mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-30 09:42:22 +06:00
478 lines
23 KiB
Python
478 lines
23 KiB
Python
# 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
|
|
|
|
import torch
|
|
|
|
from pytorch_pretrained_bert import (BertConfig, BertModel, BertForMaskedLM,
|
|
BertForNextSentencePrediction, BertForPreTraining,
|
|
BertForQuestionAnswering, BertForSequenceClassification,
|
|
BertForTokenClassification, BertForMultipleChoice)
|
|
from pytorch_pretrained_bert.modeling import PRETRAINED_MODEL_ARCHIVE_MAP
|
|
|
|
|
|
class BertModelTest(unittest.TestCase):
|
|
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):
|
|
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 = BertModelTest.ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
|
|
|
|
input_mask = None
|
|
if self.use_input_mask:
|
|
input_mask = BertModelTest.ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
|
|
|
|
token_type_ids = None
|
|
if self.use_token_type_ids:
|
|
token_type_ids = BertModelTest.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 = BertModelTest.ids_tensor([self.batch_size], self.type_sequence_label_size)
|
|
token_labels = BertModelTest.ids_tensor([self.batch_size, self.seq_length], self.num_labels)
|
|
choice_labels = BertModelTest.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 check_loss_output(self, result):
|
|
self.parent.assertListEqual(
|
|
list(result["loss"].size()),
|
|
[])
|
|
|
|
def create_bert_model(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
|
|
model = BertModel(config=config)
|
|
model.eval()
|
|
all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
|
|
outputs = {
|
|
"sequence_output": all_encoder_layers[-1],
|
|
"pooled_output": pooled_output,
|
|
"all_encoder_layers": all_encoder_layers,
|
|
}
|
|
return outputs
|
|
|
|
def check_bert_model_output(self, result):
|
|
self.parent.assertListEqual(
|
|
[size for layer in result["all_encoder_layers"] for size in layer.size()],
|
|
[self.batch_size, self.seq_length, self.hidden_size] * self.num_hidden_layers)
|
|
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_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 = model(input_ids, token_type_ids, input_mask, token_labels)
|
|
prediction_scores = model(input_ids, token_type_ids, input_mask)
|
|
outputs = {
|
|
"loss": loss,
|
|
"prediction_scores": prediction_scores,
|
|
}
|
|
return outputs
|
|
|
|
def check_bert_for_masked_lm_output(self, result):
|
|
self.parent.assertListEqual(
|
|
list(result["prediction_scores"].size()),
|
|
[self.batch_size, self.seq_length, self.vocab_size])
|
|
|
|
def create_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 = model(input_ids, token_type_ids, input_mask, sequence_labels)
|
|
seq_relationship_score = model(input_ids, token_type_ids, input_mask)
|
|
outputs = {
|
|
"loss": loss,
|
|
"seq_relationship_score": seq_relationship_score,
|
|
}
|
|
return outputs
|
|
|
|
def check_bert_for_next_sequence_prediction_output(self, result):
|
|
self.parent.assertListEqual(
|
|
list(result["seq_relationship_score"].size()),
|
|
[self.batch_size, 2])
|
|
|
|
|
|
def create_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 = model(input_ids, token_type_ids, input_mask, token_labels, sequence_labels)
|
|
prediction_scores, seq_relationship_score = model(input_ids, token_type_ids, input_mask)
|
|
outputs = {
|
|
"loss": loss,
|
|
"prediction_scores": prediction_scores,
|
|
"seq_relationship_score": seq_relationship_score,
|
|
}
|
|
return outputs
|
|
|
|
def check_bert_for_pretraining_output(self, result):
|
|
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])
|
