[ET Device Support] Annotate device attributes of CUDA backend IO tensors cuda device

backends/cuda/cuda_partitioner.py

@@ -10,6 +10,7 @@
 from executorch.backends.cuda.cuda_backend import CudaBackend  # usort: skip
 from executorch.exir._warnings import experimental
 from executorch.exir.backend.compile_spec_schema import CompileSpec
+from executorch.exir.passes.propagate_device_pass import TARGET_DEVICE_COMPILE_SPEC_KEY
 
 
 @final
@@ -19,7 +20,34 @@
 class CudaPartitioner(AotiPartitioner):
     """
     CUDA partitioner driven by AOTInductor backend.
+
+    This partitioner adds a target_device compile spec to enable device info
+    propagation. The PropagateDevicePass will read this spec and mark delegate
+    output tensors with CUDA device type, which flows through to serialization.
     """
 
-    def __init__(self, compile_spec: List[CompileSpec]) -> None:
+    def __init__(
+        self,
+        compile_spec: List[CompileSpec],
+        device_index: int = 0,
+    ) -> None:
+        """
+        Initialize the CUDA partitioner.
+
+        Args:
+            compile_spec: List of compile specs for the backend.
+            device_index: The CUDA device index (default: 0). This is used to
+                         generate the target_device compile spec (e.g., "cuda:0").
+        """
+        # Add target_device compile spec for device propagation if not already present
+        has_target_device = any(
+            spec.key == TARGET_DEVICE_COMPILE_SPEC_KEY for spec in compile_spec
+        )
+        if not has_target_device:
+            compile_spec = list(compile_spec) + [
+                CompileSpec(
+                    TARGET_DEVICE_COMPILE_SPEC_KEY,
+                    f"cuda:{device_index}".encode("utf-8"),
+                )
+            ]
         super().__init__(CudaBackend.__name__, compile_spec)

backends/cuda/runtime/cuda_backend.cpp

@@ -403,6 +403,26 @@ class ET_EXPERIMENTAL CudaBackend final
         n_outputs,
         args.size())
 
+    // Verify device info on all memory-planned, ET-driven IO tensors.
+    // All input and output tensors should have device_type = CUDA, which
+    // is set during serialization by PropagateDevicePass based on the
+    // target_device compile spec from CudaPartitioner.
+    //
+    // Note: At this stage, the tensor memory is still on CPU. The device_type
+    // is metadata indicating where the tensor *should* reside. The backend
+    // is responsible for copying data to the actual CUDA device.
+    for (size_t i = 0; i < n_inputs + n_outputs; i++) {
+      auto* tensor = &(args[i]->toTensor());
+      auto device_type = tensor->unsafeGetTensorImpl()->device_type();
+      ET_CHECK_OR_RETURN_ERROR(
+          device_type == executorch::runtime::etensor::DeviceType::CUDA,
+          InvalidArgument,
+          "Tensor %zu expected device_type=CUDA (1), got %d. "
+          "Device info may not be properly propagated from CudaPartitioner.",
+          i,
+          static_cast<int>(device_type));
+    }
+
     // NOTE: ExecuTorch tensors may be on CPU or GPU due to the skip-copy
     // optimization. We need to create GPU copies for CUDA kernel execution
     // using SlimTensor.

backends/cuda/tests/test_cuda_export.py

@@ -325,3 +325,72 @@ def test_triton_kernel_mode_off(self):
             edge_program_manager,
             "SDPA kernel export with triton_kernel_mode=OFF failed",
         )
+
+    def test_device_info_propagated_to_cuda_delegate_outputs(self):
+        """
+        Test that device info is correctly propagated from export to serialization
+        for CUDA delegate outputs.
+
+        This verifies the device propagation flow:
+        1. CudaPartitioner adds target_device="cuda:0" CompileSpec
+        2. PropagateDevicePass sets TensorSpec.device = CUDA for delegate outputs
+        3. Emitter serializes device info into ExtraTensorInfo.device_type
+        4. Serialized tensors have device_type = DeviceType.CUDA
+
+        Note: At this stage, the tensor memory is still on CPU. The CUDA backend
+        will copy data to GPU device at runtime. Device info tagging is the first
+        step toward full device-aware memory allocation.
+        """
+        from executorch.exir import schema
+
+        class AddModule(torch.nn.Module):
+            def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+                return x + y
+
+        module = AddModule()
+        module.eval()
+        inputs = (torch.randn(2, 3), torch.randn(2, 3))
+
+        # Export to CUDA with full pipeline
+        edge_program_manager = self._export_to_cuda_with_lower(module, inputs)
+        self.assertIsNotNone(edge_program_manager, "CUDA export failed")
+
+        # Convert to ExecutorTorch and access the serialized program
+        et_prog = edge_program_manager.to_executorch()
+        program = et_prog._emitter_output.program
+
+        # Get the execution plan and verify delegate exists
+        plan = program.execution_plan[0]
+        self.assertGreater(
+            len(plan.delegates),
+            0,
+            "Expected at least one delegate in the execution plan",
+        )
+
+        # Count tensors by device type
+        cpu_tensors = []
+        cuda_tensors = []
+
+        for value in plan.values:
+            if isinstance(value.val, schema.Tensor):
+                tensor = value.val
+                if (
+                    tensor.extra_tensor_info is not None
+                    and tensor.extra_tensor_info.device_type == schema.DeviceType.CUDA
+                ):
+                    cuda_tensors.append(tensor)
+                else:
+                    # Either no extra_tensor_info or device_type is CPU (default)
+                    cpu_tensors.append(tensor)
+
+        # Both input and output tensors should be on CUDA device for now.
+        self.assertEqual(
+            len(cpu_tensors),
+            0,
+            "All tensors are on CUDA device..",
+        )
+        self.assertGreater(
+            len(cuda_tensors),
+            3,
+            "Expected CUDA tensors for delegate outputs",
+        )