From ed7a1b2ba80cf46c04dc359e7aef3afd8f66fd5f Mon Sep 17 00:00:00 2001
From: pytorchbot <soumith+bot@pytorch.org>
Date: Wed, 19 Apr 2023 13:34:08 +0000
Subject: [PATCH] Generate Python docs from
 pytorch/pytorch@a470c041b0f9f01f8ab62ec80bb0b47466fe8d34

---
 docs/2.0/_images/RReLU.png                    | Bin 34208 -> 33856 bytes
 .../torch/cuda/amp/autocast_mode.html         |   2 +-
 .../torch/distributed/distributed_c10d.html   |  41 +++--
 docs/2.0/_modules/torch/nn/functional.html    |  40 ++++-
 .../_modules/torch/nn/modules/activation.html |  37 ++--
 .../torch/nn/modules/transformer.html         |   8 +-
 docs/2.0/_modules/torch/optim/adam.html       |  10 +-
 docs/2.0/_modules/torch/optim/adamw.html      |   9 +-
 docs/2.0/_modules/torch/serialization.html    |   2 +-
 docs/2.0/_modules/torch/storage.html          |  37 +++-
 .../torch/utils/data/_utils/collate.html      |   3 +-
 .../_modules/torch/utils/data/dataloader.html |  17 +-
 .../2.0/_sources/notes/extending.func.rst.txt |   9 +-
 docs/2.0/distributed.html                     |   4 +-
 .../torch.nn.MultiheadAttention.html          |  14 +-
 docs/2.0/notes/extending.func.html            |   9 +-
 .../quantization-backend-configuration.html   | 168 +++++++++---------
 docs/2.0/searchindex.js                       |   2 +-
 docs/2.0/torch.html                           |  10 +-
 19 files changed, 260 insertions(+), 162 deletions(-)

diff --git a/docs/2.0/_images/RReLU.png b/docs/2.0/_images/RReLU.png
index 75fa1b8507010c3e3bf9c3740ab2a6f87371b3a0..04f0001d9bc7375e90cad50c82d3795d3d7f308b 100644
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diff --git a/docs/2.0/_modules/torch/cuda/amp/autocast_mode.html b/docs/2.0/_modules/torch/cuda/amp/autocast_mode.html
index c233fe991454..dd4a059ce9c2 100644
--- a/docs/2.0/_modules/torch/cuda/amp/autocast_mode.html
+++ b/docs/2.0/_modules/torch/cuda/amp/autocast_mode.html
@@ -504,7 +504,7 @@ <h1>Source code for torch.cuda.amp.autocast_mode</h1><div class="highlight"><pre
     <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
         <span class="n">is_eligible</span> <span class="o">=</span> <span class="p">(</span><span class="n">value</span><span class="o">.</span><span class="n">is_floating_point</span><span class="p">()</span> <span class="ow">and</span> <span class="n">value</span><span class="o">.</span><span class="n">is_cuda</span> <span class="ow">and</span> <span class="p">(</span><span class="n">value</span><span class="o">.</span><span class="n">dtype</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">float64</span><span class="p">))</span>
         <span class="k">return</span> <span class="n">value</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span> <span class="k">if</span> <span class="n">is_eligible</span> <span class="k">else</span> <span class="n">value</span>
-    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
+    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="p">(</span><span class="nb">str</span><span class="p">,</span> <span class="nb">bytes</span><span class="p">)):</span>
         <span class="k">return</span> <span class="n">value</span>
     <span class="k">elif</span> <span class="n">HAS_NUMPY</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
         <span class="k">return</span> <span class="n">value</span>
diff --git a/docs/2.0/_modules/torch/distributed/distributed_c10d.html b/docs/2.0/_modules/torch/distributed/distributed_c10d.html
index 5e0538df744b..7694541ae08a 100644
--- a/docs/2.0/_modules/torch/distributed/distributed_c10d.html
+++ b/docs/2.0/_modules/torch/distributed/distributed_c10d.html
@@ -683,7 +683,6 @@ <h1>Source code for torch.distributed.distributed_c10d</h1><div class="highlight
 
     <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">backend</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Backend</span><span class="p">]):</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">device_backend_map</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">Backend</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="c1"># error check to make sure the config string is valid</span>
 
         <span class="c1"># Cases for when backend is a single string (without device types)</span>
         <span class="k">if</span> <span class="n">backend</span> <span class="o">==</span> <span class="n">Backend</span><span class="o">.</span><span class="n">UNDEFINED</span><span class="p">:</span>
@@ -700,13 +699,24 @@ <h1>Source code for torch.distributed.distributed_c10d</h1><div class="highlight
                 <span class="s2">&quot;cuda&quot;</span><span class="p">:</span> <span class="n">backend_val</span><span class="p">,</span>
             <span class="p">}</span>
         <span class="k">else</span><span class="p">:</span>
-            <span class="c1"># custom backend string in format of &quot;{device_type1}:{backend1},{device_type2}:{backend2}&quot;</span>
-            <span class="c1"># TODO</span>
-            <span class="k">pass</span>
-
-        <span class="n">required_devices</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;cpu&quot;</span><span class="p">,</span> <span class="s2">&quot;cuda&quot;</span><span class="p">]</span>
-        <span class="k">for</span> <span class="n">device</span> <span class="ow">in</span> <span class="n">required_devices</span><span class="p">:</span>
-            <span class="k">assert</span> <span class="n">device</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">device_backend_map</span>
+            <span class="c1"># make sure the backend string is in the correct format</span>
+            <span class="c1"># &quot;{device_type1}:{backend1},{device_type2}:{backend2}&quot;</span>
+            <span class="c1"># e.g. &quot;cpu:gloo,cuda:nccl&quot;</span>
+            <span class="n">backend_str_error_message</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;&quot;&quot;The custom backend string argument is invalid: </span><span class="si">{</span><span class="n">backend</span><span class="si">}</span><span class="s2">.</span>
+<span class="s2">                Custom backend string is an experimental feature where the backend string must be in the format:</span>
+<span class="s2">                &quot;&lt;device_type1&gt;:&lt;backend1&gt;,&lt;device_type2&gt;:&lt;backend2&gt;...&quot;. e.g. &#39;cpu:gloo,cuda:nccl&#39;&quot;&quot;&quot;</span>
+
+            <span class="c1"># parse the backend string and populate the device_backend_map</span>
+            <span class="k">for</span> <span class="n">device_backend_pair_str</span> <span class="ow">in</span> <span class="n">backend</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;,&quot;</span><span class="p">):</span>
+                <span class="n">device_backend_pair</span> <span class="o">=</span> <span class="n">device_backend_pair_str</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;:&quot;</span><span class="p">)</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">device_backend_pair</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
+                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Invalid device:backend pairing: </span><span class="se">\</span>
+<span class="s2">                                     </span><span class="si">{</span><span class="n">device_backend_pair_str</span><span class="si">}</span><span class="s2">. </span><span class="si">{</span><span class="n">backend_str_error_message</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+                <span class="n">device</span><span class="p">,</span> <span class="n">backend</span> <span class="o">=</span> <span class="n">device_backend_pair</span>
+                <span class="k">if</span> <span class="n">device</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">device_backend_map</span><span class="p">:</span>
+                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Duplicate device type </span><span class="si">{</span><span class="n">device</span><span class="si">}</span><span class="s2"> </span><span class="se">\</span>
+<span class="s2">                                     in backend string: </span><span class="si">{</span><span class="n">backend</span><span class="si">}</span><span class="s2">. </span><span class="si">{</span><span class="n">backend_str_error_message</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">device_backend_map</span><span class="p">[</span><span class="n">device</span><span class="p">]</span> <span class="o">=</span> <span class="n">Backend</span><span class="p">(</span><span class="n">backend</span><span class="p">)</span>
 
     <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="c1"># string with all the device:backend pairs separared by commas</span>
@@ -1293,7 +1303,9 @@ <h1>Source code for torch.distributed.distributed_c10d</h1><div class="highlight
 <span class="sd">    .. note:: Support for multiple backends is experimental. Currently when no backend is</span>
 <span class="sd">        specified, both ``gloo`` and ``nccl`` backends will be created. The ``gloo`` backend</span>
 <span class="sd">        will be used for collectives with CPU tensors and the ``nccl`` backend will be used</span>
-<span class="sd">        for collectives with CUDA tensors.</span>
+<span class="sd">        for collectives with CUDA tensors. A custom backend can be specified by passing in</span>
+<span class="sd">        a string with format &quot;&lt;device_type&gt;:&lt;backend_name&gt;,&lt;device_type&gt;:&lt;backend_name&gt;&quot;, e.g.</span>
+<span class="sd">        &quot;cpu:gloo,cuda:custom_backend&quot;.</span>
 
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="k">global</span> <span class="n">_world</span>
@@ -1444,6 +1456,9 @@ <h1>Source code for torch.distributed.distributed_c10d</h1><div class="highlight
             <span class="n">backend_type</span> <span class="o">=</span> <span class="n">ProcessGroup</span><span class="o">.</span><span class="n">BackendType</span><span class="o">.</span><span class="n">MPI</span>
             <span class="k">if</span> <span class="ow">not</span> <span class="n">backend_class</span><span class="p">:</span>
                 <span class="k">return</span> <span class="n">GroupMember</span><span class="o">.</span><span class="n">NON_GROUP_MEMBER</span>
+            <span class="c1"># create new process group with accurate rank and size</span>
+            <span class="k">if</span> <span class="n">pg</span><span class="o">.</span><span class="n">rank</span><span class="p">()</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span> <span class="ow">and</span> <span class="n">pg</span><span class="o">.</span><span class="n">size</span><span class="p">()</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
+                <span class="n">pg</span> <span class="o">=</span> <span class="n">ProcessGroup</span><span class="p">(</span><span class="n">backend_prefix_store</span><span class="p">,</span> <span class="n">backend_class</span><span class="o">.</span><span class="n">rank</span><span class="p">(),</span> <span class="n">backend_class</span><span class="o">.</span><span class="n">size</span><span class="p">(),</span> <span class="n">base_pg_options</span><span class="p">)</span>
         <span class="k">elif</span> <span class="n">backend_str</span> <span class="o">==</span> <span class="n">Backend</span><span class="o">.</span><span class="n">GLOO</span><span class="p">:</span>
             <span class="c1"># TODO: remove this check after lazy initialization is supported</span>
             <span class="c1"># if pg_options is not None:</span>
@@ -1527,15 +1542,15 @@ <h1>Source code for torch.distributed.distributed_c10d</h1><div class="highlight
                         <span class="n">timeout</span><span class="o">=</span><span class="n">timeout</span><span class="p">,</span>
                     <span class="p">)</span>
 
-        <span class="c1"># only create single backend pg when backend is set to gloo, nccl, mpi, and ucc</span>
-        <span class="k">if</span> <span class="n">backend</span> <span class="ow">in</span> <span class="p">[</span><span class="n">Backend</span><span class="o">.</span><span class="n">GLOO</span><span class="p">,</span> <span class="n">Backend</span><span class="o">.</span><span class="n">NCCL</span><span class="p">,</span> <span class="n">Backend</span><span class="o">.</span><span class="n">UCC</span><span class="p">,</span> <span class="n">Backend</span><span class="o">.</span><span class="n">MPI</span><span class="p">]:</span>
+        <span class="c1"># register only a single backend when all get_device_backend_map values are the same</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">backend_config</span><span class="o">.</span><span class="n">get_device_backend_map</span><span class="p">()</span><span class="o">.</span><span class="n">values</span><span class="p">()))</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
             <span class="k">for</span> <span class="n">device</span> <span class="ow">in</span> <span class="n">backend_config</span><span class="o">.</span><span class="n">get_device_backend_map</span><span class="p">()</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
                 <span class="n">pg</span><span class="o">.</span><span class="n">_register_backend</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> <span class="n">backend_type</span><span class="p">,</span> <span class="n">backend_class</span><span class="p">)</span>
 
             <span class="c1"># break out of outer loop to not create any more backends</span>
             <span class="k">break</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">pg</span><span class="o">.</span><span class="n">_register_backend</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> <span class="n">backend_type</span><span class="p">,</span> <span class="n">backend_class</span><span class="p">)</span>
+
+        <span class="n">pg</span><span class="o">.</span><span class="n">_register_backend</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> <span class="n">backend_type</span><span class="p">,</span> <span class="n">backend_class</span><span class="p">)</span>
 
     <span class="c1"># update global state</span>
     <span class="n">_world</span><span class="o">.</span><span class="n">pg_map</span><span class="p">[</span><span class="n">pg</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">backend</span><span class="p">,</span> <span class="n">prefix_store</span><span class="p">)</span>
diff --git a/docs/2.0/_modules/torch/nn/functional.html b/docs/2.0/_modules/torch/nn/functional.html
index 0fdcfaeb1ba0..1737bcf7a48c 100644
--- a/docs/2.0/_modules/torch/nn/functional.html
+++ b/docs/2.0/_modules/torch/nn/functional.html
@@ -5513,11 +5513,21 @@ <h1>Source code for torch.nn.functional</h1><div class="highlight"><pre>
 <span class="sd">            be ignored by the attention. This is an binary mask. When the value is True,</span>
 <span class="sd">            the corresponding value on the attention layer will be filled with -inf.</span>
 <span class="sd">        need_weights: output attn_output_weights.</span>
+<span class="sd">            Default: `True`</span>
+<span class="sd">            Note: `needs_weight` defaults to `True`, but should be set to `False`</span>
+<span class="sd">            For best performance when attention weights are not nedeeded.</span>
+<span class="sd">            *Setting needs_weights to `True`</span>
+<span class="sd">            leads to a significant performance degradation.*</span>
 <span class="sd">        attn_mask: 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all</span>
 <span class="sd">            the batches while a 3D mask allows to specify a different mask for the entries of each batch.</span>
 <span class="sd">        is_causal: If specified, applies a causal mask as attention mask, and ignores</span>
 <span class="sd">            attn_mask for computing scaled dot product attention.</span>
 <span class="sd">            Default: ``False``.</span>
+<span class="sd">            .. warning::</span>
+<span class="sd">                is_causal is provides a hint that the attn_mask is the</span>
+<span class="sd">                causal mask.Providing incorrect hints can result in</span>
+<span class="sd">                incorrect execution, including forward and backward</span>
+<span class="sd">                compatibility.</span>
 <span class="sd">        use_separate_proj_weight: the function accept the proj. weights for query, key,</span>
 <span class="sd">            and value in different forms. If false, in_proj_weight will be used, which is</span>
 <span class="sd">            a combination of q_proj_weight, k_proj_weight, v_proj_weight.</span>
@@ -5617,8 +5627,33 @@ <h1>Source code for torch.nn.functional</h1><div class="highlight"><pre>
         <span class="n">target_type</span><span class="o">=</span><span class="n">query</span><span class="o">.</span><span class="n">dtype</span>
     <span class="p">)</span>
 
