342 lines
15 KiB
Python
342 lines
15 KiB
Python
# coding=utf-8
|
|
# 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
|
|
|
|
from transformers import MobileBertConfig, is_tf_available
|
|
from transformers.testing_utils import require_tf, slow
|
|
|
|
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 (
|
|
TFMobileBertForMaskedLM,
|
|
TFMobileBertForMultipleChoice,
|
|
TFMobileBertForNextSentencePrediction,
|
|
TFMobileBertForPreTraining,
|
|
TFMobileBertForQuestionAnswering,
|
|
TFMobileBertForSequenceClassification,
|
|
TFMobileBertForTokenClassification,
|
|
TFMobileBertModel,
|
|
)
|
|
|
|
|
|
@require_tf
|
|
class TFMobileBertModelTest(TFModelTesterMixin, unittest.TestCase):
|
|
|
|
all_model_classes = (
|
|
(
|
|
TFMobileBertModel,
|
|
TFMobileBertForMaskedLM,
|
|
TFMobileBertForNextSentencePrediction,
|
|
TFMobileBertForPreTraining,
|
|
TFMobileBertForQuestionAnswering,
|
|
TFMobileBertForSequenceClassification,
|
|
TFMobileBertForTokenClassification,
|
|
TFMobileBertForMultipleChoice,
|
|
)
|
|
if is_tf_available()
|
|
else ()
|
|
)
|
|
test_head_masking = False
|
|
test_onnx = False
|
|
|
|
class TFMobileBertModelTester(object):
|
|
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,
|
|
num_choices=4,
|
|
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.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.num_choices = num_choices
|
|
self.scope = scope
|
|
self.embedding_size = embedding_size
|
|
|
|
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 = MobileBertConfig(
|
|
vocab_size=self.vocab_size,
|
|
hidden_size=self.hidden_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,
|
|
embedding_size=self.embedding_size,
|
|
)
|
|
|
|
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
|
|
|
def create_and_check_mobilebert_model(
|
|
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
|
):
|
|
model = TFMobileBertModel(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.hidden_size)
|
|
)
|
|
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
|
|
|
|
def create_and_check_mobilebert_for_masked_lm(
|
|
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
|
):
|
|
model = TFMobileBertForMaskedLM(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_mobilebert_for_next_sequence_prediction(
|
|
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
|
):
|
|
model = TFMobileBertForNextSentencePrediction(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, 2))
|
|
|
|
def create_and_check_mobilebert_for_pretraining(
|
|
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
|
):
|
|
model = TFMobileBertForPreTraining(config=config)
|
|
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
|
|
result = model(inputs)
|
|
self.parent.assertEqual(
|
|
result.prediction_logits.shape, (self.batch_size, self.seq_length, self.vocab_size)
|
|
)
|
|
self.parent.assertEqual(result.seq_relationship_logits.shape, (self.batch_size, 2))
|
|
|
|
def create_and_check_mobilebert_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 = TFMobileBertForSequenceClassification(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_mobilebert_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 = TFMobileBertForMultipleChoice(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_mobilebert_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 = TFMobileBertForTokenClassification(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_mobilebert_for_question_answering(
|
|
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
|
):
|
|
model = TFMobileBertForQuestionAnswering(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
|
|
|
|
def setUp(self):
|
|
self.model_tester = TFMobileBertModelTest.TFMobileBertModelTester(self)
|
|
self.config_tester = ConfigTester(self, config_class=MobileBertConfig, hidden_size=37)
|
|
|
|
def test_config(self):
|
|
self.config_tester.run_common_tests()
|
|
|
|
def test_mobilebert_model(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_mobilebert_model(*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_mobilebert_for_masked_lm(*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_mobilebert_for_multiple_choice(*config_and_inputs)
|
|
|
|
def test_for_next_sequence_prediction(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*config_and_inputs)
|
|
|
|
def test_for_pretraining(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_mobilebert_for_pretraining(*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_mobilebert_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_mobilebert_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_mobilebert_for_token_classification(*config_and_inputs)
|
|
|
|
def test_model_common_attributes(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
list_lm_models = [TFMobileBertForMaskedLM, TFMobileBertForPreTraining]
|
|
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config)
|
|
assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer)
|
|
|
|
if model_class in list_lm_models:
|
|
x = model.get_output_embeddings()
|
|
assert isinstance(x, tf.keras.layers.Layer)
|
|
name = model.get_bias()
|
|
assert isinstance(name, dict)
|
|
for k, v in name.items():
|
|
assert isinstance(v, tf.Variable)
|
|
else:
|
|
x = model.get_output_embeddings()
|
|
assert x is None
|
|
name = model.get_bias()
|
|
assert name is None
|
|
|
|
def test_saved_model_creation(self):
|
|
# This test is too long (>30sec) and makes fail the CI
|
|
pass
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
# for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
|
|
for model_name in ["google/mobilebert-uncased"]:
|
|
model = TFMobileBertModel.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
|
|
|
|
@require_tf
|
|
class TFMobileBertModelIntegrationTest(unittest.TestCase):
|
|
@slow
|
|
def test_inference_masked_lm(self):
|
|
model = TFMobileBertForPreTraining.from_pretrained("google/mobilebert-uncased")
|
|
input_ids = tf.constant([[0, 1, 2, 3, 4, 5]])
|
|
output = model(input_ids)[0]
|
|
|
|
expected_shape = [1, 6, 30522]
|
|
self.assertEqual(output.shape, expected_shape)
|
|
|
|
expected_slice = tf.constant(
|
|
[
|
|
[
|
|
[-4.5919547, -9.248295, -9.645256],
|
|
[-6.7306175, -6.440284, -6.6052837],
|
|
[-7.2743506, -6.7847915, -6.024673],
|
|
]
|
|
]
|
|
)
|
|
tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=1e-4)
|