update category term (#2615)

cpu/2.2.0+cpu/tutorials/api_doc.html

@@ -78,7 +78,7 @@
 <li class="toctree-l3"><a class="reference internal" href="#ipex.verbose"><code class="docutils literal notranslate"><span class="pre">verbose</span></code></a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="#fast-bert-experimental">Fast Bert (Experimental)</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#fast-bert-prototype">Fast Bert (Prototype)</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#ipex.fast_bert"><code class="docutils literal notranslate"><span class="pre">fast_bert()</span></code></a></li>
 </ul>
 </li>
@@ -232,7 +232,7 @@ <h2>General<a class="headerlink" href="#general" title="Permalink to this headin
 <code class="docutils literal notranslate"><span class="pre">True</span></code>. You might get better performance at the cost of extra memory usage.
 The default value is <code class="docutils literal notranslate"><span class="pre">None</span></code>. Explicitly setting this knob overwrites the
 configuration set by <code class="docutils literal notranslate"><span class="pre">level</span></code> knob.</p></li>
-<li><p><strong>graph_mode</strong> – (bool) [experimental]: It will automatically apply a combination of methods
+<li><p><strong>graph_mode</strong> – (bool) [prototype]: It will automatically apply a combination of methods
 to generate graph or multiple subgraphs if True. The default value is <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
 <li><p><strong>concat_linear</strong> (<em>bool</em>) – Whether to perform <code class="docutils literal notranslate"><span class="pre">concat_linear</span></code>. It only
 works for inference model. The default value is <code class="docutils literal notranslate"><span class="pre">None</span></code>. Explicitly
@@ -387,12 +387,12 @@ <h2>General<a class="headerlink" href="#general" title="Permalink to this headin
 </dd></dl>
 
 </section>
-<section id="fast-bert-experimental">
-<h2>Fast Bert (Experimental)<a class="headerlink" href="#fast-bert-experimental" title="Permalink to this heading"></a></h2>
+<section id="fast-bert-prototype">
+<h2>Fast Bert (Prototype)<a class="headerlink" href="#fast-bert-prototype" title="Permalink to this heading"></a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="ipex.fast_bert">
 <span class="sig-prename descclassname"><span class="pre">ipex.</span></span><span class="sig-name descname"><span class="pre">fast_bert</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.float32</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unpad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#ipex.fast_bert" title="Permalink to this definition"></a></dt>
-<dd><p>Use TPP to speedup training/inference. fast_bert API is still a experimental
+<dd><p>Use TPP to speedup training/inference. fast_bert API is still a prototype
 feature and now only optimized for bert model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -556,7 +556,7 @@ <h2>Graph Optimization<a class="headerlink" href="#graph-optimization" title="Pe
 </dl>
 </dd></dl>
 
-<p>Experimental API, introduction is avaiable at <a class="reference external" href="./features/int8_recipe_tuning_api.html">feature page</a>.</p>
+<p>Prototype API, introduction is avaiable at <a class="reference external" href="./features/int8_recipe_tuning_api.html">feature page</a>.</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="ipex.quantization.autotune">
 <span class="sig-prename descclassname"><span class="pre">ipex.quantization.</span></span><span class="sig-name descname"><span class="pre">autotune</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calib_dataloader</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calib_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op_type_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">smoothquant_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sampling_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">accuracy_criterion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tuning_time</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#ipex.quantization.autotune" title="Permalink to this definition"></a></dt>
@@ -810,4 +810,4 @@ <h2>Graph Optimization<a class="headerlink" href="#graph-optimization" title="Pe
   </script> 
 
