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#include "caffe2/operators/softplus_op.h"
#include "caffe2/utils/eigen_utils.h"
#include "caffe2/utils/math.h"
namespace caffe2 {
template <>
bool SoftplusOp<float, CPUContext>::RunOnDevice() {
auto& X = Input(0);
auto* Y = Output(0, X.sizes(), at::dtype<float>());
EigenVectorMap<float>(Y->template mutable_data<float>(), X.numel()) =
(ConstEigenVectorMap<float>(X.data<float>(), X.numel()).array().exp() +
1.0f)
.log();
return true;
}
template <>
bool SoftplusGradientOp<float, CPUContext>::RunOnDevice() {
auto& Y = Input(0);
auto& dY = Input(1);
TORCH_DCHECK_EQ(dY.numel(), Y.numel());
auto* dX = Output(0, Y.sizes(), at::dtype<float>());
const float* Ydata = Y.data<float>();
const float* dYdata = dY.data<float>();
float* dXdata = dX->template mutable_data<float>();
EigenVectorArrayMap<float> dXvec(dXdata, dX->numel());
ConstEigenVectorArrayMap<float> Yvec(Ydata, Y.numel());
ConstEigenVectorArrayMap<float> dYvec(dYdata, dY.numel());
dXvec = dYvec * (1.0 - (-Yvec).exp());
return true;
}
REGISTER_CPU_OPERATOR(Softplus, SoftplusOp<float, CPUContext>);
REGISTER_CPU_OPERATOR(SoftplusGradient, SoftplusGradientOp<float, CPUContext>);
// Input: X, output: Y
OPERATOR_SCHEMA(Softplus)
.NumInputs(1)
.NumOutputs(1)
.AllowInplace({{0, 0}})
.IdenticalTypeAndShape()
.SetDoc(R"DOC(
Softplus takes one input data tensor $X$ and produces one output data tensor $Y,$ where the softplus function, $y = ln(e^x + 1)$, is applied to $X$ elementwise.
Github Links:
- https://github.com/pytorch/pytorch/blob/master/caffe2/operators/softplus_op.h
- https://github.com/pytorch/pytorch/blob/master/caffe2/operators/softplus_op.cc
<details>
<summary> <b>Example</b> </summary>
**Code**
```
workspace.ResetWorkspace()
op = core.CreateOperator(
"Softplus",
["X"],
["Y"],
)
workspace.FeedBlob("X", np.random.randn(3, 3).astype(np.float32))
print("X:\n", workspace.FetchBlob("X"), "\n")
workspace.RunOperatorOnce(op)
print("Y:\n", workspace.FetchBlob("Y"))
```
**Result**
```
X:
[[-0.5380011 0.65190786 0.55673236]
[-0.16272168 0.5451048 0.30880353]
[-0.76606876 -0.6238556 -0.40444514]]
Y:
[[0.4598992 1.0713093 1.0097669 ]
[0.61509246 1.0023911 0.8594219 ]
[0.38174385 0.42909983 0.5112337 ]]
```
</details>
)DOC")
.Input(0, "X", "Input data blob to be operated on.")
.Output(0, "Y", "Output data blob with same shape as input.")
.InheritOnnxSchema();
// Input: Y, dY, output: dX
OPERATOR_SCHEMA(SoftplusGradient)
.NumInputs(2)
.NumOutputs(1)
.AllowInplace({{1, 0}});
class GetSoftplusGradient : public GradientMakerBase {
using GradientMakerBase::GradientMakerBase;
vector<OperatorDef> GetGradientDefs() override {
return SingleGradientDef(
"SoftplusGradient",
"",
vector<string>{O(0), GO(0)},
vector<string>{GI(0)});
}
};
REGISTER_GRADIENT(Softplus, GetSoftplusGradient);
} // namespace caffe2
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