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-rw-r--r--nerv/Makefile3
-rw-r--r--nerv/layer/duplicate.lua41
-rw-r--r--nerv/layer/graph.lua156
-rw-r--r--nerv/layer/gru.lua4
-rw-r--r--nerv/layer/identity.lua30
-rw-r--r--nerv/layer/init.lua12
-rw-r--r--nerv/layer/lstm.lua52
-rw-r--r--nerv/layer/rnn.lua38
-rw-r--r--nerv/nn/init.lua1
-rw-r--r--nerv/nn/network.lua498
10 files changed, 806 insertions, 29 deletions
diff --git a/nerv/Makefile b/nerv/Makefile
index e8bcad2..421eda0 100644
--- a/nerv/Makefile
+++ b/nerv/Makefile
@@ -42,7 +42,8 @@ LUA_LIBS := matrix/init.lua io/init.lua init.lua \
layer/init.lua layer/affine.lua layer/sigmoid.lua layer/tanh.lua layer/softmax_ce.lua layer/softmax.lua \
layer/window.lua layer/bias.lua layer/combiner.lua layer/mse.lua \
layer/elem_mul.lua layer/lstm.lua layer/lstm_gate.lua layer/dropout.lua layer/gru.lua \
- nn/init.lua nn/layer_repo.lua nn/param_repo.lua nn/layer_dag.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/layer_dag.lua nn/network.lua \
io/sgd_buffer.lua \
tnn/init.lua tnn/sutil.lua tnn/tnn.lua
diff --git a/nerv/layer/duplicate.lua b/nerv/layer/duplicate.lua
new file mode 100644
index 0000000..137472b
--- /dev/null
+++ b/nerv/layer/duplicate.lua
@@ -0,0 +1,41 @@
+local DuplicateLayer = nerv.class('nerv.DuplicateLayer', 'nerv.Layer')
+
+function DuplicateLayer:__init(id, global_conf, layer_conf)
+ nerv.Layer.__init(self, id, global_conf, layer_conf)
+ self:check_dim_len(1, -1)
+ if #self.dim_out < 1 then
+ nerv.error('no output specified')
+ end
+ for i = 1, #self.dim_out do
+ if self.dim_out[i] ~= self.dim_in[1] then
+ nerv.error('mismatching dimensions of outputs')
+ end
+ end
+end
+
+function DuplicateLayer:init()
+end
+
+function DuplicateLayer:batch_resize()
+end
+
+function DuplicateLayer:propagate(input, output)
+ for i = 1, #self.dim_out do
+ output[i]:copy_from(input[1])
+ -- FIXME: use reference copy to speed up
+ end
+end
+
+function DuplicateLayer:back_propagate(bp_err, next_bp_err)
+ next_bp_err[1]:copy_from(bp_err[1])
+ for i = 2, #self.dim_out do
+ next_bp_err[1]:add(next_bp_err[1], bp_err[i], 1.0, 1.0)
+ end
+end
+
+function DuplicateLayer:update()
+end
+
+function DuplicateLayer:get_params()
+ return nerv.ParamRepo({}, self.loc_type)
+end
diff --git a/nerv/layer/graph.lua b/nerv/layer/graph.lua
new file mode 100644
index 0000000..5f42fca
--- /dev/null
+++ b/nerv/layer/graph.lua
@@ -0,0 +1,156 @@
+local GraphLayer = nerv.class('nerv.GraphLayer', 'nerv.Layer')
+
+function GraphLayer:__init(id, global_conf, layer_conf)
+ nerv.Layer.__init(self, id, global_conf, layer_conf)
+ self:graph_init(layer_conf.layer_repo, layer_conf.connections)
+end
+
+local function parse_id(str)
+ local id, port, _
+ _, _, id, port = string.find(str, "([a-zA-Z0-9_.]+)%[([0-9]+)%]")
+ if id == nil or port == nil then
+ _, _, id, port = string.find(str, "(.+)%[([0-9]+)%]")
+ if not (id == "<input>" or id == "<output>") then
+ nerv.error("wrong format of connection id")
+ end
+ end
+ port = tonumber(port)
