diff options
| -rw-r--r-- | nerv/examples/lmptb/lstmlm_ptb_main.lua | 15 | ||||
| -rw-r--r-- | nerv/examples/lmptb/tnn/tnn.lua | 28 |
2 files changed, 29 insertions, 14 deletions
diff --git a/nerv/examples/lmptb/lstmlm_ptb_main.lua b/nerv/examples/lmptb/lstmlm_ptb_main.lua index 42b541f..69f26f5 100644 --- a/nerv/examples/lmptb/lstmlm_ptb_main.lua +++ b/nerv/examples/lmptb/lstmlm_ptb_main.lua @@ -153,8 +153,9 @@ function prepare_tnn(global_conf, layerRepo) end ]]-- - local tnn = nerv.TNN("TNN", global_conf, {["dim_in"] = {1, global_conf.vocab:size()}, ["dim_out"] = {1}, ["sub_layers"] = layerRepo, - ["connections"] = connections_t, + local tnn = nerv.TNN("TNN", global_conf, {["dim_in"] = {1, global_conf.vocab:size()}, + ["dim_out"] = {1}, ["sub_layers"] = layerRepo, + ["connections"] = connections_t, ["clip_t"] = global_conf.clip_t, }) tnn:init(global_conf.batch_size, global_conf.chunk_size) @@ -183,12 +184,12 @@ test_fn = data_dir .. '/ptb.test.txt.adds' vocab_fn = data_dir .. '/vocab' global_conf = { - lrate = 1, wcost = 1e-6, momentum = 0, + lrate = 0.001, wcost = 1e-6, momentum = 0, clip_t = 0.01, cumat_type = nerv.CuMatrixFloat, mmat_type = nerv.MMatrixFloat, nn_act_default = 0, - hidden_size = 400, --set to 400 for a stable good test PPL + hidden_size = 200, --set to 400 for a stable good test PPL chunk_size = 15, batch_size = 10, max_iter = 35, @@ -200,9 +201,9 @@ global_conf = { test_fn = test_fn, vocab_fn = vocab_fn, sche_log_pre = "[SCHEDULER]:", - log_w_num = 40000, --give a message when log_w_num words have been processed + log_w_num = 400, --give a message when log_w_num words have been processed timer = nerv.Timer(), - work_dir_base = '/home/slhome/txh18/workspace/nerv/play/ptbEXP/tnn_test' + work_dir_base = '/home/slhome/txh18/workspace/nerv/play/ptbEXP/tnn_lstm_test' } elseif (set == "msr_sc") then @@ -278,7 +279,7 @@ else printf("%s no user setting, all default...\n", global_conf.sche_log_pre) end -global_conf.work_dir = global_conf.work_dir_base .. 'h' .. global_conf.hidden_size .. 'ch' .. global_conf.chunk_size .. 'ba' .. global_conf.batch_size .. 'slr' .. global_conf.lrate .. 'wc' .. global_conf.wcost +global_conf.work_dir = global_conf.work_dir_base .. 'h' .. global_conf.hidden_size --.. 'ch' .. global_conf.chunk_size .. 'ba' .. global_conf.batch_size .. 'slr' .. global_conf.lrate .. 'wc' .. global_conf.wcost global_conf.train_fn_shuf = global_conf.work_dir .. '/train_fn_shuf' global_conf.train_fn_shuf_bak = global_conf.train_fn_shuf .. '_bak' global_conf.param_fn = global_conf.work_dir .. "/params" diff --git a/nerv/examples/lmptb/tnn/tnn.lua b/nerv/examples/lmptb/tnn/tnn.lua index c87f963..db6cdd7 100644 --- a/nerv/examples/lmptb/tnn/tnn.lua +++ b/nerv/examples/lmptb/tnn/tnn.lua @@ -31,6 +31,7 @@ local function discover(id, layers, layer_repo) local dim_in, dim_out = layer:get_dim() ref = { layer = layer, + id = layer.id, inputs_m = {}, --storage for computation, inputs_m[time][port] inputs_b = {}, --inputs_g[time][port], whether this input can been computed inputs_matbak_p = {}, --which is a back-up space to handle some cross-border computation, inputs_p_matbak[port] @@ -89,6 +90,10 @@ function TNN:out_of_feedrange(t) --out of chunk, or no input, for the current fe end function TNN:__init(id, global_conf, layer_conf) + self.clip_t = layer_conf.clip_t + if self.clip_t > 0 then + nerv.info("tnn(%s) will clip gradient across time with %f...", id, self.clip_t) + end local layers = {} local inputs_p = {} --map:port of the TNN to layer ref and port local outputs_p = {} @@ -429,7 +434,7 @@ end --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 + if do_update == nil then nerv.error("do_update should not be nil") end for t = 1, self.chunk_size, 1 do @@ -445,7 +450,7 @@ function TNN:net_backpropagate(do_update) --propagate according to feeds_now 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 + 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 @@ -457,10 +462,10 @@ function TNN:net_backpropagate(do_update) --propagate according to feeds_now local flag_out = true for t = 1, self.chunk_size do --check whether every output has been computed - if (bit.band(feeds_now.flagsPack_now[t], nerv.TNN.FC.HAS_INPUT) > 0) then + if bit.band(feeds_now.flagsPack_now[t], nerv.TNN.FC.HAS_INPUT) > 0 then for i = 1, #self.dim_in do local ref = self.inputs_p[i].ref - if (ref.err_outputs_b[t][1] ~= true) then + if ref.err_outputs_b[t][1] ~= true then flag_out = false break end @@ -475,10 +480,10 @@ end --ref: the TNN_ref of a layer --t: the current time to propagate function TNN:backpropagate_dfs(ref, t, do_update) - if (self:out_of_feedrange(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 + if ref.err_outputs_b[t][1] == true then --already back_propagated, 1 is just a random port return end @@ -501,7 +506,16 @@ function TNN:backpropagate_dfs(ref, t, do_update) if (do_update == false) then self.gconf.timer:tic("tnn_actual_layer_backpropagate") ref.layer:back_propagate(ref.err_inputs_m[t], ref.err_outputs_m[t], ref.inputs_m[t], ref.outputs_m[t], t) - self.gconf.timer:toc("tnn_actual_layer_backpropagate") + self.gconf.timer:toc("tnn_actual_layer_backpropagate") + if self.clip_t > 0 then + for _, conn in pairs(ref.i_conns_p) do + local p = conn.dst.port --port for ref + if conn.time ~= 0 then + --print("debug clip_t tnn", ref.id, "port:", p, "clip:", self.clip_t) + ref.err_outputs_m[t][p]:clip(-self.clip_t, self.clip_t) + end + end + end else --print(ref.err_inputs_m[t][1]) self.gconf.timer:tic("tnn_actual_layer_update") |