working on TNN:net_propagate

1 files changed, 71 insertions, 22 deletions

diff --git a/nerv/examples/lmptb/rnn/tnn.lua b/nerv/examples/lmptb/rnn/tnn.lua
index 3f192b5..8037918 100644
--- a/nerv/examples/lmptb/rnn/tnn.lua
+++ b/nerv/examples/lmptb/rnn/tnn.lua
@@ -32,10 +32,14 @@ local function discover(id, layers, layer_repo)
         local dim_in, dim_out = layer:get_dim()
         ref = {
             layer = layer,
-            inputs_m = {}, --storage for computation, inputs_m[port][time]
+            inputs_m = {}, --storage for computation, inputs_m[time][port]
+            inputs_b = {}, --inputs_g[time][port], whether this input can been computed
             outputs_m = {},
+            outputs_b = {},
             err_inputs_m = {},
+            err_inputs_b = {},
             err_outputs_m = {},
+            err_outputs_b = {},
             conns_i = {}, --list of inputing connections
             conns_o = {}, --list of outputing connections
             dim_in = dim_in, --list of dimensions of ports
@@ -53,7 +57,7 @@ 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 DAGLayer.makeInitialStore(st, p, dim, batch_size, chunk_size, global_conf, st_c, p_c)
+function TNN.makeInitialStore(st, p, dim, batch_size, chunk_size, global_conf, st_c, p_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")
@@ -73,7 +77,7 @@ function DAGLayer.makeInitialStore(st, p, dim, batch_size, chunk_size, global_co
     end
 end
 
-function DAGLayer:__init(id, global_conf, layer_conf)
+function TNN:__init(id, global_conf, layer_conf)
     local layers = {}
     local inputs_p = {} --map:port of the TDAGLayer to layer ref and port
     local outputs_p = {}
@@ -129,7 +133,9 @@ function DAGLayer:__init(id, global_conf, layer_conf)
     self.gconf = global_conf
 end
 
-function DAGLayer:init(batch_size, chunk_size)
+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
@@ -141,8 +147,10 @@ function DAGLayer:init(batch_size, chunk_size)
         end
 
         print("TNN initing storage", ref_from.layer.id, "->", ref_to.layer.id)
-        self.makeInitialStore(ref_from.outputs_m, port_from, dim, batch_size, chunk_size, global_conf, ref_to.inputs_m, port_to)
-        self.makeInitialStore(ref_from.err_inputs_m, port_from, dim, batch_size, chunk_size, global_conf, ref_to.err_outputs_m, port_to)
+        ref_to.inputs_p_matbak[port_to] = self.gconf.cumat_type(batch_size, dim)
+        self.makeInitialStore(ref_from.outputs_m, port_from, dim, batch_size, chunk_size, self.gconf, ref_to.inputs_m, port_to)
+        ref_from.err_inputs_p_matbak[port_from] =  self.gconf.cumat_type(batch_size, dim)
+        self.makeInitialStore(ref_from.err_inputs_m, port_from, dim, batch_size, chunk_size, self.gconf, ref_to.err_outputs_m, port_to)
         
     end
 
@@ -180,13 +188,18 @@ function DAGLayer:init(batch_size, chunk_size)
     end
 
     local flags_now = {}
+    local flagsPack_now = {}
     for i = 1, chunk_size do
         flags_now[i] = {}
+        flagsPack_now[i] = 0
     end
 
     self.feeds_now = {} --feeds is for the reader to fill
     self.feeds_now.inputs_m = self.inputs_m
     self.feeds_now.flags_now = flags_now
+    self.feeds_now.flagsPack_now = flagsPack_now
+
+    self:flush_all()
 end
 
 --[[
@@ -218,13 +231,61 @@ function DAGLayer:batch_resize(batch_size)
 end
 ]]--
 
+function TNN:flush_all() --flush all history and activation
+    local _, ref
+    for _, ref in pairs(self.layers) do
+        for i = 1, #ref.dim_in do
+            for t = 1 - self.chunk_size, self.chunk_size * 2 do 
+                ref.inputs_m[t][i]:fill(self.gconf.nn_act_default)
+                if (ref.inputs_b[t] == nil) then
+                    ref.inputs_b[t] = {}
+                end
+                ref.inputs_b[t][i] = false
+                ref.err_outputs_m[t][i]:fill(0)
+                if (ref.err_outputs_b[t] == nil) then
+                    ref.err_outputs_b[t] = {}
+                end
+                ref.err_outputs_b[t][i] = false
+            end
+        end
+        for i = 1, #ref.dim_out do
+            for t = 1 - self.chunk_size, self.chunk_size * 2 do 
+                ref.outputs_m[t][i]:fill(self.gconf.nn_act_default)
+                if (ref.outputs_b[t] == nil) then
+                    ref.outputs_b[t] = {}
+                end
+                ref.outputs_b[t][i] = false
+                ref.err_inputs_m[t][i]:fill(0)
+                if (ref.err_inputs_b[t] == nil) then
+                    ref.err_inputs_b[t] = {}
+                end
+                ref.err_inputs_b[t][i] = false
+            end
+        end
+    end
+end
+
 --reader: some reader
 --Returns: bool, whether has new feed
 --Returns: feeds, a table that will be filled with the reader's feeds
-function DAGLayer:getFeedFromReader(reader)
-    local feeds = self.feeds_now
-    local got_new = reader:get_batch(feeds)
-    return got_new, feeds
+function TNN:getFeedFromReader(reader)
+    local feeds_now = self.feeds_now
+    local got_new = reader:get_batch(feeds_now)
+    return got_new, feeds_now
+end
+
+function TNN:net_propagate() --propagate according to feeds_now
+    local feeds_now = self.feeds_now
+    for t = 1, chunk_size do
+        if (bit.band(feeds_now.flagsPack_now[t], nerv.TNN.FC.HAS_INPUT) > 0) then
+            for i = 1, #self.dim_in do
+                local ref = inputs_p[i].ref
+                local p = inputs_p[i].port
+                ref.inputs_b[t][p] = true
+            end
+            --TODO
+        end
+    end
 end
 
 function DAGLayer:update(bp_err, input, output)
@@ -238,18 +299,6 @@ function DAGLayer:update(bp_err, input, output)
     end
 end
 
-function DAGLayer:propagate(input, output)
-    self:set_inputs(input)
-    self:set_outputs(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)
-    end
-    return ret
-end
-
 function DAGLayer:back_propagate(bp_err, next_bp_err, input, output)
     self:set_err_outputs(next_bp_err)
     self:set_err_inputs(bp_err)