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ffCudaNn.cpp
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ffCudaNn.cpp
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#include "ffCudaNn.h"
#include <cuda_runtime.h>
#include <assert.h>
#include <random>
#include <chrono>
#define K_THREAD_PER_BLOCK 512
#define K_SMALL_THREAD_PER_BLOCK 64
namespace ff
{
///////////////////////////////////////////////////////////////////////
//std::default_random_engine g_generator;
std::default_random_engine g_generator(static_cast<int>(std::chrono::steady_clock::now().time_since_epoch().count()));
std::uniform_real_distribution<float> g_uniformDistribution;
static std::normal_distribution<float> g_normalDistribution(0.0f, 1.0f);
CudaTensor::CudaTensor() : _d0(0), _d1(0), _d2(0), _d3(0), _dataSize(0), _dataGpu(nullptr), _dataGpuSize(0)
{
}
CudaTensor::CudaTensor(int d0, int d1, int d2, int d3) : _dataGpu(nullptr), _dataGpuSize(0)
{
ResetTensor(d0, d1, d2, d3);
}
CudaTensor::~CudaTensor()
{
if (nullptr != _dataGpu) cudaFree(_dataGpu);
}
CudaTensor::CudaTensor(const CudaTensor& rhs)
{
assert(0); // DISALLOW_COPY_AND_ASSIGN
}
CudaTensor& CudaTensor::operator=(const CudaTensor& rhs)
{
assert(0); // DISALLOW_COPY_AND_ASSIGN
return *this;
}
void CudaTensor::ResetTensor(int d0, int d1, int d2, int d3)
{
_d0 = d0; _d1 = d1; _d2 = d2; _d3 = d3;
_dataSize = _d0 * _d1 * _d2 * _d3;
_data.resize(_dataSize);
if (_dataGpuSize < _dataSize)
{
_dataGpuSize = _dataSize;
if (_dataGpu) cudaFree(_dataGpu);
cudaError_t err = cudaMalloc(&_dataGpu, _dataGpuSize * sizeof(float));
assert(err == cudaSuccess);
//printf("%s\n", cudaGetErrorName(cudaGetLastError()));
//printf("%s\n", cudaGetErrorString(cudaGetLastError()));
}
}
void CudaTensor::Reshape(int d0, int d1, int d2, int d3)
{
assert((d0 * d1 * d2 * d3) == _dataSize);
_d0 = d0; _d1 = d1; _d2 = d2; _d3 = d3;
}
void CudaTensor::SetRandom(const float multiplier)
{
for (int i = 0; i < _dataSize; ++i)
{
_data[i] = (g_uniformDistribution(g_generator) * 2.0f - 1.0f) * multiplier;
}
PushToGpu();
}
void CudaTensor::SetZero()
{
memset(&_data[0], 0, _data.size() * sizeof(float));
PushToGpu();
}
void CudaTensor::SetDropoutMask(float zeroRatio)
{
assert(zeroRatio > 0.0f && zeroRatio < 1.0f);
float s = 1.0f / (1.0f - zeroRatio);
for (int i = 0; i < _dataSize; ++i)
{
_data[i] = 0.0f;
if (g_uniformDistribution(g_generator) > zeroRatio)
_data[i] = s;
}
PushToGpu();
}
void CudaTensor::PushToGpu()
{
cudaError_t err = cudaMemcpy(_dataGpu, &_data[0], _dataSize * sizeof(float), cudaMemcpyKind::cudaMemcpyHostToDevice);
assert(err == cudaSuccess);
}
void CudaTensor::PullFromGpu()
{
cudaError_t err = cudaMemcpy(&_data[0], _dataGpu, _dataSize * sizeof(float), cudaMemcpyKind::cudaMemcpyDeviceToHost);
assert(err == cudaSuccess);
}
///////////////////////////////////////////////////////////////////////
FcLayer::FcLayer(CudaNn* nn, int inDim, int outDim) : CudaLayer(nn), _pX(nullptr)
{
_w.ResetTensor(outDim, inDim);
_w.SetRandom(1.0f / sqrtf((float)inDim));
_wG.ResetTensor(outDim, inDim);
_wG_m.ResetTensor(outDim, inDim);
_wG_m.SetZero();
_wG_v.ResetTensor(outDim, inDim);
_wG_v.SetZero();
_b.ResetTensor(outDim);
_b.SetRandom(1.0f / sqrtf((float)inDim));
_bG.ResetTensor(outDim);
_bG_m.ResetTensor(outDim);
_bG_m.SetZero();
_bG_v.ResetTensor(outDim);
_bG_v.SetZero();
}
__global__ void LinearTransform_Cuda(float* y, const float* x, const float* w, const float* b, int nColX, int nColW, int nJobs)
{
const int yIndex = blockIdx.x * blockDim.x + threadIdx.x;
if (yIndex >= nJobs) return;
int rowX = yIndex / nColW;
int colW = yIndex % nColW;
float v = 0.0f;
int xBaseIndex = rowX * nColX;
for (int i = 0; i < nColX; ++i)
{
