Merge pull request #16 from cloudygoose/txh18/rnnlm

Added an example of bi-lstm

12 files changed, 973 insertions, 94 deletions

diff --git a/nerv/examples/lmptb/bilstmlm_ptb_main.lua b/nerv/examples/lmptb/bilstmlm_ptb_main.lua
new file mode 100644
index 0000000..0472588
--- /dev/null
+++ b/nerv/examples/lmptb/bilstmlm_ptb_main.lua
@@ -0,0 +1,517 @@
+require 'lmptb.lmvocab'
+require 'lmptb.lmfeeder'
+require 'lmptb.lmutil'
+require 'lmptb.layer.init'
+--require 'tnn.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)
+    nerv.printf("%s preparing parameters...\n", global_conf.sche_log_pre) 
+    
+    global_conf.paramRepo = nerv.ParamRepo()
+    local paramRepo = global_conf.paramRepo
+
+    if iter == -1 then --first time
+        nerv.printf("%s first time, prepare some pre-set parameters, and leaving other parameters to auto-generation...\n", global_conf.sche_log_pre) 
+        local f = nerv.ChunkFile(global_conf.param_fn .. '.0', 'w')
+        f:close()
+        --[[
+        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
+    
+    nerv.printf("%s loading parameter from file %s...\n", global_conf.sche_log_pre, global_conf.param_fn .. '.' .. tostring(iter)) 
+    paramRepo:import({global_conf.param_fn .. '.' .. tostring(iter)}, nil, global_conf)
+
+    nerv.printf("%s preparing parameters end.\n", global_conf.sche_log_pre)
+
+    return nil
+end
+
+--global_conf: table
+--Returns: nerv.LayerRepo
+function prepare_layers(global_conf)
+    nerv.printf("%s preparing layers...\n", global_conf.sche_log_pre)
+    
+    local pr = global_conf.paramRepo
+
+    local du = false
+
+    local layers = {
+        ["nerv.LSTMLayerT"] = {
+            ["lstmFL1"] = {{}, {["dim_in"] = {global_conf.hidden_size, global_conf.hidden_size, global_conf.hidden_size}, ["dim_out"] = {global_conf.hidden_size, global_conf.hidden_size}, ["pr"] = pr}}, 
+            ["lstmRL1"] = {{}, {["dim_in"] = {global_conf.hidden_size, global_conf.hidden_size, global_conf.hidden_size}, ["dim_out"] = {global_conf.hidden_size, global_conf.hidden_size}, ["pr"] = pr}}, 
+        },
+        
+        ["nerv.DropoutLayerT"] = {
+            ["dropoutL1"] = {{}, {["dim_in"] = {global_conf.hidden_size}, ["dim_out"] = {global_conf.hidden_size}}}, 
+        },
+
+        ["nerv.SelectLinearLayer"] = {
+            ["selectL1"] = {{}, {["dim_in"] = {1}, ["dim_out"] = {global_conf.hidden_size}, ["vocab"] = global_conf.vocab, ["pr"] = pr}},
+        },
+        
+        ["nerv.CombinerLayer"] = {
+            ["combinerXL1"] = {{}, {["dim_in"] = {global_conf.hidden_size}, ["dim_out"] = {global_conf.hidden_size, global_conf.hidden_size}, ["lambda"] = {1}}},
+            ["combinerHFL1"] = {{}, {["dim_in"] = {global_conf.hidden_size}, ["dim_out"] = {global_conf.hidden_size, global_conf.hidden_size}, ["lambda"] = {1}}},
+            ["combinerHRL1"] = {{}, {["dim_in"] = {global_conf.hidden_size}, ["dim_out"] = {global_conf.hidden_size, global_conf.hidden_size}, ["lambda"] = {1}}},
+        },
+
+        ["nerv.AffineLayer"] = {
+            ["biAffineL1"] = {{}, {["dim_in"] = {global_conf.hidden_size, global_conf.hidden_size}, ["dim_out"] = {global_conf.hidden_size}, ["pr"] = pr, ["lambda"] = {1, 1}}},
+            ["outputL"] = {{}, {["dim_in"] = {global_conf.hidden_size}, ["dim_out"] = {global_conf.vocab:size()}, ["direct_update"] = du, ["pr"] = pr}},
+        },
+
+        ["nerv.TanhLayer"] = {
+            ["biTanhL1"] = {{}, {["dim_in"] = {global_conf.hidden_size}, ["dim_out"] = {global_conf.hidden_size}}},
+        },
+
+        ["nerv.SoftmaxCELayerT"] = {
+            ["softmaxL"] = {{}, {["dim_in"] = {global_conf.vocab:size(), global_conf.vocab:size()}, ["dim_out"] = {1}}},
+        },
+    }
+
+    if global_conf.layer_num > 1 then
+        nerv.error("this script currently do not support more than one layer")
+    end
+    --[[ 
+    for l = 2, global_conf.layer_num do 
+        layers["nerv.DropoutLayerT"]["dropoutL" .. l] = {{}, {["dim_in"] = {global_conf.hidden_size}, ["dim_out"] = {global_conf.hidden_size}}}
+        layers["nerv.LSTMLayerT"]["lstmL" .. l] = {{}, {["dim_in"] = {global_conf.hidden_size, global_conf.hidden_size, global_conf.hidden_size}, ["dim_out"] = {global_conf.hidden_size, global_conf.hidden_size}, ["pr"] = pr}}
