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# 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 Splinter model."""
import copy
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import SplinterConfig, SplinterForPreTraining, SplinterForQuestionAnswering, SplinterModel
class SplinterModelTester:
def __init__(
self,
parent,
batch_size=13,
num_questions=3,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
question_token_id=1,
num_hidden_layers=2,
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.num_questions = num_questions
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.question_token_id = question_token_id
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_ids[:, 1] = self.question_token_id
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)
start_positions = None
end_positions = None
question_positions = None
if self.use_labels:
start_positions = ids_tensor([self.batch_size, self.num_questions], self.type_sequence_label_size)
end_positions = ids_tensor([self.batch_size, self.num_questions], self.type_sequence_label_size)
question_positions = ids_tensor([self.batch_size, self.num_questions], self.num_labels)
config = SplinterConfig(
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,
question_token_id=self.question_token_id,
)
return (config, input_ids, token_type_ids, input_mask, start_positions, end_positions, question_positions)
def create_and_check_model(
self,
config,
input_ids,
token_type_ids,
input_mask,
start_positions,
end_positions,
question_positions,
):
model = SplinterModel(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_question_answering(
self,
config,
input_ids,
token_type_ids,
input_mask,
start_positions,
end_positions,
question_positions,
):
model = SplinterForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=start_positions[:, 0],
end_positions=end_positions[:, 0],
)
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_pretraining(
self,
config,
input_ids,
token_type_ids,
input_mask,
start_positions,
end_positions,
question_positions,
):
model = SplinterForPreTraining(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=start_positions,
end_positions=end_positions,
question_positions=question_positions,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.num_questions, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.num_questions, 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,
start_positions,
end_positions,
question_positions,
) = 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 SplinterModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
SplinterModel,
SplinterForQuestionAnswering,
SplinterForPreTraining,
)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{"feature-extraction": SplinterModel, "question-answering": SplinterForQuestionAnswering}
if is_torch_available()
else {}
)
# TODO: Fix the failed tests when this model gets more usage
def is_pipeline_test_to_skip(
self,
pipeline_test_case_name,
config_class,
model_architecture,
tokenizer_name,
image_processor_name,
feature_extractor_name,
processor_name,
):
if pipeline_test_case_name == "QAPipelineTests":
return True
elif pipeline_test_case_name == "FeatureExtractionPipelineTests" and tokenizer_name.endswith("Fast"):
return True
return False
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = copy.deepcopy(inputs_dict)
if return_labels:
if issubclass(model_class, SplinterForPreTraining):
inputs_dict["start_positions"] = torch.zeros(
self.model_tester.batch_size,
self.model_tester.num_questions,
dtype=torch.long,
device=torch_device,
)
inputs_dict["end_positions"] = torch.zeros(
self.model_tester.batch_size,
self.model_tester.num_questions,
dtype=torch.long,
device=torch_device,
)
inputs_dict["question_positions"] = torch.zeros(
self.model_tester.batch_size,
self.model_tester.num_questions,
dtype=torch.long,
device=torch_device,
)
elif issubclass(model_class, SplinterForQuestionAnswering):
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 = SplinterModelTester(self)
self.config_tester = ConfigTester(self, config_class=SplinterConfig, 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_model_various_embeddings(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
config_and_inputs[0].position_embedding_type = type
self.model_tester.create_and_check_model(*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_pretraining(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*config_and_inputs)
def test_inputs_embeds(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
if not self.is_encoder_decoder:
input_ids = inputs["input_ids"]
del inputs["input_ids"]
else:
encoder_input_ids = inputs["input_ids"]
decoder_input_ids = inputs.get("decoder_input_ids", encoder_input_ids)
del inputs["input_ids"]
inputs.pop("decoder_input_ids", None)
wte = model.get_input_embeddings()
if not self.is_encoder_decoder:
inputs["inputs_embeds"] = wte(input_ids)
else:
inputs["inputs_embeds"] = wte(encoder_input_ids)
inputs["decoder_inputs_embeds"] = wte(decoder_input_ids)
with torch.no_grad():
if isinstance(model, SplinterForPreTraining):
with self.assertRaises(TypeError):
# question_positions must not be None.
model(**inputs)[0]
else:
model(**inputs)[0]
@slow
def test_model_from_pretrained(self):
model_name = "tau/splinter-base"
model = SplinterModel.from_pretrained(model_name)
self.assertIsNotNone(model)
