demo now works (without random shuffle)

16 files changed, 361 insertions, 95 deletions

diff --git a/Makefile b/Makefile
index 934235f..f0d319f 100644
--- a/Makefile
+++ b/Makefile
@@ -9,7 +9,8 @@ LUA_LIBS := matrix/init.lua io/init.lua nerv.lua \
 			pl/utils.lua pl/compat.lua \
 			layer/init.lua layer/affine.lua layer/sigmoid.lua layer/softmax_ce.lua \
 			layer/window.lua layer/bias.lua \
-			nn/init.lua nn/layer_repo.lua nn/param_repo.lua nn/layer_dag.lua
+			nn/init.lua nn/layer_repo.lua nn/param_repo.lua nn/layer_dag.lua \
+			io/sgd_buffer.lua
 INCLUDE := -I build/luajit-2.0/include/luajit-2.0/ -DLUA_USE_APICHECK
 CUDA_BASE := /usr/local/cuda-6.5
 CUDA_INCLUDE := -I $(CUDA_BASE)/include/
@@ -53,7 +54,7 @@ $(OBJ_DIR)/matrix/cukernel.o: matrix/generic/cukernel.cu
 
 speech:
 	-mkdir -p build/objs/speech/tnet_io
-	$(MAKE) -C speech/ BUILD_DIR=$(BUILD_DIR) LIB_DIR=$(LIB_DIR) OBJ_DIR=$(CURDIR)/build/objs/speech/
+	$(MAKE) -C speech/ BUILD_DIR=$(BUILD_DIR) LIB_DIR=$(LIB_DIR) OBJ_DIR=$(CURDIR)/build/objs/speech/ LUA_DIR=$(LUA_DIR)
 
 clean:
 	-rm -rf $(OBJ_DIR)
diff --git a/examples/test_dnn_layers.lua b/examples/test_dnn_layers.lua
index 6e4d98d..f306807 100644
--- a/examples/test_dnn_layers.lua
+++ b/examples/test_dnn_layers.lua
@@ -3,7 +3,7 @@ require 'layer.sigmoid'
 require 'layer.softmax_ce'
 
 global_conf = {lrate = 0.8, wcost = 1e-6,
-                momentum = 0.9, mat_type = nerv.CuMatrixFloat}
+                momentum = 0.9, cumat_type = nerv.CuMatrixFloat}
 
 pf = nerv.ChunkFile("affine.param", "r")
 ltp = pf:read_chunk("a", global_conf)
diff --git a/examples/test_nn_lib.lua b/examples/test_nn_lib.lua
index ec338fe..9600917 100644
--- a/examples/test_nn_lib.lua
+++ b/examples/test_nn_lib.lua
@@ -1,14 +1,24 @@
--- require 'layer.affine'
--- require 'layer.sigmoid'
--- require 'layer.softmax_ce'
-
+require 'speech.init'
 gconf = {lrate = 0.8, wcost = 1e-6, momentum = 0.9,
-        mat_type = nerv.CuMatrixFloat,
-        batch_size = 10}
+        cumat_type = nerv.CuMatrixFloat,
+        mmat_type = nerv.MMatrixFloat,
+        batch_size = 256}
 
-param_repo = nerv.ParamRepo({"converted.nerv"})
+param_repo = nerv.ParamRepo({"converted.nerv", "global_transf.nerv"})
 sublayer_repo = nerv.LayerRepo(
     {
+        -- global transf
+        ["nerv.BiasLayer"] =
+        {
+            blayer1 = {{bias = "bias1"}, {dim_in = {429}, dim_out = {429}}},
+            blayer2 = {{bias = "bias2"}, {dim_in = {429}, dim_out = {429}}}
+        },
+        ["nerv.WindowLayer"] =
+        {
+            wlayer1 = {{window = "window1"}, {dim_in = {429}, dim_out = {429}}},
+            wlayer2 = {{window = "window2"}, {dim_in = {429}, dim_out = {429}}}
+        },
+        -- biased linearity
         ["nerv.AffineLayer"] =
         {
             affine0 = {{ltp = "affine0_ltp", bp = "affine0_bp"},
@@ -40,7 +50,7 @@ sublayer_repo = nerv.LayerRepo(
         },
         ["nerv.SoftmaxCELayer"] =
         {
-            softmax_ce0 = {{}, {dim_in = {3001, 3001}, dim_out = {}}}
+            softmax_ce0 = {{}, {dim_in = {3001, 1}, dim_out = {}, compressed = true}}
         }
     }, param_repo, gconf)
 
