wehub-resource-syncYNNPACK Delegate for LiteRT
Warning
The YNNPACK delegate is experimental and under active development. Expect bugs and performance issues when using it.
The YNNPACK delegate allows LiteRT (formerly TensorFlow Lite) to offload supported operators to YNNPACK.
YNNPACK aims to provide great flexibility with good performance.
Delegate Provider Options
When using LiteRT tooling (e.g., benchmarks, evaluation tools) that link the
ynnpack_delegate_provider, the following command-line flags are exposed to
configure the YNNPACK delegate:
Core Options
-
--use_ynnpack=true|false(default:false): Explicitly apply the YNNPACK delegate to the model. -
--num_threads=N(default:0or1depending on tool): The number of threads to use for execution. Note that YNNPACK will only use a thread pool fornum_threads > 1. A value of0or1disables the thread pool (single-threaded execution).
YNNPACK Specific Options
-
--ynnpack_static_shape=true|false(default:false): Make input shapes static instead of dynamic. Enabling this may improve execution (Invoke) performance by allowing YNNPACK to optimize for fixed shapes, but it makes model reshaping (ResizeInputTensor) much more expensive. -
--ynnpack_fast_math=true|false(default:false): EnableYNN_FLAG_FAST_MATH. This allows YNNPACK to use faster but potentially less precise mathematical approximations. -
--ynnpack_consistent_arithmetic=true|false(default:false): EnableYNN_FLAG_CONSISTENT_ARITHMETIC. YNNPACK will attempt to provide numerically consistent results for all hardware the same build of YNNPACK runs on. It does not guarantee consistency across different builds (which means it does not guarantee consistency across different platforms, which are necessarily different builds). -
--ynnpack_no_excess_precision=true|false(default:false): EnableYNN_FLAG_NO_EXCESS_PRECISION. YNNPACK will not promote tensors to higher precision as a performance optimization.