/// <summary>
/// Reshape the bottom (input) and top (output) blobs.
/// </summary>
/// <param name="colBottom">Specifies the collection of bottom (input) Blobs.</param>
/// <param name="colTop">Specifies the collection of top (output) Blobs.</param>
public override void Reshape(BlobCollection <T> colBottom, BlobCollection <T> colTop)
{
base.Reshape(colBottom, colTop);
if (!m_param.convolution_param.useCudnn(m_nNumSpatialAxes))
{
return;
}
m_log.CHECK_EQ(2, m_nNumSpatialAxes, "cuDNN Convolution input must have 2 spatial axes (e.g., height and width). Use 'engine: CAFFE' for general ND convolution.");
m_nBottomOffset = m_nBottomDim / m_nGroup;
m_nTopOffset = m_nTopDim / m_nGroup;
int nHeight = colBottom[0].shape(m_nChannelAxis + 1);
int nWidth = colBottom[0].shape(m_nChannelAxis + 2);
int nHeightOut = colTop[0].shape(m_nChannelAxis + 1);
int nWidthOut = colTop[0].shape(m_nChannelAxis + 2);
Size szPad = size_at(m_blobPad);
Size szStride = size_at(m_blobStride);
// Specify workspace limit for kernels directly until we have a
// planning strategy and a rewrite of Caffe's GPU memory management.
// default = 1024 * 1024 * 8;
long lWorkspaceLimitBytes = m_param.convolution_param.cudnn_workspace_limit * 8;
// BUG Work Around
// With cuDNN 7.0.5 and above we are seeing memory overwrite errors (from CUDA)
// when using more than 1 group and the workspace.
// * also confirmed in cuDNN 7.1.4 and CUDA 9.2 on driver 397.64
if (m_nGroup > 1)
{
lWorkspaceLimitBytes = 0; // sets option to NO_WORKSPACE for Bwd Filter and Data
}
for (int i = 0; i < colBottom.Count; i++)
{
m_cuda.SetTensorDesc(m_rghBottomDesc[i], m_nNum, m_nChannels / m_nGroup, nHeight, nWidth, m_nChannels * nHeight * nWidth, nHeight * nWidth, nWidth, 1);
m_cuda.SetTensorDesc(m_rghTopDesc[i], m_nNum, m_nNumOutput / m_nGroup, nHeightOut, nWidthOut, m_nNumOutput * m_nOutSpatialDim, m_nOutSpatialDim, nWidthOut, 1);
m_cuda.SetConvolutionDesc(m_rghConvDesc[i], szPad.Height, szPad.Width, szStride.Height, szStride.Width);
// Get the algorithms and workspace sizes needed.
CONV_FWD_ALGO algoFwd = (CONV_FWD_ALGO)0;
CONV_BWD_FILTER_ALGO algoBwdFilter = (CONV_BWD_FILTER_ALGO)0;
CONV_BWD_DATA_ALGO algoBwdData = (CONV_BWD_DATA_ALGO)0;
long lWsSizeFwd = 0;
long lWsSizeBwdFilter = 0;
long lWsSizeBwdData = 0;
m_cuda.GetConvolutionInfo(m_rghCudnn[0], m_rghBottomDesc[i], m_hFilterDesc, m_rghConvDesc[i], m_rghTopDesc[i], lWorkspaceLimitBytes, out algoFwd, out lWsSizeFwd, out algoBwdFilter, out lWsSizeBwdFilter, out algoBwdData, out lWsSizeBwdData);
m_rgfwdAlgo[i] = algoFwd;
m_rglWorkspaceFwdSizes[i] = lWsSizeFwd;
m_rgbwdFilterAlgo[i] = algoBwdFilter;
m_rglWorkspaceBwdFilterSizes[i] = lWsSizeBwdFilter;
m_rgbwdDataAlgo[i] = algoBwdData;
m_rglWorkspaceBwdDataSizes[i] = lWsSizeBwdData;
}
// reduce over all workspace sizes to get a maximum to allocate / reallocate
long lTotalWsFwd = 0;
long lTotalWsBwdFilter = 0;
long lTotalWsBwdData = 0;
for (int i = 0; i < colBottom.Count; i++)
{
lTotalWsFwd = Math.Max(lTotalWsFwd, m_rglWorkspaceFwdSizes[i]);
lTotalWsBwdFilter = Math.Max(lTotalWsBwdFilter, m_rglWorkspaceBwdFilterSizes[i]);
lTotalWsBwdData = Math.Max(lTotalWsBwdData, m_rglWorkspaceBwdDataSizes[i]);
}
// Get max over all oeprations.
long lMaxWorkspace = Math.Max(lTotalWsFwd, Math.Max(lTotalWsBwdFilter, lTotalWsBwdData));
// Ensure all groups have enough workspace.
long lTotalMaxWorkspace = lMaxWorkspace * m_nGroup * CUDNN_STREAMS_PER_GROUP;
// Initialize the workspace data.
