mirror of
https://github.com/huggingface/transformers.git
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295 lines
13 KiB
Python
295 lines
13 KiB
Python
# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import unittest
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import json
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import random
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import torch
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from pytorch_pretrained_bert import (BertConfig, BertModel, BertForMaskedLM,
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BertForNextSentencePrediction, BertForPreTraining,
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BertForQuestionAnswering, BertForSequenceClassification,
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BertForTokenClassification)
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class BertModelTest(unittest.TestCase):
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class BertModelTester(object):
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def __init__(self,
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parent,
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batch_size=13,
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seq_length=7,
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is_training=True,
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use_input_mask=True,
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use_token_type_ids=True,
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use_labels=True,
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vocab_size=99,
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hidden_size=32,
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num_hidden_layers=5,
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num_attention_heads=4,
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intermediate_size=37,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=16,
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type_sequence_label_size=2,
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initializer_range=0.02,
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num_labels=3,
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scope=None):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.use_input_mask = use_input_mask
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self.use_token_type_ids = use_token_type_ids
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.type_sequence_label_size = type_sequence_label_size
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self.initializer_range = initializer_range
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self.num_labels = num_labels
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self.scope = scope
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def prepare_config_and_inputs(self):
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input_ids = BertModelTest.ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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input_mask = None
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if self.use_input_mask:
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input_mask = BertModelTest.ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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token_type_ids = None
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if self.use_token_type_ids:
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token_type_ids = BertModelTest.ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
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sequence_labels = None
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token_labels = None
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if self.use_labels:
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sequence_labels = BertModelTest.ids_tensor([self.batch_size], self.type_sequence_label_size)
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token_labels = BertModelTest.ids_tensor([self.batch_size, self.seq_length], self.num_labels)
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config = BertConfig(
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vocab_size_or_config_json_file=self.vocab_size,
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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hidden_act=self.hidden_act,
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hidden_dropout_prob=self.hidden_dropout_prob,
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attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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max_position_embeddings=self.max_position_embeddings,
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type_vocab_size=self.type_vocab_size,
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initializer_range=self.initializer_range)
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return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels
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def check_loss_output(self, result):
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self.parent.assertListEqual(
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list(result["loss"].size()),
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[])
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def create_bert_model(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels):
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model = BertModel(config=config)
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all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
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outputs = {
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"sequence_output": all_encoder_layers[-1],
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"pooled_output": pooled_output,
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"all_encoder_layers": all_encoder_layers,
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}
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return outputs
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def check_bert_model_output(self, result):
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self.parent.assertListEqual(
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[size for layer in result["all_encoder_layers"] for size in layer.size()],
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[self.batch_size, self.seq_length, self.hidden_size] * self.num_hidden_layers)
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self.parent.assertListEqual(
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list(result["sequence_output"].size()),
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[self.batch_size, self.seq_length, self.hidden_size])
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self.parent.assertListEqual(list(result["pooled_output"].size()), [self.batch_size, self.hidden_size])
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def create_bert_for_masked_lm(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels):
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model = BertForMaskedLM(config=config)
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loss = model(input_ids, token_type_ids, input_mask, token_labels)
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prediction_scores = model(input_ids, token_type_ids, input_mask)
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outputs = {
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"loss": loss,
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"prediction_scores": prediction_scores,
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}
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return outputs
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def check_bert_for_masked_lm_output(self, result):
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self.parent.assertListEqual(
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list(result["prediction_scores"].size()),
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[self.batch_size, self.seq_length, self.vocab_size])
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def create_bert_for_next_sequence_prediction(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels):
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model = BertForNextSentencePrediction(config=config)
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loss = model(input_ids, token_type_ids, input_mask, sequence_labels)
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seq_relationship_score = model(input_ids, token_type_ids, input_mask)
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outputs = {
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"loss": loss,
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"seq_relationship_score": seq_relationship_score,
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}
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return outputs
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def check_bert_for_next_sequence_prediction_output(self, result):
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self.parent.assertListEqual(
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list(result["seq_relationship_score"].size()),
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[self.batch_size, 2])
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def create_bert_for_pretraining(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels):
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model = BertForPreTraining(config=config)
