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- # coding=utf-8
- # Copyright 2020 HuggingFace Inc. team.
- #
- # 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 FunnelConfig, is_tf_available
- from transformers.testing_utils import require_tf
-
- 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 (
- TFFunnelBaseModel,
- TFFunnelForMaskedLM,
- TFFunnelForMultipleChoice,
- TFFunnelForPreTraining,
- TFFunnelForQuestionAnswering,
- TFFunnelForSequenceClassification,
- TFFunnelForTokenClassification,
- TFFunnelModel,
- )
-
-
- class TFFunnelModelTester:
- """You can also import this e.g, from .test_modeling_funnel import FunnelModelTester"""
-
- def __init__(
- self,
- parent,
- batch_size=13,
- seq_length=7,
- is_training=True,
- use_input_mask=True,
- use_token_type_ids=True,
- use_labels=True,
- vocab_size=99,
- block_sizes=[1, 1, 2],
- num_decoder_layers=1,
- d_model=32,
- n_head=4,
- d_head=8,
- d_inner=37,
- hidden_act="gelu_new",
- hidden_dropout=0.1,
- attention_dropout=0.1,
- activation_dropout=0.0,
- max_position_embeddings=512,
- type_vocab_size=3,
- num_labels=3,
- num_choices=4,
- scope=None,
- base=False,
- ):
- self.parent = parent
- self.batch_size = batch_size
- self.seq_length = seq_length
- self.is_training = is_training
- self.use_input_mask = use_input_mask
- self.use_token_type_ids = use_token_type_ids
- self.use_labels = use_labels
- self.vocab_size = vocab_size
- self.block_sizes = block_sizes
- self.num_decoder_layers = num_decoder_layers
- self.d_model = d_model
- self.n_head = n_head
- self.d_head = d_head
- self.d_inner = d_inner
- self.hidden_act = hidden_act
- self.hidden_dropout = hidden_dropout
- self.attention_dropout = attention_dropout
- self.activation_dropout = activation_dropout
- self.max_position_embeddings = max_position_embeddings
- self.type_vocab_size = type_vocab_size
- self.type_sequence_label_size = 2
- self.num_labels = num_labels
- self.num_choices = num_choices
- self.scope = scope
-
- # Used in the tests to check the size of the first attention layer
- self.num_attention_heads = n_head
- # Used in the tests to check the size of the first hidden state
- self.hidden_size = self.d_model
- # Used in the tests to check the number of output hidden states/attentions
- self.num_hidden_layers = sum(self.block_sizes) + (0 if base else self.num_decoder_layers)
- # FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
- # the last hidden state of the first block (which is the first hidden state of the decoder).
- if not base:
- self.expected_num_hidden_layers = self.num_hidden_layers + 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 = 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 = FunnelConfig(
- vocab_size=self.vocab_size,
- block_sizes=self.block_sizes,
- num_decoder_layers=self.num_decoder_layers,
- d_model=self.d_model,
- n_head=self.n_head,
- d_head=self.d_head,
- d_inner=self.d_inner,
- hidden_act=self.hidden_act,
- hidden_dropout=self.hidden_dropout,
- attention_dropout=self.attention_dropout,
- activation_dropout=self.activation_dropout,
- max_position_embeddings=self.max_position_embeddings,
- type_vocab_size=self.type_vocab_size,
- )
-
- return (
- config,
- input_ids,
- token_type_ids,
- input_mask,
- sequence_labels,
- token_labels,
- choice_labels,
- )
-
- def create_and_check_model(
- self,
- config,
- input_ids,
- token_type_ids,
- input_mask,
- sequence_labels,
- token_labels,
- choice_labels,
- ):
- model = TFFunnelModel(config=config)
- inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
- result = model(inputs)
-
- inputs = [input_ids, input_mask]
- result = model(inputs)
-
- result = model(input_ids)
- self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model))
-
- config.truncate_seq = False
- model = TFFunnelModel(config=config)
- result = model(input_ids)
- self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model))
-
- config.separate_cls = False
- model = TFFunnelModel(config=config)
- result = model(input_ids)
- self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model))
-
- def create_and_check_base_model(
- self,
- config,
- input_ids,
- token_type_ids,
- input_mask,
- sequence_labels,
- token_labels,
- choice_labels,
- ):
- model = TFFunnelBaseModel(config=config)
- inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
- result = model(inputs)
-
- inputs = [input_ids, input_mask]
- result = model(inputs)
-
- result = model(input_ids)
- self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 2, self.d_model))
-
- config.truncate_seq = False
- model = TFFunnelBaseModel(config=config)
- result = model(input_ids)
- self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 3, self.d_model))
-
- config.separate_cls = False
- model = TFFunnelBaseModel(config=config)
- result = model(input_ids)
