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

* rm test_sdpa_equivalence

* make fixup

---------

Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>

tests/models/aya_vision/test_modeling_aya_vision.py

@@ -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

tests/models/cohere2/test_modeling_cohere2.py

@@ -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

tests/models/deepseek_v3/test_modeling_deepseek_v3.py

@@ -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

tests/models/gemma/test_modeling_gemma.py

@@ -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

tests/models/gemma2/test_modeling_gemma2.py

@@ -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"

tests/models/nemotron/test_modeling_nemotron.py

@@ -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