+        layers["nerv.CombinerLayer"]["combinerL" .. l] = {{}, {["dim_in"] = {global_conf.hidden_size}, ["dim_out"] = {global_conf.hidden_size, global_conf.hidden_size}, ["lambda"] = {1}}}
+    end
+    ]]--
+
+    local layerRepo = nerv.LayerRepo(layers, pr, global_conf)
+    nerv.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)
+    nerv.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},
+        
+        {"selectL1[1]", "combinerXL1[1]", 0},
+        {"combinerXL1[1]", "lstmFL1[1]", 0},
+        {"lstmFL1[1]", "combinerHFL1[1]", 0},
+        {"combinerHFL1[1]", "lstmFL1[2]", 1},
+        {"lstmFL1[2]", "lstmFL1[3]", 1},
+        {"combinerXL1[2]", "lstmRL1[1]", 0},
+        {"lstmRL1[1]", "combinerHRL1[1]", 0},
+        {"combinerHRL1[1]", "lstmRL1[2]", -1},
+        {"lstmRL1[2]", "lstmRL1[3]", -1},
+        {"combinerHFL1[2]", "biAffineL1[1]", 0},
+        {"combinerHRL1[2]", "biAffineL1[2]", 0},
+        {"biAffineL1[1]", "biTanhL1[1]", 0},
+        {"biTanhL1[1]", "dropoutL1[1]", 0},
+
+        {"dropoutL"..global_conf.layer_num.."[1]", "outputL[1]", 0},
+        {"outputL[1]", "softmaxL[1]", 0},
+        {"<input>[2]", "softmaxL[2]", 0},
+        {"softmaxL[1]", "<output>[1]", 0}
+    }
+
+    --[[    
+    for l = 2, global_conf.layer_num do
+        table.insert(connections_t, {"dropoutL"..(l-1).."[1]", "lstmL"..l.."[1]", 0})
+        table.insert(connections_t, {"lstmL"..l.."[2]", "lstmL"..l.."[3]", 1})
+        table.insert(connections_t, {"lstmL"..l.."[1]", "combinerL"..l.."[1]", 0})
+        table.insert(connections_t, {"combinerL"..l.."[1]", "lstmL"..l.."[2]", 1})
+        table.insert(connections_t, {"combinerL"..l.."[2]", "dropoutL"..l.."[1]", 0})
+    end
+    ]]--
+
+    --[[
+    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, ["clip_t"] = global_conf.clip_t,
+        })
+
+    tnn:init(global_conf.batch_size, global_conf.chunk_size)
+
+    nerv.printf("%s Initing TNN end.\n", global_conf.sche_log_pre)
+    return tnn
+end
+
+function load_net(global_conf, next_iter)
+    prepare_parameters(global_conf, next_iter)
+    local layerRepo = prepare_layers(global_conf)
+    local tnn = prepare_tnn(global_conf, layerRepo)
+    return tnn
+end
+
+local train_fn, valid_fn, test_fn
+global_conf = {}
+local set = arg[1] --"test"
+
+if (set == "ptb") then
+
+root_dir = '/home/slhome/txh18/workspace'
+data_dir = root_dir .. '/ptb/DATA'
+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'
+
+qdata_dir = root_dir .. '/ptb/questionGen/gen'
+
+global_conf = {
+    lrate = 0.015, wcost = 1e-5, momentum = 0, clip_t = 5,
+    cumat_type = nerv.CuMatrixFloat,
+    mmat_type = nerv.MMatrixFloat,
+    nn_act_default = 0, 
+
+    hidden_size = 300,
+    layer_num = 1,
+    chunk_size = 90,
+    batch_size = 20, 
+    max_iter = 35,
+    lr_decay = 1.003,
+    decay_iter = 10,
+    param_random = function() return (math.random() / 5 - 0.1) end,
+    dropout_str = "0",
+
+    train_fn = train_fn,
+    valid_fn = valid_fn,
+    test_fn = test_fn,
+    vocab_fn = vocab_fn,
+    max_sen_len = 90,
+    sche_log_pre = "[SCHEDULER]:",
+    log_w_num = 40000, --give a message when log_w_num words have been processed
+    timer = nerv.Timer(),
+    work_dir_base = '/home/slhome/txh18/workspace/ptb/EXP-nerv/bilstmlm_v1.0'
+}
+
+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,
+    layer_num = 1,
+    chunk_size = 15,
+    batch_size = 10, 
+    max_iter = 30,
+    decay_iter = 10,
+    lr_decay = 1.003,
+    param_random = function() return (math.random() / 5 - 0.1) end,
+    dropout_str = "0",
+
+    train_fn = train_fn,
+    valid_fn = valid_fn,
+    test_fn = test_fn,
+    vocab_fn = vocab_fn,
+    sche_log_pre = "[SCHEDULER]:",
+    log_w_num = 400000, --give a message when log_w_num words have been processed
+    timer = nerv.Timer(),
+    work_dir_base = '/home/slhome/txh18/workspace/sentenceCompletion/EXP-Nerv/rnnlm_test'
+}
+
+elseif (set == "twitter") then
+
+root_dir = '/home/slhome/txh18/workspace'
+data_dir = root_dir .. '/twitter_new/DATA'
+train_fn = data_dir .. '/twitter.choose.adds'
+valid_fn = data_dir .. '/twitter.valid.adds'
+test_fn = data_dir .. '/comm.test.choose-ppl.adds'
+vocab_fn = data_dir .. '/twitter.choose.train.vocab'
+
+--qdata_dir = root_dir .. '/ptb/questionGen/gen'
+
+global_conf = {
+    lrate = 0.15, wcost = 1e-5, momentum = 0, clip_t = 5,
