`>= 7.5` (Volta).
return_attention_mask:
(optional) Set to False to avoid returning attention mask (default: set to model specifics)
"""
# Load from model defaults
if return_attention_mask is None:
return_attention_mask = "attention_mask" in self.model_input_names
if padding_strategy == PaddingStrategy.LONGEST:
max_length = len(encoded_inputs["input_ids"])
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
needs_to_be_padded = (
padding_strategy != PaddingStrategy.DO_NOT_PAD and len(encoded_inputs["input_ids"]) != max_length
)
# Initialize attention mask if not present.
if return_attention_mask and "attention_mask" not in encoded_inputs:
encoded_inputs["attention_mask"] = [1] * len(encoded_inputs["input_ids"])
if needs_to_be_padded:
difference = max_length - len(encoded_inputs["input_ids"])
if self.padding_side == "right":
if return_attention_mask:
encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [0] * difference
if "token_type_ids" in encoded_inputs:
encoded_inputs["token_type_ids"] = (
encoded_inputs["token_type_ids"] + [[self.pad_token_type_id] * 7] * difference
)
if "labels" in encoded_inputs:
encoded_inputs["labels"] = encoded_inputs["labels"] + [0] * difference
if "numeric_values" in encoded_inputs:
encoded_inputs["numeric_values"] = encoded_inputs["numeric_values"] + [float("nan")] * difference
if "numeric_values_scale" in encoded_inputs:
encoded_inputs["numeric_values_scale"] = (
encoded_inputs["numeric_values_scale"] + [1.0] * difference
)
if "special_tokens_mask" in encoded_inputs:
encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference
encoded_inputs["input_ids"] = encoded_inputs["input_ids"] + [self.pad_token_id] * difference
elif self.padding_side == "left":
if return_attention_mask:
encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
if "token_type_ids" in encoded_inputs:
encoded_inputs["token_type_ids"] = [[self.pad_token_type_id] * 7] * difference + encoded_inputs[
"token_type_ids"
]
if "labels" in encoded_inputs:
encoded_inputs["labels"] = [0] * difference + encoded_inputs["labels"]
if "numeric_values" in encoded_inputs:
encoded_inputs["numeric_values"] = [float("nan")] * difference + encoded_inputs["numeric_values"]
if "numeric_values_scale" in encoded_inputs:
encoded_inputs["numeric_values_scale"] = [1.0] * difference + encoded_inputs[
"numeric_values_scale"
]
if "special_tokens_mask" in encoded_inputs:
encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
encoded_inputs["input_ids"] = [self.pad_token_id] * difference + encoded_inputs["input_ids"]
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side))
return encoded_inputs
# Everything related to converting logits to predictions
def _get_cell_token_probs(self, probabilities, segment_ids, row_ids, column_ids):
for i, p in enumerate(probabilities):
segment_id = segment_ids[i]
col = column_ids[i] - 1
row = row_ids[i] - 1
if col >= 0 and row >= 0 and segment_id == 1:
yield i, p
def _get_mean_cell_probs(self, probabilities, segment_ids, row_ids, column_ids):
"""Computes average probability per cell, aggregating over tokens."""
coords_to_probs = collections.defaultdict(list)
for i, prob in self._get_cell_token_probs(probabilities, segment_ids, row_ids, column_ids):
col = column_ids[i] - 1
row = row_ids[i] - 1
coords_to_probs[(col, row)].append(prob)
return {coords: np.array(cell_probs).mean() for coords, cell_probs in coords_to_probs.items()}
def convert_logits_to_predictions(self, data, logits, logits_agg=None, cell_classification_threshold=0.5):
"""
Converts logits of [`TapasForQuestionAnswering`] to actual predicted answer coordinates and optional
aggregation indices.
The original implementation, on which this function is based, can be found
[here](https://github.com/google-research/tapas/blob/4908213eb4df7aa988573350278b44c4dbe3f71b/tapas/experiments/prediction_utils.py#L288).
Args:
data (`dict`):
Dictionary mapping features to actual values. Should be created using [`TapasTokenizer`].
logits (`torch.Tensor` or `tf.Tensor` of shape `(batch_size, sequence_length)`):
Tensor containing the logits at the token level.
logits_agg (`torch.Tensor` or `tf.Tensor` of shape `(batch_size, num_aggregation_labels)`, *optional*):
Tensor containing the aggregation logits.
cell_classification_threshold (`float`, *optional*, defaults to 0.5):
Threshold to be used for cell selection. All table cells for which their probability is larger than
this threshold will be selected.
Returns:
`tuple` comprising various elements depending on the inputs:
- predicted_answer_coordinates (`List[List[[tuple]]` of length `batch_size`): Predicted answer coordinates
as a list of lists of tuples. Each element in the list contains the predicted answer coordinates of a
single example in the batch, as a list of tuples. Each tuple is a cell, i.e. (row index, column index).
- predicted_aggregation_indices (`List[int]`of length `batch_size`, *optional*, returned when
`logits_aggregation` is provided): Predicted aggregation operator indices of the aggregation head.
"""
# converting to numpy arrays to work with PT/TF
logits = logits.numpy()
if logits_agg is not None:
logits_agg = logits_agg.numpy()
data = {key: value.numpy() for key, value in data.items() if key != "training"}
# input data is of type float32
# np.log(np.finfo(np.float32).max) = 88.72284
# Any value over 88.72284 will overflow when passed through the exponential, sending a warning
# We disable this warning by truncating the logits.
logits[logits < -88.7] = -88.7
# Compute probabilities from token logits
probabilities = 1 / (1 + np.exp(-logits)) * data["attention_mask"]
token_types = [
"segment_ids",
"column_ids",
"row_ids",
"prev_labels",
"column_ranks",
"inv_column_ranks",
"numeric_relations",
]
# collect input_ids, segment ids, row ids and column ids of batch. Shape (batch_size, seq_len)
input_ids = data["input_ids"]
segment_ids = data["token_type_ids"][:, :, token_types.index("segment_ids")]
row_ids = data["token_type_ids"][:, :, token_types.index("row_ids")]
column_ids = data["token_type_ids"][:, :, token_types.index("column_ids")]
# next, get answer coordinates for every example in the batch
num_batch = input_ids.shape[0]
predicted_answer_coordinates = []
for i in range(num_batch):
probabilities_example = probabilities[i].tolist()
segment_ids_example = segment_ids[i]
row_ids_example = row_ids[i]
column_ids_example = column_ids[i]
max_width = column_ids_example.max()
max_height = row_ids_example.max()
if max_width == 0 and max_height == 0:
continue
cell_coords_to_prob = self._get_mean_cell_probs(
probabilities_example,
segment_ids_example.tolist(),
row_ids_example.tolist(),
column_ids_example.tolist(),
)
# Select the answers above the classification threshold.
answer_coordinates = []
for col in range(max_width):
for row in range(max_height):
cell_prob = cell_coords_to_prob.get((col, row), None)
if cell_prob is not None:
if cell_prob > cell_classification_threshold:
answer_coordinates.append((row, col))
answer_coordinates = sorted(answer_coordinates)
predicted_answer_coordinates.append(answer_coordinates)
output = (predicted_answer_coordinates,)
if logits_agg is not None:
predicted_aggregation_indices = logits_agg.argmax(axis=-1)
output = (predicted_answer_coordinates, predicted_aggregation_indices.tolist())
return output
# End of everything related to converting logits to predictions
# Copied from transformers.models.bert.tokenization_bert.BasicTokenizer
class BasicTokenizer(object):
"""
Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
Args:
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
never_split (`Iterable`, *optional*):
Collection of tokens which will never be split during tokenization. Only has an effect when
`do_basic_tokenize=True`
tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
Whether or not to tokenize Chinese characters.
This should likely be deactivated for Japanese (see this
[issue](https://github.com/huggingface/transformers/issues/328)).
strip_accents (`bool`, *optional*):
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for `lowercase` (as in the original BERT).
do_split_on_punc (`bool`, *optional*, defaults to `True`):
In some instances we want to skip the basic punctuation splitting so that later tokenization can capture
the full context of the words, such as contractions.
"""
def __init__(
self,
do_lower_case=True,
never_split=None,
tokenize_chinese_chars=True,
strip_accents=None,
do_split_on_punc=True,
):
if never_split is None:
never_split = []
self.do_lower_case = do_lower_case
self.never_split = set(never_split)
self.tokenize_chinese_chars = tokenize_chinese_chars
self.strip_accents = strip_accents
self.do_split_on_punc = do_split_on_punc
def tokenize(self, text, never_split=None):
"""
Basic Tokenization of a piece of text. For sub-word tokenization, see WordPieceTokenizer.
Args:
never_split (`List[str]`, *optional*)
Kept for backward compatibility purposes. Now implemented directly at the base class level (see
[`PreTrainedTokenizer.tokenize`]) List of token not to split.
"""
# union() returns a new set by concatenating the two sets.
never_split = self.never_split.union(set(never_split)) if never_split else self.never_split
text = self._clean_text(text)
# This was added on November 1st, 2018 for the multilingual and Chinese
# models. This is also applied to the English models now, but it doesn't
# matter since the English models were not trained on any Chinese data
# and generally don't have any Chinese data in them (there are Chinese
# characters in the vocabulary because Wikipedia does have some Chinese
# words in the English Wikipedia.).
if self.tokenize_chinese_chars:
text = self._tokenize_chinese_chars(text)
# prevents treating the same character with different unicode codepoints as different characters
unicode_normalized_text = unicodedata.normalize("NFC", text)
orig_tokens = whitespace_tokenize(unicode_normalized_text)
split_tokens = []
for token in orig_tokens:
if token not in never_split:
if self.do_lower_case:
token = token.lower()
if self.strip_accents is not False:
token = self._run_strip_accents(token)
elif self.strip_accents:
token = self._run_strip_accents(token)
split_tokens.extend(self._run_split_on_punc(token, never_split))
output_tokens = whitespace_tokenize(" ".join(split_tokens))
return output_tokens
def _run_strip_accents(self, text):
"""Strips accents from a piece of text."""
text = unicodedata.normalize("NFD", text)
output = []
for char in text:
cat = unicodedata.category(char)
if cat == "Mn":
continue
output.append(char)
return "".join(output)
def _run_split_on_punc(self, text, never_split=None):
"""Splits punctuation on a piece of text."""
if not self.do_split_on_punc or (never_split is not None and text in never_split):
return [text]
chars = list(text)
i = 0
start_new_word = True
output = []
while i < len(chars):
char = chars[i]
if _is_punctuation(char):
output.append([char])
start_new_word = True
else:
if start_new_word:
output.append([])
start_new_word = False
output[-1].append(char)
i += 1
return ["".join(x) for x in output]
def _tokenize_chinese_chars(self, text):
"""Adds whitespace around any CJK character."""
output = []
for char in text:
cp = ord(char)
if self._is_chinese_char(cp):
output.append(" ")
output.append(char)
output.append(" ")
else:
output.append(char)
return "".join(output)
def _is_chinese_char(self, cp):
"""Checks whether CP is the codepoint of a CJK character."""
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if (
(cp >= 0x4E00 and cp <= 0x9FFF)
or (cp >= 0x3400 and cp <= 0x4DBF) #
or (cp >= 0x20000 and cp <= 0x2A6DF) #
or (cp >= 0x2A700 and cp <= 0x2B73F) #
or (cp >= 0x2B740 and cp <= 0x2B81F) #
or (cp >= 0x2B820 and cp <= 0x2CEAF) #
or (cp >= 0xF900 and cp <= 0xFAFF)
or (cp >= 0x2F800 and cp <= 0x2FA1F) #
): #
return True
return False
def _clean_text(self, text):
"""Performs invalid character removal and whitespace cleanup on text."""
output = []
for char in text:
cp = ord(char)
if cp == 0 or cp == 0xFFFD or _is_control(char):
continue
if _is_whitespace(char):
output.append(" ")
else:
output.append(char)
return "".join(output)
# Copied from transformers.models.bert.tokenization_bert.WordpieceTokenizer
class WordpieceTokenizer(object):
"""Runs WordPiece tokenization."""
def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
self.vocab = vocab
self.unk_token = unk_token
self.max_input_chars_per_word = max_input_chars_per_word
def tokenize(self, text):
"""
Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform
tokenization using the given vocabulary.
For example, `input = "unaffable"` wil return as output `["un", "##aff", "##able"]`.
Args:
text: A single token or whitespace separated tokens. This should have
already been passed through *BasicTokenizer*.
Returns:
A list of wordpiece tokens.
"""
output_tokens = []
for token in whitespace_tokenize(text):
chars = list(token)
if len(chars) > self.max_input_chars_per_word:
output_tokens.append(self.unk_token)
continue
is_bad = False
start = 0
sub_tokens = []
while start < len(chars):
end = len(chars)
cur_substr = None
while start < end:
substr = "".join(chars[start:end])
if start > 0:
substr = "##" + substr
if substr in self.vocab:
cur_substr = substr
break
end -= 1
if cur_substr is None:
is_bad = True
break
sub_tokens.append(cur_substr)
start = end
if is_bad:
output_tokens.append(self.unk_token)
else:
output_tokens.extend(sub_tokens)
return output_tokens
# Below: utilities for TAPAS tokenizer (independent from PyTorch/Tensorflow).
# This includes functions to parse numeric values (dates and numbers) from both the table and questions in order
# to create the column_ranks, inv_column_ranks, numeric_values, numeric values_scale and numeric_relations in
# prepare_for_model of TapasTokenizer.
# These are meant to be used in an academic setup, for production use cases Gold mine or Aqua should be used.
# taken from constants.py of the original implementation
# URL: https://github.com/google-research/tapas/blob/master/tapas/utils/constants.py
class Relation(enum.Enum):
HEADER_TO_CELL = 1 # Connects header to cell.
CELL_TO_HEADER = 2 # Connects cell to header.
QUERY_TO_HEADER = 3 # Connects query to headers.
QUERY_TO_CELL = 4 # Connects query to cells.
ROW_TO_CELL = 5 # Connects row to cells.
CELL_TO_ROW = 6 # Connects cells to row.
EQ = 7 # Annotation value is same as cell value
LT = 8 # Annotation value is less than cell value
GT = 9 # Annotation value is greater than cell value
@dataclass
class Date:
year: Optional[int] = None
month: Optional[int] = None
day: Optional[int] = None
@dataclass
class NumericValue:
float_value: Optional[float] = None
date: Optional[Date] = None
@dataclass
class NumericValueSpan:
begin_index: int = None
end_index: int = None
values: List[NumericValue] = None
@dataclass
class Cell:
text: Text
numeric_value: Optional[NumericValue] = None
@dataclass
class Question:
original_text: Text # The original raw question string.
text: Text # The question string after normalization.
numeric_spans: Optional[List[NumericValueSpan]] = None
# Below: all functions from number_utils.py as well as 2 functions (namely get_all_spans and normalize_for_match)
# from text_utils.py of the original implementation. URL's:
# - https://github.com/google-research/tapas/blob/master/tapas/utils/number_utils.py
# - https://github.com/google-research/tapas/blob/master/tapas/utils/text_utils.py
# Constants for parsing date expressions.
# Masks that specify (by a bool) which of (year, month, day) will be populated.
_DateMask = collections.namedtuple("_DateMask", ["year", "month", "day"])
_YEAR = _DateMask(True, False, False)
_YEAR_MONTH = _DateMask(True, True, False)
_YEAR_MONTH_DAY = _DateMask(True, True, True)
_MONTH = _DateMask(False, True, False)
_MONTH_DAY = _DateMask(False, True, True)
# Pairs of patterns to pass to 'datetime.strptime' and masks specifying which
# fields will be set by the corresponding pattern.
_DATE_PATTERNS = (
("%B", _MONTH),
("%Y", _YEAR),
("%Ys", _YEAR),
("%b %Y", _YEAR_MONTH),
("%B %Y", _YEAR_MONTH),
("%B %d", _MONTH_DAY),
("%b %d", _MONTH_DAY),
("%d %b", _MONTH_DAY),
("%d %B", _MONTH_DAY),
("%B %d, %Y", _YEAR_MONTH_DAY),
("%d %B %Y", _YEAR_MONTH_DAY),
("%m-%d-%Y", _YEAR_MONTH_DAY),
("%Y-%m-%d", _YEAR_MONTH_DAY),
("%Y-%m", _YEAR_MONTH),
("%B %Y", _YEAR_MONTH),
("%d %b %Y", _YEAR_MONTH_DAY),
("%Y-%m-%d", _YEAR_MONTH_DAY),
("%b %d, %Y", _YEAR_MONTH_DAY),
("%d.%m.%Y", _YEAR_MONTH_DAY),
("%A, %b %d", _MONTH_DAY),
("%A, %B %d", _MONTH_DAY),
)
# This mapping is used to convert date patterns to regex patterns.
_FIELD_TO_REGEX = (
("%A", r"\w+"), # Weekday as locale’s full name.
("%B", r"\w+"), # Month as locale’s full name.
("%Y", r"\d{4}"), # Year with century as a decimal number.
("%b", r"\w{3}"), # Month as locale’s abbreviated name.
("%d", r"\d{1,2}"), # Day of the month as a zero-padded decimal number.
("%m", r"\d{1,2}"), # Month as a zero-padded decimal number.
)
def _process_date_pattern(dp):
"""Compute a regex for each date pattern to use as a prefilter."""
pattern, mask = dp
regex = pattern
regex = regex.replace(".", re.escape("."))
regex = regex.replace("-", re.escape("-"))
regex = regex.replace(" ", r"\s+")
for field, field_regex in _FIELD_TO_REGEX:
regex = regex.replace(field, field_regex)
# Make sure we didn't miss any of the fields.
assert "%" not in regex, regex
return pattern, mask, re.compile("^" + regex + "$")
def _process_date_patterns():
return tuple(_process_date_pattern(dp) for dp in _DATE_PATTERNS)
_PROCESSED_DATE_PATTERNS = _process_date_patterns()
_MAX_DATE_NGRAM_SIZE = 5
# Following DynSp:
# https://github.com/Microsoft/DynSP/blob/master/util.py#L414.
_NUMBER_WORDS = [
"zero",
"one",
"two",
"three",
"four",
"five",
"six",
"seven",
"eight",
"nine",
"ten",
"eleven",
"twelve",
]
_ORDINAL_WORDS = [
"zeroth",
"first",
"second",
"third",
"fourth",
"fith",
"sixth",
"seventh",
"eighth",
"ninth",
"tenth",
"eleventh",
"twelfth",
]
_ORDINAL_SUFFIXES = ["st", "nd", "rd", "th"]
_NUMBER_PATTERN = re.compile(r"((^|\s)[+-])?((\.\d+)|(\d+(,\d\d\d)*(\.\d*)?))")
# Following DynSp:
# https://github.com/Microsoft/DynSP/blob/master/util.py#L293.
_MIN_YEAR = 1700
_MAX_YEAR = 2016
_INF = float("INF")
def _get_numeric_value_from_date(date, mask):
"""Converts date (datetime Python object) to a NumericValue object with a Date object value."""
if date.year < _MIN_YEAR or date.year > _MAX_YEAR:
raise ValueError(f"Invalid year: {date.year}")
new_date = Date()
if mask.year:
new_date.year = date.year
if mask.month:
new_date.month = date.month
if mask.day:
new_date.day = date.day
return NumericValue(date=new_date)
def _get_span_length_key(span):
"""Sorts span by decreasing length first and increasing first index second."""
return span[1] - span[0], -span[0]
def _get_numeric_value_from_float(value):
"""Converts float (Python) to a NumericValue object with a float value."""
return NumericValue(float_value=value)
# Doesn't parse ordinal expressions such as '18th of february 1655'.
def _parse_date(text):
"""Attempts to format a text as a standard date string (yyyy-mm-dd)."""
text = re.sub(r"Sept\b", "Sep", text)
for in_pattern, mask, regex in _PROCESSED_DATE_PATTERNS:
if not regex.match(text):
continue
try:
date = datetime.datetime.strptime(text, in_pattern).date()
except ValueError:
continue
try:
return _get_numeric_value_from_date(date, mask)
except ValueError:
continue
return None
def _parse_number(text):
"""Parses simple cardinal and ordinals numbers."""
for suffix in _ORDINAL_SUFFIXES:
if text.endswith(suffix):
text = text[: -len(suffix)]
break
text = text.replace(",", "")
try:
value = float(text)
except ValueError:
return None
if math.isnan(value):
return None
if value == _INF:
return None
return value
def get_all_spans(text, max_ngram_length):
"""
Split a text into all possible ngrams up to 'max_ngram_length'. Split points are white space and punctuation.
Args:
text: Text to split.
max_ngram_length: maximal ngram length.
Yields:
Spans, tuples of begin-end index.
"""
start_indexes = []
for index, char in enumerate(text):
if not char.isalnum():
continue
if index == 0 or not text[index - 1].isalnum():
start_indexes.append(index)
if index + 1 == len(text) or not text[index + 1].isalnum():
for start_index in start_indexes[-max_ngram_length:]:
yield start_index, index + 1
def normalize_for_match(text):
return " ".join(text.lower().split())
def format_text(text):
"""Lowercases and strips punctuation."""
text = text.lower().strip()
if text == "n/a" or text == "?" or text == "nan":
text = EMPTY_TEXT
text = re.sub(r"[^\w\d]+", " ", text).replace("_", " ")
text = " ".join(text.split())
text = text.strip()
if text:
return text
return EMPTY_TEXT
def parse_text(text):
"""
Extracts longest number and date spans.
Args:
text: text to annotate
Returns:
List of longest numeric value spans.
"""
span_dict = collections.defaultdict(list)
for match in _NUMBER_PATTERN.finditer(text):
span_text = text[match.start() : match.end()]
number = _parse_number(span_text)
if number is not None:
span_dict[match.span()].append(_get_numeric_value_from_float(number))
for begin_index, end_index in get_all_spans(text, max_ngram_length=1):
if (begin_index, end_index) in span_dict:
continue
span_text = text[begin_index:end_index]
number = _parse_number(span_text)
if number is not None:
span_dict[begin_index, end_index].append(_get_numeric_value_from_float(number))
for number, word in enumerate(_NUMBER_WORDS):
if span_text == word:
span_dict[begin_index, end_index].append(_get_numeric_value_from_float(float(number)))
break
for number, word in enumerate(_ORDINAL_WORDS):
if span_text == word:
span_dict[begin_index, end_index].append(_get_numeric_value_from_float(float(number)))
break
for begin_index, end_index in get_all_spans(text, max_ngram_length=_MAX_DATE_NGRAM_SIZE):
span_text = text[begin_index:end_index]
date = _parse_date(span_text)
if date is not None:
span_dict[begin_index, end_index].append(date)
spans = sorted(span_dict.items(), key=lambda span_value: _get_span_length_key(span_value[0]), reverse=True)
selected_spans = []
for span, value in spans:
for selected_span, _ in selected_spans:
if selected_span[0] <= span[0] and span[1] <= selected_span[1]:
break
else:
selected_spans.append((span, value))
selected_spans.sort(key=lambda span_value: span_value[0][0])
numeric_value_spans = []
for span, values in selected_spans:
numeric_value_spans.append(NumericValueSpan(begin_index=span[0], end_index=span[1], values=values))
return numeric_value_spans
# Below: all functions from number_annotation_utils.py and 2 functions (namely filter_invalid_unicode
# and filter_invalid_unicode_from_table) from text_utils.py of the original implementation. URL's:
# - https://github.com/google-research/tapas/blob/master/tapas/utils/number_annotation_utils.py
# - https://github.com/google-research/tapas/blob/master/tapas/utils/text_utils.py
_PrimitiveNumericValue = Union[float, Tuple[Optional[float], Optional[float], Optional[float]]]
_SortKeyFn = Callable[[NumericValue], Tuple[float, Ellipsis]]
_DATE_TUPLE_SIZE = 3
EMPTY_TEXT = "EMPTY"
NUMBER_TYPE = "number"
DATE_TYPE = "date"
def _get_value_type(numeric_value):
if numeric_value.float_value is not None:
return NUMBER_TYPE
elif numeric_value.date is not None:
return DATE_TYPE
raise ValueError(f"Unknown type: {numeric_value}")
def _get_value_as_primitive_value(numeric_value):
"""Maps a NumericValue proto to a float or tuple of float."""
if numeric_value.float_value is not None:
return numeric_value.float_value
if numeric_value.date is not None:
date = numeric_value.date
value_tuple = [None, None, None]
# All dates fields are cased to float to produce a simple primitive value.
if date.year is not None:
value_tuple[0] = float(date.year)
if date.month is not None:
value_tuple[1] = float(date.month)
if date.day is not None:
value_tuple[2] = float(date.day)
return tuple(value_tuple)
raise ValueError(f"Unknown type: {numeric_value}")
def _get_all_types(numeric_values):
return {_get_value_type(value) for value in numeric_values}
def get_numeric_sort_key_fn(numeric_values):
"""
Creates a function that can be used as a sort key or to compare the values. Maps to primitive types and finds the
biggest common subset. Consider the values "05/05/2010" and "August 2007". With the corresponding primitive values
(2010.,5.,5.) and (2007.,8., None). These values can be compared by year and date so we map to the sequence (2010.,
5.), (2007., 8.). If we added a third value "2006" with primitive value (2006., None, None), we could only compare
by the year so we would map to (2010.,), (2007.,) and (2006.,).
Args:
numeric_values: Values to compare
Returns:
A function that can be used as a sort key function (mapping numeric values to a comparable tuple)
Raises:
ValueError if values don't have a common type or are not comparable.
"""
value_types = _get_all_types(numeric_values)
if len(value_types) != 1:
raise ValueError(f"No common value type in {numeric_values}")
value_type = next(iter(value_types))
if value_type == NUMBER_TYPE:
# Primitive values are simple floats, nothing to do here.
return _get_value_as_primitive_value
# The type can only be Date at this point which means the primitive type
# is a float triple.
valid_indexes = set(range(_DATE_TUPLE_SIZE))
for numeric_value in numeric_values:
value = _get_value_as_primitive_value(numeric_value)
assert isinstance(value, tuple)
for tuple_index, inner_value in enumerate(value):
if inner_value is None:
valid_indexes.discard(tuple_index)
if not valid_indexes:
raise ValueError(f"No common value in {numeric_values}")
def _sort_key_fn(numeric_value):
value = _get_value_as_primitive_value(numeric_value)
return tuple(value[index] for index in valid_indexes)
return _sort_key_fn
def _consolidate_numeric_values(row_index_to_values, min_consolidation_fraction, debug_info):
"""
Finds the most common numeric values in a column and returns them
Args:
row_index_to_values:
For each row index all the values in that cell.
min_consolidation_fraction:
Fraction of cells that need to have consolidated value.
debug_info:
Additional information only used for logging
Returns:
For each row index the first value that matches the most common value. Rows that don't have a matching value
are dropped. Empty list if values can't be consolidated.
"""
type_counts = collections.Counter()
for numeric_values in row_index_to_values.values():
type_counts.update(_get_all_types(numeric_values))
if not type_counts:
return {}
max_count = max(type_counts.values())
if max_count < len(row_index_to_values) * min_consolidation_fraction:
# logging.log_every_n(logging.INFO, f'Can\'t consolidate types: {debug_info} {row_index_to_values} {max_count}', 100)
return {}
valid_types = set()
for value_type, count in type_counts.items():
if count == max_count:
valid_types.add(value_type)
if len(valid_types) > 1:
assert DATE_TYPE in valid_types
max_type = DATE_TYPE
else:
max_type = next(iter(valid_types))
new_row_index_to_value = {}
for index, values in row_index_to_values.items():
# Extract the first matching value.
for value in values:
if _get_value_type(value) == max_type:
new_row_index_to_value[index] = value
break
return new_row_index_to_value
def _get_numeric_values(text):
"""Parses text and returns numeric values."""
numeric_spans = parse_text(text)
return itertools.chain(*(span.values for span in numeric_spans))
def _get_column_values(table, col_index):
"""
Parses text in column and returns a dict mapping row_index to values. This is the _get_column_values function from
number_annotation_utils.py of the original implementation
Args:
table: Pandas dataframe
col_index: integer, indicating the index of the column to get the numeric values of
"""
index_to_values = {}
for row_index, row in table.iterrows():
text = normalize_for_match(row[col_index].text)
index_to_values[row_index] = list(_get_numeric_values(text))
return index_to_values
def get_numeric_relation(value, other_value, sort_key_fn):
"""Compares two values and returns their relation or None."""
value = sort_key_fn(value)
other_value = sort_key_fn(other_value)
if value == other_value:
return Relation.EQ
if value < other_value:
return Relation.LT
if value > other_value:
return Relation.GT
return None
def add_numeric_values_to_question(question):
"""Adds numeric value spans to a question."""
original_text = question
question = normalize_for_match(question)
numeric_spans = parse_text(question)
return Question(original_text=original_text, text=question, numeric_spans=numeric_spans)
def filter_invalid_unicode(text):
"""Return an empty string and True if 'text' is in invalid unicode."""
return ("", True) if isinstance(text, bytes) else (text, False)
def filter_invalid_unicode_from_table(table):
"""
Removes invalid unicode from table. Checks whether a table cell text contains an invalid unicode encoding. If yes,
reset the table cell text to an empty str and log a warning for each invalid cell
Args:
table: table to clean.
"""
# to do: add table id support
if not hasattr(table, "table_id"):
table.table_id = 0
for row_index, row in table.iterrows():
for col_index, cell in enumerate(row):
cell, is_invalid = filter_invalid_unicode(cell)
if is_invalid:
logging.warning(
f"Scrub an invalid table body @ table_id: {table.table_id}, row_index: {row_index}, "
f"col_index: {col_index}",
)
for col_index, column in enumerate(table.columns):
column, is_invalid = filter_invalid_unicode(column)
if is_invalid:
logging.warning(f"Scrub an invalid table header @ table_id: {table.table_id}, col_index: {col_index}")
def add_numeric_table_values(table, min_consolidation_fraction=0.7, debug_info=None):
"""
Parses text in table column-wise and adds the consolidated values. Consolidation refers to finding values with a
common types (date or number)
Args:
table:
Table to annotate.
min_consolidation_fraction:
Fraction of cells in a column that need to have consolidated value.
debug_info:
Additional information used for logging.
"""
table = table.copy()
# First, filter table on invalid unicode
filter_invalid_unicode_from_table(table)
# Second, replace cell values by Cell objects
for row_index, row in table.iterrows():
for col_index, cell in enumerate(row):
table.iloc[row_index, col_index] = Cell(text=cell)
# Third, add numeric_value attributes to these Cell objects
for col_index, column in enumerate(table.columns):
column_values = _consolidate_numeric_values(
_get_column_values(table, col_index),
min_consolidation_fraction=min_consolidation_fraction,
debug_info=(debug_info, column),
)
for row_index, numeric_value in column_values.items():
table.iloc[row_index, col_index].numeric_value = numeric_value
return table
| transformers/src/transformers/models/tapas/tokenization_tapas.py/0 | {
"file_path": "transformers/src/transformers/models/tapas/tokenization_tapas.py",
"repo_id": "transformers",
"token_count": 53968
} | 125 |
# flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
is_vision_available,
)
_import_structure = {
"configuration_tvlt": ["TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP", "TvltConfig"],
"feature_extraction_tvlt": ["TvltFeatureExtractor"],
"processing_tvlt": ["TvltProcessor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tvlt"] = [
"TVLT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TvltModel",
"TvltForPreTraining",
"TvltForAudioVisualClassification",
"TvltPreTrainedModel",
]
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["image_processing_tvlt"] = ["TvltImageProcessor"]
if TYPE_CHECKING:
from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
from .processing_tvlt import TvltProcessor
from .feature_extraction_tvlt import TvltFeatureExtractor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tvlt import (
TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
TvltForAudioVisualClassification,
TvltForPreTraining,
TvltModel,
TvltPreTrainedModel,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_tvlt import TvltImageProcessor
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| transformers/src/transformers/models/tvlt/__init__.py/0 | {
"file_path": "transformers/src/transformers/models/tvlt/__init__.py",
"repo_id": "transformers",
"token_count": 1013
} | 126 |
# coding=utf-8
# Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" UniSpeech model configuration"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"microsoft/unispeech-large-1500h-cv": (
"https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json"
),
# See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}
class UniSpeechConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`UniSpeechModel`]. It is used to instantiate an
UniSpeech model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the UniSpeech
[microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 32):
Vocabulary size of the UniSpeech model. Defines the number of different tokens that can be represented by
the `inputs_ids` passed when calling [`UniSpeechModel`]. Vocabulary size of the model. Defines the
different tokens that can be represented by the *inputs_ids* passed to the forward method of
[`UniSpeechModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
activation_dropout (`float`, *optional*, defaults to 0.1):
The dropout ratio for activations inside the fully connected layer.
attention_dropout (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
feat_proj_dropout (`float`, *optional*, defaults to 0.0):
The dropout probability for output of the feature encoder.
feat_quantizer_dropout (`float`, *optional*, defaults to 0.0):
The dropout probability for the output of the feature encoder that's used by the quantizer.
final_dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for the final projection layer of [`UniSpeechForCTC`].
layerdrop (`float`, *optional*, defaults to 0.1):
The LayerDrop probability. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more
details.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the layer normalization layers.
feat_extract_norm (`str`, *optional*, defaults to `"group"`):
The norm to be applied to 1D convolutional layers in feature encoder. One of `"group"` for group
normalization of only the first 1D convolutional layer or `"layer"` for layer normalization of all 1D
convolutional layers.
feat_extract_activation (`str, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the 1D convolutional layers of the feature
extractor. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported.
conv_dim (`Tuple[int]` or `List[int]`, *optional*, defaults to `(512, 512, 512, 512, 512, 512, 512)`):
A tuple of integers defining the number of input and output channels of each 1D convolutional layer in the
feature encoder. The length of *conv_dim* defines the number of 1D convolutional layers.
conv_stride (`Tuple[int]` or `List[int]`, *optional*, defaults to `(5, 2, 2, 2, 2, 2, 2)`):
A tuple of integers defining the stride of each 1D convolutional layer in the feature encoder. The length
of *conv_stride* defines the number of convolutional layers and has to match the length of *conv_dim*.
conv_kernel (`Tuple[int]` or `List[int]`, *optional*, defaults to `(10, 3, 3, 3, 3, 2, 2)`):
A tuple of integers defining the kernel size of each 1D convolutional layer in the feature encoder. The
length of *conv_kernel* defines the number of convolutional layers and has to match the length of
*conv_dim*.
conv_bias (`bool`, *optional*, defaults to `False`):
Whether the 1D convolutional layers have a bias.
num_conv_pos_embeddings (`int`, *optional*, defaults to 128):
Number of convolutional positional embeddings. Defines the kernel size of 1D convolutional positional
embeddings layer.
num_conv_pos_embedding_groups (`int`, *optional*, defaults to 16):
Number of groups of 1D convolutional positional embeddings layer.
do_stable_layer_norm (`bool`, *optional*, defaults to `False`):
Whether to apply *stable* layer norm architecture of the Transformer encoder. `do_stable_layer_norm is
True` corresponds to applying layer norm before the attention layer, whereas `do_stable_layer_norm is
False` corresponds to applying layer norm after the attention layer.
apply_spec_augment (`bool`, *optional*, defaults to `True`):
Whether to apply *SpecAugment* data augmentation to the outputs of the feature encoder. For reference see
[SpecAugment: A Simple Data Augmentation Method for Automatic Speech
Recognition](https://arxiv.org/abs/1904.08779).
mask_time_prob (`float`, *optional*, defaults to 0.05):
Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking
procecure generates ''mask_time_prob*len(time_axis)/mask_time_length'' independent masks over the axis. If
reasoning from the propability of each feature vector to be chosen as the start of the vector span to be
masked, *mask_time_prob* should be `prob_vector_start*mask_time_length`. Note that overlap may decrease the
actual percentage of masked vectors. This is only relevant if `apply_spec_augment is True`.
mask_time_length (`int`, *optional*, defaults to 10):
Length of vector span along the time axis.
mask_time_min_masks (`int`, *optional*, defaults to 2):
The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step,
irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length <
mask_time_min_masks''
mask_feature_prob (`float`, *optional*, defaults to 0.0):
Percentage (between 0 and 1) of all feature vectors along the feature axis which will be masked. The
masking procecure generates ''mask_feature_prob*len(feature_axis)/mask_time_length'' independent masks over
the axis. If reasoning from the propability of each feature vector to be chosen as the start of the vector
span to be masked, *mask_feature_prob* should be `prob_vector_start*mask_feature_length`. Note that overlap
may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is
True`.
mask_feature_length (`int`, *optional*, defaults to 10):
Length of vector span along the feature axis.
mask_feature_min_masks (`int`, *optional*, defaults to 0):
The minimum number of masks of length `mask_feature_length` generated along the feature axis, each time
step, irrespectively of `mask_feature_prob`. Only relevant if
''mask_feature_prob*len(feature_axis)/mask_feature_length < mask_feature_min_masks''
num_codevectors_per_group (`int`, *optional*, defaults to 320):
Number of entries in each quantization codebook (group).
num_codevector_groups (`int`, *optional*, defaults to 2):
Number of codevector groups for product codevector quantization.
contrastive_logits_temperature (`float`, *optional*, defaults to 0.1):
The temperature *kappa* in the contrastive loss.
num_negatives (`int`, *optional*, defaults to 100):
Number of negative samples for the contrastive loss.
codevector_dim (`int`, *optional*, defaults to 256):
Dimensionality of the quantized feature vectors.
proj_codevector_dim (`int`, *optional*, defaults to 256):
Dimensionality of the final projection of both the quantized and the transformer features.
diversity_loss_weight (`int`, *optional*, defaults to 0.1):
The weight of the codebook diversity loss component.
ctc_loss_reduction (`str`, *optional*, defaults to `"mean"`):
Specifies the reduction to apply to the output of `torch.nn.CTCLoss`. Only relevant when training an
instance of [`UniSpeechForCTC`].
ctc_zero_infinity (`bool`, *optional*, defaults to `False`):
Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`. Infinite losses mainly
occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance
of [`UniSpeechForCTC`].
use_weighted_layer_sum (`bool`, *optional*, defaults to `False`):
Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an
instance of [`UniSpeechForSequenceClassification`].
classifier_proj_size (`int`, *optional*, defaults to 256):
Dimensionality of the projection before token mean-pooling for classification.
num_ctc_classes (`int`, *optional*, defaults to 80):
Specifies the number of classes (phoneme tokens and blank token) for phoneme-level CTC loss. Only relevant
when using an instance of [`UniSpeechForPreTraining`].
pad_token_id (`int`, *optional*, defaults to 0):
The id of the padding token.
bos_token_id (`int`, *optional*, defaults to 1):
The id of the "beginning-of-sequence" token.
eos_token_id (`int`, *optional*, defaults to 2):
The id of the "end-of-sequence" token.
replace_prob (`float`, *optional*, defaults to 0.5):
Propability that transformer feature is replaced by quantized feature for pretraining.
Example:
```python
>>> from transformers import UniSpeechConfig, UniSpeechModel
>>> # Initializing a UniSpeech facebook/unispeech-base-960h style configuration
>>> configuration = UniSpeechConfig()
>>> # Initializing a model (with random weights) from the facebook/unispeech-base-960h style configuration
>>> model = UniSpeechModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "unispeech"
def __init__(
self,
vocab_size=32,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout=0.1,
activation_dropout=0.1,
attention_dropout=0.1,
feat_proj_dropout=0.0,
feat_quantizer_dropout=0.0,
final_dropout=0.1,
layerdrop=0.1,
initializer_range=0.02,
layer_norm_eps=1e-5,
feat_extract_norm="group",
feat_extract_activation="gelu",
conv_dim=(512, 512, 512, 512, 512, 512, 512),
conv_stride=(5, 2, 2, 2, 2, 2, 2),
conv_kernel=(10, 3, 3, 3, 3, 2, 2),
conv_bias=False,
num_conv_pos_embeddings=128,
num_conv_pos_embedding_groups=16,
do_stable_layer_norm=False,
apply_spec_augment=True,
mask_time_prob=0.05,
mask_time_length=10,
mask_time_min_masks=2,
mask_feature_prob=0.0,
mask_feature_length=10,
mask_feature_min_masks=0,
num_codevectors_per_group=320,
num_codevector_groups=2,
contrastive_logits_temperature=0.1,
num_negatives=100,
codevector_dim=256,
proj_codevector_dim=256,
diversity_loss_weight=0.1,
ctc_loss_reduction="mean",
ctc_zero_infinity=False,
use_weighted_layer_sum=False,
classifier_proj_size=256,
num_ctc_classes=80,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
replace_prob=0.5,
**kwargs,
):
super().__init__(**kwargs, pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id)
self.hidden_size = hidden_size
self.feat_extract_norm = feat_extract_norm
self.feat_extract_activation = feat_extract_activation
self.conv_dim = list(conv_dim)
self.conv_stride = list(conv_stride)
self.conv_kernel = list(conv_kernel)
self.conv_bias = conv_bias
self.num_conv_pos_embeddings = num_conv_pos_embeddings
self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups
self.num_feat_extract_layers = len(self.conv_dim)
self.num_hidden_layers = num_hidden_layers
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.num_attention_heads = num_attention_heads
self.hidden_dropout = hidden_dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.feat_proj_dropout = feat_proj_dropout
self.final_dropout = final_dropout
self.layerdrop = layerdrop
self.layer_norm_eps = layer_norm_eps
self.initializer_range = initializer_range
self.num_ctc_classes = num_ctc_classes
self.vocab_size = vocab_size
self.do_stable_layer_norm = do_stable_layer_norm
self.use_weighted_layer_sum = use_weighted_layer_sum
self.classifier_proj_size = classifier_proj_size
if (
(len(self.conv_stride) != self.num_feat_extract_layers)
or (len(self.conv_kernel) != self.num_feat_extract_layers)
or (len(self.conv_dim) != self.num_feat_extract_layers)
):
raise ValueError(
"Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
" `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
f" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"
f" `len(config.conv_kernel) = {len(self.conv_kernel)}`."
)
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
self.apply_spec_augment = apply_spec_augment
self.mask_time_prob = mask_time_prob
self.mask_time_length = mask_time_length
self.mask_time_min_masks = mask_time_min_masks
self.mask_feature_prob = mask_feature_prob
self.mask_feature_length = mask_feature_length
self.mask_feature_min_masks = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
self.num_codevectors_per_group = num_codevectors_per_group
self.num_codevector_groups = num_codevector_groups
self.contrastive_logits_temperature = contrastive_logits_temperature
self.feat_quantizer_dropout = feat_quantizer_dropout
self.num_negatives = num_negatives
self.codevector_dim = codevector_dim
self.proj_codevector_dim = proj_codevector_dim
self.diversity_loss_weight = diversity_loss_weight
# ctc loss
self.ctc_loss_reduction = ctc_loss_reduction
self.ctc_zero_infinity = ctc_zero_infinity
# pretraining loss
self.replace_prob = replace_prob
@property
def inputs_to_logits_ratio(self):
return functools.reduce(operator.mul, self.conv_stride, 1)
| transformers/src/transformers/models/unispeech/configuration_unispeech.py/0 | {
"file_path": "transformers/src/transformers/models/unispeech/configuration_unispeech.py",
"repo_id": "transformers",
"token_count": 6896
} | 127 |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert Swin Transformer + UperNet checkpoints from mmsegmentation.
URL: https://github.com/open-mmlab/mmsegmentation/tree/master/configs/swin
"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def get_upernet_config(model_name):
auxiliary_in_channels = 384
window_size = 7
if "tiny" in model_name:
embed_dim = 96
depths = (2, 2, 6, 2)
num_heads = (3, 6, 12, 24)
elif "small" in model_name:
embed_dim = 96
depths = (2, 2, 18, 2)
num_heads = (3, 6, 12, 24)
elif "base" in model_name:
embed_dim = 128
depths = (2, 2, 18, 2)
num_heads = (4, 8, 16, 32)
window_size = 12
auxiliary_in_channels = 512
elif "large" in model_name:
embed_dim = 192
depths = (2, 2, 18, 2)
num_heads = (6, 12, 24, 48)
window_size = 12
auxiliary_in_channels = 768
# set label information
num_labels = 150
repo_id = "huggingface/label-files"
filename = "ade20k-id2label.json"
id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
id2label = {int(k): v for k, v in id2label.items()}
label2id = {v: k for k, v in id2label.items()}
backbone_config = SwinConfig(
embed_dim=embed_dim,
depths=depths,
num_heads=num_heads,
window_size=window_size,
out_features=["stage1", "stage2", "stage3", "stage4"],
)
config = UperNetConfig(
backbone_config=backbone_config,
auxiliary_in_channels=auxiliary_in_channels,
num_labels=num_labels,
id2label=id2label,
label2id=label2id,
)
return config
# here we list all keys to be renamed (original name on the left, our name on the right)
def create_rename_keys(config):
rename_keys = []
# fmt: off
# stem
rename_keys.append(("backbone.patch_embed.projection.weight", "backbone.embeddings.patch_embeddings.projection.weight"))
rename_keys.append(("backbone.patch_embed.projection.bias", "backbone.embeddings.patch_embeddings.projection.bias"))
rename_keys.append(("backbone.patch_embed.norm.weight", "backbone.embeddings.norm.weight"))
rename_keys.append(("backbone.patch_embed.norm.bias", "backbone.embeddings.norm.bias"))
# stages
for i in range(len(config.backbone_config.depths)):
for j in range(config.backbone_config.depths[i]):
rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm1.weight", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight"))
rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm1.bias", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias"))
rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table"))
rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index"))
rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight", f"backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight"))
rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias", f"backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias"))
rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm2.weight", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight"))
rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm2.bias", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias"))
rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight", f"backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight"))
rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias", f"backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias"))
rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight", f"backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight"))
rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias", f"backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias"))
if i < 3:
rename_keys.append((f"backbone.stages.{i}.downsample.reduction.weight", f"backbone.encoder.layers.{i}.downsample.reduction.weight"))
rename_keys.append((f"backbone.stages.{i}.downsample.norm.weight", f"backbone.encoder.layers.{i}.downsample.norm.weight"))
rename_keys.append((f"backbone.stages.{i}.downsample.norm.bias", f"backbone.encoder.layers.{i}.downsample.norm.bias"))
rename_keys.append((f"backbone.norm{i}.weight", f"backbone.hidden_states_norms.stage{i+1}.weight"))
rename_keys.append((f"backbone.norm{i}.bias", f"backbone.hidden_states_norms.stage{i+1}.bias"))
# decode head
rename_keys.extend(
[
("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
]
)
# fmt: on
return rename_keys
def rename_key(dct, old, new):
val = dct.pop(old)
dct[new] = val
# we split up the matrix of each encoder layer into queries, keys and values
def read_in_q_k_v(state_dict, backbone_config):
num_features = [int(backbone_config.embed_dim * 2**i) for i in range(len(backbone_config.depths))]
for i in range(len(backbone_config.depths)):
dim = num_features[i]
for j in range(backbone_config.depths[i]):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
in_proj_weight = state_dict.pop(f"backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight")
in_proj_bias = state_dict.pop(f"backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias")
# next, add query, keys and values (in that order) to the state dict
state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.query.weight"] = in_proj_weight[:dim, :]
state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.query.bias"] = in_proj_bias[: dim]
state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.key.weight"] = in_proj_weight[
dim : dim * 2, :
]
state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.key.bias"] = in_proj_bias[
dim : dim * 2
]
state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.value.weight"] = in_proj_weight[
-dim :, :
]
state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.value.bias"] = in_proj_bias[-dim :]
# fmt: on
def correct_unfold_reduction_order(x):
out_channel, in_channel = x.shape
x = x.reshape(out_channel, 4, in_channel // 4)
x = x[:, [0, 2, 1, 3], :].transpose(1, 2).reshape(out_channel, in_channel)
return x
def reverse_correct_unfold_reduction_order(x):
out_channel, in_channel = x.shape
x = x.reshape(out_channel, in_channel // 4, 4)
x = x[:, :, [0, 2, 1, 3]].transpose(1, 2).reshape(out_channel, in_channel)
return x
def correct_unfold_norm_order(x):
in_channel = x.shape[0]
x = x.reshape(4, in_channel // 4)
x = x[[0, 2, 1, 3], :].transpose(0, 1).reshape(in_channel)
return x
# there was an incompatibility with this version, due to a new implementation of their downsampling operation using nn.Unfold.
# was resolved as seen here:
# https://github.com/open-mmlab/mmdetection/blob/31c84958f54287a8be2b99cbf87a6dcf12e57753/mmdet/models/utils/ckpt_convert.py#L96.
def reverse_correct_unfold_norm_order(x):
in_channel = x.shape[0]
x = x.reshape(in_channel // 4, 4)
x = x[:, [0, 2, 1, 3]].transpose(0, 1).reshape(in_channel)
return x
def convert_upernet_checkpoint(model_name, pytorch_dump_folder_path, push_to_hub):
model_name_to_url = {
"upernet-swin-tiny": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth",
"upernet-swin-small": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth",
"upernet-swin-base": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth",
"upernet-swin-large": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth",
}
checkpoint_url = model_name_to_url[model_name]
state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu", file_name=model_name)[
"state_dict"
]
for name, param in state_dict.items():
print(name, param.shape)
config = get_upernet_config(model_name)
model = UperNetForSemanticSegmentation(config)
model.eval()
# replace "bn" => "batch_norm"
for key in state_dict.copy().keys():
val = state_dict.pop(key)
if "bn" in key:
key = key.replace("bn", "batch_norm")
state_dict[key] = val
# rename keys
rename_keys = create_rename_keys(config)
for src, dest in rename_keys:
rename_key(state_dict, src, dest)
read_in_q_k_v(state_dict, config.backbone_config)
# fix downsample parameters
for key, value in state_dict.items():
if "downsample" in key:
if "reduction" in key:
state_dict[key] = reverse_correct_unfold_reduction_order(value)
if "norm" in key:
state_dict[key] = reverse_correct_unfold_norm_order(value)
model.load_state_dict(state_dict)
# verify on image
url = "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
processor = SegformerImageProcessor()
pixel_values = processor(image, return_tensors="pt").pixel_values
with torch.no_grad():
outputs = model(pixel_values)
logits = outputs.logits
print(logits.shape)
print("First values of logits:", logits[0, 0, :3, :3])
# assert values
if model_name == "upernet-swin-tiny":
expected_slice = torch.tensor(
[[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]
)
elif model_name == "upernet-swin-small":
expected_slice = torch.tensor(
[[-7.1921, -7.1921, -6.9532], [-7.1921, -7.1921, -6.9532], [-7.0908, -7.0908, -6.8534]]
)
elif model_name == "upernet-swin-base":
expected_slice = torch.tensor(
[[-6.5851, -6.5851, -6.4330], [-6.5851, -6.5851, -6.4330], [-6.4763, -6.4763, -6.3254]]
)
elif model_name == "upernet-swin-large":
expected_slice = torch.tensor(
[[-7.5297, -7.5297, -7.3802], [-7.5297, -7.5297, -7.3802], [-7.4044, -7.4044, -7.2586]]
)
print("Logits:", outputs.logits[0, 0, :3, :3])
assert torch.allclose(outputs.logits[0, 0, :3, :3], expected_slice, atol=1e-4)
print("Looks ok!")
if pytorch_dump_folder_path is not None:
print(f"Saving model {model_name} to {pytorch_dump_folder_path}")
model.save_pretrained(pytorch_dump_folder_path)
print(f"Saving processor to {pytorch_dump_folder_path}")
processor.save_pretrained(pytorch_dump_folder_path)
if push_to_hub:
print(f"Pushing model and processor for {model_name} to hub")
model.push_to_hub(f"openmmlab/{model_name}")
processor.push_to_hub(f"openmmlab/{model_name}")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="upernet-swin-tiny",
type=str,
choices=[f"upernet-swin-{size}" for size in ["tiny", "small", "base", "large"]],
help="Name of the Swin + UperNet model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
args = parser.parse_args()
convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| transformers/src/transformers/models/upernet/convert_swin_upernet_to_pytorch.py/0 | {
"file_path": "transformers/src/transformers/models/upernet/convert_swin_upernet_to_pytorch.py",
"repo_id": "transformers",
"token_count": 6234
} | 128 |
# coding=utf-8
# Copyright 2023 Microsoft Research & University of Wisconsin-Madison and the HuggingFace Inc. team. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" VipLlava model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
logger = logging.get_logger(__name__)
VIPLLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"ybelkada/vip-llava-7b-hf": "https://huggingface.co/llava-hf/vip-llava-7b-hf/resolve/main/config.json",
}
class VipLlavaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`VipLlavaForConditionalGeneration`]. It is used to instantiate an
VipLlava model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the VipLlava-9B.
e.g. [ybelkada/vip-llava-7b-hf](https://huggingface.co/ybelkada/vip-llava-7b-hf)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vision_config (`VipLlavaVisionConfig`, *optional*):
Custom vision config or dict
text_config (`Union[AutoConfig, dict]`, *optional*):
The config object of the text backbone. Can be any of `LlamaConfig` or `MistralConfig`.
ignore_index (`int`, *optional*, defaults to -100):
The ignore index for the loss function.
image_token_index (`int`, *optional*, defaults to 32000):
The image token index to encode the image prompt.
projector_hidden_act (`str`, *optional*, defaults to `"gelu"`):
The activation function used by the multimodal projector.
projector_layernorm_eps (`float`, *optional*, defaults to 1e-05):
The layer norm epsilon of the projector layernorm
vision_feature_layers (`List[int]`, *optional*, defaults to `[-2, -5, -8, -11, 6]`):
The list of layers to select the vision features from.
vocab_size (`int`, *optional*, defaults to 32000):
Vocabulary size of the VipLlava model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`~VipLlavaForConditionalGeneration`]
Example:
```python
>>> from transformers import VipLlavaForConditionalGeneration, VipLlavaConfig, CLIPVisionConfig, LlamaConfig
>>> # Initializing a CLIP-vision config
>>> vision_config = CLIPVisionConfig()
>>> # Initializing a Llama config
>>> text_config = LlamaConfig()
>>> # Initializing a VipLlava vipllava-7b style configuration
>>> configuration = VipLlavaConfig(vision_config, text_config)
>>> # Initializing a model from the vipllava-7b style configuration
>>> model = VipLlavaForConditionalGeneration(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "vipllava"
is_composition = False
def __init__(
self,
vision_config=None,
text_config=None,
ignore_index=-100,
image_token_index=32000,
projector_hidden_act="gelu",
projector_layernorm_eps=1e-5,
vision_feature_layers=[-2, -5, -8, -11, 6],
vocab_size=32000,
**kwargs,
):
self.ignore_index = ignore_index
self.image_token_index = image_token_index
self.projector_hidden_act = projector_hidden_act
self.projector_layernorm_eps = projector_layernorm_eps
self.vision_feature_layers = vision_feature_layers
self.vocab_size = vocab_size
self.vision_config = vision_config
if isinstance(self.vision_config, dict):
vision_config["model_type"] = (
vision_config["model_type"] if "model_type" in vision_config else "clip_vision_model"
)
self.vision_config = CONFIG_MAPPING[vision_config["model_type"]](**vision_config)
elif vision_config is None:
self.vision_config = CONFIG_MAPPING["clip_vision_model"](
intermediate_size=4096,
hidden_size=1024,
patch_size=14,
image_size=336,
num_hidden_layers=24,
num_attention_heads=16,
vocab_size=32000,
projection_dim=768,
)
self.vocab_size = self.vocab_size
self.text_config = text_config
if isinstance(self.text_config, dict):
text_config["model_type"] = text_config["model_type"] if "model_type" in text_config else "llama"
self.text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
self.vocab_size = self.text_config.vocab_size
elif text_config is None:
self.text_config = CONFIG_MAPPING["llama"]()
super().__init__(**kwargs)
| transformers/src/transformers/models/vipllava/configuration_vipllava.py/0 | {
"file_path": "transformers/src/transformers/models/vipllava/configuration_vipllava.py",
"repo_id": "transformers",
"token_count": 2161
} | 129 |
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert VisualBert checkpoint."""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
rename_keys_prefix = [
("bert.bert", "visual_bert"),
("bert.cls", "cls"),
("bert.classifier", "cls"),
("token_type_embeddings_visual", "visual_token_type_embeddings"),
("position_embeddings_visual", "visual_position_embeddings"),
("projection", "visual_projection"),
]
ACCEPTABLE_CHECKPOINTS = [
"nlvr2_coco_pre_trained.th",
"nlvr2_fine_tuned.th",
"nlvr2_pre_trained.th",
"vcr_coco_pre_train.th",
"vcr_fine_tune.th",
"vcr_pre_train.th",
"vqa_coco_pre_trained.th",
"vqa_fine_tuned.th",
"vqa_pre_trained.th",
]
def load_state_dict(checkpoint_path):
sd = torch.load(checkpoint_path, map_location="cpu")
return sd
def get_new_dict(d, config, rename_keys_prefix=rename_keys_prefix):
new_d = OrderedDict()
new_d["visual_bert.embeddings.position_ids"] = torch.arange(config.max_position_embeddings).expand((1, -1))
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
new_key = key
for name_pair in rename_keys_prefix:
new_key = new_key.replace(name_pair[0], name_pair[1])
new_d[new_key] = d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
new_d["cls.predictions.decoder.bias"] = new_d["cls.predictions.bias"]
return new_d
@torch.no_grad()
def convert_visual_bert_checkpoint(checkpoint_path, pytorch_dump_folder_path):
"""
Copy/paste/tweak model's weights to our VisualBERT structure.
"""
assert (
checkpoint_path.split("/")[-1] in ACCEPTABLE_CHECKPOINTS
), f"The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}."
# Get Config
if "pre" in checkpoint_path:
model_type = "pretraining"
if "vcr" in checkpoint_path:
config_params = {"visual_embedding_dim": 512}
elif "vqa_advanced" in checkpoint_path:
config_params = {"visual_embedding_dim": 2048}
elif "vqa" in checkpoint_path:
config_params = {"visual_embedding_dim": 2048}
elif "nlvr" in checkpoint_path:
config_params = {"visual_embedding_dim": 1024}
else:
raise NotImplementedError(f"No implementation found for `{checkpoint_path}`.")
else:
if "vcr" in checkpoint_path:
config_params = {"visual_embedding_dim": 512}
model_type = "multichoice"
elif "vqa_advanced" in checkpoint_path:
config_params = {"visual_embedding_dim": 2048}
model_type = "vqa_advanced"
elif "vqa" in checkpoint_path:
config_params = {"visual_embedding_dim": 2048, "num_labels": 3129}
model_type = "vqa"
elif "nlvr" in checkpoint_path:
config_params = {
"visual_embedding_dim": 1024,
"num_labels": 2,
}
model_type = "nlvr"
config = VisualBertConfig(**config_params)
# Load State Dict
state_dict = load_state_dict(checkpoint_path)
new_state_dict = get_new_dict(state_dict, config)
if model_type == "pretraining":
model = VisualBertForPreTraining(config)
elif model_type == "vqa":
model = VisualBertForQuestionAnswering(config)
elif model_type == "nlvr":
model = VisualBertForVisualReasoning(config)
elif model_type == "multichoice":
model = VisualBertForMultipleChoice(config)
model.load_state_dict(new_state_dict)
# Save Checkpoints
Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
model.save_pretrained(pytorch_dump_folder_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument("orig_checkpoint_path", type=str, help="A path to .th on local filesystem.")
parser.add_argument("pytorch_dump_folder_path", type=str, help="Path to the output PyTorch model.")
args = parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| transformers/src/transformers/models/visual_bert/convert_visual_bert_original_pytorch_checkpoint_to_pytorch.py/0 | {
"file_path": "transformers/src/transformers/models/visual_bert/convert_visual_bert_original_pytorch_checkpoint_to_pytorch.py",
"repo_id": "transformers",
"token_count": 2115
} | 130 |
# coding=utf-8
# Copyright 2023 HUST-VL and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch ViTMatte model."""
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from ...modeling_utils import PreTrainedModel
from ...utils import (
ModelOutput,
add_start_docstrings,
add_start_docstrings_to_model_forward,
replace_return_docstrings,
)
from ...utils.backbone_utils import load_backbone
from .configuration_vitmatte import VitMatteConfig
VITMATTE_PRETRAINED_MODEL_ARCHIVE_LIST = [
"hustvl/vitmatte-small-composition-1k",
# See all VitMatte models at https://huggingface.co/models?filter=vitmatte
]
# General docstring
_CONFIG_FOR_DOC = "VitMatteConfig"
@dataclass
class ImageMattingOutput(ModelOutput):
"""
Class for outputs of image matting models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Loss.
alphas (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Estimated alpha values.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each stage) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states
(also called feature maps) of the model at the output of each stage.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, patch_size,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[torch.FloatTensor] = None
alphas: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
class VitMattePreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = VitMatteConfig
main_input_name = "pixel_values"
supports_gradient_checkpointing = True
def _init_weights(self, module):
if isinstance(module, nn.Conv2d):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
class VitMatteBasicConv3x3(nn.Module):
"""
Basic convolution layers including: Conv3x3, BatchNorm2d, ReLU layers.
"""
def __init__(self, config, in_channels, out_channels, stride=2, padding=1):
super().__init__()
self.conv = nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=3,
stride=stride,
padding=padding,
bias=False,
)
self.batch_norm = nn.BatchNorm2d(out_channels, eps=config.batch_norm_eps)
self.relu = nn.ReLU()
def forward(self, hidden_state):
hidden_state = self.conv(hidden_state)
hidden_state = self.batch_norm(hidden_state)
hidden_state = self.relu(hidden_state)
return hidden_state
class VitMatteConvStream(nn.Module):
"""
Simple ConvStream containing a series of basic conv3x3 layers to extract detail features.
"""
def __init__(self, config):
super().__init__()
in_channels = config.backbone_config.num_channels
out_channels = config.convstream_hidden_sizes
self.convs = nn.ModuleList()
self.conv_chans = [in_channels] + out_channels
for i in range(len(self.conv_chans) - 1):
in_chan_ = self.conv_chans[i]
out_chan_ = self.conv_chans[i + 1]
self.convs.append(VitMatteBasicConv3x3(config, in_chan_, out_chan_))
def forward(self, pixel_values):
out_dict = {"detailed_feature_map_0": pixel_values}
embeddings = pixel_values
for i in range(len(self.convs)):
embeddings = self.convs[i](embeddings)
name_ = "detailed_feature_map_" + str(i + 1)
out_dict[name_] = embeddings
return out_dict
class VitMatteFusionBlock(nn.Module):
"""
Simple fusion block to fuse features from ConvStream and Plain Vision Transformer.
"""
def __init__(self, config, in_channels, out_channels):
super().__init__()
self.conv = VitMatteBasicConv3x3(config, in_channels, out_channels, stride=1, padding=1)
def forward(self, features, detailed_feature_map):
upscaled_features = nn.functional.interpolate(features, scale_factor=2, mode="bilinear", align_corners=False)
out = torch.cat([detailed_feature_map, upscaled_features], dim=1)
out = self.conv(out)
return out
class VitMatteHead(nn.Module):
"""
Simple Matting Head, containing only conv3x3 and conv1x1 layers.
"""
def __init__(self, config):
super().__init__()
in_channels = config.fusion_hidden_sizes[-1]
mid_channels = 16
self.matting_convs = nn.Sequential(
nn.Conv2d(in_channels, mid_channels, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(mid_channels),
nn.ReLU(True),
nn.Conv2d(mid_channels, 1, kernel_size=1, stride=1, padding=0),
)
def forward(self, hidden_state):
hidden_state = self.matting_convs(hidden_state)
return hidden_state
class VitMatteDetailCaptureModule(nn.Module):
"""
Simple and lightweight Detail Capture Module for ViT Matting.
"""
def __init__(self, config):
super().__init__()
if len(config.fusion_hidden_sizes) != len(config.convstream_hidden_sizes) + 1:
raise ValueError(
"The length of fusion_hidden_sizes should be equal to the length of convstream_hidden_sizes + 1."
)
self.config = config
self.convstream = VitMatteConvStream(config)
self.conv_chans = self.convstream.conv_chans
self.fusion_blocks = nn.ModuleList()
self.fusion_channels = [config.hidden_size] + config.fusion_hidden_sizes
for i in range(len(self.fusion_channels) - 1):
self.fusion_blocks.append(
VitMatteFusionBlock(
config=config,
in_channels=self.fusion_channels[i] + self.conv_chans[-(i + 1)],
out_channels=self.fusion_channels[i + 1],
)
)
self.matting_head = VitMatteHead(config)
def forward(self, features, pixel_values):
detail_features = self.convstream(pixel_values)
for i in range(len(self.fusion_blocks)):
detailed_feature_map_name = "detailed_feature_map_" + str(len(self.fusion_blocks) - i - 1)
features = self.fusion_blocks[i](features, detail_features[detailed_feature_map_name])
alphas = torch.sigmoid(self.matting_head(features))
return alphas
VITMATTE_START_DOCSTRING = r"""
Parameters:
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
config ([`UperNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
VITMATTE_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
[`AutoImageProcessor`]. See [`VitMatteImageProcessor.__call__`] for details.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See
`attentions` under returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under
returned tensors for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"""ViTMatte framework leveraging any vision backbone e.g. for ADE20k, CityScapes.""",
VITMATTE_START_DOCSTRING,
)
class VitMatteForImageMatting(VitMattePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.backbone = load_backbone(config)
self.decoder = VitMatteDetailCaptureModule(config)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(VITMATTE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=ImageMattingOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
return_dict: Optional[bool] = None,
):
"""
labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
Ground truth image matting for computing the loss.
Returns:
Examples:
```python
>>> from transformers import VitMatteImageProcessor, VitMatteForImageMatting
>>> import torch
>>> from PIL import Image
>>> from huggingface_hub import hf_hub_download
>>> processor = VitMatteImageProcessor.from_pretrained("hustvl/vitmatte-small-composition-1k")
>>> model = VitMatteForImageMatting.from_pretrained("hustvl/vitmatte-small-composition-1k")
>>> filepath = hf_hub_download(
... repo_id="hf-internal-testing/image-matting-fixtures", filename="image.png", repo_type="dataset"
... )
>>> image = Image.open(filepath).convert("RGB")
>>> filepath = hf_hub_download(
... repo_id="hf-internal-testing/image-matting-fixtures", filename="trimap.png", repo_type="dataset"
... )
>>> trimap = Image.open(filepath).convert("L")
>>> # prepare image + trimap for the model
>>> inputs = processor(images=image, trimaps=trimap, return_tensors="pt")
>>> with torch.no_grad():
... alphas = model(**inputs).alphas
>>> print(alphas.shape)
torch.Size([1, 1, 640, 960])
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
outputs = self.backbone.forward_with_filtered_kwargs(
pixel_values, output_hidden_states=output_hidden_states, output_attentions=output_attentions
)
features = outputs.feature_maps[-1]
alphas = self.decoder(features, pixel_values)
loss = None
if labels is not None:
raise NotImplementedError("Training is not yet supported")
if not return_dict:
output = (alphas,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return ImageMattingOutput(
loss=loss,
alphas=alphas,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
| transformers/src/transformers/models/vitmatte/modeling_vitmatte.py/0 | {
"file_path": "transformers/src/transformers/models/vitmatte/modeling_vitmatte.py",
"repo_id": "transformers",
"token_count": 5253
} | 131 |
# coding=utf-8
# Copyright 2021 The Fairseq Authors and the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Flax Wav2Vec2 model."""
from functools import partial
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
import numpy as np
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.linen.attention import dot_product_attention_weights
from flax.traverse_util import flatten_dict, unflatten_dict
from jax import lax
from ...modeling_flax_outputs import FlaxBaseModelOutput, FlaxCausalLMOutput
from ...modeling_flax_utils import (
ACT2FN,
FlaxPreTrainedModel,
append_replace_return_docstrings,
overwrite_call_docstring,
)
from ...utils import ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_wav2vec2 import Wav2Vec2Config
logger = logging.get_logger(__name__)
@flax.struct.dataclass
class FlaxWav2Vec2BaseModelOutput(ModelOutput):
"""
Output type of [`FlaxWav2Vec2BaseModelOutput`], with potential hidden states and attentions.
Args:
last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
extract_features (`jnp.ndarray` of shape `(batch_size, sequence_length, last_conv_dim)`):
Sequence of extracted feature vectors of the last convolutional layer of the model with `last_conv_dim`
being the dimension of the last convolutional layer.
hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape
`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
last_hidden_state: jnp.ndarray = None
extract_features: jnp.ndarray = None
hidden_states: Optional[Tuple[jnp.ndarray]] = None
attentions: Optional[Tuple[jnp.ndarray]] = None
@flax.struct.dataclass
class FlaxWav2Vec2ForPreTrainingOutput(ModelOutput):
"""
Output type of [`FlaxWav2Vec2ForPreTrainingOutput`], with potential hidden states and attentions.
Args:
loss (*optional*, returned when model is in train mode, `jnp.ndarray` of shape `(1,)`):
Total loss as the sum of the contrastive loss (L_m) and the diversity loss (L_d) as stated in the [official
paper](https://arxiv.org/pdf/2006.11477.pdf) . (classification) loss.
projected_states (`jnp.ndarray` of shape `(batch_size, sequence_length, config.proj_codevector_dim)`):
Hidden-states of the model projected to *config.proj_codevector_dim* that can be used to predict the masked
projected quantized states.
projected_quantized_states (`jnp.ndarray` of shape `(batch_size, sequence_length, config.proj_codevector_dim)`):
Quantized extracted feature vectors projected to *config.proj_codevector_dim* representing the positive
target vectors for contrastive loss.
hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape
`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
projected_states: jnp.ndarray = None
projected_quantized_states: jnp.ndarray = None
codevector_perplexity: jnp.ndarray = None
hidden_states: Optional[Tuple[jnp.ndarray]] = None
attentions: Optional[Tuple[jnp.ndarray]] = None
def _compute_mask_indices(
shape: Tuple[int, int],
mask_prob: float,
mask_length: int,
attention_mask: Optional[np.ndarray] = None,
min_masks: int = 0,
) -> np.ndarray:
"""
Computes random mask spans for a given shape. Used to implement [SpecAugment: A Simple Data Augmentation Method for
ASR](https://arxiv.org/abs/1904.08779). Note that this method is not optimized to run on TPU and should be run on
CPU as part of the preprocessing during training.
Args:
shape: the shape for which to compute masks.
should be of size 2 where first element is batch size and 2nd is timesteps
mask_prob:
probability for each token to be chosen as start of the span to be masked. this will be multiplied by
number of timesteps divided by length of mask span to mask approximately this percentage of all elements.
however due to overlaps, the actual number will be smaller (unless no_overlap is True)
mask_length: size of the mask
min_masks: minimum number of masked spans
"""
batch_size, sequence_length = shape
if mask_length < 1:
raise ValueError("`mask_length` has to be bigger than 0.")
if mask_length > sequence_length:
raise ValueError(
f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and"
f" `sequence_length`: {sequence_length}`"
)
# compute number of masked spans in batch
num_masked_spans = int(mask_prob * sequence_length / mask_length + np.random.rand(1).item())
num_masked_spans = max(num_masked_spans, min_masks)
# make sure num masked indices <= sequence_length
if num_masked_spans * mask_length > sequence_length:
num_masked_spans = sequence_length // mask_length
# SpecAugment mask to fill
spec_aug_mask = np.zeros((batch_size, sequence_length), dtype=bool)
# get random indices to mask
spec_aug_mask_idxs = np.array(
[
np.random.choice(np.arange(sequence_length - (mask_length - 1)), num_masked_spans, replace=False)
for _ in range(batch_size)
]
)
# expand masked indices to masked spans
spec_aug_mask_idxs = np.broadcast_to(spec_aug_mask_idxs[:, :, None], (batch_size, num_masked_spans, mask_length))
spec_aug_mask_idxs = spec_aug_mask_idxs.reshape(batch_size, num_masked_spans * mask_length)
offsets = np.arange(mask_length)[None, None, :]
offsets = np.broadcast_to(offsets, (batch_size, num_masked_spans, mask_length)).reshape(
batch_size, num_masked_spans * mask_length
)
spec_aug_mask_idxs = spec_aug_mask_idxs + offsets
# scatter indices to mask
np.put_along_axis(spec_aug_mask, spec_aug_mask_idxs, 1, -1)
if attention_mask is not None:
# make sure padded input ids cannot be masked
spec_aug_mask = np.where(attention_mask, spec_aug_mask, False)
return spec_aug_mask
def _sample_negative_indices(features_shape: Tuple, num_negatives: int, attention_mask: Optional[np.ndarray] = None):
"""
Sample `num_negatives` vectors from feature vectors.
"""
batch_size, sequence_length, hidden_size = features_shape
if sequence_length <= 1:
raise ValueError(
"`features should have `sequence_length` > 1, but are of shape "
f"(batch_size, sequence_length, hidden_size) = ({batch_size, sequence_length, hidden_size})."
)
# get `num_negatives` random vector indices from the same utterance
sampled_negative_indices = []
for batch_idx in range(batch_size):
high = attention_mask[batch_idx].sum() - 1 if attention_mask is not None else sequence_length - 1
sampled_indices_slice = np.random.randint(0, high, size=(num_negatives * sequence_length,))
sampled_negative_indices.append(sampled_indices_slice)
sampled_negative_indices = np.asarray(sampled_negative_indices, dtype=np.int32)
# generate indices of the positive vectors themselves, repeat them `num_negatives` times
feature_indices = np.broadcast_to(np.arange(sequence_length)[:, None], (sequence_length, num_negatives)).flatten()
# avoid sampling the same positive vector, but keep the distribution uniform
sampled_negative_indices[sampled_negative_indices >= feature_indices] += 1
# correct for batch size
for batch_idx in range(1, batch_size):
sampled_negative_indices[batch_idx] += batch_idx * sequence_length
return sampled_negative_indices
WAV_2_VEC_2_START_DOCSTRING = r"""
Wav2Vec2 was proposed in [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael
Auli.
This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a Flax Linen
[flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a
regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.
Finally, this model supports inherent JAX features such as:
- [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
- [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
- [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
- [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
Parameters:
config ([`Wav2Vec2Config`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights.
dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
`jax.numpy.bfloat16` (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given `dtype`.
**Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.**
If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and
[`~FlaxPreTrainedModel.to_bf16`].
"""
WAV_2_VEC_2_INPUTS_DOCSTRING = r"""
Args:
input_values (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
Float values of input raw speech waveform. Values can be obtained by loading a `.flac` or `.wav` audio file
into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* via the soundfile library (`pip install
soundfile`). To prepare the array into `input_values`, the [`AutoProcessor`] should be used for padding and
conversion into a tensor of type `jnp.ndarray`. See [`Wav2Vec2Processor.__call__`] for details.
attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing convolution and attention on padding token indices. Mask values selected in `[0,
1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask) .. warning:: `attention_mask` should only be passed
if the corresponding processor has `config.return_attention_mask == True`. For all models whose processor
has `config.return_attention_mask == False`, such as
[wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base-960h), `attention_mask` should **not** be
passed to avoid degraded performance when doing batched inference. For such models `input_values` should
simply be padded with 0 and passed without `attention_mask`. Be aware that these models also yield slightly
different results depending on whether `input_values` is padded or not.
mask_time_indices (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Indices to mask extracted features for contrastive loss. When in training mode, model learns to predict
masked extracted features in *config.proj_codevector_dim* space.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
class FlaxWav2Vec2LayerNormConvLayer(nn.Module):
config: Wav2Vec2Config
layer_id: int = 0
dtype: jnp.dtype = jnp.float32
def setup(self):
self.in_conv_dim = self.config.conv_dim[self.layer_id] if self.layer_id > 0 else 1
self.out_conv_dim = self.config.conv_dim[self.layer_id]
self.conv = nn.Conv(
features=self.config.conv_dim[self.layer_id],
kernel_size=(self.config.conv_kernel[self.layer_id],),
strides=(self.config.conv_stride[self.layer_id],),
use_bias=self.config.conv_bias,
kernel_init=jax.nn.initializers.he_normal(),
padding="VALID",
dtype=self.dtype,
)
self.layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
self.activation = ACT2FN[self.config.feat_extract_activation]
def __call__(self, hidden_states):
hidden_states = self.conv(hidden_states)
hidden_states = self.layer_norm(hidden_states)
hidden_states = self.activation(hidden_states)
return hidden_states
class FlaxConvWithWeightNorm(nn.Module):
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.conv = nn.Conv(
features=self.config.hidden_size,
kernel_size=(self.config.num_conv_pos_embeddings,),
kernel_init=jax.nn.initializers.he_normal(),
padding="VALID",
feature_group_count=self.config.num_conv_pos_embedding_groups,
dtype=self.dtype,
)
weight_shape = (
self.conv.features,
self.conv.features // self.conv.feature_group_count,
self.conv.kernel_size[0],
)
self.weight_v = self.param("weight_v", jax.nn.initializers.he_normal(), weight_shape)
self.weight_g = self.param("weight_g", lambda _: jnp.linalg.norm(self.weight_v, axis=(0, 1))[None, None, :])
self.bias = self.param("bias", jax.nn.initializers.zeros, (self.conv.features,))
self.prev_padding = self.conv.kernel_size[0] // 2
def _get_normed_weights(self):
weight_v_norm = jnp.linalg.norm(self.weight_v, axis=(0, 1))[None, None, :]
normed_weight_v = jnp.divide(self.weight_v, weight_v_norm)
normed_kernel = jnp.multiply(normed_weight_v, self.weight_g)
return normed_kernel
def __call__(self, hidden_states):
kernel = self._get_normed_weights()
hidden_states = jnp.pad(hidden_states, ((0, 0), (self.prev_padding, self.prev_padding), (0, 0)))
hidden_states = self.conv.apply({"params": {"kernel": kernel.T, "bias": self.bias}}, hidden_states)
return hidden_states
class FlaxWav2Vec2PositionalConvEmbedding(nn.Module):
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.conv = FlaxConvWithWeightNorm(self.config, dtype=self.dtype)
self.activation = ACT2FN[self.config.feat_extract_activation]
self.num_pad_remove = 1 if self.config.num_conv_pos_embeddings % 2 == 0 else 0
def __call__(self, hidden_states):
hidden_states = hidden_states.transpose((0, 1, 2))
hidden_states = self.conv(hidden_states)
if self.num_pad_remove > 0:
hidden_states = hidden_states[:, : -self.num_pad_remove, :]
hidden_states = self.activation(hidden_states)
hidden_states = hidden_states.transpose((0, 1, 2))
return hidden_states
class FlaxConvLayersCollection(nn.Module):
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
if self.config.feat_extract_norm == "layer":
self.layers = [
FlaxWav2Vec2LayerNormConvLayer(self.config, layer_id=i, name=str(i), dtype=self.dtype)
for i in range(self.config.num_feat_extract_layers)
]
elif self.config.feat_extract_norm == "group":
raise NotImplementedError("At the moment only ``config.feat_extact_norm == 'layer'`` is supported")
else:
raise ValueError(
f"`config.feat_extract_norm` is {self.config.feat_extract_norm}, but has to be one of ['group',"
" 'layer']"
)
def __call__(self, hidden_states):
for i, conv_layer in enumerate(self.layers):
hidden_states = conv_layer(hidden_states)
return hidden_states
class FlaxWav2Vec2FeatureEncoder(nn.Module):
"""Construct the features from raw audio waveform"""
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.conv_layers = FlaxConvLayersCollection(self.config, dtype=self.dtype)
def __call__(self, input_values, freeze_feature_encoder=False):
hidden_states = input_values[:, :, None]
hidden_states = self.conv_layers(hidden_states)
if freeze_feature_encoder:
hidden_states = jax.lax.stop_gradient(hidden_states)
return hidden_states
class FlaxWav2Vec2FeatureProjection(nn.Module):
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
self.projection = nn.Dense(
self.config.hidden_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
dtype=self.dtype,
)
self.dropout = nn.Dropout(rate=self.config.feat_proj_dropout)
def __call__(self, hidden_states, deterministic=True):
norm_hidden_states = self.layer_norm(hidden_states)
hidden_states = self.projection(norm_hidden_states)
hidden_states = self.dropout(hidden_states, deterministic=deterministic)
return hidden_states, norm_hidden_states
class FlaxWav2Vec2Attention(nn.Module):
config: Wav2Vec2Config
embed_dim: int
num_heads: int
dropout: float = 0.0
bias: bool = True
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self) -> None:
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
f" {self.num_heads})."
