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Avoid using uncessary get_values(MODEL_MAPPING) (#29362)

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* more fixes

* more fixes

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
2024-02-29 17:19:17 +08:00
committed by GitHub
parent b647acdb53
commit 44fe1a1cc4
14 changed files with 94 additions and 85 deletions
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51
tests/models/perceiver/test_modeling_perceiver.py
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@@ -26,7 +26,6 @@ import numpy as np
from datasets import load_dataset
from transformers import PerceiverConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available
@@ -40,11 +39,6 @@ if is_torch_available():
from torch import nn
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
PerceiverForImageClassificationConvProcessing,
PerceiverForImageClassificationFourier,
PerceiverForImageClassificationLearned,
@@ -55,6 +49,13 @@ if is_torch_available():
PerceiverModel,
PerceiverTokenizer,
)
from transformers.models.auto.modeling_auto import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_MASKED_LM_MAPPING_NAMES,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
MODEL_MAPPING_NAMES,
)
from transformers.models.perceiver.modeling_perceiver import PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST
@@ -317,16 +318,19 @@ class PerceiverModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCas
inputs_dict["subsampled_output_points"] = self.model_tester.subsampling
if return_labels:
if model_class in [
*get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING),
*get_values(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING),
if model_class.__name__ in [
*MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES.values(),
"PerceiverForImageClassificationLearned",
"PerceiverForImageClassificationFourier",
"PerceiverForImageClassificationConvProcessing",
*MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES.values(),
]:
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_MASKED_LM_MAPPING),
elif model_class.__name__ in [
*MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES.values(),
*MODEL_FOR_MASKED_LM_MAPPING_NAMES.values(),
]:
inputs_dict["labels"] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
@@ -380,10 +384,10 @@ class PerceiverModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCas
return
for model_class in self.all_model_classes:
if model_class in [
*get_values(MODEL_MAPPING),
PerceiverForOpticalFlow,
PerceiverForMultimodalAutoencoding,
if model_class.__name__ in [
*MODEL_MAPPING_NAMES.values(),
"PerceiverForOpticalFlow",
"PerceiverForMultimodalAutoencoding",
]:
continue
@@ -727,11 +731,14 @@ class PerceiverModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCas
for model_class in self.all_model_classes:
# most Perceiver models don't have a typical head like is the case with BERT
if model_class in [
PerceiverForOpticalFlow,
PerceiverForMultimodalAutoencoding,
*get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING),
*get_values(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING),
if model_class.__name__ in [
"PerceiverForOpticalFlow",
"PerceiverForMultimodalAutoencoding",
*MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES.values(),
"PerceiverForImageClassificationLearned",
"PerceiverForImageClassificationFourier",
"PerceiverForImageClassificationConvProcessing",
*MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES.values(),
]:
continue
@@ -753,7 +760,7 @@ class PerceiverModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCas
]
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 MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES.values():
continue
config, inputs, input_mask, _, _ = self.model_tester.prepare_config_and_inputs(model_class=model_class)