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1c2ed4eb23
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/13942 Codemod generated with clangr shard mode, 25 files per diff, motivation: https://github.com/pytorch/pytorch/pull/12407 Reviewed By: smessmer Differential Revision: D13054770 fbshipit-source-id: a9e86e5dfcb4f7cebf5243e1d359fad064561bed
56 lines
1.7 KiB
C++
56 lines
1.7 KiB
C++
#ifndef CAFFE2_OPERATORS_BYTE_WEIGHT_DEQUANT_OP_H_
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#define CAFFE2_OPERATORS_BYTE_WEIGHT_DEQUANT_OP_H_
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#include "caffe2/core/operator.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 <typename Context>
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class ByteWeightDequantOp : public Operator<Context> {
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public:
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ByteWeightDequantOp(const OperatorDef& operator_def, Workspace* ws)
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: Operator<Context>(operator_def, ws),
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min_(this->template GetSingleArgument<float>("min", -3)),
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max_(this->template GetSingleArgument<float>("max", 3)),
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shape_(this->template GetRepeatedArgument<int64_t>("shape")) {}
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USE_OPERATOR_FUNCTIONS(Context);
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using Operator<Context>::Operator;
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bool RunOnDevice() override {
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const auto& WI = Input(0);
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auto* Y = Output(0, shape_, at::dtype<float>());
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float bin_interval = (max_ - min_) / 255.0;
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int total = 1;
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for (int i = 0; i < shape_.size(); i++) {
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total *= Y->size(i);
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}
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const uint8_t* Xdata;
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if (WI.template IsType<uint8_t>()) {
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CAFFE_ENFORCE(total, WI.nbytes());
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Xdata = WI.template data<uint8_t>();
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} else {
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CAFFE_ENFORCE(total, WI.template data<std::string>()[0].size());
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Xdata = reinterpret_cast<const uint8_t*>(
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WI.template data<std::string>()[0].c_str());
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}
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auto* Ydata = Y->template mutable_data<float>();
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ConstEigenVectorMap<uint8_t> index(&Xdata[0], total);
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EigenVectorMap<float> weights(&Ydata[0], total);
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weights = (index.cast<float>().array() * bin_interval) + min_;
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return true;
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}
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private:
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float min_;
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float max_;
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std::vector<int64_t> shape_;
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};
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} // namespace caffe2
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#endif // CAFFE2_OPERATORS_BYTE_WEIGHT_DEQUANT_OP_H_
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