class ScheduleInitTest(unittest.TestCase):
m = torch.nn.Linear(50, 50) if is_torch_available() else None
optimizer = AdamW(m.parameters(), lr=10.) if is_torch_available() else None
num_steps = 10
def assertListAlmostEqual(self, list1, list2, tol):
self.assertEqual(len(list1), len(list2))
for a, b in zip(list1, list2):
self.assertAlmostEqual(a, b, delta=tol)
def test_constant_scheduler(self):
scheduler = get_constant_schedule(self.optimizer)
lrs = unwrap_schedule(scheduler, self.num_steps)
expected_learning_rates = [10.] * self.num_steps
self.assertEqual(len(lrs[0]), 1)
self.assertListEqual([l[0] for l in lrs], expected_learning_rates)
scheduler = get_constant_schedule(self.optimizer)
lrs_2 = unwrap_and_save_reload_schedule(scheduler, self.num_steps)
self.assertListEqual([l[0] for l in lrs], [l[0] for l in lrs_2])
def test_warmup_constant_scheduler(self):
scheduler = get_constant_schedule_with_warmup(self.optimizer,
num_warmup_steps=4)
lrs = unwrap_schedule(scheduler, self.num_steps)
expected_learning_rates = [
2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0
]
self.assertEqual(len(lrs[0]), 1)
self.assertListEqual([l[0] for l in lrs], expected_learning_rates)
scheduler = get_constant_schedule_with_warmup(self.optimizer,
num_warmup_steps=4)
lrs_2 = unwrap_and_save_reload_schedule(scheduler, self.num_steps)
self.assertListEqual([l[0] for l in lrs], [l[0] for l in lrs_2])
def test_warmup_linear_scheduler(self):
scheduler = get_linear_schedule_with_warmup(self.optimizer,
num_warmup_steps=2,
num_training_steps=10)
lrs = unwrap_schedule(scheduler, self.num_steps)
expected_learning_rates = [
5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25, 0.0
]
self.assertEqual(len(lrs[0]), 1)
self.assertListEqual([l[0] for l in lrs], expected_learning_rates)
scheduler = get_linear_schedule_with_warmup(self.optimizer,
num_warmup_steps=2,
num_training_steps=10)
lrs_2 = unwrap_and_save_reload_schedule(scheduler, self.num_steps)
self.assertListEqual([l[0] for l in lrs], [l[0] for l in lrs_2])
def test_warmup_cosine_scheduler(self):
scheduler = get_cosine_schedule_with_warmup(self.optimizer,
num_warmup_steps=2,
num_training_steps=10)
lrs = unwrap_schedule(scheduler, self.num_steps)
expected_learning_rates = [
5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38, 0.0
]
self.assertEqual(len(lrs[0]), 1)
self.assertListAlmostEqual([l[0] for l in lrs],
expected_learning_rates,
tol=1e-2)
scheduler = get_cosine_schedule_with_warmup(self.optimizer,
num_warmup_steps=2,
num_training_steps=10)
lrs_2 = unwrap_and_save_reload_schedule(scheduler, self.num_steps)
self.assertListEqual([l[0] for l in lrs], [l[0] for l in lrs_2])
def test_warmup_cosine_hard_restart_scheduler(self):
scheduler = get_cosine_with_hard_restarts_schedule_with_warmup(
self.optimizer,
num_warmup_steps=2,
num_cycles=2,
num_training_steps=10)
lrs = unwrap_schedule(scheduler, self.num_steps)
expected_learning_rates = [
5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46, 0.0
]
self.assertEqual(len(lrs[0]), 1)
self.assertListAlmostEqual([l[0] for l in lrs],
expected_learning_rates,
tol=1e-2)
scheduler = get_cosine_with_hard_restarts_schedule_with_warmup(
self.optimizer,
num_warmup_steps=2,
num_cycles=2,
num_training_steps=10)
lrs_2 = unwrap_and_save_reload_schedule(scheduler, self.num_steps)
self.assertListEqual([l[0] for l in lrs], [l[0] for l in lrs_2])
class BertModelTest(CommonTestCases.CommonModelTester):
all_model_classes = (
BertModel, BertForMaskedLM, BertForNextSentencePrediction,
BertForPreTraining, BertForQuestionAnswering,
BertForSequenceClassification,
BertForTokenClassification) if is_torch_available() else ()
class BertModelTester(object):
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,
device='cpu',
):
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
self.device = device
# 1. superset of bert input/output descs
# see BertPreTrainedModel doc
self.input_ids_desc = IODescription(
'input_ids', ['batch', 'max_seq_len_in_batch'],
torch.int64,
num_classes=self.vocab_size)
self.attention_mask_desc = IODescription(
'attention_mask', ['batch', 'max_seq_len_in_batch'],
torch.int64,
num_classes=2)
self.token_type_ids_desc = IODescription(
'token_type_ids', ['batch', 'max_seq_len_in_batch'],
torch.int64,
num_classes=2)
self.position_ids_desc = IODescription(
'position_ids', ['batch', 'max_seq_len_in_batch'],
torch.int64,
num_classes=self.max_position_embeddings)
self.head_mask_desc = IODescription(
'head_mask',
[self.num_hidden_layers, self.num_attention_heads],
torch.int64,
num_classes=2)
self.inputs_embeds_desc = IODescription(
'inputs_embeds',
['batch', 'max_seq_len_in_batch', self.hidden_size],
torch.float32)
self.encoder_hidden_states_desc = IODescription(
'encoder_hidden_states',
['batch', 'max_seq_len_in_batch', self.hidden_size],
torch.float32)
self.encoder_attention_mask_desc = IODescription(
'encoder_attention_mask', ['batch', 'max_seq_len_in_batch'],
torch.float32)
# see BertForPreTraining doc
self.masked_lm_labels_desc = IODescription(
'masked_lm_labels', ['batch', 'max_seq_len_in_batch'],
torch.int64,
num_classes=self.vocab_size)
self.next_sentence_label_desc = IODescription(
'next_sentence_label', [
'batch',
], torch.int64, num_classes=2)
# outputs
self.loss_desc = IODescription('loss', [
1,
], torch.float32)
self.prediction_scores_desc = IODescription(
'prediction_scores',
['batch', 'max_seq_len_in_batch', self.vocab_size],
torch.float32)
self.seq_relationship_scores_desc = IODescription(
'seq_relationship_scores', ['batch', 2], torch.float32
) # IODescription('seq_relationship_scores', ['batch', 'max_seq_len_in_batch', 2], torch.float32)
self.hidden_states_desc = IODescription('hidden_states', [
self.num_hidden_layers, 'batch', 'max_seq_len_in_batch',
self.hidden_size
], torch.float32)
self.attentions_desc = IODescription('attentions', [
self.num_hidden_layers, 'batch', self.num_attention_heads,
'max_seq_len_in_batch', 'max_seq_len_in_batch'
], torch.float32)
self.last_hidden_state_desc = IODescription(
'last_hidden_state',
['batch', 'max_seq_len_in_batch', self.hidden_size],
torch.float32)
self.pooler_output_desc = IODescription(
'pooler_output', ['batch', self.hidden_size], torch.float32)
# BertForPreTraining forward:
# def forward(self, input_ids=None, attention_mask=None, token_type_ids=None,
# position_ids??=None, head_mask??=None, inputs_embeds??=None,
# masked_lm_labels=None, next_sentence_label=None):
#
# create_and_check_bert_for_pretraining calls BertForPreTraining:
# model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids,
# masked_lm_labels=token_labels, next_sentence_label=sequence_labels)
def BertForPreTraining_descs(self):
return ModelDescription(
[
self.input_ids_desc, self.attention_mask_desc,
self.token_type_ids_desc, self.masked_lm_labels_desc,
self.next_sentence_label_desc
],
# returns loss_desc if both masked_lm_labels_desc, next_sentence_label are provided
# hidden_states_desc, attentions_desc shall be included according to config.output_attentions, config.output_hidden_states
[
self.loss_desc,
self.prediction_scores_desc,
self.seq_relationship_scores_desc,
#hidden_states_desc, attentions_desc
])
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length],
self.vocab_size).to(self.device)
input_mask = None
if self.use_input_mask:
input_mask = ids_tensor([self.batch_size, self.seq_length],
vocab_size=2).to(self.device)
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).to(
self.device)
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).to(
self.device)
token_labels = ids_tensor([self.batch_size, self.seq_length],
self.num_labels).to(self.device)
choice_labels = ids_tensor([self.batch_size],
self.num_choices).to(self.device)
config = BertConfig(
vocab_size_or_config_json_file=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)
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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 check_loss_output(self, result):
self.parent.assertListEqual(list(result["loss"].size()), [])
def create_and_check_bert_model(self, config, input_ids,
token_type_ids, input_mask,
sequence_labels, token_labels,
choice_labels):
model = BertModel(config=config)
model.to(input_ids.device)
model.eval()
sequence_output, pooled_output = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids)
# failed because there is not loss output
model_desc = ModelDescription([
self.input_ids_desc, self.attention_mask_desc,
self.token_type_ids_desc
], [self.last_hidden_state_desc, self.pooler_output_desc])
args_gradient_accumulation_steps = 8
args_local_rank = 0
args_world_size = 1
args_fp16 = True
args_allreduce_post_accumulation = True
model = ORTTrainer(
model,
None,
model_desc,
"LambOptimizer",
map_optimizer_attributes=map_optimizer_attributes,
learning_rate_description=IODescription(
'Learning_Rate', [
1,
], torch.float32),
device=self.device,
postprocess_model=postprocess_model,
gradient_accumulation_steps=args_gradient_accumulation_steps,
world_rank=args_local_rank,
world_size=args_world_size,
use_mixed_precision=True if args_fp16 else False,
allreduce_post_accumulation=True
if args_allreduce_post_accumulation else False)
sequence_output, pooled_output = model(
input_ids, token_type_ids=token_type_ids)
sequence_output, pooled_output = model(input_ids)
result = {
"sequence_output": sequence_output,
"pooled_output": pooled_output,
}
self.parent.assertListEqual(
list(result["sequence_output"].size()),
[self.batch_size, self.seq_length, self.hidden_size])
self.parent.assertListEqual(list(result["pooled_output"].size()),
[self.batch_size, self.hidden_size])
def create_and_check_bert_model_as_decoder(self, config, input_ids,
token_type_ids, input_mask,
sequence_labels,
token_labels, choice_labels,
encoder_hidden_states,
encoder_attention_mask):
model = BertModel(config)
model.eval()
sequence_output, pooled_output = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask)
sequence_output, pooled_output = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states)
sequence_output, pooled_output = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids)
result = {
"sequence_output": sequence_output,
"pooled_output": pooled_output,
}
self.parent.assertListEqual(
list(result["sequence_output"].size()),
[self.batch_size, self.seq_length, self.hidden_size])
self.parent.assertListEqual(list(result["pooled_output"].size()),
[self.batch_size, self.hidden_size])
def create_and_check_bert_for_masked_lm(self, config, input_ids,
token_type_ids, input_mask,
sequence_labels, token_labels,
choice_labels):
model = BertForMaskedLM(config=config)
model.eval()
loss, prediction_scores = model(input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
masked_lm_labels=token_labels)
#####
model_desc = ModelDescription([
self.input_ids_desc, self.attention_mask_desc,
self.token_type_ids_desc, self.masked_lm_labels_desc
], [self.loss_desc, self.prediction_scores_desc])
args_gradient_accumulation_steps = 8
args_local_rank = 0
args_world_size = 1
args_fp16 = True
args_allreduce_post_accumulation = True
model = ORTTrainer(
model,
None,
model_desc,
"LambOptimizer",
map_optimizer_attributes=map_optimizer_attributes,
learning_rate_description=IODescription(
'Learning_Rate', [
1,
], torch.float32),
device=self.device,
postprocess_model=postprocess_model,
gradient_accumulation_steps=args_gradient_accumulation_steps,
world_rank=args_local_rank,
world_size=args_world_size,
use_mixed_precision=True if args_fp16 else False,
allreduce_post_accumulation=True
if args_allreduce_post_accumulation else False)
model(input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
masked_lm_labels=token_labels)
