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https://github.com/zebrajr/pytorch.git
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dbe850af5b
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/33851 Rationale and context described in #33828. Script to reproduce the move: https://gist.github.com/suo/16cbefaaeb67ca5a7c6caffd49b7f6e9 ghstack-source-id: 99079645 Test Plan: Make sure CI passes Reviewed By: jamesr66a Differential Revision: D20133869 fbshipit-source-id: 390e9241a9c85366d9005c492ac31f10aa96488e
30 lines
838 B
C++
30 lines
838 B
C++
#pragma once
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#include <torch/csrc/WindowsTorchApiMacro.h>
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#include <torch/csrc/jit/codegen/fuser/arg_spec.h>
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#include <torch/csrc/jit/codegen/fuser/partition_desc.h>
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#include <torch/csrc/jit/codegen/fuser/tensor_desc.h>
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#include <torch/csrc/jit/ir/ir.h>
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#include <iostream>
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#include <string>
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#include <tuple>
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#include <vector>
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namespace torch {
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namespace jit {
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namespace fuser {
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// Creates a CPU or CUDA kernel for the given graph.
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// Returns the C++ or CUDA string implementing the kernel.
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TORCH_API std::string generateKernel(
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const std::string& name,
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const Graph& graph,
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const std::vector<std::pair<const Value*, const c10::optional<TensorDesc>>>& inputs,
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const std::vector<std::pair<const Value*, const TensorDesc>>& outputs,
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const bool use_cuda);
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} // namespace fuser
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} // namespace jit
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} // namespace torch
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