refactor kaldi_decode

3 files changed, 20 insertions, 34 deletions

diff --git a/kaldi_decode/src/Makefile b/kaldi_decode/src/Makefile
deleted file mode 100644
index 0897798..0000000
--- a/kaldi_decode/src/Makefile
+++ /dev/null
@@ -1,12 +0,0 @@
-# Change KDIR to `kaldi-trunk' path (Kaldi must be compiled with --share)
-KDIR := /speechlab/tools/KALDI/kaldi-master/
-NERVDIR := /speechlab/users/mfy43/nerv/
-CUDADIR := /usr/local/cuda/
-
-nnet-forward:
-	g++ -msse -msse2 -Wall -I $(KDIR)/src/ -pthread -DKALDI_DOUBLEPRECISION=0 -DHAVE_POSIX_MEMALIGN -Wno-sign-compare -Wno-unused-local-typedefs -Winit-self -DHAVE_EXECINFO_H=1 -rdynamic -DHAVE_CXXABI_H -DHAVE_ATLAS -I $(KDIR)/tools/ATLAS/include -I $(KDIR)/tools/openfst/include -Wno-sign-compare -g  -fPIC -DHAVE_CUDA -I $(CUDADIR)/include  -DKALDI_NO_EXPF  -I $(NERVDIR)/install//include/luajit-2.0/ -I $(NERVDIR)/install/include/ -DLUA_USE_APICHECK -c -o nnet-forward.o nnet-forward.cc
-	g++ -rdynamic -Wl,-rpath=$(KDIR)/tools/openfst/lib -L$(CUDADIR)/lib64 -Wl,-rpath,$(CUDADIR)/lib64 -Wl,-rpath=$(KDIR)/src/lib -L.  -L$(KDIR)/src/nnet/   -L$(KDIR)/src/cudamatrix/   -L$(KDIR)/src/lat/   -L$(KDIR)/src/hmm/   -L$(KDIR)/src/tree/   -L$(KDIR)/src/matrix/   -L$(KDIR)/src/util/   -L$(KDIR)/src/base/ nnet-forward.o $(KDIR)/src/nnet//libkaldi-nnet.so $(KDIR)/src/cudamatrix//libkaldi-cudamatrix.so $(KDIR)/src/lat//libkaldi-lat.so $(KDIR)/src/hmm//libkaldi-hmm.so $(KDIR)/src/tree//libkaldi-tree.so $(KDIR)/src/matrix//libkaldi-matrix.so $(KDIR)/src/util//libkaldi-util.so $(KDIR)/src/base//libkaldi-base.so   -L$(KDIR)/tools/openfst/lib -lfst -lm -lpthread -ldl   -lkaldi-nnet   -lkaldi-cudamatrix   -lkaldi-lat   -lkaldi-hmm   -lkaldi-tree   -lkaldi-matrix   -lkaldi-util   -lkaldi-base -lstdc++ -L$(NERVDIR)/install/lib -Wl,-rpath=$(NERVDIR)/install/lib -lnervcore -lluaT -rdynamic -Wl,-rpath=$(KDIR)//tools/openfst/lib -L$(DUDADIR)/lib64 -Wl,-rpath,$(CUDADIR)/lib64 -Wl,-rpath=$(KDIR)//src/lib -lfst -lm -lpthread -ldl -L $(NERVDIR)/luajit-2.0/src/ -lluajit -o nnet-forward -L/home/intel/mkl/lib/intel64/ -Wl,-rpath=/home/intel/mkl/lib/intel64/ -lmkl_rt
-
-clean:
-	-rm nnet-forward.o nnet-forward
-
diff --git a/kaldi_decode/src/nerv4decode.lua b/kaldi_decode/src/asr_propagator.lua
index 898b5a8..5d0ad7c 100644
--- a/kaldi_decode/src/nerv4decode.lua
+++ b/kaldi_decode/src/asr_propagator.lua
@@ -15,19 +15,18 @@ local function _add_profile_method(cls)
 end
 _add_profile_method(nerv.MMatrix)
 
