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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">os</span>
<span class="kn">import</span> <span class="nn">sys</span>
<span class="kn">import</span> <span class="nn">typing</span>
<span class="kn">import</span> <span class="nn">warnings</span>
<span class="kn">import</span> <span class="nn">zipfile</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.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._ops</span> <span class="kn">import</span> <span class="n">OpOverload</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">&quot;Constraint&quot;</span><span class="p">,</span>
    <span class="s2">&quot;Dim&quot;</span><span class="p">,</span>
    <span class="s2">&quot;ExportBackwardSignature&quot;</span><span class="p">,</span>
    <span class="s2">&quot;ExportGraphSignature&quot;</span><span class="p">,</span>
    <span class="s2">&quot;ExportedProgram&quot;</span><span class="p">,</span>
    <span class="s2">&quot;CustomDecompTable&quot;</span><span class="p">,</span>
    <span class="s2">&quot;ModuleCallEntry&quot;</span><span class="p">,</span>
    <span class="s2">&quot;ModuleCallSignature&quot;</span><span class="p">,</span>
    <span class="s2">&quot;default_decompositions&quot;</span><span class="p">,</span>
    <span class="s2">&quot;dims&quot;</span><span class="p">,</span>
    <span class="s2">&quot;export&quot;</span><span class="p">,</span>
    <span class="s2">&quot;export_for_training&quot;</span><span class="p">,</span>
    <span class="s2">&quot;export_for_inference&quot;</span><span class="p">,</span>
    <span class="s2">&quot;load&quot;</span><span class="p">,</span>
    <span class="s2">&quot;register_dataclass&quot;</span><span class="p">,</span>
    <span class="s2">&quot;save&quot;</span><span class="p">,</span>
    <span class="s2">&quot;unflatten&quot;</span><span class="p">,</span>
    <span class="s2">&quot;FlatArgsAdapter&quot;</span><span class="p">,</span>
    <span class="s2">&quot;UnflattenedModule&quot;</span><span class="p">,</span>
<span class="p">]</span>


<span class="kn">from</span> <span class="nn">.decomp_utils</span> <span class="kn">import</span> <span class="n">CustomDecompTable</span>
<span class="kn">from</span> <span class="nn">.dynamic_shapes</span> <span class="kn">import</span> <span class="n">Constraint</span><span class="p">,</span> <span class="n">Dim</span><span class="p">,</span> <span class="n">dims</span><span class="p">,</span> <span class="n">ShapesCollection</span>
<span class="kn">from</span> <span class="nn">.exported_program</span> <span class="kn">import</span> <span class="p">(</span>
    <span class="n">default_decompositions</span><span class="p">,</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="p">,</span>
<span class="p">)</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="kn">from</span> <span class="nn">.unflatten</span> <span class="kn">import</span> <span class="n">FlatArgsAdapter</span><span class="p">,</span> <span class="n">unflatten</span><span class="p">,</span> <span class="n">UnflattenedModule</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="k">def</span> <span class="nf">export_for_training</span><span class="p">(</span>
    <span class="n">mod</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</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">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="n">List</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">-&gt;</span> <span class="n">ExportedProgram</span><span class="p">:</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    :func:`export_for_training` takes any nn.Module 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 all 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. This API is intended for PT2 quantization training use cases</span>
<span class="sd">    and will soon be the default IR of torch.export.export in the near future. To read further about</span>
<span class="sd">    the motivation behind this change, please refer to</span>
<span class="sd">    https://dev-discuss.pytorch.org/t/why-pytorch-does-not-need-a-new-standardized-operator-set/2206</span>
<span class="sd">    With this API, and :func:`run_decompositions()`, you should be able to get inference IR with</span>
<span class="sd">    your custom decomposition behaviour.</span>

<span class="sd">    **Soundness Guarantee**</span>

<span class="sd">    See :func:`export()` docstring for more details.</span>

<span class="sd">    Args:</span>
<span class="sd">        mod: We will trace the forward method of this module.</span>