# overwrite from common since `SplinterForPreTraining` could contain different number of question tokens in inputs.
# When the batch is distributed to multiple devices, each replica could get different values for the maximal number
# of question tokens (see `SplinterForPreTraining._prepare_question_positions()`), and the model returns different
# shape along dimension 1 (i.e. `num_questions`) that could not be combined into a single tensor as an output.
@require_torch_multi_gpu
def test_multi_gpu_data_parallel_forward(self):
from torch import nn
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
# some params shouldn't be scattered by nn.DataParallel
# so just remove them if they are present.
blacklist_non_batched_params = ["head_mask", "decoder_head_mask", "cross_attn_head_mask"]
for k in blacklist_non_batched_params:
inputs_dict.pop(k, None)
# move input tensors to cuda:O
for k, v in inputs_dict.items():
if torch.is_tensor(v):
inputs_dict[k] = v.to(0)
for model_class in self.all_model_classes:
# Skip this case since it will fail sometimes, as described above.
if model_class == SplinterForPreTraining:
continue
model = model_class(config=config)
model.to(0)
model.eval()
# Wrap model in nn.DataParallel
model = nn.DataParallel(model)
with torch.no_grad():
_ = model(**self._prepare_for_class(inputs_dict, model_class))
@unittest.skip(
"Splinter GC with `use_reentrant` fails after #38751, FIXME raushan after deprecated args are removed"
)
def test_training_gradient_checkpointing(self):
pass
@unittest.skip(
"Splinter GC with `use_reentrant` fails after #38751, FIXME raushan after deprecated args are removed"
)
def test_training_gradient_checkpointing_use_reentrant(self):
pass
@require_torch
class SplinterModelIntegrationTest(unittest.TestCase):
@slow
def test_splinter_question_answering(self):
model = SplinterForQuestionAnswering.from_pretrained("tau/splinter-base-qass")
# Input: "[CLS] Brad was born in [QUESTION] . He returned to the United Kingdom later . [SEP]"
# Output should be the span "the United Kingdom"
input_ids = torch.tensor(
[[101, 7796, 1108, 1255, 1107, 104, 119, 1124, 1608, 1106, 1103, 1244, 2325, 1224, 119, 102]]
)
output = model(input_ids)
expected_shape = torch.Size((1, 16))
self.assertEqual(output.start_logits.shape, expected_shape)
self.assertEqual(output.end_logits.shape, expected_shape)
self.assertEqual(torch.argmax(output.start_logits), 10)
self.assertEqual(torch.argmax(output.end_logits), 12)
@slow
def test_splinter_pretraining(self):
model = SplinterForPreTraining.from_pretrained("tau/splinter-base-qass")
# Input: "[CLS] [QUESTION] was born in [QUESTION] . Brad returned to the United Kingdom later . [SEP]"
# Output should be the spans "Brad" and "the United Kingdom"
input_ids = torch.tensor(
[[101, 104, 1108, 1255, 1107, 104, 119, 7796, 1608, 1106, 1103, 1244, 2325, 1224, 119, 102]]
)
question_positions = torch.tensor([[1, 5]], dtype=torch.long)
output = model(input_ids, question_positions=question_positions)
expected_shape = torch.Size((1, 2, 16))
self.assertEqual(output.start_logits.shape, expected_shape)
self.assertEqual(output.end_logits.shape, expected_shape)
self.assertEqual(torch.argmax(output.start_logits[0, 0]), 7)
self.assertEqual(torch.argmax(output.end_logits[0, 0]), 7)
self.assertEqual(torch.argmax(output.start_logits[0, 1]), 10)
self.assertEqual(torch.argmax(output.end_logits[0, 1]), 12)
@slow
def test_splinter_pretraining_loss_requires_question_positions(self):
model = SplinterForPreTraining.from_pretrained("tau/splinter-base-qass")
# Input: "[CLS] [QUESTION] was born in [QUESTION] . Brad returned to the United Kingdom later . [SEP]"
# Output should be the spans "Brad" and "the United Kingdom"
input_ids = torch.tensor(
