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require 'lmptb.lmvocab'
require 'lmptb.lmfeeder'
require 'lmptb.lmutil'
require 'lmptb.layer.init'
require 'rnn.init'
require 'lmptb.lmseqreader'
require 'lm_trainer'
--[[global function rename]]--
local printf = nerv.printf
local LMTrainer = nerv.LMTrainer
--[[global function rename ends]]--
--global_conf: table
--first_time: bool
--Returns: a ParamRepo
function prepare_parameters(global_conf, iter)
printf("%s preparing parameters...\n", global_conf.sche_log_pre)
if (iter == -1) then --first time
printf("%s first time, generating parameters...\n", global_conf.sche_log_pre)
ltp_ih = nerv.LinearTransParam("ltp_ih", global_conf)
ltp_ih.trans = global_conf.cumat_type(global_conf.vocab:size(), global_conf.hidden_size) --index 0 is for zero, others correspond to vocab index(starting from 1)
ltp_ih.trans:generate(global_conf.param_random)
ltp_hh = nerv.LinearTransParam("ltp_hh", global_conf)
ltp_hh.trans = global_conf.cumat_type(global_conf.hidden_size, global_conf.hidden_size)
ltp_hh.trans:generate(global_conf.param_random)
ltp_ho = nerv.LinearTransParam("ltp_ho", global_conf)
ltp_ho.trans = global_conf.cumat_type(global_conf.hidden_size, global_conf.vocab:size())
ltp_ho.trans:generate(global_conf.param_random)
bp_h = nerv.BiasParam("bp_h", global_conf)
bp_h.trans = global_conf.cumat_type(1, global_conf.hidden_size)
bp_h.trans:generate(global_conf.param_random)
bp_o = nerv.BiasParam("bp_o", global_conf)
bp_o.trans = global_conf.cumat_type(1, global_conf.vocab:size())
bp_o.trans:generate(global_conf.param_random)
local f = nerv.ChunkFile(global_conf.param_fn .. '.0', 'w')
f:write_chunk(ltp_ih)
f:write_chunk(ltp_hh)
f:write_chunk(ltp_ho)
f:write_chunk(bp_h)
f:write_chunk(bp_o)
f:close()
return nil
end
printf("%s loading parameter from file %s...\n", global_conf.sche_log_pre, global_conf.param_fn .. '.' .. tostring(iter))
local paramRepo = nerv.ParamRepo()
paramRepo:import({global_conf.param_fn .. '.' .. tostring(iter)}, nil, global_conf)
printf("%s preparing parameters end.\n", global_conf.sche_log_pre)
return paramRepo
end
--global_conf: table
--Returns: nerv.LayerRepo
function prepare_layers(global_conf, paramRepo)
printf("%s preparing layers...\n", global_conf.sche_log_pre)
local du = true
--local recurrentLconfig = {{["bp"] = "bp_h", ["ltp_hh"] = "ltp_hh"}, {["dim_in"] = {global_conf.hidden_size, global_conf.hidden_size}, ["dim_out"] = {global_conf.hidden_size}, ["break_id"] = global_conf.vocab:get_sen_entry().id, ["independent"] = global_conf.independent, ["clip"] = 10}}
local recurrentLconfig = {{["bp"] = "bp_h", ["ltp_hh"] = "ltp_hh"}, {["dim_in"] = {global_conf.hidden_size, global_conf.hidden_size}, ["dim_out"] = {global_conf.hidden_size}, ["clip"] = 10, ["direct_update"] = du}}
local layers = {
["nerv.AffineRecurrentLayer"] = {
["recurrentL1"] = recurrentLconfig,
},
["nerv.SelectLinearLayer"] = {
["selectL1"] = {{["ltp"] = "ltp_ih"}, {["dim_in"] = {1}, ["dim_out"] = {global_conf.hidden_size}}},
},
["nerv.SigmoidLayer"] = {
["sigmoidL1"] = {{}, {["dim_in"] = {global_conf.hidden_size}, ["dim_out"] = {global_conf.hidden_size}}}
},
["nerv.CombinerLayer"] = {
["combinerL1"] = {{}, {["dim_in"] = {global_conf.hidden_size}, ["dim_out"] = {global_conf.hidden_size, global_conf.hidden_size}, ["lambda"] = {1}}}
},
["nerv.AffineLayer"] = {