<span class="sd">        args: Example positional inputs.</span>

<span class="sd">        kwargs: Optional example keyword inputs.</span>

<span class="sd">        dynamic_shapes:</span>
<span class="sd">         An optional argument where the type 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">    &quot;&quot;&quot;</span>
    <span class="kn">from</span> <span class="nn">._trace</span> <span class="kn">import</span> <span class="n">_export_for_training</span>

    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
            <span class="sa">f</span><span class="s2">&quot;Expected `mod` to be an instance of `torch.nn.Module`, got </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">mod</span><span class="p">)</span><span class="si">}</span><span class="s2">.&quot;</span>
        <span class="p">)</span>
    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">ScriptModule</span><span class="p">):</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
            <span class="s2">&quot;Exporting a ScriptModule is not supported. &quot;</span>
            <span class="s2">&quot;Maybe try converting your ScriptModule to an ExportedProgram &quot;</span>
            <span class="s2">&quot;using `TS2EPConverter(mod, args, kwargs).convert()` instead.&quot;</span>
        <span class="p">)</span>
    <span class="k">return</span> <span class="n">_export_for_training</span><span class="p">(</span>
        <span class="n">mod</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="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">def</span> <span class="nf">export_for_inference</span><span class="p">(</span>
    <span class="n">mod</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</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">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="n">List</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="n">decomp_table</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="s2">&quot;OpOverload&quot;</span><span class="p">,</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Callable</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">-&gt;</span> <span class="n">ExportedProgram</span><span class="p">:</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    :func:`export_for_inference` takes any nn.Module 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 ATen operator set</span>
<span class="sd">    (as well as any user-specified custom operators) which is customizable via decomp_table,</span>
<span class="sd">    (2) has eliminated all Python control flow and data structures (with certain exceptions),</span>
<span class="sd">    and (3) records the set of shape constraints needed to show that this normalization and control-flow</span>
<span class="sd">    elimination is sound for future inputs. This API is for convenience use as it combines :func:`export_for_training` and</span>
<span class="sd">    :func:`run_decompositions`.</span>

<span class="sd">    **Soundness Guarantee**</span>

<span class="sd">    See :func:`export()` docstring for more details.</span>

<span class="sd">    Args:</span>
<span class="sd">        mod: We will trace the forward method of this module.</span>

<span class="sd">        args: Example positional inputs.</span>

<span class="sd">        kwargs: Optional example keyword inputs.</span>

<span class="sd">        dynamic_shapes:</span>
<span class="sd">         An optional argument where the type 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">        decomp_table: See :func:`run_decompositions` for more details.</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">    &quot;&quot;&quot;</span>

    <span class="n">ep_for_training</span> <span class="o">=</span> <span class="n">export_for_training</span><span class="p">(</span>
        <span class="n">mod</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>

    <span class="k">return</span> <span class="n">ep_for_training</span><span class="o">.</span><span class="n">run_decompositions</span><span class="p">(</span><span class="n">decomp_table</span><span class="o">=</span><span class="n">decomp_table</span><span class="p">)</span>


<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">mod</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</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">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="n">List</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">-&gt;</span> <span class="n">ExportedProgram</span><span class="p">:</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    :func:`export` takes any nn.Module 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(&quot;dim0_x&quot;)``::</span>

<span class="sd">        dim = Dim(&quot;dim0_x&quot;, 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">        mod: We will trace the forward method of this module.</span>

<span class="sd">        args: Example positional inputs.</span>

<span class="sd">        kwargs: Optional example keyword inputs.</span>

<span class="sd">        dynamic_shapes:</span>
<span class="sd">         An optional argument where the type 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">    &quot;&quot;&quot;</span>
    <span class="kn">from</span> <span class="nn">._trace</span> <span class="kn">import</span> <span class="n">_export</span>

