# 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 CTRLConfig, is_torch_available from transformers.testing_utils import require_torch, slow, torch_device from ...generation.test_generation_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask if is_torch_available(): import torch from transformers import ( CTRL_PRETRAINED_MODEL_ARCHIVE_LIST, CTRLForSequenceClassification, CTRLLMHeadModel, CTRLModel, ) class CTRLModelTester: def __init__( self, parent, ): self.parent = parent self.batch_size = 14 self.seq_length = 7 self.is_training = True self.use_token_type_ids = True self.use_input_mask = True self.use_labels = True self.use_mc_token_ids = True self.vocab_size = 99 self.hidden_size = 32 self.num_hidden_layers = 5 self.num_attention_heads = 4 self.intermediate_size = 37 self.hidden_act = "gelu" self.hidden_dropout_prob = 0.1 self.attention_probs_dropout_prob = 0.1 self.max_position_embeddings = 512 self.type_vocab_size = 16 self.type_sequence_label_size = 2 self.initializer_range = 0.02 self.num_labels = 3 self.num_choices = 4 self.scope = None self.pad_token_id = self.vocab_size - 1 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 = random_attention_mask([self.batch_size, self.seq_length]) 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 = self.get_config() 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 get_config(self): return 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, # type_vocab_size=self.type_vocab_size, # initializer_range=self.initializer_range, pad_token_id=self.pad_token_id, ) 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) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertEqual(len(result.past_key_values), 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() result = model(input_ids, token_type_ids=token_type_ids, labels=input_ids) self.parent.assertEqual(result.loss.shape, ()) self.parent.assertEqual(result.logits.shape, (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 create_and_check_ctrl_for_sequence_classification(self, config, input_ids, head_mask, token_type_ids, *args): config.num_labels = self.num_labels model = CTRLForSequenceClassification(config) model.to(torch_device) model.eval() sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) result = model(input_ids, token_type_ids=token_type_ids, labels=sequence_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) @require_torch class CTRLModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): all_model_classes = (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else () all_generative_model_classes = (CTRLLMHeadModel,) if is_torch_available() else () test_pruning = True test_resize_embeddings = False test_head_masking = False def setUp(self): self.model_tester = 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) @slow def test_model_from_pretrained(self): for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = CTRLModel.from_pretrained(model_name) self.assertIsNotNone(model) @require_torch class CTRLModelLanguageGenerationTest(unittest.TestCase): @slow def test_lm_generate_ctrl(self): model = CTRLLMHeadModel.from_pretrained("ctrl") model.to(torch_device) input_ids = torch.tensor( [[11859, 0, 1611, 8]], dtype=torch.long, device=torch_device ) # Legal the president is expected_output_ids = [ 11859, 0, 1611, 8, 5, 150, 26449, 2, 19, 348, 469, 3, 2595, 48, 20740, 246533, 246533, 19, 30, 5, ] # Legal the president is a good guy and I don't want to lose my job. \n \n I have a output_ids = model.generate(input_ids, do_sample=False) self.assertListEqual(output_ids[0].tolist(), expected_output_ids)