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<h1>Source code for torch.export</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">builtins</span>
<span class="kn">import</span> <span class="nn">copy</span>
<span class="kn">import</span> <span class="nn">dataclasses</span>
<span class="kn">import</span> <span class="nn">inspect</span>
<span class="kn">import</span> <span class="nn">io</span>
<span class="kn">import</span> <span class="nn">math</span>
<span class="kn">import</span> <span class="nn">pathlib</span>
<span class="kn">import</span> <span class="nn">sys</span>
<span class="kn">import</span> <span class="nn">typing</span>
<span class="kn">from</span> <span class="nn">enum</span> <span class="kn">import</span> <span class="n">auto</span><span class="p">,</span> <span class="n">Enum</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="p">(</span>
<span class="n">Any</span><span class="p">,</span>
<span class="n">Callable</span><span class="p">,</span>
<span class="n">Dict</span><span class="p">,</span>
<span class="n">Iterator</span><span class="p">,</span>
<span class="n">List</span><span class="p">,</span>
<span class="n">Optional</span><span class="p">,</span>
<span class="n">Tuple</span><span class="p">,</span>
<span class="n">Type</span><span class="p">,</span>
<span class="n">TYPE_CHECKING</span><span class="p">,</span>
<span class="n">Union</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.fx._pytree</span> <span class="k">as</span> <span class="nn">fx_pytree</span>
<span class="kn">import</span> <span class="nn">torch.utils._pytree</span> <span class="k">as</span> <span class="nn">pytree</span>
<span class="kn">from</span> <span class="nn">torch.fx._compatibility</span> <span class="kn">import</span> <span class="n">compatibility</span>
<span class="kn">from</span> <span class="nn">torch.fx.passes.infra.pass_base</span> <span class="kn">import</span> <span class="n">PassResult</span>
<span class="kn">from</span> <span class="nn">torch.fx.passes.infra.pass_manager</span> <span class="kn">import</span> <span class="n">PassManager</span>
<span class="kn">from</span> <span class="nn">torch.utils._pytree</span> <span class="kn">import</span> <span class="p">(</span>
<span class="n">FlattenFunc</span><span class="p">,</span>
<span class="n">FromDumpableContextFn</span><span class="p">,</span>
<span class="n">ToDumpableContextFn</span><span class="p">,</span>
<span class="n">UnflattenFunc</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">if</span> <span class="n">TYPE_CHECKING</span><span class="p">:</span>
<span class="c1"># Import the following modules during type checking to enable code intelligence features,</span>
<span class="c1"># Do not import unconditionally, as they import sympy and importing sympy is very slow</span>
<span class="kn">from</span> <span class="nn">torch.fx.experimental.symbolic_shapes</span> <span class="kn">import</span> <span class="n">StrictMinMaxConstraint</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span>
<span class="s2">"Constraint"</span><span class="p">,</span>
<span class="s2">"Dim"</span><span class="p">,</span>
<span class="s2">"ExportBackwardSignature"</span><span class="p">,</span>
<span class="s2">"ExportGraphSignature"</span><span class="p">,</span>
<span class="s2">"ExportedProgram"</span><span class="p">,</span>
<span class="s2">"ModuleCallEntry"</span><span class="p">,</span>
<span class="s2">"ModuleCallSignature"</span><span class="p">,</span>
<span class="s2">"dims"</span><span class="p">,</span>
<span class="s2">"dynamic_dim"</span><span class="p">,</span>
<span class="s2">"export"</span><span class="p">,</span>
<span class="s2">"load"</span><span class="p">,</span>
<span class="s2">"register_dataclass"</span><span class="p">,</span>
<span class="s2">"save"</span><span class="p">,</span>
<span class="p">]</span>
<span class="kn">from</span> <span class="nn">.exported_program</span> <span class="kn">import</span> <span class="n">ExportedProgram</span><span class="p">,</span> <span class="n">ModuleCallEntry</span><span class="p">,</span> <span class="n">ModuleCallSignature</span>
<span class="kn">from</span> <span class="nn">.graph_signature</span> <span class="kn">import</span> <span class="n">ExportBackwardSignature</span><span class="p">,</span> <span class="n">ExportGraphSignature</span>
<span class="n">PassType</span> <span class="o">=</span> <span class="n">Callable</span><span class="p">[[</span><span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">],</span> <span class="n">Optional</span><span class="p">[</span><span class="n">PassResult</span><span class="p">]]</span>
<span class="nd">@dataclasses</span><span class="o">.</span><span class="n">dataclass</span>
<span class="k">class</span> <span class="nc">_ConstraintTarget</span><span class="p">:</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> This represents input tensor dimensions. Don't create this</span>
<span class="sd"> class directly; instead, use :func:`dynamic_dim`.</span>
<span class="sd"> """</span>
<span class="n">w_tensor</span><span class="p">:</span> <span class="n">Any</span> <span class="c1"># weakref to torch.Tensor</span>
<span class="c1"># TODO: We don't need t_id; we can get it off of w_tensor</span>
<span class="n">t_id</span><span class="p">:</span> <span class="nb">int</span>