@@ -48,8 +58,19 @@ layer_repo = nerv.LayerRepo(
     {
         ["nerv.DAGLayer"] =
         {
+            global_transf = {{}, {
+                dim_in = {429}, dim_out = {429},
+                sub_layers = sublayer_repo,
+                connections = {
+                    ["<input>[1]"] = "blayer1[1]",
+                    ["blayer1[1]"] = "wlayer1[1]",
+                    ["wlayer1[1]"] = "blayer2[1]",
+                    ["blayer2[1]"] = "wlayer2[1]",
+                    ["wlayer2[1]"] = "<output>[1]"
+                }
+            }},
             main = {{}, {
-                dim_in = {429, 3001}, dim_out = {},
+                dim_in = {429, 1}, dim_out = {},
                 sub_layers = sublayer_repo,
                 connections = {
                     ["<input>[1]"] = "affine0[1]",
@@ -74,24 +95,52 @@ layer_repo = nerv.LayerRepo(
         }
     }, param_repo, gconf)
 
-df = nerv.ChunkFile("input.param", "r")
-label = nerv.CuMatrixFloat(10, 3001)
-label:fill(0)
-for i = 0, 9 do
-    label[i][i] = 1.0
-end
+tnet_reader = nerv.TNetReader(gconf,
+    {
+        id = "main_scp",
+        scp_file = "/slfs1/users/mfy43/swb_ivec/train_bp.scp",
+--        scp_file = "t.scp",
+        conf_file = "/slfs1/users/mfy43/swb_ivec/plp_0_d_a.conf",
+        frm_ext = 5,
+        mlfs = {
+            ref = {
+                file = "/slfs1/users/mfy43/swb_ivec/ref.mlf",
+                format = "map",
+                format_arg = "/slfs1/users/mfy43/swb_ivec/dict",
+                dir = "*/",
+                ext = "lab"
+            }
+        },
+        global_transf = layer_repo:get_layer("global_transf")
+    })
+
+buffer = nerv.SGDBuffer(gconf,
+    {
+        buffer_size = 8192,
+        readers = {
+            { reader = tnet_reader,
+              data = {main_scp = 429, ref = 1}}
+        }
+    })
 
