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<div class="section" id="torch-fx-experimental">
<h1>torch.fx.experimental<a class="headerlink" href="#torch-fx-experimental" title="Permalink to this heading">¶</a></h1>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>These APIs are experimental and subject to change without notice.</p>
</div>
<div class="section" id="module-torch.fx.experimental.symbolic_shapes">
<span id="torch-fx-experimental-symbolic-shapes"></span><h2>torch.fx.experimental.symbolic_shapes<a class="headerlink" href="#module-torch.fx.experimental.symbolic_shapes" title="Permalink to this heading">¶</a></h2>
<p><code class="docutils literal notranslate"><span class="pre">torch.fx.experimental.symbolic_shapes</span></code> provides interfaces for interacting with
our symbolic shapes reasoning system that is used heavily in torch.compile. Although
this is not generally considered public API, when writing framework code in PyTorch
as well as extensions to PyTorch (e.g., in custom operator implementations), you may
need to make use of these APIs to setup dynamic shapes support appropriately.</p>
<table class="autosummary longtable docutils colwidths-auto align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.fx.experimental.symbolic_shapes.ShapeEnv"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.ShapeEnv.html#torch.fx.experimental.symbolic_shapes.ShapeEnv" title="torch.fx.experimental.symbolic_shapes.ShapeEnv"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ShapeEnv</span></code></a></p></td>
<td><p></p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.fx.experimental.symbolic_shapes.DimDynamic"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.DimDynamic.html#torch.fx.experimental.symbolic_shapes.DimDynamic" title="torch.fx.experimental.symbolic_shapes.DimDynamic"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DimDynamic</span></code></a></p></td>
<td><p>Controls how to perform symbol allocation for a dimension.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.fx.experimental.symbolic_shapes.StrictMinMaxConstraint"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.StrictMinMaxConstraint.html#torch.fx.experimental.symbolic_shapes.StrictMinMaxConstraint" title="torch.fx.experimental.symbolic_shapes.StrictMinMaxConstraint"><code class="xref py py-obj docutils literal notranslate"><span class="pre">StrictMinMaxConstraint</span></code></a></p></td>
<td><p>For clients: the size at this dimension must be within 'vr' (which specifies a lower and upper bound, inclusive-inclusive) AND it must be non-negative and should not be 0 or 1 (but see NB below).</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.fx.experimental.symbolic_shapes.RelaxedUnspecConstraint"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.RelaxedUnspecConstraint.html#torch.fx.experimental.symbolic_shapes.RelaxedUnspecConstraint" title="torch.fx.experimental.symbolic_shapes.RelaxedUnspecConstraint"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RelaxedUnspecConstraint</span></code></a></p></td>
<td><p>For clients: no explicit constraint; constraint is whatever is implicitly inferred by guards from tracing.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.fx.experimental.symbolic_shapes.EqualityConstraint"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.EqualityConstraint.html#torch.fx.experimental.symbolic_shapes.EqualityConstraint" title="torch.fx.experimental.symbolic_shapes.EqualityConstraint"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EqualityConstraint</span></code></a></p></td>
<td><p>Represent and decide various kinds of equality constraints between input sources.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.fx.experimental.symbolic_shapes.SymbolicContext"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.SymbolicContext.html#torch.fx.experimental.symbolic_shapes.SymbolicContext" title="torch.fx.experimental.symbolic_shapes.SymbolicContext"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SymbolicContext</span></code></a></p></td>
<td><p>Data structure specifying how we should create symbols in <code class="docutils literal notranslate"><span class="pre">create_symbolic_sizes_strides_storage_offset</span></code>; e.g., should they be static or dynamic.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.fx.experimental.symbolic_shapes.StatelessSymbolicContext"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.StatelessSymbolicContext.html#torch.fx.experimental.symbolic_shapes.StatelessSymbolicContext" title="torch.fx.experimental.symbolic_shapes.StatelessSymbolicContext"><code class="xref py py-obj docutils literal notranslate"><span class="pre">StatelessSymbolicContext</span></code></a></p></td>
