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601e7dc0bb
This PR fixes typos in comments and messages of `.cc` and `.h` files under `caffe2/operators` directory Pull Request resolved: https://github.com/pytorch/pytorch/pull/98235 Approved by: https://github.com/kit1980
51 lines
1.5 KiB
C++
51 lines
1.5 KiB
C++
#include "caffe2/operators/async_net_barrier_op.h"
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namespace caffe2 {
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namespace {
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std::pair<std::vector<DeviceOption>, std::vector<DeviceOption>>
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asyncBarrierOpDevInfer(const OperatorDef& def) {
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auto op_device =
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def.has_device_option() ? def.device_option() : DeviceOption();
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ArgumentHelper helper(def);
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auto cross_device = helper.GetSingleArgument<int>("cross_device", 0);
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std::vector<DeviceOption> opt;
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for (int i = 0; i < def.input().size(); ++i) {
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if (cross_device == 1) {
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DeviceOption dev;
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dev.set_device_type(op_device.device_type());
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dev.set_device_id(i);
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opt.push_back(dev);
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} else {
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opt.push_back(op_device);
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}
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}
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return std::make_pair(opt, opt);
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}
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}
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OPERATOR_SCHEMA(AsyncNetBarrier)
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.NumInputs(1, INT_MAX)
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.NumOutputs(1, INT_MAX)
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.IdenticalTypeAndShape()
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.InputsCanCrossDevices()
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.AllowOneToOneInplace()
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.DeviceInferenceFunction(asyncBarrierOpDevInfer)
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.SetDoc(R"DOC(
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This is a pretty much no-op operator, since it's only purposes is make sure that
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async_scheduling will schedule certain operations earlier than others.
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Exaple where this operator can work well - mixture of data-parallel and model-
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parallel training, where one wants to force that all copies are started before
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data-parallel part starts.
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)DOC")
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.Arg(
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"cross_device",
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"Specifies either inputs should be across different devices in dev inference options");
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SHOULD_NOT_DO_GRADIENT(AsyncNetBarrier);
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REGISTER_CPU_OPERATOR(AsyncNetBarrier, AsyncNetBarrierOp<CPUContext>);
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} // namespace caffe2
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