Deploy to GitHub Pages: 04806ffe

develop/doc/api/v2/fluid/layers.html

@@ -2601,8 +2601,9 @@ will be named automatically.</li>
 <dl class="function">
 <dt>
 <code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">matmul</code><span class="sig-paren">(</span><em>x</em>, <em>y</em>, <em>transpose_x=False</em>, <em>transpose_y=False</em>, <em>name=None</em><span class="sig-paren">)</span></dt>
-<dd><p>Applies matrix multipication to two tensors. Currently only rank 1 to rank
-3 input tensors are supported.</p>
+<dd><p>Applies matrix multiplication to two tensors. Currently, the input
+tensors&#8217; rank can be any, but when the rank of anyone inputs is
+bigger than 3, this two inputs&#8217; rank should be equal.</p>
 <p>The actual behavior depends on the shapes of <span class="math">\(x\)</span>, <span class="math">\(y\)</span> and the
 flag values of <code class="xref py py-attr docutils literal"><span class="pre">transpose_x</span></code>, <code class="xref py py-attr docutils literal"><span class="pre">transpose_y</span></code>. Specifically:</p>
 <ul class="simple">
@@ -2612,10 +2613,10 @@ are transposed. If the tensor is rank-1 of shape <span class="math">\([D]\)</spa
 <span class="math">\([D, 1]\)</span> in transposed form, whereas for <span class="math">\(y\)</span> it is the
 opposite: It is treated as <span class="math">\([D, 1]\)</span> in nontransposed form and as
 <span class="math">\([1, D]\)</span> in transposed form.</li>
-<li>After transpose, the two tensors are 2-D or 3-D and matrix multipication
+<li>After transpose, the two tensors are 2-D or n-D and matrix multiplication
 performs in the following way.<ul>
 <li>If both are 2-D, they are multiplied like conventional matrices.</li>
-<li>If either is 3-D, it is treated as a stack of matrices residing in the
+<li>If either is n-D, it is treated as a stack of matrices residing in the
 last two dimensions and a batched matrix multiply supporting broadcast
 applies on the two tensors.</li>
 </ul>
@@ -2623,7 +2624,7 @@ applies on the two tensors.</li>
 </ul>
 <p>Also note that if the raw tensor <span class="math">\(x\)</span> or <span class="math">\(y\)</span> is rank-1 and
 nontransposed, the prepended or appended dimension <span class="math">\(1\)</span> will be
-removed after matrix multipication.</p>
+removed after matrix multiplication.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
@@ -2648,6 +2649,8 @@ will be named automatically.</li>
 </table>
 <p class="rubric">Examples</p>
 <div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1"># Examples to clarify shapes of the inputs and output</span>
+<span class="c1"># x: [B, ..., M, K], y: [B, ..., K, N]</span>
+<span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>  <span class="c1"># out: [B, ..., M, N]</span>
 <span class="c1"># x: [B, M, K], y: [B, K, N]</span>
 <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>  <span class="c1"># out: [B, M, N]</span>
 <span class="c1"># x: [B, M, K], y: [K, N]</span>

