Generate Python docs from pytorch/pytorch@53fdd60

Author: pytorchbot

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@@ -1456,7 +1456,7 @@ <h1>Source code for torch._tensor</h1><div class="highlight"><pre>
             <span class="n">torch</span><span class="o">.</span><span class="n">int64</span><span class="p">:</span> <span class="s2">&quot;&lt;i8&quot;</span><span class="p">,</span>
         <span class="p">}[</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">]</span>
 
-        <span class="n">itemsize</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">storage</span><span class="p">()</span><span class="o">.</span><span class="n">element_size</span><span class="p">()</span>
+        <span class="n">itemsize</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">element_size</span><span class="p">()</span>
 
         <span class="n">shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_contiguous</span><span class="p">():</span>
@@ -1745,9 +1745,10 @@ <h1>Source code for torch._tensor</h1><div class="highlight"><pre>
             <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s2">&quot;cuda&quot;</span><span class="p">:</span>
                 <span class="n">stream</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">ExternalStream</span><span class="p">(</span><span class="n">stream</span><span class="p">)</span>
                 <span class="c1"># Only synchronize on different streams</span>
-                <span class="k">if</span> <span class="n">stream</span> <span class="o">!=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">current_stream</span><span class="p">:</span>
+                <span class="n">sync_stream</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">current_stream</span><span class="p">()</span>
+                <span class="k">if</span> <span class="n">stream</span> <span class="o">!=</span> <span class="n">sync_stream</span><span class="p">:</span>
                     <span class="n">event</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">Event</span><span class="p">()</span>
-                    <span class="n">event</span><span class="o">.</span><span class="n">record</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">current_stream</span><span class="p">())</span>
+                    <span class="n">event</span><span class="o">.</span><span class="n">record</span><span class="p">(</span><span class="n">sync_stream</span><span class="p">)</span>
                     <span class="n">stream</span><span class="o">.</span><span class="n">wait_event</span><span class="p">(</span><span class="n">event</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">to_dlpack</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
 

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@@ -596,6 +596,12 @@ <h1>Source code for torch.autograd</h1><div class="highlight"><pre>
 <span class="sd">            not provided, the gradient is accumulated into all the leaf Tensors that</span>
 <span class="sd">            were used to compute the attr::tensors.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_are_functorch_transforms_active</span><span class="p">():</span>
+        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
+            <span class="s2">&quot;backward() called inside a functorch transform. This is not &quot;</span>
+            <span class="s2">&quot;supported, please use functorch.grad or functorch.vjp instead &quot;</span>
+            <span class="s2">&quot;or call backward() outside of functorch transforms.&quot;</span><span class="p">)</span>
+
     <span class="k">if</span> <span class="n">grad_variables</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;&#39;grad_variables&#39; is deprecated. Use &#39;grad_tensors&#39; instead.&quot;</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">grad_tensors</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>