+ return id, port
+end
+
+function GraphLayer:add_prefix(layers, connections)
+ local function ap(name)
+ return self.id .. '.' .. name
+ end
+
+ for layer_type, sublayers in pairs(layers) do
+ local tmp = {}
+ for name, layer_config in pairs(sublayers) do
+ tmp[ap(name)] = layer_config
+ end
+ layers[layer_type] = tmp
+ end
+
+ for i = 1, #connections do
+ local from, to = connections[i][1], connections[i][2]
+ if parse_id(from) ~= '<input>' then
+ connections[i][1] = ap(from)
+ end
+ if parse_id(to) ~= '<output>' then
+ connections[i][2] = ap(to)
+ end
+ end
+end
+
+function GraphLayer:discover(id, layer_repo)
+ if id == '<output>' then
+ id = '<input>'
+ end
+ local layers = self.layers
+ local ref = layers[id]
+ if ref == nil then
+ local layer = layer_repo:get_layer(id)
+ local dim_in, dim_out = layer:get_dim()
+ self.layer_num = self.layer_num + 1
+ ref = {
+ layer = layer,
+ inputs = {},
+ outputs = {},
+ dim_in = dim_in,
+ dim_out = dim_out,
+ id = self.layer_num,
+ }
+ layers[id] = ref
+ end
+ return ref
+end
+
+function GraphLayer:graph_init(layer_repo, connections)
+ local layers = {}
+ layers['<input>'] = {
+ inputs = {},
+ outputs = {},
+ dim_in = self.dim_out,
+ dim_out = self.dim_in,
+ id = 0,
+ }
+ self.layers = layers
+ self.layer_num = 0
+ self.connections = {}
+
+ -- check data dimension between connected ports
+ for _, edge in pairs(connections) do
+ local from, to, time = edge[1], edge[2], edge[3]
+ local id_from, port_from = parse_id(from)
+ local id_to, port_to = parse_id(to)
+ local ref_from = self:discover(id_from, layer_repo)
+ local ref_to = self:discover(id_to, layer_repo)
+ if ref_from.outputs[port_from] ~= nil then
+ nerv.error('%s has already been attached', from)
+ end
+ if ref_to.inputs[port_to] ~= nil then
+ nerv.error('%s has already been attached', to)
+ end
+ if ref_from.dim_out[port_from] ~= ref_to.dim_in[port_to] then
+ nerv.error('mismatching data dimension between %s and %s', from, to)
+ end
+ if ref_from.id == 0 and ref_to.id == 0 then
+ nerv.error('short-circuit connection between <input> and <output>')
+ end
+ ref_from.outputs[port_from] = true
+ ref_to.inputs[port_to] = true
+ table.insert(self.connections, {ref_from.id, port_from, ref_to.id, port_to, time})
+ end
+
+ -- check dangling ports
+ for id, ref in pairs(layers) do
+ if id ~= '<input>' then
+ for i = 1, #ref.dim_in do
+ if ref.inputs[i] == nil then
+ nerv.error('dangling input port %d of layer %s', i, id)
+ end
+ 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)
+ end
+ end
+ end
+ end
+ for i = 1, #self.dim_in do
+ if layers['<input>'].outputs[i] == nil then
+ nerv.error('dangling port %d of layer <input>', i)
+ end
+ end
+ for i = 1, #self.dim_out do
+ if layers['<input>'].inputs[i] == nil then
+ nerv.error('dangling port %d of layer <output>', i)
+ end
+ end
+end
+
+function GraphLayer:set_attr(name, value)
+ self[name] = value
+ for id, ref in pairs(self.layers) do
+ if id ~= '<input>' then
+ ref.layer:set_attr(name, value)
+ end