-input = {df:read_chunk("input", gconf).trans, label}
-output = {}
-err_input = {}
-err_output = {input[1]:create()}
 sm = sublayer_repo:get_layer("softmax_ce0")
 main = layer_repo:get_layer("main")
-main:init()
-for i = 0, 3 do
+main:init(gconf.batch_size)
+cnt = 0
+for data in buffer.get_data, buffer do
+    if cnt == 1000 then break end
+    cnt = cnt + 1
+    input = {data.main_scp, data.ref}
+    output = {}
+    err_input = {}
+    err_output = {input[1]:create()}
+    
     main:propagate(input, output)
     main:back_propagate(err_output, err_input, input, output)
     main:update(err_input, input, output)
+
     nerv.utils.printf("cross entropy: %.8f\n", sm.total_ce)
     nerv.utils.printf("frames: %.8f\n", sm.total_frames)
+    nerv.utils.printf("err/frm: %.8f\n", sm.total_ce / sm.total_frames)
+    collectgarbage("collect")
 end
diff --git a/io/init.lua b/io/init.lua
index 4a663a7..9bbd51a 100644
--- a/io/init.lua
+++ b/io/init.lua
@@ -28,3 +28,25 @@ function nerv.ChunkFile:read_chunk(id, global_conf)
     chunk:read(self:get_chunkdata(id))
     return chunk
 end
+
+local DataReader = nerv.class("nerv.DataReader")
+
+function DataReader:__init(global_conf, reader_conf)
+    nerv.error_method_not_implemented()
+end
+
+function DataReader:get_data()
+    nerv.error_method_not_implemented()
+end
+
+local DataBuffer = nerv.class("nerv.DataBuffer")
+
+function DataBuffer:__init(global_conf, buffer_conf)
+    nerv.error_method_not_implemented()
+end
+
+function DataBuffer:get_batch()
+    nerv.error_method_not_implemented()
+end
+
+require 'io.sgd_buffer'
diff --git a/io/sgd_buffer.lua b/io/sgd_buffer.lua
new file mode 100644
index 0000000..dadcf67
--- /dev/null
+++ b/io/sgd_buffer.lua
@@ -0,0 +1,99 @@
+local SGDBuffer = nerv.class("nerv.SGDBuffer", "nerv.DataBuffer")
+
+function SGDBuffer:__init(global_conf, buffer_conf)
+    self.gconf = global_conf
+    self.buffer_size = math.floor(buffer_conf.buffer_size /
+                                global_conf.batch_size) * global_conf.batch_size
+    self.head = 0
+    self.tail = 0
+    self.readers = {}
+    for i, reader_spec in ipairs(buffer_conf.readers) do
+        local buffs = {}
+        for id, width in pairs(reader_spec.data) do
+            buffs[id] = {data = global_conf.mmat_type(self.buffer_size, width),
+                        leftover = {},
+                        width = width}
+        end
+        table.insert(self.readers, {buffs = buffs,
+                                    reader = reader_spec.reader,
+                                    tail = 0,
+                                    has_leftover = false})
+    end
+end
+
+function SGDBuffer:saturate()
+    local buffer_size = self.buffer_size
+    self.head = 0
+    self.tail = buffer_size
+    for i, reader in ipairs(self.readers) do
+        reader.tail = 0
+        if reader.has_leftover then
+            local lrow
+            for id, buff in pairs(reader.buffs) do
+                lrow = buff.leftover:nrow()
+                if lrow > buffer_size then
+                    nerv.error("buffer size is too small to contain leftovers")
+                end
+                buff.data:copy_from(buff.leftover, 0, lrow)
+            end
+            reader.tail = lrow
+            reader.has_leftover = false
+        end
+        while reader.tail < buffer_size do
+            local data = reader.reader:get_data()
+            if data == nil then
+                break
+            end
+            local drow = nil
+            for id, d in pairs(data) do
+                if drow == nil then
+                    drow = d:nrow()
+                elseif d:nrow() ~= drow then
+                    nerv.error("reader provides with inconsistent rows of data")
+                end
+            end
+            local remain = buffer_size - reader.tail
+            if drow > remain then
+                for id, buff in pairs(reader.buffs) do
+                    local d = data[id]
+                    if d == nil then
+                        nerv.error("reader does not provide data for %s", id)
+                    end
+                    buff.leftover = self.gconf.mmat_type(drow - remain,
+                                                        buff.width)
+                    buff.leftover:copy_from(d, remain, drow)
+                end
+                drow = remain
+                reader.has_leftover = true
+            end
+            for id, buff in pairs(reader.buffs) do
+                buff.data:copy_from(data[id], 0, drow, reader.tail)
+            end
+            reader.tail = reader.tail + drow
+        end
+        self.tail = math.min(self.tail, reader.tail)
+    end
+    return self.tail >= self.gconf.batch_size
+end
+
+function SGDBuffer:get_data()
+    local batch_size = self.gconf.batch_size
+    if self.head >= self.tail then -- buffer is empty
+        if not self:saturate() then
+            return nil -- the remaining data cannot build a batch
+        end
+    end
+    if self.head + batch_size > self.tail then
+        return nil -- the remaining data cannot build a batch
+    end
+    local res = {}
+    for i, reader in ipairs(self.readers) do
+        for id, buff in pairs(reader.buffs) do
+            local batch = self.gconf.cumat_type(batch_size, buff.width)
+            batch:copy_fromh(buff.data, self.head, self.head + batch_size)
+            res[id] = batch
+        end
+    end
+    self.head = self.head + batch_size
+    return res
+end
diff --git a/layer/affine.lua b/layer/affine.lua
index 90a1d16..59a0e91 100644
--- a/layer/affine.lua
+++ b/layer/affine.lua
@@ -4,7 +4,7 @@ local BiasParam = nerv.class('nerv.BiasParam', 'nerv.MatrixParam')
 local AffineLayer = nerv.class('nerv.AffineLayer', 'nerv.Layer')
 
 function MatrixParam:read(pcdata)
-    self.trans = self.gconf.mat_type.new_from_host(
+    self.trans = self.gconf.cumat_type.new_from_host(
                     nerv.MMatrixFloat.load(pcdata))
 end
 
diff --git a/layer/init.lua b/layer/init.lua
index c8c691b..38bcd7f 100644
--- a/layer/init.lua
+++ b/layer/init.lua
@@ -2,50 +2,50 @@
 
 local Param = nerv.class('nerv.Param')
 
-function nerv.Param:__init(id, global_conf)
+function Param:__init(id, global_conf)
     self.id = id
     self.gconf = global_conf
 end
 
-function nerv.Param:get_info()
+function Param:get_info()
     return self.info
 end
 
-function nerv.Param:set_info(info)
+function Param:set_info(info)
     self.info = info
 end
 
-function nerv.Param:read(pfhandle)
+function Param:read(pfhandle)
     nerv.error_method_not_implemented()
 end
 
