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# coding=utf-8
# Copyright 2020 The HuggingFace Team Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a clone of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import time
import unittest
from transformers import AutoTokenizer, is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
EosTokenCriteria,
MaxLengthCriteria,
MaxNewTokensCriteria,
MaxTimeCriteria,
StoppingCriteriaList,
StopStringCriteria,
validate_stopping_criteria,
)
@require_torch
class StoppingCriteriaTestCase(unittest.TestCase):
def _get_tensors(self, length):
batch_size = 3
vocab_size = 250
input_ids = ids_tensor((batch_size, length), vocab_size)
scores = torch.ones((batch_size, length), device=torch_device, dtype=torch.float) / length
return input_ids, scores
def test_list_criteria(self):
input_ids, scores = self._get_tensors(5)
criteria = StoppingCriteriaList(
[
MaxLengthCriteria(max_length=10),
MaxTimeCriteria(max_time=0.1),
]
)
self.assertFalse(all(criteria(input_ids, scores)))
input_ids, scores = self._get_tensors(9)
self.assertFalse(all(criteria(input_ids, scores)))
input_ids, scores = self._get_tensors(10)
self.assertTrue(all(criteria(input_ids, scores)))
def test_max_length_criteria(self):
criteria = MaxLengthCriteria(max_length=10)
input_ids, scores = self._get_tensors(5)
self.assertFalse(all(criteria(input_ids, scores)))
input_ids, scores = self._get_tensors(9)
self.assertFalse(all(criteria(input_ids, scores)))
input_ids, scores = self._get_tensors(10)
self.assertTrue(all(criteria(input_ids, scores)))
def test_max_new_tokens_criteria(self):
criteria = MaxNewTokensCriteria(start_length=5, max_new_tokens=5)
input_ids, scores = self._get_tensors(5)
self.assertFalse(all(criteria(input_ids, scores)))
input_ids, scores = self._get_tensors(9)
self.assertFalse(all(criteria(input_ids, scores)))
input_ids, scores = self._get_tensors(10)
self.assertTrue(all(criteria(input_ids, scores)))
criteria_list = StoppingCriteriaList([criteria])
self.assertEqual(criteria_list.max_length, 10)
def test_max_time_criteria(self):
input_ids, scores = self._get_tensors(5)
criteria = MaxTimeCriteria(max_time=0.1)
self.assertFalse(all(criteria(input_ids, scores)))
criteria = MaxTimeCriteria(max_time=0.1, initial_timestamp=time.time() - 0.2)
self.assertTrue(all(criteria(input_ids, scores)))
def test_eos_token_criteria(self):
criteria = EosTokenCriteria(eos_token_id=0)
input_ids, scores = self._get_tensors(5)
input_ids[:, -1] = 0
self.assertTrue(all(criteria(input_ids, scores)))
input_ids, scores = self._get_tensors(5)
input_ids[:2, -1] = 0
input_ids[2, -1] = 1
self.assertListEqual(criteria(input_ids, scores).tolist(), [True, True, False])
input_ids, scores = self._get_tensors(5)
input_ids[:, -1] = 1
self.assertListEqual(criteria(input_ids, scores).tolist(), [False, False, False])
def test_validate_stopping_criteria(self):
validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10)]), 10)
with self.assertWarns(UserWarning):
validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10)]), 11)
stopping_criteria = validate_stopping_criteria(StoppingCriteriaList(), 11)
self.assertEqual(len(stopping_criteria), 1)
def test_stop_string_criteria(self):
true_strings = [
"<|im_start|><|im_end|>",
"<|im_start|><|im_end|<|im_end|>",
">><|im_start|>>stop",
"stop",
"e nd",
]
false_strings = [
"<|im_start|><|im_end|",
"<|im_start|><|im_end|<|im_end|",
"<|im_end|><|im_start|>",
"<|im_end|<>stop<|im_end|",
"end",
"en d",
"eNd",
"<|im_end|",
"|im_end|>",
"s",
]
stop_strings = ["<|im_end|>", "stop", "e nd"]
# Use a tokenizer that won't actually have special tokens for these
tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
tokenizer.pad_token_id = tokenizer.eos_token_id
tokenizer.padding_side = "left"
true_input_ids = tokenizer(true_strings, return_tensors="pt", padding="longest", add_special_tokens=False)
false_input_ids = tokenizer(false_strings, return_tensors="pt", padding="longest", add_special_tokens=False)
scores = None
criteria = StopStringCriteria(tokenizer=tokenizer, stop_strings=stop_strings)
for i in range(len(true_strings)):
