added lm_sampler, todo:test it

2 files changed, 104 insertions, 6 deletions

diff --git a/nerv/examples/lmptb/lm_sampler.lua b/nerv/examples/lmptb/lm_sampler.lua
new file mode 100644
index 0000000..c165127
--- /dev/null
+++ b/nerv/examples/lmptb/lm_sampler.lua
@@ -0,0 +1,98 @@
+local LMSampler = nerv.class('nerv.LMSampler')
+
+function LMSampler:__init(global_conf)
+    self.log_pre = "LMSampler"
+    self.gconf = global_conf
+    self.vocab = self.gconf.vocab
+    self.sen_end_token = self.vocab.sen_end_token
+    self.sen_end_id = self.vocab:get_word_str(self.sen_end_token).id 
+end
+
+function LMSampler:load_dagL(dagL)
+    self.batch_size = self.gconf.batch_size
+    self.chunk_size = self.gconf.chunk_size
+    
+    nerv.printf("%s loading dagL\n", self.log_pre)
+
+    self.dagL = dagL
+
+    self.dagL_inputs = {}
+    self.dagL_inputs[1] = global_conf.cumat_type(global_conf.batch_size, 1)
+    self.dagL_inputs[1]:fill(self.sen_end_id - 1)
+    self.dagL_inputs[2] = global_conf.cumat_type(global_conf.batch_size, global_conf.hidden_size)
+    self.dagL_inputs[2]:fill(0)
+    
+    self.dagL_outputs = {}
+    self.dagL_outputs[1] = global_conf.cumat_type(global_conf.batch_size, global_conf.vocab:size())
+    self.dagL_outputs[2] = global_conf.cumat_type(global_conf.batch_size, global_conf.hidden_size)
+    
+    self.smout_d = global_conf.cumat_type(self.batch_size, self.vocab:size())
+    self.smout_h = global_conf.mmat_type(self.batch_size, self.vocab:size())
+
+    self.store = {}
+    for i = 1, self.batch_size do
+        self.store[i] = {}
+        self.store[i][1] = {}
+        self.store[i][1].w = self.sen_end_token
+        self.store[i][1].id = self.sen_end_id
+        self.store[i][1].p = 0
+    end
+end
+
+function LMSampler:sample_to_store(smout)
+    for i = 1, self.batch_size do
+        local ran = math.random()
+        local s = 0, id = self.vocab:size()
+        for j = 0, self.vocab:size() - 1 do
+            s = s + smout[i][j]
+            if s >= ran then 
+                id = j + 1
+                break
+            end
+        end
+        if #self.store[i] >= self.chunk_size - 2 then
+            id = self.sen_end_id
+        end
+        local tmp = {}
+        tmp.w = self.vocab:get_word_id(id).str
+        tmp.id = id
+        tmp.p = smout[i][id]
+        table.insert(self.store[i], tmp)
+    end
+end
+
+--Returns: LMResult
+function LMSampler:lm_sample_rnn_dagL(sample_num, p_conf)
+    local dagL = self.dagL
+    local inputs = self.dagL_inputs
+    local outputs = self.dagL_outputs
+
+    local res = {}
+    while #res < sample_num do
+        dagL:propagate(inputs, outputs)
+        inputs[2]:copy_fromd(outputs[2]) --copy hidden activation
+    
+        self.smout_d:softmax(outputs[1])
+        self.smout_d:copy_toh(self.smout_h)
+        
+        self:sample_to_store(self.smout_h)
+        for i = 1, self.batch_size do
+            inputs[1][i - 1][0] = self.store[i][#self.store[i]].id - 1
+            if self.store[i][#self.store[i]].id == self.sen_end_id then --meet a sentence end
+                if #self.store[i] >= 3 then
+                    res[#res + 1] = self.store[i]
+                end
+                inputs[2][i - 1]:fill(0)
+                self.store[i] = {}
+                self.store[i][1] = {}
+                self.store[i][1].w = self.sen_end_token
+                self.store[i][1].id = self.sen_end_id
+                self.store[i][1].p = 0
+            end
+        end
+
+        collectgarbage("collect")                                              
+    end
+
+    return res
+end 
diff --git a/nerv/examples/lmptb/sample_grulm_ptb_main.lua b/nerv/examples/lmptb/sample_grulm_ptb_main.lua
index 86209da..b6351bd 100644
--- a/nerv/examples/lmptb/sample_grulm_ptb_main.lua
+++ b/nerv/examples/lmptb/sample_grulm_ptb_main.lua
@@ -5,6 +5,7 @@ require 'lmptb.layer.init'
 --require 'tnn.init'
 require 'lmptb.lmseqreader'
 require 'lm_trainer'
+require 'lm_sampler'
 
 --[[global function rename]]--
 --local printf = nerv.printf
@@ -134,7 +135,7 @@ function prepare_tnn(global_conf, layerRepo)
 end
 
 function prepare_dagL(global_conf, layerRepo)
-    nerv.printf("%s Generate and initing TNN ...\n", global_conf.sche_log_pre)
+    nerv.printf("%s Generate and initing dagL ...\n", global_conf.sche_log_pre)
 
     --input: input_w, input_w, ... input_w_now, last_activation
     local connections_t = {
@@ -376,15 +377,12 @@ commands = nerv.SUtil.parse_commands_set(commands_str)
 if start_lr ~= nil then
     global_conf.lrate = start_lr
 end
- 
-nerv.printf("%s creating work_dir(%s)...\n", global_conf.sche_log_pre, global_conf.work_dir)
-nerv.LMUtil.wait(2)
-os.execute("mkdir -p "..global_conf.work_dir)
-os.execute("cp " .. global_conf.train_fn .. " " .. global_conf.train_fn_shuf)
 
+--[[ 
 --redirecting log outputs!
 nerv.SUtil.log_redirect(global_conf.log_fn)
 nerv.LMUtil.wait(2)
+]]--
 
 ----------------printing options---------------------------------
 nerv.printf("%s printing global_conf...\n", global_conf.sche_log_pre)
@@ -424,6 +422,8 @@ if commands["sampling"] == 1 then
     nerv.printf("===SAMPLE===\n") 
     global_conf.sche_log_pre = "[SCHEDULER SAMPLING]:" 
     local dagL = load_net_dagL(global_conf, global_conf.fn_to_sample) 
+    local sampler = nerv.LMSampler(global_conf)
+    sampler:load_dagL(dagL)
     --global_conf.dropout_rate = 0
     --LMTrainer.lm_process_file_rnn(global_conf, global_conf.test_fn, tnn, false) --false update!
 end --if commands["sampling"]