exp_code/1_benchmark/CausVid/causvid/util.py

from torch.distributed.fsdp.wrap import transformer_auto_wrap_policy
from torch.distributed.fsdp.wrap import size_based_auto_wrap_policy
from torch.distributed.fsdp.wrap import ModuleWrapPolicy
from torch.distributed.fsdp import (
    FullStateDictConfig,
    StateDictType,
    MixedPrecision,
    ShardingStrategy,
    FullyShardedDataParallel as FSDP
)
from torchvision.utils import make_grid
from datetime import timedelta, datetime
import torch.distributed as dist
from omegaconf import OmegaConf
from functools import partial
import numpy as np
import random
import torch
import wandb
import os


def launch_distributed_job(backend: str = "nccl"):
    rank = int(os.environ["RANK"])
    local_rank = int(os.environ["LOCAL_RANK"])
    world_size = int(os.environ["WORLD_SIZE"])
    host = os.environ["MASTER_ADDR"]
    port = int(os.environ["MASTER_PORT"])

    if ":" in host:  # IPv6
        init_method = f"tcp://[{host}]:{port}"
    else:  # IPv4
        init_method = f"tcp://{host}:{port}"
    dist.init_process_group(rank=rank, world_size=world_size, backend=backend,
                            init_method=init_method, timeout=timedelta(minutes=30))
    torch.cuda.set_device(local_rank)


def set_seed(seed: int, deterministic: bool = False):
    """
    Helper function for reproducible behavior to set the seed in `random`, `numpy`, `torch`.

    Args:
        seed (`int`):
            The seed to set.
        deterministic (`bool`, *optional*, defaults to `False`):
            Whether to use deterministic algorithms where available. Can slow down training.
    """
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)

    if deterministic:
        torch.use_deterministic_algorithms(True)


def init_logging_folder(args):
    date = str(datetime.now()).replace(" ", "-").replace(":", "-")
    output_path = os.path.join(
        args.output_path,
        f"{date}_seed{args.seed}"
    )
    os.makedirs(output_path, exist_ok=False)

    os.makedirs(args.output_path, exist_ok=True)
    wandb.login(host=args.wandb_host, key=args.wandb_key)
    run = wandb.init(config=OmegaConf.to_container(args, resolve=True), dir=args.output_path, **
                     {"mode": "online", "entity": args.wandb_entity, "project": args.wandb_project})
    wandb.run.log_code(".")
    wandb.run.name = args.wandb_name
    print(f"run dir: {run.dir}")
    wandb_folder = run.dir
    os.makedirs(wandb_folder, exist_ok=True)

    return output_path, wandb_folder


def fsdp_wrap(module, sharding_strategy="full", mixed_precision=False, wrap_strategy="size", min_num_params=int(5e7), transformer_module=None):
    if mixed_precision:
        mixed_precision_policy = MixedPrecision(
            param_dtype=torch.bfloat16,
            reduce_dtype=torch.float32,
            buffer_dtype=torch.float32,
            cast_forward_inputs=False
        )
    else:
        mixed_precision_policy = None

    if wrap_strategy == "transformer":
        auto_wrap_policy = partial(
            transformer_auto_wrap_policy,
            transformer_layer_cls=transformer_module
        )
    elif wrap_strategy == "size":
        auto_wrap_policy = partial(
            size_based_auto_wrap_policy,
            min_num_params=min_num_params
        )
    else:
        raise ValueError(f"Invalid wrap strategy: {wrap_strategy}")

    os.environ["NCCL_CROSS_NIC"] = "1"

    sharding_strategy = {
        "full": ShardingStrategy.FULL_SHARD,
        "hybrid_full": ShardingStrategy.HYBRID_SHARD,
        "hybrid_zero2": ShardingStrategy._HYBRID_SHARD_ZERO2,
        "no_shard": ShardingStrategy.NO_SHARD,
    }[sharding_strategy]

    module = FSDP(
        module,
        auto_wrap_policy=auto_wrap_policy,
        sharding_strategy=sharding_strategy,
        mixed_precision=mixed_precision_policy,
        device_id=torch.cuda.current_device(),
        limit_all_gathers=True,
        sync_module_states=False  # Load ckpt on rank 0 and sync to other ranks
    )
    return module


def cycle(dl):
    while True:
        for data in dl:
            yield data


def fsdp_state_dict(model):
    fsdp_fullstate_save_policy = FullStateDictConfig(
        offload_to_cpu=True, rank0_only=True
    )
    with FSDP.state_dict_type(
        model, StateDictType.FULL_STATE_DICT, fsdp_fullstate_save_policy
    ):
        checkpoint = model.state_dict()

    return checkpoint


def barrier():
    if dist.is_initialized():
        dist.barrier()


def prepare_for_saving(tensor, fps=16, caption=None):
    # Convert range [-1, 1] to [0, 1]
    tensor = (tensor * 0.5 + 0.5).clamp(0, 1).detach()

    if tensor.ndim == 4:
        # Assuming it's an image and has shape [batch_size, 3, height, width]
        tensor = make_grid(tensor, 4, padding=0, normalize=False)
        return wandb.Image((tensor * 255).cpu().numpy().astype(np.uint8), caption=caption)
    elif tensor.ndim == 5:
        # Assuming it's a video and has shape [batch_size, num_frames, 3, height, width]
        return wandb.Video((tensor * 255).cpu().numpy().astype(np.uint8), fps=fps, format="webm", caption=caption)
    else:
        raise ValueError("Unsupported tensor shape for saving. Expected 4D (image) or 5D (video) tensor.")