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10 changed file
--- a/develop/doc/_sources/api/v2/fluid/layers.rst.txt
+++ b/develop/doc/_sources/api/v2/fluid/layers.rst.txt
@@ -358,6 +358,12 @@ reduce_min
 ..  autofunction:: paddle.v2.fluid.layers.reduce_min
    :noindex:

+
+split
+-----
+..  autofunction:: paddle.v2.fluid.layers.split
+    :noindex:
+
 logsigmoid
 ----------
 ..  autofunction:: paddle.v2.fluid.layers.logsigmoid

--- a/develop/doc/_sources/api/v2/fluid/nets.rst.txt
+++ b/develop/doc/_sources/api/v2/fluid/nets.rst.txt
@@ -20,3 +20,8 @@ sequence_conv_pool
    :noindex:


+glu
+---
+..  autofunction:: paddle.v2.fluid.nets.glu
+    :noindex:
+
--- a/develop/doc/api/v2/fluid/layers.html
+++ b/develop/doc/api/v2/fluid/layers.html
@@ -2434,6 +2434,52 @@ than the <code class="xref py py-attr docutils literal"><span class="pre">input<
 </div>
 </dd></dl>

+</div>
+<div class="section" id="split">
+<h2>split<a class="headerlink" href="#split" title="Permalink to this headline">¶</a></h2>
+<dl class="function">
+<dt>
+<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">split</code><span class="sig-paren">(</span><em>input</em>, <em>num_or_sections</em>, <em>dim=-1</em><span class="sig-paren">)</span></dt>
+<dd><p>Splits the tensor into multiple sub-tensors.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>input</strong> (<em>Variable</em>) &#8211; The input variable which is a Tensor or LoDTensor.</li>
+<li><strong>num_or_sections</strong> (<em>int|list</em>) &#8211; If <code class="xref py py-attr docutils literal"><span class="pre">num_or_sections</span></code> is an integer,
+then the integer indicates the number of equal sized sub-tensors
+that the tensor will be divided into. If <code class="xref py py-attr docutils literal"><span class="pre">num_or_sections</span></code>
+is a list of integers, the length of list indicates the number of
+sub-tensors and the integers indicate the sizes of sub-tensors&#8217;
+<code class="xref py py-attr docutils literal"><span class="pre">dim</span></code> dimension orderly.</li>
+<li><strong>dim</strong> (<em>int</em>) &#8211; The dimension along which to split. If <span class="math">\(dim &lt; 0\)</span>, the
+dimension to split along is <span class="math">\(rank(input) + dim\)</span>.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The list of segmented tensor variables.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">List</p>
+</td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Examples</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1"># x is a Tensor variable with shape [3, 9, 5]:</span>
+<span class="n">x0</span><span class="p">,</span> <span class="n">x1</span><span class="p">,</span> <span class="n">x2</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">num_or_sections</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">x0</span><span class="o">.</span><span class="n">shape</span>  <span class="c1"># [3, 3, 5]</span>
+<span class="n">x1</span><span class="o">.</span><span class="n">shape</span>  <span class="c1"># [3, 3, 5]</span>
+<span class="n">x2</span><span class="o">.</span><span class="n">shape</span>  <span class="c1"># [3, 3, 5]</span>
+<span class="n">x0</span><span class="p">,</span> <span class="n">x1</span><span class="p">,</span> <span class="n">x2</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">num_or_sections</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">x0</span><span class="o">.</span><span class="n">shape</span>  <span class="c1"># [3, 2, 5]</span>
+<span class="n">x1</span><span class="o">.</span><span class="n">shape</span>  <span class="c1"># [3, 3, 5]</span>
+<span class="n">x2</span><span class="o">.</span><span class="n">shape</span>  <span class="c1"># [3, 4, 5]</span>
+</pre></div>
+</div>
+</dd></dl>
+
 </div>
 <div class="section" id="logsigmoid">
 <h2>logsigmoid<a class="headerlink" href="#logsigmoid" title="Permalink to this headline">¶</a></h2>

--- a/develop/doc/api/v2/fluid/nets.html
+++ b/develop/doc/api/v2/fluid/nets.html
@@ -242,6 +242,46 @@
 <code class="descclassname">paddle.v2.fluid.nets.</code><code class="descname">sequence_conv_pool</code><span class="sig-paren">(</span><em>input</em>, <em>num_filters</em>, <em>filter_size</em>, <em>param_attr=None</em>, <em>act='sigmoid'</em>, <em>pool_type='max'</em><span class="sig-paren">)</span></dt>
 <dd></dd></dl>