<td><p>Create symbols in <code class="docutils literal notranslate"><span class="pre">create_symbolic_sizes_strides_storage_offset</span></code> via a symbolic_context determination as given by <code class="docutils literal notranslate"><span class="pre">DimDynamic</span></code> and <code class="docutils literal notranslate"><span class="pre">DimConstraint</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.fx.experimental.symbolic_shapes.StatefulSymbolicContext"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.StatefulSymbolicContext.html#torch.fx.experimental.symbolic_shapes.StatefulSymbolicContext" title="torch.fx.experimental.symbolic_shapes.StatefulSymbolicContext"><code class="xref py py-obj docutils literal notranslate"><span class="pre">StatefulSymbolicContext</span></code></a></p></td>
<td><p>Create symbols in <code class="docutils literal notranslate"><span class="pre">create_symbolic_sizes_strides_storage_offset</span></code> via a symbolic_context determination as given by a cache of Source:Symbol.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.fx.experimental.symbolic_shapes.SubclassSymbolicContext"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.SubclassSymbolicContext.html#torch.fx.experimental.symbolic_shapes.SubclassSymbolicContext" title="torch.fx.experimental.symbolic_shapes.SubclassSymbolicContext"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SubclassSymbolicContext</span></code></a></p></td>
<td><p>The correct symbolic context for a given inner tensor of a traceable tensor subclass may differ from that of the outer symbolic context.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.fx.experimental.symbolic_shapes.DimConstraints"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.DimConstraints.html#torch.fx.experimental.symbolic_shapes.DimConstraints" title="torch.fx.experimental.symbolic_shapes.DimConstraints"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DimConstraints</span></code></a></p></td>
<td><p>Custom solver for a system of constraints on symbolic dimensions.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.fx.experimental.symbolic_shapes.hint_int"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.hint_int.html#torch.fx.experimental.symbolic_shapes.hint_int" title="torch.fx.experimental.symbolic_shapes.hint_int"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hint_int</span></code></a></p></td>
<td><p>Retrieve the hint for an int (based on the underlying real values as observed at runtime).</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.fx.experimental.symbolic_shapes.is_concrete_int"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.is_concrete_int.html#torch.fx.experimental.symbolic_shapes.is_concrete_int" title="torch.fx.experimental.symbolic_shapes.is_concrete_int"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_concrete_int</span></code></a></p></td>
<td><p>Utility to check if underlying object in SymInt is concrete value.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.fx.experimental.symbolic_shapes.is_concrete_bool"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.is_concrete_bool.html#torch.fx.experimental.symbolic_shapes.is_concrete_bool" title="torch.fx.experimental.symbolic_shapes.is_concrete_bool"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_concrete_bool</span></code></a></p></td>
<td><p>Utility to check if underlying object in SymBool is concrete value.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.fx.experimental.symbolic_shapes.has_free_symbols"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.has_free_symbols.html#torch.fx.experimental.symbolic_shapes.has_free_symbols" title="torch.fx.experimental.symbolic_shapes.has_free_symbols"><code class="xref py py-obj docutils literal notranslate"><span class="pre">has_free_symbols</span></code></a></p></td>
<td><p>Faster version of bool(free_symbols(val))</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.fx.experimental.symbolic_shapes.definitely_true"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.definitely_true.html#torch.fx.experimental.symbolic_shapes.definitely_true" title="torch.fx.experimental.symbolic_shapes.definitely_true"><code class="xref py py-obj docutils literal notranslate"><span class="pre">definitely_true</span></code></a></p></td>
<td><p>Returns True only if we can tell that a is True, possibly introducing a guard in the process.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.fx.experimental.symbolic_shapes.definitely_false"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.definitely_false.html#torch.fx.experimental.symbolic_shapes.definitely_false" title="torch.fx.experimental.symbolic_shapes.definitely_false"><code class="xref py py-obj docutils literal notranslate"><span class="pre">definitely_false</span></code></a></p></td>
