diff options
Diffstat (limited to 'nerv/examples/lmptb/rnn')
| -rw-r--r-- | nerv/examples/lmptb/rnn/tnn.lua | 70 |
1 files changed, 56 insertions, 14 deletions
diff --git a/nerv/examples/lmptb/rnn/tnn.lua b/nerv/examples/lmptb/rnn/tnn.lua index 460fcc4..019d24c 100644 --- a/nerv/examples/lmptb/rnn/tnn.lua +++ b/nerv/examples/lmptb/rnn/tnn.lua @@ -34,16 +34,16 @@ 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] + inputs_matbak_p = {}, --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_matbak_p = {}, --which is a back-up space to handle some cross-border computation err_inputs_b = {}, err_outputs_m = {}, err_outputs_b = {}, - conns_i = {}, --list of inputing connections - conns_o = {}, --list of outputing connections + i_conns_p = {}, --list of inputing connections + o_conns_p = {}, --list of outputing connections dim_in = dim_in, --list of dimensions of ports dim_out = dim_out, } @@ -130,8 +130,8 @@ function TNN:__init(id, global_conf, layer_conf) nerv.error("mismatch dimension or wrong time %s,%s,%d", ll[1], ll[2], ll[3]) end table.insert(parsed_conns, conn_now) - table.insert(ref_to.conns_i, conn_now) - table.insert(ref_from.conns_o, conn_now) + ref_to.i_conns_p[conn_now.dst.port] = conn_now + ref_from.o_conns_p[conn_now.src.port] = conn_now end end @@ -161,9 +161,9 @@ function TNN:init(batch_size, chunk_size) end 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) + ref_to.inputs_matbak_p[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, time) - ref_from.err_inputs_p_matbak[port_from] = self.gconf.cumat_type(batch_size, dim) + ref_from.err_inputs_matbak_p[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, time) end @@ -289,7 +289,7 @@ function TNN:getFeedFromReader(reader) 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 t = 1, self.chunk_size, 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]) @@ -336,7 +336,7 @@ function TNN:propagate_dfs(ref, t) --print("debug dfs", ref.layer.id, t) local flag = true --whether have all inputs - for _, conn in pairs(ref.conns_i) do + for _, conn in pairs(ref.i_conns_p) do local p = conn.dst.port if (not (ref.inputs_b[t][p] or self:outOfFeedRange(t - conn.time))) then flag = false @@ -349,7 +349,36 @@ function TNN:propagate_dfs(ref, t) --ok, do propagate --print("debug ok, propagating"); - ref.layer:propagate(ref.inputs_m[t], ref.outputs_m[t]) + if (bit.bor(self.feeds_now.flagsPack_now[t], bit.bor(nerv.TNN.FC.SEQ_START, nerv.TNN.FC.SEQ_END)) > 0) then --flush cross-border history + for i = 1, self.batch_size do + local seq_start = bit.bor(self.feeds_now.flags_now[t][i], nerv.TNN.FC.SEQ_START) + local seq_end = bit.bor(self.feeds_now.flags_now[t][i], nerv.TNN.FC.SEQ_END) + if (seq_start > 0 or seq_end > 0) then + for p = 1, #ref.i_conns_p do + if ((ref.i_conns_p[p].time > 0 and seq_start > 0) or (ref.i_conns_p[p].time < 0 and seq_end > 0)) then --cross-border, set to default + ref.inputs_matbak_p[p][i - 1]:copy_fromd(ref.inputs_m[t][p][i - 1]) + ref.inputs_m[t][p][i - 1]:fill(self.gconf.nn_act_default) + end + end + end + end + end + ref.layer:propagate(ref.inputs_m[t], ref.outputs_m[t]) --propagate! + if (bit.bor(self.feeds_now.flagsPack_now[t], bit.bor(nerv.TNN.FC.SEQ_START, nerv.TNN.FC.SEQ_END)) > 0) then --restore cross-border history + for i = 1, self.batch_size do + local seq_start = bit.bor(self.feeds_now.flags_now[t][i], nerv.TNN.FC.SEQ_START) + local seq_end = bit.bor(self.feeds_now.flags_now[t][i], nerv.TNN.FC.SEQ_END) + if (seq_start > 0 or seq_end > 0) then + for p = 1, #ref.i_conns_p do + if ((ref.i_conns_p[p].time > 0 and seq_start > 0) or (ref.i_conns_p[p].time < 0 and seq_end > 0)) then + ref.inputs_m[t][p][i - 1]:copy_fromd(ref.inputs_matbak_p[p][i - 1]) + end + end + end + end + end + + --set input flag for future layers 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") @@ -358,7 +387,7 @@ function TNN:propagate_dfs(ref, t) end --try dfs for further layers - for _, conn in pairs(ref.conns_o) do + for _, conn in pairs(ref.o_conns_p) 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) @@ -407,7 +436,7 @@ function TNN:backpropagate_dfs(ref, t, do_update) --print("debug dfs", ref.layer.id, t) local flag = true --whether have all inputs - for _, conn in pairs(ref.conns_o) do + for _, conn in pairs(ref.o_conns_p) do local p = conn.src.port if (not (ref.err_inputs_b[t][p] or self:outOfFeedRange(t + conn.time))) then flag = false @@ -420,6 +449,19 @@ function TNN:backpropagate_dfs(ref, t, do_update) --ok, do back_propagate --print("debug ok, back-propagating(or updating)") + if (bit.bor(self.feeds_now.flagsPack_now[t], bit.bor(nerv.TNN.FC.SEQ_START, nerv.TNN.FC.SEQ_END)) > 0) then --flush cross-border errors + for i = 1, self.batch_size do + local seq_start = bit.bor(self.feeds_now.flags_now[t][i], nerv.TNN.FC.SEQ_START) + local seq_end = bit.bor(self.feeds_now.flags_now[t][i], nerv.TNN.FC.SEQ_END) + if (seq_start > 0 or seq_end > 0) then + for p = 1, #ref.o_conns_p do + if ((ref.o_conns_p[p].time > 0 and seq_end > 0) or (ref.o_conns_p[p].time < 0 and seq_start > 0)) then --cross-border, set to zero + ref.err_inputs_m[t][p][i - 1]:fill(0) + end + end + end + end + end 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 @@ -434,7 +476,7 @@ function TNN:backpropagate_dfs(ref, t, do_update) end --try dfs for further layers - for _, conn in pairs(ref.conns_i) do + for _, conn in pairs(ref.i_conns_p) 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) |