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WindowMixture.lua
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WindowMixture.lua
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local WindowMixture, parent = torch.class('nn.WindowMixture', 'nn.Module')
-- 2 modes
-- Dense input, sparse output:
-- The input is a tensor of activations
-- outputs is a table of 2 tensors: {output, outputIndice}
WindowMixture.DENSE_SPARSE = 1
-- Sparse input, sparse output:
-- Input is a table of 2 tensors: {input, inputIndice}
-- Output is a ttable of 2 tensors: {output, outputIndice}
WindowMixture.SPARSE_SPARSE = 2
-- Note: SPARSE_DENSE is not supported (no gater is required),
-- just use WindowSparse + ElementTable
function WindowMixture:__init(expert, gater, mode, mixture)
parent.__init(self)
self.gater = gater
self.expert = expert
self.mode = mode or self.SPARSE_SPARSE
self.cmul = mixture or nn.CMulTable()
self.modules = {gater, expert, cmul}
self.output = {}
self._gradInput = torch.Tensor()
self.gradInput = (self.mode == self.DENSE_SPARSE) and self._gradInput or {}
self.batchSize = 0
-- for dense inputs or outputs
self.inputIndice = torch.LongTensor()
end
function WindowMixture:updateOutput(inputTable)
local input, inputIndice = self:unpackInput(inputTable)
if self.batchSize ~= input:size(1) then
self.inputIndice:resize(input:size(1)):fill(1)
self.batchSize = input:size(1)
end
self.gaterOutput = self.gater:updateOutput(inputTable)
self.expertInput = {input, inputIndice, self.gaterOutput[1]}
self.expertOutput = self.expert:updateOutput(self.expertInput)
self.mixtureInput = {self.expertOutput[1], self.gaterOutput[2]}
self.mixtureOutput = self.cmul:updateOutput(self.mixtureInput)
self:packOutput(self.mixtureOutput, self.gaterOutput[1])
return self.output
end
function WindowMixture:updateGradInput(inputTable, gradOutputTable)
local input, inputIndice = self:unpackInput(inputTable)
local gradOutput = self:unpackGradOutput(gradOutputTable)
self.mixtureGradInput = self.cmul:updateGradInput(self.mixtureInput, gradOutput)
self.expertGradInput = self.expert:updateGradInput(self.expertInput, {self.mixtureGradInput[1]})
self.gaterGradInput = self.gater:updateGradInput(inputTable, self.mixtureGradInput[2])
local gaterGradInput = self:unpackInput(self.gaterGradInput)
self._gradInput:resizeAs(input)
self._gradInput:copy(self.expertGradInput[1])
self._gradInput:add(gaterGradInput)
return self:packGradInput(self._gradInput)
end
function WindowMixture:accGradParameters(inputTable, gradOutputTable, scale)
scale = scale or 1
self.expert:accGradParameters(self.expertInput, {self.mixtureGradInput[2]}, scale)
self.gater:accGradParameters(inputTable, self.mixtureGradInput[1], scale)
end
function WindowMixture:accUpdateGradParameters(inputTable, gradOutputTable, lr)
self.expert:accUpdateGradParameters(self.expertInput, {self.mixtureGradInput[2]}, lr)
self.gater:accUpdateGradParameters(inputTable, self.mixtureGradInput[1], lr)
end
function WindowMixture:zeroGradParameters()
self.expert:zeroGradParameters()
self.gater:zeroGradParameters()
end
function WindowMixture:updateParameters(learningRate)
self.expert:updateParameters(learningRate)
self.gater:updateParameters(learningRate)
end
function WindowMixture:share(mlp,...)
for i=1,#self.modules do
self.modules[i]:share(mlp.modules[i],...);
end
end
function WindowMixture:parameters()
local function tinsert(to, from)
if type(from) == 'table' then
for i=1,#from do
tinsert(to,from[i])
end
else
table.insert(to,from)
end
end
local w = {}
local gw = {}
for i=1,#self.modules do
local mw,mgw = self.modules[i]:parameters()
if mw then
tinsert(w,mw)
tinsert(gw,mgw)
end
end
return w,gw
end
function WindowMixture:type(type)
self.expert:type(type)
self.gater:type(type)
self.cmul:type(type)
self._gradInput = self._gradInput:type(type)
self.gradInput = (self.mode == self.DENSE_SPARSE) and self._gradInput or {}
end
function WindowMixture:unpackInput(inputTable)
if self.mode == self.DENSE_SPARSE then
return inputTable, self.inputIndice
end
return unpack(inputTable)
end
function WindowMixture:unpackGradOutput(gradOutputTable)
return gradOutputTable[1]
end
function WindowMixture:packGradInput(gradInput)
if self.mode ~= self.DENSE_SPARSE then
self.gradInput[1] = gradInput
end
return self.gradInput
end
function WindowMixture:packOutput(output, outputIndice)
self.output[1] = output
self.output[2] = outputIndice
return self.output
end