607 lines
26 KiB
Python
607 lines
26 KiB
Python
# coding=utf-8
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# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Testing suite for the PyTorch FNet model."""
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import unittest
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from typing import Dict, List, Tuple
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from transformers import FNetConfig, is_torch_available
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from transformers.models.auto import get_values
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from transformers.testing_utils import require_tokenizers, require_torch, slow, torch_device
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
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from ...test_pipeline_mixin import PipelineTesterMixin
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if is_torch_available():
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import torch
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from transformers import (
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MODEL_FOR_PRETRAINING_MAPPING,
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FNetForMaskedLM,
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FNetForMultipleChoice,
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FNetForNextSentencePrediction,
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FNetForPreTraining,
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FNetForQuestionAnswering,
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FNetForSequenceClassification,
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FNetForTokenClassification,
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FNetModel,
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FNetTokenizerFast,
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)
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from transformers.models.fnet.modeling_fnet import (
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FNetBasicFourierTransform,
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is_scipy_available,
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)
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# Override ConfigTester
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class FNetConfigTester(ConfigTester):
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def create_and_test_config_common_properties(self):
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config = self.config_class(**self.inputs_dict)
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if self.has_text_modality:
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self.parent.assertTrue(hasattr(config, "vocab_size"))
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self.parent.assertTrue(hasattr(config, "hidden_size"))
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self.parent.assertTrue(hasattr(config, "num_hidden_layers"))
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class FNetModelTester:
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def __init__(
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self,
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parent,
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batch_size=13,
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seq_length=7,
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is_training=True,
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use_token_type_ids=True,
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use_labels=True,
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vocab_size=99,
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hidden_size=32,
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num_hidden_layers=2,
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intermediate_size=37,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=16,
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type_sequence_label_size=2,
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initializer_range=0.02,
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num_labels=3,
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num_choices=4,
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scope=None,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.use_token_type_ids = use_token_type_ids
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.type_sequence_label_size = type_sequence_label_size
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self.initializer_range = initializer_range
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self.num_labels = num_labels
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self.num_choices = num_choices
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self.scope = scope
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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token_type_ids = None
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if self.use_token_type_ids:
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token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
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sequence_labels = None
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token_labels = None
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choice_labels = None
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if self.use_labels:
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sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
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token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
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choice_labels = ids_tensor([self.batch_size], self.num_choices)
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config = self.get_config()
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return config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels
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def get_config(self):
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return FNetConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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intermediate_size=self.intermediate_size,
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hidden_act=self.hidden_act,
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hidden_dropout_prob=self.hidden_dropout_prob,
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max_position_embeddings=self.max_position_embeddings,
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type_vocab_size=self.type_vocab_size,
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initializer_range=self.initializer_range,
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tpu_short_seq_length=self.seq_length,
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)
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@require_torch
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def create_and_check_fourier_transform(self, config):
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hidden_states = floats_tensor([self.batch_size, self.seq_length, config.hidden_size])
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transform = FNetBasicFourierTransform(config)
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fftn_output = transform(hidden_states)
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config.use_tpu_fourier_optimizations = True
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if is_scipy_available():
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transform = FNetBasicFourierTransform(config)
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dft_output = transform(hidden_states)
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config.max_position_embeddings = 4097
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transform = FNetBasicFourierTransform(config)
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fft_output = transform(hidden_states)
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if is_scipy_available():
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self.parent.assertTrue(torch.allclose(fftn_output[0][0], dft_output[0][0], atol=1e-4))
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self.parent.assertTrue(torch.allclose(fft_output[0][0], dft_output[0][0], atol=1e-4))
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self.parent.assertTrue(torch.allclose(fftn_output[0][0], fft_output[0][0], atol=1e-4))
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def create_and_check_model(self, config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels):
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model = FNetModel(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, token_type_ids=token_type_ids)
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result = model(input_ids)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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def create_and_check_for_pretraining(
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self, config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels
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):
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model = FNetForPreTraining(config=config)
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model.to(torch_device)
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model.eval()
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result = model(
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input_ids,
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token_type_ids=token_type_ids,
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labels=token_labels,
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next_sentence_label=sequence_labels,
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)
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self.parent.assertEqual(result.prediction_logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
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self.parent.assertEqual(result.seq_relationship_logits.shape, (self.batch_size, 2))
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def create_and_check_for_masked_lm(
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self, config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels
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):
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model = FNetForMaskedLM(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, token_type_ids=token_type_ids, labels=token_labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
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def create_and_check_for_next_sentence_prediction(
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self, config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels
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):
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model = FNetForNextSentencePrediction(config=config)
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model.to(torch_device)
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model.eval()
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result = model(
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input_ids,
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token_type_ids=token_type_ids,
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next_sentence_label=sequence_labels,
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)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, 2))
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def create_and_check_for_question_answering(
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self, config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels
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):
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model = FNetForQuestionAnswering(config=config)
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model.to(torch_device)