<span class="n">dim</span><span class="p">:</span> <span class="nb">int</span>
<span class="k">class</span> <span class="nc">_ConstraintFactory</span><span class="p">(</span><span class="nb">type</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Metaclass that ensures a private constructor for :class:`Constraint`</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="bp">cls</span><span class="o">.</span><span class="vm">__module__</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="bp">cls</span><span class="o">.</span><span class="vm">__qualname__</span><span class="si">}</span><span class="s2"> has no public constructor. "</span>
<span class="sa">f</span><span class="s2">"Please use torch.export.dynamic_dim() to create one"</span>
<span class="p">)</span>
<span class="k">def</span> <span class="nf">_create</span><span class="p">(</span>
<span class="bp">cls</span><span class="p">,</span> <span class="n">w_tensor</span><span class="p">,</span> <span class="n">t_id</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span> <span class="n">constraint_range</span><span class="p">,</span> <span class="n">shared</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">debug_name</span><span class="o">=</span><span class="kc">None</span>
<span class="p">):</span>
<span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__call__</span><span class="p">(</span>
<span class="n">w_tensor</span><span class="p">,</span> <span class="n">t_id</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span> <span class="n">constraint_range</span><span class="p">,</span> <span class="n">shared</span><span class="p">,</span> <span class="n">debug_name</span>
<span class="p">)</span>
<span class="k">def</span> <span class="nf">_create_constraint</span><span class="p">(</span>
<span class="n">w_tensor</span><span class="p">,</span> <span class="n">t_id</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span> <span class="n">constraint_range</span><span class="p">,</span> <span class="n">shared</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">debug_name</span><span class="o">=</span><span class="kc">None</span>
<span class="p">):</span>
<span class="k">return</span> <span class="n">Constraint</span><span class="o">.</span><span class="n">_create</span><span class="p">(</span><span class="n">w_tensor</span><span class="p">,</span> <span class="n">t_id</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span> <span class="n">constraint_range</span><span class="p">,</span> <span class="n">shared</span><span class="p">,</span> <span class="n">debug_name</span><span class="p">)</span>
<div class="viewcode-block" id="Constraint"><a class="viewcode-back" href="../../export.html#torch.export.Constraint">[docs]</a><span class="nd">@dataclasses</span><span class="o">.</span><span class="n">dataclass</span>
<span class="k">class</span> <span class="nc">Constraint</span><span class="p">(</span><span class="n">_ConstraintTarget</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">_ConstraintFactory</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> .. warning::</span>
<span class="sd"> Do not construct :class:`Constraint` directly, use :func:`dynamic_dim` instead.</span>
<span class="sd"> This represents constraints on input tensor dimensions, e.g., requiring</span>
<span class="sd"> them to be fully polymorphic or within some range.</span>
<span class="sd"> """</span>
<span class="c1"># NOTE(avik): In the future, this could be Union[StrictMinMaxConstraint, <other kinds>]</span>
<span class="n">constraint_range</span><span class="p">:</span> <span class="s2">"StrictMinMaxConstraint"</span>
<span class="c1"># Represent that `constraint_range` is shared with another _ConstraintTarget, which</span>
<span class="c1"># typically arises because of a specified equality with another dynamic dimension.</span>
<span class="n">shared</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">_ConstraintTarget</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
<span class="n">debug_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">def</span> <span class="nf">_clone_with_range</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lower</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">upper</span><span class="o">=</span><span class="n">math</span><span class="o">.</span><span class="n">inf</span><span class="p">):</span>
<span class="c1"># Import sympy locally</span>
<span class="kn">from</span> <span class="nn">torch.fx.experimental.symbolic_shapes</span> <span class="kn">import</span> <span class="n">StrictMinMaxConstraint</span>
<span class="kn">from</span> <span class="nn">torch.utils._sympy.value_ranges</span> <span class="kn">import</span> <span class="n">ValueRanges</span>
<span class="n">constraint_range</span> <span class="o">=</span> <span class="n">StrictMinMaxConstraint</span><span class="p">(</span>
<span class="n">vr</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">constraint_range</span><span class="o">.</span><span class="n">vr</span> <span class="o">&</span> <span class="n">ValueRanges</span><span class="p">(</span><span class="n">lower</span><span class="o">=</span><span class="n">lower</span><span class="p">,</span> <span class="n">upper</span><span class="o">=</span><span class="n">upper</span><span class="p">),</span>
