# 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()