- self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 2, self.d_model))
-
- def create_and_check_for_pretraining(
- self,
- config,
- input_ids,
- token_type_ids,
- input_mask,
- sequence_labels,
- token_labels,
- choice_labels,
- ):
- model = TFFunnelForPreTraining(config=config)
- inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
- result = model(inputs)
- self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length))
-
- def create_and_check_for_masked_lm(
- self,
- config,
- input_ids,
- token_type_ids,
- input_mask,
- sequence_labels,
- token_labels,
- choice_labels,
- ):
- model = TFFunnelForMaskedLM(config=config)
- inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
- result = model(inputs)
- self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
-
- 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 = TFFunnelForSequenceClassification(config=config)
- inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
- result = model(inputs)
- self.parent.assertEqual(result.logits.shape, (self.batch_size, 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 = TFFunnelForMultipleChoice(config=config)
- multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1))
- multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 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))
- inputs = {
- "input_ids": multiple_choice_inputs_ids,
- "attention_mask": multiple_choice_input_mask,
- "token_type_ids": multiple_choice_token_type_ids,
- }
- result = model(inputs)
- self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
-
- 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 = TFFunnelForTokenClassification(config=config)
- inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
- result = model(inputs)
- self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
-
- def create_and_check_for_question_answering(
- self,
- config,
- input_ids,
- token_type_ids,
- input_mask,
- sequence_labels,
- token_labels,
- choice_labels,
- ):
- model = TFFunnelForQuestionAnswering(config=config)
- inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
- result = model(inputs)
- self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
- self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
-
- def prepare_config_and_inputs_for_common(self):
- config_and_inputs = self.prepare_config_and_inputs()
- (
- config,
- input_ids,
- token_type_ids,
- input_mask,
- sequence_labels,
- token_labels,
- choice_labels,
- ) = config_and_inputs
- inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
- return config, inputs_dict
-
-
- @require_tf
- class TFFunnelModelTest(TFModelTesterMixin, unittest.TestCase):
- all_model_classes = (
- (
- TFFunnelModel,
- TFFunnelForMaskedLM,
- TFFunnelForPreTraining,
- TFFunnelForQuestionAnswering,
- TFFunnelForTokenClassification,
- )
- if is_tf_available()
- else ()
- )
- test_head_masking = False
- test_onnx = False
-
- def setUp(self):
- self.model_tester = TFFunnelModelTester(self)
- self.config_tester = ConfigTester(self, config_class=FunnelConfig)
-
- def test_config(self):
- self.config_tester.run_common_tests()
-
- def test_model(self):
- config_and_inputs = self.model_tester.prepare_config_and_inputs()
- self.model_tester.create_and_check_model(*config_and_inputs)
-
- def test_for_pretraining(self):
- config_and_inputs = self.model_tester.prepare_config_and_inputs()
- self.model_tester.create_and_check_for_pretraining(*config_and_inputs)
-
- def test_for_masked_lm(self):
- config_and_inputs = self.model_tester.prepare_config_and_inputs()
- self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
-
- def test_for_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_question_answering(self):
- config_and_inputs = self.model_tester.prepare_config_and_inputs()
- self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
-
- def test_saved_model_creation(self):
- # This test is too long (>30sec) and makes fail the CI
- pass
-
- def test_compile_tf_model(self):
- # This test fails the CI. TODO Lysandre re-enable it
- pass
-
-
- @require_tf
- class TFFunnelBaseModelTest(TFModelTesterMixin, unittest.TestCase):
- all_model_classes = (
- (TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
- )
- test_head_masking = False
- test_onnx = False
-
- def setUp(self):
- self.model_tester = TFFunnelModelTester(self, base=True)
- self.config_tester = ConfigTester(self, config_class=FunnelConfig)
-
- def test_config(self):
- self.config_tester.run_common_tests()
-
- def test_base_model(self):
- config_and_inputs = self.model_tester.prepare_config_and_inputs()
- self.model_tester.create_and_check_base_model(*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_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
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