-    <span class="k">if</span> <span class="n">is_causal</span><span class="p">:</span>
+    <span class="k">if</span> <span class="n">is_causal</span> <span class="ow">and</span> <span class="n">attn_mask</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
+            <span class="s2">&quot;Need attn_mask if specifying the is_causal hint. &quot;</span>
+            <span class="s2">&quot;You may use the Transformer module method &quot;</span>
+            <span class="s2">&quot;`generate_square_subsequent_mask` to create this mask.&quot;</span>
+        <span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">is_causal</span> <span class="ow">and</span> <span class="n">key_padding_mask</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">need_weights</span><span class="p">:</span>
+        <span class="c1"># when we have a kpm or need weights, we need attn_mask</span>
+        <span class="c1"># Otherwise, we use the is_causal hint go as is_causal</span>
+        <span class="c1"># indicator to SDPA.</span>
         <span class="n">attn_mask</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">attn_mask</span> <span class="o">=</span> <span class="n">_canonical_mask</span><span class="p">(</span>
+            <span class="n">mask</span><span class="o">=</span><span class="n">attn_mask</span><span class="p">,</span>
+            <span class="n">mask_name</span><span class="o">=</span><span class="s2">&quot;attn_mask&quot;</span><span class="p">,</span>
+            <span class="n">other_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+            <span class="n">other_name</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">,</span>
+            <span class="n">target_type</span><span class="o">=</span><span class="n">query</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span>
+            <span class="n">check_other</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">key_padding_mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="c1"># We have the attn_mask, and use that to merge kpm into it.</span>
+            <span class="c1"># Turn off use of is_causal hint, as the merged mask is no</span>
+            <span class="c1"># longer causal.</span>
+            <span class="n">is_causal</span> <span class="o">=</span> <span class="kc">False</span>
 
     <span class="k">assert</span> <span class="n">embed_dim</span> <span class="o">==</span> <span class="n">embed_dim_to_check</span><span class="p">,</span> \
         <span class="sa">f</span><span class="s2">&quot;was expecting embedding dimension of </span><span class="si">{</span><span class="n">embed_dim_to_check</span><span class="si">}</span><span class="s2">, but got </span><span class="si">{</span><span class="n">embed_dim</span><span class="si">}</span><span class="s2">&quot;</span>
@@ -5748,6 +5783,9 @@ <h1>Source code for torch.nn.functional</h1><div class="highlight"><pre>
     <span class="k">if</span> <span class="n">need_weights</span><span class="p">:</span>
         <span class="n">B</span><span class="p">,</span> <span class="n">Nt</span><span class="p">,</span> <span class="n">E</span> <span class="o">=</span> <span class="n">q</span><span class="o">.</span><span class="n">shape</span>
         <span class="n">q_scaled</span> <span class="o">=</span> <span class="n">q</span> <span class="o">/</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">E</span><span class="p">)</span>
+
+        <span class="k">assert</span> <span class="ow">not</span> <span class="p">(</span><span class="n">is_causal</span> <span class="ow">and</span> <span class="n">attn_mask</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">),</span> <span class="s2">&quot;FIXME: is_causal not implemented for need_weights&quot;</span>
+
         <span class="k">if</span> <span class="n">attn_mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">attn_output_weights</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">baddbmm</span><span class="p">(</span><span class="n">attn_mask</span><span class="p">,</span> <span class="n">q_scaled</span><span class="p">,</span> <span class="n">k</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">))</span>
         <span class="k">else</span><span class="p">:</span>
diff --git a/docs/2.0/_modules/torch/nn/modules/activation.html b/docs/2.0/_modules/torch/nn/modules/activation.html
index dac76c8328b2..d152507f86df 100644
--- a/docs/2.0/_modules/torch/nn/modules/activation.html
+++ b/docs/2.0/_modules/torch/nn/modules/activation.html
@@ -1353,9 +1353,8 @@ <h1>Source code for torch.nn.modules.activation</h1><div class="highlight"><pre>
 
 <span class="sd">    where :math:`head_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V)`.</span>
 
-<span class="sd">    ``forward()`` will use the optimized implementation described in</span>
-<span class="sd">    `FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness`_ if all of the following</span>
-<span class="sd">    conditions are met:</span>
+<span class="sd">    ``nn.MultiHeadAttention`` will use the optimized implementations of</span>
+<span class="sd">    ``scaled_dot_product_attention()`` when possible.</span>
 
 <span class="sd">    - self attention is being computed (i.e., ``query``, ``key``, and ``value`` are the same tensor. This</span>
 <span class="sd">      restriction will be loosened in the future.)</span>
@@ -1510,8 +1509,13 @@ <h1>Source code for torch.nn.modules.activation</h1><div class="highlight"><pre>
 <span class="sd">            corresponding position is not allowed to attend. For a float mask, the mask values will be added to</span>
 <span class="sd">            the attention weight.</span>
 <span class="sd">            If both attn_mask and key_padding_mask are supplied, their types should match.</span>
-<span class="sd">        is_causal: If specified, applies a causal mask as attention mask. Mutually exclusive with providing attn_mask.</span>
+<span class="sd">        is_causal: If specified, applies a causal mask as attention mask.</span>
 <span class="sd">            Default: ``False``.</span>
+<span class="sd">            Warning:</span>
+<span class="sd">            ``is_causal`` provides a hint that ``attn_mask`` is the</span>
+<span class="sd">            causal mask. Providing incorrect hints can result in</span>
+<span class="sd">            incorrect execution, including forward and backward</span>
+<span class="sd">            compatibility.</span>
 <span class="sd">        average_attn_weights: If true, indicates that the returned ``attn_weights`` should be averaged across</span>
 <span class="sd">            heads. Otherwise, ``attn_weights`` are provided separately per head. Note that this flag only has an</span>
 <span class="sd">            effect when ``need_weights=True``. Default: ``True`` (i.e. average weights across heads)</span>
@@ -1530,8 +1534,6 @@ <h1>Source code for torch.nn.modules.activation</h1><div class="highlight"><pre>
 <span class="sd">        .. note::</span>
 <span class="sd">            `batch_first` argument is ignored for unbatched inputs.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">attn_mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">is_causal</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span><span class="s2">&quot;Only allow causal mask or attn_mask&quot;</span><span class="p">)</span>
 
         <span class="n">is_batched</span> <span class="o">=</span> <span class="n">query</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">==</span> <span class="mi">3</span>
 
@@ -1684,20 +1686,27 @@ <h1>Source code for torch.nn.modules.activation</h1><div class="highlight"><pre>
             <span class="n">check_other</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
         <span class="p">)</span>
 
-        <span class="k">if</span> <span class="n">attn_mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">mask_type</span> <span class="o">=</span> <span class="mi">0</span>
-            <span class="n">merged_mask</span> <span class="o">=</span> <span class="n">attn_mask</span>
         <span class="k">if</span> <span class="n">key_padding_mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">mask_type</span> <span class="o">=</span> <span class="mi">1</span>
             <span class="n">merged_mask</span> <span class="o">=</span> <span class="n">key_padding_mask</span>
-        <span class="k">if</span> <span class="p">(</span><span class="n">attn_mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">key_padding_mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="n">attn_mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="c1"># In this branch query can&#39;t be a nested tensor, so it has a shape</span>
             <span class="n">batch_size</span><span class="p">,</span> <span class="n">seq_len</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">query</span><span class="o">.</span><span class="n">shape</span>
             <span class="n">mask_type</span> <span class="o">=</span> <span class="mi">2</span>
-            <span class="n">key_padding_mask_expanded</span> <span class="o">=</span> <span class="n">key_padding_mask</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">seq_len</span><span class="p">)</span> \
-                                                        <span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_heads</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
-            <span class="n">attn_mask_expanded</span> <span class="o">=</span> <span class="n">attn_mask</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">seq_len</span><span class="p">,</span> <span class="n">seq_len</span><span class="p">)</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_heads</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
-            <span class="n">merged_mask</span> <span class="o">=</span> <span class="n">attn_mask_expanded</span> <span class="o">+</span> <span class="n">key_padding_mask_expanded</span>
+
+            <span class="c1"># Always expands attn_mask to 4D</span>
+            <span class="k">if</span> <span class="n">attn_mask</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+                <span class="n">attn_mask_expanded</span> <span class="o">=</span> <span class="n">attn_mask</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">seq_len</span><span class="p">,</span> <span class="n">seq_len</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>  <span class="c1"># attn_mask.dim() == 2:</span>
+                <span class="n">attn_mask_expanded</span> <span class="o">=</span> <span class="n">attn_mask</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">seq_len</span><span class="p">,</span> <span class="n">seq_len</span><span class="p">)</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_heads</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+            <span class="n">merged_mask</span> <span class="o">=</span> <span class="n">attn_mask_expanded</span>
+
+            <span class="k">if</span> <span class="n">key_padding_mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">key_padding_mask_expanded</span> <span class="o">=</span> <span class="n">key_padding_mask</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">seq_len</span><span class="p">)</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_heads</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+                <span class="n">merged_mask</span> <span class="o">=</span> <span class="n">attn_mask_expanded</span> <span class="o">+</span> <span class="n">key_padding_mask_expanded</span>
+
+        <span class="c1"># no attn_mask and no key_padding_mask, returns None, None</span>
         <span class="k">return</span> <span class="n">merged_mask</span><span class="p">,</span> <span class="n">mask_type</span></div></div>
 
 
diff --git a/docs/2.0/_modules/torch/nn/modules/transformer.html b/docs/2.0/_modules/torch/nn/modules/transformer.html
index 4ea7205f7fff..02cb886a2e77 100644
--- a/docs/2.0/_modules/torch/nn/modules/transformer.html
+++ b/docs/2.0/_modules/torch/nn/modules/transformer.html
@@ -1022,21 +1022,21 @@ <h1>Source code for torch.nn.modules.transformer</h1><div class="highlight"><pre
 
         <span class="n">x</span> <span class="o">=</span> <span class="n">src</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">norm_first</span><span class="p">:</span>
-            <span class="n">x</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sa_block</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">norm1</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="n">src_mask</span><span class="p">,</span> <span class="n">src_key_padding_mask</span><span class="p">)</span>
+            <span class="n">x</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sa_block</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">norm1</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="n">src_mask</span><span class="p">,</span> <span class="n">src_key_padding_mask</span><span class="p">,</span> <span class="n">is_causal</span><span class="o">=</span><span class="n">is_causal</span><span class="p">)</span>
             <span class="n">x</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ff_block</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">norm2</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
         <span class="k">else</span><span class="p">:</span>
-            <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">norm1</span><span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sa_block</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">src_mask</span><span class="p">,</span> <span class="n">src_key_padding_mask</span><span class="p">))</span>
+            <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">norm1</span><span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sa_block</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">src_mask</span><span class="p">,</span> <span class="n">src_key_padding_mask</span><span class="p">,</span> <span class="n">is_causal</span><span class="o">=</span><span class="n">is_causal</span><span class="p">))</span>
             <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">norm2</span><span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ff_block</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
 
         <span class="k">return</span> <span class="n">x</span></div>
 
     <span class="c1"># self-attention block</span>
     <span class="k">def</span> <span class="nf">_sa_block</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span>
-                  <span class="n">attn_mask</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">key_padding_mask</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Tensor</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="n">Tensor</span><span class="p">:</span>
+                  <span class="n">attn_mask</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">key_padding_mask</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">is_causal</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tensor</span><span class="p">:</span>
         <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">self_attn</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span>
                            <span class="n">attn_mask</span><span class="o">=</span><span class="n">attn_mask</span><span class="p">,</span>
                            <span class="n">key_padding_mask</span><span class="o">=</span><span class="n">key_padding_mask</span><span class="p">,</span>
-                           <span class="n">need_weights</span><span class="o">=</span><span class="kc">False</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+                           <span class="n">need_weights</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">is_causal</span><span class="o">=</span><span class="n">is_causal</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">dropout1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
 
