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local DAGLayerT = nerv.class("nerv.DAGLayerT", "nerv.LayerT")
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 = {
id = layer.id,
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 DAGLayerT:__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 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}
if ref_to.inputs[1] == nil then
ref_to.inputs[1] = {}
end
if ref_to.inputs[1][port_to] ~= nil then
nerv.error("port(%d) for layer(%s) already attached", port_to, to)
end
ref_to.inputs[1][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}
if ref_from.outputs[1] == nil then
ref_from.outputs[1] = {}
end
if ref_from.outputs[1][port_from] ~= nil then
nerv.error("port(%d) for layer(%s) already attached", port_from, from)
end
ref_from.outputs[1] = {}
ref_from.outputs[1][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 %s", queue[i].layer, 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.id = id
self.dim_in = dim_in
self.dim_out = dim_out
self.parsed_conn = parsed_conn
self.queue = queue
self.gconf = global_conf
end
function DAGLayerT:init(batch_size, chunk_size)
nerv.info("initing DAGLayerT %s...", self.id)
if chunk_size == nil then
chunk_size = 1
nerv.info("(Initing DAGLayerT) chunk_size is nil, setting it to default 1\n")
end
self.chunk_size = chunk_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 dim = 1
if output_dim[port_from] > 0 then
dim = output_dim[port_from]
end
for t = 1, chunk_size do
local mid = self.gconf.cumat_type(batch_size, dim)
local err_mid = mid:create()
if ref_from.outputs[t] == nil then
ref_from.outputs[t] = {}
end
if ref_to.inputs[t] == nil then
ref_to.inputs[t] = {}
end
if ref_to.err_outputs[t] == nil then
ref_to.err_outputs[t] = {}
end
if ref_from.err_inputs[t] == nil then
ref_from.err_inputs[t] = {}
end
ref_from.outputs[t][port_from] = mid
ref_to.inputs[t][port_to] = mid
ref_from.err_inputs[t][port_from] = err_mid
ref_to.err_outputs[t][port_to] = err_mid
end
end
for id, ref in pairs(self.layers) do
for i = 1, ref.input_len do
if ref.inputs[1][i] == nil then --peek at time 1
nerv.error("dangling input port %d of layer %s", i, id)
end
end
for i = 1, ref.output_len do
if ref.outputs[1][i] == nil then --peek at time 1
nerv.error("dangling output port %d of layer %s", i, id)
end
end
-- initialize sub layers
ref.layer:init(batch_size, chunk_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 DAGLayerT:batch_resize(batch_size, chunk_size)
if chunk_size == nil then
chunk_size = 1
end
if batch_size ~= self.gconf.batch_size
or chunk_size ~= self.gconf.chunk_size then
nerv.printf("warn: in DAGLayerT:batch_resize, the batch_size ~= gconf.batch_size, or chunk_size ~= gconf.chunk_size")
end
self.gconf.batch_size = batch_size
self.gconf.chunk_size = chunk_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()
for t = 1, chunk_size do
if ref_from.outputs[t] == nil then
ref_from.outputs[t] = {}
end
if ref_to.inputs[t] == nil then
ref_to.inputs[t] = {}
end
if ref_from.err_outputs[t] == nil then
ref_from.err_outputs[t] = {}
end
if ref_from.err_inputs[t] == nil then
ref_from.err_inputs[t] = {}
end
local mid = self.gconf.cumat_type(batch_size, dim)
local err_mid = mid:create()
ref_from.outputs[t][port_from] = mid
ref_to.inputs[t][port_to] = mid
ref_from.err_inputs[t][port_from] = err_mid
ref_to.err_outputs[t][port_to] = err_mid
end
end
for id, ref in pairs(self.layers) do
ref.layer:batch_resize(batch_size, chunk_size)
end
collectgarbage("collect")
end
function DAGLayerT:set_inputs(input, t)
for i = 1, #self.dim_in do
if input[i] == nil then
nerv.error("some input is not provided");
end
local layer = self.inputs[i][1]
local port = self.inputs[i][2]
if layer.inputs[t] == nil then
layer.inputs[t] = {}
end
layer.inputs[t][port] = input[i]
end
end
function DAGLayerT:set_outputs(output, t)
for i = 1, #self.dim_out do
if output[i] == nil then
nerv.error("some output is not provided");
end
local layer = self.outputs[i][1]
local port = self.outputs[i][2]
if layer.outputs[t] == nil then
layer.outputs[t] = {}
end
layer.outputs[t][port] = output[i]
end
end
function DAGLayerT:set_err_inputs(bp_err, t)
for i = 1, #self.dim_out do
local layer = self.outputs[i][1]
local port = self.outputs[i][2]
if layer.err_inputs[t] == nil then
layer.err_inputs[t] = {}
end
layer.err_inputs[t][port] = bp_err[i]
end
end
function DAGLayerT:set_err_outputs(next_bp_err, t)
for i = 1, #self.dim_in do
local layer = self.inputs[i][1]
local port = self.inputs[i][2]
if layer.err_outputs[t] == nil then
layer.err_outputs[t] = {}
end
layer.err_outputs[t][port] = next_bp_err[i]
end
end
function DAGLayerT:update(bp_err, input, output, t)
if t == nil then
t = 1
end
self:set_err_inputs(bp_err, t)
self:set_inputs(input, t)
self:set_outputs(output, t)
for id, ref in pairs(self.queue) do
ref.layer:update(ref.err_inputs[t], ref.inputs[t], ref.outputs[t], t)
end
end
function DAGLayerT:propagate(input, output, t)
if t == nil then
t = 1
end
self:set_inputs(input, t)
self:set_outputs(output, t)
local ret = false
for i = 1, #self.queue do
local ref = self.queue[i]
--print("debug DAGLAyerT:propagate", ref.id, t)
ret = ref.layer:propagate(ref.inputs[t], ref.outputs[t], t)
end
return ret
end
function DAGLayerT:back_propagate(bp_err, next_bp_err, input, output, t)
if t == nil then
t = 1
end
self:set_err_outputs(next_bp_err, t)
self:set_err_inputs(bp_err, t)
self:set_inputs(input, t)
self:set_outputs(output, t)
for i = #self.queue, 1, -1 do
local ref = self.queue[i]
ref.layer:back_propagate(ref.err_inputs[t], ref.err_outputs[t], ref.inputs[t], ref.outputs[t], t)
end
end
function DAGLayerT: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
DAGLayerT.PORT_TYPES = {
INPUT = {},
OUTPUT = {},
ERR_INPUT = {},
ERR_OUTPUT = {}
}
function DAGLayerT:get_intermediate(id, port_type)
if id == "<input>" or id == "<output>" then
nerv.error("an actual real layer id is expected")
end
local layer = self.layers[id]
if layer == nil then
nerv.error("layer id %s not found", id)
end
if port_type == DAGLayerT.PORT_TYPES.INPUT then
return layer.inputs
elseif port_type == DAGLayerT.PORT_TYPES.OUTPUT then
return layer.outputs
elseif port_type == DAGLayerT.PORT_TYPES.ERR_INPUT then
return layer.err_inputs
elseif port_type == DAGLayerT.PORT_TYPES.ERR_OUTPUT then
return layer.err_outputs
end
nerv.error("unrecognized port type")
end
|