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DeeBERT
|
DeeBERT-master/transformers/tokenization_auto.py
|
# 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.
""" Auto Model class. """
from __future__ import absolute_import, division, print_function, unicode_literals
import logging
from .tokenization_bert import BertTokenizer
from .tokenization_openai import OpenAIGPTTokenizer
from .tokenization_gpt2 import GPT2Tokenizer
from .tokenization_ctrl import CTRLTokenizer
from .tokenization_transfo_xl import TransfoXLTokenizer
from .tokenization_xlnet import XLNetTokenizer
from .tokenization_xlm import XLMTokenizer
from .tokenization_roberta import RobertaTokenizer
from .tokenization_distilbert import DistilBertTokenizer
from .tokenization_camembert import CamembertTokenizer
logger = logging.getLogger(__name__)
class AutoTokenizer(object):
r""":class:`~transformers.AutoTokenizer` is a generic tokenizer class
that will be instantiated as one of the tokenizer classes of the library
when created with the `AutoTokenizer.from_pretrained(pretrained_model_name_or_path)`
class method.
The `from_pretrained()` method take care of returning the correct tokenizer class instance
using pattern matching on the `pretrained_model_name_or_path` string.
The tokenizer class to instantiate is selected as the first pattern matching
in the `pretrained_model_name_or_path` string (in the following order):
- contains `camembert`: CamembertTokenizer (CamemBERT model)
- contains `distilbert`: DistilBertTokenizer (DistilBert model)
- contains `roberta`: RobertaTokenizer (RoBERTa model)
- contains `bert`: BertTokenizer (Bert model)
- contains `openai-gpt`: OpenAIGPTTokenizer (OpenAI GPT model)
- contains `gpt2`: GPT2Tokenizer (OpenAI GPT-2 model)
- contains `ctrl`: CTRLTokenizer (Salesforce CTRL model)
- contains `transfo-xl`: TransfoXLTokenizer (Transformer-XL model)
- contains `xlnet`: XLNetTokenizer (XLNet model)
- contains `xlm`: XLMTokenizer (XLM model)
This class cannot be instantiated using `__init__()` (throw an error).
"""
def __init__(self):
raise EnvironmentError("AutoTokenizer is designed to be instantiated "
"using the `AutoTokenizer.from_pretrained(pretrained_model_name_or_path)` method.")
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs):
r""" Instantiate a one of the tokenizer classes of the library
from a pre-trained model vocabulary.
The tokenizer class to instantiate is selected as the first pattern matching
in the `pretrained_model_name_or_path` string (in the following order):
- contains `camembert`: CamembertTokenizer (CamemBERT model)
- contains `distilbert`: DistilBertTokenizer (DistilBert model)
- contains `roberta`: RobertaTokenizer (RoBERTa model)
- contains `bert`: BertTokenizer (Bert model)
- contains `openai-gpt`: OpenAIGPTTokenizer (OpenAI GPT model)
- contains `gpt2`: GPT2Tokenizer (OpenAI GPT-2 model)
- contains `ctrl`: CTRLTokenizer (Salesforce CTRL model)
- contains `transfo-xl`: TransfoXLTokenizer (Transformer-XL model)
- contains `xlnet`: XLNetTokenizer (XLNet model)
- contains `xlm`: XLMTokenizer (XLM model)
Params:
pretrained_model_name_or_path: either:
- a string with the `shortcut name` of a predefined tokenizer to load from cache or download, e.g.: ``bert-base-uncased``.
- a path to a `directory` containing vocabulary files required by the tokenizer, for instance saved using the :func:`~transformers.PreTrainedTokenizer.save_pretrained` method, e.g.: ``./my_model_directory/``.
- (not applicable to all derived classes) a path or url to a single saved vocabulary file if and only if the tokenizer only requires a single vocabulary file (e.g. Bert, XLNet), e.g.: ``./my_model_directory/vocab.txt``.
cache_dir: (`optional`) string:
Path to a directory in which a downloaded predefined tokenizer vocabulary files should be cached if the standard cache should not be used.
force_download: (`optional`) boolean, default False:
Force to (re-)download the vocabulary files and override the cached versions if they exists.
proxies: (`optional`) dict, default None:
A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
The proxies are used on each request.
inputs: (`optional`) positional arguments: will be passed to the Tokenizer ``__init__`` method.
kwargs: (`optional`) keyword arguments: will be passed to the Tokenizer ``__init__`` method. Can be used to set special tokens like ``bos_token``, ``eos_token``, ``unk_token``, ``sep_token``, ``pad_token``, ``cls_token``, ``mask_token``, ``additional_special_tokens``. See parameters in the doc string of :class:`~transformers.PreTrainedTokenizer` for details.
Examples::
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased') # Download vocabulary from S3 and cache.
tokenizer = AutoTokenizer.from_pretrained('./test/bert_saved_model/') # E.g. tokenizer was saved using `save_pretrained('./test/saved_model/')`
"""
if 'distilbert' in pretrained_model_name_or_path:
return DistilBertTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
elif 'camembert' in pretrained_model_name_or_path:
return CamembertTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
elif 'roberta' in pretrained_model_name_or_path:
return RobertaTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
elif 'bert' in pretrained_model_name_or_path:
return BertTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
elif 'openai-gpt' in pretrained_model_name_or_path:
return OpenAIGPTTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
elif 'gpt2' in pretrained_model_name_or_path:
return GPT2Tokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
elif 'transfo-xl' in pretrained_model_name_or_path:
return TransfoXLTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
elif 'xlnet' in pretrained_model_name_or_path:
return XLNetTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
elif 'xlm' in pretrained_model_name_or_path:
return XLMTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
elif 'ctrl' in pretrained_model_name_or_path:
return CTRLTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
raise ValueError("Unrecognized model identifier in {}. Should contains one of "
"'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', "
"'xlm', 'roberta', 'camembert', 'ctrl'".format(pretrained_model_name_or_path))
| 7,877 | 59.6 | 372 |
py
|
DeeBERT
|
DeeBERT-master/transformers/configuration_distilbert.py
|
# 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 __future__ import (absolute_import, division, print_function,
unicode_literals)
import sys
import json
import logging
from io import open
from .configuration_utils import PretrainedConfig
logger = logging.getLogger(__name__)
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
'distilbert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-config.json",
'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-config.json"
}
class DistilBertConfig(PretrainedConfig):
pretrained_config_archive_map = DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
def __init__(self,
vocab_size_or_config_json_file=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,
tie_weights_=True,
qa_dropout=0.1,
seq_classif_dropout=0.2,
**kwargs):
super(DistilBertConfig, self).__init__(**kwargs)
if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
and isinstance(vocab_size_or_config_json_file, unicode)):
with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader:
json_config = json.loads(reader.read())
for key, value in json_config.items():
self.__dict__[key] = value
elif isinstance(vocab_size_or_config_json_file, int):
self.vocab_size = vocab_size_or_config_json_file
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.tie_weights_ = tie_weights_
self.qa_dropout = qa_dropout
self.seq_classif_dropout = seq_classif_dropout
else:
raise ValueError("First argument must be either a vocabulary size (int)"
" or the path to a pretrained model config file (str)")
@property
def hidden_size(self):
return self.dim
@property
def num_attention_heads(self):
return self.n_heads
@property
def num_hidden_layers(self):
return self.n_layers
| 3,568 | 38.655556 | 152 |
py
|
DeeBERT
|
DeeBERT-master/transformers/modeling_gpt2.py
|
# 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.
"""PyTorch OpenAI GPT-2 model."""
from __future__ import absolute_import, division, print_function, unicode_literals
import collections
import json
import logging
import math
import os
import sys
from io import open
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss
from torch.nn.parameter import Parameter
from .modeling_utils import PreTrainedModel, Conv1D, prune_conv1d_layer, SequenceSummary
from .configuration_gpt2 import GPT2Config
from .file_utils import add_start_docstrings
logger = logging.getLogger(__name__)
GPT2_PRETRAINED_MODEL_ARCHIVE_MAP = {"gpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-pytorch_model.bin",
"gpt2-medium": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-pytorch_model.bin",
"gpt2-large": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-large-pytorch_model.bin",
"gpt2-xl": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-xl-pytorch_model.bin",
"distilgpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/distilgpt2-pytorch_model.bin",}
def load_tf_weights_in_gpt2(model, config, gpt2_checkpoint_path):
""" Load tf checkpoints in a pytorch model
"""
try:
import re
import numpy as np
import tensorflow as tf
except ImportError:
logger.error("Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
"https://www.tensorflow.org/install/ for installation instructions.")
raise
tf_path = os.path.abspath(gpt2_checkpoint_path)
logger.info("Converting TensorFlow checkpoint from {}".format(tf_path))
# Load weights from TF model
init_vars = tf.train.list_variables(tf_path)
names = []
arrays = []
for name, shape in init_vars:
logger.info("Loading TF weight {} with shape {}".format(name, shape))
array = tf.train.load_variable(tf_path, name)
names.append(name)
arrays.append(array.squeeze())
for name, array in zip(names, arrays):
name = name[6:] # skip "model/"
name = name.split('/')
pointer = model
for m_name in name:
if re.fullmatch(r'[A-Za-z]+\d+', m_name):
l = re.split(r'(\d+)', m_name)
else:
l = [m_name]
if l[0] == 'w' or l[0] == 'g':
pointer = getattr(pointer, 'weight')
elif l[0] == 'b':
pointer = getattr(pointer, 'bias')
elif l[0] == 'wpe' or l[0] == 'wte':
pointer = getattr(pointer, l[0])
pointer = getattr(pointer, 'weight')
else:
pointer = getattr(pointer, l[0])
if len(l) >= 2:
num = int(l[1])
pointer = pointer[num]
try:
assert pointer.shape == array.shape
except AssertionError as e:
e.args += (pointer.shape, array.shape)
raise
logger.info("Initialize PyTorch weight {}".format(name))
pointer.data = torch.from_numpy(array)
return model
def gelu(x):
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
class Attention(nn.Module):
def __init__(self, nx, n_ctx, config, scale=False):
super(Attention, self).__init__()
self.output_attentions = config.output_attentions
n_state = nx # in Attention: n_state=768 (nx=n_embd)
# [switch nx => n_state from Block to Attention to keep identical to TF implem]
assert n_state % config.n_head == 0
self.register_buffer("bias", torch.tril(torch.ones(n_ctx, n_ctx)).view(1, 1, n_ctx, n_ctx))
self.n_head = config.n_head
self.split_size = n_state
self.scale = scale
self.c_attn = Conv1D(n_state * 3, nx)
self.c_proj = Conv1D(n_state, nx)
self.attn_dropout = nn.Dropout(config.attn_pdrop)
self.resid_dropout = nn.Dropout(config.resid_pdrop)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
mask = torch.ones(self.n_head, self.split_size // self.n_head)
heads = set(heads) - self.pruned_heads # Convert to set and emove already pruned heads
for head in heads:
# Compute how many pruned heads are before the head and move the index accordingly
head = head - sum(1 if h < head else 0 for h in self.pruned_heads)
mask[head] = 0
mask = mask.view(-1).contiguous().eq(1)
index = torch.arange(len(mask))[mask].long()
index_attn = torch.cat([index, index + self.split_size, index + (2*self.split_size)])
# Prune conv1d layers
self.c_attn = prune_conv1d_layer(self.c_attn, index_attn, dim=1)
self.c_proj = prune_conv1d_layer(self.c_proj, index, dim=0)
# Update hyper params
self.split_size = (self.split_size // self.n_head) * (self.n_head - len(heads))
self.n_head = self.n_head - len(heads)
self.pruned_heads = self.pruned_heads.union(heads)
def _attn(self, q, k, v, attention_mask=None, head_mask=None):
w = torch.matmul(q, k)
if self.scale:
w = w / math.sqrt(v.size(-1))
nd, ns = w.size(-2), w.size(-1)
b = self.bias[:, :, ns-nd:ns, :ns]
w = w * b - 1e4 * (1 - b)
if attention_mask is not None:
# Apply the attention mask
w = w + attention_mask
w = nn.Softmax(dim=-1)(w)
w = self.attn_dropout(w)
# Mask heads if we want to
if head_mask is not None:
w = w * head_mask
outputs = [torch.matmul(w, v)]
if self.output_attentions:
outputs.append(w)
return outputs
def merge_heads(self, x):
x = x.permute(0, 2, 1, 3).contiguous()
new_x_shape = x.size()[:-2] + (x.size(-2) * x.size(-1),)
return x.view(*new_x_shape) # in Tensorflow implem: fct merge_states
def split_heads(self, x, k=False):
new_x_shape = x.size()[:-1] + (self.n_head, x.size(-1) // self.n_head)
x = x.view(*new_x_shape) # in Tensorflow implem: fct split_states
if k:
return x.permute(0, 2, 3, 1) # (batch, head, head_features, seq_length)
else:
return x.permute(0, 2, 1, 3) # (batch, head, seq_length, head_features)
def forward(self, x, layer_past=None, attention_mask=None, head_mask=None):
x = self.c_attn(x)
query, key, value = x.split(self.split_size, dim=2)
query = self.split_heads(query)
key = self.split_heads(key, k=True)
value = self.split_heads(value)
if layer_past is not None:
past_key, past_value = layer_past[0].transpose(-2, -1), layer_past[1] # transpose back cf below
key = torch.cat((past_key, key), dim=-1)
value = torch.cat((past_value, value), dim=-2)
present = torch.stack((key.transpose(-2, -1), value)) # transpose to have same shapes for stacking
attn_outputs = self._attn(query, key, value, attention_mask, head_mask)
a = attn_outputs[0]
a = self.merge_heads(a)
a = self.c_proj(a)
a = self.resid_dropout(a)
outputs = [a, present] + attn_outputs[1:]
return outputs # a, present, (attentions)
class MLP(nn.Module):
def __init__(self, n_state, config): # in MLP: n_state=3072 (4 * n_embd)
super(MLP, self).__init__()
nx = config.n_embd
self.c_fc = Conv1D(n_state, nx)
self.c_proj = Conv1D(nx, n_state)
self.act = gelu
self.dropout = nn.Dropout(config.resid_pdrop)
def forward(self, x):
h = self.act(self.c_fc(x))
h2 = self.c_proj(h)
return self.dropout(h2)
class Block(nn.Module):
def __init__(self, n_ctx, config, scale=False):
super(Block, self).__init__()
nx = config.n_embd
self.ln_1 = nn.LayerNorm(nx, eps=config.layer_norm_epsilon)
self.attn = Attention(nx, n_ctx, config, scale)
self.ln_2 = nn.LayerNorm(nx, eps=config.layer_norm_epsilon)
self.mlp = MLP(4 * nx, config)
def forward(self, x, layer_past=None, attention_mask=None, head_mask=None):
output_attn = self.attn(self.ln_1(x),
layer_past=layer_past,
attention_mask=attention_mask,
head_mask=head_mask)
a = output_attn[0] # output_attn: a, present, (attentions)
x = x + a
m = self.mlp(self.ln_2(x))
x = x + m
outputs = [x] + output_attn[1:]
return outputs # x, present, (attentions)
class GPT2PreTrainedModel(PreTrainedModel):
""" An abstract class to handle weights initialization and
a simple interface for dowloading and loading pretrained models.
"""
config_class = GPT2Config
pretrained_model_archive_map = GPT2_PRETRAINED_MODEL_ARCHIVE_MAP
load_tf_weights = load_tf_weights_in_gpt2
base_model_prefix = "transformer"
def __init__(self, *inputs, **kwargs):
super(GPT2PreTrainedModel, self).__init__(*inputs, **kwargs)
def _init_weights(self, module):
""" Initialize the weights.
"""
if isinstance(module, (nn.Linear, nn.Embedding, 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 isinstance(module, (nn.Linear, Conv1D)) and module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
GPT2_START_DOCSTRING = r""" OpenAI GPT-2 model was proposed in
`Language Models are Unsupervised Multitask Learners`_
by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
It's a causal (unidirectional) transformer pre-trained using language modeling on a very large
corpus of ~40 GB of text data.
This model is a PyTorch `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.
.. _`Language Models are Unsupervised Multitask Learners`:
https://openai.com/blog/better-language-models/
.. _`torch.nn.Module`:
https://pytorch.org/docs/stable/nn.html#module
Parameters:
config (:class:`~transformers.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 :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""
GPT2_INPUTS_DOCSTRING = r""" Inputs:
**input_ids**: ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Indices of input sequence tokens in the vocabulary.
GPT-2 is a model with absolute position embeddings so it's usually advised to pad the inputs on
the right rather than the left.
Indices can be obtained using :class:`transformers.GPT2Tokenizer`.
See :func:`transformers.PreTrainedTokenizer.encode` and
:func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
**past**:
list of ``torch.FloatTensor`` (one for each layer):
that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
(see `past` 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**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:
Mask to avoid performing attention on padding token indices.
Mask values selected in ``[0, 1]``:
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
**token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
A parallel sequence of tokens (can be used to indicate various portions of the inputs).
The embeddings from these tokens will be summed with the respective token embeddings.
Indices are selected in the vocabulary (unlike BERT which has a specific vocabulary for segment indices).
**position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Indices of positions of each input sequence tokens in the position embeddings.
Selected in the range ``[0, config.max_position_embeddings - 1]``.
**head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
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**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, embedding_dim)``:
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.
"""
@add_start_docstrings("The bare GPT2 Model transformer outputting raw hidden-states without any specific head on top.",
GPT2_START_DOCSTRING, GPT2_INPUTS_DOCSTRING)
class GPT2Model(GPT2PreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**last_hidden_state**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)``
Sequence of hidden-states at the last layer of the model.
**past**:
list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
that contains pre-computed hidden-states (key and values in the attention blocks).
Can be used (see `past` input) 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.
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2Model.from_pretrained('gpt2')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
outputs = model(input_ids)
last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
"""
def __init__(self, config):
super(GPT2Model, self).__init__(config)
self.output_hidden_states = config.output_hidden_states
self.output_attentions = config.output_attentions
self.output_past = config.output_past
self.wte = nn.Embedding(config.vocab_size, config.n_embd)
self.wpe = nn.Embedding(config.n_positions, config.n_embd)
self.drop = nn.Dropout(config.embd_pdrop)
self.h = nn.ModuleList([Block(config.n_ctx, config, scale=True) for _ in range(config.n_layer)])
self.ln_f = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
self.init_weights()
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}
"""
for layer, heads in heads_to_prune.items():
self.h[layer].attn.prune_heads(heads)
def forward(self, input_ids=None, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None):
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 = 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 token_type_ids is not None:
token_type_ids = token_type_ids.view(-1, input_shape[-1])
if position_ids is not None:
position_ids = position_ids.view(-1, input_shape[-1])
if past is None:
past_length = 0
past = [None] * len(self.h)
else:
past_length = past[0][0].size(-2)
if position_ids is None:
device = input_ids.device if input_ids is not None else inputs_embeds.device
position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
# Attention mask.
if attention_mask is not None:
attention_mask = attention_mask.view(-1, input_shape[-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 -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.
attention_mask = attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility
attention_mask = (1.0 - attention_mask) * -10000.0
# 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
# head_mask has shape n_layer x batch x n_heads x N x N
if head_mask is not None:
if head_mask.dim() == 1:
head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
head_mask = head_mask.expand(self.config.n_layer, -1, -1, -1, -1)
elif head_mask.dim() == 2:
head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1) # We can specify head_mask for each layer
head_mask = head_mask.to(dtype=next(self.parameters()).dtype) # switch to fload if need + fp16 compatibility
else:
head_mask = [None] * self.config.n_layer
if inputs_embeds is None:
inputs_embeds = self.wte(input_ids)
position_embeds = self.wpe(position_ids)
if token_type_ids is not None:
token_type_embeds = self.wte(token_type_ids)
else:
token_type_embeds = 0
hidden_states = inputs_embeds + position_embeds + token_type_embeds
hidden_states = self.drop(hidden_states)
output_shape = input_shape + (hidden_states.size(-1),)
presents = ()
all_attentions = []
all_hidden_states = ()
for i, (block, layer_past) in enumerate(zip(self.h, past)):
if self.output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states.view(*output_shape),)
outputs = block(hidden_states,
layer_past=layer_past,
attention_mask=attention_mask,
head_mask=head_mask[i])
hidden_states, present = outputs[:2]
if self.output_past:
presents = presents + (present,)
if self.output_attentions:
all_attentions.append(outputs[2])
hidden_states = self.ln_f(hidden_states)
hidden_states = hidden_states.view(*output_shape)
# Add last hidden state
if self.output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
outputs = (hidden_states,)
if self.output_past:
outputs = outputs + (presents,)
if self.output_hidden_states:
outputs = outputs + (all_hidden_states,)
if self.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,) + all_attentions[0].shape[-2:]
all_attentions = tuple(t.view(*attention_output_shape) for t in all_attentions)
outputs = outputs + (all_attentions,)
return outputs # last hidden state, (presents), (all hidden_states), (attentions)
@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, GPT2_INPUTS_DOCSTRING)
class GPT2LMHeadModel(GPT2PreTrainedModel):
r"""
**labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Labels for language modeling.
Note that the labels **are shifted** inside the model, i.e. you can set ``lm_labels = input_ids``
Indices are selected in ``[-1, 0, ..., config.vocab_size]``
All labels set to ``-1`` are ignored (masked), the loss is only
computed for labels in ``[0, ..., config.vocab_size]``
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Language modeling loss.
**prediction_scores**: ``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).
**past**:
list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
that contains pre-computed hidden-states (key and values in the attention blocks).
Can be used (see `past` input) 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.
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2LMHeadModel.from_pretrained('gpt2')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
outputs = model(input_ids, labels=input_ids)
loss, logits = outputs[:2]
"""
def __init__(self, config):
super(GPT2LMHeadModel, self).__init__(config)
self.transformer = GPT2Model(config)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
self.init_weights()
def get_output_embeddings(self):
return self.lm_head
def forward(self, input_ids=None, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None,
labels=None):
transformer_outputs = self.transformer(input_ids,
past=past,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds)
hidden_states = transformer_outputs[0]
lm_logits = self.lm_head(hidden_states)
outputs = (lm_logits,) + transformer_outputs[1:]
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(ignore_index=-1)
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)),
shift_labels.view(-1))
outputs = (loss,) + outputs
return outputs # (loss), lm_logits, presents, (all hidden_states), (attentions)
@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, GPT2_INPUTS_DOCSTRING)
class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
r"""
**mc_token_ids**: (`optional`, default to index of the last token of the input) ``torch.LongTensor`` of shape ``(batch_size, num_choices)``:
Index of the classification token in each input sequence.
Selected in the range ``[0, input_ids.size(-1) - 1[``.
**lm_labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Labels for language modeling.
Note that the labels **are shifted** inside the model, i.e. you can set ``lm_labels = input_ids``
Indices are selected in ``[-1, 0, ..., config.vocab_size]``
All labels set to ``-1`` are ignored (masked), the loss is only
computed for labels in ``[0, ..., config.vocab_size]``
**mc_labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size)``:
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)
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**lm_loss**: (`optional`, returned when ``lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Language modeling loss.
**mc_loss**: (`optional`, returned when ``multiple_choice_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Multiple choice classification loss.
**lm_prediction_scores**: ``torch.FloatTensor`` 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_prediction_scores**: ``torch.FloatTensor`` of shape ``(batch_size, num_choices)``
Prediction scores of the multiplechoice classification head (scores for each choice before SoftMax).
**past**:
list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
that contains pre-computed hidden-states (key and values in the attention blocks).
Can be used (see `past` input) 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.
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
import torch
from transformers import GPT2Tokenizer, GPT2DoubleHeadsModel
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2DoubleHeadsModel.from_pretrained('gpt2')
# Add a [CLS] to the vocabulary (we should train it also!)
tokenizer.add_special_tokens({'cls_token': '[CLS]'})
model.resize_token_embeddings(len(tokenizer)) # Update the model embeddings with the new vocabulary size
print(tokenizer.cls_token_id, len(tokenizer)) # The newly token the last token of the vocabulary
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 = torch.tensor(encoded_choices).unsqueeze(0) # Batch size: 1, number of choices: 2
mc_token_ids = torch.tensor([cls_token_location]) # Batch size: 1
outputs = model(input_ids, mc_token_ids=mc_token_ids)
lm_prediction_scores, mc_prediction_scores = outputs[:2]
"""
def __init__(self, config):
super(GPT2DoubleHeadsModel, self).__init__(config)
self.transformer = GPT2Model(config)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
self.multiple_choice_head = SequenceSummary(config)
self.init_weights()
def get_output_embeddings(self):
return self.lm_head
def forward(self, input_ids=None, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None,
mc_token_ids=None, lm_labels=None, mc_labels=None):
transformer_outputs = self.transformer(input_ids,
past=past,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds)
hidden_states = transformer_outputs[0]
lm_logits = self.lm_head(hidden_states)
mc_logits = self.multiple_choice_head(hidden_states, mc_token_ids).squeeze(-1)
outputs = (lm_logits, mc_logits) + transformer_outputs[1:]
if mc_labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(mc_logits.view(-1, mc_logits.size(-1)),
mc_labels.view(-1))
outputs = (loss,) + outputs
if lm_labels is not None:
shift_logits = lm_logits[..., :-1, :].contiguous()
shift_labels = lm_labels[..., 1:].contiguous()
loss_fct = CrossEntropyLoss(ignore_index=-1)
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)),
shift_labels.view(-1))
outputs = (loss,) + outputs
return outputs # (lm loss), (mc loss), lm logits, mc logits, presents, (all hidden_states), (attentions)
| 34,421 | 49.920118 | 148 |
py
|
DeeBERT
|
DeeBERT-master/transformers/modeling_openai.py
|
# 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.
"""PyTorch OpenAI GPT model."""
from __future__ import absolute_import, division, print_function, unicode_literals
import collections
import json
import logging
import math
import os
import sys
from io import open
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss
from torch.nn.parameter import Parameter
from .modeling_utils import PreTrainedModel, Conv1D, prune_conv1d_layer, SequenceSummary
from .configuration_openai import OpenAIGPTConfig
from .file_utils import add_start_docstrings
logger = logging.getLogger(__name__)
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP = {"openai-gpt": "https://s3.amazonaws.com/models.huggingface.co/bert/openai-gpt-pytorch_model.bin"}
def load_tf_weights_in_openai_gpt(model, config, openai_checkpoint_folder_path):
""" Load tf pre-trained weights in a pytorch model (from NumPy arrays here)
"""
import re
import numpy as np
if '.ckpt' in openai_checkpoint_folder_path:
openai_checkpoint_folder_path = os.path.dirname(openai_checkpoint_folder_path)
logger.info("Loading weights from {}".format(openai_checkpoint_folder_path))
names = json.load(open(openai_checkpoint_folder_path + '/parameters_names.json', "r", encoding='utf-8'))
shapes = json.load(open(openai_checkpoint_folder_path + '/params_shapes.json', "r", encoding='utf-8'))
offsets = np.cumsum([np.prod(shape) for shape in shapes])
init_params = [np.load(openai_checkpoint_folder_path + '/params_{}.npy'.format(n)) for n in range(10)]
init_params = np.split(np.concatenate(init_params, 0), offsets)[:-1]
init_params = [param.reshape(shape) for param, shape in zip(init_params, shapes)]
# This was used when we had a single embedding matrix for positions and tokens
# init_params[0] = np.concatenate([init_params[1], init_params[0]], 0)
# del init_params[1]
init_params = [arr.squeeze() for arr in init_params]
try:
assert model.tokens_embed.weight.shape == init_params[1].shape
assert model.positions_embed.weight.shape == init_params[0].shape
except AssertionError as e:
e.args += (model.tokens_embed.weight.shape, init_params[1].shape)
e.args += (model.positions_embed.weight.shape, init_params[0].shape)
raise
model.tokens_embed.weight.data = torch.from_numpy(init_params[1])
model.positions_embed.weight.data = torch.from_numpy(init_params[0])
names.pop(0)
# Pop position and token embedding arrays
init_params.pop(0)
init_params.pop(0)
for name, array in zip(names, init_params): # names[1:n_transfer], init_params[1:n_transfer]):
name = name[6:] # skip "model/"
assert name[-2:] == ":0"
name = name[:-2]
name = name.split('/')
pointer = model
for m_name in name:
if re.fullmatch(r'[A-Za-z]+\d+', m_name):
l = re.split(r'(\d+)', m_name)
else:
l = [m_name]
if l[0] == 'g':
pointer = getattr(pointer, 'weight')
elif l[0] == 'b':
pointer = getattr(pointer, 'bias')
elif l[0] == 'w':
pointer = getattr(pointer, 'weight')
else:
pointer = getattr(pointer, l[0])
if len(l) >= 2:
num = int(l[1])
pointer = pointer[num]
try:
assert pointer.shape == array.shape
except AssertionError as e:
e.args += (pointer.shape, array.shape)
raise
try:
assert pointer.shape == array.shape
except AssertionError as e:
e.args += (pointer.shape, array.shape)
raise
logger.info("Initialize PyTorch weight {}".format(name))
pointer.data = torch.from_numpy(array)
return model
def gelu(x):
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
def swish(x):
return x * torch.sigmoid(x)
ACT_FNS = {"relu": nn.ReLU, "swish": swish, "gelu": gelu}
class Attention(nn.Module):
def __init__(self, nx, n_ctx, config, scale=False):
super(Attention, self).__init__()
n_state = nx # in Attention: n_state=768 (nx=n_embd)
# [switch nx => n_state from Block to Attention to keep identical to TF implem]
assert n_state % config.n_head == 0
self.register_buffer("bias", torch.tril(torch.ones(n_ctx, n_ctx)).view(1, 1, n_ctx, n_ctx))
self.n_head = config.n_head
self.split_size = n_state
self.scale = scale
self.output_attentions = config.output_attentions
self.c_attn = Conv1D(n_state * 3, nx)
self.c_proj = Conv1D(n_state, nx)
self.attn_dropout = nn.Dropout(config.attn_pdrop)
self.resid_dropout = nn.Dropout(config.resid_pdrop)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
mask = torch.ones(self.n_head, self.split_size // self.n_head)
heads = set(heads) - self.pruned_heads
for head in heads:
head -= sum(1 if h < head else 0 for h in self.pruned_heads)
mask[head] = 0
mask = mask.view(-1).contiguous().eq(1)
index = torch.arange(len(mask))[mask].long()
index_attn = torch.cat([index, index + self.split_size, index + (2*self.split_size)])
# Prune conv1d layers
self.c_attn = prune_conv1d_layer(self.c_attn, index_attn, dim=1)
self.c_proj = prune_conv1d_layer(self.c_proj, index, dim=0)
# Update hyper params
self.split_size = (self.split_size // self.n_head) * (self.n_head - len(heads))
self.n_head = self.n_head - len(heads)
self.pruned_heads = self.pruned_heads.union(heads)
def _attn(self, q, k, v, attention_mask=None, head_mask=None):
w = torch.matmul(q, k)
if self.scale:
w = w / math.sqrt(v.size(-1))
# w = w * self.bias + -1e9 * (1 - self.bias) # TF implem method: mask_attn_weights
# XD: self.b may be larger than w, so we need to crop it
b = self.bias[:, :, : w.size(-2), : w.size(-1)]
w = w * b + - 1e4 * (1 - b)
if attention_mask is not None:
# Apply the attention mask
w = w + attention_mask
w = nn.Softmax(dim=-1)(w)
w = self.attn_dropout(w)
# Mask heads if we want to
if head_mask is not None:
w = w * head_mask
outputs = [torch.matmul(w, v)]
if self.output_attentions:
outputs.append(w)
return outputs
def merge_heads(self, x):
x = x.permute(0, 2, 1, 3).contiguous()
new_x_shape = x.size()[:-2] + (x.size(-2) * x.size(-1),)
return x.view(*new_x_shape) # in Tensorflow implem: fct merge_states
def split_heads(self, x, k=False):
new_x_shape = x.size()[:-1] + (self.n_head, x.size(-1) // self.n_head)
x = x.view(*new_x_shape) # in Tensorflow implem: fct split_states
if k:
return x.permute(0, 2, 3, 1)
else:
return x.permute(0, 2, 1, 3)
def forward(self, x, attention_mask=None, head_mask=None):
x = self.c_attn(x)
query, key, value = x.split(self.split_size, dim=2)
query = self.split_heads(query)
key = self.split_heads(key, k=True)
value = self.split_heads(value)
attn_outputs = self._attn(query, key, value, attention_mask, head_mask)
a = attn_outputs[0]
a = self.merge_heads(a)
a = self.c_proj(a)
a = self.resid_dropout(a)
outputs = [a] + attn_outputs[1:]
return outputs # a, (attentions)
class MLP(nn.Module):
def __init__(self, n_state, config): # in MLP: n_state=3072 (4 * n_embd)
super(MLP, self).__init__()
nx = config.n_embd
self.c_fc = Conv1D(n_state, nx)
self.c_proj = Conv1D(nx, n_state)
self.act = ACT_FNS[config.afn]
self.dropout = nn.Dropout(config.resid_pdrop)
def forward(self, x):
h = self.act(self.c_fc(x))
h2 = self.c_proj(h)
return self.dropout(h2)
class Block(nn.Module):
def __init__(self, n_ctx, config, scale=False):
super(Block, self).__init__()
nx = config.n_embd
self.attn = Attention(nx, n_ctx, config, scale)
self.ln_1 = nn.LayerNorm(nx, eps=config.layer_norm_epsilon)
self.mlp = MLP(4 * nx, config)
self.ln_2 = nn.LayerNorm(nx, eps=config.layer_norm_epsilon)
def forward(self, x, attention_mask=None, head_mask=None):
attn_outputs = self.attn(x, attention_mask=attention_mask, head_mask=head_mask)
a = attn_outputs[0]
n = self.ln_1(x + a)
m = self.mlp(n)
h = self.ln_2(n + m)
outputs = [h] + attn_outputs[1:]
return outputs
class OpenAIGPTPreTrainedModel(PreTrainedModel):
""" An abstract class to handle weights initialization and
a simple interface for dowloading and loading pretrained models.
"""
config_class = OpenAIGPTConfig
pretrained_model_archive_map = OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP
load_tf_weights = load_tf_weights_in_openai_gpt
base_model_prefix = "transformer"
def _init_weights(self, module):
""" Initialize the weights.
"""
if isinstance(module, (nn.Linear, nn.Embedding, 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 isinstance(module, (nn.Linear, Conv1D)) and module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
OPENAI_GPT_START_DOCSTRING = r""" OpenAI GPT model was proposed in
`Improving Language Understanding by Generative Pre-Training`_
by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
It's a causal (unidirectional) transformer pre-trained using language modeling on a large
corpus will long range dependencies, the Toronto Book Corpus.
This model is a PyTorch `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.
.. _`Improving Language Understanding by Generative Pre-Training`:
https://openai.com/blog/language-unsupervised/
.. _`torch.nn.Module`:
https://pytorch.org/docs/stable/nn.html#module
Parameters:
config (:class:`~transformers.OpenAIGPTConfig`): 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 :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""
OPENAI_GPT_INPUTS_DOCSTRING = r""" Inputs:
**input_ids**: ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Indices of input sequence tokens in the vocabulary.
GPT is a model with absolute position embeddings so it's usually advised to pad the inputs on
the right rather than the left.
Indices can be obtained using :class:`transformers.BPT2Tokenizer`.
See :func:`transformers.PreTrainedTokenizer.encode` and
:func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
**attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:
Mask to avoid performing attention on padding token indices.
Mask values selected in ``[0, 1]``:
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
**token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
A parallel sequence of tokens (can be used to indicate various portions of the inputs).
The embeddings from these tokens will be summed with the respective token embeddings.
Indices are selected in the vocabulary (unlike BERT which has a specific vocabulary for segment indices)
**position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Indices of positions of each input sequence tokens in the position embeddings.
Selected in the range ``[0, config.max_position_embeddings - 1]``.
**head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
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**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, embedding_dim)``:
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.
"""
@add_start_docstrings("The bare OpenAI GPT transformer model outputting raw hidden-states without any specific head on top.",
OPENAI_GPT_START_DOCSTRING, OPENAI_GPT_INPUTS_DOCSTRING)
class OpenAIGPTModel(OpenAIGPTPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**last_hidden_state**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)``
Sequence of hidden-states at the last layer of the model.
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
tokenizer = OpenAIGPTTokenizer.from_pretrained('openai-gpt')
model = OpenAIGPTModel.from_pretrained('openai-gpt')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
outputs = model(input_ids)
last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
"""
def __init__(self, config):
super(OpenAIGPTModel, self).__init__(config)
self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states
self.tokens_embed = nn.Embedding(config.vocab_size, config.n_embd)
self.positions_embed = nn.Embedding(config.n_positions, config.n_embd)
self.drop = nn.Dropout(config.embd_pdrop)
self.h = nn.ModuleList([Block(config.n_ctx, config, scale=True) for _ in range(config.n_layer)])
self.init_weights()
def get_input_embeddings(self):
return self.tokens_embed
def set_input_embeddings(self, new_embeddings):
self.tokens_embed = 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].attn.prune_heads(heads)
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None):
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 = 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 position_ids is None:
# Code is different from when we had a single embedding matrice from position and token embeddings
device = input_ids.device if input_ids is not None else inputs_embeds.device
position_ids = torch.arange(input_shape[-1], dtype=torch.long, device=device)
position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
# Attention mask.
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 = 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 -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.
attention_mask = attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility
attention_mask = (1.0 - attention_mask) * -10000.0
# 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
# head_mask has shape n_layer x batch x n_heads x N x N
if head_mask is not None:
if head_mask.dim() == 1:
head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
head_mask = head_mask.expand(self.config.n_layer, -1, -1, -1, -1)
elif head_mask.dim() == 2:
head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1) # We can specify head_mask for each layer
head_mask = head_mask.to(dtype=next(self.parameters()).dtype) # switch to fload if need + fp16 compatibility
else:
head_mask = [None] * self.config.n_layer
if inputs_embeds is None:
inputs_embeds = self.tokens_embed(input_ids)
position_embeds = self.positions_embed(position_ids)
if token_type_ids is not None:
token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1))
token_type_embeds = self.tokens_embed(token_type_ids)
else:
token_type_embeds = 0
hidden_states = inputs_embeds + position_embeds + token_type_embeds
hidden_states = self.drop(hidden_states)
output_shape = input_shape + (hidden_states.size(-1),)
all_attentions = ()
all_hidden_states = ()
for i, block in enumerate(self.h):
if self.output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states.view(*output_shape),)
outputs = block(hidden_states, attention_mask, head_mask[i])
hidden_states = outputs[0]
if self.output_attentions:
all_attentions = all_attentions + (outputs[1],)
# Add last layer
if self.output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states.view(*output_shape),)
outputs = (hidden_states.view(*output_shape),)
if self.output_hidden_states:
outputs = outputs + (all_hidden_states,)
if self.output_attentions:
outputs = outputs + (all_attentions,)
return outputs # last hidden state, (all hidden states), (all attentions)
@add_start_docstrings("""OpenAI GPT Model transformer with a language modeling head on top
(linear layer with weights tied to the input embeddings). """, OPENAI_GPT_START_DOCSTRING, OPENAI_GPT_INPUTS_DOCSTRING)
class OpenAIGPTLMHeadModel(OpenAIGPTPreTrainedModel):
r"""
**labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
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 ``[-1, 0, ..., config.vocab_size]``
All labels set to ``-1`` are ignored (masked), the loss is only
computed for labels in ``[0, ..., config.vocab_size]``
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Language modeling loss.
**prediction_scores**: ``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).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
tokenizer = OpenAIGPTTokenizer.from_pretrained('openai-gpt')
model = OpenAIGPTLMHeadModel.from_pretrained('openai-gpt')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
outputs = model(input_ids, labels=input_ids)
loss, logits = outputs[:2]
"""
def __init__(self, config):
super(OpenAIGPTLMHeadModel, self).__init__(config)
self.transformer = OpenAIGPTModel(config)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
self.init_weights()
def get_output_embeddings(self):
return self.lm_head
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None,
labels=None):
transformer_outputs = self.transformer(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds)
hidden_states = transformer_outputs[0]
lm_logits = self.lm_head(hidden_states)
outputs = (lm_logits,) + transformer_outputs[1:]
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(ignore_index=-1)
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)),
shift_labels.view(-1))
outputs = (loss,) + outputs
return outputs # (loss), lm_logits, (all hidden states), (all attentions)
@add_start_docstrings("""OpenAI GPT 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).
""", OPENAI_GPT_START_DOCSTRING, OPENAI_GPT_INPUTS_DOCSTRING)
class OpenAIGPTDoubleHeadsModel(OpenAIGPTPreTrainedModel):
r"""
**mc_token_ids**: (`optional`, default to index of the last token of the input) ``torch.LongTensor`` of shape ``(batch_size, num_choices)``:
Index of the classification token in each input sequence.
Selected in the range ``[0, input_ids.size(-1) - 1[``.
**lm_labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Labels for language modeling.
Note that the labels **are shifted** inside the model, i.e. you can set ``lm_labels = input_ids``
Indices are selected in ``[-1, 0, ..., config.vocab_size]``
All labels set to ``-1`` are ignored (masked), the loss is only
computed for labels in ``[0, ..., config.vocab_size]``
**mc_labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size)``:
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)
`multiple_choice_labels`: optional multiple choice labels: ``torch.LongTensor`` of shape [batch_size]
with indices selected in [0, ..., num_choices].
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**lm_loss**: (`optional`, returned when ``lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Language modeling loss.
**mc_loss**: (`optional`, returned when ``multiple_choice_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Multiple choice classification loss.
**lm_prediction_scores**: ``torch.FloatTensor`` 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_prediction_scores**: ``torch.FloatTensor`` of shape ``(batch_size, num_choices)``
Prediction scores of the multiplechoice classification head (scores for each choice before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
tokenizer = OpenAIGPTTokenizer.from_pretrained('openai-gpt')
model = OpenAIGPTDoubleHeadsModel.from_pretrained('openai-gpt')
tokenizer.add_special_tokens({'cls_token': '[CLS]'}) # Add a [CLS] to the vocabulary (we should train it also!)
model.resize_token_embeddings(len(tokenizer))
choices = ["Hello, my dog is cute [CLS]", "Hello, my cat is cute [CLS]"]
input_ids = torch.tensor([tokenizer.encode(s) for s in choices]).unsqueeze(0) # Batch size 1, 2 choices
mc_token_ids = torch.tensor([input_ids.size(-1)-1, input_ids.size(-1)-1]).unsqueeze(0) # Batch size 1
outputs = model(input_ids, mc_token_ids=mc_token_ids)
lm_prediction_scores, mc_prediction_scores = outputs[:2]
"""
def __init__(self, config):
super(OpenAIGPTDoubleHeadsModel, self).__init__(config)
self.transformer = OpenAIGPTModel(config)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
self.multiple_choice_head = SequenceSummary(config)
self.init_weights()
def get_output_embeddings(self):
return self.lm_head
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None,
mc_token_ids=None, lm_labels=None, mc_labels=None):
transformer_outputs = self.transformer(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds)
hidden_states = transformer_outputs[0]
lm_logits = self.lm_head(hidden_states)
mc_logits = self.multiple_choice_head(hidden_states, mc_token_ids).squeeze(-1)
outputs = (lm_logits, mc_logits) + transformer_outputs[1:]
if mc_labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(mc_logits.view(-1, mc_logits.size(-1)),
mc_labels.view(-1))
outputs = (loss,) + outputs
if lm_labels is not None:
shift_logits = lm_logits[..., :-1, :].contiguous()
shift_labels = lm_labels[..., 1:].contiguous()
loss_fct = CrossEntropyLoss(ignore_index=-1)
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)),
shift_labels.view(-1))
outputs = (loss,) + outputs
return outputs # (lm loss), (mc loss), lm logits, mc logits, (all hidden_states), (attentions)
| 31,203 | 48.687898 | 148 |
py
|
DeeBERT
|
DeeBERT-master/transformers/convert_gpt2_original_tf_checkpoint_to_pytorch.py
|
# 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 OpenAI GPT checkpoint."""
from __future__ import absolute_import, division, print_function
import argparse
from io import open
import torch
from transformers import (CONFIG_NAME, WEIGHTS_NAME,
GPT2Config,
GPT2Model,
load_tf_weights_in_gpt2)
import logging
logging.basicConfig(level=logging.INFO)
def convert_gpt2_checkpoint_to_pytorch(gpt2_checkpoint_path, gpt2_config_file, pytorch_dump_folder_path):
# Construct model
if gpt2_config_file == "":
config = GPT2Config()
else:
config = GPT2Config.from_json_file(gpt2_config_file)
model = GPT2Model(config)
# Load weights from numpy
load_tf_weights_in_gpt2(model, config, gpt2_checkpoint_path)
# Save pytorch-model
pytorch_weights_dump_path = pytorch_dump_folder_path + '/' + WEIGHTS_NAME
pytorch_config_dump_path = pytorch_dump_folder_path + '/' + CONFIG_NAME
print("Save PyTorch model to {}".format(pytorch_weights_dump_path))
torch.save(model.state_dict(), pytorch_weights_dump_path)
print("Save configuration file to {}".format(pytorch_config_dump_path))
with open(pytorch_config_dump_path, "w", encoding="utf-8") as f:
f.write(config.to_json_string())
if __name__ == "__main__":
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--gpt2_checkpoint_path",
default = None,
type = str,
required = True,
help = "Path to the TensorFlow checkpoint path.")
parser.add_argument("--pytorch_dump_folder_path",
default = None,
type = str,
required = True,
help = "Path to the output PyTorch model.")
parser.add_argument("--gpt2_config_file",
default = "",
type = str,
help = "An optional config json file corresponding to the pre-trained OpenAI model. \n"
"This specifies the model architecture.")
args = parser.parse_args()
convert_gpt2_checkpoint_to_pytorch(args.gpt2_checkpoint_path,
args.gpt2_config_file,
args.pytorch_dump_folder_path)
| 3,074 | 39.460526 | 111 |
py
|
DeeBERT
|
DeeBERT-master/transformers/modeling_tf_roberta.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team 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.
""" TF 2.0 RoBERTa model. """
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import logging
import numpy as np
import tensorflow as tf
from .configuration_roberta import RobertaConfig
from .modeling_tf_utils import TFPreTrainedModel, get_initializer
from .file_utils import add_start_docstrings
from .modeling_tf_bert import TFBertEmbeddings, TFBertMainLayer, gelu, gelu_new
logger = logging.getLogger(__name__)
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP = {
'roberta-base': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-tf_model.h5",
'roberta-large': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-tf_model.h5",
'roberta-large-mnli': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-mnli-tf_model.h5",
'distilroberta-base': "https://s3.amazonaws.com/models.huggingface.co/bert/distilroberta-base-tf_model.h5",
}
class TFRobertaEmbeddings(TFBertEmbeddings):
"""
Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
"""
def __init__(self, config, **kwargs):
super(TFRobertaEmbeddings, self).__init__(config, **kwargs)
self.padding_idx = 1
def _embedding(self, inputs, training=False):
"""Applies embedding based on inputs tensor."""
input_ids, position_ids, token_type_ids, inputs_embeds = inputs
if input_ids is not None:
seq_length = tf.shape(input_ids)[1]
else:
seq_length = tf.shape(inputs_embeds)[1]
if position_ids is None:
position_ids = tf.range(self.padding_idx+1, seq_length+self.padding_idx+1, dtype=tf.int32)[tf.newaxis, :]
return super(TFRobertaEmbeddings, self)._embedding([input_ids, position_ids, token_type_ids, inputs_embeds], training=training)
class TFRobertaMainLayer(TFBertMainLayer):
"""
Same as TFBertMainLayer but uses TFRobertaEmbeddings.
"""
def __init__(self, config, **kwargs):
super(TFRobertaMainLayer, self).__init__(config, **kwargs)
self.embeddings = TFRobertaEmbeddings(config, name='embeddings')
def get_input_embeddings(self):
return self.embeddings
class TFRobertaPreTrainedModel(TFPreTrainedModel):
""" An abstract class to handle weights initialization and
a simple interface for dowloading and loading pretrained models.
"""
config_class = RobertaConfig
pretrained_model_archive_map = TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
base_model_prefix = "roberta"
ROBERTA_START_DOCSTRING = r""" The RoBERTa model was proposed in
`RoBERTa: A Robustly Optimized BERT Pretraining Approach`_
by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer,
Veselin Stoyanov. It is based on Google's BERT model released in 2018.
It builds on BERT and modifies key hyperparameters, removing the next-sentence pretraining
objective and training with much larger mini-batches and learning rates.
This implementation is the same as BertModel with a tiny embeddings tweak as well as a setup for Roberta pretrained
models.
This model is a tf.keras.Model `tf.keras.Model`_ sub-class. 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.
.. _`RoBERTa: A Robustly Optimized BERT Pretraining Approach`:
https://arxiv.org/abs/1907.11692
.. _`tf.keras.Model`:
https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/Model
Note on the model inputs:
TF 2.0 models accepts two formats as inputs:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional arguments.
This second option is usefull when using `tf.keras.Model.fit()` method which currently requires having all the tensors in the first argument of the model call function: `model(inputs)`.
If you choose this second option, 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(inputs_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 associaed to the input names given in the docstring:
`model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`
Parameters:
config (:class:`~transformers.RobertaConfig`): 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 :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""
ROBERTA_INPUTS_DOCSTRING = r"""
Inputs:
**input_ids**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
Indices of input sequence tokens in the vocabulary.
To match pre-training, RoBERTa input sequence should be formatted with <s> and </s> tokens as follows:
(a) For sequence pairs:
``tokens: <s> Is this Jacksonville ? </s> </s> No it is not . </s>``
(b) For single sequences:
``tokens: <s> the dog is hairy . </s>``
Fully encoded sequences or sequence pairs can be obtained using the RobertaTokenizer.encode function with
the ``add_special_tokens`` parameter set to ``True``.
RoBERTa is a model with absolute position embeddings so it's usually advised to pad the inputs on
the right rather than the left.
See :func:`transformers.PreTrainedTokenizer.encode` and
:func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
**attention_mask**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
Mask to avoid performing attention on padding token indices.
Mask values selected in ``[0, 1]``:
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
**token_type_ids**: (`optional` need to be trained) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
Optional segment token indices to indicate first and second portions of the inputs.
This embedding matrice is not trained (not pretrained during RoBERTa pretraining), you will have to train it
during finetuning.
Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
corresponds to a `sentence B` token
(see `BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding`_ for more details).
**position_ids**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
Indices of positions of each input sequence tokens in the position embeddings.
Selected in the range ``[0, config.max_position_embeddings - 1[``.
**head_mask**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
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**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length, embedding_dim)``:
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.
"""
@add_start_docstrings("The bare RoBERTa Model transformer outputing raw hidden-states without any specific head on top.",
ROBERTA_START_DOCSTRING, ROBERTA_INPUTS_DOCSTRING)
class TFRobertaModel(TFRobertaPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**last_hidden_state**: ``tf.Tensor`` of shape ``(batch_size, sequence_length, hidden_size)``
Sequence of hidden-states at the output of the last layer of the model.
**pooler_output**: ``tf.Tensor`` of shape ``(batch_size, hidden_size)``
Last layer hidden-state of the first token of the sequence (classification token)
further processed by a Linear layer and a Tanh activation function. The Linear
layer weights are trained from the next sentence prediction (classification)
objective during Bert pretraining. This output is usually *not* a good summary
of the semantic content of the input, you're often better with averaging or pooling
the sequence of hidden-states for the whole input sequence.
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``tf.Tensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
import tensorflow as tf
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = TFRobertaModel.from_pretrained('roberta-base')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
outputs = model(input_ids)
last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
"""
def __init__(self, config, *inputs, **kwargs):
super(TFRobertaModel, self).__init__(config, *inputs, **kwargs)
self.roberta = TFRobertaMainLayer(config, name='roberta')
def call(self, inputs, **kwargs):
outputs = self.roberta(inputs, **kwargs)
return outputs
class TFRobertaLMHead(tf.keras.layers.Layer):
"""Roberta Head for masked language modeling."""
def __init__(self, config, input_embeddings, **kwargs):
super(TFRobertaLMHead, self).__init__(**kwargs)
self.vocab_size = config.vocab_size
self.dense = tf.keras.layers.Dense(config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
name='dense')
self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name='layer_norm')
self.act = tf.keras.layers.Activation(gelu)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = input_embeddings
def build(self, input_shape):
self.bias = self.add_weight(shape=(self.vocab_size,),
initializer='zeros',
trainable=True,
name='bias')
super(TFRobertaLMHead, self).build(input_shape)
def call(self, features):
x = self.dense(features)
x = self.act(x)
x = self.layer_norm(x)
# project back to size of vocabulary with bias
x = self.decoder(x, mode="linear") + self.bias
return x
@add_start_docstrings("""RoBERTa Model with a `language modeling` head on top. """,
ROBERTA_START_DOCSTRING, ROBERTA_INPUTS_DOCSTRING)
class TFRobertaForMaskedLM(TFRobertaPreTrainedModel):
r"""
**masked_lm_labels**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
Labels for computing the masked language modeling loss.
Indices should be in ``[-1, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)
Tokens with indices set to ``-1`` are ignored (masked), the loss is only computed for the tokens with labels
in ``[0, ..., config.vocab_size]``
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**loss**: (`optional`, returned when ``masked_lm_labels`` is provided) ``tf.Tensor`` of shape ``(1,)``:
Masked language modeling loss.
**prediction_scores**: ``tf.Tensor`` of shape ``(batch_size, sequence_length, config.vocab_size)``
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``tf.Tensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
import tensorflow as tf
from transformers import RobertaTokenizer, TFRobertaForMaskedLM
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = TFRobertaForMaskedLM.from_pretrained('roberta-base')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
outputs = model(input_ids, masked_lm_labels=input_ids)
prediction_scores = outputs[0]
"""
def __init__(self, config, *inputs, **kwargs):
super(TFRobertaForMaskedLM, self).__init__(config, *inputs, **kwargs)
self.roberta = TFRobertaMainLayer(config, name="roberta")
self.lm_head = TFRobertaLMHead(config, self.roberta.embeddings, name="lm_head")
def get_output_embeddings(self):
return self.lm_head.decoder
def call(self, inputs, **kwargs):
outputs = self.roberta(inputs, **kwargs)
sequence_output = outputs[0]
prediction_scores = self.lm_head(sequence_output)
outputs = (prediction_scores,) + outputs[2:] # Add hidden states and attention if they are here
return outputs # prediction_scores, (hidden_states), (attentions)
class TFRobertaClassificationHead(tf.keras.layers.Layer):
"""Head for sentence-level classification tasks."""
def __init__(self, config, **kwargs):
super(TFRobertaClassificationHead, self).__init__(config, **kwargs)
self.dense = tf.keras.layers.Dense(config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
activation='tanh',
name="dense")
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
self.out_proj = tf.keras.layers.Dense(config.num_labels,
kernel_initializer=get_initializer(config.initializer_range),
name="out_proj")
def call(self, features, training=False):
x = features[:, 0, :] # take <s> token (equiv. to [CLS])
x = self.dropout(x, training=training)
x = self.dense(x)
x = self.dropout(x, training=training)
x = self.out_proj(x)
return x
@add_start_docstrings("""RoBERTa Model transformer with a sequence classification/regression head on top (a linear layer
on top of the pooled output) e.g. for GLUE tasks. """,
ROBERTA_START_DOCSTRING, ROBERTA_INPUTS_DOCSTRING)
class TFRobertaForSequenceClassification(TFRobertaPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**logits**: ``tf.Tensor`` of shape ``(batch_size, config.num_labels)``
Classification (or regression if config.num_labels==1) scores (before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``tf.Tensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
import tensorflow as tf
from transformers import RobertaTokenizer, TFRobertaForSequenceClassification
tokenizer = RoertaTokenizer.from_pretrained('roberta-base')
model = TFRobertaForSequenceClassification.from_pretrained('roberta-base')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
labels = tf.constant([1])[None, :] # Batch size 1
outputs = model(input_ids)
logits = outputs[0]
"""
def __init__(self, config, *inputs, **kwargs):
super(TFRobertaForSequenceClassification, self).__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.roberta = TFRobertaMainLayer(config, name="roberta")
self.classifier = TFRobertaClassificationHead(config, name="classifier")
def call(self, inputs, **kwargs):
outputs = self.roberta(inputs, **kwargs)
sequence_output = outputs[0]
logits = self.classifier(sequence_output, training=kwargs.get('training', False))
outputs = (logits,) + outputs[2:]
return outputs # logits, (hidden_states), (attentions)
@add_start_docstrings("""RoBERTa 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. """,
ROBERTA_START_DOCSTRING, ROBERTA_INPUTS_DOCSTRING)
class TFRobertaForTokenClassification(TFRobertaPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**scores**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length, config.num_labels)``
Classification scores (before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``Numpy array`` or ``tf.Tensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list of ``Numpy array`` or ``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.
Examples::
import tensorflow as tf
from transformers import RobertaTokenizer, TFRobertaForTokenClassification
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = TFRobertaForTokenClassification.from_pretrained('roberta-base')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :] # Batch size 1
outputs = model(input_ids)
scores = outputs[0]
"""
def __init__(self, config, *inputs, **kwargs):
super(TFRobertaForTokenClassification, self).__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.roberta = TFRobertaMainLayer(config, name='roberta')
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
self.classifier = tf.keras.layers.Dense(config.num_labels,
kernel_initializer=get_initializer(config.initializer_range),
name='classifier')
def call(self, inputs, **kwargs):
outputs = self.roberta(inputs, **kwargs)
sequence_output = outputs[0]
sequence_output = self.dropout(sequence_output, training=kwargs.get('training', False))
logits = self.classifier(sequence_output)
outputs = (logits,) + outputs[2:] # add hidden states and attention if they are here
return outputs # scores, (hidden_states), (attentions)
| 22,329 | 51.789598 | 193 |
py
|
DeeBERT
|
DeeBERT-master/transformers/configuration_auto.py
|
# 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.
""" Auto Model class. """
from __future__ import absolute_import, division, print_function, unicode_literals
import logging
from .configuration_bert import BertConfig
from .configuration_openai import OpenAIGPTConfig
from .configuration_gpt2 import GPT2Config
from .configuration_transfo_xl import TransfoXLConfig
from .configuration_xlnet import XLNetConfig
from .configuration_xlm import XLMConfig
from .configuration_roberta import RobertaConfig
from .configuration_distilbert import DistilBertConfig
from .configuration_ctrl import CTRLConfig
from .configuration_camembert import CamembertConfig
logger = logging.getLogger(__name__)
class AutoConfig(object):
r""":class:`~transformers.AutoConfig` is a generic configuration class
that will be instantiated as one of the configuration classes of the library
when created with the `AutoConfig.from_pretrained(pretrained_model_name_or_path)`
class method.
The `from_pretrained()` method take care of returning the correct model class instance
using pattern matching on the `pretrained_model_name_or_path` string.
The base model class to instantiate is selected as the first pattern matching
in the `pretrained_model_name_or_path` string (in the following order):
- contains `distilbert`: DistilBertConfig (DistilBERT model)
- contains `bert`: BertConfig (Bert model)
- contains `openai-gpt`: OpenAIGPTConfig (OpenAI GPT model)
- contains `gpt2`: GPT2Config (OpenAI GPT-2 model)
- contains `transfo-xl`: TransfoXLConfig (Transformer-XL model)
- contains `xlnet`: XLNetConfig (XLNet model)
- contains `xlm`: XLMConfig (XLM model)
- contains `roberta`: RobertaConfig (RoBERTa model)
- contains `camembert`: CamembertConfig (CamemBERT model)
- contains `ctrl` : CTRLConfig (CTRL model)
This class cannot be instantiated using `__init__()` (throw an error).
"""
def __init__(self):
raise EnvironmentError("AutoConfig is designed to be instantiated "
"using the `AutoConfig.from_pretrained(pretrained_model_name_or_path)` method.")
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
r""" Instantiate a one of the configuration classes of the library
from a pre-trained model configuration.
The configuration class to instantiate is selected as the first pattern matching
in the `pretrained_model_name_or_path` string (in the following order):
- contains `distilbert`: DistilBertConfig (DistilBERT model)
- contains `bert`: BertConfig (Bert model)
- contains `openai-gpt`: OpenAIGPTConfig (OpenAI GPT model)
- contains `gpt2`: GPT2Config (OpenAI GPT-2 model)
- contains `transfo-xl`: TransfoXLConfig (Transformer-XL model)
- contains `xlnet`: XLNetConfig (XLNet model)
- contains `xlm`: XLMConfig (XLM model)
- contains `roberta`: RobertaConfig (RoBERTa model)
- contains `camembert`: CamembertConfig (CamemBERT model)
- contains `ctrl` : CTRLConfig (CTRL model)
Params:
pretrained_model_name_or_path: either:
- a string with the `shortcut name` of a pre-trained model configuration to load from cache or download, e.g.: ``bert-base-uncased``.
- a path to a `directory` containing a configuration file saved using the :func:`~transformers.PretrainedConfig.save_pretrained` method, e.g.: ``./my_model_directory/``.
- a path or url to a saved configuration JSON `file`, e.g.: ``./my_model_directory/configuration.json``.
cache_dir: (`optional`) string:
Path to a directory in which a downloaded pre-trained model
configuration should be cached if the standard cache should not be used.
kwargs: (`optional`) dict: key/value pairs with which to update the configuration object after loading.
- The values in kwargs of any keys which are configuration attributes will be used to override the loaded values.
- Behavior concerning key/value pairs whose keys are *not* configuration attributes is controlled by the `return_unused_kwargs` keyword parameter.
force_download: (`optional`) boolean, default False:
Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
proxies: (`optional`) dict, default None:
A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
The proxies are used on each request.
return_unused_kwargs: (`optional`) bool:
- If False, then this function returns just the final configuration object.
- If True, then this functions returns a tuple `(config, unused_kwargs)` where `unused_kwargs` is a dictionary consisting of the key/value pairs whose keys are not configuration attributes: ie the part of kwargs which has not been used to update `config` and is otherwise ignored.
Examples::
config = AutoConfig.from_pretrained('bert-base-uncased') # Download configuration from S3 and cache.
config = AutoConfig.from_pretrained('./test/bert_saved_model/') # E.g. config (or model) was saved using `save_pretrained('./test/saved_model/')`
config = AutoConfig.from_pretrained('./test/bert_saved_model/my_configuration.json')
config = AutoConfig.from_pretrained('bert-base-uncased', output_attention=True, foo=False)
assert config.output_attention == True
config, unused_kwargs = AutoConfig.from_pretrained('bert-base-uncased', output_attention=True,
foo=False, return_unused_kwargs=True)
assert config.output_attention == True
assert unused_kwargs == {'foo': False}
"""
if 'distilbert' in pretrained_model_name_or_path:
return DistilBertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
elif 'camembert' in pretrained_model_name_or_path:
return CamembertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
elif 'roberta' in pretrained_model_name_or_path:
return RobertaConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
elif 'bert' in pretrained_model_name_or_path:
return BertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
elif 'openai-gpt' in pretrained_model_name_or_path:
return OpenAIGPTConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
elif 'gpt2' in pretrained_model_name_or_path:
return GPT2Config.from_pretrained(pretrained_model_name_or_path, **kwargs)
elif 'transfo-xl' in pretrained_model_name_or_path:
return TransfoXLConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
elif 'xlnet' in pretrained_model_name_or_path:
return XLNetConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
elif 'xlm' in pretrained_model_name_or_path:
return XLMConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
elif 'ctrl' in pretrained_model_name_or_path:
return CTRLConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
raise ValueError("Unrecognized model identifier in {}. Should contains one of "
"'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', "
"'xlm', 'roberta', 'camembert', 'ctrl'".format(pretrained_model_name_or_path))
| 8,468 | 58.223776 | 296 |
py
|
DeeBERT
|
DeeBERT-master/transformers/convert_roberta_original_pytorch_checkpoint_to_pytorch.py
|
# 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."""
from __future__ import absolute_import, division, print_function
import argparse
import logging
import numpy as np
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from transformers.modeling_bert import (BertConfig, BertEncoder,
BertIntermediate, BertLayer,
BertModel, BertOutput,
BertSelfAttention,
BertSelfOutput)
from transformers.modeling_roberta import (RobertaEmbeddings,
RobertaForMaskedLM,
RobertaForSequenceClassification,
RobertaModel)
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
SAMPLE_TEXT = 'Hello world! cécé herlolip'
def convert_roberta_checkpoint_to_pytorch(roberta_checkpoint_path, pytorch_dump_folder_path, classification_head):
"""
Copy/paste/tweak roberta's weights to our BERT structure.
"""
roberta = FairseqRobertaModel.from_pretrained(roberta_checkpoint_path)
roberta.eval() # disable dropout
config = BertConfig(
vocab_size_or_config_json_file=50265,
hidden_size=roberta.args.encoder_embed_dim,
num_hidden_layers=roberta.args.encoder_layers,
num_attention_heads=roberta.args.encoder_attention_heads,
intermediate_size=roberta.args.encoder_ffn_embed_dim,
max_position_embeddings=514,
type_vocab_size=1,
layer_norm_eps=1e-5, # PyTorch default used in fairseq
)
if classification_head:
config.num_labels = roberta.args.num_classes
print("Our BERT config:", config)
model = RobertaForSequenceClassification(config) if classification_head else RobertaForMaskedLM(config)
model.eval()
# Now let's copy all the weights.
# Embeddings
roberta_sent_encoder = roberta.model.decoder.sentence_encoder
model.roberta.embeddings.word_embeddings.weight = roberta_sent_encoder.embed_tokens.weight
model.roberta.embeddings.position_embeddings.weight = roberta_sent_encoder.embed_positions.weight
model.roberta.embeddings.token_type_embeddings.weight.data = torch.zeros_like(model.roberta.embeddings.token_type_embeddings.weight) # just zero them out b/c RoBERTa doesn't use them.
model.roberta.embeddings.LayerNorm.weight = roberta_sent_encoder.emb_layer_norm.weight
model.roberta.embeddings.LayerNorm.bias = roberta_sent_encoder.emb_layer_norm.bias
for i in range(config.num_hidden_layers):
# Encoder: start of layer
layer: BertLayer = model.roberta.encoder.layer[i]
roberta_layer: TransformerSentenceEncoderLayer = roberta_sent_encoder.layers[i]
### self attention
self_attn: BertSelfAttention = layer.attention.self
assert(
roberta_layer.self_attn.in_proj_weight.shape == torch.Size((3 * config.hidden_size, config.hidden_size))
)
# we use three distinct linear layers so we split the source layer here.
self_attn.query.weight.data = roberta_layer.self_attn.in_proj_weight[:config.hidden_size, :]
self_attn.query.bias.data = roberta_layer.self_attn.in_proj_bias[:config.hidden_size]
self_attn.key.weight.data = roberta_layer.self_attn.in_proj_weight[config.hidden_size:2*config.hidden_size, :]
self_attn.key.bias.data = roberta_layer.self_attn.in_proj_bias[config.hidden_size:2*config.hidden_size]
self_attn.value.weight.data = roberta_layer.self_attn.in_proj_weight[2*config.hidden_size:, :]
self_attn.value.bias.data = roberta_layer.self_attn.in_proj_bias[2*config.hidden_size:]
### self-attention output
self_output: BertSelfOutput = layer.attention.output
assert(
self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
)
self_output.dense.weight = roberta_layer.self_attn.out_proj.weight
self_output.dense.bias = roberta_layer.self_attn.out_proj.bias
self_output.LayerNorm.weight = roberta_layer.self_attn_layer_norm.weight
self_output.LayerNorm.bias = roberta_layer.self_attn_layer_norm.bias
### intermediate
intermediate: BertIntermediate = layer.intermediate
assert(
intermediate.dense.weight.shape == roberta_layer.fc1.weight.shape
)
intermediate.dense.weight = roberta_layer.fc1.weight
intermediate.dense.bias = roberta_layer.fc1.bias
### output
bert_output: BertOutput = layer.output
assert(
bert_output.dense.weight.shape == roberta_layer.fc2.weight.shape
)
bert_output.dense.weight = roberta_layer.fc2.weight
bert_output.dense.bias = roberta_layer.fc2.bias
bert_output.LayerNorm.weight = roberta_layer.final_layer_norm.weight
bert_output.LayerNorm.bias = roberta_layer.final_layer_norm.bias
#### end of layer
if classification_head:
model.classifier.dense.weight = roberta.model.classification_heads['mnli'].dense.weight
model.classifier.dense.bias = roberta.model.classification_heads['mnli'].dense.bias
model.classifier.out_proj.weight = roberta.model.classification_heads['mnli'].out_proj.weight
model.classifier.out_proj.bias = roberta.model.classification_heads['mnli'].out_proj.bias
else:
# LM Head
model.lm_head.dense.weight = roberta.model.decoder.lm_head.dense.weight
model.lm_head.dense.bias = roberta.model.decoder.lm_head.dense.bias
model.lm_head.layer_norm.weight = roberta.model.decoder.lm_head.layer_norm.weight
model.lm_head.layer_norm.bias = roberta.model.decoder.lm_head.layer_norm.bias
model.lm_head.decoder.weight = roberta.model.decoder.lm_head.weight
model.lm_head.bias = roberta.model.decoder.lm_head.bias
# Let's check that we get the same results.
input_ids: torch.Tensor = roberta.encode(SAMPLE_TEXT).unsqueeze(0) # batch of size 1
our_output = model(input_ids)[0]
if classification_head:
their_output = roberta.model.classification_heads['mnli'](roberta.extract_features(input_ids))
else:
their_output = roberta.model(input_ids)[0]
print(our_output.shape, their_output.shape)
max_absolute_diff = torch.max(torch.abs(our_output - their_output)).item()
print(f"max_absolute_diff = {max_absolute_diff}") # ~ 1e-7
success = torch.allclose(our_output, their_output, atol=1e-3)
print(
"Do both models output the same tensors?",
"🔥" if success else "💩"
)
if not success:
raise Exception("Something went wRoNg")
print(f"Saving model to {pytorch_dump_folder_path}")
model.save_pretrained(pytorch_dump_folder_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--roberta_checkpoint_path",
default = None,
type = str,
required = True,
help = "Path the official PyTorch dump.")
parser.add_argument("--pytorch_dump_folder_path",
default = None,
type = str,
required = True,
help = "Path to the output PyTorch model.")
parser.add_argument("--classification_head",
action = "store_true",
help = "Whether to convert a final classification head.")
args = parser.parse_args()
convert_roberta_checkpoint_to_pytorch(
args.roberta_checkpoint_path,
args.pytorch_dump_folder_path,
args.classification_head
)
| 8,486 | 45.889503 | 188 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tokenization_bert.py
|
# 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.
"""Tokenization classes."""
from __future__ import absolute_import, division, print_function, unicode_literals
import collections
import logging
import os
import unicodedata
from io import open
from .tokenization_utils import PreTrainedTokenizer
logger = logging.getLogger(__name__)
VOCAB_FILES_NAMES = {'vocab_file': 'vocab.txt'}
PRETRAINED_VOCAB_FILES_MAP = {
'vocab_file':
{
'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt",
'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt",
'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-vocab.txt",
'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-vocab.txt",
'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-vocab.txt",
'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-vocab.txt",
'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-vocab.txt",
'bert-base-german-cased': "https://int-deepset-models-bert.s3.eu-central-1.amazonaws.com/pytorch/bert-base-german-cased-vocab.txt",
'bert-large-uncased-whole-word-masking': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-vocab.txt",
'bert-large-cased-whole-word-masking': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-vocab.txt",
'bert-large-uncased-whole-word-masking-finetuned-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-finetuned-squad-vocab.txt",
'bert-large-cased-whole-word-masking-finetuned-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-finetuned-squad-vocab.txt",
'bert-base-cased-finetuned-mrpc': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-finetuned-mrpc-vocab.txt",
'bert-base-german-dbmdz-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-cased-vocab.txt",
'bert-base-german-dbmdz-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-uncased-vocab.txt",
}
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
'bert-base-uncased': 512,
'bert-large-uncased': 512,
'bert-base-cased': 512,
'bert-large-cased': 512,
'bert-base-multilingual-uncased': 512,
'bert-base-multilingual-cased': 512,
'bert-base-chinese': 512,
'bert-base-german-cased': 512,
'bert-large-uncased-whole-word-masking': 512,
'bert-large-cased-whole-word-masking': 512,
'bert-large-uncased-whole-word-masking-finetuned-squad': 512,
'bert-large-cased-whole-word-masking-finetuned-squad': 512,
'bert-base-cased-finetuned-mrpc': 512,
'bert-base-german-dbmdz-cased': 512,
'bert-base-german-dbmdz-uncased': 512,
}
PRETRAINED_INIT_CONFIGURATION = {
'bert-base-uncased': {'do_lower_case': True},
'bert-large-uncased': {'do_lower_case': True},
'bert-base-cased': {'do_lower_case': False},
'bert-large-cased': {'do_lower_case': False},
'bert-base-multilingual-uncased': {'do_lower_case': True},
'bert-base-multilingual-cased': {'do_lower_case': False},
'bert-base-chinese': {'do_lower_case': False},
'bert-base-german-cased': {'do_lower_case': False},
'bert-large-uncased-whole-word-masking': {'do_lower_case': True},
'bert-large-cased-whole-word-masking': {'do_lower_case': False},
'bert-large-uncased-whole-word-masking-finetuned-squad': {'do_lower_case': True},
'bert-large-cased-whole-word-masking-finetuned-squad': {'do_lower_case': False},
'bert-base-cased-finetuned-mrpc': {'do_lower_case': False},
'bert-base-german-dbmdz-cased': {'do_lower_case': False},
'bert-base-german-dbmdz-uncased': {'do_lower_case': True},
}
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
class BertTokenizer(PreTrainedTokenizer):
r"""
Constructs a BertTokenizer.
:class:`~transformers.BertTokenizer` runs end-to-end tokenization: punctuation splitting + wordpiece
Args:
vocab_file: Path to a one-wordpiece-per-line vocabulary file
do_lower_case: Whether to lower case the input. Only has an effect when do_wordpiece_only=False
do_basic_tokenize: Whether to do basic tokenization before wordpiece.
max_len: An artificial maximum length to truncate tokenized sequences to; Effective maximum length is always the
minimum of this value (if specified) and the underlying BERT model's sequence length.
never_split: List of tokens which will never be split during tokenization. Only has an effect when
do_wordpiece_only=False
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
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]", tokenize_chinese_chars=True, **kwargs):
"""Constructs a BertTokenizer.
Args:
**vocab_file**: Path to a one-wordpiece-per-line vocabulary file
**do_lower_case**: (`optional`) boolean (default True)
Whether to lower case the input
Only has an effect when do_basic_tokenize=True
**do_basic_tokenize**: (`optional`) boolean (default True)
Whether to do basic tokenization before wordpiece.
**never_split**: (`optional`) list of string
List of tokens which will never be split during tokenization.
Only has an effect when do_basic_tokenize=True
**tokenize_chinese_chars**: (`optional`) boolean (default True)
Whether to tokenize Chinese characters.
This should likely be deactivated for Japanese:
see: https://github.com/huggingface/pytorch-pretrained-BERT/issues/328
"""
super(BertTokenizer, self).__init__(unk_token=unk_token, sep_token=sep_token,
pad_token=pad_token, cls_token=cls_token,
mask_token=mask_token, **kwargs)
self.max_len_single_sentence = self.max_len - 2 # take into account special tokens
self.max_len_sentences_pair = self.max_len - 3 # take into account special tokens
if not os.path.isfile(vocab_file):
raise ValueError(
"Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained "
"model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format(vocab_file))
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)
self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=self.unk_token)
@property
def vocab_size(self):
return len(self.vocab)
def _tokenize(self, text):
split_tokens = []
if self.do_basic_tokenize:
for token in self.basic_tokenizer.tokenize(text, never_split=self.all_special_tokens):
for sub_token in self.wordpiece_tokenizer.tokenize(token):
split_tokens.append(sub_token)
else:
split_tokens = self.wordpiece_tokenizer.tokenize(text)
return split_tokens
def _convert_token_to_id(self, token):
""" Converts a token (str/unicode) 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 (string/unicode) 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 build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks
by concatenating and adding special tokens.
A BERT sequence has the following format:
single sequence: [CLS] X [SEP]
pair of sequences: [CLS] A [SEP] B [SEP]
"""
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 + token_ids_1 + sep
def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False):
"""
Retrieves 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`` or ``encode_plus`` methods.
Args:
token_ids_0: list of ids (must not contain special tokens)
token_ids_1: Optional list of ids (must not contain special tokens), necessary when fetching sequence ids
for sequence pairs
already_has_special_tokens: (default False) Set to True if the token list is already formated with
special tokens for the model
Returns:
A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
if token_ids_1 is not None:
raise ValueError("You should not supply a second sequence if the provided sequence of "
"ids is already formated with special tokens for the model.")
return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))
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, token_ids_1=None):
"""
Creates a mask from the two sequences passed to be used in a sequence-pair classification task.
A BERT sequence pair mask has the following format:
0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1
| first sequence | second sequence
if token_ids_1 is None, only returns the first portion of the mask (0'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, vocab_path):
"""Save the tokenizer vocabulary to a directory or file."""
index = 0
if os.path.isdir(vocab_path):
vocab_file = os.path.join(vocab_path, VOCAB_FILES_NAMES['vocab_file'])
else:
vocab_file = vocab_path
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("Saving vocabulary to {}: vocabulary indices are not consecutive."
" Please check that the vocabulary is not corrupted!".format(vocab_file))
index = token_index
writer.write(token + u'\n')
index += 1
return (vocab_file,)
class BasicTokenizer(object):
"""Runs basic tokenization (punctuation splitting, lower casing, etc.)."""
def __init__(self, do_lower_case=True, never_split=None, tokenize_chinese_chars=True):
""" Constructs a BasicTokenizer.
Args:
**do_lower_case**: Whether to lower case the input.
**never_split**: (`optional`) list of str
Kept for backward compatibility purposes.
Now implemented directly at the base class level (see :func:`PreTrainedTokenizer.tokenize`)
List of token not to split.
**tokenize_chinese_chars**: (`optional`) boolean (default True)
Whether to tokenize Chinese characters.
This should likely be deactivated for Japanese:
see: https://github.com/huggingface/pytorch-pretrained-BERT/issues/328
"""
if never_split is None:
never_split = []
self.do_lower_case = do_lower_case
self.never_split = never_split
self.tokenize_chinese_chars = tokenize_chinese_chars
def tokenize(self, text, never_split=None):
""" Basic Tokenization of a piece of text.
Split on "white spaces" only, for sub-word tokenization, see WordPieceTokenizer.
Args:
**never_split**: (`optional`) list of str
Kept for backward compatibility purposes.
Now implemented directly at the base class level (see :func:`PreTrainedTokenizer.tokenize`)
List of token not to split.
"""
never_split = self.never_split + (never_split if never_split is not None else [])
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)
orig_tokens = whitespace_tokenize(text)
split_tokens = []
for token in orig_tokens:
if self.do_lower_case and token not in never_split:
token = token.lower()
token = self._run_strip_accents(token)
split_tokens.extend(self._run_split_on_punc(token))
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 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)
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"
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
def _is_whitespace(char):
"""Checks whether `chars` is a whitespace character."""
# \t, \n, and \r are technically contorl characters but we treat them
# as whitespace since they are generally considered as such.
if char == " " or char == "\t" or char == "\n" or char == "\r":
return True
cat = unicodedata.category(char)
if cat == "Zs":
return True
return False
def _is_control(char):
"""Checks whether `chars` is a control character."""
# These are technically control characters but we count them as whitespace
# characters.
if char == "\t" or char == "\n" or char == "\r":
return False
cat = unicodedata.category(char)
if cat.startswith("C"):
return True
return False
def _is_punctuation(char):
"""Checks whether `chars` is a punctuation character."""
cp = ord(char)
# We treat all non-letter/number ASCII as punctuation.
# Characters such as "^", "$", and "`" are not in the Unicode
# Punctuation class but we treat them as punctuation anyways, for
# consistency.
if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or
(cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)):
return True
cat = unicodedata.category(char)
if cat.startswith("P"):
return True
return False
| 22,451 | 43.636183 | 183 |
py
|
DeeBERT
|
DeeBERT-master/transformers/convert_transfo_xl_original_tf_checkpoint_to_pytorch.py
|
# 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 Transformer XL checkpoint and datasets."""
from __future__ import absolute_import, division, print_function
import argparse
import os
import sys
from io import open
import torch
import transformers.tokenization_transfo_xl as data_utils
from transformers import CONFIG_NAME, WEIGHTS_NAME
from transformers import (TransfoXLConfig, TransfoXLLMHeadModel,
load_tf_weights_in_transfo_xl)
from transformers.tokenization_transfo_xl import (CORPUS_NAME, VOCAB_FILES_NAMES)
if sys.version_info[0] == 2:
import cPickle as pickle
else:
import pickle
import logging
logging.basicConfig(level=logging.INFO)
# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
data_utils.Vocab = data_utils.TransfoXLTokenizer
data_utils.Corpus = data_utils.TransfoXLCorpus
sys.modules['data_utils'] = data_utils
sys.modules['vocabulary'] = data_utils
def convert_transfo_xl_checkpoint_to_pytorch(tf_checkpoint_path,
transfo_xl_config_file,
pytorch_dump_folder_path,
transfo_xl_dataset_file):
if transfo_xl_dataset_file:
# Convert a pre-processed corpus (see original TensorFlow repo)
with open(transfo_xl_dataset_file, "rb") as fp:
corpus = pickle.load(fp, encoding="latin1")
# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
pytorch_vocab_dump_path = pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
print("Save vocabulary to {}".format(pytorch_vocab_dump_path))
corpus_vocab_dict = corpus.vocab.__dict__
torch.save(corpus_vocab_dict, pytorch_vocab_dump_path)
corpus_dict_no_vocab = corpus.__dict__
corpus_dict_no_vocab.pop('vocab', None)
pytorch_dataset_dump_path = pytorch_dump_folder_path + '/' + CORPUS_NAME
print("Save dataset to {}".format(pytorch_dataset_dump_path))
torch.save(corpus_dict_no_vocab, pytorch_dataset_dump_path)
if tf_checkpoint_path:
# Convert a pre-trained TensorFlow model
config_path = os.path.abspath(transfo_xl_config_file)
tf_path = os.path.abspath(tf_checkpoint_path)
print("Converting Transformer XL checkpoint from {} with config at {}".format(tf_path, config_path))
# Initialise PyTorch model
if transfo_xl_config_file == "":
config = TransfoXLConfig()
else:
config = TransfoXLConfig.from_json_file(transfo_xl_config_file)
print("Building PyTorch model from configuration: {}".format(str(config)))
model = TransfoXLLMHeadModel(config)
model = load_tf_weights_in_transfo_xl(model, config, tf_path)
# Save pytorch-model
pytorch_weights_dump_path = os.path.join(pytorch_dump_folder_path, WEIGHTS_NAME)
pytorch_config_dump_path = os.path.join(pytorch_dump_folder_path, CONFIG_NAME)
print("Save PyTorch model to {}".format(os.path.abspath(pytorch_weights_dump_path)))
torch.save(model.state_dict(), pytorch_weights_dump_path)
print("Save configuration file to {}".format(os.path.abspath(pytorch_config_dump_path)))
with open(pytorch_config_dump_path, "w", encoding="utf-8") as f:
f.write(config.to_json_string())
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path",
default = None,
type = str,
required = True,
help = "Path to the folder to store the PyTorch model or dataset/vocab.")
parser.add_argument("--tf_checkpoint_path",
default = "",
type = str,
help = "An optional path to a TensorFlow checkpoint path to be converted.")
parser.add_argument("--transfo_xl_config_file",
default = "",
type = str,
help = "An optional config json file corresponding to the pre-trained BERT model. \n"
"This specifies the model architecture.")
parser.add_argument("--transfo_xl_dataset_file",
default = "",
type = str,
help = "An optional dataset file to be converted in a vocabulary.")
args = parser.parse_args()
convert_transfo_xl_checkpoint_to_pytorch(args.tf_checkpoint_path,
args.transfo_xl_config_file,
args.pytorch_dump_folder_path,
args.transfo_xl_dataset_file)
| 5,518 | 45.771186 | 121 |
py
|
DeeBERT
|
DeeBERT-master/transformers/configuration_ctrl.py
|
# 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.
""" Salesforce CTRL configuration """
from __future__ import absolute_import, division, print_function, unicode_literals
import json
import logging
import sys
from io import open
from .configuration_utils import PretrainedConfig
logger = logging.getLogger(__name__)
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP = {"ctrl": "https://storage.googleapis.com/sf-ctrl/pytorch/ctrl-config.json"}
class CTRLConfig(PretrainedConfig):
"""Configuration class to store the configuration of a `CTRLModel`.
Args:
vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `CTRLModel` or a configuration json file.
n_positions: Number of positional embeddings.
n_ctx: Size of the causal mask (usually same as n_positions).
dff: Size of the inner dimension of the FFN.
n_embd: Dimensionality of the embeddings and hidden states.
n_layer: Number of hidden layers in the Transformer encoder.
n_head: Number of attention heads for each attention layer in
the Transformer encoder.
layer_norm_epsilon: epsilon to use in the layer norm layers
resid_pdrop: The dropout probabilitiy for all fully connected
layers in the embeddings, encoder, and pooler.
attn_pdrop: The dropout ratio for the attention
probabilities.
embd_pdrop: The dropout ratio for the embeddings.
initializer_range: The sttdev of the truncated_normal_initializer for
initializing all weight matrices.
"""
pretrained_config_archive_map = CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP
def __init__(
self,
vocab_size_or_config_json_file=246534,
n_positions=256,
n_ctx=256,
n_embd=1280,
dff=8192,
n_layer=48,
n_head=16,
resid_pdrop=0.1,
embd_pdrop=0.1,
attn_pdrop=0.1,
layer_norm_epsilon=1e-6,
initializer_range=0.02,
num_labels=1,
summary_type='cls_index',
summary_use_proj=True,
summary_activation=None,
summary_proj_to_labels=True,
summary_first_dropout=0.1,
**kwargs
):
"""Constructs CTRLConfig.
Args:
vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `CTRLModel` or a configuration json file.
n_positions: Number of positional embeddings.
n_ctx: Size of the causal mask (usually same as n_positions).
dff: Size of the inner dimension of the FFN.
n_embd: Dimensionality of the embeddings and hidden states.
n_layer: Number of hidden layers in the Transformer encoder.
n_head: Number of attention heads for each attention layer in
the Transformer encoder.
layer_norm_epsilon: epsilon to use in the layer norm layers
resid_pdrop: The dropout probabilitiy for all fully connected
layers in the embeddings, encoder, and pooler.
attn_pdrop: The dropout ratio for the attention
probabilities.
embd_pdrop: The dropout ratio for the embeddings.
initializer_range: The sttdev of the truncated_normal_initializer for
initializing all weight matrices.
"""
super(CTRLConfig, self).__init__(**kwargs)
self.vocab_size = vocab_size_or_config_json_file if isinstance(vocab_size_or_config_json_file, int) else -1
self.n_ctx = n_ctx
self.n_positions = n_positions
self.n_embd = n_embd
self.n_layer = n_layer
self.n_head = n_head
self.dff = dff
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.num_labels = num_labels
self.summary_type = summary_type
self.summary_use_proj = summary_use_proj
self.summary_activation = summary_activation
self.summary_first_dropout = summary_first_dropout
self.summary_proj_to_labels = summary_proj_to_labels
if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
and isinstance(vocab_size_or_config_json_file, unicode)):
with open(vocab_size_or_config_json_file, "r", encoding="utf-8") as reader:
json_config = json.loads(reader.read())
for key, value in json_config.items():
self.__dict__[key] = value
elif not isinstance(vocab_size_or_config_json_file, int):
raise ValueError(
"First argument must be either a vocabulary size (int)"
"or the path to a pretrained model config file (str)"
)
@property
def max_position_embeddings(self):
return self.n_positions
@property
def hidden_size(self):
return self.n_embd
@property
def num_attention_heads(self):
return self.n_head
@property
def num_hidden_layers(self):
return self.n_layer
| 5,775 | 39.111111 | 120 |
py
|
DeeBERT
|
DeeBERT-master/transformers/modeling_tf_transfo_xl_utilities.py
|
# 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.
""" A TF 2.0 Adaptive Softmax for Transformer XL model.
"""
from collections import defaultdict
import numpy as np
import tensorflow as tf
from .modeling_tf_utils import shape_list
class TFAdaptiveSoftmaxMask(tf.keras.layers.Layer):
def __init__(self, n_token, d_embed, d_proj, cutoffs, div_val=1,
keep_order=False, **kwargs):
super(TFAdaptiveSoftmaxMask, self).__init__(**kwargs)
self.n_token = n_token
self.d_embed = d_embed
self.d_proj = d_proj
self.cutoffs = cutoffs + [n_token]
self.cutoff_ends = [0] + self.cutoffs
self.div_val = div_val
self.shortlist_size = self.cutoffs[0]
self.n_clusters = len(self.cutoffs) - 1
self.head_size = self.shortlist_size + self.n_clusters
self.keep_order = keep_order
self.out_layers = []
self.out_projs = []
def build(self, input_shape):
if self.n_clusters > 0:
self.cluster_weight = self.add_weight(shape=(self.n_clusters, self.d_embed),
initializer='zeros',
trainable=True,
name='cluster_weight')
self.cluster_bias = self.add_weight(shape=(self.n_clusters,),
initializer='zeros',
trainable=True,
name='cluster_bias')
if self.div_val == 1:
for i in range(len(self.cutoffs)):
if self.d_proj != self.d_embed:
weight = self.add_weight(shape=(self.d_embed, self.d_proj),
initializer='zeros',
trainable=True,
name='out_projs_._{}'.format(i))
self.out_projs.append(weight)
else:
self.out_projs.append(None)
weight = self.add_weight(shape=(self.n_token, self.d_embed,),
initializer='zeros',
trainable=True,
name='out_layers_._{}_._weight'.format(i))
bias = self.add_weight(shape=(self.n_token,),
initializer='zeros',
trainable=True,
name='out_layers_._{}_._bias'.format(i))
self.out_layers.append((weight, bias))
else:
for i in range(len(self.cutoffs)):
l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i+1]
d_emb_i = self.d_embed // (self.div_val ** i)
weight = self.add_weight(shape=(d_emb_i, self.d_proj),
initializer='zeros',
trainable=True,
name='out_projs_._{}'.format(i))
self.out_projs.append(weight)
weight = self.add_weight(shape=(r_idx-l_idx, d_emb_i,),
initializer='zeros',
trainable=True,
name='out_layers_._{}_._weight'.format(i))
bias = self.add_weight(shape=(r_idx-l_idx,),
initializer='zeros',
trainable=True,
name='out_layers_._{}_._bias'.format(i))
self.out_layers.append((weight, bias))
super(TFAdaptiveSoftmaxMask, self).build(input_shape)
@staticmethod
def _logit(x, W, b, proj=None):
y = x
if proj is not None:
y = tf.einsum('ibd,ed->ibe', y, proj)
return tf.einsum('ibd,nd->ibn', y, W) + b
@staticmethod
def _gather_logprob(logprob, target):
lp_size = tf.shape(logprob)
r = tf.range(lp_size[0])
idx = tf.stack([r, target], 1)
return tf.gather_nd(logprob, idx)
def call(self, inputs, return_mean=True, training=False):
hidden, target = inputs
head_logprob = 0
if self.n_clusters == 0:
softmax_b = tf.get_variable('bias', [n_token], initializer=tf.zeros_initializer())
output = self._logit(hidden, self.out_layers[0][0], self.out_layers[0][1], self.out_projs[0])
if target is not None:
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=target, logits=output)
out = tf.nn.log_softmax(output, axis=-1)
else:
hidden_sizes = shape_list(hidden)
out = []
loss = tf.zeros(hidden_sizes[:2], dtype=tf.float32)
for i in range(len(self.cutoffs)):
l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1]
if target is not None:
mask = (target >= l_idx) & (target < r_idx)
mask_idx = tf.where(mask)
cur_target = tf.boolean_mask(target, mask) - l_idx
if self.div_val == 1:
cur_W = self.out_layers[0][0][l_idx:r_idx]
cur_b = self.out_layers[0][1][l_idx:r_idx]
else:
cur_W = self.out_layers[i][0]
cur_b = self.out_layers[i][1]
if i == 0:
cur_W = tf.concat([cur_W, self.cluster_weight], 0)
cur_b = tf.concat([cur_b, self.cluster_bias], 0)
head_logit = self._logit(hidden, cur_W, cur_b, self.out_projs[0])
head_logprob = tf.nn.log_softmax(head_logit)
out.append(head_logprob[..., :self.cutoffs[0]])
if target is not None:
cur_head_logprob = tf.boolean_mask(head_logprob, mask)
cur_logprob = self._gather_logprob(cur_head_logprob, cur_target)
else:
tail_logit = self._logit(hidden, cur_W, cur_b, self.out_projs[i])
tail_logprob = tf.nn.log_softmax(tail_logit)
cluster_prob_idx = self.cutoffs[0] + i - 1 # No probability for the head cluster
logprob_i = head_logprob[..., cluster_prob_idx, None] + tail_logprob
out.append(logprob_i)
if target is not None:
cur_head_logprob = tf.boolean_mask(head_logprob, mask)
cur_tail_logprob = tf.boolean_mask(tail_logprob, mask)
cur_logprob = self._gather_logprob(cur_tail_logprob, cur_target)
cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
if target is not None:
loss += tf.scatter_nd(mask_idx, -cur_logprob, tf.cast(tf.shape(loss), dtype=tf.int64))
out = tf.concat(out, axis=-1)
if target is not None:
if return_mean:
loss = tf.reduce_mean(loss)
# Add the training-time loss value to the layer using `self.add_loss()`.
self.add_loss(loss)
# Log the loss as a metric (we could log arbitrary metrics,
# including different metrics for training and inference.
self.add_metric(loss, name=self.name, aggregation='mean' if return_mean else '')
return out
| 8,325 | 46.306818 | 110 |
py
|
DeeBERT
|
DeeBERT-master/transformers/modeling_auto.py
|
# 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.
""" Auto Model class. """
from __future__ import absolute_import, division, print_function, unicode_literals
import logging
from .modeling_bert import BertModel, BertForMaskedLM, BertForSequenceClassification, BertForQuestionAnswering
from .modeling_openai import OpenAIGPTModel, OpenAIGPTLMHeadModel
from .modeling_gpt2 import GPT2Model, GPT2LMHeadModel
from .modeling_ctrl import CTRLModel, CTRLLMHeadModel
from .modeling_transfo_xl import TransfoXLModel, TransfoXLLMHeadModel
from .modeling_xlnet import XLNetModel, XLNetLMHeadModel, XLNetForSequenceClassification, XLNetForQuestionAnswering
from .modeling_xlm import XLMModel, XLMWithLMHeadModel, XLMForSequenceClassification, XLMForQuestionAnswering
from .modeling_roberta import RobertaModel, RobertaForMaskedLM, RobertaForSequenceClassification
from .modeling_distilbert import DistilBertModel, DistilBertForQuestionAnswering, DistilBertForMaskedLM, DistilBertForSequenceClassification
from .modeling_utils import PreTrainedModel, SequenceSummary
from .file_utils import add_start_docstrings
logger = logging.getLogger(__name__)
class AutoModel(object):
r"""
:class:`~transformers.AutoModel` is a generic model class
that will be instantiated as one of the base model classes of the library
when created with the `AutoModel.from_pretrained(pretrained_model_name_or_path)`
class method.
The `from_pretrained()` method takes care of returning the correct model class instance
using pattern matching on the `pretrained_model_name_or_path` string.
The base model class to instantiate is selected as the first pattern matching
in the `pretrained_model_name_or_path` string (in the following order):
- contains `distilbert`: DistilBertModel (DistilBERT model)
- contains `roberta`: RobertaModel (RoBERTa model)
- contains `bert`: BertModel (Bert model)
- contains `openai-gpt`: OpenAIGPTModel (OpenAI GPT model)
- contains `gpt2`: GPT2Model (OpenAI GPT-2 model)
- contains `ctrl`: CTRLModel (Salesforce CTRL model)
- contains `transfo-xl`: TransfoXLModel (Transformer-XL model)
- contains `xlnet`: XLNetModel (XLNet model)
- contains `xlm`: XLMModel (XLM model)
This class cannot be instantiated using `__init__()` (throws an error).
"""
def __init__(self):
raise EnvironmentError("AutoModel is designed to be instantiated "
"using the `AutoModel.from_pretrained(pretrained_model_name_or_path)` method.")
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
r""" Instantiates one of the base model classes of the library
from a pre-trained model configuration.
The model class to instantiate is selected as the first pattern matching
in the `pretrained_model_name_or_path` string (in the following order):
- contains `distilbert`: DistilBertModel (DistilBERT model)
- contains `roberta`: RobertaModel (RoBERTa model)
- contains `bert`: BertModel (Bert model)
- contains `openai-gpt`: OpenAIGPTModel (OpenAI GPT model)
- contains `gpt2`: GPT2Model (OpenAI GPT-2 model)
- contains `ctrl`: CTRLModel (Salesforce CTRL model)
- contains `transfo-xl`: TransfoXLModel (Transformer-XL model)
- contains `xlnet`: XLNetModel (XLNet model)
- contains `xlm`: XLMModel (XLM model)
The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
To train the model, you should first set it back in training mode with `model.train()`
Params:
pretrained_model_name_or_path: either:
- a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
- a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
- a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
model_args: (`optional`) Sequence of positional arguments:
All remaning positional arguments will be passed to the underlying model's ``__init__`` method
config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
- the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
- the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
- the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
state_dict: (`optional`) dict:
an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
This option can be used if you want to create a model from a pretrained configuration but load your own weights.
In this case though, you should check if using :func:`~transformers.PreTrainedModel.save_pretrained` and :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option.
cache_dir: (`optional`) string:
Path to a directory in which a downloaded pre-trained model
configuration should be cached if the standard cache should not be used.
force_download: (`optional`) boolean, default False:
Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
proxies: (`optional`) dict, default None:
A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
The proxies are used on each request.
output_loading_info: (`optional`) boolean:
Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
kwargs: (`optional`) Remaining dictionary of keyword arguments:
Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
- If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
- If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
Examples::
model = AutoModel.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache.
model = AutoModel.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
model = AutoModel.from_pretrained('bert-base-uncased', output_attention=True) # Update configuration during loading
assert model.config.output_attention == True
# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
model = AutoModel.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
"""
if 'distilbert' in pretrained_model_name_or_path:
return DistilBertModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
elif 'roberta' in pretrained_model_name_or_path:
return RobertaModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
elif 'bert' in pretrained_model_name_or_path:
return BertModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
elif 'openai-gpt' in pretrained_model_name_or_path:
return OpenAIGPTModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
elif 'gpt2' in pretrained_model_name_or_path:
return GPT2Model.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
elif 'transfo-xl' in pretrained_model_name_or_path:
return TransfoXLModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
elif 'xlnet' in pretrained_model_name_or_path:
return XLNetModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
elif 'xlm' in pretrained_model_name_or_path:
return XLMModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
elif 'ctrl' in pretrained_model_name_or_path:
return CTRLModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
raise ValueError("Unrecognized model identifier in {}. Should contains one of "
"'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', "
"'xlm', 'roberta, 'ctrl'".format(pretrained_model_name_or_path))
class AutoModelWithLMHead(object):
r"""
:class:`~transformers.AutoModelWithLMHead` is a generic model class
that will be instantiated as one of the language modeling model classes of the library
when created with the `AutoModelWithLMHead.from_pretrained(pretrained_model_name_or_path)`
class method.
The `from_pretrained()` method takes care of returning the correct model class instance
using pattern matching on the `pretrained_model_name_or_path` string.
The model class to instantiate is selected as the first pattern matching
in the `pretrained_model_name_or_path` string (in the following order):
- contains `distilbert`: DistilBertForMaskedLM (DistilBERT model)
- contains `roberta`: RobertaForMaskedLM (RoBERTa model)
- contains `bert`: BertForMaskedLM (Bert model)
- contains `openai-gpt`: OpenAIGPTLMHeadModel (OpenAI GPT model)
- contains `gpt2`: GPT2LMHeadModel (OpenAI GPT-2 model)
- contains `ctrl`: CTRLLMModel (Salesforce CTRL model)
- contains `transfo-xl`: TransfoXLLMHeadModel (Transformer-XL model)
- contains `xlnet`: XLNetLMHeadModel (XLNet model)
- contains `xlm`: XLMWithLMHeadModel (XLM model)
This class cannot be instantiated using `__init__()` (throws an error).
"""
def __init__(self):
raise EnvironmentError("AutoModelWithLMHead is designed to be instantiated "
"using the `AutoModelWithLMHead.from_pretrained(pretrained_model_name_or_path)` method.")
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
r""" Instantiates one of the language modeling model classes of the library
from a pre-trained model configuration.
The `from_pretrained()` method takes care of returning the correct model class instance
using pattern matching on the `pretrained_model_name_or_path` string.
The model class to instantiate is selected as the first pattern matching
in the `pretrained_model_name_or_path` string (in the following order):
- contains `distilbert`: DistilBertForMaskedLM (DistilBERT model)
- contains `roberta`: RobertaForMaskedLM (RoBERTa model)
- contains `bert`: BertForMaskedLM (Bert model)
- contains `openai-gpt`: OpenAIGPTLMHeadModel (OpenAI GPT model)
- contains `gpt2`: GPT2LMHeadModel (OpenAI GPT-2 model)
- contains `transfo-xl`: TransfoXLLMHeadModel (Transformer-XL model)
- contains `xlnet`: XLNetLMHeadModel (XLNet model)
- contains `xlm`: XLMWithLMHeadModel (XLM model)
The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
To train the model, you should first set it back in training mode with `model.train()`
Params:
pretrained_model_name_or_path: either:
- a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
- a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
- a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
model_args: (`optional`) Sequence of positional arguments:
All remaning positional arguments will be passed to the underlying model's ``__init__`` method
config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
- the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
- the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
- the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
state_dict: (`optional`) dict:
an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
This option can be used if you want to create a model from a pretrained configuration but load your own weights.
