wehub-resource-sync
399 lines
15 KiB
Python
399 lines
15 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2020 The HuggingFace 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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import inspect
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import unittest
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import paddle
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from paddlenlp.transformers import (
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FunnelConfig,
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FunnelForQuestionAnswering,
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FunnelForSequenceClassification,
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FunnelForTokenClassification,
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FunnelModel,
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)
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from ..test_modeling_common import ModelTesterMixin, ids_tensor
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class 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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vocab_size=99,
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block_sizes=[4, 4, 4],
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block_repeats=None,
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num_decoder_layers=2,
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d_model=32,
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n_head=4,
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d_head=4,
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d_inner=32,
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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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initializer_range=0.1,
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initializer_std=None,
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layer_norm_eps=1e-9,
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num_labels=2,
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return_dict=True,
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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.vocab_size = vocab_size
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self.hidden_act = hidden_act
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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.initializer_range = initializer_range
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self.block_sizes = block_sizes
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self.block_repeats = block_repeats
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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.hidden_size = 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_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.initializer_std = initializer_std
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self.layer_norm_eps = layer_norm_eps
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self.num_hidden_layers = sum(self.block_sizes)
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self.num_attention_heads = n_head
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self.num_labels = num_labels
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self.return_dict = return_dict
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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 = paddle.ones([self.batch_size, self.seq_length], dtype="int32")
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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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config = self.get_config()
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return_dict = self.return_dict
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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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return_dict,
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)
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def get_config(self):
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return FunnelConfig(
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vocab_size=self.vocab_size,
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hidden_act=self.hidden_act,
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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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block_sizes=self.block_sizes,
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block_repeats=self.block_repeats,
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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_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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initializer_std=self.initializer_std,
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layer_norm_eps=self.layer_norm_eps,
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num_labels=self.num_labels,
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)
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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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input_mask,
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token_type_ids,
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return_dict,
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) = config_and_inputs
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inputs_dict = {
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"input_ids": input_ids,
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"attention_mask": input_mask,
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"token_type_ids": token_type_ids,
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"return_dict": return_dict,
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}
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return config, inputs_dict
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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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return_dict,
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):
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model = FunnelModel(config)
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model.eval()
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result = model(
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input_ids=input_ids,
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attention_mask=input_mask,
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token_type_ids=token_type_ids,
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return_dict=return_dict,
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)
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self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.d_model])
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def create_and_check_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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return_dict,
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):
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model = FunnelForQuestionAnswering(config)
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model.eval()
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result = model(
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input_ids=input_ids,
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attention_mask=input_mask,
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token_type_ids=token_type_ids,
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output_attentions=True,
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output_hidden_states=True,
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return_dict=return_dict,
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)
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self.parent.assertEqual(result[1].shape, [self.batch_size, self.seq_length])
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def create_and_check_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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return_dict,
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):
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model = FunnelForSequenceClassification(config)
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model.eval()
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result = model(
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input_ids=input_ids,
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attention_mask=input_mask,
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token_type_ids=token_type_ids,
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output_attentions=True,
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output_hidden_states=True,
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)
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self.parent.assertEqual(result[0].shape, [self.batch_size, self.num_labels])
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def create_and_check_token_classification(self, config, input_ids, token_type_ids, input_mask, return_dict):
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model = FunnelForTokenClassification(config)
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model.eval()
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result = model(
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input_ids=input_ids,
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attention_mask=input_mask,
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token_type_ids=token_type_ids,
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output_attentions=True,
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output_hidden_states=True,
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return_dict=return_dict,
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)
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self.parent.assertEqual(result.shape, [self.batch_size, self.seq_length, self.num_labels])
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class FunnelModelTest(ModelTesterMixin, unittest.TestCase):
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base_model_class = FunnelModel
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return_dict: bool = True
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use_labels: bool = False
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use_test_inputs_embeds: bool = False
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all_model_classes = (FunnelModel,)
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def setUp(self):
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self.model_tester = FunnelModelTester(self)
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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_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_question_answering(*config_and_inputs)
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def test_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_sequence_classification(*config_and_inputs)
