src/utils/random.py

def set_seed(seed: int, device_specific: bool = False):
    """
    Helper function for reproducible behavior to set the seed in `random`, `numpy`, `torch`.
    Args:
        seed (`int`):
            The seed to set.
        device_specific (`bool`, *optional*, defaults to `False`):
            Whether to differ the seed on each device slightly with `self.process_index`.
    """
    if device_specific:
        seed += AcceleratorState().process_index
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    if is_xpu_available():
        torch.xpu.manual_seed_all(seed)
    elif is_npu_available():
        torch.npu.manual_seed_all(seed)
    else:
        torch.cuda.manual_seed_all(seed)
    # ^^ safe to call this function even if cuda is not available
    if is_tpu_available():
        xm.set_rng_state(seed)
def synchronize_rng_state(rng_type: Optional[RNGType] = None, generator: Optional[torch.Generator] = None):
    # Get the proper rng state
    if rng_type == RNGType.TORCH:
        rng_state = torch.get_rng_state()
    elif rng_type == RNGType.CUDA:
        rng_state = torch.cuda.get_rng_state()
    elif rng_type == RNGType.XLA:
        assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
        rng_state = torch.tensor(xm.get_rng_state())
    elif rng_type == RNGType.NPU:
        assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
        rng_state = torch.npu.get_rng_state()
    elif rng_type == RNGType.XPU:
        assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
        rng_state = torch.xpu.get_rng_state()
    elif rng_type == RNGType.GENERATOR:
        assert generator is not None, "Need a generator to synchronize its seed."
        rng_state = generator.get_state()
    # Broadcast the rng state from device 0 to other devices
    state = AcceleratorState()
    if state.distributed_type == DistributedType.TPU:
        rng_state = rng_state.to(xm.xla_device())
        xm.collective_broadcast([rng_state])
        xm.mark_step()
        rng_state = rng_state.cpu()
    elif (
        state.distributed_type in CUDA_DISTRIBUTED_TYPES
        or state.distributed_type == DistributedType.MULTI_NPU
        or state.distributed_type == DistributedType.MULTI_XPU
    ):
        rng_state = rng_state.to(state.device)
        torch.distributed.broadcast(rng_state, 0)
        rng_state = rng_state.cpu()
    elif state.distributed_type == DistributedType.MULTI_CPU:
        torch.distributed.broadcast(rng_state, 0)
    # Set the broadcast rng state
    if rng_type == RNGType.TORCH:
        torch.set_rng_state(rng_state)
    elif rng_type == RNGType.CUDA:
        torch.cuda.set_rng_state(rng_state)
    elif rng_type == RNGType.NPU:
        torch.npu.set_rng_state(rng_state)
    elif rng_type == RNGType.XPU:
        torch.xpu.set_rng_state(rng_state)
    elif rng_type == RNGType.XLA:
        xm.set_rng_state(rng_state.item())
    elif rng_type == RNGType.GENERATOR:
        generator.set_state(rng_state)
def synchronize_rng_states(rng_types: List[Union[str, RNGType]], generator: Optional[torch.Generator] = None):
    for rng_type in rng_types:
        synchronize_rng_state(RNGType(rng_type), generator=generator)