Source code for catalyst.contrib.dl.callbacks.gradnorm_logger

from typing import Dict

from torch.nn import DataParallel
from torch.nn.parallel import DistributedDataParallel

from catalyst.core.callback import Callback, CallbackNode, CallbackOrder
from catalyst.core.runner import IRunner
from import Model

[docs]class GradNormLogger(Callback): """Callback for logging model gradients."""
[docs] def __init__( self, norm_type: int = 2, accumulation_steps: int = 1, ): """ Args: norm_type (int): norm type used to calculate norm of gradients. If `OptimizerCallback` provides non-default argument `grad_clip_params` with custom norm type, then corresponding norm type should be used in this class. accumulation_steps (int): number of steps before ``model.zero_grad()``. Should be the same as in `OptimizerCallback`. """ super().__init__( order=CallbackOrder.optimizer + 1, node=CallbackNode.all ) self.grad_norm_prefix = "_grad_norm" self.norm_type = norm_type self.accumulation_steps: int = accumulation_steps self._accumulation_counter: int = 0
[docs] @staticmethod def grad_norm(*, model: Model, prefix: str, norm_type: int) -> Dict: """Computes gradient norms for a given model. Args: model (Model): model which gradients to be saved. prefix (str): prefix for keys in resulting dictionary. norm_type (int): norm type of gradient norm. Returns: Dict: dictionary in which gradient norms are stored. """ if isinstance(model, (DataParallel, DistributedDataParallel)): model = model.module total_norm = 0.0 grad_norm = {} for tag, value in model.named_parameters(): tag = tag.replace(".", "/") metrics_tag = f"{prefix}/{tag}" param_norm = total_norm += param_norm ** norm_type grad_norm[metrics_tag] = param_norm total_norm = total_norm ** (1.0 / norm_type) metrics_tag = f"{prefix}/total" grad_norm[metrics_tag] = total_norm return grad_norm
[docs] def on_batch_end(self, runner: IRunner) -> None: """On batch end event Args: runner (IRunner): current runner """ if not runner.is_train_loader: return self._accumulation_counter += 1 need_gradient_step = ( self._accumulation_counter % self.accumulation_steps == 0 ) if need_gradient_step: grad_norm = self.grad_norm( model=runner.model, prefix=self.grad_norm_prefix, norm_type=self.norm_type, ) runner.batch_metrics.update(**grad_norm) self._accumulation_counter = 0
__all__ = ["GradNormLogger"]