diff options
| -rw-r--r-- | lua/config.lua | 67 | ||||
| -rw-r--r-- | lua/main.lua | 43 | ||||
| -rw-r--r-- | lua/network.lua | 109 | ||||
| -rw-r--r-- | lua/reader.lua | 112 | ||||
| -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, 562 insertions, 0 deletions
diff --git a/lua/config.lua b/lua/config.lua new file mode 100644 index 0000000..9d73b64 --- /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 = 1, + param_random = function() return (math.random() / 5 - 0.1) end, + dropout = 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}}, + }, + } + 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..684efac --- /dev/null +++ b/lua/main.lua @@ -0,0 +1,43 @@ +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') diff --git a/lua/network.lua b/lua/network.lua new file mode 100644 index 0000000..6280f24 --- /dev/null +++ b/lua/network.lua @@ -0,0 +1,109 @@ +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) + self.gconf.dropout_rate = 0 + 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].info = {} + ret[i].info.seq_length = data[i].seq_len + ret[i].info.new_seq = {} + for j = 1, self.gconf.batch_size do + if data[i].seq_start[j] then + table.insert(ret[i].info.new_seq, j) + end + end + end + return ret +end + +function nn:process(data, do_train) + local total_err = 0 + local total_frame = 0 + for id = 1, #data do + if do_train then + self.gconf.dropout_rate = self.gconf.dropout + else + self.gconf.dropout_rate = 0 + end + self.network:mini_batch_init(data[id].info) + local input = {} + for t = 1, self.gconf.chunk_size do + input[t] = {data[id].input[t][1], data[id].input[t][2]:decompress(self.gconf.vocab_size)} + end + self.network:propagate(input, data[id].output) + 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].info.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 + self.network:back_propagate(data[id].err_input, data[id].err_output, input, data[id].output) + self.network:update(data[id].err_input, input, data[id].output) + 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 diff --git a/lua/reader.lua b/lua/reader.lua new file mode 100644 index 0000000..2e51a9c --- /dev/null +++ b/lua/reader.lua @@ -0,0 +1,112 @@ +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 + 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 |