     <span class="c1"># feed forward block</span>
diff --git a/docs/2.0/_modules/torch/optim/adam.html b/docs/2.0/_modules/torch/optim/adam.html
index d9c8680f64e7..885d39ff2447 100644
--- a/docs/2.0/_modules/torch/optim/adam.html
+++ b/docs/2.0/_modules/torch/optim/adam.html
@@ -984,7 +984,6 @@ <h1>Source code for torch.optim.adam</h1><div class="highlight"><pre>
     <span class="n">capturable</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>  <span class="c1"># Needed for consistency.</span>
     <span class="n">differentiable</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
-    <span class="n">grouped_tensors</span> <span class="o">=</span> <span class="n">_group_tensors_by_device_and_dtype</span><span class="p">([</span><span class="n">params</span><span class="p">,</span> <span class="n">grads</span><span class="p">,</span> <span class="n">exp_avgs</span><span class="p">,</span> <span class="n">exp_avg_sqs</span><span class="p">,</span> <span class="n">max_exp_avg_sqs</span><span class="p">,</span> <span class="n">state_steps</span><span class="p">])</span>
     <span class="n">grad_scale_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">grad_scale</span><span class="o">.</span><span class="n">device</span><span class="p">:</span> <span class="n">grad_scale</span><span class="p">}</span> <span class="k">if</span> <span class="n">grad_scale</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="kc">None</span>
     <span class="n">found_inf_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">found_inf</span><span class="o">.</span><span class="n">device</span><span class="p">:</span> <span class="n">found_inf</span><span class="p">}</span> <span class="k">if</span> <span class="n">found_inf</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="kc">None</span>
     <span class="n">grouped_tensors</span> <span class="o">=</span> <span class="n">_group_tensors_by_device_and_dtype</span><span class="p">([</span><span class="n">params</span><span class="p">,</span> <span class="n">grads</span><span class="p">,</span> <span class="n">exp_avgs</span><span class="p">,</span> <span class="n">exp_avg_sqs</span><span class="p">,</span> <span class="n">max_exp_avg_sqs</span><span class="p">,</span> <span class="n">state_steps</span><span class="p">])</span>
@@ -997,16 +996,15 @@ <h1>Source code for torch.optim.adam</h1><div class="highlight"><pre>
             <span class="n">device_max_exp_avg_sqs</span><span class="p">,</span>
             <span class="n">device_state_steps</span><span class="p">,</span>
         <span class="p">)</span> <span class="o">=</span> <span class="n">grouped_tensors</span><span class="p">[(</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="p">)]</span>
-        <span class="k">if</span> <span class="n">grad_scale</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">found_inf</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">device_grad_scale</span><span class="p">,</span> <span class="n">device_found_inf</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span>
+        <span class="k">if</span> <span class="n">grad_scale</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="k">if</span> <span class="n">device</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">grad_scale_dict</span><span class="p">:</span>
                 <span class="n">grad_scale_dict</span><span class="p">[</span><span class="n">device</span><span class="p">]</span> <span class="o">=</span> <span class="n">grad_scale</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">,</span> <span class="n">non_blocking</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+            <span class="n">device_grad_scale</span> <span class="o">=</span> <span class="n">grad_scale_dict</span><span class="p">[</span><span class="n">device</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">found_inf</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="k">if</span> <span class="n">found_inf</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">found_inf_dict</span><span class="p">:</span>
                 <span class="n">found_inf_dict</span><span class="p">[</span><span class="n">device</span><span class="p">]</span> <span class="o">=</span> <span class="n">found_inf</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">,</span> <span class="n">non_blocking</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-            <span class="n">device_grad_scale</span> <span class="o">=</span> <span class="n">grad_scale_dict</span><span class="p">[</span><span class="n">device</span><span class="p">]</span>
             <span class="n">device_found_inf</span> <span class="o">=</span> <span class="n">found_inf_dict</span><span class="p">[</span><span class="n">device</span><span class="p">]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">device_grad_scale</span> <span class="o">=</span> <span class="kc">None</span>
-            <span class="n">device_found_inf</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="n">torch</span><span class="o">.</span><span class="n">_foreach_add_</span><span class="p">(</span><span class="n">device_state_steps</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
         <span class="n">torch</span><span class="o">.</span><span class="n">_fused_adam_</span><span class="p">(</span>
             <span class="n">device_params</span><span class="p">,</span>
diff --git a/docs/2.0/_modules/torch/optim/adamw.html b/docs/2.0/_modules/torch/optim/adamw.html
index 204bf9a06a32..5506ca655aa2 100644
--- a/docs/2.0/_modules/torch/optim/adamw.html
+++ b/docs/2.0/_modules/torch/optim/adamw.html
@@ -1057,16 +1057,15 @@ <h1>Source code for torch.optim.adamw</h1><div class="highlight"><pre>
             <span class="n">device_max_exp_avg_sqs</span><span class="p">,</span>
             <span class="n">device_state_steps</span><span class="p">,</span>
         <span class="p">)</span> <span class="o">=</span> <span class="n">grouped_tensors</span><span class="p">[(</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="p">)]</span>
-        <span class="k">if</span> <span class="n">grad_scale</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">found_inf</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">device_grad_scale</span><span class="p">,</span> <span class="n">device_found_inf</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span>
+        <span class="k">if</span> <span class="n">grad_scale</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="k">if</span> <span class="n">device</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">grad_scale_dict</span><span class="p">:</span>
                 <span class="n">grad_scale_dict</span><span class="p">[</span><span class="n">device</span><span class="p">]</span> <span class="o">=</span> <span class="n">grad_scale</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">,</span> <span class="n">non_blocking</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+            <span class="n">device_grad_scale</span> <span class="o">=</span> <span class="n">grad_scale_dict</span><span class="p">[</span><span class="n">device</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">found_inf</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="k">if</span> <span class="n">found_inf</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">found_inf_dict</span><span class="p">:</span>
                 <span class="n">found_inf_dict</span><span class="p">[</span><span class="n">device</span><span class="p">]</span> <span class="o">=</span> <span class="n">found_inf</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">,</span> <span class="n">non_blocking</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-            <span class="n">device_grad_scale</span> <span class="o">=</span> <span class="n">grad_scale_dict</span><span class="p">[</span><span class="n">device</span><span class="p">]</span>
             <span class="n">device_found_inf</span> <span class="o">=</span> <span class="n">found_inf_dict</span><span class="p">[</span><span class="n">device</span><span class="p">]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">device_grad_scale</span> <span class="o">=</span> <span class="kc">None</span>
-            <span class="n">device_found_inf</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="n">torch</span><span class="o">.</span><span class="n">_foreach_add_</span><span class="p">(</span><span class="n">device_state_steps</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
         <span class="n">torch</span><span class="o">.</span><span class="n">_fused_adamw_</span><span class="p">(</span>
             <span class="n">device_params</span><span class="p">,</span>
diff --git a/docs/2.0/_modules/torch/serialization.html b/docs/2.0/_modules/torch/serialization.html
index 828eefb3877e..37b75a1bf0a2 100644
--- a/docs/2.0/_modules/torch/serialization.html
+++ b/docs/2.0/_modules/torch/serialization.html
@@ -1533,7 +1533,7 @@ <h1>Source code for torch.serialization</h1><div class="highlight"><pre>
         <span class="k">def</span> <span class="nf">restore_location</span><span class="p">(</span><span class="n">storage</span><span class="p">,</span> <span class="n">location</span><span class="p">):</span>
             <span class="n">location</span> <span class="o">=</span> <span class="n">map_location</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">location</span><span class="p">,</span> <span class="n">location</span><span class="p">)</span>
             <span class="k">return</span> <span class="n">default_restore_location</span><span class="p">(</span><span class="n">storage</span><span class="p">,</span> <span class="n">location</span><span class="p">)</span>
-    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">map_location</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
+    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">map_location</span><span class="p">,</span> <span class="p">(</span><span class="nb">str</span><span class="p">,</span> <span class="nb">bytes</span><span class="p">)):</span>
         <span class="k">def</span> <span class="nf">restore_location</span><span class="p">(</span><span class="n">storage</span><span class="p">,</span> <span class="n">location</span><span class="p">):</span>
             <span class="k">return</span> <span class="n">default_restore_location</span><span class="p">(</span><span class="n">storage</span><span class="p">,</span> <span class="n">map_location</span><span class="p">)</span>
     <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">map_location</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">):</span>
diff --git a/docs/2.0/_modules/torch/storage.html b/docs/2.0/_modules/torch/storage.html
index 808613bfc3fe..9f1ee809d93e 100644
--- a/docs/2.0/_modules/torch/storage.html
+++ b/docs/2.0/_modules/torch/storage.html
@@ -761,14 +761,37 @@ <h1>Source code for torch.storage</h1><div class="highlight"><pre>
     <span class="k">else</span><span class="p">:</span>
         <span class="k">return</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="nb">int</span><span class="p">)</span>
 
+<span class="n">_always_warn_typed_storage_removal</span> <span class="o">=</span> <span class="kc">False</span>
+
+<span class="k">def</span> <span class="nf">_get_always_warn_typed_storage_removal</span><span class="p">():</span>
+    <span class="k">return</span> <span class="n">_always_warn_typed_storage_removal</span>
+
+<span class="k">def</span> <span class="nf">_set_always_warn_typed_storage_removal</span><span class="p">(</span><span class="n">always_warn</span><span class="p">):</span>
+    <span class="k">global</span> <span class="n">_always_warn_typed_storage_removal</span>
+    <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">always_warn</span><span class="p">,</span> <span class="nb">bool</span><span class="p">)</span>
+    <span class="n">_always_warn_typed_storage_removal</span> <span class="o">=</span> <span class="n">always_warn</span>
+
 <span class="k">def</span> <span class="nf">_warn_typed_storage_removal</span><span class="p">(</span><span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
-    <span class="n">message</span> <span class="o">=</span> <span class="p">(</span>
-        <span class="s2">&quot;TypedStorage is deprecated. It will be removed in the future and &quot;</span>
-        <span class="s2">&quot;UntypedStorage will be the only storage class. This should only matter &quot;</span>
-        <span class="s2">&quot;to you if you are using storages directly.  To access UntypedStorage &quot;</span>
-        <span class="s2">&quot;directly, use tensor.untyped_storage() instead of tensor.storage()&quot;</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="n">message</span><span class="p">,</span> <span class="ne">UserWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="n">stacklevel</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="k">global</span> <span class="n">_always_warn_typed_storage_removal</span>
+
+    <span class="k">def</span> <span class="nf">is_first_time</span><span class="p">():</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">_warn_typed_storage_removal</span><span class="p">,</span> <span class="s1">&#39;has_warned&#39;</span><span class="p">):</span>
+            <span class="k">return</span> <span class="kc">True</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="ow">not</span> <span class="n">_warn_typed_storage_removal</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s1">&#39;has_warned&#39;</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="n">_get_always_warn_typed_storage_removal</span><span class="p">()</span> <span class="ow">or</span> <span class="n">is_first_time</span><span class="p">():</span>
+        <span class="n">message</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="s2">&quot;TypedStorage is deprecated. It will be removed in the future and &quot;</span>
+            <span class="s2">&quot;UntypedStorage will be the only storage class. This should only matter &quot;</span>
+            <span class="s2">&quot;to you if you are using storages directly.  To access UntypedStorage &quot;</span>
+            <span class="s2">&quot;directly, use tensor.untyped_storage() instead of tensor.storage()&quot;</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="n">message</span><span class="p">,</span> <span class="ne">UserWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="n">stacklevel</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">_warn_typed_storage_removal</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s1">&#39;has_warned&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
+
+<span class="k">def</span> <span class="nf">_reset_warn_typed_storage_removal</span><span class="p">():</span>
+    <span class="n">_warn_typed_storage_removal</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s1">&#39;has_warned&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span>
 
 <div class="viewcode-block" id="TypedStorage"><a class="viewcode-back" href="../../storage.html#torch.TypedStorage">[docs]</a><span class="k">class</span> <span class="nc">TypedStorage</span><span class="p">:</span>
     <span class="n">is_sparse</span> <span class="o">=</span> <span class="kc">False</span>
diff --git a/docs/2.0/_modules/torch/utils/data/_utils/collate.html b/docs/2.0/_modules/torch/utils/data/_utils/collate.html
index 87f898a5af4e..e3907f7d8d88 100644
--- a/docs/2.0/_modules/torch/utils/data/_utils/collate.html
+++ b/docs/2.0/_modules/torch/utils/data/_utils/collate.html
@@ -524,7 +524,7 @@ <h1>Source code for torch.utils.data._utils.collate</h1><div class="highlight"><
         <span class="k">return</span> <span class="n">elem_type</span><span class="p">(</span><span class="o">*</span><span class="p">(</span><span class="n">default_convert</span><span class="p">(</span><span class="n">d</span><span class="p">)</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">data</span><span class="p">))</span>
     <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>
         <span class="k">return</span> <span class="p">[</span><span class="n">default_convert</span><span class="p">(</span><span class="n">d</span><span class="p">)</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">data</span><span class="p">]</span>  <span class="c1"># Backwards compatibility.</span>
-    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">collections</span><span class="o">.</span><span class="n">abc</span><span class="o">.</span><span class="n">Sequence</span><span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
+    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">collections</span><span class="o">.</span><span class="n">abc</span><span class="o">.</span><span class="n">Sequence</span><span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="p">(</span><span class="nb">str</span><span class="p">,</span> <span class="nb">bytes</span><span class="p">)):</span>
         <span class="k">try</span><span class="p">:</span>
             <span class="k">return</span> <span class="n">elem_type</span><span class="p">([</span><span class="n">default_convert</span><span class="p">(</span><span class="n">d</span><span class="p">)</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">data</span><span class="p">])</span>
         <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span>
@@ -653,6 +653,7 @@ <h1>Source code for torch.utils.data._utils.collate</h1><div class="highlight"><
 <span class="n">default_collate_fn_map</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="n">collate_float_fn</span>
 <span class="n">default_collate_fn_map</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="n">collate_int_fn</span>
 <span class="n">default_collate_fn_map</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">collate_str_fn</span>
+<span class="n">default_collate_fn_map</span><span class="p">[</span><span class="nb">bytes</span><span class="p">]</span> <span class="o">=</span> <span class="n">collate_str_fn</span>
 
 
 <div class="viewcode-block" id="default_collate"><a class="viewcode-back" href="../../../../../data.html#torch.utils.data._utils.collate.default_collate">[docs]</a><span class="k">def</span> <span class="nf">default_collate</span><span class="p">(</span><span class="n">batch</span><span class="p">):</span>
diff --git a/docs/2.0/_modules/torch/utils/data/dataloader.html b/docs/2.0/_modules/torch/utils/data/dataloader.html
index 8faf4e767bf3..a9ec570aff46 100644
--- a/docs/2.0/_modules/torch/utils/data/dataloader.html
+++ b/docs/2.0/_modules/torch/utils/data/dataloader.html
@@ -489,7 +489,6 @@ <h1>Source code for torch.utils.data.dataloader</h1><div class="highlight"><pre>
     <span class="n">Dataset</span><span class="p">,)</span>
 
