1 files changed, 398 insertions, 0 deletions
diff --git a/contrib/lua-torch/nn/IndexLinear.lua b/contrib/lua-torch/nn/IndexLinear.lua
new file mode 100644
index 000000000..928e5d3f2
--- /dev/null
+++ b/contrib/lua-torch/nn/IndexLinear.lua
@@ -0,0 +1,398 @@
+local ffi  = require 'ffi'
+local IndexLinear, parent = torch.class('nn.IndexLinear', 'nn.Module')
+
+
+
+function IndexLinear:__init(inputSize, outputSize, doGradInput, keysOffset, weight, bias, normalize)
+   parent.__init(self)
+
+   -- We need for 3 extra parameters per feature
+   -- if we normalize:
+   -- * The max-abs value
+   -- * The inverse of the max-abs value
+   -- * The per-feature bias
+   -- We keep an extra placeholder for further per learning rate feature manipulation.
+   -- So it's 4 total.
+   self.normalize = normalize and 4 or 0
+
+   -- This is important to keep the possibility of sharing a weight
+   -- directly, without having to allocate it first.
+   -- The reason is these weights can be very large.
+   self.weight = weight or torch.Tensor(inputSize, outputSize + self.normalize):zero()
+   self.bias = bias or torch.Tensor(outputSize):zero()
+   self.inputSize = self.weight and self.weight:size(1) or inputSize
+   self.outputSize = self.weight and (self.weight:size(2)-self.normalize) or outputSize
+
+   -- gradWeight is not initialized as we're doing dense gradient accumulation
+   -- This is more efficient and avoids allocating a giant useless gradWeight
+   self.gradWeight = torch.Tensor()
+
+   -- gradBias still works the same as it's already dense
+   self.gradBias = torch.Tensor(self.outputSize):zero()
+
+   -- Buffers
+   self.gradWeightBuffer = torch.Tensor()
+   self.valuesBuffer = torch.Tensor()
+   self.normalizedValues = torch.Tensor()
+
+   -- That is used to accumulate keys and gradWeight
+   -- when doing gradients accumulations
+   self.running = {
+      cumSumSizes = {},
+      keys = {},
+      gradWeight = {},
+      counter = 1,
+   }
+
+   -- self.sizes, self.cumSumSizes are calculated on the CPU even when using CUDA.
+   -- These two tables make it easier to resize these buffers instead of re-allocating them.
+   -- self.*Cache[1] always contains values on CPU.
+   -- If CUDA is being used, self.*Cache[2] contains values on GPU.
+   self.sizesCache = {}
+   self.cumSumSizesCache = {}
+
+   -- A few options
+   self.weightDecay = 0
+   self.doGradInput = doGradInput or false
+   self.offset = keysOffset and keysOffset-1 or -1 -- if this adds self.offset to indices
+end
+
+-- Reset all the parameters needed
+-- for normalization to 0
+function IndexLinear:reset(stdv)
+   if stdv then
+      stdv = stdv * math.sqrt(3)
+   else
+      stdv = 1./math.sqrt(self.weight:size(2))
+   end
+   self.weight:uniform(-stdv, stdv)
+   self.bias:uniform(-stdv, stdv):mul(0.000001)
+   if self.normalize and self.normalize > 0 then
+      self.weight[{{}, {1,self.normalize}}]:zero()
+   end
+end
+
+function IndexLinear:reshapeInput(input)
+   assert(type(input) == 'table')
+
+   local ninputs = 0
+   for _, v in ipairs(input) do
+      ninputs = ninputs + 1
+   end
+
+   assert(ninputs == 2 or ninputs == 3)
+
+   -- If format is:
+   -- {
+   --   torch.LongTensor(size1+size2+...+sizeN), -- concatenated batch of keys
+   --   torch.Tensor(size1+size2+...+sizeN), -- concatenated batch of values
+   --   torch.LongTensor(N), -- keys/values sizes (values are {size1, ..., sizeN})