WorkspaceArgs wsArgs = getWorkspace();
// This is the total amount of storage needed over all groups + streams.
if (lTotalMaxWorkspace > wsArgs.Size)
{
setWorkspace(lTotalMaxWorkspace);
}
// if we succedd in the allocation, set the offsets for the workspaces.
for (int g = 0; g < (m_nGroup * CUDNN_STREAMS_PER_GROUP); g++)
{
m_rglWorkspaceFwdOffsets[g] = g * lTotalWsFwd;
m_rglWorkspaceBwdFilterOffsets[g] = g * lTotalWsBwdFilter;
m_rglWorkspaceBwdDataOffsets[g] = g * lTotalWsBwdData;
}
// Tensor descriptor for bias.
if (m_bBiasTerm)
{
m_cuda.SetTensorDesc(m_hBiasDesc, 1, m_nNumOutput / m_nGroup, 1, 1);
}
}
/// <summary>
/// Reshape the bottom (input) and top (output) blobs.
/// </summary>
/// <param name="colBottom">Specifies the collection of bottom (input) Blobs.</param>
/// <param name="colTop">Specifies the collection of top (output) Blobs.</param>
public override void Reshape(BlobCollection <T> colBottom, BlobCollection <T> colTop)
{
base.Reshape(colBottom, colTop);
if (!m_param.convolution_param.useCudnn(m_nNumSpatialAxes))
{
return;
}
m_log.CHECK_EQ(2, m_nNumSpatialAxes, "cuDNN Deconvolution input must have 2 spatial axes (e.g., height and width). Use 'engine: CAFFE' for general ND deconvolution.");
m_nBottomOffset = m_nBottomDim / m_nGroup;
m_nTopOffset = m_nTopDim / m_nGroup;
int nHeight = colBottom[0].shape(m_nChannelAxis + 1);
int nWidth = colBottom[0].shape(m_nChannelAxis + 2);
int nHeightOut = colTop[0].shape(m_nChannelAxis + 1);
int nWidthOut = colTop[0].shape(m_nChannelAxis + 2);
Size szPad = size_at(m_blobPad);
Size szStride = size_at(m_blobStride);
// Specify workspace limit for kernels directly until we have a
// planning strategy and a rewrite of Caffe's GPU memory management.
// default = 1024 * 1024 * 8;
long lWorkspaceLimitBytes = m_param.convolution_param.cudnn_workspace_limit * 8;
for (int i = 0; i < colBottom.Count; i++)
{
m_cuda.SetTensorDesc(m_rghBottomDesc[i], m_nNum, m_nChannels / m_nGroup, nHeight, nWidth, m_nChannels * nHeight * nWidth, nHeight * nWidth, nWidth, 1);
m_cuda.SetTensorDesc(m_rghTopDesc[i], m_nNum, m_nNumOutput / m_nGroup, nHeightOut, nWidthOut, m_nNumOutput * nHeightOut * nWidthOut, nHeightOut * nWidthOut, nWidthOut, 1);
m_cuda.SetConvolutionDesc(m_rghConvDesc[i], szPad.Height, szPad.Width, szStride.Height, szStride.Width);
// NOTE: The native Caffe team has found that CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM is
// buggy (in deconvolution). Thus, if this algo was chosen (by CuDnn), we attempt to use winograd
// instead. If winograd is not supported, or the workspace is larger than the threshold, we
// use implicit_gemm instead.
CONV_FWD_ALGO algoFwdPreferred = CONV_FWD_ALGO.ALGO_WINOGRAD;
// Get the algorithms and workspace sizes needed.
