| <!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta http-equiv="X-UA-Compatible" content="IE=edge"/><title>Half Precision · Apache SINGA</title><meta name="viewport" content="width=device-width"/><meta name="generator" content="Docusaurus"/><meta name="description" content="<!--- Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to you under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. -->"/><meta name="docsearch:version" content="3.0.0"/><meta name="docsearch:language" content="en"/><meta property="og:title" content="Half Precision · Apache SINGA"/><meta property="og:type" content="website"/><meta property="og:url" content="https://singa.apache.org/"/><meta property="og:description" content="<!--- Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to you under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. -->"/><meta property="og:image" content="https://singa.apache.org/img/singa_twitter_banner.jpeg"/><meta name="twitter:card" content="summary"/><meta name="twitter:image" content="https://singa.apache.org/img/singa_twitter_banner.jpeg"/><link rel="shortcut icon" href="/img/favicon.ico"/><link rel="stylesheet" href="https://cdn.jsdelivr.net/docsearch.js/1/docsearch.min.css"/><link rel="stylesheet" href="//cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/styles/atom-one-dark.min.css"/><link rel="alternate" type="application/atom+xml" href="https://singa.apache.org/blog/atom.xml" title="Apache SINGA Blog ATOM Feed"/><link rel="alternate" type="application/rss+xml" href="https://singa.apache.org/blog/feed.xml" title="Apache SINGA Blog RSS Feed"/><link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Source+Sans+Pro:400,400i,700"/><link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=Baloo+Paaji+2&family=Source+Sans+Pro:wght@200;300&display=swap"/><script type="text/javascript" src="https://buttons.github.io/buttons.js"></script><script src="https://unpkg.com/vanilla-back-to-top@7.1.14/dist/vanilla-back-to-top.min.js"></script><script> |
| document.addEventListener('DOMContentLoaded', function() { |
| addBackToTop( |
| {"zIndex":100} |
| ) |
| }); |
| </script><script src="/js/scrollSpy.js"></script><link rel="stylesheet" href="/css/main.css"/><script src="/js/codetabs.js"></script></head><body class="sideNavVisible separateOnPageNav"><div class="fixedHeaderContainer"><div class="headerWrapper wrapper"><header><a href="/"><img class="logo" src="/img/singa.png" alt="Apache SINGA"/></a><a href="/versions"><h3>3.0.0</h3></a><div class="navigationWrapper navigationSlider"><nav class="slidingNav"><ul class="nav-site nav-site-internal"><li class=""><a href="/docs/3.0.0/installation" target="_self">Docs</a></li><li class=""><a href="/docs/3.0.0/source-repository" target="_self">Community</a></li><li class=""><a href="/blog/" target="_self">News</a></li><li class=""><a href="https://apache-singa.readthedocs.io/en/latest/" target="_self">API</a></li><li class="navSearchWrapper reactNavSearchWrapper"><input type="text" id="search_input_react" placeholder="Search" title="Search"/></li><li class=""><a href="https://github.com/apache/singa" target="_self">GitHub</a></li></ul></nav></div></header></div></div><div class="navPusher"><div class="docMainWrapper wrapper"><div class="container mainContainer docsContainer"><div class="wrapper"><div class="post"><header class="postHeader"><a class="edit-page-link button" href="https://github.com/apache/singa-doc/blob/master/docs-site/docs/half-precision.md" target="_blank" rel="noreferrer noopener">Edit</a><h1 id="__docusaurus" class="postHeaderTitle">Half Precision</h1></header><article><div><span><!--- Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to you under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> |
| <p>Half precision training 优点:</p> |
| <ul> |
| <li>CPU内存使用低, 网络支持大。</li> |
| <li>训练速度快。</li> |
| </ul> |
| <h2><a class="anchor" aria-hidden="true" id="half-data-type"></a><a href="#half-data-type" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Half data type</h2> |
| <h3><a class="anchor" aria-hidden="true" id="half-data-type-定义"></a><a href="#half-data-type-定义" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Half data type 定义</h3> |
| <p>在 IEEE 754 标准中明确binary16有如下格式： |
| <a href="https://en.wikipedia.org/wiki/Half-precision_floating-point_format">format</a>: |
| Sign bit: 1 bit |
| Exponent width: 5 bits |
| Significand precision: 11 bits (10 explicitly stored)</p> |