def create_and_check_bert_model_for_masked_lm_as_decoder(
self, config, input_ids, token_type_ids, input_mask,
sequence_labels, token_labels, choice_labels,
encoder_hidden_states, encoder_attention_mask):
model = BertForMaskedLM(config=config)
model.eval()
loss, prediction_scores = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
masked_lm_labels=token_labels,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask)
loss, prediction_scores = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
masked_lm_labels=token_labels,
encoder_hidden_states=encoder_hidden_states)
result = {
"loss": loss,
"prediction_scores": prediction_scores,
}
self.parent.assertListEqual(
list(result["prediction_scores"].size()),
[self.batch_size, self.seq_length, self.vocab_size])
self.check_loss_output(result)
def create_and_check_bert_for_next_sequence_prediction(
self, config, input_ids, token_type_ids, input_mask,
sequence_labels, token_labels, choice_labels):
model = BertForNextSentencePrediction(config=config)
model.eval()
loss, seq_relationship_score = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
next_sentence_label=sequence_labels)
result = {
"loss": loss,
"seq_relationship_score": seq_relationship_score,
}
self.parent.assertListEqual(
list(result["seq_relationship_score"].size()),
[self.batch_size, 2])
self.check_loss_output(result)
def create_and_check_bert_for_pretraining(self, config, input_ids,
token_type_ids, input_mask,
sequence_labels,
token_labels, choice_labels):
model = BertForPreTraining(config=config)
model.eval()
loss, prediction_scores, seq_relationship_score = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
masked_lm_labels=token_labels,
next_sentence_label=sequence_labels)
model_desc = ModelDescription([
self.input_ids_desc, self.attention_mask_desc,
self.token_type_ids_desc, self.masked_lm_labels_desc,
self.next_sentence_label_desc
], [
self.loss_desc, self.prediction_scores_desc,
self.seq_relationship_scores_desc
])
import argparse
args_ = argparse.Namespace(fp16=True, amp_opt_level='O1')
from collections import namedtuple
MyArgs = namedtuple(
"MyArgs",
"local_rank world_size max_steps learning_rate warmup_proportion batch_size seq_len"
)
args = MyArgs(local_rank=0,
world_size=1,
max_steps=100,
learning_rate=0.00001,
warmup_proportion=0.01,
batch_size=13,
seq_len=7)
from train_with_ort_trainer import get_lr
def get_lr_this_step(global_step):
return get_lr(args, global_step)
loss_scaler = LossScaler('loss_scale_input_name',
True,
up_scale_window=2000)
option_gradient_accumulation_steps = [8]
option_fp16 = [True, False]
option_allreduce_post_accumulation = True
option_use_internal_get_lr_this_step = False
option_use_internal_loss_scaler = False
# TODO: with with fetches
for gradient_accumulation_steps in option_gradient_accumulation_steps:
for fp16 in option_fp16:
for option_split_batch in BatchArgsOption:
loss_ort, prediction_scores_ort, seq_relationship_score_ort =\
run_test(model, model_desc, self.device, args, gradient_accumulation_steps, fp16,
option_allreduce_post_accumulation,
get_lr_this_step, option_use_internal_get_lr_this_step,
loss_scaler, option_use_internal_loss_scaler,
option_split_batch)
print(loss_ort)
print(prediction_scores_ort)
print(seq_relationship_score_ort)
def create_and_check_bert_for_question_answering(
self, config, input_ids, token_type_ids, input_mask,
sequence_labels, token_labels, choice_labels):
model = BertForQuestionAnswering(config=config)
model.eval()
loss, start_logits, end_logits = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels)
result = {
"loss": loss,
"start_logits": start_logits,
"end_logits": end_logits,
}
self.parent.assertListEqual(list(result["start_logits"].size()),
[self.batch_size, self.seq_length])
self.parent.assertListEqual(list(result["end_logits"].size()),
[self.batch_size, self.seq_length])
self.check_loss_output(result)
def create_and_check_bert_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 = BertForSequenceClassification(config)
model.eval()
loss, logits = model(input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=sequence_labels)
result = {
"loss": loss,
"logits": logits,
}
self.parent.assertListEqual(list(result["logits"].size()),
[self.batch_size, self.num_labels])
self.check_loss_output(result)
def create_and_check_bert_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 = BertForTokenClassification(config=config)
model.eval()
loss, logits = model(input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=token_labels)
result = {
"loss": loss,
"logits": logits,
}
self.parent.assertListEqual(
list(result["logits"].size()),
[self.batch_size, self.seq_length, self.num_labels])
self.check_loss_output(result)
def create_and_check_bert_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 = BertForMultipleChoice(config=config)
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()
loss, logits = model(multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels)
result = {
"loss": loss,
"logits": logits,
}
self.parent.assertListEqual(list(result["logits"].size()),
[self.batch_size, self.num_choices])
self.check_loss_output(result)
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
def setUp(self):
self.model_tester = BertModelTest.BertModelTester(self)
self.config_tester = ConfigTester(self,
config_class=BertConfig,
hidden_size=37)
# def test_config(self):
# self.config_tester.run_common_tests()
# def test_bert_model(self, use_cuda=False):
# # ^^ This could be a real fixture
# if use_cuda:
# self.model_tester.device = "cuda"
# config_and_inputs = self.model_tester.prepare_config_and_inputs()
# self.model_tester.create_and_check_bert_model(*config_and_inputs)
# def test_bert_model_as_decoder(self):
# config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
# self.model_tester.create_and_check_bert_model_as_decoder(*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_bert_for_masked_lm(
*config_and_inputs)
# def test_for_masked_lm_decoder(self):
# config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
# self.model_tester.create_and_check_bert_model_for_masked_lm_as_decoder(*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_bert_for_multiple_choice(*config_and_inputs)
# def test_for_next_sequence_prediction(self):
# config_and_inputs = self.model_tester.prepare_config_and_inputs()
# self.model_tester.create_and_check_bert_for_next_sequence_prediction(*config_and_inputs)
def test_for_pretraining(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_bert_for_pretraining(
*config_and_inputs)
class TapasModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = ((
TapasModel,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
) if is_torch_available() else None)
test_pruning = False
test_torchscript = False
test_resize_embeddings = True
test_head_masking = False
def _prepare_for_class(self,
inputs_dict,
model_class,
return_labels=False):
inputs_dict = copy.deepcopy(inputs_dict)
if model_class in get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
inputs_dict = {
k: v.unsqueeze(1).expand(-1, self.model_tester.num_choices,
-1).contiguous()
if isinstance(v, torch.Tensor) and v.ndim > 1 else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
inputs_dict["labels"] = torch.ones(
self.model_tester.batch_size,
dtype=torch.long,
device=torch_device)
elif model_class in get_values(
MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING):
inputs_dict["labels"] = torch.zeros(
(self.model_tester.batch_size,
self.model_tester.seq_length),
dtype=torch.long,
device=torch_device)
inputs_dict["aggregation_labels"] = torch.zeros(
self.model_tester.batch_size,
dtype=torch.long,
device=torch_device)
inputs_dict["numeric_values"] = torch.zeros(
(self.model_tester.batch_size,
self.model_tester.seq_length),
dtype=torch.float,
device=torch_device,
)
inputs_dict["numeric_values_scale"] = torch.zeros(
(self.model_tester.batch_size,
self.model_tester.seq_length),
dtype=torch.float,
device=torch_device,
)
inputs_dict["float_answer"] = torch.zeros(
self.model_tester.batch_size,
dtype=torch.float,
device=torch_device)
elif model_class in [
*get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING),
*get_values(MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING),
]:
inputs_dict["labels"] = torch.zeros(
self.model_tester.batch_size,
dtype=torch.long,
device=torch_device)
elif model_class in [
*get_values(MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING),
*get_values(MODEL_FOR_CAUSAL_LM_MAPPING),
*get_values(MODEL_FOR_MASKED_LM_MAPPING),
*get_values(MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING),
]:
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 = TapasModelTester(self)
self.config_tester = ConfigTester(self,
config_class=TapasConfig,
dim=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_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 main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--models",
required=False,
type=str,
default="all",
help="Model checkpoints to be provided "
"to the AutoModel classes. Leave "
"blank to benchmark the base version "
"of all available model "
"architectures.",
)
parser.add_argument("--torch",
required=False,
action="store_true",
help="Benchmark the Pytorch version of the "
"models")
parser.add_argument("--torch_cuda",
required=False,
action="store_true",
help="Pytorch only: run on available "
"cuda devices")
parser.add_argument(
"--torchscript",
required=False,
action="store_true",
help="Pytorch only: trace the models "
"using torchscript",
)
parser.add_argument(
"--tensorflow",
required=False,
action="store_true",
help="Benchmark the TensorFlow version "
"of the models. Will run on GPU if "
"the correct dependencies are "
"installed",
)
parser.add_argument("--xla",
required=False,
action="store_true",
help="TensorFlow only: use XLA acceleration.")
parser.add_argument(
"--amp",
required=False,
action="store_true",
help="TensorFlow only: use automatic mixed precision acceleration.",
)
parser.add_argument("--fp16",
required=False,
action="store_true",
help="PyTorch only: use FP16 to accelerate inference.")
parser.add_argument(
"--keras_predict",
required=False,
action="store_true",
help="Whether to use model.predict "
"instead of model() to do a "
"forward pass.",
)
parser.add_argument("--save_to_csv",
required=False,
action="store_true",
help="Save to a CSV file.")
parser.add_argument("--csv_filename",
required=False,
default=None,
help="CSV filename used if saving results to csv.")
parser.add_argument("--average_over",
required=False,
default=30,
type=int,
help="Times an experiment will be run.")
args = parser.parse_args()
if args.models == "all":
args.models = [
"gpt2",
"bert-base-cased",
"xlnet-base-cased",
"xlm-mlm-en-2048",
"transfo-xl-wt103",
"openai-gpt",
"distilbert-base-uncased",
"distilgpt2",
"roberta-base",
"ctrl",
]
else:
args.models = args.models.split()
print("Running with arguments", args)
if args.torch:
if is_torch_available():
create_setup_and_compute(
model_names=args.models,
tensorflow=False,
gpu=args.torch_cuda,
torchscript=args.torchscript,
fp16=args.fp16,
save_to_csv=args.save_to_csv,
csv_filename=args.csv_filename,
average_over=args.average_over,
)
else:
raise ImportError(
"Trying to run a PyTorch benchmark but PyTorch was not found in the environment."
)
if args.tensorflow:
if is_tf_available():
create_setup_and_compute(
model_names=args.models,
tensorflow=True,
xla=args.xla,
amp=args.amp,
save_to_csv=args.save_to_csv,
csv_filename=args.csv_filename,
average_over=args.average_over,
)
else:
raise ImportError(
"Trying to run a TensorFlow benchmark but TensorFlow was not found in the environment."
