diff --git a/pytorch_transformers/tests/modeling_common_test.py b/pytorch_transformers/tests/modeling_common_test.py index e974ae865d6..8a183c30da9 100644 --- a/pytorch_transformers/tests/modeling_common_test.py +++ b/pytorch_transformers/tests/modeling_common_test.py @@ -49,6 +49,7 @@ class CommonTestCases: test_torchscript = True test_pruning = True test_resize_embeddings = True + test_head_masking = True def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() @@ -159,6 +160,9 @@ class CommonTestCases: def test_headmasking(self): + if not self.test_head_masking: + return + config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_attentions = True @@ -282,6 +286,9 @@ class CommonTestCases: self.assertTrue(models_equal) def test_tie_model_weights(self): + if not self.test_torchscript: + return + config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() def check_same_values(layer_1, layer_2): diff --git a/pytorch_transformers/tests/modeling_dilbert_test.py b/pytorch_transformers/tests/modeling_dilbert_test.py new file mode 100644 index 00000000000..0cbef7e0833 --- /dev/null +++ b/pytorch_transformers/tests/modeling_dilbert_test.py @@ -0,0 +1,219 @@ +# 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 shutil +import pytest + +from pytorch_transformers import (DilBertConfig, DilBertModel, DilBertForMaskedLM, + DilBertForQuestionAnswering, DilBertForSequenceClassification) +from pytorch_transformers.modeling_dilbert import DILBERT_PRETRAINED_MODEL_ARCHIVE_MAP + +from .modeling_common_test import (CommonTestCases, ConfigTester, ids_tensor) + + +class DilBertModelTest(CommonTestCases.CommonModelTester): + + all_model_classes = (DilBertModel, DilBertForMaskedLM, DilBertForQuestionAnswering, + DilBertForSequenceClassification) + test_pruning = False + test_torchscript = False + test_resize_embeddings = False + test_head_masking = False + + class DilBertModelTester(object): + + def __init__(self, + parent, + batch_size=13, + seq_length=7, + is_training=True, + use_input_mask=True, + use_token_type_ids=False, + 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 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) + + input_mask = None + if self.use_input_mask: + input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2) + + sequence_labels = None + token_labels = None + choice_labels = None + if self.use_labels: + sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) + token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) + choice_labels = ids_tensor([self.batch_size], self.num_choices) + + config = DilBertConfig( + vocab_size_or_config_json_file=self.vocab_size, + dim=self.hidden_size, + n_layers=self.num_hidden_layers, + n_heads=self.num_attention_heads, + hidden_dim=self.intermediate_size, + hidden_act=self.hidden_act, + dropout=self.hidden_dropout_prob, + attention_dropout=self.attention_probs_dropout_prob, + max_position_embeddings=self.max_position_embeddings, + initializer_range=self.initializer_range) + + return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels + + def check_loss_output(self, result): + self.parent.assertListEqual( + list(result["loss"].size()), + []) + + def create_and_check_dilbert_model(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels): + model = DilBertModel(config=config) + model.eval() + sequence_output, pooled_output = model(input_ids, input_mask) + sequence_output, pooled_output = model(input_ids) + + result = { + "sequence_output": sequence_output, + "pooled_output": pooled_output, + } + 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_and_check_dilbert_for_masked_lm(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels): + model = DilBertForMaskedLM(config=config) + model.eval() + loss, prediction_scores = model(input_ids, input_mask, token_labels) + result = { + "loss": loss, + "prediction_scores": prediction_scores, + } + self.parent.assertListEqual( + list(result["prediction_scores"].size()), + [self.batch_size, self.seq_length, self.vocab_size]) + self.check_loss_output(result) + + def create_and_check_dilbert_for_question_answering(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels): + model = DilBertForQuestionAnswering(config=config) + model.eval() + loss, start_logits, end_logits = model(input_ids, input_mask, sequence_labels, sequence_labels) + result = { + "loss": loss, + "start_logits": start_logits, + "end_logits": end_logits, + } + 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]) + self.check_loss_output(result) + + def create_and_check_dilbert_for_sequence_classification(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels): + config.num_labels = self.num_labels + model = DilBertForSequenceClassification(config) + model.eval() + loss, logits = model(input_ids, input_mask, sequence_labels) + result = { + "loss": loss, + "logits": logits, + } + self.parent.assertListEqual( + list(result["logits"].size()), + [self.batch_size, self.num_labels]) + self.check_loss_output(result) + + def prepare_config_and_inputs_for_common(self): + config_and_inputs = self.prepare_config_and_inputs() + (config, input_ids, input_mask, sequence_labels, token_labels, choice_labels) = config_and_inputs + inputs_dict = {'input_ids': input_ids, 'attention_mask': input_mask} + return config, inputs_dict + + def setUp(self): + self.model_tester = DilBertModelTest.DilBertModelTester(self) + self.config_tester = ConfigTester(self, config_class=DilBertConfig, dim=37) + + def test_config(self): + self.config_tester.run_common_tests() + + def test_dilbert_model(self): + config_and_inputs = self.model_tester.prepare_config_and_inputs() + self.model_tester.create_and_check_dilbert_model(*config_and_inputs) + + def test_for_masked_lm(self): + config_and_inputs = self.model_tester.prepare_config_and_inputs() + self.model_tester.create_and_check_dilbert_for_masked_lm(*config_and_inputs) + + def test_for_question_answering(self): + config_and_inputs = self.model_tester.prepare_config_and_inputs() + self.model_tester.create_and_check_dilbert_for_question_answering(*config_and_inputs) + + def test_for_sequence_classification(self): + config_and_inputs = self.model_tester.prepare_config_and_inputs() + self.model_tester.create_and_check_dilbert_for_sequence_classification(*config_and_inputs) + + # @pytest.mark.slow + # def test_model_from_pretrained(self): + # cache_dir = "/tmp/pytorch_transformers_test/" + # for model_name in list(DILBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: + # model = DilBertModel.from_pretrained(model_name, cache_dir=cache_dir) + # shutil.rmtree(cache_dir) + # self.assertIsNotNone(model) + +if __name__ == "__main__": + unittest.main()