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//
// SparseConvolutionTiledExecutor
// MNN
//
// Created by MNN on 2021/04/06.
// Copyright © 2018-2021 Alibaba Group Holding Limited.
//
#include "SparseConvolutionTiledExecutor.hpp"
#include <MNN/AutoTime.hpp>
#include "backend/cpu/CPUBackend.hpp"
#include "CommonOptFunction.h"
#include "core/Concurrency.h"
#include "ConvOpt.h"
#include "core/Macro.h"
#include "core/TensorUtils.hpp"
#include "math/Vec.hpp"
#include "core/BufferAllocator.hpp"
#include "common/MemoryFormater.h"
#include "common/CommonCompute.hpp"
using Vec4 = MNN::Math::Vec<float, 4>;
namespace MNN {
/*
source: source matrix is h x l
transpose: if false, export compressed matrix as h x l, other export as l x h.
*/
static int _fillIndex(int32_t* targetIndexes, uint32_t begin, uint32_t end, const uint32_t* indexes, uint32_t indexSize, int indexStart) {
int mid = -1;
int current = -1;
for (int i=indexStart; i<indexSize; ++i) {
if (indexes[i] >= begin) {
mid = i;
current = indexes[i];
break;
}
}
uint32_t number = end - begin;
for (uint32_t i=0; i<number; ++i) {
targetIndexes[i] = -1;
}
auto offset = current - begin;
do {
if (current < begin || current >= end) {
break;
}
targetIndexes[current - begin] = mid;
mid++;
if (mid >= indexSize) {
break;
}
current = indexes[mid];
} while (true);
return mid;
}
static void MNNGetOptimalBlockShape(size_t& weightNNZElement, size_t& weightBlockNumber, const uint32_t* indexes, uint32_t indexSize, int sparseBlockOC, size_t h, size_t l) {
size_t nnzBlock = 0;
size_t nnzTail = 0;
int ocEven = (h / sparseBlockOC) * sparseBlockOC;
std::vector<int32_t> tempIndexes(sparseBlockOC * l);
size_t ioc = 0;
int offset = 0;
for (; ioc < ocEven; ioc += sparseBlockOC) {
offset = _fillIndex(tempIndexes.data(), ioc * l, (ioc+sparseBlockOC) * l, indexes, indexSize, offset);
for (size_t i = 0; i < l; i++) {
bool allZero = true;
for (int u=0; u<sparseBlockOC; ++u) {
if (tempIndexes[u*l + i] >= 0) {
allZero = false;
break;
}
}
if (!allZero) {
nnzBlock++;
}
}
}
for (; ioc < h; ioc++) {
offset = _fillIndex(tempIndexes.data(), ioc * l, (ioc+1) * l, indexes, indexSize, offset);
for (size_t i = 0; i < l; i++) {
if (tempIndexes[i] >= 0) {
nnzTail++;
}
}
}
weightNNZElement = nnzBlock * sparseBlockOC + nnzTail;
weightBlockNumber = nnzBlock + nnzTail;
return;
}
static void MNNPackForSparseMatMul_B(float* dest, unsigned int* NNZMap, int* dataOffsetMap, int sparseBlockOC, const float* source, const uint32_t* indexes, uint32_t indexSize, size_t h, size_t ic, size_t kernelSize, const int eP) {
// 1. in convolution, source B layout is OC x (KH * KW * IC),
// the dest layout of weight is BCSC(block compressed sparse colum) format, which is OC(!=0) x (KH*KW*IC!=0), as a canceled result, just do BCSR, transpose should be false.
