fish_speech/callbacks/grad_norm.py

from typing import Optional, Union

import lightning.pytorch as pl
import torch
from lightning import LightningModule, Trainer
from lightning.pytorch.callbacks import Callback
from torch import Tensor, nn
from torch.utils._foreach_utils import (
    _group_tensors_by_device_and_dtype,
    _has_foreach_support,
)


@torch.no_grad()
def grad_norm(
    parameters: Union[Tensor, list[Tensor]],
    norm_type: float = 2.0,
) -> float:
    """
    Returns the norm of the gradients of the given parameters.

    Args:
        parameters (Iterable[Tensor] or Tensor): an iterable of Tensors or a
            single Tensor that will have gradients normalized
        norm_type (float): type of the used p-norm.

    Returns:
        Total norm of the parameter gradients (viewed as a single vector).
    """  # noqa: E501

    if isinstance(parameters, Tensor):
        parameters = [parameters]

    grads = [p.grad for p in parameters if p.grad is not None]
    if len(grads) == 0:
        return None

    first_device = grads[0].device
    grouped_grads: dict[
        tuple[torch.device, torch.dtype], list[list[Tensor]]
    ] = _group_tensors_by_device_and_dtype(
        [[g.detach() for g in grads]]
    )  # type: ignore[assignment]

    norms = []
    for (device, _), ([grads], _) in grouped_grads.items():
        if _has_foreach_support(grads, device=device):
            norms.extend(torch._foreach_norm(grads, norm_type))
        else:
            norms.extend([torch.norm(g, norm_type) for g in grads])

    return torch.norm(torch.stack([norm.to(first_device) for norm in norms]), norm_type)


class GradNormMonitor(Callback):
    """
    Callback that computes the gradient norm of the model parameters.
    """

    def __init__(
        self,
        norm_type: float = 2.0,
        logging_interval: str = "step",
        sub_module: Optional[Union[str, list[str]]] = None,
    ) -> None:
        """
        Args:
            norm_type (float): type of the used p-norm.
            logging_interval (str): "step" or "epoch".
        """
        super().__init__()

        self.norm_type = norm_type
        self.logging_interval = logging_interval
        self.sub_module = sub_module

    def on_after_backward(self, trainer: Trainer, model: LightningModule) -> None:
        """
        Computes the gradient norm of the model parameters and logs it to the logger.

        Args:
            trainer (Trainer): The trainer object
            model (LightningModule): The current lightningModule
        """

        lightning_model = model

        if self.sub_module is None:
            return self.log_sub_module_grad_norm(lightning_model, model, "")

        sub_modules = self.sub_module
        if isinstance(sub_modules, str):
            sub_modules = [sub_modules]

        for sub_module in sub_modules:
            self.log_sub_module_grad_norm(
                lightning_model, getattr(model, sub_module), f"/{sub_module}"
            )

    def log_sub_module_grad_norm(
        self, lightning_model: LightningModule, model: nn.Module, path: str
    ) -> None:
        grad_norm_val = grad_norm(model.parameters(), self.norm_type)
        if grad_norm_val is None:
            return

        on_step = self.logging_interval == "step"
        lightning_model.log(
            f"train{path}/grad_norm",
            grad_norm_val,
            on_step=on_step,
            on_epoch=not on_step,
        )