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1a73255102
Fixes #ISSUE_NUMBER Pull Request resolved: https://github.com/pytorch/pytorch/pull/138976 Approved by: https://github.com/Skylion007
40 lines
1.4 KiB
C++
40 lines
1.4 KiB
C++
#include <torch/csrc/jit/codegen/onednn/guard_shape.h>
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#include <torch/csrc/jit/jit_log.h>
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#include <torch/csrc/jit/passes/tensorexpr_fuser.h>
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#include <torch/csrc/jit/passes/utils/subgraph_utils.h>
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#include <torch/csrc/jit/runtime/graph_executor.h>
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namespace torch::jit::fuser::onednn {
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//! [ Note -- prepareFusionGroupAndGuardOutputs implementation ]
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//! shamelessly copying code from NNC (tensorexpr_fuser) with very little
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//! modification, original code at:
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//! `torch/csrc/jit/passes/tensorexpr_fuser.cpp:prepareFusionGroupAndGuardOutputs`
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//!
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//! We have the assumption that LLGA does not have operators
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//! depending on the content of the tensor.
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void prepareFusionGroupAndGuardOutputs(Block* block) {
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std::vector<Node*> fusion_groups;
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for (Node* n : block->nodes()) {
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for (Block* b : n->blocks()) {
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prepareFusionGroupAndGuardOutputs(b);
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}
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if (n->kind() == prim::oneDNNFusionGroup) {
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fusion_groups.push_back(n);
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}
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}
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for (Node* fusion_group : fusion_groups) {
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// TODO: add further optimization pass to removeOutputsUsedOnlyInSize,
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// refer to
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// `torch/csrc/jit/passes/tensorexpr_fuser.cpp:removeOutputsUsedOnlyInSize`
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// removeOutputsUsedOnlyInSize(fusion_group);
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insertTypeGuard(
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fusion_group,
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[](const TensorTypePtr& t) { return t; },
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prim::oneDNNFusionGuard);
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}
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}
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} // namespace torch::jit::fuser::onednn
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