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<div class="section" id="ux-limitations">
<span id="id1"></span><h1>UX Limitations<a class="headerlink" href="#ux-limitations" title="Permalink to this heading">¶</a></h1>
<p>torch.func, like <a class="reference external" href="https://github.com/google/jax">JAX</a>, has restrictions around
what can be transformed. In general, JAX’s limitations are that transforms
only work with pure functions: that is, functions where the output is completely
determined by the input and that do not involve side effects (like mutation).</p>
<p>We have a similar guarantee: our transforms work well with pure functions.
However, we do support certain in-place operations. On one hand, writing code
compatible with function transforms may involve changing how you write PyTorch
code, on the other hand, you may find that our transforms let you express things
that were previously difficult to express in PyTorch.</p>
<div class="section" id="general-limitations">
<h2>General limitations<a class="headerlink" href="#general-limitations" title="Permalink to this heading">¶</a></h2>
<p>All torch.func transforms share a limitation in that a function should not
assign to global variables. Instead, all outputs to a function must be returned
from the function. This restriction comes from how torch.func is implemented:
each transform wraps Tensor inputs in special torch.func Tensor subclasses
that facilitate the transform.</p>
<p>So, instead of the following:</p>
<div class="highlight-default 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">torch.func</span> <span class="kn">import</span> <span class="n">grad</span>
<span class="c1"># Don't do this</span>
<span class="n">intermediate</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">global</span> <span class="n">intermediate</span>
<span class="n">intermediate</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">sin</span><span class="p">()</span>
<span class="n">z</span> <span class="o">=</span> <span class="n">intermediate</span><span class="o">.</span><span class="n">sin</span><span class="p">()</span>
<span class="k">return</span> <span class="n">z</span>
<span class="n">x</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="n">grad_x</span> <span class="o">=</span> <span class="n">grad</span><span class="p">(</span><span class="n">f</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
<p>Please rewrite <code class="docutils literal notranslate"><span class="pre">f</span></code> to return <code class="docutils literal notranslate"><span class="pre">intermediate</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="n">intermediate</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">sin</span><span class="p">()</span>
<span class="n">z</span> <span class="o">=</span> <span class="n">intermediate</span><span class="o">.</span><span class="n">sin</span><span class="p">()</span>
<span class="k">return</span> <span class="n">z</span><span class="p">,</span> <span class="n">intermediate</span>
<span class="n">grad_x</span><span class="p">,</span> <span class="n">intermediate</span> <span class="o">=</span> <span class="n">grad</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">has_aux</span><span class="o">=</span><span class="kc">True</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="torch-autograd-apis">
<h2>torch.autograd APIs<a class="headerlink" href="#torch-autograd-apis" title="Permalink to this heading">¶</a></h2>
<p>If you are trying to use a <code class="docutils literal notranslate"><span class="pre">torch.autograd</span></code> API like <code class="docutils literal notranslate"><span class="pre">torch.autograd.grad</span></code>
or <code class="docutils literal notranslate"><span class="pre">torch.autograd.backward</span></code> inside of a function being transformed by
<a class="reference internal" href="generated/torch.func.vmap.html#torch.func.vmap" title="torch.func.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">vmap()</span></code></a> or one of torch.func’s AD transforms (<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">vjp()</span></code></a>, <a class="reference internal" href="generated/torch.func.jvp.html#torch.func.jvp" title="torch.func.jvp"><code class="xref py py-func docutils literal notranslate"><span class="pre">jvp()</span></code></a>,
<a class="reference internal" href="generated/torch.func.jacrev.html#torch.func.jacrev" title="torch.func.jacrev"><code class="xref py py-func docutils literal notranslate"><span class="pre">jacrev()</span></code></a>, <a class="reference internal" href="generated/torch.func.jacfwd.html#torch.func.jacfwd" title="torch.func.jacfwd"><code class="xref py py-func docutils literal notranslate"><span class="pre">jacfwd()</span></code></a>), the transform may not be able to transform over it.
