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Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/46780 This is in prototype status, but pretty functional. There are two major parts. - Model converter. This is a pure Python component that consumes a model in TorchScript format, converts the operations into NNAPI semantics, and serializes the model in a custom format. It then wraps the result in a new TorchScript model that can invoke NNAPI under the hood. - Runtime. This is a TorchBind object that deserializes the model and sends the result to NNAPI. This is fairly simple since the serialized format is basically just a list of NNAPI calls to make, so most of the code is spent on bounds checking. A few notes on the design. - Currently, all tensor sizes need to be fixed, and those fixed sizes are burned directly into the serialized model. This will probably need to change. NNAPI supports variable-sized tensors, but the important hardware backends do not. However, we're seeing use cases crop up where the input size is not known until around the time that the model is loaded (for example, it might depend on the camera aspect ratio). I think the proper fix here is to remove the code in the converter that eagerly calculates the sizes of the intermediate tensors and replace it with a code generator that will generate some TorchScript code that will perform those calculations at model load time. This way, we will be able to support models that have variable-sized inputs while still only showing fixed-sized operands to NNAPI. - The important hardware backends want operands to be in NHWC order, but PyTorch natively represents all tensors and NCHW. The strategy for this is to keep NCHW during most of the conversion process, but track and additional value per operand representing the "dimension order". The dimension order gets propagated through convolutions and pointwise ops. When we're ready to serialize the model, we reorder the dimensions for "channels last" operands to NHWC. Test Plan: Some local testing with FB prod models. I'll need to add some examples and automated tests. Reviewed By: iseeyuan Differential Revision: D24574040 Pulled By: dreiss fbshipit-source-id: 6adc8571b234877ee3666ec0c0de24da35c38a1f
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