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require 'lmptb.lmvocab'
require 'lmptb.lmutil'
--require 'tnn.init'
local LMReader = nerv.class("nerv.LMSeqReader")
local printf = nerv.printf
--global_conf: table
--batch_size: int
--vocab: nerv.LMVocab
function LMReader:__init(global_conf, batch_size, chunk_size, vocab, r_conf)
self.gconf = global_conf
self.fh = nil --file handle to read, nil means currently no file
self.batch_size = batch_size
self.chunk_size = chunk_size
self.log_pre = "[LOG]LMSeqReader:"
self.vocab = vocab
self.streams = nil
if r_conf == nil then
r_conf = {}
end
self.se_mode = false --sentence end mode, when a sentence end is met, the stream after will be null
if r_conf.se_mode == true then
self.se_mode = true
end
self.compressed_label = false
if r_conf.compressed_label == true then
self.compressed_label = true
end
self.same_io = false
if r_conf.same_io == true then --can be used to train P(wi|w1..(i-1),(i+1)..n)
self.same_io = true
end
end
--fn: string
--Initialize all streams
function LMReader:open_file(fn)
if (self.fh ~= nil) then
nerv.error("%s error: in open_file(fn is %s), file handle not nil.", self.log_pre, fn)
end
nerv.printf("%s opening file %s...\n", self.log_pre, fn)
nerv.printf("%s batch_size:%d chunk_size:%d\n", self.log_pre, self.batch_size, self.chunk_size)
nerv.printf("%s se_mode:%s same_io:%s\n", self.log_pre, tostring(self.se_mode), tostring(self.same_io))
self.fh = io.open(fn, "r")
self.streams = {}
for i = 1, self.batch_size, 1 do
self.streams[i] = {["store"] = {}, ["head"] = 1, ["tail"] = 0}
end
self.stat = {} --stat collected during file reading
self.stat.al_sen_start = true --check whether it's always sentence_start at the begining of a minibatch
self.bak_inputs_m = {} --backup MMatrix for temporary storey(then copy to TNN CuMatrix)
for j = 1, self.chunk_size, 1 do
self.bak_inputs_m[j] = {}
self.bak_inputs_m[j][1] = self.gconf.mmat_type(self.batch_size, 1)
if self.compressed_label == true then
self.bak_inputs_m[j][2] = self.gconf.mmat_type(self.batch_size, 1)
end
--self.bak_inputs_m[j][2] = self.gconf.mmat_type(self.batch_size, self.vocab:size()) --since MMatrix does not yet have fill, this m[j][2] is not used
end
end
--id: int
--Refresh stream id, read a line from file, will check whether this line is cntklm-style
function LMReader:refresh_stream(id)
if (self.streams[id] == nil) then
nerv.error("stream %d does not exit.", id)
end
local st = self.streams[id]
if (st.store[st.head] ~= nil) then return end
if (self.fh == nil) then return end
local list = nerv.LMUtil.read_line(self.fh)
if (list == nil) then --file has end
printf("%s file expires, closing.\n", self.log_pre)
self.fh:close()
self.fh = nil
return
end
--some sanity check
if (list[1] ~= self.vocab.sen_end_token or list[#list] ~= self.vocab.sen_end_token) then --check for cntklm style input
nerv.error("%s sentence not begin or end with </s> : %s", self.log_pre, table.tostring(list));
end
for i = 2, #list - 1, 1 do
if (list[i] == self.vocab.sen_end_token) then
nerv.error("%s Got </s> in the middle of a line(%s) in file", self.log_pre, table.tostring(list))
end
end
for i = 1, #list, 1 do
st.tail = st.tail + 1
st.store[st.tail] = list[i]
end
end
--feeds: a table that will be filled by the reader
--Returns: bool
function LMReader:get_batch(feeds)
if (feeds == nil or type(feeds) ~= "table") then
nerv.error("feeds is not a table")
end
feeds["inputs_s"] = {}
feeds["labels_s"] = {}
local inputs_s = feeds.inputs_s
local labels_s = feeds.labels_s
for i = 1, self.chunk_size, 1 do
inputs_s[i] = {}
labels_s[i] = {}
end
