blob: 9513ae89a4553218516c5e52f538ba2eebb67bdb [file] [log] [blame]
/**
* Copyright (c) 2016-present, Facebook, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "caffe2/operators/math_ops.h"
#include "caffe2/utils/math.h"
namespace caffe2 {
struct SqrCPUFunctor {
template <typename T>
inline void
operator()(const int n, const T* x, T* y, CPUContext* device_context) {
math::Sqr<T, CPUContext>(n, x, y, device_context);
}
};
REGISTER_CPU_OPERATOR(
Sqr,
UnaryElementwiseOp<TensorTypes<float>, CPUContext, SqrCPUFunctor>);
OPERATOR_SCHEMA(Sqr)
.NumInputs(1)
.NumOutputs(1)
.AllowInplace({{0, 0}})
.IdenticalTypeAndShape()
.SetDoc("Square (x^2) the elements of the input")
.Input(0, "input", "Input tensor")
.Output(0, "output", "Squared elements of the input");
class GetSqrGradient : public GradientMakerBase {
using GradientMakerBase::GradientMakerBase;
vector<OperatorDef> GetGradientDefs() override {
Argument scale_arg;
scale_arg.set_name("scale");
scale_arg.set_f(2.0);
return vector<OperatorDef>{CreateOperatorDef(
"Scale",
"",
std::vector<string>{GO(0)},
std::vector<string>{GO(0)},
std::vector<Argument>{scale_arg}),
CreateOperatorDef(
"Mul",
"",
std::vector<string>{GO(0), I(0)},
std::vector<string>{GI(0)})};
}
};
REGISTER_GRADIENT(Sqr, GetSqrGradient);
struct SignCPUFunctor {
template <typename T>
inline void
operator()(const int n, const T* x, T* y, CPUContext* device_context) {
for (int i = 0; i < n; ++i) {
y[i] = (-T(1) * (x[i] < 0)) + (x[i] > 0);
}
}
};
REGISTER_CPU_OPERATOR(
Sign,
UnaryElementwiseOp<TensorTypes<float>, CPUContext, SignCPUFunctor>);
OPERATOR_SCHEMA(Sign)
.NumInputs(1)
.NumOutputs(1)
.SetDoc("Computes sign for each element of the input: -1, 0 or 1.")
.IdenticalTypeAndShape();
SHOULD_NOT_DO_GRADIENT(Sign);
REGISTER_CPU_OPERATOR(
Pow,
UnaryElementwiseWithArgsOp<TensorTypes<float>, CPUContext, PowFunctor>);
OPERATOR_SCHEMA(Pow)
.NumInputs(1)
.NumOutputs(1)
.Arg("exponent", "The exponent of the power function.")
.AllowInplace({{0, 0}})
.IdenticalTypeAndShape()
.SetDoc(R"DOC(
Pow takes input data (Tensor<T>) and an argument exponent, and
produces one output data (Tensor<T>) where the function `f(x) = x^exponent`,
is applied to the data tensor elementwise.
)DOC")
.Input(0, "X", "Input tensor of any shape")
.Output(0, "Y", "Output tensor (same size as X)");
class GetPowGradient : public GradientMakerBase {
using GradientMakerBase::GradientMakerBase;
vector<OperatorDef> GetGradientDefs() override {
ArgumentHelper arg_helper(def_);
float exponent = arg_helper.GetSingleArgument<float>("exponent", 0.0);
Argument scale_arg;
scale_arg.set_name("scale");
scale_arg.set_f(exponent);
Argument pow_arg;
pow_arg.set_name("exponent");
pow_arg.set_f(exponent - 1);
return vector<OperatorDef>{CreateOperatorDef(
"Pow",
"",
std::vector<string>{I(0)},
std::vector<string>{GI(0)},
std::vector<Argument>{pow_arg}),
CreateOperatorDef(
"Mul",
"",
std::vector<string>{GI(0), GO(0)},
std::vector<string>{GI(0)}),
CreateOperatorDef(
"Scale",
"",
std::vector<string>{GI(0)},
std::vector<string>{GI(0)},
std::vector<Argument>{scale_arg})};
}
virtual bool CopyArguments() const override {
return false;
}
};
REGISTER_GRADIENT(Pow, GetPowGradient);
} // namespace caffe2