Ignore updateGradInput if self.gradInput is nil

Change suggested by Natalia Gimelshein

To align behaviour on other modules (linear, spatial convolution, etc.).
This allows model definitions such as:

local lenet = nn.Sequential()
lenet:add(nn.MulConstant(0.00390625))
lenet:add(nn.SpatialConvolution(1,20,5,5,1,1,0)) -- 1*28*28 -> 20*24*24
lenet:add(nn.SpatialMaxPooling(2, 2, 2, 2)) -- 20*24*24 -> 20*12*12
lenet:add(nn.SpatialConvolution(20,50,5,5,1,1,0)) -- 20*12*12 -> 50*8*8
lenet:add(nn.SpatialMaxPooling(2,2,2,2)) --  50*8*8 -> 50*4*4
lenet:add(nn.View(-1):setNumInputDims(3))  -- 50*4*4 -> 800
lenet:add(nn.Linear(800,500))  -- 800 -> 500
lenet:add(nn.ReLU())
lenet:add(nn.Linear(500, 10))  -- 500 -> 10
lenet:add(nn.LogSoftMax())
lenet:get(1).gradInput = nil
lenet:get(2).gradInput = nil

Setting gradInput to nil on the first two layers removes unnecessary
dgrad computations and saves about 5% of compute utilization.

MulConstant.lua

@@ -4,7 +4,7 @@ function MulConstant:__init(constant_scalar,ip)
   parent.__init(self)
   assert(type(constant_scalar) == 'number', 'input is not scalar!')
   self.constant_scalar = constant_scalar
-    
+
   -- default for inplace is false
    self.inplace = ip or false
    if (ip and type(ip) ~= 'boolean') then
@@ -22,18 +22,20 @@ function MulConstant:updateOutput(input)
     self.output:mul(self.constant_scalar)
   end
   return self.output
-end 
+end
 
 function MulConstant:updateGradInput(input, gradOutput)
-  if self.inplace then
-    gradOutput:mul(self.constant_scalar)
-    self.gradInput = gradOutput
-    -- restore previous input value
-    input:div(self.constant_scalar)
-  else
-    self.gradInput:resizeAs(gradOutput)
-    self.gradInput:copy(gradOutput)
-    self.gradInput:mul(self.constant_scalar)
+  if self.gradInput then
+    if self.inplace then
+      gradOutput:mul(self.constant_scalar)
+      self.gradInput = gradOutput
+      -- restore previous input value
+      input:div(self.constant_scalar)
+    else
+      self.gradInput:resizeAs(gradOutput)
+      self.gradInput:copy(gradOutput)
+      self.gradInput:mul(self.constant_scalar)
+    end
+    return self.gradInput
   end
-  return self.gradInput
 end