)
class T5ModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (
T5Model, T5ForConditionalGeneration) if is_torch_available() else ()
all_generative_model_classes = (
T5ForConditionalGeneration, ) if is_torch_available() else ()
test_pruning = False
test_torchscript = False
test_resize_embeddings = False
is_encoder_decoder = True
def setUp(self):
self.model_tester = T5ModelTester(self)
self.config_tester = ConfigTester(self,
config_class=T5Config,
d_model=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_shift_right(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_prepare_lm_labels_via_shift_left(
*config_and_inputs)
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_with_lm_head(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_with_lm_head(*config_and_inputs)
def test_decoder_model_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past(
*config_and_inputs)
def test_decoder_model_past_with_attn_mask(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_attention_mask_past(
*config_and_inputs)
def test_generate_with_past_key_value_states(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_generate_with_past_key_value_states(
*config_and_inputs)
def test_encoder_decoder_shared_weights(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_encoder_decoder_shared_weights(
*config_and_inputs)
@unittest.skipIf(torch_device == "cpu", "Cant do half precision")
def test_model_fp16_forward(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fp16_forward(
*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = T5Model.from_pretrained(model_name)
self.assertIsNotNone(model)
def test_export_to_onnx(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
model = T5Model(config_and_inputs[0]).to(torch_device)
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
model,
config_and_inputs[1],
f"{tmpdirname}/t5_test.onnx",
export_params=True,
opset_version=9,
)
class ElectraModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = ((
ElectraModel,
ElectraForPreTraining,
ElectraForMaskedLM,
ElectraForCausalLM,
ElectraForMultipleChoice,
ElectraForTokenClassification,
ElectraForSequenceClassification,
ElectraForQuestionAnswering,
) if is_torch_available() else ())
all_generative_model_classes = (
ElectraForCausalLM, ) if is_torch_available() else ()
fx_ready_model_classes = all_model_classes
fx_dynamic_ready_model_classes = all_model_classes
# special case for ForPreTraining model
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 in get_values(MODEL_FOR_PRETRAINING_MAPPING):
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 = ElectraModelTester(self)
self.config_tester = ConfigTester(self,
config_class=ElectraConfig,
hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
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_as_decoder(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder(
)
self.model_tester.create_and_check_electra_model_as_decoder(
*config_and_inputs)
def test_electra_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_electra_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_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_pre_training(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_electra_for_pretraining(
*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)
@slow
def test_model_from_pretrained(self):
for model_name in ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = ElectraModel.from_pretrained(model_name)
self.assertIsNotNone(model)
def test_for_causal_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder(
)
self.model_tester.create_and_check_electra_for_causal_lm(
*config_and_inputs)
class GPT2ModelTest(ModelTesterMixin, GenerationTesterMixin,
unittest.TestCase):
all_model_classes = ((GPT2Model, GPT2LMHeadModel, GPT2DoubleHeadsModel,
GPT2ForSequenceClassification)
if is_torch_available() else ())
all_generative_model_classes = (
GPT2LMHeadModel, GPT2DoubleHeadsModel) if is_torch_available() else ()
all_parallelizable_model_classes = (
GPT2LMHeadModel, ) if is_torch_available() else ()
test_missing_keys = False
test_model_parallel = True
# special case for DoubleHeads model
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__ == "GPT2DoubleHeadsModel":
inputs_dict["labels"] = torch.zeros(
(self.model_tester.batch_size,
self.model_tester.num_choices,
self.model_tester.seq_length),
dtype=torch.long,
device=torch_device,
)
inputs_dict["input_ids"] = inputs_dict["labels"]
inputs_dict["token_type_ids"] = inputs_dict["labels"]
inputs_dict["mc_token_ids"] = torch.zeros(
(self.model_tester.batch_size,
self.model_tester.num_choices),
dtype=torch.long,
device=torch_device,
)
inputs_dict["mc_labels"] = torch.zeros(
self.model_tester.batch_size,
dtype=torch.long,
device=torch_device)
return inputs_dict
def setUp(self):
self.model_tester = GPT2ModelTester(self)
self.config_tester = ConfigTester(self,
config_class=GPT2Config,
n_embd=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_gpt2_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gpt2_model(*config_and_inputs)
def test_gpt2_model_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gpt2_model_past(*config_and_inputs)
def test_gpt2_model_att_mask_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gpt2_model_attention_mask_past(
*config_and_inputs)
def test_gpt2_model_past_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gpt2_model_past_large_inputs(
*config_and_inputs)
def test_gpt2_lm_head_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*config_and_inputs)
def test_gpt2_double_lm_head_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(
*config_and_inputs)
def test_gpt2_sequence_classification_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gpt2_for_sequence_classification(
*config_and_inputs)
def test_gpt2_gradient_checkpointing(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs(
gradient_checkpointing=True)
self.model_tester.create_and_check_forward_and_backwards(
*config_and_inputs)
@slow
def test_batch_generation(self):
model = GPT2LMHeadModel.from_pretrained("gpt2")
model.to(torch_device)
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
tokenizer.padding_side = "left"
# Define PAD Token = EOS Token = 50256
tokenizer.pad_token = tokenizer.eos_token
model.config.pad_token_id = model.config.eos_token_id
# use different length sentences to test batching
sentences = [
"Hello, my dog is a little",
"Today, I",
]
inputs = tokenizer(sentences, return_tensors="pt", padding=True)
input_ids = inputs["input_ids"].to(torch_device)
token_type_ids = torch.cat(
[
input_ids.new_full(
(input_ids.shape[0], input_ids.shape[1] - 1), 0),
input_ids.new_full((input_ids.shape[0], 1), 500),
],
dim=-1,
)
outputs = model.generate(
input_ids=input_ids,
attention_mask=inputs["attention_mask"].to(torch_device),
)
outputs_tt = model.generate(
input_ids=input_ids,
attention_mask=inputs["attention_mask"].to(torch_device),
token_type_ids=token_type_ids,
)
inputs_non_padded = tokenizer(
sentences[0], return_tensors="pt").input_ids.to(torch_device)
output_non_padded = model.generate(input_ids=inputs_non_padded)
num_paddings = inputs_non_padded.shape[-1] - inputs["attention_mask"][
-1].long().sum().cpu().item()
inputs_padded = tokenizer(
sentences[1], return_tensors="pt").input_ids.to(torch_device)
output_padded = model.generate(input_ids=inputs_padded,
max_length=model.config.max_length -
num_paddings)
batch_out_sentence = tokenizer.batch_decode(outputs,
skip_special_tokens=True)
batch_out_sentence_tt = tokenizer.batch_decode(
outputs_tt, skip_special_tokens=True)
non_padded_sentence = tokenizer.decode(output_non_padded[0],
skip_special_tokens=True)
padded_sentence = tokenizer.decode(output_padded[0],
skip_special_tokens=True)
expected_output_sentence = [
"Hello, my dog is a little bit of a mess. I'm not sure if he's going",
"Today, I'm going to be doing a lot of research on this. I",
]
self.assertListEqual(expected_output_sentence, batch_out_sentence)
self.assertTrue(
batch_out_sentence_tt !=
batch_out_sentence) # token_type_ids should change output
self.assertListEqual(expected_output_sentence,
[non_padded_sentence, padded_sentence])
@slow
def test_batch_generation_2heads(self):
model = GPT2DoubleHeadsModel.from_pretrained("gpt2")
model.to(torch_device)
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
tokenizer.padding_side = "left"
# This tokenizer has no pad token, so we have to set it in some way
# Define PAD Token = EOS Token = 50256
tokenizer.pad_token = tokenizer.eos_token
model.config.pad_token_id = model.config.eos_token_id
# use different length sentences to test batching
sentences = [
"Hello, my dog is a little",
"Today, I",
]
inputs = tokenizer(sentences, return_tensors="pt", padding=True)
input_ids = inputs["input_ids"].to(torch_device)
token_type_ids = torch.cat(
[
input_ids.new_full(
(input_ids.shape[0], input_ids.shape[1] - 1), 0),
input_ids.new_full((input_ids.shape[0], 1), 500),
],
dim=-1,
)
outputs = model.generate(
input_ids=input_ids,
attention_mask=inputs["attention_mask"].to(torch_device),
)
outputs_tt = model.generate(
input_ids=input_ids,
attention_mask=inputs["attention_mask"].to(torch_device),
token_type_ids=token_type_ids,
)
inputs_non_padded = tokenizer(
sentences[0], return_tensors="pt").input_ids.to(torch_device)
output_non_padded = model.generate(input_ids=inputs_non_padded)
num_paddings = inputs_non_padded.shape[-1] - inputs["attention_mask"][
-1].long().sum().cpu().item()
inputs_padded = tokenizer(
sentences[1], return_tensors="pt").input_ids.to(torch_device)
output_padded = model.generate(input_ids=inputs_padded,
max_length=model.config.max_length -
num_paddings)
batch_out_sentence = tokenizer.batch_decode(outputs,
skip_special_tokens=True)
batch_out_sentence_tt = tokenizer.batch_decode(
outputs_tt, skip_special_tokens=True)
non_padded_sentence = tokenizer.decode(output_non_padded[0],
skip_special_tokens=True)
padded_sentence = tokenizer.decode(output_padded[0],
skip_special_tokens=True)
expected_output_sentence = [
"Hello, my dog is a little bit of a mess. I'm not sure if he's going",
"Today, I'm going to be doing a lot of research on this. I",
]
self.assertListEqual(expected_output_sentence, batch_out_sentence)
self.assertTrue(
batch_out_sentence_tt !=
batch_out_sentence) # token_type_ids should change output
self.assertListEqual(expected_output_sentence,
[non_padded_sentence, padded_sentence])
@slow
def test_model_from_pretrained(self):
for model_name in GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = GPT2Model.from_pretrained(model_name)
self.assertIsNotNone(model)
class CTRLModelTest(CommonTestCases.CommonModelTester):
all_model_classes = (CTRLModel,
CTRLLMHeadModel) if is_torch_available() else ()
test_pruning = False
test_torchscript = False
test_resize_embeddings = False
test_head_masking = False
class CTRLModelTester(object):
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_token_type_ids=True,
use_input_mask=True,
use_labels=True,
use_mc_token_ids=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_token_type_ids = use_token_type_ids
self.use_input_mask = use_input_mask
self.use_labels = use_labels
self.use_mc_token_ids = use_mc_token_ids
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 = 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)
mc_token_ids = None
if self.use_mc_token_ids:
mc_token_ids = ids_tensor([self.batch_size, self.num_choices],
self.seq_length)
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 = CTRLConfig(
vocab_size_or_config_json_file=self.vocab_size,
n_embd=self.hidden_size,
n_layer=self.num_hidden_layers,
n_head=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,
n_positions=self.max_position_embeddings,
n_ctx=self.max_position_embeddings
# type_vocab_size=self.type_vocab_size,
# initializer_range=self.initializer_range
)
head_mask = ids_tensor(
[self.num_hidden_layers, self.num_attention_heads], 2)
return config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, token_labels, choice_labels
def check_loss_output(self, result):
self.parent.assertListEqual(list(result["loss"].size()), [])
def create_and_check_ctrl_model(self, config, input_ids, input_mask,
head_mask, token_type_ids, *args):
model = CTRLModel(config=config)
model.eval()
model(input_ids,
token_type_ids=token_type_ids,
head_mask=head_mask)
model(input_ids, token_type_ids=token_type_ids)
sequence_output, presents = model(input_ids)
result = {
"sequence_output": sequence_output,
"presents": presents,
}
self.parent.assertListEqual(
list(result["sequence_output"].size()),
[self.batch_size, self.seq_length, self.hidden_size])
self.parent.assertEqual(len(result["presents"]), config.n_layer)
def create_and_check_lm_head_model(self, config, input_ids, input_mask,
head_mask, token_type_ids, *args):
model = CTRLLMHeadModel(config)
model.eval()
loss, lm_logits, _ = model(input_ids,
token_type_ids=token_type_ids,
labels=input_ids)
result = {"loss": loss, "lm_logits": lm_logits}
self.parent.assertListEqual(list(result["loss"].size()), [])
self.parent.assertListEqual(
list(result["lm_logits"].size()),
[self.batch_size, self.seq_length, self.vocab_size])
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids, input_mask, head_mask, token_type_ids,
mc_token_ids, sequence_labels, token_labels,
choice_labels) = config_and_inputs
inputs_dict = {
'input_ids': input_ids,
'token_type_ids': token_type_ids,
'head_mask': head_mask
}
return config, inputs_dict
def setUp(self):
self.model_tester = CTRLModelTest.CTRLModelTester(self)
self.config_tester = ConfigTester(self,
config_class=CTRLConfig,
n_embd=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_ctrl_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_ctrl_model(*config_and_inputs)
def test_ctrl_lm_head_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*config_and_inputs)
@pytest.mark.slow
def test_model_from_pretrained(self):
cache_dir = "/tmp/transformers_test/"
for model_name in list(CTRL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = CTRLModel.from_pretrained(model_name, cache_dir=cache_dir)
shutil.rmtree(cache_dir)
self.assertIsNotNone(model)
class GPTNeoModelTest(ModelTesterMixin, GenerationTesterMixin,
unittest.TestCase):
all_model_classes = ((GPTNeoModel, GPTNeoForCausalLM,
GPTNeoForSequenceClassification)
if is_torch_available() else ())
all_generative_model_classes = (
GPTNeoForCausalLM, ) if is_torch_available() else ()
fx_compatible = True
test_missing_keys = False
test_pruning = False
test_model_parallel = False
# special case for DoubleHeads model
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)
return inputs_dict
def setUp(self):
self.model_tester = GPTNeoModelTester(self)
self.config_tester = ConfigTester(self,
config_class=GPTNeoConfig,
n_embd=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_gpt_neo_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gpt_neo_model(*config_and_inputs)
def test_gpt_neo_model_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gpt_neo_model_past(
*config_and_inputs)
def test_gpt_neo_model_att_mask_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gpt_neo_model_attention_mask_past(
*config_and_inputs)
def test_gpt_neo_model_past_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gpt_neo_model_past_large_inputs(
*config_and_inputs)
def test_gpt_neo_lm_head_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*config_and_inputs)
def test_gpt_neo_sequence_classification_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gpt_neo_for_sequence_classification(
*config_and_inputs)
def test_gpt_neo_gradient_checkpointing(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_forward_and_backwards(
*config_and_inputs, gradient_checkpointing=True)
def _get_hidden_states(self):
return torch.tensor(
[[
[0.4983, -0.7584, -1.6944, 0.5440],
[2.6918, 0.4206, 0.4176, 0.2055],
[-0.0071, -0.0405, -1.4920, -0.3630],
[1.0492, 0.1599, -1.7648, 0.2419],
[-1.8348, 2.0514, -0.1946, 0.3203],
[0.7672, -1.1600, -1.7118, -0.9056],
[0.2986, 0.5372, 0.7729, -0.1927],
[0.0285, 0.2629, -1.1156, -1.1992],
]],
dtype=torch.float32,
device=torch_device,
)
def test_local_attn_probs(self):
model = GPTNeoModel.from_pretrained(
"valhalla/gpt-neo-random-tiny").eval()
layer = model.h[1].attn.attention.to(torch_device)
hidden_states = self._get_hidden_states()
hidden_states = torch.cat([hidden_states, hidden_states - 0.5], dim=2)
batch_size, seq_length, _ = hidden_states.shape
mask_tokens = 2
attention_mask = torch.ones(batch_size,
seq_length,
device=torch_device,
dtype=torch.long)
attention_mask[:, -mask_tokens:] = 0 # dont attend last mask_tokens
attention_mask = attention_mask.view(batch_size, -1)
attention_mask = attention_mask[:, None, None, :]
attention_mask = (1.0 - attention_mask) * -10000.0
attn_probs = layer(hidden_states,
attention_mask=attention_mask,
output_attentions=True)[-1]
# the last 2 tokens are masked, and should have 0 attn_probs
self.assertTrue(
torch.all(attn_probs[:, :, -mask_tokens:, -mask_tokens:] == 0))
# in loacal attention each token can only attend to the previous window_size tokens (inlcuding itself)
# here window_size is 4, so a token at index 5 can only attend to indcies [2, 3, 4, 5]
# and the attn_probs should be 0 for token [0, 1]
self.assertTrue(torch.all(attn_probs[:, :, 5, 2:6] != 0))
self.assertTrue(torch.all(attn_probs[:, :, 5, :2] == 0))
class DPRModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = ((
DPRContextEncoder,
DPRQuestionEncoder,
DPRReader,
) if is_torch_available() else ())
test_resize_embeddings = False
test_missing_keys = False # why?