<span class="n">warn_only</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">_create_constraint</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">w_tensor</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">t_id</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">dim</span><span class="p">,</span>
<span class="n">constraint_range</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">shared</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">debug_name</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">def</span> <span class="fm">__ge__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lower</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_clone_with_range</span><span class="p">(</span><span class="n">lower</span><span class="o">=</span><span class="n">lower</span><span class="p">)</span>
<span class="k">def</span> <span class="fm">__gt__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lower</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_clone_with_range</span><span class="p">(</span><span class="n">lower</span><span class="o">=</span><span class="n">lower</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">def</span> <span class="fm">__le__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">upper</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_clone_with_range</span><span class="p">(</span><span class="n">upper</span><span class="o">=</span><span class="n">upper</span><span class="p">)</span>
<span class="k">def</span> <span class="fm">__lt__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">upper</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_clone_with_range</span><span class="p">(</span><span class="n">upper</span><span class="o">=</span><span class="n">upper</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">def</span> <span class="fm">__bool__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="c1"># NOTE(avik): We do not support compound expressions like a <= x <= b.</span>
<span class="c1"># This is because Python implicitly desugars them into bool(a <= x) and bool(x <= b),</span>
<span class="c1"># and moreover, enforces that any overload of __bool__ must return True or False.</span>
<span class="c1"># FWIW, sympy also raises TypeError in this case.</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
<span class="s2">"Cannot determine truth value of Constraint. "</span>
<span class="s2">"If you are trying to combine Constraint's with logical connectives, "</span>
<span class="s2">"you can specify them separately instead."</span>
<span class="p">)</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">serializable_spec</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="c1"># We need a serialization compatible format of the constraint so that it</span>
<span class="c1"># can be savedin the graph module w/o breaking the module serialization.</span>
<span class="c1"># The saved constraints will be used directly for the post-exporting pass</span>
<span class="c1"># that converts constraints to runtime assertion. The saved constraints</span>
<span class="c1"># will not be saved in the serialized module.</span>
<span class="c1"># TODO: A better way is needed. Currently we use 't_id' to map the constraint,</span>
<span class="c1"># which is not reliable</span>
<span class="k">return</span> <span class="p">{</span>
<span class="s2">"t_id"</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">t_id</span><span class="p">,</span>
<span class="s2">"dim"</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim</span><span class="p">,</span>
<span class="s2">"min"</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">constraint_range</span><span class="o">.</span><span class="n">vr</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span>
<span class="s2">"max"</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">constraint_range</span><span class="o">.</span><span class="n">vr</span><span class="o">.</span><span class="n">upper</span><span class="p">,</span>
<span class="s2">"shared"</span><span class="p">:</span> <span class="p">(</span>
<span class="kc">None</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">shared</span> <span class="ow">is</span> <span class="kc">None</span>
<span class="k">else</span> <span class="p">{</span>
<span class="s2">"t_id"</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">shared</span><span class="o">.</span><span class="n">t_id</span><span class="p">,</span>
<span class="s2">"dim"</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">shared</span><span class="o">.</span><span class="n">dim</span><span class="p">,</span>
<span class="p">}</span>
<span class="p">),</span>
<span class="p">}</span>
<span class="k">def</span> <span class="fm">__eq__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">Constraint</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
<span class="s2">"A dynamic dim can be specified equal only to another dynamic dim. "</span>
<span class="sa">f</span><span class="s2">"Equality with </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">other</span><span class="p">)</span><span class="si">}</span><span class="s2"> is not supported."</span>
<span class="p">)</span>
<span class="c1"># import sympy locally</span>