    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
            <span class="sa">f</span><span class="s2">&quot;Expected `mod` to be an instance of `torch.nn.Module`, got </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">mod</span><span class="p">)</span><span class="si">}</span><span class="s2">.&quot;</span>
        <span class="p">)</span>
    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">ScriptModule</span><span class="p">):</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
            <span class="s2">&quot;Exporting a ScriptModule is not supported. &quot;</span>
            <span class="s2">&quot;Maybe try converting your ScriptModule to an ExportedProgram &quot;</span>
            <span class="s2">&quot;using `TS2EPConverter(mod, args, kwargs).convert()` instead.&quot;</span>
        <span class="p">)</span>
    <span class="k">return</span> <span class="n">_export</span><span class="p">(</span>
        <span class="n">mod</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="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="n">pre_dispatch</span><span class="o">=</span><span class="kc">True</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">os</span><span class="o">.</span><span class="n">PathLike</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">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;</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 &lt;torch.export.load&gt;`.</span>

<span class="sd">    Args:</span>
<span class="sd">        ep (ExportedProgram): The exported program to save.</span>

<span class="sd">        f (Union[str, os.PathLike, 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, &#39;exported_program.pt2&#39;)</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 = {&#39;foo.txt&#39;: b&#39;bar&#39;.decode(&#39;utf-8&#39;)}</span>
<span class="sd">        torch.export.save(ep, &#39;exported_program.pt2&#39;, extra_files=extra_files)</span>

<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</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="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
            <span class="sa">f</span><span class="s2">&quot;The &#39;ep&#39; parameter must be an instance of &#39;ExportedProgram&#39;, got &#39;</span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">ep</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">&#39; instead.&quot;</span>
        <span class="p">)</span>

    <span class="kn">from</span> <span class="nn">torch._export.serde.schema</span> <span class="kn">import</span> <span class="n">SCHEMA_VERSION</span>
    <span class="kn">from</span> <span class="nn">torch._export.serde.serialize</span> <span class="kn">import</span> <span class="n">serialize</span><span class="p">,</span> <span class="n">SerializedArtifact</span>

    <span class="n">artifact</span><span class="p">:</span> <span class="n">SerializedArtifact</span> <span class="o">=</span> <span class="n">serialize</span><span class="p">(</span><span class="n">ep</span><span class="p">,</span> <span class="n">opset_version</span><span class="p">)</span>

    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="p">(</span><span class="nb">str</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">PathLike</span><span class="p">)):</span>
        <span class="n">f</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">fspath</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>

    <span class="k">with</span> <span class="n">zipfile</span><span class="o">.</span><span class="n">ZipFile</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">zipf</span><span class="p">:</span>
        <span class="c1"># Save every field in the SerializedArtifact to a file.</span>
        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">artifact</span><span class="o">.</span><span class="n">exported_program</span><span class="p">,</span> <span class="nb">bytes</span><span class="p">)</span>
        <span class="n">zipf</span><span class="o">.</span><span class="n">writestr</span><span class="p">(</span><span class="s2">&quot;serialized_exported_program.json&quot;</span><span class="p">,</span> <span class="n">artifact</span><span class="o">.</span><span class="n">exported_program</span><span class="p">)</span>
        <span class="n">zipf</span><span class="o">.</span><span class="n">writestr</span><span class="p">(</span><span class="s2">&quot;serialized_state_dict.pt&quot;</span><span class="p">,</span> <span class="n">artifact</span><span class="o">.</span><span class="n">state_dict</span><span class="p">)</span>
        <span class="n">zipf</span><span class="o">.</span><span class="n">writestr</span><span class="p">(</span><span class="s2">&quot;serialized_constants.pt&quot;</span><span class="p">,</span> <span class="n">artifact</span><span class="o">.</span><span class="n">constants</span><span class="p">)</span>
        <span class="n">zipf</span><span class="o">.</span><span class="n">writestr</span><span class="p">(</span><span class="s2">&quot;serialized_example_inputs.pt&quot;</span><span class="p">,</span> <span class="n">artifact</span><span class="o">.</span><span class="n">example_inputs</span><span class="p">)</span>