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model.eval()
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result = model(
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input_ids,
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token_type_ids=token_type_ids,
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start_positions=sequence_labels,
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end_positions=sequence_labels,
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)
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self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
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self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
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def create_and_check_for_sequence_classification(
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self, config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels
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):
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config.num_labels = self.num_labels
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model = FNetForSequenceClassification(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, token_type_ids=token_type_ids, labels=sequence_labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
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def create_and_check_for_token_classification(
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self, config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels
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):
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config.num_labels = self.num_labels
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model = FNetForTokenClassification(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, token_type_ids=token_type_ids, labels=token_labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
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def create_and_check_for_multiple_choice(
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self, config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels
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):
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config.num_choices = self.num_choices
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model = FNetForMultipleChoice(config=config)
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model.to(torch_device)
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model.eval()
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multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
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multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
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result = model(
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multiple_choice_inputs_ids,
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token_type_ids=multiple_choice_token_type_ids,
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labels=choice_labels,
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)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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(
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config,
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input_ids,
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token_type_ids,
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sequence_labels,
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token_labels,
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choice_labels,
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) = config_and_inputs
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inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids}
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return config, inputs_dict
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@require_torch
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class FNetModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (
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(
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FNetModel,
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FNetForPreTraining,
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FNetForMaskedLM,
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FNetForNextSentencePrediction,
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FNetForMultipleChoice,
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FNetForQuestionAnswering,
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FNetForSequenceClassification,
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FNetForTokenClassification,
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)
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if is_torch_available()
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else ()
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)
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pipeline_model_mapping = (
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{
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"feature-extraction": FNetModel,
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"fill-mask": FNetForMaskedLM,
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"question-answering": FNetForQuestionAnswering,
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"text-classification": FNetForSequenceClassification,
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"token-classification": FNetForTokenClassification,
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"zero-shot": FNetForSequenceClassification,
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}
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if is_torch_available()
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else {}
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)
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# Skip Tests
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test_pruning = False
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test_head_masking = False
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test_pruning = False
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# TODO: Fix the failed tests
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def is_pipeline_test_to_skip(
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self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
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):
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if pipeline_test_casse_name == "QAPipelineTests" and not tokenizer_name.endswith("Fast"):
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return True
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return False
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# special case for ForPreTraining model
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def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
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inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
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if return_labels:
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if model_class in get_values(MODEL_FOR_PRETRAINING_MAPPING):
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inputs_dict["labels"] = torch.zeros(
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(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
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)
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inputs_dict["next_sentence_label"] = torch.zeros(
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self.model_tester.batch_size, dtype=torch.long, device=torch_device
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)
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return inputs_dict
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# Overriden Tests
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def test_attention_outputs(self):
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pass
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@unittest.skip(
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reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
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)
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def test_training_gradient_checkpointing(self):
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pass
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@unittest.skip(
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reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
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)
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def test_training_gradient_checkpointing_use_reentrant(self):
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pass
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@unittest.skip(
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reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
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)
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def test_training_gradient_checkpointing_use_reentrant_false(self):
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pass
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def test_model_outputs_equivalence(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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def set_nan_tensor_to_zero(t):
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t[t != t] = 0
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return t
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def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
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with torch.no_grad():
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tuple_output = model(**tuple_inputs, return_dict=False, **additional_kwargs)
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dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs).to_tuple()
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def recursive_check(tuple_object, dict_object):
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if isinstance(tuple_object, (List, Tuple)):
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for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
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recursive_check(tuple_iterable_value, dict_iterable_value)
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elif isinstance(tuple_object, Dict):
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for tuple_iterable_value, dict_iterable_value in zip(
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tuple_object.values(), dict_object.values()
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):
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recursive_check(tuple_iterable_value, dict_iterable_value)
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elif tuple_object is None:
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return
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else:
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self.assertTrue(
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torch.allclose(
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set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5
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),
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msg=(
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"Tuple and dict output are not equal. Difference:"
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f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:"
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f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has"
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f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}."