In this case though, you should check if using :func:`~transformers.PreTrainedModel.save_pretrained` and :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option.
cache_dir: (`optional`) string:
Path to a directory in which a downloaded pre-trained model
configuration should be cached if the standard cache should not be used.
force_download: (`optional`) boolean, default False:
Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
proxies: (`optional`) dict, default None:
A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
The proxies are used on each request.
output_loading_info: (`optional`) boolean:
Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
kwargs: (`optional`) Remaining dictionary of keyword arguments:
Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
- If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
- If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
Examples::
model = AutoModelWithLMHead.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache.
model = AutoModelWithLMHead.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
model = AutoModelWithLMHead.from_pretrained('bert-base-uncased', output_attention=True) # Update configuration during loading
assert model.config.output_attention == True
# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
model = AutoModelWithLMHead.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
"""
if 'distilbert' in pretrained_model_name_or_path:
return DistilBertForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
elif 'roberta' in pretrained_model_name_or_path:
return RobertaForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
elif 'bert' in pretrained_model_name_or_path:
return BertForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
elif 'openai-gpt' in pretrained_model_name_or_path:
return OpenAIGPTLMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
elif 'gpt2' in pretrained_model_name_or_path:
return GPT2LMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
elif 'transfo-xl' in pretrained_model_name_or_path:
return TransfoXLLMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
elif 'xlnet' in pretrained_model_name_or_path:
return XLNetLMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
elif 'xlm' in pretrained_model_name_or_path:
return XLMWithLMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
elif 'ctrl' in pretrained_model_name_or_path:
return CTRLLMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
raise ValueError("Unrecognized model identifier in {}. Should contains one of "
"'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', "
"'xlm', 'roberta','ctrl'".format(pretrained_model_name_or_path))
class AutoModelForSequenceClassification(object):
r"""
:class:`~transformers.AutoModelForSequenceClassification` is a generic model class
that will be instantiated as one of the sequence classification model classes of the library
when created with the `AutoModelForSequenceClassification.from_pretrained(pretrained_model_name_or_path)`
class method.
The `from_pretrained()` method takes care of returning the correct model class instance
using pattern matching on the `pretrained_model_name_or_path` string.
The model class to instantiate is selected as the first pattern matching
in the `pretrained_model_name_or_path` string (in the following order):
- contains `distilbert`: DistilBertForSequenceClassification (DistilBERT model)
- contains `roberta`: RobertaForSequenceClassification (RoBERTa model)
- contains `bert`: BertForSequenceClassification (Bert model)
- contains `xlnet`: XLNetForSequenceClassification (XLNet model)
- contains `xlm`: XLMForSequenceClassification (XLM model)
This class cannot be instantiated using `__init__()` (throws an error).
"""
def __init__(self):
raise EnvironmentError("AutoModelWithLMHead is designed to be instantiated "
"using the `AutoModelWithLMHead.from_pretrained(pretrained_model_name_or_path)` method.")
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
r""" Instantiates one of the sequence classification model classes of the library
from a pre-trained model configuration.
The `from_pretrained()` method takes care of returning the correct model class instance
using pattern matching on the `pretrained_model_name_or_path` string.
The model class to instantiate is selected as the first pattern matching
in the `pretrained_model_name_or_path` string (in the following order):
- contains `distilbert`: DistilBertForSequenceClassification (DistilBERT model)
- contains `roberta`: RobertaForSequenceClassification (RoBERTa model)
- contains `bert`: BertForSequenceClassification (Bert model)
- contains `xlnet`: XLNetForSequenceClassification (XLNet model)
- contains `xlm`: XLMForSequenceClassification (XLM model)
The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
To train the model, you should first set it back in training mode with `model.train()`
Params:
pretrained_model_name_or_path: either:
- a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
- a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
- a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
model_args: (`optional`) Sequence of positional arguments:
All remaning positional arguments will be passed to the underlying model's ``__init__`` method
config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
- the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
- the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
- the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
state_dict: (`optional`) dict:
an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
This option can be used if you want to create a model from a pretrained configuration but load your own weights.
In this case though, you should check if using :func:`~transformers.PreTrainedModel.save_pretrained` and :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option.
cache_dir: (`optional`) string:
Path to a directory in which a downloaded pre-trained model
configuration should be cached if the standard cache should not be used.
force_download: (`optional`) boolean, default False:
Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
proxies: (`optional`) dict, default None:
A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
The proxies are used on each request.
output_loading_info: (`optional`) boolean:
Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
kwargs: (`optional`) Remaining dictionary of keyword arguments:
Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
- If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
- If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
Examples::
model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache.
model = AutoModelForSequenceClassification.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased', output_attention=True) # Update configuration during loading
assert model.config.output_attention == True
# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
model = AutoModelForSequenceClassification.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
"""
if 'distilbert' in pretrained_model_name_or_path:
return DistilBertForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
elif 'roberta' in pretrained_model_name_or_path:
return RobertaForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
elif 'bert' in pretrained_model_name_or_path:
return BertForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
elif 'xlnet' in pretrained_model_name_or_path:
return XLNetForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
elif 'xlm' in pretrained_model_name_or_path:
return XLMForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
raise ValueError("Unrecognized model identifier in {}. Should contains one of "
"'bert', 'xlnet', 'xlm', 'roberta'".format(pretrained_model_name_or_path))
class AutoModelForQuestionAnswering(object):
r"""
:class:`~transformers.AutoModelForQuestionAnswering` is a generic model class
that will be instantiated as one of the question answering model classes of the library
when created with the `AutoModelForQuestionAnswering.from_pretrained(pretrained_model_name_or_path)`
class method.
The `from_pretrained()` method takes care of returning the correct model class instance
using pattern matching on the `pretrained_model_name_or_path` string.
The model class to instantiate is selected as the first pattern matching
in the `pretrained_model_name_or_path` string (in the following order):
- contains `distilbert`: DistilBertForQuestionAnswering (DistilBERT model)
- contains `bert`: BertForQuestionAnswering (Bert model)
- contains `xlnet`: XLNetForQuestionAnswering (XLNet model)
- contains `xlm`: XLMForQuestionAnswering (XLM model)
This class cannot be instantiated using `__init__()` (throws an error).
"""
def __init__(self):
raise EnvironmentError("AutoModelWithLMHead is designed to be instantiated "
"using the `AutoModelWithLMHead.from_pretrained(pretrained_model_name_or_path)` method.")
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
r""" Instantiates one of the question answering model classes of the library
from a pre-trained model configuration.
The `from_pretrained()` method takes care of returning the correct model class instance
using pattern matching on the `pretrained_model_name_or_path` string.
The model class to instantiate is selected as the first pattern matching
in the `pretrained_model_name_or_path` string (in the following order):
- contains `distilbert`: DistilBertForQuestionAnswering (DistilBERT model)
- contains `bert`: BertForQuestionAnswering (Bert model)
- contains `xlnet`: XLNetForQuestionAnswering (XLNet model)
- contains `xlm`: XLMForQuestionAnswering (XLM model)
The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
To train the model, you should first set it back in training mode with `model.train()`
Params:
pretrained_model_name_or_path: either:
- a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
- a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
- a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
model_args: (`optional`) Sequence of positional arguments:
All remaning positional arguments will be passed to the underlying model's ``__init__`` method
config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
- the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
- the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
- the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
state_dict: (`optional`) dict:
an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
This option can be used if you want to create a model from a pretrained configuration but load your own weights.
In this case though, you should check if using :func:`~transformers.PreTrainedModel.save_pretrained` and :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option.
cache_dir: (`optional`) string:
Path to a directory in which a downloaded pre-trained model
configuration should be cached if the standard cache should not be used.
force_download: (`optional`) boolean, default False:
Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
proxies: (`optional`) dict, default None:
A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
The proxies are used on each request.
output_loading_info: (`optional`) boolean:
Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
kwargs: (`optional`) Remaining dictionary of keyword arguments:
Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
- If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
- If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
Examples::
model = AutoModelForQuestionAnswering.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache.
model = AutoModelForQuestionAnswering.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
model = AutoModelForQuestionAnswering.from_pretrained('bert-base-uncased', output_attention=True) # Update configuration during loading
assert model.config.output_attention == True
# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
model = AutoModelForQuestionAnswering.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
"""
if 'distilbert' in pretrained_model_name_or_path:
return DistilBertForQuestionAnswering.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
elif 'bert' in pretrained_model_name_or_path:
return BertForQuestionAnswering.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
elif 'xlnet' in pretrained_model_name_or_path:
return XLNetForQuestionAnswering.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
elif 'xlm' in pretrained_model_name_or_path:
return XLMForQuestionAnswering.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
raise ValueError("Unrecognized model identifier in {}. Should contains one of "
"'bert', 'xlnet', 'xlm'".format(pretrained_model_name_or_path))
| 36,985 | 72.384921 | 472 |
py
|
DeeBERT
|
DeeBERT-master/transformers/modeling_tf_xlnet.py
|
# 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.
""" TF 2.0 XLNet model.
"""
from __future__ import absolute_import, division, print_function, unicode_literals
import json
import logging
import math
import os
import sys
from io import open
import numpy as np
import tensorflow as tf
from .configuration_xlnet import XLNetConfig
from .modeling_tf_utils import TFPreTrainedModel, TFSharedEmbeddings, TFSequenceSummary, shape_list, get_initializer
from .file_utils import add_start_docstrings
logger = logging.getLogger(__name__)
TF_XLNET_PRETRAINED_MODEL_ARCHIVE_MAP = {
'xlnet-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-base-cased-tf_model.h5",
'xlnet-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-large-cased-tf_model.h5",
}
def gelu(x):
""" Implementation of the gelu activation function.
XLNet is using OpenAI GPT's gelu
Also see https://arxiv.org/abs/1606.08415
"""
cdf = 0.5 * (1.0 + tf.tanh(
(np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3)))))
return x * cdf
def swish(x):
return x * tf.sigmoid(x)
ACT2FN = {"gelu": tf.keras.layers.Activation(gelu),
"relu": tf.keras.activations.relu,
"swish": tf.keras.layers.Activation(swish)}
class TFXLNetRelativeAttention(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super(TFXLNetRelativeAttention, self).__init__(**kwargs)
self.output_attentions = config.output_attentions
if config.d_model % config.n_head != 0:
raise ValueError(
"The hidden size (%d) is not a multiple of the number of attention "
"heads (%d)" % (config.d_model, config.n_head))
self.n_head = config.n_head
self.d_head = config.d_head
self.d_model = config.d_model
self.scale = 1 / (config.d_head ** 0.5)
self.initializer_range = config.initializer_range
self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name='layer_norm')
self.dropout = tf.keras.layers.Dropout(config.dropout)
def build(self, input_shape):
initializer = get_initializer(self.initializer_range)
self.q = self.add_weight(shape=(self.d_model, self.n_head, self.d_head),
initializer=initializer,
trainable=True, name='q')
self.k = self.add_weight(shape=(self.d_model, self.n_head, self.d_head),
initializer=initializer,
trainable=True, name='k')
self.v = self.add_weight(shape=(self.d_model, self.n_head, self.d_head),
initializer=initializer,
trainable=True, name='v')
self.o = self.add_weight(shape=(self.d_model, self.n_head, self.d_head),
initializer=initializer,
trainable=True, name='o')
self.r = self.add_weight(shape=(self.d_model, self.n_head, self.d_head),
initializer=initializer,
trainable=True, name='r')
self.r_r_bias = self.add_weight(shape=(self.n_head, self.d_head),
initializer='zeros',
trainable=True, name='r_r_bias')
self.r_s_bias = self.add_weight(shape=(self.n_head, self.d_head),
initializer='zeros',
trainable=True, name='r_s_bias')
self.r_w_bias = self.add_weight(shape=(self.n_head, self.d_head),
initializer='zeros',
trainable=True, name='r_w_bias')
self.seg_embed = self.add_weight(shape=(2, self.n_head, self.d_head),
initializer=initializer,
trainable=True, name='seg_embed')
super(TFXLNetRelativeAttention, self).build(input_shape)
def prune_heads(self, heads):
raise NotImplementedError
@staticmethod
def rel_shift(x, klen=-1):
"""perform relative shift to form the relative attention score."""
x_size = shape_list(x)
x = tf.reshape(x, (x_size[1], x_size[0], x_size[2], x_size[3]))
x = x[1:, ...]
x = tf.reshape(x, (x_size[0], x_size[1] - 1, x_size[2], x_size[3]))
x = x[:, 0:klen, :, :]
# x = torch.index_select(x, 1, torch.arange(klen, device=x.device, dtype=torch.long))
return x
def rel_attn_core(self, inputs, training=False):
"""Core relative positional attention operations."""
q_head, k_head_h, v_head_h, k_head_r, seg_mat, attn_mask, head_mask = inputs
# content based attention score
ac = tf.einsum('ibnd,jbnd->ijbn', q_head + self.r_w_bias, k_head_h)
# position based attention score
bd = tf.einsum('ibnd,jbnd->ijbn', q_head + self.r_r_bias, k_head_r)
bd = self.rel_shift(bd, klen=ac.shape[1])
# segment based attention score
if seg_mat is None:
ef = 0
else:
ef = tf.einsum('ibnd,snd->ibns', q_head + self.r_s_bias, self.seg_embed)
ef = tf.einsum('ijbs,ibns->ijbn', seg_mat, ef)
# merge attention scores and perform masking
attn_score = (ac + bd + ef) * self.scale
if attn_mask is not None:
# attn_score = attn_score * (1 - attn_mask) - 1e30 * attn_mask
if attn_mask.dtype == tf.float16:
attn_score = attn_score - 65500 * attn_mask
else:
attn_score = attn_score - 1e30 * attn_mask
# attention probability
attn_prob = tf.nn.softmax(attn_score, axis=1)
attn_prob = self.dropout(attn_prob, training=training)
# Mask heads if we want to
if head_mask is not None:
attn_prob = attn_prob * head_mask
# attention output
attn_vec = tf.einsum('ijbn,jbnd->ibnd', attn_prob, v_head_h)
if self.output_attentions:
return attn_vec, attn_prob
return attn_vec
def post_attention(self, inputs, residual=True, training=False):
"""Post-attention processing."""
# post-attention projection (back to `d_model`)
h, attn_vec = inputs
attn_out = tf.einsum('ibnd,hnd->ibh', attn_vec, self.o)
attn_out = self.dropout(attn_out, training=training)
if residual:
attn_out = attn_out + h
output = self.layer_norm(attn_out)
return output
def call(self, inputs, training=False):
(h, g, attn_mask_h, attn_mask_g,
r, seg_mat, mems, target_mapping, head_mask) = inputs
if g is not None:
###### Two-stream attention with relative positional encoding.
# content based attention score
if mems is not None and mems.shape.ndims > 1:
cat = tf.concat([mems, h], axis=0)
else:
cat = h
# content-based key head
k_head_h = tf.einsum('ibh,hnd->ibnd', cat, self.k)
# content-based value head
v_head_h = tf.einsum('ibh,hnd->ibnd', cat, self.v)
# position-based key head
k_head_r = tf.einsum('ibh,hnd->ibnd', r, self.r)
##### h-stream
# content-stream query head
q_head_h = tf.einsum('ibh,hnd->ibnd', h, self.q)
# core attention ops
attn_vec_h = self.rel_attn_core(
[q_head_h, k_head_h, v_head_h, k_head_r, seg_mat, attn_mask_h, head_mask],
training=training)
if self.output_attentions:
attn_vec_h, attn_prob_h = attn_vec_h
# post processing
output_h = self.post_attention([h, attn_vec_h], training=training)
##### g-stream
# query-stream query head
q_head_g = tf.einsum('ibh,hnd->ibnd', g, self.q)
# core attention ops
if target_mapping is not None:
q_head_g = tf.einsum('mbnd,mlb->lbnd', q_head_g, target_mapping)
attn_vec_g = self.rel_attn_core(
[q_head_g, k_head_h, v_head_h, k_head_r, seg_mat, attn_mask_g, head_mask],
training=training)
if self.output_attentions:
attn_vec_g, attn_prob_g = attn_vec_g
attn_vec_g = tf.einsum('lbnd,mlb->mbnd', attn_vec_g, target_mapping)
else:
attn_vec_g = self.rel_attn_core(
[q_head_g, k_head_h, v_head_h, k_head_r, seg_mat, attn_mask_g, head_mask],
training=training)
if self.output_attentions:
attn_vec_g, attn_prob_g = attn_vec_g
# post processing
output_g = self.post_attention([g, attn_vec_g], training=training)
if self.output_attentions:
attn_prob = attn_prob_h, attn_prob_g
else:
###### Multi-head attention with relative positional encoding
if mems is not None and mems.shape.ndims > 1:
cat = tf.concat([mems, h], axis=0)
else:
cat = h
# content heads
q_head_h = tf.einsum('ibh,hnd->ibnd', h, self.q)
k_head_h = tf.einsum('ibh,hnd->ibnd', cat, self.k)
v_head_h = tf.einsum('ibh,hnd->ibnd', cat, self.v)
# positional heads
k_head_r = tf.einsum('ibh,hnd->ibnd', r, self.r)
# core attention ops
attn_vec = self.rel_attn_core(
[q_head_h, k_head_h, v_head_h, k_head_r, seg_mat, attn_mask_h, head_mask],
training=training)
if self.output_attentions:
attn_vec, attn_prob = attn_vec
# post processing
output_h = self.post_attention([h, attn_vec], training=training)
output_g = None
outputs = (output_h, output_g)
if self.output_attentions:
outputs = outputs + (attn_prob,)
return outputs
class TFXLNetFeedForward(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super(TFXLNetFeedForward, self).__init__(**kwargs)
self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name='layer_norm')
self.layer_1 = tf.keras.layers.Dense(config.d_inner,
kernel_initializer=get_initializer(config.initializer_range),
name='layer_1')
self.layer_2 = tf.keras.layers.Dense(config.d_model,
kernel_initializer=get_initializer(config.initializer_range),
name='layer_2')
self.dropout = tf.keras.layers.Dropout(config.dropout)
if isinstance(config.ff_activation, str) or \
(sys.version_info[0] == 2 and isinstance(config.ff_activation, unicode)):
self.activation_function = ACT2FN[config.ff_activation]
else:
self.activation_function = config.ff_activation
def call(self, inp, training=False):
output = inp
output = self.layer_1(output)
output = self.activation_function(output)
output = self.dropout(output, training=training)
output = self.layer_2(output)
output = self.dropout(output, training=training)
output = self.layer_norm(output + inp)
return output
class TFXLNetLayer(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super(TFXLNetLayer, self).__init__(**kwargs)
self.rel_attn = TFXLNetRelativeAttention(config, name='rel_attn')
self.ff = TFXLNetFeedForward(config, name='ff')
self.dropout = tf.keras.layers.Dropout(config.dropout)
def call(self, inputs, training=False):
outputs = self.rel_attn(inputs, training=training)
output_h, output_g = outputs[:2]
if output_g is not None:
output_g = self.ff(output_g, training=training)
output_h = self.ff(output_h, training=training)
outputs = (output_h, output_g) + outputs[2:] # Add again attentions if there are there
return outputs
class TFXLNetLMHead(tf.keras.layers.Layer):
def __init__(self, config, input_embeddings, **kwargs):
super(TFXLNetLMHead, self).__init__(**kwargs)
self.vocab_size = config.vocab_size
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.input_embeddings = input_embeddings
def build(self, input_shape):
self.bias = self.add_weight(shape=(self.vocab_size,),
initializer='zeros',
trainable=True,
name='bias')
super(TFXLNetLMHead, self).build(input_shape)
def call(self, hidden_states):
hidden_states = self.input_embeddings(hidden_states, mode="linear")
hidden_states = hidden_states + self.bias
return hidden_states
class TFXLNetMainLayer(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super(TFXLNetMainLayer, self).__init__(**kwargs)
self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states
self.output_past = config.output_past
self.mem_len = config.mem_len
self.reuse_len = config.reuse_len
self.d_model = config.d_model
self.same_length = config.same_length
self.attn_type = config.attn_type
self.bi_data = config.bi_data
self.clamp_len = config.clamp_len
self.n_layer = config.n_layer
self.use_bfloat16 = config.use_bfloat16
self.initializer_range = config.initializer_range
self.word_embedding = TFSharedEmbeddings(config.n_token, config.d_model, initializer_range=config.initializer_range, name='word_embedding')
self.layer = [TFXLNetLayer(config, name='layer_._{}'.format(i)) for i in range(config.n_layer)]
self.dropout = tf.keras.layers.Dropout(config.dropout)
def get_input_embeddings(self):
return self.word_embedding
def build(self, input_shape):
initializer = get_initializer(self.initializer_range)
self.mask_emb = self.add_weight(shape=(1, 1, self.d_model),
initializer=initializer,
trainable=True, name='mask_emb')
def _resize_token_embeddings(self, new_num_tokens):
raise NotImplementedError
def _prune_heads(self, heads_to_prune):
raise NotImplementedError
def create_mask(self, qlen, mlen, dtype=tf.float32):
"""
Creates causal attention mask. Float mask where 1.0 indicates masked, 0.0 indicates not-masked.
Args:
qlen: TODO Lysandre didn't fill
mlen: TODO Lysandre didn't fill
::
same_length=False: same_length=True:
<mlen > < qlen > <mlen > < qlen >
^ [0 0 0 0 0 1 1 1 1] [0 0 0 0 0 1 1 1 1]
[0 0 0 0 0 0 1 1 1] [1 0 0 0 0 0 1 1 1]
qlen [0 0 0 0 0 0 0 1 1] [1 1 0 0 0 0 0 1 1]
[0 0 0 0 0 0 0 0 1] [1 1 1 0 0 0 0 0 1]
v [0 0 0 0 0 0 0 0 0] [1 1 1 1 0 0 0 0 0]
"""
attn_mask = tf.ones([qlen, qlen], dtype=dtype)
mask_u = tf.matrix_band_part(attn_mask, 0, -1)
mask_dia = tf.matrix_band_part(attn_mask, 0, 0)
attn_mask_pad = tf.zeros([qlen, mlen], dtype=dtype)
ret = tf.concat([attn_mask_pad, mask_u - mask_dia], 1)
if self.same_length:
mask_l = tf.matrix_band_part(attn_mask, -1, 0)
ret = tf.concat([ret[:, :qlen] + mask_l - mask_dia, ret[:, qlen:]], 1)
return ret
def cache_mem(self, curr_out, prev_mem):
"""cache hidden states into memory."""
if self.reuse_len is not None and self.reuse_len > 0:
curr_out = curr_out[:self.reuse_len]
if prev_mem is None:
new_mem = curr_out[-self.mem_len:]
else:
new_mem = tf.concat([prev_mem, curr_out], 0)[-self.mem_len:]
return tf.stop_gradient(new_mem)
@staticmethod
def positional_embedding(pos_seq, inv_freq, bsz=None):
sinusoid_inp = tf.einsum('i,d->id', pos_seq, inv_freq)
pos_emb = tf.concat([tf.sin(sinusoid_inp), tf.cos(sinusoid_inp)], axis=-1)
pos_emb = pos_emb[:, None, :]
if bsz is not None:
pos_emb = tf.tile(pos_emb, [1, bsz, 1])
return pos_emb
def relative_positional_encoding(self, qlen, klen, bsz=None, dtype=None):
"""create relative positional encoding."""
freq_seq = tf.range(0, self.d_model, 2.0)
if dtype is not None and dtype != tf.float32:
freq_seq = tf.cast(freq_seq, dtype=dtype)
inv_freq = 1 / (10000 ** (freq_seq / self.d_model))
if self.attn_type == 'bi':
# beg, end = klen - 1, -qlen
beg, end = klen, -qlen
elif self.attn_type == 'uni':
# beg, end = klen - 1, -1
beg, end = klen, -1
else:
raise ValueError('Unknown `attn_type` {}.'.format(self.attn_type))
if self.bi_data:
fwd_pos_seq = tf.range(beg, end, -1.0)
bwd_pos_seq = tf.range(-beg, -end, 1.0)
if dtype is not None and dtype != tf.float32:
fwd_pos_seq = tf.cast(fwd_pos_seq, dtype=dtype)
bwd_pos_seq = tf.cast(bwd_pos_seq, dtype=dtype)
if self.clamp_len > 0:
fwd_pos_seq = tf.clip_by_value(fwd_pos_seq, -self.clamp_len, self.clamp_len)
bwd_pos_seq = tf.clip_by_value(bwd_pos_seq, -self.clamp_len, self.clamp_len)
if bsz is not None:
# With bi_data, the batch size should be divisible by 2.
assert bsz%2 == 0
fwd_pos_emb = self.positional_embedding(fwd_pos_seq, inv_freq, bsz//2)
bwd_pos_emb = self.positional_embedding(bwd_pos_seq, inv_freq, bsz//2)
else:
fwd_pos_emb = self.positional_embedding(fwd_pos_seq, inv_freq)
bwd_pos_emb = self.positional_embedding(bwd_pos_seq, inv_freq)
pos_emb = tf.concat([fwd_pos_emb, bwd_pos_emb], axis=1)
else:
fwd_pos_seq = tf.range(beg, end, -1.0)
if dtype is not None and dtype != tf.float32:
fwd_pos_seq = tf.cast(fwd_pos_seq, dtype=dtype)
if self.clamp_len > 0:
fwd_pos_seq = tf.clip_by_value(fwd_pos_seq, -clamp_len, clamp_len)
pos_emb = self.positional_embedding(fwd_pos_seq, inv_freq, bsz)
return pos_emb
def call(self, inputs, attention_mask=None, mems=None, perm_mask=None, target_mapping=None,
token_type_ids=None, input_mask=None, head_mask=None, inputs_embeds=None, training=False):
if isinstance(inputs, (tuple, list)):
input_ids = inputs[0]
attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
mems = inputs[2] if len(inputs) > 2 else mems
perm_mask = inputs[3] if len(inputs) > 3 else perm_mask
target_mapping = inputs[4] if len(inputs) > 4 else target_mapping
token_type_ids = inputs[5] if len(inputs) > 5 else token_type_ids
input_mask = inputs[6] if len(inputs) > 6 else input_mask
head_mask = inputs[7] if len(inputs) > 7 else head_mask
inputs_embeds = inputs[8] if len(inputs) > 8 else inputs_embeds
assert len(inputs) <= 9, "Too many inputs."
elif isinstance(inputs, dict):
input_ids = inputs.get('input_ids')
attention_mask = inputs.get('attention_mask', attention_mask)
mems = inputs.get('mems', mems)
perm_mask = inputs.get('perm_mask', perm_mask)
target_mapping = inputs.get('target_mapping', target_mapping)
token_type_ids = inputs.get('token_type_ids', token_type_ids)
input_mask = inputs.get('input_mask', input_mask)
head_mask = inputs.get('head_mask', head_mask)
inputs_embeds = inputs.get('inputs_embeds', inputs_embeds)
assert len(inputs) <= 9, "Too many inputs."
else:
input_ids = inputs
# the original code for XLNet uses shapes [len, bsz] with the batch dimension at the end
# but we want a unified interface in the library with the batch size on the first dimension
# so we move here the first dimension (batch) to the end
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_ids = tf.transpose(input_ids, perm=(1, 0))
qlen, bsz = shape_list(input_ids)[:2]
elif inputs_embeds is not None:
inputs_embeds = tf.transpose(inputs_embeds, perm=(1, 0, 2))
qlen, bsz = shape_list(inputs_embeds)[:2]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
token_type_ids = tf.transpose(token_type_ids, perm=(1, 0)) if token_type_ids is not None else None
input_mask = tf.transpose(input_mask, perm=(1, 0)) if input_mask is not None else None
attention_mask = tf.transpose(attention_mask, perm=(1, 0)) if attention_mask is not None else None
perm_mask = tf.transpose(perm_mask, perm=(1, 2, 0)) if perm_mask is not None else None
target_mapping = tf.transpose(target_mapping, perm=(1, 2, 0)) if target_mapping is not None else None
mlen = shape_list(mems[0])[0] if mems is not None and mems[0] is not None else 0
klen = mlen + qlen
dtype_float = tf.bfloat16 if self.use_bfloat16 else tf.float32
##### Attention mask
# causal attention mask
if self.attn_type == 'uni':
attn_mask = self.create_mask(qlen, mlen)
attn_mask = attn_mask[:, :, None, None]
elif self.attn_type == 'bi':
attn_mask = None
else:
raise ValueError('Unsupported attention type: {}'.format(self.attn_type))
# data mask: input mask & perm mask
assert input_mask is None or attention_mask is None, "You can only use one of input_mask (uses 1 for padding) " \
"or attention_mask (uses 0 for padding, added for compatbility with BERT). Please choose one."
if input_mask is None and attention_mask is not None:
input_mask = 1.0 - attention_mask
if input_mask is not None and perm_mask is not None:
data_mask = input_mask[None] + perm_mask
elif input_mask is not None and perm_mask is None:
data_mask = input_mask[None]
elif input_mask is None and perm_mask is not None:
data_mask = perm_mask
else:
data_mask = None
if data_mask is not None:
# all mems can be attended to
mems_mask = tf.zeros([tf.shape(data_mask)[0], mlen, bsz],
dtype=dtype_float)
data_mask = tf.concat([mems_mask, data_mask], axis=1)
if attn_mask is None:
attn_mask = data_mask[:, :, :, None]
else:
attn_mask += data_mask[:, :, :, None]
if attn_mask is not None:
attn_mask = tf.cast(attn_mask > 0, dtype=dtype_float)
if attn_mask is not None:
non_tgt_mask = -tf.eye(qlen, dtype=dtype_float)
non_tgt_mask = tf.concat([tf.zeros([qlen, mlen], dtype=dtype_float), non_tgt_mask], axis=-1)
non_tgt_mask = tf.cast((attn_mask + non_tgt_mask[:, :, None, None]) > 0, dtype=dtype_float)
else:
non_tgt_mask = None
##### Word embeddings and prepare h & g hidden states
if inputs_embeds is not None:
word_emb_k = inputs_embeds
else:
word_emb_k = self.word_embedding(input_ids)
output_h = self.dropout(word_emb_k, training=training)
if target_mapping is not None:
word_emb_q = tf.tile(self.mask_emb, [tf.shape(target_mapping)[0], bsz, 1])
# else: # We removed the inp_q input which was same as target mapping
# inp_q_ext = inp_q[:, :, None]
# word_emb_q = inp_q_ext * self.mask_emb + (1 - inp_q_ext) * word_emb_k
output_g = self.dropout(word_emb_q, training=training)
else:
output_g = None
##### Segment embedding
if token_type_ids is not None:
# Convert `token_type_ids` to one-hot `seg_mat`
mem_pad = tf.zeros([mlen, bsz], dtype=tf.int32)
cat_ids = tf.concat([mem_pad, token_type_ids], 0)
# `1` indicates not in the same segment [qlen x klen x bsz]
seg_mat = tf.cast(
tf.logical_not(tf.equal(token_type_ids[:, None], cat_ids[None, :])),
tf.int32)
seg_mat = tf.one_hot(seg_mat, 2, dtype=dtype_float)
else:
seg_mat = None
##### Positional encoding
pos_emb = self.relative_positional_encoding(qlen, klen, bsz=bsz, dtype=dtype_float)
pos_emb = self.dropout(pos_emb, training=training)
# 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] (a head_mask for each layer)
# and head_mask is converted to shape [num_hidden_layers x qlen x klen x bsz x n_head]
if head_mask is not None:
if head_mask.dim() == 1:
head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(0).unsqueeze(0)
head_mask = head_mask.expand(self.n_layer, -1, -1, -1, -1)
elif head_mask.dim() == 2:
head_mask = head_mask.unsqueeze(1).unsqueeze(1).unsqueeze(1)
head_mask = head_mask.to(dtype=next(self.parameters()).dtype) # switch to fload if need + fp16 compatibility
else:
head_mask = [None] * self.n_layer
new_mems = ()
if mems is None:
mems = [None] * len(self.layer)
attentions = []
hidden_states = []
for i, layer_module in enumerate(self.layer):
# cache new mems
if self.mem_len is not None and self.mem_len > 0 and self.output_past:
new_mems = new_mems + (self.cache_mem(output_h, mems[i]),)
if self.output_hidden_states:
hidden_states.append((output_h, output_g) if output_g is not None else output_h)
outputs = layer_module([output_h, output_g, non_tgt_mask, attn_mask,
pos_emb, seg_mat, mems[i], target_mapping,
head_mask[i]], training=training)
output_h, output_g = outputs[:2]
if self.output_attentions:
attentions.append(outputs[2])
# Add last hidden state
if self.output_hidden_states:
hidden_states.append((output_h, output_g) if output_g is not None else output_h)
output = self.dropout(output_g if output_g is not None else output_h, training=training)
# Prepare outputs, we transpose back here to shape [bsz, len, hidden_dim] (cf. beginning of forward() method)
outputs = (tf.transpose(output, perm=(1, 0, 2)),)
if self.mem_len is not None and self.mem_len > 0 and self.output_past:
outputs = outputs + (new_mems,)
if self.output_hidden_states:
if output_g is not None:
hidden_states = tuple(tf.transpose(h, perm=(1, 0, 2)) for hs in hidden_states for h in hs)
else:
hidden_states = tuple(tf.transpose(hs, perm=(1, 0, 2)) for hs in hidden_states)
outputs = outputs + (hidden_states,)
if self.output_attentions:
attentions = tuple(tf.transpose(t, perm=(2, 3, 0, 1)) for t in attentions)
outputs = outputs + (attentions,)
return outputs # outputs, (new_mems), (hidden_states), (attentions)
class TFXLNetPreTrainedModel(TFPreTrainedModel):
""" An abstract class to handle weights initialization and
a simple interface for dowloading and loading pretrained models.
"""
config_class = XLNetConfig
pretrained_model_archive_map = TF_XLNET_PRETRAINED_MODEL_ARCHIVE_MAP
base_model_prefix = "transformer"
XLNET_START_DOCSTRING = r""" The XLNet model was proposed in
`XLNet: Generalized Autoregressive Pretraining for Language Understanding`_
by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
XLnet is an extension of the Transformer-XL model pre-trained using an autoregressive method
to learn bidirectional contexts by maximizing the expected likelihood over all permutations
of the input sequence factorization order.
The specific attention pattern can be controlled at training and test time using the `perm_mask` input.
Do to the difficulty of training a fully auto-regressive model over various factorization order,
XLNet is pretrained using only a sub-set of the output tokens as target which are selected
with the `target_mapping` input.
To use XLNet for sequential decoding (i.e. not in fully bi-directional setting), use the `perm_mask` and
`target_mapping` inputs to control the attention span and outputs (see examples in `examples/run_generation.py`)
This model is a tf.keras.Model `tf.keras.Model`_ sub-class. 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.
.. _`XLNet: Generalized Autoregressive Pretraining for Language Understanding`:
http://arxiv.org/abs/1906.08237
.. _`tf.keras.Model`:
https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/Model
Note on the model inputs:
TF 2.0 models accepts two formats as inputs:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional arguments.
This second option is usefull when using `tf.keras.Model.fit()` method which currently requires having all the tensors in the first argument of the model call function: `model(inputs)`.
If you choose this second option, 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(inputs_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 associaed to the input names given in the docstring:
`model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`
Parameters:
config (:class:`~transformers.XLNetConfig`): 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 :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""
XLNET_INPUTS_DOCSTRING = r"""
Inputs:
**input_ids**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
Indices of input sequence tokens in the vocabulary.
XLNet is a model with relative position embeddings so you can either pad the inputs on
the right or on the left.
Indices can be obtained using :class:`transformers.XLNetTokenizer`.
See :func:`transformers.PreTrainedTokenizer.encode` and
:func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
**attention_mask**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
Mask to avoid performing attention on padding token indices.
Mask values selected in ``[0, 1]``:
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
**mems**: (`optional`)
list of ``Numpy array`` or ``tf.Tensor`` (one for each layer):
that contains pre-computed hidden-states (key and values in the attention blocks) as output by the model
(see `mems` output below). Can be used to speed up sequential decoding and attend to longer context.
To activate mems you need to set up config.mem_len to a positive value which will be the max number of tokens in
the memory output by the model. E.g. `model = XLNetModel.from_pretrained('xlnet-base-case, mem_len=1024)` will
instantiate a model which can use up to 1024 tokens of memory (in addition to the input it self).
**perm_mask**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length, sequence_length)``:
Mask to indicate the attention pattern for each input token with values selected in ``[0, 1]``:
If ``perm_mask[k, i, j] = 0``, i attend to j in batch k;
if ``perm_mask[k, i, j] = 1``, i does not attend to j in batch k.
If None, each token attends to all the others (full bidirectional attention).
Only used during pretraining (to define factorization order) or for sequential decoding (generation).
**target_mapping**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, num_predict, sequence_length)``:
Mask to indicate the output tokens to use.
If ``target_mapping[k, i, j] = 1``, the i-th predict in batch k is on the j-th token.
Only used during pretraining for partial prediction or for sequential decoding (generation).
**token_type_ids**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
A parallel sequence of tokens (can be used to indicate various portions of the inputs).
The type indices in XLNet are NOT selected in the vocabulary, they can be arbitrary numbers and
the important thing is that they should be different for tokens which belong to different segments.
The model will compute relative segment differences from the given type indices:
0 if the segment id of two tokens are the same, 1 if not.
**input_mask**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
Mask to avoid performing attention on padding token indices.
Negative of `attention_mask`, i.e. with 0 for real tokens and 1 for padding.
Kept for compatibility with the original code base.
You can only uses one of `input_mask` and `attention_mask`
Mask values selected in ``[0, 1]``:
``1`` for tokens that are MASKED, ``0`` for tokens that are NOT MASKED.
**head_mask**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
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**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length, embedding_dim)``:
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.
"""
@add_start_docstrings("The bare XLNet Model transformer outputing raw hidden-states without any specific head on top.",
XLNET_START_DOCSTRING, XLNET_INPUTS_DOCSTRING)
class TFXLNetModel(TFXLNetPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**last_hidden_state**: ``tf.Tensor`` of shape ``(batch_size, sequence_length, hidden_size)``
Sequence of hidden-states at the last layer of the model.
**mems**: (`optional`, returned when ``config.mem_len > 0``)
list of ``tf.Tensor`` (one for each layer):
that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
if config.mem_len > 0 else tuple of None. Can be used to speed up sequential decoding and attend to longer context.
See details in the docstring of the `mems` input above.
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``tf.Tensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
import tensorflow as tf
from transformers import XLNetTokenizer, TFXLNetModel
tokenizer = XLNetTokenizer.from_pretrained('xlnet-large-cased')
model = TFXLNetModel.from_pretrained('xlnet-large-cased')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
outputs = model(input_ids)
last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
"""
def __init__(self, config, *inputs, **kwargs):
super(TFXLNetModel, self).__init__(config, *inputs, **kwargs)
self.transformer = TFXLNetMainLayer(config, name='transformer')
def call(self, inputs, **kwargs):
outputs = self.transformer(inputs, **kwargs)
return outputs
@add_start_docstrings("""XLNet Model with a language modeling head on top
(linear layer with weights tied to the input embeddings). """,
XLNET_START_DOCSTRING, XLNET_INPUTS_DOCSTRING)
class TFXLNetLMHeadModel(TFXLNetPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**prediction_scores**: ``tf.Tensor`` of shape ``(batch_size, sequence_length, config.vocab_size)``
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
**mems**: (`optional`, returned when ``config.mem_len > 0``)
list of ``tf.Tensor`` (one for each layer):
that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
if config.mem_len > 0 else tuple of None. Can be used to speed up sequential decoding and attend to longer context.
See details in the docstring of the `mems` input above.
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``tf.Tensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
import tensorflow as tf
from transformers import XLNetTokenizer, TFXLNetLMHeadModel
tokenizer = XLNetTokenizer.from_pretrained('xlnet-large-cased')
model = TFXLNetLMHeadModel.from_pretrained('xlnet-large-cased')
# We show how to setup inputs to predict a next token using a bi-directional context.
input_ids = tf.constant(tokenizer.encode("Hello, my dog is very <mask>"))[None, :] # We will predict the masked token
perm_mask = tf.zeros((1, input_ids.shape[1], input_ids.shape[1]))
perm_mask[:, :, -1] = 1.0 # Previous tokens don't see last token
target_mapping = tf.zeros((1, 1, input_ids.shape[1])) # Shape [1, 1, seq_length] => let's predict one token
target_mapping[0, 0, -1] = 1.0 # Our first (and only) prediction will be the last token of the sequence (the masked token)
outputs = model(input_ids, perm_mask=perm_mask, target_mapping=target_mapping)
next_token_logits = outputs[0] # Output has shape [target_mapping.size(0), target_mapping.size(1), config.vocab_size]
"""
def __init__(self, config, *inputs, **kwargs):
super(TFXLNetLMHeadModel, self).__init__(config, *inputs, **kwargs)
self.transformer = TFXLNetMainLayer(config, name='transformer')
self.lm_loss = TFXLNetLMHead(config, self.transformer.word_embedding, name='lm_loss')
def get_output_embeddings(self):
return self.lm_loss.input_embeddings
def call(self, inputs, **kwargs):
transformer_outputs = self.transformer(inputs, **kwargs)
hidden_state = transformer_outputs[0]
logits = self.lm_loss(hidden_state)
outputs = (logits,) + transformer_outputs[1:] # Keep mems, hidden states, attentions if there are in it
return outputs # return logits, (mems), (hidden states), (attentions)
@add_start_docstrings("""XLNet Model with a sequence classification/regression head on top (a linear layer on top of
the pooled output) e.g. for GLUE tasks. """,
XLNET_START_DOCSTRING, XLNET_INPUTS_DOCSTRING)
class TFXLNetForSequenceClassification(TFXLNetPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**logits**: ``tf.Tensor`` of shape ``(batch_size, config.num_labels)``
Classification (or regression if config.num_labels==1) scores (before SoftMax).
**mems**: (`optional`, returned when ``config.mem_len > 0``)
list of ``tf.Tensor`` (one for each layer):
that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
if config.mem_len > 0 else tuple of None. Can be used to speed up sequential decoding and attend to longer context.
See details in the docstring of the `mems` input above.
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``tf.Tensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
import tensorflow as tf
from transformers import XLNetTokenizer, TFXLNetForSequenceClassification
tokenizer = XLNetTokenizer.from_pretrained('xlnet-large-cased')
model = TFXLNetForSequenceClassification.from_pretrained('xlnet-large-cased')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
outputs = model(input_ids)
logits = outputs[0]
"""
def __init__(self, config, *inputs, **kwargs):
super(TFXLNetForSequenceClassification, self).__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.transformer = TFXLNetMainLayer(config, name='transformer')
self.sequence_summary = TFSequenceSummary(config, initializer_range=config.initializer_range, name='sequence_summary')
self.logits_proj = tf.keras.layers.Dense(config.num_labels,
kernel_initializer=get_initializer(config.initializer_range),
name='logits_proj')
def call(self, inputs, **kwargs):
transformer_outputs = self.transformer(inputs, **kwargs)
output = transformer_outputs[0]
output = self.sequence_summary(output)
logits = self.logits_proj(output)
outputs = (logits,) + transformer_outputs[1:] # Keep mems, hidden states, attentions if there are in it
return outputs # return logits, (mems), (hidden states), (attentions)
# @add_start_docstrings("""XLNet 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`). """,
# XLNET_START_DOCSTRING, XLNET_INPUTS_DOCSTRING)
# class TFXLNetForQuestionAnswering(TFXLNetPreTrainedModel):
class TFXLNetForQuestionAnsweringSimple(TFXLNetPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**start_scores**: ``tf.Tensor`` of shape ``(batch_size, sequence_length,)``
Span-start scores (before SoftMax).
**end_scores**: ``tf.Tensor`` of shape ``(batch_size, sequence_length,)``
Span-end scores (before SoftMax).
**mems**: (`optional`, returned when ``config.mem_len > 0``)
list of ``tf.Tensor`` (one for each layer):
that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
if config.mem_len > 0 else tuple of None. Can be used to speed up sequential decoding and attend to longer context.
See details in the docstring of the `mems` input above.
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``tf.Tensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
import tensorflow as tf
from transformers import XLNetTokenizer, TFXLNetForQuestionAnsweringSimple
tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased')
model = TFXLNetForQuestionAnsweringSimple.from_pretrained('xlnet-base-cased')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
outputs = model(input_ids)
start_scores, end_scores = outputs[:2]
"""
def __init__(self, config, *inputs, **kwargs):
super(TFXLNetForQuestionAnsweringSimple, self).__init__(config, *inputs, **kwargs)
self.transformer = TFXLNetMainLayer(config, name='transformer')
self.qa_outputs = tf.keras.layers.Dense(config.num_labels,
kernel_initializer=get_initializer(config.initializer_range),
name='qa_outputs')
def call(self, inputs, **kwargs):
transformer_outputs = self.transformer(inputs, **kwargs)
sequence_output = transformer_outputs[0]
logits = self.qa_outputs(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)
outputs = (start_logits, end_logits,) + transformer_outputs[1:] # Keep mems, hidden states, attentions if there are in it
return outputs # start_logits, end_logits, (mems), (hidden_states), (attentions)
# @add_start_docstrings("""XLNet 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`). """,
# XLNET_START_DOCSTRING, XLNET_INPUTS_DOCSTRING)
# class TFXLNetForQuestionAnswering(TFXLNetPreTrainedModel):
# r"""
# Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
# **start_top_log_probs**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
# ``tf.Tensor`` of shape ``(batch_size, config.start_n_top)``
# Log probabilities for the top config.start_n_top start token possibilities (beam-search).
# **start_top_index**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
# ``torch.LongTensor`` of shape ``(batch_size, config.start_n_top)``
# Indices for the top config.start_n_top start token possibilities (beam-search).
# **end_top_log_probs**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
# ``tf.Tensor`` of shape ``(batch_size, config.start_n_top * config.end_n_top)``
# Log probabilities for the top ``config.start_n_top * config.end_n_top`` end token possibilities (beam-search).
# **end_top_index**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
# ``torch.LongTensor`` of shape ``(batch_size, config.start_n_top * config.end_n_top)``
# Indices for the top ``config.start_n_top * config.end_n_top`` end token possibilities (beam-search).
# **cls_logits**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
# ``tf.Tensor`` of shape ``(batch_size,)``
# Log probabilities for the ``is_impossible`` label of the answers.
# **mems**:
# list of ``tf.Tensor`` (one for each layer):
# that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
# if config.mem_len > 0 else tuple of None. Can be used to speed up sequential decoding and attend to longer context.
# See details in the docstring of the `mems` input above.
# **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
# list of ``tf.Tensor`` (one for the output of each layer + the output of the embeddings)
# 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**: (`optional`, returned when ``config.output_attentions=True``)
# list 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.
# Examples::
# tokenizer = XLMTokenizer.from_pretrained('xlm-mlm-en-2048')
# model = XLMForQuestionAnswering.from_pretrained('xlnet-large-cased')
# input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
# start_positions = tf.constant([1])
# end_positions = tf.constant([3])
# outputs = model(input_ids, start_positions=start_positions, end_positions=end_positions)
# loss, start_scores, end_scores = outputs[:2]
# """
# def __init__(self, config, *inputs, **kwargs):
# super(TFXLNetForQuestionAnswering, self).__init__(config, *inputs, **kwargs)
# self.start_n_top = config.start_n_top
# self.end_n_top = config.end_n_top
# self.transformer = TFXLNetMainLayer(config, name='transformer')
# self.start_logits = TFPoolerStartLogits(config, name='start_logits')
# self.end_logits = TFPoolerEndLogits(config, name='end_logits')
# self.answer_class = TFPoolerAnswerClass(config, name='answer_class')
# def call(self, inputs, training=False):
# transformer_outputs = self.transformer(inputs, training=training)
# hidden_states = transformer_outputs[0]
# start_logits = self.start_logits(hidden_states, p_mask=p_mask)
# outputs = transformer_outputs[1:] # Keep mems, hidden states, attentions if there are in it
# if start_positions is not None and end_positions is not None:
# # If we are on multi-GPU, let's remove the dimension added by batch splitting
# for x in (start_positions, end_positions, cls_index, is_impossible):
# if x is not None and x.dim() > 1:
# x.squeeze_(-1)
# # during training, compute the end logits based on the ground truth of the start position
# end_logits = self.end_logits(hidden_states, start_positions=start_positions, p_mask=p_mask)
# loss_fct = CrossEntropyLoss()
# start_loss = loss_fct(start_logits, start_positions)
# end_loss = loss_fct(end_logits, end_positions)
# total_loss = (start_loss + end_loss) / 2
# if cls_index is not None and is_impossible is not None:
# # Predict answerability from the representation of CLS and START
# cls_logits = self.answer_class(hidden_states, start_positions=start_positions, cls_index=cls_index)
# loss_fct_cls = nn.BCEWithLogitsLoss()
# cls_loss = loss_fct_cls(cls_logits, is_impossible)
# # note(zhiliny): by default multiply the loss by 0.5 so that the scale is comparable to start_loss and end_loss
# total_loss += cls_loss * 0.5
# outputs = (total_loss,) + outputs
# else:
# # during inference, compute the end logits based on beam search
# bsz, slen, hsz = hidden_states.size()
# start_log_probs = F.softmax(start_logits, dim=-1) # shape (bsz, slen)
# start_top_log_probs, start_top_index = torch.topk(start_log_probs, self.start_n_top, dim=-1) # shape (bsz, start_n_top)
# start_top_index_exp = start_top_index.unsqueeze(-1).expand(-1, -1, hsz) # shape (bsz, start_n_top, hsz)
# start_states = torch.gather(hidden_states, -2, start_top_index_exp) # shape (bsz, start_n_top, hsz)
# start_states = start_states.unsqueeze(1).expand(-1, slen, -1, -1) # shape (bsz, slen, start_n_top, hsz)
# hidden_states_expanded = hidden_states.unsqueeze(2).expand_as(start_states) # shape (bsz, slen, start_n_top, hsz)
# p_mask = p_mask.unsqueeze(-1) if p_mask is not None else None
# end_logits = self.end_logits(hidden_states_expanded, start_states=start_states, p_mask=p_mask)
# end_log_probs = F.softmax(end_logits, dim=1) # shape (bsz, slen, start_n_top)
# end_top_log_probs, end_top_index = torch.topk(end_log_probs, self.end_n_top, dim=1) # shape (bsz, end_n_top, start_n_top)
# end_top_log_probs = end_top_log_probs.view(-1, self.start_n_top * self.end_n_top)
# end_top_index = end_top_index.view(-1, self.start_n_top * self.end_n_top)
# start_states = torch.einsum("blh,bl->bh", hidden_states, start_log_probs) # get the representation of START as weighted sum of hidden states
# cls_logits = self.answer_class(hidden_states, start_states=start_states, cls_index=cls_index) # Shape (batch size,): one single `cls_logits` for each sample
# outputs = (start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits) + outputs
# # return start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits
# # or (if labels are provided) (total_loss,)
# return outputs
| 57,790 | 50.92363 | 193 |
py
|
DeeBERT
|
DeeBERT-master/transformers/convert_openai_original_tf_checkpoint_to_pytorch.py
|
# 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 OpenAI GPT checkpoint."""
from __future__ import absolute_import, division, print_function
import argparse
from io import open
import torch
from transformers import (CONFIG_NAME, WEIGHTS_NAME,
OpenAIGPTConfig,
OpenAIGPTModel,
load_tf_weights_in_openai_gpt)
import logging
logging.basicConfig(level=logging.INFO)
def convert_openai_checkpoint_to_pytorch(openai_checkpoint_folder_path, openai_config_file, pytorch_dump_folder_path):
# Construct model
if openai_config_file == "":
config = OpenAIGPTConfig()
else:
config = OpenAIGPTConfig.from_json_file(openai_config_file)
model = OpenAIGPTModel(config)
# Load weights from numpy
load_tf_weights_in_openai_gpt(model, config, openai_checkpoint_folder_path)
# Save pytorch-model
pytorch_weights_dump_path = pytorch_dump_folder_path + '/' + WEIGHTS_NAME
pytorch_config_dump_path = pytorch_dump_folder_path + '/' + CONFIG_NAME
print("Save PyTorch model to {}".format(pytorch_weights_dump_path))
torch.save(model.state_dict(), pytorch_weights_dump_path)
print("Save configuration file to {}".format(pytorch_config_dump_path))
with open(pytorch_config_dump_path, "w", encoding="utf-8") as f:
f.write(config.to_json_string())
if __name__ == "__main__":
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--openai_checkpoint_folder_path",
default = None,
type = str,
required = True,
help = "Path to the TensorFlow checkpoint path.")
parser.add_argument("--pytorch_dump_folder_path",
default = None,
type = str,
required = True,
help = "Path to the output PyTorch model.")
parser.add_argument("--openai_config_file",
default = "",
type = str,
help = "An optional config json file corresponding to the pre-trained OpenAI model. \n"
"This specifies the model architecture.")
args = parser.parse_args()
convert_openai_checkpoint_to_pytorch(args.openai_checkpoint_folder_path,
args.openai_config_file,
args.pytorch_dump_folder_path)
| 3,161 | 40.605263 | 118 |
py
|
DeeBERT
|
DeeBERT-master/transformers/modeling_camembert.py
|
# coding=utf-8
# Copyright 2019 Inria, Facebook AI Research 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.
"""PyTorch CamemBERT model. """
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import logging
from .modeling_roberta import RobertaModel, RobertaForMaskedLM, RobertaForSequenceClassification, RobertaForMultipleChoice, RobertaForTokenClassification
from .configuration_camembert import CamembertConfig
from .file_utils import add_start_docstrings
logger = logging.getLogger(__name__)
CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
'camembert-base': "https://s3.amazonaws.com/models.huggingface.co/bert/camembert-base-pytorch_model.bin",
}
CAMEMBERT_START_DOCSTRING = r""" The CamemBERT model was proposed in
`CamemBERT: a Tasty French Language Model`_
by Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah, and Benoît Sagot. It is based on Facebook's RoBERTa model released in 2019.
It is a model trained on 138GB of French text.
This implementation is the same as RoBERTa.
This model is a PyTorch `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.
.. _`CamemBERT: a Tasty French Language Model`:
https://arxiv.org/abs/1911.03894
.. _`torch.nn.Module`:
https://pytorch.org/docs/stable/nn.html#module
Parameters:
config (:class:`~transformers.CamembertConfig`): 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 :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""
CAMEMBERT_INPUTS_DOCSTRING = r"""
Inputs:
**input_ids**: ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Indices of input sequence tokens in the vocabulary.
To match pre-training, CamemBERT input sequence should be formatted with <s> and </s> tokens as follows:
(a) For sequence pairs:
``tokens: <s> Is this Jacksonville ? </s> </s> No it is not . </s>``
(b) For single sequences:
``tokens: <s> the dog is hairy . </s>``
Fully encoded sequences or sequence pairs can be obtained using the CamembertTokenizer.encode function with
the ``add_special_tokens`` parameter set to ``True``.
CamemBERT is a model with absolute position embeddings so it's usually advised to pad the inputs on
the right rather than the left.
See :func:`transformers.PreTrainedTokenizer.encode` and
:func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
**attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:
Mask to avoid performing attention on padding token indices.
Mask values selected in ``[0, 1]``:
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
**token_type_ids**: (`optional` need to be trained) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Optional segment token indices to indicate first and second portions of the inputs.
This embedding matrice is not trained (not pretrained during CamemBERT pretraining), you will have to train it
during finetuning.
Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
corresponds to a `sentence B` token
(see `BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding`_ for more details).
**position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Indices of positions of each input sequence tokens in the position embeddings.
Selected in the range ``[0, config.max_position_embeddings - 1[``.
**head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
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**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, embedding_dim)``:
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.
"""
@add_start_docstrings("The bare CamemBERT Model transformer outputting raw hidden-states without any specific head on top.",
CAMEMBERT_START_DOCSTRING, CAMEMBERT_INPUTS_DOCSTRING)
class CamembertModel(RobertaModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**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.
**pooler_output**: ``torch.FloatTensor`` of shape ``(batch_size, hidden_size)``
Last layer hidden-state of the first token of the sequence (classification token)
further processed by a Linear layer and a Tanh activation function. The Linear
layer weights are trained from the next sentence prediction (classification)
eo match pre-training, CamemBERT input sequence should be formatted with [CLS] and [SEP] tokens as follows:
(a) For sequence pairs:
``tokens: [CLS] is this jack ##son ##ville ? [SEP] [SEP] no it is not . [SEP]``
``token_type_ids: 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1``
(b) For single sequences:
``tokens: [CLS] the dog is hairy . [SEP]``
``token_type_ids: 0 0 0 0 0 0 0``
objective during Bert pretraining. This output is usually *not* a good summary
of the semantic content of the input, you're often better with averaging or pooling
the sequence of hidden-states for the whole input sequence.
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
tokenizer = CamembertTokenizer.from_pretrained('camembert-base')
model = CamembertModel.from_pretrained('camembert-base')
input_ids = torch.tensor(tokenizer.encode("J'aime le camembert !")).unsqueeze(0) # Batch size 1
outputs = model(input_ids)
last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
"""
config_class = CamembertConfig
pretrained_model_archive_map = CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP
@add_start_docstrings("""CamemBERT Model with a `language modeling` head on top. """,
CAMEMBERT_START_DOCSTRING, CAMEMBERT_INPUTS_DOCSTRING)
class CamembertForMaskedLM(RobertaForMaskedLM):
r"""
**masked_lm_labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Labels for computing the masked language modeling loss.
Indices should be in ``[-1, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)
Tokens with indices set to ``-1`` are ignored (masked), the loss is only computed for the tokens with labels
in ``[0, ..., config.vocab_size]``
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**loss**: (`optional`, returned when ``masked_lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Masked language modeling loss.
**prediction_scores**: ``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).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
tokenizer = CamembertTokenizer.from_pretrained('camembert-base')
model = CamembertForMaskedLM.from_pretrained('camembert-base')
input_ids = torch.tensor(tokenizer.encode("J'aime le camembert !")).unsqueeze(0) # Batch size 1
outputs = model(input_ids, masked_lm_labels=input_ids)
loss, prediction_scores = outputs[:2]
"""
config_class = CamembertConfig
pretrained_model_archive_map = CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP
@add_start_docstrings("""CamemBERT Model transformer with a sequence classification/regression head on top (a linear layer
on top of the pooled output) e.g. for GLUE tasks. """,
CAMEMBERT_START_DOCSTRING, CAMEMBERT_INPUTS_DOCSTRING)
class CamembertForSequenceClassification(RobertaForSequenceClassification):
r"""
**labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
Labels for computing the sequence classification/regression loss.
Indices should be in ``[0, ..., config.num_labels]``.
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: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Classification (or regression if config.num_labels==1) loss.
**logits**: ``torch.FloatTensor`` of shape ``(batch_size, config.num_labels)``
Classification (or regression if config.num_labels==1) scores (before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
tokenizer = CamembertTokenizer.from_pretrained('camembert-base')
model = CamembertForSequenceClassification.from_pretrained('camembert-base')
input_ids = torch.tensor(tokenizer.encode("J'aime le camembert !")).unsqueeze(0) # Batch size 1
labels = torch.tensor([1]).unsqueeze(0) # Batch size 1
outputs = model(input_ids, labels=labels)
loss, logits = outputs[:2]
"""
config_class = CamembertConfig
pretrained_model_archive_map = CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP
@add_start_docstrings("""CamemBERT 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. """,
CAMEMBERT_START_DOCSTRING, CAMEMBERT_INPUTS_DOCSTRING)
class CamembertForMultipleChoice(RobertaForMultipleChoice):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Classification loss.
**classification_scores**: ``torch.FloatTensor`` of shape ``(batch_size, num_choices)`` where `num_choices` is the size of the second dimension
of the input tensors. (see `input_ids` above).
Classification scores (before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
tokenizer = CamembertTokenizer.from_pretrained('camembert-base')
model = CamembertForMultipleChoice.from_pretrained('camembert-base')
choices = ["J'aime le camembert !", "Je deteste le camembert !"]
input_ids = torch.tensor([tokenizer.encode(s, add_special_tokens=True) for s in choices]).unsqueeze(0) # Batch size 1, 2 choices
labels = torch.tensor(1).unsqueeze(0) # Batch size 1
outputs = model(input_ids, labels=labels)
loss, classification_scores = outputs[:2]
"""
config_class = CamembertConfig
pretrained_model_archive_map = CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP
@add_start_docstrings("""CamemBERT 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. """,
CAMEMBERT_START_DOCSTRING, CAMEMBERT_INPUTS_DOCSTRING)
class CamembertForTokenClassification(RobertaForTokenClassification):
r"""
**labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Labels for computing the token classification loss.
Indices should be in ``[0, ..., config.num_labels - 1]``.
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Classification loss.
**scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, config.num_labels)``
Classification scores (before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
tokenizer = CamembertTokenizer.from_pretrained('camembert-base')
model = CamembertForTokenClassification.from_pretrained('camembert-base')
input_ids = torch.tensor(tokenizer.encode("J'aime le camembert !", add_special_tokens=True)).unsqueeze(0) # Batch size 1
labels = torch.tensor([1] * input_ids.size(1)).unsqueeze(0) # Batch size 1
outputs = model(input_ids, labels=labels)
loss, scores = outputs[:2]
"""
config_class = CamembertConfig
pretrained_model_archive_map = CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP
| 17,760 | 59.411565 | 217 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tokenization_xlnet.py
|
# 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 XLNet model."""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import logging
import os
from shutil import copyfile
import unicodedata
import six
from .tokenization_utils import PreTrainedTokenizer
logger = logging.getLogger(__name__)
VOCAB_FILES_NAMES = {'vocab_file': 'spiece.model'}
PRETRAINED_VOCAB_FILES_MAP = {
'vocab_file':
{
'xlnet-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-base-cased-spiece.model",
'xlnet-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-large-cased-spiece.model",
}
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
'xlnet-base-cased': None,
'xlnet-large-cased': None,
}
SPIECE_UNDERLINE = u'▁'
# Segments (not really needed)
SEG_ID_A = 0
SEG_ID_B = 1
SEG_ID_CLS = 2
SEG_ID_SEP = 3
SEG_ID_PAD = 4
class XLNetTokenizer(PreTrainedTokenizer):
"""
SentencePiece based tokenizer. Peculiarities:
- requires `SentencePiece <https://github.com/google/sentencepiece>`_
"""
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=False, remove_space=True, keep_accents=False,
bos_token="<s>", eos_token="</s>", unk_token="<unk>", sep_token="<sep>",
pad_token="<pad>", cls_token="<cls>", mask_token="<mask>",
additional_special_tokens=["<eop>", "<eod>"], **kwargs):
super(XLNetTokenizer, self).__init__(bos_token=bos_token, eos_token=eos_token,
unk_token=unk_token, sep_token=sep_token,
pad_token=pad_token, cls_token=cls_token,
mask_token=mask_token, additional_special_tokens=
additional_special_tokens, **kwargs)
self.max_len_single_sentence = self.max_len - 2 # take into account special tokens
self.max_len_sentences_pair = self.max_len - 3 # take into account special tokens
try:
import sentencepiece as spm
except ImportError:
logger.warning("You need to install SentencePiece to use XLNetTokenizer: https://github.com/google/sentencepiece"
"pip install sentencepiece")
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.Load(vocab_file)
@property
def vocab_size(self):
return len(self.sp_model)
def __getstate__(self):
state = self.__dict__.copy()
state["sp_model"] = None
return state
def __setstate__(self, d):
self.__dict__ = d
try:
import sentencepiece as spm
except ImportError:
logger.warning("You need to install SentencePiece to use XLNetTokenizer: https://github.com/google/sentencepiece"
"pip install sentencepiece")
self.sp_model = spm.SentencePieceProcessor()
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 six.PY2 and isinstance(outputs, str):
outputs = outputs.decode('utf-8')
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, return_unicode=True, sample=False):
""" Tokenize a string.
return_unicode is used only for py2
"""
text = self.preprocess_text(text)
# note(zhiliny): in some systems, sentencepiece only accepts str for py2
if six.PY2 and isinstance(text, unicode):
text = text.encode('utf-8')
if not sample:
pieces = self.sp_model.EncodeAsPieces(text)
else:
pieces = self.sp_model.SampleEncodeAsPieces(text, 64, 0.1)
new_pieces = []
for piece in pieces:
if len(piece) > 1 and piece[-1] == ',' 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)
# note(zhiliny): convert back to unicode for py2
if six.PY2 and return_unicode:
ret_pieces = []
for piece in new_pieces:
if isinstance(piece, str):
piece = piece.decode('utf-8')
ret_pieces.append(piece)
new_pieces = ret_pieces
return new_pieces
def _convert_token_to_id(self, token):
""" Converts a token (str/unicode) in an id using the vocab. """
return self.sp_model.PieceToId(token)
def _convert_id_to_token(self, index, return_unicode=True):
"""Converts an index (integer) in a token (string/unicode) using the vocab."""
token = self.sp_model.IdToPiece(index)
if six.PY2 and return_unicode and isinstance(token, str):
token = token.decode('utf-8')
return token
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (strings for sub-words) in a single string."""
out_string = ''.join(tokens).replace(SPIECE_UNDERLINE, ' ').strip()
return out_string
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks
by concatenating and adding special tokens.
A RoBERTa sequence has the following format:
single sequence: <s> X </s>
pair of sequences: <s> A </s></s> B </s>
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return token_ids_0 + sep + cls
return token_ids_0 + sep + token_ids_1 + sep + cls
def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False):
"""
Retrieves 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`` or ``encode_plus`` methods.
Args:
token_ids_0: list of ids (must not contain special tokens)
token_ids_1: Optional list of ids (must not contain special tokens), necessary when fetching sequence ids
for sequence pairs
already_has_special_tokens: (default False) Set to True if the token list is already formated with
special tokens for the model
Returns:
A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
if token_ids_1 is not None:
raise ValueError("You should not supply a second sequence if the provided sequence of "
"ids is already formated with special tokens for the model.")
return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))
if token_ids_1 is not None:
return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1, 1]
return ([0] * len(token_ids_0)) + [1, 1]
def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None):
"""
Creates a mask from the two sequences passed to be used in a sequence-pair classification task.
A BERT sequence pair mask has the following format:
0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 2
| first sequence | second sequence | CLS segment ID
if token_ids_1 is None, only returns the first portion of the mask (0's).
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
cls_segment_id = [2]
if token_ids_1 is None:
return len(token_ids_0 + sep + cls) * [0]
return len(token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1] + cls_segment_id
def save_vocabulary(self, save_directory):
""" Save the sentencepiece vocabulary (copy original file) and special tokens file
to a directory.
"""
if not os.path.isdir(save_directory):
logger.error("Vocabulary path ({}) should be a directory".format(save_directory))
return
out_vocab_file = os.path.join(save_directory, 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,)
| 10,317 | 39.622047 | 125 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tokenization_distilbert.py
|
# 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.
"""Tokenization classes for DistilBERT."""
from __future__ import absolute_import, division, print_function, unicode_literals
import collections
import logging
import os
import unicodedata
from io import open
from .tokenization_bert import BertTokenizer
logger = logging.getLogger(__name__)
VOCAB_FILES_NAMES = {'vocab_file': 'vocab.txt'}
PRETRAINED_VOCAB_FILES_MAP = {
'vocab_file':
{
'distilbert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt",
'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt",
}
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
'distilbert-base-uncased': 512,
'distilbert-base-uncased-distilled-squad': 512,
}
class DistilBertTokenizer(BertTokenizer):
r"""
Constructs a DistilBertTokenizer.
:class:`~transformers.DistilBertTokenizer` is identical to BertTokenizer and runs end-to-end tokenization: punctuation splitting + wordpiece
Args:
vocab_file: Path to a one-wordpiece-per-line vocabulary file
do_lower_case: Whether to lower case the input. Only has an effect when do_wordpiece_only=False
do_basic_tokenize: Whether to do basic tokenization before wordpiece.
max_len: An artificial maximum length to truncate tokenized sequences to; Effective maximum length is always the
minimum of this value (if specified) and the underlying BERT model's sequence length.
never_split: List of tokens which will never be split during tokenization. Only has an effect when
do_wordpiece_only=False
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
| 2,438 | 37.714286 | 144 |
py
|
DeeBERT
|
DeeBERT-master/transformers/convert_xlm_original_pytorch_checkpoint_to_pytorch.py
|
# 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 OpenAI GPT checkpoint."""
from __future__ import absolute_import, division, print_function
import argparse
import json
from io import open
import torch
import numpy
from transformers import CONFIG_NAME, WEIGHTS_NAME
from transformers.tokenization_xlm import VOCAB_FILES_NAMES
import logging
logging.basicConfig(level=logging.INFO)
def convert_xlm_checkpoint_to_pytorch(xlm_checkpoint_path, pytorch_dump_folder_path):
# Load checkpoint
chkpt = torch.load(xlm_checkpoint_path, map_location='cpu')
state_dict = chkpt['model']
# We have the base model one level deeper than the original XLM repository
two_levels_state_dict = {}
for k, v in state_dict.items():
if 'pred_layer' in k:
two_levels_state_dict[k] = v
else:
two_levels_state_dict['transformer.' + k] = v
config = chkpt['params']
config = dict((n, v) for n, v in config.items() if not isinstance(v, (torch.FloatTensor, numpy.ndarray)))
vocab = chkpt['dico_word2id']
vocab = dict((s + '</w>' if s.find('@@') == -1 and i > 13 else s.replace('@@', ''), i) for s, i in vocab.items())
# Save pytorch-model
pytorch_weights_dump_path = pytorch_dump_folder_path + '/' + WEIGHTS_NAME
pytorch_config_dump_path = pytorch_dump_folder_path + '/' + CONFIG_NAME
pytorch_vocab_dump_path = pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['vocab_file']
print("Save PyTorch model to {}".format(pytorch_weights_dump_path))
torch.save(two_levels_state_dict, pytorch_weights_dump_path)
print("Save configuration file to {}".format(pytorch_config_dump_path))
with open(pytorch_config_dump_path, "w", encoding="utf-8") as f:
f.write(json.dumps(config, indent=2) + "\n")
print("Save vocab file to {}".format(pytorch_config_dump_path))
with open(pytorch_vocab_dump_path, "w", encoding="utf-8") as f:
f.write(json.dumps(vocab, indent=2) + "\n")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--xlm_checkpoint_path",
default = None,
type = str,
required = True,
help = "Path the official PyTorch dump.")
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_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)
| 3,235 | 37.52381 | 117 |
py
|
DeeBERT
|
DeeBERT-master/transformers/modeling_tf_xlm.py
|
# coding=utf-8
# Copyright 2019-present, 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.
""" TF 2.0 XLM model.
"""
from __future__ import absolute_import, division, print_function, unicode_literals
import logging
import math
import os
import itertools
import numpy as np
import tensorflow as tf
from .configuration_xlm import XLMConfig
from .modeling_tf_utils import TFPreTrainedModel, TFSharedEmbeddings, TFSequenceSummary, shape_list, get_initializer, DUMMY_INPUTS
from .file_utils import add_start_docstrings
logger = logging.getLogger(__name__)
TF_XLM_PRETRAINED_MODEL_ARCHIVE_MAP = {
'xlm-mlm-en-2048': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-en-2048-tf_model.h5",
'xlm-mlm-ende-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-ende-1024-tf_model.h5",
'xlm-mlm-enfr-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-enfr-1024-tf_model.h5",
'xlm-mlm-enro-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-enro-1024-tf_model.h5",
'xlm-mlm-tlm-xnli15-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-tlm-xnli15-1024-tf_model.h5",
'xlm-mlm-xnli15-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-xnli15-1024-tf_model.h5",
'xlm-clm-enfr-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-clm-enfr-1024-tf_model.h5",
'xlm-clm-ende-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-clm-ende-1024-tf_model.h5",
'xlm-mlm-17-1280': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-17-1280-tf_model.h5",
'xlm-mlm-100-1280': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-100-1280-tf_model.h5",
}
def create_sinusoidal_embeddings(n_pos, dim, out):
position_enc = np.array([
[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)]
for pos in range(n_pos)
])
out[:, 0::2] = tf.constant(np.sin(position_enc[:, 0::2]))
out[:, 1::2] = tf.constant(np.cos(position_enc[:, 1::2]))
def gelu(x):
""" Gaussian Error Linear Unit.
Original Implementation of the gelu activation function in Google Bert repo when initially created.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
Also see https://arxiv.org/abs/1606.08415
"""
cdf = 0.5 * (1.0 + tf.math.erf(x / tf.math.sqrt(2.0)))
return x * cdf
def get_masks(slen, lengths, causal, padding_mask=None, dtype=tf.float32):
"""
Generate hidden states mask, and optionally an attention mask.
"""
bs = shape_list(lengths)[0]
if padding_mask is not None:
mask = padding_mask
else:
# assert lengths.max().item() <= slen
alen = tf.range(slen)
mask = tf.math.less(alen, lengths[:, tf.newaxis])
# attention mask is the same as mask, or triangular inferior attention (causal)
if causal:
attn_mask = tf.less_equal(tf.tile(alen[tf.newaxis, tf.newaxis, :], (bs, slen, 1)),
alen[tf.newaxis, :, tf.newaxis])
else:
attn_mask = mask
# sanity check
# assert shape_list(mask) == [bs, slen]
tf.debugging.assert_equal(shape_list(mask), [bs, slen])
assert causal is False or shape_list(attn_mask) == [bs, slen, slen]
mask = tf.cast(mask, dtype=dtype)
attn_mask = tf.cast(attn_mask, dtype=dtype)
return mask, attn_mask
class TFMultiHeadAttention(tf.keras.layers.Layer):
NEW_ID = itertools.count()
def __init__(self, n_heads, dim, config, **kwargs):
super(TFMultiHeadAttention, self).__init__(**kwargs)
self.layer_id = next(TFMultiHeadAttention.NEW_ID)
self.output_attentions = config.output_attentions
self.dim = dim
self.n_heads = n_heads
assert self.dim % self.n_heads == 0
self.q_lin = tf.keras.layers.Dense(dim, kernel_initializer=get_initializer(config.init_std), name='q_lin')
self.k_lin = tf.keras.layers.Dense(dim, kernel_initializer=get_initializer(config.init_std), name='k_lin')
self.v_lin = tf.keras.layers.Dense(dim, kernel_initializer=get_initializer(config.init_std), name='v_lin')
self.out_lin = tf.keras.layers.Dense(dim, kernel_initializer=get_initializer(config.init_std), name='out_lin')
self.dropout = tf.keras.layers.Dropout(config.attention_dropout)
self.pruned_heads = set()
def prune_heads(self, heads):
raise NotImplementedError
def call(self, inputs, training=False):
"""
Self-attention (if kv is None) or attention over source sentence (provided by kv).
"""
input, mask, kv, cache, head_mask = inputs
# Input is (bs, qlen, dim)
# Mask is (bs, klen) (non-causal) or (bs, klen, klen)
bs, qlen, dim = shape_list(input)
if kv is None:
klen = qlen if cache is None else cache['slen'] + qlen
else:
klen = shape_list(kv)[1]
# assert dim == self.dim, 'Dimensions do not match: %s input vs %s configured' % (dim, self.dim)
n_heads = self.n_heads
dim_per_head = self.dim // n_heads
mask_reshape = (bs, 1, qlen, klen) if len(shape_list(mask)) == 3 else (bs, 1, 1, klen)
def shape(x):
""" projection """
return tf.transpose(tf.reshape(x, (bs, -1, self.n_heads, dim_per_head)), perm=(0, 2, 1, 3))
def unshape(x):
""" compute context """
return tf.reshape(tf.transpose(x, perm=(0, 2, 1, 3)), (bs, -1, self.n_heads * dim_per_head))
q = shape(self.q_lin(input)) # (bs, n_heads, qlen, dim_per_head)
if kv is None:
k = shape(self.k_lin(input)) # (bs, n_heads, qlen, dim_per_head)
v = shape(self.v_lin(input)) # (bs, n_heads, qlen, dim_per_head)
elif cache is None or self.layer_id not in cache:
k = v = kv
k = shape(self.k_lin(k)) # (bs, n_heads, qlen, dim_per_head)
v = shape(self.v_lin(v)) # (bs, n_heads, qlen, dim_per_head)
if cache is not None:
if self.layer_id in cache:
if kv is None:
k_, v_ = cache[self.layer_id]
k = tf.concat([k_, k], axis=2) # (bs, n_heads, klen, dim_per_head)
v = tf.concat([v_, v], axis=2) # (bs, n_heads, klen, dim_per_head)
else:
k, v = cache[self.layer_id]
cache[self.layer_id] = (k, v)
q = q / math.sqrt(dim_per_head) # (bs, n_heads, qlen, dim_per_head)
scores = tf.matmul(q, k, transpose_b=True) # (bs, n_heads, qlen, klen)
mask = tf.reshape(mask, mask_reshape) # (bs, n_heads, qlen, klen)
# scores.masked_fill_(mask, -float('inf')) # (bs, n_heads, qlen, klen)
scores = scores - 1e30 * (1.0 - mask)
weights = tf.nn.softmax(scores, axis=-1) # (bs, n_heads, qlen, klen)
weights = self.dropout(weights, training=training) # (bs, n_heads, qlen, klen)
# Mask heads if we want to
if head_mask is not None:
weights = weights * head_mask
context = tf.matmul(weights, v) # (bs, n_heads, qlen, dim_per_head)
context = unshape(context) # (bs, qlen, dim)
outputs = (self.out_lin(context),)
if self.output_attentions:
outputs = outputs + (weights,)
return outputs
class TFTransformerFFN(tf.keras.layers.Layer):
def __init__(self, in_dim, dim_hidden, out_dim, config, **kwargs):
super(TFTransformerFFN, self).__init__(**kwargs)
self.lin1 = tf.keras.layers.Dense(dim_hidden, kernel_initializer=get_initializer(config.init_std), name='lin1')
self.lin2 = tf.keras.layers.Dense(out_dim, kernel_initializer=get_initializer(config.init_std), name='lin2')
self.act = tf.keras.layers.Activation(gelu) if config.gelu_activation else tf.keras.activations.relu
self.dropout = tf.keras.layers.Dropout(config.dropout)
def call(self, input, training=False):
x = self.lin1(input)
x = self.act(x)
x = self.lin2(x)
x = self.dropout(x, training=training)
return x
class TFXLMMainLayer(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super(TFXLMMainLayer, self).__init__(**kwargs)
self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states
# encoder / decoder, output layer
self.is_encoder = config.is_encoder
self.is_decoder = not config.is_encoder
if self.is_decoder:
raise NotImplementedError("Currently XLM can only be used as an encoder")
# self.with_output = with_output
self.causal = config.causal
# dictionary / languages
self.n_langs = config.n_langs
self.use_lang_emb = config.use_lang_emb
self.n_words = config.n_words
self.eos_index = config.eos_index
self.pad_index = config.pad_index
# self.dico = dico
# self.id2lang = config.id2lang
# self.lang2id = config.lang2id
# assert len(self.dico) == self.n_words
# assert len(self.id2lang) == len(self.lang2id) == self.n_langs
# model parameters
self.dim = config.emb_dim # 512 by default
self.hidden_dim = self.dim * 4 # 2048 by default
self.n_heads = config.n_heads # 8 by default
self.n_layers = config.n_layers
assert self.dim % self.n_heads == 0, 'transformer dim must be a multiple of n_heads'
# embeddings
self.dropout = tf.keras.layers.Dropout(config.dropout)
self.attention_dropout = tf.keras.layers.Dropout(config.attention_dropout)
self.position_embeddings = tf.keras.layers.Embedding(config.max_position_embeddings,
self.dim,
embeddings_initializer=get_initializer(config.embed_init_std),
name='position_embeddings')
if config.sinusoidal_embeddings:
raise NotImplementedError
# create_sinusoidal_embeddings(config.max_position_embeddings, self.dim, out=self.position_embeddings.weight)
if config.n_langs > 1 and config.use_lang_emb:
self.lang_embeddings = tf.keras.layers.Embedding(self.n_langs,
self.dim,
embeddings_initializer=get_initializer(config.embed_init_std),
name='lang_embeddings')
self.embeddings = TFSharedEmbeddings(self.n_words, self.dim, initializer_range=config.embed_init_std, name='embeddings') # padding_idx=self.pad_index)
self.layer_norm_emb = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name='layer_norm_emb')
# transformer layers
self.attentions = []
self.layer_norm1 = []
self.ffns = []
self.layer_norm2 = []
# if self.is_decoder:
# self.layer_norm15 = []
# self.encoder_attn = []
for i in range(self.n_layers):
self.attentions.append(TFMultiHeadAttention(self.n_heads, self.dim, config=config, name='attentions_._{}'.format(i)))
self.layer_norm1.append(tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name='layer_norm1_._{}'.format(i)))
# if self.is_decoder:
# self.layer_norm15.append(nn.LayerNorm(self.dim, eps=config.layer_norm_eps))
# self.encoder_attn.append(MultiHeadAttention(self.n_heads, self.dim, dropout=self.attention_dropout))
self.ffns.append(TFTransformerFFN(self.dim, self.hidden_dim, self.dim, config=config, name='ffns_._{}'.format(i)))
self.layer_norm2.append(tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name='layer_norm2_._{}'.format(i)))
if hasattr(config, "pruned_heads"):
pruned_heads = config.pruned_heads.copy().items()
config.pruned_heads = {}
for layer, heads in pruned_heads:
if self.attentions[int(layer)].n_heads == config.n_heads:
self.prune_heads({int(layer): list(map(int, heads))})
def get_input_embeddings(self):
return self.embeddings
def _resize_token_embeddings(self, new_num_tokens):
raise NotImplementedError
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 call(self, inputs, attention_mask=None, langs=None, token_type_ids=None,
position_ids=None, lengths=None, cache=None, head_mask=None, inputs_embeds=None,
training=False): # removed: src_enc=None, src_len=None
if isinstance(inputs, (tuple, list)):
input_ids = inputs[0]
attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
langs = inputs[2] if len(inputs) > 2 else langs
token_type_ids = inputs[3] if len(inputs) > 3 else token_type_ids
position_ids = inputs[4] if len(inputs) > 4 else position_ids
lengths = inputs[5] if len(inputs) > 5 else lengths
cache = inputs[6] if len(inputs) > 6 else cache
head_mask = inputs[7] if len(inputs) > 7 else head_mask
inputs_embeds = inputs[8] if len(inputs) > 8 else inputs_embeds
assert len(inputs) <= 9, "Too many inputs."
elif isinstance(inputs, dict):
input_ids = inputs.get('input_ids')
attention_mask = inputs.get('attention_mask', attention_mask)
langs = inputs.get('langs', langs)
token_type_ids = inputs.get('token_type_ids', token_type_ids)
position_ids = inputs.get('position_ids', position_ids)
lengths = inputs.get('lengths', lengths)
cache = inputs.get('cache', cache)
head_mask = inputs.get('head_mask', head_mask)
inputs_embeds = inputs.get('inputs_embeds', inputs_embeds)
assert len(inputs) <= 9, "Too many inputs."
else:
input_ids = inputs
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:
bs, slen = shape_list(input_ids)
elif inputs_embeds is not None:
bs, slen = shape_list(inputs_embeds)[:2]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if lengths is None:
if input_ids is not None:
lengths = tf.reduce_sum(tf.cast(tf.not_equal(input_ids, self.pad_index), dtype=tf.int32), axis=1)
else:
lengths = tf.convert_to_tensor([slen]*bs, tf.int32)
# mask = input_ids != self.pad_index
# check inputs
# assert shape_list(lengths)[0] == bs
tf.debugging.assert_equal(shape_list(lengths)[0], bs)
# assert lengths.max().item() <= slen
# input_ids = input_ids.transpose(0, 1) # batch size as dimension 0
# assert (src_enc is None) == (src_len is None)
# if src_enc is not None:
# assert self.is_decoder
# assert src_enc.size(0) == bs
# generate masks
mask, attn_mask = get_masks(slen, lengths, self.causal, padding_mask=attention_mask)
# if self.is_decoder and src_enc is not None:
# src_mask = torch.arange(src_len.max(), dtype=torch.long, device=lengths.device) < src_len[:, None]
# position_ids
if position_ids is None:
position_ids = tf.expand_dims(tf.range(slen), axis=0)
else:
# assert shape_list(position_ids) == [bs, slen] # (slen, bs)
tf.debugging.assert_equal(shape_list(position_ids), [bs, slen])
# position_ids = position_ids.transpose(0, 1)
# langs
if langs is not None:
# assert shape_list(langs) == [bs, slen] # (slen, bs)
tf.debugging.assert_equal(shape_list(langs), [bs, slen])
# langs = langs.transpose(0, 1)
# 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 qlen x klen]
if head_mask is not None:
raise NotImplementedError
else:
head_mask = [None] * self.n_layers
# do not recompute cached elements
if cache is not None and input_ids is not None:
_slen = slen - cache['slen']
input_ids = input_ids[:, -_slen:]
position_ids = position_ids[:, -_slen:]
if langs is not None:
langs = langs[:, -_slen:]
mask = mask[:, -_slen:]
attn_mask = attn_mask[:, -_slen:]
# embeddings
if inputs_embeds is None:
inputs_embeds = self.embeddings(input_ids)
tensor = inputs_embeds + self.position_embeddings(position_ids)
if langs is not None and self.use_lang_emb:
tensor = tensor + self.lang_embeddings(langs)
if token_type_ids is not None:
tensor = tensor + self.embeddings(token_type_ids)
tensor = self.layer_norm_emb(tensor)
tensor = self.dropout(tensor, training=training)
tensor = tensor * mask[..., tf.newaxis]
# transformer layers
hidden_states = ()
attentions = ()
for i in range(self.n_layers):
if self.output_hidden_states:
hidden_states = hidden_states + (tensor,)
# self attention
attn_outputs = self.attentions[i]([tensor, attn_mask, None, cache, head_mask[i]], training=training)
attn = attn_outputs[0]
if self.output_attentions:
attentions = attentions + (attn_outputs[1],)
attn = self.dropout(attn, training=training)
tensor = tensor + attn
tensor = self.layer_norm1[i](tensor)
# encoder attention (for decoder only)
# if self.is_decoder and src_enc is not None:
# attn = self.encoder_attn[i](tensor, src_mask, kv=src_enc, cache=cache)
# attn = F.dropout(attn, p=self.dropout, training=self.training)
# tensor = tensor + attn
# tensor = self.layer_norm15[i](tensor)
# FFN
tensor = tensor + self.ffns[i](tensor)
tensor = self.layer_norm2[i](tensor)
tensor = tensor * mask[..., tf.newaxis]
# Add last hidden state
if self.output_hidden_states:
hidden_states = hidden_states + (tensor,)
# update cache length
if cache is not None:
cache['slen'] += tensor.size(1)
# move back sequence length to dimension 0
# tensor = tensor.transpose(0, 1)
outputs = (tensor,)
if self.output_hidden_states:
outputs = outputs + (hidden_states,)
if self.output_attentions:
outputs = outputs + (attentions,)
return outputs # outputs, (hidden_states), (attentions)
class TFXLMPreTrainedModel(TFPreTrainedModel):
""" An abstract class to handle weights initialization and
a simple interface for dowloading and loading pretrained models.
"""
config_class = XLMConfig
pretrained_model_archive_map = TF_XLM_PRETRAINED_MODEL_ARCHIVE_MAP
base_model_prefix = "transformer"
@property
def dummy_inputs(self):
# Sometimes XLM has language embeddings so don't forget to build them as well if needed
inputs_list = tf.constant([[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]])
attns_list = tf.constant([[1, 1, 0, 0, 1], [1, 1, 1, 0, 0], [1, 0, 0, 1, 1]])
if self.config.use_lang_emb and self.config.n_langs > 1:
langs_list = tf.constant([[1, 1, 0, 0, 1], [1, 1, 1, 0, 0], [1, 0, 0, 1, 1]])
else:
langs_list = None
return [inputs_list, attns_list, langs_list]
XLM_START_DOCSTRING = r""" The XLM model was proposed in
`Cross-lingual Language Model Pretraining`_
by Guillaume Lample*, Alexis Conneau*. It's a transformer pre-trained using one of the following objectives:
- a causal language modeling (CLM) objective (next token prediction),
- a masked language modeling (MLM) objective (Bert-like), or
- a Translation Language Modeling (TLM) object (extension of Bert's MLM to multiple language inputs)
Original code can be found `here`_.
This model is a tf.keras.Model `tf.keras.Model`_ sub-class. 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.
.. _`Cross-lingual Language Model Pretraining`:
https://arxiv.org/abs/1901.07291
.. _`here`:
https://github.com/facebookresearch/XLM
.. _`tf.keras.Model`:
https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/Model
Note on the model inputs:
TF 2.0 models accepts two formats as inputs:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional arguments.
This second option is usefull when using `tf.keras.Model.fit()` method which currently requires having all the tensors in the first argument of the model call function: `model(inputs)`.
If you choose this second option, 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(inputs_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 associaed to the input names given in the docstring:
`model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`
Parameters:
config (:class:`~transformers.XLMConfig`): 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 :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""
XLM_INPUTS_DOCSTRING = r"""
Inputs:
**input_ids**: ```Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
Indices of input sequence tokens in the vocabulary.
XLM is a model with absolute position embeddings so it's usually advised to pad the inputs on
the right rather than the left.
Indices can be obtained using :class:`transformers.XLMTokenizer`.
See :func:`transformers.PreTrainedTokenizer.encode` and
:func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
**attention_mask**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
Mask to avoid performing attention on padding token indices.
Mask values selected in ``[0, 1]``:
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
**langs**: (`optional`) ```Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
A parallel sequence of tokens to be used to indicate the language of each token in the input.
Indices are languages ids which can be obtained from the language names by using two conversion mappings
provided in the configuration of the model (only provided for multilingual models).
More precisely, the `language name -> language id` mapping is in `model.config.lang2id` (dict str -> int) and
the `language id -> language name` mapping is `model.config.id2lang` (dict int -> str).
**token_type_ids**: (`optional`) ```Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
A parallel sequence of tokens (can be used to indicate various portions of the inputs).
The embeddings from these tokens will be summed with the respective token embeddings.
Indices are selected in the vocabulary (unlike BERT which has a specific vocabulary for segment indices).
**position_ids**: (`optional`) ```Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
Indices of positions of each input sequence tokens in the position embeddings.
Selected in the range ``[0, config.max_position_embeddings - 1]``.
**lengths**: (`optional`) ```Numpy array`` or ``tf.Tensor`` of shape ``(batch_size,)``:
Length of each sentence that can be used to avoid performing attention on padding token indices.
You can also use `attention_mask` for the same result (see above), kept here for compatbility.
Indices selected in ``[0, ..., input_ids.size(-1)]``:
**cache**:
dictionary with ``Numpy array`` or ``tf.Tensor`` that contains pre-computed
hidden-states (key and values in the attention blocks) as computed by the model
(see `cache` output below). Can be used to speed up sequential decoding.
The dictionary object will be modified in-place during the forward pass to add newly computed hidden-states.
**head_mask**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
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**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length, embedding_dim)``:
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.
"""
@add_start_docstrings("The bare XLM Model transformer outputing raw hidden-states without any specific head on top.",
XLM_START_DOCSTRING, XLM_INPUTS_DOCSTRING)
class TFXLMModel(TFXLMPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**last_hidden_state**: ``tf.Tensor`` of shape ``(batch_size, sequence_length, hidden_size)``
Sequence of hidden-states at the last layer of the model.
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``tf.Tensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
import tensorflow as tf
from transformers import XLMTokenizer, TFXLMModel
tokenizer = XLMTokenizer.from_pretrained('xlm-mlm-en-2048')
model = TFXLMModel.from_pretrained('xlm-mlm-en-2048')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
outputs = model(input_ids)
last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
"""
def __init__(self, config, *inputs, **kwargs):
super(TFXLMModel, self).__init__(config, *inputs, **kwargs)
self.transformer = TFXLMMainLayer(config, name='transformer')
def call(self, inputs, **kwargs):
outputs = self.transformer(inputs, **kwargs)
return outputs
class TFXLMPredLayer(tf.keras.layers.Layer):
"""
Prediction layer (cross_entropy or adaptive_softmax).
"""
def __init__(self, config, input_embeddings, **kwargs):
super(TFXLMPredLayer, self).__init__(**kwargs)
self.asm = config.asm
self.n_words = config.n_words
self.pad_index = config.pad_index
if config.asm is False:
self.input_embeddings = input_embeddings
else:
raise NotImplementedError
# self.proj = nn.AdaptiveLogSoftmaxWithLoss(
# in_features=dim,
# n_classes=config.n_words,
# cutoffs=config.asm_cutoffs,
# div_value=config.asm_div_value,
# head_bias=True, # default is False
# )
def build(self, input_shape):
# The output weights are the same as the input embeddings, but there is an output-only bias for each token.
self.bias = self.add_weight(shape=(self.n_words,),
initializer='zeros',
trainable=True,
name='bias')
super(TFXLMPredLayer, self).build(input_shape)
def call(self, hidden_states):
hidden_states = self.input_embeddings(hidden_states, mode="linear")
hidden_states = hidden_states + self.bias
return hidden_states
@add_start_docstrings("""The XLM Model transformer with a language modeling head on top
(linear layer with weights tied to the input embeddings). """,
XLM_START_DOCSTRING, XLM_INPUTS_DOCSTRING)
class TFXLMWithLMHeadModel(TFXLMPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**prediction_scores**: ``tf.Tensor`` of shape ``(batch_size, sequence_length, config.vocab_size)``
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``tf.Tensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
import tensorflow as tf
from transformers import XLMTokenizer, TFXLMWithLMHeadModel
tokenizer = XLMTokenizer.from_pretrained('xlm-mlm-en-2048')
model = TFXLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
outputs = model(input_ids)
last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
"""
def __init__(self, config, *inputs, **kwargs):
super(TFXLMWithLMHeadModel, self).__init__(config, *inputs, **kwargs)
self.transformer = TFXLMMainLayer(config, name='transformer')
self.pred_layer = TFXLMPredLayer(config, self.transformer.embeddings, name='pred_layer_._proj')
def get_output_embeddings(self):
return self.pred_layer.input_embeddings
def call(self, inputs, **kwargs):
transformer_outputs = self.transformer(inputs, **kwargs)
output = transformer_outputs[0]
outputs = self.pred_layer(output)
outputs = (outputs,) + transformer_outputs[1:] # Keep new_mems and attention/hidden states if they are here
return outputs
@add_start_docstrings("""XLM Model with a sequence classification/regression head on top (a linear layer on top of
the pooled output) e.g. for GLUE tasks. """,
XLM_START_DOCSTRING, XLM_INPUTS_DOCSTRING)
class TFXLMForSequenceClassification(TFXLMPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**logits**: ``tf.Tensor`` of shape ``(batch_size, config.num_labels)``
Classification (or regression if config.num_labels==1) scores (before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``tf.Tensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
import tensorflow as tf
from transformers import XLMTokenizer, TFXLMForSequenceClassification
tokenizer = XLMTokenizer.from_pretrained('xlm-mlm-en-2048')
model = TFXLMForSequenceClassification.from_pretrained('xlm-mlm-en-2048')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
labels = tf.constant([1])[None, :] # Batch size 1
outputs = model(input_ids)
logits = outputs[0]
"""
def __init__(self, config, *inputs, **kwargs):
super(TFXLMForSequenceClassification, self).__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.transformer = TFXLMMainLayer(config, name='transformer')
self.sequence_summary = TFSequenceSummary(config, initializer_range=config.init_std, name='sequence_summary')
def call(self, inputs, **kwargs):
transformer_outputs = self.transformer(inputs, **kwargs)
output = transformer_outputs[0]
logits = self.sequence_summary(output)
outputs = (logits,) + transformer_outputs[1:] # Keep new_mems and attention/hidden states if they are here
return outputs
@add_start_docstrings("""XLM 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`). """,
XLM_START_DOCSTRING, XLM_INPUTS_DOCSTRING)
class TFXLMForQuestionAnsweringSimple(TFXLMPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**start_scores**: ``tf.Tensor`` of shape ``(batch_size, sequence_length,)``
Span-start scores (before SoftMax).
**end_scores**: ``tf.Tensor`` of shape ``(batch_size, sequence_length,)``
Span-end scores (before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``tf.Tensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
import tensorflow as tf
from transformers import XLMTokenizer, TFXLMForQuestionAnsweringSimple
tokenizer = XLMTokenizer.from_pretrained('xlm-mlm-en-2048')
model = TFXLMForQuestionAnsweringSimple.from_pretrained('xlm-mlm-en-2048')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
outputs = model(input_ids)
start_scores, end_scores = outputs[:2]
"""
def __init__(self, config, *inputs, **kwargs):
super(TFXLMForQuestionAnsweringSimple, self).__init__(config, *inputs, **kwargs)
self.transformer = TFXLMMainLayer(config, name='transformer')
self.qa_outputs = tf.keras.layers.Dense(config.num_labels,
kernel_initializer=get_initializer(config.init_std),
name='qa_outputs')
def call(self, inputs, **kwargs):
transformer_outputs = self.transformer(inputs, **kwargs)
sequence_output = transformer_outputs[0]
logits = self.qa_outputs(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)
outputs = (start_logits, end_logits,) + transformer_outputs[1:] # Keep mems, hidden states, attentions if there are in it
return outputs # start_logits, end_logits, (hidden_states), (attentions)
| 39,276 | 49.942931 | 193 |
py
|
DeeBERT
|
DeeBERT-master/transformers/modeling_beam_search.py
|
# coding=utf-8
# Copyright (c) 2019 Yang Liu
# 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.
"""
A general wrapper around models with LM heads to generate sequences
using beam search.
"""
import torch
from torch import nn
class TransformerBeamSearch(nn.Module):
def __init__(
self,
model,
tokenizer,
batch_size,
beam_size,
min_length,
max_length,
alpha=0,
block_repeating_trigram=True,
):
"""
Attributes:
mask_word_id: token id that corresponds to the mask
"""
super(TransformerBeamSearch, self).__init__()
self.model = model
self.tokenizer = tokenizer
self.start_token_id = tokenizer.start_token_id
self.end_token_id = tokenizer.end_token_id
self.pad_token_id = tokenizer.pad_token_id
self.beam_size = beam_size
self.min_length = min_length
self.max_length = max_length
self.block_repeating_trigram = block_repeating_trigram
self.apply_length_penalty = False if alpha == 0 else True
self.alpha = alpha
# State of the beam
self.hypotheses = [[] for _ in range(batch_size)]
self.batch_offset = torch.arange(batch_size, dtype=torch.long)
self.beam_offset = torch.arange(
0, batch_size * self.beam_size, step=self.beam_size, dtype=torch.long
)
self.growing_beam = torch.full(
(batch_size * self.beam_size, 1), self.start_token_id, dtype=torch.long
)
self.topk_log_probabilities = torch.tensor(
[0.0] + [float("-inf")] * (self.beam_size - 1), dtype=torch.float
).repeat(batch_size)
self.results = {
"prediction": [[] for _ in batch_size],
"scores": [[] for _ in batch_size],
}
self._step = 0
self.is_done = False
def step(self, log_probabilities):
""" Grows the beam by one step. """
self._step += 1
# The batch size changes as some beams finish so we define _B
vocab_size = log_probabilities.size(-1)
_B = log_probabilities.size(0) // self.beam_size
# Multiply each beam probability with the probability of the
# next token (conditioned on the words in the beam).
log_probabilities += self.topk_log_probabilities.view(-1, 1)
self.enforce_min_length(log_probabilities)
if self.block_repeating_trigram:
self.remove_repeating_trigrams(log_probabilities, _B)
# Find the `beam_size` (previous_beam + token) combinations with
# the highest score
topk_log_probabilities, topk_ids = log_probabilities.topk(
log_probabilities.view(_B, self.beam_size * vocab_size),
self.beam_size,
dim=1,
)
# Apply the length penalty. The +1 accounts for the [EOS] token
# that will be added if the beam ends.
topk_scores = topk_log_probabilities / self.length_penalty()
# Retrieve the corresponding respective beam and token id
# topk_token_ids[i] will be added to topk_beam_ids[i]
topk_beam_ids = topk_ids.div(vocab_size)
topk_token_ids = topk_ids.fmod(vocab_size)
# Retrieve the row index of the surviving beams in the original
# view of the log_probabilities tensor
surviving_beams_rows = (topk_beam_ids + self.beam_offset[:_B].view(-1, 1)).view(
-1
)
# Append the last predictions
self.growing_beam = torch.cat(
[
self.growing_beam.index_select(0, surviving_beams_rows),
topk_token_ids.view(-1, 1),
],
1,
)
# Check if any of the beam searches has ended during this
# growth step. Also if top beam (most probable) has ended
# for one element of the batch.
is_finished = topk_token_ids.eq(self.end_token_id)
self.enforce_max_length()
is_top_beam_finished = is_finished[:, 0].eq(1)
# Save the finished searches
if is_finished.any():
predictions = self.growing_beam.view(
-1, self.beam_size, self.growing_beam.size(1)
)
for i in range(is_finished.size(0)):
if is_top_beam_finished[i]:
is_finished[i].fill_(1)
finished_hyp = is_finished[i].nonzero().view(-1)
# Store finished hypotheses for this batch.
b = self.batch_offset[i]
for j in finished_hyp:
self.hypotheses[b].append((topk_scores[i, j], predictions[i, j, :]))
# If the batch reached the end, save the best hypotheses
# in terms of length-penalized score.
if is_top_beam_finished[i]:
best_hyp = sorted(
self.hypotheses[b], key=lambda x: x[0], reverse=True
)
best_score, best_prediction = best_hyp[0]
self.results["scores"][b].append(best_score)
self.results["predictions"][b].append(best_prediction)
non_finished = is_top_beam_finished.eq(0).nonzero().view(-1)
if len(non_finished) == 0:
self.is_done = True
# Remove finished batches for the next step.
topk_log_probabilities = topk_log_probabilities.index_select(
0, non_finished
)
self.batch_offset = self.batch_offset.index_select(0, non_finished)
self.growing_beam = predictions.index_select(0, non_finished).view(
-1, self.growing_beam.size(-1)
)
surviving_beams_rows = surviving_beams_rows.index_select(0, non_finished)
return surviving_beams_rows
def forward(self, encoder_input_ids, **kwargs):
# keyword arguments come in 3 flavors: encoder-specific (prefixed by
# `encoder_`), decoder-specific (prefixed by `decoder_`) and those
# that apply to the model as whole.
# We let the specific kwargs override the common ones in case of conflict.
kwargs_encoder = {
argument[len("encoder_"):]: value
for argument, value in kwargs.items()
if argument.startswith("encoder_")
}
kwargs_decoder = {
argument[len("decoder_"):]: value
for argument, value in kwargs.items()
if argument.startswith("decoder_")
}
kwargs_common = {
argument: value
for argument, value in kwargs.items()
if not (argument.startswith("encoder_") or argument.startswith("decoder_"))
}
kwargs_decoder = dict(kwargs_common, **kwargs_decoder)
kwargs_encoder = dict(kwargs_common, **kwargs_encoder)
# forward pass on the encoder
encoder_outputs = self.model.encoder.forward(encoder_input_ids, kwargs_encoder)
kwargs_decoder["encoder_hidden_states"] = tile(
encoder_outputs, self.beam_size, dim=0
)
# grow the beam by generating sequences in an autoregressive way
self.growing_beam = torch.full(
(self.batch_size * self.beam_size, 1), self.start_token_id, dtype=torch.long
)
for step in range(self.max_length):
decoder_input = self.growing_beam[:, -1]
outputs = self.model.decoder(decoder_input, kwargs_decoder)
log_probabilities = torch.nn.functional.log_softmax(outputs[1])
surviving_beams_rows = self.step(log_probabilities)
if self.is_done:
break
kwargs_decoder["encoder_hidden_states"] = kwargs_decoder[
"encoder_hidden_states"
].index_select(0, surviving_beams_rows)
return self.results
def remove_repeating_trigrams(self, log_probabilities, _B):
if(self._step + 1 > 3):
for i in range(_B * self.beam_size):
tokens = [t for t in self.growing_beam[i]]
trigrams = [(tokens[i-1], tokens[i], tokens[i+1]) for i in range(1, len(words) - 1)]
last_trigram = tuple(trigrams[-1])
if last_trigram in trigrams[:-1]:
log_probabilities[i] = -1e20
def enforce_min_length(self):
if self._step < self.min_length:
self.log_probabilities[self.end_token_id] = -1e20
def enforce_max_length(self):
if self._step + 1 == self.max_length:
self.is_finished.fill_(1)
def length_penalty(self):
return ((5.0 + (self._step + 1)) / 6.0) ** self.alpha
def tile(x, count, dim=0):
"""
Tiles `x` along dimension `dim` `count` times.