|
|
|
|
|
def create_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 = model(input_ids, token_type_ids, input_mask, sequence_labels, sequence_labels)
|
|
start_logits, end_logits = model(input_ids, token_type_ids, input_mask)
|
|
outputs = {
|
|
"loss": loss,
|
|
"start_logits": start_logits,
|
|
"end_logits": end_logits,
|
|
}
|
|
return outputs
|
|
|
|
def check_bert_for_question_answering_output(self, result):
|
|
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])
|
|
|
|
|
|
def create_bert_for_sequence_classification(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
|
|
model = BertForSequenceClassification(config=config, num_labels=self.num_labels)
|
|
model.eval()
|
|
loss = model(input_ids, token_type_ids, input_mask, sequence_labels)
|
|
logits = model(input_ids, token_type_ids, input_mask)
|
|
outputs = {
|
|
"loss": loss,
|
|
"logits": logits,
|
|
}
|
|
return outputs
|
|
|
|
def check_bert_for_sequence_classification_output(self, result):
|
|
self.parent.assertListEqual(
|
|
list(result["logits"].size()),
|
|
[self.batch_size, self.num_labels])
|
|
|
|
|
|
def create_bert_for_token_classification(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
|
|
model = BertForTokenClassification(config=config, num_labels=self.num_labels)
|
|
model.eval()
|
|
loss = model(input_ids, token_type_ids, input_mask, token_labels)
|
|
logits = model(input_ids, token_type_ids, input_mask)
|
|
outputs = {
|
|
"loss": loss,
|
|
"logits": logits,
|
|
}
|
|
return outputs
|
|
|
|
def check_bert_for_token_classification_output(self, result):
|
|
self.parent.assertListEqual(
|
|
list(result["logits"].size()),
|
|
[self.batch_size, self.seq_length, self.num_labels])
|
|
|
|
|
|
def create_bert_for_multiple_choice(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
|
|
model = BertForMultipleChoice(config=config, num_choices=self.num_choices)
|
|
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 = model(multiple_choice_inputs_ids,
|
|
multiple_choice_token_type_ids,
|
|
multiple_choice_input_mask,
|
|
choice_labels)
|
|
logits = model(multiple_choice_inputs_ids,
|
|
multiple_choice_token_type_ids,
|
|
multiple_choice_input_mask)
|
|
outputs = {
|
|
"loss": loss,
|
|
"logits": logits,
|
|
}
|
|
return outputs
|
|
|
|
def check_bert_for_multiple_choice(self, result):
|
|
self.parent.assertListEqual(
|
|
list(result["logits"].size()),
|
|
[self.batch_size, self.num_choices])
|
|
|
|
|
|
def create_and_check_bert_for_attentions(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
|
|
for model_class in (BertModel, BertForMaskedLM, BertForNextSentencePrediction,
|
|
BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification,
|
|
BertForTokenClassification):
|
|
if model_class in [BertForSequenceClassification,
|
|
BertForTokenClassification]:
|
|
model = model_class(config=config, num_labels=self.num_labels, output_attentions=True)
|
|
else:
|
|
model = model_class(config=config, output_attentions=True)
|
|
model.eval()
|
|
output = model(input_ids, token_type_ids, input_mask)
|
|
attentions = output[0]
|
|
self.parent.assertEqual(len(attentions), self.num_hidden_layers)
|
|
self.parent.assertListEqual(
|
|
list(attentions[0].size()),
|
|
[self.batch_size, self.num_attention_heads, self.seq_length, self.seq_length])
|
|
|
|
|
|
def create_and_check_bert_for_headmasking(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
|
|
for model_class in (BertModel, BertForMaskedLM, BertForNextSentencePrediction,
|
|
BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification,
|
|
BertForTokenClassification):
|
|
if model_class in [BertForSequenceClassification,
