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-rw-r--r--Makefile4
-rw-r--r--nerv/Makefile16
-rw-r--r--nerv/config.ld8
-rw-r--r--nerv/doc/lua/index.html71
-rw-r--r--nerv/doc/lua/ldoc_pale.css304
-rw-r--r--nerv/doc/lua/modules/layer.affine.html145
-rw-r--r--nerv/doc/lua/modules/matrix.html409
-rw-r--r--nerv/doc/lua/modules/nerv.html428
-rw-r--r--nerv/examples/asr_trainer.lua18
-rw-r--r--nerv/init.lua64
-rw-r--r--nerv/io/sgd_buffer.lua31
-rw-r--r--nerv/layer/affine.lua63
-rw-r--r--nerv/layer/bias.lua2
-rw-r--r--nerv/layer/combiner.lua13
-rw-r--r--nerv/layer/init.lua35
-rw-r--r--nerv/layer/mse.lua17
-rw-r--r--nerv/layer/softmax_ce.lua11
-rw-r--r--nerv/layer/window.lua2
-rw-r--r--nerv/lib/common.c1
-rw-r--r--nerv/lib/io/chunk_file.c3
-rw-r--r--nerv/lib/matrix/cuda_helper.h1
-rw-r--r--nerv/lib/matrix/cukernel.h11
-rw-r--r--nerv/lib/matrix/cumatrix.c4
-rw-r--r--nerv/lib/matrix/generic/cukernel.cu14
-rw-r--r--nerv/lib/matrix/generic/cumatrix.c2
-rw-r--r--nerv/lib/matrix/generic/cumatrix.h11
-rw-r--r--nerv/lib/matrix/generic/matrix.c8
-rw-r--r--nerv/lib/matrix/generic/mmatrix.c505
-rw-r--r--nerv/lib/matrix/generic/mmatrix.h45
-rw-r--r--nerv/lib/matrix/matrix.h4
-rw-r--r--nerv/lib/matrix/mmatrix.c5
-rw-r--r--nerv/matrix/generic/cumatrix.c294
-rw-r--r--nerv/matrix/generic/matrix.c213
-rw-r--r--nerv/matrix/generic/mmatrix.c60
-rw-r--r--nerv/matrix/init.lua57
-rw-r--r--nerv/matrix/mmatrix.c3
-rw-r--r--nerv/nerv-scm-1.rockspec2
-rw-r--r--nerv/nn/layer_dag.lua9
-rw-r--r--nerv/nn/layer_repo.lua3
-rw-r--r--nerv/nn/param_repo.lua6
-rw-r--r--nerv/test/cumatrix_func.lua2
-rw-r--r--nerv/test/cumatrix_func.out1952
-rw-r--r--nerv/test/matrix_func.lua168
-rw-r--r--nerv/test/mmatrix_func.lua2
-rw-r--r--nerv/test/mmatrix_func.out1952
45 files changed, 6604 insertions, 374 deletions
diff --git a/Makefile b/Makefile
index 72a5915..0982295 100644
--- a/Makefile
+++ b/Makefile
@@ -11,5 +11,9 @@ install:
speech:
cd speech/speech_utils; $(PREFIX)/bin/luarocks make
cd speech/htk_io; $(PREFIX)/bin/luarocks make
+ cd speech/kaldi_io; $(PREFIX)/bin/luarocks make
clean:
cd nerv && make clean
+ cd speech/speech_utils && make clean
+ cd speech/htk_io && make clean
+ cd speech/kaldi_io && make clean
diff --git a/nerv/Makefile b/nerv/Makefile
index db5df22..a472cfc 100644
--- a/nerv/Makefile
+++ b/nerv/Makefile
@@ -32,11 +32,11 @@ LIBS := $(INST_LIBDIR)/libnerv.so $(LIB_PATH)/libnervcore.so $(LIB_PATH)/libluaT
LUA_LIBS := matrix/init.lua io/init.lua init.lua \
layer/init.lua layer/affine.lua layer/sigmoid.lua layer/tanh.lua layer/softmax_ce.lua layer/softmax.lua \
layer/window.lua layer/bias.lua layer/combiner.lua layer/mse.lua layer/affine_recurrent.lua \
- layer/elem_mul.lua layer/gate_fff.lua \
+ layer/elem_mul.lua layer/gate_fff.lua \
nn/init.lua nn/layer_repo.lua nn/param_repo.lua nn/layer_dag.lua \
io/sgd_buffer.lua \
- tnn/init.lua tnn/layer_dag_t.lua tnn/sutil.lua tnn/tnn.lua \
- tnn/layersT/dropout_t.lua tnn/layersT/lstm_t.lua tnn/layersT/gru_t.lua tnn/layersT/softmax_ce_t.lua
+ tnn/init.lua tnn/layer_dag_t.lua tnn/sutil.lua tnn/tnn.lua \
+ tnn/layersT/dropout_t.lua tnn/layersT/lstm_t.lua tnn/layersT/gru_t.lua tnn/layersT/softmax_ce_t.lua
INCLUDE := -I $(LUA_INCDIR) -DLUA_USE_APICHECK
#CUDA_BASE := /usr/local/cuda-7.0
@@ -45,9 +45,11 @@ CUDA_INCLUDE := -I $(CUDA_BASE)/include/
INCLUDE += $(CUDA_INCLUDE)
LDFLAGS := -L$(CUDA_BASE)/lib64/ -Wl,-rpath=$(CUDA_BASE)/lib64/ -lcudart -lcublas -lcurand
-CFLAGS := -Wall -Wextra -O2
NVCC := $(CUDA_BASE)/bin/nvcc
-NVCC_FLAGS := -Xcompiler -fPIC,-Wall,-Wextra
+EMPTY :=
+SPACE := $(EMPTY) $(EMPTY)
+COMMA := ,
+NVCC_FLAGS := -Xcompiler -fPIC -Xcompiler $(subst $(SPACE),$(COMMA),$(CFLAGS))
LUA_LIBS := $(addprefix $(LUA_DIR)/,$(LUA_LIBS))
@@ -58,12 +60,12 @@ $(OBJ_DIR) $(LUA_DIR) $(OBJ_SUBDIR) $(LUA_SUBDIR) $(INC_SUBDIR):
$(OBJ_DIR)/%.o: %.c $(patsubst /%.o,/%.c,$@)
gcc -c -o $@ $< $(INCLUDE) -fPIC $(CFLAGS)
$(OBJ_DIR)/lib/matrix/cukernel.o: lib/matrix/cukernel.cu
- $(NVCC) -c -o $@ $< $(INCLUDE) $(NVCC_FLAGS) $(CFLAGS)
+ $(NVCC) -c -o $@ $< $(INCLUDE) $(NVCC_FLAGS)
$(LUA_DIR)/%.lua: %.lua
cp $< $@
$(LIB_PATH)/libnervcore.so: $(CORE_OBJS)
- gcc -shared -o $@ $^ $(LDFLAGS)
+ gcc -shared -o $@ $^ $(LDFLAGS) -lcblas
$(LIB_PATH)/libluaT.so: $(LUAT_OBJS)
gcc -shared -o $@ $^ $(LDFLAGS)
$(INST_LIBDIR)/libnerv.so: $(NERV_OBJS) $(LIB_PATH)/libnervcore.so $(LIB_PATH)/libluaT.so
diff --git a/nerv/config.ld b/nerv/config.ld
new file mode 100644
index 0000000..f60b53f
--- /dev/null
+++ b/nerv/config.ld
@@ -0,0 +1,8 @@
+project = 'NERV'
+title = 'NERV documentation'
+description = 'A Lua-based toolkit for high-performance deep learning'
+backtick_references = true
+file = {'./', exclude = {'./lib/luaT'}}
+dir = 'doc/lua'
+style = '!pale'
+kind_names = {topic = 'Manual',script = 'Programs'}
diff --git a/nerv/doc/lua/index.html b/nerv/doc/lua/index.html
new file mode 100644
index 0000000..56212cc
--- /dev/null
+++ b/nerv/doc/lua/index.html
@@ -0,0 +1,71 @@
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN"
+ "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd">
+<html>
+<meta http-equiv="Content-Type" content="text/html; charset=UTF-8"/>
+<head>
+ <title>NERV documentation</title>
+ <link rel="stylesheet" href="ldoc_pale.css" type="text/css" />
+</head>
+<body>
+
+<div id="container">
+
+<div id="product">
+ <div id="product_logo"></div>
+ <div id="product_name"><big><b></b></big></div>
+ <div id="product_description"></div>
+</div> <!-- id="product" -->
+
+
+<div id="main">
+
+
+<!-- Menu -->
+
+<div id="navigation">
+<br/>
+<h1>NERV</h1>
+
+
+
+
+<h2>Modules</h2>
+<ul class="$(kind=='Topics' and '' or 'nowrap'">
+ <li><a href="modules/nerv.html">nerv</a></li>
+ <li><a href="modules/layer.affine.html">layer.affine</a></li>
+ <li><a href="modules/matrix.html">matrix</a></li>
+</ul>
+
+</div>
+
+<div id="content">
+
+
+ <h2>A Lua-based toolkit for high-performance deep learning</h2>
+
+<h2>Modules</h2>
+<table class="module_list">
+ <tr>
+ <td class="name" nowrap><a href="modules/nerv.html">nerv</a></td>
+ <td class="summary">NERV: a Lua-based toolkit for high-performance deep learning.</td>
+ </tr>
+ <tr>
+ <td class="name" nowrap><a href="modules/layer.affine.html">layer.affine</a></td>
+ <td class="summary">Parameter and layer classes related to linear transform.</td>
+ </tr>
+ <tr>
+ <td class="name" nowrap><a href="modules/matrix.html">matrix</a></td>
+ <td class="summary">Implements a fraction of matrix operations (methods) in Lua, while
+ others are implemented in C extension.</td>
+ </tr>
+</table>
+
+</div> <!-- id="content" -->
+</div> <!-- id="main" -->
+<div id="about">
+<i>generated by <a href="http://github.com/stevedonovan/LDoc">LDoc 1.4.3</a></i>
+<i style="float:right;">Last updated 2016-01-15 14:56:30 </i>
+</div> <!-- id="about" -->
+</div> <!-- id="container" -->
+</body>
+</html>
diff --git a/nerv/doc/lua/ldoc_pale.css b/nerv/doc/lua/ldoc_pale.css
new file mode 100644
index 0000000..b071110
--- /dev/null
+++ b/nerv/doc/lua/ldoc_pale.css
@@ -0,0 +1,304 @@
+/* BEGIN RESET
+
+Copyright (c) 2010, Yahoo! Inc. All rights reserved.
+Code licensed under the BSD License:
+http://developer.yahoo.com/yui/license.html
+version: 2.8.2r1
+*/
+html {
+ color: #000;
+ background: #FFF;
+}
+body,div,dl,dt,dd,ul,ol,li,h1,h2,h3,h4,h5,h6,pre,code,form,fieldset,legend,input,button,textarea,p,blockquote,th,td {
+ margin: 0;
+ padding: 0;
+}
+table {
+ border-collapse: collapse;
+ border-spacing: 0;
+}
+fieldset,img {
+ border: 0;
+}
+address,caption,cite,code,dfn,em,strong,th,var,optgroup {
+ font-style: inherit;
+ font-weight: inherit;
+}
+del,ins {
+ text-decoration: none;
+}
+li {
+ list-style: disc;
+ margin-left: 20px;
+}
+caption,th {
+ text-align: left;
+}
+h1,h2,h3,h4,h5,h6 {
+ font-size: 100%;
+ font-weight: bold;
+}
+q:before,q:after {
+ content: '';
+}
+abbr,acronym {
+ border: 0;
+ font-variant: normal;
+}
+sup {
+ vertical-align: baseline;
+}
+sub {
+ vertical-align: baseline;
+}
+legend {
+ color: #000;
+}
+input,button,textarea,select,optgroup,option {
+ font-family: inherit;
+ font-size: inherit;
+ font-style: inherit;
+ font-weight: inherit;
+}
+input,button,textarea,select {*font-size:100%;
+}
+/* END RESET */
+
+body {
+ margin-left: 1em;
+ margin-right: 1em;
+ font-family: arial, helvetica, geneva, sans-serif;
+ background-color: #ffffff; margin: 0px;
+}
+
+code, tt { font-family: monospace; font-size: 1.1em; }
+span.parameter { font-family:monospace; }
+span.parameter:after { content:":"; }
+span.types:before { content:"("; }
+span.types:after { content:")"; }
+.type { font-weight: bold; font-style:italic }
+
+body, p, td, th { font-size: .95em; line-height: 1.2em;}
+
+p, ul { margin: 10px 0 0 0px;}
+
+strong { font-weight: bold;}
+
+em { font-style: italic;}
+
+h1 {
+ font-size: 1.5em;
+ margin: 0 0 20px 0;
+}
+h2, h3, h4 { margin: 15px 0 10px 0; }
+h2 { font-size: 1.25em; }
+h3 { font-size: 1.15em; }
+h4 { font-size: 1.06em; }
+
+a:link { font-weight: bold; color: #004080; text-decoration: none; }
+a:visited { font-weight: bold; color: #006699; text-decoration: none; }
+a:link:hover { text-decoration: underline; }
+
+hr {
+ color:#cccccc;
+ background: #00007f;
+ height: 1px;
+}
+
+blockquote { margin-left: 3em; }
+
+ul { list-style-type: disc; }
+
+p.name {
+ font-family: "Andale Mono", monospace;
+ padding-top: 1em;
+}
+
+pre {
+ background-color: rgb(245, 245, 245);
+ border: 1px solid #C0C0C0; /* silver */
+ padding: 10px;
+ margin: 10px 0 10px 0;
+ overflow: auto;
+ font-family: "Andale Mono", monospace;
+}
+
+pre.example {
+ font-size: .85em;
+}
+
+table.index { border: 1px #00007f; }
+table.index td { text-align: left; vertical-align: top; }
+
+#container {
+ margin-left: 1em;
+ margin-right: 1em;
+ background-color: #ffffff;
+}
+
+#product {
+ text-align: center;
+ border-bottom: 1px solid #cccccc;
+ background-color: #ffffff;
+}
+
+#product big {
+ font-size: 2em;
+}
+
+#main {
+ background-color:#FFFFFF; // #f0f0f0;
+ //border-left: 2px solid #cccccc;
+}
+
+#navigation {
+ float: left;
+ width: 14em;
+ vertical-align: top;
+ background-color:#FFFFFF; // #f0f0f0;
+ border-right: 2px solid #cccccc;
+ overflow: visible;
+}
+
+#navigation h2 {
+ background-color:#FFFFFF;//:#e7e7e7;
+ font-size:1.1em;
+ color:#000000;
+ text-align: left;
+ padding:0.2em;
+ //border-top:1px solid #dddddd;
+ border-bottom:1px solid #dddddd;
+}
+
+#navigation ul
+{
+ font-size:1em;
+ list-style-type: none;
+ margin: 1px 1px 10px 1px;
+}
+
+#navigation li {
+ text-indent: -1em;
+ display: block;
+ margin: 3px 0px 0px 22px;
+}
+
+#navigation li li a {
+ margin: 0px 3px 0px -1em;
+}
+
+#content {
+ margin-left: 14em;
+ padding: 1em;
+ width: 700px;
+ border-left: 2px solid #cccccc;
+ // border-right: 2px solid #cccccc;
+ background-color: #ffffff;
+}
+
+#about {
+ clear: both;
+ padding: 5px;
+ border-top: 2px solid #cccccc;
+ background-color: #ffffff;
+}
+
+@media print {
+ body {
+ font: 12pt "Times New Roman", "TimeNR", Times, serif;
+ }
+ a { font-weight: bold; color: #004080; text-decoration: underline; }
+
+ #main {
+ background-color: #ffffff;
+ border-left: 0px;
+ }
+
+ #container {
+ margin-left: 2%;
+ margin-right: 2%;
+ background-color: #ffffff;
+ }
+
+ #content {
+ padding: 1em;
+ background-color: #ffffff;
+ }
+
+ #navigation {
+ display: none;
+ }
+ pre.example {
+ font-family: "Andale Mono", monospace;
+ font-size: 10pt;
+ page-break-inside: avoid;
+ }
+}
+
+table.module_list {
+ border-width: 1px;
+ border-style: solid;
+ border-color: #cccccc;
+ border-collapse: collapse;
+}
+table.module_list td {
+ border-width: 1px;
+ padding: 3px;
+ border-style: solid;
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+}
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+table.module_list td.summary { width: 100%; }
+
+table.function_list {
+ border-width: 1px;
+ border-style: solid;
+ border-color: #cccccc;
+ border-collapse: collapse;
+}
+table.function_list td {
+ border-width: 1px;
+ padding: 3px;
+ border-style: solid;
+ border-color: #cccccc;
+}
+table.function_list td.name { background-color: #f6f6ff; ; min-width: 200px; }
+table.function_list td.summary { width: 100%; }
+
+dl.table dt, dl.function dt {border-top: 1px solid #ccc; padding-top: 1em;}
+dl.table dd, dl.function dd {padding-bottom: 1em; margin: 10px 0 0 20px;}
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+
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+ overflow:auto;
+ whitespace:nowrap;
+}
+
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+ul ol { margin-top: 0px; }
+
+/* make the target distinct; helps when we're navigating to a function */
+a:target + * {
+ background-color: #FF9;
+}
+
+
+/* styles for prettification of source */
+pre .comment { color: #558817; }
+pre .constant { color: #a8660d; }
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+pre .prompt { color: #558817; }
+pre .url { color: #272fc2; text-decoration: underline; }
+
diff --git a/nerv/doc/lua/modules/layer.affine.html b/nerv/doc/lua/modules/layer.affine.html
new file mode 100644
index 0000000..817fc37
--- /dev/null
+++ b/nerv/doc/lua/modules/layer.affine.html
@@ -0,0 +1,145 @@
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN"
+ "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd">
+<html>
+<meta http-equiv="Content-Type" content="text/html; charset=UTF-8"/>
+<head>
+ <title>NERV documentation</title>
+ <link rel="stylesheet" href="../ldoc_pale.css" type="text/css" />
+</head>
+<body>
+
+<div id="container">
+
+<div id="product">
+ <div id="product_logo"></div>
+ <div id="product_name"><big><b></b></big></div>
+ <div id="product_description"></div>
+</div> <!-- id="product" -->
+
+
+<div id="main">
+
+
+<!-- Menu -->
+
+<div id="navigation">
+<br/>
+<h1>NERV</h1>
+
+<ul>
+ <li><a href="../index.html">Index</a></li>
+</ul>
+
+<h2>Contents</h2>
+<ul>
+<li><a href="#Class_nerv_MatrixParam">Class nerv.MatrixParam </a></li>
+<li><a href="#Class_nerv_AffineLayer">Class nerv.AffineLayer </a></li>
+</ul>
+
+
+<h2>Modules</h2>
+<ul class="$(kind=='Topics' and '' or 'nowrap'">
+ <li><a href="../modules/nerv.html">nerv</a></li>
+ <li><strong>layer.affine</strong></li>
+ <li><a href="../modules/matrix.html">matrix</a></li>
+</ul>
+
+</div>
+
+<div id="content">
+
+<h1>Module <code>layer.affine</code></h1>
+<p>Parameter and layer classes related to linear transform.</p>
+<p></p>
+
+
+<h2><a href="#Class_nerv_MatrixParam">Class nerv.MatrixParam </a></h2>
+<table class="function_list">
+ <tr>
+ <td class="name" nowrap><a href="#MatrixParam:read">MatrixParam:read (handle)</a></td>
+ <td class="summary">Read from a file handle.</td>
+ </tr>
+</table>
+<h2><a href="#Class_nerv_AffineLayer">Class nerv.AffineLayer </a></h2>
+<table class="function_list">
+ <tr>
+ <td class="name" nowrap><a href="#AffineLayer:__init">AffineLayer:__init (id, global_conf, layer_conf)</a></td>
+ <td class="summary">The constructor.</td>
+ </tr>
+</table>
+
+<br/>
+<br/>
+
+
+ <h2 class="section-header has-description"><a name="Class_nerv_MatrixParam"></a>Class nerv.MatrixParam </h2>
+
+ <div class="section-description">
+ A parameter that consists of a single matrix
+ </div>
+ <dl class="function">
+ <dt>
+ <a name = "MatrixParam:read"></a>
+ <strong>MatrixParam:read (handle)</strong>
+ </dt>
+ <dd>
+ Read from a file handle.