<span class="kn">from</span> <span class="nn">torch.fx.experimental.symbolic_shapes</span> <span class="kn">import</span> <span class="n">StrictMinMaxConstraint</span>
<span class="n">constraint_range</span> <span class="o">=</span> <span class="n">StrictMinMaxConstraint</span><span class="p">(</span>
<span class="n">vr</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">constraint_range</span><span class="o">.</span><span class="n">vr</span> <span class="o">&</span> <span class="n">other</span><span class="o">.</span><span class="n">constraint_range</span><span class="o">.</span><span class="n">vr</span><span class="p">,</span>
<span class="n">warn_only</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">debug_name</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">debug_name</span> <span class="o">=</span> <span class="n">other</span><span class="o">.</span><span class="n">debug_name</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">assert</span> <span class="n">other</span><span class="o">.</span><span class="n">debug_name</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">debug_name</span> <span class="o">==</span> <span class="n">other</span><span class="o">.</span><span class="n">debug_name</span>
<span class="n">debug_name</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">debug_name</span>
<span class="k">return</span> <span class="n">_create_constraint</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">w_tensor</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">t_id</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">dim</span><span class="p">,</span>
<span class="n">constraint_range</span><span class="p">,</span>
<span class="n">shared</span><span class="o">=</span><span class="n">_ConstraintTarget</span><span class="p">(</span><span class="n">other</span><span class="o">.</span><span class="n">w_tensor</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">t_id</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">dim</span><span class="p">),</span>
<span class="n">debug_name</span><span class="o">=</span><span class="n">debug_name</span><span class="p">,</span>
<span class="p">)</span></div>
<div class="viewcode-block" id="dynamic_dim"><a class="viewcode-back" href="../../export.html#torch.export.dynamic_dim">[docs]</a><span class="k">def</span> <span class="nf">dynamic_dim</span><span class="p">(</span><span class="n">t</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">index</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> .. warning::</span>
<span class="sd"> (This feature is DEPRECATED. See :func:`Dim` instead.)</span>
<span class="sd"> :func:`dynamic_dim` constructs a :class:`Constraint` object that describes the dynamism of</span>
<span class="sd"> a dimension ``index`` of tensor ``t``. :class:`Constraint` objects should be passed to</span>
<span class="sd"> ``constraints`` argument of :func:`export`.</span>
<span class="sd"> Args:</span>
<span class="sd"> t (torch.Tensor): Example input tensor that have dynamic dimension size(s)</span>
<span class="sd"> index (int): Index of dynamic dimension</span>
<span class="sd"> Returns:</span>
<span class="sd"> A :class:`Constraint` object that describes shape dynamism. It can be passed to :func:`export` so</span>
<span class="sd"> that :func:`export` does not assume static size of specified tensor, i.e. keeping it dynamic</span>
<span class="sd"> as a symbolic size rather than specializing according to size of example tracing input.</span>
<span class="sd"> Specifically :func:`dynamic_dim` can be used to express following types of dynamism.</span>
<span class="sd"> - Size of a dimension is dynamic and unbounded::</span>
<span class="sd"> t0 = torch.rand(2, 3)</span>
<span class="sd"> t1 = torch.rand(3, 4)</span>
<span class="sd"> # First dimension of t0 can be dynamic size rather than always being static size 2</span>
<span class="sd"> constraints = [dynamic_dim(t0, 0)]</span>
<span class="sd"> ep = export(fn, (t0, t1), constraints=constraints)</span>
<span class="sd"> - Size of a dimension is dynamic with a lower bound::</span>
<span class="sd"> t0 = torch.rand(10, 3)</span>
<span class="sd"> t1 = torch.rand(3, 4)</span>
<span class="sd"> # First dimension of t0 can be dynamic size with a lower bound of 5 (inclusive)</span>
<span class="sd"> # Second dimension of t1 can be dynamic size with a lower bound of 2 (exclusive)</span>
<span class="sd"> constraints = [</span>
<span class="sd"> dynamic_dim(t0, 0) >= 5,</span>
<span class="sd"> dynamic_dim(t1, 1) > 2,</span>
<span class="sd"> ]</span>
<span class="sd"> ep = export(fn, (t0, t1), constraints=constraints)</span>
<span class="sd"> - Size of a dimension is dynamic with an upper bound::</span>
<span class="sd"> t0 = torch.rand(10, 3)</span>
<span class="sd"> t1 = torch.rand(3, 4)</span>
<span class="sd"> # First dimension of t0 can be dynamic size with a upper bound of 16 (inclusive)</span>
<span class="sd"> # Second dimension of t1 can be dynamic size with a upper bound of 8 (exclusive)</span>
<span class="sd"> constraints = [</span>
<span class="sd"> dynamic_dim(t0, 0) <= 16,</span>
<span class="sd"> dynamic_dim(t1, 1) < 8,</span>
<span class="sd"> ]</span>
<span class="sd"> ep = export(fn, (t0, t1), constraints=constraints)</span>
