wehub-resource-sync
624 lines
22 KiB
Python
624 lines
22 KiB
Python
# Copyright (c) 2022 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 random
|
|
import tempfile
|
|
import unittest
|
|
|
|
import numpy as np
|
|
import paddle
|
|
from parameterized import parameterized, parameterized_class
|
|
|
|
from paddlenlp.transformers import (
|
|
ElectraConfig,
|
|
ElectraDiscriminator,
|
|
ElectraForMaskedLM,
|
|
ElectraForMultipleChoice,
|
|
ElectraForPretraining,
|
|
ElectraForQuestionAnswering,
|
|
ElectraForSequenceClassification,
|
|
ElectraForTokenClassification,
|
|
ElectraGenerator,
|
|
ElectraModel,
|
|
ElectraPretrainedModel,
|
|
)
|
|
from tests.testing_utils import require_package, slow
|
|
from tests.transformers.test_modeling_common import (
|
|
ModelTesterMixin,
|
|
floats_tensor,
|
|
ids_tensor,
|
|
random_attention_mask,
|
|
)
|
|
|
|
|
|
class ElectraModelTester:
|
|
def __init__(
|
|
self,
|
|
parent,
|
|
):
|
|
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_inputs_embeds = False
|
|
self.vocab_size = 99
|
|
self.embedding_size = 32
|
|
self.hidden_size = 32
|
|
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 = 2
|
|
self.initializer_range = 0.02
|
|
self.pad_token_id = 0
|
|
self.layer_norm_eps = 1e-12
|
|
self.type_sequence_label_size = 2
|
|
self.num_classes = 3
|
|
self.num_choices = 2
|
|
|
|
def prepare_config_and_inputs(self):
|
|
input_ids = None
|
|
inputs_embeds = None
|
|
if self.use_inputs_embeds:
|
|
inputs_embeds = floats_tensor([self.batch_size, self.seq_length, self.embedding_size])
|
|
else:
|
|
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.parent.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_classes)
|
|
choice_labels = ids_tensor([self.batch_size], self.num_choices)
|
|
|
|
config = self.get_config()
|
|
return (
|
|
config,
|
|
input_ids,
|
|
token_type_ids,
|
|
input_mask,
|
|
inputs_embeds,
|
|
sequence_labels,
|
|
token_labels,
|
|
choice_labels,
|
|
)
|
|
|
|
def get_config(self):
|
|
return ElectraConfig(
|
|
vocab_size=self.vocab_size,
|
|
embedding_size=self.embedding_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,
|
|
pad_token_id=self.pad_token_id,
|
|
)
|
|
|
|
def create_and_check_electra_model(
|
|
self,
|
|
config,
|
|
input_ids,
|
|
token_type_ids,
|
|
input_mask,
|
|
inputs_embeds,
|
|
sequence_labels,
|
|
token_labels,
|
|
choice_labels,
|
|
):
|
|
model = ElectraModel(config)
|
|
model.eval()
|
|
result = model(
|
|
input_ids,
|
|
attention_mask=input_mask,
|
|
token_type_ids=token_type_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
return_dict=self.parent.return_dict,
|
|
)
|
|
result = model(input_ids, token_type_ids=token_type_ids)
|
|
result = model(input_ids, return_dict=self.parent.return_dict)
|
|
|
|
if paddle.is_tensor(result):
|
|
result = [result]
|
|
|
|
self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.hidden_size])
|
|
|
|
def create_and_check_electra_model_cache(
|
|
self,
|
|
config,
|
|
input_ids,
|
|
token_type_ids,
|
|
input_mask,
|
|
inputs_embeds,
|
|
sequence_labels,
|
|
token_labels,
|
|
choice_labels,
|
|
):
|
|
model = ElectraModel(config)
|
|
model.eval()
|
|
|
|
input_ids = ids_tensor((self.batch_size, self.seq_length), self.vocab_size)
|
|
input_token_types = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
|
|
|
