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local ffi = require 'ffi'
local THNN = require 'nn.THNN'
local THCUNN = {}
-- load libTHCUNN
THCUNN.C = ffi.load(package.searchpath('libTHCUNN', package.cpath))
-- load THC
local THC = ffi.os == 'Windows' and ffi.load('THC') or ffi.C
local THCState_ptr = ffi.typeof('THCState*')
function THCUNN.getState()
return THCState_ptr(cutorch.getState());
end
local THCUNN_generic_h = require 'cunn.THCUNN_generic_h'
-- strip all lines starting with #
-- to remove preprocessor directives originally present
-- in THNN.h
THCUNN_generic_h = THCUNN_generic_h:gsub("\n#[^\n]*", "")
THCUNN_generic_h = THCUNN_generic_h:gsub("^#[^\n]*\n", "")
local preprocessed_generic = string.gsub(THCUNN_generic_h, 'TH_API void THNN_%(([%a%d_]+)%)', 'void THNN_TYPE%1')
local replacements =
{
{
['THTensor'] = 'THCudaTensor',
['THCIndexTensor'] = 'THCudaLongTensor',
['THIndex_t'] = 'long',
['THInteger_t'] = 'float'
}
}
local cct2lt = {
['THCudaFloatTensor'] = 'torch.CudaTensor',
['THCudaDoubleTensor'] = 'torch.CudaDoubleTensor',
}
local replacements_generic =
{
{
['THCTensor'] = 'THCudaTensor',
['THCIndexTensor'] = 'THCudaLongTensor',
['TYPE'] = 'Cuda',
['real'] = 'float'
},
{
['THCTensor'] = 'THCudaDoubleTensor',
['THCIndexTensor'] = 'THCudaLongTensor',
['TYPE'] = 'CudaDouble',
['real'] = 'double',
}
}
if cutorch.hasHalf then
ffi.cdef("half THC_float2half(float a);")
ffi.cdef("float THC_half2float(half a);")
cct2lt['THCudaHalfTensor'] = 'torch.CudaHalfTensor'
local half_replacement = {
['THCTensor'] = 'THCudaHalfTensor',
['THCIndexTensor'] = 'THCudaLongTensor',
['TYPE'] = 'CudaHalf',
['real'] = 'half'
}
table.insert(replacements_generic, half_replacement)
end
for i=1,#replacements_generic do
local r = replacements_generic[i]
local s = preprocessed_generic
for k,v in pairs(r) do
s = string.gsub(s, k, v)
end
ffi.cdef(s)
end
local function extract_function_names_generic(s)
local t = {}
for n in string.gmatch(s, 'TH_API void THNN_%(([%a%d_]+)%)') do
t[#t+1] = n
end
return t
end
local function find_positions(s, p)
local begin = 0
local positions = {}
while true do
local start, stop = string.find(s, p, begin)
if (start == nil) then break end
positions[#positions+1] = start
begin = stop + 1
end
return positions
end
local function extract_function_names_and_real_args(s)
local t = {}
for n in string.gmatch(s, 'TH_API ([^;]+)') do
local func_name = string.match(n, 'void THNN_%(([%a%d_]+)%)')
local param_positions = find_positions(n, ',')
local positions = {}
for x,y in ipairs(find_positions(n, 'real')) do
local found = false
for cn,cp in ipairs(param_positions) do
if cp > y then
positions[#positions+1] = cn
found = true
break
end
end
-- it is the last param
if not found then positions[#positions+1] = #param_positions + 1 end
end
t[func_name] = positions
end
return t
end
local real_args = extract_function_names_and_real_args(THCUNN_generic_h)
-- build function table
local function_names_generic = extract_function_names_generic(THCUNN_generic_h)
THNN.kernels['torch.CudaTensor'] = THNN.bind(THCUNN.C, function_names_generic, 'Cuda', THCUNN.getState)
torch.getmetatable('torch.CudaTensor').THNN = THNN.kernels['torch.CudaTensor']
THNN.kernels['torch.CudaDoubleTensor'] = THNN.bind(THCUNN.C, function_names_generic, 'CudaDouble', THCUNN.getState)
torch.getmetatable('torch.CudaDoubleTensor').THNN = THNN.kernels['torch.CudaDoubleTensor']
if cutorch.hasHalf then
-- in order to call 'half' functions from lua, convert real arguments from
-- to half since there is no other defined conversion
local transform_reals_to_half = function(func_name, real_args, ...)
t = {}
-- this select logic is necessary to deal with nil arguments
for i = 1, select('#', ...) do
t[i] = select(i, ...)
end
for k,v in ipairs(real_args[func_name]) do
-- first argument (THCState) is added implicitly by bind
t[v-1] = THC.THC_float2half(t[v-1])
end
return t
end
local raw_half_functions = THNN.bind(THCUNN.C, function_names_generic, 'CudaHalf', THCUNN.getState)
for k,v in pairs(raw_half_functions) do
-- select required in case there are trailing nils
raw_half_functions[k] = function(...) v(unpack(transform_reals_to_half(k, real_args, ...), 1, select("#",...)))
end
end
THNN.kernels['torch.CudaHalfTensor'] = raw_half_functions
torch.getmetatable('torch.CudaHalfTensor').THNN = THNN.kernels['torch.CudaHalfTensor']
end
local function Module__converter(type)
return function(self)
return self:type(type)
end
end
rawset(torch.getmetatable('nn.Module'), 'cudaDouble', Module__converter('torch.CudaDoubleTensor'))
if cutorch.hasHalf then
rawset(torch.getmetatable('nn.Module'), 'cudaHalf', Module__converter('torch.CudaHalfTensor'))
end
return THCUNN
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