+ end
+end
+
+function GraphLayer:get_sublayer(id)
+ if self.layers[id] == nil or id == '<input>' then
+ nerv.error('layer with id %s not found', id)
+ end
+ return self.layers[id].layer
+end
+
+function GraphLayer:get_params()
+ local param_repos = {}
+ for id, ref in pairs(self.layers) do
+ if id ~= '<input>' then
+ table.insert(param_repos, ref.layer:get_params())
+ end
+ end
+ return nerv.ParamRepo.merge(param_repos, self.loc_type)
+end
diff --git a/nerv/layer/gru.lua b/nerv/layer/gru.lua
index a590a67..71718d7 100644
--- a/nerv/layer/gru.lua
+++ b/nerv/layer/gru.lua
@@ -13,7 +13,7 @@ function GRULayer:__init(id, global_conf, layer_conf)
-- prepare a DAGLayer to hold the lstm structure
local pr = layer_conf.pr
if pr == nil then
- pr = nerv.ParamRepo(nil, self.loc_type)
+ pr = nerv.ParamRepo({}, self.loc_type)
end
local function ap(str)
@@ -102,7 +102,7 @@ end
function GRULayer:bind_params()
local pr = layer_conf.pr
if pr == nil then
- pr = nerv.ParamRepo(nil, self.loc_type)
+ pr = nerv.ParamRepo({}, self.loc_type)
end
self.lrepo:rebind(pr)
end
diff --git a/nerv/layer/identity.lua b/nerv/layer/identity.lua
new file mode 100644
index 0000000..d56337d
--- /dev/null
+++ b/nerv/layer/identity.lua
@@ -0,0 +1,30 @@
+local IdentityLayer = nerv.class('nerv.IdentityLayer', 'nerv.Layer')
+
+function IdentityLayer:__init(id, global_conf, layer_conf)
+ nerv.Layer.__init(self, id, global_conf, layer_conf)
+ self:check_dim_len(1, 1)
+ if self.dim_in[1] ~= self.dim_out[1] then
+ nerv.error('mismatching dimensions of input and output')
+ end
+end
+
+function IdentityLayer:init()
+end
+
+function IdentityLayer:batch_resize()
+end
+
+function IdentityLayer:propagate(input, output)
+ output[1]:copy_from(input[1])
+end
+
+function IdentityLayer:back_propagate(bp_err, next_bp_err)
+ next_bp_err[1]:copy_from(bp_err[1])
+end
+
+function IdentityLayer:update()
+end
+
+function IdentityLayer:get_params()
+ return nerv.ParamRepo({}, self.loc_type)
+end
diff --git a/nerv/layer/init.lua b/nerv/layer/init.lua
index 146ad8c..475ef62 100644
--- a/nerv/layer/init.lua
+++ b/nerv/layer/init.lua
@@ -85,6 +85,14 @@ function Layer:get_dim()
return self.dim_in, self.dim_out
end
+function Layer:set_attr(name, value)
+ self[name] = value
+end
+
+function Layer:get_sublayer(id)
+ nerv.error('primitive layer does not have sublayers')
+end
+
function Layer:find_param(plist, lconf, gconf, p_type, p_dim)
if type(plist) == "string" then
plist = {plist}
@@ -119,6 +127,7 @@ function Layer:find_param(plist, lconf, gconf, p_type, p_dim)
return p
end
+nerv.include('graph.lua')
nerv.include('affine.lua')
nerv.include('sigmoid.lua')
nerv.include('tanh.lua')
@@ -133,6 +142,9 @@ nerv.include('lstm.lua')
nerv.include('lstm_gate.lua')
nerv.include('dropout.lua')
nerv.include('gru.lua')
+nerv.include('rnn.lua')
+nerv.include('duplicate.lua')
+nerv.include('identity.lua')
-- The following lines are for backward compatibility, and will be removed in
-- the future. The use of these names are deprecated.
