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-rw-r--r--nerv/nn/layer_dag.lua249
1 files changed, 249 insertions, 0 deletions
diff --git a/nerv/nn/layer_dag.lua b/nerv/nn/layer_dag.lua
new file mode 100644
index 0000000..8e30216
--- /dev/null
+++ b/nerv/nn/layer_dag.lua
@@ -0,0 +1,249 @@
+local DAGLayer = nerv.class("nerv.DAGLayer", "nerv.Layer")
+
+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
+
+local function discover(id, layers, layer_repo)
+ local ref = layers[id]
+ if id == "<input>" or id == "<output>" then
+ return nil
+ end
+ if ref == nil then
+ local layer = layer_repo:get_layer(id)
+ local dim_in, dim_out = layer:get_dim()
+ ref = {
+ layer = layer,
+ inputs = {},
+ outputs = {},
+ err_inputs = {},
+ err_outputs = {},
+ next_layers = {},
+ input_len = #dim_in,
+ output_len = #dim_out,
+ in_deg = 0,
+ visited = false
+ }
+ layers[id] = ref
+ end
+ return ref
+end
+
+function DAGLayer:__init(id, global_conf, layer_conf)
+ local layers = {}
+ local inputs = {}
+ local outputs = {}
+ local dim_in = layer_conf.dim_in
+ local dim_out = layer_conf.dim_out
+ local parsed_conn = {}
+ for from, to in pairs(layer_conf.connections) do
+ local id_from, port_from = parse_id(from)
+ local id_to, port_to = parse_id(to)
+ local ref_from = discover(id_from, layers, layer_conf.sub_layers)
+ local ref_to = discover(id_to, layers, layer_conf.sub_layers)
+ local input_dim, output_dim, _
+ if ref_from and ref_from.outputs[port_from] ~= nil then
+ nerv.error("%s has already been attached", from)
+ end
+ if ref_to and ref_to.inputs[port_to] ~= nil then
+ nerv.error("%s has already been attached", to)
+ end
+ if id_from == "<input>" then
+ input_dim, _ = ref_to.layer:get_dim()
+ if dim_in[port_from] ~= input_dim[port_to] then
+ nerv.error("mismatching data dimension between %s and %s", from, to)
+ end
+ inputs[port_from] = {ref_to, port_to}
+ ref_to.inputs[port_to] = inputs -- just a place holder
+ elseif id_to == "<output>" then
+ _, output_dim = ref_from.layer:get_dim()
+ if output_dim[port_from] ~= dim_out[port_to] then
+ nerv.error("mismatching data dimension between %s and %s", from, to)
+ end
+ outputs[port_to] = {ref_from, port_from}
+ ref_from.outputs[port_from] = outputs -- just a place holder
+ else
+ _, output_dim = ref_from.layer:get_dim()
+ input_dim, _ = ref_to.layer:get_dim()
+ if output_dim[port_from] ~= input_dim[port_to] then
+ nerv.error("mismatching data dimension between %s and %s", from, to)
+ end
+
+ table.insert(parsed_conn,
+ {{ref_from, port_from}, {ref_to, port_to}})
+ table.insert(ref_from.next_layers, ref_to) -- add edge
+ ref_to.in_deg = ref_to.in_deg + 1 -- increase the in-degree of the target layer
+ end
+ end
+
+ -- topology sort
+ local queue = {}
+ local l = 1
+ local r = 1
+ for id, ref in pairs(layers) do
+ if ref.in_deg == 0 then
+ table.insert(queue, ref)
+ nerv.info("adding source layer: %s", id)
+ r = r + 1
+ end
+ end
+ if l == r then
+ nerv.error("loop detected")
+ end
+ while l < r do
+ local cur = queue[l]
+ cur.visited = true
+ l = l + 1
+ for _, nl in pairs(cur.next_layers) do
+ nl.in_deg = nl.in_deg - 1
+ if nl.in_deg == 0 then
+ table.insert(queue, nl)
+ r = r + 1
+ end
+ end
+ end
+ for i = 1, #queue do
+ nerv.info("enqueued layer: %s", queue[i].layer.id)
+ end
+
+ for id, ref in pairs(layers) do
