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GPU.lua
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GPU.lua
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------------------------------------------------------------------------
--[[ GPU ]]--
-- Decorates a module such that its parameters are
-- hosted on a specified GPU device.
-- The operations are also executed on that device.
-- Arguments input and gradOutput are converted to the specified device
-- before being fed to the decorated module.
-- Returned output is on the specified outdevice (defaults to device).
-- Returned gradInput is allocated on the same device as the input.
-- The unit test is located in cunn.
------------------------------------------------------------------------
local GPU, parent = torch.class("nn.GPU", "nn.Container")
function GPU:__init(module, device, outdevice)
parent.__init(self)
assert(torch.type(device) == 'number')
self.device = device
self.outdevice = outdevice or device
assert(torch.isTypeOf(module, 'nn.Module'))
self.modules[1] = module
if module:type():find('torch%.Cuda.*Tensor') then
self:type(module:type())
end
end
function GPU.recursiveModuleDevice(obj, device)
if type(obj) == 'table' and not torch.isTypeOf(obj, 'nn.GPU') and not obj.__noGPU__ then
for k,v in pairs(obj) do
obj[k] = GPU.recursiveModuleDevice(v, device)
end
elseif torch.type(obj):match('torch.Cuda.*Tensor') then
if obj:getDevice() ~= device then
obj = obj:clone() -- this will reallocate it to device
local newdevice = obj:getDevice()
-- when nElement() == 0 newdevice is 0
assert(newdevice == device or newdevice == 0)
end
end
assert(obj ~= nil)
return obj
end
-- set the device of the decorated module
function GPU:setDevice(device)
self.device = device or self.device
assert(self.modules[1])
self.modules[1] = cutorch.withDevice(self.device, function()
return self.recursiveModuleDevice(self.modules[1], self.device)
end)
return self
end
-- when proto is a device number, returns a dst that has device device for each element in src
-- otherwise, if proto is a table/tensor, makes sure dst is a identical to src, yet on the same device as proto
function GPU.recursiveSetDevice(dst, src, proto)
local device, prototable
if torch.isTensor(proto) then
device = proto:getDevice()
elseif torch.type(proto) == 'number' then
device = proto
elseif torch.type(proto) == 'table' then
prototable = true
else
error"Expecting number, table or tensor for arg 3 (proto)"
end
if torch.type(src) == 'table' then
dst = torch.type(dst) == 'table' and dst or {}
for k,v in ipairs(src) do
dst[k] = GPU.recursiveSetDevice(dst[k], v, prototable and proto[k] or device)
end
for k=#src+1,#dst do
dst[k] = nil
end
elseif torch.type(src):match('torch.Cuda.*Tensor') and src:getDevice() ~= device and src:getDevice() ~= 0 then
if not (torch.type(dst):match('torch.Cuda.*Tensor') and dst:getDevice() == device) then
dst = src.new()
end
cutorch.withDevice(device, function() dst:resizeAs(src):copy(src) end)
else
dst = src
end
return dst
end
function GPU:updateOutput(input)
if self._type:find('torch%.Cuda.*Tensor') then
self._input = self.recursiveSetDevice(self._input, input, self.device)
local output = cutorch.withDevice(self.device, function()
return self.modules[1]:updateOutput(self._input)
end)
if self.device ~= self.outdevice then
self.output = self.recursiveSetDevice(self.output, output, self.outdevice)
else
self.output = output
end
else
self.output = self.modules[1]:updateOutput(input)
end
return self.output
end
function GPU:updateGradInput(input, gradOutput)
if self._type:find('torch%.Cuda.*Tensor') then
self._gradOutput = self.recursiveSetDevice(self._gradOutput, gradOutput, self.device)
local gradInput = cutorch.withDevice(self.device, function()
return self.modules[1]:updateGradInput(self._input, self._gradOutput)
end)
self.gradInput = self.recursiveSetDevice(self.gradInput, gradInput, input)