Example:
>> ex = torch.tensor([1,2],[3,4])
>> tile(ex, 2, 0)
torch.Tensor([[1,2],[1,2],[3,4],[3,4]])
"""
perm = list(range(len(x.size())))
if dim != 0:
perm[0], perm[dim] = perm[dim], perm[0]
x = x.permute(perm).contiguous()
out_size = list(x.size())
out_size[0] *= count
batch = x.size(0)
x = (
x.view(batch, -1)
.transpose(0, 1)
.repeat(count, 1)
.transpose(0, 1)
.contiguous()
.view(*out_size)
)
if dim != 0:
x = x.permute(perm).contiguous()
return x
| 10,385 | 37.183824 | 100 |
py
|
DeeBERT
|
DeeBERT-master/transformers/configuration_transfo_xl.py
|
# 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.
""" Transformer XL configuration """
from __future__ import absolute_import, division, print_function, unicode_literals
import json
import logging
import sys
from io import open
from .configuration_utils import PretrainedConfig
logger = logging.getLogger(__name__)
TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP = {
'transfo-xl-wt103': "https://s3.amazonaws.com/models.huggingface.co/bert/transfo-xl-wt103-config.json",
}
class TransfoXLConfig(PretrainedConfig):
"""Configuration class to store the configuration of a `TransfoXLModel`.
Args:
vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `TransfoXLModel` or a configuration json file.
cutoffs: cutoffs for the adaptive softmax
d_model: Dimensionality of the model's hidden states.
d_embed: Dimensionality of the embeddings
d_head: Dimensionality of the model's heads.
div_val: divident value for adapative input and softmax
pre_lnorm: apply LayerNorm to the input instead of the output
d_inner: Inner dimension in FF
n_layer: Number of hidden layers in the Transformer encoder.
n_head: Number of attention heads for each attention layer in
the Transformer encoder.
tgt_len: number of tokens to predict
ext_len: length of the extended context
mem_len: length of the retained previous heads
same_length: use the same attn length for all tokens
proj_share_all_but_first: True to share all but first projs, False not to share.
attn_type: attention type. 0 for Transformer-XL, 1 for Shaw et al, 2 for Vaswani et al, 3 for Al Rfou et al.
clamp_len: use the same pos embeddings after clamp_len
sample_softmax: number of samples in sampled softmax
adaptive: use adaptive softmax
tie_weight: tie the word embedding and softmax weights
dropout: The dropout probabilitiy for all fully connected
layers in the embeddings, encoder, and pooler.
dropatt: The dropout ratio for the attention probabilities.
untie_r: untie relative position biases
embd_pdrop: The dropout ratio for the embeddings.
init: parameter initializer to use
init_range: parameters initialized by U(-init_range, init_range).
proj_init_std: parameters initialized by N(0, init_std)
init_std: parameters initialized by N(0, init_std)
"""
pretrained_config_archive_map = TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP
def __init__(self,
vocab_size_or_config_json_file=267735,
cutoffs=[20000, 40000, 200000],
d_model=1024,
d_embed=1024,
n_head=16,
d_head=64,
d_inner=4096,
div_val=4,
pre_lnorm=False,
n_layer=18,
tgt_len=128,
ext_len=0,
mem_len=1600,
clamp_len=1000,
same_length=True,
proj_share_all_but_first=True,
attn_type=0,
sample_softmax=-1,
adaptive=True,
tie_weight=True,
dropout=0.1,
dropatt=0.0,
untie_r=True,
init="normal",
init_range=0.01,
proj_init_std=0.01,
init_std=0.02,
layer_norm_epsilon=1e-5,
**kwargs):
"""Constructs TransfoXLConfig.
"""
super(TransfoXLConfig, self).__init__(**kwargs)
self.n_token = vocab_size_or_config_json_file if isinstance(vocab_size_or_config_json_file, int) else -1
self.cutoffs = []
self.cutoffs.extend(cutoffs)
self.tie_weight = tie_weight
if proj_share_all_but_first:
self.tie_projs = [False] + [True] * len(self.cutoffs)
else:
self.tie_projs = [False] + [False] * len(self.cutoffs)
self.d_model = d_model
self.d_embed = d_embed
self.d_head = d_head
self.d_inner = d_inner
self.div_val = div_val
self.pre_lnorm = pre_lnorm
self.n_layer = n_layer
self.n_head = n_head
self.tgt_len = tgt_len
self.ext_len = ext_len
self.mem_len = mem_len
self.same_length = same_length
self.attn_type = attn_type
self.clamp_len = clamp_len
self.sample_softmax = sample_softmax
self.adaptive = adaptive
self.dropout = dropout
self.dropatt = dropatt
self.untie_r = untie_r
self.init = init
self.init_range = init_range
self.proj_init_std = proj_init_std
self.init_std = init_std
self.layer_norm_epsilon = layer_norm_epsilon
if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
and isinstance(vocab_size_or_config_json_file, unicode)):
with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader:
json_config = json.loads(reader.read())
for key, value in json_config.items():
self.__dict__[key] = value
elif not isinstance(vocab_size_or_config_json_file, int):
raise ValueError("First argument must be either a vocabulary size (int)"
" or the path to a pretrained model config file (str)")
@property
def max_position_embeddings(self):
return self.tgt_len + self.ext_len + self.mem_len
@property
def vocab_size(self):
return self.n_token
@vocab_size.setter
def vocab_size(self, value):
self.n_token = value
@property
def hidden_size(self):
return self.d_model
@property
def num_attention_heads(self):
return self.n_head
@property
def num_hidden_layers(self):
return self.n_layer
| 6,819 | 39.35503 | 125 |
py
|
DeeBERT
|
DeeBERT-master/transformers/file_utils.py
|
"""
Utilities for working with the local dataset cache.
This file is adapted from the AllenNLP library at https://github.com/allenai/allennlp
Copyright by the AllenNLP authors.
"""
from __future__ import (absolute_import, division, print_function, unicode_literals)
import sys
import json
import logging
import os
import six
import shutil
import tempfile
import fnmatch
from functools import wraps
from hashlib import sha256
from io import open
import boto3
from botocore.config import Config
from botocore.exceptions import ClientError
import requests
from tqdm import tqdm
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
try:
import tensorflow as tf
assert hasattr(tf, '__version__') and int(tf.__version__[0]) >= 2
_tf_available = True # pylint: disable=invalid-name
logger.info("TensorFlow version {} available.".format(tf.__version__))
except (ImportError, AssertionError):
_tf_available = False # pylint: disable=invalid-name
try:
import torch
_torch_available = True # pylint: disable=invalid-name
logger.info("PyTorch version {} available.".format(torch.__version__))
except ImportError:
_torch_available = False # pylint: disable=invalid-name
try:
from torch.hub import _get_torch_home
torch_cache_home = _get_torch_home()
except ImportError:
torch_cache_home = os.path.expanduser(
os.getenv('TORCH_HOME', os.path.join(
os.getenv('XDG_CACHE_HOME', '~/.cache'), 'torch')))
default_cache_path = os.path.join(torch_cache_home, 'transformers')
try:
from urllib.parse import urlparse
except ImportError:
from urlparse import urlparse
try:
from pathlib import Path
PYTORCH_PRETRAINED_BERT_CACHE = Path(
os.getenv('PYTORCH_TRANSFORMERS_CACHE', os.getenv('PYTORCH_PRETRAINED_BERT_CACHE', default_cache_path)))
except (AttributeError, ImportError):
PYTORCH_PRETRAINED_BERT_CACHE = os.getenv('PYTORCH_TRANSFORMERS_CACHE',
os.getenv('PYTORCH_PRETRAINED_BERT_CACHE',
default_cache_path))
PYTORCH_TRANSFORMERS_CACHE = PYTORCH_PRETRAINED_BERT_CACHE # Kept for backward compatibility
TRANSFORMERS_CACHE = PYTORCH_PRETRAINED_BERT_CACHE # Kept for backward compatibility
WEIGHTS_NAME = "pytorch_model.bin"
TF2_WEIGHTS_NAME = 'tf_model.h5'
TF_WEIGHTS_NAME = 'model.ckpt'
CONFIG_NAME = "config.json"
def is_torch_available():
return _torch_available
def is_tf_available():
return _tf_available
if not six.PY2:
def add_start_docstrings(*docstr):
def docstring_decorator(fn):
fn.__doc__ = ''.join(docstr) + fn.__doc__
return fn
return docstring_decorator
def add_end_docstrings(*docstr):
def docstring_decorator(fn):
fn.__doc__ = fn.__doc__ + ''.join(docstr)
return fn
return docstring_decorator
else:
# Not possible to update class docstrings on python2
def add_start_docstrings(*docstr):
def docstring_decorator(fn):
return fn
return docstring_decorator
def add_end_docstrings(*docstr):
def docstring_decorator(fn):
return fn
return docstring_decorator
def url_to_filename(url, etag=None):
"""
Convert `url` into a hashed filename in a repeatable way.
If `etag` is specified, append its hash to the url's, delimited
by a period.
If the url ends with .h5 (Keras HDF5 weights) ands '.h5' to the name
so that TF 2.0 can identify it as a HDF5 file
(see https://github.com/tensorflow/tensorflow/blob/00fad90125b18b80fe054de1055770cfb8fe4ba3/tensorflow/python/keras/engine/network.py#L1380)
"""
url_bytes = url.encode('utf-8')
url_hash = sha256(url_bytes)
filename = url_hash.hexdigest()
if etag:
etag_bytes = etag.encode('utf-8')
etag_hash = sha256(etag_bytes)
filename += '.' + etag_hash.hexdigest()
if url.endswith('.h5'):
filename += '.h5'
return filename
def filename_to_url(filename, cache_dir=None):
"""
Return the url and etag (which may be ``None``) stored for `filename`.
Raise ``EnvironmentError`` if `filename` or its stored metadata do not exist.
"""
if cache_dir is None:
cache_dir = TRANSFORMERS_CACHE
if sys.version_info[0] == 3 and isinstance(cache_dir, Path):
cache_dir = str(cache_dir)
cache_path = os.path.join(cache_dir, filename)
if not os.path.exists(cache_path):
raise EnvironmentError("file {} not found".format(cache_path))
meta_path = cache_path + '.json'
if not os.path.exists(meta_path):
raise EnvironmentError("file {} not found".format(meta_path))
with open(meta_path, encoding="utf-8") as meta_file:
metadata = json.load(meta_file)
url = metadata['url']
etag = metadata['etag']
return url, etag
def cached_path(url_or_filename, cache_dir=None, force_download=False, proxies=None):
"""
Given something that might be a URL (or might be a local path),
determine which. If it's a URL, download the file and cache it, and
return the path to the cached file. If it's already a local path,
make sure the file exists and then return the path.
Args:
cache_dir: specify a cache directory to save the file to (overwrite the default cache dir).
force_download: if True, re-dowload the file even if it's already cached in the cache dir.
"""
if cache_dir is None:
cache_dir = TRANSFORMERS_CACHE
if sys.version_info[0] == 3 and isinstance(url_or_filename, Path):
url_or_filename = str(url_or_filename)
if sys.version_info[0] == 3 and isinstance(cache_dir, Path):
cache_dir = str(cache_dir)
parsed = urlparse(url_or_filename)
if parsed.scheme in ('http', 'https', 's3'):
# URL, so get it from the cache (downloading if necessary)
return get_from_cache(url_or_filename, cache_dir=cache_dir, force_download=force_download, proxies=proxies)
elif os.path.exists(url_or_filename):
# File, and it exists.
return url_or_filename
elif parsed.scheme == '':
# File, but it doesn't exist.
raise EnvironmentError("file {} not found".format(url_or_filename))
else:
# Something unknown
raise ValueError("unable to parse {} as a URL or as a local path".format(url_or_filename))
def split_s3_path(url):
"""Split a full s3 path into the bucket name and path."""
parsed = urlparse(url)
if not parsed.netloc or not parsed.path:
raise ValueError("bad s3 path {}".format(url))
bucket_name = parsed.netloc
s3_path = parsed.path
# Remove '/' at beginning of path.
if s3_path.startswith("/"):
s3_path = s3_path[1:]
return bucket_name, s3_path
def s3_request(func):
"""
Wrapper function for s3 requests in order to create more helpful error
messages.
"""
@wraps(func)
def wrapper(url, *args, **kwargs):
try:
return func(url, *args, **kwargs)
except ClientError as exc:
if int(exc.response["Error"]["Code"]) == 404:
raise EnvironmentError("file {} not found".format(url))
else:
raise
return wrapper
@s3_request
def s3_etag(url, proxies=None):
"""Check ETag on S3 object."""
s3_resource = boto3.resource("s3", config=Config(proxies=proxies))
bucket_name, s3_path = split_s3_path(url)
s3_object = s3_resource.Object(bucket_name, s3_path)
return s3_object.e_tag
@s3_request
def s3_get(url, temp_file, proxies=None):
"""Pull a file directly from S3."""
s3_resource = boto3.resource("s3", config=Config(proxies=proxies))
bucket_name, s3_path = split_s3_path(url)
s3_resource.Bucket(bucket_name).download_fileobj(s3_path, temp_file)
def http_get(url, temp_file, proxies=None):
req = requests.get(url, stream=True, proxies=proxies)
content_length = req.headers.get('Content-Length')
total = int(content_length) if content_length is not None else None
progress = tqdm(unit="B", total=total)
for chunk in req.iter_content(chunk_size=1024):
if chunk: # filter out keep-alive new chunks
progress.update(len(chunk))
temp_file.write(chunk)
progress.close()
def get_from_cache(url, cache_dir=None, force_download=False, proxies=None, etag_timeout=10):
"""
Given a URL, look for the corresponding dataset in the local cache.
If it's not there, download it. Then return the path to the cached file.
"""
if cache_dir is None:
cache_dir = TRANSFORMERS_CACHE
if sys.version_info[0] == 3 and isinstance(cache_dir, Path):
cache_dir = str(cache_dir)
if sys.version_info[0] == 2 and not isinstance(cache_dir, str):
cache_dir = str(cache_dir)
if not os.path.exists(cache_dir):
os.makedirs(cache_dir)
# Get eTag to add to filename, if it exists.
if url.startswith("s3://"):
etag = s3_etag(url, proxies=proxies)
else:
try:
response = requests.head(url, allow_redirects=True, proxies=proxies, timeout=etag_timeout)
if response.status_code != 200:
etag = None
else:
etag = response.headers.get("ETag")
except (EnvironmentError, requests.exceptions.Timeout):
etag = None
if sys.version_info[0] == 2 and etag is not None:
etag = etag.decode('utf-8')
filename = url_to_filename(url, etag)
# get cache path to put the file
cache_path = os.path.join(cache_dir, filename)
# If we don't have a connection (etag is None) and can't identify the file
# try to get the last downloaded one
if not os.path.exists(cache_path) and etag is None:
matching_files = fnmatch.filter(os.listdir(cache_dir), filename + '.*')
matching_files = list(filter(lambda s: not s.endswith('.json'), matching_files))
if matching_files:
cache_path = os.path.join(cache_dir, matching_files[-1])
if not os.path.exists(cache_path) or force_download:
# Download to temporary file, then copy to cache dir once finished.
# Otherwise you get corrupt cache entries if the download gets interrupted.
with tempfile.NamedTemporaryFile() as temp_file:
logger.info("%s not found in cache or force_download set to True, downloading to %s", url, temp_file.name)
# GET file object
if url.startswith("s3://"):
s3_get(url, temp_file, proxies=proxies)
else:
http_get(url, temp_file, proxies=proxies)
# we are copying the file before closing it, so flush to avoid truncation
temp_file.flush()
# shutil.copyfileobj() starts at the current position, so go to the start
temp_file.seek(0)
logger.info("copying %s to cache at %s", temp_file.name, cache_path)
with open(cache_path, 'wb') as cache_file:
shutil.copyfileobj(temp_file, cache_file)
logger.info("creating metadata file for %s", cache_path)
meta = {'url': url, 'etag': etag}
meta_path = cache_path + '.json'
with open(meta_path, 'w') as meta_file:
output_string = json.dumps(meta)
if sys.version_info[0] == 2 and isinstance(output_string, str):
output_string = unicode(output_string, 'utf-8') # The beauty of python 2
meta_file.write(output_string)
logger.info("removing temp file %s", temp_file.name)
return cache_path
| 11,692 | 34.978462 | 144 |
py
|
DeeBERT
|
DeeBERT-master/transformers/modeling_tf_ctrl.py
|
# 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.
""" TF 2.0 CTRL model."""
from __future__ import absolute_import, division, print_function, unicode_literals
import logging
import os
import sys
from io import open
import numpy as np
import tensorflow as tf
from .configuration_ctrl import CTRLConfig
from .modeling_tf_utils import TFPreTrainedModel, get_initializer, shape_list, TFSharedEmbeddings
from .file_utils import add_start_docstrings
logger = logging.getLogger(__name__)
TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP = {"ctrl": "https://s3.amazonaws.com/models.huggingface.co/bert/ctrl-tf_model.h5"}
def angle_defn(pos, i, d_model_size):
angle_rates = 1 / np.power(10000, (2 * (i//2)) / np.float32(d_model_size))
return pos * angle_rates
def positional_encoding(position, d_model_size):
# create the sinusoidal pattern for the positional encoding
angle_rads = angle_defn(np.arange(position)[:, np.newaxis],
np.arange(d_model_size)[np.newaxis, :],
d_model_size)
sines = np.sin(angle_rads[:, 0::2])
cosines = np.cos(angle_rads[:, 1::2])
# pos_encoding = tf.cast(np.concatenate([sines, cosines], axis=-1)[np.newaxis, ...], dtype=tf.float32)
pos_encoding = tf.cast(np.concatenate([sines, cosines], axis=-1), dtype=tf.float32)
return pos_encoding
def scaled_dot_product_attention(q, k, v, mask, attention_mask=None, head_mask=None):
# calculate attention
matmul_qk = tf.matmul(q, k, transpose_b=True)
dk = tf.cast(shape_list(k)[-1], tf.float32)
scaled_attention_logits = matmul_qk / tf.math.sqrt(dk)
if mask is not None:
scaled_attention_logits += (mask * -1e4)
if attention_mask is not None:
# Apply the attention mask
scaled_attention_logits = scaled_attention_logits + attention_mask
attention_weights = tf.nn.softmax(scaled_attention_logits, axis=-1)
# Mask heads if we want to
if head_mask is not None:
attention_weights = attention_weights * head_mask
output = tf.matmul(attention_weights, v)
return output, attention_weights
class TFMultiHeadAttention(tf.keras.layers.Layer):
def __init__(self, d_model_size, num_heads, output_attentions=False, **kwargs):
super(TFMultiHeadAttention, self).__init__(**kwargs)
self.output_attentions = output_attentions
self.num_heads = num_heads
self.d_model_size = d_model_size
self.depth = int(d_model_size / self.num_heads)
self.Wq = tf.keras.layers.Dense(d_model_size, name='Wq')
self.Wk = tf.keras.layers.Dense(d_model_size, name='Wk')
self.Wv = tf.keras.layers.Dense(d_model_size, name='Wv')
self.dense = tf.keras.layers.Dense(d_model_size, name='dense')
def split_into_heads(self, x, batch_size):
x = tf.reshape(x, (batch_size, -1, self.num_heads, self.depth))
return tf.transpose(x, perm=[0, 2, 1, 3])
def call(self, inputs, training=False):
v, k, q, mask, layer_past, attention_mask, head_mask = inputs
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 = tf.unstack(layer_past, axis=1)
k = tf.concat((past_key, k), dim=-2)
v = tf.concat((past_value, v), dim=-2)
present = tf.stack((k, v), axis=1)
output = scaled_dot_product_attention(q, k, v, mask, attention_mask, head_mask)
scaled_attention = tf.transpose(output[0], perm=[0, 2, 1, 3])
attn = output[1]
original_size_attention = tf.reshape(scaled_attention, (batch_size, -1, self.d_model_size))
output = self.dense(original_size_attention)
outputs = (output, present)
if self.output_attentions:
outputs = outputs + (attn,)
return outputs
def point_wise_feed_forward_network(d_model_size, dff, name=""):
return tf.keras.Sequential([
tf.keras.layers.Dense(dff, activation='relu', name="0"),
tf.keras.layers.Dense(d_model_size, name="2")
], name="ffn")
class TFEncoderLayer(tf.keras.layers.Layer):
def __init__(self, d_model_size, num_heads, dff, rate=0.1, layer_norm_epsilon=1e-6, output_attentions=False, **kwargs):
super(TFEncoderLayer, self).__init__(**kwargs)
self.multi_head_attention = TFMultiHeadAttention(d_model_size,
num_heads,
output_attentions,
name="multi_head_attention")
self.ffn = point_wise_feed_forward_network(d_model_size, dff, name="ffn")
self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=layer_norm_epsilon, name="layernorm1")
self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=layer_norm_epsilon, name="layernorm2")
self.dropout1 = tf.keras.layers.Dropout(rate)
self.dropout2 = tf.keras.layers.Dropout(rate)
def call(self, inputs, training=False):
x, mask, layer_past, attention_mask, head_mask = inputs
normed = self.layernorm1(x)
attn_outputs = self.multi_head_attention([normed, normed, normed, mask, layer_past,
attention_mask, head_mask], training=training)
attn_output = attn_outputs[0]
attn_output = self.dropout1(attn_output, training=training)
out1 = x + attn_output
out2 = self.layernorm2(out1)
ffn_output = self.ffn(out2)
ffn_output = self.dropout2(ffn_output, training=training)
out2 = out1 + ffn_output
outputs = (out2,) + attn_outputs[1:]
return outputs
class TFCTRLMainLayer(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super(TFCTRLMainLayer, self).__init__(**kwargs)
self.output_hidden_states = config.output_hidden_states
self.output_attentions = config.output_attentions
self.output_past = config.output_past
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)
self.w = TFSharedEmbeddings(config.vocab_size,
config.n_embd,
initializer_range=config.initializer_range,
name="w")
self.dropout = tf.keras.layers.Dropout(config.embd_pdrop)
self.h = [TFEncoderLayer(config.n_embd,
config.n_head,
config.dff,
config.resid_pdrop,
config.layer_norm_epsilon,
config.output_attentions,
name='h_._{}'.format(i)) for i in range(config.n_layer)]
self.layernorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="layernorm")
def get_input_embeddings(self):
return self.w
def _resize_token_embeddings(self, new_num_tokens):
raise NotImplementedError
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
def call(self, inputs, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, training=False):
if isinstance(inputs, (tuple, list)):
input_ids = inputs[0]
past = inputs[1] if len(inputs) > 1 else past
attention_mask = inputs[2] if len(inputs) > 2 else attention_mask
token_type_ids = inputs[3] if len(inputs) > 3 else token_type_ids
position_ids = inputs[4] if len(inputs) > 4 else position_ids
head_mask = inputs[5] if len(inputs) > 5 else head_mask
inputs_embeds = inputs[6] if len(inputs) > 6 else inputs_embeds
assert len(inputs) <= 7, "Too many inputs."
elif isinstance(inputs, dict):
input_ids = inputs.get('input_ids')
past = inputs.get('past', past)
attention_mask = inputs.get('attention_mask', attention_mask)
token_type_ids = inputs.get('token_type_ids', token_type_ids)
position_ids = inputs.get('position_ids', position_ids)
head_mask = inputs.get('head_mask', head_mask)
inputs_embeds = inputs.get('inputs_embeds', inputs_embeds)
assert len(inputs) <= 7, "Too many inputs."
else:
input_ids = inputs
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 is None:
past_length = 0
past = [None] * len(self.h)
else:
past_length = shape_list(past[0][0])[-2]
if position_ids is None:
position_ids = tf.range(past_length, input_shape[-1] + past_length, dtype=tf.int32)[tf.newaxis, :]
position_ids = tf.tile(position_ids, [input_shape[0], 1])
# Attention mask.
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 = attention_mask[:, tf.newaxis, tf.newaxis, :]
# 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.
attention_mask = tf.cast(attention_mask, tf.float32)
attention_mask = (1.0 - attention_mask) * -10000.0
else:
attention_mask = None
# 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
# head_mask has shape n_layer x batch x n_heads x N x N
if head_mask is not None:
raise NotImplementedError
else:
head_mask = [None] * self.num_layers
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.w(token_type_ids, mode='embedding')
token_type_embeds *= tf.math.sqrt(tf.cast(self.d_model_size, tf.float32))
else:
token_type_embeds = 0
position_ids = tf.reshape(position_ids, [-1, shape_list(position_ids)[-1]])
if inputs_embeds is None:
inputs_embeds = self.w(input_ids, mode='embedding')
seq_len = input_shape[-1]
mask = 1 - tf.linalg.band_part(tf.ones((seq_len, seq_len)), -1, 0)
inputs_embeds *= tf.math.sqrt(tf.cast(self.d_model_size, tf.float32))
pos_embeds = tf.gather(self.pos_encoding, position_ids)
hidden_states = inputs_embeds + pos_embeds + token_type_embeds
hidden_states = self.dropout(hidden_states, training=training)
output_shape = input_shape + [shape_list(hidden_states)[-1]]
presents = ()
all_hidden_states = ()
all_attentions = []
for i, (h, layer_past) in enumerate(zip(self.h, past)):
if self.output_hidden_states:
all_hidden_states = all_hidden_states + (tf.reshape(hidden_states, output_shape),)
outputs = h([hidden_states, mask, layer_past, attention_mask, head_mask[i]], training=training)
hidden_states, present = outputs[:2]
if self.output_past:
presents = presents + (present,)
if self.output_attentions:
all_attentions.append(outputs[2])
hidden_states = self.layernorm(hidden_states)
hidden_states = tf.reshape(hidden_states, output_shape)
if self.output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
outputs = (hidden_states,)
if self.output_past:
outputs = outputs + (presents,)
if self.output_hidden_states:
outputs = outputs + (all_hidden_states,)
if self.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)
outputs = outputs + (all_attentions,)
return outputs
class TFCTRLPreTrainedModel(TFPreTrainedModel):
""" An abstract class to handle weights initialization and
a simple interface for dowloading and loading pretrained models.
"""
config_class = CTRLConfig
pretrained_model_archive_map = TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP
base_model_prefix = "transformer"
CTRL_START_DOCSTRING = r""" CTRL model was proposed in
`CTRL: A Conditional Transformer Language Model for Controllable Generation`_
by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher.
It's a causal (unidirectional) transformer pre-trained using language modeling on a very large
corpus of ~140 GB of text data with the first token reserved as a control code (such as Links, Books, Wikipedia etc.).
This model is a PyTorch `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.
.. _`CTRL: A Conditional Transformer Language Model for Controllable Generation`:
https://www.github.com/salesforce/ctrl
.. _`torch.nn.Module`:
https://pytorch.org/docs/stable/nn.html#module
Parameters:
config (:class:`~transformers.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 :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""
CTRL_INPUTS_DOCSTRING = r""" Inputs:
**input_ids**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
Indices of input sequence tokens in the vocabulary.
CTRL is a model with absolute position embeddings so it's usually advised to pad the inputs on
the right rather than the left.
Indices can be obtained using :class:`transformers.CTRLTokenizer`.
See :func:`transformers.PreTrainedTokenizer.encode` and
:func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
**past**:
list of ``Numpy array`` or ``tf.Tensor`` (one for each layer):
that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
(see `past` output below). Can be used to speed up sequential decoding.
**attention_mask**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
Mask to avoid performing attention on padding token indices.
Mask values selected in ``[0, 1]``:
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
**token_type_ids**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
A parallel sequence of tokens (can be used to indicate various portions of the inputs).
The embeddings from these tokens will be summed with the respective token embeddings.
Indices are selected in the vocabulary (unlike BERT which has a specific vocabulary for segment indices).
**position_ids**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
Indices of positions of each input sequence tokens in the position embeddings.
Selected in the range ``[0, config.max_position_embeddings - 1]``.
**head_mask**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
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**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length, embedding_dim)``:
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.
"""
@add_start_docstrings("The bare CTRL Model transformer outputting raw hidden-states without any specific head on top.",
CTRL_START_DOCSTRING, CTRL_INPUTS_DOCSTRING)
class TFCTRLModel(TFCTRLPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**last_hidden_state**: ``tf.Tensor`` of shape ``(batch_size, sequence_length, hidden_size)``
Sequence of hidden-states at the last layer of the model.
**past**:
list of ``tf.Tensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
that contains pre-computed hidden-states (key and values in the attention blocks).
Can be used (see `past` input) to speed up sequential decoding.
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``tf.Tensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
import tensorflow as tf
from transformers import CTRLTokenizer, TFCTRLModel
tokenizer = CTRLTokenizer.from_pretrained('ctrl')
model = TFCTRLModel.from_pretrained('ctrl')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
outputs = model(input_ids)
last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
"""
def __init__(self, config, *inputs, **kwargs):
super(TFCTRLModel, self).__init__(config, *inputs, **kwargs)
self.transformer = TFCTRLMainLayer(config, name='transformer')
def call(self, inputs, **kwargs):
outputs = self.transformer(inputs, **kwargs)
return outputs
class TFCTRLLMHead(tf.keras.layers.Layer):
def __init__(self, config, input_embeddings, **kwargs):
super(TFCTRLLMHead, self).__init__(**kwargs)
self.vocab_size = config.vocab_size
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.input_embeddings = input_embeddings
def build(self, input_shape):
self.bias = self.add_weight(shape=(self.vocab_size,),
initializer='zeros',
trainable=True,
name='bias')
super(TFCTRLLMHead, self).build(input_shape)
def call(self, hidden_states):
hidden_states = self.input_embeddings(hidden_states, mode="linear")
hidden_states = hidden_states + self.bias
return hidden_states
@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, CTRL_INPUTS_DOCSTRING)
class TFCTRLLMHeadModel(TFCTRLPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**prediction_scores**: ``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).
**past**:
list of ``tf.Tensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
that contains pre-computed hidden-states (key and values in the attention blocks).
Can be used (see `past` input) to speed up sequential decoding.
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``tf.Tensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
import torch
from transformers import CTRLTokenizer, TFCTRLLMHeadModel
tokenizer = CTRLTokenizer.from_pretrained('ctrl')
model = TFCTRLLMHeadModel.from_pretrained('ctrl')
input_ids = torch.tensor(tokenizer.encode("Links Hello, my dog is cute")).unsqueeze(0) # Batch size 1
outputs = model(input_ids, labels=input_ids)
loss, logits = outputs[:2]
"""
def __init__(self, config, *inputs, **kwargs):
super(TFCTRLLMHeadModel, self).__init__(config, *inputs, **kwargs)
self.transformer = TFCTRLMainLayer(config, name='transformer')
self.lm_head = TFCTRLLMHead(config, self.transformer.w, name="lm_head")
def get_output_embeddings(self):
return self.lm_head.input_embeddings
def call(self, inputs, **kwargs):
transformer_outputs = self.transformer(inputs, **kwargs)
hidden_states = transformer_outputs[0]
lm_logits = self.lm_head(hidden_states)
outputs = (lm_logits,) + transformer_outputs[1:]
return outputs # lm_logits, presents, (all hidden_states), (attentions)
| 24,528 | 47.380671 | 151 |
py
|
DeeBERT
|
DeeBERT-master/transformers/__init__.py
|
__version__ = "2.1.1"
# Work around to update TensorFlow's absl.logging threshold which alters the
# default Python logging output behavior when present.
# see: https://github.com/abseil/abseil-py/issues/99
# and: https://github.com/tensorflow/tensorflow/issues/26691#issuecomment-500369493
try:
import absl.logging
absl.logging.set_verbosity('info')
absl.logging.set_stderrthreshold('info')
absl.logging._warn_preinit_stderr = False
except:
pass
import logging
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
# Files and general utilities
from .file_utils import (TRANSFORMERS_CACHE, PYTORCH_TRANSFORMERS_CACHE, PYTORCH_PRETRAINED_BERT_CACHE,
cached_path, add_start_docstrings, add_end_docstrings,
WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME, CONFIG_NAME,
is_tf_available, is_torch_available)
from .data import (is_sklearn_available,
InputExample, InputFeatures, DataProcessor,
glue_output_modes, glue_convert_examples_to_features,
glue_processors, glue_tasks_num_labels)
if is_sklearn_available():
from .data import glue_compute_metrics
# Tokenizers
from .tokenization_utils import (PreTrainedTokenizer)
from .tokenization_auto import AutoTokenizer
from .tokenization_bert import BertTokenizer, BasicTokenizer, WordpieceTokenizer
from .tokenization_openai import OpenAIGPTTokenizer
from .tokenization_transfo_xl import (TransfoXLTokenizer, TransfoXLCorpus)
from .tokenization_gpt2 import GPT2Tokenizer
from .tokenization_ctrl import CTRLTokenizer
from .tokenization_xlnet import XLNetTokenizer, SPIECE_UNDERLINE
from .tokenization_xlm import XLMTokenizer
from .tokenization_roberta import RobertaTokenizer
from .tokenization_distilbert import DistilBertTokenizer
from .tokenization_camembert import CamembertTokenizer
# Configurations
from .configuration_utils import PretrainedConfig
from .configuration_auto import AutoConfig
from .configuration_bert import BertConfig, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_openai import OpenAIGPTConfig, OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_transfo_xl import TransfoXLConfig, TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_gpt2 import GPT2Config, GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_ctrl import CTRLConfig, CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_xlnet import XLNetConfig, XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_ctrl import CTRLConfig, CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_xlm import XLMConfig, XLM_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_roberta import RobertaConfig, ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_distilbert import DistilBertConfig, DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_camembert import CamembertConfig, CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
# Modeling
if is_torch_available():
from .modeling_utils import (PreTrainedModel, prune_layer, Conv1D)
from .modeling_auto import (AutoModel, AutoModelForSequenceClassification, AutoModelForQuestionAnswering,
AutoModelWithLMHead)
from .modeling_bert import (BertPreTrainedModel, BertModel, BertForPreTraining,
BertForMaskedLM, BertForNextSentencePrediction,
BertForSequenceClassification, BertForMultipleChoice,
BertForTokenClassification, BertForQuestionAnswering,
load_tf_weights_in_bert, BERT_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_openai import (OpenAIGPTPreTrainedModel, OpenAIGPTModel,
OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel,
load_tf_weights_in_openai_gpt, OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_transfo_xl import (TransfoXLPreTrainedModel, TransfoXLModel, TransfoXLLMHeadModel,
AdaptiveEmbedding,
load_tf_weights_in_transfo_xl, TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_gpt2 import (GPT2PreTrainedModel, GPT2Model,
GPT2LMHeadModel, GPT2DoubleHeadsModel,
load_tf_weights_in_gpt2, GPT2_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_ctrl import (CTRLPreTrainedModel, CTRLModel,
CTRLLMHeadModel,
CTRL_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_xlnet import (XLNetPreTrainedModel, XLNetModel, XLNetLMHeadModel,
XLNetForSequenceClassification, XLNetForMultipleChoice,
XLNetForQuestionAnsweringSimple, XLNetForQuestionAnswering,
load_tf_weights_in_xlnet, XLNET_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_xlm import (XLMPreTrainedModel , XLMModel,
XLMWithLMHeadModel, XLMForSequenceClassification,
XLMForQuestionAnswering, XLMForQuestionAnsweringSimple,
XLM_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_roberta import (RobertaForMaskedLM, RobertaModel,
RobertaForSequenceClassification, RobertaForMultipleChoice,
RobertaForTokenClassification,
ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_distilbert import (DistilBertForMaskedLM, DistilBertModel,
DistilBertForSequenceClassification, DistilBertForQuestionAnswering,
DistilBertForTokenClassification,
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_camembert import (CamembertForMaskedLM, CamembertModel,
CamembertForSequenceClassification, CamembertForMultipleChoice,
CamembertForTokenClassification,
CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_encoder_decoder import PreTrainedEncoderDecoder, Model2Model
# Optimization
from .optimization import (AdamW, get_constant_schedule, get_constant_schedule_with_warmup, get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup, get_linear_schedule_with_warmup)
# TensorFlow
if is_tf_available():
from .modeling_tf_utils import TFPreTrainedModel, TFSharedEmbeddings, TFSequenceSummary
from .modeling_tf_auto import (TFAutoModel, TFAutoModelForSequenceClassification, TFAutoModelForQuestionAnswering,
TFAutoModelWithLMHead)
from .modeling_tf_bert import (TFBertPreTrainedModel, TFBertMainLayer, TFBertEmbeddings,
TFBertModel, TFBertForPreTraining,
TFBertForMaskedLM, TFBertForNextSentencePrediction,
TFBertForSequenceClassification, TFBertForMultipleChoice,
TFBertForTokenClassification, TFBertForQuestionAnswering,
TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_tf_gpt2 import (TFGPT2PreTrainedModel, TFGPT2MainLayer,
TFGPT2Model, TFGPT2LMHeadModel, TFGPT2DoubleHeadsModel,
TF_GPT2_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_tf_openai import (TFOpenAIGPTPreTrainedModel, TFOpenAIGPTMainLayer,
TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel,
TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_tf_transfo_xl import (TFTransfoXLPreTrainedModel, TFTransfoXLMainLayer,
TFTransfoXLModel, TFTransfoXLLMHeadModel,
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_tf_xlnet import (TFXLNetPreTrainedModel, TFXLNetMainLayer,
TFXLNetModel, TFXLNetLMHeadModel,
TFXLNetForSequenceClassification,
TFXLNetForQuestionAnsweringSimple,
TF_XLNET_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_tf_xlm import (TFXLMPreTrainedModel, TFXLMMainLayer,
TFXLMModel, TFXLMWithLMHeadModel,
TFXLMForSequenceClassification,
TFXLMForQuestionAnsweringSimple,
TF_XLM_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_tf_roberta import (TFRobertaPreTrainedModel, TFRobertaMainLayer,
TFRobertaModel, TFRobertaForMaskedLM,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_tf_distilbert import (TFDistilBertPreTrainedModel, TFDistilBertMainLayer,
TFDistilBertModel, TFDistilBertForMaskedLM,
TFDistilBertForSequenceClassification,
TFDistilBertForQuestionAnswering,
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_tf_ctrl import (TFCTRLPreTrainedModel, TFCTRLModel,
TFCTRLLMHeadModel,
TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP)
# TF 2.0 <=> PyTorch conversion utilities
from .modeling_tf_pytorch_utils import (convert_tf_weight_name_to_pt_weight_name,
load_pytorch_checkpoint_in_tf2_model,
load_pytorch_weights_in_tf2_model,
load_pytorch_model_in_tf2_model,
load_tf2_checkpoint_in_pytorch_model,
load_tf2_weights_in_pytorch_model,
load_tf2_model_in_pytorch_model)
if not is_tf_available() and not is_torch_available():
logger.warning("Neither PyTorch nor TensorFlow >= 2.0 have been found."
"Models won't be available and only tokenizers, configuration"
"and file/data utilities can be used.")
| 10,661 | 58.898876 | 128 |
py
|
DeeBERT
|
DeeBERT-master/transformers/configuration_gpt2.py
|
# 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.
""" OpenAI GPT-2 configuration """
from __future__ import absolute_import, division, print_function, unicode_literals
import json
import logging
import sys
from io import open
from .configuration_utils import PretrainedConfig
logger = logging.getLogger(__name__)
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP = {"gpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-config.json",
"gpt2-medium": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-config.json",
"gpt2-large": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-large-config.json",
"gpt2-xl": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-xl-config.json",
"distilgpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/distilgpt2-config.json",}
class GPT2Config(PretrainedConfig):
"""Configuration class to store the configuration of a `GPT2Model`.
Args:
vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `GPT2Model` or a configuration json file.
n_positions: Number of positional embeddings.
n_ctx: Size of the causal mask (usually same as n_positions).
n_embd: Dimensionality of the embeddings and hidden states.
n_layer: Number of hidden layers in the Transformer encoder.
n_head: Number of attention heads for each attention layer in
the Transformer encoder.
layer_norm_epsilon: epsilon to use in the layer norm layers
resid_pdrop: The dropout probabilitiy for all fully connected
layers in the embeddings, encoder, and pooler.
attn_pdrop: The dropout ratio for the attention
probabilities.
embd_pdrop: The dropout ratio for the embeddings.
initializer_range: The sttdev of the truncated_normal_initializer for
initializing all weight matrices.
"""
pretrained_config_archive_map = GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP
def __init__(
self,
vocab_size_or_config_json_file=50257,
n_positions=1024,
n_ctx=1024,
n_embd=768,
n_layer=12,
n_head=12,
resid_pdrop=0.1,
embd_pdrop=0.1,
attn_pdrop=0.1,
layer_norm_epsilon=1e-5,
initializer_range=0.02,
num_labels=1,
summary_type='cls_index',
summary_use_proj=True,
summary_activation=None,
summary_proj_to_labels=True,
summary_first_dropout=0.1,
**kwargs
):
"""Constructs GPT2Config.
Args:
vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `GPT2Model` or a configuration json file.
n_positions: Number of positional embeddings.
n_ctx: Size of the causal mask (usually same as n_positions).
n_embd: Dimensionality of the embeddings and hidden states.
n_layer: Number of hidden layers in the Transformer encoder.
n_head: Number of attention heads for each attention layer in
the Transformer encoder.
layer_norm_epsilon: epsilon to use in the layer norm layers
resid_pdrop: The dropout probabilitiy for all fully connected
layers in the embeddings, encoder, and pooler.
attn_pdrop: The dropout ratio for the attention
probabilities.
embd_pdrop: The dropout ratio for the embeddings.
initializer_range: The sttdev of the truncated_normal_initializer for
initializing all weight matrices.
"""
super(GPT2Config, self).__init__(**kwargs)
if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
and isinstance(vocab_size_or_config_json_file, unicode)):
with open(vocab_size_or_config_json_file, "r", encoding="utf-8") as reader:
json_config = json.loads(reader.read())
for key, value in json_config.items():
self.__dict__[key] = value
elif isinstance(vocab_size_or_config_json_file, int):
self.vocab_size = vocab_size_or_config_json_file
self.n_ctx = n_ctx
self.n_positions = n_positions
self.n_embd = n_embd
self.n_layer = n_layer
self.n_head = n_head
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.num_labels = num_labels
self.summary_type = summary_type
self.summary_use_proj = summary_use_proj
self.summary_activation = summary_activation
self.summary_first_dropout = summary_first_dropout
self.summary_proj_to_labels = summary_proj_to_labels
else:
raise ValueError(
"First argument must be either a vocabulary size (int)"
"or the path to a pretrained model config file (str)"
)
@property
def max_position_embeddings(self):
return self.n_positions
@property
def hidden_size(self):
return self.n_embd
@property
def num_attention_heads(self):
return self.n_head
@property
def num_hidden_layers(self):
return self.n_layer
| 6,177 | 41.315068 | 131 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tokenization_gpt2.py
|
# coding=utf-8
# 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 OpenAI GPT."""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import sys
import json
import logging
import os
import regex as re
from io import open
try:
from functools import lru_cache
except ImportError:
# Just a dummy decorator to get the checks to run on python2
# because honestly I don't want to support a byte-level unicode BPE tokenizer on python 2 right now.
def lru_cache():
return lambda func: func
from .tokenization_utils import PreTrainedTokenizer
logger = logging.getLogger(__name__)
VOCAB_FILES_NAMES = {
'vocab_file': 'vocab.json',
'merges_file': 'merges.txt',
}
PRETRAINED_VOCAB_FILES_MAP = {
'vocab_file':
{
'gpt2': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json",
'gpt2-medium': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-vocab.json",
'gpt2-large': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-large-vocab.json",
'gpt2-xl': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-xl-vocab.json",
'distilgpt2': "https://s3.amazonaws.com/models.huggingface.co/bert/distilgpt2-vocab.json",
},
'merges_file':
{
'gpt2': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-merges.txt",
'gpt2-medium': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-merges.txt",
'gpt2-large': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-large-merges.txt",
'gpt2-xl': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-xl-merges.txt",
'distilgpt2': "https://s3.amazonaws.com/models.huggingface.co/bert/distilgpt2-merges.txt",
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
'gpt2': 1024,
'gpt2-medium': 1024,
'gpt2-large': 1024,
'gpt2-xl': 1024,
'distilgpt2': 1024,
}
@lru_cache()
def bytes_to_unicode():
"""
Returns list of utf-8 byte and a mapping to unicode strings.
We specifically avoids mapping to whitespace/control characters the bpe code barfs on.
The reversible bpe codes work on unicode strings.
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
This is a signficant percentage of your normal, say, 32K bpe vocab.
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
"""
_chr = unichr if sys.version_info[0] == 2 else chr
bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
cs = bs[:]
n = 0
for b in range(2**8):
if b not in bs:
bs.append(b)
cs.append(2**8+n)
n += 1
cs = [_chr(n) for n in cs]
return dict(zip(bs, cs))
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 GPT2Tokenizer(PreTrainedTokenizer):
"""
GPT-2 BPE tokenizer. Peculiarities:
- Byte-level Byte-Pair-Encoding
- Requires a space to start the input string => the encoding methods should be called with the
``add_prefix_space`` flag set to ``True``.
Otherwise, this tokenizer ``encode`` and ``decode`` method will not conserve
the absence of a space at the beginning of a string: `tokenizer.decode(tokenizer.encode("Hello")) = " Hello"`
"""
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, errors='replace', unk_token="<|endoftext|>",
bos_token="<|endoftext|>", eos_token="<|endoftext|>", **kwargs):
super(GPT2Tokenizer, self).__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, **kwargs)
self.max_len_single_sentence = self.max_len # no default special tokens - you can update this value if you add special tokens
self.max_len_sentences_pair = self.max_len # no default special tokens - you can update this value if you add special tokens
self.encoder = json.load(open(vocab_file, encoding="utf-8"))
self.decoder = {v: k for k, v in self.encoder.items()}
self.errors = errors # how to handle errors in decoding
self.byte_encoder = bytes_to_unicode()
self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
bpe_data = open(merges_file, encoding='utf-8').read().split('\n')[1:-1]
bpe_merges = [tuple(merge.split()) for merge in bpe_data]
self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
self.cache = {}
# Should haved added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
@property
def vocab_size(self):
return len(self.encoder)
def bpe(self, token):
if token in self.cache:
return self.cache[token]
word = tuple(token)
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)
new_word.extend(word[i:j])
i = j
except:
new_word.extend(word[i:])
break
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)
self.cache[token] = word
return word
def _tokenize(self, text, add_prefix_space=False):
""" Tokenize a string.
Args:
- add_prefix_space (boolean, default False):
Begin the sentence with at least one space toto get invariance to word order in GPT-2 (and RoBERTa) tokenizers.
"""
if add_prefix_space:
text = ' ' + text
bpe_tokens = []
for token in re.findall(self.pat, text):
if sys.version_info[0] == 2:
token = ''.join(self.byte_encoder[ord(b)] for b in token) # Maps all our bytes to unicode strings, avoiding controle tokens of the BPE (spaces in our case)
else:
token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8')) # Maps all our bytes to unicode strings, avoiding controle tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(' '))
return bpe_tokens
def _convert_token_to_id(self, token):
""" Converts a token (str/unicode) 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 (string/unicode) using the vocab."""
return self.decoder.get(index)
def convert_tokens_to_string(self, tokens):
""" Converts a sequence of tokens (string) in a single string. """
text = ''.join(tokens)
text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors=self.errors)
return text
def save_vocabulary(self, save_directory):
"""Save the tokenizer vocabulary and merge files to a directory."""
if not os.path.isdir(save_directory):
logger.error("Vocabulary path ({}) should be a directory".format(save_directory))
return
vocab_file = os.path.join(save_directory, VOCAB_FILES_NAMES['vocab_file'])
merge_file = os.path.join(save_directory, VOCAB_FILES_NAMES['merges_file'])
with open(vocab_file, 'w', encoding='utf-8') as f:
f.write(json.dumps(self.encoder, ensure_ascii=False))
index = 0
with open(merge_file, "w", encoding="utf-8") as writer:
writer.write(u'#version: 0.2\n')
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning("Saving vocabulary to {}: BPE merge indices are not consecutive."
" Please check that the tokenizer is not corrupted!".format(merge_file))
index = token_index
writer.write(' '.join(bpe_tokens) + u'\n')
index += 1
return vocab_file, merge_file
| 10,009 | 41.236287 | 182 |
py
|
DeeBERT
|
DeeBERT-master/transformers/modeling_tf_bert.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team 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.
""" TF 2.0 BERT model. """
from __future__ import absolute_import, division, print_function, unicode_literals
import json
import logging
import math
import os
import sys
from io import open
import numpy as np
import tensorflow as tf
from .configuration_bert import BertConfig
from .modeling_tf_utils import TFPreTrainedModel, get_initializer
from .file_utils import add_start_docstrings
logger = logging.getLogger(__name__)
TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-tf_model.h5",
'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-tf_model.h5",
'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-tf_model.h5",
'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-tf_model.h5",
'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-tf_model.h5",
'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-tf_model.h5",
'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-tf_model.h5",
'bert-base-german-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-cased-tf_model.h5",
'bert-large-uncased-whole-word-masking': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-tf_model.h5",
'bert-large-cased-whole-word-masking': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-tf_model.h5",
'bert-large-uncased-whole-word-masking-finetuned-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-finetuned-squad-tf_model.h5",
'bert-large-cased-whole-word-masking-finetuned-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-finetuned-squad-tf_model.h5",
'bert-base-cased-finetuned-mrpc': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-finetuned-mrpc-tf_model.h5",
}
def gelu(x):
""" Gaussian Error Linear Unit.
Original Implementation of the gelu activation function in Google Bert repo when initially created.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
Also see https://arxiv.org/abs/1606.08415
"""
cdf = 0.5 * (1.0 + tf.math.erf(x / tf.math.sqrt(2.0)))
return x * cdf
def gelu_new(x):
"""Gaussian Error Linear Unit.
This is a smoother version of the RELU.
Original paper: https://arxiv.org/abs/1606.08415
Args:
x: float Tensor to perform activation.
Returns:
`x` with the GELU activation applied.
"""
cdf = 0.5 * (1.0 + tf.tanh(
(np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3)))))
return x * cdf
def swish(x):
return x * tf.sigmoid(x)
ACT2FN = {"gelu": tf.keras.layers.Activation(gelu),
"relu": tf.keras.activations.relu,
"swish": tf.keras.layers.Activation(swish),
"gelu_new": tf.keras.layers.Activation(gelu_new)}
class TFBertEmbeddings(tf.keras.layers.Layer):
"""Construct the embeddings from word, position and token_type embeddings.
"""
def __init__(self, config, **kwargs):
super(TFBertEmbeddings, self).__init__(**kwargs)
self.vocab_size = config.vocab_size
self.hidden_size = config.hidden_size
self.initializer_range = config.initializer_range
self.position_embeddings = tf.keras.layers.Embedding(config.max_position_embeddings,
config.hidden_size,
embeddings_initializer=get_initializer(self.initializer_range),
name='position_embeddings')
self.token_type_embeddings = tf.keras.layers.Embedding(config.type_vocab_size,
config.hidden_size,
embeddings_initializer=get_initializer(self.initializer_range),
name='token_type_embeddings')
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
# any TensorFlow checkpoint file
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name='LayerNorm')
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
def build(self, input_shape):
"""Build shared word embedding layer """
with tf.name_scope("word_embeddings"):
# Create and initialize weights. The random normal initializer was chosen
# arbitrarily, and works well.
self.word_embeddings = self.add_weight(
"weight",
shape=[self.vocab_size, self.hidden_size],
initializer=get_initializer(self.initializer_range))
super(TFBertEmbeddings, self).build(input_shape)
def call(self, inputs, mode="embedding", training=False):
"""Get token embeddings of inputs.
Args:
inputs: list of three int64 tensors with shape [batch_size, length]: (input_ids, position_ids, token_type_ids)
mode: string, a valid value is one of "embedding" and "linear".
Returns:
outputs: (1) If mode == "embedding", output embedding tensor, float32 with
shape [batch_size, length, embedding_size]; (2) mode == "linear", output
linear tensor, float32 with shape [batch_size, length, vocab_size].
Raises:
ValueError: if mode is not valid.
Shared weights logic adapted from
https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
"""
if mode == "embedding":
return self._embedding(inputs, training=training)
elif mode == "linear":
return self._linear(inputs)
else:
raise ValueError("mode {} is not valid.".format(mode))
def _embedding(self, inputs, training=False):
"""Applies embedding based on inputs tensor."""
input_ids, position_ids, token_type_ids, inputs_embeds = inputs
if input_ids is not None:
input_shape = tf.shape(input_ids)
else:
input_shape = tf.shape(inputs_embeds)[:-1]
seq_length = input_shape[1]
if position_ids is None:
position_ids = tf.range(seq_length, dtype=tf.int32)[tf.newaxis, :]
if token_type_ids is None:
token_type_ids = tf.fill(input_shape, 0)
if inputs_embeds is None:
inputs_embeds = tf.gather(self.word_embeddings, input_ids)
position_embeddings = self.position_embeddings(position_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + position_embeddings + token_type_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings, training=training)
return embeddings
def _linear(self, inputs):
"""Computes logits by running inputs through a linear layer.
Args:
inputs: A float32 tensor with shape [batch_size, length, hidden_size]
Returns:
float32 tensor with shape [batch_size, length, vocab_size].
"""
batch_size = tf.shape(inputs)[0]
length = tf.shape(inputs)[1]
x = tf.reshape(inputs, [-1, self.hidden_size])
logits = tf.matmul(x, self.word_embeddings, transpose_b=True)
return tf.reshape(logits, [batch_size, length, self.vocab_size])
class TFBertSelfAttention(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super(TFBertSelfAttention, self).__init__(**kwargs)
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
"The hidden size (%d) is not a multiple of the number of attention "
"heads (%d)" % (config.hidden_size, config.num_attention_heads))
self.output_attentions = config.output_attentions
self.num_attention_heads = config.num_attention_heads
assert config.hidden_size % config.num_attention_heads == 0
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.query = tf.keras.layers.Dense(self.all_head_size,
kernel_initializer=get_initializer(config.initializer_range),
name='query')
self.key = tf.keras.layers.Dense(self.all_head_size,
kernel_initializer=get_initializer(config.initializer_range),
name='key')
self.value = tf.keras.layers.Dense(self.all_head_size,
kernel_initializer=get_initializer(config.initializer_range),
name='value')
self.dropout = tf.keras.layers.Dropout(config.attention_probs_dropout_prob)
def transpose_for_scores(self, x, batch_size):
x = tf.reshape(x, (batch_size, -1, self.num_attention_heads, self.attention_head_size))
return tf.transpose(x, perm=[0, 2, 1, 3])
def call(self, inputs, training=False):
hidden_states, attention_mask, head_mask = inputs
batch_size = tf.shape(hidden_states)[0]
mixed_query_layer = self.query(hidden_states)
mixed_key_layer = self.key(hidden_states)
mixed_value_layer = self.value(hidden_states)
query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)
key_layer = self.transpose_for_scores(mixed_key_layer, batch_size)
value_layer = self.transpose_for_scores(mixed_value_layer, batch_size)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True) # (batch size, num_heads, seq_len_q, seq_len_k)
dk = tf.cast(tf.shape(key_layer)[-1], tf.float32) # scale attention_scores
attention_scores = attention_scores / tf.math.sqrt(dk)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in TFBertModel call() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = tf.nn.softmax(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(attention_probs, training=training)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = tf.matmul(attention_probs, value_layer)
context_layer = tf.transpose(context_layer, perm=[0, 2, 1, 3])
context_layer = tf.reshape(context_layer,
(batch_size, -1, self.all_head_size)) # (batch_size, seq_len_q, all_head_size)
outputs = (context_layer, attention_probs) if self.output_attentions else (context_layer,)
return outputs
class TFBertSelfOutput(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super(TFBertSelfOutput, self).__init__(**kwargs)
self.dense = tf.keras.layers.Dense(config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
name='dense')
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name='LayerNorm')
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
def call(self, inputs, training=False):
hidden_states, input_tensor = inputs
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class TFBertAttention(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super(TFBertAttention, self).__init__(**kwargs)
self.self_attention = TFBertSelfAttention(config, name='self')
self.dense_output = TFBertSelfOutput(config, name='output')
def prune_heads(self, heads):
raise NotImplementedError
def call(self, inputs, training=False):
input_tensor, attention_mask, head_mask = inputs
self_outputs = self.self_attention([input_tensor, attention_mask, head_mask], training=training)
attention_output = self.dense_output([self_outputs[0], input_tensor], training=training)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
class TFBertIntermediate(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super(TFBertIntermediate, self).__init__(**kwargs)
self.dense = tf.keras.layers.Dense(config.intermediate_size,
kernel_initializer=get_initializer(config.initializer_range),
name='dense')
if isinstance(config.hidden_act, str) or (sys.version_info[0] == 2 and isinstance(config.hidden_act, unicode)):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
def call(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
class TFBertOutput(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super(TFBertOutput, self).__init__(**kwargs)
self.dense = tf.keras.layers.Dense(config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
name='dense')
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name='LayerNorm')
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
def call(self, inputs, training=False):
hidden_states, input_tensor = inputs
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class TFBertLayer(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super(TFBertLayer, self).__init__(**kwargs)
self.attention = TFBertAttention(config, name='attention')
self.intermediate = TFBertIntermediate(config, name='intermediate')
self.bert_output = TFBertOutput(config, name='output')
def call(self, inputs, training=False):
hidden_states, attention_mask, head_mask = inputs
attention_outputs = self.attention([hidden_states, attention_mask, head_mask], training=training)
attention_output = attention_outputs[0]
intermediate_output = self.intermediate(attention_output)
layer_output = self.bert_output([intermediate_output, attention_output], training=training)
outputs = (layer_output,) + attention_outputs[1:] # add attentions if we output them
return outputs
class TFBertEncoder(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super(TFBertEncoder, self).__init__(**kwargs)
self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states
self.layer = [TFBertLayer(config, name='layer_._{}'.format(i)) for i in range(config.num_hidden_layers)]
def call(self, inputs, training=False):
hidden_states, attention_mask, head_mask = inputs
all_hidden_states = ()
all_attentions = ()
for i, layer_module in enumerate(self.layer):
if self.output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_outputs = layer_module([hidden_states, attention_mask, head_mask[i]], training=training)
hidden_states = layer_outputs[0]
if self.output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
# Add last layer
if self.output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
outputs = (hidden_states,)
if self.output_hidden_states:
outputs = outputs + (all_hidden_states,)
if self.output_attentions:
outputs = outputs + (all_attentions,)
return outputs # outputs, (hidden states), (attentions)
class TFBertPooler(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super(TFBertPooler, self).__init__(**kwargs)
self.dense = tf.keras.layers.Dense(config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
activation='tanh',
name='dense')
def call(self, hidden_states):
# 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)
return pooled_output
class TFBertPredictionHeadTransform(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super(TFBertPredictionHeadTransform, self).__init__(**kwargs)
self.dense = tf.keras.layers.Dense(config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
name='dense')
if isinstance(config.hidden_act, str) or (sys.version_info[0] == 2 and isinstance(config.hidden_act, unicode)):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
self.transform_act_fn = config.hidden_act
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name='LayerNorm')
def call(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.transform_act_fn(hidden_states)
hidden_states = self.LayerNorm(hidden_states)
return hidden_states
class TFBertLMPredictionHead(tf.keras.layers.Layer):
def __init__(self, config, input_embeddings, **kwargs):
super(TFBertLMPredictionHead, self).__init__(**kwargs)
self.vocab_size = config.vocab_size
self.transform = TFBertPredictionHeadTransform(config, name='transform')
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.input_embeddings = input_embeddings
def build(self, input_shape):
self.bias = self.add_weight(shape=(self.vocab_size,),
initializer='zeros',
trainable=True,
name='bias')
super(TFBertLMPredictionHead, self).build(input_shape)
def call(self, hidden_states):
hidden_states = self.transform(hidden_states)
hidden_states = self.input_embeddings(hidden_states, mode="linear")
hidden_states = hidden_states + self.bias
return hidden_states
class TFBertMLMHead(tf.keras.layers.Layer):
def __init__(self, config, input_embeddings, **kwargs):
super(TFBertMLMHead, self).__init__(**kwargs)
self.predictions = TFBertLMPredictionHead(config, input_embeddings, name='predictions')
def call(self, sequence_output):
prediction_scores = self.predictions(sequence_output)
return prediction_scores
class TFBertNSPHead(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super(TFBertNSPHead, self).__init__(**kwargs)
self.seq_relationship = tf.keras.layers.Dense(2,
kernel_initializer=get_initializer(config.initializer_range),
name='seq_relationship')
def call(self, pooled_output):
seq_relationship_score = self.seq_relationship(pooled_output)
return seq_relationship_score
class TFBertMainLayer(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super(TFBertMainLayer, self).__init__(**kwargs)
self.num_hidden_layers = config.num_hidden_layers
self.embeddings = TFBertEmbeddings(config, name='embeddings')
self.encoder = TFBertEncoder(config, name='encoder')
self.pooler = TFBertPooler(config, name='pooler')
def get_input_embeddings(self):
return self.embeddings
def _resize_token_embeddings(self, new_num_tokens):
raise NotImplementedError
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 call(self, inputs, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, training=False):
if isinstance(inputs, (tuple, list)):
input_ids = inputs[0]
attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
token_type_ids = inputs[2] if len(inputs) > 2 else token_type_ids
position_ids = inputs[3] if len(inputs) > 3 else position_ids
head_mask = inputs[4] if len(inputs) > 4 else head_mask
inputs_embeds = inputs[5] if len(inputs) > 5 else inputs_embeds
assert len(inputs) <= 6, "Too many inputs."
elif isinstance(inputs, dict):
input_ids = inputs.get('input_ids')
attention_mask = inputs.get('attention_mask', attention_mask)
token_type_ids = inputs.get('token_type_ids', token_type_ids)
position_ids = inputs.get('position_ids', position_ids)
head_mask = inputs.get('head_mask', head_mask)
inputs_embeds = inputs.get('inputs_embeds', inputs_embeds)
assert len(inputs) <= 6, "Too many inputs."
else:
input_ids = inputs
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 = input_ids.shape
elif inputs_embeds is not None:
input_shape = inputs_embeds.shape[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if attention_mask is None:
attention_mask = tf.fill(input_shape, 1)
if token_type_ids is None:
token_type_ids = tf.fill(input_shape, 0)
# 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.
extended_attention_mask = attention_mask[:, tf.newaxis, tf.newaxis, :]
# 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, tf.float32)
extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
# 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 not head_mask is None:
raise NotImplementedError
else:
head_mask = [None] * self.num_hidden_layers
# head_mask = tf.constant([0] * self.num_hidden_layers)
embedding_output = self.embeddings([input_ids, position_ids, token_type_ids, inputs_embeds], training=training)
encoder_outputs = self.encoder([embedding_output, extended_attention_mask, head_mask], training=training)
sequence_output = encoder_outputs[0]
pooled_output = self.pooler(sequence_output)
outputs = (sequence_output, pooled_output,) + encoder_outputs[1:] # add hidden_states and attentions if they are here
return outputs # sequence_output, pooled_output, (hidden_states), (attentions)
class TFBertPreTrainedModel(TFPreTrainedModel):
""" An abstract class to handle weights initialization and
a simple interface for dowloading and loading pretrained models.
"""
config_class = BertConfig
pretrained_model_archive_map = TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP
base_model_prefix = "bert"
BERT_START_DOCSTRING = r""" The BERT model was proposed in
`BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding`_
by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova. It's a bidirectional transformer
pre-trained using a combination of masked language modeling objective and next sentence prediction
on a large corpus comprising the Toronto Book Corpus and Wikipedia.
This model is a tf.keras.Model `tf.keras.Model`_ sub-class. 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.
.. _`BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding`:
https://arxiv.org/abs/1810.04805
.. _`tf.keras.Model`:
https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/Model
Note on the model inputs:
TF 2.0 models accepts two formats as inputs:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional arguments.
This second option is usefull when using `tf.keras.Model.fit()` method which currently requires having all the tensors in the first argument of the model call function: `model(inputs)`.
If you choose this second option, 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(inputs_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 associaed to the input names given in the docstring:
`model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`
Parameters:
config (:class:`~transformers.BertConfig`): 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 :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""
BERT_INPUTS_DOCSTRING = r"""
Inputs:
**input_ids**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
Indices of input sequence tokens in the vocabulary.
To match pre-training, BERT input sequence should be formatted with [CLS] and [SEP] tokens as follows:
(a) For sequence pairs:
``tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]``
``token_type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1``
(b) For single sequences:
``tokens: [CLS] the dog is hairy . [SEP]``
``token_type_ids: 0 0 0 0 0 0 0``
Bert is a model with absolute position embeddings so it's usually advised to pad the inputs on
the right rather than the left.
Indices can be obtained using :class:`transformers.BertTokenizer`.
See :func:`transformers.PreTrainedTokenizer.encode` and
:func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
**attention_mask**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
Mask to avoid performing attention on padding token indices.
Mask values selected in ``[0, 1]``:
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
**token_type_ids**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
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
(see `BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding`_ for more details).
**position_ids**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
Indices of positions of each input sequence tokens in the position embeddings.
Selected in the range ``[0, config.max_position_embeddings - 1]``.
**head_mask**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
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**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length, embedding_dim)``:
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.
"""
@add_start_docstrings("The bare Bert Model transformer outputing raw hidden-states without any specific head on top.",
BERT_START_DOCSTRING, BERT_INPUTS_DOCSTRING)
class TFBertModel(TFBertPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**last_hidden_state**: ``tf.Tensor`` of shape ``(batch_size, sequence_length, hidden_size)``
Sequence of hidden-states at the output of the last layer of the model.
**pooler_output**: ``tf.Tensor`` of shape ``(batch_size, hidden_size)``
Last layer hidden-state of the first token of the sequence (classification token)
further processed by a Linear layer and a Tanh activation function. The Linear
layer weights are trained from the next sentence prediction (classification)
objective during Bert pretraining. This output is usually *not* a good summary
of the semantic content of the input, you're often better with averaging or pooling
the sequence of hidden-states for the whole input sequence.
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``tf.Tensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
import tensorflow as tf
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = TFBertModel.from_pretrained('bert-base-uncased')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
outputs = model(input_ids)
last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
"""
def __init__(self, config, *inputs, **kwargs):
super(TFBertModel, self).__init__(config, *inputs, **kwargs)
self.bert = TFBertMainLayer(config, name='bert')
def call(self, inputs, **kwargs):
outputs = self.bert(inputs, **kwargs)
return outputs
@add_start_docstrings("""Bert Model with two heads on top as done during the pre-training:
a `masked language modeling` head and a `next sentence prediction (classification)` head. """,
BERT_START_DOCSTRING, BERT_INPUTS_DOCSTRING)
class TFBertForPreTraining(TFBertPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**prediction_scores**: ```tf.Tensor`` of shape ``(batch_size, sequence_length, config.vocab_size)``
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
**seq_relationship_scores**: ```tf.Tensor`` of shape ``(batch_size, sequence_length, 2)``
Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ```tf.Tensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
import tensorflow as tf
from transformers import BertTokenizer, TFBertForPreTraining
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = TFBertForPreTraining.from_pretrained('bert-base-uncased')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
outputs = model(input_ids)
prediction_scores, seq_relationship_scores = outputs[:2]
"""
def __init__(self, config, *inputs, **kwargs):
super(TFBertForPreTraining, self).__init__(config, *inputs, **kwargs)
self.bert = TFBertMainLayer(config, name='bert')
self.nsp = TFBertNSPHead(config, name='nsp___cls')
self.mlm = TFBertMLMHead(config, self.bert.embeddings, name='mlm___cls')
def get_output_embeddings(self):
return self.bert.embeddings
def call(self, inputs, **kwargs):
outputs = self.bert(inputs, **kwargs)
sequence_output, pooled_output = outputs[:2]
prediction_scores = self.mlm(sequence_output, training=kwargs.get('training', False))
seq_relationship_score = self.nsp(pooled_output)
outputs = (prediction_scores, seq_relationship_score,) + outputs[2:] # add hidden states and attention if they are here
return outputs # prediction_scores, seq_relationship_score, (hidden_states), (attentions)
@add_start_docstrings("""Bert Model with a `language modeling` head on top. """,
BERT_START_DOCSTRING, BERT_INPUTS_DOCSTRING)
class TFBertForMaskedLM(TFBertPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**prediction_scores**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length, config.vocab_size)``
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``Numpy array`` or ``tf.Tensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list of ``Numpy array`` or ``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.
Examples::
import tensorflow as tf
from transformers import BertTokenizer, TFBertForMaskedLM
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = TFBertForMaskedLM.from_pretrained('bert-base-uncased')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
outputs = model(input_ids)
prediction_scores = outputs[0]
"""
def __init__(self, config, *inputs, **kwargs):
super(TFBertForMaskedLM, self).__init__(config, *inputs, **kwargs)
self.bert = TFBertMainLayer(config, name='bert')
self.mlm = TFBertMLMHead(config, self.bert.embeddings, name='mlm___cls')
def get_output_embeddings(self):
return self.bert.embeddings
def call(self, inputs, **kwargs):
outputs = self.bert(inputs, **kwargs)
sequence_output = outputs[0]
prediction_scores = self.mlm(sequence_output, training=kwargs.get('training', False))
outputs = (prediction_scores,) + outputs[2:] # Add hidden states and attention if they are here
return outputs # prediction_scores, (hidden_states), (attentions)
@add_start_docstrings("""Bert Model with a `next sentence prediction (classification)` head on top. """,
BERT_START_DOCSTRING, BERT_INPUTS_DOCSTRING)
class TFBertForNextSentencePrediction(TFBertPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**seq_relationship_scores**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length, 2)``
Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``Numpy array`` or ``tf.Tensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list of ``Numpy array`` or ``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.
Examples::
import tensorflow as tf
from transformers import BertTokenizer, TFBertForNextSentencePrediction
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = TFBertForNextSentencePrediction.from_pretrained('bert-base-uncased')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
outputs = model(input_ids)
seq_relationship_scores = outputs[0]
"""
def __init__(self, config, *inputs, **kwargs):
super(TFBertForNextSentencePrediction, self).__init__(config, *inputs, **kwargs)
self.bert = TFBertMainLayer(config, name='bert')
self.nsp = TFBertNSPHead(config, name='nsp___cls')
def call(self, inputs, **kwargs):
outputs = self.bert(inputs, **kwargs)
pooled_output = outputs[1]
seq_relationship_score = self.nsp(pooled_output)
outputs = (seq_relationship_score,) + outputs[2:] # add hidden states and attention if they are here
return outputs # seq_relationship_score, (hidden_states), (attentions)
@add_start_docstrings("""Bert Model transformer with a sequence classification/regression head on top (a linear layer on top of
the pooled output) e.g. for GLUE tasks. """,
BERT_START_DOCSTRING, BERT_INPUTS_DOCSTRING)
class TFBertForSequenceClassification(TFBertPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**logits**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, config.num_labels)``
Classification (or regression if config.num_labels==1) scores (before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``Numpy array`` or ``tf.Tensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list of ``Numpy array`` or ``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.
Examples::
import tensorflow as tf
from transformers import BertTokenizer, TFBertForSequenceClassification
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = TFBertForSequenceClassification.from_pretrained('bert-base-uncased')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
outputs = model(input_ids)
logits = outputs[0]
"""
def __init__(self, config, *inputs, **kwargs):
super(TFBertForSequenceClassification, self).__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.bert = TFBertMainLayer(config, name='bert')
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
self.classifier = tf.keras.layers.Dense(config.num_labels,
kernel_initializer=get_initializer(config.initializer_range),
name='classifier')
def call(self, inputs, **kwargs):
outputs = self.bert(inputs, **kwargs)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output, training=kwargs.get('training', False))
logits = self.classifier(pooled_output)
outputs = (logits,) + outputs[2:] # add hidden states and attention if they are here
return outputs # logits, (hidden_states), (attentions)
@add_start_docstrings("""Bert 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. """,
BERT_START_DOCSTRING, BERT_INPUTS_DOCSTRING)
class TFBertForMultipleChoice(TFBertPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**classification_scores**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, num_choices)`` where `num_choices` is the size of the second dimension
of the input tensors. (see `input_ids` above).
Classification scores (before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``Numpy array`` or ``tf.Tensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list of ``Numpy array`` or ``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.
Examples::
import tensorflow as tf
from transformers import BertTokenizer, TFBertForMultipleChoice
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = TFBertForMultipleChoice.from_pretrained('bert-base-uncased')
choices = ["Hello, my dog is cute", "Hello, my cat is amazing"]
input_ids = tf.constant([tokenizer.encode(s) for s in choices])[None, :] # Batch size 1, 2 choices
outputs = model(input_ids)
classification_scores = outputs[0]
"""
def __init__(self, config, *inputs, **kwargs):
super(TFBertForMultipleChoice, self).__init__(config, *inputs, **kwargs)
self.bert = TFBertMainLayer(config, name='bert')
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
self.classifier = tf.keras.layers.Dense(1,
kernel_initializer=get_initializer(config.initializer_range),
name='classifier')
def call(self, inputs, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, training=False):
if isinstance(inputs, (tuple, list)):
input_ids = inputs[0]
attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
token_type_ids = inputs[2] if len(inputs) > 2 else token_type_ids
position_ids = inputs[3] if len(inputs) > 3 else position_ids
head_mask = inputs[4] if len(inputs) > 4 else head_mask
inputs_embeds = inputs[5] if len(inputs) > 5 else inputs_embeds
assert len(inputs) <= 6, "Too many inputs."
elif isinstance(inputs, dict):
input_ids = inputs.get('input_ids')
attention_mask = inputs.get('attention_mask', attention_mask)
token_type_ids = inputs.get('token_type_ids', token_type_ids)
position_ids = inputs.get('position_ids', position_ids)
head_mask = inputs.get('head_mask', head_mask)
inputs_embeds = inputs.get('inputs_embeds', inputs_embeds)
assert len(inputs) <= 6, "Too many inputs."
else:
input_ids = inputs
if input_ids is not None:
num_choices = tf.shape(input_ids)[1]
seq_length = tf.shape(input_ids)[2]
else:
num_choices = tf.shape(inputs_embeds)[1]
seq_length = tf.shape(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 = [flat_input_ids, flat_attention_mask, flat_token_type_ids, flat_position_ids, head_mask, inputs_embeds]
outputs = self.bert(flat_inputs, training=training)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output, training=training)
logits = self.classifier(pooled_output)
reshaped_logits = tf.reshape(logits, (-1, num_choices))
outputs = (reshaped_logits,) + outputs[2:] # add hidden states and attention if they are here
return outputs # reshaped_logits, (hidden_states), (attentions)
@add_start_docstrings("""Bert 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. """,
BERT_START_DOCSTRING, BERT_INPUTS_DOCSTRING)
class TFBertForTokenClassification(TFBertPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**scores**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length, config.num_labels)``
Classification scores (before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``Numpy array`` or ``tf.Tensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list of ``Numpy array`` or ``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.
Examples::
import tensorflow as tf
from transformers import BertTokenizer, TFBertForTokenClassification
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = TFBertForTokenClassification.from_pretrained('bert-base-uncased')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
outputs = model(input_ids)
scores = outputs[0]
"""
def __init__(self, config, *inputs, **kwargs):
super(TFBertForTokenClassification, self).__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.bert = TFBertMainLayer(config, name='bert')
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
self.classifier = tf.keras.layers.Dense(config.num_labels,
kernel_initializer=get_initializer(config.initializer_range),
name='classifier')
def call(self, inputs, **kwargs):
outputs = self.bert(inputs, **kwargs)
sequence_output = outputs[0]
sequence_output = self.dropout(sequence_output, training=kwargs.get('training', False))
logits = self.classifier(sequence_output)
outputs = (logits,) + outputs[2:] # add hidden states and attention if they are here
return outputs # scores, (hidden_states), (attentions)
@add_start_docstrings("""Bert 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`). """,
BERT_START_DOCSTRING, BERT_INPUTS_DOCSTRING)
class TFBertForQuestionAnswering(TFBertPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**start_scores**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length,)``
Span-start scores (before SoftMax).
**end_scores**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length,)``
Span-end scores (before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``Numpy array`` or ``tf.Tensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list of ``Numpy array`` or ``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.
Examples::
import tensorflow as tf
from transformers import BertTokenizer, TFBertForQuestionAnswering
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = TFBertForQuestionAnswering.from_pretrained('bert-base-uncased')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
outputs = model(input_ids)
start_scores, end_scores = outputs[:2]
"""
def __init__(self, config, *inputs, **kwargs):
super(TFBertForQuestionAnswering, self).__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.bert = TFBertMainLayer(config, name='bert')
self.qa_outputs = tf.keras.layers.Dense(config.num_labels,
kernel_initializer=get_initializer(config.initializer_range),
name='qa_outputs')
def call(self, inputs, **kwargs):
outputs = self.bert(inputs, **kwargs)
sequence_output = outputs[0]
logits = self.qa_outputs(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)
outputs = (start_logits, end_logits,) + outputs[2:]
return outputs # start_logits, end_logits, (hidden_states), (attentions)
| 55,995 | 51.283847 | 193 |
py
|
DeeBERT
|
DeeBERT-master/transformers/configuration_roberta.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team 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.
""" RoBERTa configuration """
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import logging
from .configuration_bert import BertConfig
logger = logging.getLogger(__name__)
ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP = {
'roberta-base': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-config.json",
'roberta-large': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-config.json",
'roberta-large-mnli': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-mnli-config.json",
'distilroberta-base': "https://s3.amazonaws.com/models.huggingface.co/bert/distilroberta-base-config.json",
'roberta-base-openai-detector': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-openai-detector-config.json",
'roberta-large-openai-detector': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-openai-detector-config.json",
}
class RobertaConfig(BertConfig):
pretrained_config_archive_map = ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP
| 1,793 | 45 | 133 |
py
|
DeeBERT
|
DeeBERT-master/transformers/modeling_highway_roberta.py
|
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss, MSELoss
from .modeling_roberta import RobertaEmbeddings
from .modeling_highway_bert import BertModel, BertPreTrainedModel, entropy, HighwayException
from .configuration_roberta import RobertaConfig
ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP = {
'roberta-base': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-pytorch_model.bin",
'roberta-large': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-pytorch_model.bin",
'roberta-large-mnli': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-mnli-pytorch_model.bin",
'distilroberta-base': "https://s3.amazonaws.com/models.huggingface.co/bert/distilroberta-base-pytorch_model.bin",
'roberta-base-openai-detector': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-openai-detector-pytorch_model.bin",
'roberta-large-openai-detector': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-openai-detector-pytorch_model.bin",
}
class RobertaModel(BertModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**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.
**pooler_output**: ``torch.FloatTensor`` of shape ``(batch_size, hidden_size)``
Last layer hidden-state of the first token of the sequence (classification token)
further processed by a Linear layer and a Tanh activation function. The Linear
layer weights are trained from the next sentence prediction (classification)
objective during Bert pretraining. This output is usually *not* a good summary
of the semantic content of the input, you're often better with averaging or pooling
the sequence of hidden-states for the whole input sequence.
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = RobertaModel.from_pretrained('roberta-base')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
outputs = model(input_ids)
last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
"""
config_class = RobertaConfig
pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
base_model_prefix = "roberta"
def __init__(self, config):
super(RobertaModel, self).__init__(config)
self.embeddings = RobertaEmbeddings(config)
self.init_weights()
def get_input_embeddings(self):
return self.embeddings.word_embeddings
def set_input_embeddings(self, value):
self.embeddings.word_embeddings = value
class RobertaForSequenceClassification(BertPreTrainedModel):
r"""
**labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
Labels for computing the sequence classification/regression loss.
Indices should be in ``[0, ..., config.num_labels]``.
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: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Classification (or regression if config.num_labels==1) loss.
**logits**: ``torch.FloatTensor`` of shape ``(batch_size, config.num_labels)``
Classification (or regression if config.num_labels==1) scores (before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = RobertaForSequenceClassification.from_pretrained('roberta-base')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
labels = torch.tensor([1]).unsqueeze(0) # Batch size 1
outputs = model(input_ids, labels=labels)
loss, logits = outputs[:2]
"""
config_class = RobertaConfig
pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
base_model_prefix = "roberta"
def __init__(self, config):
super(RobertaForSequenceClassification, self).__init__(config)
self.num_labels = config.num_labels
self.num_layers = config.num_hidden_layers
self.roberta = RobertaModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, self.config.num_labels)
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None,
inputs_embeds=None,
labels=None,
output_layer=-1, train_highway=False):
exit_layer = self.num_layers
try:
outputs = self.roberta(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
outputs = (logits,) + outputs[2:] # add hidden states and attention if they are here
except HighwayException as e:
outputs = e.message
exit_layer = e.exit_layer
logits = outputs[0]
if not self.training:
original_entropy = entropy(logits)
highway_entropy = []
highway_logits_all = []
if labels is not None:
if self.num_labels == 1:
# We are doing regression
loss_fct = MSELoss()
loss = loss_fct(logits.view(-1), labels.view(-1))
else:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
# work with highway exits
highway_losses = []
for highway_exit in outputs[-1]:
highway_logits = highway_exit[0]
if not self.training:
highway_logits_all.append(highway_logits)
highway_entropy.append(highway_exit[2])
if self.num_labels == 1:
# We are doing regression
loss_fct = MSELoss()
highway_loss = loss_fct(highway_logits.view(-1),
labels.view(-1))
else:
loss_fct = CrossEntropyLoss()
highway_loss = loss_fct(highway_logits.view(-1, self.num_labels),
labels.view(-1))
highway_losses.append(highway_loss)
if train_highway:
outputs = (sum(highway_losses[:-1]),) + outputs
# exclude the final highway, of course
else:
outputs = (loss,) + outputs
if not self.training:
outputs = outputs + ((original_entropy, highway_entropy), exit_layer)
if output_layer >= 0:
outputs = (outputs[0],) + \
(highway_logits_all[output_layer],) + \
outputs[2:] ## use the highway of the last layer
return outputs # (loss), logits, (hidden_states), (attentions), entropy
| 9,356 | 50.983333 | 139 |
py
|
DeeBERT
|
DeeBERT-master/transformers/configuration_openai.py
|
# 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.
""" OpenAI GPT configuration """
from __future__ import absolute_import, division, print_function, unicode_literals
import json
import logging
import sys
from io import open
from .configuration_utils import PretrainedConfig
logger = logging.getLogger(__name__)
OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"openai-gpt": "https://s3.amazonaws.com/models.huggingface.co/bert/openai-gpt-config.json"
}
class OpenAIGPTConfig(PretrainedConfig):
"""
Configuration class to store the configuration of a `OpenAIGPTModel`.
Args:
vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `OpenAIGPTModel` or a configuration json file.
n_positions: Number of positional embeddings.
n_ctx: Size of the causal mask (usually same as n_positions).
n_embd: Dimensionality of the embeddings and hidden states.
n_layer: Number of hidden layers in the Transformer encoder.
n_head: Number of attention heads for each attention layer in
the Transformer encoder.
afn: The non-linear activation function (function or string) in the
encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
resid_pdrop: The dropout probabilitiy for all fully connected
layers in the embeddings, encoder, and pooler.
attn_pdrop: The dropout ratio for the attention
probabilities.
embd_pdrop: The dropout ratio for the embeddings.
layer_norm_epsilon: epsilon to use in the layer norm layers
initializer_range: The sttdev of the truncated_normal_initializer for
initializing all weight matrices.
predict_special_tokens: should we predict special tokens (when the model has a LM head)
"""
pretrained_config_archive_map = OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP
def __init__(
self,
vocab_size_or_config_json_file=40478,
n_positions=512,
n_ctx=512,
n_embd=768,
n_layer=12,
n_head=12,
afn="gelu",
resid_pdrop=0.1,
embd_pdrop=0.1,
attn_pdrop=0.1,
layer_norm_epsilon=1e-5,
initializer_range=0.02,
predict_special_tokens=True,
num_labels=1,
summary_type='cls_index',
summary_use_proj=True,
summary_activation=None,
summary_proj_to_labels=True,
summary_first_dropout=0.1,
**kwargs
):
"""Constructs OpenAIGPTConfig.
"""
super(OpenAIGPTConfig, self).__init__(**kwargs)
if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
and isinstance(vocab_size_or_config_json_file, unicode)):
with open(vocab_size_or_config_json_file, "r", encoding="utf-8") as reader:
json_config = json.loads(reader.read())
for key, value in json_config.items():
self.__dict__[key] = value
elif isinstance(vocab_size_or_config_json_file, int):
self.vocab_size = vocab_size_or_config_json_file
self.n_ctx = n_ctx
self.n_positions = n_positions
self.n_embd = n_embd
self.n_layer = n_layer
self.n_head = n_head
self.afn = afn
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.predict_special_tokens = predict_special_tokens
self.num_labels = num_labels
self.summary_type = summary_type
self.summary_use_proj = summary_use_proj
self.summary_activation = summary_activation
self.summary_first_dropout = summary_first_dropout
self.summary_proj_to_labels = summary_proj_to_labels
else:
raise ValueError(
"First argument must be either a vocabulary size (int)"
"or the path to a pretrained model config file (str)"
)
@property
def max_position_embeddings(self):
return self.n_positions
@property
def hidden_size(self):
return self.n_embd
@property
def num_attention_heads(self):
return self.n_head
@property
def num_hidden_layers(self):
return self.n_layer
| 5,100 | 36.785185 | 121 |
py
|
DeeBERT
|
DeeBERT-master/transformers/convert_bert_original_tf_checkpoint_to_pytorch.py
|
# 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 BERT checkpoint."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
import logging
logging.basicConfig(level=logging.INFO)
def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytorch_dump_path):
# Initialise PyTorch model
config = BertConfig.from_json_file(bert_config_file)
print("Building PyTorch model from configuration: {}".format(str(config)))
model = BertForPreTraining(config)
# Load weights from tf checkpoint
load_tf_weights_in_bert(model, config, tf_checkpoint_path)
# Save pytorch-model
print("Save PyTorch model to {}".format(pytorch_dump_path))
torch.save(model.state_dict(), pytorch_dump_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--tf_checkpoint_path",
default = None,
type = str,
required = True,
help = "Path to the TensorFlow checkpoint path.")
parser.add_argument("--bert_config_file",
default = None,
type = str,
required = True,
help = "The config json file corresponding to the pre-trained BERT model. \n"
"This specifies the model architecture.")
parser.add_argument("--pytorch_dump_path",
default = None,
type = str,
required = True,
help = "Path to the output PyTorch model.")
args = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path,
args.bert_config_file,
args.pytorch_dump_path)
| 2,577 | 38.060606 | 101 |
py
|
DeeBERT
|
DeeBERT-master/transformers/modeling_tf_distilbert.py
|
# 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.
""" TF 2.0 DistilBERT model
"""
from __future__ import absolute_import, division, print_function, unicode_literals
import json
import logging
import math
import copy
import sys
from io import open
import itertools
import numpy as np
import tensorflow as tf
from .configuration_distilbert import DistilBertConfig
from .modeling_tf_utils import TFPreTrainedModel, TFSharedEmbeddings, shape_list, get_initializer
from .file_utils import add_start_docstrings
logger = logging.getLogger(__name__)
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
'distilbert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-tf_model.h5",
'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-tf_model.h5"
}
### UTILS AND BUILDING BLOCKS OF THE ARCHITECTURE ###
def gelu(x):
""" Gaussian Error Linear Unit.
Original Implementation of the gelu activation function in Google Bert repo when initially created.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
Also see https://arxiv.org/abs/1606.08415
"""
cdf = 0.5 * (1.0 + tf.math.erf(x / tf.math.sqrt(2.0)))
return x * cdf
def gelu_new(x):
"""Gaussian Error Linear Unit.
This is a smoother version of the RELU.
Original paper: https://arxiv.org/abs/1606.08415
Args:
x: float Tensor to perform activation.
Returns:
`x` with the GELU activation applied.
"""
cdf = 0.5 * (1.0 + tf.tanh(
(np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3)))))
return x * cdf
class TFEmbeddings(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super(TFEmbeddings, self).__init__(**kwargs)
self.vocab_size = config.vocab_size
self.dim = config.dim
self.initializer_range = config.initializer_range
self.word_embeddings = TFSharedEmbeddings(config.vocab_size,
config.dim,
initializer_range=config.initializer_range,
name='word_embeddings') # padding_idx=0)
self.position_embeddings = tf.keras.layers.Embedding(config.max_position_embeddings,
config.dim,
embeddings_initializer=get_initializer(config.initializer_range),
name='position_embeddings')
if config.sinusoidal_pos_embds:
raise NotImplementedError
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=1e-12, name="LayerNorm")
self.dropout = tf.keras.layers.Dropout(config.dropout)
def build(self, input_shape):
"""Build shared word embedding layer """
with tf.name_scope("word_embeddings"):
# Create and initialize weights. The random normal initializer was chosen
# arbitrarily, and works well.
self.word_embeddings = self.add_weight(
"weight",
shape=[self.vocab_size, self.dim],
initializer=get_initializer(self.initializer_range))
super(TFEmbeddings, self).build(input_shape)
def call(self, inputs, inputs_embeds=None, mode="embedding", training=False):
"""Get token embeddings of inputs.
Args:
inputs: list of three int64 tensors with shape [batch_size, length]: (input_ids, position_ids, token_type_ids)
mode: string, a valid value is one of "embedding" and "linear".
Returns:
outputs: (1) If mode == "embedding", output embedding tensor, float32 with
shape [batch_size, length, embedding_size]; (2) mode == "linear", output
linear tensor, float32 with shape [batch_size, length, vocab_size].
Raises:
ValueError: if mode is not valid.
Shared weights logic adapted from
https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
"""
if mode == "embedding":
return self._embedding(inputs, inputs_embeds=inputs_embeds, training=training)
elif mode == "linear":
return self._linear(inputs)
else:
raise ValueError("mode {} is not valid.".format(mode))
def _embedding(self, inputs, inputs_embeds=None, training=False):
"""
Parameters
----------
input_ids: tf.Tensor(bs, max_seq_length)
The token ids to embed.
Outputs
-------
embeddings: tf.Tensor(bs, max_seq_length, dim)
The embedded tokens (plus position embeddings, no token_type embeddings)
"""
if not isinstance(inputs, (tuple, list)):
input_ids = inputs
position_ids = None
else:
input_ids, position_ids = inputs
if input_ids is not None:
seq_length = tf.shape(input_ids)[1]
else:
seq_length = tf.shape(inputs_embeds)[1]
if position_ids is None:
position_ids = tf.range(seq_length, dtype=tf.int32)[tf.newaxis, :]
if inputs_embeds is None:
inputs_embeds = tf.gather(self.word_embeddings, input_ids)
position_embeddings = self.position_embeddings(position_ids) # (bs, max_seq_length, dim)
embeddings = inputs_embeds + position_embeddings # (bs, max_seq_length, dim)
embeddings = self.LayerNorm(embeddings) # (bs, max_seq_length, dim)
embeddings = self.dropout(embeddings, training=training) # (bs, max_seq_length, dim)
return embeddings
def _linear(self, inputs):
"""Computes logits by running inputs through a linear layer.
Args:
inputs: A float32 tensor with shape [batch_size, length, hidden_size]
Returns:
float32 tensor with shape [batch_size, length, vocab_size].
"""
batch_size = tf.shape(inputs)[0]
length = tf.shape(inputs)[1]
x = tf.reshape(inputs, [-1, self.dim])
logits = tf.matmul(x, self.word_embeddings, transpose_b=True)
return tf.reshape(logits, [batch_size, length, self.vocab_size])
class TFMultiHeadSelfAttention(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super(TFMultiHeadSelfAttention, self).__init__(**kwargs)
self.n_heads = config.n_heads
self.dim = config.dim
self.dropout = tf.keras.layers.Dropout(config.attention_dropout)
self.output_attentions = config.output_attentions
assert self.dim % self.n_heads == 0
self.q_lin = tf.keras.layers.Dense(config.dim,
kernel_initializer=get_initializer(config.initializer_range),
name="q_lin")
self.k_lin = tf.keras.layers.Dense(config.dim,
kernel_initializer=get_initializer(config.initializer_range),
name="k_lin")
self.v_lin = tf.keras.layers.Dense(config.dim,
kernel_initializer=get_initializer(config.initializer_range),
name="v_lin")
self.out_lin = tf.keras.layers.Dense(config.dim,
kernel_initializer=get_initializer(config.initializer_range),
name="out_lin")
self.pruned_heads = set()
def prune_heads(self, heads):
raise NotImplementedError
def call(self, inputs, training=False):
"""
Parameters
----------
query: tf.Tensor(bs, seq_length, dim)
key: tf.Tensor(bs, seq_length, dim)
value: tf.Tensor(bs, seq_length, dim)
mask: tf.Tensor(bs, seq_length)
Outputs
-------
weights: tf.Tensor(bs, n_heads, seq_length, seq_length)
Attention weights
context: tf.Tensor(bs, seq_length, dim)
Contextualized layer. Optional: only if `output_attentions=True`
"""
query, key, value, mask, head_mask = inputs
bs, q_length, dim = shape_list(query)
k_length = shape_list(key)[1]
# assert dim == self.dim, 'Dimensions do not match: %s input vs %s configured' % (dim, self.dim)
# assert key.size() == value.size()
dim_per_head = self.dim // self.n_heads
mask_reshape = [bs, 1, 1, k_length]
def shape(x):
""" separate heads """
return tf.transpose(tf.reshape(x, (bs, -1, self.n_heads, dim_per_head)), perm=(0, 2, 1, 3))
def unshape(x):
""" group heads """
return tf.reshape(tf.transpose(x, perm=(0, 2, 1, 3)), (bs, -1, self.n_heads * dim_per_head))
q = shape(self.q_lin(query)) # (bs, n_heads, q_length, dim_per_head)
k = shape(self.k_lin(key)) # (bs, n_heads, k_length, dim_per_head)
v = shape(self.v_lin(value)) # (bs, n_heads, k_length, dim_per_head)
q = q / math.sqrt(dim_per_head) # (bs, n_heads, q_length, dim_per_head)
scores = tf.matmul(q, k, transpose_b=True) # (bs, n_heads, q_length, k_length)
mask = tf.reshape(mask, mask_reshape) # (bs, n_heads, qlen, klen)
# scores.masked_fill_(mask, -float('inf')) # (bs, n_heads, q_length, k_length)
scores = scores - 1e30 * (1.0 - mask)
weights = tf.nn.softmax(scores, axis=-1) # (bs, n_heads, qlen, klen)
weights = self.dropout(weights, training=training) # (bs, n_heads, qlen, klen)
# Mask heads if we want to
if head_mask is not None:
weights = weights * head_mask
context = tf.matmul(weights, v) # (bs, n_heads, qlen, dim_per_head)
context = unshape(context) # (bs, q_length, dim)
context = self.out_lin(context) # (bs, q_length, dim)
if self.output_attentions:
return (context, weights)
else:
return (context,)
class TFFFN(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super(TFFFN, self).__init__(**kwargs)
self.dropout = tf.keras.layers.Dropout(config.dropout)
self.lin1 = tf.keras.layers.Dense(config.hidden_dim,
kernel_initializer=get_initializer(config.initializer_range),
name="lin1")
self.lin2 = tf.keras.layers.Dense(config.dim,
kernel_initializer=get_initializer(config.initializer_range),
name="lin2")
assert config.activation in ['relu', 'gelu'], "activation ({}) must be in ['relu', 'gelu']".format(config.activation)
self.activation = tf.keras.layers.Activation(gelu) if config.activation=='gelu' else tf.keras.activations.relu
def call(self, input, training=False):
x = self.lin1(input)
x = self.activation(x)
x = self.lin2(x)
x = self.dropout(x, training=training)
return x
class TFTransformerBlock(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super(TFTransformerBlock, self).__init__(**kwargs)
self.n_heads = config.n_heads
self.dim = config.dim
self.hidden_dim = config.hidden_dim
self.dropout = tf.keras.layers.Dropout(config.dropout)
self.activation = config.activation
self.output_attentions = config.output_attentions
assert config.dim % config.n_heads == 0
self.attention = TFMultiHeadSelfAttention(config, name="attention")
self.sa_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-12, name="sa_layer_norm")
self.ffn = TFFFN(config, name="ffn")
self.output_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-12, name="output_layer_norm")
def call(self, inputs, training=False): # removed: src_enc=None, src_len=None
"""
Parameters
----------
x: tf.Tensor(bs, seq_length, dim)
attn_mask: tf.Tensor(bs, seq_length)
Outputs
-------
sa_weights: tf.Tensor(bs, n_heads, seq_length, seq_length)
The attention weights
ffn_output: tf.Tensor(bs, seq_length, dim)
The output of the transformer block contextualization.
"""
x, attn_mask, head_mask = inputs
# Self-Attention
sa_output = self.attention([x, x, x, attn_mask, head_mask], training=training)
if self.output_attentions:
sa_output, sa_weights = sa_output # (bs, seq_length, dim), (bs, n_heads, seq_length, seq_length)
else: # To handle these `output_attention` or `output_hidden_states` cases returning tuples
# assert type(sa_output) == tuple
sa_output = sa_output[0]
sa_output = self.sa_layer_norm(sa_output + x) # (bs, seq_length, dim)
# Feed Forward Network
ffn_output = self.ffn(sa_output, training=training) # (bs, seq_length, dim)
ffn_output = self.output_layer_norm(ffn_output + sa_output) # (bs, seq_length, dim)
output = (ffn_output,)
if self.output_attentions:
output = (sa_weights,) + output
return output
class TFTransformer(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super(TFTransformer, self).__init__(**kwargs)
self.n_layers = config.n_layers
self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states
self.layer = [TFTransformerBlock(config, name='layer_._{}'.format(i))
for i in range(config.n_layers)]
def call(self, inputs, training=False):
"""
Parameters
----------
x: tf.Tensor(bs, seq_length, dim)
Input sequence embedded.
attn_mask: tf.Tensor(bs, seq_length)
Attention mask on the sequence.
Outputs
-------
hidden_state: tf.Tensor(bs, seq_length, dim)
Sequence of hiddens states in the last (top) layer
all_hidden_states: Tuple[tf.Tensor(bs, seq_length, dim)]
Tuple of length n_layers with the hidden states from each layer.
Optional: only if output_hidden_states=True
all_attentions: Tuple[tf.Tensor(bs, n_heads, seq_length, seq_length)]
Tuple of length n_layers with the attention weights from each layer
Optional: only if output_attentions=True
"""
x, attn_mask, head_mask = inputs
all_hidden_states = ()
all_attentions = ()
hidden_state = x
for i, layer_module in enumerate(self.layer):
if self.output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_state,)
layer_outputs = layer_module([hidden_state, attn_mask, head_mask[i]], training=training)
hidden_state = layer_outputs[-1]
if self.output_attentions:
assert len(layer_outputs) == 2
attentions = layer_outputs[0]
all_attentions = all_attentions + (attentions,)
else:
assert len(layer_outputs) == 1
# Add last layer
if self.output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_state,)
outputs = (hidden_state,)
if self.output_hidden_states:
outputs = outputs + (all_hidden_states,)
if self.output_attentions:
outputs = outputs + (all_attentions,)
return outputs # last-layer hidden state, (all hidden states), (all attentions)
class TFDistilBertMainLayer(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super(TFDistilBertMainLayer, self).__init__(**kwargs)
self.num_hidden_layers = config.num_hidden_layers
self.embeddings = TFEmbeddings(config, name="embeddings") # Embeddings
self.transformer = TFTransformer(config, name="transformer") # Encoder
def get_input_embeddings(self):
return self.embeddings
def _resize_token_embeddings(self, new_num_tokens):
raise NotImplementedError
def _prune_heads(self, heads_to_prune):
raise NotImplementedError
def call(self, inputs, attention_mask=None, head_mask=None, inputs_embeds=None, training=False):
if isinstance(inputs, (tuple, list)):
input_ids = inputs[0]
attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
head_mask = inputs[2] if len(inputs) > 2 else head_mask
inputs_embeds = inputs[3] if len(inputs) > 3 else inputs_embeds
assert len(inputs) <= 4, "Too many inputs."
elif isinstance(inputs, dict):
input_ids = inputs.get('input_ids')
attention_mask = inputs.get('attention_mask', attention_mask)
head_mask = inputs.get('head_mask', head_mask)
inputs_embeds = inputs.get('inputs_embeds', inputs_embeds)
assert len(inputs) <= 4, "Too many inputs."
else:
input_ids = inputs
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")
if attention_mask is None:
attention_mask = tf.ones(input_shape) # (bs, seq_length)
attention_mask = tf.cast(attention_mask, dtype=tf.float32)
# 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
embedding_output = self.embeddings(input_ids, inputs_embeds=inputs_embeds) # (bs, seq_length, dim)
tfmr_output = self.transformer([embedding_output, attention_mask, head_mask], training=training)
return tfmr_output # last-layer hidden-state, (all hidden_states), (all attentions)
### INTERFACE FOR ENCODER AND TASK SPECIFIC MODEL ###
class TFDistilBertPreTrainedModel(TFPreTrainedModel):
""" An abstract class to handle weights initialization and
a simple interface for downloading and loading pretrained models.
