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chore: import upstream snapshot with attribution

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2026-07-13 13:33:03 +08:00
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//
// CPUTFQuantizedConv2D.cpp
// MNN
//
// Created by MNN on 2018/08/02.
// Copyright © 2018, Alibaba Group Holding Limited
//
#include "backend/cpu/CPUBackend.hpp"
#ifdef MNN_SUPPORT_DEPRECATED_OP
#include "backend/cpu/CPUTFQuantizedConv2D.hpp"
#include <math.h>
#include "backend/cpu/CPUFixedPoint.hpp"
#include "backend/cpu/CPUQuantizationUtils.hpp"
#include "backend/cpu/compute/CommonOptFunction.h"
#include "core/Concurrency.h"
#include "core/TensorUtils.hpp"
#ifdef MNN_USE_NEON
#include <arm_neon.h>
#endif
#define UNIT 4
#define SRC_UNIT 16
//SRC_UNIT/UNIT
#define SRC_C4_UNIT 4
// ugly macro compatible with MNNGemmInt8ToFloat32_XX
#ifdef DST_XUNIT
#undef DST_XUNIT
#endif
// One Tile Compute DST_XUNIT * outputChannel 's number
#ifdef __aarch64__
#define DST_XUNIT 4
#else
#define DST_XUNIT 2
#endif
extern "C" {
void MNNQuanToDestUint8(uint8_t* outputInTile, const int32_t* gemmOutputAddr, const int32_t* biasData, size_t ocUnit,
size_t realDstCount, size_t dstZStep, size_t srcZstep,
const MNN::CPUTFQuantizedConv2D::QuanParameter* parameter);
void MNNLoadU8AndSum(int32_t* inputSum, int8_t* colAddr, const uint8_t* inputOrigin, size_t srcZStep, size_t icDiv8,
size_t realDstCount, size_t mFilterOffset);
void MNNGemmint8to32_8x4_Unit(int32_t* dst, const int8_t* src, const int8_t* weight, const int32_t* inputSummer, size_t src_depth_quad,
size_t dst_step, size_t dst_depth_quad);
}
#ifndef MNN_USE_NEON
void MNNGemmint8to32_8x4_Unit(int32_t* dst, const int8_t* src, const int8_t* weight, const int32_t* inputSummer, size_t src_depth_quad,
size_t dst_step, size_t dst_depth_quad) {
for (int dz = 0; dz < dst_depth_quad; ++dz) {
auto weight_dz = weight + src_depth_quad * dz * SRC_UNIT * UNIT;
auto dst_z = dst + dz * dst_step / sizeof(int32_t);
for (int w = 0; w < DST_XUNIT; ++w) {
auto dst_x = dst_z + 4 * w;
::memset(dst_x, 0, UNIT * sizeof(int32_t));
auto src_x = src + SRC_UNIT * w;
for (int sz = 0; sz < src_depth_quad; ++sz) {
auto weight_sz = weight_dz +SRC_UNIT * UNIT * sz;
auto src_z = src_x + sz * DST_XUNIT * SRC_UNIT;
for (int j = 0; j < UNIT; ++j) {
auto weight_j = weight_sz + j * SRC_UNIT;
for (int i = 0; i < SRC_UNIT; ++i) {
auto s0 = (int32_t)(src_z[i+0]);
auto s1 = (int32_t)(weight_j[i+0]);
dst_x[j] += s0 * s1;
}
}
}
for (int j = 0; j < UNIT; ++j) {
dst_x[j] -= inputSummer[w];
}
}
}
}
void MNNLoadU8AndSum(int32_t* inputSum, int8_t* colAddr, const uint8_t* inputOrigin, size_t srcZStep, size_t icDiv8,
size_t realDstCount, size_t mFilterOffset) {
for (int i = 0; i < realDstCount; ++i) {
inputSum[i] = 0;
auto colAddrI = colAddr + SRC_UNIT * i;
auto inputK = inputOrigin + UNIT * i;
for (int sz = 0; sz < icDiv8; ++sz) {
auto inputZ0 = inputK + srcZStep * (SRC_C4_UNIT * sz + 0);
auto inputZ1 = inputK + srcZStep * (SRC_C4_UNIT * sz + 1);
