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@ -98,7 +98,7 @@ bool QuantStrategy::CanOpPostQuantized(AnfNodePtr &node) const {
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static const std::vector<schema::PrimitiveType> uint8OpList = {
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schema::PrimitiveType_Nchw2Nhwc, schema::PrimitiveType_Nhwc2Nchw, schema::PrimitiveType_Conv2D,
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schema::PrimitiveType_DepthwiseConv2D, schema::PrimitiveType_Add, schema::PrimitiveType_Pooling,
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schema::PrimitiveType_Concat, schema::PrimitiveType_SoftMax, schema::PrimitiveType_Reshape,
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schema::PrimitiveType_Concat, /*schema::PrimitiveType_SoftMax,*/ schema::PrimitiveType_Reshape,
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schema::PrimitiveType_Activation};
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return IsContain(uint8OpList, type);
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}
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@ -242,64 +242,122 @@ STATUS CalQuantizationParams(std::unique_ptr<AnfQuantParam> &quantParam, double
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return RET_OK;
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}
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STATUS QuantFilter(ParamValueLitePtr &weightPtr, QuantType quantType, int quant_max, int quant_min, size_t bitNum) {
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STATUS QuantFilter(ParamValueLitePtr &weightPtr, QuantType quantType, int quant_max, int quant_min, size_t bitNum,
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bool per_channel) {
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if (per_channel) {
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// per channel
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auto dims = weightPtr->tensor_shape();
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if (dims.size() < 1) {
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MS_LOG(ERROR) << "weight dims size error";
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return RET_ERROR;
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MS_LOG(ERROR) << "weight dims size error";
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return RET_ERROR;
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}
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uint32_t channels = dims[0];
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// todo(x)
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uint32_t channels = dims[3];
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if (channels == 0) {
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MS_LOG(ERROR) << "channels error 0";
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return RET_ERROR;
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MS_LOG(ERROR) << "channels error 0";
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return RET_ERROR;
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}
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size_t shapeSize = weightPtr->tensor_shape_size();
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size_t oneFilterSize = shapeSize / channels;
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auto *rawDatas = reinterpret_cast<const float *>(weightPtr->tensor_addr());
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if (rawDatas == nullptr) {
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MS_LOG(ERROR) << "rawDatas is nullptr";
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return RET_ERROR;
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MS_LOG(ERROR) << "rawDatas is nullptr";
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return RET_ERROR;
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}
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weightPtr->quant_param().clear();
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vector<uint8_t> qDatas(shapeSize);
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vector<int8_t> qDatas(shapeSize);
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for (uint32_t i = 0; i < channels; i++) {
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float min = 0;
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float max = 0;
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// find min and max
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for (uint32_t j = 0; j < oneFilterSize; j++) {
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min = std::min(min, rawDatas[j + i * oneFilterSize]);
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max = std::max(max, rawDatas[j + i * oneFilterSize]);
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}
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float min = 0;
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float max = 0;
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// find min and max
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for (uint32_t j = 0; j < oneFilterSize; j++) {
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min = std::min(min, rawDatas[j + i * oneFilterSize]);
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max = std::max(max, rawDatas[j + i * oneFilterSize]);
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}
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std::unique_ptr<AnfQuantParam> quantParam = std::unique_ptr<AnfQuantParam>(new AnfQuantParam);
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STATUS status = CalQuantizationParams(quantParam, min, max, false, quant_max, quant_min, bitNum);
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if (status != RET_OK) {
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MS_LOG(ERROR) << "CalQuantizationParams failed" << status;
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return status;
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}
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// update data and datatype
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for (uint32_t j = 0; j < oneFilterSize; j++) {
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float rawData = rawDatas[j + i * oneFilterSize];
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auto qData = QuantizeData<int8_t>(rawData, quantParam.get(), quant_max, quant_min);
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qDatas[j + i * oneFilterSize] = qData;
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}
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weightPtr->set_quant_param(quantParam);
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}
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auto ret = memcpy_s(const_cast<float*>(rawDatas), weightPtr->tensor_size(),
