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local TNN = nerv.class("nerv.TNN")
local function parse_id(str)
--used to parse layerid[portid],time
local id, port, time, _
_, _, id, port, time = string.find(str, "([a-zA-Z0-9_]+)%[([0-9]+)%][,]*([0-9]*)")
if id == nil or port == nil then
_, _, id, port, time = string.find(str, "(.+)%[([0-9]+)%][,]*([0-9]*)")
if not (id == "<input>" or id == "<output>") then
nerv.error("wrong format of connection id")
end
end
--print(str, id, port, time)
port = tonumber(port)
if (time == nil) then
time = 0
else
time = tonumber(time)
end
--now time don't need to be parsed
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,
id = layer.id,
inputs_m = {}, --storage for computation, inputs_m[time][port]
inputs_b = {}, --inputs_g[time][port], whether this input can been computed
inputs_matbak_p = {}, --which is a back-up space to handle some cross-border computation, inputs_p_matbak[port]
outputs_m = {},
outputs_b = {},
err_inputs_m = {},
err_inputs_matbak_p = {}, --which is a back-up space to handle some cross-border computation
err_inputs_b = {},
err_outputs_m = {},
err_outputs_b = {},
i_conns_p = {}, --list of inputing connections
o_conns_p = {}, --list of outputing connections
dim_in = dim_in, --list of dimensions of ports
dim_out = dim_out,
}
layers[id] = ref
end
return ref
end
nerv.TNN.FC = {} --flag const
nerv.TNN.FC.SEQ_START = 4
nerv.TNN.FC.SEQ_END = 8
nerv.TNN.FC.HAS_INPUT = 1
nerv.TNN.FC.HAS_LABEL = 2
nerv.TNN.FC.SEQ_NORM = bit.bor(nerv.TNN.FC.HAS_INPUT, nerv.TNN.FC.HAS_LABEL) --This instance have both input and label
function TNN.make_initial_store(st, p, dim, batch_size, chunk_size, global_conf, st_c, p_c, t_c)
--Return a table of matrix storage from time (1-chunk_size)..(2*chunk_size)
if (type(st) ~= "table") then
nerv.error("st should be a table")
end
for i = 1 - chunk_size - 1, chunk_size * 2 + 1 do --intentionally allocated more time, should be [1-chunk_size, chunk_size*2]
if (st[i] == nil) then
st[i] = {}
end
st[i][p] = global_conf.cumat_type(batch_size, dim)
st[i][p]:fill(0)
if (st_c ~= nil) then
if (st_c[i + t_c] == nil) then
st_c[i + t_c] = {}
end
st_c[i + t_c][p_c] = st[i][p]
end
end
end
function TNN:out_of_feedrange(t) --out of chunk, or no input, for the current feed
if (t < 1 or t > self.chunk_size) then
return true
end
if (self.feeds_now.flagsPack_now[t] == 0 or self.feeds_now.flagsPack_now[t] == nil) then
return true
end
return false
end
function TNN:__init(id, global_conf, layer_conf)
self.clip_t = layer_conf.clip_t
if self.clip_t > 0 then
nerv.info("tnn(%s) will clip gradient across time with %f...", id, self.clip_t)
end
local layers = {}
local inputs_p = {} --map:port of the TNN to layer ref and port
local outputs_p = {}
local dim_in = layer_conf.dim_in
local dim_out = layer_conf.dim_out
local parsed_conns = {}
local _
for _, ll in pairs(layer_conf.connections) do
local id_from, port_from = parse_id(ll[1])
local id_to, port_to = parse_id(ll[2])
local time_to = ll[3]
print(id_from, id_to, time_to)
local ref_from = discover(id_from, layers, layer_conf.sub_layers)
local ref_to = discover(id_to, layers, layer_conf.sub_layers)
if (id_from == "<input>") then
