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enable 6 rt_detr_v2 cases on xpu (#37548)

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* enable 6 rt_detr_v2 cases on xpu

Signed-off-by: YAO Matrix <matrix.yao@intel.com>

* fix style

Signed-off-by: YAO Matrix <matrix.yao@intel.com>

---------

Signed-off-by: YAO Matrix <matrix.yao@intel.com>
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
This commit is contained in:
Yao Matrix
2025-04-16 17:23:56 +08:00
committed by GitHub
parent d91858c232
commit ed53809ac5
1 changed files with 9 additions and 3 deletions
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12
tests/models/rt_detr_v2/test_modeling_rt_detr_v2.py
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@@ -27,7 +27,13 @@ from transformers import (
is_torch_available, is_torch_available,
is_vision_available, is_vision_available,
) )
from transformers.testing_utils import require_torch, require_torch_gpu, require_vision, slow, torch_device from transformers.testing_utils import (
require_torch,
require_torch_accelerator,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester from ...test_configuration_common import ConfigTester
@@ -636,7 +642,7 @@ class RTDetrV2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase
self.assertTrue(not failed_cases, message) self.assertTrue(not failed_cases, message)
@parameterized.expand(["float32", "float16", "bfloat16"]) @parameterized.expand(["float32", "float16", "bfloat16"])
@require_torch_gpu @require_torch_accelerator
@slow @slow
def test_inference_with_different_dtypes(self, torch_dtype_str): def test_inference_with_different_dtypes(self, torch_dtype_str):
torch_dtype = { torch_dtype = {
@@ -658,7 +664,7 @@ class RTDetrV2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase
_ = model(**self._prepare_for_class(inputs_dict, model_class)) _ = model(**self._prepare_for_class(inputs_dict, model_class))
@parameterized.expand(["float32", "float16", "bfloat16"]) @parameterized.expand(["float32", "float16", "bfloat16"])
@require_torch_gpu @require_torch_accelerator
@slow @slow
def test_inference_equivalence_for_static_and_dynamic_anchors(self, torch_dtype_str): def test_inference_equivalence_for_static_and_dynamic_anchors(self, torch_dtype_str):
torch_dtype = { torch_dtype = {