["outputL"] = {{["ltp"] = "ltp_ho", ["bp"] = "bp_o"}, {["dim_in"] = {global_conf.hidden_size}, ["dim_out"] = {global_conf.vocab:size()}, ["direct_update"] = du}},
},
["nerv.SoftmaxCELayerT"] = {
["softmaxL"] = {{}, {["dim_in"] = {global_conf.vocab:size(), global_conf.vocab:size()}, ["dim_out"] = {1}}},
},
}
--[[ --we do not need those in the new rnn framework
printf("%s adding %d bptt layers...\n", global_conf.sche_log_pre, global_conf.bptt)
for i = 1, global_conf.bptt do
layers["nerv.IndRecurrentLayer"]["recurrentL" .. (i + 1)] = recurrentLconfig
layers["nerv.SigmoidLayer"]["sigmoidL" .. (i + 1)] = {{}, {["dim_in"] = {global_conf.hidden_size}, ["dim_out"] = {global_conf.hidden_size}}}
layers["nerv.SelectLinearLayer"]["selectL" .. (i + 1)] = {{["ltp"] = "ltp_ih"}, {["dim_in"] = {1}, ["dim_out"] = {global_conf.hidden_size}}}
end
--]]
local layerRepo = nerv.LayerRepo(layers, paramRepo, global_conf)
printf("%s preparing layers end.\n", global_conf.sche_log_pre)
return layerRepo
end
--global_conf: table
--layerRepo: nerv.LayerRepo
--Returns: a nerv.TNN
function prepare_tnn(global_conf, layerRepo)
printf("%s Generate and initing TNN ...\n", global_conf.sche_log_pre)
--input: input_w, input_w, ... input_w_now, last_activation
local connections_t = {
{"<input>[1]", "selectL1[1]", 0},
{"selectL1[1]", "recurrentL1[1]", 0},
{"recurrentL1[1]", "sigmoidL1[1]", 0},
{"sigmoidL1[1]", "combinerL1[1]", 0},
{"combinerL1[1]", "recurrentL1[2]", 1},
{"combinerL1[2]", "outputL[1]", 0},
{"outputL[1]", "softmaxL[1]", 0},
{"<input>[2]", "softmaxL[2]", 0},
{"softmaxL[1]", "<output>[1]", 0}
}
--[[
printf("%s printing DAG connections:\n", global_conf.sche_log_pre)
for key, value in pairs(connections_t) do
printf("\t%s->%s\n", key, value)
end
]]--
local tnn = nerv.TNN("TNN", global_conf, {["dim_in"] = {1, global_conf.vocab:size()}, ["dim_out"] = {1}, ["sub_layers"] = layerRepo,
["connections"] = connections_t,
})
tnn:init(global_conf.batch_size, global_conf.chunk_size)
printf("%s Initing TNN end.\n", global_conf.sche_log_pre)
return tnn
end
function load_net(global_conf, next_iter)
local paramRepo = prepare_parameters(global_conf, next_iter)
local layerRepo = prepare_layers(global_conf, paramRepo)
local tnn = prepare_tnn(global_conf, layerRepo)
return tnn, paramRepo
end
local train_fn, valid_fn, test_fn
global_conf = {}
local set = arg[1] --"test"
if (set == "ptb") then
data_dir = '/home/slhome/txh18/workspace/nerv/nerv/nerv/examples/lmptb/PTBdata'
train_fn = data_dir .. '/ptb.train.txt.adds'
valid_fn = data_dir .. '/ptb.valid.txt.adds'
test_fn = data_dir .. '/ptb.test.txt.adds'
vocab_fn = data_dir .. '/vocab'
global_conf = {
lrate = 1, wcost = 1e-5, momentum = 0,
cumat_type = nerv.CuMatrixFloat,
mmat_type = nerv.MMatrixFloat,
nn_act_default = 0,
hidden_size = 400, --set to 400 for a stable good test PPL
chunk_size = 15,
batch_size = 10,
max_iter = 35,
decay_iter = 16,
param_random = function() return (math.random() / 5 - 0.1) end,
train_fn = train_fn,
valid_fn = valid_fn,
test_fn = test_fn,
vocab_fn = vocab_fn,
sche_log_pre = "[SCHEDULER]:",
log_w_num = 40000, --give a message when log_w_num words have been processed
timer = nerv.Timer(),
work_dir = '/home/slhome/txh18/workspace/nerv/play/dagL_test'
}
elseif (set == "msr_sc") then
data_dir = '/home/slhome/txh18/workspace/sentenceCompletion/DATA_PV2'
train_fn = data_dir .. '/normed_all.sf.len60.adds.train'
