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130 lines
4.3 KiB
130 lines
4.3 KiB
/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef QUANTIZER_UTIL_H
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#define QUANTIZER_UTIL_H
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#include <memory>
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#include <string>
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#include <cmath>
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#include <array>
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#include "tools/converter/quantizer/quantizer.h"
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#include "src/ops/primitive_c.h"
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#include "include/errorcode.h"
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#include "ir/func_graph.h"
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#include "ir/anf.h"
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#include "include/model.h"
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#include "base/base.h"
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#include "ir/primitive.h"
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#include "abstract/dshape.h"
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namespace mindspore {
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namespace lite {
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namespace quant {
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static constexpr size_t UINT8_QUANTIZATION = 8;
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/**
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* 1. when op's weight size > mWeightSize just skip
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* 2. only do conv/deconv/convdepthwise/deconvdepthwise/mul/matmul/batchmatmul quantization
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* 3. when conv/deconv/convdepthwise/deconvdepthwise ops' weight channel size > covWeightQuantChannelThreshold just skip
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* */
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class QuantStrategy {
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public:
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explicit QuantStrategy(size_t weightSize, size_t covWeightQuantChannelThreshold = 16);
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~QuantStrategy() = default;
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bool CanConvOpQuantized(const CNodePtr &node) const;
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bool CanMulOpQuantized(const CNodePtr &node) const;
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bool CanOpPostQuantized(AnfNodePtr &node) const;
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private:
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size_t mWeightSize;
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size_t mConvWeightQuantChannelThreshold;
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static const std::array<std::string, 4> mConvTypes;
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static const std::array<std::string, 4> mMulTypes;
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};
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STATUS CalQuantizationParams(schema::QuantParamT *quantParam, double mMin, double mMax, bool narrowRange, int quant_max,
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int quant_min, int num_bits);
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STATUS CalQuantizationParams(schema::QuantParamT *quantParam, double mMin, double mMax, bool narrowRange = false,
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int numBits = UINT8_QUANTIZATION);
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template <typename T>
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T QuantizeData(const float originData, const schema::QuantParamT *quantParam) {
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MS_ASSERT(quantParam != nullptr);
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MS_ASSERT(quantParam->inited);
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const auto scale = quantParam->scale;
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const auto zeroPoint = quantParam->zeroPoint;
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const auto numBit = quantParam->numBits;
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const auto narrowRange = quantParam->narrowRange;
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const double maxLimit = static_cast<float>((1 << (unsigned int)numBit) - 1 - zeroPoint) * scale;
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double minLimit;
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if (narrowRange) {
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minLimit = static_cast<float>(1 - zeroPoint) * scale;
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} else {
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minLimit = static_cast<float>(0 - zeroPoint) * scale;
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}
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return [maxLimit, minLimit, zeroPoint, scale, narrowRange, originData] {
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double tmp = 0.0f;
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if (originData > maxLimit) {
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tmp = maxLimit;
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} else if (originData < minLimit) {
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tmp = minLimit;
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} else {
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tmp = originData;
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}
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auto quantData = static_cast<T>(std::round(tmp / scale + zeroPoint));
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if (quantData == 0 && narrowRange) {
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quantData++;
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}
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return quantData;
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}();
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}
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template <typename T>
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T QuantizeData(float originData, const schema::QuantParamT &quantParam, int quant_max, int quant_min) {
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MS_ASSERT(quantParam != nullptr);
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MS_ASSERT(quantParam->inited);
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const auto scale = quantParam.scale;
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const int zeroPoint = quantParam.zeroPoint;
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const auto narrowRange = quantParam.narrowRange;
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const int maxLimit = quant_max;
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const int minLimit = quant_min;
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return [maxLimit, minLimit, zeroPoint, scale, narrowRange, originData] {
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int quant_data = std::round(originData / scale + zeroPoint);
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if (quant_data > maxLimit) {
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quant_data = maxLimit;
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} else if (quant_data < minLimit) {
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quant_data = minLimit;
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}
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return static_cast<T>(quant_data);
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}();
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}
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STATUS QuantFilter(ParamValueLitePtr weight, std::shared_ptr<PrimitiveC> primitive_c, QuantType quantType,
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int quant_max, int quant_min, size_t bitNum = UINT8_QUANTIZATION, bool per_channel = false,
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bool depth_wise = false);
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STATUS PostBitPack(float *weights, size_t shapeSize, size_t bitNum = UINT8_QUANTIZATION);
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} // namespace quant
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} // namespace lite
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} // namespace mindspore
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#endif
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