# 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 json import random import torch from pytorch_pretrained_bert import (BertConfig, BertModel, BertForMaskedLM, BertForNextSentencePrediction, BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification, BertForTokenClassification) 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, 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.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 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) 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 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): 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): 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): 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): 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): 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): 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): 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 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 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) @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()