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qDatas.data(), shapeSize * sizeof(int8_t));
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if (ret != EOK) {
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MS_LOG(ERROR) << "memcpy error: " << ret;
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return RET_ERROR;
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}
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if (quantType == QuantType_WeightQuant) {
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PostBitPack(const_cast<float*>(rawDatas), shapeSize, bitNum);
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}
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std::unique_ptr<AnfQuantParam> quantParam = std::unique_ptr<AnfQuantParam>(new AnfQuantParam);
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STATUS status = CalQuantizationParams(quantParam, min, max, false, quant_max, quant_min, bitNum);
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if (status != RET_OK) {
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MS_LOG(ERROR) << "CalQuantizationParams failed" << status;
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return status;
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}
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// update data and datatype
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for (uint32_t j = 0; j < oneFilterSize; j++) {
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float rawData = rawDatas[j + i * oneFilterSize];
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auto qData = QuantizeData<uint8_t>(rawData, quantParam.get());
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qDatas[j + i * oneFilterSize] = qData;
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}
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weightPtr->set_tensor_type(kNumberTypeInt8);
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weightPtr->set_tensor_size(shapeSize * sizeof(int8_t));
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} else {
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// per layer
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size_t shapeSize = weightPtr->tensor_shape_size();
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auto *rawDatas = static_cast<float *>(weightPtr->tensor_addr());
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if (rawDatas == nullptr) {
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MS_LOG(ERROR) << "rawDatas is nullptr";
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return RET_ERROR;
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}
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weightPtr->set_quant_param(quantParam);
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weightPtr->quant_param().clear();
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vector<int8_t> qDatas(shapeSize);
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float min = 0;
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float max = 0;
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for (uint32_t i = 0; i < shapeSize; i++) {
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// find max min
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min = std::min(min, rawDatas[i]);
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max = std::max(max, rawDatas[i]);
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}
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auto ret = memcpy_s(const_cast<float*>(rawDatas), weightPtr->tensor_size(),
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qDatas.data(), shapeSize * sizeof(uint8_t));
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std::unique_ptr<AnfQuantParam> quantParam = std::unique_ptr<AnfQuantParam>(new AnfQuantParam);
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STATUS status = CalQuantizationParams(quantParam, min, max, false, quant_max, quant_min, bitNum);
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if (status != RET_OK) {
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MS_LOG(ERROR) << "CalQuantizationParams failed" << status;
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return status;
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}
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// update data and datatype
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for (uint32_t i = 0; i < shapeSize; i++) {
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float rawData = rawDatas[i];
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auto quant_data = std::round(rawData / quantParam->scale + quantParam->zeroPoint);
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if (quant_data > quant_max) {
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qDatas[i] = quant_max;
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} else if (quant_data < quant_min) {
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qDatas[i] = quant_min;
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} else {
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qDatas[i] = static_cast<int8_t>(quant_data);
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}
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}
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weightPtr->set_quant_param(quantParam);
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auto ret = memcpy_s(rawDatas, weightPtr->tensor_size() * sizeof(int8_t),
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qDatas.data(), shapeSize * sizeof(int8_t));
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if (ret != EOK) {
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MS_LOG(ERROR) << "memcpy error: " << ret;
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return RET_ERROR;
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MS_LOG(ERROR) << "memcpy error: " << ret;
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return RET_ERROR;
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}
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if (quantType == QuantType_WeightQuant) {
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PostBitPack(const_cast<float*>(rawDatas), shapeSize, bitNum);
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PostBitPack(rawDatas, shapeSize, bitNum);
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}
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weightPtr->set_tensor_type(kNumberTypeInt8);
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weightPtr->set_tensor_size(shapeSize * sizeof(int8_t));
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}
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return RET_OK;
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}
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mindspore-ci-bot
5 years ago
committed by
Gitee