+
+
+ <h3>Parameters:</h3>
+ <ul>
+ <li><span class="parameter">handle</span>
+ the file handle
+ </li>
+ </ul>
+
+
+
+
+
+</dd>
+</dl>
+ <h2 class="section-header has-description"><a name="Class_nerv_AffineLayer"></a>Class nerv.AffineLayer </h2>
+
+ <div class="section-description">
+ A fully-connected linear transform layer.
+ </div>
+ <dl class="function">
+ <dt>
+ <a name = "AffineLayer:__init"></a>
+ <strong>AffineLayer:__init (id, global_conf, layer_conf)</strong>
+ </dt>
+ <dd>
+ The constructor.
+
+
+ <h3>Parameters:</h3>
+ <ul>
+ <li><span class="parameter">id</span>
+
+ </li>
+ <li><span class="parameter">global_conf</span>
+
+ </li>
+ <li><span class="parameter">layer_conf</span>
+
+ </li>
+ </ul>
+
+
+
+
+
+</dd>
+</dl>
+
+
+</div> <!-- id="content" -->
+</div> <!-- id="main" -->
+<div id="about">
+<i>generated by <a href="http://github.com/stevedonovan/LDoc">LDoc 1.4.3</a></i>
+<i style="float:right;">Last updated 2016-01-15 14:56:30 </i>
+</div> <!-- id="about" -->
+</div> <!-- id="container" -->
+</body>
+</html>
diff --git a/nerv/doc/lua/modules/matrix.html b/nerv/doc/lua/modules/matrix.html
new file mode 100644
index 0000000..2840e59
--- /dev/null
+++ b/nerv/doc/lua/modules/matrix.html
@@ -0,0 +1,409 @@
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN"
+ "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd">
+<html>
+<meta http-equiv="Content-Type" content="text/html; charset=UTF-8"/>
+<head>
+ <title>NERV documentation</title>
+ <link rel="stylesheet" href="../ldoc_pale.css" type="text/css" />
+</head>
+<body>
+
+<div id="container">
+
+<div id="product">
+ <div id="product_logo"></div>
+ <div id="product_name"><big><b></b></big></div>
+ <div id="product_description"></div>
+</div> <!-- id="product" -->
+
+
+<div id="main">
+
+
+<!-- Menu -->
+
+<div id="navigation">
+<br/>
+<h1>NERV</h1>
+
+<ul>
+ <li><a href="../index.html">Index</a></li>
+</ul>
+
+<h2>Contents</h2>
+<ul>
+<li><a href="#Class_nerv_Matrix">Class nerv.Matrix </a></li>
+<li><a href="#Class_nerv_CuMatrixFloat">Class nerv.CuMatrixFloat </a></li>
+<li><a href="#Class_nerv_CuMatrixDouble">Class nerv.CuMatrixDouble </a></li>
+<li><a href="#Class_nerv_MMatrix">Class nerv.MMatrix </a></li>
+</ul>
+
+
+<h2>Modules</h2>
+<ul class="$(kind=='Topics' and '' or 'nowrap'">
+ <li><a href="../modules/nerv.html">nerv</a></li>
+ <li><a href="../modules/layer.affine.html">layer.affine</a></li>
+ <li><strong>matrix</strong></li>
+</ul>
+
+</div>
+
+<div id="content">
+
+<h1>Module <code>matrix</code></h1>
+<p>Implements a fraction of matrix operations (methods) in Lua, while
+ others are implemented in C extension.</p>
+<p></p>
+ <h3>Info:</h3>
+ <ul>
+ <li><strong>Author</strong>: Ted Yin <[email protected]></li>
+ </ul>
+
+
+<h2><a href="#Class_nerv_Matrix">Class nerv.Matrix </a></h2>
+<table class="function_list">
+ <tr>
+ <td class="name" nowrap><a href="#nerv.Matrix:__tostring__">nerv.Matrix:__tostring__ ()</a></td>
+ <td class="summary">Convert the matrix object to a string.</td>
+ </tr>
+ <tr>
+ <td class="name" nowrap><a href="#nerv.Matrix:generate">nerv.Matrix:generate (gen)</a></td>
+ <td class="summary">Assign each element in a matrix using the value returned by a callback <code>gen</code>.</td>
+ </tr>
+ <tr>
+ <td class="name" nowrap><a href="#nerv.Matrix:create">nerv.Matrix:create (nrow, ncol)</a></td>
+ <td class="summary">Create a fresh new matrix of the same matrix type (as <code>self</code>).</td>
+ </tr>
+ <tr>
+ <td class="name" nowrap><a href="#nerv.Matrix:__add__">nerv.Matrix:__add__ (b)</a></td>
+ <td class="summary">Operator overloading of <code>+</code>.</td>
+ </tr>
+ <tr>
+ <td class="name" nowrap><a href="#nerv.Matrix:__sub__">nerv.Matrix:__sub__ (b)</a></td>
+ <td class="summary">Operator overloading of <code>-</code>.</td>
+ </tr>
+ <tr>
+ <td class="name" nowrap><a href="#nerv.Matrix:__mul__">nerv.Matrix:__mul__ (b)</a></td>
+ <td class="summary">Operator overloading of <code>*</code>.</td>
+ </tr>
+</table>
+<h2><a href="#Class_nerv_CuMatrixFloat">Class nerv.CuMatrixFloat </a></h2>
+<table class="function_list">
+ <tr>
+ <td class="name" nowrap><a href="#nerv.CuMatrixFloat.new_from_host">nerv.CuMatrixFloat.new_from_host (mat)</a></td>
+ <td class="summary">Create a CUDA matrix copy of the host matrix (in memory)</td>
+ </tr>
+ <tr>
+ <td class="name" nowrap><a href="#nerv.CuMatrixFloat:new_to_host">nerv.CuMatrixFloat:new_to_host ()</a></td>
+ <td class="summary">Create a host matrix copy of the CUDA matrix</td>
+ </tr>
+</table>
+<h2><a href="#Class_nerv_CuMatrixDouble">Class nerv.CuMatrixDouble </a></h2>
+<table class="function_list">
+ <tr>
+ <td class="name" nowrap><a href="#nerv.CuMatrixDouble.new_from_host">nerv.CuMatrixDouble.new_from_host (mat)</a></td>
+ <td class="summary">Create a CUDA matrix copy of the host matrix (in memory)</td>
+ </tr>
+ <tr>
+ <td class="name" nowrap><a href="#nerv.CuMatrixDouble:new_to_host">nerv.CuMatrixDouble:new_to_host ()</a></td>
+ <td class="summary">Create a host matrix copy of the CUDA matrix</td>
+ </tr>
+</table>
+<h2><a href="#Class_nerv_MMatrix">Class nerv.MMatrix </a></h2>
+<table class="function_list">
+ <tr>
+ <td class="name" nowrap><a href="#nerv.MMatrix:copy_toh">nerv.MMatrix:copy_toh (b, ...)</a></td>
+ <td class="summary">A wrapper function for <code>copy_fromh</code></td>
+ </tr>
+ <tr>
+ <td class="name" nowrap><a href="#nerv.MMatrix.print_profile">nerv.MMatrix.print_profile ()</a></td>
+ <td class="summary">Print profiling info of host matrices</td>
+ </tr>
+ <tr>
+ <td class="name" nowrap><a href="#nerv.MMatrix.clear_profile">nerv.MMatrix.clear_profile ()</a></td>
+ <td class="summary">Clear profiling info of host matrices</td>
+ </tr>
+</table>
+
+<br/>
+<br/>
+
+
+ <h2 class="section-header has-description"><a name="Class_nerv_Matrix"></a>Class nerv.Matrix </h2>
+
+ <div class="section-description">
+ The base class for all matrices.
+ </div>
+ <dl class="function">
+ <dt>
+ <a name = "nerv.Matrix:__tostring__"></a>
+ <strong>nerv.Matrix:__tostring__ ()</strong>
+ </dt>
+ <dd>
+ Convert the matrix object to a string.
+
+
+
+
+
+
+
+</dd>
+ <dt>
+ <a name = "nerv.Matrix:generate"></a>
+ <strong>nerv.Matrix:generate (gen)</strong>
+ </dt>
+ <dd>
+ Assign each element in a matrix using the value returned by a callback <code>gen</code>.
+
+
+ <h3>Parameters:</h3>
+ <ul>
+ <li><span class="parameter">gen</span>
+ the callback used to generated the values in the matrix, to which
+ the indices of row and column will be passed (e.g., <code>gen(i, j)</code>)
+ </li>
+ </ul>
+
+
+
+
+
+</dd>
+ <dt>
+ <a name = "nerv.Matrix:create"></a>
+ <strong>nerv.Matrix:create (nrow, ncol)</strong>
+ </dt>
+ <dd>
+ Create a fresh new matrix of the same matrix type (as <code>self</code>).
+
+
+ <h3>Parameters:</h3>
+ <ul>
+ <li><span class="parameter">nrow</span>
+ optional, the number of rows in the created matrix if specified,
+ otherwise <code>self:nrow()</code> will be used
+ </li>
+ <li><span class="parameter">ncol</span>
+ optional, the number of columns in the created matrix if specified,
+ otherwise <code>self:ncol()</code> will be used
+ </li>
+ </ul>
+
+
+
+
+
+</dd>
+ <dt>
+ <a name = "nerv.Matrix:__add__"></a>
+ <strong>nerv.Matrix:__add__ (b)</strong>
+ </dt>
+ <dd>
+ Operator overloading of <code>+</code>.
+
+
+ <h3>Parameters:</h3>
+ <ul>
+ <li><span class="parameter">b</span>
+
+ </li>
+ </ul>
+
+
+
+
+
+</dd>
+ <dt>
+ <a name = "nerv.Matrix:__sub__"></a>
+ <strong>nerv.Matrix:__sub__ (b)</strong>
+ </dt>
+ <dd>
+ Operator overloading of <code>-</code>.
+
+
+ <h3>Parameters:</h3>
+ <ul>
+ <li><span class="parameter">b</span>
+
+ </li>
+ </ul>
+
+
+
+
+
+</dd>
+ <dt>
+ <a name = "nerv.Matrix:__mul__"></a>
+ <strong>nerv.Matrix:__mul__ (b)</strong>
+ </dt>
+ <dd>
+ Operator overloading of <code>*</code>.
+
+
+ <h3>Parameters:</h3>
+ <ul>
+ <li><span class="parameter">b</span>
+
+ </li>
+ </ul>
+
+
+
+
+
+</dd>
+</dl>
+ <h2 class="section-header has-description"><a name="Class_nerv_CuMatrixFloat"></a>Class nerv.CuMatrixFloat </h2>
+
+ <div class="section-description">
+ CUDA float matrices
+ </div>
+ <dl class="function">
+ <dt>
+ <a name = "nerv.CuMatrixFloat.new_from_host"></a>
+ <strong>nerv.CuMatrixFloat.new_from_host (mat)</strong>
+ </dt>
+ <dd>
+ Create a CUDA matrix copy of the host matrix (in memory)
+
+
+ <h3>Parameters:</h3>
+ <ul>
+ <li><span class="parameter">mat</span>
+ the host matrix
+ </li>
+ </ul>
+
+
+
+
+
+</dd>
+ <dt>
+ <a name = "nerv.CuMatrixFloat:new_to_host"></a>
+ <strong>nerv.CuMatrixFloat:new_to_host ()</strong>
+ </dt>
+ <dd>
+ Create a host matrix copy of the CUDA matrix
+
+
+
+
+
+
+
+</dd>
+</dl>
+ <h2 class="section-header has-description"><a name="Class_nerv_CuMatrixDouble"></a>Class nerv.CuMatrixDouble </h2>
+
+ <div class="section-description">
+ CUDA double matrices
+ </div>
+ <dl class="function">
+ <dt>
+ <a name = "nerv.CuMatrixDouble.new_from_host"></a>
+ <strong>nerv.CuMatrixDouble.new_from_host (mat)</strong>
+ </dt>
+ <dd>
+ Create a CUDA matrix copy of the host matrix (in memory)
+
+
+ <h3>Parameters:</h3>
+ <ul>
+ <li><span class="parameter">mat</span>
+ the host matrix
+ </li>
+ </ul>
+
+
+
+
+
+</dd>
+ <dt>
+ <a name = "nerv.CuMatrixDouble:new_to_host"></a>
+ <strong>nerv.CuMatrixDouble:new_to_host ()</strong>
+ </dt>
+ <dd>
+ Create a host matrix copy of the CUDA matrix
+
+
+
+
+
+
+
+</dd>
+</dl>
+ <h2 class="section-header has-description"><a name="Class_nerv_MMatrix"></a>Class nerv.MMatrix </h2>
+
+ <div class="section-description">
+ The base class for all host (in-memory) matrices
+ </div>
+ <dl class="function">
+ <dt>
+ <a name = "nerv.MMatrix:copy_toh"></a>
+ <strong>nerv.MMatrix:copy_toh (b, ...)</strong>
+ </dt>
+ <dd>
+ A wrapper function for <code>copy_fromh</code>
+
+
+ <h3>Parameters:</h3>
+ <ul>
+ <li><span class="parameter">b</span>
+
+ </li>
+ <li><span class="parameter">...</span>
+
+ </li>
+ </ul>
+
+
+
+
+
+</dd>
+ <dt>
+ <a name = "nerv.MMatrix.print_profile"></a>
+ <strong>nerv.MMatrix.print_profile ()</strong>
+ </dt>
+ <dd>
+ Print profiling info of host matrices
+
+
+
+
+
+
+
+</dd>
+ <dt>
+ <a name = "nerv.MMatrix.clear_profile"></a>
+ <strong>nerv.MMatrix.clear_profile ()</strong>
+ </dt>
+ <dd>
+ Clear profiling info of host matrices
+
+
+
+
+
+
+
+</dd>
+</dl>
+
+
+</div> <!-- id="content" -->
+</div> <!-- id="main" -->
+<div id="about">
+<i>generated by <a href="http://github.com/stevedonovan/LDoc">LDoc 1.4.3</a></i>
+<i style="float:right;">Last updated 2016-01-15 14:56:30 </i>
+</div> <!-- id="about" -->
+</div> <!-- id="container" -->
+</body>
+</html>
diff --git a/nerv/doc/lua/modules/nerv.html b/nerv/doc/lua/modules/nerv.html
new file mode 100644
index 0000000..91baf9e
--- /dev/null
+++ b/nerv/doc/lua/modules/nerv.html
@@ -0,0 +1,428 @@
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN"
+ "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd">
+<html>
+<meta http-equiv="Content-Type" content="text/html; charset=UTF-8"/>
+<head>
+ <title>NERV documentation</title>
+ <link rel="stylesheet" href="../ldoc_pale.css" type="text/css" />
+</head>
+<body>
+
+<div id="container">
+
+<div id="product">
+ <div id="product_logo"></div>
+ <div id="product_name"><big><b></b></big></div>
+ <div id="product_description"></div>
+</div> <!-- id="product" -->
+
+
+<div id="main">
+
+
+<!-- Menu -->
+
+<div id="navigation">
+<br/>
+<h1>NERV</h1>
+
+<ul>
+ <li><a href="../index.html">Index</a></li>
+</ul>
+
+<h2>Contents</h2>
+<ul>
+<li><a href="#Functions">Functions</a></li>
+</ul>
+
+
+<h2>Modules</h2>
+<ul class="$(kind=='Topics' and '' or 'nowrap'">
+ <li><strong>nerv</strong></li>
+ <li><a href="../modules/layer.affine.html">layer.affine</a></li>
+ <li><a href="../modules/matrix.html">matrix</a></li>
+</ul>
+
+</div>
+
+<div id="content">
+
+<h1>Module <code>nerv</code></h1>
+<p>NERV: a Lua-based toolkit for high-performance deep learning.</p>
+<p>
+ This file contains misc utility functions of NERV and finally initializes
+ NERV by including <code>init.lua</code> of other basic modules.</p>
+ <h3>Info:</h3>
+ <ul>
+ <li><strong>Author</strong>: Ted Yin <[email protected]></li>
+ </ul>
+
+
+<h2><a href="#Functions">Functions</a></h2>
+<table class="function_list">
+ <tr>
+ <td class="name" nowrap><a href="#error_method_not_implemented">error_method_not_implemented ()</a></td>
+ <td class="summary">Dummy function.</td>
+ </tr>
+ <tr>
+ <td class="name" nowrap><a href="#sprintf">sprintf (fmt, ...)</a></td>
+ <td class="summary">Format a string just like <a href="../modules/nerv.html#sprintf">sprintf</a> in C.</td>
+ </tr>
+ <tr>
+ <td class="name" nowrap><a href="#printf">printf (fmt, ...)</a></td>
+ <td class="summary">Print a formatted string to stdout.</td>
+ </tr>
+ <tr>
+ <td class="name" nowrap><a href="#error">error (fmt, ...)</a></td>
+ <td class="summary">Raise an global error with the formatted message.</td>
+ </tr>
+ <tr>
+ <td class="name" nowrap><a href="#info">info (fmt, ...)</a></td>
+ <td class="summary">Print a notification message that begins with "info" and a timestamp.</td>
+ </tr>
+ <tr>
+ <td class="name" nowrap><a href="#warning">warning (fmt, ...)</a></td>
+ <td class="summary">Print a warning message that begins with "warning" and a timestamp.</td>
+ </tr>
+ <tr>
+ <td class="name" nowrap><a href="#class">class (tname, parenttname)</a></td>
+ <td class="summary">Create a class (Torch-compatible).</td>
+ </tr>
+ <tr>
+ <td class="name" nowrap><a href="#table.tostring">table.tostring (tbl)</a></td>
+ <td class="summary">Get the string representation of a table, which can be executed as a valid
+ piece of Lua code.</td>
+ </tr>
+ <tr>
+ <td class="name" nowrap><a href="#get_type">get_type (tname)</a></td>
+ <td class="summary">Get the class by name.</td>
+ </tr>
+ <tr>
+ <td class="name" nowrap><a href="#is_type">is_type (obj, tname)</a></td>
+ <td class="summary">Check if the object is of the certain class.</td>
+ </tr>
+ <tr>
+ <td class="name" nowrap><a href="#dirname">dirname (filename)</a></td>
+ <td class="summary">Strip last component from file name.</td>
+ </tr>
+ <tr>
+ <td class="name" nowrap><a href="#include">include (filename)</a></td>
+ <td class="summary">Include a script file (chunk) into the current script.</td>
+ </tr>
+</table>
+
+<br/>
+<br/>
+
+
+ <h2 class="section-header "><a name="Functions"></a>Functions</h2>
+
+ <dl class="function">
+ <dt>
+ <a name = "error_method_not_implemented"></a>
+ <strong>error_method_not_implemented ()</strong>
+ </dt>
+ <dd>
+ Dummy function.
+ Display a friendly error message when user attempts to invoke a
+ non-implemented function.
+
+
+
+
+
+
+
+</dd>
+ <dt>
+ <a name = "sprintf"></a>
+ <strong>sprintf (fmt, ...)</strong>
+ </dt>
+ <dd>
+ Format a string just like <a href="../modules/nerv.html#sprintf">sprintf</a> in C.
+
+
+ <h3>Parameters:</h3>
+ <ul>
+ <li><span class="parameter">fmt</span>
+ the format string
+ </li>
+ <li><span class="parameter">...</span>
+ args, the data to be formatted
+ </li>
+ </ul>
+
+ <h3>Returns:</h3>
+ <ol>
+
+ the formatted string
+ </ol>
+
+
+
+
+</dd>
+ <dt>
+ <a name = "printf"></a>
+ <strong>printf (fmt, ...)</strong>
+ </dt>
+ <dd>
+ Print a formatted string to stdout.
+
+
+ <h3>Parameters:</h3>
+ <ul>
+ <li><span class="parameter">fmt</span>
+ the format string
+ </li>
+ <li><span class="parameter">...</span>
+ args, the data to be formatted
+ </li>
+ </ul>
+
+
+
+
+
+</dd>
+ <dt>
+ <a name = "error"></a>
+ <strong>error (fmt, ...)</strong>
+ </dt>
+ <dd>
+ Raise an global error with the formatted message.
+
+
+ <h3>Parameters:</h3>
+ <ul>
+ <li><span class="parameter">fmt</span>
+ the format string
+ </li>
+ <li><span class="parameter">...</span>
+ args, the data to be formatted
+ </li>
+ </ul>
+
+
+
+
+
+</dd>
+ <dt>
+ <a name = "info"></a>
+ <strong>info (fmt, ...)</strong>
+ </dt>
+ <dd>
+ Print a notification message that begins with "info" and a timestamp.
+ Instead of using <a href="../modules/nerv.html#printf">nerv.printf</a>, normal users should use this to print any
+ notification information.