v += x[i + xBaseIndex] * w[colW + i * nColW];
}
y[yIndex] = v + b[colW];
}
const CudaTensor* FcLayer::Forward(const CudaTensor* x)
{
int x_d0 = x->_d0;
int x_d1 = x->_d1;
if (x_d0 != _w._d1)
{
x_d0 = x->_d0 * x->_d1 * x->_d2;
x_d1 = x->_d3;
}
assert(x_d0 == _w._d1);
_pX = x;
_y.ResetTensor(_w._d0, x_d1);
// y = xw+b
int nJobs = x_d1 * _w._d0;
int numBlocks = (nJobs + K_THREAD_PER_BLOCK - 1) / K_THREAD_PER_BLOCK;
dim3 block(numBlocks), threads(K_THREAD_PER_BLOCK);
LinearTransform_Cuda <<< block, threads >>> (_y._dataGpu, _pX->_dataGpu, _w._dataGpu, _b._dataGpu, x_d0, _w._d0, nJobs);
assert(cudaGetLastError() == cudaSuccess);
return &_y;
}
__global__ void BackwardFc_Wg_Cuda(float* wG, const float* x, const float* yG, int nXcol, int nXrow, int nWcol, int nJobs)
{
const int wGindex = blockIdx.x * K_THREAD_PER_BLOCK + threadIdx.x;
if (wGindex >= nJobs) return;
int r = wGindex / nWcol;
int c = wGindex % nWcol;
// wG = x.T * yG
float val = 0.0f;
for (int i = 0; i < nXrow; ++i)
{
val += x[r + i * nXcol] * yG[c + i * nWcol];
}
//wG[wGindex] = val / nXrow;
wG[wGindex] = val;
}
__global__ void BackwardFc_Bg_Cuda(float* bG, const float* yG, int nYgCol, int nYgRow)
{
const int c = blockIdx.x * blockDim.x + threadIdx.x;
if (c >= nYgCol) return;
float val = 0.0f;
for (int i = 0; i < nYgRow; ++i)
{
val += yG[c + i * nYgCol];
}
//bG[c] = val / nYgRow;
bG[c] = val;
}
__global__ void BackwardFc_Xg_Cuda(float* xG, const float* yG, const float* w, int yGw, int wTh, int xGw, int nJobs)
{
const int xGindex = blockIdx.x * blockDim.x + threadIdx.x;
if (xGindex >= nJobs) return;
int r = xGindex / xGw;
int c = xGindex % xGw;
// xG = yG * w.T
float val = 0.0f;
int yGbaseIndex = r * yGw;
int wBaseIndex = c * wTh;
for (int i = 0; i < yGw; ++i)
{
val += yG[i + yGbaseIndex] * w[i + wBaseIndex];
}
xG[xGindex] = val;
}
const CudaTensor* FcLayer::Backward(const CudaTensor* yG, const int layerIndex)
{
assert(yG->_d0 == _wG._d0);
int x_d0 = _pX->_d0;
int x_d1 = _pX->_d1;
if (x_d0 != _w._d1)
{
x_d0 = _pX->_d0 * _pX->_d1 * _pX->_d2;
x_d1 = _pX->_d3;
}
{
// wG = x.T * yG
int nJobs = _wG._d1 * _wG._d0;
int numBlocks = (nJobs + K_THREAD_PER_BLOCK - 1) / K_THREAD_PER_BLOCK;
dim3 block(numBlocks), threads(K_THREAD_PER_BLOCK);
BackwardFc_Wg_Cuda << < block, threads >> > (_wG._dataGpu, _pX->_dataGpu, yG->_dataGpu, x_d0, x_d1, _wG._d0, nJobs);
assert(cudaGetLastError() == cudaSuccess);
}
{
int numBlocks = (_b._d0 + K_THREAD_PER_BLOCK - 1) / K_THREAD_PER_BLOCK;
dim3 block(numBlocks), threads(K_THREAD_PER_BLOCK);
BackwardFc_Bg_Cuda << < block, threads >> > (_bG._dataGpu, yG->_dataGpu, yG->_d0, yG->_d1);
assert(cudaGetLastError() == cudaSuccess);
}
if (layerIndex > 0)
{
assert(yG->_d1 == x_d1);
_xG.ResetTensor(x_d0, x_d1);
// xG = yG * w.T
int nJobs = _xG._d1 * _xG._d0;
int numBlocks = (nJobs + K_THREAD_PER_BLOCK - 1) / K_THREAD_PER_BLOCK;
dim3 block(numBlocks), threads(K_THREAD_PER_BLOCK);
BackwardFc_Xg_Cuda << < block, threads >> > (_xG._dataGpu, yG->_dataGpu, _w._dataGpu, yG->_d0, _w._d0, _xG._d0, nJobs);
assert(cudaGetLastError() == cudaSuccess);
}
return &_xG;
}
__global__ void UpdateWs_Cuda(float learningRate, float beta1, float beta2, float beta1t, float beta2t,
float* w, const float* wG, float* wG_m, float* wG_v, int nJobs)
{
int index = blockIdx.x * blockDim.x + threadIdx.x;
if (index >= nJobs) return;
// Vanilla
//w[index] -= wG[index] * learningRate;
// Adam
float g = wG[index];
float m = wG_m[index];
float v = wG_v[index];
m = beta1 * m + (1.0f - beta1) * g;
v = beta2 * v + (1.0f - beta2) * g * g;
float unbiased_m = m / (1.0f - beta1t);
float unbiased_v = v / (1.0f - beta2t);
wG_m[index] = m;
wG_v[index] = v;
float impv = learningRate * unbiased_m / (sqrtf(unbiased_v) + 1e-8f);
