Move test model folders (#17034)
* move test model folders (TODO: fix imports and others) * fix (potentially partially) imports (in model test modules) * fix (potentially partially) imports (in tokenization test modules) * fix (potentially partially) imports (in feature extraction test modules) * fix import utils.test_modeling_tf_core * fix path ../fixtures/ * fix imports about generation.test_generation_flax_utils * fix more imports * fix fixture path * fix get_test_dir * update module_to_test_file * fix get_tests_dir from wrong transformers.utils * update config.yml (CircleCI) * fix style * remove missing imports * update new model script * update check_repo * update SPECIAL_MODULE_TO_TEST_MAP * fix style * add __init__ * update self-scheduled * fix add_new_model scripts * check one way to get location back * python setup.py build install * fix import in test auto * update self-scheduled.yml * update slack notification script * Add comments about artifact names * fix for yolos Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
0
tests/models/convbert/__init__.py
Normal file
0
tests/models/convbert/__init__.py
Normal file
456
tests/models/convbert/test_modeling_convbert.py
Normal file
456
tests/models/convbert/test_modeling_convbert.py
Normal file
@@ -0,0 +1,456 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2021 The HuggingFace Inc. 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.
|
||||
""" Testing suite for the PyTorch ConvBERT model. """
|
||||
import os
|
||||
import tempfile
|
||||
import unittest
|
||||
|
||||
from transformers import ConvBertConfig, is_torch_available
|
||||
from transformers.models.auto import get_values
|
||||
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
|
||||
|
||||
from ...test_configuration_common import ConfigTester
|
||||
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
|
||||
|
||||
|
||||
if is_torch_available():
|
||||
import torch
|
||||
|
||||
from transformers import (
|
||||
MODEL_FOR_QUESTION_ANSWERING_MAPPING,
|
||||
ConvBertForMaskedLM,
|
||||
ConvBertForMultipleChoice,
|
||||
ConvBertForQuestionAnswering,
|
||||
ConvBertForSequenceClassification,
|
||||
ConvBertForTokenClassification,
|
||||
ConvBertModel,
|
||||
)
|
||||
from transformers.models.convbert.modeling_convbert import CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST
|
||||
|
||||
|
||||
class ConvBertModelTester:
|
||||
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,
|
||||
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
|
||||
|
||||
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 = 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
|
||||
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 = self.get_config()
|
||||
|
||||
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
||||
|
||||
def get_config(self):
|
||||
return ConvBertConfig(
|
||||
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,
|
||||
is_decoder=False,
|
||||
initializer_range=self.initializer_range,
|
||||
)
|
||||
|
||||
def prepare_config_and_inputs_for_decoder(self):
|
||||
(
|
||||
config,
|
||||
input_ids,
|
||||
token_type_ids,
|
||||
input_mask,
|
||||
sequence_labels,
|
||||
token_labels,
|
||||
choice_labels,
|
||||
) = self.prepare_config_and_inputs()
|
||||
|
||||
config.is_decoder = True
|
||||
encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
|
||||
encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
|
||||
|
||||
return (
|
||||
config,
|
||||
input_ids,
|
||||
token_type_ids,
|
||||
input_mask,
|
||||
sequence_labels,
|
||||
token_labels,
|
||||
choice_labels,
|
||||
encoder_hidden_states,
|
||||
encoder_attention_mask,
|
||||
)
|
||||
|
||||
def create_and_check_model(
|
||||
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
||||
):
|
||||
model = ConvBertModel(config=config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
|
||||
result = model(input_ids, token_type_ids=token_type_ids)
|
||||
result = model(input_ids)
|
||||
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
|
||||
|
||||
def create_and_check_for_masked_lm(
|
||||
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
||||
):
|
||||
model = ConvBertForMaskedLM(config=config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
|
||||
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
|
||||
|
||||
def create_and_check_for_question_answering(
|
||||
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
||||
):
|
||||
model = ConvBertForQuestionAnswering(config=config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
