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1433160a36
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/66742 Modified loops in files under fbsource/fbcode/caffe2/ from the format `for(TYPE var=x0;var<x_max;x++)` to the format `for(const auto var: irange(xmax))` This was achieved by running r-barnes's loop upgrader script (D28874212) with some modification to exclude all files under /torch/jit and a number of reversions or unused variable suppression warnings added by hand. Test Plan: Sandcastle Reviewed By: malfet Differential Revision: D31705366 fbshipit-source-id: be58222426c192406a7f93c21582c3f6f2082401
106 lines
3.0 KiB
C++
106 lines
3.0 KiB
C++
#ifndef CAFFE2_OPERATORS_NORMALIZE_OP_H_
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#define CAFFE2_OPERATORS_NORMALIZE_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/eigen_utils.h"
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#include "caffe2/utils/math.h"
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#define KEPS 1e-12f
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namespace caffe2 {
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template <typename T, class Context>
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class NormalizeOp final : public Operator<Context> {
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public:
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USE_OPERATOR_CONTEXT_FUNCTIONS;
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template <class... Args>
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explicit NormalizeOp(Args&&... args)
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: Operator<Context>(std::forward<Args>(args)...) {}
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bool RunOnDevice() override {
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const auto& x = Input(0);
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const auto* xData = x.template data<T>();
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auto* y = Output(0, x.sizes(), at::dtype<T>());
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auto* yData = y->template mutable_data<T>();
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const auto canonical_axis = x.canonical_axis_index(
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this->template GetSingleArgument<int>("axis", -1));
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const int64_t m = x.dim(canonical_axis);
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const size_t n = x.numel() / m;
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const size_t sf = x.size_from_dim(canonical_axis + 1);
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DoNormalize(xData, yData, m, n, sf);
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return true;
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}
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private:
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const T kEps_ = KEPS;
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void DoNormalize(
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const T* xData,
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T* yData,
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const int m,
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const int n,
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const int sf) {
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using InnerStride = Eigen::InnerStride<Eigen::Dynamic>;
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using StridedVec =
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Eigen::Map<Eigen::Matrix<T, 1, Eigen::Dynamic>, 0, InnerStride>;
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using ConstStridedVec =
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Eigen::Map<const Eigen::Matrix<T, 1, Eigen::Dynamic>, 0, InnerStride>;
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for (const auto i : c10::irange(n)) {
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auto base = (i / sf) * sf * m + (i % sf);
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ConstStridedVec xVec(xData + base, 1, m, InnerStride(sf));
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auto norm = xVec.template lpNorm<2>();
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norm = std::max(norm, kEps_);
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StridedVec yVec(yData + base, 1, m, InnerStride(sf));
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yVec = xVec / norm;
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}
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}
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};
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template <typename T, class Context>
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class NormalizeGradientOp final : public Operator<Context> {
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public:
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USE_OPERATOR_CONTEXT_FUNCTIONS;
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template <class... Args>
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explicit NormalizeGradientOp(Args&&... args)
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: Operator<Context>(std::forward<Args>(args)...) {}
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bool RunOnDevice() override {
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const auto& x = Input(0);
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const auto& gOut = Input(GRAD_OUT);
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auto* gIn = Output(GRAD_IN, gOut.sizes(), at::dtype<T>());
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const auto* xData = x.template data<T>();
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const auto* gOutData = gOut.template data<T>();
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auto* gInData = gIn->template mutable_data<T>();
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const auto canonical_axis = x.canonical_axis_index(
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this->template GetSingleArgument<int>("axis", -1));
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const int m = x.dim32(canonical_axis);
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const int n = x.numel() / m;
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const int sf = x.size_from_dim(canonical_axis + 1);
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DoNormalize(xData, gOutData, gInData, m, n, sf);
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return true;
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}
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private:
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const T kEps_ = KEPS;
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void DoNormalize(
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const T* xData,
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const T* gOutData,
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T* gInData,
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const int m,
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const int n,
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const int sf);
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INPUT_TAGS(INPUT, GRAD_OUT);
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OUTPUT_TAGS(GRAD_IN);
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};
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} // namespace caffe2
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#endif // CAFFE2_OPERATORS_NORMALIZE_OP_H_
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