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Summary: Sub-step of my attempt to split up the torch_cuda library, as it is huge. Please look at https://github.com/pytorch/pytorch/issues/49050 for details on the split and which files are in which target. This PR introduces two new macros for Windows DLL purposes, TORCH_CUDA_CPP_API and TORCH_CUDA_CU_API. Both are defined as TORCH_CUDA_API for the time being. Pull Request resolved: https://github.com/pytorch/pytorch/pull/50627 Reviewed By: mruberry Differential Revision: D25955441 Pulled By: janeyx99 fbshipit-source-id: ff226026833b8fb2fb7c77df6f2d6c824f006869
48 lines
1.4 KiB
C++
48 lines
1.4 KiB
C++
#pragma once
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#include <torch/csrc/WindowsTorchApiMacro.h>
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#include <torch/csrc/jit/ir/ir.h>
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#include <torch/csrc/jit/codegen/cuda/fusion.h>
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/*
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* This file handles Parsing PyTorch jit ir;
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*
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* It is used in two places:
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* 1. When partitioning PyTorch jit ir to create prim::CudaFusionGroup, each
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* node is queried by `isNodeParsible` to determine whether the node could
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* be handled by the fuser (whether a given PyTorch jit operator should be
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* merged);
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* 2. lowering PyTorch jit ir to CUDA codegen ir.
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* creates a `Fusion` by traversing a PyTorch jit graph.
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*
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* TODO: we could consider exposing API to allow custom registration of parsing
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* rules for a given PyTorch jit operator.
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*/
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namespace torch {
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namespace jit {
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namespace fuser {
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namespace cuda {
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constexpr int kPwThreadX = 128;
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constexpr int kFcdReductionThreadX = 128;
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constexpr int kNonFcdReductionThreadX = 32;
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constexpr int kNonFcdReductionThreadY = 32;
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TORCH_CUDA_CU_API bool hasReductionNode(const Block* block);
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TORCH_CUDA_CU_API bool isReductionNode(const Node* node);
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// returns whether or not a parsing function exists for the given node type.
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TORCH_CUDA_CU_API bool isNodeParsible(const Node* node);
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// lowers PyTorch jit graph to `Fusion`.
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TORCH_CUDA_CU_API std::unique_ptr<Fusion> parseJitIR(
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const std::shared_ptr<Graph>& graph);
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} // namespace cuda
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} // namespace fuser
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} // namespace jit
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} // namespace torch
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