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authorTed Yin <[email protected]>2016-03-12 13:17:38 +0800
committerTed Yin <[email protected]>2016-03-12 13:17:38 +0800
commit2b03555ea53a47e87d03a79feb866c868d424f01 (patch)
tree63cd01ee70d056d3a159a1e7d9aa4ea6f414d213
parente8b1007d99691c08dd1b71f5733eb3cd2827dc64 (diff)
parent2660af7f6a9ac243a8ad38bf3375ef0fd292bf52 (diff)
Merge pull request #31 from liuq901/master
modfiy param generate & rewrite LSTM layer
-rw-r--r--nerv/Makefile2
-rw-r--r--nerv/examples/network_debug/config.lua62
-rw-r--r--nerv/examples/network_debug/main.lua45
-rw-r--r--nerv/examples/network_debug/network.lua110
-rw-r--r--nerv/examples/network_debug/reader.lua113
-rw-r--r--nerv/examples/network_debug/select_linear.lua59
-rw-r--r--nerv/examples/network_debug/timer.lua33
-rw-r--r--nerv/examples/network_debug/tnn.lua136
-rw-r--r--nerv/io/init.lua3
-rw-r--r--nerv/io/seq_buffer.lua0
-rw-r--r--nerv/layer/dropout.lua11
-rw-r--r--nerv/layer/graph.lua2
-rw-r--r--nerv/layer/lstm.lua191
-rw-r--r--nerv/layer/lstm_gate.lua7
-rw-r--r--nerv/layer/rnn.lua20
-rw-r--r--nerv/lib/matrix/generic/cukernel.cu18
-rw-r--r--nerv/lib/matrix/generic/cumatrix.c9
-rw-r--r--nerv/lib/matrix/generic/cumatrix.h2
-rw-r--r--nerv/lib/matrix/generic/mmatrix.c17
-rw-r--r--nerv/lib/matrix/generic/mmatrix.h1
-rw-r--r--nerv/matrix/generic/cumatrix.c1
-rw-r--r--nerv/matrix/generic/matrix.c10
-rw-r--r--nerv/matrix/generic/mmatrix.c1
-rw-r--r--nerv/matrix/init.lua18
24 files changed, 728 insertions, 143 deletions
diff --git a/nerv/Makefile b/nerv/Makefile
index 7921bd9..68465a1 100644
--- a/nerv/Makefile
+++ b/nerv/Makefile
@@ -44,7 +44,7 @@ LUA_LIBS := matrix/init.lua io/init.lua init.lua \
layer/elem_mul.lua layer/lstm.lua layer/lstm_gate.lua layer/dropout.lua layer/gru.lua \
layer/graph.lua layer/rnn.lua layer/duplicate.lua layer/identity.lua \
nn/init.lua nn/layer_repo.lua nn/param_repo.lua nn/network.lua \
- io/sgd_buffer.lua
+ io/sgd_buffer.lua io/seq_buffer.lua
INCLUDE := -I $(LUA_INCDIR) -DLUA_USE_APICHECK
CUDA_INCLUDE := -I $(CUDA_BASE)/include/
diff --git a/nerv/examples/network_debug/config.lua b/nerv/examples/network_debug/config.lua
new file mode 100644
index 0000000..e20d5a9
--- /dev/null
+++ b/nerv/examples/network_debug/config.lua
@@ -0,0 +1,62 @@
+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.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}, dim_out = {global_conf.hidden_size}, pr = pr}
+ layers['nerv.DropoutLayer']['dropout' .. i] = {dim_in = {global_conf.hidden_size}, dim_out = {global_conf.hidden_size}}
+ 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]', '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/nerv/examples/network_debug/main.lua b/nerv/examples/network_debug/main.lua
new file mode 100644
index 0000000..790c404
--- /dev/null
+++ b/nerv/examples/network_debug/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 = '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/nerv/examples/network_debug/network.lua b/nerv/examples/network_debug/network.lua
