diff options
Diffstat (limited to 'nerv/examples/lmptb/rnn/tnn.lua')
| -rw-r--r-- | nerv/examples/lmptb/rnn/tnn.lua | 42 |
1 files changed, 26 insertions, 16 deletions
diff --git a/nerv/examples/lmptb/rnn/tnn.lua b/nerv/examples/lmptb/rnn/tnn.lua index d6bf42e..c2e397c 100644 --- a/nerv/examples/lmptb/rnn/tnn.lua +++ b/nerv/examples/lmptb/rnn/tnn.lua @@ -58,7 +58,7 @@ 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 TNN.makeInitialStore(st, p, dim, batch_size, chunk_size, global_conf, st_c, p_c, t_c) +function TNN.make_initial_store(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") @@ -78,7 +78,7 @@ function TNN.makeInitialStore(st, p, dim, batch_size, chunk_size, global_conf, s end end -function TNN:outOfFeedRange(t) --out of chunk, or no input, for the current feed +function TNN:out_of_feedrange(t) --out of chunk, or no input, for the current feed if (t < 1 or t > self.chunk_size) then return true end @@ -165,9 +165,9 @@ function TNN:init(batch_size, chunk_size) print("TNN initing storage", ref_from.layer.id, "->", ref_to.layer.id) 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) + self.make_initial_store(ref_from.outputs_m, port_from, dim, batch_size, chunk_size, self.gconf, ref_to.inputs_m, port_to, time) 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) + self.make_initial_store(ref_from.err_inputs_m, port_from, dim, batch_size, chunk_size, self.gconf, ref_to.err_outputs_m, port_to, time) end @@ -176,8 +176,8 @@ 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, 0) - self.makeInitialStore(ref.err_inputs_m, p, self.dim_out[i], batch_size, chunk_size, self.gconf, self.err_inputs_m, i, 0) + self.make_initial_store(ref.outputs_m, p, self.dim_out[i], batch_size, chunk_size, self.gconf, self.outputs_m, i, 0) + self.make_initial_store(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 = {} @@ -185,8 +185,8 @@ 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, 0) - self.makeInitialStore(ref.err_outputs_m, p, self.dim_in[i], batch_size, chunk_size, self.gconf, self.err_outputs_m, i, 0) + self.make_initial_store(ref.inputs_m, p, self.dim_in[i], batch_size, chunk_size, self.gconf, self.inputs_m, i, 0) + self.make_initial_store(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 @@ -285,17 +285,27 @@ end --reader: some reader --Returns: bool, whether has new feed --Returns: feeds, a table that will be filled with the reader's feeds -function TNN:getFeedFromReader(reader) +function TNN:getfeed_from_reader(reader) local feeds_now = self.feeds_now local got_new = reader:get_batch(feeds_now) return got_new, feeds_now end -function TNN:moveRightToNextMB() --move output history activations of 1..chunk_size to 1-chunk_size..0 - for t = 1, self.chunk_size, 1 do +function TNN:move_right_to_nextmb(list_t) --move output history activations of 1..chunk_size to 1-chunk_size..0 + if list_t == nil then + list_t = {} + for i = 1, self.chunk_size do + list_t[i] = i - self.chunk_size + end + end + for i = 1, #list_t do + t = list_t[i] + if t < 1 - self.chunk_size or t > 0 then + nerv.error("MB move range error") + end 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]) + ref.outputs_m[t][p]:copy_fromd(ref.outputs_m[t + self.chunk_size][p]) end end end @@ -345,7 +355,7 @@ end --ref: the TNN_ref of a layer --t: the current time to propagate function TNN:propagate_dfs(ref, t) - if (self:outOfFeedRange(t)) then + if (self:out_of_feedrange(t)) then return end if (ref.outputs_b[t][1] == true) then --already propagated, 1 is just a random port @@ -357,7 +367,7 @@ function TNN:propagate_dfs(ref, t) local flag = true --whether have all inputs 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 + if (not (ref.inputs_b[t][p] or self:out_of_feedrange(t - conn.time))) then flag = false break end @@ -465,7 +475,7 @@ 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 + if (self:out_of_feedrange(t)) then return end if (ref.err_outputs_b[t][1] == true) then --already back_propagated, 1 is just a random port @@ -477,7 +487,7 @@ function TNN:backpropagate_dfs(ref, t, do_update) local flag = true --whether have all inputs 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 + if (not (ref.err_inputs_b[t][p] or self:out_of_feedrange(t + conn.time))) then flag = false break end |