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Unverified Commit c3d9ac76 authored by Lysandre Debut's avatar Lysandre Debut Committed by GitHub
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Expose get_config() on ModelTesters (#12812) 

* Expose get_config() on ModelTesters

* Typo
parent cabcc751
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Showing with 385 additions and 406 deletions
tests/test_modeling_reformer.py
View file @ c3d9ac76
......@@ -15,7 +15,7 @@
import unittest
from transformers import is_torch_available
from transformers import ReformerConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
......@@ -36,7 +36,6 @@ if is_torch_available():
from transformers import (
REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
ReformerConfig,
ReformerForMaskedLM,
ReformerForQuestionAnswering,
ReformerForSequenceClassification,
......@@ -51,44 +50,44 @@ class ReformerModelTester:
def __init__(
self,
parent,
batch_size=None,
seq_length=None,
is_training=None,
is_decoder=None,
use_input_mask=None,
use_labels=None,
vocab_size=None,
attention_head_size=None,
hidden_size=None,
num_attention_heads=None,
local_attn_chunk_length=None,
local_num_chunks_before=None,
local_num_chunks_after=None,
batch_size=13,
seq_length=32,
is_training=True,
is_decoder=True,
use_input_mask=True,
use_labels=True,
vocab_size=32,
attention_head_size=16,
hidden_size=32,
num_attention_heads=2,
local_attn_chunk_length=4,
local_num_chunks_before=1,
local_num_chunks_after=0,
num_buckets=None,
num_hashes=1,
lsh_attn_chunk_length=None,
lsh_num_chunks_before=None,
lsh_num_chunks_after=None,
chunk_size_lm_head=None,
chunk_size_feed_forward=None,
feed_forward_size=None,
hidden_act=None,
hidden_dropout_prob=None,
local_attention_probs_dropout_prob=None,
chunk_size_lm_head=0,
chunk_size_feed_forward=0,
feed_forward_size=32,
hidden_act="gelu",
hidden_dropout_prob=0.1,
local_attention_probs_dropout_prob=0.1,
lsh_attention_probs_dropout_prob=None,
max_position_embeddings=None,
initializer_range=None,
axial_norm_std=None,
layer_norm_eps=None,
axial_pos_embds=None,
axial_pos_shape=None,
axial_pos_embds_dim=None,
attn_layers=None,
pad_token_id=None,
eos_token_id=None,
max_position_embeddings=512,
initializer_range=0.02,
axial_norm_std=1.0,
layer_norm_eps=1e-12,
axial_pos_embds=True,
axial_pos_shape=[4, 8],
axial_pos_embds_dim=[16, 16],
attn_layers=["local", "local", "local", "local"],
pad_token_id=0,
eos_token_id=2,
scope=None,
hash_seed=None,
num_labels=None,
hash_seed=0,
num_labels=2,
):
self.parent = parent
self.batch_size = batch_size
......@@ -101,7 +100,7 @@ class ReformerModelTester:
self.attention_head_size = attention_head_size
self.hidden_size = hidden_size
self.num_attention_heads = num_attention_heads
self.num_hidden_layers = len(attn_layers)
self.num_hidden_layers = len(attn_layers) if attn_layers is not None else 0
self.local_attn_chunk_length = local_attn_chunk_length
self.local_num_chunks_after = local_num_chunks_after
self.local_num_chunks_before = local_num_chunks_before
......@@ -149,7 +148,17 @@ class ReformerModelTester:
if self.use_labels:
choice_labels = ids_tensor([self.batch_size], 2)
config = ReformerConfig(
config = self.get_config()
return (
config,
input_ids,
input_mask,
choice_labels,
)
def get_config(self):
return ReformerConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
......@@ -177,13 +186,6 @@ class ReformerModelTester:
hash_seed=self.hash_seed,
)
return (
config,
input_ids,
input_mask,
choice_labels,
)