develop/doc/operators.json

@@ -4120,7 +4120,7 @@
  } ] 
 },{
  "type" : "matmul",
- "comment" : "\nMatMul Operator.\n\n\nThis operator is used to perform (batched) matrix multiplication\nover the last two dimensions of the input tensors `X` and `Y`.\n\nIf a transpose flag is specified, the last two dimensions of the\ntensor are transposed. If the tensor is rank-1 of shape [D], then\nfor `X` it is treated as [1, D] in nontransposed form and as [D, 1]\nin transposed form, whereas for `Y` it is the opposite: It is treated\nas [D, 1] in nontransposed form and as [1, D] in transposed form.\n\nExamples without transpose:\n- X: [K], Y: [K] => Out: [1]\n- X: [K], Y: [K, N] => Out: [N]\n- X: [B, M, K], Y: [K] => Out: [B, M]\n- X: [M, K], Y: [B, K, N] => Out: [B, M, N]\n- X: [B, M, K], Y: [B, K, N] => Out: [B, M, N]\n\nThe behavior is designed to be similar to the `numpy.matmul` function.\nThe differences are:\n- Currently only rank 1 to rank 3 input tensors are supported.\n- We add `transpose_X` and `transpose_Y` flags.\n\nBoth the input `X` and `Y` can carry the LoD (Level of Details) information,\nor not. But the output only shares the LoD information with input `X`.\n\n",
+ "comment" : "\nMatMul Operator.\n\n\nThis operator is used to perform (batched) matrix multiplication\nover the last two dimensions of the input tensors `X` and `Y`.\n\nIf a transpose flag is specified, the last two dimensions of the\ntensor are transposed. If the tensor is rank-1 of shape [D], then\nfor `X` it is treated as [1, D] in nontransposed form and as [D, 1]\nin transposed form, whereas for `Y` it is the opposite: It is treated\nas [D, 1] in nontransposed form and as [1, D] in transposed form.\n\nExamples without transpose:\n- X: [K], Y: [K] => Out: [1]\n- X: [K], Y: [K, N] => Out: [N]\n- X: [B, M, K], Y: [K] => Out: [B, M]\n- X: [M, K], Y: [B, K, N] => Out: [B, M, N]\n- X: [B, M, K], Y: [B, K, N] => Out: [B, M, N]\n- X: [B, ..., M, K], Y: [B, ..., K, N] => Out: [B, ..., M, N]\n\nThe behavior is designed to be similar to the `numpy.matmul` function.\nThe differences are:\n- When the rank of the input data is less than or equal to 3, it\n  is similar to the `numpy.matmul` function.\n- When the rank of the input is greater than 3, the rank of X and\n  Y must be equal, and the first `rank - 2` dimensions must be equal.\n- We add `transpose_X` and `transpose_Y` flags.\n\nBoth the input `X` and `Y` can carry the LoD (Level of Details) information,\nor not. But the output only shares the LoD information with input `X`.\n\n",
  "inputs" : [ 
  { 
    "name" : "X",

develop/doc_cn/api/v2/fluid/layers.html

@@ -2620,8 +2620,9 @@ will be named automatically.</li>
 <dl class="function">
 <dt>
 <code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">matmul</code><span class="sig-paren">(</span><em>x</em>, <em>y</em>, <em>transpose_x=False</em>, <em>transpose_y=False</em>, <em>name=None</em><span class="sig-paren">)</span></dt>
-<dd><p>Applies matrix multipication to two tensors. Currently only rank 1 to rank
-3 input tensors are supported.</p>
+<dd><p>Applies matrix multiplication to two tensors. Currently, the input
+tensors&#8217; rank can be any, but when the rank of anyone inputs is
+bigger than 3, this two inputs&#8217; rank should be equal.</p>
 <p>The actual behavior depends on the shapes of <span class="math">\(x\)</span>, <span class="math">\(y\)</span> and the
 flag values of <code class="xref py py-attr docutils literal"><span class="pre">transpose_x</span></code>, <code class="xref py py-attr docutils literal"><span class="pre">transpose_y</span></code>. Specifically:</p>
 <ul class="simple">
@@ -2631,10 +2632,10 @@ are transposed. If the tensor is rank-1 of shape <span class="math">\([D]\)</spa
 <span class="math">\([D, 1]\)</span> in transposed form, whereas for <span class="math">\(y\)</span> it is the
 opposite: It is treated as <span class="math">\([D, 1]\)</span> in nontransposed form and as
 <span class="math">\([1, D]\)</span> in transposed form.</li>
-<li>After transpose, the two tensors are 2-D or 3-D and matrix multipication
+<li>After transpose, the two tensors are 2-D or n-D and matrix multiplication
 performs in the following way.<ul>
 <li>If both are 2-D, they are multiplied like conventional matrices.</li>
-<li>If either is 3-D, it is treated as a stack of matrices residing in the
+<li>If either is n-D, it is treated as a stack of matrices residing in the
 last two dimensions and a batched matrix multiply supporting broadcast
 applies on the two tensors.</li>
 </ul>
@@ -2642,7 +2643,7 @@ applies on the two tensors.</li>
 </ul>
 <p>Also note that if the raw tensor <span class="math">\(x\)</span> or <span class="math">\(y\)</span> is rank-1 and
 nontransposed, the prepended or appended dimension <span class="math">\(1\)</span> will be
-removed after matrix multipication.</p>
+removed after matrix multiplication.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
@@ -2667,6 +2668,8 @@ will be named automatically.</li>
 </table>
 <p class="rubric">Examples</p>
 <div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1"># Examples to clarify shapes of the inputs and output</span>
+<span class="c1"># x: [B, ..., M, K], y: [B, ..., K, N]</span>
+<span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>  <span class="c1"># out: [B, ..., M, N]</span>
 <span class="c1"># x: [B, M, K], y: [B, K, N]</span>
 <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>  <span class="c1"># out: [B, M, N]</span>
 <span class="c1"># x: [B, M, K], y: [K, N]</span>