+   -- }
+   if ninputs == 3 then
+      local fkeys = input[1]
+      local fvals = input[2]
+      local fsizes = torch.isTensor(input[3]) and input[3] or fkeys.new{input[3]}
+      assert(fkeys:nElement() == fvals:nElement(), 'Keys and values should be of same size')
+      assert(fkeys:dim() == 1, 'Keys and values should be 1D')
+      self.isFlat = true
+      self.noBatch = false
+      return fkeys, fvals, fsizes
+   end
+
+   local keys = input[1]
+   local values = input[2]
+   local lkeys, lvalues
+
+   -- If format is:
+   -- {
+   --   { torch.LongTensor(size1), torch.LongTensor(size2), ..., torch.LongTensor(sizeN) }, -- batch of keys
+   --   { torch.Tensor(size1), torch.Tensor(size2), ..., torch.Tensor(sizeN) }, -- batch of values,
+   -- }
+   if type(keys) == 'table' and type(values) == 'table' then
+      lkeys, lvalues = keys, values
+      self.isFlat = false
+      self.noBatch = false
+
+   -- If format is not a batch:
+   -- {
+   --   torch.LongTensor(size1), -- keys
+   --   torch.Tensor(size1), -- values,
+   -- }
+   elseif torch.isTensor(keys) and torch.isTensor(values) then
+      lkeys, lvalues = {keys}, {values}
+      self.isFlat = false
+      self.noBatch = true
+   else
+      error('Wrong input format.')
+   end
+
+   for i=1,#lkeys do
+      assert(lvalues[i]:dim() == 1 and lkeys[i]:dim() == 1, "keys and values should be 1D")
+   end
+
+   return lkeys, lvalues
+end
+
+function IndexLinear:longTensor(...)
+   if (self:type() == 'torch.CudaTensor') then
+      return torch.CudaLongTensor(...)
+   else
+      return torch.LongTensor(...)
+   end
+end
+
+function IndexLinear:flattenInputs(input)
+   local lkeys, lvalues, sizes = self:reshapeInput(input)
+
+   local counter = self.running.counter
+
+   -- Ensure everything is of the right type
+   local isCuda = (self:type() == 'torch.CudaTensor')
+   self.running.keys[counter] = self.running.keys[counter] or self:longTensor()
+   self.keys = self.running.keys[counter]
+
+   if self.isFlat then
+      self.values = self.values or lvalues.new()
+      self.sizes = self.sizes or self:longTensor()
+
+      self.keys:resize(lkeys:size()):copy(lkeys)
+      self.values:resize(lvalues:size()):copy(lvalues)
+      self.sizes = sizes
+      self.cumSumSizes = self.cumSumSizes or self.sizes.new()
+      self.cumSumSizes:cumsum(self.sizes)
+   else
+      self.values = self.values or lvalues[1].new()
+
+      self.lkeys = lkeys
+      self.lvalues = lvalues
+      local batchSize = #self.lkeys
+
+      self.sizesCache[1] = self.sizesCache[1] or torch.LongTensor(batchSize)
+      self.cumSumSizesCache[1] = self.cumSumSizesCache[1] or torch.LongTensor(batchSize)
+
+      self.sizes = self.sizesCache[1]
+      self.cumSumSizes = self.cumSumSizesCache[1]
+
+      self.sizes:resize(batchSize)
+      self.cumSumSizes:resize(batchSize)
+
+      for i = 1,batchSize do
+         self.sizes[i] = self.lkeys[i]:size(1)
+      end
+      self.cumSumSizes:cumsum(self.sizes)
+
+      self.keys:cat(self.lkeys, 1)
+      self.values:cat(self.lvalues, 1)
+
+      if isCuda then
+         -- Get the GPU cache
+         self.sizesCache[2] = self.sizesCache[2] or torch.CudaLongTensor()
+         self.cumSumSizesCache[2] = self.cumSumSizesCache[2] or torch.CudaLongTensor()
+
+         self.sizes = self.sizesCache[2]
