Minor cleanup

Author: GregoryComer

docs/source/backends/template/backend-partitioner.md

@@ -1,3 +1,15 @@
+==========================
+{BACKEND_NAME} Partitioner API
+==========================
+
+Document the partitioner API for the backend, including configuration options and compile specs.
+
+- ``option1``: Description of the option and values.
+- ``option2``: Description of the second option.
+- ``option3``: Description of the third option.
+
+{ADDITIONAL_PARTITIONER_DETAILS}
+
 ================
 Operator Support
 ================

docs/source/backends/template/backend-op-support.rst renamed to docs/source/backends/template/backend-partitioner.rst

@@ -82,21 +82,18 @@ No additional steps are necessary to use the backend beyond linking the target.
 
 **→{doc}`xnnpack-troubleshooting` — Debug common issues.**
 
-**→{doc}`xnnpack-partitioner` — Partitioner options.**
+**→{doc}`xnnpack-partitioner` — Partitioner options and supported operators.**
 
 **→{doc}`xnnpack-quantization` — Supported quantization schemes.**
 
-**→{doc}`xnnpack-op-support` — Supported operators.**
-
 **→{doc}`xnnpack-arch-internals` — XNNPACK backend internals.**
 
 ```{toctree}
 :maxdepth: 2
 :hidden:
 :caption: XNNPACK Backend
 
-xnnpack-troubleshooting
 xnnpack-partitioner
 xnnpack-quantization
-xnnpack-op-support
+xnnpack-troubleshooting
 xnnpack-arch-internals

docs/source/backends/xnnpack/xnnpack-op-support.rst

@@ -0,0 +1,24 @@
+===============
+Partitioner API
+===============
+
+The XNNPACK partitioner API allows for configuration of the model delegation to XNNPACK. Passing an ``XnnpackPartitioner`` instance with no additional parameters will run as much of the model as possible on the XNNPACK backend. This is the most common use-case. For advanced use cases, the partitioner exposes the following options via the `constructor <https://github.com/pytorch/executorch/blob/release/0.6/backends/xnnpack/partition/xnnpack_partitioner.py#L31>`_:
+
+- ``configs``: Control which operators are delegated to XNNPACK. By default, all available operators all delegated. See `../config/__init__.py <https://github.com/pytorch/executorch/blob/release/0.6/backends/xnnpack/partition/config/__init__.py#L66>`_ for an exhaustive list of available operator configs.
+- ``config_precisions``: Filter operators by data type. By default, delegate all precisions. One or more of ``ConfigPrecisionType.FP32``, ``ConfigPrecisionType.STATIC_QUANT``, or ``ConfigPrecisionType.DYNAMIC_QUANT``. See `ConfigPrecisionType <https://github.com/pytorch/executorch/blob/release/0.6/backends/xnnpack/partition/config/xnnpack_config.py#L24>`_.
+- ``per_op_mode``: If true, emit individual delegate calls for every operator. This is an advanced option intended to reduce memory overhead in some contexts at the cost of a small amount of runtime overhead. Defaults to false.
+- ``verbose``: If true, print additional information during lowering.
+
+================
+Operator Support
+================
+
+This section lists the operators supported by the XNNPACK backend. Operators are the building blocks of the ML model. See `IRs <https://docs.pytorch.org/docs/stable/torch.compiler_ir.html>`_ for more information on the PyTorch operator set.
+
+All operators support dynamic input shapes unless otherwise noted.
+
+.. csv-table:: Operator Support
+   :file: op-support.csv
+   :header-rows: 1
+   :widths: 20 15 30 30
+   :align: center