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<a href="https://github.com/tinygrad/tinygrad/edit/master/docs/developer/developer.md" title="Edit this page" class="md-content__button md-icon" rel="edit">
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<h1>Intro</h1>
<p>The tinygrad framework has four pieces</p>
<ul>
<li>a PyTorch like <b>frontend</b>.</li>
<li>a <b>scheduler</b> which breaks the compute into kernels.</li>
<li>a <b>lowering</b> engine which converts ASTs into code that can run on the accelerator.</li>
<li>an <b>execution</b> engine which can run that code.</li>
</ul>
<p>There is a good <a href="https://mesozoic-egg.github.io/tinygrad-notes/">bunch of tutorials</a> by Di Zhu that go over tinygrad internals.</p>
<p>There's also a <a href="../speed/">doc describing speed</a></p>
<h2 id="frontend">Frontend<a class="headerlink" href="#frontend" title="Permanent link">¤</a></h2>
<p>Everything in <a href="../../tensor/">Tensor</a> is syntactic sugar around constructing a graph of <a href="../uop/">UOps</a>.</p>
<p>The <code class="language-python highlight"><span class="n">UOp</span></code> graph specifies the compute in terms of low level tinygrad ops. Not all UOps will actually become realized. There's two types of UOps, base and view. base contains compute into a contiguous buffer, and view is a view. Inputs to a base can be either base or view, inputs to a view can only be a single base.</p>
<h2 id="scheduling">Scheduling<a class="headerlink" href="#scheduling" title="Permanent link">¤</a></h2>
<p>The <a href="https://github.com/tinygrad/tinygrad/tree/master/tinygrad/schedule/__init__.py">scheduler</a> converts the graph of UOps into a <code class="language-python highlight"><span class="n">LINEAR</span></code> UOp whose <code class="language-python highlight"><span class="n">src</span></code> is a list of <code class="language-python highlight"><span class="n">CALL</span></code> UOps. One <code class="language-python highlight"><span class="n">CALL</span></code> is one kernel on the GPU, and the scheduler is responsible for breaking the large compute graph into subgraphs that can fit in a kernel. The <code class="language-python highlight"><span class="n">CALL</span></code>'s <code class="language-python highlight"><span class="n">src</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span></code> (a <code class="language-python highlight"><span class="n">SINK</span></code> ast) specifies what compute to run, and the remaining <code class="language-python highlight"><span class="n">src</span></code> are the buffers to run it on.</p>
<h2 id="lowering">Lowering<a class="headerlink" href="#lowering" title="Permanent link">¤</a></h2>
<p>The code in <a href="https://github.com/tinygrad/tinygrad/tree/master/tinygrad/engine/realize.py">realize</a> lowers each <code class="language-python highlight"><span class="n">CALL</span></code> by compiling its ast into a <code class="language-python highlight"><span class="n">PROGRAM</span></code> and running it.</p>
<div class="doc doc-object doc-function">
<h3 id="tinygrad.engine.realize.run_linear" class="doc doc-heading">
<code class="doc-symbol doc-symbol-heading doc-symbol-function"></code> <span class="doc doc-object-name doc-function-name">run_linear</span>
<a href="#tinygrad.engine.realize.run_linear" class="headerlink" title="Permanent link">¤</a></h3>
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">run_linear</span><span class="p">(</span>
<span class="n">linear</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-class&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-class-name&quot;&gt;UOp&lt;/span&gt; (&lt;code&gt;tinygrad.uop.ops.UOp&lt;/code&gt;)" href="../uop/#tinygrad.uop.ops.UOp">UOp</a></span><span class="p">,</span>
<span class="n">var_vals</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/stdtypes.html#dict">dict</a></span><span class="p">[</span><span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/stdtypes.html#str">str</a></span><span class="p">,</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></span><span class="p">]</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">input_uops</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" title="&lt;code&gt;typing.Sequence&lt;/code&gt;" href="https://docs.python.org/3/library/typing.html#typing.Sequence">Sequence</a></span><span class="p">[</span><span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-class&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-class-name&quot;&gt;UOp&lt;/span&gt; (&lt;code&gt;tinygrad.uop.ops.UOp&lt;/code&gt;)" href="../uop/#tinygrad.uop.ops.UOp">UOp</a></span><span class="p">]</span> <span class="o">=</span> <span class="p">(),</span>
<span class="n">update_stats</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">jit</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">wait</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="p">)</span>
</code></pre></div>
<div class="doc doc-contents first">
<details class="mkdocstrings-source">