if (dim_in[port_from] ~= ref_to.dim_in[port_to] or time_to ~= 0) then
nerv.error("mismatch dimension or wrong time %s,%s,%d", ll[1], ll[2], ll[3])
end
inputs_p[port_from] = {["ref"] = ref_to, ["port"] = port_to}
ref_to.inputs_m[port_to] = {} --just a place holder
elseif (id_to == "<output>") then
if (dim_out[port_to] ~= ref_from.dim_out[port_from] or time_to ~= 0) then
nerv.error("mismatch dimension or wrong time %s,%s,%d", ll[1], ll[2], ll[3])
end
outputs_p[port_to] = {["ref"] = ref_from, ["port"] = port_from}
ref_from.outputs_m[port_from] = {} --just a place holder
else
local conn_now = {
["src"] = {["ref"] = ref_from, ["port"] = port_from},
["dst"] = {["ref"] = ref_to, ["port"] = port_to},
["time"] = time_to
}
if (ref_to.dim_in[port_to] ~= ref_from.dim_out[port_from]) then
nerv.error("mismatch dimension or wrong time %s,%s,%d", ll[1], ll[2], ll[3])
end
table.insert(parsed_conns, conn_now)
ref_to.i_conns_p[conn_now.dst.port] = conn_now
ref_from.o_conns_p[conn_now.src.port] = conn_now
end
end
for id, ref in pairs(layers) do
print(id, "#dim_in:", #ref.dim_in, "#dim_out:", #ref.dim_out, "#i_conns_p:", #ref.i_conns_p, "#o_conns_p", #ref.o_conns_p)
end
self.layers = layers
self.inputs_p = inputs_p
self.outputs_p = outputs_p
self.id = id
self.dim_in = dim_in
self.dim_out = dim_out
self.parsed_conns = parsed_conns
self.gconf = global_conf
end
function TNN:init(batch_size, chunk_size)
self.batch_size = batch_size
self.chunk_size = chunk_size
for i, conn in ipairs(self.parsed_conns) do --init storage for connections inside the NN
local _, output_dim
local ref_from, port_from, ref_to, port_to, time
ref_from, port_from = conn.src.ref, conn.src.port
ref_to, port_to = conn.dst.ref, conn.dst.port
time = conn.time
local dim = ref_from.dim_out[port_from]
if (dim == 0) then
nerv.error("layer %s has a zero dim port", ref_from.layer.id)
end
print("TNN initing storage", ref_from.layer.id, "->", ref_to.layer.id)
ref_to.inputs_matbak_p[port_to] = self.gconf.cumat_type(batch_size, dim)
self.make_initial_store(ref_from.outputs_m, port_from, dim, batch_size, chunk_size, self.gconf, ref_to.inputs_m, port_to, time)
ref_from.err_inputs_matbak_p[port_from] = self.gconf.cumat_type(batch_size, dim)
self.make_initial_store(ref_from.err_inputs_m, port_from, dim, batch_size, chunk_size, self.gconf, ref_to.err_outputs_m, port_to, time)
end
self.outputs_m = {}
self.err_inputs_m = {}
for i = 1, #self.dim_out do --Init storage for output ports
local ref = self.outputs_p[i].ref
local p = self.outputs_p[i].port
self.make_initial_store(ref.outputs_m, p, self.dim_out[i], batch_size, chunk_size, self.gconf, self.outputs_m, i, 0)
self.make_initial_store(ref.err_inputs_m, p, self.dim_out[i], batch_size, chunk_size, self.gconf, self.err_inputs_m, i, 0)
end
self.inputs_m = {}
self.err_outputs_m = {}
for i = 1, #self.dim_in do --Init storage for input ports
local ref = self.inputs_p[i].ref
local p = self.inputs_p[i].port
self.make_initial_store(ref.inputs_m, p, self.dim_in[i], batch_size, chunk_size, self.gconf, self.inputs_m, i, 0)
self.make_initial_store(ref.err_outputs_m, p, self
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