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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
38 lines
1004 B
C++
38 lines
1004 B
C++
#include "caffe2/operators/quantized/int8_relu_op.h"
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namespace caffe2 {
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namespace {
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OpSchema::Cost CostInferenceForRelu(
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const OperatorDef& def,
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const vector<TensorShape>& in) {
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struct OpSchema::Cost cost = PointwiseCostInference<0>(def, in);
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cost.params_bytes = 0;
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return cost;
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}
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} // namespace
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REGISTER_CPU_OPERATOR(Int8Relu, int8::Int8ReluOp);
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// Input: X, output: Y
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OPERATOR_SCHEMA(Int8Relu)
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.NumInputs(1)
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.NumOutputs(1)
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.Arg("Y_scale", "Output tensor quantization scale")
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.Arg("Y_zero_point", "Output tensor quantization offset")
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.AllowInplace({{0, 0}})
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.CostInferenceFunction(CostInferenceForRelu)
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.IdenticalTypeAndShape()
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.SetDoc(R"DOC(
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Relu takes one input data (Tensor<T>) and produces one output data
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(Tensor<T>) where the rectified linear function, y = max(0, x), is applied to
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the tensor elementwise.
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)DOC")
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.Input(0, "X", "1D input tensor")
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.Output(0, "Y", "1D input tensor")
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.InheritOnnxSchema("Relu");
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} // namespace caffe2
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