Speedup and reduce memory usage in Normalize

[fmassa]

It's almost 40x faster on CPU, and doesn't use extra memory. Should
be faster on GPU as well, but I haven't benchmarked.

• Instead of creating a temporary matrix b1*b2 of size (n,d,d), rearrange the computations to avoid creating this huge matrix which was eating all the memory.
• forward and backward supports varying dimensionality (which wasn't the case before because of the eye matrix)

Should be of interest to @ffmpbgrnn and @bamos.

[fmassa]

Just did a quick test on the GPU, backward seems to be much faster than the previous version as well.
For an input of dimensions (32,4096), this version is 100x faster than the old one (and the old version used 2GB of temporary buffers).

[bamos]

Hi, I ran @ffmpbgrnn's test from #341 for some simple profiling of this PR on a Tesla K40 GPU and 3.70GHz CPU. The speedup is amazing!

require 'nn'
require 'cutorch'
require 'cunn'

local module = nn.Normalize(2):cuda()
module:fastMode(false)
local input = torch.rand(64, 2400):cuda()
local t = torch.Timer()
for i = 1, 100 do
    module:forward(input)
    module:backward(input, input)
    print(i)
end
print(t:time().real/100)

Current Master

• Commit: b80bda2
• md5sum of Normalize.lua: 4b1f217a796f00ff8cfe575da8e4a409
• CPU mean execution time: 3.82 s
• K40 mean execution time: 0.108 s

This PR

• md5sum of Normalize.lua: a9c7d3f642c08de361369be201234fca
• CPU: 0.00634 s
• K40: 0.000624 s

[soumith]

this is super awesome. If unit tests pass, and it's exactly the same implementation as before, why not!!!