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
|
-- Weight Normalization
-- https://arxiv.org/pdf/1602.07868v3.pdf
local WeightNorm, parent = torch.class("nn.WeightNorm", "nn.Decorator")
function WeightNorm:__init(module, outputDim)
-- this container will apply Weight Normalization to any module it wraps
-- it accepts parameter ``outputDim`` that represents the dimension of the output of the weight
-- if outputDim is not 1, the container will transpose the weight
-- if the weight is not 2D, the container will view the weight into a 2D shape
-- that is nOut x (nIn x kw x dw x ...)
parent.__init(self, module)
assert(module.weight)
if module.bias then
self.bias = module.bias
self.gradBias = module.gradBias
end
self.gradWeight = module.gradWeight
self.weight = module.weight
self.outputDim = outputDim or 1
-- track the non-output weight dimensions
self.otherDims = 1
for i = 1, self.weight:dim() do
if i ~= self.outputDim then
self.otherDims = self.otherDims * self.weight:size(i)
end
end
-- view size for weight norm 2D calculations
self.viewIn = torch.LongStorage({self.weight:size(self.outputDim), self.otherDims})
-- view size back to original weight
self.viewOut = self.weight:size()
self.weightSize = self.weight:size()
-- bubble outputDim size up to the front
for i = self.outputDim - 1, 1, -1 do
self.viewOut[i], self.viewOut[i + 1] = self.viewOut[i + 1], self.viewOut[i]
end
-- weight is reparametrized to decouple the length from the direction
-- such that w = g * ( v / ||v|| )
self.v = torch.Tensor(self.viewIn[1], self.viewIn[2])
self.g = torch.Tensor(self.viewIn[1])
self._norm = torch.Tensor(self.viewIn[1])
self._scale = torch.Tensor(self.viewIn[1])
-- gradient of g
self.gradG = torch.Tensor(self.viewIn[1]):zero()
-- gradient of v
self.gradV = torch.Tensor(self.viewIn)
self:resetInit()
end
function WeightNorm:permuteIn(inpt)
local ans = inpt
for i = self.outputDim - 1, 1, -1 do
ans = ans:transpose(i, i+1)
end
return ans
end
function WeightNorm:permuteOut(inpt)
local ans = inpt
for i = 1, self.outputDim - 1 do
ans = ans:transpose(i, i+1)
end
return ans
end
function WeightNorm:resetInit(inputSize, outputSize)
self.v:normal(0, math.sqrt(2/self.viewIn[2]))
self.g:norm(self.v, 2, 2)
if self.bias then
self.bias:zero()
end
end
function WeightNorm:evaluate()
if not(self.train == false) then
self:updateWeight()
parent.evaluate(self)
end
end
function WeightNorm:updateWeight()
-- view to 2D when weight norm container operates
self.gradV:copy(self:permuteIn(self.weight))
self.gradV = self.gradV:view(self.viewIn)
-- ||w||
self._norm:norm(self.v, 2, 2):pow(2):add(10e-5):sqrt()
-- g * w / ||w||
self.gradV:copy(self.v)
self._scale:copy(self.g):cdiv(self._norm)
self.gradV:cmul(self._scale:view(self.viewIn[1], 1)
:expand(self.viewIn[1], self.viewIn[2]))
-- otherwise maintain size of original module weight
self.gradV = self.gradV:view(self.viewOut)
self.weight:copy(self:permuteOut(self.gradV))
end
function WeightNorm:updateOutput(input)
if not(self.train == false) then
self:updateWeight()
end
self.output:set(self.modules[1]:updateOutput(input))
return self.output
end
function WeightNorm:accGradParameters(input, gradOutput, scale)
scale = scale or 1
self.modules[1]:accGradParameters(input, gradOutput, scale)
self.weight:copy(self:permuteIn(self.weight))
self.gradV:copy(self:permuteIn(self.gradWeight))
self.weight = self.weight:view(self.viewIn)
local norm = self._norm:view(self.viewIn[1], 1):expand(self.viewIn[1], self.viewIn[2])
local scale = self._scale:view(self.viewIn[1], 1):expand(self.viewIn[1], self.viewIn[2])
-- dL / dw * (w / ||w||)
self.weight:copy(self.gradV)
self.weight:cmul(self.v):cdiv(norm)
self.gradG:sum(self.weight, 2)
-- dL / dw * g / ||w||
self.gradV:cmul(scale)
-- dL / dg * (w * g / ||w||^2)
self.weight:copy(self.v):cmul(scale):cdiv(norm)
self.weight:cmul(self.gradG:view(self.viewIn[1], 1)
:expand(self.viewIn[1], self.viewIn[2]))
-- dL / dv update
self.gradV:add(-1, self.weight)
self.gradV = self.gradV:view(self.viewOut)
self.weight = self.weight:view(self.viewOut)
self.gradWeight:copy(self:permuteOut(self.gradV))
end
function WeightNorm:updateGradInput(input, gradOutput)
self.gradInput:set(self.modules[1]:updateGradInput(input, gradOutput))
return self.gradInput
end
function WeightNorm:zeroGradParameters()
self.modules[1]:zeroGradParameters()
self.gradV:zero()
self.gradG:zero()
end
function WeightNorm:updateParameters(lr)
self.modules[1]:updateParameters(lr)
self.g:add(-lr, self.gradG)
self.v:add(-lr, self.gradV)
end
function WeightNorm:parameters()
if self.bias then
return {self.v, self.g, self.bias}, {self.gradV, self.gradG, self.gradBias}
else
return {self.v, self.g}, {self.gradV, self.gradG}
end
end
function WeightNorm:write(file)
-- Don't save weight and gradWeight since we can easily re-compute it from v
-- and g.
local weight = self.modules[1].weight
local gradWeight = self.modules[1].gradWeight
self.weight = nil
self.gradWeight = nil
self.modules[1].weight = nil
self.modules[1].gradWeight = nil
if not self.weightSize then
self.weightSize = weight:size()
end
parent.write(self, file)
self.modules[1].weight = weight
self.modules[1].gradWeight = gradWeight
self.weight = weight
self.gradWeight = gradWeight
end
function WeightNorm:read(file)
parent.read(self, file)
-- Re-compute weight and gradWeight
if not self.weight then
self.modules[1].weight = self.v.new(self.weightSize)
self.modules[1].gradWeight = self.v.new(self.weightSize)
self.weight = self.modules[1].weight
self.gradWeight = self.modules[1].gradWeight
self:updateWeight()
self.gradWeight:copy(self:permuteOut(self.gradV))
end
end
|