test_pruning = False
test_head_masking = False
def setUp(self):
self.model_tester = DPRModelTester(self)
self.config_tester = ConfigTester(self,
config_class=DPRConfig,
hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_context_encoder_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_context_encoder(*config_and_inputs)
def test_question_encoder_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_question_encoder(*config_and_inputs)
def test_reader_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reader(*config_and_inputs)
def test_init_changed_config(self):
config = self.model_tester.prepare_config_and_inputs()[0]
model = DPRQuestionEncoder(config=config)
model.to(torch_device)
model.eval()
with tempfile.TemporaryDirectory() as tmp_dirname:
model.save_pretrained(tmp_dirname)
model = DPRQuestionEncoder.from_pretrained(tmp_dirname,
projection_dim=512)
self.assertIsNotNone(model)
@slow
def test_model_from_pretrained(self):
for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:
1]:
model = DPRContextEncoder.from_pretrained(model_name)
self.assertIsNotNone(model)
for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:
1]:
model = DPRContextEncoder.from_pretrained(model_name)
self.assertIsNotNone(model)
for model_name in DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:
1]:
model = DPRQuestionEncoder.from_pretrained(model_name)
self.assertIsNotNone(model)
for model_name in DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = DPRReader.from_pretrained(model_name)
self.assertIsNotNone(model)
class XLMModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
all_model_classes = ((
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
) if is_torch_available() else ())
all_generative_model_classes = (
(XLMWithLMHeadModel, ) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
test_sequence_classification_problem_types = True
# XLM 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__ == "XLMForQuestionAnswering":
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 = XLMModelTester(self)
self.config_tester = ConfigTester(self,
config_class=XLMConfig,
emb_dim=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_xlm_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*config_and_inputs)
def test_xlm_lm_head(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*config_and_inputs)
def test_xlm_simple_qa(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*config_and_inputs)
def test_xlm_qa(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*config_and_inputs)
def test_xlm_sequence_classif(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(
*config_and_inputs)
def test_xlm_token_classif(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(
*config_and_inputs)
def test_xlm_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(
*config_and_inputs)
def _check_attentions_for_generate(self,
batch_size,
attentions,
min_length,
max_length,
config,
use_cache=False,
num_beam_groups=1):
self.assertIsInstance(attentions, tuple)
self.assertListEqual([
isinstance(iter_attentions, tuple)
for iter_attentions in attentions
], [True] * len(attentions))
self.assertEqual(len(attentions),
(max_length - min_length) * num_beam_groups)
for idx, iter_attentions in enumerate(attentions):
# adds PAD dummy token
tgt_len = min_length + idx + 1
src_len = min_length + idx + 1
expected_shape = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions],
[expected_shape] * len(iter_attentions))
def _check_hidden_states_for_generate(self,
batch_size,
hidden_states,
min_length,
max_length,
config,
use_cache=False,
num_beam_groups=1):
self.assertIsInstance(hidden_states, tuple)
self.assertListEqual(
[
isinstance(iter_hidden_states, tuple)
for iter_hidden_states in hidden_states
],
[True] * len(hidden_states),
)
self.assertEqual(len(hidden_states),
(max_length - min_length) * num_beam_groups)
for idx, iter_hidden_states in enumerate(hidden_states):
# adds PAD dummy token
seq_len = min_length + idx + 1
expected_shape = (batch_size * num_beam_groups, seq_len,
config.hidden_size)
# check hidden size
self.assertListEqual(
[
layer_hidden_states.shape
for layer_hidden_states in iter_hidden_states
],
[expected_shape] * len(iter_hidden_states),
)
pass
@slow
def test_model_from_pretrained(self):
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = XLMModel.from_pretrained(model_name)
self.assertIsNotNone(model)
class T5ModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (T5Model, T5ForConditionalGeneration) if is_torch_available() else ()
all_generative_model_classes = (T5ForConditionalGeneration,) if is_torch_available() else ()
test_pruning = False
test_torchscript = False
test_resize_embeddings = False
is_encoder_decoder = True
class T5ModelTester(object):
def __init__(
self,
parent,
batch_size=13,
encoder_seq_length=7,
decoder_seq_length=9,
is_training=True,
use_attention_mask=True,
use_labels=True,
vocab_size=99,
n_positions=14,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
d_ff=37,
relative_attention_num_buckets=8,
dropout_rate=0.1,
initializer_factor=0.002,
eos_token_id=1,
pad_token_id=0,
decoder_start_token_id=0,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.encoder_seq_length = encoder_seq_length
self.decoder_seq_length = decoder_seq_length
self.is_training = is_training
self.use_attention_mask = use_attention_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.n_positions = n_positions
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.d_ff = d_ff
self.relative_attention_num_buckets = relative_attention_num_buckets
self.dropout_rate = dropout_rate
self.initializer_factor = initializer_factor
self.scope = scope
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.decoder_start_token_id = decoder_start_token_id
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size)
decoder_input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
attention_mask = None
decoder_attention_mask = None
if self.use_attention_mask:
attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2)
decoder_attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2)
lm_labels = None
if self.use_labels:
lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
config = T5Config(
vocab_size=self.vocab_size,
n_positions=self.n_positions,
d_model=self.hidden_size,
d_ff=self.d_ff,
d_kv=self.hidden_size // self.num_attention_heads,
num_layers=self.num_hidden_layers,
num_heads=self.num_attention_heads,
relative_attention_num_buckets=self.relative_attention_num_buckets,
dropout_rate=self.dropout_rate,
initializer_factor=self.initializer_factor,
eos_token_id=self.eos_token_id,
bos_token_id=self.pad_token_id,
pad_token_id=self.pad_token_id,
decoder_start_token_id=self.decoder_start_token_id,
)
return (
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
)
def check_loss_output(self, result):
self.parent.assertListEqual(list(result["loss"].size()), [])
def check_prepare_lm_labels_via_shift_left(
self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels,
):
model = T5Model(config=config)
model.to(torch_device)
model.eval()
# make sure that lm_labels are correctly padded from the right
lm_labels.masked_fill_((lm_labels == self.decoder_start_token_id), self.eos_token_id)
# add casaul pad token mask
triangular_mask = torch.tril(lm_labels.new_ones(lm_labels.shape)).logical_not()
lm_labels.masked_fill_(triangular_mask, self.pad_token_id)
decoder_input_ids = model._shift_right(lm_labels)
for i, (decoder_input_ids_slice, lm_labels_slice) in enumerate(zip(decoder_input_ids, lm_labels)):
# first item
self.parent.assertEqual(decoder_input_ids_slice[0].item(), self.decoder_start_token_id)
if i < decoder_input_ids_slice.shape[-1]:
if i < decoder_input_ids.shape[-1] - 1:
# items before diagonal
self.parent.assertListEqual(
decoder_input_ids_slice[1 : i + 1].tolist(), lm_labels_slice[:i].tolist()
)
# pad items after diagonal
if i < decoder_input_ids.shape[-1] - 2:
self.parent.assertListEqual(
decoder_input_ids_slice[i + 2 :].tolist(), lm_labels_slice[i + 1 : -1].tolist()
)
else:
# all items after square
self.parent.assertListEqual(decoder_input_ids_slice[1:].tolist(), lm_labels_slice[:-1].tolist())
def create_and_check_t5_model(
self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels,
):
model = T5Model(config=config)
model.to(torch_device)
model.eval()
decoder_output, decoder_past, encoder_output = model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
decoder_output, decoder_past, encoder_output = model(
input_ids=input_ids, decoder_input_ids=decoder_input_ids
)
result = {
"encoder_output": encoder_output,
"decoder_output": decoder_output,
"decoder_past": decoder_past,
}
self.parent.assertListEqual(
list(result["encoder_output"].size()), [self.batch_size, self.encoder_seq_length, self.hidden_size]
)
self.parent.assertListEqual(
list(result["decoder_output"].size()), [self.batch_size, self.decoder_seq_length, self.hidden_size]
)
self.parent.assertEqual(len(decoder_past), 2)
# decoder_past[0] should correspond to encoder output
self.parent.assertTrue(torch.all(decoder_past[0][0] == encoder_output))
# There should be `num_layers` key value embeddings stored in decoder_past[1]
self.parent.assertEqual(len(decoder_past[1]), config.num_layers)
# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past[1] tuple
self.parent.assertEqual(len(decoder_past[1][0]), 4)
def create_and_check_t5_with_lm_head(
self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels,
):
model = T5ForConditionalGeneration(config=config)
model.to(torch_device)
model.eval()
outputs = model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
lm_labels=lm_labels,
)
loss, prediction_scores, _, _ = outputs
self.parent.assertEqual(len(outputs), 4)
result = {
"loss": loss,
"prediction_scores": prediction_scores,
}
self.parent.assertListEqual(
list(result["prediction_scores"].size()), [self.batch_size, self.decoder_seq_length, self.vocab_size]
)
self.check_loss_output(result)
def create_and_check_t5_decoder_model_past(
self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels,
):
model = T5Model(config=config).get_decoder()
model.to(torch_device)
model.eval()
# first forward pass
output, past_key_value_states = model(input_ids, use_cache=True)
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
output_from_no_past = model(next_input_ids)[0]
output_from_past = model(next_tokens, past_key_value_states=past_key_value_states)[0]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_t5_decoder_model_attention_mask_past(
self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels,
):
model = T5Model(config=config).get_decoder()
model.to(torch_device)
model.eval()
# create attention mask
attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
half_seq_length = input_ids.shape[-1] // 2
attn_mask[:, half_seq_length:] = 0
# first forward pass
output, past_key_value_states = model(input_ids, attention_mask=attn_mask, use_cache=True)
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# change a random masked slice from input_ids
random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1
random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1)
input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens
# append to next input_ids and attn_mask
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
attn_mask = torch.cat(
[attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)], dim=1,
)
# get two different outputs
output_from_no_past = model(next_input_ids, attention_mask=attn_mask)[0]
output_from_past = model(
next_tokens, past_key_value_states=past_key_value_states, attention_mask=attn_mask
)[0]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_t5_and_check_t5_generate_with_past_key_value_states(
self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels,
):
model = T5ForConditionalGeneration(config=config)
model.to(torch_device)
model.eval()
torch.manual_seed(0)
output_without_past_cache = model.generate(
input_ids[:1], num_beams=2, max_length=5, do_sample=True, use_cache=False
)
torch.manual_seed(0)
output_with_past_cache = model.generate(input_ids[:1], num_beams=2, max_length=5, do_sample=True)
self.parent.assertTrue(torch.all(output_with_past_cache == output_without_past_cache))
def create_and_check_t5_model_fp16_forward(
self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels,
):
model = T5Model(config=config)
model.to(torch_device)
model.half()
model.eval()
output = model(input_ids, decoder_input_ids=input_ids, attention_mask=attention_mask)[0]
self.parent.assertFalse(torch.isnan(output).any().item())
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"use_cache": False,
}
return config, inputs_dict
def setUp(self):
self.model_tester = T5ModelTest.T5ModelTester(self)
self.config_tester = ConfigTester(self, config_class=T5Config, d_model=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_shift_right(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_prepare_lm_labels_via_shift_left(*config_and_inputs)
def test_t5_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_t5_model(*config_and_inputs)
def test_with_lm_head(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_t5_with_lm_head(*config_and_inputs)
def test_t5_decoder_model_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_t5_decoder_model_past(*config_and_inputs)
def test_t5_decoder_model_past_with_attn_mask(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_t5_decoder_model_attention_mask_past(*config_and_inputs)
def test_t5_generate_with_past_key_value_states(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_t5_and_check_t5_generate_with_past_key_value_states(*config_and_inputs)
@unittest.skipIf(torch_device == "cpu", "Cant do half precision")
