# coding=utf-8 # 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. from __future__ import annotations import unittest from transformers import is_tf_available from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import tensorflow as tf from transformers import ( LongformerConfig, TFLongformerForMaskedLM, TFLongformerForMultipleChoice, TFLongformerForQuestionAnswering, TFLongformerForSequenceClassification, TFLongformerForTokenClassification, TFLongformerModel, TFLongformerSelfAttention, ) from transformers.tf_utils import shape_list class TFLongformerModelTester: def __init__( self, parent, ): self.parent = parent self.batch_size = 13 self.seq_length = 7 self.is_training = True self.use_input_mask = True self.use_token_type_ids = True self.use_labels = True self.vocab_size = 99 self.hidden_size = 32 self.num_hidden_layers = 2 self.num_attention_heads = 4 self.intermediate_size = 37 self.hidden_act = "gelu" self.hidden_dropout_prob = 0.1 self.attention_probs_dropout_prob = 0.1 self.max_position_embeddings = 512 self.type_vocab_size = 16 self.type_sequence_label_size = 2 self.initializer_range = 0.02 self.num_labels = 3 self.num_choices = 4 self.scope = None self.attention_window = 4 # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size # [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1] # because its local attention only attends to `self.attention_window` and one before and one after 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 = 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, ) return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def create_and_check_attention_mask_determinism( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TFLongformerModel(config=config) attention_mask = tf.ones(input_ids.shape, dtype=tf.int64) output_with_mask = model(input_ids, attention_mask=attention_mask)[0] output_without_mask = model(input_ids)[0] tf.debugging.assert_near(output_with_mask[0, 0, :5], output_without_mask[0, 0, :5], rtol=1e-4) def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.return_dict = True model = TFLongformerModel(config=config) 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.assertListEqual( shape_list(result.last_hidden_state), [self.batch_size, self.seq_length, self.hidden_size] ) self.parent.assertListEqual(shape_list(result.pooler_output), [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 ): config.return_dict = True model = TFLongformerModel(config=config) half_input_mask_length = shape_list(input_mask)[-1] // 2 global_attention_mask = tf.concat( [ tf.zeros_like(input_mask)[:, :half_input_mask_length], tf.ones_like(input_mask)[:, half_input_mask_length:], ], axis=-1, ) 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.assertListEqual( shape_list(result.last_hidden_state), [self.batch_size, self.seq_length, self.hidden_size] ) self.parent.assertListEqual(shape_list(result.pooler_output), [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 ): config.return_dict = True model = TFLongformerForMaskedLM(config=config) result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertListEqual(shape_list(result.logits), [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 ): config.return_dict = True model = TFLongformerForQuestionAnswering(config=config) result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, start_positions=sequence_labels, end_positions=sequence_labels, ) self.parent.assertListEqual(shape_list(result.start_logits), [self.batch_size, self.seq_length]) self.parent.assertListEqual(shape_list(result.end_logits), [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 = TFLongformerForSequenceClassification(config=config) output = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels ).logits self.parent.assertListEqual(shape_list(output), [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 = TFLongformerForTokenClassification(config=config) output = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels).logits self.parent.assertListEqual(shape_list(output), [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 = TFLongformerForMultipleChoice(config=config) multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1)) multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1)) multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1)) output = 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, ).logits self.parent.assertListEqual(list(output.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 has to be partly defined # to trace all weights global_attention_mask = tf.concat( [tf.zeros_like(input_ids)[:, :-1], tf.ones_like(input_ids)[:, -1:]], axis=-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 = tf.where(input_ids == config.sep_token_id, 0, input_ids) # Make sure there are exactly three sep_token_id input_ids = tf.concat([input_ids[:, :-3], tf.ones_like(input_ids)[:, -3:] * config.sep_token_id], axis=-1) input_mask = tf.ones_like(input_ids) return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels @require_tf class TFLongformerModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( TFLongformerModel, TFLongformerForMaskedLM, TFLongformerForQuestionAnswering, TFLongformerForSequenceClassification, TFLongformerForMultipleChoice, TFLongformerForTokenClassification, ) if is_tf_available() else () ) pipeline_model_mapping = ( { "feature-extraction": TFLongformerModel, "fill-mask": TFLongformerForMaskedLM, "question-answering": TFLongformerForQuestionAnswering, "text-classification": TFLongformerForSequenceClassification, "token-classification": TFLongformerForTokenClassification, "zero-shot": TFLongformerForSequenceClassification, } if is_tf_available() else {} ) test_head_masking = False test_onnx = False # TODO: Fix the failed tests def is_pipeline_test_to_skip( self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name ): if ( pipeline_test_casse_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 = TFLongformerModelTester(self) self.config_tester = ConfigTester(self, config_class=LongformerConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() 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(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*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("Longformer keeps using potentially symbolic tensors in conditionals and breaks tracing.") def test_saved_model_creation(self): pass @unittest.skip("Longformer keeps using potentially symbolic tensors in conditionals and breaks tracing.") def test_compile_tf_model(self): pass @require_tf @require_sentencepiece @require_tokenizers class TFLongformerModelIntegrationTest(unittest.TestCase): def _get_hidden_states(self): return tf.convert_to_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=tf.float32, ) def test_diagonalize(self): hidden_states = self._get_hidden_states() hidden_states = tf.reshape(hidden_states, (1, 8, 4)) # set seq length = 8, hidden dim = 4 chunked_hidden_states = TFLongformerSelfAttention._chunk(hidden_states, window_overlap=2) window_overlap_size = shape_list(chunked_hidden_states)[2] self.assertTrue(window_overlap_size == 4) padded_hidden_states = TFLongformerSelfAttention._pad_and_diagonalize(chunked_hidden_states) self.assertTrue( shape_list(padded_hidden_states)[-1] == shape_list(chunked_hidden_states)[-1] + window_overlap_size - 1 ) # first row => [0.4983, 2.6918, -0.0071, 1.0492, 0.0000, 0.0000, 0.0000] tf.debugging.assert_near(padded_hidden_states[0, 0, 0, :4], chunked_hidden_states[0, 0, 0], rtol=1e-3) tf.debugging.assert_near(padded_hidden_states[0, 0, 0, 4:], tf.zeros((3,), dtype=tf.float32), rtol=1e-3) # last row => [0.0000, 