diff --git a/nerv/layer/lstm.lua b/nerv/layer/lstm.lua
index d4c9212..641d5dc 100644
--- a/nerv/layer/lstm.lua
+++ b/nerv/layer/lstm.lua
@@ -8,7 +8,7 @@ function LSTMLayer:__init(id, global_conf, layer_conf)
-- prepare a DAGLayer to hold the lstm structure
local pr = layer_conf.pr
if pr == nil then
- pr = nerv.ParamRepo(nil, self.loc_type)
+ pr = nerv.ParamRepo({}, self.loc_type)
end
local function ap(str)
@@ -18,47 +18,47 @@ function LSTMLayer:__init(id, global_conf, layer_conf)
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},
+ [ap("inputXDup")] = {dim_in = {din1},
dim_out = {din1, din1, din1, din1},
- lambda = {1}}},
+ lambda = {1}},
- [ap("inputHDup")] = {{}, {dim_in = {din2},
+ [ap("inputHDup")] = {dim_in = {din2},
dim_out = {din2, din2, din2, din2},
- lambda = {1}}},
+ lambda = {1}},
- [ap("inputCDup")] = {{}, {dim_in = {din3},
+ [ap("inputCDup")] = {dim_in = {din3},
dim_out = {din3, din3, din3},
- lambda = {1}}},
+ lambda = {1}},
- [ap("mainCDup")] = {{}, {dim_in = {din3, din3},
+ [ap("mainCDup")] = {dim_in = {din3, din3},
dim_out = {din3, din3, din3},
- lambda = {1, 1}}},
+ lambda = {1, 1}},
},
["nerv.AffineLayer"] = {
- [ap("mainAffineL")] = {{}, {dim_in = {din1, din2},
+ [ap("mainAffineL")] = {dim_in = {din1, din2},
dim_out = {dout1},
- pr = pr}},
+ pr = pr},
},
["nerv.TanhLayer"] = {
- [ap("mainTanhL")] = {{}, {dim_in = {dout1}, dim_out = {dout1}}},
- [ap("outputTanhL")] = {{}, {dim_in = {dout1}, dim_out = {dout1}}},
+ [ap("mainTanhL")] = {dim_in = {dout1}, dim_out = {dout1}},
+ [ap("outputTanhL")] = {dim_in = {dout1}, dim_out = {dout1}},
},
["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}},
+ [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.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}}},
+ [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}},
},
}
@@ -114,7 +114,7 @@ end
function LSTMLayer:bind_params()
local pr = layer_conf.pr
if pr == nil then
- pr = nerv.ParamRepo(nil, self.loc_type)
+ pr = nerv.ParamRepo({}, self.loc_type)
end
self.lrepo:rebind(pr)
end
diff --git a/nerv/layer/rnn.lua b/nerv/layer/rnn.lua
new file mode 100644
index 0000000..e59cf5b
--- /dev/null
+++ b/nerv/layer/rnn.lua
@@ -0,0 +1,38 @@
+local RNNLayer = nerv.class('nerv.RNNLayer', 'nerv.GraphLayer')
+
+function RNNLayer:__init(id, global_conf, layer_conf)
+ nerv.Layer.__init(self, id, global_conf, layer_conf)
+ 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 layers = {
+ ['nerv.AffineLayer'] = {
+ main = {dim_in = {din, dout}, dim_out = {dout}, pr = pr},
+ },
+ ['nerv.SigmoidLayer'] = {
+ sigmoid = {dim_in = {dout}, dim_out = {dout}},
+ },
+ ['nerv.DuplicateLayer'] = {
+ dup = {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},
+ }
+
+ 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/nn/init.lua b/nerv/nn/init.lua
index cbaf52b..c32ea09 100644
--- a/nerv/nn/init.lua
+++ b/nerv/nn/init.lua
@@ -1,3 +1,4 @@
nerv.include('layer_repo.lua')
nerv.include('param_repo.lua')
nerv.include('layer_dag.lua')
+nerv.include('network.lua')
diff --git a/nerv/nn/network.lua b/nerv/nn/network.lua
new file mode 100644
index 0000000..35e11e3
--- /dev/null
+++ b/nerv/nn/network.lua
@@ -0,0 +1,498 @@
+local network = nerv.class('nerv.Network')
+
+function network:__init(id, global_conf, network_conf)
+ self.id = id
+ self.network = network_conf.network
+ self.dim_in = self.network.dim_in
+ self.dim_out = self.network.dim_out
