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MNN/backupcode/cpubackend/CPULSTM.cpp

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//
// CPULSTM.cpp
// MNN
//
// Created by MNN on 2018/07/17.
// Copyright © 2018, Alibaba Group Holding Limited
//
#include "backend/cpu/CPULSTM.hpp"
#include <math.h>
#include "backend/cpu/CPUBackend.hpp"
#include "core/BufferAllocator.hpp"
#include "backend/cpu/compute/CommonOptFunction.h"
#include "core/Concurrency.h"
#include "core/Macro.h"
#include "core/TensorUtils.hpp"
#ifdef MNN_USE_NEON
#include <arm_neon.h>
#endif
namespace MNN {
static inline float sigmoid(float x) {
return 1. / (1. + expf(-x));
}
// copy data from src matrix to dst matrix, and align up to 4x4
static void copyWeightAlignUp4x4(float* dst, const float* src, int numUnits, int numFeatures, int devide) {
int permuteIndex[] = {0, 1, 2, 3};
if (devide) {
permuteIndex[2] = 3;
permuteIndex[3] = 2;
}
for (int i = 0; i < 4; ++i) {
const float* srcData = src + permuteIndex[i] * numUnits * numFeatures;
float* dstData = dst + i * numUnits * ALIGN_UP4(numFeatures);
int w = 0, outputIndex = 0;
for (; w + 3 < numFeatures; w += 4) {
for (int h = 0, inputIndex = w; h < numUnits; ++h, outputIndex += 4, inputIndex += numFeatures) {
dstData[outputIndex] = srcData[inputIndex];
dstData[outputIndex + 1] = srcData[inputIndex + 1];
dstData[outputIndex + 2] = srcData[inputIndex + 2];
dstData[outputIndex + 3] = srcData[inputIndex + 3];
}
}
if (w < numFeatures) {
for (int h = 0, inputIndex = w, ww; h < numUnits; ++h, inputIndex += numFeatures) {
for (ww = 0; ww < numFeatures - w; ++ww) {
dstData[outputIndex++] = srcData[inputIndex + ww];
}
for (; ww < 4; ++ww) {
dstData[outputIndex++] = 0;
}
}
}
}
}
CPULSTM::CPULSTM(Backend *backend, const LSTM *LSTM) : Execution(backend), mLSTM(LSTM) {
const int hiddenSize = mLSTM->outputCount();
int biasLength = 0;
if(mLSTM->bias()){
biasLength = mLSTM->bias()->float32s()->size();
}
mGateHaveBias = biasLength == 8 * hiddenSize;
}
CPULSTM::~CPULSTM() {
if (mInit) {
backend()->onReleaseBuffer(mWeightH.get(), Backend::STATIC);
backend()->onReleaseBuffer(mWeightI.get(), Backend::STATIC);
backend()->onReleaseBuffer(mBiasC.get(), Backend::STATIC);
}
}
ErrorCode CPULSTM::onResize(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
auto &input = inputs[0];
auto &output = outputs[0];
MNN_ASSERT(TensorUtils::getDescribe(input)->dimensionFormat == MNN_DATA_FORMAT_NC4HW4);
const int batch = input->buffer().dim[0].extent;
const int timeSteps = input->buffer().dim[1].extent;
const int numFeatures = input->buffer().dim[3].extent;
const int numUnits = output->buffer().dim[3].extent;
int eP, lP, hP;
MNNGetMatMulPackMode(&eP, &lP, &hP);
//MNN_PRINT("%d - %d - %d - %d\n", batch, timeSteps, numFeatures, numUnits);
mInput.buffer().dim[0].extent = batch * UP_DIV(timeSteps, hP);
mInput.buffer().dim[1].extent = numFeatures;
mInput.buffer().dim[2].extent = hP;
mInput.buffer().dimensions = 3;
TensorUtils::setLinearLayout(&mInput); // We must invoke setLinearLayout on mInput, otherwise stride value of tensor is incorrect
bool success = backend()->onAcquireBuffer(&mInput, Backend::DYNAMIC);
mTransposeInputFunction = [batch, timeSteps, numFeatures, hP](const float* src, float* dst) {
