wehub-resource-sync
283 lines
10 KiB
Python
283 lines
10 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
|
|
# Copyright 2020 The HuggingFace Team. All rights reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import unittest
|
|
|
|
import paddle
|
|
from parameterized import parameterized_class
|
|
|
|
from paddlenlp.transformers import (
|
|
MobileBertConfig,
|
|
MobileBertForQuestionAnswering,
|
|
MobileBertForSequenceClassification,
|
|
MobileBertModel,
|
|
PretrainedModel,
|
|
)
|
|
|
|
from ...testing_utils import slow
|
|
from ..test_configuration_common import ConfigTester
|
|
from ..test_modeling_common import (
|
|
ModelTesterMixin,
|
|
ModelTesterPretrainedMixin,
|
|
ids_tensor,
|
|
random_attention_mask,
|
|
)
|
|
|
|
|
|
class MobileBertModelTester:
|
|
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,
|
|
hidden_size=32,
|
|
embedding_size=32,
|
|
num_hidden_layers=5,
|
|
num_attention_heads=4,
|
|
intermediate_size=37,
|
|
hidden_act="gelu",
|
|
hidden_dropout_prob=0.1,
|
|
attention_probs_dropout_prob=0.1,
|
|
max_position_embeddings=512,
|
|
type_vocab_size=16,
|
|
type_sequence_label_size=2,
|
|
initializer_range=0.02,
|
|
num_labels=3,
|
|
scope=None,
|
|
):
|
|
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.hidden_size = hidden_size
|
|
self.embedding_size = embedding_size
|
|
self.num_hidden_layers = num_hidden_layers
|
|
self.num_attention_heads = num_attention_heads
|
|
self.intermediate_size = intermediate_size
|
|
self.hidden_act = hidden_act
|
|
self.hidden_dropout_prob = hidden_dropout_prob
|
|
self.attention_probs_dropout_prob = attention_probs_dropout_prob
|
|
self.max_position_embeddings = max_position_embeddings
|
|
self.type_vocab_size = type_vocab_size
|
|
self.type_sequence_label_size = type_sequence_label_size
|
|
self.initializer_range = initializer_range
|
|
self.num_labels = num_labels
|
|
self.scope = scope
|
|
|
|
def prepare_config_and_inputs(self):
|
|
inputs = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
|
|
|
|
input_mask = None
|
|
if self.use_input_mask:
|
|
input_mask = random_attention_mask([self.batch_size, self.seq_length])
|
|
|
|
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
|
|
if self.use_labels:
|
|
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
|
|
|
|
config = self.get_config()
|
|
|
|
return config, inputs, token_type_ids, input_mask, sequence_labels
|
|
|
|
def get_config(self):
|
|
return MobileBertConfig(
|
|
vocab_size=self.vocab_size,
|
|
hidden_size=self.hidden_size,
|
|
embedding_size=self.embedding_size,
|
|
num_hidden_layers=self.num_hidden_layers,
|
|
num_attention_heads=self.num_attention_heads,
|
|
intermediate_size=self.intermediate_size,
|
|
hidden_act=self.hidden_act,
|
|
hidden_dropout_prob=self.hidden_dropout_prob,
|
|
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
|
|
max_position_embeddings=self.max_position_embeddings,
|
|
type_vocab_size=self.type_vocab_size,
|
|
initializer_range=self.initializer_range,
|
|
)
|
|
|
|
def create_and_check_model(self, config, input_ids, token_type_ids, input_mask, sequence_labels):
|
|
model = MobileBertModel(config=config)
|
|
model.eval()
|
|
result = model(
|
|
input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, return_dict=self.parent.return_dict
|
|
)
|
|
result = model(input_ids, return_dict=self.parent.return_dict)
|
|
self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.hidden_size])
|
|
self.parent.assertEqual(result[1].shape, [self.batch_size, self.hidden_size])
|
|
|
|
def create_and_check_for_question_answering(self, config, input_ids, token_type_ids, input_mask, sequence_labels):
|
|
model = MobileBertForQuestionAnswering(config=config)
|
|
model.eval()
|
|
result = model(
|
|
input_ids,
|
|
attention_mask=input_mask,
|
|
token_type_ids=token_type_ids,
|
|
start_positions=sequence_labels,
|
|
end_positions=sequence_labels,
|
|
return_dict=self.parent.return_dict,
|
|
)
|
|
if sequence_labels is not None:
|