If it is unable to do so, you’ll receive an error message.</p>
<p>This is a fundamental design limitation in how PyTorch’s AD support is implemented
and the reason why we designed the torch.func library. Please instead use the torch.func
equivalents of the <code class="docutils literal notranslate"><span class="pre">torch.autograd</span></code> APIs:
- <code class="docutils literal notranslate"><span class="pre">torch.autograd.grad</span></code>, <code class="docutils literal notranslate"><span class="pre">Tensor.backward</span></code> -> <code class="docutils literal notranslate"><span class="pre">torch.func.vjp</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.func.grad</span></code>
- <code class="docutils literal notranslate"><span class="pre">torch.autograd.functional.jvp</span></code> -> <code class="docutils literal notranslate"><span class="pre">torch.func.jvp</span></code>
- <code class="docutils literal notranslate"><span class="pre">torch.autograd.functional.jacobian</span></code> -> <code class="docutils literal notranslate"><span class="pre">torch.func.jacrev</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.func.jacfwd</span></code>
- <code class="docutils literal notranslate"><span class="pre">torch.autograd.functional.hessian</span></code> -> <code class="docutils literal notranslate"><span class="pre">torch.func.hessian</span></code></p>
</div>
<div class="section" id="vmap-limitations">
<h2>vmap limitations<a class="headerlink" href="#vmap-limitations" title="Permalink to this heading">¶</a></h2>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p><a class="reference internal" href="generated/torch.func.vmap.html#torch.func.vmap" title="torch.func.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">vmap()</span></code></a> is our most restrictive transform.
The grad-related transforms (<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">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">vjp()</span></code></a>, <a class="reference internal" href="generated/torch.func.jvp.html#torch.func.jvp" title="torch.func.jvp"><code class="xref py py-func docutils literal notranslate"><span class="pre">jvp()</span></code></a>) do not
have these limitations. <a class="reference internal" href="generated/torch.func.jacfwd.html#torch.func.jacfwd" title="torch.func.jacfwd"><code class="xref py py-func docutils literal notranslate"><span class="pre">jacfwd()</span></code></a> (and <a class="reference internal" href="generated/torch.func.hessian.html#torch.func.hessian" title="torch.func.hessian"><code class="xref py py-func docutils literal notranslate"><span class="pre">hessian()</span></code></a>, which is
implemented with <a class="reference internal" href="generated/torch.func.jacfwd.html#torch.func.jacfwd" title="torch.func.jacfwd"><code class="xref py py-func docutils literal notranslate"><span class="pre">jacfwd()</span></code></a>) is a composition of <a class="reference internal" href="generated/torch.func.vmap.html#torch.func.vmap" title="torch.func.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">vmap()</span></code></a> and
<a class="reference internal" href="generated/torch.func.jvp.html#torch.func.jvp" title="torch.func.jvp"><code class="xref py py-func docutils literal notranslate"><span class="pre">jvp()</span></code></a> so it also has these limitations.</p>
</div>
<p><code class="docutils literal notranslate"><span class="pre">vmap(func)</span></code> is a transform that returns a function that maps <code class="docutils literal notranslate"><span class="pre">func</span></code> over
some new dimension of each input Tensor. The mental model for vmap is that it is
like running a for-loop: for pure functions (i.e. in the absence of side
effects), <code class="docutils literal notranslate"><span class="pre">vmap(f)(x)</span></code> is equivalent to:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">([</span><span class="n">f</span><span class="p">(</span><span class="n">x_i</span><span class="p">)</span> <span class="k">for</span> <span class="n">x_i</span> <span class="ow">in</span> <span class="n">x</span><span class="o">.</span><span class="n">unbind</span><span class="p">(</span><span class="mi">0</span><span class="p">)])</span>
</pre></div>
</div>
<div class="section" id="mutation-arbitrary-mutation-of-python-data-structures">
<h3>Mutation: Arbitrary mutation of Python data structures<a class="headerlink" href="#mutation-arbitrary-mutation-of-python-data-structures" title="Permalink to this heading">¶</a></h3>
<p>In the presence of side effects, <a class="reference internal" href="generated/torch.func.vmap.html#torch.func.vmap" title="torch.func.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">vmap()</span></code></a> no longer acts like it is running
a for-loop. For example, the following function:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
<span class="nb">list</span><span class="o">.</span><span class="n">pop</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"hello!"</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">x</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">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">lst</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="mi">3</span><span class="p">]</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">vmap</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">in_dims</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="kc">None</span><span class="p">))(</span><span class="n">x</span><span class="p">,</span> <span class="n">lst</span><span class="p">)</span>
</pre></div>
</div>
<p>will print “hello!” once and pop only one element from <code class="docutils literal notranslate"><span class="pre">lst</span></code>.</p>
<p><a class="reference internal" href="generated/torch.func.vmap.html#torch.func.vmap" title="torch.func.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">vmap()</span></code></a> executes <code class="docutils literal notranslate"><span class="pre">f</span></code> a single time, so all side effects only happen once.</p>
<p>This is a consequence of how vmap is implemented. torch.func has a special,
internal BatchedTensor class. <code class="docutils literal notranslate"><span class="pre">vmap(f)(*inputs)</span></code> takes all Tensor inputs,
turns them into BatchedTensors, and calls <code class="docutils literal notranslate"><span class="pre">f(*batched_tensor_inputs)</span></code>.