+ self.gconf = global_conf
+ if self.gconf.use_cpu then
+ self.mat_type = self.gconf.mmat_type
+ else
+ self.mat_type = self.gconf.cumat_type
+ end
+ self.clip = network_conf.clip
+ self.nn_act_default = network_conf.nn_act_default
+ if self.nn_act_default == nil then
+ self.nn_act_default = 0
+ end
+ self.layers = {}
+ self.input_conn = {}
+ self.output_conn = {}
+ self.socket = self:compile(self.network)
+ for i = 1, #self.dim_in do
+ local edge = self.socket.inputs[i]
+ local id, port, time = edge[1], edge[2], edge[3]
+ if self.input_conn[id][port] ~= nil then
+ nerv.error('duplicate edge')
+ end
+ self.input_conn[id][port] = {0, i, time}
+ end
+ for i = 1, #self.dim_out do
+ local edge = self.socket.outputs[i]
+ local id, port, time = edge[1], edge[2], edge[3]
+ if self.output_conn[id][port] ~= nil then
+ nerv.error('duplicate edge')
+ end
+ self.output_conn[id][port] = {0, i, time}
+ end
+ self.delay = 0
+ for i = 1, #self.layers do
+ local dim_in, _ = self.layers[i]:get_dim()
+ for j = 1, #dim_in do
+ local time = self.input_conn[i][j][3]
+ if math.abs(time) > self.delay then
+ self.delay = math.abs(time)
+ end
+ end
+ end
+end
+
+function network:compile(layer)
+ local socket = {inputs = {}, outputs = {}}
+ if not nerv.is_type(layer, 'nerv.GraphLayer') then
+ table.insert(self.layers, layer)
+ local id = #self.layers
+ self.input_conn[id] = {}
+ self.output_conn[id] = {}
+ local dim_in, dim_out = layer:get_dim()
+ for i = 1, #dim_in do
+ socket.inputs[i] = {id, i, 0}
+ end
+ for i = 1, #dim_out do
+ socket.outputs[i] = {id, i, 0}
+ end
+ else
+ local sublayer_socket = {}
+ for id, sublayer in pairs(layer.layers) do
+ if id ~= '<input>' then
+ sublayer_socket[sublayer.id] = self:compile(sublayer.layer)
+ end
+ end
+ for _, edge in pairs(layer.connections) do
+ -- id = 0 means <input> or <output>
+ local id_from, port_from = edge[1], edge[2]
+ local id_to, port_to = edge[3], edge[4]
+ local time = edge[5]
+ if id_from == 0 then
+ if socket.inputs[port_from] ~= nil then
+ nerv.error('duplicate input socket')
+ end
+ local input = sublayer_socket[id_to].inputs[port_to]
+ local id, port, t = input[1], input[2], input[3] + time
+ socket.inputs[port_from] = {id, port, t}
+ else
+ local output = sublayer_socket[id_from].outputs[port_from]
+ local id, port, t = output[1], output[2], output[3] + time
+ if id_to == 0 then
+ if socket.outputs[port_to] ~= nil then
+ nerv.error('duplicate output socket')
+ end
+ socket.outputs[port_to] = {id, port, t}
+ else
+ local input = sublayer_socket[id_to].inputs[port_to]
+ local id1, port1, t1 = input[1], input[2], input[3]
+ if self.input_conn[id1][port1] ~= nil or self.output_conn[id][port] ~= nil then
+ nerv.error('duplicate edge')
+ end
+ self.input_conn[id1][port1] = {id, port, t + t1}
+ self.output_conn[id][port] = {id1, port1, t + t1}
+ end
+ end
+ end
+ end
+ return socket
+end
+
+function network:init(batch_size, chunk_size)
+ self.batch_size = batch_size
+ self.chunk_size = chunk_size
+
+ self:topsort()
+
+ self:make_initial_store()
+ collectgarbage('collect')
+
+ for i = 1, #self.layers do
+ self.layers[i]:init(batch_size, chunk_size)
+ end
+end
+
+function network:topsort()
+ nerv.info('network topology sort')
+ local degree = {}
+ for t = 1, self.chunk_size do
+ degree[t] = {}
+ for i = 1, #self.layers do
+ degree[t][i] = 0
+ end
+ end
+
+ for t = 1, self.chunk_size do
