pytorch/caffe2/operators/byte_weight_dequant_op.h
Richard Barnes 1433160a36 use irange for loops 6 (#66742)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66742

Modified loops in files under fbsource/fbcode/caffe2/ from the format

`for(TYPE var=x0;var<x_max;x++)`

to the format

`for(const auto var: irange(xmax))`

This was achieved by running r-barnes's loop upgrader script (D28874212) with some modification to exclude all files under /torch/jit and a number of reversions or unused variable suppression warnings added by hand.

Test Plan: Sandcastle

Reviewed By: malfet

Differential Revision: D31705366

fbshipit-source-id: be58222426c192406a7f93c21582c3f6f2082401
2021-12-07 16:07:50 -08:00

56 lines
1.7 KiB
C++

#ifndef CAFFE2_OPERATORS_BYTE_WEIGHT_DEQUANT_OP_H_
#define CAFFE2_OPERATORS_BYTE_WEIGHT_DEQUANT_OP_H_
#include "caffe2/core/operator.h"
#include "caffe2/utils/eigen_utils.h"
#include "caffe2/utils/math.h"
namespace caffe2 {
template <typename Context>
class ByteWeightDequantOp : public Operator<Context> {
public:
ByteWeightDequantOp(const OperatorDef& operator_def, Workspace* ws)
: Operator<Context>(operator_def, ws),
min_(this->template GetSingleArgument<float>("min", -3)),
max_(this->template GetSingleArgument<float>("max", 3)),
shape_(this->template GetRepeatedArgument<int64_t>("shape")) {}
USE_OPERATOR_FUNCTIONS(Context);
using Operator<Context>::Operator;
bool RunOnDevice() override {
const auto& WI = Input(0);
auto* Y = Output(0, shape_, at::dtype<float>());
float bin_interval = (max_ - min_) / 255.0;
int total = 1;
for (const auto i : c10::irange(0U, shape_.size())) {
total *= Y->size(i);
}
const uint8_t* Xdata;
if (WI.template IsType<uint8_t>()) {
CAFFE_ENFORCE(total, WI.nbytes());
Xdata = WI.template data<uint8_t>();
} else {
CAFFE_ENFORCE(total, WI.template data<std::string>()[0].size());
Xdata = reinterpret_cast<const uint8_t*>(
WI.template data<std::string>()[0].c_str());
}
auto* Ydata = Y->template mutable_data<float>();
ConstEigenVectorMap<uint8_t> index(&Xdata[0], total);
EigenVectorMap<float> weights(&Ydata[0], total);
weights = (index.cast<float>().array() * bin_interval) + min_;
return true;
}
private:
float min_;
float max_;
std::vector<int64_t> shape_;
};
} // namespace caffe2
#endif // CAFFE2_OPERATORS_BYTE_WEIGHT_DEQUANT_OP_H_