blob: 4c44ce6d2420f1ddb8b16bb36e6c7b56341458fe [file] [log] [blame]
#include "caffe2/operators/asin_op.h"
#include "caffe2/utils/eigen_utils.h"
#include <algorithm>
#include <functional>
namespace caffe2 {
template <>
template <typename T>
bool AsinGradientFunctor<CPUContext>::Forward(
const std::vector<int>& X_dims,
const std::vector<int>& /* dY_dims */,
const T* X,
const T* dY,
T* dX,
CPUContext* /* context */) const {
const int size = std::accumulate(
// NOLINTNEXTLINE(modernize-use-transparent-functors)
X_dims.cbegin(), X_dims.cend(), 1, std::multiplies<int>());
ConstEigenVectorArrayMap<T> dY_arr(dY, size);
ConstEigenVectorArrayMap<T> X_arr(X, size);
EigenVectorMap<T>(dX, size) = dY_arr * (T(1) - X_arr.square()).rsqrt();
return true;
}
REGISTER_CPU_OPERATOR(
Asin,
UnaryElementwiseOp<
TensorTypes<float>,
CPUContext,
AsinFunctor<CPUContext>>);
REGISTER_CPU_OPERATOR(
AsinGradient,
BinaryElementwiseOp<
TensorTypes<float>,
CPUContext,
AsinGradientFunctor<CPUContext>>);
OPERATOR_SCHEMA(Asin)
.NumInputs(1)
.NumOutputs(1)
.IdenticalTypeAndShape()
.SetDoc(R"DOC(
Calculates the arcsine of the given input tensor, element-wise.
)DOC")
.Input(0, "input", "Input tensor")
.Output(
0,
"output",
"The arcsine of the input tensor computed element-wise");
OPERATOR_SCHEMA(AsinGradient)
.NumInputs(2)
.NumOutputs(1)
.IdenticalTypeAndShape();
namespace {
class GetAsinGradient : public GradientMakerBase {
using GradientMakerBase::GradientMakerBase;
std::vector<OperatorDef> GetGradientDefs() override {
return SingleGradientDef(
"AsinGradient",
"",
std::vector<std::string>{I(0), GO(0)},
std::vector<std::string>{GI(0)});
}
};
} // namespace
REGISTER_GRADIENT(Asin, GetAsinGradient);
} // namespace caffe2