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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"
namespace caffe2 {
using namespace std;
using dnnlowp::TensorQuantizationParams;
struct Int8QuantSchemeBlob {
public:
Int8QuantSchemeBlob(std::string quantization_kind, bool preserve_sparsity)
: quantization_kind_(quantization_kind),
preserve_sparsity_(preserve_sparsity) {}
std::string quantization_kind_;
bool preserve_sparsity_;
};
struct Int8QuantParamsBlob {
public:
Int8QuantParamsBlob(float scale, int zero_point) {
qparam.scale = scale;
qparam.zero_point = zero_point;
}
dnnlowp::TensorQuantizationParams qparam;
};
template <class Context, class Engine = DefaultEngine>
class Int8GenQuantParamsOp final : public Operator<Context> {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
Int8GenQuantParamsOp(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 auto& input_data = Input(0);
const auto* quant_scheme =
this->template Input<unique_ptr<Int8QuantSchemeBlob>>(1).get();
CAFFE_ENFORCE(input_data.dim() > 0);
CAFFE_ENFORCE(quant_scheme);
std::string quant_kind = quant_scheme->quantization_kind_;
bool preserve_sparsity = quant_scheme->preserve_sparsity_;
dnnlowp::QuantizationFactory* qfactory =
dnnlowp::QuantizationFactory::GetDefaultInstance();
TensorQuantizationParams qparam = qfactory->ChooseQuantizationParams(
input_data.template data<float>(),
input_data.numel(),
dnnlowp::StringToKind(quant_kind),
8,
preserve_sparsity);
auto* output_qparam =
this->template Output<unique_ptr<Int8QuantParamsBlob>>(0);
output_qparam->reset(
new Int8QuantParamsBlob(qparam.scale, qparam.zero_point));
return true;
}
}; // class Int8GenQuantParamsOp
} // namespace caffe2
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