new file mode 100644
index 0000000..5518e27
--- /dev/null
+++ b/nerv/examples/network_debug/network.lua
@@ -0,0 +1,110 @@
+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
+ self.network:epoch_init()
+ 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 tmp = self.gconf.dropout_rate
+ self.gconf.dropout_rate = 0
+ local val_error = self:process(self.val_data, false)
+ self.gconf.dropout_rate = tmp
+ return train_error, val_error
+end
diff --git a/nerv/examples/network_debug/reader.lua b/nerv/examples/network_debug/reader.lua
new file mode 100644
index 0000000..b10baaf
--- /dev/null
+++ b/nerv/examples/network_debug/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, 26 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/nerv/examples/network_debug/select_linear.lua b/nerv/examples/network_debug/select_linear.lua
new file mode 100644
index 0000000..91beedf
--- /dev/null
+++ b/nerv/examples/network_debug/select_linear.lua
@@ -0,0 +1,59 @@
+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)
+ nerv.Layer.__init(self, id, global_conf, layer_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/nerv/examples/network_debug/timer.lua b/nerv/examples/network_debug/timer.lua
new file mode 100644
index 0000000..2c54ca8
--- /dev/null
+++ b/nerv/examples/network_debug/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/nerv/examples/network_debug/tnn.lua b/nerv/examples/network_debug/tnn.lua
new file mode 100644
index 0000000..bf9f118
--- /dev/null
+++ b/nerv/examples/network_debug/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
diff --git a/nerv/io/init.lua b/nerv/io/init.lua
index eb2e3e5..c36d850 100644
--- a/nerv/io/init.lua
+++ b/nerv/io/init.lua
@@ -52,8 +52,9 @@ function DataBuffer:__init(global_conf, buffer_conf)
nerv.error_method_not_implemented()
end
-function DataBuffer:get_batch()
+function DataBuffer:get_data()
nerv.error_method_not_implemented()
end
nerv.include('sgd_buffer.lua')
+nerv.include('seq_buffer.lua')
diff --git a/nerv/io/seq_buffer.lua b/nerv/io/seq_buffer.lua
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/nerv/io/seq_buffer.lua
diff --git a/nerv/layer/dropout.lua b/nerv/layer/dropout.lua
index 1a379c9..de0fb64 100644
--- a/nerv/layer/dropout.lua
+++ b/nerv/layer/dropout.lua
@@ -2,8 +2,7 @@ local DropoutLayer = nerv.class("nerv.DropoutLayer", "nerv.Layer")
function DropoutLayer:__init(id, global_conf, layer_conf)
nerv.Layer.__init(self, id, global_conf, layer_conf)
- self.rate = layer_conf.dropout_rate or global_conf.dropout_rate
- if self.rate == nil then
+ if self.gconf.dropout_rate == nil then
nerv.warning("[DropoutLayer:propagate] dropout rate is not set")
end
self:check_dim_len(1, 1) -- two inputs: nn output and label
@@ -41,12 +40,12 @@ function DropoutLayer:propagate(input, output, t)
if t == nil then
t = 1
end
