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fix UT failures on XPU w/ stock PyTorch 2.7 & 2.8 (#39116)

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* fix UT failures on XPU w/ stock PyTorch 2.7 & 2.8

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

* zamba2

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

* xx

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

* internvl

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

* tp cases

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

---------

Signed-off-by: YAO Matrix <matrix.yao@intel.com>
This commit is contained in:
Yao Matrix
2025-06-30 17:49:03 +08:00
committed by GitHub
parent ccf2ca162e
commit 2100ee6545
8 changed files with 119 additions and 51 deletions
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79
tests/models/grounding_dino/test_modeling_grounding_dino.py
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@@ -29,6 +29,7 @@ from transformers import (
)
from transformers.file_utils import cached_property
from transformers.testing_utils import (
Expectations,
is_flaky,
require_timm,
require_torch,
@@ -804,34 +805,62 @@ class GroundingDinoModelIntegrationTests(unittest.TestCase):
with torch.no_grad():
outputs = model(**text_inputs, **image_inputs)
# Loss differs by CPU and GPU, also this can be changed in future.
expected_loss_dict = {
"loss_ce": torch.tensor(1.1147),
"loss_bbox": torch.tensor(0.2031),
"loss_giou": torch.tensor(0.5819),
"loss_ce_0": torch.tensor(1.1941),
"loss_bbox_0": torch.tensor(0.1978),
"loss_giou_0": torch.tensor(0.5524),
"loss_ce_1": torch.tensor(1.1621),
"loss_bbox_1": torch.tensor(0.1909),
"loss_giou_1": torch.tensor(0.5892),
"loss_ce_2": torch.tensor(1.1641),
"loss_bbox_2": torch.tensor(0.1892),
"loss_giou_2": torch.tensor(0.5626),
"loss_ce_3": torch.tensor(1.1943),
"loss_bbox_3": torch.tensor(0.1941),
"loss_giou_3": torch.tensor(0.5607),
"loss_ce_4": torch.tensor(1.0956),
"loss_bbox_4": torch.tensor(0.2008),
"loss_giou_4": torch.tensor(0.5836),
"loss_ce_enc": torch.tensor(16226.3164),
"loss_bbox_enc": torch.tensor(0.3063),
"loss_giou_enc": torch.tensor(0.7380),
}
# Loss differs by CPU and accelerator, also this can be changed in future.
expected_loss_dicts = Expectations(
{
("xpu", 3): {
"loss_ce": torch.tensor(1.1147),
"loss_bbox": torch.tensor(0.2031),
"loss_giou": torch.tensor(0.5819),
"loss_ce_0": torch.tensor(1.1941),
"loss_bbox_0": torch.tensor(0.1978),
"loss_giou_0": torch.tensor(0.5524),
"loss_ce_1": torch.tensor(1.1621),
"loss_bbox_1": torch.tensor(0.1909),
"loss_giou_1": torch.tensor(0.5892),
"loss_ce_2": torch.tensor(1.1641),
"loss_bbox_2": torch.tensor(0.1892),
"loss_giou_2": torch.tensor(0.5626),
"loss_ce_3": torch.tensor(1.1943),
"loss_bbox_3": torch.tensor(0.1941),
"loss_giou_3": torch.tensor(0.5592),
"loss_ce_4": torch.tensor(1.0956),
"loss_bbox_4": torch.tensor(0.2037),
"loss_giou_4": torch.tensor(0.5813),
"loss_ce_enc": torch.tensor(16226.3164),
"loss_bbox_enc": torch.tensor(0.3063),
"loss_giou_enc": torch.tensor(0.7380),
},
("cuda", None): {
"loss_ce": torch.tensor(1.1147),
"loss_bbox": torch.tensor(0.2031),
"loss_giou": torch.tensor(0.5819),
"loss_ce_0": torch.tensor(1.1941),
"loss_bbox_0": torch.tensor(0.1978),
"loss_giou_0": torch.tensor(0.5524),
"loss_ce_1": torch.tensor(1.1621),
"loss_bbox_1": torch.tensor(0.1909),
"loss_giou_1": torch.tensor(0.5892),
"loss_ce_2": torch.tensor(1.1641),
"loss_bbox_2": torch.tensor(0.1892),
"loss_giou_2": torch.tensor(0.5626),
"loss_ce_3": torch.tensor(1.1943),
"loss_bbox_3": torch.tensor(0.1941),
"loss_giou_3": torch.tensor(0.5607),
"loss_ce_4": torch.tensor(1.0956),
"loss_bbox_4": torch.tensor(0.2008),
"loss_giou_4": torch.tensor(0.5836),
"loss_ce_enc": torch.tensor(16226.3164),
"loss_bbox_enc": torch.tensor(0.3063),
"loss_giou_enc": torch.tensor(0.7380),
},
}
) # fmt: skip
expected_loss_dict = expected_loss_dicts.get_expectation()
expected_loss = torch.tensor(32482.2305)
for key in expected_loss_dict:
self.assertTrue(torch.allclose(outputs.loss_dict[key], expected_loss_dict[key], atol=1e-3))
torch.testing.assert_close(outputs.loss_dict[key], expected_loss_dict[key], rtol=1e-5, atol=1e-3)
self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=1e-3))