)
dense = partial(
nn.Dense,
self.embed_dim,
use_bias=self.bias,
dtype=self.dtype,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
)
self.q_proj, self.k_proj, self.v_proj = dense(), dense(), dense()
self.out_proj = dense()
self.dropout_layer = nn.Dropout(rate=self.dropout)
def _split_heads(self, hidden_states):
return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads, self.head_dim))
def _merge_heads(self, hidden_states):
return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,))
def __call__(
self,
hidden_states: jnp.ndarray,
key_value_states: Optional[jnp.ndarray] = None,
attention_mask: Optional[jnp.ndarray] = None,
deterministic: bool = True,
) -> Tuple[jnp.ndarray]:
"""Input shape: Batch x Time x Channel"""
# get query proj
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = self._split_heads(query_states)
key_states = self._split_heads(key_states)
value_states = self._split_heads(value_states)
if attention_mask is not None:
attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))
# Convert the boolean attention mask to an attention bias.
if attention_mask is not None:
# attention mask in the form of attention bias
attention_bias = lax.select(
attention_mask > 0,
jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype),
)
else:
attention_bias = None
dropout_rng = None
if not deterministic and self.dropout > 0.0:
dropout_rng = self.make_rng("dropout")
attn_weights = dot_product_attention_weights(
query_states,
key_states,
bias=attention_bias,
dropout_rng=dropout_rng,
dropout_rate=self.dropout,
broadcast_dropout=True,
deterministic=deterministic,
dtype=self.dtype,
precision=None,
)
attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
attn_output = self._merge_heads(attn_output)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights
class FlaxWav2Vec2FeedForward(nn.Module):
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.intermediate_dropout = nn.Dropout(rate=self.config.activation_dropout)
self.intermediate_dense = nn.Dense(
self.config.intermediate_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
dtype=self.dtype,
)
if isinstance(self.config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[self.config.hidden_act]
else:
self.intermediate_act_fn = self.config.hidden_act
self.output_dense = nn.Dense(
self.config.hidden_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
dtype=self.dtype,
)
self.output_dropout = nn.Dropout(rate=self.config.hidden_dropout)
def __call__(self, hidden_states, deterministic=True):
hidden_states = self.intermediate_dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
hidden_states = self.intermediate_dropout(hidden_states, deterministic=deterministic)
hidden_states = self.output_dense(hidden_states)
hidden_states = self.output_dropout(hidden_states, deterministic=deterministic)
return hidden_states
class FlaxWav2Vec2EncoderLayerStableLayerNorm(nn.Module):
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.attention = FlaxWav2Vec2Attention(
config=self.config,
embed_dim=self.config.hidden_size,
num_heads=self.config.num_attention_heads,
dropout=self.config.attention_dropout,
dtype=self.dtype,
)
self.dropout = nn.Dropout(rate=self.config.hidden_dropout)
self.layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
self.feed_forward = FlaxWav2Vec2FeedForward(self.config, dtype=self.dtype)
self.final_layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
def __call__(self, hidden_states, attention_mask=None, deterministic=True, output_attentions=False):
attn_residual = hidden_states
hidden_states = self.layer_norm(hidden_states)
hidden_states, attn_weights = self.attention(
hidden_states, attention_mask=attention_mask, deterministic=deterministic
)
hidden_states = self.dropout(hidden_states, deterministic=deterministic)
hidden_states = attn_residual + hidden_states
hidden_states = hidden_states + self.feed_forward(
self.final_layer_norm(hidden_states), deterministic=deterministic
)
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
class FlaxWav2Vec2EncoderLayerStableLayerNormCollection(nn.Module):
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.layers = [
FlaxWav2Vec2EncoderLayerStableLayerNorm(self.config, name=str(i), dtype=self.dtype)
for i in range(self.config.num_hidden_layers)
]
def __call__(
self,
hidden_states,
attention_mask=None,
deterministic: bool = True,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
all_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
for i, layer in enumerate(self.layers):
if output_hidden_states:
all_hidden_states += (hidden_states,)
layer_outputs = layer(
hidden_states, attention_mask, deterministic=deterministic, output_attentions=output_attentions
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions += (layer_outputs[1],)
if output_hidden_states:
all_hidden_states += (hidden_states,)
outputs = (hidden_states, all_hidden_states, all_attentions)
if not return_dict:
return tuple(v for v in outputs if v is not None)
return FlaxBaseModelOutput(
last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
)
class FlaxWav2Vec2StableLayerNormEncoder(nn.Module):
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.pos_conv_embed = FlaxWav2Vec2PositionalConvEmbedding(self.config, dtype=self.dtype)
self.layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
self.dropout = nn.Dropout(rate=self.config.hidden_dropout)
self.layers = FlaxWav2Vec2EncoderLayerStableLayerNormCollection(self.config, dtype=self.dtype)
def __call__(
self,
hidden_states,
attention_mask=None,
deterministic=True,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
):
if attention_mask is not None:
# make sure padded tokens are not attended to
hidden_states = jnp.where(
jnp.broadcast_to(attention_mask[:, :, None], hidden_states.shape), hidden_states, 0
)
position_embeddings = self.pos_conv_embed(hidden_states)
hidden_states = hidden_states + position_embeddings
hidden_states = self.dropout(hidden_states, deterministic=deterministic)
outputs = self.layers(
hidden_states,
attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_state = self.layer_norm(outputs[0])
# update the last element in `hidden_states` after applying `layernorm` above
hidden_states = None
if output_hidden_states:
hidden_states = outputs[1]
hidden_states = hidden_states[:-1] + (last_hidden_state,)
if not return_dict:
outputs = (last_hidden_state, hidden_states) + (outputs[2:] if output_hidden_states else outputs[1:])
return tuple(v for v in outputs if v is not None)
return FlaxBaseModelOutput(
last_hidden_state=last_hidden_state, hidden_states=hidden_states, attentions=outputs.attentions
)
class FlaxWav2Vec2GumbelVectorQuantizer(nn.Module):
"""
Vector quantization using gumbel softmax. See [CATEGORICAL REPARAMETERIZATION WITH
GUMBEL-SOFTMAX](https://arxiv.org/pdf/1611.01144.pdf) for more information.
"""
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.num_groups = self.config.num_codevector_groups
self.num_vars = self.config.num_codevectors_per_group
if self.config.codevector_dim % self.num_groups != 0:
raise ValueError(
f"`config.codevector_dim {self.config.codevector_dim} must be divisible by"
f" `config.num_codevector_groups` {self.num_groups} for concatenation"
)
# storage for codebook variables (codewords)
self.codevectors = self.param(
"codevectors",
jax.nn.initializers.uniform(),
(1, self.num_groups * self.num_vars, self.config.codevector_dim // self.num_groups),
)
self.weight_proj = nn.Dense(
self.num_groups * self.num_vars,
kernel_init=jax.nn.initializers.normal(1.0),
dtype=self.dtype,
)
@staticmethod
def _compute_perplexity(probs, mask=None):
if mask is not None:
mask_extended = jnp.broadcast_to(mask.flatten()[:, None, None], probs.shape)
probs = jnp.where(mask_extended, probs, jnp.zeros_like(probs))
marginal_probs = probs.sum(axis=0) / mask.sum()
else:
marginal_probs = probs.mean(axis=0)
perplexity = jnp.exp(-jnp.sum(marginal_probs * jnp.log(marginal_probs + 1e-7), axis=-1)).sum()
return perplexity
def __call__(self, hidden_states, mask_time_indices=None, deterministic=True, temperature=1):
batch_size, sequence_length, hidden_size = hidden_states.shape
# project to codevector dim
hidden_states = self.weight_proj(hidden_states)
hidden_states = hidden_states.reshape(batch_size * sequence_length * self.num_groups, -1)
if not deterministic:
# sample code vector probs via gumbel in differentiateable way
gumbel_rng = self.make_rng("gumbel")
gumbels = jax.random.gumbel(gumbel_rng, hidden_states.shape)
codevector_probs = nn.softmax((hidden_states + gumbels) / temperature)
# compute perplexity
codevector_soft_dist = nn.softmax(
hidden_states.reshape(batch_size * sequence_length, self.num_groups, -1), axis=-1
)
perplexity = self._compute_perplexity(codevector_soft_dist, mask_time_indices)
else:
# take argmax in non-differentiable way
# comptute hard codevector distribution (one hot)
codevector_idx = hidden_states.argmax(axis=-1)
codevector_probs = jax.nn.one_hot(codevector_idx, hidden_states.shape[-1]) * 1.0
codevector_probs = codevector_probs.reshape(batch_size * sequence_length, self.num_groups, -1)
perplexity = self._compute_perplexity(codevector_probs, mask_time_indices)
codevector_probs = codevector_probs.reshape(batch_size * sequence_length, -1)
# use probs to retrieve codevectors
codevectors_per_group = jnp.expand_dims(codevector_probs, axis=-1) * self.codevectors
codevectors = codevectors_per_group.reshape(batch_size * sequence_length, self.num_groups, self.num_vars, -1)
codevectors = codevectors.sum(-2).reshape(batch_size, sequence_length, -1)
return codevectors, perplexity
class FlaxWav2Vec2Adapter(nn.Module):
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
# hidden_states require down-projection if feature dims don't match
if self.config.output_hidden_size != self.config.hidden_size:
self.proj = nn.Dense(
self.config.output_hidden_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
dtype=self.dtype,
)
self.proj_layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
else:
self.proj = self.proj_layer_norm = None
self.layers = FlaxWav2Vec2AdapterLayersCollection(self.config, dtype=self.dtype)
def __call__(self, hidden_states, deterministic=True):
# down-project hidden_states if required
if self.proj is not None and self.proj_layer_norm is not None:
hidden_states = self.proj(hidden_states)
hidden_states = self.proj_layer_norm(hidden_states)
hidden_states = self.layers(hidden_states)
return hidden_states
class FlaxWav2Vec2AdapterLayer(nn.Module):
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.conv = nn.Conv(
features=2 * self.config.output_hidden_size,
kernel_size=(self.config.adapter_kernel_size,),
strides=(self.config.adapter_stride,),
padding=((1, 1),),
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
dtype=self.dtype,
)
def __call__(self, hidden_states):
hidden_states = self.conv(hidden_states)
hidden_states = nn.glu(hidden_states, axis=2)
return hidden_states
class FlaxWav2Vec2AdapterLayersCollection(nn.Module):
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.layers = [
FlaxWav2Vec2AdapterLayer(self.config, name=str(i), dtype=self.dtype)
for i in range(self.config.num_adapter_layers)
]
def __call__(self, hidden_states):
for conv_layer in self.layers:
hidden_states = conv_layer(hidden_states)
return hidden_states
class FlaxWav2Vec2PreTrainedModel(FlaxPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = Wav2Vec2Config
base_model_prefix: str = "wav2vec2"
main_input_name = "input_values"
module_class: nn.Module = None
def __init__(
self,
config: Wav2Vec2Config,
input_shape: Tuple = (1, 1024),
seed: int = 0,
dtype: jnp.dtype = jnp.float32,
_do_init: bool = True,
**kwargs,
):
module = self.module_class(config=config, dtype=dtype, **kwargs)
super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
# init input tensors
input_values = jnp.zeros(input_shape, dtype="i4")
attention_mask = jnp.ones_like(input_values)
params_rng, dropout_rng = jax.random.split(rng, 2)
rngs = {"params": params_rng, "dropout": dropout_rng}
random_params = self.module.init(rngs, input_values, attention_mask, return_dict=False)["params"]
if params is not None:
random_params = flatten_dict(unfreeze(random_params))
params = flatten_dict(unfreeze(params))
for missing_key in self._missing_keys:
params[missing_key] = random_params[missing_key]
self._missing_keys = set()
return freeze(unflatten_dict(params))
else:
return random_params
@add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING)
def __call__(
self,
input_values,
attention_mask=None,
mask_time_indices=None,
params: dict = None,
dropout_rng: jax.random.PRNGKey = None,
train: bool = False,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
freeze_feature_encoder: bool = False,
return_dict: Optional[bool] = None,
):
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.return_dict
batch_size, sequence_length = input_values.shape
if attention_mask is None:
attention_mask = jnp.ones((batch_size, sequence_length))
# Handle any PRNG if needed
rngs = {}
if dropout_rng is not None:
rngs["dropout"] = dropout_rng
inputs = {"params": params or self.params}
return self.module.apply(
inputs,
jnp.array(input_values, dtype="f4"),
jnp.array(attention_mask, dtype="i4"),
mask_time_indices,
not train,
output_attentions,
output_hidden_states,
freeze_feature_encoder,
return_dict,
rngs=rngs,
)
def _get_feat_extract_output_lengths(
self, input_lengths: Union[jnp.ndarray, int], add_adapter: Optional[bool] = None
):
return self.module._get_feat_extract_output_lengths(input_lengths, add_adapter=add_adapter)
class FlaxWav2Vec2Module(nn.Module):
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.feature_extractor = FlaxWav2Vec2FeatureEncoder(self.config, dtype=self.dtype)
self.feature_projection = FlaxWav2Vec2FeatureProjection(self.config, dtype=self.dtype)
self.masked_spec_embed = self.param(
"masked_spec_embed", jax.nn.initializers.uniform(), (self.config.hidden_size,)
)
if self.config.do_stable_layer_norm:
self.encoder = FlaxWav2Vec2StableLayerNormEncoder(self.config, dtype=self.dtype)
else:
raise NotImplementedError("``config.do_stable_layer_norm is False`` is currently not supported.")
self.adapter = FlaxWav2Vec2Adapter(self.config, dtype=self.dtype) if self.config.add_adapter else None
def __call__(
self,
input_values,
attention_mask=None,
mask_time_indices=None,
deterministic=True,
output_attentions=None,
output_hidden_states=None,
freeze_feature_encoder=False,
return_dict=None,
):
extract_features = self.feature_extractor(input_values, freeze_feature_encoder=freeze_feature_encoder)
# make sure that no loss is computed on padded inputs
if attention_mask is not None:
# compute reduced attention_mask corresponding to feature vectors
attention_mask = self._get_feature_vector_attention_mask(
extract_features.shape[1], attention_mask, add_adapter=False
)
hidden_states, extract_features = self.feature_projection(extract_features, deterministic=deterministic)
if mask_time_indices is not None: # apply SpecAugment along time axis with given indices
hidden_states = jnp.where(
jnp.broadcast_to(mask_time_indices[:, :, None], hidden_states.shape),
jnp.broadcast_to(self.masked_spec_embed[None, None, :], hidden_states.shape),
hidden_states,
)
encoder_outputs = self.encoder(
hidden_states,
attention_mask=attention_mask,
deterministic=deterministic,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = encoder_outputs[0]
if self.adapter is not None:
hidden_states = self.adapter(hidden_states)
if not return_dict:
return (hidden_states, extract_features) + encoder_outputs[1:]
return FlaxWav2Vec2BaseModelOutput(
last_hidden_state=hidden_states,
extract_features=extract_features,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
def _get_feat_extract_output_lengths(
self, input_lengths: Union[jnp.ndarray, int], add_adapter: Optional[bool] = None
):
"""
Computes the output length of the convolutional layers
"""
add_adapter = self.config.add_adapter if add_adapter is None else add_adapter
def _conv_out_length(input_length, kernel_size, stride):
# 1D convolutional layer output length formula taken
# from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html
return (input_length - kernel_size) // stride + 1
for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride):
input_lengths = _conv_out_length(input_lengths, kernel_size, stride)
if add_adapter:
for _ in range(self.config.num_adapter_layers):
input_lengths = _conv_out_length(input_lengths, 1, self.config.adapter_stride)
return input_lengths
def _get_feature_vector_attention_mask(
self, feature_vector_length: int, attention_mask: jnp.ndarray, add_adapter=None
):
# Effectively attention_mask.sum(-1), but not inplace to be able to run
# on inference mode.
non_padded_lengths = attention_mask.cumsum(axis=-1)[:, -1]
output_lengths = self._get_feat_extract_output_lengths(non_padded_lengths, add_adapter=add_adapter)
batch_size = attention_mask.shape[0]
attention_mask = jnp.zeros((batch_size, feature_vector_length), dtype=attention_mask.dtype)
# these two operations makes sure that all values
# before the output lengths indices are attended to
attention_mask = attention_mask.at[jnp.arange(attention_mask.shape[0]), output_lengths - 1].set(1)
attention_mask = jnp.flip(jnp.flip(attention_mask, -1).cumsum(-1), -1).astype("bool")
return attention_mask
@add_start_docstrings(
"The bare Wav2Vec2 Model transformer outputting raw hidden-states without any specific head on top.",
WAV_2_VEC_2_START_DOCSTRING,
)
class FlaxWav2Vec2Model(FlaxWav2Vec2PreTrainedModel):
module_class = FlaxWav2Vec2Module
FLAX_WAV2VEC2_MODEL_DOCSTRING = """
Returns:
Example:
```python
>>> from transformers import AutoProcessor, FlaxWav2Vec2Model
>>> from datasets import load_dataset
>>> import soundfile as sf
>>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-large-lv60")
>>> model = FlaxWav2Vec2Model.from_pretrained("facebook/wav2vec2-large-lv60")
>>> def map_to_array(batch):
... speech, _ = sf.read(batch["file"])
... batch["speech"] = speech
... return batch
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> ds = ds.map(map_to_array)
>>> input_values = processor(
... ds["speech"][0], sampling_rate=16_000, return_tensors="np"
... ).input_values # Batch size 1
>>> hidden_states = model(input_values).last_hidden_state
```
"""
overwrite_call_docstring(
FlaxWav2Vec2Model,
WAV_2_VEC_2_INPUTS_DOCSTRING + FLAX_WAV2VEC2_MODEL_DOCSTRING,
)
append_replace_return_docstrings(
FlaxWav2Vec2Model, output_type=FlaxWav2Vec2BaseModelOutput, config_class=Wav2Vec2Config
)
class FlaxWav2Vec2ForCTCModule(nn.Module):
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.wav2vec2 = FlaxWav2Vec2Module(self.config, dtype=self.dtype)
self.dropout = nn.Dropout(rate=self.config.final_dropout)
self.lm_head = nn.Dense(
self.config.vocab_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
dtype=self.dtype,
)
def __call__(
self,
input_values,
attention_mask=None,
mask_time_indices=None,
deterministic=True,
output_attentions=None,
output_hidden_states=None,
freeze_feature_encoder=False,
return_dict=None,
):
outputs = self.wav2vec2(
input_values,
attention_mask=attention_mask,
mask_time_indices=mask_time_indices,
deterministic=deterministic,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
freeze_feature_encoder=freeze_feature_encoder,
return_dict=return_dict,
)
hidden_states = outputs[0]
hidden_states = self.dropout(hidden_states, deterministic=deterministic)
logits = self.lm_head(hidden_states)
if not return_dict:
return (logits,) + outputs[2:]
return FlaxCausalLMOutput(logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
def _get_feat_extract_output_lengths(
self,
input_lengths: Union[jnp.ndarray, int],
add_adapter: Optional[bool] = None,
):
"""
Computes the output length of the convolutional layers
"""
add_adapter = self.config.add_adapter if add_adapter is None else add_adapter
def _conv_out_length(input_length, kernel_size, stride):
# 1D convolutional layer output length formula taken
# from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html
return (input_length - kernel_size) // stride + 1
for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride):
input_lengths = _conv_out_length(input_lengths, kernel_size, stride)
if add_adapter:
for _ in range(self.config.num_adapter_layers):
input_lengths = _conv_out_length(input_lengths, 1, self.config.adapter_stride)
return input_lengths
@add_start_docstrings(
"Wav2Vec2 Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).",
WAV_2_VEC_2_START_DOCSTRING,
)
class FlaxWav2Vec2ForCTC(FlaxWav2Vec2PreTrainedModel):
module_class = FlaxWav2Vec2ForCTCModule
FLAX_WAV2VEC2_FOR_CTC_DOCSTRING = """
Returns:
Example:
```python
>>> import jax.numpy as jnp
>>> from transformers import AutoProcessor, FlaxWav2Vec2ForCTC
>>> from datasets import load_dataset
>>> import soundfile as sf
>>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-large-960h-lv60")
>>> model = FlaxWav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h-lv60")
>>> def map_to_array(batch):
... speech, _ = sf.read(batch["file"])
... batch["speech"] = speech
... return batch
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> ds = ds.map(map_to_array)
>>> input_values = processor(
... ds["speech"][0], sampling_rate=16_000, return_tensors="np"
... ).input_values # Batch size 1
>>> logits = model(input_values).logits
>>> predicted_ids = jnp.argmax(logits, axis=-1)
>>> transcription = processor.decode(predicted_ids[0])
>>> # should give: "A MAN SAID TO THE UNIVERSE SIR I EXIST"
```
"""
overwrite_call_docstring(
FlaxWav2Vec2ForCTC,
WAV_2_VEC_2_INPUTS_DOCSTRING + FLAX_WAV2VEC2_FOR_CTC_DOCSTRING,
)
append_replace_return_docstrings(FlaxWav2Vec2ForCTC, output_type=FlaxCausalLMOutput, config_class=Wav2Vec2Config)
class FlaxWav2Vec2ForPreTrainingModule(nn.Module):
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.wav2vec2 = FlaxWav2Vec2Module(self.config, dtype=self.dtype)
self.dropout_features = nn.Dropout(self.config.feat_quantizer_dropout)
self.quantizer = FlaxWav2Vec2GumbelVectorQuantizer(self.config, dtype=self.dtype)
self.project_q = nn.Dense(
self.config.proj_codevector_dim,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
dtype=self.dtype,
)
self.project_hid = nn.Dense(
self.config.proj_codevector_dim,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
dtype=self.dtype,
)
def __call__(
self,
input_values,
attention_mask=None,
mask_time_indices=None,
gumbel_temperature: int = 1,
deterministic: bool = True,
output_attentions=None,
output_hidden_states=None,
freeze_feature_encoder=False,
return_dict=None,
):
r"""
Returns:
Example:
```python
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.wav2vec2(
input_values,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
mask_time_indices=mask_time_indices,
deterministic=deterministic,
freeze_feature_encoder=freeze_feature_encoder,
return_dict=return_dict,
)
# project all transformed features (including masked) to final vq dim
transformer_features = self.project_hid(outputs[0])
# quantize all (unmasked) extracted features and project to final vq dim
extract_features = self.dropout_features(outputs[1], deterministic=deterministic)
quantized_features, codevector_perplexity = self.quantizer(
extract_features, mask_time_indices, deterministic=deterministic, temperature=gumbel_temperature
)
quantized_features = self.project_q(quantized_features)
if not return_dict:
return (transformer_features, quantized_features, codevector_perplexity) + outputs[2:]
return FlaxWav2Vec2ForPreTrainingOutput(
projected_states=transformer_features,
projected_quantized_states=quantized_features,
codevector_perplexity=codevector_perplexity,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def _get_feat_extract_output_lengths(
self, input_lengths: Union[jnp.ndarray, int], add_adapter: Optional[bool] = None
):
"""
Computes the output length of the convolutional layers
"""
add_adapter = self.config.add_adapter if add_adapter is None else add_adapter
def _conv_out_length(input_length, kernel_size, stride):
# 1D convolutional layer output length formula taken
# from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html
return (input_length - kernel_size) // stride + 1
for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride):
input_lengths = _conv_out_length(input_lengths, kernel_size, stride)
if add_adapter:
for _ in range(self.config.num_adapter_layers):
input_lengths = _conv_out_length(input_lengths, 1, self.config.adapter_stride)
return input_lengths
@add_start_docstrings("""Wav2Vec2 Model with a quantizer and `VQ` head on top.""", WAV_2_VEC_2_START_DOCSTRING)
class FlaxWav2Vec2ForPreTraining(FlaxWav2Vec2PreTrainedModel):
module_class = FlaxWav2Vec2ForPreTrainingModule
@add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING)
# overwrite since has `gumbel_temperature` input
def __call__(
self,
input_values,
attention_mask=None,
mask_time_indices=None,
gumbel_temperature: int = 1,
params: dict = None,
dropout_rng: jax.random.PRNGKey = None,
gumbel_rng: jax.random.PRNGKey = None,
train: bool = False,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
freeze_feature_encoder: bool = False,
return_dict: Optional[bool] = None,
):
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.return_dict
batch_size, sequence_length = input_values.shape
if attention_mask is None:
attention_mask = jnp.ones((batch_size, sequence_length))
# Handle any PRNG if needed
rngs = {}
if dropout_rng is not None:
rngs["dropout"] = dropout_rng
if gumbel_rng is not None:
rngs["gumbel"] = gumbel_rng
inputs = {"params": params or self.params}
return self.module.apply(
inputs,
jnp.array(input_values, dtype="f4"),
jnp.array(attention_mask, dtype="i4"),
mask_time_indices,
gumbel_temperature,
not train,
output_attentions,
output_hidden_states,
freeze_feature_encoder,
return_dict,
rngs=rngs,
)
FLAX_WAV2VEC2_FOR_PRETRAINING_DOCSTRING = """
Returns:
Example:
```python
>>> import optax
>>> import numpy as np
>>> import jax.numpy as jnp
>>> from transformers import AutoFeatureExtractor, FlaxWav2Vec2ForPreTraining
>>> from transformers.models.wav2vec2.modeling_flax_wav2vec2 import _compute_mask_indices
>>> from datasets import load_dataset
>>> import soundfile as sf
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-large-lv60")
>>> model = FlaxWav2Vec2ForPreTraining.from_pretrained("facebook/wav2vec2-large-lv60")
>>> def map_to_array(batch):
... speech, _ = sf.read(batch["file"])
... batch["speech"] = speech
... return batch
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> ds = ds.map(map_to_array)
>>> input_values = feature_extractor(ds["speech"][0], return_tensors="np").input_values # Batch size 1
>>> # compute masked indices
>>> batch_size, raw_sequence_length = input_values.shape
>>> sequence_length = model._get_feat_extract_output_lengths(raw_sequence_length)
>>> mask_time_indices = _compute_mask_indices((batch_size, sequence_length), mask_prob=0.2, mask_length=2)
>>> outputs = model(input_values, mask_time_indices=mask_time_indices)
>>> # compute cosine similarity between predicted (=projected_states) and target (=projected_quantized_states)
>>> cosine_sim = optax.cosine_similarity(outputs.projected_states, outputs.projected_quantized_states)
>>> # show that cosine similarity is much higher than random
>>> assert np.asarray(cosine_sim)[mask_time_indices].mean() > 0.5
```
"""
overwrite_call_docstring(
FlaxWav2Vec2ForPreTraining,
WAV_2_VEC_2_INPUTS_DOCSTRING + FLAX_WAV2VEC2_FOR_PRETRAINING_DOCSTRING,
)
append_replace_return_docstrings(
FlaxWav2Vec2ForPreTraining, output_type=FlaxWav2Vec2ForPreTrainingOutput, config_class=Wav2Vec2Config
)
| transformers/src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py/0 | {
"file_path": "transformers/src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py",
"repo_id": "transformers",
"token_count": 24375
} | 132 |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Speech processor class for Whisper
"""
from ...processing_utils import ProcessorMixin
class WhisperProcessor(ProcessorMixin):
r"""
Constructs a Whisper processor which wraps a Whisper feature extractor and a Whisper tokenizer into a single
processor.
[`WhisperProcessor`] offers all the functionalities of [`WhisperFeatureExtractor`] and [`WhisperTokenizer`]. See
the [`~WhisperProcessor.__call__`] and [`~WhisperProcessor.decode`] for more information.
Args:
feature_extractor (`WhisperFeatureExtractor`):
An instance of [`WhisperFeatureExtractor`]. The feature extractor is a required input.
tokenizer (`WhisperTokenizer`):
An instance of [`WhisperTokenizer`]. The tokenizer is a required input.
"""
feature_extractor_class = "WhisperFeatureExtractor"
tokenizer_class = "WhisperTokenizer"
def __init__(self, feature_extractor, tokenizer):
super().__init__(feature_extractor, tokenizer)
self.current_processor = self.feature_extractor
self._in_target_context_manager = False
def get_decoder_prompt_ids(self, task=None, language=None, no_timestamps=True):
return self.tokenizer.get_decoder_prompt_ids(task=task, language=language, no_timestamps=no_timestamps)
def __call__(self, *args, **kwargs):
"""
Forwards the `audio` argument to WhisperFeatureExtractor's [`~WhisperFeatureExtractor.__call__`] and the `text`
argument to [`~WhisperTokenizer.__call__`]. Please refer to the doctsring of the above two methods for more
information.
"""
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*args, **kwargs)
audio = kwargs.pop("audio", None)
sampling_rate = kwargs.pop("sampling_rate", None)
text = kwargs.pop("text", None)
if len(args) > 0:
audio = args[0]
args = args[1:]
if audio is None and text is None:
raise ValueError("You need to specify either an `audio` or `text` input to process.")
if audio is not None:
inputs = self.feature_extractor(audio, *args, sampling_rate=sampling_rate, **kwargs)
if text is not None:
encodings = self.tokenizer(text, **kwargs)
if text is None:
return inputs
elif audio is None:
return encodings
else:
inputs["labels"] = encodings["input_ids"]
return inputs
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to WhisperTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to WhisperTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
def get_prompt_ids(self, text: str, return_tensors="np"):
return self.tokenizer.get_prompt_ids(text, return_tensors=return_tensors)
| transformers/src/transformers/models/whisper/processing_whisper.py/0 | {
"file_path": "transformers/src/transformers/models/whisper/processing_whisper.py",
"repo_id": "transformers",
"token_count": 1465
} | 133 |
# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License
""" Tokenization classes for XLM-RoBERTa model."""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
XLMRobertaTokenizer = None
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"FacebookAI/xlm-roberta-base": "https://huggingface.co/FacebookAI/xlm-roberta-base/resolve/main/sentencepiece.bpe.model",
"FacebookAI/xlm-roberta-large": "https://huggingface.co/FacebookAI/xlm-roberta-large/resolve/main/sentencepiece.bpe.model",
"FacebookAI/xlm-roberta-large-finetuned-conll02-dutch": (
"https://huggingface.co/FacebookAI/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"
),
"FacebookAI/xlm-roberta-large-finetuned-conll02-spanish": (
"https://huggingface.co/FacebookAI/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"
),
"FacebookAI/xlm-roberta-large-finetuned-conll03-english": (
"https://huggingface.co/FacebookAI/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"
),
"FacebookAI/xlm-roberta-large-finetuned-conll03-german": (
"https://huggingface.co/FacebookAI/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"
),
},
"tokenizer_file": {
"FacebookAI/xlm-roberta-base": "https://huggingface.co/FacebookAI/xlm-roberta-base/resolve/main/tokenizer.json",
"FacebookAI/xlm-roberta-large": "https://huggingface.co/FacebookAI/xlm-roberta-large/resolve/main/tokenizer.json",
"FacebookAI/xlm-roberta-large-finetuned-conll02-dutch": (
"https://huggingface.co/FacebookAI/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json"
),
"FacebookAI/xlm-roberta-large-finetuned-conll02-spanish": (
"https://huggingface.co/FacebookAI/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json"
),
"FacebookAI/xlm-roberta-large-finetuned-conll03-english": (
"https://huggingface.co/FacebookAI/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json"
),
"FacebookAI/xlm-roberta-large-finetuned-conll03-german": (
"https://huggingface.co/FacebookAI/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json"
),
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"FacebookAI/xlm-roberta-base": 512,
"FacebookAI/xlm-roberta-large": 512,
"FacebookAI/xlm-roberta-large-finetuned-conll02-dutch": 512,
"FacebookAI/xlm-roberta-large-finetuned-conll02-spanish": 512,
"FacebookAI/xlm-roberta-large-finetuned-conll03-english": 512,
"FacebookAI/xlm-roberta-large-finetuned-conll03-german": 512,
}
class XLMRobertaTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" XLM-RoBERTa tokenizer (backed by HuggingFace's *tokenizers* library). Adapted from
[`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on
[BPE](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=BPE#models).
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
Path to the vocabulary file.
bos_token (`str`, *optional*, defaults to `"<s>"`):
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the `cls_token`.
</Tip>
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the `sep_token`.
</Tip>
sep_token (`str`, *optional*, defaults to `"</s>"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
cls_token (`str`, *optional*, defaults to `"<s>"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
mask_token (`str`, *optional*, defaults to `"<mask>"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
additional_special_tokens (`List[str]`, *optional*, defaults to `["<s>NOTUSED", "</s>NOTUSED"]`):
Additional special tokens used by the tokenizer.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ["input_ids", "attention_mask"]
slow_tokenizer_class = XLMRobertaTokenizer
def __init__(
self,
vocab_file=None,
tokenizer_file=None,
bos_token="<s>",
eos_token="</s>",
sep_token="</s>",
cls_token="<s>",
unk_token="<unk>",
pad_token="<pad>",
mask_token="<mask>",
**kwargs,
):
# Mask token behave like a normal word, i.e. include the space before it
mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
super().__init__(
vocab_file,
tokenizer_file=tokenizer_file,
bos_token=bos_token,
eos_token=eos_token,
sep_token=sep_token,
cls_token=cls_token,
unk_token=unk_token,
pad_token=pad_token,
mask_token=mask_token,
**kwargs,
)
self.vocab_file = vocab_file
@property
def can_save_slow_tokenizer(self) -> bool:
return os.path.isfile(self.vocab_file) if self.vocab_file else False
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. An XLM-RoBERTa sequence has the following format:
- single sequence: `<s> X </s>`
- pair of sequences: `<s> A </s></s> B </s>`
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
if token_ids_1 is None:
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
cls = [self.cls_token_id]
sep = [self.sep_token_id]
return cls + token_ids_0 + sep + sep + token_ids_1 + sep
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. XLM-RoBERTa does
not make use of token type ids, therefore a list of zeros is returned.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of zeros.
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer."
)
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory.")
return
out_vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
copyfile(self.vocab_file, out_vocab_file)
return (out_vocab_file,)
| transformers/src/transformers/models/xlm_roberta/tokenization_xlm_roberta_fast.py/0 | {
"file_path": "transformers/src/transformers/models/xlm_roberta/tokenization_xlm_roberta_fast.py",
"repo_id": "transformers",
"token_count": 4527
} | 134 |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch optimization for BERT model."""
import math
import warnings
from functools import partial
from typing import Callable, Iterable, Optional, Tuple, Union
import torch
from torch import nn
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR, ReduceLROnPlateau
from .trainer_utils import SchedulerType
from .utils import logging
from .utils.versions import require_version
logger = logging.get_logger(__name__)
def _get_constant_lambda(_=None):
return 1
def get_constant_schedule(optimizer: Optimizer, last_epoch: int = -1):
"""
Create a schedule with a constant learning rate, using the learning rate set in optimizer.
Args:
optimizer ([`~torch.optim.Optimizer`]):
The optimizer for which to schedule the learning rate.
last_epoch (`int`, *optional*, defaults to -1):
The index of the last epoch when resuming training.
Return:
`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
"""
return LambdaLR(optimizer, _get_constant_lambda, last_epoch=last_epoch)
def get_reduce_on_plateau_schedule(optimizer: Optimizer, **kwargs):
"""
Create a schedule with a constant learning rate that decreases when a metric has stopped improving.
Args:
optimizer ([`~torch.optim.Optimizer`]):
The optimizer for which to schedule the learning rate.
kwargs (`dict`, *optional*):
Extra parameters to be passed to the scheduler. See `torch.optim.lr_scheduler.ReduceLROnPlateau`
for possible parameters.
Return:
`torch.optim.lr_scheduler.ReduceLROnPlateau` with the appropriate schedule.
"""
return ReduceLROnPlateau(optimizer, **kwargs)
def _get_constant_schedule_with_warmup_lr_lambda(current_step: int, *, num_warmup_steps: int):
if current_step < num_warmup_steps:
return float(current_step) / float(max(1.0, num_warmup_steps))
return 1.0
def get_constant_schedule_with_warmup(optimizer: Optimizer, num_warmup_steps: int, last_epoch: int = -1):
"""
Create a schedule with a constant learning rate preceded by a warmup period during which the learning rate
increases linearly between 0 and the initial lr set in the optimizer.
Args:
optimizer ([`~torch.optim.Optimizer`]):
The optimizer for which to schedule the learning rate.
num_warmup_steps (`int`):
The number of steps for the warmup phase.
last_epoch (`int`, *optional*, defaults to -1):
The index of the last epoch when resuming training.
Return:
`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
"""
lr_lambda = partial(_get_constant_schedule_with_warmup_lr_lambda, num_warmup_steps=num_warmup_steps)
return LambdaLR(optimizer, lr_lambda, last_epoch=last_epoch)
def _get_linear_schedule_with_warmup_lr_lambda(current_step: int, *, num_warmup_steps: int, num_training_steps: int):
if current_step < num_warmup_steps:
return float(current_step) / float(max(1, num_warmup_steps))
return max(0.0, float(num_training_steps - current_step) / float(max(1, num_training_steps - num_warmup_steps)))
def get_linear_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps, last_epoch=-1):
"""
Create a schedule with a learning rate that decreases linearly from the initial lr set in the optimizer to 0, after
a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer.
Args:
optimizer ([`~torch.optim.Optimizer`]):
The optimizer for which to schedule the learning rate.
num_warmup_steps (`int`):
The number of steps for the warmup phase.
num_training_steps (`int`):
The total number of training steps.
last_epoch (`int`, *optional*, defaults to -1):
The index of the last epoch when resuming training.
Return:
`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
"""
lr_lambda = partial(
_get_linear_schedule_with_warmup_lr_lambda,
num_warmup_steps=num_warmup_steps,
num_training_steps=num_training_steps,
)
return LambdaLR(optimizer, lr_lambda, last_epoch)
def _get_cosine_schedule_with_warmup_lr_lambda(
current_step: int, *, num_warmup_steps: int, num_training_steps: int, num_cycles: float
):
if current_step < num_warmup_steps:
return float(current_step) / float(max(1, num_warmup_steps))
progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))
return max(0.0, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress)))
def get_cosine_schedule_with_warmup(
optimizer: Optimizer, num_warmup_steps: int, num_training_steps: int, num_cycles: float = 0.5, last_epoch: int = -1
):
"""
Create a schedule with a learning rate that decreases following the values of the cosine function between the
initial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the
initial lr set in the optimizer.
Args:
optimizer ([`~torch.optim.Optimizer`]):
The optimizer for which to schedule the learning rate.
num_warmup_steps (`int`):
The number of steps for the warmup phase.
num_training_steps (`int`):
The total number of training steps.
num_cycles (`float`, *optional*, defaults to 0.5):
The number of waves in the cosine schedule (the defaults is to just decrease from the max value to 0
following a half-cosine).
last_epoch (`int`, *optional*, defaults to -1):
The index of the last epoch when resuming training.
Return:
`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
"""
lr_lambda = partial(
_get_cosine_schedule_with_warmup_lr_lambda,
num_warmup_steps=num_warmup_steps,
num_training_steps=num_training_steps,
num_cycles=num_cycles,
)
return LambdaLR(optimizer, lr_lambda, last_epoch)
def _get_cosine_with_hard_restarts_schedule_with_warmup_lr_lambda(
current_step: int, *, num_warmup_steps: int, num_training_steps: int, num_cycles: int
):
if current_step < num_warmup_steps:
return float(current_step) / float(max(1, num_warmup_steps))
progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))
if progress >= 1.0:
return 0.0
return max(0.0, 0.5 * (1.0 + math.cos(math.pi * ((float(num_cycles) * progress) % 1.0))))
def get_cosine_with_hard_restarts_schedule_with_warmup(
optimizer: Optimizer, num_warmup_steps: int, num_training_steps: int, num_cycles: int = 1, last_epoch: int = -1
):
"""
Create a schedule with a learning rate that decreases following the values of the cosine function between the
initial lr set in the optimizer to 0, with several hard restarts, after a warmup period during which it increases
linearly between 0 and the initial lr set in the optimizer.
Args:
optimizer ([`~torch.optim.Optimizer`]):
The optimizer for which to schedule the learning rate.
num_warmup_steps (`int`):
The number of steps for the warmup phase.
num_training_steps (`int`):
The total number of training steps.
num_cycles (`int`, *optional*, defaults to 1):
The number of hard restarts to use.
last_epoch (`int`, *optional*, defaults to -1):
The index of the last epoch when resuming training.
Return:
`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
"""
lr_lambda = partial(
_get_cosine_with_hard_restarts_schedule_with_warmup_lr_lambda,
num_warmup_steps=num_warmup_steps,
num_training_steps=num_training_steps,
num_cycles=num_cycles,
)
return LambdaLR(optimizer, lr_lambda, last_epoch)
def _get_polynomial_decay_schedule_with_warmup_lr_lambda(
current_step: int,
*,
num_warmup_steps: int,
num_training_steps: int,
lr_end: float,
power: float,
lr_init: int,
):
if current_step < num_warmup_steps:
return float(current_step) / float(max(1, num_warmup_steps))
elif current_step > num_training_steps:
return lr_end / lr_init # as LambdaLR multiplies by lr_init
else:
lr_range = lr_init - lr_end
decay_steps = num_training_steps - num_warmup_steps
pct_remaining = 1 - (current_step - num_warmup_steps) / decay_steps
decay = lr_range * pct_remaining**power + lr_end
return decay / lr_init # as LambdaLR multiplies by lr_init
def get_polynomial_decay_schedule_with_warmup(
optimizer, num_warmup_steps, num_training_steps, lr_end=1e-7, power=1.0, last_epoch=-1
):
"""
Create a schedule with a learning rate that decreases as a polynomial decay from the initial lr set in the
optimizer to end lr defined by *lr_end*, after a warmup period during which it increases linearly from 0 to the
initial lr set in the optimizer.
Args:
optimizer ([`~torch.optim.Optimizer`]):
The optimizer for which to schedule the learning rate.
num_warmup_steps (`int`):
The number of steps for the warmup phase.
num_training_steps (`int`):
The total number of training steps.
lr_end (`float`, *optional*, defaults to 1e-7):
The end LR.
power (`float`, *optional*, defaults to 1.0):
Power factor.
last_epoch (`int`, *optional*, defaults to -1):
The index of the last epoch when resuming training.
Note: *power* defaults to 1.0 as in the fairseq implementation, which in turn is based on the original BERT
implementation at
https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/optimization.py#L37
Return:
`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
"""
lr_init = optimizer.defaults["lr"]
if not (lr_init > lr_end):
raise ValueError(f"lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})")
lr_lambda = partial(
_get_polynomial_decay_schedule_with_warmup_lr_lambda,
num_warmup_steps=num_warmup_steps,
num_training_steps=num_training_steps,
lr_end=lr_end,
power=power,
lr_init=lr_init,
)
return LambdaLR(optimizer, lr_lambda, last_epoch)
def _get_inverse_sqrt_schedule_lr_lambda(current_step: int, *, num_warmup_steps: int, timescale: int = None):
if current_step < num_warmup_steps:
return float(current_step) / float(max(1, num_warmup_steps))
shift = timescale - num_warmup_steps
decay = 1.0 / math.sqrt((current_step + shift) / timescale)
return decay
def get_inverse_sqrt_schedule(
optimizer: Optimizer, num_warmup_steps: int, timescale: int = None, last_epoch: int = -1
):
"""
Create a schedule with an inverse square-root learning rate, from the initial lr set in the optimizer, after a
warmup period which increases lr linearly from 0 to the initial lr set in the optimizer.
Args:
optimizer ([`~torch.optim.Optimizer`]):
The optimizer for which to schedule the learning rate.
num_warmup_steps (`int`):
The number of steps for the warmup phase.
timescale (`int`, *optional*, defaults to `num_warmup_steps`):
Time scale.
last_epoch (`int`, *optional*, defaults to -1):
The index of the last epoch when resuming training.
Return:
`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
"""
# Note: this implementation is adapted from
# https://github.com/google-research/big_vision/blob/f071ce68852d56099437004fd70057597a95f6ef/big_vision/utils.py#L930
if timescale is None:
timescale = num_warmup_steps
lr_lambda = partial(_get_inverse_sqrt_schedule_lr_lambda, num_warmup_steps=num_warmup_steps, timescale=timescale)
return LambdaLR(optimizer, lr_lambda, last_epoch=last_epoch)
TYPE_TO_SCHEDULER_FUNCTION = {
SchedulerType.LINEAR: get_linear_schedule_with_warmup,
SchedulerType.COSINE: get_cosine_schedule_with_warmup,
SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
SchedulerType.CONSTANT: get_constant_schedule,
SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
SchedulerType.INVERSE_SQRT: get_inverse_sqrt_schedule,
SchedulerType.REDUCE_ON_PLATEAU: get_reduce_on_plateau_schedule,
}
def get_scheduler(
name: Union[str, SchedulerType],
optimizer: Optimizer,
num_warmup_steps: Optional[int] = None,
num_training_steps: Optional[int] = None,
scheduler_specific_kwargs: Optional[dict] = None,
):
"""
Unified API to get any scheduler from its name.
Args:
name (`str` or `SchedulerType`):
The name of the scheduler to use.
optimizer (`torch.optim.Optimizer`):
The optimizer that will be used during training.
num_warmup_steps (`int`, *optional*):
The number of warmup steps to do. This is not required by all schedulers (hence the argument being
optional), the function will raise an error if it's unset and the scheduler type requires it.
num_training_steps (`int``, *optional*):
The number of training steps to do. This is not required by all schedulers (hence the argument being
optional), the function will raise an error if it's unset and the scheduler type requires it.
scheduler_specific_kwargs (`dict`, *optional*):
Extra parameters for schedulers such as cosine with restarts. Mismatched scheduler types and scheduler
parameters will cause the scheduler function to raise a TypeError.
"""
name = SchedulerType(name)
schedule_func = TYPE_TO_SCHEDULER_FUNCTION[name]
if name == SchedulerType.CONSTANT:
return schedule_func(optimizer)
if scheduler_specific_kwargs is None:
scheduler_specific_kwargs = {}
if name == SchedulerType.REDUCE_ON_PLATEAU:
return schedule_func(optimizer, **scheduler_specific_kwargs)
# All other schedulers require `num_warmup_steps`
if num_warmup_steps is None:
raise ValueError(f"{name} requires `num_warmup_steps`, please provide that argument.")
if name == SchedulerType.CONSTANT_WITH_WARMUP:
return schedule_func(optimizer, num_warmup_steps=num_warmup_steps)
if name == SchedulerType.INVERSE_SQRT:
return schedule_func(optimizer, num_warmup_steps=num_warmup_steps)
# All other schedulers require `num_training_steps`
if num_training_steps is None:
raise ValueError(f"{name} requires `num_training_steps`, please provide that argument.")
return schedule_func(
optimizer,
num_warmup_steps=num_warmup_steps,
num_training_steps=num_training_steps,
**scheduler_specific_kwargs,
)
class AdamW(Optimizer):
"""
Implements Adam algorithm with weight decay fix as introduced in [Decoupled Weight Decay
Regularization](https://arxiv.org/abs/1711.05101).
Parameters:
params (`Iterable[nn.parameter.Parameter]`):
Iterable of parameters to optimize or dictionaries defining parameter groups.
lr (`float`, *optional*, defaults to 0.001):
The learning rate to use.
betas (`Tuple[float,float]`, *optional*, defaults to `(0.9, 0.999)`):
Adam's betas parameters (b1, b2).
eps (`float`, *optional*, defaults to 1e-06):
Adam's epsilon for numerical stability.
weight_decay (`float`, *optional*, defaults to 0.0):
Decoupled weight decay to apply.
correct_bias (`bool`, *optional*, defaults to `True`):
Whether or not to correct bias in Adam (for instance, in Bert TF repository they use `False`).
no_deprecation_warning (`bool`, *optional*, defaults to `False`):
A flag used to disable the deprecation warning (set to `True` to disable the warning).
"""
def __init__(
self,
params: Iterable[nn.parameter.Parameter],
lr: float = 1e-3,
betas: Tuple[float, float] = (0.9, 0.999),
eps: float = 1e-6,
weight_decay: float = 0.0,
correct_bias: bool = True,
no_deprecation_warning: bool = False,
):
if not no_deprecation_warning:
warnings.warn(
"This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch"
" implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this"
" warning",
FutureWarning,
)
require_version("torch>=1.5.0") # add_ with alpha
if lr < 0.0:
raise ValueError(f"Invalid learning rate: {lr} - should be >= 0.0")
if not 0.0 <= betas[0] < 1.0:
raise ValueError(f"Invalid beta parameter: {betas[0]} - should be in [0.0, 1.0)")
if not 0.0 <= betas[1] < 1.0:
raise ValueError(f"Invalid beta parameter: {betas[1]} - should be in [0.0, 1.0)")
if not 0.0 <= eps:
raise ValueError(f"Invalid epsilon value: {eps} - should be >= 0.0")
defaults = {"lr": lr, "betas": betas, "eps": eps, "weight_decay": weight_decay, "correct_bias": correct_bias}
super().__init__(params, defaults)
@torch.no_grad()
def step(self, closure: Callable = None):
"""
Performs a single optimization step.
Arguments:
closure (`Callable`, *optional*): A closure that reevaluates the model and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
for p in group["params"]:
if p.grad is None:
continue
grad = p.grad
if grad.is_sparse:
raise RuntimeError("Adam does not support sparse gradients, please consider SparseAdam instead")
state = self.state[p]
# State initialization
if len(state) == 0:
state["step"] = 0
# Exponential moving average of gradient values
state["exp_avg"] = torch.zeros_like(p)
# Exponential moving average of squared gradient values
state["exp_avg_sq"] = torch.zeros_like(p)
exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
beta1, beta2 = group["betas"]
state["step"] += 1
# Decay the first and second moment running average coefficient
# In-place operations to update the averages at the same time
exp_avg.mul_(beta1).add_(grad, alpha=(1.0 - beta1))
exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1.0 - beta2)
denom = exp_avg_sq.sqrt().add_(group["eps"])
step_size = group["lr"]
if group["correct_bias"]: # No bias correction for Bert
bias_correction1 = 1.0 - beta1 ** state["step"]
bias_correction2 = 1.0 - beta2 ** state["step"]
step_size = step_size * math.sqrt(bias_correction2) / bias_correction1
p.addcdiv_(exp_avg, denom, value=-step_size)
# Just adding the square of the weights to the loss function is *not*
# the correct way of using L2 regularization/weight decay with Adam,
# since that will interact with the m and v parameters in strange ways.
#
# Instead we want to decay the weights in a manner that doesn't interact
# with the m/v parameters. This is equivalent to adding the square
# of the weights to the loss with plain (non-momentum) SGD.
# Add weight decay at the end (fixed version)
if group["weight_decay"] > 0.0:
p.add_(p, alpha=(-group["lr"] * group["weight_decay"]))
return loss
class Adafactor(Optimizer):
"""
AdaFactor pytorch implementation can be used as a drop in replacement for Adam original fairseq code:
https://github.com/pytorch/fairseq/blob/master/fairseq/optim/adafactor.py
Paper: *Adafactor: Adaptive Learning Rates with Sublinear Memory Cost* https://arxiv.org/abs/1804.04235 Note that
this optimizer internally adjusts the learning rate depending on the `scale_parameter`, `relative_step` and
`warmup_init` options. To use a manual (external) learning rate schedule you should set `scale_parameter=False` and
`relative_step=False`.
Arguments:
params (`Iterable[nn.parameter.Parameter]`):
Iterable of parameters to optimize or dictionaries defining parameter groups.
lr (`float`, *optional*):
The external learning rate.
eps (`Tuple[float, float]`, *optional*, defaults to `(1e-30, 0.001)`):
Regularization constants for square gradient and parameter scale respectively
clip_threshold (`float`, *optional*, defaults to 1.0):
Threshold of root mean square of final gradient update
decay_rate (`float`, *optional*, defaults to -0.8):
Coefficient used to compute running averages of square
beta1 (`float`, *optional*):
Coefficient used for computing running averages of gradient
weight_decay (`float`, *optional*, defaults to 0.0):
Weight decay (L2 penalty)
scale_parameter (`bool`, *optional*, defaults to `True`):
If True, learning rate is scaled by root mean square
relative_step (`bool`, *optional*, defaults to `True`):
If True, time-dependent learning rate is computed instead of external learning rate
warmup_init (`bool`, *optional*, defaults to `False`):
Time-dependent learning rate computation depends on whether warm-up initialization is being used
This implementation handles low-precision (FP16, bfloat) values, but we have not thoroughly tested.
Recommended T5 finetuning settings (https://discuss.huggingface.co/t/t5-finetuning-tips/684/3):
- Training without LR warmup or clip_threshold is not recommended.
- use scheduled LR warm-up to fixed LR
- use clip_threshold=1.0 (https://arxiv.org/abs/1804.04235)
- Disable relative updates
- Use scale_parameter=False
- Additional optimizer operations like gradient clipping should not be used alongside Adafactor
Example:
```python
Adafactor(model.parameters(), scale_parameter=False, relative_step=False, warmup_init=False, lr=1e-3)
```
Others reported the following combination to work well:
```python
Adafactor(model.parameters(), scale_parameter=True, relative_step=True, warmup_init=True, lr=None)
```
When using `lr=None` with [`Trainer`] you will most likely need to use [`~optimization.AdafactorSchedule`]
scheduler as following:
```python
from transformers.optimization import Adafactor, AdafactorSchedule
optimizer = Adafactor(model.parameters(), scale_parameter=True, relative_step=True, warmup_init=True, lr=None)
lr_scheduler = AdafactorSchedule(optimizer)
trainer = Trainer(..., optimizers=(optimizer, lr_scheduler))
```
Usage:
```python
# replace AdamW with Adafactor
optimizer = Adafactor(
model.parameters(),
lr=1e-3,
eps=(1e-30, 1e-3),
clip_threshold=1.0,
decay_rate=-0.8,
beta1=None,
weight_decay=0.0,
relative_step=False,
scale_parameter=False,
warmup_init=False,
)
```"""
def __init__(
self,
params,
lr=None,
eps=(1e-30, 1e-3),
clip_threshold=1.0,
decay_rate=-0.8,
beta1=None,
weight_decay=0.0,
scale_parameter=True,
relative_step=True,
warmup_init=False,
):
require_version("torch>=1.5.0") # add_ with alpha
if lr is not None and relative_step:
raise ValueError("Cannot combine manual `lr` and `relative_step=True` options")
if warmup_init and not relative_step:
raise ValueError("`warmup_init=True` requires `relative_step=True`")
defaults = {
"lr": lr,
"eps": eps,
"clip_threshold": clip_threshold,
"decay_rate": decay_rate,
"beta1": beta1,
"weight_decay": weight_decay,
"scale_parameter": scale_parameter,
"relative_step": relative_step,
"warmup_init": warmup_init,
}
super().__init__(params, defaults)
@staticmethod
def _get_lr(param_group, param_state):
rel_step_sz = param_group["lr"]
if param_group["relative_step"]:
min_step = 1e-6 * param_state["step"] if param_group["warmup_init"] else 1e-2
rel_step_sz = min(min_step, 1.0 / math.sqrt(param_state["step"]))
param_scale = 1.0
if param_group["scale_parameter"]:
param_scale = max(param_group["eps"][1], param_state["RMS"])
return param_scale * rel_step_sz
@staticmethod
def _get_options(param_group, param_shape):
factored = len(param_shape) >= 2
use_first_moment = param_group["beta1"] is not None
return factored, use_first_moment
@staticmethod
def _rms(tensor):
return tensor.norm(2) / (tensor.numel() ** 0.5)
@staticmethod
def _approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col):
# copy from fairseq's adafactor implementation:
# https://github.com/huggingface/transformers/blob/8395f14de6068012787d83989c3627c3df6a252b/src/transformers/optimization.py#L505
r_factor = (exp_avg_sq_row / exp_avg_sq_row.mean(dim=-1, keepdim=True)).rsqrt_().unsqueeze(-1)
c_factor = exp_avg_sq_col.unsqueeze(-2).rsqrt()
return torch.mul(r_factor, c_factor)
@torch.no_grad()
def step(self, closure=None):
"""
Performs a single optimization step
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
for p in group["params"]:
if p.grad is None:
continue
grad = p.grad
if grad.dtype in {torch.float16, torch.bfloat16}:
grad = grad.float()
if grad.is_sparse:
raise RuntimeError("Adafactor does not support sparse gradients.")
state = self.state[p]
grad_shape = grad.shape
factored, use_first_moment = self._get_options(group, grad_shape)
# State Initialization
if len(state) == 0:
state["step"] = 0
if use_first_moment:
# Exponential moving average of gradient values
state["exp_avg"] = torch.zeros_like(grad)
if factored:
state["exp_avg_sq_row"] = torch.zeros(grad_shape[:-1]).to(grad)
state["exp_avg_sq_col"] = torch.zeros(grad_shape[:-2] + grad_shape[-1:]).to(grad)
else:
state["exp_avg_sq"] = torch.zeros_like(grad)
state["RMS"] = 0
else:
if use_first_moment:
state["exp_avg"] = state["exp_avg"].to(grad)
if factored:
state["exp_avg_sq_row"] = state["exp_avg_sq_row"].to(grad)
state["exp_avg_sq_col"] = state["exp_avg_sq_col"].to(grad)
else:
state["exp_avg_sq"] = state["exp_avg_sq"].to(grad)
p_data_fp32 = p
if p.dtype in {torch.float16, torch.bfloat16}:
p_data_fp32 = p_data_fp32.float()
state["step"] += 1
state["RMS"] = self._rms(p_data_fp32)
lr = self._get_lr(group, state)
beta2t = 1.0 - math.pow(state["step"], group["decay_rate"])
update = (grad**2) + group["eps"][0]
if factored:
exp_avg_sq_row = state["exp_avg_sq_row"]
exp_avg_sq_col = state["exp_avg_sq_col"]
exp_avg_sq_row.mul_(beta2t).add_(update.mean(dim=-1), alpha=(1.0 - beta2t))
exp_avg_sq_col.mul_(beta2t).add_(update.mean(dim=-2), alpha=(1.0 - beta2t))
# Approximation of exponential moving average of square of gradient
update = self._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col)
update.mul_(grad)
else:
exp_avg_sq = state["exp_avg_sq"]
exp_avg_sq.mul_(beta2t).add_(update, alpha=(1.0 - beta2t))
update = exp_avg_sq.rsqrt().mul_(grad)
update.div_((self._rms(update) / group["clip_threshold"]).clamp_(min=1.0))
update.mul_(lr)
if use_first_moment:
exp_avg = state["exp_avg"]
exp_avg.mul_(group["beta1"]).add_(update, alpha=(1 - group["beta1"]))
update = exp_avg
if group["weight_decay"] != 0:
p_data_fp32.add_(p_data_fp32, alpha=(-group["weight_decay"] * lr))
p_data_fp32.add_(-update)
if p.dtype in {torch.float16, torch.bfloat16}:
p.copy_(p_data_fp32)
return loss
class AdafactorSchedule(LambdaLR):
"""
Since [`~optimization.Adafactor`] performs its own scheduling, if the training loop relies on a scheduler (e.g.,
for logging), this class creates a proxy object that retrieves the current lr values from the optimizer.
It returns `initial_lr` during startup and the actual `lr` during stepping.
"""
def __init__(self, optimizer, initial_lr=0.0):
def lr_lambda(_):
return initial_lr
for group in optimizer.param_groups:
group["initial_lr"] = initial_lr
super().__init__(optimizer, lr_lambda)
for group in optimizer.param_groups:
del group["initial_lr"]
def get_lr(self):
opt = self.optimizer
lrs = [
opt._get_lr(group, opt.state[group["params"][0]])
for group in opt.param_groups
if group["params"][0].grad is not None
]
if len(lrs) == 0:
lrs = self.base_lrs # if called before stepping
return lrs
def get_adafactor_schedule(optimizer, initial_lr=0.0):
"""
Get a proxy schedule for [`~optimization.Adafactor`]
Args:
optimizer ([`~torch.optim.Optimizer`]):
The optimizer for which to schedule the learning rate.
initial_lr (`float`, *optional*, defaults to 0.0):
Initial lr
Return:
[`~optimization.Adafactor`] proxy schedule object.
"""
return AdafactorSchedule(optimizer, initial_lr)
| transformers/src/transformers/optimization.py/0 | {
"file_path": "transformers/src/transformers/optimization.py",
"repo_id": "transformers",
"token_count": 13951
} | 135 |
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import Pipeline, build_pipeline_init_args
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES
logger = logging.get_logger(__name__)
@add_end_docstrings(build_pipeline_init_args(has_tokenizer=True, has_image_processor=True))
class ImageToTextPipeline(Pipeline):
"""
Image To Text pipeline using a `AutoModelForVision2Seq`. This pipeline predicts a caption for a given image.
Example:
```python
>>> from transformers import pipeline
>>> captioner = pipeline(model="ydshieh/vit-gpt2-coco-en")
>>> captioner("https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png")
[{'generated_text': 'two birds are standing next to each other '}]
```
Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
This image to text pipeline can currently be loaded from pipeline() using the following task identifier:
"image-to-text".
See the list of available models on
[huggingface.co/models](https://huggingface.co/models?pipeline_tag=image-to-text).
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
requires_backends(self, "vision")
self.check_model_type(
TF_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES if self.framework == "tf" else MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES
)
def _sanitize_parameters(self, max_new_tokens=None, generate_kwargs=None, prompt=None, timeout=None):
forward_kwargs = {}
preprocess_params = {}
if prompt is not None:
preprocess_params["prompt"] = prompt
if timeout is not None:
preprocess_params["timeout"] = timeout
if generate_kwargs is not None:
forward_kwargs["generate_kwargs"] = generate_kwargs
if max_new_tokens is not None:
if "generate_kwargs" not in forward_kwargs:
forward_kwargs["generate_kwargs"] = {}
if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
raise ValueError(
"'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"
" please use only one"
)
forward_kwargs["generate_kwargs"]["max_new_tokens"] = max_new_tokens
return preprocess_params, forward_kwargs, {}
def __call__(self, images: Union[str, List[str], "Image.Image", List["Image.Image"]], **kwargs):
"""
Assign labels to the image(s) passed as inputs.
Args:
images (`str`, `List[str]`, `PIL.Image` or `List[PIL.Image]`):
The pipeline handles three types of images:
- A string containing a HTTP(s) link pointing to an image
- A string containing a local path to an image
- An image loaded in PIL directly
The pipeline accepts either a single image or a batch of images.
max_new_tokens (`int`, *optional*):
The amount of maximum tokens to generate. By default it will use `generate` default.
generate_kwargs (`Dict`, *optional*):
Pass it to send all of these arguments directly to `generate` allowing full control of this function.
timeout (`float`, *optional*, defaults to None):
The maximum time in seconds to wait for fetching images from the web. If None, no timeout is set and
the call may block forever.
Return:
A list or a list of list of `dict`: Each result comes as a dictionary with the following key:
- **generated_text** (`str`) -- The generated text.
"""
return super().__call__(images, **kwargs)
def preprocess(self, image, prompt=None, timeout=None):
image = load_image(image, timeout=timeout)
if prompt is not None:
if not isinstance(prompt, str):
raise ValueError(
f"Received an invalid text input, got - {type(prompt)} - but expected a single string. "
"Note also that one single text can be provided for conditional image to text generation."
)
model_type = self.model.config.model_type
if model_type == "git":
model_inputs = self.image_processor(images=image, return_tensors=self.framework)
input_ids = self.tokenizer(text=prompt, add_special_tokens=False).input_ids
input_ids = [self.tokenizer.cls_token_id] + input_ids
input_ids = torch.tensor(input_ids).unsqueeze(0)
model_inputs.update({"input_ids": input_ids})
elif model_type == "pix2struct":
model_inputs = self.image_processor(images=image, header_text=prompt, return_tensors=self.framework)
elif model_type != "vision-encoder-decoder":
# vision-encoder-decoder does not support conditional generation
model_inputs = self.image_processor(images=image, return_tensors=self.framework)
text_inputs = self.tokenizer(prompt, return_tensors=self.framework)
model_inputs.update(text_inputs)
else:
raise ValueError(f"Model type {model_type} does not support conditional text generation")
else:
model_inputs = self.image_processor(images=image, return_tensors=self.framework)
if self.model.config.model_type == "git" and prompt is None:
model_inputs["input_ids"] = None
return model_inputs
def _forward(self, model_inputs, generate_kwargs=None):
# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
if (
"input_ids" in model_inputs
and isinstance(model_inputs["input_ids"], list)
and all(x is None for x in model_inputs["input_ids"])
):
model_inputs["input_ids"] = None
if generate_kwargs is None:
generate_kwargs = {}
# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
# parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
# the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
# in the `_prepare_model_inputs` method.
inputs = model_inputs.pop(self.model.main_input_name)
model_outputs = self.model.generate(inputs, **model_inputs, **generate_kwargs)
return model_outputs
def postprocess(self, model_outputs):
records = []
for output_ids in model_outputs:
record = {
"generated_text": self.tokenizer.decode(
output_ids,
skip_special_tokens=True,
)
}
records.append(record)
return records
| transformers/src/transformers/pipelines/image_to_text.py/0 | {
"file_path": "transformers/src/transformers/pipelines/image_to_text.py",
"repo_id": "transformers",
"token_count": 3389
} | 136 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import ChunkPipeline, build_pipeline_init_args
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from transformers.modeling_outputs import BaseModelOutput
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES
logger = logging.get_logger(__name__)
@add_end_docstrings(build_pipeline_init_args(has_image_processor=True))
class ZeroShotObjectDetectionPipeline(ChunkPipeline):
"""
Zero shot object detection pipeline using `OwlViTForObjectDetection`. This pipeline predicts bounding boxes of
objects when you provide an image and a set of `candidate_labels`.
Example:
```python
>>> from transformers import pipeline
>>> detector = pipeline(model="google/owlvit-base-patch32", task="zero-shot-object-detection")
>>> detector(
... "http://images.cocodataset.org/val2017/000000039769.jpg",
... candidate_labels=["cat", "couch"],
... )
[{'score': 0.287, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}}, {'score': 0.254, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}}, {'score': 0.121, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}}]
>>> detector(
... "https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png",
... candidate_labels=["head", "bird"],
... )
[{'score': 0.119, 'label': 'bird', 'box': {'xmin': 71, 'ymin': 170, 'xmax': 410, 'ymax': 508}}]
```
Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
This object detection pipeline can currently be loaded from [`pipeline`] using the following task identifier:
`"zero-shot-object-detection"`.
See the list of available models on
[huggingface.co/models](https://huggingface.co/models?filter=zero-shot-object-detection).
"""
def __init__(self, **kwargs):
super().__init__(**kwargs)
if self.framework == "tf":
raise ValueError(f"The {self.__class__} is only available in PyTorch.")
requires_backends(self, "vision")
self.check_model_type(MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES)
def __call__(
self,
image: Union[str, "Image.Image", List[Dict[str, Any]]],
candidate_labels: Union[str, List[str]] = None,
**kwargs,
):
"""
Detect objects (bounding boxes & classes) in the image(s) passed as inputs.
Args:
image (`str`, `PIL.Image` or `List[Dict[str, Any]]`):
The pipeline handles three types of images:
- A string containing an http url pointing to an image
- A string containing a local path to an image
- An image loaded in PIL directly
You can use this parameter to send directly a list of images, or a dataset or a generator like so:
```python
>>> from transformers import pipeline
>>> detector = pipeline(model="google/owlvit-base-patch32", task="zero-shot-object-detection")
>>> detector(
... [
... {
... "image": "http://images.cocodataset.org/val2017/000000039769.jpg",
... "candidate_labels": ["cat", "couch"],
... },
... {
... "image": "http://images.cocodataset.org/val2017/000000039769.jpg",
... "candidate_labels": ["cat", "couch"],
... },
... ]
... )
[[{'score': 0.287, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}}, {'score': 0.25, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}}, {'score': 0.121, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}}], [{'score': 0.287, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}}, {'score': 0.254, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}}, {'score': 0.121, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}}]]
```
candidate_labels (`str` or `List[str]` or `List[List[str]]`):
What the model should recognize in the image.
threshold (`float`, *optional*, defaults to 0.1):
The probability necessary to make a prediction.
top_k (`int`, *optional*, defaults to None):
The number of top predictions that will be returned by the pipeline. If the provided number is `None`
or higher than the number of predictions available, it will default to the number of predictions.
timeout (`float`, *optional*, defaults to None):
The maximum time in seconds to wait for fetching images from the web. If None, no timeout is set and
the call may block forever.
Return:
A list of lists containing prediction results, one list per input image. Each list contains dictionaries
with the following keys:
- **label** (`str`) -- Text query corresponding to the found object.
- **score** (`float`) -- Score corresponding to the object (between 0 and 1).
- **box** (`Dict[str,int]`) -- Bounding box of the detected object in image's original size. It is a
dictionary with `x_min`, `x_max`, `y_min`, `y_max` keys.
"""
if "text_queries" in kwargs:
candidate_labels = kwargs.pop("text_queries")
if isinstance(image, (str, Image.Image)):
inputs = {"image": image, "candidate_labels": candidate_labels}
else:
inputs = image
results = super().__call__(inputs, **kwargs)
return results
def _sanitize_parameters(self, **kwargs):
preprocess_params = {}
if "timeout" in kwargs:
preprocess_params["timeout"] = kwargs["timeout"]
postprocess_params = {}
if "threshold" in kwargs:
postprocess_params["threshold"] = kwargs["threshold"]
if "top_k" in kwargs:
postprocess_params["top_k"] = kwargs["top_k"]
return preprocess_params, {}, postprocess_params
def preprocess(self, inputs, timeout=None):
image = load_image(inputs["image"], timeout=timeout)
candidate_labels = inputs["candidate_labels"]
if isinstance(candidate_labels, str):
candidate_labels = candidate_labels.split(",")
target_size = torch.tensor([[image.height, image.width]], dtype=torch.int32)
for i, candidate_label in enumerate(candidate_labels):
text_inputs = self.tokenizer(candidate_label, return_tensors=self.framework)
image_features = self.image_processor(image, return_tensors=self.framework)
yield {
"is_last": i == len(candidate_labels) - 1,
"target_size": target_size,
"candidate_label": candidate_label,
**text_inputs,
**image_features,
}
def _forward(self, model_inputs):
target_size = model_inputs.pop("target_size")
candidate_label = model_inputs.pop("candidate_label")
is_last = model_inputs.pop("is_last")
outputs = self.model(**model_inputs)
model_outputs = {"target_size": target_size, "candidate_label": candidate_label, "is_last": is_last, **outputs}
return model_outputs
def postprocess(self, model_outputs, threshold=0.1, top_k=None):
results = []
for model_output in model_outputs:
label = model_output["candidate_label"]
model_output = BaseModelOutput(model_output)
outputs = self.image_processor.post_process_object_detection(
outputs=model_output, threshold=threshold, target_sizes=model_output["target_size"]
)[0]
for index in outputs["scores"].nonzero():
score = outputs["scores"][index].item()
box = self._get_bounding_box(outputs["boxes"][index][0])
result = {"score": score, "label": label, "box": box}
results.append(result)
results = sorted(results, key=lambda x: x["score"], reverse=True)
if top_k:
results = results[:top_k]
return results
def _get_bounding_box(self, box: "torch.Tensor") -> Dict[str, int]:
"""
Turns list [xmin, xmax, ymin, ymax] into dict { "xmin": xmin, ... }
Args:
box (`torch.Tensor`): Tensor containing the coordinates in corners format.
Returns:
bbox (`Dict[str, int]`): Dict containing the coordinates in corners format.
"""
if self.framework != "pt":
raise ValueError("The ZeroShotObjectDetectionPipeline is only available in PyTorch.")
xmin, ymin, xmax, ymax = box.int().tolist()
bbox = {
"xmin": xmin,
"ymin": ymin,
"xmax": xmax,
"ymax": ymax,
}
return bbox
| transformers/src/transformers/pipelines/zero_shot_object_detection.py/0 | {
"file_path": "transformers/src/transformers/pipelines/zero_shot_object_detection.py",
"repo_id": "transformers",
"token_count": 4201
} | 137 |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import collections
import contextlib
import doctest
import functools
import importlib
import inspect
import logging
import multiprocessing
import os
import re
import shlex
import shutil
import subprocess
import sys
import tempfile
import time
import unittest
from collections import defaultdict
from collections.abc import Mapping
from io import StringIO
from pathlib import Path
from typing import Callable, Dict, Iterable, Iterator, List, Optional, Union
from unittest import mock
from unittest.mock import patch
import urllib3
from transformers import logging as transformers_logging
from .integrations import (
is_clearml_available,
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_tensorboard_available,
is_wandb_available,
)
from .integrations.deepspeed import is_deepspeed_available
from .utils import (
is_accelerate_available,
is_apex_available,
is_aqlm_available,
is_auto_awq_available,
is_auto_gptq_available,
is_bitsandbytes_available,
is_bs4_available,
is_cv2_available,
is_cython_available,
is_decord_available,
is_detectron2_available,
is_essentia_available,
is_faiss_available,
is_flash_attn_2_available,
is_flax_available,
is_fsdp_available,
is_ftfy_available,
is_g2p_en_available,
is_ipex_available,
is_jieba_available,
is_jinja_available,
is_jumanpp_available,
is_keras_nlp_available,
is_levenshtein_available,
is_librosa_available,
is_natten_available,
is_nltk_available,
is_onnx_available,
is_optimum_available,
is_pandas_available,
is_peft_available,
is_phonemizer_available,
is_pretty_midi_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytest_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_safetensors_available,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_soundfile_availble,
is_spacy_available,
is_sudachi_available,
is_sudachi_projection_available,
is_tensorflow_probability_available,
is_tensorflow_text_available,
is_tf2onnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bf16_available_on_device,
is_torch_bf16_cpu_available,
is_torch_bf16_gpu_available,
is_torch_fp16_available_on_device,
is_torch_neuroncore_available,
is_torch_npu_available,
is_torch_sdpa_available,
is_torch_tensorrt_fx_available,
is_torch_tf32_available,
is_torch_tpu_available,
is_torch_xpu_available,
is_torchaudio_available,
is_torchdynamo_available,
is_torchvision_available,
is_vision_available,
strtobool,
)
if is_accelerate_available():
from accelerate.state import AcceleratorState, PartialState
if is_pytest_available():
from _pytest.doctest import (
Module,
_get_checker,
_get_continue_on_failure,
_get_runner,
_is_mocked,
_patch_unwrap_mock_aware,
get_optionflags,
import_path,
)
from _pytest.outcomes import skip
from pytest import DoctestItem
else:
Module = object
DoctestItem = object
SMALL_MODEL_IDENTIFIER = "julien-c/bert-xsmall-dummy"
DUMMY_UNKNOWN_IDENTIFIER = "julien-c/dummy-unknown"
DUMMY_DIFF_TOKENIZER_IDENTIFIER = "julien-c/dummy-diff-tokenizer"
# Used to test Auto{Config, Model, Tokenizer} model_type detection.
# Used to test the hub
USER = "__DUMMY_TRANSFORMERS_USER__"
ENDPOINT_STAGING = "https://hub-ci.huggingface.co"
# Not critical, only usable on the sandboxed CI instance.
TOKEN = "hf_94wBhPGp6KrrTH3KDchhKpRxZwd6dmHWLL"
def parse_flag_from_env(key, default=False):
try:
value = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
_value = default
else:
# KEY is set, convert it to True or False.
try:
_value = strtobool(value)
except ValueError:
# More values are supported, but let's keep the message simple.
raise ValueError(f"If set, {key} must be yes or no.")
return _value
def parse_int_from_env(key, default=None):
try:
value = os.environ[key]
except KeyError:
_value = default
else:
try:
_value = int(value)
except ValueError:
raise ValueError(f"If set, {key} must be a int.")
return _value
_run_slow_tests = parse_flag_from_env("RUN_SLOW", default=False)
_run_pt_tf_cross_tests = parse_flag_from_env("RUN_PT_TF_CROSS_TESTS", default=True)
_run_pt_flax_cross_tests = parse_flag_from_env("RUN_PT_FLAX_CROSS_TESTS", default=True)
_run_custom_tokenizers = parse_flag_from_env("RUN_CUSTOM_TOKENIZERS", default=False)
_run_staging = parse_flag_from_env("HUGGINGFACE_CO_STAGING", default=False)
_tf_gpu_memory_limit = parse_int_from_env("TF_GPU_MEMORY_LIMIT", default=None)
_run_pipeline_tests = parse_flag_from_env("RUN_PIPELINE_TESTS", default=True)
_run_tool_tests = parse_flag_from_env("RUN_TOOL_TESTS", default=False)
_run_third_party_device_tests = parse_flag_from_env("RUN_THIRD_PARTY_DEVICE_TESTS", default=False)
def is_pt_tf_cross_test(test_case):
"""
Decorator marking a test as a test that control interactions between PyTorch and TensorFlow.