std::shared_ptr<Tensor> tempBuffer(Tensor::create<float>({timeSteps, numFeatures}));
for (int n=0; n<batch; ++n) {
auto source = src + n * ALIGN_UP4(timeSteps) * numFeatures;
auto temp = tempBuffer->host<float>();
auto dest = dst + n * UP_DIV(timeSteps, hP) * numFeatures * hP;
MNNUnpackC4(temp, source, numFeatures, timeSteps);
MNNPackForMatMul_B(dest, temp, timeSteps, 1, numFeatures, true);
}
};
// cont transform space
if (inputs.size() > 1) {
auto &cont = inputs[1];
TensorUtils::copyShape(cont, &mCont);
success = success && backend()->onAcquireBuffer(&mCont, Backend::DYNAMIC);
}
mOutput.buffer().dim[0].extent = timeSteps * numUnits;
mOutput.buffer().dimensions = 1;
success = success && backend()->onAcquireBuffer(&mOutput, Backend::DYNAMIC);
// divide weight & bias if needed
auto weightI = mLSTM->weightI();
auto weightH = mLSTM->weightH();
int weightSize = weightI->dims()->data()[0];
// gate space
mGates.buffer().dim[0].extent = batch * ALIGN_UP4(timeSteps) * numUnits * 4;
mGates.buffer().dimensions = 1;
success = success && backend()->onAcquireBuffer(&mGates, Backend::DYNAMIC);
memset(mGates.host<float>(), 0, mGates.size());
//MNN_PRINT("%d, %d\n", batch * ALIGN_UP4(timeSteps) * numUnits * 4, mGates.elementSize());
// cell space
mCell.buffer().dim[0].extent = numUnits;
mCell.buffer().dimensions = 1;
success = success && backend()->onAcquireBuffer(&mCell, Backend::DYNAMIC);
if (!success) {
return OUT_OF_MEMORY;
}
if (!mInit) {
mInit = true;
auto devide = weightI && !weightH && weightSize == 4 * numUnits * (numFeatures + numUnits + 2);
mWeightI.reset(Tensor::createDevice<float>(std::vector<int>{4, UP_DIV(numFeatures, 4), numUnits, 4}));
mWeightH.reset(Tensor::createDevice<float>(std::vector<int>{numUnits * numUnits * 4}));
if(mLSTM->weightH()){
MNN_ASSERT(mLSTM->weightH()->float32s()->size() == mWeightH->elementSize());
}
int biasSize = numUnits * 4;
if(mGateHaveBias){
biasSize = numUnits * 8;
}
mBiasC.reset(Tensor::createDevice<float>(std::vector<int>{biasSize}));
success = success && backend()->onAcquireBuffer(mWeightH.get(), Backend::STATIC);
success = success && backend()->onAcquireBuffer(mWeightI.get(), Backend::STATIC);
success = success && backend()->onAcquireBuffer(mBiasC.get(), Backend::STATIC);
if (!success) {
return OUT_OF_MEMORY;
}
copyWeightAlignUp4x4(mWeightI->host<float>(), mLSTM->weightI()->float32s()->data(), numUnits, numFeatures, devide);
if (devide) {
auto data = weightI->float32s()->data() + 4 * numUnits * numFeatures;
{
float *to = mWeightH->host<float>();
int step = numUnits * numUnits;
memcpy(to, data, 2 * step * sizeof(float));
to += 2 * step;
data += 2 * step; // IF
memcpy(to, data + step, step * sizeof(float)); // O
memcpy(to + step, data, step * sizeof(float)); // G
data += 2 * step;
}
{
float *to = mBiasC->host<float>();
int step = numUnits;
memcpy(to, data, 2 * step * sizeof(float));
to += 2 * step;
data += 2 * step; // IF
memcpy(to, data + step, step * sizeof(float)); // O
memcpy(to + step, data, step * sizeof(float)); // G
// data += 2 * step;
}
} else {
::memcpy(mBiasC->host<float>(), mLSTM->bias()->float32s()->data(), mBiasC->size());
::memcpy(mWeightH->host<float>(), mLSTM->weightH()->float32s()->data(), mWeightH->size());
}
if (mGateHaveBias) {
// Merge bias
auto biasPtr = mBiasC->host<float>();