+</div>
+<div class="section" id="glu">
+<h2>glu<a class="headerlink" href="#glu" title="Permalink to this headline">¶</a></h2>
+<dl class="function">
+<dt>
+<code class="descclassname">paddle.v2.fluid.nets.</code><code class="descname">glu</code><span class="sig-paren">(</span><em>input</em>, <em>dim=-1</em><span class="sig-paren">)</span></dt>
+<dd><p>The gated linear unit composed by split and elementwise multiplication.
+Specifically, Split the input into two equal sized parts <span class="math">\(a\)</span> and
+<span class="math">\(b\)</span> along the given dimension and then compute as following:</p>
+<blockquote>
+<div><div class="math">
+\[{GLU}(a, b)= a \otimes \sigma(b)\]</div>
+</div></blockquote>
+<p>Refer to <a class="reference external" href="https://arxiv.org/pdf/1612.08083.pdf">Language Modeling with Gated Convolutional Networks</a>.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>input</strong> (<em>Variable</em>) &#8211; The input variable which is a Tensor or LoDTensor.</li>
+<li><strong>dim</strong> (<em>int</em>) &#8211; The dimension along which to split. If <span class="math">\(dim &lt; 0\)</span>, the
+dimension to split along is <span class="math">\(rank(input) + dim\)</span>.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The Tensor variable with half the size of input.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">Variable</p>
+</td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Examples</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1"># x is a Tensor variable with shape [3, 6, 9]</span>
+<span class="n">fluid</span><span class="o">.</span><span class="n">nets</span><span class="o">.</span><span class="n">glu</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>  <span class="c1"># shape of output: [3, 3, 9]</span>
+</pre></div>
+</div>
+</dd></dl>
+
 </div>
 </div>


--- a/develop/doc/searchindex.js
+++ b/develop/doc/searchindex.js
--- a/develop/doc_cn/_sources/api/v2/fluid/layers.rst.txt
+++ b/develop/doc_cn/_sources/api/v2/fluid/layers.rst.txt
@@ -358,6 +358,12 @@ reduce_min
 ..  autofunction:: paddle.v2.fluid.layers.reduce_min
    :noindex:

+
+split
+-----
+..  autofunction:: paddle.v2.fluid.layers.split
+    :noindex:
+
 logsigmoid
 ----------
 ..  autofunction:: paddle.v2.fluid.layers.logsigmoid

--- a/develop/doc_cn/_sources/api/v2/fluid/nets.rst.txt
+++ b/develop/doc_cn/_sources/api/v2/fluid/nets.rst.txt
@@ -20,3 +20,8 @@ sequence_conv_pool
    :noindex:


+glu
+---
+..  autofunction:: paddle.v2.fluid.nets.glu
+    :noindex:
+
--- a/develop/doc_cn/api/v2/fluid/layers.html
+++ b/develop/doc_cn/api/v2/fluid/layers.html
@@ -2453,6 +2453,52 @@ than the <code class="xref py py-attr docutils literal"><span class="pre">input<
 </div>
 </dd></dl>