+    cumat_type = nerv.CuMatrixFloat,
+    mmat_type = nerv.MMatrixFloat,
+    nn_act_default = 0, 
+
+    hidden_size = 300,
+    layer_num = 1,
+    chunk_size = 15,
+    batch_size = 20, 
+    max_iter = 35,
+    lr_decay = 1.003,
+    decay_iter = 10,
+    param_random = function() return (math.random() / 5 - 0.1) end,
+    dropout_str = "0",
+
+    train_fn = train_fn,
+    valid_fn = valid_fn,
+    test_fn = test_fn,
+    vocab_fn = vocab_fn,
+    max_sen_len = 90,
+    sche_log_pre = "[SCHEDULER]:",
+    log_w_num = 40000, --give a message when log_w_num words have been processed
+    timer = nerv.Timer(),
+    work_dir_base = root_dir .. '/twitter_new/EXP-nerv/bilstmlm_v1.0'
+}
+
+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 = 0.01, wcost = 1e-5, momentum = 0,
+    cumat_type = nerv.CuMatrixFloat,
+    mmat_type = nerv.MMatrixFloat,
+    nn_act_default = 0, 
+
+    hidden_size = 20,
+    layer_num = 1,
+    chunk_size = 20,
+    batch_size = 10, 
+    max_iter = 2,
+    param_random = function() return (math.random() / 5 - 0.1) end,
+    dropout_str = "0",
+
+    train_fn = train_fn,
+    valid_fn = valid_fn,
+    test_fn = test_fn,
+    max_sen_len = 80,
+    lr_decay = 1.003,
+    decay_iter = 10,
+    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_base = '/home/slhome/txh18/workspace/nerv/play/testEXP/tnn_bilstmlm_test'
+}
+
+end
+
+lr_half = false --can not be local, to be set by loadstring
+start_iter = -1
+start_lr = global_conf.lrate
+ppl_last = 100000
+commands_str = "train:test"
+commands = {}
+test_iter = -1
+
+--for testout(question)
+q_file = "/home/slhome/txh18/workspace/ptb/questionGen/gen/ptb.test.txt.q10rs1_Msss.adds"
+
+if arg[2] ~= nil then
+    nerv.printf("%s applying arg[2](%s)...\n", global_conf.sche_log_pre, arg[2])
+    loadstring(arg[2])() 
+    nerv.LMUtil.wait(0.5)
+else
+    nerv.printf("%s no user setting, all default...\n", global_conf.sche_log_pre)
+end
+
+global_conf.work_dir = global_conf.work_dir_base .. 'h' .. global_conf.hidden_size .. 'l' .. global_conf.layer_num .. 'ch' .. global_conf.chunk_size .. 'ba' .. global_conf.batch_size .. 'slr' ..   global_conf.lrate .. 'wc' .. global_conf.wcost .. 'dr' .. global_conf.dropout_str 
+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"
+global_conf.dropout_list = nerv.SUtil.parse_schedule(global_conf.dropout_str)
+global_conf.log_fn = global_conf.work_dir .. '/log_lstm_tnn_' .. commands_str ..os.date("_TT%m_%d_%X",os.time())
+global_conf.log_fn, _ = string.gsub(global_conf.log_fn, ':', '-')
+commands = nerv.SUtil.parse_commands_set(commands_str)
+
+global_conf.lrate = start_lr --starting lr can be set by user(arg[2])
+
+nerv.printf("%s creating work_dir(%s)...\n", global_conf.sche_log_pre, global_conf.work_dir)
+nerv.LMUtil.wait(2)
+os.execute("mkdir -p "..global_conf.work_dir)
+os.execute("cp " .. global_conf.train_fn .. " " .. global_conf.train_fn_shuf)
+
+--redirecting log outputs!
+nerv.SUtil.log_redirect(global_conf.log_fn)
+nerv.LMUtil.wait(2)
+
+----------------printing options---------------------------------
+nerv.printf("%s printing global_conf...\n", global_conf.sche_log_pre)
+for id, value in pairs(global_conf) do
+    nerv.printf("%s:\t%s\n", id, tostring(value))
+end
+nerv.LMUtil.wait(2)
+
+nerv.printf("%s printing training scheduling options...\n", global_conf.sche_log_pre)
+nerv.printf("lr_half:\t%s\n", tostring(lr_half))
+nerv.printf("start_iter:\t%s\n", tostring(start_iter))
+nerv.printf("ppl_last:\t%s\n", tostring(ppl_last))
+nerv.printf("commands_str:\t%s\n", commands_str)
+nerv.printf("test_iter:\t%s\n", tostring(test_iter))
+nerv.printf("%s printing training scheduling end.\n", global_conf.sche_log_pre)
+nerv.LMUtil.wait(2)
+------------------printing options end------------------------------
+
+math.randomseed(1)
+
+local vocab = nerv.LMVocab()
+global_conf["vocab"] = vocab
+nerv.printf("%s building vocab...\n", global_conf.sche_log_pre)
+global_conf.vocab:build_file(global_conf.vocab_fn, false)
+ppl_rec = {} 
+
+local final_iter = -1
+if commands["train"] == 1 then
+    if start_iter == -1 then 
+        prepare_parameters(global_conf, -1) --write pre_generated params to param.0 file
+    end
+    
+    if start_iter == -1 or start_iter == 0 then
+        nerv.printf("===INITIAL VALIDATION===\n") 
+        local tnn = load_net(global_conf, 0)