w[index] -= impv;
}
void FcLayer::UpdateWs(float learningRate, float beta1, float beta2, float beta1t, float beta2t)
{
{
int nJobs = _w._d1 * _w._d0;
int numBlocks = (nJobs + K_THREAD_PER_BLOCK - 1) / K_THREAD_PER_BLOCK;
dim3 block(numBlocks), threads(K_THREAD_PER_BLOCK);
UpdateWs_Cuda <<<block, threads >>> (learningRate, beta1, beta2, beta1t, beta2t, _w._dataGpu, _wG._dataGpu, _wG_m._dataGpu, _wG_v._dataGpu, nJobs);
assert(cudaGetLastError() == cudaSuccess);
}
{
int numBlocks = (_b._d0 + K_THREAD_PER_BLOCK - 1) / K_THREAD_PER_BLOCK;
dim3 block(numBlocks), threads(K_THREAD_PER_BLOCK);
UpdateWs_Cuda <<< block, threads >>> (learningRate, beta1, beta2, beta1t, beta2t, _b._dataGpu, _bG._dataGpu, _bG_m._dataGpu, _bG_v._dataGpu, _b._d0);
assert(cudaGetLastError() == cudaSuccess);
}
}
void FcLayer::Pull()
{
_xG.PullFromGpu();
_w.PullFromGpu();
_wG.PullFromGpu();
_wG_m.PullFromGpu();
_wG_v.PullFromGpu();
_b.PullFromGpu();
_bG.PullFromGpu();
_bG_m.PullFromGpu();
_bG_v.PullFromGpu();
_y.PullFromGpu();
}
Conv2dLayer::Conv2dLayer(CudaNn* nn, int kernelSize, int nInChannel, int nOutChannel, int stride, int padding) :
CudaLayer(nn), _kernelSize(kernelSize), _stride(stride), _padding(padding)
{
_w.ResetTensor(_kernelSize, _kernelSize, nInChannel, nOutChannel);
_w.SetRandom(1.0f / sqrtf(float(_kernelSize * _kernelSize * nInChannel)));
_wG.ResetTensor(_kernelSize, _kernelSize, nInChannel, nOutChannel);
_wG_m.ResetTensor(_kernelSize, _kernelSize, nInChannel, nOutChannel);
_wG_m.SetZero();
_wG_v.ResetTensor(_kernelSize, _kernelSize, nInChannel, nOutChannel);
_wG_v.SetZero();
_b.ResetTensor(nOutChannel);
_b.SetRandom(1.0f / sqrtf(float(_kernelSize * _kernelSize * nInChannel)));
_bG.ResetTensor(nOutChannel);
_bG_m.ResetTensor(nOutChannel);
_bG_m.SetZero();
_bG_v.ResetTensor(nOutChannel);
_bG_v.SetZero();
}
__global__ void ForwardConv2d_Cuda(
float* y, const float* x, const float* w, const float* b,
int nOutChannel, int nRowY, int nColY,
int nInChannel, int nRowX, int nColX,
int kernelSize, int stride, int padding, int nJobs)
{
int index = blockIdx.x * blockDim.x + threadIdx.x;
if (index >= nJobs) return;
const int yIndex = index;
int image = index / (nOutChannel * nRowY * nColY);
index -= image * (nOutChannel * nRowY * nColY);
int outChannel = index / (nRowY * nColY);
index -= outChannel * (nRowY * nColY);
int rowY = index / nColY;
int colY = index % nColY;
float val = 0.0f;
int startRowX = rowY * stride - padding;
int startColX = colY * stride - padding;
int startRowKernel= max(0, padding - (rowY * stride));
int startColKernel = max(0, padding - (colY * stride));
for (int rowX = startRowX + startRowKernel; rowX < startRowX + kernelSize; ++rowX)
{
if (rowX >= nRowX) break;
for (int colX = startColX + startColKernel; colX < startColX + kernelSize; ++colX)
{
if (colX >= nColX) break;
for (int inChannel = 0; inChannel < nInChannel; ++inChannel)
{
int xBaseIndex = image * (nInChannel * nRowX * nColX) + inChannel * (nRowX * nColX) + rowX * nColX;
int wBaseIndex = outChannel * (nInChannel * kernelSize * kernelSize) + inChannel * (kernelSize * kernelSize) + (rowX - startRowX) * kernelSize;
val += x[xBaseIndex + colX] * w[wBaseIndex + (colX - startColX)];
}
}
}
y[yIndex] = val + b[outChannel];
}
const CudaTensor* Conv2dLayer::Forward(const CudaTensor* x)
{
assert(x->_d2 == _w._d2); // Check # of input channels
// (N - F) / stride + 1
_pX = x;
int nColY = (_pX->_d0 + _padding * 2 - _w._d0) / _stride + 1;
int nRowY = (_pX->_d1 + _padding * 2 - _w._d1) / _stride + 1;
// x x y x nOutChannel x nImages
_y.ResetTensor(nColY, nRowY, _w._d3, _pX->_d3);
int nJobs = _y._dataSize;
int numBlocks = (nJobs + K_THREAD_PER_BLOCK - 1) / K_THREAD_PER_BLOCK;