+
+
+ <h3>Parameters:</h3>
+ <ul>
+ <li><span class="parameter">fmt</span>
+ the format string
+ </li>
+ <li><span class="parameter">...</span>
+ args, the data to be formatted
+ </li>
+ </ul>
+
+
+
+
+
+</dd>
+ <dt>
+ <a name = "warning"></a>
+ <strong>warning (fmt, ...)</strong>
+ </dt>
+ <dd>
+ Print a warning message that begins with "warning" and a timestamp.
+ Instead of using <a href="../modules/nerv.html#printf">nerv.printf</a>, normal users should use this to print any
+ warnings.
+
+
+ <h3>Parameters:</h3>
+ <ul>
+ <li><span class="parameter">fmt</span>
+ the format string
+ </li>
+ <li><span class="parameter">...</span>
+ args, the data to be formatted
+ </li>
+ </ul>
+
+
+
+
+
+</dd>
+ <dt>
+ <a name = "class"></a>
+ <strong>class (tname, parenttname)</strong>
+ </dt>
+ <dd>
+ Create a class (Torch-compatible).
+ Use this to create a class in NERV.
+
+
+ <h3>Parameters:</h3>
+ <ul>
+ <li><span class="parameter">tname</span>
+ the class name
+ </li>
+ <li><span class="parameter">parenttname</span>
+ the parent class name (from which it inherits)
+ </li>
+ </ul>
+
+ <h3>Returns:</h3>
+ <ol>
+
+ the created class
+ </ol>
+
+
+
+
+</dd>
+ <dt>
+ <a name = "table.tostring"></a>
+ <strong>table.tostring (tbl)</strong>
+ </dt>
+ <dd>
+ Get the string representation of a table, which can be executed as a valid
+ piece of Lua code.
+
+
+ <h3>Parameters:</h3>
+ <ul>
+ <li><span class="parameter">tbl</span>
+ the table
+ </li>
+ </ul>
+
+ <h3>Returns:</h3>
+ <ol>
+
+ the string representation which will result in a Lua table entity
+ when evaluated
+ </ol>
+
+
+
+
+</dd>
+ <dt>
+ <a name = "get_type"></a>
+ <strong>get_type (tname)</strong>
+ </dt>
+ <dd>
+ Get the class by name.
+
+
+ <h3>Parameters:</h3>
+ <ul>
+ <li><span class="parameter">tname</span>
+ the name of the class
+ </li>
+ </ul>
+
+ <h3>Returns:</h3>
+ <ol>
+
+ the class entity
+ </ol>
+
+
+
+
+</dd>
+ <dt>
+ <a name = "is_type"></a>
+ <strong>is_type (obj, tname)</strong>
+ </dt>
+ <dd>
+ Check if the object is of the certain class.
+
+
+ <h3>Parameters:</h3>
+ <ul>
+ <li><span class="parameter">obj</span>
+ the object ("class instance")
+ </li>
+ <li><span class="parameter">tname</span>
+ the class name ("type name")
+ </li>
+ </ul>
+
+
+
+
+
+</dd>
+ <dt>
+ <a name = "dirname"></a>
+ <strong>dirname (filename)</strong>
+ </dt>
+ <dd>
+ Strip last component from file name.
+
+
+ <h3>Parameters:</h3>
+ <ul>
+ <li><span class="parameter">filename</span>
+ the path to a file
+ </li>
+ </ul>
+
+ <h3>Returns:</h3>
+ <ol>
+
+ the path to the containing directory
+ </ol>
+
+
+
+
+</dd>
+ <dt>
+ <a name = "include"></a>
+ <strong>include (filename)</strong>
+ </dt>
+ <dd>
+ Include a script file (chunk) into the current script.
+ An analogy to <code>#include</code> in C. Note that the effect is the same as executing
+ <code>dofile(filename)</code> at the current line.
+
+
+ <h3>Parameters:</h3>
+ <ul>
+ <li><span class="parameter">filename</span>
+ the path to a file
+ </li>
+ </ul>
+
+ <h3>Returns:</h3>
+ <ol>
+
+ all values returned by the chunk
+ </ol>
+
+
+
+
+</dd>
+</dl>
+
+
+</div> <!-- id="content" -->
+</div> <!-- id="main" -->
+<div id="about">
+<i>generated by <a href="http://github.com/stevedonovan/LDoc">LDoc 1.4.3</a></i>
+<i style="float:right;">Last updated 2016-01-15 14:56:30 </i>
+</div> <!-- id="about" -->
+</div> <!-- id="container" -->
+</body>
+</html>
diff --git a/nerv/examples/asr_trainer.lua b/nerv/examples/asr_trainer.lua
index 69cfeed..3fa2653 100644
--- a/nerv/examples/asr_trainer.lua
+++ b/nerv/examples/asr_trainer.lua
@@ -5,6 +5,12 @@ function build_trainer(ifname)
local network = get_network(layer_repo)
local global_transf = get_global_transf(layer_repo)
local input_order = get_input_order()
+ local mat_type
+ if gconf.use_cpu then
+ mat_type = gconf.mmat_type
+ else
+ mat_type = gconf.cumat_type
+ end
local iterative_trainer = function (prefix, scp_file, bp)
gconf.randomize = bp
-- build buffer
@@ -12,15 +18,15 @@ function build_trainer(ifname)
-- initialize the network
network:init(gconf.batch_size)
gconf.cnt = 0
- err_input = {nerv.CuMatrixFloat(gconf.batch_size, 1)}
+ err_input = {mat_type(gconf.batch_size, 1)}
err_input[1]:fill(1)
for data in buffer.get_data, buffer do
-- prine stat periodically
gconf.cnt = gconf.cnt + 1
if gconf.cnt == 1000 then
print_stat(layer_repo)
- nerv.CuMatrix.print_profile()
- nerv.CuMatrix.clear_profile()
+ mat_type.print_profile()
+ mat_type.clear_profile()
gconf.cnt = 0
-- break
end
@@ -42,7 +48,7 @@ function build_trainer(ifname)
end
table.insert(input, transformed)
end
- local output = {nerv.CuMatrixFloat(gconf.batch_size, 1)}
+ local output = {mat_type(gconf.batch_size, 1)}
err_output = {}
for i = 1, #input do
table.insert(err_output, input[i]:create())
@@ -56,8 +62,8 @@ function build_trainer(ifname)
collectgarbage("collect")
end
print_stat(layer_repo)
- nerv.CuMatrix.print_profile()
- nerv.CuMatrix.clear_profile()
+ mat_type.print_profile()
+ mat_type.clear_profile()
if (not bp) and prefix ~= nil then
nerv.info("writing back...")
local fname = string.format("%s_cv%.3f.nerv",
diff --git a/nerv/init.lua b/nerv/init.lua
index b5d20a2..6312df1 100644
--- a/nerv/init.lua
+++ b/nerv/init.lua
@@ -1,40 +1,67 @@
+--- NERV: a Lua-based toolkit for high-performance deep learning.
+-- This file contains misc utility functions of NERV and finally initializes
+-- NERV by including `init.lua` of other basic modules.
+-- @author Ted Yin <[email protected]>
+-- @module nerv
+
require 'libnerv'
+--- Dummy function.
+-- Display a friendly error message when user attempts to invoke a
+-- non-implemented function.
function nerv.error_method_not_implemented()
nerv.error("method not implemented");
end
+--- Format a string just like `sprintf` in C.
+-- @param fmt the format string
+-- @param ... args, the data to be formatted
+-- @return the formatted string
function nerv.sprintf(fmt, ...)
return string.format(fmt, ...)
end
+--- Print a formatted string to stdout.
+-- @param fmt the format string
+-- @param ... args, the data to be formatted
function nerv.printf(fmt, ...)
io.write(nerv.sprintf(fmt, ...))
end
+--- Raise an global error with the formatted message.
+-- @param fmt the format string
+-- @param ... args, the data to be formatted
function nerv.error(fmt, ...)
error(nerv.sprintf("[nerv] internal error: " .. fmt .. "\n", ...))
end
-function nerv.mesg_with_timestamp(fmt, ...)
- nerv.printf(
- string.format("(%s)[nerv] info: %s\n",
- os.date("%H:%M:%S %F"), fmt), ...)
-end
-
+--- Print a notification message that begins with "info" and a timestamp.
+-- Instead of using `nerv.printf`, normal users should use this to print any
+-- notification information.
+-- @param fmt the format string
+-- @param ... args, the data to be formatted
function nerv.info(fmt, ...)
nerv.printf(
string.format("(%s)[nerv] info: %s\n",
os.date("%H:%M:%S %F"), fmt), ...)
end
+--- Print a warning message that begins with "warning" and a timestamp.
+-- Instead of using `nerv.printf`, normal users should use this to print any
+-- warnings.
+-- @param fmt the format string
+-- @param ... args, the data to be formatted
function nerv.warning(fmt, ...)
nerv.printf(
string.format("(%s)[nerv] warning: %s\n",
os.date("%H:%M:%S %F"), fmt), ...)
end
--- Torch C API wrapper
+--- Create a class (Torch-compatible).
+-- Use this to create a class in NERV.
+-- @param tname the class name
+-- @param parenttname the parent class name (from which it inherits)
+-- @return the created class
function nerv.class(tname, parenttname)
local function constructor(...)
@@ -81,6 +108,11 @@ function table.key_to_str (k)
end
end
+--- Get the string representation of a table, which can be executed as a valid
+-- piece of Lua code.
+-- @param tbl the table
+-- @return the string representation which will result in a Lua table entity
+-- when evaluated
function table.tostring(tbl)
local result, done = {}, {}
for k, v in ipairs(tbl) do
@@ -96,10 +128,16 @@ function table.tostring(tbl)
return "{" .. table.concat(result, ",") .. "}"
end
+--- Get the class by name.
+-- @param tname the name of the class
+-- @return the class entity
function nerv.get_type(tname)
return assert(loadstring("return " .. tname))()
end
+--- Check if the object is of the certain class.
+-- @param obj the object ("class instance")
+-- @param tname the class name ("type name")
function nerv.is_type(obj, tname)
local mt0 = nerv.getmetatable(tname)
local mt = getmetatable(obj)
@@ -112,6 +150,9 @@ function nerv.is_type(obj, tname)
return false
end
+--- Strip last component from file name.
+-- @param filename the path to a file
+-- @return the path to the containing directory
function nerv.dirname(filename)
if filename:match(".-/.-") then
local name = string.gsub(filename, "(.*/)(.*)", "%1")
@@ -121,11 +162,18 @@ function nerv.dirname(filename)
end
end
+--- Include a script file (chunk) into the current script.
+-- An analogy to `#include` in C. Note that the effect is the same as executing
+-- `dofile(filename)` at the current line.
+-- @param filename the path to a file
+-- @return all values returned by the chunk
function nerv.include(filename)
local caller = debug.getinfo(2, "S").source:sub(2)
- dofile(nerv.dirname(caller) .. filename)
+ return dofile(nerv.dirname(caller) .. filename)
end
+-- the following lines trigger the initialization of basic modules
+
nerv.include('matrix/init.lua')
nerv.include('io/init.lua')
nerv.include('layer/init.lua')
diff --git a/nerv/io/sgd_buffer.lua b/nerv/io/sgd_buffer.lua
index 74c4934..3cf4f5a 100644
--- a/nerv/io/sgd_buffer.lua
+++ b/nerv/io/sgd_buffer.lua
@@ -7,21 +7,38 @@ function SGDBuffer:__init(global_conf, buffer_conf)
self.randomize = buffer_conf.randomize
self.consume = buffer_conf.consume
local cumat_type = global_conf.cumat_type
+ if self.gconf.use_cpu then
+ self.output_mat_type = self.gconf.mmat_type
+ else
+ self.output_mat_type = self.gconf.cumat_type
+ end
if buffer_conf.use_gpu then
self.mat_type = cumat_type
- self.copy_rows_from_by_idx = cumat_type.copy_rows_fromd_by_idx
- self.copy_from = cumat_type.copy_fromd
- self.copy_from_reader = cumat_type.copy_fromh
+ if self.gconf.use_cpu then
+ -- gpu buffer -> cpu training
+ nerv.error("not implemeted")
+ else
+ -- gpu buffer -> gpu training
+ self.copy_rows_from_by_idx = cumat_type.copy_rows_fromd_by_idx
+ self.copy_from = cumat_type.copy_fromd
+ end
self.perm_gen = function (x)
return cumat_type.new_from_host(nerv.MMatrixFloat.perm_gen(x))
end
else
self.mat_type = global_conf.mmat_type
- self.copy_rows_from_by_idx = cumat_type.copy_rows_fromh_by_idx
- self.copy_from = cumat_type.copy_fromh
+ if self.gconf.use_cpu then
+ -- cpu buffer -> cpu training
+ self.copy_rows_from_by_idx = gconf.mmat_type.copy_rows_fromh_by_idx
+ self.copy_from = gconf.mmat_type.copy_fromh
+ else
+ -- cpu buffer -> gpu training
+ self.copy_rows_from_by_idx = cumat_type.copy_rows_fromh_by_idx
+ self.copy_from = cumat_type.copy_fromh
+ end
self.perm_gen = nerv.MMatrixFloat.perm_gen
- self.copy_from_reader = self.mat_type.copy_from
end
+ self.copy_from_reader = self.mat_type.copy_fromh
self.head = 0
self.tail = 0
self.readers = {}
@@ -117,7 +134,7 @@ function SGDBuffer:get_data()
local res = {}
for i, reader in ipairs(self.readers) do
for id, buff in pairs(reader.buffs) do
- local batch = self.gconf.cumat_type(actual_batch_size, buff.width)
+ local batch = self.output_mat_type(actual_batch_size, buff.width)
if self.randomize then
self.copy_rows_from_by_idx(batch, buff.data, self.rand_map, self.head)
else
diff --git a/nerv/layer/affine.lua b/nerv/layer/affine.lua
index 566e9bc..4156dde 100644
--- a/nerv/layer/affine.lua
+++ b/nerv/layer/affine.lua
@@ -1,15 +1,28 @@
+--- Parameter and layer classes related to linear transform.
+
local MatrixParam = nerv.class('nerv.MatrixParam', 'nerv.Param')
local LinearTransParam = nerv.class('nerv.LinearTransParam', 'nerv.MatrixParam')
local BiasParam = nerv.class('nerv.BiasParam', 'nerv.MatrixParam')
local AffineLayer = nerv.class('nerv.AffineLayer', 'nerv.Layer')
+--- A parameter that consists of a single matrix
+-- @type nerv.MatrixParam
+
+--- Read from a file handle.
+-- @param handle the file handle
function MatrixParam:read(handle)
- self.trans = self.gconf.cumat_type.new_from_host(
- self.gconf.mmat_type.load(handle))
+ self.trans = self.gconf.mmat_type.load(handle)
+ if not self.gconf.use_cpu then
+ self.trans = self.gconf.cumat_type.new_from_host(self.trans)
+ end
end
function MatrixParam:write(handle)
- self.trans:new_to_host():save(handle)
+ local trans = self.trans
+ if not self.gconf.use_cpu then
+ trans = self.trans:new_to_host()
+ end
+ trans:save(handle)
end
function MatrixParam:train_init()
@@ -59,15 +72,28 @@ function LinearTransParam:update_by_err_input(err, input)
self:_update_by_err_input(err, input, l2, l2)
end
+--- A fully-connected linear transform layer.
+-- @type nerv.AffineLayer
+
+--- The constructor.
function AffineLayer:__init(id, global_conf, layer_conf)
self.id = id
self.dim_in = layer_conf.dim_in
self.dim_out = layer_conf.dim_out
- self.ltp = self:find_param("ltp", layer_conf, global_conf, nerv.LinearTransParam, {self.dim_in[1], self.dim_out[1]}) --layer_conf.ltp
- for i = 2, #self.dim_in do
- self["ltp" .. i] = self:find_param("ltp" .. i, layer_conf, global_conf, nerv.LinearTransParam, {self.dim_in[i], self.dim_out[1]})
+ if layer_conf.ltp ~= nil and layer_conf.ltp1 == nil then
+ layer_conf.ltp1 = layer_conf.ltp
+ end
+ for i = 1, #self.dim_in do
+ local pid = "ltp" .. i
+ local pid_list = i == 1 and {pid, "ltp"} or pid
+ self["ltp" .. i] = self:find_param(pid_list, layer_conf, global_conf,
+ nerv.LinearTransParam,
+ {self.dim_in[i], self.dim_out[1]})
end
- self.bp = self:find_param("bp", layer_conf, global_conf, nerv.BiasParam, {1, self.dim_out[1]}) --layer_conf.bp
+ self.ltp = self.ltp1 -- alias of ltp1
+ self.bp = self:find_param("bp", layer_conf, global_conf,
+ nerv.BiasParam,
+ {1, self.dim_out[1]})
self.gconf = global_conf
self:check_dim_len(-1, 1) -- exactly one output, allow multiple inputs
end
@@ -76,15 +102,7 @@ function AffineLayer:init(batch_size)
if self.ltp.trans:ncol() ~= self.bp.trans:ncol() then
nerv.error("mismatching dimensions of linear transform and bias paramter")
end
- self.bp:train_init()
- if self.dim_in[1] ~= self.ltp.trans:nrow() then
- nerv.error("mismatching dimensions of linear transform parameter and input")
- end
- if self.dim_out[1] ~= self.ltp.trans:ncol() then
- nerv.error("mismatching dimensions of linear transform parameter and output")
- end
- self.ltp:train_init()
- for i = 2, #self.dim_in do
+ for i = 1, #self.dim_in do
if self.dim_in[i] ~= self["ltp" .. i].trans:nrow() then
nerv.error("mismatching dimensions of linear transform parameter and input")
end
@@ -93,6 +111,7 @@ function AffineLayer:init(batch_size)
end
self["ltp" .. i]:train_init()
end
+ self.bp:train_init()
end
function AffineLayer:batch_resize(batch_size)
@@ -100,30 +119,30 @@ function AffineLayer:batch_resize(batch_size)
end
function AffineLayer:update(bp_err, input, output)
- self.ltp:update_by_err_input(bp_err[1], input[1])
- for i = 2, #self.dim_in do
+ for i = 1, #self.dim_in do
self["ltp" .. i]:update_by_err_input(bp_err[1], input[i])
end
self.bp:update_by_gradient(bp_err[1]:colsum())
end
function AffineLayer:propagate(input, output)
- output[1]:mul(input[1], self.ltp.trans, 1.0, 0.0, 'N', 'N')
+ -- apply linear transform
+ output[1]:mul(input[1], self.ltp1.trans, 1.0, 0.0, 'N', 'N')
for i = 2, #self.dim_in do
output[1]:mul(input[i], self["ltp" .. i].trans, 1.0, 1.0, 'N', 'N')
end
+ -- add bias
output[1]:add_row(self.bp.trans, 1.0)
end
function AffineLayer:back_propagate(bp_err, next_bp_err, input, output)
- next_bp_err[1]:mul(bp_err[1], self.ltp.trans, 1.0, 0.0, 'N', 'T')
- for i = 2, #self.dim_in do
+ for i = 1, #self.dim_in do
next_bp_err[i]:mul(bp_err[1], self["ltp" .. i].trans, 1.0, 0.0, 'N', 'T')
end
end
function AffineLayer:get_params()
- local pr = nerv.ParamRepo({self.ltp, self.bp})
+ local pr = nerv.ParamRepo({self.ltp1, self.bp})
for i = 2, #self.dim_in do
pr:add(self["ltp" .. i].id, self["ltp" .. i])
end
diff --git a/nerv/layer/bias.lua b/nerv/layer/bias.lua
index 7e9fd46..924c3da 100644
--- a/nerv/layer/bias.lua
+++ b/nerv/layer/bias.lua
@@ -23,7 +23,7 @@ function BiasLayer:batch_resize(batch_size)
end
function BiasLayer:propagate(input, output)
- output[1]:copy_fromd(input[1])
+ output[1]:copy_from(input[1])
output[1]:add_row(self.bias.trans, 1.0)
end
diff --git a/nerv/layer/combiner.lua b/nerv/layer/combiner.lua
index 1bcfdfb..22e89a9 100644
--- a/nerv/layer/combiner.lua
+++ b/nerv/layer/combiner.lua
@@ -6,6 +6,11 @@ function CombinerLayer:__init(id, global_conf, layer_conf)
self.dim_in = layer_conf.dim_in
self.dim_out = layer_conf.dim_out
self.gconf = global_conf
+ if self.gconf.use_cpu then
+ self.mat_type = self.gconf.mmat_type
+ else
+ self.mat_type = self.gconf.cumat_type
+ end
self:check_dim_len(#self.lambda, -1)
if #self.dim_in < 1 then
nerv.error("no input specified")
@@ -27,12 +32,12 @@ function CombinerLayer:init(batch_size)
nerv.error("mismatching dimensions of inputs/outputs")
end
end
- self.sum = self.gconf.cumat_type(batch_size, dim)
+ self.sum = self.mat_type(batch_size, dim)
end
function CombinerLayer:batch_resize(batch_size)
if self.sum:nrow() ~= batch_size then
- self.sum = self.gconf.cumat_type(batch_size, self.dim_in[1])
+ self.sum = self.mat_type(batch_size, self.dim_in[1])
end
end
@@ -45,13 +50,13 @@ function CombinerLayer:propagate(input, output)
output[1]:add(output[1], input[i], 1.0, self.lambda[i])
end
for i = 2, #self.dim_out do
- output[i]:copy_fromd(output[1])
+ output[i]:copy_from(output[1])
end
end
function CombinerLayer:back_propagate(bp_err, next_bp_err, input, output)
local sum = self.sum
- sum:copy_fromd(bp_err[1])
+ sum:copy_from(bp_err[1])
for i = 2, #self.dim_out do
sum:add(sum, bp_err[i], 1.0, 1.0)
end
diff --git a/nerv/layer/init.lua b/nerv/layer/init.lua
index 23606e1..43c2250 100644
--- a/nerv/layer/init.lua
+++ b/nerv/layer/init.lua
@@ -70,22 +70,33 @@ function Layer:get_dim()
return self.dim_in, self.dim_out
end
-function Layer:find_param(pid, l_conf, gconf, p_type, p_dim)
- if l_conf[pid] ~= nil then
- nerv.info("Param [%s] of layer [%s] found in layer_conf.", pid, self.id)
- return l_conf[pid]
+function Layer:find_param(pid_list, lconf, gconf, p_type, p_dim)
+ if type(pid_list) == "string" then
+ pid_list = {pid_list}
end
- local pid_g = self.id .. '_' .. pid --global identifier
- local pr = l_conf.pr
- local p
- if pr ~= nil and pr:has_param(pid_g) == true then
- nerv.info("Param [%s] of layer [%s] found in layer_conf.paramRepo.", pid, self.id)
- p = pr:get_param(pid_g)
- return p
+ pid_list_str = table.tostring(pid_list)
+ for i, pid in ipairs(pid_list) do
+ if lconf[pid] ~= nil then
+ nerv.info("param [%s] of layer [%s] found in `layer_conf`.", pid, self.id)
+ return lconf[pid]
+ end
+ local pid_g = self.id .. '_' .. pid --global identifier
+ local pr = lconf.pr
+ local p
+ if pr ~= nil and pr:has_param(pid_g) == true then
+ nerv.info("param [%s] of layer [%s] found in `layer_conf.pr`.", pid_list_str, self.id)
+ p = pr:get_param(pid_g)
+ return p
+ end
end
- nerv.info("Param [%s] of layer [%s] is not found in layer_conf or layer_conf.paramRepo, switch to auto-generate.", pid, self.id)
+ nerv.info("param [%s] of layer [%s] is not found in `layer_conf` or `layer_conf.pr`, " ..
