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# SPDX-FileCopyrightText: Copyright 2022-2023, Arm Limited and/or its affiliates.
# SPDX-License-Identifier: Apache-2.0
"""Module for the API functions."""
from __future__ import annotations
import logging
from pathlib import Path
from typing import Any
from mlia.core.advisor import InferenceAdvisor
from mlia.core.common import AdviceCategory
from mlia.core.context import ExecutionContext
from mlia.target.registry import profile
from mlia.target.registry import registry as target_registry
logger = logging.getLogger(__name__)
def get_advice(
target_profile: str,
model: str | Path,
category: set[str],
optimization_targets: list[dict[str, Any]] | None = None,
context: ExecutionContext | None = None,
backends: list[str] | None = None,
) -> None:
"""Get the advice.
This function represents an entry point to the library API.
Based on provided parameters it will collect and analyze the data
and produce the advice.
:param target_profile: target profile identifier
:param model: path to the NN model
:param category: set of categories of the advice. MLIA supports three categories:
"compatibility", "performance", "optimization". If not provided
category "compatibility" is used by default.
:param optimization_targets: optional model optimization targets that
could be used for generating advice in "optimization" category.
:param context: optional parameter which represents execution context,
could be used for advanced use cases
:param backends: A list of backends that should be used for the given
target. Default settings will be used if None.
Examples:
NB: Before launching MLIA, the logging functionality should be configured!
Getting the advice for the provided target profile and the model
>>> get_advice("ethos-u55-256", "path/to/the/model",
{"optimization", "compatibility"})
Getting the advice for the category "performance".
>>> get_advice("ethos-u55-256", "path/to/the/model", {"performance"})
"""
advice_category = AdviceCategory.from_string(category)
if context is not None:
context.advice_category = advice_category
if context is None:
context = ExecutionContext(advice_category=advice_category)
advisor = get_advisor(
context,
target_profile,
model,
optimization_targets=optimization_targets,
backends=backends,
)
advisor.run(context)
def get_advisor(
context: ExecutionContext,
target_profile: str | Path,
model: str | Path,
**extra_args: Any,
) -> InferenceAdvisor:
"""Find appropriate advisor for the target."""
target = profile(target_profile).target
factory_function = target_registry.items[target].advisor_factory_func
return factory_function(
context,
target_profile,
model,
**extra_args,
)
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