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[tests] remove test_sdpa_equivalence (redundant) (#37911)

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* rm test_sdpa_equivalence

* make fixup

---------

Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
This commit is contained in:
Joao Gante 2025-05-16 18:37:27 +01:00 committed by GitHub
parent ea29f61ed9
commit 40a493c7ed
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6 changed files with 0 additions and 81 deletions
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4
tests/models/aya_vision/test_modeling_aya_vision.py
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@ -297,10 +297,6 @@ class AyaVisionModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTester
def test_multi_gpu_data_parallel_forward(self):
pass
@unittest.skip("Cohere2's eager attn/sdpa attn outputs are expected to be different")
def test_sdpa_equivalence(self):
pass
@unittest.skip(reason="SiglipVisionModel does not support standalone training")
def test_training(self):
pass

4
tests/models/cohere2/test_modeling_cohere2.py
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@ -127,10 +127,6 @@ class Cohere2ModelTest(CohereModelTest, unittest.TestCase):
def test_generate_continue_from_inputs_embeds(self):
pass
@unittest.skip("Cohere2's eager attn/sdpa attn outputs are expected to be different")
def test_sdpa_equivalence(self):
pass
@slow
@require_read_token

4
tests/models/deepseek_v3/test_modeling_deepseek_v3.py
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@ -300,10 +300,6 @@ class DeepseekV3ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTeste
def test_generate_continue_from_inputs_embeds(self):
pass
@unittest.skip("DeepseekV3's eager attn/sdpa attn outputs are expected to be different")
def test_sdpa_equivalence(self):
pass
@unittest.skip("Deepseek-V3 uses MLA so it is not compatible with the standard cache format")
def test_beam_search_generate_dict_outputs_use_cache(self):
pass

32
tests/models/gemma/test_modeling_gemma.py
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@ -303,38 +303,6 @@ class GemmaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi
def test_flash_attn_2_inference_equivalence_right_padding(self):
self.skipTest(reason="Gemma flash attention does not support right padding")
@require_torch_sdpa
@require_torch_accelerator
@slow
def test_sdpa_equivalence(self):
for model_class in self.all_model_classes:
if not model_class._supports_sdpa:
self.skipTest(reason="Model does not support SDPA")
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
model = model_class(config)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model_sdpa = model_class.from_pretrained(
tmpdirname, torch_dtype=torch.float16, attn_implementation="sdpa"
)
model_sdpa.to(torch_device)
model = model_class.from_pretrained(tmpdirname, torch_dtype=torch.float16, attn_implementation="eager")
model.to(torch_device)
dummy_input = inputs_dict[model_class.main_input_name]
dummy_input = dummy_input.to(torch_device)
outputs = model(dummy_input, output_hidden_states=True)
outputs_sdpa = model_sdpa(dummy_input, output_hidden_states=True)
logits = outputs.hidden_states[-1]
logits_sdpa = outputs_sdpa.hidden_states[-1]
# gemma sdpa needs a high tolerance
assert torch.allclose(logits_sdpa, logits, atol=3e-3)
@require_flash_attn
@require_torch_gpu
@pytest.mark.flash_attn_test

4
tests/models/gemma2/test_modeling_gemma2.py
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@ -143,10 +143,6 @@ class Gemma2ModelTest(GemmaModelTest, unittest.TestCase):
def test_generate_continue_from_inputs_embeds(self):
pass
@unittest.skip("Gemma2's eager attn/sdpa attn outputs are expected to be different")
def test_sdpa_equivalence(self):
pass
@unittest.skip(
reason="HybridCache can't be gathered because it is not iterable. Adding a simple iter and dumping `distributed_iterator`"
" as in Dynamic Cache doesn't work. NOTE: @gante all cache objects would need better compatibility with multi gpu setting"

33
tests/models/nemotron/test_modeling_nemotron.py
View File

@ -28,7 +28,6 @@ from transformers.testing_utils import (
require_torch,
require_torch_accelerator,
require_torch_gpu,
require_torch_sdpa,
slow,
torch_device,
)
@ -102,38 +101,6 @@ class NemotronModelTest(GemmaModelTest):
def test_model_outputs_equivalence(self, **kwargs):
pass
@require_torch_sdpa
@require_torch_accelerator
@slow
def test_sdpa_equivalence(self):
for model_class in self.all_model_classes:
if not model_class._supports_sdpa:
self.skipTest(reason="Model does not support SDPA")
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
model = model_class(config)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model_sdpa = model_class.from_pretrained(
tmpdirname, torch_dtype=torch.float16, attn_implementation="sdpa"
)
model_sdpa.to(torch_device)
model = model_class.from_pretrained(tmpdirname, torch_dtype=torch.float16, attn_implementation="eager")
model.to(torch_device)
dummy_input = inputs_dict[model_class.main_input_name]
dummy_input = dummy_input.to(torch_device)
outputs = model(dummy_input, output_hidden_states=True)
outputs_sdpa = model_sdpa(dummy_input, output_hidden_states=True)
logits = outputs.hidden_states[-1]
logits_sdpa = outputs_sdpa.hidden_states[-1]
# nemotron sdpa needs a high tolerance
assert torch.allclose(logits_sdpa, logits, atol=1e-2)
@require_flash_attn
@require_torch_gpu
@pytest.mark.flash_attn_test