dim3 blocks(numBlocks), threads(K_THREAD_PER_BLOCK);
ForwardConv2d_Cuda<<<blocks, threads>>>(
_y._dataGpu, _pX->_dataGpu, _w._dataGpu, _b._dataGpu,
_y._d2, _y._d1, _y._d0,
_pX->_d2, _pX->_d1, _pX->_d0,
_kernelSize, _stride, _padding, nJobs);
assert(cudaGetLastError() == cudaSuccess);
return &_y;
}
__global__ void BackwardConv2d_Wg_Cuda(
float* wG, const float* x, const float* yG,
int nOutChannel, int nInChannel,
int nImages, int nRowY, int nColY, int nRowX, int nColX,
int kernelSize, int stride, int padding, int nJobs)
{
int index = blockIdx.x * blockDim.x + threadIdx.x;
if (index >= nJobs) return;
const int wIndex = index;
int outChannel = index / (nInChannel * kernelSize * kernelSize);
index -= outChannel * (nInChannel * kernelSize * kernelSize);
int inChannel = index / (kernelSize * kernelSize);
index -= inChannel * (kernelSize * kernelSize);
int rowW = index / kernelSize;
int colW = index % kernelSize;
float val = 0.0f;
int startRowY = max(int(ceil((padding - rowW) / float(stride))), 0);
int startColY = max(int(ceil((padding - colW) / float(stride))), 0);
for (int image = 0; image < nImages; ++image)
{
int yBaseIndex = image * (nOutChannel * nRowY * nColY) + outChannel * (nRowY * nColY);
int xBaseIndex = image * (nInChannel * nRowX * nColX) + inChannel * (nRowX * nColX);
for (int rowY = startRowY; rowY < nRowY; ++rowY)
{
int rowX = rowY * stride - padding + rowW;
if (rowX >= nRowX) break;
for (int colY = startColY; colY < nColY; ++colY)
{
int colX = colY * stride - padding + colW;
if (colX >= nColX) break;
int yIndex = yBaseIndex + rowY * nColY + colY;
int xIndex = xBaseIndex + rowX * nColX + colX;
val += x[xIndex] * yG[yIndex];
}
}
}
//wG[wIndex] = val / nImages;
wG[wIndex] = val;
}
__global__ void BackwardConv2d_Bg_Cuda(float* bG, const float* yG, int nImages, int nOutChannel, int nRowY, int nColY)
{
const int outChannel = blockIdx.x * blockDim.x + threadIdx.x;
if (outChannel >= nOutChannel) return;
float val = 0.0f;
for (int image = 0; image < nImages; ++image)
{
for (int rowY = 0; rowY < nRowY; ++rowY)
{
int yBaseIndex = image * (nOutChannel * nRowY * nColY) + outChannel * (nRowY * nColY) + rowY * nColY;
for (int colY = 0; colY < nColY; ++colY)
{
val += yG[yBaseIndex + colY];
}
}
}
//bG[outChannel] = val / nImages;
bG[outChannel] = val;
}
__global__ void BackwardConv2d_Xg_Cuda(
float* xG, const float* w, const float* yG,
int nImages, int nInChannel, int nRowX, int nColX,
int nOutChannel, int nRowY, int nColY,
int kernelSize, int stride, int padding, int nJobs)
{
int index = blockIdx.x * blockDim.x + threadIdx.x;
if (index >= nJobs) return;
int xIndex = index;
int image = index / (nInChannel * nRowX * nColX);
index -= image * (nInChannel * nRowX * nColX);
int inChannel = index / (nRowX * nColX);
index -= inChannel * (nRowX * nColX);
int rowX = index / nColX;
int colX = index % nColX;
float val = 0.0f;
// Note(dongwook): Iterate all y's which use the current x
int startRowY = max(0, int(floor(float(rowX + padding - kernelSize) / stride)) + 1);
int startColY = max(0, int(floor(float(colX + padding - kernelSize) / stride)) + 1);
for (int rowY = startRowY; rowY < nRowY; ++rowY)
{
int upperX = rowY * stride - padding;
if (rowX < upperX) break;
int rowW = rowX - upperX;
for (int colY = startColY; colY < nColY; ++colY)
{
int leftX = colY * stride - padding;
if (colX < leftX) break;
int colW = colX - leftX;
for (int outChannel = 0; outChannel < nOutChannel; ++outChannel)
{
int yIndex = image * (nOutChannel * nRowY * nColY) + outChannel * (nRowY * nColY) + rowY * nColY + colY;
int wIndex = outChannel * (nInChannel * kernelSize * kernelSize) + inChannel * (kernelSize * kernelSize) + rowW * kernelSize + colW;
