mirror of
https://github.com/zebrajr/pytorch.git
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246f5c412e
Summary:
This reverts commit d73c830e23.
We have observed significant perf drop when training ResNext101 with multiple amd GPUs:
Before:
https://ci.pytorch.org/jenkins/job/caffe2-builds/job/py2-clang7-rocmdeb-ubuntu16.04-bench/1636/console
2 GPUs ResNext training got 150\~160 imgs/sec
4 GPUs ResNext training got 270\~280 imgs/sec
After:
https://ci.pytorch.org/jenkins/job/caffe2-builds/job/py2-clang7-rocmdeb-ubuntu16.04-bench/1637/console
Both 2 and 4 GPUs ResNext training drop to 110\~120 imgs/sec
Similar perf drop are seen on ResNet50 training jobs as well.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18680
Differential Revision: D14702941
Pulled By: bddppq
fbshipit-source-id: 828141805afc23f25c08d4a2eb6d4b99f817c128
40 lines
1.1 KiB
C++
40 lines
1.1 KiB
C++
#ifndef CAFFE2_OPERATORS_COPY_OP_H_
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#define CAFFE2_OPERATORS_COPY_OP_H_
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#include "caffe2/core/context.h"
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#include "caffe2/core/operator.h"
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namespace caffe2 {
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template <class Context, class DstContext, class SrcContext>
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class CopyOp : public Operator<Context> {
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public:
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USE_OPERATOR_CONTEXT_FUNCTIONS;
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USE_SIMPLE_CTOR_DTOR(CopyOp)
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bool RunOnDevice() override {
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auto& input = this->template Input<Tensor>(0, SrcContext::GetDeviceType());
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auto* output =
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this->template Output<Tensor>(0, DstContext::GetDeviceType());
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output->ResizeLike(input);
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this->context_.template CopyItems<SrcContext, DstContext>(
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input.dtype(),
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input.numel(),
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input.raw_data(),
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output->raw_mutable_data(input.dtype()));
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return true;
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}
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};
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template <class Context, class DstContext, class SrcContext>
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class CopyOnDeviceLikeOp : public CopyOp<Context, DstContext, SrcContext> {
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public:
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template <class... Args>
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explicit CopyOnDeviceLikeOp(Args&&... args)
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: CopyOp<Context, DstContext, SrcContext>(std::forward<Args>(args)...) {}
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};
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} // namespace caffe2
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#endif // CAFFE2_OPERATORS_COPY_OP_H_
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