# Copyright 2021 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 BigBird model.""" import unittest from transformers import BigBirdConfig, is_torch_available from transformers.models.auto import get_values from transformers.models.big_bird.tokenization_big_bird import BigBirdTokenizer 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, BigBirdForCausalLM, BigBirdForMaskedLM, BigBirdForMultipleChoice, BigBirdForPreTraining, BigBirdForQuestionAnswering, BigBirdForSequenceClassification, BigBirdForTokenClassification, BigBirdModel, ) class BigBirdModelTester: def __init__( self, parent, batch_size=7, seq_length=128, 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_new", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=256, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, num_labels=3, num_choices=4, attention_type="block_sparse", use_bias=True, rescale_embeddings=False, block_size=8, num_rand_blocks=3, 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 self.attention_type = attention_type self.use_bias = use_bias self.rescale_embeddings = rescale_embeddings self.block_size = block_size self.num_rand_blocks = num_rand_blocks 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 BigBirdConfig( 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, is_encoder_decoder=False, initializer_range=self.initializer_range, attention_type=self.attention_type, use_bias=self.use_bias, rescale_embeddings=self.rescale_embeddings, block_size=self.block_size, num_random_blocks=self.num_rand_blocks, ) 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 = BigBirdModel(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)) def create_and_check_for_pretraining( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = BigBirdForPreTraining(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, next_sentence_label=sequence_labels, ) self.parent.assertEqual(result.prediction_logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) self.parent.assertEqual(result.seq_relationship_logits.shape, (self.batch_size, config.num_labels)) 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 = BigBirdModel(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)) def create_and_check_for_masked_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = BigBirdForMaskedLM(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 = BigBirdForCausalLM(config=config) model.to(torch_device) model.eval() # 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) 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, use_cache=False, 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, use_cache=True, 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, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = BigBirdForQuestionAnswering(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 create_and_check_for_sequence_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = BigBirdForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_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, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = BigBirdForTokenClassification(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 = BigBirdForMultipleChoice(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 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 def create_and_check_for_auto_padding( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ): model = BigBirdModel(config) model.to(torch_device) model.eval() result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_for_change_to_full_attn( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ): model = BigBirdModel(config) model.to(torch_device) model.eval() result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) # the config should not be changed self.parent.assertTrue(model.config.attention_type == "block_sparse") @require_torch class BigBirdModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( BigBirdModel, BigBirdForPreTraining, BigBirdForMaskedLM, BigBirdForCausalLM, BigBirdForMultipleChoice, BigBirdForQuestionAnswering, BigBirdForSequenceClassification, BigBirdForTokenClassification, ) 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": BigBirdModel, "fill-mask": BigBirdForMaskedLM, "question-answering": BigBirdForQuestionAnswering, "text-classification": BigBirdForSequenceClassification, "text-generation": BigBirdForCausalLM, "token-classification": BigBirdForTokenClassification, "zero-shot": BigBirdForSequenceClassification, } if is_torch_available() else {} ) # 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"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device ) inputs_dict["next_sentence_label"] = torch.zeros( self.model_tester.batch_size, dtype=torch.long, device=torch_device ) return inputs_dict def setUp(self): self.model_tester = BigBirdModelTester(self) self.config_tester = ConfigTester(self, config_class=BigBirdConfig, 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_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_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_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_retain_grad_hidden_states_attentions(self): # bigbird cannot keep gradients in attentions when `attention_type=block_sparse` if self.model_tester.attention_type == "original_full": super().test_retain_grad_hidden_states_attentions() @slow def test_model_from_pretrained(self): model_name = "google/bigbird-roberta-base" model = BigBirdForPreTraining.from_pretrained(model_name) self.assertIsNotNone(model) def test_model_various_attn_type(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() for type in ["original_full", "block_sparse"]: config_and_inputs[0].attention_type = type self.model_tester.create_and_check_model(*config_and_inputs) def test_fast_integration(self): # fmt: off input_ids = torch.tensor( [[6, 117, 33, 36, 70, 22, 63, 31, 71, 72, 88, 58, 109, 49, 48, 116, 92, 6, 19, 95, 118, 100, 80, 111, 93, 2, 31, 84, 26, 5, 6, 82, 46, 96, 109, 4, 39, 19, 109, 13, 92, 31, 36, 90, 111, 18, 75, 6, 56, 74, 16, 42, 56, 92, 69, 108, 127, 81, 82, 41, 106, 19, 44, 24, 82, 121, 120, 65, 36, 26, 72, 13, 36, 98, 43, 64, 8, 53, 100, 92, 51, 122, 66, 17, 61, 50, 104, 127, 26, 35, 94, 23, 110, 71, 80, 67, 109, 111, 44, 19, 51, 41, 86, 71, 76, 44, 18, 68, 44, 77, 107, 81, 98, 126, 100, 2, 49, 98, 84, 39, 23, 98, 52, 46, 10, 82, 121, 73],[6, 117, 33, 36, 70, 22, 63, 31, 71, 72, 88, 58, 109, 49, 48, 116, 92, 6, 19, 95, 118, 100, 80, 111, 93, 2, 31, 84, 26, 5, 6, 82, 46, 96, 109, 4, 39, 19, 109, 13, 92, 31, 36, 90, 111, 18, 75, 6, 56, 74, 16, 42, 56, 92, 69, 108, 127, 81, 82, 41, 106, 19, 44, 24, 82, 121, 120, 65, 36, 26, 72, 13, 36, 98, 43, 64, 8, 53, 100, 92, 51, 12, 66, 17, 61, 50, 104, 127, 26, 35, 94, 23, 110, 71, 80, 67, 109, 111, 44, 19, 51, 41, 86, 71, 76, 28, 18, 68, 44, 77, 107, 81, 98, 126, 100, 2, 49, 18, 84, 39, 23, 98, 52, 46, 10, 82, 121, 73]], # noqa: E231 dtype=torch.long, device=torch_device, ) # fmt: on input_ids = input_ids % self.model_tester.vocab_size input_ids[1] = input_ids[1] - 1 attention_mask = torch.ones((input_ids.shape), device=torch_device) attention_mask[:, :-10] = 0 config, _, _, _, _, _, _ = self.model_tester.prepare_config_and_inputs() torch.manual_seed(0) model = BigBirdModel(config).eval().to(torch_device) with torch.no_grad(): hidden_states = model(input_ids, attention_mask=attention_mask).last_hidden_state self.assertTrue( torch.allclose( hidden_states[0, 0, :5], torch.tensor([1.4825, 0.0774, 0.8226, -0.2962, -0.9593], device=torch_device), atol=1e-3, ) ) def test_auto_padding(self): self.model_tester.seq_length = 241 config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_auto_padding(*config_and_inputs) def test_for_change_to_full_attn(self): self.model_tester.seq_length = 9 config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_change_to_full_attn(*config_and_inputs) @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass @require_torch @slow class BigBirdModelIntegrationTest(unittest.TestCase): # we can have this true once block_sparse attn_probs works accurately test_attention_probs = False def _get_dummy_input_ids(self): # fmt: off ids = torch.tensor( [[6, 117, 33, 36, 70, 22, 63, 31, 71, 72, 88, 58, 109, 49, 48, 116, 92, 6, 19, 95, 118, 100, 80, 111, 93, 2, 31, 84, 26, 5, 6, 82, 46, 96, 109, 4, 39, 19, 109, 13, 92, 31, 36, 90, 111, 18, 75, 6, 56, 74, 16, 42, 56, 92, 69, 108, 127, 81, 82, 41, 106, 19, 44, 24, 82, 121, 120, 65, 36, 26, 72, 13, 36, 98, 43, 64, 8, 53, 100, 92, 51, 122, 66, 17, 61, 50, 104, 127, 26, 35, 94, 23, 110, 71, 80, 67, 109, 111, 44, 19, 51, 41, 86, 71, 76, 44, 18, 68, 44, 77, 107, 81, 98, 126, 100, 2, 49, 98, 84, 39, 23, 98, 52, 46, 10, 82, 121, 73]], # noqa: E231 dtype=torch.long, device=torch_device, ) # fmt: on return ids def test_inference_block_sparse_pretraining(self): model = BigBirdForPreTraining.from_pretrained("google/bigbird-roberta-base", attention_type="block_sparse") model.to(torch_device) input_ids = torch.tensor([[20920, 232, 328, 1437] * 1024], dtype=torch.long, device=torch_device) with torch.no_grad(): outputs = model(input_ids) prediction_logits = outputs.prediction_logits seq_relationship_logits = outputs.seq_relationship_logits self.assertEqual(prediction_logits.shape, torch.Size((1, 4096, 50358))) self.assertEqual(seq_relationship_logits.shape, torch.Size((1, 2))) expected_prediction_logits_slice = torch.tensor( [ [-0.5583, 0.0475, -0.2508, 7.4423], [0.7409, 1.4460, -0.7593, 7.7010], [1.9150, 3.1395, 5.8840, 9.3498], [-0.1854, -1.4640, -2.2052, 3.7968], ], device=torch_device, ) torch.testing.assert_close( prediction_logits[0, 128:132, 128:132], expected_prediction_logits_slice, rtol=1e-4, atol=1e-4 ) expected_seq_relationship_logits = torch.tensor([[46.9465, 47.9517]], device=torch_device) torch.testing.assert_close(seq_relationship_logits, expected_seq_relationship_logits, rtol=1e-4, atol=1e-4) def test_inference_full_pretraining(self): model = BigBirdForPreTraining.from_pretrained("google/bigbird-roberta-base", attention_type="original_full") model.to(torch_device) input_ids = torch.tensor([[20920, 232, 328, 1437] * 512], dtype=torch.long, device=torch_device) with torch.no_grad(): outputs = model(input_ids) prediction_logits = outputs.prediction_logits seq_relationship_logits = outputs.seq_relationship_logits self.assertEqual(prediction_logits.shape, torch.Size((1, 512 * 4, 50358))) self.assertEqual(seq_relationship_logits.shape, torch.Size((1, 2))) expected_prediction_logits_slice = torch.tensor( [ [0.1499, -1.1217, 0.1990, 8.4499], [-2.7757, -3.0687, -4.8577, 7.5156], [1.5446, 0.1982, 4.3016, 10.4281], [-1.3705, -4.0130, -3.9629, 5.1526], ], device=torch_device, ) torch.testing.assert_close( prediction_logits[0, 128:132, 128:132], expected_prediction_logits_slice, rtol=1e-4, atol=1e-4 ) expected_seq_relationship_logits = torch.tensor([[41.4503, 41.2406]], device=torch_device) torch.testing.assert_close(seq_relationship_logits, expected_seq_relationship_logits, rtol=1e-4, atol=1e-4) def test_block_sparse_attention_probs(self): """ Asserting if outputted attention matrix is similar to hard coded attention matrix """ if not self.test_attention_probs: self.skipTest("test_attention_probs is set to False") model = BigBirdModel.from_pretrained( "google/bigbird-roberta-base", attention_type="block_sparse", num_random_blocks=3, block_size=16 ) model.to(torch_device) model.eval() config = model.config input_ids = self._get_dummy_input_ids() hidden_states = model.embeddings(input_ids) batch_size, seqlen, _ = hidden_states.size() attn_mask = torch.ones(batch_size, seqlen, device=torch_device, dtype=torch.float) to_seq_length = from_seq_length = seqlen from_block_size = to_block_size = config.block_size blocked_mask, band_mask, from_mask, to_mask = model.create_masks_for_block_sparse_attn( attn_mask, config.block_size ) from_blocked_mask = to_blocked_mask = blocked_mask for i in range(config.num_hidden_layers): pointer = model.encoder.layer[i].attention.self query_layer = pointer.transpose_for_scores(pointer.query(hidden_states)) key_layer = pointer.transpose_for_scores(pointer.key(hidden_states)) value_layer = pointer.transpose_for_scores(pointer.value(hidden_states)) context_layer, attention_probs = pointer.bigbird_block_sparse_attention( query_layer, key_layer, value_layer, band_mask, from_mask, to_mask, from_blocked_mask, to_blocked_mask, pointer.num_attention_heads, pointer.num_random_blocks, pointer.attention_head_size, from_block_size, to_block_size, batch_size, from_seq_length, to_seq_length, seed=pointer.seed, plan_from_length=None, plan_num_rand_blocks=None, output_attentions=True, ) context_layer = context_layer.contiguous().view(batch_size, from_seq_length, -1) cl = torch.einsum("bhqk,bhkd->bhqd", attention_probs, value_layer) cl = cl.view(context_layer.size()) torch.testing.assert_close(context_layer, cl, rtol=0.001, atol=0.001) def test_block_sparse_context_layer(self): model = BigBirdModel.from_pretrained( "google/bigbird-roberta-base", attention_type="block_sparse", num_random_blocks=3, block_size=16 ) model.to(torch_device) model.eval() config = model.config input_ids = self._get_dummy_input_ids() dummy_hidden_states = model.embeddings(input_ids) attn_mask = torch.ones_like(input_ids, device=torch_device) blocked_mask, band_mask, from_mask, to_mask = model.create_masks_for_block_sparse_attn( attn_mask, config.block_size ) targeted_cl = torch.tensor( [ [0.1870, 1.5248, 0.2333, -0.0483, -0.0952, 1.8359, -0.0142, 0.1239, 0.0083, -0.0045], [-0.0601, 0.1243, 0.1329, -0.1524, 0.2347, 0.0894, -0.2248, -0.2461, -0.0645, -0.0109], [-0.0418, 0.1463, 0.1290, -0.1638, 0.2489, 0.0799, -0.2341, -0.2406, -0.0524, 0.0106], [0.1859, 1.5182, 0.2324, -0.0473, -0.0952, 1.8295, -0.0148, 0.1242, 0.0080, -0.0045], [0.1879, 1.5300, 0.2334, -0.0480, -0.0967, 1.8428, -0.0137, 0.1256, 0.0087, -0.0050], [0.1852, 1.5149, 0.2330, -0.0492, -0.0936, 1.8236, -0.0154, 0.1210, 0.0080, -0.0048], [0.1857, 1.5186, 0.2331, -0.0484, -0.0940, 1.8285, -0.0148, 0.1224, 0.0077, -0.0045], [0.1884, 1.5336, 0.2334, -0.0469, -0.0974, 1.8477, -0.0132, 0.1266, 0.0085, -0.0046], [0.1881, 1.5308, 0.2334, -0.0479, -0.0969, 1.8438, -0.0136, 0.1258, 0.0088, -0.0050], [0.1849, 1.5143, 0.2329, -0.0491, -0.0930, 1.8230, -0.0156, 0.1209, 0.0074, -0.0047], [0.1878, 1.5299, 0.2333, -0.0472, -0.0967, 1.8434, -0.0137, 0.1257, 0.0084, -0.0048], [0.1873, 1.5260, 0.2333, -0.0478, -0.0961, 1.8383, -0.0142, 0.1245, 0.0083, -0.0048], [0.1849, 1.5145, 0.2327, -0.0491, -0.0935, 1.8237, -0.0156, 0.1215, 0.0083, -0.0046], [0.1866, 1.5232, 0.2332, -0.0488, -0.0950, 1.8342, -0.0143, 0.1237, 0.0084, -0.0047], ], device=torch_device, ) context_layer = model.encoder.layer[0].attention.self( dummy_hidden_states, band_mask=band_mask, from_mask=from_mask, to_mask=to_mask, from_blocked_mask=blocked_mask, to_blocked_mask=blocked_mask, ) context_layer = context_layer[0] self.assertEqual(context_layer.shape, torch.Size((1, 128, 768))) torch.testing.assert_close(context_layer[0, 64:78, 300:310], targeted_cl, rtol=0.0001, atol=0.0001) def test_tokenizer_inference(self): tokenizer = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base") model = BigBirdModel.from_pretrained( "google/bigbird-roberta-base", attention_type="block_sparse", num_random_blocks=3, block_size=16 ) model.to(torch_device) text = [ "Transformer-based models are unable to process long sequences due to their self-attention operation," " which scales quadratically with