[[101, 104, 1108, 1255, 1107, 104, 119, 7796, 1608, 1106, 1103, 1244, 2325, 1224, 119, 102]]
)
start_positions = torch.tensor([[7, 10]], dtype=torch.long)
end_positions = torch.tensor([7, 12], dtype=torch.long)
with self.assertRaises(TypeError):
model(
input_ids,
start_positions=start_positions,
end_positions=end_positions,
)
@slow
def test_splinter_pretraining_loss(self):
model = SplinterForPreTraining.from_pretrained("tau/splinter-base-qass")
# Input: "[CLS] [QUESTION] was born in [QUESTION] . Brad returned to the United Kingdom later . [SEP]"
# Output should be the spans "Brad" and "the United Kingdom"
input_ids = torch.tensor(
[
[101, 104, 1108, 1255, 1107, 104, 119, 7796, 1608, 1106, 1103, 1244, 2325, 1224, 119, 102],
[101, 104, 1108, 1255, 1107, 104, 119, 7796, 1608, 1106, 1103, 1244, 2325, 1224, 119, 102],
]
)
start_positions = torch.tensor([[7, 10], [7, 10]], dtype=torch.long)
end_positions = torch.tensor([[7, 12], [7, 12]], dtype=torch.long)
question_positions = torch.tensor([[1, 5], [1, 5]], dtype=torch.long)
output = model(
input_ids,
start_positions=start_positions,
end_positions=end_positions,
question_positions=question_positions,
)
self.assertAlmostEqual(output.loss.item(), 0.0024, 4)
@slow
def test_splinter_pretraining_loss_with_padding(self):
model = SplinterForPreTraining.from_pretrained("tau/splinter-base-qass")
# Input: "[CLS] [QUESTION] was born in [QUESTION] . Brad returned to the United Kingdom later . [SEP]"
# Output should be the spans "Brad" and "the United Kingdom"
input_ids = torch.tensor(
[
[101, 104, 1108, 1255, 1107, 104, 119, 7796, 1608, 1106, 1103, 1244, 2325, 1224, 119, 102],
]
)
start_positions = torch.tensor([[7, 10]], dtype=torch.long)
end_positions = torch.tensor([7, 12], dtype=torch.long)
question_positions = torch.tensor([[1, 5]], dtype=torch.long)
start_positions_with_padding = torch.tensor([[7, 10, 0]], dtype=torch.long)
end_positions_with_padding = torch.tensor([7, 12, 0], dtype=torch.long)
question_positions_with_padding = torch.tensor([[1, 5, 0]], dtype=torch.long)
output = model(
input_ids,
start_positions=start_positions,
end_positions=end_positions,
question_positions=question_positions,
)
output_with_padding = model(
input_ids,
start_positions=start_positions_with_padding,
end_positions=end_positions_with_padding,
question_positions=question_positions_with_padding,
)
self.assertAlmostEqual(output.loss.item(), output_with_padding.loss.item(), 4)
# Note that the original code uses 0 to denote padded question tokens
# and their start and end positions. As the pad_token_id of the model's
# config is used for the losse's ignore_index in SplinterForPreTraining,
# we add this test to ensure anybody making changes to the default
# value of the config, will be aware of the implication.
self.assertEqual(model.config.pad_token_id, 0)
@slow
def test_splinter_pretraining_prepare_question_positions(self):
model = SplinterForPreTraining.from_pretrained("tau/splinter-base-qass")
input_ids = torch.tensor(
[
[101, 104, 1, 2, 104, 3, 4, 102],
[101, 1, 104, 2, 104, 3, 104, 102],
[101, 1, 2, 104, 104, 3, 4, 102],
[101, 1, 2, 3, 4, 5, 104, 102],
]
)
question_positions = torch.tensor([[1, 4, 0], [2, 4, 6], [3, 4, 0], [6, 0, 0]], dtype=torch.long)
output_without_positions = model(input_ids)
output_with_positions = model(input_ids, question_positions=question_positions)
self.assertTrue((output_without_positions.start_logits == output_with_positions.start_logits).all())
self.assertTrue((output_without_positions.end_logits == output_with_positions.end_logits).all())