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@@ -387,6 +387,7 @@ <h1>Source code for torch_xla.experimental.eager</h1><div class="highlight"><pre
 <span class="kn">from</span> <span class="nn">contextlib</span> <span class="kn">import</span> <span class="n">contextmanager</span>
 
 <span class="kn">import</span> <span class="nn">torch_xla</span>
+<span class="kn">import</span> <span class="nn">logging</span>
 
 
 <div class="viewcode-block" id="eager_mode"><a class="viewcode-back" href="../../../index.html#torch_xla.experimental.eager_mode">[docs]</a><span class="k">def</span> <span class="nf">eager_mode</span><span class="p">(</span><span class="n">enable</span><span class="p">:</span> <span class="nb">bool</span><span class="p">):</span>
@@ -416,33 +417,12 @@ <h1>Source code for torch_xla.experimental.eager</h1><div class="highlight"><pre
     <span class="n">eager_mode</span><span class="p">(</span><span class="n">saved_eager_mode</span><span class="p">)</span>
 
 
-<div class="viewcode-block" id="compile"><a class="viewcode-back" href="../../../index.html#torch_xla.experimental.compile">[docs]</a><span class="k">def</span> <span class="nf">compile</span><span class="p">(</span><span class="n">func</span><span class="p">):</span>
-<span class="w">  </span><span class="sd">&quot;&quot;&quot;Compile the func with Lazy Tensor.</span>
-
-<span class="sd">  Return the optimized function that takes exact same input. Compile will</span>
-<span class="sd">  run the target func under the tracing mode using Lazy tensor.</span>
-<span class="sd">  &quot;&quot;&quot;</span>
-
-  <span class="nd">@functools</span><span class="o">.</span><span class="n">wraps</span><span class="p">(</span><span class="n">func</span><span class="p">)</span>  <span class="c1"># Keep function&#39;s name, docstring, etc.</span>
-  <span class="k">def</span> <span class="nf">wrapper</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-    <span class="n">saved_eager_mode_status</span> <span class="o">=</span> <span class="n">torch_xla</span><span class="o">.</span><span class="n">_XLAC</span><span class="o">.</span><span class="n">_get_use_eager_mode</span><span class="p">()</span>
-    <span class="n">torch_xla</span><span class="o">.</span><span class="n">_XLAC</span><span class="o">.</span><span class="n">_set_use_eager_mode</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
-    <span class="c1"># clear the pending graph if any</span>
-    <span class="n">torch_xla</span><span class="o">.</span><span class="n">sync</span><span class="p">()</span>
-    <span class="k">try</span><span class="p">:</span>
-      <span class="c1"># Target Function Execution</span>
-      <span class="n">result</span> <span class="o">=</span> <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-      <span class="c1"># Sync the graph generated by the target function.</span>
-      <span class="n">torch_xla</span><span class="o">.</span><span class="n">sync</span><span class="p">()</span>
-    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
-      <span class="c1"># Handle exceptions (if needed)</span>
-      <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Error in target function: </span><span class="si">{</span><span class="n">e</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-      <span class="k">raise</span>  <span class="c1"># Re-raise the exception</span>
-    <span class="n">torch_xla</span><span class="o">.</span><span class="n">_XLAC</span><span class="o">.</span><span class="n">_set_use_eager_mode</span><span class="p">(</span><span class="n">saved_eager_mode_status</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">result</span>
-
-  <span class="k">return</span> <span class="n">wrapper</span></div>
+<span class="k">def</span> <span class="nf">compile</span><span class="p">(</span><span class="n">func</span><span class="p">):</span>
+  <span class="c1"># can&#39;s use deprecated wrapper at import time due to circular dependency</span>
+  <span class="n">logging</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
+      <span class="s1">&#39;torch_xla.experimental.compile is deprecated. Use torch_xla.compile instead.&#39;</span>
+  <span class="p">)</span>
+  <span class="k">return</span> <span class="n">torch_xla</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">func</span><span class="p">)</span>
 </pre></div>
 
              </article>

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master/_modules/torch_xla/torch_xla.html

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@@ -446,12 +446,43 @@ <h1>Source code for torch_xla.torch_xla</h1><div class="highlight"><pre>
   <span class="n">xm</span><span class="o">.</span><span class="n">mark_step</span><span class="p">()</span></div>
 