+         self.cumSumSizes = self.cumSumSizesCache[2]
+
+         -- Resize and copy to GPU
+         self.sizes:resize(batchSize):copy(self.sizesCache[1])
+         self.cumSumSizes:resize(batchSize):copy(self.cumSumSizesCache[1])
+      end
+   end
+   self.running.cumSumSizes[counter] = self.cumSumSizes
+end
+
+function IndexLinear:updateOutput(input)
+
+   self:flattenInputs(input)
+
+   self.values.THNN.IndexLinear_updateOutput(
+      self.keys:cdata(),
+      self.offset,
+      self.values:cdata(),
+      self.sizes:cdata(),
+      self.cumSumSizes:cdata(),
+      self.output:cdata(),
+      self.weight:cdata(),
+      self.bias:cdata(),
+      self.normalizedValues:cdata(),
+      self.train and 1 or 0
+      )
+
+   if self.noBatch then
+      self.output:resize(self.output:size(2))
+   end
+   return self.output
+end
+
+function IndexLinear:accUpdateGradParameters(input, gradOutput, scale)
+   self.values.THNN.IndexLinear_accUpdateGradParameters(
+      self.keys:cdata(),
+      self.offset,
+      self.normalize > 0 and self.normalizedValues:cdata() or self.values:cdata(),
+      self.sizes:cdata(),
+      self.cumSumSizes:cdata(),
+      gradOutput:cdata(),
+      self.weight:cdata(),
+      self.bias:cdata(),
+      self.weightDecay or 0,
+      scale or 1
+   )
+end
+
+function IndexLinear:accGradParameters(input, gradOutput, scale)
+
+   local counter = self.running.counter
+
+   -- Same as the running.keys in the updateOutput function,
+   -- get a table of dense running.gradWeight
+   self.running.gradWeight[counter] = self.running.gradWeight[counter] or self.values.new()
+   self.values.THNN.IndexLinear_accGradParameters(
+      self.keys:cdata(),
+      self.offset,
+      self.normalize > 0 and self.normalizedValues:cdata() or self.values:cdata(),
+      self.sizes:cdata(),
+      self.cumSumSizes:cdata(),
+      gradOutput:cdata(),
+      self.running.gradWeight[counter]:cdata(),
+      self.gradBias:cdata(),
+      self.weight:cdata(),
+      self.bias:cdata(),
+      self.valuesBuffer:cdata(),
+      self.weightDecay or 0,
+      scale or 1
+   )
+
+   -- Increment the running counter to create a new buffer
+   -- if we don't flush them in zerogradParameters
+   self.running.counter = self.running.counter + 1
+end
+
+function IndexLinear:updateGradInput(input, gradOutput)
+   self.gradInput = {}
+   -- Revamped from nn.SparseLinear.updateGradInput
+   if self.doGradInput and self.normalize > 0 then
+      error('updateGradInput is not implemented in max-normalize mode')
+   end
+
+   local ini = self.weight:size(1)
+
+   if self.doGradInput then
+      local gi = gradOutput.new()
+      if gradOutput:dim() == 1 then
+         gi:resize(self.weight:size(1))
+         gi:mv(self.weight,gradOutput)
+         gi:resize(1, self.weight:size(1))
+      elseif gradOutput:dim() == 2 then
+         gi:resize(gradOutput:size(1), self.weight:size(1))
+         gi:mm(gradOutput, self.weight:t())
+      end
+
+      local indices = self.running.keys[1].new(ini):range(1, ini)
+
+      if self.isFlat then
+         self.gradInput[1] = torch.repeatTensor(indices, gi:size(1), 1)
+         self.gradInput[2] = gi
+      else
+         self.gradInput[1] = {}
+         self.gradInput[2] = {}
+         for i = 1,gi:size(1) do
+            self.gradInput[1][i] = self.running.keys[1].new(ini)
+            self.gradInput[1][i]:copy(indices)