|
# first forward pass
|
|
first_pass_outputs = model(input_ids, token_type_ids=input_token_types, use_cache=True, return_dict=True)
|
|
past_key_values = first_pass_outputs.past_key_values
|
|
|
|
# fully-visible attention mask
|
|
attention_mask = paddle.ones([self.batch_size, self.seq_length * 2])
|
|
|
|
# second forward pass with past_key_values with visible mask
|
|
second_pass_outputs = model(
|
|
input_ids,
|
|
token_type_ids=input_token_types,
|
|
attention_mask=attention_mask,
|
|
past_key_values=past_key_values,
|
|
return_dict=self.parent.return_dict,
|
|
)
|
|
|
|
# last_hidden_state should have the same shape but different values when given past_key_values
|
|
if self.parent.return_dict:
|
|
self.parent.assertEqual(
|
|
second_pass_outputs.last_hidden_state.shape, first_pass_outputs.last_hidden_state.shape
|
|
)
|
|
self.parent.assertFalse(
|
|
paddle.allclose(second_pass_outputs.last_hidden_state, first_pass_outputs.last_hidden_state)
|
|
)
|
|
else:
|
|
self.parent.assertEqual(second_pass_outputs.shape, first_pass_outputs[0].shape)
|
|
self.parent.assertFalse(paddle.allclose(second_pass_outputs, first_pass_outputs[0]))
|
|
|
|
def create_and_check_electra_for_masked_lm(
|
|
self,
|
|
config,
|
|
input_ids,
|
|
token_type_ids,
|
|
input_mask,
|
|
inputs_embeds,
|
|
sequence_labels,
|
|
token_labels,
|
|
choice_labels,
|
|
):
|
|
model = ElectraForMaskedLM(config)
|
|
model.eval()
|
|
result = model(
|
|
input_ids,
|
|
attention_mask=input_mask,
|
|
token_type_ids=token_type_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
labels=token_labels,
|
|
return_dict=self.parent.return_dict,
|
|
)
|
|
if not self.parent.return_dict and token_labels is None:
|
|
self.parent.assertTrue(paddle.is_tensor(result))
|
|
|
|
if paddle.is_tensor(result):
|
|
result = [result]
|
|
elif token_labels is not None:
|
|
result = result[1:]
|
|
|
|
self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.vocab_size])
|
|
|
|
def create_and_check_electra_for_token_classification(
|
|
self,
|
|
config,
|
|
input_ids,
|
|
token_type_ids,
|
|
input_mask,
|
|
inputs_embeds,
|
|
sequence_labels,
|
|
token_labels,
|
|
choice_labels,
|
|
):
|
|
config.num_classes = self.num_classes
|
|
model = ElectraForTokenClassification(config)
|
|
model.eval()
|
|
result = model(
|
|
input_ids,
|
|
attention_mask=input_mask,
|
|
token_type_ids=token_type_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
labels=token_labels,
|
|
return_dict=self.parent.return_dict,
|
|
)
|
|
|
|
if not self.parent.return_dict and token_labels is None:
|
|
self.parent.assertTrue(paddle.is_tensor(result))
|
|
|
|
if paddle.is_tensor(result):
|
|
result = [result]
|
|
elif token_labels is not None:
|
|
result = result[1:]
|
|
|
|
self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.num_classes])
|
|
|
|
def create_and_check_electra_for_pretraining(
|
|
self,
|
|
config,
|
|
input_ids,
|
|
token_type_ids,
|
|
input_mask,
|
|
inputs_embeds,
|
|
sequence_labels,
|
|
token_labels,
|
|
choice_labels,
|
|
):
|
|
model = ElectraForPretraining(config)
|
|
model.eval()
|
|
|
|
generator_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
|
|
raw_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
|
|
|
|
result = model(
|
|
input_ids,
|
|
attention_mask=input_mask,
|
|
raw_input_ids=raw_input_ids,