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def test_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_token_classification(*config_and_inputs)
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def test_attention_outputs(self):
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"attention include encoder and decoder"
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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seq_len = getattr(self.model_tester, "seq_length", None)
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decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
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encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
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decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length)
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encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
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for model_class in self.all_model_classes:
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signature = inspect.signature(model_class.forward)
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# signature.parameters is an OrderedDict => so arg_names order is deterministic
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arg_names = [*signature.parameters.keys()]
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if not all(name in arg_names for name in ["output_attentions", "output_hidden_states", "return_dict"]):
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continue
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = False
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inputs_dict["return_dict"] = True
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model = self._make_model_instance(config, model_class)
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model.eval()
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with paddle.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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attentions = outputs.encoder_attentions if self.is_encoder_decoder else outputs.attentions
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self.assertEqual(
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len(attentions), self.model_tester.num_hidden_layers + self.model_tester.num_decoder_layers
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)
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# TODO(guosheng): check that output_attentions also work using config
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self.assertListEqual(
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list(attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
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)
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out_len = len(outputs)
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if self.is_encoder_decoder:
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correct_outlen = 5
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# loss is at first position
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if "labels" in inputs_dict:
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correct_outlen += 1 # loss is added to beginning
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# Question Answering model returns start_logits and end_logits
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if model_class.__name__.endswith("ForQuestionAnswering"):
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correct_outlen += 1 # start_logits and end_logits instead of only 1 output
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if "past_key_values" in outputs:
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correct_outlen += 1 # past_key_values have been returned
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self.assertEqual(out_len, correct_outlen)
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# decoder attentions
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decoder_attentions = outputs.decoder_attentions
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self.assertIsInstance(decoder_attentions, (list, tuple))
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self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
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self.assertListEqual(
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list(decoder_attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
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)
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# cross attentions
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cross_attentions = outputs.cross_attentions
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self.assertIsInstance(cross_attentions, (list, tuple))
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self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
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self.assertListEqual(
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list(cross_attentions[0].shape[-3:]),
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[
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self.model_tester.num_attention_heads,
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decoder_seq_length,
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encoder_key_length,
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],
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)
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# Check attention is always last and order is fine
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = True
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model = self._make_model_instance(config, model_class)
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model.eval()
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with paddle.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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if hasattr(self.model_tester, "num_hidden_states_types"):
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added_hidden_states = self.model_tester.num_hidden_states_types
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elif self.is_encoder_decoder:
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added_hidden_states = 2
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else:
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added_hidden_states = 1
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self.assertEqual(out_len + added_hidden_states, len(outputs))
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self_attentions = outputs.encoder_attentions if self.is_encoder_decoder else outputs.attentions
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self.assertEqual(
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len(self_attentions), self.model_tester.num_hidden_layers + self.model_tester.num_decoder_layers
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)
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self.assertListEqual(
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list(self_attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
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)
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def test_hidden_states_output(self):
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"hidden state include encoder and decoder"
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def check_hidden_states_output(inputs_dict, config, model_class):
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model = self._make_model_instance(config, model_class)
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model.eval()
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with paddle.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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hidden_states = outputs.encoder_hidden_states if self.is_encoder_decoder else outputs.hidden_states
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expected_num_layers = getattr(
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self.model_tester,
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"expected_num_hidden_layers",
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self.model_tester.num_hidden_layers + 1 + self.model_tester.num_decoder_layers + 1,
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)
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self.assertEqual(len(hidden_states), expected_num_layers)
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if hasattr(self.model_tester, "encoder_seq_length"):
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seq_length = self.model_tester.encoder_seq_length
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else:
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seq_length = self.model_tester.seq_length
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self.assertListEqual(
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list(hidden_states[0].shape[-2:]),
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[seq_length, self.model_tester.hidden_size],
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)
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if self.is_encoder_decoder:
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hidden_states = outputs.decoder_hidden_states
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self.assertIsInstance(hidden_states, (list, tuple))
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self.assertEqual(len(hidden_states), expected_num_layers)
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seq_len = getattr(self.model_tester, "seq_length", None)
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decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
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self.assertListEqual(
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list(hidden_states[0].shape[-2:]),
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[decoder_seq_length, self.model_tester.hidden_size],
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)
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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inputs_dict["return_dict"] = True
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for model_class in self.all_model_classes:
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signature = inspect.signature(model_class.forward)
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# signature.parameters is an OrderedDict => so arg_names order is deterministic
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arg_names = [*signature.parameters.keys()]
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if not all(name in arg_names for name in ["output_attentions", "output_hidden_states", "return_dict"]):
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continue
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inputs_dict["output_hidden_states"] = True
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check_hidden_states_output(inputs_dict, config, model_class)
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# TODO(guosheng): check that output_hidden_states also work using config
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