+ -- check wether the graph is connected
+ if ref.visited == false then
+ nerv.warning("layer %s is ignored", id)
+ end
+ end
+
+ self.layers = layers
+ self.inputs = inputs
+ self.outputs = outputs
+ self.dim_in = dim_in
+ self.dim_out = dim_out
+ self.parsed_conn = parsed_conn
+ self.queue = queue
+ self.gconf = global_conf
+end
+
+function DAGLayer:init(batch_size)
+ for i, conn in ipairs(self.parsed_conn) do
+ local _, output_dim
+ local ref_from, port_from, ref_to, port_to
+ ref_from, port_from = unpack(conn[1])
+ ref_to, port_to = unpack(conn[2])
+ _, output_dim = ref_from.layer:get_dim()
+ local mid = self.gconf.cumat_type(batch_size,
+ output_dim[port_from])
+ local err_mid = mid:create()
+
+ ref_from.outputs[port_from] = mid
+ ref_to.inputs[port_to] = mid
+
+ ref_from.err_inputs[port_from] = err_mid
+ ref_to.err_outputs[port_to] = err_mid
+ end
+ for id, ref in pairs(self.layers) do
+ for i = 1, ref.input_len do
+ if ref.inputs[i] == nil then
+ nerv.error("dangling input port %d of layer %s", i, id)
+ end
+ end
+ for i = 1, ref.output_len do
+ if ref.outputs[i] == nil then
+ nerv.error("dangling output port %d of layer %s", i, id)
+ end
+ end
+ -- initialize sub layers
+ ref.layer:init(batch_size)
+ end
+ for i = 1, #self.dim_in do
+ if self.inputs[i] == nil then
+ nerv.error("dangling port %d of layer <input>", i)
+ end
+ end
+ for i = 1, #self.dim_out do
+ if self.outputs[i] == nil then
+ nerv.error("dangling port %d of layer <output>", i)
+ end
+ end
+end
+
+function DAGLayer:set_inputs(input)
+ for i = 1, #self.dim_in do
+ local layer = self.inputs[i][1]
+ local port = self.inputs[i][2]
+ layer.inputs[port] = input[i]
+ end
+end
+
+function DAGLayer:set_outputs(output)
+ for i = 1, #self.dim_out do
+ local layer = self.outputs[i][1]
+ local port = self.outputs[i][2]
+ layer.outputs[port] = output[i]
+ end
+end
+
+function DAGLayer:set_err_inputs(bp_err)
+ for i = 1, #self.dim_out do
+ local layer = self.outputs[i][1]
+ local port = self.outputs[i][2]
+ layer.err_inputs[port] = bp_err[i]
+ end
+end
+
+function DAGLayer:set_err_outputs(next_bp_err)
+ for i = 1, #self.dim_in do
+ local layer = self.inputs[i][1]
+ local port = self.inputs[i][2]
+ layer.err_outputs[port] = next_bp_err[i]
+ end
+end
+
+function DAGLayer:update(bp_err, input, output)
+ self:set_err_inputs(bp_err)
+ self:set_inputs(input)
+ self:set_outputs(output)
+ -- print("update")
+ for id, ref in pairs(self.queue) do
+ -- print(ref.layer.id)
+ ref.layer:update(ref.err_inputs, ref.inputs, ref.outputs)
+ end
+end
+
+function DAGLayer:propagate(input, output)
+ self:set_inputs(input)
+ self:set_outputs(output)
+ for i = 1, #self.queue do
+ local ref = self.queue[i]
+ -- print(ref.layer.id)
+ ref.layer:propagate(ref.inputs, ref.outputs)
+ end
+end
+
+function DAGLayer:back_propagate(bp_err, next_bp_err, input, output)
+ self:set_err_outputs(next_bp_err)
+ self:set_err_inputs(bp_err)
+ self:set_inputs(input)
+ self:set_outputs(output)
+ for i = #self.queue, 1, -1 do
+ local ref = self.queue[i]
+ -- print(ref.layer.id)
+ ref.layer:back_propagate(ref.err_inputs, ref.err_outputs, ref.inputs, ref.outputs)
+ end
+end
+
+function DAGLayer:get_params()
+ local param_repos = {}
+ for id, ref in pairs(self.queue) do
+ table.insert(param_repos, ref.layer:get_params())
+ end
+ return nerv.ParamRepo.merge(param_repos)
+end