[executorch] Propagate device metadata from partitioner result onto TensorSpecs

[Gasoonjia]

Stack from ghstack (oldest at bottom):

• [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] Propagate device info from TensorSpec into serialized Tensor #18079
• -> [executorch] Propagate device metadata from partitioner result onto TensorSpecs #18078
• [ET Device Support] TensorImpl carries device info #17534
• [ET Device Support] Schema changes: device info on Tensor and buffer-level device array #17533

Add end-to-end device type annotation support from export to runtime. Currently we only support one device per graph

The overall pipeline is:
a. Partitioner use compile_spec to determine which device the partitoned blob is runing on
b. after lowered partitioned graph to backend, the new-introed propagate_device_pass will annotate the input and output tensors of delegate blob as target device.

Differential Revision: D95842511

[pytorch-bot]

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[digantdesai]

This effectively assumes that we know the device 'name' AoT. In theory, we can have a multi-device delegate then the runtime might interpret this name differently and that can cause some confusion i.e cuda:0 device on Metal.

I am not sure about using generic names like 'gpu' but also not sure about following PyTorch's eager/jit style naming convention where you won't switch devices underneath.

[Gasoonjia]

May I have your suggestions on the executorch device name?

Currently we set up the device name AOT and intentionally decouple dour device attribute with pytorch/pytorch device concept; we created a enum in the etensor schema for all devices we are supporting right now. In this way we can support as much as device as we want.

For the situaton you mentioned, if other backend like vulken need its own gpu device, they should add a new one to the enum. We should avoid using generic names like 'gpu'.