+            self.gradInput[2][i] = gradOutput.new(ini)
+            self.gradInput[2][i]:copy(gi[i])
+         end
+      end
+   end
+
+   if self.noBatch then
+      if self.isFlat then
+         self.gradInput = {self.gradInput[1]:resize(ini), self.gradInput[2]:resize(ini)}
+      else
+         self.gradInput = {self.gradInput[1][1], self.gradInput[2][1]}
+      end
+   end
+   return self.gradInput
+end
+
+function IndexLinear:updateParameters(lr)
+   local counter = self.running.counter
+   if counter > 1 then
+      if counter == 2 then
+         self.updateKeys = self.running.keys[1]
+         self.gradWeight = self.running.gradWeight[1]
+      else
+         self.updateKeysBuffer = self.updateKeysBuffer or self:longTensor()
+         local lkeys = {}
+         local lgweights = {}
+         local totalSize = 0
+         local lCumSumSizes = {}
+         for i=1,counter-1 do
+            lkeys[i] = self.running.keys[i]
+            -- Change layout to take advantage of the 1-D contiguous torch.cat
+            lgweights[i] = self.running.gradWeight[i]:contiguous()
+            lgweights[i]:resize(lgweights[i]:nElement())
+            lCumSumSizes[i] = totalSize + self.running.cumSumSizes[i]
+            totalSize = totalSize + lkeys[i]:size(1)
+         end
+
+         self.updateKeysBuffer:cat(lkeys, 1)
+         self.gradWeightBuffer:cat(lgweights, 1)
+         self.cumSumSizes:cat(lCumSumSizes, 1)
+         self.gradWeightBuffer:resize(totalSize, self.outputSize)
+         self.gradWeight = self.gradWeightBuffer
+         self.updateKeys = self.updateKeysBuffer
+      end
+      self.values.THNN.IndexLinear_updateParameters(
+            self.gradWeight:cdata(),
+            self.gradBias:cdata(),
+            self.weight:cdata(),
+            self.bias:cdata(),
+            self.updateKeys:cdata(),
+            self.cumSumSizes:cdata(),
+            self.offset,
+            self.weightDecay or 0,
+            lr or error('You must specify a learning rate')
+         )
+   end
+end
+
+function IndexLinear:zeroGradParameters()
+   -- No need to do anything here as gradWeight is dense
+   self.gradBias:zero()
+
+   -- The below piece of code would reset
+   -- the smart scaling parameters for each features
+   -- each time we call zeroGradParameters
+   -- TODO: decide what to do with that piece of code.
+   -- NB: this should be commented along with the corresponding
+   -- piece of code in lib/THNN/generic/IndexLinear.c, in the accUpdateGradParameters function.
+
+   --[[
+   local w = self.weight:select(2, 3)
+   if self.updateKeys and self.updateKeys:nElement() > 0 then
+      self.updateKeysBuffer:resizeAs(self.updateKeys):copy(self.updateKeys):add(self.offset+1)
+      w:indexFill(1, self.updateKeysBuffer, 0)
+   end
+   ]]--
+   self.running.counter = 1
+end
+
+function IndexLinear:parameters()
+   return {self.weight, self.bias}, {self.running, self.gradBias}
+end
+
+function IndexLinear:clearState()
+   self.running.keys = {}
+   self.running.gradWeight = {}
+   self.keys = nil
+   self.zerokeys = nil
+   self.updateKeys = nil
+   self.values = nil
+   self.sizes = nil
+   self.lkeys = {}
+   self.lvalues = {}
+   self.gradWeightBuffer = self.gradWeightBuffer.new()
+   self.valuesBuffer = self.valuesBuffer.new()
+   self.updateKeysBuffer = nil
+   self.values = nil
+   return parent.clearState(self)
+end