-function nerv.Param:write(pfhandle)
+function Param:write(pfhandle)
     nerv.error_method_not_implemented()
 end
 
 local Layer = nerv.class('nerv.Layer')
 
-function nerv.Layer:__init(id, global_conf, ...)
+function Layer:__init(id, global_conf, layer_conf)
     nerv.error_method_not_implemented()
 end
 
-function nerv.Layer:init(id)
+function Layer:init(id)
     nerv.error_method_not_implemented()
 end
 
-function nerv.Layer:update(bp_err, input, output)
+function Layer:update(bp_err, input, output)
     nerv.error_method_not_implemented()
 end
 
-function nerv.Layer:propagate(input, output)
+function Layer:propagate(input, output)
     nerv.error_method_not_implemented()
 end
 
-function nerv.Layer:back_propagate(next_bp_err, bp_err, input, output)
+function Layer:back_propagate(next_bp_err, bp_err, input, output)
     nerv.error_method_not_implemented()
 end
 
-function nerv.Layer:check_dim_len(len_in, len_out)
+function Layer:check_dim_len(len_in, len_out)
     local expected_in = #self.dim_in
     local expected_out = #self.dim_out
     if len_in > 0 and expected_in ~= len_in then
@@ -58,7 +58,7 @@ function nerv.Layer:check_dim_len(len_in, len_out)
     end
 end
 
-function nerv.Layer:get_dim()
+function Layer:get_dim()
     return self.dim_in, self.dim_out
 end
 
diff --git a/layer/softmax_ce.lua b/layer/softmax_ce.lua
index 09eb3a9..cf98c45 100644
--- a/layer/softmax_ce.lua
+++ b/layer/softmax_ce.lua
@@ -5,6 +5,10 @@ function SoftmaxCELayer:__init(id, global_conf, layer_conf)
     self.gconf = global_conf
     self.dim_in = layer_conf.dim_in
     self.dim_out = layer_conf.dim_out
+    self.compressed = layer_conf.compressed
+    if self.compressed == nil then
+        self.compressed = false
+    end
     self:check_dim_len(2, -1) -- two inputs: nn output and label
 end
 
@@ -26,15 +30,21 @@ function SoftmaxCELayer:propagate(input, output)
     soutput:softmax(input[1])
     local ce = soutput:create()
     ce:log_elem(soutput)
-    ce:mul_elem(ce, input[2])
---     print(input[1][0])
---     print(soutput[1][0])
-     -- add total ce
+    local label = input[2]
+    if self.compressed then
+        label = label:decompress(input[1]:ncol())
+    end
+    ce:mul_elem(ce, label)
+    -- add total ce
     self.total_ce = self.total_ce - ce:rowsum():colsum()[0]
     self.total_frames = self.total_frames + soutput:nrow()
 end
 
 function SoftmaxCELayer:back_propagate(next_bp_err, bp_err, input, output)
     -- softmax output - label
-    next_bp_err[1]:add(self.soutput, input[2], 1.0, -1.0)
+    local label = input[2]
+    if self.compressed then
+        label = label:decompress(input[1]:ncol())
+    end
+    next_bp_err[1]:add(self.soutput, label, 1.0, -1.0)
 end
diff --git a/matrix/cuda_helper.h b/matrix/cuda_helper.h
index c0fa618..cedc643 100644
--- a/matrix/cuda_helper.h
+++ b/matrix/cuda_helper.h
@@ -23,7 +23,7 @@
 #define CHECK_SAME_DIMENSION(a, b) \
     do { \
         if (!(a->nrow == b->nrow && a->ncol == b->ncol)) \
-            nerv_error(L, "Matrices should be of the same dimension"); \
+            nerv_error(L, "matrices should be of the same dimension"); \
     } while (0)
 
 static const char *cublasGetErrorString(cublasStatus_t err) {
diff --git a/matrix/cukernel.h b/matrix/cukernel.h
index 178b7d3..7d2168e 100644
--- a/matrix/cukernel.h
+++ b/matrix/cukernel.h
@@ -13,4 +13,5 @@ void cudak_(cuda_fill)(Matrix *a, double val);
 void cudak_(cuda_expand_frm)(const Matrix *a, Matrix *b, int context);
 void cudak_(cuda_rearrange_frm)(const Matrix *a, Matrix *b, int step);
 void cudak_(cuda_scale_row)(const Matrix *a, Matrix *b);
+void cudak_(cuda_decompress)(const Matrix *a, Matrix *b);
 #endif
diff --git a/matrix/generic/cukernel.cu b/matrix/generic/cukernel.cu
index 1d8b983..05a1e78 100644
--- a/matrix/generic/cukernel.cu
+++ b/matrix/generic/cukernel.cu
@@ -187,6 +187,15 @@ __global__ void cudak_(scale_row)(const MATRIX_ELEM *a, MATRIX_ELEM *b,
     b[j + i * stride] *= a[j];
 }
 