auto inputZ2 = inputK + srcZStep * (SRC_C4_UNIT * sz + 2);
auto inputZ3 = inputK + srcZStep * (SRC_C4_UNIT * sz + 3);
auto indexOutside = sz;
auto dstK0 = colAddrI + indexOutside * SRC_UNIT * DST_XUNIT;
auto dstK1 = dstK0 + UNIT;
auto dstK2 = dstK1 + UNIT;
auto dstK3 = dstK2 + UNIT;
for (int u = 0; u < UNIT; ++u) {
dstK0[u] = (int)inputZ0[u] - 128;
dstK1[u] = (int)inputZ1[u] - 128;
dstK2[u] = (int)inputZ2[u] - 128;
dstK3[u] = (int)inputZ3[u] - 128;
inputSum[i] += ((int32_t)dstK0[u] + (int32_t)dstK1[u] + (int32_t)dstK2[u] + (int32_t)dstK3[u]) * mFilterOffset;
}
}
}
}
void MNNQuanToDestUint8(uint8_t* outputInTile, const int32_t* gemmOutputAddr, const int32_t* biasData, size_t ocUnit,
size_t realDstCount, size_t dstZStep, size_t srcZstep,
const MNN::CPUTFQuantizedConv2D::QuanParameter* parameter) {
dstZStep = dstZStep / sizeof(uint8_t);
srcZstep = srcZstep / sizeof(int32_t);
for (int dz = 0; dz < ocUnit; ++dz) {
auto dstZ = outputInTile + dz * dstZStep;
auto srcZ = gemmOutputAddr + dz * srcZstep;
auto biasZ = biasData + dz * UNIT;
for (int x = 0; x < realDstCount; ++x) {
auto dstX = dstZ + x * UNIT;
auto srcX = srcZ + x * UNIT;
for (int i = 0; i < UNIT; i++) {
int result = srcX[i];
int acc = result + biasZ[i];
acc = MNN::RoundingDivideByPOT(
MNN::SaturatingRoundingDoublingHighMul(acc * (1 << parameter->mOutputShiftBefore),
parameter->mOutputMultiplier),
-parameter->mOutputShiftAfter);
acc += parameter->mOutputOffset;
acc = std::max(acc, parameter->mOutputActivationMin);
acc = std::min(acc, parameter->mOutputActivationMax);
dstX[i] = static_cast<uint8_t>(acc);
}
}
}
}
#endif
namespace MNN {
CPUTFQuantizedConv2D::CPUTFQuantizedConv2D(Backend* backend, const Op* TFQuantizedConv2DOp) : Execution(backend) {
mTfQuantizedConv2D_param = TFQuantizedConv2DOp->main_as_TfQuantizedConv2D();
// Input filter is of the following dimensions:
// [ filter_rows, filter_cols, in_depth, out_depth]
auto outputChannel = mTfQuantizedConv2D_param->common()->outputCount();
auto kx = mTfQuantizedConv2D_param->common()->kernelX();
auto ky = mTfQuantizedConv2D_param->common()->kernelY();
int inputChannel = mTfQuantizedConv2D_param->weight()->size() / outputChannel / kx / ky;
auto outputChannelUnit = UP_DIV(outputChannel, UNIT);
auto inputChannelUnit = UP_DIV(inputChannel, UNIT);
mIm2ColParamter = new ConvolutionCommon::Im2ColParameter;
mIm2ColParamter->dilateX = mTfQuantizedConv2D_param->common()->dilateX();
mIm2ColParamter->dilateY = mTfQuantizedConv2D_param->common()->dilateY();
mIm2ColParamter->strideX = mTfQuantizedConv2D_param->common()->strideX();
mIm2ColParamter->strideY = mTfQuantizedConv2D_param->common()->strideY();
mIm2ColParamter->kernelX = mTfQuantizedConv2D_param->common()->kernelX();
mIm2ColParamter->kernelY = mTfQuantizedConv2D_param->common()->kernelY();
mIm2ColParamter->padX = mTfQuantizedConv2D_param->common()->padX();
mIm2ColParamter->padY = mTfQuantizedConv2D_param->common()->padY();
mIm2ColParamter->icDiv4 = inputChannelUnit;
mIm2ColParamter->kernelCountUnit = UP_DIV(inputChannelUnit * kx * ky, SRC_C4_UNIT);