def create_and_check_reformer_model(self, config, input_ids, input_mask, choice_labels):
model = ReformerModel(config=config)
model.to(torch_device)
......@@ -593,45 +595,8 @@ class ReformerLocalAttnModelTest(ReformerTesterMixin, GenerationTesterMixin, Mod
test_torchscript = False
test_sequence_classification_problem_types = True
def prepare_kwargs(self):
return {
"batch_size": 13,
"seq_length": 32,
"is_training": True,
"is_decoder": True,
"use_input_mask": True,
"use_labels": True,
"vocab_size": 32,
"attention_head_size": 16,
"hidden_size": 32,
"num_attention_heads": 2,
"local_attn_chunk_length": 4,
"local_num_chunks_before": 1,
"local_num_chunks_after": 0,
"chunk_size_lm_head": 0,
"chunk_size_feed_forward": 0,
"feed_forward_size": 32,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"local_attention_probs_dropout_prob": 0.1,
"max_position_embeddings": 512,
"initializer_range": 0.02,
"axial_norm_std": 1.0,
"layer_norm_eps": 1e-12,
"axial_pos_embds": True,
"axial_pos_shape": [4, 8],
"axial_pos_embds_dim": [16, 16],
"attn_layers": ["local", "local", "local", "local"],
"pad_token_id": 0,
"eos_token_id": 2,
"scope": None,
"hash_seed": 0,
"num_labels": 2,
}
def setUp(self):
tester_kwargs = self.prepare_kwargs()
self.model_tester = ReformerModelTester(self, **tester_kwargs)
self.model_tester = ReformerModelTester(self)
self.config_tester = ConfigTester(self, config_class=ReformerConfig, hidden_size=37)
@slow
......@@ -716,49 +681,46 @@ class ReformerLSHAttnModelTest(ReformerTesterMixin, ModelTesterMixin, Generation
test_headmasking = False
test_torchscript = False
def prepare_kwargs(self):
return {
"batch_size": 13,
"seq_length": 13,
"use_input_mask": True,
"use_labels": True,
"is_training": False,
"is_decoder": True,
"vocab_size": 32,
"attention_head_size": 16,
"hidden_size": 64,
"num_attention_heads": 2,
"num_buckets": 2,
"num_hashes": 4,
"lsh_attn_chunk_length": 4,
"lsh_num_chunks_before": 1,
"lsh_num_chunks_after": 0,
"chunk_size_lm_head": 5,
"chunk_size_feed_forward": 6,
"feed_forward_size": 32,
"hidden_act": "relu",
"hidden_dropout_prob": 0.1,
"lsh_attention_probs_dropout_prob": 0.1,
"max_position_embeddings": 512,
"initializer_range": 0.02,
"axial_norm_std": 1.0,
"layer_norm_eps": 1e-12,
"axial_pos_embds": True,
"axial_pos_shape": [4, 8],
"axial_pos_embds_dim": [16, 48],
# sanotheu
# "attn_layers": ["lsh", "lsh", "lsh", "lsh"],
"attn_layers": ["lsh"],
"pad_token_id": 0,
"eos_token_id": 2,
"scope": None,
"hash_seed": 0,
"num_labels": 2,
}
def setUp(self):
tester_kwargs = self.prepare_kwargs()
self.model_tester = ReformerModelTester(self, **tester_kwargs)
self.model_tester = ReformerModelTester(
self,
batch_size=13,
seq_length=13,
use_input_mask=True,
use_labels=True,
is_training=False,
is_decoder=True,
vocab_size=32,
attention_head_size=16,
hidden_size=64,
num_attention_heads=2,
num_buckets=2,
num_hashes=4,
lsh_attn_chunk_length=4,
lsh_num_chunks_before=1,
lsh_num_chunks_after=0,
chunk_size_lm_head=5,
chunk_size_feed_forward=6,
feed_forward_size=32,
hidden_act="relu",
hidden_dropout_prob=0.1,
lsh_attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
initializer_range=0.02,
axial_norm_std=1.0,
layer_norm_eps=1e-12,
axial_pos_embds=True,
axial_pos_shape=[4, 8],
axial_pos_embds_dim=[16, 48],
# sanotheu
# attn_layers=[lsh,lsh,lsh,lsh],
attn_layers=["lsh"],
pad_token_id=0,
eos_token_id=2,
scope=None,
hash_seed=0,
num_labels=2,
)
self.config_tester = ConfigTester(self, config_class=ReformerConfig, hidden_size=37)
def _check_attentions_for_generate(
......