result = model(
|
||||
input_ids,
|
||||
attention_mask=input_mask,
|
||||
token_type_ids=token_type_ids,
|
||||
start_positions=sequence_labels,
|
||||
end_positions=sequence_labels,
|
||||
)
|
||||
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 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 = ConvBertForSequenceClassification(config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
|
||||
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
|
||||
|
||||
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 = ConvBertForTokenClassification(config=config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
|
||||
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, 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 = ConvBertForMultipleChoice(config=config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
|
||||
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
|
||||
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
|
||||
result = model(
|
||||
multiple_choice_inputs_ids,
|
||||
attention_mask=multiple_choice_input_mask,
|
||||
token_type_ids=multiple_choice_token_type_ids,
|
||||
labels=choice_labels,
|
||||
)
|
||||
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
|
||||
|
||||
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_torch
|
||||
class ConvBertModelTest(ModelTesterMixin, unittest.TestCase):
|
||||
|
||||
all_model_classes = (
|
||||
(
|
||||
ConvBertModel,
|
||||
ConvBertForMaskedLM,
|
||||
ConvBertForMultipleChoice,
|
||||
ConvBertForQuestionAnswering,
|
||||
ConvBertForSequenceClassification,
|
||||
ConvBertForTokenClassification,
|
||||
)
|
||||
if is_torch_available()
|
||||
else ()
|
||||
)
|
||||
test_pruning = False
|
||||
test_head_masking = False
|
||||
|
||||
def setUp(self):
|
||||
self.model_tester = ConvBertModelTester(self)
|
||||
self.config_tester = ConfigTester(self, config_class=ConvBertConfig, hidden_size=37)
|
||||
|
||||
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_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_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_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_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_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)
|
||||
|
||||
@slow
|
||||
def test_model_from_pretrained(self):
|
||||
for model_name in CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
|
||||
model = ConvBertModel.from_pretrained(model_name)
|
||||
self.assertIsNotNone(model)
|
||||
|
||||
def test_attention_outputs(self):
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
config.return_dict = True
|
||||
|
||||
seq_len = getattr(self.model_tester, "seq_length", None)
|
||||
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
|
||||
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
|
||||
decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length)
|
||||
encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
|
||||
chunk_length = getattr(self.model_tester, "chunk_length", None)
|
||||
if chunk_length is not None and hasattr(self.model_tester, "num_hashes"):
|
||||
encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes
|
||||
|
||||
for model_class in self.all_model_classes:
|
||||
inputs_dict["output_attentions"] = True
|
||||
inputs_dict["output_hidden_states"] = False
|
||||
config.return_dict = True
|
||||
model = model_class(config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
with torch.no_grad():
|
||||
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
||||
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
|
||||
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
|
||||
|
||||
# check that output_attentions also work using config
|
||||
del inputs_dict["output_attentions"]
|
||||
config.output_attentions = True
|
||||
model = model_class(config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
with torch.no_grad():
|
||||
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
||||
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
|
||||
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
|
||||
|
||||
if chunk_length is not None:
|
||||
self.assertListEqual(
|
||||
list(attentions[0].shape[-4:]),
|
||||
[self.model_tester.num_attention_heads / 2, encoder_seq_length, chunk_length, encoder_key_length],
|
||||
)
|
||||
else:
|
||||
self.assertListEqual(
|
||||
list(attentions[0].shape[-3:]),
|
||||
[self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length],
|
||||
)
|
||||
out_len = len(outputs)