def test_t5_model_fp16_forward(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_t5_model_fp16_forward(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in list(T5_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = T5Model.from_pretrained(model_name)
self.assertIsNotNone(model)
class CanineModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (
(
CanineModel,
CanineForMultipleChoice,
CanineForQuestionAnswering,
CanineForSequenceClassification,
CanineForTokenClassification,
)
if is_torch_available()
else ()
)
test_torchscript = False
test_mismatched_shapes = False
test_resize_embeddings = False
test_pruning = False
def setUp(self):
self.model_tester = CanineModelTester(self)
# we set has_text_modality to False as the config has no vocab_size attribute
self.config_tester = ConfigTester(self, config_class=CanineConfig, has_text_modality=False, 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_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)
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))
hidden_states = outputs.hidden_states
# expected_num_layers equals num_hidden_layers of the deep encoder + 1, + 2 for the first shallow encoder, + 2
# for the final shallow encoder
expected_num_layers = self.model_tester.num_hidden_layers + 1 + 2 + 2
self.assertEqual(len(hidden_states), expected_num_layers)
seq_length = self.model_tester.seq_length
for i in range(expected_num_layers):
if (i < 2) or ((expected_num_layers - i) < 3):
# the expected length of the hidden_states of the first and final shallow encoders
# is equal to the seq_length
self.assertListEqual(
list(hidden_states[i].shape[-2:]),
[seq_length, self.model_tester.hidden_size],
)
else:
# the expected length of the hidden_states of the deep encoder need to be updated
# for CANINE since the seq length is downsampled
self.assertListEqual(
list(hidden_states[i].shape[-2:]),
[seq_length // config.downsampling_rate, 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_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)
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.attentions
# we add + 2 due to the 2 shallow encoders
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers + 2)
# 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.attentions
# we add + 2 due to the 2 shallow encoders
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers + 2)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, seq_len, seq_len],
)
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))
if hasattr(self.model_tester, "num_hidden_states_types"):
added_hidden_states = self.model_tester.num_hidden_states_types
else:
added_hidden_states = 1
self.assertEqual(out_len + added_hidden_states, len(outputs))
self_attentions = outputs.attentions
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers + 2)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, seq_len, seq_len],
)
def test_model_outputs_equivalence(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def set_nan_tensor_to_zero(t):
t[t != t] = 0
return t
def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
with torch.no_grad():
tuple_output = model(**tuple_inputs, return_dict=False, **additional_kwargs)
dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs).to_tuple()
def recursive_check(tuple_object, dict_object):
if isinstance(tuple_object, (List, Tuple)):
for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
recursive_check(tuple_iterable_value, dict_iterable_value)
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5
),
msg=f"Tuple and dict output are not equal. Difference: {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`: {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}.",
)
recursive_check(tuple_output, dict_output)
for model_class in self.all_model_classes:
print(model_class)
model = model_class(config)
model.to(torch_device)
model.eval()
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs)
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs)
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True})
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True})
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(
model, tuple_inputs, dict_inputs, {"output_hidden_states": True, "output_attentions": True}
)
def test_headmasking(self):
if not self.test_head_masking:
return
global_rng.seed(42)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
global_rng.seed()
inputs_dict["output_attentions"] = True
config.output_hidden_states = True
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
model.to(torch_device)
model.eval()
# Prepare head_mask
# Set require_grad after having prepared the tensor to avoid error (leaf variable has been moved into the graph interior)
head_mask = torch.ones(
self.model_tester.num_hidden_layers,
self.model_tester.num_attention_heads,
device=torch_device,
)
head_mask[0, 0] = 0
head_mask[-1, :-1] = 0
head_mask.requires_grad_(requires_grad=True)
inputs = self._prepare_for_class(inputs_dict, model_class).copy()
inputs["head_mask"] = head_mask
outputs = model(**inputs, return_dict=True)
# Test that we can get a gradient back for importance score computation
output = sum(t.sum() for t in outputs[0])
output = output.sum()
output.backward()
multihead_outputs = head_mask.grad
self.assertIsNotNone(multihead_outputs)
self.assertEqual(len(multihead_outputs), self.model_tester.num_hidden_layers)
def check_attentions_validity(attentions):
# Remove Nan
for t in attentions:
self.assertLess(
torch.sum(torch.isnan(t)), t.numel() / 4
) # Check we don't have more than 25% nans (arbitrary)
attentions = [
t.masked_fill(torch.isnan(t), 0.0) for t in attentions
] # remove them (the test is less complete)
self.assertAlmostEqual(attentions[1][..., 0, :, :].flatten().sum().item(), 0.0)
self.assertNotEqual(attentions[1][..., -1, :, :].flatten().sum().item(), 0.0)
self.assertAlmostEqual(attentions[-2][..., -2, :, :].flatten().sum().item(), 0.0)
self.assertNotEqual(attentions[-2][..., -1, :, :].flatten().sum().item(), 0.0)
check_attentions_validity(outputs.attentions)
@unittest.skip("CANINE does not have a get_input_embeddings() method.")
def test_inputs_embeds(self):
# ViT does not use inputs_embeds
pass
@unittest.skip("CANINE does not have a get_input_embeddings() method.")
def test_model_common_attributes(self):
pass
@slow
def test_model_from_pretrained(self):
for model_name in CANINE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = CanineModel.from_pretrained(model_name)
self.assertIsNotNone(model)
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 get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING):
inputs_dict["labels"] = torch.zeros(
self.model_tester.batch_size,
dtype=torch.long,
device=torch_device)
elif model_class in get_values(MODEL_FOR_PRETRAINING_MAPPING):
# 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)
def prepare_tf_inputs_from_pt_inputs(self, pt_inputs_dict):
tf_inputs_dict = {}
for key, value in pt_inputs_dict.items():
# skip key that does not exist in tf
if isinstance(value, dict):
tf_inputs_dict[key] = self.prepare_pt_inputs_from_tf_inputs(
value)
elif isinstance(value, (list, tuple)):
tf_inputs_dict[key] = (
self.prepare_pt_inputs_from_tf_inputs(iter_value)
for iter_value in value)
elif type(value) == bool:
tf_inputs_dict[key] = value
elif key == "input_values":
tf_inputs_dict[key] = tf.convert_to_tensor(value.cpu().numpy(),
dtype=tf.float32)
elif key == "pixel_values":
tf_inputs_dict[key] = tf.convert_to_tensor(value.cpu().numpy(),
dtype=tf.float32)
elif key == "input_features":
tf_inputs_dict[key] = tf.convert_to_tensor(value.cpu().numpy(),
dtype=tf.float32)
# other general float inputs
elif value.is_floating_point():
tf_inputs_dict[key] = tf.convert_to_tensor(value.cpu().numpy(),
dtype=tf.float32)
else:
tf_inputs_dict[key] = tf.convert_to_tensor(value.cpu().numpy(),
dtype=tf.int32)
return tf_inputs_dict
class PoolFormerModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (
PoolFormerModel,
PoolFormerForImageClassification) if is_torch_available() else ()
test_head_masking = False
test_pruning = False
test_resize_embeddings = False
test_torchscript = False
def setUp(self):
self.model_tester = PoolFormerModelTester(self)
self.config_tester = PoolFormerConfigTester(
self, config_class=PoolFormerConfig)
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)
@unittest.skip("PoolFormer does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip(
"PoolFormer does not have get_input_embeddings method and get_output_embeddings methods"
)
def test_model_common_attributes(self):
pass
def test_retain_grad_hidden_states_attentions(self):
# Since poolformer doesn't use Attention
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common(
)
config.output_hidden_states = 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)
output = outputs[0]
hidden_states = outputs.hidden_states[0]
hidden_states.retain_grad()
output.flatten()[0].backward(retain_graph=True)
self.assertIsNotNone(hidden_states.grad)
def test_model_outputs_equivalence(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common(
)
def set_nan_tensor_to_zero(t):
t[t != t] = 0
return t
def check_equivalence(model,
tuple_inputs,
dict_inputs,
additional_kwargs={}):
with torch.no_grad():
tuple_output = model(**tuple_inputs,
return_dict=False,
**additional_kwargs)
dict_output = model(**dict_inputs,
return_dict=True,
**additional_kwargs).to_tuple()
def recursive_check(tuple_object, dict_object):
if isinstance(tuple_object, (List, Tuple)):
for tuple_iterable_value, dict_iterable_value in zip(
tuple_object, dict_object):
recursive_check(tuple_iterable_value,
dict_iterable_value)
elif isinstance(tuple_object, Dict):
for tuple_iterable_value, dict_iterable_value in zip(
tuple_object.values(), dict_object.values()):
recursive_check(tuple_iterable_value,
dict_iterable_value)
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(tuple_object),
set_nan_tensor_to_zero(dict_object),
atol=1e-5),
msg=
f"Tuple and dict output are not equal. Difference: {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`: {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}.",
)
recursive_check(tuple_output, dict_output)
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs)
tuple_inputs = self._prepare_for_class(inputs_dict,
model_class,
return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict,
model_class,
return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs)
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs,
{"output_hidden_states": True})
tuple_inputs = self._prepare_for_class(inputs_dict,
model_class,
return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict,
model_class,
return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs,
{"output_hidden_states": True})
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
@unittest.skip("PoolFormer does not have attention")
def test_attention_outputs(self):
pass
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))
hidden_states = outputs.hidden_states
expected_num_layers = self.model_tester.num_encoder_blocks
self.assertEqual(len(hidden_states), expected_num_layers)
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:]),
[
self.model_tester.hidden_sizes[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
],
)
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_training(self):
if not self.model_tester.is_training:
return
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common(
)
config.return_dict = True
for model_class in self.all_model_classes:
if model_class in get_values(MODEL_MAPPING):
continue
model = model_class(config)
model.to(torch_device)
model.train()
inputs = self._prepare_for_class(inputs_dict,
model_class,
return_labels=True)
loss = model(**inputs).loss
loss.backward()
@slow
def test_model_from_pretrained(self):
for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = PoolFormerModel.from_pretrained(model_name)
self.assertIsNotNone(model)
class AlbertModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = ((
AlbertModel,
AlbertForPreTraining,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertForQuestionAnswering,
) if is_torch_available() else ())
# special case for ForPreTraining model
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 in MODEL_FOR_PRETRAINING_MAPPING.values():
inputs_dict["labels"] = torch.zeros(
(self.model_tester.batch_size,
self.model_tester.seq_length),
dtype=torch.long,
device=torch_device)
inputs_dict["sentence_order_label"] = torch.zeros(
self.model_tester.batch_size,
dtype=torch.long,
device=torch_device)
return inputs_dict
def setUp(self):
self.model_tester = AlbertModelTester(self)
self.config_tester = ConfigTester(self,
config_class=AlbertConfig,
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_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_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_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)
@slow
def test_model_from_pretrained(self):
for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = AlbertModel.from_pretrained(model_name)
self.assertIsNotNone(model)
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))
class QDQBertModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = ((
QDQBertModel,
QDQBertForMaskedLM,
QDQBertForMultipleChoice,
QDQBertForNextSentencePrediction,
QDQBertForQuestionAnswering,
QDQBertForSequenceClassification,
QDQBertForTokenClassification,
QDQBertLMHeadModel,
) if is_torch_available() else ())
all_generative_model_classes = (
QDQBertLMHeadModel, ) if is_torch_available() else ()
def setUp(self):
self.model_tester = QDQBertModelTester(self)
self.config_tester = ConfigTester(self,
config_class=QDQBertConfig,
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_model_as_decoder(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder(
