# 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 from transformers import LongformerConfig, is_torch_available from transformers.testing_utils import ( is_flaky, require_sentencepiece, require_tokenizers, require_torch, slow, torch_device, ) 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 ( LongformerForMaskedLM, LongformerForMultipleChoice, LongformerForQuestionAnswering, LongformerForSequenceClassification, LongformerForTokenClassification, LongformerModel, ) from transformers.models.longformer.modeling_longformer import LongformerSelfAttention class LongformerModelTester: 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, attention_window=4, ): 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_window = attention_window # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size # [num_attention_heads, encoder_seq_length, encoder_key_length], but LongformerSelfAttention # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1] # because its local attention only attends to `self.attention_window + 1` locations # (assuming no token with global attention, otherwise the last dimension of attentions # is x + self.attention_window + 1, where x is the number of tokens with global attention) self.key_length = self.attention_window + 2 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 LongformerConfig( 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, attention_window=self.attention_window, ) def get_pipeline_config(self): config = self.get_config() config.vocab_size = 300 return config def create_and_check_attention_mask_determinism( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = LongformerModel(config=config) model.to(torch_device) model.eval() attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device) output_with_mask = model(input_ids, attention_mask=attention_mask)["last_hidden_state"] output_without_mask = model(input_ids)["last_hidden_state"] self.parent.assertTrue(torch.allclose(output_with_mask[0, 0, :5], output_without_mask[0, 0, :5], atol=1e-4)) def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = LongformerModel(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_with_global_attention_mask( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = LongformerModel(config=config) model.to(torch_device) model.eval() global_attention_mask = input_mask.clone() global_attention_mask[:, input_mask.shape[-1] // 2] = 0 global_attention_mask = global_attention_mask.to(torch_device) result = model( input_ids, attention_mask=input_mask, global_attention_mask=global_attention_mask, token_type_ids=token_type_ids, ) result = model(input_ids, token_type_ids=token_type_ids, global_attention_mask=global_attention_mask) result = model(input_ids, global_attention_mask=global_attention_mask) 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_masked_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = LongformerForMaskedLM(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_question_answering( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = LongformerForQuestionAnswering(config=config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, global_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 = LongformerForSequenceClassification(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 = LongformerForTokenClassification(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 = LongformerForMultipleChoice(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() 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, global_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 global_attention_mask = torch.zeros_like(input_ids) global_attention_mask[:, -1] = 1 inputs_dict = { "input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask, "global_attention_mask": global_attention_mask, } return config, inputs_dict def prepare_config_and_inputs_for_question_answering(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 # Replace sep_token_id by some random id input_ids[input_ids == config.sep_token_id] = torch.randint(0, config.vocab_size, (1,)).item() # Make sure there are exactly three sep_token_id input_ids[:, -3:] = config.sep_token_id input_mask = torch.ones_like(input_ids) return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels @require_torch class LongformerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( LongformerModel, LongformerForMaskedLM, LongformerForSequenceClassification, LongformerForQuestionAnswering, LongformerForTokenClassification, LongformerForMultipleChoice, ) if is_torch_available() else () ) pipeline_model_mapping = ( { "feature-extraction": LongformerModel, "fill-mask": LongformerForMaskedLM, "question-answering": LongformerForQuestionAnswering, "text-classification": LongformerForSequenceClassification, "token-classification": LongformerForTokenClassification, "zero-shot": LongformerForSequenceClassification, } if is_torch_available() else {} ) # Need to use `0.6` instead of `0.5` for `test_disk_offload` model_split_percents = [0.6, 0.7, 0.9] # 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 == "QAPipelineTests" and tokenizer_name is not None and not tokenizer_name.endswith("Fast") ): # `QAPipelineTests` fails for a few models when the slower tokenizer are used. # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework) # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer return True return False def setUp(self): self.model_tester = LongformerModelTester(self) self.config_tester = ConfigTester(self, config_class=LongformerConfig, hidden_size=37) # Without this, 0.01% failure rate. @is_flaky( max_attempts=2, description="When `inputs_dict['attention_mask'][:, -1]` is all `0`s, we get shorter length along the last dimension of the output's `attentions`.", ) def test_attention_outputs(self): super().test_attention_outputs() 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_attention_mask_determinism(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_attention_mask_determinism(*config_and_inputs) def test_model_global_attention_mask(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_with_global_attention_mask(*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_question_answering(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_question_answering() 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_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) @unittest.skip(reason="Longformer cannot keep gradients in attention or hidden states") def test_retain_grad_hidden_states_attentions(self): return @unittest.skip(reason="LongFormer calculates global attn only when attn_mask has non-zero elements") def test_batching_equivalence(self): return @require_torch @require_sentencepiece @require_tokenizers class LongformerModelIntegrationTest(unittest.TestCase): def _get_hidden_states(self): return torch.tensor( [ [ [ 4.98332758e-01, 2.69175139e00, -7.08081422e-03, 1.04915401e00, -1.83476661e00, 7.67220476e-01, 2.98580543e-01, 2.84803992e-02, ], [ -7.58357372e-01, 4.20635998e-01, -4.04739919e-02, 1.59924145e-01, 2.05135748e00, -1.15997978e00, 5.37166397e-01, 2.62873606e-01, ], [ -1.69438001e00, 4.17574660e-01, -1.49196962e00, -1.76483717e00, -1.94566312e-01, -1.71183858e00, 7.72903565e-01, -1.11557056e00, ], [ 5.44028163e-01, 2.05466114e-01, -3.63045868e-01, 2.41865062e-01, 3.20348382e-01, -9.05611176e-01, -1.92690727e-01, -1.19917547e00, ], ] ], dtype=torch.float32, device=torch_device, ) def test_diagonalize(self): hidden_states = self._get_hidden_states() hidden_states = hidden_states.reshape((1, 8, 4)) # set seq length = 8, hidden dim = 4 chunked_hidden_states = LongformerSelfAttention._chunk(hidden_states, window_overlap=2) window_overlap_size = chunked_hidden_states.shape[2] self.assertTrue(window_overlap_size == 4) padded_hidden_states = LongformerSelfAttention._pad_and_diagonalize(chunked_hidden_states) self.assertTrue(padded_hidden_states.shape[-1] == chunked_hidden_states.shape[-1] + window_overlap_size - 1) # first row => [0.4983, 2.6918, -0.0071, 1.0492, 0.0000, 0.0000, 0.0000] torch.testing.assert_close( padded_hidden_states[0, 0, 0, :4], chunked_hidden_states[0, 0, 0], rtol=1e-3, atol=1e-3 ) self.assertTrue( torch.allclose( padded_hidden_states[0, 0, 0, 4:], torch.zeros((3,), device=torch_device, dtype=torch.float32), atol=1e-3, ) ) # last row => [0.0000, 0.0000, 0.0000, 2.0514, -1.1600, 0.5372, 0.2629] torch.testing.assert_close( padded_hidden_states[0, 0, -1, 3:], chunked_hidden_states[0, 0, -1], rtol=1e-3, atol=1e-3 ) self.assertTrue( torch.allclose( padded_hidden_states[0, 0, -1, :3], torch.zeros((3,), device=torch_device, dtype=torch.float32), atol=1e-3, ) ) def test_pad_and_transpose_last_two_dims(self): hidden_states = self._get_hidden_states() self.assertEqual(hidden_states.shape, (1, 4, 8)) padding = (0, 0, 0, 1) padded_hidden_states = LongformerSelfAttention._pad_and_transpose_last_two_dims(hidden_states, padding) self.assertEqual(padded_hidden_states.shape, (1, 8, 5)) expected_added_dim = torch.zeros((5,), device=torch_device, dtype=torch.float32) torch.testing.assert_close(expected_added_dim, padded_hidden_states[0, -1, :], rtol=1e-6, atol=1e-6) torch.testing.assert_close( hidden_states[0, -1, :], padded_hidden_states.view(1, -1)[0, 