valid_fn = data_dir .. '/normed_all.sf.len60.adds.dev'
test_fn = data_dir .. '/answer_normed.adds'
vocab_fn = data_dir .. '/normed_all.choose.vocab30000.addqvocab'
global_conf = {
lrate = 1, wcost = 1e-6, momentum = 0,
cumat_type = nerv.CuMatrixFloat,
mmat_type = nerv.MMatrixFloat,
nn_act_default = 0,
hidden_size = 300,
chunk_size = 15,
batch_size = 10,
max_iter = 30,
decay_iter = 10,
param_random = function() return (math.random() / 5 - 0.1) end,
train_fn = train_fn,
valid_fn = valid_fn,
test_fn = test_fn,
vocab_fn = vocab_fn,
sche_log_pre = "[SCHEDULER]:",
log_w_num = 40000, --give a message when log_w_num words have been processed
timer = nerv.Timer(),
work_dir = '/home/slhome/txh18/workspace/sentenceCompletion/EXP-Nerv/rnnlm_test'
}
else
valid_fn = '/home/slhome/txh18/workspace/nerv/nerv/nerv/examples/lmptb/m-tests/some-text'
train_fn = '/home/slhome/txh18/workspace/nerv/nerv/nerv/examples/lmptb/m-tests/some-text'
test_fn = '/home/slhome/txh18/workspace/nerv/nerv/nerv/examples/lmptb/m-tests/some-text'
vocab_fn = '/home/slhome/txh18/workspace/nerv/nerv/nerv/examples/lmptb/m-tests/some-text'
global_conf = {
lrate = 1, wcost = 1e-5, momentum = 0,
cumat_type = nerv.CuMatrixFloat,
mmat_type = nerv.CuMatrixFloat,
nn_act_default = 0,
hidden_size = 20,
chunk_size = 2,
batch_size = 3,
max_iter = 3,
param_random = function() return (math.random() / 5 - 0.1) end,
train_fn = train_fn,
valid_fn = valid_fn,
test_fn = test_fn,
vocab_fn = vocab_fn,
sche_log_pre = "[SCHEDULER]:",
log_w_num = 10, --give a message when log_w_num words have been processed
timer = nerv.Timer(),
work_dir = '/home/slhome/txh18/workspace/nerv/play/dagL_test'
}
end
global_conf.train_fn_shuf = global_conf.work_dir .. '/train_fn_shuf'
global_conf.train_fn_shuf_bak = global_conf.train_fn_shuf .. '_bak'
global_conf.param_fn = global_conf.work_dir .. "/params"
lr_half = false --can not be local, to be set by loadstring
start_iter = -1
ppl_last = 100000
if (arg[2] ~= nil) then
printf("%s applying arg[2](%s)...\n", global_conf.sche_log_pre, arg[2])
loadstring(arg[2])()
nerv.LMUtil.wait(0.5)
else
printf("%s not user setting, all default...\n", global_conf.sche_log_pre)
end
----------------printing options---------------------------------
printf("%s printing global_conf...\n", global_conf.sche_log_pre)
for id, value in pairs(global_conf) do
print(id, value)
end
nerv.LMUtil.wait(2)
printf("%s printing training scheduling options...\n", global_conf.sche_log_pre)
print("lr_half", lr_half)
print("start_iter", start_iter)
print("ppl_last", ppl_last)
printf("%s printing training scheduling end.\n", global_conf.sche_log_pre)
nerv.LMUtil.wait(2)
------------------printing options end------------------------------
math.randomseed(1)
printf("%s creating work_dir...\n", global_conf.sche_log_pre)
os.execute("mkdir -p "..global_conf.work_dir)
os.execute("cp " .. global_conf.train_fn .. " " .. global_conf.train_fn_shuf)
local vocab = nerv.LMVocab()
global_conf["vocab"] = vocab
printf("%s building vocab...\n", global_conf.sche_log_pre)
global_conf.vocab:build_file(global_conf.vocab_fn, false)
ppl_rec = {}
if (start_iter == -1) then
prepare_parameters(global_conf, -1) --randomly generate parameters
print("===INITIAL VALIDATION===")
local tnn, paramRepo = load_net(global_conf, 0)
local result = LMTrainer.lm_process_file(global_conf, global_conf.valid_fn, tnn, false) --false update!