+        global_conf.paramRepo = tnn:get_params() --get auto-generted params
+        global_conf.paramRepo:export(global_conf.param_fn .. '.0', nil)  --some parameters are auto-generated, saved again to param.0 file
+        global_conf.dropout_rate = 0
+        local result = LMTrainer.lm_process_file_birnn(global_conf, global_conf.valid_fn, tnn, false) --false update!
+        nerv.LMUtil.wait(1)
+        ppl_rec[0] = {} 
+        ppl_rec[0].valid = result:ppl_all("birnn")  
+        ppl_last = ppl_rec[0].valid 
+        ppl_rec[0].train = 0 
+        ppl_rec[0].test = 0
+        ppl_rec[0].lr = 0 
+    
+        start_iter = 1
+    
+        nerv.printf("\n") 
+    end
+    
+    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 = load_net(global_conf, iter - 1) 
+        nerv.printf("===ITERATION %d LR %f===\n", iter, global_conf.lrate) 
+        global_conf.dropout_rate = nerv.SUtil.sche_get(global_conf.dropout_list, iter)
+        result = LMTrainer.lm_process_file_birnn(global_conf, global_conf.train_fn_shuf, tnn, true) --true update!
+        global_conf.dropout_rate = 0
+        ppl_rec[iter] = {}
+        ppl_rec[iter].train = result:ppl_all("birnn")
+        --shuffling training file
+        nerv.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)
+        nerv.printf("===PEEK ON TEST %d===\n", iter) 
+        result = LMTrainer.lm_process_file_birnn(global_conf, global_conf.test_fn, tnn, false) --false update!
+        ppl_rec[iter].test = result:ppl_all("birnn")  
+        nerv.printf("===VALIDATION %d===\n", iter) 
+        result = LMTrainer.lm_process_file_birnn(global_conf, global_conf.valid_fn, tnn, false) --false update!
+        ppl_rec[iter].valid = result:ppl_all("birnn") 
+        ppl_rec[iter].lr = global_conf.lrate 
+        if ((ppl_last / ppl_rec[iter].valid < global_conf.lr_decay 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 
+            nerv.printf("%s PPL improves, saving net to file %s.%d...\n", global_conf.sche_log_pre, global_conf.param_fn, iter) 
+            global_conf.paramRepo:export(global_conf.param_fn .. '.' .. tostring(iter), nil) 
+        else 
+            nerv.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))
+        end 
+        if ppl_last / ppl_rec[iter].valid < global_conf.lr_decay or lr_half == true then
+            lr_half = true
+        end
+        if ppl_rec[iter].valid < ppl_last then
+            ppl_last = ppl_rec[iter].valid
+        end
+        nerv.printf("\n") 
+        nerv.LMUtil.wait(2) 
+    end
+    nerv.info("saving final nn to param.final")
+    os.execute('cp ' .. global_conf.param_fn .. '.' .. tostring(final_iter) .. ' ' .. global_conf.param_fn .. '.final')
+    
+    nerv.printf("===VALIDATION PPL record===\n") 
+    for i, _ in pairs(ppl_rec) do 
+        nerv.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 
+    nerv.printf("\n")
+end --if commands["train"]
+
+if commands["test"] == 1 then
+    nerv.printf("===FINAL TEST===\n") 
+    global_conf.sche_log_pre = "[SCHEDULER FINAL_TEST]:" 
+    if final_iter ~= -1 and test_iter == -1 then
+        test_iter = final_iter
+    end
+    if test_iter == -1 then
+        test_iter = "final"
+    end
+    tnn = load_net(global_conf, test_iter) 
+    global_conf.dropout_rate = 0
+    LMTrainer.lm_process_file_birnn(global_conf, global_conf.test_fn, tnn, false) --false update!
+end --if commands["test"]
+
+if commands["testout"] == 1 then
+    nerv.printf("===TEST OUT===\n") 
+    nerv.printf("q_file:\t%s\n", q_file)    
+    local q_fn = q_file --qdata_dir .. '/' .. q_file
+    global_conf.sche_log_pre = "[SCHEDULER FINAL_TEST]:" 
+    if final_iter ~= -1 and test_iter == -1 then
+        test_iter = final_iter
+    end
+    if test_iter == -1 then
+        test_iter = "final"
+    end
+    tnn = load_net(global_conf, test_iter) 
+    global_conf.dropout_rate = 0
+    LMTrainer.lm_process_file_birnn(global_conf, q_fn, tnn, false,
+            {["one_sen_report"] = true}) --false update!
+end --if commands["testout"]
+
+
diff --git a/nerv/examples/lmptb/lm_trainer.lua b/nerv/examples/lmptb/lm_trainer.lua
index 9ef4794..3b8b5c3 100644
--- a/nerv/examples/lmptb/lm_trainer.lua
+++ b/nerv/examples/lmptb/lm_trainer.lua
@@ -17,9 +17,30 @@ function nerv.BiasParam:update_by_gradient(gradient)
 end
 