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),
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)
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recursive_check(tuple_output, dict_output)
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for model_class in self.all_model_classes:
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
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dict_inputs = self._prepare_for_class(inputs_dict, model_class)
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check_equivalence(model, tuple_inputs, dict_inputs)
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tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
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dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
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check_equivalence(model, tuple_inputs, dict_inputs)
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# tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
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# dict_inputs = self._prepare_for_class(inputs_dict, model_class)
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# check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
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tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
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dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
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check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
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def test_retain_grad_hidden_states_attentions(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.output_hidden_states = True
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config.output_attentions = True
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# no need to test all models as different heads yield the same functionality
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model_class = self.all_model_classes[0]
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model = model_class(config)
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model.to(torch_device)
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inputs = self._prepare_for_class(inputs_dict, model_class)
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outputs = model(**inputs)
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output = outputs[0]
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hidden_states = outputs.hidden_states[0]
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hidden_states.retain_grad()
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output.flatten()[0].backward(retain_graph=True)
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self.assertIsNotNone(hidden_states.grad)
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def setUp(self):
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self.model_tester = FNetModelTester(self)
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self.config_tester = FNetConfigTester(self, config_class=FNetConfig, hidden_size=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_for_pretraining(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_pretraining(*config_and_inputs)
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def test_for_masked_lm(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
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def test_for_multiple_choice(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
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def test_for_question_answering(self):
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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)
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
model_name = "google/fnet-base"
|
|
model = FNetModel.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
|
|
|
|
@require_torch
|
|
class FNetModelIntegrationTest(unittest.TestCase):
|
|
@slow
|
|
def test_inference_for_masked_lm(self):
|
|
"""
|
|
For comparison:
|
|
1. Modify the pre-training model `__call__` to skip computing metrics and return masked_lm_output like so:
|
|
```
|
|
...
|
|
sequence_output, pooled_output = EncoderModel(
|
|
self.config, random_seed=self.random_seed, name="encoder")(
|
|
input_ids, input_mask, type_ids, deterministic=deterministic)
|
|
|
|
masked_lm_output = nn.Dense(
|
|
self.config.d_emb,
|
|
kernel_init=default_kernel_init,
|
|
name="predictions_dense")(
|
|
sequence_output)
|
|
masked_lm_output = nn.gelu(masked_lm_output)
|
|
masked_lm_output = nn.LayerNorm(
|
|
epsilon=LAYER_NORM_EPSILON, name="predictions_layer_norm")(
|
|
masked_lm_output)
|
|
masked_lm_logits = layers.OutputProjection(
|
|
kernel=self._get_embedding_table(), name="predictions_output")(
|
|
masked_lm_output)
|
|
|
|
next_sentence_logits = layers.OutputProjection(
|
|
n_out=2, kernel_init=default_kernel_init, name="classification")(
|
|
pooled_output)
|
|
|
|
return masked_lm_logits
|
|
...
|
|
```
|
|
2. Run the following:
|
|
>>> import jax.numpy as jnp
|
|
>>> import sentencepiece as spm
|
|
>>> from flax.training import checkpoints
|
|
>>> from f_net.models import PreTrainingModel
|
|
>>> from f_net.configs.pretraining import get_config, ModelArchitecture
|
|
|
|
>>> pretrained_params = checkpoints.restore_checkpoint('./f_net/f_net_checkpoint', None) # Location of original checkpoint
|
|
>>> pretrained_config = get_config()
|
|
>>> pretrained_config.model_arch = ModelArchitecture.F_NET
|
|
|
|
>>> vocab_filepath = "./f_net/c4_bpe_sentencepiece.model" # Location of the sentence piece model
|
|
>>> tokenizer = spm.SentencePieceProcessor()
|
|
>>> tokenizer.Load(vocab_filepath)
|
|
>>> with pretrained_config.unlocked():
|
|
>>> pretrained_config.vocab_size = tokenizer.GetPieceSize()
|
|
>>> tokens = jnp.array([[0, 1, 2, 3, 4, 5]])
|
|
>>> type_ids = jnp.zeros_like(tokens, dtype="i4")
|
|
>>> attention_mask = jnp.ones_like(tokens) # Dummy. This gets deleted inside the model.
