cellmap_flow.inference.graph
============================

.. py:module:: cellmap_flow.inference.graph

.. autoapi-nested-parse::

   The graph a torch model computes, for its output activation (``portable.activation``).

   ``module_graph(model)`` reads the graph of what the inference server runs,
   as ``portable.activation.output_activation`` takes it: ``(nodes, output)``,
   each node ``{"op", "inputs", "outputs"}`` (a clip's ``min`` and ``max``
   where they are constants), ``output`` the name of the value the model
   returns. The op names are the format's own; ``activation.canonical`` reads
   them.

   - A TorchScript module (a cellmap-models or Hugging Face export's model.ts,
     a fly .ts): its ``inlined_graph``, every submodule's call inlined. Its
     constants and attribute reads (the weights) are values no node writes; a
     list built for ``aten::cat`` is the cat's inputs.
   - A torch.export program, or a module with an fx graph of its own (an
     ExportedProgram's ``module()``, ``torch.export.unflatten``'s): that graph,
     each submodule called with a graph of its own inlined.
   - Any other module (a script's, a DaCapo run's, a fly checkpoint's network in
     a Sequential with its sigmoid): a torch.fx symbolic trace, torch.nn's own
     modules and TorchScript submodules (a fly .ts inside its Sequential) left
     whole and read as above. A forward that branches on its input's values
     cannot be traced: no graph.

   An in-place op (``aten::sigmoid_``, ``x.sigmoid_()``) changes the value it
   is given, and a traced graph goes on reading that value by its old name
   (``x.sigmoid_(); return x`` returns the name the sigmoid was given): each
   later read of it is read as the in-place op's output instead.

   torch is imported when a graph is first read: the runner imports this module.



Attributes
----------

.. autoapisummary::

   cellmap_flow.inference.graph.logger


Functions
---------

.. autoapisummary::

   cellmap_flow.inference.graph.module_graph


Module Contents
---------------

.. py:data:: logger

.. py:function:: module_graph(model)

   ``(nodes, output)`` of ``model``, a torch module or torch.export program; None when it cannot be read.