+ "switch to auto-generate.", pid_list_str, self.id)
+ local pid_g = self.id .. '_' .. pid_list[1]
p = p_type(pid_g, gconf)
p.trans = gconf.cumat_type(unpack(p_dim))
+ if type(gconf.param_random) ~= "function" then
+ nerv.error("a param generate function is needed")
+ end
p.trans:generate(gconf.param_random)
return p
end
diff --git a/nerv/layer/mse.lua b/nerv/layer/mse.lua
index 0ee3080..1c218d0 100644
--- a/nerv/layer/mse.lua
+++ b/nerv/layer/mse.lua
@@ -5,6 +5,11 @@ function MSELayer:__init(id, global_conf, layer_conf)
self.dim_in = layer_conf.dim_in
self.dim_out = layer_conf.dim_out
self.gconf = global_conf
+ if self.gconf.use_cpu then
+ self.mat_type = self.gconf.mmat_type
+ else
+ self.mat_type = self.gconf.cumat_type
+ end
self:check_dim_len(2, -1)
end
@@ -15,15 +20,15 @@ function MSELayer:init(batch_size)
self.scale = 1 / self.dim_in[1]
self.total_mse = 0.0
self.total_frames = 0
- self.mse = self.gconf.cumat_type(batch_size, self.dim_in[1])
- self.mse_sum = self.gconf.cumat_type(batch_size, 1)
+ self.mse = self.mat_type(batch_size, self.dim_in[1])
+ self.mse_sum = self.mat_type(batch_size, 1)
self.diff = self.mse:create()
end
function MSELayer:batch_resize(batch_size)
if self.mse:nrow() ~= batch_resize then
- self.mse = self.gconf.cumat_type(batch_size, self.dim_in[1])
- self.mse_sum = self.gconf.cumat_type(batch_size, 1)
+ self.mse = self.mat_type(batch_size, self.dim_in[1])
+ self.mse_sum = self.mat_type(batch_size, 1)
self.diff = self.mse:create()
end
end
@@ -36,11 +41,11 @@ function MSELayer:propagate(input, output)
local mse = self.mse
local mse_sum = self.mse_sum
mse:add(input[1], input[2], 1.0, -1.0)
- self.diff:copy_fromd(mse)
+ self.diff:copy_from(mse)
mse:mul_elem(mse, mse)
mse_sum:add(mse_sum, mse:rowsum(mse), 0.0, self.scale)
if output[1] ~= nil then
- output[1]:copy_fromd(mse_sum)
+ output[1]:copy_from(mse_sum)
end
self.total_mse = self.total_mse + mse_sum:colsum()[0][0]
self.total_frames = self.total_frames + mse_sum:nrow()
diff --git a/nerv/layer/softmax_ce.lua b/nerv/layer/softmax_ce.lua
index 9071e86..31a2ad7 100644
--- a/nerv/layer/softmax_ce.lua
+++ b/nerv/layer/softmax_ce.lua
@@ -3,6 +3,11 @@ local SoftmaxCELayer = nerv.class("nerv.SoftmaxCELayer", "nerv.Layer")
function SoftmaxCELayer:__init(id, global_conf, layer_conf)
self.id = id
self.gconf = global_conf
+ if self.gconf.use_cpu then
+ self.mat_type = self.gconf.mmat_type
+ else
+ self.mat_type = self.gconf.cumat_type
+ end
self.dim_in = layer_conf.dim_in
self.dim_out = layer_conf.dim_out
self.compressed = layer_conf.compressed
@@ -19,13 +24,13 @@ function SoftmaxCELayer:init(batch_size)
self.total_ce = 0.0
self.total_correct = 0
self.total_frames = 0
- self.softmax = self.gconf.cumat_type(batch_size, self.dim_in[1])
+ self.softmax = self.mat_type(batch_size, self.dim_in[1])
self.ce = self.softmax:create()
end
function SoftmaxCELayer:batch_resize(batch_size)
if self.softmax:nrow() ~= batch_resize then
- self.softmax = self.gconf.cumat_type(batch_size, self.dim_in[1])
+ self.softmax = self.mat_type(batch_size, self.dim_in[1])
self.ce = self.softmax:create()
end
end
@@ -46,7 +51,7 @@ function SoftmaxCELayer:propagate(input, output)
ce:mul_elem(ce, label)
ce = ce:rowsum()
if output[1] ~= nil then
- output[1]:copy_fromd(ce)
+ output[1]:copy_from(ce)
end
-- add total ce
self.total_ce = self.total_ce - ce:colsum()[0][0]
diff --git a/nerv/layer/window.lua b/nerv/layer/window.lua
index 8eed352..4933de0 100644
--- a/nerv/layer/window.lua
+++ b/nerv/layer/window.lua
@@ -23,7 +23,7 @@ function WindowLayer:batch_resize(batch_size)
end
function WindowLayer:propagate(input, output)
- output[1]:copy_fromd(input[1])
+ output[1]:copy_from(input[1])
output[1]:scale_rows_by_row(self.window.trans)
end
diff --git a/nerv/lib/common.c b/nerv/lib/common.c
index db667b2..d977f8d 100644
--- a/nerv/lib/common.c
+++ b/nerv/lib/common.c
@@ -41,6 +41,7 @@ int nerv_error_status(lua_State *L, Status *status) {
status->file, status->lineno);
else
nerv_error(L, "%s @%s:%d", mmesg, status->file, status->lineno);
+ return 0;
}
int nerv_error_method_not_implemented(lua_State *L) {
diff --git a/nerv/lib/io/chunk_file.c b/nerv/lib/io/chunk_file.c
index 71db820..5c50ccd 100644
--- a/nerv/lib/io/chunk_file.c
+++ b/nerv/lib/io/chunk_file.c
@@ -2,6 +2,7 @@
#include "chunk_file.h"
#include <stdlib.h>
#include <string.h>
+#include <ctype.h>
#define PARAM_HEADER_SIZE 16
static size_t read_chunk_header_plain(FILE *fp, Status *status) {
@@ -112,7 +113,7 @@ static ChunkFile *open_read(const char *fn, Status *status) {
for (i = 0;; offset += chunk_len, i++)
{
ChunkInfo *cip;
- fprintf(stdout, "reading chunk %d from %d\n", i, (int)offset);
+ fprintf(stderr, "reading chunk %d from %d\n", i, (int)offset);
/* skip to the begining of chunk i */
if (fseeko(fp, offset, SEEK_SET) != 0)
{
diff --git a/nerv/lib/matrix/cuda_helper.h b/nerv/lib/matrix/cuda_helper.h
index 8041efb..13d5728 100644
--- a/nerv/lib/matrix/cuda_helper.h
+++ b/nerv/lib/matrix/cuda_helper.h
@@ -4,6 +4,7 @@
#include "cuda_runtime.h"
#include "driver_types.h"
#include "cublas_v2.h"
+#include "curand.h"
#define CUBLAS_SAFE_SYNC_CALL_RET(call, status) \
do { \
diff --git a/nerv/lib/matrix/cukernel.h b/nerv/lib/matrix/cukernel.h
index fe682d3..4654d29 100644
--- a/nerv/lib/matrix/cukernel.h
+++ b/nerv/lib/matrix/cukernel.h
@@ -3,7 +3,7 @@ void cudak_(cuda_mul_elem)(const Matrix *a, const Matrix *b, Matrix *c);
void cudak_(cuda_log_elem)(const Matrix *a, Matrix *b);
void cudak_(cuda_sigmoid)(const Matrix *a, Matrix *b);
void cudak_(cuda_sigmoid_grad)(const Matrix *output, const Matrix *err, Matrix *nerr);
-void cudak_(cuda_rand_uniform)(const Matrix *a); //a's curand_gen may be modified
+void cudak_(cuda_rand_uniform)(const Matrix *a); /* a's curand_gen may be modified */
void cudak_(cuda_thres_mask)(const Matrix *a, const Matrix *b, double thres, double low, double high);
void cudak_(cuda_tanh)(const Matrix *a, Matrix *b);
void cudak_(cuda_tanh_grad)(const Matrix *output, const Matrix *err, Matrix *nerr);
@@ -25,4 +25,13 @@ void cudak_(cuda_scale_rows_by_col)(const Matrix *a, Matrix *b);
void cudak_(cuda_decompress)(const Matrix *a, Matrix *b);
void cudak_(cuda_copy_rows_by_idx)(const Matrix *a, Matrix *b,
const Matrix *idx, int b_begin);
+void cudak_(cuda_copy_rows_by_colidx)(const Matrix *a, Matrix *b,
+ const Matrix *idx, int b_begin);
+#ifdef __NERV_FUTURE_CUDA_7
+ void cudak_(cuda_update_select_rows_by_rowidx)(Matrix *c, const Matrix *a, const Matrix *idx,
+ double alpha, double beta);
+ void cudak_(cuda_update_select_rows_by_colidx)(Matrix *c, const Matrix *a, const Matrix *idx,
+ double alpha, double beta);
+#endif
+
#endif
diff --git a/nerv/lib/matrix/cumatrix.c b/nerv/lib/matrix/cumatrix.c
index ea7a518..58bdfe7 100644
--- a/nerv/lib/matrix/cumatrix.c
+++ b/nerv/lib/matrix/cumatrix.c
@@ -2,9 +2,11 @@
#include "../common.h"
#include "cuda_helper.h"
#include <string.h>
+#include <time.h>
#define PROFILE_HASHMAP_SIZE 123457
static cublasHandle_t cublas_handle;
static cudaEvent_t profile_start, profile_stop;
+curandGenerator_t curand_gen;
static HashMap *profile;
void nerv_cumatrix_select_gpu(int dev, Status *status) {
@@ -45,6 +47,8 @@ void accu_profile(const char *name, float delta) {
void nerv_cumatrix_init() {
cublasCreate(&cublas_handle);
+ curandCreateGenerator(&curand_gen, CURAND_RNG_PSEUDO_DEFAULT);
+ curandSetPseudoRandomGeneratorSeed(curand_gen, time(NULL));
cudaEventCreate(&profile_start);
cudaEventCreate(&profile_stop);
profile = hashmap_create(PROFILE_HASHMAP_SIZE, bkdr_hash, strcmp);
diff --git a/nerv/lib/matrix/generic/cukernel.cu b/nerv/lib/matrix/generic/cukernel.cu
index 995059c..51e3b6a 100644
--- a/nerv/lib/matrix/generic/cukernel.cu
+++ b/nerv/lib/matrix/generic/cukernel.cu
@@ -4,6 +4,7 @@
#include "../matrix.h"
#include "cuda.h"
#include "float.h"
+#include "curand.h"
#define CUDA_THREADS_N 16
#define CUDA_THREADS_NN ((CUDA_THREADS_N) * (CUDA_THREADS_N))
#define CEIL_DIV(a, b) (((a) + (b) - 1) / (b))
@@ -356,11 +357,17 @@ __global__ void cudak_(copy_rows_by_idx)(const MATRIX_ELEM *a, MATRIX_ELEM *b,
int j = blockIdx.x * blockDim.x + threadIdx.x;
int i = blockIdx.y * blockDim.y + threadIdx.y;
if (i >= nrow || j >= ncol) return;
+ /*
int k = lrintf(idx[i]);
if (k < 0 || k >= a_nrow) {
printf("error in kernel copy_rows_by_idx k(%d) out of range\n", k);
}
b[j + i * stride] = a[j + k * stride];
+ */
+ /* NOTE: in most cases it is guaranteed
+ * the idx is within the range, checking
+ * would bring some overhead. */
+ b[j + i * stride] = a[j + lrintf(idx[i]) * stride];
}
__global__ void cudak_(copy_rows_by_colidx)(const MATRIX_ELEM *a, MATRIX_ELEM *b,
@@ -391,8 +398,6 @@ __global__ void cudak_(prefixsum_row_reduce)(const MATRIX_ELEM *a, MATRIX_ELEM *
b[idx_b] = a[idx_a];
}
-
-
extern "C" {
#include "../cukernel.h"
void cudak_(cuda_log_elem)(const Matrix *a, Matrix *b) {
@@ -440,12 +445,13 @@ extern "C" {
cudaStreamSynchronize(0);
}
+ extern curandGenerator_t curand_gen;
void cudak_(cuda_rand_uniform)(const Matrix *a) {
#ifdef MATRIX_USE_FLOAT
- curandGenerateUniform(*(a->curand_gen), MATRIX_ELEM_PTR(a), a->nrow * a->stride / sizeof(MATRIX_ELEM));
+ curandGenerateUniform(curand_gen, MATRIX_ELEM_PTR(a), a->nrow * a->stride / sizeof(MATRIX_ELEM));
#endif
#ifdef MATRIX_USE_DOUBLE
- curandGenerateUniformDouble(*(a->curand_gen), MATRIX_ELEM_PTR(a), a->nrow * a->stride / sizeof(MATRIX_ELEM));
+ curandGenerateUniformDouble(curand_gen, MATRIX_ELEM_PTR(a), a->nrow * a->stride / sizeof(MATRIX_ELEM));
#endif
}
diff --git a/nerv/lib/matrix/generic/cumatrix.c b/nerv/lib/matrix/generic/cumatrix.c
index dafadb2..7b70607 100644
--- a/nerv/lib/matrix/generic/cumatrix.c
+++ b/nerv/lib/matrix/generic/cumatrix.c
@@ -41,7 +41,7 @@ void nerv_matrix_(mul)(Matrix *c, const Matrix *a, const Matrix *b,
int bm = b->nrow, bn = b->ncol;
if (ta == CUBLAS_OP_T) SWAP(am, an);
if (tb == CUBLAS_OP_T) SWAP(bm, bn);
- if (an != bm)
+ if (an != bm || (am != c->nrow && bn != c->ncol))
NERV_EXIT_STATUS(status, MAT_WRONG_MULT_DIM, 0);
/* Because matrix in Nerv is row-major, here b comes first */
PROFILE_START
diff --git a/nerv/lib/matrix/generic/cumatrix.h b/nerv/lib/matrix/generic/cumatrix.h
index 41e9e35..5b8076f 100644
--- a/nerv/lib/matrix/generic/cumatrix.h
+++ b/nerv/lib/matrix/generic/cumatrix.h
@@ -62,3 +62,14 @@ void nerv_matrix_(scale_rows_by_col)(Matrix *a, const Matrix *b,
void nerv_matrix_(scale_rows_by_row)(Matrix *a, const Matrix *b,
Status *status);
void nerv_matrix_(prefixsum_row)(Matrix *a, const Matrix *b, Status *status);
+void nerv_matrix_(thres_mask)(Matrix *a, Matrix *b,
+ double thres, double low, double high,
+ Status *status);
+void nerv_matrix_(rand_uniform)(Matrix *a, Status *status);
+
+#ifdef __NERV_FUTURE_CUDA_7
+void nerv_matrix_(update_select_rows_by_rowidx)(Matrix *c, const Matrix *a, const Matrix *idx,
+ double alpha, double beta, Status *status);
+void nerv_matrix_(update_select_rows_by_colidx)(Matrix *c, const Matrix *a, const Matrix *idx,
+ double alpha, double beta, Status *status);
+#endif
diff --git a/nerv/lib/matrix/generic/matrix.c b/nerv/lib/matrix/generic/matrix.c
index 004d9aa..998d107 100644
--- a/nerv/lib/matrix/generic/matrix.c
+++ b/nerv/lib/matrix/generic/matrix.c
@@ -9,8 +9,6 @@ void nerv_matrix_(data_free)(Matrix *self, Status *status) {
{
/* free matrix data */
MATRIX_DATA_FREE(MATRIX_ELEM_PTR_BASE(self), status);
- curandDestroyGenerator(*(self->curand_gen));
- free(self->curand_gen);
free(self->data_ref);
free(self);
}
@@ -40,11 +38,6 @@ Matrix *nerv_matrix_(create)(long nrow, long ncol, Status *status) {
}
self->data_ref = (long *)malloc(sizeof(long));
*self->data_ref = 0;
-
- self->curand_gen = (curandGenerator_t*)malloc(sizeof(curandGenerator_t));
- curandCreateGenerator(self->curand_gen, CURAND_RNG_PSEUDO_DEFAULT);
- curandSetPseudoRandomGeneratorSeed(*(self->curand_gen), time(NULL));
-
self->offset = 0;
nerv_matrix_(data_retain)(self);
NERV_SET_STATUS(status, NERV_NORMAL, 0);
@@ -64,7 +57,6 @@ Matrix *nerv_matrix_(getrow)(Matrix *self, int row) {
prow->nmax = prow->ncol;
prow->data = self->data;
prow->data_ref = self->data_ref;
- prow->curand_gen = self->curand_gen;
prow->offset = row * self->stride;
nerv_matrix_(data_retain)(prow);
return prow;
diff --git a/nerv/lib/matrix/generic/mmatrix.c b/nerv/lib/matrix/generic/mmatrix.c
index 225079e..fa1dc5f 100644
--- a/nerv/lib/matrix/generic/mmatrix.c
+++ b/nerv/lib/matrix/generic/mmatrix.c
@@ -5,9 +5,477 @@
#define MATRIX_DATA_ALLOC(dptr, stride, width, height, status) \
host_matrix_(alloc)(dptr, stride, width, height, status)
#define NERV_GENERIC_MATRIX
+#include "../cuda_helper.h"
#include "../../common.h"
#include "../../io/chunk_file.h"
-#include "string.h"
+#include <string.h>
+#include <cblas.h>
+#include <float.h>
+
+Matrix *nerv_matrix_(colsum)(Matrix *a, Status *status) {
+ Matrix *b = nerv_matrix_(create)(1, a->ncol, status);
+ if (status->err_code != NERV_NORMAL)
+ return NULL;
+ MATRIX_ELEM *arow = MATRIX_ELEM_PTR(a),
+ *brow = MATRIX_ELEM_PTR(b);
+ int i, j;
+ size_t astride = a->stride;
+ memset(brow, 0, sizeof(MATRIX_ELEM) * b->ncol);
+ for (i = 0; i < a->nrow; i++)
+ {
+ for (j = 0; j < a->ncol; j++)
+ brow[j] += arow[j];
+ arow = MATRIX_NEXT_ROW_PTR(arow, astride);
+ }
+ NERV_SET_STATUS(status, NERV_NORMAL, 0);
+ return b;
+}
+
+Matrix *nerv_matrix_(colsame)(Matrix *a, const Matrix *ref, Status *status) {
+ Matrix *b = nerv_matrix_(create)(1, a->ncol, status);
+ if (status->err_code != NERV_NORMAL)
+ return NULL;
+ CHECK_SAME_DIMENSION_RET(a, ref, status);
+ int i, j;
+ size_t astride = a->stride, cstride = ref->stride;
+ MATRIX_ELEM *arow = MATRIX_ELEM_PTR(a),
+ *brow = MATRIX_ELEM_PTR(b);
+ const MATRIX_ELEM *crow = MATRIX_ELEM_PTR(ref);
+ memset(brow, 0, sizeof(MATRIX_ELEM) * b->ncol);
+ for (i = 0; i < a->nrow; i++)
+ {
+ for (j = 0; j < a->ncol; j++)
+ {
+ brow[j] += (int)(arow[j] == crow[j]);
+ }
+ arow = MATRIX_NEXT_ROW_PTR(arow, astride);
+ crow = MATRIX_NEXT_ROW_PTR(crow, cstride);
+ }
+ NERV_SET_STATUS(status, NERV_NORMAL, 0);
+ return b;
+}
+
+Matrix *nerv_matrix_(rowsum)(Matrix *a, Status *status) {
+ Matrix *b = nerv_matrix_(create)(a->nrow, 1, status);
+ if (status->err_code != NERV_NORMAL)
+ return NULL;
+ MATRIX_ELEM *arow = MATRIX_ELEM_PTR(a),
+ *brow = MATRIX_ELEM_PTR(b);
+ int i, j;
+ size_t astride = a->stride, bstride = b->stride;
+ memset(brow, 0, b->stride * b->nrow);
+ for (i = 0; i < a->nrow; i++)
+ {
+ for (j = 0; j < a->ncol; j++)
+ brow[0] += arow[j];