- if self.rate then
+ if self.gconf.dropout_rate then
self.mask[t]:rand_uniform()
-- since we will lose a portion of the actvations, we multiply the
-- activations by 1 / (1 - rate) to compensate
- self.mask[t]:thres_mask(self.mask[t], self.rate,
- 0, 1 / (1.0 - self.rate))
+ self.mask[t]:thres_mask(self.mask[t], self.gconf.dropout_rate,
+ 0, 1 / (1.0 - self.gconf.dropout_rate))
output[1]:mul_elem(input[1], self.mask[t])
else
output[1]:copy_fromd(input[1])
@@ -61,7 +60,7 @@ function DropoutLayer:back_propagate(bp_err, next_bp_err, input, output, t)
if t == nil then
t = 1
end
- if self.rate then
+ if self.gconf.dropout_rate then
next_bp_err[1]:mul_elem(bp_err[1], self.mask[t])
else
next_bp_err[1]:copy_fromd(bp_err[1])
diff --git a/nerv/layer/graph.lua b/nerv/layer/graph.lua
index 5f42fca..68d5f51 100644
--- a/nerv/layer/graph.lua
+++ b/nerv/layer/graph.lua
@@ -112,7 +112,7 @@ function GraphLayer:graph_init(layer_repo, connections)
end
for i = 1, #ref.dim_out do
if ref.outputs[i] == nil then
- nerv.error('dangling output port %d os layer %s', i, id)
+ nerv.error('dangling output port %d of layer %s', i, id)
end
end
end
diff --git a/nerv/layer/lstm.lua b/nerv/layer/lstm.lua
index 641d5dc..56f674a 100644
--- a/nerv/layer/lstm.lua
+++ b/nerv/layer/lstm.lua
@@ -1,144 +1,85 @@
-local LSTMLayer = nerv.class('nerv.LSTMLayer', 'nerv.Layer')
+local LSTMLayer = nerv.class('nerv.LSTMLayer', 'nerv.GraphLayer')
function LSTMLayer:__init(id, global_conf, layer_conf)
- -- input1:x
- -- input2:h
- -- input3:c
nerv.Layer.__init(self, id, global_conf, layer_conf)
- -- prepare a DAGLayer to hold the lstm structure
+ self:check_dim_len(1, 1)
+
+ local din = layer_conf.dim_in[1]
+ local dout = layer_conf.dim_out[1]
+
local pr = layer_conf.pr
if pr == nil then
pr = nerv.ParamRepo({}, self.loc_type)
end
-
- local function ap(str)
- return self.id .. '.' .. str
- end
- local din1, din2, din3 = self.dim_in[1], self.dim_in[2], self.dim_in[3]
- local dout1, dout2, dout3 = self.dim_out[1], self.dim_out[2], self.dim_out[3]
- local layers = {
- ["nerv.CombinerLayer"] = {
- [ap("inputXDup")] = {dim_in = {din1},
- dim_out = {din1, din1, din1, din1},
- lambda = {1}},
- [ap("inputHDup")] = {dim_in = {din2},
- dim_out = {din2, din2, din2, din2},
- lambda = {1}},
-
- [ap("inputCDup")] = {dim_in = {din3},
- dim_out = {din3, din3, din3},
- lambda = {1}},
-
- [ap("mainCDup")] = {dim_in = {din3, din3},
- dim_out = {din3, din3, din3},
- lambda = {1, 1}},
+ local layers = {
+ ['nerv.CombinerLayer'] = {
+ mainCombine = {dim_in = {dout, dout}, dim_out = {dout}, lambda = {1, 1}},
},
- ["nerv.AffineLayer"] = {
- [ap("mainAffineL")] = {dim_in = {din1, din2},
- dim_out = {dout1},
- pr = pr},
+ ['nerv.DuplicateLayer'] = {
+ inputDup = {dim_in = {din}, dim_out = {din, din, din, din}},
+ outputDup = {dim_in = {dout}, dim_out = {dout, dout, dout, dout, dout}},
+ cellDup = {dim_in = {dout}, dim_out = {dout, dout, dout, dout, dout}},
},