tests/test_modeling_roberta.py
View file @ c3d9ac76
......@@ -17,7 +17,7 @@
import unittest
from copy import deepcopy
from transformers import is_torch_available
from transformers import RobertaConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from .test_configuration_common import ConfigTester
......@@ -29,7 +29,6 @@ if is_torch_available():
import torch
from transformers import (
RobertaConfig,
RobertaForCausalLM,
RobertaForMaskedLM,
RobertaForMultipleChoice,
......@@ -94,7 +93,12 @@ class RobertaModelTester:
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = RobertaConfig(
config = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
return RobertaConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
......@@ -108,8 +112,6 @@ class RobertaModelTester:
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,
......
tests/test_modeling_roformer.py
View file @ c3d9ac76
......@@ -18,7 +18,7 @@
import unittest
from tests.test_modeling_common import floats_tensor
from transformers import is_torch_available
from transformers import RoFormerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from .test_configuration_common import ConfigTester
......@@ -29,7 +29,6 @@ if is_torch_available():
import torch
from transformers import (
RoFormerConfig,
RoFormerForCausalLM,
RoFormerForMaskedLM,
RoFormerForMultipleChoice,
......@@ -113,7 +112,12 @@ class RoFormerModelTester:
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = RoFormerConfig(
config = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
return RoFormerConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
......@@ -128,8 +132,6 @@ class RoFormerModelTester:
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,
......
tests/test_modeling_speech_to_text.py
View file @ c3d9ac76
......@@ -14,13 +14,13 @@
# limitations under the License.
""" Testing suite for the PyTorch Speech2Text model. """
import copy
import inspect
import os
import tempfile
import unittest
from transformers import Speech2TextConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import (
is_torch_available,
......@@ -40,12 +40,7 @@ from .test_modeling_common import ModelTesterMixin, _config_zero_init, floats_te
if is_torch_available():
import torch
from transformers import (
Speech2TextConfig,
Speech2TextForConditionalGeneration,
Speech2TextModel,
Speech2TextProcessor,
)
from transformers import Speech2TextForConditionalGeneration, Speech2TextModel, Speech2TextProcessor
from transformers.models.speech_to_text.modeling_speech_to_text import Speech2TextDecoder, Speech2TextEncoder
......@@ -142,7 +137,17 @@ class Speech2TextModelTester:
attention_mask = torch.ones([self.batch_size, self.seq_length], dtype=torch.long, device=torch_device)
decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp(2)
config = Speech2TextConfig(
config = self.get_config()
inputs_dict = prepare_speech_to_text_inputs_dict(
config,
input_features=input_features,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
)
return config, inputs_dict
def get_config(self):
return Speech2TextConfig(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
......@@ -165,13 +170,6 @@ class Speech2TextModelTester:
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
)
inputs_dict = prepare_speech_to_text_inputs_dict(
config,
input_features=input_features,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
)
return config, inputs_dict
def prepare_config_and_inputs_for_common(self):
config, inputs_dict = self.prepare_config_and_inputs()
......