CONV_FWD_ALGO algoFwd = (CONV_FWD_ALGO)0;
CONV_BWD_FILTER_ALGO algoBwdFilter = (CONV_BWD_FILTER_ALGO)0;
CONV_BWD_DATA_ALGO algoBwdData = (CONV_BWD_DATA_ALGO)0;
long lWsSizeFwd = 0;
long lWsSizeBwdFilter = 0;
long lWsSizeBwdData = 0;
m_cuda.GetConvolutionInfo(m_rghCudnn[0], m_rghTopDesc[i], m_hFilterDesc, m_rghConvDesc[i], m_rghBottomDesc[i], lWorkspaceLimitBytes, out algoFwd, out lWsSizeFwd, out algoBwdFilter, out lWsSizeBwdFilter, out algoBwdData, out lWsSizeBwdData, algoFwdPreferred);
m_rgfwdAlgo[i] = algoFwd;
m_rglWorkspaceFwdSizes[i] = lWsSizeFwd;
m_rgbwdFilterAlgo[i] = algoBwdFilter;
m_rglWorkspaceBwdFilterSizes[i] = lWsSizeBwdFilter;
m_rgbwdDataAlgo[i] = algoBwdData;
m_rglWorkspaceBwdDataSizes[i] = lWsSizeBwdData;
}
// reduce over all workspace sizes to get a maximum to allocate / reallocate
long lTotalWsFwd = 0;
long lTotalWsBwdFilter = 0;
long lTotalWsBwdData = 0;
for (int i = 0; i < colBottom.Count; i++)
{
lTotalWsFwd = Math.Max(lTotalWsFwd, m_rglWorkspaceFwdSizes[i]);
lTotalWsBwdFilter = Math.Max(lTotalWsBwdFilter, m_rglWorkspaceBwdFilterSizes[i]);
lTotalWsBwdData = Math.Max(lTotalWsBwdData, m_rglWorkspaceBwdDataSizes[i]);
}
// Get max over all oeprations.
long lMaxWorkspace = Math.Max(lTotalWsFwd, Math.Max(lTotalWsBwdFilter, lTotalWsBwdData));
// Ensure all groups have enough workspace.
long lTotalMaxWorkspace = lMaxWorkspace * m_nGroup * CUDNN_STREAMS_PER_GROUP;
// Initialize the workspace data.
WorkspaceArgs wsArgs = getWorkspace();
// This is the total amount of storage needed over all groups + streams.
if (lTotalMaxWorkspace > wsArgs.Size)
{
setWorkspace(lTotalMaxWorkspace);
}
// if we succedd in the allocation, set the offsets for the workspaces.
for (int g = 0; g < (m_nGroup * CUDNN_STREAMS_PER_GROUP); g++)
{
m_rglWorkspaceFwdOffsets[g] = g * lTotalWsFwd;
m_rglWorkspaceBwdFilterOffsets[g] = g * lTotalWsBwdFilter;
m_rglWorkspaceBwdDataOffsets[g] = g * lTotalWsBwdData;
}
// Tensor descriptor for bias.
if (m_bBiasTerm)
{
m_cuda.SetTensorDesc(m_hBiasDesc, 1, m_nNumOutput / m_nGroup, 1, 1);
}
}
/// <summary>
/// Reshape the bottom (input) and top (output) blobs.
/// </summary>
/// <param name="colBottom">Specifies the collection of bottom (input) Blobs.</param>
/// <param name="colTop">Specifies the collection of top (output) Blobs.</param>
public override void Reshape(BlobCollection <T> colBottom, BlobCollection <T> colTop)
{
base.Reshape(colBottom, colTop);
if (!m_param.convolution_param.useCudnn(m_nNumSpatialAxes))
{
return;
}
m_log.CHECK_EQ(2, m_nNumSpatialAxes, "cuDNN Convolution input must have 2 spatial axes (e.g., height and width). Use 'engine: CAFFE' for general ND convolution.");
m_nBottomOffset = m_nBottomDim / m_nGroup;
m_nTopOffset = m_nTopDim / m_nGroup;
int nHeight = colBottom[0].shape(m_nChannelAxis + 1);
int nWidth = colBottom[0].shape(m_nChannelAxis + 2);
int nHeightOut = colTop[0].shape(m_nChannelAxis + 1);
int nWidthOut = colTop[0].shape(m_nChannelAxis + 2);
Size szPad = size_at(m_blobPad);
Size szStride = size_at(m_blobStride);
ulong lWorkspaceLimitBytes = getWorkspaceLimitInBytes();
for (int i = 0; i < colBottom.Count; i++)
{
m_cuda.SetTensorDesc(m_rghBottomDesc[i], m_nNum, m_nChannels / m_nGroup, nHeight, nWidth, m_nChannels * nHeight * nWidth, nHeight * nWidth, nWidth, 1, m_bUseHalfSize);
m_cuda.SetTensorDesc(m_rghTopDesc[i], m_nNum, m_nNumOutput / m_nGroup, nHeightOut, nWidthOut, m_nNumOutput * m_nOutSpatialDim, m_nOutSpatialDim, nWidthOut, 1, m_bUseHalfSize);
m_cuda.SetConvolutionDesc(m_rghConvDesc[i], szPad.Height, szPad.Width, szStride.Height, szStride.Width, m_bUseHalfSize);
// Get the algorithms and workspace sizes needed.
