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# SPDX-FileCopyrightText: Copyright 2022-2024, Arm Limited and/or its affiliates.
# SPDX-License-Identifier: Apache-2.0
"""Data collector support for performance optimizations."""
from __future__ import annotations
import logging
from abc import abstractmethod
from functools import partial
from pathlib import Path
from typing import Any
from typing import Callable
from mlia.core.common import DataItem
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.core.performance import P
from mlia.core.performance import PerformanceEstimator
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 KerasModel
from mlia.nn.tensorflow.config import TFLiteModel
from mlia.nn.tensorflow.utils import save_keras_model
from mlia.nn.tensorflow.utils import save_tflite_model
from mlia.target.config import TargetProfile
from mlia.utils.types import is_list_of
logger = logging.getLogger(__name__)
class OptimizingDataCollector(ContextAwareDataCollector):
"""Collect performance metrics for the optimizations."""
def __init__(
self,
model: Path,
target_config: TargetProfile,
backends: list[str] | None = None,
) -> None:
"""Init performance optimizations data collector."""
self.model = model
self.target = target_config
self.backends = backends
def collect_data(self) -> DataItem:
"""Collect performance metrics for the optimizations."""
logger.info("Estimate performance ...")
optimizations = self._get_optimization_settings(self.context)
training_parameters = self._get_training_settings(self.context)
if not optimizations or optimizations == [[]]:
raise FunctionalityNotSupportedError(
reason="No optimization targets provided",
description="Unable to estimate model optimizations impact",
)
opt_settings = self._parse_optimization_params(optimizations)
optimization_types = {
setting.optimization_type for opt in opt_settings for setting in opt
}
if optimization_types != {"rewrite"}:
try:
model = get_keras_model(self.model, self.context)
except NotImplementedError as err:
raise FunctionalityNotSupportedError(
reason=f"{self.model} is not a Keras model and "
"could not be converted to a Keras model",
description="Unable to run model optimizations",
) from err
else:
model = self.model # type: ignore
optimizers: list[Callable] = [
partial(self.optimize_model, opts, training_parameters)
for opts in opt_settings
]
return self.optimize_and_estimate_performance(model, optimizers, opt_settings)
def optimize_model(
self,
opt_settings: list[OptimizationSettings],
training_parameters: dict | None,
model: KerasModel | TFLiteModel,
) -> Any:
"""Run optimization."""
optimizer = get_optimizer(
model, opt_settings, training_parameters=training_parameters
)
opts_as_str = ", ".join(str(opt) for opt in opt_settings)
logger.info("Applying model optimizations - [%s]", opts_as_str)
optimizer.apply_optimization()
model = optimizer.get_model() # type: ignore
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)
def _get_optimization_settings(self, context: Context) -> list[list[dict]]:
"""Get optimization settings."""
return self.get_parameter( # type: ignore
OptimizingDataCollector.name(),
"optimizations",
expected_type=list,
expected=False,
context=context,
)
def _get_training_settings(self, context: Context) -> dict:
"""Get optimization settings."""
return self.get_parameter( # type: ignore
OptimizingDataCollector.name(),
"training_parameters",
expected_type=dict,
expected=False,
context=context,
)
@staticmethod
def _parse_optimization_params(
optimizations: list[list[dict]],
) -> list[list[OptimizationSettings]]:
"""Parse optimization parameters."""
if not is_list_of(optimizations, list):
raise TypeError("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
]
def optimize_and_estimate_performance(
self,
model: KerasModel | Path,
optimizers: list[Callable],
_: list[list[OptimizationSettings]],
) -> DataItem:
"""Run optimizers and estimate perfomance on the results."""
for optimizer in optimizers:
optimizer(model)
return {}
@classmethod
def name(cls) -> str:
"""Return name of the collector."""
return "common_optimizations"
class OptimizingPerformaceDataCollector(OptimizingDataCollector):
"""Collect performance metrics for the optimizations."""
@abstractmethod
def create_estimator(self) -> PerformanceEstimator:
"""Create a PerformanceEstimator, to be overridden in subclasses."""
@abstractmethod
def create_optimization_performance_metrics(
self, original_metrics: P, optimizations_perf_metrics: list[P]
) -> Any:
"""Create an optimization metrics object."""
def optimize_and_estimate_performance(
self,
model: KerasModel | Path,
optimizers: list[Callable],
opt_settings: list[list[OptimizationSettings]],
) -> Any:
"""Run optimizers and estimate perfomance on the results."""
estimator = self.create_estimator()
original_metrics, *optimized_metrics = estimate_performance(
model, estimator, optimizers
)
return self.create_optimization_performance_metrics(
original_metrics,
list(zip(opt_settings, optimized_metrics)),
)
_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 add_common_optimization_params(advisor_parameters: dict, extra_args: dict) -> None:
"""Add common optimization parameters."""
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 TypeError("Optimization targets value has wrong format.")
rewrite_parameters = extra_args.get("optimization_profile")
training_parameters = None
if rewrite_parameters:
if not isinstance(rewrite_parameters, dict):
raise TypeError("Training Parameter values has wrong format.")
training_parameters = extra_args["optimization_profile"].get("training")
advisor_parameters.update(
{
"common_optimizations": {
"optimizations": [optimization_targets],
"training_parameters": training_parameters,
},
}
)
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