+ arow = MATRIX_NEXT_ROW_PTR(arow, astride);
+ brow = MATRIX_NEXT_ROW_PTR(brow, bstride);
+ }
+ NERV_SET_STATUS(status, NERV_NORMAL, 0);
+ return b;
+}
+
+Matrix *nerv_matrix_(rowmax)(Matrix *a, Status *status) {
+ Matrix *b = nerv_matrix_(create)(a->nrow, 1, status);
+ if (status->err_code != NERV_NORMAL)
+ return NULL;
+ MATRIX_ELEM *arow = MATRIX_ELEM_PTR(a),
+ *brow = MATRIX_ELEM_PTR(b);
+ int i, j;
+ size_t astride = a->stride, bstride = b->stride;
+ for (i = 0; i < a->nrow; i++)
+ {
+ brow[0] = arow[0];
+ for (j = 1; j < a->ncol; j++)
+ if (arow[j] > brow[0])
+ brow[0] = arow[j];
+ arow = MATRIX_NEXT_ROW_PTR(arow, astride);
+ brow = MATRIX_NEXT_ROW_PTR(brow, bstride);
+ }
+ NERV_SET_STATUS(status, NERV_NORMAL, 0);
+ return b;
+}
+
+void nerv_matrix_(rowmax_idx)(Matrix *a, Matrix **b, Matrix **idx, Status *status) {
+ *b = nerv_matrix_(create)(a->nrow, 1, status);
+ if (status->err_code != NERV_NORMAL)
+ return;
+ *idx = nerv_matrix_(create)(a->nrow, 1, status);
+ if (status->err_code != NERV_NORMAL)
+ {
+ /* FIXME: destroy may also fail! */
+ nerv_matrix_(destroy)(*b, status);
+ return;
+ }
+ MATRIX_ELEM *arow = MATRIX_ELEM_PTR(a),
+ *brow = MATRIX_ELEM_PTR(*b),
+ *crow = MATRIX_ELEM_PTR(*idx);
+ int i, j;
+ size_t astride = a->stride,
+ bstride = (*b)->stride,
+ cstride = (*idx)->stride;
+ for (i = 0; i < a->nrow; i++)
+ {
+ brow[0] = arow[(int)(crow[0] = 0)];
+ for (j = 1; j < a->ncol; j++)
+ if (arow[j] > brow[0])
+ brow[0] = arow[(int)(crow[0] = j)];
+ arow = MATRIX_NEXT_ROW_PTR(arow, astride);
+ brow = MATRIX_NEXT_ROW_PTR(brow, bstride);
+ crow = MATRIX_NEXT_ROW_PTR(crow, cstride);
+ }
+ NERV_SET_STATUS(status, NERV_NORMAL, 0);
+}
+
+Matrix *nerv_matrix_(trans)(Matrix *a, Status *status) {
+ Matrix *b = nerv_matrix_(create)(a->ncol, a->nrow, status);
+ if (status->err_code != NERV_NORMAL)
+ return NULL;
+ MATRIX_ELEM *arow = MATRIX_ELEM_PTR(a);
+ int i, j;
+ size_t astride = a->stride, bstride = b->stride;
+ for (i = 0; i < a->nrow; i++)
+ {
+ MATRIX_ELEM *brow = MATRIX_ELEM_PTR(b) + i;
+ for (j = 0; j < a->ncol; j++)
+ {
+ brow[0] = arow[j];
+ brow = MATRIX_NEXT_ROW_PTR(brow, bstride);
+ }
+ arow = MATRIX_NEXT_ROW_PTR(arow, astride);
+ }
+ NERV_SET_STATUS(status, NERV_NORMAL, 0);
+ return b;
+}
+
+Matrix *nerv_matrix_(decompress)(const Matrix *a, int orig_col, Status *status) {
+ Matrix *b;
+ if (a->ncol != 1)
+ {
+ NERV_SET_STATUS(status, MAT_COL_VECTOR_EXP, 0);
+ return NULL;
+ }
+ b = nerv_matrix_(create)(a->nrow, orig_col, status);
+ if (status->err_code != NERV_NORMAL)
+ return NULL;
+ int i;
+ size_t astride = a->stride, bstride = b->stride;
+ const MATRIX_ELEM *arow = MATRIX_ELEM_PTR(a);
+ MATRIX_ELEM *brow = MATRIX_ELEM_PTR(b);
+ memset(brow, 0, b->stride * b->nrow);
+ for (i = 0; i < a->nrow; i++)
+ {
+ brow[lrintf(arow[0])] = 1;
+ arow = MATRIX_NEXT_ROW_PTR(arow, astride);
+ brow = MATRIX_NEXT_ROW_PTR(brow, bstride);
+ }
+ NERV_SET_STATUS(status, NERV_NORMAL, 0);
+ return b;
+}
+
+void nerv_matrix_(add)(Matrix *c, const Matrix *a, const Matrix *b, MATRIX_ELEM alpha, MATRIX_ELEM beta, Status *status) {
+ CHECK_SAME_DIMENSION(a, b, status);
+ CHECK_SAME_DIMENSION(a, c, status);
+ int i, j;
+ size_t astride = a->stride,
+ bstride = b->stride,
+ cstride = c->stride;
+ const MATRIX_ELEM *arow = MATRIX_ELEM_PTR(a),
+ *brow = MATRIX_ELEM_PTR(b);
+ MATRIX_ELEM *crow = MATRIX_ELEM_PTR(c);
+ for (i = 0; i < a->nrow; i++)
+ {
+ for (j = 0; j < a->ncol; j++)
+ crow[j] = arow[j] * alpha + brow[j] * beta;
+ arow = MATRIX_NEXT_ROW_PTR(arow, astride);
+ brow = MATRIX_NEXT_ROW_PTR(brow, bstride);
+ crow = MATRIX_NEXT_ROW_PTR(crow, cstride);
+ }
+ NERV_SET_STATUS(status, NERV_NORMAL, 0);
+}
+
+
+void nerv_matrix_(mul)(Matrix *c, const Matrix *a, const Matrix *b,
+ MATRIX_ELEM alpha, MATRIX_ELEM beta,
+ int ta, int tb, Status *status) {
+#define SWAP(a, b) \
+ do { int t = (a); (a) = (b); (b) = t; } while (0)
+
+ int am = a->nrow, an = a->ncol;
+ int bm = b->nrow, bn = b->ncol;
+ if (ta == CblasTrans) SWAP(am, an);
+ if (tb == CblasTrans) SWAP(bm, bn);
+ if (an != bm || (am != c->nrow && bn != c->ncol))
+ NERV_EXIT_STATUS(status, MAT_WRONG_MULT_DIM, 0);
+ /* Because matrix in Nerv is row-major, here b comes first */
+ NERV_CBLAS_(gemm)(CblasRowMajor, ta, tb, am, bn, an,
+ alpha,
+ MATRIX_ELEM_PTR(a), a->stride / sizeof(MATRIX_ELEM),
+ MATRIX_ELEM_PTR(b), b->stride / sizeof(MATRIX_ELEM),
+ beta,
+ MATRIX_ELEM_PTR(c), c->stride / sizeof(MATRIX_ELEM));
+ NERV_SET_STATUS(status, NERV_NORMAL, 0);
+}
+
+void nerv_matrix_(add_row)(Matrix *b, const Matrix *a, double beta,
+ Status *status) {
+ if (a->ncol != b->ncol)
+ NERV_EXIT_STATUS(status, MAT_MISMATCH_DIM, 0);
+ if (a->nrow != 1)
+ NERV_EXIT_STATUS(status, MAT_ROW_VECTOR_EXP, 0);
+ const MATRIX_ELEM *arow = MATRIX_ELEM_PTR(a);
+ MATRIX_ELEM *brow = MATRIX_ELEM_PTR(b);
+ int i, j;
+ size_t bstride = b->stride;
+ for (i = 0; i < b->nrow; i++)
+ {
+ for (j = 0; j < b->ncol; j++)
+ brow[j] += arow[j] * beta;
+ brow = MATRIX_NEXT_ROW_PTR(brow, bstride);
+ }
+ NERV_SET_STATUS(status, NERV_NORMAL, 0);
+}
+
+void nerv_matrix_(clip)(Matrix *self, double val_1, double val_2, Status *status) {
+ int i, j;
+ size_t astride = self->stride;
+ MATRIX_ELEM *arow = MATRIX_ELEM_PTR(self);
+ for (i = 0; i < self->nrow; i++)
+ {
+ for (j = 0; j < self->ncol; j++)
+ if (arow[j] > val_2)
+ arow[j] = val_2;
+ else if (arow[j] < val_1)
+ arow[j] = val_1;
+ arow = MATRIX_NEXT_ROW_PTR(arow, astride);
+ }
+ NERV_SET_STATUS(status, NERV_NORMAL, 0);
+}
+
+void nerv_matrix_(fill)(Matrix *self, double val, Status *status) {
+ int i, j;
+ size_t astride = self->stride;
+ MATRIX_ELEM *arow = MATRIX_ELEM_PTR(self);
+ for (i = 0; i < self->nrow; i++)
+ {
+ for (j = 0; j < self->ncol; j++)
+ arow[j] = val;
+ arow = MATRIX_NEXT_ROW_PTR(arow, astride);
+ }
+ NERV_SET_STATUS(status, NERV_NORMAL, 0);
+}
+
+void nerv_matrix_(sigmoid)(Matrix *a, const Matrix *b, Status *status) {
+ CHECK_SAME_DIMENSION(a, b, status);
+ int i, j;
+ size_t astride = a->stride, bstride = b->stride;
+ MATRIX_ELEM *arow = MATRIX_ELEM_PTR(a);
+ const MATRIX_ELEM *brow = MATRIX_ELEM_PTR(b);
+ for (i = 0; i < a->nrow; i++)
+ {
+ for (j = 0; j < a->ncol; j++)
+ arow[j] = 1.0 / (1.0 + exp(-brow[j]));
+ arow = MATRIX_NEXT_ROW_PTR(arow, astride);
+ brow = MATRIX_NEXT_ROW_PTR(brow, bstride);
+ }
+ NERV_SET_STATUS(status, NERV_NORMAL, 0);
+}
+
+void nerv_matrix_(sigmoid_grad)(Matrix *nerr, const Matrix *err,
+ const Matrix *output, Status *status) {
+ CHECK_SAME_DIMENSION(nerr, err, status);
+ CHECK_SAME_DIMENSION(nerr, output, status);
+ int i, j;
+ size_t nerr_stride = nerr->stride,
+ err_stride = err->stride,
+ out_stride = output->stride;
+ MATRIX_ELEM *nerr_row = MATRIX_ELEM_PTR(nerr);
+ const MATRIX_ELEM *err_row = MATRIX_ELEM_PTR(err),
+ *out_row = MATRIX_ELEM_PTR(output);
+ for (i = 0; i < nerr->nrow; i++)
+ {
+ for (j = 0; j < nerr->ncol; j++)
+ nerr_row[j] = out_row[j] * (1.0 - out_row[j]) * err_row[j];
+ nerr_row = MATRIX_NEXT_ROW_PTR(nerr_row, nerr_stride);
+ err_row = MATRIX_NEXT_ROW_PTR(err_row, err_stride);
+ out_row = MATRIX_NEXT_ROW_PTR(out_row, out_stride);
+ }
+ NERV_SET_STATUS(status, NERV_NORMAL, 0);
+}
+
+Matrix *nerv_matrix_(softmax)(Matrix *b, const Matrix *a, Status *status) {
+ Matrix *max_idx;
+ CHECK_SAME_DIMENSION_RET(a, b, status);
+ max_idx = nerv_matrix_(create)(a->nrow, 1, status);
+ if (status->err_code != NERV_NORMAL)
+ return NULL;
+ int i, j;
+ size_t astride = a->stride,
+ bstride = b->stride,
+ cstride = max_idx->stride;
+ const MATRIX_ELEM *arow = MATRIX_ELEM_PTR(a);
+ MATRIX_ELEM *brow = MATRIX_ELEM_PTR(b),
+ *crow = MATRIX_ELEM_PTR(max_idx);
+ for (i = 0; i < b->nrow; i++)
+ {
+ MATRIX_ELEM max = arow[(int)(crow[0] = 0)];
+ for (j = 1; j < b->ncol; j++)
+ if (arow[j] > max)
+ max = arow[(int)(crow[0] = j)];
+
+ MATRIX_ELEM sum = 0;
+ for (j = 0; j < b->ncol; j++)
+ sum += exp(arow[j] - max);
+ for (j = 0; j < b->ncol; j++)
+ brow[j] = exp(arow[j] - max) / sum;
+ arow = MATRIX_NEXT_ROW_PTR(arow, astride);
+ brow = MATRIX_NEXT_ROW_PTR(brow, bstride);
+ crow = MATRIX_NEXT_ROW_PTR(crow, cstride);
+ }
+ NERV_SET_STATUS(status, NERV_NORMAL, 0);
+ return max_idx;
+}
+
+void nerv_matrix_(mul_elem)(Matrix *c, const Matrix *a, const Matrix *b,
+ Status *status) {
+ CHECK_SAME_DIMENSION(a, b, status);
+ CHECK_SAME_DIMENSION(a, c, status);
+ int i, j;
+ size_t astride = a->stride,
+ bstride = b->stride,
+ cstride = c->stride;
+ const MATRIX_ELEM *arow = MATRIX_ELEM_PTR(a),
+ *brow = MATRIX_ELEM_PTR(b);
+ MATRIX_ELEM *crow = MATRIX_ELEM_PTR(c);
+ for (i = 0; i < c->nrow; i++)
+ {
+ for (j = 0; j < c->ncol; j++)
+ crow[j] = arow[j] * brow[j];
+ arow = MATRIX_NEXT_ROW_PTR(arow, astride);
+ brow = MATRIX_NEXT_ROW_PTR(brow, bstride);
+ crow = MATRIX_NEXT_ROW_PTR(crow, cstride);
+ }
+ NERV_SET_STATUS(status, NERV_NORMAL, 0);
+}
+
+void nerv_matrix_(log_elem)(Matrix *b, const Matrix *a, Status *status) {
+ CHECK_SAME_DIMENSION(a, b, status);
+ int i, j;
+ size_t astride = a->stride, bstride = b->stride;
+ const MATRIX_ELEM *arow = MATRIX_ELEM_PTR(a);
+ MATRIX_ELEM *brow = MATRIX_ELEM_PTR(b);
+ MATRIX_ELEM limit =
+#ifdef MATRIX_USE_FLOAT
+ FLT_MIN;
+#elif defined (MATRIX_USE_DOUBLE)
+ DBL_MIN;
+#elif defined (MATRIX_USE_INT)
+ 1;
+#endif
+ for (i = 0; i < b->nrow; i++)
+ {
+ for (j = 0; j < b->ncol; j++)
+ brow[j] = log(arow[j] < limit ? limit : arow[j]);
+ arow = MATRIX_NEXT_ROW_PTR(arow, astride);
+ brow = MATRIX_NEXT_ROW_PTR(brow, bstride);
+ }
+ NERV_SET_STATUS(status, NERV_NORMAL, 0);
+}
+
+void nerv_matrix_(expand_frm)(Matrix *a, const Matrix *b,
+ int context, Status *status) {
+ if (a->nrow != b->nrow)
+ NERV_EXIT_STATUS(status, MAT_MISMATCH_DIM, 0);
+ if (a->ncol != b->ncol * (context * 2 + 1))
+ NERV_EXIT_STATUS(status, MAT_GENERAL_ERR,
+ "the width should be 2 * context + 1");
+ int i, j, k;
+ size_t astride = a->stride, bstride = b->stride;
+ MATRIX_ELEM *arow = MATRIX_ELEM_PTR(a);
+ for (i = 0; i < a->nrow; i++)
+ {
+ MATRIX_ELEM *a_subrow = arow;
+ int start = i - context;
+ if (start < 0) start = 0;
+ const MATRIX_ELEM *brow = MATRIX_ROW_PTR(b, start);
+ for (j = i - context; j <= i + context; j++)
+ {
+ for (k = 0; k < b->ncol; k++)
+ a_subrow[k] = brow[k];
+ if (0 <= j && j < b->nrow - 1)
+ brow = MATRIX_NEXT_ROW_PTR(brow, bstride);
+ a_subrow += b->ncol;
+ }
+ arow = MATRIX_NEXT_ROW_PTR(arow, astride);
+ }
+ NERV_SET_STATUS(status, NERV_NORMAL, 0);
+}
+
+void nerv_matrix_(rearrange_frm)(Matrix *a, const Matrix *b,
+ int step, Status *status) {
+ CHECK_SAME_DIMENSION(a, b, status);
+ if (b->ncol % step)
+ NERV_EXIT_STATUS(status, MAT_GENERAL_ERR,
+ "the dimension of columns is not divisible by step");
+ int i, j, k;
+ size_t astride = a->stride, bstride = b->stride;
+ MATRIX_ELEM *arow = MATRIX_ELEM_PTR(a);
+ const MATRIX_ELEM *brow = MATRIX_ELEM_PTR(b);
+ int gsize = b->ncol / step; /* group size of each step */
+ for (i = 0; i < a->nrow; i++)
+ {
+ MATRIX_ELEM *a_subrow = arow;
+ for (k = 0; k < gsize; k++)
+ {
+ const MATRIX_ELEM *b_subrow = brow + k;
+ for (j = 0; j < step;
+ j++, a_subrow++, b_subrow += gsize)
+ *a_subrow = *b_subrow;
+ }
+ arow = MATRIX_NEXT_ROW_PTR(arow, astride);
+ brow = MATRIX_NEXT_ROW_PTR(brow, bstride);
+ }
+ NERV_SET_STATUS(status, NERV_NORMAL, 0);
+}
+
+void nerv_matrix_(scale_rows_by_row)(Matrix *a, const Matrix *b,
+ Status *status) {
+ if (a->ncol != b->ncol)
+ NERV_EXIT_STATUS(status, MAT_MISMATCH_DIM, 0);
+ if (b->nrow != 1)
+ NERV_EXIT_STATUS(status, MAT_ROW_VECTOR_EXP, 0);
+ int i, j;
+ size_t astride = a->stride;
+ MATRIX_ELEM *arow = MATRIX_ELEM_PTR(a);
+ const MATRIX_ELEM *brow = MATRIX_ELEM_PTR(b);
+ for (i = 0; i < a->nrow; i++)
+ {
+ for (j = 0; j < a->ncol; j++)
+ arow[j] *= brow[j];
+ arow = MATRIX_NEXT_ROW_PTR(arow, astride);
+ }
+ NERV_SET_STATUS(status, NERV_NORMAL, 0);
+}
+
+void nerv_matrix_(scale_rows_by_col)(Matrix *a, const Matrix *b,
+ Status *status) {
+ if (a->nrow != b->nrow)
+ NERV_EXIT_STATUS(status, MAT_MISMATCH_DIM, 0);
+ if (b->ncol != 1)
+ NERV_EXIT_STATUS(status, MAT_COL_VECTOR_EXP, 0);
+ int i, j;
+ size_t astride = a->stride, bstride = b->stride;
+ MATRIX_ELEM *arow = MATRIX_ELEM_PTR(a),
+ *brow = MATRIX_ELEM_PTR(b);
+ for (i = 0; i < a->nrow; i++)
+ {
+ for (j = 0; j < a->ncol; j++)
+ arow[j] *= brow[0];
+ arow = MATRIX_NEXT_ROW_PTR(arow, astride);
+ brow = MATRIX_NEXT_ROW_PTR(brow, bstride);
+ }
+ NERV_SET_STATUS(status, NERV_NORMAL, 0);
+}
static void host_matrix_(free)(MATRIX_ELEM *ptr, Status *status) {
free(ptr);
@@ -29,7 +497,10 @@ Matrix *nerv_matrix_(load)(ChunkData *cdp, Status *status) {
FILE *fp = cdp->fp;
Matrix *self;
if (fscanf(fp, "%ld %ld", &nrow, &ncol) != 2)
- NERV_EXIT_STATUS(status, MAT_INVALID_FORMAT, 0);
+ {
+ NERV_SET_STATUS(status, MAT_INVALID_FORMAT, 0);
+ return 0;
+ }
self = nerv_matrix_(create)(nrow, ncol, status);
if (status->err_code != NERV_NORMAL)
return NULL;
@@ -40,7 +511,8 @@ Matrix *nerv_matrix_(load)(ChunkData *cdp, Status *status) {
if (fscanf(fp, MATRIX_ELEM_FMT, row + j) != 1)
{
free(self);
- NERV_EXIT_STATUS(status, MAT_INVALID_FORMAT, 0);
+ NERV_SET_STATUS(status, MAT_INVALID_FORMAT, 0);
+ return 0;
}
}
NERV_SET_STATUS(status, NERV_NORMAL, 0);
@@ -65,7 +537,8 @@ void nerv_matrix_(save)(Matrix *self, ChunkFile *cfp, Status *status) {
NERV_SET_STATUS(status, NERV_NORMAL, 0);
}
-void nerv_matrix_(copy_from)(Matrix *a, const Matrix *b,
+
+void nerv_matrix_(copy_fromh)(Matrix *a, const Matrix *b,
int a_begin, int b_begin, int b_end,
Status *status) {
if (!(0 <= b_begin && b_begin < b_end && b_end <= b->nrow &&
@@ -75,7 +548,29 @@ void nerv_matrix_(copy_from)(Matrix *a, const Matrix *b,
NERV_EXIT_STATUS(status, MAT_MISMATCH_DIM, 0);
memmove(MATRIX_ROW_PTR(a, a_begin),
MATRIX_ROW_PTR(b, b_begin),
- sizeof(MATRIX_ELEM) * b->ncol * (b_end - b_begin));
+ b->stride * (b_end - b_begin));
+ NERV_SET_STATUS(status, NERV_NORMAL, 0);
+}
+
+void nerv_matrix_(copy_rows_fromh_by_idx)(Matrix *a, const Matrix *b,
+ const Matrix *idx, int b_begin, Status *status) {
+ if (!(0 <= b_begin && b_begin + a->nrow <= idx->ncol))
+ NERV_EXIT_STATUS(status, MAT_INVALID_COPY_INTERVAL, 0);
+ if (idx->nrow != 1)
+ NERV_EXIT_STATUS(status, MAT_IDX_VECTOR_EXP, 0);
+ if (a->ncol != b->ncol)
+ NERV_EXIT_STATUS(status, MAT_MISMATCH_DIM, 0);
+ int i;
+ MATRIX_ELEM *idx_row = MATRIX_ELEM_PTR(idx) + b_begin;
+ for (i = 0; i < a->nrow; i++)
+ {
+ int src_idx = lrintf(idx_row[i]);
+ /* assume the index is valid to improve performance
+ if (!(0 <= src_idx && src_idx < b->nrow))
+ NERV_EXIT_STATUS(status, MAT_INVALID_IDX, 0);
