Source code for nvalchemi.hooks._context

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"""Hook context for passing state to hooks."""

from __future__ import annotations

from dataclasses import dataclass
from typing import TYPE_CHECKING, Any

import torch

if TYPE_CHECKING:
    from nvalchemi.data.batch import Batch
    from nvalchemi.models.base import BaseModelMixin




@dataclass
class HookContext:
    """Context object passed to hooks at each stage.

    Attributes
    ----------
    batch : Batch
        Current batch being processed.
    step_count : int
        Current step number in the workflow.
    model : BaseModelMixin | None
        Model being used (if applicable).
    loss : torch.Tensor | None
        Current loss value (training only).
    optimizer : torch.optim.Optimizer | None
        Optimizer being used (training only).
    lr_scheduler : object | None
        Learning rate scheduler (training only).
    gradients : dict[str, torch.Tensor] | None
        Parameter gradients (training only).
    converged_mask : torch.Tensor | None
        Boolean mask of converged samples (dynamics only).
    epoch : int | None
        Current epoch number (training only).
    global_rank : int
        Distributed rank of this process.
    workflow : Any
        Back-reference to the engine running the hooks (e.g. a
        ``BaseDynamics`` instance).  ``None`` when the workflow does
        not inject itself.
    """

    batch: Batch
    step_count: int
    model: BaseModelMixin | None = None
    loss: torch.Tensor | None = None
    optimizer: torch.optim.Optimizer | None = None
    lr_scheduler: object | None = None
    gradients: dict[str, torch.Tensor] | None = None
    converged_mask: torch.Tensor | None = None
    epoch: int | None = None
    global_rank: int = 0
    workflow: Any = None