448 lines
16 KiB
Python
448 lines
16 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 os
|
|
import tempfile
|
|
import unittest
|
|
|
|
from transformers import FlaubertConfig, is_torch_available
|
|
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, ids_tensor, random_attention_mask
|
|
|
|
|
|
if is_torch_available():
|
|
import torch
|
|
|
|
from transformers import (
|
|
FlaubertForMultipleChoice,
|
|
FlaubertForQuestionAnswering,
|
|
FlaubertForQuestionAnsweringSimple,
|
|
FlaubertForSequenceClassification,
|
|
FlaubertForTokenClassification,
|
|
FlaubertModel,
|
|
FlaubertWithLMHeadModel,
|
|
)
|
|
from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST
|
|
|
|
|
|
class FlaubertModelTester(object):
|
|
def __init__(
|
|
self,
|
|
parent,
|
|
):
|
|
self.parent = parent
|
|
self.batch_size = 13
|
|
self.seq_length = 7
|
|
self.is_training = True
|
|
self.use_input_lengths = True
|
|
self.use_token_type_ids = True
|
|
self.use_labels = True
|
|
self.gelu_activation = True
|
|
self.sinusoidal_embeddings = False
|
|
self.causal = False
|
|
self.asm = False
|
|
self.n_langs = 2
|
|
self.vocab_size = 99
|
|
self.n_special = 0
|
|
self.hidden_size = 32
|
|
self.num_hidden_layers = 5
|
|
self.num_attention_heads = 4
|
|
self.hidden_dropout_prob = 0.1
|
|
self.attention_probs_dropout_prob = 0.1
|
|
self.max_position_embeddings = 512
|
|
self.type_vocab_size = 12
|
|
self.type_sequence_label_size = 2
|
|
self.initializer_range = 0.02
|
|
self.num_labels = 3
|
|
self.num_choices = 4
|
|
self.summary_type = "last"
|
|
self.use_proj = None
|
|
self.scope = None
|
|
|
|
def prepare_config_and_inputs(self):
|
|
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
|
|
input_mask = random_attention_mask([self.batch_size, self.seq_length])
|
|
|
|
input_lengths = None
|
|
if self.use_input_lengths:
|
|
input_lengths = (
|
|
ids_tensor([self.batch_size], vocab_size=2) + self.seq_length - 2
|
|
) # small variation of seq_length
|
|
|
|
token_type_ids = None
|
|
if self.use_token_type_ids:
|
|
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.n_langs)
|
|
|
|
sequence_labels = None
|
|
token_labels = None
|
|
is_impossible_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)
|
|
is_impossible_labels = ids_tensor([self.batch_size], 2).float()
|
|
choice_labels = ids_tensor([self.batch_size], self.num_choices)
|
|
|
|
config = self.get_config()
|
|
|
|
return (
|
|
config,
|
|
input_ids,
|
|
token_type_ids,
|
|
input_lengths,
|
|
sequence_labels,
|
|
token_labels,
|
|
is_impossible_labels,
|
|
choice_labels,
|
|
input_mask,
|
|
)
|
|
|
|
def get_config(self):
|
|
return FlaubertConfig(
|
|
vocab_size=self.vocab_size,
|
|
n_special=self.n_special,
|
|
emb_dim=self.hidden_size,
|
|
n_layers=self.num_hidden_layers,
|
|
n_heads=self.num_attention_heads,
|
|
dropout=self.hidden_dropout_prob,
|
|
attention_dropout=self.attention_probs_dropout_prob,
|
|
gelu_activation=self.gelu_activation,
|
|
sinusoidal_embeddings=self.sinusoidal_embeddings,
|
|
asm=self.asm,
|
|
causal=self.causal,
|
|
n_langs=self.n_langs,
|
|
max_position_embeddings=self.max_position_embeddings,
|
|
initializer_range=self.initializer_range,
|
|
summary_type=self.summary_type,
|
|
use_proj=self.use_proj,
|
|
)
|
|
|
|
def create_and_check_flaubert_model(
|
|
self,
|
|
config,
|
|
input_ids,
|
|
token_type_ids,
|
|
input_lengths,
|
|
sequence_labels,
|
|
token_labels,
|
|
is_impossible_labels,
|
|
choice_labels,
|
|
input_mask,
|
|
):
|
|
model = FlaubertModel(config=config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
result = model(input_ids, lengths=input_lengths, langs=token_type_ids)
|
|