+__global__ void cudak_(decompress)(const MATRIX_ELEM *a, MATRIX_ELEM *b,
+                                    int nrow, int ncol,
+                                    int stride_a, int stride_b) {
+    int j = blockIdx.x * blockDim.x + threadIdx.x;
+    int i = blockIdx.y * blockDim.y + threadIdx.y;
+    if (i >= nrow || j >= ncol) return;
+    b[lrintf(a[j + i * stride_a]) + i * stride_b] = 1.0;
+}
+
 extern "C" {
 #include "../cukernel.h"
     void cudak_(cuda_log_elem)(const Matrix *a, Matrix *b) {
@@ -385,5 +394,15 @@ extern "C" {
             (MATRIX_ELEM_PTR(a), MATRIX_ELEM_PTR(b),
              b->nrow, b->ncol, b->stride / sizeof(MATRIX_ELEM));
     }
+
+    void cudak_(cuda_decompress)(const Matrix *a, Matrix *b) {
+        dim3 threadsPerBlock(1, CUDA_THREADS_NN);
+        dim3 numBlocks(1, CEIL_DIV(a->nrow, threadsPerBlock.y));
+        cudak_(decompress)<<<numBlocks, threadsPerBlock>>> \
+            (MATRIX_ELEM_PTR(a), MATRIX_ELEM_PTR(b),
+             a->nrow, a->ncol,
+             a->stride / sizeof(MATRIX_ELEM),
+             b->stride / sizeof(MATRIX_ELEM));
+    }
 }
 #endif
diff --git a/matrix/generic/cumatrix.c b/matrix/generic/cumatrix.c
index 0df1bd7..373fc42 100644
--- a/matrix/generic/cumatrix.c
+++ b/matrix/generic/cumatrix.c
@@ -74,7 +74,8 @@ static int nerv_matrix_(mul)(lua_State *L) {
     if (an != bm)
         nerv_error(L, "Wrong dimension of multipliers");
 /*    MATRIX_ELEM alpha = 1.0f, beta = 0.0f; */
-    CUBLAS_SAFE_CALL( //Because matrix in Nerv is row-major, here b comes first
+    /* Because matrix in Nerv is row-major, here b comes first */
+    CUBLAS_SAFE_CALL(
             NERV_CUBLAS_(gemm)(cublas_handle, tb, ta,
                 bn, am, bm,
                 &alpha,
@@ -113,9 +114,11 @@ static int nerv_matrix_(sigmoid_grad)(lua_State *L) {
 static int nerv_matrix_(softmax)(lua_State *L) {
     Matrix *a = luaT_checkudata(L, 2, nerv_matrix_(tname));
     Matrix *b = luaT_checkudata(L, 1, nerv_matrix_(tname));
-    Matrix *max = nerv_matrix_(new_)(L, a->nrow, 1);
-    Matrix *dno = nerv_matrix_(new_)(L, a->nrow, 1);
+    Matrix *max;
+    Matrix *dno;
     CHECK_SAME_DIMENSION(a, b);
+    max = nerv_matrix_(new_)(L, a->nrow, 1);
+    dno = nerv_matrix_(new_)(L, a->nrow, 1);
     cudak_(cuda_rowmax)(a, max);
     cudak_(cuda_softmax_denominator)(a, max, dno);
     cudak_(cuda_softmax_final)(a, max, dno, b);
@@ -168,26 +171,22 @@ static int nerv_matrix_(fill)(lua_State *L) {
     return 0;
 }
 
