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require 'cutorch'
require 'nn'
cudnn = require 'cudnn.env'
require('cudnn.ffi')
local C = cudnn.C
local ffi = require 'ffi'
cudnn.benchmark = false
cudnn.fastest = false
local maxStreamsPerDevice = 1024
local numDevices = cutorch.getDeviceCount()
-- this tensor keeps track of whether a handle has been initialized or not
local handleStatus = torch.ByteTensor(numDevices,
maxStreamsPerDevice):zero()
-- here we create an array of cudnn handle structs
cudnn.handle = ffi.new('struct cudnnContext*[?]', numDevices*maxStreamsPerDevice)
local function destroy(handle)
local currentDevice = cutorch.getDevice()
for i=1,numDevices do
cutorch.setDevice(i)
-- streams go from 0 to maxStreamsPerDevice - 1
for j=0,maxStreamsPerDevice - 1 do
if handleStatus[i][j + 1] == 1 then -- if handle was created
cudnn.errcheck('cudnnDestroy', handle[(((i-1)*maxStreamsPerDevice) + j)]);
end
end
end
cutorch.setDevice(currentDevice)
end
ffi.gc(cudnn.handle, destroy)
cudnn.typemap = {
['torch.CudaHalfTensor'] = 'CUDNN_DATA_HALF',
['torch.CudaTensor'] = 'CUDNN_DATA_FLOAT',
['torch.CudaDoubleTensor'] = 'CUDNN_DATA_DOUBLE',
}
local sizeofmap = {
['torch.CudaHalfTensor'] = cutorch.hasHalf and ffi.sizeof('half') or 2,
['torch.CudaTensor'] = ffi.sizeof('float'),
['torch.CudaDoubleTensor'] = ffi.sizeof('double'),
}
function cudnn.sizeof(t)
return sizeofmap[torch.type(t)]
end
local onemap = {
['torch.CudaHalfTensor'] = torch.FloatTensor({1}),
['torch.CudaTensor'] = torch.FloatTensor({1}),
['torch.CudaDoubleTensor'] = torch.DoubleTensor({1}),
}
local zeromap = {
['torch.CudaHalfTensor'] = torch.FloatTensor({0}),
['torch.CudaTensor'] = torch.FloatTensor({0}),
['torch.CudaDoubleTensor'] = torch.DoubleTensor({0}),
}
function cudnn.scalar(t, val)
if val == 1 then
return onemap[torch.type(t)]:data()
elseif val == 0 then
return zeromap[torch.type(t)]:data()
else
error('unknown scalar')
end
end
-- TODO: determine if device supports true half and use true half on it
-- so far use float for half and float, double for double
local function determineHalfCapability(dev)
local prop = cutorch.getDeviceProperties(dev)
if prop.major >= 6 or prop.name:find'X1' then
return 'CUDNN_DATA_HALF'
else
return 'CUDNN_DATA_FLOAT'
end
end
local configmaps = {}
for i=1,cutorch.getDeviceCount() do
configmaps[i] = {
['torch.CudaHalfTensor'] = determineHalfCapability(i),
['torch.CudaTensor'] = 'CUDNN_DATA_FLOAT',
['torch.CudaDoubleTensor'] = 'CUDNN_DATA_DOUBLE',
}
end
cudnn.configmap = function(tensortype)
return configmaps[cutorch.getDevice()][tensortype]
end
function cudnn.getHandle()
local device = cutorch.getDevice()
local stream = cutorch.getStream() -- starts from 0
assert(stream < maxStreamsPerDevice, 'cudnn bindings only support max of : '
.. maxStreamsPerDevice .. ' streams per device')
-- lazy initialization of handles
if handleStatus[device][stream + 1] == 0 then
local status = C['cudnnCreate'](cudnn.handle
+ (((device-1) * maxStreamsPerDevice)
+ stream))
if status ~= ffi.C.CUDNN_STATUS_SUCCESS then
local str = ffi.string(C.cudnnGetErrorString(status))
error('Error in CuDNN: ' .. str)
end
handleStatus[device][stream + 1] = 1 -- mark handle as initialized
end
return cudnn.handle[(((device-1)*maxStreamsPerDevice) + stream)]
end
local errcheck = function(f, ...)
