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// Copyright 2004-present Facebook. All Rights Reserved.
#pragma once
#include "caffe2/quantization/server/caffe2_dnnlowp_utils.h"
#include "caffe2/quantization/server/dnnlowp.h"
#include "caffe2/quantization/server/int8_gen_quant_params.h"
#include <glog/logging.h>
namespace caffe2 {
using namespace std;
using dnnlowp::TensorQuantizationParams;
template <class Context, class Engine = DefaultEngine>
class Int8GenQuantParamsMinMaxOp final : public Operator<Context> {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
Int8GenQuantParamsMinMaxOp(const OperatorDef& operator_def, Workspace* ws)
: Operator<Context>(operator_def, ws) {}
bool RunOnDevice() override {
// Generate Int8 quant params based on the input data (last N samples of the
// activations) and the quant scheme
const float min =
OperatorBase::Input<Tensor>(0, CPU).template data<float>()[0];
const float max =
OperatorBase::Input<Tensor>(1, CPU).template data<float>()[0];
bool preserve_sparsity = false;
if (InputSize() == 3){
const auto* quant_scheme =
this->template Input<unique_ptr<Int8QuantSchemeBlob>>(2).get();
preserve_sparsity = quant_scheme->preserve_sparsity_;
}
dnnlowp::QuantizationFactory* qfactory =
dnnlowp::QuantizationFactory::GetDefaultInstance();
TensorQuantizationParams qparam = qfactory->ChooseQuantizationParams(
min,
max,
8,
preserve_sparsity);
auto* output_qparam =
this->template Output<unique_ptr<Int8QuantParamsBlob>>(0);
output_qparam->reset(
new Int8QuantParamsBlob(qparam.scale, qparam.zero_point));
LOG_EVERY_N(INFO, 1) << "scale and bias are " << qparam.scale << "," << qparam.zero_point;
return true;
}
}; // class Int8GenQuantParamsOp
} // namespace caffe2
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