|
start_logits, end_logits = result[1], result[2]
|
|
else:
|
|
start_logits, end_logits = result[0], result[1]
|
|
|
|
self.parent.assertEqual(start_logits.shape, [self.batch_size, self.seq_length])
|
|
self.parent.assertEqual(end_logits.shape, [self.batch_size, self.seq_length])
|
|
|
|
def create_and_check_for_sequence_classification(
|
|
self, config, input_ids, token_type_ids, input_mask, sequence_labels
|
|
):
|
|
config.num_labels = self.num_labels
|
|
model = MobileBertForSequenceClassification(config)
|
|
model.eval()
|
|
result = model(
|
|
input_ids,
|
|
attention_mask=input_mask,
|
|
token_type_ids=token_type_ids,
|
|
labels=sequence_labels,
|
|
return_dict=self.parent.return_dict,
|
|
)
|
|
if sequence_labels is not None:
|
|
result = result[1:]
|
|
elif paddle.is_tensor(result):
|
|
result = [result]
|
|
self.parent.assertEqual(result[0].shape, [self.batch_size, self.num_labels])
|
|
|
|
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,
|
|
) = config_and_inputs
|
|
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
|
|
return config, inputs_dict
|
|
|
|
|
|
@parameterized_class(
|
|
("return_dict", "use_labels"),
|
|
[
|
|
[False, False],
|
|
[False, True],
|
|
[True, False],
|
|
[True, True],
|
|
],
|
|
)
|
|
class MobileBertModelTest(ModelTesterMixin, unittest.TestCase):
|
|
base_model_class = MobileBertModel
|
|
return_dict = False
|
|
use_labels = False
|
|
is_decoder = True
|
|
|
|
all_model_classes = (
|
|
MobileBertModel,
|
|
MobileBertForSequenceClassification,
|
|
MobileBertForQuestionAnswering,
|
|
)
|
|
|
|
def setUp(self):
|
|
self.model_tester = MobileBertModelTester(self)
|
|
self.config_tester = ConfigTester(self, config_class=MobileBertConfig, vocab_size=256, hidden_size=24)
|
|
|
|
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_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_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_model_from_pretrained(self):
|
|
for model_name in MobileBertModel.pretrained_init_configuration.keys():
|
|
model = MobileBertModel.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
|
|
|
|
class MobileBertModelIntegrationTest(unittest.TestCase, ModelTesterPretrainedMixin):
|
|
base_model_class: PretrainedModel = MobileBertModel
|
|
# hf_remote_test_model_path: str = "google/mobilebert-uncased"
|
|
paddlehub_remote_test_model_name: str = "mobilebert-uncased"
|
|
|
|
@slow
|
|
def test_inference_no_attention(self):
|
|
model = MobileBertModel.from_pretrained("mobilebert-uncased")
|
|
model.eval()
|
|
input_ids = paddle.to_tensor([[101, 7110, 1005, 1056, 2023, 11333, 17413, 1029, 102]])
|
|
with paddle.no_grad():
|
|
output = model(input_ids)[0]
|
|
expected_shape = [1, 9, 512]
|
|
self.assertEqual(output.shape, expected_shape)
|
|
expected_slice = paddle.to_tensor(
|
|
[
|
|
[
|
|
[-2.4736526e07, 8.2691656e04, 1.6521838e05],
|
|
[-5.7541704e-01, 3.9056022e00, 4.4011507e00],
|
|
[2.6047359e00, 1.5677652e00, -1.7324188e-01],
|
|
]
|
|
]
|
|
)
|
|
lower_bound = paddle.all((expected_slice / output[..., :3, :3]) >= 1 - 1e-3)
|
|
upper_bound = paddle.all((expected_slice / output[..., :3, :3]) <= 1 + 1e-3)
|
|
|
|
self.assertTrue(lower_bound and upper_bound)
|
|
|
|
@slow
|
|
def test_inference_with_attention(self):
|
|
model = MobileBertModel.from_pretrained("mobilebert-uncased")
|
|
model.eval()
|
|
input_ids = paddle.to_tensor([[101, 7110, 1005, 1056, 2023, 11333, 17413, 1029, 102]])
|
|
attention_mask = paddle.to_tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1]])
|
|
with paddle.no_grad():
|
|
output = model(input_ids, attention_mask=attention_mask)[0]
|
|
expected_shape = [1, 9, 512]
|
|
self.assertEqual(output.shape, expected_shape)
|
|
|
|
expected_slice = paddle.to_tensor(
|
|
[
|
|
[
|
|
[2.96605349, 3.73147392, -0.20700839],
|
|
[2.02441382, 0.04513174, 3.61004543],
|
|
[4.02399778, -0.25662401, 1.62328660],
|
|
]
|
|
]
|
|
)
|
|
self.assertTrue(paddle.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|