// 2. in ordinary sparse MatMul, transpose is corresponding to BCSR or BCSC
auto l = ic * kernelSize;
int columOffset = 0;
int i = 0;
std::vector<int32_t> tempIndexes(sparseBlockOC * l);
int offset = 0;
for (; i + sparseBlockOC <= h; i += sparseBlockOC) {
*NNZMap = 0;
offset = _fillIndex(tempIndexes.data(), i * l, (i+sparseBlockOC) * l, indexes, indexSize, offset);
// Origin weight is oc, ic, kernelSize, new weight order is oc, kernelsize, ic
for (int x=0; x<kernelSize; ++x) {
for (int y=0; y<ic; ++y) {
auto j = y * kernelSize + x;
bool allZero = true;
for (int u=0; u<sparseBlockOC; ++u) {
if (tempIndexes[u*l + j] >= 0) {
allZero = false;
break;
}
}
if (!allZero) {
for (int ioc = 0; ioc < sparseBlockOC; ioc++) {
auto index = tempIndexes[ioc*l + j];
if (index >= 0) {
*dest = source[index];
} else {
*dest = 0.0f;
}
dest++;
}
*NNZMap = *NNZMap + 1;
*dataOffsetMap = columOffset;
dataOffsetMap++;
columOffset = 0;
}
columOffset += eP;
}
}
NNZMap++;
columOffset -= l * eP;
}
for (; i < h; i++) {
*NNZMap = 0;
offset = _fillIndex(tempIndexes.data(), i * l, (i+1) * l, indexes, indexSize, offset);
for (int x=0; x<kernelSize; ++x) {
for (int y=0; y<ic; ++y) {
auto j = y * kernelSize + x;
auto index = tempIndexes[j];
if (index >= 0) {
*dest = source[index];
dest++;
*NNZMap = *NNZMap + 1;
*dataOffsetMap = columOffset;
dataOffsetMap++;
columOffset = 0;
}
columOffset += eP;
}
}
NNZMap++;
columOffset -= l * eP;
}
*dataOffsetMap = columOffset; //
return;
}
void SparseConvolutionTiledExecutor::initWeight(float* dest, unsigned int* NNZMap, int* dataOffsetMap,
int sparseBlockOC, const float* source, const uint32_t* indexes, uint32_t indexSize, int depth,
int outputCount, int kernelSize, int eP, size_t weightNNZElement,
size_t weightBlockNumber, const CoreFunctions* function) {
MNNPackForSparseMatMul_B(dest, NNZMap, dataOffsetMap, sparseBlockOC, source, indexes, indexSize, outputCount, depth, kernelSize, eP);
// MNN_PRINT("\nBCSR origin weight:");
// formatMatrix(source, {outputCount, kernelSize * depth});
// MNN_PRINT("\nBCSR new weight:");
// formatMatrix(dest, {static_cast<int>(weightNNZElement)});
// MNN_PRINT("\nBCSR weight nnzmap:");
// formatMatrix(NNZMap, {outputCount / sparseBlockOC + outputCount % sparseBlockOC});
// MNN_PRINT("\nBCSR weight dataOffsetMap:");
// formatMatrix(dataOffsetMap, {static_cast<int>(weightBlockNumber + 1)});
}
SparseConvolutionTiledExecutor::SparseConvolutionTiledExecutor(const Convolution2DCommon *common, Backend* b,
const IDSTQuan* weight, const SparseCommon* sparseCommon,
const float* bias, size_t biasSize)
: ConvolutionTiledExecutor(b, bias, biasSize) {
auto outputCount = (int)biasSize;
// Don't use common->inputCount for old model common->inputCount is zero
auto lSize = weight->weightSize() / outputCount;
auto srcCount = lSize / (common->kernelX() * common->kernelY());
int eP, lP, hP;
auto core = static_cast<CPUBackend*>(b)->functions();
int bytes = core->bytes;
core->MNNGetSparseMatMulPackMode(&eP, &lP, &hP);
auto sparseBlockOC = sparseCommon->args()->LookupByKey("sparseBlockOC")->i();
size_t weightNNZElement = sparseCommon->args()->LookupByKey("NNZElement")->i();
size_t weightBlockNumber = sparseCommon->args()->LookupByKey("blockNumber")->i();
int optimalSparseBlockOC = sparseBlockOC;
MNNPackedSparseMatMul packedSparseMatmul = nullptr;
core->MNNAdjustOptimalSparseKernel(optimalSparseBlockOC, packedSparseMatmul);
if (optimalSparseBlockOC != sparseBlockOC) {
size_t optimalWeightNNZElement = weightNNZElement;