+ for i = 1, #self.layers do
+ local _, dim_out = self.layers[i]:get_dim()
+ for j = 1, #dim_out do
+ if self.output_conn[i][j] ~= nil then
+ local edge = self.output_conn[i][j]
+ local id, time = edge[1], edge[3] + t
+ if time >= 1 and time <= self.chunk_size and id ~= 0 then
+ degree[time][id] = degree[time][id] + 1
+ end
+ end
+ end
+ end
+ end
+
+ self.queue = {}
+ local l = 1
+ local r = 0
+ for t = 1, self.chunk_size do
+ for i = 1, #self.layers do
+ if degree[t][i] == 0 then
+ r = r + 1
+ self.queue[r] = {chunk = t, id = i}
+ end
+ end
+ end
+ while l<=r do
+ local t, i = self.queue[l].chunk, self.queue[l].id
+ l = l + 1
+ local _, dim_out = self.layers[i]:get_dim()
+ for j = 1, #dim_out do
+ if self.output_conn[i][j] ~= nil then
+ local edge = self.output_conn[i][j]
+ local id, time = edge[1], edge[3] + t
+ if time >= 1 and time <= self.chunk_size and id ~= 0 then
+ degree[time][id] = degree[time][id] - 1
+ if degree[time][id] == 0 then
+ r = r + 1
+ self.queue[r] = {chunk = time, id = id}
+ end
+ end
+ end
+ end
+ end
+
+ if r ~= self.chunk_size * #self.layers then
+ nerv.error('loop detected')
+ end
+end
+
+function network:make_initial_store()
+ nerv.info('network initing storage')
+
+ -- allocate memory
+ local memory = {}
+ local err_memory = {}
+ for t = 1 - self.delay, self.chunk_size + self.delay do
+ memory[t] = {}
+ err_memory[t] = {}
+ for i = 1, #self.layers do
+ memory[t][i] = {}
+ err_memory[t][i] = {}
+ local dim_in, dim_out = self.layers[i]:get_dim()
+ for j = 1, #dim_in do
+ err_memory[t][i][j] = self.mat_type(self.batch_size, dim_in[j])
+ err_memory[t][i][j]:fill(0)
+ end
+ for j = 1, #dim_out do
+ memory[t][i][j] = self.mat_type(self.batch_size, dim_out[j])
+ memory[t][i][j]:fill(self.nn_act_default)
+ end
+ end
+ -- memory[t][0] stores network input
+ memory[t][0] = {}
+ for j = 1, #self.dim_in do
+ memory[t][0][j] = self.mat_type(self.batch_size, self.dim_in[j])
+ memory[t][0][j]:fill(self.nn_act_default)
+ end
+ -- err_memory[t][0] stores network err_input
+ err_memory[t][0] = {}
+ for j = 1, #self.dim_out do
+ err_memory[t][0][j] = self.mat_type(self.batch_size, self.dim_out[j])
+ err_memory[t][0][j]:fill(0)
+ end
+ end
+
+ -- connect memory and reference
+ self.input = {}
+ self.output = {}
+ self.err_input = {}
+ self.err_output = {}
+ for t = 1, self.chunk_size do
+ self.input[t] = {}
+ self.output[t] = {}
+ self.err_input[t] = {}
+ self.err_output[t] = {}
+ for i = 1, #self.layers do
+ self.input[t][i] = {}
+ self.output[t][i] = {}
+ self.err_input[t][i] = {}
+ self.err_output[t][i] = {}
+ local dim_in, dim_out = self.layers[i]:get_dim()
+ for j = 1, #dim_in do
+ local edge = self.input_conn[i][j]
+ local id, port, time = edge[1], edge[2], edge[3]
+ if id ~= 0 or t - time < 1 or t - time > self.chunk_size then
+ self.input[t][i][j] = memory[t - time][id][port]
+ end
+ if id ~= 0 then
+ self.err_output[t][i][j] = err_memory[t][i][j]
+ end
+ end
+ for j = 1, #dim_out do
+ local edge = self.output_conn[i][j]
+ local id, port, time = edge[1], edge[2], edge[3]
+ if id ~= 0 then
+ self.output[t][i][j] = memory[t][i][j]
+ end
+ if id ~= 0 or t + time < 1 or t + time > self.chunk_size then
+ self.err_input[t][i][j] = err_memory[t + time][id][port]
+ end
+ end
+ end
+ end
+
+ -- check dangling reference
+ for t = 1, self.chunk_size do
+ for i = 1, #self.dim_in do
+ local edge = self.socket.inputs[i]