auto biasPtr2 = biasPtr + 4 * numUnits;
for (int i=0; i<4*numUnits; ++i) {
biasPtr[i] = biasPtr[i] + biasPtr2[i];
}
}
}
if (inputs.size() > 1) {
backend()->onReleaseBuffer(&mCont, Backend::DYNAMIC);
}
backend()->onReleaseBuffer(&mOutput, Backend::DYNAMIC);
backend()->onReleaseBuffer(&mCell, Backend::DYNAMIC);
const int maxDepth = 5;
BufferAllocator* memoryPool = ((CPUBackend *)backend())->getBufferAllocator();
memoryPool->barrierBegin();
std::shared_ptr<void> __a(nullptr, [memoryPool](void *) { memoryPool->barrierEnd(); });
for (int i = 0; i < 4; ++i) {
float* weightData = mWeightI->host<float>() + i * mWeightI->stride(0);
mUnits[i].mTempWeight.reset(Tensor::create<float>(std::vector<int>{UP_DIV(numFeatures, 4), numUnits, 4}, weightData));
float* gateData = mGates.host<float>() + i * batch * ALIGN_UP4(timeSteps) * numUnits;
mUnits[i].mTempGates.reset(Tensor::create<float>(std::vector<int>{batch * UP_DIV(timeSteps, 4), numUnits, 4}, gateData));
mUnits[i].mTempInputVector = std::vector<Tensor*>{mUnits[i].mTempWeight.get(), &mInput};
mUnits[i].mTempOutputVector = std::vector<Tensor*>{mUnits[i].mTempGates.get()};
mUnits[i].mStracssenComputor.reset(new StrassenMatrixComputor(backend(), false, maxDepth));
mUnits[i].mStracssenComputor->onReset();
memoryPool->beginGroup();
std::shared_ptr<void> __b(nullptr, [memoryPool](void *) { memoryPool->endGroup(); });
mUnits[i].mStracssenComputor->onEncode(mUnits[i].mTempInputVector, mUnits[i].mTempOutputVector);
}
Tensor tempInternalTensor; // just for acquire memory efficiently
tempInternalTensor.buffer().dim[0].extent = 4 * batch * ALIGN_UP4(timeSteps) * numUnits;
tempInternalTensor.buffer().dimensions = 1;
success = success && backend()->onAcquireBuffer(&tempInternalTensor, Backend::DYNAMIC);
if (!success) {
return OUT_OF_MEMORY;
}
float* tempData = tempInternalTensor.host<float>();
backend()->onReleaseBuffer(&tempInternalTensor, Backend::DYNAMIC);
mRetriveOutputFunction = [batch, timeSteps, numUnits, tempData](float* gateData, const float* bias) {
const int itemSize = batch * ALIGN_UP4(timeSteps) * numUnits;
for (int i = 0; i < 4; ++i) {
MNNUnpackC4(tempData + i * itemSize, gateData + i * itemSize, numUnits, batch * ALIGN_UP4(timeSteps));
}
if(bias){
for (int i = 0, outputIndex = 0; i < itemSize; ++i, outputIndex += 4) {
const int biasIndex = i % numUnits;
gateData[outputIndex] = tempData[i] + bias[biasIndex]; // I
gateData[outputIndex + 1] = tempData[i + itemSize] + bias[biasIndex + numUnits]; // F
gateData[outputIndex + 2] = tempData[i + 2 * itemSize] + bias[biasIndex + 2 * numUnits]; // O
gateData[outputIndex + 3] = tempData[i + 3 * itemSize] + bias[biasIndex + 3 * numUnits]; // G
}
}else{
for (int i = 0, outputIndex = 0; i < itemSize; ++i, outputIndex += 4) {
gateData[outputIndex] = tempData[i]; // I
gateData[outputIndex + 1] = tempData[i + itemSize]; // F
gateData[outputIndex + 2] = tempData[i + 2 * itemSize]; // O
gateData[outputIndex + 3] = tempData[i + 3 * itemSize]; // G
}
}
};
backend()->onReleaseBuffer(&mInput, Backend::DYNAMIC);
backend()->onReleaseBuffer(&mGates, Backend::DYNAMIC);
return NO_ERROR;
}
ErrorCode CPULSTM::onExecute(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
auto &input = inputs[0];
auto &output = outputs[0];
const int batch = input->buffer().dim[0].extent;
