Source code for emerging_optimizers.utils

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from contextlib import contextmanager
from typing import Generator

import torch

from .eig import *


__all__ = ["fp32_matmul_precision", "get_pg_size", "get_pg_rank"]




@contextmanager
def fp32_matmul_precision(precision: str = "highest") -> Generator[None, None, None]:
    """Context manager for setting the precision of matmuls.

    Args:
        precision: Precision of matmuls (defaults to "highest")
    """
    prev_val = torch.get_float32_matmul_precision()
    torch.set_float32_matmul_precision(precision)
    try:
        yield
    finally:
        torch.set_float32_matmul_precision(prev_val)





def get_pg_size(group: torch.distributed.ProcessGroup | None = None) -> int:
    """Get world size for a distributed group with fallback"""
    if not torch.distributed.is_initialized() or group is None:
        return 1
    return group.size()





def get_pg_rank(group: torch.distributed.ProcessGroup | None = None) -> int:
    """Get rank for a distributed group with fallback"""
    if not torch.distributed.is_initialized() or group is None:
        return 0
    return group.rank()