C.cudnnSetStream(cudnn.getHandle(),
ffi.C.THCState_getCurrentStream(cutorch.getState()))
local status = C[f](...)
if status ~= ffi.C.CUDNN_STATUS_SUCCESS then
local str = ffi.string(C.cudnnGetErrorString(status))
error('Error in CuDNN: ' .. str .. ' ('..f..')')
end
end
cudnn.errcheck = errcheck
function cudnn.toDescriptor(t)
local typename = torch.typename(t)
assert(cudnn.typemap[typename])
local descriptor = ffi.new('struct cudnnTensorStruct*[1]')
-- create descriptor
errcheck('cudnnCreateTensorDescriptor', descriptor)
-- set gc hook
local function destroy(d)
errcheck('cudnnDestroyTensorDescriptor', d[0]);
end
ffi.gc(descriptor, destroy)
-- view 2D and 3D as 4D
if t:dim() == 2 then
t = t:view(t:size(1), t:size(2), 1, 1)
elseif t:dim() == 3 then
t = t:view(t:size(1), t:size(2), t:size(3), 1)
end
-- set descriptor
local size = torch.LongTensor(t:size()):int()
local stride = torch.LongTensor(t:stride()):int()
errcheck('cudnnSetTensorNdDescriptor', descriptor[0], cudnn.typemap[typename],
t:dim(), size:data(), stride:data())
return descriptor
end
local sharedBuffer = {}
for i=1,numDevices do
sharedBuffer[i] = {}
end
function cudnn.getSharedWorkspace()
local device = cutorch.getDevice()
local stream = cutorch.getStream() -- starts from 0
if not sharedBuffer[device][stream] then
sharedBuffer[device][stream] = torch.CudaTensor(1)
end
return sharedBuffer[device][stream]
end
-- Creates a clone of luaStr that can be used to prevent side
-- effects when passing char* to C functions.
function cudnn.externalizeString(luaStr)
local cStr = ffi.new("char[?]", #luaStr+1)
ffi.copy(cStr, luaStr)
return cStr
end
require('cudnn.SpatialConvolution')
require('cudnn.VolumetricConvolution')
require('cudnn.SpatialFullConvolution')
require('cudnn.Pooling')
require('cudnn.SpatialMaxPooling')
require('cudnn.SpatialAveragePooling')
require('cudnn.Pooling3D')
require('cudnn.VolumetricMaxPooling')
require('cudnn.VolumetricAveragePooling')
require('cudnn.Pointwise')
require('cudnn.ReLU')
require('cudnn.ClippedReLU')
require('cudnn.Tanh')
require('cudnn.Sigmoid')
require('cudnn.SpatialSoftMax')
require('cudnn.SpatialLogSoftMax')
require('cudnn.VolumetricSoftMax')
require('cudnn.VolumetricLogSoftMax')
require('cudnn.SoftMax')
require('cudnn.LogSoftMax')
require('cudnn.SpatialCrossMapLRN')
require('cudnn.BatchNormalization')
require('cudnn.SpatialBatchNormalization')
require('cudnn.VolumetricBatchNormalization')
require('cudnn.SpatialCrossEntropyCriterion')
require('cudnn.VolumetricCrossEntropyCriterion')
require('cudnn.TemporalConvolution')
require('cudnn.RNN')
require('cudnn.RNNTanh')
require('cudnn.RNNReLU')
require('cudnn.BLSTM')
require('cudnn.LSTM')
require('cudnn.BGRU')
require('cudnn.GRU')
require('cudnn.functional')
require('cudnn.convert')
return cudnn
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