const int timeSteps = input->buffer().dim[1].extent;
const int numUnits = output->buffer().dim[3].extent;
const int threadNumber = ((CPUBackend*)backend())->threadNumber();
mTransposeInputFunction(input->host<float>(), mInput.host<float>());
MNN_CONCURRENCY_BEGIN(index, 4) {
mUnits[index].mStracssenComputor->onExecute();
}
MNN_CONCURRENCY_END();
float* biasStartPtr = mBiasC->host<float>();
mRetriveOutputFunction(mGates.host<float>(), biasStartPtr);
// tranform
const float *contData = nullptr;
if (inputs.size() > 1) {
auto &cont = inputs[1];
MNNUnpackC4(mCont.host<float>(), cont->host<float>(), cont->width() * cont->height(), cont->channel());
contData = mCont.host<float>();
}
// calc weightHC
auto cellData = mCell.host<float>();
memset(cellData, 0, numUnits * sizeof(float));
const auto hcStep = batch * numUnits * numUnits;
for (int batchIndex = 0; batchIndex < batch; ++batchIndex) {
for (int ic = 0; ic < timeSteps; ic++) {
// clip hidden by continuation indicator
auto cont = ic > 0 && (!contData || contData[ic]);
auto outChannel = mOutput.host<float>() + ic * numUnits;
MNN_CONCURRENCY_BEGIN(tId, threadNumber) {
auto gatesPtr = mGates.host<const float>() + ic * numUnits * 4 + tId * 4 + batchIndex * timeSteps * numUnits * 4;
auto weightHCI = mWeightH->host<const float>() + numUnits * tId;
for (int oc = (int)tId; oc < numUnits; oc += threadNumber, gatesPtr += 4 * threadNumber, weightHCI += numUnits * threadNumber) {
float I = gatesPtr[0], F = gatesPtr[1], O = gatesPtr[2], G = gatesPtr[3];
// hidden
if (cont) {
auto weightHCF = weightHCI + hcStep;
auto weightHCO = weightHCF + hcStep;
auto weightHCG = weightHCO + hcStep;
auto hiddenPtr = mOutput.host<float>() + (ic - 1) * numUnits;
int i = 0;
#ifdef MNN_USE_NEON
float32x4_t Ix4 = vdupq_n_f32(0);
float32x4_t Fx4 = vdupq_n_f32(0);
float32x4_t Ox4 = vdupq_n_f32(0);
float32x4_t Gx4 = vdupq_n_f32(0);
for (; i + 3 < numUnits; i += 4) {
const float32x4_t hiddenData = vld1q_f32(hiddenPtr + i);
Ix4 += vld1q_f32(weightHCI + i) * hiddenData;
Fx4 += vld1q_f32(weightHCF + i) * hiddenData;
Ox4 += vld1q_f32(weightHCO + i) * hiddenData;
Gx4 += vld1q_f32(weightHCG + i) * hiddenData;
}
#if !(defined(__ARM_FEATURE_FMA) && defined(__aarch64__))
#define vaddvq_f32(__v4) (__v4[0] + __v4[1] + __v4[2] + __v4[3]) // support A64 only
#endif
I += vaddvq_f32(Ix4);
F += vaddvq_f32(Fx4);
O += vaddvq_f32(Ox4);
G += vaddvq_f32(Gx4);
#endif
for (; i < numUnits; i++) {
const float hiddenData = hiddenPtr[i];
I += weightHCI[i] * hiddenData;
F += weightHCF[i] * hiddenData;
O += weightHCO[i] * hiddenData;
G += weightHCG[i] * hiddenData;
}
}
// add bias
//MNN_PRINT("%f, %f, %f, %f\n", I, O, F, G);
I = sigmoid(I);
F = sigmoid(F);
O = sigmoid(O);
G = tanhf(G);
auto newCell = F * cellData[oc] + I * G;
cellData[oc] = newCell;
auto H = O * tanhf(newCell);
outChannel[oc] = H;
}
}
MNN_CONCURRENCY_END();
}
MNNPackC4(output->host<float>() + batchIndex * output->stride(0), mOutput.host<float>(), output->width() * output->height(), output->channel());
}
return NO_ERROR;
}
class CPULSTMCreator : 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 CPULSTM(backend, op->main_as_LSTM());
}
};
REGISTER_CPU_OP_CREATOR(CPULSTMCreator, OpType_LSTM);
} // namespace MNN
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