PT+TF tests are skipped by default and we can run only them by setting RUN_PT_TF_CROSS_TESTS environment variable
to a truthy value and selecting the is_pt_tf_cross_test pytest mark.
"""
if not _run_pt_tf_cross_tests or not is_torch_available() or not is_tf_available():
return unittest.skip("test is PT+TF test")(test_case)
else:
try:
import pytest # We don't need a hard dependency on pytest in the main library
except ImportError:
return test_case
else:
return pytest.mark.is_pt_tf_cross_test()(test_case)
def is_pt_flax_cross_test(test_case):
"""
Decorator marking a test as a test that control interactions between PyTorch and Flax
PT+FLAX tests are skipped by default and we can run only them by setting RUN_PT_FLAX_CROSS_TESTS environment
variable to a truthy value and selecting the is_pt_flax_cross_test pytest mark.
"""
if not _run_pt_flax_cross_tests or not is_torch_available() or not is_flax_available():
return unittest.skip("test is PT+FLAX test")(test_case)
else:
try:
import pytest # We don't need a hard dependency on pytest in the main library
except ImportError:
return test_case
else:
return pytest.mark.is_pt_flax_cross_test()(test_case)
def is_staging_test(test_case):
"""
Decorator marking a test as a staging test.
Those tests will run using the staging environment of huggingface.co instead of the real model hub.
"""
if not _run_staging:
return unittest.skip("test is staging test")(test_case)
else:
try:
import pytest # We don't need a hard dependency on pytest in the main library
except ImportError:
return test_case
else:
return pytest.mark.is_staging_test()(test_case)
def is_pipeline_test(test_case):
"""
Decorator marking a test as a pipeline test. If RUN_PIPELINE_TESTS is set to a falsy value, those tests will be
skipped.
"""
if not _run_pipeline_tests:
return unittest.skip("test is pipeline test")(test_case)
else:
try:
import pytest # We don't need a hard dependency on pytest in the main library
except ImportError:
return test_case
else:
return pytest.mark.is_pipeline_test()(test_case)
def is_tool_test(test_case):
"""
Decorator marking a test as a tool test. If RUN_TOOL_TESTS is set to a falsy value, those tests will be skipped.
"""
if not _run_tool_tests:
return unittest.skip("test is a tool test")(test_case)
else:
try:
import pytest # We don't need a hard dependency on pytest in the main library
except ImportError:
return test_case
else:
return pytest.mark.is_tool_test()(test_case)
def slow(test_case):
"""
Decorator marking a test as slow.
Slow tests are skipped by default. Set the RUN_SLOW environment variable to a truthy value to run them.
"""
return unittest.skipUnless(_run_slow_tests, "test is slow")(test_case)
def tooslow(test_case):
"""
Decorator marking a test as too slow.
Slow tests are skipped while they're in the process of being fixed. No test should stay tagged as "tooslow" as
these will not be tested by the CI.
"""
return unittest.skip("test is too slow")(test_case)
def custom_tokenizers(test_case):
"""
Decorator marking a test for a custom tokenizer.
Custom tokenizers require additional dependencies, and are skipped by default. Set the RUN_CUSTOM_TOKENIZERS
environment variable to a truthy value to run them.
"""
return unittest.skipUnless(_run_custom_tokenizers, "test of custom tokenizers")(test_case)
def require_bs4(test_case):
"""
Decorator marking a test that requires BeautifulSoup4. These tests are skipped when BeautifulSoup4 isn't installed.
"""
return unittest.skipUnless(is_bs4_available(), "test requires BeautifulSoup4")(test_case)
def require_cv2(test_case):
"""
Decorator marking a test that requires OpenCV.
These tests are skipped when OpenCV isn't installed.
"""
return unittest.skipUnless(is_cv2_available(), "test requires OpenCV")(test_case)
def require_levenshtein(test_case):
"""
Decorator marking a test that requires Levenshtein.
These tests are skipped when Levenshtein isn't installed.
"""
return unittest.skipUnless(is_levenshtein_available(), "test requires Levenshtein")(test_case)
def require_nltk(test_case):
"""
Decorator marking a test that requires NLTK.
These tests are skipped when NLTK isn't installed.
"""
return unittest.skipUnless(is_nltk_available(), "test requires NLTK")(test_case)
def require_accelerate(test_case):
"""
Decorator marking a test that requires accelerate. These tests are skipped when accelerate isn't installed.
"""
return unittest.skipUnless(is_accelerate_available(), "test requires accelerate")(test_case)
def require_fsdp(test_case, min_version: str = "1.12.0"):
"""
Decorator marking a test that requires fsdp. These tests are skipped when fsdp isn't installed.
"""
return unittest.skipUnless(is_fsdp_available(min_version), f"test requires torch version >= {min_version}")(
test_case
)
def require_g2p_en(test_case):
"""
Decorator marking a test that requires g2p_en. These tests are skipped when SentencePiece isn't installed.
"""
return unittest.skipUnless(is_g2p_en_available(), "test requires g2p_en")(test_case)
def require_safetensors(test_case):
"""
Decorator marking a test that requires safetensors. These tests are skipped when safetensors isn't installed.
"""
return unittest.skipUnless(is_safetensors_available(), "test requires safetensors")(test_case)
def require_rjieba(test_case):
"""
Decorator marking a test that requires rjieba. These tests are skipped when rjieba isn't installed.
"""
return unittest.skipUnless(is_rjieba_available(), "test requires rjieba")(test_case)
def require_jieba(test_case):
"""
Decorator marking a test that requires jieba. These tests are skipped when jieba isn't installed.
"""
return unittest.skipUnless(is_jieba_available(), "test requires jieba")(test_case)
def require_jinja(test_case):
"""
Decorator marking a test that requires jinja. These tests are skipped when jinja isn't installed.
"""
return unittest.skipUnless(is_jinja_available(), "test requires jinja")(test_case)
def require_tf2onnx(test_case):
return unittest.skipUnless(is_tf2onnx_available(), "test requires tf2onnx")(test_case)
def require_onnx(test_case):
return unittest.skipUnless(is_onnx_available(), "test requires ONNX")(test_case)
def require_timm(test_case):
"""
Decorator marking a test that requires Timm.
These tests are skipped when Timm isn't installed.
"""
return unittest.skipUnless(is_timm_available(), "test requires Timm")(test_case)
def require_natten(test_case):
"""
Decorator marking a test that requires NATTEN.
These tests are skipped when NATTEN isn't installed.
"""
return unittest.skipUnless(is_natten_available(), "test requires natten")(test_case)
def require_torch(test_case):
"""
Decorator marking a test that requires PyTorch.
These tests are skipped when PyTorch isn't installed.
"""
return unittest.skipUnless(is_torch_available(), "test requires PyTorch")(test_case)
def require_flash_attn(test_case):
"""
Decorator marking a test that requires Flash Attention.
These tests are skipped when Flash Attention isn't installed.
"""
return unittest.skipUnless(is_flash_attn_2_available(), "test requires Flash Attention")(test_case)
def require_torch_sdpa(test_case):
"""
Decorator marking a test that requires PyTorch's SDPA.
These tests are skipped when requirements are not met (torch version).
"""
return unittest.skipUnless(is_torch_sdpa_available(), "test requires PyTorch SDPA")(test_case)
def require_peft(test_case):
"""
Decorator marking a test that requires PEFT.
These tests are skipped when PEFT isn't installed.
"""
return unittest.skipUnless(is_peft_available(), "test requires PEFT")(test_case)
def require_torchvision(test_case):
"""
Decorator marking a test that requires Torchvision.
These tests are skipped when Torchvision isn't installed.
"""
return unittest.skipUnless(is_torchvision_available(), "test requires Torchvision")(test_case)
def require_torch_or_tf(test_case):
"""
Decorator marking a test that requires PyTorch or TensorFlow.
These tests are skipped when neither PyTorch not TensorFlow is installed.
"""
return unittest.skipUnless(is_torch_available() or is_tf_available(), "test requires PyTorch or TensorFlow")(
test_case
)
def require_intel_extension_for_pytorch(test_case):
"""
Decorator marking a test that requires Intel Extension for PyTorch.
These tests are skipped when Intel Extension for PyTorch isn't installed or it does not match current PyTorch
version.
"""
return unittest.skipUnless(
is_ipex_available(),
"test requires Intel Extension for PyTorch to be installed and match current PyTorch version, see"
" https://github.com/intel/intel-extension-for-pytorch",
)(test_case)
def require_tensorflow_probability(test_case):
"""
Decorator marking a test that requires TensorFlow probability.
These tests are skipped when TensorFlow probability isn't installed.
"""
return unittest.skipUnless(is_tensorflow_probability_available(), "test requires TensorFlow probability")(
test_case
)
def require_torchaudio(test_case):
"""
Decorator marking a test that requires torchaudio. These tests are skipped when torchaudio isn't installed.
"""
return unittest.skipUnless(is_torchaudio_available(), "test requires torchaudio")(test_case)
def require_tf(test_case):
"""
Decorator marking a test that requires TensorFlow. These tests are skipped when TensorFlow isn't installed.
"""
return unittest.skipUnless(is_tf_available(), "test requires TensorFlow")(test_case)
def require_flax(test_case):
"""
Decorator marking a test that requires JAX & Flax. These tests are skipped when one / both are not installed
"""
return unittest.skipUnless(is_flax_available(), "test requires JAX & Flax")(test_case)
def require_sentencepiece(test_case):
"""
Decorator marking a test that requires SentencePiece. These tests are skipped when SentencePiece isn't installed.
"""
return unittest.skipUnless(is_sentencepiece_available(), "test requires SentencePiece")(test_case)
def require_seqio(test_case):
"""
Decorator marking a test that requires SentencePiece. These tests are skipped when SentencePiece isn't installed.
"""
return unittest.skipUnless(is_seqio_available(), "test requires Seqio")(test_case)
def require_scipy(test_case):
"""
Decorator marking a test that requires Scipy. These tests are skipped when SentencePiece isn't installed.
"""
return unittest.skipUnless(is_scipy_available(), "test requires Scipy")(test_case)
def require_tokenizers(test_case):
"""
Decorator marking a test that requires 🤗 Tokenizers. These tests are skipped when 🤗 Tokenizers isn't installed.
"""
return unittest.skipUnless(is_tokenizers_available(), "test requires tokenizers")(test_case)
def require_tensorflow_text(test_case):
"""
Decorator marking a test that requires tensorflow_text. These tests are skipped when tensroflow_text isn't
installed.
"""
return unittest.skipUnless(is_tensorflow_text_available(), "test requires tensorflow_text")(test_case)
def require_keras_nlp(test_case):
"""
Decorator marking a test that requires keras_nlp. These tests are skipped when keras_nlp isn't installed.
"""
return unittest.skipUnless(is_keras_nlp_available(), "test requires keras_nlp")(test_case)
def require_pandas(test_case):
"""
Decorator marking a test that requires pandas. These tests are skipped when pandas isn't installed.
"""
return unittest.skipUnless(is_pandas_available(), "test requires pandas")(test_case)
def require_pytesseract(test_case):
"""
Decorator marking a test that requires PyTesseract. These tests are skipped when PyTesseract isn't installed.
"""
return unittest.skipUnless(is_pytesseract_available(), "test requires PyTesseract")(test_case)
def require_pytorch_quantization(test_case):
"""
Decorator marking a test that requires PyTorch Quantization Toolkit. These tests are skipped when PyTorch
Quantization Toolkit isn't installed.
"""
return unittest.skipUnless(is_pytorch_quantization_available(), "test requires PyTorch Quantization Toolkit")(
test_case
)
def require_vision(test_case):
"""
Decorator marking a test that requires the vision dependencies. These tests are skipped when torchaudio isn't
installed.
"""
return unittest.skipUnless(is_vision_available(), "test requires vision")(test_case)
def require_ftfy(test_case):
"""
Decorator marking a test that requires ftfy. These tests are skipped when ftfy isn't installed.
"""
return unittest.skipUnless(is_ftfy_available(), "test requires ftfy")(test_case)
def require_spacy(test_case):
"""
Decorator marking a test that requires SpaCy. These tests are skipped when SpaCy isn't installed.
"""
return unittest.skipUnless(is_spacy_available(), "test requires spacy")(test_case)
def require_decord(test_case):
"""
Decorator marking a test that requires decord. These tests are skipped when decord isn't installed.
"""
return unittest.skipUnless(is_decord_available(), "test requires decord")(test_case)
def require_torch_multi_gpu(test_case):
"""
Decorator marking a test that requires a multi-GPU setup (in PyTorch). These tests are skipped on a machine without
multiple GPUs.
To run *only* the multi_gpu tests, assuming all test names contain multi_gpu: $ pytest -sv ./tests -k "multi_gpu"
"""
if not is_torch_available():
return unittest.skip("test requires PyTorch")(test_case)
import torch
return unittest.skipUnless(torch.cuda.device_count() > 1, "test requires multiple GPUs")(test_case)
def require_torch_multi_accelerator(test_case):
"""
Decorator marking a test that requires a multi-accelerator (in PyTorch). These tests are skipped on a machine
without multiple accelerators. To run *only* the multi_accelerator tests, assuming all test names contain
multi_accelerator: $ pytest -sv ./tests -k "multi_accelerator"
"""
if not is_torch_available():
return unittest.skip("test requires PyTorch")(test_case)
return unittest.skipUnless(backend_device_count(torch_device) > 1, "test requires multiple accelerators")(
test_case
)
def require_torch_non_multi_gpu(test_case):
"""
Decorator marking a test that requires 0 or 1 GPU setup (in PyTorch).
"""
if not is_torch_available():
return unittest.skip("test requires PyTorch")(test_case)
import torch
return unittest.skipUnless(torch.cuda.device_count() < 2, "test requires 0 or 1 GPU")(test_case)
def require_torch_non_multi_accelerator(test_case):
"""
Decorator marking a test that requires 0 or 1 accelerator setup (in PyTorch).
"""
if not is_torch_available():
return unittest.skip("test requires PyTorch")(test_case)
return unittest.skipUnless(backend_device_count(torch_device) < 2, "test requires 0 or 1 accelerator")(test_case)
def require_torch_up_to_2_gpus(test_case):
"""
Decorator marking a test that requires 0 or 1 or 2 GPU setup (in PyTorch).
"""
if not is_torch_available():
return unittest.skip("test requires PyTorch")(test_case)
import torch
return unittest.skipUnless(torch.cuda.device_count() < 3, "test requires 0 or 1 or 2 GPUs")(test_case)
def require_torch_up_to_2_accelerators(test_case):
"""
Decorator marking a test that requires 0 or 1 or 2 accelerator setup (in PyTorch).
"""
if not is_torch_available():
return unittest.skip("test requires PyTorch")(test_case)
return unittest.skipUnless(backend_device_count(torch_device) < 3, "test requires 0 or 1 or 2 accelerators")
(test_case)
def require_torch_tpu(test_case):
"""
Decorator marking a test that requires a TPU (in PyTorch).
"""
return unittest.skipUnless(is_torch_tpu_available(check_device=False), "test requires PyTorch TPU")(test_case)
def require_torch_neuroncore(test_case):
"""
Decorator marking a test that requires NeuronCore (in PyTorch).
"""
return unittest.skipUnless(is_torch_neuroncore_available(check_device=False), "test requires PyTorch NeuronCore")(
test_case
)
def require_torch_npu(test_case):
"""
Decorator marking a test that requires NPU (in PyTorch).
"""
return unittest.skipUnless(is_torch_npu_available(), "test requires PyTorch NPU")(test_case)
def require_torch_multi_npu(test_case):
"""
Decorator marking a test that requires a multi-NPU setup (in PyTorch). These tests are skipped on a machine without
multiple NPUs.
To run *only* the multi_npu tests, assuming all test names contain multi_npu: $ pytest -sv ./tests -k "multi_npu"
"""
if not is_torch_npu_available():
return unittest.skip("test requires PyTorch NPU")(test_case)
return unittest.skipUnless(torch.npu.device_count() > 1, "test requires multiple NPUs")(test_case)
def require_torch_xpu(test_case):
"""
Decorator marking a test that requires XPU and IPEX.
These tests are skipped when Intel Extension for PyTorch isn't installed or it does not match current PyTorch
version.
"""
return unittest.skipUnless(is_torch_xpu_available(), "test requires IPEX and an XPU device")(test_case)
def require_torch_multi_xpu(test_case):
"""
Decorator marking a test that requires a multi-XPU setup with IPEX and atleast one XPU device. These tests are
skipped on a machine without IPEX or multiple XPUs.
To run *only* the multi_xpu tests, assuming all test names contain multi_xpu: $ pytest -sv ./tests -k "multi_xpu"
"""
if not is_torch_xpu_available():
return unittest.skip("test requires IPEX and atleast one XPU device")(test_case)
return unittest.skipUnless(torch.xpu.device_count() > 1, "test requires multiple XPUs")(test_case)
if is_torch_available():
# Set env var CUDA_VISIBLE_DEVICES="" to force cpu-mode
import torch
if "TRANSFORMERS_TEST_BACKEND" in os.environ:
backend = os.environ["TRANSFORMERS_TEST_BACKEND"]
try:
_ = importlib.import_module(backend)
except ModuleNotFoundError as e:
raise ModuleNotFoundError(
f"Failed to import `TRANSFORMERS_TEST_BACKEND` '{backend}'! This should be the name of an installed module. The original error (look up to see its"
f" traceback):\n{e}"
) from e
if "TRANSFORMERS_TEST_DEVICE" in os.environ:
torch_device = os.environ["TRANSFORMERS_TEST_DEVICE"]
try:
# try creating device to see if provided device is valid
_ = torch.device(torch_device)
except RuntimeError as e:
raise RuntimeError(
f"Unknown testing device specified by environment variable `TRANSFORMERS_TEST_DEVICE`: {torch_device}"
) from e
elif torch.cuda.is_available():
torch_device = "cuda"
elif _run_third_party_device_tests and is_torch_npu_available():
torch_device = "npu"
elif _run_third_party_device_tests and is_torch_xpu_available():
torch_device = "xpu"
else:
torch_device = "cpu"
else:
torch_device = None
if is_tf_available():
import tensorflow as tf
if is_flax_available():
import jax
jax_device = jax.default_backend()
else:
jax_device = None
def require_torchdynamo(test_case):
"""Decorator marking a test that requires TorchDynamo"""
return unittest.skipUnless(is_torchdynamo_available(), "test requires TorchDynamo")(test_case)
def require_torch_tensorrt_fx(test_case):
"""Decorator marking a test that requires Torch-TensorRT FX"""
return unittest.skipUnless(is_torch_tensorrt_fx_available(), "test requires Torch-TensorRT FX")(test_case)
def require_torch_gpu(test_case):
"""Decorator marking a test that requires CUDA and PyTorch."""
return unittest.skipUnless(torch_device == "cuda", "test requires CUDA")(test_case)
def require_torch_accelerator(test_case):
"""Decorator marking a test that requires an accessible accelerator and PyTorch."""
return unittest.skipUnless(torch_device is not None and torch_device != "cpu", "test requires accelerator")(
test_case
)
def require_torch_fp16(test_case):
"""Decorator marking a test that requires a device that supports fp16"""
return unittest.skipUnless(
is_torch_fp16_available_on_device(torch_device), "test requires device with fp16 support"
)(test_case)
def require_torch_bf16(test_case):
"""Decorator marking a test that requires a device that supports bf16"""
return unittest.skipUnless(
is_torch_bf16_available_on_device(torch_device), "test requires device with bf16 support"
)(test_case)
def require_torch_bf16_gpu(test_case):
"""Decorator marking a test that requires torch>=1.10, using Ampere GPU or newer arch with cuda>=11.0"""
return unittest.skipUnless(
is_torch_bf16_gpu_available(),
"test requires torch>=1.10, using Ampere GPU or newer arch with cuda>=11.0",
)(test_case)
def require_torch_bf16_cpu(test_case):
"""Decorator marking a test that requires torch>=1.10, using CPU."""
return unittest.skipUnless(
is_torch_bf16_cpu_available(),
"test requires torch>=1.10, using CPU",
)(test_case)
def require_torch_tf32(test_case):
"""Decorator marking a test that requires Ampere or a newer GPU arch, cuda>=11 and torch>=1.7."""
return unittest.skipUnless(
is_torch_tf32_available(), "test requires Ampere or a newer GPU arch, cuda>=11 and torch>=1.7"
)(test_case)
def require_detectron2(test_case):
"""Decorator marking a test that requires detectron2."""
return unittest.skipUnless(is_detectron2_available(), "test requires `detectron2`")(test_case)
def require_faiss(test_case):
"""Decorator marking a test that requires faiss."""
return unittest.skipUnless(is_faiss_available(), "test requires `faiss`")(test_case)
def require_optuna(test_case):
"""
Decorator marking a test that requires optuna.
These tests are skipped when optuna isn't installed.
"""
return unittest.skipUnless(is_optuna_available(), "test requires optuna")(test_case)
def require_ray(test_case):
"""
Decorator marking a test that requires Ray/tune.
These tests are skipped when Ray/tune isn't installed.
"""
return unittest.skipUnless(is_ray_available(), "test requires Ray/tune")(test_case)
def require_sigopt(test_case):
"""
Decorator marking a test that requires SigOpt.
These tests are skipped when SigOpt isn't installed.
"""
return unittest.skipUnless(is_sigopt_available(), "test requires SigOpt")(test_case)
def require_wandb(test_case):
"""
Decorator marking a test that requires wandb.
These tests are skipped when wandb isn't installed.
"""
return unittest.skipUnless(is_wandb_available(), "test requires wandb")(test_case)
def require_clearml(test_case):
"""
Decorator marking a test requires clearml.
These tests are skipped when clearml isn't installed.
"""
return unittest.skipUnless(is_clearml_available(), "test requires clearml")(test_case)
def require_soundfile(test_case):
"""
Decorator marking a test that requires soundfile
These tests are skipped when soundfile isn't installed.
"""
return unittest.skipUnless(is_soundfile_availble(), "test requires soundfile")(test_case)
def require_deepspeed(test_case):
"""
Decorator marking a test that requires deepspeed
"""
return unittest.skipUnless(is_deepspeed_available(), "test requires deepspeed")(test_case)
def require_apex(test_case):
"""
Decorator marking a test that requires apex
"""
return unittest.skipUnless(is_apex_available(), "test requires apex")(test_case)
def require_aqlm(test_case):
"""
Decorator marking a test that requires aqlm
"""
return unittest.skipUnless(is_aqlm_available(), "test requires aqlm")(test_case)
def require_bitsandbytes(test_case):
"""
Decorator marking a test that requires the bitsandbytes library. Will be skipped when the library or its hard dependency torch is not installed.
"""
if is_bitsandbytes_available() and is_torch_available():
try:
import pytest
return pytest.mark.bitsandbytes(test_case)
except ImportError:
return test_case
else:
return unittest.skip("test requires bitsandbytes and torch")(test_case)
def require_optimum(test_case):
"""
Decorator for optimum dependency
"""
return unittest.skipUnless(is_optimum_available(), "test requires optimum")(test_case)
def require_tensorboard(test_case):
"""
Decorator for `tensorboard` dependency
"""
return unittest.skipUnless(is_tensorboard_available(), "test requires tensorboard")
def require_auto_gptq(test_case):
"""
Decorator for auto_gptq dependency
"""
return unittest.skipUnless(is_auto_gptq_available(), "test requires auto-gptq")(test_case)
def require_auto_awq(test_case):
"""
Decorator for auto_awq dependency
"""
return unittest.skipUnless(is_auto_awq_available(), "test requires autoawq")(test_case)
def require_phonemizer(test_case):
"""
Decorator marking a test that requires phonemizer
"""
return unittest.skipUnless(is_phonemizer_available(), "test requires phonemizer")(test_case)
def require_pyctcdecode(test_case):
"""
Decorator marking a test that requires pyctcdecode
"""
return unittest.skipUnless(is_pyctcdecode_available(), "test requires pyctcdecode")(test_case)
def require_librosa(test_case):
"""
Decorator marking a test that requires librosa
"""
return unittest.skipUnless(is_librosa_available(), "test requires librosa")(test_case)
def require_essentia(test_case):
"""
Decorator marking a test that requires essentia
"""
return unittest.skipUnless(is_essentia_available(), "test requires essentia")(test_case)
def require_pretty_midi(test_case):
"""
Decorator marking a test that requires pretty_midi
"""
return unittest.skipUnless(is_pretty_midi_available(), "test requires pretty_midi")(test_case)
def cmd_exists(cmd):
return shutil.which(cmd) is not None
def require_usr_bin_time(test_case):
"""
Decorator marking a test that requires `/usr/bin/time`
"""
return unittest.skipUnless(cmd_exists("/usr/bin/time"), "test requires /usr/bin/time")(test_case)
def require_sudachi(test_case):
"""
Decorator marking a test that requires sudachi
"""
return unittest.skipUnless(is_sudachi_available(), "test requires sudachi")(test_case)
def require_sudachi_projection(test_case):
"""
Decorator marking a test that requires sudachi_projection
"""
return unittest.skipUnless(is_sudachi_projection_available(), "test requires sudachi which supports projection")(
test_case
)
def require_jumanpp(test_case):
"""
Decorator marking a test that requires jumanpp
"""
return unittest.skipUnless(is_jumanpp_available(), "test requires jumanpp")(test_case)
def require_cython(test_case):
"""
Decorator marking a test that requires jumanpp
"""
return unittest.skipUnless(is_cython_available(), "test requires cython")(test_case)
def get_gpu_count():
"""
Return the number of available gpus (regardless of whether torch, tf or jax is used)
"""
if is_torch_available():
import torch
return torch.cuda.device_count()
elif is_tf_available():
import tensorflow as tf
return len(tf.config.list_physical_devices("GPU"))
elif is_flax_available():
import jax
return jax.device_count()
else:
return 0
def get_tests_dir(append_path=None):
"""
Args:
append_path: optional path to append to the tests dir path
Return:
The full path to the `tests` dir, so that the tests can be invoked from anywhere. Optionally `append_path` is
joined after the `tests` dir the former is provided.
"""
# this function caller's __file__
caller__file__ = inspect.stack()[1][1]
tests_dir = os.path.abspath(os.path.dirname(caller__file__))
while not tests_dir.endswith("tests"):
tests_dir = os.path.dirname(tests_dir)
if append_path:
return os.path.join(tests_dir, append_path)
else:
return tests_dir
#
# Helper functions for dealing with testing text outputs
# The original code came from:
# https://github.com/fastai/fastai/blob/master/tests/utils/text.py
# When any function contains print() calls that get overwritten, like progress bars,
# a special care needs to be applied, since under pytest -s captured output (capsys
# or contextlib.redirect_stdout) contains any temporary printed strings, followed by
# \r's. This helper function ensures that the buffer will contain the same output
# with and without -s in pytest, by turning:
# foo bar\r tar mar\r final message
# into:
# final message
# it can handle a single string or a multiline buffer
def apply_print_resets(buf):
return re.sub(r"^.*\r", "", buf, 0, re.M)
def assert_screenout(out, what):
out_pr = apply_print_resets(out).lower()
match_str = out_pr.find(what.lower())
assert match_str != -1, f"expecting to find {what} in output: f{out_pr}"
class CaptureStd:
"""
Context manager to capture:
- stdout: replay it, clean it up and make it available via `obj.out`
- stderr: replay it and make it available via `obj.err`
Args:
out (`bool`, *optional*, defaults to `True`): Whether to capture stdout or not.
err (`bool`, *optional*, defaults to `True`): Whether to capture stderr or not.
replay (`bool`, *optional*, defaults to `True`): Whether to replay or not.
By default each captured stream gets replayed back on context's exit, so that one can see what the test was
doing. If this is a not wanted behavior and the captured data shouldn't be replayed, pass `replay=False` to
disable this feature.
Examples:
```python
# to capture stdout only with auto-replay
with CaptureStdout() as cs:
print("Secret message")
assert "message" in cs.out
# to capture stderr only with auto-replay
import sys
with CaptureStderr() as cs:
print("Warning: ", file=sys.stderr)
assert "Warning" in cs.err
# to capture both streams with auto-replay
with CaptureStd() as cs:
print("Secret message")
print("Warning: ", file=sys.stderr)
assert "message" in cs.out
assert "Warning" in cs.err
# to capture just one of the streams, and not the other, with auto-replay
with CaptureStd(err=False) as cs:
print("Secret message")
assert "message" in cs.out
# but best use the stream-specific subclasses
# to capture without auto-replay
with CaptureStd(replay=False) as cs:
print("Secret message")
assert "message" in cs.out
```"""
def __init__(self, out=True, err=True, replay=True):
self.replay = replay
if out:
self.out_buf = StringIO()
self.out = "error: CaptureStd context is unfinished yet, called too early"
else:
self.out_buf = None
self.out = "not capturing stdout"
if err:
self.err_buf = StringIO()
self.err = "error: CaptureStd context is unfinished yet, called too early"
else:
self.err_buf = None
self.err = "not capturing stderr"
def __enter__(self):
if self.out_buf:
self.out_old = sys.stdout
sys.stdout = self.out_buf
if self.err_buf:
self.err_old = sys.stderr
sys.stderr = self.err_buf
return self
def __exit__(self, *exc):
if self.out_buf:
sys.stdout = self.out_old
captured = self.out_buf.getvalue()
if self.replay:
sys.stdout.write(captured)
self.out = apply_print_resets(captured)
if self.err_buf:
sys.stderr = self.err_old
captured = self.err_buf.getvalue()
if self.replay:
sys.stderr.write(captured)
self.err = captured
def __repr__(self):
msg = ""
if self.out_buf:
msg += f"stdout: {self.out}\n"
if self.err_buf:
msg += f"stderr: {self.err}\n"
return msg
# in tests it's the best to capture only the stream that's wanted, otherwise
# it's easy to miss things, so unless you need to capture both streams, use the
# subclasses below (less typing). Or alternatively, configure `CaptureStd` to
# disable the stream you don't need to test.
class CaptureStdout(CaptureStd):
"""Same as CaptureStd but captures only stdout"""
def __init__(self, replay=True):
super().__init__(err=False, replay=replay)
class CaptureStderr(CaptureStd):
"""Same as CaptureStd but captures only stderr"""
def __init__(self, replay=True):
super().__init__(out=False, replay=replay)
class CaptureLogger:
"""
Context manager to capture `logging` streams
Args:
logger: 'logging` logger object
Returns:
The captured output is available via `self.out`
Example:
```python
>>> from transformers import logging
>>> from transformers.testing_utils import CaptureLogger
>>> msg = "Testing 1, 2, 3"
>>> logging.set_verbosity_info()
>>> logger = logging.get_logger("transformers.models.bart.tokenization_bart")
>>> with CaptureLogger(logger) as cl:
... logger.info(msg)
>>> assert cl.out, msg + "\n"
```
"""
def __init__(self, logger):
self.logger = logger
self.io = StringIO()
self.sh = logging.StreamHandler(self.io)
self.out = ""
def __enter__(self):
self.logger.addHandler(self.sh)
return self
def __exit__(self, *exc):
self.logger.removeHandler(self.sh)
self.out = self.io.getvalue()
def __repr__(self):
return f"captured: {self.out}\n"
@contextlib.contextmanager
def LoggingLevel(level):
"""
This is a context manager to temporarily change transformers modules logging level to the desired value and have it
restored to the original setting at the end of the scope.
Example:
```python
with LoggingLevel(logging.INFO):
AutoModel.from_pretrained("openai-community/gpt2") # calls logger.info() several times
```
"""
orig_level = transformers_logging.get_verbosity()
try:
transformers_logging.set_verbosity(level)
yield
finally:
transformers_logging.set_verbosity(orig_level)
@contextlib.contextmanager
# adapted from https://stackoverflow.com/a/64789046/9201239
def ExtendSysPath(path: Union[str, os.PathLike]) -> Iterator[None]:
"""
Temporary add given path to `sys.path`.
Usage :
```python
with ExtendSysPath("/path/to/dir"):
mymodule = importlib.import_module("mymodule")
```
"""
path = os.fspath(path)
try:
sys.path.insert(0, path)
yield
finally:
sys.path.remove(path)
class TestCasePlus(unittest.TestCase):
"""
This class extends *unittest.TestCase* with additional features.
Feature 1: A set of fully resolved important file and dir path accessors.
In tests often we need to know where things are relative to the current test file, and it's not trivial since the
test could be invoked from more than one directory or could reside in sub-directories with different depths. This
class solves this problem by sorting out all the basic paths and provides easy accessors to them:
- `pathlib` objects (all fully resolved):
- `test_file_path` - the current test file path (=`__file__`)
- `test_file_dir` - the directory containing the current test file
- `tests_dir` - the directory of the `tests` test suite
- `examples_dir` - the directory of the `examples` test suite
- `repo_root_dir` - the directory of the repository
- `src_dir` - the directory of `src` (i.e. where the `transformers` sub-dir resides)
- stringified paths---same as above but these return paths as strings, rather than `pathlib` objects:
- `test_file_path_str`
- `test_file_dir_str`
- `tests_dir_str`
- `examples_dir_str`
- `repo_root_dir_str`
- `src_dir_str`
Feature 2: Flexible auto-removable temporary dirs which are guaranteed to get removed at the end of test.
1. Create a unique temporary dir:
```python
def test_whatever(self):
tmp_dir = self.get_auto_remove_tmp_dir()
```
`tmp_dir` will contain the path to the created temporary dir. It will be automatically removed at the end of the
test.
2. Create a temporary dir of my choice, ensure it's empty before the test starts and don't
empty it after the test.
```python
def test_whatever(self):
tmp_dir = self.get_auto_remove_tmp_dir("./xxx")
```
This is useful for debug when you want to monitor a specific directory and want to make sure the previous tests
didn't leave any data in there.
3. You can override the first two options by directly overriding the `before` and `after` args, leading to the
following behavior:
`before=True`: the temporary dir will always be cleared at the beginning of the test.
`before=False`: if the temporary dir already existed, any existing files will remain there.
`after=True`: the temporary dir will always be deleted at the end of the test.
`after=False`: the temporary dir will always be left intact at the end of the test.
Note 1: In order to run the equivalent of `rm -r` safely, only subdirs of the project repository checkout are
allowed if an explicit `tmp_dir` is used, so that by mistake no `/tmp` or similar important part of the filesystem
will get nuked. i.e. please always pass paths that start with `./`
Note 2: Each test can register multiple temporary dirs and they all will get auto-removed, unless requested
otherwise.
Feature 3: Get a copy of the `os.environ` object that sets up `PYTHONPATH` specific to the current test suite. This
is useful for invoking external programs from the test suite - e.g. distributed training.
```python
def test_whatever(self):
env = self.get_env()
```"""
def setUp(self):
# get_auto_remove_tmp_dir feature:
self.teardown_tmp_dirs = []
# figure out the resolved paths for repo_root, tests, examples, etc.
self._test_file_path = inspect.getfile(self.__class__)
path = Path(self._test_file_path).resolve()
self._test_file_dir = path.parents[0]
for up in [1, 2, 3]:
tmp_dir = path.parents[up]
if (tmp_dir / "src").is_dir() and (tmp_dir / "tests").is_dir():
break
if tmp_dir:
self._repo_root_dir = tmp_dir
else:
raise ValueError(f"can't figure out the root of the repo from {self._test_file_path}")
self._tests_dir = self._repo_root_dir / "tests"
self._examples_dir = self._repo_root_dir / "examples"
self._src_dir = self._repo_root_dir / "src"
@property
def test_file_path(self):
return self._test_file_path
@property
def test_file_path_str(self):
return str(self._test_file_path)
@property
def test_file_dir(self):
return self._test_file_dir
@property
def test_file_dir_str(self):
return str(self._test_file_dir)
@property
def tests_dir(self):
return self._tests_dir
@property
def tests_dir_str(self):
return str(self._tests_dir)
@property
def examples_dir(self):
return self._examples_dir
@property
def examples_dir_str(self):
return str(self._examples_dir)
@property
def repo_root_dir(self):
return self._repo_root_dir
@property
def repo_root_dir_str(self):
return str(self._repo_root_dir)
@property
def src_dir(self):
return self._src_dir
@property
def src_dir_str(self):
return str(self._src_dir)
def get_env(self):
"""
Return a copy of the `os.environ` object that sets up `PYTHONPATH` correctly, depending on the test suite it's
invoked from. This is useful for invoking external programs from the test suite - e.g. distributed training.
It always inserts `./src` first, then `./tests` or `./examples` depending on the test suite type and finally
the preset `PYTHONPATH` if any (all full resolved paths).
"""
env = os.environ.copy()
paths = [self.src_dir_str]
if "/examples" in self.test_file_dir_str:
paths.append(self.examples_dir_str)
else:
paths.append(self.tests_dir_str)
paths.append(env.get("PYTHONPATH", ""))
env["PYTHONPATH"] = ":".join(paths)
return env
def get_auto_remove_tmp_dir(self, tmp_dir=None, before=None, after=None):
"""
Args:
tmp_dir (`string`, *optional*):
if `None`:
- a unique temporary path will be created
- sets `before=True` if `before` is `None`
- sets `after=True` if `after` is `None`
else:
- `tmp_dir` will be created
- sets `before=True` if `before` is `None`
- sets `after=False` if `after` is `None`
before (`bool`, *optional*):
If `True` and the `tmp_dir` already exists, make sure to empty it right away if `False` and the
`tmp_dir` already exists, any existing files will remain there.
after (`bool`, *optional*):
If `True`, delete the `tmp_dir` at the end of the test if `False`, leave the `tmp_dir` and its contents
intact at the end of the test.
Returns:
tmp_dir(`string`): either the same value as passed via *tmp_dir* or the path to the auto-selected tmp dir
"""
if tmp_dir is not None:
# defining the most likely desired behavior for when a custom path is provided.
# this most likely indicates the debug mode where we want an easily locatable dir that:
# 1. gets cleared out before the test (if it already exists)
# 2. is left intact after the test
if before is None:
before = True
if after is None:
after = False
# using provided path
path = Path(tmp_dir).resolve()
# to avoid nuking parts of the filesystem, only relative paths are allowed
if not tmp_dir.startswith("./"):
raise ValueError(
f"`tmp_dir` can only be a relative path, i.e. `./some/path`, but received `{tmp_dir}`"
)
# ensure the dir is empty to start with
if before is True and path.exists():
shutil.rmtree(tmp_dir, ignore_errors=True)
path.mkdir(parents=True, exist_ok=True)
else:
# defining the most likely desired behavior for when a unique tmp path is auto generated
# (not a debug mode), here we require a unique tmp dir that:
# 1. is empty before the test (it will be empty in this situation anyway)
# 2. gets fully removed after the test
if before is None:
before = True
if after is None:
after = True
# using unique tmp dir (always empty, regardless of `before`)
tmp_dir = tempfile.mkdtemp()
if after is True:
# register for deletion
self.teardown_tmp_dirs.append(tmp_dir)
return tmp_dir
def python_one_liner_max_rss(self, one_liner_str):
"""
Runs the passed python one liner (just the code) and returns how much max cpu memory was used to run the
program.
Args:
one_liner_str (`string`):
a python one liner code that gets passed to `python -c`
Returns:
max cpu memory bytes used to run the program. This value is likely to vary slightly from run to run.
Requirements:
this helper needs `/usr/bin/time` to be installed (`apt install time`)
Example:
```
one_liner_str = 'from transformers import AutoModel; AutoModel.from_pretrained("google-t5/t5-large")'
max_rss = self.python_one_liner_max_rss(one_liner_str)
```
"""
if not cmd_exists("/usr/bin/time"):
raise ValueError("/usr/bin/time is required, install with `apt install time`")
cmd = shlex.split(f"/usr/bin/time -f %M python -c '{one_liner_str}'")
with CaptureStd() as cs:
execute_subprocess_async(cmd, env=self.get_env())
# returned data is in KB so convert to bytes
max_rss = int(cs.err.split("\n")[-2].replace("stderr: ", "")) * 1024
return max_rss
def tearDown(self):
# get_auto_remove_tmp_dir feature: remove registered temp dirs
for path in self.teardown_tmp_dirs:
shutil.rmtree(path, ignore_errors=True)
self.teardown_tmp_dirs = []
if is_accelerate_available():
AcceleratorState._reset_state()
PartialState._reset_state()
# delete all the env variables having `ACCELERATE` in them
for k in list(os.environ.keys()):
if "ACCELERATE" in k:
del os.environ[k]
def mockenv(**kwargs):
"""
this is a convenience wrapper, that allows this ::
@mockenv(RUN_SLOW=True, USE_TF=False) def test_something():
run_slow = os.getenv("RUN_SLOW", False) use_tf = os.getenv("USE_TF", False)
"""
return mock.patch.dict(os.environ, kwargs)
# from https://stackoverflow.com/a/34333710/9201239
@contextlib.contextmanager
def mockenv_context(*remove, **update):
"""
Temporarily updates the `os.environ` dictionary in-place. Similar to mockenv
The `os.environ` dictionary is updated in-place so that the modification is sure to work in all situations.
Args:
remove: Environment variables to remove.
update: Dictionary of environment variables and values to add/update.
"""
env = os.environ
update = update or {}
remove = remove or []
# List of environment variables being updated or removed.
stomped = (set(update.keys()) | set(remove)) & set(env.keys())
# Environment variables and values to restore on exit.
update_after = {k: env[k] for k in stomped}
# Environment variables and values to remove on exit.
remove_after = frozenset(k for k in update if k not in env)
try:
env.update(update)
[env.pop(k, None) for k in remove]
yield
finally:
env.update(update_after)
[env.pop(k) for k in remove_after]
# --- pytest conf functions --- #
# to avoid multiple invocation from tests/conftest.py and examples/conftest.py - make sure it's called only once
pytest_opt_registered = {}
def pytest_addoption_shared(parser):
"""
This function is to be called from `conftest.py` via `pytest_addoption` wrapper that has to be defined there.
It allows loading both `conftest.py` files at once without causing a failure due to adding the same `pytest`
option.
"""
option = "--make-reports"
if option not in pytest_opt_registered:
parser.addoption(
option,
action="store",
default=False,
help="generate report files. The value of this option is used as a prefix to report names",
)
pytest_opt_registered[option] = 1
def pytest_terminal_summary_main(tr, id):
"""
Generate multiple reports at the end of test suite run - each report goes into a dedicated file in the current
directory. The report files are prefixed with the test suite name.
This function emulates --duration and -rA pytest arguments.
This function is to be called from `conftest.py` via `pytest_terminal_summary` wrapper that has to be defined
there.
Args:
- tr: `terminalreporter` passed from `conftest.py`
- id: unique id like `tests` or `examples` that will be incorporated into the final reports filenames - this is
needed as some jobs have multiple runs of pytest, so we can't have them overwrite each other.
NB: this functions taps into a private _pytest API and while unlikely, it could break should pytest do internal
changes - also it calls default internal methods of terminalreporter which can be hijacked by various `pytest-`
plugins and interfere.
"""
from _pytest.config import create_terminal_writer
if not len(id):
id = "tests"
config = tr.config
orig_writer = config.get_terminal_writer()
orig_tbstyle = config.option.tbstyle
orig_reportchars = tr.reportchars
dir = f"reports/{id}"
Path(dir).mkdir(parents=True, exist_ok=True)
report_files = {
k: f"{dir}/{k}.txt"
for k in [
"durations",
"errors",
"failures_long",
"failures_short",
"failures_line",
"passes",
"stats",
"summary_short",
"warnings",
]
}
# custom durations report
# note: there is no need to call pytest --durations=XX to get this separate report
# adapted from https://github.com/pytest-dev/pytest/blob/897f151e/src/_pytest/runner.py#L66
dlist = []
for replist in tr.stats.values():
for rep in replist:
if hasattr(rep, "duration"):
dlist.append(rep)
if dlist:
dlist.sort(key=lambda x: x.duration, reverse=True)
with open(report_files["durations"], "w") as f:
durations_min = 0.05 # sec
f.write("slowest durations\n")
for i, rep in enumerate(dlist):
if rep.duration < durations_min:
f.write(f"{len(dlist)-i} durations < {durations_min} secs were omitted")
break
f.write(f"{rep.duration:02.2f}s {rep.when:<8} {rep.nodeid}\n")
def summary_failures_short(tr):
# expecting that the reports were --tb=long (default) so we chop them off here to the last frame
reports = tr.getreports("failed")
if not reports:
return
tr.write_sep("=", "FAILURES SHORT STACK")
for rep in reports:
msg = tr._getfailureheadline(rep)
tr.write_sep("_", msg, red=True, bold=True)
# chop off the optional leading extra frames, leaving only the last one
longrepr = re.sub(r".*_ _ _ (_ ){10,}_ _ ", "", rep.longreprtext, 0, re.M | re.S)
tr._tw.line(longrepr)
# note: not printing out any rep.sections to keep the report short
# use ready-made report funcs, we are just hijacking the filehandle to log to a dedicated file each
# adapted from https://github.com/pytest-dev/pytest/blob/897f151e/src/_pytest/terminal.py#L814
# note: some pytest plugins may interfere by hijacking the default `terminalreporter` (e.g.
# pytest-instafail does that)
# report failures with line/short/long styles
config.option.tbstyle = "auto" # full tb
with open(report_files["failures_long"], "w") as f:
tr._tw = create_terminal_writer(config, f)
tr.summary_failures()
# config.option.tbstyle = "short" # short tb
with open(report_files["failures_short"], "w") as f:
tr._tw = create_terminal_writer(config, f)
summary_failures_short(tr)
config.option.tbstyle = "line" # one line per error
with open(report_files["failures_line"], "w") as f:
tr._tw = create_terminal_writer(config, f)
tr.summary_failures()
with open(report_files["errors"], "w") as f:
tr._tw = create_terminal_writer(config, f)
tr.summary_errors()
with open(report_files["warnings"], "w") as f:
tr._tw = create_terminal_writer(config, f)
tr.summary_warnings() # normal warnings
tr.summary_warnings() # final warnings
tr.reportchars = "wPpsxXEf" # emulate -rA (used in summary_passes() and short_test_summary())
# Skip the `passes` report, as it starts to take more than 5 minutes, and sometimes it timeouts on CircleCI if it
# takes > 10 minutes (as this part doesn't generate any output on the terminal).
# (also, it seems there is no useful information in this report, and we rarely need to read it)
# with open(report_files["passes"], "w") as f:
# tr._tw = create_terminal_writer(config, f)
# tr.summary_passes()
with open(report_files["summary_short"], "w") as f:
tr._tw = create_terminal_writer(config, f)
tr.short_test_summary()
with open(report_files["stats"], "w") as f:
tr._tw = create_terminal_writer(config, f)
tr.summary_stats()
# restore:
tr._tw = orig_writer
tr.reportchars = orig_reportchars
config.option.tbstyle = orig_tbstyle
# --- distributed testing functions --- #
# adapted from https://stackoverflow.com/a/59041913/9201239
import asyncio # noqa
class _RunOutput:
def __init__(self, returncode, stdout, stderr):
self.returncode = returncode
self.stdout = stdout
self.stderr = stderr
async def _read_stream(stream, callback):
while True:
line = await stream.readline()
if line:
callback(line)
else:
break
async def _stream_subprocess(cmd, env=None, stdin=None, timeout=None, quiet=False, echo=False) -> _RunOutput:
if echo:
print("\nRunning: ", " ".join(cmd))
p = await asyncio.create_subprocess_exec(
cmd[0],
*cmd[1:],
stdin=stdin,
stdout=asyncio.subprocess.PIPE,
stderr=asyncio.subprocess.PIPE,
env=env,
)
# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
#
# If it starts hanging, will need to switch to the following code. The problem is that no data
# will be seen until it's done and if it hangs for example there will be no debug info.
# out, err = await p.communicate()
# return _RunOutput(p.returncode, out, err)
out = []
err = []
def tee(line, sink, pipe, label=""):
line = line.decode("utf-8").rstrip()
sink.append(line)
if not quiet:
print(label, line, file=pipe)
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
_read_stream(p.stdout, lambda l: tee(l, out, sys.stdout, label="stdout:")),
_read_stream(p.stderr, lambda l: tee(l, err, sys.stderr, label="stderr:")),
],
timeout=timeout,
)
return _RunOutput(await p.wait(), out, err)
def execute_subprocess_async(cmd, env=None, stdin=None, timeout=180, quiet=False, echo=True) -> _RunOutput:
loop = asyncio.get_event_loop()
result = loop.run_until_complete(
_stream_subprocess(cmd, env=env, stdin=stdin, timeout=timeout, quiet=quiet, echo=echo)
)
cmd_str = " ".join(cmd)
if result.returncode > 0:
stderr = "\n".join(result.stderr)
raise RuntimeError(
f"'{cmd_str}' failed with returncode {result.returncode}\n\n"
f"The combined stderr from workers follows:\n{stderr}"
)
# check that the subprocess actually did run and produced some output, should the test rely on
# the remote side to do the testing
if not result.stdout and not result.stderr:
raise RuntimeError(f"'{cmd_str}' produced no output.")
return result
def pytest_xdist_worker_id():
"""
Returns an int value of worker's numerical id under `pytest-xdist`'s concurrent workers `pytest -n N` regime, or 0
if `-n 1` or `pytest-xdist` isn't being used.
"""
worker = os.environ.get("PYTEST_XDIST_WORKER", "gw0")
worker = re.sub(r"^gw", "", worker, 0, re.M)
return int(worker)
def get_torch_dist_unique_port():
"""
Returns a port number that can be fed to `torch.distributed.launch`'s `--master_port` argument.
Under `pytest-xdist` it adds a delta number based on a worker id so that concurrent tests don't try to use the same
port at once.
"""
port = 29500
uniq_delta = pytest_xdist_worker_id()
return port + uniq_delta
def nested_simplify(obj, decimals=3):
"""
Simplifies an object by rounding float numbers, and downcasting tensors/numpy arrays to get simple equality test
within tests.
"""
import numpy as np
if isinstance(obj, list):
return [nested_simplify(item, decimals) for item in obj]
if isinstance(obj, tuple):
return tuple([nested_simplify(item, decimals) for item in obj])
elif isinstance(obj, np.ndarray):
return nested_simplify(obj.tolist())
elif isinstance(obj, Mapping):
return {nested_simplify(k, decimals): nested_simplify(v, decimals) for k, v in obj.items()}
elif isinstance(obj, (str, int, np.int64)):
return obj
elif obj is None:
return obj
elif is_torch_available() and isinstance(obj, torch.Tensor):
return nested_simplify(obj.tolist(), decimals)
elif is_tf_available() and tf.is_tensor(obj):
return nested_simplify(obj.numpy().tolist())
elif isinstance(obj, float):
return round(obj, decimals)
elif isinstance(obj, (np.int32, np.float32)):
return nested_simplify(obj.item(), decimals)
else:
raise Exception(f"Not supported: {type(obj)}")
def check_json_file_has_correct_format(file_path):
with open(file_path, "r") as f:
lines = f.readlines()
if len(lines) == 1:
# length can only be 1 if dict is empty
assert lines[0] == "{}"
else:
# otherwise make sure json has correct format (at least 3 lines)
assert len(lines) >= 3
# each key one line, ident should be 2, min length is 3
assert lines[0].strip() == "{"
for line in lines[1:-1]:
left_indent = len(lines[1]) - len(lines[1].lstrip())
assert left_indent == 2
assert lines[-1].strip() == "}"
def to_2tuple(x):
if isinstance(x, collections.abc.Iterable):
return x
return (x, x)
# These utils relate to ensuring the right error message is received when running scripts
class SubprocessCallException(Exception):
pass
def run_command(command: List[str], return_stdout=False):
"""
Runs `command` with `subprocess.check_output` and will potentially return the `stdout`. Will also properly capture
if an error occured while running `command`
"""
try:
output = subprocess.check_output(command, stderr=subprocess.STDOUT)
if return_stdout:
if hasattr(output, "decode"):
output = output.decode("utf-8")
return output
except subprocess.CalledProcessError as e:
raise SubprocessCallException(
f"Command `{' '.join(command)}` failed with the following error:\n\n{e.output.decode()}"
) from e
class RequestCounter:
"""
Helper class that will count all requests made online.
Might not be robust if urllib3 changes its logging format but should be good enough for us.
Usage:
```py
with RequestCounter() as counter:
_ = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert")
assert counter["GET"] == 0
assert counter["HEAD"] == 1
assert counter.total_calls == 1
```
"""
def __enter__(self):
self._counter = defaultdict(int)
self.patcher = patch.object(urllib3.connectionpool.log, "debug", wraps=urllib3.connectionpool.log.debug)
self.mock = self.patcher.start()
return self
def __exit__(self, *args, **kwargs) -> None:
for call in self.mock.call_args_list:
log = call.args[0] % call.args[1:]
for method in ("HEAD", "GET", "POST", "PUT", "DELETE", "CONNECT", "OPTIONS", "TRACE", "PATCH"):
if method in log:
self._counter[method] += 1
break
self.patcher.stop()
def __getitem__(self, key: str) -> int:
return self._counter[key]
@property
def total_calls(self) -> int:
return sum(self._counter.values())
def is_flaky(max_attempts: int = 5, wait_before_retry: Optional[float] = None, description: Optional[str] = None):
"""
To decorate flaky tests. They will be retried on failures.
Args:
max_attempts (`int`, *optional*, defaults to 5):
The maximum number of attempts to retry the flaky test.
wait_before_retry (`float`, *optional*):
If provided, will wait that number of seconds before retrying the test.
description (`str`, *optional*):
A string to describe the situation (what / where / why is flaky, link to GH issue/PR comments, errors,
etc.)
"""
def decorator(test_func_ref):
@functools.wraps(test_func_ref)
def wrapper(*args, **kwargs):
retry_count = 1
while retry_count < max_attempts:
try:
return test_func_ref(*args, **kwargs)
except Exception as err:
print(f"Test failed with {err} at try {retry_count}/{max_attempts}.", file=sys.stderr)
if wait_before_retry is not None:
time.sleep(wait_before_retry)
retry_count += 1
return test_func_ref(*args, **kwargs)
return wrapper
return decorator
def run_test_in_subprocess(test_case, target_func, inputs=None, timeout=None):
"""
To run a test in a subprocess. In particular, this can avoid (GPU) memory issue.
Args:
test_case (`unittest.TestCase`):
The test that will run `target_func`.
target_func (`Callable`):
The function implementing the actual testing logic.
inputs (`dict`, *optional*, defaults to `None`):
The inputs that will be passed to `target_func` through an (input) queue.
timeout (`int`, *optional*, defaults to `None`):
The timeout (in seconds) that will be passed to the input and output queues. If not specified, the env.
variable `PYTEST_TIMEOUT` will be checked. If still `None`, its value will be set to `600`.
"""
if timeout is None:
timeout = int(os.environ.get("PYTEST_TIMEOUT", 600))
start_methohd = "spawn"
ctx = multiprocessing.get_context(start_methohd)
input_queue = ctx.Queue(1)
output_queue = ctx.JoinableQueue(1)
# We can't send `unittest.TestCase` to the child, otherwise we get issues regarding pickle.
input_queue.put(inputs, timeout=timeout)
process = ctx.Process(target=target_func, args=(input_queue, output_queue, timeout))
process.start()
# Kill the child process if we can't get outputs from it in time: otherwise, the hanging subprocess prevents
# the test to exit properly.
try:
results = output_queue.get(timeout=timeout)
output_queue.task_done()
except Exception as e:
process.terminate()
test_case.fail(e)
process.join(timeout=timeout)
if results["error"] is not None:
test_case.fail(f'{results["error"]}')
"""
The following contains utils to run the documentation tests without having to overwrite any files.
The `preprocess_string` function adds `# doctest: +IGNORE_RESULT` markers on the fly anywhere a `load_dataset` call is
made as a print would otherwise fail the corresonding line.
To skip cuda tests, make sure to call `SKIP_CUDA_DOCTEST=1 pytest --doctest-modules <path_to_files_to_test>
"""
def preprocess_string(string, skip_cuda_tests):
"""Prepare a docstring or a `.md` file to be run by doctest.
The argument `string` would be the whole file content if it is a `.md` file. For a python file, it would be one of
its docstring. In each case, it may contain multiple python code examples. If `skip_cuda_tests` is `True` and a
cuda stuff is detective (with a heuristic), this method will return an empty string so no doctest will be run for
`string`.
"""
codeblock_pattern = r"(```(?:python|py)\s*\n\s*>>> )((?:.*?\n)*?.*?```)"
codeblocks = re.split(re.compile(codeblock_pattern, flags=re.MULTILINE | re.DOTALL), string)
is_cuda_found = False
for i, codeblock in enumerate(codeblocks):
if "load_dataset(" in codeblock and "# doctest: +IGNORE_RESULT" not in codeblock:
codeblocks[i] = re.sub(r"(>>> .*load_dataset\(.*)", r"\1 # doctest: +IGNORE_RESULT", codeblock)
if (
(">>>" in codeblock or "..." in codeblock)
and re.search(r"cuda|to\(0\)|device=0", codeblock)
and skip_cuda_tests
):
is_cuda_found = True
break
modified_string = ""
if not is_cuda_found:
modified_string = "".join(codeblocks)
return modified_string
class HfDocTestParser(doctest.DocTestParser):
"""
Overwrites the DocTestParser from doctest to properly parse the codeblocks that are formatted with black. This
means that there are no extra lines at the end of our snippets. The `# doctest: +IGNORE_RESULT` marker is also
added anywhere a `load_dataset` call is made as a print would otherwise fail the corresponding line.
Tests involving cuda are skipped base on a naive pattern that should be updated if it is not enough.
"""
# This regular expression is used to find doctest examples in a
# string. It defines three groups: `source` is the source code
# (including leading indentation and prompts); `indent` is the
# indentation of the first (PS1) line of the source code; and
# `want` is the expected output (including leading indentation).
# fmt: off
_EXAMPLE_RE = re.compile(r'''
# Source consists of a PS1 line followed by zero or more PS2 lines.
(?P<source>
(?:^(?P<indent> [ ]*) >>> .*) # PS1 line
(?:\n [ ]* \.\.\. .*)*) # PS2 lines
\n?
# Want consists of any non-blank lines that do not start with PS1.
(?P<want> (?:(?![ ]*$) # Not a blank line
(?![ ]*>>>) # Not a line starting with PS1
# !!!!!!!!!!! HF Specific !!!!!!!!!!!
(?:(?!```).)* # Match any character except '`' until a '```' is found (this is specific to HF because black removes the last line)
# !!!!!!!!!!! HF Specific !!!!!!!!!!!
(?:\n|$) # Match a new line or end of string
)*)
''', re.MULTILINE | re.VERBOSE
)
# fmt: on
# !!!!!!!!!!! HF Specific !!!!!!!!!!!
skip_cuda_tests: bool = bool(os.environ.get("SKIP_CUDA_DOCTEST", False))
# !!!!!!!!!!! HF Specific !!!!!!!!!!!
def parse(self, string, name="<string>"):
"""
Overwrites the `parse` method to incorporate a skip for CUDA tests, and remove logs and dataset prints before
calling `super().parse`
"""
string = preprocess_string(string, self.skip_cuda_tests)
return super().parse(string, name)
class HfDoctestModule(Module):
"""
Overwrites the `DoctestModule` of the pytest package to make sure the HFDocTestParser is used when discovering
tests.
"""
def collect(self) -> Iterable[DoctestItem]:
class MockAwareDocTestFinder(doctest.DocTestFinder):
"""A hackish doctest finder that overrides stdlib internals to fix a stdlib bug.
https://github.com/pytest-dev/pytest/issues/3456 https://bugs.python.org/issue25532
"""
def _find_lineno(self, obj, source_lines):
"""Doctest code does not take into account `@property`, this
is a hackish way to fix it. https://bugs.python.org/issue17446
Wrapped Doctests will need to be unwrapped so the correct line number is returned. This will be
reported upstream. #8796
"""
if isinstance(obj, property):
obj = getattr(obj, "fget", obj)
if hasattr(obj, "__wrapped__"):
# Get the main obj in case of it being wrapped
obj = inspect.unwrap(obj)
# Type ignored because this is a private function.
return super()._find_lineno( # type:ignore[misc]
obj,
source_lines,
)
def _find(self, tests, obj, name, module, source_lines, globs, seen) -> None:
if _is_mocked(obj):
return
with _patch_unwrap_mock_aware():
# Type ignored because this is a private function.
super()._find( # type:ignore[misc]
tests, obj, name, module, source_lines, globs, seen
)
if self.path.name == "conftest.py":
module = self.config.pluginmanager._importconftest(
self.path,
self.config.getoption("importmode"),
rootpath=self.config.rootpath,
)
else:
try:
module = import_path(
self.path,
root=self.config.rootpath,
mode=self.config.getoption("importmode"),
)
except ImportError:
if self.config.getvalue("doctest_ignore_import_errors"):
skip("unable to import module %r" % self.path)
else:
raise
# !!!!!!!!!!! HF Specific !!!!!!!!!!!
finder = MockAwareDocTestFinder(parser=HfDocTestParser())
# !!!!!!!!!!! HF Specific !!!!!!!!!!!
optionflags = get_optionflags(self)
runner = _get_runner(
verbose=False,
optionflags=optionflags,
checker=_get_checker(),
continue_on_failure=_get_continue_on_failure(self.config),
)
for test in finder.find(module, module.__name__):
if test.examples: # skip empty doctests and cuda
yield DoctestItem.from_parent(self, name=test.name, runner=runner, dtest=test)
def _device_agnostic_dispatch(device: str, dispatch_table: Dict[str, Callable], *args, **kwargs):
if device not in dispatch_table:
return dispatch_table["default"](*args, **kwargs)
fn = dispatch_table[device]
# Some device agnostic functions return values. Need to guard against `None`
# instead at user level.
if fn is None:
return None
return fn(*args, **kwargs)
if is_torch_available():
# Mappings from device names to callable functions to support device agnostic
# testing.
BACKEND_MANUAL_SEED = {"cuda": torch.cuda.manual_seed, "cpu": torch.manual_seed, "default": torch.manual_seed}
BACKEND_EMPTY_CACHE = {"cuda": torch.cuda.empty_cache, "cpu": None, "default": None}
BACKEND_DEVICE_COUNT = {"cuda": torch.cuda.device_count, "cpu": lambda: 0, "default": lambda: 1}
def backend_manual_seed(device: str, seed: int):
return _device_agnostic_dispatch(device, BACKEND_MANUAL_SEED, seed)
def backend_empty_cache(device: str):
return _device_agnostic_dispatch(device, BACKEND_EMPTY_CACHE)
def backend_device_count(device: str):
return _device_agnostic_dispatch(device, BACKEND_DEVICE_COUNT)
if is_torch_available():
# If `TRANSFORMERS_TEST_DEVICE_SPEC` is enabled we need to import extra entries
# into device to function mappings.
if "TRANSFORMERS_TEST_DEVICE_SPEC" in os.environ:
device_spec_path = os.environ["TRANSFORMERS_TEST_DEVICE_SPEC"]
if not Path(device_spec_path).is_file():
raise ValueError(
f"Specified path to device spec file is not a file or not found. Received '{device_spec_path}"
)
# Try to strip extension for later import – also verifies we are importing a
# python file.
try:
import_name = device_spec_path[: device_spec_path.index(".py")]
except ValueError as e:
raise ValueError(f"Provided device spec file was not a Python file! Received '{device_spec_path}") from e
device_spec_module = importlib.import_module(import_name)
# Imported file must contain `DEVICE_NAME`. If it doesn't, terminate early.
try:
device_name = device_spec_module.DEVICE_NAME
except AttributeError as e:
raise AttributeError("Device spec file did not contain `DEVICE_NAME`") from e
if "TRANSFORMERS_TEST_DEVICE" in os.environ and torch_device != device_name:
msg = f"Mismatch between environment variable `TRANSFORMERS_TEST_DEVICE` '{torch_device}' and device found in spec '{device_name}'\n"
msg += "Either unset `TRANSFORMERS_TEST_DEVICE` or ensure it matches device spec name."
raise ValueError(msg)
torch_device = device_name
def update_mapping_from_spec(device_fn_dict: Dict[str, Callable], attribute_name: str):
try:
# Try to import the function directly
spec_fn = getattr(device_spec_module, attribute_name)
device_fn_dict[torch_device] = spec_fn
except AttributeError as e:
# If the function doesn't exist, and there is no default, throw an error
if "default" not in device_fn_dict:
raise AttributeError(
f"`{attribute_name}` not found in '{device_spec_path}' and no default fallback function found."
) from e
# Add one entry here for each `BACKEND_*` dictionary.
update_mapping_from_spec(BACKEND_MANUAL_SEED, "MANUAL_SEED_FN")
update_mapping_from_spec(BACKEND_EMPTY_CACHE, "EMPTY_CACHE_FN")
update_mapping_from_spec(BACKEND_DEVICE_COUNT, "DEVICE_COUNT_FN")
| transformers/src/transformers/testing_utils.py/0 | {
"file_path": "transformers/src/transformers/testing_utils.py",
"repo_id": "transformers",
"token_count": 32147
} | 138 |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import ast
import difflib
from collections.abc import Mapping
from typing import Any, Callable, Dict
class InterpretorError(ValueError):
"""
An error raised when the interpretor cannot evaluate a Python expression, due to syntax error or unsupported
operations.
"""
pass
def evaluate(code: str, tools: Dict[str, Callable], state=None, chat_mode=False):
"""
Evaluate a python expression using the content of the variables stored in a state and only evaluating a given set
of functions.
This function will recurse through the nodes of the tree provided.
Args:
code (`str`):
The code to evaluate.
tools (`Dict[str, Callable]`):
The functions that may be called during the evaluation. Any call to another function will fail with an
`InterpretorError`.
state (`Dict[str, Any]`):
A dictionary mapping variable names to values. The `state` should contain the initial inputs but will be
updated by this function to contain all variables as they are evaluated.
chat_mode (`bool`, *optional*, defaults to `False`):
Whether or not the function is called from `Agent.chat`.
"""
try:
expression = ast.parse(code)
except SyntaxError as e:
print("The code generated by the agent is not valid.\n", e)
return
if state is None:
state = {}
result = None
for idx, node in enumerate(expression.body):
try:
line_result = evaluate_ast(node, state, tools)
except InterpretorError as e:
msg = f"Evaluation of the code stopped at line {idx} before the end because of the following error"
if chat_mode:
msg += (
f". Copy paste the following error message and send it back to the agent:\nI get an error: '{e}'"
)
else:
msg += f":\n{e}"
print(msg)
break
if line_result is not None:
result = line_result
return result
def evaluate_ast(expression: ast.AST, state: Dict[str, Any], tools: Dict[str, Callable]):
"""
Evaluate an absract syntax tree using the content of the variables stored in a state and only evaluating a given
set of functions.
This function will recurse trough the nodes of the tree provided.
Args:
expression (`ast.AST`):
The code to evaluate, as an abastract syntax tree.
state (`Dict[str, Any]`):
A dictionary mapping variable names to values. The `state` is updated if need be when the evaluation
encounters assignements.
tools (`Dict[str, Callable]`):
The functions that may be called during the evaluation. Any call to another function will fail with an
`InterpretorError`.
"""
if isinstance(expression, ast.Assign):
# Assignement -> we evaluate the assignement which should update the state
# We return the variable assigned as it may be used to determine the final result.
return evaluate_assign(expression, state, tools)
elif isinstance(expression, ast.Call):
# Function call -> we return the value of the function call
return evaluate_call(expression, state, tools)
elif isinstance(expression, ast.Constant):
# Constant -> just return the value
return expression.value
elif isinstance(expression, ast.Dict):
# Dict -> evaluate all keys and values
keys = [evaluate_ast(k, state, tools) for k in expression.keys]
values = [evaluate_ast(v, state, tools) for v in expression.values]
return dict(zip(keys, values))
elif isinstance(expression, ast.Expr):
# Expression -> evaluate the content
return evaluate_ast(expression.value, state, tools)
elif isinstance(expression, ast.For):
# For loop -> execute the loop
return evaluate_for(expression, state, tools)
elif isinstance(expression, ast.FormattedValue):
# Formatted value (part of f-string) -> evaluate the content and return
return evaluate_ast(expression.value, state, tools)
elif isinstance(expression, ast.If):
# If -> execute the right branch
return evaluate_if(expression, state, tools)
elif hasattr(ast, "Index") and isinstance(expression, ast.Index):
return evaluate_ast(expression.value, state, tools)
elif isinstance(expression, ast.JoinedStr):
return "".join([str(evaluate_ast(v, state, tools)) for v in expression.values])
elif isinstance(expression, ast.List):
# List -> evaluate all elements
return [evaluate_ast(elt, state, tools) for elt in expression.elts]
elif isinstance(expression, ast.Name):
# Name -> pick up the value in the state
return evaluate_name(expression, state, tools)
elif isinstance(expression, ast.Subscript):
# Subscript -> return the value of the indexing
return evaluate_subscript(expression, state, tools)
else:
# For now we refuse anything else. Let's add things as we need them.
raise InterpretorError(f"{expression.__class__.__name__} is not supported.")
def evaluate_assign(assign, state, tools):
var_names = assign.targets
result = evaluate_ast(assign.value, state, tools)
if len(var_names) == 1:
state[var_names[0].id] = result
else:
if len(result) != len(var_names):
raise InterpretorError(f"Expected {len(var_names)} values but got {len(result)}.")
for var_name, r in zip(var_names, result):
state[var_name.id] = r
return result
def evaluate_call(call, state, tools):
if not isinstance(call.func, ast.Name):
raise InterpretorError(
f"It is not permitted to evaluate other functions than the provided tools (tried to execute {call.func} of "
f"type {type(call.func)}."
)
func_name = call.func.id
if func_name not in tools:
raise InterpretorError(
f"It is not permitted to evaluate other functions than the provided tools (tried to execute {call.func.id})."
)
func = tools[func_name]
# Todo deal with args
args = [evaluate_ast(arg, state, tools) for arg in call.args]
kwargs = {keyword.arg: evaluate_ast(keyword.value, state, tools) for keyword in call.keywords}
return func(*args, **kwargs)
def evaluate_subscript(subscript, state, tools):
index = evaluate_ast(subscript.slice, state, tools)
value = evaluate_ast(subscript.value, state, tools)
if isinstance(value, (list, tuple)):
return value[int(index)]
if index in value:
return value[index]
if isinstance(index, str) and isinstance(value, Mapping):
close_matches = difflib.get_close_matches(index, list(value.keys()))
if len(close_matches) > 0:
return value[close_matches[0]]
raise InterpretorError(f"Could not index {value} with '{index}'.")
def evaluate_name(name, state, tools):
if name.id in state:
return state[name.id]
close_matches = difflib.get_close_matches(name.id, list(state.keys()))
if len(close_matches) > 0:
return state[close_matches[0]]
raise InterpretorError(f"The variable `{name.id}` is not defined.")
def evaluate_condition(condition, state, tools):
if len(condition.ops) > 1:
raise InterpretorError("Cannot evaluate conditions with multiple operators")
left = evaluate_ast(condition.left, state, tools)
comparator = condition.ops[0]
right = evaluate_ast(condition.comparators[0], state, tools)
if isinstance(comparator, ast.Eq):
return left == right
elif isinstance(comparator, ast.NotEq):
return left != right
elif isinstance(comparator, ast.Lt):
return left < right
elif isinstance(comparator, ast.LtE):
return left <= right
elif isinstance(comparator, ast.Gt):
return left > right
elif isinstance(comparator, ast.GtE):
return left >= right
elif isinstance(comparator, ast.Is):
return left is right
elif isinstance(comparator, ast.IsNot):
return left is not right
elif isinstance(comparator, ast.In):
return left in right
elif isinstance(comparator, ast.NotIn):
return left not in right
else:
raise InterpretorError(f"Operator not supported: {comparator}")
def evaluate_if(if_statement, state, tools):
result = None
if evaluate_condition(if_statement.test, state, tools):
for line in if_statement.body:
line_result = evaluate_ast(line, state, tools)
if line_result is not None:
result = line_result
else:
for line in if_statement.orelse:
line_result = evaluate_ast(line, state, tools)
if line_result is not None:
result = line_result
return result
def evaluate_for(for_loop, state, tools):
result = None
iterator = evaluate_ast(for_loop.iter, state, tools)
for counter in iterator:
state[for_loop.target.id] = counter
for expression in for_loop.body:
line_result = evaluate_ast(expression, state, tools)
if line_result is not None:
result = line_result
return result
| transformers/src/transformers/tools/python_interpreter.py/0 | {
"file_path": "transformers/src/transformers/tools/python_interpreter.py",
"repo_id": "transformers",
"token_count": 3737
} | 139 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Collection of utils to be used by backbones and their components."""
import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union
class BackboneType(enum.Enum):
TIMM = "timm"
TRANSFORMERS = "transformers"
def verify_out_features_out_indices(
out_features: Optional[Iterable[str]], out_indices: Optional[Iterable[int]], stage_names: Optional[Iterable[str]]
):
"""
Verify that out_indices and out_features are valid for the given stage_names.
"""
if stage_names is None:
raise ValueError("Stage_names must be set for transformers backbones")
if out_features is not None:
if not isinstance(out_features, (list,)):
raise ValueError(f"out_features must be a list got {type(out_features)}")
if any(feat not in stage_names for feat in out_features):
raise ValueError(f"out_features must be a subset of stage_names: {stage_names} got {out_features}")
if len(out_features) != len(set(out_features)):
raise ValueError(f"out_features must not contain any duplicates, got {out_features}")
if out_features != (sorted_feats := [feat for feat in stage_names if feat in out_features]):
raise ValueError(
f"out_features must be in the same order as stage_names, expected {sorted_feats} got {out_features}"
)
if out_indices is not None:
if not isinstance(out_indices, (list, tuple)):
raise ValueError(f"out_indices must be a list or tuple, got {type(out_indices)}")
# Convert negative indices to their positive equivalent: [-1,] -> [len(stage_names) - 1,]
positive_indices = tuple(idx % len(stage_names) if idx < 0 else idx for idx in out_indices)
if any(idx for idx in positive_indices if idx not in range(len(stage_names))):
raise ValueError(f"out_indices must be valid indices for stage_names {stage_names}, got {out_indices}")
if len(positive_indices) != len(set(positive_indices)):
msg = f"out_indices must not contain any duplicates, got {out_indices}"
msg += f"(equivalent to {positive_indices}))" if positive_indices != out_indices else ""
raise ValueError(msg)
if positive_indices != tuple(sorted(positive_indices)):
sorted_negative = tuple(idx for _, idx in sorted(zip(positive_indices, out_indices), key=lambda x: x[0]))
raise ValueError(
f"out_indices must be in the same order as stage_names, expected {sorted_negative} got {out_indices}"
)
if out_features is not None and out_indices is not None:
if len(out_features) != len(out_indices):
raise ValueError("out_features and out_indices should have the same length if both are set")
if out_features != [stage_names[idx] for idx in out_indices]:
raise ValueError("out_features and out_indices should correspond to the same stages if both are set")
def _align_output_features_output_indices(
out_features: Optional[List[str]],
out_indices: Optional[Union[List[int], Tuple[int]]],
stage_names: List[str],
):
"""
Finds the corresponding `out_features` and `out_indices` for the given `stage_names`.