0.0000, 0.0000, 2.0514, -1.1600, 0.5372, 0.2629] tf.debugging.assert_near(padded_hidden_states[0, 0, -1, 3:], chunked_hidden_states[0, 0, -1], rtol=1e-3) tf.debugging.assert_near(padded_hidden_states[0, 0, -1, :3], tf.zeros((3,), dtype=tf.float32), rtol=1e-3) def test_pad_and_transpose_last_two_dims(self): hidden_states = self._get_hidden_states() self.assertEqual(shape_list(hidden_states), [1, 4, 8]) # pad along seq length dim paddings = tf.constant([[0, 0], [0, 0], [0, 1], [0, 0]], dtype=tf.int64) hidden_states = TFLongformerSelfAttention._chunk(hidden_states, window_overlap=2) padded_hidden_states = TFLongformerSelfAttention._pad_and_transpose_last_two_dims(hidden_states, paddings) self.assertTrue(shape_list(padded_hidden_states) == [1, 1, 8, 5]) expected_added_dim = tf.zeros((5,), dtype=tf.float32) tf.debugging.assert_near(expected_added_dim, padded_hidden_states[0, 0, -1, :], rtol=1e-6) tf.debugging.assert_near( hidden_states[0, 0, -1, :], tf.reshape(padded_hidden_states, (1, -1))[0, 24:32], rtol=1e-6 ) def test_mask_invalid_locations(self): hidden_states = self._get_hidden_states() batch_size = 1 seq_length = 8 hidden_size = 4 hidden_states = tf.reshape(hidden_states, (batch_size, seq_length, hidden_size)) hidden_states = TFLongformerSelfAttention._chunk(hidden_states, window_overlap=2) hid_states_1 = TFLongformerSelfAttention._mask_invalid_locations(hidden_states, 1) hid_states_2 = TFLongformerSelfAttention._mask_invalid_locations(hidden_states, 2) hid_states_3 = TFLongformerSelfAttention._mask_invalid_locations(hidden_states[:, :, :, :3], 2) hid_states_4 = TFLongformerSelfAttention._mask_invalid_locations(hidden_states[:, :, 2:, :], 2) self.assertTrue(tf.math.reduce_sum(tf.cast(tf.math.is_inf(hid_states_1), tf.int64)) == 8) self.assertTrue(tf.math.reduce_sum(tf.cast(tf.math.is_inf(hid_states_2), tf.int64)) == 24) self.assertTrue(tf.math.reduce_sum(tf.cast(tf.math.is_inf(hid_states_3), tf.int64)) == 24) self.assertTrue(tf.math.reduce_sum(tf.cast(tf.math.is_inf(hid_states_4), tf.int64)) == 12) def test_chunk(self): hidden_states = self._get_hidden_states() batch_size = 1 seq_length = 8 hidden_size = 4 hidden_states = tf.reshape(hidden_states, (batch_size, seq_length, hidden_size)) chunked_hidden_states = TFLongformerSelfAttention._chunk(hidden_states, window_overlap=2) # expected slices across chunk and seq length dim expected_slice_along_seq_length = tf.convert_to_tensor([0.4983, -0.7584, -1.6944], dtype=tf.float32) expected_slice_along_chunk = tf.convert_to_tensor([0.4983, -1.8348, -0.7584, 2.0514], dtype=tf.float32) self.assertTrue(shape_list(chunked_hidden_states) == [1, 3, 4, 4]) tf.debugging.assert_near( chunked_hidden_states[0, :, 0, 0], expected_slice_along_seq_length, rtol=1e-3, atol=1e-4 ) tf.debugging.assert_near(chunked_hidden_states[0, 0, :, 0], expected_slice_along_chunk, rtol=1e-3, atol=1e-4) def test_layer_local_attn(self): model = TFLongformerModel.from_pretrained("patrickvonplaten/longformer-random-tiny") layer = model.longformer.encoder.layer[0].attention.self_attention hidden_states = self._get_hidden_states() batch_size, seq_length, hidden_size = hidden_states.shape attention_mask = tf.zeros((batch_size, seq_length), dtype=tf.float32) is_index_global_attn = tf.math.greater(attention_mask, 1) is_global_attn = tf.math.reduce_any(is_index_global_attn) attention_mask = tf.where(tf.range(4)[None, :, None, None] > 1, -10000.0, attention_mask[:, :, None, None]) is_index_masked = tf.math.less(attention_mask[:, :, 0, 0], 0) layer_head_mask = None output_hidden_states = layer( [hidden_states, attention_mask, layer_head_mask, is_index_masked, is_index_global_attn, is_global_attn] )[0] expected_slice = tf.convert_to_tensor( [0.00188, 0.012196, -0.017051, -0.025571, -0.02996, 0.017297, -0.011521, 0.004848], dtype=tf.float32 ) self.assertEqual(output_hidden_states.shape, (1, 4, 8)) tf.debugging.assert_near(output_hidden_states[0, 1], expected_slice, rtol=1e-3, atol=1e-4) def test_layer_global_attn(self): model = TFLongformerModel.from_pretrained("patrickvonplaten/longformer-random-tiny") layer = model.longformer.encoder.layer[0].attention.self_attention hidden_states = self._get_hidden_states() hidden_states = tf.concat([self._get_hidden_states(), self._get_hidden_states() - 0.5], axis=0) batch_size, seq_length, hidden_size = hidden_states.shape # create attn mask attention_mask_1 = tf.zeros((1, 1, 1, seq_length), dtype=tf.float32) attention_mask_2 = tf.zeros((1, 1, 1, seq_length), dtype=tf.float32) attention_mask_1 = tf.where(tf.range(4)[None, :, None, None] > 1, 10000.0, attention_mask_1) attention_mask_1 = tf.where(tf.range(4)[None, :, None, None] > 2, -10000.0, attention_mask_1) attention_mask_2 = tf.where(tf.range(4)[None, :, None, None] > 0, 10000.0, attention_mask_2) attention_mask = tf.concat([attention_mask_1, attention_mask_2], axis=0) is_index_masked = tf.math.less(attention_mask[:, :, 0, 0], 0) is_index_global_attn = tf.math.greater(attention_mask[:, :, 0, 0], 0) is_global_attn = tf.math.reduce_any(is_index_global_attn) layer_head_mask = None output_hidden_states = layer( [ hidden_states, -tf.math.abs(attention_mask), layer_head_mask, is_index_masked, is_index_global_attn, is_global_attn, ] )[0] self.assertEqual(output_hidden_states.shape, (2, 4, 8)) expected_slice_0 = tf.convert_to_tensor( [-0.06508, -0.039306, 0.030934, -0.03417, -0.00656, -0.01553, -0.02088, -0.04938], dtype=tf.float32 ) expected_slice_1 = tf.convert_to_tensor( [-0.04055, -0.038399, 0.0396, -0.03735, -0.03415, 0.01357, 0.00145, -0.05709], dtype=tf.float32 ) tf.debugging.assert_near(output_hidden_states[0, 2], expected_slice_0, rtol=1e-3, atol=1e-4) tf.debugging.assert_near(output_hidden_states[1, -2], expected_slice_1, rtol=1e-3, atol=1e-4) def test_layer_attn_probs(self): model = TFLongformerModel.from_pretrained("patrickvonplaten/longformer-random-tiny") layer = model.longformer.encoder.layer[0].attention.self_attention hidden_states = tf.concat([self._get_hidden_states(), self._get_hidden_states() - 0.5], axis=0) batch_size, seq_length, hidden_size = hidden_states.shape # create attn mask attention_mask_1 = tf.zeros((1, 1, 1, seq_length), dtype=tf.float32) attention_mask_2 = tf.zeros((1, 1, 1, seq_length), dtype=tf.float32) attention_mask_1 = tf.where(tf.range(4)[None, :, None, None] > 1, 10000.0, attention_mask_1) attention_mask_1 = tf.where(tf.range(4)[None, :, None, None] > 2, -10000.0, attention_mask_1) attention_mask_2 = tf.where(tf.range(4)[None, :, None, None] > 0, 10000.0, attention_mask_2) attention_mask = tf.concat([attention_mask_1, attention_mask_2], axis=0) is_index_masked = tf.math.less(attention_mask[:, :, 0, 0], 0) is_index_global_attn = tf.math.greater(attention_mask[:, :, 0, 0], 0) is_global_attn = tf.math.reduce_any(is_index_global_attn) layer_head_mask = None output_hidden_states, local_attentions, global_attentions = layer( [ hidden_states, -tf.math.abs(attention_mask), layer_head_mask, is_index_masked, is_index_global_attn, is_global_attn, ] ) self.assertEqual(local_attentions.shape, (2, 4, 2, 8)) self.assertEqual(global_attentions.shape, (2, 2, 3, 4)) self.assertTrue((local_attentions[0, 2:4, :, :] == 0).numpy().tolist()) self.assertTrue((local_attentions[1, 1:4, :, :] == 0).numpy().tolist()) # # The weight of all tokens with local attention must sum to 1. self.assertTrue( (tf.math.abs(tf.math.reduce_sum(global_attentions[0, :, :2, :], axis=-1) - 1) < 1e-6).numpy().tolist() ) self.assertTrue( (tf.math.abs(tf.math.reduce_sum(global_attentions[1, :, :1, :], axis=-1) - 1) < 1e-6).numpy().tolist() ) tf.debugging.assert_near( local_attentions[0, 0, 0, :], tf.convert_to_tensor([0.3328, 