 </body>
-</html>
+</html>

cpu/2.2.0+cpu/tutorials/examples.html

@@ -89,7 +89,7 @@
 <li class="toctree-l6"><a class="reference internal" href="#id3">BERT</a></li>
 </ul>
 </li>
-<li class="toctree-l5"><a class="reference internal" href="#torchdynamo-mode-experimental-new-feature-from-2-0-0">TorchDynamo Mode (Experimental, <em>NEW feature from 2.0.0</em>)</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="#torchdynamo-mode-beta-new-feature-from-2-0-0">TorchDynamo Mode (Beta, <em>NEW feature from 2.0.0</em>)</a><ul>
 <li class="toctree-l6"><a class="reference internal" href="#id4">Resnet50</a></li>
 <li class="toctree-l6"><a class="reference internal" href="#id5">BERT</a></li>
 </ul>
@@ -107,14 +107,14 @@
 <li class="toctree-l6"><a class="reference internal" href="#id12">BERT</a></li>
 </ul>
 </li>
-<li class="toctree-l5"><a class="reference internal" href="#id13">TorchDynamo Mode (Experimental, <em>NEW feature from 2.0.0</em>)</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="#id13">TorchDynamo Mode (Beta, <em>NEW feature from 2.0.0</em>)</a><ul>
 <li class="toctree-l6"><a class="reference internal" href="#id14">Resnet50</a></li>
 <li class="toctree-l6"><a class="reference internal" href="#id15">BERT</a></li>
 </ul>
 </li>
 </ul>
 </li>
-<li class="toctree-l4"><a class="reference internal" href="#fast-bert-experimental">Fast Bert (<em>Experimental</em>)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#fast-bert-beta">Fast Bert (<em>Beta</em>)</a></li>
 <li class="toctree-l4"><a class="reference internal" href="#int8">INT8</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="#calibration">Calibration</a><ul>
 <li class="toctree-l6"><a class="reference internal" href="#static-quantization">Static Quantization</a></li>
@@ -217,7 +217,7 @@ <h4>Single-instance Training<a class="headerlink" href="#single-instance-trainin
 <span class="n">model</span><span class="p">,</span> <span class="n">optimizer</span> <span class="o">=</span> <span class="n">ipex</span><span class="o">.</span><span class="n">optimize</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="n">optimizer</span><span class="p">)</span>
 <span class="c1"># For BFloat16</span>
 <span class="n">model</span><span class="p">,</span> <span class="n">optimizer</span> <span class="o">=</span> <span class="n">ipex</span><span class="o">.</span><span class="n">optimize</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">bfloat16</span><span class="p">)</span>
-<span class="c1"># Invoke the code below to enable experimental feature torch.compile</span>
+<span class="c1"># Invoke the code below to enable beta feature torch.compile</span>
 <span class="n">model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s2">&quot;ipex&quot;</span><span class="p">)</span>
 <span class="o">...</span>
 <span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
@@ -259,7 +259,7 @@ <h5>Float32<a class="headerlink" href="#float32" title="Permalink to this headin
 <span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
 
 <span class="n">model</span><span class="p">,</span> <span class="n">optimizer</span> <span class="o">=</span> <span class="n">ipex</span><span class="o">.</span><span class="n">optimize</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="n">optimizer</span><span class="p">)</span>
-<span class="c1"># Uncomment the code below to enable experimental feature torch.compile</span>
+<span class="c1"># Uncomment the code below to enable beta feature torch.compile</span>
 <span class="c1"># model = torch.compile(model, backend=&quot;ipex&quot;)</span>
 
 <span class="k">for</span> <span class="n">batch_idx</span><span class="p">,</span> <span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">train_loader</span><span class="p">):</span>
@@ -312,7 +312,7 @@ <h5>BFloat16<a class="headerlink" href="#bfloat16" title="Permalink to this head
 <span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
 
 <span class="n">model</span><span class="p">,</span> <span class="n">optimizer</span> <span class="o">=</span> <span class="n">ipex</span><span class="o">.</span><span class="n">optimize</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">bfloat16</span><span class="p">)</span>
-<span class="c1"># Uncomment the code below to enable experimental feature torch.compile</span>
+<span class="c1"># Uncomment the code below to enable beta feature torch.compile</span>
 <span class="c1"># model = torch.compile(model, backend=&quot;ipex&quot;)</span>
 
 <span class="k">for</span> <span class="n">batch_idx</span><span class="p">,</span> <span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">train_loader</span><span class="p">):</span>
@@ -513,8 +513,8 @@ <h6>BERT<a class="headerlink" href="#id3" title="Permalink to this heading"><
 </div>
 </section>
 </section>
-<section id="torchdynamo-mode-experimental-new-feature-from-2-0-0">
-<h5>TorchDynamo Mode (Experimental, <em>NEW feature from 2.0.0</em>)<a class="headerlink" href="#torchdynamo-mode-experimental-new-feature-from-2-0-0" title="Permalink to this heading"></a></h5>
+<section id="torchdynamo-mode-beta-new-feature-from-2-0-0">
+<h5>TorchDynamo Mode (Beta, <em>NEW feature from 2.0.0</em>)<a class="headerlink" href="#torchdynamo-mode-beta-new-feature-from-2-0-0" title="Permalink to this heading"></a></h5>
 <section id="id4">
 <h6>Resnet50<a class="headerlink" href="#id4" title="Permalink to this heading"></a></h6>
 <p><strong>Note:</strong> You need to install <code class="docutils literal notranslate"><span class="pre">torchvision</span></code> Python package to run the following example.</p>
@@ -525,7 +525,7 @@ <h6>Resnet50<a class="headerlink" href="#id4" title="Permalink to this heading">
 <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
 <span class="n">data</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">)</span>
 