|
|
BertForTokenClassification]:
|
|
model = model_class(config=config,
|
|
num_labels=self.num_labels,
|
|
keep_multihead_output=True)
|
|
else:
|
|
model = model_class(config=config, keep_multihead_output=True)
|
|
model.eval()
|
|
head_mask = torch.zeros(self.num_hidden_layers, self.num_attention_heads).to(input_ids.device)
|
|
head_mask[0, 1:-1] = 1.0 # Mask all but the first and last heads on the first layer
|
|
head_mask[-1, 1:] = 1.0 # Mask all but the first head on the last layer
|
|
output = model(input_ids, token_type_ids, input_mask, head_mask=head_mask)
|
|
|
|
if isinstance(model, BertModel):
|
|
output = sum(t.sum() for t in output[0])
|
|
elif isinstance(output, (list, tuple)):
|
|
output = sum(t.sum() for t in output)
|
|
output = output.sum()
|
|
output.backward()
|
|
multihead_outputs = (model if isinstance(model, BertModel) else model.bert).get_multihead_outputs()
|
|
|
|
self.parent.assertEqual(len(multihead_outputs), self.num_hidden_layers)
|
|
self.parent.assertListEqual(
|
|
list(multihead_outputs[0].size()),
|
|
[self.batch_size, self.num_attention_heads,
|
|
self.seq_length, self.hidden_size // self.num_attention_heads])
|
|
self.parent.assertEqual(
|
|
len(multihead_outputs[0][:, 1:(self.num_attention_heads-1), :, :].nonzero()),
|
|
0)
|
|
self.parent.assertEqual(
|
|
len(multihead_outputs[0][:, 0, :, :].nonzero()),
|
|
self.batch_size * self.seq_length * self.hidden_size // self.num_attention_heads)
|
|
self.parent.assertEqual(
|
|
len(multihead_outputs[0][:, self.num_attention_heads-1, :, :].nonzero()),
|
|
self.batch_size * self.seq_length * self.hidden_size // self.num_attention_heads)
|
|
|
|
self.parent.assertListEqual(
|
|
list(multihead_outputs[1].size()),
|
|
[self.batch_size, self.num_attention_heads,
|
|
self.seq_length, self.hidden_size // self.num_attention_heads])
|
|
self.parent.assertEqual(
|
|
len(multihead_outputs[1].nonzero()),
|
|
multihead_outputs[1].numel())
|
|
|
|
self.parent.assertListEqual(
|
|
list(multihead_outputs[-1].size()),
|
|
[self.batch_size, self.num_attention_heads,
|
|
self.seq_length, self.hidden_size // self.num_attention_heads])
|
|
self.parent.assertEqual(
|
|
len(multihead_outputs[-1][:, 1:, :, :].nonzero()),
|
|
0)
|
|
self.parent.assertEqual(
|
|
len(multihead_outputs[-1][:, 0, :, :].nonzero()),
|
|
self.batch_size * self.seq_length * self.hidden_size // self.num_attention_heads)
|
|
|
|
|
|
def create_and_check_bert_for_head_pruning(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
|
|
for model_class in (BertModel, BertForMaskedLM, BertForNextSentencePrediction,
|
|
BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification,
|
|
BertForTokenClassification):
|
|
if model_class in [BertForSequenceClassification,
|
|
BertForTokenClassification]:
|
|
model = model_class(config=config,
|
|
num_labels=self.num_labels,
|
|
keep_multihead_output=True)
|
|
else:
|
|
model = model_class(config=config, keep_multihead_output=True)
|
|
model.eval()
|
|
bert_model = model if isinstance(model, BertModel) else model.bert
|
|
heads_to_prune = {0: list(range(1, self.num_attention_heads)),
|
|
-1: [0]}
|
|
bert_model.prune_heads(heads_to_prune)
|
|
output = model(input_ids, token_type_ids, input_mask)
|
|
|
|
if isinstance(model, BertModel):
|
|
output = sum(t.sum() for t in output[0])
|
|
elif isinstance(output, (list, tuple)):
|
|
output = sum(t.sum() for t in output)
|
|
output = output.sum()
|
|
output.backward()
|
|
multihead_outputs = bert_model.get_multihead_outputs()
|
|
|
|
self.parent.assertEqual(len(multihead_outputs), self.num_hidden_layers)
|
|
self.parent.assertListEqual(