-static int nerv_matrix_(copy_fromd)(lua_State *L) { 
+static int nerv_matrix_(copy_fromd)(lua_State *L) {
     Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
     Matrix *b = luaT_checkudata(L, 2, nerv_matrix_(tname));
-    CHECK_SAME_DIMENSION(a, b);
-    CUDA_SAFE_SYNC_CALL(
-            cudaMemcpy2D(MATRIX_ELEM_PTR(a), a->stride,
-                MATRIX_ELEM_PTR(b), b->stride,
-                sizeof(MATRIX_ELEM) * b->ncol, b->nrow,
-                cudaMemcpyDeviceToDevice));
-    return 0;
-}
-
-static int nerv_matrix_(copy_tod)(lua_State *L) {
-    Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
-    Matrix *b = luaT_checkudata(L, 2, nerv_matrix_(tname));
-    CHECK_SAME_DIMENSION(a, b);
+    int nargs = lua_gettop(L);
+    int b_begin = nargs > 2 ? luaL_checkinteger(L, 3) : 0;
+    int b_end = nargs > 3 ? luaL_checkinteger(L, 4) : b->nrow;
+    int a_begin = nargs > 4 ? luaL_checkinteger(L, 5) : 0;
+    if (!(0 <= b_begin && b_begin < b_end && b_end <= b->nrow &&
+            a_begin + b_end - b_begin <= a->nrow))
+        nerv_error(L, "invalid copy interval");
+    if (a->ncol != b->ncol)
+        nerv_error(L, "matrices should be of the same dimension");
     CUDA_SAFE_SYNC_CALL(
-            cudaMemcpy2D(MATRIX_ELEM_PTR(b), b->stride,
-                MATRIX_ELEM_PTR(a), a->stride,
-                sizeof(MATRIX_ELEM) * a->ncol, a->nrow,
+            cudaMemcpy2D(MATRIX_ROW_PTR(a, a_begin), a->stride,
+                MATRIX_ROW_PTR(b, b_begin), b->stride,
+                sizeof(MATRIX_ELEM) * b->ncol, b_end - b_begin,
                 cudaMemcpyDeviceToDevice));
     return 0;
 }
@@ -196,11 +195,19 @@ extern const char *MATRIX_CUMATRIX_HOST_TNAME;
 static int nerv_matrix_(copy_fromh)(lua_State *L) { 
     Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
     Matrix *b = luaT_checkudata(L, 2, MATRIX_CUMATRIX_HOST_TNAME);
-    CHECK_SAME_DIMENSION(a, b);
+    int nargs = lua_gettop(L);
+    int b_begin = nargs > 2 ? luaL_checkinteger(L, 3) : 0;
+    int b_end = nargs > 3 ? luaL_checkinteger(L, 4) : b->nrow;
+    int a_begin = nargs > 4 ? luaL_checkinteger(L, 5) : 0;
+    if (!(0 <= b_begin && b_begin < b_end && b_end <= b->nrow &&
+            a_begin + b_end - b_begin <= a->nrow))
+        nerv_error(L, "invalid copy interval");
+    if (a->ncol != b->ncol)
+        nerv_error(L, "matrices should be of the same dimension");
     CUDA_SAFE_SYNC_CALL(
-            cudaMemcpy2D(MATRIX_ELEM_PTR(a), a->stride,
-                MATRIX_ELEM_PTR(b), b->stride,
-                sizeof(MATRIX_ELEM) * b->ncol, b->nrow,
+            cudaMemcpy2D(MATRIX_ROW_PTR(a, a_begin), a->stride,
+                MATRIX_ROW_PTR(b, b_begin), b->stride,
+                sizeof(MATRIX_ELEM) * b->ncol, b_end - b_begin,
                 cudaMemcpyHostToDevice));
     return 0;
 }
@@ -208,11 +215,19 @@ static int nerv_matrix_(copy_fromh)(lua_State *L) {
 static int nerv_matrix_(copy_toh)(lua_State *L) {
     Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
     Matrix *b = luaT_checkudata(L, 2, MATRIX_CUMATRIX_HOST_TNAME);
-    CHECK_SAME_DIMENSION(a, b);
+    int nargs = lua_gettop(L);
+    int a_begin = nargs > 2 ? luaL_checkinteger(L, 3) : 0;
+    int a_end = nargs > 3 ? luaL_checkinteger(L, 4) : a->nrow;
+    int b_begin = nargs > 4 ? luaL_checkinteger(L, 5) : 0;
+    if (!(0 <= a_begin && a_begin < a_end && a_end <= a->nrow &&
+            b_begin + a_end - a_begin <= b->nrow))
+        nerv_error(L, "invalid copy interval");
+    if (b->ncol != a->ncol)
+        nerv_error(L, "matrices should be of the same dimension");
     CUDA_SAFE_SYNC_CALL(
-            cudaMemcpy2D(MATRIX_ELEM_PTR(b), b->stride,
-                MATRIX_ELEM_PTR(a), a->stride,
-                sizeof(MATRIX_ELEM) * a->ncol, a->nrow,
+            cudaMemcpy2D(MATRIX_ROW_PTR(b, b_begin), b->stride,
+                MATRIX_ROW_PTR(a, a_begin), a->stride,
+                sizeof(MATRIX_ELEM) * a->ncol, a_end - a_begin,
                 cudaMemcpyDeviceToHost));
     return 0;
 }
@@ -221,6 +236,7 @@ static int nerv_matrix_(trans)(lua_State *L) {
     Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
     Matrix *b = nerv_matrix_(new_)(L, a->ncol, a->nrow);
     MATRIX_ELEM alpha = 1, beta = 0;
+    /* FIXME: possible memory leak when lua error is raised */
     CUBLAS_SAFE_CALL(
             NERV_CUBLAS_(geam)(cublas_handle, CUBLAS_OP_T, CUBLAS_OP_T,
                 a->nrow, a->ncol,
@@ -251,6 +267,19 @@ static int nerv_matrix_(log_elem)(lua_State *L) {
     return 0;
 }
 