 <span class="kn">from</span> <span class="nn">torch.utils.data.datapipes.datapipe</span> <span class="kn">import</span> <span class="n">_IterDataPipeSerializationWrapper</span><span class="p">,</span> <span class="n">_MapDataPipeSerializationWrapper</span>
-<span class="kn">from</span> <span class="nn">torch.utils.data.datapipes.iter.sharding</span> <span class="kn">import</span> <span class="n">SHARDING_PRIORITIES</span>
 
 <span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_utils</span>
 
@@ -559,6 +558,7 @@ <h1>Source code for torch.utils.data.dataloader</h1><div class="highlight"><pre>
 
 
 <span class="k">def</span> <span class="nf">_sharding_worker_init_fn</span><span class="p">(</span><span class="n">worker_init_fn</span><span class="p">,</span> <span class="n">world_size</span><span class="p">,</span> <span class="n">rank_id</span><span class="p">,</span> <span class="n">worker_id</span><span class="p">):</span>
+    <span class="n">global_worker_id</span> <span class="o">=</span> <span class="n">worker_id</span>
     <span class="n">info</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">get_worker_info</span><span class="p">()</span>
     <span class="k">assert</span> <span class="n">info</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
     <span class="n">total_workers</span> <span class="o">=</span> <span class="n">info</span><span class="o">.</span><span class="n">num_workers</span>
@@ -566,10 +566,10 @@ <h1>Source code for torch.utils.data.dataloader</h1><div class="highlight"><pre>
     <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">datapipe</span><span class="p">,</span> <span class="p">(</span><span class="n">IterDataPipe</span><span class="p">,</span> <span class="n">MapDataPipe</span><span class="p">))</span>
     <span class="c1"># To distribute elements across distributed process evenly, we should shard data on distributed</span>
     <span class="c1"># processes first then shard on worker processes</span>
-    <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">graph_settings</span><span class="o">.</span><span class="n">apply_sharding</span><span class="p">(</span>
-        <span class="n">datapipe</span><span class="p">,</span> <span class="n">world_size</span><span class="p">,</span> <span class="n">rank_id</span><span class="p">,</span> <span class="n">sharding_group</span><span class="o">=</span><span class="n">SHARDING_PRIORITIES</span><span class="o">.</span><span class="n">DISTRIBUTED</span><span class="p">)</span>
-    <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">graph_settings</span><span class="o">.</span><span class="n">apply_sharding</span><span class="p">(</span>
-        <span class="n">datapipe</span><span class="p">,</span> <span class="n">total_workers</span><span class="p">,</span> <span class="n">worker_id</span><span class="p">,</span> <span class="n">sharding_group</span><span class="o">=</span><span class="n">SHARDING_PRIORITIES</span><span class="o">.</span><span class="n">MULTIPROCESSING</span><span class="p">)</span>
+    <span class="n">total_workers</span> <span class="o">*=</span> <span class="n">world_size</span>
+    <span class="n">global_worker_id</span> <span class="o">=</span> <span class="n">global_worker_id</span> <span class="o">*</span> <span class="n">world_size</span> <span class="o">+</span> <span class="n">rank_id</span>
+    <span class="c1"># For BC, use default SHARDING_PRIORITIES</span>
+    <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">graph_settings</span><span class="o">.</span><span class="n">apply_sharding</span><span class="p">(</span><span class="n">datapipe</span><span class="p">,</span> <span class="n">total_workers</span><span class="p">,</span> <span class="n">global_worker_id</span><span class="p">)</span>
     <span class="k">if</span> <span class="n">worker_init_fn</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">worker_init_fn</span><span class="p">(</span><span class="n">worker_id</span><span class="p">)</span>
 
@@ -857,8 +857,7 @@ <h1>Source code for torch.utils.data.dataloader</h1><div class="highlight"><pre>
                             <span class="p">(</span><span class="s1">&#39;multiprocessing_context option &#39;</span>
                              <span class="s1">&#39;should specify a valid start method in </span><span class="si">{!r}</span><span class="s1">, but got &#39;</span>
                              <span class="s1">&#39;multiprocessing_context=</span><span class="si">{!r}</span><span class="s1">&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">valid_start_methods</span><span class="p">,</span> <span class="n">multiprocessing_context</span><span class="p">))</span>
-                    <span class="c1"># error: Argument 1 to &quot;get_context&quot; has incompatible type &quot;Union[str, bytes]&quot;; expected &quot;str&quot;  [arg-type]</span>
-                    <span class="n">multiprocessing_context</span> <span class="o">=</span> <span class="n">multiprocessing</span><span class="o">.</span><span class="n">get_context</span><span class="p">(</span><span class="n">multiprocessing_context</span><span class="p">)</span>  <span class="c1"># type: ignore[arg-type]</span>
+                    <span class="n">multiprocessing_context</span> <span class="o">=</span> <span class="n">multiprocessing</span><span class="o">.</span><span class="n">get_context</span><span class="p">(</span><span class="n">multiprocessing_context</span><span class="p">)</span>
 
                 <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">multiprocessing_context</span><span class="p">,</span> <span class="n">python_multiprocessing</span><span class="o">.</span><span class="n">context</span><span class="o">.</span><span class="n">BaseContext</span><span class="p">):</span>
                     <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">((</span><span class="s1">&#39;multiprocessing_context option should be a valid context &#39;</span>
@@ -1122,8 +1121,8 @@ <h1>Source code for torch.utils.data.dataloader</h1><div class="highlight"><pre>
         <span class="c1"># Adds forward compatibilities so classic DataLoader can work with DataPipes:</span>
         <span class="c1">#   Taking care of distributed sharding</span>
         <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_dataset</span><span class="p">,</span> <span class="p">(</span><span class="n">IterDataPipe</span><span class="p">,</span> <span class="n">MapDataPipe</span><span class="p">)):</span>
-            <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">graph_settings</span><span class="o">.</span><span class="n">apply_sharding</span><span class="p">(</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_dataset</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_world_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_rank</span><span class="p">,</span> <span class="n">sharding_group</span><span class="o">=</span><span class="n">SHARDING_PRIORITIES</span><span class="o">.</span><span class="n">DISTRIBUTED</span><span class="p">)</span>
+            <span class="c1"># For BC, use default SHARDING_PRIORITIES</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">graph_settings</span><span class="o">.</span><span class="n">apply_sharding</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_dataset</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_world_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_rank</span><span class="p">)</span>
 
         <span class="bp">self</span><span class="o">.</span><span class="n">_dataset_fetcher</span> <span class="o">=</span> <span class="n">_DatasetKind</span><span class="o">.</span><span class="n">create_fetcher</span><span class="p">(</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">_dataset_kind</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dataset</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_auto_collation</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_collate_fn</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_drop_last</span><span class="p">)</span>
diff --git a/docs/2.0/_sources/notes/extending.func.rst.txt b/docs/2.0/_sources/notes/extending.func.rst.txt
index b3a044f8c2f4..21603ba1becf 100644
--- a/docs/2.0/_sources/notes/extending.func.rst.txt
+++ b/docs/2.0/_sources/notes/extending.func.rst.txt
@@ -37,8 +37,13 @@ Only the latter is supported with function transforms:
   (by calling ``ctx.save_for_backward(*tensors)``), or save non-Tensors
   (by assigning them to the ``ctx`` object).
 
-Any intermediates that need to be saved must be returned as an output from
-:meth:`~Function.forward`.
+Because :meth:`~Function.setup_context` accepts only ``inputs`` and ``output``,
+the only quantities that can be saved are either objects (such as Tensors) in
+the inputs or outputs or quantities (like ``Tensor.shape``) derived from them.
+If you wish to save a non-input intermediate activation from
+:meth:`Function.forward` for backward, then you'll need to return it as an
+output from :meth:`~Function.forward` so that it gets passed to
+:meth:`~Function.setup_context`.
 
 Depending on the transform,
 
diff --git a/docs/2.0/distributed.html b/docs/2.0/distributed.html
index 52d99629322a..76e14e919f3e 100644
--- a/docs/2.0/distributed.html
+++ b/docs/2.0/distributed.html
@@ -800,7 +800,9 @@ <h2>Initialization<a class="headerlink" href="#initialization" title="Permalink
 <p>Support for multiple backends is experimental. Currently when no backend is
 specified, both <code class="docutils literal notranslate"><span class="pre">gloo</span></code> and <code class="docutils literal notranslate"><span class="pre">nccl</span></code> backends will be created. The <code class="docutils literal notranslate"><span class="pre">gloo</span></code> backend
 will be used for collectives with CPU tensors and the <code class="docutils literal notranslate"><span class="pre">nccl</span></code> backend will be used
-for collectives with CUDA tensors.</p>
+for collectives with CUDA tensors. A custom backend can be specified by passing in
+a string with format “&lt;device_type&gt;:&lt;backend_name&gt;,&lt;device_type&gt;:&lt;backend_name&gt;”, e.g.
+“cpu:gloo,cuda:custom_backend”.</p>
 </div>
 </dd></dl>
 
diff --git a/docs/2.0/generated/torch.nn.MultiheadAttention.html b/docs/2.0/generated/torch.nn.MultiheadAttention.html
index c398bca93d78..33e9c36bad23 100644
--- a/docs/2.0/generated/torch.nn.MultiheadAttention.html
+++ b/docs/2.0/generated/torch.nn.MultiheadAttention.html
@@ -469,9 +469,8 @@ <h1>MultiheadAttention<a class="headerlink" href="#multiheadattention" title="Pe
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mtext>MultiHead</mtext><mo stretchy="false">(</mo><mi>Q</mi><mo separator="true">,</mo><mi>K</mi><mo separator="true">,</mo><mi>V</mi><mo stretchy="false">)</mo><mo>=</mo><mtext>Concat</mtext><mo stretchy="false">(</mo><mi>h</mi><mi>e</mi><mi>a</mi><msub><mi>d</mi><mn>1</mn></msub><mo separator="true">,</mo><mo>…</mo><mo separator="true">,</mo><mi>h</mi><mi>e</mi><mi>a</mi><msub><mi>d</mi><mi>h</mi></msub><mo stretchy="false">)</mo><msup><mi>W</mi><mi>O</mi></msup></mrow><annotation encoding="application/x-tex">\text{MultiHead}(Q, K, V) = \text{Concat}(head_1,\dots,head_h)W^O
 
