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# SPDX-FileCopyrightText: Copyright 2022-2024, Arm Limited and/or its affiliates.
# SPDX-License-Identifier: Apache-2.0
"""Module for optimization selection."""
from __future__ import annotations
import math
from pathlib import Path
from typing import Any
from typing import cast
from typing import List
from typing import NamedTuple
import tensorflow as tf
from mlia.core.errors import ConfigurationError
from mlia.nn.common import Optimizer
from mlia.nn.common import OptimizerConfiguration
from mlia.nn.rewrite.core.rewrite import RewriteConfiguration
from mlia.nn.rewrite.core.rewrite import RewritingOptimizer
from mlia.nn.rewrite.core.rewrite import TrainingParameters
from mlia.nn.tensorflow.config import KerasModel
from mlia.nn.tensorflow.config import TFLiteModel
from mlia.nn.tensorflow.optimizations.clustering import Clusterer
from mlia.nn.tensorflow.optimizations.clustering import ClusteringConfiguration
from mlia.nn.tensorflow.optimizations.pruning import Pruner
from mlia.nn.tensorflow.optimizations.pruning import PruningConfiguration
from mlia.utils.types import is_list_of
class OptimizationSettings(NamedTuple):
"""Optimization settings."""
optimization_type: str
optimization_target: int | float
layers_to_optimize: list[str] | None
dataset: Path | None = None
@staticmethod
def create_from(
optimizer_params: list[tuple[str, float]],
layers_to_optimize: list[str] | None = None,
dataset: Path | None = None,
) -> list[OptimizationSettings]:
"""Create optimization settings from the provided parameters."""
return [
OptimizationSettings(
optimization_type=opt_type,
optimization_target=opt_target,
layers_to_optimize=layers_to_optimize,
dataset=dataset,
)
for opt_type, opt_target in optimizer_params
]
def __str__(self) -> str:
"""Return string representation."""
return f"{self.optimization_type}: {self.optimization_target}"
def next_target(self) -> OptimizationSettings:
"""Return next optimization target."""
if self.optimization_type == "pruning":
next_target = round(min(self.optimization_target + 0.1, 0.9), 2)
return OptimizationSettings(
self.optimization_type, next_target, self.layers_to_optimize
)
if self.optimization_type == "clustering":
# return next lowest power of two for clustering
next_target = math.log(self.optimization_target, 2)
if next_target.is_integer():
next_target -= 1
next_target = max(int(2 ** int(next_target)), 4)
return OptimizationSettings(
self.optimization_type, next_target, self.layers_to_optimize
)
if self.optimization_type == "rewrite":
return OptimizationSettings(
self.optimization_type,
self.optimization_target,
self.layers_to_optimize,
self.dataset,
)
raise ValueError(f"Optimization type {self.optimization_type} is unknown.")
class MultiStageOptimizer(Optimizer):
"""Optimizer with multiply stages."""
def __init__(
self,
model: tf.keras.Model,
optimizations: list[OptimizerConfiguration],
) -> None:
"""Init MultiStageOptimizer instance."""
self.model = model
self.optimizations = optimizations
def optimization_config(self) -> str:
"""Return string representation of the optimization config."""
return " - ".join(str(opt) for opt in self.optimizations)
def get_model(self) -> Any:
"""Return optimized model."""
return self.model
def apply_optimization(self) -> None:
"""Apply optimization to the model."""
for config in self.optimizations:
optimizer = get_optimizer(self.model, config)
optimizer.apply_optimization()
self.model = optimizer.get_model()
def get_optimizer(
model: tf.keras.Model | KerasModel | TFLiteModel,
config: OptimizerConfiguration | OptimizationSettings | list[OptimizationSettings],
training_parameters: list[dict | None] | None = None,
) -> Optimizer:
"""Get optimizer for provided configuration."""
if isinstance(model, KerasModel):
model = model.get_keras_model()
if isinstance(model, TFLiteModel):
model = model.model_path
if isinstance(config, PruningConfiguration):
return Pruner(model, config)
if isinstance(config, ClusteringConfiguration):
return Clusterer(model, config)
if isinstance(config, RewriteConfiguration):
return RewritingOptimizer(model, config)
if isinstance(config, OptimizationSettings):
return _get_optimizer(
model, cast(OptimizationSettings, config), training_parameters
)
if is_list_of(config, OptimizationSettings):
return _get_optimizer(
model, cast(List[OptimizationSettings], config), training_parameters
)
raise ConfigurationError(f"Unknown optimization configuration {config}")
def _get_optimizer(
model: tf.keras.Model | Path,
optimization_settings: OptimizationSettings | list[OptimizationSettings],
training_parameters: list[dict | None] | None = None,
) -> Optimizer:
if isinstance(optimization_settings, OptimizationSettings):
optimization_settings = [optimization_settings]
optimizer_configs = []
for opt_type, opt_target, layers_to_optimize, dataset in optimization_settings:
_check_optimizer_params(opt_type, opt_target)
opt_config = _get_optimizer_configuration(
opt_type, opt_target, layers_to_optimize, dataset, training_parameters
)
optimizer_configs.append(opt_config)
if len(optimizer_configs) == 1:
return get_optimizer(model, optimizer_configs[0])
return MultiStageOptimizer(model, optimizer_configs)
def _get_rewrite_params(
training_parameters: list[dict | None] | None = None,
) -> list:
"""Get the rewrite TrainingParameters.
Return the default constructed TrainingParameters() per default, but can be
overwritten in the unit tests.
"""
if training_parameters is None:
return [TrainingParameters()]
if training_parameters[0] is None:
train_params = TrainingParameters()
else:
train_params = TrainingParameters(**training_parameters[0])
return [train_params]
def _get_optimizer_configuration(
optimization_type: str,
optimization_target: int | float | str,
layers_to_optimize: list[str] | None = None,
dataset: Path | None = None,
training_parameters: list[dict | None] | None = None,
) -> OptimizerConfiguration:
"""Get optimizer configuration for provided parameters."""
_check_optimizer_params(optimization_type, optimization_target)
opt_type = optimization_type.lower()
if opt_type == "pruning":
return PruningConfiguration(float(optimization_target), layers_to_optimize)
if opt_type == "clustering":
# make sure an integer is given as clustering target
if optimization_target == int(optimization_target):
return ClusteringConfiguration(int(optimization_target), layers_to_optimize)
raise ConfigurationError(
"Optimization target should be a positive integer. "
f"Optimization target provided: {optimization_target}"
)
if opt_type == "rewrite":
if isinstance(optimization_target, str):
rewrite_params = _get_rewrite_params(training_parameters)
return RewriteConfiguration(
optimization_target=str(optimization_target),
layers_to_optimize=layers_to_optimize,
dataset=dataset,
train_params=rewrite_params[0],
)
raise ConfigurationError(
"Optimization target should be a string indicating a"
"choice from rewrite library. "
f"Optimization target provided: {optimization_target}"
)
raise ConfigurationError(f"Unsupported optimization type: {optimization_type}")
def _check_optimizer_params(
optimization_type: str, optimization_target: int | float | str
) -> None:
"""Check optimizer params."""
if not optimization_target:
raise ConfigurationError("Optimization target is not provided")
if not optimization_type:
raise ConfigurationError("Optimization type is not provided")
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