- ["nerv.TanhLayer"] = {
- [ap("mainTanhL")] = {dim_in = {dout1}, dim_out = {dout1}},
- [ap("outputTanhL")] = {dim_in = {dout1}, dim_out = {dout1}},
+ ['nerv.AffineLayer'] = {
+ mainAffine = {dim_in = {din, dout}, dim_out = {dout}, pr = pr},
},
- ["nerv.LSTMGateLayer"] = {
- [ap("forgetGateL")] = {dim_in = {din1, din2, din3},
- dim_out = {din3}, pr = pr},
- [ap("inputGateL")] = {dim_in = {din1, din2, din3},
- dim_out = {din3}, pr = pr},
- [ap("outputGateL")] = {dim_in = {din1, din2, din3},
- dim_out = {din3}, pr = pr},
-
+ ['nerv.TanhLayer'] = {
+ mainTanh = {dim_in = {dout}, dim_out = {dout}},
+ outputTanh = {dim_in = {dout}, dim_out = {dout}},
},
- ["nerv.ElemMulLayer"] = {
- [ap("inputGMulL")] = {dim_in = {din3, din3},
- dim_out = {din3}},
- [ap("forgetGMulL")] = {dim_in = {din3, din3},
- dim_out = {din3}},
- [ap("outputGMulL")] = {dim_in = {din3, din3},
- dim_out = {din3}},
+ ['nerv.LSTMGateLayer'] = {
+ forgetGate = {dim_in = {din, dout, dout}, dim_out = {dout}, param_type = {'N', 'N', 'D'}, pr = pr},
+ inputGate = {dim_in = {din, dout, dout}, dim_out = {dout}, param_type = {'N', 'N', 'D'}, pr = pr},
+ outputGate = {dim_in = {din, dout, dout}, dim_out = {dout}, param_type = {'N', 'N', 'D'}, pr = pr},
+ },
+ ['nerv.ElemMulLayer'] = {
+ inputGateMul = {dim_in = {dout, dout}, dim_out = {dout}},
+ forgetGateMul = {dim_in = {dout, dout}, dim_out = {dout}},
+ outputGateMul = {dim_in = {dout, dout}, dim_out = {dout}},
},
}
- self.lrepo = nerv.LayerRepo(layers, pr, global_conf)
-
local connections = {
- ["<input>[1]"] = ap("inputXDup[1]"),
- ["<input>[2]"] = ap("inputHDup[1]"),
- ["<input>[3]"] = ap("inputCDup[1]"),
-
- [ap("inputXDup[1]")] = ap("mainAffineL[1]"),
- [ap("inputHDup[1]")] = ap("mainAffineL[2]"),
- [ap("mainAffineL[1]")] = ap("mainTanhL[1]"),
-
- [ap("inputXDup[2]")] = ap("inputGateL[1]"),
- [ap("inputHDup[2]")] = ap("inputGateL[2]"),
- [ap("inputCDup[1]")] = ap("inputGateL[3]"),
-
- [ap("inputXDup[3]")] = ap("forgetGateL[1]"),
- [ap("inputHDup[3]")] = ap("forgetGateL[2]"),
- [ap("inputCDup[2]")] = ap("forgetGateL[3]"),
-
- [ap("mainTanhL[1]")] = ap("inputGMulL[1]"),
- [ap("inputGateL[1]")] = ap("inputGMulL[2]"),
-
- [ap("inputCDup[3]")] = ap("forgetGMulL[1]"),
- [ap("forgetGateL[1]")] = ap("forgetGMulL[2]"),
-
- [ap("inputGMulL[1]")] = ap("mainCDup[1]"),
- [ap("forgetGMulL[1]")] = ap("mainCDup[2]"),
-
- [ap("inputXDup[4]")] = ap("outputGateL[1]"),
- [ap("inputHDup[4]")] = ap("outputGateL[2]"),
- [ap("mainCDup[3]")] = ap("outputGateL[3]"),
-
- [ap("mainCDup[2]")] = "<output>[2]",
- [ap("mainCDup[1]")] = ap("outputTanhL[1]"),
-
- [ap("outputTanhL[1]")] = ap("outputGMulL[1]"),
- [ap("outputGateL[1]")] = ap("outputGMulL[2]"),
-
- [ap("outputGMulL[1]")] = "<output>[1]",
+ -- lstm input
+ {'<input>[1]', 'inputDup[1]', 0},
+
+ -- input gate
+ {'inputDup[1]', 'inputGate[1]', 0},
+ {'outputDup[1]', 'inputGate[2]', 1},
+ {'cellDup[1]', 'inputGate[3]', 1},
+
+ -- forget gate