|
|
token_type_ids=token_type_ids,
|
|
generator_labels=generator_labels,
|
|
)
|
|
self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.vocab_size])
|
|
self.parent.assertEqual(result[1].shape, [self.batch_size, self.seq_length])
|
|
self.parent.assertEqual(result[2].shape, [self.batch_size, self.seq_length])
|
|
|
|
def create_and_check_electra_for_sequence_classification(
|
|
self,
|
|
config,
|
|
input_ids,
|
|
token_type_ids,
|
|
input_mask,
|
|
inputs_embeds,
|
|
sequence_labels,
|
|
token_labels,
|
|
choice_labels,
|
|
):
|
|
config.num_classes = self.type_sequence_label_size
|
|
model = ElectraForSequenceClassification(config)
|
|
model.eval()
|
|
result = model(
|
|
input_ids,
|
|
attention_mask=input_mask,
|
|
token_type_ids=token_type_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
labels=sequence_labels,
|
|
return_dict=self.parent.return_dict,
|
|
)
|
|
if not self.parent.return_dict and token_labels is None:
|
|
self.parent.assertTrue(paddle.is_tensor(result))
|
|
|
|
if paddle.is_tensor(result):
|
|
result = [result]
|
|
elif token_labels is not None:
|
|
result = result[1:]
|
|
|
|
self.parent.assertEqual(result[0].shape, [self.batch_size, self.type_sequence_label_size])
|
|
|
|
def create_and_check_electra_for_question_answering(
|
|
self,
|
|
config,
|
|
input_ids,
|
|
token_type_ids,
|
|
input_mask,
|
|
inputs_embeds,
|
|
sequence_labels,
|
|
token_labels,
|
|
choice_labels,
|
|
):
|
|
model = ElectraForQuestionAnswering(config)
|
|
model.eval()
|
|
result = model(
|
|
input_ids,
|
|
attention_mask=input_mask,
|
|
token_type_ids=token_type_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
start_positions=sequence_labels,
|
|
end_positions=sequence_labels,
|
|
return_dict=self.parent.return_dict,
|
|
)
|
|
|
|
if token_labels is not None:
|
|
result = result[1:]
|
|
|
|
self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length])
|
|
self.parent.assertEqual(result[1].shape, [self.batch_size, self.seq_length])
|
|
|
|
def create_and_check_electra_for_multiple_choice(
|
|
self,
|
|
config,
|
|
input_ids,
|
|
token_type_ids,
|
|
input_mask,
|
|
inputs_embeds,
|
|
sequence_labels,
|
|
token_labels,
|
|
choice_labels,
|
|
):
|
|
config.num_choices = self.num_choices
|
|
model = ElectraForMultipleChoice(config)
|
|
model.eval()
|
|
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand([-1, self.num_choices, -1])
|
|
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand([-1, self.num_choices, -1])
|
|
multiple_choice_input_mask = input_mask.unsqueeze(1).expand([-1, self.num_choices, -1])
|
|
result = model(
|
|
multiple_choice_inputs_ids,
|
|
attention_mask=multiple_choice_input_mask,
|
|
token_type_ids=multiple_choice_token_type_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
labels=choice_labels,
|
|
return_dict=self.parent.return_dict,
|
|
)
|
|
|
|
if not self.parent.return_dict and token_labels is None:
|
|
self.parent.assertTrue(paddle.is_tensor(result))
|
|
|
|
if paddle.is_tensor(result):
|
|
result = [result]
|
|
elif token_labels is not None:
|
|
result = result[1:]
|
|
|
|
self.parent.assertEqual(result[0].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,
|
|
inputs_embeds,
|
|
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,
|
|
"inputs_embeds": inputs_embeds,
|
|
}
|
|
