[test_models_transformer_hunyuan_video] help us test torch.compile() for impactful models (#11431)

* Update test_models_transformer_hunyuan_video.py

* update

---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

tests/models/transformers/test_models_transformer_hunyuan_video.py

@@ -17,7 +17,14 @@ import unittest
 import torch
 
 from diffusers import HunyuanVideoTransformer3DModel
-from diffusers.utils.testing_utils import enable_full_determinism, torch_device
+from diffusers.utils.testing_utils import (
+    enable_full_determinism,
+    is_torch_compile,
+    require_torch_2,
+    require_torch_gpu,
+    slow,
+    torch_device,
+)
 
 from ..test_modeling_common import ModelTesterMixin
 
@@ -89,6 +96,21 @@ class HunyuanVideoTransformer3DTests(ModelTesterMixin, unittest.TestCase):
         expected_set = {"HunyuanVideoTransformer3DModel"}
         super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
 
+    @require_torch_gpu
+    @require_torch_2
+    @is_torch_compile
+    @slow
+    def test_torch_compile_recompilation_and_graph_break(self):
+        torch._dynamo.reset()
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+
+        model = self.model_class(**init_dict).to(torch_device)
+        model = torch.compile(model, fullgraph=True)
+
+        with torch._dynamo.config.patch(error_on_recompile=True), torch.no_grad():
+            _ = model(**inputs_dict)
+            _ = model(**inputs_dict)
+
 
 class HunyuanSkyreelsImageToVideoTransformer3DTests(ModelTesterMixin, unittest.TestCase):
     model_class = HunyuanVideoTransformer3DModel
@@ -157,6 +179,21 @@ class HunyuanSkyreelsImageToVideoTransformer3DTests(ModelTesterMixin, unittest.T
         expected_set = {"HunyuanVideoTransformer3DModel"}
         super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
 
+    @require_torch_gpu
+    @require_torch_2
+    @is_torch_compile
+    @slow
+    def test_torch_compile_recompilation_and_graph_break(self):
+        torch._dynamo.reset()
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+
+        model = self.model_class(**init_dict).to(torch_device)
+        model = torch.compile(model, fullgraph=True)
+
+        with torch._dynamo.config.patch(error_on_recompile=True), torch.no_grad():
+            _ = model(**inputs_dict)
+            _ = model(**inputs_dict)
+
 
 class HunyuanVideoImageToVideoTransformer3DTests(ModelTesterMixin, unittest.TestCase):
     model_class = HunyuanVideoTransformer3DModel
@@ -223,6 +260,21 @@ class HunyuanVideoImageToVideoTransformer3DTests(ModelTesterMixin, unittest.Test
         expected_set = {"HunyuanVideoTransformer3DModel"}
         super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
 
+    @require_torch_gpu
+    @require_torch_2
+    @is_torch_compile
+    @slow
+    def test_torch_compile_recompilation_and_graph_break(self):
+        torch._dynamo.reset()
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+
+        model = self.model_class(**init_dict).to(torch_device)
+        model = torch.compile(model, fullgraph=True)
+
+        with torch._dynamo.config.patch(error_on_recompile=True), torch.no_grad():
+            _ = model(**inputs_dict)
+            _ = model(**inputs_dict)
+
 
 class HunyuanVideoTokenReplaceImageToVideoTransformer3DTests(ModelTesterMixin, unittest.TestCase):
     model_class = HunyuanVideoTransformer3DModel
@@ -290,3 +342,18 @@ class HunyuanVideoTokenReplaceImageToVideoTransformer3DTests(ModelTesterMixin, u
     def test_gradient_checkpointing_is_applied(self):
         expected_set = {"HunyuanVideoTransformer3DModel"}
         super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
+
+    @require_torch_gpu
+    @require_torch_2
+    @is_torch_compile
+    @slow
+    def test_torch_compile_recompilation_and_graph_break(self):
+        torch._dynamo.reset()
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+
+        model = self.model_class(**init_dict).to(torch_device)
+        model = torch.compile(model, fullgraph=True)
+
+        with torch._dynamo.config.patch(error_on_recompile=True), torch.no_grad():
+            _ = model(**inputs_dict)
+            _ = model(**inputs_dict)