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Fix more flaky test_initialization (#38932)

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* try

* try

* fix

* fix

* fix

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar 2025-06-20 17:28:32 +02:00 committed by GitHub
parent 5ee60f970a
commit b8059e1f8f
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5 changed files with 56 additions and 5 deletions
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13
tests/models/blip/test_modeling_blip.py
View File

@ -475,8 +475,19 @@ class BlipModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
else:
# See PR #38607 (to avoid flakiness)
data = torch.flatten(param.data)
n_elements = torch.numel(data)
# skip 2.5% of elements on each side to avoid issues caused by `nn.init.trunc_normal_` described in
# https://github.com/huggingface/transformers/pull/27906#issuecomment-1846951332
n_elements_to_skip_on_each_side = int(n_elements * 0.025)
data_to_check = torch.sort(data).values
if n_elements_to_skip_on_each_side > 0:
data_to_check = data_to_check[
n_elements_to_skip_on_each_side:-n_elements_to_skip_on_each_side
]
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
((data_to_check.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)

13
tests/models/depth_pro/test_modeling_depth_pro.py
View File

@ -311,8 +311,19 @@ class DepthProModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase
]
if param.requires_grad:
if any(x in name for x in non_uniform_init_parms):
# See PR #38607 (to avoid flakiness)
data = torch.flatten(param.data)
n_elements = torch.numel(data)
# skip 2.5% of elements on each side to avoid issues caused by `nn.init.trunc_normal_` described in
# https://github.com/huggingface/transformers/pull/27906#issuecomment-1846951332
n_elements_to_skip_on_each_side = int(n_elements * 0.025)
data_to_check = torch.sort(data).values
if n_elements_to_skip_on_each_side > 0:
data_to_check = data_to_check[
n_elements_to_skip_on_each_side:-n_elements_to_skip_on_each_side
]
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
((data_to_check.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)

11
tests/models/dinov2_with_registers/test_modeling_dinov2_with_registers.py
View File

@ -252,8 +252,17 @@ class Dinov2WithRegistersModelTest(ModelTesterMixin, PipelineTesterMixin, unitte
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
if param.requires_grad and "register_tokens" not in name:
# See PR #38607 (to avoid flakiness)
data = torch.flatten(param.data)
n_elements = torch.numel(data)
# skip 2.5% of elements on each side to avoid issues caused by `nn.init.trunc_normal_` described in
# https://github.com/huggingface/transformers/pull/27906#issuecomment-1846951332
n_elements_to_skip_on_each_side = int(n_elements * 0.025)
data_to_check = torch.sort(data).values
if n_elements_to_skip_on_each_side > 0:
data_to_check = data_to_check[n_elements_to_skip_on_each_side:-n_elements_to_skip_on_each_side]
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
((data_to_check.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)

13
tests/models/pix2struct/test_modeling_pix2struct.py
View File

@ -544,8 +544,19 @@ class Pix2StructModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTeste
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
else:
# See PR #38607 (to avoid flakiness)
data = torch.flatten(param.data)
n_elements = torch.numel(data)
# skip 2.5% of elements on each side to avoid issues caused by `nn.init.trunc_normal_` described in
# https://github.com/huggingface/transformers/pull/27906#issuecomment-1846951332
n_elements_to_skip_on_each_side = int(n_elements * 0.025)
data_to_check = torch.sort(data).values
if n_elements_to_skip_on_each_side > 0:
data_to_check = data_to_check[
n_elements_to_skip_on_each_side:-n_elements_to_skip_on_each_side
]
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
((data_to_check.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)

11
tests/models/swin2sr/test_modeling_swin2sr.py
View File

@ -249,8 +249,17 @@ class Swin2SRModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase)
if "logit_scale" in name:
continue
if param.requires_grad:
# See PR #38607 (to avoid flakiness)
data = torch.flatten(param.data)
n_elements = torch.numel(data)
# skip 2.5% of elements on each side to avoid issues caused by `nn.init.trunc_normal_` described in
# https://github.com/huggingface/transformers/pull/27906#issuecomment-1846951332
n_elements_to_skip_on_each_side = int(n_elements * 0.025)
data_to_check = torch.sort(data).values
if n_elements_to_skip_on_each_side > 0:
data_to_check = data_to_check[n_elements_to_skip_on_each_side:-n_elements_to_skip_on_each_side]
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
((data_to_check.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)