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-rw-r--r--lua/config.lua67
-rw-r--r--lua/main.lua45
-rw-r--r--lua/network.lua106
-rw-r--r--lua/reader.lua113
-rw-r--r--lua/select_linear.lua62
-rw-r--r--lua/timer.lua33
-rw-r--r--lua/tnn.lua136
7 files changed, 562 insertions, 0 deletions
diff --git a/lua/config.lua b/lua/config.lua
new file mode 100644
index 0000000..ff98ae0
--- /dev/null
+++ b/lua/config.lua
@@ -0,0 +1,67 @@
+function get_global_conf()
+ local global_conf = {
+ lrate = 0.15,
+ wcost = 1e-5,
+ momentum = 0,
+ clip = 5,
+ cumat_type = nerv.CuMatrixFloat,
+ mmat_type = nerv.MMatrixFloat,
+ vocab_size = 10000,
+ nn_act_default = 0,
+ hidden_size = 300,
+ layer_num = 1,
+ chunk_size = 15,
+ batch_size = 20,
+ max_iter = 35,
+ param_random = function() return (math.random() / 5 - 0.1) end,
+ dropout_rate = 0.5,
+ timer = nerv.Timer(),
+ pr = nerv.ParamRepo(),
+ }
+ return global_conf
+end
+
+function get_layers(global_conf)
+ local pr = global_conf.pr
+ local layers = {
+ ['nerv.LSTMLayer'] = {},
+ ['nerv.DropoutLayer'] = {},
+ ['nerv.SelectLinearLayer'] = {
+ ['select'] = {dim_in = {1}, dim_out = {global_conf.hidden_size}, vocab = global_conf.vocab_size, pr = pr},
+ },
+ ['nerv.CombinerLayer'] = {},
+ ['nerv.AffineLayer'] = {
+ output = {dim_in = {global_conf.hidden_size}, dim_out = {global_conf.vocab_size}, pr = pr}
+ },
+ ['nerv.SoftmaxCELayer'] = {
+ softmax = {dim_in = {global_conf.vocab_size, global_conf.vocab_size}, dim_out = {1}, compressed = true},
+ },
+ }
+ for i = 1, global_conf.layer_num do
+ layers['nerv.LSTMLayer']['lstm' .. i] = {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.DropoutLayer']['dropout' .. i] = {dim_in = {global_conf.hidden_size}, dim_out = {global_conf.hidden_size}}
+ layers['nerv.CombinerLayer']['dup' .. i] = {dim_in = {global_conf.hidden_size}, dim_out = {global_conf.hidden_size, global_conf.hidden_size}, lambda = {1}}
+ end
+ return layers
+end
+
+function get_connections(global_conf)
+ local connections = {
+ {'<input>[1]', 'select[1]', 0},
+ {'select[1]', 'lstm1[1]', 0},
+ {'dropout' .. global_conf.layer_num .. '[1]', 'output[1]', 0},
+ {'output[1]', 'softmax[1]', 0},
+ {'<input>[2]', 'softmax[2]', 0},
+ {'softmax[1]', '<output>[1]', 0},
+ }
+ for i = 1, global_conf.layer_num do
+ table.insert(connections, {'lstm' .. i .. '[1]', 'dup' .. i .. '[1]', 0})
+ table.insert(connections, {'lstm' .. i .. '[2]', 'lstm' .. i .. '[3]', 1})
+ table.insert(connections, {'dup' .. i .. '[1]', 'lstm' .. i .. '[2]', 1})
+ table.insert(connections, {'dup' .. i .. '[2]', 'dropout' .. i .. '[1]', 0})
+ if i > 1 then
+ table.insert(connections, {'dropout' .. (i - 1) .. '[1]', 'lstm' .. i .. '[1]', 0})
+ end
+ end
+ return connections
+end
diff --git a/lua/main.lua b/lua/main.lua
new file mode 100644
index 0000000..39818aa
--- /dev/null
+++ b/lua/main.lua
@@ -0,0 +1,45 @@
+nerv.include('reader.lua')
+nerv.include('timer.lua')
+nerv.include('config.lua')
+nerv.include(arg[1])
+
+local global_conf = get_global_conf()
+local timer = global_conf.timer
+
+timer:tic('IO')
+
+local data_path = 'nerv/nerv/examples/lmptb/PTBdata/'
+local train_reader = nerv.Reader(data_path .. 'vocab', data_path .. 'ptb.train.txt.adds')
+local val_reader = nerv.Reader(data_path .. 'vocab', data_path .. 'ptb.valid.txt.adds')
+
+local train_data = train_reader:get_all_batch(global_conf)
+local val_data = val_reader:get_all_batch(global_conf)
+
+local layers = get_layers(global_conf)
+local connections = get_connections(global_conf)
+
+local NN = nerv.NN(global_conf, train_data, val_data, layers, connections)
+
+timer:toc('IO')
+timer:check('IO')
+io.flush()
+
+timer:tic('global')
+local best_cv = 1e10
+for i = 1, global_conf.max_iter do
+ timer:tic('Epoch' .. i)
