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Use model_class.__name__ and compare against XXX_MAPPING_NAMES (#21304)

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* update

* update all

* clean up

* make quality

* clean up

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
2023-01-26 11:31:31 +01:00
committed by GitHub
parent d18a1cba24
commit 4e41b87e3d
1 changed files with 53 additions and 49 deletions
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102
tests/test_modeling_common.py
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@@ -44,6 +44,26 @@ from transformers import (
logging, logging,
) )
from transformers.models.auto import get_values from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import (
MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES,
MODEL_FOR_BACKBONE_MAPPING_NAMES,
MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING_NAMES,
MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING_NAMES,
MODEL_FOR_MASKED_LM_MAPPING_NAMES,
MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES,
MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
MODEL_MAPPING_NAMES,
)
from transformers.testing_utils import ( from transformers.testing_utils import (
TOKEN, TOKEN,
USER, USER,
@@ -93,23 +113,6 @@ if is_torch_available():
from test_module.custom_modeling import CustomModel, NoSuperInitModel from test_module.custom_modeling import CustomModel, NoSuperInitModel
from transformers import ( from transformers import (
BERT_PRETRAINED_MODEL_ARCHIVE_LIST, BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
MODEL_FOR_AUDIO_XVECTOR_MAPPING,
MODEL_FOR_BACKBONE_MAPPING,
MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
MODEL_FOR_CAUSAL_LM_MAPPING,
MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
MODEL_FOR_QUESTION_ANSWERING_MAPPING,
MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
MODEL_MAPPING, MODEL_MAPPING,
AdaptiveEmbedding, AdaptiveEmbedding,
AutoModelForCausalLM, AutoModelForCausalLM,
@@ -199,22 +202,22 @@ class ModelTesterMixin:
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = copy.deepcopy(inputs_dict) inputs_dict = copy.deepcopy(inputs_dict)
if model_class in get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING): if model_class.__name__ in get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES):
inputs_dict = { inputs_dict = {
k: v.unsqueeze(1).expand(-1, self.model_tester.num_choices, -1).contiguous() k: v.unsqueeze(1).expand(-1, self.model_tester.num_choices, -1).contiguous()
if isinstance(v, torch.Tensor) and v.ndim > 1 if isinstance(v, torch.Tensor) and v.ndim > 1
else v else v
for k, v in inputs_dict.items() for k, v in inputs_dict.items()
} }
elif model_class in get_values(MODEL_FOR_AUDIO_XVECTOR_MAPPING): elif model_class.__name__ in get_values(MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES):
inputs_dict.pop("attention_mask") inputs_dict.pop("attention_mask")
if return_labels: if return_labels:
if model_class in get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING): if model_class.__name__ in get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES):
inputs_dict["labels"] = torch.ones(self.model_tester.batch_size, dtype=torch.long, device=torch_device) inputs_dict["labels"] = torch.ones(self.model_tester.batch_size, dtype=torch.long, device=torch_device)
elif model_class in [ elif model_class.__name__ in [
*get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING), *get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES),
*get_values(MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING), *get_values(MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES),
]: ]:
inputs_dict["start_positions"] = torch.zeros( inputs_dict["start_positions"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device self.model_tester.batch_size, dtype=torch.long, device=torch_device
@@ -222,32 +225,32 @@ class ModelTesterMixin:
inputs_dict["end_positions"] = torch.zeros( inputs_dict["end_positions"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device self.model_tester.batch_size, dtype=torch.long, device=torch_device
) )
elif model_class in [ elif model_class.__name__ in [
*get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING), *get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES),
*get_values(MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING), *get_values(MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES),
*get_values(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING), *get_values(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES),
*get_values(MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING), *get_values(MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES),
*get_values(MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING), *get_values(MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES),
]: ]:
inputs_dict["labels"] = torch.zeros( inputs_dict["labels"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device self.model_tester.batch_size, dtype=torch.long, device=torch_device
) )
elif model_class in [ elif model_class.__name__ in [
