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e98dd95446
Summary: `SerialGraphExecutor` inherits from `GraphExecutorBase` and executes all nodes in the graph in a serial manner Test Plan: CI Rollback Plan: Differential Revision: D76917966 Pull Request resolved: https://github.com/pytorch/pytorch/pull/156459 Approved by: https://github.com/zhxchen17, https://github.com/jingsh
34 lines
1.1 KiB
C++
34 lines
1.1 KiB
C++
#include <torch/nativert/executor/ExecutionPlanner.h>
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#include <torch/nativert/executor/ExecutorConfig.h>
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#include <torch/nativert/executor/SerialGraphExecutor.h>
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namespace torch::nativert {
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std::vector<c10::IValue> SerialGraphExecutor::execute(
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ExecutionFrame& executionFrame,
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std::vector<c10::IValue> inputs) {
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fillUserInputs(executionFrame, std::move(inputs));
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return executeWithPrefilledFrame(executionFrame);
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}
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std::vector<c10::IValue> SerialGraphExecutor::executeWithPrefilledFrame(
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ExecutionFrame& executionFrame) {
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// Execute kernels for all nodes except prim.Input and prim.Output
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for (NodeIndex nodeIdx = 1; nodeIdx < nodeKernels_.size() - 1; ++nodeIdx) {
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nodeKernels_[nodeIdx]->compute(executionFrame);
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// don't free intermediate values when static memory planning is enabled
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if (!executorConfig_.enableStaticMemoryPlanning) {
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// Free the intermediate values that are no used anymore
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for (const auto& valueKey : execPlan_->valuesToFree[nodeIdx]) {
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executionFrame.releaseValue(valueKey);
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}
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}
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}
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return executionFrame.tryMoveUserOutputs();
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}
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} // namespace torch::nativert
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