+ */
+ memmove(MATRIX_ROW_PTR(a, i), MATRIX_ROW_PTR(b, src_idx), b->stride);
+ }
NERV_SET_STATUS(status, NERV_NORMAL, 0);
}
diff --git a/nerv/lib/matrix/generic/mmatrix.h b/nerv/lib/matrix/generic/mmatrix.h
index f00a04d..c54c4e5 100644
--- a/nerv/lib/matrix/generic/mmatrix.h
+++ b/nerv/lib/matrix/generic/mmatrix.h
@@ -1,8 +1,47 @@
#include "../../common.h"
#include "../../io/chunk_file.h"
-Matrix *nerv_matrix_(load)(ChunkData *cdp, Status *status);
-void nerv_matrix_(save)(Matrix *self, ChunkFile *cfp, Status *status);
-void nerv_matrix_(copy_from)(Matrix *a, const Matrix *b,
+void nerv_matrix_(add)(Matrix *c, const Matrix *a, const Matrix *b,
+ MATRIX_ELEM alpha, MATRIX_ELEM beta,
+ Status *status);
+void nerv_matrix_(mul)(Matrix *c, const Matrix *a, const Matrix *b,
+ MATRIX_ELEM alpha, MATRIX_ELEM beta,
+ int ta, int tb, Status *status);
+void nerv_matrix_(sigmoid)(Matrix *a, const Matrix *b, Status *status);
+void nerv_matrix_(sigmoid_grad)(Matrix *nerr, const Matrix *err,
+ const Matrix *output, Status *status);
+
+Matrix *nerv_matrix_(softmax)(Matrix *b, const Matrix *a, Status *status);
+Matrix *nerv_matrix_(rowsum)(Matrix *a, Status *status);
+Matrix *nerv_matrix_(colsum)(Matrix *a, Status *status);
+Matrix *nerv_matrix_(colsame)(Matrix *a, const Matrix *ref,
+ Status *status);
+Matrix *nerv_matrix_(rowmax)(Matrix *a, Status *status);
+void nerv_matrix_(rowmax_idx)(Matrix *a, Matrix **b, Matrix **idx,
+ Status *status);
+void nerv_matrix_(add_row)(Matrix *b, const Matrix *a, double beta,
+ Status *status);
+void nerv_matrix_(clip)(Matrix *self, double val_1, double val_2, Status *status);
+void nerv_matrix_(fill)(Matrix *self, double val, Status *status);
+void nerv_matrix_(copy_fromh)(Matrix *a, const Matrix *b,
int a_begin, int b_begin, int b_end,
Status *status);
+Matrix *nerv_matrix_(trans)(Matrix *a, Status *status);
+void nerv_matrix_(mul_elem)(Matrix *c, const Matrix *a, const Matrix *b,
+ Status *status);
+
+void nerv_matrix_(log_elem)(Matrix *b, const Matrix *a, Status *status);
+
+Matrix *nerv_matrix_(decompress)(const Matrix *a, int orig_col, Status *status);
+void nerv_matrix_(copy_rows_fromh_by_idx)(Matrix *a, const Matrix *b,
+ const Matrix *idx, int b_begin, Status *status);
+void nerv_matrix_(expand_frm)(Matrix *a, const Matrix *b,
+ int context, Status *status);
+void nerv_matrix_(rearrange_frm)(Matrix *a, const Matrix *b,
+ int step, Status *status);
+void nerv_matrix_(scale_rows_by_col)(Matrix *a, const Matrix *b,
+ Status *status);
+void nerv_matrix_(scale_rows_by_row)(Matrix *a, const Matrix *b,
+ Status *status);
+Matrix *nerv_matrix_(load)(ChunkData *cdp, Status *status);
+void nerv_matrix_(save)(Matrix *self, ChunkFile *cfp, Status *status);
diff --git a/nerv/lib/matrix/matrix.h b/nerv/lib/matrix/matrix.h
index a28fd97..35b698f 100644
--- a/nerv/lib/matrix/matrix.h
+++ b/nerv/lib/matrix/matrix.h
@@ -2,7 +2,6 @@
#define NERV_GENERIC_MATRIX_H
#include <stddef.h>
-#include <curand.h>
typedef struct Matrix {
size_t stride; /* size of a row */
@@ -15,9 +14,10 @@ typedef struct Matrix {
} data; /* pointer to actual storage */
unsigned long offset; /* the actual beginning of the matrix */
long *data_ref;
- curandGenerator_t *curand_gen;
} Matrix;
#define MATRIX_ROW_PTR(self, row) \
(MATRIX_ELEM *)((char *)MATRIX_ELEM_PTR(self) + (row) * (self)->stride)
+#define MATRIX_NEXT_ROW_PTR(self, stride) \
+ (MATRIX_ELEM *)((char *)self + stride)
#endif
diff --git a/nerv/lib/matrix/mmatrix.c b/nerv/lib/matrix/mmatrix.c
index b5670f2..3125ab6 100644
--- a/nerv/lib/matrix/mmatrix.c
+++ b/nerv/lib/matrix/mmatrix.c
@@ -5,6 +5,7 @@
#define MATRIX_USE_FLOAT
#define host_matrix_(NAME) host_matrix_float_##NAME
#define nerv_matrix_(NAME) nerv_matrix_host_float_##NAME
+#define NERV_CBLAS_(NAME) cblas_s##NAME
#include "generic/matrix.h"
#include "generic/elem_type.h"
#include "generic/mmatrix.c"
@@ -29,6 +30,7 @@ Matrix *nerv_matrix_(perm_gen)(int ncol, Status *status) {
#undef nerv_matrix_
#undef host_matrix_
+#undef NERV_CBLAS_
#undef MATRIX_USE_FLOAT
#undef MATRIX_ELEM
#undef MATRIX_ELEM_PTR
@@ -40,9 +42,11 @@ Matrix *nerv_matrix_(perm_gen)(int ncol, Status *status) {
#define MATRIX_USE_DOUBLE
#define host_matrix_(NAME) host_matrix_double_##NAME
#define nerv_matrix_(NAME) nerv_matrix_host_double_##NAME
+#define NERV_CBLAS_(NAME) cblas_d##NAME
#include "generic/mmatrix.c"
#undef nerv_matrix_
#undef host_matrix_
+#undef NERV_CBLAS_
#undef MATRIX_USE_DOUBLE
#undef MATRIX_ELEM
#undef MATRIX_ELEM_PTR
@@ -54,4 +58,5 @@ Matrix *nerv_matrix_(perm_gen)(int ncol, Status *status) {
#define MATRIX_USE_INT
#define host_matrix_(NAME) host_matrix_int_##NAME
#define nerv_matrix_(NAME) nerv_matrix_host_int_##NAME
+#define NERV_CBLAS_(NAME) (void)
#include "generic/mmatrix.c"
diff --git a/nerv/matrix/generic/cumatrix.c b/nerv/matrix/generic/cumatrix.c
index 29fa7b1..b706c21 100644
--- a/nerv/matrix/generic/cumatrix.c
+++ b/nerv/matrix/generic/cumatrix.c
@@ -10,48 +10,11 @@
#include "../../lib/matrix/generic/matrix.h"
#include "../../lib/matrix/generic/cumatrix.h"
-static int nerv_matrix_(lua_add)(lua_State *L) {
- Status status;
- Matrix *c = luaT_checkudata(L, 1, nerv_matrix_(tname));
- const Matrix *a = luaT_checkudata(L, 2, nerv_matrix_(tname));
- const Matrix *b = luaT_checkudata(L, 3, nerv_matrix_(tname));
- MATRIX_ELEM alpha = luaL_checknumber(L, 4);
- MATRIX_ELEM beta = luaL_checknumber(L, 5);
- nerv_matrix_(add)(c, a, b, alpha, beta, &status);
- NERV_LUA_CHECK_STATUS(L, status);
- return 0;
-}
-
-static int nerv_matrix_(lua_get_cublas_op)(char ch) {
+#define BLAS_OP_N CUBLAS_OP_N
+static int nerv_matrix_(lua_get_blas_op)(char ch) {
return (ch == 'T' || ch == 't') ? CUBLAS_OP_T : CUBLAS_OP_N;
}
-static int nerv_matrix_(lua_mul)(lua_State *L) {
- Status status;
- Matrix *c = luaT_checkudata(L, 1, nerv_matrix_(tname));
- Matrix *a = luaT_checkudata(L, 2, nerv_matrix_(tname));
- Matrix *b = luaT_checkudata(L, 3, nerv_matrix_(tname));
- MATRIX_ELEM alpha = luaL_checknumber(L, 4);
- MATRIX_ELEM beta = luaL_checknumber(L, 5);
- int nargs = lua_gettop(L);
- int ta = nargs > 5 ? nerv_matrix_(lua_get_cublas_op)(*luaL_checkstring(L, 6)) \
- : CUBLAS_OP_N;
- int tb = nargs > 6 ? nerv_matrix_(lua_get_cublas_op)(*luaL_checkstring(L, 7)) \
- : CUBLAS_OP_N;
- nerv_matrix_(mul)(c, a, b, alpha, beta, ta, tb, &status);
- NERV_LUA_CHECK_STATUS(L, status);
- return 0;
-}
-
-static int nerv_matrix_(lua_sigmoid)(lua_State *L) {
- Status status;
- Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
- Matrix *b = luaT_checkudata(L, 2, nerv_matrix_(tname));
- nerv_matrix_(sigmoid)(a, b, &status);
- NERV_LUA_CHECK_STATUS(L, status);
- return 0;
-}
-
static int nerv_matrix_(lua_prefixsum_row)(lua_State *L) {
Status status;
Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
@@ -61,16 +24,6 @@ static int nerv_matrix_(lua_prefixsum_row)(lua_State *L) {
return 0;
}
-static int nerv_matrix_(lua_sigmoid_grad)(lua_State *L) {
- Status status;
- Matrix *nerr = luaT_checkudata(L, 1, nerv_matrix_(tname));
- Matrix *err = luaT_checkudata(L, 2, nerv_matrix_(tname));
- Matrix *output = luaT_checkudata(L, 3, nerv_matrix_(tname));
- nerv_matrix_(sigmoid_grad)(nerr, err, output, &status);
- NERV_LUA_CHECK_STATUS(L, status);
- return 0;
-}
-
static int nerv_matrix_(lua_thres_mask)(lua_State *L) {
Status status;
Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
@@ -110,107 +63,6 @@ static int nerv_matrix_(lua_tanh_grad)(lua_State *L) {
return 0;
}
-static int nerv_matrix_(lua_softmax)(lua_State *L) {
- Status status;
- Matrix *a = luaT_checkudata(L, 2, nerv_matrix_(tname));
- Matrix *b = luaT_checkudata(L, 1, nerv_matrix_(tname));
- Matrix *max_idx = nerv_matrix_(softmax)(b, a, &status);
- NERV_LUA_CHECK_STATUS(L, status);
- luaT_pushudata(L, max_idx, nerv_matrix_(tname));
- return 1;
-}
-
-static int nerv_matrix_(lua_rowsum)(lua_State *L) {
- Status status;
- Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
- Matrix *b = nerv_matrix_(rowsum)(a, &status);
- NERV_LUA_CHECK_STATUS(L, status);
- luaT_pushudata(L, b, nerv_matrix_(tname));
- return 1;
-}
-
-static int nerv_matrix_(lua_colsum)(lua_State *L) {
- Status status;
- Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
- Matrix *b = nerv_matrix_(colsum)(a, &status);
- NERV_LUA_CHECK_STATUS(L, status);
- luaT_pushudata(L, b, nerv_matrix_(tname));
- return 1;
-}
-
-static int nerv_matrix_(lua_colsame)(lua_State *L) {
- Status status;
- Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
- const Matrix *ref = luaT_checkudata(L, 2, nerv_matrix_(tname));
- Matrix *b = nerv_matrix_(colsame)(a, ref, &status);
- NERV_LUA_CHECK_STATUS(L, status);
- luaT_pushudata(L, b, nerv_matrix_(tname));
- return 1;
-}
-
-static int nerv_matrix_(lua_rowmax)(lua_State *L) {
- Status status;
- Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
- Matrix *b = nerv_matrix_(rowmax)(a, &status);
- NERV_LUA_CHECK_STATUS(L, status);
- luaT_pushudata(L, b, nerv_matrix_(tname));
- return 1;
-}
-
-static int nerv_matrix_(lua_rowmax_idx)(lua_State *L) {
- Status status;
- Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
- Matrix *b;
- Matrix *idx;
- nerv_matrix_(rowmax_idx)(a, &b, &idx, &status);
- NERV_LUA_CHECK_STATUS(L, status);
- luaT_pushudata(L, b, nerv_matrix_(tname));
- luaT_pushudata(L, idx, nerv_matrix_(tname));
- return 2;
-}
-
-static int nerv_matrix_(lua_add_row)(lua_State *L) {
- Status status;
- const Matrix *a = luaT_checkudata(L, 2, nerv_matrix_(tname));
- Matrix *b = luaT_checkudata(L, 1, nerv_matrix_(tname));
- double beta = luaL_checknumber(L, 3);
- nerv_matrix_(add_row)(b, a, beta, &status);
- NERV_LUA_CHECK_STATUS(L, status);
- return 0;
-}
-
-static int nerv_matrix_(lua_fill)(lua_State *L) {
- Status status;
- Matrix *self = luaT_checkudata(L, 1, nerv_matrix_(tname));
- double val = luaL_checknumber(L, 2);
- nerv_matrix_(fill)(self, val, &status);
- NERV_LUA_CHECK_STATUS(L, status);
- return 0;
-}
-
-static int nerv_matrix_(lua_clip)(lua_State *L) {
- Status status;
- Matrix *self = luaT_checkudata(L, 1, nerv_matrix_(tname));
- double val_1 = luaL_checknumber(L, 2);
- double val_2 = luaL_checknumber(L, 3);
- nerv_matrix_(clip)(self, val_1, val_2, &status);
- NERV_LUA_CHECK_STATUS(L, status);
- return 0;
-}
-
-static int nerv_matrix_(lua_copy_fromd)(lua_State *L) {
- Status status;
- Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
- const 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;
- nerv_matrix_(copy_fromd)(a, b, a_begin, b_begin, b_end, &status);
- NERV_LUA_CHECK_STATUS(L, status);
- return 0;
-}
-
extern const char *MATRIX_CUMATRIX_HOST_TNAME;
static int nerv_matrix_(lua_copy_fromh)(lua_State *L) {
Status status;
@@ -238,44 +90,19 @@ static int nerv_matrix_(lua_copy_toh)(lua_State *L) {
return 0;
}
-static int nerv_matrix_(lua_trans)(lua_State *L) {
+static int nerv_matrix_(lua_copy_fromd)(lua_State *L) {
Status status;
Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
- Matrix *b = nerv_matrix_(trans)(a, &status);
- NERV_LUA_CHECK_STATUS(L, status);
- luaT_pushudata(L, b, nerv_matrix_(tname));
- return 1;
-}
-
-static int nerv_matrix_(lua_mul_elem)(lua_State *L) {
- Status status;
- const Matrix *a = luaT_checkudata(L, 2, nerv_matrix_(tname));
- const Matrix *b = luaT_checkudata(L, 3, nerv_matrix_(tname));
- Matrix *c = luaT_checkudata(L, 1, nerv_matrix_(tname));
- nerv_matrix_(mul_elem)(c, a, b, &status);
- NERV_LUA_CHECK_STATUS(L, status);
- return 0;
-}
-
-static int nerv_matrix_(lua_log_elem)(lua_State *L) {
- Status status;
- const Matrix *a = luaT_checkudata(L, 2, nerv_matrix_(tname));
- Matrix *b = luaT_checkudata(L, 1, nerv_matrix_(tname));
- nerv_matrix_(log_elem)(b, a, &status);
+ const 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;
+ nerv_matrix_(copy_fromd)(a, b, a_begin, b_begin, b_end, &status);
NERV_LUA_CHECK_STATUS(L, status);
return 0;
}
-static int nerv_matrix_(lua_decompress)(lua_State *L) {
- Status status;
- const Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
- int orig_col = luaL_checkinteger(L, 2);
- Matrix *b = nerv_matrix_(decompress)(a, orig_col, &status);
- NERV_LUA_CHECK_STATUS(L, status);
- luaT_pushudata(L, b, nerv_matrix_(tname));
- return 1;
-}
-
extern const char *nerv_matrix_host_float_tname;
static int nerv_matrix_(lua_copy_rows_fromh_by_idx)(lua_State *L) {
Status status;
@@ -313,44 +140,6 @@ static int nerv_matrix_(lua_copy_rows_fromd_by_colidx)(lua_State *L) {
return 0;
}
-static int nerv_matrix_(lua_expand_frm)(lua_State *L) {
- Status status;
- Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
- const Matrix *b = luaT_checkudata(L, 2, nerv_matrix_(tname));
- int context = luaL_checkinteger(L, 3);
- nerv_matrix_(expand_frm)(a, b, context, &status);
- NERV_LUA_CHECK_STATUS(L, status);
- return 0;
-}
-
-static int nerv_matrix_(lua_rearrange_frm)(lua_State *L) {
- Status status;
- Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
- const Matrix *b = luaT_checkudata(L, 2, nerv_matrix_(tname));
- int step = luaL_checkinteger(L, 3);
- nerv_matrix_(rearrange_frm)(a, b, step, &status);
- NERV_LUA_CHECK_STATUS(L, status);
- return 0;
-}
-
-static int nerv_matrix_(lua_scale_rows_by_col)(lua_State *L) {
- Status status;
- Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
- const Matrix *b = luaT_checkudata(L, 2, nerv_matrix_(tname));
- nerv_matrix_(scale_rows_by_col)(a, b, &status);
- NERV_LUA_CHECK_STATUS(L, status);
- return 0;
-}
-
-static int nerv_matrix_(lua_scale_rows_by_row)(lua_State *L) {
- Status status;
- Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
- const Matrix *b = luaT_checkudata(L, 2, nerv_matrix_(tname));
- nerv_matrix_(scale_rows_by_row)(a, b, &status);
- NERV_LUA_CHECK_STATUS(L, status);
- return 0;
-}
-
#ifdef __NERV_FUTURE_CUDA_7
static int nerv_matrix_(lua_update_select_rows_by_rowidx)(lua_State *L) {
/* update c's select rows,
@@ -381,6 +170,39 @@ static int nerv_matrix_(lua_update_select_rows_by_colidx)(lua_State *L) {
}
#endif
+int nerv_matrix_(lua_get_elem)(lua_State *L) {
+ return nerv_error_method_not_implemented(L);
+}
+
+int nerv_matrix_(lua_set_elem)(lua_State *L) {
+ return nerv_error_method_not_implemented(L);
+}
+
+static MATRIX_ELEM cuda_matrix_(read)(lua_State *L, MATRIX_ELEM *data,
+ int idx) {
+ cudaError_t err;
+ MATRIX_ELEM res;
+ err = cudaMemcpy(&res, data + idx,
+ sizeof(MATRIX_ELEM), cudaMemcpyDeviceToHost);
+ if (err != cudaSuccess)
+ nerv_error(L, "cuda error: error while reading element");
+ cudaDeviceSynchronize();