+ local train_ppl, val_ppl = NN:epoch()
+ if val_ppl < best_cv then
+ best_cv = val_ppl
+ else
+ global_conf.lrate = global_conf.lrate / 2.0
+ end
+ nerv.printf('Epoch %d: %f %f %f\n', i, global_conf.lrate, train_ppl, val_ppl)
+ timer:toc('Epoch' .. i)
+ timer:check('Epoch' .. i)
+ io.flush()
+end
+timer:toc('global')
+timer:check('global')
+timer:check('network')
+timer:check('gc')
diff --git a/lua/network.lua b/lua/network.lua
new file mode 100644
index 0000000..d106ba1
--- /dev/null
+++ b/lua/network.lua
@@ -0,0 +1,106 @@
+nerv.include('select_linear.lua')
+
+local nn = nerv.class('nerv.NN')
+
+function nn:__init(global_conf, train_data, val_data, layers, connections)
+ self.gconf = global_conf
+ self.network = self:get_network(layers, connections)
+ self.train_data = self:get_data(train_data)
+ self.val_data = self:get_data(val_data)
+end
+
+function nn:get_network(layers, connections)
+ local layer_repo = nerv.LayerRepo(layers, self.gconf.pr, self.gconf)
+ local graph = nerv.GraphLayer('graph', self.gconf,
+ {dim_in = {1, self.gconf.vocab_size}, dim_out = {1},
+ layer_repo = layer_repo, connections = connections})
+ local network = nerv.Network('network', self.gconf,
+ {network = graph, clip = self.gconf.clip})
+ network:init(self.gconf.batch_size, self.gconf.chunk_size)
+ return network
+end
+
+function nn:get_data(data)
+ local err_output = {}
+ local softmax_output = {}
+ local output = {}
+ for i = 1, self.gconf.chunk_size do
+ err_output[i] = self.gconf.cumat_type(self.gconf.batch_size, 1)
+ softmax_output[i] = self.gconf.cumat_type(self.gconf.batch_size, self.gconf.vocab_size)
+ output[i] = self.gconf.cumat_type(self.gconf.batch_size, 1)
+ end
+ local ret = {}
+ for i = 1, #data do
+ ret[i] = {}
+ ret[i].input = {}
+ ret[i].output = {}
+ ret[i].err_input = {}
+ ret[i].err_output = {}
+ for t = 1, self.gconf.chunk_size do
+ ret[i].input[t] = {}
+ ret[i].output[t] = {}
+ ret[i].err_input[t] = {}
+ ret[i].err_output[t] = {}
+ ret[i].input[t][1] = data[i].input[t]
+ ret[i].input[t][2] = data[i].output[t]
+ ret[i].output[t][1] = output[t]
+ local err_input = self.gconf.mmat_type(self.gconf.batch_size, 1)
+ for j = 1, self.gconf.batch_size do
+ if t <= data[i].seq_len[j] then
+ err_input[j - 1][0] = 1
+ else
+ err_input[j - 1][0] = 0
+ end
+ end
+ ret[i].err_input[t][1] = self.gconf.cumat_type.new_from_host(err_input)
+ ret[i].err_output[t][1] = err_output[t]
+ ret[i].err_output[t][2] = softmax_output[t]
+ end
+ ret[i].seq_length = data[i].seq_len
+ ret[i].new_seq = {}
+ for j = 1, self.gconf.batch_size do
+ if data[i].seq_start[j] then
+ table.insert(ret[i].new_seq, j)
+ end
+ end
+ end
+ return ret
+end
+
+function nn:process(data, do_train)
+ local timer = self.gconf.timer
+ local total_err = 0
+ local total_frame = 0
+ for id = 1, #data do
+ data[id].do_train = do_train
+ timer:tic('network')
+ self.network:mini_batch_init(data[id])
+ self.network:propagate()
+ timer:toc('network')
+ for t = 1, self.gconf.chunk_size do
+ local tmp = data[id].output[t][1]:new_to_host()
+ for i = 1, self.gconf.batch_size do
+ if t <= data[id].seq_length[i] then
+ total_err = total_err + math.log10(math.exp(tmp[i - 1][0]))
+ total_frame = total_frame + 1
+ end
+ end
+ end
+ if do_train then
+ timer:tic('network')
+ self.network:back_propagate()
+ self.network:update()
+ timer:toc('network')
+ end
+ timer:tic('gc')
+ collectgarbage('collect')
+ timer:toc('gc')
+ end
+ return math.pow(10, - total_err / total_frame)
+end
+
+function nn:epoch()
+ local train_error = self:process(self.train_data, true)
+ local val_error = self:process(self.val_data, false)
+ return train_error, val_error
+end
diff --git a/lua/reader.lua b/lua/reader.lua
new file mode 100644