val += w[wIndex] * yG[yIndex];
}
}
}
xG[xIndex] = val;
}
const CudaTensor* Conv2dLayer::Backward(const CudaTensor* yG, const int layerIndex)
{
assert(_y._dataSize == yG->_dataSize);
_xG.ResetTensor(_pX->_d0, _pX->_d1, _pX->_d2, _pX->_d3);
{
assert(_y._d3 <= 256);
int nJobs = _wG._dataSize;
int numBlocks = (nJobs + K_SMALL_THREAD_PER_BLOCK - 1) / K_SMALL_THREAD_PER_BLOCK;
dim3 blocks(numBlocks), threads(K_SMALL_THREAD_PER_BLOCK);
BackwardConv2d_Wg_Cuda <<<blocks, threads >>> (
_wG._dataGpu, _pX->_dataGpu, yG->_dataGpu,
_wG._d3, _wG._d2,
_y._d3, _y._d1, _y._d0, _pX->_d1, _pX->_d0,
_kernelSize, _stride, _padding, nJobs);
assert(cudaGetLastError() == cudaSuccess);
}
{
assert(_bG._d0 == _y._d2);
int nJobs = _bG._d0;
int numBlocks = (nJobs + K_THREAD_PER_BLOCK - 1) / K_THREAD_PER_BLOCK;
dim3 blocks(numBlocks), threads(K_THREAD_PER_BLOCK);
BackwardConv2d_Bg_Cuda <<< blocks, threads >>> (_bG._dataGpu, yG->_dataGpu, _y._d3, _y._d2, _y._d1, _y._d0);
assert(cudaGetLastError() == cudaSuccess);
}
if (layerIndex > 0)
{
int nJobs = _xG._dataSize;
int numBlocks = (nJobs + K_THREAD_PER_BLOCK - 1) / K_THREAD_PER_BLOCK;
dim3 blocks(numBlocks), threads(K_THREAD_PER_BLOCK);
BackwardConv2d_Xg_Cuda <<<blocks, threads >>> (
_xG._dataGpu, _w._dataGpu, yG->_dataGpu,
_xG._d3, _xG._d2, _xG._d1, _xG._d0,
_y._d2, _y._d1, _y._d0,
_kernelSize, _stride, _padding, nJobs);
assert(cudaGetLastError() == cudaSuccess);
}
return &_xG;
}
void Conv2dLayer::UpdateWs(float learningRate, float beta1, float beta2, float beta1t, float beta2t)
{
{
int nJobs = _w._dataSize;
int numBlocks = (nJobs + K_THREAD_PER_BLOCK - 1) / K_THREAD_PER_BLOCK;
dim3 block(numBlocks), threads(K_THREAD_PER_BLOCK);
UpdateWs_Cuda <<< block, threads >>> (learningRate, beta1, beta2, beta1t, beta2t, _w._dataGpu, _wG._dataGpu, _wG_m._dataGpu, _wG_v._dataGpu, nJobs);
assert(cudaGetLastError() == cudaSuccess);
}
{
int numBlocks = (_b._d0 + K_THREAD_PER_BLOCK - 1) / K_THREAD_PER_BLOCK;
dim3 block(numBlocks), threads(K_THREAD_PER_BLOCK);
UpdateWs_Cuda <<< block, threads >>> (learningRate, beta1, beta2, beta1t, beta2t, _b._dataGpu, _bG._dataGpu, _bG_m._dataGpu, _bG_v._dataGpu, _b._d0);
assert(cudaGetLastError() == cudaSuccess);
}
}
void Conv2dLayer::Pull()
{
_xG.PullFromGpu();
_w.PullFromGpu();
_wG.PullFromGpu();
_wG_m.PullFromGpu();
_wG_v.PullFromGpu();
_b.PullFromGpu();
_bG.PullFromGpu();
_bG_m.PullFromGpu();
_bG_v.PullFromGpu();
_y.PullFromGpu();
}
__global__ void ForwardMaxPool_Cuda(float* y, float* maxIndex, const float* x, int nImages, int nInChannel, int nRow, int nCol)
{
int index = blockIdx.x * blockDim.x + threadIdx.x;
int nJobs = nImages * nInChannel * nRow * nCol;
if (nJobs <= index) return;
const int yIndex = index;
int image = index / (nInChannel * nRow * nCol);
index -= image * (nInChannel * nRow * nCol);
int inChannel = index / (nRow * nCol);
index -= inChannel * (nRow * nCol);
int row = index / nCol;
int col = index % nCol;
const int offset[4][4] = {
0, 1, nCol * 2, nCol * 2 + 1,
1, nCol * 2, nCol * 2 + 1, 0,
nCol * 2, nCol * 2 + 1, 0, 1,
nCol * 2 + 1, 0, 1, nCol * 2
};
int s = nJobs % 4;
int xIndex = image * (nInChannel * nRow * nCol * 4) + inChannel * (nRow * nCol * 4) + row * nCol * 4 + col * 2;
float maxVal = x[xIndex + offset[s][0]], maxIdx = (float)offset[s][0];
if (maxVal < x[xIndex + offset[s][1]])
{
maxIdx = (float)offset[s][1];
maxVal = x[xIndex + offset[s][1]];
}
if (maxVal < x[xIndex + offset[s][2]])
{
maxIdx = (float)offset[s][2];
maxVal = x[xIndex + offset[s][2]];
}
if (maxVal < x[xIndex + offset[s][3]])
{
maxIdx = (float)offset[s][3];
maxVal = x[xIndex + offset[s][3]];
}
maxIndex[yIndex] = maxIdx;
y[yIndex] = maxVal;
}
const CudaTensor* MaxPoolLayer::Forward(const CudaTensor* x)
