char-rnn/char-rnn-chinese
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char-rnn-chinese/train.lua

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--[[
This file trains a character-level multi-layer RNN on text data
Code is based on implementation in
https://github.com/oxford-cs-ml-2015/practical6
but modified to have multi-layer support, GPU support, as well as
many other common model/optimization bells and whistles.
The practical6 code is in turn based on
https://github.com/wojciechz/learning_to_execute
which is turn based on other stuff in Torch, etc... (long lineage)
]]--
require 'torch'
require 'nn'
require 'nngraph'
require 'optim'
require 'lfs'
require 'util.OneHot'
require 'util.misc'
local CharSplitLMMinibatchLoader = require 'util.CharSplitLMMinibatchLoader'
local model_utils = require 'util.model_utils'
local LSTM = require 'model.LSTM'
local GRU = require 'model.GRU'
local RNN = require 'model.RNN'
cmd = torch.CmdLine()
cmd:text()
cmd:text('Train a character-level language model')
cmd:text()
cmd:text('Options')
-- data
cmd:option('-data_dir','data/tinyshakespeare','data directory. Should contain the file input.txt with input data')
cmd:option('-min_freq',0,'min frequent of character')
-- model params
cmd:option('-rnn_size', 128, 'size of LSTM internal state')
cmd:option('-num_layers', 2, 'number of layers in the LSTM')
cmd:option('-model', 'lstm', 'for now only lstm is supported. keep fixed')
-- optimization
cmd:option('-learning_rate',2e-3,'learning rate')
cmd:option('-learning_rate_decay',0.97,'learning rate decay')
cmd:option('-learning_rate_decay_after',10,'in number of epochs, when to start decaying the learning rate')
cmd:option('-decay_rate',0.95,'decay rate for rmsprop')
cmd:option('-dropout',0,'dropout for regularization, used after each RNN hidden layer. 0 = no dropout')
cmd:option('-seq_length',50,'number of timesteps to unroll for')
cmd:option('-batch_size',50,'number of sequences to train on in parallel')
cmd:option('-max_epochs',50,'number of full passes through the training data')
cmd:option('-grad_clip',5,'clip gradients at this value')
cmd:option('-train_frac',0.95,'fraction of data that goes into train set')
cmd:option('-val_frac',0.05,'fraction of data that goes into validation set')
-- test_frac will be computed as (1 - train_frac - val_frac)
cmd:option('-init_from', '', 'initialize network parameters from checkpoint at this path')
-- bookkeeping
cmd:option('-seed',123,'torch manual random number generator seed')
cmd:option('-print_every',1,'how many steps/minibatches between printing out the loss')
cmd:option('-eval_val_every',2000,'every how many iterations should we evaluate on validation data?')
cmd:option('-checkpoint_dir', 'cv', 'output directory where checkpoints get written')
cmd:option('-savefile','lstm','filename to autosave the checkpont to. Will be inside checkpoint_dir/')
cmd:option('-accurate_gpu_timing',0,'set this flag to 1 to get precise timings when using GPU. Might make code bit slower but reports accurate timings.')
-- GPU/CPU
cmd:option('-gpuid',0,'which gpu to use. -1 = use CPU')
cmd:option('-opencl',0,'use OpenCL (instead of CUDA)')
-- Scheduled Sampling
cmd:option('-use_ss', 1, 'whether use scheduled sampling during training')
cmd:option('-start_ss', 1, 'start amount of truth data to be given to the model when using ss')
cmd:option('-decay_ss', 0.005, 'ss amount decay rate of each epoch')
cmd:option('-min_ss', 0.9, 'minimum amount of truth data to be given to the model when using ss')
cmd:text()
-- parse input params
opt = cmd:parse(arg)
torch.manualSeed(opt.seed)
math.randomseed(opt.seed)
-- Xperimental -- Vladislav Khorev vladislav.khorev@fishrungames.com/oxford-cs-ml-2015/practical6
--[[
opt.rnn_size = 16
opt.gpuid = -1
opt.seq_length = 2
opt.batch_size = 2
opt.data_dir='data/hongloumeng'
opt.num_layers = 1
]]
opt.min_freq = 10
opt.rnn_size = 512
opt.gpuid = 0
opt.seq_length = 50
opt.batch_size = 50
opt.data_dir='data/hongloumeng'
opt.num_layers = 2
-- train / val / test split for data, in fractions
local test_frac = math.max(0, 1 - (opt.train_frac + opt.val_frac))
local split_sizes = {opt.train_frac, opt.val_frac, test_frac}
-- initialize cunn/cutorch for training on the GPU and fall back to CPU gracefully
if opt.gpuid >= 0 and opt.opencl == 0 then
local ok, cunn = pcall(require, 'cunn')
local ok2, cutorch = pcall(require, 'cutorch')
if not ok then print('package cunn not found!') end
if not ok2 then print('package cutorch not found!') end
if ok and ok2 then
print('using CUDA on GPU ' .. opt.gpuid .. '...')