<span class="sd"> - Size of a dimension is dynamic and it is always equal to size of another dynamic dimension::</span>
<span class="sd"> t0 = torch.rand(10, 3)</span>
<span class="sd"> t1 = torch.rand(3, 4)</span>
<span class="sd"> # Sizes of second dimension of t0 and first dimension are always equal</span>
<span class="sd"> constraints = [</span>
<span class="sd"> dynamic_dim(t0, 1) == dynamic_dim(t1, 0),</span>
<span class="sd"> ]</span>
<span class="sd"> ep = export(fn, (t0, t1), constraints=constraints)</span>
<span class="sd"> - Mix and match all types above as long as they do not express conflicting requirements</span>
<span class="sd"> """</span>
<span class="kn">from</span> <span class="nn">torch._export</span> <span class="kn">import</span> <span class="n">dynamic_dim</span>
<span class="k">return</span> <span class="n">dynamic_dim</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">index</span><span class="p">)</span></div>
<span class="k">class</span> <span class="nc">_Dim</span><span class="p">(</span><span class="nb">type</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Metaclass for :func:`Dim` types.</span>
<span class="sd"> """</span>
<span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">readable</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">min_</span><span class="p">,</span> <span class="n">max_</span><span class="p">):</span>
<span class="k">if</span> <span class="n">min_</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
<span class="n">min_</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">if</span> <span class="n">max_</span> <span class="o">==</span> <span class="n">sys</span><span class="o">.</span><span class="n">maxsize</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">max_</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">if</span> <span class="n">min_</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">max_</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span> <span class="sa">f</span><span class="s2">"Dim('</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">')"</span>
<span class="k">if</span> <span class="n">min_</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span> <span class="sa">f</span><span class="s2">"Dim('</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">', max=</span><span class="si">{</span><span class="n">max_</span><span class="si">}</span><span class="s2">)"</span>
<span class="k">if</span> <span class="n">max_</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span> <span class="sa">f</span><span class="s2">"Dim('</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">', min=</span><span class="si">{</span><span class="n">min_</span><span class="si">}</span><span class="s2">)"</span>
<span class="k">return</span> <span class="sa">f</span><span class="s2">"Dim('</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">', min=</span><span class="si">{</span><span class="n">min_</span><span class="si">}</span><span class="s2">, max=</span><span class="si">{</span><span class="n">max_</span><span class="si">}</span><span class="s2">)"</span>
<div class="viewcode-block" id="Dim"><a class="viewcode-back" href="../../export.html#torch.export.Dim">[docs]</a><span class="k">def</span> <span class="nf">Dim</span><span class="p">(</span><span class="n">name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="nb">min</span><span class="p">:</span> <span class="n">Optional</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="p">,</span> <span class="nb">max</span><span class="p">:</span> <span class="n">Optional</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="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> :func:`Dim` constructs a type analogous to a named symbolic integer with a range.</span>
<span class="sd"> It can be used to describe multiple possible values of a dynamic tensor dimension.</span>
<span class="sd"> Note that different dynamic dimensions of the same tensor, or of different tensors,</span>
<span class="sd"> can be described by the same type.</span>
<span class="sd"> Args:</span>
<span class="sd"> name (str): Human-readable name for debugging.</span>
<span class="sd"> min (Optional[int]): Minimum possible value of given symbol (inclusive)</span>
<span class="sd"> max (Optional[int]): Maximum possible value of given symbol (inclusive)</span>
<span class="sd"> Returns:</span>
<span class="sd"> A type that can be used in dynamic shape specifications for tensors.</span>
<span class="sd"> """</span>
<span class="n">_min</span> <span class="o">=</span> <span class="mi">2</span> <span class="k">if</span> <span class="nb">min</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">builtins</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="nb">min</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">_max</span> <span class="o">=</span> <span class="n">sys</span><span class="o">.</span><span class="n">maxsize</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">if</span> <span class="nb">max</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">builtins</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="nb">max</span><span class="p">,</span> <span class="n">sys</span><span class="o">.</span><span class="n">maxsize</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">assert</span> <span class="n">_max</span> <span class="o">></span> <span class="n">_min</span><span class="p">,</span> <span class="sa">f</span><span class="s2">"Cannot create Dim with inconsistent min=</span><span class="si">{</span><span class="nb">min</span><span class="si">}</span><span class="s2">, max=</span><span class="si">{</span><span class="nb">max</span><span class="si">}</span><span class="s2">"</span>