"""
config_class = DistilBertConfig
pretrained_model_archive_map = TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP
base_model_prefix = "distilbert"
DISTILBERT_START_DOCSTRING = r"""
DistilBERT is a small, fast, cheap and light Transformer model
trained by distilling Bert base. It has 40% less parameters than
`bert-base-uncased`, runs 60% faster while preserving over 95% of
Bert's performances as measured on the GLUE language understanding benchmark.
Here are the differences between the interface of Bert and DistilBert:
- DistilBert doesn't have `token_type_ids`, you don't need to indicate which token belongs to which segment. Just separate your segments with the separation token `tokenizer.sep_token` (or `[SEP]`)
- DistilBert doesn't have options to select the input positions (`position_ids` input). This could be added if necessary though, just let's us know if you need this option.
For more information on DistilBERT, please refer to our
`detailed blog post`_
This model is a tf.keras.Model `tf.keras.Model`_ sub-class. 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.
.. _`detailed blog post`:
https://medium.com/huggingface/distilbert-8cf3380435b5
.. _`tf.keras.Model`:
https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/Model
Note on the model inputs:
TF 2.0 models accepts two formats as inputs:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional arguments.
This second option is usefull when using `tf.keras.Model.fit()` method which currently requires having all the tensors in the first argument of the model call function: `model(inputs)`.
If you choose this second option, 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(inputs_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 associaed to the input names given in the docstring:
`model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`
Parameters:
config (:class:`~transformers.DistilBertConfig`): 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 :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""
DISTILBERT_INPUTS_DOCSTRING = r"""
Inputs:
**input_ids** ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
Indices of input sequence tokens in the vocabulary.
The input sequences should start with `[CLS]` and end with `[SEP]` tokens.
For now, ONLY BertTokenizer(`bert-base-uncased`) is supported and you should use this tokenizer when using DistilBERT.
**attention_mask**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
Mask to avoid performing attention on padding token indices.
Mask values selected in ``[0, 1]``:
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
**head_mask**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
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**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length, embedding_dim)``:
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.
"""
@add_start_docstrings("The bare DistilBERT encoder/transformer outputing raw hidden-states without any specific head on top.",
DISTILBERT_START_DOCSTRING, DISTILBERT_INPUTS_DOCSTRING)
class TFDistilBertModel(TFDistilBertPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**last_hidden_state**: ``tf.Tensor`` of shape ``(batch_size, sequence_length, hidden_size)``
Sequence of hidden-states at the output of the last layer of the model.
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``tf.Tensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
import tensorflow as tf
from transformers import DistilBertTokenizer, TFDistilBertModel
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
model = TFDistilBertModel.from_pretrained('distilbert-base-uncased')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
outputs = model(input_ids)
last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
"""
def __init__(self, config, *inputs, **kwargs):
super(TFDistilBertModel, self).__init__(config, *inputs, **kwargs)
self.distilbert = TFDistilBertMainLayer(config, name="distilbert") # Embeddings
def call(self, inputs, **kwargs):
outputs = self.distilbert(inputs, **kwargs)
return outputs
class TFDistilBertLMHead(tf.keras.layers.Layer):
def __init__(self, config, input_embeddings, **kwargs):
super(TFDistilBertLMHead, self).__init__(**kwargs)
self.vocab_size = config.vocab_size
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.input_embeddings = input_embeddings
def build(self, input_shape):
self.bias = self.add_weight(shape=(self.vocab_size,),
initializer='zeros',
trainable=True,
name='bias')
super(TFDistilBertLMHead, self).build(input_shape)
def call(self, hidden_states):
hidden_states = self.input_embeddings(hidden_states, mode="linear")
hidden_states = hidden_states + self.bias
return hidden_states
@add_start_docstrings("""DistilBert Model with a `masked language modeling` head on top. """,
DISTILBERT_START_DOCSTRING, DISTILBERT_INPUTS_DOCSTRING)
class TFDistilBertForMaskedLM(TFDistilBertPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**prediction_scores**: ``tf.Tensor`` of shape ``(batch_size, sequence_length, config.vocab_size)``
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``tf.Tensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
import tensorflow as tf
from transformers import DistilBertTokenizer, TFDistilBertForMaskedLM
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
model = TFDistilBertForMaskedLM.from_pretrained('distilbert-base-uncased')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
outputs = model(input_ids)
prediction_scores = outputs[0]
"""
def __init__(self, config, *inputs, **kwargs):
super(TFDistilBertForMaskedLM, self).__init__(config, *inputs, **kwargs)
self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states
self.vocab_size = config.vocab_size
self.distilbert = TFDistilBertMainLayer(config, name="distilbert")
self.vocab_transform = tf.keras.layers.Dense(config.dim,
kernel_initializer=get_initializer(config.initializer_range),
name="vocab_transform")
self.act = tf.keras.layers.Activation(gelu)
self.vocab_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-12, name="vocab_layer_norm")
self.vocab_projector = TFDistilBertLMHead(config, self.distilbert.embeddings, name="vocab_projector")
def get_output_embeddings(self):
return self.vocab_projector.input_embeddings
def call(self, inputs, **kwargs):
distilbert_output = self.distilbert(inputs, **kwargs)
hidden_states = distilbert_output[0] # (bs, seq_length, dim)
prediction_logits = self.vocab_transform(hidden_states) # (bs, seq_length, dim)
prediction_logits = self.act(prediction_logits) # (bs, seq_length, dim)
prediction_logits = self.vocab_layer_norm(prediction_logits) # (bs, seq_length, dim)
prediction_logits = self.vocab_projector(prediction_logits)
outputs = (prediction_logits,) + distilbert_output[1:]
return outputs # logits, (hidden_states), (attentions)
@add_start_docstrings("""DistilBert Model transformer with a sequence classification/regression head on top (a linear layer on top of
the pooled output) e.g. for GLUE tasks. """,
DISTILBERT_START_DOCSTRING, DISTILBERT_INPUTS_DOCSTRING)
class TFDistilBertForSequenceClassification(TFDistilBertPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**logits**: ``tf.Tensor`` of shape ``(batch_size, config.num_labels)``
Classification (or regression if config.num_labels==1) scores (before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``tf.Tensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
import tensorflow as tf
from transformers import BertTokenizer, TFDistilBertForSequenceClassification
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
model = TFDistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
outputs = model(input_ids)
logits = outputs[0]
"""
def __init__(self, config, *inputs, **kwargs):
super(TFDistilBertForSequenceClassification, self).__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.distilbert = TFDistilBertMainLayer(config, name="distilbert")
self.pre_classifier = tf.keras.layers.Dense(config.dim,
kernel_initializer=get_initializer(config.initializer_range),
activation='relu',
name="pre_classifier")
self.classifier = tf.keras.layers.Dense(config.num_labels,
kernel_initializer=get_initializer(config.initializer_range),
name="classifier")
self.dropout = tf.keras.layers.Dropout(config.seq_classif_dropout)
def call(self, inputs, **kwargs):
distilbert_output = self.distilbert(inputs, **kwargs)
hidden_state = distilbert_output[0] # (bs, seq_len, dim)
pooled_output = hidden_state[:, 0] # (bs, dim)
pooled_output = self.pre_classifier(pooled_output) # (bs, dim)
pooled_output = self.dropout(pooled_output, training=kwargs.get('training', False)) # (bs, dim)
logits = self.classifier(pooled_output) # (bs, dim)
outputs = (logits,) + distilbert_output[1:]
return outputs # logits, (hidden_states), (attentions)
@add_start_docstrings("""DistilBert 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`). """,
DISTILBERT_START_DOCSTRING, DISTILBERT_INPUTS_DOCSTRING)
class TFDistilBertForQuestionAnswering(TFDistilBertPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**start_scores**: ``tf.Tensor`` of shape ``(batch_size, sequence_length,)``
Span-start scores (before SoftMax).
**end_scores**: ``tf.Tensor`` of shape ``(batch_size, sequence_length,)``
Span-end scores (before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``tf.Tensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
import tensorflow as tf
from transformers import BertTokenizer, TFDistilBertForQuestionAnswering
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
model = TFDistilBertForQuestionAnswering.from_pretrained('distilbert-base-uncased')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
outputs = model(input_ids)
start_scores, end_scores = outputs[:2]
"""
def __init__(self, config, *inputs, **kwargs):
super(TFDistilBertForQuestionAnswering, self).__init__(config, *inputs, **kwargs)
self.distilbert = TFDistilBertMainLayer(config, name="distilbert")
self.qa_outputs = tf.keras.layers.Dense(config.num_labels,
kernel_initializer=get_initializer(config.initializer_range),
name='qa_outputs')
assert config.num_labels == 2
self.dropout = tf.keras.layers.Dropout(config.qa_dropout)
def call(self, inputs, **kwargs):
distilbert_output = self.distilbert(inputs, **kwargs)
hidden_states = distilbert_output[0] # (bs, max_query_len, dim)
hidden_states = self.dropout(hidden_states, training=kwargs.get('training', False)) # (bs, max_query_len, dim)
logits = self.qa_outputs(hidden_states) # (bs, max_query_len, 2)
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)
outputs = (start_logits, end_logits,) + distilbert_output[1:]
return outputs # start_logits, end_logits, (hidden_states), (attentions)
| 37,652 | 48.674142 | 201 |
py
|
DeeBERT
|
DeeBERT-master/transformers/convert_bert_pytorch_checkpoint_to_original_tf.py
|
# 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 Huggingface Pytorch checkpoint to Tensorflow checkpoint."""
import os
import argparse
import torch
import numpy as np
import tensorflow as tf
from transformers import BertModel
def convert_pytorch_checkpoint_to_tf(model:BertModel, ckpt_dir:str, model_name:str):
"""
:param model:BertModel Pytorch model instance to be converted
:param ckpt_dir: Tensorflow model directory
:param model_name: model name
:return:
Currently supported HF models:
Y BertModel
N BertForMaskedLM
N BertForPreTraining
N BertForMultipleChoice
N BertForNextSentencePrediction
N BertForSequenceClassification
N BertForQuestionAnswering
"""
tensors_to_transpose = (
"dense.weight",
"attention.self.query",
"attention.self.key",
"attention.self.value"
)
var_map = (
('layer.', 'layer_'),
('word_embeddings.weight', 'word_embeddings'),
('position_embeddings.weight', 'position_embeddings'),
('token_type_embeddings.weight', 'token_type_embeddings'),
('.', '/'),
('LayerNorm/weight', 'LayerNorm/gamma'),
('LayerNorm/bias', 'LayerNorm/beta'),
('weight', 'kernel')
)
if not os.path.isdir(ckpt_dir):
os.makedirs(ckpt_dir)
state_dict = model.state_dict()
def to_tf_var_name(name:str):
for patt, repl in iter(var_map):
name = name.replace(patt, repl)
return 'bert/{}'.format(name)
def create_tf_var(tensor:np.ndarray, name:str, session:tf.Session):
tf_dtype = tf.dtypes.as_dtype(tensor.dtype)
tf_var = tf.get_variable(dtype=tf_dtype, shape=tensor.shape, name=name, initializer=tf.zeros_initializer())
session.run(tf.variables_initializer([tf_var]))
session.run(tf_var)
return tf_var
tf.reset_default_graph()
with tf.Session() as session:
for var_name in state_dict:
tf_name = to_tf_var_name(var_name)
torch_tensor = state_dict[var_name].numpy()
if any([x in var_name for x in tensors_to_transpose]):
torch_tensor = torch_tensor.T
tf_var = create_tf_var(tensor=torch_tensor, name=tf_name, session=session)
tf.keras.backend.set_value(tf_var, torch_tensor)
tf_weight = session.run(tf_var)
print("Successfully created {}: {}".format(tf_name, np.allclose(tf_weight, torch_tensor)))
saver = tf.train.Saver(tf.trainable_variables())
saver.save(session, os.path.join(ckpt_dir, model_name.replace("-", "_") + ".ckpt"))
def main(raw_args=None):
parser = argparse.ArgumentParser()
parser.add_argument("--model_name",
type=str,
required=True,
help="model name e.g. bert-base-uncased")
parser.add_argument("--cache_dir",
type=str,
default=None,
required=False,
help="Directory containing pytorch model")
parser.add_argument("--pytorch_model_path",
type=str,
required=True,
help="/path/to/<pytorch-model-name>.bin")
parser.add_argument("--tf_cache_dir",
type=str,
required=True,
help="Directory in which to save tensorflow model")
args = parser.parse_args(raw_args)
model = BertModel.from_pretrained(
pretrained_model_name_or_path=args.model_name,
state_dict=torch.load(args.pytorch_model_path),
cache_dir=args.cache_dir
)
convert_pytorch_checkpoint_to_tf(
model=model,
ckpt_dir=args.tf_cache_dir,
model_name=args.model_name
)
if __name__ == "__main__":
main()
| 4,478 | 33.19084 | 115 |
py
|
DeeBERT
|
DeeBERT-master/transformers/modeling_transfo_xl.py
|
# 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.
""" PyTorch Transformer XL model.
Adapted from https://github.com/kimiyoung/transformer-xl.
In particular https://github.com/kimiyoung/transformer-xl/blob/master/pytorch/mem_transformer.py
"""
from __future__ import absolute_import, division, print_function, unicode_literals
import os
import json
import math
import logging
import collections
import sys
from io import open
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import CrossEntropyLoss
from torch.nn.parameter import Parameter
from .modeling_utils import PreTrainedModel, Conv1D, prune_conv1d_layer, SequenceSummary
from .configuration_transfo_xl import TransfoXLConfig
from .modeling_transfo_xl_utilities import ProjectedAdaptiveLogSoftmax, sample_logits
from .file_utils import add_start_docstrings
logger = logging.getLogger(__name__)
TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP = {
'transfo-xl-wt103': "https://s3.amazonaws.com/models.huggingface.co/bert/transfo-xl-wt103-pytorch_model.bin",
}
def build_tf_to_pytorch_map(model, config):
""" A map of modules from TF to PyTorch.
This time I use a map to keep the PyTorch model as identical to the original PyTorch model as possible.
"""
tf_to_pt_map = {}
if hasattr(model, 'transformer'):
# We are loading in a TransfoXLLMHeadModel => we will load also the Adaptive Softmax
tf_to_pt_map.update({
"transformer/adaptive_softmax/cutoff_0/cluster_W": model.crit.cluster_weight,
"transformer/adaptive_softmax/cutoff_0/cluster_b": model.crit.cluster_bias})
for i, (out_l, proj_l, tie_proj) in enumerate(zip(
model.crit.out_layers,
model.crit.out_projs,
config.tie_projs)):
layer_str = "transformer/adaptive_softmax/cutoff_%d/" % i
if config.tie_weight:
tf_to_pt_map.update({
layer_str + 'b': out_l.bias})
else:
raise NotImplementedError
# I don't think this is implemented in the TF code
tf_to_pt_map.update({
layer_str + 'lookup_table': out_l.weight,
layer_str + 'b': out_l.bias})
if not tie_proj:
tf_to_pt_map.update({
layer_str + 'proj': proj_l
})
# Now load the rest of the transformer
model = model.transformer
# Embeddings
for i, (embed_l, proj_l) in enumerate(zip(model.word_emb.emb_layers, model.word_emb.emb_projs)):
layer_str = "transformer/adaptive_embed/cutoff_%d/" % i
tf_to_pt_map.update({
layer_str + 'lookup_table': embed_l.weight,
layer_str + 'proj_W': proj_l
})
# Transformer blocks
for i, b in enumerate(model.layers):
layer_str = "transformer/layer_%d/" % i
tf_to_pt_map.update({
layer_str + "rel_attn/LayerNorm/gamma": b.dec_attn.layer_norm.weight,
layer_str + "rel_attn/LayerNorm/beta": b.dec_attn.layer_norm.bias,
layer_str + "rel_attn/o/kernel": b.dec_attn.o_net.weight,
layer_str + "rel_attn/qkv/kernel": b.dec_attn.qkv_net.weight,
layer_str + "rel_attn/r/kernel": b.dec_attn.r_net.weight,
layer_str + "ff/LayerNorm/gamma": b.pos_ff.layer_norm.weight,
layer_str + "ff/LayerNorm/beta": b.pos_ff.layer_norm.bias,
layer_str + "ff/layer_1/kernel": b.pos_ff.CoreNet[0].weight,
layer_str + "ff/layer_1/bias": b.pos_ff.CoreNet[0].bias,
layer_str + "ff/layer_2/kernel": b.pos_ff.CoreNet[3].weight,
layer_str + "ff/layer_2/bias": b.pos_ff.CoreNet[3].bias,
})
# Relative positioning biases
if config.untie_r:
r_r_list = []
r_w_list = []
for b in model.layers:
r_r_list.append(b.dec_attn.r_r_bias)
r_w_list.append(b.dec_attn.r_w_bias)
else:
r_r_list = [model.r_r_bias]
r_w_list = [model.r_w_bias]
tf_to_pt_map.update({
'transformer/r_r_bias': r_r_list,
'transformer/r_w_bias': r_w_list})
return tf_to_pt_map
def load_tf_weights_in_transfo_xl(model, config, tf_path):
""" Load tf checkpoints in a pytorch model
"""
try:
import numpy as np
import tensorflow as tf
except ImportError:
logger.error("Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
"https://www.tensorflow.org/install/ for installation instructions.")
raise
# Build TF to PyTorch weights loading map
tf_to_pt_map = build_tf_to_pytorch_map(model, config)
# Load weights from TF model
init_vars = tf.train.list_variables(tf_path)
tf_weights = {}
for name, shape in init_vars:
logger.info("Loading TF weight {} with shape {}".format(name, shape))
array = tf.train.load_variable(tf_path, name)
tf_weights[name] = array
for name, pointer in tf_to_pt_map.items():
assert name in tf_weights
array = tf_weights[name]
# adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
# which are not required for using pretrained model
if 'kernel' in name or 'proj' in name:
array = np.transpose(array)
if ('r_r_bias' in name or 'r_w_bias' in name) and len(pointer) > 1:
# Here we will split the TF weigths
assert len(pointer) == array.shape[0]
for i, p_i in enumerate(pointer):
arr_i = array[i, ...]
try:
assert p_i.shape == arr_i.shape
except AssertionError as e:
e.args += (p_i.shape, arr_i.shape)
raise
logger.info("Initialize PyTorch weight {} for layer {}".format(name, i))
p_i.data = torch.from_numpy(arr_i)
else:
try:
assert pointer.shape == array.shape
except AssertionError as e:
e.args += (pointer.shape, array.shape)
raise
logger.info("Initialize PyTorch weight {}".format(name))
pointer.data = torch.from_numpy(array)
tf_weights.pop(name, None)
tf_weights.pop(name + '/Adam', None)
tf_weights.pop(name + '/Adam_1', None)
logger.info("Weights not copied to PyTorch model: {}".format(', '.join(tf_weights.keys())))
return model
class PositionalEmbedding(nn.Module):
def __init__(self, demb):
super(PositionalEmbedding, self).__init__()
self.demb = demb
inv_freq = 1 / (10000 ** (torch.arange(0.0, demb, 2.0) / demb))
self.register_buffer('inv_freq', inv_freq)
def forward(self, pos_seq, bsz=None):
sinusoid_inp = torch.ger(pos_seq, self.inv_freq)
pos_emb = torch.cat([sinusoid_inp.sin(), sinusoid_inp.cos()], dim=-1)
if bsz is not None:
return pos_emb[:,None,:].expand(-1, bsz, -1)
else:
return pos_emb[:,None,:]
class PositionwiseFF(nn.Module):
def __init__(self, d_model, d_inner, dropout, pre_lnorm=False, layer_norm_epsilon=1e-5):
super(PositionwiseFF, self).__init__()
self.d_model = d_model
self.d_inner = d_inner
self.dropout = dropout
self.CoreNet = nn.Sequential(
nn.Linear(d_model, d_inner), nn.ReLU(inplace=True),
nn.Dropout(dropout),
nn.Linear(d_inner, d_model),
nn.Dropout(dropout),
)
self.layer_norm = nn.LayerNorm(d_model, eps=layer_norm_epsilon)
self.pre_lnorm = pre_lnorm
def forward(self, inp):
if self.pre_lnorm:
##### layer normalization + positionwise feed-forward
core_out = self.CoreNet(self.layer_norm(inp))
##### residual connection
output = core_out + inp
else:
##### positionwise feed-forward
core_out = self.CoreNet(inp)
##### residual connection + layer normalization
output = self.layer_norm(inp + core_out)
return output
class RelPartialLearnableMultiHeadAttn(nn.Module):
def __init__(self, n_head, d_model, d_head, dropout, dropatt=0,
tgt_len=None, ext_len=None, mem_len=None, pre_lnorm=False,
r_r_bias=None, r_w_bias=None, output_attentions=False,
layer_norm_epsilon=1e-5):
super(RelPartialLearnableMultiHeadAttn, self).__init__()
self.output_attentions = output_attentions
self.n_head = n_head
self.d_model = d_model
self.d_head = d_head
self.dropout = dropout
self.qkv_net = nn.Linear(d_model, 3 * n_head * d_head, bias=False)
self.drop = nn.Dropout(dropout)
self.dropatt = nn.Dropout(dropatt)
self.o_net = nn.Linear(n_head * d_head, d_model, bias=False)
self.layer_norm = nn.LayerNorm(d_model, eps=layer_norm_epsilon)
self.scale = 1 / (d_head ** 0.5)
self.pre_lnorm = pre_lnorm
if r_r_bias is None or r_w_bias is None: # Biases are not shared
self.r_r_bias = nn.Parameter(torch.FloatTensor(self.n_head, self.d_head))
self.r_w_bias = nn.Parameter(torch.FloatTensor(self.n_head, self.d_head))
else:
self.r_r_bias = r_r_bias
self.r_w_bias = r_w_bias
self.r_net = nn.Linear(self.d_model, self.n_head * self.d_head, bias=False)
def _rel_shift(self, x):
zero_pad_shape = (x.size(0), 1) + x.size()[2:]
zero_pad = torch.zeros(zero_pad_shape, device=x.device, dtype=x.dtype)
x_padded = torch.cat([zero_pad, x], dim=1)
x_padded_shape = (x.size(1) + 1, x.size(0)) + x.size()[2:]
x_padded = x_padded.view(*x_padded_shape)
x = x_padded[1:].view_as(x)
return x
def forward(self, w, r, attn_mask=None, mems=None, head_mask=None):
qlen, rlen, bsz = w.size(0), r.size(0), w.size(1)
if mems is not None:
cat = torch.cat([mems, w], 0)
if self.pre_lnorm:
w_heads = self.qkv_net(self.layer_norm(cat))
else:
w_heads = self.qkv_net(cat)
r_head_k = self.r_net(r)
w_head_q, w_head_k, w_head_v = torch.chunk(w_heads, 3, dim=-1)
w_head_q = w_head_q[-qlen:]
else:
if self.pre_lnorm:
w_heads = self.qkv_net(self.layer_norm(w))
else:
w_heads = self.qkv_net(w)
r_head_k = self.r_net(r)
w_head_q, w_head_k, w_head_v = torch.chunk(w_heads, 3, dim=-1)
klen = w_head_k.size(0)
w_head_q = w_head_q.view(qlen, bsz, self.n_head, self.d_head) # qlen x bsz x n_head x d_head
w_head_k = w_head_k.view(klen, bsz, self.n_head, self.d_head) # qlen x bsz x n_head x d_head
w_head_v = w_head_v.view(klen, bsz, self.n_head, self.d_head) # qlen x bsz x n_head x d_head
r_head_k = r_head_k.view(rlen, self.n_head, self.d_head) # qlen x n_head x d_head
#### compute attention score
rw_head_q = w_head_q + self.r_w_bias # qlen x bsz x n_head x d_head
AC = torch.einsum('ibnd,jbnd->ijbn', (rw_head_q, w_head_k)) # qlen x klen x bsz x n_head
rr_head_q = w_head_q + self.r_r_bias
BD = torch.einsum('ibnd,jnd->ijbn', (rr_head_q, r_head_k)) # qlen x klen x bsz x n_head
BD = self._rel_shift(BD)
# [qlen x klen x bsz x n_head]
attn_score = AC + BD
attn_score.mul_(self.scale)
#### compute attention probability
if attn_mask is not None and torch.sum(attn_mask).item():
attn_mask = (attn_mask == 1) # Switch to bool
if attn_mask.dim() == 2:
if next(self.parameters()).dtype == torch.float16:
attn_score = attn_score.float().masked_fill(
attn_mask[None,:,:,None], -65000).type_as(attn_score)
else:
attn_score = attn_score.float().masked_fill(
attn_mask[None,:,:,None], -1e30).type_as(attn_score)
elif attn_mask.dim() == 3:
if next(self.parameters()).dtype == torch.float16:
attn_score = attn_score.float().masked_fill(
attn_mask[:,:,:,None], -65000).type_as(attn_score)
else:
attn_score = attn_score.float().masked_fill(
attn_mask[:,:,:,None], -1e30).type_as(attn_score)
# [qlen x klen x bsz x n_head]
attn_prob = F.softmax(attn_score, dim=1)
attn_prob = self.dropatt(attn_prob)
# Mask heads if we want to
if head_mask is not None:
attn_prob = attn_prob * head_mask
#### compute attention vector
attn_vec = torch.einsum('ijbn,jbnd->ibnd', (attn_prob, w_head_v))
# [qlen x bsz x n_head x d_head]
attn_vec = attn_vec.contiguous().view(
attn_vec.size(0), attn_vec.size(1), self.n_head * self.d_head)
##### linear projection
attn_out = self.o_net(attn_vec)
attn_out = self.drop(attn_out)
if self.pre_lnorm:
##### residual connection
outputs = [w + attn_out]
else:
##### residual connection + layer normalization
outputs = [self.layer_norm(w + attn_out)]
if self.output_attentions:
outputs.append(attn_prob)
return outputs
class RelPartialLearnableDecoderLayer(nn.Module):
def __init__(self, n_head, d_model, d_head, d_inner, dropout, layer_norm_epsilon=1e-5,
**kwargs):
super(RelPartialLearnableDecoderLayer, self).__init__()
self.dec_attn = RelPartialLearnableMultiHeadAttn(n_head, d_model,
d_head, dropout, layer_norm_epsilon=layer_norm_epsilon, **kwargs)
self.pos_ff = PositionwiseFF(d_model, d_inner, dropout,
pre_lnorm=kwargs.get('pre_lnorm'),
layer_norm_epsilon=layer_norm_epsilon)
def forward(self, dec_inp, r, dec_attn_mask=None, mems=None, head_mask=None):
attn_outputs = self.dec_attn(dec_inp, r,
attn_mask=dec_attn_mask,
mems=mems, head_mask=head_mask)
ff_output = self.pos_ff(attn_outputs[0])
outputs = [ff_output] + attn_outputs[1:]
return outputs
class AdaptiveEmbedding(nn.Module):
def __init__(self, n_token, d_embed, d_proj, cutoffs, div_val=1,
sample_softmax=False):
super(AdaptiveEmbedding, self).__init__()
self.n_token = n_token
self.d_embed = d_embed
self.cutoffs = cutoffs + [n_token]
self.div_val = div_val
self.d_proj = d_proj
self.emb_scale = d_proj ** 0.5
self.cutoff_ends = [0] + self.cutoffs
self.emb_layers = nn.ModuleList()
self.emb_projs = nn.ParameterList()
if div_val == 1:
self.emb_layers.append(
nn.Embedding(n_token, d_embed, sparse=sample_softmax>0)
)
if d_proj != d_embed:
self.emb_projs.append(nn.Parameter(torch.FloatTensor(d_proj, d_embed)))
else:
for i in range(len(self.cutoffs)):
l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i+1]
d_emb_i = d_embed // (div_val ** i)
self.emb_layers.append(nn.Embedding(r_idx-l_idx, d_emb_i))
self.emb_projs.append(nn.Parameter(torch.FloatTensor(d_proj, d_emb_i)))
def forward(self, inp):
if self.div_val == 1:
embed = self.emb_layers[0](inp)
if self.d_proj != self.d_embed:
embed = F.linear(embed, self.emb_projs[0])
else:
param = next(self.parameters())
inp_flat = inp.view(-1)
emb_flat = torch.zeros([inp_flat.size(0), self.d_proj],
dtype=param.dtype, device=param.device)
for i in range(len(self.cutoffs)):
l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1]
mask_i = (inp_flat >= l_idx) & (inp_flat < r_idx)
indices_i = mask_i.nonzero().squeeze()
if indices_i.numel() == 0:
continue
inp_i = inp_flat.index_select(0, indices_i) - l_idx
emb_i = self.emb_layers[i](inp_i)
emb_i = F.linear(emb_i, self.emb_projs[i])
emb_flat.index_copy_(0, indices_i, emb_i)
embed_shape = inp.size() + (self.d_proj,)
embed = emb_flat.view(embed_shape)
embed.mul_(self.emb_scale)
return embed
class TransfoXLPreTrainedModel(PreTrainedModel):
""" An abstract class to handle weights initialization and
a simple interface for dowloading and loading pretrained models.
"""
config_class = TransfoXLConfig
pretrained_model_archive_map = TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP
load_tf_weights = load_tf_weights_in_transfo_xl
base_model_prefix = "transformer"
def _init_weight(self, weight):
if self.config.init == 'uniform':
nn.init.uniform_(weight, -self.config.init_range, self.config.init_range)
elif self.config.init == 'normal':
nn.init.normal_(weight, 0.0, self.config.init_std)
def _init_bias(self, bias):
nn.init.constant_(bias, 0.0)
def _init_weights(self, m):
""" Initialize the weights.
"""
classname = m.__class__.__name__
if classname.find('Linear') != -1:
if hasattr(m, 'weight') and m.weight is not None:
self._init_weight(m.weight)
if hasattr(m, 'bias') and m.bias is not None:
self._init_bias(m.bias)
elif classname.find('AdaptiveEmbedding') != -1:
if hasattr(m, 'emb_projs'):
for i in range(len(m.emb_projs)):
if m.emb_projs[i] is not None:
nn.init.normal_(m.emb_projs[i], 0.0, self.config.proj_init_std)
elif classname.find('Embedding') != -1:
if hasattr(m, 'weight'):
self._init_weight(m.weight)
elif classname.find('ProjectedAdaptiveLogSoftmax') != -1:
if hasattr(m, 'cluster_weight') and m.cluster_weight is not None:
self._init_weight(m.cluster_weight)
if hasattr(m, 'cluster_bias') and m.cluster_bias is not None:
self._init_bias(m.cluster_bias)
if hasattr(m, 'out_projs'):
for i in range(len(m.out_projs)):
if m.out_projs[i] is not None:
nn.init.normal_(m.out_projs[i], 0.0, self.config.proj_init_std)
elif classname.find('LayerNorm') != -1:
if hasattr(m, 'weight'):
nn.init.normal_(m.weight, 1.0, self.config.init_std)
if hasattr(m, 'bias') and m.bias is not None:
self._init_bias(m.bias)
else:
if hasattr(m, 'r_emb'):
self._init_weight(m.r_emb)
if hasattr(m, 'r_w_bias'):
self._init_weight(m.r_w_bias)
if hasattr(m, 'r_r_bias'):
self._init_weight(m.r_r_bias)
if hasattr(m, 'r_bias'):
self._init_bias(m.r_bias)
TRANSFO_XL_START_DOCSTRING = r""" The Transformer-XL model was proposed in
`Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context`_
by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
It's a causal (uni-directional) transformer with relative positioning (sinusoïdal) embeddings which can reuse
previously computed hidden-states to attend to longer context (memory).
This model also uses adaptive softmax inputs and outputs (tied).
This model is a PyTorch `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.
.. _`Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context`:
https://arxiv.org/abs/1901.02860
.. _`torch.nn.Module`:
https://pytorch.org/docs/stable/nn.html#module
Parameters:
config (:class:`~transformers.TransfoXLConfig`): 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 :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""
TRANSFO_XL_INPUTS_DOCSTRING = r"""
Inputs:
**input_ids**: ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Indices of input sequence tokens in the vocabulary.
Transformer-XL is a model with relative position embeddings so you can either pad the inputs on
the right or on the left.
Indices can be obtained using :class:`transformers.TransfoXLTokenizer`.
See :func:`transformers.PreTrainedTokenizer.encode` and
:func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
**mems**: (`optional`)
list of ``torch.FloatTensor`` (one for each layer):
that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
(see `mems` output below). Can be used to speed up sequential decoding and attend to longer context.
**head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
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**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, embedding_dim)``:
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.
"""
@add_start_docstrings("The bare Bert Model transformer outputting raw hidden-states without any specific head on top.",
TRANSFO_XL_START_DOCSTRING, TRANSFO_XL_INPUTS_DOCSTRING)
class TransfoXLModel(TransfoXLPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**last_hidden_state**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)``
Sequence of hidden-states at the last layer of the model.
**mems**:
list of ``torch.FloatTensor`` (one for each layer):
that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
(see `mems` input above). Can be used to speed up sequential decoding and attend to longer context.
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
tokenizer = TransfoXLTokenizer.from_pretrained('transfo-xl-wt103')
model = TransfoXLModel.from_pretrained('transfo-xl-wt103')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
outputs = model(input_ids)
last_hidden_states, mems = outputs[:2]
"""
def __init__(self, config):
super(TransfoXLModel, self).__init__(config)
self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states
self.n_token = config.n_token
self.d_embed = config.d_embed
self.d_model = config.d_model
self.n_head = config.n_head
self.d_head = config.d_head
self.word_emb = AdaptiveEmbedding(config.n_token, config.d_embed, config.d_model, config.cutoffs,
div_val=config.div_val)
self.drop = nn.Dropout(config.dropout)
self.n_layer = config.n_layer
self.tgt_len = config.tgt_len
self.mem_len = config.mem_len
self.ext_len = config.ext_len
self.max_klen = config.tgt_len + config.ext_len + config.mem_len
self.attn_type = config.attn_type
if not config.untie_r:
self.r_w_bias = nn.Parameter(torch.FloatTensor(self.n_head, self.d_head))
self.r_r_bias = nn.Parameter(torch.FloatTensor(self.n_head, self.d_head))
self.layers = nn.ModuleList()
if config.attn_type == 0: # the default attention
for i in range(config.n_layer):
self.layers.append(
RelPartialLearnableDecoderLayer(
config.n_head, config.d_model, config.d_head, config.d_inner, config.dropout,
tgt_len=config.tgt_len, ext_len=config.ext_len, mem_len=config.mem_len,
dropatt=config.dropatt, pre_lnorm=config.pre_lnorm,
r_w_bias=None if config.untie_r else self.r_w_bias,
r_r_bias=None if config.untie_r else self.r_r_bias,
output_attentions=self.output_attentions,
layer_norm_epsilon=config.layer_norm_epsilon)
)
else: # learnable embeddings and absolute embeddings are not used in our pretrained checkpoints
raise NotImplementedError # Removed them to avoid maintaining dead code
self.same_length = config.same_length
self.clamp_len = config.clamp_len
if self.attn_type == 0: # default attention
self.pos_emb = PositionalEmbedding(self.d_model)
else: # learnable embeddings and absolute embeddings
raise NotImplementedError # Removed these to avoid maintaining dead code - They are not used in our pretrained checkpoint
self.init_weights()
def get_input_embeddings(self):
return self.word_emb
def set_input_embeddings(self, new_embeddings):
self.word_emb = new_embeddings
def backward_compatible(self):
self.sample_softmax = -1
def reset_length(self, tgt_len, ext_len, mem_len):
self.tgt_len = tgt_len
self.mem_len = mem_len
self.ext_len = ext_len
def _prune_heads(self, heads):
logger.info("Head pruning is not implemented for Transformer-XL model")
pass
def init_mems(self, bsz):
if self.mem_len > 0:
mems = []
param = next(self.parameters())
for i in range(self.n_layer):
empty = torch.zeros(self.mem_len, bsz, self.config.d_model,
dtype=param.dtype, device=param.device)
mems.append(empty)
return mems
else:
return None
def _update_mems(self, hids, mems, qlen, mlen):
# does not deal with None
if mems is None: return None
# mems is not None
assert len(hids) == len(mems), 'len(hids) != len(mems)'
# There are `mlen + qlen` steps that can be cached into mems
# For the next step, the last `ext_len` of the `qlen` tokens
# will be used as the extended context. Hence, we only cache
# the tokens from `mlen + qlen - self.ext_len - self.mem_len`
# to `mlen + qlen - self.ext_len`.
with torch.no_grad():
new_mems = []
end_idx = mlen + max(0, qlen - 0 - self.ext_len)
beg_idx = max(0, end_idx - self.mem_len)
for i in range(len(hids)):
cat = torch.cat([mems[i], hids[i]], dim=0)
new_mems.append(cat[beg_idx:end_idx].detach())
return new_mems
def forward(self, input_ids=None, mems=None, head_mask=None, inputs_embeds=None):
# the original code for Transformer-XL used shapes [len, bsz] but we want a unified interface in the library
# so we transpose here from shape [bsz, len] to shape [len, bsz]
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_ids = input_ids.transpose(0, 1).contiguous()
qlen, bsz = input_ids.size()
elif inputs_embeds is not None:
inputs_embeds = inputs_embeds.transpose(0, 1).contiguous()
qlen, bsz = inputs_embeds.shape[0], inputs_embeds.shape[1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if mems is None:
mems = self.init_mems(bsz)
# 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] (a head_mask for each layer)
# and head_mask is converted to shape [num_hidden_layers x qlen x klen x bsz x n_head]
if head_mask is not None:
if head_mask.dim() == 1:
head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(0).unsqueeze(0)
head_mask = head_mask.expand(self.n_layer, -1, -1, -1, -1)
elif head_mask.dim() == 2:
head_mask = head_mask.unsqueeze(1).unsqueeze(1).unsqueeze(1)
head_mask = head_mask.to(dtype=next(self.parameters()).dtype) # switch to fload if need + fp16 compatibility
else:
head_mask = [None] * self.n_layer
if inputs_embeds is not None:
word_emb = inputs_embeds
else:
word_emb = self.word_emb(input_ids)
mlen = mems[0].size(0) if mems is not None else 0
klen = mlen + qlen
if self.same_length:
all_ones = word_emb.new_ones((qlen, klen), dtype=torch.uint8)
mask_len = klen - self.mem_len
if mask_len > 0:
mask_shift_len = qlen - mask_len
else:
mask_shift_len = qlen
dec_attn_mask = (torch.triu(all_ones, 1+mlen)
+ torch.tril(all_ones, -mask_shift_len))[:, :, None] # -1
else:
dec_attn_mask = torch.triu(
word_emb.new_ones((qlen, klen), dtype=torch.uint8), diagonal=1+mlen)[:,:,None]
hids = []
attentions = []
if self.attn_type == 0: # default
pos_seq = torch.arange(klen-1, -1, -1.0, device=word_emb.device,
dtype=word_emb.dtype)
if self.clamp_len > 0:
pos_seq.clamp_(max=self.clamp_len)
pos_emb = self.pos_emb(pos_seq)
core_out = self.drop(word_emb)
pos_emb = self.drop(pos_emb)
for i, layer in enumerate(self.layers):
hids.append(core_out)
mems_i = None if mems is None else mems[i]
layer_outputs = layer(core_out, pos_emb, dec_attn_mask=dec_attn_mask,
mems=mems_i, head_mask=head_mask[i])
core_out = layer_outputs[0]
if self.output_attentions:
attentions.append(layer_outputs[1])
else: # learnable embeddings and absolute embeddings
raise NotImplementedError # Removed these to avoid maintaining dead code - They are not used in our pretrained checkpoint
core_out = self.drop(core_out)
new_mems = self._update_mems(hids, mems, mlen, qlen)
# We transpose back here to shape [bsz, len, hidden_dim]
outputs = [core_out.transpose(0, 1).contiguous(), new_mems]
if self.output_hidden_states:
# Add last layer and transpose to library standard shape [bsz, len, hidden_dim]
hids.append(core_out)
hids = list(t.transpose(0, 1).contiguous() for t in hids)
outputs.append(hids)
if self.output_attentions:
# Transpose to library standard shape [bsz, n_heads, query_seq_len, key_seq_len]
attentions = list(t.permute(2, 3, 0, 1).contiguous() for t in attentions)
outputs.append(attentions)
return outputs # last hidden state, new_mems, (all hidden states), (all attentions)
@add_start_docstrings("""The Transformer-XL Model with a language modeling head on top
(adaptive softmax with weights tied to the adaptive input embeddings)""",
TRANSFO_XL_START_DOCSTRING, TRANSFO_XL_INPUTS_DOCSTRING)
class TransfoXLLMHeadModel(TransfoXLPreTrainedModel):
r"""
**lm_labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Labels for language modeling.
Note that the labels **are shifted** inside the model, i.e. you can set ``lm_labels = input_ids``
Indices are selected in ``[-1, 0, ..., config.vocab_size]``
All labels set to ``-1`` are ignored (masked), the loss is only
computed for labels in ``[0, ..., config.vocab_size]``
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**loss**: (`optional`, returned when ``lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Language modeling loss.
**prediction_scores**: ``None`` if ``lm_labels`` is provided else ``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).
We don't output them when the loss is computed to speedup adaptive softmax decoding.
**mems**:
list of ``torch.FloatTensor`` (one for each layer):
that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
(see `mems` input above). Can be used to speed up sequential decoding and attend to longer context.
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
tokenizer = TransfoXLTokenizer.from_pretrained('transfo-xl-wt103')
model = TransfoXLLMHeadModel.from_pretrained('transfo-xl-wt103')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
outputs = model(input_ids)
prediction_scores, mems = outputs[:2]
"""
def __init__(self, config):
super(TransfoXLLMHeadModel, self).__init__(config)
self.transformer = TransfoXLModel(config)
self.sample_softmax = config.sample_softmax
# use sampled softmax
if config.sample_softmax > 0:
self.out_layer = nn.Linear(config.d_model, config.n_token)
self.sampler = LogUniformSampler(config.n_token, config.sample_softmax)
# use adaptive softmax (including standard softmax)
else:
self.crit = ProjectedAdaptiveLogSoftmax(config.n_token, config.d_embed, config.d_model,
config.cutoffs, div_val=config.div_val)
self.init_weights()
def tie_weights(self):
"""
Run this to be sure output and input (adaptive) softmax weights are tied
"""
# sampled softmax
if self.sample_softmax > 0:
if self.config.tie_weight:
self.out_layer.weight = self.transformer.word_emb.weight
# adaptive softmax (including standard softmax)
else:
if self.config.tie_weight:
for i in range(len(self.crit.out_layers)):
self._tie_or_clone_weights(self.crit.out_layers[i],
self.transformer.word_emb.emb_layers[i])
if self.config.tie_projs:
for i, tie_proj in enumerate(self.config.tie_projs):
if tie_proj and self.config.div_val == 1 and self.config.d_model != self.config.d_embed:
if self.config.torchscript:
self.crit.out_projs[i] = nn.Parameter(self.transformer.word_emb.emb_projs[0].clone())
else:
self.crit.out_projs[i] = self.transformer.word_emb.emb_projs[0]
elif tie_proj and self.config.div_val != 1:
if self.config.torchscript:
self.crit.out_projs[i] = nn.Parameter(self.transformer.word_emb.emb_projs[i].clone())
else:
self.crit.out_projs[i] = self.transformer.word_emb.emb_projs[i]
def reset_length(self, tgt_len, ext_len, mem_len):
self.transformer.reset_length(tgt_len, ext_len, mem_len)
def init_mems(self, bsz):
return self.transformer.init_mems(bsz)
def forward(self, input_ids=None, mems=None, head_mask=None, inputs_embeds=None, labels=None):
if input_ids is not None:
bsz, tgt_len = input_ids.size(0), input_ids.size(1)
elif inputs_embeds is not None:
bsz, tgt_len = inputs_embeds.size(0), inputs_embeds.size(1)
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
transformer_outputs = self.transformer(input_ids, mems=mems, head_mask=head_mask, inputs_embeds=inputs_embeds)
last_hidden = transformer_outputs[0]
pred_hid = last_hidden[:, -tgt_len:]
outputs = transformer_outputs[1:]
if self.sample_softmax > 0 and self.training:
assert self.config.tie_weight
logit = sample_logits(self.transformer.word_emb, self.out_layer.bias, labels, pred_hid, self.sampler)
softmax_output = -F.log_softmax(logit, -1)[:, :, 0]
outputs = [softmax_output] + outputs
if labels is not None:
# TODO: This is not implemented
raise NotImplementedError
else:
softmax_output = self.crit(pred_hid.view(-1, pred_hid.size(-1)), labels)
if labels is None:
softmax_output = softmax_output.view(bsz, tgt_len, -1)
outputs = [softmax_output] + outputs
else:
softmax_output = softmax_output.view(bsz, tgt_len)
outputs = [softmax_output, None] + outputs
return outputs # (loss), logits or None if labels is not None (speed up adaptive softmax), new_mems, (all hidden states), (all attentions)
| 41,005 | 44.012075 | 157 |
py
|
DeeBERT
|
DeeBERT-master/transformers/convert_xlnet_original_tf_checkpoint_to_pytorch.py
|
# 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 BERT checkpoint."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import argparse
import torch
from transformers import (CONFIG_NAME, WEIGHTS_NAME,
XLNetConfig,
XLNetLMHeadModel, XLNetForQuestionAnswering,
XLNetForSequenceClassification,
load_tf_weights_in_xlnet)
GLUE_TASKS_NUM_LABELS = {
"cola": 2,
"mnli": 3,
"mrpc": 2,
"sst-2": 2,
"sts-b": 1,
"qqp": 2,
"qnli": 2,
"rte": 2,
"wnli": 2,
}
import logging
logging.basicConfig(level=logging.INFO)
def convert_xlnet_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytorch_dump_folder_path, finetuning_task=None):
# Initialise PyTorch model
config = XLNetConfig.from_json_file(bert_config_file)
finetuning_task = finetuning_task.lower() if finetuning_task is not None else ""
if finetuning_task in GLUE_TASKS_NUM_LABELS:
print("Building PyTorch XLNetForSequenceClassification model from configuration: {}".format(str(config)))
config.finetuning_task = finetuning_task
config.num_labels = GLUE_TASKS_NUM_LABELS[finetuning_task]
model = XLNetForSequenceClassification(config)
elif 'squad' in finetuning_task:
config.finetuning_task = finetuning_task
model = XLNetForQuestionAnswering(config)
else:
model = XLNetLMHeadModel(config)
# Load weights from tf checkpoint
load_tf_weights_in_xlnet(model, config, tf_checkpoint_path)
# Save pytorch-model
pytorch_weights_dump_path = os.path.join(pytorch_dump_folder_path, WEIGHTS_NAME)
pytorch_config_dump_path = os.path.join(pytorch_dump_folder_path, CONFIG_NAME)
print("Save PyTorch model to {}".format(os.path.abspath(pytorch_weights_dump_path)))
torch.save(model.state_dict(), pytorch_weights_dump_path)
print("Save configuration file to {}".format(os.path.abspath(pytorch_config_dump_path)))
with open(pytorch_config_dump_path, "w", encoding="utf-8") as f:
f.write(config.to_json_string())
if __name__ == "__main__":
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--tf_checkpoint_path",
default = None,
type = str,
required = True,
help = "Path to the TensorFlow checkpoint path.")
parser.add_argument("--xlnet_config_file",
default = None,
type = str,
required = True,
help = "The config json file corresponding to the pre-trained XLNet model. \n"
"This specifies the model architecture.")
parser.add_argument("--pytorch_dump_folder_path",
default = None,
type = str,
required = True,
help = "Path to the folder to store the PyTorch model or dataset/vocab.")
parser.add_argument("--finetuning_task",
default = None,
type = str,
help = "Name of a task on which the XLNet TensorFloaw model was fine-tuned")
args = parser.parse_args()
print(args)
convert_xlnet_checkpoint_to_pytorch(args.tf_checkpoint_path,
args.xlnet_config_file,
args.pytorch_dump_folder_path,
args.finetuning_task)
| 4,334 | 40.285714 | 126 |
py
|
DeeBERT
|
DeeBERT-master/transformers/modeling_xlnet.py
|
# 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.
""" PyTorch XLNet model.
"""
from __future__ import absolute_import, division, print_function, unicode_literals
import json
import logging
import math
import os
import sys
from io import open
import torch
from torch import nn
from torch.nn import functional as F
from torch.nn import CrossEntropyLoss, MSELoss
from .modeling_utils import PreTrainedModel, prune_linear_layer, SequenceSummary, PoolerAnswerClass, PoolerEndLogits, PoolerStartLogits
from .configuration_xlnet import XLNetConfig
from .file_utils import add_start_docstrings
logger = logging.getLogger(__name__)
XLNET_PRETRAINED_MODEL_ARCHIVE_MAP = {
'xlnet-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-base-cased-pytorch_model.bin",
'xlnet-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-large-cased-pytorch_model.bin",
}
def build_tf_xlnet_to_pytorch_map(model, config, tf_weights=None):
""" A map of modules from TF to PyTorch.
I use a map to keep the PyTorch model as
identical to the original PyTorch model as possible.
"""
tf_to_pt_map = {}
if hasattr(model, 'transformer'):
if hasattr(model, 'lm_loss'):
# We will load also the output bias
tf_to_pt_map['model/lm_loss/bias'] = model.lm_loss.bias
if hasattr(model, 'sequence_summary') and 'model/sequnece_summary/summary/kernel' in tf_weights:
# We will load also the sequence summary
tf_to_pt_map['model/sequnece_summary/summary/kernel'] = model.sequence_summary.summary.weight
tf_to_pt_map['model/sequnece_summary/summary/bias'] = model.sequence_summary.summary.bias
if hasattr(model, 'logits_proj') and config.finetuning_task is not None \
and 'model/regression_{}/logit/kernel'.format(config.finetuning_task) in tf_weights:
tf_to_pt_map['model/regression_{}/logit/kernel'.format(config.finetuning_task)] = model.logits_proj.weight
tf_to_pt_map['model/regression_{}/logit/bias'.format(config.finetuning_task)] = model.logits_proj.bias
# Now load the rest of the transformer
model = model.transformer
# Embeddings and output
tf_to_pt_map.update({'model/transformer/word_embedding/lookup_table': model.word_embedding.weight,
'model/transformer/mask_emb/mask_emb': model.mask_emb})
# Transformer blocks
for i, b in enumerate(model.layer):
layer_str = "model/transformer/layer_%d/" % i
tf_to_pt_map.update({
layer_str + "rel_attn/LayerNorm/gamma": b.rel_attn.layer_norm.weight,
layer_str + "rel_attn/LayerNorm/beta": b.rel_attn.layer_norm.bias,
layer_str + "rel_attn/o/kernel": b.rel_attn.o,
layer_str + "rel_attn/q/kernel": b.rel_attn.q,
layer_str + "rel_attn/k/kernel": b.rel_attn.k,
layer_str + "rel_attn/r/kernel": b.rel_attn.r,
layer_str + "rel_attn/v/kernel": b.rel_attn.v,
layer_str + "ff/LayerNorm/gamma": b.ff.layer_norm.weight,
layer_str + "ff/LayerNorm/beta": b.ff.layer_norm.bias,
layer_str + "ff/layer_1/kernel": b.ff.layer_1.weight,
layer_str + "ff/layer_1/bias": b.ff.layer_1.bias,
layer_str + "ff/layer_2/kernel": b.ff.layer_2.weight,
layer_str + "ff/layer_2/bias": b.ff.layer_2.bias,
})
# Relative positioning biases
if config.untie_r:
r_r_list = []
r_w_list = []
r_s_list = []
seg_embed_list = []
for b in model.layer:
r_r_list.append(b.rel_attn.r_r_bias)
r_w_list.append(b.rel_attn.r_w_bias)
r_s_list.append(b.rel_attn.r_s_bias)
seg_embed_list.append(b.rel_attn.seg_embed)
else:
r_r_list = [model.r_r_bias]
r_w_list = [model.r_w_bias]
r_s_list = [model.r_s_bias]
seg_embed_list = [model.seg_embed]
tf_to_pt_map.update({
'model/transformer/r_r_bias': r_r_list,
'model/transformer/r_w_bias': r_w_list,
'model/transformer/r_s_bias': r_s_list,
'model/transformer/seg_embed': seg_embed_list})
return tf_to_pt_map
def load_tf_weights_in_xlnet(model, config, tf_path):
""" Load tf checkpoints in a pytorch model
"""
try:
import numpy as np
import tensorflow as tf
except ImportError:
logger.error("Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
"https://www.tensorflow.org/install/ for installation instructions.")
raise
# Load weights from TF model
init_vars = tf.train.list_variables(tf_path)
tf_weights = {}
for name, shape in init_vars:
logger.info("Loading TF weight {} with shape {}".format(name, shape))
array = tf.train.load_variable(tf_path, name)
tf_weights[name] = array
# Build TF to PyTorch weights loading map
tf_to_pt_map = build_tf_xlnet_to_pytorch_map(model, config, tf_weights)
for name, pointer in tf_to_pt_map.items():
logger.info("Importing {}".format(name))
if name not in tf_weights:
logger.info("{} not in tf pre-trained weights, skipping".format(name))
continue
array = tf_weights[name]
# adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
# which are not required for using pretrained model
if 'kernel' in name and ('ff' in name or 'summary' in name or 'logit' in name):
logger.info("Transposing")
array = np.transpose(array)
if isinstance(pointer, list):
# Here we will split the TF weigths
assert len(pointer) == array.shape[0]
for i, p_i in enumerate(pointer):
arr_i = array[i, ...]
try:
assert p_i.shape == arr_i.shape
except AssertionError as e:
e.args += (p_i.shape, arr_i.shape)
raise
logger.info("Initialize PyTorch weight {} for layer {}".format(name, i))
p_i.data = torch.from_numpy(arr_i)
else:
try:
assert pointer.shape == array.shape
except AssertionError as e:
e.args += (pointer.shape, array.shape)
raise
logger.info("Initialize PyTorch weight {}".format(name))
pointer.data = torch.from_numpy(array)
tf_weights.pop(name, None)
tf_weights.pop(name + '/Adam', None)
tf_weights.pop(name + '/Adam_1', None)
logger.info("Weights not copied to PyTorch model: {}".format(', '.join(tf_weights.keys())))
return model
def gelu(x):
""" Implementation of the gelu activation function.
XLNet is using OpenAI GPT's gelu (not exactly the same as BERT)
Also see https://arxiv.org/abs/1606.08415
"""
cdf = 0.5 * (1.0 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
return x * cdf
def swish(x):
return x * torch.sigmoid(x)
ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish}
XLNetLayerNorm = nn.LayerNorm
class XLNetRelativeAttention(nn.Module):
def __init__(self, config):
super(XLNetRelativeAttention, self).__init__()
self.output_attentions = config.output_attentions
if config.d_model % config.n_head != 0:
raise ValueError(
"The hidden size (%d) is not a multiple of the number of attention "
"heads (%d)" % (config.d_model, config.n_head))
self.n_head = config.n_head
self.d_head = config.d_head
self.d_model = config.d_model
self.scale = 1 / (config.d_head ** 0.5)
self.q = nn.Parameter(torch.FloatTensor(config.d_model, self.n_head, self.d_head))
self.k = nn.Parameter(torch.FloatTensor(config.d_model, self.n_head, self.d_head))
self.v = nn.Parameter(torch.FloatTensor(config.d_model, self.n_head, self.d_head))
self.o = nn.Parameter(torch.FloatTensor(config.d_model, self.n_head, self.d_head))
self.r = nn.Parameter(torch.FloatTensor(config.d_model, self.n_head, self.d_head))
self.r_r_bias = nn.Parameter(torch.FloatTensor(self.n_head, self.d_head))
self.r_s_bias = nn.Parameter(torch.FloatTensor(self.n_head, self.d_head))
self.r_w_bias = nn.Parameter(torch.FloatTensor(self.n_head, self.d_head))
self.seg_embed = nn.Parameter(torch.FloatTensor(2, self.n_head, self.d_head))
self.layer_norm = XLNetLayerNorm(config.d_model, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.dropout)
def prune_heads(self, heads):
raise NotImplementedError
@staticmethod
def rel_shift(x, klen=-1):
"""perform relative shift to form the relative attention score."""
x_size = x.shape
x = x.reshape(x_size[1], x_size[0], x_size[2], x_size[3])
x = x[1:, ...]
x = x.reshape(x_size[0], x_size[1] - 1, x_size[2], x_size[3])
# x = x[:, 0:klen, :, :]
x = torch.index_select(x, 1, torch.arange(klen, device=x.device, dtype=torch.long))
return x
@staticmethod
def rel_shift_bnij(x, klen=-1):
x_size = x.shape
x = x.reshape(x_size[0], x_size[1], x_size[3], x_size[2])
x = x[:, :, 1:, :]
x = x.reshape(x_size[0], x_size[1], x_size[2], x_size[3]-1)
# Note: the tensor-slice form was faster in my testing than torch.index_select
# However, tracing doesn't like the nature of the slice, and if klen changes
# during the run then it'll fail, whereas index_select will be fine.
x = torch.index_select(x, 3, torch.arange(klen, device=x.device, dtype=torch.long))
# x = x[:, :, :, :klen]
return x
def rel_attn_core(self, q_head, k_head_h, v_head_h, k_head_r, seg_mat=None, attn_mask=None, head_mask=None):
"""Core relative positional attention operations."""
# content based attention score
ac = torch.einsum('ibnd,jbnd->bnij', q_head + self.r_w_bias, k_head_h)
# position based attention score
bd = torch.einsum('ibnd,jbnd->bnij', q_head + self.r_r_bias, k_head_r)
bd = self.rel_shift_bnij(bd, klen=ac.shape[3])
# segment based attention score
if seg_mat is None:
ef = 0
else:
ef = torch.einsum('ibnd,snd->ibns', q_head + self.r_s_bias, self.seg_embed)
ef = torch.einsum('ijbs,ibns->bnij', seg_mat, ef)
# merge attention scores and perform masking
attn_score = (ac + bd + ef) * self.scale
if attn_mask is not None:
# attn_score = attn_score * (1 - attn_mask) - 1e30 * attn_mask
if attn_mask.dtype == torch.float16:
attn_score = attn_score - 65500 * torch.einsum('ijbn->bnij', attn_mask)
else:
attn_score = attn_score - 1e30 * torch.einsum('ijbn->bnij', attn_mask)
# attention probability
attn_prob = F.softmax(attn_score, dim=3)
attn_prob = self.dropout(attn_prob)
# Mask heads if we want to
if head_mask is not None:
attn_prob = attn_prob * torch.einsum('ijbn->bnij', head_mask)
# attention output
attn_vec = torch.einsum('bnij,jbnd->ibnd', attn_prob, v_head_h)
if self.output_attentions:
return attn_vec, torch.einsum('bnij->ijbn', attn_prob)
return attn_vec
def post_attention(self, h, attn_vec, residual=True):
"""Post-attention processing."""
# post-attention projection (back to `d_model`)
attn_out = torch.einsum('ibnd,hnd->ibh', attn_vec, self.o)
attn_out = self.dropout(attn_out)
if residual:
attn_out = attn_out + h
output = self.layer_norm(attn_out)
return output
def forward(self, h, g,
attn_mask_h, attn_mask_g,
r, seg_mat,
mems=None, target_mapping=None, head_mask=None):
if g is not None:
###### Two-stream attention with relative positional encoding.
# content based attention score
if mems is not None and mems.dim() > 1:
cat = torch.cat([mems, h], dim=0)
else:
cat = h
# content-based key head
k_head_h = torch.einsum('ibh,hnd->ibnd', cat, self.k)
# content-based value head
v_head_h = torch.einsum('ibh,hnd->ibnd', cat, self.v)
# position-based key head
k_head_r = torch.einsum('ibh,hnd->ibnd', r, self.r)
##### h-stream
# content-stream query head
q_head_h = torch.einsum('ibh,hnd->ibnd', h, self.q)
# core attention ops
attn_vec_h = self.rel_attn_core(
q_head_h, k_head_h, v_head_h, k_head_r, seg_mat=seg_mat, attn_mask=attn_mask_h, head_mask=head_mask)
if self.output_attentions:
attn_vec_h, attn_prob_h = attn_vec_h
# post processing
output_h = self.post_attention(h, attn_vec_h)
##### g-stream
# query-stream query head
q_head_g = torch.einsum('ibh,hnd->ibnd', g, self.q)
# core attention ops
if target_mapping is not None:
q_head_g = torch.einsum('mbnd,mlb->lbnd', q_head_g, target_mapping)
attn_vec_g = self.rel_attn_core(
q_head_g, k_head_h, v_head_h, k_head_r, seg_mat=seg_mat, attn_mask=attn_mask_g, head_mask=head_mask)
if self.output_attentions:
attn_vec_g, attn_prob_g = attn_vec_g
attn_vec_g = torch.einsum('lbnd,mlb->mbnd', attn_vec_g, target_mapping)
else:
attn_vec_g = self.rel_attn_core(
q_head_g, k_head_h, v_head_h, k_head_r, seg_mat=seg_mat, attn_mask=attn_mask_g, head_mask=head_mask)
if self.output_attentions:
attn_vec_g, attn_prob_g = attn_vec_g
# post processing
output_g = self.post_attention(g, attn_vec_g)
if self.output_attentions:
attn_prob = attn_prob_h, attn_prob_g
else:
###### Multi-head attention with relative positional encoding
if mems is not None and mems.dim() > 1:
cat = torch.cat([mems, h], dim=0)
else:
cat = h
# content heads
q_head_h = torch.einsum('ibh,hnd->ibnd', h, self.q)
k_head_h = torch.einsum('ibh,hnd->ibnd', cat, self.k)
v_head_h = torch.einsum('ibh,hnd->ibnd', cat, self.v)
# positional heads
k_head_r = torch.einsum('ibh,hnd->ibnd', r, self.r)
# core attention ops
attn_vec = self.rel_attn_core(
q_head_h, k_head_h, v_head_h, k_head_r, seg_mat=seg_mat, attn_mask=attn_mask_h, head_mask=head_mask)
if self.output_attentions:
attn_vec, attn_prob = attn_vec
# post processing
output_h = self.post_attention(h, attn_vec)
output_g = None
outputs = (output_h, output_g)
if self.output_attentions:
outputs = outputs + (attn_prob,)
return outputs
class XLNetFeedForward(nn.Module):
def __init__(self, config):
super(XLNetFeedForward, self).__init__()
self.layer_norm = XLNetLayerNorm(config.d_model, eps=config.layer_norm_eps)
self.layer_1 = nn.Linear(config.d_model, config.d_inner)
self.layer_2 = nn.Linear(config.d_inner, config.d_model)
self.dropout = nn.Dropout(config.dropout)
if isinstance(config.ff_activation, str) or \
(sys.version_info[0] == 2 and isinstance(config.ff_activation, unicode)):
self.activation_function = ACT2FN[config.ff_activation]
else:
self.activation_function = config.ff_activation
def forward(self, inp):
output = inp
output = self.layer_1(output)
output = self.activation_function(output)
output = self.dropout(output)
output = self.layer_2(output)
output = self.dropout(output)
output = self.layer_norm(output + inp)
return output
class XLNetLayer(nn.Module):
def __init__(self, config):
super(XLNetLayer, self).__init__()
self.rel_attn = XLNetRelativeAttention(config)
self.ff = XLNetFeedForward(config)
self.dropout = nn.Dropout(config.dropout)
def forward(self, output_h, output_g,
attn_mask_h, attn_mask_g,
r, seg_mat, mems=None, target_mapping=None, head_mask=None):
outputs = self.rel_attn(output_h, output_g, attn_mask_h, attn_mask_g,
r, seg_mat, mems=mems, target_mapping=target_mapping,
head_mask=head_mask)
output_h, output_g = outputs[:2]
if output_g is not None:
output_g = self.ff(output_g)
output_h = self.ff(output_h)
outputs = (output_h, output_g) + outputs[2:] # Add again attentions if there are there
return outputs
class XLNetPreTrainedModel(PreTrainedModel):
""" An abstract class to handle weights initialization and
a simple interface for dowloading and loading pretrained models.
"""
config_class = XLNetConfig
pretrained_model_archive_map = XLNET_PRETRAINED_MODEL_ARCHIVE_MAP
load_tf_weights = load_tf_weights_in_xlnet
base_model_prefix = "transformer"
def _init_weights(self, module):
""" Initialize the weights.
"""
if isinstance(module, (nn.Linear, nn.Embedding)):
# 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 isinstance(module, nn.Linear) and module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, XLNetLayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
elif isinstance(module, XLNetRelativeAttention):
for param in [module.q, module.k, module.v, module.o, module.r,
module.r_r_bias, module.r_s_bias, module.r_w_bias,
module.seg_embed]:
param.data.normal_(mean=0.0, std=self.config.initializer_range)
elif isinstance(module, XLNetModel):
module.mask_emb.data.normal_(mean=0.0, std=self.config.initializer_range)
XLNET_START_DOCSTRING = r""" The XLNet model was proposed in
`XLNet: Generalized Autoregressive Pretraining for Language Understanding`_
by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
XLnet is an extension of the Transformer-XL model pre-trained using an autoregressive method
to learn bidirectional contexts by maximizing the expected likelihood over all permutations
of the input sequence factorization order.
The specific attention pattern can be controlled at training and test time using the `perm_mask` input.
Do to the difficulty of training a fully auto-regressive model over various factorization order,
XLNet is pretrained using only a sub-set of the output tokens as target which are selected
with the `target_mapping` input.
To use XLNet for sequential decoding (i.e. not in fully bi-directional setting), use the `perm_mask` and
`target_mapping` inputs to control the attention span and outputs (see examples in `examples/run_generation.py`)
This model is a PyTorch `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.
.. _`XLNet: Generalized Autoregressive Pretraining for Language Understanding`:
http://arxiv.org/abs/1906.08237
.. _`torch.nn.Module`:
https://pytorch.org/docs/stable/nn.html#module
Parameters:
config (:class:`~transformers.XLNetConfig`): 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 :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""
XLNET_INPUTS_DOCSTRING = r"""
Inputs:
**input_ids**: ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Indices of input sequence tokens in the vocabulary.
XLNet is a model with relative position embeddings so you can either pad the inputs on
the right or on the left.
Indices can be obtained using :class:`transformers.XLNetTokenizer`.
See :func:`transformers.PreTrainedTokenizer.encode` and
:func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
**token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
A parallel sequence of tokens (can be used to indicate various portions of the inputs).
The type indices in XLNet are NOT selected in the vocabulary, they can be arbitrary numbers and
the important thing is that they should be different for tokens which belong to different segments.
The model will compute relative segment differences from the given type indices:
0 if the segment id of two tokens are the same, 1 if not.
**attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:
Mask to avoid performing attention on padding token indices.
Mask values selected in ``[0, 1]``:
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
**mems**: (`optional`)
list of ``torch.FloatTensor`` (one for each layer):
that contains pre-computed hidden-states (key and values in the attention blocks) as output by the model
(see `mems` output below). Can be used to speed up sequential decoding and attend to longer context.
To activate mems you need to set up config.mem_len to a positive value which will be the max number of tokens in
the memory output by the model. E.g. `model = XLNetModel.from_pretrained('xlnet-base-case, mem_len=1024)` will
instantiate a model which can use up to 1024 tokens of memory (in addition to the input it self).
**perm_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, sequence_length)``:
Mask to indicate the attention pattern for each input token with values selected in ``[0, 1]``:
If ``perm_mask[k, i, j] = 0``, i attend to j in batch k;
if ``perm_mask[k, i, j] = 1``, i does not attend to j in batch k.
If None, each token attends to all the others (full bidirectional attention).
Only used during pretraining (to define factorization order) or for sequential decoding (generation).
**target_mapping**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, num_predict, sequence_length)``:
Mask to indicate the output tokens to use.
If ``target_mapping[k, i, j] = 1``, the i-th predict in batch k is on the j-th token.
Only used during pretraining for partial prediction or for sequential decoding (generation).
**token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
A parallel sequence of tokens (can be used to indicate various portions of the inputs).
The type indices in XLNet are NOT selected in the vocabulary, they can be arbitrary numbers and
the important thing is that they should be different for tokens which belong to different segments.
The model will compute relative segment differences from the given type indices:
0 if the segment id of two tokens are the same, 1 if not.
**input_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:
Mask to avoid performing attention on padding token indices.
Negative of `attention_mask`, i.e. with 0 for real tokens and 1 for padding.
Kept for compatibility with the original code base.
You can only uses one of `input_mask` and `attention_mask`
Mask values selected in ``[0, 1]``:
``1`` for tokens that are MASKED, ``0`` for tokens that are NOT MASKED.
**head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
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**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, embedding_dim)``:
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.
"""
@add_start_docstrings("The bare XLNet Model transformer outputting raw hidden-states without any specific head on top.",
XLNET_START_DOCSTRING, XLNET_INPUTS_DOCSTRING)
class XLNetModel(XLNetPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**last_hidden_state**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)``
Sequence of hidden-states at the last layer of the model.
**mems**: (`optional`, returned when ``config.mem_len > 0``)
list of ``torch.FloatTensor`` (one for each layer):
that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
if config.mem_len > 0 else tuple of None. Can be used to speed up sequential decoding and attend to longer context.
See details in the docstring of the `mems` input above.
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
tokenizer = XLNetTokenizer.from_pretrained('xlnet-large-cased')
model = XLNetModel.from_pretrained('xlnet-large-cased')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
outputs = model(input_ids)
last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
"""
def __init__(self, config):
super(XLNetModel, self).__init__(config)
self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states
self.output_past = config.output_past
self.mem_len = config.mem_len
self.reuse_len = config.reuse_len
self.d_model = config.d_model
self.same_length = config.same_length
self.attn_type = config.attn_type
self.bi_data = config.bi_data
self.clamp_len = config.clamp_len
self.n_layer = config.n_layer
self.word_embedding = nn.Embedding(config.n_token, config.d_model)
self.mask_emb = nn.Parameter(torch.FloatTensor(1, 1, config.d_model))
self.layer = nn.ModuleList([XLNetLayer(config) for _ in range(config.n_layer)])
self.dropout = nn.Dropout(config.dropout)
self.init_weights()
def get_input_embeddings(self):
return self.word_embedding
def set_input_embeddings(self, new_embeddings):
self.word_embedding = new_embeddings
def _prune_heads(self, heads_to_prune):
raise NotImplementedError
def create_mask(self, qlen, mlen):
"""
Creates causal attention mask. Float mask where 1.0 indicates masked, 0.0 indicates not-masked.
Args:
qlen: TODO Lysandre didn't fill
mlen: TODO Lysandre didn't fill
::
same_length=False: same_length=True:
<mlen > < qlen > <mlen > < qlen >
^ [0 0 0 0 0 1 1 1 1] [0 0 0 0 0 1 1 1 1]
[0 0 0 0 0 0 1 1 1] [1 0 0 0 0 0 1 1 1]
qlen [0 0 0 0 0 0 0 1 1] [1 1 0 0 0 0 0 1 1]
[0 0 0 0 0 0 0 0 1] [1 1 1 0 0 0 0 0 1]
v [0 0 0 0 0 0 0 0 0] [1 1 1 1 0 0 0 0 0]
"""
attn_mask = torch.ones([qlen, qlen])
mask_up = torch.triu(attn_mask, diagonal=1)
attn_mask_pad = torch.zeros([qlen, mlen])
ret = torch.cat([attn_mask_pad, mask_up], dim=1)
if self.same_length:
mask_lo = torch.tril(attn_mask, diagonal=-1)
ret = torch.cat([ret[:, :qlen] + mask_lo, ret[:, qlen:]], dim=1)
ret = ret.to(next(self.parameters()))
return ret
def cache_mem(self, curr_out, prev_mem):
"""cache hidden states into memory."""
if self.reuse_len is not None and self.reuse_len > 0:
curr_out = curr_out[:self.reuse_len]
if prev_mem is None:
new_mem = curr_out[-self.mem_len:]
else:
new_mem = torch.cat([prev_mem, curr_out], dim=0)[-self.mem_len:]
return new_mem.detach()
@staticmethod
def positional_embedding(pos_seq, inv_freq, bsz=None):
sinusoid_inp = torch.einsum('i,d->id', pos_seq, inv_freq)
pos_emb = torch.cat([torch.sin(sinusoid_inp), torch.cos(sinusoid_inp)], dim=-1)
pos_emb = pos_emb[:, None, :]
if bsz is not None:
pos_emb = pos_emb.expand(-1, bsz, -1)
return pos_emb
def relative_positional_encoding(self, qlen, klen, bsz=None):
"""create relative positional encoding."""
freq_seq = torch.arange(0, self.d_model, 2.0, dtype=torch.float)
inv_freq = 1 / torch.pow(10000, (freq_seq / self.d_model))
if self.attn_type == 'bi':
# beg, end = klen - 1, -qlen
beg, end = klen, -qlen
elif self.attn_type == 'uni':
# beg, end = klen - 1, -1
beg, end = klen, -1
else:
raise ValueError('Unknown `attn_type` {}.'.format(self.attn_type))
if self.bi_data:
fwd_pos_seq = torch.arange(beg, end, -1.0, dtype=torch.float)
bwd_pos_seq = torch.arange(-beg, -end, 1.0, dtype=torch.float)
if self.clamp_len > 0:
fwd_pos_seq = fwd_pos_seq.clamp(-self.clamp_len, self.clamp_len)
bwd_pos_seq = bwd_pos_seq.clamp(-self.clamp_len, self.clamp_len)
if bsz is not None:
fwd_pos_emb = self.positional_embedding(fwd_pos_seq, inv_freq, bsz//2)
bwd_pos_emb = self.positional_embedding(bwd_pos_seq, inv_freq, bsz//2)
else:
fwd_pos_emb = self.positional_embedding(fwd_pos_seq, inv_freq)
bwd_pos_emb = self.positional_embedding(bwd_pos_seq, inv_freq)
pos_emb = torch.cat([fwd_pos_emb, bwd_pos_emb], dim=1)
else:
fwd_pos_seq = torch.arange(beg, end, -1.0)
if self.clamp_len > 0:
fwd_pos_seq = fwd_pos_seq.clamp(-self.clamp_len, self.clamp_len)
pos_emb = self.positional_embedding(fwd_pos_seq, inv_freq, bsz)
pos_emb = pos_emb.to(next(self.parameters()))
return pos_emb
def forward(self, input_ids=None, attention_mask=None, mems=None, perm_mask=None, target_mapping=None,
token_type_ids=None, input_mask=None, head_mask=None, inputs_embeds=None):
# the original code for XLNet uses shapes [len, bsz] with the batch dimension at the end
# but we want a unified interface in the library with the batch size on the first dimension
# so we move here the first dimension (batch) to the end
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_ids = input_ids.transpose(0, 1).contiguous()
qlen, bsz = input_ids.shape[0], input_ids.shape[1]
elif inputs_embeds is not None:
inputs_embeds.transpose(0, 1).contiguous()
qlen, bsz = inputs_embeds.shape[0], inputs_embeds.shape[1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
token_type_ids = token_type_ids.transpose(0, 1).contiguous() if token_type_ids is not None else None
input_mask = input_mask.transpose(0, 1).contiguous() if input_mask is not None else None
attention_mask = attention_mask.transpose(0, 1).contiguous() if attention_mask is not None else None
perm_mask = perm_mask.permute(1, 2, 0).contiguous() if perm_mask is not None else None
target_mapping = target_mapping.permute(1, 2, 0).contiguous() if target_mapping is not None else None
mlen = mems[0].shape[0] if mems is not None and mems[0] is not None else 0
klen = mlen + qlen
dtype_float = next(self.parameters()).dtype
device = next(self.parameters()).device
##### Attention mask
# causal attention mask
if self.attn_type == 'uni':
attn_mask = self.create_mask(qlen, mlen)
attn_mask = attn_mask[:, :, None, None]
elif self.attn_type == 'bi':
attn_mask = None
else:
raise ValueError('Unsupported attention type: {}'.format(self.attn_type))
# data mask: input mask & perm mask
assert input_mask is None or attention_mask is None, "You can only use one of input_mask (uses 1 for padding) "
"or attention_mask (uses 0 for padding, added for compatbility with BERT). Please choose one."
if input_mask is None and attention_mask is not None:
input_mask = 1.0 - attention_mask
if input_mask is not None and perm_mask is not None:
data_mask = input_mask[None] + perm_mask
elif input_mask is not None and perm_mask is None:
data_mask = input_mask[None]
elif input_mask is None and perm_mask is not None:
data_mask = perm_mask
else:
data_mask = None
if data_mask is not None:
# all mems can be attended to
if mlen > 0:
mems_mask = torch.zeros([data_mask.shape[0], mlen, bsz]).to(data_mask)
data_mask = torch.cat([mems_mask, data_mask], dim=1)
if attn_mask is None:
attn_mask = data_mask[:, :, :, None]
else:
attn_mask += data_mask[:, :, :, None]
if attn_mask is not None:
attn_mask = (attn_mask > 0).to(dtype_float)
if attn_mask is not None:
non_tgt_mask = -torch.eye(qlen).to(attn_mask)
if mlen > 0:
non_tgt_mask = torch.cat([torch.zeros([qlen, mlen]).to(attn_mask), non_tgt_mask], dim=-1)
non_tgt_mask = ((attn_mask + non_tgt_mask[:, :, None, None]) > 0).to(attn_mask)
else:
non_tgt_mask = None
##### Word embeddings and prepare h & g hidden states
if inputs_embeds is not None:
word_emb_k = inputs_embeds
else:
word_emb_k = self.word_embedding(input_ids)
output_h = self.dropout(word_emb_k)
if target_mapping is not None:
word_emb_q = self.mask_emb.expand(target_mapping.shape[0], bsz, -1)
# else: # We removed the inp_q input which was same as target mapping
# inp_q_ext = inp_q[:, :, None]
# word_emb_q = inp_q_ext * self.mask_emb + (1 - inp_q_ext) * word_emb_k
output_g = self.dropout(word_emb_q)
else:
output_g = None
##### Segment embedding
if token_type_ids is not None:
# Convert `token_type_ids` to one-hot `seg_mat`
if mlen > 0:
mem_pad = torch.zeros([mlen, bsz], dtype=torch.long, device=device)
cat_ids = torch.cat([mem_pad, token_type_ids], dim=0)
else:
cat_ids = token_type_ids
# `1` indicates not in the same segment [qlen x klen x bsz]
seg_mat = (token_type_ids[:, None] != cat_ids[None, :]).long()
seg_mat = F.one_hot(seg_mat, num_classes=2).to(dtype_float)
else:
seg_mat = None
##### Positional encoding
pos_emb = self.relative_positional_encoding(qlen, klen, bsz=bsz)
pos_emb = self.dropout(pos_emb)
# 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] (a head_mask for each layer)
# and head_mask is converted to shape [num_hidden_layers x qlen x klen x bsz x n_head]
if head_mask is not None:
if head_mask.dim() == 1:
head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(0).unsqueeze(0)
head_mask = head_mask.expand(self.n_layer, -1, -1, -1, -1)
elif head_mask.dim() == 2:
head_mask = head_mask.unsqueeze(1).unsqueeze(1).unsqueeze(1)
head_mask = head_mask.to(dtype=next(self.parameters()).dtype) # switch to fload if need + fp16 compatibility
else:
head_mask = [None] * self.n_layer
new_mems = ()
if mems is None:
mems = [None] * len(self.layer)
attentions = []
hidden_states = []
for i, layer_module in enumerate(self.layer):
if self.mem_len is not None and self.mem_len > 0 and self.output_past:
# cache new mems
new_mems = new_mems + (self.cache_mem(output_h, mems[i]),)
if self.output_hidden_states:
hidden_states.append((output_h, output_g) if output_g is not None else output_h)
outputs = layer_module(output_h, output_g, attn_mask_h=non_tgt_mask, attn_mask_g=attn_mask,
r=pos_emb, seg_mat=seg_mat, mems=mems[i], target_mapping=target_mapping,
head_mask=head_mask[i])
output_h, output_g = outputs[:2]
if self.output_attentions:
attentions.append(outputs[2])
# Add last hidden state
if self.output_hidden_states:
hidden_states.append((output_h, output_g) if output_g is not None else output_h)
output = self.dropout(output_g if output_g is not None else output_h)
# Prepare outputs, we transpose back here to shape [bsz, len, hidden_dim] (cf. beginning of forward() method)
outputs = (output.permute(1, 0, 2).contiguous(),)
if self.mem_len is not None and self.mem_len > 0 and self.output_past:
outputs = outputs + (new_mems,)
if self.output_hidden_states:
if output_g is not None:
hidden_states = tuple(h.permute(1, 0, 2).contiguous() for hs in hidden_states for h in hs)
else:
hidden_states = tuple(hs.permute(1, 0, 2).contiguous() for hs in hidden_states)
outputs = outputs + (hidden_states,)
if self.output_attentions:
attentions = tuple(t.permute(2, 3, 0, 1).contiguous() for t in attentions)
outputs = outputs + (attentions,)
return outputs # outputs, (new_mems), (hidden_states), (attentions)
@add_start_docstrings("""XLNet Model with a language modeling head on top
(linear layer with weights tied to the input embeddings). """,
XLNET_START_DOCSTRING, XLNET_INPUTS_DOCSTRING)
class XLNetLMHeadModel(XLNetPreTrainedModel):
r"""
**labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Labels for language modeling.
Note that the labels **are shifted** inside the model, i.e. you can set ``lm_labels = input_ids``
Indices are selected in ``[-1, 0, ..., config.vocab_size]``
All labels set to ``-1`` are ignored (masked), the loss is only
computed for labels in ``[0, ..., config.vocab_size]``
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Language modeling loss.
**prediction_scores**: ``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).
**mems**: (`optional`, returned when ``config.mem_len > 0``)
list of ``torch.FloatTensor`` (one for each layer):
that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
if config.mem_len > 0 else tuple of None. Can be used to speed up sequential decoding and attend to longer context.
See details in the docstring of the `mems` input above.
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
tokenizer = XLNetTokenizer.from_pretrained('xlnet-large-cased')
model = XLNetLMHeadModel.from_pretrained('xlnet-large-cased')
# We show how to setup inputs to predict a next token using a bi-directional context.
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is very <mask>")).unsqueeze(0) # We will predict the masked token
perm_mask = torch.zeros((1, input_ids.shape[1], input_ids.shape[1]), dtype=torch.float)
perm_mask[:, :, -1] = 1.0 # Previous tokens don't see last token
target_mapping = torch.zeros((1, 1, input_ids.shape[1]), dtype=torch.float) # Shape [1, 1, seq_length] => let's predict one token
target_mapping[0, 0, -1] = 1.0 # Our first (and only) prediction will be the last token of the sequence (the masked token)
outputs = model(input_ids, perm_mask=perm_mask, target_mapping=target_mapping)
next_token_logits = outputs[0] # Output has shape [target_mapping.size(0), target_mapping.size(1), config.vocab_size]
"""
def __init__(self, config):
super(XLNetLMHeadModel, self).__init__(config)
self.attn_type = config.attn_type
self.same_length = config.same_length
self.transformer = XLNetModel(config)
self.lm_loss = nn.Linear(config.d_model, config.n_token, bias=True)
self.init_weights()
def get_output_embeddings(self):
return self.lm_loss
def forward(self, input_ids=None, attention_mask=None, mems=None, perm_mask=None, target_mapping=None,
token_type_ids=None, input_mask=None, head_mask=None, inputs_embeds=None, labels=None):
transformer_outputs = self.transformer(input_ids,
attention_mask=attention_mask,
mems=mems,
perm_mask=perm_mask,
target_mapping=target_mapping,
token_type_ids=token_type_ids,
input_mask=input_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds)
logits = self.lm_loss(transformer_outputs[0])
outputs = (logits,) + transformer_outputs[1:] # Keep mems, hidden states, attentions if there are in it
if labels is not None:
# Flatten the tokens
loss_fct = CrossEntropyLoss(ignore_index=-1)
loss = loss_fct(logits.view(-1, logits.size(-1)),
labels.view(-1))
outputs = (loss,) + outputs
return outputs # return (loss), logits, (mems), (hidden states), (attentions)
@add_start_docstrings("""XLNet Model with a sequence classification/regression head on top (a linear layer on top of
the pooled output) e.g. for GLUE tasks. """,
XLNET_START_DOCSTRING, XLNET_INPUTS_DOCSTRING)
class XLNetForSequenceClassification(XLNetPreTrainedModel):
r"""
**labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
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: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Classification (or regression if config.num_labels==1) loss.
**logits**: ``torch.FloatTensor`` of shape ``(batch_size, config.num_labels)``
Classification (or regression if config.num_labels==1) scores (before SoftMax).
**mems**: (`optional`, returned when ``config.mem_len > 0``)
list of ``torch.FloatTensor`` (one for each layer):
that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
if config.mem_len > 0 else tuple of None. Can be used to speed up sequential decoding and attend to longer context.
See details in the docstring of the `mems` input above.
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
tokenizer = XLNetTokenizer.from_pretrained('xlnet-large-cased')
model = XLNetForSequenceClassification.from_pretrained('xlnet-large-cased')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
labels = torch.tensor([1]).unsqueeze(0) # Batch size 1
outputs = model(input_ids, labels=labels)
loss, logits = outputs[:2]
"""
def __init__(self, config):
super(XLNetForSequenceClassification, self).__init__(config)
self.num_labels = config.num_labels
self.transformer = XLNetModel(config)
self.sequence_summary = SequenceSummary(config)
self.logits_proj = nn.Linear(config.d_model, config.num_labels)
self.init_weights()
def forward(self, input_ids=None, attention_mask=None, mems=None, perm_mask=None, target_mapping=None,
token_type_ids=None, input_mask=None, head_mask=None, inputs_embeds=None, labels=None):
transformer_outputs = self.transformer(input_ids,
attention_mask=attention_mask,
mems=mems,
perm_mask=perm_mask,
target_mapping=target_mapping,
token_type_ids=token_type_ids,
input_mask=input_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds)
output = transformer_outputs[0]
output = self.sequence_summary(output)
logits = self.logits_proj(output)
outputs = (logits,) + transformer_outputs[1:] # Keep mems, hidden states, attentions if there are in it
if labels is not None:
if self.num_labels == 1:
# We are doing regression
loss_fct = MSELoss()
loss = loss_fct(logits.view(-1), labels.view(-1))
else:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
outputs = (loss,) + outputs
return outputs # return (loss), logits, (mems), (hidden states), (attentions)
@add_start_docstrings("""XLNet Model with a multiple choice classification head on top (a linear layer on top of
the pooled output and a softmax) e.g. for RACE/SWAG tasks. """,
XLNET_START_DOCSTRING, XLNET_INPUTS_DOCSTRING)
class XLNetForMultipleChoice(XLNetPreTrainedModel):
r"""
Inputs:
**input_ids**: ``torch.LongTensor`` of shape ``(batch_size, num_choices, sequence_length)``:
Indices of input sequence tokens in the vocabulary.
The second dimension of the input (`num_choices`) indicates the number of choices to scores.
**token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, num_choices, sequence_length)``:
Segment token indices to indicate first and second portions of the inputs.
The second dimension of the input (`num_choices`) indicates the number of choices to score.
Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
**attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, num_choices, sequence_length)``:
Mask to avoid performing attention on padding token indices.
The second dimension of the input (`num_choices`) indicates the number of choices to score.
Mask values selected in ``[0, 1]``:
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
**head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
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**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, embedding_dim)``:
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.
**labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
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)
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Classification loss.
**classification_scores**: ``torch.FloatTensor`` of shape ``(batch_size, num_choices)`` where `num_choices` is the size of the second dimension
of the input tensors. (see `input_ids` above).
Classification scores (before SoftMax).
**mems**: (`optional`, returned when ``config.mem_len > 0``)
list of ``torch.FloatTensor`` (one for each layer):
that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
if config.mem_len > 0 else tuple of None. Can be used to speed up sequential decoding and attend to longer context.
See details in the docstring of the `mems` input above.
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased')
model = XLNetForMultipleChoice.from_pretrained('xlnet-base-cased')
choices = ["Hello, my dog is cute", "Hello, my cat is amazing"]
input_ids = torch.tensor([tokenizer.encode(s) for s in choices]).unsqueeze(0) # Batch size 1, 2 choices
labels = torch.tensor(1).unsqueeze(0) # Batch size 1
outputs = model(input_ids, labels=labels)
loss, classification_scores = outputs[:2]
"""
def __init__(self, config):
super(XLNetForMultipleChoice, self).__init__(config)
self.transformer = XLNetModel(config)
self.sequence_summary = SequenceSummary(config)
self.logits_proj = nn.Linear(config.d_model, 1)
self.init_weights()
def forward(self, input_ids=None, token_type_ids=None, input_mask=None, attention_mask=None,
mems=None, perm_mask=None, target_mapping=None,
labels=None, head_mask=None, inputs_embeds=None):
num_choices = input_ids.shape[1]
flat_input_ids = input_ids.view(-1, input_ids.size(-1))
flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_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_input_mask = input_mask.view(-1, input_mask.size(-1)) if input_mask is not None else None
transformer_outputs = self.transformer(flat_input_ids, token_type_ids=flat_token_type_ids,
input_mask=flat_input_mask, attention_mask=flat_attention_mask,
mems=mems, perm_mask=perm_mask, target_mapping=target_mapping,
head_mask=head_mask, inputs_embeds=inputs_embeds)
output = transformer_outputs[0]
output = self.sequence_summary(output)
logits = self.logits_proj(output)
reshaped_logits = logits.view(-1, num_choices)
outputs = (reshaped_logits,) + transformer_outputs[1:] # Keep mems, hidden states, attentions if there are in it
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits, labels.view(-1))
outputs = (loss,) + outputs
return outputs # return (loss), logits, (mems), (hidden states), (attentions)
@add_start_docstrings("""XLNet 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`). """,
XLNET_START_DOCSTRING, XLNET_INPUTS_DOCSTRING)
class XLNetForQuestionAnsweringSimple(XLNetPreTrainedModel):
r"""
**start_positions**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
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**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
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.
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**loss**: (`optional`, returned if both ``start_positions`` and ``end_positions`` are provided) ``torch.FloatTensor`` of shape ``(1,)``:
Classification loss as the sum of start token, end token (and is_impossible if provided) classification losses.
**start_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length,)``
Span-start scores (before SoftMax).
**end_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length,)``
Span-end scores (before SoftMax).
**mems**: (`optional`, returned when ``config.mem_len > 0``)
list of ``torch.FloatTensor`` (one for each layer):
that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
if config.mem_len > 0 else tuple of None. Can be used to speed up sequential decoding and attend to longer context.
See details in the docstring of the `mems` input above.
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
tokenizer = XLMTokenizer.from_pretrained('xlm-mlm-en-2048')
model = XLMForQuestionAnswering.from_pretrained('xlnet-large-cased')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
start_positions = torch.tensor([1])
end_positions = torch.tensor([3])
outputs = model(input_ids, start_positions=start_positions, end_positions=end_positions)
loss, start_scores, end_scores = outputs[:2]
"""
def __init__(self, config):
super(XLNetForQuestionAnsweringSimple, self).__init__(config)
self.num_labels = config.num_labels
self.transformer = XLNetModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
self.init_weights()
def forward(self, input_ids=None, attention_mask=None, mems=None, perm_mask=None, target_mapping=None,
token_type_ids=None, input_mask=None, head_mask=None, inputs_embeds=None,
start_positions=None, end_positions=None):
outputs = self.transformer(input_ids,
attention_mask=attention_mask,
mems=mems,
perm_mask=perm_mask,
target_mapping=target_mapping,
token_type_ids=token_type_ids,
input_mask=input_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds)
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)
end_logits = end_logits.squeeze(-1)
outputs = (start_logits, end_logits,) + outputs[2:]
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.clamp_(0, ignored_index)
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
outputs = (total_loss,) + outputs
return outputs # (loss), start_logits, end_logits, (mems), (hidden_states), (attentions)
@add_start_docstrings("""XLNet 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`). """,
XLNET_START_DOCSTRING, XLNET_INPUTS_DOCSTRING)
class XLNetForQuestionAnswering(XLNetPreTrainedModel):
r"""
**start_positions**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
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**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
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.
**is_impossible**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
Labels whether a question has an answer or no answer (SQuAD 2.0)
**cls_index**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
Labels for position (index) of the classification token to use as input for computing plausibility of the answer.
**p_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:
Optional mask of tokens which can't be in answers (e.g. [CLS], [PAD], ...).
1.0 means token should be masked. 0.0 mean token is not masked.
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**loss**: (`optional`, returned if both ``start_positions`` and ``end_positions`` are provided) ``torch.FloatTensor`` of shape ``(1,)``:
Classification loss as the sum of start token, end token (and is_impossible if provided) classification losses.
**start_top_log_probs**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
``torch.FloatTensor`` of shape ``(batch_size, config.start_n_top)``
Log probabilities for the top config.start_n_top start token possibilities (beam-search).
**start_top_index**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
``torch.LongTensor`` of shape ``(batch_size, config.start_n_top)``
Indices for the top config.start_n_top start token possibilities (beam-search).
**end_top_log_probs**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
``torch.FloatTensor`` of shape ``(batch_size, config.start_n_top * config.end_n_top)``
Log probabilities for the top ``config.start_n_top * config.end_n_top`` end token possibilities (beam-search).
**end_top_index**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
``torch.LongTensor`` of shape ``(batch_size, config.start_n_top * config.end_n_top)``
Indices for the top ``config.start_n_top * config.end_n_top`` end token possibilities (beam-search).
**cls_logits**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
``torch.FloatTensor`` of shape ``(batch_size,)``
Log probabilities for the ``is_impossible`` label of the answers.
**mems**: (`optional`, returned when ``config.mem_len > 0``)
list of ``torch.FloatTensor`` (one for each layer):
that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
if config.mem_len > 0 else tuple of None. Can be used to speed up sequential decoding and attend to longer context.
See details in the docstring of the `mems` input above.
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
tokenizer = XLNetTokenizer.from_pretrained('xlnet-large-cased')
model = XLMForQuestionAnswering.from_pretrained('xlnet-large-cased')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
start_positions = torch.tensor([1])
end_positions = torch.tensor([3])
outputs = model(input_ids, start_positions=start_positions, end_positions=end_positions)
loss, start_scores, end_scores = outputs[:2]
"""
def __init__(self, config):
super(XLNetForQuestionAnswering, self).__init__(config)
self.start_n_top = config.start_n_top
self.end_n_top = config.end_n_top
self.transformer = XLNetModel(config)
self.start_logits = PoolerStartLogits(config)
self.end_logits = PoolerEndLogits(config)
self.answer_class = PoolerAnswerClass(config)
self.init_weights()
def forward(self, input_ids=None, attention_mask=None, mems=None, perm_mask=None, target_mapping=None,
token_type_ids=None, input_mask=None, head_mask=None, inputs_embeds=None,
start_positions=None, end_positions=None, is_impossible=None, cls_index=None, p_mask=None,):
transformer_outputs = self.transformer(input_ids,
attention_mask=attention_mask,
mems=mems,
perm_mask=perm_mask,
target_mapping=target_mapping,
token_type_ids=token_type_ids,
input_mask=input_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds)
hidden_states = transformer_outputs[0]
start_logits = self.start_logits(hidden_states, p_mask=p_mask)
outputs = transformer_outputs[1:] # Keep mems, hidden states, attentions if there are in it
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, let's remove the dimension added by batch splitting
for x in (start_positions, end_positions, cls_index, is_impossible):
if x is not None and x.dim() > 1:
x.squeeze_(-1)
# during training, compute the end logits based on the ground truth of the start position
end_logits = self.end_logits(hidden_states, start_positions=start_positions, p_mask=p_mask)
loss_fct = CrossEntropyLoss()
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
if cls_index is not None and is_impossible is not None:
# Predict answerability from the representation of CLS and START
cls_logits = self.answer_class(hidden_states, start_positions=start_positions, cls_index=cls_index)
loss_fct_cls = nn.BCEWithLogitsLoss()
cls_loss = loss_fct_cls(cls_logits, is_impossible)
# note(zhiliny): by default multiply the loss by 0.5 so that the scale is comparable to start_loss and end_loss
total_loss += cls_loss * 0.5
outputs = (total_loss,) + outputs
else:
# during inference, compute the end logits based on beam search
bsz, slen, hsz = hidden_states.size()
start_log_probs = F.softmax(start_logits, dim=-1) # shape (bsz, slen)
start_top_log_probs, start_top_index = torch.topk(start_log_probs, self.start_n_top, dim=-1) # shape (bsz, start_n_top)
start_top_index_exp = start_top_index.unsqueeze(-1).expand(-1, -1, hsz) # shape (bsz, start_n_top, hsz)
start_states = torch.gather(hidden_states, -2, start_top_index_exp) # shape (bsz, start_n_top, hsz)
start_states = start_states.unsqueeze(1).expand(-1, slen, -1, -1) # shape (bsz, slen, start_n_top, hsz)
hidden_states_expanded = hidden_states.unsqueeze(2).expand_as(start_states) # shape (bsz, slen, start_n_top, hsz)
p_mask = p_mask.unsqueeze(-1) if p_mask is not None else None
end_logits = self.end_logits(hidden_states_expanded, start_states=start_states, p_mask=p_mask)
end_log_probs = F.softmax(end_logits, dim=1) # shape (bsz, slen, start_n_top)
end_top_log_probs, end_top_index = torch.topk(end_log_probs, self.end_n_top, dim=1) # shape (bsz, end_n_top, start_n_top)
end_top_log_probs = end_top_log_probs.view(-1, self.start_n_top * self.end_n_top)
end_top_index = end_top_index.view(-1, self.start_n_top * self.end_n_top)
start_states = torch.einsum("blh,bl->bh", hidden_states, start_log_probs) # get the representation of START as weighted sum of hidden states
cls_logits = self.answer_class(hidden_states, start_states=start_states, cls_index=cls_index) # Shape (batch size,): one single `cls_logits` for each sample
outputs = (start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits) + outputs
# return start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits
# or (if labels are provided) (total_loss,)
return outputs
| 74,020 | 52.252518 | 169 |
py
|
DeeBERT
|
DeeBERT-master/transformers/modeling_xlm.py
|
# coding=utf-8
# Copyright 2019-present, 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.
""" PyTorch XLM model.
"""
from __future__ import absolute_import, division, print_function, unicode_literals
import logging
import math
import itertools
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from torch.nn import CrossEntropyLoss, MSELoss
from .modeling_utils import PreTrainedModel, prune_linear_layer, SequenceSummary, SQuADHead
from .configuration_xlm import XLMConfig
from .file_utils import add_start_docstrings
logger = logging.getLogger(__name__)
XLM_PRETRAINED_MODEL_ARCHIVE_MAP = {
'xlm-mlm-en-2048': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-en-2048-pytorch_model.bin",
'xlm-mlm-ende-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-ende-1024-pytorch_model.bin",
'xlm-mlm-enfr-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-enfr-1024-pytorch_model.bin",
'xlm-mlm-enro-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-enro-1024-pytorch_model.bin",
'xlm-mlm-tlm-xnli15-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-tlm-xnli15-1024-pytorch_model.bin",
'xlm-mlm-xnli15-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-xnli15-1024-pytorch_model.bin",
'xlm-clm-enfr-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-clm-enfr-1024-pytorch_model.bin",
'xlm-clm-ende-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-clm-ende-1024-pytorch_model.bin",
'xlm-mlm-17-1280': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-17-1280-pytorch_model.bin",
'xlm-mlm-100-1280': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-100-1280-pytorch_model.bin",
}
def create_sinusoidal_embeddings(n_pos, dim, out):
position_enc = np.array([
[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)]
for pos in range(n_pos)
])
out[:, 0::2] = torch.FloatTensor(np.sin(position_enc[:, 0::2]))
out[:, 1::2] = torch.FloatTensor(np.cos(position_enc[:, 1::2]))
out.detach_()
out.requires_grad = False
def gelu(x):
"""
GELU activation
https://arxiv.org/abs/1606.08415
https://github.com/huggingface/pytorch-openai-transformer-lm/blob/master/model_pytorch.py#L14
https://github.com/huggingface/transformers/blob/master/modeling.py
"""
# return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
return 0.5 * x * (1.0 + torch.erf(x / math.sqrt(2.0)))
def get_masks(slen, lengths, causal, padding_mask=None):
"""
Generate hidden states mask, and optionally an attention mask.