cutorch.setDevice(opt.gpuid + 1) -- note +1 to make it 0 indexed! sigh lua
cutorch.manualSeed(opt.seed)
else
print('If cutorch and cunn are installed, your CUDA toolkit may be improperly configured.')
print('Check your CUDA toolkit installation, rebuild cutorch and cunn, and try again.')
print('Falling back on CPU mode')
opt.gpuid = -1 -- overwrite user setting
end
end
-- initialize clnn/cltorch for training on the GPU and fall back to CPU gracefully
if opt.gpuid >= 0 and opt.opencl == 1 then
local ok, cunn = pcall(require, 'clnn')
local ok2, cutorch = pcall(require, 'cltorch')
if not ok then print('package clnn not found!') end
if not ok2 then print('package cltorch not found!') end
if ok and ok2 then
print('using OpenCL on GPU ' .. opt.gpuid .. '...')
cltorch.setDevice(opt.gpuid + 1) -- note +1 to make it 0 indexed! sigh lua
torch.manualSeed(opt.seed)
else
print('If cltorch and clnn are installed, your OpenCL driver may be improperly configured.')
print('Check your OpenCL driver installation, check output of clinfo command, and try again.')
print('Falling back on CPU mode')
opt.gpuid = -1 -- overwrite user setting
end
end
-- create the data loader class
local loader = CharSplitLMMinibatchLoader.create(opt.data_dir, opt.batch_size, opt.seq_length, split_sizes, opt.min_freq)
local vocab_size = loader.vocab_size -- the number of distinct characters
local vocab = loader.vocab_mapping
print('vocab size: ' .. vocab_size)
-- make sure output directory exists
if not path.exists(opt.checkpoint_dir) then lfs.mkdir(opt.checkpoint_dir) end
-- define the model: prototypes for one timestep, then clone them in time
local do_random_init = true
if string.len(opt.init_from) > 0 then
print('loading an LSTM from checkpoint ' .. opt.init_from)
local checkpoint = torch.load(opt.init_from, 'ascii')
protos = checkpoint.protos
-- make sure the vocabs are the same
local vocab_compatible = true
for c,i in pairs(checkpoint.vocab) do
if not vocab[c] == i then
vocab_compatible = false
end
end
assert(vocab_compatible, 'error, the character vocabulary for this dataset and the one in the saved checkpoint are not the same. This is trouble.')
-- overwrite model settings based on checkpoint to ensure compatibility
print('overwriting rnn_size=' .. checkpoint.opt.rnn_size .. ', num_layers=' .. checkpoint.opt.num_layers .. ' based on the checkpoint.')
