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Add common test for torch.export and fix some vision models (#35124)

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* Add is_torch_greater_or_equal test decorator

* Add common test for torch.export

* Fix bit

* Fix focalnet

* Fix imagegpt

* Fix seggpt

* Fix swin2sr

* Enable torch.export test for vision models

* Enable test for video models

* Remove json

* Enable for hiera

* Enable for ijepa

* Fix detr

* Fic conditional_detr

* Fix maskformer

* Enable test maskformer

* Fix test for deformable detr

* Fix custom kernels for export in rt-detr and deformable-detr

* Enable test for all DPT

* Remove custom test for deformable detr

* Simplify test to use only kwargs for export

* Add comment

* Move compile_compatible_method_lru_cache to utils

* Fix beit export

* Fix deformable detr

* Fix copies data2vec<->beit

* Fix typos, update test to work with dict

* Add seed to the test

* Enable test for vit_mae

* Fix beit tests

* [run-slow] beit, bit, conditional_detr, data2vec, deformable_detr, detr, focalnet, imagegpt, maskformer, rt_detr, seggpt, swin2sr

* Add vitpose test

* Add textnet test

* Add dinov2 with registers

* Update tests/test_modeling_common.py

* Switch to torch.testing.assert_close

* Fix masformer

* Remove save-load from test

* Add dab_detr

* Add depth_pro

* Fix and test RT-DETRv2

* Fix dab_detr
This commit is contained in:
Pavel Iakubovskii
2025-02-11 11:37:31 +00:00
committed by GitHub
parent 1779f5180e
commit f42d46ccb4
77 changed files with 305 additions and 151 deletions
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16
tests/models/vitpose_backbone/test_modeling_vitpose_backbone.py
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@@ -18,7 +18,7 @@ import inspect
import unittest
from transformers import VitPoseBackboneConfig
from transformers.testing_utils import require_torch
from transformers.testing_utils import require_torch, torch_device
from transformers.utils import is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
@@ -27,6 +27,8 @@ from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
if is_torch_available():
import torch
from transformers import VitPoseBackbone
@@ -129,6 +131,7 @@ class VitPoseBackboneModelTest(ModelTesterMixin, unittest.TestCase):
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
test_torch_exportable = True
def setUp(self):
self.model_tester = VitPoseBackboneModelTester(self)
@@ -187,6 +190,17 @@ class VitPoseBackboneModelTest(ModelTesterMixin, unittest.TestCase):
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_torch_export(self):
# Dense architecture
super().test_torch_export()
# MOE architecture
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.num_experts = 2
config.part_features = config.hidden_size // config.num_experts
inputs_dict["dataset_index"] = torch.tensor([0] * self.model_tester.batch_size, device=torch_device)
super().test_torch_export(config=config, inputs_dict=inputs_dict)
@require_torch
class VitPoseBackboneTest(unittest.TestCase, BackboneTesterMixin):