first small tnn test seems to work

1 files changed, 165 insertions, 35 deletions

diff --git a/nerv/examples/lmptb/rnn/tnn.lua b/nerv/examples/lmptb/rnn/tnn.lua
index 8037918..460fcc4 100644
--- a/nerv/examples/lmptb/rnn/tnn.lua
+++ b/nerv/examples/lmptb/rnn/tnn.lua
@@ -34,9 +34,11 @@ local function discover(id, layers, layer_repo)
             layer = layer,
             inputs_m = {}, --storage for computation, inputs_m[time][port]
             inputs_b = {}, --inputs_g[time][port], whether this input can been computed
+            inputs_p_matbak = {}, --which is a back-up space to handle some cross-border computation, inputs_p_matbak[port]
             outputs_m = {},
             outputs_b = {},
             err_inputs_m = {},
+            err_inputs_p_matbak = {}, --which is a back-up space to handle some cross-border computation
             err_inputs_b = {},
             err_outputs_m = {},
             err_outputs_b = {},
@@ -57,26 +59,36 @@ 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 TNN.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, t_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")
     end
-    for i = 1 - chunk_size, chunk_size * 2 do
+    for i = 1 - chunk_size - 1, chunk_size * 2 + 1 do --intentionally allocated more time, should be [1-chunk_size, chunk_size*2]
         if (st[i] == nil) then
             st[i] = {}
         end
         st[i][p] = global_conf.cumat_type(batch_size, dim)
         st[i][p]:fill(0)
         if (st_c ~= nil) then
-            if (st_c[i] == nil) then
-                st_c[i] = {}
+            if (st_c[i + t_c] == nil) then
+                st_c[i + t_c] = {}
             end
-            st_c[i][p_c] = st[i][p]
+            st_c[i + t_c][p_c] = st[i][p]
         end
     end
 end
 
+function TNN:outOfFeedRange(t) --out of chunk, or no input, for the current feed
+    if (t < 1 or t > self.chunk_size) then
+        return true
+    end
+    if (self.feeds_now.flagsPack_now[t] == 0 or self.feeds_now.flagsPack_now[t] == nil) then
+        return true
+    end
+    return false
+end
+
 function TNN:__init(id, global_conf, layer_conf)
     local layers = {}
     local inputs_p = {} --map:port of the TDAGLayer to layer ref and port
@@ -109,7 +121,7 @@ function TNN:__init(id, global_conf, layer_conf)
             outputs_p[port_to] = {["ref"] = ref_from, ["port"] = port_from}
             ref_from.outputs_m[port_from] = {} --just a place holder
         else
-            conn_now = { 
+            local conn_now = { 
                 ["src"] = {["ref"] = ref_from, ["port"] = port_from},
                 ["dst"] = {["ref"] = ref_to, ["port"] = port_to},
                 ["time"] = time_to
@@ -138,9 +150,11 @@ function TNN:init(batch_size, chunk_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
+        local ref_from, port_from, ref_to, port_to, time
         ref_from, port_from = conn.src.ref, conn.src.port
         ref_to, port_to = conn.dst.ref, conn.dst.port
+        time = conn.time
+
         local dim = ref_from.dim_out[port_from]
         if (dim == 0) then
             nerv.error("layer %s has a zero dim port", ref_from.layer.id)
@@ -148,9 +162,9 @@ function TNN:init(batch_size, chunk_size)
 
         print("TNN initing storage", ref_from.layer.id, "->", ref_to.layer.id)
         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)
+        self.makeInitialStore(ref_from.outputs_m, port_from, dim, batch_size, chunk_size, self.gconf, ref_to.inputs_m, port_to, time)
         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)
+        self.makeInitialStore(ref_from.err_inputs_m, port_from, dim, batch_size, chunk_size, self.gconf, ref_to.err_outputs_m, port_to, time)
         