opt.rnn_size = checkpoint.opt.rnn_size
opt.num_layers = checkpoint.opt.num_layers
do_random_init = false
else
print('creating an LSTM with ' .. opt.num_layers .. ' layers')
protos = {}
protos.rnn = LSTM.lstm(vocab_size, opt.rnn_size, opt.num_layers, opt.dropout)
protos.criterion = nn.ClassNLLCriterion()
end
-- the initial state of the cell/hidden states
init_state = {}
for L=1,opt.num_layers do
local h_init = torch.zeros(opt.batch_size, opt.rnn_size)
if opt.gpuid >=0 and opt.opencl == 0 then h_init = h_init:cuda() end
if opt.gpuid >=0 and opt.opencl == 1 then h_init = h_init:cl() end
table.insert(init_state, h_init:clone())
table.insert(init_state, h_init:clone())
end
-- ship the model to the GPU if desired
if opt.gpuid >= 0 and opt.opencl == 0 then
for k,v in pairs(protos) do v:cuda() end
end
if opt.gpuid >= 0 and opt.opencl == 1 then
for k,v in pairs(protos) do v:cl() end
end
-- put the above things into one flattened parameters tensor
params, grad_params = model_utils.combine_all_parameters(protos.rnn)
-- initialization
if do_random_init then
params:uniform(-0.08, 0.08) -- small numbers uniform
end
-- initialize the LSTM forget gates with slightly higher biases to encourage remembering in the beginning
if opt.model == 'lstm' then
for layer_idx = 1, opt.num_layers do
for _,node in ipairs(protos.rnn.forwardnodes) do
if node.data.annotations.name == "i2h_" .. layer_idx then
print('setting forget gate biases to 1 in LSTM layer ' .. layer_idx)
-- the gates are, in order, i,f,o,g, so f is the 2nd block of weights
node.data.module.bias[{{opt.rnn_size+1, 2*opt.rnn_size}}]:fill(1.0)
end
end
end
end
print('number of parameters in the model: ' .. params:nElement())
-- make a bunch of clones after flattening, as that reallocates memory
clones = {}
for name,proto in pairs(protos) do
print('cloning ' .. name)
clones[name] = model_utils.clone_many_times(proto, opt.seq_length, not proto.parameters)
end
-- preprocessing helper function
function prepro(x,y)
x = x:transpose(1,2):contiguous() -- swap the axes for faster indexing
y = y:transpose(1,2):contiguous()
if opt.gpuid >= 0 and opt.opencl == 0 then -- ship the input arrays to GPU
-- have to convert to float because integers can't be cuda()'d
x = x:float():cuda()
y = y:float():cuda()
end
if opt.gpuid >= 0 and opt.opencl == 1 then -- ship the input arrays to GPU
x = x:cl()
y = y:cl()
end
return x,y
end
-- evaluate the loss over an entire split
function eval_split(split_index, max_batches)
print('evaluating loss over split index ' .. split_index)
local n = loader.split_sizes[split_index]
if max_batches ~= nil then n = math.min(max_batches, n) end
loader:reset_batch_pointer(split_index) -- move batch iteration pointer for this split to front
local loss = 0
local rnn_state = {[0] = init_state}
for i = 1,n do -- iterate over batches in the split
-- fetch a batch
local x, y = loader:next_batch(split_index)
x,y = prepro(x,y)
-- forward pass
for t=1,opt.seq_length do
clones.rnn[t]:evaluate() -- for dropout proper functioning
local lst = clones.rnn[t]:forward{x[t], unpack(rnn_state[t-1])}
rnn_state[t] = {}
for i=1,#init_state do table.insert(rnn_state[t], lst[i]) end
prediction = lst[#lst]
loss = loss + clones.criterion[t]:forward(prediction, y[t])
end
-- carry over lstm state
rnn_state[0] = rnn_state[#rnn_state]
-- print(i .. '/' .. n .. '...')
end
loss = loss / opt.seq_length / n
return loss
end
-- do fwd/bwd and return loss, grad_params
local init_state_global = clone_list(init_state)
function feval(x)
if x ~= params then
params:copy(x)
end
grad_params:zero()
------------------ get minibatch -------------------
local x, y = loader:next_batch(1)
x,y = prepro(x,y)
------------------- forward pass -------------------
local rnn_state = {[0] = init_state_global}
local predictions = {} -- softmax outputs
local loss = 0
for t=1,opt.seq_length do
clones.rnn[t]:training() -- make sure we are in correct mode (this is cheap, sets flag)
if opt.use_ss == 1 and t > 1 and math.random() > ss_current then
local probs = torch.exp(predictions[t-1]):squeeze()
_,samples = torch.max(probs,2)
xx = samples:view(samples:nElement())
else
xx = x[t]
end
-- print(x[{{},t}])
local lst = clones.rnn[t]:forward{xx, unpack(rnn_state[t-1])}
rnn_state[t] = {}
for i=1,#init_state do table.insert(rnn_state[t], lst[i]) end -- extract the state, without output
predictions[t] = lst[#lst] -- last element is the prediction
loss = loss + clones.criterion[t]:forward(predictions[t], y[t])
end
loss = loss / opt.seq_length
------------------ backward pass -------------------
-- initialize gradient at time t to be zeros (there's no influence from future)
local drnn_state = {[opt.seq_length] = clone_list(init_state, true)} -- true also zeros the clones
for t=opt.seq_length,1,-1 do
-- backprop through loss, and softmax/linear
local doutput_t = clones.criterion[t]:backward(predictions[t], y[t])
table.insert(drnn_state[t], doutput_t)
local dlst = clones.rnn[t]:backward({x[t], unpack(rnn_state[t-1])}, drnn_state[t])
drnn_state[t-1] = {}
for k,v in pairs(dlst) do
if k > 1 then -- k == 1 is gradient on x, which we dont need
-- note we do k-1 because first item is dembeddings, and then follow the
-- derivatives of the state, starting at index 2. I know...