| <h3><a class="anchor" aria-hidden="true" id="half-data-type-运算"></a><a href="#half-data-type-运算" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Half data type 运算</h3> |
| <p>以fp32形式加载数据，快速转换成fp16。</p> |
| <pre><code class="hljs css language-python"><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> singa <span class="hljs-keyword">import</span> tensor, device |
| <span class="hljs-meta">>>> </span>dev = device.create_cuda_gpu() |
| <span class="hljs-meta">>>> </span>x = tensor.random((<span class="hljs-number">2</span>,<span class="hljs-number">3</span>),dev) |
| <span class="hljs-meta">>>> </span>x |
| [[<span class="hljs-number">0.7703407</span> <span class="hljs-number">0.42764223</span> <span class="hljs-number">0.5872884</span> ] |
| [<span class="hljs-number">0.78362167</span> <span class="hljs-number">0.70469785</span> <span class="hljs-number">0.64975065</span>]], float32 |
| <span class="hljs-meta">>>> </span>y = x.as_type(tensor.float16) |
| <span class="hljs-meta">>>> </span>y |
| [[<span class="hljs-number">0.7705</span> <span class="hljs-number">0.4277</span> <span class="hljs-number">0.5874</span>] |
| [<span class="hljs-number">0.7837</span> <span class="hljs-number">0.7046</span> <span class="hljs-number">0.65</span> ]], float16 |
| </code></pre> |
| <p>初级运算支持fp16格式。</p> |
| <pre><code class="hljs css language-python"><span class="hljs-meta">>>> </span>y+y |
| [[<span class="hljs-number">1.541</span> <span class="hljs-number">0.8555</span> <span class="hljs-number">1.175</span> ] |
| [<span class="hljs-number">1.567</span> <span class="hljs-number">1.409</span> <span class="hljs-number">1.3</span> ]], float16 |
| </code></pre> |
| <h2><a class="anchor" aria-hidden="true" id="training-in-half"></a><a href="#training-in-half" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Training in Half</h2> |
| <h3><a class="anchor" aria-hidden="true" id="training-in-half-三个步骤"></a><a href="#training-in-half-三个步骤" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Training in Half 三个步骤</h3> |
| <p>半精度训练只需要如下三个步骤:</p> |
| <ol> |
| <li>加载数据并且转换成半精度数据</li> |
| <li>设置数据优化类型</li> |
| <li>启动训练模型</li> |
| </ol> |
| <pre><code class="hljs css language-python"><span class="hljs-comment"># cast input data to fp16</span> |
| x = load_data() |
| x = x.astype(np.float16) |
| tx = tensor.from_numpy(x) |
| |
| <span class="hljs-comment"># load model</span> |
| model = build_model() |
| <span class="hljs-comment"># set optimizer dtype to fp16</span> |
| sgd = opt.SGD(lr=<span class="hljs-number">0.1</span>, dtype=tensor.float16) |
| |
| <span class="hljs-comment"># train as usual</span> |
| out, loss = model(tx, ty) |
| </code></pre> |
| <h3><a class="anchor" aria-hidden="true" id="示例"></a><a href="#示例" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>示例</h3> |
| <p>提供示例脚本<code>train_cnn.py</code>，可执行下面的命令语句开始半精度模型训练。</p> |
| <pre><code class="hljs css language-python">python examples/cnn/train_cnn.py cnn mnist -pfloat16 |
| </code></pre> |
| <h2><a class="anchor" aria-hidden="true" id="实现"></a><a href="#实现" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>实现</h2> |
| <h3><a class="anchor" aria-hidden="true" id="half-type-依赖性"></a><a href="#half-type-依赖性" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Half Type 依赖性</h3> |
| <p>该半精度实现方式就像一半半精度模型支持的一样，是被整合在C++后端来实现的。</p> |
| <p>在GPU上跑的时候，<code>__half</code>可用在uda math API中，为了支持<code>__half</code>数学运算，需要编译Nvidia compute arch > 6.0(Pascal)</p> |
| <h3><a class="anchor" aria-hidden="true" id="nvidia-hardware-acceleration-tensor-core"></a><a href="#nvidia-hardware-acceleration-tensor-core" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Nvidia Hardware Acceleration: Tensor Core</h3> |
| <p>Nvidia发布Tensor Core后进一步加速了半精度和倍数吞吐量的运算，如GEMM(CuBlas) and convolution(CuDNN)。要启用Tensor core的运算，在GEMM方面有一些要求，比如：卷积通道大小，Cuda版本和GPU版本（图灵或更高版本）等等。</p> |
| <h3><a class="anchor" aria-hidden="true" id="implement-operations"></a><a href="#implement-operations" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Implement Operations</h3> |
| <p>半精度运算起初被整合在<code>tensor_math_cuda.h</code>中，专门提供半精度类型运算模版和实现方式，用来实现低数据量的计算。</p> |
| <p>示例, GEMM 运算实现如下:</p> |
| <pre><code class="hljs css language-c++"><span class="hljs-keyword">template</span> <> |
| <span class="hljs-keyword">void</span> GEMM<half_float::half, lang::Cuda>(<span class="hljs-keyword">const</span> half_float::half alpha, |
| <span class="hljs-keyword">const</span> Tensor& A, <span class="hljs-keyword">const</span> Tensor& B, |
| <span class="hljs-keyword">const</span> half_float::half beta, Tensor* C, |
