Multi gpu nce

CMakeLists.txt

@@ -10,5 +10,5 @@ FIND_PACKAGE(Torch REQUIRED)
 
 SET(src)
 FILE(GLOB luasrc *.lua)
-SET(luasrc ${luasrc} test/test.lua test/mnistsample.t7)
+SET(luasrc ${luasrc} test/test.lua test/mnistsample.t7 test/bigtest.lua)
 ADD_TORCH_PACKAGE(rnn "${src}" "${luasrc}" "Recurrent Neural Networks")

examples/multigpu-nce-rnnlm.lua

@@ -0,0 +1,315 @@
+require 'paths'
+require 'rnn'
+require 'nngraph'
+local dl = require 'dataload'
+assert(nn.NCEModule and nn.NCEModule.version and nn.NCEModule.version >= 4, "update dpnn : luarocks install dpnn")
+require 'cunn'
+
+--[[ command line arguments ]]--
+cmd = torch.CmdLine()
+cmd:text()
+cmd:text('Train a multi-GPU Language Model using stacked LSTM on Google Billion Words dataset')
+cmd:text('Example:')
+cmd:text("th examples/multigpu-nce-rnnlm.lua --progress --earlystop 50 --device 2 --seqlen 20 --hiddensize '{200,200}' --batchsize 20 --startlr 1 --uniform 0.1 --cutoff 5 --schedule '{[5]=0.5,[6]=0.25,[7]=0.125,[8]=0.0625,[9]=0.03125,[10]=0.015625,[11]=0.0078125,[12]=0.00390625}'")
+cmd:text("th examples/multigpu-nce-rnnlm.lua.lua --trainsize 400000 --validsize 40000 --cutoff 10 --batchsize 128 --seqlen 100 --hiddensize '{250,250}' --progress --device 2")
+cmd:text("th scripts/evaluate-rnnlm.lua --xplogpath /data/save/rnnlm/ptb:atlas:1458081269:1.t7 --cuda")
+cmd:text('Options:')
+-- training
+cmd:option('--startlr', 0.7, 'learning rate at t=0')
+cmd:option('--minlr', 0.001, 'minimum learning rate')
+cmd:option('--saturate', 300, 'epoch at which linear decayed LR will reach minlr')
+cmd:option('--schedule', '', 'learning rate schedule. e.g. {[5] = 0.004, [6] = 0.001}')
+cmd:option('--momentum', -1, 'momentum (requires an additional copy of all params)')
+cmd:option('--maxnormout', -1, 'max l2-norm of each layer\'s output neuron weights')
+cmd:option('--cutoff', 10, 'max l2-norm of concatenation of all gradParam tensors')
+cmd:option('--device', 1, 'sets the device (GPU) to use')
+cmd:option('--profile', false, 'profile updateOutput,updateGradInput and accGradParameters in Sequential')
+cmd:option('--maxepoch', 1000, 'maximum number of epochs to run')
+cmd:option('--earlystop', 50, 'maximum number of epochs to wait to find a better local minima for early-stopping')
+cmd:option('--progress', false, 'print progress bar')
+cmd:option('--silent', false, 'don\'t print anything to stdout')
+cmd:option('--uniform', 0.1, 'initialize parameters using uniform distribution between -uniform and uniform. -1 means default initialization')
+cmd:option('--k', 400, 'how many noise samples to use for NCE')
+cmd:option('--continue', '', 'path to model for which training should be continued. Note that current options (except for device, cuda and tiny) will be ignored.')
+cmd:option('--Z', -1, 'normalization constant for NCE module (-1 approximates it from first batch).')
+cmd:option('--rownoise', false, 'sample k noise samples for each row for NCE module')
+-- rnn layer 
+cmd:option('--seqlen', 50, 'sequence length : back-propagate through time (BPTT) for this many time-steps')
+cmd:option('--inputsize', -1, 'size of lookup table embeddings. -1 defaults to hiddensize[1]')
+cmd:option('--hiddensize', '{200,200}', 'number of hidden units used at output of each recurrent layer. When more than one is specified, RNN/LSTMs/GRUs are stacked')
+cmd:option('--dropout', 0, 'ancelossy dropout with this probability after each rnn layer. dropout <= 0 disables it.')
+-- data
+cmd:option('--batchsize', 32, 'number of examples per batch')
+cmd:option('--trainsize', -1, 'number of train time-steps seen between each epoch')
+cmd:option('--validsize', -1, 'number of valid time-steps used for early stopping and cross-validation') 
+cmd:option('--savepath', paths.concat(dl.SAVE_PATH, 'rnnlm'), 'path to directory where experiment log (includes model) will be saved')
+cmd:option('--id', '', 'id string of this experiment (used to name output file) (defaults to a unique id)')
+cmd:option('--tiny', false, 'use train_tiny.th7 training file')
+cmd:option('--dontsave', false, 'dont save the model')
