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57d2d4bcff
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/9992 Optimize reduce ops for 2d and 3d Reviewed By: houseroad Differential Revision: D9042505 fbshipit-source-id: 62af2125aa6439106293e59bdf6a2b920792fd2d
61 lines
1.7 KiB
C++
61 lines
1.7 KiB
C++
#ifndef CAFFE2_OPERATORS_SQUARE_ROOT_DIVIDE_OP_H_
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#define CAFFE2_OPERATORS_SQUARE_ROOT_DIVIDE_OP_H_
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#include "caffe2/core/context.h"
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#include "caffe2/core/operator.h"
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#include "caffe2/utils/math.h"
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namespace caffe2 {
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template <class Context>
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class SquareRootDivideOp final : public Operator<Context> {
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public:
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USE_OPERATOR_CONTEXT_FUNCTIONS;
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USE_DISPATCH_HELPER;
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SquareRootDivideOp(const OperatorDef& operator_def, Workspace* ws)
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: Operator<Context>(operator_def, ws) {}
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bool RunOnDevice() override {
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return DispatchHelper<TensorTypes<float>>::call(this, Input(DATA));
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}
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private:
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template <typename TData>
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bool DoRunWithType() {
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return DispatchHelper<TensorTypes2<float, int32_t, int64_t>, TData>::call(
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this, Input(SCALE));
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}
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template <typename TData, typename TScale>
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bool DoRunWithType2() {
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auto& data = Input(DATA);
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auto& scale = Input(SCALE);
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auto* Y = Output(0);
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Y->ResizeLike(data);
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size_t batchSize = data.dim(0);
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size_t exampleSize = data.size_from_dim(1);
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CAFFE_ENFORCE(batchSize == scale.dim(0), batchSize, " != ", scale.dim(0));
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auto* scalePtr = scale.template data<TScale>();
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auto* dataPtr = data.template data<TData>();
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auto* yPtr = Y->template mutable_data<TData>();
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for (auto i = 0; i < batchSize; ++i) {
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auto scale = scalePtr[i];
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CAFFE_ENFORCE(scale >= 0, scale, " < 0");
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auto multiplier = scale == 0 ? 1.0 : 1 / std::sqrt(scale);
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math::Scale<float, TData, Context>(
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exampleSize,
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multiplier,
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dataPtr + i * exampleSize,
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yPtr + i * exampleSize,
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&context_);
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}
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return true;
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}
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INPUT_TAGS(DATA, SCALE);
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};
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} // namespace caffe2
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#endif // CAFFE2_OPERATORS_SQUARE_ROOT_DIVIDE_OP_H_
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