+ return res;
+}
+
+static void cuda_matrix_(write)(lua_State *L, MATRIX_ELEM *data,
+ int idx, MATRIX_ELEM val) {
+ cudaError_t err;
+ err = cudaMemcpy(data + idx, &val,
+ sizeof(MATRIX_ELEM), cudaMemcpyHostToDevice);
+ if (err != cudaSuccess)
+ nerv_error(L, "cuda error: error while writing element");
+ cudaDeviceSynchronize();
+}
+
+static void cuda_matrix_(init)(lua_State *L);
+#include "matrix.c"
+
static const luaL_Reg nerv_matrix_(extra_methods)[] = {
{"colsum", nerv_matrix_(lua_colsum)},
{"colsame", nerv_matrix_(lua_colsame)},
@@ -411,6 +233,7 @@ static const luaL_Reg nerv_matrix_(extra_methods)[] = {
{"thres_mask", nerv_matrix_(lua_thres_mask)},
{"copy_rows_fromh_by_idx", nerv_matrix_(lua_copy_rows_fromh_by_idx)},
{"copy_rows_fromd_by_idx", nerv_matrix_(lua_copy_rows_fromd_by_idx)},
+ {"copy_rows_from_by_idx", nerv_matrix_(lua_copy_rows_fromd_by_idx)},
{"copy_rows_fromd_by_colidx", nerv_matrix_(lua_copy_rows_fromd_by_colidx)},
{"expand_frm", nerv_matrix_(lua_expand_frm)},
{"rearrange_frm", nerv_matrix_(lua_rearrange_frm)},
@@ -431,35 +254,4 @@ static void cuda_matrix_(init)(lua_State *L) {
#endif
}
-int nerv_matrix_(lua_get_elem)(lua_State *L) {
- return nerv_error_method_not_implemented(L);
-}
-
-int nerv_matrix_(lua_set_elem)(lua_State *L) {
- return nerv_error_method_not_implemented(L);
-}
-
-static MATRIX_ELEM cuda_matrix_(read)(lua_State *L, MATRIX_ELEM *data,
- int idx) {
- cudaError_t err;
- MATRIX_ELEM res;
- err = cudaMemcpy(&res, data + idx,
- sizeof(MATRIX_ELEM), cudaMemcpyDeviceToHost);
- if (err != cudaSuccess)
- nerv_error(L, "cuda error: error while reading element");
- cudaDeviceSynchronize();
- return res;
-}
-
-static void cuda_matrix_(write)(lua_State *L, MATRIX_ELEM *data,
- int idx, MATRIX_ELEM val) {
- cudaError_t err;
- err = cudaMemcpy(data + idx, &val,
- sizeof(MATRIX_ELEM), cudaMemcpyHostToDevice);
- if (err != cudaSuccess)
- nerv_error(L, "cuda error: error while writing element");
- cudaDeviceSynchronize();
-}
-
-#include "matrix.c"
#endif
diff --git a/nerv/matrix/generic/matrix.c b/nerv/matrix/generic/matrix.c
index 8efe608..c1da774 100644
--- a/nerv/matrix/generic/matrix.c
+++ b/nerv/matrix/generic/matrix.c
@@ -125,4 +125,217 @@ void nerv_matrix_(lua_init)(lua_State *L) {
#endif
lua_pop(L, 1);
}
+
+static int nerv_matrix_(lua_add)(lua_State *L) {
+ Status status;
+ Matrix *c = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ const Matrix *a = luaT_checkudata(L, 2, nerv_matrix_(tname));
+ const Matrix *b = luaT_checkudata(L, 3, nerv_matrix_(tname));
+ MATRIX_ELEM alpha = luaL_checknumber(L, 4);
+ MATRIX_ELEM beta = luaL_checknumber(L, 5);
+ nerv_matrix_(add)(c, a, b, alpha, beta, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
+ return 0;
+}
+
+static int nerv_matrix_(lua_mul)(lua_State *L) {
+ Status status;
+ Matrix *c = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ Matrix *a = luaT_checkudata(L, 2, nerv_matrix_(tname));
+ Matrix *b = luaT_checkudata(L, 3, nerv_matrix_(tname));
+ MATRIX_ELEM alpha = luaL_checknumber(L, 4);
+ MATRIX_ELEM beta = luaL_checknumber(L, 5);
+ int nargs = lua_gettop(L);
+ int ta = nargs > 5 ? nerv_matrix_(lua_get_blas_op)(*luaL_checkstring(L, 6)) \
+ : BLAS_OP_N;
+ int tb = nargs > 6 ? nerv_matrix_(lua_get_blas_op)(*luaL_checkstring(L, 7)) \
+ : BLAS_OP_N;
+ nerv_matrix_(mul)(c, a, b, alpha, beta, ta, tb, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
+ return 0;
+}
+
+static int nerv_matrix_(lua_sigmoid)(lua_State *L) {
+ Status status;
+ Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ Matrix *b = luaT_checkudata(L, 2, nerv_matrix_(tname));
+ nerv_matrix_(sigmoid)(a, b, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
+ return 0;
+}
+
+static int nerv_matrix_(lua_sigmoid_grad)(lua_State *L) {
+ Status status;
+ Matrix *nerr = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ Matrix *err = luaT_checkudata(L, 2, nerv_matrix_(tname));
+ Matrix *output = luaT_checkudata(L, 3, nerv_matrix_(tname));
+ nerv_matrix_(sigmoid_grad)(nerr, err, output, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
+ return 0;
+}
+
+static int nerv_matrix_(lua_softmax)(lua_State *L) {
+ Status status;
+ Matrix *a = luaT_checkudata(L, 2, nerv_matrix_(tname));
+ Matrix *b = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ Matrix *max_idx = nerv_matrix_(softmax)(b, a, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
+ luaT_pushudata(L, max_idx, nerv_matrix_(tname));
+ return 1;
+}
+
+static int nerv_matrix_(lua_rowsum)(lua_State *L) {
+ Status status;
+ Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ Matrix *b = nerv_matrix_(rowsum)(a, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
+ luaT_pushudata(L, b, nerv_matrix_(tname));
+ return 1;
+}
+
+static int nerv_matrix_(lua_colsum)(lua_State *L) {
+ Status status;
+ Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ Matrix *b = nerv_matrix_(colsum)(a, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
+ luaT_pushudata(L, b, nerv_matrix_(tname));
+ return 1;
+}
+
+static int nerv_matrix_(lua_colsame)(lua_State *L) {
+ Status status;
+ Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ const Matrix *ref = luaT_checkudata(L, 2, nerv_matrix_(tname));
+ Matrix *b = nerv_matrix_(colsame)(a, ref, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
+ luaT_pushudata(L, b, nerv_matrix_(tname));
+ return 1;
+}
+
+static int nerv_matrix_(lua_rowmax)(lua_State *L) {
+ Status status;
+ Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ Matrix *b = nerv_matrix_(rowmax)(a, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
+ luaT_pushudata(L, b, nerv_matrix_(tname));
+ return 1;
+}
+
+static int nerv_matrix_(lua_rowmax_idx)(lua_State *L) {
+ Status status;
+ Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ Matrix *b;
+ Matrix *idx;
+ nerv_matrix_(rowmax_idx)(a, &b, &idx, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
+ luaT_pushudata(L, b, nerv_matrix_(tname));
+ luaT_pushudata(L, idx, nerv_matrix_(tname));
+ return 2;
+}
+
+static int nerv_matrix_(lua_add_row)(lua_State *L) {
+ Status status;
+ const Matrix *a = luaT_checkudata(L, 2, nerv_matrix_(tname));
+ Matrix *b = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ double beta = luaL_checknumber(L, 3);
+ nerv_matrix_(add_row)(b, a, beta, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
+ return 0;
+}
+
+static int nerv_matrix_(lua_fill)(lua_State *L) {
+ Status status;
+ Matrix *self = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ double val = luaL_checknumber(L, 2);
+ nerv_matrix_(fill)(self, val, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
+ return 0;
+}
+
+static int nerv_matrix_(lua_clip)(lua_State *L) {
+ Status status;
+ Matrix *self = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ double val_1 = luaL_checknumber(L, 2);
+ double val_2 = luaL_checknumber(L, 3);
+ nerv_matrix_(clip)(self, val_1, val_2, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
+ return 0;
+}
+
+static int nerv_matrix_(lua_trans)(lua_State *L) {
+ Status status;
+ Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ Matrix *b = nerv_matrix_(trans)(a, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
+ luaT_pushudata(L, b, nerv_matrix_(tname));
+ return 1;
+}
+
+static int nerv_matrix_(lua_mul_elem)(lua_State *L) {
+ Status status;
+ const Matrix *a = luaT_checkudata(L, 2, nerv_matrix_(tname));
+ const Matrix *b = luaT_checkudata(L, 3, nerv_matrix_(tname));
+ Matrix *c = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ nerv_matrix_(mul_elem)(c, a, b, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
+ return 0;
+}
+
+static int nerv_matrix_(lua_log_elem)(lua_State *L) {
+ Status status;
+ const Matrix *a = luaT_checkudata(L, 2, nerv_matrix_(tname));
+ Matrix *b = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ nerv_matrix_(log_elem)(b, a, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
+ return 0;
+}
+
+static int nerv_matrix_(lua_decompress)(lua_State *L) {
+ Status status;
+ const Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ int orig_col = luaL_checkinteger(L, 2);
+ Matrix *b = nerv_matrix_(decompress)(a, orig_col, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
+ luaT_pushudata(L, b, nerv_matrix_(tname));
+ return 1;
+}
+
+static int nerv_matrix_(lua_expand_frm)(lua_State *L) {
+ Status status;
+ Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ const Matrix *b = luaT_checkudata(L, 2, nerv_matrix_(tname));
+ int context = luaL_checkinteger(L, 3);
+ nerv_matrix_(expand_frm)(a, b, context, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
+ return 0;
+}
+
+static int nerv_matrix_(lua_rearrange_frm)(lua_State *L) {
+ Status status;
+ Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ const Matrix *b = luaT_checkudata(L, 2, nerv_matrix_(tname));
+ int step = luaL_checkinteger(L, 3);
+ nerv_matrix_(rearrange_frm)(a, b, step, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
+ return 0;
+}
+
+static int nerv_matrix_(lua_scale_rows_by_col)(lua_State *L) {
+ Status status;
+ Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ const Matrix *b = luaT_checkudata(L, 2, nerv_matrix_(tname));
+ nerv_matrix_(scale_rows_by_col)(a, b, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
+ return 0;
+}
+
+static int nerv_matrix_(lua_scale_rows_by_row)(lua_State *L) {
+ Status status;
+ Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ const Matrix *b = luaT_checkudata(L, 2, nerv_matrix_(tname));
+ nerv_matrix_(scale_rows_by_row)(a, b, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
+ return 0;
+}
+
#endif
diff --git a/nerv/matrix/generic/mmatrix.c b/nerv/matrix/generic/mmatrix.c
index a4e8489..93562d0 100644
--- a/nerv/matrix/generic/mmatrix.c
+++ b/nerv/matrix/generic/mmatrix.c
@@ -9,9 +9,15 @@
#include "../../lib/common.h"
#include "../../lib/matrix/generic/mmatrix.h"
#include "../../io/chunk_file.h"
-#include "string.h"
+#include <string.h>
+#include <cblas.h>
-int nerv_matrix_(lua_get_elem)(lua_State *L) {
+#define BLAS_OP_N CblasNoTrans
+static int nerv_matrix_(lua_get_blas_op)(char ch) {
+ return (ch == 'T' || ch == 't') ? CblasTrans : CblasNoTrans;
+}
+
+static int nerv_matrix_(lua_get_elem)(lua_State *L) {
Matrix *self = luaT_checkudata(L, 1, nerv_matrix_(tname));
int idx = luaL_checkinteger(L, 2);
if (idx < 0 || idx >= self->nmax)
@@ -20,7 +26,7 @@ int nerv_matrix_(lua_get_elem)(lua_State *L) {
return 1;
}
-int nerv_matrix_(lua_set_elem)(lua_State *L) {
+static int nerv_matrix_(lua_set_elem)(lua_State *L) {
Matrix *self = luaT_checkudata(L, 1, nerv_matrix_(tname));
int idx = luaL_checkinteger(L, 2);
MATRIX_ELEM v = luaL_checknumber(L, 3);
@@ -40,7 +46,7 @@ static void host_matrix_(init)(lua_State *L) {
#include "matrix.c"
-int nerv_matrix_(lua_load)(lua_State *L) {
+static int nerv_matrix_(lua_load)(lua_State *L) {
Status status;
ChunkData *cdp = luaT_checkudata(L, 1, nerv_chunk_data_tname);
Matrix *self = nerv_matrix_(load)(cdp, &status);
@@ -49,7 +55,7 @@ int nerv_matrix_(lua_load)(lua_State *L) {
return 1;
}
-int nerv_matrix_(lua_save)(lua_State *L) {
+static int nerv_matrix_(lua_save)(lua_State *L) {
Status status;
ChunkFile *cfp = luaT_checkudata(L, 2,
nerv_chunk_file_handle_tname);
@@ -59,7 +65,7 @@ int nerv_matrix_(lua_save)(lua_State *L) {
return 0;
}
-int nerv_matrix_(lua_copy_from)(lua_State *L) {
+static int nerv_matrix_(lua_copy_fromh)(lua_State *L) {
Status status;
Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
const Matrix *b = luaT_checkudata(L, 2, nerv_matrix_(tname));
@@ -67,15 +73,53 @@ int nerv_matrix_(lua_copy_from)(lua_State *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;
- nerv_matrix_(copy_from)(a, b, a_begin, b_begin, b_end, &status);
+ nerv_matrix_(copy_fromh)(a, b, a_begin, b_begin, b_end, &status);
NERV_LUA_CHECK_STATUS(L, status);
return 0;
}
+static int nerv_matrix_(lua_copy_rows_fromh_by_idx)(lua_State *L)
+{
+ Status status;
+ Matrix *a=luaT_checkudata(L,1,nerv_matrix_(tname));
+ const Matrix *b=luaT_checkudata(L,2,nerv_matrix_(tname));
+ const Matrix *idx=luaT_checkudata(L,3,nerv_matrix_(tname));
+ int b_begin=lua_gettop(L)>3?luaL_checkinteger(L,4):0;
+ nerv_matrix_(copy_rows_fromh_by_idx)(a,b,idx,b_begin,&status);
+ NERV_LUA_CHECK_STATUS(L,status);
+ return 0;
+}
+
static const luaL_Reg nerv_matrix_(extra_methods)[] = {
+ {"colsum", nerv_matrix_(lua_colsum)},
+ {"colsame", nerv_matrix_(lua_colsame)},
+ {"rowsum", nerv_matrix_(lua_rowsum)},
+ {"rowmax", nerv_matrix_(lua_rowmax)},
+ {"rowmax_idx", nerv_matrix_(lua_rowmax_idx)},
+ {"trans", nerv_matrix_(lua_trans)},
+ {"decompress", nerv_matrix_(lua_decompress)},
+ /* in-place calc */
+ {"copy_fromh", nerv_matrix_(lua_copy_fromh)},
+ /* alias for copy_from */
+ {"copy_from", nerv_matrix_(lua_copy_fromh)},
+ {"add", nerv_matrix_(lua_add)},
+ {"mul", nerv_matrix_(lua_mul)},
+ {"add_row", nerv_matrix_(lua_add_row)},
+ {"clip", nerv_matrix_(lua_clip)},
+ {"fill", nerv_matrix_(lua_fill)},
+ {"sigmoid", nerv_matrix_(lua_sigmoid)},
+ {"sigmoid_grad", nerv_matrix_(lua_sigmoid_grad)},
+ {"softmax", nerv_matrix_(lua_softmax)},
+ {"mul_elem", nerv_matrix_(lua_mul_elem)},
+ {"log_elem", nerv_matrix_(lua_log_elem)},
+ {"copy_rows_fromh_by_idx", nerv_matrix_(lua_copy_rows_fromh_by_idx)},
+ {"copy_rows_from_by_idx", nerv_matrix_(lua_copy_rows_fromh_by_idx)},
+ {"expand_frm", nerv_matrix_(lua_expand_frm)},
+ {"rearrange_frm", nerv_matrix_(lua_rearrange_frm)},
+ {"scale_rows_by_row", nerv_matrix_(lua_scale_rows_by_row)},
+ {"scale_rows_by_col", nerv_matrix_(lua_scale_rows_by_col)},
{"load", nerv_matrix_(lua_load)},
{"save", nerv_matrix_(lua_save)},
- {"copy_from", nerv_matrix_(lua_copy_from)},
{NULL, NULL}
};
diff --git a/nerv/matrix/init.lua b/nerv/matrix/init.lua
index 14b83d4..da76e1b 100644
--- a/nerv/matrix/init.lua
+++ b/nerv/matrix/init.lua
@@ -1,3 +1,12 @@
+--- Implements a fraction of matrix operations (methods) in Lua, while
+-- others are implemented in C extension.
+-- @author Ted Yin <[email protected]>
+
+
+--- The base class for all matrices.
+-- @type nerv.Matrix
+
+--- Convert the matrix object to a string.
function nerv.Matrix:__tostring__()
local ncol = self:ncol()
local nrow = self:nrow()
@@ -28,8 +37,9 @@ function nerv.Matrix:__tostring__()
return table.concat(strt)
end
--- gen: a function takes take indices of the matrix and return the generated
--- all entrys in the matrix will be assigned by calling gen(i, j), for a vector, gen(j) will be called.
+--- Assign each element in a matrix using the value returned by a callback `gen`.
