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# SPDX-FileCopyrightText: Copyright 2022-2023, Arm Limited and/or its affiliates.
# SPDX-License-Identifier: Apache-2.0
"""Ethos-U MLIA module."""
from __future__ import annotations
from pathlib import Path
from typing import Any
from typing import cast
from mlia.core.advice_generation import AdviceProducer
from mlia.core.advisor import DefaultInferenceAdvisor
from mlia.core.advisor import InferenceAdvisor
from mlia.core.common import AdviceCategory
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.nn.tensorflow.utils import is_tflite_model
from mlia.target.ethos_u.advice_generation import EthosUAdviceProducer
from mlia.target.ethos_u.advice_generation import EthosUStaticAdviceProducer
from mlia.target.ethos_u.config import EthosUConfiguration
from mlia.target.ethos_u.data_analysis import EthosUDataAnalyzer
from mlia.target.ethos_u.data_collection import EthosUOperatorCompatibility
from mlia.target.ethos_u.data_collection import EthosUOptimizationPerformance
from mlia.target.ethos_u.data_collection import EthosUPerformance
from mlia.target.ethos_u.events import EthosUAdvisorStartedEvent
from mlia.target.ethos_u.handlers import EthosUEventHandler
from mlia.target.registry import profile
from mlia.utils.types import is_list_of
class EthosUInferenceAdvisor(DefaultInferenceAdvisor):
"""Ethos-U Inference Advisor."""
@classmethod
def name(cls) -> str:
"""Return name of the advisor."""
return "ethos_u_inference_advisor"
def get_collectors(self, context: Context) -> list[DataCollector]:
"""Return list of the data collectors."""
model = self.get_model(context)
target_config = self._get_target_config(context)
backends = self._get_backends(context)
collectors: list[DataCollector] = []
if context.category_enabled(AdviceCategory.COMPATIBILITY):
collectors.append(EthosUOperatorCompatibility(model, target_config))
# Performance and optimization are mutually exclusive.
# Decide which one to use (taking into account the model format).
if is_tflite_model(model):
# TensorFlow Lite models do not support optimization (only performance)!
if context.category_enabled(AdviceCategory.OPTIMIZATION):
optimization_settings = self._get_optimization_settings(context)
optimization_types = {
opt["optimization_type"] for opt in optimization_settings[0]
}
if optimization_types != {"rewrite"}:
raise RuntimeError(
"Only 'rewrite' is supported for TensorFlow Lite files."
)
collectors.append(
EthosUOptimizationPerformance(
model, target_config, optimization_settings, backends
)
)
if context.category_enabled(AdviceCategory.PERFORMANCE):
collectors.append(EthosUPerformance(model, target_config, backends))
else:
# Keras/SavedModel: Prefer optimization
if context.category_enabled(AdviceCategory.OPTIMIZATION):
optimization_settings = self._get_optimization_settings(context)
collectors.append(
EthosUOptimizationPerformance(
model, target_config, optimization_settings, backends
)
)
elif context.category_enabled(AdviceCategory.PERFORMANCE):
collectors.append(EthosUPerformance(model, target_config, backends))
return collectors
def get_analyzers(self, context: Context) -> list[DataAnalyzer]:
"""Return list of the data analyzers."""
return [
EthosUDataAnalyzer(),
]
def get_producers(self, context: Context) -> list[AdviceProducer]:
"""Return list of the advice producers."""
return [
EthosUAdviceProducer(),
EthosUStaticAdviceProducer(),
]
def get_events(self, context: Context) -> list[Event]:
"""Return list of the startup events."""
model = self.get_model(context)
target_config = self._get_target_config(context)
return [
EthosUAdvisorStartedEvent(target_config=target_config, model=model),
]
def _get_target_config(self, context: Context) -> EthosUConfiguration:
"""Get target configuration."""
target_profile = self.get_target_profile(context)
return cast(EthosUConfiguration, profile(target_profile))
def _get_optimization_settings(self, context: Context) -> list[list[dict]]:
"""Get optimization settings."""
return self.get_parameter( # type: ignore
EthosUOptimizationPerformance.name(),
"optimizations",
expected_type=list,
expected=False,
context=context,
)
def _get_backends(self, context: Context) -> list[str] | None:
"""Get list of backends."""
return self.get_parameter( # type: ignore
self.name(),
"backends",
expected_type=list,
expected=False,
context=context,
)
def configure_and_get_ethosu_advisor(
context: ExecutionContext,
target_profile: str | Path,
model: str | Path,
**extra_args: Any,
) -> InferenceAdvisor:
"""Create and configure Ethos-U advisor."""
if context.event_handlers is None:
context.event_handlers = [EthosUEventHandler()]
if context.config_parameters is None:
context.config_parameters = _get_config_parameters(
model, target_profile, **extra_args
)
return EthosUInferenceAdvisor()
_DEFAULT_OPTIMIZATION_TARGETS = [
{
"optimization_type": "pruning",
"optimization_target": 0.5,
"layers_to_optimize": None,
},
{
"optimization_type": "clustering",
"optimization_target": 32,
"layers_to_optimize": None,
},
]
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] = {
"ethos_u_inference_advisor": {
"model": model,
"target_profile": target_profile,
},
}
# Specifying backends is optional (default is used)
backends = extra_args.get("backends")
if backends is not None:
if not is_list_of(backends, str):
raise ValueError("Backends value has wrong format.")
advisor_parameters["ethos_u_inference_advisor"]["backends"] = backends
optimization_targets = extra_args.get("optimization_targets")
if not optimization_targets:
optimization_targets = _DEFAULT_OPTIMIZATION_TARGETS
if not is_list_of(optimization_targets, dict):
raise ValueError("Optimization targets value has wrong format.")
advisor_parameters.update(
{
"ethos_u_model_optimizations": {
"optimizations": [optimization_targets],
},
}
)
return advisor_parameters
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