+ local id, port, time = edge[1], edge[2], edge[3]
+ if t + time >= 1 and t + time <= self.chunk_size then
+ if self.input[t + time][id][port] ~= nil then
+ nerv.error('input reference not nil')
+ end
+ self.input[t + time][id][port] = true -- just a place holder
+ if self.err_output[t + time][id][port] ~= nil then
+ nerv.error('err_output reference not nil')
+ end
+ self.err_output[t + time][id][port] = true -- just a place holder
+ end
+ end
+ for i = 1, #self.dim_out do
+ local edge = self.socket.outputs[i]
+ local id, port, time = edge[1], edge[2], edge[3]
+ if t - time >= 1 and t - time <= self.chunk_size then
+ if self.output[t - time][id][port] ~= nil then
+ nerv.error('output reference not nil')
+ end
+ self.output[t - time][id][port] = true -- just a place holder
+ if self.err_input[t - time][id][port] ~= nil then
+ nerv.error('err_output reference not nil')
+ end
+ self.err_input[t - time][id][port] = true -- just a place holder
+ end
+ end
+ end
+ for t = 1, self.chunk_size do
+ for i = 1, #self.layers do
+ local dim_in, dim_out = self.layers[i]:get_dim()
+ for j = 1, #dim_in do
+ if self.input[t][i][j] == nil then
+ nerv.error('input reference dangling')
+ end
+ if self.err_output[t][i][j] == nil then
+ nerv.error('err_output reference dangling')
+ end
+ end
+ for j = 1, #dim_out do
+ if self.output[t][i][j] == nil then
+ nerv.error('output reference dangling')
+ end
+ if self.err_input[t][i][j] == nil then
+ nerv.error('err_input reference dangling')
+ end
+ end
+ end
+ end
+
+ -- allocate reference for legacy of previous mini-batch
+ self.legacy = {}
+ for t = 1 - self.delay, 0 do
+ self.legacy[t] = {}
+ for i = 1, #self.layers do
+ self.legacy[t][i] = {}
+ local _, dim_out = self.layers[i]:get_dim()
+ for j = 1, #dim_out do
+ self.legacy[t][i][j] = memory[t][i][j]
+ end
+ end
+ end
+end
+
+function network:set_input(input)
+ for t = 1, self.chunk_size do
+ for i = 1, #self.dim_in do
+ local edge = self.socket.inputs[i]
+ local id, port, time = edge[1], edge[2], edge[3]
+ if t + time >= 1 and t + time <= self.chunk_size then
+ self.input[t + time][id][port] = input[t][i]
+ end
+ end
+ end
+end
+
+function network:set_output(output)
+ for t = 1, self.chunk_size do
+ for i = 1, #self.dim_out do
+ local edge = self.socket.outputs[i]
+ local id, port, time = edge[1], edge[2], edge[3]
+ if t - time >= 1 and t - time <= self.chunk_size then
+ self.output[t - time][id][port] = output[t][i]
+ end
+ end
+ end
+end
+
+function network:set_err_input(err_input)
+ for t = 1, self.chunk_size do
+ for i = 1, #self.dim_out do
+ local edge = self.socket.outputs[i]
+ local id, port, time = edge[1], edge[2], edge[3]
+ if t - time >= 1 and t - time <= self.chunk_size then
+ self.err_input[t - time][id][port] = err_input[t][i]
+ end
+ end
+ end
+end
+
+function network:set_err_output(err_output)
+ for t = 1, self.chunk_size do
+ for i = 1, #self.dim_in do
+ local edge = self.socket.inputs[i]
+ local id, port, time = edge[1], edge[2], edge[3]
+ if t + time >= 1 and t + time <= self.chunk_size then
+ self.err_output[t + time][id][port] = err_output[t][i]
+ end
+ end
+ end
+end
+
+--[[
+ [info] is a table that contains information of current mini-batch. These fields must be contained:
+ [input], [output] : matrix array which stores the network input and output
+ [seq_length] : a table contains the length of every sequences
