diff options
Diffstat (limited to 'lua')
| -rw-r--r-- | lua/config.lua | 67 | ||||
| -rw-r--r-- | lua/main.lua | 45 | ||||
| -rw-r--r-- | lua/network.lua | 106 | ||||
| -rw-r--r-- | lua/reader.lua | 113 | ||||
| -rw-r--r-- | lua/select_linear.lua | 62 | ||||
| -rw-r--r-- | lua/timer.lua | 33 | ||||
| -rw-r--r-- | lua/tnn.lua | 136 |
7 files changed, 0 insertions, 562 deletions
diff --git a/lua/config.lua b/lua/config.lua deleted file mode 100644 index ff98ae0..0000000 --- a/lua/config.lua +++ /dev/null @@ -1,67 +0,0 @@ -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 deleted file mode 100644 index 39818aa..0000000 --- a/lua/main.lua +++ /dev/null @@ -1,45 +0,0 @@ -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 deleted file mode 100644 index d106ba1..0000000 --- a/lua/network.lua +++ /dev/null @@ -1,106 +0,0 @@ -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 deleted file mode 100644 index d2624d3..0000000 --- a/lua/reader.lua +++ /dev/null @@ -1,113 +0,0 @@ -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 deleted file mode 100644 index a7e20cc..0000000 --- a/lua/select_linear.lua +++ /dev/null @@ -1,62 +0,0 @@ -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 deleted file mode 100644 index 2c54ca8..0000000 --- a/lua/timer.lua +++ /dev/null @@ -1,33 +0,0 @@ -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 deleted file mode 100644 index bf9f118..0000000 --- a/lua/tnn.lua +++ /dev/null @@ -1,136 +0,0 @@ -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 |