)
self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
def test_model_as_decoder_with_default_input_mask(self):
# This regression test was failing with PyTorch < 1.3
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
) = self.model_tester.prepare_config_and_inputs_for_decoder()
input_mask = None
self.model_tester.create_and_check_model_as_decoder(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def test_for_causal_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder(
)
self.model_tester.create_and_check_for_causal_lm(*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_causal_lm_decoder(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder(
)
self.model_tester.create_and_check_model_for_causal_lm_as_decoder(
*config_and_inputs)
def test_decoder_model_past_with_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder(
)
self.model_tester.create_and_check_decoder_model_past_large_inputs(
*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_next_sequence_prediction(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_next_sequence_prediction(
*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 QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = QDQBertModel.from_pretrained(model_name)
self.assertIsNotNone(model)
# Override
def test_feed_forward_chunking(self):
# feed forward chunking is not supported in QDQBert
pass
class LongformerModelTest(ModelTesterMixin, unittest.TestCase):
test_pruning = False # pruning is not supported
test_torchscript = False
all_model_classes = ((
LongformerModel,
LongformerForMaskedLM,
LongformerForSequenceClassification,
LongformerForQuestionAnswering,
LongformerForTokenClassification,
LongformerForMultipleChoice,
) if is_torch_available() else ())
def setUp(self):
self.model_tester = LongformerModelTester(self)
self.config_tester = ConfigTester(self,
config_class=LongformerConfig,
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_attention_mask_determinism(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_attention_mask_determinism(
*config_and_inputs)
def test_model_global_attention_mask(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_with_global_attention_mask(
*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_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_question_answering(
)
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)
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_retain_grad_hidden_states_attentions(self):
# longformer cannot keep gradients in attentions or hidden states
return
class XLMModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = ((
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
) if is_torch_available() else ())
all_generative_model_classes = (
(XLMWithLMHeadModel, ) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
class XLMModelTester(object):
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_lengths=True,
use_token_type_ids=True,
use_labels=True,
gelu_activation=True,
sinusoidal_embeddings=False,
causal=False,
asm=False,
n_langs=2,
vocab_size=99,
n_special=0,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
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,
summary_type="last",
use_proj=True,
scope=None,
bos_token_id=0,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_lengths = use_input_lengths
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.gelu_activation = gelu_activation
self.sinusoidal_embeddings = sinusoidal_embeddings
self.asm = asm
self.n_langs = n_langs
self.vocab_size = vocab_size
self.n_special = n_special
self.summary_type = summary_type
self.causal = causal
self.use_proj = use_proj
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.n_langs = n_langs
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.summary_type = summary_type
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
self.bos_token_id = bos_token_id
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length],
self.vocab_size)
input_mask = ids_tensor([self.batch_size, self.seq_length],
2).float()
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()
config = XLMConfig(
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,
bos_token_id=self.bos_token_id,
)
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
input_mask,
)
def check_loss_output(self, result):
self.parent.assertListEqual(list(result["loss"].size()), [])
def create_and_check_xlm_model(
self,
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
input_mask,
):
model = XLMModel(config=config)
model.to(torch_device)
model.eval()
outputs = model(input_ids,
lengths=input_lengths,
langs=token_type_ids)
outputs = model(input_ids, langs=token_type_ids)
outputs = model(input_ids)
sequence_output = outputs[0]
result = {
"sequence_output": sequence_output,
}
self.parent.assertListEqual(
list(result["sequence_output"].size()),
[self.batch_size, self.seq_length, self.hidden_size])
def create_and_check_xlm_lm_head(
self,
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
input_mask,
):
model = XLMWithLMHeadModel(config)
model.to(torch_device)
model.eval()
loss, logits = model(input_ids,
token_type_ids=token_type_ids,
labels=token_labels)
result = {
"loss": loss,
"logits": logits,
}
self.parent.assertListEqual(list(result["loss"].size()), [])
self.parent.assertListEqual(
list(result["logits"].size()),
[self.batch_size, self.seq_length, self.vocab_size])
def create_and_check_xlm_simple_qa(
self,
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
input_mask,
):
model = XLMForQuestionAnsweringSimple(config)
model.to(torch_device)
model.eval()
outputs = model(input_ids)
outputs = model(input_ids,
start_positions=sequence_labels,
end_positions=sequence_labels)
loss, start_logits, end_logits = outputs
result = {
"loss": loss,
"start_logits": start_logits,
"end_logits": end_logits,
}
self.parent.assertListEqual(list(result["start_logits"].size()),
[self.batch_size, self.seq_length])
self.parent.assertListEqual(list(result["end_logits"].size()),
[self.batch_size, self.seq_length])
self.check_loss_output(result)
def create_and_check_xlm_qa(
self,
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
input_mask,
):
model = XLMForQuestionAnswering(config)
model.to(torch_device)
model.eval()
outputs = model(input_ids)
start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits = outputs
outputs = model(
input_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
cls_index=sequence_labels,
is_impossible=is_impossible_labels,
p_mask=input_mask,
)
outputs = model(
input_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
cls_index=sequence_labels,
is_impossible=is_impossible_labels,
)
(total_loss, ) = outputs
outputs = model(input_ids,
start_positions=sequence_labels,
end_positions=sequence_labels)
(total_loss, ) = outputs
result = {
"loss": total_loss,
"start_top_log_probs": start_top_log_probs,
"start_top_index": start_top_index,
"end_top_log_probs": end_top_log_probs,
"end_top_index": end_top_index,
"cls_logits": cls_logits,
}
self.parent.assertListEqual(list(result["loss"].size()), [])
self.parent.assertListEqual(
list(result["start_top_log_probs"].size()),
[self.batch_size, model.config.start_n_top])
self.parent.assertListEqual(
list(result["start_top_index"].size()),
[self.batch_size, model.config.start_n_top])
self.parent.assertListEqual(
list(result["end_top_log_probs"].size()),
[
self.batch_size,
model.config.start_n_top * model.config.end_n_top
],
)
self.parent.assertListEqual(
list(result["end_top_index"].size()),
[
self.batch_size,
model.config.start_n_top * model.config.end_n_top
],
)
self.parent.assertListEqual(list(result["cls_logits"].size()),
[self.batch_size])
def create_and_check_xlm_sequence_classif(
self,
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
input_mask,
):
model = XLMForSequenceClassification(config)
model.to(torch_device)
model.eval()
(logits, ) = model(input_ids)
loss, logits = model(input_ids, labels=sequence_labels)
result = {
"loss": loss,
"logits": logits,
}
self.parent.assertListEqual(list(result["loss"].size()), [])
self.parent.assertListEqual(
list(result["logits"].size()),
[self.batch_size, self.type_sequence_label_size])
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,
input_mask,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"token_type_ids": token_type_ids,
"lengths": input_lengths
}
return config, inputs_dict
def setUp(self):
self.model_tester = XLMModelTest.XLMModelTester(self)
self.config_tester = ConfigTester(self,
config_class=XLMConfig,
emb_dim=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_xlm_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*config_and_inputs)
def test_xlm_lm_head(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*config_and_inputs)
def test_xlm_simple_qa(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*config_and_inputs)
def test_xlm_qa(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*config_and_inputs)
def test_xlm_sequence_classif(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(
*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in list(XLM_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = XLMModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
self.assertIsNotNone(model)
class BARTModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (
BartModel, BartForMaskedLM,
BartForSequenceClassification) if is_torch_available() else ()
is_encoder_decoder = True
# TODO(SS): fix the below in a separate PR
test_pruning = False
test_torchscript = False
test_head_masking = False
test_resize_embeddings = False # This requires inputs_dict['input_ids']
def setUp(self):
self.model_tester = ModelTester(self)
self.config_tester = ConfigTester(self, config_class=BartConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_advanced_inputs(self):
# (config, input_ids, token_type_ids, input_mask, *unused) = \
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common(
)
decoder_input_ids, decoder_attn_mask = _prepare_bart_decoder_inputs(
config, inputs_dict["input_ids"])
model = BartModel(config)
model.to(torch_device)
model.eval()
# test init
self.assertTrue(
(model.encoder.embed_tokens.weight == model.shared.weight
).all().item())
def _check_var(module):
"""Check that we initialized various parameters from N(0, config.init_std)."""
self.assertAlmostEqual(
torch.std(module.weight).item(), config.init_std, 2)
_check_var(model.encoder.embed_tokens)
_check_var(model.encoder.layers[0].self_attn.k_proj)
_check_var(model.encoder.layers[0].fc1)
_check_var(model.encoder.embed_positions)
decoder_features_with_created_mask = model.forward(**inputs_dict)[0]
decoder_features_with_passed_mask = model.forward(
decoder_attention_mask=decoder_attn_mask,
decoder_input_ids=decoder_input_ids,
**inputs_dict)[0]
_assert_tensors_equal(decoder_features_with_passed_mask,
decoder_features_with_created_mask)
useless_mask = torch.zeros_like(decoder_attn_mask)
decoder_features = model.forward(decoder_attention_mask=useless_mask,
**inputs_dict)[0]
self.assertTrue(isinstance(
decoder_features, torch.Tensor)) # no hidden states or attentions
self.assertEqual(decoder_features.size(),
(self.model_tester.batch_size,
self.model_tester.seq_length, config.d_model))
if decoder_attn_mask.min().item() < -1e3: # some tokens were masked
self.assertFalse(
(decoder_features_with_created_mask == decoder_features
).all().item())
# Test different encoder attention masks
decoder_features_with_long_encoder_mask = model.forward(
inputs_dict["input_ids"],
attention_mask=inputs_dict["attention_mask"].long())[0]
_assert_tensors_equal(decoder_features_with_long_encoder_mask,
decoder_features_with_created_mask)
def test_save_load_strict(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)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model2, info = model_class.from_pretrained(
tmpdirname, output_loading_info=True)
self.assertEqual(info["missing_keys"], [])
@unittest.skip("Passing inputs_embeds not implemented for Bart.")
def test_inputs_embeds(self):
pass
class FSMTModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (
FSMTModel,
FSMTForConditionalGeneration) if is_torch_available() else ()
all_generative_model_classes = (
FSMTForConditionalGeneration, ) if is_torch_available() else ()
is_encoder_decoder = True
# TODO(SS): fix the below in a separate PR
test_pruning = False
test_torchscript = True
test_head_masking = False
test_resize_embeddings = True # This requires inputs_dict['input_ids']
test_missing_keys = False # because FSMTForConditionalGeneration and FSMTModel now have identical state_dict
def setUp(self):
self.model_tester = ModelTester(self)
self.langs = ["en", "ru"]
config = {
"langs": self.langs,
"src_vocab_size": 10,
"tgt_vocab_size": 20,
}
# XXX: hack to appease to all other models requiring `vocab_size`
config["vocab_size"] = 99 # no such thing in FSMT
self.config_tester = ConfigTester(self,
config_class=FSMTConfig,
**config)
def test_config(self):
self.config_tester.run_common_tests()
# XXX: override test_model_common_attributes / different Embedding type
def test_model_common_attributes(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)
self.assertIsInstance(model.get_input_embeddings(),
(torch.nn.Embedding))
model.set_input_embeddings(torch.nn.Embedding(10, 10))
x = model.get_output_embeddings()
self.assertTrue(
x is None or isinstance(x, torch.nn.modules.sparse.Embedding))
def test_initialization_more(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common(
)
model = FSMTModel(config)
model.to(torch_device)
model.eval()
# test init
# self.assertTrue((model.encoder.embed_tokens.weight == model.shared.weight).all().item())
def _check_var(module):
"""Check that we initialized various parameters from N(0, config.init_std)."""