 </annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">MultiHead</span></span><span class="mopen">(</span><span class="mord mathnormal">Q</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">K</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.22222em;">V</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.1413em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">Concat</span></span><span class="mopen">(</span><span class="mord mathnormal">h</span><span class="mord mathnormal">e</span><span class="mord mathnormal">a</span><span class="mord"><span class="mord mathnormal">d</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">1</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="minner">…</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">h</span><span class="mord mathnormal">e</span><span class="mord mathnormal">a</span><span class="mord"><span class="mord mathnormal">d</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3361em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">h</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mclose">)</span><span class="mord"><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8913em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight" style="margin-right:0.02778em;">O</span></span></span></span></span></span></span></span></span></span></span></span></div><p>where <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>h</mi><mi>e</mi><mi>a</mi><msub><mi>d</mi><mi>i</mi></msub><mo>=</mo><mtext>Attention</mtext><mo stretchy="false">(</mo><mi>Q</mi><msubsup><mi>W</mi><mi>i</mi><mi>Q</mi></msubsup><mo separator="true">,</mo><mi>K</mi><msubsup><mi>W</mi><mi>i</mi><mi>K</mi></msubsup><mo separator="true">,</mo><mi>V</mi><msubsup><mi>W</mi><mi>i</mi><mi>V</mi></msubsup><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">head_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.8444em;vertical-align:-0.15em;"></span><span class="mord mathnormal">h</span><span class="mord mathnormal">e</span><span class="mord mathnormal">a</span><span class="mord"><span class="mord mathnormal">d</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3117em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">i</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.2361em;vertical-align:-0.2769em;"></span><span class="mord text"><span class="mord">Attention</span></span><span class="mopen">(</span><span class="mord mathnormal">Q</span><span class="mord"><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.9592em;"><span style="top:-2.4231em;margin-left:-0.1389em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">i</span></span></span><span style="top:-3.1809em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">Q</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.2769em;"><span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">K</span><span class="mord"><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.8413em;"><span style="top:-2.4413em;margin-left:-0.1389em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">i</span></span></span><span style="top:-3.063em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight" style="margin-right:0.07153em;">K</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.2587em;"><span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.22222em;">V</span><span class="mord"><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.8413em;"><span style="top:-2.4413em;margin-left:-0.1389em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">i</span></span></span><span style="top:-3.063em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight" style="margin-right:0.22222em;">V</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.2587em;"><span></span></span></span></span></span></span><span class="mclose">)</span></span></span></span></span>.</p>
-<p><code class="docutils literal notranslate"><span class="pre">forward()</span></code> will use the optimized implementation described in
-<a class="reference external" href="https://arxiv.org/abs/2205.14135">FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness</a> if all of the following
-conditions are met:</p>
+<p><code class="docutils literal notranslate"><span class="pre">nn.MultiHeadAttention</span></code> will use the optimized implementations of
+<code class="docutils literal notranslate"><span class="pre">scaled_dot_product_attention()</span></code> when possible.</p>
 <ul class="simple">
 <li><p>self attention is being computed (i.e., <code class="docutils literal notranslate"><span class="pre">query</span></code>, <code class="docutils literal notranslate"><span class="pre">key</span></code>, and <code class="docutils literal notranslate"><span class="pre">value</span></code> are the same tensor. This
 restriction will be loosened in the future.)</p></li>
@@ -549,8 +548,13 @@ <h1>MultiheadAttention<a class="headerlink" href="#multiheadattention" title="Pe
 corresponding position is not allowed to attend. For a float mask, the mask values will be added to
 the attention weight.
 If both attn_mask and key_padding_mask are supplied, their types should match.</p></li>
-<li><p><strong>is_causal</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><em>bool</em></a>) – If specified, applies a causal mask as attention mask. Mutually exclusive with providing attn_mask.
-Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
+<li><p><strong>is_causal</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><em>bool</em></a>) – If specified, applies a causal mask as attention mask.
+Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.
+Warning:
+<code class="docutils literal notranslate"><span class="pre">is_causal</span></code> provides a hint that <code class="docutils literal notranslate"><span class="pre">attn_mask</span></code> is the
+causal mask. Providing incorrect hints can result in
+incorrect execution, including forward and backward
+compatibility.</p></li>
 <li><p><strong>average_attn_weights</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><em>bool</em></a>) – If true, indicates that the returned <code class="docutils literal notranslate"><span class="pre">attn_weights</span></code> should be averaged across
 heads. Otherwise, <code class="docutils literal notranslate"><span class="pre">attn_weights</span></code> are provided separately per head. Note that this flag only has an
 effect when <code class="docutils literal notranslate"><span class="pre">need_weights=True</span></code>. Default: <code class="docutils literal notranslate"><span class="pre">True</span></code> (i.e. average weights across heads)</p></li>
diff --git a/docs/2.0/notes/extending.func.html b/docs/2.0/notes/extending.func.html
index 2aa252938bed..2d7fca29adb3 100644
--- a/docs/2.0/notes/extending.func.html
+++ b/docs/2.0/notes/extending.func.html
@@ -483,8 +483,13 @@ <h2>Basic Usage<a class="headerlink" href="#basic-usage" title="Permalink to thi
 (by calling <code class="docutils literal notranslate"><span class="pre">ctx.save_for_backward(*tensors)</span></code>), or save non-Tensors
 (by assigning them to the <code class="docutils literal notranslate"><span class="pre">ctx</span></code> object).</p></li>
 </ul>
-<p>Any intermediates that need to be saved must be returned as an output from
-<a class="reference internal" href="../generated/torch.autograd.Function.forward.html#torch.autograd.Function.forward" title="torch.autograd.Function.forward"><code class="xref py py-meth docutils literal notranslate"><span class="pre">forward()</span></code></a>.</p>
+<p>Because <code class="xref py py-meth docutils literal notranslate"><span class="pre">setup_context()</span></code> accepts only <code class="docutils literal notranslate"><span class="pre">inputs</span></code> and <code class="docutils literal notranslate"><span class="pre">output</span></code>,
+the only quantities that can be saved are either objects (such as Tensors) in
+the inputs or outputs or quantities (like <code class="docutils literal notranslate"><span class="pre">Tensor.shape</span></code>) derived from them.
+If you wish to save a non-input intermediate activation from
+<a class="reference internal" href="../generated/torch.autograd.Function.forward.html#torch.autograd.Function.forward" title="torch.autograd.Function.forward"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Function.forward()</span></code></a> for backward, then you’ll need to return it as an
+output from <a class="reference internal" href="../generated/torch.autograd.Function.forward.html#torch.autograd.Function.forward" title="torch.autograd.Function.forward"><code class="xref py py-meth docutils literal notranslate"><span class="pre">forward()</span></code></a> so that it gets passed to
+<code class="xref py py-meth docutils literal notranslate"><span class="pre">setup_context()</span></code>.</p>
 <p>Depending on the transform,</p>
 <ul class="simple">
 <li><p>to support reverse-mode AD (<a class="reference internal" href="../generated/torch.func.grad.html#torch.func.grad" title="torch.func.grad"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.func.grad()</span></code></a>, <a class="reference internal" href="../generated/torch.func.vjp.html#torch.func.vjp" title="torch.func.vjp"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.func.vjp()</span></code></a>),
diff --git a/docs/2.0/quantization-backend-configuration.html b/docs/2.0/quantization-backend-configuration.html
index 26a7d09ff41f..4265b51b7c55 100644
--- a/docs/2.0/quantization-backend-configuration.html
+++ b/docs/2.0/quantization-backend-configuration.html
@@ -479,7 +479,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method adaptive_avg_pool1d of type object at 0x7fb9d1c71f60&gt;,
+  &#39;pattern&#39;: &lt;built-in method adaptive_avg_pool1d of type object at 0x7f6a857c8f60&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -499,7 +499,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function adaptive_avg_pool2d at 0x7fb97fc99790&gt;,
+  &#39;pattern&#39;: &lt;function adaptive_avg_pool2d at 0x7f6a337ec8b0&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -519,7 +519,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function adaptive_avg_pool3d at 0x7fb97fc99820&gt;,
+  &#39;pattern&#39;: &lt;function adaptive_avg_pool3d at 0x7f6a337ec940&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -544,7 +544,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method add of type object at 0x7fb9d1c71f60&gt;,
+  &#39;pattern&#39;: &lt;built-in method add of type object at 0x7f6a857c8f60&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -569,7 +569,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method avg_pool1d of type object at 0x7fb9d1c71f60&gt;,
+  &#39;pattern&#39;: &lt;built-in method avg_pool1d of type object at 0x7f6a857c8f60&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -630,7 +630,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvBn1d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function fuse_conv_bn at 0x7fb97ee14310&gt;,
+  &#39;fuser_method&#39;: &lt;function fuse_conv_bn at 0x7f6a3296a550&gt;,
 },
 {
   &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.ConvTranspose1d&#39;&gt;, &lt;class &#39;torch.nn.modules.batchnorm.BatchNorm1d&#39;&gt;),
@@ -645,7 +645,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;root_module&#39;: &lt;class &#39;torch.nn.modules.conv.ConvTranspose1d&#39;&gt;,
   &#39;reference_quantized_module_for_root&#39;: &lt;class &#39;torch.ao.nn.quantized.reference.modules.conv.ConvTranspose1d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function fuse_convtranspose_bn at 0x7fb97ee144c0&gt;,
+  &#39;fuser_method&#39;: &lt;function fuse_convtranspose_bn at 0x7f6a3296a700&gt;,
 },
 {
   &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.linear.Linear&#39;&gt;, &lt;class &#39;torch.nn.modules.batchnorm.BatchNorm1d&#39;&gt;),
@@ -673,7 +673,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.LinearBn1d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function fuse_linear_bn at 0x7fb97ee14430&gt;,
+  &#39;fuser_method&#39;: &lt;function fuse_linear_bn at 0x7f6a3296a670&gt;,
 },
 {
   &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv2d&#39;&gt;, &lt;class &#39;torch.nn.modules.batchnorm.BatchNorm2d&#39;&gt;),
@@ -687,7 +687,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvBn2d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function fuse_conv_bn at 0x7fb97ee14310&gt;,
+  &#39;fuser_method&#39;: &lt;function fuse_conv_bn at 0x7f6a3296a550&gt;,
 },
 {
   &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.ConvTranspose2d&#39;&gt;, &lt;class &#39;torch.nn.modules.batchnorm.BatchNorm2d&#39;&gt;),
@@ -702,7 +702,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;root_module&#39;: &lt;class &#39;torch.nn.modules.conv.ConvTranspose2d&#39;&gt;,
   &#39;reference_quantized_module_for_root&#39;: &lt;class &#39;torch.ao.nn.quantized.reference.modules.conv.ConvTranspose2d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function fuse_convtranspose_bn at 0x7fb97ee144c0&gt;,
+  &#39;fuser_method&#39;: &lt;function fuse_convtranspose_bn at 0x7f6a3296a700&gt;,
 },
 {
   &#39;pattern&#39;: &lt;class &#39;torch.nn.modules.batchnorm.BatchNorm2d&#39;&gt;,
@@ -726,7 +726,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvBn3d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function fuse_conv_bn at 0x7fb97ee14310&gt;,
+  &#39;fuser_method&#39;: &lt;function fuse_conv_bn at 0x7f6a3296a550&gt;,
 },
 {
   &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.ConvTranspose3d&#39;&gt;, &lt;class &#39;torch.nn.modules.batchnorm.BatchNorm3d&#39;&gt;),
@@ -741,7 +741,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;root_module&#39;: &lt;class &#39;torch.nn.modules.conv.ConvTranspose3d&#39;&gt;,
   &#39;reference_quantized_module_for_root&#39;: &lt;class &#39;torch.ao.nn.quantized.reference.modules.conv.ConvTranspose3d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function fuse_convtranspose_bn at 0x7fb97ee144c0&gt;,
+  &#39;fuser_method&#39;: &lt;function fuse_convtranspose_bn at 0x7f6a3296a700&gt;,
 },
 {