 --Returns: LMResult
-function LMTrainer.lm_process_file_rnn(global_conf, fn, tnn, do_train)
-    local reader = nerv.LMSeqReader(global_conf, global_conf.batch_size, global_conf.chunk_size, global_conf.vocab)
+function LMTrainer.lm_process_file_rnn(global_conf, fn, tnn, do_train, p_conf)
+    if p_conf == nil then
+        p_conf = {}
+    end
+    local reader
+    local r_conf = {}
+    local chunk_size, batch_size
+    if p_conf.one_sen_report == true then --report log prob one by one sentence
+        if do_train == true then
+            nerv.warning("LMTrainer.lm_process_file_rnn: warning, one_sen_report is true while do_train is also true, strange")
+        end
+        nerv.printf("lm_process_file_rnn: one_sen report mode, set batch_size to 1 and chunk_size to max_sen_len(%d)\n", 
+                global_conf.max_sen_len)
+        batch_size = 1 
+        chunk_size = global_conf.max_sen_len
+        r_conf["se_mode"] = true
+    else
+        batch_size = global_conf.batch_size
+        chunk_size = global_conf.chunk_size
+    end
+
+    reader = nerv.LMSeqReader(global_conf, batch_size, chunk_size, global_conf.vocab, r_conf)
     reader:open_file(fn)
+
     local result = nerv.LMResult(global_conf, global_conf.vocab)
     result:init("rnn")
     if global_conf.dropout_rate ~= nil then
@@ -27,11 +48,13 @@ function LMTrainer.lm_process_file_rnn(global_conf, fn, tnn, do_train)
     end
         