+static int nerv_matrix_(decompress)(lua_State *L) {
+    Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
+    Matrix *b;
+    int orig_col = luaL_checkinteger(L, 2);
+    if (a->ncol != 1)
+        nerv_error(L, "the compressed matrix must be a column vector");
+    b = nerv_matrix_(new_)(L, a->nrow, orig_col);
+    cudak_(cuda_fill)(b, 0.0);
+    cudak_(cuda_decompress)(a, b);
+    luaT_pushudata(L, b, nerv_matrix_(tname));
+    return 1;
+}
+
 extern const char *nerv_matrix_host_int_tname;
 static int nerv_matrix_(copy_rows_fromh_by_idx)(lua_State *L) {
     Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
@@ -322,11 +351,11 @@ static const luaL_Reg nerv_matrix_(extra_methods)[] = {
     {"rowsum", nerv_matrix_(rowsum)},
     {"rowmax", nerv_matrix_(rowmax)},
     {"trans", nerv_matrix_(trans)},
+    {"decompress", nerv_matrix_(decompress)},
     /* in-place calc */
     {"copy_fromh", nerv_matrix_(copy_fromh)},
     {"copy_fromd", nerv_matrix_(copy_fromd)},
     {"copy_toh", nerv_matrix_(copy_toh)},
-    {"copy_tod", nerv_matrix_(copy_tod)},
     {"add", nerv_matrix_(add)},
     {"mul", nerv_matrix_(mul)},
     {"add_row", nerv_matrix_(add_row)},
diff --git a/matrix/generic/mmatrix.c b/matrix/generic/mmatrix.c
index 3a9ae79..4b722f3 100644
--- a/matrix/generic/mmatrix.c
+++ b/matrix/generic/mmatrix.c
@@ -11,6 +11,7 @@
 #define NERV_GENERIC_MATRIX
 #include "../../common.h"
 #include "../../io/chunk_file.h"
+#include "string.h"
 
 static void host_matrix_(alloc)(lua_State *L,
                                 MATRIX_ELEM **dptr, size_t *stride,
@@ -96,10 +97,27 @@ int nerv_matrix_(save)(lua_State *L) {
     return 0;
 }
 
-
+static int nerv_matrix_(copy_from)(lua_State *L) {
+    Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
+    Matrix *b = luaT_checkudata(L, 2, nerv_matrix_(tname));
+    int nargs = lua_gettop(L);
+    int b_begin = nargs > 2 ? luaL_checkinteger(L, 3) : 0;
+    int b_end = nargs > 3 ? luaL_checkinteger(L, 4) : b->nrow;
+    int a_begin = nargs > 4 ? luaL_checkinteger(L, 5) : 0;
+    if (!(0 <= b_begin && b_begin < b_end && b_end <= b->nrow &&
+            a_begin + b_end - b_begin <= a->nrow))
+        nerv_error(L, "invalid copy interval");
+    if (a->ncol != b->ncol)
+        nerv_error(L, "matrices should be of the same dimension");
+    memmove(MATRIX_ROW_PTR(a, a_begin),
+            MATRIX_ROW_PTR(b, b_begin),
+            sizeof(MATRIX_ELEM) * b->ncol * (b_end - b_begin));
+    return 0;
+}
 static const luaL_Reg nerv_matrix_(extra_methods)[] = {
     {"load", nerv_matrix_(load)},
     {"save", nerv_matrix_(save)},
+    {"copy_from", nerv_matrix_(copy_from)},
     {NULL, NULL}
 };
 
