path: root/nerv/examples/lmptb/bilstmlm_ptb_main.lua

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},