BatchedTensor overrides the PyTorch API to produce batched (i.e. vectorized)
behavior for each PyTorch operator.</p>
</div>
<div class="section" id="mutation-in-place-pytorch-operations">
<h3>Mutation: in-place PyTorch Operations<a class="headerlink" href="#mutation-in-place-pytorch-operations" title="Permalink to this heading">¶</a></h3>
<p>You might be here due to receiving an error about vmap-incompatible in-place
operations. <a class="reference internal" href="generated/torch.func.vmap.html#torch.func.vmap" title="torch.func.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">vmap()</span></code></a> will raise an error if it encounters an unsupported PyTorch
in-place operation and it will succeed otherwise. Unsupported operations
are those that would cause a Tensor with more elements to be written to a
Tensor with fewer elements. Here’s an example of how this can occur:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">f</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">x</span><span class="o">.</span><span class="n">add_</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x</span>
<span class="n">x</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">1</span><span class="p">)</span>
<span class="n">y</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">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="c1"># When vmapped over, looks like it has shape [1]</span>
<span class="c1"># Raises an error because `x` has fewer elements than `y`.</span>
<span class="n">vmap</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">in_dims</span><span class="o">=</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="mi">0</span><span class="p">))(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
</pre></div>
</div>
<p><code class="docutils literal notranslate"><span class="pre">x</span></code> is a Tensor with one element, <code class="docutils literal notranslate"><span class="pre">y</span></code> is a Tensor with three elements.
<code class="docutils literal notranslate"><span class="pre">x</span> <span class="pre">+</span> <span class="pre">y</span></code> has three elements (due to broadcasting), but attempting to write
three elements back into <code class="docutils literal notranslate"><span class="pre">x</span></code>, which only has one element, raises an error
due to attempting to write three elements into a Tensor with a single element.</p>
<p>There is no problem if the Tensor being written to is batched under
<a class="reference internal" href="generated/torch.vmap.html#torch.vmap" title="torch.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">vmap()</span></code></a> (i.e. it is being vmapped over).</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">f</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">x</span><span class="o">.</span><span class="n">add_</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x</span>
<span class="n">x</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">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">y</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">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">expected</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span>
<span class="c1"># Does not raise an error because x is being vmapped over.</span>
<span class="n">vmap</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">in_dims</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</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="k">assert</span> <span class="n">torch</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">expected</span><span class="p">)</span>
</pre></div>
</div>
<p>One common fix for this is to replace calls to factory functions with
their “new_*” equivalent. For example:</p>
<ul class="simple">
<li><p>Replace <a class="reference internal" href="generated/torch.zeros.html#torch.zeros" title="torch.zeros"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.zeros()</span></code></a> with <code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.new_zeros()</span></code></p></li>
<li><p>Replace <a class="reference internal" href="generated/torch.empty.html#torch.empty" title="torch.empty"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.empty()</span></code></a> with <code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.new_empty()</span></code></p></li>
</ul>
<p>To see why this helps, consider the following.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">diag_embed</span><span class="p">(</span><span class="n">vec</span><span class="p">):</span>
<span class="k">assert</span> <span class="n">vec</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">==</span> <span class="mi">1</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">vec</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">vec</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">result</span><span class="o">.</span><span class="n">diagonal</span><span class="p">()</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">vec</span><span class="p">)</span>
<span class="k">return</span> <span class="n">result</span>
<span class="n">vecs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([[</span><span class="mf">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="p">[</span><span class="mf">3.</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">]])</span>
<span class="c1"># RuntimeError: vmap: inplace arithmetic(self, *extra_args) is not possible ...</span>
<span class="n">vmap</span><span class="p">(</span><span class="n">diag_embed</span><span class="p">)(</span><span class="n">vecs</span><span class="p">)</span>
</pre></div>
</div>
<p>Inside of <a class="reference internal" href="generated/torch.vmap.html#torch.vmap" title="torch.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">vmap()</span></code></a>, <code class="docutils literal notranslate"><span class="pre">result</span></code> is a Tensor of shape [3, 3].