0.0000, 0.0000, 0.0000, 0.0000, 0.3355, 0.3318, 0.0000], dtype=tf.float32), rtol=1e-3, atol=1e-4, ) tf.debugging.assert_near( local_attentions[1, 0, 0, :], tf.convert_to_tensor([0.2492, 0.2502, 0.2502, 0.0000, 0.0000, 0.2505, 0.0000, 0.0000], dtype=tf.float32), rtol=1e-3, atol=1e-4, ) # All the global attention weights must sum to 1. self.assertTrue((tf.math.abs(tf.math.reduce_sum(global_attentions, axis=-1) - 1) < 1e-6).numpy().tolist()) tf.debugging.assert_near( global_attentions[0, 0, 1, :], tf.convert_to_tensor([0.2500, 0.2500, 0.2500, 0.2500], dtype=tf.float32), rtol=1e-3, atol=1e-4, ) tf.debugging.assert_near( global_attentions[1, 0, 0, :], tf.convert_to_tensor([0.2497, 0.2500, 0.2499, 0.2504], dtype=tf.float32), rtol=1e-3, atol=1e-4, ) @slow def test_inference_no_head(self): model = TFLongformerModel.from_pretrained("allenai/longformer-base-4096") # 'Hello world!' input_ids = tf.convert_to_tensor([[0, 20920, 232, 328, 1437, 2]], dtype=tf.int64) attention_mask = tf.ones(shape_list(input_ids), dtype=tf.int64) output = model(input_ids, attention_mask=attention_mask)[0] output_without_mask = model(input_ids)[0] expected_output_slice = tf.convert_to_tensor([0.0549, 0.1087, -0.1119, -0.0368, 0.0250], dtype=tf.float32) tf.debugging.assert_near(output[0, 0, -5:], expected_output_slice, rtol=1e-3, atol=1e-4) tf.debugging.assert_near(output_without_mask[0, 0, -5:], expected_output_slice, rtol=1e-3, atol=1e-4) @slow def test_inference_no_head_long(self): model = TFLongformerModel.from_pretrained("allenai/longformer-base-4096") # 'Hello world! ' repeated 1000 times input_ids = tf.convert_to_tensor([[0] + [20920, 232, 328, 1437] * 1000 + [2]], dtype=tf.int64) attention_mask = tf.ones(shape_list(input_ids), dtype=tf.int64) global_attention_mask = tf.zeros(shape_list(input_ids), dtype=tf.int64) # Set global attention on a few random positions global_attention_mask = tf.tensor_scatter_nd_update( global_attention_mask, tf.constant([[0, 1], [0, 4], [0, 21]], dtype=tf.int64), tf.constant([1, 1, 1], dtype=tf.int64), ) output = model(input_ids, attention_mask=attention_mask, global_attention_mask=global_attention_mask)[0] expected_output_sum = tf.constant(74585.875) expected_output_mean = tf.constant(0.024267) # assert close tf.debugging.assert_near(tf.reduce_sum(output), expected_output_sum, rtol=1e-4, atol=1e-4) tf.debugging.assert_near(tf.reduce_mean(output), expected_output_mean, rtol=1e-4, atol=1e-4) @slow def test_inference_masked_lm_long(self): model = TFLongformerForMaskedLM.from_pretrained("allenai/longformer-base-4096") # 'Hello world! ' repeated 1000 times input_ids = tf.convert_to_tensor([[0] + [20920, 232, 328, 1437] * 1000 + [2]], dtype=tf.int64) output = model(input_ids, labels=input_ids) loss = output.loss prediction_scores = output.logits expected_loss = tf.constant(0.0073798) expected_prediction_scores_sum = tf.constant(-610476600.0) expected_prediction_scores_mean = tf.constant(-3.03477) # assert close tf.debugging.assert_near(tf.reduce_mean(loss), expected_loss, rtol=1e-4, atol=1e-4) tf.debugging.assert_near( tf.reduce_sum(prediction_scores), expected_prediction_scores_sum, rtol=1e-4, atol=1e-4 ) tf.debugging.assert_near( tf.reduce_mean(prediction_scores), expected_prediction_scores_mean, rtol=1e-4, atol=1e-4 ) @slow def test_inference_masked_lm(self): model = TFLongformerForMaskedLM.from_pretrained("lysandre/tiny-longformer-random") input_ids = tf.constant([[0, 1, 2, 3, 4, 5]]) output = model(input_ids)[0] expected_shape = [1, 6, 10] self.assertEqual(output.shape, expected_shape) print(output[:, :3, :3]) expected_slice = tf.constant( [ [ [-0.04926379, 0.0367098, 0.02099686], [0.03940692, 0.01547744, -0.01448723], [0.03495252, -0.05900355, -0.01675752], ] ] ) tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=1e-4)