size_t optimalWeightBlockNumber = weightBlockNumber;
MNNGetOptimalBlockShape(optimalWeightNNZElement, optimalWeightBlockNumber, weight->index()->data(), weight->index()->size(), optimalSparseBlockOC, outputCount, lSize);
MNN_ASSERT(sparseBlockOC == 1 || sparseBlockOC == 2 || sparseBlockOC == 4 || sparseBlockOC == 8);
// MNN_PRINT("caution: sparsity changed!!!\nsparseBlockOC:%d -> %d weightNNZElement:%zu -> %zu, weightBlockNumber:%zu -> %zu, outputCount:%d, divide:%d, tail:%d\n",
// sparseBlockOC, optimalSparseBlockOC, weightNNZElement, optimalWeightNNZElement, weightBlockNumber, optimalWeightBlockNumber, outputCount, outputCount / optimalSparseBlockOC, outputCount % optimalSparseBlockOC);
sparseBlockOC = optimalSparseBlockOC;
weightNNZElement = optimalWeightNNZElement;
weightBlockNumber = optimalWeightBlockNumber;
}
MNN_ASSERT(weightNNZElement > 0);
MNN_ASSERT(weightBlockNumber > 0);
mSparseIndexData.reset(new SparseIndexData(sparseBlockOC, weightNNZElement, weightBlockNumber, backend()));
mResource->mWeight.reset(Tensor::createDevice<uint8_t>(
{ static_cast<int>(weightNNZElement + 1) * bytes })); // one more element in case of weight are all zeros
mSparseIndexData->mNNZMap.reset(Tensor::createDevice<unsigned int>({outputCount / sparseBlockOC + outputCount % sparseBlockOC}));
mSparseIndexData->mDataOffsetMap.reset(Tensor::createDevice<int>({static_cast<int>(weightBlockNumber + 1)}));
mValid = backend()->onAcquireBuffer(mResource->mWeight.get(), Backend::STATIC);
mValid = mValid && backend()->onAcquireBuffer(mSparseIndexData->mNNZMap.get(), Backend::STATIC);
mValid = mValid && backend()->onAcquireBuffer(mSparseIndexData->mDataOffsetMap.get(), Backend::STATIC);
if (!mValid) {
return;
}
initWeight(mResource->mWeight->host<float>(), mSparseIndexData->mNNZMap->host<unsigned int>(), mSparseIndexData->mDataOffsetMap->host<int>(), sparseBlockOC, weight->alpha()->data(), weight->index()->data(), weight->index()->size(), srcCount, outputCount, common->kernelX() * common->kernelY(), eP, weightNNZElement, weightBlockNumber, core);
mProxy.reset(new SparseConvolutionTiledImpl(common, packedSparseMatmul, sparseBlockOC, b));
}
SparseConvolutionTiledExecutor::SparseConvolutionTiledExecutor(std::shared_ptr<CPUConvolution::Resource> res,
std::shared_ptr<SparseIndexData> sparseIndexData,
const Convolution2DCommon *common,
CoreFunctions::MNNPackedSparseMatMul packedSparseMatmul,
int sparseBlockOC, Backend* b)
:mSparseIndexData(sparseIndexData),
ConvolutionTiledExecutor(res, b) {
mProxy.reset(new SparseConvolutionTiledImpl(common, packedSparseMatmul, sparseBlockOC, b));
}
SparseConvolutionTiledExecutor::~SparseConvolutionTiledExecutor() {
}
bool SparseConvolutionTiledExecutor::onClone(Backend* bn, const Op* op, Execution** dst) {
if (!mValid) {
return false;
}
if (nullptr == dst) {
return true;
}
*dst = new SparseConvolutionTiledExecutor(mResource, mSparseIndexData, op->main_as_Convolution2D()->common(), mProxy->mPackedSparseMatmul, mProxy->mSparseBlockOC, bn);
return true;
}
void SparseConvolutionTiledImpl::getPackParameter(int* eP, int* lP, int* hP, const CoreFunctions* core) {
core->MNNGetSparseMatMulPackMode(eP, lP, hP);
return;
}
ErrorCode SparseConvolutionTiledImpl::onResize(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs,
Tensor* NNZMap, Tensor* dataOffsetMap) {
CPUConvolution::onResize(inputs, outputs);
auto input = inputs[0];
auto weight = inputs[1];
Tensor *bias = nullptr;
auto core = static_cast<CPUBackend *>(backend())->functions();
