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<section id="getting-started">
<h1>Getting Started<a class="headerlink" href="#getting-started" title="Permalink to this heading">¶</a></h1>
<p>Let’s start with a simple example. Note that you are likely to see more
significant speedups the newer your GPU is.</p>
<p>The below is a tutorial for inference, for a training specific tutorial, make sure to checkout <a class="reference external" href="https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html">example on training</a></p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="k">def</span> <span class="nf">fn</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="k">return</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
<span class="n">new_fn</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">fn</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s2">"inductor"</span><span class="p">)</span>
<span class="n">input_tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">10000</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="s2">"cuda:0"</span><span class="p">)</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">new_fn</span><span class="p">(</span><span class="n">input_tensor</span><span class="p">,</span> <span class="n">input_tensor</span><span class="p">)</span>
</pre></div>
</div>
<p>This example will not actually run faster. Its purpose is to demonstrate
the <code class="docutils literal notranslate"><span class="pre">torch.cos()</span></code> and <code class="docutils literal notranslate"><span class="pre">torch.sin()</span></code> features which are
examples of pointwise ops as in they operate element by element on a
vector. A more famous pointwise op you might want to use would
be something like <code class="docutils literal notranslate"><span class="pre">torch.relu()</span></code>. Pointwise ops in eager mode are
suboptimal because each one would need to read a tensor from
memory, make some changes, and then write back those changes. The single
most important optimization that inductor does is fusion. So back to our
example we can turn 2 reads and 2 writes into 1 read and 1 write which
is crucial especially for newer GPUs where the bottleneck is memory
bandwidth (how quickly you can send data to a GPU) rather than compute
(how quickly your GPU can crunch floating point operations).</p>
<p>Another major optimization that inductor makes available is automatic
support for CUDA graphs.
CUDA graphs help eliminate the overhead from launching individual
kernels from a Python program which is especially relevant for newer GPUs.</p>
<p>TorchDynamo supports many different backends but inductor specifically works
by generating <a class="reference external" href="https://github.com/openai/triton">Triton</a> kernels and
we can inspect them by running <code class="docutils literal notranslate"><span class="pre">TORCH_COMPILE_DEBUG=1</span> <span class="pre">python</span> <span class="pre">trig.py</span></code>
with the actual generated kernel being</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@pointwise</span><span class="p">(</span><span class="n">size_hints</span><span class="o">=</span><span class="p">[</span><span class="mi">16384</span><span class="p">],</span> <span class="n">filename</span><span class="o">=</span><span class="vm">__file__</span><span class="p">,</span> <span class="n">meta</span><span class="o">=</span><span class="p">{</span><span class="s1">'signature'</span><span class="p">:</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="s1">'*fp32'</span><span class="p">,</span> <span class="mi">1</span><span class="p">:</span> <span class="s1">'*fp32'</span><span class="p">,</span> <span class="mi">2</span><span class="p">:</span> <span class="s1">'i32'</span><span class="p">},</span> <span class="s1">'device'</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s1">'constants'</span><span class="p">:</span> <span class="p">{},</span> <span class="s1">'configs'</span><span class="p">:</span> <span class="p">[</span><span class="n">instance_descriptor</span><span class="p">(</span><span class="n">divisible_by_16</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">equal_to_1</span><span class="o">=</span><span class="p">())]})</span>
<span class="nd">@triton</span><span class="o">.</span><span class="n">jit</span>
<span class="k">def</span> <span class="nf">kernel</span><span class="p">(</span><span class="n">in_ptr0</span><span class="p">,</span> <span class="n">out_ptr0</span><span class="p">,</span> <span class="n">xnumel</span><span class="p">,</span> <span class="n">XBLOCK</span> <span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">):</span>
<span class="n">xnumel</span> <span class="o">=</span> <span class="mi">10000</span>
<span class="n">xoffset</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">program_id</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">*</span> <span class="n">XBLOCK</span>
<span class="n">xindex</span> <span class="o">=</span> <span class="n">xoffset</span> <span class="o">+</span> <span class="n">tl</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">XBLOCK</span><span class="p">),</span> <span class="p">[</span><span class="n">XBLOCK</span><span class="p">])</span>
<span class="n">xmask</span> <span class="o">=</span> <span class="n">xindex</span> <span class="o"><</span> <span class="n">xnumel</span>