result = model(input_ids, langs=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_flaubert_lm_head(
|
|
self,
|
|
config,
|
|
input_ids,
|
|
token_type_ids,
|
|
input_lengths,
|
|
sequence_labels,
|
|
token_labels,
|
|
is_impossible_labels,
|
|
choice_labels,
|
|
input_mask,
|
|
):
|
|
model = FlaubertWithLMHeadModel(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
result = model(input_ids, token_type_ids=token_type_ids, labels=token_labels)
|
|
self.parent.assertEqual(result.loss.shape, ())
|
|
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
|
|
|
|
def create_and_check_flaubert_simple_qa(
|
|
self,
|
|
config,
|
|
input_ids,
|
|
token_type_ids,
|
|
input_lengths,
|
|
sequence_labels,
|
|
token_labels,
|
|
is_impossible_labels,
|
|
choice_labels,
|
|
input_mask,
|
|
):
|
|
model = FlaubertForQuestionAnsweringSimple(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
result = model(input_ids)
|
|
|
|
result = model(input_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_flaubert_qa(
|
|
self,
|
|
config,
|
|
input_ids,
|
|
token_type_ids,
|
|
input_lengths,
|
|
sequence_labels,
|
|
token_labels,
|
|
is_impossible_labels,
|
|
choice_labels,
|
|
input_mask,
|
|
):
|
|
model = FlaubertForQuestionAnswering(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
result = model(input_ids)
|
|
|
|
result_with_labels = model(
|
|
input_ids,
|
|
start_positions=sequence_labels,
|
|
end_positions=sequence_labels,
|
|
cls_index=sequence_labels,
|
|
is_impossible=is_impossible_labels,
|
|
p_mask=input_mask,
|
|
)
|
|
|
|
result_with_labels = model(
|
|
input_ids,
|
|
start_positions=sequence_labels,
|
|
end_positions=sequence_labels,
|
|
cls_index=sequence_labels,
|
|
is_impossible=is_impossible_labels,
|
|
)
|
|
|
|
(total_loss,) = result_with_labels.to_tuple()
|
|
|
|
result_with_labels = model(input_ids, start_positions=sequence_labels, end_positions=sequence_labels)
|
|
|
|
(total_loss,) = result_with_labels.to_tuple()
|
|
|
|
self.parent.assertEqual(result_with_labels.loss.shape, ())
|
|
self.parent.assertEqual(result.start_top_log_probs.shape, (self.batch_size, model.config.start_n_top))
|
|
self.parent.assertEqual(result.start_top_index.shape, (self.batch_size, model.config.start_n_top))
|
|
self.parent.assertEqual(
|
|
result.end_top_log_probs.shape, (self.batch_size, model.config.start_n_top * model.config.end_n_top)
|
|
)
|
|
self.parent.assertEqual(
|
|
result.end_top_index.shape, (self.batch_size, model.config.start_n_top * model.config.end_n_top)
|
|
)
|
|
self.parent.assertEqual(result.cls_logits.shape, (self.batch_size,))
|
|
|
|
def create_and_check_flaubert_sequence_classif(
|
|
self,
|
|
config,
|
|
input_ids,
|
|
token_type_ids,
|
|
input_lengths,
|
|
sequence_labels,
|
|
token_labels,
|
|
is_impossible_labels,
|
|
choice_labels,
|
|
input_mask,
|
|
):
|
|
model = FlaubertForSequenceClassification(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
result = model(input_ids)
|
|
result = model(input_ids, labels=sequence_labels)
|
|
|
|
self.parent.assertEqual(result.loss.shape, ())
|
|
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
|
|
|
|
def create_and_check_flaubert_token_classif(
|
|
self,
|
|
config,
|
|
input_ids,
|
|
token_type_ids,
|
|
input_lengths,
|
|
sequence_labels,
|
|
token_labels,
|
|
is_impossible_labels,
|
|
choice_labels,
|
|
input_mask,
|
|
):
|
|
config.num_labels = self.num_labels
|
|
model = FlaubertForTokenClassification(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
|
|
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
|
|
|
|
def create_and_check_flaubert_multiple_choice(
|
|
self,
|
|
config,
|
|
input_ids,
|
|
token_type_ids,
|
|
input_lengths,