self.assertTrue(criteria(true_input_ids["input_ids"][i : i + 1], scores))
for i in range(len(false_strings)):
self.assertFalse(criteria(false_input_ids["input_ids"][i : i + 1], scores))
# Now try it with a tokenizer where those are actually special tokens
tokenizer = AutoTokenizer.from_pretrained("cognitivecomputations/dolphin-2.5-mixtral-8x7b")
tokenizer.padding_side = "left"
true_input_ids = tokenizer(true_strings, return_tensors="pt", padding="longest", add_special_tokens=False)
false_input_ids = tokenizer(false_strings, return_tensors="pt", padding="longest", add_special_tokens=False)
criteria = StopStringCriteria(tokenizer=tokenizer, stop_strings=stop_strings)
for i in range(len(true_strings)):
self.assertTrue(criteria(true_input_ids["input_ids"][i : i + 1], scores))
for i in range(len(false_strings)):
self.assertFalse(criteria(false_input_ids["input_ids"][i : i + 1], scores))
def test_stop_string_matching_positions(self):
stop_string = "stop"
token_list = ["last", "top", "topper", "s", "p"]
token_indices = list(range(len(token_list)))
all_token_valid_positions, all_token_end_overlaps = StopStringCriteria._stop_string_get_matching_positions(
token_list=token_list, token_indices=token_indices, stop_strings=[stop_string]
)
valid_positions = {
token_list[idx]: positions for idx, positions in all_token_valid_positions[stop_string].items()
}
end_overlaps = {token_list[idx]: overlaps for idx, overlaps in all_token_end_overlaps[stop_string].items()}
self.assertEqual(valid_positions, {"s": [3], "last": [2]})
self.assertEqual(end_overlaps, {"top": [3], "topper": [3], "p": [1]})
def test_stop_string_embedding_vecs(self):
stop_string = "stop"
token_list = ["last", "top", "topper", "s", "p"]
token_indices = list(range(len(token_list)))
embedding_vec, max_valid_positions, max_valid_end_lens = StopStringCriteria._stop_string_create_embedding_vec(
token_list=token_list, token_indices=token_indices, stop_strings=[stop_string]
)
# Positions inside the stop string where the token matches (excluding end overlaps)
valid_positions = embedding_vec[:, 0].tolist()
self.assertEqual(valid_positions, [2, -1, -1, 3, -1])
# Overlap lengths between end of stop string and start of token
end_overlaps = embedding_vec[:, 1].tolist()
self.assertEqual(end_overlaps, [-1, 3, 3, -1, 1])
# Length of each token
token_lengths = embedding_vec[:, 2].tolist()
self.assertEqual(token_lengths, [len(token) for token in token_list])
def test_criterias_per_row(self):
text = "They completed the challenging puzzle, revealing the hidden image at the end"
stop_strings = ["end"]
tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
tokenizer.pad_token_id = tokenizer.eos_token_id
inputs = tokenizer(text, return_tensors="pt", add_special_tokens=False)
scores = None
criteria = StoppingCriteriaList(
[
MaxLengthCriteria(max_length=20),
StopStringCriteria(tokenizer=tokenizer, stop_strings=stop_strings),
]
)
# trigger stopping when at leat one criteria is satisfied, one value per batch
self.assertTrue(criteria(inputs["input_ids"], scores))
# return False when neither is satisfied
self.assertFalse(criteria(inputs["input_ids"][:, :-1], scores))
def test_criterias_per_row_batched(self):
text = [
"They completed the challenging puzzle, revealing the hidden image at the end",
"Today a dragon flew over France",
"The aroma of freshly baked pizza filled the kitchen",
]
stop_strings = ["end"]
tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
tokenizer.pad_token_id = tokenizer.eos_token_id
tokenizer.padding_side = "left"
inputs = tokenizer(text, return_tensors="pt", padding="longest", add_special_tokens=False)
scores = None
criteria = StoppingCriteriaList(
[
MaxLengthCriteria(max_length=20),
StopStringCriteria(tokenizer=tokenizer, stop_strings=stop_strings),
]
)
# trigger stopping when at leat one criteria is satisfied
self.assertListEqual(criteria(inputs["input_ids"], scores).tolist(), [True, False, False])
# False when neither is satisfied
self.assertListEqual(criteria(inputs["input_ids"][:, :-1], scores).tolist(), [False, False, False])
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