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# SPDX-FileCopyrightText: Copyright 2022-2023, Arm Limited and/or its affiliates.
# SPDX-License-Identifier: Apache-2.0
"""TOSA advisor."""
from __future__ import annotations
from pathlib import Path
from typing import Any
from typing import cast
from mlia.core.advice_generation import AdviceCategory
from mlia.core.advice_generation import AdviceProducer
from mlia.core.advisor import DefaultInferenceAdvisor
from mlia.core.advisor import InferenceAdvisor
from mlia.core.context import Context
from mlia.core.context import ExecutionContext
from mlia.core.data_analysis import DataAnalyzer
from mlia.core.data_collection import DataCollector
from mlia.core.events import Event
from mlia.target.common.optimization import add_common_optimization_params
from mlia.target.common.optimization import OptimizingDataCollector
from mlia.target.registry import profile
from mlia.target.tosa.advice_generation import TOSAAdviceProducer
from mlia.target.tosa.config import TOSAConfiguration
from mlia.target.tosa.data_analysis import TOSADataAnalyzer
from mlia.target.tosa.data_collection import TOSAOperatorCompatibility
from mlia.target.tosa.events import TOSAAdvisorStartedEvent
from mlia.target.tosa.handlers import TOSAEventHandler
from mlia.target.tosa.reporters import MetadataDisplay
class TOSAInferenceAdvisor(DefaultInferenceAdvisor):
"""TOSA inference advisor."""
@classmethod
def name(cls) -> str:
"""Return name of the advisor."""
return "tosa_inference_advisor"
def get_collectors(self, context: Context) -> list[DataCollector]:
"""Return list of the data collectors."""
model = self.get_model(context)
collectors: list[DataCollector] = []
if context.category_enabled(AdviceCategory.COMPATIBILITY):
collectors.append(TOSAOperatorCompatibility(model))
if context.category_enabled(AdviceCategory.PERFORMANCE):
raise RuntimeError(
"Performance estimation is currently not supported for TOSA."
)
if context.category_enabled(AdviceCategory.OPTIMIZATION):
target_profile = self.get_target_profile(context)
target_config = profile(target_profile)
collectors.append(OptimizingDataCollector(model, target_config))
return collectors
def get_analyzers(self, context: Context) -> list[DataAnalyzer]:
"""Return list of the data analyzers."""
return [
TOSADataAnalyzer(),
]
def get_producers(self, context: Context) -> list[AdviceProducer]:
"""Return list of the advice producers."""
return [
TOSAAdviceProducer(),
]
def get_events(self, context: Context) -> list[Event]:
"""Return list of the startup events."""
model = self.get_model(context)
target_profile = self.get_target_profile(context)
return [
TOSAAdvisorStartedEvent(
model,
cast(TOSAConfiguration, profile(target_profile)),
MetadataDisplay(model),
)
]
def configure_and_get_tosa_advisor(
context: ExecutionContext,
target_profile: str | Path,
model: str | Path,
**extra_args: Any,
) -> InferenceAdvisor:
"""Create and configure TOSA advisor."""
if context.event_handlers is None:
context.event_handlers = [TOSAEventHandler()]
if context.config_parameters is None:
context.config_parameters = _get_config_parameters(
model, target_profile, **extra_args
)
return TOSAInferenceAdvisor()
def _get_config_parameters(
model: str | Path, target_profile: str | Path, **extra_args: Any
) -> dict[str, Any]:
"""Get configuration parameters for the advisor."""
advisor_parameters: dict[str, Any] = {
"tosa_inference_advisor": {
"model": str(model),
"target_profile": target_profile,
}
}
add_common_optimization_params(advisor_parameters, extra_args)
return advisor_parameters
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