# Copyright 2022 The HuggingFace Inc. 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. """Testing suite for the PyTorch RoCBert model.""" import inspect import tempfile import unittest from transformers import RoCBertConfig, is_torch_available from transformers.models.auto import get_values from transformers.testing_utils import require_torch, slow, torch_device 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 ( MODEL_FOR_PRETRAINING_MAPPING, DataCollatorWithFlattening, RoCBertForCausalLM, RoCBertForMaskedLM, RoCBertForMultipleChoice, RoCBertForPreTraining, RoCBertForQuestionAnswering, RoCBertForSequenceClassification, RoCBertForTokenClassification, RoCBertModel, ) class RoCBertModelTester: 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, pronunciation_vocab_size=99, shape_vocab_size=99, pronunciation_embed_dim=32, shape_embed_dim=32, 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.pronunciation_vocab_size = pronunciation_vocab_size self.shape_vocab_size = shape_vocab_size self.pronunciation_embed_dim = pronunciation_embed_dim self.shape_embed_dim = shape_embed_dim 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_shape_ids = ids_tensor([self.batch_size, self.seq_length], self.shape_vocab_size) input_pronunciation_ids = ids_tensor([self.batch_size, self.seq_length], self.pronunciation_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, input_shape_ids, input_pronunciation_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) def get_config(self): return RoCBertConfig( vocab_size=self.vocab_size, shape_vocab_size=self.shape_vocab_size, pronunciation_vocab_size=self.pronunciation_vocab_size, shape_embed_dim=self.shape_embed_dim, pronunciation_embed_dim=self.pronunciation_embed_dim, 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, is_decoder=False, initializer_range=self.initializer_range, ) def prepare_config_and_inputs_for_decoder(self): ( config, input_ids, input_shape_ids, input_pronunciation_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, input_shape_ids, input_pronunciation_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, input_shape_ids, input_pronunciation_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ): model = RoCBertModel(config=config) model.to(torch_device) model.eval() result = model( input_ids, input_shape_ids=input_shape_ids, input_pronunciation_ids=input_pronunciation_ids, attention_mask=input_mask, token_type_ids=token_type_ids, ) result = model( input_ids, input_shape_ids=input_shape_ids, input_pronunciation_ids=input_pronunciation_ids, token_type_ids=token_type_ids, ) result = model(input_ids, input_shape_ids=input_shape_ids, input_pronunciation_ids=input_pronunciation_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_model_as_decoder( self, config, input_ids, input_shape_ids, input_pronunciation_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.add_cross_attention = True model = RoCBertModel(config) model.to(torch_device) model.eval() result = model( input_ids, input_shape_ids=input_shape_ids, input_pronunciation_ids=input_pronunciation_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, input_shape_ids=input_shape_ids, input_pronunciation_ids=input_pronunciation_ids, attention_mask=input_mask, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states, ) result = model( input_ids, input_shape_ids=input_shape_ids, input_pronunciation_ids=input_pronunciation_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)) def create_and_check_for_masked_lm( self, config, input_ids, input_shape_ids, input_pronunciation_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ): model = RoCBertForMaskedLM(config=config) model.to(torch_device) model.eval() result = model( input_ids, input_shape_ids=input_shape_ids, input_pronunciation_ids=input_pronunciation_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, input_shape_ids, input_pronunciation_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 = RoCBertForCausalLM(config=config) model.to(torch_device) model.eval() # first forward pass outputs = model( input_ids, input_shape_ids=input_shape_ids, input_pronunciation_ids=input_pronunciation_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) next_shape_tokens = ids_tensor((self.batch_size, 3), config.shape_vocab_size) next_pronunciation_tokens = ids_tensor((self.batch_size, 3), config.pronunciation_vocab_size) 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_input_shape_ids = torch.cat([input_shape_ids, next_shape_tokens], dim=-1) next_input_pronunciation_ids = torch.cat([input_pronunciation_ids, next_pronunciation_tokens], dim=-1) next_attention_mask = torch.cat([input_mask, next_mask], dim=-1) output_from_no_past = model( next_input_ids, input_shape_ids=next_input_shape_ids, input_pronunciation_ids=next_input_pronunciation_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, input_shape_ids=next_shape_tokens, input_pronunciation_ids=next_pronunciation_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_question_answering( self, config, input_ids, input_shape_ids, input_pronunciation_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ): model = RoCBertForQuestionAnswering(config=config) model.to(torch_device) model.eval() result = model( input_ids, input_shape_ids=input_shape_ids, input_pronunciation_ids=input_pronunciation_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 create_and_check_for_sequence_classification( self, config, input_ids, input_shape_ids, input_pronunciation_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ): config.num_labels = self.num_labels model = RoCBertForSequenceClassification(config) model.to(torch_device) model.eval() result = model( input_ids, input_shape_ids=input_shape_ids, input_pronunciation_ids=input_pronunciation_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def create_and_check_for_token_classification( self, config, input_ids, input_shape_ids, input_pronunciation_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ): config.num_labels = self.num_labels model = RoCBertForTokenClassification(config=config) model.to(torch_device) model.eval() result = model( input_ids, input_shape_ids=input_shape_ids, input_pronunciation_ids=input_pronunciation_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, input_shape_ids, input_pronunciation_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ): config.num_choices = self.num_choices model = RoCBertForMultipleChoice(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_inputs_shape_ids = input_shape_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() multiple_choice_inputs_pronunciation_ids = ( input_pronunciation_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, input_shape_ids=multiple_choice_inputs_shape_ids, input_pronunciation_ids=multiple_choice_inputs_pronunciation_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 prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, input_shape_ids, input_pronunciation_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = { "input_ids": input_ids, "input_shape_ids": input_shape_ids, "input_pronunciation_ids": input_pronunciation_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask, } return config, inputs_dict def create_and_check_for_pretraining( self, config, input_ids, input_shape_ids, input_pronunciation_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ): model = RoCBertForPreTraining(config=config) model.to(torch_device) model.eval() result = model( input_ids, input_shape_ids, input_pronunciation_ids, attention_mask=input_mask, token_type_ids=token_type_ids, attack_input_ids=input_ids, attack_input_shape_ids=input_shape_ids, attack_input_pronunciation_ids=input_pronunciation_ids, attack_attention_mask=input_mask, attack_token_type_ids=token_type_ids, labels_input_ids=token_labels, labels_input_shape_ids=input_shape_ids, labels_input_pronunciation_ids=input_pronunciation_ids, labels_attention_mask=input_mask, labels_token_type_ids=token_type_ids, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) @require_torch class RoCBertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( RoCBertModel, RoCBertForMaskedLM, RoCBertForCausalLM, RoCBertForMultipleChoice, RoCBertForQuestionAnswering, RoCBertForSequenceClassification, RoCBertForTokenClassification, RoCBertForPreTraining, ) if is_torch_available() else () ) # Doesn't run generation tests. There are interface mismatches when using `generate` -- TODO @gante all_generative_model_classes = () pipeline_model_mapping = ( { "feature-extraction": RoCBertModel, "fill-mask": RoCBertForMaskedLM, "question-answering": RoCBertForQuestionAnswering, "text-classification": RoCBertForSequenceClassification, "text-generation": RoCBertForCausalLM, "token-classification": RoCBertForTokenClassification, "zero-shot": RoCBertForSequenceClassification, } if is_torch_available() else {} ) # TODO: Fix the failed tests when this model gets more usage 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 in [ "FillMaskPipelineTests", "FeatureExtractionPipelineTests", "TextClassificationPipelineTests", "TokenClassificationPipelineTests", ]: # Get error: IndexError: index out of range in self. # `word_shape_file` and `word_pronunciation_file` should be shrunk during tiny model creation, # otherwise `IndexError` could occur in some embedding layers. Skip for now until this model has # more usage. return True return False # 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 # special case for ForPreTraining model def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels) if return_labels: if model_class in get_values(MODEL_FOR_PRETRAINING_MAPPING): inputs_dict["labels_input_ids"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device ) inputs_dict["labels_input_shape_ids"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device ) inputs_dict["labels_input_pronunciation_ids"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device ) inputs_dict["attack_input_ids"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device ) inputs_dict["attack_input_shape_ids"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device ) inputs_dict["attack_input_pronunciation_ids"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device ) return inputs_dict def setUp(self): self.model_tester = RoCBertModelTester(self) self.config_tester = ConfigTester(self, config_class=RoCBertConfig, hidden_size=37) 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_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_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_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_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) def test_for_sequence_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_sequence_classification(*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_pretraining(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_pretraining(*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, input_shape_ids, input_pronunciation_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, input_shape_ids, input_pronunciation_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) @slow def test_model_from_pretrained(self): model_name = "weiweishi/roc-bert-base-zh" model = RoCBertModel.from_pretrained(model_name) self.assertIsNotNone(model) def attention_mask_padding_matches_padding_free_with_position_ids( self, attn_implementation: str, fa_kwargs: bool = False ): """ Overwritten to account for the embeddings that rely on position ids. """ if not self.has_attentions: self.skipTest(reason="Model architecture does not support attentions") max_new_tokens = 30 support_flag = { "sdpa": "_supports_sdpa", "flash_attention_2": "_supports_flash_attn", "flash_attention_3": "_supports_flash_attn", } for model_class in self.all_generative_model_classes: if attn_implementation != "eager" and not getattr(model_class, support_flag[attn_implementation]): self.skipTest(f"{model_class.