<span class="n">x0</span> <span class="o">=</span> <span class="n">xindex</span>
<span class="n">tmp0</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">in_ptr0</span> <span class="o">+</span> <span class="p">(</span><span class="n">x0</span><span class="p">),</span> <span class="n">xmask</span><span class="p">)</span>
<span class="n">tmp1</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">tmp0</span><span class="p">)</span>
<span class="n">tmp2</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">tmp1</span><span class="p">)</span>
<span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">out_ptr0</span> <span class="o">+</span> <span class="p">(</span><span class="n">x0</span> <span class="o">+</span> <span class="n">tl</span><span class="o">.</span><span class="n">zeros</span><span class="p">([</span><span class="n">XBLOCK</span><span class="p">],</span> <span class="n">tl</span><span class="o">.</span><span class="n">int32</span><span class="p">)),</span> <span class="n">tmp2</span><span class="p">,</span> <span class="n">xmask</span><span class="p">)</span>
</pre></div>
</div>
<p>And you can verify that fusing the two <code class="docutils literal notranslate"><span class="pre">sin</span></code> did actually occur
because the two <code class="docutils literal notranslate"><span class="pre">sin</span></code> operations occur within a single Triton kernel
and the temporary variables are held in registers with very fast access.</p>
<p>You can read up a lot more on Triton’s performance
<a class="reference external" href="https://openai.com/blog/triton/">here</a> but the key is it’s in Python
so you can easily understand it even if you have not written all that
many CUDA kernels.</p>
<p>Next, let’s try a real model like resnet50 from the PyTorch
hub.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">hub</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">'pytorch/vision:v0.10.0'</span><span class="p">,</span> <span class="s1">'resnet18'</span><span class="p">,</span> <span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">opt_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s2">"inductor"</span><span class="p">)</span>
<span class="n">model</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">64</span><span class="p">,</span><span class="mi">64</span><span class="p">))</span>
</pre></div>
</div>
<p>And that is not the only available backend, you can run in a REPL
<code class="docutils literal notranslate"><span class="pre">torch._dynamo.list_backends()</span></code> to see all the available backends. Try out the
<code class="docutils literal notranslate"><span class="pre">cudagraphs</span></code> or <code class="docutils literal notranslate"><span class="pre">nvfuser</span></code> next as inspiration.</p>
<p>Let’s do something a bit more interesting now, our community frequently
uses pretrained models from
<a class="reference external" href="https://github.com/huggingface/transformers">transformers</a> or
<a class="reference external" href="https://github.com/rwightman/pytorch-image-models">TIMM</a> and one of
our design goals is for Dynamo and inductor to work out of the box with
any model that people would like to author.</p>
<p>So we will directly download a pretrained model from the
HuggingFace hub and optimize it:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">BertTokenizer</span><span class="p">,</span> <span class="n">BertModel</span>
<span class="c1"># Copy pasted from here https://huggingface.co/bert-base-uncased</span>
<span class="n">tokenizer</span> <span class="o">=</span> <span class="n">BertTokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="s1">'bert-base-uncased'</span><span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">BertModel</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="s2">"bert-base-uncased"</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="s2">"cuda:0"</span><span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s2">"inductor"</span><span class="p">)</span> <span class="c1"># This is the only line of code that we changed</span>
<span class="n">text</span> <span class="o">=</span> <span class="s2">"Replace me by any text you'd like."</span>
<span class="n">encoded_input</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="n">text</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s1">'pt'</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="s2">"cuda:0"</span><span class="p">)</span>
<span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="o">**</span><span class="n">encoded_input</span><span class="p">)</span>
</pre></div>
</div>
<p>If you remove the <code class="docutils literal notranslate"><span class="pre">to(device="cuda:0")</span></code> from the model and
<code class="docutils literal notranslate"><span class="pre">encoded_input</span></code>, then Triton will generate C++ kernels that will be
optimized for running on your CPU. You can inspect both Triton or C++
kernels for BERT, they’re obviously more complex than the trigonometry
example we had above but you can similarly skim it and understand if you
understand PyTorch.</p>
<p>Similarly let’s try out a TIMM example</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">timm</span>
