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e4c9f6d9a2
Summary: Torch Native Runtime RFC: https://github.com/pytorch/rfcs/pull/72 As part of the effort to open source TorchNativeRuntime (or what we call Sigmoid), we are moving the Pytree implementation to torch/: fbcode/sigmoid/kernels -> fbcode/caffe2/torch/nativert/kernels Test Plan: ``` buck run fbcode//mode/dev-nosan //caffe2/test/cpp/nativert:c10_kernel_test ``` Differential Revision: D76825830 Pull Request resolved: https://github.com/pytorch/pytorch/pull/156208 Approved by: https://github.com/zhxchen17
86 lines
2.8 KiB
C++
86 lines
2.8 KiB
C++
#pragma once
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#include <ATen/core/dispatch/Dispatcher.h>
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#include <ATen/core/function_schema.h>
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#include <c10/core/Device.h>
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#include <torch/nativert/executor/memory/FunctionSchema.h>
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#include <torch/nativert/executor/ExecutionFrame.h>
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#include <torch/nativert/executor/OpKernel.h>
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#include <torch/nativert/graph/Graph.h>
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namespace torch::nativert {
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// Implementation of Kernel for ATen operators
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//
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// This class exists to amortize per-kernel overhead by computing things during
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// initialization instead of on every execution. Right now we are only
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// amortizing schema resolution, and static arguments parsing,
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// but in the future this could be extended to avoid operator dispatch and
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// do better "Register" allocation (e.g. convert input/outputs to directly
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// array accesses onto a set of registers, in concert with memory planning)
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class C10Kernel : public OpKernel {
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public:
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C10Kernel() = delete; // deleted default constructor
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C10Kernel(
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const Node* node,
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c10::Device device,
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OpKernelKind kind = OpKernelKind::kInterpreterFallbackKernel,
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AliasingSpec&& aliasingSpec = {});
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virtual ~C10Kernel() override = default;
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[[nodiscard]] const c10::IValue& input(
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uint32_t i,
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ExecutionFrame& executionFrame) const override {
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if (Value* dynamicArg = arguments_.findDynamic(i)) {
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return executionFrame.getIValue(dynamicArg->id());
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}
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return attribute(i);
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}
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[[nodiscard]] const c10::IValue& attribute(uint32_t i) const {
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return arguments_.getStatic(i);
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}
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C10_ALWAYS_INLINE const FunctionSchema& schema() const {
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return schema_;
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}
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void computeInternal(ExecutionFrame& executionFrame) const override;
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private:
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c10::OperatorHandle op_;
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FunctionSchema schema_;
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Arguments arguments_;
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};
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class SymIntOpKernel : public OpKernel {
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public:
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explicit SymIntOpKernel(const Node* node) : OpKernel(node) {}
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void computeInternal(ExecutionFrame& executionFrame) const override final;
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};
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class SymBoolOpKernel : public OpKernel {
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public:
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explicit SymBoolOpKernel(const Node* node) : OpKernel(node) {}
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void computeInternal(ExecutionFrame& executionFrame) const override final;
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};
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class SymFloatOpKernel : public OpKernel {
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public:
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explicit SymFloatOpKernel(const Node* node) : OpKernel(node) {}
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void computeInternal(ExecutionFrame& executionFrame) const override final;
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};
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// ScalarOpKernel does binary arithmetic operations on scalar values.
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// Integers and floats are supported as input types. The output will be
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// promoted to float if and only if there's at least one float input.
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class ScalarBinaryOpKernel : public OpKernel {
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public:
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explicit ScalarBinaryOpKernel(const Node* node) : OpKernel(node) {}
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void computeInternal(ExecutionFrame& executionFrame) const override final;
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};
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} // namespace torch::nativert
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