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#include "caffe2/operators/elementwise_op.h"
#include "caffe2/utils/math.h"
namespace caffe2 {
struct SigmoidCPUFunctor {
template <typename T>
inline void
operator()(const int n, const T* x, T* y, CPUContext* /*device_context*/) {
ConstEigenVectorArrayMap<T> xM(x, n);
EigenVectorArrayMap<T>(y, n) = 1. / (1. + (-xM).exp());
}
};
struct SigmoidGradientCPUFunctor {
template <typename T>
inline void Run(
const int n,
const T* y,
const T* dy,
T* dx,
CPUContext* /*device_context*/) {
ConstEigenVectorArrayMap<T> yM(y, n), dyM(dy, n);
EigenVectorArrayMap<T>(dx, n) = dyM * yM * (1. - yM);
}
};
REGISTER_CPU_OPERATOR(
Sigmoid, UnaryElementwiseOp<
TensorTypes<float>, CPUContext, SigmoidCPUFunctor>);
REGISTER_CPU_OPERATOR(
SigmoidGradient,
BinaryElementwiseOp<
TensorTypes<float>,
CPUContext,
WithoutBroadcast<SigmoidGradientCPUFunctor>>);
// Input: X, output: Y
OPERATOR_SCHEMA(Sigmoid)
.NumInputs(1)
.NumOutputs(1)
.AllowInplace({{0, 0}})
.IdenticalTypeAndShape()
.SetDoc(R"DOC(
Sigmoid takes one input data (Tensor<T>) and produces one output data
(Tensor<T>) where the sigmoid function, y = 1 / (1 + exp(-x)), is applied to the
tensor elementwise.
)DOC")
.Input(0, "X", "1D input tensor")
.Output(0, "Y", "1D output tensor")
.InheritOnnxSchema("Sigmoid");
// Input: Y, dY, output: dX
OPERATOR_SCHEMA(SigmoidGradient)
.NumInputs(2)
.NumOutputs(1)
.AllowInplace({{1, 0}})
.SetDoc(R"DOC(
SigmoidGradient takes both Y and dY and uses this to update dX according to the
chain rule and derivatives of the sigmoid function.
)DOC");
class GetSigmoidGradient : public GradientMakerBase {
using GradientMakerBase::GradientMakerBase;
vector<OperatorDef> GetGradientDefs() override {
return SingleGradientDef(
"SigmoidGradient", "",
vector<string>{O(0), GO(0)},
vector<string>{GI(0)});
}
};
REGISTER_GRADIENT(Sigmoid, GetSigmoidGradient);
} // namespace caffe2
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