diff options
Diffstat (limited to 'src/mlia/nn/rewrite/core/rewrite.py')
| -rw-r--r-- | src/mlia/nn/rewrite/core/rewrite.py | 67 |
1 files changed, 42 insertions, 25 deletions
diff --git a/src/mlia/nn/rewrite/core/rewrite.py b/src/mlia/nn/rewrite/core/rewrite.py index 6d915c6..78fa533 100644 --- a/src/mlia/nn/rewrite/core/rewrite.py +++ b/src/mlia/nn/rewrite/core/rewrite.py @@ -8,6 +8,7 @@ import tempfile from abc import ABC from abc import abstractmethod from dataclasses import dataclass +from inspect import getfullargspec from pathlib import Path from typing import Any from typing import Callable @@ -36,7 +37,7 @@ from mlia.nn.tensorflow.config import TFLiteModel from mlia.utils.registry import Registry logger = logging.getLogger(__name__) -RewriteCallable = Callable[[Any, Any], keras.Model] +RewriteCallable = Callable[..., keras.Model] class Rewrite(ABC): @@ -47,10 +48,23 @@ class Rewrite(ABC): self.name = name self.function = rewrite_fn - def __call__(self, input_shape: Any, output_shape: Any) -> keras.Model: - """Return an instance of the rewrite model.""" + def __call__( + self, input_shape: Any, output_shape: Any, **kwargs: Any + ) -> keras.Model: + """Perform the rewrite operation using the configured function.""" try: - return self.function(input_shape, output_shape) + return self.function(input_shape, output_shape, **kwargs) + except TypeError as ex: + expected_args = getfullargspec(self.function).args + if "input_shape" in expected_args: + expected_args.remove("input_shape") + if "output_shape" in expected_args: + expected_args.remove("output_shape") + raise KeyError( + f"Found unexpected parameters for rewrite. Expected (sub)set " + f"of {expected_args} found unexpected parameter(s) " + f"{list(set(list(kwargs.keys())) - set(expected_args))}" + ) from ex except Exception as ex: raise RuntimeError(f"Rewrite '{self.name}' failed.") from ex @@ -67,7 +81,7 @@ class Rewrite(ABC): """Return post-processing rewrite option.""" @abstractmethod - def check_optimization(self, model: keras.Model, **kwargs: dict) -> bool: + def check_optimization(self, model: keras.Model) -> bool: """Check if the optimization has produced the correct result.""" @@ -86,7 +100,7 @@ class GenericRewrite(Rewrite): """Return default post-processing rewrite option.""" return model - def check_optimization(self, model: keras.Model, **kwargs: Any) -> bool: + def check_optimization(self, model: keras.Model) -> bool: """Not needed here.""" return True @@ -100,8 +114,8 @@ class QuantizeAwareTrainingRewrite(Rewrite, ABC): return model -class Sparsity24Rewrite(QuantizeAwareTrainingRewrite): - """Rewrite class for sparsity rewrite e.g. fully-connected-sparsity24.""" +class SparsityRewrite(QuantizeAwareTrainingRewrite): + """Rewrite class for sparsity rewrite e.g. fully-connected-sparsity.""" pruning_callback = tfmot.sparsity.keras.UpdatePruningStep @@ -132,17 +146,25 @@ class Sparsity24Rewrite(QuantizeAwareTrainingRewrite): return model - def check_optimization(self, model: keras.Model, **kwargs: Any) -> bool: + def check_optimization( + self, + model: keras.Model, + sparsity_m: int = 2, + sparsity_n: int = 4, + **_: Any, + ) -> bool: """Check if sparity has produced the correct result.""" for layer in model.layers: for weight in layer.weights: if "kernel" in weight.name: if "kernel_min" in weight.name or "kernel_max" in weight.name: continue - if not is_pruned_m_by_n(weight, m_by_n=(2, 4)): + if not is_pruned_m_by_n(weight, m_by_n=(sparsity_m, sparsity_n)): logger.warning( - "\nWARNING: Could not find (2,4) sparsity, " + "\nWARNING: Could not find (%d, %d) sparsity, " "in layer %s for weight %s \n", + sparsity_m, + sparsity_n, layer.name, weight.name, ) @@ -164,27 +186,21 @@ class ClusteringRewrite(QuantizeAwareTrainingRewrite): ) return cqat_model - def check_optimization(self, model: keras.Model, **kwargs: Any) -> bool: + def check_optimization( + self, model: keras.Model, num_clusters: int = 2, **_: Any + ) -> bool: """Check if clustering has produced the correct result.""" - number_of_clusters = kwargs.get("number_of_clusters") - if not number_of_clusters: - raise ValueError( - """ - Expected check_optimization to have argument number_of_clusters. - """ - ) - for layer in model.layers: for weight in layer.weights: if "kernel" in weight.name: if "kernel_min" in weight.name or "kernel_max" in weight.name: continue number_of_found_clusters = len(np.unique(weight)) - if number_of_found_clusters != number_of_clusters: + if number_of_found_clusters != num_clusters: logger.warning( "\nWARNING: Expected %d cluster(s), found %d " "cluster(s) in layer %s for weight %s \n", - number_of_clusters, + num_clusters, number_of_found_clusters, layer.name, weight.name, @@ -228,6 +244,7 @@ class RewriteConfiguration(OptimizerConfiguration): layers_to_optimize: list[str] | None = None dataset: Path | None = None train_params: TrainingParameters = TrainingParameters() + rewrite_specific_params: dict | None = None def __str__(self) -> str: """Return string representation of the configuration.""" @@ -240,10 +257,10 @@ class RewritingOptimizer(Optimizer): registry = RewriteRegistry( [ GenericRewrite("fully-connected", fc_rewrite), - Sparsity24Rewrite("fully-connected-sparsity24", fc_sparsity_rewrite), + SparsityRewrite("fully-connected-sparsity", fc_sparsity_rewrite), ClusteringRewrite("fully-connected-clustering", fc_clustering_rewrite), ClusteringRewrite("conv2d-clustering", conv2d_clustering_rewrite), - Sparsity24Rewrite("conv2d-sparsity24", conv2d_sparsity_rewrite), + SparsityRewrite("conv2d-sparsity", conv2d_sparsity_rewrite), ] ) @@ -265,7 +282,6 @@ class RewritingOptimizer(Optimizer): 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) @@ -287,6 +303,7 @@ class RewritingOptimizer(Optimizer): 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, + rewrite_specific_params=self.optimizer_configuration.rewrite_specific_params, # pylint: disable=line-too-long ) if orig_vs_repl_stats: |