index 0000000..d2624d3
--- /dev/null
+++ b/lua/reader.lua
@@ -0,0 +1,113 @@
+local Reader = nerv.class('nerv.Reader')
+
+function Reader:__init(vocab_file, input_file)
+ self:get_vocab(vocab_file)
+ self:get_seq(input_file)
+end
+
+function Reader:get_vocab(vocab_file)
+ local f = io.open(vocab_file, 'r')
+ local id = 0
+ self.vocab = {}
+ while true do
+ local word = f:read()
+ if word == nil then
+ break
+ end
+ self.vocab[word] = id
+ id = id + 1
+ end
+ self.size = id
+end
+
+function Reader:split(s, t)
+ local ret = {}
+ for x in (s .. t):gmatch('(.-)' .. t) do
+ table.insert(ret, x)
+ end
+ return ret
+end
+
+function Reader:get_seq(input_file)
+ local f = io.open(input_file, 'r')
+ self.seq = {}
+ while true do
+ local seq = f:read()
+ if seq == nil then
+ break
+ end
+ seq = self:split(seq, ' ')
+ local tmp = {}
+ for i = 1, #seq do
+ if seq[i] ~= '' then
+ table.insert(tmp, self.vocab[seq[i]])
+ end
+ end
+ table.insert(self.seq, tmp)
+ end
+end
+
+function Reader:get_in_out(id, pos)
+ return self.seq[id][pos], self.seq[id][pos + 1], pos + 1 == #self.seq[id]
+end
+
+function Reader:get_all_batch(global_conf)
+ local data = {}
+ local pos = {}
+ local offset = 1
+ for i = 1, global_conf.batch_size do
+ pos[i] = nil
+ end
+ while true do
+ --for i = 1, 100 do
+ local input = {}
+ local output = {}
+ for i = 1, global_conf.chunk_size do
+ input[i] = global_conf.mmat_type(global_conf.batch_size, 1)
+ input[i]:fill(global_conf.nn_act_default)
+ output[i] = global_conf.mmat_type(global_conf.batch_size, 1)
+ output[i]:fill(global_conf.nn_act_default)
+ end
+ local seq_start = {}
+ local seq_end = {}
+ local seq_len = {}
+ for i = 1, global_conf.batch_size do
+ seq_start[i] = false
+ seq_end[i] = false
+ seq_len[i] = 0
+ end
+ local has_new = false
+ for i = 1, global_conf.batch_size do
+ if pos[i] == nil then
+ if offset < #self.seq then
+ seq_start[i] = true
+ pos[i] = {offset, 1}
+ offset = offset + 1
+ end
+ end
+ if pos[i] ~= nil then
+ has_new = true
+ for j = 1, global_conf.chunk_size do
+ local final
+ input[j][i-1][0], output[j][i-1][0], final = self:get_in_out(pos[i][1], pos[i][2])
+ seq_len[i] = j
+ if final then
+ seq_end[i] = true
+ pos[i] = nil
+ break
+ end
+ pos[i][2] = pos[i][2] + 1
+ end
+ end
+ end
+ if not has_new then
+ break
+ end
+ for i = 1, global_conf.chunk_size do
+ input[i] = global_conf.cumat_type.new_from_host(input[i])
+ output[i] = global_conf.cumat_type.new_from_host(output[i])
+ end
+ table.insert(data, {input = input, output = output, seq_start = seq_start, seq_end = seq_end, seq_len = seq_len})
+ end
+ return data
+end
diff --git a/lua/select_linear.lua b/lua/select_linear.lua
new file mode 100644
index 0000000..a7e20cc
--- /dev/null
+++ b/lua/select_linear.lua
@@ -0,0 +1,62 @@
+local SL = nerv.class('nerv.SelectLinearLayer', 'nerv.Layer')
+
+--id: string
+--global_conf: table
+--layer_conf: table
+--Get Parameters
+function SL:__init(id, global_conf, layer_conf)
+ self.id = id
+ self.dim_in = layer_conf.dim_in
+ self.dim_out = layer_conf.dim_out
+ self.gconf = global_conf
+
+ self.vocab = layer_conf.vocab
+ self.ltp = self:find_param("ltp", layer_conf, global_conf, nerv.LinearTransParam, {self.vocab, self.dim_out[1]}) --layer_conf.ltp
+
+ self:check_dim_len(1, 1)
+end
+
+--Check parameter
+function SL:init(batch_size)
+ if (self.dim_in[1] ~= 1) then --one word id
+ nerv.error("mismatching dimensions of ltp and input")
+ end
+ if (self.dim_out[1] ~= self.ltp.trans:ncol()) then
+ nerv.error("mismatching dimensions of bp and output")
+ end
+
+ self.batch_size = bath_size
+ self.ltp:train_init()
+end
+
+function SL:update(bp_err, input, output)
+ --use this to produce reproducable result, don't forget to set the dropout to zero!
