diff options
| -rw-r--r-- | nerv/examples/lmptb/tnn/init.lua (renamed from nerv/examples/lmptb/rnn/init.lua) | 1 | ||||
| -rw-r--r-- | nerv/examples/lmptb/tnn/layer_dag_t.lua | 369 | ||||
| -rw-r--r-- | nerv/examples/lmptb/tnn/layers/gate_fff.lua (renamed from nerv/examples/lmptb/rnn/layers/gate_fff.lua) | 0 | ||||
| -rw-r--r-- | nerv/examples/lmptb/tnn/layersT/softmax_ce_t.lua (renamed from nerv/examples/lmptb/rnn/layersT/softmax_ce_t.lua) | 0 | ||||
| -rw-r--r-- | nerv/examples/lmptb/tnn/tnn.lua (renamed from nerv/examples/lmptb/rnn/tnn.lua) | 0 | ||||
| -rw-r--r-- | nerv/examples/lmptb/tnn_ptb_main.lua | 2 |
6 files changed, 371 insertions, 1 deletions
diff --git a/nerv/examples/lmptb/rnn/init.lua b/nerv/examples/lmptb/tnn/init.lua index 6507582..a069527 100644 --- a/nerv/examples/lmptb/rnn/init.lua +++ b/nerv/examples/lmptb/tnn/init.lua @@ -44,3 +44,4 @@ end nerv.include('tnn.lua') nerv.include('layersT/softmax_ce_t.lua') nerv.include('layers/gate_fff.lua') +nerv.include('layer_dag_t.lua') diff --git a/nerv/examples/lmptb/tnn/layer_dag_t.lua b/nerv/examples/lmptb/tnn/layer_dag_t.lua new file mode 100644 index 0000000..1a89816 --- /dev/null +++ b/nerv/examples/lmptb/tnn/layer_dag_t.lua @@ -0,0 +1,369 @@ +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 = { + 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 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 %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.printf("initing DAGLayerT\n") + if chunk_size == nil then + chunk_size = 1 + nerv.printf("(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[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, 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] + 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] + 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] + 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] + 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) + -- print("update") + for id, ref in pairs(self.queue) do + -- print(ref.layer.id) + ref.layer:update(ref.err_inputs, ref.inputs, ref.outputs, t) + end +end + +function DAGLayerT:propagate(input, output) + 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(ref.layer.id) + ret = ref.layer:propagate(ref.inputs, ref.outputs, 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] + -- print(ref.layer.id) + ref.layer:back_propagate(ref.err_inputs, ref.err_outputs, ref.inputs, ref.outputs, 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 diff --git a/nerv/examples/lmptb/rnn/layers/gate_fff.lua b/nerv/examples/lmptb/tnn/layers/gate_fff.lua index 751dde1..751dde1 100644 --- a/nerv/examples/lmptb/rnn/layers/gate_fff.lua +++ b/nerv/examples/lmptb/tnn/layers/gate_fff.lua diff --git a/nerv/examples/lmptb/rnn/layersT/softmax_ce_t.lua b/nerv/examples/lmptb/tnn/layersT/softmax_ce_t.lua index dddb05a..dddb05a 100644 --- a/nerv/examples/lmptb/rnn/layersT/softmax_ce_t.lua +++ b/nerv/examples/lmptb/tnn/layersT/softmax_ce_t.lua diff --git a/nerv/examples/lmptb/rnn/tnn.lua b/nerv/examples/lmptb/tnn/tnn.lua index c2e397c..c2e397c 100644 --- a/nerv/examples/lmptb/rnn/tnn.lua +++ b/nerv/examples/lmptb/tnn/tnn.lua diff --git a/nerv/examples/lmptb/tnn_ptb_main.lua b/nerv/examples/lmptb/tnn_ptb_main.lua index 3096a3f..66c7317 100644 --- a/nerv/examples/lmptb/tnn_ptb_main.lua +++ b/nerv/examples/lmptb/tnn_ptb_main.lua @@ -2,7 +2,7 @@ require 'lmptb.lmvocab' require 'lmptb.lmfeeder' require 'lmptb.lmutil' require 'lmptb.layer.init' -require 'rnn.init' +require 'tnn.init' require 'lmptb.lmseqreader' require 'lm_trainer' |