Embedding.lua

local Embedding, parent = torch.class('Embedding', 'nn.Module')

function Embedding:__init(inputSize, outputSize)
  parent.__init(self)
  self.outputSize = outputSize
  self.weight = torch.Tensor(inputSize, outputSize)
  self.gradWeight = torch.Tensor(inputSize, outputSize)
end

function Embedding:updateOutput(input)
  self.output:resize(input:size(1), self.outputSize)
  for i = 1, input:size(1) do
    self.output[i]:copy(self.weight[input[i]])
  end
  return self.output
end

function Embedding:updateGradInput(input, gradOutput)
  if self.gradInput then
    self.gradInput:resize(input:size())
    return self.gradInput
  end
end

function Embedding:accGradParameters(input, gradOutput, scale)
  scale = scale or 1
  if scale == 0 then
    self.gradWeight:zero()
  end
  for i = 1, input:size(1) do
    local word = input[i]
    self.gradWeight[word]:add(gradOutput[i])
  end
end

-- we do not need to accumulate parameters when sharing
Embedding.sharedAccUpdateGradParameters = Embedding.accUpdateGradParameters