{
assert(0 == (x->_d0 % 2) && 0 == (x->_d1 % 2));
_pX = x;
_y.ResetTensor(x->_d0 / 2, x->_d1 / 2, x->_d2, x->_d3);
_maxIndex.ResetTensor(_y._d0, _y._d1, _y._d2, _y._d3);
int nJobs = _y._dataSize;
int numBlocks = (nJobs + K_THREAD_PER_BLOCK - 1) / K_THREAD_PER_BLOCK;
dim3 block(numBlocks), threads(K_THREAD_PER_BLOCK);
ForwardMaxPool_Cuda <<< block, threads >>> (_y._dataGpu, _maxIndex._dataGpu, _pX->_dataGpu, _y._d3, _y._d2, _y._d1, _y._d0);
assert(cudaGetLastError() == cudaSuccess);
return &_y;
}
__global__ void BackwardMaxPool_Cuda(float* xG, const float* yG, const float* maxIndex, int nImages, int nOutChannel, int nRow, int nCol)
{
int index = blockIdx.x * blockDim.x + threadIdx.x;
int nJobs = nImages * nOutChannel * nRow * nCol;
if (nJobs <= index) return;
int yGindex = index;
int image = index / (nOutChannel * nRow * nCol);
index -= image * (nOutChannel * nRow * nCol);
int outChannel = index / (nRow * nCol);
index -= outChannel * (nRow * nCol);
int row = index / nCol;
int col = index % nCol;
int xGindex = image * (nOutChannel * nRow * nCol * 4) + outChannel * (nRow * nCol * 4) + row * nCol * 4 + col * 2;
xG[xGindex] = 0.0f;
xG[xGindex + 1] = 0.0f;
xG[xGindex + nCol * 2] = 0.0f;
xG[xGindex + nCol * 2 + 1] = 0.0f;
xG[xGindex + (int)maxIndex[yGindex]] = yG[yGindex];
}
const CudaTensor* MaxPoolLayer::Backward(const CudaTensor* yG, const int layerIndex)
{
assert(_y._dataSize == yG->_dataSize);
_xG.ResetTensor(2 * _y._d0, 2 * _y._d1, _y._d2, _y._d3);
if (layerIndex > 0)
{
int nJobs = _y._dataSize;
int numBlocks = (nJobs + K_THREAD_PER_BLOCK - 1) / K_THREAD_PER_BLOCK;
dim3 block(numBlocks), threads(K_THREAD_PER_BLOCK);
BackwardMaxPool_Cuda <<< block, threads >>> (_xG._dataGpu, yG->_dataGpu, _maxIndex._dataGpu, _y._d3, _y._d2, _y._d1, _y._d0);
assert(cudaGetLastError() == cudaSuccess);
}
return &_xG;
}
void MaxPoolLayer::Pull()
{
_maxIndex.PullFromGpu();
_xG.PullFromGpu();
_y.PullFromGpu();
}
__global__ void ForwardRelu_Cuda(float* relu_x, const float* x, int nJobs)
{
int jobIndex = blockIdx.x * blockDim.x + threadIdx.x;
if (jobIndex >= nJobs) return;
relu_x[jobIndex] = fmaxf(x[jobIndex], 0.0f);
}
const CudaTensor* ReluLayer::Forward(const CudaTensor* x)
{
_pX = x;
_xRelu.ResetTensor(_pX->_d0, _pX->_d1, _pX->_d2, _pX->_d3);
int nJobs = _xRelu._dataSize;
int numBlocks = (nJobs + K_THREAD_PER_BLOCK - 1) / K_THREAD_PER_BLOCK;
dim3 block(numBlocks), threads(K_THREAD_PER_BLOCK);
ForwardRelu_Cuda <<< block, threads >>> (_xRelu._dataGpu, _pX->_dataGpu, nJobs);
assert(cudaGetLastError() == cudaSuccess);
return &_xRelu;
}
__global__ void BackwardRelu_Cuda(float* xG, const float* yG, const float* x, int nJobs)
{
int jobIndex = blockIdx.x * blockDim.x + threadIdx.x;
if (jobIndex >= nJobs) return;
xG[jobIndex] = x[jobIndex] < 0.0f ? 0.0f : yG[jobIndex];
}
const CudaTensor* ReluLayer::Backward(const CudaTensor* yG, const int layerIndex)
{
if (layerIndex > 0)
{
_xG.ResetTensor(_xRelu._d0, _xRelu._d1, _xRelu._d2, _xRelu._d3);
int nJobs = _xRelu._dataSize;
int numBlocks = (nJobs + K_THREAD_PER_BLOCK - 1) / K_THREAD_PER_BLOCK;
dim3 block(numBlocks), threads(K_THREAD_PER_BLOCK);
BackwardRelu_Cuda <<< block, threads >>> (_xG._dataGpu, yG->_dataGpu, _pX->_dataGpu, nJobs);
assert(cudaGetLastError() == cudaSuccess);
}
return &_xG;
}
void ReluLayer::Pull()
{
_xRelu.PullFromGpu();
_xG.PullFromGpu();
}
BatchNorm2dLayer::BatchNorm2dLayer(CudaNn* nn, int inDim) : CudaLayer(nn), _pX(nullptr), _accCount(0)
{
_meanAndVariance.ResetTensor(2, inDim);
_meanAndVarianceAcc.ResetTensor(2, inDim);
_meanAndVarianceAcc.SetZero();
_meanAndVarianceG.ResetTensor(2, inDim);
_w.ResetTensor(2, inDim);
for (int i = 0; i < inDim; ++i)
{
_w._data[i*2] = 1.0f; // alpha