   &#39;pattern&#39;: &lt;class &#39;torch.nn.modules.batchnorm.BatchNorm3d&#39;&gt;,
@@ -774,7 +774,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method cat of type object at 0x7fb9d1c71f60&gt;,
+  &#39;pattern&#39;: &lt;built-in method cat of type object at 0x7f6a857c8f60&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -784,7 +784,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method clamp of type object at 0x7fb9d1c71f60&gt;,
+  &#39;pattern&#39;: &lt;built-in method clamp of type object at 0x7f6a857c8f60&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -843,7 +843,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;reference_quantized_module_for_root&#39;: &lt;class &#39;torch.ao.nn.quantized.reference.modules.conv.Conv1d&#39;&gt;,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method conv1d of type object at 0x7fb9d1c71f60&gt;,
+  &#39;pattern&#39;: &lt;built-in method conv1d of type object at 0x7f6a857c8f60&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -885,7 +885,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;reference_quantized_module_for_root&#39;: &lt;class &#39;torch.ao.nn.quantized.reference.modules.conv.Conv2d&#39;&gt;,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method conv2d of type object at 0x7fb9d1c71f60&gt;,
+  &#39;pattern&#39;: &lt;built-in method conv2d of type object at 0x7f6a857c8f60&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -927,7 +927,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;reference_quantized_module_for_root&#39;: &lt;class &#39;torch.ao.nn.quantized.reference.modules.conv.Conv3d&#39;&gt;,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method conv3d of type object at 0x7fb9d1c71f60&gt;,
+  &#39;pattern&#39;: &lt;built-in method conv3d of type object at 0x7f6a857c8f60&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1255,7 +1255,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function dropout at 0x7fb97fc998b0&gt;,
+  &#39;pattern&#39;: &lt;function dropout at 0x7f6a337ec9d0&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1275,7 +1275,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function elu at 0x7fb97fc99ee0&gt;,
+  &#39;pattern&#39;: &lt;function elu at 0x7f6a337f0040&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1359,7 +1359,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;reference_quantized_module_for_root&#39;: &lt;class &#39;torch.ao.nn.quantized.reference.modules.sparse.EmbeddingBag&#39;&gt;,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method flatten of type object at 0x7fb9d1c71f60&gt;,
+  &#39;pattern&#39;: &lt;built-in method flatten of type object at 0x7f6a857c8f60&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1379,7 +1379,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function group_norm at 0x7fb97fc9cdc0&gt;,
+  &#39;pattern&#39;: &lt;function group_norm at 0x7f6a337f0ee0&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1444,7 +1444,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function hardsigmoid at 0x7fb97fc9c700&gt;,
+  &#39;pattern&#39;: &lt;function hardsigmoid at 0x7f6a337f0820&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=0.00390625, zero_point_exact_match=0),
@@ -1484,7 +1484,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function hardswish at 0x7fb97fc9c8b0&gt;,
+  &#39;pattern&#39;: &lt;function hardswish at 0x7f6a337f09d0&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1504,7 +1504,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function hardtanh at 0x7fb97fc99dc0&gt;,
+  &#39;pattern&#39;: &lt;function hardtanh at 0x7f6a337ecee0&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1534,7 +1534,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function instance_norm at 0x7fb97fc9cca0&gt;,
+  &#39;pattern&#39;: &lt;function instance_norm at 0x7f6a337f0dc0&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1575,7 +1575,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function interpolate at 0x7fb97fc390d0&gt;,
+  &#39;pattern&#39;: &lt;function interpolate at 0x7f6a33787280&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1597,7 +1597,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function layer_norm at 0x7fb97fc9cd30&gt;,
+  &#39;pattern&#39;: &lt;function layer_norm at 0x7f6a337f0e50&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1620,7 +1620,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function leaky_relu at 0x7fb97fc9c0d0&gt;,
+  &#39;pattern&#39;: &lt;function leaky_relu at 0x7f6a337f01f0&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1872,7 +1872,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;reference_quantized_module_for_root&#39;: &lt;class &#39;torch.ao.nn.quantized.reference.modules.rnn.LSTMCell&#39;&gt;,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method matmul of type object at 0x7fb9d1c71f60&gt;,
+  &#39;pattern&#39;: &lt;built-in method matmul of type object at 0x7f6a857c8f60&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1942,7 +1942,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method mean of type object at 0x7fb9d1c71f60&gt;,
+  &#39;pattern&#39;: &lt;built-in method mean of type object at 0x7f6a857c8f60&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1977,7 +1977,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method mul of type object at 0x7fb9d1c71f60&gt;,
+  &#39;pattern&#39;: &lt;built-in method mul of type object at 0x7f6a857c8f60&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2023,10 +2023,10 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvReLU1d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7fb9d8d79430&gt;,
+  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7f6a8c8d0430&gt;,
 },
 {
-  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv1d&#39;&gt;, &lt;function relu at 0x7fb97fc99ca0&gt;),
+  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv1d&#39;&gt;, &lt;function relu at 0x7f6a337ecdc0&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2037,10 +2037,10 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvReLU1d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7fb97d2631f0&gt;,
+  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7f6a30dbb430&gt;,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in method conv1d of type object at 0x7fb9d1c71f60&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
+  &#39;pattern&#39;: (&lt;built-in method conv1d of type object at 0x7f6a857c8f60&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2052,7 +2052,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in method conv1d of type object at 0x7fb9d1c71f60&gt;, &lt;function relu at 0x7fb97fc99ca0&gt;),
+  &#39;pattern&#39;: (&lt;built-in method conv1d of type object at 0x7f6a857c8f60&gt;, &lt;function relu at 0x7f6a337ecdc0&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2075,10 +2075,10 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvBnReLU1d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function fuse_conv_bn_relu at 0x7fb97ee143a0&gt;,
+  &#39;fuser_method&#39;: &lt;function fuse_conv_bn_relu at 0x7f6a3296a5e0&gt;,
 },
 {
-  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv1d&#39;&gt;, &lt;class &#39;torch.nn.modules.batchnorm.BatchNorm1d&#39;&gt;, &lt;function relu at 0x7fb97fc99ca0&gt;),
+  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv1d&#39;&gt;, &lt;class &#39;torch.nn.modules.batchnorm.BatchNorm1d&#39;&gt;, &lt;function relu at 0x7f6a337ecdc0&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2090,7 +2090,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;root_module&#39;: &lt;class &#39;torch.nn.modules.conv.Conv1d&#39;&gt;,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvBnReLU1d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function fuse_conv_bn_relu at 0x7fb97ee143a0&gt;,
+  &#39;fuser_method&#39;: &lt;function fuse_conv_bn_relu at 0x7f6a3296a5e0&gt;,
 },
 {
   &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv2d&#39;&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
@@ -2104,10 +2104,10 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvReLU2d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7fb97d263280&gt;,
+  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7f6a30dbb4c0&gt;,
 },
 {
-  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv2d&#39;&gt;, &lt;function relu at 0x7fb97fc99ca0&gt;),
+  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv2d&#39;&gt;, &lt;function relu at 0x7f6a337ecdc0&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2118,10 +2118,10 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvReLU2d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7fb97d263310&gt;,
+  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7f6a30dbb550&gt;,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in method conv2d of type object at 0x7fb9d1c71f60&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
+  &#39;pattern&#39;: (&lt;built-in method conv2d of type object at 0x7f6a857c8f60&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2133,7 +2133,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in method conv2d of type object at 0x7fb9d1c71f60&gt;, &lt;function relu at 0x7fb97fc99ca0&gt;),
+  &#39;pattern&#39;: (&lt;built-in method conv2d of type object at 0x7f6a857c8f60&gt;, &lt;function relu at 0x7f6a337ecdc0&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2156,10 +2156,10 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvBnReLU2d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function fuse_conv_bn_relu at 0x7fb97ee143a0&gt;,
+  &#39;fuser_method&#39;: &lt;function fuse_conv_bn_relu at 0x7f6a3296a5e0&gt;,
 },
 {
-  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv2d&#39;&gt;, &lt;class &#39;torch.nn.modules.batchnorm.BatchNorm2d&#39;&gt;, &lt;function relu at 0x7fb97fc99ca0&gt;),
+  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv2d&#39;&gt;, &lt;class &#39;torch.nn.modules.batchnorm.BatchNorm2d&#39;&gt;, &lt;function relu at 0x7f6a337ecdc0&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2171,7 +2171,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;root_module&#39;: &lt;class &#39;torch.nn.modules.conv.Conv2d&#39;&gt;,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvBnReLU2d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function fuse_conv_bn_relu at 0x7fb97ee143a0&gt;,
+  &#39;fuser_method&#39;: &lt;function fuse_conv_bn_relu at 0x7f6a3296a5e0&gt;,
 },
 {
   &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv3d&#39;&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
@@ -2185,10 +2185,10 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvReLU3d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7fb97d2633a0&gt;,
+  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7f6a30dbb5e0&gt;,
 },
 {
-  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv3d&#39;&gt;, &lt;function relu at 0x7fb97fc99ca0&gt;),
+  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv3d&#39;&gt;, &lt;function relu at 0x7f6a337ecdc0&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2199,10 +2199,10 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvReLU3d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7fb97d263430&gt;,
+  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7f6a30dbb670&gt;,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in method conv3d of type object at 0x7fb9d1c71f60&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
+  &#39;pattern&#39;: (&lt;built-in method conv3d of type object at 0x7f6a857c8f60&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2214,7 +2214,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in method conv3d of type object at 0x7fb9d1c71f60&gt;, &lt;function relu at 0x7fb97fc99ca0&gt;),
+  &#39;pattern&#39;: (&lt;built-in method conv3d of type object at 0x7f6a857c8f60&gt;, &lt;function relu at 0x7f6a337ecdc0&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2237,10 +2237,10 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvBnReLU3d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function fuse_conv_bn_relu at 0x7fb97ee143a0&gt;,
+  &#39;fuser_method&#39;: &lt;function fuse_conv_bn_relu at 0x7f6a3296a5e0&gt;,
 },
 {
-  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv3d&#39;&gt;, &lt;class &#39;torch.nn.modules.batchnorm.BatchNorm3d&#39;&gt;, &lt;function relu at 0x7fb97fc99ca0&gt;),
+  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv3d&#39;&gt;, &lt;class &#39;torch.nn.modules.batchnorm.BatchNorm3d&#39;&gt;, &lt;function relu at 0x7f6a337ecdc0&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2252,7 +2252,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;root_module&#39;: &lt;class &#39;torch.nn.modules.conv.Conv3d&#39;&gt;,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvBnReLU3d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function fuse_conv_bn_relu at 0x7fb97ee143a0&gt;,
+  &#39;fuser_method&#39;: &lt;function fuse_conv_bn_relu at 0x7f6a3296a5e0&gt;,
 },
 {
   &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.linear.Linear&#39;&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
@@ -2280,10 +2280,10 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.LinearReLU&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7fb97d2634c0&gt;,
+  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7f6a30dbb700&gt;,
 },
 {
-  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.linear.Linear&#39;&gt;, &lt;function relu at 0x7fb97fc99ca0&gt;),
+  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.linear.Linear&#39;&gt;, &lt;function relu at 0x7f6a337ecdc0&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2308,7 +2308,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.LinearReLU&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7fb97d263550&gt;,
+  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7f6a30dbb790&gt;,
 },
 {
   &#39;pattern&#39;: (&lt;built-in function linear&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
@@ -2337,7 +2337,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in function linear&gt;, &lt;function relu at 0x7fb97fc99ca0&gt;),