drnn_state[t-1][k-1] = v
end
end
end
------------------------ misc ----------------------
-- transfer final state to initial state (BPTT)
init_state_global = rnn_state[#rnn_state] -- NOTE: I don't think this needs to be a clone, right?
-- clip gradient element-wise
grad_params:clamp(-opt.grad_clip, opt.grad_clip)
return loss, grad_params
end
-- start optimization here
train_losses = {}
val_losses = {}
local optim_state = {learningRate = opt.learning_rate, alpha = opt.decay_rate}
local iterations = opt.max_epochs * loader.ntrain
local iterations_per_epoch = loader.ntrain
local loss0 = nil
ss_current = opt.start_ss
for i = 1, iterations do
local epoch = i / loader.ntrain
local timer = torch.Timer()
local _, loss = optim.rmsprop(feval, params, optim_state)
if opt.accurate_gpu_timing == 1 and opt.gpuid >= 0 then
--[[
Note on timing: The reported time can be off because the GPU is invoked async. If one
wants to have exactly accurate timings one must call cutorch.synchronize() right here.
I will avoid doing so by default because this can incur computational overhead.
--]]
cutorch.synchronize()
end
local time = timer:time().real
local train_loss = loss[1] -- the loss is inside a list, pop it
train_losses[i] = train_loss
-- exponential learning rate decay
if i % loader.ntrain == 0 and opt.learning_rate_decay < 1 then
if epoch >= opt.learning_rate_decay_after then
local decay_factor = opt.learning_rate_decay
optim_state.learningRate = optim_state.learningRate * decay_factor -- decay it
print('decayed learning rate by a factor ' .. decay_factor .. ' to ' .. optim_state.learningRate)
end
end
-- decay schedule sampling amount
if opt.use_ss == 1 and i % loader.ntrain == 0 and ss_current > opt.min_ss then
ss_current = opt.start_ss - opt.decay_ss * epoch
print('decay schedule sampling amount to ' .. ss_current)
end
-- every now and then or on last iteration
if i % opt.eval_val_every == 0 or i == iterations then
-- evaluate loss on validation data
local val_loss = eval_split(2) -- 2 = validation
val_losses[i] = val_loss
local savefile = string.format('%s/lm_%s_epoch%.2f_%.4f.t7', opt.checkpoint_dir, opt.savefile, epoch, val_loss)
print('saving checkpoint to ' .. savefile)
local checkpoint = {}
checkpoint.protos = protos
checkpoint.opt = opt
checkpoint.train_losses = train_losses
checkpoint.val_loss = val_loss
checkpoint.val_losses = val_losses
checkpoint.i = i
checkpoint.epoch = epoch
checkpoint.vocab = loader.vocab_mapping
torch.save(savefile, checkpoint, 'ascii')
end
if i % opt.print_every == 0 then
print(string.format("%d/%d (epoch %.3f), train_loss = %6.8f, grad/param norm = %6.4e, time/batch = %.4fs", i, iterations, epoch, train_loss, grad_params:norm() / params:norm(), time))
end
if i % 10 == 0 then collectgarbage() end
-- handle early stopping if things are going really bad
if loss[1] ~= loss[1] then
print('loss is NaN. This usually indicates a bug. Please check the issues page for existing issues, or create a new issue, if none exist. Ideally, please state: your operating system, 32-bit/64-bit, your blas version, cpu/cuda/cl?')
break -- halt
end
if loss0 == nil then loss0 = loss[1] end
if loss[1] > loss0 * 3 then
print('loss is exploding, aborting.')
break -- halt
end
end
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