     end
 
@@ -159,8 +173,8 @@ function TNN:init(batch_size, chunk_size)
     for i = 1, #self.dim_out do --Init storage for output ports 
         local ref = self.outputs_p[i].ref
         local p = self.outputs_p[i].port
-        self.makeInitialStore(ref.outputs_m, p, self.dim_out[i], batch_size, chunk_size, self.gconf, self.outputs_m, i)
-        self.makeInitialStore(ref.err_inputs_m, p, self.dim_out[i], batch_size, chunk_size, self.gconf, self.err_inputs_m, i)
+        self.makeInitialStore(ref.outputs_m, p, self.dim_out[i], batch_size, chunk_size, self.gconf, self.outputs_m, i, 0)
+        self.makeInitialStore(ref.err_inputs_m, p, self.dim_out[i], batch_size, chunk_size, self.gconf, self.err_inputs_m, i, 0)
     end 
 
     self.inputs_m = {}
@@ -168,8 +182,8 @@ function TNN:init(batch_size, chunk_size)
     for i = 1, #self.dim_in do --Init storage for input ports 
         local ref = self.inputs_p[i].ref
         local p = self.inputs_p[i].port
-        self.makeInitialStore(ref.inputs_m, p, self.dim_in[i], batch_size, chunk_size, self.gconf, self.inputs_m, i)
-        self.makeInitialStore(ref.err_outputs_m, p, self.dim_in[i], batch_size, chunk_size, self.gconf, self.err_outputs_m, i)
+        self.makeInitialStore(ref.inputs_m, p, self.dim_in[i], batch_size, chunk_size, self.gconf, self.inputs_m, i, 0)
+        self.makeInitialStore(ref.err_outputs_m, p, self.dim_in[i], batch_size, chunk_size, self.gconf, self.err_outputs_m, i, 0)
     end 
 
     for id, ref in pairs(self.layers) do --Calling init for child layers
@@ -274,45 +288,161 @@ function TNN:getFeedFromReader(reader)
     return got_new, feeds_now
 end
 
+function TNN:moveRightToNextMB() --move output history activations of 1..chunk_size to 1-chunk_size..0 
+    for t = self.chunk_size, 1, -1 do
+        for id, ref in pairs(self.layers) do
+            for p = 1, #ref.dim_out do
+                ref.outputs_m[t - self.chunk_size][p]:copy_fromd(ref.outputs_m[t][p])
+            end
+        end
+    end
+end
+
 function TNN:net_propagate() --propagate according to feeds_now
+    for t = 1, self.chunk_size, 1 do
+        for id, ref in pairs(self.layers) do
+            for p = 1, #ref.dim_out do
+                ref.outputs_b[t][p] = false
+            end
+            for p = 1, #ref.dim_in do
+                ref.inputs_b[t][p] = false
+            end
+        end
+    end
+
     local feeds_now = self.feeds_now
-    for t = 1, chunk_size do
+    for t = 1, self.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
+                local ref = self.inputs_p[i].ref
+                local p = self.inputs_p[i].port
                 ref.inputs_b[t][p] = true
+                self:propagate_dfs(ref, t)
             end
-            --TODO
         end
     end
 end
 