return config, inputs_dict
|
|
|
|
|
|
@parameterized_class(
|
|
("return_dict", "use_labels", "use_inputs_embeds"),
|
|
[
|
|
[False, False, True],
|
|
[False, False, False],
|
|
[False, True, False],
|
|
[True, False, False],
|
|
[True, True, False],
|
|
],
|
|
)
|
|
class ElectraModelTest(ModelTesterMixin, unittest.TestCase):
|
|
test_resize_embeddings = False
|
|
test_tie_weights = True
|
|
base_model_class = ElectraModel
|
|
|
|
use_labels = False
|
|
return_dict = False
|
|
|
|
all_model_classes = (
|
|
ElectraModel,
|
|
ElectraForMaskedLM,
|
|
ElectraForMultipleChoice,
|
|
ElectraForTokenClassification,
|
|
ElectraForSequenceClassification,
|
|
ElectraForQuestionAnswering,
|
|
ElectraDiscriminator,
|
|
ElectraGenerator,
|
|
)
|
|
|
|
def setUp(self):
|
|
self.model_tester = ElectraModelTester(self)
|
|
|
|
# set attribute in setUp to overwrite the static attribute
|
|
self.test_resize_embeddings = False
|
|
|
|
def test_electra_model(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_electra_model(*config_and_inputs)
|
|
|
|
def test_electra_model_cache(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_electra_model_cache(*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_electra_for_masked_lm(*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_electra_for_token_classification(*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_electra_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_electra_for_question_answering(*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_electra_for_multiple_choice(*config_and_inputs)
|
|
|
|
def test_for_electra_for_pretraining(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_electra_for_pretraining(*config_and_inputs)
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
for model_name in list(ElectraPretrainedModel.pretrained_init_configuration)[:1]:
|
|
model = ElectraModel.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
|
|
|
|
class ElectraModelCompatibilityTest(unittest.TestCase):
|
|
model_id = "hf-internal-testing/tiny-random-ElectraModel"
|
|
|
|
@require_package("transformers", "torch")
|
|
def test_electra_converter(self):
|
|
with tempfile.TemporaryDirectory() as tempdir:
|
|
# 1. create input
|
|
input_ids = np.random.randint(100, 200, [1, 20])
|
|
|
|
# 2. forward the paddle model
|
|
from paddlenlp.transformers import ElectraModel
|
|
|
|
paddle_model = ElectraModel.from_pretrained(self.model_id, from_hf_hub=True, cache_dir=tempdir)
|
|
paddle_model.eval()
|
|
paddle_logit = paddle_model(paddle.to_tensor(input_ids))[0]
|
|
|
|
# 3. forward the torch model
|
|
import torch
|
|
from transformers import ElectraModel
|
|
|
|
torch_model = ElectraModel.from_pretrained(self.model_id, cache_dir=tempdir)
|
|
torch_model.eval()
|
|
torch_logit = torch_model(torch.tensor(input_ids), return_dict=False)[0]
|
|
|
|
# 4. compare results
|
|
self.assertTrue(
|
|
np.allclose(
|
|
paddle_logit.detach().cpu().reshape([-1])[:9].numpy(),
|
|
torch_logit.detach().cpu().reshape([-1])[:9].numpy(),
|
|
rtol=1e-4,
|
|
)
|
|
)
|
|
|
|
@require_package("transformers", "torch")
|
|
def test_electra_converter_from_local_dir(self):
|
|
with tempfile.TemporaryDirectory() as tempdir:
|
|
|
|
# 1. create common input
|
|
input_ids = np.random.randint(100, 200, [1, 20])
|
|
|
|
# 2. forward the torch model
|