nerv.LMUtil.wait(1)
ppl_rec[0] = {}
ppl_rec[0].valid = result:ppl_all("rnn")
ppl_last = ppl_rec[0].valid
ppl_rec[0].train = 0
ppl_rec[0].test = 0
ppl_rec[0].lr = 0
start_iter = 1
print()
end
if (start_iter == 0) then
nerv.error("start_iter should not be zero")
end
local final_iter
for iter = start_iter, global_conf.max_iter, 1 do
final_iter = iter --for final testing
global_conf.sche_log_pre = "[SCHEDULER ITER"..iter.." LR"..global_conf.lrate.."]:"
tnn, paramRepo = load_net(global_conf, iter - 1)
printf("===ITERATION %d LR %f===\n", iter, global_conf.lrate)
result = LMTrainer.lm_process_file(global_conf, global_conf.train_fn_shuf, tnn, true) --true update!
ppl_rec[iter] = {}
ppl_rec[iter].train = result:ppl_all("rnn")
--shuffling training file
printf("%s shuffling training file\n", global_conf.sche_log_pre)
os.execute('cp ' .. global_conf.train_fn_shuf .. ' ' .. global_conf.train_fn_shuf_bak)
os.execute('cat ' .. global_conf.train_fn_shuf_bak .. ' | sort -R --random-source=/dev/zero > ' .. global_conf.train_fn_shuf)
printf("===PEEK ON TEST %d===\n", iter)
result = LMTrainer.lm_process_file(global_conf, global_conf.test_fn, tnn, false) --false update!
ppl_rec[iter].test = result:ppl_all("rnn")
printf("===VALIDATION %d===\n", iter)
result = LMTrainer.lm_process_file(global_conf, global_conf.valid_fn, tnn, false) --false update!
ppl_rec[iter].valid = result:ppl_all("rnn")
ppl_rec[iter].lr = global_conf.lrate
if ((ppl_last / ppl_rec[iter].valid < 1.0003 or lr_half == true) and iter > global_conf.decay_iter) then
global_conf.lrate = (global_conf.lrate * 0.6)
end
if (ppl_rec[iter].valid < ppl_last) then
printf("%s PPL improves, saving net to file %s.%d...\n", global_conf.sche_log_pre, global_conf.param_fn, iter)
paramRepo:export(global_conf.param_fn .. '.' .. tostring(iter), nil)
else
printf("%s PPL did not improve, rejected, copying param file of last iter...\n", global_conf.sche_log_pre)
os.execute('cp ' .. global_conf.param_fn..'.'..tostring(iter - 1) .. ' ' .. global_conf.param_fn..'.'..tostring(iter))
--if (lr_half == true) then
-- printf("%s LR is already halfing, end training...\n", global_conf.sche_log_pre)
-- break
--end
end
if (ppl_last / ppl_rec[iter].valid < 1.0003 or lr_half == true) then
lr_half = true
end
if (ppl_rec[iter].valid < ppl_last) then
ppl_last = ppl_rec[iter].valid
end
printf("\n")
nerv.LMUtil.wait(2)
end
printf("===VALIDATION PPL record===\n")
for i, _ in pairs(ppl_rec) do
printf("<ITER%d LR%.5f train:%.3f valid:%.3f test:%.3f> \n", i, ppl_rec[i].lr, ppl_rec[i].train, ppl_rec[i].valid, ppl_rec[i].test)
end
printf("\n")
printf("===FINAL TEST===\n")
global_conf.sche_log_pre = "[SCHEDULER FINAL_TEST]:"
tnn, paramRepo = load_net(global_conf, final_iter)
LMTrainer.lm_process_file(global_conf, global_conf.test_fn, tnn, false) --false update!
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