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test_modeling_lxmert.py
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# coding=utf-8
# Copyright 2018 LXMERT Authors, The Hugging Face Team.
#
# 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 copy
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from .test_configuration_common import ConfigTester
from .test_modeling_common import ModelTesterMixin, ids_tensor
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MODEL_FOR_QUESTION_ANSWERING_MAPPING,
LxmertConfig,
LxmertForPreTraining,
LxmertForQuestionAnswering,
LxmertModel,
)
from transformers.models.lxmert.modeling_lxmert import LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST
class LxmertModelTester:
"""You can also import this e.g from .test_modeling_bart import BartModelTester """
def __init__(
self,
parent,
vocab_size=300,
hidden_size=28,
num_attention_heads=2,
num_labels=2,
intermediate_size=64,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
layer_norm_eps=1e-12,
pad_token_id=0,
num_qa_labels=30,
num_object_labels=16,
num_attr_labels=4,
num_visual_features=10,
l_layers=2,
x_layers=1,
r_layers=1,
visual_feat_dim=128,
visual_pos_dim=4,
visual_loss_normalizer=6.67,
seq_length=20,
batch_size=4,
is_training=True,
task_matched=True,
task_mask_lm=True,
task_obj_predict=True,
task_qa=True,
visual_obj_loss=True,
visual_attr_loss=True,
visual_feat_loss=True,
use_token_type_ids=True,
use_lang_mask=True,
output_attentions=False,
output_hidden_states=False,
scope=None,
):
self.parent = parent
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_attention_heads = num_attention_heads
self.num_labels = num_labels
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.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.pad_token_id = pad_token_id
self.num_qa_labels = num_qa_labels
self.num_object_labels = num_object_labels
self.num_attr_labels = num_attr_labels
self.l_layers = l_layers
self.x_layers = x_layers
self.r_layers = r_layers
self.visual_feat_dim = visual_feat_dim
self.visual_pos_dim = visual_pos_dim
self.visual_loss_normalizer = visual_loss_normalizer
self.seq_length = seq_length
self.batch_size = batch_size
self.is_training = is_training
self.use_lang_mask = use_lang_mask
self.task_matched = task_matched
self.task_mask_lm = task_mask_lm
self.task_obj_predict = task_obj_predict
self.task_qa = task_qa
self.visual_obj_loss = visual_obj_loss
self.visual_attr_loss = visual_attr_loss
self.visual_feat_loss = visual_feat_loss
self.num_visual_features = num_visual_features
self.use_token_type_ids = use_token_type_ids
self.output_attentions = output_attentions
self.output_hidden_states = output_hidden_states
self.scope = scope
self.num_hidden_layers = {"vision": r_layers, "cross_encoder": x_layers, "language": l_layers}
def prepare_config_and_inputs(self):
output_attentions = self.output_attentions
input_ids = ids_tensor([self.batch_size, self.seq_length], vocab_size=self.vocab_size)
visual_feats = torch.rand(self.batch_size, self.num_visual_features, self.visual_feat_dim, device=torch_device)
bounding_boxes = torch.rand(self.batch_size, self.num_visual_features, 4, device=torch_device)
input_mask = None
if self.use_lang_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)
obj_labels = None
if self.task_obj_predict:
obj_labels = {}
if self.visual_attr_loss and self.task_obj_predict:
obj_labels["attr"] = (
ids_tensor([self.batch_size, self.num_visual_features], self.num_attr_labels),
ids_tensor([self.batch_size, self.num_visual_features], self.num_attr_labels),
)
if self.visual_feat_loss and self.task_obj_predict:
obj_labels["feat"] = (
ids_tensor(
[self.batch_size, self.num_visual_features, self.visual_feat_dim], self.num_visual_features
),
ids_tensor([self.batch_size, self.num_visual_features], self.num_visual_features),
)
if self.visual_obj_loss and self.task_obj_predict:
obj_labels["obj"] = (
ids_tensor([self.batch_size, self.num_visual_features], self.num_object_labels),
ids_tensor([self.batch_size, self.num_visual_features], self.num_object_labels),
)
ans = None
if self.task_qa:
ans = ids_tensor([self.batch_size], self.num_qa_labels)
masked_lm_labels = None
if self.task_mask_lm:
masked_lm_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
matched_label = None
if self.task_matched:
matched_label = ids_tensor([self.batch_size], self.num_labels)
config = LxmertConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_attention_heads=self.num_attention_heads,
num_labels=self.num_labels,
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,
layer_norm_eps=self.layer_norm_eps,
pad_token_id=self.pad_token_id,
num_qa_labels=self.num_qa_labels,
num_object_labels=self.num_object_labels,
num_attr_labels=self.num_attr_labels,
l_layers=self.l_layers,
x_layers=self.x_layers,
r_layers=self.r_layers,
visual_feat_dim=self.visual_feat_dim,
visual_pos_dim=self.visual_pos_dim,
visual_loss_normalizer=self.visual_loss_normalizer,
task_matched=self.task_matched,
task_mask_lm=self.task_mask_lm,
task_obj_predict=self.task_obj_predict,
task_qa=self.task_qa,
visual_obj_loss=self.visual_obj_loss,
visual_attr_loss=self.visual_attr_loss,
visual_feat_loss=self.visual_feat_loss,
output_attentions=self.output_attentions,
output_hidden_states=self.output_hidden_states,
)
return (
config,
input_ids,
visual_feats,
bounding_boxes,
token_type_ids,
input_mask,
obj_labels,
masked_lm_labels,
matched_label,
ans,
output_attentions,
)
def create_and_check_lxmert_model(
self,
config,
input_ids,
visual_feats,
bounding_boxes,
token_type_ids,
input_mask,
obj_labels,
masked_lm_labels,
matched_label,
ans,
output_attentions,
):
model = LxmertModel(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
output_attentions=output_attentions,
)
result = model(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
output_attentions=not output_attentions,
)
result = model(input_ids, visual_feats, bounding_boxes, return_dict=False)
result = model(input_ids, visual_feats, bounding_boxes, return_dict=True)
self.parent.assertEqual(result.language_output.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(
result.vision_output.shape, (self.batch_size, self.num_visual_features, self.hidden_size)
)
self.parent.assertEqual(result.pooled_output.shape, (self.batch_size, self.hidden_size))
def create_and_check_lxmert_for_question_answering(
self,
config,
input_ids,
visual_feats,
bounding_boxes,
token_type_ids,
input_mask,
obj_labels,
masked_lm_labels,
matched_label,
ans,
output_attentions,
):
model = LxmertForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
labels=ans,
output_attentions=output_attentions,
)
result = model(input_ids, visual_feats, bounding_boxes, labels=ans)
result = model(
input_ids,
visual_feats,
bounding_boxes,
labels=ans,
token_type_ids=token_type_ids,
attention_mask=input_mask,
output_attentions=output_attentions,
)
result = model(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
labels=ans,
output_attentions=not output_attentions,
)
self.parent.assertEqual(result.question_answering_score.shape, (self.batch_size, self.num_qa_labels))
def create_and_check_lxmert_for_pretraining(
self,
config,
input_ids,
visual_feats,
bounding_boxes,
token_type_ids,
input_mask,
obj_labels,
masked_lm_labels,
matched_label,
ans,
output_attentions,
):
model = LxmertForPreTraining(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
masked_lm_labels=masked_lm_labels,
obj_labels=obj_labels,
matched_label=matched_label,
ans=ans,
output_attentions=output_attentions,
)
result = model(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
masked_lm_labels=masked_lm_labels,
output_attentions=not output_attentions,
return_dict=False,
)
result = model(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
masked_lm_labels=masked_lm_labels,
)
result = model(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
obj_labels=obj_labels,
)
result = model(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
matched_label=matched_label,
)
result = model(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
ans=ans,
)
result = model(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
masked_lm_labels=masked_lm_labels,
obj_labels=obj_labels,
matched_label=matched_label,
ans=ans,
output_attentions=not output_attentions,
)
self.parent.assertEqual(result.prediction_logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def resize_lxmert_num_qa_labels(
self,
config,
input_ids,
visual_feats,
bounding_boxes,
token_type_ids,
input_mask,
obj_labels,
masked_lm_labels,
matched_label,
ans,
output_attentions,
):
start_labels = config.num_qa_labels
num_large_labels = config.num_qa_labels * 2
