chore: import upstream snapshot with attribution

2026-07-13 13:33:03 +08:00
commit 5b57521aa1
8226 changed files with 3425766 additions and 0 deletions
@@ -0,0 +1,245 @@
+//
+//  revertMNNModel.cpp
+//  MNN
+//
+//  Created by MNN on 2019/01/31.
+//  Copyright © 2018, Alibaba Group Holding Limited
+//
+
+#include <cstdlib>
+#include <random>
+#include <ctime>
+#include <fstream>
+#include <iostream>
+
+#include <string.h>
+#include <stdlib.h>
+#include <MNN/MNNDefine.h>
+#include "revertMNNModel.hpp"
+#include "core/CommonCompute.hpp"
+#include "core/MemoryFormater.h"
+#include "core/IDSTEncoder.hpp"
+#include "core/ConvolutionCommon.hpp"
+
+int SymmetricQuantizeWeight(const float* weight, const int size, int8_t* quantizedWeight, float* scale,
+                            const int channels, float weightClampValue) {
+    const int channelStride     = size / channels;
+    const int quantizedMaxValue = weightClampValue;
+
+    for (int c = 0; c < channels; ++c) {
+        const auto weightChannelStart    = weight + c * channelStride;
+        auto quantizedWeightChannelStart = quantizedWeight + c * channelStride;
+        auto minmaxValue                 = std::minmax_element(weightChannelStart, weightChannelStart + channelStride);
+        const float dataAbsMax           = std::fmax(std::fabs(*minmaxValue.first), std::fabs(*minmaxValue.second));
+
+        float scaleDataToInt8 = 1.0f;
+        if (dataAbsMax == 0) {
+            scale[c] = 0.0f;
+        } else {
+            scale[c]        = dataAbsMax / quantizedMaxValue;
+            scaleDataToInt8 = quantizedMaxValue / dataAbsMax;
+        }
+
+        for (int i = 0; i < channelStride; ++i) {
+            const int32_t quantizedInt8Value = static_cast<int32_t>(roundf(weightChannelStart[i] * scaleDataToInt8));
+            quantizedWeightChannelStart[i] =
+                std::min(quantizedMaxValue, std::max(-quantizedMaxValue, quantizedInt8Value));
+        }
+    }
+
+    return 0;
+}
+
+
+Revert::Revert(const char* originalModelFileName) {
+    std::ifstream inputFile(originalModelFileName, std::ios::binary);
+    inputFile.seekg(0, std::ios::end);
+    const auto size = inputFile.tellg();
+    inputFile.seekg(0, std::ios::beg);
+
+    char* buffer = new char[size];
+    inputFile.read(buffer, size);
+    inputFile.close();
+    mMNNNet = MNN::UnPackNet(buffer);
+    delete[] buffer;
+    MNN_ASSERT(mMNNNet->oplists.size() > 0);
+}
+
+Revert::~Revert() {
+}
+
+void* Revert::getBuffer() const {
+    return reinterpret_cast<void*>(mBuffer.get());
+}
+
+const size_t Revert::getBufferSize() const {
+    return mBufferSize;
+}
+
+void Revert::writeExtraDescribeTensor(float* scale, float* offset) {
+    int opCounts = static_cast<int32_t>(mMNNNet->oplists.size());
+    for (int opIndex = 0; opIndex < opCounts; ++opIndex) {
+        std::unique_ptr<MNN::TensorDescribeT> describe(new MNN::TensorDescribeT);
+        describe->index = opIndex;
+        describe->quantInfo.reset(new MNN::TensorQuantInfoT);
+        describe->quantInfo->scale = *scale;
+        describe->quantInfo->zero = *offset;
+        describe->quantInfo->min = -127;
+        describe->quantInfo->max = 127;
+        describe->quantInfo->type = MNN::DataType_DT_INT8;
+        mMNNNet->extraTensorDescribe.emplace_back(std::move(describe));
+    }
+    for (const auto& op: mMNNNet->oplists) {
+        const auto opType = op->type;
+        if (opType != MNN::OpType_Convolution && opType != MNN::OpType_ConvolutionDepthwise && opType != MNN::OpType_Deconvolution) {
+            continue;
+        }
+        // Conv/ConvDepthwise/Deconv weight quant.
+        const float inputScale = *scale;
+        const float outputScale = *scale;
+        const int outputChannel = static_cast<int32_t>(op->outputIndexes.size());
+
+        auto param = op->main.AsConvolution2D();
+        float* originWeight = param->weight.data();
+        const int channels = param->common->outputCount;
+        param->symmetricQuan.reset(new MNN::QuantizedFloatParamT);
+        param->symmetricQuan->nbits = 8;
+        const int weightSize = static_cast<int32_t>(param->weight.size());
+        param->common->inputCount = weightSize / (channels * param->common->kernelX * param->common->kernelY);
+        std::vector<int8_t> quantizedWeight(weightSize);
+        std::vector<float> quantizedWeightScale(channels);
+
+        if (originWeight[0] == 0.f && originWeight[1] == 0.f) { // Process weight is null.