<span class="n">dim</span> <span class="o">=</span> <span class="n">_Dim</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="p">(</span><span class="nb">int</span><span class="p">,),</span> <span class="p">{</span><span class="s2">"min"</span><span class="p">:</span> <span class="n">_min</span><span class="p">,</span> <span class="s2">"max"</span><span class="p">:</span> <span class="n">_max</span><span class="p">})</span>
<span class="n">dim</span><span class="o">.</span><span class="vm">__module__</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span>
<span class="n">inspect</span><span class="o">.</span><span class="n">getmodule</span><span class="p">(</span><span class="n">inspect</span><span class="o">.</span><span class="n">stack</span><span class="p">()[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]),</span> <span class="s2">"__name__"</span><span class="p">,</span> <span class="s2">"__main__"</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">dim</span></div>
<div class="viewcode-block" id="dims"><a class="viewcode-back" href="../../export.html#torch.export.dims">[docs]</a><span class="k">def</span> <span class="nf">dims</span><span class="p">(</span><span class="o">*</span><span class="n">names</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="nb">min</span><span class="p">:</span> <span class="n">Optional</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="p">,</span> <span class="nb">max</span><span class="p">:</span> <span class="n">Optional</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="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Util to create multiple :func:`Dim` types.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">Dim</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="nb">min</span><span class="o">=</span><span class="nb">min</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="nb">max</span><span class="p">)</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">names</span><span class="p">)</span></div>
<div class="viewcode-block" id="export"><a class="viewcode-back" href="../../export.html#torch.export.export">[docs]</a><span class="k">def</span> <span class="nf">export</span><span class="p">(</span>
<span class="n">f</span><span class="p">:</span> <span class="n">Callable</span><span class="p">,</span>
<span class="n">args</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span>
<span class="n">kwargs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="o">*</span><span class="p">,</span>
<span class="n">constraints</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="n">Constraint</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">dynamic_shapes</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">],</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Any</span><span class="p">]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">strict</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
<span class="n">preserve_module_call_signature</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="o">=</span> <span class="p">(),</span>
<span class="p">)</span> <span class="o">-></span> <span class="n">ExportedProgram</span><span class="p">:</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> :func:`export` takes an arbitrary Python callable (an nn.Module, a function or</span>
<span class="sd"> a method) along with example inputs, and produces a traced graph representing</span>
<span class="sd"> only the Tensor computation of the function in an Ahead-of-Time (AOT) fashion,</span>
<span class="sd"> which can subsequently be executed with different inputs or serialized. The</span>
<span class="sd"> traced graph (1) produces normalized operators in the functional ATen operator set</span>
<span class="sd"> (as well as any user-specified custom operators), (2) has eliminated all Python control</span>
<span class="sd"> flow and data structures (with certain exceptions), and (3) records the set of</span>
<span class="sd"> shape constraints needed to show that this normalization and control-flow elimination</span>
<span class="sd"> is sound for future inputs.</span>
<span class="sd"> **Soundness Guarantee**</span>
<span class="sd"> While tracing, :func:`export()` takes note of shape-related assumptions</span>
<span class="sd"> made by the user program and the underlying PyTorch operator kernels.</span>
<span class="sd"> The output :class:`ExportedProgram` is considered valid only when these</span>
<span class="sd"> assumptions hold true.</span>
<span class="sd"> Tracing makes assumptions on the shapes (not values) of input tensors.</span>
<span class="sd"> Such assumptions must be validated at graph capture time for :func:`export`</span>
<span class="sd"> to succeed. Specifically:</span>
<span class="sd"> - Assumptions on static shapes of input tensors are automatically validated without additional effort.</span>
<span class="sd"> - Assumptions on dynamic shape of input tensors require explicit specification</span>