ConvolutionTiledExecutor::setIm2ColParameter(mIm2ColParameters, mCommon, input, outputs[0], mPadX, mPadY, core, nullptr);
auto sparseMatmul = mPackedSparseMatmul;
int bytes = core->bytes;
int unit = core->pack;
auto packA = core->MNNPackC4ForMatMul_A;
int eP, lP, hP;
getPackParameter(&eP, &lP, &hP, core);
auto weightPtr = weight->host<float>();
auto NNZMapPtr = NNZMap->host<unsigned int>();
auto dataOffsetPtr = dataOffsetMap->host<int>();
auto output = outputs[0];
auto batch = output->batch();
int threadNumber = ((CPUBackend *)backend())->threadNumber();
auto icC4 = UP_DIV(input->channel(), unit);
auto ic = input->channel();
auto L = ic * mCommon->kernelY() * mCommon->kernelX();
const float *biasPtr = nullptr;
if (inputs.size() > 2) {
bias = inputs[2];
biasPtr = bias->host<float>();
}
auto kernelSize = mCommon->kernelX() * mCommon->kernelY();
mTempBufferTranspose.buffer().type = halide_type_of<uint8_t>();
mTempBufferTranspose.buffer().dimensions = 2;
mTempBufferTranspose.buffer().dim[0].extent = threadNumber;
mTempBufferTranspose.buffer().dim[1].extent = UP_DIV(L, lP) * lP * eP * bytes;
TensorUtils::setLinearLayout(&mTempBufferTranspose);
auto plane = mIm2ColParameters.ow * mIm2ColParameters.oh * batch;
int tileCount = UP_DIV(plane, eP);
bool success = backend()->onAcquireBuffer(&mTempBufferTranspose, Backend::DYNAMIC);
if (!success) {
return OUT_OF_MEMORY;
}
auto outputChannel = output->channel();
auto oC4 = UP_DIV(outputChannel, unit);
auto bufferAlloc = static_cast<CPUBackend *>(backend())->getBufferAllocator();
auto maxLine = UP_DIV(eP, mIm2ColParameters.ow) + 1;
auto tempPtr = bufferAlloc->alloc(ConvolutionTiledExecutor::computeBlitInfoSize(eP, mIm2ColParameters.ow, mIm2ColParameters.kernelX * mIm2ColParameters.kernelY, threadNumber).first);
if (tempPtr.invalid()) {
return OUT_OF_MEMORY;
}
backend()->onReleaseBuffer(&mTempBufferTranspose, Backend::DYNAMIC);
bufferAlloc->free(tempPtr);
auto threadNumberFirst = std::min(threadNumber, tileCount);
auto postParameters = getPostParameters();
mFunction.first = threadNumberFirst;
mFunction.second = [=](int tId) {
auto gemmBuffer = mTempBufferTranspose.host<uint8_t>() + mTempBufferTranspose.stride(0) * tId;
auto srcPtr = (float const **)(tempPtr.ptr() + tId * kernelSize * maxLine * (4 * sizeof(int32_t) + sizeof(float *)));
auto el = (int32_t *)(srcPtr + kernelSize * maxLine);
int32_t info[4];
info[1] = mIm2ColParameters.iw * mIm2ColParameters.ih * batch;
info[2] = eP;
info[3] = mIm2ColParameters.strideX;
size_t parameters[6];
parameters[0] = eP * bytes;
parameters[1] = L;
parameters[2] = outputChannel;
parameters[3] = plane * unit * bytes;
parameters[4] = 0;
parameters[5] = 0;
auto dstOrigin = output->host<uint8_t>();
auto srcOrigin = input->host<uint8_t>();
for (int x = (int)tId; x < tileCount; x += threadNumberFirst) {
int start = (int)x * eP;
int remain = plane - start;
int xC = remain > eP ? eP : remain;
auto res = ConvolutionTiledExecutor::turnIm2ColToBlitInfo(srcPtr, el, start, xC, mIm2ColParameters, srcOrigin, bytes);
auto number = res.first;
auto needZero = res.second;
info[0] = number;
if (needZero || lP != 1) {
::memset(gemmBuffer, 0, mTempBufferTranspose.stride(0));
}
if (number > 0) {
packA((float *)gemmBuffer, srcPtr, info, el);
}
sparseMatmul((float*)(dstOrigin + start * unit * bytes), (float*)gemmBuffer, weightPtr, xC, parameters, postParameters.data(), biasPtr, NNZMapPtr, dataOffsetPtr);
}
};
return NO_ERROR;
}
} // namespace MNN
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