=============== 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 `_: - ``configs``: Control which operators are delegated to XNNPACK. By default, all available operators all delegated. See `../config/__init__.py `_ 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 `_. - ``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 `_ 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