self.assertAlmostEqual(
torch.std(module.weight).item(), config.init_std, 2)
_check_var(model.encoder.embed_tokens)
_check_var(model.encoder.layers[0].self_attn.k_proj)
_check_var(model.encoder.layers[0].fc1)
# XXX: different std for fairseq version of SinusoidalPositionalEmbedding
# self.assertAlmostEqual(torch.std(model.encoder.embed_positions.weights).item(), config.init_std, 2)
def test_advanced_inputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common(
)
config.use_cache = False
inputs_dict["input_ids"][:, -2:] = config.pad_token_id
decoder_input_ids, decoder_attn_mask, causal_mask = _prepare_fsmt_decoder_inputs(
config, inputs_dict["input_ids"])
model = FSMTModel(config).to(torch_device).eval()
decoder_features_with_created_mask = model(**inputs_dict)[0]
decoder_features_with_passed_mask = model(
decoder_attention_mask=invert_mask(decoder_attn_mask),
decoder_input_ids=decoder_input_ids,
**inputs_dict)[0]
_assert_tensors_equal(decoder_features_with_passed_mask,
decoder_features_with_created_mask)
useless_mask = torch.zeros_like(decoder_attn_mask)
decoder_features = model(decoder_attention_mask=useless_mask,
**inputs_dict)[0]
self.assertTrue(isinstance(
decoder_features, torch.Tensor)) # no hidden states or attentions
self.assertEqual(
decoder_features.size(),
(self.model_tester.batch_size, self.model_tester.seq_length,
config.tgt_vocab_size),
)
if decoder_attn_mask.min().item() < -1e3: # some tokens were masked
self.assertFalse(
(decoder_features_with_created_mask == decoder_features
).all().item())
# Test different encoder attention masks
decoder_features_with_long_encoder_mask = model(
inputs_dict["input_ids"],
attention_mask=inputs_dict["attention_mask"].long())[0]
_assert_tensors_equal(decoder_features_with_long_encoder_mask,
decoder_features_with_created_mask)
def test_save_load_strict(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)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model2, info = model_class.from_pretrained(
tmpdirname, output_loading_info=True)
self.assertEqual(info["missing_keys"], [])
def test_save_load_no_save_keys(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)
state_dict_no_save_keys = getattr(model, "state_dict_no_save_keys",
None)
if state_dict_no_save_keys is None:
continue
# check the keys are in the original state_dict
for k in state_dict_no_save_keys:
self.assertIn(k, model.state_dict())
# check that certain keys didn't get saved with the model
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
output_model_file = os.path.join(tmpdirname, WEIGHTS_NAME)
state_dict_saved = torch.load(output_model_file)
for k in state_dict_no_save_keys:
self.assertNotIn(k, state_dict_saved)
@unittest.skip("can't be implemented for FSMT due to dual vocab.")
def test_resize_tokens_embeddings(self):
pass
@unittest.skip("Passing inputs_embeds not implemented for FSMT.")
def test_inputs_embeds(self):
pass
@unittest.skip("model weights aren't tied in FSMT.")
def test_tie_model_weights(self):
pass
def test_pt_tf_model_equivalence(self):
if not is_torch_available():
return
import torch
import transformers
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
pt_model_class_name = model_class.__name__[2:] # Skip the "TF" at the beggining
pt_model_class = getattr(transformers, pt_model_class_name)
config.output_hidden_states = True
tf_model = model_class(config)
pt_model = pt_model_class(config)
# Check we can load pt model in tf and vice-versa with model => model functions
tf_model = transformers.load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=inputs_dict)
pt_model = transformers.load_tf2_model_in_pytorch_model(pt_model, tf_model)
# Check predictions on first output (logits/hidden-states) are close enought given low-level computational differences
pt_model.eval()
pt_inputs_dict = dict(
(name, torch.from_numpy(key.numpy()).to(torch.long)) for name, key in inputs_dict.items()
)
with torch.no_grad():
pto = pt_model(**pt_inputs_dict)
tfo = tf_model(inputs_dict, training=False)
tf_hidden_states = tfo[0].numpy()
pt_hidden_states = pto[0].numpy()
tf_nans = np.copy(np.isnan(tf_hidden_states))
pt_nans = np.copy(np.isnan(pt_hidden_states))
pt_hidden_states[tf_nans] = 0
tf_hidden_states[tf_nans] = 0
pt_hidden_states[pt_nans] = 0
tf_hidden_states[pt_nans] = 0
max_diff = np.amax(np.abs(tf_hidden_states - pt_hidden_states))
# Debug info (remove when fixed)
if max_diff >= 2e-2:
print("===")
print(model_class)
print(config)
print(inputs_dict)
print(pt_inputs_dict)
self.assertLessEqual(max_diff, 2e-2)
# Check we can load pt model in tf and vice-versa with checkpoint => model functions
with tempfile.TemporaryDirectory() as tmpdirname:
pt_checkpoint_path = os.path.join(tmpdirname, "pt_model.bin")
torch.save(pt_model.state_dict(), pt_checkpoint_path)
tf_model = transformers.load_pytorch_checkpoint_in_tf2_model(tf_model, pt_checkpoint_path)
tf_checkpoint_path = os.path.join(tmpdirname, "tf_model.h5")
tf_model.save_weights(tf_checkpoint_path)
pt_model = transformers.load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path)
# Check predictions on first output (logits/hidden-states) are close enought given low-level computational differences
pt_model.eval()
pt_inputs_dict = dict(
(name, torch.from_numpy(key.numpy()).to(torch.long)) for name, key in inputs_dict.items()
)
with torch.no_grad():
pto = pt_model(**pt_inputs_dict)
tfo = tf_model(inputs_dict)
tfo = tfo[0].numpy()
pto = pto[0].numpy()
tf_nans = np.copy(np.isnan(tfo))
pt_nans = np.copy(np.isnan(pto))
pto[tf_nans] = 0
tfo[tf_nans] = 0
pto[pt_nans] = 0
tfo[pt_nans] = 0
max_diff = np.amax(np.abs(tfo - pto))
self.assertLessEqual(max_diff, 2e-2)
class Wav2Vec2RobustModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (Wav2Vec2ForCTC, Wav2Vec2Model,
Wav2Vec2ForMaskedLM) if is_torch_available() else ()
test_pruning = False
test_headmasking = False
test_torchscript = False
def setUp(self):
self.model_tester = Wav2Vec2ModelTester(self,
conv_stride=(3, 3, 3),
feat_extract_norm="layer",
do_stable_layer_norm=True)
self.config_tester = ConfigTester(self,
config_class=Wav2Vec2Config,
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_batched_inference(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_batch_inference(*config_and_inputs)
def test_ctc_loss_inference(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_ctc_loss(*config_and_inputs)
def test_train(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_training(*config_and_inputs)
# Wav2Vec2 has no inputs_embeds
def test_inputs_embeds(self):
pass
# `input_ids` is renamed to `input_values`
def test_forward_signature(self):
pass
# Wav2Vec2 cannot resize token embeddings
# since it has no tokens embeddings
def test_resize_tokens_embeddings(self):
pass
# Wav2Vec2 has no inputs_embeds
# and thus the `get_input_embeddings` fn
# is not implemented
def test_model_common_attributes(self):
pass
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)
# set layer drop to 0
model.config.layerdrop = 0.0
input_values = inputs_dict["input_values"]
input_lengths = torch.tensor(
[input_values.shape[1] for _ in range(input_values.shape[0])],
dtype=torch.long,
device=torch_device)
output_lengths = model._get_feat_extract_output_lengths(input_lengths)
labels = ids_tensor((input_values.shape[0], output_lengths[0] - 2),
self.model_tester.vocab_size)
inputs_dict["attention_mask"] = torch.ones_like(
inputs_dict["attention_mask"])
inputs_dict["labels"] = labels
outputs = model(**inputs_dict)
output = outputs[0]
# Encoder-/Decoder-only models
hidden_states = outputs.hidden_states[0]
attentions = outputs.attentions[0]
hidden_states.retain_grad()
attentions.retain_grad()
output.flatten()[0].backward(retain_graph=True)
self.assertIsNotNone(hidden_states.grad)
self.assertIsNotNone(attentions.grad)
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common(
)
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
if param.requires_grad:
if "conv.weight" in name or "masked_spec_embed" in name:
self.assertTrue(
-1.0 <=
((param.data.mean() * 1e9).round() / 1e9).item() <=
1.0,
msg=
f"Parameter {name} of model {model_class} seems not properly initialized",
)
else:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=
f"Parameter {name} of model {model_class} seems not properly initialized",
)
# overwrite from test_modeling_common
def _mock_init_weights(self, module):
if hasattr(module, "weight") and module.weight is not None:
module.weight.data.fill_(3)
if hasattr(module, "weight_g") and module.weight is not None:
module.weight_g.data.fill_(3)
if hasattr(module, "bias") and module.bias is not None:
module.bias.data.fill_(3)
@slow
def test_model_from_pretrained(self):
model = Wav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h")
self.assertIsNotNone(model)
class TransfoXLModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (TransfoXLModel,
TransfoXLLMHeadModel) if is_torch_available() else ()
all_generative_model_classes = (
TransfoXLLMHeadModel, ) if is_torch_available() else ()
test_pruning = False
test_torchscript = False
test_resize_embeddings = False
class TransfoXLModelTester(object):
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
mem_len=30,
clamp_len=15,
is_training=True,
use_labels=True,
vocab_size=99,
cutoffs=[10, 50, 80],
hidden_size=32,
d_embed=32,
num_attention_heads=4,
d_head=8,
d_inner=128,
div_val=2,
num_hidden_layers=5,
scope=None,
seed=1,
eos_token_id=0,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.mem_len = mem_len
self.key_length = seq_length + mem_len
self.clamp_len = clamp_len
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.cutoffs = cutoffs
self.hidden_size = hidden_size
self.d_embed = d_embed
self.num_attention_heads = num_attention_heads
self.d_head = d_head
self.d_inner = d_inner
self.div_val = div_val
self.num_hidden_layers = num_hidden_layers
self.scope = scope
self.seed = seed
self.eos_token_id = eos_token_id
def prepare_config_and_inputs(self):
input_ids_1 = ids_tensor([self.batch_size, self.seq_length],
self.vocab_size)
input_ids_2 = ids_tensor([self.batch_size, self.seq_length],
self.vocab_size)
lm_labels = None
if self.use_labels:
lm_labels = ids_tensor([self.batch_size, self.seq_length],
self.vocab_size)
config = TransfoXLConfig(
vocab_size=self.vocab_size,
mem_len=self.mem_len,
clamp_len=self.clamp_len,
cutoffs=self.cutoffs,
d_model=self.hidden_size,
d_embed=self.d_embed,
n_head=self.num_attention_heads,
d_head=self.d_head,
d_inner=self.d_inner,
div_val=self.div_val,
n_layer=self.num_hidden_layers,
eos_token_ids=self.eos_token_id,
)
return (config, input_ids_1, input_ids_2, lm_labels)
def set_seed(self):
random.seed(self.seed)
torch.manual_seed(self.seed)
def create_transfo_xl_model(self, config, input_ids_1, input_ids_2,
lm_labels):
model = TransfoXLModel(config)
model.to(torch_device)
model.eval()
hidden_states_1, mems_1 = model(input_ids_1)
hidden_states_2, mems_2 = model(input_ids_2, mems_1)
outputs = {
"hidden_states_1": hidden_states_1,
"mems_1": mems_1,
"hidden_states_2": hidden_states_2,
"mems_2": mems_2,
}
return outputs
def check_transfo_xl_model_output(self, result):
self.parent.assertListEqual(
list(result["hidden_states_1"].size()),
[self.batch_size, self.seq_length, self.hidden_size],
)
self.parent.assertListEqual(
list(result["hidden_states_2"].size()),
[self.batch_size, self.seq_length, self.hidden_size],
)
self.parent.assertListEqual(
list(list(mem.size()) for mem in result["mems_1"]),
[[self.mem_len, self.batch_size, self.hidden_size]] *
self.num_hidden_layers,
)
self.parent.assertListEqual(
list(list(mem.size()) for mem in result["mems_2"]),
[[self.mem_len, self.batch_size, self.hidden_size]] *
self.num_hidden_layers,
)
def create_transfo_xl_lm_head(self, config, input_ids_1, input_ids_2,
lm_labels):
model = TransfoXLLMHeadModel(config)
model.to(torch_device)
model.eval()
lm_logits_1, mems_1 = model(input_ids_1)
loss_1, _, mems_1 = model(input_ids_1, labels=lm_labels)
lm_logits_2, mems_2 = model(input_ids_2, mems=mems_1)
loss_2, _, mems_2 = model(input_ids_2,
labels=lm_labels,
mems=mems_1)
outputs = {
"loss_1": loss_1,
"mems_1": mems_1,
"lm_logits_1": lm_logits_1,
"loss_2": loss_2,
"mems_2": mems_2,
"lm_logits_2": lm_logits_2,
}
return outputs
def check_transfo_xl_lm_head_output(self, result):
self.parent.assertListEqual(list(result["loss_1"].size()),
[self.batch_size, self.seq_length])
self.parent.assertListEqual(
list(result["lm_logits_1"].size()),
[self.batch_size, self.seq_length, self.vocab_size],
)
self.parent.assertListEqual(
list(list(mem.size()) for mem in result["mems_1"]),
[[self.mem_len, self.batch_size, self.hidden_size]] *
self.num_hidden_layers,
)
self.parent.assertListEqual(list(result["loss_2"].size()),
[self.batch_size, self.seq_length])
self.parent.assertListEqual(
list(result["lm_logits_2"].size()),
[self.batch_size, self.seq_length, self.vocab_size],
)
self.parent.assertListEqual(
list(list(mem.size()) for mem in result["mems_2"]),
[[self.mem_len, self.batch_size, self.hidden_size]] *
self.num_hidden_layers,
)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids_1, input_ids_2, lm_labels) = config_and_inputs
inputs_dict = {"input_ids": input_ids_1}
return config, inputs_dict
def setUp(self):
self.model_tester = TransfoXLModelTest.TransfoXLModelTester(self)
self.config_tester = ConfigTester(self,
config_class=TransfoXLConfig,
d_embed=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_transfo_xl_model(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
output_result = self.model_tester.create_transfo_xl_model(
*config_and_inputs)
self.model_tester.check_transfo_xl_model_output(output_result)
def test_transfo_xl_lm_head(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
output_result = self.model_tester.create_transfo_xl_lm_head(
*config_and_inputs)
self.model_tester.check_transfo_xl_lm_head_output(output_result)
@slow
def test_model_from_pretrained(self):
for model_name in list(
TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = TransfoXLModel.from_pretrained(model_name,
cache_dir=CACHE_DIR)
self.assertIsNotNone(model)
class BARTModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (
(BartModel, BartForConditionalGeneration, BartForSequenceClassification, BartForQuestionAnswering)
if is_torch_available()
else ()
)
all_generative_model_classes = (BartForConditionalGeneration,) if is_torch_available() else ()
is_encoder_decoder = True
# TODO(SS): fix the below in a separate PR
test_pruning = False
test_torchscript = True
test_head_masking = False
test_resize_embeddings = True # This requires inputs_dict['input_ids']
test_missing_keys = False # because BartForConditionalGeneration and BartModel now have identical state_dict
def setUp(self):
self.model_tester = ModelTester(self)
self.config_tester = ConfigTester(self, config_class=BartConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_initialization_more(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
model = BartModel(config)
model.to(torch_device)
model.eval()
# test init
self.assertTrue((model.encoder.embed_tokens.weight == model.shared.weight).all().item())
def _check_var(module):
"""Check that we initialized various parameters from N(0, config.init_std)."""