     global_conf.timer:flush()
+    tnn:init(batch_size, chunk_size)
     tnn:flush_all() --caution: will also flush the inputs from the reader!
 
     local next_log_wcn = global_conf.log_w_num
-    local neto_bakm = global_conf.mmat_type(global_conf.batch_size, 1) --space backup matrix for network output
-
+    local neto_bakm = global_conf.mmat_type(batch_size, 1) --space backup matrix for network output
+    
+    nerv.info("LMTrainer.lm_process_file_rnn: begin processing...")
     while (1) do
         global_conf.timer:tic('most_out_loop_lmprocessfile')
 
@@ -42,9 +65,9 @@ function LMTrainer.lm_process_file_rnn(global_conf, fn, tnn, do_train)
             break 
         end
     
-        for t = 1, global_conf.chunk_size do
+        for t = 1, chunk_size do
             tnn.err_inputs_m[t][1]:fill(1)
-            for i = 1, global_conf.batch_size do
+            for i = 1, batch_size do
                 if bit.band(feeds.flags_now[t][i], nerv.TNN.FC.HAS_LABEL) == 0 then
                     tnn.err_inputs_m[t][1][i - 1][0] = 0
                 end
@@ -70,15 +93,26 @@ function LMTrainer.lm_process_file_rnn(global_conf, fn, tnn, do_train)
         end
 
         global_conf.timer:tic('tnn_afterprocess')
-        for t = 1, global_conf.chunk_size, 1 do
+        local sen_logp = {}
+        for t = 1, chunk_size, 1 do
             tnn.outputs_m[t][1]:copy_toh(neto_bakm)
-            for i = 1, global_conf.batch_size, 1 do
+            for i = 1, batch_size, 1 do
                 if (feeds.labels_s[t][i] ~= global_conf.vocab.null_token) then
                     --result:add("rnn", feeds.labels_s[t][i], math.exp(tnn.outputs_m[t][1][i - 1][0]))
                     result:add("rnn", feeds.labels_s[t][i], math.exp(neto_bakm[i - 1][0]))
+                    if sen_logp[i] == nil then
+                        sen_logp[i] = 0
+                    end
+                    sen_logp[i] = sen_logp[i] + neto_bakm[i - 1][0]
                 end
             end            
         end
+        if p_conf.one_sen_report == true then
+            for i = 1, batch_size do
+                nerv.printf("LMTrainer.lm_process_file_rnn: one_sen_report_output, %f\n", sen_logp[i])    
+            end
+        end
+
         tnn:move_right_to_nextmb({0}) --only copy for time 0
         global_conf.timer:toc('tnn_afterprocess')
 
@@ -102,7 +136,6 @@ function LMTrainer.lm_process_file_rnn(global_conf, fn, tnn, do_train)
         end
         ]]--
 
-
         collectgarbage("collect")                                              
 
         --break --debug
@@ -115,4 +148,135 @@ function LMTrainer.lm_process_file_rnn(global_conf, fn, tnn, do_train)
     return result
 end
 
