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bfeb45e46b
Follows #132604 Pull Request resolved: https://github.com/pytorch/pytorch/pull/132753 Approved by: https://github.com/Skylion007
36 lines
1.2 KiB
C++
36 lines
1.2 KiB
C++
#pragma once
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#include <torch/csrc/jit/ir/ir.h>
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namespace torch::jit {
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TORCH_API bool canFuseOnCPULegacy();
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TORCH_API void overrideCanFuseOnCPULegacy(bool value);
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// NB: Be sure to run DCE before fusion, because dead instructions
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// can prevent fusion opportunities from being exploited.
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// On Windows will noop, NYI
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TORCH_API void FuseGraph(
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std::shared_ptr<Graph>& graph,
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bool strict_fuser_check = false);
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// \brief Custom fusion pass using a node-level callback to
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// determine the inclusion of nodes in a subgraph.
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//
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// This helper omits aliased inputs and fusion across control flow
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// boundaries.
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//
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// \arg graph The graph to be modified in-place
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// \arg is_fusable A callback run on each fusable node in the graph.
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// \arg kind The label given to the resultant fused subgraph
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// \arg arg_limit The maximum number of args the resultant fused subgraph
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// should have. Note: This will likely develop into a general
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// post condition on the fused subgraph.
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TORCH_API void CustomFuseGraph(
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std::shared_ptr<Graph>& graph,
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const std::function<bool(Node*)>& is_fusable,
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Symbol kind,
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size_t arg_limit = std::numeric_limits<size_t>::max());
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} // namespace torch::jit
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