self.assertAlmostEqual(torch.std(module.weight).item(), config.init_std, 2)
_check_var(model.encoder.embed_tokens)
_check_var(model.encoder.layers[0].self_attn.k_proj)
_check_var(model.encoder.layers[0].fc1)
_check_var(model.encoder.embed_positions)
def test_advanced_inputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.use_cache = False
inputs_dict["input_ids"][:, -2:] = config.pad_token_id
decoder_input_ids, decoder_attn_mask, causal_mask = _prepare_bart_decoder_inputs(
config, inputs_dict["input_ids"]
)
model = BartModel(config).to(torch_device).eval()
decoder_features_with_created_mask = model(**inputs_dict)[0]
decoder_features_with_passed_mask = model(
decoder_attention_mask=invert_mask(decoder_attn_mask), decoder_input_ids=decoder_input_ids, **inputs_dict
)[0]
_assert_tensors_equal(decoder_features_with_passed_mask, decoder_features_with_created_mask)
useless_mask = torch.zeros_like(decoder_attn_mask)
decoder_features = model(decoder_attention_mask=useless_mask, **inputs_dict)[0]
self.assertTrue(isinstance(decoder_features, torch.Tensor)) # no hidden states or attentions
self.assertEqual(
decoder_features.size(), (self.model_tester.batch_size, self.model_tester.seq_length, config.d_model)
)
if decoder_attn_mask.min().item() < -1e3: # some tokens were masked
self.assertFalse((decoder_features_with_created_mask == decoder_features).all().item())
# Test different encoder attention masks
decoder_features_with_long_encoder_mask = model(
inputs_dict["input_ids"], attention_mask=inputs_dict["attention_mask"].long()
)[0]
_assert_tensors_equal(decoder_features_with_long_encoder_mask, decoder_features_with_created_mask)
def test_save_load_strict(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)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
self.assertEqual(info["missing_keys"], [])
@unittest.skip("Passing inputs_embeds not implemented for Bart.")
def test_inputs_embeds(self):
pass
def test_tiny_model(self):
model_name = "sshleifer/bart-tiny-random"
tiny = AutoModel.from_pretrained(model_name) # same vocab size
tok = AutoTokenizer.from_pretrained(model_name) # same tokenizer
inputs_dict = tok.batch_encode_plus(["Hello my friends"], return_tensors="pt")
with torch.no_grad():
tiny(**inputs_dict)
def test_pt_tf_model_equivalence(self):
from transformers import is_torch_available
if not is_torch_available():
return
import torch
import transformers
for model_class in self.all_model_classes:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common(
return_obj_labels="PreTraining" in model_class.__name__
)
pt_model_class_name = model_class.__name__[2:] # Skip the "TF" at the beginning
pt_model_class = getattr(transformers, pt_model_class_name)
config.output_hidden_states = True
config.task_obj_predict = False
tf_model = model_class(config)
pt_model = pt_model_class(config)
# Check we can load pt model in tf and vice-versa with model => model functions
tf_model = transformers.load_pytorch_model_in_tf2_model(
tf_model, pt_model, tf_inputs=self._prepare_for_class(inputs_dict, model_class)
)
pt_model = transformers.load_tf2_model_in_pytorch_model(pt_model, tf_model)
# Check predictions on first output (logits/hidden-states) are close enought given low-level computational differences
pt_model.eval()
# Delete obj labels as we want to compute the hidden states and not the loss
if "obj_labels" in inputs_dict:
del inputs_dict["obj_labels"]
def torch_type(key):
if key in ("visual_feats", "visual_pos"):
return torch.float32
else:
return torch.long
def recursive_numpy_convert(iterable):
return_dict = {}
for key, value in iterable.items():
if isinstance(value, dict):
return_dict[key] = recursive_numpy_convert(value)
else:
if isinstance(value, (list, tuple)):
return_dict[key] = (
torch.from_numpy(iter_value.numpy()).to(torch_type(key)) for iter_value in value
)
else:
return_dict[key] = torch.from_numpy(value.numpy()).to(torch_type(key))
return return_dict
pt_inputs_dict = recursive_numpy_convert(self._prepare_for_class(inputs_dict, model_class))
# need to rename encoder-decoder "inputs" for PyTorch
if "inputs" in pt_inputs_dict and self.is_encoder_decoder:
pt_inputs_dict["input_ids"] = pt_inputs_dict.pop("inputs")
with torch.no_grad():
pto = pt_model(**pt_inputs_dict)
tfo = tf_model(self._prepare_for_class(inputs_dict, model_class), training=False)
tf_hidden_states = tfo[0].numpy()
pt_hidden_states = pto[0].numpy()
import numpy as np
tf_nans = np.copy(np.isnan(tf_hidden_states))
pt_nans = np.copy(np.isnan(pt_hidden_states))
pt_hidden_states[tf_nans] = 0
tf_hidden_states[tf_nans] = 0
pt_hidden_states[pt_nans] = 0
tf_hidden_states[pt_nans] = 0
max_diff = np.amax(np.abs(tf_hidden_states - pt_hidden_states))
# Debug info (remove when fixed)
if max_diff >= 2e-2:
print("===")
print(model_class)
print(config)
print(inputs_dict)
print(pt_inputs_dict)
self.assertLessEqual(max_diff, 6e-2)
# Check we can load pt model in tf and vice-versa with checkpoint => model functions
with tempfile.TemporaryDirectory() as tmpdirname:
import os
pt_checkpoint_path = os.path.join(tmpdirname, "pt_model.bin")
torch.save(pt_model.state_dict(), pt_checkpoint_path)
tf_model = transformers.load_pytorch_checkpoint_in_tf2_model(tf_model, pt_checkpoint_path)
tf_checkpoint_path = os.path.join(tmpdirname, "tf_model.h5")
tf_model.save_weights(tf_checkpoint_path)
pt_model = transformers.load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path)
# Check predictions on first output (logits/hidden-states) are close enought given low-level computational differences
pt_model.eval()
pt_inputs_dict = dict(
(name, torch.from_numpy(key.numpy()).to(torch.long))
for name, key in self._prepare_for_class(inputs_dict, model_class).items()
)
for key, value in pt_inputs_dict.items():
if key in ("visual_feats", "visual_pos"):
pt_inputs_dict[key] = value.to(torch.float32)
else:
pt_inputs_dict[key] = value.to(torch.long)
with torch.no_grad():
pto = pt_model(**pt_inputs_dict)
tfo = tf_model(self._prepare_for_class(inputs_dict, model_class))
tfo = tfo[0].numpy()
pto = pto[0].numpy()
tf_nans = np.copy(np.isnan(tfo))
pt_nans = np.copy(np.isnan(pto))
pto[tf_nans] = 0
tfo[tf_nans] = 0
pto[pt_nans] = 0
tfo[pt_nans] = 0
max_diff = np.amax(np.abs(tfo - pto))
self.assertLessEqual(max_diff, 6e-2)
class Wav2Vec2RobustModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (Wav2Vec2Model, Wav2Vec2ForMaskedLM, Wav2Vec2ForCTC) if is_torch_available() else ()
test_pruning = False
test_headmasking = False
test_torchscript = False
def setUp(self):
self.model_tester = Wav2Vec2ModelTester(
self, conv_stride=(3, 3, 3), feat_extract_norm="layer", do_stable_layer_norm=True
)
self.config_tester = ConfigTester(self, config_class=Wav2Vec2Config, 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)
# Wav2Vec2 has no inputs_embeds
def test_inputs_embeds(self):
pass
# `input_ids` is renamed to `input_values`
def test_forward_signature(self):
pass
# Wav2Vec2 cannot resize token embeddings
# since it has no tokens embeddings
def test_resize_tokens_embeddings(self):
pass
# Wav2Vec2 has no inputs_embeds
# and thus the `get_input_embeddings` fn
# is not implemented
def test_model_common_attributes(self):
pass
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
if param.requires_grad:
if "conv.weight" in name:
self.assertTrue(
-1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0,
msg="Parameter {} of model {} seems not properly initialized".format(name, model_class),
)
else:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg="Parameter {} of model {} seems not properly initialized".format(name, model_class),
)
@slow
def test_model_from_pretrained(self):
model = Wav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h")
self.assertIsNotNone(model)
# If checking the tensors placement
# tf.debugging.set_log_device_placement(True)
from typing import List
import timeit
from transformers import is_tf_available, is_torch_available
from time import time
import argparse
import csv
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModel
if is_torch_available():
import torch
from transformers import AutoModel
from transformers import AutoConfig, AutoTokenizer
input_text = """Bent over their instruments, three hundred Fertilizers were plunged, as
the Director of Hatcheries and Conditioning entered the room, in the
scarcely breathing silence, the absent-minded, soliloquizing hum or
whistle, of absorbed concentration. A troop of newly arrived students,
very young, pink and callow, followed nervously, rather abjectly, at the
Director's heels. Each of them carried a notebook, in which, whenever
the great man spoke, he desperately scribbled. Straight from the
class BertModelTest(ModelTesterMixin, GenerationTesterMixin,
unittest.TestCase):
all_model_classes = ((
BertModel,
BertLMHeadModel,
BertForMaskedLM,
BertForMultipleChoice,
BertForNextSentencePrediction,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertForTokenClassification,
) if is_torch_available() else ())
all_generative_model_classes = (
BertLMHeadModel, ) if is_torch_available() else ()
# special case for ForPreTraining model
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 in MODEL_FOR_PRETRAINING_MAPPING.values():
inputs_dict["labels"] = torch.zeros(
(self.model_tester.batch_size,
self.model_tester.seq_length),
dtype=torch.long,
device=torch_device)
inputs_dict["next_sentence_label"] = torch.zeros(
self.model_tester.batch_size,
dtype=torch.long,
device=torch_device)
return inputs_dict
def setUp(self):
self.model_tester = BertModelTester(self)
self.config_tester = ConfigTester(self,
config_class=BertConfig,
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_model_as_decoder(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder(
)
self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
def test_model_as_decoder_with_default_input_mask(self):
# This regression test was failing with PyTorch < 1.3
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
) = self.model_tester.prepare_config_and_inputs_for_decoder()
input_mask = None
self.model_tester.create_and_check_model_as_decoder(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def test_for_causal_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder(
)
self.model_tester.create_and_check_for_causal_lm(*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_causal_lm_decoder(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder(
)
self.model_tester.create_and_check_model_for_causal_lm_as_decoder(
*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_next_sequence_prediction(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_next_sequence_prediction(
*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_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 BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = BertModel.from_pretrained(model_name)
self.assertIsNotNone(model)