24:32], rtol=1e-6, atol=1e-6 ) def test_chunk(self): hidden_states = self._get_hidden_states() batch_size = 1 seq_length = 8 hidden_size = 4 hidden_states = hidden_states.reshape((batch_size, seq_length, hidden_size)) chunked_hidden_states = LongformerSelfAttention._chunk(hidden_states, window_overlap=2) # expected slices across chunk and seq length dim expected_slice_along_seq_length = torch.tensor( [0.4983, -0.7584, -1.6944], device=torch_device, dtype=torch.float32 ) expected_slice_along_chunk = torch.tensor( [0.4983, -1.8348, -0.7584, 2.0514], device=torch_device, dtype=torch.float32 ) torch.testing.assert_close( chunked_hidden_states[0, :, 0, 0], expected_slice_along_seq_length, rtol=1e-3, atol=1e-3 ) torch.testing.assert_close(chunked_hidden_states[0, 0, :, 0], expected_slice_along_chunk, rtol=1e-3, atol=1e-3) self.assertEqual(chunked_hidden_states.shape, (1, 3, 4, 4)) def test_mask_invalid_locations(self): hidden_states = self._get_hidden_states() batch_size = 1 seq_length = 8 hidden_size = 4 hidden_states = hidden_states.reshape((batch_size, seq_length, hidden_size)) chunked_hidden_states = LongformerSelfAttention._chunk(hidden_states, window_overlap=2) hid_states_1 = chunked_hidden_states.clone() LongformerSelfAttention._mask_invalid_locations(hid_states_1, 1) self.assertTrue(torch.isinf(hid_states_1).sum().item() == 8) hid_states_2 = chunked_hidden_states.clone() LongformerSelfAttention._mask_invalid_locations(hid_states_2, 2) self.assertTrue(torch.isinf(hid_states_2).sum().item() == 24) hid_states_3 = chunked_hidden_states.clone()[:, :, :, :3] LongformerSelfAttention._mask_invalid_locations(hid_states_3, 2) self.assertTrue(torch.isinf(hid_states_3).sum().item() == 24) hid_states_4 = chunked_hidden_states.clone()[:, :, 2:, :] LongformerSelfAttention._mask_invalid_locations(hid_states_4, 2) self.assertTrue(torch.isinf(hid_states_4).sum().item() == 12) def test_layer_local_attn(self): model = LongformerModel.from_pretrained("patrickvonplaten/longformer-random-tiny") model.eval() layer = model.encoder.layer[0].attention.self.to(torch_device) hidden_states = self._get_hidden_states() batch_size, seq_length, hidden_size = hidden_states.size() attention_mask = torch.zeros((batch_size, seq_length), dtype=torch.float32, device=torch_device) attention_mask[:, -2:] = -10000 is_index_masked = attention_mask < 0 is_index_global_attn = attention_mask > 0 is_global_attn = is_index_global_attn.flatten().any().item() output_hidden_states = layer( hidden_states, attention_mask=attention_mask, is_index_masked=is_index_masked, is_index_global_attn=is_index_global_attn, is_global_attn=is_global_attn, )[0] self.assertEqual(output_hidden_states.shape, (1, 4, 8)) self.assertTrue( torch.allclose( output_hidden_states[0, 1], torch.tensor( [0.0019, 0.0122, -0.0171, -0.0256, -0.0300, 0.0173, -0.0115, 0.0048], dtype=torch.float32, device=torch_device, ), atol=1e-3, ) ) def test_layer_global_attn(self): model = LongformerModel.from_pretrained("patrickvonplaten/longformer-random-tiny") model.eval() layer = model.encoder.layer[0].attention.self.to(torch_device) hidden_states = torch.cat([self._get_hidden_states(), self._get_hidden_states() - 0.5], dim=0) batch_size, seq_length, hidden_size = hidden_states.size() attention_mask = torch.zeros((batch_size, seq_length), dtype=torch.float32, device=torch_device) # create attn mask attention_mask[0, -2:] = 10000.0 attention_mask[0, -1:] = -10000.0 attention_mask[1, 1:] = 10000.0 is_index_masked = attention_mask < 0 is_index_global_attn = attention_mask > 0 is_global_attn = is_index_global_attn.flatten().any().item() output_hidden_states = layer( hidden_states, attention_mask=attention_mask, is_index_masked=is_index_masked, is_index_global_attn=is_index_global_attn, is_global_attn=is_global_attn, )[0] self.assertEqual(output_hidden_states.shape, (2, 4, 8)) self.assertTrue( torch.allclose( output_hidden_states[0, 2], torch.tensor( [-0.0651, -0.0393, 0.0309, -0.0342, -0.0066, -0.0155, -0.0209, -0.0494], dtype=torch.float32, device=torch_device, ), atol=1e-3, ) ) self.assertTrue( torch.allclose( output_hidden_states[1, -2], torch.tensor( [-0.0405, -0.0384, 0.0396, -0.0374, -0.0341, 0.0136, 0.0014, -0.0571], dtype=torch.float32, device=torch_device, ), atol=1e-3, ) ) def test_layer_attn_probs(self): model = LongformerModel.from_pretrained("patrickvonplaten/longformer-random-tiny") model.eval() layer = model.encoder.layer[0].attention.self.to(torch_device) hidden_states = torch.cat([self._get_hidden_states(), self._get_hidden_states() - 0.5], dim=0) batch_size, seq_length, hidden_size = hidden_states.size() attention_mask = torch.zeros((batch_size, seq_length), dtype=torch.float32, device=torch_device) # create attn mask attention_mask[0, -2:] = 10000.0 attention_mask[0, -1:] = -10000.0 attention_mask[1, 1:] = 10000.0 is_index_masked = attention_mask < 0 is_index_global_attn = attention_mask > 0 is_global_attn = is_index_global_attn.flatten().any().item() output_hidden_states, local_attentions, global_attentions = layer( hidden_states, attention_mask=attention_mask, is_index_masked=is_index_masked, is_index_global_attn=is_index_global_attn, is_global_attn=is_global_attn, output_attentions=True, ) self.assertEqual(local_attentions.shape, (2, 4, 2, 8)) self.assertEqual(global_attentions.shape, (2, 2, 3, 4)) # All tokens with global attention have weight 0 in local attentions. self.assertTrue(torch.all(local_attentions[0, 2:4, :, :] == 0)) self.assertTrue(torch.all(local_attentions[1, 1:4, :, :] == 0)) # The weight of all tokens with local attention must sum to 1. self.assertTrue(torch.all(torch.abs(global_attentions[0, :, :2, :].sum(dim=-1) - 1) < 1e-6)) self.assertTrue(torch.all(torch.abs(global_attentions[1, :, :1, :].sum(dim=-1) - 1) < 1e-6)) self.assertTrue( torch.allclose( local_attentions[0, 0, 0, :], torch.tensor( [0.3328, 0.0000, 0.0000, 0.0000, 0.0000, 0.3355, 0.3318, 0.0000], dtype=torch.float32, device=torch_device, ), atol=1e-3, ) ) self.assertTrue( torch.allclose( local_attentions[1, 0, 0, :], torch.tensor( [0.2492, 0.2502, 0.2502, 0.0000, 0.0000, 0.2505, 0.0000, 0.0000], dtype=torch.float32, device=torch_device, ), atol=1e-3, ) ) # All the global attention weights must sum to 1. self.assertTrue(torch.all(torch.abs(global_attentions.sum(dim=-1) - 1) < 1e-6)) self.assertTrue( torch.allclose( global_attentions[0, 0, 1, :], torch.tensor( [0.2500, 0.2500, 0.2500, 0.2500], dtype=torch.float32, device=torch_device, ), atol=1e-3, ) ) self.assertTrue( torch.allclose( global_attentions[1, 0, 0, :], torch.tensor( [0.2497, 0.2500, 0.2499, 0.2504], dtype=torch.float32, device=torch_device, ), atol=1e-3, ) ) @slow def test_inference_no_head(self): model = LongformerModel.from_pretrained("allenai/longformer-base-4096") model.to(torch_device) # 'Hello world!' input_ids = torch.tensor([[0, 20920, 232, 328, 1437, 2]], dtype=torch.long, device=torch_device) attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device) output = model(input_ids, attention_mask=attention_mask)[0] output_without_mask = model(input_ids)[0] expected_output_slice = torch.tensor([0.0549, 0.1087, -0.1119, -0.0368, 0.0250], device=torch_device) torch.testing.assert_close(output[0, 0, -5:], expected_output_slice, rtol=1e-4, atol=1e-4) torch.testing.assert_close(output_without_mask[0, 0, -5:], expected_output_slice, rtol=1e-4, atol=1e-4) @slow def test_inference_no_head_long(self): model = LongformerModel.from_pretrained("allenai/longformer-base-4096") model.to(torch_device) # 'Hello world! ' repeated 1000 times input_ids = torch.tensor( [[0] + [20920, 232, 328, 1437] * 1000 + [2]], dtype=torch.long, device=torch_device ) # long input attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=input_ids.device) global_attention_mask = torch.zeros(input_ids.shape, dtype=torch.long, device=input_ids.device) global_attention_mask[:, [1, 4, 21]] = 1 # Set global attention on a few random positions output = model(input_ids, attention_mask=attention_mask, global_attention_mask=global_attention_mask)[0] expected_output_sum = torch.tensor(74585.8594, device=torch_device) expected_output_mean = torch.tensor(0.0243, device=torch_device) torch.testing.assert_close(output.sum(), expected_output_sum, rtol=1e-4, atol=1e-4) torch.testing.assert_close(output.mean(), expected_output_mean, rtol=1e-4, atol=1e-4) @slow def test_inference_masked_lm_long(self): model = LongformerForMaskedLM.from_pretrained("allenai/longformer-base-4096") model.to(torch_device) # 'Hello world! ' repeated 1000 times input_ids = torch.tensor( [[0] + [20920, 232, 328, 1437] * 1000 + [2]], dtype=torch.long, device=torch_device ) # long input input_ids = input_ids.to(torch_device) loss, prediction_scores = model(input_ids, labels=input_ids).to_tuple() expected_loss = torch.tensor(0.0074, device=torch_device) expected_prediction_scores_sum = torch.tensor(-6.1048e08, device=torch_device) expected_prediction_scores_mean = torch.tensor(-3.0348, device=torch_device) torch.testing.assert_close(loss, expected_loss, rtol=1e-4, atol=1e-4) torch.testing.assert_close(prediction_scores.sum(), expected_prediction_scores_sum, rtol=1e-4, atol=1e-4) torch.testing.assert_close(prediction_scores.mean(), expected_prediction_scores_mean, rtol=1e-4, atol=1e-4)