[tests] add a test on torch compile for varied resolutions

tests/models/test_modeling_common.py

@@ -76,6 +76,7 @@
     require_torch_accelerator_with_training,
     require_torch_gpu,
     require_torch_multi_accelerator,
+    require_torch_version_greater,
     run_test_in_subprocess,
     slow,
     torch_all_close,
@@ -1908,6 +1909,8 @@ def test_push_to_hub_library_name(self):
 @is_torch_compile
 @slow
 class TorchCompileTesterMixin:
+    different_shapes_for_compilation = None
+
     def setUp(self):
         # clean up the VRAM before each test
         super().setUp()
@@ -1961,6 +1964,21 @@ def test_compile_with_group_offloading(self):
             _ = model(**inputs_dict)
             _ = model(**inputs_dict)
 
+    @require_torch_version_greater("2.7.1")
+    def test_compile_on_different_shapes(self):
+        if self.different_shapes_for_compilation is None:
+            pytest.skip(f"Skipping as `different_shapes_for_compilation` is not set for {self.__class__.__name__}.")
+        torch.fx.experimental._config.use_duck_shape = False
+
+        init_dict, _ = self.prepare_init_args_and_inputs_for_common()
+        model = self.model_class(**init_dict).to(torch_device)
+        model = torch.compile(model, fullgraph=True, dynamic=True)
+
+        for height, width in self.different_shapes_for_compilation:
+            with torch._dynamo.config.patch(error_on_recompile=True), torch.no_grad():
+                inputs_dict = self.prepare_dummy_input(height=height, width=width)
+                _ = model(**inputs_dict)
+
 
 @slow
 @require_torch_2

tests/models/transformers/test_models_transformer_flux.py

@@ -91,10 +91,20 @@ class FluxTransformerTests(ModelTesterMixin, unittest.TestCase):
 
     @property
     def dummy_input(self):
+        return self.prepare_dummy_input()
+
+    @property
+    def input_shape(self):
+        return (16, 4)
+
+    @property
+    def output_shape(self):
+        return (16, 4)
+
+    def prepare_dummy_input(self, height=4, width=4):
         batch_size = 1
         num_latent_channels = 4
         num_image_channels = 3
-        height = width = 4
         sequence_length = 48
         embedding_dim = 32
 
@@ -114,14 +124,6 @@ def dummy_input(self):
             "timestep": timestep,
         }
 
-    @property
-    def input_shape(self):
-        return (16, 4)
-
-    @property
-    def output_shape(self):
-        return (16, 4)
-
     def prepare_init_args_and_inputs_for_common(self):
         init_dict = {
             "patch_size": 1,
@@ -173,10 +175,14 @@ def test_gradient_checkpointing_is_applied(self):
 
 class FluxTransformerCompileTests(TorchCompileTesterMixin, unittest.TestCase):
     model_class = FluxTransformer2DModel
+    different_shapes_for_compilation = [(4, 4), (4, 8), (8, 8)]
 
     def prepare_init_args_and_inputs_for_common(self):
         return FluxTransformerTests().prepare_init_args_and_inputs_for_common()
 
+    def prepare_dummy_input(self, height, width):
+        return FluxTransformerTests().prepare_dummy_input(height=height, width=width)
+
 
 class FluxTransformerLoRAHotSwapTests(LoraHotSwappingForModelTesterMixin, unittest.TestCase):
     model_class = FluxTransformer2DModel