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tests/models/ctrl/test_modeling_ctrl.py
279 строк
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Sai-Suraj-27
Fix failing `salesforce-ctrl`, `xlm` & `gpt-neo` model generation tests (#43180)
09 янв 2026, 18:28
Не верифицирован
09 янв 2026, 18:28
44c2c0f
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# 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 cleanup, require_torch, slow, torch_device from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( CTRLForSequenceClassification, CTRLLMHeadModel, CTRLModel, ) class CTRLModelTester: def __init__( self, parent, batch_size=14, 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=2, 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 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() return ( config, input_ids, input_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, dff=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, token_type_ids, *args): model = CTRLModel(config=config) model.to(torch_device) model.eval() model(input_ids, token_type_ids=token_type_ids) 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, 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, 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} return config, inputs_dict @require_torch class CTRLModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else () pipeline_model_mapping = ( { "feature-extraction": CTRLModel, "text-classification": CTRLForSequenceClassification, "text-generation": CTRLLMHeadModel, "zero-shot": CTRLForSequenceClassification, } if is_torch_available() else {} ) test_resize_embeddings = False # TODO: Fix the failed tests def is_pipeline_test_to_skip( self, pipeline_test_case_name, config_class, model_architecture, tokenizer_name, image_processor_name, feature_extractor_name, processor_name, ): if pipeline_test_case_name == "ZeroShotClassificationPipelineTests": # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers. # `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny # config could not be created. return True return False def setUp(self): self.model_tester = CTRLModelTester(self) self.config_tester = ConfigTester(self, config_class=CTRLConfig, n_embd=37) def tearDown(self): super().tearDown() # clean-up as much as possible GPU memory occupied by PyTorch cleanup(torch_device) 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): model_name = "Salesforce/ctrl" model = CTRLModel.from_pretrained(model_name) self.assertIsNotNone(model) @require_torch class CTRLModelLanguageGenerationTest(unittest.TestCase): def tearDown(self): super().tearDown() # clean-up as much as possible GPU memory occupied by PyTorch cleanup(torch_device, gc_collect=True) @slow def test_lm_generate_ctrl(self): model = CTRLLMHeadModel.from_pretrained("Salesforce/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 # We limit the generation output to (max_length - input_length) while by default 20 new tokens will be generated. output_ids = model.generate(input_ids, do_sample=False, max_length=20) self.assertListEqual(output_ids[0].tolist(), expected_output_ids)