diff options
Diffstat (limited to 'src/mlia/target/ethos_u')
| -rw-r--r-- | src/mlia/target/ethos_u/advisor.py | 41 | ||||
| -rw-r--r-- | src/mlia/target/ethos_u/data_collection.py | 135 |
2 files changed, 24 insertions, 152 deletions
diff --git a/src/mlia/target/ethos_u/advisor.py b/src/mlia/target/ethos_u/advisor.py index 321734c..9f5b3a6 100644 --- a/src/mlia/target/ethos_u/advisor.py +++ b/src/mlia/target/ethos_u/advisor.py @@ -17,6 +17,8 @@ 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.common.optimization import add_common_optimization_params +from mlia.target.common.optimization import OptimizingDataCollector 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 @@ -65,9 +67,7 @@ class EthosUInferenceAdvisor(DefaultInferenceAdvisor): ) collectors.append( - EthosUOptimizationPerformance( - model, target_config, optimization_settings, backends - ) + EthosUOptimizationPerformance(model, target_config, backends) ) if context.category_enabled(AdviceCategory.PERFORMANCE): collectors.append(EthosUPerformance(model, target_config, backends)) @@ -76,9 +76,7 @@ class EthosUInferenceAdvisor(DefaultInferenceAdvisor): if context.category_enabled(AdviceCategory.OPTIMIZATION): optimization_settings = self._get_optimization_settings(context) collectors.append( - EthosUOptimizationPerformance( - model, target_config, optimization_settings, backends - ) + EthosUOptimizationPerformance(model, target_config, backends) ) elif context.category_enabled(AdviceCategory.PERFORMANCE): collectors.append(EthosUPerformance(model, target_config, backends)) @@ -115,7 +113,7 @@ class EthosUInferenceAdvisor(DefaultInferenceAdvisor): def _get_optimization_settings(self, context: Context) -> list[list[dict]]: """Get optimization settings.""" return self.get_parameter( # type: ignore - EthosUOptimizationPerformance.name(), + OptimizingDataCollector.name(), "optimizations", expected_type=list, expected=False, @@ -151,20 +149,6 @@ def configure_and_get_ethosu_advisor( 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, @@ -186,19 +170,6 @@ def _get_config_parameters( 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], - }, - } - ) + add_common_optimization_params(advisor_parameters, extra_args) return advisor_parameters diff --git a/src/mlia/target/ethos_u/data_collection.py b/src/mlia/target/ethos_u/data_collection.py index 4ea6120..2b41d7d 100644 --- a/src/mlia/target/ethos_u/data_collection.py +++ b/src/mlia/target/ethos_u/data_collection.py @@ -6,30 +6,23 @@ from __future__ import annotations import logging from pathlib import Path from typing import Any +from typing import cast 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.core.performance import P +from mlia.core.performance import PerformanceEstimator 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.common.optimization import OptimizingPerformaceDataCollector 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__) @@ -96,118 +89,26 @@ class EthosUPerformance(ContextAwareDataCollector): return "ethos_u_performance" -class OptimizeModel: - """Helper class for model optimization.""" +# pylint: disable=too-many-ancestors +class EthosUOptimizationPerformance(OptimizingPerformaceDataCollector): + """Collect performance metrics for performance optimizations.""" - def __init__( - self, context: Context, opt_settings: list[OptimizationSettings] - ) -> None: - """Init helper.""" - self.context = context - self.opt_settings = opt_settings - - def __call__(self, model: KerasModel | TFLiteModel) -> Any: - """Run optimization.""" - optimizer = get_optimizer(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() # 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) - - -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( + def create_estimator(self) -> PerformanceEstimator: + """Create a PerformanceEstimator, to be overridden in subclasses.""" + return EthosUPerformanceEstimator( self.context, - self.target, + cast(EthosUConfiguration, 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)), + def create_optimization_performance_metrics( + self, original_metrics: P, optimizations_perf_metrics: list[P] + ) -> Any: + """Create an optimization metrics object.""" + return OptimizationPerformanceMetrics( + original_perf_metrics=original_metrics, # type: ignore + optimizations_perf_metrics=optimizations_perf_metrics, # type: ignore ) - 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: |