num_small_labels = int(config.num_qa_labels * 2)
less_labels_ans = ids_tensor([self.batch_size], num_small_labels)
more_labels_ans = ids_tensor([self.batch_size], num_large_labels)
model_pretrain = LxmertForPreTraining(config=config).to(torch_device)
model_qa = LxmertForQuestionAnswering(config=config).to(torch_device)
config.num_labels = num_small_labels
end_labels = config.num_labels
result_pretrain = model_pretrain(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
ans=ans,
)
result_qa = model_qa(
input_ids,
visual_feats,
bounding_boxes,
labels=ans,
token_type_ids=token_type_ids,
attention_mask=input_mask,
)
model_pretrain.resize_num_qa_labels(num_small_labels)
model_qa.resize_num_qa_labels(num_small_labels)
result_pretrain_less = model_pretrain(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
ans=less_labels_ans,
)
result_qa_less = model_qa(
input_ids,
visual_feats,
bounding_boxes,
labels=less_labels_ans,
token_type_ids=token_type_ids,
attention_mask=input_mask,
)
model_pretrain.resize_num_qa_labels(num_large_labels)
model_qa.resize_num_qa_labels(num_large_labels)
result_pretrain_more = model_pretrain(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
ans=more_labels_ans,
)
result_qa_more = model_qa(
input_ids,
visual_feats,
bounding_boxes,
labels=more_labels_ans,
token_type_ids=token_type_ids,
attention_mask=input_mask,
)
model_qa_labels = model_qa.num_qa_labels
self.parent.assertNotEqual(start_labels, end_labels)
self.parent.assertNotEqual(model_qa_labels, start_labels)
self.parent.assertEqual(result_qa.question_answering_score.shape, (self.batch_size, start_labels))
self.parent.assertEqual(result_pretrain.question_answering_score.shape, (self.batch_size, start_labels))
self.parent.assertEqual(result_qa_less.question_answering_score.shape, (self.batch_size, num_small_labels))
self.parent.assertEqual(
result_pretrain_less.question_answering_score.shape, (self.batch_size, num_small_labels)
)
self.parent.assertEqual(result_qa_more.question_answering_score.shape, (self.batch_size, num_large_labels))
self.parent.assertEqual(
result_pretrain_more.question_answering_score.shape, (self.batch_size, num_large_labels)
)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
visual_feats,
bounding_boxes,
token_type_ids,
input_mask,
obj_labels,
masked_lm_labels,
matched_label,
ans,
output_attentions,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"visual_feats": visual_feats,
"visual_pos": bounding_boxes,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_torch
class LxmertModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (LxmertModel, LxmertForPreTraining, LxmertForQuestionAnswering) if is_torch_available() else ()
test_head_masking = False
test_pruning = False
test_torchscript = False
# overwrite function because qa models takes different input label shape
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = copy.deepcopy(inputs_dict)
if return_labels:
if model_class in MODEL_FOR_QUESTION_ANSWERING_MAPPING.values():
inputs_dict["labels"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
elif model_class in MODEL_FOR_PRETRAINING_MAPPING.values():
# special case for models like BERT that use multi-loss training for PreTraining
inputs_dict["labels"] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
)
return inputs_dict
def setUp(self):
self.model_tester = LxmertModelTester(self)
self.config_tester = ConfigTester(self, config_class=LxmertConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_lxmert_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lxmert_model(*config_and_inputs)
def test_lxmert_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lxmert_for_question_answering(*config_and_inputs)
def test_lxmert_pretraining(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lxmert_for_pretraining(*config_and_inputs)
def test_lxmert_question_answering_labels_resize(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.resize_lxmert_num_qa_labels(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = LxmertModel.from_pretrained(model_name)
model.to(torch_device)
self.assertIsNotNone(model)
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
seq_len = getattr(self.model_tester, "seq_length", None)
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