<span class="kn">import</span> <span class="nn">torch._dynamo</span> <span class="k">as</span> <span class="nn">dynamo</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">timm</span><span class="o">.</span><span class="n">create_model</span><span class="p">(</span><span class="s1">'resnext101_32x8d'</span><span class="p">,</span> <span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">num_classes</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="n">opt_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s2">"inductor"</span><span class="p">)</span>
<span class="n">opt_model</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">7</span><span class="p">,</span><span class="mi">7</span><span class="p">))</span>
</pre></div>
</div>
<p>Our goal with Dynamo and inductor is to build the highest coverage ML compiler
which should work with any model you throw at it.</p>
<section id="existing-backends">
<h2>Existing Backends<a class="headerlink" href="#existing-backends" title="Permalink to this heading">¶</a></h2>
<p>TorchDynamo has a growing list of backends, which can be found in the
<a class="reference external" href="https://github.com/pytorch/pytorch/blob/master/torch/_dynamo/backends/">backends</a> folder
or <code class="docutils literal notranslate"><span class="pre">torch._dynamo.list_backends()</span></code> each of which with its optional dependencies.</p>
<p>Some of the most commonly used backends include:</p>
<dl class="simple">
<dt><strong>Training & inference backends</strong>:</dt><dd><ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">torch.compile(m,</span> <span class="pre">backend="inductor")</span></code> - Uses <code class="docutils literal notranslate"><span class="pre">TorchInductor</span></code> backend. <a class="reference external" href="https://dev-discuss.pytorch.org/t/torchinductor-a-pytorch-native-compiler-with-define-by-run-ir-and-symbolic-shapes/747">Read more</a></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">torch.compile(m,</span> <span class="pre">backend="aot_ts_nvfuser")</span></code> - nvFuser with AotAutograd/TorchScript. <a class="reference external" href="https://dev-discuss.pytorch.org/t/tracing-with-primitives-update-1-nvfuser-and-its-primitives/593">Read more</a></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">torch.compile(m,</span> <span class="pre">backend=""nvprims_nvfuser")</span></code> - nvFuser with PrimTorch. <a class="reference external" href="https://dev-discuss.pytorch.org/t/tracing-with-primitives-update-1-nvfuser-and-its-primitives/593">Read more</a></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">torch.compile(m,</span> <span class="pre">backend="cudagraphs")</span></code> - cudagraphs with AotAutograd. <a class="reference external" href="https://github.com/pytorch/torchdynamo/pull/757">Read more</a></p></li>
</ul>
</dd>
<dt><strong>Inference-only backends</strong>:</dt><dd><ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">torch.compile(m,</span> <span class="pre">backend="onnxrt")</span></code> - Uses ONNXRT for inference on CPU/GPU. <a class="reference external" href="https://onnxruntime.ai/">Read more</a></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">torch.compile(m,</span> <span class="pre">backend="tensorrt")</span></code> - Uses ONNXRT to run TensorRT for inference optimizations. <a class="reference external" href="https://github.com/onnx/onnx-tensorrt">Read more</a></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">torch.compile(m,</span> <span class="pre">backend="ipex")</span></code> - Uses IPEX for inference on CPU. <a class="reference external" href="https://github.com/intel/intel-extension-for-pytorch">Read more</a></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">torch.compile(m,</span> <span class="pre">backend="tvm")</span></code> - Uses Apache TVM for inference optimizations. <a class="reference external" href="https://tvm.apache.org/">Read more</a></p></li>
</ul>
</dd>
</dl>
<section id="why-do-you-need-another-way-of-optimizing-pytorch-code">
<h3>Why do you need another way of optimizing PyTorch code?<a class="headerlink" href="#why-do-you-need-another-way-of-optimizing-pytorch-code" title="Permalink to this heading">¶</a></h3>
<p>While a number of other code optimization tools exist in the PyTorch
ecosystem, each of them has its own flow.
Here is a few examples of existing methods and their limitations:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">torch.jit.trace()</span></code> is silently wrong if it cannot trace, for example:
during control flow</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">torch.jit.script()</span></code> requires modifications to user or library code
by adding type annotations and removing non PyTorch code</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">torch.fx.symbolic_trace()</span></code> either traces correctly or gives a hard
error but it’s limited to traceable code so still can’t handle
control flow</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">torch._dynamo</span></code> works out of the box and produces partial graphs.
It still has the option of producing a single graph with
<code class="docutils literal notranslate"><span class="pre">nopython=True</span></code> which are needed for <a class="reference external" href="./documentation/FAQ.md#do-i-still-need-to-export-whole-graphs">some
situations</a>
but allows a smoother transition where partial graphs can be
optimized without code modification</p></li>
</ul>
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