|
||||
|
||||
if self.is_encoder_decoder:
|
||||
correct_outlen = 5
|
||||
|
||||
# loss is at first position
|
||||
if "labels" in inputs_dict:
|
||||
correct_outlen += 1 # loss is added to beginning
|
||||
# Question Answering model returns start_logits and end_logits
|
||||
if model_class in get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING):
|
||||
correct_outlen += 1 # start_logits and end_logits instead of only 1 output
|
||||
if "past_key_values" in outputs:
|
||||
correct_outlen += 1 # past_key_values have been returned
|
||||
|
||||
self.assertEqual(out_len, correct_outlen)
|
||||
|
||||
# decoder attentions
|
||||
decoder_attentions = outputs.decoder_attentions
|
||||
self.assertIsInstance(decoder_attentions, (list, tuple))
|
||||
self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
|
||||
self.assertListEqual(
|
||||
list(decoder_attentions[0].shape[-3:]),
|
||||
[self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
|
||||
)
|
||||
|
||||
# cross attentions
|
||||
cross_attentions = outputs.cross_attentions
|
||||
self.assertIsInstance(cross_attentions, (list, tuple))
|
||||
self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
|
||||
self.assertListEqual(
|
||||
list(cross_attentions[0].shape[-3:]),
|
||||
[
|
||||
self.model_tester.num_attention_heads,
|
||||
decoder_seq_length,
|
||||
encoder_key_length,
|
||||
],
|
||||
)
|
||||
|
||||
# Check attention is always last and order is fine
|
||||
inputs_dict["output_attentions"] = True
|
||||
inputs_dict["output_hidden_states"] = True
|
||||
model = model_class(config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
with torch.no_grad():
|
||||
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
||||
|
||||
if hasattr(self.model_tester, "num_hidden_states_types"):
|
||||
added_hidden_states = self.model_tester.num_hidden_states_types
|
||||
elif self.is_encoder_decoder:
|
||||
added_hidden_states = 2
|
||||
else:
|
||||
added_hidden_states = 1
|
||||
self.assertEqual(out_len + added_hidden_states, len(outputs))
|
||||
|
||||
self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
|
||||
|
||||
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
|
||||
if chunk_length is not None:
|
||||
self.assertListEqual(
|
||||
list(self_attentions[0].shape[-4:]),
|
||||
[self.model_tester.num_attention_heads / 2, encoder_seq_length, chunk_length, encoder_key_length],
|
||||
)
|
||||
else:
|
||||
self.assertListEqual(
|
||||
list(self_attentions[0].shape[-3:]),
|
||||
[self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length],
|
||||
)
|
||||
|
||||
@slow
|
||||
@require_torch_gpu
|
||||
def test_torchscript_device_change(self):
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
for model_class in self.all_model_classes:
|
||||
|
||||
# ConvBertForMultipleChoice behaves incorrectly in JIT environments.
|
||||
if model_class == ConvBertForMultipleChoice:
|
||||
return
|
||||
|
||||
config.torchscript = True
|
||||
model = model_class(config=config)
|
||||
|
||||
inputs_dict = self._prepare_for_class(inputs_dict, model_class)
|
||||
traced_model = torch.jit.trace(
|
||||
model, (inputs_dict["input_ids"].to("cpu"), inputs_dict["attention_mask"].to("cpu"))
|
||||
)
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmp:
|
||||
torch.jit.save(traced_model, os.path.join(tmp, "traced_model.pt"))
|
||||
loaded = torch.jit.load(os.path.join(tmp, "traced_model.pt"), map_location=torch_device)
|
||||
loaded(inputs_dict["input_ids"].to(torch_device), inputs_dict["attention_mask"].to(torch_device))
|
||||
|
||||
|
||||
@require_torch
|
||||
class ConvBertModelIntegrationTest(unittest.TestCase):
|
||||
@slow
|
||||
def test_inference_no_head(self):
|
||||
model = ConvBertModel.from_pretrained("YituTech/conv-bert-base")
|
||||
input_ids = torch.tensor([[1, 2, 3, 4, 5, 6]])
|
||||
output = model(input_ids)[0]
|
||||
|
||||
expected_shape = torch.Size((1, 6, 768))
|
||||
self.assertEqual(output.shape, expected_shape)
|
||||
|
||||
expected_slice = torch.tensor(
|
||||
[[[-0.0864, -0.4898, -0.3677], [0.1434, -0.2952, -0.7640], [-0.0112, -0.4432, -0.5432]]]
|
||||
)
|
||||
|
||||
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
|
||||
410
tests/models/convbert/test_modeling_tf_convbert.py
Normal file
410
tests/models/convbert/test_modeling_tf_convbert.py
Normal file
@@ -0,0 +1,410 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2021 The HuggingFace Inc. 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 os
|
||||
import tempfile
|
||||
import unittest
|