<summary>Source code in <code>tinygrad/engine/realize.py</code></summary>
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">278</span>
<span class="normal">279</span>
<span class="normal">280</span>
<span class="normal">281</span>
<span class="normal">282</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">run_linear</span><span class="p">(</span><span class="n">linear</span><span class="p">:</span><span class="n">UOp</span><span class="p">,</span> <span class="n">var_vals</span><span class="p">:</span><span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">int</span><span class="p">]</span><span class="o">|</span><span class="kc">None</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">input_uops</span><span class="p">:</span><span class="n">Sequence</span><span class="p">[</span><span class="n">UOp</span><span class="p">]</span><span class="o">=</span><span class="p">(),</span> <span class="n">update_stats</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">jit</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">wait</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="n">inputs</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">input_uops</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">jit</span><span class="p">:</span> <span class="n">linear</span> <span class="o">=</span> <span class="n">link_linear</span><span class="p">(</span><span class="n">compile_linear</span><span class="p">(</span><span class="n">linear</span><span class="p">,</span> <span class="n">validate</span><span class="o">=</span><span class="n">VALIDATE_WITH_CPU</span><span class="p">,</span> <span class="n">input_uops</span><span class="o">=</span><span class="n">inputs</span><span class="p">))</span>
<span class="n">ctx</span> <span class="o">=</span> <span class="n">ExecContext</span><span class="p">(</span><span class="n">var_vals</span> <span class="ow">or</span> <span class="p">{},</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">inputs</span><span class="p">),</span> <span class="n">update_stats</span><span class="p">,</span> <span class="n">jit</span><span class="p">,</span> <span class="n">wait</span> <span class="ow">or</span> <span class="n">DEBUG</span><span class="o">&gt;=</span><span class="mi">2</span><span class="p">)</span>
<span class="k">for</span> <span class="n">call</span> <span class="ow">in</span> <span class="n">linear</span><span class="o">.</span><span class="n">src</span><span class="p">:</span> <span class="n">pm_exec</span><span class="o">.</span><span class="n">rewrite</span><span class="p">(</span><span class="n">call</span><span class="p">,</span> <span class="n">ctx</span><span class="p">)</span>
</code></pre></div></td></tr></table></div>
</details>
</div>
</div><p>There's a ton of complexity hidden behind this, see the <code class="language-python highlight"><span class="n">codegen</span><span class="o">/</span></code> directory.</p>
<p>First we lower the AST to UOps, which is a linear list of the compute to be run. This is where the BEAM search happens.</p>
<p>Then we render the UOps into code with a <code class="language-python highlight"><span class="n">Renderer</span></code>, then we compile the code to binary with a <code class="language-python highlight"><span class="n">Compiler</span></code>.</p>
<h2 id="execution">Execution<a class="headerlink" href="#execution" title="Permanent link">¤</a></h2>
<p><code class="language-python highlight"><span class="n">run_linear</span></code> walks the <code class="language-python highlight"><span class="n">LINEAR</span></code> UOp, dispatching each <code class="language-python highlight"><span class="n">CALL</span></code> to a runner (kernel, copy, view, encdec, or graph).</p>
<h2 id="runtime">Runtime<a class="headerlink" href="#runtime" title="Permanent link">¤</a></h2>
<p>Runtimes are responsible for device-specific interactions. They handle tasks such as initializing devices, allocating memory, loading/launching programs, and more. You can find more information about the runtimes API on the <a href="../runtime/">runtime overview page</a>.</p>
<p>All runtime implementations can be found in the <a href="https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime">runtime directory</a>.</p>
<h3 id="hcq-compatible-runtimes">HCQ Compatible Runtimes<a class="headerlink" href="#hcq-compatible-runtimes" title="Permanent link">¤</a></h3>
<p>HCQ API is a lower-level API for defining runtimes. Interaction with HCQ-compatible devices occurs at a lower level, with commands issued directly to hardware queues. Some examples of such backends are <a href="https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_nv.py">NV</a> and <a href="https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_amd.py">AMD</a>, which are userspace drivers for NVIDIA and AMD devices respectively. You can find more information about the API on <a href="../hcq/">HCQ overview page</a></p>
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