the sequence length. To address this limitation, we introduce the" " Longformer with an attention mechanism that scales linearly with sequence length, making it easy to" " process documents of thousands of tokens or longer. Longformer’s attention mechanism is a drop-in" " replacement for the standard self-attention and combines a local windowed attention with a task" " motivated global attention. Following prior work on long-sequence transformers, we evaluate Longformer" " on character-level language modeling and achieve state-of-the-art results on text8 and enwik8. In" " contrast to most prior work, we also pretrain Longformer and finetune it on a variety of downstream" " tasks. Our pretrained Longformer consistently outperforms RoBERTa on long document tasks and sets new" " state-of-the-art results on WikiHop and TriviaQA." ] inputs = tokenizer(text) for k in inputs: inputs[k] = torch.tensor(inputs[k], device=torch_device, dtype=torch.long) prediction = model(**inputs) prediction = prediction[0] self.assertEqual(prediction.shape, torch.Size((1, 199, 768))) expected_prediction = torch.tensor( [ [0.1887, -0.0474, 0.2604, 0.1453], [0.0651, 0.1999, 0.1797, 0.1161], [0.2833, -0.3036, 0.6910, 0.1123], [0.2836, -0.4644, -0.0111, 0.1530], [0.3919, -0.2823, 0.4192, 0.1687], [0.2168, -0.1956, 0.4050, 0.0925], [0.2597, -0.0884, 0.1258, 0.1119], [0.1127, -0.1203, 0.1924, 0.2859], [0.1362, -0.1315, 0.2693, 0.1027], [-0.3169, -0.2266, 0.4419, 0.6740], [0.2366, -0.1452, 0.2589, 0.0579], [0.0358, -0.2021, 0.3112, -0.1392], ], device=torch_device, ) torch.testing.assert_close(prediction[0, 52:64, 320:324], expected_prediction, rtol=1e-4, atol=1e-4) def test_inference_question_answering(self): tokenizer = BigBirdTokenizer.from_pretrained("google/bigbird-base-trivia-itc") model = BigBirdForQuestionAnswering.from_pretrained( "google/bigbird-base-trivia-itc", attention_type="block_sparse", block_size=16, num_random_blocks=3 ) model.to(torch_device) context = ( "The BigBird model was proposed in Big Bird: Transformers for Longer Sequences by Zaheer, Manzil and" " Guruganesh, Guru and Dubey, Kumar Avinava and Ainslie, Joshua and Alberti, Chris and Ontanon, Santiago" " and Pham, Philip and Ravula, Anirudh and Wang, Qifan and Yang, Li and others. BigBird, is a" " sparse-attention based transformer which extends Transformer based models, such as BERT to much longer" " sequences. In addition to sparse attention, BigBird also applies global attention as well as random" " attention to the input sequence. Theoretically, it has been shown that applying sparse, global, and" " random attention approximates full attention, while being computationally much more efficient for longer" " sequences. As a consequence of the capability to handle longer context, BigBird has shown improved" " performance on various long document NLP tasks, such as question answering and summarization, compared" " to BERT or RoBERTa." ) question = [ "Which is better for longer sequences- BigBird or BERT?", "What is the benefit of using BigBird over BERT?", ] inputs = tokenizer( question, [context, context], padding=True, return_tensors="pt", add_special_tokens=True, max_length=256, truncation=True, ) inputs = {k: v.to(torch_device) for k, v in inputs.items()} start_logits, end_logits = model(**inputs).to_tuple() # fmt: off target_start_logits = torch.tensor( [[-8.5622, -9.6209, -14.3351, -8.7032, -11.8596, -7.7446, -9.6730, -13.6063, -8.9651, -11.7417, -8.2641, -8.7056, -13.4116, -5.6600, -8.8316, -10.4148, -12.2180, -7.7979, -12.5274, -6.0685, -10.3373, -11.3128, -6.6456, -14.4030, -6.8292, -14.5383, -11.5638, -6.3326, 11.5293, -1.8434, -10.0013, -7.6150], [-10.7384, -13.1179, -10.1837, -13.7700, -10.0186, -11.7335, -13.3411, -10.0188, -13.4235, -9.9381, -10.4252, -13.1281, -8.2022, -10.4326, -11.5542, -14.1549, -10.7546, -13.4691, -8.2744, -11.4324, -13.3773, -9.8284, -14.5825, -8.7471, -14.7050, -8.0364, -11.3627, -6.4638, -11.7031, -14.3446, -9.9425, -8.0088]], # noqa: E231 device=torch_device, ) target_end_logits = torch.tensor( [[-12.1736, -8.8487, -14.8877, -11.6713, -15.1165, -12.2396, -7.6828, -15.4153, -12.2528, -14.3671, -12.3596, -7.4272, -14.9615, -13.6356, -11.7939, -9.9767, -14.8112, -8.9567, -15.8798, -11.5291, -9.4249, -14.7544, -7.9387, -16.2789, -8.9702, -15.3111, -11.5585, -7.9992, -4.1127, 10.3209, -8.3926, -10.2005], [-11.1375, -15.4027, -12.6861, -16.9884, -13.7093, -10.3560, -15.7228, -12.9290, -15.8519, -13.7953, -10.2460, -15.7198, -14.2078, -12.8477, -11.4861, -16.1017, -11.8900, -16.4488, -13.2959, -10.3980, -15.4874, -10.3539, -16.8263, -10.9973, -17.0344, -9.2751, -10.1196, -13.8907, -12.1025, -13.0628, -12.8530, -13.8173]], device=torch_device, ) # fmt: on torch.testing.assert_close(start_logits[:, 64:96], target_start_logits, rtol=1e-4, atol=1e-4) torch.testing.assert_close(end_logits[:, 64:96], target_end_logits, rtol=1e-4, atol=1e-4) input_ids = inputs["input_ids"].tolist() answer = [ input_ids[i][torch.argmax(start_logits, dim=-1)[i] : torch.argmax(end_logits, dim=-1)[i] + 1] for i in range(len(input_ids)) ] answer = tokenizer.batch_decode(answer) self.assertTrue(answer == ["BigBird", "global attention"]) def test_fill_mask(self): tokenizer = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base") model = BigBirdForMaskedLM.from_pretrained("google/bigbird-roberta-base") model.to(torch_device) input_ids = tokenizer("The goal of life is [MASK] .", return_tensors="pt").input_ids.to(torch_device) logits = model(input_ids).logits # [MASK] is token at 6th position pred_token = tokenizer.decode(torch.argmax(logits[0, 6:7], axis=-1)) self.assertEqual(pred_token, "happiness") def test_auto_padding(self): model = BigBirdModel.from_pretrained( "google/bigbird-roberta-base", attention_type="block_sparse", num_random_blocks=3, block_size=16 ) model.to(torch_device) model.eval() input_ids = torch.tensor([200 * [10] + 40 * [2] + [1]], device=torch_device, dtype=torch.long) with torch.no_grad(): output = model(input_ids).to_tuple()[0] # fmt: off target = torch.tensor( [[-0.129420, -0.164740, 0.042422, -0.336030, 0.094379, 0.033794, 0.384590, 0.229660, -0.196500, 0.108020], [-0.000154, -0.168800, 0.165820, -0.313670, 0.101240, 0.035145, 0.381880, 0.213730, -0.201080, 0.077443], [0.053754, -0.166350, 0.225520, -0.272900, 0.119670, 0.019987, 0.348670, 0.199190, -0.181600, 0.084640], [0.063636, -0.187110, 0.237010, -0.297380, 0.126300, 0.020025, 0.268490, 0.191820, -0.192300, 0.035077], [0.073893, -0.184790, 0.188870, -0.297860, 0.134280, 0.028972, 0.174650, 0.186890, -0.180530, 0.006851], [0.005253, -0.169360, 0.123100, -0.302550, 0.126930, 0.024188, 0.133410, 0.200600, -0.168210, -0.001006], [-0.093336, -0.175370, -0.004768, -0.333170, 0.114330, 0.034168, 0.120960, 0.203570, -0.162810, -0.005757], [-0.160210, -0.169310, -0.049064, -0.331950, 0.115730, 0.027062, 0.143600, 0.205310, -0.144580, 0.026746], [-0.193200, -0.156820, -0.079422, -0.351600, 0.106450, 0.032174, 0.245690, 0.210250, -0.173480, 0.043914], [-0.167980, -0.153050, -0.059764, -0.357890,0.103910, 0.031481, 0.334190, 0.208960,-0.178180, 0.072165], [-0.136990, -0.156950, -0.012099, -0.353140,0.096996, 0.025864, 0.376340, 0.216050, -0.171820, 0.089963], [-0.041143, -0.167060, 0.079754, -0.353220, 0.093247, 0.019867, 0.385810, 0.214340, -0.191800, 0.065946],[0.040373, -0.158610, 0.152570, -0.312930, 0.110590, 0.012282, 0.345270, 0.204040, -0.176500, 0.064972], [0.043762, -0.166450, 0.179500, -0.317930, 0.117280, -0.004040, 0.304490, 0.201380, -0.182780, 0.044000]], # noqa: E231 device=torch_device, ) # fmt: on self.assertEqual(output.shape, torch.Size((1, 241, 768))) torch.testing.assert_close(output[0, 64:78, 300:310], target, rtol=0.0001, atol=0.0001)