"""
alen = torch.arange(slen, dtype=torch.long, device=lengths.device)
if padding_mask is not None:
mask = padding_mask
else:
assert lengths.max().item() <= slen
mask = alen < lengths[:, None]
# attention mask is the same as mask, or triangular inferior attention (causal)
bs = lengths.size(0)
if causal:
attn_mask = alen[None, None, :].repeat(bs, slen, 1) <= alen[None, :, None]
else:
attn_mask = mask
# sanity check
assert mask.size() == (bs, slen)
assert causal is False or attn_mask.size() == (bs, slen, slen)
return mask, attn_mask
class MultiHeadAttention(nn.Module):
NEW_ID = itertools.count()
def __init__(self, n_heads, dim, config):
super(MultiHeadAttention, self).__init__()
self.layer_id = next(MultiHeadAttention.NEW_ID)
self.output_attentions = config.output_attentions
self.dim = dim
self.n_heads = n_heads
self.dropout = config.attention_dropout
assert self.dim % self.n_heads == 0
self.q_lin = nn.Linear(dim, dim)
self.k_lin = nn.Linear(dim, dim)
self.v_lin = nn.Linear(dim, dim)
self.out_lin = nn.Linear(dim, dim)
self.pruned_heads = set()
def prune_heads(self, heads):
attention_head_size = self.dim // self.n_heads
if len(heads) == 0:
return
mask = torch.ones(self.n_heads, attention_head_size)
heads = set(heads) - self.pruned_heads
for head in heads:
head -= sum(1 if h < head else 0 for h in self.pruned_heads)
mask[head] = 0
mask = mask.view(-1).contiguous().eq(1)
index = torch.arange(len(mask))[mask].long()
# Prune linear layers
self.q_lin = prune_linear_layer(self.q_lin, index)
self.k_lin = prune_linear_layer(self.k_lin, index)
self.v_lin = prune_linear_layer(self.v_lin, index)
self.out_lin = prune_linear_layer(self.out_lin, index, dim=1)
# Update hyper params
self.n_heads = self.n_heads - len(heads)
self.dim = attention_head_size * self.n_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(self, input, mask, kv=None, cache=None, head_mask=None):
"""
Self-attention (if kv is None) or attention over source sentence (provided by kv).
"""
# Input is (bs, qlen, dim)
# Mask is (bs, klen) (non-causal) or (bs, klen, klen)
bs, qlen, dim = input.size()
if kv is None:
klen = qlen if cache is None else cache['slen'] + qlen
else:
klen = kv.size(1)
# assert dim == self.dim, 'Dimensions do not match: %s input vs %s configured' % (dim, self.dim)
n_heads = self.n_heads
dim_per_head = self.dim // n_heads
mask_reshape = (bs, 1, qlen, klen) if mask.dim() == 3 else (bs, 1, 1, klen)
def shape(x):
""" projection """
return x.view(bs, -1, self.n_heads, dim_per_head).transpose(1, 2)
def unshape(x):
""" compute context """
return x.transpose(1, 2).contiguous().view(bs, -1, self.n_heads * dim_per_head)
q = shape(self.q_lin(input)) # (bs, n_heads, qlen, dim_per_head)
if kv is None:
k = shape(self.k_lin(input)) # (bs, n_heads, qlen, dim_per_head)
v = shape(self.v_lin(input)) # (bs, n_heads, qlen, dim_per_head)
elif cache is None or self.layer_id not in cache:
k = v = kv
k = shape(self.k_lin(k)) # (bs, n_heads, qlen, dim_per_head)
v = shape(self.v_lin(v)) # (bs, n_heads, qlen, dim_per_head)
if cache is not None:
if self.layer_id in cache:
if kv is None:
k_, v_ = cache[self.layer_id]
k = torch.cat([k_, k], dim=2) # (bs, n_heads, klen, dim_per_head)
v = torch.cat([v_, v], dim=2) # (bs, n_heads, klen, dim_per_head)
else:
k, v = cache[self.layer_id]
cache[self.layer_id] = (k, v)
q = q / math.sqrt(dim_per_head) # (bs, n_heads, qlen, dim_per_head)
scores = torch.matmul(q, k.transpose(2, 3)) # (bs, n_heads, qlen, klen)
mask = (mask == 0).view(mask_reshape).expand_as(scores) # (bs, n_heads, qlen, klen)
scores.masked_fill_(mask, -float('inf')) # (bs, n_heads, qlen, klen)
weights = F.softmax(scores.float(), dim=-1).type_as(scores) # (bs, n_heads, qlen, klen)
weights = F.dropout(weights, p=self.dropout, training=self.training) # (bs, n_heads, qlen, klen)
# Mask heads if we want to
if head_mask is not None:
weights = weights * head_mask
context = torch.matmul(weights, v) # (bs, n_heads, qlen, dim_per_head)
context = unshape(context) # (bs, qlen, dim)
outputs = (self.out_lin(context),)
if self.output_attentions:
outputs = outputs + (weights,)
return outputs
class TransformerFFN(nn.Module):
def __init__(self, in_dim, dim_hidden, out_dim, config):
super(TransformerFFN, self).__init__()
self.dropout = config.dropout
self.lin1 = nn.Linear(in_dim, dim_hidden)
self.lin2 = nn.Linear(dim_hidden, out_dim)
self.act = gelu if config.gelu_activation else F.relu
def forward(self, input):
x = self.lin1(input)
x = self.act(x)
x = self.lin2(x)
x = F.dropout(x, p=self.dropout, training=self.training)
return x
class XLMPreTrainedModel(PreTrainedModel):
""" An abstract class to handle weights initialization and
a simple interface for dowloading and loading pretrained models.
"""
config_class = XLMConfig
pretrained_model_archive_map = XLM_PRETRAINED_MODEL_ARCHIVE_MAP
load_tf_weights = None
base_model_prefix = "transformer"
def __init__(self, *inputs, **kwargs):
super(XLMPreTrainedModel, self).__init__(*inputs, **kwargs)
def _init_weights(self, module):
""" Initialize the weights. """
if isinstance(module, nn.Embedding):
if self.config is not None and self.config.embed_init_std is not None:
nn.init.normal_(module.weight, mean=0, std=self.config.embed_init_std)
if isinstance(module, nn.Linear):
if self.config is not None and self.config.init_std is not None:
nn.init.normal_(module.weight, mean=0, std=self.config.init_std)
if hasattr(module, 'bias') and module.bias is not None:
nn.init.constant_(module.bias, 0.)
if isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
XLM_START_DOCSTRING = r""" The XLM model was proposed in
`Cross-lingual Language Model Pretraining`_
by Guillaume Lample*, Alexis Conneau*. It's a transformer pre-trained using one of the following objectives:
- a causal language modeling (CLM) objective (next token prediction),
- a masked language modeling (MLM) objective (Bert-like), or
- a Translation Language Modeling (TLM) object (extension of Bert's MLM to multiple language inputs)
Original code can be found `here`_.
This model is a PyTorch `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.
.. _`Cross-lingual Language Model Pretraining`:
https://arxiv.org/abs/1901.07291
.. _`torch.nn.Module`:
https://pytorch.org/docs/stable/nn.html#module
.. _`here`:
https://github.com/facebookresearch/XLM
Parameters:
config (:class:`~transformers.XLMConfig`): 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 :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""
XLM_INPUTS_DOCSTRING = r"""
Inputs:
**input_ids**: ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Indices of input sequence tokens in the vocabulary.
XLM is a model with absolute position embeddings so it's usually advised to pad the inputs on
the right rather than the left.
Indices can be obtained using :class:`transformers.XLMTokenizer`.
See :func:`transformers.PreTrainedTokenizer.encode` and
:func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
**attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:
Mask to avoid performing attention on padding token indices.
Mask values selected in ``[0, 1]``:
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
**langs**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
A parallel sequence of tokens to be used to indicate the language of each token in the input.
Indices are languages ids which can be obtained from the language names by using two conversion mappings
provided in the configuration of the model (only provided for multilingual models).
More precisely, the `language name -> language id` mapping is in `model.config.lang2id` (dict str -> int) and
the `language id -> language name` mapping is `model.config.id2lang` (dict int -> str).
**token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
A parallel sequence of tokens (can be used to indicate various portions of the inputs).
The embeddings from these tokens will be summed with the respective token embeddings.
Indices are selected in the vocabulary (unlike BERT which has a specific vocabulary for segment indices).
**position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Indices of positions of each input sequence tokens in the position embeddings.
Selected in the range ``[0, config.max_position_embeddings - 1]``.
**lengths**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
Length of each sentence that can be used to avoid performing attention on padding token indices.
You can also use `attention_mask` for the same result (see above), kept here for compatbility.
Indices selected in ``[0, ..., input_ids.size(-1)]``:
**cache**:
dictionary with ``torch.FloatTensor`` that contains pre-computed
hidden-states (key and values in the attention blocks) as computed by the model
(see `cache` output below). Can be used to speed up sequential decoding.
The dictionary object will be modified in-place during the forward pass to add newly computed hidden-states.
**head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
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**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, embedding_dim)``:
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.
"""
@add_start_docstrings("The bare XLM Model transformer outputting raw hidden-states without any specific head on top.",
XLM_START_DOCSTRING, XLM_INPUTS_DOCSTRING)
class XLMModel(XLMPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**last_hidden_state**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)``
Sequence of hidden-states at the last layer of the model.
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
tokenizer = XLMTokenizer.from_pretrained('xlm-mlm-en-2048')
model = XLMModel.from_pretrained('xlm-mlm-en-2048')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
outputs = model(input_ids)
last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
"""
def __init__(self, config): #, dico, is_encoder, with_output):
super(XLMModel, self).__init__(config)
self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states
# encoder / decoder, output layer
self.is_encoder = config.is_encoder
self.is_decoder = not config.is_encoder
if self.is_decoder:
raise NotImplementedError("Currently XLM can only be used as an encoder")
# self.with_output = with_output
self.causal = config.causal
# dictionary / languages
self.n_langs = config.n_langs
self.use_lang_emb = config.use_lang_emb
self.n_words = config.n_words
self.eos_index = config.eos_index
self.pad_index = config.pad_index
# self.dico = dico
# self.id2lang = config.id2lang
# self.lang2id = config.lang2id
# assert len(self.dico) == self.n_words
# assert len(self.id2lang) == len(self.lang2id) == self.n_langs
# model parameters
self.dim = config.emb_dim # 512 by default
self.hidden_dim = self.dim * 4 # 2048 by default
self.n_heads = config.n_heads # 8 by default
self.n_layers = config.n_layers
self.dropout = config.dropout
self.attention_dropout = config.attention_dropout
assert self.dim % self.n_heads == 0, 'transformer dim must be a multiple of n_heads'
# embeddings
self.position_embeddings = nn.Embedding(config.max_position_embeddings, self.dim)
if config.sinusoidal_embeddings:
create_sinusoidal_embeddings(config.max_position_embeddings, self.dim, out=self.position_embeddings.weight)
if config.n_langs > 1 and config.use_lang_emb:
self.lang_embeddings = nn.Embedding(self.n_langs, self.dim)
self.embeddings = nn.Embedding(self.n_words, self.dim, padding_idx=self.pad_index)
self.layer_norm_emb = nn.LayerNorm(self.dim, eps=config.layer_norm_eps)
# transformer layers
self.attentions = nn.ModuleList()
self.layer_norm1 = nn.ModuleList()
self.ffns = nn.ModuleList()
self.layer_norm2 = nn.ModuleList()
# if self.is_decoder:
# self.layer_norm15 = nn.ModuleList()
# self.encoder_attn = nn.ModuleList()
for _ in range(self.n_layers):
self.attentions.append(MultiHeadAttention(self.n_heads, self.dim, config=config))
self.layer_norm1.append(nn.LayerNorm(self.dim, eps=config.layer_norm_eps))
# if self.is_decoder:
# self.layer_norm15.append(nn.LayerNorm(self.dim, eps=config.layer_norm_eps))
# self.encoder_attn.append(MultiHeadAttention(self.n_heads, self.dim, dropout=self.attention_dropout))
self.ffns.append(TransformerFFN(self.dim, self.hidden_dim, self.dim, config=config))
self.layer_norm2.append(nn.LayerNorm(self.dim, eps=config.layer_norm_eps))
if hasattr(config, "pruned_heads"):
pruned_heads = config.pruned_heads.copy().items()
config.pruned_heads = {}
for layer, heads in pruned_heads:
if self.attentions[int(layer)].n_heads == config.n_heads:
self.prune_heads({int(layer): list(map(int, heads))})
self.init_weights()
def get_input_embeddings(self):
return self.embeddings
def set_input_embeddings(self, new_embeddings):
self.embeddings = 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}
See base class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.attentions[layer].prune_heads(heads)
def forward(self, input_ids=None, attention_mask=None, langs=None, token_type_ids=None, position_ids=None,
lengths=None, cache=None, head_mask=None, inputs_embeds=None): # removed: src_enc=None, src_len=None
if input_ids is not None:
bs, slen = input_ids.size()
else:
bs, slen = inputs_embeds.size()[:-1]
if lengths is None:
if input_ids is not None:
lengths = (input_ids != self.pad_index).sum(dim=1).long()
else:
lengths = torch.LongTensor([slen]*bs)
# mask = input_ids != self.pad_index
# check inputs
assert lengths.size(0) == bs
assert lengths.max().item() <= slen
# input_ids = input_ids.transpose(0, 1) # batch size as dimension 0
# assert (src_enc is None) == (src_len is None)
# if src_enc is not None:
# assert self.is_decoder
# assert src_enc.size(0) == bs
# generate masks
mask, attn_mask = get_masks(slen, lengths, self.causal, padding_mask=attention_mask)
# if self.is_decoder and src_enc is not None:
# src_mask = torch.arange(src_len.max(), dtype=torch.long, device=lengths.device) < src_len[:, None]
device = input_ids.device if input_ids is not None else inputs_embeds.device
# position_ids
if position_ids is None:
position_ids = torch.arange(slen, dtype=torch.long, device=device)
position_ids = position_ids.unsqueeze(0).expand((bs, slen))
else:
assert position_ids.size() == (bs, slen) # (slen, bs)
# position_ids = position_ids.transpose(0, 1)
# langs
if langs is not None:
assert langs.size() == (bs, slen) # (slen, bs)
# langs = langs.transpose(0, 1)
# 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 qlen x klen]
if head_mask is not None:
if head_mask.dim() == 1:
head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
head_mask = head_mask.expand(self.n_layers, -1, -1, -1, -1)
elif head_mask.dim() == 2:
head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1) # We can specify head_mask for each layer
head_mask = head_mask.to(dtype=next(self.parameters()).dtype) # switch to fload if need + fp16 compatibility
else:
head_mask = [None] * self.n_layers
# do not recompute cached elements
if cache is not None and input_ids is not None:
_slen = slen - cache['slen']
input_ids = input_ids[:, -_slen:]
position_ids = position_ids[:, -_slen:]
if langs is not None:
langs = langs[:, -_slen:]
mask = mask[:, -_slen:]
attn_mask = attn_mask[:, -_slen:]
# embeddings
if inputs_embeds is None:
inputs_embeds = self.embeddings(input_ids)
tensor = inputs_embeds + self.position_embeddings(position_ids).expand_as(inputs_embeds)
if langs is not None and self.use_lang_emb:
tensor = tensor + self.lang_embeddings(langs)
if token_type_ids is not None:
tensor = tensor + self.embeddings(token_type_ids)
tensor = self.layer_norm_emb(tensor)
tensor = F.dropout(tensor, p=self.dropout, training=self.training)
tensor *= mask.unsqueeze(-1).to(tensor.dtype)
# transformer layers
hidden_states = ()
attentions = ()
for i in range(self.n_layers):
if self.output_hidden_states:
hidden_states = hidden_states + (tensor,)
# self attention
attn_outputs = self.attentions[i](tensor, attn_mask, cache=cache, head_mask=head_mask[i])
attn = attn_outputs[0]
if self.output_attentions:
attentions = attentions + (attn_outputs[1],)
attn = F.dropout(attn, p=self.dropout, training=self.training)
tensor = tensor + attn
tensor = self.layer_norm1[i](tensor)
# encoder attention (for decoder only)
# if self.is_decoder and src_enc is not None:
# attn = self.encoder_attn[i](tensor, src_mask, kv=src_enc, cache=cache)
# attn = F.dropout(attn, p=self.dropout, training=self.training)
# tensor = tensor + attn
# tensor = self.layer_norm15[i](tensor)
# FFN
tensor = tensor + self.ffns[i](tensor)
tensor = self.layer_norm2[i](tensor)
tensor *= mask.unsqueeze(-1).to(tensor.dtype)
# Add last hidden state
if self.output_hidden_states:
hidden_states = hidden_states + (tensor,)
# update cache length
if cache is not None:
cache['slen'] += tensor.size(1)
# move back sequence length to dimension 0
# tensor = tensor.transpose(0, 1)
outputs = (tensor,)
if self.output_hidden_states:
outputs = outputs + (hidden_states,)
if self.output_attentions:
outputs = outputs + (attentions,)
return outputs # outputs, (hidden_states), (attentions)
class XLMPredLayer(nn.Module):
"""
Prediction layer (cross_entropy or adaptive_softmax).
"""
def __init__(self, config):
super(XLMPredLayer, self).__init__()
self.asm = config.asm
self.n_words = config.n_words
self.pad_index = config.pad_index
dim = config.emb_dim
if config.asm is False:
self.proj = nn.Linear(dim, config.n_words, bias=True)
else:
self.proj = nn.AdaptiveLogSoftmaxWithLoss(
in_features=dim,
n_classes=config.n_words,
cutoffs=config.asm_cutoffs,
div_value=config.asm_div_value,
head_bias=True, # default is False
)
def forward(self, x, y=None):
""" Compute the loss, and optionally the scores.
"""
outputs = ()
if self.asm is False:
scores = self.proj(x)
outputs = (scores,) + outputs
if y is not None:
loss = F.cross_entropy(scores.view(-1, self.n_words), y.view(-1), reduction='elementwise_mean')
outputs = (loss,) + outputs
else:
scores = self.proj.log_prob(x)
outputs = (scores,) + outputs
if y is not None:
_, loss = self.proj(x, y)
outputs = (loss,) + outputs
return outputs
@add_start_docstrings("""The XLM Model transformer with a language modeling head on top
(linear layer with weights tied to the input embeddings). """,
XLM_START_DOCSTRING, XLM_INPUTS_DOCSTRING)
class XLMWithLMHeadModel(XLMPreTrainedModel):
r"""
**labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Labels for language modeling.
Note that the labels **are shifted** inside the model, i.e. you can set ``lm_labels = input_ids``
Indices are selected in ``[-1, 0, ..., config.vocab_size]``
All labels set to ``-1`` are ignored (masked), the loss is only
computed for labels in ``[0, ..., config.vocab_size]``
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Language modeling loss.
**prediction_scores**: ``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).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
tokenizer = XLMTokenizer.from_pretrained('xlm-mlm-en-2048')
model = XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
outputs = model(input_ids)
last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
"""
def __init__(self, config):
super(XLMWithLMHeadModel, self).__init__(config)
self.transformer = XLMModel(config)
self.pred_layer = XLMPredLayer(config)
self.init_weights()
def get_output_embeddings(self):
return self.pred_layer.proj
def forward(self, input_ids=None, attention_mask=None, langs=None, token_type_ids=None, position_ids=None,
lengths=None, cache=None, head_mask=None, inputs_embeds=None, labels=None):
transformer_outputs = self.transformer(input_ids,
attention_mask=attention_mask,
langs=langs,
token_type_ids=token_type_ids,
position_ids=position_ids,
lengths=lengths,
cache=cache,
head_mask=head_mask,
inputs_embeds=inputs_embeds)
output = transformer_outputs[0]
outputs = self.pred_layer(output, labels)
outputs = outputs + transformer_outputs[1:] # Keep new_mems and attention/hidden states if they are here
return outputs
@add_start_docstrings("""XLM Model with a sequence classification/regression head on top (a linear layer on top of
the pooled output) e.g. for GLUE tasks. """,
XLM_START_DOCSTRING, XLM_INPUTS_DOCSTRING)
class XLMForSequenceClassification(XLMPreTrainedModel):
r"""
**labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
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: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Classification (or regression if config.num_labels==1) loss.
**logits**: ``torch.FloatTensor`` of shape ``(batch_size, config.num_labels)``
Classification (or regression if config.num_labels==1) scores (before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
tokenizer = XLMTokenizer.from_pretrained('xlm-mlm-en-2048')
model = XLMForSequenceClassification.from_pretrained('xlm-mlm-en-2048')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
labels = torch.tensor([1]).unsqueeze(0) # Batch size 1
outputs = model(input_ids, labels=labels)
loss, logits = outputs[:2]
"""
def __init__(self, config):
super(XLMForSequenceClassification, self).__init__(config)
self.num_labels = config.num_labels
self.transformer = XLMModel(config)
self.sequence_summary = SequenceSummary(config)
self.init_weights()
def forward(self, input_ids=None, attention_mask=None, langs=None, token_type_ids=None, position_ids=None,
lengths=None, cache=None, head_mask=None, inputs_embeds=None, labels=None):
transformer_outputs = self.transformer(input_ids,
attention_mask=attention_mask,
langs=langs,
token_type_ids=token_type_ids,
position_ids=position_ids,
lengths=lengths,
cache=cache,
head_mask=head_mask,
inputs_embeds=inputs_embeds)
output = transformer_outputs[0]
logits = self.sequence_summary(output)
outputs = (logits,) + transformer_outputs[1:] # Keep new_mems and attention/hidden states if they are here
if labels is not None:
if self.num_labels == 1:
# We are doing regression
loss_fct = MSELoss()
loss = loss_fct(logits.view(-1), labels.view(-1))
else:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
outputs = (loss,) + outputs
return outputs
@add_start_docstrings("""XLM 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`). """,
XLM_START_DOCSTRING, XLM_INPUTS_DOCSTRING)
class XLMForQuestionAnsweringSimple(XLMPreTrainedModel):
r"""
**start_positions**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
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**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
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.
**is_impossible**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
Labels whether a question has an answer or no answer (SQuAD 2.0)
**cls_index**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
Labels for position (index) of the classification token to use as input for computing plausibility of the answer.
**p_mask**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Optional mask of tokens which can't be in answers (e.g. [CLS], [PAD], ...)
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
**start_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length,)``
Span-start scores (before SoftMax).
**end_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length,)``
Span-end scores (before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
tokenizer = XLMTokenizer.from_pretrained('xlm-mlm-en-2048')
model = XLMForQuestionAnsweringSimple.from_pretrained('xlm-mlm-en-2048')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
start_positions = torch.tensor([1])
end_positions = torch.tensor([3])
outputs = model(input_ids, start_positions=start_positions, end_positions=end_positions)
loss, start_scores, end_scores = outputs[:2]
"""
def __init__(self, config):
super(XLMForQuestionAnsweringSimple, self).__init__(config)
self.transformer = XLMModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
self.init_weights()
def forward(self, input_ids=None, attention_mask=None, langs=None, token_type_ids=None, position_ids=None,
lengths=None, cache=None, head_mask=None, inputs_embeds=None, start_positions=None, end_positions=None):
transformer_outputs = self.transformer(input_ids,
attention_mask=attention_mask,
langs=langs,
token_type_ids=token_type_ids,
position_ids=position_ids,
lengths=lengths,
cache=cache,
head_mask=head_mask,
inputs_embeds=inputs_embeds)
sequence_output = transformer_outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1)
end_logits = end_logits.squeeze(-1)
outputs = (start_logits, end_logits,)
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.clamp_(0, ignored_index)
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
outputs = (total_loss,) + outputs
outputs = outputs + transformer_outputs[1:] # Keep new_mems and attention/hidden states if they are here
return outputs
@add_start_docstrings("""XLM Model with a beam-search 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`). """,
XLM_START_DOCSTRING, XLM_INPUTS_DOCSTRING)
class XLMForQuestionAnswering(XLMPreTrainedModel):
r"""
**start_positions**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
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**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
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.
**is_impossible**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
Labels whether a question has an answer or no answer (SQuAD 2.0)
**cls_index**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
Labels for position (index) of the classification token to use as input for computing plausibility of the answer.
**p_mask**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Optional mask of tokens which can't be in answers (e.g. [CLS], [PAD], ...)
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
**start_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length,)``
Span-start scores (before SoftMax).
**end_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length,)``
Span-end scores (before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
tokenizer = XLMTokenizer.from_pretrained('xlm-mlm-en-2048')
model = XLMForQuestionAnswering.from_pretrained('xlm-mlm-en-2048')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
start_positions = torch.tensor([1])
end_positions = torch.tensor([3])
outputs = model(input_ids, start_positions=start_positions, end_positions=end_positions)
loss, start_scores, end_scores = outputs[:2]
"""
def __init__(self, config):
super(XLMForQuestionAnswering, self).__init__(config)
self.transformer = XLMModel(config)
self.qa_outputs = SQuADHead(config)
self.init_weights()
def forward(self, input_ids=None, attention_mask=None, langs=None, token_type_ids=None, position_ids=None,
lengths=None, cache=None, head_mask=None, inputs_embeds=None, start_positions=None, end_positions=None,
is_impossible=None, cls_index=None, p_mask=None):
transformer_outputs = self.transformer(input_ids,
attention_mask=attention_mask,
langs=langs,
token_type_ids=token_type_ids,
position_ids=position_ids,
lengths=lengths,
cache=cache,
head_mask=head_mask,
inputs_embeds=inputs_embeds)
output = transformer_outputs[0]
outputs = self.qa_outputs(output, start_positions=start_positions, end_positions=end_positions,
cls_index=cls_index, is_impossible=is_impossible, p_mask=p_mask)
outputs = outputs + transformer_outputs[1:] # Keep new_mems and attention/hidden states if they are here
return outputs
| 46,645 | 50.542541 | 163 |
py
|
DeeBERT
|
DeeBERT-master/transformers/modeling_tf_utils.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team 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.
"""TF general model utils."""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import logging
import os
import tensorflow as tf
from .configuration_utils import PretrainedConfig
from .file_utils import cached_path, WEIGHTS_NAME, TF_WEIGHTS_NAME, TF2_WEIGHTS_NAME
from .modeling_tf_pytorch_utils import load_pytorch_checkpoint_in_tf2_model
logger = logging.getLogger(__name__)
DUMMY_INPUTS = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
class TFPreTrainedModel(tf.keras.Model):
r""" Base class for all TF models.
:class:`~transformers.TFPreTrainedModel` takes care of storing the configuration of the models and handles methods for loading/downloading/saving models
as well as a few methods common to all models to (i) resize the input embeddings and (ii) prune heads in the self-attention heads.
Class attributes (overridden by derived classes):
- ``config_class``: a class derived from :class:`~transformers.PretrainedConfig` to use as configuration class for this model architecture.
- ``pretrained_model_archive_map``: a python ``dict`` of with `short-cut-names` (string) as keys and `url` (string) of associated pretrained weights as values.
- ``load_tf_weights``: a python ``method`` for loading a TensorFlow checkpoint in a PyTorch model, taking as arguments:
- ``model``: an instance of the relevant subclass of :class:`~transformers.PreTrainedModel`,
- ``config``: an instance of the relevant subclass of :class:`~transformers.PretrainedConfig`,
- ``path``: a path (string) to the TensorFlow checkpoint.
- ``base_model_prefix``: a string indicating the attribute associated to the base model in derived classes of the same architecture adding modules on top of the base model.
"""
config_class = None
pretrained_model_archive_map = {}
base_model_prefix = ""
dummy_inputs = tf.constant(DUMMY_INPUTS) # dummy inputs to build the network
def __init__(self, config, *inputs, **kwargs):
super(TFPreTrainedModel, self).__init__(*inputs, **kwargs)
if not isinstance(config, PretrainedConfig):
raise ValueError(
"Parameter config in `{}(config)` should be an instance of class `PretrainedConfig`. "
"To create a model from a pretrained model use "
"`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format(
self.__class__.__name__, self.__class__.__name__
))
# Save config in model
self.config = config
def get_input_embeddings(self):
""" Get model's input embeddings
"""
base_model = getattr(self, self.base_model_prefix, self)
if base_model is not self:
return base_model.get_input_embeddings()
else:
raise NotImplementedError
def get_output_embeddings(self):
""" Get model's output embeddings
Return None if the model doesn't have output embeddings
"""
return None # Overwrite for models with output embeddings
def _get_resized_embeddings(self, old_embeddings, new_num_tokens=None):
""" Build a resized Embedding Variable from a provided token Embedding Module.
Increasing the size will add newly initialized vectors at the end
Reducing the size will remove vectors from the end
Args:
new_num_tokens: (`optional`) int
New number of tokens in the embedding matrix.
Increasing the size will add newly initialized vectors at the end
Reducing the size will remove vectors from the end
If not provided or None: return the provided token Embedding Module.
Return: ``tf.Variable``
Pointer to the resized Embedding Module or the old Embedding Module if new_num_tokens is None
"""
# if new_num_tokens is None:
# return old_embeddings
# old_num_tokens, old_embedding_dim = old_embeddings.weight.size()
# if old_num_tokens == new_num_tokens:
# return old_embeddings
# # Build new embeddings
# new_embeddings = nn.Embedding(new_num_tokens, old_embedding_dim)
# new_embeddings.to(old_embeddings.weight.device)
# # initialize all new embeddings (in particular added tokens)
# self._init_weights(new_embeddings)
# # Copy word embeddings from the previous weights
# num_tokens_to_copy = min(old_num_tokens, new_num_tokens)
# new_embeddings.weight.data[:num_tokens_to_copy, :] = old_embeddings.weight.data[:num_tokens_to_copy, :]
# return new_embeddings
def resize_token_embeddings(self, new_num_tokens=None):
""" Resize input token embeddings matrix of the model if new_num_tokens != config.vocab_size.
Take care of tying weights embeddings afterwards if the model class has a `tie_weights()` method.
Arguments:
new_num_tokens: (`optional`) int:
New number of tokens in the embedding matrix. Increasing the size will add newly initialized vectors at the end. Reducing the size will remove vectors from the end.
If not provided or None: does nothing and just returns a pointer to the input tokens ``tf.Variable`` Module of the model.
Return: ``tf.Variable``
Pointer to the input tokens Embeddings Module of the model
"""
raise NotImplementedError
def prune_heads(self, heads_to_prune):
""" Prunes heads of the base model.
Arguments:
heads_to_prune: dict with keys being selected layer indices (`int`) and associated values being the list of heads to prune in said layer (list of `int`).
"""
raise NotImplementedError
def save_pretrained(self, save_directory):
""" Save a model and its configuration file to a directory, so that it
can be re-loaded using the `:func:`~transformers.PreTrainedModel.from_pretrained`` class method.
"""
assert os.path.isdir(save_directory), "Saving path should be a directory where the model and configuration can be saved"
# Save configuration file
self.config.save_pretrained(save_directory)
# If we save using the predefined names, we can load using `from_pretrained`
output_model_file = os.path.join(save_directory, TF2_WEIGHTS_NAME)
self.save_weights(output_model_file)
logger.info("Model weights saved in {}".format(output_model_file))
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
r"""Instantiate a pretrained TF 2.0 model from a pre-trained model configuration.
The model is set in evaluation mode by default using ``model.eval()`` (Dropout modules are deactivated)
To train the model, you should first set it back in training mode with ``model.train()``
The warning ``Weights from XXX not initialized from pretrained model`` means that the weights of XXX do not come pre-trained with the rest of the model.
It is up to you to train those weights with a downstream fine-tuning task.
The warning ``Weights from XXX not used in YYY`` means that the layer XXX is not used by YYY, therefore those weights are discarded.
Parameters:
pretrained_model_name_or_path: either:
- a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
- a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
- a path or url to a `PyTorch state_dict save file` (e.g. `./pt_model/pytorch_model.bin`). In this case, ``from_pt`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the PyTorch checkpoint in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.
model_args: (`optional`) Sequence of positional arguments:
All remaning positional arguments will be passed to the underlying model's ``__init__`` method
config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
- the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
- the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
- the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
from_pt: (`optional`) boolean, default False:
Load the model weights from a PyTorch state_dict save file (see docstring of pretrained_model_name_or_path argument).
cache_dir: (`optional`) string:
Path to a directory in which a downloaded pre-trained model
configuration should be cached if the standard cache should not be used.
force_download: (`optional`) boolean, default False:
Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
proxies: (`optional`) dict, default None:
A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
The proxies are used on each request.
kwargs: (`optional`) Remaining dictionary of keyword arguments:
Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
- If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
- If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
Examples::
model = BertModel.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache.
model = BertModel.from_pretrained('./test/saved_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
model = BertModel.from_pretrained('bert-base-uncased', output_attention=True) # Update configuration during loading
assert model.config.output_attention == True
# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = BertConfig.from_json_file('./tf_model/my_tf_model_config.json')
model = BertModel.from_pretrained('./tf_model/my_tf_checkpoint.ckpt.index', from_pt=True, config=config)
"""
config = kwargs.pop('config', None)
cache_dir = kwargs.pop('cache_dir', None)
from_pt = kwargs.pop('from_pt', False)
force_download = kwargs.pop('force_download', False)
proxies = kwargs.pop('proxies', None)
# Load config
if config is None:
config, model_kwargs = cls.config_class.from_pretrained(
pretrained_model_name_or_path, *model_args,
cache_dir=cache_dir, return_unused_kwargs=True,
force_download=force_download,
**kwargs
)
else:
model_kwargs = kwargs
# Load model
if pretrained_model_name_or_path is not None:
if pretrained_model_name_or_path in cls.pretrained_model_archive_map:
archive_file = cls.pretrained_model_archive_map[pretrained_model_name_or_path]
elif os.path.isdir(pretrained_model_name_or_path):
if os.path.isfile(os.path.join(pretrained_model_name_or_path, TF2_WEIGHTS_NAME)):
# Load from a TF 2.0 checkpoint
archive_file = os.path.join(pretrained_model_name_or_path, TF2_WEIGHTS_NAME)
elif from_pt and os.path.isfile(os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)):
# Load from a PyTorch checkpoint
archive_file = os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)
else:
raise EnvironmentError("Error no file named {} found in directory {} or `from_pt` set to False".format(
[WEIGHTS_NAME, TF2_WEIGHTS_NAME],
pretrained_model_name_or_path))
elif os.path.isfile(pretrained_model_name_or_path):
archive_file = pretrained_model_name_or_path
else:
raise EnvironmentError("Error file {} not found".format(pretrained_model_name_or_path))
# redirect to the cache, if necessary
try:
resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir, force_download=force_download, proxies=proxies)
except EnvironmentError as e:
if pretrained_model_name_or_path in cls.pretrained_model_archive_map:
logger.error(
"Couldn't reach server at '{}' to download pretrained weights.".format(
archive_file))
else:
logger.error(
"Model name '{}' was not found in model name list ({}). "
"We assumed '{}' was a path or url but couldn't find any file "
"associated to this path or url.".format(
pretrained_model_name_or_path,
', '.join(cls.pretrained_model_archive_map.keys()),
archive_file))
raise e
if resolved_archive_file == archive_file:
logger.info("loading weights file {}".format(archive_file))
else:
logger.info("loading weights file {} from cache at {}".format(
archive_file, resolved_archive_file))
else:
resolved_archive_file = None
# Instantiate model.
model = cls(config, *model_args, **model_kwargs)
if from_pt:
# Load from a PyTorch checkpoint
return load_pytorch_checkpoint_in_tf2_model(model, resolved_archive_file)
ret = model(model.dummy_inputs, training=False) # build the network with dummy inputs
assert os.path.isfile(resolved_archive_file), "Error retrieving file {}".format(resolved_archive_file)
# 'by_name' allow us to do transfer learning by skipping/adding layers
# see https://github.com/tensorflow/tensorflow/blob/00fad90125b18b80fe054de1055770cfb8fe4ba3/tensorflow/python/keras/engine/network.py#L1339-L1357
model.load_weights(resolved_archive_file, by_name=True)
ret = model(model.dummy_inputs, training=False) # Make sure restore ops are run
return model
class TFConv1D(tf.keras.layers.Layer):
def __init__(self, nf, nx, initializer_range=0.02, **kwargs):
""" TFConv1D layer as defined by Radford et al. for OpenAI GPT (and also used in GPT-2)
Basically works like a Linear layer but the weights are transposed
"""
super(TFConv1D, self).__init__(**kwargs)
self.nf = nf
self.nx = nx
self.initializer_range = initializer_range
def build(self, input_shape):
self.weight = self.add_weight(
"weight",
shape=[self.nx, self.nf],
initializer=get_initializer(self.initializer_range))
self.bias = self.add_weight(
"bias",
shape=[1, self.nf],
initializer=tf.zeros_initializer())
def call(self, x):
bz, sl = shape_list(x)[:2]
x = tf.reshape(x, [-1, self.nx])
x = tf.matmul(x, self.weight) + self.bias
x = tf.reshape(x, [bz, sl, self.nf])
return x
class TFSharedEmbeddings(tf.keras.layers.Layer):
"""Construct shared token embeddings.
"""
def __init__(self, vocab_size, hidden_size, initializer_range=None, **kwargs):
super(TFSharedEmbeddings, self).__init__(**kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.initializer_range = hidden_size**-0.5 if initializer_range is None else initializer_range
def build(self, input_shape):
"""Build shared word embedding layer
Shared weights logic adapted from
https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
"""
self.weight = self.add_weight(
"weight",
shape=[self.vocab_size, self.hidden_size],
initializer=get_initializer(self.initializer_range))
super(TFSharedEmbeddings, self).build(input_shape)
def call(self, inputs, mode="embedding"):
"""Get token embeddings of inputs.
Args:
inputs: list of three int64 tensors with shape [batch_size, length]: (input_ids, position_ids, token_type_ids)
mode: string, a valid value is one of "embedding" and "linear".
Returns:
outputs: (1) If mode == "embedding", output embedding tensor, float32 with
shape [batch_size, length, embedding_size]; (2) mode == "linear", output
linear tensor, float32 with shape [batch_size, length, vocab_size].
Raises:
ValueError: if mode is not valid.
Shared weights logic adapted from
https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
"""
if mode == "embedding":
return self._embedding(inputs)
elif mode == "linear":
return self._linear(inputs)
else:
raise ValueError("mode {} is not valid.".format(mode))
def _embedding(self, input_ids):
"""Applies embedding based on inputs tensor."""
return tf.gather(self.weight, input_ids)
def _linear(self, inputs):
"""Computes logits by running inputs through a linear layer.
Args:
inputs: A float32 tensor with shape [..., hidden_size]
Returns:
float32 tensor with shape [..., vocab_size].
"""
first_dims = shape_list(inputs)[:-1]
x = tf.reshape(inputs, [-1, self.hidden_size])
logits = tf.matmul(x, self.weight, transpose_b=True)
return tf.reshape(logits, first_dims + [self.vocab_size])
class TFSequenceSummary(tf.keras.layers.Layer):
r""" Compute a single vector summary of a sequence hidden states according to various possibilities:
Args of the config class:
summary_type:
- 'last' => [default] 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 (GPT/GPT-2)
- 'attn' => Not implemented now, use multi-head attention
summary_use_proj: Add a projection after the vector extraction
summary_proj_to_labels: If True, the projection outputs to config.num_labels classes (otherwise to hidden_size). Default: False.
summary_activation: 'tanh' => add a tanh activation to the output, Other => no activation. Default
summary_first_dropout: Add a dropout before the projection and activation
summary_last_dropout: Add a dropout after the projection and activation
"""
def __init__(self, config, initializer_range=0.02, **kwargs):
super(TFSequenceSummary, self).__init__(**kwargs)
self.summary_type = config.summary_type if hasattr(config, 'summary_use_proj') else 'last'
if self.summary_type == 'attn':
# We should use a standard multi-head attention module with absolute positional embedding for that.
# Cf. https://github.com/zihangdai/xlnet/blob/master/modeling.py#L253-L276
# We can probably just use the multi-head attention module of PyTorch >=1.1.0
raise NotImplementedError
self.has_summary = hasattr(config, 'summary_use_proj') and config.summary_use_proj
if self.has_summary:
if hasattr(config, 'summary_proj_to_labels') and config.summary_proj_to_labels and config.num_labels > 0:
num_classes = config.num_labels
else:
num_classes = config.hidden_size
self.summary = tf.keras.layers.Dense(num_classes,
kernel_initializer=get_initializer(initializer_range),
name='summary')
self.has_activation = hasattr(config, 'summary_activation') and config.summary_activation == 'tanh'
if self.has_activation:
self.activation = tf.keras.activations.tanh
self.has_first_dropout = hasattr(config, 'summary_first_dropout') and config.summary_first_dropout > 0
if self.has_first_dropout:
self.first_dropout = tf.keras.layers.Dropout(config.summary_first_dropout)
self.has_last_dropout = hasattr(config, 'summary_last_dropout') and config.summary_last_dropout > 0
if self.has_last_dropout:
self.last_dropout = tf.keras.layers.Dropout(config.summary_last_dropout)
def call(self, inputs, training=False):
""" hidden_states: float Tensor in shape [bsz, seq_len, hidden_size], the hidden-states of the last layer.
cls_index: [optional] position of the classification token if summary_type == 'cls_index',
shape (bsz,) or more generally (bsz, ...) where ... are optional leading dimensions of hidden_states.
if summary_type == 'cls_index' and cls_index is None:
we take the last token of the sequence as classification token
"""
if not isinstance(inputs, (dict, tuple, list)):
hidden_states = inputs
cls_index = None
elif isinstance(inputs, (tuple, list)):
hidden_states = inputs[0]
cls_index = inputs[1] if len(inputs) > 1 else None
assert len(inputs) <= 2, "Too many inputs."
else:
input_ids = inputs.get('input_ids')
cls_index = inputs.get('cls_index', None)
if self.summary_type == 'last':
output = hidden_states[:, -1]
elif self.summary_type == 'first':
output = hidden_states[:, 0]
elif self.summary_type == 'mean':
output = tf.mean(hidden_states, axis=1)
elif self.summary_type == 'cls_index':
hidden_shape = shape_list(hidden_states) # e.g. [batch, num choices, seq length, hidden dims]
if cls_index is None:
cls_index = tf.fill(hidden_shape[:-2], hidden_shape[-2] - 1) # A tensor full of shape [batch] or [batch, num choices] full of sequence length
cls_shape = shape_list(cls_index)
if len(cls_shape) <= len(hidden_shape) - 2:
cls_index = cls_index[..., tf.newaxis]
# else:
# cls_index = cls_index[..., tf.newaxis]
# cls_index = cls_index.expand((-1,) * (cls_index.dim()-1) + (hidden_states.size(-1),))
# shape of cls_index: (bsz, XX, 1, hidden_size) where XX are optional leading dim of hidden_states
output = tf.gather(hidden_states, cls_index, batch_dims=len(hidden_shape) - 2)
output = tf.squeeze(output, axis=len(hidden_shape) - 2) # shape of output: (batch, num choices, hidden_size)
elif self.summary_type == 'attn':
raise NotImplementedError
if self.has_first_dropout:
output = self.first_dropout(output, training=training)
if self.has_summary:
output = self.summary(output)
if self.has_activation:
output = self.activation(output)
if self.has_last_dropout:
output = self.last_dropout(output, training=training)
return output
def shape_list(x):
"""Deal with dynamic shape in tensorflow cleanly."""
static = x.shape.as_list()
dynamic = tf.shape(x)
return [dynamic[i] if s is None else s for i, s in enumerate(static)]
def get_initializer(initializer_range=0.02):
"""Creates a `tf.initializers.truncated_normal` with the given range.
Args:
initializer_range: float, initializer range for stddev.
Returns:
TruncatedNormal initializer with stddev = `initializer_range`.
"""
return tf.keras.initializers.TruncatedNormal(stddev=initializer_range)
| 26,511 | 51.812749 | 472 |
py
|
DeeBERT
|
DeeBERT-master/transformers/modeling_ctrl.py
|
# 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 __future__ import absolute_import, division, print_function, unicode_literals
import collections
import json
import logging
import math
import os
import sys
from io import open
import numpy as np
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss
from torch.nn.parameter import Parameter
from .modeling_utils import PreTrainedModel, Conv1D, prune_conv1d_layer, SequenceSummary
from .configuration_ctrl import CTRLConfig
from .file_utils import add_start_docstrings
logger = logging.getLogger(__name__)
CTRL_PRETRAINED_MODEL_ARCHIVE_MAP = {"ctrl": "https://storage.googleapis.com/sf-ctrl/pytorch/seqlen256_v1.bin"}
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=dtype).unsqueeze(1),
torch.arange(d_model_size, dtype=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:
scaled_attention_logits += (mask * -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(torch.nn.Module):
def __init__(self, d_model_size, num_heads, output_attentions=False):
super(MultiHeadAttention, self).__init__()
self.output_attentions = output_attentions
self.num_heads = num_heads
self.d_model_size = d_model_size
self.depth = int(d_model_size / self.num_heads)
self.Wq = torch.nn.Linear(d_model_size, d_model_size)
self.Wk = torch.nn.Linear(d_model_size, d_model_size)
self.Wv = torch.nn.Linear(d_model_size, d_model_size)
self.dense = torch.nn.Linear(d_model_size, d_model_size)
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):
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)
present = torch.stack((k, v))
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 self.output_attentions:
outputs = outputs + (attn,)
return outputs
def point_wise_feed_forward_network(d_model_size, dff):
return torch.nn.Sequential(torch.nn.Linear(d_model_size, dff),
torch.nn.ReLU(),
torch.nn.Linear(dff, d_model_size))
class EncoderLayer(torch.nn.Module):
def __init__(self, d_model_size, num_heads, dff, rate=0.1, output_attentions=False):
super(EncoderLayer, self).__init__()
self.multi_head_attention = MultiHeadAttention(d_model_size, num_heads, output_attentions)
self.ffn = point_wise_feed_forward_network(d_model_size, dff)
self.layernorm1 = torch.nn.LayerNorm(d_model_size, eps=1e-6)
self.layernorm2 = torch.nn.LayerNorm(d_model_size, eps=1e-6)
self.dropout1 = torch.nn.Dropout(rate)
self.dropout2 = torch.nn.Dropout(rate)
def forward(self, x, mask, layer_past=None, attention_mask=None, head_mask=None):
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)
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 dowloading and loading pretrained models.
"""
config_class = CTRLConfig
pretrained_model_archive_map = CTRL_PRETRAINED_MODEL_ARCHIVE_MAP
base_model_prefix = "transformer"
def _init_weights(self, module):
""" Initialize the weights.
"""
if isinstance(module, (nn.Linear, nn.Embedding, 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 isinstance(module, (nn.Linear, Conv1D)) and module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
CTRL_START_DOCSTRING = r""" CTRL model was proposed in
`CTRL: A Conditional Transformer Language Model for Controllable Generation`_
by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher.
It's a causal (unidirectional) transformer pre-trained using language modeling on a very large
corpus of ~140 GB of text data with the first token reserved as a control code (such as Links, Books, Wikipedia etc.).
This model is a PyTorch `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.
.. _`CTRL: A Conditional Transformer Language Model for Controllable Generation`:
https://www.github.com/salesforce/ctrl
.. _`torch.nn.Module`:
https://pytorch.org/docs/stable/nn.html#module
Parameters:
config (:class:`~transformers.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 :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""
CTRL_INPUTS_DOCSTRING = r""" Inputs:
**input_ids**: ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Indices of input sequence tokens in the vocabulary.
CTRL is a model with absolute position embeddings so it's usually advised to pad the inputs on
the right rather than the left.
Indices can be obtained using :class:`transformers.CTRLTokenizer`.
See :func:`transformers.PreTrainedTokenizer.encode` and
:func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
**past**:
list of ``torch.FloatTensor`` (one for each layer):
that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
(see `past` 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**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:
Mask to avoid performing attention on padding token indices.
Mask values selected in ``[0, 1]``:
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
**token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
A parallel sequence of tokens (can be used to indicate various portions of the inputs).
The embeddings from these tokens will be summed with the respective token embeddings.
Indices are selected in the vocabulary (unlike BERT which has a specific vocabulary for segment indices).
**position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Indices of positions of each input sequence tokens in the position embeddings.
Selected in the range ``[0, config.max_position_embeddings - 1]``.
**head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
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**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, embedding_dim)``:
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.
"""
@add_start_docstrings("The bare CTRL Model transformer outputting raw hidden-states without any specific head on top.",
CTRL_START_DOCSTRING, CTRL_INPUTS_DOCSTRING)
class CTRLModel(CTRLPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**last_hidden_state**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)``
Sequence of hidden-states at the last layer of the model.
**past**:
list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
that contains pre-computed hidden-states (key and values in the attention blocks).
Can be used (see `past` input) 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.
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
tokenizer = CTRLTokenizer.from_pretrained('ctrl')
model = CTRLModel.from_pretrained('ctrl')
input_ids = torch.tensor(tokenizer.encode("Links Hello, my dog is cute")).unsqueeze(0) # Batch size 1
outputs = model(input_ids)
last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
"""
def __init__(self, config):
super(CTRLModel, self).__init__(config)
self.output_hidden_states = config.output_hidden_states
self.output_attentions = config.output_attentions
self.output_past = config.output_past
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,
config.output_attentions) for _ in range(config.n_layer)])
self.layernorm = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
self.init_weights()
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].attn.prune_heads(heads)
def forward(self, input_ids=None, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None):
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 = 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 past is None:
past_length = 0
past = [None] * len(self.h)
else:
past_length = past[0][0].size(-2)
if position_ids is None:
device = input_ids.device if input_ids is not None else inputs_embeds.device
position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
# Attention mask.
if attention_mask is not None:
attention_mask = attention_mask.view(-1, input_shape[-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 -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.
attention_mask = attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility
attention_mask = (1.0 - attention_mask) * -10000.0
# 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
# head_mask has shape n_layer x batch x n_heads x N x N
if head_mask is not None:
if head_mask.dim() == 1:
head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
head_mask = head_mask.expand(self.config.n_layer, -1, -1, -1, -1)
elif head_mask.dim() == 2:
head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1) # We can specify head_mask for each layer
head_mask = head_mask.to(dtype=next(self.parameters()).dtype) # switch to fload if need + fp16 compatibility
else:
head_mask = [None] * 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
position_ids = position_ids.view(-1, input_shape[-1])
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, seq_len), 1).to(inputs_embeds.device)
inputs_embeds *= np.sqrt(self.d_model_size)
pos_embeds = self.pos_encoding[position_ids, :].to(inputs_embeds.device)
hidden_states = inputs_embeds + pos_embeds + token_type_embeds
hidden_states = self.dropout(hidden_states)
output_shape = input_shape + (inputs_embeds.size(-1),)
presents = ()
all_hidden_states = ()
all_attentions = []
for i, (h, layer_past) in enumerate(zip(self.h, past)):
if self.output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states.view(*output_shape),)
outputs = h(hidden_states,
mask,
layer_past=layer_past,
attention_mask=attention_mask,
head_mask=head_mask[i])
hidden_states, present = outputs[:2]
if self.output_past:
presents = presents + (present,)
if self.output_attentions:
all_attentions.append(outputs[2])
hidden_states = self.layernorm(hidden_states)
hidden_states = hidden_states.view(*output_shape)
if self.output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
outputs = (hidden_states,)
if self.output_past:
outputs = outputs + (presents,)
if self.output_hidden_states:
outputs = outputs + (all_hidden_states,)
if self.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,) + all_attentions[0].shape[-2:]
all_attentions = tuple(t.view(*attention_output_shape) for t in all_attentions)
outputs = outputs + (all_attentions,)
return outputs
@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, CTRL_INPUTS_DOCSTRING)
class CTRLLMHeadModel(CTRLPreTrainedModel):
r"""
**labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Labels for language modeling.
Note that the labels **are shifted** inside the model, i.e. you can set ``lm_labels = input_ids``
Indices are selected in ``[-1, 0, ..., config.vocab_size]``
All labels set to ``-1`` are ignored (masked), the loss is only
computed for labels in ``[0, ..., config.vocab_size]``
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Language modeling loss.
**prediction_scores**: ``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).
**past**:
list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
that contains pre-computed hidden-states (key and values in the attention blocks).
Can be used (see `past` input) 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.
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
import torch
from transformers import CTRLTokenizer, CTRLLMHeadModel
tokenizer = CTRLTokenizer.from_pretrained('ctrl')
model = CTRLLMHeadModel.from_pretrained('ctrl')
input_ids = torch.tensor(tokenizer.encode("Links Hello, my dog is cute")).unsqueeze(0) # Batch size 1
outputs = model(input_ids, labels=input_ids)
loss, logits = outputs[:2]
"""
def __init__(self, config):
super(CTRLLMHeadModel, self).__init__(config)
self.transformer = CTRLModel(config)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=True)
self.init_weights()
def get_output_embeddings(self):
return self.lm_head
def forward(self, input_ids=None, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None,
labels=None):
transformer_outputs = self.transformer(input_ids,
past=past,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds)
hidden_states = transformer_outputs[0]
lm_logits = self.lm_head(hidden_states)
outputs = (lm_logits,) + transformer_outputs[1:]
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(ignore_index=-1)
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)),
shift_labels.view(-1))
outputs = (loss,) + outputs
return outputs # (loss), lm_logits, presents, (all hidden_states), (attentions)
| 24,667 | 48.139442 | 146 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tokenization_transfo_xl.py
|
# 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.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import glob
import logging
import os
import sys
from collections import Counter, OrderedDict
from io import open
import numpy as np
from .file_utils import cached_path
from .tokenization_utils import PreTrainedTokenizer
try:
import torch
except ImportError:
pass
# if sys.version_info[0] == 2:
# import cPickle as pickle
# else:
# import pickle
logger = logging.getLogger(__name__)
VOCAB_FILES_NAMES = {'pretrained_vocab_file': 'vocab.bin', 'vocab_file': 'vocab.txt'}
PRETRAINED_VOCAB_FILES_MAP = {
'pretrained_vocab_file':
{
'transfo-xl-wt103': "https://s3.amazonaws.com/models.huggingface.co/bert/transfo-xl-wt103-vocab.bin",
}
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
'transfo-xl-wt103': None,
}
PRETRAINED_CORPUS_ARCHIVE_MAP = {
'transfo-xl-wt103': "https://s3.amazonaws.com/models.huggingface.co/bert/transfo-xl-wt103-corpus.bin",
}
CORPUS_NAME = 'corpus.bin'
class TransfoXLTokenizer(PreTrainedTokenizer):
"""
Transformer-XL tokenizer adapted from Vocab class in https://github.com/kimiyoung/transformer-xl
"""
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, special=None, min_freq=0, max_size=None, lower_case=False,
delimiter=None, vocab_file=None, pretrained_vocab_file=None,
never_split=None, unk_token="<unk>", eos_token="<eos>",
additional_special_tokens=["<formula>"], **kwargs):
super(TransfoXLTokenizer, self).__init__(unk_token=unk_token, eos_token=eos_token,
additional_special_tokens=additional_special_tokens,
**kwargs)
self.max_len_single_sentence = self.max_len # no default special tokens - you can update this value if you add special tokens
self.max_len_sentences_pair = self.max_len # no default special tokens - you can update this value if you add special tokens
if never_split is None:
never_split = self.all_special_tokens
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.never_split = never_split
if pretrained_vocab_file is not None:
# Hack because, honestly this tokenizer was not made to be used
# in a library like ours, at all.
vocab_dict = torch.load(pretrained_vocab_file)
for key, value in vocab_dict.items():
if key not in self.__dict__:
self.__dict__[key] = value
if vocab_file is not None:
self.build_vocab()
def count_file(self, path, verbose=False, add_eos=False):
if verbose: logger.info('counting file {} ...'.format(path))
assert os.path.exists(path)
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(' line {}'.format(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('counting {} sents ...'.format(len(sents)))
for idx, symbols in enumerate(sents):
if verbose and idx > 0 and idx % 500000 == 0:
logger.info(' line {}'.format(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('No <unkown> token in vocabulary')
def save_vocabulary(self, vocab_path):
"""Save the tokenizer vocabulary to a directory or file."""
if os.path.isdir(vocab_path):
vocab_file = os.path.join(vocab_path, VOCAB_FILES_NAMES['pretrained_vocab_file'])
torch.save(self.__dict__, vocab_file)
return (vocab_file,)
def build_vocab(self):
if self.vocab_file:
logger.info('building vocab from {}'.format(self.vocab_file))
self._build_from_file(self.vocab_file)
logger.info('final vocab size {}'.format(len(self)))
else:
logger.info('building vocab with min_freq={}, max_size={}'.format(
self.min_freq, 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('final vocab size {} from {} unique tokens'.format(
len(self), len(self.counter)))
def encode_file(self, path, ordered=False, verbose=False, add_eos=True,
add_double_eos=False):
if verbose: logger.info('encoding file {} ...'.format(path))
assert os.path.exists(path)
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(' line {}'.format(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
def encode_sents(self, sents, ordered=False, verbose=False):
if verbose: logger.info('encoding {} sents ...'.format(len(sents)))
encoded = []
for idx, symbols in enumerate(sents):
if verbose and idx > 0 and idx % 500000 == 0:
logger.info(' line {}'.format(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, '{}_idx'.format(sym.strip('<>')), 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 _convert_id_to_token(self, idx):
"""Converts an id in a token (BPE) using the vocab."""
assert 0 <= idx < len(self), 'Index {} out of vocabulary range'.format(idx)
return self.idx2sym[idx]
def _convert_token_to_id(self, sym):
""" Converts a token (str/unicode) in an id using the vocab. """
if sym in self.sym2idx:
return self.sym2idx[sym]
else:
# logger.info('encounter unk {}'.format(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. """
out_string = ' '.join(tokens).strip()
return out_string
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 _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 = line.split(self.delimiter)
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.
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]
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
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)
if pretrained_model_name_or_path in PRETRAINED_CORPUS_ARCHIVE_MAP:
corpus_file = PRETRAINED_CORPUS_ARCHIVE_MAP[pretrained_model_name_or_path]
else:
corpus_file = os.path.join(pretrained_model_name_or_path, CORPUS_NAME)
# redirect to the cache, if necessary
try:
resolved_corpus_file = cached_path(corpus_file, cache_dir=cache_dir)
except EnvironmentError:
logger.error(
"Corpus '{}' was not found in corpus list ({}). "
"We assumed '{}' was a path or url but couldn't find files {} "
"at this path or url.".format(
pretrained_model_name_or_path,
', '.join(PRETRAINED_CORPUS_ARCHIVE_MAP.keys()),
pretrained_model_name_or_path,
corpus_file))
return None
if resolved_corpus_file == corpus_file:
logger.info("loading corpus file {}".format(corpus_file))
else:
logger.info("loading corpus file {} from cache at {}".format(
corpus_file, 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)
return data_iter
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...')
with open(fn, "rb") as fp:
corpus = pickle.load(fp)
else:
logger.info('Producing dataset {}...'.format(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
| 21,824 | 36.62931 | 133 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tokenization_openai.py
|
# coding=utf-8
# 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 OpenAI GPT."""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import json
import logging
import os
import re
from io import open
from .tokenization_utils import PreTrainedTokenizer
from .tokenization_bert import BasicTokenizer
logger = logging.getLogger(__name__)
VOCAB_FILES_NAMES = {
'vocab_file': 'vocab.json',
'merges_file': 'merges.txt',
}
PRETRAINED_VOCAB_FILES_MAP = {
'vocab_file':
{
'openai-gpt': "https://s3.amazonaws.com/models.huggingface.co/bert/openai-gpt-vocab.json",
},
'merges_file':
{
'openai-gpt': "https://s3.amazonaws.com/models.huggingface.co/bert/openai-gpt-merges.txt",
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
'openai-gpt': 512,
}
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
def text_standardize(text):
"""
fixes some issues the spacy tokenizer had on books corpus
also does some whitespace standardization
"""
text = text.replace('—', '-')
text = text.replace('–', '-')
text = text.replace('―', '-')
text = text.replace('…', '...')
text = text.replace('´', "'")
text = re.sub(r'''(-+|~+|!+|"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)''', r' \1 ', text)
text = re.sub(r'\s*\n\s*', ' \n ', text)
text = re.sub(r'[^\S\n]+', ' ', text)
return text.strip()
class OpenAIGPTTokenizer(PreTrainedTokenizer):
"""
BPE tokenizer. Peculiarities:
- lower case all inputs
- uses SpaCy tokenizer and ftfy for pre-BPE tokenization if they are installed, fallback to BERT's BasicTokenizer if not.
"""
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, unk_token="<unk>", **kwargs):
super(OpenAIGPTTokenizer, self).__init__(unk_token=unk_token, **kwargs)
self.max_len_single_sentence = self.max_len # no default special tokens - you can update this value if you add special tokens
self.max_len_sentences_pair = self.max_len # no default special tokens - you can update this value if you add special tokens
try:
import ftfy
from spacy.lang.en import English
_nlp = English()
self.nlp = _nlp.Defaults.create_tokenizer(_nlp)
self.fix_text = ftfy.fix_text
except ImportError:
logger.warning("ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.")
self.nlp = BasicTokenizer(do_lower_case=True)
self.fix_text = None
self.encoder = json.load(open(vocab_file, encoding="utf-8"))
self.decoder = {v:k for k,v in self.encoder.items()}
merges = open(merges_file, encoding='utf-8').read().split('\n')[1:-1]
merges = [tuple(merge.split()) for merge in merges]
self.bpe_ranks = dict(zip(merges, range(len(merges))))
self.cache = {}
@property
def vocab_size(self):
return len(self.encoder)
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)
new_word.extend(word[i:j])
i = j
except:
new_word.extend(word[i:])
break
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):
""" Tokenize a string. """
split_tokens = []
if self.fix_text is None:
# Using BERT's BasicTokenizer
text = self.nlp.tokenize(text)
for token in text:
split_tokens.extend([t for t in self.bpe(token).split(' ')])
else:
# Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
text = self.nlp(text_standardize(self.fix_text(text)))
for token in text:
split_tokens.extend([t for t in self.bpe(token.text.lower()).split(' ')])
return split_tokens
def _convert_token_to_id(self, token):
""" Converts a token (str/unicode) 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 id in a token (BPE) 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('</w>', ' ').strip()
return out_string
def save_vocabulary(self, save_directory):
"""Save the tokenizer vocabulary and merge files to a directory."""
if not os.path.isdir(save_directory):
logger.error("Vocabulary path ({}) should be a directory".format(save_directory))
return
vocab_file = os.path.join(save_directory, VOCAB_FILES_NAMES['vocab_file'])
merge_file = os.path.join(save_directory, VOCAB_FILES_NAMES['merges_file'])
with open(vocab_file, 'w', encoding='utf-8') as f:
f.write(json.dumps(self.encoder, ensure_ascii=False))
index = 0
with open(merge_file, "w", encoding="utf-8") as writer:
writer.write(u'#version: 0.2\n')
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning("Saving vocabulary to {}: BPE merge indices are not consecutive."
" Please check that the tokenizer is not corrupted!".format(merge_file))
index = token_index
writer.write(' '.join(bpe_tokens) + u'\n')
index += 1
return vocab_file, merge_file
| 7,784 | 36.248804 | 133 |
py
|
DeeBERT
|
DeeBERT-master/transformers/modeling_transfo_xl_utilities.py
|
# 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.
""" Utilities for PyTorch Transformer XL model.
Directly adapted from https://github.com/kimiyoung/transformer-xl.
"""
from collections import defaultdict
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
# CUDA_MAJOR = int(torch.version.cuda.split('.')[0])
# CUDA_MINOR = int(torch.version.cuda.split('.')[1])
class ProjectedAdaptiveLogSoftmax(nn.Module):
def __init__(self, n_token, d_embed, d_proj, cutoffs, div_val=1,
keep_order=False):
super(ProjectedAdaptiveLogSoftmax, self).__init__()
self.n_token = n_token
self.d_embed = d_embed
self.d_proj = d_proj
self.cutoffs = cutoffs + [n_token]
self.cutoff_ends = [0] + self.cutoffs
self.div_val = div_val
self.shortlist_size = self.cutoffs[0]
self.n_clusters = len(self.cutoffs) - 1
self.head_size = self.shortlist_size + self.n_clusters
if self.n_clusters > 0:
self.cluster_weight = nn.Parameter(torch.zeros(self.n_clusters, self.d_embed))
self.cluster_bias = nn.Parameter(torch.zeros(self.n_clusters))
self.out_layers = nn.ModuleList()
self.out_projs = nn.ParameterList()
if div_val == 1:
for i in range(len(self.cutoffs)):
if d_proj != d_embed:
self.out_projs.append(
nn.Parameter(torch.FloatTensor(d_proj, d_embed))
)
else:
self.out_projs.append(None)
self.out_layers.append(nn.Linear(d_embed, n_token))
else:
for i in range(len(self.cutoffs)):
l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i+1]
d_emb_i = d_embed // (div_val ** i)
self.out_projs.append(
nn.Parameter(torch.FloatTensor(d_proj, d_emb_i))
)
self.out_layers.append(nn.Linear(d_emb_i, r_idx-l_idx))
self.keep_order = keep_order
def _compute_logit(self, hidden, weight, bias, proj):
if proj is None:
logit = F.linear(hidden, weight, bias=bias)
else:
# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
proj_hid = F.linear(hidden, proj.t().contiguous())
logit = F.linear(proj_hid, weight, bias=bias)
# else:
# logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
# if bias is not None:
# logit = logit + bias
return logit
def forward(self, hidden, labels=None, keep_order=False):
'''
Params:
hidden :: [len*bsz x d_proj]
labels :: [len*bsz]
Return:
if labels is None:
out :: [len*bsz] Negative log likelihood
else:
out :: [len*bsz x n_tokens] log probabilities of tokens over the vocabulary
We could replace this implementation by the native PyTorch one
if their's had an option to set bias on all clusters in the native one.
here: https://github.com/pytorch/pytorch/blob/dbe6a7a9ff1a364a8706bf5df58a1ca96d2fd9da/torch/nn/modules/adaptive.py#L138
'''
if labels is not None:
labels = labels.view(-1)
if hidden.size(0) != labels.size(0):
raise RuntimeError('Input and labels should have the same size '
'in the batch dimension.')
if self.n_clusters == 0:
logit = self._compute_logit(hidden, self.out_layers[0].weight,
self.out_layers[0].bias, self.out_projs[0])
if labels is not None:
out = -F.log_softmax(logit, dim=-1) \
.gather(1, labels.unsqueeze(1)).squeeze(1)
else:
out = F.log_softmax(logit, dim=-1)
else:
# construct weights and biases
weights, biases = [], []
for i in range(len(self.cutoffs)):
if self.div_val == 1:
l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1]
weight_i = self.out_layers[0].weight[l_idx:r_idx]
bias_i = self.out_layers[0].bias[l_idx:r_idx]
else:
weight_i = self.out_layers[i].weight
bias_i = self.out_layers[i].bias
if i == 0:
weight_i = torch.cat(
[weight_i, self.cluster_weight], dim=0)
bias_i = torch.cat(
[bias_i, self.cluster_bias], dim=0)
weights.append(weight_i)
biases.append(bias_i)
head_weight, head_bias, head_proj = weights[0], biases[0], self.out_projs[0]
head_logit = self._compute_logit(hidden, head_weight, head_bias, head_proj)
head_logprob = F.log_softmax(head_logit, dim=1)
if labels is None:
out = hidden.new_empty((head_logit.size(0), self.n_token))
else:
out = torch.zeros_like(labels, dtype=hidden.dtype, device=hidden.device)
offset = 0
cutoff_values = [0] + self.cutoffs
for i in range(len(cutoff_values) - 1):
l_idx, r_idx = cutoff_values[i], cutoff_values[i + 1]
if labels is not None:
mask_i = (labels >= l_idx) & (labels < r_idx)
indices_i = mask_i.nonzero().squeeze()
if indices_i.numel() == 0:
continue
target_i = labels.index_select(0, indices_i) - l_idx
head_logprob_i = head_logprob.index_select(0, indices_i)
hidden_i = hidden.index_select(0, indices_i)
else:
hidden_i = hidden
if i == 0:
if labels is not None:
logprob_i = head_logprob_i.gather(1, target_i[:, None]).squeeze(1)
else:
out[:, :self.cutoffs[0]] = head_logprob[:, :self.cutoffs[0]]
else:
weight_i, bias_i, proj_i = weights[i], biases[i], self.out_projs[i]
tail_logit_i = self._compute_logit(hidden_i, weight_i, bias_i, proj_i)
tail_logprob_i = F.log_softmax(tail_logit_i, dim=1)
cluster_prob_idx = self.cutoffs[0] + i - 1 # No probability for the head cluster
if labels is not None:
logprob_i = head_logprob_i[:, cluster_prob_idx] \
+ tail_logprob_i.gather(1, target_i[:, None]).squeeze(1)
else:
logprob_i = head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
out[:, l_idx:r_idx] = logprob_i
if labels is not None:
if (hasattr(self, 'keep_order') and self.keep_order) or keep_order:
out.index_copy_(0, indices_i, -logprob_i)
else:
out[offset:offset+logprob_i.size(0)].copy_(-logprob_i)
offset += logprob_i.size(0)
return out
def log_prob(self, hidden):
r""" Computes log probabilities for all :math:`n\_classes`
From: https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/adaptive.py
Args:
hidden (Tensor): a minibatch of examples
Returns:
log-probabilities of for each class :math:`c`
in range :math:`0 <= c <= n\_classes`, where :math:`n\_classes` is a
parameter passed to ``AdaptiveLogSoftmaxWithLoss`` constructor.
Shape:
- Input: :math:`(N, in\_features)`
- Output: :math:`(N, n\_classes)`
"""
if self.n_clusters == 0:
logit = self._compute_logit(hidden, self.out_layers[0].weight,
self.out_layers[0].bias, self.out_projs[0])
return F.log_softmax(logit, dim=-1)
else:
# construct weights and biases
weights, biases = [], []
for i in range(len(self.cutoffs)):
if self.div_val == 1:
l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1]
weight_i = self.out_layers[0].weight[l_idx:r_idx]
bias_i = self.out_layers[0].bias[l_idx:r_idx]
else:
weight_i = self.out_layers[i].weight
bias_i = self.out_layers[i].bias
if i == 0:
weight_i = torch.cat(
[weight_i, self.cluster_weight], dim=0)
bias_i = torch.cat(
[bias_i, self.cluster_bias], dim=0)
weights.append(weight_i)
biases.append(bias_i)
head_weight, head_bias, head_proj = weights[0], biases[0], self.out_projs[0]
head_logit = self._compute_logit(hidden, head_weight, head_bias, head_proj)
out = hidden.new_empty((head_logit.size(0), self.n_token))
head_logprob = F.log_softmax(head_logit, dim=1)
cutoff_values = [0] + self.cutoffs
for i in range(len(cutoff_values) - 1):
start_idx, stop_idx = cutoff_values[i], cutoff_values[i + 1]
if i == 0:
out[:, :self.cutoffs[0]] = head_logprob[:, :self.cutoffs[0]]
else:
weight_i, bias_i, proj_i = weights[i], biases[i], self.out_projs[i]
tail_logit_i = self._compute_logit(hidden, weight_i, bias_i, proj_i)
tail_logprob_i = F.log_softmax(tail_logit_i, dim=1)
logprob_i = head_logprob[:, -i] + tail_logprob_i
out[:, start_idx, stop_idx] = logprob_i
return out
class LogUniformSampler(object):
def __init__(self, range_max, n_sample):
"""
Reference : https://github.com/tensorflow/tensorflow/blob/r1.10/tensorflow/python/ops/candidate_sampling_ops.py
`P(class) = (log(class + 2) - log(class + 1)) / log(range_max + 1)`
expected count can be approximated by 1 - (1 - p)^n
and we use a numerically stable version -expm1(num_tries * log1p(-p))
Our implementation fixes num_tries at 2 * n_sample, and the actual #samples will vary from run to run
"""
with torch.no_grad():
self.range_max = range_max
log_indices = torch.arange(1., range_max+2., 1.).log_()
self.dist = (log_indices[1:] - log_indices[:-1]) / log_indices[-1]
self.log_q = (- (-self.dist.double().log1p_() * 2 * n_sample).expm1_()).log_().float()
self.n_sample = n_sample
def sample(self, labels):
"""
labels: [b1, b2]
Return
true_log_probs: [b1, b2]
samp_log_probs: [n_sample]
neg_samples: [n_sample]
"""
# neg_samples = torch.empty(0).long()
n_sample = self.n_sample
n_tries = 2 * n_sample
with torch.no_grad():
neg_samples = torch.multinomial(self.dist, n_tries, replacement=True).unique()
device = labels.device
neg_samples = neg_samples.to(device)
true_log_probs = self.log_q[labels].to(device)
samp_log_probs = self.log_q[neg_samples].to(device)
return true_log_probs, samp_log_probs, neg_samples
def sample_logits(embedding, bias, labels, inputs, sampler):
"""
embedding: an nn.Embedding layer
bias: [n_vocab]
labels: [b1, b2]
inputs: [b1, b2, n_emb]
sampler: you may use a LogUniformSampler
Return
logits: [b1, b2, 1 + n_sample]
"""
true_log_probs, samp_log_probs, neg_samples = sampler.sample(labels)
n_sample = neg_samples.size(0)
b1, b2 = labels.size(0), labels.size(1)
all_ids = torch.cat([labels.view(-1), neg_samples])
all_w = embedding(all_ids)
true_w = all_w[: -n_sample].view(b1, b2, -1)
sample_w = all_w[- n_sample:].view(n_sample, -1)
all_b = bias[all_ids]
true_b = all_b[: -n_sample].view(b1, b2)
sample_b = all_b[- n_sample:]
hit = (labels[:, :, None] == neg_samples).detach()
true_logits = torch.einsum('ijk,ijk->ij',
[true_w, inputs]) + true_b - true_log_probs
sample_logits = torch.einsum('lk,ijk->ijl',
[sample_w, inputs]) + sample_b - samp_log_probs
sample_logits.masked_fill_(hit, -1e30)
logits = torch.cat([true_logits[:, :, None], sample_logits], -1)
return logits
| 13,568 | 39.747748 | 132 |
py
|
DeeBERT
|
DeeBERT-master/transformers/convert_pytorch_checkpoint_to_tf2.py
|
# 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 pytorch checkpoints to TensorFlow """
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import argparse
import tensorflow as tf
from transformers import is_torch_available, cached_path
from transformers import (load_pytorch_checkpoint_in_tf2_model,
BertConfig, TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2Config, TFGPT2LMHeadModel, GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLNetConfig, TFXLNetLMHeadModel, XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLMConfig, TFXLMWithLMHeadModel, XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
TransfoXLConfig, TFTransfoXLLMHeadModel, TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
OpenAIGPTConfig, TFOpenAIGPTLMHeadModel, OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
RobertaConfig, TFRobertaForMaskedLM, TFRobertaForSequenceClassification, ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
DistilBertConfig, TFDistilBertForMaskedLM, TFDistilBertForQuestionAnswering, DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRLConfig, TFCTRLLMHeadModel, CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP)
if is_torch_available():
import torch
import numpy as np
from transformers import (BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification, BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
GPT2LMHeadModel, GPT2_PRETRAINED_MODEL_ARCHIVE_MAP,
XLNetLMHeadModel, XLNET_PRETRAINED_MODEL_ARCHIVE_MAP,
XLMWithLMHeadModel, XLM_PRETRAINED_MODEL_ARCHIVE_MAP,
TransfoXLLMHeadModel, TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP,
OpenAIGPTLMHeadModel, OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP,
RobertaForMaskedLM, RobertaForSequenceClassification, ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
DistilBertForMaskedLM, DistilBertForQuestionAnswering, DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
CTRLLMHeadModel, CTRL_PRETRAINED_MODEL_ARCHIVE_MAP)
else:
(BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification, BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
GPT2LMHeadModel, GPT2_PRETRAINED_MODEL_ARCHIVE_MAP,
XLNetLMHeadModel, XLNET_PRETRAINED_MODEL_ARCHIVE_MAP,
XLMWithLMHeadModel, XLM_PRETRAINED_MODEL_ARCHIVE_MAP,
TransfoXLLMHeadModel, TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP,
OpenAIGPTLMHeadModel, OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP,
RobertaForMaskedLM, RobertaForSequenceClassification, ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
DistilBertForMaskedLM, DistilBertForQuestionAnswering, DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
CTRLLMHeadModel, CTRL_PRETRAINED_MODEL_ARCHIVE_MAP) = (
None, None, None, None,
None, None,
None, None,
None, None,
None, None,
None, None,
None, None, None,
None, None, None,
None, None)
import logging
logging.basicConfig(level=logging.INFO)
MODEL_CLASSES = {
'bert': (BertConfig, TFBertForPreTraining, BertForPreTraining, BERT_PRETRAINED_MODEL_ARCHIVE_MAP, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP),
'bert-large-uncased-whole-word-masking-finetuned-squad': (BertConfig, TFBertForQuestionAnswering, BertForQuestionAnswering, BERT_PRETRAINED_MODEL_ARCHIVE_MAP, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP),
'bert-large-cased-whole-word-masking-finetuned-squad': (BertConfig, TFBertForQuestionAnswering, BertForQuestionAnswering, BERT_PRETRAINED_MODEL_ARCHIVE_MAP, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP),
'bert-base-cased-finetuned-mrpc': (BertConfig, TFBertForSequenceClassification, BertForSequenceClassification, BERT_PRETRAINED_MODEL_ARCHIVE_MAP, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP),
'gpt2': (GPT2Config, TFGPT2LMHeadModel, GPT2LMHeadModel, GPT2_PRETRAINED_MODEL_ARCHIVE_MAP, GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP),
'xlnet': (XLNetConfig, TFXLNetLMHeadModel, XLNetLMHeadModel, XLNET_PRETRAINED_MODEL_ARCHIVE_MAP, XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP),
'xlm': (XLMConfig, TFXLMWithLMHeadModel, XLMWithLMHeadModel, XLM_PRETRAINED_MODEL_ARCHIVE_MAP, XLM_PRETRAINED_CONFIG_ARCHIVE_MAP),
'transfo-xl': (TransfoXLConfig, TFTransfoXLLMHeadModel, TransfoXLLMHeadModel, TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP, TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP),
'openai-gpt': (OpenAIGPTConfig, TFOpenAIGPTLMHeadModel, OpenAIGPTLMHeadModel, OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP, OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP),
'roberta': (RobertaConfig, TFRobertaForMaskedLM, RobertaForMaskedLM, ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP, ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP),
'roberta-large-mnli': (RobertaConfig, TFRobertaForSequenceClassification, RobertaForSequenceClassification, ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP, ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP),
'distilbert': (DistilBertConfig, TFDistilBertForMaskedLM, DistilBertForMaskedLM, DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP, DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP),
'distilbert-base-uncased-distilled-squad': (DistilBertConfig, TFDistilBertForQuestionAnswering, DistilBertForQuestionAnswering, DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP, DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP),
'ctrl': (CTRLConfig, TFCTRLLMHeadModel, CTRLLMHeadModel, CTRL_PRETRAINED_MODEL_ARCHIVE_MAP, CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP)
}
def convert_pt_checkpoint_to_tf(model_type, pytorch_checkpoint_path, config_file, tf_dump_path, compare_with_pt_model=False, use_cached_models=True):
if model_type not in MODEL_CLASSES:
raise ValueError("Unrecognized model type, should be one of {}.".format(list(MODEL_CLASSES.keys())))
config_class, model_class, pt_model_class, aws_model_maps, aws_config_map = MODEL_CLASSES[model_type]
# Initialise TF model
if config_file in aws_config_map:
config_file = cached_path(aws_config_map[config_file], force_download=not use_cached_models)
config = config_class.from_json_file(config_file)
config.output_hidden_states = True
config.output_attentions = True
print("Building TensorFlow model from configuration: {}".format(str(config)))
tf_model = model_class(config)
# Load weights from tf checkpoint
if pytorch_checkpoint_path in aws_model_maps:
pytorch_checkpoint_path = cached_path(aws_model_maps[pytorch_checkpoint_path], force_download=not use_cached_models)
# Load PyTorch checkpoint in tf2 model:
tf_model = load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path)
if compare_with_pt_model:
inputs_list = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
tf_inputs = tf.constant(inputs_list)
tfo = tf_model(tf_inputs, training=False) # build the network
pt_model = pt_model_class.from_pretrained(None,
config=config,
state_dict=torch.load(pytorch_checkpoint_path,
map_location='cpu'))
pt_inputs = torch.tensor(inputs_list)
with torch.no_grad():
pto = pt_model(pt_inputs)
np_pt = pto[0].detach().numpy()
np_tf = tfo[0].numpy()
diff = np.amax(np.abs(np_pt - np_tf))
print("Max absolute difference between models outputs {}".format(diff))
assert diff <= 2e-2, "Error, model absolute difference is >2e-2"
# Save pytorch-model
print("Save TensorFlow model to {}".format(tf_dump_path))
tf_model.save_weights(tf_dump_path, save_format='h5')
def convert_all_pt_checkpoints_to_tf(args_model_type, tf_dump_path, model_shortcut_names_or_path=None, config_shortcut_names_or_path=None,
compare_with_pt_model=False, use_cached_models=False, only_convert_finetuned_models=False):
assert os.path.isdir(args.tf_dump_path), "--tf_dump_path should be a directory"
if args_model_type is None:
model_types = list(MODEL_CLASSES.keys())
else:
model_types = [args_model_type]
for j, model_type in enumerate(model_types, start=1):
print("=" * 100)
print(" Converting model type {}/{}: {}".format(j, len(model_types), model_type))
print("=" * 100)
if model_type not in MODEL_CLASSES:
raise ValueError("Unrecognized model type {}, should be one of {}.".format(model_type, list(MODEL_CLASSES.keys())))
config_class, model_class, pt_model_class, aws_model_maps, aws_config_map = MODEL_CLASSES[model_type]
if model_shortcut_names_or_path is None:
model_shortcut_names_or_path = list(aws_model_maps.keys())
if config_shortcut_names_or_path is None:
config_shortcut_names_or_path = model_shortcut_names_or_path
for i, (model_shortcut_name, config_shortcut_name) in enumerate(
zip(model_shortcut_names_or_path, config_shortcut_names_or_path), start=1):
print("-" * 100)
if '-squad' in model_shortcut_name or '-mrpc' in model_shortcut_name or '-mnli' in model_shortcut_name:
if not only_convert_finetuned_models:
print(" Skipping finetuned checkpoint {}".format(model_shortcut_name))
continue
model_type = model_shortcut_name
elif only_convert_finetuned_models:
print(" Skipping not finetuned checkpoint {}".format(model_shortcut_name))
continue
print(" Converting checkpoint {}/{}: {} - model_type {}".format(i, len(aws_config_map), model_shortcut_name, model_type))
print("-" * 100)
if config_shortcut_name in aws_config_map:
config_file = cached_path(aws_config_map[config_shortcut_name], force_download=not use_cached_models)
else:
config_file = cached_path(config_shortcut_name, force_download=not use_cached_models)
if model_shortcut_name in aws_model_maps:
model_file = cached_path(aws_model_maps[model_shortcut_name], force_download=not use_cached_models)
else:
model_file = cached_path(model_shortcut_name, force_download=not use_cached_models)
if os.path.isfile(model_shortcut_name):
model_shortcut_name = 'converted_model'
convert_pt_checkpoint_to_tf(model_type=model_type,
pytorch_checkpoint_path=model_file,
config_file=config_file,
tf_dump_path=os.path.join(tf_dump_path, model_shortcut_name + '-tf_model.h5'),
compare_with_pt_model=compare_with_pt_model)
os.remove(config_file)
os.remove(model_file)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--tf_dump_path",
default = None,
type = str,
required = True,
help = "Path to the output Tensorflow dump file.")
parser.add_argument("--model_type",
default = None,
type = str,
help = "Model type selected in the list of {}. If not given, will download and convert all the models from AWS.".format(list(MODEL_CLASSES.keys())))
parser.add_argument("--pytorch_checkpoint_path",
default = None,
type = str,
help = "Path to the PyTorch checkpoint path or shortcut name to download from AWS. "
"If not given, will download and convert all the checkpoints from AWS.")
parser.add_argument("--config_file",
default = None,
type = str,
help = "The config json file corresponding to the pre-trained model. \n"
"This specifies the model architecture. If not given and "
"--pytorch_checkpoint_path is not given or is a shortcut name"
"use the configuration associated to the shortcut name on the AWS")
parser.add_argument("--compare_with_pt_model",
action='store_true',
help = "Compare Tensorflow and PyTorch model predictions.")
parser.add_argument("--use_cached_models",
action='store_true',
help = "Use cached models if possible instead of updating to latest checkpoint versions.")
parser.add_argument("--only_convert_finetuned_models",
action='store_true',
help = "Only convert finetuned models.")
args = parser.parse_args()
# if args.pytorch_checkpoint_path is not None:
# convert_pt_checkpoint_to_tf(args.model_type.lower(),
# args.pytorch_checkpoint_path,
# args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
# args.tf_dump_path,
# compare_with_pt_model=args.compare_with_pt_model,
# use_cached_models=args.use_cached_models)
# else:
convert_all_pt_checkpoints_to_tf(args.model_type.lower() if args.model_type is not None else None,
args.tf_dump_path,
model_shortcut_names_or_path=[args.pytorch_checkpoint_path] if args.pytorch_checkpoint_path is not None else None,
config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
compare_with_pt_model=args.compare_with_pt_model,
use_cached_models=args.use_cached_models,
only_convert_finetuned_models=args.only_convert_finetuned_models)
| 15,133 | 61.02459 | 215 |
py
|
DeeBERT
|
DeeBERT-master/transformers/modeling_roberta.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team 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.
"""PyTorch RoBERTa model. """
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import logging
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss, MSELoss
from .modeling_bert import BertEmbeddings, BertLayerNorm, BertModel, BertPreTrainedModel, gelu
from .configuration_roberta import RobertaConfig
from .file_utils import add_start_docstrings
logger = logging.getLogger(__name__)
ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP = {
'roberta-base': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-pytorch_model.bin",
'roberta-large': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-pytorch_model.bin",
'roberta-large-mnli': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-mnli-pytorch_model.bin",
'distilroberta-base': "https://s3.amazonaws.com/models.huggingface.co/bert/distilroberta-base-pytorch_model.bin",
'roberta-base-openai-detector': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-openai-detector-pytorch_model.bin",
'roberta-large-openai-detector': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-openai-detector-pytorch_model.bin",
}
class RobertaEmbeddings(BertEmbeddings):
"""
Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
"""
def __init__(self, config):
super(RobertaEmbeddings, self).__init__(config)
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)
def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
if input_ids is not None:
input_shape = input_ids.size()
else:
input_shape = inputs_embeds.size()[:-1]
seq_length = input_shape[1]
device = input_ids.device if input_ids is not None else inputs_embeds.device
if position_ids is None:
# Position numbers begin at padding_idx+1. Padding symbols are ignored.
# cf. fairseq's `utils.make_positions`
position_ids = torch.arange(self.padding_idx+1, seq_length+self.padding_idx+1, dtype=torch.long, device=device)
position_ids = position_ids.unsqueeze(0).expand(input_shape)
return super(RobertaEmbeddings, self).forward(input_ids,
token_type_ids=token_type_ids,
position_ids=position_ids,
inputs_embeds=inputs_embeds)
ROBERTA_START_DOCSTRING = r""" The RoBERTa model was proposed in
`RoBERTa: A Robustly Optimized BERT Pretraining Approach`_
by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer,
Veselin Stoyanov. It is based on Google's BERT model released in 2018.
It builds on BERT and modifies key hyperparameters, removing the next-sentence pretraining
objective and training with much larger mini-batches and learning rates.
This implementation is the same as BertModel with a tiny embeddings tweak as well as a setup for Roberta pretrained
models.
This model is a PyTorch `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.
.. _`RoBERTa: A Robustly Optimized BERT Pretraining Approach`:
https://arxiv.org/abs/1907.11692
.. _`torch.nn.Module`:
https://pytorch.org/docs/stable/nn.html#module
Parameters:
config (:class:`~transformers.RobertaConfig`): 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 :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""
ROBERTA_INPUTS_DOCSTRING = r"""
Inputs:
**input_ids**: ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Indices of input sequence tokens in the vocabulary.
To match pre-training, RoBERTa input sequence should be formatted with <s> and </s> tokens as follows:
(a) For sequence pairs:
``tokens: <s> Is this Jacksonville ? </s> </s> No it is not . </s>``
(b) For single sequences:
``tokens: <s> the dog is hairy . </s>``
Fully encoded sequences or sequence pairs can be obtained using the RobertaTokenizer.encode function with
the ``add_special_tokens`` parameter set to ``True``.
RoBERTa is a model with absolute position embeddings so it's usually advised to pad the inputs on
the right rather than the left.
See :func:`transformers.PreTrainedTokenizer.encode` and
:func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
**attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:
Mask to avoid performing attention on padding token indices.
Mask values selected in ``[0, 1]``:
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
**token_type_ids**: (`optional` need to be trained) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Optional segment token indices to indicate first and second portions of the inputs.
This embedding matrice is not trained (not pretrained during RoBERTa pretraining), you will have to train it
during finetuning.
Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
corresponds to a `sentence B` token
(see `BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding`_ for more details).
**position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Indices of positions of each input sequence tokens in the position embeddings.
Selected in the range ``[0, config.max_position_embeddings - 1[``.
**head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
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**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, embedding_dim)``:
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.
"""
@add_start_docstrings("The bare RoBERTa Model transformer outputting raw hidden-states without any specific head on top.",
ROBERTA_START_DOCSTRING, ROBERTA_INPUTS_DOCSTRING)
class RobertaModel(BertModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**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.
**pooler_output**: ``torch.FloatTensor`` of shape ``(batch_size, hidden_size)``
Last layer hidden-state of the first token of the sequence (classification token)
further processed by a Linear layer and a Tanh activation function. The Linear
layer weights are trained from the next sentence prediction (classification)
objective during Bert pretraining. This output is usually *not* a good summary
of the semantic content of the input, you're often better with averaging or pooling
the sequence of hidden-states for the whole input sequence.
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = RobertaModel.from_pretrained('roberta-base')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
outputs = model(input_ids)
last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
"""
config_class = RobertaConfig
pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
base_model_prefix = "roberta"
def __init__(self, config):
super(RobertaModel, self).__init__(config)
self.embeddings = RobertaEmbeddings(config)
self.init_weights()
def get_input_embeddings(self):
return self.embeddings.word_embeddings
def set_input_embeddings(self, value):
self.embeddings.word_embeddings = value
@add_start_docstrings("""RoBERTa Model with a `language modeling` head on top. """,
ROBERTA_START_DOCSTRING, ROBERTA_INPUTS_DOCSTRING)
class RobertaForMaskedLM(BertPreTrainedModel):
r"""
**masked_lm_labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Labels for computing the masked language modeling loss.
Indices should be in ``[-1, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)
Tokens with indices set to ``-1`` are ignored (masked), the loss is only computed for the tokens with labels
in ``[0, ..., config.vocab_size]``
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**loss**: (`optional`, returned when ``masked_lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Masked language modeling loss.
**prediction_scores**: ``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).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = RobertaForMaskedLM.from_pretrained('roberta-base')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
outputs = model(input_ids, masked_lm_labels=input_ids)
loss, prediction_scores = outputs[:2]
"""
config_class = RobertaConfig
pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
base_model_prefix = "roberta"
def __init__(self, config):
super(RobertaForMaskedLM, self).__init__(config)
self.roberta = RobertaModel(config)
self.lm_head = RobertaLMHead(config)
self.init_weights()
def get_output_embeddings(self):
return self.lm_head.decoder
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None,
masked_lm_labels=None):
outputs = self.roberta(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds)
sequence_output = outputs[0]
prediction_scores = self.lm_head(sequence_output)
outputs = (prediction_scores,) + outputs[2:] # Add hidden states and attention if they are here
if masked_lm_labels is not None:
loss_fct = CrossEntropyLoss(ignore_index=-1)
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), masked_lm_labels.view(-1))
outputs = (masked_lm_loss,) + outputs
return outputs # (masked_lm_loss), prediction_scores, (hidden_states), (attentions)
class RobertaLMHead(nn.Module):
"""Roberta Head for masked language modeling."""
def __init__(self, config):
super(RobertaLMHead, self).__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.layer_norm = BertLayerNorm(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))
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) + self.bias
return x
@add_start_docstrings("""RoBERTa Model transformer with a sequence classification/regression head on top (a linear layer
on top of the pooled output) e.g. for GLUE tasks. """,
ROBERTA_START_DOCSTRING, ROBERTA_INPUTS_DOCSTRING)
class RobertaForSequenceClassification(BertPreTrainedModel):
r"""
**labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
Labels for computing the sequence classification/regression loss.
Indices should be in ``[0, ..., config.num_labels]``.
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: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Classification (or regression if config.num_labels==1) loss.
**logits**: ``torch.FloatTensor`` of shape ``(batch_size, config.num_labels)``
Classification (or regression if config.num_labels==1) scores (before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = RobertaForSequenceClassification.from_pretrained('roberta-base')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
labels = torch.tensor([1]).unsqueeze(0) # Batch size 1
outputs = model(input_ids, labels=labels)
loss, logits = outputs[:2]
"""
config_class = RobertaConfig
pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
base_model_prefix = "roberta"
def __init__(self, config):
super(RobertaForSequenceClassification, self).__init__(config)
self.num_labels = config.num_labels
self.roberta = RobertaModel(config)
self.classifier = RobertaClassificationHead(config)
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None,
labels=None):
outputs = self.roberta(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds)
sequence_output = outputs[0]
logits = self.classifier(sequence_output)
outputs = (logits,) + outputs[2:]
if labels is not None:
if self.num_labels == 1:
# We are doing regression
loss_fct = MSELoss()
loss = loss_fct(logits.view(-1), labels.view(-1))
else:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
outputs = (loss,) + outputs
return outputs # (loss), logits, (hidden_states), (attentions)
@add_start_docstrings("""Roberta 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. """,
ROBERTA_START_DOCSTRING, ROBERTA_INPUTS_DOCSTRING)
class RobertaForMultipleChoice(BertPreTrainedModel):
r"""
Inputs:
**input_ids**: ``torch.LongTensor`` of shape ``(batch_size, num_choices, sequence_length)``:
Indices of input sequence tokens in the vocabulary.
The second dimension of the input (`num_choices`) indicates the number of choices to score.
To match pre-training, RoBerta input sequence should be formatted with [CLS] and [SEP] tokens as follows:
(a) For sequence pairs:
``tokens: [CLS] is this jack ##son ##ville ? [SEP] [SEP] no it is not . [SEP]``
``token_type_ids: 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1``
(b) For single sequences:
``tokens: [CLS] the dog is hairy . [SEP]``
``token_type_ids: 0 0 0 0 0 0 0``
Indices can be obtained using :class:`transformers.BertTokenizer`.
See :func:`transformers.PreTrainedTokenizer.encode` and
:func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
**token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, num_choices, sequence_length)``:
Segment token indices to indicate first and second portions of the inputs.
The second dimension of the input (`num_choices`) indicates the number of choices to score.
Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
**attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, num_choices, sequence_length)``:
Mask to avoid performing attention on padding token indices.
The second dimension of the input (`num_choices`) indicates the number of choices to score.
Mask values selected in ``[0, 1]``:
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
**head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
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**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, embedding_dim)``:
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.
**labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
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)
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Classification loss.
**classification_scores**: ``torch.FloatTensor`` of shape ``(batch_size, num_choices)`` where `num_choices` is the size of the second dimension
of the input tensors. (see `input_ids` above).
Classification scores (before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = RobertaForMultipleChoice.from_pretrained('roberta-base')
choices = ["Hello, my dog is cute", "Hello, my cat is amazing"]
input_ids = torch.tensor([tokenizer.encode(s, add_special_tokens=True) for s in choices]).unsqueeze(0) # Batch size 1, 2 choices
labels = torch.tensor(1).unsqueeze(0) # Batch size 1
outputs = model(input_ids, labels=labels)
loss, classification_scores = outputs[:2]
"""
config_class = RobertaConfig
pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
base_model_prefix = "roberta"
def __init__(self, config):
super(RobertaForMultipleChoice, self).__init__(config)
self.roberta = RobertaModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
self.init_weights()
def forward(self, input_ids=None, token_type_ids=None, attention_mask=None, labels=None,
position_ids=None, head_mask=None, inputs_embeds=None):
num_choices = input_ids.shape[1]
flat_input_ids = input_ids.view(-1, input_ids.size(-1))
flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_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
outputs = self.roberta(flat_input_ids, position_ids=flat_position_ids, token_type_ids=flat_token_type_ids,
attention_mask=flat_attention_mask, head_mask=head_mask)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
reshaped_logits = logits.view(-1, num_choices)
outputs = (reshaped_logits,) + outputs[2:] # add hidden states and attention if they are here
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits, labels)
outputs = (loss,) + outputs
return outputs # (loss), reshaped_logits, (hidden_states), (attentions)
@add_start_docstrings("""Roberta 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. """,
ROBERTA_START_DOCSTRING, ROBERTA_INPUTS_DOCSTRING)
class RobertaForTokenClassification(BertPreTrainedModel):
r"""
**labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Labels for computing the token classification loss.
Indices should be in ``[0, ..., config.num_labels - 1]``.
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Classification loss.
**scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, config.num_labels)``
Classification scores (before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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**: (`optional`, returned when ``config.output_attentions=True``)
list 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.
Examples::
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = RobertaForTokenClassification.from_pretrained('roberta-base')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1
labels = torch.tensor([1] * input_ids.size(1)).unsqueeze(0) # Batch size 1
outputs = model(input_ids, labels=labels)
loss, scores = outputs[:2]
"""
config_class = RobertaConfig
pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
base_model_prefix = "roberta"
def __init__(self, config):
super(RobertaForTokenClassification, self).__init__(config)
self.num_labels = config.num_labels
self.roberta = RobertaModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
self.init_weights()
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None,
position_ids=None, head_mask=None, inputs_embeds=None, labels=None):
outputs = self.roberta(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds)
sequence_output = outputs[0]
sequence_output = self.dropout(sequence_output)
logits = self.classifier(sequence_output)
outputs = (logits,) + outputs[2:] # add hidden states and attention if they are here
if labels is not None:
loss_fct = CrossEntropyLoss()
# Only keep active parts of the loss
if attention_mask is not None:
active_loss = attention_mask.view(-1) == 1
active_logits = logits.view(-1, self.num_labels)[active_loss]
active_labels = labels.view(-1)[active_loss]
loss = loss_fct(active_logits, active_labels)
else:
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
outputs = (loss,) + outputs
return outputs # (loss), scores, (hidden_states), (attentions)
class RobertaClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super(RobertaClassificationHead, self).__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 [CLS])
x = self.dropout(x)
x = self.dense(x)
x = torch.tanh(x)
x = self.dropout(x)
x = self.out_proj(x)
return x
| 30,485 | 53.634409 | 151 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tokenization_utils.py
|
# coding=utf-8
# 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 OpenAI GPT."""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import logging
import os
import json
import six
import copy
import itertools
from io import open
from .file_utils import cached_path, is_tf_available, is_torch_available
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
logger = logging.getLogger(__name__)
SPECIAL_TOKENS_MAP_FILE = 'special_tokens_map.json'
ADDED_TOKENS_FILE = 'added_tokens.json'
TOKENIZER_CONFIG_FILE = 'tokenizer_config.json'
class PreTrainedTokenizer(object):
""" Base class for all tokenizers.
Handle all the shared methods for tokenization and special tokens as well as methods dowloading/caching/loading pretrained tokenizers as well as adding tokens to the vocabulary.
This class also contain the added tokens in a unified way on top of all tokenizers so we don't have to handle the specific vocabulary augmentation methods of the various underlying dictionary structures (BPE, sentencepiece...).
Class attributes (overridden by derived classes):
- ``vocab_files_names``: a python ``dict`` with, as keys, the ``__init__`` keyword name of each vocabulary file required by the model, and as associated values, the filename for saving the associated file (string).
- ``pretrained_vocab_files_map``: a python ``dict of dict`` the high-level keys being the ``__init__`` keyword name of each vocabulary file required by the model, the low-level being the `short-cut-names` (string) of the pretrained models with, as associated values, the `url` (string) to the associated pretrained vocabulary file.
- ``max_model_input_sizes``: a python ``dict`` with, as keys, the `short-cut-names` (string) of the pretrained models, and as associated values, the maximum length of the sequence inputs of this model, or None if the model has no maximum input size.
- ``pretrained_init_configuration``: a python ``dict`` with, as keys, the `short-cut-names` (string) of the pretrained models, and as associated values, a dictionnary of specific arguments to pass to the ``__init__``method of the tokenizer class for this pretrained model when loading the tokenizer with the ``from_pretrained()`` method.