*get_values(MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING), *get_values(MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES),
*get_values(MODEL_FOR_CAUSAL_LM_MAPPING), *get_values(MODEL_FOR_CAUSAL_LM_MAPPING_NAMES),
*get_values(MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING), *get_values(MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING_NAMES),
*get_values(MODEL_FOR_MASKED_LM_MAPPING), *get_values(MODEL_FOR_MASKED_LM_MAPPING_NAMES),
*get_values(MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING), *get_values(MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES),
]: ]:
inputs_dict["labels"] = torch.zeros( inputs_dict["labels"] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
) )
elif model_class in get_values(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING): elif model_class.__name__ in get_values(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING_NAMES):
num_patches = self.model_tester.image_size // self.model_tester.patch_size num_patches = self.model_tester.image_size // self.model_tester.patch_size
inputs_dict["bool_masked_pos"] = torch.zeros( inputs_dict["bool_masked_pos"] = torch.zeros(
(self.model_tester.batch_size, num_patches**2), dtype=torch.long, device=torch_device (self.model_tester.batch_size, num_patches**2), dtype=torch.long, device=torch_device
) )
elif model_class in get_values(MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING): elif model_class.__name__ in get_values(MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES):
batch_size, num_channels, height, width = inputs_dict["pixel_values"].shape batch_size, num_channels, height, width = inputs_dict["pixel_values"].shape
inputs_dict["labels"] = torch.zeros( inputs_dict["labels"] = torch.zeros(
[self.model_tester.batch_size, height, width], device=torch_device [self.model_tester.batch_size, height, width], device=torch_device
@@ -527,9 +530,9 @@ class ModelTesterMixin:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True config.return_dict = True
if model_class in [ if model_class.__name__ in [
*get_values(MODEL_MAPPING), *get_values(MODEL_MAPPING_NAMES),
*get_values(MODEL_FOR_BACKBONE_MAPPING), *get_values(MODEL_FOR_BACKBONE_MAPPING_NAMES),
]: ]:
continue continue
@@ -550,7 +553,8 @@ class ModelTesterMixin:
config.return_dict = True config.return_dict = True
if ( if (
model_class in [*get_values(MODEL_MAPPING), *get_values(MODEL_FOR_BACKBONE_MAPPING)] model_class.__name__
in [*get_values(MODEL_MAPPING_NAMES), *get_values(MODEL_FOR_BACKBONE_MAPPING_NAMES)]
or not model_class.supports_gradient_checkpointing or not model_class.supports_gradient_checkpointing
): ):
continue continue
@@ -620,9 +624,9 @@ class ModelTesterMixin:
if "labels" in inputs_dict: if "labels" in inputs_dict:
correct_outlen += 1 # loss is added to beginning correct_outlen += 1 # loss is added to beginning
# Question Answering model returns start_logits and end_logits # Question Answering model returns start_logits and end_logits
if model_class in [ if model_class.__name__ in [
*get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING), *get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES),
*get_values(MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING), *get_values(MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES),
]: ]:
correct_outlen += 1 # start_logits and end_logits instead of only 1 output correct_outlen += 1 # start_logits and end_logits instead of only 1 output
if "past_key_values" in outputs: if "past_key_values" in outputs:
@@ -875,7 +879,7 @@ class ModelTesterMixin:
filtered_inputs = {k: v for (k, v) in inputs.items() if k in input_names} filtered_inputs = {k: v for (k, v) in inputs.items() if k in input_names}
input_names = list(filtered_inputs.keys()) input_names = list(filtered_inputs.keys())
if isinstance(model, tuple(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.values())) and ( if model.__class__.__name__ in set(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES.values()) and (
not hasattr(model.config, "problem_type") or model.config.problem_type is None not hasattr(model.config, "problem_type") or model.config.problem_type is None
): ):
model.config.problem_type = "single_label_classification" model.config.problem_type = "single_label_classification"
@@ -2532,9 +2536,9 @@ class ModelTesterMixin:
] ]
for model_class in self.all_model_classes: for model_class in self.all_model_classes:
if model_class not in [ if model_class.__name__ not in [
*get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING), *get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES),
*get_values(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING), *get_values(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES),
]: ]:
continue continue
@@ -2575,7 +2579,7 @@ class ModelTesterMixin:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes: for model_class in self.all_model_classes:
if model_class not in get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING): if model_class.__name__ not in get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES):
continue continue
with self.subTest(msg=f"Testing {model_class}"): with self.subTest(msg=f"Testing {model_class}"):