+ --for i = 1, input[1]:nrow(), 1 do
+ -- local word_vec = self.ltp.trans[input[1][i - 1][0]]
+ -- word_vec:add(word_vec, bp_err[1][i - 1], 1, - self.gconf.lrate / self.gconf.batch_size)
+ --end
+
+ --I tried the update_select_rows kernel which uses atomicAdd, but it generates unreproducable result
+ self.ltp.trans:update_select_rows_by_colidx(bp_err[1], input[1], - self.gconf.lrate / self.gconf.batch_size, 0)
+ self.ltp.trans:add(self.ltp.trans, self.ltp.trans, 1.0, - self.gconf.lrate * self.gconf.wcost)
+end
+
+function SL:propagate(input, output)
+ --for i = 0, input[1]:ncol() - 1, 1 do
+ -- if (input[1][0][i] > 0) then
+ -- output[1][i]:copy_fromd(self.ltp.trans[input[1][0][i]])
+ -- else
+ -- output[1][i]:fill(0)
+ -- end
+ --end
+ output[1]:copy_rows_fromd_by_colidx(self.ltp.trans, input[1])
+end
+
+function SL:back_propagate(bp_err, next_bp_err, input, output)
+ --input is compressed, do nothing
+end
+
+function SL:get_params()
+ local paramRepo = nerv.ParamRepo({self.ltp})
+ return paramRepo
+end
diff --git a/lua/timer.lua b/lua/timer.lua
new file mode 100644
index 0000000..2c54ca8
--- /dev/null
+++ b/lua/timer.lua
@@ -0,0 +1,33 @@
+local Timer = nerv.class("nerv.Timer")
+
+function Timer:__init()
+ self.last = {}
+ self.rec = {}
+end
+
+function Timer:tic(item)
+ self.last[item] = os.clock()
+end
+
+function Timer:toc(item)
+ if (self.last[item] == nil) then
+ nerv.error("item not there")
+ end
+ if (self.rec[item] == nil) then
+ self.rec[item] = 0
+ end
+ self.rec[item] = self.rec[item] + os.clock() - self.last[item]
+end
+
+function Timer:check(item)
+ if self.rec[item]==nil then
+ nerv.error('item not there')
+ end
+ nerv.printf('"%s" lasts for %f secs.\n',item,self.rec[item])
+end
+
+function Timer:flush()
+ for key, value in pairs(self.rec) do
+ self.rec[key] = nil
+ end
+end
diff --git a/lua/tnn.lua b/lua/tnn.lua
new file mode 100644
index 0000000..bf9f118
--- /dev/null
+++ b/lua/tnn.lua
@@ -0,0 +1,136 @@
+nerv.include('select_linear.lua')
+
+local reader = nerv.class('nerv.TNNReader')
+
+function reader:__init(global_conf, data)
+ self.gconf = global_conf
+ self.offset = 0
+ self.data = data
+end
+
+function reader:get_batch(feeds)
+ self.offset = self.offset + 1
+ if self.offset > #self.data then
+ return false
+ end
+ for i = 1, self.gconf.chunk_size do
+ feeds.inputs_m[i][1]:copy_from(self.data[self.offset].input[i])
+ feeds.inputs_m[i][2]:copy_from(self.data[self.offset].output[i]:decompress(self.gconf.vocab_size))
+ end
+ feeds.flags_now = self.data[self.offset].flags
+ feeds.flagsPack_now = self.data[self.offset].flagsPack
+ return true
+end
+
+function reader:has_data(t, i)
+ return t <= self.data[self.offset].seq_len[i]
+end
+
+function reader:get_err_input()
+ return self.data[self.offset].err_input
+end
+
+local nn = nerv.class('nerv.NN')
+
+function nn:__init(global_conf, train_data, val_data, layers, connections)
