[ET Device Support] Propagate device info from TensorSpec into serialized Tensor (#18894)

This PR was created by the merge bot to help merge the original PR into
the main branch.
ghstack PR number: #18079 by
@Gasoonjia
^ Please use this as the source of truth for the PR details, comments,
and reviews
ghstack PR base:
https://github.com/pytorch/executorch/tree/gh/gasoonjia/136/base
ghstack PR head:
https://github.com/pytorch/executorch/tree/gh/gasoonjia/136/head
Merge bot PR base:
https://github.com/pytorch/executorch/tree/gh/gasoonjia/135/orig
Merge bot PR head:
https://github.com/pytorch/executorch/tree/gh/gasoonjia/136/orig
Differential Revision:
[D95899706](https://our.internmc.facebook.com/intern/diff/D95899706/)
@diff-train-skip-merge

---------

Co-authored-by: gasoonjia <gasoonjia@icloud.com>

Author: pytorchbot

exir/emit/test/BUCK

@@ -26,10 +26,15 @@ fbcode_target(_kind = runtime.python_test,
         "//executorch/exir:schema",
         "//executorch/exir/backend/test/demos/rpc:executor_backend_partitioner",
         "//executorch/exir/backend:backend_api",
+        "//executorch/exir/backend:compile_spec_schema",
+        "//executorch/exir/backend:partitioner",
+        "//executorch/exir/backend/canonical_partitioners:canonical_partitioner_lib",
+        "//executorch/exir/backend/test:backend_with_compiler_demo",
         "//executorch/exir/emit:lib",
         "//executorch/exir/passes:const_prop_pass",
         "//executorch/exir/passes:constant_prop_pass",
         "//executorch/exir/passes:init_mutable_pass",
+        "//executorch/exir/passes:propagate_device_pass",
         "//executorch/exir/tests:lib",
         "//executorch/exir/tests:models",
         "//executorch/extension/pybindings:portable_lib",