||||
|
||||
from transformers import ConvBertConfig, 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, random_attention_mask
|
||||
|
||||
|
||||
if is_tf_available():
|
||||
import tensorflow as tf
|
||||
|
||||
from transformers import (
|
||||
TFConvBertForMaskedLM,
|
||||
TFConvBertForMultipleChoice,
|
||||
TFConvBertForQuestionAnswering,
|
||||
TFConvBertForSequenceClassification,
|
||||
TFConvBertForTokenClassification,
|
||||
TFConvBertModel,
|
||||
)
|
||||
|
||||
|
||||
class TFConvBertModelTester:
|
||||
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,
|
||||
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 = 13
|
||||
self.seq_length = 7
|
||||
self.is_training = True
|
||||
self.use_input_mask = True
|
||||
self.use_token_type_ids = True
|
||||
self.use_labels = True
|
||||
self.vocab_size = 99
|
||||
self.hidden_size = 384
|
||||
self.num_hidden_layers = 5
|
||||
self.num_attention_heads = 4
|
||||
self.intermediate_size = 37
|
||||
self.hidden_act = "gelu"
|
||||
self.hidden_dropout_prob = 0.1
|
||||
self.attention_probs_dropout_prob = 0.1
|
||||
self.max_position_embeddings = 512
|
||||
self.type_vocab_size = 16
|
||||
self.type_sequence_label_size = 2
|
||||
self.initializer_range = 0.02
|
||||
self.num_labels = 3
|
||||
self.num_choices = 4
|
||||
self.embedding_size = 128
|
||||
self.head_ratio = 2
|
||||
self.conv_kernel_size = 9
|
||||
self.num_groups = 1
|
||||
self.scope = None
|
||||
|
||||
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 = 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
|
||||
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 = ConvBertConfig(
|
||||
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,
|
||||
return_dict=True,
|
||||
)
|
||||
|
||||
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 = TFConvBertModel(config=config)
|
||||
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
|
||||
|
||||
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))
|
||||
|
||||
def create_and_check_for_masked_lm(
|
||||
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
||||
):
|
||||
model = TFConvBertForMaskedLM(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 = TFConvBertForSequenceClassification(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 = TFConvBertForMultipleChoice(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 = TFConvBertForTokenClassification(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 = TFConvBertForQuestionAnswering(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 TFConvBertModelTest(TFModelTesterMixin, unittest.TestCase):
|
||||
|
||||
all_model_classes = (
|
||||
(
|
||||
TFConvBertModel,
|
||||
TFConvBertForMaskedLM,
|
||||
TFConvBertForQuestionAnswering,
|
||||
TFConvBertForSequenceClassification,
|
||||
TFConvBertForTokenClassification,
|
||||
TFConvBertForMultipleChoice,
|
||||
)
|
||||
if is_tf_available()
|
||||
else ()
|
||||
)
|
||||
test_pruning = False
|
||||
test_head_masking = False
|
||||
test_onnx = False
|
||||
|
||||
def setUp(self):
|
||||
self.model_tester = TFConvBertModelTester(self)
|
||||
self.config_tester = ConfigTester(self, config_class=ConvBertConfig, hidden_size=37)
|
||||
|
||||
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_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_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_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_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_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)
|
||||
|
||||
@slow
|
||||
def test_saved_model_creation_extended(self):
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
config.output_hidden_states = True
|
||||
config.output_attentions = True
|
||||
|
||||
if hasattr(config, "use_cache"):
|
||||
config.use_cache = True
|
||||
|
||||
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", self.model_tester.seq_length)
|
||||
encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
|
||||
|
||||
for model_class in self.all_model_classes:
|
||||
class_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
|
||||
model = model_class(config)
|
||||
num_out = len(model(class_inputs_dict))
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmpdirname:
|
||||
model.save_pretrained(tmpdirname, saved_model=True)
|
||||
saved_model_dir = os.path.join(tmpdirname, "saved_model", "1")
|
||||
model = tf.keras.models.load_model(saved_model_dir)
|
||||
outputs = model(class_inputs_dict)
|
||||
|
||||
if self.is_encoder_decoder:
|
||||