__name__} does not support {attn_implementation}") # can't infer if new attn mask API is supported by assume that only model with attention backend support it if not model_class._supports_attention_backend: self.skipTest(f"{model_class.__name__} does not support new attention mask API") if model_class._is_stateful: # non-transformer models most probably have no packing support self.skipTest(f"{model_class.__name__} doesn't support packing!") config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() if config.is_encoder_decoder: self.skipTest("Model is an encoder-decoder") if 0 not in inputs_dict.get("attention_mask", []) or "attention_mask" not in inputs_dict: self.skipTest("Model dummy inputs should contain padding in their attention mask") if "input_ids" not in inputs_dict or inputs_dict["input_ids"].ndim != 2: self.skipTest("Model dummy inputs should contain text input ids") # make sure that all models have enough positions for generation dummy_input_ids = inputs_dict["input_ids"] if hasattr(config, "max_position_embeddings"): config.max_position_embeddings = max_new_tokens + dummy_input_ids.shape[1] + 1 model = model_class(config) if "position_ids" not in inspect.signature(model.forward).parameters: self.skipTest("Model does not support position_ids") if (not fa_kwargs) and "position_ids" not in inspect.signature(model.forward).parameters: continue # this model doesn't accept position ids as input with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) # Drop all keys except for the minimal set. Hard to manipulate with multimodals etc inputs_dict = {k: v for k, v in inputs_dict.items() if k in ["input_ids", "attention_mask"]} # Ensure left padding, to adapt for some models if 0 in inputs_dict["attention_mask"][:, -1]: inputs_dict["attention_mask"] = inputs_dict["attention_mask"].flip(1) dummy_attention_mask = inputs_dict["attention_mask"] dummy_input_ids[~dummy_attention_mask.bool()] = config.get_text_config().pad_token_id # Main difference to other models, we need to prepare position ids according to the attention mask # as we use it to extract embeddings that rely on the correct position - naively increasing sequences do # not suffice anymore atp. The solution here calculates an increasing sequences for all 1s and puts 0s else. inputs_dict["position_ids"] = ((inputs_dict["attention_mask"] == 1).long().cumsum(dim=1) - 1) * ( inputs_dict["attention_mask"] == 1 ).long() model = ( model_class.from_pretrained( tmpdirname, dtype=torch.bfloat16, attn_implementation=attn_implementation, ) .to(torch_device) .eval() ) if fa_kwargs: # flatten features = [ {"input_ids": i[a.bool()].tolist()} for i, a in zip(dummy_input_ids, dummy_attention_mask) ] # add position_ids + fa_kwargs data_collator = DataCollatorWithFlattening(return_tensors="pt", return_flash_attn_kwargs=True) batch = data_collator(features) padfree_inputs_dict = { k: t.to(torch_device) if torch.is_tensor(t) else t for k, t in batch.items() } else: # create packed position_ids position_ids = ( torch.cat([torch.arange(length) for length in dummy_attention_mask.sum(1).tolist()]) .long() .unsqueeze(0) .to(torch_device) ) padfree_inputs_dict = { "input_ids": dummy_input_ids[dummy_attention_mask.bool()].unsqueeze(0), "position_ids": position_ids, } # We need to do simple forward without cache in order to trigger packed SDPA/flex/eager attention path res_padded = model(**inputs_dict, use_cache=False) res_padfree = model(**padfree_inputs_dict, use_cache=False) logits_padded = res_padded.logits[dummy_attention_mask.bool()] logits_padfree = res_padfree.logits[0] # acceptable numerical instability tol = torch.finfo(torch.bfloat16).eps torch.testing.assert_close(logits_padded, logits_padfree, rtol=tol, atol=tol) def flash_attn_inference_equivalence( self, attn_implementation: str, padding_side: str, atol: float = 4e-2, rtol: float = 4e-2 ): super().flash_attn_inference_equivalence( attn_implementation, padding_side, # relaxing the tolerance here atol=6e-2, rtol=4e-2, ) @require_torch class RoCBertModelIntegrationTest(unittest.TestCase): @slow def test_inference_masked_lm(self): model = RoCBertForMaskedLM.from_pretrained("weiweishi/roc-bert-base-zh") # input_text: ['[CLS]', 'b', 'a', '里', '系', '[MASK]', '国', '的', '首', '都', '[SEP]'] is the adversarial text # of ['[CLS]', '巴', '黎', '是', '[MASK]', '国', '的', '首', '都', '[SEP]'], means # "Paris is the [MASK] of France" in English input_ids = torch.tensor([[101, 144, 143, 7027, 5143, 103, 1744, 4638, 7674, 6963, 102]]) input_shape_ids = torch.tensor([[2, 20324, 23690, 8740, 706, 1, 10900, 23343, 20205, 5850, 2]]) input_pronunciation_ids = torch.tensor([[2, 718, 397, 52, 61, 1, 168, 273, 180, 243, 2]]) output = model(input_ids, input_shape_ids, input_pronunciation_ids) output_ids = torch.argmax(output.logits, dim=2) # convert to tokens is: ['[CLS]', '巴', '*', '黎', '是', '法', '国', '的', '首', '都', '[SEP]'] expected_output = torch.tensor([[101, 2349, 115, 7944, 3221, 3791, 1744, 4638, 7674, 6963, 102]]) assert torch.allclose(output_ids, expected_output)