_w._data[i*2+1] = 0.0f; // beta
}
_w.PushToGpu();
_wG.ResetTensor(2, inDim);
_wG_m.ResetTensor(2, inDim);
_wG_m.SetZero();
_wG_v.ResetTensor(2, inDim);
_wG_v.SetZero();
}
template<int BLOCK_SIZE> __global__ void ForwardBatchNorm2d_Train_Cuda(
float* meanAndVariance, float* meanAndVarianceAcc, float* xHat, float* y,
const float* w, const float* x, int nRow, int nCol)
{
int nChannel = gridDim.x;
int nImages = blockDim.x;
int ch = blockIdx.x;
int image = threadIdx.x;
int mDash = nImages * nRow * nCol;
int imageStride = nChannel * nRow * nCol;
int channelStride = nRow * nCol;
int currChBaseIndex = ch * channelStride;
int baseIndex = image * imageStride + currChBaseIndex;
__shared__ float meanArr[BLOCK_SIZE];
meanArr[image] = 0.0f;
for (int i = 0; i < channelStride; ++i)
{
meanArr[image] += x[baseIndex + i];
}
__syncthreads();
float mean = 0.0f;
for (int i = 0; i < nImages; ++i)
{
mean += meanArr[i];
}
mean /= mDash;
__shared__ float varianceArr[BLOCK_SIZE];
varianceArr[image] = 0.0f;
for (int i = 0; i < channelStride; ++i)
{
float a = x[baseIndex + i] - mean;
varianceArr[image] += (a * a);
}
__syncthreads();
float variance = 0.0f;
for (int i = 0; i < nImages; ++i)
{
variance += varianceArr[i];
}
variance /= mDash;
float alpha = w[ch * 2 + 0];
float beta = w[ch * 2 + 1];
float d = sqrtf(variance) + 1e-8f;
for (int i = 0; i < channelStride; ++i)
{
float xDash = (x[baseIndex + i] - mean) / d;
xHat[baseIndex + i] = xDash;
y[baseIndex + i] = alpha * xDash + beta;
}
if (threadIdx.x == 0)
{
meanAndVariance[ch * 2 + 0] = mean;
meanAndVariance[ch * 2 + 1] = variance;
meanAndVarianceAcc[ch * 2 + 0] += mean;
meanAndVarianceAcc[ch * 2 + 1] += variance;
}
}
__global__ void ForwardBatchNorm2d_Cuda(
float* y, const float* meanAndVarianceAcc, int accCount,
const float* w, const float* x, int nRow, int nCol)
{
int nChannel = gridDim.x;
int ch = blockIdx.x;
int image = threadIdx.x;
int imageStride = nChannel * nRow * nCol;
int channelStride = nRow * nCol;
int currChBaseIndex = ch * channelStride;
int baseIndex = image * imageStride + currChBaseIndex;
float alpha = w[ch * 2 + 0];
float beta = w[ch * 2 + 1];
#if 1 // Note(dongwook): Deteministic network
float mean = meanAndVarianceAcc[ch * 2 + 0] / accCount;
float variance = meanAndVarianceAcc[ch * 2 + 1] / (accCount - 1);
//float variance = (meanAndVarianceAcc[ch * 2 + 1] / (accCount));
#else // Note(dongwook): Non-deteministic network for batch input.
int nImages = blockDim.x;
int mDash = nImages * nRow * nCol;
__shared__ float meanArr[100];
meanArr[image] = 0.0f;
for (int i = 0; i < channelStride; ++i)
{
meanArr[image] += x[baseIndex + i];
}
__syncthreads();
float mean = 0.0f;
for (int i = 0; i < nImages; ++i)
{
mean += meanArr[i];
}
mean /= mDash;
__shared__ float varianceArr[100];
varianceArr[image] = 0.0f;
for (int i = 0; i < channelStride; ++i)
{
float a = x[baseIndex + i] - mean;
varianceArr[image] += (a * a);
}
__syncthreads();
float variance = 0.0f;
for (int i = 0; i < nImages; ++i)
{
variance += varianceArr[i];
}
variance /= mDash;
#endif
float d = 1.0f / (sqrtf(variance) + 1e-8f);
float a = alpha * d;
float b = beta - (alpha * mean) * d;
for (int i = 0; i < channelStride; ++i)
{
y[baseIndex + i] = a * x[baseIndex + i] + b;
}
}
const CudaTensor* BatchNorm2dLayer::Forward(const CudaTensor* x)
{
assert(_w._d1 == x->_d2);
_pX = x;
_xHat.ResetTensor(x->_d0, x->_d1, x->_d2, x->_d3);
_y.ResetTensor(x->_d0, x->_d1, x->_d2, x->_d3);
if (_nn->IsTraining())
{
assert(x->_d3 <= 256);
ForwardBatchNorm2d_Train_Cuda <256> <<< x->_d2, x->_d3 >>> (
_meanAndVariance._dataGpu, _meanAndVarianceAcc._dataGpu, _xHat._dataGpu, _y._dataGpu,
_w._dataGpu, x->_dataGpu, x->_d1, x->_d0);
assert(cudaGetLastError() == cudaSuccess);