+</div>
+<div class="section" id="split">
+<h2>split<a class="headerlink" href="#split" title="永久链接至标题">¶</a></h2>
+<dl class="function">
+<dt>
+<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">split</code><span class="sig-paren">(</span><em>input</em>, <em>num_or_sections</em>, <em>dim=-1</em><span class="sig-paren">)</span></dt>
+<dd><p>Splits the tensor into multiple sub-tensors.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">参数:</th><td class="field-body"><ul class="first simple">
+<li><strong>input</strong> (<em>Variable</em>) &#8211; The input variable which is a Tensor or LoDTensor.</li>
+<li><strong>num_or_sections</strong> (<em>int|list</em>) &#8211; If <code class="xref py py-attr docutils literal"><span class="pre">num_or_sections</span></code> is an integer,
+then the integer indicates the number of equal sized sub-tensors
+that the tensor will be divided into. If <code class="xref py py-attr docutils literal"><span class="pre">num_or_sections</span></code>
+is a list of integers, the length of list indicates the number of
+sub-tensors and the integers indicate the sizes of sub-tensors&#8217;
+<code class="xref py py-attr docutils literal"><span class="pre">dim</span></code> dimension orderly.</li>
+<li><strong>dim</strong> (<em>int</em>) &#8211; The dimension along which to split. If <span class="math">\(dim &lt; 0\)</span>, the
+dimension to split along is <span class="math">\(rank(input) + dim\)</span>.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">The list of segmented tensor variables.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">返回类型:</th><td class="field-body"><p class="first last">List</p>
+</td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Examples</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1"># x is a Tensor variable with shape [3, 9, 5]:</span>
+<span class="n">x0</span><span class="p">,</span> <span class="n">x1</span><span class="p">,</span> <span class="n">x2</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">num_or_sections</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">x0</span><span class="o">.</span><span class="n">shape</span>  <span class="c1"># [3, 3, 5]</span>
+<span class="n">x1</span><span class="o">.</span><span class="n">shape</span>  <span class="c1"># [3, 3, 5]</span>
+<span class="n">x2</span><span class="o">.</span><span class="n">shape</span>  <span class="c1"># [3, 3, 5]</span>
+<span class="n">x0</span><span class="p">,</span> <span class="n">x1</span><span class="p">,</span> <span class="n">x2</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">num_or_sections</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">x0</span><span class="o">.</span><span class="n">shape</span>  <span class="c1"># [3, 2, 5]</span>
+<span class="n">x1</span><span class="o">.</span><span class="n">shape</span>  <span class="c1"># [3, 3, 5]</span>
+<span class="n">x2</span><span class="o">.</span><span class="n">shape</span>  <span class="c1"># [3, 4, 5]</span>
+</pre></div>
+</div>
+</dd></dl>
+
 </div>
 <div class="section" id="logsigmoid">
 <h2>logsigmoid<a class="headerlink" href="#logsigmoid" title="永久链接至标题">¶</a></h2>

--- a/develop/doc_cn/api/v2/fluid/nets.html
+++ b/develop/doc_cn/api/v2/fluid/nets.html
@@ -261,6 +261,46 @@
 <code class="descclassname">paddle.v2.fluid.nets.</code><code class="descname">sequence_conv_pool</code><span class="sig-paren">(</span><em>input</em>, <em>num_filters</em>, <em>filter_size</em>, <em>param_attr=None</em>, <em>act='sigmoid'</em>, <em>pool_type='max'</em><span class="sig-paren">)</span></dt>
 <dd></dd></dl>

+</div>
+<div class="section" id="glu">
+<h2>glu<a class="headerlink" href="#glu" title="永久链接至标题">¶</a></h2>
+<dl class="function">
+<dt>
+<code class="descclassname">paddle.v2.fluid.nets.</code><code class="descname">glu</code><span class="sig-paren">(</span><em>input</em>, <em>dim=-1</em><span class="sig-paren">)</span></dt>
+<dd><p>The gated linear unit composed by split and elementwise multiplication.
+Specifically, Split the input into two equal sized parts <span class="math">\(a\)</span> and
+<span class="math">\(b\)</span> along the given dimension and then compute as following:</p>
+<blockquote>
+<div><div class="math">
+\[{GLU}(a, b)= a \otimes \sigma(b)\]</div>
+</div></blockquote>
+<p>Refer to <a class="reference external" href="https://arxiv.org/pdf/1612.08083.pdf">Language Modeling with Gated Convolutional Networks</a>.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">参数:</th><td class="field-body"><ul class="first simple">
+<li><strong>input</strong> (<em>Variable</em>) &#8211; The input variable which is a Tensor or LoDTensor.</li>
+<li><strong>dim</strong> (<em>int</em>) &#8211; The dimension along which to split. If <span class="math">\(dim &lt; 0\)</span>, the
+dimension to split along is <span class="math">\(rank(input) + dim\)</span>.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">The Tensor variable with half the size of input.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">返回类型:</th><td class="field-body"><p class="first last">Variable</p>
+</td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Examples</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1"># x is a Tensor variable with shape [3, 6, 9]</span>
+<span class="n">fluid</span><span class="o">.</span><span class="n">nets</span><span class="o">.</span><span class="n">glu</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>  <span class="c1"># shape of output: [3, 3, 9]</span>
+</pre></div>
+</div>
+</dd></dl>
+
 </div>
 </div>


--- a/develop/doc_cn/searchindex.js
+++ b/develop/doc_cn/searchindex.js