+ {'inputDup[2]', 'forgetGate[1]', 0},
+ {'outputDup[2]', 'forgetGate[2]', 1},
+ {'cellDup[2]', 'forgetGate[3]', 1},
+
+ -- lstm cell
+ {'forgetGate[1]', 'forgetGateMul[1]', 0},
+ {'cellDup[3]', 'forgetGateMul[2]', 1},
+ {'inputDup[3]', 'mainAffine[1]', 0},
+ {'outputDup[3]', 'mainAffine[2]', 1},
+ {'mainAffine[1]', 'mainTanh[1]', 0},
+ {'inputGate[1]', 'inputGateMul[1]', 0},
+ {'mainTanh[1]', 'inputGateMul[2]', 0},
+ {'inputGateMul[1]', 'mainCombine[1]', 0},
+ {'forgetGateMul[1]', 'mainCombine[2]', 0},
+ {'mainCombine[1]', 'cellDup[1]', 0},
+
+ -- forget gate
+ {'inputDup[4]', 'outputGate[1]', 0},
+ {'outputDup[4]', 'outputGate[2]', 1},
+ {'cellDup[4]', 'outputGate[3]', 0},
+
+ -- lstm output
+ {'cellDup[5]', 'outputTanh[1]', 0},
+ {'outputGate[1]', 'outputGateMul[1]', 0},
+ {'outputTanh[1]', 'outputGateMul[2]', 0},
+ {'outputGateMul[1]', 'outputDup[1]', 0},
+ {'outputDup[5]', '<output>[1]', 0},
}
- self.dag = nerv.DAGLayer(self.id, global_conf,
- {dim_in = self.dim_in,
- dim_out = self.dim_out,
- sub_layers = self.lrepo,
- connections = connections})
-
- self:check_dim_len(3, 2) -- x, h, c and h, c
-end
-
-function LSTMLayer:bind_params()
- local pr = layer_conf.pr
- if pr == nil then
- pr = nerv.ParamRepo({}, self.loc_type)
- end
- self.lrepo:rebind(pr)
-end
-
-function LSTMLayer:init(batch_size, chunk_size)
- self.dag:init(batch_size, chunk_size)
-end
-
-function LSTMLayer:batch_resize(batch_size, chunk_size)
- self.dag:batch_resize(batch_size, chunk_size)
-end
-
-function LSTMLayer:update(bp_err, input, output, t)
- self.dag:update(bp_err, input, output, t)
-end
-
-function LSTMLayer:propagate(input, output, t)
- self.dag:propagate(input, output, t)
-end
-
-function LSTMLayer:back_propagate(bp_err, next_bp_err, input, output, t)
- self.dag:back_propagate(bp_err, next_bp_err, input, output, t)
-end
-function LSTMLayer:get_params()
- return self.dag:get_params()
+ self:add_prefix(layers, connections)
+ local layer_repo = nerv.LayerRepo(layers, pr, global_conf)
+ self:graph_init(layer_repo, connections)
end
diff --git a/nerv/layer/lstm_gate.lua b/nerv/layer/lstm_gate.lua
index 7a27bab..e690721 100644
--- a/nerv/layer/lstm_gate.lua
+++ b/nerv/layer/lstm_gate.lua
@@ -3,6 +3,7 @@ local LSTMGateLayer = nerv.class('nerv.LSTMGateLayer', 'nerv.Layer')
function LSTMGateLayer:__init(id, global_conf, layer_conf)
nerv.Layer.__init(self, id, global_conf, layer_conf)
+ self.param_type = layer_conf.param_type
self:check_dim_len(-1, 1) --accept multiple inputs
self:bind_params()
end
@@ -12,6 +13,9 @@ function LSTMGateLayer:bind_params()
self["ltp" .. i] = self:find_param("ltp" .. i, self.lconf, self.gconf,
nerv.LinearTransParam,
{self.dim_in[i], self.dim_out[1]})
+ if self.param_type[i] == 'D' then
+ self["ltp" .. i].trans:diagonalize()
+ end
end
self.bp = self:find_param("bp", self.lconf, self.gconf,
nerv.BiasParam, {1, self.dim_out[1]})
@@ -63,6 +67,9 @@ function LSTMGateLayer:update(bp_err, input, output)