tests/test_modeling_squeezebert.py
View file @ c3d9ac76
......@@ -16,7 +16,7 @@
import unittest
from transformers import is_torch_available
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from .test_configuration_common import ConfigTester
......@@ -28,7 +28,6 @@ if is_torch_available():
from transformers import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertConfig,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
......@@ -37,179 +36,181 @@ if is_torch_available():
SqueezeBertModel,
)
class SqueezeBertModelTester(object):
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=False,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=64,
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,
q_groups=2,
k_groups=2,
v_groups=2,
post_attention_groups=2,
intermediate_groups=4,
output_groups=1,
):
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.q_groups = q_groups
self.k_groups = k_groups
self.v_groups = v_groups
self.post_attention_groups = post_attention_groups
self.intermediate_groups = intermediate_groups
self.output_groups = output_groups
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 = random_attention_mask([self.batch_size, 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 = SqueezeBertConfig(
embedding_size=self.hidden_size,
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
attention_probs_dropout_prob=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
initializer_range=self.initializer_range,
q_groups=self.q_groups,
k_groups=self.k_groups,
v_groups=self.v_groups,
post_attention_groups=self.post_attention_groups,
intermediate_groups=self.intermediate_groups,
output_groups=self.output_groups,
)
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def create_and_check_squeezebert_model(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = SqueezeBertModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, input_mask)
result = model(input_ids)
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)
)
def create_and_check_squeezebert_for_masked_lm(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = SqueezeBertForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_squeezebert_for_question_answering(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = SqueezeBertForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids, attention_mask=input_mask, start_positions=sequence_labels, end_positions=sequence_labels
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def create_and_check_squeezebert_for_sequence_classification(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = SqueezeBertForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_squeezebert_for_token_classification(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = SqueezeBertForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_squeezebert_for_multiple_choice(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_choices = self.num_choices
model = SqueezeBertForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
result = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
labels=choice_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids, input_mask, sequence_labels, token_labels, choice_labels) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
class SqueezeBertModelTester(object):
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=False,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=64,
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,
q_groups=2,
k_groups=2,
v_groups=2,
post_attention_groups=2,
intermediate_groups=4,
output_groups=1,
):
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.q_groups = q_groups
self.k_groups = k_groups
self.v_groups = v_groups
self.post_attention_groups = post_attention_groups
self.intermediate_groups = intermediate_groups
self.output_groups = output_groups
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 = random_attention_mask([self.batch_size, 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 = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
return SqueezeBertConfig(
embedding_size=self.hidden_size,
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
attention_probs_dropout_prob=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
initializer_range=self.initializer_range,
q_groups=self.q_groups,
k_groups=self.k_groups,
v_groups=self.v_groups,
post_attention_groups=self.post_attention_groups,
intermediate_groups=self.intermediate_groups,
output_groups=self.output_groups,
)
def create_and_check_squeezebert_model(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = SqueezeBertModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, input_mask)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_squeezebert_for_masked_lm(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = SqueezeBertForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_squeezebert_for_question_answering(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = SqueezeBertForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids, attention_mask=input_mask, start_positions=sequence_labels, end_positions=sequence_labels
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def create_and_check_squeezebert_for_sequence_classification(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = SqueezeBertForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_squeezebert_for_token_classification(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = SqueezeBertForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_squeezebert_for_multiple_choice(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_choices = self.num_choices
model = SqueezeBertForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
result = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
labels=choice_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids, input_mask, sequence_labels, token_labels, choice_labels) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
......
tests/test_modeling_t5.py
View file @ c3d9ac76
......@@ -18,7 +18,7 @@ import copy
import tempfile
import unittest
from transformers import is_torch_available
from transformers import T5Config, is_torch_available
from transformers.file_utils import cached_property
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
......@@ -30,7 +30,7 @@ from .test_modeling_common import ModelTesterMixin, ids_tensor
if is_torch_available():
import torch
from transformers import ByT5Tokenizer, T5Config, T5EncoderModel, T5ForConditionalGeneration, T5Model, T5Tokenizer
from transformers import ByT5Tokenizer, T5EncoderModel, T5ForConditionalGeneration, T5Model, T5Tokenizer
from transformers.models.t5.modeling_t5 import T5_PRETRAINED_MODEL_ARCHIVE_LIST
......@@ -100,7 +100,19 @@ class T5ModelTester:
if self.use_labels:
lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
config = T5Config(
config = self.get_config()
return (
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
)
def get_config(self):
return T5Config(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
d_ff=self.d_ff,
......@@ -117,15 +129,6 @@ class T5ModelTester:
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_prepare_lm_labels_via_shift_left(
self,
config,
......