+
+cmd:text()
+local opt = cmd:parse(arg or {})
+opt.hiddensize = loadstring(" return "..opt.hiddensize)()
+opt.schedule = loadstring(" return "..opt.schedule)()
+opt.inputsize = opt.inputsize == -1 and opt.hiddensize[1] or opt.inputsize
+if not opt.silent then
+   table.print(opt)
+end
+opt.id = opt.id == '' and ('gbw' .. ':' .. dl.uniqueid()) or opt.id
+opt.version = 1
+
+cutorch.setDevice(opt.device)
+
+local xplog, lm, criterion, targetmodule
+if opt.continue ~= '' then
+   xplog = torch.load(opt.continue)
+   xplog.opt.cuda = true
+   xplog.opt.device = opt.device
+   xplog.opt.tiny = opt.tiny
+   opt = xplog.opt
+   lm = xplog.model.module
+   -- prevent re-casting bug
+   for i,lookup in ipairs(lm:findModules('nn.LookupTableMaskZero')) do
+      lookup.__input = nil
+   end
+   criterion = xplog.criterion
+   targetmodule = xplog.targetmodule
+   assert(opt)
+end
+
+--[[ data set ]]--
+
+local trainset, validset, testset = dl.loadGBW({opt.batchsize,opt.batchsize,opt.batchsize}, opt.tiny and 'train_tiny.th7' or nil)
+if not opt.silent then 
+   print("Vocabulary size : "..#trainset.ivocab) 
+   print("Train set split into "..opt.batchsize.." sequences of length "..trainset:size())
+end
+
+--[[ language model ]]--
+
+if not lm then
+   assert(opt.maxnormout <= 0)
+   lm = nn.Sequential()
+   lm:add(nn.Convert())
+
+   -- input layer (i.e. word embedding space)
+   local concat = nn.Concat(3)
+   for device=1,2 do
+      local inputsize = device == 1 and torch.floor(opt.inputsize/2) or torch.ceil(opt.inputsize/2)
+      local lookup = nn.LookupTableMaskZero(#trainset.ivocab, inputsize)
+      lookup.maxnormout = -1 -- prevent weird maxnormout behaviour
+      concat:add(nn.GPU(lookup, device):cuda()) -- input is seqlen x batchsize
+   end
+
+   lm:add(nn.GPU(concat, 2):cuda())
+   if opt.dropout > 0 then
+      lm:add(nn.GPU(nn.Dropout(opt.dropout), 2):cuda())
+   end
+
+   -- rnn layers
+   local inputsize = opt.inputsize
+   for i,hiddensize in ipairs(opt.hiddensize) do
+      -- this is a faster version of nn.Sequencer(nn.FastLSTM(inpusize, hiddensize))
+      local rnn = nn.SeqLSTM(inputsize, hiddensize)
+      rnn.maskzero = true
+      local device = i <= #opt.hiddensize/2 and 1 or 2
+      lm:add(nn.GPU(rnn, device):cuda())
+      if opt.dropout > 0 then
+         lm:add(nn.GPU(nn.Dropout(opt.dropout), device):cuda())
+      end
+      inputsize = hiddensize
+   end
+
+   lm:add(nn.GPU(nn.SplitTable(1), 3):cuda())
+
+   if opt.uniform > 0 then
+      for k,param in ipairs(lm:parameters()) do
+         assert(torch.type(param) == 'torch.CudaTensor')
+         cutorch.withDevice(param:getDevice(), function() param:uniform(-opt.uniform, opt.uniform) end)
+      end
+   end
+
+   -- output layer
+   local unigram = trainset.wordfreq:float()
+   ncemodule = nn.NCEModule(inputsize, #trainset.ivocab, opt.k, unigram, opt.Z)
+   ncemodule:reset() -- initializes bias to get approx. Z = 1
+   ncemodule.batchnoise = not opt.rownoise
+   -- distribute weight, gradWeight and momentum on devices 3 and 4
+   ncemodule:multicuda(3,4) 
+
+   -- NCE requires {input, target} as inputs
+   lm = nn.Sequential()
+      :add(nn.ParallelTable()
+         :add(lm):add(nn.Identity()))
+      :add(nn.ZipTable()) -- {{x1,x2,...}, {t1,t2,...}} -> {{x1,t1},{x2,t2},...}
+
+   -- encapsulate stepmodule into a Sequencer
+   local masked = nn.MaskZero(ncemodule, 1):cuda()
+   lm:add(nn.GPU(nn.Sequencer(masked), 3, opt.device):cuda())
+
+   -- remember previous state between batches
+   lm:remember()
+end
+
+if opt.profile then
+   lm:profile()
+end
+
+if not opt.silent then
+   print"Language Model:"
+   print(lm)
+end
+
+if not (criterion and targetmodule) then
+   --[[ loss function ]]--
+
+   local crit = nn.MaskZeroCriterion(nn.NCECriterion(), 0)
+
+   -- target is also seqlen x batchsize.
+   targetmodule = nn.Sequential()
+      :add(nn.Convert())
+      :add(nn.SplitTable(1))
+
+   criterion = nn.SequencerCriterion(crit)
+end
+
+--[[ CUDA ]]--
+
+lm:cuda()
+criterion:cuda()
+targetmodule:cuda()
+