However, although <code class="docutils literal notranslate"><span class="pre">vec</span></code> looks like it has shape [3], <code class="docutils literal notranslate"><span class="pre">vec</span></code> actually has
underlying shape [2, 3].
It is not possible to copy <code class="docutils literal notranslate"><span class="pre">vec</span></code> into <code class="docutils literal notranslate"><span class="pre">result.diagonal()</span></code>, which has
shape [3], because it has too many elements.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">diag_embed</span><span class="p">(</span><span class="n">vec</span><span class="p">):</span>
<span class="k">assert</span> <span class="n">vec</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">==</span> <span class="mi">1</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">vec</span><span class="o">.</span><span class="n">new_zeros</span><span class="p">(</span><span class="n">vec</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">vec</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">result</span><span class="o">.</span><span class="n">diagonal</span><span class="p">()</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">vec</span><span class="p">)</span>
<span class="k">return</span> <span class="n">result</span>
<span class="n">vecs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([[</span><span class="mf">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="p">[</span><span class="mf">3.</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">]])</span>
<span class="n">vmap</span><span class="p">(</span><span class="n">diag_embed</span><span class="p">)(</span><span class="n">vecs</span><span class="p">)</span>
</pre></div>
</div>
<p>Replacing <a class="reference internal" href="generated/torch.zeros.html#torch.zeros" title="torch.zeros"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.zeros()</span></code></a> with <code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.new_zeros()</span></code> makes it so that
<code class="docutils literal notranslate"><span class="pre">result</span></code> has an underlying Tensor of shape [2, 3, 3], so it is now possible
to copy <code class="docutils literal notranslate"><span class="pre">vec</span></code>, which has underlying shape [2, 3], into <code class="docutils literal notranslate"><span class="pre">result.diagonal()</span></code>.</p>
</div>
<div class="section" id="mutation-out-pytorch-operations">
<h3>Mutation: out= PyTorch Operations<a class="headerlink" href="#mutation-out-pytorch-operations" title="Permalink to this heading">¶</a></h3>
<p><a class="reference internal" href="generated/torch.func.vmap.html#torch.func.vmap" title="torch.func.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">vmap()</span></code></a> doesn’t support the <code class="docutils literal notranslate"><span class="pre">out=</span></code> keyword argument in PyTorch operations.
It will error out gracefully if it encounters that in your code.</p>
<p>This is not a fundamental limitation; we could theoretically support this in the
future but we have chosen not to for now.</p>
</div>
<div class="section" id="data-dependent-python-control-flow">
<h3>Data-dependent Python control flow<a class="headerlink" href="#data-dependent-python-control-flow" title="Permalink to this heading">¶</a></h3>
<p>We don’t yet support <code class="docutils literal notranslate"><span class="pre">vmap</span></code> over data-dependent control flow. Data-dependent
control flow is when the condition of an if-statement, while-loop, or
for-loop is a Tensor that is being <code class="docutils literal notranslate"><span class="pre">vmap</span></code>’ed over. For example, the
following will raise an error message:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">relu</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">if</span> <span class="n">x</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
<span class="k">return</span> <span class="n">x</span>
<span class="k">return</span> <span class="mi">0</span>
<span class="n">x</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">3</span><span class="p">)</span>
<span class="n">vmap</span><span class="p">(</span><span class="n">relu</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
<p>However, any control flow that is not dependent on the values in <code class="docutils literal notranslate"><span class="pre">vmap</span></code>’ed
tensors will work:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">custom_dot</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">if</span> <span class="n">x</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">dot</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="k">return</span> <span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="n">x</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">3</span><span class="p">)</span>
<span class="n">vmap</span><span class="p">(</span><span class="n">custom_dot</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
<p>JAX supports transforming over
<a class="reference external" href="https://jax.readthedocs.io/en/latest/jax.lax.html#control-flow-operators">data-dependent control flow</a>
using special control flow operators (e.g. <code class="docutils literal notranslate"><span class="pre">jax.lax.cond</span></code>, <code class="docutils literal notranslate"><span class="pre">jax.lax.while_loop</span></code>).
