Keep the Lua implementation for the Linear module

1 files changed, 94 insertions, 0 deletions

diff --git a/test/LinearTHNN.lua b/test/LinearTHNN.lua
new file mode 100644
index 0000000..4ac944e
--- /dev/null
+++ b/test/LinearTHNN.lua
@@ -0,0 +1,94 @@
+local Linear, parent = torch.class('nn.Linear', 'nn.Module')
+
+function Linear:__init(inputSize, outputSize, bias)
+   parent.__init(self)
+   local bias = ((bias == nil) and true) or bias
+   self.weight = torch.Tensor(outputSize, inputSize)
+   self.gradWeight = torch.Tensor(outputSize, inputSize)
+   if bias then
+      self.bias = torch.Tensor(outputSize)
+      self.gradBias = torch.Tensor(outputSize)
+   end
+   self.addBuffer = torch.Tensor(outputSize)
+   self:reset()
+end
+
+function Linear:noBias()
+   self.bias = nil
+   self.gradBias = nil
+   return self
+end
+
+function Linear:reset(stdv)
+   if stdv then
+      stdv = stdv * math.sqrt(3)
+   else
+      stdv = 1./math.sqrt(self.weight:size(2))
+   end
+   if nn.oldSeed then
+      for i=1,self.weight:size(1) do
+         self.weight:select(1, i):apply(function()
+            return torch.uniform(-stdv, stdv)
+         end)
+      end
+      if self.bias then
+         for i=1,self.bias:nElement() do
+            self.bias[i] = torch.uniform(-stdv, stdv)
+         end
+      end
+   else
+      self.weight:uniform(-stdv, stdv)
+      if self.bias then self.bias:uniform(-stdv, stdv) end
+   end
+   return self
+end
+
+function Linear:updateOutput(input)
+   input.THNN.Linear_updateOutput(
+      input:cdata(),
+      self.output:cdata(),
+      self.weight:cdata(),
+      self.bias and self.bias:cdata(),
+      self.addBuffer:cdata()
+   )
+   return self.output
+end
+
+function Linear:updateGradInput(input, gradOutput)
+   input.THNN.Linear_updateGradInput(
+      input:cdata(),
+      gradOutput:cdata(),
+      self.gradInput:cdata(),
+      self.weight:cdata()
+   )
+   return self.gradInput
+end
+
+function Linear:accGradParameters(input, gradOutput, scale)
+   input.THNN.Linear_accGradParameters(
+      input:cdata(),
+      gradOutput:cdata(),
+      self.gradInput:cdata(),
+      self.weight:cdata(),
+      self.bias and self.bias:cdata(),
+      self.gradWeight:cdata(),
+      self.bias and self.gradBias:cdata(),
+      self.addBuffer:cdata(),
+      scale or 1
+   )
+   return self.gradWeight
+end
+
+-- we do not need to accumulate parameters when sharing
+Linear.sharedAccUpdateGradParameters = Linear.accUpdateGradParameters
+
+function Linear:clearState()
+   if self.addBuffer then self.addBuffer:set() end
+   return parent.clearState(self)
+end
+
+function Linear:__tostring__()
+  return torch.type(self) ..
+      string.format('(%d -> %d)', self.weight:size(2), self.weight:size(1)) ..
+      (self.bias == nil and ' without bias' or '')
+end