Kernel inventory and usage#

registered_kernel_names() → dict[str, str]#: Returns a {op_type: kernel name} mapping for accelerated registered kernels, for example {"Abs": "onnx_light_cpu::Abs"}. Use it to confirm that accelerated rather than built-in kernels are registered. The mapping is derived from registered_kernels() rather than a separate operator list.

class RegisteredKernel#

Immutable record describing one kernel registration.

domain: str#: ONNX operator domain, e.g. "ai.onnx".

op_type: str#: ONNX operator type name, e.g. "Abs".

device: str#: Device the kernel runs on, e.g. "CPU".

kernel_name: str#: Library-qualified C++ kernel class name, e.g. "onnx_light_cpu::Abs".

types: tuple[str, ...]#: Element type names accepted for primary tensor operands.

since_version: int | None#: Inclusive opset lower bound, or None without a lower bound.

until_version: int | None#: Inclusive opset upper bound, or None without an upper bound.

registered_kernels() → tuple[RegisteredKernel, ...]#: Returns registrations collected from the C++ CollectRegisteredKernels() inventory without executing kernels.

used_kernel_names() → list[str]#: Returns, in invocation order, the accelerated kernels run since the last clear_used_kernel_names() call.

clear_used_kernel_names() → None#: Clears the recorded accelerated-kernel invocations.

set_kernel_usage_recording(enabled) → None#: Enables or disables per-invocation kernel usage recording.