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tests/models/roberta/test_modeling_roberta.py
609 строк
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Tom Aarsen
Allow position_ids_start=2 on DataCollatorWithFlattening for RoBERTa etc. (#47525)
30 июл 2026, 14:43
Не верифицирован
30 июл 2026, 14:43
4f01369
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# Copyright 2020 The HuggingFace Team. All rights reserved. # # 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 import pytest from transformers import DataCollatorWithFlattening, RobertaConfig, is_torch_available from transformers.testing_utils import ( TestCasePlus, require_flash_attn, require_torch, require_torch_accelerator, slow, torch_device, ) from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( RobertaForCausalLM, RobertaForMaskedLM, RobertaForMultipleChoice, RobertaForQuestionAnswering, RobertaForSequenceClassification, RobertaForTokenClassification, RobertaModel, ) from transformers.models.roberta.modeling_roberta import RobertaEmbeddings ROBERTA_TINY = "sshleifer/tiny-distilroberta-base" class RobertaModelTester: 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=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_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 = 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) 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, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def get_config(self): return RobertaConfig( vocab_size=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, ) def get_pipeline_config(self): config = self.get_config() config.vocab_size = 300 return config def prepare_config_and_inputs_for_decoder(self): ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = self.prepare_config_and_inputs() config.is_decoder = True encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]) encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2) return ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = RobertaModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) result = 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(result.pooler_output.shape, (self.batch_size, self.hidden_size)) def create_and_check_model_as_decoder( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.add_cross_attention = True model = RobertaModel(config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, ) result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states, ) result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size)) def create_and_check_for_causal_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): model = RobertaForCausalLM(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_decoder_model_past_large_inputs( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.is_decoder = True config.add_cross_attention = True model = RobertaForCausalLM(config=config).to(torch_device).eval() # make sure that ids don't start with pad token mask = input_ids.ne(config.pad_token_id).long() input_ids = input_ids * mask # first forward pass outputs = model( input_ids, attention_mask=input_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, use_cache=True, ) past_key_values = outputs.past_key_values # create hypothetical multiple next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) # make sure that ids don't start with pad token mask = next_tokens.ne(config.pad_token_id).long() next_tokens = next_tokens * mask next_mask = ids_tensor((self.batch_size, 3), vocab_size=2) # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) next_attention_mask = torch.cat([input_mask, next_mask], dim=-1) output_from_no_past = model( next_input_ids, attention_mask=next_attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_hidden_states=True, )["hidden_states"][0] output_from_past = model( next_tokens, attention_mask=next_attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_values=past_key_values, output_hidden_states=True, )["hidden_states"][0] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach() output_from_past_slice = output_from_past[:, :, random_slice_idx].detach() self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]) # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) def create_and_check_for_masked_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = RobertaForMaskedLM(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_for_token_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = RobertaForTokenClassification(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def create_and_check_for_multiple_choice( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_choices = self.num_choices model = RobertaForMultipleChoice(config=config) model.to(torch_device) 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() result = model( multiple_choice_inputs_ids, attention_mask=multiple_choice_input_mask, token_type_ids=multiple_choice_token_type_ids, labels=choice_labels, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices)) def create_and_check_for_question_answering( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = RobertaForQuestionAnswering(config=config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, start_positions=sequence_labels, end_positions=sequence_labels, ) self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length)) self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class RobertaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( RobertaForCausalLM, RobertaForMaskedLM, RobertaModel, RobertaForSequenceClassification, RobertaForTokenClassification, RobertaForMultipleChoice, RobertaForQuestionAnswering, ) if is_torch_available() else () ) pipeline_model_mapping = ( { "feature-extraction": RobertaModel, "fill-mask": RobertaForMaskedLM, "text-classification": RobertaForSequenceClassification, "text-generation": RobertaForCausalLM, "token-classification": RobertaForTokenClassification, "zero-shot": RobertaForSequenceClassification, } if is_torch_available() else {} ) model_split_percents = [0.5, 0.8, 0.9] # Overwriting to add `is_decoder` flag def prepare_config_and_inputs_for_generate(self, batch_size=2): config, inputs = super().prepare_config_and_inputs_for_generate(batch_size) config.is_decoder = True return config, inputs def setUp(self): self.model_tester = RobertaModelTester(self) self.config_tester = ConfigTester(self, config_class=RobertaConfig, hidden_size=32) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_model_as_decoder(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_model_as_decoder(*config_and_inputs) def test_model_as_decoder_with_default_input_mask(self): ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) = self.model_tester.prepare_config_and_inputs_for_decoder() input_mask = None self.model_tester.create_and_check_model_as_decoder( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) def test_for_causal_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_for_causal_lm(*config_and_inputs) def test_decoder_model_past_with_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_decoder_model_past_large_inputs(*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_for_masked_lm(*config_and_inputs) def test_for_token_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*config_and_inputs) def test_for_multiple_choice(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_multiple_choice(*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_for_question_answering(*config_and_inputs) @slow def test_model_from_pretrained(self): model_name = "FacebookAI/roberta-base" model = RobertaModel.from_pretrained(model_name) self.assertIsNotNone(model) def test_create_position_ids_respects_padding_index(self): """This is a regression test for https://github.com/huggingface/transformers/issues/1761 The position ids should be masked with the embedding object's padding index. Therefore, the first available non-padding position index is RobertaEmbeddings.padding_idx + 1 """ config = self.model_tester.prepare_config_and_inputs()[0] model = RobertaEmbeddings(config=config) input_ids = torch.as_tensor([[12, 31, 13, model.padding_idx]]) expected_positions = torch.as_tensor( [[0 + model.padding_idx + 1, 1 + model.padding_idx + 1, 2 + model.padding_idx + 1, model.padding_idx]] ) position_ids = RobertaEmbeddings.create_position_ids_from_input_ids(input_ids, model.padding_idx) self.assertEqual(position_ids.shape, expected_positions.shape) self.assertTrue(torch.all(torch.eq(position_ids, expected_positions))) def test_create_position_ids_from_inputs_embeds(self): """This is a regression test for https://github.com/huggingface/transformers/issues/1761 The position ids should be masked with the embedding object's padding index. Therefore, the first available non-padding position index is RobertaEmbeddings.padding_idx + 1 """ config = self.model_tester.prepare_config_and_inputs()[0] embeddings = RobertaEmbeddings(config=config) inputs_embeds = torch.empty(2, 4, 30) expected_single_positions = [ 0 + embeddings.padding_idx + 1, 1 + embeddings.padding_idx + 1, 2 + embeddings.padding_idx + 1, 3 + embeddings.padding_idx + 1, ] expected_positions = torch.as_tensor([expected_single_positions, expected_single_positions]) position_ids = embeddings.create_position_ids_from_inputs_embeds(inputs_embeds, embeddings.padding_idx) self.assertEqual(position_ids.shape, expected_positions.shape) self.assertTrue(torch.all(torch.eq(position_ids, expected_positions))) @require_flash_attn @require_torch_accelerator @pytest.mark.flash_attn_test def test_flash_attn_padding_free_position_ids_start(self): """Test that flattening a batch with position_ids_start=pad_token_id + 1 matches the padded batched run.""" config, input_ids, _, attention_mask = self.model_tester.prepare_config_and_inputs()[:4] model = RobertaModel(config).to(dtype=torch.float16, device=torch_device).eval() model.set_attn_implementation("flash_attention_2") # Modify the random attention mask into right padding, the packed run takes the prefix of each sequence attention_mask = attention_mask.sort(dim=-1, descending=True).values # Replace accidental pad tokens, they would shift the position ids the model computes input_ids[input_ids == config.pad_token_id] += 1 input_ids[attention_mask == 0] = config.pad_token_id with torch.no_grad(): batched = model(input_ids, attention_mask=attention_mask).last_hidden_state collator = DataCollatorWithFlattening(position_ids_start=config.pad_token_id + 1) lengths = attention_mask.sum(dim=-1) packed = collator([{"input_ids": ids[:length]} for ids, length in zip(input_ids, lengths)]) expected_position_ids = [pos + config.pad_token_id + 1 for length in lengths for pos in range(length)] self.assertEqual(packed["position_ids"][0].tolist(), expected_position_ids) # Without attention_mask and cu_seq_lens, FlashAttention infers the boundaries from position_ids with torch.no_grad(): flattened = model( input_ids=packed["input_ids"].to(torch_device), position_ids=packed["position_ids"].to(torch_device) ).last_hidden_state # The padded and packed runs match bitwise on the hardware tested, wrong position ids differ by >2 offset = 0 for i, length in enumerate(lengths): flattened_seq = flattened[0, offset : offset + length] torch.testing.assert_close(batched[i, :length], flattened_seq, atol=1e-3, rtol=1e-3) offset += length @require_torch class RobertaModelIntegrationTest(TestCasePlus): @slow def test_inference_masked_lm(self): model = RobertaForMaskedLM.from_pretrained("FacebookAI/roberta-base") input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) with torch.no_grad(): output = model(input_ids)[0] expected_shape = torch.Size((1, 11, 50265)) self.assertEqual(output.shape, expected_shape) # compare the actual values for a slice. expected_slice = torch.tensor( [[[33.8802, -4.3103, 22.7761], [4.6539, -2.8098, 13.6253], [1.8228, -3.6898, 8.8600]]] ) # roberta = torch.hub.load('pytorch/fairseq', 'roberta.base') # roberta.eval() # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach() torch.testing.assert_close(output[:, :3, :3], expected_slice, rtol=1e-4, atol=1e-4) @slow def test_inference_no_head(self): model = RobertaModel.from_pretrained("FacebookAI/roberta-base") input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) with torch.no_grad(): output = model(input_ids)[0] # compare the actual values for a slice. expected_slice = torch.tensor( [[[-0.0231, 0.0782, 0.0074], [-0.1854, 0.0540, -0.0175], [0.0548, 0.0799, 0.1687]]] ) # roberta = torch.hub.load('pytorch/fairseq', 'roberta.base') # roberta.eval() # expected_slice = roberta.extract_features(input_ids)[:, :3, :3].detach() torch.testing.assert_close(output[:, :3, :3], expected_slice, rtol=1e-4, atol=1e-4) @slow def test_inference_classification_head(self): model = RobertaForSequenceClassification.from_pretrained("FacebookAI/roberta-large-mnli") input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) with torch.no_grad(): output = model(input_ids)[0] expected_shape = torch.Size((1, 3)) self.assertEqual(output.shape, expected_shape) expected_tensor = torch.tensor([[-0.9469, 0.3913, 0.5118]]) # roberta = torch.hub.load('pytorch/fairseq', 'roberta.large.mnli') # roberta.eval() # expected_tensor = roberta.predict("mnli", input_ids, return_logits=True).detach() torch.testing.assert_close(output, expected_tensor, rtol=1e-4, atol=1e-4)