diff options
| -rw-r--r-- | nerv/examples/lmptb/lmptb/layer/select_linear.lua | 4 | ||||
| -rw-r--r-- | nerv/examples/lmptb/lmptb/lmseqreader.lua | 2 | ||||
| -rw-r--r-- | nerv/examples/lmptb/m-tests/dagl_test.lua | 180 | ||||
| -rw-r--r-- | nerv/examples/lmptb/m-tests/some-text | 2 | ||||
| -rw-r--r-- | nerv/examples/lmptb/main.lua | 2 | ||||
| -rw-r--r-- | nerv/examples/lmptb/rnn/tnn.lua | 200 |
6 files changed, 171 insertions, 219 deletions
diff --git a/nerv/examples/lmptb/lmptb/layer/select_linear.lua b/nerv/examples/lmptb/lmptb/layer/select_linear.lua index d4cff0b..efbaf20 100644 --- a/nerv/examples/lmptb/lmptb/layer/select_linear.lua +++ b/nerv/examples/lmptb/lmptb/layer/select_linear.lua @@ -36,7 +36,7 @@ function SL:update(bp_err, input, output) --word_vec:add(word_vec, bp_err[1][i - 1], 1, - self.gconf.lrate / self.gconf.batch_size) -- end --end - self.ltp.trans:update_select_rows(bp_err[1], input[1], - self.gconf.lrate / self.gconf.batch_size, 0) + self.ltp.trans:update_select_rows(bp_err[1], input[1]:trans(), - self.gconf.lrate / self.gconf.batch_size, 0) end function SL:propagate(input, output) @@ -47,7 +47,7 @@ function SL:propagate(input, output) -- output[1][i]:fill(0) -- end --end - output[1]:copy_rows_fromd_by_idx(self.ltp.trans, input[1]) + output[1]:copy_rows_fromd_by_idx(self.ltp.trans, input[1]:trans()) end function SL:back_propagate(bp_err, next_bp_err, input, output) diff --git a/nerv/examples/lmptb/lmptb/lmseqreader.lua b/nerv/examples/lmptb/lmptb/lmseqreader.lua index 6cbd0e9..41e3903 100644 --- a/nerv/examples/lmptb/lmptb/lmseqreader.lua +++ b/nerv/examples/lmptb/lmptb/lmseqreader.lua @@ -121,7 +121,7 @@ function LMReader:get_batch(feeds) end end end - + for j = 1, self.chunk_size, 1 do flagsPack[j] = 0 for i = 1, self.batch_size, 1 do diff --git a/nerv/examples/lmptb/m-tests/dagl_test.lua b/nerv/examples/lmptb/m-tests/dagl_test.lua deleted file mode 100644 index 6bd11c8..0000000 --- a/nerv/examples/lmptb/m-tests/dagl_test.lua +++ /dev/null @@ -1,180 +0,0 @@ -require 'lmptb.lmvocab' -require 'lmptb.lmfeeder' -require 'lmptb.lmutil' -require 'lmptb.layer.init' -require 'lmptb.lmseqreader' -require 'rnn.tnn' - ---[[global function rename]]-- -printf = nerv.printf ---[[global function rename ends]]-- - ---global_conf: table ---first_time: bool ---Returns: a ParamRepo -function prepare_parameters(global_conf, first_time) - printf("%s preparing parameters...\n", global_conf.sche_log_pre) - - if (first_time) then - ltp_ih = nerv.LinearTransParam("ltp_ih", global_conf) - ltp_ih.trans = global_conf.cumat_type(global_conf.vocab:size() + 1, global_conf.hidden_size) --index 0 is for zero, others correspond to vocab index(starting from 1) - ltp_ih.trans:generate(global_conf.param_random) - ltp_ih.trans[0]:fill(0) - - ltp_hh = nerv.LinearTransParam("ltp_hh", global_conf) - ltp_hh.trans = global_conf.cumat_type(global_conf.hidden_size, global_conf.hidden_size) - ltp_hh.trans:generate(global_conf.param_random) - - ltp_ho = nerv.LinearTransParam("ltp_ho", global_conf) - ltp_ho.trans = global_conf.cumat_type(global_conf.hidden_size, global_conf.vocab:size()) - ltp_ho.trans:generate(global_conf.param_random) - - bp_h = nerv.BiasParam("bp_h", global_conf) - bp_h.trans = global_conf.cumat_type(1, global_conf.hidden_size) - bp_h.trans:generate(global_conf.param_random) - - bp_o = nerv.BiasParam("bp_o", global_conf) - bp_o.trans = global_conf.cumat_type(1, global_conf.vocab:size()) - bp_o.trans:generate(global_conf.param_random) - - local f = nerv.ChunkFile(global_conf.param_fn, 'w') - f:write_chunk(ltp_ih) - f:write_chunk(ltp_hh) - f:write_chunk(ltp_ho) - f:write_chunk(bp_h) - f:write_chunk(bp_o) - f:close() - end - - local paramRepo = nerv.ParamRepo() - paramRepo:import({global_conf.param_fn}, nil, global_conf) - - printf("%s preparing parameters end.\n", global_conf.sche_log_pre) - - return paramRepo -end - ---global_conf: table ---Returns: nerv.LayerRepo -function prepare_layers(global_conf, paramRepo) - printf("%s preparing layers...\n", global_conf.sche_log_pre) - - local recurrentLconfig = {{["bp"] = "bp_h", ["ltp_hh"] = "ltp_hh"}, {["dim_in"] = {global_conf.hidden_size, global_conf.hidden_size}, ["dim_out"] = {global_conf.hidden_size}, ["break_id"] = global_conf.vocab:get_sen_entry().id, ["independent"] = global_conf.independent, ["clip"] = 10}} - - local layers = { - ["nerv.IndRecurrentLayer"] = { - ["recurrentL1"] = recurrentLconfig, - }, - - ["nerv.SelectLinearLayer"] = { - ["selectL1"] = {{["ltp"] = "ltp_ih"}, {["dim_in"] = {1}, ["dim_out"] = {global_conf.hidden_size}}}, - }, - - ["nerv.SigmoidLayer"] = { - ["sigmoidL1"] = {{}, {["dim_in"] = {global_conf.hidden_size}, ["dim_out"] = {global_conf.hidden_size}}} - }, - - ["nerv.AffineLayer"] = { - ["outputL"] = {{["ltp"] = "ltp_ho", ["bp"] = "bp_o"}, {["dim_in"] = {global_conf.hidden_size}, ["dim_out"] = {global_conf.vocab:size()}}}, - }, - - ["nerv.SoftmaxCELayer"] = { - ["softmaxL"] = {{}, {["dim_in"] = {global_conf.vocab:size(), global_conf.vocab:size()}, ["dim_out"] = {1}}}, - }, - } - - --[[ --we do not need those in the new rnn framework - printf("%s adding %d bptt layers...\n", global_conf.sche_log_pre, global_conf.bptt) - for i = 1, global_conf.bptt do - layers["nerv.IndRecurrentLayer"]["recurrentL" .. (i + 1)] = recurrentLconfig - layers["nerv.SigmoidLayer"]["sigmoidL" .. (i + 1)] = {{}, {["dim_in"] = {global_conf.hidden_size}, ["dim_out"] = {global_conf.hidden_size}}} - layers["nerv.SelectLinearLayer"]["selectL" .. (i + 1)] = {{["ltp"] = "ltp_ih"}, {["dim_in"] = {1}, ["dim_out"] = {global_conf.hidden_size}}} - end - --]] - - local layerRepo = nerv.LayerRepo(layers, paramRepo, global_conf) - printf("%s preparing layers end.\n", global_conf.sche_log_pre) - return layerRepo -end - ---global_conf: table ---layerRepo: nerv.LayerRepo ---Returns: a nerv.TNN -function prepare_dagLayer(global_conf, layerRepo) - printf("%s Initing TNN ...