self.err_bakm:sigmoid_grad(bp_err[1], output[1])
for i = 1, #self.dim_in do
self["ltp" .. i]:update_by_err_input(self.err_bakm, input[i])
+ if self.param_type[i] == 'D' then
+ self["ltp" .. i].trans:diagonalize()
+ end
end
self.bp:update_by_gradient(self.err_bakm:colsum())
end
diff --git a/nerv/layer/rnn.lua b/nerv/layer/rnn.lua
index e59cf5b..0b5ccaa 100644
--- a/nerv/layer/rnn.lua
+++ b/nerv/layer/rnn.lua
@@ -4,6 +4,10 @@ function RNNLayer:__init(id, global_conf, layer_conf)
nerv.Layer.__init(self, id, global_conf, layer_conf)
self:check_dim_len(1, 1)
+ if layer_conf.activation == nil then
+ layer_conf.activation = 'nerv.SigmoidLayer'
+ end
+
local din = layer_conf.dim_in[1]
local dout = layer_conf.dim_out[1]
@@ -16,20 +20,20 @@ function RNNLayer:__init(id, global_conf, layer_conf)
['nerv.AffineLayer'] = {
main = {dim_in = {din, dout}, dim_out = {dout}, pr = pr},
},
- ['nerv.SigmoidLayer'] = {
- sigmoid = {dim_in = {dout}, dim_out = {dout}},
+ [layers.activation] = {
+ activation = {dim_in = {dout}, dim_out = {dout}},
},
['nerv.DuplicateLayer'] = {
- dup = {dim_in = {dout}, dim_out = {dout, dout}},
- }
+ duplicate = {dim_in = {dout}, dim_out = {dout, dout}},
+ },
}
local connections = {
{'<input>[1]', 'main[1]', 0},
- {'main[1]', 'sigmoid[1]', 0},
- {'sigmoid[1]', 'dup[1]', 0},
- {'dup[1]', 'main[2]', 1},
- {'dup[2]', '<output>[1]', 0},
+ {'main[1]', 'activation[1]', 0},
+ {'activation[1]', 'duplicate[1]', 0},
+ {'duplicate[1]', 'main[2]', 1},
+ {'duplicate[2]', '<output>[1]', 0},
}
self:add_prefix(layers, connections)
diff --git a/nerv/lib/matrix/generic/cukernel.cu b/nerv/lib/matrix/generic/cukernel.cu
index 0e09cfa..93121dc 100644
--- a/nerv/lib/matrix/generic/cukernel.cu
+++ b/nerv/lib/matrix/generic/cukernel.cu
@@ -250,6 +250,14 @@ __global__ void cudak_(fill)(MATRIX_ELEM *a,
a[j + i * stride] = val;
}
+__global__ void cudak_(diagonalize)(MATRIX_ELEM *a,
+ int nrow, int ncol, int stride) {
+ int j = blockIdx.x * blockDim.x + threadIdx.x;
+ int i = blockIdx.y * blockDim.y + threadIdx.y;
+ if (i >= nrow || j >= ncol || i == j) return;
+ a[j + i * stride] = 0;
+}
+
__global__ void cudak_(clip)(MATRIX_ELEM *a,
int nrow, int ncol, int stride, double val_1, double val_2) {
int j = blockIdx.x * blockDim.x + threadIdx.x;
@@ -678,6 +686,16 @@ extern "C" {
cudaStreamSynchronize(0);
}
+ void cudak_(cuda_diagonalize)(Matrix *a) {
+ dim3 threadsPerBlock(CUDA_THREADS_N, CUDA_THREADS_N);
+ dim3 numBlocks(CEIL_DIV(a->ncol, threadsPerBlock.x),
+ CEIL_DIV(a->nrow, threadsPerBlock.y));
+ cudak_(diagonalize)<<<numBlocks, threadsPerBlock>>> \
+ (MATRIX_ELEM_PTR(a), a->nrow, a->ncol,
+ a->stride / sizeof(MATRIX_ELEM));
+ cudaStreamSynchronize(0);
+ }
+
void cudak_(cuda_clip)(Matrix *a, double val_1, double val_2) {
dim3 threadsPerBlock(CUDA_THREADS_N, CUDA_THREADS_N);
dim3 numBlocks(CEIL_DIV(a->ncol, threadsPerBlock.x),
diff --git a/nerv/lib/matrix/generic/cumatrix.c b/nerv/lib/matrix/generic/cumatrix.c