-function build_trainer(ifname, feature)
+function build_propagator(ifname, feature)
     local param_repo = nerv.ParamRepo()
     param_repo:import(ifname, nil, gconf)
     local layer_repo = make_layer_repo(param_repo)
     local network = get_decode_network(layer_repo)
     local global_transf = get_global_transf(layer_repo)
-    local input_order = get_input_order()
-    local readers = make_readers(feature, layer_repo)
-    network:init(1)
+    local input_order = get_decode_input_order()
+    local readers = make_decode_readers(feature, layer_repo)
 
-    local iterative_trainer = function()
+    local batch_propagator = function()
         local data = nil
-        for ri = 1, #readers, 1 do
+        for ri = 1, #readers do
             data = readers[ri].reader:get_data()
             if data ~= nil then
                 break
@@ -38,6 +37,9 @@ function build_trainer(ifname, feature)
             return "", nil
         end
 
+        gconf.batch_size = data[input_order[1].id]:nrow()
+        network:init(gconf.batch_size)
+
         local input = {}
         for i, e in ipairs(input_order) do
             local id = e.id
@@ -47,16 +49,15 @@ function build_trainer(ifname, feature)
             local transformed
             if e.global_transf then
                 transformed = nerv.speech_utils.global_transf(data[id],
-                global_transf,
-                gconf.frm_ext or 0, 0,
-                gconf)
+                                    global_transf,
+                                    gconf.frm_ext or 0, 0,
+                                    gconf)
             else
                 transformed = data[id]
             end
             table.insert(input, transformed)
         end
         local output = {nerv.MMatrixFloat(input[1]:nrow(), network.dim_out[1])}
-        network:batch_resize(input[1]:nrow())
         network:propagate(input, output)
         
         local utt = data["key"]
@@ -64,20 +65,17 @@ function build_trainer(ifname, feature)
             nerv.error("no key found.")
         end
 
-        local mat = nerv.MMatrixFloat(output[1]:nrow(), output[1]:ncol())
-        output[1]:copy_toh(mat)
-
         collectgarbage("collect")
-        return utt, mat
+        return utt, output[1]
     end
 
-    return iterative_trainer
+    return batch_propagator
 end
 
 function init(config, feature)
     dofile(config)
     gconf.use_cpu = true -- use CPU to decode
-    trainer = build_trainer(gconf.decode_param, feature)
+    trainer = build_propagator(gconf.decode_param, feature)
 end
 
 function feed()
diff --git a/kaldi_decode/src/nnet-forward.cc b/kaldi_decode/src/nnet-forward.cc
index 4911791..8781705 100644
--- a/kaldi_decode/src/nnet-forward.cc
+++ b/kaldi_decode/src/nnet-forward.cc
@@ -46,7 +46,7 @@ int main(int argc, char *argv[]) {
         const char *usage =
             "Perform forward pass through Neural Network.\n"
             "\n"
-            "Usage:  nnet-forward [options] <nerv-config> <feature-rspecifier> <feature-wspecifier> [nerv4decode.lua]\n"
+            "Usage:  nnet-forward [options] <nerv-config> <feature-rspecifier> <feature-wspecifier> [asr_propagator.lua]\n"
             "e.g.: \n"
             " nnet-forward config.lua ark:features.ark ark:mlpoutput.ark\n";
 
@@ -78,9 +78,9 @@ int main(int argc, char *argv[]) {
         std::string config = po.GetArg(1),
             feature_rspecifier = po.GetArg(2),
             feature_wspecifier = po.GetArg(3),
-            nerv4decode = "src/nerv4decode.lua";
-        if(po.NumArgs() >= 4)
-            nerv4decode = po.GetArg(4);
+            propagator = "src/asr_propagator.lua";
+            if(po.NumArgs() >= 4)
+                propagator = po.GetArg(4);
 
         //Select the GPU
 #if HAVE_CUDA==1
@@ -99,8 +99,8 @@ int main(int argc, char *argv[]) {
 
         lua_State *L = lua_open();
         luaL_openlibs(L);
-        if(luaL_loadfile(L, nerv4decode.c_str()))
-            KALDI_ERR << "luaL_loadfile() " << nerv4decode << " failed " << lua_tostring(L, -1);
+        if(luaL_loadfile(L, propagator.c_str()))
+            KALDI_ERR << "luaL_loadfile() " << propagator << " failed " << lua_tostring(L, -1);
 
         if(lua_pcall(L, 0, 0, 0))
             KALDI_ERR << "lua_pall failed " << lua_tostring(L, -1);