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# SPDX-FileCopyrightText: Copyright 2023-2024, Arm Limited and/or its affiliates.
# SPDX-License-Identifier: Apache-2.0
"""Contains class RewritingOptimizer to replace a subgraph/layer of a model."""
from __future__ import annotations
import importlib
import logging
import tempfile
from dataclasses import dataclass
from pathlib import Path
from typing import Any
from typing import Callable
from typing import cast
from keras.api._v2 import keras # Temporary workaround for now: MLIA-1107
from mlia.core.errors import ConfigurationError
from mlia.core.reporting import Column
from mlia.core.reporting import Format
from mlia.core.reporting import Table
from mlia.nn.common import Optimizer
from mlia.nn.common import OptimizerConfiguration
from mlia.nn.rewrite.core.train import train
from mlia.nn.rewrite.core.train import TrainingParameters
from mlia.nn.tensorflow.config import TFLiteModel
from mlia.utils.registry import Registry
logger = logging.getLogger(__name__)
RewriteCallable = Callable[[Any, Any], keras.Model]
class Rewrite:
"""Graph rewrite logic to be used by RewritingOptimizer."""
def __init__(self, name: str, rewrite_fn: RewriteCallable):
"""Initialize a Rewrite instance with a given name and an optional function."""
self.name = name
self.function = rewrite_fn
def __call__(self, input_shape: Any, output_shape: Any) -> keras.Model:
"""Perform the rewrite operation using the configured function."""
try:
return self.function(input_shape, output_shape)
except Exception as ex:
raise RuntimeError(f"Rewrite '{self.name}' failed.") from ex
@dataclass
class DynamicallyLoadedRewrite(Rewrite):
"""A rewrite which can load logic from a function loaded dynamically."""
def __init__(self, name: str, function_name: str):
"""Initialize."""
def load_and_run(input_shape: Any, output_shape: Any) -> keras.Model:
"""Load the function from a file dynamically."""
self.load_function(function_name)
return self.function(input_shape, output_shape)
super().__init__(name, load_and_run)
def load_function(self, function_name: str) -> RewriteCallable:
"""Return the rewrite function. Import using the auto_load attr if necessary."""
try:
name_parts = function_name.split(".")
module_name = ".".join(name_parts[:-1])
fn_name = name_parts[-1]
module = importlib.import_module(module_name)
self.function = cast(RewriteCallable, getattr(module, fn_name))
return self.function
except Exception as ex:
raise RuntimeError(
f"Unable to load rewrite function '{function_name}' for '{self.name}'."
) from ex
class RewriteRegistry(Registry[Rewrite]):
"""Registry rewrite functions."""
def __init__(self, rewrites: list[Rewrite] | None = None):
"""Set up a rewrite registry.
Can optionally initialise with name->function pairs
to be automatically loaded on demand
"""
super().__init__()
if rewrites:
for rewrite in rewrites:
self.register_rewrite(rewrite)
def register_rewrite(self, rewrite: Rewrite) -> bool:
"""Register a rewrite."""
return super().register(rewrite.name, rewrite)
@dataclass
class RewriteConfiguration(OptimizerConfiguration):
"""Rewrite configuration."""
optimization_target: str
layers_to_optimize: list[str] | None = None
dataset: Path | None = None
train_params: TrainingParameters = TrainingParameters()
def __str__(self) -> str:
"""Return string representation of the configuration."""
return f"rewrite: {self.optimization_target}"
class RewritingOptimizer(Optimizer):
"""RewritingOptimizer class for basic rewrite flow."""
registry = RewriteRegistry(
[
DynamicallyLoadedRewrite(
"fully-connected", "mlia.nn.rewrite.library.fc_layer.get_keras_model"
)
]
)
def __init__(
self, tflite_model_path: Path, optimizer_configuration: RewriteConfiguration
):
"""Init RewritingOptimizer instance."""
self.model = TFLiteModel(tflite_model_path)
self.model_path = tflite_model_path
self.optimizer_configuration = optimizer_configuration
@classmethod
def builtin_rewrite_names(cls) -> list:
"""Return all registered rewrite names."""
return cls.registry.names()
def apply_optimization(self) -> None: # pylint: disable=too-many-locals
"""Apply the rewrite flow."""
rewrite = RewritingOptimizer.registry.items[
self.optimizer_configuration.optimization_target
]
use_unmodified_model = True
tflite_model = self.model.model_path
tfrecord = str(self.optimizer_configuration.dataset)
tmp_dir = tempfile.mkdtemp()
tmp_output = Path(tmp_dir, "output.tflite")
if not self.optimizer_configuration.layers_to_optimize:
raise ConfigurationError(
"Input and output tensor names need to be set for rewrite."
)
orig_vs_repl_stats, total_stats = train(
source_model=tflite_model,
unmodified_model=tflite_model if use_unmodified_model else None,
output_model=str(tmp_output),
input_tfrec=str(tfrecord),
replace_fn=rewrite,
input_tensors=[self.optimizer_configuration.layers_to_optimize[0]],
output_tensors=[self.optimizer_configuration.layers_to_optimize[1]],
train_params=self.optimizer_configuration.train_params,
)
if orig_vs_repl_stats:
orig_vs_repl = ["Replaced sub-graph only"] + [
f"{stat:.3f}" for stat in orig_vs_repl_stats
]
total = ["Total model"] + [f"{stat:.3f}" for stat in total_stats]
notes = (
"These metrics show the difference between original model\n"
"and the model optimized by the rewrite. The models are\n"
"compared at two positions: directly after the replaced\n"
"sub-graph and at the model output.\n"
"MAE = Mean Absolute Error\n"
"NRMSE = Normalized Root Mean Square Error"
)
table = Table(
columns=[
Column(
"Original vs. optimized",
alias="metric",
fmt=Format(wrap_width=40),
),
Column("MAE", alias="value", fmt=Format(wrap_width=15)),
Column("NRMSE", alias="value", fmt=Format(wrap_width=15)),
],
rows=[orig_vs_repl, total],
name="Rewrite performance metrics",
alias="rewrite_performance_metrics",
notes=notes,
)
logger.info(table.to_plain_text(show_title=True))
def get_model(self) -> TFLiteModel:
"""Return optimized model."""
return self.model
def optimization_config(self) -> str:
"""Optimization configurations."""
return str(self.optimizer_configuration)
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