395 lines
14 KiB
Python
395 lines
14 KiB
Python
# coding=utf-8
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# Copyright 2020 HuggingFace Inc. team.
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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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import unittest
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from transformers import FunnelConfig, is_tf_available
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from transformers.testing_utils import require_tf
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from .test_configuration_common import ConfigTester
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from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
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if is_tf_available():
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import tensorflow as tf
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from transformers.modeling_tf_funnel import (
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TFFunnelBaseModel,
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TFFunnelForMaskedLM,
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TFFunnelForMultipleChoice,
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TFFunnelForPreTraining,
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TFFunnelForQuestionAnswering,
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TFFunnelForSequenceClassification,
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TFFunnelForTokenClassification,
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TFFunnelModel,
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)
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class TFFunnelModelTester:
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"""You can also import this e.g, from .test_modeling_funnel import FunnelModelTester """
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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_input_mask=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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block_sizes=[1, 1, 2],
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num_decoder_layers=1,
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d_model=32,
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n_head=4,
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d_head=8,
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d_inner=37,
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hidden_act="gelu_new",
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hidden_dropout=0.1,
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attention_dropout=0.1,
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activation_dropout=0.0,
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max_position_embeddings=512,
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type_vocab_size=3,
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num_labels=3,
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num_choices=4,
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scope=None,
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base=False,
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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_input_mask = use_input_mask
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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.block_sizes = block_sizes
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self.num_decoder_layers = num_decoder_layers
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self.d_model = d_model
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self.n_head = n_head
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self.d_head = d_head
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self.d_inner = d_inner
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self.hidden_act = hidden_act
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self.hidden_dropout = hidden_dropout
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self.attention_dropout = attention_dropout
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self.activation_dropout = activation_dropout
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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 = 2
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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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# Used in the tests to check the size of the first attention layer
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self.num_attention_heads = n_head
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# Used in the tests to check the size of the first hidden state
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self.hidden_size = self.d_model
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# Used in the tests to check the number of output hidden states/attentions
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self.num_hidden_layers = sum(self.block_sizes) + (0 if base else self.num_decoder_layers)
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# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
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# the last hidden state of the first block (which is the first hidden state of the decoder).
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if not base:
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self.expected_num_hidden_layers = self.num_hidden_layers + 2
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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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input_mask = None
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if self.use_input_mask:
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input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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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 = FunnelConfig(
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vocab_size=self.vocab_size,
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block_sizes=self.block_sizes,
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num_decoder_layers=self.num_decoder_layers,
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d_model=self.d_model,
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n_head=self.n_head,
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d_head=self.d_head,
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d_inner=self.d_inner,
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hidden_act=self.hidden_act,
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hidden_dropout=self.hidden_dropout,
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attention_dropout=self.attention_dropout,
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activation_dropout=self.activation_dropout,
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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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return_dict=True,
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)
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return (
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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)
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def create_and_check_model(
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self,
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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):
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model = TFFunnelModel(config=config)
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inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
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result = model(inputs)
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inputs = [input_ids, input_mask]
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result = model(inputs)
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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.d_model))
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config.truncate_seq = False
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model = TFFunnelModel(config=config)
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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.d_model))
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config.separate_cls = False
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model = TFFunnelModel(config=config)
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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.d_model))
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def create_and_check_base_model(
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self,
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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):
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model = TFFunnelBaseModel(config=config)
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inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
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result = model(inputs)
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inputs = [input_ids, input_mask]
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result = model(inputs)
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result = model(input_ids)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 2, self.d_model))
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config.truncate_seq = False
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model = TFFunnelBaseModel(config=config)
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result = model(input_ids)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 3, self.d_model))
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config.separate_cls = False
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model = TFFunnelBaseModel(config=config)
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result = model(input_ids)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 2, self.d_model))
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def create_and_check_for_pretraining(
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self,
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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):
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model = TFFunnelForPreTraining(config=config)
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inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
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result = model(inputs)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length))
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def create_and_check_for_masked_lm(
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self,
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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):
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model = TFFunnelForMaskedLM(config=config)
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inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
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result = model(inputs)
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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_sequence_classification(
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self,
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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):
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config.num_labels = self.num_labels
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model = TFFunnelForSequenceClassification(config=config)
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inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
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result = model(inputs)
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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_multiple_choice(
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self,
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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):
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config.num_choices = self.num_choices
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model = TFFunnelForMultipleChoice(config=config)
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multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1))
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multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1))
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multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1))
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inputs = {
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"input_ids": multiple_choice_inputs_ids,
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"attention_mask": multiple_choice_input_mask,
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"token_type_ids": multiple_choice_token_type_ids,
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}
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result = model(inputs)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
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def create_and_check_for_token_classification(
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self,
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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):
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config.num_labels = self.num_labels
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model = TFFunnelForTokenClassification(config=config)
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inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
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result = model(inputs)
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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_question_answering(
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self,
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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):
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model = TFFunnelForQuestionAnswering(config=config)
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inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
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result = model(inputs)
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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 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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input_mask,
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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, "attention_mask": input_mask}
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return config, inputs_dict
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@require_tf
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class TFFunnelModelTest(TFModelTesterMixin, unittest.TestCase):
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all_model_classes = (
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(
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TFFunnelModel,
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TFFunnelForMaskedLM,
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TFFunnelForPreTraining,
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TFFunnelForQuestionAnswering,
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TFFunnelForTokenClassification,
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)
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if is_tf_available()
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else ()
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)
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def setUp(self):
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self.model_tester = TFFunnelModelTester(self)
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self.config_tester = ConfigTester(self, config_class=FunnelConfig)
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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_token_classification(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_token_classification(*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()
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self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
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@require_tf
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class TFFunnelBaseModelTest(TFModelTesterMixin, unittest.TestCase):
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all_model_classes = (
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(TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
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)
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def setUp(self):
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self.model_tester = TFFunnelModelTester(self, base=True)
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self.config_tester = ConfigTester(self, config_class=FunnelConfig)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_base_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_base_model(*config_and_inputs)
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def test_for_sequence_classification(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_sequence_classification(*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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