index 6342d90..6d84663 100644
--- a/nerv/lib/matrix/generic/cumatrix.c
+++ b/nerv/lib/matrix/generic/cumatrix.c
@@ -515,6 +515,15 @@ void nerv_matrix_(prefixsum_row)(Matrix *a, const Matrix *b,
NERV_SET_STATUS(status, NERV_NORMAL, 0);
}
+void nerv_matrix_(diagonalize)(Matrix *a, CuContext * context, Status *status) {
+ if (a->nrow != a->ncol)
+ NERV_EXIT_STATUS(status, MAT_MISMATCH_DIM, 0);
+ PROFILE_START
+ cudak_(cuda_diagonalize)(a);
+ PROFILE_STOP
+ NERV_SET_STATUS(status, NERV_NORMAL, 0);
+}
+
static void cuda_matrix_(free)(MATRIX_ELEM *ptr, CuContext *context, Status *status) {
CUDA_SAFE_SYNC_CALL(cudaFree(ptr), status);
NERV_SET_STATUS(status, NERV_NORMAL, 0);
diff --git a/nerv/lib/matrix/generic/cumatrix.h b/nerv/lib/matrix/generic/cumatrix.h
index fe83b5d..de3a09e 100644
--- a/nerv/lib/matrix/generic/cumatrix.h
+++ b/nerv/lib/matrix/generic/cumatrix.h
@@ -33,6 +33,8 @@ void nerv_matrix_(clip)(Matrix *self, double val1, double val2,
CuContext *context, Status *status);
void nerv_matrix_(fill)(Matrix *self, double val,
CuContext *context, Status *status);
+void nerv_matrix_(diagonalize)(Matrix *self,
+ CuContext *context, Status *status);
void nerv_matrix_(copy_fromd)(Matrix *a, const Matrix *b,
int a_begin, int b_begin, int b_end,
CuContext *context, Status *status);
diff --git a/nerv/lib/matrix/generic/mmatrix.c b/nerv/lib/matrix/generic/mmatrix.c
index fb99b53..badddbd 100644
--- a/nerv/lib/matrix/generic/mmatrix.c
+++ b/nerv/lib/matrix/generic/mmatrix.c
@@ -274,6 +274,23 @@ void nerv_matrix_(fill)(Matrix *self, double val,
NERV_SET_STATUS(status, NERV_NORMAL, 0);
}
+void nerv_matrix_(diagonalize)(Matrix *self,
+ MContext *context, Status *status) {
+ if (self->nrow != self->ncol)
+ NERV_EXIT_STATUS(status, MAT_MISMATCH_DIM, 0);
+ int i, j;
+ size_t astride = self->stride;
+ MATRIX_ELEM *arow = MATRIX_ELEM_PTR(self);
+ for (i = 0; i < self->nrow; i++)
+ {
+ for (j = 0; j < self->ncol; j++)
+ if (i != j)
+ arow[j] = 0;
+ arow = MATRIX_NEXT_ROW_PTR(arow, astride);
+ }
+ NERV_SET_STATUS(status, NERV_NORMAL, 0);
+}
+
void nerv_matrix_(sigmoid)(Matrix *a, const Matrix *b,
MContext *context, Status *status) {
CHECK_SAME_DIMENSION(a, b, status);
diff --git a/nerv/lib/matrix/generic/mmatrix.h b/nerv/lib/matrix/generic/mmatrix.h
index 6e0589a..6d17c99 100644
--- a/nerv/lib/matrix/generic/mmatrix.h
+++ b/nerv/lib/matrix/generic/mmatrix.h
@@ -27,6 +27,7 @@ void nerv_matrix_(add_row)(Matrix *b, const Matrix *a, double beta,
MContext *context, Status *status);
void nerv_matrix_(clip)(Matrix *self, double val1, double val2,
MContext *context, Status *status);
+void nerv_matrix_(diagonalize)(Matrix *self, MContext *context, Status *status);
void nerv_matrix_(fill)(Matrix *self, double val, MContext *context, Status *status);
void nerv_matrix_(copy_fromh)(Matrix *a, const Matrix *b,
int a_begin, int b_begin, int b_end,