|
|
list(multihead_outputs[0].size()),
|
|
[self.batch_size, 1,
|
|
self.seq_length, self.hidden_size // self.num_attention_heads])
|
|
self.parent.assertListEqual(
|
|
list(multihead_outputs[1].size()),
|
|
[self.batch_size, self.num_attention_heads,
|
|
self.seq_length, self.hidden_size // self.num_attention_heads])
|
|
self.parent.assertListEqual(
|
|
list(multihead_outputs[-1].size()),
|
|
[self.batch_size, self.num_attention_heads-1,
|
|
self.seq_length, self.hidden_size // self.num_attention_heads])
|
|
|
|
|
|
def test_default(self):
|
|
self.run_tester(BertModelTest.BertModelTester(self))
|
|
|
|
def test_config_to_json_string(self):
|
|
config = BertConfig(vocab_size_or_config_json_file=99, hidden_size=37)
|
|
obj = json.loads(config.to_json_string())
|
|
self.assertEqual(obj["vocab_size"], 99)
|
|
self.assertEqual(obj["hidden_size"], 37)
|
|
|
|
def test_config_to_json_file(self):
|
|
config_first = BertConfig(vocab_size_or_config_json_file=99, hidden_size=37)
|
|
json_file_path = "/tmp/config.json"
|
|
config_first.to_json_file(json_file_path)
|
|
config_second = BertConfig.from_json_file(json_file_path)
|
|
os.remove(json_file_path)
|
|
self.assertEqual(config_second.to_dict(), config_first.to_dict())
|
|
|
|
@pytest.mark.slow
|
|
def test_model_from_pretrained(self):
|
|
cache_dir = "/tmp/pytorch_pretrained_bert_test/"
|
|
for model_name in list(PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
|
|
model = BertModel.from_pretrained(model_name, cache_dir=cache_dir)
|
|
shutil.rmtree(cache_dir)
|
|
self.assertIsNotNone(model)
|
|
|
|
def run_tester(self, tester):
|
|
config_and_inputs = tester.prepare_config_and_inputs()
|
|
output_result = tester.create_bert_model(*config_and_inputs)
|
|
tester.check_bert_model_output(output_result)
|
|
|
|
output_result = tester.create_bert_for_masked_lm(*config_and_inputs)
|
|
tester.check_bert_for_masked_lm_output(output_result)
|
|
tester.check_loss_output(output_result)
|
|
|
|
output_result = tester.create_bert_for_next_sequence_prediction(*config_and_inputs)
|
|
tester.check_bert_for_next_sequence_prediction_output(output_result)
|
|
tester.check_loss_output(output_result)
|
|
|
|
output_result = tester.create_bert_for_pretraining(*config_and_inputs)
|
|
tester.check_bert_for_pretraining_output(output_result)
|
|
tester.check_loss_output(output_result)
|
|
|
|
output_result = tester.create_bert_for_question_answering(*config_and_inputs)
|
|
tester.check_bert_for_question_answering_output(output_result)
|
|
tester.check_loss_output(output_result)
|
|
|
|
output_result = tester.create_bert_for_sequence_classification(*config_and_inputs)
|
|
tester.check_bert_for_sequence_classification_output(output_result)
|
|
tester.check_loss_output(output_result)
|
|
|
|
output_result = tester.create_bert_for_token_classification(*config_and_inputs)
|
|
tester.check_bert_for_token_classification_output(output_result)
|
|
tester.check_loss_output(output_result)
|
|
|
|
output_result = tester.create_bert_for_multiple_choice(*config_and_inputs)
|
|
tester.check_bert_for_multiple_choice(output_result)
|
|
tester.check_loss_output(output_result)
|
|
|
|
tester.create_and_check_bert_for_attentions(*config_and_inputs)
|
|
tester.create_and_check_bert_for_headmasking(*config_and_inputs)
|
|
tester.create_and_check_bert_for_head_pruning(*config_and_inputs)
|
|
|
|
@classmethod
|
|
def ids_tensor(cls, shape, vocab_size, rng=None, name=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))
|
|
|
|
return torch.tensor(data=values, dtype=torch.long).view(shape).contiguous()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|