-<span class="c1"># Experimental Feature</span>
+<span class="c1"># Beta Feature</span>
 <span class="c1">#################### code changes ####################  # noqa F401</span>
 <span class="kn">import</span> <span class="nn">intel_extension_for_pytorch</span> <span class="k">as</span> <span class="nn">ipex</span>
 <span class="n">model</span> <span class="o">=</span> <span class="n">ipex</span><span class="o">.</span><span class="n">optimize</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">weights_prepack</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
@@ -553,7 +553,7 @@ <h6>BERT<a class="headerlink" href="#id5" title="Permalink to this heading"><
 <span class="n">seq_length</span> <span class="o">=</span> <span class="mi">512</span>
 <span class="n">data</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="n">vocab_size</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">[</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">seq_length</span><span class="p">])</span>
 
-<span class="c1"># Experimental Feature</span>
+<span class="c1"># Beta Feature</span>
 <span class="c1">#################### code changes ####################  # noqa F401</span>
 <span class="kn">import</span> <span class="nn">intel_extension_for_pytorch</span> <span class="k">as</span> <span class="nn">ipex</span>
 <span class="n">model</span> <span class="o">=</span> <span class="n">ipex</span><span class="o">.</span><span class="n">optimize</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">weights_prepack</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
@@ -685,7 +685,7 @@ <h6>BERT<a class="headerlink" href="#id12" title="Permalink to this heading">
 </section>
 </section>
 <section id="id13">
-<h5>TorchDynamo Mode (Experimental, <em>NEW feature from 2.0.0</em>)<a class="headerlink" href="#id13" title="Permalink to this heading"></a></h5>
+<h5>TorchDynamo Mode (Beta, <em>NEW feature from 2.0.0</em>)<a class="headerlink" href="#id13" title="Permalink to this heading"></a></h5>
 <section id="id14">
 <h6>Resnet50<a class="headerlink" href="#id14" title="Permalink to this heading"></a></h6>
 <p><strong>Note:</strong> You need to install <code class="docutils literal notranslate"><span class="pre">torchvision</span></code> Python package to run the following example.</p>
@@ -696,7 +696,7 @@ <h6>Resnet50<a class="headerlink" href="#id14" title="Permalink to this heading"
 <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
 <span class="n">data</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">)</span>
 
-<span class="c1"># Experimental Feature</span>
+<span class="c1"># Beta Feature</span>
 <span class="c1">#################### code changes ####################  # noqa F401</span>
 <span class="kn">import</span> <span class="nn">intel_extension_for_pytorch</span> <span class="k">as</span> <span class="nn">ipex</span>
 <span class="n">model</span> <span class="o">=</span> <span class="n">ipex</span><span class="o">.</span><span class="n">optimize</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">bfloat16</span><span class="p">,</span> <span class="n">weights_prepack</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
@@ -724,7 +724,7 @@ <h6>BERT<a class="headerlink" href="#id15" title="Permalink to this heading">
 <span class="n">seq_length</span> <span class="o">=</span> <span class="mi">512</span>
 <span class="n">data</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="n">vocab_size</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">[</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">seq_length</span><span class="p">])</span>
 
-<span class="c1"># Experimental Feature</span>
+<span class="c1"># Beta Feature</span>
 <span class="c1">#################### code changes ####################  # noqa F401</span>
 <span class="kn">import</span> <span class="nn">intel_extension_for_pytorch</span> <span class="k">as</span> <span class="nn">ipex</span>
 <span class="n">model</span> <span class="o">=</span> <span class="n">ipex</span><span class="o">.</span><span class="n">optimize</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">bfloat16</span><span class="p">,</span> <span class="n">weights_prepack</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
@@ -740,8 +740,8 @@ <h6>BERT<a class="headerlink" href="#id15" title="Permalink to this heading">
 </section>
 </section>
 </section>
-<section id="fast-bert-experimental">
-<h4>Fast Bert (<em>Experimental</em>)<a class="headerlink" href="#fast-bert-experimental" title="Permalink to this heading"></a></h4>
+<section id="fast-bert-beta">
+<h4>Fast Bert (<em>Beta</em>)<a class="headerlink" href="#fast-bert-beta" title="Permalink to this heading"></a></h4>
 <p><strong>Note:</strong> You need to install <code class="docutils literal notranslate"><span class="pre">transformers</span></code> Python package to run the following example.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
 <span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">BertModel</span>
@@ -1061,4 +1061,4 @@ <h2>Intel® AI Reference Models<a class="headerlink" href="#intel-ai-reference-m
   </script> 
 
 </body>
-</html>
+</html>