+  &#39;pattern&#39;: (&lt;built-in function linear&gt;, &lt;function relu at 0x7f6a337ecdc0&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2378,7 +2378,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in function add&gt;, &lt;function relu at 0x7fb97fc99ca0&gt;),
+  &#39;pattern&#39;: (&lt;built-in function add&gt;, &lt;function relu at 0x7f6a337ecdc0&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2393,7 +2393,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in function add&gt;, &lt;built-in method relu of type object at 0x7fb9d1c71f60&gt;),
+  &#39;pattern&#39;: (&lt;built-in function add&gt;, &lt;built-in method relu of type object at 0x7f6a857c8f60&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2408,7 +2408,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in method add of type object at 0x7fb9d1c71f60&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
+  &#39;pattern&#39;: (&lt;built-in method add of type object at 0x7f6a857c8f60&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2423,7 +2423,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in method add of type object at 0x7fb9d1c71f60&gt;, &lt;function relu at 0x7fb97fc99ca0&gt;),
+  &#39;pattern&#39;: (&lt;built-in method add of type object at 0x7f6a857c8f60&gt;, &lt;function relu at 0x7f6a337ecdc0&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2438,7 +2438,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in method add of type object at 0x7fb9d1c71f60&gt;, &lt;built-in method relu of type object at 0x7fb9d1c71f60&gt;),
+  &#39;pattern&#39;: (&lt;built-in method add of type object at 0x7f6a857c8f60&gt;, &lt;built-in method relu of type object at 0x7f6a857c8f60&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2468,7 +2468,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in function mul&gt;, &lt;function relu at 0x7fb97fc99ca0&gt;),
+  &#39;pattern&#39;: (&lt;built-in function mul&gt;, &lt;function relu at 0x7f6a337ecdc0&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2483,7 +2483,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in function mul&gt;, &lt;built-in method relu of type object at 0x7fb9d1c71f60&gt;),
+  &#39;pattern&#39;: (&lt;built-in function mul&gt;, &lt;built-in method relu of type object at 0x7f6a857c8f60&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2498,7 +2498,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in method mul of type object at 0x7fb9d1c71f60&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
+  &#39;pattern&#39;: (&lt;built-in method mul of type object at 0x7f6a857c8f60&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2513,7 +2513,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in method mul of type object at 0x7fb9d1c71f60&gt;, &lt;function relu at 0x7fb97fc99ca0&gt;),
+  &#39;pattern&#39;: (&lt;built-in method mul of type object at 0x7f6a857c8f60&gt;, &lt;function relu at 0x7f6a337ecdc0&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2528,7 +2528,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in method mul of type object at 0x7fb9d1c71f60&gt;, &lt;built-in method relu of type object at 0x7fb9d1c71f60&gt;),
+  &#39;pattern&#39;: (&lt;built-in method mul of type object at 0x7f6a857c8f60&gt;, &lt;built-in method relu of type object at 0x7f6a857c8f60&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2553,7 +2553,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function relu at 0x7fb97fc99ca0&gt;,
+  &#39;pattern&#39;: &lt;function relu at 0x7f6a337ecdc0&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2592,10 +2592,10 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.BNReLU2d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7fb97d2635e0&gt;,
+  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7f6a30dbb820&gt;,
 },
 {
-  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.batchnorm.BatchNorm2d&#39;&gt;, &lt;function relu at 0x7fb97fc99ca0&gt;),
+  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.batchnorm.BatchNorm2d&#39;&gt;, &lt;function relu at 0x7f6a337ecdc0&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2604,7 +2604,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.BNReLU2d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7fb97d263670&gt;,
+  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7f6a30dbb8b0&gt;,
 },
 {
   &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.batchnorm.BatchNorm3d&#39;&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
@@ -2616,10 +2616,10 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.BNReLU3d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7fb97d263700&gt;,
+  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7f6a30dbb940&gt;,
 },
 {
-  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.batchnorm.BatchNorm3d&#39;&gt;, &lt;function relu at 0x7fb97fc99ca0&gt;),
+  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.batchnorm.BatchNorm3d&#39;&gt;, &lt;function relu at 0x7f6a337ecdc0&gt;),
   &#39;dtype_configs&#39;: [
     {
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@@ -2628,7 +2628,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.BNReLU3d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7fb97d263790&gt;,
+  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7f6a30dbb9d0&gt;,
 },
 {
   &#39;pattern&#39;: &lt;class &#39;torch.nn.modules.activation.ReLU6&#39;&gt;,
@@ -2641,7 +2641,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function relu6 at 0x7fb97fc99e50&gt;,
+  &#39;pattern&#39;: &lt;function relu6 at 0x7f6a337ecf70&gt;,
   &#39;dtype_configs&#39;: [
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@@ -2661,7 +2661,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method repeat_interleave of type object at 0x7fb9d1c71f60&gt;,
+  &#39;pattern&#39;: &lt;built-in method repeat_interleave of type object at 0x7f6a857c8f60&gt;,
   &#39;dtype_configs&#39;: [
     {
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@@ -2743,7 +2743,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method sigmoid of type object at 0x7fb9d1c71f60&gt;,
+  &#39;pattern&#39;: &lt;built-in method sigmoid of type object at 0x7f6a857c8f60&gt;,
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     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=0.00390625, zero_point_exact_match=0),
@@ -2793,7 +2793,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method squeeze of type object at 0x7fb9d1c71f60&gt;,
+  &#39;pattern&#39;: &lt;built-in method squeeze of type object at 0x7f6a857c8f60&gt;,
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     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2823,7 +2823,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method stack of type object at 0x7fb9d1c71f60&gt;,
+  &#39;pattern&#39;: &lt;built-in method stack of type object at 0x7f6a857c8f60&gt;,
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     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2843,7 +2843,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method tanh of type object at 0x7fb9d1c71f60&gt;,
+  &#39;pattern&#39;: &lt;built-in method tanh of type object at 0x7f6a857c8f60&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=0.0078125, zero_point_exact_match=128),
@@ -2873,7 +2873,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method transpose of type object at 0x7fb9d1c71f60&gt;,
+  &#39;pattern&#39;: &lt;built-in method transpose of type object at 0x7f6a857c8f60&gt;,
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     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2893,7 +2893,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method unsqueeze of type object at 0x7fb9d1c71f60&gt;,
+  &#39;pattern&#39;: &lt;built-in method unsqueeze of type object at 0x7f6a857c8f60&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
diff --git a/docs/2.0/searchindex.js b/docs/2.0/searchindex.js
index f8ded58ed8a0..b34e897fea1f 100644
--- a/docs/2.0/searchindex.js
+++ b/docs/2.0/searchindex.js
@@ -1 +1 @@
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1396, 1397, 1398, 1452, 1762, 1846, 1851, 1854, 1855, 1881, 1883], "purpos": [21, 23, 31, 34, 35, 38, 61, 455, 482, 889, 1180, 1265, 1323, 1406, 1571, 1572, 1573, 1725, 1841, 1846, 1851, 1873, 1882], "unaffect": [21, 482, 483, 1354], "ddp_model": [21, 23, 1574, 1855], "fp16_compress_hook": 21, "compress": [21, 53, 196, 569, 570, 1066, 1067, 1076, 1077, 1574, 1759, 1760, 1761, 1763, 1764, 1797, 1827, 1870], "decompress": [21, 1826, 1840], "bf16_compress_hook": 21, "nccl": [21, 24, 25, 49, 53, 1574, 1860], "brain": [21, 1888, 1891], "wrapper": [21, 23, 28, 29, 53, 60, 61, 606, 738, 771, 805, 948, 950, 951, 953, 1018, 1020, 1090, 1091, 1110, 1171, 1337, 1829, 1831, 1832, 1841, 1843, 1854, 1855, 1869, 1874], "fp16_compress_wrapp": 21, "powersgdst": 21, "matrix_approximation_rank": 21, "start_powersgd_it": 21, "powersgd_hook": 21, "bf16_compress_wrapp": 21, "wikipedia": [21, 1534, 1851, 1859, 1897], "wiki": [21, 1897], "bfloat16_float": 21, "point_format": 21, "vogel": 21, "et": [21, 29, 53, 1323, 1412, 1413, 1448, 1449, 1646, 1741, 1778, 1849], "al": [21, 29, 53, 1323, 1412, 1413, 1448, 1449, 1646, 1741, 1778, 1849], "neurip": [21, 29], "2019": [21, 29, 1003], "bandwidth": [21, 23, 34, 47, 49, 1876, 1881], "hyperparamet": [21, 61, 1890], "1000": [21, 1059, 1064, 1090, 1091, 1308, 1343, 1449, 1678, 1705, 1725, 1791, 1851, 1867, 1869, 1890], "min_compression_r": 21, "use_error_feedback": 21, "warm_start": 21, "orthogonalization_epsilon": 21, "random_se": 21, "compression_stats_logging_frequ": 21, "batch_tensors_with_same_shap": 21, "tune": [21, 23, 27, 33, 38, 1003, 1630, 1631, 1632, 1633, 1634, 1635, 1636, 1637, 1638, 1643, 1644, 1645, 1646, 1647, 1851, 1854, 1863, 1876], "stronger": 21, "threshold": [21, 61, 1225, 1228, 1361, 1431, 1436, 1555, 1561, 1660, 1725, 1830, 1850, 1854, 1871, 1890], "exponenti": [21, 245, 1054, 1222, 1248, 1249, 1250, 1258, 1322, 1342, 1486, 1827, 1832, 1871, 1872, 1886, 1893], "grid": [21, 1064, 1272, 1462, 1497, 1828, 1830, 1854, 1890], "satisfactori": 21, "nlp": [21, 33, 1363, 1364, 1365, 1372], "appendix": [21, 1827], "defer": [21, 53, 1854, 1874], "hybrid": [21, 53, 206, 525, 565, 566, 567, 569, 570, 1135], "scheme": [21, 48, 462, 783, 800, 801, 802, 803, 806, 808, 1862], "sensit": [21, 1361, 1431, 1869, 1873, 1882], "suboptim": [21, 34], "trajectori": 21, "irrecover": 21, "impact": [21, 23, 33, 38, 1003, 1720, 1839, 1841, 1851, 1876, 1889], "warm": [21, 27, 33, 992, 1650, 1651, 1854, 1875], "num_row": 21, "num_col": 21, "1e": [21, 61, 101, 331, 675, 693, 694, 695, 696, 697, 698, 704, 705, 717, 718, 730, 732, 733, 734, 735, 736, 889, 890, 925, 926, 1160, 1186, 1187, 1237, 1318, 1319, 1320, 1335, 1354, 1355, 1363, 1364, 1365, 1372, 1373, 1374, 1375, 1382, 1383, 1384, 1386, 1409, 1414, 1439, 1443, 1445, 1447, 1448, 1467, 1479, 1494, 1498, 1499, 1507, 1511, 1533, 1536, 1540, 1563, 1582, 1615, 1629, 1630, 1631, 1632, 1633, 1634, 1635, 1636, 1637, 1643, 1644, 1645, 1647, 1660, 1668, 1721, 1829, 1830, 1856, 1862, 1869, 1872, 1886, 1892], "orthogon": [21, 1207, 1210, 1229, 1234, 1243, 1664, 1676, 1849, 1851, 1862, 1885], "div": [21, 223, 1040, 1089, 1092, 1308, 1412, 1413, 1696, 1805, 1828, 1830, 1836, 1844, 1871, 1885, 1888], "0s": [21, 1630, 1631, 1632, 1633, 1634, 1635, 1636, 1637, 1642, 1643, 1644, 1645, 1646, 1647], "batch": [21, 23, 29, 33, 38, 49, 51, 54, 55, 58, 59, 61, 565, 566, 567, 569, 570, 667, 683, 684, 685, 686, 687, 688, 716, 741, 760, 766, 888, 889, 890, 902, 914, 915, 922, 925, 926, 927, 947, 1032, 1034, 1044, 1105, 1106, 1112, 1200, 1201, 1202, 1203, 1204, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1238, 1239, 1240, 1243, 1251, 1260, 1261, 1265, 1316, 1317, 1318, 1319, 1320, 1323, 1328, 1329, 1334, 1336, 1337, 1339, 1340, 1341, 1343, 1344, 1345, 1347, 1352, 1353, 1354, 1355, 1360, 1361, 1363, 1364, 1365, 1366, 1367, 1370, 1371, 1372, 1373, 1374, 1375, 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21, "apex": 21, "uncompress": [21, 1885], "p": [21, 25, 29, 53, 61, 142, 221, 273, 410, 411, 437, 474, 475, 590, 667, 670, 902, 904, 914, 922, 1038, 1191, 1202, 1208, 1209, 1217, 1218, 1260, 1262, 1265, 1275, 1293, 1312, 1338, 1339, 1340, 1341, 1343, 1344, 1345, 1366, 1368, 1369, 1405, 1409, 1411, 1448, 1449, 1463, 1482, 1483, 1484, 1485, 1488, 1489, 1490, 1518, 1519, 1529, 1533, 1536, 1537, 1563, 1578, 1584, 1586, 1592, 1601, 1621, 1646, 1697, 1754, 1828, 1830, 1831, 1850, 1851, 1854, 1859, 1864, 1875, 1885, 1886], "pq": 21, "ps": [21, 1858], "mq": [21, 1876, 1895], "qs": 21, "tp": [21, 28], "speedup": [21, 34, 35, 1406, 1447], "awai": [21, 61, 1497, 1827, 1845, 1851, 1857], "config": [21, 32, 33, 34, 35, 38, 53, 61, 773, 774, 775, 776, 840, 842, 1855, 1868, 1879], "comm": [21, 23, 1855], "handler": [21, 23, 41, 42, 1593, 1841, 1861, 1873, 1896], "batched_powersgd_hook": 21, "destroi": [21, 48, 1337, 1851, 1881], "squar": [21, 29, 533, 720, 721, 722, 723, 760, 766, 775, 946, 1031, 1033, 1168, 1202, 1204, 1206, 1207, 1208, 1211, 1212, 1216, 1218, 1220, 1222, 1224, 1228, 1230, 1231, 1233, 1234, 1238, 1251, 1260, 1306, 1310, 1314, 1315, 1329, 1330, 1332, 1333, 1348, 1349, 1361, 1369, 1391, 1394, 1395, 1398, 1431, 1443, 1464, 1473, 1476, 1492, 1493, 1497, 1506, 1508, 1528, 1551, 1566, 1581, 1631, 1633, 1634, 1635, 1637, 1643, 1644, 1647, 1707, 1766, 1800, 1813, 1830, 1871, 1885], "truncat": [21, 1089, 1806, 1846, 1849, 1858, 1865], "impli": [21, 48, 1843, 1851, 1869, 1874, 1879, 1881], "debugging_hook": 21, "noop_hook": 21, "noop": [21, 1574], "headroom": 21, "desynchron": [21, 23], "trainer": [21, 39, 41, 46, 49, 50, 1574, 1881], "restart": [21, 32, 39, 47, 49, 51, 1650, 1651, 1843, 1890], "__setstate__": 21, "__getstate__": 21, "reload": [21, 27, 1826], "os": [21, 23, 24, 38, 49, 50, 51, 61, 1242, 1574, 1708, 1826, 1843, 1851, 1854, 1855, 1868, 1873, 1874, 1881], "sy": [21, 40, 49, 51, 1826, 1854, 1873], "tempfil": 21, "simplemodel": 21, "24": [21, 23, 29, 32, 907, 1229, 1388, 1462, 1614, 1676, 1793, 1829, 1849, 1853, 1886], "fc2": [21, 1569, 1874], "master_addr": [21, 23, 39, 49, 51, 1855, 1874, 1881, 1882], "localhost": [21, 23, 48, 49, 1855, 1874, 1881, 