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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
95 lines
2.6 KiB
C++
95 lines
2.6 KiB
C++
#include "caffe2/operators/swish_op.h"
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#include <string>
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#include <vector>
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#include "caffe2/core/types.h"
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#include "caffe2/utils/eigen_utils.h"
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#include "caffe2/utils/math.h"
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namespace caffe2 {
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template <>
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template <typename T>
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bool SwishFunctor<CPUContext>::
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operator()(const int N, const T* X, T* Y, CPUContext* /* context */) const {
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ConstEigenVectorArrayMap<T> X_arr(X, N);
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EigenVectorArrayMap<T>(Y, N) = X_arr / (T(1) + (-X_arr).exp());
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return true;
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}
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template <>
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template <typename T>
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bool SwishGradientOp<CPUContext>::DoRunWithType() {
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auto& Xin = Input(X);
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auto& Yin = Input(Y);
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auto& DYin = Input(DY);
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CAFFE_ENFORCE_EQ(Xin.numel(), Yin.numel());
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CAFFE_ENFORCE_EQ(DYin.numel(), Yin.numel());
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auto* DXout = Output(DX, Yin.sizes(), at::dtype<float>());
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const float* Xdata = Xin.template data<float>();
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const float* Ydata = Yin.template data<float>();
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const float* dYdata = DYin.template data<float>();
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float* dXdata = DXout->template mutable_data<float>();
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EigenVectorArrayMap<float> dXvec(dXdata, DXout->numel());
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ConstEigenVectorArrayMap<float> Xvec(Xdata, Xin.numel());
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ConstEigenVectorArrayMap<float> Yvec(Ydata, Yin.numel());
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ConstEigenVectorArrayMap<float> dYvec(dYdata, DYin.numel());
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// dx = dy * (y + sigmoid(x)*(1-y))
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dXvec = dYvec * (Yvec + (T(1) / (T(1) + (-Xvec).exp())) * (T(1) - Yvec));
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return true;
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}
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REGISTER_CPU_OPERATOR(
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Swish,
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UnaryElementwiseOp<
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TensorTypes<float>,
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CPUContext,
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SwishFunctor<CPUContext>>);
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REGISTER_CPU_OPERATOR(SwishGradient, SwishGradientOp<CPUContext>);
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// Input: X, output: Y
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OPERATOR_SCHEMA(Swish)
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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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Swish takes one input data (Tensor) and produces one output data
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(Tensor) where the swish function, y = x / (1 + exp(-x)), is applied to the
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tensor elementwise.
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)DOC")
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.Input(0, "X", "1D input tensor")
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.Output(0, "Y", "1D output tensor");
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// Input: X, Y, dY, output: dX
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OPERATOR_SCHEMA(SwishGradient)
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.NumInputs(3)
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.NumOutputs(1)
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.AllowInplace({{2, 0}})
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.SetDoc(R"DOC(
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SwishGradient takes X, Y and dY and uses this to update dX according to the
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chain rule and derivatives of the swish function.
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)DOC");
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namespace {
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class GetSwishGradient : 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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"SwishGradient",
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"",
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std::vector<std::string>{I(0), O(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(Swish, GetSwishGradient);
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} // namespace caffe2
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