        <span class="n">zipf</span><span class="o">.</span><span class="n">writestr</span><span class="p">(</span><span class="s2">&quot;version&quot;</span><span class="p">,</span> <span class="s2">&quot;.&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="nb">str</span><span class="p">,</span> <span class="n">SCHEMA_VERSION</span><span class="p">)))</span>

        <span class="c1"># Add extra files if provided</span>
        <span class="k">if</span> <span class="n">extra_files</span><span class="p">:</span>
            <span class="k">for</span> <span class="n">extra_file_name</span><span class="p">,</span> <span class="n">content</span> <span class="ow">in</span> <span class="n">extra_files</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
                <span class="n">encoded_content</span> <span class="o">=</span> <span class="n">content</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="s2">&quot;utf-8&quot;</span><span class="p">)</span>
                <span class="n">zipf</span><span class="o">.</span><span class="n">writestr</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;extra_files/</span><span class="si">{</span><span class="n">extra_file_name</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">encoded_content</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">os</span><span class="o">.</span><span class="n">PathLike</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">-&gt;</span> <span class="n">ExportedProgram</span><span class="p">:</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;</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">    Loads an :class:`ExportedProgram` previously saved with</span>
<span class="sd">    :func:`torch.export.save &lt;torch.export.save&gt;`.</span>

<span class="sd">    Args:</span>
<span class="sd">        ep (ExportedProgram): The exported program to save.</span>

<span class="sd">        f (Union[str, os.PathLike, 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]]): The extra filenames given in</span>
<span class="sd">         this map would be loaded and their content would be stored in the</span>
<span class="sd">         provided map.</span>

<span class="sd">        expected_opset_version (Optional[Dict[str, int]]): A map of opset names</span>
<span class="sd">         to expected opset versions</span>

<span class="sd">    Returns:</span>
<span class="sd">        An :class:`ExportedProgram` object</span>

<span class="sd">    Example::</span>

<span class="sd">        import torch</span>
<span class="sd">        import io</span>

<span class="sd">        # Load ExportedProgram from file</span>
<span class="sd">        ep = torch.export.load(&#39;exported_program.pt2&#39;)</span>

<span class="sd">        # Load ExportedProgram from io.BytesIO object</span>
<span class="sd">        with open(&#39;exported_program.pt2&#39;, &#39;rb&#39;) as f:</span>
<span class="sd">            buffer = io.BytesIO(f.read())</span>
<span class="sd">        buffer.seek(0)</span>
<span class="sd">        ep = torch.export.load(buffer)</span>

<span class="sd">        # Load with extra files.</span>
<span class="sd">        extra_files = {&#39;foo.txt&#39;: &#39;&#39;}  # values will be replaced with data</span>
<span class="sd">        ep = torch.export.load(&#39;exported_program.pt2&#39;, extra_files=extra_files)</span>
<span class="sd">        print(extra_files[&#39;foo.txt&#39;])</span>
<span class="sd">        print(ep(torch.randn(5)))</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="p">(</span><span class="nb">str</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">PathLike</span><span class="p">)):</span>
        <span class="n">f</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">fspath</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="ow">or</span> <span class="p">{}</span>

    <span class="k">with</span> <span class="n">zipfile</span><span class="o">.</span><span class="n">ZipFile</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">zipf</span><span class="p">:</span>
        <span class="c1"># Check the version</span>
        <span class="n">version</span> <span class="o">=</span> <span class="n">zipf</span><span class="o">.</span><span class="n">read</span><span class="p">(</span><span class="s2">&quot;version&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">decode</span><span class="p">()</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)</span>
        <span class="kn">from</span> <span class="nn">torch._export.serde.schema</span> <span class="kn">import</span> <span class="n">SCHEMA_VERSION</span>

