Source code for cerebras.modelzoo.trainer.callbacks.sparsity

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"""Contains the SparsityCallback class that applies sparsity to the model."""

from typing import Optional

import cerebras.pytorch as cstorch
from cerebras.modelzoo.trainer.callbacks import Callback


[docs]class SparsityCallback(Callback): """Callback class that applies sparsity to the model and optimizer.""" def __init__( self, sparsity: Optional[cstorch.sparse.SparsityAlgorithm] = None ): """ Args: sparsity: Sparsity algorithm instance. """ self.sparsity = sparsity def setup(self, trainer): if self.sparsity is None: return elif isinstance(self.sparsity, cstorch.sparse.SparsityAlgorithm): pass else: self.sparsity = self.sparsity() if self.sparsity is None: return trainer.model.apply(self.sparsity) if trainer.optimizer is not None: trainer.optimizer.apply(self.sparsity) def on_save_checkpoint(self, trainer, state_dict): if self.sparsity: state_dict["sparsity"] = self.sparsity.state_dict() def on_load_checkpoint(self, trainer, state_dict): if self.sparsity: if ( "model" in state_dict and not trainer.checkpoint.disable_strict_checkpoint_loading and not any( k.endswith("_mask") and k[: -len("_mask")] in state_dict["model"] for k in state_dict["model"].keys() ) ): raise RuntimeError( "Did not find any sparsity masks in the model checkpoint." " Please ensure that you're using a checkpoint with the" " correct sparsity config from a previous run." ) if "sparsity" in state_dict: self.sparsity.load_state_dict(state_dict["sparsity"]) trainer.logger.info( f"Sparsity state found in checkpoint and loaded successfully." ) else: trainer.logger.info( "Sparsity state not found in the checkpoint. " "Using default initialized state." )
[docs]class LogSparsity(Callback): """Log target and actual sparsity levels."""
[docs] def setup(self, trainer): if trainer.optimizer is not None: sparsity = trainer.callbacks["sparsity"].sparsity if sparsity is None: return sparsity.register_target_sparsity_hook( lambda _, name, target: trainer.log_metrics( **{f"sparsity/{name}/target": target} ) ) sparsity.register_computed_sparsity_hook( lambda _, name, actual: trainer.log_metrics( **{f"sparsity/{name}/actual": actual} ) )