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
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
language_attentions, vision_attentions, cross_encoder_attentions = (outputs[-3], outputs[-2], outputs[-1])
self.assertEqual(len(language_attentions), self.model_tester.num_hidden_layers["language"])
self.assertEqual(len(vision_attentions), self.model_tester.num_hidden_layers["vision"])
self.assertEqual(len(cross_encoder_attentions), self.model_tester.num_hidden_layers["cross_encoder"])
# 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))
language_attentions, vision_attentions, cross_encoder_attentions = (outputs[-3], outputs[-2], outputs[-1])
self.assertEqual(len(language_attentions), self.model_tester.num_hidden_layers["language"])
self.assertEqual(len(vision_attentions), self.model_tester.num_hidden_layers["vision"])
self.assertEqual(len(cross_encoder_attentions), self.model_tester.num_hidden_layers["cross_encoder"])
attentions = [language_attentions, vision_attentions, cross_encoder_attentions]
attention_shapes = [
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
[
self.model_tester.num_attention_heads,
self.model_tester.num_visual_features,
self.model_tester.num_visual_features,
],
[self.model_tester.num_attention_heads, encoder_key_length, self.model_tester.num_visual_features],
]
for attention, attention_shape in zip(attentions, attention_shapes):
self.assertListEqual(list(attention[0].shape[-3:]), attention_shape)
out_len = len(outputs)
# 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))
# 2 hidden states were added
self.assertEqual(out_len + 2, len(outputs))
language_attentions, vision_attentions, cross_encoder_attentions = (outputs[-3], outputs[-2], outputs[-1])
self.assertEqual(len(language_attentions), self.model_tester.num_hidden_layers["language"])
self.assertEqual(len(vision_attentions), self.model_tester.num_hidden_layers["vision"])
self.assertEqual(len(cross_encoder_attentions), self.model_tester.num_hidden_layers["cross_encoder"])
attentions = [language_attentions, vision_attentions, cross_encoder_attentions]
attention_shapes = [
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
[
self.model_tester.num_attention_heads,
self.model_tester.num_visual_features,
self.model_tester.num_visual_features,
],
[self.model_tester.num_attention_heads, encoder_key_length, self.model_tester.num_visual_features],
]
for attention, attention_shape in zip(attentions, attention_shapes):
self.assertListEqual(list(attention[0].shape[-3:]), attention_shape)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
language_hidden_states, vision_hidden_states = outputs[-2], outputs[-1]
self.assertEqual(len(language_hidden_states), self.model_tester.num_hidden_layers["language"] + 1)
self.assertEqual(len(vision_hidden_states), self.model_tester.num_hidden_layers["vision"] + 1)
seq_length = self.model_tester.seq_length
num_visual_features = self.model_tester.num_visual_features
self.assertListEqual(
list(language_hidden_states[0].shape[-2:]),
[seq_length, self.model_tester.hidden_size],
)
self.assertListEqual(
list(vision_hidden_states[0].shape[-2:]),
[num_visual_features, self.model_tester.hidden_size],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
def test_retain_grad_hidden_states_attentions(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = True
config.output_attentions = True
# no need to test all models as different heads yield the same functionality
model_class = self.all_model_classes[0]
model = model_class(config)
model.to(torch_device)
inputs = self._prepare_for_class(inputs_dict, model_class)
outputs = model(**inputs)
hidden_states_lang = outputs.language_hidden_states[0]
attentions_lang = outputs.language_attentions[0]
hidden_states_vision = outputs.vision_hidden_states[0]
attentions_vision = outputs.vision_attentions[0]
hidden_states_lang.retain_grad()
attentions_lang.retain_grad()
hidden_states_vision.retain_grad()
attentions_vision.retain_grad()
outputs.language_output.flatten()[0].backward(retain_graph=True)
outputs.vision_output.flatten()[0].backward(retain_graph=True)
self.assertIsNotNone(hidden_states_lang.grad)
self.assertIsNotNone(attentions_vision.grad)
self.assertIsNotNone(hidden_states_vision.grad)
self.assertIsNotNone(attentions_vision.grad)

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