 
-<div class="viewcode-block" id="step"><a class="viewcode-back" href="../../index.html#torch_xla.step">[docs]</a><span class="k">def</span> <span class="nf">step</span><span class="p">(</span><span class="n">f</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Callable</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+<span class="k">def</span> <span class="nf">step</span><span class="p">():</span>
 <span class="w">  </span><span class="sd">&quot;&quot;&quot;Wraps code that should be dispatched to the runtime.</span>
 
 <span class="sd">  Experimental: `xla.step` is still a work in progress. Some code that currently</span>
 <span class="sd">  works with `xla.step` but does not follow best practices will become errors in</span>
 <span class="sd">  future releases. See https://github.com/pytorch/xla/issues/6751 for context.</span>
+<span class="sd">  &quot;&quot;&quot;</span>
+  <span class="k">return</span> <span class="nb">compile</span><span class="p">()</span>
+
+
+<div class="viewcode-block" id="compile"><a class="viewcode-back" href="../../index.html#torch_xla.compile">[docs]</a><span class="k">def</span> <span class="nf">compile</span><span class="p">(</span><span class="n">f</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Callable</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+<span class="w">  </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">  Optimizes given model/function using torch_xla&#39;s LazyTensor tracing mode.</span>
+<span class="sd">  PyTorch/XLA will trace the given function with given inputs and then generate</span>
+<span class="sd">  graphs to represent the pytorch operations happens within this function. This</span>
+<span class="sd">  graph will be compiled by the XLA and executed on the accelerator(decided by the</span>
+<span class="sd">  tensor&#39;s device). Eager mode will be disabled for the compiled region of the funciton.</span>
+
+<span class="sd">  Args:</span>
+<span class="sd">      model (Callable): Module/function to optimize, if not passed this function will</span>
+<span class="sd">        act as a context manager.</span>
+
+<span class="sd">  Example::</span>
+
+<span class="sd">      # usage 1</span>
+<span class="sd">      @torch_xla.compile()</span>
+<span class="sd">      def foo(x):</span>
+<span class="sd">        return torch.sin(x) + torch.cos(x)</span>
+
+<span class="sd">      def foo2(x):</span>
+<span class="sd">        return torch.sin(x) + torch.cos(x)</span>
+<span class="sd">      # usage 2</span>
+<span class="sd">      compiled_foo2 = torch_xla.compile(foo2)</span>
+
+<span class="sd">      # usage 3</span>
+<span class="sd">      with torch_xla.compile():</span>
+<span class="sd">        res = foo2(x)</span>
 <span class="sd">  &quot;&quot;&quot;</span>
 
   <span class="nd">@contextlib</span><span class="o">.</span><span class="n">contextmanager</span>

master/_sources/index.rst.txt

@@ -28,7 +28,7 @@ torch_xla
 .. autofunction:: devices
 .. autofunction:: device_count
 .. autofunction:: sync
-.. autofunction:: step
+.. autofunction:: compile
 .. autofunction:: manual_seed
 
 runtime
@@ -96,7 +96,6 @@ experimental
 ----------------------------------
 .. automodule:: torch_xla.experimental
 .. autofunction:: eager_mode
-.. autofunction:: compile
 
 debug
 ----------------------------------

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@@ -428,7 +428,7 @@ <h2 id="C">C</h2>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="index.html#torch_xla.distributed.spmd.clear_sharding">clear_sharding() (in module torch_xla.distributed.spmd)</a>
 </li>
-      <li><a href="index.html#torch_xla.experimental.compile">compile() (in module torch_xla.experimental)</a>
+      <li><a href="index.html#torch_xla.compile">compile() (in module torch_xla)</a>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
@@ -610,8 +610,6 @@ <h2 id="S">S</h2>
       <li><a href="index.html#torch_xla.debug.metrics.short_metrics_report">short_metrics_report() (in module torch_xla.debug.metrics)</a>
 </li>
       <li><a href="index.html#torch_xla.distributed.xla_multiprocessing.spawn">spawn() (in module torch_xla.distributed.xla_multiprocessing)</a>
-</li>
-      <li><a href="index.html#torch_xla.step">step() (in module torch_xla)</a>
 </li>
       <li><a href="index.html#torch_xla.sync">sync() (in module torch_xla)</a>
 </li>

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