Parameters:
- ``bos_token``: (`Optional`) string: a beginning of sentence token. Will be associated to ``self.bos_token`` and ``self.bos_token_id``
- ``eos_token``: (`Optional`) string: an end of sentence token. Will be associated to ``self.eos_token`` and ``self.eos_token_id``
- ``unk_token``: (`Optional`) string: an unknown token. Will be associated to ``self.unk_token`` and ``self.unk_token_id``
- ``sep_token``: (`Optional`) string: a separation token (e.g. to separate context and query in an input sequence). Will be associated to ``self.sep_token`` and ``self.sep_token_id``
- ``pad_token``: (`Optional`) string: a padding token. Will be associated to ``self.pad_token`` and ``self.pad_token_id``
- ``cls_token``: (`Optional`) string: a classification token (e.g. to extract a summary of an input sequence leveraging self-attention along the full depth of the model). Will be associated to ``self.cls_token`` and ``self.cls_token_id``
- ``mask_token``: (`Optional`) string: a masking token (e.g. when training a model with masked-language modeling). Will be associated to ``self.mask_token`` and ``self.mask_token_id``
- ``additional_special_tokens``: (`Optional`) list: a list of additional special tokens. Adding all special tokens here ensure they won't be split by the tokenization process. Will be associated to ``self.additional_special_tokens`` and ``self.additional_special_tokens_ids``
"""
vocab_files_names = {}
pretrained_vocab_files_map = {}
pretrained_init_configuration = {}
max_model_input_sizes = {}
SPECIAL_TOKENS_ATTRIBUTES = ["bos_token", "eos_token", "unk_token", "sep_token",
"pad_token", "cls_token", "mask_token",
"additional_special_tokens"]
@property
def bos_token(self):
""" Beginning of sentence token (string). Log an error if used while not having been set. """
if self._bos_token is None:
logger.error("Using bos_token, but it is not set yet.")
return self._bos_token
@property
def eos_token(self):
""" End of sentence token (string). Log an error if used while not having been set. """
if self._eos_token is None:
logger.error("Using eos_token, but it is not set yet.")
return self._eos_token
@property
def unk_token(self):
""" Unknown token (string). Log an error if used while not having been set. """
if self._unk_token is None:
logger.error("Using unk_token, but it is not set yet.")
return self._unk_token
@property
def sep_token(self):
""" Separation token (string). E.g. separate context and query in an input sequence. Log an error if used while not having been set. """
if self._sep_token is None:
logger.error("Using sep_token, but it is not set yet.")
return self._sep_token
@property
def pad_token(self):
""" Padding token (string). Log an error if used while not having been set. """
if self._pad_token is None:
logger.error("Using pad_token, but it is not set yet.")
return self._pad_token
@property
def cls_token(self):
""" Classification token (string). E.g. to extract a summary of an input sequence leveraging self-attention along the full depth of the model. Log an error if used while not having been set. """
if self._cls_token is None:
logger.error("Using cls_token, but it is not set yet.")
return self._cls_token
@property
def mask_token(self):
""" Mask token (string). E.g. when training a model with masked-language modeling. Log an error if used while not having been set. """
if self._mask_token is None:
logger.error("Using mask_token, but it is not set yet.")
return self._mask_token
@property
def additional_special_tokens(self):
""" All the additional special tokens you may want to use (list of strings). Log an error if used while not having been set. """
if self._additional_special_tokens is None:
logger.error("Using additional_special_tokens, but it is not set yet.")
return self._additional_special_tokens
@bos_token.setter
def bos_token(self, value):
self._bos_token = value
@eos_token.setter
def eos_token(self, value):
self._eos_token = value
@unk_token.setter
def unk_token(self, value):
self._unk_token = value
@sep_token.setter
def sep_token(self, value):
self._sep_token = value
@pad_token.setter
def pad_token(self, value):
self._pad_token = value
@cls_token.setter
def cls_token(self, value):
self._cls_token = value
@mask_token.setter
def mask_token(self, value):
self._mask_token = value
@additional_special_tokens.setter
def additional_special_tokens(self, value):
self._additional_special_tokens = value
@property
def bos_token_id(self):
""" Id of the beginning of sentence token in the vocabulary. Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.bos_token)
@property
def eos_token_id(self):
""" Id of the end of sentence token in the vocabulary. Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.eos_token)
@property
def unk_token_id(self):
""" Id of the unknown token in the vocabulary. Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.unk_token)
@property
def sep_token_id(self):
""" Id of the separation token in the vocabulary. E.g. separate context and query in an input sequence. Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.sep_token)
@property
def pad_token_id(self):
""" Id of the padding token in the vocabulary. Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.pad_token)
@property
def cls_token_id(self):
""" Id of the classification token in the vocabulary. E.g. to extract a summary of an input sequence leveraging self-attention along the full depth of the model. Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.cls_token)
@property
def mask_token_id(self):
""" Id of the mask token in the vocabulary. E.g. when training a model with masked-language modeling. Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.mask_token)
@property
def additional_special_tokens_ids(self):
""" Ids of all the additional special tokens in the vocabulary (list of integers). Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.additional_special_tokens)
def __init__(self, max_len=None, **kwargs):
self._bos_token = None
self._eos_token = None
self._unk_token = None
self._sep_token = None
self._pad_token = None
self._cls_token = None
self._mask_token = None
self._additional_special_tokens = []
self.max_len = max_len if max_len is not None else int(1e12)
# Added tokens
self.added_tokens_encoder = {}
self.added_tokens_decoder = {}
# inputs and kwargs for saving and re-loading (see ``from_pretrained`` and ``save_pretrained``)
self.init_inputs = ()
self.init_kwargs = {}
for key, value in kwargs.items():
if key in self.SPECIAL_TOKENS_ATTRIBUTES:
if key == 'additional_special_tokens':
assert isinstance(value, (list, tuple)) and all(isinstance(t, str) or (six.PY2 and isinstance(t, unicode)) for t in value)
else:
assert isinstance(value, str) or (six.PY2 and isinstance(value, unicode))
setattr(self, key, value)
@classmethod
def from_pretrained(cls, *inputs, **kwargs):
r"""
Instantiate a :class:`~transformers.PreTrainedTokenizer` (or a derived class) from a predefined tokenizer.
Args:
pretrained_model_name_or_path: either:
- a string with the `shortcut name` of a predefined tokenizer to load from cache or download, e.g.: ``bert-base-uncased``.
- a path to a `directory` containing vocabulary files required by the tokenizer, for instance saved using the :func:`~transformers.PreTrainedTokenizer.save_pretrained` method, e.g.: ``./my_model_directory/``.
- (not applicable to all derived classes) a path or url to a single saved vocabulary file if and only if the tokenizer only requires a single vocabulary file (e.g. Bert, XLNet), e.g.: ``./my_model_directory/vocab.txt``.
cache_dir: (`optional`) string:
Path to a directory in which a downloaded predefined tokenizer vocabulary files should be cached if the standard cache should not be used.
force_download: (`optional`) boolean, default False:
Force to (re-)download the vocabulary files and override the cached versions if they exists.
proxies: (`optional`) dict, default None:
A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
The proxies are used on each request.
inputs: (`optional`) positional arguments: will be passed to the Tokenizer ``__init__`` method.
kwargs: (`optional`) keyword arguments: will be passed to the Tokenizer ``__init__`` method. Can be used to set special tokens like ``bos_token``, ``eos_token``, ``unk_token``, ``sep_token``, ``pad_token``, ``cls_token``, ``mask_token``, ``additional_special_tokens``. See parameters in the doc string of :class:`~transformers.PreTrainedTokenizer` for details.
Examples::
# We can't instantiate directly the base class `PreTrainedTokenizer` so let's show our examples on a derived class: BertTokenizer
# Download vocabulary from S3 and cache.
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
# If vocabulary files are in a directory (e.g. tokenizer was saved using `save_pretrained('./test/saved_model/')`)
tokenizer = BertTokenizer.from_pretrained('./test/saved_model/')
# If the tokenizer uses a single vocabulary file, you can point directly to this file
tokenizer = BertTokenizer.from_pretrained('./test/saved_model/my_vocab.txt')
# You can link tokens to special vocabulary when instantiating
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', unk_token='<unk>')
# You should be sure '<unk>' is in the vocabulary when doing that.
# Otherwise use tokenizer.add_special_tokens({'unk_token': '<unk>'}) instead)
assert tokenizer.unk_token == '<unk>'
"""
return cls._from_pretrained(*inputs, **kwargs)
@classmethod
def _from_pretrained(cls, pretrained_model_name_or_path, *init_inputs, **kwargs):
cache_dir = kwargs.pop('cache_dir', None)
force_download = kwargs.pop('force_download', False)
proxies = kwargs.pop('proxies', None)
s3_models = list(cls.max_model_input_sizes.keys())
vocab_files = {}
init_configuration = {}
if pretrained_model_name_or_path in s3_models:
# Get the vocabulary from AWS S3 bucket
for file_id, map_list in cls.pretrained_vocab_files_map.items():
vocab_files[file_id] = map_list[pretrained_model_name_or_path]
if cls.pretrained_init_configuration and pretrained_model_name_or_path in cls.pretrained_init_configuration:
init_configuration = cls.pretrained_init_configuration[pretrained_model_name_or_path]
else:
# Get the vocabulary from local files
logger.info(
"Model name '{}' not found in model shortcut name list ({}). "
"Assuming '{}' is a path or url to a directory containing tokenizer files.".format(
pretrained_model_name_or_path, ', '.join(s3_models),
pretrained_model_name_or_path))
# Look for the tokenizer main vocabulary files
for file_id, file_name in cls.vocab_files_names.items():
if os.path.isdir(pretrained_model_name_or_path):
# If a directory is provided we look for the standard filenames
full_file_name = os.path.join(pretrained_model_name_or_path, file_name)
else:
# If a path to a file is provided we use it (will only work for non-BPE tokenizer using a single vocabulary file)
full_file_name = pretrained_model_name_or_path
if not os.path.exists(full_file_name):
logger.info("Didn't find file {}. We won't load it.".format(full_file_name))
full_file_name = None
vocab_files[file_id] = full_file_name
# Look for the additional tokens files
additional_files_names = {'added_tokens_file': ADDED_TOKENS_FILE,
'special_tokens_map_file': SPECIAL_TOKENS_MAP_FILE,
'tokenizer_config_file': TOKENIZER_CONFIG_FILE,
}
# If a path to a file was provided, get the parent directory
saved_directory = pretrained_model_name_or_path
if os.path.exists(saved_directory) and not os.path.isdir(saved_directory):
saved_directory = os.path.dirname(saved_directory)
for file_id, file_name in additional_files_names.items():
full_file_name = os.path.join(saved_directory, file_name)
if not os.path.exists(full_file_name):
logger.info("Didn't find file {}. We won't load it.".format(full_file_name))
full_file_name = None
vocab_files[file_id] = full_file_name
if all(full_file_name is None for full_file_name in vocab_files.values()):
raise EnvironmentError(
"Model name '{}' was not found in tokenizers model name list ({}). "
"We assumed '{}' was a path or url to a directory containing vocabulary files "
"named {} but couldn't find such vocabulary files at this path or url.".format(
pretrained_model_name_or_path, ', '.join(s3_models),
pretrained_model_name_or_path,
list(cls.vocab_files_names.values())))
# Get files from url, cache, or disk depending on the case
try:
resolved_vocab_files = {}
for file_id, file_path in vocab_files.items():
if file_path is None:
resolved_vocab_files[file_id] = None
else:
resolved_vocab_files[file_id] = cached_path(file_path, cache_dir=cache_dir, force_download=force_download, proxies=proxies)
except EnvironmentError:
if pretrained_model_name_or_path in s3_models:
msg = "Couldn't reach server at '{}' to download vocabulary files."
else:
msg = "Model name '{}' was not found in tokenizers model name list ({}). " \
"We assumed '{}' was a path or url to a directory containing vocabulary files " \
"named {}, but couldn't find such vocabulary files at this path or url.".format(
pretrained_model_name_or_path, ', '.join(s3_models),
pretrained_model_name_or_path,
list(cls.vocab_files_names.values()))
raise EnvironmentError(msg)
for file_id, file_path in vocab_files.items():
if file_path == resolved_vocab_files[file_id]:
logger.info("loading file {}".format(file_path))
else:
logger.info("loading file {} from cache at {}".format(
file_path, resolved_vocab_files[file_id]))
# Prepare tokenizer initialization kwargs
# Did we saved some inputs and kwargs to reload ?
tokenizer_config_file = resolved_vocab_files.pop('tokenizer_config_file', None)
if tokenizer_config_file is not None:
init_kwargs = json.load(open(tokenizer_config_file, encoding="utf-8"))
saved_init_inputs = init_kwargs.pop('init_inputs', ())
if not init_inputs:
init_inputs = saved_init_inputs
else:
init_kwargs = init_configuration
# Update with newly provided kwargs
init_kwargs.update(kwargs)
# Set max length if needed
if pretrained_model_name_or_path in cls.max_model_input_sizes:
# if we're using a pretrained model, ensure the tokenizer
# wont index sequences longer than the number of positional embeddings
max_len = cls.max_model_input_sizes[pretrained_model_name_or_path]
if max_len is not None and isinstance(max_len, (int, float)):
init_kwargs['max_len'] = min(init_kwargs.get('max_len', int(1e12)), max_len)
# Merge resolved_vocab_files arguments in init_kwargs.
added_tokens_file = resolved_vocab_files.pop('added_tokens_file', None)
special_tokens_map_file = resolved_vocab_files.pop('special_tokens_map_file', None)
for args_name, file_path in resolved_vocab_files.items():
if args_name not in init_kwargs:
init_kwargs[args_name] = file_path
if special_tokens_map_file is not None:
special_tokens_map = json.load(open(special_tokens_map_file, encoding="utf-8"))
for key, value in special_tokens_map.items():
if key not in init_kwargs:
init_kwargs[key] = value
# Instantiate tokenizer.
tokenizer = cls(*init_inputs, **init_kwargs)
# Save inputs and kwargs for saving and re-loading with ``save_pretrained``
tokenizer.init_inputs = init_inputs
tokenizer.init_kwargs = init_kwargs
# Add supplementary tokens.
if added_tokens_file is not None:
added_tok_encoder = json.load(open(added_tokens_file, encoding="utf-8"))
added_tok_decoder = {v:k for k, v in added_tok_encoder.items()}
tokenizer.added_tokens_encoder.update(added_tok_encoder)
tokenizer.added_tokens_decoder.update(added_tok_decoder)
return tokenizer
def save_pretrained(self, save_directory):
""" Save the tokenizer vocabulary files together with:
- added tokens,
- special-tokens-to-class-attributes-mapping,
- tokenizer instantiation positional and keywords inputs (e.g. do_lower_case for Bert).
This won't save modifications other than (added tokens and special token mapping) you may have
applied to the tokenizer after the instantiation (e.g. modifying tokenizer.do_lower_case after creation).
This method make sure the full tokenizer can then be re-loaded using the :func:`~transformers.PreTrainedTokenizer.from_pretrained` class method.
"""
if not os.path.isdir(save_directory):
logger.error("Saving directory ({}) should be a directory".format(save_directory))
return
special_tokens_map_file = os.path.join(save_directory, SPECIAL_TOKENS_MAP_FILE)
added_tokens_file = os.path.join(save_directory, ADDED_TOKENS_FILE)
tokenizer_config_file = os.path.join(save_directory, TOKENIZER_CONFIG_FILE)
tokenizer_config = copy.deepcopy(self.init_kwargs)
tokenizer_config['init_inputs'] = copy.deepcopy(self.init_inputs)
for file_id in self.vocab_files_names.keys():
tokenizer_config.pop(file_id, None)
with open(tokenizer_config_file, 'w', encoding='utf-8') as f:
f.write(json.dumps(tokenizer_config, ensure_ascii=False))
with open(special_tokens_map_file, 'w', encoding='utf-8') as f:
f.write(json.dumps(self.special_tokens_map, ensure_ascii=False))
with open(added_tokens_file, 'w', encoding='utf-8') as f:
if self.added_tokens_encoder:
out_str = json.dumps(self.added_tokens_encoder, ensure_ascii=False)
else:
out_str = u"{}"
f.write(out_str)
vocab_files = self.save_vocabulary(save_directory)
return vocab_files + (special_tokens_map_file, added_tokens_file)
def save_vocabulary(self, save_directory):
""" Save the tokenizer vocabulary to a directory. This method does *NOT* save added tokens
and special token mappings.
Please use :func:`~transformers.PreTrainedTokenizer.save_pretrained` `()` to save the full Tokenizer state if you want to reload it using the :func:`~transformers.PreTrainedTokenizer.from_pretrained` class method.
"""
raise NotImplementedError
def vocab_size(self):
""" Size of the base vocabulary (without the added tokens) """
raise NotImplementedError
def __len__(self):
""" Size of the full vocabulary with the added tokens """
return self.vocab_size + len(self.added_tokens_encoder)
def add_tokens(self, new_tokens):
"""
Add a list of new tokens to the tokenizer class. If the new tokens are not in the
vocabulary, they are added to it with indices starting from length of the current vocabulary.
Args:
new_tokens: list of string. Each string is a token to add. Tokens are only added if they are not already in the vocabulary (tested by checking if the tokenizer assign the index of the ``unk_token`` to them).
Returns:
Number of tokens added to the vocabulary.
Examples::
# Let's see how to increase the vocabulary of Bert model and tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')
num_added_toks = tokenizer.add_tokens(['new_tok1', 'my_new-tok2'])
print('We have added', num_added_toks, 'tokens')
model.resize_token_embeddings(len(tokenizer)) # Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e. the length of the tokenizer.
"""
if not new_tokens:
return 0
to_add_tokens = []
for token in new_tokens:
assert isinstance(token, str) or (six.PY2 and isinstance(token, unicode))
if token != self.unk_token and \
self.convert_tokens_to_ids(token) == self.convert_tokens_to_ids(self.unk_token) and \
token not in to_add_tokens:
to_add_tokens.append(token)
logger.info("Adding %s to the vocabulary", token)
added_tok_encoder = dict((tok, len(self) + i) for i, tok in enumerate(to_add_tokens))
added_tok_decoder = {v:k for k, v in added_tok_encoder.items()}
self.added_tokens_encoder.update(added_tok_encoder)
self.added_tokens_decoder.update(added_tok_decoder)
return len(to_add_tokens)
def num_added_tokens(self, pair=False):
"""
Returns the number of added tokens when encoding a sequence with special tokens.
Note:
This encodes inputs and checks the number of added tokens, and is therefore not efficient. Do not put this
inside your training loop.
Args:
pair: Returns the number of added tokens in the case of a sequence pair if set to True, returns the
number of added tokens in the case of a single sequence if set to False.
Returns:
Number of tokens added to sequences
"""
token_ids_0 = []
token_ids_1 = []
return len(self.build_inputs_with_special_tokens(token_ids_0, token_ids_1 if pair else None))
def add_special_tokens(self, special_tokens_dict):
"""
Add a dictionary of special tokens (eos, pad, cls...) to the encoder and link them
to class attributes. If special tokens are NOT in the vocabulary, they are added
to it (indexed starting from the last index of the current vocabulary).
Using `add_special_tokens` will ensure your special tokens can be used in several ways:
- special tokens are carefully handled by the tokenizer (they are never split)
- you can easily refer to special tokens using tokenizer class attributes like `tokenizer.cls_token`. This makes it easy to develop model-agnostic training and fine-tuning scripts.
When possible, special tokens are already registered for provided pretrained models (ex: BertTokenizer cls_token is already registered to be '[CLS]' and XLM's one is also registered to be '</s>')
Args:
special_tokens_dict: dict of string. Keys should be in the list of predefined special attributes:
[``bos_token``, ``eos_token``, ``unk_token``, ``sep_token``, ``pad_token``, ``cls_token``, ``mask_token``,
``additional_special_tokens``].
Tokens are only added if they are not already in the vocabulary (tested by checking if the tokenizer assign the index of the ``unk_token`` to them).
Returns:
Number of tokens added to the vocabulary.
Examples::
# Let's see how to add a new classification token to GPT-2
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2Model.from_pretrained('gpt2')
special_tokens_dict = {'cls_token': '<CLS>'}
num_added_toks = tokenizer.add_special_tokens(special_tokens_dict)
print('We have added', num_added_toks, 'tokens')
model.resize_token_embeddings(len(tokenizer)) # Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e. the length of the tokenizer.
assert tokenizer.cls_token == '<CLS>'
"""
if not special_tokens_dict:
return 0
added_tokens = 0
for key, value in special_tokens_dict.items():
assert key in self.SPECIAL_TOKENS_ATTRIBUTES
if key == 'additional_special_tokens':
assert isinstance(value, (list, tuple)) and all(isinstance(t, str) or (six.PY2 and isinstance(t, unicode)) for t in value)
added_tokens += self.add_tokens(value)
else:
assert isinstance(value, str) or (six.PY2 and isinstance(value, unicode))
added_tokens += self.add_tokens([value])
logger.info("Assigning %s to the %s key of the tokenizer", value, key)
setattr(self, key, value)
return added_tokens
def tokenize(self, text, **kwargs):
""" Converts a string in a sequence of tokens (string), using the tokenizer.
Split in words for word-based vocabulary or sub-words for sub-word-based
vocabularies (BPE/SentencePieces/WordPieces).
Take care of added tokens.
"""
def split_on_token(tok, text):
result = []
split_text = text.split(tok)
for i, sub_text in enumerate(split_text):
sub_text = sub_text.strip()
if i == 0 and not sub_text:
result += [tok]
elif i == len(split_text) - 1:
if sub_text:
result += [sub_text]
else:
pass
else:
if sub_text:
result += [sub_text]
result += [tok]
return result
def split_on_tokens(tok_list, text):
if not text:
return []
if not tok_list:
return self._tokenize(text, **kwargs)
tokenized_text = []
text_list = [text]
for tok in tok_list:
tokenized_text = []
for sub_text in text_list:
if sub_text not in self.added_tokens_encoder \
and sub_text not in self.all_special_tokens:
tokenized_text += split_on_token(tok, sub_text)
else:
tokenized_text += [sub_text]
text_list = tokenized_text
return list(itertools.chain.from_iterable((self._tokenize(token, **kwargs) if token not \
in self.added_tokens_encoder and token not in self.all_special_tokens \
else [token] for token in tokenized_text)))
added_tokens = list(self.added_tokens_encoder.keys()) + self.all_special_tokens
tokenized_text = split_on_tokens(added_tokens, text)
return tokenized_text
def _tokenize(self, text, **kwargs):
""" Converts a string in a sequence of tokens (string), using the tokenizer.
Split in words for word-based vocabulary or sub-words for sub-word-based
vocabularies (BPE/SentencePieces/WordPieces).
Do NOT take care of added tokens.
"""
raise NotImplementedError
def convert_tokens_to_ids(self, tokens):
""" Converts a single token, or a sequence of tokens, (str/unicode) in a single integer id
(resp. a sequence of ids), using the vocabulary.
"""
if tokens is None:
return None
if isinstance(tokens, str) or (six.PY2 and isinstance(tokens, unicode)):
return self._convert_token_to_id_with_added_voc(tokens)
ids = []
for token in tokens:
ids.append(self._convert_token_to_id_with_added_voc(token))
return ids
def _convert_token_to_id_with_added_voc(self, token):
if token is None:
return None
if token in self.added_tokens_encoder:
return self.added_tokens_encoder[token]
return self._convert_token_to_id(token)
def _convert_token_to_id(self, token):
raise NotImplementedError
def encode(self,
text,
text_pair=None,
add_special_tokens=True,
max_length=None,
stride=0,
truncation_strategy='longest_first',
return_tensors=None,
**kwargs):
"""
Converts a string in a sequence of ids (integer), using the tokenizer and vocabulary.
Same as doing ``self.convert_tokens_to_ids(self.tokenize(text))``.
Args:
text: The first sequence to be encoded. This can be a string, a list of strings (tokenized string using
the `tokenize` method) or a list of integers (tokenized string ids using the `convert_tokens_to_ids`
method)
text_pair: Optional second sequence to be encoded. This can be a string, a list of strings (tokenized
string using the `tokenize` method) or a list of integers (tokenized string ids using the
`convert_tokens_to_ids` method)
add_special_tokens: if set to ``True``, the sequences will be encoded with the special tokens relative
to their model.
max_length: if set to a number, will limit the total sequence returned so that it has a maximum length.
If there are overflowing tokens, those will be added to the returned dictionary
stride: if set to a number along with max_length, the overflowing tokens returned will contain some tokens
from the main sequence returned. The value of this argument defines the number of additional tokens.
truncation_strategy: string selected in the following options:
- 'longest_first' (default) Iteratively reduce the inputs sequence until the input is under max_length
starting from the longest one at each token (when there is a pair of input sequences)
- 'only_first': Only truncate the first sequence
- 'only_second': Only truncate the second sequence
- 'do_not_truncate': Does not truncate (raise an error if the input sequence is longer than max_length)
return_tensors: (optional) can be set to 'tf' or 'pt' to return respectively TensorFlow tf.constant
or PyTorch torch.Tensor instead of a list of python integers.
**kwargs: passed to the `self.tokenize()` method
"""
encoded_inputs = self.encode_plus(text,
text_pair=text_pair,
max_length=max_length,
add_special_tokens=add_special_tokens,
stride=stride,
truncation_strategy=truncation_strategy,
return_tensors=return_tensors,
**kwargs)
return encoded_inputs["input_ids"]
def encode_plus(self,
text,
text_pair=None,
add_special_tokens=True,
max_length=None,
stride=0,
truncation_strategy='longest_first',
return_tensors=None,
**kwargs):
"""
Returns a dictionary containing the encoded sequence or sequence pair and additional informations:
the mask for sequence classification and the overflowing elements if a ``max_length`` is specified.
Args:
text: The first sequence to be encoded. This can be a string, a list of strings (tokenized string using
the `tokenize` method) or a list of integers (tokenized string ids using the `convert_tokens_to_ids`
method)
text_pair: Optional second sequence to be encoded. This can be a string, a list of strings (tokenized
string using the `tokenize` method) or a list of integers (tokenized string ids using the
`convert_tokens_to_ids` method)
add_special_tokens: if set to ``True``, the sequences will be encoded with the special tokens relative
to their model.
max_length: if set to a number, will limit the total sequence returned so that it has a maximum length.
If there are overflowing tokens, those will be added to the returned dictionary
stride: if set to a number along with max_length, the overflowing tokens returned will contain some tokens
from the main sequence returned. The value of this argument defines the number of additional tokens.
truncation_strategy: string selected in the following options:
- 'longest_first' (default) Iteratively reduce the inputs sequence until the input is under max_length
starting from the longest one at each token (when there is a pair of input sequences)
- 'only_first': Only truncate the first sequence
- 'only_second': Only truncate the second sequence
- 'do_not_truncate': Does not truncate (raise an error if the input sequence is longer than max_length)
return_tensors: (optional) can be set to 'tf' or 'pt' to return respectively TensorFlow tf.constant
or PyTorch torch.Tensor instead of a list of python integers.
**kwargs: passed to the `self.tokenize()` method
"""
def get_input_ids(text):
if isinstance(text, six.string_types):
return self.convert_tokens_to_ids(self.tokenize(text, **kwargs))
elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], six.string_types):
return self.convert_tokens_to_ids(text)
elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], int):
return text
else:
raise ValueError("Input is not valid. Should be a string, a list/tuple of strings or a list/tuple of integers.")
first_ids = get_input_ids(text)
second_ids = get_input_ids(text_pair) if text_pair is not None else None
return self.prepare_for_model(first_ids,
pair_ids=second_ids,
max_length=max_length,
add_special_tokens=add_special_tokens,
stride=stride,
truncation_strategy=truncation_strategy,
return_tensors=return_tensors)
def prepare_for_model(self, ids, pair_ids=None, max_length=None, add_special_tokens=True, stride=0,
truncation_strategy='longest_first', return_tensors=None):
"""
Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model.
It adds special tokens, truncates
sequences if overflowing while taking into account the special tokens and manages a window stride for
overflowing tokens
Args:
ids: list of tokenized input ids. Can be obtained from a string by chaining the
`tokenize` and `convert_tokens_to_ids` methods.
pair_ids: Optional second list of input ids. Can be obtained from a string by chaining the
`tokenize` and `convert_tokens_to_ids` methods.
max_length: maximum length of the returned list. Will truncate by taking into account the special tokens.
add_special_tokens: if set to ``True``, the sequences will be encoded with the special tokens relative
to their model.
stride: window stride for overflowing tokens. Can be useful for edge effect removal when using sequential
list of inputs.
truncation_strategy: string selected in the following options:
- 'longest_first' (default) Iteratively reduce the inputs sequence until the input is under max_length
starting from the longest one at each token (when there is a pair of input sequences)
- 'only_first': Only truncate the first sequence
- 'only_second': Only truncate the second sequence
- 'do_not_truncate': Does not truncate (raise an error if the input sequence is longer than max_length)
return_tensors: (optional) can be set to 'tf' or 'pt' to return respectively TensorFlow tf.constant
or PyTorch torch.Tensor instead of a list of python integers.
Return:
A Dictionary of shape::
{
input_ids: list[int],
overflowing_tokens: list[int] if a ``max_length`` is specified, else None
special_tokens_mask: list[int] if ``add_special_tokens`` if set to ``True``
}
With the fields:
``input_ids``: list of tokens to be fed to a model
``overflowing_tokens``: list of overflowing tokens if a max length is specified.
``special_tokens_mask``: if adding special tokens, this is a list of [0, 1], with 0 specifying special added
tokens and 1 specifying sequence tokens.
"""
pair = bool(pair_ids is not None)
len_ids = len(ids)
len_pair_ids = len(pair_ids) if pair else 0
encoded_inputs = {}
total_len = len_ids + len_pair_ids + (self.num_added_tokens(pair=pair) if add_special_tokens else 0)
if max_length and total_len > max_length:
ids, pair_ids, overflowing_tokens = self.truncate_sequences(ids, pair_ids=pair_ids,
num_tokens_to_remove=total_len-max_length,
truncation_strategy=truncation_strategy,
stride=stride)
encoded_inputs["overflowing_tokens"] = overflowing_tokens
encoded_inputs["num_truncated_tokens"] = total_len - max_length
if add_special_tokens:
sequence = self.build_inputs_with_special_tokens(ids, pair_ids)
token_type_ids = self.create_token_type_ids_from_sequences(ids, pair_ids)
encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(ids, pair_ids)
else:
sequence = ids + pair_ids if pair else ids
token_type_ids = [0] * len(ids) + ([1] * len(pair_ids) if pair else [])
if return_tensors == 'tf' and is_tf_available():
sequence = tf.constant([sequence])
token_type_ids = tf.constant([token_type_ids])
elif return_tensors == 'pt' and is_torch_available():
sequence = torch.tensor([sequence])
token_type_ids = torch.tensor([token_type_ids])
elif return_tensors is not None:
logger.warning("Unable to convert output to tensors format {}, PyTorch or TensorFlow is not available.".format(return_tensors))
encoded_inputs["input_ids"] = sequence
encoded_inputs["token_type_ids"] = token_type_ids
if max_length and len(encoded_inputs["input_ids"]) > max_length:
encoded_inputs["input_ids"] = encoded_inputs["input_ids"][:max_length]
encoded_inputs["token_type_ids"] = encoded_inputs["token_type_ids"][:max_length]
encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"][:max_length]
if max_length is None and len(encoded_inputs["input_ids"]) > self.max_len:
logger.warning("Token indices sequence length is longer than the specified maximum sequence length "
"for this model ({} > {}). Running this sequence through the model will result in "
"indexing errors".format(len(ids), self.max_len))
return encoded_inputs
def truncate_sequences(self, ids, pair_ids=None, num_tokens_to_remove=0, truncation_strategy='longest_first', stride=0):
"""Truncates a sequence pair in place to the maximum length.
truncation_strategy: string selected in the following options:
- 'longest_first' (default) Iteratively reduce the inputs sequence until the input is under max_length
starting from the longest one at each token (when there is a pair of input sequences).
Overflowing tokens only contains overflow from the first sequence.
- 'only_first': Only truncate the first sequence. raise an error if the first sequence is shorter or equal to than num_tokens_to_remove.
- 'only_second': Only truncate the second sequence
- 'do_not_truncate': Does not truncate (raise an error if the input sequence is longer than max_length)
"""
if num_tokens_to_remove <= 0:
return ids, pair_ids, []
if truncation_strategy == 'longest_first':
overflowing_tokens = []
for _ in range(num_tokens_to_remove):
if pair_ids is None or len(ids) > len(pair_ids):
overflowing_tokens = [ids[-1]] + overflowing_tokens
ids = ids[:-1]
else:
pair_ids = pair_ids[:-1]
window_len = min(len(ids), stride)
if window_len > 0:
overflowing_tokens = ids[-window_len:] + overflowing_tokens
elif truncation_strategy == 'only_first':
assert len(ids) > num_tokens_to_remove
window_len = min(len(ids), stride + num_tokens_to_remove)
overflowing_tokens = ids[-window_len:]
ids = ids[:-num_tokens_to_remove]
elif truncation_strategy == 'only_second':
assert pair_ids is not None and len(pair_ids) > num_tokens_to_remove
window_len = min(len(pair_ids), stride + num_tokens_to_remove)
overflowing_tokens = pair_ids[-window_len:]
pair_ids = pair_ids[:-num_tokens_to_remove]
elif truncation_strategy == 'do_not_truncate':
raise ValueError("Input sequence are too long for max_length. Please select a truncation strategy.")
else:
raise ValueError("Truncation_strategy should be selected in ['longest_first', 'only_first', 'only_second', 'do_not_truncate']")
return (ids, pair_ids, overflowing_tokens)
def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None):
logger.warning("This tokenizer does not make use of special tokens.")
if token_ids_1 is None:
return len(token_ids_0) * [0]
return [0] * len(token_ids_0) + [1] * len(token_ids_1)
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks
by concatenating and adding special tokens.
A RoBERTa sequence has the following format:
single sequence: <s> X </s>
pair of sequences: <s> A </s></s> B </s>
"""
logger.warning("This tokenizer does not make use of special tokens. Input is returned with no modification.")
if token_ids_1 is None:
return token_ids_0
return token_ids_0 + token_ids_1
def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False):
"""
Retrieves 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`` or ``encode_plus`` methods.
Args:
token_ids_0: list of ids (must not contain special tokens)
token_ids_1: Optional list of ids (must not contain special tokens), necessary when fetching sequence ids
for sequence pairs
already_has_special_tokens: (default False) Set to True if the token list is already formated with
special tokens for the model
Returns:
A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
return [0] * ((len(token_ids_1) if token_ids_1 else 0) + len(token_ids_0))
def convert_ids_to_tokens(self, ids, skip_special_tokens=False):
""" Converts a single index or a sequence of indices (integers) in a token "
(resp.) a sequence of tokens (str/unicode), using the vocabulary and added tokens.
Args:
skip_special_tokens: Don't decode special tokens (self.all_special_tokens). Default: False
"""
if isinstance(ids, int):
if ids in self.added_tokens_decoder:
return self.added_tokens_decoder[ids]
else:
return self._convert_id_to_token(ids)
tokens = []
for index in ids:
if skip_special_tokens and index in self.all_special_ids:
continue
if index in self.added_tokens_decoder:
tokens.append(self.added_tokens_decoder[index])
else:
tokens.append(self._convert_id_to_token(index))
return tokens
def _convert_id_to_token(self, index):
raise NotImplementedError
def convert_tokens_to_string(self, tokens):
""" Converts a sequence of tokens (string) in a single string.
The most simple way to do it is ' '.join(self.convert_ids_to_tokens(token_ids))
but we often want to remove sub-word tokenization artifacts at the same time.
"""
return ' '.join(self.convert_ids_to_tokens(tokens))
def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
"""
Converts a sequence of ids (integer) in a string, using the tokenizer and vocabulary
with options to remove special tokens and clean up tokenization spaces.
Similar to doing ``self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))``.
Args:
token_ids: list of tokenized input ids. Can be obtained using the `encode` or `encode_plus` methods.
skip_special_tokens: if set to True, will replace special tokens.
clean_up_tokenization_spaces: if set to True, will clean up the tokenization spaces.
"""
filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens)
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separatly for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
sub_texts = []
current_sub_text = []
for token in filtered_tokens:
if skip_special_tokens and token in self.all_special_ids:
continue
if token in self.added_tokens_encoder:
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(current_sub_text))
current_sub_text = []
sub_texts.append(" " + token)
else:
current_sub_text.append(token)
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(current_sub_text))
text = ''.join(sub_texts)
if clean_up_tokenization_spaces:
clean_text = self.clean_up_tokenization(text)
return clean_text
else:
return text
@property
def special_tokens_map(self):
""" A dictionary mapping special token class attribute (cls_token, unk_token...) to their
values ('<unk>', '<cls>'...)
"""
set_attr = {}
for attr in self.SPECIAL_TOKENS_ATTRIBUTES:
attr_value = getattr(self, "_" + attr)
if attr_value:
set_attr[attr] = attr_value
return set_attr
@property
def all_special_tokens(self):
""" List all the special tokens ('<unk>', '<cls>'...) mapped to class attributes
(cls_token, unk_token...).
"""
all_toks = []
set_attr = self.special_tokens_map
for attr_value in set_attr.values():
all_toks = all_toks + (list(attr_value) if isinstance(attr_value, (list, tuple)) else [attr_value])
all_toks = list(set(all_toks))
return all_toks
@property
def all_special_ids(self):
""" List the vocabulary indices of the special tokens ('<unk>', '<cls>'...) mapped to
class attributes (cls_token, unk_token...).
"""
all_toks = self.all_special_tokens
all_ids = self.convert_tokens_to_ids(all_toks)
return all_ids
@staticmethod
def clean_up_tokenization(out_string):
""" Clean up a list of simple English tokenization artifacts like spaces before punctuations and abreviated forms.
"""
out_string = out_string.replace(' .', '.').replace(' ?', '?').replace(' !', '!').replace(' ,', ','
).replace(" ' ", "'").replace(" n't", "n't").replace(" 'm", "'m").replace(" do not", " don't"
).replace(" 's", "'s").replace(" 've", "'ve").replace(" 're", "'re")
return out_string
| 55,072 | 50.422035 | 372 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tests/modeling_transfo_xl_test.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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 absolute_import
from __future__ import division
from __future__ import print_function
import unittest
import random
import shutil
import pytest
from transformers import is_torch_available
if is_torch_available():
import torch
from transformers import (TransfoXLConfig, TransfoXLModel, TransfoXLLMHeadModel)
from transformers.modeling_transfo_xl import TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP
else:
pytestmark = pytest.mark.skip("Require Torch")
from .modeling_common_test import (CommonTestCases, ids_tensor)
from .configuration_common_test import ConfigTester
class TransfoXLModelTest(CommonTestCases.CommonModelTester):
all_model_classes = (TransfoXLModel, TransfoXLLMHeadModel) if is_torch_available() else ()
test_pruning = False
test_torchscript = False
test_resize_embeddings = False
class TransfoXLModelTester(object):
def __init__(self,
parent,
batch_size=13,
seq_length=7,
mem_len=30,
clamp_len=15,
is_training=True,
use_labels=True,
vocab_size=99,
cutoffs=[10, 50, 80],
hidden_size=32,
d_embed=32,
num_attention_heads=4,
d_head=8,
d_inner=128,
div_val=2,
num_hidden_layers=5,
scope=None,
seed=1,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.mem_len = mem_len
self.key_len = seq_length + mem_len
self.clamp_len = clamp_len
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.cutoffs = cutoffs
self.hidden_size = hidden_size
self.d_embed = d_embed
self.num_attention_heads = num_attention_heads
self.d_head = d_head
self.d_inner = d_inner
self.div_val = div_val
self.num_hidden_layers = num_hidden_layers
self.scope = scope
self.seed = seed
def prepare_config_and_inputs(self):
input_ids_1 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_ids_2 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
lm_labels = None
if self.use_labels:
lm_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
config = TransfoXLConfig(
vocab_size_or_config_json_file=self.vocab_size,
mem_len=self.mem_len,
clamp_len=self.clamp_len,
cutoffs=self.cutoffs,
d_model=self.hidden_size,
d_embed=self.d_embed,
n_head=self.num_attention_heads,
d_head=self.d_head,
d_inner=self.d_inner,
div_val=self.div_val,
n_layer=self.num_hidden_layers)
return (config, input_ids_1, input_ids_2, lm_labels)
def set_seed(self):
random.seed(self.seed)
torch.manual_seed(self.seed)
def create_transfo_xl_model(self, config, input_ids_1, input_ids_2, lm_labels):
model = TransfoXLModel(config)
model.eval()
hidden_states_1, mems_1 = model(input_ids_1)
hidden_states_2, mems_2 = model(input_ids_2, mems_1)
outputs = {
"hidden_states_1": hidden_states_1,
"mems_1": mems_1,
"hidden_states_2": hidden_states_2,
"mems_2": mems_2,
}
return outputs
def check_transfo_xl_model_output(self, result):
self.parent.assertListEqual(
list(result["hidden_states_1"].size()),
[self.batch_size, self.seq_length, self.hidden_size])
self.parent.assertListEqual(
list(result["hidden_states_2"].size()),
[self.batch_size, self.seq_length, self.hidden_size])
self.parent.assertListEqual(
list(list(mem.size()) for mem in result["mems_1"]),
[[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
self.parent.assertListEqual(
list(list(mem.size()) for mem in result["mems_2"]),
[[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
def create_transfo_xl_lm_head(self, config, input_ids_1, input_ids_2, lm_labels):
model = TransfoXLLMHeadModel(config)
model.eval()
lm_logits_1, mems_1 = model(input_ids_1)
loss_1, _, mems_1 = model(input_ids_1, labels=lm_labels)
lm_logits_2, mems_2 = model(input_ids_2, mems=mems_1)
loss_2, _, mems_2 = model(input_ids_2, labels=lm_labels, mems=mems_1)
outputs = {
"loss_1": loss_1,
"mems_1": mems_1,
"lm_logits_1": lm_logits_1,
"loss_2": loss_2,
"mems_2": mems_2,
"lm_logits_2": lm_logits_2,
}
return outputs
def check_transfo_xl_lm_head_output(self, result):
self.parent.assertListEqual(
list(result["loss_1"].size()),
[self.batch_size, self.seq_length])
self.parent.assertListEqual(
list(result["lm_logits_1"].size()),
[self.batch_size, self.seq_length, self.vocab_size])
self.parent.assertListEqual(
list(list(mem.size()) for mem in result["mems_1"]),
[[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
self.parent.assertListEqual(
list(result["loss_2"].size()),
[self.batch_size, self.seq_length])
self.parent.assertListEqual(
list(result["lm_logits_2"].size()),
[self.batch_size, self.seq_length, self.vocab_size])
self.parent.assertListEqual(
list(list(mem.size()) for mem in result["mems_2"]),
[[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids_1, input_ids_2, lm_labels) = config_and_inputs
inputs_dict = {'input_ids': input_ids_1}
return config, inputs_dict
def setUp(self):
self.model_tester = TransfoXLModelTest.TransfoXLModelTester(self)
self.config_tester = ConfigTester(self, config_class=TransfoXLConfig, d_embed=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_transfo_xl_model(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
output_result = self.model_tester.create_transfo_xl_model(*config_and_inputs)
self.model_tester.check_transfo_xl_model_output(output_result)
def test_transfo_xl_lm_head(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
output_result = self.model_tester.create_transfo_xl_lm_head(*config_and_inputs)
self.model_tester.check_transfo_xl_lm_head_output(output_result)
@pytest.mark.slow
def test_model_from_pretrained(self):
cache_dir = "/tmp/transformers_test/"
for model_name in list(TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = TransfoXLModel.from_pretrained(model_name, cache_dir=cache_dir)
shutil.rmtree(cache_dir)
self.assertIsNotNone(model)
if __name__ == "__main__":
unittest.main()
| 8,700 | 38.912844 | 94 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tests/tokenization_transfo_xl_test.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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 absolute_import, division, print_function, unicode_literals
import os
import unittest
import pytest
from io import open
from transformers import is_torch_available
if is_torch_available():
import torch
from transformers.tokenization_transfo_xl import TransfoXLTokenizer, VOCAB_FILES_NAMES
else:
pytestmark = pytest.mark.skip("Require Torch") # TODO: untangle Transfo-XL tokenizer from torch.load and torch.save
from .tokenization_tests_commons import CommonTestCases
class TransfoXLTokenizationTest(CommonTestCases.CommonTokenizerTester):
tokenizer_class = TransfoXLTokenizer if is_torch_available() else None
def setUp(self):
super(TransfoXLTokenizationTest, self).setUp()
vocab_tokens = [
"<unk>", "[CLS]", "[SEP]", "want", "unwanted", "wa", "un",
"running", ",", "low", "l",
]
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_tokenizer(self, **kwargs):
kwargs['lower_case'] = True
return TransfoXLTokenizer.from_pretrained(self.tmpdirname, **kwargs)
def get_input_output_texts(self):
input_text = u"<unk> UNwanted , running"
output_text = u"<unk> unwanted, running"
return input_text, output_text
def test_full_tokenizer(self):
tokenizer = TransfoXLTokenizer(vocab_file=self.vocab_file, lower_case=True)
tokens = tokenizer.tokenize(u"<unk> UNwanted , running")
self.assertListEqual(tokens, ["<unk>", "unwanted", ",", "running"])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(tokens), [0, 4, 8, 7])
def test_full_tokenizer_lower(self):
tokenizer = TransfoXLTokenizer(lower_case=True)
self.assertListEqual(
tokenizer.tokenize(u" \tHeLLo ! how \n Are yoU ? "),
["hello", "!", "how", "are", "you", "?"])
def test_full_tokenizer_no_lower(self):
tokenizer = TransfoXLTokenizer(lower_case=False)
self.assertListEqual(
tokenizer.tokenize(u" \tHeLLo ! how \n Are yoU ? "),
["HeLLo", "!", "how", "Are", "yoU", "?"])
if __name__ == '__main__':
unittest.main()
| 2,975 | 35.292683 | 120 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tests/tokenization_tests_commons.py
|
# coding=utf-8
# Copyright 2019 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.
from __future__ import absolute_import, division, print_function, unicode_literals
import os
import sys
from io import open
import tempfile
import shutil
import unittest
if sys.version_info[0] == 2:
import cPickle as pickle
class TemporaryDirectory(object):
"""Context manager for tempfile.mkdtemp() so it's usable with "with" statement."""
def __enter__(self):
self.name = tempfile.mkdtemp()
return self.name
def __exit__(self, exc_type, exc_value, traceback):
shutil.rmtree(self.name)
else:
import pickle
TemporaryDirectory = tempfile.TemporaryDirectory
unicode = str
class CommonTestCases:
class CommonTokenizerTester(unittest.TestCase):
tokenizer_class = None
def setUp(self):
self.tmpdirname = tempfile.mkdtemp()
def tearDown(self):
shutil.rmtree(self.tmpdirname)
def get_tokenizer(self, **kwargs):
raise NotImplementedError
def get_input_output_texts(self):
raise NotImplementedError
def test_tokenizers_common_properties(self):
tokenizer = self.get_tokenizer()
attributes_list = ["bos_token", "eos_token", "unk_token", "sep_token",
"pad_token", "cls_token", "mask_token"]
for attr in attributes_list:
self.assertTrue(hasattr(tokenizer, attr))
self.assertTrue(hasattr(tokenizer, attr + "_id"))
self.assertTrue(hasattr(tokenizer, "additional_special_tokens"))
self.assertTrue(hasattr(tokenizer, 'additional_special_tokens_ids'))
attributes_list = ["max_len", "init_inputs", "init_kwargs", "added_tokens_encoder",
"added_tokens_decoder"]
for attr in attributes_list:
self.assertTrue(hasattr(tokenizer, attr))
def test_save_and_load_tokenizer(self):
# safety check on max_len default value so we are sure the test works
tokenizer = self.get_tokenizer()
self.assertNotEqual(tokenizer.max_len, 42)
# Now let's start the test
tokenizer = self.get_tokenizer(max_len=42)
before_tokens = tokenizer.encode(u"He is very happy, UNwant\u00E9d,running", add_special_tokens=False)
with TemporaryDirectory() as tmpdirname:
tokenizer.save_pretrained(tmpdirname)
tokenizer = self.tokenizer_class.from_pretrained(tmpdirname)
after_tokens = tokenizer.encode(u"He is very happy, UNwant\u00E9d,running", add_special_tokens=False)
self.assertListEqual(before_tokens, after_tokens)
self.assertEqual(tokenizer.max_len, 42)
tokenizer = self.tokenizer_class.from_pretrained(tmpdirname, max_len=43)
self.assertEqual(tokenizer.max_len, 43)
def test_pickle_tokenizer(self):
tokenizer = self.get_tokenizer()
self.assertIsNotNone(tokenizer)
text = u"Munich and Berlin are nice cities"
subwords = tokenizer.tokenize(text)
with TemporaryDirectory() as tmpdirname:
filename = os.path.join(tmpdirname, u"tokenizer.bin")
pickle.dump(tokenizer, open(filename, "wb"))
tokenizer_new = pickle.load(open(filename, "rb"))
subwords_loaded = tokenizer_new.tokenize(text)
self.assertListEqual(subwords, subwords_loaded)
def test_add_tokens_tokenizer(self):
tokenizer = self.get_tokenizer()
vocab_size = tokenizer.vocab_size
all_size = len(tokenizer)
self.assertNotEqual(vocab_size, 0)
self.assertEqual(vocab_size, all_size)
new_toks = ["aaaaa bbbbbb", "cccccccccdddddddd"]
added_toks = tokenizer.add_tokens(new_toks)
vocab_size_2 = tokenizer.vocab_size
all_size_2 = len(tokenizer)
self.assertNotEqual(vocab_size_2, 0)
self.assertEqual(vocab_size, vocab_size_2)
self.assertEqual(added_toks, len(new_toks))
self.assertEqual(all_size_2, all_size + len(new_toks))
tokens = tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l", add_special_tokens=False)
out_string = tokenizer.decode(tokens)
self.assertGreaterEqual(len(tokens), 4)
self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
self.assertGreater(tokens[-2], tokenizer.vocab_size - 1)
new_toks_2 = {'eos_token': ">>>>|||<||<<|<<",
'pad_token': "<<<<<|||>|>>>>|>"}
added_toks_2 = tokenizer.add_special_tokens(new_toks_2)
vocab_size_3 = tokenizer.vocab_size
all_size_3 = len(tokenizer)
self.assertNotEqual(vocab_size_3, 0)
self.assertEqual(vocab_size, vocab_size_3)
self.assertEqual(added_toks_2, len(new_toks_2))
self.assertEqual(all_size_3, all_size_2 + len(new_toks_2))
tokens = tokenizer.encode(">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l",
add_special_tokens=False)
out_string = tokenizer.decode(tokens)
self.assertGreaterEqual(len(tokens), 6)
self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
self.assertGreater(tokens[0], tokens[1])
self.assertGreater(tokens[-2], tokenizer.vocab_size - 1)
self.assertGreater(tokens[-2], tokens[-3])
self.assertEqual(tokens[0], tokenizer.eos_token_id)
self.assertEqual(tokens[-2], tokenizer.pad_token_id)
def test_add_special_tokens(self):
tokenizer = self.get_tokenizer()
input_text, output_text = self.get_input_output_texts()
special_token = "[SPECIAL TOKEN]"
tokenizer.add_special_tokens({"cls_token": special_token})
encoded_special_token = tokenizer.encode(special_token, add_special_tokens=False)
assert len(encoded_special_token) == 1
text = " ".join([input_text, special_token, output_text])
encoded = tokenizer.encode(text, add_special_tokens=False)
input_encoded = tokenizer.encode(input_text, add_special_tokens=False)
output_encoded = tokenizer.encode(output_text, add_special_tokens=False)
special_token_id = tokenizer.encode(special_token, add_special_tokens=False)
assert encoded == input_encoded + special_token_id + output_encoded
decoded = tokenizer.decode(encoded, skip_special_tokens=True)
assert special_token not in decoded
def test_required_methods_tokenizer(self):
tokenizer = self.get_tokenizer()
input_text, output_text = self.get_input_output_texts()
tokens = tokenizer.tokenize(input_text)
ids = tokenizer.convert_tokens_to_ids(tokens)
ids_2 = tokenizer.encode(input_text, add_special_tokens=False)
self.assertListEqual(ids, ids_2)
tokens_2 = tokenizer.convert_ids_to_tokens(ids)
text_2 = tokenizer.decode(ids)
self.assertEqual(text_2, output_text)
self.assertNotEqual(len(tokens_2), 0)
self.assertIsInstance(text_2, (str, unicode))
def test_pretrained_model_lists(self):
weights_list = list(self.tokenizer_class.max_model_input_sizes.keys())
weights_lists_2 = []
for file_id, map_list in self.tokenizer_class.pretrained_vocab_files_map.items():
weights_lists_2.append(list(map_list.keys()))
for weights_list_2 in weights_lists_2:
self.assertListEqual(weights_list, weights_list_2)
def test_mask_output(self):
if sys.version_info <= (3, 0):
return
tokenizer = self.get_tokenizer()
if tokenizer.build_inputs_with_special_tokens.__qualname__.split('.')[0] != "PreTrainedTokenizer":
seq_0 = "Test this method."
seq_1 = "With these inputs."
information = tokenizer.encode_plus(seq_0, seq_1, add_special_tokens=True)
sequences, mask = information["input_ids"], information["token_type_ids"]
self.assertEqual(len(sequences), len(mask))
def test_number_of_added_tokens(self):
tokenizer = self.get_tokenizer()
seq_0 = "Test this method."
seq_1 = "With these inputs."
sequences = tokenizer.encode(seq_0, seq_1, add_special_tokens=False)
attached_sequences = tokenizer.encode(seq_0, seq_1, add_special_tokens=True)
# Method is implemented (e.g. not GPT-2)
if len(attached_sequences) != 2:
self.assertEqual(tokenizer.num_added_tokens(pair=True), len(attached_sequences) - len(sequences))
def test_maximum_encoding_length_single_input(self):
tokenizer = self.get_tokenizer()
seq_0 = "This is a sentence to be encoded."
stride = 2
sequence = tokenizer.encode(seq_0, add_special_tokens=False)
num_added_tokens = tokenizer.num_added_tokens()
total_length = len(sequence) + num_added_tokens
information = tokenizer.encode_plus(seq_0, max_length=total_length - 2, add_special_tokens=True, stride=stride)
truncated_sequence = information["input_ids"]
overflowing_tokens = information["overflowing_tokens"]
self.assertEqual(len(overflowing_tokens), 2 + stride)
self.assertEqual(overflowing_tokens, sequence[-(2 + stride):])
self.assertEqual(len(truncated_sequence), total_length - 2)
self.assertEqual(truncated_sequence, tokenizer.build_inputs_with_special_tokens(sequence[:-2]))
def test_maximum_encoding_length_pair_input(self):
tokenizer = self.get_tokenizer()
seq_0 = "This is a sentence to be encoded."
seq_1 = "This is another sentence to be encoded."
stride = 2
sequence_0_no_special_tokens = tokenizer.encode(seq_0, add_special_tokens=False)
sequence_1_no_special_tokens = tokenizer.encode(seq_1, add_special_tokens=False)
sequence = tokenizer.encode(seq_0, seq_1, add_special_tokens=True)
truncated_second_sequence = tokenizer.build_inputs_with_special_tokens(
tokenizer.encode(seq_0, add_special_tokens=False),
tokenizer.encode(seq_1, add_special_tokens=False)[:-2]
)
information = tokenizer.encode_plus(seq_0, seq_1, max_length=len(sequence) - 2, add_special_tokens=True,
stride=stride, truncation_strategy='only_second')
information_first_truncated = tokenizer.encode_plus(seq_0, seq_1, max_length=len(sequence) - 2,
add_special_tokens=True, stride=stride,
truncation_strategy='only_first')
truncated_sequence = information["input_ids"]
overflowing_tokens = information["overflowing_tokens"]
overflowing_tokens_first_truncated = information_first_truncated["overflowing_tokens"]
self.assertEqual(len(overflowing_tokens), 2 + stride)
self.assertEqual(overflowing_tokens, sequence_1_no_special_tokens[-(2 + stride):])
self.assertEqual(overflowing_tokens_first_truncated, sequence_0_no_special_tokens[-(2 + stride):])
self.assertEqual(len(truncated_sequence), len(sequence) - 2)
self.assertEqual(truncated_sequence, truncated_second_sequence)
def test_encode_input_type(self):
tokenizer = self.get_tokenizer()
sequence = "Let's encode this sequence"
tokens = tokenizer.tokenize(sequence)
input_ids = tokenizer.convert_tokens_to_ids(tokens)
formatted_input = tokenizer.encode(sequence, add_special_tokens=True)
self.assertEqual(tokenizer.encode(tokens, add_special_tokens=True), formatted_input)
self.assertEqual(tokenizer.encode(input_ids, add_special_tokens=True), formatted_input)
def test_special_tokens_mask(self):
tokenizer = self.get_tokenizer()
sequence_0 = "Encode this."
sequence_1 = "This one too please."
# Testing single inputs
encoded_sequence = tokenizer.encode(sequence_0, add_special_tokens=False)
encoded_sequence_dict = tokenizer.encode_plus(sequence_0, add_special_tokens=True)
encoded_sequence_w_special = encoded_sequence_dict["input_ids"]
special_tokens_mask = encoded_sequence_dict["special_tokens_mask"]
self.assertEqual(len(special_tokens_mask), len(encoded_sequence_w_special))
filtered_sequence = [(x if not special_tokens_mask[i] else None) for i, x in enumerate(encoded_sequence_w_special)]
filtered_sequence = [x for x in filtered_sequence if x is not None]
self.assertEqual(encoded_sequence, filtered_sequence)
# Testing inputs pairs
encoded_sequence = tokenizer.encode(sequence_0, add_special_tokens=False) + tokenizer.encode(sequence_1,
add_special_tokens=False)
encoded_sequence_dict = tokenizer.encode_plus(sequence_0, sequence_1, add_special_tokens=True)
encoded_sequence_w_special = encoded_sequence_dict["input_ids"]
special_tokens_mask = encoded_sequence_dict["special_tokens_mask"]
self.assertEqual(len(special_tokens_mask), len(encoded_sequence_w_special))
filtered_sequence = [(x if not special_tokens_mask[i] else None) for i, x in enumerate(encoded_sequence_w_special)]
filtered_sequence = [x for x in filtered_sequence if x is not None]
self.assertEqual(encoded_sequence, filtered_sequence)
# Testing with already existing special tokens
if tokenizer.cls_token_id == tokenizer.unk_token_id and tokenizer.cls_token_id == tokenizer.unk_token_id:
tokenizer.add_special_tokens({'cls_token': '</s>', 'sep_token': '<s>'})
encoded_sequence_dict = tokenizer.encode_plus(sequence_0, add_special_tokens=True)
encoded_sequence_w_special = encoded_sequence_dict["input_ids"]
special_tokens_mask_orig = encoded_sequence_dict["special_tokens_mask"]
special_tokens_mask = tokenizer.get_special_tokens_mask(encoded_sequence_w_special, already_has_special_tokens=True)
self.assertEqual(len(special_tokens_mask), len(encoded_sequence_w_special))
self.assertEqual(special_tokens_mask_orig, special_tokens_mask)
| 15,663 | 45.343195 | 130 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tests/modeling_tf_auto_test.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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 absolute_import
from __future__ import division
from __future__ import print_function
import unittest
import shutil
import pytest
import logging
from transformers import is_tf_available
if is_tf_available():
from transformers import (AutoConfig, BertConfig,
TFAutoModel, TFBertModel,
TFAutoModelWithLMHead, TFBertForMaskedLM,
TFAutoModelForSequenceClassification, TFBertForSequenceClassification,
TFAutoModelForQuestionAnswering, TFBertForQuestionAnswering)
from transformers.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP
from .modeling_common_test import (CommonTestCases, ids_tensor)
from .configuration_common_test import ConfigTester
else:
pytestmark = pytest.mark.skip("Require TensorFlow")
class TFAutoModelTest(unittest.TestCase):
def test_model_from_pretrained(self):
import h5py
self.assertTrue(h5py.version.hdf5_version.startswith("1.10"))
logging.basicConfig(level=logging.INFO)
# for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
for model_name in ['bert-base-uncased']:
config = AutoConfig.from_pretrained(model_name, force_download=True)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = TFAutoModel.from_pretrained(model_name, force_download=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertModel)
def test_lmhead_model_from_pretrained(self):
logging.basicConfig(level=logging.INFO)
# for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
for model_name in ['bert-base-uncased']:
config = AutoConfig.from_pretrained(model_name, force_download=True)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = TFAutoModelWithLMHead.from_pretrained(model_name, force_download=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertForMaskedLM)
def test_sequence_classification_model_from_pretrained(self):
logging.basicConfig(level=logging.INFO)
# for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
for model_name in ['bert-base-uncased']:
config = AutoConfig.from_pretrained(model_name, force_download=True)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = TFAutoModelForSequenceClassification.from_pretrained(model_name, force_download=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertForSequenceClassification)
def test_question_answering_model_from_pretrained(self):
logging.basicConfig(level=logging.INFO)
# for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
for model_name in ['bert-base-uncased']:
config = AutoConfig.from_pretrained(model_name, force_download=True)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = TFAutoModelForQuestionAnswering.from_pretrained(model_name, force_download=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertForQuestionAnswering)
if __name__ == "__main__":
unittest.main()
| 4,154 | 42.736842 | 108 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tests/modeling_tf_ctrl_test.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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 absolute_import
from __future__ import division
from __future__ import print_function
import unittest
import shutil
import pytest
import sys
from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
from .configuration_common_test import ConfigTester
from transformers import CTRLConfig, is_tf_available
if is_tf_available():
import tensorflow as tf
from transformers.modeling_tf_ctrl import (TFCTRLModel, TFCTRLLMHeadModel,
TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP)
else:
pytestmark = pytest.mark.skip("Require TensorFlow")
class TFCTRLModelTest(TFCommonTestCases.TFCommonModelTester):
all_model_classes = (TFCTRLModel, TFCTRLLMHeadModel) if is_tf_available() else ()
class TFCTRLModelTester(object):
def __init__(self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_token_type_ids=True,
use_input_mask=True,
use_labels=True,
use_mc_token_ids=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
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_token_type_ids = use_token_type_ids
self.use_input_mask = use_input_mask
self.use_labels = use_labels
self.use_mc_token_ids = use_mc_token_ids
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 = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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)
mc_token_ids = None
if self.use_mc_token_ids:
mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length)
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 = CTRLConfig(
vocab_size_or_config_json_file=self.vocab_size,
n_embd=self.hidden_size,
n_layer=self.num_hidden_layers,
n_head=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,
n_positions=self.max_position_embeddings,
n_ctx=self.max_position_embeddings
# type_vocab_size=self.type_vocab_size,
# initializer_range=self.initializer_range
)
head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
return config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, token_labels, choice_labels
def create_and_check_ctrl_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = TFCTRLModel(config=config)
inputs = {'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids}
sequence_output = model(inputs)[0]
inputs = [input_ids, None, input_mask] # None is the input for 'past'
sequence_output = model(inputs)[0]
sequence_output = model(input_ids)[0]
result = {
"sequence_output": sequence_output.numpy(),
}
self.parent.assertListEqual(
list(result["sequence_output"].shape),
[self.batch_size, self.seq_length, self.hidden_size])
def create_and_check_ctrl_lm_head(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = TFCTRLLMHeadModel(config=config)
inputs = {'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids}
prediction_scores = model(inputs)[0]
result = {
"prediction_scores": prediction_scores.numpy(),
}
self.parent.assertListEqual(
list(result["prediction_scores"].shape),
[self.batch_size, self.seq_length, self.vocab_size])
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids, input_mask, head_mask, token_type_ids,
mc_token_ids, 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
def setUp(self):
self.model_tester = TFCTRLModelTest.TFCTRLModelTester(self)
self.config_tester = ConfigTester(self, config_class=CTRLConfig, n_embd=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_ctrl_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_ctrl_model(*config_and_inputs)
def test_ctrl_lm_head(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_ctrl_lm_head(*config_and_inputs)
@pytest.mark.slow
def test_model_from_pretrained(self):
cache_dir = "/tmp/transformers_test/"
for model_name in list(TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = TFCTRLModel.from_pretrained(model_name, cache_dir=cache_dir)
shutil.rmtree(cache_dir)
self.assertIsNotNone(model)
if __name__ == "__main__":
unittest.main()
| 8,453 | 40.851485 | 135 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tests/modeling_tf_gpt2_test.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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 absolute_import
from __future__ import division
from __future__ import print_function
import unittest
import shutil
import pytest
import sys
from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
from .configuration_common_test import ConfigTester
from transformers import GPT2Config, is_tf_available
if is_tf_available():
import tensorflow as tf
from transformers.modeling_tf_gpt2 import (TFGPT2Model, TFGPT2LMHeadModel,
TFGPT2DoubleHeadsModel,
TF_GPT2_PRETRAINED_MODEL_ARCHIVE_MAP)
else:
pytestmark = pytest.mark.skip("Require TensorFlow")
class TFGPT2ModelTest(TFCommonTestCases.TFCommonModelTester):
all_model_classes = (TFGPT2Model, TFGPT2LMHeadModel,
TFGPT2DoubleHeadsModel) if is_tf_available() else ()
# all_model_classes = (TFGPT2Model, TFGPT2LMHeadModel) if is_tf_available() else ()
class TFGPT2ModelTester(object):
def __init__(self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_token_type_ids=True,
use_input_mask=True,
use_labels=True,
use_mc_token_ids=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
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_token_type_ids = use_token_type_ids
self.use_input_mask = use_input_mask
self.use_labels = use_labels
self.use_mc_token_ids = use_mc_token_ids
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 = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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)
mc_token_ids = None
if self.use_mc_token_ids:
mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length)
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 = GPT2Config(
vocab_size_or_config_json_file=self.vocab_size,
n_embd=self.hidden_size,
n_layer=self.num_hidden_layers,
n_head=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,
n_positions=self.max_position_embeddings,
n_ctx=self.max_position_embeddings
# type_vocab_size=self.type_vocab_size,
# initializer_range=self.initializer_range
)
head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
return config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, token_labels, choice_labels
def create_and_check_gpt2_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = TFGPT2Model(config=config)
inputs = {'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids}
sequence_output = model(inputs)[0]
inputs = [input_ids, None, input_mask] # None is the input for 'past'
sequence_output = model(inputs)[0]
sequence_output = model(input_ids)[0]
result = {
"sequence_output": sequence_output.numpy(),
}
self.parent.assertListEqual(
list(result["sequence_output"].shape),
[self.batch_size, self.seq_length, self.hidden_size])
def create_and_check_gpt2_lm_head(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = TFGPT2LMHeadModel(config=config)
inputs = {'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids}
prediction_scores = model(inputs)[0]
result = {
"prediction_scores": prediction_scores.numpy(),
}
self.parent.assertListEqual(
list(result["prediction_scores"].shape),
[self.batch_size, self.seq_length, self.vocab_size])
def create_and_check_gpt2_double_head(self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, *args):
model = TFGPT2DoubleHeadsModel(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,
'mc_token_ids': mc_token_ids,
'attention_mask': multiple_choice_input_mask,
'token_type_ids': multiple_choice_token_type_ids}
lm_logits, mc_logits = model(inputs)[:2]
result = {
"lm_logits": lm_logits.numpy(),
"mc_logits": mc_logits.numpy()
}
self.parent.assertListEqual(
list(result["lm_logits"].shape),
[self.batch_size, self.num_choices, self.seq_length, self.vocab_size])
self.parent.assertListEqual(
list(result["mc_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, input_mask, head_mask, token_type_ids,
mc_token_ids, 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
def setUp(self):
self.model_tester = TFGPT2ModelTest.TFGPT2ModelTester(self)
self.config_tester = ConfigTester(self, config_class=GPT2Config, n_embd=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_gpt2_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gpt2_model(*config_and_inputs)
def test_gpt2_lm_head(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gpt2_lm_head(*config_and_inputs)
def test_gpt2_double_head(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gpt2_double_head(*config_and_inputs)
@pytest.mark.slow
def test_model_from_pretrained(self):
cache_dir = "/tmp/transformers_test/"
for model_name in list(TF_GPT2_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = TFGPT2Model.from_pretrained(model_name, cache_dir=cache_dir)
shutil.rmtree(cache_dir)
self.assertIsNotNone(model)
if __name__ == "__main__":
unittest.main()
| 10,147 | 42.553648 | 135 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tests/conftest.py
|
# content of conftest.py
import pytest
def pytest_addoption(parser):
parser.addoption(
"--runslow", action="store_true", default=False, help="run slow tests"
)
parser.addoption(
"--use_cuda", action="store_true", default=False, help="run tests on gpu"
)
def pytest_configure(config):
config.addinivalue_line("markers", "slow: mark test as slow to run")
def pytest_collection_modifyitems(config, items):
if config.getoption("--runslow"):
# --runslow given in cli: do not skip slow tests
return
skip_slow = pytest.mark.skip(reason="need --runslow option to run")
for item in items:
if "slow" in item.keywords:
item.add_marker(skip_slow)
@pytest.fixture
def use_cuda(request):
""" Run test on gpu """
return request.config.getoption("--use_cuda")
| 844 | 25.40625 | 81 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tests/modeling_openai_test.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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 absolute_import
from __future__ import division
from __future__ import print_function
import unittest
import pytest
import shutil
from transformers import is_torch_available
if is_torch_available():
from transformers import (OpenAIGPTConfig, OpenAIGPTModel, OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP,
OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel)
else:
pytestmark = pytest.mark.skip("Require Torch")
from .modeling_common_test import (CommonTestCases, ids_tensor)
from .configuration_common_test import ConfigTester
class OpenAIGPTModelTest(CommonTestCases.CommonModelTester):
all_model_classes = (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel) if is_torch_available() else ()
class OpenAIGPTModelTester(object):
def __init__(self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
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_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)
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 = OpenAIGPTConfig(
vocab_size_or_config_json_file=self.vocab_size,
n_embd=self.hidden_size,
n_layer=self.num_hidden_layers,
n_head=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,
n_positions=self.max_position_embeddings,
n_ctx=self.max_position_embeddings
# type_vocab_size=self.type_vocab_size,
# initializer_range=self.initializer_range
)
head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
return config, input_ids, head_mask, token_type_ids, sequence_labels, token_labels, choice_labels
def check_loss_output(self, result):
self.parent.assertListEqual(
list(result["loss"].size()),
[])
def create_and_check_openai_gpt_model(self, config, input_ids, head_mask, token_type_ids, *args):
model = OpenAIGPTModel(config=config)
model.eval()
model(input_ids, token_type_ids=token_type_ids, head_mask=head_mask)
model(input_ids, token_type_ids=token_type_ids)
(sequence_output,) = model(input_ids)
result = {
"sequence_output": sequence_output
}
self.parent.assertListEqual(
list(result["sequence_output"].size()),
[self.batch_size, self.seq_length, self.hidden_size])
def create_and_check_lm_head_model(self, config, input_ids, head_mask, token_type_ids, *args):
model = OpenAIGPTLMHeadModel(config)
model.eval()
loss, lm_logits = model(input_ids, token_type_ids=token_type_ids, labels=input_ids)
result = {
"loss": loss,
"lm_logits": lm_logits
}
self.parent.assertListEqual(
list(result["loss"].size()),
[])
self.parent.assertListEqual(
list(result["lm_logits"].size()),
[self.batch_size, self.seq_length, self.vocab_size])
def create_and_check_double_lm_head_model(self, config, input_ids, head_mask, token_type_ids, *args):
model = OpenAIGPTDoubleHeadsModel(config)
model.eval()
loss, lm_logits, mc_logits = model(input_ids, token_type_ids=token_type_ids, lm_labels=input_ids)
result = {
"loss": loss,
"lm_logits": lm_logits
}
self.parent.assertListEqual(
list(result["loss"].size()),
[])
self.parent.assertListEqual(
list(result["lm_logits"].size()),
[self.batch_size, self.seq_length, self.vocab_size])
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids, head_mask, token_type_ids, sequence_labels, token_labels, choice_labels) = config_and_inputs
inputs_dict = {
'input_ids': input_ids,
'token_type_ids': token_type_ids,
'head_mask': head_mask
}
return config, inputs_dict
def setUp(self):
self.model_tester = OpenAIGPTModelTest.OpenAIGPTModelTester(self)
self.config_tester = ConfigTester(self, config_class=OpenAIGPTConfig, n_embd=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_openai_gpt_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*config_and_inputs)
def test_openai_gpt_lm_head_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*config_and_inputs)
def test_openai_gpt_double_lm_head_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*config_and_inputs)
@pytest.mark.slow
def test_model_from_pretrained(self):
cache_dir = "/tmp/transformers_test/"
for model_name in list(OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = OpenAIGPTModel.from_pretrained(model_name, cache_dir=cache_dir)
shutil.rmtree(cache_dir)
self.assertIsNotNone(model)
if __name__ == "__main__":
unittest.main()
| 8,855 | 39.81106 | 124 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tests/modeling_roberta_test.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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 absolute_import
from __future__ import division
from __future__ import print_function
import unittest
import shutil
import pytest
from transformers import is_torch_available
if is_torch_available():
import torch
from transformers import (RobertaConfig, RobertaModel, RobertaForMaskedLM,
RobertaForSequenceClassification, RobertaForTokenClassification)
from transformers.modeling_roberta import ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
else:
pytestmark = pytest.mark.skip("Require Torch")
from .modeling_common_test import (CommonTestCases, ids_tensor)
from .configuration_common_test import ConfigTester
class RobertaModelTest(CommonTestCases.CommonModelTester):
all_model_classes = (RobertaForMaskedLM, RobertaModel) if is_torch_available() else ()
class RobertaModelTester(object):
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=5,
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 = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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 = RobertaConfig(
vocab_size_or_config_json_file=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, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def check_loss_output(self, result):
self.parent.assertListEqual(
list(result["loss"].size()),
[])
def create_and_check_roberta_model(self, config, input_ids, token_type_ids, input_mask, sequence_labels,
token_labels, choice_labels):
model = RobertaModel(config=config)
model.eval()
sequence_output, pooled_output = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
sequence_output, pooled_output = model(input_ids, token_type_ids=token_type_ids)
sequence_output, pooled_output = model(input_ids)
result = {
"sequence_output": sequence_output,
"pooled_output": pooled_output,
}
self.parent.assertListEqual(
list(result["sequence_output"].size()),
[self.batch_size, self.seq_length, self.hidden_size])
self.parent.assertListEqual(list(result["pooled_output"].size()), [self.batch_size, self.hidden_size])
def create_and_check_roberta_for_masked_lm(self, config, input_ids, token_type_ids, input_mask, sequence_labels,
token_labels, choice_labels):
model = RobertaForMaskedLM(config=config)
model.eval()
loss, prediction_scores = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, masked_lm_labels=token_labels)
result = {
"loss": loss,
"prediction_scores": prediction_scores,
}
self.parent.assertListEqual(
list(result["prediction_scores"].size()),
[self.batch_size, self.seq_length, self.vocab_size])
self.check_loss_output(result)
def create_and_check_roberta_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 = RobertaForTokenClassification(config=config)
model.eval()
loss, logits = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids,
labels=token_labels)
result = {
"loss": loss,
"logits": logits,
}
self.parent.assertListEqual(
list(result["logits"].size()),
[self.batch_size, self.seq_length, self.num_labels])
self.check_loss_output(result)
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
def setUp(self):
self.model_tester = RobertaModelTest.RobertaModelTester(self)
self.config_tester = ConfigTester(self, config_class=RobertaConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_roberta_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_roberta_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_roberta_for_masked_lm(*config_and_inputs)
@pytest.mark.slow
def test_model_from_pretrained(self):
cache_dir = "/tmp/transformers_test/"
for model_name in list(ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = RobertaModel.from_pretrained(model_name, cache_dir=cache_dir)
shutil.rmtree(cache_dir)
self.assertIsNotNone(model)
class RobertaModelIntegrationTest(unittest.TestCase):
@pytest.mark.slow
def test_inference_masked_lm(self):
model = RobertaForMaskedLM.from_pretrained('roberta-base')
input_ids = torch.tensor([[ 0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
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(
[[[33.8843, -4.3107, 22.7779],
[ 4.6533, -2.8099, 13.6252],
[ 1.8222, -3.6898, 8.8600]]]
)
self.assertTrue(
torch.allclose(output[:, :3, :3], expected_slice, atol=1e-3)
)
@pytest.mark.slow
def test_inference_no_head(self):
model = RobertaModel.from_pretrained('roberta-base')
input_ids = torch.tensor([[ 0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
output = model(input_ids)[0]
# compare the actual values for a slice.
expected_slice = torch.Tensor(
[[[-0.0231, 0.0782, 0.0074],
[-0.1854, 0.0539, -0.0174],
[ 0.0548, 0.0799, 0.1687]]]
)
self.assertTrue(
torch.allclose(output[:, :3, :3], expected_slice, atol=1e-3)
)
@pytest.mark.slow
def test_inference_classification_head(self):
model = RobertaForSequenceClassification.from_pretrained('roberta-large-mnli')
input_ids = torch.tensor([[ 0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
output = model(input_ids)[0]
expected_shape = torch.Size((1, 3))
self.assertEqual(
output.shape,
expected_shape
)
expected_tensor = torch.Tensor([[-0.9469, 0.3913, 0.5118]])
self.assertTrue(
torch.allclose(output, expected_tensor, atol=1e-3)
)
if __name__ == "__main__":
unittest.main()
| 11,393 | 41.834586 | 143 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tests/tokenization_utils_test.py
|
# coding=utf-8
# Copyright 2018 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import unittest
import six
import pytest
from transformers import PreTrainedTokenizer
from transformers.tokenization_gpt2 import GPT2Tokenizer
class TokenizerUtilsTest(unittest.TestCase):
@pytest.mark.slow
def check_tokenizer_from_pretrained(self, tokenizer_class):
s3_models = list(tokenizer_class.max_model_input_sizes.keys())
for model_name in s3_models[:1]:
tokenizer = tokenizer_class.from_pretrained(model_name)
self.assertIsNotNone(tokenizer)
self.assertIsInstance(tokenizer, tokenizer_class)
self.assertIsInstance(tokenizer, PreTrainedTokenizer)
for special_tok in tokenizer.all_special_tokens:
if six.PY2:
self.assertIsInstance(special_tok, unicode)
else:
self.assertIsInstance(special_tok, str)
special_tok_id = tokenizer.convert_tokens_to_ids(special_tok)
self.assertIsInstance(special_tok_id, int)
def test_pretrained_tokenizers(self):
self.check_tokenizer_from_pretrained(GPT2Tokenizer)
if __name__ == "__main__":
unittest.main()
| 1,858 | 36.938776 | 77 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tests/modeling_common_test.py
|
# coding=utf-8
# Copyright 2019 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import sys
import os
import shutil
import tempfile
import json
import random
import uuid
import unittest
import logging
import pytest
from transformers import is_torch_available
if is_torch_available():
import torch
import numpy as np
from transformers import (AdaptiveEmbedding, PretrainedConfig, PreTrainedModel,
BertModel, BertConfig, BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
GPT2LMHeadModel, GPT2Config, GPT2_PRETRAINED_MODEL_ARCHIVE_MAP)
else:
pytestmark = pytest.mark.skip("Require Torch")
if sys.version_info[0] == 2:
import cPickle as pickle
class TemporaryDirectory(object):
"""Context manager for tempfile.mkdtemp() so it's usable with "with" statement."""
def __enter__(self):
self.name = tempfile.mkdtemp()
return self.name
def __exit__(self, exc_type, exc_value, traceback):
shutil.rmtree(self.name)
else:
import pickle
TemporaryDirectory = tempfile.TemporaryDirectory
unicode = str
def _config_zero_init(config):
configs_no_init = copy.deepcopy(config)
for key in configs_no_init.__dict__.keys():
if '_range' in key or '_std' in key:
setattr(configs_no_init, key, 0.0)
return configs_no_init
class CommonTestCases:
class CommonModelTester(unittest.TestCase):
model_tester = None
all_model_classes = ()
test_torchscript = True
test_pruning = True
test_resize_embeddings = True
test_head_masking = True
def test_save_load(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
model.eval()
with torch.no_grad():
outputs = model(**inputs_dict)
with TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model = model_class.from_pretrained(tmpdirname)
with torch.no_grad():
after_outputs = model(**inputs_dict)
# Make sure we don't have nans
out_1 = after_outputs[0].numpy()
out_2 = outputs[0].numpy()
out_1 = out_1[~np.isnan(out_1)]
out_2 = out_2[~np.isnan(out_2)]
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
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():
if param.requires_grad:
self.assertIn(param.data.mean().item(), [0.0, 1.0],
msg="Parameter {} of model {} seems not properly initialized".format(name, model_class))
def test_determinism(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
model.eval()
first, second = model(inputs_dict["input_ids"])[0], model(inputs_dict["input_ids"])[0]
self.assertEqual(first.ne(second).sum().item(), 0)
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
config.output_attentions = True
config.output_hidden_states = False
model = model_class(config)
model.eval()
outputs = model(**inputs_dict)
attentions = outputs[-1]
self.assertEqual(model.config.output_attentions, True)
self.assertEqual(model.config.output_hidden_states, False)
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads,
self.model_tester.seq_length,
self.model_tester.key_len if hasattr(self.model_tester, 'key_len') else self.model_tester.seq_length])
out_len = len(outputs)
# Check attention is always last and order is fine
config.output_attentions = True
config.output_hidden_states = True
model = model_class(config)
model.eval()
outputs = model(**inputs_dict)
self.assertEqual(out_len+1, len(outputs))
self.assertEqual(model.config.output_attentions, True)
self.assertEqual(model.config.output_hidden_states, True)
attentions = outputs[-1]
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads,
self.model_tester.seq_length,
self.model_tester.key_len if hasattr(self.model_tester, 'key_len') else self.model_tester.seq_length])
def test_torchscript(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
self._create_and_check_torchscript(config, inputs_dict)
def test_torchscript_output_attentions(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_attentions = True
self._create_and_check_torchscript(config, inputs_dict)
def test_torchscript_output_hidden_state(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = True
self._create_and_check_torchscript(config, inputs_dict)
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.eval()
inputs = inputs_dict['input_ids'] # Let's keep only input_ids
try:
torch.jit.trace(model, inputs)
except RuntimeError:
self.fail("Couldn't trace module.")
try:
traced_gpt2 = torch.jit.trace(model, inputs)
torch.jit.save(traced_gpt2, "traced_model.pt")
except RuntimeError:
self.fail("Couldn't save module.")
try:
loaded_model = torch.jit.load("traced_model.pt")
os.remove("traced_model.pt")
except ValueError:
self.fail("Couldn't load module.")
model.eval()
loaded_model.eval()
model_params = model.parameters()
loaded_model_params = loaded_model.parameters()
models_equal = True
for p1, p2 in zip(model_params, loaded_model_params):
if p1.data.ne(p2.data).sum() > 0:
models_equal = False
self.assertTrue(models_equal)
def test_headmasking(self):
if not self.test_head_masking:
return
global_rng.seed(42)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
global_rng.seed()
config.output_attentions = True
config.output_hidden_states = True
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
model.eval()
# Prepare head_mask
# Set require_grad after having prepared the tensor to avoid error (leaf variable has been moved into the graph interior)
head_mask = torch.ones(self.model_tester.num_hidden_layers, self.model_tester.num_attention_heads)
head_mask[0, 0] = 0
head_mask[-1, :-1] = 0
head_mask.requires_grad_(requires_grad=True)
inputs = inputs_dict.copy()
inputs['head_mask'] = head_mask
outputs = model(**inputs)
# Test that we can get a gradient back for importance score computation
output = sum(t.sum() for t in outputs[0])
output = output.sum()
output.backward()
multihead_outputs = head_mask.grad
attentions = outputs[-1]
hidden_states = outputs[-2]
# Remove Nan
for t in attentions:
self.assertLess(torch.sum(torch.isnan(t)), t.numel() / 4) # Check we don't have more than 25% nans (arbitrary)
attentions = [t.masked_fill(torch.isnan(t), 0.0) for t in attentions] # remove them (the test is less complete)
self.assertIsNotNone(multihead_outputs)
self.assertEqual(len(multihead_outputs), self.model_tester.num_hidden_layers)
self.assertAlmostEqual(
attentions[0][..., 0, :, :].flatten().sum().item(), 0.0)
self.assertNotEqual(
attentions[0][..., -1, :, :].flatten().sum().item(), 0.0)
self.assertNotEqual(
attentions[1][..., 0, :, :].flatten().sum().item(), 0.0)
self.assertAlmostEqual(
attentions[-1][..., -2, :, :].flatten().sum().item(), 0.0)
self.assertNotEqual(
attentions[-1][..., -1, :, :].flatten().sum().item(), 0.0)
def test_head_pruning(self):
if not self.test_pruning:
return
for model_class in self.all_model_classes:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
if "head_mask" in inputs_dict:
del inputs_dict["head_mask"]
config.output_attentions = True
config.output_hidden_states = False
model = model_class(config=config)
model.eval()
heads_to_prune = {0: list(range(1, self.model_tester.num_attention_heads)),
-1: [0]}
model.prune_heads(heads_to_prune)
outputs = model(**inputs_dict)
attentions = outputs[-1]
self.assertEqual(
attentions[0].shape[-3], 1)
self.assertEqual(
attentions[1].shape[-3], self.model_tester.num_attention_heads)
self.assertEqual(
attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1)
def test_head_pruning_save_load_from_pretrained(self):
if not self.test_pruning:
return
for model_class in self.all_model_classes:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
if "head_mask" in inputs_dict:
del inputs_dict["head_mask"]
config.output_attentions = True
config.output_hidden_states = False
model = model_class(config=config)
model.eval()
heads_to_prune = {0: list(range(1, self.model_tester.num_attention_heads)),
-1: [0]}
model.prune_heads(heads_to_prune)
directory = "pruned_model"
if not os.path.exists(directory):
os.makedirs(directory)
model.save_pretrained(directory)
model = model_class.from_pretrained(directory)
outputs = model(**inputs_dict)
attentions = outputs[-1]
self.assertEqual(attentions[0].shape[-3], 1)
self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads)
self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1)
shutil.rmtree(directory)
def test_head_pruning_save_load_from_config_init(self):
if not self.test_pruning:
return
for model_class in self.all_model_classes:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
if "head_mask" in inputs_dict:
del inputs_dict["head_mask"]
config.output_attentions = True
config.output_hidden_states = False
heads_to_prune = {0: list(range(1, self.model_tester.num_attention_heads)),
-1: [0]}
config.pruned_heads = heads_to_prune
model = model_class(config=config)
model.eval()
outputs = model(**inputs_dict)
attentions = outputs[-1]
self.assertEqual(attentions[0].shape[-3], 1)
self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads)
self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1)
def test_head_pruning_integration(self):
if not self.test_pruning:
return
for model_class in self.all_model_classes:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
if "head_mask" in inputs_dict:
del inputs_dict["head_mask"]
config.output_attentions = True
config.output_hidden_states = False
heads_to_prune = {0: [0], 1: [1, 2]}
config.pruned_heads = heads_to_prune
model = model_class(config=config)
model.eval()
outputs = model(**inputs_dict)
attentions = outputs[-1]
self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1)
self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2)
self.assertEqual(attentions[2].shape[-3], self.model_tester.num_attention_heads)
self.assertEqual(attentions[3].shape[-3], self.model_tester.num_attention_heads)
directory = "pruned_model"
if not os.path.exists(directory):
os.makedirs(directory)
model.save_pretrained(directory)
model = model_class.from_pretrained(directory)
shutil.rmtree(directory)
outputs = model(**inputs_dict)
attentions = outputs[-1]
self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1)
self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2)
self.assertEqual(attentions[2].shape[-3], self.model_tester.num_attention_heads)
self.assertEqual(attentions[3].shape[-3], self.model_tester.num_attention_heads)
heads_to_prune = {0: [0], 2: [1, 2]}
model.prune_heads(heads_to_prune)
outputs = model(**inputs_dict)
attentions = outputs[-1]
self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads -1)
self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2)
self.assertEqual(attentions[2].shape[-3], self.model_tester.num_attention_heads - 2)
self.assertEqual(attentions[3].shape[-3], self.model_tester.num_attention_heads)
self.assertDictEqual(model.config.pruned_heads, {0: [0], 1: [1, 2], 2: [1, 2]})
def test_hidden_states_output(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
config.output_hidden_states = True
config.output_attentions = False
model = model_class(config)
model.eval()
outputs = model(**inputs_dict)
hidden_states = outputs[-1]
self.assertEqual(model.config.output_attentions, False)
self.assertEqual(model.config.output_hidden_states, True)
self.assertEqual(len(hidden_states), self.model_tester.num_hidden_layers + 1)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[self.model_tester.seq_length, self.model_tester.hidden_size])
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_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 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)
# 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_model_common_attributes(self):
config, inputs_dict = 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(),
(torch.nn.Embedding, AdaptiveEmbedding)
)
model.set_input_embeddings(torch.nn.Embedding(10, 10))
x = model.get_output_embeddings()
self.assertTrue(
x is None or isinstance(x, torch.nn.Linear)
)
def test_tie_model_weights(self):
if not self.test_torchscript:
return
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def check_same_values(layer_1, layer_2):
equal = True
for p1, p2 in zip(layer_1.weight, layer_2.weight):
if p1.data.ne(p2.data).sum() > 0:
equal = False
return equal
for model_class in self.all_model_classes:
config.torchscript = True
model_not_tied = model_class(config)
if model_not_tied.get_output_embeddings() is None:
continue
params_not_tied = list(model_not_tied.parameters())
config_tied = copy.deepcopy(config)
config_tied.torchscript = False
model_tied = model_class(config_tied)
params_tied = list(model_tied.parameters())
# Check that the embedding layer and decoding layer are the same in size and in value
self.assertGreater(len(params_not_tied), len(params_tied))
# self.assertTrue(check_same_values(embeddings, decoding))
# # Check that after modification, they remain the same.
# embeddings.weight.data.div_(2)
# # Check that the embedding layer and decoding layer are the same in size and in value
# self.assertTrue(embeddings.weight.shape, decoding.weight.shape)
# self.assertTrue(check_same_values(embeddings, decoding))
# # Check that after modification, they remain the same.
# decoding.weight.data.div_(4)
# # Check that the embedding layer and decoding layer are the same in size and in value
# self.assertTrue(embeddings.weight.shape, decoding.weight.shape)
# self.assertTrue(check_same_values(embeddings, decoding))
# Check that after resize they remain tied.
model_tied.resize_token_embeddings(config.vocab_size + 10)
params_tied_2 = list(model_tied.parameters())
self.assertGreater(len(params_not_tied), len(params_tied))
self.assertEqual(len(params_tied_2), len(params_tied))
# decoding.weight.data.mul_(20)
# # Check that the embedding layer and decoding layer are the same in size and in value
# self.assertTrue(model.transformer.wte.weight.shape, model.lm_head.weight.shape)
# self.assertTrue(check_same_values(model.transformer.wte, model.lm_head))
def test_inputs_embeds(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
input_ids = inputs_dict["input_ids"]
del inputs_dict["input_ids"]
for model_class in self.all_model_classes:
model = model_class(config)
model.eval()
wte = model.get_input_embeddings()
inputs_dict["inputs_embeds"] = wte(input_ids)
outputs = model(**inputs_dict)
class GPTModelTester(CommonModelTester):
def __init__(self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_position_ids=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
n_positions=33,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
n_choices=3,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
scope=None,
config_class=None,
base_model_class=None,
lm_head_model_class=None,
double_head_model_class=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_position_ids = use_position_ids
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.n_positions = n_positions
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.n_choices = n_choices
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.scope = scope
self.config_class = config_class
self.base_model_class = base_model_class
self.lm_head_model_class = lm_head_model_class
self.double_head_model_class = double_head_model_class
self.all_model_classes = (base_model_class, lm_head_model_class, double_head_model_class)
def prepare_config_and_inputs(self):
total_num_tokens = self.vocab_size
input_ids = ids_tensor([self.batch_size, self.n_choices, self.seq_length], total_num_tokens)
position_ids = None
if self.use_position_ids:
position_ids = ids_tensor([self.batch_size, self.n_choices, self.seq_length], self.n_positions)
token_type_ids = None
if self.use_token_type_ids:
total_voc = self.vocab_size
token_type_ids = ids_tensor([self.batch_size, self.n_choices, self.seq_length], total_voc)
mc_labels = None
lm_labels = None
mc_token_ids = None
if self.use_labels:
mc_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
lm_labels = ids_tensor([self.batch_size, self.n_choices, self.seq_length], self.num_labels)
mc_token_ids = ids_tensor([self.batch_size, self.n_choices], self.seq_length)
config = self.config_class(
vocab_size_or_config_json_file=self.vocab_size,
n_positions=self.n_positions,
n_embd=self.hidden_size,
n_layer=self.num_hidden_layers,
n_head=self.num_attention_heads,
initializer_range=self.initializer_range)
return (config, input_ids, token_type_ids, position_ids,
mc_labels, lm_labels, mc_token_ids)
def create_and_check_base_model(self, config, input_ids, token_type_ids, position_ids,
mc_labels, lm_labels, mc_token_ids):
model = self.base_model_class(config)
model.eval()
outputs = model(input_ids, position_ids, token_type_ids)
outputs = model(input_ids, position_ids)
outputs = model(input_ids)
hidden_state = outputs[0]
self.parent.assertListEqual(
list(hidden_state.size()),
[self.batch_size, self.n_choices, self.seq_length, self.hidden_size])
def create_and_check_lm_head(self, config, input_ids, token_type_ids, position_ids,
mc_labels, lm_labels, mc_token_ids):
model = self.lm_head_model_class(config)
model.eval()
outputs = model(input_ids, position_ids, token_type_ids, lm_labels)
loss, lm_logits = outputs[:2]
total_voc = self.vocab_size
self.parent.assertListEqual(
list(lm_logits.size()),
[self.batch_size, self.n_choices, self.seq_length, total_voc])
self.parent.assertListEqual(
list(loss.size()),
[])
def create_and_check_presents(self, config, input_ids, token_type_ids, position_ids,
mc_labels, lm_labels, mc_token_ids):
for model_class in self.all_model_classes:
model = model_class(config)
model.eval()
outputs = model(input_ids)
presents = outputs[-1]
self.parent.assertEqual(self.num_hidden_layers, len(presents))
self.parent.assertListEqual(
list(presents[0].size()),
[2, self.batch_size * self.n_choices, self.num_attention_heads,
self.seq_length, self.hidden_size // self.num_attention_heads])
def create_and_check_double_heads(self, config, input_ids, token_type_ids, position_ids,
mc_labels, lm_labels, mc_token_ids):
model = self.double_head_model_class(config)
model.eval()
outputs = model(input_ids, mc_token_ids, lm_labels=lm_labels, mc_labels=mc_labels,
token_type_ids=token_type_ids, position_ids=position_ids)
lm_loss, mc_loss, lm_logits, mc_logits = outputs[:4]
loss = [lm_loss, mc_loss]
total_voc = self.vocab_size
self.parent.assertListEqual(
list(lm_logits.size()),
[self.batch_size, self.n_choices, self.seq_length, total_voc])
self.parent.assertListEqual(
list(mc_logits.size()),
[self.batch_size, self.n_choices])
self.parent.assertListEqual(
[list(l.size()) for l in loss],
[[], []])
def create_and_check_model_from_pretrained(self):
cache_dir = "/tmp/transformers_test/"
for model_name in list(self.base_model_class.pretrained_model_archive_map.keys())[:1]:
model = self.base_model_class.from_pretrained(model_name, cache_dir=cache_dir)
shutil.rmtree(cache_dir)
self.parent.assertIsNotNone(model)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids, token_type_ids, position_ids,
mc_labels, lm_labels, mc_token_ids) = config_and_inputs
inputs_dict = {'input_ids': input_ids}
return config, inputs_dict
def run_common_tests(self, test_presents=False):
config_and_inputs = self.prepare_config_and_inputs()
self.create_and_check_base_model(*config_and_inputs)
config_and_inputs = self.prepare_config_and_inputs()
self.create_and_check_lm_head(*config_and_inputs)
config_and_inputs = self.prepare_config_and_inputs()
self.create_and_check_double_heads(*config_and_inputs)
if test_presents:
config_and_inputs = self.prepare_config_and_inputs()
self.create_and_check_presents(*config_and_inputs)
@pytest.mark.slow
def run_slow_tests(self):
self.create_and_check_model_from_pretrained()
class ConfigTester(object):
def __init__(self, parent, config_class=None, **kwargs):
self.parent = parent
self.config_class = config_class
self.inputs_dict = kwargs
def create_and_test_config_common_properties(self):
config = self.config_class(**self.inputs_dict)
self.parent.assertTrue(hasattr(config, 'vocab_size'))
self.parent.assertTrue(hasattr(config, 'hidden_size'))
self.parent.assertTrue(hasattr(config, 'num_attention_heads'))
self.parent.assertTrue(hasattr(config, 'num_hidden_layers'))
def create_and_test_config_to_json_string(self):
config = self.config_class(**self.inputs_dict)
obj = json.loads(config.to_json_string())
for key, value in self.inputs_dict.items():
self.parent.assertEqual(obj[key], value)
def create_and_test_config_to_json_file(self):
config_first = self.config_class(**self.inputs_dict)
json_file_path = os.path.join(os.getcwd(), "config_" + str(uuid.uuid4()) + ".json")
config_first.to_json_file(json_file_path)
config_second = self.config_class.from_json_file(json_file_path)
os.remove(json_file_path)
self.parent.assertEqual(config_second.to_dict(), config_first.to_dict())
def run_common_tests(self):
self.create_and_test_config_common_properties()
self.create_and_test_config_to_json_string()
self.create_and_test_config_to_json_file()
global_rng = random.Random()
def ids_tensor(shape, vocab_size, rng=None, name=None):
"""Creates a random int32 tensor of the shape within the vocab size."""
if rng is None:
rng = global_rng
total_dims = 1
for dim in shape:
total_dims *= dim
values = []
for _ in range(total_dims):
values.append(rng.randint(0, vocab_size - 1))
return torch.tensor(data=values, dtype=torch.long).view(shape).contiguous()
def floats_tensor(shape, scale=1.0, rng=None, name=None):
"""Creates a random float32 tensor of the shape within the vocab size."""