|
|
|
|
>>> flax_pretraining_model = PreTrainingModel(pretrained_config)
|
|
>>> pretrained_model_params = freeze(pretrained_params['target'])
|
|
>>> flax_model_outputs = flax_pretraining_model.apply({"params": pretrained_model_params}, tokens, attention_mask, type_ids, None, None, None, None, deterministic=True)
|
|
>>> masked_lm_logits[:, :3, :3]
|
|
"""
|
|
|
|
model = FNetForMaskedLM.from_pretrained("google/fnet-base")
|
|
model.to(torch_device)
|
|
|
|
input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]], device=torch_device)
|
|
with torch.no_grad():
|
|
output = model(input_ids)[0]
|
|
|
|
vocab_size = 32000
|
|
|
|
expected_shape = torch.Size((1, 6, vocab_size))
|
|
self.assertEqual(output.shape, expected_shape)
|
|
|
|
expected_slice = torch.tensor(
|
|
[[[-1.7819, -7.7384, -7.5002], [-3.4746, -8.5943, -7.7762], [-3.2052, -9.0771, -8.3468]]],
|
|
device=torch_device,
|
|
)
|
|
|
|
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
|
|
|
|
@slow
|
|
@require_tokenizers
|
|
def test_inference_long_sentence(self):
|
|
tokenizer = FNetTokenizerFast.from_pretrained("google/fnet-base")
|
|
|
|
inputs = tokenizer(
|
|
"the man worked as a [MASK].",
|
|
"this is his [MASK].",
|
|
return_tensors="pt",
|
|
padding="max_length",
|
|
max_length=512,
|
|
)
|
|
|
|
torch.testing.assert_close(inputs["input_ids"], torch.tensor([[4, 13, 283, 2479, 106, 8, 6, 845, 5, 168, 65, 367, 6, 845, 5, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3]])) # fmt: skip
|
|
|
|
inputs = {k: v.to(torch_device) for k, v in inputs.items()}
|
|
|
|
model = FNetForMaskedLM.from_pretrained("google/fnet-base")
|
|
model.to(torch_device)
|
|
logits = model(**inputs).logits
|
|
predictions_mask_1 = tokenizer.decode(logits[0, 6].topk(5).indices)
|
|
predictions_mask_2 = tokenizer.decode(logits[0, 12].topk(5).indices)
|
|
|
|
self.assertEqual(predictions_mask_1.split(" "), ["man", "child", "teacher", "woman", "model"])
|
|
self.assertEqual(predictions_mask_2.split(" "), ["work", "wife", "job", "story", "name"])
|
|
|
|
@slow
|
|
def test_inference_for_next_sentence_prediction(self):
|
|
model = FNetForNextSentencePrediction.from_pretrained("google/fnet-base")
|
|
model.to(torch_device)
|
|
|
|
input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]], device=torch_device)
|
|
with torch.no_grad():
|
|
output = model(input_ids)[0]
|
|
|
|
expected_shape = torch.Size((1, 2))
|
|
self.assertEqual(output.shape, expected_shape)
|
|
|
|
expected_slice = torch.tensor([[-0.2234, -0.0226]], device=torch_device)
|
|
|
|
self.assertTrue(torch.allclose(output, expected_slice, atol=1e-4))
|
|
|
|
@slow
|
|
def test_inference_model(self):
|
|
model = FNetModel.from_pretrained("google/fnet-base")
|
|
model.to(torch_device)
|
|
|
|
input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]], device=torch_device)
|
|
with torch.no_grad():
|
|
output = model(input_ids)[0]
|
|
|
|
expected_shape = torch.Size((1, 6, model.config.hidden_size))
|
|
self.assertEqual(output.shape, expected_shape)
|
|
|
|
expected_slice = torch.tensor(
|
|
[[[4.1541, -0.1051, -0.1667], [-0.9144, 0.2939, -0.0086], [-0.8472, -0.7281, 0.0256]]], device=torch_device
|
|
)
|
|
|
|
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
|