|
import torch
|
|
from transformers import ElectraModel
|
|
|
|
torch_model = ElectraModel.from_pretrained(self.model_id)
|
|
torch_model.eval()
|
|
torch_model.save_pretrained(tempdir)
|
|
torch_logit = torch_model(torch.tensor(input_ids), return_dict=False)[0]
|
|
|
|
# 2. forward the paddle model
|
|
from paddlenlp.transformers import ElectraModel
|
|
|
|
paddle_model = ElectraModel.from_pretrained(tempdir, convert_from_torch=True)
|
|
paddle_model.eval()
|
|
paddle_logit = paddle_model(paddle.to_tensor(input_ids))[0]
|
|
|
|
self.assertTrue(
|
|
np.allclose(
|
|
paddle_logit.detach().cpu().reshape([-1])[:9].numpy(),
|
|
torch_logit.detach().cpu().reshape([-1])[:9].numpy(),
|
|
rtol=1e-4,
|
|
)
|
|
)
|
|
|
|
@parameterized.expand(
|
|
[
|
|
("ElectraModel",),
|
|
# ("ElectraForMaskedLM",), TODO: need to tie weights
|
|
# ("ElectraForPretraining",), TODO: need to tie weights
|
|
("ElectraForMultipleChoice",),
|
|
("ElectraForQuestionAnswering",),
|
|
("ElectraForSequenceClassification",),
|
|
("ElectraForTokenClassification",),
|
|
]
|
|
)
|
|
@require_package("transformers", "torch")
|
|
def test_electra_classes_from_local_dir(self, class_name, pytorch_class_name=None):
|
|
pytorch_class_name = pytorch_class_name or class_name
|
|
with tempfile.TemporaryDirectory() as tempdir:
|
|
|
|
# 1. create common input
|
|
input_ids = np.random.randint(100, 200, [1, 20])
|
|
|
|
# 2. forward the torch model
|
|
import torch
|
|
import transformers
|
|
|
|
torch_model_class = getattr(transformers, pytorch_class_name)
|
|
torch_model = torch_model_class.from_pretrained(self.model_id)
|
|
torch_model.eval()
|
|
|
|
if "MultipleChoice" in class_name:
|
|
# construct input for MultipleChoice Model
|
|
torch_model.config.num_choices = random.randint(2, 10)
|
|
input_ids = (
|
|
paddle.to_tensor(input_ids)
|
|
.unsqueeze(1)
|
|
.expand([-1, torch_model.config.num_choices, -1])
|
|
.cpu()
|
|
.numpy()
|
|
)
|
|
|
|
torch_model.save_pretrained(tempdir)
|
|
torch_logit = torch_model(torch.tensor(input_ids), return_dict=False)[0]
|
|
|
|
# 3. forward the paddle model
|
|
from paddlenlp import transformers
|
|
|
|
paddle_model_class = getattr(transformers, class_name)
|
|
paddle_model = paddle_model_class.from_pretrained(tempdir, convert_from_torch=True)
|
|
paddle_model.eval()
|
|
|
|
paddle_logit = paddle_model(paddle.to_tensor(input_ids), return_dict=False)[0]
|
|
|
|
self.assertTrue(
|
|
np.allclose(
|
|
paddle_logit.detach().cpu().reshape([-1])[:9].numpy(),
|
|
torch_logit.detach().cpu().reshape([-1])[:9].numpy(),
|
|
atol=1e-3,
|
|
)
|
|
)
|
|
|
|
|
|
class ElectraModelIntegrationTest(unittest.TestCase):
|
|
@slow
|
|
def test_inference_no_head_absolute_embedding(self):
|
|
model = ElectraModel.from_pretrained("electra-small")
|
|
model.eval()
|
|
input_ids = paddle.to_tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
|
|
attention_mask = paddle.to_tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
|
|
output = model(input_ids, attention_mask=attention_mask)
|
|
expected_shape = [1, 11, 256]
|
|
self.assertEqual(output.shape, expected_shape)
|
|
expected_slice = paddle.to_tensor(
|
|
[[[0.4471, 0.6821, -0.3265], [0.4627, 0.5255, -0.3668], [0.4532, 0.3313, -0.4344]]]
|
|
)
|
|
self.assertTrue(paddle.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))
|