The logic is as follows:
- `out_features` not set, `out_indices` set: `out_features` is set to the `out_features` corresponding to the
`out_indices`.
- `out_indices` not set, `out_features` set: `out_indices` is set to the `out_indices` corresponding to the
`out_features`.
- `out_indices` and `out_features` not set: `out_indices` and `out_features` are set to the last stage.
- `out_indices` and `out_features` set: input `out_indices` and `out_features` are returned.
Args:
out_features (`List[str]`): The names of the features for the backbone to output.
out_indices (`List[int]` or `Tuple[int]`): The indices of the features for the backbone to output.
stage_names (`List[str]`): The names of the stages of the backbone.
"""
if out_indices is None and out_features is None:
out_indices = [len(stage_names) - 1]
out_features = [stage_names[-1]]
elif out_indices is None and out_features is not None:
out_indices = [stage_names.index(layer) for layer in out_features]
elif out_features is None and out_indices is not None:
out_features = [stage_names[idx] for idx in out_indices]
return out_features, out_indices
def get_aligned_output_features_output_indices(
out_features: Optional[List[str]],
out_indices: Optional[Union[List[int], Tuple[int]]],
stage_names: List[str],
) -> Tuple[List[str], List[int]]:
"""
Get the `out_features` and `out_indices` so that they are aligned.
The logic is as follows:
- `out_features` not set, `out_indices` set: `out_features` is set to the `out_features` corresponding to the
`out_indices`.
- `out_indices` not set, `out_features` set: `out_indices` is set to the `out_indices` corresponding to the
`out_features`.
- `out_indices` and `out_features` not set: `out_indices` and `out_features` are set to the last stage.
- `out_indices` and `out_features` set: they are verified to be aligned.
Args:
out_features (`List[str]`): The names of the features for the backbone to output.
out_indices (`List[int]` or `Tuple[int]`): The indices of the features for the backbone to output.
stage_names (`List[str]`): The names of the stages of the backbone.
"""
# First verify that the out_features and out_indices are valid
verify_out_features_out_indices(out_features=out_features, out_indices=out_indices, stage_names=stage_names)
output_features, output_indices = _align_output_features_output_indices(
out_features=out_features, out_indices=out_indices, stage_names=stage_names
)
# Verify that the aligned out_features and out_indices are valid
verify_out_features_out_indices(out_features=output_features, out_indices=output_indices, stage_names=stage_names)
return output_features, output_indices
class BackboneMixin:
backbone_type: Optional[BackboneType] = None
def _init_timm_backbone(self, config) -> None:
"""
Initialize the backbone model from timm The backbone must already be loaded to self._backbone
"""
if getattr(self, "_backbone", None) is None:
raise ValueError("self._backbone must be set before calling _init_timm_backbone")
# These will diagree with the defaults for the transformers models e.g. for resnet50
# the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
# the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
self.stage_names = [stage["module"] for stage in self._backbone.feature_info.info]
self.num_features = [stage["num_chs"] for stage in self._backbone.feature_info.info]
out_indices = self._backbone.feature_info.out_indices
out_features = self._backbone.feature_info.module_name()
# We verify the out indices and out features are valid
verify_out_features_out_indices(
out_features=out_features, out_indices=out_indices, stage_names=self.stage_names
)
self._out_features, self._out_indices = out_features, out_indices
def _init_transformers_backbone(self, config) -> None:
stage_names = getattr(config, "stage_names")
out_features = getattr(config, "out_features", None)
out_indices = getattr(config, "out_indices", None)
self.stage_names = stage_names
self._out_features, self._out_indices = get_aligned_output_features_output_indices(
out_features=out_features, out_indices=out_indices, stage_names=stage_names
)
# Number of channels for each stage. This is set in the transformer backbone model init
self.num_features = None
def _init_backbone(self, config) -> None:
"""
Method to initialize the backbone. This method is called by the constructor of the base class after the
pretrained model weights have been loaded.
"""
self.config = config
self.use_timm_backbone = getattr(config, "use_timm_backbone", False)
self.backbone_type = BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS
if self.backbone_type == BackboneType.TIMM:
self._init_timm_backbone(config)
elif self.backbone_type == BackboneType.TRANSFORMERS:
self._init_transformers_backbone(config)
else:
raise ValueError(f"backbone_type {self.backbone_type} not supported.")
@property
def out_features(self):
return self._out_features
@out_features.setter
def out_features(self, out_features: List[str]):
"""
Set the out_features attribute. This will also update the out_indices attribute to match the new out_features.
"""
self._out_features, self._out_indices = get_aligned_output_features_output_indices(
out_features=out_features, out_indices=None, stage_names=self.stage_names
)
@property
def out_indices(self):
return self._out_indices
@out_indices.setter
def out_indices(self, out_indices: Union[Tuple[int], List[int]]):
"""
Set the out_indices attribute. This will also update the out_features attribute to match the new out_indices.
"""
self._out_features, self._out_indices = get_aligned_output_features_output_indices(
out_features=None, out_indices=out_indices, stage_names=self.stage_names
)
@property
def out_feature_channels(self):
# the current backbones will output the number of channels for each stage
# even if that stage is not in the out_features list.
return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names)}
@property
def channels(self):
return [self.out_feature_channels[name] for name in self.out_features]
def forward_with_filtered_kwargs(self, *args, **kwargs):
signature = dict(inspect.signature(self.forward).parameters)
filtered_kwargs = {k: v for k, v in kwargs.items() if k in signature}
return self(*args, **filtered_kwargs)
def forward(
self,
pixel_values,
output_hidden_states: Optional[bool] = None,
output_attentions: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
raise NotImplementedError("This method should be implemented by the derived class.")
def to_dict(self):
"""
Serializes this instance to a Python dictionary. Override the default `to_dict()` from `PretrainedConfig` to
include the `out_features` and `out_indices` attributes.
"""
output = super().to_dict()
output["out_features"] = output.pop("_out_features")
output["out_indices"] = output.pop("_out_indices")
return output
class BackboneConfigMixin:
"""
A Mixin to support handling the `out_features` and `out_indices` attributes for the backbone configurations.
"""
@property
def out_features(self):
return self._out_features
@out_features.setter
def out_features(self, out_features: List[str]):
"""
Set the out_features attribute. This will also update the out_indices attribute to match the new out_features.
"""
self._out_features, self._out_indices = get_aligned_output_features_output_indices(
out_features=out_features, out_indices=None, stage_names=self.stage_names
)
@property
def out_indices(self):
return self._out_indices
@out_indices.setter
def out_indices(self, out_indices: Union[Tuple[int], List[int]]):
"""
Set the out_indices attribute. This will also update the out_features attribute to match the new out_indices.
"""
self._out_features, self._out_indices = get_aligned_output_features_output_indices(
out_features=None, out_indices=out_indices, stage_names=self.stage_names
)
def to_dict(self):
"""
Serializes this instance to a Python dictionary. Override the default `to_dict()` from `PretrainedConfig` to
include the `out_features` and `out_indices` attributes.
"""
output = super().to_dict()
output["out_features"] = output.pop("_out_features")
output["out_indices"] = output.pop("_out_indices")
return output
def load_backbone(config):
"""
Loads the backbone model from a config object.
If the config is from the backbone model itself, then we return a backbone model with randomly initialized
weights.
If the config is from the parent model of the backbone model itself, then we load the pretrained backbone weights
if specified.
"""
from transformers import AutoBackbone, AutoConfig
backbone_config = getattr(config, "backbone_config", None)
use_timm_backbone = getattr(config, "use_timm_backbone", None)
use_pretrained_backbone = getattr(config, "use_pretrained_backbone", None)
backbone_checkpoint = getattr(config, "backbone", None)
backbone_kwargs = getattr(config, "backbone_kwargs", None)
backbone_kwargs = {} if backbone_kwargs is None else backbone_kwargs
if backbone_kwargs and backbone_config is not None:
raise ValueError("You can't specify both `backbone_kwargs` and `backbone_config`.")
# If there is a backbone_config and a backbone checkpoint, and use_pretrained_backbone=False then the desired
# behaviour is ill-defined: do you want to load from the checkpoint's config or the backbone_config?
if backbone_config is not None and backbone_checkpoint is not None and use_pretrained_backbone is not None:
raise ValueError("Cannot specify both config.backbone_config and config.backbone")
# If any of thhe following are set, then the config passed in is from a model which contains a backbone.
if (
backbone_config is None
and use_timm_backbone is None
and backbone_checkpoint is None
and backbone_checkpoint is None
):
return AutoBackbone.from_config(config=config, **backbone_kwargs)
# config from the parent model that has a backbone
if use_timm_backbone:
if backbone_checkpoint is None:
raise ValueError("config.backbone must be set if use_timm_backbone is True")
# Because of how timm backbones were originally added to models, we need to pass in use_pretrained_backbone
# to determine whether to load the pretrained weights.
backbone = AutoBackbone.from_pretrained(
backbone_checkpoint,
use_timm_backbone=use_timm_backbone,
use_pretrained_backbone=use_pretrained_backbone,
**backbone_kwargs,
)
elif use_pretrained_backbone:
if backbone_checkpoint is None:
raise ValueError("config.backbone must be set if use_pretrained_backbone is True")
backbone = AutoBackbone.from_pretrained(backbone_checkpoint, **backbone_kwargs)
else:
if backbone_config is None and backbone_checkpoint is None:
raise ValueError("Either config.backbone_config or config.backbone must be set")
if backbone_config is None:
backbone_config = AutoConfig.from_pretrained(backbone_checkpoint, **backbone_kwargs)
backbone = AutoBackbone.from_config(config=backbone_config)
return backbone
| transformers/src/transformers/utils/backbone_utils.py/0 | {
"file_path": "transformers/src/transformers/utils/backbone_utils.py",
"repo_id": "transformers",
"token_count": 6094
} | 140 |
# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
class ImageProcessingMixin(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class ImageFeatureExtractionMixin(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class BeitFeatureExtractor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class BeitImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class BitImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class BlipImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class BridgeTowerImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class ChineseCLIPFeatureExtractor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class ChineseCLIPImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class CLIPFeatureExtractor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class CLIPImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class ConditionalDetrFeatureExtractor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class ConditionalDetrImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class ConvNextFeatureExtractor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class ConvNextImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class DeformableDetrFeatureExtractor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class DeformableDetrImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class DeiTFeatureExtractor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class DeiTImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class DetaImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class DetrFeatureExtractor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class DetrImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class DonutFeatureExtractor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class DonutImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class DPTFeatureExtractor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class DPTImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class EfficientFormerImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class EfficientNetImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class FlavaFeatureExtractor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class FlavaImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class FlavaProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class FuyuImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class FuyuProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class GLPNFeatureExtractor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class GLPNImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class IdeficsImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class ImageGPTFeatureExtractor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class ImageGPTImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class LayoutLMv2FeatureExtractor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class LayoutLMv2ImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class LayoutLMv3FeatureExtractor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class LayoutLMv3ImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class LevitFeatureExtractor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class LevitImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class Mask2FormerImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class MaskFormerFeatureExtractor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class MaskFormerImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class MobileNetV1FeatureExtractor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class MobileNetV1ImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class MobileNetV2FeatureExtractor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class MobileNetV2ImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class MobileViTFeatureExtractor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class MobileViTImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class NougatImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class OneFormerImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class Owlv2ImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class OwlViTFeatureExtractor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class OwlViTImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class PerceiverFeatureExtractor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class PerceiverImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class Pix2StructImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class PoolFormerFeatureExtractor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class PoolFormerImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class PvtImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class SamImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class SegformerFeatureExtractor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class SegformerImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class SiglipImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class Swin2SRImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class TvltImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class TvpImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class VideoMAEFeatureExtractor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class VideoMAEImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class ViltFeatureExtractor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class ViltImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class ViltProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class ViTFeatureExtractor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class ViTImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class ViTHybridImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class VitMatteImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class VivitImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class YolosFeatureExtractor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class YolosImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
| transformers/src/transformers/utils/dummy_vision_objects.py/0 | {
"file_path": "transformers/src/transformers/utils/dummy_vision_objects.py",
"repo_id": "transformers",
"token_count": 5489
} | 141 |
{
"modelname": "BrandNewBERT",
"uppercase_modelname": "BRAND_NEW_BERT",
"lowercase_modelname": "brand_new_bert",
"camelcase_modelname": "BrandNewBert",
"has_slow_class": ["True", "False"],
"has_fast_class": ["True", "False"],
"slow_tokenizer_use_sentencepiece": ["True", "False"],
"authors": "The HuggingFace Team"
}
| transformers/templates/adding_a_missing_tokenization_test/cookiecutter.json/0 | {
"file_path": "transformers/templates/adding_a_missing_tokenization_test/cookiecutter.json",
"repo_id": "transformers",
"token_count": 129
} | 142 |
# coding=utf-8
# Copyright 2022 {{cookiecutter.authors}} and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization classes for {{cookiecutter.modelname}}."""
{%- if cookiecutter.tokenizer_type == "Based on BERT" %}
from ...utils import logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_{{cookiecutter.lowercase_modelname}} import {{cookiecutter.camelcase_modelname}}Tokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"{{cookiecutter.checkpoint_identifier}}": "https://huggingface.co/{{cookiecutter.checkpoint_identifier}}/resolve/main/vocab.txt",
}
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"{{cookiecutter.checkpoint_identifier}}": 512,
}
PRETRAINED_INIT_CONFIGURATION = {
"{{cookiecutter.checkpoint_identifier}}": {"do_lower_case": False},
}
class {{cookiecutter.camelcase_modelname}}TokenizerFast(BertTokenizerFast):
r"""
Construct a "fast" {{cookiecutter.modelname}} tokenizer (backed by HuggingFace's *tokenizers* library).
[`~{{cookiecutter.camelcase_modelname}}TokenizerFast`] is identical to [`BertTokenizerFast`] and runs
end-to-end tokenization: punctuation splitting and wordpiece.
Refer to superclass [`BertTokenizerFast`] for usage examples and documentation concerning
parameters.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
slow_tokenizer_class = {{cookiecutter.camelcase_modelname}}Tokenizer
{%- elif cookiecutter.tokenizer_type == "Based on BART" %}
from ...utils import logging
from ..bart.tokenization_bart_fast import BartTokenizerFast
from .tokenization_{{cookiecutter.lowercase_modelname}} import {{cookiecutter.camelcase_modelname}}Tokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"{{cookiecutter.checkpoint_identifier}}": "https://huggingface.co/{{cookiecutter.checkpoint_identifier}}/resolve/main/vocab.json",
},
"merges_file": {
"{{cookiecutter.checkpoint_identifier}}": "https://huggingface.co/{{cookiecutter.checkpoint_identifier}}/resolve/main/merges.txt",
},
"tokenizer_file": {
"{{cookiecutter.checkpoint_identifier}}": "https://huggingface.co/{{cookiecutter.checkpoint_identifier}}/resolve/main/tokenizer.json",
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"{{cookiecutter.checkpoint_identifier}}": 1024,
}
class {{cookiecutter.camelcase_modelname}}TokenizerFast(BartTokenizerFast):
r"""
Construct a "fast" {{cookiecutter.modelname}} tokenizer (backed by HuggingFace's *tokenizers* library).
[`~{{cookiecutter.camelcase_modelname}}TokenizerFast`] is identical to [`BartTokenizerFast`] and runs
end-to-end tokenization: punctuation splitting and wordpiece.
Refer to superclass [`BartTokenizerFast`] for usage examples and documentation concerning
parameters.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
slow_tokenizer_class = {{cookiecutter.camelcase_modelname}}Tokenizer
{%- elif cookiecutter.tokenizer_type == "Standalone" %}
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_{{cookiecutter.lowercase_modelname}} import {{cookiecutter.camelcase_modelname}}Tokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"{{cookiecutter.checkpoint_identifier}}": "https://huggingface.co/{{cookiecutter.checkpoint_identifier}}/resolve/main/vocab.txt",
},
"tokenizer_file": {
"{{cookiecutter.checkpoint_identifier}}": "https://huggingface.co/{{cookiecutter.checkpoint_identifier}}/resolve/main/tokenizer.json",
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"{{cookiecutter.checkpoint_identifier}}": 1024,
}
class {{cookiecutter.camelcase_modelname}}TokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" {{cookiecutter.modelname}} tokenizer (backed by HuggingFace's *tokenizers* library).
Args:
vocab_file (`str`):
Path to the vocabulary file.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
slow_tokenizer_class = {{cookiecutter.camelcase_modelname}}Tokenizer
def __init__(
self,
vocab_file,
merges_file,
unk_token="<|endoftext|>",
bos_token="<|endoftext|>",
eos_token="<|endoftext|>",
add_prefix_space=False,
trim_offsets=True,
**kwargs
):
super().__init__(
ByteLevelBPETokenizer(
vocab_file=vocab_file,
merges_file=merges_file,
add_prefix_space=add_prefix_space,
trim_offsets=trim_offsets,
),
bos_token=bos_token,
eos_token=eos_token,
unk_token=unk_token,
**kwargs,
)
self.add_prefix_space = add_prefix_space
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
output = [self.bos_token_id] + token_ids_0 + [self.eos_token_id]
if token_ids_1 is None:
return output
return output + [self.eos_token_id] + token_ids_1 + [self.eos_token_id]
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task.
{{cookiecutter.modelname}} does not make use of token type ids, therefore a list of zeros is returned.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of zeros.
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
{% endif %}
| transformers/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/tokenization_fast_{{cookiecutter.lowercase_modelname}}.py/0 | {
"file_path": "transformers/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/tokenization_fast_{{cookiecutter.lowercase_modelname}}.py",
"repo_id": "transformers",
"token_count": 2997
} | 143 |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class BenchmarkTest(unittest.TestCase):
def check_results_dict_not_empty(self, results):
for model_result in results.values():
for batch_size, sequence_length in zip(model_result["bs"], model_result["ss"]):
result = model_result["result"][batch_size][sequence_length]
self.assertIsNotNone(result)
def test_inference_no_configs(self):
MODEL_ID = "sshleifer/tiny-gpt2"
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=False,
inference=True,
sequence_lengths=[8],
batch_sizes=[1],
multi_process=False,
)
benchmark = PyTorchBenchmark(benchmark_args)
results = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def test_inference_no_configs_only_pretrain(self):
MODEL_ID = "sgugger/tiny-distilbert-classification"
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=False,
inference=True,
sequence_lengths=[8],
batch_sizes=[1],
multi_process=False,
only_pretrain_model=True,
)
benchmark = PyTorchBenchmark(benchmark_args)
results = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def test_inference_torchscript(self):
MODEL_ID = "sshleifer/tiny-gpt2"
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=False,
inference=True,
torchscript=True,
sequence_lengths=[8],
batch_sizes=[1],
multi_process=False,
)
benchmark = PyTorchBenchmark(benchmark_args)
results = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
@unittest.skipIf(torch_device == "cpu", "Cant do half precision")
def test_inference_fp16(self):
MODEL_ID = "sshleifer/tiny-gpt2"
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=False,
inference=True,
fp16=True,
sequence_lengths=[8],
batch_sizes=[1],
multi_process=False,
)
benchmark = PyTorchBenchmark(benchmark_args)
results = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def test_inference_no_model_no_architectures(self):
MODEL_ID = "sshleifer/tiny-gpt2"
config = AutoConfig.from_pretrained(MODEL_ID)
# set architectures equal to `None`
config.architectures = None
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=True,
inference=True,
sequence_lengths=[8],
batch_sizes=[1],
multi_process=False,
)
benchmark = PyTorchBenchmark(benchmark_args, configs=[config])
results = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def test_train_no_configs(self):
MODEL_ID = "sshleifer/tiny-gpt2"
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=True,
inference=False,
sequence_lengths=[8],
batch_sizes=[1],
multi_process=False,
)
benchmark = PyTorchBenchmark(benchmark_args)
results = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result)
self.check_results_dict_not_empty(results.memory_train_result)
@unittest.skipIf(torch_device == "cpu", "Can't do half precision")
def test_train_no_configs_fp16(self):
MODEL_ID = "sshleifer/tiny-gpt2"
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=True,
inference=False,
sequence_lengths=[8],
batch_sizes=[1],
fp16=True,
multi_process=False,
)
benchmark = PyTorchBenchmark(benchmark_args)
results = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result)
self.check_results_dict_not_empty(results.memory_train_result)
def test_inference_with_configs(self):
MODEL_ID = "sshleifer/tiny-gpt2"
config = AutoConfig.from_pretrained(MODEL_ID)
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=False,
inference=True,
sequence_lengths=[8],
batch_sizes=[1],
multi_process=False,
)
benchmark = PyTorchBenchmark(benchmark_args, configs=[config])
results = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def test_inference_encoder_decoder_with_configs(self):
MODEL_ID = "sshleifer/tinier_bart"
config = AutoConfig.from_pretrained(MODEL_ID)
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=False,
inference=True,
sequence_lengths=[8],
batch_sizes=[1],
multi_process=False,
)
benchmark = PyTorchBenchmark(benchmark_args, configs=[config])
results = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def test_train_with_configs(self):
MODEL_ID = "sshleifer/tiny-gpt2"
config = AutoConfig.from_pretrained(MODEL_ID)
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=True,
inference=False,
sequence_lengths=[8],
batch_sizes=[1],
multi_process=False,
)
benchmark = PyTorchBenchmark(benchmark_args, configs=[config])
results = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result)
self.check_results_dict_not_empty(results.memory_train_result)
def test_train_encoder_decoder_with_configs(self):
MODEL_ID = "sshleifer/tinier_bart"
config = AutoConfig.from_pretrained(MODEL_ID)
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=True,
inference=True,
sequence_lengths=[8],
batch_sizes=[1],
multi_process=False,
)
benchmark = PyTorchBenchmark(benchmark_args, configs=[config])
results = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result)
self.check_results_dict_not_empty(results.memory_train_result)
def test_save_csv_files(self):
MODEL_ID = "sshleifer/tiny-gpt2"
with tempfile.TemporaryDirectory() as tmp_dir:
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=True,
inference=True,
save_to_csv=True,
sequence_lengths=[8],
batch_sizes=[1],
inference_time_csv_file=os.path.join(tmp_dir, "inf_time.csv"),
train_memory_csv_file=os.path.join(tmp_dir, "train_mem.csv"),
inference_memory_csv_file=os.path.join(tmp_dir, "inf_mem.csv"),
train_time_csv_file=os.path.join(tmp_dir, "train_time.csv"),
env_info_csv_file=os.path.join(tmp_dir, "env.csv"),
multi_process=False,
)
benchmark = PyTorchBenchmark(benchmark_args)
benchmark.run()
self.assertTrue(Path(os.path.join(tmp_dir, "inf_time.csv")).exists())
self.assertTrue(Path(os.path.join(tmp_dir, "train_time.csv")).exists())
self.assertTrue(Path(os.path.join(tmp_dir, "inf_mem.csv")).exists())
self.assertTrue(Path(os.path.join(tmp_dir, "train_mem.csv")).exists())
self.assertTrue(Path(os.path.join(tmp_dir, "env.csv")).exists())
def test_trace_memory(self):
MODEL_ID = "sshleifer/tiny-gpt2"
def _check_summary_is_not_empty(summary):
self.assertTrue(hasattr(summary, "sequential"))
self.assertTrue(hasattr(summary, "cumulative"))
self.assertTrue(hasattr(summary, "current"))
self.assertTrue(hasattr(summary, "total"))
with tempfile.TemporaryDirectory() as tmp_dir:
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=True,
inference=True,
sequence_lengths=[8],
batch_sizes=[1],
log_filename=os.path.join(tmp_dir, "log.txt"),
log_print=True,
trace_memory_line_by_line=True,
multi_process=False,
)
benchmark = PyTorchBenchmark(benchmark_args)
result = benchmark.run()
_check_summary_is_not_empty(result.inference_summary)
_check_summary_is_not_empty(result.train_summary)
self.assertTrue(Path(os.path.join(tmp_dir, "log.txt")).exists())
| transformers/tests/benchmark/test_benchmark.py/0 | {
"file_path": "transformers/tests/benchmark/test_benchmark.py",
"repo_id": "transformers",
"token_count": 4937
} | 144 |
{
"feature_extractor_type": "Wav2Vec2FeatureExtractor",
"processor_class": "Wav2Vec2Processor"
} | transformers/tests/fixtures/preprocessor_config.json/0 | {
"file_path": "transformers/tests/fixtures/preprocessor_config.json",
"repo_id": "transformers",
"token_count": 41
} | 145 |
# coding=utf-8
# Copyright 2020 The HuggingFace Team Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a clone of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import tempfile
import unittest
import warnings
import numpy as np
from parameterized import parameterized
from transformers import is_torch_available, pipeline, set_seed
from transformers.testing_utils import (
is_flaky,
require_accelerate,
require_torch,
require_torch_multi_accelerator,
slow,
torch_device,
)
from ..test_modeling_common import floats_tensor, ids_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin
if is_torch_available():
import torch
from transformers import (
AutoModelForCausalLM,
AutoModelForSeq2SeqLM,
AutoModelForSpeechSeq2Seq,
AutoModelForVision2Seq,
AutoTokenizer,
BartForCausalLM,
BartForConditionalGeneration,
BartTokenizer,
GPT2LMHeadModel,
GPT2Tokenizer,
ImageGPTForCausalImageModeling,
SpeechEncoderDecoderModel,
top_k_top_p_filtering,
)
from transformers.cache_utils import DynamicCache
from transformers.generation import (
BeamSampleDecoderOnlyOutput,
BeamSampleEncoderDecoderOutput,
BeamSearchDecoderOnlyOutput,
BeamSearchEncoderDecoderOutput,
BeamSearchScorer,
ConstrainedBeamSearchScorer,
DisjunctiveConstraint,
ForcedBOSTokenLogitsProcessor,
ForcedEOSTokenLogitsProcessor,
GenerateBeamDecoderOnlyOutput,
GenerateBeamEncoderDecoderOutput,
GenerateDecoderOnlyOutput,
GenerateEncoderDecoderOutput,
GreedySearchDecoderOnlyOutput,
GreedySearchEncoderDecoderOutput,
HammingDiversityLogitsProcessor,
InfNanRemoveLogitsProcessor,
LogitsProcessorList,
MaxLengthCriteria,
MinLengthLogitsProcessor,
NoBadWordsLogitsProcessor,
NoRepeatNGramLogitsProcessor,
PhrasalConstraint,
RepetitionPenaltyLogitsProcessor,
SampleDecoderOnlyOutput,
SampleEncoderDecoderOutput,
StoppingCriteria,
StoppingCriteriaList,
TemperatureLogitsWarper,
TopKLogitsWarper,
TopPLogitsWarper,
)
from transformers.generation.utils import _speculative_sampling
class GenerationTesterMixin:
model_tester = None
all_generative_model_classes = ()
input_name = "input_ids"
def _get_input_ids_and_config(self, batch_size=2):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
input_ids = inputs_dict[self.input_name]
# cut to half length & take max batch_size 3
sequence_length = input_ids.shape[-1] // 2
input_ids = input_ids[:batch_size, :sequence_length]
# generate max 3 tokens
max_length = input_ids.shape[-1] + 3
if config.eos_token_id is not None and config.pad_token_id is None:
# hack to allow generate for models such as GPT2 as is done in `generate()`
if isinstance(config.eos_token_id, int):
config.eos_token_id = [config.eos_token_id]
config.pad_token_id = config.eos_token_id[0]
attention_mask = torch.ones_like(input_ids, dtype=torch.long)[:batch_size, :sequence_length]
return config, input_ids, attention_mask, max_length
@staticmethod
def _get_logits_processor_and_kwargs(
input_length,
eos_token_id,
forced_bos_token_id=None,
forced_eos_token_id=None,
max_length=None,
diversity_penalty=None,
):
process_kwargs = {
"min_length": input_length + 1 if max_length is None else max_length - 1,
"bad_words_ids": [[1, 0]],
"repetition_penalty": 1.2,
"remove_invalid_values": True,
}
# NoRepeatNGramLogitsProcessor + forced tokens may result in no valid continuations
if forced_bos_token_id is None and forced_eos_token_id is None:
process_kwargs["no_repeat_ngram_size"] = 2
# NOTE: the order of operations here should match `generate` for accurate testing
logits_processor = LogitsProcessorList(
(
[
HammingDiversityLogitsProcessor(diversity_penalty, num_beams=2, num_beam_groups=2),
]
if diversity_penalty is not None
else []
)
+ (
[
MinLengthLogitsProcessor(process_kwargs["min_length"], eos_token_id),
]
if eos_token_id is not None
else []
)
+ (
[
ForcedBOSTokenLogitsProcessor(forced_bos_token_id),
]
if forced_bos_token_id is not None
else []
)
+ (
[ForcedEOSTokenLogitsProcessor(max_length, forced_eos_token_id)]
if forced_eos_token_id is not None
else []
)
+ [NoBadWordsLogitsProcessor(process_kwargs["bad_words_ids"], eos_token_id)]
+ (
[NoRepeatNGramLogitsProcessor(process_kwargs["no_repeat_ngram_size"])]
if forced_bos_token_id is None and forced_eos_token_id is None
else []
)
+ [RepetitionPenaltyLogitsProcessor(process_kwargs["repetition_penalty"])]
+ [InfNanRemoveLogitsProcessor()] # prevent flaky generation test failures
)
return process_kwargs, logits_processor
@staticmethod
def _get_warper_and_kwargs(num_beams):
warp_kwargs = {"top_k": 10, "top_p": 0.7, "temperature": 0.7}
logits_warper = LogitsProcessorList(
[
TemperatureLogitsWarper(warp_kwargs["temperature"]),
TopKLogitsWarper(top_k=warp_kwargs["top_k"], min_tokens_to_keep=(2 if num_beams > 1 else 1)),
TopPLogitsWarper(top_p=warp_kwargs["top_p"], min_tokens_to_keep=(2 if num_beams > 1 else 1)),
]
)
return warp_kwargs, logits_warper
@staticmethod
def _get_beam_scorer_and_kwargs(batch_size, max_length, num_return_sequences=1):
beam_kwargs = {
"early_stopping": False,
"length_penalty": 2.0,
"num_beams": 2,
"num_return_sequences": num_return_sequences,
}
beam_scorer = BeamSearchScorer(
batch_size=batch_size,
num_beams=beam_kwargs["num_beams"],
device=torch_device,
length_penalty=beam_kwargs["length_penalty"],
do_early_stopping=beam_kwargs["early_stopping"],
num_beam_hyps_to_keep=num_return_sequences,
)
return beam_kwargs, beam_scorer
@staticmethod
def _get_diverse_beam_scorer_and_kwargs(batch_size, max_length, num_return_sequences=1):
beam_kwargs = {
"early_stopping": False,
"length_penalty": 2.0,
"num_beams": 2,
"num_return_sequences": num_return_sequences,
"num_beam_groups": 2, # one beam per group
"diversity_penalty": 2.0,
}
beam_scorer = BeamSearchScorer(
batch_size=batch_size,
num_beams=beam_kwargs["num_beams"],
device=torch_device,
length_penalty=beam_kwargs["length_penalty"],
do_early_stopping=beam_kwargs["early_stopping"],
num_beam_hyps_to_keep=num_return_sequences,
num_beam_groups=beam_kwargs["num_beam_groups"],
)
return beam_kwargs, beam_scorer
@staticmethod
def _get_constrained_beam_scorer_and_kwargs(batch_size, max_length, constraints, num_return_sequences=1):
beam_kwargs = {
"early_stopping": False,
"length_penalty": 2.0,
"num_beams": num_return_sequences * 4,
"num_return_sequences": num_return_sequences,
}
beam_scorer = ConstrainedBeamSearchScorer(
batch_size=batch_size,
constraints=constraints,
num_beams=beam_kwargs["num_beams"],
device=torch_device,
length_penalty=beam_kwargs["length_penalty"],
do_early_stopping=beam_kwargs["early_stopping"],
num_beam_hyps_to_keep=num_return_sequences,
)
return beam_kwargs, beam_scorer
@staticmethod
def _get_encoder_outputs(
model, input_ids, attention_mask, output_attentions=None, output_hidden_states=None, num_interleave=1
):
encoder = model.get_encoder()
encoder_outputs = encoder(
input_ids,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
encoder_outputs["last_hidden_state"] = encoder_outputs.last_hidden_state.repeat_interleave(
num_interleave, dim=0
)
input_ids = torch.zeros_like(input_ids[:, :1]) + model._get_decoder_start_token_id()
attention_mask = None
return encoder_outputs, input_ids, attention_mask
def _greedy_generate(
self,
model,
input_ids,
attention_mask,
max_length,
output_scores=False,
output_logits=False,
output_attentions=False,
output_hidden_states=False,
return_dict_in_generate=False,
):
if model.config.is_encoder_decoder:
max_length = 4
logits_process_kwargs, logits_processor = self._get_logits_processor_and_kwargs(
input_ids.shape[-1],
eos_token_id=model.config.eos_token_id,
forced_bos_token_id=model.config.forced_bos_token_id,
forced_eos_token_id=model.config.forced_eos_token_id,
max_length=max_length,
)
kwargs = {}
model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
output_generate = model.generate(
input_ids,
do_sample=False,
num_beams=1,
max_length=max_length,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
output_scores=output_scores,
output_logits=output_logits,
return_dict_in_generate=return_dict_in_generate,
**logits_process_kwargs,
**model_kwargs,
)
if model.config.is_encoder_decoder:
encoder_outputs, input_ids, attention_mask = self._get_encoder_outputs(
model,
input_ids,
attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
kwargs["encoder_outputs"] = encoder_outputs
with torch.no_grad():
model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
output_greedy = model.greedy_search(
input_ids,
max_length=max_length,
logits_processor=logits_processor,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
output_scores=output_scores,
output_logits=output_logits,
return_dict_in_generate=return_dict_in_generate,
**kwargs,
**model_kwargs,
)
return output_greedy, output_generate
def _sample_generate(
self,
model,
input_ids,
attention_mask,
max_length,
num_return_sequences,
logits_processor,
logits_warper,
logits_warper_kwargs,
process_kwargs,
output_scores=False,
output_logits=False,
output_attentions=False,
output_hidden_states=False,
return_dict_in_generate=False,
):
torch.manual_seed(0)
model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
output_generate = model.generate(
input_ids,
do_sample=True,
num_beams=1,
max_length=max_length,
num_return_sequences=num_return_sequences,
output_scores=output_scores,
output_logits=output_logits,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict_in_generate=return_dict_in_generate,
**logits_warper_kwargs,
**process_kwargs,
**model_kwargs,
)
torch.manual_seed(0)
kwargs = {}
if model.config.is_encoder_decoder:
encoder_outputs, input_ids, attention_mask = self._get_encoder_outputs(
model,
input_ids,
attention_mask,
num_interleave=num_return_sequences,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
kwargs["encoder_outputs"] = encoder_outputs
elif attention_mask is not None:
attention_mask = attention_mask.repeat_interleave(num_return_sequences, dim=0)
with torch.no_grad():
model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
output_sample = model.sample(
input_ids.repeat_interleave(num_return_sequences, dim=0),
max_length=max_length,
logits_processor=logits_processor,
logits_warper=logits_warper,
output_scores=output_scores,
output_logits=output_logits,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict_in_generate=return_dict_in_generate,
**kwargs,
**model_kwargs,
)
return output_sample, output_generate
def _beam_search_generate(
self,
model,
input_ids,
attention_mask,
max_length,
beam_scorer,
beam_kwargs,
logits_processor,
logits_process_kwargs,
output_scores=False,
output_logits=False,
output_attentions=False,
output_hidden_states=False,
return_dict_in_generate=False,
):
model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
output_generate = model.generate(
input_ids,
do_sample=False,
max_length=max_length,
output_scores=output_scores,
output_logits=output_logits,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict_in_generate=return_dict_in_generate,
**beam_kwargs,
**logits_process_kwargs,
**model_kwargs,
)
# beam_search does not automatically interleave `batch_size` dim for `num_beams`
kwargs = {}
if model.config.is_encoder_decoder:
encoder_outputs, input_ids, attention_mask = self._get_encoder_outputs(
model,
input_ids,
attention_mask,
num_interleave=beam_scorer.num_beams,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
kwargs["encoder_outputs"] = encoder_outputs
elif attention_mask is not None:
attention_mask = attention_mask.repeat_interleave(beam_scorer.num_beams, dim=0)
with torch.no_grad():
model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
output_beam_search = model.beam_search(
input_ids.repeat_interleave(beam_scorer.num_beams, dim=0),
beam_scorer,
max_length=max_length,
logits_processor=logits_processor,
output_scores=output_scores,
output_logits=output_logits,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict_in_generate=return_dict_in_generate,
**kwargs,
**model_kwargs,
)
return output_generate, output_beam_search
def _beam_sample_generate(
self,
model,
input_ids,
attention_mask,
max_length,
beam_scorer,
beam_kwargs,
logits_warper,
logits_warper_kwargs,
output_scores=False,
output_logits=False,
output_attentions=False,
output_hidden_states=False,
return_dict_in_generate=False,
):
torch.manual_seed(0)
model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
output_generate = model.generate(
input_ids,
do_sample=True,
max_length=max_length,
output_scores=output_scores,
output_logits=output_logits,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict_in_generate=return_dict_in_generate,
**beam_kwargs,
**logits_warper_kwargs,
**model_kwargs,
)
# beam_search does not automatically interleave `batch_size` dim for `num_beams`
torch.manual_seed(0)
kwargs = {}
if model.config.is_encoder_decoder:
encoder_outputs, input_ids, attention_mask = self._get_encoder_outputs(
model,
input_ids,
attention_mask,
num_interleave=beam_scorer.num_beams,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
kwargs["encoder_outputs"] = encoder_outputs
elif attention_mask is not None:
attention_mask = attention_mask.repeat_interleave(beam_scorer.num_beams, dim=0)
# prevent flaky generation test failures
logits_processor = LogitsProcessorList()
logits_processor.append(InfNanRemoveLogitsProcessor())
with torch.no_grad():
model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
output_beam_sample = model.beam_sample(
input_ids.repeat_interleave(beam_scorer.num_beams, dim=0),
beam_scorer,
max_length=max_length,
logits_warper=logits_warper,
logits_processor=logits_processor,
output_scores=output_scores,
output_logits=output_logits,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict_in_generate=return_dict_in_generate,
**kwargs,
**model_kwargs,
)
return output_generate, output_beam_sample
def _group_beam_search_generate(
self,
model,
input_ids,
attention_mask,
max_length,
beam_scorer,
beam_kwargs,
logits_processor,
logits_process_kwargs,
output_scores=False,
output_logits=False,
output_attentions=False,
output_hidden_states=False,
return_dict_in_generate=False,
):
model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
output_generate = model.generate(
input_ids,
do_sample=False,
max_length=max_length,
output_scores=output_scores,
output_logits=output_logits,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict_in_generate=return_dict_in_generate,
**beam_kwargs,
**logits_process_kwargs,
**model_kwargs,
)
# group_beam_search does not automatically interleave `batch_size` dim for `num_beams`
kwargs = {}
if model.config.is_encoder_decoder:
encoder_outputs, input_ids, attention_mask = self._get_encoder_outputs(
model,
input_ids,
attention_mask,
num_interleave=beam_scorer.num_beams,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
kwargs["encoder_outputs"] = encoder_outputs
elif attention_mask is not None:
attention_mask = attention_mask.repeat_interleave(beam_scorer.num_beams, dim=0)
with torch.no_grad():
model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
output_group_beam_search = model.group_beam_search(
input_ids.repeat_interleave(beam_scorer.num_beams, dim=0),
beam_scorer,
max_length=max_length,
logits_processor=logits_processor,
output_scores=output_scores,
output_logits=output_logits,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict_in_generate=return_dict_in_generate,
**kwargs,
**model_kwargs,
)
return output_generate, output_group_beam_search
def _constrained_beam_search_generate(
self,
model,
input_ids,
attention_mask,
max_length,
constrained_beam_scorer,
constraints,
beam_kwargs,
logits_processor,
logits_process_kwargs,
output_scores=False,
output_logits=False,
output_attentions=False,
output_hidden_states=False,
return_dict_in_generate=False,
):
model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
output_generate = model.generate(
input_ids,
do_sample=False,
max_length=max_length,
output_scores=output_scores,
output_logits=output_logits,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict_in_generate=return_dict_in_generate,
constraints=constraints,
**beam_kwargs,
**logits_process_kwargs,
**model_kwargs,
)
# group_beam_search does not automatically interleave `batch_size` dim for `num_beams`
kwargs = {}
if model.config.is_encoder_decoder:
encoder_outputs, input_ids, attention_mask = self._get_encoder_outputs(
model,
input_ids,
attention_mask,
num_interleave=constrained_beam_scorer.num_beams,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
kwargs["encoder_outputs"] = encoder_outputs
elif attention_mask is not None:
attention_mask = attention_mask.repeat_interleave(constrained_beam_scorer.num_beams, dim=0)
with torch.no_grad():
model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
output_group_beam_search = model.constrained_beam_search(
input_ids.repeat_interleave(constrained_beam_scorer.num_beams, dim=0),
constrained_beam_scorer,
max_length=max_length,
logits_processor=logits_processor,
output_scores=output_scores,
output_logits=output_logits,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict_in_generate=return_dict_in_generate,
**kwargs,
**model_kwargs,
)
return output_generate, output_group_beam_search
def _contrastive_generate(
self,
model,
input_ids,
attention_mask,
max_length,
output_scores=False,
output_logits=False,
output_attentions=False,
output_hidden_states=False,
return_dict_in_generate=False,
):
contrastive_search_kwargs = {
"penalty_alpha": 0.6,
"top_k": 5,
}
if model.config.is_encoder_decoder:
max_length = 4
logits_process_kwargs, logits_processor = self._get_logits_processor_and_kwargs(
input_ids.shape[-1],
eos_token_id=model.config.eos_token_id,
forced_bos_token_id=model.config.forced_bos_token_id,
forced_eos_token_id=model.config.forced_eos_token_id,
max_length=max_length,
)
kwargs = {}
model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
output_generate = model.generate(
input_ids,
do_sample=False,
num_beams=1,
max_length=max_length,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
output_scores=output_scores,
output_logits=output_logits,
return_dict_in_generate=return_dict_in_generate,
**logits_process_kwargs,
**model_kwargs,
**contrastive_search_kwargs,
)
if model.config.is_encoder_decoder:
encoder_outputs, input_ids, attention_mask = self._get_encoder_outputs(
model,
input_ids,
attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
kwargs["encoder_outputs"] = encoder_outputs
with torch.no_grad():
model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
stopping_criteria = StoppingCriteriaList([MaxLengthCriteria(max_length=max_length)])
output_contrastive = model.contrastive_search(
input_ids,
stopping_criteria=stopping_criteria,
logits_processor=logits_processor,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
output_scores=output_scores,
output_logits=output_logits,
return_dict_in_generate=return_dict_in_generate,
**kwargs,
**model_kwargs,
**contrastive_search_kwargs,
)
return output_contrastive, output_generate
def test_greedy_generate(self):
# check `generate()` and `greedy_search()` are equal
for model_class in self.all_generative_model_classes:
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
# test old generation output for backwards compatibility
model = model_class(config).to(torch_device).eval()
output_greedy, output_generate = self._greedy_generate(
model=model, input_ids=input_ids, attention_mask=attention_mask, max_length=max_length
)
self.assertListEqual(output_greedy.tolist(), output_generate.tolist())
def test_greedy_generate_dict_outputs(self):
for model_class in self.all_generative_model_classes:
# disable cache
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
config.use_cache = False
model = model_class(config).to(torch_device).eval()
output_greedy, output_generate = self._greedy_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
output_scores=True,
output_logits=True,
output_hidden_states=True,
output_attentions=True,
return_dict_in_generate=True,
)
if model.config.is_encoder_decoder:
self.assertIsInstance(output_greedy, GenerateEncoderDecoderOutput)
self.assertIsInstance(output_generate, GenerateEncoderDecoderOutput)
# Retrocompatibility check
self.assertIsInstance(output_greedy, GreedySearchEncoderDecoderOutput)
self.assertIsInstance(output_generate, GreedySearchEncoderDecoderOutput)
else:
self.assertIsInstance(output_greedy, GenerateDecoderOnlyOutput)
self.assertIsInstance(output_generate, GenerateDecoderOnlyOutput)
# Retrocompatibility check
self.assertIsInstance(output_greedy, GreedySearchDecoderOnlyOutput)
self.assertIsInstance(output_generate, GreedySearchDecoderOnlyOutput)
self.assertListEqual(output_generate.sequences.tolist(), output_greedy.sequences.tolist())
for output in (output_greedy, output_generate):
self._check_outputs(output, input_ids, model.config)
def test_greedy_generate_dict_outputs_use_cache(self):
for model_class in self.all_generative_model_classes:
# enable cache
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
if not hasattr(config, "use_cache"):
self.skipTest("This model doesn't support caching")
config.use_cache = True
config.is_decoder = True
model = model_class(config).to(torch_device).eval()
output_greedy, output_generate = self._greedy_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
output_scores=True,
output_logits=True,
output_hidden_states=True,
output_attentions=True,
return_dict_in_generate=True,
)
self.assertListEqual(output_generate.sequences.tolist(), output_greedy.sequences.tolist())
for output in (output_greedy, output_generate):
self._check_outputs(output, input_ids, model.config, use_cache=True)
def test_sample_generate(self):
for model_class in self.all_generative_model_classes:
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
model = model_class(config).to(torch_device).eval()
if model.config.is_encoder_decoder:
max_length = 4
process_kwargs, logits_processor = self._get_logits_processor_and_kwargs(
input_ids.shape[-1],
model.config.eos_token_id,
forced_bos_token_id=model.config.forced_bos_token_id,
forced_eos_token_id=model.config.forced_eos_token_id,
max_length=max_length,
)
logits_warper_kwargs, logits_warper = self._get_warper_and_kwargs(num_beams=2)
# check `generate()` and `sample()` are equal
output_sample, output_generate = self._sample_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
num_return_sequences=1,
logits_processor=logits_processor,
logits_warper=logits_warper,
logits_warper_kwargs=logits_warper_kwargs,
process_kwargs=process_kwargs,
)
self.assertListEqual(output_sample.tolist(), output_generate.tolist())
# check `generate()` and `sample()` yield equal results for `num_return_sequences`
output_sample, output_generate = self._sample_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
num_return_sequences=3,
logits_processor=logits_processor,
logits_warper=logits_warper,
logits_warper_kwargs=logits_warper_kwargs,
process_kwargs=process_kwargs,
)
self.assertListEqual(output_sample.tolist(), output_generate.tolist())
def test_sample_generate_dict_output(self):
for model_class in self.all_generative_model_classes:
# disable cache
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
config.use_cache = False
model = model_class(config).to(torch_device).eval()
if model.config.is_encoder_decoder:
max_length = 4
process_kwargs, logits_processor = self._get_logits_processor_and_kwargs(
input_ids.shape[-1],
model.config.eos_token_id,
forced_bos_token_id=model.config.forced_bos_token_id,
forced_eos_token_id=model.config.forced_eos_token_id,
max_length=max_length,
)
logits_warper_kwargs, logits_warper = self._get_warper_and_kwargs(num_beams=1)
output_sample, output_generate = self._sample_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
num_return_sequences=2,
logits_processor=logits_processor,
logits_warper=logits_warper,
logits_warper_kwargs=logits_warper_kwargs,
process_kwargs=process_kwargs,
output_scores=True,
output_logits=True,
output_hidden_states=True,
output_attentions=True,
return_dict_in_generate=True,
)
if model.config.is_encoder_decoder:
self.assertIsInstance(output_sample, GenerateEncoderDecoderOutput)
self.assertIsInstance(output_generate, GenerateEncoderDecoderOutput)
# Retrocompatibility check
self.assertIsInstance(output_sample, SampleEncoderDecoderOutput)
self.assertIsInstance(output_generate, SampleEncoderDecoderOutput)
else:
self.assertIsInstance(output_sample, GenerateDecoderOnlyOutput)
self.assertIsInstance(output_generate, GenerateDecoderOnlyOutput)
# Retrocompatibility check
self.assertIsInstance(output_sample, SampleDecoderOnlyOutput)
self.assertIsInstance(output_generate, SampleDecoderOnlyOutput)
self.assertListEqual(output_generate.sequences.tolist(), output_sample.sequences.tolist())
for output in (output_sample, output_generate):
self._check_outputs(output, input_ids, model.config, num_return_sequences=2)
def test_beam_search_generate(self):
for model_class in self.all_generative_model_classes:
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
# It is important set set the eos_token_id to None to ensure that no sequences
# shorter than `max_length` can be generated which could lead to flaky circle ci
# failures if the top `num_return_sequences` beams are all shorter than the longest beam
config.eos_token_id = None
config.forced_eos_token_id = None
model = model_class(config).to(torch_device).eval()
if model.config.is_encoder_decoder:
max_length = 4
logits_process_kwargs, logits_processor = self._get_logits_processor_and_kwargs(
input_ids.shape[-1],
config.eos_token_id,
config.forced_bos_token_id,
config.forced_eos_token_id,
max_length,
)
beam_kwargs, beam_scorer = self._get_beam_scorer_and_kwargs(input_ids.shape[0], max_length)
# check `generate()` and `beam_search()` are equal
output_generate, output_beam_search = self._beam_search_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
beam_scorer=beam_scorer,
beam_kwargs=beam_kwargs,
logits_process_kwargs=logits_process_kwargs,
logits_processor=logits_processor,
)
self.assertListEqual(output_generate.tolist(), output_beam_search.tolist())
if model.config.is_encoder_decoder:
max_length = 4
beam_kwargs, beam_scorer = self._get_beam_scorer_and_kwargs(input_ids.shape[0], max_length)
output_generate, output_beam_search = self._beam_search_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
beam_scorer=beam_scorer,
beam_kwargs=beam_kwargs,
logits_process_kwargs=logits_process_kwargs,
logits_processor=logits_processor,
)
self.assertListEqual(output_generate.tolist(), output_beam_search.tolist())
def test_beam_search_generate_dict_output(self):
for model_class in self.all_generative_model_classes:
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
# disable cache
config.use_cache = False
# It is important set set the eos_token_id to None to ensure that no sequences
# shorter than `max_length` can be generated which could lead to flaky circle ci
# failures if the top `num_return_sequences` beams are all shorter than the longest beam
config.eos_token_id = None
config.forced_eos_token_id = None
model = model_class(config).to(torch_device).eval()
if model.config.is_encoder_decoder:
max_length = 4
logits_process_kwargs, logits_processor = self._get_logits_processor_and_kwargs(
input_ids.shape[-1],
config.eos_token_id,
config.forced_bos_token_id,
config.forced_eos_token_id,
max_length,
)
beam_kwargs, beam_scorer = self._get_beam_scorer_and_kwargs(input_ids.shape[0], max_length)
output_generate, output_beam_search = self._beam_search_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
beam_scorer=beam_scorer,
beam_kwargs=beam_kwargs,
logits_process_kwargs=logits_process_kwargs,
logits_processor=logits_processor,
output_scores=True,
output_logits=True,
output_hidden_states=True,
output_attentions=True,
return_dict_in_generate=True,
)
if model.config.is_encoder_decoder:
self.assertIsInstance(output_beam_search, GenerateBeamEncoderDecoderOutput)
self.assertIsInstance(output_generate, GenerateBeamEncoderDecoderOutput)
# Retrocompatibility check
self.assertIsInstance(output_beam_search, BeamSearchEncoderDecoderOutput)
self.assertIsInstance(output_generate, BeamSearchEncoderDecoderOutput)
else:
self.assertIsInstance(output_beam_search, GenerateBeamDecoderOnlyOutput)
self.assertIsInstance(output_generate, GenerateBeamDecoderOnlyOutput)
# Retrocompatibility check
self.assertIsInstance(output_beam_search, BeamSearchDecoderOnlyOutput)
self.assertIsInstance(output_generate, BeamSearchDecoderOnlyOutput)
self.assertListEqual(output_generate.sequences.tolist(), output_beam_search.sequences.tolist())
self.assertTrue(
torch.allclose(output_generate["sequences_scores"], output_beam_search["sequences_scores"], atol=1e-3)
)
self.assertTrue(output_generate["sequences_scores"].shape == (output_generate["sequences"].shape[0],))
self.assertTrue((output_generate["sequences_scores"] < 0).all().item())
for output in (output_beam_search, output_generate):
self._check_outputs(output, input_ids, model.config, num_return_sequences=beam_scorer.num_beams)
def test_beam_search_generate_dict_outputs_use_cache(self):
for model_class in self.all_generative_model_classes:
# enable cache
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
# It is important set set the eos_token_id to None to ensure that no sequences
# shorter than `max_length` can be generated which could lead to flaky circle ci
# failures if the top `num_return_sequences` beams are all shorter than the longest beam
config.eos_token_id = None
config.forced_eos_token_id = None
if not hasattr(config, "use_cache"):
self.skipTest("This model doesn't support caching")
model = model_class(config).to(torch_device).eval()
if model.config.is_encoder_decoder:
max_length = 4
logits_process_kwargs, logits_processor = self._get_logits_processor_and_kwargs(
input_ids.shape[-1],
config.eos_token_id,
config.forced_bos_token_id,
config.forced_eos_token_id,
max_length,
)
beam_kwargs, beam_scorer = self._get_beam_scorer_and_kwargs(input_ids.shape[0], max_length)
config.use_cache = True
config.is_decoder = True
model = model_class(config).to(torch_device).eval()
output_beam, output_generate = self._beam_search_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
beam_scorer=beam_scorer,
beam_kwargs=beam_kwargs,
logits_process_kwargs=logits_process_kwargs,
logits_processor=logits_processor,
output_scores=True,
output_logits=True,
output_hidden_states=True,
output_attentions=True,
return_dict_in_generate=True,
)
self.assertListEqual(output_generate.sequences.tolist(), output_beam.sequences.tolist())
for output in (output_beam, output_generate):
self._check_outputs(
output, input_ids, model.config, use_cache=True, num_return_sequences=beam_scorer.num_beams
)
@require_accelerate
@require_torch_multi_accelerator
def test_model_parallel_beam_search(self):
for model_class in self.all_generative_model_classes:
if model_class._no_split_modules is None:
continue
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
model = model_class(config).eval()
with tempfile.TemporaryDirectory() as tmp_dir:
model.cpu().save_pretrained(tmp_dir)
new_model = model_class.from_pretrained(tmp_dir, device_map="auto")
new_model.generate(
input_ids,
attention_mask=attention_mask,
max_length=max_length,
num_beams=2,
)
def test_beam_sample_generate(self):
for model_class in self.all_generative_model_classes:
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
# It is important set set the eos_token_id to None to ensure that no sequences
# shorter than `max_length` can be generated which could lead to flaky circle ci
# failures if the top `num_return_sequences` beams are all shorter than the longest beam
config.eos_token_id = None
config.forced_eos_token_id = None
logits_warper_kwargs, logits_warper = self._get_warper_and_kwargs(num_beams=1)
model = model_class(config).to(torch_device).eval()
# check `generate()` and `beam_search()` are equal
if model.config.is_encoder_decoder:
max_length = 4
beam_kwargs, beam_scorer = self._get_beam_scorer_and_kwargs(input_ids.shape[0], max_length)
output_generate, output_beam_sample = self._beam_sample_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
beam_scorer=beam_scorer,
beam_kwargs=beam_kwargs,
logits_warper=logits_warper,
logits_warper_kwargs=logits_warper_kwargs,
)
self.assertListEqual(output_generate.tolist(), output_beam_sample.tolist())
def test_beam_sample_generate_dict_output(self):
for model_class in self.all_generative_model_classes:
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
# disable cache
config.use_cache = False
# It is important set set the eos_token_id to None to ensure that no sequences
# shorter than `max_length` can be generated which could lead to flaky circle ci
# failures if the top `num_return_sequences` beams are all shorter than the longest beam
config.eos_token_id = None
config.forced_eos_token_id = None
model = model_class(config).to(torch_device).eval()
logits_warper_kwargs, logits_warper = self._get_warper_and_kwargs(num_beams=1)
if model.config.is_encoder_decoder:
max_length = 4
beam_kwargs, beam_scorer = self._get_beam_scorer_and_kwargs(input_ids.shape[0], max_length)
output_beam_sample, output_generate = self._beam_sample_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
beam_scorer=beam_scorer,
beam_kwargs=beam_kwargs,
logits_warper=logits_warper,
logits_warper_kwargs=logits_warper_kwargs,
output_scores=True,
output_logits=True,
output_hidden_states=True,
output_attentions=True,
return_dict_in_generate=True,
)
if model.config.is_encoder_decoder:
self.assertIsInstance(output_beam_sample, GenerateBeamEncoderDecoderOutput)
self.assertIsInstance(output_generate, GenerateBeamEncoderDecoderOutput)
# Retrocompatibility check
self.assertIsInstance(output_beam_sample, BeamSampleEncoderDecoderOutput)
self.assertIsInstance(output_generate, BeamSampleEncoderDecoderOutput)
else:
self.assertIsInstance(output_beam_sample, GenerateBeamDecoderOnlyOutput)
self.assertIsInstance(output_generate, GenerateBeamDecoderOnlyOutput)
# Retrocompatibility check
self.assertIsInstance(output_beam_sample, BeamSampleDecoderOnlyOutput)
self.assertIsInstance(output_generate, BeamSampleDecoderOnlyOutput)
self.assertListEqual(output_generate.sequences.tolist(), output_beam_sample.sequences.tolist())
self.assertTrue(
torch.allclose(output_generate["sequences_scores"], output_beam_sample["sequences_scores"], atol=1e-3)
)
self.assertTrue(output_generate["sequences_scores"].shape == (output_generate["sequences"].shape[0],))
self.assertTrue((output_generate["sequences_scores"] < 0).all().item())
for output in (output_beam_sample, output_generate):
self._check_outputs(output, input_ids, model.config, num_return_sequences=beam_scorer.num_beams)
def test_generate_without_input_ids(self):
config, _, _, max_length = self._get_input_ids_and_config()
# if no bos token id => cannot generate from None
if config.bos_token_id is None:
return
for model_class in self.all_generative_model_classes:
model = model_class(config).to(torch_device)
model.eval()
output_ids_generate = model.generate(do_sample=False, max_length=max_length, remove_invalid_values=True)
self.assertIsNotNone(output_ids_generate)
def test_group_beam_search_generate(self):
for model_class in self.all_generative_model_classes:
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
# It is important set set the eos_token_id to None to ensure that no sequences
# shorter than `max_length` can be generated which could lead to flaky circle ci
# failures if the top `num_return_sequences` beams are all shorter than the longest beam
config.eos_token_id = None
config.forced_eos_token_id = None
model = model_class(config).to(torch_device).eval()
if model.config.is_encoder_decoder:
max_length = 4
logits_process_kwargs, logits_processor = self._get_logits_processor_and_kwargs(
input_ids.shape[-1],
config.eos_token_id,
config.forced_bos_token_id,
config.forced_eos_token_id,
max_length,
diversity_penalty=2.0,
)
# check `generate()` and `group_beam_search()` are equal
beam_kwargs, beam_scorer = self._get_diverse_beam_scorer_and_kwargs(input_ids.shape[0], max_length)
output_generate, output_group_beam_search = self._group_beam_search_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
beam_scorer=beam_scorer,
beam_kwargs=beam_kwargs,
logits_processor=logits_processor,
logits_process_kwargs=logits_process_kwargs,
)
self.assertListEqual(output_generate.tolist(), output_group_beam_search.tolist())
# check `generate()` and `group_beam_search()` are equal for `num_return_sequences`
num_return_sequences = 2
if model.config.is_encoder_decoder:
max_length = 4
beam_kwargs, beam_scorer = self._get_diverse_beam_scorer_and_kwargs(
input_ids.shape[0], max_length, num_return_sequences=num_return_sequences
)
output_generate, output_group_beam_search = self._group_beam_search_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
beam_scorer=beam_scorer,
beam_kwargs=beam_kwargs,
logits_processor=logits_processor,
logits_process_kwargs=logits_process_kwargs,
)
self.assertListEqual(output_generate.tolist(), output_group_beam_search.tolist())
def test_group_beam_search_generate_dict_output(self):
for model_class in self.all_generative_model_classes:
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
config.use_cache = False
# It is important set set the eos_token_id to None to ensure that no sequences
# shorter than `max_length` can be generated which could lead to flaky circle ci
# failures if the top `num_return_sequences` beams are all shorter than the longest beam
config.eos_token_id = None
config.forced_eos_token_id = None
model = model_class(config).to(torch_device).eval()
if model.config.is_encoder_decoder:
max_length = 4
logits_process_kwargs, logits_processor = self._get_logits_processor_and_kwargs(
input_ids.shape[-1],
config.eos_token_id,
config.forced_bos_token_id,
config.forced_eos_token_id,
max_length,
diversity_penalty=2.0,
)
num_return_sequences = 1
beam_kwargs, beam_scorer = self._get_diverse_beam_scorer_and_kwargs(
input_ids.shape[0], max_length, num_return_sequences=num_return_sequences
)
output_generate, output_group_beam_search = self._group_beam_search_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
beam_scorer=beam_scorer,
beam_kwargs=beam_kwargs,
logits_processor=logits_processor,
logits_process_kwargs=logits_process_kwargs,
output_scores=True,
output_logits=True,
output_hidden_states=True,
output_attentions=True,
return_dict_in_generate=True,
)
if model.config.is_encoder_decoder:
self.assertIsInstance(output_group_beam_search, GenerateBeamEncoderDecoderOutput)
self.assertIsInstance(output_generate, GenerateBeamEncoderDecoderOutput)
# Retrocompatibility check
self.assertIsInstance(output_group_beam_search, BeamSearchEncoderDecoderOutput)
self.assertIsInstance(output_generate, BeamSearchEncoderDecoderOutput)
else:
self.assertIsInstance(output_group_beam_search, GenerateBeamDecoderOnlyOutput)
self.assertIsInstance(output_generate, GenerateBeamDecoderOnlyOutput)
# Retrocompatibility check
self.assertIsInstance(output_group_beam_search, BeamSearchDecoderOnlyOutput)
self.assertIsInstance(output_generate, BeamSearchDecoderOnlyOutput)
self.assertListEqual(output_generate.sequences.tolist(), output_group_beam_search.sequences.tolist())
self.assertTrue(
torch.allclose(
output_generate["sequences_scores"], output_group_beam_search["sequences_scores"], atol=1e-3
)
)
self.assertTrue(output_generate["sequences_scores"].shape == (output_generate["sequences"].shape[0],))
self.assertTrue((output_generate["sequences_scores"] < 0).all().item())
for output in (output_group_beam_search, output_generate):
self._check_outputs(
output, input_ids, model.config, num_return_sequences=num_return_sequences * beam_scorer.num_beams
)
# TODO: @gante
@is_flaky()
def test_constrained_beam_search_generate(self):
for model_class in self.all_generative_model_classes:
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
# It is important set set the eos_token_id to None to ensure that no sequences
# shorter than `max_length` can be generated which could lead to flaky circle ci
# failures if the top `num_return_sequences` beams are all shorter than the longest beam
config.eos_token_id = None
config.forced_eos_token_id = None
model = model_class(config).to(torch_device).eval()
max_length = 20
logits_process_kwargs, logits_processor = self._get_logits_processor_and_kwargs(
input_ids.shape[-1],
config.eos_token_id,
config.forced_bos_token_id,
config.forced_eos_token_id,
max_length,
)
# check `generate()` and `constrained_beam_search()` are equal
# Sample constraints
min_id = 3
max_id = config.vocab_size
force_tokens = torch.randint(min_id, max_id, (1, 2)).tolist()[0]
constraints = [
PhrasalConstraint(force_tokens),
]
beam_kwargs, beam_scorer = self._get_constrained_beam_scorer_and_kwargs(
input_ids.shape[0], max_length, constraints, num_return_sequences=1
)
output_generate, output_beam_search = self._constrained_beam_search_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
constrained_beam_scorer=beam_scorer,
constraints=constraints,
beam_kwargs=beam_kwargs,
logits_processor=logits_processor,
logits_process_kwargs=logits_process_kwargs,
)
self.assertListEqual(output_generate.tolist(), output_beam_search.tolist())
for generation_output in output_generate:
self._check_sequence_inside_sequence(force_tokens, generation_output)
# check `generate()` and `constrained_beam_search()` are equal for `num_return_sequences`
# Sample constraints
force_tokens = torch.randint(min_id, max_id, (1, 2)).tolist()[0]
constraints = [
PhrasalConstraint(force_tokens),
]
num_return_sequences = 2
max_length = 20
beam_kwargs, beam_scorer = self._get_constrained_beam_scorer_and_kwargs(
input_ids.shape[0], max_length, constraints, num_return_sequences=num_return_sequences
)
output_generate, output_beam_search = self._constrained_beam_search_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
constrained_beam_scorer=beam_scorer,
constraints=constraints,
beam_kwargs=beam_kwargs,
logits_processor=logits_processor,
logits_process_kwargs=logits_process_kwargs,
)
self.assertListEqual(output_generate.tolist(), output_beam_search.tolist())
for generation_output in output_generate:
self._check_sequence_inside_sequence(force_tokens, generation_output)
def test_constrained_beam_search_generate_dict_output(self):
for model_class in self.all_generative_model_classes:
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
# disable cache
config.use_cache = False
# It is important set set the eos_token_id to None to ensure that no sequences
# shorter than `max_length` can be generated which could lead to flaky circle ci
# failures if the top `num_return_sequences` beams are all shorter than the longest beam
config.eos_token_id = None
config.forced_eos_token_id = None
model = model_class(config).to(torch_device).eval()
if model.config.is_encoder_decoder:
max_length = 20
logits_process_kwargs, logits_processor = self._get_logits_processor_and_kwargs(
input_ids.shape[-1],
config.eos_token_id,
config.forced_bos_token_id,
config.forced_eos_token_id,
max_length,
)
# Sample constraints
min_id = 3
max_id = model.config.vocab_size
force_tokens = torch.randint(min_id, max_id, (1, 2)).tolist()[0]
constraints = [
PhrasalConstraint(force_tokens),
]
beam_kwargs, beam_scorer = self._get_constrained_beam_scorer_and_kwargs(
input_ids.shape[0], max_length, constraints, num_return_sequences=1
)
output_generate, output_beam_search = self._constrained_beam_search_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
constrained_beam_scorer=beam_scorer,
constraints=constraints,
beam_kwargs=beam_kwargs,
logits_processor=logits_processor,
logits_process_kwargs=logits_process_kwargs,
output_scores=True,
output_logits=True,
output_hidden_states=True,
output_attentions=True,
return_dict_in_generate=True,
)
if model.config.is_encoder_decoder:
self.assertIsInstance(output_beam_search, GenerateBeamEncoderDecoderOutput)
self.assertIsInstance(output_generate, GenerateBeamEncoderDecoderOutput)
# Retrocompatibility check
self.assertIsInstance(output_beam_search, BeamSearchEncoderDecoderOutput)
self.assertIsInstance(output_generate, BeamSearchEncoderDecoderOutput)
else:
self.assertIsInstance(output_beam_search, GenerateBeamDecoderOnlyOutput)
self.assertIsInstance(output_generate, GenerateBeamDecoderOnlyOutput)
# Retrocompatibility check
self.assertIsInstance(output_beam_search, BeamSearchDecoderOnlyOutput)
self.assertIsInstance(output_generate, BeamSearchDecoderOnlyOutput)
self.assertListEqual(output_generate.sequences.tolist(), output_beam_search.sequences.tolist())
self.assertTrue(
torch.allclose(output_generate["sequences_scores"], output_beam_search["sequences_scores"], atol=1e-3)
)
self.assertTrue(output_generate["sequences_scores"].shape == (output_generate["sequences"].shape[0],))
self.assertTrue((output_generate["sequences_scores"] < 0).all().item())
for output in (output_beam_search, output_generate):
self._check_outputs(output, input_ids, model.config, num_return_sequences=beam_scorer.num_beams)
def test_contrastive_generate(self):
# check `generate()` and `contrastive_search()` are equal
for model_class in self.all_generative_model_classes:
# won't fix: FSMT and Reformer have a different cache variable type (and format).
if any(model_name in model_class.__name__.lower() for model_name in ["fsmt", "reformer"]):
self.skipTest("Won't fix: old model with different cache format")
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
# NOTE: contrastive search only works with cache on at the moment.
if not hasattr(config, "use_cache"):
self.skipTest("This model doesn't support caching")
config.use_cache = True
config.is_decoder = True
# test old generation output for backwards compatibility
model = model_class(config).to(torch_device).eval()
output_contrastive, output_generate = self._contrastive_generate(
model=model, input_ids=input_ids, attention_mask=attention_mask, max_length=max_length
)
self.assertListEqual(output_contrastive.tolist(), output_generate.tolist())
def test_contrastive_generate_dict_outputs_use_cache(self):
for model_class in self.all_generative_model_classes:
# won't fix: FSMT and Reformer have a different cache variable type (and format).
if any(model_name in model_class.__name__.lower() for model_name in ["fsmt", "reformer"]):
self.skipTest("Won't fix: old model with different cache format")
# enable cache
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
# NOTE: contrastive search only works with cache on at the moment.
if not hasattr(config, "use_cache"):
self.skipTest("This model doesn't support caching")
config.use_cache = True
config.is_decoder = True
model = model_class(config).to(torch_device).eval()
output_contrastive, output_generate = self._contrastive_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
output_scores=True,
output_logits=True,
output_hidden_states=True,
output_attentions=True,
return_dict_in_generate=True,
)
self.assertListEqual(output_generate.sequences.tolist(), output_contrastive.sequences.tolist())
for output in (output_contrastive, output_generate):
self._check_outputs(output, input_ids, model.config, use_cache=True)
def test_contrastive_generate_low_memory(self):
# Check that choosing 'low_memory' does not change the model output
for model_class in self.all_generative_model_classes:
if any(model_name in model_class.__name__.lower() for model_name in ["fsmt", "reformer", "speech2text"]):
self.skipTest("Won't fix: old model with different cache format")
if any(model_name in model_class.__name__.lower() for model_name in ["gptbigcode"]):
self.skipTest("TODO: fix me")
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config(batch_size=1)
# NOTE: contrastive search only works with cache on at the moment.
if not hasattr(config, "use_cache"):
self.skipTest("This model doesn't support caching")
config.use_cache = True
config.is_decoder = True
# test output equality of low versus high memory
model = model_class(config).to(torch_device).eval()
low_output = model.generate(
input_ids,
top_k=4,
penalty_alpha=0.6,
low_memory=True,
max_length=max_length,
attention_mask=attention_mask,
)
high_output = model.generate(
input_ids,
top_k=4,
penalty_alpha=0.6,
low_memory=False,
max_length=max_length,
attention_mask=attention_mask,
)
self.assertListEqual(low_output.tolist(), high_output.tolist())
def test_beam_search_low_memory(self):
# Check that choosing 'low_memory' does not change the model output
for model_class in self.all_generative_model_classes:
if any(model_name in model_class.__name__.lower() for model_name in ["fsmt", "reformer"]):
self.skipTest("Won't fix: old model with different cache format")
if any(
model_name in model_class.__name__.lower()
for model_name in [
"bloom",
"ctrl",
"gptbigcode",
"transo_xl",
"xlnet",
"cpm",
]
):
self.skipTest("May fix in the future: need model-specific fixes")
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config(batch_size=2)
# batch_size=1 is ok, but batch_size>1 will cause non-identical output
config.use_cache = True
config.is_decoder = True
# test output equality of low versus high memory
model = model_class(config).to(torch_device).eval()
low_output = model.generate(input_ids, max_new_tokens=8, num_beams=5, early_stopping=True, low_memory=True)
high_output = model.generate(
input_ids, max_new_tokens=8, num_beams=5, early_stopping=True, low_memory=False
)
self.assertListEqual(low_output.tolist(), high_output.tolist())
@is_flaky() # Read NOTE (1) below. If there are API issues, all attempts will fail.
def test_assisted_decoding_matches_greedy_search(self):
# This test ensures that the assisted generation does not introduce output changes over greedy search.
# NOTE (1): The sentence above is true most of the time, there is a tiny difference in the logits due to matmul
# shape differences -- and it may result in a different output. The input shape difference happens in the
# main model, that runs the forward pass with several candidates at once (as opposed to generating one token at
# a time). See https://github.com/huggingface/transformers/issues/25420#issuecomment-1775317535 for more info.
# NOTE (2): It breaks the pattern in the tests above, for multiple reasons:
# - assisted_decoding, contrarily to the other methods, can't be called on its own (e.g. needs to
# prepare the assistant encoder outputs in the main generate body);
# - assisted_decoding does not support `use_cache = False`
# - assisted_decoding does not support `batch_size > 1`
for model_class in self.all_generative_model_classes:
if any(model_name in model_class.__name__.lower() for model_name in ["fsmt", "reformer"]):
self.skipTest("Won't fix: old model with different cache format")
if any(
model_name in model_class.__name__.lower()
for model_name in [
"bigbirdpegasus",
"led",
"mega",
"speech2text",
"git",
"prophetnet",
"seamlessm4t",
"clvp",
]
):
self.skipTest("May fix in the future: need model-specific fixes")
# enable cache
config, input_ids, attention_mask, _ = self._get_input_ids_and_config(batch_size=1)
# NOTE: assisted generation only works with cache on at the moment.
if not hasattr(config, "use_cache"):
self.skipTest("This model doesn't support caching")
config.use_cache = True
config.is_decoder = True
model = model_class(config).to(torch_device).eval()
# Sets assisted generation arguments such that:
# a) no EOS is generated, to ensure generation doesn't break early
# b) the assistant model always generates two tokens when it is called, to ensure the input preparation of
# the assistant model is correct
# c) there are at least two forward passes in the main model, to ensure the input preparation of
# the main model is correct
generation_kwargs = {
"eos_token_id": -1, # see a)
"max_new_tokens": 4, # see c)
"num_beams": 1,
"do_sample": False,
"output_scores": True,
"output_logits": True,
"output_hidden_states": True,
"output_attentions": True,
"return_dict_in_generate": True,
}
output_greedy = model.generate(input_ids, attention_mask=attention_mask, **generation_kwargs)
assistant_model = model
assistant_model.generation_config.num_assistant_tokens = 2 # see b)
assistant_model.generation_config.num_assistant_tokens_schedule = "constant" # see b)
generation_kwargs.update({"assistant_model": assistant_model})
output_assisted = model.generate(input_ids, attention_mask=attention_mask, **generation_kwargs)
# The two outputs must match and their shape must be as expected
self.assertListEqual(output_greedy.sequences.tolist(), output_assisted.sequences.tolist())
for output in (output_greedy, output_assisted):
self._check_outputs(output, input_ids, model.config, use_cache=True)
@is_flaky()
def test_prompt_lookup_decoding_matches_greedy_search(self):
# This test ensures that the prompt lookup generation does not introduce output changes over greedy search.
# This test is mostly a copy of test_assisted_decoding_matches_greedy_search
for model_class in self.all_generative_model_classes:
if any(model_name in model_class.__name__.lower() for model_name in ["fsmt", "reformer"]):
self.skipTest("Won't fix: old model with different cache format")
if any(
model_name in model_class.__name__.lower()
for model_name in [
"bigbirdpegasus",
"led",
"mega",
"speech2text",
"git",
"prophetnet",
"seamlessm4t",
"clvp",
]
):
self.skipTest("May fix in the future: need model-specific fixes")
# enable cache
config, input_ids, attention_mask, _ = self._get_input_ids_and_config(batch_size=1)
# NOTE: assisted generation only works with cache on at the moment.
if not hasattr(config, "use_cache"):
self.skipTest("This model doesn't support caching")
config.use_cache = True
config.is_decoder = True
model = model_class(config).to(torch_device).eval()
# Sets assisted generation arguments such that:
# a) no EOS is generated, to ensure generation doesn't break early
# b) the prompt lookup tries to give the model 2 tokens, to ensure the input preparation of
# prompt lookup is correct
# c) there are at least two forward passes in the main model, to ensure the input preparation of
# the main model is correct
generation_kwargs = {
"eos_token_id": -1, # see a)
"max_new_tokens": 4, # see c)
"num_beams": 1,
"do_sample": False,
"output_scores": True,
"output_logits": True,
"output_hidden_states": True,
"output_attentions": True,
"return_dict_in_generate": True,
}
output_greedy = model.generate(input_ids, attention_mask=attention_mask, **generation_kwargs)
generation_kwargs.update({"prompt_lookup_num_tokens": 2}) # see b)
output_prompt_lookup = model.generate(input_ids, attention_mask=attention_mask, **generation_kwargs)
# The two outputs must match and their shape must be as expected
self.assertListEqual(output_greedy.sequences.tolist(), output_prompt_lookup.sequences.tolist())
for output in (output_greedy, output_prompt_lookup):
self._check_outputs(output, input_ids, model.config, use_cache=True)
def test_assisted_decoding_sample(self):
# In this test we don't check assisted vs non-assisted output -- seeded assisted decoding with sample will not
# match sample for the same seed, as the forward pass does not return the exact same logits (due to matmul with
# different shapes, see https://github.com/huggingface/transformers/issues/25420#issuecomment-1775317535).
for model_class in self.all_generative_model_classes:
if any(model_name in model_class.__name__.lower() for model_name in ["fsmt", "reformer"]):
self.skipTest("Won't fix: old model with different cache format")
if any(
model_name in model_class.__name__.lower()
for model_name in [
"bigbirdpegasus",
"led",
"mega",
"speech2text",
"git",
"prophetnet",
"seamlessm4t",
"clvp",
]
):
self.skipTest("May fix in the future: need model-specific fixes")
# enable cache
config, input_ids, attention_mask, _ = self._get_input_ids_and_config(batch_size=1)
# NOTE: assisted generation only works with cache on at the moment.
if not hasattr(config, "use_cache"):
self.skipTest("This model doesn't support caching")
config.use_cache = True
config.is_decoder = True
model = model_class(config).to(torch_device).eval()
# Sets assisted generation arguments such that:
# a) no EOS is generated, to ensure generation doesn't break early
# b) the assistant model always generates two tokens when it is called, to ensure the input preparation of
# the assistant model is correct
# c) there are at least two forward passes in the main model, to ensure the input preparation of
# the main model is correct
assistant_model = model
assistant_model.generation_config.num_assistant_tokens = 2 # see b)
assistant_model.generation_config.num_assistant_tokens_schedule = "constant" # see b)
generation_kwargs = {
"eos_token_id": -1, # see a)
"max_new_tokens": 4, # see c)
"num_beams": 1,
"do_sample": True,
"assistant_model": assistant_model,
"output_scores": True,
"output_logits": True,
"output_hidden_states": True,
"output_attentions": True,
"return_dict_in_generate": True,
}
output_assisted = model.generate(input_ids, attention_mask=attention_mask, **generation_kwargs)
self._check_outputs(output_assisted, input_ids, model.config, use_cache=True)
def test_generate_with_head_masking(self):
"""Test designed for encoder-decoder models to ensure the attention head masking is used."""
