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https://github.com/zebrajr/pytorch.git
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a9b0a921d5
Summary: As GoogleTest `TEST` macro is non-compliant with it as well as `DEFINE_DISPATCH` All changes but the ones to `.clang-tidy` are generated using following script: ``` for i in `find . -type f -iname "*.c*" -or -iname "*.h"|xargs grep cppcoreguidelines-avoid-non-const-global-variables|cut -f1 -d:|sort|uniq`; do sed -i "/\/\/ NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)/d" $i; done ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/62008 Reviewed By: driazati, r-barnes Differential Revision: D29838584 Pulled By: malfet fbshipit-source-id: 1b2f8602c945bd4ce50a9bfdd204755556e31d13
76 lines
2.0 KiB
C++
76 lines
2.0 KiB
C++
#include "caffe2/operators/log1p_op.h"
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#include "caffe2/utils/eigen_utils.h"
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#include <algorithm>
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#include <functional>
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namespace caffe2 {
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template <>
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template <typename T>
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bool Log1pGradientFunctor<CPUContext>::Forward(
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const std::vector<int>& X_dims,
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const std::vector<int>& /* dY_dims */,
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const T* X,
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const T* dY,
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T* dX,
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CPUContext* /* context */) const {
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const int size = std::accumulate(
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// NOLINTNEXTLINE(modernize-use-transparent-functors)
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X_dims.cbegin(), X_dims.cend(), 1, std::multiplies<int>());
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ConstEigenVectorArrayMap<T> dY_arr(dY, size);
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ConstEigenVectorArrayMap<T> X_arr(X, size);
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EigenVectorMap<T>(dX, size) = dY_arr / (T(1) + X_arr);
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return true;
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}
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REGISTER_CPU_OPERATOR(
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Log1p,
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UnaryElementwiseOp<TensorTypes<float>, CPUContext, Log1pFunctor<CPUContext>>);
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REGISTER_CPU_OPERATOR(
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Log1pGradient,
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BinaryElementwiseOp<
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TensorTypes<float>,
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CPUContext,
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Log1pGradientFunctor<CPUContext>>);
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OPERATOR_SCHEMA(Log1p)
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.NumInputs(1)
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.NumOutputs(1)
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.IdenticalTypeAndShape()
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.SetDoc(R"DOC(
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Calculates Log1p of the given input tensor element-wise. This
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operation can be done in an in-place fashion too, by providing the same input
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and output blobs.
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Github Link:
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- https://github.com/pytorch/pytorch/blob/master/caffe2/operators/log1p_op.cc
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)DOC")
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.Input(0, "input", "Input data blob to be operated on.")
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.Output(0, "output", "Output data blob with same shape as input")
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.InheritOnnxSchema();
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OPERATOR_SCHEMA(Log1pGradient)
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.NumInputs(2)
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.NumOutputs(1)
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.IdenticalTypeAndShapeOfInput(0);
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namespace {
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class GetLog1pGradient : public GradientMakerBase {
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using GradientMakerBase::GradientMakerBase;
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std::vector<OperatorDef> GetGradientDefs() override {
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return SingleGradientDef(
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"Log1pGradient",
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"",
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std::vector<std::string>{I(0), GO(0)},
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std::vector<std::string>{GI(0)});
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}
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};
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} // namespace
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REGISTER_GRADIENT(Log1p, GetLog1pGradient);
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} // namespace caffe2
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