still working on dagL_T

2 files changed, 29 insertions, 23 deletions

diff --git a/nerv/examples/lmptb/tnn/layer_dag_t.lua b/nerv/examples/lmptb/tnn/layer_dag_t.lua
index 1a89816..cd5fba7 100644
--- a/nerv/examples/lmptb/tnn/layer_dag_t.lua
+++ b/nerv/examples/lmptb/tnn/layer_dag_t.lua
@@ -22,6 +22,7 @@ local function discover(id, layers, layer_repo)
         local layer = layer_repo:get_layer(id)
         local dim_in, dim_out = layer:get_dim()
         ref = {
+            id = layer.id, 
             layer = layer,
             inputs = {},
             outputs = {},
@@ -51,26 +52,33 @@ function DAGLayerT:__init(id, global_conf, layer_conf)
         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
+            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}
-            ref_from.outputs[port_from] = outputs -- just a place holder
+            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()
@@ -134,10 +142,10 @@ function DAGLayerT:__init(id, global_conf, layer_conf)
 end
 
 function DAGLayerT:init(batch_size, chunk_size)
-    nerv.printf("initing DAGLayerT\n")
+    nerv.info("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")
+        nerv.info("(Initing DAGLayerT) chunk_size is nil, setting it to default 1\n")
     end
 
     self.chunk_size = chunk_size
@@ -179,12 +187,12 @@ function DAGLayerT:init(batch_size, chunk_size)
     end
     for id, ref in pairs(self.layers) do
         for i = 1, ref.input_len do
-            if ref.inputs[i] == nil then
+            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[i] == nil then
+            if ref.outputs[1][i] == nil then --peek at time 1
                 nerv.error("dangling output port %d of layer %s", i, id)
             end
         end
@@ -258,6 +266,7 @@ function DAGLayerT:set_inputs(input, t)
         end
         local layer = self.inputs[i][1]
         local port = self.inputs[i][2]
+        
         layer.inputs[t][port] = input[i]
     end
 end
@@ -296,10 +305,8 @@ function DAGLayerT:update(bp_err, input, output, t)
     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)
+        ref.layer:update(ref.err_inputs[t], ref.inputs[t], ref.outputs[t], t)
     end
 end
 
@@ -312,8 +319,7 @@ function DAGLayerT:propagate(input, output)
     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)
+        ret = ref.layer:propagate(ref.inputs[t], ref.outputs[t], t)
     end
     return ret
 end
@@ -328,8 +334,7 @@ function DAGLayerT:back_propagate(bp_err, next_bp_err, input, output, 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)
+        ref.layer:back_propagate(ref.err_inputs[t], ref.err_outputs[t], ref.inputs[t], ref.outputs[t], t)
     end
 end
 
diff --git a/nerv/examples/lmptb/unfold_ptb_main.lua b/nerv/examples/lmptb/unfold_ptb_main.lua
index 6c4ead3..5affadf 100644
--- a/nerv/examples/lmptb/unfold_ptb_main.lua
+++ b/nerv/examples/lmptb/unfold_ptb_main.lua
@@ -8,6 +8,7 @@
 require 'lmptb.lmvocab'
 require 'lmptb.lmfeeder'
 require 'lmptb.lmutil'
+require 'tnn.init'
 nerv.include('lmptb/layer/init.lua')
 
 --[[global function rename]]--
@@ -70,7 +71,7 @@ function prepare_layers(global_conf, paramRepo)
         },
 
         ["nerv.SelectLinearLayer"] = {
-            ["selectL1"] = {{["ltp"] = "ltp_ih"}, {["dim_in"] = {1}, ["dim_out"] = {global_conf.hidden_size}}},
+            ["selectL1"] = {{["ltp"] = "ltp_ih"}, {["dim_in"] = {1}, ["dim_out"] = {global_conf.hidden_size}, ["vocab"] = global_conf.vocab}},
         },
 
         ["nerv.SigmoidLayer"] = {
@@ -90,7 +91,7 @@ function prepare_layers(global_conf, paramRepo)
     for i = 1, global_conf.bptt do
         layers["nerv.IndRecurrentLayer"]["recurrentL" .. (i + 1)] = recurrentLconfig 
         layers["nerv.SigmoidLayer"]["sigmoidL" .. (i + 1)] = {{}, {["dim_in"] = {global_conf.hidden_size}, ["dim_out"] = {global_conf.hidden_size}}}
-        layers["nerv.SelectLinearLayer"]["selectL" .. (i + 1)] = {{["ltp"] = "ltp_ih"}, {["dim_in"] = {1}, ["dim_out"] = {global_conf.hidden_size}}}
+        layers["nerv.SelectLinearLayer"]["selectL" .. (i + 1)] = {{["ltp"] = "ltp_ih"}, {["dim_in"] = {1}, ["dim_out"] = {global_conf.hidden_size}, ["vocab"] = global_conf.vocab}}
     end
     local layerRepo = nerv.LayerRepo(layers, paramRepo, global_conf)
     printf("%s preparing layers end.\n", global_conf.sche_log_pre)
@@ -138,7 +139,7 @@ function prepare_dagLayer(global_conf, layerRepo)
         printf("\t%s->%s\n", key, value)
     end
 
-    local dagL = nerv.DAGLayer("dagL", global_conf, {["dim_in"] = dim_in_t, ["dim_out"] = {1}, ["sub_layers"] = layerRepo,
+    local dagL = nerv.DAGLayerT("dagL", global_conf, {["dim_in"] = dim_in_t, ["dim_out"] = {1}, ["sub_layers"] = layerRepo,
             ["connections"] = connections_t, 
         })
     dagL:init(global_conf.batch_size)
@@ -277,7 +278,7 @@ if (set == "ptb") then
     global_conf = {
         lrate = 1, wcost = 1e-6, momentum = 0,
         cumat_type = nerv.CuMatrixFloat,
-        mmat_type = nerv.CuMatrixFloat,
+        mmat_type = nerv.MMatrixFloat,
 
         hidden_size = 200,
         batch_size = 10,