attention_names = ["encoder_attentions", "decoder_attentions", "cross_attentions"]
for model_class in self.all_generative_model_classes:
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
# We want to test only encoder-decoder models
if not config.is_encoder_decoder:
continue
model = model_class(config).to(torch_device)
head_masking = {
"head_mask": torch.zeros(config.encoder_layers, config.encoder_attention_heads, device=torch_device),
"decoder_head_mask": torch.zeros(
config.decoder_layers, config.decoder_attention_heads, device=torch_device
),
"cross_attn_head_mask": torch.zeros(
config.decoder_layers, config.decoder_attention_heads, device=torch_device
),
}
signature = inspect.signature(model.forward)
# We want to test only models where encoder/decoder head masking is implemented
if not set(head_masking.keys()) < {*signature.parameters.keys()}:
continue
for attn_name, (name, mask) in zip(attention_names, head_masking.items()):
out = model.generate(
input_ids,
attention_mask=attention_mask,
num_beams=1,
output_attentions=True,
return_dict_in_generate=True,
remove_invalid_values=True,
**{name: mask},
)
# We check the state of decoder_attentions and cross_attentions just from the last step
attn_weights = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
self.assertEqual(sum([w.sum().item() for w in attn_weights]), 0.0)
def test_left_padding_compatibility(self):
# The check done in this test is fairly difficult -- depending on the model architecture, passing the right
# position index for the position embeddings can still result in a different output, due to numerical masking.
# On the other hand, for some types of position embeddings, an incorrect position index can have a minimal
# impact on the output.
# There are two tricks employed to check whether left-padding compatibility is in place:
# 1 - To reduce the negative impact of the numerical attention mask on a correct position index, we set the
# padding size to 1.
# 2 - To reduce the chance of false positives (i.e. passing when it should be failing), we run the check
# multiple times with random inputs, and it has to pass with all of them.
# NOTE: because of 2), there is some chance of false positives in this test.
for model_class in self.all_generative_model_classes:
config, _, _, _ = self._get_input_ids_and_config()
if config.is_encoder_decoder:
continue # skip for encoder-decoder models -- they don't need left-padding compatibility
model = model_class(config).to(torch_device).eval()
signature = inspect.signature(model.forward).parameters.keys()
no_failures = True
for _ in range(10): # there may be false positives with 10 runs, we rely on the CI to catch the flakiness
_, input_ids, attention_mask, _ = self._get_input_ids_and_config()
model_kwargs = {"input_ids": input_ids, "attention_mask": attention_mask}
if "position_ids" in signature:
position_ids = torch.cumsum(attention_mask, dim=-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
model_kwargs["position_ids"] = position_ids
next_logits_wo_padding = model(**model_kwargs).logits[:, -1, :]
pad_size = (input_ids.shape[0], 1)
padding = torch.ones(pad_size, dtype=input_ids.dtype, device=torch_device) * config.pad_token_id
padded_input_ids = torch.cat((padding, input_ids), dim=1)
padded_attention_mask = torch.cat((torch.zeros_like(padding), attention_mask), dim=1)
model_kwargs = {"input_ids": padded_input_ids, "attention_mask": padded_attention_mask}
if "position_ids" in signature:
position_ids = torch.cumsum(padded_attention_mask, dim=-1) - 1
position_ids.masked_fill_(padded_attention_mask == 0, 1)
model_kwargs["position_ids"] = position_ids
next_logits_with_padding = model(**model_kwargs).logits[:, -1, :]
if not torch.allclose(next_logits_wo_padding, next_logits_with_padding, atol=1e-7):
no_failures = False
break
self.assertTrue(no_failures)
def test_past_key_values_format(self):
# Test that the KV cache is formatted correctly. Exceptions need to explicitly overwrite this test. Having a
# standard KV cache format is important for a consistent API (and for advanced generation methods).
for model_class in self.all_generative_model_classes:
config, inputs = self.model_tester.prepare_config_and_inputs_for_common()
# If it doesn't support cache, pass the test
if not hasattr(config, "use_cache"):
self.skipTest("This model doesn't support caching")
model = model_class(config).to(torch_device)
if "use_cache" not in inputs:
inputs["use_cache"] = True
outputs = model(**inputs)
# If "past_key_values" is not returned, pass the test (e.g. RWKV uses a different cache name and format)
if "past_key_values" not in outputs:
self.skipTest("This model doesn't return `past_key_values`")
num_hidden_layers = (
getattr(config, "decoder_layers", None)
or getattr(config, "num_decoder_layers", None)
or config.num_hidden_layers
)
num_attention_heads = getattr(config, "decoder_attention_heads", config.num_attention_heads)
embed_dim = getattr(config, "d_model", config.hidden_size)
per_head_embed_dim = embed_dim // num_attention_heads
past_kv = outputs["past_key_values"]
self.assertEqual(len(past_kv), num_hidden_layers)
# Encoder-Decoder checks
if config.is_encoder_decoder:
encoder_num_attention_heads = config.encoder_attention_heads
encoder_per_head_embed_dim = embed_dim // encoder_num_attention_heads
batch_size, seq_length = inputs["decoder_input_ids"].shape
for i in range(num_hidden_layers):
self.assertEqual(len(past_kv[i]), 4) # K V for the decoder + K V for the encoder = 4
self.assertEqual(
past_kv[i][0].shape, (batch_size, num_attention_heads, seq_length, per_head_embed_dim)
)
self.assertEqual(
past_kv[i][1].shape, (batch_size, num_attention_heads, seq_length, per_head_embed_dim)
)
# The sequence length for the encoder K V depends on the model. Since it is not manipulated in
# autoregressive generation, I'm keeping the test general and not checking the 3rd dim
self.assertEqual(
(past_kv[i][2].shape[0], past_kv[i][2].shape[1], past_kv[i][2].shape[3]),
(batch_size, encoder_num_attention_heads, encoder_per_head_embed_dim),
)
self.assertEqual(
(past_kv[i][3].shape[0], past_kv[i][3].shape[1], past_kv[i][3].shape[3]),
(batch_size, encoder_num_attention_heads, encoder_per_head_embed_dim),
)
# Decoder-only checks
else:
# TODO: this line is only needed because of imagegpt, where "pixel_values" = "input_ids". Fix the
# tests in imagegpt such that `prepare_config_and_inputs_for_common` returns the later (and the other
# tests use it)
key = "input_ids" if "input_ids" in inputs else "pixel_values"
batch_size, seq_length = inputs[key].shape
for i in range(num_hidden_layers):
self.assertEqual(len(past_kv[0]), 2) # K V for the decoder = 2
self.assertEqual(
past_kv[i][0].shape, (batch_size, num_attention_heads, seq_length, per_head_embed_dim)
)
self.assertEqual(
past_kv[i][1].shape, (batch_size, num_attention_heads, seq_length, per_head_embed_dim)
)
def test_generate_from_inputs_embeds_decoder_only(self):
# When supported, tests that the decoder model can generate from `inputs_embeds` instead of `input_ids`
# if fails, you should probably update the `prepare_inputs_for_generation` function
for model_class in self.all_generative_model_classes:
config, input_ids, _, _ = self._get_input_ids_and_config()
# Ignore:
# a) eos (to always output 20 tokens) and pad (so we don't try to infer the attn mask from the input_ids,
# which would cause a mismatch),
config.pad_token_id = config.eos_token_id = -1
# b) embedding scaling, the scaling factor applied after embeding from input_ids (requires knowledge of the
# variable that holds the scaling factor, which is model-dependent)
if hasattr(config, "scale_embedding"):
config.scale_embedding = False
# This test is for decoder-only models (encoder-decoder models have native input embeddings support in the
# decoder)
if config.is_encoder_decoder:
continue
# Skip models without explicit support
model = model_class(config).to(torch_device).eval()
if "inputs_embeds" not in inspect.signature(model.prepare_inputs_for_generation).parameters.keys():
continue
# Traditional way of generating text
outputs_from_ids = model.generate(input_ids)
self.assertEqual(outputs_from_ids.shape, (2, 20))
# Same thing, but from input embeddings (`input_ids` is passed so the prompt is present in the output)
inputs_embeds = model.get_input_embeddings()(input_ids)
outputs_from_embeds = model.generate(input_ids, inputs_embeds=inputs_embeds)
self.assertListEqual(outputs_from_ids.tolist(), outputs_from_embeds.tolist())
# But if we pass different inputs_embeds, we should get different outputs
torch.manual_seed(0)
random_embeds = torch.rand_like(inputs_embeds)
outputs_from_rand_embeds = model.generate(input_ids, inputs_embeds=random_embeds)
with self.assertRaises(AssertionError):
self.assertListEqual(outputs_from_rand_embeds.tolist(), outputs_from_embeds.tolist())
# input_ids is not a required input -- if we don't pass it, the newly generated tokens will be the same
outputs_from_embeds_wo_ids = model.generate(
inputs_embeds=inputs_embeds, max_new_tokens=20 - inputs_embeds.shape[1]
)
self.assertListEqual(
outputs_from_embeds[:, inputs_embeds.shape[1] :].tolist(),
outputs_from_embeds_wo_ids.tolist(),
)
def test_generate_continue_from_past_key_values(self):
# Tests that we can continue generating from past key values, returned from a previous `generate` call
for model_class in self.all_generative_model_classes:
if any(model_name in model_class.__name__.lower() for model_name in ["imagegpt"]):
self.skipTest("Won't fix: old model with unique inputs/caches/other")
if any(model_name in model_class.__name__.lower() for model_name in ["umt5"]):
self.skipTest("TODO: needs modeling or test input preparation fixes for compatibility")
config, inputs = self.model_tester.prepare_config_and_inputs_for_common()
if not hasattr(config, "use_cache"):
self.skipTest("This model doesn't support caching")
# Let's make it always:
# 1. use cache (for obvious reasons)
# 2. generate to max length (which can be achieved by setting the eos token to an invalid value), which
# would make the test flaky (e.g. EOS is generated on iteration 1 on both generations, but the
# continuation would force it to generate beyond an EOS token)
# 3. ignore `token_type_ids` for simplicity
# 4. ignore `forced_eos_token_id`, which requires further manipulation of the continuation inputs and is
# active by default on some models
config.use_cache = True
if "token_type_ids" in inputs:
del inputs["token_type_ids"]
model = model_class(config).to(torch_device)
model.eval()
model.generation_config.pad_token_id = model.generation_config.eos_token_id = -1
model.generation_config.forced_eos_token_id = None
# If "past_key_values" is not returned, skip the test (e.g. RWKV uses a different cache name and format)
outputs = model(**inputs)
if "past_key_values" not in outputs:
self.skipTest("This model doesn't return `past_key_values`")
# Traditional way of generating text, with `return_dict_in_generate` to return the past key values
outputs = model.generate(**inputs, do_sample=False, max_new_tokens=4, return_dict_in_generate=True)
# Let's generate again, but passing the past key values in between (3 + 1 = 4 tokens). Note that the
# inputs may need to be tweaked across `generate` calls (like the attention mask).
outputs_cached = model.generate(**inputs, do_sample=False, max_new_tokens=3, return_dict_in_generate=True)
# Continue from the tokens generated above, preparing the inputs accordingly
inputs["past_key_values"] = outputs_cached.past_key_values
new_attention_len = outputs_cached.sequences.shape[-1]
if config.is_encoder_decoder:
inputs["decoder_input_ids"] = outputs_cached.sequences
if "decoder_attention_mask" in inputs:
inputs["decoder_attention_mask"] = torch.nn.functional.pad(
inputs["decoder_attention_mask"],
(0, new_attention_len - inputs["decoder_attention_mask"].shape[1]),
mode="constant",
value=1,
)
else:
inputs["input_ids"] = outputs_cached.sequences
if "attention_mask" in inputs:
inputs["attention_mask"] = torch.nn.functional.pad(
inputs["attention_mask"],
(0, new_attention_len - inputs["attention_mask"].shape[1]),
mode="constant",
value=1,
)
outputs_cached = model.generate(**inputs, do_sample=False, max_new_tokens=1, return_dict_in_generate=True)
# The two sets of generated text and past kv should be equal to each other
self.assertListEqual(outputs.sequences.tolist(), outputs_cached.sequences.tolist())
for layer_idx in range(len(outputs_cached.past_key_values)):
for kv_idx in range(len(outputs_cached.past_key_values[layer_idx])):
self.assertTrue(
torch.allclose(
outputs.past_key_values[layer_idx][kv_idx],
outputs_cached.past_key_values[layer_idx][kv_idx],
)
)
@parameterized.expand([(1, False), (1, True), (4, False)])
def test_new_cache_format(self, num_beams, do_sample):
# Tests that generating with the new format is exactly the same as the legacy one (for models that support it).
# 👉 tests with and without beam search so that we can test with and without cache reordering.
# 👉 tests with and without sampling so we can cover the most common use cases.
for model_class in self.all_generative_model_classes:
if not model_class._supports_cache_class:
self.skipTest("This model does not support the new cache format")
config, input_ids, attention_mask, _ = self._get_input_ids_and_config()
config.use_cache = True
config.is_decoder = True
model = model_class(config).to(torch_device).eval()
generation_kwargs = {
"max_new_tokens": 5,
"do_sample": do_sample,
"num_beams": num_beams,
"num_return_sequences": num_beams,
"return_dict_in_generate": True, # Required to return `past_key_values`
}
# Sets seed before calling `generate` for the case with do_sample=True
seed = torch.randint(0, 1000000, (1,)).item()
set_seed(seed)
legacy_results = model.generate(input_ids, attention_mask=attention_mask, **generation_kwargs)
set_seed(seed)
new_results = model.generate(
input_ids, attention_mask=attention_mask, past_key_values=DynamicCache(), **generation_kwargs
)
# The two sets of generated sequences must match, despite the cache format between forward passes being
# different
self.assertListEqual(legacy_results.sequences.tolist(), new_results.sequences.tolist())
self.assertTrue(isinstance(legacy_results.past_key_values, tuple))
self.assertTrue(isinstance(new_results.past_key_values, DynamicCache))
# The contents of the two caches, when converted to the same format (in both directions!), must match
legacy_cache = legacy_results.past_key_values
new_cache_converted = new_results.past_key_values.to_legacy_cache()
for layer_idx in range(len(legacy_cache)):
for kv_idx in range(len(legacy_cache[layer_idx])):
self.assertTrue(
torch.allclose(
legacy_cache[layer_idx][kv_idx],
new_cache_converted[layer_idx][kv_idx],
)
)
new_cache = new_results.past_key_values
legacy_cache_converted = DynamicCache.from_legacy_cache(legacy_results.past_key_values)
for layer_idx in range(len(new_cache)):
for kv_idx in range(len(new_cache[layer_idx])):
self.assertTrue(
torch.allclose(
new_cache[layer_idx][kv_idx],
legacy_cache_converted[layer_idx][kv_idx],
)
)
def _check_outputs(self, output, input_ids, config, use_cache=False, num_return_sequences=1):
batch_size, seq_length = input_ids.shape
num_sequences_in_output = batch_size * num_return_sequences
gen_len = (
output.sequences.shape[-1] - 1 if config.is_encoder_decoder else output.sequences.shape[-1] - seq_length
)
# scores
self._check_scores(num_sequences_in_output, output.scores, length=gen_len, config=config)
# unprocessed logits
self._check_logits(num_sequences_in_output, output.logits, config=config)
# Attentions
if config.is_encoder_decoder:
# encoder
self._check_encoder_attention_for_generate(output.encoder_attentions, batch_size, config, seq_length)
# decoder
self._check_attentions_for_generate(
num_sequences_in_output,
output.decoder_attentions,
min_length=1,
max_length=output.sequences.shape[-1],
config=config,
use_cache=use_cache,
)
else:
# if use_cache first input is equal to no use_cache, so skip here
attentions = output.attentions if not use_cache else output.attentions[1:]
min_length = seq_length if not use_cache else seq_length + 1
self._check_attentions_for_generate(
num_sequences_in_output,
attentions=attentions,
min_length=min_length,
max_length=output.sequences.shape[-1],
config=config,
use_cache=use_cache,
)
# Hidden States
if config.is_encoder_decoder:
# encoder
self._check_encoder_hidden_states_for_generate(
output.encoder_hidden_states, batch_size, config, seq_length
)
# decoder
self._check_hidden_states_for_generate(
num_sequences_in_output,
output.decoder_hidden_states,
min_length=1,
max_length=output.sequences.shape[-1],
config=config,
use_cache=use_cache,
)
else:
# if use_cache first input is equal to no use_cache, so skip here
hidden_states = output.hidden_states if not use_cache else output.hidden_states[1:]
min_length = seq_length if not use_cache else seq_length + 1
self._check_hidden_states_for_generate(
num_sequences_in_output,
hidden_states,
min_length=min_length,
max_length=output.sequences.shape[-1],
config=config,
use_cache=use_cache,
)
# Past Key Value States -- two notes here:
# 1. Its inner sequence length is with respect to the inputs of the latest forward pass, hence the "-1"
# 2. Some old models still return `output.past_key_values` even without `use_cache=True`
# 3. TODO (joao): A few models have different formats, skipping those until the cache refactor is complete
models_without_standard_cache = ("bloom", "ctrl", "fsmt", "gptbigcode", "mega", "reformer")
has_standard_cache = not any(
model_name in config.__class__.__name__.lower() for model_name in models_without_standard_cache
)
if use_cache and has_standard_cache:
past_key_values = output.past_key_values
past_sequence_length = output.sequences.shape[-1] - 1
self._check_past_key_values_for_generate(
num_sequences_in_output,
past_key_values,
seq_length=past_sequence_length,
config=config,
)
def _check_scores(self, batch_size, scores, length, config):
expected_shape = (batch_size, config.vocab_size)
self.assertIsInstance(scores, tuple)
self.assertEqual(len(scores), length)
self.assertListEqual([iter_scores.shape for iter_scores in scores], [expected_shape] * len(scores))
def _check_logits(self, batch_size, scores, config):
self.assertIsInstance(scores, tuple)
self.assertListEqual([iter_scores.shape[0] for iter_scores in scores], [batch_size] * len(scores))
# vocabulary difference equal to one (imagegptmodel?) or zero (all other models)
vocab_diff = config.vocab_size - scores[0].shape[-1]
self.assertTrue(vocab_diff in [0, 1])
self.assertListEqual([config.vocab_size - score.shape[-1] for score in scores], [vocab_diff] * len(scores))
def _check_attentions_for_generate(
self, batch_size, attentions, min_length, max_length, config, use_cache=False, num_beam_groups=1
):
self.assertIsInstance(attentions, tuple)
self.assertListEqual(
[isinstance(iter_attentions, tuple) for iter_attentions in attentions], [True] * len(attentions)
)
self.assertEqual(len(attentions), (max_length - min_length) * num_beam_groups)
for idx, iter_attentions in enumerate(attentions):
tgt_len = min_length + idx if not use_cache else 1
src_len = min_length + idx
expected_shape = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions], [expected_shape] * len(iter_attentions)
)
def _check_encoder_attention_for_generate(self, attentions, batch_size, config, seq_length):
encoder_expected_shape = (batch_size, config.num_attention_heads, seq_length, seq_length)
self.assertIsInstance(attentions, tuple)
self.assertListEqual(
[layer_attentions.shape for layer_attentions in attentions],
[encoder_expected_shape] * len(attentions),
)
def _check_hidden_states_for_generate(
self, batch_size, hidden_states, min_length, max_length, config, use_cache=False, num_beam_groups=1
):
self.assertIsInstance(hidden_states, tuple)
self.assertListEqual(
[isinstance(iter_hidden_states, tuple) for iter_hidden_states in hidden_states],
[True] * len(hidden_states),
)
self.assertEqual(len(hidden_states), (max_length - min_length) * num_beam_groups)
for idx, iter_hidden_states in enumerate(hidden_states):
seq_len = min_length + idx if not use_cache else 1
expected_shape = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states],
[expected_shape] * len(iter_hidden_states),
)
def _check_encoder_hidden_states_for_generate(self, hidden_states, batch_size, config, seq_length):
encoder_expected_shape = (batch_size, seq_length, config.hidden_size)
self.assertIsInstance(hidden_states, tuple)
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in hidden_states],
[encoder_expected_shape] * len(hidden_states),
)
def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config, num_beam_groups=1):
self.assertIsInstance(past_key_values, tuple)
self.assertListEqual(
[isinstance(iter_past_key_values, tuple) for iter_past_key_values in past_key_values],
[True] * len(past_key_values),
)
# (batch, head, seq_length, head_features)
expected_shape = (
batch_size * num_beam_groups,
config.num_key_value_heads if hasattr(config, "num_key_value_heads") else config.num_attention_heads,
seq_length,
config.hidden_size // config.num_attention_heads,
)
# check shape key, value
self.assertListEqual(
[layer_past_key_values[0].shape for layer_past_key_values in past_key_values],
[expected_shape] * len(past_key_values),
)
self.assertListEqual(
[layer_past_key_values[1].shape for layer_past_key_values in past_key_values],
[expected_shape] * len(past_key_values),
)
def _check_sequence_inside_sequence(self, tensor_1, tensor_2):
# check if tensor_1 inside tensor_2 or tensor_2 inside tensor_1.
# set to same device. we don't care what device.
if not isinstance(tensor_1, list):
tensor_1 = tensor_1.cpu().tolist()
if not isinstance(tensor_2, list):
tensor_2 = tensor_2.cpu().tolist()
in_order = len(tensor_1) <= len(tensor_2)
longer = tensor_2 if in_order else tensor_1
shorter = tensor_1 if in_order else tensor_2
flag = False
chunk_size = len(shorter)
for chunk_idx in range(len(longer) - chunk_size + 1):
subseq = longer[chunk_idx : chunk_idx + chunk_size]
if subseq == shorter:
flag = True
break
self.assertTrue(flag)
@require_torch
class UtilsFunctionsTest(unittest.TestCase):
# tests whether the top_k_top_p function behaves as expected
def test_top_k_top_p_filtering(self):
logits = torch.tensor(
[
[
8.2220991, # 3rd highest value; idx. 0
-0.5620044,
5.23229752,
4.0386393,
-6.8798378,
-0.54785802,
-3.2012153,
2.92777176,
1.88171953,
7.35341276,
8.43207833, # 2nd highest value; idx. 10
-9.85711836,
-5.96209236,
-1.13039161,
-7.1115294,
-0.8369633,
-5.3186408,
7.06427407,
0.81369344,
-0.82023817,
-5.9179796,
0.58813443,
-6.99778438,
4.71551189,
-0.18771637,
7.44020759, # 4th highest value; idx. 25
9.38450987, # 1st highest value; idx. 26
2.12662941,
-9.32562038,
2.35652522,
], # cummulative prob of 4 highest values <= 0.6
[
0.58425518,
4.53139238,
-5.57510464,
-6.28030699,
-7.19529503,
-4.02122551,
1.39337037,
-6.06707057,
1.59480517,
-9.643119,
0.03907799,
0.67231762,
-8.88206726,
6.27115922, # 4th highest value; idx. 13
2.28520723,
4.82767506,
4.30421368,
8.8275313, # 2nd highest value; idx. 17
5.44029958,
-4.4735794,
7.38579536, # 3rd highest value; idx. 20
-2.91051663,
2.61946077,
-2.5674762,
-9.48959302,
-4.02922645,
-1.35416918,
9.67702323, # 1st highest value; idx. 27
-5.89478553,
1.85370467,
], # cummulative prob of 4 highest values <= 0.6
],
dtype=torch.float,
device=torch_device,
)
non_inf_expected_idx = torch.tensor(
[[0, 0], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 20], [1, 27]],
dtype=torch.long,
device=torch_device,
) # expected non filtered idx as noted above
non_inf_expected_output = torch.tensor(
[
8.2221,
8.4321,
7.4402,
9.3845,
6.2712,
8.8275,
7.3858,
9.6770,
], # expected non filtered values as noted above
dtype=torch.float,
device=torch_device,
)
output = top_k_top_p_filtering(logits, top_k=10, top_p=0.6, min_tokens_to_keep=4)
non_inf_output = output[output != -float("inf")].to(device=torch_device)
non_inf_idx = (output != -float("inf")).nonzero().to(device=torch_device)
self.assertTrue(torch.allclose(non_inf_expected_output, non_inf_output, atol=1e-12))
self.assertTrue(torch.all(torch.eq(non_inf_expected_idx, non_inf_idx)))
# tests whether the function uses filter_value instead of default -inf
def test_top_k_top_p_filtering_with_filter_value(self):
logits = torch.tensor(
[
[
1,
1,
1,
0.99, # get filtered by top-p filtering
0.98, # get filtered by top-k filtering
]
],
dtype=torch.float,
device=torch_device,
)
expected_output = torch.tensor(
[[1, 1, 1, 0, 0]],
dtype=torch.float,
device=torch_device,
)
output = top_k_top_p_filtering(logits, top_k=4, top_p=0.5, filter_value=0.0)
self.assertTrue(torch.allclose(expected_output, output, atol=1e-12))
def test_speculative_sampling(self):
# assume vocab size 10, input length 5 + 3 generated candidates
candidate_input_ids = torch.tensor([[8, 0, 3, 9, 8, 1, 4, 5]]) # input tokens
candidate_logits = torch.tensor(
[
[
[-10.0, 10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0], # generated 1
[-10.0, -10.0, -10.0, -10.0, 10.0, -10.0, -10.0, -10.0, -10.0, -10.0], # generated 4
[-10.0, -10.0, -10.0, -10.0, -10.0, 10.0, -10.0, -10.0, -10.0, -10.0], # generated 5
]
]
)
candidate_length = 3
inf = float("inf")
new_logits = torch.tensor(
[
[
[-10.0, 10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0], # accepts 1
[-10.0, -10.0, -10.0, -10.0, 10.0, -10.0, -10.0, -10.0, -10.0, -10.0], # accepts 4
[-inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, 10.0, -inf], # rejects 5, accepts 8
[-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0], # N/A
]
]
)
last_assistant_token_is_eos = False
max_matches = 5
validated_tokens, n_matches = _speculative_sampling(
candidate_input_ids,
candidate_logits,
candidate_length,
new_logits,
last_assistant_token_is_eos,
max_matches,
)
self.assertTrue(n_matches.item() == 2)
self.assertTrue(validated_tokens.tolist()[0] == [1, 4, 8])
@require_torch
class GenerationIntegrationTests(unittest.TestCase, GenerationIntegrationTestsMixin):
# setting framework_dependent_parameters needs to be gated, just like its contents' imports
if is_torch_available():
framework_dependent_parameters = {
"AutoModelForCausalLM": AutoModelForCausalLM,
"AutoModelForSpeechSeq2Seq": AutoModelForSpeechSeq2Seq,
"AutoModelForSeq2SeqLM": AutoModelForSeq2SeqLM,
"AutoModelForVision2Seq": AutoModelForVision2Seq,
"LogitsProcessorList": LogitsProcessorList,
"MinLengthLogitsProcessor": MinLengthLogitsProcessor,
"create_tensor_fn": torch.tensor,
"floats_tensor": floats_tensor,
"return_tensors": "pt",
}
@slow
def test_diverse_beam_search(self):
# PT-only test: TF doesn't have a diverse beam search implementation
article = """Justin Timberlake and Jessica Biel, welcome to parenthood.
The celebrity couple announced the arrival of their son, Silas Randall Timberlake, in statements to People.
"Silas was the middle name of Timberlake's maternal grandfather Bill Bomar, who died in 2012, while Randall is the musician's own middle name, as well as his father's first," People reports.
The couple announced the pregnancy in January, with an Instagram post. It is the first baby for both."""
bart_tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn")
bart_model = BartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn").to(torch_device)
input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
outputs = bart_model.generate(
input_ids,
num_beams=4,
num_return_sequences=2,
num_beam_groups=4,
diversity_penalty=2.0,
remove_invalid_values=True,
)
generated_text = bart_tokenizer.batch_decode(outputs, skip_special_tokens=True)
self.assertListEqual(
generated_text,
[
"The couple announced the birth of their son, Silas Randall Timberlake, in a statement. Silas was the"
" middle name of Timberlake's maternal grandfather Bill Bomar. Randall is the musician's own middle"
" name, as well as his father's first. It is the first baby for both of them.",
"Justin Timberlake and Jessica Biel have a son. The baby is named Silas Randall Timberlake. It is the"
" first child for both. The couple announced the pregnancy in January. The name Silas is the middle"
" name of Timberlake's maternal grandfather. It's also his own middle name.",
],
)
def test_max_length_backward_compat_greedy(self):
# PT-only test: TF doesn't have StoppingCriteria
article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
bart_tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
bart_model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart").to(
torch_device
)
input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
max_length = 20
input_ids = input_ids.expand(2, -1)
model_kwargs = bart_model._prepare_encoder_decoder_kwargs_for_generation(input_ids, {})
input_ids, model_kwargs = bart_model._prepare_decoder_input_ids_for_generation(
batch_size=input_ids.shape[0],
model_input_name=bart_model.main_input_name,
model_kwargs=model_kwargs,
decoder_start_token_id=bart_model.config.decoder_start_token_id,
bos_token_id=bart_model.config.bos_token_id,
)
with self.assertWarns(UserWarning):
bart_model.greedy_search(
input_ids,
max_length=max_length,
pad_token_id=bart_model.config.pad_token_id,
eos_token_id=bart_model.config.eos_token_id,
**model_kwargs,
)
def test_max_length_backward_compat_sample(self):
# PT-only test: TF doesn't have StoppingCriteria
article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
bart_tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
bart_model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart").to(
torch_device
)
input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
max_length = 20
input_ids = input_ids.expand(2, -1)
model_kwargs = bart_model._prepare_encoder_decoder_kwargs_for_generation(input_ids, {})
input_ids, model_kwargs = bart_model._prepare_decoder_input_ids_for_generation(
batch_size=input_ids.shape[0],
model_input_name=bart_model.main_input_name,
model_kwargs=model_kwargs,
decoder_start_token_id=bart_model.config.decoder_start_token_id,
bos_token_id=bart_model.config.bos_token_id,
)
with torch.no_grad():
with self.assertWarns(UserWarning):
bart_model.sample(
input_ids,
max_length=max_length,
pad_token_id=bart_model.config.pad_token_id,
eos_token_id=bart_model.config.eos_token_id,
**model_kwargs,
)
def test_max_length_backward_compat_beam_search(self):
# PT-only test: TF doesn't have StoppingCriteria
article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
bart_tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
bart_model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart").to(
torch_device
)
input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
batch_size = 1
max_length = 20
num_beams = 2
input_ids = input_ids.expand(2, -1)
model_kwargs = bart_model._prepare_encoder_decoder_kwargs_for_generation(input_ids, {})
input_ids, model_kwargs = bart_model._prepare_decoder_input_ids_for_generation(
batch_size=input_ids.shape[0],
model_input_name=bart_model.main_input_name,
model_kwargs=model_kwargs,
decoder_start_token_id=bart_model.config.decoder_start_token_id,
bos_token_id=bart_model.config.bos_token_id,
)
beam_scorer = BeamSearchScorer(
batch_size=batch_size,
num_beams=num_beams,
device=torch_device,
)
with self.assertWarns(UserWarning):
_ = bart_model.beam_search(
input_ids, num_beams=num_beams, max_length=max_length, beam_scorer=beam_scorer, **model_kwargs
)
def test_max_length_backward_compat_group_beam_search(self):
# PT-only test: TF doesn't have StoppingCriteria & group beam search
article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
bart_tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
bart_model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart").to(
torch_device
)
input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
batch_size = 1
max_length = 20
num_beams = 6
num_beam_groups = 3
num_return_sequences = num_beams * batch_size
input_ids = input_ids.expand(6, -1)
model_kwargs = bart_model._prepare_encoder_decoder_kwargs_for_generation(input_ids, {})
input_ids, model_kwargs = bart_model._prepare_decoder_input_ids_for_generation(
batch_size=input_ids.shape[0],
model_input_name=bart_model.main_input_name,
model_kwargs=model_kwargs,
decoder_start_token_id=bart_model.config.decoder_start_token_id,
bos_token_id=bart_model.config.bos_token_id,
)
diverse_beam_scorer = BeamSearchScorer(
batch_size=batch_size,
num_beams=num_beams,
device=torch_device,
num_beam_hyps_to_keep=num_return_sequences,
num_beam_groups=num_beam_groups,
)
with self.assertWarns(UserWarning):
bart_model.group_beam_search(
input_ids, diverse_beam_scorer, num_beams=num_beams, max_length=max_length, **model_kwargs
)
def test_max_length_warning_if_different(self):
# PT-only test: TF doesn't have StoppingCriteria
article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
bart_tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
bart_model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart").to(
torch_device
)
input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
batch_size = 1
max_length = 20
num_beams = 6
num_beam_groups = 3
num_return_sequences = num_beams * batch_size
stopping_criteria_max_length = 18
stopping_criteria = StoppingCriteriaList([MaxLengthCriteria(max_length=stopping_criteria_max_length)])
# Greedy
input_ids = input_ids.expand(6, -1)
model_kwargs = bart_model._prepare_encoder_decoder_kwargs_for_generation(input_ids, {})
input_ids, model_kwargs = bart_model._prepare_decoder_input_ids_for_generation(
batch_size=input_ids.shape[0],
model_input_name=bart_model.main_input_name,
model_kwargs=model_kwargs,
decoder_start_token_id=bart_model.config.decoder_start_token_id,
bos_token_id=bart_model.config.bos_token_id,
)
with self.assertWarns(UserWarning):
bart_model.greedy_search(
input_ids,
max_length=max_length,
pad_token_id=bart_model.config.pad_token_id,
stopping_criteria=stopping_criteria,
eos_token_id=bart_model.config.eos_token_id,
**model_kwargs,
)
# Sample
with self.assertWarns(UserWarning):
with torch.no_grad():
bart_model.sample(
input_ids,
max_length=max_length,
stopping_criteria=stopping_criteria,
pad_token_id=bart_model.config.pad_token_id,
eos_token_id=bart_model.config.eos_token_id,
**model_kwargs,
)
# Beam
beam_scorer = BeamSearchScorer(
batch_size=batch_size,
num_beams=num_beams,
device=torch_device,
)
with self.assertWarns(UserWarning):
with torch.no_grad():
bart_model.beam_search(
input_ids,
num_beams=num_beams,
stopping_criteria=stopping_criteria,
max_length=max_length,
beam_scorer=beam_scorer,
**model_kwargs,
)
# Grouped beam search
diverse_beam_scorer = BeamSearchScorer(
batch_size=batch_size,
num_beams=num_beams,
device=torch_device,
num_beam_hyps_to_keep=num_return_sequences,
num_beam_groups=num_beam_groups,
)
with self.assertWarns(UserWarning):
bart_model.group_beam_search(
input_ids,
diverse_beam_scorer,
stopping_criteria=stopping_criteria,
num_beams=num_beams,
max_length=max_length,
**model_kwargs,
)
def test_max_length_if_input_embeds(self):
# PT-only test: TF doesn't have StoppingCriteria
article = "Today a dragon flew over Paris."
model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
input_ids = tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
inputs_embeds = model.get_input_embeddings()(input_ids)
max_length = 20
input_len = input_ids.shape[-1]
out_gen = model.generate(input_ids=input_ids, max_length=max_length)
out_gen_embeds = model.generate(inputs_embeds=inputs_embeds, max_length=max_length)
self.assertEqual(out_gen.shape[-1], input_len + out_gen_embeds.shape[-1])
def test_custom_stopping_criteria_overload_error(self):
# PT-only test: TF doesn't have StoppingCriteria
article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
bart_tokenizer = BartTokenizer.from_pretrained("sshleifer/bart-tiny-random")
bart_model = BartForConditionalGeneration.from_pretrained("sshleifer/bart-tiny-random").to(torch_device)
input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
stopping_criteria = StoppingCriteriaList()
stopping_criteria.append(MaxLengthCriteria(max_length=42))
with self.assertRaises(ValueError):
bart_model.generate(input_ids, stopping_criteria=stopping_criteria)
with self.assertRaises(ValueError):
bart_model.generate(input_ids, stopping_criteria=stopping_criteria, max_length=32)
def test_custom_stopping_criteria(self):
# PT-only test: TF doesn't have StoppingCriteria
article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
bart_tokenizer = BartTokenizer.from_pretrained("sshleifer/bart-tiny-random")
bart_model = BartForConditionalGeneration.from_pretrained("sshleifer/bart-tiny-random").to(torch_device)
input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
class DummyCriteria(StoppingCriteria):
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
return input_ids.shape[-1] >= 20
stopping_criteria = StoppingCriteriaList()
stopping_criteria.append(DummyCriteria())
self.assertEqual(
list(bart_model.generate(input_ids, stopping_criteria=stopping_criteria, max_length=22).shape),
[1, 20],
)
self.assertEqual(
list(bart_model.generate(input_ids, stopping_criteria=stopping_criteria, max_length=18).shape),
[1, 18],
)
def test_stop_sequence_stopping_criteria(self):
# PT-only test: TF doesn't have StoppingCriteria
prompt = """Hello I believe in"""
generator = pipeline("text-generation", model="hf-internal-testing/tiny-random-bart")
output = generator(prompt)
self.assertEqual(
output,
[
{
"generated_text": (
"Hello I believe in in in number number number number number number number number number"
)
}
],
)
output = generator(prompt, stop_sequence=" number")
self.assertEqual(output, [{"generated_text": "Hello I believe in in in number"}])
def test_generate_non_nlp_input_ids_as_kwarg(self):
# PT-only test: AFAIK there's no non-NLP model architecture in TF that supports `input_ids` as its only input
model = ImageGPTForCausalImageModeling.from_pretrained(
"hf-internal-testing/tiny-random-imagegpt", max_length=10
).to(torch_device)
input_ids = ids_tensor((3, 5), vocab_size=10)
output_sequences_kwargs = model.generate(input_ids=input_ids).cpu()
output_sequences = model.generate(input_ids).cpu()
self.assertListEqual(output_sequences.tolist(), output_sequences_kwargs.tolist())
self.assertEqual(output_sequences.shape, (3, 10))
def test_generate_input_values_as_encoder_kwarg(self):
# PT-only test: AFAIK there's no generate-capable architecture in TF that supports `input_values` as its input
input_values = floats_tensor((2, 250))
model = SpeechEncoderDecoderModel.from_pretrained("hf-internal-testing/tiny-random-speech-encoder-decoder")
model = model.to(torch_device)
output_sequences_kwargs = model.generate(input_values=input_values, max_length=5).cpu()
output_sequences = model.generate(input_values, max_length=5).cpu()
self.assertListEqual(output_sequences.tolist(), output_sequences_kwargs.tolist())
self.assertEqual(output_sequences.shape, (2, 5))
def test_transition_scores_group_beam_search_encoder_decoder(self):
# PT-only test: TF doesn't have group beam search
articles = [
"Justin Timberlake and Jessica Biel, welcome to parenthood.",
"Michael Phelps is arguably the most decorated Olympian of all time.",
]
tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
model = BartForConditionalGeneration.from_pretrained(
"hf-internal-testing/tiny-random-bart",
max_length=10,
num_beams=2,
num_beam_groups=2,
num_return_sequences=2,
diversity_penalty=1.0,
eos_token_id=None,
return_dict_in_generate=True,
output_scores=True,
length_penalty=0.0,
)
model = model.to(torch_device)
input_ids = tokenizer(articles, return_tensors="pt", padding=True).input_ids.to(torch_device)
outputs = model.generate(input_ids=input_ids)
transition_scores = model.compute_transition_scores(outputs.sequences, outputs.scores, outputs.beam_indices)
transition_scores_sum = transition_scores.sum(-1)
self.assertTrue(torch.allclose(transition_scores_sum, outputs.sequences_scores, atol=1e-3))
def test_beam_search_low_memory(self):
tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2")
model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
tokenizer.pad_token_id = tokenizer.eos_token_id
model_inputs = tokenizer("I", return_tensors="pt")["input_ids"]
low_output = model.generate(model_inputs, max_new_tokens=40, num_beams=5, early_stopping=True, low_memory=True)
high_output = model.generate(
model_inputs, max_new_tokens=40, num_beams=5, early_stopping=True, low_memory=False
)
self.assertListEqual(low_output.tolist(), high_output.tolist())
@slow
def test_beam_search_example_integration(self):
# PT-only test: TF doesn't have a BeamSearchScorer
# exactly the example provided in the docstrings of beam search, which previously
# failed after directly copying from it. Refer to PR #15555
tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-base")
model = AutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-base")
encoder_input_str = "translate English to German: How old are you?"
encoder_input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids
# lets run beam search using 3 beams
num_beams = 3
# define decoder start token ids
input_ids = torch.ones((num_beams, 1), device=model.device, dtype=torch.long)
input_ids = input_ids * model.config.decoder_start_token_id
# add encoder_outputs to model keyword arguments
model_kwargs = {
"encoder_outputs": model.get_encoder()(
encoder_input_ids.repeat_interleave(num_beams, dim=0), return_dict=True
)
}
# instantiate beam scorer
beam_scorer = BeamSearchScorer(
batch_size=1,
num_beams=num_beams,
device=model.device,
)
# instantiate logits processors
logits_processor = LogitsProcessorList(
[
MinLengthLogitsProcessor(5, eos_token_id=model.config.eos_token_id),
]
)
outputs = model.beam_search(input_ids, beam_scorer, logits_processor=logits_processor, **model_kwargs)
outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)
self.assertListEqual(outputs, ["Wie alt bist du?"])
@slow
def test_constrained_beam_search(self):
# PT-only test: TF doesn't have constrained beam search
model = GPT2LMHeadModel.from_pretrained("openai-community/gpt2").to(torch_device)
tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2")
force_tokens = tokenizer("scared", add_prefix_space=True, add_special_tokens=False).input_ids
force_tokens_2 = tokenizer("big weapons", add_prefix_space=True, add_special_tokens=False).input_ids
constraints = [
PhrasalConstraint(force_tokens),
PhrasalConstraint(force_tokens_2),
]
starting_text = ["The soldiers were not prepared and"]
input_ids = tokenizer(starting_text, return_tensors="pt").input_ids.to(torch_device)
outputs = model.generate(
input_ids,
constraints=constraints,
num_beams=10,
num_return_sequences=1,
no_repeat_ngram_size=1,
max_length=30,
remove_invalid_values=True,
)
generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)
self.assertListEqual(
generated_text,
[
"The soldiers were not prepared and didn't know what to do. They had no idea how they would react if"
" the enemy attacked them, big weapons scared"
],
)
@slow
def test_constrained_beam_search_mixed(self):
# PT-only test: TF doesn't have constrained beam search
model = GPT2LMHeadModel.from_pretrained("openai-community/gpt2").to(torch_device)
tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2")
force_phrase = tokenizer("scared", add_prefix_space=True, add_special_tokens=False).input_ids
flexible_phrases = tokenizer(
["scream", "screams", "screaming", "screamed"], add_prefix_space=True, add_special_tokens=False
).input_ids
constraints = [
PhrasalConstraint(force_phrase),
DisjunctiveConstraint(flexible_phrases),
]
starting_text = ["The soldiers", "The child"]
input_ids = tokenizer(starting_text, return_tensors="pt").input_ids.to(torch_device)
outputs = model.generate(
input_ids,
constraints=constraints,
num_beams=10,
num_return_sequences=1,
no_repeat_ngram_size=1,
# max_length=20,
remove_invalid_values=True,
)
generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)
self.assertListEqual(
generated_text,
[
"The soldiers, who had been stationed at the base for more than a year before being evacuated"
" screaming scared",
"The child was taken to a local hospital where he died.\n 'I don't think screaming scared",
],
)
@slow
def test_constrained_beam_search_mixed_mixin(self):
# PT-only test: TF doesn't have constrained beam search
model = GPT2LMHeadModel.from_pretrained("openai-community/gpt2").to(torch_device)
tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2")
force_word = "scared"
force_flexible = ["scream", "screams", "screaming", "screamed"]
force_words_ids = [
tokenizer([force_word], add_prefix_space=True, add_special_tokens=False).input_ids,
tokenizer(force_flexible, add_prefix_space=True, add_special_tokens=False).input_ids,
]
starting_text = ["The soldiers", "The child"]
input_ids = tokenizer(starting_text, return_tensors="pt").input_ids.to(torch_device)
outputs = model.generate(
input_ids,
force_words_ids=force_words_ids,
num_beams=10,
num_return_sequences=1,
no_repeat_ngram_size=1,
remove_invalid_values=True,
)
generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)
self.assertListEqual(
generated_text,
[
"The soldiers, who had been stationed at the base for more than a year before being evacuated"
" screaming scared",
"The child was taken to a local hospital where he died.\n 'I don't think screaming scared",
],
)
@slow
def test_cfg_mixin(self):
model = GPT2LMHeadModel.from_pretrained("openai-community/gpt2").to(torch_device)
tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2")
input = tokenizer(["The dragon flew over Paris,"], return_tensors="pt", return_attention_mask=True)
input["input_ids"] = input["input_ids"].to(torch_device)
input["attention_mask"] = input["attention_mask"].to(torch_device)
outputs = model.generate(**input, max_new_tokens=32, guidance_scale=1.5)
generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)
self.assertListEqual(
generated_text,
[
"The dragon flew over Paris, landing in the Rue de la Bastille. The crowd was so excited "
'that they had to leave the city.\n\n"We\'re going to Paris!"\n'
],
)
neg = tokenizer(["France,"], return_tensors="pt", return_attention_mask=True)
neg["input_ids"] = neg["input_ids"].to(torch_device)
neg["attention_mask"] = neg["attention_mask"].to(torch_device)
outputs = model.generate(
**input,
max_new_tokens=32,
guidance_scale=1.5,
negative_prompt_ids=neg["input_ids"],
negative_prompt_attention_mask=neg["attention_mask"],
)
generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)
self.assertListEqual(
generated_text,
[
'The dragon flew over Paris, landing on the pavement.\n\n"Paris!"\n\n"Paris!"\n\n"'
'Paris!"\n\n"Paris!"\n\n"Paris!"\n\n'
],
)
@slow
def test_constrained_beam_search_example_translation_mixin(self):
# PT-only test: TF doesn't have constrained beam search
tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-base")
model = AutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-base")
encoder_input_str = "translate English to German: How old are you?"
force_words = ["sind"]
input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids
force_words_ids = tokenizer(force_words, add_special_tokens=False).input_ids
outputs = model.generate(
input_ids,
force_words_ids=force_words_ids,
num_beams=10,
num_return_sequences=1,
no_repeat_ngram_size=1,
remove_invalid_values=True,
)
outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)
self.assertListEqual(outputs, ["Wie alt sind Sie?"])
@slow
def test_constrained_beam_search_example_integration(self):
# PT-only test: TF doesn't have constrained beam search
tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-base")
model = AutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-base")
encoder_input_str = "translate English to German: How old are you?"
encoder_input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids
# lets run beam search using 5 beams
num_beams = 5
# define decoder start token ids
input_ids = torch.ones((num_beams, 1), device=model.device, dtype=torch.long)
input_ids = input_ids * model.config.decoder_start_token_id
# add encoder_outputs to model keyword arguments
model_kwargs = {
"encoder_outputs": model.get_encoder()(
encoder_input_ids.repeat_interleave(num_beams, dim=0), return_dict=True
)
}
constraint_str = "sind"
constraint_token_ids = tokenizer.encode(constraint_str)[:-1] # remove eos token
constraints = [PhrasalConstraint(token_ids=constraint_token_ids)]
# instantiate beam scorer
beam_scorer = ConstrainedBeamSearchScorer(
batch_size=1, num_beams=num_beams, device=model.device, constraints=constraints
)
# instantiate logits processors
logits_processor = LogitsProcessorList(
[
MinLengthLogitsProcessor(5, eos_token_id=model.config.eos_token_id),
]
)
outputs = model.constrained_beam_search(
input_ids, beam_scorer, constraints=constraints, logits_processor=logits_processor, **model_kwargs
)
outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)
self.assertListEqual(outputs, ["Wie alt sind Sie?"])
def test_constrained_beam_search_mixin_type_checks(self):
# PT-only test: TF doesn't have constrained beam search
tokenizer = AutoTokenizer.from_pretrained("patrickvonplaten/t5-tiny-random")
model = AutoModelForSeq2SeqLM.from_pretrained("patrickvonplaten/t5-tiny-random")
encoder_input_str = "translate English to German: How old are you?"
input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids
with self.assertRaises(ValueError):
force_words = ["sind"]
force_words_ids = tokenizer(force_words, return_tensors="pt").input_ids
model.generate(
input_ids,
force_words_ids=force_words_ids,
num_beams=10,
num_return_sequences=1,
no_repeat_ngram_size=1,
remove_invalid_values=True,
)
with self.assertRaises(ValueError):
force_words = ["sind"]
force_words_ids = [tokenizer(force_words, return_tensors="pt").input_ids]
model.generate(
input_ids,
force_words_ids=force_words_ids,
num_beams=10,
num_return_sequences=1,
no_repeat_ngram_size=1,
remove_invalid_values=True,
)
with self.assertRaises(ValueError):
model.generate(input_ids, force_words_ids=[])
with self.assertRaises(ValueError):
model.generate(input_ids, force_words_ids=[[-1]])
with self.assertRaises(ValueError):
model.generate(input_ids, force_words_ids=[[[-1]]])
def test_batched_decoder_start_id(self):
# PT-only test: TF doesn't support batched_decoder_start_id
articles = [
"Justin Timberlake and Jessica Biel, welcome to parenthood.",
"Michael Phelps is arguably the most decorated Olympian of all time.",
]
bart_tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
bart_model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart").to(
torch_device
)
input_ids = bart_tokenizer(articles, return_tensors="pt", padding=True).input_ids.to(torch_device)
decoder_start_token_id = bart_model.generation_config.decoder_start_token_id
decoder_start_token_id_batch = [decoder_start_token_id] * input_ids.shape[0]
outputs = bart_model.generate(input_ids, decoder_start_token_id=decoder_start_token_id)
outputs_batched_ids = bart_model.generate(input_ids, decoder_start_token_id=decoder_start_token_id_batch)
self.assertListEqual(outputs.tolist(), outputs_batched_ids.tolist())
def test_contrastive_search_batched(self):
# PT-only test: TF doesn't have constrained beam search
# Tests that contrastive search works with batched inputs (i.e. has the same output as for non-batched inputs)
articles = ["Foo", "Bar Baz"]
tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart").to(torch_device)
model.config.eos_token_id = None
input_ids_batched = tokenizer(articles, padding=True, return_tensors="pt").input_ids.to(torch_device)
input_ids = tokenizer(articles[1], return_tensors="pt").input_ids.to(torch_device)
output_sequences_batched = model.generate(
input_ids=input_ids_batched, penalty_alpha=0.6, top_k=4, return_dict_in_generate=True, output_scores=True
)
output_sequences = model.generate(
input_ids=input_ids, penalty_alpha=0.6, top_k=4, return_dict_in_generate=True, output_scores=True
)
batched_out = tokenizer.decode(output_sequences_batched.sequences[1], skip_special_tokens=True)
out = tokenizer.decode(output_sequences.sequences[0], skip_special_tokens=True)
self.assertEqual(batched_out, out)
# output_sequences_batched.scores[0][1] -> 1st set of logits, 2nd sequence
max_score_diff = (output_sequences_batched.scores[0][1] - output_sequences.scores[0][0]).abs().max()
self.assertTrue(max_score_diff < 1e-5)
def test_eos_token_id_int_and_list_top_k_top_sampling(self):
# Has TF equivalent: this test relies on random sampling
generation_kwargs = {
"do_sample": True,
"num_beams": 1,
"top_p": 0.7,
"top_k": 10,
"temperature": 0.7,
}
expectation = 20
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
text = """Hello, my dog is cute and"""
tokens = tokenizer(text, return_tensors="pt").to(torch_device)
model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
# Only some seeds will work both on CPU/GPU for a fixed `expectation` value.
# The selected seed is not guaranteed to work on all torch versions.
torch.manual_seed(1)
eos_token_id = 846
generated_tokens = model.generate(**tokens, eos_token_id=eos_token_id, **generation_kwargs)
self.assertTrue(expectation == len(generated_tokens[0]))
torch.manual_seed(1)
eos_token_id = [846, 198]
generated_tokens = model.generate(**tokens, eos_token_id=eos_token_id, **generation_kwargs)
self.assertTrue(expectation == len(generated_tokens[0]))
def test_model_kwarg_encoder_signature_filtering(self):
# Has TF equivalent: ample use of framework-specific code
bart_tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
article = """Hugging Face is a technology company based in New York and Paris."""
input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
bart_model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart").to(
torch_device
)
output = bart_model.generate(input_ids).cpu().numpy()
# Let's create a fake model that has a different signature. In particular, this fake model accepts "foo" as an
# argument. Because "foo" is not in the encoder signature and doesn't start with "decoder_", it will be part of
# the encoder kwargs prior to signature filtering, which would lead to an exception. But filtering kicks in and
# saves the day.
class FakeBart(BartForConditionalGeneration):
def forward(self, input_ids, foo=None, **kwargs):
return super().forward(input_ids, **kwargs)
bart_model = FakeBart.from_pretrained("hf-internal-testing/tiny-random-bart").to(torch_device)
fake_output = bart_model.generate(input_ids, foo="bar").cpu().numpy()
self.assertTrue(np.array_equal(output, fake_output))
# Encoder signature filtering only kicks in if it doesn't accept wildcard kwargs. The following test will fail
# because it doesn't do signature filtering.
class FakeEncoder(bart_model.model.encoder.__class__):
def forward(self, input_ids, **kwargs):
return super().forward(input_ids, **kwargs)
fake_encoder = FakeEncoder(bart_model.config, bart_model.model.shared).to(torch_device)
bart_model.model.encoder = fake_encoder
# Normal generation still works (the output will be different because the encoder weights are different)
fake_output = bart_model.generate(input_ids).cpu().numpy()
with self.assertRaises(TypeError):
# FakeEncoder.forward() accepts **kwargs -> no filtering -> type error due to unexpected input "foo"
bart_model.generate(input_ids, foo="bar")
def test_default_max_length_warning(self):
model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
model.config.pad_token_id = tokenizer.eos_token_id
text = "Hello world"
tokenized_inputs = tokenizer([text], return_tensors="pt")
input_ids = tokenized_inputs.input_ids.to(torch_device)
# Default generation config value of 20 -> emits warning
with self.assertWarns(UserWarning):
model.generate(input_ids)
# Explicitly setting max_length to 20 -> no warning
with warnings.catch_warnings(record=True) as warning_list:
model.generate(input_ids, max_length=20)
self.assertEqual(len(warning_list), 0)
# Generation config max_length != 20 -> no warning
with warnings.catch_warnings(record=True) as warning_list:
# generation_config is modified -> legacy mode is disabled = generation_config takes precedence
model.generation_config.max_length = 10
model.generate(input_ids)
self.assertEqual(len(warning_list), 0)
def test_model_kwarg_assisted_decoding_decoder_only(self):
# PT-only test: TF doesn't support assisted decoding yet.
model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
model.config.pad_token_id = tokenizer.eos_token_id
text = "Hello world"
tokenized_inputs = tokenizer([text], return_tensors="pt")
input_ids = tokenized_inputs.input_ids.to(torch_device)
# Traditional way of generating text
outputs_normal = model.generate(input_ids)
self.assertEqual(outputs_normal.shape, (1, 20))
# Should be different with token_type_ids
outputs_tti = model.generate(
input_ids,
token_type_ids=torch.zeros(input_ids.shape, dtype=torch.long).to(torch_device),
)
with self.assertRaises(AssertionError):
self.assertListEqual(outputs_tti.tolist(), outputs_normal.tolist())
# Assistant model
assistant = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
assistant.config.pad_token_id = tokenizer.eos_token_id
# If assisted generation passes model_kwargs correctly, should be same as previous
outputs_assisted = model.generate(
input_ids,
token_type_ids=torch.zeros(input_ids.shape, dtype=torch.long).to(torch_device),
assistant_model=assistant,
)
self.assertListEqual(outputs_assisted.tolist(), outputs_tti.tolist())
def test_model_kwarg_assisted_decoding_encoder_decoder(self):
"""
Tests that the following scenario is compatible with assisted generation:
1. encoder-decoder main model
2. encoder-decoder assistant model
3. both have a custom input
(e.g. Whisper)
"""
# PT-only test: TF doesn't support assisted decoding yet.
# Bart subclass with a kwarg that distorts the output
class FakeBart(BartForConditionalGeneration):
def forward(self, input_ids, past_key_values, foo=False, **kwargs):
outs = super().forward(input_ids, past_key_values=past_key_values, **kwargs)
if foo:
outs["logits"][:, :, :] = 0.0
return outs
def prepare_inputs_for_generation(self, *args, foo=False, encoder_outputs=None, **kwargs):
kwargs["encoder_outputs"] = encoder_outputs
inputs = super().prepare_inputs_for_generation(*args, **kwargs)
inputs["foo"] = foo
return inputs
model = FakeBart.from_pretrained("hf-internal-testing/tiny-random-BartForConditionalGeneration").to(
torch_device
)
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-BartForConditionalGeneration")
text = "Hello world"
tokenized_inputs = tokenizer([text], return_tensors="pt")
input_ids = tokenized_inputs.input_ids.to(torch_device)
# Traditional way of generating text
outputs_normal = model.generate(input_ids)
self.assertEqual(outputs_normal.shape, (1, 20))
# Should be different with foo
outputs_foo = model.generate(input_ids, foo=True)
with self.assertRaises(AssertionError):
self.assertListEqual(outputs_foo.tolist(), outputs_normal.tolist())
# Assistant model
assistant = FakeBart.from_pretrained("hf-internal-testing/tiny-random-BartForConditionalGeneration").to(
torch_device
)
# If assisted generation passes model_kwargs correctly, should be same as previous
outputs_assisted = model.generate(
input_ids,
foo=True,
assistant_model=assistant,
)
self.assertListEqual(outputs_assisted.tolist(), outputs_foo.tolist())
# Check that passing encoder_outputs directly also works as expected
encoder_outputs = assistant.get_encoder()(input_ids)
outputs_assisted = model.generate(
foo=True,
assistant_model=assistant,
encoder_outputs=encoder_outputs,
assistant_encoder_outputs=encoder_outputs,
)
self.assertListEqual(outputs_assisted.tolist(), outputs_foo.tolist())
def test_assisted_decoding_encoder_decoder_shared_encoder(self):
"""
Tests that the following scenario is compatible with assisted generation:
1. encoder-decoder main model
2. decoder-only assistant model
3. both have a custom input
(e.g. DistilWhisper)
"""
# PT-only test: TF doesn't support assisted decoding yet.
# Bart subclass with a kwarg called foo that distorts the output
class FakeBartSeq2Seq(BartForConditionalGeneration):
def forward(self, input_ids, foo=False, **kwargs):
outs = super().forward(input_ids, **kwargs)
if foo:
outs["logits"][:, :, :] = 0.0
return outs
def prepare_inputs_for_generation(self, *args, foo=False, encoder_outputs=None, **kwargs):
kwargs["encoder_outputs"] = encoder_outputs
inputs = super().prepare_inputs_for_generation(*args, **kwargs)
inputs["foo"] = foo
return inputs
class FakeBartCausalLM(BartForCausalLM):
def forward(self, input_ids, attention_mask, past_key_values, foo=False, **kwargs):
outs = super().forward(input_ids, attention_mask, past_key_values=past_key_values, **kwargs)
if foo:
outs["logits"][:, :, :] = 0.0
return outs
def prepare_inputs_for_generation(self, *args, foo=False, encoder_outputs=None, **kwargs):
kwargs["encoder_outputs"] = encoder_outputs
inputs = super().prepare_inputs_for_generation(*args, **kwargs)
inputs["foo"] = foo
return inputs
model = FakeBartSeq2Seq.from_pretrained("hf-internal-testing/tiny-random-BartForConditionalGeneration").to(
torch_device
)
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-BartForConditionalGeneration")
text = "Hello world"
tokenized_inputs = tokenizer([text], return_tensors="pt")
input_ids = tokenized_inputs.input_ids.to(torch_device)
# Traditional way of generating text
outputs_normal = model.generate(input_ids)
self.assertEqual(outputs_normal.shape, (1, 20))
# Should be different with foo
outputs_foo = model.generate(input_ids, foo=True)
with self.assertRaises(AssertionError):
self.assertListEqual(outputs_foo.tolist(), outputs_normal.tolist())
# Assistant model
assistant = FakeBartCausalLM.from_pretrained(
"hf-internal-testing/tiny-random-BartForConditionalGeneration"
).to(torch_device)
# If assisted generation passes model_kwargs correctly, should be same as previous
outputs_assisted = model.generate(
input_ids,
foo=True,
assistant_model=assistant,
)
self.assertListEqual(outputs_assisted.tolist(), outputs_foo.tolist())
# Check that passing encoder_outputs directly also works as expected
encoder_outputs = model.get_encoder()(input_ids)
outputs_assisted = model.generate(
foo=True,
assistant_model=assistant,
encoder_outputs=encoder_outputs,
)
self.assertListEqual(outputs_assisted.tolist(), outputs_foo.tolist())
def test_assisted_decoding_num_assistant_tokens_heuristic_schedule(self):
# This test ensures that the assisted generation num_assistant_tokens 'heuristic' schedule works properly.
prompt = "Alice and Bob"
checkpoint = "EleutherAI/pythia-160m-deduped"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
inputs = tokenizer(prompt, return_tensors="pt")
model = AutoModelForCausalLM.from_pretrained(checkpoint)
assistant_model = model
assistant_model.generation_config.num_assistant_tokens = 5
assistant_model.generation_config.num_assistant_tokens_schedule = "heuristic"
generation_kwargs = {
"eos_token_id": -1,
"max_new_tokens": 5,
"do_sample": False,
"assistant_model": assistant_model,
}
model.generate(**inputs, **generation_kwargs)
# update_candidate_strategy is called only once and therefore, assistant_model.generation_config.num_assistant_tokens should be either 4 or 7
self.assertTrue(assistant_model.generation_config.num_assistant_tokens in (4, 7))
def test_assisted_decoding_num_assistant_tokens_heuristic_transient_schedule(self):
# This test ensures that the assisted generation num_assistant_tokens 'heuristic' schedule works properly.
prompt = "Alice and Bob"
checkpoint = "EleutherAI/pythia-160m-deduped"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
inputs = tokenizer(prompt, return_tensors="pt")
model = AutoModelForCausalLM.from_pretrained(checkpoint)
assistant_model = model
assistant_model.generation_config.num_assistant_tokens = 5
assistant_model.generation_config.num_assistant_tokens_schedule = "heuristic_transient"
generation_kwargs = {
"eos_token_id": -1,
"max_new_tokens": 5,
"do_sample": False,
"assistant_model": assistant_model,
}
model.generate(**inputs, **generation_kwargs)
# update_candidate_strategy is called once but assistant_model.generation_config.num_assistant_tokens should stay 5
self.assertEqual(assistant_model.generation_config.num_assistant_tokens, 5)
def test_compare_unprocessed_logit_scores(self):
# Get unprocessed logit scores back from model generate function.
# Assert that unprocessed logits from generate() are same as those from modal eval()
# tell model to generate text and return unprocessed/unwarped logit scores
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
text = "generate yes or no: "
input_ids = tokenizer([text], return_tensors="pt").input_ids.to(torch_device)
model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
with torch.no_grad():
# Get logits for the next token from fwd pass
logits_fwd = model(input_ids).logits[:, -1, :][0]
# Get logits for the next token from generate function
outputs = model.generate(
input_ids=input_ids,
return_dict_in_generate=True,
output_logits=True,
max_new_tokens=1,
do_sample=True,
)
logits_gen = outputs.logits[0][0]
# assert that unprocessed logits from generate() are same as those from modal eval()
self.assertListEqual(logits_fwd.tolist(), logits_gen.tolist())
def test_return_unprocessed_logit_scores(self):
# tell model to generate text and return unprocessed/unwarped logit scores
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
text = "generate yes or no: "
input_ids = tokenizer([text], return_tensors="pt").input_ids.to(torch_device)
model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
outputs = model.generate(
input_ids=input_ids, return_dict_in_generate=True, output_logits=True, max_new_tokens=3
)
# perform dummy check if unpreprocessed logits make sense.
# do preselection on high probabilities; find scores of y and n tokens
probs_all = torch.nn.functional.softmax(outputs.logits[2][0], dim=-1)
indices = torch.argwhere(probs_all > 0.001)
indices = indices[:, -1]
tokens_max = tokenizer.batch_decode(indices, skip_special_tokens=True)
probs_max = probs_all[probs_all > 0.001]
self.assertTrue(len(indices) >= 2)
next_token_dict = {str(t): p for t, p in zip(tokens_max, probs_max)}
self.assertTrue("n" in next_token_dict)
self.assertTrue("y" in next_token_dict)
y_prob = next_token_dict["y"]
n_prob = next_token_dict["n"]
self.assertTrue(y_prob > 0.001 and n_prob > 0.001)
self.assertTrue(y_prob <= 1.0 and n_prob <= 1.0)
| transformers/tests/generation/test_utils.py/0 | {
"file_path": "transformers/tests/generation/test_utils.py",
"repo_id": "transformers",
"token_count": 78940
} | 146 |
# coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir
sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
SAMPLE_ROBERTA_CONFIG = get_tests_dir("fixtures/dummy-config.json")
class AutoConfigTest(unittest.TestCase):
def setUp(self):
transformers.dynamic_module_utils.TIME_OUT_REMOTE_CODE = 0
def test_module_spec(self):
self.assertIsNotNone(transformers.models.auto.__spec__)
self.assertIsNotNone(importlib.util.find_spec("transformers.models.auto"))
def test_config_from_model_shortcut(self):
config = AutoConfig.from_pretrained("google-bert/bert-base-uncased")
self.assertIsInstance(config, BertConfig)
def test_config_model_type_from_local_file(self):
config = AutoConfig.from_pretrained(SAMPLE_ROBERTA_CONFIG)
self.assertIsInstance(config, RobertaConfig)
def test_config_model_type_from_model_identifier(self):
config = AutoConfig.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER)
self.assertIsInstance(config, RobertaConfig)
def test_config_for_model_str(self):
config = AutoConfig.for_model("roberta")
self.assertIsInstance(config, RobertaConfig)
def test_pattern_matching_fallback(self):
with tempfile.TemporaryDirectory() as tmp_dir:
# This model name contains bert and roberta, but roberta ends up being picked.
folder = os.path.join(tmp_dir, "fake-roberta")
os.makedirs(folder, exist_ok=True)
with open(os.path.join(folder, "config.json"), "w") as f:
f.write(json.dumps({}))
config = AutoConfig.from_pretrained(folder)
self.assertEqual(type(config), RobertaConfig)
def test_new_config_registration(self):
try:
AutoConfig.register("custom", CustomConfig)
# Wrong model type will raise an error
with self.assertRaises(ValueError):
AutoConfig.register("model", CustomConfig)
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(ValueError):
AutoConfig.register("bert", BertConfig)
# Now that the config is registered, it can be used as any other config with the auto-API
config = CustomConfig()
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(tmp_dir)
new_config = AutoConfig.from_pretrained(tmp_dir)
self.assertIsInstance(new_config, CustomConfig)
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
def test_repo_not_found(self):
with self.assertRaisesRegex(
EnvironmentError, "bert-base is not a local folder and is not a valid model identifier"
):
_ = AutoConfig.from_pretrained("bert-base")
def test_revision_not_found(self):
with self.assertRaisesRegex(
EnvironmentError, r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"
):
_ = AutoConfig.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER, revision="aaaaaa")
def test_configuration_not_found(self):
with self.assertRaisesRegex(
EnvironmentError,
"hf-internal-testing/no-config-test-repo does not appear to have a file named config.json.",
):
_ = AutoConfig.from_pretrained("hf-internal-testing/no-config-test-repo")
def test_from_pretrained_dynamic_config(self):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(ValueError):
config = AutoConfig.from_pretrained("hf-internal-testing/test_dynamic_model")
# If remote code is disabled, we can't load this config.
with self.assertRaises(ValueError):
config = AutoConfig.from_pretrained("hf-internal-testing/test_dynamic_model", trust_remote_code=False)
config = AutoConfig.from_pretrained("hf-internal-testing/test_dynamic_model", trust_remote_code=True)
self.assertEqual(config.__class__.__name__, "NewModelConfig")
# Test config can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(tmp_dir)
reloaded_config = AutoConfig.from_pretrained(tmp_dir, trust_remote_code=True)
self.assertEqual(reloaded_config.__class__.__name__, "NewModelConfig")
def test_from_pretrained_dynamic_config_conflict(self):
class NewModelConfigLocal(BertConfig):
model_type = "new-model"
try:
AutoConfig.register("new-model", NewModelConfigLocal)
# If remote code is not set, the default is to use local
config = AutoConfig.from_pretrained("hf-internal-testing/test_dynamic_model")
self.assertEqual(config.__class__.__name__, "NewModelConfigLocal")
# If remote code is disabled, we load the local one.
config = AutoConfig.from_pretrained("hf-internal-testing/test_dynamic_model", trust_remote_code=False)
self.assertEqual(config.__class__.__name__, "NewModelConfigLocal")
# If remote is enabled, we load from the Hub
config = AutoConfig.from_pretrained("hf-internal-testing/test_dynamic_model", trust_remote_code=True)
self.assertEqual(config.__class__.__name__, "NewModelConfig")
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
| transformers/tests/models/auto/test_configuration_auto.py/0 | {
"file_path": "transformers/tests/models/auto/test_configuration_auto.py",
"repo_id": "transformers",
"token_count": 2613
} | 147 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BartConfig, BartTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
os.environ["XLA_PYTHON_CLIENT_ALLOCATOR"] = "platform"
import jax
import jax.numpy as jnp
from transformers.models.bart.modeling_flax_bart import (
FlaxBartForConditionalGeneration,
FlaxBartForQuestionAnswering,
FlaxBartForSequenceClassification,
FlaxBartModel,
shift_tokens_right,
)
def prepare_bart_inputs_dict(
config,
input_ids,
decoder_input_ids=None,
attention_mask=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
):
if attention_mask is None:
attention_mask = np.where(input_ids != config.pad_token_id, 1, 0)
if decoder_attention_mask is None:
decoder_attention_mask = np.where(decoder_input_ids != config.pad_token_id, 1, 0)
if head_mask is None:
head_mask = np.ones((config.encoder_layers, config.encoder_attention_heads))
if decoder_head_mask is None:
decoder_head_mask = np.ones((config.decoder_layers, config.decoder_attention_heads))
if cross_attn_head_mask is None:
cross_attn_head_mask = np.ones((config.decoder_layers, config.decoder_attention_heads))
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
}
class FlaxBartModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_labels=False,
vocab_size=99,
hidden_size=16,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=4,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=32,
eos_token_id=2,
pad_token_id=1,
bos_token_id=0,
initializer_range=0.02,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
self.initializer_range = initializer_range
def prepare_config_and_inputs(self):
input_ids = np.clip(ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size), 3, self.vocab_size)
input_ids = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1), dtype=np.int64)), -1)
decoder_input_ids = shift_tokens_right(input_ids, 1, 2)
config = BartConfig(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
encoder_ffn_dim=self.intermediate_size,
decoder_ffn_dim=self.intermediate_size,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
initializer_range=self.initializer_range,
use_cache=False,
)
inputs_dict = prepare_bart_inputs_dict(config, input_ids, decoder_input_ids)
return config, inputs_dict
def prepare_config_and_inputs_for_common(self):
config, inputs_dict = self.prepare_config_and_inputs()
return config, inputs_dict
def check_use_cache_forward(self, model_class_name, config, inputs_dict):
max_decoder_length = 20
model = model_class_name(config)
encoder_outputs = model.encode(inputs_dict["input_ids"])
decoder_input_ids, decoder_attention_mask = (
inputs_dict["decoder_input_ids"],
inputs_dict["decoder_attention_mask"],
)
past_key_values = model.init_cache(decoder_input_ids.shape[0], max_decoder_length, encoder_outputs)
decoder_attention_mask = jnp.ones((decoder_input_ids.shape[0], max_decoder_length), dtype="i4")
decoder_position_ids = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :],
(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),
)
outputs_cache = model.decode(
decoder_input_ids[:, :-1],
encoder_outputs,
decoder_attention_mask=decoder_attention_mask,
past_key_values=past_key_values,
decoder_position_ids=decoder_position_ids,
)
decoder_position_ids = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]], dtype="i4")
outputs_cache_next = model.decode(
decoder_input_ids[:, -1:],
encoder_outputs,
decoder_attention_mask=decoder_attention_mask,
past_key_values=outputs_cache.past_key_values,
decoder_position_ids=decoder_position_ids,
)
outputs = model.decode(decoder_input_ids, encoder_outputs)
diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}")
def check_use_cache_forward_with_attn_mask(self, model_class_name, config, inputs_dict):
max_decoder_length = 20
model = model_class_name(config)
encoder_outputs = model.encode(inputs_dict["input_ids"])
decoder_input_ids, decoder_attention_mask = (
inputs_dict["decoder_input_ids"],
inputs_dict["decoder_attention_mask"],
)
decoder_attention_mask_cache = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
],
axis=-1,
)
past_key_values = model.init_cache(decoder_input_ids.shape[0], max_decoder_length, encoder_outputs)
decoder_position_ids = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :],
(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),
)
outputs_cache = model.decode(
decoder_input_ids[:, :-1],
encoder_outputs,
decoder_attention_mask=decoder_attention_mask_cache,
past_key_values=past_key_values,
decoder_position_ids=decoder_position_ids,
)
decoder_position_ids = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]], dtype="i4")
outputs_cache_next = model.decode(
decoder_input_ids[:, -1:],
encoder_outputs,
past_key_values=outputs_cache.past_key_values,
decoder_attention_mask=decoder_attention_mask_cache,
decoder_position_ids=decoder_position_ids,
)
outputs = model.decode(decoder_input_ids, encoder_outputs, decoder_attention_mask=decoder_attention_mask)
diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}")
@require_flax
class BartHeadTests(unittest.TestCase):
vocab_size = 99
def _get_config_and_data(self):
input_ids = np.array(
[
[71, 82, 18, 33, 46, 91, 2],
[68, 34, 26, 58, 30, 82, 2],
[5, 97, 17, 39, 94, 40, 2],
[76, 83, 94, 25, 70, 78, 2],
[87, 59, 41, 35, 48, 66, 2],
[55, 13, 16, 58, 5, 2, 1], # note padding
[64, 27, 31, 51, 12, 75, 2],
[52, 64, 86, 17, 83, 39, 2],
[48, 61, 9, 24, 71, 82, 2],
[26, 1, 60, 48, 22, 13, 2],
[21, 5, 62, 28, 14, 76, 2],
[45, 98, 37, 86, 59, 48, 2],
[70, 70, 50, 9, 28, 0, 2],
],
dtype=np.int64,
)
batch_size = input_ids.shape[0]
config = BartConfig(
vocab_size=self.vocab_size,
d_model=24,
encoder_layers=2,
decoder_layers=2,
encoder_attention_heads=2,
decoder_attention_heads=2,
encoder_ffn_dim=32,
decoder_ffn_dim=32,
max_position_embeddings=48,
eos_token_id=2,
pad_token_id=1,
bos_token_id=0,
)
return config, input_ids, batch_size
def test_sequence_classification_forward(self):
config, input_ids, batch_size = self._get_config_and_data()
model = FlaxBartForSequenceClassification(config)
outputs = model(input_ids=input_ids, decoder_input_ids=input_ids)
expected_shape = (batch_size, config.num_labels)
self.assertEqual(outputs["logits"].shape, expected_shape)
def test_question_answering_forward(self):
config, input_ids, batch_size = self._get_config_and_data()
model = FlaxBartForQuestionAnswering(config)
outputs = model(input_ids=input_ids)
self.assertEqual(outputs["start_logits"].shape, input_ids.shape)
self.assertEqual(outputs["end_logits"].shape, input_ids.shape)
# @timeout_decorator.timeout(1) # not working with the decorator so far
def test_lm_forward(self):
config, input_ids, batch_size = self._get_config_and_data()
lm_model = FlaxBartForConditionalGeneration(config)
outputs = lm_model(input_ids=input_ids)
expected_shape = (batch_size, input_ids.shape[1], config.vocab_size)
self.assertEqual(outputs["logits"].shape, expected_shape)
def test_lm_uneven_forward(self):
config = BartConfig(
vocab_size=self.vocab_size,
d_model=14,
encoder_layers=2,
decoder_layers=2,
encoder_attention_heads=2,
decoder_attention_heads=2,
encoder_ffn_dim=8,
decoder_ffn_dim=8,
max_position_embeddings=48,
)
lm_model = FlaxBartForConditionalGeneration(config)
context = np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]], dtype=np.int64)
summary = np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]], dtype=np.int64)
outputs = lm_model(input_ids=context, decoder_input_ids=summary)
expected_shape = (*summary.shape, config.vocab_size)
self.assertEqual(outputs["logits"].shape, expected_shape)
def test_shift_tokens_right(self):
input_ids = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]], dtype=np.int64)
shifted = shift_tokens_right(input_ids, 1, 2)
n_pad_before = np.equal(input_ids, 1).astype(np.float32).sum()
n_pad_after = np.equal(shifted, 1).astype(np.float32).sum()
self.assertEqual(shifted.shape, input_ids.shape)
self.assertEqual(n_pad_after, n_pad_before - 1)
self.assertTrue(np.equal(shifted[:, 0], 2).all())
@require_flax
class FlaxBartModelTest(FlaxModelTesterMixin, unittest.TestCase, FlaxGenerationTesterMixin):
is_encoder_decoder = True
all_model_classes = (
(
FlaxBartModel,
FlaxBartForConditionalGeneration,
FlaxBartForSequenceClassification,
FlaxBartForQuestionAnswering,
)
if is_flax_available()
else ()
)
all_generative_model_classes = (FlaxBartForConditionalGeneration,) if is_flax_available() else ()
def setUp(self):
self.model_tester = FlaxBartModelTester(self)
def test_use_cache_forward(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(model_class, config, inputs_dict)
def test_use_cache_forward_with_attn_mask(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(model_class, config, inputs_dict)
def test_encode(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
model = model_class(config)
@jax.jit
def encode_jitted(input_ids, attention_mask=None, **kwargs):
return model.encode(input_ids=input_ids, attention_mask=attention_mask)
with self.subTest("JIT Enabled"):
jitted_outputs = encode_jitted(**prepared_inputs_dict).to_tuple()
with self.subTest("JIT Disabled"):
with jax.disable_jit():
outputs = encode_jitted(**prepared_inputs_dict).to_tuple()
self.assertEqual(len(outputs), len(jitted_outputs))
for jitted_output, output in zip(jitted_outputs, outputs):
self.assertEqual(jitted_output.shape, output.shape)
def test_decode(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
model = model_class(config)
encoder_outputs = model.encode(inputs_dict["input_ids"], inputs_dict["attention_mask"])
prepared_inputs_dict = {
"decoder_input_ids": inputs_dict["decoder_input_ids"],
"decoder_attention_mask": inputs_dict["decoder_attention_mask"],
"encoder_outputs": encoder_outputs,
}
@jax.jit
def decode_jitted(decoder_input_ids, decoder_attention_mask, encoder_outputs):
return model.decode(
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
encoder_outputs=encoder_outputs,
)
with self.subTest("JIT Enabled"):
jitted_outputs = decode_jitted(**prepared_inputs_dict).to_tuple()
with self.subTest("JIT Disabled"):
with jax.disable_jit():
outputs = decode_jitted(**prepared_inputs_dict).to_tuple()
self.assertEqual(len(outputs), len(jitted_outputs))
for jitted_output, output in zip(jitted_outputs, outputs):
self.assertEqual(jitted_output.shape, output.shape)
@slow
def test_model_from_pretrained(self):
for model_class_name in self.all_model_classes:
model = model_class_name.from_pretrained("facebook/bart-base", from_pt=True)
# FlaxBartForSequenceClassification expects eos token in input_ids
input_ids = np.ones((1, 1)) * model.config.eos_token_id
outputs = model(input_ids)
self.assertIsNotNone(outputs)
@slow
def test_summarization_fast(self):
model = FlaxBartForConditionalGeneration.from_pretrained("sshleifer/distilbart-cnn-6-6")
tokenizer = BartTokenizer.from_pretrained("sshleifer/distilbart-cnn-6-6")
input_str = (
"This sentence is made of three parts. Each part is important on its own. One part is about animals, the"
" other part about planes, and the last part about housing."