+--Returns: LMResult
+function LMTrainer.lm_process_file_birnn(global_conf, fn, tnn, do_train, p_conf)
+    if p_conf == nil then
+        p_conf = {}
+    end
+    local reader
+    local chunk_size, batch_size
+    local r_conf = {["se_mode"] = true}
+    if p_conf.one_sen_report == true then --report log prob one by one sentence
+        if do_train == true then
+            nerv.warning("LMTrainer.lm_process_file_birnn: warning, one_sen_report is true while do_train is also true, strange")
+        end
+        nerv.printf("lm_process_file_birnn: one_sen report mode, set batch_size to 1 and chunk_size to max_sen_len(%d)\n", 
+                global_conf.max_sen_len)
+        batch_size = 1 
+        chunk_size = global_conf.max_sen_len
+    else
+        batch_size = global_conf.batch_size
+        chunk_size = global_conf.chunk_size
+    end
+
+    reader = nerv.LMSeqReader(global_conf, batch_size, chunk_size, global_conf.vocab, r_conf)
+    reader:open_file(fn)
+
+    local result = nerv.LMResult(global_conf, global_conf.vocab)
+    result:init("birnn")
+    if global_conf.dropout_rate ~= nil then
+        nerv.info("LMTrainer.lm_process_file_birnn: dropout_rate is %f", global_conf.dropout_rate)
+    end
+        
+    global_conf.timer:flush()
+    tnn:init(batch_size, chunk_size)
+    tnn:flush_all() --caution: will also flush the inputs from the reader!
+
+    local next_log_wcn = global_conf.log_w_num
+    local neto_bakm = global_conf.mmat_type(batch_size, 1) --space backup matrix for network output
+
+    nerv.info("LMTrainer.lm_process_file_birnn: begin processing...")
+
+    while (1) do
+        global_conf.timer:tic('most_out_loop_lmprocessfile')
+
+        local r, feeds
+        global_conf.timer:tic('tnn_beforeprocess')
+        r, feeds = tnn:getfeed_from_reader(reader)
+        if r == false then 
+            break 
+        end
+    
+        for t = 1, chunk_size do
+            tnn.err_inputs_m[t][1]:fill(1)
+            for i = 1, batch_size do
+                if bit.band(feeds.flags_now[t][i], nerv.TNN.FC.HAS_LABEL) == 0 then
+                    tnn.err_inputs_m[t][1][i - 1][0] = 0
+                end
+            end
+        end
+        global_conf.timer:toc('tnn_beforeprocess')
+
+        --[[
+        for j = 1, global_conf.chunk_size, 1 do
+            for i = 1, global_conf.batch_size, 1 do
+                printf("%s[L(%s)] ", feeds.inputs_s[j][i], feeds.labels_s[j][i])   --vocab:get_word_str(input[i][j]).id
+            end
+            printf("\n")
+        end
+        printf("\n")
+        ]]--
+
+        tnn:net_propagate()
+ 
+        if do_train == true then
+            tnn:net_backpropagate(false) 
+            tnn:net_backpropagate(true)
+        end
+
+        global_conf.timer:tic('tnn_afterprocess')
+        local sen_logp = {}
+        for t = 1, chunk_size, 1 do
+            tnn.outputs_m[t][1]:copy_toh(neto_bakm)
+            for i = 1, batch_size, 1 do
+                if (feeds.labels_s[t][i] ~= global_conf.vocab.null_token) then
+                    result:add("birnn", feeds.labels_s[t][i], math.exp(neto_bakm[i - 1][0]))
+                    if sen_logp[i] == nil then
+                        sen_logp[i] = 0
+                    end
+                    sen_logp[i] = sen_logp[i] + neto_bakm[i - 1][0]
+                end
+            end            
+        end
+        if p_conf.one_sen_report == true then
+            for i = 1, batch_size do
+                nerv.printf("LMTrainer.lm_process_file_birnn: one_sen_report_output, %f\n", sen_logp[i])    
+            end
+        end
+
+        --tnn:move_right_to_nextmb({0}) --do not need history for bi directional model
+        global_conf.timer:toc('tnn_afterprocess')
+
+        global_conf.timer:toc('most_out_loop_lmprocessfile')
+
+        --print log
+        if result["birnn"].cn_w > next_log_wcn then
+            next_log_wcn = next_log_wcn + global_conf.log_w_num
+            nerv.printf("%s %d words processed %s.\n", global_conf.sche_log_pre, result["birnn"].cn_w, os.date()) 
+            nerv.printf("\t%s log prob per sample :%f.\n", global_conf.sche_log_pre, result:logp_sample("birnn"))
+            for key, value in pairs(global_conf.timer.rec) do
+                nerv.printf("\t [global_conf.timer]: time spent on %s:%.5f clock time\n", key, value)
+            end
+            global_conf.timer:flush()
+            nerv.LMUtil.wait(0.1)
+        end
+         
+        --[[
+        for t = 1, global_conf.chunk_size do
+            print(tnn.outputs_m[t][1])
+        end
+        ]]--
+
+        collectgarbage("collect")                                              
+
+        --break --debug
+    end
+    
+    nerv.printf("%s Displaying result:\n", global_conf.sche_log_pre)
+    nerv.printf("%s %s\n", global_conf.sche_log_pre, result:status("birnn"))
+    nerv.printf("%s Doing on %s end.\n", global_conf.sche_log_pre, fn)
+    
+    return result
+end
+
 
