[Modular] Test for catching dtype and device issues with AutoModel type hints (#13287)

* update

* update

* update

Author: DN6

tests/modular_pipelines/test_modular_pipelines_custom_blocks.py

@@ -31,7 +31,41 @@
     WanModularPipeline,
 )
 
-from ..testing_utils import nightly, require_torch, slow
+from ..testing_utils import nightly, require_torch, require_torch_accelerator, slow, torch_device
+
+
+def _create_tiny_model_dir(model_dir):
+    TINY_MODEL_CODE = (
+        "import torch\n"
+        "from diffusers import ModelMixin, ConfigMixin\n"
+        "from diffusers.configuration_utils import register_to_config\n"
+        "\n"
+        "class TinyModel(ModelMixin, ConfigMixin):\n"
+        "    @register_to_config\n"
+        "    def __init__(self, hidden_size=4):\n"
+        "        super().__init__()\n"
+        "        self.linear = torch.nn.Linear(hidden_size, hidden_size)\n"
+        "\n"
+        "    def forward(self, x):\n"
+        "        return self.linear(x)\n"
+    )
+
+    with open(os.path.join(model_dir, "modeling.py"), "w") as f:
+        f.write(TINY_MODEL_CODE)
+
+    config = {
+        "_class_name": "TinyModel",
+        "_diffusers_version": "0.0.0",
+        "auto_map": {"AutoModel": "modeling.TinyModel"},
+        "hidden_size": 4,
+    }
+    with open(os.path.join(model_dir, "config.json"), "w") as f:
+        json.dump(config, f)
+
+    torch.save(
+        {"linear.weight": torch.randn(4, 4), "linear.bias": torch.randn(4)},
+        os.path.join(model_dir, "diffusion_pytorch_model.bin"),
+    )
 
 
 class DummyCustomBlockSimple(ModularPipelineBlocks):
@@ -341,6 +375,81 @@ def __call__(self, components, state: PipelineState) -> PipelineState:
             loaded_pipe.update_components(custom_model=custom_model)
             assert getattr(loaded_pipe, "custom_model", None) is not None
 
+    def test_automodel_type_hint_preserves_torch_dtype(self, tmp_path):
+        """Regression test for #13271: torch_dtype was incorrectly removed when type_hint is AutoModel."""
+        from diffusers import AutoModel
+
+        model_dir = str(tmp_path / "model")
+        os.makedirs(model_dir)
+        _create_tiny_model_dir(model_dir)
+
+        class DtypeTestBlock(ModularPipelineBlocks):
+            @property
+            def expected_components(self):
+                return [ComponentSpec("model", AutoModel, pretrained_model_name_or_path=model_dir)]
+
+            @property
+            def inputs(self) -> List[InputParam]:
+                return [InputParam("prompt", type_hint=str, required=True)]
+
+            @property
+            def intermediate_inputs(self) -> List[InputParam]:
+                return []
+
+            @property
+            def intermediate_outputs(self) -> List[OutputParam]:
+                return [OutputParam("output", type_hint=str)]
+
+            def __call__(self, components, state: PipelineState) -> PipelineState:
+                block_state = self.get_block_state(state)
+                block_state.output = "test"
+                self.set_block_state(state, block_state)
+                return components, state
+
+        block = DtypeTestBlock()
+        pipe = block.init_pipeline()
+        pipe.load_components(torch_dtype=torch.float16, trust_remote_code=True)
+
+        assert pipe.model.dtype == torch.float16
+
+    @require_torch_accelerator
+    def test_automodel_type_hint_preserves_device(self, tmp_path):
+        """Test that ComponentSpec with AutoModel type_hint correctly passes device_map."""
+        from diffusers import AutoModel
+
+        model_dir = str(tmp_path / "model")
+        os.makedirs(model_dir)
+        _create_tiny_model_dir(model_dir)
+
+        class DeviceTestBlock(ModularPipelineBlocks):
+            @property
+            def expected_components(self):
+                return [ComponentSpec("model", AutoModel, pretrained_model_name_or_path=model_dir)]
+
+            @property
+            def inputs(self) -> List[InputParam]:
+                return [InputParam("prompt", type_hint=str, required=True)]
+
+            @property
+            def intermediate_inputs(self) -> List[InputParam]:
+                return []
+
+            @property
+            def intermediate_outputs(self) -> List[OutputParam]:
+                return [OutputParam("output", type_hint=str)]
+
+            def __call__(self, components, state: PipelineState) -> PipelineState:
+                block_state = self.get_block_state(state)
+                block_state.output = "test"
+                self.set_block_state(state, block_state)
+                return components, state
+
+        block = DeviceTestBlock()
+        pipe = block.init_pipeline()
+        pipe.load_components(device_map=torch_device, trust_remote_code=True)
+
+        assert pipe.model.device.type == torch_device
+
     def test_custom_block_loads_from_hub(self):
         repo_id = "hf-internal-testing/tiny-modular-diffusers-block"
         block = ModularPipelineBlocks.from_pretrained(repo_id, trust_remote_code=True)