)
input_ids = tokenizer(input_str, return_tensors="np").input_ids
sequences = model.generate(input_ids, num_beams=2, min_length=None, max_length=20).sequences
output_str = tokenizer.batch_decode(sequences)[0]
assert (
output_str == "</s><s>This sentence is made of three parts. One part is about animals, the other part</s>"
)
@slow
def test_cnn_summarization_same_as_fairseq(self):
model = FlaxBartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn")
tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn")
FRANCE_ARTICLE = ( # @noq
" Marseille, France (CNN)The French prosecutor leading an investigation into the crash of Germanwings"
" Flight 9525 insisted Wednesday that he was not aware of any video footage from on board the plane."
' Marseille prosecutor Brice Robin told CNN that "so far no videos were used in the crash investigation."'
' He added, "A person who has such a video needs to immediately give it to the investigators." Robin\'s'
" comments follow claims by two magazines, German daily Bild and French Paris Match, of a cell phone video"
" showing the harrowing final seconds from on board Germanwings Flight 9525 as it crashed into the French"
" Alps. All 150 on board were killed. Paris Match and Bild reported that the video was recovered from a"
" phone at the wreckage site. The two publications described the supposed video, but did not post it on"
" their websites. The publications said that they watched the video, which was found by a source close to"
" the investigation. \"One can hear cries of 'My God' in several languages,\" Paris Match reported."
' "Metallic banging can also be heard more than three times, perhaps of the pilot trying to open the'
" cockpit door with a heavy object. Towards the end, after a heavy shake, stronger than the others, the"
' screaming intensifies. Then nothing." "It is a very disturbing scene," said Julian Reichelt,'
" editor-in-chief of Bild online. An official with France's accident investigation agency, the BEA, said"
" the agency is not aware of any such video. Lt. Col. Jean-Marc Menichini, a French Gendarmerie spokesman"
" in charge of communications on rescue efforts around the Germanwings crash site, told CNN that the"
' reports were "completely wrong" and "unwarranted." Cell phones have been collected at the site, he said,'
' but that they "hadn\'t been exploited yet." Menichini said he believed the cell phones would need to be'
" sent to the Criminal Research Institute in Rosny sous-Bois, near Paris, in order to be analyzed by"
" specialized technicians working hand-in-hand with investigators. But none of the cell phones found so"
" far have been sent to the institute, Menichini said. Asked whether staff involved in the search could"
' have leaked a memory card to the media, Menichini answered with a categorical "no." Reichelt told "Erin'
' Burnett: Outfront" that he had watched the video and stood by the report, saying Bild and Paris Match'
' are "very confident" that the clip is real. He noted that investigators only revealed they\'d recovered'
' cell phones from the crash site after Bild and Paris Match published their reports. "That is something'
" we did not know before. ... Overall we can say many things of the investigation weren't revealed by the"
' investigation at the beginning," he said. What was mental state of Germanwings co-pilot? German airline'
" Lufthansa confirmed Tuesday that co-pilot Andreas Lubitz had battled depression years before he took the"
" controls of Germanwings Flight 9525, which he's accused of deliberately crashing last week in the"
' French Alps. Lubitz told his Lufthansa flight training school in 2009 that he had a "previous episode of'
' severe depression," the airline said Tuesday. Email correspondence between Lubitz and the school'
" discovered in an internal investigation, Lufthansa said, included medical documents he submitted in"
" connection with resuming his flight training. The announcement indicates that Lufthansa, the parent"
" company of Germanwings, knew of Lubitz's battle with depression, allowed him to continue training and"
" ultimately put him in the cockpit. Lufthansa, whose CEO Carsten Spohr previously said Lubitz was 100%"
' fit to fly, described its statement Tuesday as a "swift and seamless clarification" and said it was'
" sharing the information and documents -- including training and medical records -- with public"
" prosecutors. Spohr traveled to the crash site Wednesday, where recovery teams have been working for the"
" past week to recover human remains and plane debris scattered across a steep mountainside. He saw the"
" crisis center set up in Seyne-les-Alpes, laid a wreath in the village of Le Vernet, closer to the crash"
" site, where grieving families have left flowers at a simple stone memorial. Menichini told CNN late"
" Tuesday that no visible human remains were left at the site but recovery teams would keep searching."
" French President Francois Hollande, speaking Tuesday, said that it should be possible to identify all"
" the victims using DNA analysis by the end of the week, sooner than authorities had previously suggested."
" In the meantime, the recovery of the victims' personal belongings will start Wednesday, Menichini said."
" Among those personal belongings could be more cell phones belonging to the 144 passengers and six crew"
" on board. Check out the latest from our correspondents . The details about Lubitz's correspondence with"
" the flight school during his training were among several developments as investigators continued to"
" delve into what caused the crash and Lubitz's possible motive for downing the jet. A Lufthansa"
" spokesperson told CNN on Tuesday that Lubitz had a valid medical certificate, had passed all his"
' examinations and "held all the licenses required." Earlier, a spokesman for the prosecutor\'s office in'
" Dusseldorf, Christoph Kumpa, said medical records reveal Lubitz suffered from suicidal tendencies at"
" some point before his aviation career and underwent psychotherapy before he got his pilot's license."
" Kumpa emphasized there's no evidence suggesting Lubitz was suicidal or acting aggressively before the"
" crash. Investigators are looking into whether Lubitz feared his medical condition would cause him to"
" lose his pilot's license, a European government official briefed on the investigation told CNN on"
' Tuesday. While flying was "a big part of his life," the source said, it\'s only one theory being'
" considered. Another source, a law enforcement official briefed on the investigation, also told CNN that"
" authorities believe the primary motive for Lubitz to bring down the plane was that he feared he would"
" not be allowed to fly because of his medical problems. Lubitz's girlfriend told investigators he had"
" seen an eye doctor and a neuropsychologist, both of whom deemed him unfit to work recently and concluded"
" he had psychological issues, the European government official said. But no matter what details emerge"
" about his previous mental health struggles, there's more to the story, said Brian Russell, a forensic"
' psychologist. "Psychology can explain why somebody would turn rage inward on themselves about the fact'
" that maybe they weren't going to keep doing their job and they're upset about that and so they're"
' suicidal," he said. "But there is no mental illness that explains why somebody then feels entitled to'
" also take that rage and turn it outward on 149 other people who had nothing to do with the person's"
' problems." Germanwings crash compensation: What we know . Who was the captain of Germanwings Flight'
" 9525? CNN's Margot Haddad reported from Marseille and Pamela Brown from Dusseldorf, while Laura"
" Smith-Spark wrote from London. CNN's Frederik Pleitgen, Pamela Boykoff, Antonia Mortensen, Sandrine"
" Amiel and Anna-Maja Rappard contributed to this report."
)
SHORTER_ARTICLE = (
" (CNN)The Palestinian Authority officially became the 123rd member of the International Criminal Court on"
" Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The"
" formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based."
" The Palestinians signed the ICC's founding Rome Statute in January, when they also accepted its"
' jurisdiction over alleged crimes committed "in the occupied Palestinian territory, including East'
' Jerusalem, since June 13, 2014." Later that month, the ICC opened a preliminary examination into the'
" situation in Palestinian territories, paving the way for possible war crimes investigations against"
" Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and"
" the United States, neither of which is an ICC member, opposed the Palestinians' efforts to join the"
" body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday's ceremony, said it was a"
' move toward greater justice. "As Palestine formally becomes a State Party to the Rome Statute today, the'
' world is also a step closer to ending a long era of impunity and injustice," he said, according to an'
' ICC news release. "Indeed, today brings us closer to our shared goals of justice and peace." Judge'
" Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the"
' Palestinians. "As the Rome Statute today enters into force for the State of Palestine, Palestine'
" acquires all the rights as well as responsibilities that come with being a State Party to the Statute."
' These are substantive commitments, which cannot be taken lightly," she said. Rights group Human Rights'
' Watch welcomed the development. "Governments seeking to penalize Palestine for joining the ICC should'
" immediately end their pressure, and countries that support universal acceptance of the court's treaty"
' should speak out to welcome its membership," said Balkees Jarrah, international justice counsel for the'
" group. \"What's objectionable is the attempts to undermine international justice, not Palestine's"
' decision to join a treaty to which over 100 countries around the world are members." In January, when'
" the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an"
' outrage, saying the court was overstepping its boundaries. The United States also said it "strongly"'
" disagreed with the court's decision. \"As we have said repeatedly, we do not believe that Palestine is a"
' state and therefore we do not believe that it is eligible to join the ICC," the State Department said in'
' a statement. It urged the warring sides to resolve their differences through direct negotiations. "We'
' will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace,"'
" it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the"
' territories as "Palestine." While a preliminary examination is not a formal investigation, it allows the'
" court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou"
' Bensouda said her office would "conduct its analysis in full independence and impartiality." The war'
" between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry"
" will include alleged war crimes committed since June. The International Criminal Court was set up in"
" 2002 to prosecute genocide, crimes against humanity and war crimes. CNN's Vasco Cotovio, Kareem Khadder"
" and Faith Karimi contributed to this report."
)
# The below article tests that we don't add any hypotheses outside of the top n_beams
IRAN_ARTICLE = (
" (CNN)The United States and its negotiating partners reached a very strong framework agreement with Iran"
" in Lausanne, Switzerland, on Thursday that limits Iran's nuclear program in such a way as to effectively"
" block it from building a nuclear weapon. Expect pushback anyway, if the recent past is any harbinger."
" Just last month, in an attempt to head off such an agreement, House Speaker John Boehner invited Israeli"
" Prime Minister Benjamin Netanyahu to preemptively blast it before Congress, and 47 senators sent a"
" letter to the Iranian leadership warning them away from a deal. The debate that has already begun since"
" the announcement of the new framework will likely result in more heat than light. It will not be helped"
" by the gathering swirl of dubious assumptions and doubtful assertions. Let us address some of these: ."
" The most misleading assertion, despite universal rejection by experts, is that the negotiations'"
" objective at the outset was the total elimination of any nuclear program in Iran. That is the position"
" of Netanyahu and his acolytes in the U.S. Congress. But that is not and never was the objective. If it"
" had been, there would have been no Iranian team at the negotiating table. Rather, the objective has"
" always been to structure an agreement or series of agreements so that Iran could not covertly develop a"
" nuclear arsenal before the United States and its allies could respond. The new framework has exceeded"
" expectations in achieving that goal. It would reduce Iran's low-enriched uranium stockpile, cut by"
" two-thirds its number of installed centrifuges and implement a rigorous inspection regime. Another"
" dubious assumption of opponents is that the Iranian nuclear program is a covert weapons program. Despite"
" sharp accusations by some in the United States and its allies, Iran denies having such a program, and"
" U.S. intelligence contends that Iran has not yet made the decision to build a nuclear weapon. Iran's"
" continued cooperation with International Atomic Energy Agency inspections is further evidence on this"
" point, and we'll know even more about Iran's program in the coming months and years because of the deal."
" In fact, the inspections provisions that are part of this agreement are designed to protect against any"
" covert action by the Iranians. What's more, the rhetoric of some members of Congress has implied that"
" the negotiations have been between only the United States and Iran (i.e., the 47 senators' letter"
" warning that a deal might be killed by Congress or a future president). This of course is not the case."
" The talks were between Iran and the five permanent members of the U.N. Security Council (United States,"
" United Kingdom, France, China and Russia) plus Germany, dubbed the P5+1. While the United States has"
" played a leading role in the effort, it negotiated the terms alongside its partners. If the agreement"
" reached by the P5+1 is rejected by Congress, it could result in an unraveling of the sanctions on Iran"
" and threaten NATO cohesion in other areas. Another questionable assertion is that this agreement"
" contains a sunset clause, after which Iran will be free to do as it pleases. Again, this is not the"
" case. Some of the restrictions on Iran's nuclear activities, such as uranium enrichment, will be eased"
" or eliminated over time, as long as 15 years. But most importantly, the framework agreement includes"
" Iran's ratification of the Additional Protocol, which allows IAEA inspectors expanded access to nuclear"
" sites both declared and nondeclared. This provision will be permanent. It does not sunset. Thus, going"
" forward, if Iran decides to enrich uranium to weapons-grade levels, monitors will be able to detect such"
" a move in a matter of days and alert the U.N. Security Council. Many in Congress have said that the"
' agreement should be a formal treaty requiring the Senate to "advise and consent." But the issue is not'
" suited for a treaty. Treaties impose equivalent obligations on all signatories. For example, the New"
" START treaty limits Russia and the United States to 1,550 deployed strategic warheads. But any agreement"
" with Iran will not be so balanced. The restrictions and obligations in the final framework agreement"
" will be imposed almost exclusively on Iran. The P5+1 are obligated only to ease and eventually remove"
" most but not all economic sanctions, which were imposed as leverage to gain this final deal. Finally"
" some insist that any agreement must address Iranian missile programs, human rights violations or support"
" for Hamas or Hezbollah. As important as these issues are, and they must indeed be addressed, they are"
" unrelated to the most important aim of a nuclear deal: preventing a nuclear Iran. To include them in"
" the negotiations would be a poison pill. This agreement should be judged on its merits and on how it"
" affects the security of our negotiating partners and allies, including Israel. Those judgments should be"
" fact-based, not based on questionable assertions or dubious assumptions."
)
ARTICLE_SUBWAY = (
" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York. A"
" year later, she got married again in Westchester County, but to a different man and without divorcing"
" her first husband. Only 18 days after that marriage, she got hitched yet again. Then, Barrientos"
' declared "I do" five more times, sometimes only within two weeks of each other. In 2010, she married'
" once more, this time in the Bronx. In an application for a marriage license, she stated it was her"
' "first and only" marriage. Barrientos, now 39, is facing two criminal counts of "offering a false'
' instrument for filing in the first degree," referring to her false statements on the 2010 marriage'
" license application, according to court documents. Prosecutors said the marriages were part of an"
" immigration scam. On Friday, she pleaded not guilty at State Supreme Court in the Bronx, according to"
" her attorney, Christopher Wright, who declined to comment further. After leaving court, Barrientos was"
" arrested and charged with theft of service and criminal trespass for allegedly sneaking into the New"
" York subway through an emergency exit, said Detective Annette Markowski, a police spokeswoman. In total,"
" Barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002. All"
" occurred either in Westchester County, Long Island, New Jersey or the Bronx. She is believed to still be"
" married to four men, and at one time, she was married to eight men at once, prosecutors say. Prosecutors"
" said the immigration scam involved some of her husbands, who filed for permanent residence status"
" shortly after the marriages. Any divorces happened only after such filings were approved. It was"
" unclear whether any of the men will be prosecuted. The case was referred to the Bronx District"
" Attorney's Office by Immigration and Customs Enforcement and the Department of Homeland Security's"
' Investigation Division. Seven of the men are from so-called "red-flagged" countries, including Egypt,'
" Turkey, Georgia, Pakistan and Mali. Her eighth husband, Rashid Rajput, was deported in 2006 to his"
" native Pakistan after an investigation by the Joint Terrorism Task Force. If convicted, Barrientos faces"
" up to four years in prison. Her next court appearance is scheduled for May 18."
)
dct = tokenizer.batch_encode_plus(
[FRANCE_ARTICLE, SHORTER_ARTICLE, IRAN_ARTICLE, ARTICLE_SUBWAY],
max_length=1024,
padding="max_length",
truncation_strategy="only_first",
truncation=True,
return_tensors="np",
)
self.assertEqual(1024, dct["input_ids"].shape[1])
hypotheses_batch = model.generate(
input_ids=dct["input_ids"],
attention_mask=dct["attention_mask"],
num_beams=2,
).sequences
assert (hypotheses_batch[:, 1] == 0).all().item()
EXPECTED = [
"A French prosecutor says he is not aware of any video footage from on board the plane. Two German"
" magazines claim to have found a cell phone video showing the crash. The publications say they watched"
" the video, which was found by a source close to the investigation. All 150 on board the Germanwings"
" flight were killed.",
"Palestinian Authority becomes 123rd member of the International Criminal Court. The move gives the court"
" jurisdiction over alleged crimes in Palestinian territories. Israel and the United States opposed the"
" Palestinians' efforts to join the body. But Palestinian Foreign Minister Riad al-Malki said it was a"
" move toward greater justice.",
"U.S. and its negotiating partners reached a strong framework agreement with Iran. Peter Bergen: The"
" debate that has already begun will likely result in more heat than light. Bergen: The most misleading"
" assertion is that the negotiations' objective at the outset was the total elimination of any nuclear"
" program.",
"Liana Barrientos, 39, has been married 10 times, sometimes within two weeks of each other. Prosecutors"
" say the marriages were part of an immigration scam. She pleaded not guilty at State Supreme Court in the"
" Bronx on Friday. If convicted, Barrientos faces up to four years in prison.",
]
generated_summaries = tokenizer.batch_decode(
hypotheses_batch.tolist(), clean_up_tokenization_spaces=True, skip_special_tokens=True
)
assert generated_summaries == EXPECTED
class FlaxBartStandaloneDecoderModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_attention_mask=True,
use_labels=False,
vocab_size=99,
hidden_size=16,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=4,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=32,
eos_token_id=2,
pad_token_id=1,
bos_token_id=0,
initializer_range=0.02,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_attention_mask = use_attention_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
self.initializer_range = initializer_range
def prepare_config_and_inputs(self):
input_ids = jnp.clip(ids_tensor([self.batch_size, self.seq_length], self.vocab_size), 3, self.vocab_size)
attention_mask = None
if self.use_attention_mask:
attention_mask = random_attention_mask([self.batch_size, self.seq_length])
config = BartConfig(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
encoder_ffn_dim=self.intermediate_size,
decoder_ffn_dim=self.intermediate_size,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
initializer_range=self.initializer_range,
use_cache=False,
)
return config, input_ids, attention_mask
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, input_ids, attention_mask = config_and_inputs
inputs_dict = {"input_ids": input_ids, "attention_mask": attention_mask}
return config, inputs_dict
def prepare_config_and_inputs_for_decoder(self):
config, input_ids, attention_mask = self.prepare_config_and_inputs()
encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
return (
config,
input_ids,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
)
| transformers/tests/models/bart/test_modeling_flax_bart.py/0 | {
"file_path": "transformers/tests/models/bart/test_modeling_flax_bart.py",
"repo_id": "transformers",
"token_count": 17353
} | 148 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
if is_tf_available():
import tensorflow as tf
from transformers.modeling_tf_utils import keras
if is_tensorflow_text_available():
from transformers.models.bert import TFBertTokenizer
TOKENIZER_CHECKPOINTS = ["google-bert/bert-base-uncased", "google-bert/bert-base-cased"]
TINY_MODEL_CHECKPOINT = "hf-internal-testing/tiny-bert-tf-only"
if is_tf_available():
from transformers.modeling_tf_utils import keras
class ModelToSave(keras.Model):
def __init__(self, tokenizer):
super().__init__()
self.tokenizer = tokenizer
config = AutoConfig.from_pretrained(TINY_MODEL_CHECKPOINT)
self.bert = TFAutoModel.from_config(config)
def call(self, inputs):
tokenized = self.tokenizer(inputs)
out = self.bert(tokenized)
return out["pooler_output"]
@require_tf
@require_tensorflow_text
class BertTokenizationTest(unittest.TestCase):
# The TF tokenizers are usually going to be used as pretrained tokenizers from existing model checkpoints,
# so that's what we focus on here.
def setUp(self):
super().setUp()
self.tokenizers = [BertTokenizer.from_pretrained(checkpoint) for checkpoint in TOKENIZER_CHECKPOINTS]
self.tf_tokenizers = [TFBertTokenizer.from_pretrained(checkpoint) for checkpoint in TOKENIZER_CHECKPOINTS]
assert len(self.tokenizers) == len(self.tf_tokenizers)
self.test_sentences = [
"This is a straightforward English test sentence.",
"This one has some weird characters\rto\nsee\r\nif those\u00E9break things.",
"Now we're going to add some Chinese: 一 二 三 一二三",
"And some much more rare Chinese: 齉 堃 齉堃",
"Je vais aussi écrire en français pour tester les accents",
"Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ",
]
self.paired_sentences = list(zip(self.test_sentences, self.test_sentences[::-1]))
def test_output_equivalence(self):
for tokenizer, tf_tokenizer in zip(self.tokenizers, self.tf_tokenizers):
for test_inputs in (self.test_sentences, self.paired_sentences):
python_outputs = tokenizer(test_inputs, return_tensors="tf", padding="longest")
tf_outputs = tf_tokenizer(test_inputs)
for key in python_outputs.keys():
self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape))
self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key], tf.int64) == tf_outputs[key]))
@slow
def test_different_pairing_styles(self):
for tf_tokenizer in self.tf_tokenizers:
merged_outputs = tf_tokenizer(self.paired_sentences)
separated_outputs = tf_tokenizer(
text=[sentence[0] for sentence in self.paired_sentences],
text_pair=[sentence[1] for sentence in self.paired_sentences],
)
for key in merged_outputs.keys():
self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key], tf.int64) == separated_outputs[key]))
@slow
def test_graph_mode(self):
for tf_tokenizer in self.tf_tokenizers:
compiled_tokenizer = tf.function(tf_tokenizer)
for test_inputs in (self.test_sentences, self.paired_sentences):
test_inputs = tf.constant(test_inputs)
compiled_outputs = compiled_tokenizer(test_inputs)
eager_outputs = tf_tokenizer(test_inputs)
for key in eager_outputs.keys():
self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]))
@slow
def test_export_for_inference(self):
for tf_tokenizer in self.tf_tokenizers:
model = ModelToSave(tokenizer=tf_tokenizer)
test_inputs = tf.convert_to_tensor(self.test_sentences)
out = model(test_inputs) # Build model with some sample inputs
with TemporaryDirectory() as tempdir:
save_path = Path(tempdir) / "saved.model"
model.export(save_path)
loaded_model = tf.saved_model.load(save_path)
loaded_output = loaded_model.serve(test_inputs)
# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output)), 1e-5)
| transformers/tests/models/bert/test_tokenization_bert_tf.py/0 | {
"file_path": "transformers/tests/models/bert/test_tokenization_bert_tf.py",
"repo_id": "transformers",
"token_count": 2067
} | 149 |
# coding=utf-8
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class BioGptTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = BioGptTokenizer
test_rust_tokenizer = False
def setUp(self):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
vocab = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"w</w>",
"r</w>",
"t</w>",
"lo",
"low",
"er</w>",
"low</w>",
"lowest</w>",
"newer</w>",
"wider</w>",
"<unk>",
]
vocab_tokens = dict(zip(vocab, range(len(vocab))))
merges = ["l o 123", "lo w 1456", "e r</w> 1789", ""]
self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
with open(self.vocab_file, "w") as fp:
fp.write(json.dumps(vocab_tokens))
with open(self.merges_file, "w") as fp:
fp.write("\n".join(merges))
def get_input_output_texts(self, tokenizer):
input_text = "lower newer"
output_text = "lower newer"
return input_text, output_text
def test_full_tokenizer(self):
"""Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt"""
tokenizer = BioGptTokenizer(self.vocab_file, self.merges_file)
text = "lower"
bpe_tokens = ["low", "er</w>"]
tokens = tokenizer.tokenize(text)
self.assertListEqual(tokens, bpe_tokens)
input_tokens = tokens + ["<unk>"]
input_bpe_tokens = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
@slow
def test_sequence_builders(self):
tokenizer = BioGptTokenizer.from_pretrained("microsoft/biogpt")
text = tokenizer.encode("sequence builders", add_special_tokens=False)
text_2 = tokenizer.encode("multi-sequence build", add_special_tokens=False)
encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
self.assertTrue(encoded_sentence == [2] + text)
self.assertTrue(encoded_pair == [2] + text + [2] + text_2)
| transformers/tests/models/biogpt/test_tokenization_biogpt.py/0 | {
"file_path": "transformers/tests/models/biogpt/test_tokenization_biogpt.py",
"repo_id": "transformers",
"token_count": 1509
} | 150 |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch Blip model. """
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import BlipTextModel
from transformers.models.blip.modeling_blip import BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class BlipTextModelTester:
def __init__(
self,
parent,
batch_size=12,
seq_length=7,
is_training=True,
use_input_mask=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
projection_dim=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
dropout=0.1,
attention_dropout=0.1,
max_position_embeddings=512,
initializer_range=0.02,
bos_token_id=0,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.projection_dim = projection_dim
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.dropout = dropout
self.attention_dropout = attention_dropout
self.max_position_embeddings = max_position_embeddings
self.initializer_range = initializer_range
self.scope = scope
self.bos_token_id = bos_token_id
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
if input_mask is not None:
batch_size, seq_length = input_mask.shape
rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,))
for batch_idx, start_index in enumerate(rnd_start_indices):
input_mask[batch_idx, :start_index] = 1
input_mask[batch_idx, start_index:] = 0
config = self.get_config()
return config, input_ids, input_mask
def get_config(self):
return BlipTextConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
projection_dim=self.projection_dim,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
dropout=self.dropout,
attention_dropout=self.attention_dropout,
max_position_embeddings=self.max_position_embeddings,
initializer_range=self.initializer_range,
bos_token_id=self.bos_token_id,
)
def create_and_check_model(self, config, input_ids, input_mask):
model = BlipTextModel(config=config)
model.to(torch_device)
model.eval()
with torch.no_grad():
result = model(input_ids, attention_mask=input_mask)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, input_ids, input_mask = config_and_inputs
inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class BlipTextModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (BlipTextModel,) if is_torch_available() else ()
fx_compatible = False
test_pruning = False
test_head_masking = False
def setUp(self):
self.model_tester = BlipTextModelTester(self)
self.config_tester = ConfigTester(self, config_class=BlipTextConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_training(self):
pass
def test_training_gradient_checkpointing(self):
pass
@unittest.skip(
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
)
def test_training_gradient_checkpointing_use_reentrant(self):
pass
@unittest.skip(
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
)
def test_training_gradient_checkpointing_use_reentrant_false(self):
pass
@unittest.skip(reason="Blip does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING")
def test_save_load_fast_init_from_base(self):
pass
@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING")
def test_save_load_fast_init_to_base(self):
pass
@slow
def test_model_from_pretrained(self):
for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = BlipTextModel.from_pretrained(model_name)
self.assertIsNotNone(model)
def test_pt_tf_model_equivalence(self):
super().test_pt_tf_model_equivalence(allow_missing_keys=True)
| transformers/tests/models/blip/test_modeling_blip_text.py/0 | {
"file_path": "transformers/tests/models/blip/test_modeling_blip_text.py",
"repo_id": "transformers",
"token_count": 2774
} | 151 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sentencepiece
class ClapProcessorTest(unittest.TestCase):
def setUp(self):
self.checkpoint = "laion/clap-htsat-unfused"
self.tmpdirname = tempfile.mkdtemp()
def get_tokenizer(self, **kwargs):
return RobertaTokenizer.from_pretrained(self.checkpoint, **kwargs)
def get_feature_extractor(self, **kwargs):
return ClapFeatureExtractor.from_pretrained(self.checkpoint, **kwargs)
def tearDown(self):
shutil.rmtree(self.tmpdirname)
def test_save_load_pretrained_default(self):
tokenizer = self.get_tokenizer()
feature_extractor = self.get_feature_extractor()
processor = ClapProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
processor.save_pretrained(self.tmpdirname)
processor = ClapProcessor.from_pretrained(self.tmpdirname)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
self.assertIsInstance(processor.tokenizer, RobertaTokenizerFast)
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string())
self.assertIsInstance(processor.feature_extractor, ClapFeatureExtractor)
def test_save_load_pretrained_additional_features(self):
processor = ClapProcessor(tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor())
processor.save_pretrained(self.tmpdirname)
tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
feature_extractor_add_kwargs = self.get_feature_extractor(do_normalize=False, padding_value=1.0)
processor = ClapProcessor.from_pretrained(
self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
self.assertIsInstance(processor.tokenizer, RobertaTokenizerFast)
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string())
self.assertIsInstance(processor.feature_extractor, ClapFeatureExtractor)
def test_feature_extractor(self):
feature_extractor = self.get_feature_extractor()
tokenizer = self.get_tokenizer()
processor = ClapProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
raw_speech = floats_list((3, 1000))
input_feat_extract = feature_extractor(raw_speech, return_tensors="np")
input_processor = processor(audios=raw_speech, return_tensors="np")
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
def test_tokenizer(self):
feature_extractor = self.get_feature_extractor()
tokenizer = self.get_tokenizer()
processor = ClapProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
input_str = "This is a test string"
encoded_processor = processor(text=input_str)
encoded_tok = tokenizer(input_str)
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key], encoded_processor[key])
def test_tokenizer_decode(self):
feature_extractor = self.get_feature_extractor()
tokenizer = self.get_tokenizer()
processor = ClapProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
decoded_processor = processor.batch_decode(predicted_ids)
decoded_tok = tokenizer.batch_decode(predicted_ids)
self.assertListEqual(decoded_tok, decoded_processor)
def test_model_input_names(self):
feature_extractor = self.get_feature_extractor()
tokenizer = self.get_tokenizer()
processor = ClapProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
self.assertListEqual(
processor.model_input_names[2:],
feature_extractor.model_input_names,
msg="`processor` and `feature_extractor` model input names do not match",
)
| transformers/tests/models/clap/test_processor_clap.py/0 | {
"file_path": "transformers/tests/models/clap/test_processor_clap.py",
"repo_id": "transformers",
"token_count": 1895
} | 152 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch ConvNextV2 model. """
import unittest
from transformers import ConvNextV2Config
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import ConvNextV2Backbone, ConvNextV2ForImageClassification, ConvNextV2Model
from transformers.models.convnextv2.modeling_convnextv2 import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class ConvNextV2ModelTester:
def __init__(
self,
parent,
batch_size=13,
image_size=32,
num_channels=3,
num_stages=4,
hidden_sizes=[10, 20, 30, 40],
depths=[2, 2, 3, 2],
is_training=True,
use_labels=True,
intermediate_size=37,
hidden_act="gelu",
num_labels=10,
initializer_range=0.02,
out_features=["stage2", "stage3", "stage4"],
out_indices=[2, 3, 4],
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.num_channels = num_channels
self.num_stages = num_stages
self.hidden_sizes = hidden_sizes
self.depths = depths
self.is_training = is_training
self.use_labels = use_labels
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.num_labels = num_labels
self.initializer_range = initializer_range
self.out_features = out_features
self.out_indices = out_indices
self.scope = scope
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size], self.num_labels)
config = self.get_config()
return config, pixel_values, labels
def get_config(self):
return ConvNextV2Config(
num_channels=self.num_channels,
hidden_sizes=self.hidden_sizes,
depths=self.depths,
num_stages=self.num_stages,
hidden_act=self.hidden_act,
is_decoder=False,
initializer_range=self.initializer_range,
out_features=self.out_features,
out_indices=self.out_indices,
num_labels=self.num_labels,
)
def create_and_check_model(self, config, pixel_values, labels):
model = ConvNextV2Model(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape,
(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),
)
def create_and_check_for_image_classification(self, config, pixel_values, labels):
model = ConvNextV2ForImageClassification(config)
model.to(torch_device)
model.eval()
result = model(pixel_values, labels=labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_backbone(self, config, pixel_values, labels):
model = ConvNextV2Backbone(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
# verify hidden states
self.parent.assertEqual(len(result.feature_maps), len(config.out_features))
self.parent.assertListEqual(list(result.feature_maps[0].shape), [self.batch_size, self.hidden_sizes[1], 4, 4])
# verify channels
self.parent.assertEqual(len(model.channels), len(config.out_features))
self.parent.assertListEqual(model.channels, config.hidden_sizes[1:])
# verify backbone works with out_features=None
config.out_features = None
model = ConvNextV2Backbone(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
# verify feature maps
self.parent.assertEqual(len(result.feature_maps), 1)
self.parent.assertListEqual(list(result.feature_maps[0].shape), [self.batch_size, self.hidden_sizes[-1], 1, 1])
# verify channels
self.parent.assertEqual(len(model.channels), 1)
self.parent.assertListEqual(model.channels, [config.hidden_sizes[-1]])
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, labels = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
def prepare_config_and_inputs_with_labels(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, labels = config_and_inputs
inputs_dict = {"pixel_values": pixel_values, "labels": labels}
return config, inputs_dict
@require_torch
class ConvNextV2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_common.py, as ConvNextV2 does not use input_ids, inputs_embeds,
attention_mask and seq_length.
"""
all_model_classes = (
(
ConvNextV2Model,
ConvNextV2ForImageClassification,
ConvNextV2Backbone,
)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{"image-feature-extraction": ConvNextV2Model, "image-classification": ConvNextV2ForImageClassification}
if is_torch_available()
else {}
)
fx_compatible = False
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
has_attentions = False
def setUp(self):
self.model_tester = ConvNextV2ModelTester(self)
self.config_tester = ConfigTester(self, config_class=ConvNextV2Config, has_text_modality=False, hidden_size=37)
def test_config(self):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def create_and_test_config_common_properties(self):
return
@unittest.skip(reason="ConvNextV2 does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="ConvNextV2 does not support input and output embeddings")
def test_model_common_attributes(self):
pass
@unittest.skip(reason="ConvNextV2 does not use feedforward chunking")
def test_feed_forward_chunking(self):
pass
def test_training(self):
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_with_labels()
config.return_dict = True
if model_class.__name__ in [
*get_values(MODEL_MAPPING_NAMES),
*get_values(MODEL_FOR_BACKBONE_MAPPING_NAMES),
]:
continue
model = model_class(config)
model.to(torch_device)
model.train()
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
loss = model(**inputs).loss
loss.backward()
def test_training_gradient_checkpointing(self):
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_with_labels()
config.use_cache = False
config.return_dict = True
if (
model_class.__name__
in [*get_values(MODEL_MAPPING_NAMES), *get_values(MODEL_FOR_BACKBONE_MAPPING_NAMES)]
or not model_class.supports_gradient_checkpointing
):
continue
model = model_class(config)
model.to(torch_device)
model.gradient_checkpointing_enable()
model.train()
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
loss = model(**inputs).loss
loss.backward()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
expected_num_stages = self.model_tester.num_stages
self.assertEqual(len(hidden_states), expected_num_stages + 1)
# ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[self.model_tester.image_size // 4, self.model_tester.image_size // 4],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
def test_for_image_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = ConvNextV2Model.from_pretrained(model_name)
self.assertIsNotNone(model)
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_torch
@require_vision
class ConvNextV2ModelIntegrationTest(unittest.TestCase):
@cached_property
def default_image_processor(self):
return AutoImageProcessor.from_pretrained("facebook/convnextv2-tiny-1k-224") if is_vision_available() else None
@slow
def test_inference_image_classification_head(self):
model = ConvNextV2ForImageClassification.from_pretrained("facebook/convnextv2-tiny-1k-224").to(torch_device)
preprocessor = self.default_image_processor
image = prepare_img()
inputs = preprocessor(images=image, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
# verify the logits
expected_shape = torch.Size((1, 1000))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = torch.tensor([0.9996, 0.1966, -0.4386]).to(torch_device)
self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))
| transformers/tests/models/convnextv2/test_modeling_convnextv2.py/0 | {
"file_path": "transformers/tests/models/convnextv2/test_modeling_convnextv2.py",
"repo_id": "transformers",
"token_count": 5523
} | 153 |
# coding=utf-8
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch Data2VecAudio model. """
import unittest
from tests.test_modeling_common import floats_tensor, ids_tensor, random_attention_mask
from transformers import Data2VecTextConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
Data2VecTextForCausalLM,
Data2VecTextForMaskedLM,
Data2VecTextForMultipleChoice,
Data2VecTextForQuestionAnswering,
Data2VecTextForSequenceClassification,
Data2VecTextForTokenClassification,
Data2VecTextModel,
)
from transformers.models.data2vec.modeling_data2vec_text import (
DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
Data2VecTextForTextEmbeddings,
create_position_ids_from_input_ids,
)
class Data2VecTextModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
return Data2VecTextConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range,
)
def prepare_config_and_inputs_for_decoder(self):
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = self.prepare_config_and_inputs()
config.is_decoder = True
encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = Data2VecTextModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def create_and_check_model_as_decoder(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.add_cross_attention = True
model = Data2VecTextModel(config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
)
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
)
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def create_and_check_for_causal_lm(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
model = Data2VecTextForCausalLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_decoder_model_past_large_inputs(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.is_decoder = True
config.add_cross_attention = True
model = Data2VecTextForCausalLM(config=config).to(torch_device).eval()
# make sure that ids don't start with pad token
mask = input_ids.ne(config.pad_token_id).long()
input_ids = input_ids * mask
# first forward pass
outputs = model(
input_ids,
attention_mask=input_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=True,
)
past_key_values = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
# make sure that ids don't start with pad token
mask = next_tokens.ne(config.pad_token_id).long()
next_tokens = next_tokens * mask
next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
output_from_no_past = model(
next_input_ids,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_hidden_states=True,
)["hidden_states"][0]
output_from_past = model(
next_tokens,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
output_hidden_states=True,
)["hidden_states"][0]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_for_masked_lm(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = Data2VecTextForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_token_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = Data2VecTextForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_multiple_choice(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_choices = self.num_choices
model = Data2VecTextForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
result = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def create_and_check_for_question_answering(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = Data2VecTextForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class Data2VecTextModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
Data2VecTextForCausalLM,
Data2VecTextForMaskedLM,
Data2VecTextModel,
Data2VecTextForSequenceClassification,
Data2VecTextForTokenClassification,
Data2VecTextForMultipleChoice,
Data2VecTextForQuestionAnswering,
)
if is_torch_available()
else ()
)
all_generative_model_classes = (Data2VecTextForCausalLM,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"feature-extraction": Data2VecTextModel,
"fill-mask": Data2VecTextForMaskedLM,
"question-answering": Data2VecTextForQuestionAnswering,
"text-classification": Data2VecTextForSequenceClassification,
"text-generation": Data2VecTextForCausalLM,
"token-classification": Data2VecTextForTokenClassification,
"zero-shot": Data2VecTextForSequenceClassification,
}
if is_torch_available()
else {}
)
model_split_percents = [0.5, 0.9]
def setUp(self):
self.model_tester = Data2VecTextModelTester(self)
self.config_tester = ConfigTester(self, config_class=Data2VecTextConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_various_embeddings(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
config_and_inputs[0].position_embedding_type = type
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_as_decoder(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
def test_model_as_decoder_with_default_input_mask(self):
# This regression test was failing with PyTorch < 1.3
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
) = self.model_tester.prepare_config_and_inputs_for_decoder()
input_mask = None
self.model_tester.create_and_check_model_as_decoder(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def test_for_causal_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_for_causal_lm(*config_and_inputs)
def test_decoder_model_past_with_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_decoder_model_past_with_large_inputs_relative_pos_emb(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
config_and_inputs[0].position_embedding_type = "relative_key"
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = Data2VecTextModel.from_pretrained(model_name)
self.assertIsNotNone(model)
def test_create_position_ids_respects_padding_index(self):
"""Ensure that the default position ids only assign a sequential . This is a regression
test for https://github.com/huggingface/transformers/issues/1761
The position ids should be masked with the embedding object's padding index. Therefore, the
first available non-padding position index is Data2VecTextForTextEmbeddings.padding_idx + 1
"""
config = self.model_tester.prepare_config_and_inputs()[0]
model = Data2VecTextForTextEmbeddings(config=config)
input_ids = torch.as_tensor([[12, 31, 13, model.padding_idx]])
expected_positions = torch.as_tensor(
[[0 + model.padding_idx + 1, 1 + model.padding_idx + 1, 2 + model.padding_idx + 1, model.padding_idx]]
)
position_ids = create_position_ids_from_input_ids(input_ids, model.padding_idx)
self.assertEqual(position_ids.shape, expected_positions.shape)
self.assertTrue(torch.all(torch.eq(position_ids, expected_positions)))
def test_create_position_ids_from_inputs_embeds(self):
"""Ensure that the default position ids only assign a sequential . This is a regression
test for https://github.com/huggingface/transformers/issues/1761
The position ids should be masked with the embedding object's padding index. Therefore, the
first available non-padding position index is Data2VecTextForTextEmbeddings.padding_idx + 1
"""
config = self.model_tester.prepare_config_and_inputs()[0]
embeddings = Data2VecTextForTextEmbeddings(config=config)
inputs_embeds = torch.empty(2, 4, 30)
expected_single_positions = [
0 + embeddings.padding_idx + 1,
1 + embeddings.padding_idx + 1,
2 + embeddings.padding_idx + 1,
3 + embeddings.padding_idx + 1,
]
expected_positions = torch.as_tensor([expected_single_positions, expected_single_positions])
position_ids = embeddings.create_position_ids_from_inputs_embeds(inputs_embeds)
self.assertEqual(position_ids.shape, expected_positions.shape)
self.assertTrue(torch.all(torch.eq(position_ids, expected_positions)))
@require_torch
class Data2VecTextModelIntegrationTest(TestCasePlus):
@slow
def test_inference_masked_lm(self):
model = Data2VecTextForMaskedLM.from_pretrained("facebook/data2vec-text-base")
input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
with torch.no_grad():
output = model(input_ids)[0]
expected_shape = torch.Size((1, 11, 50265))
self.assertEqual(output.shape, expected_shape)
# compare the actual values for a slice.
expected_slice = torch.tensor([[[0.2328, 0.0000, 1.1710], [2.2525, 0.0000, 1.9937], [2.1280, 0.0000, 1.8691]]])
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
@slow
def test_inference_no_head(self):
model = Data2VecTextModel.from_pretrained("facebook/data2vec-text-base")
input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
with torch.no_grad():
output = model(input_ids)[0]
# compare the actual values for a slice.
expected_slice = torch.tensor(
[[[0.1998, -0.0379, 0.0024], [-0.0971, -0.2214, -0.1798], [-0.0789, -0.2400, -0.1898]]]
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
| transformers/tests/models/data2vec/test_modeling_data2vec_text.py/0 | {
"file_path": "transformers/tests/models/data2vec/test_modeling_data2vec_text.py",
"repo_id": "transformers",
"token_count": 10072
} | 154 |
# coding=utf-8
# Copyright 2022 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
from transformers.file_utils import is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
if is_vision_available():
from transformers import DPTImageProcessor
class DPTImageProcessingTester(unittest.TestCase):
def __init__(
self,
parent,
batch_size=7,
num_channels=3,
image_size=18,
min_resolution=30,
max_resolution=400,
do_resize=True,
size=None,
do_normalize=True,
image_mean=[0.5, 0.5, 0.5],
image_std=[0.5, 0.5, 0.5],
):
size = size if size is not None else {"height": 18, "width": 18}
self.parent = parent
self.batch_size = batch_size
self.num_channels = num_channels
self.image_size = image_size
self.min_resolution = min_resolution
self.max_resolution = max_resolution
self.do_resize = do_resize
self.size = size
self.do_normalize = do_normalize
self.image_mean = image_mean
self.image_std = image_std
def prepare_image_processor_dict(self):
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
def expected_output_image_shape(self, images):
return self.num_channels, self.size["height"], self.size["width"]
def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
return prepare_image_inputs(
batch_size=self.batch_size,
num_channels=self.num_channels,
min_resolution=self.min_resolution,
max_resolution=self.max_resolution,
equal_resolution=equal_resolution,
numpify=numpify,
torchify=torchify,
)
@require_torch
@require_vision
class DPTImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = DPTImageProcessor if is_vision_available() else None
def setUp(self):
self.image_processor_tester = DPTImageProcessingTester(self)
@property
def image_processor_dict(self):
return self.image_processor_tester.prepare_image_processor_dict()
def test_image_processor_properties(self):
image_processing = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(image_processing, "image_mean"))
self.assertTrue(hasattr(image_processing, "image_std"))
self.assertTrue(hasattr(image_processing, "do_normalize"))
self.assertTrue(hasattr(image_processing, "do_resize"))
self.assertTrue(hasattr(image_processing, "size"))
self.assertTrue(hasattr(image_processing, "do_rescale"))
self.assertTrue(hasattr(image_processing, "rescale_factor"))
self.assertTrue(hasattr(image_processing, "do_pad"))
self.assertTrue(hasattr(image_processing, "size_divisor"))
def test_image_processor_from_dict_with_kwargs(self):
image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
self.assertEqual(image_processor.size, {"height": 18, "width": 18})
image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42)
self.assertEqual(image_processor.size, {"height": 42, "width": 42})
def test_padding(self):
image_processing = self.image_processing_class(**self.image_processor_dict)
image = np.random.randn(3, 249, 491)
# test individual method
image = image_processing.pad_image(image, size_divisor=4)
self.assertTrue(image.shape[1] % 4 == 0)
self.assertTrue(image.shape[2] % 4 == 0)
# test by calling
pixel_values = image_processing.preprocess(
image, do_rescale=False, do_resize=False, do_pad=True, size_divisor=4, return_tensors="pt"
).pixel_values
self.assertTrue(pixel_values.shape[2] % 4 == 0)
self.assertTrue(pixel_values.shape[3] % 4 == 0)
def test_keep_aspect_ratio(self):
size = {"height": 512, "width": 512}
image_processor = DPTImageProcessor(size=size, keep_aspect_ratio=True, ensure_multiple_of=32)
image = np.zeros((489, 640, 3))
pixel_values = image_processor(image, return_tensors="pt").pixel_values
self.assertEqual(list(pixel_values.shape), [1, 3, 512, 672])
| transformers/tests/models/dpt/test_image_processing_dpt.py/0 | {
"file_path": "transformers/tests/models/dpt/test_image_processing_dpt.py",
"repo_id": "transformers",
"token_count": 2095
} | 155 |
# coding=utf-8
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import tempfile
import unittest
from typing import List
from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
@require_tokenizers
class ESMTokenizationTest(unittest.TestCase):
tokenizer_class = EsmTokenizer
def setUp(self):
super().setUp()
self.tmpdirname = tempfile.mkdtemp()
vocab_tokens: List[str] = ["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"] # fmt: skip
self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
def get_tokenizers(self, **kwargs) -> List[PreTrainedTokenizerBase]:
return [self.get_tokenizer(**kwargs)]
def get_tokenizer(self, **kwargs) -> PreTrainedTokenizer:
return self.tokenizer_class.from_pretrained(self.tmpdirname, **kwargs)
def test_tokenizer_single_example(self):
tokenizer = self.tokenizer_class(self.vocab_file)
tokens = tokenizer.tokenize("LAGVS")
self.assertListEqual(tokens, ["L", "A", "G", "V", "S"])
self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [4, 5, 6, 7, 8])
def test_tokenizer_encode_single(self):
tokenizer = self.tokenizer_class(self.vocab_file)
seq = "LAGVS"
self.assertListEqual(tokenizer.encode(seq), [0, 4, 5, 6, 7, 8, 2])
def test_tokenizer_call_no_pad(self):
tokenizer = self.tokenizer_class(self.vocab_file)
seq_batch = ["LAGVS", "WCB"]
tokens_batch = tokenizer(seq_batch, padding=False)["input_ids"]
self.assertListEqual(tokens_batch, [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]])
def test_tokenizer_call_pad(self):
tokenizer = self.tokenizer_class(self.vocab_file)
seq_batch = ["LAGVS", "WCB"]
tokens_batch = tokenizer(seq_batch, padding=True)["input_ids"]
self.assertListEqual(tokens_batch, [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]])
def test_tokenize_special_tokens(self):
"""Test `tokenize` with special tokens."""
tokenizers = self.get_tokenizers(fast=True)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
SPECIAL_TOKEN_1 = "<unk>"
SPECIAL_TOKEN_2 = "<mask>"
token_1 = tokenizer.tokenize(SPECIAL_TOKEN_1)
token_2 = tokenizer.tokenize(SPECIAL_TOKEN_2)
self.assertEqual(len(token_1), 1)
self.assertEqual(len(token_2), 1)
self.assertEqual(token_1[0], SPECIAL_TOKEN_1)
self.assertEqual(token_2[0], SPECIAL_TOKEN_2)
def test_add_tokens(self):
tokenizer = self.tokenizer_class(self.vocab_file)
vocab_size = len(tokenizer)
self.assertEqual(tokenizer.add_tokens(""), 0)
self.assertEqual(tokenizer.add_tokens("testoken"), 1)
self.assertEqual(tokenizer.add_tokens(["testoken1", "testtoken2"]), 2)
self.assertEqual(len(tokenizer), vocab_size + 3)
self.assertEqual(tokenizer.add_special_tokens({}), 0)
self.assertEqual(tokenizer.add_special_tokens({"bos_token": "[BOS]", "eos_token": "[EOS]"}), 2)
self.assertRaises(AssertionError, tokenizer.add_special_tokens, {"additional_special_tokens": "<testtoken1>"})
self.assertEqual(tokenizer.add_special_tokens({"additional_special_tokens": ["<testtoken2>"]}), 1)
self.assertEqual(
tokenizer.add_special_tokens({"additional_special_tokens": ["<testtoken3>", "<testtoken4>"]}), 2
)
self.assertIn("<testtoken3>", tokenizer.special_tokens_map["additional_special_tokens"])
self.assertIsInstance(tokenizer.special_tokens_map["additional_special_tokens"], list)
self.assertGreaterEqual(len(tokenizer.special_tokens_map["additional_special_tokens"]), 2)
self.assertEqual(len(tokenizer), vocab_size + 8)
| transformers/tests/models/esm/test_tokenization_esm.py/0 | {
"file_path": "transformers/tests/models/esm/test_tokenization_esm.py",
"repo_id": "transformers",
"token_count": 2117
} | 156 |
# coding=utf-8
# Copyright 2018 The Microsoft Research Asia LayoutLM Team Authors, The Hugging Face Team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.layoutlm.modeling_tf_layoutlm import (
TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLayoutLMForMaskedLM,
TFLayoutLMForQuestionAnswering,
TFLayoutLMForSequenceClassification,
TFLayoutLMForTokenClassification,
TFLayoutLMModel,
)
class TFLayoutLMModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
range_bbox=1000,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
self.range_bbox = range_bbox
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
# convert bbox to numpy since TF does not support item assignment
bbox = ids_tensor([self.batch_size, self.seq_length, 4], self.range_bbox).numpy()
# Ensure that bbox is legal
for i in range(bbox.shape[0]):
for j in range(bbox.shape[1]):
if bbox[i, j, 3] < bbox[i, j, 1]:
t = bbox[i, j, 3]
bbox[i, j, 3] = bbox[i, j, 1]
bbox[i, j, 1] = t
if bbox[i, j, 2] < bbox[i, j, 0]:
t = bbox[i, j, 2]
bbox[i, j, 2] = bbox[i, j, 0]
bbox[i, j, 0] = t
bbox = tf.convert_to_tensor(bbox)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = LayoutLMConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range,
)
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def create_and_check_model(
self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = TFLayoutLMModel(config=config)
result = model(input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, bbox, token_type_ids=token_type_ids)
result = model(input_ids, bbox)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def create_and_check_for_masked_lm(
self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = TFLayoutLMForMaskedLM(config=config)
result = model(input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_sequence_classification(
self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = TFLayoutLMForSequenceClassification(config=config)
result = model(input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_token_classification(
self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = TFLayoutLMForTokenClassification(config=config)
result = model(input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_question_answering(
self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = TFLayoutLMForQuestionAnswering(config=config)
result = model(input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
bbox,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"bbox": bbox,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_tf
class TFLayoutLMModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
TFLayoutLMModel,
TFLayoutLMForMaskedLM,
TFLayoutLMForTokenClassification,
TFLayoutLMForSequenceClassification,
TFLayoutLMForQuestionAnswering,
)
if is_tf_available()
else ()
)
pipeline_model_mapping = (
{
"feature-extraction": TFLayoutLMModel,
"fill-mask": TFLayoutLMForMaskedLM,
"text-classification": TFLayoutLMForSequenceClassification,
"token-classification": TFLayoutLMForTokenClassification,
"zero-shot": TFLayoutLMForSequenceClassification,
}
if is_tf_available()
else {}
)
test_head_masking = False
test_onnx = True
onnx_min_opset = 10
def setUp(self):
self.model_tester = TFLayoutLMModelTester(self)
self.config_tester = ConfigTester(self, config_class=LayoutLMConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = TFLayoutLMModel.from_pretrained(model_name)
self.assertIsNotNone(model)
# TODO (Joao): fix me
@unittest.skip("Onnx compliancy broke with TF 2.10")
def test_onnx_compliancy(self):
pass
def prepare_layoutlm_batch_inputs():
# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
# fmt: off
input_ids = tf.convert_to_tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]) # noqa: E231
attention_mask = tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]) # noqa: E231
bbox = tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]) # noqa: E231
token_type_ids = tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]) # noqa: E231
# these are sequence labels (i.e. at the token level)
labels = tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]) # noqa: E231
# fmt: on
return input_ids, attention_mask, bbox, token_type_ids, labels
@require_tf
class TFLayoutLMModelIntegrationTest(unittest.TestCase):
@slow
def test_forward_pass_no_head(self):
model = TFLayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased")
input_ids, attention_mask, bbox, token_type_ids, labels = prepare_layoutlm_batch_inputs()
# forward pass
outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids)
# test the sequence output on [0, :3, :3]
expected_slice = tf.convert_to_tensor(
[[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]],
)
self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-3))
# test the pooled output on [1, :3]
expected_slice = tf.convert_to_tensor([-0.6580, -0.0214, 0.8552])
self.assertTrue(np.allclose(outputs.pooler_output[1, :3], expected_slice, atol=1e-3))
@slow
def test_forward_pass_sequence_classification(self):
# initialize model with randomly initialized sequence classification head
model = TFLayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased", num_labels=2)
input_ids, attention_mask, bbox, token_type_ids, _ = prepare_layoutlm_batch_inputs()
# forward pass
outputs = model(
input_ids=input_ids,
bbox=bbox,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
labels=tf.convert_to_tensor([1, 1]),
)
# test whether we get a loss as a scalar
loss = outputs.loss
expected_shape = (2,)
self.assertEqual(loss.shape, expected_shape)
# test the shape of the logits
logits = outputs.logits
expected_shape = (2, 2)
self.assertEqual(logits.shape, expected_shape)
@slow
def test_forward_pass_token_classification(self):
# initialize model with randomly initialized token classification head
model = TFLayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased", num_labels=13)
input_ids, attention_mask, bbox, token_type_ids, labels = prepare_layoutlm_batch_inputs()
# forward pass
outputs = model(
input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids, labels=labels
)
# test the shape of the logits
logits = outputs.logits
expected_shape = tf.convert_to_tensor((2, 25, 13))
self.assertEqual(logits.shape, expected_shape)
@slow
def test_forward_pass_question_answering(self):
# initialize model with randomly initialized token classification head
model = TFLayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased")
input_ids, attention_mask, bbox, token_type_ids, labels = prepare_layoutlm_batch_inputs()
# forward pass
outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids)
# test the shape of the logits
expected_shape = tf.convert_to_tensor((2, 25))
self.assertEqual(outputs.start_logits.shape, expected_shape)
self.assertEqual(outputs.end_logits.shape, expected_shape)
| transformers/tests/models/layoutlm/test_modeling_tf_layoutlm.py/0 | {
"file_path": "transformers/tests/models/layoutlm/test_modeling_tf_layoutlm.py",
"repo_id": "transformers",
"token_count": 7442
} | 157 |
# coding=utf-8
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import LongformerConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
LongformerForMaskedLM,
LongformerForMultipleChoice,
LongformerForQuestionAnswering,
LongformerForSequenceClassification,
LongformerForTokenClassification,
LongformerModel,
LongformerSelfAttention,
)
class LongformerModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
attention_window=4,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
self.attention_window = attention_window
# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
# [num_attention_heads, encoder_seq_length, encoder_key_length], but LongformerSelfAttention
# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
# because its local attention only attends to `self.attention_window + 1` locations
# (assuming no token with global attention, otherwise the last dimension of attentions
# is x + self.attention_window + 1, where x is the number of tokens with global attention)
self.key_length = self.attention_window + 2
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
return LongformerConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range,
attention_window=self.attention_window,
)
def get_pipeline_config(self):
config = self.get_config()
config.vocab_size = 300
return config
def create_and_check_attention_mask_determinism(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = LongformerModel(config=config)
model.to(torch_device)
model.eval()
attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
output_with_mask = model(input_ids, attention_mask=attention_mask)["last_hidden_state"]
output_without_mask = model(input_ids)["last_hidden_state"]
self.parent.assertTrue(torch.allclose(output_with_mask[0, 0, :5], output_without_mask[0, 0, :5], atol=1e-4))
def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = LongformerModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def create_and_check_model_with_global_attention_mask(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = LongformerModel(config=config)
model.to(torch_device)
model.eval()
global_attention_mask = input_mask.clone()
global_attention_mask[:, input_mask.shape[-1] // 2] = 0
global_attention_mask = global_attention_mask.to(torch_device)
result = model(
input_ids,
attention_mask=input_mask,
global_attention_mask=global_attention_mask,
token_type_ids=token_type_ids,
)
result = model(input_ids, token_type_ids=token_type_ids, global_attention_mask=global_attention_mask)
result = model(input_ids, global_attention_mask=global_attention_mask)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def create_and_check_for_masked_lm(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = LongformerForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_question_answering(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = LongformerForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
global_attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def create_and_check_for_sequence_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = LongformerForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_token_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = LongformerForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_multiple_choice(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_choices = self.num_choices
model = LongformerForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
result = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
global_attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
global_attention_mask = torch.zeros_like(input_ids)
global_attention_mask[:, -1] = 1
inputs_dict = {
"input_ids": input_ids,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
"global_attention_mask": global_attention_mask,
}
return config, inputs_dict
def prepare_config_and_inputs_for_question_answering(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
# Replace sep_token_id by some random id
input_ids[input_ids == config.sep_token_id] = torch.randint(0, config.vocab_size, (1,)).item()
# Make sure there are exactly three sep_token_id
input_ids[:, -3:] = config.sep_token_id
input_mask = torch.ones_like(input_ids)
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
@require_torch
class LongformerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
test_pruning = False # pruning is not supported
test_torchscript = False
all_model_classes = (
(
LongformerModel,
LongformerForMaskedLM,
LongformerForSequenceClassification,
LongformerForQuestionAnswering,
LongformerForTokenClassification,
LongformerForMultipleChoice,
)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{
"feature-extraction": LongformerModel,
"fill-mask": LongformerForMaskedLM,
"question-answering": LongformerForQuestionAnswering,
"text-classification": LongformerForSequenceClassification,
"token-classification": LongformerForTokenClassification,
"zero-shot": LongformerForSequenceClassification,
}
if is_torch_available()
else {}
)
# Need to use `0.6` instead of `0.5` for `test_disk_offload`
model_split_percents = [0.6, 0.7, 0.9]
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("Fast")
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def setUp(self):
self.model_tester = LongformerModelTester(self)
self.config_tester = ConfigTester(self, config_class=LongformerConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_attention_mask_determinism(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_attention_mask_determinism(*config_and_inputs)
def test_model_global_attention_mask(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_with_global_attention_mask(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_question_answering()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
def test_retain_grad_hidden_states_attentions(self):
# longformer cannot keep gradients in attentions or hidden states
return
@require_torch
@require_sentencepiece
@require_tokenizers
class LongformerModelIntegrationTest(unittest.TestCase):
def _get_hidden_states(self):
return torch.tensor(
[
[
[
4.98332758e-01,
2.69175139e00,
-7.08081422e-03,
1.04915401e00,
-1.83476661e00,
7.67220476e-01,
2.98580543e-01,
2.84803992e-02,
],
[
-7.58357372e-01,
4.20635998e-01,
-4.04739919e-02,
1.59924145e-01,
2.05135748e00,
-1.15997978e00,
5.37166397e-01,
2.62873606e-01,
],
[
-1.69438001e00,
4.17574660e-01,
-1.49196962e00,
-1.76483717e00,
-1.94566312e-01,
-1.71183858e00,
7.72903565e-01,
-1.11557056e00,
],
[
5.44028163e-01,
2.05466114e-01,
-3.63045868e-01,
2.41865062e-01,
3.20348382e-01,
-9.05611176e-01,
-1.92690727e-01,
-1.19917547e00,
],
]
],
dtype=torch.float32,
device=torch_device,
)
def test_diagonalize(self):
hidden_states = self._get_hidden_states()
hidden_states = hidden_states.reshape((1, 8, 4)) # set seq length = 8, hidden dim = 4
chunked_hidden_states = LongformerSelfAttention._chunk(hidden_states, window_overlap=2)
window_overlap_size = chunked_hidden_states.shape[2]
self.assertTrue(window_overlap_size == 4)
padded_hidden_states = LongformerSelfAttention._pad_and_diagonalize(chunked_hidden_states)
self.assertTrue(padded_hidden_states.shape[-1] == chunked_hidden_states.shape[-1] + window_overlap_size - 1)
# first row => [0.4983, 2.6918, -0.0071, 1.0492, 0.0000, 0.0000, 0.0000]
self.assertTrue(torch.allclose(padded_hidden_states[0, 0, 0, :4], chunked_hidden_states[0, 0, 0], atol=1e-3))
self.assertTrue(
torch.allclose(
padded_hidden_states[0, 0, 0, 4:],
torch.zeros((3,), device=torch_device, dtype=torch.float32),
atol=1e-3,
)
)
# last row => [0.0000, 0.0000, 0.0000, 2.0514, -1.1600, 0.5372, 0.2629]
self.assertTrue(torch.allclose(padded_hidden_states[0, 0, -1, 3:], chunked_hidden_states[0, 0, -1], atol=1e-3))
self.assertTrue(
torch.allclose(
padded_hidden_states[0, 0, -1, :3],
torch.zeros((3,), device=torch_device, dtype=torch.float32),
atol=1e-3,
)
)
def test_pad_and_transpose_last_two_dims(self):
hidden_states = self._get_hidden_states()
self.assertEqual(hidden_states.shape, (1, 4, 8))
padding = (0, 0, 0, 1)
padded_hidden_states = LongformerSelfAttention._pad_and_transpose_last_two_dims(hidden_states, padding)
self.assertEqual(padded_hidden_states.shape, (1, 8, 5))
expected_added_dim = torch.zeros((5,), device=torch_device, dtype=torch.float32)
self.assertTrue(torch.allclose(expected_added_dim, padded_hidden_states[0, -1, :], atol=1e-6))
self.assertTrue(torch.allclose(hidden_states[0, -1, :], padded_hidden_states.view(1, -1)[0, 24:32], atol=1e-6))
def test_chunk(self):
hidden_states = self._get_hidden_states()
batch_size = 1
seq_length = 8
hidden_size = 4
hidden_states = hidden_states.reshape((batch_size, seq_length, hidden_size))
chunked_hidden_states = LongformerSelfAttention._chunk(hidden_states, window_overlap=2)
# expected slices across chunk and seq length dim
expected_slice_along_seq_length = torch.tensor(
[0.4983, -0.7584, -1.6944], device=torch_device, dtype=torch.float32
)
expected_slice_along_chunk = torch.tensor(
[0.4983, -1.8348, -0.7584, 2.0514], device=torch_device, dtype=torch.float32
)
self.assertTrue(torch.allclose(chunked_hidden_states[0, :, 0, 0], expected_slice_along_seq_length, atol=1e-3))
self.assertTrue(torch.allclose(chunked_hidden_states[0, 0, :, 0], expected_slice_along_chunk, atol=1e-3))
self.assertEqual(chunked_hidden_states.shape, (1, 3, 4, 4))
def test_mask_invalid_locations(self):
hidden_states = self._get_hidden_states()
batch_size = 1
seq_length = 8
hidden_size = 4
hidden_states = hidden_states.reshape((batch_size, seq_length, hidden_size))
chunked_hidden_states = LongformerSelfAttention._chunk(hidden_states, window_overlap=2)
hid_states_1 = chunked_hidden_states.clone()
LongformerSelfAttention._mask_invalid_locations(hid_states_1, 1)
self.assertTrue(torch.isinf(hid_states_1).sum().item() == 8)
hid_states_2 = chunked_hidden_states.clone()
LongformerSelfAttention._mask_invalid_locations(hid_states_2, 2)
self.assertTrue(torch.isinf(hid_states_2).sum().item() == 24)
hid_states_3 = chunked_hidden_states.clone()[:, :, :, :3]
LongformerSelfAttention._mask_invalid_locations(hid_states_3, 2)
self.assertTrue(torch.isinf(hid_states_3).sum().item() == 24)
hid_states_4 = chunked_hidden_states.clone()[:, :, 2:, :]
LongformerSelfAttention._mask_invalid_locations(hid_states_4, 2)
self.assertTrue(torch.isinf(hid_states_4).sum().item() == 12)
def test_layer_local_attn(self):
model = LongformerModel.from_pretrained("patrickvonplaten/longformer-random-tiny")
model.eval()
layer = model.encoder.layer[0].attention.self.to(torch_device)
hidden_states = self._get_hidden_states()
batch_size, seq_length, hidden_size = hidden_states.size()
attention_mask = torch.zeros((batch_size, seq_length), dtype=torch.float32, device=torch_device)
attention_mask[:, -2:] = -10000
is_index_masked = attention_mask < 0
is_index_global_attn = attention_mask > 0
is_global_attn = is_index_global_attn.flatten().any().item()
output_hidden_states = layer(
hidden_states,
attention_mask=attention_mask,
is_index_masked=is_index_masked,
is_index_global_attn=is_index_global_attn,
is_global_attn=is_global_attn,
)[0]
self.assertEqual(output_hidden_states.shape, (1, 4, 8))
self.assertTrue(
torch.allclose(
output_hidden_states[0, 1],
torch.tensor(
[0.0019, 0.0122, -0.0171, -0.0256, -0.0300, 0.0173, -0.0115, 0.0048],
dtype=torch.float32,
device=torch_device,
),
atol=1e-3,
)
)
def test_layer_global_attn(self):
model = LongformerModel.from_pretrained("patrickvonplaten/longformer-random-tiny")
model.eval()
layer = model.encoder.layer[0].attention.self.to(torch_device)
hidden_states = torch.cat([self._get_hidden_states(), self._get_hidden_states() - 0.5], dim=0)
batch_size, seq_length, hidden_size = hidden_states.size()
attention_mask = torch.zeros((batch_size, seq_length), dtype=torch.float32, device=torch_device)
# create attn mask
attention_mask[0, -2:] = 10000.0
attention_mask[0, -1:] = -10000.0
attention_mask[1, 1:] = 10000.0
is_index_masked = attention_mask < 0
is_index_global_attn = attention_mask > 0
is_global_attn = is_index_global_attn.flatten().any().item()
output_hidden_states = layer(
hidden_states,
attention_mask=attention_mask,
is_index_masked=is_index_masked,
is_index_global_attn=is_index_global_attn,
is_global_attn=is_global_attn,
)[0]
self.assertEqual(output_hidden_states.shape, (2, 4, 8))
self.assertTrue(
torch.allclose(
output_hidden_states[0, 2],
torch.tensor(
[-0.0651, -0.0393, 0.0309, -0.0342, -0.0066, -0.0155, -0.0209, -0.0494],
dtype=torch.float32,
device=torch_device,
),
atol=1e-3,
)
)
self.assertTrue(
torch.allclose(
output_hidden_states[1, -2],
torch.tensor(
[-0.0405, -0.0384, 0.0396, -0.0374, -0.0341, 0.0136, 0.0014, -0.0571],
dtype=torch.float32,
device=torch_device,
),
atol=1e-3,
)
)
def test_layer_attn_probs(self):
model = LongformerModel.from_pretrained("patrickvonplaten/longformer-random-tiny")
model.eval()
layer = model.encoder.layer[0].attention.self.to(torch_device)
hidden_states = torch.cat([self._get_hidden_states(), self._get_hidden_states() - 0.5], dim=0)
batch_size, seq_length, hidden_size = hidden_states.size()
attention_mask = torch.zeros((batch_size, seq_length), dtype=torch.float32, device=torch_device)
# create attn mask
attention_mask[0, -2:] = 10000.0
attention_mask[0, -1:] = -10000.0
attention_mask[1, 1:] = 10000.0
is_index_masked = attention_mask < 0
is_index_global_attn = attention_mask > 0
is_global_attn = is_index_global_attn.flatten().any().item()
output_hidden_states, local_attentions, global_attentions = layer(
hidden_states,
attention_mask=attention_mask,
is_index_masked=is_index_masked,
is_index_global_attn=is_index_global_attn,
is_global_attn=is_global_attn,
output_attentions=True,
)
self.assertEqual(local_attentions.shape, (2, 4, 2, 8))
self.assertEqual(global_attentions.shape, (2, 2, 3, 4))
# All tokens with global attention have weight 0 in local attentions.
self.assertTrue(torch.all(local_attentions[0, 2:4, :, :] == 0))
self.assertTrue(torch.all(local_attentions[1, 1:4, :, :] == 0))
# The weight of all tokens with local attention must sum to 1.
self.assertTrue(torch.all(torch.abs(global_attentions[0, :, :2, :].sum(dim=-1) - 1) < 1e-6))
self.assertTrue(torch.all(torch.abs(global_attentions[1, :, :1, :].sum(dim=-1) - 1) < 1e-6))
self.assertTrue(
torch.allclose(
local_attentions[0, 0, 0, :],
torch.tensor(
[0.3328, 0.0000, 0.0000, 0.0000, 0.0000, 0.3355, 0.3318, 0.0000],
dtype=torch.float32,
device=torch_device,
),
atol=1e-3,
)
)
self.assertTrue(
torch.allclose(
local_attentions[1, 0, 0, :],
torch.tensor(
[0.2492, 0.2502, 0.2502, 0.0000, 0.0000, 0.2505, 0.0000, 0.0000],
dtype=torch.float32,
device=torch_device,
),
atol=1e-3,
)
)
# All the global attention weights must sum to 1.
self.assertTrue(torch.all(torch.abs(global_attentions.sum(dim=-1) - 1) < 1e-6))
self.assertTrue(
torch.allclose(
global_attentions[0, 0, 1, :],
torch.tensor(
[0.2500, 0.2500, 0.2500, 0.2500],
dtype=torch.float32,
device=torch_device,
),
atol=1e-3,
)
)
self.assertTrue(
torch.allclose(
global_attentions[1, 0, 0, :],
torch.tensor(
[0.2497, 0.2500, 0.2499, 0.2504],
dtype=torch.float32,
device=torch_device,
),
atol=1e-3,
)
)
@slow
def test_inference_no_head(self):
model = LongformerModel.from_pretrained("allenai/longformer-base-4096")
model.to(torch_device)
# 'Hello world!'
input_ids = torch.tensor([[0, 20920, 232, 328, 1437, 2]], dtype=torch.long, device=torch_device)
attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
output = model(input_ids, attention_mask=attention_mask)[0]
output_without_mask = model(input_ids)[0]
expected_output_slice = torch.tensor([0.0549, 0.1087, -0.1119, -0.0368, 0.0250], device=torch_device)
self.assertTrue(torch.allclose(output[0, 0, -5:], expected_output_slice, atol=1e-4))
self.assertTrue(torch.allclose(output_without_mask[0, 0, -5:], expected_output_slice, atol=1e-4))
@slow
def test_inference_no_head_long(self):
model = LongformerModel.from_pretrained("allenai/longformer-base-4096")
model.to(torch_device)
# 'Hello world! ' repeated 1000 times
input_ids = torch.tensor(
[[0] + [20920, 232, 328, 1437] * 1000 + [2]], dtype=torch.long, device=torch_device
) # long input
attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=input_ids.device)
global_attention_mask = torch.zeros(input_ids.shape, dtype=torch.long, device=input_ids.device)
global_attention_mask[:, [1, 4, 21]] = 1 # Set global attention on a few random positions
output = model(input_ids, attention_mask=attention_mask, global_attention_mask=global_attention_mask)[0]
expected_output_sum = torch.tensor(74585.8594, device=torch_device)
expected_output_mean = torch.tensor(0.0243, device=torch_device)
self.assertTrue(torch.allclose(output.sum(), expected_output_sum, atol=1e-4))
self.assertTrue(torch.allclose(output.mean(), expected_output_mean, atol=1e-4))
@slow
def test_inference_masked_lm_long(self):
model = LongformerForMaskedLM.from_pretrained("allenai/longformer-base-4096")
model.to(torch_device)
# 'Hello world! ' repeated 1000 times
input_ids = torch.tensor(
[[0] + [20920, 232, 328, 1437] * 1000 + [2]], dtype=torch.long, device=torch_device
) # long input
input_ids = input_ids.to(torch_device)
loss, prediction_scores = model(input_ids, labels=input_ids).to_tuple()
expected_loss = torch.tensor(0.0074, device=torch_device)
expected_prediction_scores_sum = torch.tensor(-6.1048e08, device=torch_device)
expected_prediction_scores_mean = torch.tensor(-3.0348, device=torch_device)
self.assertTrue(torch.allclose(loss, expected_loss, atol=1e-4))
self.assertTrue(torch.allclose(prediction_scores.sum(), expected_prediction_scores_sum, atol=1e-4))
self.assertTrue(torch.allclose(prediction_scores.mean(), expected_prediction_scores_mean, atol=1e-4))
| transformers/tests/models/longformer/test_modeling_longformer.py/0 | {
"file_path": "transformers/tests/models/longformer/test_modeling_longformer.py",
"repo_id": "transformers",
"token_count": 15408
} | 158 |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch MaskFormer Swin model. """
import collections
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import MaskFormerSwinBackbone
from transformers.models.maskformer import MaskFormerSwinModel
class MaskFormerSwinModelTester:
def __init__(
self,
parent,
batch_size=13,
image_size=32,
patch_size=2,
num_channels=3,
embed_dim=16,
depths=[1, 2, 1],
num_heads=[2, 2, 4],
window_size=2,
mlp_ratio=2.0,
qkv_bias=True,
hidden_dropout_prob=0.0,
attention_probs_dropout_prob=0.0,
drop_path_rate=0.1,
hidden_act="gelu",
use_absolute_embeddings=False,
patch_norm=True,
initializer_range=0.02,
layer_norm_eps=1e-5,
is_training=True,
scope=None,
use_labels=True,
type_sequence_label_size=10,
encoder_stride=8,
out_features=["stage1", "stage2", "stage3"],
out_indices=[1, 2, 3],
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.embed_dim = embed_dim
self.depths = depths
self.num_heads = num_heads
self.window_size = window_size
self.mlp_ratio = mlp_ratio
self.qkv_bias = qkv_bias
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.drop_path_rate = drop_path_rate
self.hidden_act = hidden_act
self.use_absolute_embeddings = use_absolute_embeddings
self.patch_norm = patch_norm
self.layer_norm_eps = layer_norm_eps
self.initializer_range = initializer_range
self.is_training = is_training
self.scope = scope
self.use_labels = use_labels
self.type_sequence_label_size = type_sequence_label_size
self.encoder_stride = encoder_stride
self.out_features = out_features
self.out_indices = out_indices
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
config = self.get_config()
return config, pixel_values, labels
def get_config(self):
return MaskFormerSwinConfig(
image_size=self.image_size,
patch_size=self.patch_size,
num_channels=self.num_channels,
embed_dim=self.embed_dim,
depths=self.depths,
num_heads=self.num_heads,
window_size=self.window_size,
mlp_ratio=self.mlp_ratio,
qkv_bias=self.qkv_bias,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
drop_path_rate=self.drop_path_rate,
hidden_act=self.hidden_act,
use_absolute_embeddings=self.use_absolute_embeddings,
path_norm=self.patch_norm,
layer_norm_eps=self.layer_norm_eps,
initializer_range=self.initializer_range,
encoder_stride=self.encoder_stride,
out_features=self.out_features,
out_indices=self.out_indices,
)
def create_and_check_model(self, config, pixel_values, labels):
model = MaskFormerSwinModel(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
expected_seq_len = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
expected_dim = int(config.embed_dim * 2 ** (len(config.depths) - 1))
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, expected_seq_len, expected_dim))
def create_and_check_backbone(self, config, pixel_values, labels):
model = MaskFormerSwinBackbone(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
# verify feature maps
self.parent.assertEqual(len(result.feature_maps), len(config.out_features))
self.parent.assertListEqual(list(result.feature_maps[0].shape), [13, 16, 16, 16])
# verify channels
self.parent.assertEqual(len(model.channels), len(config.out_features))
self.parent.assertListEqual(model.channels, [16, 32, 64])
# verify ValueError
with self.parent.assertRaises(ValueError):
config.out_features = ["stem"]
model = MaskFormerSwinBackbone(config=config)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, labels = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class MaskFormerSwinModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
MaskFormerSwinModel,
MaskFormerSwinBackbone,
)
if is_torch_available()
else ()
)
pipeline_model_mapping = {"feature-extraction": MaskFormerSwinModel} if is_torch_available() else {}
fx_compatible = False
test_torchscript = False
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
def setUp(self):
self.model_tester = MaskFormerSwinModelTester(self)
self.config_tester = ConfigTester(self, config_class=MaskFormerSwinConfig, embed_dim=37)
@require_torch_multi_gpu
@unittest.skip(
reason=(
"`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn't work well with"
" `nn.DataParallel`"
)
)
def test_multi_gpu_data_parallel_forward(self):
pass
def test_config(self):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def create_and_test_config_common_properties(self):
return
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_backbone(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*config_and_inputs)
@unittest.skip("Swin does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip("Swin does not support feedforward chunking")
def test_feed_forward_chunking(self):
pass
def test_model_common_attributes(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
x = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(x, nn.Linear))
@unittest.skip(reason="MaskFormerSwin is only used as backbone and doesn't support output_attentions")
def test_attention_outputs(self):
pass
@unittest.skip(reason="MaskFormerSwin is only used as an internal backbone")
def test_save_load_fast_init_to_base(self):
pass
def check_hidden_states_output(self, inputs_dict, config, model_class, image_size):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.hidden_states
expected_num_layers = getattr(
self.model_tester, "expected_num_hidden_layers", len(self.model_tester.depths) + 1
)
self.assertEqual(len(hidden_states), expected_num_layers)
# Swin has a different seq_length
patch_size = (
config.patch_size
if isinstance(config.patch_size, collections.abc.Iterable)
else (config.patch_size, config.patch_size)
)
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[num_patches, self.model_tester.embed_dim],
)
def test_hidden_states_output(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
image_size = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size, collections.abc.Iterable)
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
self.check_hidden_states_output(inputs_dict, config, model_class, image_size)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
self.check_hidden_states_output(inputs_dict, config, model_class, image_size)
def test_hidden_states_output_with_padding(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.patch_size = 3
image_size = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size, collections.abc.Iterable)
else (self.model_tester.image_size, self.model_tester.image_size)
)
patch_size = (
config.patch_size
if isinstance(config.patch_size, collections.abc.Iterable)
else (config.patch_size, config.patch_size)
)
padded_height = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
padded_width = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
self.check_hidden_states_output(inputs_dict, config, model_class, (padded_height, padded_width))
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
self.check_hidden_states_output(inputs_dict, config, model_class, (padded_height, padded_width))
@unittest.skip(reason="MaskFormerSwin doesn't have pretrained checkpoints")
def test_model_from_pretrained(self):
pass
@unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin")
def test_initialization(self):
pass
@unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin")
def test_gradient_checkpointing_backward_compatibility(self):
pass
def test_model_outputs_equivalence(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def set_nan_tensor_to_zero(t):
t[t != t] = 0
return t
def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
with torch.no_grad():
tuple_output = model(**tuple_inputs, return_dict=False, **additional_kwargs)
dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs).to_tuple()
def recursive_check(tuple_object, dict_object):
if isinstance(tuple_object, (List, Tuple)):
for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
recursive_check(tuple_iterable_value, dict_iterable_value)
elif isinstance(tuple_object, Dict):
for tuple_iterable_value, dict_iterable_value in zip(
tuple_object.values(), dict_object.values()
):
recursive_check(tuple_iterable_value, dict_iterable_value)
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5
),
msg=(
"Tuple and dict output are not equal. Difference:"
f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:"
f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has"
f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}."