        <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">version</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">SCHEMA_VERSION</span><span class="p">)</span>
        <span class="k">if</span> <span class="n">version</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="nb">str</span><span class="p">(</span><span class="n">SCHEMA_VERSION</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
                <span class="sa">f</span><span class="s2">&quot;Serialized version </span><span class="si">{</span><span class="n">version</span><span class="si">}</span><span class="s2"> does not match our current &quot;</span>
                <span class="sa">f</span><span class="s2">&quot;schema version </span><span class="si">{</span><span class="n">SCHEMA_VERSION</span><span class="si">}</span><span class="s2">.&quot;</span>
            <span class="p">)</span>

        <span class="kn">from</span> <span class="nn">torch._export.serde.serialize</span> <span class="kn">import</span> <span class="n">deserialize</span><span class="p">,</span> <span class="n">SerializedArtifact</span>

        <span class="c1"># Load serialized_ep and serialized_state_dict from the zip file</span>

        <span class="n">serialized_exported_program</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bytes</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="n">serialized_state_dict</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bytes</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="n">serialized_constants</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bytes</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="n">serialized_example_inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bytes</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>

        <span class="k">for</span> <span class="n">file_info</span> <span class="ow">in</span> <span class="n">zipf</span><span class="o">.</span><span class="n">infolist</span><span class="p">():</span>
            <span class="n">file_content</span> <span class="o">=</span> <span class="n">zipf</span><span class="o">.</span><span class="n">read</span><span class="p">(</span><span class="n">file_info</span><span class="o">.</span><span class="n">filename</span><span class="p">)</span>

            <span class="k">if</span> <span class="n">file_info</span><span class="o">.</span><span class="n">filename</span> <span class="o">==</span> <span class="s2">&quot;serialized_exported_program.json&quot;</span><span class="p">:</span>
                <span class="n">serialized_exported_program</span> <span class="o">=</span> <span class="n">file_content</span>
            <span class="k">elif</span> <span class="n">file_info</span><span class="o">.</span><span class="n">filename</span> <span class="o">==</span> <span class="s2">&quot;serialized_state_dict.json&quot;</span><span class="p">:</span>
                <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;This version of file is deprecated&quot;</span><span class="p">)</span>
                <span class="n">serialized_state_dict</span> <span class="o">=</span> <span class="n">file_content</span>
            <span class="k">elif</span> <span class="n">file_info</span><span class="o">.</span><span class="n">filename</span> <span class="o">==</span> <span class="s2">&quot;serialized_constants.json&quot;</span><span class="p">:</span>
                <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;This version of file is deprecated&quot;</span><span class="p">)</span>
                <span class="n">serialized_constants</span> <span class="o">=</span> <span class="n">file_content</span>
            <span class="k">elif</span> <span class="n">file_info</span><span class="o">.</span><span class="n">filename</span> <span class="o">==</span> <span class="s2">&quot;serialized_state_dict.pt&quot;</span><span class="p">:</span>
                <span class="n">serialized_state_dict</span> <span class="o">=</span> <span class="n">file_content</span>
            <span class="k">elif</span> <span class="n">file_info</span><span class="o">.</span><span class="n">filename</span> <span class="o">==</span> <span class="s2">&quot;serialized_constants.pt&quot;</span><span class="p">:</span>
                <span class="n">serialized_constants</span> <span class="o">=</span> <span class="n">file_content</span>
            <span class="k">elif</span> <span class="n">file_info</span><span class="o">.</span><span class="n">filename</span> <span class="o">==</span> <span class="s2">&quot;serialized_example_inputs.pt&quot;</span><span class="p">:</span>
                <span class="n">serialized_example_inputs</span> <span class="o">=</span> <span class="n">file_content</span>
            <span class="k">elif</span> <span class="n">file_info</span><span class="o">.</span><span class="n">filename</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;extra_files&quot;</span><span class="p">):</span>
                <span class="n">filename</span> <span class="o">=</span> <span class="n">file_info</span><span class="o">.</span><span class="n">filename</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;/&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span>
                <span class="n">extra_files</span><span class="p">[</span><span class="n">filename</span><span class="p">]</span> <span class="o">=</span> <span class="n">file_content</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s2">&quot;utf-8&quot;</span><span class="p">)</span>