if rng is None:
rng = global_rng
total_dims = 1
for dim in shape:
total_dims *= dim
values = []
for _ in range(total_dims):
values.append(rng.random() * scale)
return torch.tensor(data=values, dtype=torch.float).view(shape).contiguous()
class ModelUtilsTest(unittest.TestCase):
@pytest.mark.slow
def test_model_from_pretrained(self):
logging.basicConfig(level=logging.INFO)
for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
config = BertConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, PretrainedConfig)
model = BertModel.from_pretrained(model_name)
model, loading_info = BertModel.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, PreTrainedModel)
for value in loading_info.values():
self.assertEqual(len(value), 0)
config = BertConfig.from_pretrained(model_name, output_attentions=True, output_hidden_states=True)
model = BertModel.from_pretrained(model_name, output_attentions=True, output_hidden_states=True)
self.assertEqual(model.config.output_attentions, True)
self.assertEqual(model.config.output_hidden_states, True)
self.assertEqual(model.config, config)
if __name__ == "__main__":
unittest.main()
| 35,615 | 42.593635 | 137 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tests/tokenization_ctrl_test.py
|
# coding=utf-8
# Copyright 2018 Salesforce 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.
from __future__ import absolute_import, division, print_function, unicode_literals
import os
import unittest
import json
from io import open
from transformers.tokenization_ctrl import CTRLTokenizer, VOCAB_FILES_NAMES
from .tokenization_tests_commons import CommonTestCases
class CTRLTokenizationTest(CommonTestCases.CommonTokenizerTester):
tokenizer_class = CTRLTokenizer
def setUp(self):
super(CTRLTokenizationTest, self).setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
vocab = ['adapt', 're@@', 'a@@', 'apt', 'c@@', 't', '<unk>']
vocab_tokens = dict(zip(vocab, range(len(vocab))))
merges = ["#version: 0.2", 'a p', 'ap t</w>', 'r e', 'a d', 'ad apt</w>', '']
self.special_tokens_map = {"unk_token": "<unk>"}
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", encoding="utf-8") as fp:
fp.write(json.dumps(vocab_tokens) + "\n")
with open(self.merges_file, "w", encoding="utf-8") as fp:
fp.write("\n".join(merges))
def get_tokenizer(self, **kwargs):
kwargs.update(self.special_tokens_map)
return CTRLTokenizer.from_pretrained(self.tmpdirname, **kwargs)
def get_input_output_texts(self):
input_text = u"adapt react readapt apt"
output_text = u"adapt react readapt apt"
return input_text, output_text
def test_full_tokenizer(self):
tokenizer = CTRLTokenizer(self.vocab_file, self.merges_file, **self.special_tokens_map)
text = "adapt react readapt apt"
bpe_tokens = 'adapt re@@ a@@ c@@ t re@@ adapt apt'.split()
tokens = tokenizer.tokenize(text)
self.assertListEqual(tokens, bpe_tokens)
input_tokens = tokens + [tokenizer.unk_token]
input_bpe_tokens = [0, 1, 2, 4, 5, 1, 0, 3, 6]
self.assertListEqual(
tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
if __name__ == '__main__':
unittest.main()
| 2,766 | 38.528571 | 95 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tests/tokenization_xlm_test.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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 absolute_import, division, print_function, unicode_literals
import os
import unittest
import json
import pytest
from transformers.tokenization_xlm import XLMTokenizer, VOCAB_FILES_NAMES
from .tokenization_tests_commons import CommonTestCases
class XLMTokenizationTest(CommonTestCases.CommonTokenizerTester):
tokenizer_class = XLMTokenizer
def setUp(self):
super(XLMTokenizationTest, self).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_tokenizer(self, **kwargs):
return XLMTokenizer.from_pretrained(self.tmpdirname, **kwargs)
def get_input_output_texts(self):
input_text = u"lower newer"
output_text = u"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 = XLMTokenizer(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)
@pytest.mark.slow
def test_sequence_builders(self):
tokenizer = XLMTokenizer.from_pretrained("xlm-mlm-en-2048")
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 == [1] + text + [1]
assert encoded_pair == [1] + text + [1] + text_2 + [1]
if __name__ == '__main__':
unittest.main()
| 3,278 | 37.576471 | 94 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tests/modeling_auto_test.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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 absolute_import
from __future__ import division
from __future__ import print_function
import unittest
import shutil
import pytest
import logging
from transformers import is_torch_available
if is_torch_available():
from transformers import (AutoConfig, BertConfig,
AutoModel, BertModel,
AutoModelWithLMHead, BertForMaskedLM,
AutoModelForSequenceClassification, BertForSequenceClassification,
AutoModelForQuestionAnswering, BertForQuestionAnswering)
from transformers.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_MAP
from .modeling_common_test import (CommonTestCases, ids_tensor)
from .configuration_common_test import ConfigTester
else:
pytestmark = pytest.mark.skip("Require Torch")
class AutoModelTest(unittest.TestCase):
@pytest.mark.slow
def test_model_from_pretrained(self):
logging.basicConfig(level=logging.INFO)
for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = AutoModel.from_pretrained(model_name)
model, loading_info = AutoModel.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertModel)
for value in loading_info.values():
self.assertEqual(len(value), 0)
@pytest.mark.slow
def test_lmhead_model_from_pretrained(self):
logging.basicConfig(level=logging.INFO)
for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = AutoModelWithLMHead.from_pretrained(model_name)
model, loading_info = AutoModelWithLMHead.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForMaskedLM)
@pytest.mark.slow
def test_sequence_classification_model_from_pretrained(self):
logging.basicConfig(level=logging.INFO)
for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
model, loading_info = AutoModelForSequenceClassification.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForSequenceClassification)
@pytest.mark.slow
def test_question_answering_model_from_pretrained(self):
logging.basicConfig(level=logging.INFO)
for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
model, loading_info = AutoModelForQuestionAnswering.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForQuestionAnswering)
if __name__ == "__main__":
unittest.main()
| 4,263 | 42.510204 | 122 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tests/modeling_bert_test.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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 absolute_import
from __future__ import division
from __future__ import print_function
import unittest
import shutil
import pytest
from transformers import is_torch_available
from .modeling_common_test import (CommonTestCases, ids_tensor, floats_tensor)
from .configuration_common_test import ConfigTester
if is_torch_available():
from transformers import (BertConfig, BertModel, BertForMaskedLM,
BertForNextSentencePrediction, BertForPreTraining,
BertForQuestionAnswering, BertForSequenceClassification,
BertForTokenClassification, BertForMultipleChoice)
from transformers.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_MAP
else:
pytestmark = pytest.mark.skip("Require Torch")
@pytest.mark.usefixtures("use_cuda")
class BertModelTest(CommonTestCases.CommonModelTester):
all_model_classes = (BertModel, BertForMaskedLM, BertForNextSentencePrediction,
BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification,
BertForTokenClassification) if is_torch_available() else ()
class BertModelTester(object):
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=5,
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,
device='cpu',
):
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.device = device
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).to(self.device)
input_mask = None
if self.use_input_mask:
input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2).to(self.device)
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).to(self.device)
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).to(self.device)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels).to(self.device)
choice_labels = ids_tensor([self.batch_size], self.num_choices).to(self.device)
config = BertConfig(
vocab_size_or_config_json_file=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)
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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 check_loss_output(self, result):
self.parent.assertListEqual(
list(result["loss"].size()),
[])
def create_and_check_bert_model(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
model = BertModel(config=config)
model.to(input_ids.device)
model.eval()
sequence_output, pooled_output = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
sequence_output, pooled_output = model(input_ids, token_type_ids=token_type_ids)
sequence_output, pooled_output = model(input_ids)
result = {
"sequence_output": sequence_output,
"pooled_output": pooled_output,
}
self.parent.assertListEqual(
list(result["sequence_output"].size()),
[self.batch_size, self.seq_length, self.hidden_size])
self.parent.assertListEqual(list(result["pooled_output"].size()), [self.batch_size, self.hidden_size])
def create_and_check_bert_model_as_decoder(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask):
model = BertModel(config)
model.eval()
sequence_output, pooled_output = 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)
sequence_output, pooled_output = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states)
sequence_output, pooled_output = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = {
"sequence_output": sequence_output,
"pooled_output": pooled_output,
}
self.parent.assertListEqual(
list(result["sequence_output"].size()),
[self.batch_size, self.seq_length, self.hidden_size])
self.parent.assertListEqual(list(result["pooled_output"].size()), [self.batch_size, self.hidden_size])
def create_and_check_bert_for_masked_lm(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
model = BertForMaskedLM(config=config)
model.eval()
loss, prediction_scores = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, masked_lm_labels=token_labels)
result = {
"loss": loss,
"prediction_scores": prediction_scores,
}
self.parent.assertListEqual(
list(result["prediction_scores"].size()),
[self.batch_size, self.seq_length, self.vocab_size])
self.check_loss_output(result)
def create_and_check_bert_model_for_masked_lm_as_decoder(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask):
model = BertForMaskedLM(config=config)
model.eval()
loss, prediction_scores = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, masked_lm_labels=token_labels, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask)
loss, prediction_scores = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, masked_lm_labels=token_labels, encoder_hidden_states=encoder_hidden_states)
result = {
"loss": loss,
"prediction_scores": prediction_scores,
}
self.parent.assertListEqual(
list(result["prediction_scores"].size()),
[self.batch_size, self.seq_length, self.vocab_size])
self.check_loss_output(result)
def create_and_check_bert_for_next_sequence_prediction(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
model = BertForNextSentencePrediction(config=config)
model.eval()
loss, seq_relationship_score = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, next_sentence_label=sequence_labels)
result = {
"loss": loss,
"seq_relationship_score": seq_relationship_score,
}
self.parent.assertListEqual(
list(result["seq_relationship_score"].size()),
[self.batch_size, 2])
self.check_loss_output(result)
def create_and_check_bert_for_pretraining(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
model = BertForPreTraining(config=config)
model.eval()
loss, prediction_scores, seq_relationship_score = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids,
masked_lm_labels=token_labels, next_sentence_label=sequence_labels)
result = {
"loss": loss,
"prediction_scores": prediction_scores,
"seq_relationship_score": seq_relationship_score,
}
self.parent.assertListEqual(
list(result["prediction_scores"].size()),
[self.batch_size, self.seq_length, self.vocab_size])
self.parent.assertListEqual(
list(result["seq_relationship_score"].size()),
[self.batch_size, 2])
self.check_loss_output(result)
def create_and_check_bert_for_question_answering(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
model = BertForQuestionAnswering(config=config)
model.eval()
loss, start_logits, end_logits = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids,
start_positions=sequence_labels, end_positions=sequence_labels)
result = {
"loss": loss,
"start_logits": start_logits,
"end_logits": end_logits,
}
self.parent.assertListEqual(
list(result["start_logits"].size()),
[self.batch_size, self.seq_length])
self.parent.assertListEqual(
list(result["end_logits"].size()),
[self.batch_size, self.seq_length])
self.check_loss_output(result)
def create_and_check_bert_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 = BertForSequenceClassification(config)
model.eval()
loss, logits = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
result = {
"loss": loss,
"logits": logits,
}
self.parent.assertListEqual(
list(result["logits"].size()),
[self.batch_size, self.num_labels])
self.check_loss_output(result)
def create_and_check_bert_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 = BertForTokenClassification(config=config)
model.eval()
loss, logits = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
result = {
"loss": loss,
"logits": logits,
}
self.parent.assertListEqual(
list(result["logits"].size()),
[self.batch_size, self.seq_length, self.num_labels])
self.check_loss_output(result)
def create_and_check_bert_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 = BertForMultipleChoice(config=config)
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()
loss, logits = model(multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels)
result = {
"loss": loss,
"logits": logits,
}
self.parent.assertListEqual(
list(result["logits"].size()),
[self.batch_size, self.num_choices])
self.check_loss_output(result)
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
def setUp(self):
self.model_tester = BertModelTest.BertModelTester(self)
self.config_tester = ConfigTester(self, config_class=BertConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_bert_model(self, use_cuda=False):
# ^^ This could be a real fixture
if use_cuda:
self.model_tester.device = "cuda"
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_bert_model(*config_and_inputs)
def test_bert_model_as_decoder(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_bert_model_as_decoder(*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_bert_for_masked_lm(*config_and_inputs)
def test_for_masked_lm_decoder(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_bert_model_for_masked_lm_as_decoder(*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_bert_for_multiple_choice(*config_and_inputs)
def test_for_next_sequence_prediction(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_bert_for_next_sequence_prediction(*config_and_inputs)
def test_for_pretraining(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_bert_for_pretraining(*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_bert_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_bert_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_bert_for_token_classification(*config_and_inputs)
@pytest.mark.slow
def test_model_from_pretrained(self):
cache_dir = "/tmp/transformers_test/"
for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = BertModel.from_pretrained(model_name, cache_dir=cache_dir)
shutil.rmtree(cache_dir)
self.assertIsNotNone(model)
if __name__ == "__main__":
unittest.main()
| 18,964 | 50.256757 | 235 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tests/modeling_encoder_decoder_test.py
|
# coding=utf-8
# Copyright 2018 The Hugging Face 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 logging
import unittest
import pytest
from transformers import is_torch_available
if is_torch_available():
from transformers import BertModel, BertForMaskedLM, Model2Model
from transformers.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_MAP
else:
pytestmark = pytest.mark.skip("Require Torch")
class EncoderDecoderModelTest(unittest.TestCase):
@pytest.mark.slow
def test_model2model_from_pretrained(self):
logging.basicConfig(level=logging.INFO)
for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = Model2Model.from_pretrained(model_name)
self.assertIsInstance(model.encoder, BertModel)
self.assertIsInstance(model.decoder, BertForMaskedLM)
self.assertEqual(model.decoder.config.is_decoder, True)
self.assertEqual(model.encoder.config.is_decoder, False)
def test_model2model_from_pretrained_not_bert(self):
logging.basicConfig(level=logging.INFO)
with self.assertRaises(ValueError):
_ = Model2Model.from_pretrained('roberta')
with self.assertRaises(ValueError):
_ = Model2Model.from_pretrained('distilbert')
with self.assertRaises(ValueError):
_ = Model2Model.from_pretrained('does-not-exist')
if __name__ == "__main__":
unittest.main()
| 1,960 | 35.314815 | 77 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tests/optimization_test.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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 absolute_import
from __future__ import division
from __future__ import print_function
import unittest
import os
import pytest
from transformers import is_torch_available
if is_torch_available():
import torch
from transformers import (AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup)
else:
pytestmark = pytest.mark.skip("Require Torch")
from .tokenization_tests_commons import TemporaryDirectory
def unwrap_schedule(scheduler, num_steps=10):
lrs = []
for _ in range(num_steps):
scheduler.step()
lrs.append(scheduler.get_lr())
return lrs
def unwrap_and_save_reload_schedule(scheduler, num_steps=10):
lrs = []
for step in range(num_steps):
scheduler.step()
lrs.append(scheduler.get_lr())
if step == num_steps // 2:
with TemporaryDirectory() as tmpdirname:
file_name = os.path.join(tmpdirname, 'schedule.bin')
torch.save(scheduler.state_dict(), file_name)
state_dict = torch.load(file_name)
scheduler.load_state_dict(state_dict)
return lrs
class OptimizationTest(unittest.TestCase):
def assertListAlmostEqual(self, list1, list2, tol):
self.assertEqual(len(list1), len(list2))
for a, b in zip(list1, list2):
self.assertAlmostEqual(a, b, delta=tol)
def test_adam_w(self):
w = torch.tensor([0.1, -0.2, -0.1], requires_grad=True)
target = torch.tensor([0.4, 0.2, -0.5])
criterion = torch.nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
optimizer = AdamW(params=[w], lr=2e-1, weight_decay=0.0)
for _ in range(100):
loss = criterion(w, target)
loss.backward()
optimizer.step()
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
w.grad.zero_()
self.assertListAlmostEqual(w.tolist(), [0.4, 0.2, -0.5], tol=1e-2)
class ScheduleInitTest(unittest.TestCase):
m = torch.nn.Linear(50, 50) if is_torch_available() else None
optimizer = AdamW(m.parameters(), lr=10.) if is_torch_available() else None
num_steps = 10
def assertListAlmostEqual(self, list1, list2, tol):
self.assertEqual(len(list1), len(list2))
for a, b in zip(list1, list2):
self.assertAlmostEqual(a, b, delta=tol)
def test_constant_scheduler(self):
scheduler = get_constant_schedule(self.optimizer)
lrs = unwrap_schedule(scheduler, self.num_steps)
expected_learning_rates = [10.] * self.num_steps
self.assertEqual(len(lrs[0]), 1)
self.assertListEqual([l[0] for l in lrs], expected_learning_rates)
scheduler = get_constant_schedule(self.optimizer)
lrs_2 = unwrap_and_save_reload_schedule(scheduler, self.num_steps)
self.assertListEqual([l[0] for l in lrs], [l[0] for l in lrs_2])
def test_warmup_constant_scheduler(self):
scheduler = get_constant_schedule_with_warmup(self.optimizer, num_warmup_steps=4)
lrs = unwrap_schedule(scheduler, self.num_steps)
expected_learning_rates = [2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0]
self.assertEqual(len(lrs[0]), 1)
self.assertListEqual([l[0] for l in lrs], expected_learning_rates)
scheduler = get_constant_schedule_with_warmup(self.optimizer, num_warmup_steps=4)
lrs_2 = unwrap_and_save_reload_schedule(scheduler, self.num_steps)
self.assertListEqual([l[0] for l in lrs], [l[0] for l in lrs_2])
def test_warmup_linear_scheduler(self):
scheduler = get_linear_schedule_with_warmup(self.optimizer, num_warmup_steps=2, num_training_steps=10)
lrs = unwrap_schedule(scheduler, self.num_steps)
expected_learning_rates = [5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25, 0.0]
self.assertEqual(len(lrs[0]), 1)
self.assertListEqual([l[0] for l in lrs], expected_learning_rates)
scheduler = get_linear_schedule_with_warmup(self.optimizer, num_warmup_steps=2, num_training_steps=10)
lrs_2 = unwrap_and_save_reload_schedule(scheduler, self.num_steps)
self.assertListEqual([l[0] for l in lrs], [l[0] for l in lrs_2])
def test_warmup_cosine_scheduler(self):
scheduler = get_cosine_schedule_with_warmup(self.optimizer, num_warmup_steps=2, num_training_steps=10)
lrs = unwrap_schedule(scheduler, self.num_steps)
expected_learning_rates = [5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38, 0.0]
self.assertEqual(len(lrs[0]), 1)
self.assertListAlmostEqual([l[0] for l in lrs], expected_learning_rates, tol=1e-2)
scheduler = get_cosine_schedule_with_warmup(self.optimizer, num_warmup_steps=2, num_training_steps=10)
lrs_2 = unwrap_and_save_reload_schedule(scheduler, self.num_steps)
self.assertListEqual([l[0] for l in lrs], [l[0] for l in lrs_2])
def test_warmup_cosine_hard_restart_scheduler(self):
scheduler = get_cosine_with_hard_restarts_schedule_with_warmup(self.optimizer, num_warmup_steps=2, num_cycles=2, num_training_steps=10)
lrs = unwrap_schedule(scheduler, self.num_steps)
expected_learning_rates = [5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46, 0.0]
self.assertEqual(len(lrs[0]), 1)
self.assertListAlmostEqual([l[0] for l in lrs], expected_learning_rates, tol=1e-2)
scheduler = get_cosine_with_hard_restarts_schedule_with_warmup(self.optimizer, num_warmup_steps=2, num_cycles=2, num_training_steps=10)
lrs_2 = unwrap_and_save_reload_schedule(scheduler, self.num_steps)
self.assertListEqual([l[0] for l in lrs], [l[0] for l in lrs_2])
if __name__ == "__main__":
unittest.main()
| 6,670 | 43.178808 | 143 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tests/modeling_xlnet_test.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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 absolute_import
from __future__ import division
from __future__ import print_function
import os
import unittest
import json
import random
import shutil
import pytest
from transformers import is_torch_available
if is_torch_available():
import torch
from transformers import (XLNetConfig, XLNetModel, XLNetLMHeadModel, XLNetForSequenceClassification, XLNetForQuestionAnswering)
from transformers.modeling_xlnet import XLNET_PRETRAINED_MODEL_ARCHIVE_MAP
else:
pytestmark = pytest.mark.skip("Require Torch")
from .modeling_common_test import (CommonTestCases, ids_tensor)
from .configuration_common_test import ConfigTester
class XLNetModelTest(CommonTestCases.CommonModelTester):
all_model_classes=(XLNetModel, XLNetLMHeadModel,
XLNetForSequenceClassification, XLNetForQuestionAnswering) if is_torch_available() else ()
test_pruning = False
class XLNetModelTester(object):
def __init__(self,
parent,
batch_size=13,
seq_length=7,
mem_len=10,
clamp_len=-1,
reuse_len=15,
is_training=True,
use_labels=True,
vocab_size=99,
cutoffs=[10, 50, 80],
hidden_size=32,
num_attention_heads=4,
d_inner=128,
num_hidden_layers=5,
max_position_embeddings=10,
type_sequence_label_size=2,
untie_r=True,
bi_data=False,
same_length=False,
initializer_range=0.05,
seed=1,
type_vocab_size=2,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.mem_len = mem_len
# self.key_len = seq_length + mem_len
self.clamp_len = clamp_len
self.reuse_len = reuse_len
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.cutoffs = cutoffs
self.hidden_size = hidden_size
self.num_attention_heads = num_attention_heads
self.d_inner = d_inner
self.num_hidden_layers = num_hidden_layers
self.max_position_embeddings = max_position_embeddings
self.bi_data = bi_data
self.untie_r = untie_r
self.same_length = same_length
self.initializer_range = initializer_range
self.seed = seed
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
def prepare_config_and_inputs(self):
input_ids_1 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_ids_2 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
segment_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
input_mask = ids_tensor([self.batch_size, self.seq_length], 2).float()
input_ids_q = ids_tensor([self.batch_size, self.seq_length + 1], self.vocab_size)
perm_mask = torch.zeros(self.batch_size, self.seq_length + 1, self.seq_length + 1, dtype=torch.float)
perm_mask[:, :, -1] = 1.0 # Previous tokens don't see last token
target_mapping = torch.zeros(self.batch_size, 1, self.seq_length + 1, dtype=torch.float)
target_mapping[:, 0, -1] = 1.0 # predict last token
sequence_labels = None
lm_labels = None
is_impossible_labels = None
if self.use_labels:
lm_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
is_impossible_labels = ids_tensor([self.batch_size], 2).float()
config = XLNetConfig(
vocab_size_or_config_json_file=self.vocab_size,
d_model=self.hidden_size,
n_head=self.num_attention_heads,
d_inner=self.d_inner,
n_layer=self.num_hidden_layers,
untie_r=self.untie_r,
max_position_embeddings=self.max_position_embeddings,
mem_len=self.mem_len,
clamp_len=self.clamp_len,
same_length=self.same_length,
reuse_len=self.reuse_len,
bi_data=self.bi_data,
initializer_range=self.initializer_range,
num_labels=self.type_sequence_label_size)
return (config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels)
def set_seed(self):
random.seed(self.seed)
torch.manual_seed(self.seed)
def create_and_check_xlnet_base_model(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels):
model = XLNetModel(config)
model.eval()
_, _ = model(input_ids_1, input_mask=input_mask)
_, _ = model(input_ids_1, attention_mask=input_mask)
_, _ = model(input_ids_1, token_type_ids=segment_ids)
outputs, mems_1 = model(input_ids_1)
result = {
"mems_1": mems_1,
"outputs": outputs,
}
config.mem_len = 0
model = XLNetModel(config)
model.eval()
no_mems_outputs = model(input_ids_1)
self.parent.assertEqual(len(no_mems_outputs), 1)
self.parent.assertListEqual(
list(result["outputs"].size()),
[self.batch_size, self.seq_length, self.hidden_size])
self.parent.assertListEqual(
list(list(mem.size()) for mem in result["mems_1"]),
[[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
def create_and_check_xlnet_lm_head(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels):
model = XLNetLMHeadModel(config)
model.eval()
loss_1, all_logits_1, mems_1 = model(input_ids_1, token_type_ids=segment_ids, labels=lm_labels)
loss_2, all_logits_2, mems_2 = model(input_ids_2, token_type_ids=segment_ids, labels=lm_labels, mems=mems_1)
logits, _ = model(input_ids_q, perm_mask=perm_mask, target_mapping=target_mapping)
result = {
"loss_1": loss_1,
"mems_1": mems_1,
"all_logits_1": all_logits_1,
"loss_2": loss_2,
"mems_2": mems_2,
"all_logits_2": all_logits_2,
}
self.parent.assertListEqual(
list(result["loss_1"].size()),
[])
self.parent.assertListEqual(
list(result["all_logits_1"].size()),
[self.batch_size, self.seq_length, self.vocab_size])
self.parent.assertListEqual(
list(list(mem.size()) for mem in result["mems_1"]),
[[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
self.parent.assertListEqual(
list(result["loss_2"].size()),
[])
self.parent.assertListEqual(
list(result["all_logits_2"].size()),
[self.batch_size, self.seq_length, self.vocab_size])
self.parent.assertListEqual(
list(list(mem.size()) for mem in result["mems_2"]),
[[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
def create_and_check_xlnet_qa(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels):
model = XLNetForQuestionAnswering(config)
model.eval()
outputs = model(input_ids_1)
start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits, mems = outputs
outputs = model(input_ids_1, start_positions=sequence_labels,
end_positions=sequence_labels,
cls_index=sequence_labels,
is_impossible=is_impossible_labels,
p_mask=input_mask)
outputs = model(input_ids_1, start_positions=sequence_labels,
end_positions=sequence_labels,
cls_index=sequence_labels,
is_impossible=is_impossible_labels)
total_loss, mems = outputs
outputs = model(input_ids_1, start_positions=sequence_labels,
end_positions=sequence_labels)
total_loss, mems = outputs
result = {
"loss": total_loss,
"start_top_log_probs": start_top_log_probs,
"start_top_index": start_top_index,
"end_top_log_probs": end_top_log_probs,
"end_top_index": end_top_index,
"cls_logits": cls_logits,
"mems": mems,
}
self.parent.assertListEqual(
list(result["loss"].size()),
[])
self.parent.assertListEqual(
list(result["start_top_log_probs"].size()),
[self.batch_size, model.config.start_n_top])
self.parent.assertListEqual(
list(result["start_top_index"].size()),
[self.batch_size, model.config.start_n_top])
self.parent.assertListEqual(
list(result["end_top_log_probs"].size()),
[self.batch_size, model.config.start_n_top * model.config.end_n_top])
self.parent.assertListEqual(
list(result["end_top_index"].size()),
[self.batch_size, model.config.start_n_top * model.config.end_n_top])
self.parent.assertListEqual(
list(result["cls_logits"].size()),
[self.batch_size])
self.parent.assertListEqual(
list(list(mem.size()) for mem in result["mems"]),
[[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
def create_and_check_xlnet_sequence_classif(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels):
model = XLNetForSequenceClassification(config)
model.eval()
logits, mems_1 = model(input_ids_1)
loss, logits, mems_1 = model(input_ids_1, labels=sequence_labels)
result = {
"loss": loss,
"mems_1": mems_1,
"logits": logits,
}
self.parent.assertListEqual(
list(result["loss"].size()),
[])
self.parent.assertListEqual(
list(result["logits"].size()),
[self.batch_size, self.type_sequence_label_size])
self.parent.assertListEqual(
list(list(mem.size()) for mem in result["mems_1"]),
[[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
target_mapping, segment_ids, lm_labels,
sequence_labels, is_impossible_labels) = config_and_inputs
inputs_dict = {'input_ids': input_ids_1}
return config, inputs_dict
def setUp(self):
self.model_tester = XLNetModelTest.XLNetModelTester(self)
self.config_tester = ConfigTester(self, config_class=XLNetConfig, d_inner=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_xlnet_base_model(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlnet_base_model(*config_and_inputs)
def test_xlnet_lm_head(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlnet_lm_head(*config_and_inputs)
def test_xlnet_sequence_classif(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlnet_sequence_classif(*config_and_inputs)
def test_xlnet_qa(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlnet_qa(*config_and_inputs)
@pytest.mark.slow
def test_model_from_pretrained(self):
cache_dir = "/tmp/transformers_test/"
for model_name in list(XLNET_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = XLNetModel.from_pretrained(model_name, cache_dir=cache_dir)
shutil.rmtree(cache_dir)
self.assertIsNotNone(model)
if __name__ == "__main__":
unittest.main()
| 14,578 | 42.519403 | 131 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tests/modeling_distilbert_test.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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 absolute_import
from __future__ import division
from __future__ import print_function
import unittest
import pytest
from transformers import is_torch_available
if is_torch_available():
from transformers import (DistilBertConfig, DistilBertModel, DistilBertForMaskedLM,
DistilBertForTokenClassification,
DistilBertForQuestionAnswering, DistilBertForSequenceClassification)
else:
pytestmark = pytest.mark.skip("Require Torch")
from .modeling_common_test import (CommonTestCases, ids_tensor)
from .configuration_common_test import ConfigTester
class DistilBertModelTest(CommonTestCases.CommonModelTester):
all_model_classes = (DistilBertModel, DistilBertForMaskedLM, DistilBertForQuestionAnswering,
DistilBertForSequenceClassification) if is_torch_available() else None
test_pruning = True
test_torchscript = True
test_resize_embeddings = True
test_head_masking = True
class DistilBertModelTester(object):
def __init__(self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=False,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
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 = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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 = DistilBertConfig(
vocab_size_or_config_json_file=self.vocab_size,
dim=self.hidden_size,
n_layers=self.num_hidden_layers,
n_heads=self.num_attention_heads,
hidden_dim=self.intermediate_size,
hidden_act=self.hidden_act,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
initializer_range=self.initializer_range)
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def check_loss_output(self, result):
self.parent.assertListEqual(
list(result["loss"].size()),
[])
def create_and_check_distilbert_model(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels):
model = DistilBertModel(config=config)
model.eval()
(sequence_output,) = model(input_ids, input_mask)
(sequence_output,) = model(input_ids)
result = {
"sequence_output": sequence_output,
}
self.parent.assertListEqual(
list(result["sequence_output"].size()),
[self.batch_size, self.seq_length, self.hidden_size])
def create_and_check_distilbert_for_masked_lm(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels):
model = DistilBertForMaskedLM(config=config)
model.eval()
loss, prediction_scores = model(input_ids, attention_mask=input_mask, masked_lm_labels=token_labels)
result = {
"loss": loss,
"prediction_scores": prediction_scores,
}
self.parent.assertListEqual(
list(result["prediction_scores"].size()),
[self.batch_size, self.seq_length, self.vocab_size])
self.check_loss_output(result)
def create_and_check_distilbert_for_question_answering(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels):
model = DistilBertForQuestionAnswering(config=config)
model.eval()
loss, start_logits, end_logits = model(input_ids, attention_mask=input_mask, start_positions=sequence_labels, end_positions=sequence_labels)
result = {
"loss": loss,
"start_logits": start_logits,
"end_logits": end_logits,
}
self.parent.assertListEqual(
list(result["start_logits"].size()),
[self.batch_size, self.seq_length])
self.parent.assertListEqual(
list(result["end_logits"].size()),
[self.batch_size, self.seq_length])
self.check_loss_output(result)
def create_and_check_distilbert_for_sequence_classification(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels):
config.num_labels = self.num_labels
model = DistilBertForSequenceClassification(config)
model.eval()
loss, logits = model(input_ids, attention_mask=input_mask, labels=sequence_labels)
result = {
"loss": loss,
"logits": logits,
}
self.parent.assertListEqual(
list(result["logits"].size()),
[self.batch_size, self.num_labels])
self.check_loss_output(result)
def create_and_check_distilbert_for_token_classification(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels):
config.num_labels = self.num_labels
model = DistilBertForTokenClassification(config=config)
model.eval()
loss, logits = model(input_ids, attention_mask=input_mask, labels=token_labels)
result = {
"loss": loss,
"logits": logits,
}
self.parent.assertListEqual(
list(result["logits"].size()),
[self.batch_size, self.seq_length, self.num_labels])
self.check_loss_output(result)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids, input_mask, sequence_labels, token_labels, choice_labels) = config_and_inputs
inputs_dict = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
def setUp(self):
self.model_tester = DistilBertModelTest.DistilBertModelTester(self)
self.config_tester = ConfigTester(self, config_class=DistilBertConfig, dim=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_distilbert_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_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_distilbert_for_masked_lm(*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_distilbert_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_distilbert_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_distilbert_for_token_classification(*config_and_inputs)
# @pytest.mark.slow
# def test_model_from_pretrained(self):
# cache_dir = "/tmp/transformers_test/"
# for model_name in list(DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
# model = DistilBertModel.from_pretrained(model_name, cache_dir=cache_dir)
# shutil.rmtree(cache_dir)
# self.assertIsNotNone(model)
if __name__ == "__main__":
unittest.main()
| 10,800 | 43.632231 | 152 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tests/tokenization_bert_test.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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 absolute_import, division, print_function, unicode_literals
import os
import unittest
import pytest
from io import open
from transformers.tokenization_bert import (BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control, _is_punctuation,
_is_whitespace, VOCAB_FILES_NAMES)
from .tokenization_tests_commons import CommonTestCases
class BertTokenizationTest(CommonTestCases.CommonTokenizerTester):
tokenizer_class = BertTokenizer
def setUp(self):
super(BertTokenizationTest, self).setUp()
vocab_tokens = [
"[UNK]", "[CLS]", "[SEP]", "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_tokenizer(self, **kwargs):
return BertTokenizer.from_pretrained(self.tmpdirname, **kwargs)
def get_input_output_texts(self):
input_text = u"UNwant\u00E9d,running"
output_text = u"unwanted, running"
return input_text, output_text
def test_full_tokenizer(self):
tokenizer = self.tokenizer_class(self.vocab_file)
tokens = tokenizer.tokenize(u"UNwant\u00E9d,running")
self.assertListEqual(tokens, ["un", "##want", "##ed", ",", "runn", "##ing"])
self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [7, 4, 5, 10, 8, 9])
def test_chinese(self):
tokenizer = BasicTokenizer()
self.assertListEqual(
tokenizer.tokenize(u"ah\u535A\u63A8zz"),
[u"ah", u"\u535A", u"\u63A8", u"zz"])
def test_basic_tokenizer_lower(self):
tokenizer = BasicTokenizer(do_lower_case=True)
self.assertListEqual(
tokenizer.tokenize(u" \tHeLLo!how \n Are yoU? "),
["hello", "!", "how", "are", "you", "?"])
self.assertListEqual(tokenizer.tokenize(u"H\u00E9llo"), ["hello"])
def test_basic_tokenizer_no_lower(self):
tokenizer = BasicTokenizer(do_lower_case=False)
self.assertListEqual(
tokenizer.tokenize(u" \tHeLLo!how \n Are yoU? "),
["HeLLo", "!", "how", "Are", "yoU", "?"])
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"])
def test_is_whitespace(self):
self.assertTrue(_is_whitespace(u" "))
self.assertTrue(_is_whitespace(u"\t"))
self.assertTrue(_is_whitespace(u"\r"))
self.assertTrue(_is_whitespace(u"\n"))
self.assertTrue(_is_whitespace(u"\u00A0"))
self.assertFalse(_is_whitespace(u"A"))
self.assertFalse(_is_whitespace(u"-"))
def test_is_control(self):
self.assertTrue(_is_control(u"\u0005"))
self.assertFalse(_is_control(u"A"))
self.assertFalse(_is_control(u" "))
self.assertFalse(_is_control(u"\t"))
self.assertFalse(_is_control(u"\r"))
def test_is_punctuation(self):
self.assertTrue(_is_punctuation(u"-"))
self.assertTrue(_is_punctuation(u"$"))
self.assertTrue(_is_punctuation(u"`"))
self.assertTrue(_is_punctuation(u"."))
self.assertFalse(_is_punctuation(u"A"))
self.assertFalse(_is_punctuation(u" "))
@pytest.mark.slow
def test_sequence_builders(self):
tokenizer = self.tokenizer_class.from_pretrained("bert-base-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 == [101] + text + [102]
assert encoded_pair == [101] + text + [102] + text_2 + [102]
if __name__ == '__main__':
unittest.main()
| 5,391 | 36.444444 | 90 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tests/modeling_tf_distilbert_test.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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 absolute_import
from __future__ import division
from __future__ import print_function
import unittest
import pytest
from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
from .configuration_common_test import ConfigTester
from transformers import DistilBertConfig, is_tf_available
if is_tf_available():
import tensorflow as tf
from transformers.modeling_tf_distilbert import (TFDistilBertModel,
TFDistilBertForMaskedLM,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification)
else:
pytestmark = pytest.mark.skip("Require TensorFlow")
class TFDistilBertModelTest(TFCommonTestCases.TFCommonModelTester):
all_model_classes = (TFDistilBertModel, TFDistilBertForMaskedLM, TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification) if is_tf_available() else None
test_pruning = True
test_torchscript = True
test_resize_embeddings = True
test_head_masking = True
class TFDistilBertModelTester(object):
def __init__(self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=False,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
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 = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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 = DistilBertConfig(
vocab_size_or_config_json_file=self.vocab_size,
dim=self.hidden_size,
n_layers=self.num_hidden_layers,
n_heads=self.num_attention_heads,
hidden_dim=self.intermediate_size,
hidden_act=self.hidden_act,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
initializer_range=self.initializer_range)
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def create_and_check_distilbert_model(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels):
model = TFDistilBertModel(config=config)
inputs = {'input_ids': input_ids,
'attention_mask': input_mask}
outputs = model(inputs)
sequence_output = outputs[0]
inputs = [input_ids, input_mask]
(sequence_output,) = model(inputs)
result = {
"sequence_output": sequence_output.numpy(),
}
self.parent.assertListEqual(
list(result["sequence_output"].shape),
[self.batch_size, self.seq_length, self.hidden_size])
def create_and_check_distilbert_for_masked_lm(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels):
model = TFDistilBertForMaskedLM(config=config)
inputs = {'input_ids': input_ids,
'attention_mask': input_mask}
(prediction_scores,) = model(inputs)
result = {
"prediction_scores": prediction_scores.numpy(),
}
self.parent.assertListEqual(
list(result["prediction_scores"].shape),
[self.batch_size, self.seq_length, self.vocab_size])
def create_and_check_distilbert_for_question_answering(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels):
model = TFDistilBertForQuestionAnswering(config=config)
inputs = {'input_ids': input_ids,
'attention_mask': input_mask}
start_logits, end_logits = model(inputs)
result = {
"start_logits": start_logits.numpy(),
"end_logits": end_logits.numpy(),
}
self.parent.assertListEqual(
list(result["start_logits"].shape),
[self.batch_size, self.seq_length])
self.parent.assertListEqual(
list(result["end_logits"].shape),
[self.batch_size, self.seq_length])
def create_and_check_distilbert_for_sequence_classification(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels):
config.num_labels = self.num_labels
model = TFDistilBertForSequenceClassification(config)
inputs = {'input_ids': input_ids,
'attention_mask': input_mask}
(logits,) = model(inputs)
result = {
"logits": logits.numpy(),
}
self.parent.assertListEqual(
list(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, input_ids, input_mask, sequence_labels, token_labels, choice_labels) = config_and_inputs
inputs_dict = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
def setUp(self):
self.model_tester = TFDistilBertModelTest.TFDistilBertModelTester(self)
self.config_tester = ConfigTester(self, config_class=DistilBertConfig, dim=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_distilbert_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_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_distilbert_for_masked_lm(*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_distilbert_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_distilbert_for_sequence_classification(*config_and_inputs)
# @pytest.mark.slow
# def test_model_from_pretrained(self):
# cache_dir = "/tmp/transformers_test/"
# for model_name in list(DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
# model = DistilBertModel.from_pretrained(model_name, cache_dir=cache_dir)
# shutil.rmtree(cache_dir)
# self.assertIsNotNone(model)
if __name__ == "__main__":
unittest.main()
| 9,856 | 43.201794 | 151 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tests/tokenization_auto_test.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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 absolute_import
from __future__ import division
from __future__ import print_function
import unittest
import shutil
import pytest
import logging
from transformers import AutoTokenizer, BertTokenizer, AutoTokenizer, GPT2Tokenizer
from transformers import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP
class AutoTokenizerTest(unittest.TestCase):
@pytest.mark.slow
def test_tokenizer_from_pretrained(self):
logging.basicConfig(level=logging.INFO)
for model_name in list(BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys())[:1]:
tokenizer = AutoTokenizer.from_pretrained(model_name)
self.assertIsNotNone(tokenizer)
self.assertIsInstance(tokenizer, BertTokenizer)
self.assertGreater(len(tokenizer), 0)
for model_name in list(GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys())[:1]:
tokenizer = AutoTokenizer.from_pretrained(model_name)
self.assertIsNotNone(tokenizer)
self.assertIsInstance(tokenizer, GPT2Tokenizer)
self.assertGreater(len(tokenizer), 0)
if __name__ == "__main__":
unittest.main()
| 1,775 | 36.787234 | 95 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tests/configuration_common_test.py
|
# coding=utf-8
# Copyright 2019 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import os
import shutil
import json
import random
import uuid
import unittest
import logging
class ConfigTester(object):
def __init__(self, parent, config_class=None, **kwargs):
self.parent = parent
self.config_class = config_class
self.inputs_dict = kwargs
def create_and_test_config_common_properties(self):
config = self.config_class(**self.inputs_dict)
self.parent.assertTrue(hasattr(config, 'vocab_size'))
self.parent.assertTrue(hasattr(config, 'hidden_size'))
self.parent.assertTrue(hasattr(config, 'num_attention_heads'))
self.parent.assertTrue(hasattr(config, 'num_hidden_layers'))
def create_and_test_config_to_json_string(self):
config = self.config_class(**self.inputs_dict)
obj = json.loads(config.to_json_string())
for key, value in self.inputs_dict.items():
self.parent.assertEqual(obj[key], value)
def create_and_test_config_to_json_file(self):
config_first = self.config_class(**self.inputs_dict)
json_file_path = os.path.join(os.getcwd(), "config_" + str(uuid.uuid4()) + ".json")
config_first.to_json_file(json_file_path)
config_second = self.config_class.from_json_file(json_file_path)
os.remove(json_file_path)
self.parent.assertEqual(config_second.to_dict(), config_first.to_dict())
def run_common_tests(self):
self.create_and_test_config_common_properties()
self.create_and_test_config_to_json_string()
self.create_and_test_config_to_json_file()
if __name__ == "__main__":
unittest.main()
| 2,330 | 36 | 91 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tests/tokenization_roberta_test.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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 absolute_import, division, print_function, unicode_literals
import os
import json
import unittest
import pytest
from io import open
from transformers.tokenization_roberta import RobertaTokenizer, VOCAB_FILES_NAMES
from .tokenization_tests_commons import CommonTestCases
class RobertaTokenizationTest(CommonTestCases.CommonTokenizerTester):
tokenizer_class = RobertaTokenizer
def setUp(self):
super(RobertaTokenizationTest, self).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",
"\u0120", "\u0120l", "\u0120n",
"\u0120lo", "\u0120low", "er",
"\u0120lowest", "\u0120newer", "\u0120wider", "<unk>"]
vocab_tokens = dict(zip(vocab, range(len(vocab))))
merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
self.special_tokens_map = {"unk_token": "<unk>"}
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", encoding="utf-8") as fp:
fp.write(json.dumps(vocab_tokens) + "\n")
with open(self.merges_file, "w", encoding="utf-8") as fp:
fp.write("\n".join(merges))
def get_tokenizer(self, **kwargs):
kwargs.update(self.special_tokens_map)
return RobertaTokenizer.from_pretrained(self.tmpdirname, **kwargs)
def get_input_output_texts(self):
input_text = u"lower newer"
output_text = u"lower newer"
return input_text, output_text
def test_full_tokenizer(self):
tokenizer = RobertaTokenizer(self.vocab_file, self.merges_file, **self.special_tokens_map)
text = "lower newer"
bpe_tokens = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
tokens = tokenizer.tokenize(text, add_prefix_space=True)
self.assertListEqual(tokens, bpe_tokens)
input_tokens = tokens + [tokenizer.unk_token]
input_bpe_tokens = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(
tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
def roberta_dict_integration_testing(self):
tokenizer = self.get_tokenizer()
self.assertListEqual(
tokenizer.encode('Hello world!', add_special_tokens=False),
[0, 31414, 232, 328, 2]
)
self.assertListEqual(
tokenizer.encode('Hello world! cécé herlolip 418', add_special_tokens=False),
[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]
)
@pytest.mark.slow
def test_sequence_builders(self):
tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
text = tokenizer.encode("sequence builders", add_special_tokens=False)
text_2 = tokenizer.encode("multi-sequence build", add_special_tokens=False)
encoded_text_from_decode = tokenizer.encode("sequence builders", add_special_tokens=True)
encoded_pair_from_decode = tokenizer.encode("sequence builders", "multi-sequence build", add_special_tokens=True)
encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
if __name__ == '__main__':
unittest.main()
| 4,189 | 40.485149 | 121 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tests/tokenization_xlnet_test.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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 absolute_import, division, print_function, unicode_literals
import os
import unittest
import pytest
from transformers.tokenization_xlnet import (XLNetTokenizer, SPIECE_UNDERLINE)
from .tokenization_tests_commons import CommonTestCases
SAMPLE_VOCAB = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'fixtures/test_sentencepiece.model')
class XLNetTokenizationTest(CommonTestCases.CommonTokenizerTester):
tokenizer_class = XLNetTokenizer
def setUp(self):
super(XLNetTokenizationTest, self).setUp()
# We have a SentencePiece fixture for testing
tokenizer = XLNetTokenizer(SAMPLE_VOCAB, keep_accents=True)
tokenizer.save_pretrained(self.tmpdirname)
def get_tokenizer(self, **kwargs):
return XLNetTokenizer.from_pretrained(self.tmpdirname, **kwargs)
def get_input_output_texts(self):
input_text = u"This is a test"
output_text = u"This is a test"
return input_text, output_text
def test_full_tokenizer(self):
tokenizer = XLNetTokenizer(SAMPLE_VOCAB, keep_accents=True)
tokens = tokenizer.tokenize(u'This is a test')
self.assertListEqual(tokens, [u'▁This', u'▁is', u'▁a', u'▁t', u'est'])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(tokens), [285, 46, 10, 170, 382])
tokens = tokenizer.tokenize(u"I was born in 92000, and this is falsé.")
self.assertListEqual(tokens, [SPIECE_UNDERLINE + u'I', SPIECE_UNDERLINE + u'was', SPIECE_UNDERLINE + u'b',
u'or', u'n', SPIECE_UNDERLINE + u'in', SPIECE_UNDERLINE + u'',
u'9', u'2', u'0', u'0', u'0', u',', SPIECE_UNDERLINE + u'and', SPIECE_UNDERLINE + u'this',
SPIECE_UNDERLINE + u'is', SPIECE_UNDERLINE + u'f', u'al', u's', u'é', u'.'])
ids = tokenizer.convert_tokens_to_ids(tokens)
self.assertListEqual(
ids, [8, 21, 84, 55, 24, 19, 7, 0,
602, 347, 347, 347, 3, 12, 66,
46, 72, 80, 6, 0, 4])
back_tokens = tokenizer.convert_ids_to_tokens(ids)
self.assertListEqual(back_tokens, [SPIECE_UNDERLINE + u'I', SPIECE_UNDERLINE + u'was', SPIECE_UNDERLINE + u'b',
u'or', u'n', SPIECE_UNDERLINE + u'in',
SPIECE_UNDERLINE + u'', u'<unk>', u'2', u'0', u'0', u'0', u',',
SPIECE_UNDERLINE + u'and', SPIECE_UNDERLINE + u'this',
SPIECE_UNDERLINE + u'is', SPIECE_UNDERLINE + u'f', u'al', u's',
u'<unk>', u'.'])
def test_tokenizer_lower(self):
tokenizer = XLNetTokenizer(SAMPLE_VOCAB, do_lower_case=True)
tokens = tokenizer.tokenize(u"I was born in 92000, and this is falsé.")
self.assertListEqual(tokens, [SPIECE_UNDERLINE + u'', u'i', SPIECE_UNDERLINE + u'was', SPIECE_UNDERLINE + u'b',
u'or', u'n', SPIECE_UNDERLINE + u'in', SPIECE_UNDERLINE + u'',
u'9', u'2', u'0', u'0', u'0', u',', SPIECE_UNDERLINE + u'and', SPIECE_UNDERLINE + u'this',
SPIECE_UNDERLINE + u'is', SPIECE_UNDERLINE + u'f', u'al', u'se', u'.'])
self.assertListEqual(tokenizer.tokenize(u"H\u00E9llo"), [u"▁he", u"ll", u"o"])
def test_tokenizer_no_lower(self):
tokenizer = XLNetTokenizer(SAMPLE_VOCAB, do_lower_case=False)
tokens = tokenizer.tokenize(u"I was born in 92000, and this is falsé.")
self.assertListEqual(tokens, [SPIECE_UNDERLINE + u'I', SPIECE_UNDERLINE + u'was', SPIECE_UNDERLINE + u'b', u'or',
u'n', SPIECE_UNDERLINE + u'in', SPIECE_UNDERLINE + u'',
u'9', u'2', u'0', u'0', u'0', u',', SPIECE_UNDERLINE + u'and', SPIECE_UNDERLINE + u'this',
SPIECE_UNDERLINE + u'is', SPIECE_UNDERLINE + u'f', u'al', u'se', u'.'])
@pytest.mark.slow
def test_sequence_builders(self):
tokenizer = XLNetTokenizer.from_pretrained("xlnet-base-cased")
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 + [4, 3]
assert encoded_pair == text + [4] + text_2 + [4, 3]
if __name__ == '__main__':
unittest.main()
| 5,339 | 47.990826 | 128 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tests/modeling_tf_openai_gpt_test.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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 absolute_import
from __future__ import division
from __future__ import print_function
import unittest
import shutil
import pytest
import sys
from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
from .configuration_common_test import ConfigTester
from transformers import OpenAIGPTConfig, is_tf_available
if is_tf_available():
import tensorflow as tf
from transformers.modeling_tf_openai import (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel,
TFOpenAIGPTDoubleHeadsModel,
TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP)
else:
pytestmark = pytest.mark.skip("Require TensorFlow")
class TFOpenAIGPTModelTest(TFCommonTestCases.TFCommonModelTester):
all_model_classes = (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel,
TFOpenAIGPTDoubleHeadsModel) if is_tf_available() else ()
class TFOpenAIGPTModelTester(object):
def __init__(self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_token_type_ids=True,
use_input_mask=True,
use_labels=True,
use_mc_token_ids=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
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_token_type_ids = use_token_type_ids
self.use_input_mask = use_input_mask
self.use_labels = use_labels
self.use_mc_token_ids = use_mc_token_ids
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 = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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)
mc_token_ids = None
if self.use_mc_token_ids:
mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length)
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 = OpenAIGPTConfig(
vocab_size_or_config_json_file=self.vocab_size,
n_embd=self.hidden_size,
n_layer=self.num_hidden_layers,
n_head=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,
n_positions=self.max_position_embeddings,
n_ctx=self.max_position_embeddings
# type_vocab_size=self.type_vocab_size,
# initializer_range=self.initializer_range
)
head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
return config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, token_labels, choice_labels
def create_and_check_openai_gpt_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = TFOpenAIGPTModel(config=config)
inputs = {'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids}
sequence_output = model(inputs)[0]
inputs = [input_ids, input_mask]
sequence_output = model(inputs)[0]
sequence_output = model(input_ids)[0]
result = {
"sequence_output": sequence_output.numpy(),
}
self.parent.assertListEqual(
list(result["sequence_output"].shape),
[self.batch_size, self.seq_length, self.hidden_size])
def create_and_check_openai_gpt_lm_head(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = TFOpenAIGPTLMHeadModel(config=config)
inputs = {'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids}
prediction_scores = model(inputs)[0]
result = {
"prediction_scores": prediction_scores.numpy(),
}
self.parent.assertListEqual(
list(result["prediction_scores"].shape),
[self.batch_size, self.seq_length, self.vocab_size])
def create_and_check_openai_gpt_double_head(self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, *args):
model = TFOpenAIGPTDoubleHeadsModel(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,
'mc_token_ids': mc_token_ids,
'attention_mask': multiple_choice_input_mask,
'token_type_ids': multiple_choice_token_type_ids}
lm_logits, mc_logits = model(inputs)[:2]
result = {
"lm_logits": lm_logits.numpy(),
"mc_logits": mc_logits.numpy()
}
self.parent.assertListEqual(
list(result["lm_logits"].shape),
[self.batch_size, self.num_choices, self.seq_length, self.vocab_size])
self.parent.assertListEqual(
list(result["mc_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, input_mask, head_mask, token_type_ids,
mc_token_ids, 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
def setUp(self):
self.model_tester = TFOpenAIGPTModelTest.TFOpenAIGPTModelTester(self)
self.config_tester = ConfigTester(self, config_class=OpenAIGPTConfig, n_embd=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_openai_gpt_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*config_and_inputs)
def test_openai_gpt_lm_head(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_lm_head(*config_and_inputs)
def test_openai_gpt_double_head(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_double_head(*config_and_inputs)
@pytest.mark.slow
def test_model_from_pretrained(self):
cache_dir = "/tmp/transformers_test/"
for model_name in list(TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = TFOpenAIGPTModel.from_pretrained(model_name, cache_dir=cache_dir)
shutil.rmtree(cache_dir)
self.assertIsNotNone(model)
if __name__ == "__main__":
unittest.main()
| 10,178 | 42.875 | 137 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tests/modeling_tf_transfo_xl_test.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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 absolute_import
from __future__ import division
from __future__ import print_function
import unittest
import random
import shutil
import pytest
from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
from .configuration_common_test import ConfigTester
from transformers import TransfoXLConfig, is_tf_available
if is_tf_available():
import tensorflow as tf
from transformers.modeling_tf_transfo_xl import (TFTransfoXLModel,
TFTransfoXLLMHeadModel,
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP)
else:
pytestmark = pytest.mark.skip("Require TensorFlow")
class TFTransfoXLModelTest(TFCommonTestCases.TFCommonModelTester):
all_model_classes = (TFTransfoXLModel, TFTransfoXLLMHeadModel) if is_tf_available() else ()
test_pruning = False
test_torchscript = False
test_resize_embeddings = False
class TFTransfoXLModelTester(object):
def __init__(self,
parent,
batch_size=13,
seq_length=7,
mem_len=30,
clamp_len=15,
is_training=True,
use_labels=True,
vocab_size=99,
cutoffs=[10, 50, 80],
hidden_size=32,
d_embed=32,
num_attention_heads=4,
d_head=8,
d_inner=128,
div_val=2,
num_hidden_layers=5,
scope=None,
seed=1,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.mem_len = mem_len
self.key_len = seq_length + mem_len
self.clamp_len = clamp_len
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.cutoffs = cutoffs
self.hidden_size = hidden_size
self.d_embed = d_embed
self.num_attention_heads = num_attention_heads
self.d_head = d_head
self.d_inner = d_inner
self.div_val = div_val
self.num_hidden_layers = num_hidden_layers
self.scope = scope
self.seed = seed
def prepare_config_and_inputs(self):
input_ids_1 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_ids_2 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
lm_labels = None
if self.use_labels:
lm_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
config = TransfoXLConfig(
vocab_size_or_config_json_file=self.vocab_size,
mem_len=self.mem_len,
clamp_len=self.clamp_len,
cutoffs=self.cutoffs,
d_model=self.hidden_size,
d_embed=self.d_embed,
n_head=self.num_attention_heads,
d_head=self.d_head,
d_inner=self.d_inner,
div_val=self.div_val,
n_layer=self.num_hidden_layers)
return (config, input_ids_1, input_ids_2, lm_labels)
def set_seed(self):
random.seed(self.seed)
tf.random.set_seed(self.seed)
def create_and_check_transfo_xl_model(self, config, input_ids_1, input_ids_2, lm_labels):
model = TFTransfoXLModel(config)
hidden_states_1, mems_1 = model(input_ids_1)
inputs = {'input_ids': input_ids_2,
'mems': mems_1}
hidden_states_2, mems_2 = model(inputs)
result = {
"hidden_states_1": hidden_states_1.numpy(),
"mems_1": [mem.numpy() for mem in mems_1],
"hidden_states_2": hidden_states_2.numpy(),
"mems_2": [mem.numpy() for mem in mems_2],
}
self.parent.assertListEqual(
list(result["hidden_states_1"].shape),
[self.batch_size, self.seq_length, self.hidden_size])
self.parent.assertListEqual(
list(result["hidden_states_2"].shape),
[self.batch_size, self.seq_length, self.hidden_size])
self.parent.assertListEqual(
list(list(mem.shape) for mem in result["mems_1"]),
[[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
self.parent.assertListEqual(
list(list(mem.shape) for mem in result["mems_2"]),
[[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
def create_and_check_transfo_xl_lm_head(self, config, input_ids_1, input_ids_2, lm_labels):
model = TFTransfoXLLMHeadModel(config)
lm_logits_1, mems_1 = model(input_ids_1)
inputs = {'input_ids': input_ids_1,
'labels': lm_labels}
_, mems_1 = model(inputs)
lm_logits_2, mems_2 = model([input_ids_2, mems_1])
inputs = {'input_ids': input_ids_1,
'mems': mems_1,
'labels': lm_labels}
_, mems_2 = model(inputs)
result = {
"mems_1": [mem.numpy() for mem in mems_1],
"lm_logits_1": lm_logits_1.numpy(),
"mems_2": [mem.numpy() for mem in mems_2],
"lm_logits_2": lm_logits_2.numpy(),
}
self.parent.assertListEqual(
list(result["lm_logits_1"].shape),
[self.batch_size, self.seq_length, self.vocab_size])
self.parent.assertListEqual(
list(list(mem.shape) for mem in result["mems_1"]),
[[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
self.parent.assertListEqual(
list(result["lm_logits_2"].shape),
[self.batch_size, self.seq_length, self.vocab_size])
self.parent.assertListEqual(
list(list(mem.shape) for mem in result["mems_2"]),
[[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids_1, input_ids_2, lm_labels) = config_and_inputs
inputs_dict = {'input_ids': input_ids_1}
return config, inputs_dict
def setUp(self):
self.model_tester = TFTransfoXLModelTest.TFTransfoXLModelTester(self)
self.config_tester = ConfigTester(self, config_class=TransfoXLConfig, d_embed=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_transfo_xl_model(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_model(*config_and_inputs)
def test_transfo_xl_lm_head(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_lm_head(*config_and_inputs)
@pytest.mark.slow
def test_model_from_pretrained(self):
cache_dir = "/tmp/transformers_test/"
for model_name in list(TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = TFTransfoXLModel.from_pretrained(model_name, cache_dir=cache_dir)
shutil.rmtree(cache_dir)
self.assertIsNotNone(model)
if __name__ == "__main__":
unittest.main()
| 8,483 | 37.917431 | 104 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tests/modeling_tf_common_test.py
|
# coding=utf-8
# Copyright 2019 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.
from __future__ import absolute_import, division, print_function
import os
import copy
import json
import logging
import importlib
import random
import shutil
import unittest
import uuid
import tempfile
import pytest
import sys
from transformers import is_tf_available, is_torch_available
if is_tf_available():
import tensorflow as tf
import numpy as np
from transformers import TFPreTrainedModel
# from transformers.modeling_bert import BertModel, BertConfig, BERT_PRETRAINED_MODEL_ARCHIVE_MAP
else:
pytestmark = pytest.mark.skip("Require TensorFlow")
if sys.version_info[0] == 2:
import cPickle as pickle
class TemporaryDirectory(object):
"""Context manager for tempfile.mkdtemp() so it's usable with "with" statement."""
def __enter__(self):
self.name = tempfile.mkdtemp()
return self.name
def __exit__(self, exc_type, exc_value, traceback):
shutil.rmtree(self.name)
else:
import pickle
TemporaryDirectory = tempfile.TemporaryDirectory
unicode = str
def _config_zero_init(config):
configs_no_init = copy.deepcopy(config)
for key in configs_no_init.__dict__.keys():
if '_range' in key or '_std' in key:
setattr(configs_no_init, key, 0.0)
return configs_no_init
class TFCommonTestCases:
class TFCommonModelTester(unittest.TestCase):
model_tester = None
all_model_classes = ()
test_torchscript = True
test_pruning = True
test_resize_embeddings = True
def test_initialization(self):
pass
# 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():
# if param.requires_grad:
# self.assertIn(param.data.mean().item(), [0.0, 1.0],
# msg="Parameter {} of model {} seems not properly initialized".format(name, model_class))
def test_save_load(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
outputs = model(inputs_dict)
with TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model = model_class.from_pretrained(tmpdirname)
after_outputs = model(inputs_dict)
# Make sure we don't have nans
out_1 = after_outputs[0].numpy()
out_2 = outputs[0].numpy()
out_1 = out_1[~np.isnan(out_1)]
out_2 = out_2[~np.isnan(out_2)]
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
def test_pt_tf_model_equivalence(self):
if not is_torch_available():
return
import torch
import transformers
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
pt_model_class_name = model_class.__name__[2:] # Skip the "TF" at the beggining
pt_model_class = getattr(transformers, pt_model_class_name)
config.output_hidden_states = True
tf_model = model_class(config)
pt_model = pt_model_class(config)
# Check we can load pt model in tf and vice-versa with model => model functions
tf_model = transformers.load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=inputs_dict)
pt_model = transformers.load_tf2_model_in_pytorch_model(pt_model, tf_model)
# Check predictions on first output (logits/hidden-states) are close enought given low-level computational differences
pt_model.eval()
pt_inputs_dict = dict((name, torch.from_numpy(key.numpy()).to(torch.long))
for name, key in inputs_dict.items())
with torch.no_grad():
pto = pt_model(**pt_inputs_dict)
tfo = tf_model(inputs_dict)
max_diff = np.amax(np.abs(tfo[0].numpy() - pto[0].numpy()))
self.assertLessEqual(max_diff, 2e-2)
# Check we can load pt model in tf and vice-versa with checkpoint => model functions
with TemporaryDirectory() as tmpdirname:
pt_checkpoint_path = os.path.join(tmpdirname, 'pt_model.bin')
torch.save(pt_model.state_dict(), pt_checkpoint_path)
tf_model = transformers.load_pytorch_checkpoint_in_tf2_model(tf_model, pt_checkpoint_path)
tf_checkpoint_path = os.path.join(tmpdirname, 'tf_model.h5')
tf_model.save_weights(tf_checkpoint_path)
pt_model = transformers.load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path)
# Check predictions on first output (logits/hidden-states) are close enought given low-level computational differences
pt_model.eval()
pt_inputs_dict = dict((name, torch.from_numpy(key.numpy()).to(torch.long))
for name, key in inputs_dict.items())
with torch.no_grad():
pto = pt_model(**pt_inputs_dict)
tfo = tf_model(inputs_dict)
max_diff = np.amax(np.abs(tfo[0].numpy() - pto[0].numpy()))
self.assertLessEqual(max_diff, 2e-2)
def test_compile_tf_model(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
input_ids = tf.keras.Input(batch_shape=(2, 2000), name='input_ids', dtype='int32')
optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08, clipnorm=1.0)
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
metric = tf.keras.metrics.SparseCategoricalAccuracy('accuracy')
for model_class in self.all_model_classes:
# Prepare our model
model = model_class(config)
# Let's load it from the disk to be sure we can use pretrained weights
with TemporaryDirectory() as tmpdirname:
outputs = model(inputs_dict) # build the model
model.save_pretrained(tmpdirname)
model = model_class.from_pretrained(tmpdirname)
outputs_dict = model(input_ids)
hidden_states = outputs_dict[0]
# Add a dense layer on top to test intetgration with other keras modules
outputs = tf.keras.layers.Dense(2, activation='softmax', name='outputs')(hidden_states)
# Compile extended model
extended_model = tf.keras.Model(inputs=[input_ids], outputs=[outputs])
extended_model.compile(optimizer=optimizer, loss=loss, metrics=[metric])
def test_keyword_and_dict_args(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
outputs_dict = model(inputs_dict)
inputs_keywords = copy.deepcopy(inputs_dict)
input_ids = inputs_keywords.pop('input_ids')
outputs_keywords = model(input_ids, **inputs_keywords)
output_dict = outputs_dict[0].numpy()
output_keywords = outputs_keywords[0].numpy()
self.assertLess(np.sum(np.abs(output_dict - output_keywords)), 1e-6)
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
config.output_attentions = True
config.output_hidden_states = False
model = model_class(config)
outputs = model(inputs_dict)
attentions = [t.numpy() for t in outputs[-1]]
self.assertEqual(model.config.output_attentions, True)
self.assertEqual(model.config.output_hidden_states, False)
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads,
self.model_tester.seq_length,
self.model_tester.key_len if hasattr(self.model_tester, 'key_len') else self.model_tester.seq_length])
out_len = len(outputs)
# Check attention is always last and order is fine
config.output_attentions = True
config.output_hidden_states = True
model = model_class(config)
outputs = model(inputs_dict)
self.assertEqual(out_len+1, len(outputs))
self.assertEqual(model.config.output_attentions, True)
self.assertEqual(model.config.output_hidden_states, True)
attentions = [t.numpy() for t in outputs[-1]]
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads,
self.model_tester.seq_length,
self.model_tester.key_len if hasattr(self.model_tester, 'key_len') else self.model_tester.seq_length])
def test_headmasking(self):
pass
# config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
# config.output_attentions = True
# config.output_hidden_states = True
# configs_no_init = _config_zero_init(config) # To be sure we have no Nan
# for model_class in self.all_model_classes:
# model = model_class(config=configs_no_init)
# model.eval()
# # Prepare head_mask
# # Set require_grad after having prepared the tensor to avoid error (leaf variable has been moved into the graph interior)
# head_mask = torch.ones(self.model_tester.num_hidden_layers, self.model_tester.num_attention_heads)
# head_mask[0, 0] = 0
# head_mask[-1, :-1] = 0
# head_mask.requires_grad_(requires_grad=True)
# inputs = inputs_dict.copy()
# inputs['head_mask'] = head_mask
# outputs = model(**inputs)
# # Test that we can get a gradient back for importance score computation
# output = sum(t.sum() for t in outputs[0])
# output = output.sum()
# output.backward()
# multihead_outputs = head_mask.grad
# attentions = outputs[-1]
# hidden_states = outputs[-2]
# # Remove Nan
# self.assertIsNotNone(multihead_outputs)
# self.assertEqual(len(multihead_outputs), self.model_tester.num_hidden_layers)
# self.assertAlmostEqual(
# attentions[0][..., 0, :, :].flatten().sum().item(), 0.0)
# self.assertNotEqual(
# attentions[0][..., -1, :, :].flatten().sum().item(), 0.0)
# self.assertNotEqual(
# attentions[1][..., 0, :, :].flatten().sum().item(), 0.0)
# self.assertAlmostEqual(
# attentions[-1][..., -2, :, :].flatten().sum().item(), 0.0)
# self.assertNotEqual(
# attentions[-1][..., -1, :, :].flatten().sum().item(), 0.0)
def test_head_pruning(self):
pass
# if not self.test_pruning:
# return
# config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
# for model_class in self.all_model_classes:
# config.output_attentions = True
# config.output_hidden_states = False
# model = model_class(config=config)
# model.eval()
# heads_to_prune = {0: list(range(1, self.model_tester.num_attention_heads)),
# -1: [0]}
# model.prune_heads(heads_to_prune)
# outputs = model(**inputs_dict)
# attentions = outputs[-1]
# self.assertEqual(
# attentions[0].shape[-3], 1)
# self.assertEqual(
# attentions[1].shape[-3], self.model_tester.num_attention_heads)
# self.assertEqual(
# attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1)
def test_hidden_states_output(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
config.output_hidden_states = True
config.output_attentions = False
model = model_class(config)
outputs = model(inputs_dict)
hidden_states = [t.numpy() for t in outputs[-1]]
self.assertEqual(model.config.output_attentions, False)
self.assertEqual(model.config.output_hidden_states, True)
self.assertEqual(len(hidden_states), self.model_tester.num_hidden_layers + 1)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[self.model_tester.seq_length, self.model_tester.hidden_size])
def test_resize_tokens_embeddings(self):
pass
# 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_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 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)
# # 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_model_common_attributes(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer)
x = model.get_output_embeddings()
assert x is None or isinstance(x, tf.keras.layers.Layer)
def test_tie_model_weights(self):
pass
# config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
# def check_same_values(layer_1, layer_2):
# equal = True
# for p1, p2 in zip(layer_1.weight, layer_2.weight):
# if p1.data.ne(p2.data).sum() > 0:
# equal = False
# return equal
# for model_class in self.all_model_classes:
# if not hasattr(model_class, 'tie_weights'):
# continue
# config.torchscript = True
# model_not_tied = model_class(config)
# params_not_tied = list(model_not_tied.parameters())
# config_tied = copy.deepcopy(config)
# config_tied.torchscript = False
# model_tied = model_class(config_tied)
# params_tied = list(model_tied.parameters())
# # Check that the embedding layer and decoding layer are the same in size and in value
# self.assertGreater(len(params_not_tied), len(params_tied))
# # Check that after resize they remain tied.
# model_tied.resize_token_embeddings(config.vocab_size + 10)
# params_tied_2 = list(model_tied.parameters())
# self.assertGreater(len(params_not_tied), len(params_tied))
# self.assertEqual(len(params_tied_2), len(params_tied))
def test_determinism(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
first, second = model(inputs_dict, training=False)[0], model(inputs_dict, training=False)[0]
self.assertTrue(tf.math.equal(first, second).numpy().all())
def test_inputs_embeds(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
input_ids = inputs_dict["input_ids"]
del inputs_dict["input_ids"]
for model_class in self.all_model_classes:
model = model_class(config)
wte = model.get_input_embeddings()
try:
x = wte(input_ids, mode="embedding")
except:
try:
x = wte([input_ids], mode="embedding")
except:
x = tf.ones(input_ids.shape + [self.model_tester.hidden_size], dtype=tf.dtypes.float32)
# ^^ In our TF models, the input_embeddings can take slightly different forms,
# so we try two of them and fall back to just synthetically creating a dummy tensor of ones.
inputs_dict["inputs_embeds"] = x
outputs = model(inputs_dict)
def ids_tensor(shape, vocab_size, rng=None, name=None, dtype=None):
"""Creates a random int32 tensor of the shape within the vocab size."""
if rng is None:
rng = random.Random()
total_dims = 1
for dim in shape:
total_dims *= dim
values = []
for _ in range(total_dims):
values.append(rng.randint(0, vocab_size - 1))
output = tf.constant(values,
shape=shape,
dtype=dtype if dtype is not None else tf.int32)
return output
class TFModelUtilsTest(unittest.TestCase):
@pytest.mark.skipif('tensorflow' not in sys.modules, reason="requires TensorFlow")
def test_model_from_pretrained(self):
pass
# logging.basicConfig(level=logging.INFO)
# for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
# config = BertConfig.from_pretrained(model_name)
# self.assertIsNotNone(config)
# self.assertIsInstance(config, PretrainedConfig)
# model = BertModel.from_pretrained(model_name)
# model, loading_info = BertModel.from_pretrained(model_name, output_loading_info=True)
# self.assertIsNotNone(model)
# self.assertIsInstance(model, PreTrainedModel)
# for value in loading_info.values():
# self.assertEqual(len(value), 0)
# config = BertConfig.from_pretrained(model_name, output_attentions=True, output_hidden_states=True)
# model = BertModel.from_pretrained(model_name, output_attentions=True, output_hidden_states=True)
# self.assertEqual(model.config.output_attentions, True)
# self.assertEqual(model.config.output_hidden_states, True)
# self.assertEqual(model.config, config)
if __name__ == "__main__":
unittest.main()
| 21,957 | 44.556017 | 140 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tests/__init__.py
| 0 | 0 | 0 |
py
|
|
DeeBERT
|
DeeBERT-master/transformers/tests/modeling_xlm_test.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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 absolute_import
from __future__ import division
from __future__ import print_function
import unittest
import shutil
import pytest
from transformers import is_torch_available
if is_torch_available():
from transformers import (XLMConfig, XLMModel, XLMWithLMHeadModel, XLMForQuestionAnswering,
XLMForSequenceClassification, XLMForQuestionAnsweringSimple)
from transformers.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_MAP
else:
pytestmark = pytest.mark.skip("Require Torch")
from .modeling_common_test import (CommonTestCases, ids_tensor)
from .configuration_common_test import ConfigTester
class XLMModelTest(CommonTestCases.CommonModelTester):
all_model_classes = (XLMModel, XLMWithLMHeadModel, XLMForQuestionAnswering,
XLMForSequenceClassification, XLMForQuestionAnsweringSimple) if is_torch_available() else ()
class XLMModelTester(object):
def __init__(self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_lengths=True,
use_token_type_ids=True,
use_labels=True,
gelu_activation=True,
sinusoidal_embeddings=False,
causal=False,
asm=False,
n_langs=2,
vocab_size=99,
n_special=0,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
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,
summary_type="last",
use_proj=True,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_lengths = use_input_lengths
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.gelu_activation = gelu_activation
self.sinusoidal_embeddings = sinusoidal_embeddings
self.asm = asm
self.n_langs = n_langs
self.vocab_size = vocab_size
self.n_special = n_special
self.summary_type = summary_type
self.causal = causal
self.use_proj = use_proj
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.n_langs = n_langs
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.summary_type = summary_type
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 = ids_tensor([self.batch_size, self.seq_length], 2).float()
input_lengths = None
if self.use_input_lengths:
input_lengths = ids_tensor([self.batch_size], vocab_size=2) + self.seq_length - 2 # small variation of seq_length
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.n_langs)
sequence_labels = None
token_labels = None
is_impossible_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)
is_impossible_labels = ids_tensor([self.batch_size], 2).float()
config = XLMConfig(
vocab_size_or_config_json_file=self.vocab_size,
n_special=self.n_special,
emb_dim=self.hidden_size,
n_layers=self.num_hidden_layers,
n_heads=self.num_attention_heads,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
gelu_activation=self.gelu_activation,
sinusoidal_embeddings=self.sinusoidal_embeddings,
asm=self.asm,
causal=self.causal,
n_langs=self.n_langs,
max_position_embeddings=self.max_position_embeddings,
initializer_range=self.initializer_range,
summary_type=self.summary_type,
use_proj=self.use_proj)
return config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, input_mask
def check_loss_output(self, result):
self.parent.assertListEqual(
list(result["loss"].size()),
[])
def create_and_check_xlm_model(self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, input_mask):
model = XLMModel(config=config)
model.eval()
outputs = model(input_ids, lengths=input_lengths, langs=token_type_ids)
outputs = model(input_ids, langs=token_type_ids)
outputs = model(input_ids)
sequence_output = outputs[0]
result = {
"sequence_output": sequence_output,
}
self.parent.assertListEqual(
list(result["sequence_output"].size()),
[self.batch_size, self.seq_length, self.hidden_size])
def create_and_check_xlm_lm_head(self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, input_mask):
model = XLMWithLMHeadModel(config)
model.eval()
loss, logits = model(input_ids, token_type_ids=token_type_ids, labels=token_labels)
result = {
"loss": loss,
"logits": logits,
}
self.parent.assertListEqual(
list(result["loss"].size()),
[])
self.parent.assertListEqual(
list(result["logits"].size()),
[self.batch_size, self.seq_length, self.vocab_size])
def create_and_check_xlm_simple_qa(self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, input_mask):
model = XLMForQuestionAnsweringSimple(config)
model.eval()
outputs = model(input_ids)
outputs = model(input_ids, start_positions=sequence_labels,
end_positions=sequence_labels)
loss, start_logits, end_logits = outputs
result = {
"loss": loss,
"start_logits": start_logits,
"end_logits": end_logits,
}
self.parent.assertListEqual(
list(result["start_logits"].size()),
[self.batch_size, self.seq_length])
self.parent.assertListEqual(
list(result["end_logits"].size()),
[self.batch_size, self.seq_length])
self.check_loss_output(result)
def create_and_check_xlm_qa(self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, input_mask):
model = XLMForQuestionAnswering(config)
model.eval()
outputs = model(input_ids)
start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits = outputs
outputs = model(input_ids, start_positions=sequence_labels,
end_positions=sequence_labels,
cls_index=sequence_labels,
is_impossible=is_impossible_labels,
p_mask=input_mask)
outputs = model(input_ids, start_positions=sequence_labels,
end_positions=sequence_labels,
cls_index=sequence_labels,
is_impossible=is_impossible_labels)
(total_loss,) = outputs
outputs = model(input_ids, start_positions=sequence_labels,
end_positions=sequence_labels)
(total_loss,) = outputs
result = {
"loss": total_loss,
"start_top_log_probs": start_top_log_probs,
"start_top_index": start_top_index,
"end_top_log_probs": end_top_log_probs,
"end_top_index": end_top_index,
"cls_logits": cls_logits,
}
self.parent.assertListEqual(
list(result["loss"].size()),
[])
self.parent.assertListEqual(
list(result["start_top_log_probs"].size()),
[self.batch_size, model.config.start_n_top])
self.parent.assertListEqual(
list(result["start_top_index"].size()),
[self.batch_size, model.config.start_n_top])
self.parent.assertListEqual(
list(result["end_top_log_probs"].size()),
[self.batch_size, model.config.start_n_top * model.config.end_n_top])
self.parent.assertListEqual(
list(result["end_top_index"].size()),
[self.batch_size, model.config.start_n_top * model.config.end_n_top])
self.parent.assertListEqual(
list(result["cls_logits"].size()),
[self.batch_size])
def create_and_check_xlm_sequence_classif(self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, input_mask):
model = XLMForSequenceClassification(config)
model.eval()
(logits,) = model(input_ids)
loss, logits = model(input_ids, labels=sequence_labels)
result = {
"loss": loss,
"logits": logits,
}
self.parent.assertListEqual(
list(result["loss"].size()),
[])
self.parent.assertListEqual(
list(result["logits"].size()),
[self.batch_size, self.type_sequence_label_size])
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids, token_type_ids, input_lengths,
sequence_labels, token_labels, is_impossible_labels, input_mask) = config_and_inputs
inputs_dict = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths}
return config, inputs_dict
def setUp(self):
self.model_tester = XLMModelTest.XLMModelTester(self)
self.config_tester = ConfigTester(self, config_class=XLMConfig, emb_dim=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_xlm_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*config_and_inputs)
def test_xlm_lm_head(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*config_and_inputs)
def test_xlm_simple_qa(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*config_and_inputs)
def test_xlm_qa(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*config_and_inputs)
def test_xlm_sequence_classif(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*config_and_inputs)
@pytest.mark.slow
def test_model_from_pretrained(self):
cache_dir = "/tmp/transformers_test/"
for model_name in list(XLM_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = XLMModel.from_pretrained(model_name, cache_dir=cache_dir)
shutil.rmtree(cache_dir)
self.assertIsNotNone(model)
if __name__ == "__main__":
unittest.main()
| 13,845 | 41.472393 | 171 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tests/modeling_tf_bert_test.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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 absolute_import
from __future__ import division
from __future__ import print_function
import unittest
import shutil
import pytest
import sys
from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
from .configuration_common_test import ConfigTester
from transformers import BertConfig, is_tf_available
if is_tf_available():
import tensorflow as tf
from transformers.modeling_tf_bert import (TFBertModel, TFBertForMaskedLM,
TFBertForNextSentencePrediction,
TFBertForPreTraining,
TFBertForSequenceClassification,
TFBertForMultipleChoice,
TFBertForTokenClassification,
TFBertForQuestionAnswering,
TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP)
else:
pytestmark = pytest.mark.skip("Require TensorFlow")
class TFBertModelTest(TFCommonTestCases.TFCommonModelTester):
all_model_classes = (TFBertModel, TFBertForMaskedLM, TFBertForNextSentencePrediction,
TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification,
TFBertForTokenClassification) if is_tf_available() else ()
class TFBertModelTester(object):
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=5,
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 = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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 = BertConfig(
vocab_size_or_config_json_file=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, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def create_and_check_bert_model(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
model = TFBertModel(config=config)
inputs = {'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids}
sequence_output, pooled_output = model(inputs)
inputs = [input_ids, input_mask]
sequence_output, pooled_output = model(inputs)
sequence_output, pooled_output = model(input_ids)
result = {
"sequence_output": sequence_output.numpy(),
"pooled_output": pooled_output.numpy(),
}
self.parent.assertListEqual(
list(result["sequence_output"].shape),
[self.batch_size, self.seq_length, self.hidden_size])
self.parent.assertListEqual(list(result["pooled_output"].shape), [self.batch_size, self.hidden_size])
def create_and_check_bert_for_masked_lm(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
model = TFBertForMaskedLM(config=config)
inputs = {'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids}
prediction_scores, = model(inputs)
result = {
"prediction_scores": prediction_scores.numpy(),
}
self.parent.assertListEqual(
list(result["prediction_scores"].shape),
[self.batch_size, self.seq_length, self.vocab_size])
def create_and_check_bert_for_next_sequence_prediction(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
model = TFBertForNextSentencePrediction(config=config)
inputs = {'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids}
seq_relationship_score, = model(inputs)
result = {
"seq_relationship_score": seq_relationship_score.numpy(),
}
self.parent.assertListEqual(
list(result["seq_relationship_score"].shape),
[self.batch_size, 2])
def create_and_check_bert_for_pretraining(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
model = TFBertForPreTraining(config=config)
inputs = {'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids}
prediction_scores, seq_relationship_score = model(inputs)
result = {
"prediction_scores": prediction_scores.numpy(),
"seq_relationship_score": seq_relationship_score.numpy(),
}
self.parent.assertListEqual(
list(result["prediction_scores"].shape),
[self.batch_size, self.seq_length, self.vocab_size])
self.parent.assertListEqual(
list(result["seq_relationship_score"].shape),
[self.batch_size, 2])
def create_and_check_bert_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 = TFBertForSequenceClassification(config=config)
inputs = {'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids}
logits, = model(inputs)
result = {
"logits": logits.numpy(),
}
self.parent.assertListEqual(
list(result["logits"].shape),
[self.batch_size, self.num_labels])
def create_and_check_bert_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 = TFBertForMultipleChoice(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}
logits, = model(inputs)
result = {
"logits": logits.numpy(),
}
self.parent.assertListEqual(
list(result["logits"].shape),
[self.batch_size, self.num_choices])
def create_and_check_bert_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 = TFBertForTokenClassification(config=config)
inputs = {'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids}
logits, = model(inputs)
result = {
"logits": logits.numpy(),
}
self.parent.assertListEqual(
list(result["logits"].shape),
[self.batch_size, self.seq_length, self.num_labels])
def create_and_check_bert_for_question_answering(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
model = TFBertForQuestionAnswering(config=config)
inputs = {'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids}
start_logits, end_logits = model(inputs)
result = {
"start_logits": start_logits.numpy(),
"end_logits": end_logits.numpy(),
}
self.parent.assertListEqual(
list(result["start_logits"].shape),
[self.batch_size, self.seq_length])
self.parent.assertListEqual(
list(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
def setUp(self):
self.model_tester = TFBertModelTest.TFBertModelTester(self)
self.config_tester = ConfigTester(self, config_class=BertConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_bert_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_bert_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_bert_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_bert_for_multiple_choice(*config_and_inputs)
def test_for_next_sequence_prediction(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_bert_for_next_sequence_prediction(*config_and_inputs)
def test_for_pretraining(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_bert_for_pretraining(*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_bert_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_bert_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_bert_for_token_classification(*config_and_inputs)
@pytest.mark.slow
def test_model_from_pretrained(self):
cache_dir = "/tmp/transformers_test/"
# for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
for model_name in ['bert-base-uncased']:
model = TFBertModel.from_pretrained(model_name, cache_dir=cache_dir)
shutil.rmtree(cache_dir)
self.assertIsNotNone(model)
if __name__ == "__main__":
unittest.main()
| 15,160 | 45.79321 | 162 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tests/modeling_gpt2_test.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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 absolute_import
from __future__ import division
from __future__ import print_function
import unittest
import pytest
import shutil
from transformers import is_torch_available
if is_torch_available():
from transformers import (GPT2Config, GPT2Model, GPT2_PRETRAINED_MODEL_ARCHIVE_MAP,
GPT2LMHeadModel, GPT2DoubleHeadsModel)
else:
pytestmark = pytest.mark.skip("Require Torch")
from .modeling_common_test import (CommonTestCases, ids_tensor)
from .configuration_common_test import ConfigTester
class GPT2ModelTest(CommonTestCases.CommonModelTester):
all_model_classes = (GPT2Model, GPT2LMHeadModel, GPT2DoubleHeadsModel) if is_torch_available() else ()
class GPT2ModelTester(object):
def __init__(self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_token_type_ids=True,
use_input_mask=True,
use_labels=True,
use_mc_token_ids=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
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_token_type_ids = use_token_type_ids
self.use_input_mask = use_input_mask
self.use_labels = use_labels
self.use_mc_token_ids = use_mc_token_ids
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 = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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)
mc_token_ids = None
if self.use_mc_token_ids:
mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length)
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 = GPT2Config(
vocab_size_or_config_json_file=self.vocab_size,
n_embd=self.hidden_size,
n_layer=self.num_hidden_layers,
n_head=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,
n_positions=self.max_position_embeddings,
n_ctx=self.max_position_embeddings
# type_vocab_size=self.type_vocab_size,
# initializer_range=self.initializer_range
)
head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
return config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, token_labels, choice_labels
def check_loss_output(self, result):
self.parent.assertListEqual(
list(result["loss"].size()),
[])
def create_and_check_gpt2_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = GPT2Model(config=config)
model.eval()
model(input_ids, token_type_ids=token_type_ids, head_mask=head_mask)
model(input_ids, token_type_ids=token_type_ids)
sequence_output, presents = model(input_ids)
result = {
"sequence_output": sequence_output,
"presents": presents,
}
self.parent.assertListEqual(
list(result["sequence_output"].size()),
[self.batch_size, self.seq_length, self.hidden_size])
self.parent.assertEqual(len(result["presents"]), config.n_layer)
def create_and_check_lm_head_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = GPT2LMHeadModel(config)
model.eval()
loss, lm_logits, _ = model(input_ids, token_type_ids=token_type_ids, labels=input_ids)
result = {
"loss": loss,
"lm_logits": lm_logits
}
self.parent.assertListEqual(
list(result["loss"].size()),
[])
self.parent.assertListEqual(
list(result["lm_logits"].size()),
[self.batch_size, self.seq_length, self.vocab_size])
def create_and_check_double_lm_head_model(self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, *args):
model = GPT2DoubleHeadsModel(config)
model.eval()
multiple_choice_inputs_ids = input_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_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
inputs = {'input_ids': multiple_choice_inputs_ids,
'mc_token_ids': mc_token_ids,
'attention_mask': multiple_choice_input_mask,
'token_type_ids': multiple_choice_token_type_ids,
'lm_labels': multiple_choice_inputs_ids}
loss, lm_logits, mc_logits, _ = model(**inputs)
result = {
"loss": loss,
"lm_logits": lm_logits,
"mc_logits": mc_logits
}
self.parent.assertListEqual(
list(result["loss"].size()),
[])
self.parent.assertListEqual(
list(result["lm_logits"].size()),
[self.batch_size, self.num_choices, self.seq_length, self.vocab_size])
self.parent.assertListEqual(
list(result["mc_logits"].size()),
[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, input_mask, head_mask, token_type_ids,
mc_token_ids, sequence_labels, token_labels, choice_labels) = config_and_inputs
inputs_dict = {
'input_ids': input_ids,
'token_type_ids': token_type_ids,
'head_mask': head_mask
}
return config, inputs_dict
def setUp(self):
self.model_tester = GPT2ModelTest.GPT2ModelTester(self)
self.config_tester = ConfigTester(self, config_class=GPT2Config, n_embd=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_gpt2_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gpt2_model(*config_and_inputs)
def test_gpt2_lm_head_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*config_and_inputs)
def test_gpt2_double_lm_head_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*config_and_inputs)
@pytest.mark.slow
def test_model_from_pretrained(self):
cache_dir = "/tmp/transformers_test/"
for model_name in list(GPT2_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = GPT2Model.from_pretrained(model_name, cache_dir=cache_dir)
shutil.rmtree(cache_dir)
self.assertIsNotNone(model)
if __name__ == "__main__":
unittest.main()
| 10,296 | 40.353414 | 135 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tests/modeling_tf_xlm_test.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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 absolute_import
from __future__ import division
from __future__ import print_function
import unittest
import shutil
import pytest
from transformers import is_tf_available
if is_tf_available():
import tensorflow as tf
from transformers import (XLMConfig, TFXLMModel,
TFXLMWithLMHeadModel,
TFXLMForSequenceClassification,
TFXLMForQuestionAnsweringSimple,
TF_XLM_PRETRAINED_MODEL_ARCHIVE_MAP)
else:
pytestmark = pytest.mark.skip("Require TensorFlow")
from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
from .configuration_common_test import ConfigTester
class TFXLMModelTest(TFCommonTestCases.TFCommonModelTester):
all_model_classes = (TFXLMModel, TFXLMWithLMHeadModel,
TFXLMForSequenceClassification,
TFXLMForQuestionAnsweringSimple) if is_tf_available() else ()
class TFXLMModelTester(object):
def __init__(self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_lengths=True,
use_token_type_ids=True,
use_labels=True,
gelu_activation=True,
sinusoidal_embeddings=False,
causal=False,
asm=False,
n_langs=2,
vocab_size=99,
n_special=0,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
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,
summary_type="last",
use_proj=True,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_lengths = use_input_lengths
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.gelu_activation = gelu_activation
self.sinusoidal_embeddings = sinusoidal_embeddings
self.asm = asm
self.n_langs = n_langs
self.vocab_size = vocab_size
self.n_special = n_special
self.summary_type = summary_type
self.causal = causal
self.use_proj = use_proj
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.n_langs = n_langs
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.summary_type = summary_type
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 = ids_tensor([self.batch_size, self.seq_length], 2, dtype=tf.float32)
input_lengths = None
if self.use_input_lengths:
input_lengths = ids_tensor([self.batch_size], vocab_size=2) + self.seq_length - 2 # small variation of seq_length
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.n_langs)
sequence_labels = None
token_labels = None
is_impossible_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)
is_impossible_labels = ids_tensor([self.batch_size], 2, dtype=tf.float32)
config = XLMConfig(
vocab_size_or_config_json_file=self.vocab_size,
n_special=self.n_special,
emb_dim=self.hidden_size,
n_layers=self.num_hidden_layers,
n_heads=self.num_attention_heads,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
gelu_activation=self.gelu_activation,
sinusoidal_embeddings=self.sinusoidal_embeddings,
asm=self.asm,
causal=self.causal,
n_langs=self.n_langs,
max_position_embeddings=self.max_position_embeddings,
initializer_range=self.initializer_range,
summary_type=self.summary_type,
use_proj=self.use_proj)
return config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, input_mask
def create_and_check_xlm_model(self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, input_mask):
model = TFXLMModel(config=config)
inputs = {'input_ids': input_ids,
'lengths': input_lengths,
'langs': token_type_ids}
outputs = model(inputs)
inputs = [input_ids, input_mask]
outputs = model(inputs)
sequence_output = outputs[0]
result = {
"sequence_output": sequence_output.numpy(),
}
self.parent.assertListEqual(
list(result["sequence_output"].shape),
[self.batch_size, self.seq_length, self.hidden_size])
def create_and_check_xlm_lm_head(self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, input_mask):
model = TFXLMWithLMHeadModel(config)
inputs = {'input_ids': input_ids,
'lengths': input_lengths,
'langs': token_type_ids}
outputs = model(inputs)
logits = outputs[0]
result = {
"logits": logits.numpy(),
}
self.parent.assertListEqual(
list(result["logits"].shape),
[self.batch_size, self.seq_length, self.vocab_size])
def create_and_check_xlm_qa(self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, input_mask):
model = TFXLMForQuestionAnsweringSimple(config)
inputs = {'input_ids': input_ids,
'lengths': input_lengths}
outputs = model(inputs)
start_logits, end_logits = model(inputs)
result = {
"start_logits": start_logits.numpy(),
"end_logits": end_logits.numpy(),
}
self.parent.assertListEqual(
list(result["start_logits"].shape),
[self.batch_size, self.seq_length])
self.parent.assertListEqual(
list(result["end_logits"].shape),
[self.batch_size, self.seq_length])
def create_and_check_xlm_sequence_classif(self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, input_mask):
model = TFXLMForSequenceClassification(config)
inputs = {'input_ids': input_ids,
'lengths': input_lengths}
(logits,) = model(inputs)
result = {
"logits": logits.numpy(),
}
self.parent.assertListEqual(
list(result["logits"].shape),
[self.batch_size, self.type_sequence_label_size])
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids, token_type_ids, input_lengths,
sequence_labels, token_labels, is_impossible_labels, input_mask) = config_and_inputs
inputs_dict = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'langs': token_type_ids, 'lengths': input_lengths}
return config, inputs_dict
def setUp(self):
self.model_tester = TFXLMModelTest.TFXLMModelTester(self)
self.config_tester = ConfigTester(self, config_class=XLMConfig, emb_dim=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_xlm_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*config_and_inputs)
def test_xlm_lm_head(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*config_and_inputs)
def test_xlm_qa(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*config_and_inputs)
def test_xlm_sequence_classif(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*config_and_inputs)
@pytest.mark.slow
def test_model_from_pretrained(self):
cache_dir = "/tmp/transformers_test/"
for model_name in list(TF_XLM_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = XLMModel.from_pretrained(model_name, cache_dir=cache_dir)
shutil.rmtree(cache_dir)
self.assertIsNotNone(model)
if __name__ == "__main__":
unittest.main()
| 10,924 | 40.226415 | 171 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tests/tokenization_gpt2_test.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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 absolute_import, division, print_function, unicode_literals
import os
import unittest
import json
from io import open
from transformers.tokenization_gpt2 import GPT2Tokenizer, VOCAB_FILES_NAMES
from .tokenization_tests_commons import CommonTestCases
class GPT2TokenizationTest(CommonTestCases.CommonTokenizerTester):
tokenizer_class = GPT2Tokenizer
def setUp(self):
super(GPT2TokenizationTest, self).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",
"\u0120", "\u0120l", "\u0120n",
"\u0120lo", "\u0120low", "er",
"\u0120lowest", "\u0120newer", "\u0120wider", "<unk>"]
vocab_tokens = dict(zip(vocab, range(len(vocab))))
merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
self.special_tokens_map = {"unk_token": "<unk>"}
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", encoding="utf-8") as fp:
fp.write(json.dumps(vocab_tokens) + "\n")
with open(self.merges_file, "w", encoding="utf-8") as fp:
fp.write("\n".join(merges))
def get_tokenizer(self, **kwargs):
kwargs.update(self.special_tokens_map)
return GPT2Tokenizer.from_pretrained(self.tmpdirname, **kwargs)
def get_input_output_texts(self):
input_text = u"lower newer"
output_text = u"lower newer"
return input_text, output_text
def test_full_tokenizer(self):
tokenizer = GPT2Tokenizer(self.vocab_file, self.merges_file, **self.special_tokens_map)
text = "lower newer"
bpe_tokens = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
tokens = tokenizer.tokenize(text, add_prefix_space=True)
self.assertListEqual(tokens, bpe_tokens)
input_tokens = tokens + [tokenizer.unk_token]
input_bpe_tokens = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(
tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
if __name__ == '__main__':
unittest.main()
| 2,926 | 39.09589 | 95 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tests/modeling_ctrl_test.py
|
# coding=utf-8
# Copyright 2018 Salesforce 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import unittest
import pytest
import shutil
import pdb
from transformers import is_torch_available
if is_torch_available():
from transformers import (CTRLConfig, CTRLModel, CTRL_PRETRAINED_MODEL_ARCHIVE_MAP,
CTRLLMHeadModel)
else:
pytestmark = pytest.mark.skip("Require Torch")
from .modeling_common_test import (CommonTestCases, ids_tensor)
from .configuration_common_test import ConfigTester
class CTRLModelTest(CommonTestCases.CommonModelTester):
all_model_classes = (CTRLModel, CTRLLMHeadModel) if is_torch_available() else ()
test_pruning = False
test_torchscript = False
test_resize_embeddings = False
test_head_masking = False
class CTRLModelTester(object):
def __init__(self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_token_type_ids=True,
use_input_mask=True,
use_labels=True,
use_mc_token_ids=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
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_token_type_ids = use_token_type_ids
self.use_input_mask = use_input_mask
self.use_labels = use_labels
self.use_mc_token_ids = use_mc_token_ids
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 = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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)
mc_token_ids = None
if self.use_mc_token_ids:
mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length)
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 = CTRLConfig(
vocab_size_or_config_json_file=self.vocab_size,
n_embd=self.hidden_size,
n_layer=self.num_hidden_layers,
n_head=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,
n_positions=self.max_position_embeddings,
n_ctx=self.max_position_embeddings
# type_vocab_size=self.type_vocab_size,
# initializer_range=self.initializer_range
)
head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
return config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, token_labels, choice_labels
def check_loss_output(self, result):
self.parent.assertListEqual(
list(result["loss"].size()),
[])
def create_and_check_ctrl_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = CTRLModel(config=config)
model.eval()
model(input_ids, token_type_ids=token_type_ids, head_mask=head_mask)
model(input_ids, token_type_ids=token_type_ids)
sequence_output, presents = model(input_ids)
result = {
"sequence_output": sequence_output,
"presents": presents,
}
self.parent.assertListEqual(
list(result["sequence_output"].size()),
[self.batch_size, self.seq_length, self.hidden_size])
self.parent.assertEqual(len(result["presents"]), config.n_layer)
def create_and_check_lm_head_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = CTRLLMHeadModel(config)
model.eval()
loss, lm_logits, _ = model(input_ids, token_type_ids=token_type_ids, labels=input_ids)
result = {
"loss": loss,
"lm_logits": lm_logits
}
self.parent.assertListEqual(
list(result["loss"].size()),
[])
self.parent.assertListEqual(
list(result["lm_logits"].size()),
[self.batch_size, self.seq_length, self.vocab_size])
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids, input_mask, head_mask, token_type_ids,
mc_token_ids, sequence_labels, token_labels, choice_labels) = config_and_inputs
inputs_dict = {
'input_ids': input_ids,
'token_type_ids': token_type_ids,
'head_mask': head_mask
}
return config, inputs_dict
def setUp(self):
self.model_tester = CTRLModelTest.CTRLModelTester(self)
self.config_tester = ConfigTester(self, config_class=CTRLConfig, n_embd=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_ctrl_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_ctrl_model(*config_and_inputs)
def test_ctrl_lm_head_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*config_and_inputs)
@pytest.mark.slow
def test_model_from_pretrained(self):
cache_dir = "/tmp/transformers_test/"
for model_name in list(CTRL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = CTRLModel.from_pretrained(model_name, cache_dir=cache_dir)
shutil.rmtree(cache_dir)
self.assertIsNotNone(model)
if __name__ == "__main__":
unittest.main()
| 8,667 | 39.12963 | 135 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tests/modeling_tf_xlnet_test.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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 absolute_import
from __future__ import division
from __future__ import print_function
import os
import unittest
import json
import random
import shutil
import pytest
from transformers import XLNetConfig, is_tf_available
if is_tf_available():
import tensorflow as tf
from transformers.modeling_tf_xlnet import (TFXLNetModel, TFXLNetLMHeadModel,
TFXLNetForSequenceClassification,
TFXLNetForQuestionAnsweringSimple,
TF_XLNET_PRETRAINED_MODEL_ARCHIVE_MAP)
else:
pytestmark = pytest.mark.skip("Require TensorFlow")
from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
from .configuration_common_test import ConfigTester
class TFXLNetModelTest(TFCommonTestCases.TFCommonModelTester):
all_model_classes=(TFXLNetModel, TFXLNetLMHeadModel,
TFXLNetForSequenceClassification,
TFXLNetForQuestionAnsweringSimple) if is_tf_available() else ()
test_pruning = False
class TFXLNetModelTester(object):
def __init__(self,
parent,
batch_size=13,
seq_length=7,
mem_len=10,
clamp_len=-1,
reuse_len=15,
is_training=True,
use_labels=True,
vocab_size=99,
cutoffs=[10, 50, 80],
hidden_size=32,
num_attention_heads=4,
d_inner=128,
num_hidden_layers=5,
max_position_embeddings=10,
type_sequence_label_size=2,
untie_r=True,
bi_data=False,
same_length=False,
initializer_range=0.05,
seed=1,
type_vocab_size=2,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.mem_len = mem_len
# self.key_len = seq_length + mem_len
self.clamp_len = clamp_len
self.reuse_len = reuse_len
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.cutoffs = cutoffs
self.hidden_size = hidden_size
self.num_attention_heads = num_attention_heads
self.d_inner = d_inner
self.num_hidden_layers = num_hidden_layers
self.max_position_embeddings = max_position_embeddings
self.bi_data = bi_data
self.untie_r = untie_r
self.same_length = same_length
self.initializer_range = initializer_range
self.seed = seed
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
def prepare_config_and_inputs(self):
input_ids_1 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_ids_2 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
segment_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
input_mask = ids_tensor([self.batch_size, self.seq_length], 2, dtype=tf.float32)
input_ids_q = ids_tensor([self.batch_size, self.seq_length + 1], self.vocab_size)
perm_mask = tf.zeros((self.batch_size, self.seq_length + 1, self.seq_length), dtype=tf.float32)
perm_mask_last = tf.ones((self.batch_size, self.seq_length + 1, 1), dtype=tf.float32)
perm_mask = tf.concat([perm_mask, perm_mask_last], axis=-1)
# perm_mask[:, :, -1] = 1.0 # Previous tokens don't see last token
target_mapping = tf.zeros((self.batch_size, 1, self.seq_length), dtype=tf.float32)
target_mapping_last = tf.ones((self.batch_size, 1, 1), dtype=tf.float32)
target_mapping = tf.concat([target_mapping, target_mapping_last], axis=-1)
# target_mapping[:, 0, -1] = 1.0 # predict last token
sequence_labels = None
lm_labels = None
is_impossible_labels = None
if self.use_labels:
lm_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
is_impossible_labels = ids_tensor([self.batch_size], 2, dtype=tf.float32)
config = XLNetConfig(
vocab_size_or_config_json_file=self.vocab_size,
d_model=self.hidden_size,
n_head=self.num_attention_heads,
d_inner=self.d_inner,
n_layer=self.num_hidden_layers,
untie_r=self.untie_r,
max_position_embeddings=self.max_position_embeddings,
mem_len=self.mem_len,
clamp_len=self.clamp_len,
same_length=self.same_length,
reuse_len=self.reuse_len,
bi_data=self.bi_data,
initializer_range=self.initializer_range,
num_labels=self.type_sequence_label_size)
return (config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels)
def set_seed(self):
random.seed(self.seed)
tf.random.set_seed(self.seed)
def create_and_check_xlnet_base_model(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels):
model = TFXLNetModel(config)
inputs = {'input_ids': input_ids_1,
'input_mask': input_mask,
'token_type_ids': segment_ids}
_, _ = model(inputs)
inputs = [input_ids_1, input_mask]
outputs, mems_1 = model(inputs)
result = {
"mems_1": [mem.numpy() for mem in mems_1],
"outputs": outputs.numpy(),
}
config.mem_len = 0
model = TFXLNetModel(config)
no_mems_outputs = model(inputs)
self.parent.assertEqual(len(no_mems_outputs), 1)
self.parent.assertListEqual(
list(result["outputs"].shape),
[self.batch_size, self.seq_length, self.hidden_size])
self.parent.assertListEqual(
list(list(mem.shape) for mem in result["mems_1"]),
[[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
def create_and_check_xlnet_lm_head(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels):
model = TFXLNetLMHeadModel(config)
inputs_1 = {'input_ids': input_ids_1,
'token_type_ids': segment_ids}
all_logits_1, mems_1 = model(inputs_1)
inputs_2 = {'input_ids': input_ids_2,
'mems': mems_1,
'token_type_ids': segment_ids}
all_logits_2, mems_2 = model(inputs_2)
inputs_3 = {'input_ids': input_ids_q,
'perm_mask': perm_mask,
'target_mapping': target_mapping}
logits, _ = model(inputs_3)
result = {
"mems_1": [mem.numpy() for mem in mems_1],
"all_logits_1": all_logits_1.numpy(),
"mems_2": [mem.numpy() for mem in mems_2],
"all_logits_2": all_logits_2.numpy(),
}
self.parent.assertListEqual(
list(result["all_logits_1"].shape),
[self.batch_size, self.seq_length, self.vocab_size])
self.parent.assertListEqual(
list(list(mem.shape) for mem in result["mems_1"]),
[[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
self.parent.assertListEqual(
list(result["all_logits_2"].shape),
[self.batch_size, self.seq_length, self.vocab_size])
self.parent.assertListEqual(
list(list(mem.shape) for mem in result["mems_2"]),
[[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
def create_and_check_xlnet_qa(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels):
model = TFXLNetForQuestionAnsweringSimple(config)
inputs = {'input_ids': input_ids_1,
'attention_mask': input_mask,
'token_type_ids': segment_ids}
start_logits, end_logits, mems = model(inputs)
result = {
"start_logits": start_logits.numpy(),
"end_logits": end_logits.numpy(),
"mems": [m.numpy() for m in mems],
}
self.parent.assertListEqual(
list(result["start_logits"].shape),
[self.batch_size, self.seq_length])
self.parent.assertListEqual(
list(result["end_logits"].shape),
[self.batch_size, self.seq_length])
self.parent.assertListEqual(
list(list(mem.shape) for mem in result["mems"]),
[[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
def create_and_check_xlnet_sequence_classif(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels):
model = TFXLNetForSequenceClassification(config)
logits, mems_1 = model(input_ids_1)
result = {
"mems_1": [mem.numpy() for mem in mems_1],
"logits": logits.numpy(),
}
self.parent.assertListEqual(
list(result["logits"].shape),
[self.batch_size, self.type_sequence_label_size])
self.parent.assertListEqual(
list(list(mem.shape) for mem in result["mems_1"]),
[[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
target_mapping, segment_ids, lm_labels,
sequence_labels, is_impossible_labels) = config_and_inputs
inputs_dict = {'input_ids': input_ids_1}
return config, inputs_dict
def setUp(self):
self.model_tester = TFXLNetModelTest.TFXLNetModelTester(self)
self.config_tester = ConfigTester(self, config_class=XLNetConfig, d_inner=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_xlnet_base_model(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlnet_base_model(*config_and_inputs)
def test_xlnet_lm_head(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlnet_lm_head(*config_and_inputs)
def test_xlnet_sequence_classif(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlnet_sequence_classif(*config_and_inputs)
def test_xlnet_qa(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlnet_qa(*config_and_inputs)
@pytest.mark.slow
def test_model_from_pretrained(self):
cache_dir = "/tmp/transformers_test/"
for model_name in list(TF_XLNET_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = TFXLNetModel.from_pretrained(model_name, cache_dir=cache_dir)
shutil.rmtree(cache_dir)
self.assertIsNotNone(model)
if __name__ == "__main__":
unittest.main()
| 13,322 | 42.256494 | 127 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tests/modeling_tf_roberta_test.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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 absolute_import
from __future__ import division
from __future__ import print_function
import unittest
import shutil
import pytest
from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
from .configuration_common_test import ConfigTester
from transformers import RobertaConfig, is_tf_available
if is_tf_available():
import tensorflow as tf
import numpy
from transformers.modeling_tf_roberta import (TFRobertaModel, TFRobertaForMaskedLM,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP)
else:
pytestmark = pytest.mark.skip("Require TensorFlow")
class TFRobertaModelTest(TFCommonTestCases.TFCommonModelTester):
all_model_classes = (TFRobertaModel,TFRobertaForMaskedLM,
TFRobertaForSequenceClassification) if is_tf_available() else ()
class TFRobertaModelTester(object):
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=5,
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 = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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 = RobertaConfig(
vocab_size_or_config_json_file=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, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def create_and_check_roberta_model(self, config, input_ids, token_type_ids, input_mask, sequence_labels,
token_labels, choice_labels):
model = TFRobertaModel(config=config)
inputs = {'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids}
sequence_output = model(inputs)[0]
inputs = [input_ids, input_mask]
sequence_output = model(inputs)[0]
sequence_output = model(input_ids)[0]
result = {
"sequence_output": sequence_output.numpy(),
}
self.parent.assertListEqual(
list(result["sequence_output"].shape),
[self.batch_size, self.seq_length, self.hidden_size])
def create_and_check_roberta_for_masked_lm(self, config, input_ids, token_type_ids, input_mask, sequence_labels,
token_labels, choice_labels):
model = TFRobertaForMaskedLM(config=config)
prediction_scores = model([input_ids, input_mask, token_type_ids])[0]
result = {
"prediction_scores": prediction_scores.numpy(),
}
self.parent.assertListEqual(
list(result["prediction_scores"].shape),
[self.batch_size, self.seq_length, self.vocab_size])
def create_and_check_roberta_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 = TFRobertaForTokenClassification(config=config)
inputs = {'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids}
logits, = model(inputs)
result = {
"logits": logits.numpy(),
}
self.parent.assertListEqual(
list(result["logits"].shape),
[self.batch_size, self.seq_length, self.num_labels])
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
def setUp(self):
self.model_tester = TFRobertaModelTest.TFRobertaModelTester(self)
self.config_tester = ConfigTester(self, config_class=RobertaConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_roberta_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_roberta_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_roberta_for_masked_lm(*config_and_inputs)
@pytest.mark.slow
def test_model_from_pretrained(self):
cache_dir = "/tmp/transformers_test/"
for model_name in list(TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = TFRobertaModel.from_pretrained(model_name, cache_dir=cache_dir)
shutil.rmtree(cache_dir)
self.assertIsNotNone(model)
class TFRobertaModelIntegrationTest(unittest.TestCase):
@pytest.mark.slow
def test_inference_masked_lm(self):
model = TFRobertaForMaskedLM.from_pretrained('roberta-base')
input_ids = tf.constant([[ 0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
output = model(input_ids)[0]
expected_shape = [1, 11, 50265]
self.assertEqual(
list(output.numpy().shape),
expected_shape
)
# compare the actual values for a slice.
expected_slice = tf.constant(
[[[33.8843, -4.3107, 22.7779],
[ 4.6533, -2.8099, 13.6252],
[ 1.8222, -3.6898, 8.8600]]]
)
self.assertTrue(
numpy.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-3)
)
@pytest.mark.slow
def test_inference_no_head(self):
model = TFRobertaModel.from_pretrained('roberta-base')
input_ids = tf.constant([[ 0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
output = model(input_ids)[0]
# compare the actual values for a slice.
expected_slice = tf.constant(
[[[-0.0231, 0.0782, 0.0074],
[-0.1854, 0.0539, -0.0174],
[ 0.0548, 0.0799, 0.1687]]]
)
self.assertTrue(
numpy.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-3)
)
@pytest.mark.slow
def test_inference_classification_head(self):
model = TFRobertaForSequenceClassification.from_pretrained('roberta-large-mnli')
input_ids = tf.constant([[ 0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
output = model(input_ids)[0]
expected_shape = [1, 3]
self.assertEqual(
list(output.numpy().shape),
expected_shape
)
expected_tensor = tf.constant([[-0.9469, 0.3913, 0.5118]])
self.assertTrue(
numpy.allclose(output.numpy(), expected_tensor.numpy(), atol=1e-3)
)
if __name__ == "__main__":
unittest.main()
| 11,158 | 41.591603 | 161 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tests/tokenization_openai_test.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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 absolute_import, division, print_function, unicode_literals
import os
import unittest
import json
from transformers.tokenization_openai import OpenAIGPTTokenizer, VOCAB_FILES_NAMES
from .tokenization_tests_commons import CommonTestCases
class OpenAIGPTTokenizationTest(CommonTestCases.CommonTokenizerTester):
tokenizer_class = OpenAIGPTTokenizer
def setUp(self):
super(OpenAIGPTTokenizationTest, self).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 = ["#version: 0.2", "l o", "lo w", "e r</w>", ""]
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_tokenizer(self, **kwargs):
return OpenAIGPTTokenizer.from_pretrained(self.tmpdirname, **kwargs)
def get_input_output_texts(self):
input_text = u"lower newer"
output_text = u"lower newer"
return input_text, output_text
def test_full_tokenizer(self):
tokenizer = OpenAIGPTTokenizer(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)
if __name__ == '__main__':
unittest.main()
| 2,648 | 35.287671 | 90 |
py
|
DeeBERT
|
DeeBERT-master/transformers/tests/tokenization_distilbert_test.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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 absolute_import, division, print_function, unicode_literals
import os
import unittest
import pytest
from io import open
from transformers.tokenization_distilbert import (DistilBertTokenizer)
from .tokenization_tests_commons import CommonTestCases
from .tokenization_bert_test import BertTokenizationTest
class DistilBertTokenizationTest(BertTokenizationTest):
tokenizer_class = DistilBertTokenizer
def get_tokenizer(self, **kwargs):
return DistilBertTokenizer.from_pretrained(self.tmpdirname, **kwargs)
@pytest.mark.slow
def test_sequence_builders(self):
tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-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 == [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]
if __name__ == '__main__':
unittest.main()
| 1,915 | 36.568627 | 93 |
py
|
DeeBERT
|
DeeBERT-master/transformers/data/__init__.py
|
from .processors import InputExample, InputFeatures, DataProcessor
from .processors import glue_output_modes, glue_processors, glue_tasks_num_labels, glue_convert_examples_to_features
from .metrics import is_sklearn_available
if is_sklearn_available():
from .metrics import glue_compute_metrics
| 300 | 42 | 116 |
py
|
DeeBERT
|
DeeBERT-master/transformers/data/metrics/__init__.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team 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.
import csv
import sys
import logging
logger = logging.getLogger(__name__)
try:
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import matthews_corrcoef, f1_score
_has_sklearn = True
except (AttributeError, ImportError) as e:
logger.warning("To use data.metrics please install scikit-learn. See https://scikit-learn.org/stable/index.html")
_has_sklearn = False
def is_sklearn_available():
return _has_sklearn
if _has_sklearn:
def simple_accuracy(preds, labels):
return (preds == labels).mean()
def acc_and_f1(preds, labels):
acc = simple_accuracy(preds, labels)
f1 = f1_score(y_true=labels, y_pred=preds)
return {
"acc": acc,
"f1": f1,
"acc_and_f1": (acc + f1) / 2,
}
def pearson_and_spearman(preds, labels):
pearson_corr = pearsonr(preds, labels)[0]
spearman_corr = spearmanr(preds, labels)[0]
return {
"pearson": pearson_corr,
"spearmanr": spearman_corr,
"corr": (pearson_corr + spearman_corr) / 2,
}
def glue_compute_metrics(task_name, preds, labels):
assert len(preds) == len(labels)
if task_name == "cola":
return {"mcc": matthews_corrcoef(labels, preds)}
elif task_name == "sst-2":
return {"acc": simple_accuracy(preds, labels)}
elif task_name == "mrpc":
return acc_and_f1(preds, labels)
elif task_name == "sts-b":
return pearson_and_spearman(preds, labels)
elif task_name == "qqp":
return acc_and_f1(preds, labels)
elif task_name == "mnli":
return {"acc": simple_accuracy(preds, labels)}
elif task_name == "mnli-mm":
return {"acc": simple_accuracy(preds, labels)}
elif task_name == "qnli":
return {"acc": simple_accuracy(preds, labels)}
elif task_name == "rte":
return {"acc": simple_accuracy(preds, labels)}
elif task_name == "wnli":
return {"acc": simple_accuracy(preds, labels)}
else:
raise KeyError(task_name)
| 2,870 | 33.178571 | 117 |
py
|
DeeBERT
|
DeeBERT-master/transformers/data/processors/glue.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team 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.