-function DAGLayer:update(bp_err, input, output)
-    self:set_err_inputs(bp_err)
-    self:set_inputs(input)
-    self:set_outputs(output)
-    -- print("update")
-    for id, ref in pairs(self.queue) do
-        -- print(ref.layer.id)
-        ref.layer:update(ref.err_inputs, ref.inputs, ref.outputs)
+--ref: the TNN_ref of a layer
+--t: the current time to propagate
+function TNN:propagate_dfs(ref, t)
+    if (self:outOfFeedRange(t)) then
+        return
+    end
+    if (ref.outputs_b[t][1] == true) then --already propagated, 1 is just a random port
+        return
+    end
+
+    --print("debug dfs", ref.layer.id, t)
+
+    local flag = true --whether have all inputs
+    for _, conn in pairs(ref.conns_i) do
+        local p = conn.dst.port
+        if (not (ref.inputs_b[t][p] or self:outOfFeedRange(t - conn.time))) then
+            flag = false
+            break
+        end
+    end
+    if (flag == false) then
+        return
+    end
+
+    --ok, do propagate
+    --print("debug ok, propagating");
+    ref.layer:propagate(ref.inputs_m[t], ref.outputs_m[t])
+    for i = 1, #ref.dim_out do
+        if (ref.outputs_b[t][i] == true) then
+            nerv.error("this time's outputs_b should be false")
+        end
+        ref.outputs_b[t][i] = true
+    end
+    
+    --try dfs for further layers
+    for _, conn in pairs(ref.conns_o) do
+        --print("debug dfs-searching", conn.dst.ref.layer.id)
+        conn.dst.ref.inputs_b[t + conn.time][conn.dst.port] = true
+        self:propagate_dfs(conn.dst.ref, t + conn.time)
     end
 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)
-    self:set_inputs(input)
-    self:set_outputs(output)
-    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)
+--do_update: bool, whether we are doing back-propagate or updating the parameters
+function TNN:net_backpropagate(do_update) --propagate according to feeds_now
+    if (do_update == nil) then
+        nerv.error("do_update should not be nil")
+    end
+    for t = 1, self.chunk_size, 1 do
+        for id, ref in pairs(self.layers) do
+            for p = 1, #ref.dim_out do
+                ref.err_inputs_b[t][p] = false
+            end
+            for p = 1, #ref.dim_in do
+                ref.err_outputs_b[t][p] = false
+            end
+        end
+    end
+
+    local feeds_now = self.feeds_now
+    for t = 1, self.chunk_size do
+        if (bit.band(feeds_now.flagsPack_now[t], nerv.TNN.FC.HAS_LABEL) > 0) then
+            for i = 1, #self.dim_out do
+                local ref = self.outputs_p[i].ref
+                local p = self.outputs_p[i].port
+                ref.err_inputs_b[t][p] = true
+                self:backpropagate_dfs(ref, t, do_update)
+            end
+        end
+    end
+end
+
+--ref: the TNN_ref of a layer
+--t: the current time to propagate
+function TNN:backpropagate_dfs(ref, t, do_update)
+    if (self:outOfFeedRange(t)) then
+        return
+    end
+    if (ref.err_outputs_b[t][1] == true) then --already back_propagated, 1 is just a random port
+        return
+    end
+
+    --print("debug dfs", ref.layer.id, t)
+
+    local flag = true --whether have all inputs
+    for _, conn in pairs(ref.conns_o) do
+        local p = conn.src.port
+        if (not (ref.err_inputs_b[t][p] or self:outOfFeedRange(t + conn.time))) then
+            flag = false
+            break
+        end
+    end
+    if (flag == false) then
+        return
+    end
+
+    --ok, do back_propagate
+    --print("debug ok, back-propagating(or updating)")
+    if (do_update == false) then
+        ref.layer:back_propagate(ref.err_inputs_m[t], ref.err_outputs_m[t], ref.inputs_m[t], ref.outputs_m[t])
+    else 
+        --print(ref.err_inputs_m[t][1])
+        ref.layer:update(ref.err_inputs_m[t], ref.inputs_m[t], ref.outputs_m[t])
+    end
+    for i = 1, #ref.dim_in do
+        if (ref.err_outputs_b[t][i] == true) then
+            nerv.error("this time's outputs_b should be false")
+        end
+        ref.err_outputs_b[t][i] = true
+    end
+    
+    --try dfs for further layers
+    for _, conn in pairs(ref.conns_i) do
+        --print("debug dfs-searching", conn.src.ref.layer.id)
+        conn.src.ref.err_inputs_b[t - conn.time][conn.src.port] = true
+        self:backpropagate_dfs(conn.src.ref, t - conn.time, do_update)
     end
 end
 
 --Return: nerv.ParamRepo
-function DAGLayer:get_params()
+function TNN:get_params()
     local param_repos = {}
     for id, ref in pairs(self.queue) do
         table.insert(param_repos, ref.layer:get_params())