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# SPDX-FileCopyrightText: Copyright 2022-2023, Arm Limited and/or its affiliates.
# SPDX-License-Identifier: Apache-2.0
"""Data collection module for Ethos-U."""
from __future__ import annotations
import logging
from pathlib import Path
from typing import Any
from mlia.backend.vela.compat import Operators
from mlia.backend.vela.compat import supported_operators
from mlia.core.context import Context
from mlia.core.data_collection import ContextAwareDataCollector
from mlia.core.errors import FunctionalityNotSupportedError
from mlia.core.performance import estimate_performance
from mlia.nn.select import get_optimizer
from mlia.nn.select import OptimizationSettings
from mlia.nn.tensorflow.config import get_keras_model
from mlia.nn.tensorflow.config import get_tflite_model
from mlia.nn.tensorflow.config import KerasModel
from mlia.nn.tensorflow.config import TFLiteModel
from mlia.nn.tensorflow.tflite_compat import TFLiteChecker
from mlia.nn.tensorflow.tflite_compat import TFLiteCompatibilityInfo
from mlia.nn.tensorflow.utils import is_tflite_model
from mlia.nn.tensorflow.utils import save_keras_model
from mlia.nn.tensorflow.utils import save_tflite_model
from mlia.target.ethos_u.config import EthosUConfiguration
from mlia.target.ethos_u.performance import EthosUPerformanceEstimator
from mlia.target.ethos_u.performance import OptimizationPerformanceMetrics
from mlia.target.ethos_u.performance import PerformanceMetrics
from mlia.utils.logging import log_action
from mlia.utils.types import is_list_of
logger = logging.getLogger(__name__)
class EthosUOperatorCompatibility(ContextAwareDataCollector):
"""Collect operator compatibility information."""
def __init__(self, model: Path, target_config: EthosUConfiguration) -> None:
"""Init operator compatibility data collector."""
self.model = model
self.target_config = target_config
def collect_data(self) -> Operators | TFLiteCompatibilityInfo | None:
"""Collect operator compatibility information."""
if not is_tflite_model(self.model):
with log_action("Checking TensorFlow Lite compatibility ..."):
tflite_checker = TFLiteChecker()
tflite_compat = tflite_checker.check_compatibility(self.model)
if not tflite_compat.compatible:
return tflite_compat
tflite_model = get_tflite_model(self.model, self.context)
with log_action("Checking operator compatibility ..."):
return supported_operators(
Path(tflite_model.model_path), self.target_config.compiler_options
)
@classmethod
def name(cls) -> str:
"""Return name of the collector."""
return "ethos_u_operator_compatibility"
class EthosUPerformance(ContextAwareDataCollector):
"""Collect performance metrics."""
def __init__(
self,
model: Path,
target_config: EthosUConfiguration,
backends: list[str] | None = None,
) -> None:
"""Init performance data collector."""
self.model = model
self.target_config = target_config
self.backends = backends
def collect_data(self) -> PerformanceMetrics:
"""Collect model performance metrics."""
tflite_model = get_tflite_model(self.model, self.context)
estimator = EthosUPerformanceEstimator(
self.context,
self.target_config,
self.backends,
)
return estimator.estimate(tflite_model)
@classmethod
def name(cls) -> str:
"""Return name of the collector."""
return "ethos_u_performance"
class OptimizeModel:
"""Helper class for model optimization."""
def __init__(
self, context: Context, opt_settings: list[OptimizationSettings]
) -> None:
"""Init helper."""
self.context = context
self.opt_settings = opt_settings
def __call__(self, keras_model: KerasModel) -> Any:
"""Run optimization."""
optimizer = get_optimizer(keras_model, self.opt_settings)
opts_as_str = ", ".join(str(opt) for opt in self.opt_settings)
logger.info("Applying model optimizations - [%s]", opts_as_str)
optimizer.apply_optimization()
model = optimizer.get_model()
if isinstance(model, Path):
return model
if isinstance(model, TFLiteModel):
model_path = self.context.get_model_path("optimized_model.tflite")
with open(model.model_path, "rb") as file_handle:
model_data = bytearray(file_handle.read())
save_tflite_model(model_data, model_path)
return TFLiteModel(model_path)
model_path = self.context.get_model_path("optimized_model.h5")
save_keras_model(model, model_path)
return KerasModel(model_path)
class EthosUOptimizationPerformance(ContextAwareDataCollector):
"""Collect performance metrics for the optimizations."""
def __init__(
self,
model: Path,
target_config: EthosUConfiguration,
optimizations: list[list[dict]],
backends: list[str] | None = None,
) -> None:
"""Init performance optimizations data collector."""
self.model = model
self.target = target_config
self.optimizations = optimizations
self.backends = backends
def collect_data(self) -> OptimizationPerformanceMetrics | None:
"""Collect performance metrics for the optimizations."""
logger.info("Estimate performance ...")
if not self.optimizations:
raise FunctionalityNotSupportedError(
reason="Unable to estimate model optimizations impact",
description="No optimization targets provided",
)
opt_settings = self._parse_optimization_params(self.optimizations)
if opt_settings[0][0].optimization_type != "rewrite":
try:
model = get_keras_model(self.model, self.context)
except NotImplementedError as err:
raise FunctionalityNotSupportedError(
reason="Unable to run model optimizations",
description=f"{self.model} is not a Keras model and "
"could not be converted to a Keras model",
) from err
else:
model = self.model # type: ignore
optimizers = [OptimizeModel(self.context, opts) for opts in opt_settings]
estimator = EthosUPerformanceEstimator(
self.context,
self.target,
self.backends,
)
original_metrics, *optimized_metrics = estimate_performance(
model, estimator, optimizers # type: ignore
)
result = OptimizationPerformanceMetrics(
original_perf_metrics=original_metrics,
optimizations_perf_metrics=list(zip(opt_settings, optimized_metrics)),
)
return result
@staticmethod
def _parse_optimization_params(
optimizations: list[list[dict]],
) -> list[list[OptimizationSettings]]:
"""Parse optimization parameters."""
if not is_list_of(optimizations, list):
raise ValueError("Optimization parameters expected to be a list.")
return [
[
OptimizationSettings(
item.get("optimization_type"), # type: ignore
item.get("optimization_target"), # type: ignore
item.get("layers_to_optimize"),
item.get("dataset"),
)
for item in opt_configuration
]
for opt_configuration in optimizations
]
@classmethod
def name(cls) -> str:
"""Return name of the collector."""
return "ethos_u_model_optimizations"
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