CONV_FWD_ALGO algoFwd = (CONV_FWD_ALGO)0;
CONV_BWD_FILTER_ALGO algoBwdFilter = (CONV_BWD_FILTER_ALGO)0;
CONV_BWD_DATA_ALGO algoBwdData = (CONV_BWD_DATA_ALGO)0;
ulong lWsSizeFwd = 0;
ulong lWsSizeBwdFilter = 0;
ulong lWsSizeBwdData = 0;
m_cuda.GetConvolutionInfo(m_rghCudnn[0], m_rghBottomDesc[i], m_hFilterDesc, m_rghConvDesc[i], m_rghTopDesc[i], lWorkspaceLimitBytes, out algoFwd, out lWsSizeFwd, out algoBwdFilter, out lWsSizeBwdFilter, out algoBwdData, out lWsSizeBwdData);
m_rgfwdAlgo[i] = algoFwd;
m_rglWorkspaceFwdSizes[i] = lWsSizeFwd;
m_rgbwdFilterAlgo[i] = algoBwdFilter;
m_rglWorkspaceBwdFilterSizes[i] = lWsSizeBwdFilter;
m_rgbwdDataAlgo[i] = algoBwdData;
m_rglWorkspaceBwdDataSizes[i] = lWsSizeBwdData;
}
// reduce over all workspace sizes to get a maximum to allocate / reallocate
ulong lTotalWsFwd = 0;
ulong lTotalWsBwdFilter = 0;
ulong lTotalWsBwdData = 0;
for (int i = 0; i < colBottom.Count; i++)
{
lTotalWsFwd = Math.Max(lTotalWsFwd, m_rglWorkspaceFwdSizes[i]);
lTotalWsBwdFilter = Math.Max(lTotalWsBwdFilter, m_rglWorkspaceBwdFilterSizes[i]);
lTotalWsBwdData = Math.Max(lTotalWsBwdData, m_rglWorkspaceBwdDataSizes[i]);
}
// Get max over all oeprations.
ulong lMaxWorkspace = Math.Max(lTotalWsFwd, Math.Max(lTotalWsBwdFilter, lTotalWsBwdData));
// Ensure all groups have enough workspace.
ulong lTotalMaxWorkspace = (ulong)lMaxWorkspace * (ulong)m_nGroup * (ulong)CUDNN_STREAMS_PER_GROUP;
// Initialize the workspace data.
WorkspaceArgs wsArgs = getWorkspace();
// This is the total amount of storage needed over all groups + streams.
if (lTotalMaxWorkspace > wsArgs.Size)
{
setWorkspace(lTotalMaxWorkspace);
}
// if we succedd in the allocation, set the offsets for the workspaces.
for (int g = 0; g < (m_nGroup * CUDNN_STREAMS_PER_GROUP); g++)
{
m_rglWorkspaceFwdOffsets[g] = (ulong)g * lTotalWsFwd;
m_rglWorkspaceBwdFilterOffsets[g] = (ulong)g * lTotalWsBwdFilter;
m_rglWorkspaceBwdDataOffsets[g] = (ulong)g * lTotalWsBwdData;
}
// Tensor descriptor for bias.
if (m_bBiasTerm)
{
m_cuda.SetTensorDesc(m_hBiasDesc, 1, m_nNumOutput / m_nGroup, 1, 1, m_bUseHalfSize);
}
}