),
)
recursive_check(tuple_output, dict_output)
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs)
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs)
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
@require_torch
class MaskFormerSwinBackboneTest(unittest.TestCase, BackboneTesterMixin):
all_model_classes = (MaskFormerSwinBackbone,) if is_torch_available() else ()
config_class = MaskFormerSwinConfig
def setUp(self):
self.model_tester = MaskFormerSwinModelTester(self)
# Overriding as returned hidden states are tuples of tensors instead of a single tensor
def test_backbone_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
batch_size = inputs_dict["pixel_values"].shape[0]
for backbone_class in self.all_model_classes:
backbone = backbone_class(config)
backbone.to(torch_device)
backbone.eval()
outputs = backbone(**inputs_dict)
# Test default outputs and verify feature maps
self.assertIsInstance(outputs.feature_maps, tuple)
self.assertTrue(len(outputs.feature_maps) == len(backbone.channels))
for feature_map, n_channels in zip(outputs.feature_maps, backbone.channels):
self.assertTrue(feature_map.shape[:2], (batch_size, n_channels))
self.assertIsNone(outputs.hidden_states)
self.assertIsNone(outputs.attentions)
# Test output_hidden_states=True
outputs = backbone(**inputs_dict, output_hidden_states=True)
self.assertIsNotNone(outputs.hidden_states)
self.assertTrue(len(outputs.hidden_states), len(backbone.stage_names))
# We skip the stem layer
for hidden_states, n_channels in zip(outputs.hidden_states[1:], backbone.channels):
for hidden_state in hidden_states:
# Hidden states are in the format (batch_size, (height * width), n_channels)
h_batch_size, _, h_n_channels = hidden_state.shape
self.assertTrue((h_batch_size, h_n_channels), (batch_size, n_channels))
# Test output_attentions=True
if self.has_attentions:
outputs = backbone(**inputs_dict, output_attentions=True)
self.assertIsNotNone(outputs.attentions)
| transformers/tests/models/maskformer/test_modeling_maskformer_swin.py/0 | {
"file_path": "transformers/tests/models/maskformer/test_modeling_maskformer_swin.py",
"repo_id": "transformers",
"token_count": 8052
} | 159 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the MgpstrProcessor. """
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MgpstrProcessor, ViTImageProcessor
@require_torch
@require_vision
class MgpstrProcessorTest(unittest.TestCase):
image_processing_class = ViTImageProcessor if is_vision_available() else None
@property
def image_processor_dict(self):
return self.prepare_image_processor_dict()
def setUp(self):
self.image_size = (3, 32, 128)
self.tmpdirname = tempfile.mkdtemp()
vocab = ['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z'] # fmt: skip
vocab_tokens = dict(zip(vocab, range(len(vocab))))
self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
with open(self.vocab_file, "w", encoding="utf-8") as fp:
fp.write(json.dumps(vocab_tokens) + "\n")
image_processor_map = {
"do_normalize": False,
"do_resize": True,
"image_processor_type": "ViTImageProcessor",
"resample": 3,
"size": {"height": 32, "width": 128},
}
self.image_processor_file = os.path.join(self.tmpdirname, IMAGE_PROCESSOR_NAME)
with open(self.image_processor_file, "w", encoding="utf-8") as fp:
json.dump(image_processor_map, fp)
def get_tokenizer(self, **kwargs):
return MgpstrTokenizer.from_pretrained(self.tmpdirname, **kwargs)
def get_image_processor(self, **kwargs):
return ViTImageProcessor.from_pretrained(self.tmpdirname, **kwargs)
def tearDown(self):
shutil.rmtree(self.tmpdirname)
def prepare_image_inputs(self):
"""This function prepares a list of PIL images."""
image_input = np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)
image_input = Image.fromarray(np.moveaxis(image_input, 0, -1))
return image_input
def test_save_load_pretrained_default(self):
tokenizer = self.get_tokenizer()
image_processor = self.get_image_processor()
processor = MgpstrProcessor(tokenizer=tokenizer, image_processor=image_processor)
processor.save_pretrained(self.tmpdirname)
processor = MgpstrProcessor.from_pretrained(self.tmpdirname, use_fast=False)
self.assertEqual(processor.char_tokenizer.get_vocab(), tokenizer.get_vocab())
self.assertIsInstance(processor.char_tokenizer, MgpstrTokenizer)
self.assertEqual(processor.image_processor.to_json_string(), image_processor.to_json_string())
self.assertIsInstance(processor.image_processor, ViTImageProcessor)
def test_save_load_pretrained_additional_features(self):
tokenizer = self.get_tokenizer()
image_processor = self.get_image_processor()
processor = MgpstrProcessor(tokenizer=tokenizer, image_processor=image_processor)
processor.save_pretrained(self.tmpdirname)
tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
image_processor_add_kwargs = self.get_image_processor(do_normalize=False, padding_value=1.0)
processor = MgpstrProcessor.from_pretrained(
self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
)
self.assertEqual(processor.char_tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
self.assertIsInstance(processor.char_tokenizer, MgpstrTokenizer)
self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string())
self.assertIsInstance(processor.image_processor, ViTImageProcessor)
def test_image_processor(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = MgpstrProcessor(tokenizer=tokenizer, image_processor=image_processor)
image_input = self.prepare_image_inputs()
input_image_proc = image_processor(image_input, return_tensors="np")
input_processor = processor(images=image_input, return_tensors="np")
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum(), input_processor[key].sum(), delta=1e-2)
def test_tokenizer(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = MgpstrProcessor(tokenizer=tokenizer, image_processor=image_processor)
input_str = "test"
encoded_processor = processor(text=input_str)
encoded_tok = tokenizer(input_str)
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key], encoded_processor[key])
def test_processor(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = MgpstrProcessor(tokenizer=tokenizer, image_processor=image_processor)
input_str = "test"
image_input = self.prepare_image_inputs()
inputs = processor(text=input_str, images=image_input)
self.assertListEqual(list(inputs.keys()), ["pixel_values", "labels"])
# test if it raises when no input is passed
with pytest.raises(ValueError):
processor()
def test_tokenizer_decode(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = MgpstrProcessor(tokenizer=tokenizer, image_processor=image_processor)
predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]
decoded_processor = processor.char_decode(predicted_ids)
decoded_tok = tokenizer.batch_decode(predicted_ids)
decode_strs = [seq.replace(" ", "") for seq in decoded_tok]
self.assertListEqual(decode_strs, decoded_processor)
def test_model_input_names(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = MgpstrProcessor(tokenizer=tokenizer, image_processor=image_processor)
input_str = None
image_input = self.prepare_image_inputs()
inputs = processor(text=input_str, images=image_input)
self.assertListEqual(list(inputs.keys()), processor.model_input_names)
def test_processor_batch_decode(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = MgpstrProcessor(tokenizer=tokenizer, image_processor=image_processor)
char_input = torch.randn(1, 27, 38)
bpe_input = torch.randn(1, 27, 50257)
wp_input = torch.randn(1, 27, 30522)
results = processor.batch_decode([char_input, bpe_input, wp_input])
self.assertListEqual(list(results.keys()), ["generated_text", "scores", "char_preds", "bpe_preds", "wp_preds"])
| transformers/tests/models/mgp_str/test_processor_mgp_str.py/0 | {
"file_path": "transformers/tests/models/mgp_str/test_processor_mgp_str.py",
"repo_id": "transformers",
"token_count": 3186
} | 160 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch MRA model. """
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraModel,
)
from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST
class MraModelTester:
def __init__(
self,
parent,
batch_size=2,
seq_length=8,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=16,
num_hidden_layers=2,
num_attention_heads=2,
intermediate_size=36,
hidden_act="gelu",
hidden_dropout_prob=0.0,
attention_probs_dropout_prob=0.0,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
return MraConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
is_decoder=False,
initializer_range=self.initializer_range,
)
def get_pipeline_config(self):
config = self.get_config()
config.vocab_size = 300
return config
def prepare_config_and_inputs_for_decoder(self):
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = self.prepare_config_and_inputs()
config.is_decoder = True
encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = MraModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_model_as_decoder(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.add_cross_attention = True
model = MraModel(config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
)
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
)
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_for_masked_lm(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = MraForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_question_answering(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = MraForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def create_and_check_for_sequence_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = MraForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_token_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = MraForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_multiple_choice(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_choices = self.num_choices
model = MraForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
result = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class MraModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
MraModel,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
)
if is_torch_available()
else ()
)
test_pruning = False
test_headmasking = False
test_torchscript = False
has_attentions = False
all_generative_model_classes = ()
pipeline_model_mapping = (
{
"feature-extraction": MraModel,
"fill-mask": MraForMaskedLM,
"question-answering": MraForQuestionAnswering,
"text-classification": MraForSequenceClassification,
"token-classification": MraForTokenClassification,
"zero-shot": MraForSequenceClassification,
}
if is_torch_available()
else {}
)
def setUp(self):
self.model_tester = MraModelTester(self)
self.config_tester = ConfigTester(self, config_class=MraConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_various_embeddings(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
config_and_inputs[0].position_embedding_type = type
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = MraModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@unittest.skip(reason="MRA does not output attentions")
def test_attention_outputs(self):
return
@unittest.skip(
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
)
def test_training_gradient_checkpointing(self):
pass
@unittest.skip(
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
)
def test_training_gradient_checkpointing_use_reentrant(self):
pass
@unittest.skip(
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
)
def test_training_gradient_checkpointing_use_reentrant_false(self):
pass
@require_torch
class MraModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_no_head(self):
model = MraModel.from_pretrained("uw-madison/mra-base-512-4")
input_ids = torch.arange(256).unsqueeze(0)
with torch.no_grad():
output = model(input_ids)[0]
expected_shape = torch.Size((1, 256, 768))
self.assertEqual(output.shape, expected_shape)
expected_slice = torch.tensor(
[[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]]
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
@slow
def test_inference_masked_lm(self):
model = MraForMaskedLM.from_pretrained("uw-madison/mra-base-512-4")
input_ids = torch.arange(256).unsqueeze(0)
with torch.no_grad():
output = model(input_ids)[0]
vocab_size = 50265
expected_shape = torch.Size((1, 256, vocab_size))
self.assertEqual(output.shape, expected_shape)
expected_slice = torch.tensor(
[[[9.2595, -3.6038, 11.8819], [9.3869, -3.2693, 11.0956], [11.8524, -3.4938, 13.1210]]]
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
@slow
def test_inference_masked_lm_long_input(self):
model = MraForMaskedLM.from_pretrained("uw-madison/mra-base-4096-8-d3")
input_ids = torch.arange(4096).unsqueeze(0)
with torch.no_grad():
output = model(input_ids)[0]
vocab_size = 50265
expected_shape = torch.Size((1, 4096, vocab_size))
self.assertEqual(output.shape, expected_shape)
expected_slice = torch.tensor(
[[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]]
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
| transformers/tests/models/mra/test_modeling_mra.py/0 | {
"file_path": "transformers/tests/models/mra/test_modeling_mra.py",
"repo_id": "transformers",
"token_count": 7505
} | 161 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.models.nllb.tokenization_nllb import FAIRSEQ_LANGUAGE_CODES
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
if is_torch_available():
from transformers.models.m2m_100.modeling_m2m_100 import shift_tokens_right
EN_CODE = 256047
RO_CODE = 256145
@require_sentencepiece
@require_tokenizers
class NllbTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = NllbTokenizer
rust_tokenizer_class = NllbTokenizerFast
test_rust_tokenizer = True
test_sentencepiece = True
from_pretrained_kwargs = {}
def setUp(self):
super().setUp()
# We have a SentencePiece fixture for testing
tokenizer = NllbTokenizer(SAMPLE_VOCAB, keep_accents=True)
tokenizer.save_pretrained(self.tmpdirname)
def test_full_tokenizer(self):
tokenizer = NllbTokenizer(SAMPLE_VOCAB, keep_accents=True)
tokens = tokenizer.tokenize("This is a test")
self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(tokens),
[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]],
)
tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
self.assertListEqual(
tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
],
)
ids = tokenizer.convert_tokens_to_ids(tokens)
self.assertListEqual(
ids,
[
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
],
)
back_tokens = tokenizer.convert_ids_to_tokens(ids)
self.assertListEqual(
back_tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
],
)
# overwrite from test_tokenization_common to speed up test
def test_save_pretrained(self):
self.tokenizers_list[0] = (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-nllb", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tmpdirname2 = tempfile.mkdtemp()
tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2)
tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files))
tokenizer_r_files = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f)
self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files)
# Checks everything loads correctly in the same way
tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(tokenizer_rp, key))
shutil.rmtree(tmpdirname2)
# Save tokenizer rust, legacy_format=True
tmpdirname2 = tempfile.mkdtemp()
tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2, legacy_format=True)
tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
# Checks it save with the same files
self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files)
# Checks everything loads correctly in the same way
tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(tokenizer_rp, key))
shutil.rmtree(tmpdirname2)
# Save tokenizer rust, legacy_format=False
tmpdirname2 = tempfile.mkdtemp()
tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2, legacy_format=False)
tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
# Checks it saved the tokenizer.json file
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files))
# Checks everything loads correctly in the same way
tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(tokenizer_rp, key))
shutil.rmtree(tmpdirname2)
@require_torch
def test_prepare_seq2seq_batch(self):
if not self.test_seq2seq:
return
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
# Longer text that will definitely require truncation.
src_text = [
" UN Chief Says There Is No Military Solution in Syria",
" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"
" Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"
" will only worsen the violence and misery for millions of people.",
]
tgt_text = [
"Şeful ONU declară că nu există o soluţie militară în Siria",
"Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"
' Rusiei pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi'
" că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
]
try:
batch = tokenizer.prepare_seq2seq_batch(
src_texts=src_text,
tgt_texts=tgt_text,
max_length=3,
max_target_length=10,
return_tensors="pt",
src_lang="eng_Latn",
tgt_lang="ron_Latn",
)
except NotImplementedError:
return
self.assertEqual(batch.input_ids.shape[1], 3)
self.assertEqual(batch.labels.shape[1], 10)
# max_target_length will default to max_length if not specified
batch = tokenizer.prepare_seq2seq_batch(
src_text, tgt_texts=tgt_text, max_length=3, return_tensors="pt"
)
self.assertEqual(batch.input_ids.shape[1], 3)
self.assertEqual(batch.labels.shape[1], 3)
batch_encoder_only = tokenizer.prepare_seq2seq_batch(
src_texts=src_text, max_length=3, max_target_length=10, return_tensors="pt"
)
self.assertEqual(batch_encoder_only.input_ids.shape[1], 3)
self.assertEqual(batch_encoder_only.attention_mask.shape[1], 3)
self.assertNotIn("decoder_input_ids", batch_encoder_only)
@unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece.")
def test_save_slow_from_fast_and_reload_fast(self):
pass
def test_special_tokens_initialization(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
added_tokens = [AddedToken("<special>", lstrip=True)]
tokenizer_r = self.rust_tokenizer_class.from_pretrained(
pretrained_name, additional_special_tokens=added_tokens, **kwargs
)
r_output = tokenizer_r.encode("Hey this is a <special> token")
special_token_id = tokenizer_r.encode("<special>", add_special_tokens=False)[0]
self.assertTrue(special_token_id in r_output)
if self.test_slow_tokenizer:
tokenizer_cr = self.rust_tokenizer_class.from_pretrained(
pretrained_name,
additional_special_tokens=added_tokens,
**kwargs, # , from_slow=True <- unfortunately too slow to convert
)
tokenizer_p = self.tokenizer_class.from_pretrained(
pretrained_name, additional_special_tokens=added_tokens, **kwargs
)
p_output = tokenizer_p.encode("Hey this is a <special> token")
cr_output = tokenizer_cr.encode("Hey this is a <special> token")
self.assertEqual(p_output, r_output)
self.assertEqual(cr_output, r_output)
self.assertTrue(special_token_id in p_output)
self.assertTrue(special_token_id in cr_output)
@unittest.skip("Need to fix this after #26538")
def test_training_new_tokenizer(self):
pass
def test_new_language_codes(self):
code1, code2 = "myv_Cyrl", "myv_Latn"
new_codes = FAIRSEQ_LANGUAGE_CODES + [code1, code2]
# here I create a tokenizer with the default behaviour
tok1 = NllbTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")
# here I enhance the model's vocabulary with two new language codes
tok2 = NllbTokenizer.from_pretrained("facebook/nllb-200-distilled-600M", additional_special_tokens=new_codes)
# testing that the new codes can work
self.assertEqual(len(tok2), len(tok1) + 2)
tok2.tgt_lang = code1
tok2.src_lang = code2
self.assertEqual(tok2("šumbrat!").input_ids[0], tok2.convert_tokens_to_ids(code2))
with tempfile.TemporaryDirectory() as tempdir:
# testing that saving and loading the tokenizer preserves the new behaviour
tok2.save_pretrained(tempdir)
tok3 = NllbTokenizer.from_pretrained(tempdir)
self.assertEqual(tok2.get_vocab(), tok3.get_vocab())
tok3.src_lang = code2
self.assertEqual(tok3("šumbrat!").input_ids[0], tok3.convert_tokens_to_ids(code2))
# testing that saving and loading the tokenizer preserves the new behaviour
tok2.save_pretrained(tempdir)
tok3 = NllbTokenizer(f"{tempdir}/sentencepiece.bpe.model", additional_special_tokens=None)
self.assertEqual(len(tok3), 256204) # legacy
tok4 = NllbTokenizer(f"{tempdir}/sentencepiece.bpe.model", additional_special_tokens=[])
self.assertEqual(len(tok4), 256002)
tok5 = NllbTokenizer(f"{tempdir}/sentencepiece.bpe.model", additional_special_tokens=[code1, code2])
self.assertEqual(len(tok5), 256004)
@require_torch
@require_sentencepiece
@require_tokenizers
class NllbDistilledIntegrationTest(unittest.TestCase):
checkpoint_name = "facebook/nllb-200-distilled-600M"
src_text = [
" UN Chief Says There Is No Military Solution in Syria",
""" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
]
tgt_text = [
"Şeful ONU declară că nu există o soluţie militară în Siria",
"Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
" face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
]
expected_src_tokens = [
256047,
16297,
134408,
8165,
248066,
14734,
950,
1135,
105721,
3573,
83,
27352,
108,
49486,
2,
]
@classmethod
def setUpClass(cls):
cls.tokenizer: NllbTokenizer = NllbTokenizer.from_pretrained(
cls.checkpoint_name, src_lang="eng_Latn", tgt_lang="ron_Latn"
)
cls.pad_token_id = 1
return cls
def test_language_codes(self):
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Arab"], 256001)
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Latn"], 256002)
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["fra_Latn"], 256057)
def test_enro_tokenizer_batch_encode_plus(self):
ids = self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
self.assertListEqual(self.expected_src_tokens, ids)
def test_enro_tokenizer_decode_ignores_language_codes(self):
self.assertIn(RO_CODE, self.tokenizer.all_special_ids)
generated_ids = [RO_CODE, 4254, 98068, 112923, 39072, 3909, 713, 102767, 26, 17314, 35642, 14683, 33118, 2022, 66987, 2, 256047] # fmt: skip
result = self.tokenizer.decode(generated_ids, skip_special_tokens=True)
expected_romanian = self.tokenizer.decode(generated_ids[1:], skip_special_tokens=True)
self.assertEqual(result, expected_romanian)
self.assertNotIn(self.tokenizer.eos_token, result)
def test_enro_tokenizer_truncation(self):
src_text = ["this is gunna be a long sentence " * 20]
assert isinstance(src_text[0], str)
desired_max_length = 10
ids = self.tokenizer(src_text, max_length=desired_max_length, truncation=True).input_ids[0]
self.assertEqual(ids[-1], 2)
self.assertEqual(ids[0], EN_CODE)
self.assertEqual(len(ids), desired_max_length)
def test_mask_token(self):
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]), [256203, 3])
def test_special_tokens_unaffacted_by_save_load(self):
tmpdirname = tempfile.mkdtemp()
original_special_tokens = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(tmpdirname)
new_tok = NllbTokenizer.from_pretrained(tmpdirname)
self.assertDictEqual(new_tok.fairseq_tokens_to_ids, original_special_tokens)
@require_torch
def test_enro_tokenizer_prepare_batch(self):
batch = self.tokenizer(
self.src_text,
text_target=self.tgt_text,
padding=True,
truncation=True,
max_length=len(self.expected_src_tokens),
return_tensors="pt",
)
batch["decoder_input_ids"] = shift_tokens_right(
batch["labels"], self.tokenizer.pad_token_id, self.tokenizer.convert_tokens_to_ids("ron_Latn")
)
self.assertIsInstance(batch, BatchEncoding)
self.assertEqual((2, 15), batch.input_ids.shape)
self.assertEqual((2, 15), batch.attention_mask.shape)
result = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens, result)
self.assertEqual(RO_CODE, batch.decoder_input_ids[0, 0]) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens, [EN_CODE])
self.assertEqual(self.tokenizer.suffix_tokens, [self.tokenizer.eos_token_id])
def test_seq2seq_max_length(self):
batch = self.tokenizer(self.src_text, padding=True, truncation=True, max_length=3, return_tensors="pt")
targets = self.tokenizer(
text_target=self.tgt_text, padding=True, truncation=True, max_length=10, return_tensors="pt"
)
labels = targets["input_ids"]
batch["decoder_input_ids"] = shift_tokens_right(
labels,
self.tokenizer.pad_token_id,
decoder_start_token_id=self.tokenizer.convert_tokens_to_ids(self.tokenizer.tgt_lang),
)
self.assertEqual(batch.input_ids.shape[1], 3)
self.assertEqual(batch.decoder_input_ids.shape[1], 10)
@require_torch
def test_tokenizer_translation(self):
inputs = self.tokenizer._build_translation_inputs(
"A test", return_tensors="pt", src_lang="eng_Latn", tgt_lang="fra_Latn"
)
self.assertEqual(
nested_simplify(inputs),
{
# A, test, EOS, en_XX
"input_ids": [[256047, 70, 7356, 2]],
"attention_mask": [[1, 1, 1, 1]],
# ar_AR
"forced_bos_token_id": 256057,
},
)
@require_torch
def test_legacy_behaviour(self):
self.tokenizer.legacy_behaviour = True
inputs = self.tokenizer(
"UN Chief says there is no military solution in Syria", src_lang="eng_Latn", tgt_lang="fra_Latn"
)
self.assertEqual(
inputs.input_ids, [16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2, 256047]
)
self.tokenizer.legacy_behaviour = False
inputs = self.tokenizer(
"UN Chief says there is no military solution in Syria", src_lang="eng_Latn", tgt_lang="fra_Latn"
)
self.assertEqual(
inputs.input_ids, [256047, 16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2]
)
| transformers/tests/models/nllb/test_tokenization_nllb.py/0 | {
"file_path": "transformers/tests/models/nllb/test_tokenization_nllb.py",
"repo_id": "transformers",
"token_count": 9965
} | 162 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import Pix2StructImageProcessor
class Pix2StructImageProcessingTester(unittest.TestCase):
def __init__(
self,
parent,
batch_size=7,
num_channels=3,
image_size=18,
min_resolution=30,
max_resolution=400,
size=None,
do_normalize=True,
do_convert_rgb=True,
patch_size=None,
):
size = size if size is not None else {"height": 20, "width": 20}
self.parent = parent
self.batch_size = batch_size
self.num_channels = num_channels
self.image_size = image_size
self.min_resolution = min_resolution
self.max_resolution = max_resolution
self.size = size
self.do_normalize = do_normalize
self.do_convert_rgb = do_convert_rgb
self.max_patches = [512, 1024, 2048, 4096]
self.patch_size = patch_size if patch_size is not None else {"height": 16, "width": 16}
def prepare_image_processor_dict(self):
return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}
def prepare_dummy_image(self):
img_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
return raw_image
def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
return prepare_image_inputs(
batch_size=self.batch_size,
num_channels=self.num_channels,
min_resolution=self.min_resolution,
max_resolution=self.max_resolution,
equal_resolution=equal_resolution,
numpify=numpify,
torchify=torchify,
)
@require_torch
@require_vision
class Pix2StructImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = Pix2StructImageProcessor if is_vision_available() else None
def setUp(self):
self.image_processor_tester = Pix2StructImageProcessingTester(self)
@property
def image_processor_dict(self):
return self.image_processor_tester.prepare_image_processor_dict()
def test_image_processor_properties(self):
image_processor = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(image_processor, "do_normalize"))
self.assertTrue(hasattr(image_processor, "do_convert_rgb"))
def test_expected_patches(self):
dummy_image = self.image_processor_tester.prepare_dummy_image()
image_processor = self.image_processing_class(**self.image_processor_dict)
max_patch = 2048
inputs = image_processor(dummy_image, return_tensors="pt", max_patches=max_patch)
self.assertTrue(torch.allclose(inputs.flattened_patches.mean(), torch.tensor(0.0606), atol=1e-3, rtol=1e-3))
def test_call_pil(self):
# Initialize image_processor
image_processor = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
for image in image_inputs:
self.assertIsInstance(image, Image.Image)
# Test not batched input
expected_hidden_dim = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
encoded_images = image_processor(
image_inputs[0], return_tensors="pt", max_patches=max_patch
).flattened_patches
self.assertEqual(
encoded_images.shape,
(1, max_patch, expected_hidden_dim),
)
# Test batched
encoded_images = image_processor(
image_inputs, return_tensors="pt", max_patches=max_patch
).flattened_patches
self.assertEqual(
encoded_images.shape,
(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),
)
def test_call_vqa(self):
# Initialize image_processor
image_processor = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
for image in image_inputs:
self.assertIsInstance(image, Image.Image)
# Test not batched input
expected_hidden_dim = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* self.image_processor_tester.num_channels
) + 2
image_processor.is_vqa = True
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
with self.assertRaises(ValueError):
encoded_images = image_processor(
image_inputs[0], return_tensors="pt", max_patches=max_patch
).flattened_patches
dummy_text = "Hello"
encoded_images = image_processor(
image_inputs[0], return_tensors="pt", max_patches=max_patch, header_text=dummy_text
).flattened_patches
self.assertEqual(
encoded_images.shape,
(1, max_patch, expected_hidden_dim),
)
# Test batched
encoded_images = image_processor(
image_inputs, return_tensors="pt", max_patches=max_patch, header_text=dummy_text
).flattened_patches
self.assertEqual(
encoded_images.shape,
(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),
)
def test_call_numpy(self):
# Initialize image_processor
image_processor = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
for image in image_inputs:
self.assertIsInstance(image, np.ndarray)
expected_hidden_dim = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
encoded_images = image_processor(
image_inputs[0], return_tensors="pt", max_patches=max_patch
).flattened_patches
self.assertEqual(
encoded_images.shape,
(1, max_patch, expected_hidden_dim),
)
# Test batched
encoded_images = image_processor(
image_inputs, return_tensors="pt", max_patches=max_patch
).flattened_patches
self.assertEqual(
encoded_images.shape,
(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),
)
def test_call_numpy_4_channels(self):
# Initialize image_processor
image_processor = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
self.image_processor_tester.num_channels = 4
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
for image in image_inputs:
self.assertIsInstance(image, np.ndarray)
expected_hidden_dim = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
encoded_images = image_processor(
image_inputs[0], return_tensors="pt", max_patches=max_patch, input_data_format="channels_first"
).flattened_patches
self.assertEqual(
encoded_images.shape,
(1, max_patch, expected_hidden_dim),
)
# Test batched
encoded_images = image_processor(
image_inputs, return_tensors="pt", max_patches=max_patch, input_data_format="channels_first"
).flattened_patches
self.assertEqual(
encoded_images.shape,
(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),
)
self.image_processor_tester.num_channels = 3
def test_call_pytorch(self):
# Initialize image_processor
image_processor = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
for image in image_inputs:
self.assertIsInstance(image, torch.Tensor)
# Test not batched input
expected_hidden_dim = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
encoded_images = image_processor(
image_inputs[0], return_tensors="pt", max_patches=max_patch
).flattened_patches
self.assertEqual(
encoded_images.shape,
(1, max_patch, expected_hidden_dim),
)
# Test batched
encoded_images = image_processor(
image_inputs, return_tensors="pt", max_patches=max_patch
).flattened_patches
self.assertEqual(
encoded_images.shape,
(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),
)
@require_torch
@require_vision
class Pix2StructImageProcessingTestFourChannels(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = Pix2StructImageProcessor if is_vision_available() else None
def setUp(self):
self.image_processor_tester = Pix2StructImageProcessingTester(self, num_channels=4)
self.expected_encoded_image_num_channels = 3
@property
def image_processor_dict(self):
return self.image_processor_tester.prepare_image_processor_dict()
def test_image_processor_properties(self):
image_processor = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(image_processor, "do_normalize"))
self.assertTrue(hasattr(image_processor, "do_convert_rgb"))
def test_call_pil(self):
# Initialize image_processor
image_processor = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
for image in image_inputs:
self.assertIsInstance(image, Image.Image)
# Test not batched input
expected_hidden_dim = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* (self.image_processor_tester.num_channels - 1)
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
encoded_images = image_processor(
image_inputs[0], return_tensors="pt", max_patches=max_patch
).flattened_patches
self.assertEqual(
encoded_images.shape,
(1, max_patch, expected_hidden_dim),
)
# Test batched
encoded_images = image_processor(
image_inputs, return_tensors="pt", max_patches=max_patch
).flattened_patches
self.assertEqual(
encoded_images.shape,
(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),
)
@unittest.skip("Pix2StructImageProcessor does not support 4 channels yet") # FIXME Amy
def test_call_numpy(self):
return super().test_call_numpy()
@unittest.skip("Pix2StructImageProcessor does not support 4 channels yet") # FIXME Amy
def test_call_pytorch(self):
return super().test_call_torch()
@unittest.skip("Pix2StructImageProcessor does treat numpy and PIL 4 channel images consistently") # FIXME Amy
def test_call_numpy_4_channels(self):
return super().test_call_torch()
| transformers/tests/models/pix2struct/test_image_processing_pix2struct.py/0 | {
"file_path": "transformers/tests/models/pix2struct/test_image_processing_pix2struct.py",
"repo_id": "transformers",
"token_count": 6116
} | 163 |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team, The Microsoft Research team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
class ProphetNetTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = ProphetNetTokenizer
test_rust_tokenizer = False
def setUp(self):
super().setUp()
vocab_tokens = [
"[UNK]",
"[CLS]",
"[SEP]",
"[PAD]",
"[MASK]",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
def get_input_output_texts(self, tokenizer):
input_text = "UNwant\u00E9d,running"
output_text = "unwanted, running"
return input_text, output_text
def test_full_tokenizer(self):
tokenizer = self.tokenizer_class(self.vocab_file)
tokens = tokenizer.tokenize("UNwant\u00E9d,running")
self.assertListEqual(tokens, ["un", "##want", "##ed", ",", "runn", "##ing"])
self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [9, 6, 7, 12, 10, 11])
def test_chinese(self):
tokenizer = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz"), ["ah", "\u535A", "\u63A8", "zz"])
def test_basic_tokenizer_lower(self):
tokenizer = BasicTokenizer(do_lower_case=True)
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? "), ["hello", "!", "how", "are", "you", "?"]
)
self.assertListEqual(tokenizer.tokenize("H\u00E9llo"), ["hello"])
def test_basic_tokenizer_lower_strip_accents_false(self):
tokenizer = BasicTokenizer(do_lower_case=True, strip_accents=False)
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? "), ["hällo", "!", "how", "are", "you", "?"]
)
self.assertListEqual(tokenizer.tokenize("H\u00E9llo"), ["h\u00E9llo"])
def test_basic_tokenizer_lower_strip_accents_true(self):
tokenizer = BasicTokenizer(do_lower_case=True, strip_accents=True)
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? "), ["hallo", "!", "how", "are", "you", "?"]
)
self.assertListEqual(tokenizer.tokenize("H\u00E9llo"), ["hello"])
def test_basic_tokenizer_lower_strip_accents_default(self):
tokenizer = BasicTokenizer(do_lower_case=True)
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? "), ["hallo", "!", "how", "are", "you", "?"]
)
self.assertListEqual(tokenizer.tokenize("H\u00E9llo"), ["hello"])
def test_basic_tokenizer_no_lower(self):
tokenizer = BasicTokenizer(do_lower_case=False)
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? "), ["HeLLo", "!", "how", "Are", "yoU", "?"]
)
def test_basic_tokenizer_no_lower_strip_accents_false(self):
tokenizer = BasicTokenizer(do_lower_case=False, strip_accents=False)
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? "), ["HäLLo", "!", "how", "Are", "yoU", "?"]
)
def test_basic_tokenizer_no_lower_strip_accents_true(self):
tokenizer = BasicTokenizer(do_lower_case=False, strip_accents=True)
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? "), ["HaLLo", "!", "how", "Are", "yoU", "?"]
)
def test_basic_tokenizer_respects_never_split_tokens(self):
tokenizer = BasicTokenizer(do_lower_case=False, never_split=["[UNK]"])
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? [UNK]"), ["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"]
)
def test_wordpiece_tokenizer(self):
vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]
vocab = {}
for i, token in enumerate(vocab_tokens):
vocab[token] = i
tokenizer = WordpieceTokenizer(vocab=vocab, unk_token="[UNK]")
self.assertListEqual(tokenizer.tokenize(""), [])
self.assertListEqual(tokenizer.tokenize("unwanted running"), ["un", "##want", "##ed", "runn", "##ing"])
self.assertListEqual(tokenizer.tokenize("unwantedX running"), ["[UNK]", "runn", "##ing"])
@require_torch
def test_prepare_batch(self):
tokenizer = self.tokenizer_class.from_pretrained("microsoft/prophetnet-large-uncased")
src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."]
expected_src_tokens = [1037, 2146, 20423, 2005, 7680, 7849, 3989, 1012, 102]
batch = tokenizer(src_text, padding=True, return_tensors="pt")
self.assertIsInstance(batch, BatchEncoding)
result = list(batch.input_ids.numpy()[0])
self.assertListEqual(expected_src_tokens, result)
self.assertEqual((2, 9), batch.input_ids.shape)
self.assertEqual((2, 9), batch.attention_mask.shape)
def test_is_whitespace(self):
self.assertTrue(_is_whitespace(" "))
self.assertTrue(_is_whitespace("\t"))
self.assertTrue(_is_whitespace("\r"))
self.assertTrue(_is_whitespace("\n"))
self.assertTrue(_is_whitespace("\u00A0"))
self.assertFalse(_is_whitespace("A"))
self.assertFalse(_is_whitespace("-"))
def test_is_control(self):
self.assertTrue(_is_control("\u0005"))
self.assertFalse(_is_control("A"))
self.assertFalse(_is_control(" "))
self.assertFalse(_is_control("\t"))
self.assertFalse(_is_control("\r"))
def test_is_punctuation(self):
self.assertTrue(_is_punctuation("-"))
self.assertTrue(_is_punctuation("$"))
self.assertTrue(_is_punctuation("`"))
self.assertTrue(_is_punctuation("."))
self.assertFalse(_is_punctuation("A"))
self.assertFalse(_is_punctuation(" "))
@slow
def test_sequence_builders(self):
tokenizer = self.tokenizer_class.from_pretrained("microsoft/prophetnet-large-uncased")
text = tokenizer.encode("sequence builders", add_special_tokens=False)
text_2 = tokenizer.encode("multi-sequence build", add_special_tokens=False)
encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
assert encoded_sentence == text + [102]
assert encoded_pair == text + [102] + text_2 + [102]
| transformers/tests/models/prophetnet/test_tokenization_prophetnet.py/0 | {
"file_path": "transformers/tests/models/prophetnet/test_tokenization_prophetnet.py",
"repo_id": "transformers",
"token_count": 3445
} | 164 |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer
class RealmRetrieverTest(TestCase):
def setUp(self):
self.tmpdirname = tempfile.mkdtemp()
self.num_block_records = 5
# Realm tok
vocab_tokens = [
"[UNK]",
"[CLS]",
"[SEP]",
"[PAD]",
"[MASK]",
"test",
"question",
"this",
"is",
"the",
"first",
"second",
"third",
"fourth",
"fifth",
"record",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
realm_tokenizer_path = os.path.join(self.tmpdirname, "realm_tokenizer")
os.makedirs(realm_tokenizer_path, exist_ok=True)
self.vocab_file = os.path.join(realm_tokenizer_path, VOCAB_FILES_NAMES["vocab_file"])
with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
realm_block_records_path = os.path.join(self.tmpdirname, "realm_block_records")
os.makedirs(realm_block_records_path, exist_ok=True)
def get_tokenizer(self) -> RealmTokenizer:
return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname, "realm_tokenizer"))
def tearDown(self):
shutil.rmtree(self.tmpdirname)
def get_config(self):
config = RealmConfig(num_block_records=self.num_block_records)
return config
def get_dummy_dataset(self):
dataset = Dataset.from_dict(
{
"id": ["0", "1"],
"question": ["foo", "bar"],
"answers": [["Foo", "Bar"], ["Bar"]],
}
)
return dataset
def get_dummy_block_records(self):
block_records = np.array(
[
b"This is the first record",
b"This is the second record",
b"This is the third record",
b"This is the fourth record",
b"This is the fifth record",
b"This is a longer longer longer record",
],
dtype=object,
)
return block_records
def get_dummy_retriever(self):
retriever = RealmRetriever(
block_records=self.get_dummy_block_records(),
tokenizer=self.get_tokenizer(),
)
return retriever
def test_retrieve(self):
config = self.get_config()
retriever = self.get_dummy_retriever()
tokenizer = retriever.tokenizer
retrieved_block_ids = np.array([0, 3], dtype="long")
question_input_ids = tokenizer(["Test question"]).input_ids
answer_ids = tokenizer(
["the fourth"],
add_special_tokens=False,
return_token_type_ids=False,
return_attention_mask=False,
).input_ids
max_length = config.reader_seq_len
has_answers, start_pos, end_pos, concat_inputs = retriever(
retrieved_block_ids, question_input_ids, answer_ids=answer_ids, max_length=max_length, return_tensors="np"
)
self.assertEqual(len(has_answers), 2)
self.assertEqual(len(start_pos), 2)
self.assertEqual(len(end_pos), 2)
self.assertEqual(concat_inputs.input_ids.shape, (2, 10))
self.assertEqual(concat_inputs.attention_mask.shape, (2, 10))
self.assertEqual(concat_inputs.token_type_ids.shape, (2, 10))
self.assertEqual(concat_inputs.special_tokens_mask.shape, (2, 10))
self.assertEqual(
tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0]),
["[CLS]", "test", "question", "[SEP]", "this", "is", "the", "first", "record", "[SEP]"],
)
self.assertEqual(
tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1]),
["[CLS]", "test", "question", "[SEP]", "this", "is", "the", "fourth", "record", "[SEP]"],
)
def test_block_has_answer(self):
config = self.get_config()
retriever = self.get_dummy_retriever()
tokenizer = retriever.tokenizer
retrieved_block_ids = np.array([0, 3, 5], dtype="long")
question_input_ids = tokenizer(["Test question"]).input_ids
answer_ids = tokenizer(
["the fourth", "longer longer"],
add_special_tokens=False,
return_token_type_ids=False,
return_attention_mask=False,
).input_ids
max_length = config.reader_seq_len
has_answers, start_pos, end_pos, _ = retriever(
retrieved_block_ids, question_input_ids, answer_ids=answer_ids, max_length=max_length, return_tensors="np"
)
self.assertEqual([False, True, True], has_answers)
self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]], start_pos)
self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]], end_pos)
def test_save_load_pretrained(self):
retriever = self.get_dummy_retriever()
retriever.save_pretrained(os.path.join(self.tmpdirname, "realm_block_records"))
# Test local path
retriever = retriever.from_pretrained(os.path.join(self.tmpdirname, "realm_block_records"))
self.assertEqual(retriever.block_records[0], b"This is the first record")
# Test mocked remote path
with patch("transformers.models.realm.retrieval_realm.hf_hub_download") as mock_hf_hub_download:
mock_hf_hub_download.return_value = os.path.join(
os.path.join(self.tmpdirname, "realm_block_records"), _REALM_BLOCK_RECORDS_FILENAME
)
retriever = RealmRetriever.from_pretrained("google/realm-cc-news-pretrained-openqa")
self.assertEqual(retriever.block_records[0], b"This is the first record")
| transformers/tests/models/realm/test_retrieval_realm.py/0 | {
"file_path": "transformers/tests/models/realm/test_retrieval_realm.py",
"repo_id": "transformers",
"token_count": 3280
} | 165 |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the Tensorflow ResNet model. """
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFResNetForImageClassification, TFResNetModel
from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class TFResNetModelTester:
def __init__(
self,
parent,
batch_size=3,
image_size=32,
num_channels=3,
embeddings_size=10,
hidden_sizes=[10, 20, 30, 40],
depths=[1, 1, 2, 1],
is_training=True,
use_labels=True,
hidden_act="relu",
num_labels=3,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.num_channels = num_channels
self.embeddings_size = embeddings_size
self.hidden_sizes = hidden_sizes
self.depths = depths
self.is_training = is_training
self.use_labels = use_labels
self.hidden_act = hidden_act
self.num_labels = num_labels
self.scope = scope
self.num_stages = len(hidden_sizes)
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size], self.num_labels)
config = self.get_config()
return config, pixel_values, labels
def get_config(self):
return ResNetConfig(
num_channels=self.num_channels,
embeddings_size=self.embeddings_size,
hidden_sizes=self.hidden_sizes,
depths=self.depths,
hidden_act=self.hidden_act,
num_labels=self.num_labels,
image_size=self.image_size,
)
def create_and_check_model(self, config, pixel_values, labels):
model = TFResNetModel(config=config)
result = model(pixel_values)
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape,
(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),
)
def create_and_check_for_image_classification(self, config, pixel_values, labels):
config.num_labels = self.num_labels
model = TFResNetForImageClassification(config)
result = model(pixel_values, labels=labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, labels = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class TFResNetModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_common.py, as ResNet does not use input_ids, inputs_embeds,
attention_mask and seq_length.
"""
all_model_classes = (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
pipeline_model_mapping = (
{"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
if is_tf_available()
else {}
)
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
test_onnx = False
has_attentions = False
def setUp(self):
self.model_tester = TFResNetModelTester(self)
self.config_tester = ConfigTester(self, config_class=ResNetConfig, has_text_modality=False)
def test_config(self):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def create_and_test_config_common_properties(self):
return
@unittest.skip(reason="ResNet does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="ResNet does not support input and output embeddings")
def test_model_common_attributes(self):
pass
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.call)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
expected_num_stages = self.model_tester.num_stages
self.assertEqual(len(hidden_states), expected_num_stages + 1)
# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[self.model_tester.image_size // 4, self.model_tester.image_size // 4],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
layers_type = ["basic", "bottleneck"]
for model_class in self.all_model_classes:
for layer_type in layers_type:
config.layer_type = layer_type
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
def test_for_image_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = TFResNetModel.from_pretrained(model_name)
self.assertIsNotNone(model)
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_tf
@require_vision
class TFResNetModelIntegrationTest(unittest.TestCase):
@cached_property
def default_image_processor(self):
return (
AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
if is_vision_available()
else None
)
@slow
def test_inference_image_classification_head(self):
model = TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
image_processor = self.default_image_processor
image = prepare_img()
inputs = image_processor(images=image, return_tensors="tf")
# forward pass
outputs = model(**inputs)
# verify the logits
expected_shape = tf.TensorShape((1, 1000))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = tf.constant([-11.1069, -9.7877, -8.3777])
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy(), expected_slice, atol=1e-4))
| transformers/tests/models/resnet/test_modeling_tf_resnet.py/0 | {
"file_path": "transformers/tests/models/resnet/test_modeling_tf_resnet.py",
"repo_id": "transformers",
"token_count": 3896
} | 166 |
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerModel,
)
from transformers.models.roformer.modeling_tf_roformer import (
TFRoFormerSelfAttention,
TFRoFormerSinusoidalPositionalEmbedding,
)
class TFRoFormerModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = 13
self.seq_length = 7
self.is_training = True
self.use_input_mask = True
self.use_token_type_ids = True
self.use_labels = True
self.vocab_size = 99
self.hidden_size = 32
self.num_hidden_layers = 2
self.num_attention_heads = 4
self.intermediate_size = 37
self.hidden_act = "gelu"
self.hidden_dropout_prob = 0.1
self.attention_probs_dropout_prob = 0.1
self.max_position_embeddings = 512
self.type_vocab_size = 16
self.type_sequence_label_size = 2
self.initializer_range = 0.02
self.num_labels = 3
self.num_choices = 4
self.scope = None
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = RoFormerConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range,
return_dict=True,
)
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = TFRoFormerModel(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
inputs = [input_ids, input_mask]
result = model(inputs)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_lm_head(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.is_decoder = True
model = TFRoFormerForCausalLM(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
prediction_scores = model(inputs)["logits"]
self.parent.assertListEqual(
list(prediction_scores.numpy().shape), [self.batch_size, self.seq_length, self.vocab_size]
)
def create_and_check_for_masked_lm(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = TFRoFormerForMaskedLM(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_sequence_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = TFRoFormerForSequenceClassification(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_multiple_choice(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_choices = self.num_choices
model = TFRoFormerForMultipleChoice(config=config)
multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1))
multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1))
multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1))
inputs = {
"input_ids": multiple_choice_inputs_ids,
"attention_mask": multiple_choice_input_mask,
"token_type_ids": multiple_choice_token_type_ids,
}
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def create_and_check_for_token_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = TFRoFormerForTokenClassification(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_question_answering(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = TFRoFormerForQuestionAnswering(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
result = model(inputs)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class TFRoFormerModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
TFRoFormerModel,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerForMultipleChoice,
)
if is_tf_available()
else ()
)
pipeline_model_mapping = (
{
"feature-extraction": TFRoFormerModel,
"fill-mask": TFRoFormerForMaskedLM,
"question-answering": TFRoFormerForQuestionAnswering,
"text-classification": TFRoFormerForSequenceClassification,
"text-generation": TFRoFormerForCausalLM,
"token-classification": TFRoFormerForTokenClassification,
"zero-shot": TFRoFormerForSequenceClassification,
}
if is_tf_available()
else {}
)
test_head_masking = False
test_onnx = False
# TODO: add `prepare_inputs_for_generation` for `TFRoFormerForCausalLM`
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name == "TextGenerationPipelineTests":
return True
return False
def setUp(self):
self.model_tester = TFRoFormerModelTester(self)
self.config_tester = ConfigTester(self, config_class=RoFormerConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_causal_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
model = TFRoFormerModel.from_pretrained("junnyu/roformer_chinese_base")
self.assertIsNotNone(model)
@require_tf
class TFRoFormerModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_masked_lm(self):
model = TFRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base")
input_ids = tf.constant([[0, 1, 2, 3, 4, 5]])
output = model(input_ids)[0]
# TODO Replace vocab size
vocab_size = 50000
expected_shape = [1, 6, vocab_size]
self.assertEqual(output.shape, expected_shape)
print(output[:, :3, :3])
# TODO Replace values below with what was printed above.
expected_slice = tf.constant(
[
[
[-0.12053341, -1.0264901, 0.29221946],
[-1.5133783, 0.197433, 0.15190607],
[-5.0135403, -3.900256, -0.84038764],
]
]
)
tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=1e-4)
@require_tf
class TFRoFormerSinusoidalPositionalEmbeddingTest(unittest.TestCase):
tolerance = 1e-4
def test_basic(self):
input_ids = tf.constant([[4, 10]])
emb1 = TFRoFormerSinusoidalPositionalEmbedding(num_positions=6, embedding_dim=6)
emb = emb1(input_ids.shape)
desired_weights = tf.constant(
[[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]]
)
tf.debugging.assert_near(emb, desired_weights, atol=self.tolerance)
def test_positional_emb_weights_against_roformer(self):
desired_weights = tf.constant(
[
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
[0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
]
)
emb1 = TFRoFormerSinusoidalPositionalEmbedding(num_positions=512, embedding_dim=512)
emb1([2, 16, 512])
weights = emb1.weight[:3, :5]
tf.debugging.assert_near(weights, desired_weights, atol=self.tolerance)
@require_tf
class TFRoFormerSelfAttentionRotaryPositionEmbeddingTest(unittest.TestCase):
tolerance = 1e-4
def test_apply_rotary_position_embeddings(self):
# 2,12,16,64
query_layer = tf.reshape(tf.range(2 * 12 * 16 * 64, dtype=tf.float32), shape=(2, 12, 16, 64)) / 100
key_layer = -tf.reshape(tf.range(2 * 12 * 16 * 64, dtype=tf.float32), shape=(2, 12, 16, 64)) / 100
embed_positions = TFRoFormerSinusoidalPositionalEmbedding(num_positions=32, embedding_dim=64)
sinusoidal_pos = embed_positions([2, 16, 768])[None, None, :, :]
query_layer, key_layer = TFRoFormerSelfAttention.apply_rotary_position_embeddings(
sinusoidal_pos, query_layer, key_layer
)
desired_query_layer = tf.constant(
[
[0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
[-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
[-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
[-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
[0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
[3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
]
)
desired_key_layer = tf.constant(
[
[0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
[0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
[1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
[2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
[-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
[-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
]
)
tf.debugging.assert_near(query_layer[0, 0, :6, :8], desired_query_layer, atol=self.tolerance)
tf.debugging.assert_near(key_layer[0, 0, :6, :8], desired_key_layer, atol=self.tolerance)
| transformers/tests/models/roformer/test_modeling_tf_roformer.py/0 | {
"file_path": "transformers/tests/models/roformer/test_modeling_tf_roformer.py",
"repo_id": "transformers",
"token_count": 7807
} | 167 |
# coding=utf-8
# Copyright 2021 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import SegformerImageProcessor
class SegformerImageProcessingTester(unittest.TestCase):
def __init__(
self,
parent,
batch_size=7,
num_channels=3,
min_resolution=30,
max_resolution=400,
do_resize=True,
size=None,
do_normalize=True,
image_mean=[0.5, 0.5, 0.5],
image_std=[0.5, 0.5, 0.5],
do_reduce_labels=False,
):
size = size if size is not None else {"height": 30, "width": 30}
self.parent = parent
self.batch_size = batch_size
self.num_channels = num_channels
self.min_resolution = min_resolution
self.max_resolution = max_resolution
self.do_resize = do_resize
self.size = size
self.do_normalize = do_normalize
self.image_mean = image_mean
self.image_std = image_std
self.do_reduce_labels = do_reduce_labels
def prepare_image_processor_dict(self):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_reduce_labels": self.do_reduce_labels,
}
def expected_output_image_shape(self, images):
return self.num_channels, self.size["height"], self.size["width"]
def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
return prepare_image_inputs(
batch_size=self.batch_size,
num_channels=self.num_channels,
min_resolution=self.min_resolution,
max_resolution=self.max_resolution,
equal_resolution=equal_resolution,
numpify=numpify,
torchify=torchify,
)
def prepare_semantic_single_inputs():
dataset = load_dataset("hf-internal-testing/fixtures_ade20k", split="test")
image = Image.open(dataset[0]["file"])
map = Image.open(dataset[1]["file"])
return image, map
def prepare_semantic_batch_inputs():
dataset = load_dataset("hf-internal-testing/fixtures_ade20k", split="test")
image1 = Image.open(dataset[0]["file"])
map1 = Image.open(dataset[1]["file"])
image2 = Image.open(dataset[2]["file"])
map2 = Image.open(dataset[3]["file"])
return [image1, image2], [map1, map2]
@require_torch
@require_vision
class SegformerImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = SegformerImageProcessor if is_vision_available() else None
def setUp(self):
self.image_processor_tester = SegformerImageProcessingTester(self)
@property
def image_processor_dict(self):
return self.image_processor_tester.prepare_image_processor_dict()
def test_image_processor_properties(self):
image_processing = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(image_processing, "do_resize"))
self.assertTrue(hasattr(image_processing, "size"))
self.assertTrue(hasattr(image_processing, "do_normalize"))
self.assertTrue(hasattr(image_processing, "image_mean"))
self.assertTrue(hasattr(image_processing, "image_std"))
self.assertTrue(hasattr(image_processing, "do_reduce_labels"))
def test_image_processor_from_dict_with_kwargs(self):
image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
self.assertEqual(image_processor.size, {"height": 30, "width": 30})
self.assertEqual(image_processor.do_reduce_labels, False)
image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42, reduce_labels=True)
self.assertEqual(image_processor.size, {"height": 42, "width": 42})
self.assertEqual(image_processor.do_reduce_labels, True)
def test_call_segmentation_maps(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
maps = []
for image in image_inputs:
self.assertIsInstance(image, torch.Tensor)
maps.append(torch.zeros(image.shape[-2:]).long())
# Test not batched input
encoding = image_processing(image_inputs[0], maps[0], return_tensors="pt")
self.assertEqual(
encoding["pixel_values"].shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
self.assertEqual(
encoding["labels"].shape,
(
1,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
self.assertEqual(encoding["labels"].dtype, torch.long)
self.assertTrue(encoding["labels"].min().item() >= 0)
self.assertTrue(encoding["labels"].max().item() <= 255)
# Test batched
encoding = image_processing(image_inputs, maps, return_tensors="pt")
self.assertEqual(
encoding["pixel_values"].shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
self.assertEqual(
encoding["labels"].shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
self.assertEqual(encoding["labels"].dtype, torch.long)
self.assertTrue(encoding["labels"].min().item() >= 0)
self.assertTrue(encoding["labels"].max().item() <= 255)
# Test not batched input (PIL images)
image, segmentation_map = prepare_semantic_single_inputs()
encoding = image_processing(image, segmentation_map, return_tensors="pt")
self.assertEqual(
encoding["pixel_values"].shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
self.assertEqual(
encoding["labels"].shape,
(
1,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
self.assertEqual(encoding["labels"].dtype, torch.long)
self.assertTrue(encoding["labels"].min().item() >= 0)
self.assertTrue(encoding["labels"].max().item() <= 255)
# Test batched input (PIL images)
images, segmentation_maps = prepare_semantic_batch_inputs()
encoding = image_processing(images, segmentation_maps, return_tensors="pt")
self.assertEqual(
encoding["pixel_values"].shape,
(
2,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
self.assertEqual(
encoding["labels"].shape,
(
2,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
self.assertEqual(encoding["labels"].dtype, torch.long)
self.assertTrue(encoding["labels"].min().item() >= 0)
self.assertTrue(encoding["labels"].max().item() <= 255)
def test_reduce_labels(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
image, map = prepare_semantic_single_inputs()
encoding = image_processing(image, map, return_tensors="pt")
self.assertTrue(encoding["labels"].min().item() >= 0)
self.assertTrue(encoding["labels"].max().item() <= 150)
image_processing.do_reduce_labels = True
encoding = image_processing(image, map, return_tensors="pt")
self.assertTrue(encoding["labels"].min().item() >= 0)
self.assertTrue(encoding["labels"].max().item() <= 255)
| transformers/tests/models/segformer/test_image_processing_segformer.py/0 | {
"file_path": "transformers/tests/models/segformer/test_image_processing_segformer.py",
"repo_id": "transformers",
"token_count": 4300
} | 168 |
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch Speech2Text model. """
import copy
import inspect
import os
import tempfile
import unittest
from transformers import Speech2TextConfig
from transformers.testing_utils import (
is_torch_available,
require_sentencepiece,
require_tokenizers,
require_torch,
require_torch_fp16,
require_torchaudio,
slow,
torch_device,
)
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import Speech2TextForConditionalGeneration, Speech2TextModel, Speech2TextProcessor
from transformers.models.speech_to_text.modeling_speech_to_text import Speech2TextDecoder, Speech2TextEncoder
def prepare_speech_to_text_inputs_dict(
config,
input_features,
decoder_input_ids,
attention_mask=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
):
if attention_mask is None:
attention_mask = input_features.ne(0)
if decoder_attention_mask is None:
decoder_attention_mask = decoder_input_ids.ne(config.pad_token_id)
if head_mask is None:
head_mask = torch.ones(config.encoder_layers, config.encoder_attention_heads, device=torch_device)
if decoder_head_mask is None:
decoder_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
if cross_attn_head_mask is None:
cross_attn_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
return {
# "input_ids": input_features,
"input_features": input_features,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_torch
class Speech2TextModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_labels=False,
vocab_size=99,
hidden_size=16,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=4,
num_conv_layers=2,
conv_kernel_sizes=(5, 5),
conv_channels=32,
input_feat_per_channel=24,
input_channels=1,
hidden_act="relu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=20,
max_source_positions=20,
max_target_positions=20,
eos_token_id=2,
pad_token_id=1,
bos_token_id=0,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.num_conv_layers = num_conv_layers
self.conv_kernel_sizes = conv_kernel_sizes
self.conv_channels = conv_channels
self.input_feat_per_channel = input_feat_per_channel
self.input_channels = input_channels
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.max_source_positions = max_source_positions
self.max_target_positions = max_target_positions
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
def prepare_config_and_inputs(self):
input_features = floats_tensor(
[self.batch_size, self.seq_length, self.input_feat_per_channel], self.vocab_size
)
attention_mask = torch.ones([self.batch_size, self.seq_length], dtype=torch.long, device=torch_device)
decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp(2)
config = self.get_config()
inputs_dict = prepare_speech_to_text_inputs_dict(
config,
input_features=input_features,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
)
return config, inputs_dict
def get_config(self):
return Speech2TextConfig(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
encoder_ffn_dim=self.intermediate_size,
decoder_ffn_dim=self.intermediate_size,
num_conv_layers=self.num_conv_layers,
conv_kernel_sizes=self.conv_kernel_sizes,
conv_channels=self.conv_channels,
input_feat_per_channel=self.input_feat_per_channel,
input_channels=self.input_channels,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
max_source_positions=self.max_source_positions,
max_target_positions=self.max_target_positions,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
)
def prepare_config_and_inputs_for_common(self):
config, inputs_dict = self.prepare_config_and_inputs()
return config, inputs_dict
def get_subsampled_output_lengths(self, input_lengths):
"""
Computes the output length of the convolutional layers
"""
for i in range(self.num_conv_layers):
input_lengths = (input_lengths - 1) // 2 + 1
return input_lengths
def create_and_check_model_forward(self, config, inputs_dict):
model = Speech2TextModel(config=config).to(torch_device).eval()
input_features = inputs_dict["input_features"]
decoder_input_ids = inputs_dict["decoder_input_ids"]
# first forward pass
last_hidden_state = model(input_features, decoder_input_ids=decoder_input_ids).last_hidden_state
self.parent.assertTrue(last_hidden_state.shape, (13, 7, 16))
def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict):
model = Speech2TextModel(config=config).get_decoder().to(torch_device).eval()
input_ids = inputs_dict["decoder_input_ids"]
attention_mask = inputs_dict["decoder_attention_mask"]
# first forward pass
outputs = model(input_ids, attention_mask=attention_mask, use_cache=True)
output, past_key_values = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size).clamp(2)
next_attn_mask = ids_tensor((self.batch_size, 3), 2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1)
output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"]
output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[
"last_hidden_state"
]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-2))
def check_encoder_decoder_model_standalone(self, config, inputs_dict):
model = Speech2TextModel(config=config).to(torch_device).eval()
outputs = model(**inputs_dict)
encoder_last_hidden_state = outputs.encoder_last_hidden_state
last_hidden_state = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
encoder = model.get_encoder()
encoder.save_pretrained(tmpdirname)
encoder = Speech2TextEncoder.from_pretrained(tmpdirname).to(torch_device)
encoder_last_hidden_state_2 = encoder(
inputs_dict["input_features"], attention_mask=inputs_dict["attention_mask"]
)[0]
self.parent.assertTrue((encoder_last_hidden_state_2 - encoder_last_hidden_state).abs().max().item() < 1e-3)
with tempfile.TemporaryDirectory() as tmpdirname:
decoder = model.get_decoder()
decoder.save_pretrained(tmpdirname)
decoder = Speech2TextDecoder.from_pretrained(tmpdirname).to(torch_device)
encoder_attention_mask = encoder._get_feature_vector_attention_mask(
encoder_last_hidden_state.shape[1], inputs_dict["attention_mask"]
)
last_hidden_state_2 = decoder(
input_ids=inputs_dict["decoder_input_ids"],
attention_mask=inputs_dict["decoder_attention_mask"],
encoder_hidden_states=encoder_last_hidden_state,
encoder_attention_mask=encoder_attention_mask,
)[0]
self.parent.assertTrue((last_hidden_state_2 - last_hidden_state).abs().max().item() < 1e-3)
@require_torch
class Speech2TextModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (Speech2TextModel, Speech2TextForConditionalGeneration) if is_torch_available() else ()
all_generative_model_classes = (Speech2TextForConditionalGeneration,) if is_torch_available() else ()
pipeline_model_mapping = (
{"automatic-speech-recognition": Speech2TextForConditionalGeneration, "feature-extraction": Speech2TextModel}
if is_torch_available()
else {}
)
is_encoder_decoder = True
fx_compatible = True
test_pruning = False
test_missing_keys = False
input_name = "input_features"
def setUp(self):
self.model_tester = Speech2TextModelTester(self)
self.config_tester = ConfigTester(self, config_class=Speech2TextConfig)
self.maxDiff = 3000
def test_config(self):
self.config_tester.run_common_tests()
def test_save_load_strict(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
model = model_class(config)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
self.assertEqual(info["missing_keys"], [])
def test_model_forward(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_forward(*config_and_inputs)
def test_decoder_model_past_with_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_encoder_decoder_model_standalone(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*config_and_inputs)
# not implemented currently
def test_inputs_embeds(self):
pass
# training is not supported yet
def test_training(self):
pass
def test_training_gradient_checkpointing(self):
pass
@unittest.skip(
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
)
def test_training_gradient_checkpointing_use_reentrant(self):
pass
@unittest.skip(
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
)
def test_training_gradient_checkpointing_use_reentrant_false(self):
pass
@require_torch_fp16
def test_generate_fp16(self):
config, input_dict = self.model_tester.prepare_config_and_inputs()
input_features = input_dict["input_features"]
attention_mask = input_dict["attention_mask"]
model = Speech2TextForConditionalGeneration(config).eval().to(torch_device)
input_features = input_features.half()
model.half()
model.generate(input_features, attention_mask=attention_mask)
model.generate(input_features, num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = [
"input_features",
"attention_mask",
"decoder_input_ids",
"decoder_attention_mask",
]
expected_arg_names.extend(
["head_mask", "decoder_head_mask", "cross_attn_head_mask", "encoder_outputs"]
if "head_mask" and "decoder_head_mask" and "cross_attn_head_mask" in arg_names
else ["encoder_outputs"]
)
self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
expected_num_layers = getattr(
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
)
self.assertEqual(len(hidden_states), expected_num_layers)
if hasattr(self.model_tester, "encoder_seq_length"):
seq_length = self.model_tester.encoder_seq_length
else:
seq_length = self.model_tester.seq_length
subsampled_seq_length = model._get_feat_extract_output_lengths(seq_length)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[subsampled_seq_length, self.model_tester.hidden_size],
)
if config.is_encoder_decoder:
hidden_states = outputs.decoder_hidden_states
self.assertIsInstance(hidden_states, (list, tuple))
self.assertEqual(len(hidden_states), expected_num_layers)
seq_len = getattr(self.model_tester, "seq_length", None)
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[decoder_seq_length, self.model_tester.hidden_size],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
seq_len = getattr(self.model_tester, "seq_length", None)
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length)
encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.eval()
subsampled_encoder_seq_length = model._get_feat_extract_output_lengths(encoder_seq_length)
subsampled_encoder_key_length = model._get_feat_extract_output_lengths(encoder_key_length)
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, subsampled_encoder_seq_length, subsampled_encoder_key_length],
)
out_len = len(outputs)
correct_outlen = 5
# loss is at first position
if "labels" in inputs_dict:
correct_outlen += 1 # loss is added to beginning
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
self.assertEqual(out_len, correct_outlen)
# decoder attentions
decoder_attentions = outputs.decoder_attentions
self.assertIsInstance(decoder_attentions, (list, tuple))
self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(decoder_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
)
# cross attentions
cross_attentions = outputs.cross_attentions
self.assertIsInstance(cross_attentions, (list, tuple))
self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(cross_attentions[0].shape[-3:]),
[
self.model_tester.num_attention_heads,
decoder_seq_length,
subsampled_encoder_key_length,
],
)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
added_hidden_states = 2
self.assertEqual(out_len + added_hidden_states, len(outputs))
self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, subsampled_encoder_seq_length, subsampled_encoder_key_length],
)
def test_resize_tokens_embeddings(self):
(
original_config,
inputs_dict,
) = self.model_tester.prepare_config_and_inputs_for_common()
if not self.test_resize_embeddings:
return
for model_class in self.all_model_classes:
config = copy.deepcopy(original_config)
model = model_class(config)
model.to(torch_device)
if self.model_tester.is_training is False:
model.eval()
model_vocab_size = config.vocab_size
# Retrieve the embeddings and clone theme
model_embed = model.resize_token_embeddings(model_vocab_size)
cloned_embeddings = model_embed.weight.clone()
# Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
model_embed = model.resize_token_embeddings(model_vocab_size + 10)
self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
# Check that it actually resizes the embeddings matrix
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10)
# Check that the model can still do a forward pass successfully (every parameter should be resized)
model(**self._prepare_for_class(inputs_dict, model_class))
# Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
model_embed = model.resize_token_embeddings(model_vocab_size - 15)
self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
# Check that it actually resizes the embeddings matrix
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15)
# make sure that decoder_input_ids are resized
if "decoder_input_ids" in inputs_dict:
inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1)
model(**self._prepare_for_class(inputs_dict, model_class))
# Check that adding and removing tokens has not modified the first part of the embedding matrix.
models_equal = True
for p1, p2 in zip(cloned_embeddings, model_embed.weight):
if p1.data.ne(p2.data).sum() > 0:
models_equal = False
self.assertTrue(models_equal)
def test_resize_embeddings_untied(self):
(
original_config,
inputs_dict,
) = self.model_tester.prepare_config_and_inputs_for_common()
if not self.test_resize_embeddings:
return
original_config.tie_word_embeddings = False
# if model cannot untied embeddings -> leave test
if original_config.tie_word_embeddings:
return
for model_class in self.all_model_classes:
config = copy.deepcopy(original_config)
model = model_class(config).to(torch_device)
# if no output embeddings -> leave test
if model.get_output_embeddings() is None:
continue
# Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
model_vocab_size = config.vocab_size
model.resize_token_embeddings(model_vocab_size + 10)
self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
output_embeds = model.get_output_embeddings()
self.assertEqual(output_embeds.weight.shape[0], model_vocab_size + 10)
# Check bias if present
if output_embeds.bias is not None:
self.assertEqual(output_embeds.bias.shape[0], model_vocab_size + 10)
# Check that the model can still do a forward pass successfully (every parameter should be resized)
model(**self._prepare_for_class(inputs_dict, model_class))
# Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
model.resize_token_embeddings(model_vocab_size - 15)
self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
# Check that it actually resizes the embeddings matrix
output_embeds = model.get_output_embeddings()
self.assertEqual(output_embeds.weight.shape[0], model_vocab_size - 15)
# Check bias if present
if output_embeds.bias is not None:
self.assertEqual(output_embeds.bias.shape[0], model_vocab_size - 15)
# Check that the model can still do a forward pass successfully (every parameter should be resized)
if "decoder_input_ids" in inputs_dict:
inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1)
# Check that the model can still do a forward pass successfully (every parameter should be resized)
model(**self._prepare_for_class(inputs_dict, model_class))
def test_generate_without_input_ids(self):
pass
@staticmethod
def _get_encoder_outputs(
model, input_ids, attention_mask, output_attentions=None, output_hidden_states=None, num_interleave=1
):
encoder = model.get_encoder()
encoder_outputs = encoder(
input_ids,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
encoder_outputs["last_hidden_state"] = encoder_outputs.last_hidden_state.repeat_interleave(
num_interleave, dim=0
)
input_ids = input_ids[:, :, 0]
input_ids = torch.zeros_like(input_ids[:, :1], dtype=torch.long) + model._get_decoder_start_token_id()
attention_mask = None
return encoder_outputs, input_ids, attention_mask
def _check_outputs(self, output, input_ids, config, use_cache=False, num_return_sequences=1):
batch_size, seq_length = input_ids.shape[:2]
subsampled_seq_length = self.model_tester.get_subsampled_output_lengths(seq_length)
num_sequences_in_output = batch_size * num_return_sequences
gen_len = (
output.sequences.shape[-1] - 1 if config.is_encoder_decoder else output.sequences.shape[-1] - seq_length
)
# scores
self._check_scores(num_sequences_in_output, output.scores, length=gen_len, config=config)
# Attentions
# encoder
self._check_encoder_attention_for_generate(
output.encoder_attentions, batch_size, config, subsampled_seq_length
)
# decoder
self._check_attentions_for_generate(
num_sequences_in_output,
output.decoder_attentions,
min_length=1,
max_length=output.sequences.shape[-1],
config=config,
use_cache=use_cache,
)
# Hidden States
# encoder
self._check_encoder_hidden_states_for_generate(
output.encoder_hidden_states, batch_size, config, subsampled_seq_length
)
# decoder
self._check_hidden_states_for_generate(
num_sequences_in_output,
output.decoder_hidden_states,
min_length=1,
max_length=output.sequences.shape[-1],
config=config,
use_cache=use_cache,
)
def _create_and_check_torchscript(self, config, inputs_dict):
if not self.test_torchscript:
return
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
configs_no_init.torchscript = True
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
model.to(torch_device)
model.eval()
inputs = self._prepare_for_class(inputs_dict, model_class)
try:
model.config.use_cache = False # FSTM still requires this hack -> FSTM should probably be refactored similar to BART afterward
input_features = inputs["input_features"]
attention_mask = inputs["attention_mask"]
decoder_input_ids = inputs["decoder_input_ids"]
decoder_attention_mask = inputs["decoder_attention_mask"]
traced_model = torch.jit.trace(
model, (input_features, attention_mask, decoder_input_ids, decoder_attention_mask)
)
except RuntimeError:
self.fail("Couldn't trace module.")
with tempfile.TemporaryDirectory() as tmp_dir_name:
pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
try:
torch.jit.save(traced_model, pt_file_name)
except Exception:
self.fail("Couldn't save module.")