exir/emit/test/test_emit.py

@@ -2518,3 +2518,128 @@ def forward(self):
             for j in range(2):
                 expected_storage.append(j * 16 + i)
         self.assertEqual([int(v) for v in storage_values], expected_storage)
+
+    def test_emit_device_info_propagated_to_serialized_tensor(self) -> None:
+        """Verify that device info from PropagateDevicePass flows through
+        the emitter into ExtraTensorInfo.device_type on serialized tensors."""
+        from executorch.exir.backend.canonical_partitioners.pattern_op_partitioner import (
+            generate_pattern_op_partitions,
+        )
+        from executorch.exir.backend.compile_spec_schema import CompileSpec
+        from executorch.exir.backend.partitioner import (
+            DelegationSpec,
+            Partitioner,
+            PartitionResult,
+        )
+        from executorch.exir.backend.test.backend_with_compiler_demo import (
+            BackendWithCompilerDemo,
+        )
+        from executorch.exir.passes.propagate_device_pass import (
+            TARGET_DEVICE_COMPILE_SPEC_KEY,
+        )
+        from torch.fx.passes.operator_support import any_chain, OperatorSupportBase
+
+        class AddSupport(OperatorSupportBase):
+            def is_node_supported(self, submodules, node: torch.fx.Node) -> bool:
+                return node.op == "call_function" and node.target in [
+                    exir_ops.edge.aten.add.Tensor,
+                ]
+
+        class DevicePartitioner(Partitioner):
+            def __init__(self):
+                super().__init__()
+                self.delegation_spec = DelegationSpec(
+                    BackendWithCompilerDemo.__name__,
+                    [
+                        CompileSpec("max_value", bytes([4])),
+                        CompileSpec(TARGET_DEVICE_COMPILE_SPEC_KEY, b"cuda:0"),
+                    ],
+                )
+
+            def partition(self, exported_program) -> PartitionResult:
+                partition_tags = {}
+                partition_list = generate_pattern_op_partitions(
+                    exported_program.graph_module,
+                    op_support=any_chain(AddSupport()),
+                )
+                for partition in partition_list:
+                    for node in partition.nodes:
+                        tag = f"tag{partition.id}"
+                        node.meta["delegation_tag"] = tag
+                        partition_tags[tag] = self.delegation_spec
+                return PartitionResult(
+                    tagged_exported_program=exported_program,
+                    partition_tags=partition_tags,
+                )
+
+        class Model(torch.nn.Module):
+            def forward(self, a, b):
+                return torch.add(a, b)
+
+        model = Model()
+        inputs = (torch.randn(2, 2), torch.randn(2, 2))
+
+        edge = to_edge(
+            export(model, inputs),
+            compile_config=EdgeCompileConfig(_check_ir_validity=False),
+        )
+        lowered = edge.to_backend(DevicePartitioner())
+        et_prog = lowered.to_executorch()
+        program = et_prog._emitter_output.program
+
+        plan = program.execution_plan[0]
+        self.assertGreater(len(plan.delegates), 0)
+
+        tensor_values = [v.val for v in plan.values if isinstance(v.val, Tensor)]
+        cuda_tensors = [
+            t
+            for t in tensor_values
+            if t.extra_tensor_info is not None
+            and t.extra_tensor_info.device_type == schema.DeviceType.CUDA
+        ]
+        # add(a, b) has 2 delegate inputs + 1 delegate output = 3 CUDA tensors
+        self.assertEqual(
+            len(cuda_tensors),
+            3,
+            f"Expected exactly 3 CUDA tensors (2 inputs + 1 output for delegated add), got {len(cuda_tensors)}",
+        )
+        # Verify device_index is also correctly serialized (cuda:0 → index 0)
+        for t in cuda_tensors:
+            self.assertEqual(
+                t.extra_tensor_info.device_index,
+                0,
+                "CUDA tensor device_index should be 0 for cuda:0",
+            )
+
+    def test_emit_cpu_tensors_no_extra_device_info(self) -> None:
+        """When all tensors are on CPU (default), ExtraTensorInfo should NOT be
+        created solely for device info — it should remain None for activation tensors.
+        """
+
+        class Model(torch.nn.Module):
+            def forward(self, a, b):
+                return torch.add(a, b)
+
+        model = Model()
+        inputs = (torch.randn(2, 2), torch.randn(2, 2))
+
+        edge = to_edge(
+            export(model, inputs),
+            compile_config=EdgeCompileConfig(_check_ir_validity=False),
+        )
+        et_prog = edge.to_executorch()
+        program = et_prog._emitter_output.program
+
+        plan = program.execution_plan[0]
+        tensor_values = [v.val for v in plan.values if isinstance(v.val, Tensor)]
+        non_cpu_tensors = [
+            t
+            for t in tensor_values
+            if t.extra_tensor_info is not None
+            and t.extra_tensor_info.device_type is not None
+        ]
+        self.assertEqual(
+            len(non_cpu_tensors),
+            0,
+            "No tensor should have extra device info when model runs entirely on CPU",
+        )

exir/tensor.py

@@ -366,6 +366,21 @@ def to_list(
     tensor_size = to_list(spec.shape)
     tensor_dim_order = to_list(spec.dim_order)
 
+    extra_tensor_info = spec.extra_tensor_info
+    # Propagate device from TensorSpec into ExtraTensorInfo for serialization.
+    # Note: we don't propagate Device on CPU; if no device info will be noticed,
+    # tensor_parser will automatic treat it as CPU:0, to prevent pte size
+    # regression as much as possible.
+    if spec.device != schema.DeviceType.CPU:
+        if extra_tensor_info is None:
+            extra_tensor_info = schema.ExtraTensorInfo(
+                device_type=spec.device,
+                device_index=spec.device_index,
+            )
+        else:
+            extra_tensor_info.device_type = spec.device
+            extra_tensor_info.device_index = spec.device_index
+
     flatbuffer_tensor = schema.Tensor(
         scalar_type=scalar_type_enum(spec.scalar_type),
         # The runtime currently only supports tensors with offsets of zero.
@@ -377,7 +392,7 @@ def to_list(
         allocation_info=allocation_info,
         layout=layout_enum(spec.layout),
         shape_dynamism=spec.shape_dynamism,
-        extra_tensor_info=spec.extra_tensor_info,
+        extra_tensor_info=extra_tensor_info,
     )
     return flatbuffer_tensor