<span class="sd"> by using the :func:`Dim` API to construct dynamic dimensions and by associating</span>
<span class="sd"> them with example inputs through the ``dynamic_shapes`` argument.</span>
<span class="sd"> If any assumption can not be validated, a fatal error will be raised. When that happens,</span>
<span class="sd"> the error message will include suggested fixes to the specification that are needed</span>
<span class="sd"> to validate the assumptions. For example :func:`export` might suggest the</span>
<span class="sd"> following fix to the definition of a dynamic dimension ``dim0_x``, say appearing in the</span>
<span class="sd"> shape associated with input ``x``, that was previously defined as ``Dim("dim0_x")``::</span>
<span class="sd"> dim = Dim("dim0_x", max=5)</span>
<span class="sd"> This example means the generated code requires dimension 0 of input ``x`` to be less</span>
<span class="sd"> than or equal to 5 to be valid. You can inspect the suggested fixes to dynamic dimension</span>
<span class="sd"> definitions and then copy them verbatim into your code without needing to change the</span>
<span class="sd"> ``dynamic_shapes`` argument to your :func:`export` call.</span>
<span class="sd"> Args:</span>
<span class="sd"> f: The callable to trace.</span>
<span class="sd"> args: Example positional inputs.</span>
<span class="sd"> kwargs: Optional example keyword inputs.</span>
<span class="sd"> constraints: [DEPRECATED: use ``dynamic_shapes`` instead, see below]</span>
<span class="sd"> An optional list of constraints on the dynamic arguments</span>
<span class="sd"> that specify their possible range of shapes. By default, shapes of</span>
<span class="sd"> input torch.Tensors are assumed to be static. If an input torch.Tensor</span>
<span class="sd"> is expected to have dynamic shapes, please use :func:`dynamic_dim`</span>
<span class="sd"> to define :class:`Constraint` objects that specify the dynamics and the possible</span>
<span class="sd"> range of shapes. See :func:`dynamic_dim` docstring for examples on</span>
<span class="sd"> how to use it.</span>
<span class="sd"> dynamic_shapes: Should either be:</span>
<span class="sd"> 1) a dict from argument names of ``f`` to their dynamic shape specifications,</span>
<span class="sd"> 2) a tuple that specifies dynamic shape specifications for each input in original order.</span>
<span class="sd"> If you are specifying dynamism on keyword args, you will need to pass them in the order that</span>
<span class="sd"> is defined in the original function signature.</span>
<span class="sd"> The dynamic shape of a tensor argument can be specified as either</span>
<span class="sd"> (1) a dict from dynamic dimension indices to :func:`Dim` types, where it is</span>
<span class="sd"> not required to include static dimension indices in this dict, but when they are,</span>
<span class="sd"> they should be mapped to None; or (2) a tuple / list of :func:`Dim` types or None,</span>
<span class="sd"> where the :func:`Dim` types correspond to dynamic dimensions, and static dimensions</span>
<span class="sd"> are denoted by None. Arguments that are dicts or tuples / lists of tensors are</span>
<span class="sd"> recursively specified by using mappings or sequences of contained specifications.</span>
<span class="sd"> strict: When enabled (default), the export function will trace the program through</span>
<span class="sd"> TorchDynamo which will ensure the soundness of the resulting graph. Otherwise, the</span>
<span class="sd"> exported program will not validate the implicit assumptions baked into the graph and</span>
<span class="sd"> may cause behavior divergence between the original model and the exported one. This is</span>
<span class="sd"> useful when users need to workaround bugs in the tracer, or simply want incrementally</span>
<span class="sd"> enable safety in their models. Note that this does not affect the resulting IR spec</span>
<span class="sd"> to be different and the model will be serialized in the same way regardless of what value</span>
<span class="sd"> is passed here.</span>
<span class="sd"> WARNING: This option is experimental and use this at your own risk.</span>
<span class="sd"> Returns:</span>
<span class="sd"> An :class:`ExportedProgram` containing the traced callable.</span>
<span class="sd"> **Acceptable input/output types**</span>
<span class="sd"> Acceptable types of inputs (for ``args`` and ``kwargs``) and outputs include:</span>
<span class="sd"> - Primitive types, i.e. ``torch.Tensor``, ``int``, ``float``, ``bool`` and ``str``.</span>
<span class="sd"> - Dataclasses, but they must be registered by calling :func:`register_dataclass` first.</span>
<span class="sd"> - (Nested) Data structures comprising of ``dict``, ``list``, ``tuple``, ``namedtuple`` and</span>
<span class="sd"> ``OrderedDict`` containing all above types.</span>
<span class="sd"> """</span>
<span class="kn">from</span> <span class="nn">torch._export</span> <span class="kn">import</span> <span class="n">export</span><span class="p">,</span> <span class="n">export__RC__</span>
<span class="k">if</span> <span class="n">constraints</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span> <span class="n">export</span><span class="p">(</span>
