Supported LiteRT Ops#

The following TFLite op types have a built-in converter in yobx.litert.ops. The list is generated programmatically from the live converter registry.

<<<

import re
from yobx.litert import register_litert_converters
from yobx.litert.register import LITERT_OP_CONVERTERS
from yobx.litert.litert_helper import builtin_op_name

register_litert_converters()

PATTERN = re.compile(r"TFLite\s+``(\w+)``\s+→\s+ONNX\s+(.+)")
MODULE_LABELS = {
    "yobx.litert.ops.activations": "Activations",
    "yobx.litert.ops.elementwise": "Element-wise",
    "yobx.litert.ops.nn_ops": "Neural network",
    "yobx.litert.ops.reshape_ops": "Shape / tensor manipulation",
}
MODULE_ORDER = list(MODULE_LABELS.keys())

groups = {m: [] for m in MODULE_ORDER}
for code, fn in LITERT_OP_CONVERTERS.items():
    mod = fn.__module__
    doc = (fn.__doc__ or "").strip().splitlines()[0].strip().rstrip(".")
    m = PATTERN.match(doc)
    tflite_op = (
        m.group(1) if m else (builtin_op_name(code) if isinstance(code, int) else code)
    )
    onnx_op = m.group(2).rstrip(".") if m else "?"
    if mod in groups:
        groups[mod].append((tflite_op, onnx_op, fn))

for mod in MODULE_ORDER:
    label = MODULE_LABELS[mod]
    items = sorted(groups[mod], key=lambda x: x[0])
    if not items:
        continue
    print(f"**{label}** (:mod:`{mod}`)")
    print()
    for tflite_op, onnx_op, fn in items:
        link = f":func:`{fn.__name__} <{fn.__module__}.{fn.__name__}>`"
        print(f"* ``{tflite_op}`` → {onnx_op} ({link})")
    print()

>>>

Activations (yobx.litert.ops.activations)

Element-wise (yobx.litert.ops.elementwise)

Neural network (yobx.litert.ops.nn_ops)

Shape / tensor manipulation (yobx.litert.ops.reshape_ops)

This Page