From 714eb56e1760fdfb26afccde92664d3a2f1e8435 Mon Sep 17 00:00:00 2001
From: Vsevolod Stakhov <vsevolod@highsecure.ru>
Date: Wed, 23 May 2018 18:14:15 +0100
Subject: [Minor] Move lua contrib libraries to lua- prefix

---
 contrib/lua-torch/nn/PartialLinear.lua | 114 +++++++++++++++++++++++++++++++++
 1 file changed, 114 insertions(+)
 create mode 100644 contrib/lua-torch/nn/PartialLinear.lua

(limited to 'contrib/lua-torch/nn/PartialLinear.lua')

diff --git a/contrib/lua-torch/nn/PartialLinear.lua b/contrib/lua-torch/nn/PartialLinear.lua
new file mode 100644
index 000000000..6e92cfc08
--- /dev/null
+++ b/contrib/lua-torch/nn/PartialLinear.lua
@@ -0,0 +1,114 @@
+local PartialLinear, Module = torch.class('nn.PartialLinear', 'nn.Module')
+
+--[[
+
+PartialLinear is a Linear layer that allows the user to a set a collection of
+column indices. When the column indices are set, the layer will behave like a
+Linear layer that only has those columns. Meanwhile, all parameters are
+preserved, so resetting the PartialLinear layer will result in a module that
+behaves just like a regular Linear layer.
+
+This module is useful, for instance, when you want to do forward-backward on
+only a subset of a Linear layer during training but use the full Linear layer
+at test time.
+
+]]--
+
+function PartialLinear:__init(inputsize, outputsize, bias)
+   local bias = ((bias == nil) and true) or bias
+   Module.__init(self)
+
+   -- define the layer as a small network:
+   local pt = nn.ParallelTable()
+   pt:add(nn.Identity()):add(nn.LookupTable(outputsize, inputsize))
+   self.network = nn.Sequential():add(pt):add(nn.MM(false, true))
+   if bias then
+      self.bias     = torch.Tensor(1, outputsize):zero()
+      self.gradBias = torch.Tensor(1, outputsize):zero()
+   end
+
+   -- set partition:
+   self.inputsize  = inputsize
+   self.outputsize = outputsize
+   self.allcolumns = torch.range(1, self.outputsize)
+   self:resetPartition()
+end
+
+function PartialLinear:setPartition(indices)
+   self.partition = indices:type(self.allcolumns:type())
+end
+
+function PartialLinear:resetPartition()
+   self.partition = self.allcolumns
+end
+
+function PartialLinear:parameters()
+   return {self.network:get(1):get(2).weight,     self.bias},
+          {self.network:get(1):get(2).gradWeight, self.gradBias}
+end  -- should return only the relevant partition?
+
+function PartialLinear:updateOutput(input)
+   self.output:set(self.network:forward{input, self.partition})
+   if self.bias then
+      self.output:add(
+         self.bias:index(2, self.partition:long()):expandAs(self.output)
+      )
+      self.addBuffer = self.addBuffer or input.new()
+      if self.addBuffer:nElement() ~= input:size(1) then
+         self.addBuffer:resize(input:size(1)):fill(1)
+      end
+   end
+   return self.output
+end
+
+function PartialLinear:updateGradInput(input, gradOutput)
+   if self.gradInput then
+      self.network:updateGradInput({input, self.partition}, gradOutput)
+      self.gradInput:set(self.network.gradInput[1])
+   end
+   return self.gradInput
+end
+
+function PartialLinear:accGradParameters(input, gradOutput, scale)
+   local scale = scale or 1
+   self.network:accGradParameters({input, self.partition}, gradOutput, scale)
+   if self.bias then
+      self.buffer = self.buffer or input.new()
+      self.buffer:resize(gradOutput:size(2))
+      self.buffer:mv(gradOutput:t(), self.addBuffer):mul(scale)
+      self.gradBias:indexAdd(
+         2, self.partition:long(), self.buffer:view(1, self.buffer:nElement())
+      )
+   end
+end
+
+function PartialLinear:accUpdateGradParameters(input, gradOutput, lr)
+   local gradWeight = self.network:get(1):get(2).gradWeight
+   local gradBias = self.gradBias
+   self.network:get(1):get(2).gradWeight = self.network:get(1):get(2).weight
+   self.gradBias = self.bias
+   self:accGradParameters(input, gradOutput, -lr)
+   self.network:get(1):get(2).gradWeight = gradWeight
+   self.gradBias = gradBias
+end
+
+function PartialLinear:zeroGradParameters()
+   self.network:zeroGradParameters()
+   self.gradBias:zero()
+end
+
+function PartialLinear:updateParameters(learningRate)
+   self.network:updateParameters(learningRate)
+   self.bias:add(-learningRate, self.gradBias)
+end
+
+function PartialLinear:sharedAccUpdateGradParameters(input, gradOutput, lr)
+   -- we do not need to accumulate parameters when sharing:
+   self:defaultAccUpdateGradParameters(input, gradOutput, lr)
+end
+
+function PartialLinear:__tostring__()
+   return torch.type(self) ..
+      string.format('(%d -> %d)', self.inputsize, self.outputsize) ..
+      (self.bias == nil and ' without bias' or '')
+end
-- 
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