mQuanParameter = new QuanParameter;
float inputScale = mTfQuantizedConv2D_param->inputQuantizedParam()->scale();
float filterScale = mTfQuantizedConv2D_param->filterQuantizedParam()->scale();
{
double realMultiplier = 0.0;
const double inputProductScale = inputScale * filterScale;
const double outputScale = mTfQuantizedConv2D_param->outputQuantizedParam()->scale();
MNN_ASSERT(inputProductScale >= 0);
realMultiplier = inputProductScale / outputScale;
MNN_ASSERT(realMultiplier < 1.0);
int shift = 0;
QuantizeMultiplierSmallerThanOne(realMultiplier, &mQuanParameter->mOutputMultiplier, &shift);
shift = -shift;
if (shift < 0) {
mQuanParameter->mOutputShiftBefore = 0;
mQuanParameter->mOutputShiftAfter = shift;
} else {
mQuanParameter->mOutputShiftBefore = shift;
mQuanParameter->mOutputShiftAfter = 0;
}
mFusedActivationFunction = mTfQuantizedConv2D_param->activationType();
CalculateActivationRangeUint8(mFusedActivationFunction,
mTfQuantizedConv2D_param->outputQuantizedParam()->zeroPoint(),
mTfQuantizedConv2D_param->outputQuantizedParam()->scale(),
&mQuanParameter->mOutputActivationMin, &mQuanParameter->mOutputActivationMax);
}
mQuanParameter->mOutputOffset = mTfQuantizedConv2D_param->outputQuantizedParam()->zeroPoint();
auto src = mTfQuantizedConv2D_param->weight()->data();
int32_t offsetFilter = mTfQuantizedConv2D_param->filterQuantizedParam()->zeroPoint() - 128;
auto totalKernelCountD8 = UP_DIV(inputChannelUnit * kx * ky, SRC_C4_UNIT);
mWeight.reset(Tensor::create<int8_t>(std::vector<int>{outputChannelUnit, totalKernelCountD8, UNIT, SRC_UNIT}));
::memset(mWeight->host<int8_t>(), (int8_t)offsetFilter, mWeight->size());
std::shared_ptr<Tensor> mWeightSum;
mWeightSum.reset(Tensor::create<int32_t>(std::vector<int>{outputChannelUnit, 4}));
::memset(mWeightSum->host<int32_t>(), 0, mWeightSum->size());
mQuanParameter->mFilterOffset = offsetFilter;
mQuanParameter->mInputOffset = mTfQuantizedConv2D_param->inputQuantizedParam()->zeroPoint() - 128;
mQuanParameter->mOffsetAdd =
mQuanParameter->mFilterOffset * mQuanParameter->mInputOffset * totalKernelCountD8 * SRC_UNIT;
auto dst = mWeight->host<int8_t>();
int kernelCount = kx * ky;
auto weightSum = mWeightSum->host<int32_t>();
for (int i = 0; i < outputChannel; ++i) {
weightSum[i] = (int32_t)offsetFilter * totalKernelCountD8 * SRC_UNIT;
}
// weight format : hwio -> oc/4, (hw ic/4) / 2, oc4, (hw ic/4) % 2 ic4
for (int k = 0; k < kernelCount; ++k) {
auto srcK = src + k * inputChannel * outputChannel;
for (int y = 0; y < inputChannel; ++y) {
int yOutSide = y / UNIT;
int yInside = y % UNIT;
int yIndex = yOutSide + k * inputChannelUnit;
int ySubOutside = yIndex / SRC_C4_UNIT;
int ySubInside = yIndex % SRC_C4_UNIT;
auto dstY = dst + ySubOutside * UNIT * SRC_UNIT + ySubInside * UNIT + yInside;
auto srcY = srcK + y * outputChannel;
for (int x = 0; x < outputChannel; ++x) {
int xOutSide = x / UNIT;
int xInside = x % UNIT;
auto dstX = dstY + xOutSide * mWeight->stride(0) + xInside * SRC_UNIT;
auto srcX = srcY + x;