+--[[ experiment log ]]--
+
+-- is saved to file every time a new validation minima is found
+if not xplog then
+   xplog = {}
+   xplog.opt = opt -- save all hyper-parameters and such
+   xplog.dataset = 'GoogleBillionWords'
+   xplog.vocab = trainset.vocab
+   -- will only serialize params
+   xplog.model = nn.Serial(lm)
+   xplog.model:mediumSerial()
+   xplog.criterion = criterion
+   xplog.targetmodule = targetmodule
+   -- keep a log of NLL for each epoch
+   xplog.trainnceloss = {}
+   xplog.valnceloss = {}
+   -- will be used for early-stopping
+   xplog.minvalnceloss = 99999999
+   xplog.epoch = 0
+   paths.mkdir(opt.savepath)
+end
+local ntrial = 0
+
+local epoch = xplog.epoch+1
+opt.lr = opt.lr or opt.startlr
+opt.trainsize = opt.trainsize == -1 and trainset:size() or opt.trainsize
+opt.validsize = opt.validsize == -1 and validset:size() or opt.validsize
+while opt.maxepoch <= 0 or epoch <= opt.maxepoch do
+   print("")
+   print("Epoch #"..epoch.." :")
+
+   -- 1. training
+
+   local a = torch.Timer()
+   lm:training()
+   local sumErr = 0
+   for i, inputs, targets in trainset:subiter(opt.seqlen, opt.trainsize) do
+      targets = targetmodule:forward(targets)
+      inputs = {inputs, targets}
+      -- forward
+      local outputs = lm:forward(inputs)
+      local err = criterion:forward(outputs, targets)
+      sumErr = sumErr + err
+      -- backward 
+      local gradOutputs = criterion:backward(outputs, targets)
+      local a = torch.Timer()
+      lm:zeroGradParameters()
+      lm:backward(inputs, gradOutputs)
+
+      -- update
+      if opt.cutoff > 0 then
+         local norm = lm:gradParamClip(opt.cutoff) -- affects gradParams
+         opt.meanNorm = opt.meanNorm and (opt.meanNorm*0.9 + norm*0.1) or norm
+      end
+      lm:updateGradParameters(opt.momentum) -- affects gradParams
+      lm:updateParameters(opt.lr) -- affects params
+      lm:maxParamNorm(opt.maxnormout) -- affects params
+
+      if opt.progress then
+         xlua.progress(i, opt.trainsize)
+      end
+
+      if i % 2000 == 0 then
+         collectgarbage()
+      end
+
+   end
+
+   -- learning rate decay
+   if opt.schedule then
+      opt.lr = opt.schedule[epoch] or opt.lr
+   else
+      opt.lr = opt.lr + (opt.minlr - opt.startlr)/opt.saturate
+   end
+   opt.lr = math.max(opt.minlr, opt.lr)
+
+   if not opt.silent then
+      print("learning rate", opt.lr)
+      if opt.meanNorm then
+         print("mean gradParam norm", opt.meanNorm)
+      end
+   end
+
+   if cutorch then cutorch.synchronize() end
+   local speed = opt.trainsize*opt.batchsize/a:time().real
+   print(string.format("Speed : %f words/second; %f ms/word", speed, 1000/speed))
+
+   local nceloss = sumErr/opt.trainsize
+   print("Training error : "..nceloss)
+
+   xplog.trainnceloss[epoch] = nceloss
+
+   -- 2. cross-validation
+
+   lm:evaluate()
+   local sumErr = 0
+   for i, inputs, targets in validset:subiter(opt.seqlen, opt.validsize) do
+      targets = targetmodule:forward(targets)
+      local outputs = lm:forward{inputs, targets}
+      local err = criterion:forward(outputs, targets)
+      sumErr = sumErr + err
+
+      if opt.progress then
+         xlua.progress(i, opt.validsize)
+      end
+   end
+
+   local nceloss = sumErr/opt.validsize
+   print("Validation error : "..nceloss)
+
+   xplog.valnceloss[epoch] = nceloss
+   ntrial = ntrial + 1
+
+   -- early-stopping
+   if nceloss < xplog.minvalnceloss then
+      -- save best version of model
+      xplog.minvalnceloss = nceloss
+      xplog.epoch = epoch 
+      local filename = paths.concat(opt.savepath, opt.id..'.t7')
+      if not opt.dontsave then
+         print("Found new minima. Saving to "..filename)
+         torch.save(filename, xplog)
+      end
+      ntrial = 0
+   elseif ntrial >= opt.earlystop then
+      print("No new minima found after "..ntrial.." epochs.")
+      print("Stopping experiment.")
+      print("Best model can be found in "..paths.concat(opt.savepath, opt.id..'.t7'))
+      os.exit()
+   end
+
+   collectgarbage()
+   epoch = epoch + 1
+end