1882], "master_port": [21, 23, 39, 49, 51, 1855, 1874, 1881, 1882], "12355": 21, "init_process_group": [21, 23, 24, 27, 39, 49, 51, 1574, 1854, 1855, 1881], "cleanup": 21, "destroy_process_group": 21, "run_demo": 21, "demo_fn": 21, "mp": [21, 23, 24, 50, 1574, 1827, 1855, 1864, 1876, 1882, 1887, 1888, 1895], "nproc": [21, 23, 46, 47, 49, 1843, 1855, 1882], "demo_seri": 21, "gettempdir": 21, "device_id": [21, 23, 24, 27, 49, 53, 1242, 1337, 1439, 1562, 1574, 1855], "powersgd_st": 21, "lr": [21, 24, 27, 53, 1569, 1574, 1630, 1631, 1632, 1633, 1634, 1635, 1636, 1637, 1643, 1644, 1645, 1646, 1647, 1648, 1649, 1650, 1651, 1652, 1653, 1654, 1655, 1656, 1657, 1658, 1659, 1660, 1661, 1662, 1854, 1855, 1862, 1872, 1882, 1890], "001": [21, 889, 890, 1180, 1629, 1633, 1634, 1643, 1647, 1659, 1855, 1869], "comm_hook": 21, "comm_hook_st": 21, "hook_stat": 21, "barrier": [21, 39, 48, 1871], "map_loc": [21, 1175, 1178, 1242, 1574, 1826, 1840, 1873], "n_gpu": 21, "device_count": [21, 23, 1827], "got": [21, 23, 872, 1856], "thank": [21, 29, 1856, 1874], "author": [21, 32, 34, 38, 48, 49, 1112, 1643, 1683, 1819, 1850], "thij": 21, "par": 21, "home": [22, 1854], "brief": [23, 1574, 1843, 1881], "introduct": [23, 29, 981, 1574, 1827, 1829, 1833, 1844, 1852, 1862, 1869, 1881, 1885, 1890], "mpi": [23, 1574], "gloo": [23, 49, 1574, 1855, 1860, 1881], "recv": [23, 1574, 1871, 1882], "broadcast": [23, 27, 29, 33, 53, 58, 85, 184, 384, 386, 387, 388, 496, 498, 500, 548, 666, 667, 668, 669, 670, 671, 672, 678, 716, 865, 902, 907, 910, 914, 915, 916, 917, 941, 960, 1038, 1039, 1044, 1049, 1089, 1090, 1091, 1092, 1113, 1115, 1127, 1137, 1195, 1196, 1203, 1216, 1220, 1225, 1226, 1228, 1231, 1239, 1259, 1264, 1265, 1275, 1289, 1292, 1301, 1304, 1335, 1354, 1406, 1479, 1541, 1574, 1629, 1665, 1673, 1696, 1698, 1774, 1793, 1798, 1822, 1827, 1828, 1832, 1844, 1845, 1846, 1855, 1869, 1871, 1886], "all_reduc": [23, 50, 1574], "all_gath": 23, "scatter": [23, 27, 53, 496, 498, 500, 1337, 1830, 1858, 1871, 1881], "reduce_scatt": [23, 1871], "all_to_al": 23, "v1": [23, 53, 1239, 1665, 1826, 1855, 1881], "init_method": [23, 1574, 1881], "adher": [23, 1832], "some_fil": 23, "machine_nam": 23, "share_folder_nam": 23, "tcpstore": [23, 48], "past": [23, 34, 61, 1005, 1022, 1574, 1858], "ask": [23, 57, 58, 1826, 1827, 1857, 1859], "infiniband": [23, 1574, 1881], "interconnect": 23, "gpudirect": 23, "ethernet": 23, "encount": [23, 31, 33, 35, 38, 58, 60, 61, 1574, 1829, 1832, 1834, 1853, 1865, 1873, 1876], "ip": [23, 48], "ib": 23, "upcom": [23, 33, 38, 53, 1850], "nccl_socket_ifnam": 23, "eth0": 23, "gloo_socket_ifnam": 23, "comma": [23, 1044, 1832], "eth1": 23, "eth2": 23, "eth3": 23, "imper": 23, "nccl_debug": 23, "info": [23, 32, 33, 38, 39, 42, 46, 873, 950, 951, 953, 1201, 1212, 1214, 1215, 1219, 1232, 1260, 1627, 1827, 1830, 1855, 1856, 1857, 1869, 1873], "nccl_debug_subsi": 23, "coll": 23, "hang": [23, 24, 27, 1574, 1855], "topolog": [23, 25, 28, 39, 61, 1869], "effort": [23, 1881], "socket": [23, 1843, 1881], "nccl_socket_nthread": 23, "nccl_nsocks_perthread": 23, "cloud": [23, 1885, 1890], "aw": [23, 40, 946], "gcp": 23, "primit": [23, 27, 48, 1242, 1828, 1829, 1831, 1833, 1853, 1863, 1869, 1881], "kind": [23, 35, 42, 61, 1190, 1575, 1628, 1675, 1740, 1826, 1836, 1856, 1864, 1869, 1873, 1876, 1886, 1888], "connect": [23, 39, 48, 1328, 1329, 1330, 1331, 1332, 1333, 1376, 1377, 1378, 1379, 1380, 1381, 1428, 1843, 1881], "advantag": [23, 48, 49, 1317, 1361, 1855, 1858, 1881, 1885], "redund": [23, 53, 1060, 1061, 1063, 1065, 1079, 1081, 1773], "averag": [23, 27, 716, 747, 748, 749, 750, 802, 803, 898, 900, 946, 1171, 1305, 1306, 1307, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1334, 1336, 1344, 1354, 1360, 1366, 1367, 1368, 1369, 1373, 1374, 1375, 1391, 1392, 1400, 1403, 1404, 1405, 1406, 1407, 1414, 1431, 1432, 1439, 1448, 1456, 1457, 1458, 1464, 1465, 1466, 1469, 1470, 1480, 1494, 1509, 1518, 1519, 1532, 1540, 1574, 1630, 1631, 1633, 1634, 1635, 1637, 1643, 1644, 1647, 1798, 1827, 1855, 1875], "elimin": [23, 34, 61, 592, 1809, 1810, 1866], "thrash": 23, "recurr": [23, 741, 742, 1186, 1323, 1337, 1352, 1353, 1370, 1415, 1584, 1610, 1644, 1827, 1854], "use_distribut": 23, "datetim": [23, 1841], "timedelta": [23, 48, 1841], "1800": 23, "group_nam": [23, 45], "pg_option": 23, "url": [23, 37, 48, 897, 1826, 1840, 1881], "discov": [23, 1881], "encod": [23, 39, 45, 48, 61, 1218, 1242, 1316, 1317, 1443, 1444, 1445, 1446, 1447, 1574, 1759, 1760, 1761, 1763, 1764, 1829, 1832, 1833, 1856, 1867, 1873, 1882, 1885], "ucc": 23, "provi": 23, "lowercas": 23, "deadlock": [23, 1574], "job": [23, 39, 41, 42, 44, 45, 47, 48, 49, 50, 1574, 1652, 1658, 1861, 1875, 1890], "exchang": [23, 48, 951, 1032, 1854, 1869], "nccl_blocking_wait": 23, "nccl_async_error_handl": [23, 1854], "throw": [23, 24, 28, 33, 34, 38, 53, 60, 61, 178, 310, 315, 527, 596, 1156, 1171, 1211, 1260, 1400, 1574, 1576, 1577, 1610, 1621, 1672, 1812, 1846, 1851, 1866, 1881, 1891], "abort": [23, 1854], "crash": [23, 41, 48, 1843, 1851, 1881, 1883, 1890], "caught": [23, 1574, 1843], "littl": [23, 35, 1856, 1883], "watch": 23, "dog": 23, "deprec": [23, 39, 48, 49, 53, 57, 392, 496, 539, 585, 766, 767, 768, 774, 807, 888, 924, 925, 996, 1000, 1117, 1171, 1225, 1228, 1260, 1261, 1316, 1317, 1334, 1336, 1359, 1360, 1366, 1367, 1391, 1392, 1400, 1403, 1404, 1405, 1407, 1414, 1431, 1432, 1448, 1453, 1454, 1469, 1470, 1480, 1509, 1532, 1540, 1566, 1567, 1568, 1570, 1574, 1615, 1616, 1621, 1676, 1692, 1773, 1777, 1800, 1827, 1829, 1834, 1843, 1852, 1875, 1879, 1887, 1891, 1892], "processgroupopt": 23, "processgroupnccl": [23, 1855], "is_high_priority_stream": 23, "is_initi": [23, 1827], "is_mpi_avail": 23, "is_nccl_avail": 23, "is_gloo_avail": 23, 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"type_info.rst"], "titles": ["torch._dynamo", "Automatic Mixed Precision package - torch.amp", "Automatic differentiation package - torch.autograd", "torch.backends", "Benchmark Utils - torch.utils.benchmark", "torch.utils.bottleneck", "torch.utils.checkpoint", "PyTorch Governance | Build + CI", "PyTorch Contribution Guide", "PyTorch Design Philosophy", "PyTorch Governance | Mechanics", "PyTorch Governance | Maintainers", "Complex Numbers", "torch.__config__", "torch.utils.cpp_extension", "C++", "torch.cuda", "CUDA Stream Sanitizer", "&lt;no title&gt;", "&lt;no title&gt;", "torch.utils.data", "DDP Communication Hooks", "torch::deploy has been moved to pytorch/multipy", "Distributed communication package - torch.distributed", "Generic Join Context Manager", "Distributed Checkpoint - torch.distributed.checkpoint", "Torch Distributed Elastic", "Distributed Optimizers", "Tensor Parallelism - torch.distributed.tensor.parallel", "Probability distributions - torch.distributions", 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889, 890, 1180, 1629, 1633, 1634, 1643, 1647, 1659, 1855, 1869], "comm_hook": 21, "comm_hook_st": 21, "hook_stat": 21, "barrier": [21, 39, 48, 1871], "map_loc": [21, 1175, 1178, 1242, 1574, 1826, 1840, 1873], "n_gpu": 21, "device_count": [21, 23, 1827], "got": [21, 23, 872, 1856], "thank": [21, 29, 1856, 1874], "author": [21, 32, 34, 38, 48, 49, 1112, 1643, 1683, 1819, 1850], "thij": 21, "par": 21, "home": [22, 1854], "brief": [23, 1574, 1843, 1881], "introduct": [23, 29, 981, 1574, 1827, 1829, 1833, 1844, 1852, 1862, 1869, 1881, 1885, 1890], "mpi": [23, 1574], "gloo": [23, 49, 1574, 1855, 1860, 1881], "recv": [23, 1574, 1871, 1882], "broadcast": [23, 27, 29, 33, 53, 58, 85, 184, 384, 386, 387, 388, 496, 498, 500, 548, 666, 667, 668, 669, 670, 671, 672, 678, 716, 865, 902, 907, 910, 914, 915, 916, 917, 941, 960, 1038, 1039, 1044, 1049, 1089, 1090, 1091, 1092, 1113, 1115, 1127, 1137, 1195, 1196, 1203, 1216, 1220, 1225, 1226, 1228, 1231, 1239, 1259, 1264, 1265, 1275, 1289, 1292, 1301, 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33, 38, 39, 42, 46, 873, 950, 951, 953, 1201, 1212, 1214, 1215, 1219, 1232, 1260, 1627, 1827, 1830, 1855, 1856, 1857, 1869, 1873], "nccl_debug_subsi": 23, "coll": 23, "hang": [23, 24, 27, 1574, 1855], "topolog": [23, 25, 28, 39, 61, 1869], "effort": [23, 1881], "socket": [23, 1843, 1881], "nccl_socket_nthread": 23, "nccl_nsocks_perthread": 23, "cloud": [23, 1885, 1890], "aw": [23, 40, 946], "gcp": 23, "primit": [23, 27, 48, 1242, 1828, 1829, 1831, 1833, 1853, 1863, 1869, 1881], "kind": [23, 35, 42, 61, 1190, 1575, 1628, 1675, 1740, 1826, 1836, 1856, 1864, 1869, 1873, 1876, 1886, 1888], "connect": [23, 39, 48, 1328, 1329, 1330, 1331, 1332, 1333, 1376, 1377, 1378, 1379, 1380, 1381, 1428, 1843, 1881], "advantag": [23, 48, 49, 1317, 1361, 1855, 1858, 1881, 1885], "redund": [23, 53, 1060, 1061, 1063, 1065, 1079, 1081, 1773], "averag": [23, 27, 716, 747, 748, 749, 750, 802, 803, 898, 900, 946, 1171, 1305, 1306, 1307, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1334, 1336, 1344, 1354, 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1854, 1869], "nccl_blocking_wait": 23, "nccl_async_error_handl": [23, 1854], "throw": [23, 24, 28, 33, 34, 38, 53, 60, 61, 178, 310, 315, 527, 596, 1156, 1171, 1211, 1260, 1400, 1574, 1576, 1577, 1610, 1621, 1672, 1812, 1846, 1851, 1866, 1881, 1891], "abort": [23, 1854], "crash": [23, 41, 48, 1843, 1851, 1881, 1883, 1890], "caught": [23, 1574, 1843], "littl": [23, 35, 1856, 1883], "watch": 23, "dog": 23, "deprec": [23, 39, 48, 49, 53, 57, 392, 496, 539, 585, 766, 767, 768, 774, 807, 888, 924, 925, 996, 1000, 1117, 1171, 1225, 1228, 1260, 1261, 1316, 1317, 1334, 1336, 1359, 1360, 1366, 1367, 1391, 1392, 1400, 1403, 1404, 1405, 1407, 1414, 1431, 1432, 1448, 1453, 1454, 1469, 1470, 1480, 1509, 1532, 1540, 1566, 1567, 1568, 1570, 1574, 1615, 1616, 1621, 1676, 1692, 1773, 1777, 1800, 1827, 1829, 1834, 1843, 1852, 1875, 1879, 1887, 1891, 1892], "processgroupopt": 23, "processgroupnccl": [23, 1855], "is_high_priority_stream": 23, "backend_nam": 23, "custom_backend": 23, "is_initi": [23, 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"meta": 1832, "primit": 1832, "special": [1832, 1851, 1886], "instanc": 1832, "signatur": 1832, "expr": 1832, "convers": [1832, 1846], "atom": 1832, "parenthes": 1832, "form": 1832, "dictionari": 1832, "displai": 1832, "primari": 1832, "power": 1832, "unari": [1832, 1838, 1885], "bitwis": 1832, "binari": [1832, 1838], "shift": 1832, "boolean": 1832, "condit": 1832, "augment": 1832, "rais": 1832, "assert": [1832, 1860], "del": 1832, "compound": 1832, "els": 1832, "getattr": 1832, "hasattr": 1832, "zip": [1832, 1873], "enumer": 1832, "rule": [1832, 1845, 1857, 1870], "remot": [1832, 1883], "procedur": 1832, "execut": [1832, 1851, 1854, 1873, 1874], "program": 1832, "coverag": [1833, 1844, 1856], "properti": [1834, 1837], "Not": 1834, "correctli": 1834, "bound": 1834, "schema": 1834, "between": [1834, 1873], "matrix": [1837, 1876], "decomposit": 1837, "solver": 1837, "misc": 1837, "experiment": 1837, "motiv": 1838, "reduct": [1838, 1854, 1865, 1893], "mobile_optim": 1839, "model_zoo": 1840, "strategi": [1843, 1872], "descriptor": 1843, "file_descriptor": 1843, "file_system": 1843, "subprocess": 1843, "keep": [1844, 1873], "dimens": [1844, 1845], "unifi": 1844, "contract": 1844, "awai": 1844, "factori": 1844, "variant": 1844, "semant": [1845, 1852, 1854, 1860, 1867], "explicit": 1845, "align": 1845, "current": 1845, "subsystem": 1845, "constructor": 1846, "contain": 1847, "convolut": [1847, 1848, 1865, 1866], "layer": 1847, "pool": [1847, 1848], "activ": [1847, 1848], "weight": [1847, 1872], "nonlinear": 1847, "recurr": [1847, 1858], "distanc": [1847, 1848], "loss": [1847, 1848, 1850], "vision": [1847, 1848], "shuffl": 1847, "lazi": 1847, "attent": 1848, "typic": 1850, "unscal": 1850, "accumul": 1850, "penalti": 1850, "one": 1850, "per": [1850, 1872], "particular": [1850, 1851], "dtype": [1850, 1867, 1879, 1888], "encod": 1851, "histori": 1851, "set": [1851, 1869], "No": 1851, "multithread": 1851, "concurr": 1851, "determin": [1851, 1866], "retain": 1851, "thread": [1851, 1853], "safeti": 1851, "wirting": 1851, "calculu": 1851, "pictur": 1851, "conjug": 1851, "own": 1851, "formula": 1851, "domain": 1851, "whether": [1851, 1873], "fire": 1851, "differ": [1851, 1869], "modifi": 1851, "compat": 1852, "tune": 1853, "tensorfloat": [1854, 1860, 1865], "32": [1854, 1860, 1865, 1868], "tf32": [1854, 1860, 1865], "amper": [1854, 1865], "reduc": [1854, 1865], "fp16": [1854, 1865], "gemm": [1854, 1865], "bf16": [1854, 1865], "bc": 1854, "alloc": [1854, 1858], "cufft": 1854, "plan": [1854, 1860], "just": 1854, "time": 1854, "practic": [1854, 1864, 1876], "agnost": 1854, "buffer": [1854, 1864], "instead": 1854, "captur": 1854, "partial": 1854, "9": 1854, "6": 1854, "across": [1854, 1867], "intern": [1855, 1869, 1873], "processgroup": 1855, "ddpoptim": 1855, "separ": 1856, "setup_context": 1856, "like": [1856, 1873], "subclass": 1856, "wrapper": 1856, "__torch_function__": 1856, "overrid": [1856, 1896], "specifi": 1857, "gotcha": 1857, "staticmethod": 1857, 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\ No newline at end of file
diff --git a/docs/2.0/torch.html b/docs/2.0/torch.html
index 643a2c0b9e77..626a0dfe1545 100644
--- a/docs/2.0/torch.html
+++ b/docs/2.0/torch.html
@@ -2119,15 +2119,15 @@ <h2>Operator Tags<a class="headerlink" href="#operator-tags" title="Permalink to
 <dt class="sig sig-object py" id="torch.Tag">
 <em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">Tag</span></span><a class="headerlink" href="#torch.Tag" title="Permalink to this definition">¶</a></dt>
 <dd><p>Members:</p>
-<p>nondeterministic_bitwise</p>
-<p>pointwise</p>
 <p>view_copy</p>
-<p>nondeterministic_seeded</p>
 <p>dynamic_output_shape</p>
+<p>nondeterministic_bitwise</p>
 <p>core</p>
-<p>inplace_view</p>
-<p>generated</p>
 <p>data_dependent_output</p>
+<p>generated</p>
+<p>pointwise</p>
+<p>nondeterministic_seeded</p>
+<p>inplace_view</p>
 <dl class="py property">
 <dt class="sig sig-object py" id="torch.Tag.name">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">name</span></span><a class="headerlink" href="#torch.Tag.name" title="Permalink to this definition">¶</a></dt>