<span class="n">f</span><span class="p">,</span>
<span class="n">args</span><span class="p">,</span>
<span class="n">kwargs</span><span class="p">,</span>
<span class="n">constraints</span><span class="p">,</span>
<span class="n">strict</span><span class="o">=</span><span class="n">strict</span><span class="p">,</span>
<span class="n">preserve_module_call_signature</span><span class="o">=</span><span class="n">preserve_module_call_signature</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">export__RC__</span><span class="p">(</span>
<span class="n">f</span><span class="p">,</span>
<span class="n">args</span><span class="p">,</span>
<span class="n">kwargs</span><span class="p">,</span>
<span class="n">dynamic_shapes</span><span class="o">=</span><span class="n">dynamic_shapes</span><span class="p">,</span>
<span class="n">strict</span><span class="o">=</span><span class="n">strict</span><span class="p">,</span>
<span class="n">preserve_module_call_signature</span><span class="o">=</span><span class="n">preserve_module_call_signature</span><span class="p">,</span>
<span class="p">)</span></div>
<div class="viewcode-block" id="save"><a class="viewcode-back" href="../../export.html#torch.export.save">[docs]</a><span class="k">def</span> <span class="nf">save</span><span class="p">(</span>
<span class="n">ep</span><span class="p">:</span> <span class="n">ExportedProgram</span><span class="p">,</span>
<span class="n">f</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">pathlib</span><span class="o">.</span><span class="n">Path</span><span class="p">,</span> <span class="n">io</span><span class="o">.</span><span class="n">BytesIO</span><span class="p">],</span>
<span class="o">*</span><span class="p">,</span>
<span class="n">extra_files</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">opset_version</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">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="p">,</span>
<span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> .. warning::</span>
<span class="sd"> Under active development, saved files may not be usable in newer versions</span>
<span class="sd"> of PyTorch.</span>
<span class="sd"> Saves an :class:`ExportedProgram` to a file-like object. It can then be</span>
<span class="sd"> loaded using the Python API :func:`torch.export.load <torch.export.load>`.</span>
<span class="sd"> Args:</span>
<span class="sd"> ep (ExportedProgram): The exported program to save.</span>
<span class="sd"> f (Union[str, pathlib.Path, io.BytesIO): A file-like object (has to</span>
<span class="sd"> implement write and flush) or a string containing a file name.</span>
<span class="sd"> extra_files (Optional[Dict[str, Any]]): Map from filename to contents</span>
<span class="sd"> which will be stored as part of f.</span>
<span class="sd"> opset_version (Optional[Dict[str, int]]): A map of opset names</span>
<span class="sd"> to the version of this opset</span>
<span class="sd"> Example::</span>
<span class="sd"> import torch</span>
<span class="sd"> import io</span>
<span class="sd"> class MyModule(torch.nn.Module):</span>
<span class="sd"> def forward(self, x):</span>
<span class="sd"> return x + 10</span>
<span class="sd"> ep = torch.export.export(MyModule(), (torch.randn(5),))</span>
<span class="sd"> # Save to file</span>
<span class="sd"> torch.export.save(ep, 'exported_program.pt2')</span>
<span class="sd"> # Save to io.BytesIO buffer</span>
<span class="sd"> buffer = io.BytesIO()</span>
<span class="sd"> torch.export.save(ep, buffer)</span>
<span class="sd"> # Save with extra files</span>
<span class="sd"> extra_files = {'foo.txt': b'bar'.decode('utf-8')}</span>
<span class="sd"> torch.export.save(ep, 'exported_program.pt2', extra_files=extra_files)</span>
<span class="sd"> """</span>
<span class="kn">from</span> <span class="nn">torch._export</span> <span class="kn">import</span> <span class="n">save</span>
<span class="n">save</span><span class="p">(</span><span class="n">ep</span><span class="p">,</span> <span class="n">f</span><span class="p">,</span> <span class="n">extra_files</span><span class="o">=</span><span class="n">extra_files</span><span class="p">,</span> <span class="n">opset_version</span><span class="o">=</span><span class="n">opset_version</span><span class="p">)</span></div>
<div class="viewcode-block" id="load"><a class="viewcode-back" href="../../export.html#torch.export.load">[docs]</a><span class="k">def</span> <span class="nf">load</span><span class="p">(</span>
<span class="n">f</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">pathlib</span><span class="o">.</span><span class="n">Path</span><span class="p">,</span> <span class="n">io</span><span class="o">.</span><span class="n">BytesIO</span><span class="p">],</span>
<span class="o">*</span><span class="p">,</span>
<span class="n">extra_files</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">expected_opset_version</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">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="p">,</span>
<span class="p">)</span> <span class="o">-></span> <span class="n">ExportedProgram</span><span class="p">:</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> .. warning::</span>
<span class="sd"> Under active development, saved files may not be usable in newer versions</span>