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