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loss = model(input_ids, token_type_ids, input_mask, token_labels, sequence_labels)
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prediction_scores, seq_relationship_score = model(input_ids, token_type_ids, input_mask)
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outputs = {
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"loss": loss,
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"prediction_scores": prediction_scores,
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"seq_relationship_score": seq_relationship_score,
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}
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return outputs
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def check_bert_for_pretraining_output(self, result):
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self.parent.assertListEqual(
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list(result["prediction_scores"].size()),
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[self.batch_size, self.seq_length, self.vocab_size])
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self.parent.assertListEqual(
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list(result["seq_relationship_score"].size()),
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[self.batch_size, 2])
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def create_bert_for_question_answering(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels):
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model = BertForQuestionAnswering(config=config)
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loss = model(input_ids, token_type_ids, input_mask, sequence_labels, sequence_labels)
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start_logits, end_logits = model(input_ids, token_type_ids, input_mask)
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outputs = {
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"loss": loss,
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"start_logits": start_logits,
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"end_logits": end_logits,
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}
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return outputs
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def check_bert_for_question_answering_output(self, result):
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self.parent.assertListEqual(
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list(result["start_logits"].size()),
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[self.batch_size, self.seq_length])
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self.parent.assertListEqual(
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list(result["end_logits"].size()),
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[self.batch_size, self.seq_length])
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def create_bert_for_sequence_classification(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels):
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model = BertForSequenceClassification(config=config, num_labels=self.num_labels)
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loss = model(input_ids, token_type_ids, input_mask, sequence_labels)
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logits = model(input_ids, token_type_ids, input_mask)
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outputs = {
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"loss": loss,
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"logits": logits,
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}
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return outputs
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def check_bert_for_sequence_classification_output(self, result):
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self.parent.assertListEqual(
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list(result["logits"].size()),
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[self.batch_size, self.num_labels])
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def create_bert_for_token_classification(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels):
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model = BertForTokenClassification(config=config, num_labels=self.num_labels)
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loss = model(input_ids, token_type_ids, input_mask, token_labels)
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logits = model(input_ids, token_type_ids, input_mask)
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outputs = {
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"loss": loss,
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"logits": logits,
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}
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return outputs
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def check_bert_for_token_classification_output(self, result):
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self.parent.assertListEqual(
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list(result["logits"].size()),
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[self.batch_size, self.seq_length, self.num_labels])
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def test_default(self):
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self.run_tester(BertModelTest.BertModelTester(self))
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def test_config_to_json_string(self):
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config = BertConfig(vocab_size_or_config_json_file=99, hidden_size=37)
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obj = json.loads(config.to_json_string())
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self.assertEqual(obj["vocab_size"], 99)
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self.assertEqual(obj["hidden_size"], 37)
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def run_tester(self, tester):
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config_and_inputs = tester.prepare_config_and_inputs()
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output_result = tester.create_bert_model(*config_and_inputs)
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tester.check_bert_model_output(output_result)
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output_result = tester.create_bert_for_masked_lm(*config_and_inputs)
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tester.check_bert_for_masked_lm_output(output_result)
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tester.check_loss_output(output_result)
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output_result = tester.create_bert_for_next_sequence_prediction(*config_and_inputs)
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tester.check_bert_for_next_sequence_prediction_output(output_result)
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tester.check_loss_output(output_result)
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output_result = tester.create_bert_for_pretraining(*config_and_inputs)
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tester.check_bert_for_pretraining_output(output_result)
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tester.check_loss_output(output_result)
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output_result = tester.create_bert_for_question_answering(*config_and_inputs)
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tester.check_bert_for_question_answering_output(output_result)
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tester.check_loss_output(output_result)
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output_result = tester.create_bert_for_sequence_classification(*config_and_inputs)
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tester.check_bert_for_sequence_classification_output(output_result)
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tester.check_loss_output(output_result)
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output_result = tester.create_bert_for_token_classification(*config_and_inputs)
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tester.check_bert_for_token_classification_output(output_result)
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tester.check_loss_output(output_result)
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@classmethod
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def ids_tensor(cls, shape, vocab_size, rng=None, name=None):
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"""Creates a random int32 tensor of the shape within the vocab size."""
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if rng is None:
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rng = random.Random()
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total_dims = 1
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for dim in shape:
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total_dims *= dim
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values = []
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for _ in range(total_dims):
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values.append(rng.randint(0, vocab_size - 1))
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return torch.tensor(data=values, dtype=torch.long).view(shape).contiguous()
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if __name__ == "__main__":
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unittest.main()
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