dstX[0] = (int)srcX[0] - 128;
if (dstX[0] == -128) {
dstX[0] = -127;
}
weightSum[x] += ((int32_t)dstX[0] - (int32_t)offsetFilter);
}
}
}
auto originBiasData = mTfQuantizedConv2D_param->bias()->data();
mBias.reset(outputChannelUnit * 4);
auto biasData = mBias.get();
// Sum[0, kx*ky*sz](x-x0)*(w-w0) = Sum(xw) - Sum(x)*w0 - Sum(w)*x0 + x0w0*(kx*ky*sz)
// Let bias[oz] = bias[oz] - Sum[0, kx*ky*sz](w)*x0 + x0w0*(kx*ky*sz)
for (int i = 0; i < outputChannel; ++i) {
biasData[i] = originBiasData[i] - weightSum[i] * mQuanParameter->mInputOffset + mQuanParameter->mOffsetAdd;
}
}
CPUTFQuantizedConv2D::~CPUTFQuantizedConv2D() {
delete mQuanParameter;
delete mIm2ColParamter;
}
ErrorCode CPUTFQuantizedConv2D::onResize(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs) {
auto input = inputs[0];
auto output = outputs[0];
auto outputWidth = output->width();
auto outputHeight = output->height();
auto inputWidth = input->width();
auto inputHeight = input->height();
auto common = mTfQuantizedConv2D_param->common();
auto strideX = common->strideX();
auto strideY = common->strideY();
auto filterWidth = common->kernelX();
auto filterHeight = common->kernelY();
if (common->padMode() == PadMode::PadMode_VALID) {
mIm2ColParamter->padX = ((outputWidth - 1) * strideX + filterWidth - inputWidth + 1) / 2;
mIm2ColParamter->padY = ((outputHeight - 1) * strideY + filterHeight - inputHeight + 1) / 2;
} else {
mIm2ColParamter->padX = ((outputWidth - 1) * strideX + filterWidth - inputWidth) / 2;
mIm2ColParamter->padY = ((outputHeight - 1) * strideY + filterHeight - inputHeight) / 2;
}
int outputChannel = common->outputCount();
auto outputChannelUnit = UP_DIV(outputChannel, UNIT);
auto kernelCountUnit = mIm2ColParamter->kernelCountUnit;
mIm2ColParamter->iw = inputWidth;
mIm2ColParamter->ih = inputHeight;
mIm2ColParamter->ow = outputWidth;
mIm2ColParamter->oh = outputHeight;
int tileCount = UP_DIV(outputWidth * outputHeight, DST_XUNIT);
mThreadNumber = std::max(((CPUBackend*)backend())->threadNumber(), 1);
mThreadNumber = std::min(mThreadNumber, tileCount);
mTempBuffer.buffer().type = halide_type_of<int8_t>();
mTempBuffer.buffer().dimensions = 3;
mTempBuffer.buffer().dim[0].extent = mThreadNumber;
mTempBuffer.buffer().dim[1].extent = DST_XUNIT;
mTempBuffer.buffer().dim[2].extent = kernelCountUnit * SRC_UNIT;
TensorUtils::setLinearLayout(&mTempBuffer);
mTempDstBuffer.buffer().type = halide_type_of<int32_t>();
mTempDstBuffer.buffer().dimensions = 3;
mTempDstBuffer.buffer().dim[0].extent = mThreadNumber;
mTempDstBuffer.buffer().dim[1].extent = DST_XUNIT;
mTempDstBuffer.buffer().dim[2].extent = outputChannelUnit * UNIT;
TensorUtils::setLinearLayout(&mTempDstBuffer);
mTempInputSum.buffer().type = halide_type_of<int32_t>();
mTempInputSum.buffer().dimensions = 2;
mTempInputSum.buffer().dim[0].extent = mThreadNumber;
mTempInputSum.buffer().dim[1].extent = DST_XUNIT;
TensorUtils::setLinearLayout(&mTempInputSum);
backend()->onAcquireBuffer(&mTempBuffer, Backend::DYNAMIC);
backend()->onAcquireBuffer(&mTempDstBuffer, Backend::DYNAMIC);
