diff options
Diffstat (limited to 'nerv/tnn/layer_dag_t.lua')
| -rw-r--r-- | nerv/tnn/layer_dag_t.lua | 386 |
1 files changed, 0 insertions, 386 deletions
diff --git a/nerv/tnn/layer_dag_t.lua b/nerv/tnn/layer_dag_t.lua deleted file mode 100644 index b651f4e..0000000 --- a/nerv/tnn/layer_dag_t.lua +++ /dev/null @@ -1,386 +0,0 @@ -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 |