output_hidden_states = outputs["encoder_hidden_states"]
|
||||
output_attentions = outputs["encoder_attentions"]
|
||||
else:
|
||||
output_hidden_states = outputs["hidden_states"]
|
||||
output_attentions = outputs["attentions"]
|
||||
|
||||
self.assertEqual(len(outputs), num_out)
|
||||
|
||||
expected_num_layers = getattr(
|
||||
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
|
||||
)
|
||||
|
||||
self.assertEqual(len(output_hidden_states), expected_num_layers)
|
||||
self.assertListEqual(
|
||||
list(output_hidden_states[0].shape[-2:]),
|
||||
[self.model_tester.seq_length, self.model_tester.hidden_size],
|
||||
)
|
||||
|
||||
self.assertEqual(len(output_attentions), self.model_tester.num_hidden_layers)
|
||||
self.assertListEqual(
|
||||
list(output_attentions[0].shape[-3:]),
|
||||
[self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length],
|
||||
)
|
||||
|
||||
@slow
|
||||
def test_model_from_pretrained(self):
|
||||
model = TFConvBertModel.from_pretrained("YituTech/conv-bert-base")
|
||||
self.assertIsNotNone(model)
|
||||
|
||||
def test_attention_outputs(self):
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
config.return_dict = True
|
||||
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", self.model_tester.seq_length)
|
||||
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", self.model_tester.seq_length)
|
||||
decoder_key_length = getattr(self.model_tester, "key_length", decoder_seq_length)
|
||||
encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
|
||||
|
||||
def check_decoder_attentions_output(outputs):
|
||||
out_len = len(outputs)
|
||||
self.assertEqual(out_len % 2, 0)
|
||||
decoder_attentions = outputs.decoder_attentions
|
||||
self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
|
||||
self.assertListEqual(
|
||||
list(decoder_attentions[0].shape[-3:]),
|
||||
[self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length],
|
||||
)
|
||||
|
||||
def check_encoder_attentions_output(outputs):
|
||||
attentions = [
|
||||
t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
|
||||
]
|
||||
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
|
||||
self.assertListEqual(
|
||||
list(attentions[0].shape[-3:]),
|
||||
[self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length],
|
||||
)
|
||||
|
||||
for model_class in self.all_model_classes:
|
||||
inputs_dict["output_attentions"] = True
|
||||
config.output_hidden_states = False
|
||||
model = model_class(config)
|
||||
outputs = model(self._prepare_for_class(inputs_dict, model_class))
|
||||
out_len = len(outputs)
|
||||
self.assertEqual(config.output_hidden_states, False)
|
||||
check_encoder_attentions_output(outputs)
|
||||
|
||||
if self.is_encoder_decoder:
|
||||
model = model_class(config)
|
||||
outputs = model(self._prepare_for_class(inputs_dict, model_class))
|
||||
self.assertEqual(config.output_hidden_states, False)
|
||||
check_decoder_attentions_output(outputs)
|
||||
|
||||
# Check that output attentions can also be changed via the config
|
||||
del inputs_dict["output_attentions"]
|
||||
config.output_attentions = True
|
||||
model = model_class(config)
|
||||
outputs = model(self._prepare_for_class(inputs_dict, model_class))
|
||||
self.assertEqual(config.output_hidden_states, False)
|
||||
check_encoder_attentions_output(outputs)
|
||||
|
||||
# Check attention is always last and order is fine
|
||||
inputs_dict["output_attentions"] = True
|
||||
config.output_hidden_states = True
|
||||
model = model_class(config)
|
||||
outputs = model(self._prepare_for_class(inputs_dict, model_class))
|
||||
|
||||
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1), len(outputs))
|
||||
self.assertEqual(model.config.output_hidden_states, True)
|
||||
check_encoder_attentions_output(outputs)
|
||||
|
||||
|
||||
@require_tf
|
||||
class TFConvBertModelIntegrationTest(unittest.TestCase):
|
||||
@slow
|
||||
def test_inference_masked_lm(self):
|
||||
model = TFConvBertModel.from_pretrained("YituTech/conv-bert-base")
|
||||
input_ids = tf.constant([[0, 1, 2, 3, 4, 5]])
|
||||
output = model(input_ids)[0]
|
||||
|
||||
expected_shape = [1, 6, 768]
|
||||
self.assertEqual(output.shape, expected_shape)
|
||||
|
||||
expected_slice = tf.constant(
|
||||
[
|
||||
[
|
||||
[-0.03475493, -0.4686034, -0.30638832],
|
||||
[0.22637248, -0.26988646, -0.7423424],
|
||||
[0.10324868, -0.45013508, -0.58280784],
|
||||
]
|
||||
]
|
||||
)
|
||||
tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=1e-4)
|
||||
Reference in New Issue
Block a user