We’re investigating adding equivalents of those to PyTorch.</p>
</div>
<div class="section" id="data-dependent-operations-item">
<h3>Data-dependent operations (.item())<a class="headerlink" href="#data-dependent-operations-item" title="Permalink to this heading">¶</a></h3>
<p>We do not (and will not) support vmap over a user-defined function that calls
<code class="docutils literal notranslate"><span class="pre">.item()</span></code> on a Tensor. For example, the following will raise an error message:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
<span class="n">x</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">3</span><span class="p">)</span>
<span class="n">vmap</span><span class="p">(</span><span class="n">f</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
<p>Please try to rewrite your code to not use <code class="docutils literal notranslate"><span class="pre">.item()</span></code> calls.</p>
<p>You may also encounter an error message about using <code class="docutils literal notranslate"><span class="pre">.item()</span></code> but you might
not have used it. In those cases, it is possible that PyTorch internally is
calling <code class="docutils literal notranslate"><span class="pre">.item()</span></code> – please file an issue on GitHub and we’ll fix
PyTorch internals.</p>
</div>
<div class="section" id="dynamic-shape-operations-nonzero-and-friends">
<h3>Dynamic shape operations (nonzero and friends)<a class="headerlink" href="#dynamic-shape-operations-nonzero-and-friends" title="Permalink to this heading">¶</a></h3>
<p><code class="docutils literal notranslate"><span class="pre">vmap(f)</span></code> requires that <code class="docutils literal notranslate"><span class="pre">f</span></code> applied to every “example” in your input
returns a Tensor with the same shape. Operations such as <code class="docutils literal notranslate"><span class="pre">torch.nonzero</span></code>,
<code class="docutils literal notranslate"><span class="pre">torch.is_nonzero</span></code> are not supported and will error as a result.</p>
<p>To see why, consider the following example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">xs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</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="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">]])</span>
<span class="n">vmap</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nonzero</span><span class="p">)(</span><span class="n">xs</span><span class="p">)</span>
</pre></div>
</div>
<p><code class="docutils literal notranslate"><span class="pre">torch.nonzero(xs[0])</span></code> returns a Tensor of shape 2;
but <code class="docutils literal notranslate"><span class="pre">torch.nonzero(xs[1])</span></code> returns a Tensor of shape 1.
We are unable to construct a single Tensor as an output;
the output would need to be a ragged Tensor (and PyTorch does not yet have
the concept of a ragged Tensor).</p>
</div>
</div>
<div class="section" id="randomness">
<h2>Randomness<a class="headerlink" href="#randomness" title="Permalink to this heading">¶</a></h2>
<p>The user’s intention when calling a random operation can be unclear. Specifically, some users may want
the random behavior to be the same across batches while others may want it to differ across batches.
To address this, <code class="docutils literal notranslate"><span class="pre">vmap</span></code> takes a randomness flag.</p>
<p>The flag can only be passed to vmap and can take on 3 values, “error,” “different,” or “same,” defaulting
to error. Under “error” mode, any call to a random function will produce an error asking the user to use
one of the other two flags based on their use case.</p>
<p>Under “different” randomness, elements in a batch produce different random values. For instance,</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">add_noise</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="n">y</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="c1"># y will be different across the batch</span>
<span class="k">return</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">vmap</span><span class="p">(</span><span class="n">add_noise</span><span class="p">,</span> <span class="n">randomness</span><span class="o">=</span><span class="s2">"different"</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span> <span class="c1"># we get 3 different values</span>
</pre></div>
</div>
<p>Under “same” randomness, elements in a batch produce same random values. For instance,</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">add_noise</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="n">y</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="c1"># y will be the same across the batch</span>
<span class="k">return</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">vmap</span><span class="p">(</span><span class="n">add_noise</span><span class="p">,</span> <span class="n">randomness</span><span class="o">=</span><span class="s2">"same"</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span> <span class="c1"># we get the same value, repeated 3 times</span>
</pre></div>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Our system only determine the randomness behavior of PyTorch operators and cannot control the
behavior of other libraries, like numpy. This is similar to JAX’s limitations with their solutions</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Multiple vmap calls using either type of supported randomness will not produce
the same results. Like with standard PyTorch, a user can get randomness reproducibility through
either using <code class="docutils literal notranslate"><span class="pre">torch.manual_seed()</span></code> outside of vmap or by using generators.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Finally, our randomness differs from JAX because we aren’t using a stateless PRNG, in part because PyTorch
doesn’t have full support for a stateless PRNG. Instead, we’ve introduced a flag system to allow for the
most common forms of randomness that we see. If your use case does not fit these forms of randomness, please
file an issue.</p>
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