diff --git a/matrix/init.lua b/matrix/init.lua
index f309f81..9637391 100644
--- a/matrix/init.lua
+++ b/matrix/init.lua
@@ -22,7 +22,8 @@ function nerv.Matrix:__tostring__()
             table.insert(strt, "\n")
         end
     end
-    table.insert(strt, string.format("[Matrix %d x %d]", nrow, ncol))
+    table.insert(strt, string.format(
+        "[%s %d x %d]", self.__typename, nrow, ncol))
     return table.concat(strt)
 end
 
diff --git a/nn/layer_dag.lua b/nn/layer_dag.lua
index 1ab18fa..4ee829e 100644
--- a/nn/layer_dag.lua
+++ b/nn/layer_dag.lua
@@ -44,6 +44,7 @@ function nerv.DAGLayer:__init(id, global_conf, layer_conf)
     local outputs = {}
     local dim_in = layer_conf.dim_in
     local dim_out = layer_conf.dim_out
+    local parsed_conn = {}
     for from, to in pairs(layer_conf.connections) do
         local id_from, port_from = parse_id(from)
         local id_to, port_to = parse_id(to)
@@ -76,32 +77,18 @@ function nerv.DAGLayer:__init(id, global_conf, layer_conf)
             if output_dim[port_from] ~= input_dim[port_to] then
                 nerv.error("mismatching data dimension between %s and %s", from, to)
             end
-            local mid = global_conf.mat_type(global_conf.batch_size,
-                                                output_dim[port_from])
-            local err_mid = mid:create() 
-
-            ref_from.outputs[port_from] = mid
-            ref_to.inputs[port_to] = mid
-
-            ref_from.err_inputs[port_from] = err_mid
-            ref_to.err_outputs[port_to] = err_mid
 
+            table.insert(parsed_conn,
+                {{ref_from, port_from}, {ref_to, port_to}})
             table.insert(ref_from.next_layers, ref_to) -- add edge
             ref_to.in_deg = ref_to.in_deg + 1          -- increase the in-degree of the target layer
         end
     end
-    self.layers = layers
-    self.inputs = inputs
-    self.outputs = outputs
-    self.dim_in = dim_in
-    self.dim_out = dim_out
-end
 
-function nerv.DAGLayer:init(id) -- topology sort
     local queue = {}
     local l = 1
     local r = 1
-    for id, ref in pairs(self.layers) do
+    for id, ref in pairs(layers) do
         if ref.in_deg == 0 then
             table.insert(queue, ref)
             nerv.utils.printf("adding source layer: %s\n", id)
@@ -126,20 +113,50 @@ function nerv.DAGLayer:init(id) -- topology sort
     for i = 1, #queue do
         nerv.utils.printf("queued layer: %s\n", queue[i].layer.id)
     end
-    self.queue = queue
-    for id, ref in pairs(self.layers) do
+
+    for id, ref in pairs(layers) do
         -- check wether the graph is connected
         if ref.visited == false then
             nerv.utils.printf("warning: layer %s is ignored\n", id)
         end
+    end
+
+    self.layers = layers
+    self.inputs = inputs
+    self.outputs = outputs
+    self.dim_in = dim_in
+    self.dim_out = dim_out
+    self.parsed_conn = parsed_conn
+    self.queue = queue
+    self.gconf = global_conf
+end
+
+function nerv.DAGLayer:init(batch_size) -- topology sort
+    for i, conn in ipairs(self.parsed_conn) do
+        local _, output_dim
+        local ref_from, port_from, ref_to, port_to
+        ref_from, port_from = unpack(conn[1])
+        ref_to, port_to = unpack(conn[2])
+        _, output_dim = ref_from.layer:get_dim()
+        local mid = self.gconf.cumat_type(batch_size,
+                                        output_dim[port_from])
+        local err_mid = mid:create()
+
+        ref_from.outputs[port_from] = mid
+        ref_to.inputs[port_to] = mid
+
+        ref_from.err_inputs[port_from] = err_mid
+        ref_to.err_outputs[port_to] = err_mid
+    end
+    for id, ref in pairs(self.layers) do
         for i = 1, ref.input_len do
             if ref.inputs[i] == nil then
-                nerv.error("dangling port %d of layer %s", i, id)
+                nerv.error("dangling input port %d of layer %s", i, id)
             end
         end
         for i = 1, ref.output_len do
             if ref.outputs[i] == nil then
-                nerv.error("dangling port %d of layer %s", i, id)
+                nerv.error("dangling output port %d of layer %s", i, id)
             end
         end
         -- initialize sub layers
diff --git a/speech b/speech
-Subproject 0c6ca6a17f06821cd5d612f489ca6cb68c2c4d5
+Subproject a753eca0121ac3ec81ed76bd719d3f1cb952268