|
|
sequence_labels,
|
|
token_labels,
|
|
is_impossible_labels,
|
|
choice_labels,
|
|
input_mask,
|
|
):
|
|
config.num_choices = self.num_choices
|
|
model = FlaubertForMultipleChoice(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_lengths,
|
|
sequence_labels,
|
|
token_labels,
|
|
is_impossible_labels,
|
|
choice_labels,
|
|
input_mask,
|
|
) = config_and_inputs
|
|
inputs_dict = {
|
|
"input_ids": input_ids,
|
|
"token_type_ids": token_type_ids,
|
|
"lengths": input_lengths,
|
|
"attention_mask": input_mask,
|
|
}
|
|
return config, inputs_dict
|
|
|
|
|
|
@require_torch
|
|
class FlaubertModelTest(ModelTesterMixin, unittest.TestCase):
|
|
|
|
all_model_classes = (
|
|
(
|
|
FlaubertModel,
|
|
FlaubertWithLMHeadModel,
|
|
FlaubertForQuestionAnswering,
|
|
FlaubertForQuestionAnsweringSimple,
|
|
FlaubertForSequenceClassification,
|
|
FlaubertForTokenClassification,
|
|
FlaubertForMultipleChoice,
|
|
)
|
|
if is_torch_available()
|
|
else ()
|
|
)
|
|
|
|
# Flaubert has 2 QA models -> need to manually set the correct labels for one of them here
|
|
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
|
|
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
|
|
|
|
if return_labels:
|
|
if model_class.__name__ == "FlaubertForQuestionAnswering":
|
|
inputs_dict["start_positions"] = torch.zeros(
|
|
self.model_tester.batch_size, dtype=torch.long, device=torch_device
|
|
)
|
|
inputs_dict["end_positions"] = torch.zeros(
|
|
self.model_tester.batch_size, dtype=torch.long, device=torch_device
|
|
)
|
|
|
|
return inputs_dict
|
|
|
|
def setUp(self):
|
|
self.model_tester = FlaubertModelTester(self)
|
|
self.config_tester = ConfigTester(self, config_class=FlaubertConfig, emb_dim=37)
|
|
|
|
def test_config(self):
|
|
self.config_tester.run_common_tests()
|
|
|
|
def test_flaubert_model(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_flaubert_model(*config_and_inputs)
|
|
|
|
def test_flaubert_lm_head(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_flaubert_lm_head(*config_and_inputs)
|
|
|
|
def test_flaubert_simple_qa(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_flaubert_simple_qa(*config_and_inputs)
|
|
|
|
def test_flaubert_qa(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_flaubert_qa(*config_and_inputs)
|
|
|
|
def test_flaubert_sequence_classif(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_flaubert_sequence_classif(*config_and_inputs)
|
|
|
|
def test_flaubert_token_classif(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_flaubert_token_classif(*config_and_inputs)
|
|
|
|
def test_flaubert_multiple_choice(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_flaubert_multiple_choice(*config_and_inputs)
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
|
|
model = FlaubertModel.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
|
|
@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:
|
|
|
|
# FlauBertForMultipleChoice behaves incorrectly in JIT environments.
|
|
if model_class == FlaubertForMultipleChoice:
|
|
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 FlaubertModelIntegrationTest(unittest.TestCase):
|
|
@slow
|
|
def test_inference_no_head_absolute_embedding(self):
|
|
model = FlaubertModel.from_pretrained("flaubert/flaubert_base_cased")
|
|
input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
|
|
output = model(input_ids)[0]
|
|
expected_shape = torch.Size((1, 11, 768))
|
|
self.assertEqual(output.shape, expected_shape)
|
|
expected_slice = torch.tensor(
|
|
[[[-2.6251, -1.4298, -0.0227], [-2.8510, -1.6387, 0.2258], [-2.8114, -1.1832, -0.3066]]]
|
|
)
|
|
|
|
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
|