""" GLUE processors and helpers """
import logging
import os
from .utils import DataProcessor, InputExample, InputFeatures
from ...file_utils import is_tf_available
if is_tf_available():
import tensorflow as tf
logger = logging.getLogger(__name__)
def glue_convert_examples_to_features(examples, tokenizer,
max_length=512,
task=None,
label_list=None,
output_mode=None,
pad_on_left=False,
pad_token=0,
pad_token_segment_id=0,
mask_padding_with_zero=True):
"""
Loads a data file into a list of ``InputFeatures``
Args:
examples: List of ``InputExamples`` or ``tf.data.Dataset`` containing the examples.
tokenizer: Instance of a tokenizer that will tokenize the examples
max_length: Maximum example length
task: GLUE task
label_list: List of labels. Can be obtained from the processor using the ``processor.get_labels()`` method
output_mode: String indicating the output mode. Either ``regression`` or ``classification``
pad_on_left: If set to ``True``, the examples will be padded on the left rather than on the right (default)
pad_token: Padding token
pad_token_segment_id: The segment ID for the padding token (It is usually 0, but can vary such as for XLNet where it is 4)
mask_padding_with_zero: If set to ``True``, the attention mask will be filled by ``1`` for actual values
and by ``0`` for padded values. If set to ``False``, inverts it (``1`` for padded values, ``0`` for
actual values)
Returns:
If the ``examples`` input is a ``tf.data.Dataset``, will return a ``tf.data.Dataset``
containing the task-specific features. If the input is a list of ``InputExamples``, will return
a list of task-specific ``InputFeatures`` which can be fed to the model.
"""
is_tf_dataset = False
if is_tf_available() and isinstance(examples, tf.data.Dataset):
is_tf_dataset = True
if task is not None:
processor = glue_processors[task]()
if label_list is None:
label_list = processor.get_labels()
logger.info("Using label list %s for task %s" % (label_list, task))
if output_mode is None:
output_mode = glue_output_modes[task]
logger.info("Using output mode %s for task %s" % (output_mode, task))
label_map = {label: i for i, label in enumerate(label_list)}
features = []
for (ex_index, example) in enumerate(examples):
if ex_index % 10000 == 0:
logger.info("Writing example %d" % (ex_index))
if is_tf_dataset:
example = processor.get_example_from_tensor_dict(example)
example = processor.tfds_map(example)
inputs = tokenizer.encode_plus(
example.text_a,
example.text_b,
add_special_tokens=True,
max_length=max_length,
)
input_ids, token_type_ids = inputs["input_ids"], inputs["token_type_ids"]
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
attention_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)
# Zero-pad up to the sequence length.
padding_length = max_length - len(input_ids)
if pad_on_left:
input_ids = ([pad_token] * padding_length) + input_ids
attention_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
token_type_ids = ([pad_token_segment_id] * padding_length) + token_type_ids
else:
input_ids = input_ids + ([pad_token] * padding_length)
attention_mask = attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
token_type_ids = token_type_ids + ([pad_token_segment_id] * padding_length)
assert len(input_ids) == max_length, "Error with input length {} vs {}".format(len(input_ids), max_length)
assert len(attention_mask) == max_length, "Error with input length {} vs {}".format(len(attention_mask), max_length)
assert len(token_type_ids) == max_length, "Error with input length {} vs {}".format(len(token_type_ids), max_length)
if output_mode == "classification":
label = label_map[example.label]
elif output_mode == "regression":
label = float(example.label)
else:
raise KeyError(output_mode)
if ex_index < 5:
logger.info("*** Example ***")
logger.info("guid: %s" % (example.guid))
logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
logger.info("attention_mask: %s" % " ".join([str(x) for x in attention_mask]))
logger.info("token_type_ids: %s" % " ".join([str(x) for x in token_type_ids]))
logger.info("label: %s (id = %d)" % (example.label, label))
features.append(
InputFeatures(input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
label=label))
if is_tf_available() and is_tf_dataset:
def gen():
for ex in features:
yield ({'input_ids': ex.input_ids,
'attention_mask': ex.attention_mask,
'token_type_ids': ex.token_type_ids},
ex.label)
return tf.data.Dataset.from_generator(gen,
({'input_ids': tf.int32,
'attention_mask': tf.int32,
'token_type_ids': tf.int32},
tf.int64),
({'input_ids': tf.TensorShape([None]),
'attention_mask': tf.TensorShape([None]),
'token_type_ids': tf.TensorShape([None])},
tf.TensorShape([])))
return features
class MrpcProcessor(DataProcessor):
"""Processor for the MRPC data set (GLUE version)."""
def get_example_from_tensor_dict(self, tensor_dict):
"""See base class."""
return InputExample(tensor_dict['idx'].numpy(),
tensor_dict['sentence1'].numpy().decode('utf-8'),
tensor_dict['sentence2'].numpy().decode('utf-8'),
str(tensor_dict['label'].numpy()))
def get_train_examples(self, data_dir):
"""See base class."""
logger.info("LOOKING AT {}".format(os.path.join(data_dir, "train.tsv")))
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
def get_labels(self):
"""See base class."""
return ["0", "1"]
def _create_examples(self, lines, set_type):
"""Creates examples for the training and dev sets."""
examples = []
for (i, line) in enumerate(lines):
if i == 0:
continue
guid = "%s-%s" % (set_type, i)
text_a = line[3]
text_b = line[4]
label = line[0]
examples.append(
InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
class MnliProcessor(DataProcessor):
"""Processor for the MultiNLI data set (GLUE version)."""
def get_example_from_tensor_dict(self, tensor_dict):
"""See base class."""
return InputExample(tensor_dict['idx'].numpy(),
tensor_dict['premise'].numpy().decode('utf-8'),
tensor_dict['hypothesis'].numpy().decode('utf-8'),
str(tensor_dict['label'].numpy()))
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "dev_matched.tsv")),
"dev_matched")
def get_labels(self):
"""See base class."""
return ["contradiction", "entailment", "neutral"]
def _create_examples(self, lines, set_type):
"""Creates examples for the training and dev sets."""
examples = []
for (i, line) in enumerate(lines):
if i == 0:
continue
guid = "%s-%s" % (set_type, line[0])
text_a = line[8]
text_b = line[9]
label = line[-1]
examples.append(
InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
class MnliMismatchedProcessor(MnliProcessor):
"""Processor for the MultiNLI Mismatched data set (GLUE version)."""
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "dev_mismatched.tsv")),
"dev_matched")
class ColaProcessor(DataProcessor):
"""Processor for the CoLA data set (GLUE version)."""
def get_example_from_tensor_dict(self, tensor_dict):
"""See base class."""
return InputExample(tensor_dict['idx'].numpy(),
tensor_dict['sentence'].numpy().decode('utf-8'),
None,
str(tensor_dict['label'].numpy()))
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
def get_labels(self):
"""See base class."""
return ["0", "1"]
def _create_examples(self, lines, set_type):
"""Creates examples for the training and dev sets."""
examples = []
for (i, line) in enumerate(lines):
guid = "%s-%s" % (set_type, i)
text_a = line[3]
label = line[1]
examples.append(
InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
return examples
class Sst2Processor(DataProcessor):
"""Processor for the SST-2 data set (GLUE version)."""
def get_example_from_tensor_dict(self, tensor_dict):
"""See base class."""
return InputExample(tensor_dict['idx'].numpy(),
tensor_dict['sentence'].numpy().decode('utf-8'),
None,
str(tensor_dict['label'].numpy()))
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
def get_labels(self):
"""See base class."""
return ["0", "1"]
def _create_examples(self, lines, set_type):
"""Creates examples for the training and dev sets."""
examples = []
for (i, line) in enumerate(lines):
if i == 0:
continue
guid = "%s-%s" % (set_type, i)
text_a = line[0]
label = line[1]
examples.append(
InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
return examples
class StsbProcessor(DataProcessor):
"""Processor for the STS-B data set (GLUE version)."""
def get_example_from_tensor_dict(self, tensor_dict):
"""See base class."""
return InputExample(tensor_dict['idx'].numpy(),
tensor_dict['sentence1'].numpy().decode('utf-8'),
tensor_dict['sentence2'].numpy().decode('utf-8'),
str(tensor_dict['label'].numpy()))
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
def get_labels(self):
"""See base class."""
return [None]
def _create_examples(self, lines, set_type):
"""Creates examples for the training and dev sets."""
examples = []
for (i, line) in enumerate(lines):
if i == 0:
continue
guid = "%s-%s" % (set_type, line[0])
text_a = line[7]
text_b = line[8]
label = line[-1]
examples.append(
InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
class QqpProcessor(DataProcessor):
"""Processor for the QQP data set (GLUE version)."""
def get_example_from_tensor_dict(self, tensor_dict):
"""See base class."""
return InputExample(tensor_dict['idx'].numpy(),
tensor_dict['question1'].numpy().decode('utf-8'),
tensor_dict['question2'].numpy().decode('utf-8'),
str(tensor_dict['label'].numpy()))
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
def get_labels(self):
"""See base class."""
return ["0", "1"]
def _create_examples(self, lines, set_type):
"""Creates examples for the training and dev sets."""
examples = []
for (i, line) in enumerate(lines):
if i == 0:
continue
guid = "%s-%s" % (set_type, line[0])
try:
text_a = line[3]
text_b = line[4]
label = line[5]
except IndexError:
continue
examples.append(
InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
class QnliProcessor(DataProcessor):
"""Processor for the QNLI data set (GLUE version)."""
def get_example_from_tensor_dict(self, tensor_dict):
"""See base class."""
return InputExample(tensor_dict['idx'].numpy(),
tensor_dict['question'].numpy().decode('utf-8'),
tensor_dict['sentence'].numpy().decode('utf-8'),
str(tensor_dict['label'].numpy()))
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "dev.tsv")),
"dev_matched")
def get_labels(self):
"""See base class."""
return ["entailment", "not_entailment"]
def _create_examples(self, lines, set_type):
"""Creates examples for the training and dev sets."""
examples = []
for (i, line) in enumerate(lines):
if i == 0:
continue
guid = "%s-%s" % (set_type, line[0])
text_a = line[1]
text_b = line[2]
label = line[-1]
examples.append(
InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
class RteProcessor(DataProcessor):
"""Processor for the RTE data set (GLUE version)."""
def get_example_from_tensor_dict(self, tensor_dict):
"""See base class."""
return InputExample(tensor_dict['idx'].numpy(),
tensor_dict['sentence1'].numpy().decode('utf-8'),
tensor_dict['sentence2'].numpy().decode('utf-8'),
str(tensor_dict['label'].numpy()))
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
def get_labels(self):
"""See base class."""
return ["entailment", "not_entailment"]
def _create_examples(self, lines, set_type):
"""Creates examples for the training and dev sets."""
examples = []
for (i, line) in enumerate(lines):
if i == 0:
continue
guid = "%s-%s" % (set_type, line[0])
text_a = line[1]
text_b = line[2]
label = line[-1]
examples.append(
InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
class WnliProcessor(DataProcessor):
"""Processor for the WNLI data set (GLUE version)."""
def get_example_from_tensor_dict(self, tensor_dict):
"""See base class."""
return InputExample(tensor_dict['idx'].numpy(),
tensor_dict['sentence1'].numpy().decode('utf-8'),
tensor_dict['sentence2'].numpy().decode('utf-8'),
str(tensor_dict['label'].numpy()))
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
def get_labels(self):
"""See base class."""
return ["0", "1"]
def _create_examples(self, lines, set_type):
"""Creates examples for the training and dev sets."""
examples = []
for (i, line) in enumerate(lines):
if i == 0:
continue
guid = "%s-%s" % (set_type, line[0])
text_a = line[1]
text_b = line[2]
label = line[-1]
examples.append(
InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
glue_tasks_num_labels = {
"cola": 2,
"mnli": 3,
"mrpc": 2,
"sst-2": 2,
"sts-b": 1,
"qqp": 2,
"qnli": 2,
"rte": 2,
"wnli": 2,
}
glue_processors = {
"cola": ColaProcessor,
"mnli": MnliProcessor,
"mnli-mm": MnliMismatchedProcessor,
"mrpc": MrpcProcessor,
"sst-2": Sst2Processor,
"sts-b": StsbProcessor,
"qqp": QqpProcessor,
"qnli": QnliProcessor,
"rte": RteProcessor,
"wnli": WnliProcessor,
}
glue_output_modes = {
"cola": "classification",
"mnli": "classification",
"mnli-mm": "classification",
"mrpc": "classification",
"sst-2": "classification",
"sts-b": "regression",
"qqp": "classification",
"qnli": "classification",
"rte": "classification",
"wnli": "classification",
}
| 20,708 | 36.448463 | 130 |
py
|
DeeBERT
|
DeeBERT-master/transformers/data/processors/utils.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team 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.
import csv
import sys
import copy
import json
class InputExample(object):
"""
A single training/test example for simple sequence classification.
Args:
guid: Unique id for the example.
text_a: string. The untokenized text of the first sequence. For single
sequence tasks, only this sequence must be specified.
text_b: (Optional) string. The untokenized text of the second sequence.
Only must be specified for sequence pair tasks.
label: (Optional) string. The label of the example. This should be
specified for train and dev examples, but not for test examples.
"""
def __init__(self, guid, text_a, text_b=None, label=None):
self.guid = guid
self.text_a = text_a
self.text_b = text_b
self.label = label
def __repr__(self):
return str(self.to_json_string())
def to_dict(self):
"""Serializes this instance to a Python dictionary."""
output = copy.deepcopy(self.__dict__)
return output
def to_json_string(self):
"""Serializes this instance to a JSON string."""
return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
class InputFeatures(object):
"""
A single set of features of data.
Args:
input_ids: Indices of input sequence tokens in the vocabulary.
attention_mask: Mask to avoid performing attention on padding token indices.
Mask values selected in ``[0, 1]``:
Usually ``1`` for tokens that are NOT MASKED, ``0`` for MASKED (padded) tokens.
token_type_ids: Segment token indices to indicate first and second portions of the inputs.
label: Label corresponding to the input
"""
def __init__(self, input_ids, attention_mask, token_type_ids, label):
self.input_ids = input_ids
self.attention_mask = attention_mask
self.token_type_ids = token_type_ids
self.label = label
def __repr__(self):
return str(self.to_json_string())
def to_dict(self):
"""Serializes this instance to a Python dictionary."""
output = copy.deepcopy(self.__dict__)
return output
def to_json_string(self):
"""Serializes this instance to a JSON string."""
return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
class DataProcessor(object):
"""Base class for data converters for sequence classification data sets."""
def get_example_from_tensor_dict(self, tensor_dict):
"""Gets an example from a dict with tensorflow tensors
Args:
tensor_dict: Keys and values should match the corresponding Glue
tensorflow_dataset examples.
"""
raise NotImplementedError()
def get_train_examples(self, data_dir):
"""Gets a collection of `InputExample`s for the train set."""
raise NotImplementedError()
def get_dev_examples(self, data_dir):
"""Gets a collection of `InputExample`s for the dev set."""
raise NotImplementedError()
def get_labels(self):
"""Gets the list of labels for this data set."""
raise NotImplementedError()
def tfds_map(self, example):
"""Some tensorflow_datasets datasets are not formatted the same way the GLUE datasets are.
This method converts examples to the correct format."""
if len(self.get_labels()) > 1:
example.label = self.get_labels()[int(example.label)]
return example
@classmethod
def _read_tsv(cls, input_file, quotechar=None):
"""Reads a tab separated value file."""
with open(input_file, "r", encoding="utf-8-sig") as f:
reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
lines = []
for line in reader:
if sys.version_info[0] == 2:
line = list(unicode(cell, 'utf-8') for cell in line)
lines.append(line)
return lines
| 4,706 | 35.773438 | 99 |
py
|
DeeBERT
|
DeeBERT-master/transformers/data/processors/__init__.py
|
from .utils import InputExample, InputFeatures, DataProcessor
from .glue import glue_output_modes, glue_processors, glue_tasks_num_labels, glue_convert_examples_to_features
| 174 | 42.75 | 110 |
py
|
QU-BraTS
|
QU-BraTS-main/BraTS_Seg_Uncertainty.py
|
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import argparse
import numpy as np
import pandas as pd
from sklearn.metrics import auc
import nibabel as nib
from time import time
#############################################3
### Pretty plots
import matplotlib.pyplot as plt
from matplotlib import cycler
plt.style.use("default")
plt.rcParams.update(
{"lines.linewidth": 1.5,
"axes.grid": True,
"grid.linestyle": ":",
"axes.grid.axis": "both",
"axes.prop_cycle": cycler('color',
['0071bc', 'd85218', 'ecb01f',
'7d2e8d', '76ab2f', '4cbded', 'a1132e']),
"xtick.top": True,
"xtick.minor.size": 0,
"xtick.direction": "in",
"xtick.minor.visible": True,
"ytick.right": True,
"ytick.minor.size": 0,
"ytick.direction": "in",
"ytick.minor.visible": True,
"legend.framealpha": 1.0,
"legend.edgecolor": "black",
"legend.fancybox": False,
"figure.figsize": (2.5, 2.5),
"figure.autolayout": False,
"savefig.dpi": 300,
"savefig.format": "png",
"savefig.bbox": "tight",
"savefig.pad_inches": 0.01,
"savefig.transparent": False
}
)
COLORS = plt.rcParams["axes.prop_cycle"].by_key()["color"]
###############################################
EPS = np.finfo(np.float32).eps
def dice_metric(ground_truth, predictions):
"""
Returns Dice coefficient for a single example.
Args:
ground_truth: `numpy.ndarray`, binary ground truth segmentation target,
with shape [W, H, D].
predictions: `numpy.ndarray`, binary segmentation predictions,
with shape [W, H, D].
Returns:
Dice coefficient overlap (`float` in [0.0, 1.0])
between `ground_truth` and `predictions`.
"""
# Cast to float32 type
ground_truth = ground_truth.astype("float32")
predictions = predictions.astype("float32")
# Calculate intersection and union of y_true and y_predict
intersection = np.sum(predictions * ground_truth)
union = np.sum(predictions) + np.sum(ground_truth)
# Calcualte dice metric
if intersection == 0.0 and union == 0.0:
dice = 1.0
else:
dice = (2. * intersection) / (union)
return dice
def ftp_ratio_metric(ground_truth,
predictions,
unc_mask,
brain_mask):
"""
Returns Filtered True Positive Ratio for a single example.
Args:
ground_truth: `numpy.ndarray`, binary ground truth segmentation target,
with shape [W, H, D].
predictions: `numpy.ndarray`, binary segmentatation predictions,
with shape [W, H, D].
unc_mask: `numpy.ndarray`, uncertainty binary mask, where uncertain voxels has value 0
and certain voxels has value 1, with shape [W, H, D].
brain_mask: `numpy.ndarray`, brain binary mask, where background voxels has value 0
and forground voxels has value 1, with shape [W, H, D].
Returns:
Filtered true positive ratio (`float` in [0.0, 1.0]).
"""
# Cast to float32 type
ground_truth = ground_truth.astype("float32")
predictions = predictions.astype("float32")
unc_mask = unc_mask.astype("float32")
brain_mask = brain_mask.astype("float32")
# Get filtered Filtered TP ratio (We use EPS for numeric stability)
TP = (predictions * ground_truth) * brain_mask
tp_before_filtering = TP.sum() # TP before filtering
tp_after_filtering = (TP * unc_mask).sum() # TP after filtering
ftp_ratio = (tp_before_filtering - tp_after_filtering) / (tp_before_filtering + EPS)
return ftp_ratio
def ftn_ratio_metric(ground_truth,
predictions,
unc_mask,
brain_mask):
"""
Returns Filtered True Negative Ratio for a single example.
Args:
ground_truth: `numpy.ndarray`, binary ground truth segmentation target,
with shape [W, H, D].
predictions: `numpy.ndarray`, binary segmentatation predictions,
with shape [W, H, D].
unc_mask: `numpy.ndarray`, uncertainty binary mask, where uncertain voxels has value 0
and certain voxels has value 1, with shape [W, H, D].
brain_mask: `numpy.ndarray`, brain binary mask, where background voxels has value 0
and forground voxels has value 1, with shape [W, H, D].
Returns:
Filtered true negative ratio (`float` in [0.0, 1.0]).
"""
# Cast to float32 type
ground_truth = ground_truth.astype("float32")
predictions = predictions.astype("float32")
unc_mask = unc_mask.astype("float32")
brain_mask = brain_mask.astype("float32")
# Get filtered Filtered TN ratio (We use EPS for numeric stability)
TN = ((1-predictions) * (1-ground_truth)) * brain_mask
tn_before_filtering = TN.sum() # TN before filtering
tn_after_filtering = (TN * unc_mask).sum() # TN after filtering
ftn_ratio = (tn_before_filtering - tn_after_filtering) / (tn_before_filtering + EPS)
return ftn_ratio
def make(ground_truth,
predictions,
uncertainties,
brain_mask,
thresholds):
"""
Performs evaluation for a binary segmentation task.
Args:
ground_truth: `numpy.ndarray`, binary ground truth segmentation target,
with shape [W, H, D].
predictions: `numpy.ndarray`, binary segmentatation predictions,
with shape [W, H, D].
uncertainties: `numpy.ndarray`, uncertainties for `predictions`,
with values in [0, 100] and shape [W, H, D].
brain_mask: `numpy.ndarray`, binary brain mask,
with shape [W, H, D].
thresholds: `numpy.ndarray`, the cut-off values for `uncertainties`,
with shape [K].
Returns:
dice: `numpy.ndarray`, the dice for the different uncertainty
`thresholds`, with shape [K].
ftp_ratio: `numpy.ndarray`, the FTP ratio for the different uncertainty
`thresholds`, with shape [K].
ftn_ratio: `numpy.ndarray`, the FTN ratio for the different uncertainty
`thresholds`, with shape [K].
"""
dice = list()
ftp_ratio = list()
ftn_ratio = list()
# Iterate through different uncertainty thresholds
for th in thresholds:
# Convert uncertainty to binary mask according to uncertainty threshold
# voxels with uncertainty greater than threshold are considered uncertain
# and voxels with uncertainty less than threshold are considered certain
unc_mask = np.ones_like(uncertainties, dtype='float32')
unc_mask[uncertainties > th] = 0.0
# Multiply ground truth and predictions with unc_mask his helps in filtering out uncertain voxels
# we calculate metric of interest (here, dice) only on unfiltered certain voxels
ground_truth_filtered = ground_truth * unc_mask
predictions_filtered = predictions * unc_mask
# Calculate dice
dsc_i = dice_metric(ground_truth_filtered, predictions_filtered)
dice.append(dsc_i)
# Calculate filtered true positive ratio
ftp_ratio_i = ftp_ratio_metric(ground_truth, predictions, unc_mask, brain_mask)
ftp_ratio.append(ftp_ratio_i)
# Calculate filtered true negative ratio
ftn_ratio_i = ftn_ratio_metric(ground_truth, predictions, unc_mask, brain_mask)
ftn_ratio.append(ftn_ratio_i)
return dice, ftp_ratio, ftn_ratio
def evaluate(ground_truth,
segmentation,
whole,
core,
enhance,
brain_mask,
output_file,
num_points,
return_auc=True,
return_plot=True):
"""
Evaluates a single sample from BraTS.
Args:
ground_truth: `str`, path to ground truth segmentation .
segmentation: `str`, path to segmentation map.
whole: `str`, path to uncertainty map for whole tumor.
core: `str`, path to uncertainty map for core tumor.
enhance: `str`, path to uncertainty map for enhance tumor.
brain_mask: `str`, path to brain mask.
output_file: `str`, path to output file to store statistics.
num_points: `int`, number of uncertainty threshold points.
return_auc: `bool`, if it is True it returns AUCs.
return_plot: `bool`, if it is True it returns plots (Dice vs 1 - Unc_thresholds, FTP vs 1 - Unc_thresholds, FTN vs 1 - Unc_thresholds).
Returns:
The table (`pandas.DataFrame`) that summarizes the metrics.
"""
# Define Uncertainty Threshold points
_UNC_POINTs = np.arange(0.0, 100.0 + EPS, 100.0 / num_points).tolist()
_UNC_POINTs.reverse()
# Parse NIFTI files
GT = nib.load(ground_truth).get_fdata()
PRED = nib.load(segmentation).get_fdata()
WT = nib.load(whole).get_fdata()
TC = nib.load(core).get_fdata()
ET = nib.load(enhance).get_fdata()
BM = nib.load(brain_mask).get_fdata()
# convert mask into binary.
# useful when you don't have access to the mask, but generating it from T1 image
# 0 intensity is considered background, anything else is forground
# works well with BraTS
BM[BM>0] = 1.0
# Output container
METRICS = dict()
########
# Whole Tumour: take 1,2, and 4 label as foreground, 0 as background.
# convert multi-Label GT and Pred to binary class
GT_bin = np.zeros_like(GT)
Pred_bin = np.zeros_like(PRED)
GT_bin[GT > 0] = 1.0
Pred_bin[PRED > 0] = 1.0
METRICS["WT_DICE"], METRICS["WT_FTP_RATIO"], METRICS["WT_FTN_RATIO"] = make(GT_bin, Pred_bin, WT, BM, _UNC_POINTs)
#######
# Tumour Core: take 1 and 4 label as foreground, 0 and 2 as background.
# convert multi-Label GT and Pred to binary class
GT_bin = np.zeros_like(GT)
Pred_bin = np.zeros_like(PRED)
GT_bin[GT == 1] = 1.0
GT_bin[GT == 4] = 1.0
Pred_bin[PRED == 1] = 1.0
Pred_bin[PRED == 4] = 1.0
METRICS["TC_DICE"], METRICS["TC_FTP_RATIO"], METRICS["TC_FTN_RATIO"] = make(GT_bin, Pred_bin, TC, BM, _UNC_POINTs)
##########
# Enhancing Tumour: take 4 label as foreground, 0, 1, and 2 as bacground.
# convert multi-Label GT and Pred to binary class
GT_bin = np.zeros_like(GT)
Pred_bin = np.zeros_like(PRED)
GT_bin[GT == 4] = 1.0
Pred_bin[PRED == 4] = 1.0
METRICS["ET_DICE"], METRICS["ET_FTP_RATIO"], METRICS["ET_FTN_RATIO"] = make(GT_bin, Pred_bin, ET, BM, _UNC_POINTs)
##########
# save plot
if return_plot:
# create a plot for Dice vs 100 - Unc_Thres, FTP vs 100 - Unc_Thres, FTN vs 100 - Unc_Thres for all three tumour types: WT, TC, ET
fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(12.0, 8.0), sharex=True)
# loop through Different Metrics
for j, met in enumerate(["DICE", "FTP_RATIO", "FTN_RATIO"]):
# loop throug different Tumour Type
for i, typ in enumerate(["WT", "TC", "ET"]):
# plot 100 - _Unc_threshold on X-axis and Metric on Y-axis. Calculate AUC also
axes[j,i].plot(100 - np.array(_UNC_POINTs), np.array(METRICS[typ+"_"+met]),
color=COLORS[i],
# marker='o',
label='AUC: {:.4f}'.format(auc(100 - np.array(_UNC_POINTs), np.array(METRICS[typ+"_"+met]))/100.0))
# set ylabel for first column
if i == 0:
axes[j, i].set(ylabel=met)
# set title for first row
if j == 0:
axes[j, i].set(title=typ)
# set xlabel for last row
if j == 2:
axes[j, i].set(xlabel="1 - Uncertainty Threshold")
axes[j,i].set(ylim = (0.00,1.0001))
axes[j,i].set(xlim = (0.00,100.0001))
[ax.legend() for ax in axes.flatten()]
fig.savefig(output_file+'.png', dpi=300, format="png", trasparent=True)
################
# Print to CSV
if not return_auc:
# Returns <thresholds: [DICE_{type}, FTP_RATIO_{type}, FTN_RATIO_{type}]>
METRICS["THRESHOLDS"] = _UNC_POINTs
df = pd.DataFrame(METRICS).set_index("THRESHOLDS")
df.to_csv(output_file+'.csv')
return df
else:
# Returns <{type}: [DICE_AUC, FTP_RATIO_AUC, FTN_RATIO_AUC]>
df = pd.DataFrame(index=["WT", "TC", "ET"],
columns=["DICE_AUC", "FTP_RATIO_AUC", "FTN_RATIO_AUC"],
dtype=float)
for ttype in df.index:
df.loc[ttype, "DICE_AUC"] = auc(_UNC_POINTs, METRICS["{}_DICE".format(ttype)]) / 100.0
df.loc[ttype, "FTP_RATIO_AUC"] = auc(_UNC_POINTs, METRICS["{}_FTP_RATIO".format(ttype)]) / 100.0
df.loc[ttype, "FTN_RATIO_AUC"] = auc(_UNC_POINTs, METRICS["{}_FTN_RATIO".format(ttype)]) / 100.0
df.index.names = ["TUMOR_TYPE"]
df.to_csv(output_file+'.csv')
return df
########################################################################################################
##
########################################################################################################
parser = argparse.ArgumentParser(description="Uncertainty Analysis on BraTS dataset")
parser.add_argument("-s",
"--segmentation",
type=str,
required=True,
help="Path to predicted segmentation map.")
parser.add_argument("-w",
"--whole",
type=str,
required=True,
help="Path to uncertainty map for whole tumor.")
parser.add_argument("-c",
"--core",
type=str,
required=True,
help="Path to uncertainty map for core tumor.")
parser.add_argument("-e",
"--enhance",
type=str,
required=True,
help="Path to uncertainty map for enhance tumor.")
parser.add_argument("-r",
"--ground_truth",
type=str,
required=True,
help="Path to ground truth segmentation.")
parser.add_argument("-m",
"--brain_mask",
type=str,
required=True,
help="Path to brain mask. You can also provide T1 MR images path.")
parser.add_argument("-o",
"--output_file",
type=str,
required=True,
help="Path to output file to store statistics.")
parser.add_argument("-n",
"--num_points",
type=int,
default=40,
help="Number of threshold points.")
parser.add_argument("-a",
"--return_auc",
action='store_true',
help='If this is True, then returns AUCs. Default: False')
parser.add_argument("-p",
"--return_plot",
action='store_true',
help='If this is True, then returns Plots. Default: False')
args = parser.parse_args()
# to calculate time for each subject
start = time()
evaluate(ground_truth=args.ground_truth,
segmentation=args.segmentation,
whole=args.whole,
core=args.core,
enhance=args.enhance,
brain_mask=args.brain_mask,
output_file=args.output_file,
num_points=args.num_points,
return_auc=args.return_auc,
return_plot=args.return_plot)
print("Total Analysis Time: {:.02f} \n".format(time() - start))
| 16,053 | 32.101031 | 143 |
py
|
mvksc-rkm
|
mvksc-rkm-main/kernels.py
|
import concurrent
import copy
from abc import ABC, abstractmethod
from concurrent.futures.thread import ThreadPoolExecutor
from typing import Callable, List
import numpy as np
import scipy as sp
import torch
from torch import nn
from torch.nn import Parameter
Tensor = torch.Tensor
def kernel_factory(name: str, param: dict):
assert name in ["rbf", "linear", "normpoly", "poly"]
kernel = None
if name == "rbf":
kernel = GaussianKernelTorch(**param)
elif name == "normpoly":
kernel = NormPolyKernelTorch(**param)
elif name == "poly":
kernel = PolyKernelTorch(**param)
elif name == "linear":
kernel = LinearKernel()
return kernel
class LinearKernel(nn.Module):
def __init__(self):
super(LinearKernel, self).__init__()
def forward(self, X: Tensor, Y: Tensor = None) -> Tensor:
"""
Computes the kernel matrix for some observation matrices X and Y.
:param X: d x N matrix
:param Y: d x M matrix. If not specified, it is assumed to be X.
:return: N x M kernel matrix
"""
if Y is None:
Y = X
N = X.shape[1] if len(X.shape) > 1 else 1
M = Y.shape[1] if len(Y.shape) > 1 else 1
return torch.mm(X.t(), Y)
class GaussianKernelTorch(nn.Module):
def __init__(self, sigma2=50.0):
super(GaussianKernelTorch, self).__init__()
if type(sigma2) == float:
self.sigma2 = Parameter(torch.tensor(float(sigma2)), requires_grad=False)
self.register_parameter("sigma2", self.sigma2)
else:
self.sigma2 = sigma2
def forward(self, X: Tensor, Y: Tensor = None) -> Tensor:
"""
Computes the kernel matrix for some observation matrices X and Y.
:param X: d x N matrix
:param Y: d x M matrix. If not specified, it is assumed to be X.
:return: N x M kernel matrix
"""
if Y is None:
Y = X
N = X.shape[1] if len(X.shape) > 1 else 1
M = Y.shape[1] if len(Y.shape) > 1 else 1
def my_cdist(x1, x2):
"""
Computes a matrix of the norm of the difference.
"""
x1 = torch.t(x1)
x2 = torch.t(x2)
x1_norm = x1.pow(2).sum(dim=-1, keepdim=True)
x2_norm = x2.pow(2).sum(dim=-1, keepdim=True)
res = torch.addmm(x2_norm.transpose(-2, -1), x1, x2.transpose(-2, -1), alpha=-2).add_(x1_norm)
res = res.clamp_min_(1e-30).sqrt_()
return res
D = my_cdist(X,Y)
return torch.exp(- torch.pow(D, 2) / (self.sigma2))
class PolyKernelTorch(nn.Module):
def __init__(self, d: int, t=1.0) -> None:
super().__init__()
self.d = d
self.c = t
def forward(self, X: Tensor, Y: Tensor = None) -> Tensor:
"""
Computes the kernel matrix for some observation matrices X and Y.
:param X: d x N matrix
:param Y: d x M matrix. If not specified, it is assumed to be X.
:return: N x M kernel matrix
"""
if Y is None:
Y = X
N = X.shape[1] if len(X.shape) > 1 else 1
M = Y.shape[1] if len(Y.shape) > 1 else 1
return torch.pow(torch.matmul(X.t(), Y), self.d)
class NormPolyKernelTorch(nn.Module):
def __init__(self, d: int, t=1.0) -> None:
super().__init__()
self.d = d
self.c = t
def forward(self, X: Tensor, Y: Tensor = None) -> Tensor:
"""
Computes the kernel matrix for some observation matrices X and Y.
:param X: d x N matrix
:param Y: d x M matrix. If not specified, it is assumed to be X.
:return: N x M kernel matrix
"""
if Y is None:
Y = X
X = X.t()
Y = Y.t()
D1 = torch.diag(1. / torch.sqrt(torch.pow(torch.sum(torch.pow(X, 2), dim=1) + self.c ** 2, self.d)))
D2 = torch.diag(1. / torch.sqrt(torch.pow(torch.sum(torch.pow(Y, 2), dim=1) + self.c ** 2, self.d)))
return torch.matmul(torch.matmul(D1, torch.pow((torch.mm(X, Y.t()) + self.c**2), self.d)), D2)
| 4,124 | 33.090909 | 108 |
py
|
mvksc-rkm
|
mvksc-rkm-main/ksc.py
|
import socket
import time
from argparse import Namespace
from datetime import datetime
from functools import partial
from typing import List
import scipy.sparse.linalg
import torch
import os
from definitions import OUT_DIR
from utils import my_sign
import utils
from dataloader import get_dataloader, get_dataset
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
from kernels import kernel_factory
import numpy as np
import scipy.io
import hydra
from omegaconf import DictConfig, open_dict
import logging, sys
import omegaconf
def train_ksc_helper(X, kernel, k, kpca=False):
"""
:param X: [N,d] matrix
:param kernel: just one kernel
:param k: number of clusters
:return: function taking a [Ntest,d] matrix
"""
from mvkscrkm import omega_mv, degree_matrix, centered_omegas, codebook, compute_alphaCenters, closest_code
start = datetime.now()
Omega = omega_mv([X], [X], [kernel])[0]
Dadd, Dinv, dinv = degree_matrix([Omega])
OmegaCentered = centered_omegas([Omega], Dinv, dinv)[0]
Dadd = Dadd if not kpca else np.eye(Dadd.shape[0])
try:
eigenValues, H = scipy.sparse.linalg.eigs(OmegaCentered, k=k - 1, M=Dadd, which="LM", maxiter=200)
except ValueError:
eigenValues, H = np.zeros(k-1), np.zeros((OmegaCentered.shape[0], k-1))
eigenValues, H = np.real(eigenValues), np.real(H)
eigenVectors = H
etrain = np.matmul(OmegaCentered, H)
Ktest_mem = {}
def f(x_test):
hash_X = sum([hash(str(x_test))])
if hash_X not in Ktest_mem:
Ktest_mem[hash_X] = [omega_mv([x_test], [x_test], [kernel])[0]]
Ktest = Ktest_mem[hash_X]
Omega_test = omega_mv([X], [x_test], [kernel])[0]
Dadd_test, Dinv_test, dinv_test = degree_matrix([Omega_test])
OmegaCentered_test = centered_omegas([Omega_test], Dinv_test, dinv_test, Dinv, dinv)[0]
etest = np.matmul(OmegaCentered_test, H)
codebooks = [np.array(codebook(my_sign(etrain)))]
alphaCenters = [compute_alphaCenters(H, etrain)]
q = np.array([closest_code(my_sign(etest), codebooks[0], H, alphaCenters[0])]).transpose()
return {"y_pred": q, "K": [OmegaCentered_test], "etest": [etest], "etrain": [etrain],
"Ktest": Ktest, "xtest": x_test, "k": k}
elapsed_time = datetime.now() - start
return f, {"eigs": eigenValues, "H": eigenVectors, "train_time": elapsed_time.total_seconds()}
def train_ksc_cat(X, kernel, k, kpca=False):
V = len(X)
f_cat_helper, train_dict_cat = train_ksc_helper(np.concatenate(X, axis=1), kernel, k, kpca=kpca)
def f_cat(x_test):
res = f_cat_helper(np.concatenate(x_test, axis=1))
res["y_pred"] = np.tile(res["y_pred"], (1,V))
res["xtest"] = x_test
res["kernels"] = [kernel for _ in range(V)]
return res
f_cat.method = "cat" if not kpca else "catkpca"
return [f_cat], train_dict_cat
def train_ksc_view(X, kernels, k):
V = len(X)
assert len(kernels) == V
f_views_helper = [train_ksc_helper(X[v], kernel, k) for v, kernel in zip(range(V), kernels)]
def f_view(x_test, v):
res = f_views_helper[v][0](x_test[v])
res["y_pred"] = np.tile(res["y_pred"], (1,V))
res["xtest"] = x_test
res["kernels"] = kernels
return res
f_views = [partial(f_view, v=v) for v in range(V)]
for v in range(V):
f_views[v].method = f"view{v}"
return f_views, utils.merge_dicts([x[1] for x in f_views_helper])
def best_single_view(eval_dict, val_metrics=None, test_metrics=None, traintest="train"):
if test_metrics is None:
test_metrics = ["ari", "nmi"]
if val_metrics is None:
val_metrics = ["blf000", "blf005", "blf010", "blf015", "blf020", "blf025", "blf030", "blf035", "blf040", "blf045", "blf050", "blf055", "blf060", "blf065", "blf070", "blf075", "blf080", "blf085", "blf090", "blf095", "blf100", "baf", "silhouette", "davies_bouldin"]
def best_single_view_helper(x, test_metric, val_metric):
if not f"{val_metric}_{traintest}_view0" in x.keys():
return {}
v = np.nanargmax([x.get(f"{val_metric}_{traintest}_view{v}", -np.inf) for v in range(10)])
max_v = x[f"{val_metric}_{traintest}_view{v}"]
if max_v == -np.inf or np.isnan(max_v):
return {}
res = {f"{val_metric}_{traintest}_bv": max_v,
f"{test_metric}_{traintest}_bv_{val_metric}": x[f"{test_metric}_{traintest}_view{v}"]}
return res
from functools import reduce
return reduce(utils.merge_two_dicts, [best_single_view_helper(eval_dict, test_metric, val_metric) for test_metric in test_metrics for val_metric in val_metrics])
@hydra.main(config_path='configs', config_name='config_ksc')
def main(args: DictConfig):
# Set random seed
torch.manual_seed(args.seed)
np.random.seed(args.seed)
# Set up logging
logging.basicConfig(stream=sys.stdout, level=logging.INFO, format='%(message)s')
label = "run002"
created_timestamp = int(time.time())
model_dir = OUT_DIR.joinpath(label)
model_dir.mkdir()
# Load Training Data
trainloader = get_dataloader(Namespace(**utils.merge_two_dicts(args.dataset, {"train": True})))
test_loader = get_dataloader(Namespace(**utils.merge_two_dicts(args.dataset, {"train": False})))
x_train, y_train = get_dataset(trainloader)
V = len(x_train)
x_test, y_test = get_dataset(test_loader)
# Define kernel
if 'name' in args.kernels:
kernels = [kernel_factory(args.kernels.name, args.kernels.args) for v in range(V)]
else:
kernels = [kernel_factory(kernel.name, kernel.args) for kernel in [t[1] for t in args.kernels.items()]]
assert len(kernels) == V
# k-fold cross-validation
logging.info("Starting training")
def training_fun(x):
fs, train_dict = [], []
if args.model.cat:
fs_cat, train_dict_cat = train_ksc_cat(x, kernel=kernels[0], k=args.dataset.k)
train_dict_cat = {f"{key}_cat": value for key, value in train_dict_cat.items()}
fs += fs_cat
train_dict.append(train_dict_cat)
if args.model.view:
fs_view, train_dict_view = train_ksc_view(x, kernels=kernels, k=args.dataset.k)
train_dict_view = {f"{key}_view": value for key, value in train_dict_view.items()}
fs += fs_view
train_dict.append(train_dict_view)
if args.model.kpca:
fs_catkpca, train_dict_catkpca = train_ksc_cat(x, kernel=kernels[0], k=args.dataset.k, kpca=True)
train_dict_catkpca = {f"{key}_catkpca": value for key, value in train_dict_catkpca.items()}
fs += fs_catkpca
train_dict.append(train_dict_catkpca)
return fs, utils.merge_dicts(train_dict)
eval_dict = utils.kfold_cv(training_fun, x_train, y_train, args.dataset.k_folds, x_test=x_test, y_test=y_test)
# Update best single view metrics
if args.model.view:
eval_dict.update(best_single_view(eval_dict, traintest="train"))
eval_dict.update(best_single_view(eval_dict, traintest="val"))
eval_dict.update(best_single_view(eval_dict, traintest="test"))
# Print result
[eval_dict.pop(key) for key in ["eigs_cat", "H_cat", "eigs_catkpca", "H_catkpca", "eigs_view", "H_view", "fs_output"] if key in eval_dict]
eval_dict.update({"timestamp": created_timestamp, "hostname": socket.getfqdn()})
logging.info("\n".join("{}\t{}".format(k, str(v)) for k, v in eval_dict.items()))
logging.info(f"Saved label: {label}")
return eval_dict
if __name__ == '__main__':
main()
| 7,621 | 40.423913 | 271 |
py
|
mvksc-rkm
|
mvksc-rkm-main/mvksclssvm.py
|
import socket
import time
from argparse import Namespace
from datetime import datetime
from functools import partial
import scipy.sparse.linalg
import os
from definitions import OUT_DIR
from mvkscrkm import omega_mv, degree_matrix, centered_omegas, codebook, compute_alphaCenters, closest_code, assign_uncoupled, assign_mean
from utils import my_sign
import utils
from dataloader import get_dataloader, get_dataset
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
from kernels import kernel_factory
import numpy as np
import scipy.io
import hydra
from omegaconf import DictConfig
import logging, sys
import torch
def compute_DOI(OmegasCentered, Dinv, gamma):
V = len(OmegasCentered)
N = OmegasCentered[0].shape[0]
assert len(gamma) == len(Dinv) == V
DOI = [np.eye(N) - (gamma[v]/N) * np.matmul(Dinv[v], OmegasCentered[v]) for v in range(V)]
return DOI
def train_mvksclssvm(X, kernels, k, assignment_args, gamma):
"""
:return: list of functions that take x as argument and return
{"y_pred": list of V cluster assignment [Ntest],
"K": list of V OmegaCentered_add [Ntrain,Ntest],
"etest": list of V score variables of x [Ntest,k-1],
"etrain": list of V score variables of training points X [Ntrain,k-1],
"Ktest": list of V non-centered kernel matrices [Ntest,Ntest]}
"""
V = len(X)
N = X[0].shape[0]
if type(gamma["value"]) == str and (gamma["value"] == "None" or gamma["value"] == "null"):
gamma["value"] = None
if type(gamma) == float or type(gamma) == int:
gamma = [gamma for v in range(V)]
elif type(gamma) == list:
gamma = gamma
else:
if gamma["value"] is not None:
gamma = gamma["value"]
if type(gamma) == float or type(gamma) == int:
gamma = [gamma for v in range(V)]
elif gamma["value"] is None:
gamma = [gamma[f"gamma{v+1}"] for v in range(V) if gamma[f"gamma{v+1}"] is not None]
assert len(gamma) == V
assert not np.any(np.array(gamma) < 0)
# Build Omega for each view
Omegas = omega_mv(X, X, kernels)
# Compute the kernel matrix Omega, and the degree matrix D
_, Dinv, dinv = degree_matrix(Omegas)
# Compute the centered kernel matrices
OmegasCentered = centered_omegas(Omegas, Dinv, dinv)
# Compute DOI
DOI = compute_DOI(OmegasCentered, Dinv, gamma)
# Build matrices
R = scipy.linalg.block_diag(*DOI)
assert R.shape == (V*N, V*N)
L = np.concatenate([np.concatenate(
([np.matmul(np.matmul(np.sqrt(Dinv[v]), np.sqrt(Dinv[v2])), OmegasCentered[v2]) for v2 in range(v) if v2 != v] if v > 0 else []) +
[np.zeros((N,N))] +
([np.matmul(np.matmul(np.sqrt(Dinv[v]), np.sqrt(Dinv[v2])), OmegasCentered[v2]) for v2 in range(v,V) if v2 != v] if v < V-1 else []), axis=1)
for v in range(V)], axis=0)
assert L.shape == (V*N, V*N)
eigenValues, H = scipy.sparse.linalg.eigs(L, k=k - 1, M=R, which="LM", maxiter=200)
H = np.real(H)
eigenValues = np.real(eigenValues)
eigenVectors = H
alphas = [H[v*N:(v+1)*N,:] for v in range(V)]
# Compute score variables
etrain = [np.matmul(OmegasCentered[v], alphas[v]) for v in range(V)]
def compute_etest(x_test):
Omegas_test = omega_mv(X, x_test, kernels)
_, Dinv_test, dinv_test = degree_matrix(Omegas_test)
OmegasCentered_test = centered_omegas(Omegas_test, Dinv_test, dinv_test, Dinv, dinv)
etest = [np.matmul(OmegasCentered_test[v], alphas[v]) for v in range(V)]
return etest, OmegasCentered_test
Ktest_mem = {}
def f_generic(x_test, assign_f):
hash_X = sum([hash(str(x_test[v])) for v in range(V)])
if hash_X not in Ktest_mem:
Ktest_mem[hash_X] = omega_mv(x_test, x_test, kernels)
Ktest = Ktest_mem[hash_X]
etest, OmegasCentered_test = compute_etest(x_test)
etrainused = assign_f(etrain, **assignment_args)
etestused = assign_f(etest, **assignment_args)
codebooks = [np.array(codebook(my_sign(etrainused[v]))) for v in range(V)]
alphaCenters = [compute_alphaCenters(alphas[v], etrainused[v]) for v in range(V)]
q = np.array([closest_code(my_sign(etestused[v]), codebooks[v], alphas[v], alphaCenters[v]) for v in range(V)]).transpose()
return {"y_pred": q, "K": OmegasCentered_test, "etest": etestused, "etrain": etrainused, "Ktest": Ktest, "xtest": x_test, "kernels": kernels, "k": k}
f_uncoupled = lambda x: f_generic(x, assign_uncoupled)
f_mean = lambda x: f_generic(x, assign_mean)
f_uncoupled.method = "uncoupled"
f_mean.method = "mean"
return [f_uncoupled, f_mean], {"eigs": eigenValues, "H": eigenVectors}
@hydra.main(config_path='configs', config_name='config_lssvm')
def main(args: DictConfig):
# Set random seed
torch.manual_seed(args.seed)
np.random.seed(args.seed)
# Set up logging
logging.basicConfig(stream=sys.stdout, level=logging.INFO, format='%(message)s')
label = "run001"
created_timestamp = int(time.time())
model_dir = OUT_DIR.joinpath(label)
model_dir.mkdir()
# Load Training Data
trainloader = get_dataloader(Namespace(**utils.merge_two_dicts(args.dataset, {"train": True})))
test_loader = get_dataloader(Namespace(**utils.merge_two_dicts(args.dataset, {"train": False})))
x_train, y_train = get_dataset(trainloader)
V = len(x_train)
x_test, y_test = get_dataset(test_loader)
# Define kernels for each view
if 'name' in args.kernels:
kernels = [kernel_factory(args.kernels.name, args.kernels.args) for v in range(V)]
else:
kernels = [kernel_factory(kernel.name, kernel.args) for kernel in [t[1] for t in args.kernels.items()]]
assert len(kernels) == V
# k-fold cross-validation
logging.info("Starting training")
training_fun = lambda x: train_mvksclssvm(x, kernels, k=args.dataset.k, assignment_args=args.model.assignment, gamma=args.model.gamma)
eval_dict = utils.kfold_cv(training_fun, x_train, y_train, args.dataset.k_folds, x_test=x_test, y_test=y_test)
# Print result
[eval_dict.pop(key) for key in ["eigs", "H", "fs_output"] if key in eval_dict]
eval_dict.update({"timestamp": created_timestamp, "hostname": socket.getfqdn()})
logging.info("\n".join("{}\t{}".format(k, str(v)) for k, v in eval_dict.items()))
logging.info(f"Saved label: {label}")
return eval_dict
if __name__ == '__main__':
main()
| 6,512 | 41.019355 | 157 |
py
|
mvksc-rkm
|
mvksc-rkm-main/definitions.py
|
import os
from wcmatch import pathlib
import torch
import sys
import numpy as np
torch.cuda.empty_cache()
Tensor = torch.Tensor
device = torch.device("cpu")
TensorType = torch.FloatTensor if "dataset=reuters" in sys.argv else torch.DoubleTensor
torch.set_default_tensor_type(TensorType)
ROOT_DIR = pathlib.Path(os.path.dirname(os.path.abspath(__file__))) # This is your Project Root
OUT_DIR = pathlib.Path("~/out/mvksc-rkm/").expanduser()
OUT_DIR.mkdir(parents=True, exist_ok=True)
DATA_DIR = ROOT_DIR.joinpath('data').expanduser().absolute()
| 544 | 33.0625 | 95 |
py
|
mvksc-rkm
|
mvksc-rkm-main/mvkscrkm.py
|
import socket
import time
from argparse import Namespace
from typing import List
import scipy.sparse.linalg
import torch
import os
import definitions
from definitions import OUT_DIR
from utils import my_sign
import utils
from dataloader import get_dataloader, get_dataset
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
from kernels import kernel_factory
import numpy as np
import scipy.io
import hydra
from omegaconf import DictConfig
import logging, sys
import omegaconf
def codebook(e):
"""
Finds the codebook for encoding matrix e
:param e: N x (k-1) matrix of -1, 1 entries
:return: list of the k most frequent encodings
"""
k = e.shape[1] + 1
c, counts = np.unique(e, axis=0, return_counts=True)
return [t[0] for t in sorted(zip(c, counts), key=lambda x: -x[1])[:k]]
def closest_code(e, codebook, alphat=None, alphaCenters=None):
"""
Finds closest encoding vector in codebook
:param e: N x (k-1) matrix of -1, 1 entries
:param codebook: list of the k codes of length (k-1)
:return: array of length N, closest element in codebook to e
"""
from sklearn.neighbors import DistanceMetric
dist = DistanceMetric.get_metric('hamming')
dist2 = DistanceMetric.get_metric('euclidean')
d = dist.pairwise(e, np.array(codebook))
qtrain = np.argmin(d, axis=1)
if alphat is not None and alphaCenters is not None and d.shape[1] > 1 and qtrain.shape[0] <= alphat.shape[0]:
#Break ties
sorted_d = np.sort(d, axis=1)
nidx = sorted_d[:,0] == sorted_d[:,1]
if np.sum(nidx) > 0:
nidx_test = nidx
if nidx.shape[0] < alphat.shape[0]:
nidx_test = np.concatenate([nidx, np.zeros((alphat.shape[0] - nidx.shape[0]), dtype=bool)])
d2 = dist2.pairwise(alphat[nidx_test], alphaCenters)
qtrain[nidx] = np.argmin(d2, axis=1)
return qtrain
def assign_mean(etrain: List[np.ndarray], beta=None, **kwargs):
"""
Mean decision rule.
:param etrain: list of V N x (k-1) matrices of score variables for view v
:param beta: weights for each view in the decision rule. If None, beta[i] = 1/V
:return: new score variable as a list of V N x (k-1) matrices
"""
from math import isclose
N, V = etrain[0].shape[0], len(etrain)
if beta is None:
beta = np.array([1. / V for v in range(V)])
else:
if type(beta) == omegaconf.listconfig.ListConfig or type(beta) == list:
beta = np.array(list(beta))
else:
if type(beta["value"]) == str and (beta["value"] == "None" or beta["value"] == "null"):
beta["value"] = None
if beta["value"] is not None:
beta = np.array(list(beta["value"]))
elif beta["value"] is None:
beta = np.array([beta[f"beta{v + 1}"] for v in range(V) if beta[f"beta{v + 1}"] is not None])
if len(beta) == 0:
beta = np.array([1. / V for v in range(V)])
else:
beta = beta / sum(beta)
if not isclose(sum(beta), 1.0):
beta = beta / sum(beta)
assert len(beta) == V
assert isclose(sum(beta), 1.0)
encoding_total = np.array(etrain)
dim_array = np.ones((1, encoding_total.ndim), int).ravel()
dim_array[0] = -1
beta_reshaped = beta.reshape(dim_array)
encoding_total = encoding_total * beta_reshaped
encoding_total = np.sum(encoding_total, axis=0)
return [encoding_total for v in range(V)]
def assign_uncoupled(etrain: List[np.ndarray], **kwargs):
"""
Uncoupled decision rule.
:param etrain: list of V N x (k-1) matrices of score variables for view v
:return: new score variable as a list of V N x (k-1) matrices
"""
N, V = etrain[0].shape[0], len(etrain)
return [etrain[v] for v in range(V)]
def omega_mv(X1, X2, kernels):
V = len(X1)
# Build Omega for each view
Omegas = []
for v in range(V):
Omegas_tmp = kernels[v](torch.from_numpy(X1[v]).t(), torch.from_numpy(X2[v]).t()).numpy()
Omegas.append((0.5 * (Omegas_tmp + Omegas_tmp.transpose())) if Omegas_tmp.shape[0] == Omegas_tmp.shape[1] else Omegas_tmp.T)
assert len(Omegas) == V
return Omegas
def degree_matrix(Omegas):
V = len(Omegas)
Ntest, N = Omegas[0].shape
# Compute the kernel matrix Omega, and the degree matrix D
Dinv = []
dinv = []
Dadd = np.zeros((Ntest, Ntest))
for v in range(V):
d = np.sum(Omegas[v], axis=1)
dinv.append(np.nan_to_num((1. / d)).reshape(Ntest, 1))
Dinv.append(np.nan_to_num(np.diag(1. / d)))
Dadd += np.diag(d)
assert len(Dinv) == V
assert len(dinv) == V
return Dadd, Dinv, dinv
def centered_omegas(Omegas, Dinv, dinv, Dinvtrain=None, dinvtrain=None):
Dinvtrain = Dinvtrain if Dinvtrain is not None else Dinv
dinvtrain = dinvtrain if dinvtrain is not None else dinv
V = len(Omegas)
Ntest, N = Omegas[0].shape
# Compute the centered kernel matrices
OmegasCentered = []
for v in range(V):
md = np.eye(Ntest) - np.matmul(np.ones((Ntest, 1)), dinv[v].transpose()) / np.sum(dinv[v])
kd = np.eye(N) - np.matmul(np.matmul(Dinvtrain[v], np.ones((N, 1))), np.ones((1, N))) / np.sum(dinvtrain[v])
OmegasCentered.append(np.matmul(np.matmul(md, Omegas[v]), kd))
assert len(OmegasCentered) == V
return OmegasCentered
def compute_alphaCenters(alphat, etrain):
assert alphat.shape[1] == etrain.shape[1]
assert alphat.shape[0] == etrain.shape[0]
N, d = alphat.shape
k = d + 1
alphaCenters = np.zeros((k, d))
c, m, uniquecw = np.unique(my_sign(etrain), return_index=True, return_inverse=True, axis=0)
cwsizes = np.zeros((len(m)))
for i in range(len(m)):
cwsizes[i] = np.sum(uniquecw == i)
j= np.argsort(-cwsizes, kind='mergesort')
if len(m) < k:
k = len(m)
qtrain = np.zeros((alphat.shape[0],))
for i in range(k):
qtrain[uniquecw == j[i]] = i + 1
for i in range(k):
alphaCenters[i] = np.median(alphat[qtrain == (i+1)], axis=0)
return alphaCenters
def train_mvkscrkm(X, kernels, k, eta, assignment_args, rho=1, kappa=1.0):
"""
:return: list of functions that take x as argument and return
{"y_pred": list of V cluster assignment [Ntest],
"K": list of V OmegaCentered_add [Ntrain,Ntest],
"etest": list of V score variables of x [Ntest,k-1],
"etrain": list of V score variables of training points X [Ntrain,k-1],
"Ktest": list of V non-centered kernel matrices [Ntest,Ntest]}
"""
assert 0 <= rho <= 1
V = len(X)
if type(kappa["value"]) == str and (kappa["value"] == "None" or kappa["value"] == "null"):
kappa["value"] = None
if type(kappa) == float or type(kappa) == int:
kappa = [kappa for v in range(V)]
elif type(kappa) == list:
kappa = kappa
else:
if kappa["value"] is not None:
kappa = kappa["value"]
if type(kappa) == float or type(kappa) == int:
kappa = [kappa for v in range(V)]
elif kappa["value"] is None:
kappa = [kappa[f"kappa{v+1}"] for v in range(V) if kappa[f"kappa{v+1}"] is not None]
assert len(kappa) == V
# Build Omega for each view
Omegas = omega_mv(X, X, kernels)
# Compute the kernel matrix Omega, and the degree matrix D
Dadd, Dinv, dinv = degree_matrix(Omegas)
# Compute the centered kernel matrices
OmegasCentered = centered_omegas(Omegas, Dinv, dinv)
OmegasCentered = [kappa[v] * OmegasCentered[v] for v in range(V)]
OmegasCentered_add = rho * np.add.reduce(OmegasCentered) + (1.0 - rho) * np.multiply.reduce(OmegasCentered)
# Build matrices
R = Dadd
L = 1 / eta * OmegasCentered_add
eigenValues, H = scipy.sparse.linalg.eigs(L, k=k - 1, M=R, which="LM", maxiter=200)
eigenValues = np.real(eigenValues)
eigenVectors = H
H = np.real(H)
# Compute score variables
etrain = []
for v in range(V):
etrain.append(np.matmul(OmegasCentered[v], H))
def compute_etest(x_test):
Omegas_test = omega_mv(X, x_test, kernels)
Dadd_test, Dinv_test, dinv_test = degree_matrix(Omegas_test)
OmegasCentered_test = centered_omegas(Omegas_test, Dinv_test, dinv_test, Dinv, dinv)
etest = [np.matmul(OmegasCentered_test[v], H) for v in range(V)]
return etest, OmegasCentered_test
Ktest_mem = {}
def f_generic(x_test, assign_f):
hash_X = sum([hash(str(x_test[v])) for v in range(V)])
if hash_X not in Ktest_mem:
Ktest_mem[hash_X] = omega_mv(x_test, x_test, kernels)
Ktest = Ktest_mem[hash_X]
etest, OmegasCentered_test = compute_etest(x_test)
etrainused = assign_f(etrain, **assignment_args)
etestused = assign_f(etest, **assignment_args)
codebooks = [np.array(codebook(my_sign(etrainused[v]))) for v in range(V)]
alphaCenters = [compute_alphaCenters(H, etrainused[v]) for v in range(V)]
q = np.array([closest_code(my_sign(etestused[v]), codebooks[v], H, alphaCenters[v]) for v in range(V)]).transpose()
return {"y_pred": q, "K": OmegasCentered_test, "etest": etestused, "etrain": etrainused, "Ktest": Ktest, "xtest": x_test, "kernels": kernels,
"k": k}
f_uncoupled = lambda x: f_generic(x, assign_uncoupled)
f_mean = lambda x: f_generic(x, assign_mean)
f_uncoupled.method = "uncoupled"
f_mean.method = "mean"
return [f_uncoupled, f_mean], {"eigs": eigenValues, "H": eigenVectors}
@hydra.main(config_path='configs', config_name='config_rkm')
def main(args: DictConfig):
# Set random seed
torch.manual_seed(args.seed)
np.random.seed(args.seed)
# Set up logging
logging.basicConfig(stream=sys.stdout, level=logging.INFO, format='%(message)s')
label = "run000"
created_timestamp = int(time.time())
model_dir = OUT_DIR.joinpath(label)
model_dir.mkdir()
# Load Training Data
trainloader = get_dataloader(Namespace(**utils.merge_two_dicts(args.dataset, {"train": True})))
test_loader = get_dataloader(Namespace(**utils.merge_two_dicts(args.dataset, {"train": False})))
x_train, y_train = get_dataset(trainloader)
V = len(x_train)
x_test, y_test = get_dataset(test_loader)
# Define kernels for each view
if 'name' in args.kernels:
kernels = [kernel_factory(args.kernels.name, args.kernels.args) for v in range(V)]
else:
kernels = [kernel_factory(kernel.name, kernel.args) for kernel in [t[1] for t in args.kernels.items()]]
assert len(kernels) == V
# k-fold cross-validation
training_fun = lambda x: train_mvkscrkm(x, kernels, k=args.dataset.k, eta=args.model.eta, assignment_args=args.model.assignment,
rho=args.model.rho, kappa=args.model.kappa)
eval_dict = utils.kfold_cv(training_fun, x_train, y_train, args.dataset.k_folds, x_test=x_test, y_test=y_test)
# Print result
[eval_dict.pop(key) for key in ["eigs", "H", "fs_output"] if key in eval_dict]
eval_dict.update({"timestamp": created_timestamp, "hostname": socket.getfqdn()})
logging.info("\n".join("{}\t{}".format(k, str(v)) for k, v in eval_dict.items()))
logging.info(f"Saved label: {label}")
return eval_dict
if __name__ == '__main__':
main()
| 11,411 | 39.183099 | 149 |
py
|
mvksc-rkm
|
mvksc-rkm-main/dataloader.py
|
import math
import os
from argparse import Namespace
from typing import List
import numpy as np
import scipy.io
import torch
import torchvision
from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler
from torchvision import datasets, transforms
from definitions import DATA_DIR, device, TensorType, Tensor
import random
def seed_worker(worker_id):
worker_seed = torch.initial_seed() % 2**32
np.random.seed(worker_seed)
random.seed(worker_seed)
def get_dataset(loader):
x, y = [], []
for data, labels in loader:
if type(data) == Tensor:
data = data.to(device=device)
elif type(data) == list:
data = list(map(lambda x: x.to(device), data))
labels = labels.to(device=device)
x.append(data)
y.append(labels)
if x == []:
return None, None
elif type(x[0]) == Tensor:
x = torch.cat(x).numpy()
elif type(x[0]) == list:
x = [torch.cat([x[i][v] for i in range(len(x))]).numpy() for v in range(len(x[0]))]
y = torch.cat(y)
return x, y.numpy()
def get_dataloader(args):
args = transformation_factory(args)
loader = get_dataloader_helper(args)
loader = get_dataloader_subset(loader, args)
return loader
def transformation_factory(args):
def transformation_factory_helper(transformation):
assert len(transformation.items()) == 1
d = {'normalize': transforms.Normalize}
name, args = list(transformation.items())[0]
t = d[name](**args)
return t
if "post_transformations" in args:
args.post_transformations = list(map(transformation_factory_helper, args.post_transformations))
return args
def get_dataloader_subset(loader, args):
N = args.N if args.train else args.Ntest
if N >= 0 and N < len(loader.dataset):
rng = np.random.default_rng(torch.initial_seed())
indices = list(rng.choice(range(len(loader.dataset)), N, shuffle=False, replace=N>len(loader.dataset)))
loader = DataLoader(torch.utils.data.Subset(loader.dataset, indices), batch_size=args.mb_size, pin_memory=False, num_workers=args.workers, shuffle=args.shuffle, worker_init_fn=seed_worker)
return loader
def get_dataloader_helper(args):
args_dict = vars(args)
if "post_transformations" not in args_dict:
args_dict["post_transformations"] = []
if "pre_transformations" not in args_dict:
args_dict["pre_transformations"] = []
if "train" not in args_dict:
args_dict["train"] = False
if "dataset_name" not in args_dict:
args_dict["dataset_name"] = args_dict["name"]
print(f'Loading data for {args_dict["dataset_name"]}...')
if args.dataset_name == 'toydataset3':
return get_toydataset3_dataloader(args=args)
elif args.dataset_name == 'toydataset2':
return get_toydataset2_dataloader(args=args)
elif args.dataset_name == 'data3Sources':
return get_data3Sources_dataloader(args=args)
elif args.dataset_name == 'reuters2':
return get_reuters2_dataloader(args=args)
elif args.dataset_name == 'reuters':
return get_reuters_dataloader(args=args)
elif args.dataset_name == 'ads':
return get_ads_dataloader(args=args)
elif args.dataset_name == 'kolenda':
return get_kolenda_dataloader(args=args)
elif args.dataset_name == 'nus':
return get_nus_dataloader(args=args)
elif args.dataset_name == 'game':
return get_game_dataloader(args=args)
class MultiViewDataSet(Dataset):
def __init__(self, x: List[Tensor], y):
self.V = len(x)
self.N = x[0].shape[0]
self.x = x
self.y = y
def __getitem__(self, index):
return [self.x[v][index] for v in range(self.V)], self.y[index]
def __len__(self):
return self.N
def get_toydataset3_dataloader(args):
print("Loading toydataset3.")
def generate_view(N, rng, perc_pos=0.5, mean_pos=np.array((-1.5, 0)), std_pos=np.array([[1,0],[0,1]]), mean_neg=np.array((1.5, 0)), std_neg=np.array([[1,0],[0,1]])):
N_pos = math.floor(N * perc_pos)
N_neg = N - N_pos
x = torch.cat([torch.from_numpy(rng.multivariate_normal(mean=mean_pos, cov=std_pos, size=(N_pos,))).type(TensorType),
torch.from_numpy(rng.multivariate_normal(mean=mean_neg, cov=std_neg, size=(N_neg,))).type(TensorType)])
y = torch.cat([torch.zeros(N_pos), torch.ones(N_neg)]) + 1
randperm = torch.from_numpy(rng.permutation(x.shape[0])).long()
x = x[randperm]
y = y[randperm]
return x, y
rng = np.random.default_rng(0)
N_train, N_test, perc_pos = 1000, 300, 0.8
x1, y1 = generate_view(N_train + N_test, rng, perc_pos=perc_pos, mean_pos=np.array((1,1)), mean_neg=np.array((2,2)), std_pos=np.array([[0.1,0],[0,0.3]]), std_neg=np.array([[1.5,0.4],[0.4,1.2]]))
if args.train:
x1, y1 = x1[:N_train], y1[:N_train]
else:
x1, y1 = x1[N_train:], y1[N_train:]
x2, y2 = generate_view(N_train + N_test, rng, perc_pos=perc_pos, mean_pos=np.array((2,2)), mean_neg=np.array((1,1)), std_neg=np.array([[1,0.5],[0.5,0.9]]), std_pos=np.array([[0.3,0],[0,0.6]]))
if args.train:
x2, y2 = x2[:N_train], y2[:N_train]
else:
x2, y2 = x2[N_train:], y2[N_train:]
X = [x1, x2]
if "normalize" in args and args.normalize:
X2 = []
from sklearn.preprocessing import normalize
view_size = len(X)
for view in range(view_size):
X2.append(torch.from_numpy(normalize(X[view].numpy(), norm='l2')))
X = X2
dataset = MultiViewDataSet(X, torch.stack([y1,y2]).t())
# all_transforms = transforms.Compose(
# args.pre_transformations + [transforms.ToTensor()] + args.post_transformations)
# all_transforms = None
train_loader = DataLoader(dataset, batch_size=args.mb_size, shuffle=args.shuffle,
pin_memory=True, num_workers=args.workers)
# _, c, x, y = next(iter(train_loader))[0].size()
return train_loader
def get_data3Sources_dataloader(args, path_to_data='./data'):
"""data3Sources dataloader."""
print("Loading data3Sources.")
mat = scipy.io.loadmat(DATA_DIR.joinpath('data3Sources.mat'))
X = mat['X']
y = mat['Y']
y = np.array([y[0, 0], y[0, 1], y[0, 2]]).swapaxes(0,1).reshape(169,3)
X = list(map(lambda x: torch.from_numpy(x.toarray()).type(TensorType), X[0].tolist()))
if "normalize" in args and args.normalize:
X2 = []
from sklearn.preprocessing import normalize
view_size = len(X)
for view in range(view_size):
X2.append(torch.from_numpy(normalize(X[view].numpy(), norm='l2')))
X = X2
all_transforms = transforms.Compose(args.pre_transformations + args.post_transformations)
dataset = MultiViewDataSet(X, torch.from_numpy(y).type(TensorType))
loader = DataLoader(dataset, batch_size=args.mb_size, shuffle=args.shuffle,
pin_memory=True, num_workers=args.workers, drop_last=True)
# _, c, x, y = next(iter(loader))[0].size()
return loader
def get_reuters2_dataloader(args, path_to_data='./data'):
"""reuters2 dataloader."""
print("Loading Reuters 2.")
mat = scipy.io.loadmat(DATA_DIR.joinpath('reutersMulSubset.mat'))
X = [mat['En'].toarray(), mat['Fr'].toarray(), mat['Gr'].toarray()]
y = mat['GND'].flatten()
X = list(map(lambda x: torch.from_numpy(x).type(TensorType), X))
if "normalize" in args and args.normalize:
X2 = []
from sklearn.preprocessing import normalize
view_size = len(X)
for view in range(view_size):
X2.append(torch.from_numpy(normalize(X[view].numpy(), norm='l2')))
X = X2
all_transforms = transforms.Compose(args.pre_transformations + args.post_transformations)
dataset = MultiViewDataSet(X, torch.from_numpy(y).type(TensorType))
loader = DataLoader(dataset, batch_size=args.mb_size, shuffle=args.shuffle,
num_workers=args.workers, drop_last=True)
# _, c, x, y = next(iter(loader))[0].size()
return loader
def get_reuters_dataloader(args, path_to_data='./data'):
"""reuters dataloader."""
print("Loading Large-scale Reuters.")
mat = scipy.io.loadmat(DATA_DIR.joinpath('Reuters.mat'))
X = [mat['X'][0,v].astype(np.float32).todense() for v in range(5)]
y = mat['Y'].flatten()
X = list(map(lambda x: torch.from_numpy(x).type(TensorType), X))
if "normalize" in args and args.normalize:
X2 = []
from sklearn.preprocessing import normalize
view_size = len(X)
for view in range(view_size):
X2.append(torch.from_numpy(normalize(X[view].numpy(), norm='l2')))
X = X2
all_transforms = transforms.Compose(args.pre_transformations + args.post_transformations)
dataset = MultiViewDataSet(X, torch.from_numpy(y).type(TensorType))
loader = DataLoader(dataset, batch_size=args.mb_size, shuffle=args.shuffle,
num_workers=args.workers, drop_last=True)
# _, c, x, y = next(iter(loader))[0].size()
return loader
def get_ads_dataloader(args, path_to_data='./data'):
"""ads dataloader."""
print("Loading Ads.")
mat = scipy.io.loadmat(DATA_DIR.joinpath('ad_data.mat'))
Xtrain, Xtest = mat['X'], mat['Xt']
ytrain, ytest = mat['Y'], mat['Yt']
split = 0
Xtrain, Xtest = [Xtrain[0][split][0][0], Xtrain[0][split][0][1], Xtrain[0][split][0][2]], [Xtest[0][split][0][0], Xtest[0][split][0][1], Xtest[0][split][0][2]]
Xtrain, Xtest = list(map(lambda x: torch.from_numpy(x).type(TensorType), Xtrain)), list(map(lambda x: torch.from_numpy(x).type(TensorType), Xtest))
ytrain = ytrain[0, split].flatten()
ytest = ytest[0, split].flatten()
Xtrain = [torch.cat([Xtrain[i], Xtest[i]]) for i in range(len(Xtrain))]
ytrain = np.concatenate([ytrain, ytest])
if "normalize" in args and args.normalize:
X2 = []
from sklearn.preprocessing import normalize
view_size = len(Xtrain)
for view in range(view_size):
X2.append(torch.from_numpy(normalize(Xtrain[view].numpy(), norm='l2')))
Xtrain = X2
all_transforms = transforms.Compose(args.pre_transformations + args.post_transformations)
dataset = MultiViewDataSet(Xtrain if args.train else Xtest, torch.from_numpy(ytrain if args.train else ytest).type(TensorType))
loader = DataLoader(dataset, batch_size=args.mb_size, shuffle=args.shuffle,
num_workers=args.workers, drop_last=True)
# _, c, x, y = next(iter(loader))[0].size()
return loader
def get_kolenda_dataloader(args, path_to_data='./data'):
"""kolenda dataloader."""
print("Loading Kolenda.")
mat = scipy.io.loadmat(DATA_DIR.joinpath('Kolenda_data.mat'))
Xtrain, Xtest = mat['Xs'], mat['Xts']
ytrain, ytest = mat['Ys'], mat['Yts']
split = 0
Xtrain, Xtest = [Xtrain[0][split][0][0], Xtrain[0][split][0][1], Xtrain[0][split][0][2]], [Xtest[0][split][0][0], Xtest[0][split][0][1], Xtest[0][split][0][2]]
Xtrain, Xtest = list(map(lambda x: torch.from_numpy(x.astype(float)).type(TensorType), Xtrain)), list(map(lambda x: torch.from_numpy(x.astype(float)).type(TensorType), Xtest))
ytrain = ytrain[0, split].flatten()
ytest = ytest[0, split].flatten()
Xtrain = [torch.cat([Xtrain[i], Xtest[i]]) for i in range(len(Xtrain))]
ytrain = np.concatenate([ytrain, ytest])
if "normalize" in args and args.normalize:
X2 = []
from sklearn.preprocessing import normalize
view_size = len(Xtrain)
for view in range(view_size):
X2.append(torch.from_numpy(normalize(Xtrain[view].numpy(), norm='l2')))
Xtrain = X2
all_transforms = transforms.Compose(args.pre_transformations + args.post_transformations)
dataset = MultiViewDataSet(Xtrain if args.train else Xtest, torch.from_numpy(ytrain if args.train else ytest).type(TensorType))
loader = DataLoader(dataset, batch_size=args.mb_size, shuffle=args.shuffle,
num_workers=args.workers, drop_last=True)
# _, c, x, y = next(iter(loader))[0].size()
return loader
def get_nus_dataloader(args, path_to_data='./data'):
"""nus dataloader."""
print("Loading NUS.")
mat = scipy.io.loadmat(DATA_DIR.joinpath('NUSWIDE_dataset.mat'))
Xtrain, Xtest = mat['Xs'], mat['Xts']
ytrain, ytest = mat['Ys'], mat['Yts']
split = 0
Xtrain, Xtest = [Xtrain[0][split][0][v] for v in range(5)], [Xtest[0][split][0][v] for v in range(5)]
Xtrain, Xtest = list(map(lambda x: torch.from_numpy(x).type(TensorType), Xtrain)), list(map(lambda x: torch.from_numpy(x).type(TensorType), Xtest))
ytrain = ytrain[0, split].flatten()
ytest = ytest[0, split].flatten()
Xtrain = [torch.cat([Xtrain[i], Xtest[i]]) for i in range(len(Xtrain))]
ytrain = np.concatenate([ytrain, ytest])
if "normalize" in args and args.normalize:
X2 = []
from sklearn.preprocessing import normalize
view_size = len(Xtrain)
for view in range(view_size):
X2.append(torch.from_numpy(normalize(Xtrain[view].numpy(), norm='l2')))
Xtrain = X2
all_transforms = transforms.Compose(args.pre_transformations + args.post_transformations)
dataset = MultiViewDataSet(Xtrain if args.train else Xtest, torch.from_numpy(ytrain if args.train else ytest).type(TensorType))
loader = DataLoader(dataset, batch_size=args.mb_size, shuffle=args.shuffle,
num_workers=args.workers, drop_last=True)
# _, c, x, y = next(iter(loader))[0].size()
return loader
def get_game_dataloader(args, path_to_data='./data'):
"""game dataloader."""
print("Loading Game.")
mat = scipy.io.loadmat(DATA_DIR.joinpath('GameData.mat'))
Xtrain, Xtest = mat['Xs'], mat['Xts']
ytrain, ytest = mat['Ys'], mat['Yts']
split = 0
Xtrain, Xtest = [Xtrain[0][split][0][v] for v in range(3)], [Xtest[0][split][0][v] for v in range(3)]
Xtrain, Xtest = list(map(lambda x: torch.from_numpy(x).type(TensorType), Xtrain)), list(map(lambda x: torch.from_numpy(x).type(TensorType), Xtest))
ytrain = ytrain[0, split].flatten()
ytest = ytest[0, split].flatten()
Xtrain = [torch.cat([Xtrain[i], Xtest[i]]) for i in range(len(Xtrain))]
ytrain = np.concatenate([ytrain, ytest])
if "normalize" in args and args.normalize:
X2 = []
from sklearn.preprocessing import normalize
view_size = len(Xtrain)
for view in range(view_size):
X2.append(torch.from_numpy(normalize(Xtrain[view].numpy(), norm='l2')))
Xtrain = X2
all_transforms = transforms.Compose(args.pre_transformations + args.post_transformations)
dataset = MultiViewDataSet(Xtrain if args.train else Xtest, torch.from_numpy(ytrain if args.train else ytest).type(TensorType))
loader = DataLoader(dataset, batch_size=args.mb_size, shuffle=args.shuffle,
num_workers=args.workers, drop_last=True)
# _, c, x, y = next(iter(loader))[0].size()
return loader
def get_toydataset2_dataloader(args, path_to_data='./data'):
"""toydataset2 dataloader."""
print("Loading toydataset2.")
mat = scipy.io.loadmat(DATA_DIR.joinpath('synth3views_2clusters.mat'))
X = mat['X']
y = mat['truth'].flatten()
X = list(map(lambda x: torch.from_numpy(x).type(TensorType), X[0].tolist()))
if "normalize" in args and args.normalize:
X2 = []
from sklearn.preprocessing import normalize
view_size = len(X)
for view in range(view_size):
X2.append(torch.from_numpy(normalize(X[view].numpy(), norm='l2')))
X = X2
all_transforms = transforms.Compose(args.pre_transformations + args.post_transformations)
dataset = MultiViewDataSet(X, torch.from_numpy(y).type(TensorType))
loader = DataLoader(dataset, batch_size=args.mb_size, shuffle=args.shuffle,
pin_memory=True, num_workers=args.workers, drop_last=True)
# _, c, x, y = next(iter(loader))[0].size()
return loader
| 16,212 | 40.359694 | 198 |
py
|
mvksc-rkm
|
mvksc-rkm-main/utils.py
|
import collections
import logging
import sys
from datetime import datetime
from torch.utils.data import Dataset
import numpy as np
import utils
def merge_two_dicts(x, y):
# In case of same key, it keeps the value of y
return {**x, **y}
def merge_dicts(list_of_dicts):
from functools import reduce
return reduce(merge_two_dicts, list_of_dicts)
def flatten_dict(d, parent_key='', sep='_', prefix='eval_'):
items = []
for k, v in d.items():
new_key = parent_key + sep + k if parent_key else k
if isinstance(v, collections.MutableMapping):
items.extend(flatten_dict(v, new_key, sep=sep, prefix=prefix).items())
else:
items.append((prefix + new_key, v))
return dict(items)
def float_format(f: float) -> str:
return "%+.4e" % f
def my_sign(x):
return np.sign(x) + (x == 0)
def kfold_cv(training_fun, x_train, y_train, k_folds, x_test=None, y_test=None):
eval_dict = {}
if k_folds > 2:
from sklearn.model_selection import KFold
kf = KFold(n_splits=k_folds)
eval_dicts = []
for train, val in kf.split(x_train[0]):
# Define training and validation sets
x_train_cv = [x[train] for x in x_train]
x_val_cv = [x[val] for x in x_train]
y_train_cv = y_train[train]
y_val_cv = y_train[val]
assert x_train_cv[0].shape[0] >= x_val_cv[0].shape[0] # training set must be larger or equal than validation set
# Train
fs = training_fun(x_train_cv)
train_dict = {}
if type(fs) == tuple:
fs, train_dict = fs
# Evaluate on validation set
from metrics import internal_metrics
fs_output = [(traintest, x, f(x[0]), f.method) for traintest, x in zip(["val"], [(x_val_cv, y_val_cv)]) for f in fs]
eval_dict = {f"{metric[0]}_{traintest}_{f_method}": metric[1](y_true=y, **f_output)
for metric in internal_metrics
for traintest, (x, y), f_output, f_method in fs_output}
eval_dicts.append(merge_two_dicts(eval_dict, train_dict))
# Average performance across k runs
eval_dict = {key: np.mean([d[key] for d in eval_dicts]) for key in eval_dicts[0].keys()}
# Train on full dataset
from metrics import external_metrics, internal_metrics
start = datetime.now()
fs = training_fun(x_train)
train_dict = {}
if type(fs) == tuple:
fs, train_dict = fs
elapsed_time = datetime.now() - start
logging.info("Training complete in: " + str(elapsed_time))
# Finally evaluate performance on full dataset and possibly on test set
a = zip(["train"] + (["test"] if x_test is not None else []), [(x_train, y_train)] + ([(x_test,y_test)] if x_test is not None else []))
fs_output = [(traintest, x, f(x[0]), f.method) for traintest, x in a for f in fs]
eval_dict = merge_dicts([{"train_time": elapsed_time.total_seconds()}, eval_dict, train_dict,
{f"{metric[0]}_{traintest}_{f_method}": metric[1](y_true=y, **f_output)
for metric in external_metrics + internal_metrics
for traintest, (x, y), f_output, f_method in fs_output}])
eval_dict["fs_output"] = fs_output
return eval_dict
| 3,385 | 37.044944 | 139 |
py
|
mvksc-rkm
|
mvksc-rkm-main/metrics.py
|
from functools import partial
import numpy as np
from sklearn.metrics.cluster import adjusted_rand_score, normalized_mutual_info_score
from sklearn.metrics.cluster._unsupervised import check_number_of_labels
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.metrics import silhouette_score, calinski_harabasz_score, davies_bouldin_score
import scipy.sparse.linalg
from sklearn.preprocessing import LabelEncoder
from sklearn.utils import _safe_indexing, check_X_y
import torch
import pandas
import os
def metric_helper(y_true, y_pred, metric):
res = 0
if len(y_pred.shape) != len(y_true.shape):
if len(y_true.shape) < len(y_pred.shape):
V = y_pred.shape[1]
y_true = np.tile(np.array([y_true]).transpose(), (1, V))
else:
V = y_true.shape[1]
y_pred = np.tile(np.array([y_pred]).transpose(), (1, V))
if len(y_pred.shape) > 1:
res = np.mean([metric(y_true[:, i], y_pred[:, i]) for i in range(y_pred.shape[1])])
else:
res = metric(y_true, y_pred)
return res
def ari(y_true, y_pred, **kwargs):
return metric_helper(y_true, y_pred, adjusted_rand_score)
def nmi(y_true, y_pred, **kwargs):
return metric_helper(y_true, y_pred, normalized_mutual_info_score)
def davies_bouldin(y_pred, xtest, kernels, **kwargs):
V = len(xtest)
from kernels import LinearKernel
if isinstance(kernels[0], LinearKernel):
return np.mean([davies_bouldin_score(xtest[v], y_pred[:, v]) if len(np.unique(y_pred[:, v])) > 1 else 0.0 for v in range(V)])
else:
return np.mean([my_davies_bouldin_score(xtest[v], y_pred[:, v], kernels[v]) if len(np.unique(y_pred[:,v])) > 1 else 10.0 for v in range(V)])
def my_davies_bouldin_score(X, labels, kernel):
# Adapted from https://github.com/scikit-learn/scikit-learn/blob/7e1e6d09b/sklearn/metrics/cluster/_unsupervised.py#L303
X, labels = check_X_y(X, labels)
le = LabelEncoder()
labels = le.fit_transform(labels)
n_samples, _ = X.shape
n_labels = len(le.classes_)
check_number_of_labels(n_labels, n_samples)
intra_dists = np.zeros(n_labels)
centroids = np.zeros((n_labels, len(X[0])), dtype=float)
for k in range(n_labels):
cluster_k = _safe_indexing(X, labels == k)
centroid = cluster_k.mean(axis=0)
centroids[k] = centroid
intra_dists[k] = np.average(kernel(torch.from_numpy(cluster_k).t(), torch.from_numpy(np.array([centroid])).t()).numpy())
centroid_distances = kernel(torch.from_numpy(centroids).t()).numpy()
if np.allclose(intra_dists, 0) or np.allclose(centroid_distances, 0):
return 0.0
centroid_distances[centroid_distances == 0] = np.inf
combined_intra_dists = intra_dists[:, None] + intra_dists
scores = np.max(combined_intra_dists / centroid_distances, axis=1)
return np.mean(scores)
def blf(etest, y_pred, K, k, eta=0.5, **kwargs):
"""
Compute BLF score.
:param etest: score variables of test points, dimension [Ntest,k-1]
:param y_pred: array of {-1,1} of assignments for test points, dimension [Ntest,V]
:param K: kernel matrix, dimension [Ntrain,Ntest]
:param eta: float in [0,1]
:return: float
"""
V = len(etest)
return np.mean([blf_singleview(etest[v], y_pred[:,v], K[v], k, eta=eta) for v in range(V)])
def blf_singleview(etest, y_pred, K, k, eta=0.5):
"""
Compute BLF score.
:param etest: score variables of test points, dimension [Ntest,k-1]
:param y_pred: array of {-1,1} of assignments for test points, dimension [Ntest]
:param K: kernel matrix, dimension [Ntrain,Ntest]
:param eta: float in [0,1]
:return: float
"""
assert len(y_pred.shape) == 1
assert etest.shape[0] == y_pred.shape[0]
c, counts_ = np.unique(y_pred, axis=0, return_counts=True)
counts = np.ones((k,))
counts[c] = counts_
assert etest.shape[1] == k - 1
if k == 2:
Z = [np.array([etest[y_pred == p].flatten(), np.array([]) if k > 2 else np.sum(K[y_pred == p], axis=1)]).T for p in range(k)]
else:
Z = [etest[y_pred == p] for p in range(k)]
Z = [Z[p] - np.mean(Z[p], axis=0) for p in range(k)]
C = [1. / counts[p] * np.matmul(Z[p].T, Z[p]) for p in range(k)]
zeta = [np.sort(np.real(scipy.sparse.linalg.eigs(C[p], k=k - 1, which="LM")[0]))[::-1] for p in range(k)]
if k == 2:
linefit = sum([zeta[p][0] / (zeta[p][0] + zeta[p][1]) - 0.5 for p in range(k)])
else:
linefit = sum([((k - 1.) / (k - 2.)) * (zeta[p][0] / sum(zeta[p]) - 1. / (k - 1.)) for p in range(k)]) / k
balance = np.min(counts) / np.max(counts)
res = eta * linefit + (1. - eta) * balance
res = 0.0 if np.isnan(res) else res
assert 0 <= res <= 1
return res
def baf(etest, y_pred, etrain, k, **kwargs):
"""
Compute BAF score.
:param etest: score variables of test points, dimension [Ntest,k-1]
:param y_pred: array of {-1,1} of assignments for test points, dimension [Ntest,V]
:param K: kernel matrix, dimension [Ntrain,Ntest]
:param eta: float in [0,1]
:return: float
"""
V = len(etest)
return np.mean([baf_singleview(y_pred[:,v], etest[v], etrain[v], k) for v in range(V)])
def baf_singleview(y_pred, etest, etrain, k):
"""
Compute BAF score.
:param y_pred: array of {-1,1} of assignments for test points, dimension [Ntest]
:param etest: score variables for test points, dimension [Ntest,k-1]
:param etrain: score variables for training points, dimension [Ntrain,k-1]
:return: float
"""
assert len(y_pred.shape) == 1
Ntrain, _ = etrain.shape
Ntest = y_pred.shape[0]
c, counts_ = np.unique(y_pred, axis=0, return_counts=True)
counts = np.ones((k,))
counts[c] = counts_
c = list(range(k))
from mvkscrkm import compute_alphaCenters
etrainCenters = compute_alphaCenters(etrain, etrain)
K = cosine_similarity(etest, etrainCenters) # [Ntest, k]
assert K.shape == (Ntest, k)
res = 1. / k * sum([1. / counts[i] * np.sum(np.max(K[y_pred == c[i]], axis=1)) for i in range(k)])
assert -1 <= res <= 1
return res
def silhouette(y_pred, Ktest, **kwargs):
V = len(Ktest)
return np.mean([silhouette_score(np.abs(Ktest[v]-np.diag(np.diag(Ktest[v]))), y_pred[:, v], metric="precomputed") if len(np.unique(y_pred[:,v])) > 1 else 0 for v in range(V)])
internal_metrics = list(zip(["ari", "nmi", "baf", "silhouette", "davies_bouldin"] + [("blf%03.2f" % i).replace(".","") for i in [0.05*j for j in range(21)]],
[ari, nmi, baf, silhouette, davies_bouldin] + [partial(blf, eta=i) for i in [0.05*j for j in range(21)]]))
external_metrics = list(zip(["ari", "nmi"], [ari, nmi]))
| 6,726 | 40.524691 | 179 |
py
|
more
|
more-main/cameras.py
|
"""Camera configs."""
import numpy as np
import pybullet as p
class RealSenseD435:
"""Default configuration with 3 RealSense RGB-D cameras.
https://www.intel.com/content/www/us/en/support/articles/000030385/emerging-technologies/intel-realsense-technology.html
camera_image_size = (480, 640)
camera_fov_w = 69.4 # horizontal field of view, width of image
camera_focal_length = (float(camera_image_size[1]) / 2) / np.tan((np.pi * camera_fov_w / 180) / 2)
camera_focal_length = 462.14
"""
# Mimic RealSense D435 RGB-D camera parameters.
image_size = (480, 640)
intrinsics = np.array([[462.14, 0, 320], [0, 462.14, 240], [0, 0, 1]])
# Set default camera poses.
front_position = (1.0, 0, 0.75)
front_rotation = (np.pi / 4, np.pi, -np.pi / 2)
front_rotation = p.getQuaternionFromEuler(front_rotation)
left_position = (0, 0.5, 0.75)
left_rotation = (np.pi / 4.5, np.pi, np.pi / 4)
left_rotation = p.getQuaternionFromEuler(left_rotation)
right_position = (0, -0.5, 0.75)
right_rotation = (np.pi / 4.5, np.pi, 3 * np.pi / 4)
right_rotation = p.getQuaternionFromEuler(right_rotation)
# Default camera configs.
CONFIG = [
{
"image_size": image_size,
"intrinsics": intrinsics,
"position": front_position,
"rotation": front_rotation,
"zrange": (0.01, 10.0),
"noise": False,
},
{
"image_size": image_size,
"intrinsics": intrinsics,
"position": left_position,
"rotation": left_rotation,
"zrange": (0.01, 10.0),
"noise": False,
},
{
"image_size": image_size,
"intrinsics": intrinsics,
"position": right_position,
"rotation": right_rotation,
"zrange": (0.01, 10.0),
"noise": False,
},
]
class RealSenseD455:
"""Default configuration with 3 RealSense RGB-D cameras.
https://www.intel.com/content/www/us/en/support/articles/000030385/emerging-technologies/intel-realsense-technology.html
camera_image_size = (480, 640)
camera_fov_w = 69.4 # horizontal field of view, width of image
camera_focal_length = (float(camera_image_size[1]) / 2) / np.tan((np.pi * camera_fov_w / 180) / 2)
camera_focal_length = 462.14
"""
# Mimic RealSense D455 RGB-D camera parameters.
image_size = (720, 1280)
intrinsics = np.array([[634.72, 0, 644.216], [0, 634.118, 368.458], [0, 0, 1]])
# Set default camera poses.
front_position = (0.5, 0, 0.7)
front_rotation = p.getQuaternionFromEuler((0, np.pi, -np.pi / 2))
# Default camera configs.
CONFIG = [
{
"image_size": image_size,
"intrinsics": intrinsics,
"position": front_position,
"rotation": front_rotation,
"zrange": (0.01, 2.0),
"noise": True,
},
]
class Oracle:
"""Top-down noiseless image used only by the oracle demonstrator."""
# Near-orthographic projection.
image_size = (480, 640)
intrinsics = np.array([[63e4, 0, 320], [0, 63e4, 240], [0, 0, 1]])
position = (0.5, 0, 1000.0)
rotation = p.getQuaternionFromEuler((0, np.pi, -np.pi / 2))
# Camera config.
CONFIG = [
{
"image_size": image_size,
"intrinsics": intrinsics,
"position": position,
"rotation": rotation,
"zrange": (999.7, 1001.0),
"noise": False,
}
]
| 3,578 | 32.138889 | 124 |
py
|
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