try:
loaded_model = torch.jit.load(pt_file_name)
except Exception:
self.fail("Couldn't load module.")
model.to(torch_device)
model.eval()
loaded_model.to(torch_device)
loaded_model.eval()
model_state_dict = model.state_dict()
loaded_model_state_dict = loaded_model.state_dict()
non_persistent_buffers = {}
for key in loaded_model_state_dict.keys():
if key not in model_state_dict.keys():
non_persistent_buffers[key] = loaded_model_state_dict[key]
loaded_model_state_dict = {
key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers
}
self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
model_buffers = list(model.buffers())
for non_persistent_buffer in non_persistent_buffers.values():
found_buffer = False
for i, model_buffer in enumerate(model_buffers):
if torch.equal(non_persistent_buffer, model_buffer):
found_buffer = True
break
self.assertTrue(found_buffer)
model_buffers.pop(i)
models_equal = True
for layer_name, p1 in model_state_dict.items():
p2 = loaded_model_state_dict[layer_name]
if p1.data.ne(p2.data).sum() > 0:
models_equal = False
self.assertTrue(models_equal)
def test_pt_tf_model_equivalence(self, allow_missing_keys=True):
# Allow missing keys since TF doesn't cache the sinusoidal embeddings in an attribute
super().test_pt_tf_model_equivalence(allow_missing_keys=allow_missing_keys)
@unittest.skip("Test failing, @RocketNight is looking into it")
def test_tf_from_pt_safetensors(self):
pass
@require_torch
@require_torchaudio
@require_sentencepiece
@require_tokenizers
@slow
class Speech2TextModelIntegrationTests(unittest.TestCase):
@cached_property
def default_processor(self):
return Speech2TextProcessor.from_pretrained("facebook/s2t-small-librispeech-asr")
def _load_datasamples(self, num_samples):
from datasets import load_dataset
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
# automatic decoding with librispeech
speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
def test_generation_librispeech(self):
model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr")
model.to(torch_device)
processor = self.default_processor
input_speech = self._load_datasamples(1)
input_features = processor(input_speech, return_tensors="pt").input_features.to(torch_device)
generated_ids = model.generate(input_features)
generated_transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)
EXPECTED_TRANSCRIPTIONS = [
"mister quilter is the apostle of the middle classes and we are glad to welcome his gospel"
]
self.assertListEqual(generated_transcript, EXPECTED_TRANSCRIPTIONS)
def test_generation_librispeech_batched(self):
model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr")
model.to(torch_device)
processor = self.default_processor
input_speech = self._load_datasamples(4)
inputs = processor(input_speech, return_tensors="pt", padding=True)
input_features = inputs.input_features.to(torch_device)
attention_mask = inputs.attention_mask.to(torch_device)
generated_ids = model.generate(input_features, attention_mask=attention_mask)
generated_transcripts = processor.batch_decode(generated_ids, skip_special_tokens=True)
EXPECTED_TRANSCRIPTIONS = [
"mister quilter is the apostle of the middle classes and we are glad to welcome his gospel",
"nor is mister cultar's manner less interesting than his matter",
"he tells us that at this festive season of the year with christmas and roast beef looming before us"
" similes drawn from eating and its results occur most readily to the mind",
"he has grave doubts whether sir frederick leyton's work is really greek after all and can discover in it"
" but little of rocky ithaca",
]
self.assertListEqual(generated_transcripts, EXPECTED_TRANSCRIPTIONS)
| transformers/tests/models/speech_to_text/test_modeling_speech_to_text.py/0 | {
"file_path": "transformers/tests/models/speech_to_text/test_modeling_speech_to_text.py",
"repo_id": "transformers",
"token_count": 15739
} | 169 |
# coding=utf-8
# Copyright 2020 The SqueezeBert authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class SqueezeBertTokenizationTest(BertTokenizationTest):
tokenizer_class = SqueezeBertTokenizer
rust_tokenizer_class = SqueezeBertTokenizerFast
test_rust_tokenizer = True
def get_rust_tokenizer(self, **kwargs):
return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname, **kwargs)
@slow
def test_sequence_builders(self):
tokenizer = SqueezeBertTokenizer.from_pretrained("squeezebert/squeezebert-mnli-headless")
text = tokenizer.encode("sequence builders", add_special_tokens=False)
text_2 = tokenizer.encode("multi-sequence build", add_special_tokens=False)
encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_2 + [
tokenizer.sep_token_id
]
| transformers/tests/models/squeezebert/test_tokenization_squeezebert.py/0 | {
"file_path": "transformers/tests/models/squeezebert/test_tokenization_squeezebert.py",
"repo_id": "transformers",
"token_count": 644
} | 170 |
# coding=utf-8
# Copyright 2021 Google T5 Authors and HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import tempfile
import unittest
import numpy as np
import transformers
from transformers import is_flax_available
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_sentencepiece,
require_tokenizers,
slow,
)
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
os.environ["XLA_PYTHON_CLIENT_ALLOCATOR"] = "platform"
import jax
import jax.numpy as jnp
import optax
from flax.core.frozen_dict import unfreeze
from flax.training.common_utils import onehot
from flax.traverse_util import flatten_dict
from transformers import FLAX_MODEL_MAPPING, ByT5Tokenizer, T5Config, T5Tokenizer
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.models.t5.modeling_flax_t5 import (
FlaxT5EncoderModel,
FlaxT5ForConditionalGeneration,
FlaxT5Model,
shift_tokens_right,
)
class FlaxT5ModelTester:
def __init__(
self,
parent,
vocab_size=99,
batch_size=13,
encoder_seq_length=7,
decoder_seq_length=9,
# For common tests
is_training=True,
use_attention_mask=True,
use_labels=True,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
d_ff=37,
relative_attention_num_buckets=8,
dropout_rate=0.1,
initializer_factor=0.002,
eos_token_id=1,
pad_token_id=0,
decoder_start_token_id=0,
scope=None,
decoder_layers=None,
):
self.parent = parent
self.batch_size = batch_size
self.encoder_seq_length = encoder_seq_length
self.decoder_seq_length = decoder_seq_length
# For common tests
self.seq_length = self.decoder_seq_length
self.is_training = is_training
self.use_attention_mask = use_attention_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.d_ff = d_ff
self.relative_attention_num_buckets = relative_attention_num_buckets
self.dropout_rate = dropout_rate
self.initializer_factor = initializer_factor
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.decoder_start_token_id = decoder_start_token_id
self.scope = None
self.decoder_layers = decoder_layers
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size)
decoder_input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
attention_mask = None
decoder_attention_mask = None
if self.use_attention_mask:
attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2)
decoder_attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2)
config = T5Config(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
d_ff=self.d_ff,
d_kv=self.hidden_size // self.num_attention_heads,
num_layers=self.num_hidden_layers,
num_decoder_layers=self.decoder_layers,
num_heads=self.num_attention_heads,
relative_attention_num_buckets=self.relative_attention_num_buckets,
dropout_rate=self.dropout_rate,
initializer_factor=self.initializer_factor,
eos_token_id=self.eos_token_id,
bos_token_id=self.pad_token_id,
pad_token_id=self.pad_token_id,
decoder_start_token_id=self.decoder_start_token_id,
)
return (
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
)
def create_and_check_model(
self,
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
):
model = FlaxT5Model(config=config)
result = model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
result = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
decoder_output = result.last_hidden_state
encoder_output = result.encoder_last_hidden_state
self.parent.assertEqual(encoder_output.shape, (self.batch_size, self.encoder_seq_length, self.hidden_size))
self.parent.assertEqual(decoder_output.shape, (self.batch_size, self.decoder_seq_length, self.hidden_size))
def check_use_cache_forward_with_attn_mask(
self,
model_class_name,
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
):
max_decoder_length = 20
model = model_class_name(config)
encoder_outputs = model.encode(input_ids)
# prevent fully zero'd out attention mask
decoder_attention_mask = jnp.ones_like(decoder_attention_mask)
decoder_attention_mask_cache = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
],
axis=-1,
)
past_key_values = model.init_cache(decoder_input_ids.shape[0], max_decoder_length, encoder_outputs)
outputs_cache = model.decode(
decoder_input_ids[:, :-1],
encoder_outputs,
decoder_attention_mask=decoder_attention_mask_cache,
past_key_values=past_key_values,
)
outputs_cache_next = model.decode(
decoder_input_ids[:, -1:],
encoder_outputs,
past_key_values=outputs_cache.past_key_values,
decoder_attention_mask=decoder_attention_mask_cache,
)
outputs = model.decode(decoder_input_ids, encoder_outputs, decoder_attention_mask=decoder_attention_mask)
diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}")
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
}
return config, inputs_dict
@require_flax
class FlaxT5ModelTest(FlaxModelTesterMixin, FlaxGenerationTesterMixin, unittest.TestCase):
all_model_classes = (FlaxT5Model, FlaxT5ForConditionalGeneration) if is_flax_available() else ()
all_generative_model_classes = (FlaxT5ForConditionalGeneration,) if is_flax_available() else ()
is_encoder_decoder = True
def setUp(self):
self.model_tester = FlaxT5ModelTester(self)
self.config_tester = ConfigTester(self, config_class=T5Config, d_model=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_v1_1(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
# check that gated gelu feed forward and different word embeddings work
config = config_and_inputs[0]
config.tie_word_embeddings = False
config.feed_forward_proj = "gated-gelu"
self.model_tester.create_and_check_model(config, *config_and_inputs[1:])
def test_use_cache_forward_with_attn_mask(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(model_class, *config_and_inputs)
def test_encode(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
model = model_class(config)
@jax.jit
def encode_jitted(input_ids, attention_mask=None, **kwargs):
return model.encode(input_ids=input_ids, attention_mask=attention_mask)
with self.subTest("JIT Enabled"):
jitted_outputs = encode_jitted(**prepared_inputs_dict).to_tuple()
with self.subTest("JIT Disabled"):
with jax.disable_jit():
outputs = encode_jitted(**prepared_inputs_dict).to_tuple()
self.assertEqual(len(outputs), len(jitted_outputs))
for jitted_output, output in zip(jitted_outputs, outputs):
self.assertEqual(jitted_output.shape, output.shape)
def test_decode(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
model = model_class(config)
encoder_outputs = model.encode(inputs_dict["input_ids"], inputs_dict["attention_mask"])
prepared_inputs_dict = {
"decoder_input_ids": inputs_dict["decoder_input_ids"],
"decoder_attention_mask": inputs_dict["decoder_attention_mask"],
"encoder_outputs": encoder_outputs,
}
@jax.jit
def decode_jitted(decoder_input_ids, decoder_attention_mask, encoder_outputs):
return model.decode(
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
encoder_outputs=encoder_outputs,
)
with self.subTest("JIT Enabled"):
jitted_outputs = decode_jitted(**prepared_inputs_dict).to_tuple()
with self.subTest("JIT Disabled"):
with jax.disable_jit():
outputs = decode_jitted(**prepared_inputs_dict).to_tuple()
self.assertEqual(len(outputs), len(jitted_outputs))
for jitted_output, output in zip(jitted_outputs, outputs):
self.assertEqual(jitted_output.shape, output.shape)
def test_shift_right(self):
decoder_start_token_id = 0
pad_token_id = 1
labels = np.arange(2, 102).reshape(5, 20)
labels[:2, 15:] = -100
decoder_input_ids = shift_tokens_right(labels, pad_token_id, decoder_start_token_id)
np_decoder_input_ids = np.array(decoder_input_ids)
padded_slice = np_decoder_input_ids[:2, (15 + 1) :]
self.assertTrue((padded_slice == 1).all())
not_padded_slice = np_decoder_input_ids[2:, 1:]
rolled_labels = np.roll(labels[2:], 1)[:, 1:]
self.assertTrue((not_padded_slice == rolled_labels).all())
self.assertTrue((np_decoder_input_ids[:, 0] == 0).all())
# overwrite since special base model prefix is used
def test_save_load_from_base(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
base_class = FLAX_MODEL_MAPPING[config.__class__]
for model_class in self.all_model_classes:
if model_class == base_class:
continue
model = base_class(config)
base_params = flatten_dict(unfreeze(model.params))
# check that all base model weights are loaded correctly
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
head_model = model_class.from_pretrained(tmpdirname)
base_param_from_head = flatten_dict(unfreeze(head_model.params))
for key in base_param_from_head.keys():
max_diff = (base_params[key] - base_param_from_head[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
# overwrite since special base model prefix is used
def test_save_load_to_base(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
base_class = FLAX_MODEL_MAPPING[config.__class__]
for model_class in self.all_model_classes:
if model_class == base_class:
continue
model = model_class(config)
base_params_from_head = flatten_dict(unfreeze(model.params))
# check that all base model weights are loaded correctly
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
base_model = base_class.from_pretrained(tmpdirname)
base_params = flatten_dict(unfreeze(base_model.params))
for key in base_params_from_head.keys():
max_diff = (base_params[key] - base_params_from_head[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
# overwrite since special base model prefix is used
@is_pt_flax_cross_test
def test_save_load_from_base_pt(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
base_class = FLAX_MODEL_MAPPING[config.__class__]
for model_class in self.all_model_classes:
if model_class == base_class:
continue
model = base_class(config)
base_params = flatten_dict(unfreeze(model.params))
# convert Flax model to PyTorch model
pt_model_class = getattr(transformers, base_class.__name__[4:]) # Skip the "Flax" at the beginning
pt_model = pt_model_class(config).eval()
pt_model = load_flax_weights_in_pytorch_model(pt_model, model.params)
# check that all base model weights are loaded correctly
with tempfile.TemporaryDirectory() as tmpdirname:
# save pt model
pt_model.save_pretrained(tmpdirname)
head_model = model_class.from_pretrained(tmpdirname, from_pt=True)
base_param_from_head = flatten_dict(unfreeze(head_model.params))
for key in base_param_from_head.keys():
max_diff = (base_params[key] - base_param_from_head[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
# overwrite since special base model prefix is used
@is_pt_flax_cross_test
def test_save_load_to_base_pt(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
base_class = FLAX_MODEL_MAPPING[config.__class__]
for model_class in self.all_model_classes:
if model_class == base_class:
continue
model = model_class(config)
base_params_from_head = flatten_dict(unfreeze(model.params))
# convert Flax model to PyTorch model
pt_model_class = getattr(transformers, model_class.__name__[4:]) # Skip the "Flax" at the beginning
pt_model = pt_model_class(config).eval()
pt_model = load_flax_weights_in_pytorch_model(pt_model, model.params)
# check that all base model weights are loaded correctly
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(tmpdirname)
base_model = base_class.from_pretrained(tmpdirname, from_pt=True)
base_params = flatten_dict(unfreeze(base_model.params))
for key in base_params_from_head.keys():
max_diff = (base_params[key] - base_params_from_head[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
# overwrite since special base model prefix is used
@is_pt_flax_cross_test
def test_save_load_bf16_to_base_pt(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
base_class = FLAX_MODEL_MAPPING[config.__class__]
for model_class in self.all_model_classes:
if model_class == base_class:
continue
model = model_class(config)
model.params = model.to_bf16(model.params)
base_params_from_head = flatten_dict(unfreeze(model.params))
# convert Flax model to PyTorch model
pt_model_class = getattr(transformers, model_class.__name__[4:]) # Skip the "Flax" at the beginning
pt_model = pt_model_class(config).eval()
pt_model = load_flax_weights_in_pytorch_model(pt_model, model.params)
# check that all base model weights are loaded correctly
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(tmpdirname)
base_model = base_class.from_pretrained(tmpdirname, from_pt=True)
base_params = flatten_dict(unfreeze(base_model.params))
for key in base_params_from_head.keys():
max_diff = (base_params[key] - base_params_from_head[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
class FlaxT5EncoderOnlyModelTester:
def __init__(
self,
parent,
vocab_size=99,
batch_size=13,
encoder_seq_length=7,
# For common tests
is_training=True,
use_attention_mask=True,
use_labels=True,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
d_ff=37,
relative_attention_num_buckets=8,
dropout_rate=0.1,
initializer_factor=0.002,
eos_token_id=1,
pad_token_id=0,
decoder_start_token_id=0,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.encoder_seq_length = encoder_seq_length
# For common tests
self.seq_length = self.encoder_seq_length
self.is_training = is_training
self.use_attention_mask = use_attention_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.d_ff = d_ff
self.relative_attention_num_buckets = relative_attention_num_buckets
self.dropout_rate = dropout_rate
self.initializer_factor = initializer_factor
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.decoder_start_token_id = decoder_start_token_id
self.scope = None
self.decoder_layers = 0
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size)
attention_mask = None
if self.use_attention_mask:
attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2)
config = T5Config(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
d_ff=self.d_ff,
d_kv=self.hidden_size // self.num_attention_heads,
num_layers=self.num_hidden_layers,
num_decoder_layers=self.decoder_layers,
num_heads=self.num_attention_heads,
relative_attention_num_buckets=self.relative_attention_num_buckets,
dropout_rate=self.dropout_rate,
initializer_factor=self.initializer_factor,
eos_token_id=self.eos_token_id,
bos_token_id=self.pad_token_id,
pad_token_id=self.pad_token_id,
decoder_start_token_id=self.decoder_start_token_id,
is_encoder_decoder=False,
)
return (
config,
input_ids,
attention_mask,
)
def create_and_check_model(
self,
config,
input_ids,
attention_mask,
):
model = FlaxT5EncoderModel(config=config)
result = model(
input_ids=input_ids,
attention_mask=attention_mask,
)
result = model(input_ids=input_ids)
encoder_output = result.last_hidden_state
self.parent.assertEqual(encoder_output.shape, (self.batch_size, self.encoder_seq_length, self.hidden_size))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
attention_mask,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"attention_mask": attention_mask,
}
return config, inputs_dict
@require_flax
class FlaxT5EncoderOnlyModelTest(FlaxModelTesterMixin, unittest.TestCase):
all_model_classes = (FlaxT5EncoderModel,) if is_flax_available() else ()
is_encoder_decoder = False
def setUp(self):
self.model_tester = FlaxT5EncoderOnlyModelTester(self)
self.config_tester = ConfigTester(self, config_class=T5Config, d_model=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_v1_1(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
# check that gated gelu feed forward and different word embeddings work
config = config_and_inputs[0]
config.tie_word_embeddings = False
config.feed_forward_proj = "gated-gelu"
self.model_tester.create_and_check_model(config, *config_and_inputs[1:])
def test_encode(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
model = model_class(config)
@jax.jit
def encode_jitted(input_ids, attention_mask=None, **kwargs):
return model(input_ids=input_ids, attention_mask=attention_mask)
with self.subTest("JIT Enabled"):
jitted_outputs = encode_jitted(**prepared_inputs_dict).to_tuple()
with self.subTest("JIT Disabled"):
with jax.disable_jit():
outputs = encode_jitted(**prepared_inputs_dict).to_tuple()
self.assertEqual(len(outputs), len(jitted_outputs))
for jitted_output, output in zip(jitted_outputs, outputs):
self.assertEqual(jitted_output.shape, output.shape)
# overwrite since special base model prefix is used
def test_save_load_from_base(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
base_class = FLAX_MODEL_MAPPING[config.__class__]
for model_class in self.all_model_classes:
if model_class == base_class:
continue
model = base_class(config)
base_params = flatten_dict(unfreeze(model.params))
# check that all base model weights are loaded correctly
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
head_model = model_class.from_pretrained(tmpdirname)
base_param_from_head = flatten_dict(unfreeze(head_model.params))
for key in base_param_from_head.keys():
max_diff = (base_params[key] - base_param_from_head[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
# overwrite since special base model prefix is used
def test_save_load_to_base(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
base_class = FLAX_MODEL_MAPPING[config.__class__]
for model_class in self.all_model_classes:
if model_class == base_class:
continue
model = model_class(config)
base_params_from_head = flatten_dict(unfreeze(model.params))
# check that all base model weights are loaded correctly
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
base_model = base_class.from_pretrained(tmpdirname)
base_params = flatten_dict(unfreeze(base_model.params))
for key in base_params_from_head.keys():
max_diff = (base_params[key] - base_params_from_head[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
# overwrite since special base model prefix is used
@is_pt_flax_cross_test
def test_save_load_from_base_pt(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
base_class = FLAX_MODEL_MAPPING[config.__class__]
for model_class in self.all_model_classes:
if model_class == base_class:
continue
model = base_class(config)
base_params = flatten_dict(unfreeze(model.params))
# convert Flax model to PyTorch model
pt_model_class = getattr(transformers, base_class.__name__[4:]) # Skip the "Flax" at the beginning
pt_model = pt_model_class(config).eval()
pt_model = load_flax_weights_in_pytorch_model(pt_model, model.params)
# check that all base model weights are loaded correctly
with tempfile.TemporaryDirectory() as tmpdirname:
# save pt model
pt_model.save_pretrained(tmpdirname)
head_model = model_class.from_pretrained(tmpdirname, from_pt=True)
base_param_from_head = flatten_dict(unfreeze(head_model.params))
for key in base_param_from_head.keys():
max_diff = (base_params[key] - base_param_from_head[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
# overwrite since special base model prefix is used
@is_pt_flax_cross_test
def test_save_load_to_base_pt(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
base_class = FLAX_MODEL_MAPPING[config.__class__]
for model_class in self.all_model_classes:
if model_class == base_class:
continue
model = model_class(config)
base_params_from_head = flatten_dict(unfreeze(model.params))
# convert Flax model to PyTorch model
pt_model_class = getattr(transformers, model_class.__name__[4:]) # Skip the "Flax" at the beginning
pt_model = pt_model_class(config).eval()
pt_model = load_flax_weights_in_pytorch_model(pt_model, model.params)
# check that all base model weights are loaded correctly
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(tmpdirname)
base_model = base_class.from_pretrained(tmpdirname, from_pt=True)
base_params = flatten_dict(unfreeze(base_model.params))
for key in base_params_from_head.keys():
max_diff = (base_params[key] - base_params_from_head[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
# overwrite since special base model prefix is used
@is_pt_flax_cross_test
def test_save_load_bf16_to_base_pt(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
base_class = FLAX_MODEL_MAPPING[config.__class__]
for model_class in self.all_model_classes:
if model_class == base_class:
continue
model = model_class(config)
model.params = model.to_bf16(model.params)
base_params_from_head = flatten_dict(unfreeze(model.params))
# convert Flax model to PyTorch model
pt_model_class = getattr(transformers, model_class.__name__[4:]) # Skip the "Flax" at the beginning
pt_model = pt_model_class(config).eval()
pt_model = load_flax_weights_in_pytorch_model(pt_model, model.params)
# check that all base model weights are loaded correctly
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(tmpdirname)
base_model = base_class.from_pretrained(tmpdirname, from_pt=True)
base_params = flatten_dict(unfreeze(base_model.params))
for key in base_params_from_head.keys():
max_diff = (base_params[key] - base_params_from_head[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
@require_sentencepiece
@require_tokenizers
@require_flax
class FlaxT5ModelIntegrationTests(unittest.TestCase):
@slow
def test_small_integration_test(self):
"""
For comparision run:
>>> import t5 # pip install t5==0.7.1
>>> from t5.data.sentencepiece_vocabulary import SentencePieceVocabulary
>>> path_to_mtf_small_t5_checkpoint = '<fill_in>'
>>> path_to_mtf_small_spm_model_path = '<fill_in>'
>>> t5_model = t5.models.MtfModel(model_dir=path_to_mtf_small_t5_checkpoint, batch_size=1, tpu=None)
>>> vocab = SentencePieceVocabulary(path_to_mtf_small_spm_model_path, extra_ids=100)
>>> score = t5_model.score(inputs=["Hello there"], targets=["Hi I am"], vocabulary=vocab)
"""
model = FlaxT5ForConditionalGeneration.from_pretrained("google-t5/t5-small")
tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-small")
input_ids = tokenizer("Hello there", return_tensors="np").input_ids
labels = tokenizer("Hi I am", return_tensors="np").input_ids
decoder_input_ids = shift_tokens_right(labels, model.config.pad_token_id, model.config.decoder_start_token_id)
logits = model(input_ids, decoder_input_ids=decoder_input_ids).logits
loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])).mean()
mtf_score = -(labels.shape[-1] * loss.item())
EXPECTED_SCORE = -19.0845
self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)
@slow
def test_small_v1_1_integration_test(self):
"""
For comparision run:
>>> import t5 # pip install t5==0.7.1
>>> from t5.data.sentencepiece_vocabulary import SentencePieceVocabulary
>>> path_to_mtf_small_t5_v1_1_checkpoint = '<fill_in>'
>>> path_to_mtf_small_spm_model_path = '<fill_in>'
>>> t5_model = t5.models.MtfModel(model_dir=path_to_mtf_small_t5_v1_1_checkpoint, batch_size=1, tpu=None)
>>> vocab = SentencePieceVocabulary(path_to_mtf_small_spm_model_path, extra_ids=100)
>>> score = t5_model.score(inputs=["Hello there"], targets=["Hi I am"], vocabulary=vocab)
"""
model = FlaxT5ForConditionalGeneration.from_pretrained("google/t5-v1_1-small")
tokenizer = T5Tokenizer.from_pretrained("google/t5-v1_1-small")
input_ids = tokenizer("Hello there", return_tensors="np").input_ids
labels = tokenizer("Hi I am", return_tensors="np").input_ids
decoder_input_ids = shift_tokens_right(labels, model.config.pad_token_id, model.config.decoder_start_token_id)
logits = model(input_ids, decoder_input_ids=decoder_input_ids).logits
loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])).mean()
mtf_score = -(labels.shape[-1] * loss.item())
EXPECTED_SCORE = -59.0293
self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)
@slow
def test_small_byt5_integration_test(self):
"""
For comparision run:
>>> import t5 # pip install t5==0.9.1
>>> path_to_byt5_small_checkpoint = '<fill_in>'
>>> t5_model = t5.models.MtfModel(model_dir=path_to_tf_checkpoint, batch_size=1, tpu=None)
>>> vocab = t5.data.ByteVocabulary()
>>> score = t5_model.score(inputs=["Hello there"], targets=["Hi I am"], vocabulary=vocab)
"""
model = FlaxT5ForConditionalGeneration.from_pretrained("google/byt5-small")
tokenizer = ByT5Tokenizer.from_pretrained("google/byt5-small")
input_ids = tokenizer("Hello there", return_tensors="np").input_ids
labels = tokenizer("Hi I am", return_tensors="np").input_ids
decoder_input_ids = shift_tokens_right(labels, model.config.pad_token_id, model.config.decoder_start_token_id)
logits = model(input_ids, decoder_input_ids=decoder_input_ids).logits
loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])).mean()
mtf_score = -(labels.shape[-1] * loss.item())
EXPECTED_SCORE = -60.7397
self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)
@slow
def test_small_generation(self):
model = FlaxT5ForConditionalGeneration.from_pretrained("google-t5/t5-small")
model.config.max_length = 8
model.config.num_beams = 1
model.config.do_sample = False
tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-small")
input_ids = tokenizer("summarize: Hello there", return_tensors="np").input_ids
sequences = model.generate(input_ids).sequences
output_str = tokenizer.batch_decode(sequences, skip_special_tokens=True)[0]
self.assertTrue(output_str == "Hello there!")
@slow
def test_small_generation_bfloat16(self):
model = FlaxT5ForConditionalGeneration.from_pretrained("google-t5/t5-small", dtype=jnp.bfloat16)
model.config.max_length = 8
model.config.num_beams = 1
model.config.do_sample = False
tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-small")
input_ids = tokenizer("summarize: Hello there", return_tensors="np").input_ids
sequences = model.generate(input_ids).sequences
output_str = tokenizer.batch_decode(sequences, skip_special_tokens=True)[0]
self.assertTrue(output_str == "Hello there!")
@slow
def test_summarization(self):
model = FlaxT5ForConditionalGeneration.from_pretrained("google-t5/t5-base")
tok = T5Tokenizer.from_pretrained("google-t5/t5-base")
FRANCE_ARTICLE = ( # @noqa
"Marseille, France (CNN)The French prosecutor leading an investigation into the crash of Germanwings"
" Flight 9525 insisted Wednesday that he was not aware of any video footage from on board the plane."
' Marseille prosecutor Brice Robin told CNN that "so far no videos were used in the crash investigation."'
' He added, "A person who has such a video needs to immediately give it to the investigators." Robin\'s'
" comments follow claims by two magazines, German daily Bild and French Paris Match, of a cell phone video"
" showing the harrowing final seconds from on board Germanwings Flight 9525 as it crashed into the French"
" Alps. All 150 on board were killed. Paris Match and Bild reported that the video was recovered from a"
" phone at the wreckage site. The two publications described the supposed video, but did not post it on"
" their websites. The publications said that they watched the video, which was found by a source close to"
" the investigation. \"One can hear cries of 'My God' in several languages,\" Paris Match reported."
' "Metallic banging can also be heard more than three times, perhaps of the pilot trying to open the'
" cockpit door with a heavy object. Towards the end, after a heavy shake, stronger than the others, the"
' screaming intensifies. Then nothing." "It is a very disturbing scene," said Julian Reichelt,'
" editor-in-chief of Bild online. An official with France's accident investigation agency, the BEA, said"
" the agency is not aware of any such video. Lt. Col. Jean-Marc Menichini, a French Gendarmerie spokesman"
" in charge of communications on rescue efforts around the Germanwings crash site, told CNN that the"
' reports were "completely wrong" and "unwarranted." Cell phones have been collected at the site, he said,'
' but that they "hadn\'t been exploited yet." Menichini said he believed the cell phones would need to be'
" sent to the Criminal Research Institute in Rosny sous-Bois, near Paris, in order to be analyzed by"
" specialized technicians working hand-in-hand with investigators. But none of the cell phones found so"
" far have been sent to the institute, Menichini said. Asked whether staff involved in the search could"
' have leaked a memory card to the media, Menichini answered with a categorical "no." Reichelt told "Erin'
' Burnett: Outfront" that he had watched the video and stood by the report, saying Bild and Paris Match'
' are "very confident" that the clip is real. He noted that investigators only revealed they\'d recovered'
' cell phones from the crash site after Bild and Paris Match published their reports. "That is something'
" we did not know before. ... Overall we can say many things of the investigation weren't revealed by the"
' investigation at the beginning," he said. What was mental state of Germanwings co-pilot? German airline'
" Lufthansa confirmed Tuesday that co-pilot Andreas Lubitz had battled depression years before he took the"
" controls of Germanwings Flight 9525, which he's accused of deliberately crashing last week in the"
' French Alps. Lubitz told his Lufthansa flight training school in 2009 that he had a "previous episode of'
' severe depression," the airline said Tuesday. Email correspondence between Lubitz and the school'
" discovered in an internal investigation, Lufthansa said, included medical documents he submitted in"
" connection with resuming his flight training. The announcement indicates that Lufthansa, the parent"
" company of Germanwings, knew of Lubitz's battle with depression, allowed him to continue training and"
" ultimately put him in the cockpit. Lufthansa, whose CEO Carsten Spohr previously said Lubitz was 100%"
' fit to fly, described its statement Tuesday as a "swift and seamless clarification" and said it was'
" sharing the information and documents -- including training and medical records -- with public"
" prosecutors. Spohr traveled to the crash site Wednesday, where recovery teams have been working for the"
" past week to recover human remains and plane debris scattered across a steep mountainside. He saw the"
" crisis center set up in Seyne-les-Alpes, laid a wreath in the village of Le Vernet, closer to the crash"
" site, where grieving families have left flowers at a simple stone memorial. Menichini told CNN late"
" Tuesday that no visible human remains were left at the site but recovery teams would keep searching."
" French President Francois Hollande, speaking Tuesday, said that it should be possible to identify all"
" the victims using DNA analysis by the end of the week, sooner than authorities had previously suggested."
" In the meantime, the recovery of the victims' personal belongings will start Wednesday, Menichini said."
" Among those personal belongings could be more cell phones belonging to the 144 passengers and six crew"
" on board. Check out the latest from our correspondents . The details about Lubitz's correspondence with"
" the flight school during his training were among several developments as investigators continued to"
" delve into what caused the crash and Lubitz's possible motive for downing the jet. A Lufthansa"
" spokesperson told CNN on Tuesday that Lubitz had a valid medical certificate, had passed all his"
' examinations and "held all the licenses required." Earlier, a spokesman for the prosecutor\'s office in'
" Dusseldorf, Christoph Kumpa, said medical records reveal Lubitz suffered from suicidal tendencies at"
" some point before his aviation career and underwent psychotherapy before he got his pilot's license."
" Kumpa emphasized there's no evidence suggesting Lubitz was suicidal or acting aggressively before the"
" crash. Investigators are looking into whether Lubitz feared his medical condition would cause him to"
" lose his pilot's license, a European government official briefed on the investigation told CNN on"
' Tuesday. While flying was "a big part of his life," the source said, it\'s only one theory being'
" considered. Another source, a law enforcement official briefed on the investigation, also told CNN that"
" authorities believe the primary motive for Lubitz to bring down the plane was that he feared he would"
" not be allowed to fly because of his medical problems. Lubitz's girlfriend told investigators he had"
" seen an eye doctor and a neuropsychologist, both of whom deemed him unfit to work recently and concluded"
" he had psychological issues, the European government official said. But no matter what details emerge"
" about his previous mental health struggles, there's more to the story, said Brian Russell, a forensic"
' psychologist. "Psychology can explain why somebody would turn rage inward on themselves about the fact'
" that maybe they weren't going to keep doing their job and they're upset about that and so they're"
' suicidal," he said. "But there is no mental illness that explains why somebody then feels entitled to'
" also take that rage and turn it outward on 149 other people who had nothing to do with the person's"
' problems." Germanwings crash compensation: What we know . Who was the captain of Germanwings Flight'
" 9525? CNN's Margot Haddad reported from Marseille and Pamela Brown from Dusseldorf, while Laura"
" Smith-Spark wrote from London. CNN's Frederik Pleitgen, Pamela Boykoff, Antonia Mortensen, Sandrine"
" Amiel and Anna-Maja Rappard contributed to this report."
)
SHORTER_ARTICLE = (
"(CNN)The Palestinian Authority officially became the 123rd member of the International Criminal Court on"
" Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The"
" formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based."
" The Palestinians signed the ICC's founding Rome Statute in January, when they also accepted its"
' jurisdiction over alleged crimes committed "in the occupied Palestinian territory, including East'
' Jerusalem, since June 13, 2014." Later that month, the ICC opened a preliminary examination into the'
" situation in Palestinian territories, paving the way for possible war crimes investigations against"
" Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and"
" the United States, neither of which is an ICC member, opposed the Palestinians' efforts to join the"
" body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday's ceremony, said it was a"
' move toward greater justice. "As Palestine formally becomes a State Party to the Rome Statute today, the'
' world is also a step closer to ending a long era of impunity and injustice," he said, according to an'
' ICC news release. "Indeed, today brings us closer to our shared goals of justice and peace." Judge'
" Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the"
' Palestinians. "As the Rome Statute today enters into force for the State of Palestine, Palestine'
" acquires all the rights as well as responsibilities that come with being a State Party to the Statute."
' These are substantive commitments, which cannot be taken lightly," she said. Rights group Human Rights'
' Watch welcomed the development. "Governments seeking to penalize Palestine for joining the ICC should'
" immediately end their pressure, and countries that support universal acceptance of the court's treaty"
' should speak out to welcome its membership," said Balkees Jarrah, international justice counsel for the'
" group. \"What's objectionable is the attempts to undermine international justice, not Palestine's"
' decision to join a treaty to which over 100 countries around the world are members." In January, when'
" the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an"
' outrage, saying the court was overstepping its boundaries. The United States also said it "strongly"'
" disagreed with the court's decision. \"As we have said repeatedly, we do not believe that Palestine is a"
' state and therefore we do not believe that it is eligible to join the ICC," the State Department said in'
' a statement. It urged the warring sides to resolve their differences through direct negotiations. "We'
' will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace,"'
" it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the"
' territories as "Palestine." While a preliminary examination is not a formal investigation, it allows the'
" court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou"
' Bensouda said her office would "conduct its analysis in full independence and impartiality." The war'
" between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry"
" will include alleged war crimes committed since June. The International Criminal Court was set up in"
" 2002 to prosecute genocide, crimes against humanity and war crimes. CNN's Vasco Cotovio, Kareem Khadder"
" and Faith Karimi contributed to this report."
)
IRAN_ARTICLE = (
"(CNN)The United States and its negotiating partners reached a very strong framework agreement with Iran"
" in Lausanne, Switzerland, on Thursday that limits Iran's nuclear program in such a way as to effectively"
" block it from building a nuclear weapon. Expect pushback anyway, if the recent past is any harbinger."
" Just last month, in an attempt to head off such an agreement, House Speaker John Boehner invited Israeli"
" Prime Minister Benjamin Netanyahu to preemptively blast it before Congress, and 47 senators sent a"
" letter to the Iranian leadership warning them away from a deal. The debate that has already begun since"
" the announcement of the new framework will likely result in more heat than light. It will not be helped"
" by the gathering swirl of dubious assumptions and doubtful assertions. Let us address some of these: ."
" The most misleading assertion, despite universal rejection by experts, is that the negotiations'"
" objective at the outset was the total elimination of any nuclear program in Iran. That is the position"
" of Netanyahu and his acolytes in the U.S. Congress. But that is not and never was the objective. If it"
" had been, there would have been no Iranian team at the negotiating table. Rather, the objective has"
" always been to structure an agreement or series of agreements so that Iran could not covertly develop a"
" nuclear arsenal before the United States and its allies could respond. The new framework has exceeded"
" expectations in achieving that goal. It would reduce Iran's low-enriched uranium stockpile, cut by"
" two-thirds its number of installed centrifuges and implement a rigorous inspection regime. Another"
" dubious assumption of opponents is that the Iranian nuclear program is a covert weapons program. Despite"
" sharp accusations by some in the United States and its allies, Iran denies having such a program, and"
" U.S. intelligence contends that Iran has not yet made the decision to build a nuclear weapon. Iran's"
" continued cooperation with International Atomic Energy Agency inspections is further evidence on this"
" point, and we'll know even more about Iran's program in the coming months and years because of the deal."
" In fact, the inspections provisions that are part of this agreement are designed to protect against any"
" covert action by the Iranians. What's more, the rhetoric of some members of Congress has implied that"
" the negotiations have been between only the United States and Iran (i.e., the 47 senators' letter"
" warning that a deal might be killed by Congress or a future president). This of course is not the case."
" The talks were between Iran and the five permanent members of the U.N. Security Council (United States,"
" United Kingdom, France, China and Russia) plus Germany, dubbed the P5+1. While the United States has"
" played a leading role in the effort, it negotiated the terms alongside its partners. If the agreement"
" reached by the P5+1 is rejected by Congress, it could result in an unraveling of the sanctions on Iran"
" and threaten NATO cohesion in other areas. Another questionable assertion is that this agreement"
" contains a sunset clause, after which Iran will be free to do as it pleases. Again, this is not the"
" case. Some of the restrictions on Iran's nuclear activities, such as uranium enrichment, will be eased"
" or eliminated over time, as long as 15 years. But most importantly, the framework agreement includes"
" Iran's ratification of the Additional Protocol, which allows IAEA inspectors expanded access to nuclear"
" sites both declared and nondeclared. This provision will be permanent. It does not sunset. Thus, going"
" forward, if Iran decides to enrich uranium to weapons-grade levels, monitors will be able to detect such"
" a move in a matter of days and alert the U.N. Security Council. Many in Congress have said that the"
' agreement should be a formal treaty requiring the Senate to "advise and consent." But the issue is not'
" suited for a treaty. Treaties impose equivalent obligations on all signatories. For example, the New"
" START treaty limits Russia and the United States to 1,550 deployed strategic warheads. But any agreement"
" with Iran will not be so balanced. The restrictions and obligations in the final framework agreement"
" will be imposed almost exclusively on Iran. The P5+1 are obligated only to ease and eventually remove"
" most but not all economic sanctions, which were imposed as leverage to gain this final deal. Finally"
" some insist that any agreement must address Iranian missile programs, human rights violations or support"
" for Hamas or Hezbollah. As important as these issues are, and they must indeed be addressed, they are"
" unrelated to the most important aim of a nuclear deal: preventing a nuclear Iran. To include them in"
" the negotiations would be a poison pill. This agreement should be judged on its merits and on how it"
" affects the security of our negotiating partners and allies, including Israel. Those judgments should be"
" fact-based, not based on questionable assertions or dubious assumptions."
)
ARTICLE_SUBWAY = (
"New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York. A"
" year later, she got married again in Westchester County, but to a different man and without divorcing"
" her first husband. Only 18 days after that marriage, she got hitched yet again. Then, Barrientos"
' declared "I do" five more times, sometimes only within two weeks of each other. In 2010, she married'
" once more, this time in the Bronx. In an application for a marriage license, she stated it was her"
' "first and only" marriage. Barrientos, now 39, is facing two criminal counts of "offering a false'
' instrument for filing in the first degree," referring to her false statements on the 2010 marriage'
" license application, according to court documents. Prosecutors said the marriages were part of an"
" immigration scam. On Friday, she pleaded not guilty at State Supreme Court in the Bronx, according to"
" her attorney, Christopher Wright, who declined to comment further. After leaving court, Barrientos was"
" arrested and charged with theft of service and criminal trespass for allegedly sneaking into the New"
" York subway through an emergency exit, said Detective Annette Markowski, a police spokeswoman. In total,"
" Barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002. All"
" occurred either in Westchester County, Long Island, New Jersey or the Bronx. She is believed to still be"
" married to four men, and at one time, she was married to eight men at once, prosecutors say. Prosecutors"
" said the immigration scam involved some of her husbands, who filed for permanent residence status"
" shortly after the marriages. Any divorces happened only after such filings were approved. It was"
" unclear whether any of the men will be prosecuted. The case was referred to the Bronx District"
" Attorney's Office by Immigration and Customs Enforcement and the Department of Homeland Security's"
' Investigation Division. Seven of the men are from so-called "red-flagged" countries, including Egypt,'
" Turkey, Georgia, Pakistan and Mali. Her eighth husband, Rashid Rajput, was deported in 2006 to his"
" native Pakistan after an investigation by the Joint Terrorism Task Force. If convicted, Barrientos faces"
" up to four years in prison. Her next court appearance is scheduled for May 18."
)
expected_summaries = [
'prosecutor: "so far no videos were used in the crash investigation" two magazines claim to have found a'
" cell phone video of the final seconds . \"one can hear cries of 'My God' in several languages,\" one"
" magazine says . all 150 on board were killed in the crash .",
"the formal accession was marked by a ceremony at The Hague, in the Netherlands . the ICC opened a"
" preliminary examination into the situation in the occupied Palestinian territory . as members of the"
" court, Palestinians may be subject to counter-charges as well .",
"the u.s. and its negotiating partners reached a very strong framework agreement with Iran . aaron miller:"
" the debate that has already begun since the announcement of the new framework will likely result in more"
" heat than light . he says the new framework would reduce Iran's low-enriched uranium stockpile and cut"
" centrifuges . miller: if it had been, there would have been no Iranian team at the table .",
"prosecutors say the marriages were part of an immigration scam . if convicted, barrientos faces two"
' criminal counts of "offering a false instrument for filing in the first degree" she has been married 10'
" times, with nine of her marriages occurring between 1999 and 2002 .",
]
dct = tok(
["summarize: " + x for x in [FRANCE_ARTICLE, SHORTER_ARTICLE, IRAN_ARTICLE, ARTICLE_SUBWAY]],
padding="max_length",
truncation=True,
return_tensors="np",
)
self.assertEqual(512, dct["input_ids"].shape[1])
hypotheses_batch = model.generate(
**dct,
num_beams=4,
length_penalty=2.0,
max_length=142,
min_length=56,
do_sample=False,
early_stopping=True,
).sequences
decoded = tok.batch_decode(hypotheses_batch, skip_special_tokens=True, clean_up_tokenization_spaces=False)
self.assertListEqual(
expected_summaries,
decoded,
)
| transformers/tests/models/t5/test_modeling_flax_t5.py/0 | {
"file_path": "transformers/tests/models/t5/test_modeling_flax_t5.py",
"repo_id": "transformers",
"token_count": 23924
} | 171 |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the TensorFlow ViTMAE model. """
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFViTMAEForPreTraining, TFViTMAEModel
from transformers.modeling_tf_utils import keras
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class TFViTMAEModelTester:
def __init__(
self,
parent,
batch_size=13,
image_size=30,
patch_size=2,
num_channels=3,
is_training=True,
use_labels=True,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
type_sequence_label_size=10,
initializer_range=0.02,
num_labels=3,
mask_ratio=0.6,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.is_training = is_training
self.use_labels = use_labels
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.mask_ratio = mask_ratio
self.scope = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
num_patches = (image_size // patch_size) ** 2
self.seq_length = int(math.ceil((1 - mask_ratio) * (num_patches + 1)))
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
config = self.get_config()
return config, pixel_values, labels
def get_config(self):
return ViTMAEConfig(
image_size=self.image_size,
patch_size=self.patch_size,
num_channels=self.num_channels,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
decoder_hidden_size=self.hidden_size,
decoder_num_hidden_layers=self.num_hidden_layers,
decoder_num_attention_heads=self.num_attention_heads,
decoder_intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
is_decoder=False,
initializer_range=self.initializer_range,
mask_ratio=self.mask_ratio,
)
def create_and_check_model(self, config, pixel_values, labels):
model = TFViTMAEModel(config=config)
result = model(pixel_values, training=False)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_for_pretraining(self, config, pixel_values, labels):
model = TFViTMAEForPreTraining(config)
result = model(pixel_values, training=False)
# expected sequence length = num_patches
num_patches = (self.image_size // self.patch_size) ** 2
expected_num_channels = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape, (self.batch_size, num_patches, expected_num_channels))
# test greyscale images
config.num_channels = 1
model = TFViTMAEForPreTraining(config)
pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
result = model(pixel_values, training=False)
expected_num_channels = self.patch_size**2
self.parent.assertEqual(result.logits.shape, (self.batch_size, num_patches, expected_num_channels))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, pixel_values, labels) = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class TFViTMAEModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_common.py, as ViTMAE does not use input_ids, inputs_embeds,
attention_mask and seq_length.
"""
all_model_classes = (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
pipeline_model_mapping = {"feature-extraction": TFViTMAEModel} if is_tf_available() else {}
test_pruning = False
test_onnx = False
test_resize_embeddings = False
test_head_masking = False
def setUp(self):
self.model_tester = TFViTMAEModelTester(self)
self.config_tester = ConfigTester(self, config_class=ViTMAEConfig, has_text_modality=False, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
@unittest.skip(reason="ViTMAE does not use inputs_embeds")
def test_inputs_embeds(self):
pass
def test_model_common_attributes(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
self.assertIsInstance(model.get_input_embeddings(), (keras.layers.Layer))
x = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(x, keras.layers.Layer))
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.call)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_pretraining(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*config_and_inputs)
# overwrite from common since TFViTMAEForPretraining has random masking, we need to fix the noise
# to generate masks during test
def test_keyword_and_dict_args(self):
# make the mask reproducible
np.random.seed(2)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
num_patches = int((config.image_size // config.patch_size) ** 2)
noise = np.random.uniform(size=(self.model_tester.batch_size, num_patches))
for model_class in self.all_model_classes:
model = model_class(config)
inputs = self._prepare_for_class(inputs_dict, model_class)
outputs_dict = model(inputs, noise=noise)
inputs_keywords = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
outputs_keywords = model(**inputs_keywords, noise=noise)
output_dict = outputs_dict[0].numpy()
output_keywords = outputs_keywords[0].numpy()
self.assertLess(np.sum(np.abs(output_dict - output_keywords)), 1e-6)
# overwrite from common since TFViTMAEForPretraining has random masking, we need to fix the noise
# to generate masks during test
def test_numpy_arrays_inputs(self):
# make the mask reproducible
np.random.seed(2)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
num_patches = int((config.image_size // config.patch_size) ** 2)
noise = np.random.uniform(size=(self.model_tester.batch_size, num_patches))
def prepare_numpy_arrays(inputs_dict):
inputs_np_dict = {}
for k, v in inputs_dict.items():
if tf.is_tensor(v):
inputs_np_dict[k] = v.numpy()
else:
inputs_np_dict[k] = np.array(k)
return inputs_np_dict
for model_class in self.all_model_classes:
model = model_class(config)
inputs = self._prepare_for_class(inputs_dict, model_class)
inputs_np = prepare_numpy_arrays(inputs)
output_for_dict_input = model(inputs_np, noise=noise)
output_for_kw_input = model(**inputs_np, noise=noise)
self.assert_outputs_same(output_for_dict_input, output_for_kw_input)
# overwrite from common since TFViTMAEForPretraining has random masking, we need to fix the noise
# to generate masks during test
def check_pt_tf_models(self, tf_model, pt_model, tf_inputs_dict):
# make masks reproducible
np.random.seed(2)
num_patches = int((tf_model.config.image_size // tf_model.config.patch_size) ** 2)
noise = np.random.uniform(size=(self.model_tester.batch_size, num_patches))
tf_noise = tf.constant(noise)
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
tf_inputs_dict["noise"] = tf_noise
super().check_pt_tf_models(tf_model, pt_model, tf_inputs_dict)
# overwrite from common since TFViTMAEForPretraining has random masking, we need to fix the noise
# to generate masks during test
def test_keras_save_load(self):
# make mask reproducible
np.random.seed(2)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
tf_main_layer_classes = {
module_member
for model_class in self.all_model_classes
for module in (import_module(model_class.__module__),)
for module_member_name in dir(module)
if module_member_name.endswith("MainLayer")
# This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
and module_member_name[: -len("MainLayer")] == model_class.__name__[: -len("Model")]
for module_member in (getattr(module, module_member_name),)
if isinstance(module_member, type)
and keras.layers.Layer in module_member.__bases__
and getattr(module_member, "_keras_serializable", False)
}
num_patches = int((config.image_size // config.patch_size) ** 2)
noise = np.random.uniform(size=(self.model_tester.batch_size, num_patches))
noise = tf.convert_to_tensor(noise)
inputs_dict.update({"noise": noise})
for main_layer_class in tf_main_layer_classes:
main_layer = main_layer_class(config)
symbolic_inputs = {
name: keras.Input(tensor.shape[1:], dtype=tensor.dtype) for name, tensor in inputs_dict.items()
}
model = keras.Model(symbolic_inputs, outputs=main_layer(symbolic_inputs))
outputs = model(inputs_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
filepath = os.path.join(tmpdirname, "keras_model.h5")
model.save(filepath)
model = keras.models.load_model(filepath, custom_objects={main_layer_class.__name__: main_layer_class})
assert isinstance(model, keras.Model)
after_outputs = model(inputs_dict)
self.assert_outputs_same(after_outputs, outputs)
# overwrite from common since TFViTMAEForPretraining has random masking, we need to fix the noise
# to generate masks during test
@slow
def test_save_load(self):
# make mask reproducible
np.random.seed(2)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
num_patches = int((config.image_size // config.patch_size) ** 2)
noise = np.random.uniform(size=(self.model_tester.batch_size, num_patches))
for model_class in self.all_model_classes:
model = model_class(config)
model_input = self._prepare_for_class(inputs_dict, model_class)
outputs = model(model_input, noise=noise)
if model_class.__name__ == "TFViTMAEModel":
out_2 = outputs.last_hidden_state.numpy()
out_2[np.isnan(out_2)] = 0
else:
out_2 = outputs.logits.numpy()
out_2[np.isnan(out_2)] = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname, saved_model=False)
model = model_class.from_pretrained(tmpdirname)
after_outputs = model(model_input, noise=noise)
if model_class.__name__ == "TFViTMAEModel":
out_1 = after_outputs["last_hidden_state"].numpy()
out_1[np.isnan(out_1)] = 0
else:
out_1 = after_outputs["logits"].numpy()
out_1[np.isnan(out_1)] = 0
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
# overwrite from common since TFViTMAEForPretraining has random masking, we need to fix the noise
# to generate masks during test
def test_save_load_config(self):
# make mask reproducible
np.random.seed(2)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
num_patches = int((config.image_size // config.patch_size) ** 2)
noise = np.random.uniform(size=(self.model_tester.batch_size, num_patches))
for model_class in self.all_model_classes:
model = model_class(config)
model_inputs = self._prepare_for_class(inputs_dict, model_class)
outputs = model(model_inputs, noise=noise)
model_config = model.get_config()
# make sure that returned config is jsonifiable, which is required by keras
json.dumps(model_config)
new_model = model_class.from_config(model.get_config())
# make sure it also accepts a normal config
_ = model_class.from_config(model.config)
_ = new_model(model_inputs) # Build model
new_model.set_weights(model.get_weights())
after_outputs = new_model(model_inputs, noise=noise)
self.assert_outputs_same(after_outputs, outputs)
@unittest.skip(
reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results."""
)
def test_determinism(self):
pass
@unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load""")
def test_model_outputs_equivalence(self):
pass
@slow
def test_model_from_pretrained(self):
model = TFViTMAEModel.from_pretrained("google/vit-base-patch16-224")
self.assertIsNotNone(model)
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_tf
@require_vision
class TFViTMAEModelIntegrationTest(unittest.TestCase):
@cached_property
def default_image_processor(self):
return ViTImageProcessor.from_pretrained("facebook/vit-mae-base") if is_vision_available() else None
@slow
def test_inference_for_pretraining(self):
# make random mask reproducible across the PT and TF model
np.random.seed(2)
model = TFViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base")
image_processor = self.default_image_processor
image = prepare_img()
inputs = image_processor(images=image, return_tensors="tf")
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)
vit_mae_config = ViTMAEConfig()
num_patches = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2)
noise = np.random.uniform(size=(1, num_patches))
# forward pass
outputs = model(**inputs, noise=noise)
# verify the logits
expected_shape = tf.convert_to_tensor([1, 196, 768])
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = tf.convert_to_tensor(
[[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]
)
tf.debugging.assert_near(outputs.logits[0, :3, :3], expected_slice, atol=1e-4)
| transformers/tests/models/vit_mae/test_modeling_tf_vit_mae.py/0 | {
"file_path": "transformers/tests/models/vit_mae/test_modeling_tf_vit_mae.py",
"repo_id": "transformers",
"token_count": 7979
} | 172 |
# coding=utf-8
# Copyright 2021 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, Wav2Vec2Config, Wav2Vec2FeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
global_rng = random.Random()
# Copied from tests.models.whisper.test_feature_extraction_whisper.floats_list
def floats_list(shape, scale=1.0, rng=None, name=None):
"""Creates a random float32 tensor"""
if rng is None:
rng = global_rng
values = []
for batch_idx in range(shape[0]):
values.append([])
for _ in range(shape[1]):
values[-1].append(rng.random() * scale)
return values
class Wav2Vec2FeatureExtractionTester(unittest.TestCase):
def __init__(
self,
parent,
batch_size=7,
min_seq_length=400,
max_seq_length=2000,
feature_size=1,
padding_value=0.0,
sampling_rate=16000,
return_attention_mask=True,
do_normalize=True,
):
self.parent = parent
self.batch_size = batch_size
self.min_seq_length = min_seq_length
self.max_seq_length = max_seq_length
self.seq_length_diff = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
self.feature_size = feature_size
self.padding_value = padding_value
self.sampling_rate = sampling_rate
self.return_attention_mask = return_attention_mask
self.do_normalize = do_normalize
def prepare_feat_extract_dict(self):
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def prepare_inputs_for_common(self, equal_length=False, numpify=False):
def _flatten(list_of_lists):
return list(itertools.chain(*list_of_lists))
if equal_length:
speech_inputs = floats_list((self.batch_size, self.max_seq_length))
else:
# make sure that inputs increase in size
speech_inputs = [
_flatten(floats_list((x, self.feature_size)))
for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff)
]
if numpify:
speech_inputs = [np.asarray(x) for x in speech_inputs]
return speech_inputs
class Wav2Vec2FeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):
feature_extraction_class = Wav2Vec2FeatureExtractor
def setUp(self):
self.feat_extract_tester = Wav2Vec2FeatureExtractionTester(self)
def _check_zero_mean_unit_variance(self, input_vector):
self.assertTrue(np.all(np.mean(input_vector, axis=0) < 1e-3))
self.assertTrue(np.all(np.abs(np.var(input_vector, axis=0) - 1) < 1e-3))
def test_call(self):
# Tests that all call wrap to encode_plus and batch_encode_plus
feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
# create three inputs of length 800, 1000, and 1200
speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs]
# Test not batched input
encoded_sequences_1 = feat_extract(speech_inputs[0], return_tensors="np").input_values
encoded_sequences_2 = feat_extract(np_speech_inputs[0], return_tensors="np").input_values
self.assertTrue(np.allclose(encoded_sequences_1, encoded_sequences_2, atol=1e-3))
# Test batched
encoded_sequences_1 = feat_extract(speech_inputs, return_tensors="np").input_values
encoded_sequences_2 = feat_extract(np_speech_inputs, return_tensors="np").input_values
for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
# Test 2-D numpy arrays are batched.
speech_inputs = [floats_list((1, x))[0] for x in (800, 800, 800)]
np_speech_inputs = np.asarray(speech_inputs)
encoded_sequences_1 = feat_extract(speech_inputs, return_tensors="np").input_values
encoded_sequences_2 = feat_extract(np_speech_inputs, return_tensors="np").input_values
for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
def test_zero_mean_unit_variance_normalization_np(self):
feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
paddings = ["longest", "max_length", "do_not_pad"]
max_lengths = [None, 1600, None]
for max_length, padding in zip(max_lengths, paddings):
processed = feat_extract(speech_inputs, padding=padding, max_length=max_length, return_tensors="np")
input_values = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:800])
self.assertTrue(input_values[0][800:].sum() < 1e-6)
self._check_zero_mean_unit_variance(input_values[1][:1000])
self.assertTrue(input_values[0][1000:].sum() < 1e-6)
self._check_zero_mean_unit_variance(input_values[2][:1200])
def test_zero_mean_unit_variance_normalization(self):
feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
lengths = range(800, 1400, 200)
speech_inputs = [floats_list((1, x))[0] for x in lengths]
paddings = ["longest", "max_length", "do_not_pad"]
max_lengths = [None, 1600, None]
for max_length, padding in zip(max_lengths, paddings):
processed = feat_extract(speech_inputs, max_length=max_length, padding=padding)
input_values = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:800])
self._check_zero_mean_unit_variance(input_values[1][:1000])
self._check_zero_mean_unit_variance(input_values[2][:1200])
def test_zero_mean_unit_variance_normalization_trunc_np_max_length(self):
feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
processed = feat_extract(
speech_inputs, truncation=True, max_length=1000, padding="max_length", return_tensors="np"
)
input_values = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800])
self._check_zero_mean_unit_variance(input_values[1])
self._check_zero_mean_unit_variance(input_values[2])
def test_zero_mean_unit_variance_normalization_trunc_np_longest(self):
feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
processed = feat_extract(
speech_inputs, truncation=True, max_length=1000, padding="longest", return_tensors="np"
)
input_values = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800])
self._check_zero_mean_unit_variance(input_values[1, :1000])
self._check_zero_mean_unit_variance(input_values[2])
# make sure that if max_length < longest -> then pad to max_length
self.assertTrue(input_values.shape == (3, 1000))
speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
processed = feat_extract(
speech_inputs, truncation=True, max_length=2000, padding="longest", return_tensors="np"
)
input_values = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800])
self._check_zero_mean_unit_variance(input_values[1, :1000])
self._check_zero_mean_unit_variance(input_values[2])
# make sure that if max_length > longest -> then pad to longest
self.assertTrue(input_values.shape == (3, 1200))
@require_torch
def test_double_precision_pad(self):
import torch
feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
np_speech_inputs = np.random.rand(100).astype(np.float64)
py_speech_inputs = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
np_processed = feature_extractor.pad([{"input_values": inputs}], return_tensors="np")
self.assertTrue(np_processed.input_values.dtype == np.float32)
pt_processed = feature_extractor.pad([{"input_values": inputs}], return_tensors="pt")
self.assertTrue(pt_processed.input_values.dtype == torch.float32)
@slow
@require_torch
def test_pretrained_checkpoints_are_set_correctly(self):
# this test makes sure that models that are using
# group norm don't have their feature extractor return the
# attention_mask
for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
config = Wav2Vec2Config.from_pretrained(model_id)
feat_extract = Wav2Vec2FeatureExtractor.from_pretrained(model_id)
# only "layer" feature extraction norm should make use of
# attention_mask
self.assertEqual(feat_extract.return_attention_mask, config.feat_extract_norm == "layer")
| transformers/tests/models/wav2vec2/test_feature_extraction_wav2vec2.py/0 | {
"file_path": "transformers/tests/models/wav2vec2/test_feature_extraction_wav2vec2.py",
"repo_id": "transformers",
"token_count": 4411
} | 173 |
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch WavLM model. """
import math
import unittest
import pytest
from datasets import load_dataset
from transformers import WavLMConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import (
ModelTesterMixin,
_config_zero_init,
floats_tensor,
ids_tensor,
random_attention_mask,
)
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
Wav2Vec2FeatureExtractor,
WavLMForAudioFrameClassification,
WavLMForCTC,
WavLMForSequenceClassification,
WavLMForXVector,
WavLMModel,
)
class WavLMModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=1024, # speech is longer
is_training=False,
hidden_size=16,
feat_extract_norm="group",
feat_extract_dropout=0.0,
feat_extract_activation="gelu",
conv_dim=(32, 32, 32),
conv_stride=(4, 4, 4),
conv_kernel=(8, 8, 8),
conv_bias=False,
num_conv_pos_embeddings=16,
num_conv_pos_embedding_groups=2,
num_hidden_layers=2,
num_attention_heads=2,
hidden_dropout_prob=0.1, # this is most likely not correctly set yet
intermediate_size=20,
layer_norm_eps=1e-5,
hidden_act="gelu",
initializer_range=0.02,
vocab_size=32,
do_stable_layer_norm=False,
tdnn_dim=(32, 32),
tdnn_kernel=(3, 3),
tdnn_dilation=(1, 1),
xvector_output_dim=32,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.hidden_size = hidden_size
self.feat_extract_norm = feat_extract_norm
self.feat_extract_dropout = feat_extract_dropout
self.feat_extract_activation = feat_extract_activation
self.conv_dim = conv_dim
self.conv_stride = conv_stride
self.conv_kernel = conv_kernel
self.conv_bias = conv_bias
self.num_conv_pos_embeddings = num_conv_pos_embeddings
self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_dropout_prob = hidden_dropout_prob
self.intermediate_size = intermediate_size
self.layer_norm_eps = layer_norm_eps
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.vocab_size = vocab_size
self.do_stable_layer_norm = do_stable_layer_norm
self.tdnn_dim = tdnn_dim
self.tdnn_kernel = tdnn_kernel
self.tdnn_dilation = tdnn_dilation
self.xvector_output_dim = xvector_output_dim
self.scope = scope
output_seq_length = self.seq_length
for kernel, stride in zip(self.conv_kernel, self.conv_stride):
output_seq_length = (output_seq_length - (kernel - 1)) / stride
self.output_seq_length = int(math.ceil(output_seq_length))
self.encoder_seq_length = self.output_seq_length
def prepare_config_and_inputs(self):
input_values = floats_tensor([self.batch_size, self.seq_length], scale=1.0)
attention_mask = random_attention_mask([self.batch_size, self.seq_length])
config = self.get_config()
return config, input_values, attention_mask
def get_config(self):
return WavLMConfig(
hidden_size=self.hidden_size,
feat_extract_norm=self.feat_extract_norm,
feat_extract_dropout=self.feat_extract_dropout,
feat_extract_activation=self.feat_extract_activation,
conv_dim=self.conv_dim,
conv_stride=self.conv_stride,
conv_kernel=self.conv_kernel,
conv_bias=self.conv_bias,
num_conv_pos_embeddings=self.num_conv_pos_embeddings,
num_conv_pos_embedding_groups=self.num_conv_pos_embedding_groups,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
hidden_dropout_prob=self.hidden_dropout_prob,
intermediate_size=self.intermediate_size,
layer_norm_eps=self.layer_norm_eps,
hidden_act=self.hidden_act,
initializer_range=self.initializer_range,
vocab_size=self.vocab_size,
tdnn_dim=self.tdnn_dim,
tdnn_kernel=self.tdnn_kernel,
tdnn_dilation=self.tdnn_dilation,
xvector_output_dim=self.xvector_output_dim,
)
def create_and_check_model(self, config, input_values, attention_mask):
model = WavLMModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_values, attention_mask=attention_mask)
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.output_seq_length, self.hidden_size)
)
def create_and_check_batch_inference(self, config, input_values, *args):
# test does not pass for models making use of `group_norm`
# check: https://github.com/pytorch/fairseq/issues/3227
model = WavLMModel(config=config)
model.to(torch_device)
model.eval()
input_values = input_values[:3]
attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.bool)
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
# pad input
for i in range(len(input_lengths)):
input_values[i, input_lengths[i] :] = 0.0
attention_mask[i, input_lengths[i] :] = 0.0
batch_outputs = model(input_values, attention_mask=attention_mask).last_hidden_state
for i in range(input_values.shape[0]):
input_slice = input_values[i : i + 1, : input_lengths[i]]
output = model(input_slice).last_hidden_state
batch_output = batch_outputs[i : i + 1, : output.shape[1]]
self.parent.assertTrue(torch.allclose(output, batch_output, atol=1e-3))
def check_ctc_loss(self, config, input_values, *args):
model = WavLMForCTC(config=config)
model.to(torch_device)
# make sure that dropout is disabled
model.eval()
input_values = input_values[:3]
attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long)
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
labels = ids_tensor((input_values.shape[0], min(max_length_labels) - 1), model.config.vocab_size)
# pad input
for i in range(len(input_lengths)):
input_values[i, input_lengths[i] :] = 0.0
attention_mask[i, input_lengths[i] :] = 0
model.config.ctc_loss_reduction = "sum"
sum_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()
model.config.ctc_loss_reduction = "mean"
mean_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()
self.parent.assertTrue(isinstance(sum_loss, float))
self.parent.assertTrue(isinstance(mean_loss, float))
def check_seq_classifier_loss(self, config, input_values, *args):
model = WavLMForSequenceClassification(config=config)
model.to(torch_device)
# make sure that dropout is disabled
model.eval()
input_values = input_values[:3]
attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long)
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label))
# pad input
for i in range(len(input_lengths)):
input_values[i, input_lengths[i] :] = 0.0
attention_mask[i, input_lengths[i] :] = 0
masked_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()
unmasked_loss = model(input_values, labels=labels).loss.item()
self.parent.assertTrue(isinstance(masked_loss, float))
self.parent.assertTrue(isinstance(unmasked_loss, float))
self.parent.assertTrue(masked_loss != unmasked_loss)
def check_ctc_training(self, config, input_values, *args):
config.ctc_zero_infinity = True
model = WavLMForCTC(config=config)
model.to(torch_device)
model.train()
# freeze feature encoder
model.freeze_feature_encoder()
input_values = input_values[:3]
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size)
# pad input
for i in range(len(input_lengths)):
input_values[i, input_lengths[i] :] = 0.0
if max_length_labels[i] < labels.shape[-1]:
# it's important that we make sure that target lengths are at least
# one shorter than logit lengths to prevent -inf
labels[i, max_length_labels[i] - 1 :] = -100
loss = model(input_values, labels=labels).loss
self.parent.assertFalse(torch.isinf(loss).item())
loss.backward()
def check_seq_classifier_training(self, config, input_values, *args):
config.ctc_zero_infinity = True
model = WavLMForSequenceClassification(config=config)
model.to(torch_device)
model.train()
# freeze everything but the classification head
model.freeze_base_model()
input_values = input_values[:3]
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label))
# pad input
for i in range(len(input_lengths)):
input_values[i, input_lengths[i] :] = 0.0
loss = model(input_values, labels=labels).loss
self.parent.assertFalse(torch.isinf(loss).item())
loss.backward()
def check_labels_out_of_vocab(self, config, input_values, *args):
model = WavLMForCTC(config)
model.to(torch_device)
model.train()
input_values = input_values[:3]
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size + 100)
with pytest.raises(ValueError):
model(input_values, labels=labels)
def prepare_config_and_inputs_for_common(self):
config, input_values, attention_mask = self.prepare_config_and_inputs()
inputs_dict = {"input_values": input_values, "attention_mask": attention_mask}
return config, inputs_dict
@require_torch
class WavLMModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(WavLMForCTC, WavLMModel, WavLMForAudioFrameClassification, WavLMForSequenceClassification, WavLMForXVector)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{
"audio-classification": WavLMForSequenceClassification,
"automatic-speech-recognition": WavLMForCTC,
"feature-extraction": WavLMModel,
}
if is_torch_available()
else {}
)
test_pruning = False
test_headmasking = False
def setUp(self):
self.model_tester = WavLMModelTester(self)
self.config_tester = ConfigTester(self, config_class=WavLMConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_ctc_loss_inference(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_ctc_loss(*config_and_inputs)
def test_seq_classifier_loss_inference(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_seq_classifier_loss(*config_and_inputs)
def test_ctc_train(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_ctc_training(*config_and_inputs)
def test_seq_classifier_train(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_seq_classifier_training(*config_and_inputs)
def test_labels_out_of_vocab(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_labels_out_of_vocab(*config_and_inputs)
# WavLM has no inputs_embeds
def test_inputs_embeds(self):
pass
# `input_ids` is renamed to `input_values`
def test_forward_signature(self):
pass
# WavLM cannot resize token embeddings
# since it has no tokens embeddings
def test_resize_tokens_embeddings(self):
pass
# WavLM has no inputs_embeds
# and thus the `get_input_embeddings` fn
# is not implemented
def test_model_common_attributes(self):
pass
# WavLM uses PyTorch's multi-head-attention class
# and thus can't retain gradients on attentions
def test_retain_grad_hidden_states_attentions(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = True
config.output_attentions = True
# no need to test all models as different heads yield the same functionality
model_class = self.all_model_classes[0]
model = model_class(config)
model.to(torch_device)
# set layer drop to 0
model.config.layerdrop = 0.0
input_values = inputs_dict["input_values"]
input_lengths = torch.tensor(
[input_values.shape[1] for _ in range(input_values.shape[0])], dtype=torch.long, device=torch_device
)
output_lengths = model._get_feat_extract_output_lengths(input_lengths)
labels = ids_tensor((input_values.shape[0], output_lengths[0] - 2), self.model_tester.vocab_size)
inputs_dict["attention_mask"] = torch.ones_like(inputs_dict["attention_mask"])
inputs_dict["labels"] = labels
outputs = model(**inputs_dict)
output = outputs[0]
# Encoder-/Decoder-only models
hidden_states = outputs.hidden_states[0]
hidden_states.retain_grad()
output.flatten()[0].backward(retain_graph=True)
self.assertIsNotNone(hidden_states.grad)
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
uniform_init_parms = [
"conv.weight",
"conv.parametrizations.weight",
"masked_spec_embed",
"codevectors",
"quantizer.weight_proj.weight",
"project_hid.weight",
"project_hid.bias",
"project_q.weight",
"project_q.bias",
"feature_projection.projection.weight",
"feature_projection.projection.bias",
"label_embeddings_concat",
"rel_attn_embed",
"objective.weight",
]
if param.requires_grad:
if any(x in name for x in uniform_init_parms):
self.assertTrue(
-1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0,
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
else:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
# overwrite from test_modeling_common
def _mock_init_weights(self, module):
if hasattr(module, "weight") and module.weight is not None:
module.weight.data.fill_(3)
if hasattr(module, "weight_g") and module.weight_g is not None:
module.weight_g.data.fill_(3)
if hasattr(module, "weight_v") and module.weight_v is not None:
module.weight_v.data.fill_(3)
if hasattr(module, "bias") and module.bias is not None:
module.bias.data.fill_(3)
if hasattr(module, "codevectors") and module.codevectors is not None:
module.codevectors.data.fill_(3)
if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None:
module.masked_spec_embed.data.fill_(3)
@unittest.skip(reason="Feed forward chunking is not implemented for WavLM")
def test_feed_forward_chunking(self):
pass
@slow
def test_model_from_pretrained(self):
model = WavLMModel.from_pretrained("microsoft/wavlm-base-plus")
self.assertIsNotNone(model)
@require_torch
@require_torchaudio
@slow
class WavLMModelIntegrationTest(unittest.TestCase):
def _load_datasamples(self, num_samples):
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
# automatic decoding with librispeech
speech_samples = ds.sort("id").filter(
lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)]
)[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
def _load_superb(self, task, num_samples):
ds = load_dataset("anton-l/superb_dummy", task, split="test")
return ds[:num_samples]
def test_inference_base(self):
model = WavLMModel.from_pretrained("microsoft/wavlm-base-plus").to(torch_device)
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
"microsoft/wavlm-base-plus", return_attention_mask=True
)
input_speech = self._load_datasamples(2)
inputs = feature_extractor(input_speech, return_tensors="pt", padding=True)
input_values = inputs.input_values.to(torch_device)
attention_mask = inputs.attention_mask.to(torch_device)
with torch.no_grad():
hidden_states_slice = (
model(input_values, attention_mask=attention_mask).last_hidden_state[:, -2:, -2:].cpu()
)
EXPECTED_HIDDEN_STATES_SLICE = torch.tensor(
[[[0.0577, 0.1161], [0.0579, 0.1165]], [[0.0199, 0.1237], [0.0059, 0.0605]]]
)
self.assertTrue(torch.allclose(hidden_states_slice, EXPECTED_HIDDEN_STATES_SLICE, atol=5e-2))
def test_inference_large(self):
model = WavLMModel.from_pretrained("microsoft/wavlm-large").to(torch_device)
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
"microsoft/wavlm-large", return_attention_mask=True
)
input_speech = self._load_datasamples(2)
inputs = feature_extractor(input_speech, return_tensors="pt", padding=True)
input_values = inputs.input_values.to(torch_device)
attention_mask = inputs.attention_mask.to(torch_device)
with torch.no_grad():
hidden_states_slice = (
model(input_values, attention_mask=attention_mask).last_hidden_state[:, -2:, -2:].cpu()
)
EXPECTED_HIDDEN_STATES_SLICE = torch.tensor(
[[[0.2122, 0.0500], [0.2118, 0.0563]], [[0.1353, 0.1818], [0.2453, 0.0595]]]
)
self.assertTrue(torch.allclose(hidden_states_slice, EXPECTED_HIDDEN_STATES_SLICE, rtol=5e-2))
def test_inference_diarization(self):
model = WavLMForAudioFrameClassification.from_pretrained("microsoft/wavlm-base-plus-sd").to(torch_device)
processor = Wav2Vec2FeatureExtractor.from_pretrained("microsoft/wavlm-base-plus-sd")
input_data = self._load_superb("sd", 4)
inputs = processor(input_data["speech"], return_tensors="pt", padding=True, sampling_rate=16_000)
input_values = inputs.input_values.to(torch_device)
attention_mask = inputs.attention_mask.to(torch_device)
with torch.no_grad():
outputs = model(input_values, attention_mask=attention_mask)
# labels is a one-hot array of shape (num_frames, num_speakers)
labels = (outputs.logits > 0).long()
# s3prl logits for the same batch
expected_logits = torch.tensor(
[
[[-5.9566, -8.6554], [-5.7137, -8.9386], [-5.7906, -7.0973], [-5.7829, -5.9999]],
[[-5.2086, -7.7878], [-4.8890, -7.9312], [-4.2004, -3.9101], [-5.4480, -4.6932]],
[[-4.6105, -6.7178], [-5.1930, -6.1635], [-2.6228, -4.1123], [-2.7646, -3.1576]],
[[-4.4477, -7.9206], [-3.9339, -7.3707], [-4.9528, -4.8242], [-3.6921, -2.9687]],
],
device=torch_device,
)
self.assertEqual(labels[0, :, 0].sum(), 258)
self.assertEqual(labels[0, :, 1].sum(), 647)
self.assertTrue(torch.allclose(outputs.logits[:, :4], expected_logits, atol=1e-2))
def test_inference_speaker_verification(self):
model = WavLMForXVector.from_pretrained("microsoft/wavlm-base-plus-sv").to(torch_device)
processor = Wav2Vec2FeatureExtractor.from_pretrained("microsoft/wavlm-base-plus-sv")
input_data = self._load_superb("si", 4)
inputs = processor(input_data["speech"], return_tensors="pt", padding=True)
labels = torch.tensor([5, 1, 1, 3], device=torch_device).T
with torch.no_grad():
input_values = inputs.input_values.to(torch_device)
attention_mask = inputs.attention_mask.to(torch_device)
outputs = model(input_values, attention_mask=attention_mask, labels=labels)
embeddings = torch.nn.functional.normalize(outputs.embeddings, dim=-1)
cosine_sim = torch.nn.CosineSimilarity(dim=-1)
# id10002 vs id10002
self.assertAlmostEqual(cosine_sim(embeddings[1], embeddings[2]).item(), 0.9787, 3)
# id10006 vs id10002
self.assertAlmostEqual(cosine_sim(embeddings[0], embeddings[1]).item(), 0.5064, 3)
# id10002 vs id10004
self.assertAlmostEqual(cosine_sim(embeddings[2], embeddings[3]).item(), 0.4780, 3)
self.assertAlmostEqual(outputs.loss.item(), 18.4154, 2)
| transformers/tests/models/wavlm/test_modeling_wavlm.py/0 | {
"file_path": "transformers/tests/models/wavlm/test_modeling_wavlm.py",
"repo_id": "transformers",
"token_count": 10880
} | 174 |
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