+ self.gconf = global_conf
+ self.tnn = self:get_tnn(layers, connections)
+ self.train_data = self:get_data(train_data)
+ self.val_data = self:get_data(val_data)
+end
+
+function nn:get_tnn(layers, connections)
+ self.gconf.dropout_rate = 0
+ local layer_repo = nerv.LayerRepo(layers, self.gconf.pr, self.gconf)
+ local tnn = nerv.TNN('TNN', self.gconf, {dim_in = {1, self.gconf.vocab_size},
+ dim_out = {1}, sub_layers = layer_repo, connections = connections,
+ clip = self.gconf.clip})
+ tnn:init(self.gconf.batch_size, self.gconf.chunk_size)
+ return tnn
+end
+
+function nn:get_data(data)
+ local ret = {}
+ for i = 1, #data do
+ ret[i] = {}
+ ret[i].input = data[i].input
+ ret[i].output = data[i].output
+ ret[i].flags = {}
+ ret[i].err_input = {}
+ for t = 1, self.gconf.chunk_size do
+ ret[i].flags[t] = {}
+ local err_input = self.gconf.mmat_type(self.gconf.batch_size, 1)
+ for j = 1, self.gconf.batch_size do
+ if t <= data[i].seq_len[j] then
+ ret[i].flags[t][j] = nerv.TNN.FC.SEQ_NORM
+ err_input[j - 1][0] = 1
+ else
+ ret[i].flags[t][j] = 0
+ err_input[j - 1][0] = 0
+ end
+ end
+ ret[i].err_input[t] = self.gconf.cumat_type.new_from_host(err_input)
+ end
+ for j = 1, self.gconf.batch_size do
+ if data[i].seq_start[j] then
+ ret[i].flags[1][j] = bit.bor(ret[i].flags[1][j], nerv.TNN.FC.SEQ_START)
+ end
+ if data[i].seq_end[j] then
+ local t = data[i].seq_len[j]
+ ret[i].flags[t][j] = bit.bor(ret[i].flags[t][j], nerv.TNN.FC.SEQ_END)
+ end
+ end
+ ret[i].flagsPack = {}
+ for t = 1, self.gconf.chunk_size do
+ ret[i].flagsPack[t] = 0
+ for j = 1, self.gconf.batch_size do
+ ret[i].flagsPack[t] = bit.bor(ret[i].flagsPack[t], ret[i].flags[t][j])
+ end
+ end
+ ret[i].seq_len = data[i].seq_len
+ end
+ return ret
+end
+
+function nn:process(data, do_train)
+ local total_err = 0
+ local total_frame = 0
+ local reader = nerv.TNNReader(self.gconf, data)
+ while true do
+ local r, _ = self.tnn:getfeed_from_reader(reader)
+ if not r then
+ break
+ end
+ if do_train then
+ self.gconf.dropout_rate = self.gconf.dropout
+ else
+ self.gconf.dropout_rate = 0
+ end
+ self.tnn:net_propagate()
+ for t = 1, self.gconf.chunk_size do
+ local tmp = self.tnn.outputs_m[t][1]:new_to_host()
+ for i = 1, self.gconf.batch_size do
+ if reader:has_data(t, i) then
+ total_err = total_err + math.log10(math.exp(tmp[i - 1][0]))
+ total_frame = total_frame + 1
+ end
+ end
+ end
+ if do_train then
+ local err_input = reader:get_err_input()
+ for i = 1, self.gconf.chunk_size do
+ self.tnn.err_inputs_m[i][1]:copy_from(err_input[i])
+ end
+ self.tnn:net_backpropagate(false)
+ self.tnn:net_backpropagate(true)
+ end
+ collectgarbage('collect')
+ end
+ return math.pow(10, - total_err / total_frame)
+end
+
+function nn:epoch()
+ local train_error = self:process(self.train_data, true)
+ local val_error = self:process(self.val_data, false)
+ return train_error, val_error
+end