\n", global_conf.sche_log_pre) - - --input: input_w, input_w, ... input_w_now, last_activation - local connections_t = { - {"<input>[1]", "selectL1[1]", 0}, - {"selectL1[1]", "recurrentL1[1]", 0}, - {"recurrentL1[1]", "sigmoidL1[1]", 0}, - {"sigmoidL1[1]", "outputL[1]", 0}, - {"sigmoidL1[1]", "recurrentL1[2]", 1}, - {"outputL[1]", "softmaxL[1]", 0}, - {"<input>[2]", "softmaxL[2]", 0}, - {"softmaxL[1]", "<output>[1]", 0} - } - - --[[ - printf("%s printing DAG connections:\n", global_conf.sche_log_pre) - for key, value in pairs(connections_t) do - printf("\t%s->%s\n", key, value) - end - ]]-- - - local tnn = nerv.TNN("TNN", global_conf, {["dim_in"] = {1, global_conf.vocab:size()}, ["dim_out"] = {1}, ["sub_layers"] = layerRepo, - ["connections"] = connections_t, - }) - printf("%s Initing TNN end.\n", global_conf.sche_log_pre) - return tnn -end - -local train_fn = '/home/slhome/txh18/workspace/nerv/nerv/nerv/examples/lmptb/m-tests/some-text' -local test_fn = '/home/slhome/txh18/workspace/nerv/nerv/nerv/examples/lmptb/m-tests/some-text' - -local global_conf = { - lrate = 1, wcost = 1e-6, momentum = 0, - cumat_type = nerv.CuMatrixFloat, - mmat_type = nerv.CuMatrixFloat, - nn_act_default = 0, - - hidden_size = 20, - chunk_size = 5, - batch_size = 3, - max_iter = 18, - param_random = function() return (math.random() / 5 - 0.1) end, - independent = true, - - train_fn = train_fn, - test_fn = test_fn, - sche_log_pre = "[SCHEDULER]:", - log_w_num = 10, --give a message when log_w_num words have been processed - timer = nerv.Timer() -} -global_conf.work_dir = '/home/slhome/txh18/workspace/nerv/play/dagL_test' -global_conf.param_fn = global_conf.work_dir.."/params" - -local vocab = nerv.LMVocab() -global_conf["vocab"] = vocab -global_conf.vocab:build_file(global_conf.train_fn, false) -local paramRepo = prepare_parameters(global_conf, true) -local layerRepo = prepare_layers(global_conf, paramRepo) -local tnn = prepare_dagLayer(global_conf, layerRepo) -tnn:init(global_conf.batch_size, global_conf.chunk_size) - -local reader = nerv.LMSeqReader(global_conf, global_conf.batch_size, global_conf.chunk_size, global_conf.vocab) -reader:open_file(global_conf.train_fn) - -local batch_num = 1 -while (1) do - local r, feeds - r, feeds = tnn:getFeedFromReader(reader) - if (r == false) then break end - for j = 1, global_conf.chunk_size, 1 do - for i = 1, global_conf.batch_size, 1 do - printf("%s[L(%s)] ", feeds.inputs_s[j][i], feeds.labels_s[j][i]) --vocab:get_word_str(input[i][j]).id - end - printf("\n") - end - printf("\n") -end diff --git a/nerv/examples/lmptb/m-tests/some-text b/nerv/examples/lmptb/m-tests/some-text index cdfbd2c..da4bea9 100644 --- a/nerv/examples/lmptb/m-tests/some-text +++ b/nerv/examples/lmptb/m-tests/some-text @@ -1,6 +1,6 @@ </s> aa bb cc aa bb cc aa bb cc aa bb cc aa bb cc aa </s> </s> aa bb cc aa bb cc aa bb cc aa </s> -</s> aa bb cc aa bb cc aa bb cc aa </s> +</s> bb cc aa bb cc aa bb cc aa </s> </s> aa bb cc aa </s> </s> aa bb cc aa </s> </s> aa bb cc aa </s> diff --git a/nerv/examples/lmptb/main.lua b/nerv/examples/lmptb/main.lua index 1939eda..a93c148 100644 --- a/nerv/examples/lmptb/main.lua +++ b/nerv/examples/lmptb/main.lua @@ -1,3 +1,5 @@ +--TODO: the select_linear now accepts a column vector, instead of a row vector + require 'lmptb.lmvocab' require 'lmptb.lmfeeder' require 'lmptb.lmutil' 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()) |