Fix possible division by zero in Normalize

When p < 2, if the input is 0 there was division by zero. Add a small dampening factor to avoid this. Also, small optimizations for different p.

Normalize.lua

@@ -53,11 +53,28 @@ function Normalize:updateGradInput(input, gradOutput)
   gradOutput = gradOutput:view(n,d,1)
   self._gradInput:cmul(self.normp:view(n,1,1):expand(n,d,1), gradOutput)
 
+  -- small optimizations for different p
+  -- buffer = input*|input|^(p-2)
+  if self.p % 2 ~= 0 then
+    -- for non-even p, need to add absolute value
+    if self.p < 2 then
+      -- add eps to avoid possible division by 0
+      self.buffer:abs(input):add(self.eps):pow(self.p-2):cmul(input)
+    else
+      self.buffer:abs(input):pow(self.p-2):cmul(input)
+    end
+  elseif self.p == 2 then
+    -- special case for p == 2, pow(x,0) = 1
+    self.buffer:copy(input)
+  else
+    -- p is even and > 2, pow(x,p) is always positive
+    self.buffer:pow(input,self.p-2):cmul(input)
+  end
+
   -- compute cross term in two steps
   self.cross = self.cross or input.new()
   self.cross:resize(n,1,1)
 
-  self.buffer:abs(input):pow(self.p-2):cmul(input)
   local b1 = self.buffer:view(n,d,1)
   local b2 = input:view(n,1,d)
   -- instead of having a huge temporary matrix (b1*b2),

test.lua

@@ -438,7 +438,7 @@ end
 function nntest.Normalize()
    -- compare forward against torch implementation
    -- and check gradient
-   for _,p in pairs({1,2,1.5}) do
+   for _,p in pairs({1,2,3,4,1.5}) do
       local ini = math.random(3,10)
       local input = torch.randn(ini)
       local module = nn.Normalize(p)
@@ -452,7 +452,7 @@ function nntest.Normalize()
    end
 
    -- batch mode
-   for _,p in pairs({1,2,torch.uniform()*math.random(1,10)}) do
+   for _,p in pairs({1,2,3,4,torch.uniform()*math.random(1,10)}) do
       local ini = math.random(3,5)
       local inj = math.random(3,5)
       local ink = math.random(3,5)