completed layerdag_t, now testing...

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'