local inputs_m = feeds.inputs_m --port 1 : word_id, port 2 : label
local flags = feeds.flags_now
local flagsPack = feeds.flagsPack_now
local got_new = false
for j = 1, self.chunk_size, 1 do
inputs_m[j][2]:fill(0)
end
for i = 1, self.batch_size, 1 do
local st = self.streams[i]
local end_stream = false --used for se_mode, indicating that this stream is ended
for j = 1, self.chunk_size, 1 do
flags[j][i] = 0
if end_stream == true then
if self.se_mode == false then
nerv.error("lmseqreader:getbatch: error, end_stream is true while se_mode is false")
end
inputs_s[j][i] = self.vocab.null_token
self.bak_inputs_m[j][1][i - 1][0] = 0
if self.compressed_label == true then
self.bak_inputs_m[j][2][i - 1][0] = 0
end
labels_s[j][i] = self.vocab.null_token
else
self:refresh_stream(i)
if st.store[st.head] ~= nil then
if self.same_io == false then
inputs_s[j][i] = st.store[st.head]
self.bak_inputs_m[j][1][i - 1][0] = self.vocab:get_word_str(st.store[st.head]).id - 1
else
inputs_s[j][i] = st.store[st.head + 1]
self.bak_inputs_m[j][1][i - 1][0] = self.vocab:get_word_str(st.store[st.head + 1]).id - 1
end
else
inputs_s[j][i] = self.vocab.null_token
self.bak_inputs_m[j][1][i - 1][0] = 0
end
if st.store[st.head + 1] ~= nil then
labels_s[j][i] = st.store[st.head + 1]
if self.compressed_label == true then
self.bak_inputs_m[j][2][i - 1][0] = self.vocab:get_word_str(st.store[st.head + 1]).id - 1
else
inputs_m[j][2][i - 1][self.vocab:get_word_str(st.store[st.head + 1]).id - 1] = 1
end
else
if inputs_s[j][i] ~= self.vocab.null_token then
nerv.error("reader error : input not null but label is null_token")
end
labels_s[j][i] = self.vocab.null_token
end
if inputs_s[j][i] ~= self.vocab.null_token then
if labels_s[j][i] == self.vocab.null_token then
nerv.error("reader error : label is null while input is not null")
end
flags[j][i] = bit.bor(flags[j][i], nerv.TNN.FC.SEQ_NORM) --has both input and label
got_new = true
if st.store[st.head] == self.vocab.sen_end_token then
flags[j][i] = bit.bor(flags[j][i], nerv.TNN.FC.SEQ_START)
end
st.store[st.head] = nil
st.head = st.head + 1
if labels_s[j][i] == self.vocab.sen_end_token then
flags[j][i] = bit.bor(flags[j][i], nerv.TNN.FC.SEQ_END)
st.store[st.head] = nil --sentence end is passed
st.head = st.head + 1
if self.se_mode == true then
end_stream = true --meet sentence end, this stream ends now
end
end
end
end
end
end
for j = 1, self.chunk_size, 1 do
flagsPack[j] = 0
for i = 1, self.batch_size, 1 do
flagsPack[j] = bit.bor(flagsPack[j], flags[j][i])
end
inputs_m[j][1]:copy_fromh(self.bak_inputs_m[j][1])
if self.compressed_label == true then
inputs_m[j][2]:copy_fromh(self.bak_inputs_m[j][2])
end
end
--check for self.al_sen_start
for i = 1, self.batch_size do
if bit.band(flags[1][i], nerv.TNN.FC.SEQ_START) == 0 and flags[1][i] > 0 then
self.stat.al_sen_start = false
end
end
if got_new == false then
nerv.info("lmseqreader file ends, printing stats...")
nerv.printf("al_sen_start:%s\n", tostring(self.stat.al_sen_start))
return false
else
return true
end
end
--[[
do
local test_fn = "/home/slhome/txh18/workspace/nerv/nerv/some-text"
--local test_fn = "/home/slhome/txh18/workspace/nerv-project/nerv/examples/lmptb/PTBdata/ptb.train.txt"
local vocab = nerv.LMVocab()
vocab:build_file(test_fn)
local batch_size = 3
local feeder = nerv.LMFeeder({}, batch_size, vocab)
feeder:open_file(test_fn)
while (1) do
local list = feeder:get_batch()
if (list == nil) then break end
for i = 1, batch_size, 1 do
printf("%s(%d) ", list[i], vocab:get_word_str(list[i]).id)
end
printf("\n")
end
end
]]--
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