+ [new_seq]: a table contains the batch number of new sequences
+ [do_train]: a bool value indicates do train or not
+ if [do_train] is true, these fileds also must be contained:
+ [err_input], [err_output] : matrix array which stores the network err_input and err_output
+--]]
+function network:mini_batch_init(info)
+ self.info = info
+ self:set_input(self.info.input)
+ self:set_output(self.info.output)
+
+ -- calculate border
+ self.max_length = 0
+ self.border = {}
+ for i = 1, self.chunk_size do
+ self.border[i] = {}
+ end
+ for i = 1, self.batch_size do
+ if self.info.seq_length[i] > self.max_length then
+ self.max_length = self.info.seq_length[i]
+ end
+ for t = 1, self.delay do
+ local chunk = self.info.seq_length[i] + t
+ if chunk > self.chunk_size then
+ break
+ end
+ table.insert(self.border[chunk], i)
+ end
+ end
+
+ -- copy legacy
+ for t = 1 - self.delay, 0 do
+ for i = 1, #self.layers do
+ local _, dim_out = self.layers[i]:get_dim()
+ for j = 1, #dim_out do
+ if t + self.chunk_size >= 1 and self.output_conn[i][j][1] ~= 0 then
+ self.legacy[t][i][j]:copy_from(self.output[t + self.chunk_size][i][j])
+ end
+ for k = 1, #self.info.new_seq do
+ local batch = self.info.new_seq[k]
+ self.legacy[t][i][j][batch - 1]:fill(self.nn_act_default)
+ end
+ end
+ end
+ end
+
+ if self.info.do_train then
+ self:set_err_input(self.info.err_input)
+ self:set_err_output(self.info.err_output)
+
+ -- flush border gradient
+ for t = self.max_length + 1, self.max_length + self.delay do
+ if t > self.chunk_size then
+ break
+ end
+ for i = 1, #self.layers do
+ local dim_in, _ = self.layers[i]:get_dim()
+ for j = 1, #dim_in do
+ self.err_output[t][i][j]:fill(0)
+ end
+ end
+ end
+ end
+end
+
+function network:propagate()
+ for i = 1, #self.queue do
+ local t, id = self.queue[i].chunk, self.queue[i].id
+ if t <= self.max_length then
+ self.layers[id]:propagate(self.input[t][id], self.output[t][id], t)
+ end
+ -- flush border activation
+ for j = 1, #self.border[t] do
+ local batch = self.border[t][j]
+ local _, dim_out = self.layers[id]:get_dim()
+ for k = 1, #dim_out do
+ self.output[t][id][k][batch - 1]:fill(self.nn_act_default)
+ end
+ end
+ end
+end
+
+function network:back_propagate()
+ for i = #self.queue, 1, -1 do
+ local t, id = self.queue[i].chunk, self.queue[i].id
+ if t <= self.max_length then
+ -- flush border gradient
+ for j = 1, #self.border[t] do
+ local batch = self.border[t][j]
+ local _, dim_out = self.layers[id]:get_dim()
+ for k = 1, #dim_out do
+ self.err_input[t][id][k][batch - 1]:fill(0)
+ end
+ end
+ self.layers[id]:back_propagate(self.err_input[t][id], self.err_output[t][id], self.input[t][id], self.output[t][id], t)
+ if self.clip ~= nil then
+ local dim_in, _ = self.layers[id]:get_dim()
+ for j = 1, #dim_in do
+ self.err_output[t][id][j]:clip(-self.clip, self.clip)
+ end
+ end
+ end
+ end
+end
+
+function network:update()
+ for i = 1, #self.queue do
+ local t, id = self.queue[i].chunk, self.queue[i].id
+ if t <= self.max_length then
+ self.layers[id]:update(self.err_input[t][id], self.input[t][id], self.output[t][id], t)
+ end
+ end
+end
+
+function network:set_attr(name, value)
+ self.network:set_attr(name, value)
+end
+
+function network:get_sublayer(id)
+ return self.network:get_sublayer(id)
+end
+
+function network:get_params()
+ return self.network:get_params()
+end
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