aboutsummaryrefslogtreecommitdiffstats
path: root/contrib/lua-torch/nn/GPU.lua
blob: 758618d8b28a18b4221895d35a61140365684b85 (plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
------------------------------------------------------------------------
--[[ 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