backend()->onAcquireBuffer(&mTempInputSum, Backend::DYNAMIC);
backend()->onReleaseBuffer(&mTempBuffer, Backend::DYNAMIC);
backend()->onReleaseBuffer(&mTempDstBuffer, Backend::DYNAMIC);
backend()->onReleaseBuffer(&mTempInputSum, Backend::DYNAMIC);
return NO_ERROR;
}
static void _im2ColCommon(int32_t* inputSum, int8_t* colAddr, const uint8_t* inputOrigin,
const CPUTFQuantizedConv2D::QuanParameter* quanParamter,
const ConvolutionCommon::Im2ColParameter* im2ColParameter, size_t xIndexStart,
size_t realDstCount) {
int colBufferSize = im2ColParameter->kernelCountUnit * DST_XUNIT * SRC_UNIT * sizeof(uint8_t);
::memset(colAddr, (int8_t)quanParamter->mInputOffset, colBufferSize);
auto ih = im2ColParameter->ih;
auto iw = im2ColParameter->iw;
auto kh = im2ColParameter->kernelY;
auto kw = im2ColParameter->kernelX;
auto dilateX = im2ColParameter->dilateX;
auto dilateY = im2ColParameter->dilateY;
auto icDiv4 = im2ColParameter->icDiv4;
auto srcZStep = iw * ih * UNIT;
int countSumC8 = im2ColParameter->kernelCountUnit;
for (int i = 0; i < realDstCount; ++i) {
int xIndex = (int)xIndexStart + i;
int ox = xIndex % im2ColParameter->ow;
int oy = xIndex / im2ColParameter->ow;
int sx = ox * im2ColParameter->strideX - im2ColParameter->padX;
int sy = oy * im2ColParameter->strideY - im2ColParameter->padY;
int sfy = ALIMAX(0, (UP_DIV(-sy, im2ColParameter->dilateX)));
int efy = ALIMIN(kh, UP_DIV(ih - sy, im2ColParameter->dilateY));
int sfx = ALIMAX(0, (UP_DIV(-sx, im2ColParameter->dilateX)));
int efx = ALIMIN(kw, UP_DIV(iw - sx, im2ColParameter->dilateX));
int fyC = efy - sfy;
int fxC = efx - sfx;
auto colAddrI = colAddr + SRC_UNIT * i;
auto inputOffset = inputOrigin + (sx + sy * iw) * UNIT + (sfx * dilateX) * UNIT + (sfy * dilateY) * iw * UNIT;
auto indexOffset = (sfy * kw + sfx) * icDiv4;
for (int fy = 0; fy < fyC; ++fy) {
for (int fx = 0; fx < fxC; ++fx) {
auto inputK = inputOffset + (fx * dilateX) * UNIT + (fy * dilateY) * iw * UNIT;
auto indexStart = indexOffset + (fy * kw + fx) * icDiv4;
for (int sz = 0; sz < icDiv4; ++sz) {
auto inputZ = inputK + srcZStep * sz;
auto index = indexStart + sz;
auto indexInside = index % SRC_C4_UNIT;
auto indexOutside = index / SRC_C4_UNIT;
auto dstK = colAddrI + indexOutside * SRC_UNIT * DST_XUNIT + UNIT * indexInside;
//TODO Optimize it
for (int j=0; j<UNIT; ++j) {
dstK[j] = (int32_t)inputZ[j] - 128;
}
//*((int32_t*)dstK) = *((int32_t*)inputZ);
}
}
}
int32_t inputSumValue = 0;
#ifdef MNN_USE_NEON
int32x2_t inputSumValueC4 = vmov_n_s32(0);
#endif
for (int j = 0; j < countSumC8; ++j) {
auto colAddrIJ = colAddrI + j * SRC_UNIT * DST_XUNIT;
#ifdef MNN_USE_NEON
auto p0 = vld1_s8(colAddrIJ + 0);
auto p1 = vld1_s8(colAddrIJ + 8);
auto q0 = vpaddl_s8(p0);
auto q1 = vpaddl_s8(p1);
inputSumValueC4 += vpaddl_s16(q0);
inputSumValueC4 += vpaddl_s16(q1);
#else
for (int k = 0; k < SRC_UNIT; ++k) {
inputSumValue += colAddrIJ[k];
}
#endif
}
#ifdef MNN_USE_NEON
inputSumValue = inputSumValueC4[0] + inputSumValueC4[1];
#endif
inputSum[i] = inputSumValue * quanParamter->mFilterOffset;
