remove fa2 test

tests/test_modeling_common.py

@@ -2769,8 +2769,6 @@ def check_pt_flax_outputs(self, fx_outputs, pt_outputs, model_class, tol=1e-4, n
                 attributes = tuple([f"{name}_{idx}" for idx in range(len(fx_outputs))])
 
             for fx_output, pt_output, attr in zip(fx_outputs, pt_outputs, attributes):
-                if isinstance(pt_output, DynamicCache):
-                    pt_output = pt_output.to_legacy_cache()
                 self.check_pt_flax_outputs(fx_output, pt_output, model_class, tol=tol, name=attr)
 
         elif isinstance(fx_outputs, jnp.ndarray):
@@ -3610,34 +3608,6 @@ def test_model_is_small(self):
                 num_params < 1000000
             ), f"{model_class} is too big for the common tests ({num_params})! It should have 1M max."
 
-    @require_flash_attn
-    @require_torch_gpu
-    @mark.flash_attn_test
-    @slow
-    def test_flash_attn_2_conversion(self):
-        if not self.has_attentions:
-            self.skipTest(reason="Model architecture does not support attentions")
-
-        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
-
-        for model_class in self.all_model_classes:
-            if not model_class._supports_flash_attn_2:
-                self.skipTest(f"{model_class.__name__} does not support Flash Attention 2")
-
-            model = model_class(config)
-
-            with tempfile.TemporaryDirectory() as tmpdirname:
-                model.save_pretrained(tmpdirname)
-                model = model_class.from_pretrained(
-                    tmpdirname, torch_dtype=torch.float16, attn_implementation="flash_attention_2"
-                ).to(torch_device)
-
-                for _, module in model.named_modules():
-                    if "FlashAttention" in module.__class__.__name__:
-                        return
-
-                self.assertTrue(False, "FlashAttention2 modules not found in model")
-
     @require_flash_attn
     @require_torch_gpu
     @mark.flash_attn_test