else
self.gradInput = self.modules[1]:updateGradInput(input, gradOutput)
end
return self.gradInput
end
function GPU:accGradParameters(input, gradOutput, scale)
if self._type:find('torch%.Cuda.*Tensor') then
cutorch.withDevice(self.device, function()
self.modules[1]:accGradParameters(self._input, self._gradOutput, scale)
end)
else
self.modules[1]:accGradParameters(input, gradOutput, scale)
end
end
function GPU:apply(callback)
if self._type:find('torch%.Cuda.*Tensor') then
cutorch.withDevice(self.device, function() parent.apply(self, callback) end)
else
parent.apply(self, callback)
end
end
function GPU:type(type, typecache)
if type and type:find('torch%.Cuda.*Tensor') then
cutorch.withDevice(self.device, function() parent.type(self, type, typecache) end)
self:setDevice()
else
self.output = nil
self.gradInput = nil
self._input = nil
self._gradOutput = nil
parent.type(self, type, typecache)
end
return self
end
function GPU:clearState()
nn.utils.clear(self, 'output', 'gradInput')
self._input = nil
self._gradOutput = nil
if self._type:find('torch%.Cuda.*Tensor') then
cutorch.withDevice(self.device, function() parent.clearState(self) end)
else
parent.clearState(self)
end
end
function GPU:zeroGradParameters()
if self._type:find('torch%.Cuda.*Tensor') then
cutorch.withDevice(self.device, function() parent.zeroGradParameters(self) end)
else
parent.zeroGradParameters(self)
end
end
function GPU:updateParameters(lr)
if self._type:find('torch%.Cuda.*Tensor') then
cutorch.withDevice(self.device, function() parent.updateParameters(self, lr) end)
else
parent.updateParameters(self, lr)
end
end
function GPU:training()
if self._type:find('torch%.Cuda.*Tensor') then
cutorch.withDevice(self.device, function() parent.training(self) end)
else
parent.training(self)
end
end
function GPU:evaluate()
if self._type:find('torch%.Cuda.*Tensor') then
cutorch.withDevice(self.device, function() parent.evaluate(self) end)
else
parent.evaluate(self)
end
end
function GPU:share(mlp, ...)
local args = {...}
if self._type:find('torch%.Cuda.*Tensor') then
cutorch.withDevice(self.device, function() parent.share(self, mlp, unpack(args)) end)
else
parent.share(self, mlp, unpack(args))
end
return self
end
function GPU:reset(...)
local args = {...}
if self._type:find('torch%.Cuda.*Tensor') then
cutorch.withDevice(self.device, function() parent.reset(self, unpack(args)) end)
else
parent.reset(self, unpack(args))
end
return self
end
function GPU:clone(...)
local args = {...}
if self._type:find('torch%.Cuda.*Tensor') then
return cutorch.withDevice(self.device, function() parent.clone(self, unpack(args)) end)
else
return parent.clone(self, unpack(args))
end
end
function GPU:write(file)
-- Write all values in the object as a table.
local object = {}
for k, v in pairs(self) do
object[k] = v
end
local header = {self._type, self.device}
file:writeObject(header)
file:writeObject(object)
end
function GPU:read(file)
local header = file:readObject()
local object
if header[1] and header[1]:find('torch%.Cuda.*Tensor') then
local device = header[2]
if device > cutorch.getDeviceCount() then
print"Warning : model was saved with more devices than available on current host."
print"Attempting to load module onto device 1"
device = 1
end
object = cutorch.withDevice(device, function() return file:readObject() end)
else
object = file:readObject()
end
for k, v in pairs(object) do
self[k] = v
end
end
function GPU:__tostring__()
if self.modules[1].__tostring__ then
return torch.type(self) .. '(' .. self.device ..') @ ' .. self.modules[1]:__tostring__()
else
return torch.type(self) .. '(' .. self.device ..') @ ' .. torch.type(self.modules[1])
end
end
function GPU:accUpdateGradParameters(input, gradOutput, lr)
error("Not Implemented for "..torch.type(self))
end
function GPU:sharedAccUpdateGradParameters(input, gradOutput, lr)
error("Not Implemented for "..torch.type(self))
end