| Context* ctx) { |
| <span class="hljs-comment">// ...</span> |
| CUBLAS_CHECK(cublasGemmEx(handle, transb, transa, ncolB, nrowA, ncolA, |
| alphaPtr, BPtr, Btype, ldb, APtr, Atype, lda, |
| betaPtr, CPtr, Ctype, ldc, computeType, algo)); |
| <span class="hljs-comment">// ...</span> |
| } |
| </code></pre> |
| </span></div></article></div><div class="docs-prevnext"></div></div></div><nav class="onPageNav"><ul class="toc-headings"><li><a href="#half-data-type">Half data type</a><ul class="toc-headings"><li><a href="#half-data-type-定义">Half data type 定义</a></li><li><a href="#half-data-type-运算">Half data type 运算</a></li></ul></li><li><a href="#training-in-half">Training in Half</a><ul class="toc-headings"><li><a href="#training-in-half-三个步骤">Training in Half 三个步骤</a></li><li><a href="#示例">示例</a></li></ul></li><li><a href="#实现">实现</a><ul class="toc-headings"><li><a href="#half-type-依赖性">Half Type 依赖性</a></li><li><a href="#nvidia-hardware-acceleration-tensor-core">Nvidia Hardware Acceleration: Tensor Core</a></li><li><a href="#implement-operations">Implement Operations</a></li></ul></li></ul></nav></div><footer class="nav-footer" id="footer"><section class="sitemap"><a href="/" class="nav-home"><img src="/img/singa-logo-square.png" alt="Apache SINGA" width="66" height="58"/></a><div><h5>Docs</h5><a href="/docs/installation">Getting Started</a><a href="/docs/device">Guides</a><a href="/en/https://apache-singa.readthedocs.io/en/latest/">API Reference</a><a href="/docs/examples">Examples</a><a href="/docs/download-singa">Development</a></div><div><h5>Community</h5><a href="/en/users.html">User Showcase</a><a href="/docs/history-singa">SINGA History</a><a href="/docs/team-list">SINGA Team</a><a href="/blog">SINGA News</a><a href="https://github.com/apache/singa">GitHub</a><div class="social"><a class="github-button" href="https://github.com/apache/singa" data-count-href="/apache/singa/stargazers" data-show-count="true" data-count-aria-label="# stargazers on GitHub" aria-label="Star this project on GitHub">apache/singa-doc</a></div><div class="social"><a href="https://twitter.com/ApacheSINGA" class="twitter-follow-button">Follow @ApacheSINGA</a></div></div><div><h5>Apache Software Foundation</h5><a href="https://apache.org/" target="_blank" rel="noreferrer noopener">Foundation</a><a href="http://www.apache.org/licenses/" target="_blank" rel="noreferrer noopener">License</a><a href="http://www.apache.org/foundation/sponsorship.html" target="_blank" rel="noreferrer noopener">Sponsorship</a><a href="http://www.apache.org/foundation/thanks.html" target="_blank" rel="noreferrer noopener">Thanks</a><a href="http://www.apache.org/events/current-event" target="_blank" rel="noreferrer noopener">Events</a><a href="http://www.apache.org/security/" target="_blank" rel="noreferrer noopener">Security</a></div></section><div style="width:100%;text-align:center"><a href="https://apache.org/" target="_blank" rel="noreferrer noopener" class="ApacheOpenSource"><img src="/img/asf_logo_wide.svg" alt="Apache Open Source"/></a><section class="copyright" style="max-width:60%;margin:0 auto">Copyright © 2023 |
| The Apache Software Foundation. All rights reserved. |
| Apache SINGA, Apache, the Apache feather logo, and |
| the Apache SINGA project logos are trademarks of The |
| Apache Software Foundation. All other marks mentioned |
| may be trademarks or registered trademarks of their |
| respective owners.</section></div></footer></div><script type="text/javascript" src="https://cdn.jsdelivr.net/docsearch.js/1/docsearch.min.js"></script><script>window.twttr=(function(d,s, id){var js,fjs=d.getElementsByTagName(s)[0],t=window.twttr||{};if(d.getElementById(id))return t;js=d.createElement(s);js.id=id;js.src='https://platform.twitter.com/widgets.js';fjs.parentNode.insertBefore(js, fjs);t._e = [];t.ready = function(f) {t._e.push(f);};return t;}(document, 'script', 'twitter-wjs'));</script><script> |
| document.addEventListener('keyup', function(e) { |
| if (e.target !== document.body) { |
| return; |
| } |
| // keyCode for '/' (slash) |
| if (e.keyCode === 191) { |
| const search = document.getElementById('search_input_react'); |
| search && search.focus(); |
| } |
| }); |
| </script><script> |
| var search = docsearch({ |
| |
| apiKey: '45202133606c0b5fa6d21cddc4725dd8', |
| indexName: 'apache_singa', |
| inputSelector: '#search_input_react', |
| algoliaOptions: {"facetFilters":["language:en","version:3.0.0"]} |
| }); |
| </script></body></html> |
