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| # coding=utf-8 | |
| # Copyright 2018 Salesforce and HuggingFace Inc. team. | |
| # 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. | |
| import unittest | |
| from transformers import is_torch_available | |
| from .test_configuration_common import ConfigTester | |
| from .test_modeling_common import ModelTesterMixin, ids_tensor | |
| from .utils import CACHE_DIR, require_torch, slow, torch_device | |
| if is_torch_available(): | |
| from transformers import CTRLConfig, CTRLModel, CTRL_PRETRAINED_MODEL_ARCHIVE_MAP, CTRLLMHeadModel | |
| class CTRLModelTest(ModelTesterMixin, unittest.TestCase): | |
| all_model_classes = (CTRLModel, CTRLLMHeadModel) if is_torch_available() else () | |
| test_pruning = False | |
| test_torchscript = False | |
| test_resize_embeddings = False | |
| test_head_masking = False | |
| class CTRLModelTester(object): | |
| def __init__( | |
| self, | |
| parent, | |
| batch_size=13, | |
| seq_length=7, | |
| is_training=True, | |
| use_token_type_ids=True, | |
| use_input_mask=True, | |
| use_labels=True, | |
| use_mc_token_ids=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_token_type_ids = use_token_type_ids | |
| self.use_input_mask = use_input_mask | |
| self.use_labels = use_labels | |
| self.use_mc_token_ids = use_mc_token_ids | |
| 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) | |
| token_type_ids = None | |
| if self.use_token_type_ids: | |
| token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) | |
| mc_token_ids = None | |
| if self.use_mc_token_ids: | |
| mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length) | |
| 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 = CTRLConfig( | |
| vocab_size=self.vocab_size, | |
| n_embd=self.hidden_size, | |
| n_layer=self.num_hidden_layers, | |
| n_head=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, | |
| n_positions=self.max_position_embeddings, | |
| n_ctx=self.max_position_embeddings | |
| # type_vocab_size=self.type_vocab_size, | |
| # initializer_range=self.initializer_range | |
| ) | |
| head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2) | |
| return ( | |
| config, | |
| input_ids, | |
| input_mask, | |
| head_mask, | |
| token_type_ids, | |
| mc_token_ids, | |
| sequence_labels, | |
| token_labels, | |
| choice_labels, | |
| ) | |
| def check_loss_output(self, result): | |
| self.parent.assertListEqual(list(result["loss"].size()), []) | |
| def create_and_check_ctrl_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args): | |
| model = CTRLModel(config=config) | |
| model.to(torch_device) | |
| model.eval() | |
| model(input_ids, token_type_ids=token_type_ids, head_mask=head_mask) | |
| model(input_ids, token_type_ids=token_type_ids) | |
| sequence_output, presents = model(input_ids) | |
| result = { | |
| "sequence_output": sequence_output, | |
| "presents": presents, | |
| } | |
| self.parent.assertListEqual( | |
| list(result["sequence_output"].size()), [self.batch_size, self.seq_length, self.hidden_size] | |
| ) | |
| self.parent.assertEqual(len(result["presents"]), config.n_layer) | |
| def create_and_check_lm_head_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args): | |
| model = CTRLLMHeadModel(config) | |
| model.to(torch_device) | |
| model.eval() | |
| loss, lm_logits, _ = model(input_ids, token_type_ids=token_type_ids, labels=input_ids) | |
| result = {"loss": loss, "lm_logits": lm_logits} | |
| self.parent.assertListEqual(list(result["loss"].size()), []) | |
| self.parent.assertListEqual( | |
| list(result["lm_logits"].size()), [self.batch_size, self.seq_length, self.vocab_size] | |
| ) | |
| def prepare_config_and_inputs_for_common(self): | |
| config_and_inputs = self.prepare_config_and_inputs() | |
| ( | |
| config, | |
| input_ids, | |
| input_mask, | |
| head_mask, | |
| token_type_ids, | |
| mc_token_ids, | |
| sequence_labels, | |
| token_labels, | |
| choice_labels, | |
| ) = config_and_inputs | |
| inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "head_mask": head_mask} | |
| return config, inputs_dict | |
| def setUp(self): | |
| self.model_tester = CTRLModelTest.CTRLModelTester(self) | |
| self.config_tester = ConfigTester(self, config_class=CTRLConfig, n_embd=37) | |
| def test_config(self): | |
| self.config_tester.run_common_tests() | |
| def test_ctrl_model(self): | |
| config_and_inputs = self.model_tester.prepare_config_and_inputs() | |
| self.model_tester.create_and_check_ctrl_model(*config_and_inputs) | |
| def test_ctrl_lm_head_model(self): | |
| config_and_inputs = self.model_tester.prepare_config_and_inputs() | |
| self.model_tester.create_and_check_lm_head_model(*config_and_inputs) | |
| def test_model_from_pretrained(self): | |
| for model_name in list(CTRL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: | |
| model = CTRLModel.from_pretrained(model_name, cache_dir=CACHE_DIR) | |
| self.assertIsNotNone(model) | |