Unable to do parameter sharing in Torch between [sub]networks

Question

I am trying to share the parameters between the encoder/decoder sub-networks of an architecture with another encoder/decoder in a different architecture. This is necessary for my problem since at the test time it requires a lot of computation (and time) to do a forward pass on the original architecture and then extract the decoder results. However, what I noticed was although I have explicitly asked for parameter sharing when doing clone(), the parameters are not shared and each architecture has its own parameters while training.

I am showing the difference between the results of the two architecture via some print() statements by forward-propagating some random vectors into the decoder and encoders of both architectures (you can do also compare their weights).

So I wonder, can anyone help me find out what I'm doing wrong when sharing the parameters?

Below I post a simplified version of my code:

require 'nn'
require 'nngraph'
require 'cutorch'
require 'cunn'
require 'optim'


input = nn.Identity()()
encoder = nn.Sequential():add(nn.Linear(100, 20)):add(nn.ReLU(true)):add(nn.Linear(20, 10))
decoder = nn.Sequential():add(nn.Linear(10, 20)):add(nn.ReLU(true)):add(nn.Linear(20, 100))
code = encoder(input)
reconstruction = decoder(code)

outsideCode = nn.Identity()()
decoderCloned= decoder:clone('weight', 'bias', 'gradWeight', 'gradBias')
outsideReconstruction = decoderCloned(nn.JoinTable(1)({code, outsideCode}))
dumbNet = nn.Sequential():add(nn.Linear(100, 10))
codeRecon = dumbNet(outsideReconstruction)

input2 = nn.Identity()()
encoderTestTime = encoder:clone('weight', 'bias', 'gradWeight', 'gradBias')
decoderTestTime = decoder:clone('weight', 'bias', 'gradWeight', 'gradBias')
codeTest = encoderTestTime(input2)
reconTest = decoderTestTime(codeTest)

gMod = nn.gModule({input, outsideCode}, {reconstruction, codeRecon})
gModTest = nn.gModule({input2}, {reconTest})

criterion1 = nn.BCECriterion()
criterion2 = nn.MSECriterion()

-- Okay, the module has been created. Now it's time to do some other stuff

params, gParams = gMod:getParameters()

numParams = params:nElement()
memReqForParams = numParams * 5 * 4 / 1024 / 1024 -- Convert to MBs

-- If enough memory on GPU, move stuff to the GPU
if memReqForParams <= 1000 then
    gMod = gMod:cuda()
    gModTest = gModTest:cuda()
    criterion1 = criterion1:cuda()
    criterion2 = criterion2:cuda()
    params, gParams = gMod:getParameters()
end

-- Data

Data = torch.rand(200, 100):cuda()
Data[Data:gt(0.5)] = 1
Data[Data:lt(0.5)] = 0

fakeCodes = torch.rand(400, 10):cuda()

config = {learningRate = 0.001}
state = {}

-- Start training
print ("\nEncoders before training: \n\tgMod's Encoder: " .. gMod:get(2):forward(torch.ones(1, 100):cuda()):sum() .. "\n\tgModTest's Encoder: " .. gModTest:get(2):forward(torch.ones(1, 100):cuda()):sum())
print ("\nDecoders before training: \n\tgMod's Decoder: " .. gMod:get(3):forward(torch.ones(1, 10):cuda()):sum() .. "\n\tgModTest's Decoder: " ..  gModTest:get(3):forward(torch.ones(1, 10):cuda()):sum())


gMod:training()
for i=1, Data:size(1) do
    local opfunc = function(x)
        if x ~= params then
            params:copy(x)
        end
        gMod:zeroGradParameters()
        recon, outsideRecon = unpack(gMod:forward({Data[{{i}}], fakeCodes[{{i}}]}))
        err = criterion1:forward(recon, Data[{{i}}])
        df_dw = criterion1:backward(recon, Data[{{i}}])

        errFake = criterion2:forward(outsideRecon, fakeCodes[{{i*2-1, i * 2}}])
        df_dwFake = criterion2:backward(outsideRecon, fakeCodes[{{i*2-1, i * 2}}])

        errorGrads = {df_dw, df_dwFake}
        gMod:backward({Data[{{i}}], fakeCodes[{{i*2-1, i * 2}}]}, errorGrads)

        return err, gParams
    end

    x, reconError = optim.adam(opfunc, params, config, state)
end

print ("\n\nEncoders after training: \n\tgMod's Encoder: " .. gMod:get(2):forward(torch.ones(1, 100):cuda()):sum() .. "\n\tgModTest's Encoder: " .. gModTest:get(2):forward(torch.ones(1, 100):cuda()):sum())
print ("\nDecoders after training: \n\tgMod's Decoder: " .. gMod:get(3):forward(torch.ones(1, 10):cuda()):sum() .. "\n\tgModTest's Decoder: " ..  gModTest:get(3):forward(torch.ones(1, 10):cuda()):sum())

Answer 1

I got the solution to the problem with the help of fmassa on a GitHub issue I had opened for this problem here. One can use nn.Container to resolve the issue of parameter sharing as follow:

container = nn.Container()
container:add(gMod)
container:add(gModTest)
params, gradParams = container:getParameters()