diff options
45 files changed, 6604 insertions, 374 deletions
@@ -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 { + 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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;} +dl.table h3, dl.function h3 {font-size: .95em;} + +ul.nowrap { + overflow:auto; + whitespace:nowrap; +} + +/* stop sublists from having initial vertical space */ +ul ul { margin-top: 0px; } +ol ul { margin-top: 0px; } +ol ol { margin-top: 0px; } +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; } +pre .escape { color: #844631; } +pre .keyword { color: #aa5050; font-weight: bold; } +pre .library { color: #0e7c6b; } +pre .marker { color: #512b1e; background: #fedc56; font-weight: bold; } +pre .string { color: #8080ff; } +pre .number { color: #f8660d; } +pre .operator { color: #2239a8; font-weight: bold; } +pre .preprocessor, pre .prepro { color: #a33243; } +pre .global { color: #800080; } +pre .user-keyword { color: #800080; } +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 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108.00000000 117.00000000 126.00000000 135.00000000 144.00000000 153.00000000 162.00000000 +[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 +0.00000000 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