Merge branch 'site' into loop

_news/news-item-1.md

@@ -1,5 +1,5 @@
 ---
-order: 1
+order: 2
 link: /get-started/pytorch-2.0/#ask-the-engineers-20-live-qa-series
 summary: "Ask the Engineers: 2.0 Live Q&A Series"
 ---

_news/news-item-2.md

@@ -1,5 +1,5 @@
 ---
-order: 2
+order: 3
 link: https://fb.me/e/29RoWnqBX
 summary: "Watch the PyTorch Conference online"
 ---

_news/news-item-3.md

@@ -0,0 +1,5 @@
+---
+order: 1
+link: /blog/deprecation-cuda-python-support/
+summary: "Deprecation of CUDA 11.6 and Python 3.7 Support"
+---

_posts/2023-01-27-accelerated-stable-diffusion-2.md

@@ -161,8 +161,6 @@ As the _original version_ we took the SD 2.1 release. It uses PyTorch 1.12 and a
 
 It uses `nn.MultiheadAttention` in `CrossAttention` and PyTorch 2.0.0.dev20230111+cu117. It also has a few other minor optimizations in PyTorch-related code. 
 
-Please see the appendix “Benchmarked versions definition” in [the companion page](/blog/performance-experiments-stable-diffusion/) for the precise definition of the 5 configurations and prompts triggering each of them.
-
 The table below shows runtime of each version of the code in seconds, and the percentage improvement compared to the _original with xFormers_. The compilation time is excluded.
 
 **Runtimes for batch size 1. In parenthesis - relative improvement with respect to the “Original with xFormers” row**
@@ -438,16 +436,16 @@ The table below shows runtime of each version of the code in seconds, and the pe
 </table>
 
 
-To minimize fluctuations and external influence on the performance of the benchmarked code, we ran each version of the code one after another, and then repeated this sequence 10 times: A, B, C, D, E,  A, B, … So the results of a typical run would look like the one in the picture below. For results of all runs please see appendix “Per-run data” in [the companion page](/blog/performance-experiments-stable-diffusion/). Note that one shouldn’t rely on comparison of absolute run times between different graphs, but comparison of run times _inside_ one graph is pretty reliable, thanks to our benchmarking setup.
+To minimize fluctuations and external influence on the performance of the benchmarked code, we ran each version of the code one after another, and then repeated this sequence 10 times: A, B, C, D, E,  A, B, … So the results of a typical run would look like the one in the picture below. Note that one shouldn’t rely on comparison of absolute run times between different graphs, but comparison of run times _inside_ one graph is pretty reliable, thanks to our benchmarking setup.
 
 ![Stable Diffusion 2.1 benchmarks](/assets/images/stable-diffusion/original_vs_optimized_a100_n_samples_1_n_iter_2_sd2.png){:width="80%"}
 
 
 Each run of `txt2img.py` generates several batches, which is regulated by the CLI parameter `--n_iter`. In the benchmarks we used `n_iter = 2`, but introduced an additional “warm-up” iteration, which doesn’t contribute to the run time. This was necessary for the runs with compilation, because compilation happens the first time the code runs, and so the first iteration is much longer than all subsequent. To make comparison fair, we also introduced this additional “warm-up” iteration to all other runs, which is turned on by CLI option `--skip_first` provided to the modified `txt2img.py`.
 
-The numbers in the table above are for number of iterations 2 (plus a “warm-up one”), prompt ”A photo”, seed 1, PLMS sampler, and autocast turned on. See [the companion page](/blog/performance-experiments-stable-diffusion/) for precise CLI commands in appendix “Benchmarked versions definition” and detailed results of individual runs in appendix “Per-run data”.
+The numbers in the table above are for number of iterations 2 (plus a “warm-up one”), prompt ”A photo”, seed 1, PLMS sampler, and autocast turned on.
 
-The P100, V100, and A100 benchmarks were done on Meta internal infrastructure. The T4 benchmarks were done in Google Colab Pro. The A10 benchmarks were done on g5.4xlarge AWS instances with 1 GPU.
+Benchmarks were done using P100, V100, A100, A10 and T4 GPUs.
 
 
 ## Conclusions and next steps

docs/master/_dynamo.html

@@ -237,7 +237,7 @@
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@@ -1619,6 +1619,9 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
     <span class="c1"># signatures already imported. For now these clashes are ignored; see</span>
     <span class="c1"># PR #43339 for details.</span>
     <span class="kn">from</span> <span class="nn">torch._C._VariableFunctions</span> <span class="kn">import</span> <span class="o">*</span>  <span class="c1"># type: ignore[misc] # noqa: F403</span>
+    <span class="c1"># Fixup segment_reduce visibility</span>
+    <span class="n">_segment_reduce</span> <span class="o">=</span> <span class="n">segment_reduce</span>
+    <span class="k">del</span> <span class="n">segment_reduce</span>
 
 <span class="c1"># Ops not to be exposed in `torch` namespace,</span>
 <span class="c1"># mostly helper ops.</span>
@@ -1631,6 +1634,9 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
         <span class="k">continue</span>
     <span class="n">obj</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">_VariableFunctions</span><span class="p">,</span> <span class="n">name</span><span class="p">)</span>
     <span class="n">obj</span><span class="o">.</span><span class="vm">__module__</span> <span class="o">=</span> <span class="s1">&#39;torch&#39;</span>
+    <span class="c1"># Hide some APIs that should not be public</span>
+    <span class="k">if</span> <span class="n">name</span> <span class="o">==</span> <span class="s2">&quot;segment_reduce&quot;</span><span class="p">:</span>
+        <span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;_&quot;</span> <span class="o">+</span> <span class="n">name</span>
     <span class="nb">globals</span><span class="p">()[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">obj</span>
     <span class="k">if</span> <span class="ow">not</span> <span class="n">name</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;_&quot;</span><span class="p">):</span>
         <span class="n">__all__</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>

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@@ -841,6 +841,8 @@ <h1>Source code for torch._utils</h1><div class="highlight"><pre>
     <span class="c1"># This loosely mimicks the function on the object class but since Tensor do not inherit</span>
     <span class="c1"># from it, we cannot call that function directly</span>
     <span class="c1"># https://github.com/python/cpython/blob/c83919bd635f4433f1c6ae8504996a9fe3c215e5/Objects/typeobject.c#L4891</span>
+    <span class="c1"># Note that starting with Python 3.11, this `__getstate__` is always defined and thus</span>
+    <span class="c1"># the else branch will never be taken.</span>
     <span class="n">getstate_fn</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="s2">&quot;__getstate__&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
     <span class="k">if</span> <span class="n">getstate_fn</span><span class="p">:</span>
         <span class="n">state</span> <span class="o">=</span> <span class="n">getstate_fn</span><span class="p">()</span>
@@ -871,8 +873,11 @@ <h1>Source code for torch._utils</h1><div class="highlight"><pre>
         <span class="n">dict_state</span> <span class="o">=</span> <span class="n">state</span>
         <span class="n">slots_state</span> <span class="o">=</span> <span class="kc">None</span>
 
-    <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">dict_state</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-        <span class="nb">setattr</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span>
+    <span class="c1"># Starting with Python 3.11, the __dict__ attribute is lazily created</span>
+    <span class="c1"># and is serialized as None when not needed.</span>
+    <span class="k">if</span> <span class="n">dict_state</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">dict_state</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="nb">setattr</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span>
 
     <span class="k">if</span> <span class="n">slots_state</span><span class="p">:</span>
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