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- # 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.
-
-
- 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
-
-
- if is_tf_available():
- import tensorflow as tf
-
- from transformers import (
- LongformerConfig,
- TFLongformerForMaskedLM,
- TFLongformerForMultipleChoice,
- TFLongformerForQuestionAnswering,
- TFLongformerForSequenceClassification,
- TFLongformerForTokenClassification,
- TFLongformerModel,
- TFLongformerSelfAttention,
- )
-
- def shape_list(x):
- """
- copied from transformers.modeling_tf_utils
- """
- static = x.shape.as_list()
- dynamic = tf.shape(x)
- return [dynamic[i] if s is None else s for i, s in enumerate(static)]
-
-
- 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 = 5
- 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
-
- # because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
- # the `test_attention_outputs` and `test_hidden_states_output` tests
- self.encoder_seq_length = (
- self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
- )
-
- 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 = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
-
- 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.dtypes.int32)
- 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, unittest.TestCase):
-
- all_model_classes = (
- (
- TFLongformerModel,
- TFLongformerForMaskedLM,
- TFLongformerForQuestionAnswering,
- TFLongformerForSequenceClassification,
- TFLongformerForMultipleChoice,
- TFLongformerForTokenClassification,
- )
- if is_tf_available()
- else ()
- )
- test_head_masking = False
- test_onnx = 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)
-
- def test_saved_model_creation(self):
- # This test is too long (>30sec) and makes fail the CI
- pass
-
- def test_xla_mode(self):
- # TODO JP: Make Longformer XLA compliant
- 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.dtypes.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.dtypes.float32), rtol=1e-3
- )
-
- def test_pad_and_transpose_last_two_dims(self):
- hidden_states = self._get_hidden_states()
- self.assertTrue(shape_list(hidden_states), [1, 8, 4])
-
- # pad along seq length dim
- paddings = tf.constant([[0, 0], [0, 0], [0, 1], [0, 0]], dtype=tf.dtypes.int32)
-
- 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.dtypes.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.dtypes.int32)) == 8)
- self.assertTrue(tf.math.reduce_sum(tf.cast(tf.math.is_inf(hid_states_2), tf.dtypes.int32)) == 24)
- self.assertTrue(tf.math.reduce_sum(tf.cast(tf.math.is_inf(hid_states_3), tf.dtypes.int32)) == 24)
- self.assertTrue(tf.math.reduce_sum(tf.cast(tf.math.is_inf(hid_states_4), tf.dtypes.int32)) == 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.dtypes.float32)
- expected_slice_along_chunk = tf.convert_to_tensor([0.4983, -1.8348, -0.7584, 2.0514], dtype=tf.dtypes.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)
- tf.debugging.assert_near(chunked_hidden_states[0, 0, :, 0], expected_slice_along_chunk, rtol=1e-3)
-
- 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.dtypes.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.dtypes.float32
- )
-
- self.assertTrue(output_hidden_states.shape, (1, 4, 8))
- tf.debugging.assert_near(output_hidden_states[0, 1], expected_slice, rtol=1e-3)
-
- 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.dtypes.float32)
- attention_mask_2 = tf.zeros((1, 1, 1, seq_length), dtype=tf.dtypes.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.assertTrue(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.dtypes.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.dtypes.float32
- )
-
- tf.debugging.assert_near(output_hidden_states[0, 2], expected_slice_0, rtol=1e-3)
- tf.debugging.assert_near(output_hidden_states[1, -2], expected_slice_1, rtol=1e-3)
-
- 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.dtypes.float32)
- attention_mask_2 = tf.zeros((1, 1, 1, seq_length), dtype=tf.dtypes.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.dtypes.float32
- ),
- rtol=1e-3,
- )
-
- 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.dtypes.float32
- ),
- rtol=1e-3,
- )
-
- # 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.dtypes.float32),
- rtol=1e-3,
- )
- tf.debugging.assert_near(
- global_attentions[1, 0, 0, :],
- tf.convert_to_tensor([0.2497, 0.2500, 0.2499, 0.2504], dtype=tf.dtypes.float32),
- rtol=1e-3,
- )
-
- @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.dtypes.int32)
- attention_mask = tf.ones(shape_list(input_ids), dtype=tf.dtypes.int32)
-
- 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.dtypes.float32
- )
-
- tf.debugging.assert_near(output[0, 0, -5:], expected_output_slice, rtol=1e-3)
- tf.debugging.assert_near(output_without_mask[0, 0, -5:], expected_output_slice, rtol=1e-3)
-
- @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.dtypes.int32)
-
- attention_mask = tf.ones(shape_list(input_ids), dtype=tf.dtypes.int32)
- global_attention_mask = tf.zeros(shape_list(input_ids), dtype=tf.dtypes.int32)
- # 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]]), tf.constant([1, 1, 1])
- )
-
- 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)
- tf.debugging.assert_near(tf.reduce_mean(output), expected_output_mean, rtol=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.dtypes.int32)
-
- 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)
- tf.debugging.assert_near(tf.reduce_sum(prediction_scores), expected_prediction_scores_sum, rtol=1e-4)
- tf.debugging.assert_near(tf.reduce_mean(prediction_scores), expected_prediction_scores_mean, rtol=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)
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