tests/test_modeling_tapas.py
View file @ c3d9ac76
......@@ -13,7 +13,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import unittest
......@@ -29,6 +28,7 @@ from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TapasConfig,
is_torch_available,
)
from transformers.file_utils import cached_property
......@@ -43,7 +43,6 @@ if is_torch_available():
import torch
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
......@@ -183,7 +182,24 @@ class TapasModelTester:
float_answer = floats_tensor([self.batch_size]).to(torch_device)
aggregation_labels = ids_tensor([self.batch_size], self.num_aggregation_labels).to(torch_device)
config = TapasConfig(
config = self.get_config()
return (
config,
input_ids,
input_mask,
token_type_ids,
sequence_labels,
token_labels,
labels,
numeric_values,
numeric_values_scale,
float_answer,
aggregation_labels,
)
def get_config(self):
return TapasConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
......@@ -220,20 +236,6 @@ class TapasModelTester:
disable_per_token_loss=self.disable_per_token_loss,
)
return (
config,
input_ids,
input_mask,
token_type_ids,
sequence_labels,
token_labels,
labels,
numeric_values,
numeric_values_scale,
float_answer,
aggregation_labels,
)
def create_and_check_model(
self,
config,
......
tests/test_modeling_transfo_xl.py
View file @ c3d9ac76
......@@ -17,7 +17,7 @@ import copy
import random
import unittest
from transformers import is_torch_available
from transformers import TransfoXLConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, slow, torch_device
from .test_configuration_common import ConfigTester
......@@ -29,7 +29,7 @@ if is_torch_available():
import torch
from torch import nn
from transformers import TransfoXLConfig, TransfoXLForSequenceClassification, TransfoXLLMHeadModel, TransfoXLModel
from transformers import TransfoXLForSequenceClassification, TransfoXLLMHeadModel, TransfoXLModel
from transformers.models.transfo_xl.modeling_transfo_xl import TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST
......@@ -69,7 +69,12 @@ class TransfoXLModelTester:
if self.use_labels:
lm_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
config = TransfoXLConfig(
config = self.get_config()
return (config, input_ids_1, input_ids_2, lm_labels)
def get_config(self):
return TransfoXLConfig(
vocab_size=self.vocab_size,
mem_len=self.mem_len,
clamp_len=self.clamp_len,
......@@ -85,8 +90,6 @@ class TransfoXLModelTester:
pad_token_id=self.pad_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)
......
tests/test_modeling_visual_bert.py
View file @ c3d9ac76
......@@ -14,12 +14,11 @@
# limitations under the License.
""" Testing suite for the PyTorch VisualBERT model. """
import copy
import unittest
from tests.test_modeling_common import floats_tensor
from transformers import is_torch_available
from transformers import VisualBertConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from .test_configuration_common import ConfigTester
......@@ -30,7 +29,6 @@ if is_torch_available():
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
......@@ -98,7 +96,7 @@ class VisualBertModelTester:
self.num_choices = num_choices
self.scope = scope
def prepare_config(self):
def get_config(self):
return VisualBertConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
......@@ -138,7 +136,7 @@ class VisualBertModelTester:
if self.use_visual_token_type_ids:
visual_token_type_ids = ids_tensor([self.batch_size, self.visual_seq_length], self.type_vocab_size)
config = self.prepare_config()
config = self.get_config()
return config, {
"input_ids": input_ids,
"token_type_ids": token_type_ids,
......@@ -198,7 +196,7 @@ class VisualBertModelTester:
if self.use_labels:
labels = ids_tensor([self.batch_size], self.num_choices)
config = self.prepare_config()
config = self.get_config()
return config, {
"input_ids": input_ids,
"token_type_ids": token_type_ids,
......
tests/test_modeling_vit.py
View file @ c3d9ac76
......@@ -18,6 +18,7 @@
import inspect
import unittest
from transformers import ViTConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
......@@ -29,7 +30,7 @@ if is_torch_available():
import torch
from torch import nn
from transformers import ViTConfig, ViTForImageClassification, ViTModel
from transformers import ViTForImageClassification, ViTModel
from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST, to_2tuple
......@@ -86,7 +87,12 @@ class ViTModelTester:
if self.use_labels:
labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
config = ViTConfig(
config = self.get_config()
return config, pixel_values, labels
def get_config(self):
return ViTConfig(
image_size=self.image_size,
patch_size=self.patch_size,
num_channels=self.num_channels,
......@@ -101,8 +107,6 @@ class ViTModelTester:
initializer_range=self.initializer_range,
)
return config, pixel_values, labels
def create_and_check_model(self, config, pixel_values, labels):
model = ViTModel(config=config)
model.to(torch_device)
......