++_accCount;
}
else
{
assert(x->_d3 <= 256);
ForwardBatchNorm2d_Cuda <<< x->_d2, x->_d3 >>> (
_y._dataGpu, _meanAndVarianceAcc._dataGpu, _accCount > 1 ? _accCount : 2,
_w._dataGpu, x->_dataGpu, x->_d1, x->_d0);
assert(cudaGetLastError() == cudaSuccess);
}
return &_y;
}
template<int BLOCK_SIZE> __global__ void BackwardBatchNorm2d_Cuda(
float* wG, float* xG, float* meanAndVarianceG, float* meanAndVarianceAcc,
const float* w, const float* x, const float* xHat, const float* meanAndVariance, const float* yG,
int nRow, int nCol)
{
int nChannel = gridDim.x;
int nImages = blockDim.x;
int ch = blockIdx.x;
int image = threadIdx.x;
int mDash = nImages * nRow * nCol;
int imageStride = nChannel * nRow * nCol;
int channelStride = nRow * nCol;
int currChBaseIndex = ch * channelStride;
__shared__ float alphaArr[BLOCK_SIZE], betaArr[BLOCK_SIZE];
alphaArr[image] = 0.0f;
betaArr[image] = 0.0f;
int baseIndex = image * imageStride + currChBaseIndex;
for (int i = 0; i < channelStride; ++i)
{
float currYg = yG[baseIndex + i];
alphaArr[image] += currYg * xHat[baseIndex + i];
betaArr[image] += currYg;
xG[baseIndex + i] = currYg * w[ch * 2 + 0];
}
__syncthreads();
float alpha = 0.0f, beta = 0.0f;
for (int i = 0; i < nImages; ++i)
{
alpha += alphaArr[i];
beta += betaArr[i];
}
wG[ch * 2 + 0] = alpha;
wG[ch * 2 + 1] = beta;
__shared__ float varianceGarr[BLOCK_SIZE];
varianceGarr[image] = 0.0f;
float mean = meanAndVariance[ch * 2 + 0];
float variance = meanAndVariance[ch * 2 + 1];
float b = -0.5f * __powf(variance + 1e-8f, -1.5f);
for (int i = 0; i < channelStride; ++i)
{
varianceGarr[image] += (xG[baseIndex + i] * (x[baseIndex + i] - mean) * b);
}
__syncthreads();
float varianceG = 0.0f;
for (int i = 0; i < nImages; ++i)
{
varianceG += varianceGarr[i];
}
meanAndVarianceG[ch * 2 + 1] = varianceG;
__shared__ float meanGarr[BLOCK_SIZE];
meanGarr[image] = 0.0f;
float varianceSqrtInv = 1.0f / (sqrtf(variance) + 1e-8f);
b = -varianceSqrtInv;
for (int i = 0; i < channelStride; ++i)
{
meanGarr[image] += xG[baseIndex + i] * b + varianceG * ((-2.0f * (x[baseIndex + i] - mean)) / mDash);
}
__syncthreads();
float meanG = 0.0f;
for (int i = 0; i < nImages; ++i)
{
meanG += meanGarr[i];
}
meanAndVarianceG[ch * 2 + 0] = meanG;
b = meanG / mDash;
for (int i = 0; i < channelStride; ++i)
{
float currXg = xG[baseIndex + i];
xG[baseIndex + i] = currXg * varianceSqrtInv + varianceG * ((2.0f * (x[baseIndex + i] - mean)) / mDash) + b;
}
// Reset
meanAndVarianceAcc[ch * 2 + 0] = 0.0f;
meanAndVarianceAcc[ch * 2 + 1] = 0.0f;
}
const CudaTensor* BatchNorm2dLayer::Backward(const CudaTensor* yG, const int layerIndex)
{
// _pX.shape == _xG.shape == yG.shape == _y.shape
_xG.ResetTensor(_pX->_d0, _pX->_d1, _pX->_d2, _pX->_d3);
assert(_xG._d3 <= 256);
BackwardBatchNorm2d_Cuda<256> <<< _xG._d2, _xG._d3 >>> (
_wG._dataGpu, _xG._dataGpu, _meanAndVarianceG._dataGpu, _meanAndVarianceAcc._dataGpu,
_w._dataGpu, _pX->_dataGpu, _xHat._dataGpu, _meanAndVariance._dataGpu, yG->_dataGpu,
_xG._d1, _xG._d0);
assert(cudaGetLastError() == cudaSuccess);
_accCount = 0;
return &_xG;
}
void BatchNorm2dLayer::UpdateWs(float learningRate, float beta1, float beta2, float beta1t, float beta2t)
{
{
int nJobs = _w._d1 * _w._d0;
int numBlocks = (nJobs + K_THREAD_PER_BLOCK - 1) / K_THREAD_PER_BLOCK;
dim3 block(numBlocks), threads(K_THREAD_PER_BLOCK);
UpdateWs_Cuda << <block, threads >> > (learningRate, beta1, beta2, beta1t, beta2t, _w._dataGpu, _wG._dataGpu, _wG_m._dataGpu, _wG_v._dataGpu, nJobs);
assert(cudaGetLastError() == cudaSuccess);
}
}
void BatchNorm2dLayer::Pull()
{
_meanAndVariance.PullFromGpu();
_meanAndVarianceAcc.PullFromGpu();
_meanAndVarianceG.PullFromGpu();
_w.PullFromGpu();