diff --git a/nerv/examples/lmptb/lmptb/lmseqreader.lua b/nerv/examples/lmptb/lmptb/lmseqreader.lua
index 40471d5..ed791d2 100644
--- a/nerv/examples/lmptb/lmptb/lmseqreader.lua
+++ b/nerv/examples/lmptb/lmptb/lmseqreader.lua
@@ -179,7 +179,7 @@ function LMReader:get_batch(feeds)
 
     if got_new == false then
         nerv.info("lmseqreader file ends, printing stats...")
-        print("al_sen_start:", self.stat.al_sen_start)
+        nerv.printf("al_sen_start:%s\n", tostring(self.stat.al_sen_start))
 
         return false
     else
diff --git a/nerv/examples/lmptb/lmptb/lstm_t_v2.lua b/nerv/examples/lmptb/lmptb/lstm_t_v2.lua
new file mode 100644
index 0000000..dc2fe45
--- /dev/null
+++ b/nerv/examples/lmptb/lmptb/lstm_t_v2.lua
@@ -0,0 +1,123 @@
+local LSTMLayerT = nerv.class('nerv.LSTMLayerTv2', 'nerv.LayerT') 
+--a version of LSTM that only feed h into the gates
+
+function LSTMLayerT:__init(id, global_conf, layer_conf)
+    --input1:x input2:h input3:c
+    self.id = id
+    self.dim_in = layer_conf.dim_in
+    self.dim_out = layer_conf.dim_out
+    self.gconf = global_conf
+
+    --prepare a DAGLayerT to hold the lstm structure
+    local pr = layer_conf.pr
+    if pr == nil then
+        pr = nerv.ParamRepo()
+    end
+    
+    local function ap(str)
+        return self.id .. '.' .. str
+    end
+
+    local layers = {
+        ["nerv.CombinerLayer"] = {
+            [ap("inputXDup")] = {{}, {["dim_in"] = {self.dim_in[1]}, 
+                ["dim_out"] = {self.dim_in[1], self.dim_in[1], self.dim_in[1], self.dim_in[1]}, ["lambda"] = {1}}},
+            [ap("inputHDup")] = {{}, {["dim_in"] = {self.dim_in[2]}, 
+                ["dim_out"] = {self.dim_in[2], self.dim_in[2], self.dim_in[2], self.dim_in[2]}, ["lambda"] = {1}}},
+            [ap("inputCDup")] = {{}, {["dim_in"] = {self.dim_in[3]}, 
+                ["dim_out"] = {self.dim_in[3]}, ["lambda"] = {1}}},
+            [ap("mainCDup")] = {{}, {["dim_in"] = {self.dim_in[3], self.dim_in[3]}, 
+                ["dim_out"] = {self.dim_in[3], self.dim_in[3]}, ["lambda"] = {1, 1}}},
+        },
+        ["nerv.AffineLayer"] = {
+            [ap("mainAffineL")] = {{}, {["dim_in"] = {self.dim_in[1], self.dim_in[2]}, 
+                ["dim_out"] = {self.dim_out[1]}, ["pr"] = pr}},
+        },
+        ["nerv.TanhLayer"] = {
+            [ap("mainTanhL")] = {{}, {["dim_in"] = {self.dim_out[1]}, ["dim_out"] = {self.dim_out[1]}}},
+            [ap("outputTanhL")] = {{}, {["dim_in"] = {self.dim_out[1]}, ["dim_out"] = {self.dim_out[1]}}},
+        },
+        ["nerv.GateFLayer"] = {
+            [ap("forgetGateL")] = {{}, {["dim_in"] = {self.dim_in[1], self.dim_in[2]}, 
+                ["dim_out"] = {self.dim_in[3]}, ["pr"] = pr}},
+