[ET Device Support] Device-aware memory planning: separate buffers per device type

[Gasoonjia]

Stack from ghstack (oldest at bottom):

• [ET Device Support] CUDA-native Qwen 3.5 MoE inference with device tensor pipeline #18788
• [ET Device Support] Extract shared device test utilities to reduce redundancy #18762
• [ET Device Support] Add device tensor helper functions to TensorPtr API #18761
• [ET Device Support] Add ExecutorchBackendConfig flags for skipping H2D/D2H copies #18760
• [ET Device Support] PropagateDevicePass inserts H2D/D2H copy ops at delegate boundaries #18730
• [ET Device Support] Define et_copy runtime h2d and d2h copy ops #18729
• [ET Device Support] Define AOT device copy ops registry #18728
• [ET Device Support] CudaAllocator: device memory allocator for CUDA backend #18477
• [ET Device Support] Module: allocate device memory for planned buffers #18476
• [ET Device Support] MemoryManager: add per-buffer device metadata #18475
• [ET Device Support] MethodMeta: expose per-buffer device placement API #18474
• [ET Device Support] DeviceMemoryBuffer RAII class for device memory lifetime management #18473
• [ET Device Support] Emitter reads non_const_buffer_device from graph meta #18472
• -> [ET Device Support] Device-aware memory planning: separate buffers per device type #18375
• [ET Device Support] Add NonConstBufferDevice schema for per-buffer device mapping #18330
• [ET Device Support] DeviceAllocator interface and DeviceAllocatorRegistry #17535
• [ET Device Support] Annotate device attributes of CUDA backend IO tensors cuda device #18080
• [ET Device Support] Parse device info from serialized tensor in tensor_parser #18328
• [ET Device Support] Propagate device info from TensorSpec into serialized Tensor #18079
• [executorch] Propagate device metadata from partitioner result onto TensorSpecs #18078

Extends memory planning to separate device tensors from CPU tensors into distinct
memory buffers. Non-CPU TensorSpecs (e.g., CUDA) are pre-assigned device-specific
mem_ids before the greedy/naive algorithm runs, ensuring they get planned into
independent memory buffers that never share space with CPU tensors.

Differential Revision: D97447105

[pytorch-bot]

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❌ 4 New Failures, 2 Unrelated Failures

As of commit 38f2c3c with merge base b5ae0b9 ():

NEW FAILURES - The following jobs have failed:

• pull / unittest / linux / linux-job (gh)
  exir/tests/test_joint_graph.py::TestJointGraph::test_joint_graph
• pull / unittest / macos / macos-job (gh)
  examples/models/test/test_export.py::ExportTest::test_mv3_export_to_executorch
• pull / unittest-editable / linux / linux-job (gh)
  exir/tests/test_joint_graph.py::TestJointGraph::test_joint_graph
• pull / unittest-editable / macos / macos-job (gh)
  examples/models/test/test_export.py::ExportTest::test_mv3_export_to_executorch

BROKEN TRUNK - The following jobs failed but were present on the merge base:

👉 Rebase onto the `viable/strict` branch to avoid these failures

• pull / unittest / windows / windows-job (gh) (trunk failure)
  ##[error]The operation was canceled.
• pull / unittest-editable / windows / windows-job (gh) (trunk failure)
  ##[error]The operation was canceled.

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