<td><p>Returns True only if we can tell that a is False, possibly introducing a guard in the process.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.fx.experimental.symbolic_shapes.guard_size_oblivious"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.guard_size_oblivious.html#torch.fx.experimental.symbolic_shapes.guard_size_oblivious" title="torch.fx.experimental.symbolic_shapes.guard_size_oblivious"><code class="xref py py-obj docutils literal notranslate"><span class="pre">guard_size_oblivious</span></code></a></p></td>
<td><p>Perform a guard on a symbolic boolean expression in a size oblivious way.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.fx.experimental.symbolic_shapes.parallel_or"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.parallel_or.html#torch.fx.experimental.symbolic_shapes.parallel_or" title="torch.fx.experimental.symbolic_shapes.parallel_or"><code class="xref py py-obj docutils literal notranslate"><span class="pre">parallel_or</span></code></a></p></td>
<td><p>Evaluate the logical OR of several arguments, avoiding guarding on unbacked SymInts if another argument is definitely True.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.fx.experimental.symbolic_shapes.parallel_and"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.parallel_and.html#torch.fx.experimental.symbolic_shapes.parallel_and" title="torch.fx.experimental.symbolic_shapes.parallel_and"><code class="xref py py-obj docutils literal notranslate"><span class="pre">parallel_and</span></code></a></p></td>
<td><p>Evaluate the logical FALSE of several arguments, avoiding guarding on unbacked SymInts if another argument is definitely False.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.fx.experimental.symbolic_shapes.sym_eq"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.sym_eq.html#torch.fx.experimental.symbolic_shapes.sym_eq" title="torch.fx.experimental.symbolic_shapes.sym_eq"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sym_eq</span></code></a></p></td>
<td><p>Like ==, but when run on list/tuple, it will recursively test equality and use sym_and to join the results together, without guarding.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.fx.experimental.symbolic_shapes.constrain_range"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.constrain_range.html#torch.fx.experimental.symbolic_shapes.constrain_range" title="torch.fx.experimental.symbolic_shapes.constrain_range"><code class="xref py py-obj docutils literal notranslate"><span class="pre">constrain_range</span></code></a></p></td>
<td><p>Applies a constraint that the passed in SymInt must lie between min-max inclusive-inclusive, WITHOUT introducing a guard on the SymInt (meaning that it can be used on unbacked SymInts).</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.fx.experimental.symbolic_shapes.constrain_unify"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.constrain_unify.html#torch.fx.experimental.symbolic_shapes.constrain_unify" title="torch.fx.experimental.symbolic_shapes.constrain_unify"><code class="xref py py-obj docutils literal notranslate"><span class="pre">constrain_unify</span></code></a></p></td>
<td><p>Given two SymInts, constrain them so that they must be equal.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.fx.experimental.symbolic_shapes.canonicalize_bool_expr"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.canonicalize_bool_expr.html#torch.fx.experimental.symbolic_shapes.canonicalize_bool_expr" title="torch.fx.experimental.symbolic_shapes.canonicalize_bool_expr"><code class="xref py py-obj docutils literal notranslate"><span class="pre">canonicalize_bool_expr</span></code></a></p></td>
<td><p>Canonicalize a boolean expression by transforming it into a lt / le inequality and moving all the non-constant terms to the rhs.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.fx.experimental.symbolic_shapes.statically_known_true"/><a class="reference internal" href="generated/torch.fx.experimental.symbolic_shapes.statically_known_true.html#torch.fx.experimental.symbolic_shapes.statically_known_true" title="torch.fx.experimental.symbolic_shapes.statically_known_true"><code class="xref py py-obj docutils literal notranslate"><span class="pre">statically_known_true</span></code></a></p></td>
<td><p>Returns True if x can be simplified to a constant and is true.</p></td>
</tr>
</tbody>
</table>
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