diff --git a/nerv/matrix/generic/cumatrix.c b/nerv/matrix/generic/cumatrix.c
index 00e4ee3..0c90d39 100644
--- a/nerv/matrix/generic/cumatrix.c
+++ b/nerv/matrix/generic/cumatrix.c
@@ -267,6 +267,7 @@ static const luaL_Reg nerv_matrix_(extra_methods)[] = {
{"scale_rows_by_row", nerv_matrix_(lua_scale_rows_by_row)},
{"scale_rows_by_col", nerv_matrix_(lua_scale_rows_by_col)},
{"prefixsum_row", nerv_matrix_(lua_prefixsum_row)},
+ {"diagonalize", nerv_matrix_(lua_diagonalize)},
#ifdef __NERV_FUTURE_CUDA_7
{"update_select_rows_by_rowidx", nerv_matrix_(lua_update_select_rows_by_rowidx)},
{"update_select_rows_by_colidx", nerv_matrix_(lua_update_select_rows_by_colidx)},
diff --git a/nerv/matrix/generic/matrix.c b/nerv/matrix/generic/matrix.c
index 8c2f871..fe07585 100644
--- a/nerv/matrix/generic/matrix.c
+++ b/nerv/matrix/generic/matrix.c
@@ -385,4 +385,14 @@ static int nerv_matrix_(lua_scale_rows_by_row)(lua_State *L) {
return 0;
}
+static int nerv_matrix_(lua_diagonalize)(lua_State *L) {
+ Status status;
+ MATRIX_CONTEXT *context;
+ MATRIX_GET_CONTEXT(L, 2);
+ Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ nerv_matrix_(diagonalize)(a, context, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
+ return 0;
+}
+
#endif
diff --git a/nerv/matrix/generic/mmatrix.c b/nerv/matrix/generic/mmatrix.c
index 1f37173..a5e5969 100644
--- a/nerv/matrix/generic/mmatrix.c
+++ b/nerv/matrix/generic/mmatrix.c
@@ -116,6 +116,7 @@ static const luaL_Reg nerv_matrix_(extra_methods)[] = {
{"add_row", nerv_matrix_(lua_add_row)},
{"clip", nerv_matrix_(lua_clip)},
{"fill", nerv_matrix_(lua_fill)},
+ {"diagonalize", nerv_matrix_(lua_diagonalize)},
{"sigmoid", nerv_matrix_(lua_sigmoid)},
{"sigmoid_grad", nerv_matrix_(lua_sigmoid_grad)},
{"softmax", nerv_matrix_(lua_softmax)},
diff --git a/nerv/matrix/init.lua b/nerv/matrix/init.lua
index cf85004..722c780 100644
--- a/nerv/matrix/init.lua
+++ b/nerv/matrix/init.lua
@@ -40,7 +40,8 @@ end
--- Assign each element in a matrix using the value returned by a callback `gen`.
-- @param gen the callback used to generated the values in the matrix, to which
-- the indices of row and column will be passed (e.g., `gen(i, j)`)
-function nerv.Matrix:generate(gen)
+
+function nerv.Matrix:_generate(gen)
if (self:dim() == 2) then
for i = 0, self:nrow() - 1 do
local row = self[i]
@@ -55,6 +56,21 @@ function nerv.Matrix:generate(gen)
end
end
+function nerv.Matrix:generate(gen)
+ local tmp
+ if nerv.is_type(self, 'nerv.CuMatrixFloat') then
+ tmp = nerv.MMatrixFloat(self:nrow(), self:ncol())
+ elseif nerv.is_type(self, 'nerv.CuMatrixDouble') then
+ tmp = nerv.MMatrixDouble(self:nrow(), self:ncol())
+ else
+ tmp = self
+ end
+ tmp:_generate(gen)
+ if nerv.is_type(self, 'nerv.CuMatrix') then
+ self:copy_fromh(tmp)
+ end
+end
+
--- Create a fresh new matrix of the same matrix type (as `self`).
-- @param nrow optional, the number of rows in the created matrix if specified,
-- otherwise `self:nrow()` will be used