tests/test_modeling_wav2vec2.py
View file @ c3d9ac76
......@@ -21,7 +21,7 @@ import unittest
import pytest
from tests.test_modeling_common import floats_tensor, ids_tensor, random_attention_mask
from transformers import is_torch_available
from transformers import Wav2Vec2Config, is_torch_available
from transformers.testing_utils import require_datasets, require_soundfile, require_torch, slow, torch_device
from .test_configuration_common import ConfigTester
......@@ -32,7 +32,6 @@ if is_torch_available():
import torch
from transformers import (
Wav2Vec2Config,
Wav2Vec2FeatureExtractor,
Wav2Vec2ForCTC,
Wav2Vec2ForMaskedLM,
......@@ -106,7 +105,12 @@ class Wav2Vec2ModelTester:
input_values = floats_tensor([self.batch_size, self.seq_length], self.vocab_size)
attention_mask = random_attention_mask([self.batch_size, self.seq_length])
config = Wav2Vec2Config(
config = self.get_config()
return config, input_values, attention_mask
def get_config(self):
return Wav2Vec2Config(
hidden_size=self.hidden_size,
feat_extract_norm=self.feat_extract_norm,
feat_extract_dropout=self.feat_extract_dropout,
......@@ -127,8 +131,6 @@ class Wav2Vec2ModelTester:
vocab_size=self.vocab_size,
)
return config, input_values, attention_mask
def create_and_check_model(self, config, input_values, attention_mask):
model = Wav2Vec2Model(config=config)
model.to(torch_device)
......
tests/test_modeling_xlm.py
View file @ c3d9ac76
......@@ -13,10 +13,9 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import is_torch_available
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from .test_configuration_common import ConfigTester
......@@ -28,7 +27,6 @@ if is_torch_available():
import torch
from transformers import (
XLMConfig,
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
......@@ -97,7 +95,22 @@ class XLMModelTester:
is_impossible_labels = ids_tensor([self.batch_size], 2).float()
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = XLMConfig(
config = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def get_config(self):
return XLMConfig(
vocab_size=self.vocab_size,
n_special=self.n_special,
emb_dim=self.hidden_size,
......@@ -118,18 +131,6 @@ class XLMModelTester:
bos_token_id=self.bos_token_id,
)
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def create_and_check_xlm_model(
self,
config,
......
tests/test_modeling_xlnet.py
View file @ c3d9ac76
......@@ -13,11 +13,10 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import random
import unittest
from transformers import is_torch_available
from transformers import XLNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from .test_configuration_common import ConfigTester
......@@ -29,7 +28,6 @@ if is_torch_available():
import torch
from transformers import (
XLNetConfig,
XLNetForMultipleChoice,
XLNetForQuestionAnswering,
XLNetForQuestionAnsweringSimple,
......@@ -131,7 +129,25 @@ class XLNetModelTester:
is_impossible_labels = ids_tensor([self.batch_size], 2).float()
token_labels = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
config = XLNetConfig(
config = self.get_config()
return (
config,
input_ids_1,
input_ids_2,
input_ids_q,
perm_mask,
input_mask,
target_mapping,
segment_ids,
lm_labels,
sequence_labels,
is_impossible_labels,
token_labels,
)
def get_config(self):
return XLNetConfig(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
n_head=self.num_attention_heads,
......@@ -150,21 +166,6 @@ class XLNetModelTester:
eos_token_id=self.eos_token_id,
)
return (
config,
input_ids_1,
input_ids_2,
input_ids_q,
perm_mask,
input_mask,
target_mapping,
segment_ids,
lm_labels,
sequence_labels,
is_impossible_labels,
token_labels,
)
def set_seed(self):
random.seed(self.seed)
torch.manual_seed(self.seed)
......
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