        <span class="k">assert</span> <span class="n">serialized_exported_program</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
        <span class="k">assert</span> <span class="n">serialized_state_dict</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
        <span class="k">assert</span> <span class="n">serialized_constants</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
        <span class="k">assert</span> <span class="n">serialized_example_inputs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
        <span class="n">artifact</span><span class="p">:</span> <span class="n">SerializedArtifact</span> <span class="o">=</span> <span class="n">SerializedArtifact</span><span class="p">(</span>
            <span class="n">serialized_exported_program</span><span class="p">,</span>
            <span class="n">serialized_state_dict</span><span class="p">,</span>
            <span class="n">serialized_constants</span><span class="p">,</span>
            <span class="n">serialized_example_inputs</span><span class="p">,</span>
        <span class="p">)</span>

        <span class="c1"># Deserialize ExportedProgram</span>
        <span class="n">ep</span> <span class="o">=</span> <span class="n">deserialize</span><span class="p">(</span><span class="n">artifact</span><span class="p">,</span> <span class="n">expected_opset_version</span><span class="p">)</span>

        <span class="k">return</span> <span class="n">ep</span></div>


<div class="viewcode-block" id="register_dataclass"><a class="viewcode-back" href="../../export.html#torch.export.register_dataclass">[docs]</a><span class="k">def</span> <span class="nf">register_dataclass</span><span class="p">(</span>
    <span class="bp">cls</span><span class="p">:</span> <span class="n">Type</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">serialized_type_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="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Registers a dataclass as a valid input/output type for :func:`torch.export.export`.</span>

<span class="sd">    Args:</span>
<span class="sd">        cls: the dataclass type to register</span>
<span class="sd">        serialized_type_name: The serialized name for the dataclass. This is</span>
<span class="sd">        required if you want to serialize the pytree TreeSpec containing this</span>
<span class="sd">        dataclass.</span>

<span class="sd">    Example::</span>

<span class="sd">        import torch</span>
<span class="sd">        from dataclasses import dataclass</span>

<span class="sd">        @dataclass</span>
<span class="sd">        class InputDataClass:</span>
<span class="sd">            feature: torch.Tensor</span>
<span class="sd">            bias: int</span>

<span class="sd">        @dataclass</span>
<span class="sd">        class OutputDataClass:</span>
<span class="sd">            res: torch.Tensor</span>

<span class="sd">        torch.export.register_dataclass(InputDataClass)</span>
<span class="sd">        torch.export.register_dataclass(OutputDataClass)</span>

<span class="sd">        class Mod(torch.nn.Module):</span>
<span class="sd">            def forward(self, x: InputDataClass) -&gt; OutputDataClass:</span>
<span class="sd">                res = x.feature + x.bias</span>
<span class="sd">                return OutputDataClass(res=res)</span>

<span class="sd">        ep = torch.export.export(Mod(), (InputDataClass(torch.ones(2, 2), 1), ))</span>
<span class="sd">        print(ep)</span>

<span class="sd">    &quot;&quot;&quot;</span>

    <span class="kn">from</span> <span class="nn">torch._export.utils</span> <span class="kn">import</span> <span class="n">register_dataclass_as_pytree_node</span>

    <span class="k">return</span> <span class="n">register_dataclass_as_pytree_node</span><span class="p">(</span>
        <span class="bp">cls</span><span class="p">,</span> <span class="n">serialized_type_name</span><span class="o">=</span><span class="n">serialized_type_name</span>
    <span class="p">)</span></div>
</pre></div>

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