+-- @param gen the callback used to generated the values in the matrix, to which
+-- the indices of row and column will be passed (e.g., `gen(i, j)`)
function nerv.Matrix:generate(gen)
if (self:dim() == 2) then
for i = 0, self:nrow() - 1 do
@@ -45,50 +55,87 @@ function nerv.Matrix:generate(gen)
end
end
+--- Create a fresh new matrix of the same matrix type (as `self`).
+-- @param nrow optional, the number of rows in the created matrix if specified,
+-- otherwise `self:nrow()` will be used
+-- @param ncol optional, the number of columns in the created matrix if specified,
+-- otherwise `self:ncol()` will be used
function nerv.Matrix:create(nrow, ncol)
return self.__constructor(nrow or self:nrow(), ncol or self:ncol())
end
nerv.MMatrixInt.fmt = "%d "
-function nerv.CuMatrix:__add__(b)
+--- Operator overloading of `+`.
+function nerv.Matrix:__add__(b)
c = self:create()
c:add(self, b, 1.0, 1.0)
return c
end
-function nerv.CuMatrix:__sub__(b)
+--- Operator overloading of `-`.
+function nerv.Matrix:__sub__(b)
c = self:create()
c:add(self, b, 1.0, -1.0)
return c
end
-function nerv.CuMatrix:__mul__(b)
+--- Operator overloading of `*`.
+function nerv.Matrix:__mul__(b)
c = nerv.get_type(self.__typename)(self:nrow(), b:ncol())
c:mul(self, b, 1.0, 0.0, 'N', 'N')
return c
end
+--- CUDA float matrices
+-- @type nerv.CuMatrixFloat
+
+--- Create a CUDA matrix copy of the host matrix (in memory)
+-- @param mat the host matrix
function nerv.CuMatrixFloat.new_from_host(mat)
local res = nerv.CuMatrixFloat(mat:nrow(), mat:ncol())
res:copy_fromh(mat)
return res
end
+--- Create a host matrix copy of the CUDA matrix
function nerv.CuMatrixFloat:new_to_host()
local res = nerv.MMatrixFloat(self:nrow(), self:ncol())
self:copy_toh(res)
return res
end
+--- CUDA double matrices
+-- @type nerv.CuMatrixDouble
+
+--- Create a CUDA matrix copy of the host matrix (in memory)
+-- @param mat the host matrix
function nerv.CuMatrixDouble.new_from_host(mat)
local res = nerv.CuMatrixDouble(mat:nrow(), mat:ncol())
res:copy_fromh(mat)
return res
end
+--- Create a host matrix copy of the CUDA matrix
function nerv.CuMatrixDouble:new_to_host()
local res = nerv.MMatrixDouble(self:nrow(), self:ncol())
self:copy_toh(res)
return res
end
+
+--- The base class for all host (in-memory) matrices
+-- @type nerv.MMatrix
+
+--- A wrapper function for `copy_fromh`
+function nerv.MMatrix:copy_toh(b, ...)
+ b:copy_fromh(self, ...)
+end
+
+--- Print profiling info of host matrices
+function nerv.MMatrix.print_profile()
+ nerv.info("mmatrix profile not available")
+end
+
+--- Clear profiling info of host matrices
+function nerv.MMatrix.clear_profile()
+end
diff --git a/nerv/matrix/mmatrix.c b/nerv/matrix/mmatrix.c
index 20c31d6..a68506d 100644
--- a/nerv/matrix/mmatrix.c
+++ b/nerv/matrix/mmatrix.c
@@ -15,6 +15,7 @@ void nerv_lua_mmatrix_init(lua_State *L) {
#define MATRIX_USE_FLOAT
#define host_matrix_(NAME) host_matrix_float_##NAME
#define nerv_matrix_(NAME) nerv_matrix_host_float_##NAME
+#define MATRIX_MMATRIX_CUDA_TNAME nerv_matrix_cuda_float_tname
const char *nerv_matrix_(tname) = "nerv.MMatrixFloat";
#define MMATRIX_INIT(L) host_matrix_(init_extra)(L)
@@ -49,11 +50,13 @@ static const luaL_Reg nerv_matrix_(extra_methods_int)[] = {
#undef MATRIX_ELEM_FMT
#undef MATRIX_ELEM_WRITE_FMT
#undef MMATRIX_INIT
+#undef MATRIX_MMATRIX_CUDA_TNAME
#define NERV_GENERIC_MMATRIX
#define MATRIX_USE_DOUBLE
#define host_matrix_(NAME) host_matrix_double_##NAME
#define nerv_matrix_(NAME) nerv_matrix_host_double_##NAME
+#define MATRIX_MMATRIX_CUDA_TNAME nerv_matrix_cuda_double_tname
const char *nerv_matrix_(tname) = "nerv.MMatrixDouble";
#include "generic/mmatrix.c"
#undef nerv_matrix_
diff --git a/nerv/nerv-scm-1.rockspec b/nerv/nerv-scm-1.rockspec
index 786b2df..6949f54 100644
--- a/nerv/nerv-scm-1.rockspec
+++ b/nerv/nerv-scm-1.rockspec
@@ -16,7 +16,7 @@ dependencies = {
build = {
type = "make",
build_variables = {
- CFLAGS="$(CFLAGS)",
+ CFLAGS="$(CFLAGS) -Wall -Wextra -g -O2",
LIBFLAG="$(LIBFLAG)",
LUA_LIBDIR="$(LUA_LIBDIR)",
LUA_BINDIR="$(LUA_BINDIR)",
diff --git a/nerv/nn/layer_dag.lua b/nerv/nn/layer_dag.lua
index 73bb77d..6ad7ae9 100644
--- a/nerv/nn/layer_dag.lua
+++ b/nerv/nn/layer_dag.lua
@@ -131,6 +131,11 @@ function DAGLayer:__init(id, global_conf, layer_conf)
self.parsed_conn = parsed_conn
self.queue = queue
self.gconf = global_conf
+ if self.gconf.use_cpu then
+ self.mat_type = self.gconf.mmat_type
+ else
+ self.mat_type = self.gconf.cumat_type
+ end
end
function DAGLayer:init(batch_size)
@@ -144,7 +149,7 @@ function DAGLayer:init(batch_size)
if output_dim[port_from] > 0 then
dim = output_dim[port_from]
end
- local mid = self.gconf.cumat_type(batch_size, dim)
+ local mid = self.mat_type(batch_size, dim)
local err_mid = mid:create()
ref_from.outputs[port_from] = mid
@@ -190,7 +195,7 @@ function DAGLayer:batch_resize(batch_size)
_, output_dim = ref_from.layer:get_dim()
if ref_from.outputs[port_from]:nrow() ~= batch_size and output_dim[port_from] > 0 then
- local mid = self.gconf.cumat_type(batch_size, output_dim[port_from])
+ local mid = self.mat_type(batch_size, output_dim[port_from])
local err_mid = mid:create()
ref_from.outputs[port_from] = mid
diff --git a/nerv/nn/layer_repo.lua b/nerv/nn/layer_repo.lua
index ef333a7..2f8de08 100644
--- a/nerv/nn/layer_repo.lua
+++ b/nerv/nn/layer_repo.lua
@@ -24,6 +24,9 @@ function LayerRepo:add_layers(layer_spec, param_repo, global_conf)
for pname, pid in pairs(spec[1]) do
layer_config[pname] = param_repo:get_param(pid)
end
+ if layer_config.pr == nil then
+ layer_config.pr = param_repo
+ end
layers[id] = layer_type(id, global_conf, layer_config)
end
end
diff --git a/nerv/nn/param_repo.lua b/nerv/nn/param_repo.lua
index 6d52691..c124e08 100644
--- a/nerv/nn/param_repo.lua
+++ b/nerv/nn/param_repo.lua
@@ -68,11 +68,7 @@ function ParamRepo:export(param_file, pids)
end
function ParamRepo:has_param(pid)
- if self.params[pid] ~= nil then
- return true
- else
- return false
- end
+ return self.params[pid] ~= nil
end
function ParamRepo:get_param(pid)
diff --git a/nerv/test/cumatrix_func.lua b/nerv/test/cumatrix_func.lua
new file mode 100644
index 0000000..c461dc6
--- /dev/null
+++ b/nerv/test/cumatrix_func.lua
@@ -0,0 +1,2 @@
+nerv.include("matrix_func.lua")
+test_all(nerv.CuMatrixFloat)
diff --git a/nerv/test/cumatrix_func.out b/nerv/test/cumatrix_func.out
new file mode 100644
index 0000000..2761126
--- /dev/null
+++ b/nerv/test/cumatrix_func.out
@@ -0,0 +1,1952 @@
+Greetings
+0.00000000 1.00000000 2.00000000 3.00000000
+1.00000000 2.00000000 3.00000000 4.00000000
+2.00000000 3.00000000 4.00000000 5.00000000
+[nerv.CuMatrixFloat 3 x 4]
+0.50000000 0.73105860 0.88079709 0.95257413
+0.73105860 0.88079709 0.95257413 0.98201376
+0.88079709 0.95257413 0.98201376 0.99330717
+[nerv.CuMatrixFloat 3 x 4]
+1.00000000 2.46211720 3.76159430 4.90514851
+2.46211720 3.76159430 4.90514851 5.96402740
+3.76159430 4.90514851 5.96402740 6.98661423
+[nerv.CuMatrixFloat 3 x 4]
+-1.00000000 -0.46211720 0.23840582 1.09485173
+-0.46211720 0.23840582 1.09485173 2.03597260
+0.23840582 1.09485173 2.03597260 3.01338577
+[nerv.CuMatrixFloat 3 x 4]
+0.00000000 1.00000000 2.00000000 3.00000000
+1.00000000 2.00000000 3.00000000 4.00000000
+2.00000000 3.00000000 4.00000000 5.00000000
+[nerv.CuMatrixFloat 3 x 4]
+0.50000000 0.73105860 0.88079709 0.95257413
+0.73105860 0.88079709 0.95257413 0.98201376
+0.88079709 0.95257413 0.98201376 0.99330717
+[nerv.CuMatrixFloat 3 x 4]
+0.00000000 1.00000000
+1.00000000 2.00000000
+2.00000000 3.00000000
+[nerv.CuMatrixFloat 3 x 2]
+0.00000000 1.00000000 2.00000000 3.00000000
+1.00000000 2.00000000 3.00000000 4.00000000
+[nerv.CuMatrixFloat 2 x 4]
+1.00000000 2.00000000 3.00000000 4.00000000
+2.00000000 5.00000000 8.00000000 11.00000000
+3.00000000 8.00000000 13.00000000 18.00000000
+[nerv.CuMatrixFloat 3 x 4]
+1.50000000 3.00000000 4.50000000 6.00000000
+3.00000000 7.50000000 12.00000000 16.50000000
+4.50000000 12.00000000 19.50000000 27.00000000
+[nerv.CuMatrixFloat 3 x 4]
+1.00000000 2.00000000 3.00000000
+2.00000000 5.00000000 8.00000000
+3.00000000 8.00000000 13.00000000
+4.00000000 11.00000000 18.00000000
+[nerv.CuMatrixFloat 4 x 3]
+10.00000000 26.00000000 42.00000000
+[nerv.CuMatrixFloat 1 x 3]
+4.00000000 4.00000000 4.00000000
+[nerv.CuMatrixFloat 1 x 3]
+3.00000000 3.00000000 3.00000000
+[nerv.CuMatrixFloat 1 x 3]
+6.00000000
+15.00000000
+24.00000000
+33.00000000
+[nerv.CuMatrixFloat 4 x 1]
+3.00000000
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+13.00000000
+18.00000000
+[nerv.CuMatrixFloat 4 x 1]
+9999.00000000
+9999.00000000
+9999.00000000
+9999.00000000
+[nerv.CuMatrixFloat 4 x 1]
+1.00000000 0.00000000 0.00000000
+0.00000000 1.00000000 0.00000000
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+[nerv.CuMatrixFloat 4 x 3]
+1.00000000
+1.00000000
+1.00000000
+1.00000000
+[nerv.CuMatrixFloat 4 x 1]
+0.00000000
+1.00000000
+2.00000000
+2.00000000
+[nerv.CuMatrixFloat 4 x 1]
+1.00000000 2.00000000 3.00000000
+2.00000000 5.00000000 8.00000000
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+[nerv.CuMatrixFloat 4 x 3]
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+[nerv.CuMatrixFloat 4 x 3]
+1.00000000 0.00000000 0.00000000 0.00000000
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+[nerv.CuMatrixFloat 4 x 4]
+1.00000000 0.00000000 0.00000000 0.00000000
+0.00000000 1.00000000 0.00000000 0.00000000
+0.00000000 0.00000000 1.00000000 0.00000000
+0.00000000 0.00000000 0.00000000 1.00000000
+[nerv.CuMatrixFloat 4 x 4]
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+0.00000000 1.50000000 1.00000000 1.50000000
+0.00000000 0.50000000 2.00000000 1.50000000
+0.00000000 0.50000000 1.00000000 2.50000000
+[nerv.CuMatrixFloat 4 x 4]
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+1.00000000 1.50000000 1.00000000 1.50000000
+1.00000000 1.00000000 2.00000000 1.50000000
+1.00000000 1.00000000 1.00000000 2.00000000
+[nerv.CuMatrixFloat 4 x 4]
+0.00000000 0.00000000 -4.00000000 -18.00000000
+0.00000000 -4.00000000 -18.00000000 -48.00000000
+-4.00000000 -18.00000000 -48.00000000 -100.00000000
+[nerv.CuMatrixFloat 3 x 4]
+0.00000000 1.00000000 2.00000000 3.00000000
+1.00000000 4.00000000 3.00000000 4.00000000
+2.00000000 3.00000000 8.00000000 5.00000000
+[nerv.CuMatrixFloat 3 x 4]
+0.03205860 0.08714432 0.23688281 0.64391422
+0.02059302 0.41362199 0.15216303 0.41362199
+0.00234065 0.00636253 0.94428378 0.04701312
+[nerv.CuMatrixFloat 3 x 4]
+3.00000000
+1.00000000
+2.00000000
+[nerv.CuMatrixFloat 3 x 1]
+0.00102775 0.00759413 0.05611346 0.41462553
+0.00042407 0.17108315 0.02315359 0.17108315
+0.00000548 0.00004048 0.89167184 0.00221023
+[nerv.CuMatrixFloat 3 x 4]
+-6.88037968 -4.88037968 -2.88037944 -0.88037950
+-7.76560593 -1.76560557 -3.76560545 -1.76560557
+-12.11465740 -10.11465740 -0.11465711 -6.11465740
+[nerv.CuMatrixFloat 3 x 4]
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+[nerv.CuMatrixFloat 3 x 20]
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+[nerv.CuMatrixFloat 10 x 10]
diff --git a/nerv/test/matrix_func.lua b/nerv/test/matrix_func.lua
new file mode 100644
index 0000000..3750ddd
--- /dev/null
+++ b/nerv/test/matrix_func.lua
@@ -0,0 +1,168 @@
+function _pattern_fill(mat_type, m, n)
+ local a = mat_type(m, n)
+ for i = 0, m - 1 do
+ row = a[i]
+ for j = 0, n - 1 do
+ row[j] = i + j
+ end
+ end
+ return a
+end
+
+function _test_all_shape(mat_type, m, n, k, fill)
+ local a = fill(mat_type, m, n)
+ local b = fill(mat_type, m, n)
+ local c = fill(mat_type, m, n)
+ for i = 0, m - 1 do
+ for j = 0, n - 1 do
+ a[i][j] = i + j
+ end
+ end
+ -- test sigmoid
+ b:sigmoid(a)
+ print(a)
+ print(b)
+ -- test add
+ c:add(a, b, 1.0, 2.0)
+ print(c)
+ c:add(a, b, 1.0, -2.0)
+ print(c)
+ c:add(a, b, 1.0, 0.0)
+ print(c)
+ c:add(a, b, 0.0, 1.0)
+ print(c)
+ -- test mul
+ a = fill(mat_type, m, k)
+ b = fill(mat_type, k, n)
+ print(a)
+ print(b)
+ c:mul(a, b, 1.0, 0.0, 'N', 'N')
+ print(c)
+ c:mul(a, b, 1.0, 0.5, 'N', 'N')
+ print(c)
+ c = mat_type(n, m)
+ c:mul(b, a, 1.0, 0.0, 'T', 'T')
+ print(c)
+ -- test colsum
+ print(c:colsum())
+ -- test colsame
+ print(c:colsame(c))
+ local d = c:create()
+ d:copy_from(c)
+ for j = 0, m - 1 do
+ d[math.min(j, c:nrow() - 1)][j] = -1
+ end
+ print(c:colsame(d))
+ -- test rowsum
+ print(c:rowsum())
+ -- test rowmax
+ print(c:rowmax())
+ d:copy_from(c)
+ for i = 0, n - 1 do
+ d[i][0] = 9999
+ end
+ print(d:rowmax())
+ -- test fill
+ d:fill(0)
+ for i = 0, n - 1 do
+ d[i][math.min(i, d:ncol() - 1)] = 1.0
+ end
+ print(d)
+ -- test rowmax_idx
+ x, y = d:rowmax_idx()
+ print(x)
+ print(y)
+ -- test trans
+ print(c)
+ c:mul(b:trans(), a:trans(), 1.0, 0.0, 'N', 'N')
+ print(c)
+ -- test decompress
+ c = mat_type(n, 1)
+ for i = 0, n - 1 do
+ c[i][0] = i
+ end
+ local e = c:decompress(n)
+ print(e)
+ -- test copy_from
+ d = mat_type(n, n)
+ d:copy_from(e)
+ print(d)
+ -- test add_row
+ a = mat_type(1, n)
+ for i = 0, n - 1 do
+ a[0][i] = i
+ end
+ d:add_row(a, 0.5)
+ print(d)
+ -- test clip
+ e:copy_from(d)
+ e:clip(1, 2)
+ print(e)
+ -- test sigmoid_grad
+ a = fill(mat_type, m, n)
+ b = fill(mat_type, m, n)
+ c = a:create()
+ c:sigmoid_grad(a, b)
+ print(c)
+ -- test softmax
+ for i = 0, m - 1 do
+ a[i][math.min(i, a:ncol() - 1)] = a[i][0] * n
+ end
+ print(a)
+ e = c:softmax(a)
+ print(c)
+ print(e)
+ -- test mul_elem
+ a:mul_elem(c, c)
+ print(a)
+ -- test log_elem
+ a:log_elem(a)
+ print(a)
+ -- test copy_rows_from_by_idx
+ local idx = mat_type(1, n)
+ a = fill(mat_type, n, m)
+ b = mat_type(n, m)
+ for i = 0, n - 1 do
+ idx[0][i] = n - 1 - i
+ end
+ print(a)
+ b:copy_rows_from_by_idx(a, idx)
+ b = mat_type(2, m)
+ b:copy_rows_from_by_idx(a, idx, 2, 2)
+ print(a)
+ print(b)
+ -- test expand_frm
+ a = mat_type(m, n)
+ for i = 0, m - 1 do
+ for j = 0, n - 1 do
+ a[i][j] = i
+ end
+ end
+ c = mat_type(m, n * (2 * k + 1))
+ c:expand_frm(a, k)
+ print(a)
+ print(c)
+ -- test rearrange_frm
+ a = c:create()
+ a:rearrange_frm(c, n)
+ print(a)
+ -- test scale_rows_by_row
+ a = mat_type(n, m)
+ a:fill(2)
+ b = fill(mat_type, 1, m)
+ c = a:create()
+ print(a)
+ a:scale_rows_by_row(b)
+ print(a)
+ -- test scale_rows_by_col
+ a = fill(mat_type, m, n)
+ b = fill(mat_type, m, 1)
+ print(a)
+ a:scale_rows_by_col(b)
+ print(a)
+end
+function test_all(mat_type)
+ _test_all_shape(mat_type, 3, 4, 2, _pattern_fill)
+ _test_all_shape(mat_type, 30, 40, 20, _pattern_fill)
+ _test_all_shape(mat_type, 10, 10, 10, _pattern_fill)
+end
diff --git a/nerv/test/mmatrix_func.lua b/nerv/test/mmatrix_func.lua
new file mode 100644
index 0000000..523e51a
--- /dev/null
+++ b/nerv/test/mmatrix_func.lua
@@ -0,0 +1,2 @@
+nerv.include("matrix_func.lua")
+test_all(nerv.MMatrixFloat)
diff --git a/nerv/test/mmatrix_func.out b/nerv/test/mmatrix_func.out
new file mode 100644
index 0000000..9f95989
--- /dev/null
+++ b/nerv/test/mmatrix_func.out
@@ -0,0 +1,1952 @@
+Greetings
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