}
}
ErrorCode CPUTFQuantizedConv2D::onExecute(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs) {
MNN_ASSERT(inputs.size() == 1);
MNN_ASSERT(outputs.size() == 1);
// Input tensor is of the following dimensions:
// [ batch, in_rows, in_cols, in_depth ]
const Tensor* input = inputs[0];
const int strideX = mIm2ColParamter->strideX;
const int strideY = mIm2ColParamter->strideY;
auto batchs = input->batch();
auto ic = input->channel();
auto iw = input->width();
auto ih = input->height();
auto output = outputs[0];
auto oc = output->channel();
auto oh = output->height();
auto ow = output->width();
auto ocUnit = UP_DIV(oc, UNIT);
int icDiv4 = UP_DIV(ic, UNIT);
int kh = mIm2ColParamter->kernelY;
int kw = mIm2ColParamter->kernelX;
auto kernelCountUnit = mIm2ColParamter->kernelCountUnit;
int outputCount = ow * oh;
int outputCountTile = UP_DIV(outputCount, DST_XUNIT);
bool fastMode = kw == 1 && kh == 1 && strideX == 1 && strideY == 1 && mIm2ColParamter->padY == 0 &&
mIm2ColParamter->padX == 0 && icDiv4 % SRC_C4_UNIT == 0;
auto gemmFunction = MNNGemmint8to32_8x4_Unit;
const int* biasData = mBias.get();
for (int batchIndex = 0; batchIndex < batchs; ++batchIndex) {
auto inputOrigin = input->host<uint8_t>() + batchIndex * input->stride(0);
auto weightOrigin = mWeight->host<int8_t>();
auto outputOrigin = output->host<uint8_t>() + batchIndex * output->stride(0);
MNN_CONCURRENCY_BEGIN(tId, mThreadNumber) {
auto colAddr = mTempBuffer.host<int8_t>() + tId * mTempBuffer.buffer().dim[0].stride;
auto gemmOutputAddr = mTempDstBuffer.host<int32_t>() + tId * mTempDstBuffer.buffer().dim[0].stride;
auto inputSum = mTempInputSum.host<int32_t>() + mTempInputSum.stride(0) * tId;
for (int tIndex = (int)tId; tIndex < outputCountTile; tIndex += mThreadNumber) {
int xIndexStart = tIndex * DST_XUNIT;
int realDstCount = ALIMIN(outputCount - xIndexStart, DST_XUNIT);
/*Im2Col Begin*/
if (fastMode) {
MNNLoadU8AndSum(inputSum, colAddr, inputOrigin + UNIT * xIndexStart, iw * ih * UNIT, icDiv4 / SRC_C4_UNIT,
realDstCount, mQuanParameter->mFilterOffset);
} else {
_im2ColCommon(inputSum, colAddr, inputOrigin, mQuanParameter, mIm2ColParamter, xIndexStart,
realDstCount);
}
/*Im2Col End*/
// GEMM
gemmFunction(gemmOutputAddr, colAddr, weightOrigin, inputSum, kernelCountUnit, UNIT * DST_XUNIT * sizeof(int32_t),
ocUnit);
/*Copy Data to Real Output*/
auto outputInTile = outputOrigin + xIndexStart * UNIT;
MNNQuanToDestUint8(outputInTile, gemmOutputAddr, biasData, ocUnit, realDstCount,
ow * oh * UNIT * sizeof(uint8_t), DST_XUNIT * UNIT * sizeof(int32_t),
mQuanParameter);
}
}
MNN_CONCURRENCY_END();
}
return NO_ERROR;
}
class CPUTFQuantizedConv2DCreator : public CPUBackend::Creator {
public:
virtual Execution* onCreate(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs,
const MNN::Op* op, Backend* backend) const {
return new CPUTFQuantizedConv2D(backend, op);
}
};
} // namespace MNN
#endif
namespace MNN {
REGISTER_CPU_OP_CREATOR_OLD(CPUTFQuantizedConv2DCreator, OpType_TfQuantizedConv2D);
}