+            // Initialize originWeight
+            std::uniform_real_distribution<double> u(-200, 200);
+            std::default_random_engine e(time(NULL));
+            for (int i = 0; i < weightSize; ++i) {
+                originWeight[i] = u(e);
+            }
+        }
+
+        SymmetricQuantizeWeight(originWeight, weightSize, quantizedWeight.data(), quantizedWeightScale.data(), channels, 127.0f);
+        param->quanParameter = IDSTEncoder::encode(param->weight.data(), quantizedWeightScale, weightSize/channels, channels, false, quantizedWeight.data(), -127.0f);
+        param->quanParameter->scaleIn = *scale;
+        param->quanParameter->scaleOut = *scale;
+        if (param->common->relu6) {
+            param->common->relu  = true;
+            param->common->relu6 = false;
+        }
+        param->weight.clear();
+    }
+}
+
+void Revert::packMNNNet() {
+    flatbuffers::FlatBufferBuilder builder(1024);
+    auto offset = MNN::Net::Pack(builder, mMNNNet.get());
+    builder.Finish(offset);
+    mBufferSize = builder.GetSize();
+    mBuffer.reset(new uint8_t[mBufferSize], std::default_delete<uint8_t[]>());
+    ::memcpy(mBuffer.get(), builder.GetBufferPointer(), mBufferSize);
+    mMNNNet.reset();
+}
+
+void Revert::initialize(float spasity, int sparseBlockOC, bool rewrite, bool quantizedModel) {
+    if (mMNNNet->bizCode == "benchmark" || rewrite) {
+        randStart();
+        bool useSparse = spasity > 0.5f;
+        for (auto& op : mMNNNet->oplists) {
+            const auto opType = op->type;
+            switch (opType) {
+                case MNN::OpType_Convolution:
+                case MNN::OpType_Deconvolution:
+                case MNN::OpType_ConvolutionDepthwise: {
+                    auto param           = op->main.AsConvolution2D();
+                    auto& convCommon     = param->common;
+                    const int weightReduceStride = convCommon->kernelX * convCommon->kernelY * convCommon->inputCount;
+                    const int oc = convCommon->outputCount / convCommon->group;
+                    param->weight.resize(oc * weightReduceStride);
+                    ::memset(param->weight.data(), 0, param->weight.size() * sizeof(float));
+                    param->bias.resize(convCommon->outputCount);
+                    ::memset(param->bias.data(), 0, param->bias.size() * sizeof(float));
+                    if (useSparse) {
+                        size_t weightNNZElement, weightBlockNumber = 0;
+                        MNN::CommonCompute::fillRandValueAsSparsity(weightNNZElement, weightBlockNumber, param->weight.data(), oc, weightReduceStride, spasity, sparseBlockOC);
+
+                        MNN::AttributeT* arg1(new MNN::AttributeT);
+                        arg1->key = "sparseBlockOC";
+                        arg1->i = sparseBlockOC;
+
+                        MNN::AttributeT* arg2(new MNN::AttributeT);
+                        arg2->key = "sparseBlockKernel";
+                        arg2->i = 1;
+
+                        MNN::AttributeT* arg3(new MNN::AttributeT);
+                        arg3->key = "NNZElement";
+                        arg3->i = weightNNZElement;
+
+                        MNN::AttributeT* arg4(new MNN::AttributeT);
+                        arg4->key = "blockNumber";
+                        arg4->i = weightBlockNumber;
+
+                        flatbuffers::FlatBufferBuilder builder;
+                        std::vector<flatbuffers::Offset<MNN::Attribute>> argsVector;
+                        auto sparseArg1 = MNN::CreateAttribute(builder, arg1);
+                        auto sparseArg2 = MNN::CreateAttribute(builder, arg2);
+                        auto sparseArg3 = MNN::CreateAttribute(builder, arg3);
+                        auto sparseArg4 = MNN::CreateAttribute(builder, arg4);
+
+                        argsVector.emplace_back(sparseArg1);
+                        argsVector.emplace_back(sparseArg2);
+                        argsVector.emplace_back(sparseArg3);
+                        argsVector.emplace_back(sparseArg4);
+
+                        auto sparseArgs = builder.CreateVectorOfSortedTables<MNN::Attribute>(&argsVector);
+                        MNN::SparseAlgo prune_algo_type;
+                        if (sparseBlockOC == 4) {
+                            prune_algo_type = MNN::SparseAlgo_SIMD_OC;
+                        } else {
+                            prune_algo_type = MNN::SparseAlgo_RANDOM;
+                        }
+                        auto sparseCom = MNN::CreateSparseCommon(builder, prune_algo_type, sparseArgs);
+                        builder.Finish(sparseCom);
+                        auto sparseComPtr = flatbuffers::GetRoot<MNN::SparseCommon>(builder.GetBufferPointer())->UnPack();
+                        param->sparseParameter.reset(sparseComPtr);
+                        MNN::CommonCompute::compressFloatWeightToSparse(op.get());
+                    }
+                    break;
+                }
+                case MNN::OpType_Scale: {
+                    auto param = op->main.AsScale();
+                    param->biasData.resize(param->channels);
+                    param->scaleData.resize(param->channels);
+                    fillRandValue(param->scaleData.data(), param->channels);
+                    fillRandValue(param->biasData.data(), param->channels);
+                    break;
+                }
+                default:
+                    break;
+            }
+        }
+    }
+    if (quantizedModel) {
+        int opsize =  mMNNNet->oplists.size();
+        std::vector<float> scale(opsize);
+        for (int i = 0;i < opsize; ++i) {
+            scale[i] = ((i + 1) / (opsize + 100.0f));
+        }
+        float offset = 0;
+        writeExtraDescribeTensor(scale.data(), &offset);
+    }
+    packMNNNet();
+}
+
+void Revert::fillRandValue(float * data, size_t size) {
+    unsigned int seed = 1000;
+    std::mt19937 rng(seed);
+    std::uniform_real_distribution<float> uniform_dist(-2, 2);
+
+    for (size_t i = 0; i < size; i++) {
+        *data = uniform_dist(rng);
+    }
+    return;
+}
+
+void Revert::randStart() {
+}