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Less flaky for TimmBackboneModelTest::test_batching_equivalence (#35971)

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* fix

* remove is_flaky

* fix

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
2025-01-30 16:56:26 +01:00
committed by GitHub
parent e320d5542e
commit 5757681837
2 changed files with 6 additions and 8 deletions
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10
tests/models/timm_backbone/test_modeling_timm_backbone.py
View File

@@ -18,7 +18,7 @@ import inspect
import unittest
from transformers import AutoBackbone
from transformers.testing_utils import is_flaky, require_timm, require_torch, torch_device
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
@@ -115,11 +115,9 @@ class TimmBackboneModelTest(ModelTesterMixin, BackboneTesterMixin, PipelineTeste
def test_config(self):
self.config_tester.run_common_tests()
@is_flaky(
description="`TimmBackbone` has no `_init_weights`. Timm's way of weight init. seems to give larger magnitude in the intermediate values during `forward`."
)
def test_batching_equivalence(self):
super().test_batching_equivalence()
# `TimmBackbone` has no `_init_weights`. Timm's way of weight init. seems to give larger magnitude in the intermediate values during `forward`.
def test_batching_equivalence(self, atol=1e-4, rtol=1e-4):
super().test_batching_equivalence(atol=atol, rtol=rtol)
def test_timm_transformer_backbone_equivalence(self):
timm_checkpoint = "resnet18"

4
tests/test_modeling_common.py
View File

@@ -768,7 +768,7 @@ class ModelTesterMixin:
else:
check_determinism(first, second)
def test_batching_equivalence(self):
def test_batching_equivalence(self, atol=1e-5, rtol=1e-5):
"""
Tests that the model supports batching and that the output is the nearly the same for the same input in
different batch sizes.
@@ -812,7 +812,7 @@ class ModelTesterMixin:
torch.isinf(single_row_object).any(), f"Single row output has `inf` in {model_name} for key={key}"
)
try:
torch.testing.assert_close(batched_row, single_row_object, atol=1e-5, rtol=1e-5)
torch.testing.assert_close(batched_row, single_row_object, atol=atol, rtol=rtol)
except AssertionError as e:
msg = f"Batched and Single row outputs are not equal in {model_name} for key={key}.\n\n"
msg += str(e)