Delete utils.py

utils.py

@@ -1,127 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the BSD-style license found in the
-# LICENSE file in the root directory of this source tree.
-
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the BSD-style license found in the
-# LICENSE file in the root directory of this source tree.
-
-import json
-import os
-
-import torch
-import torch.distributed as dist
-from torch import nn
-
-
-def setup_for_distributed(is_master):
-    """
-    This function disables printing when not in master process
-    """
-    import builtins as __builtin__
-
-    builtin_print = __builtin__.print
-
-    def print(*args, **kwargs):
-        force = kwargs.pop("force", False)
-        if is_master or force:
-            builtin_print(*args, **kwargs)
-
-    __builtin__.print = print
-
-
-def is_dist_avail_and_initialized():
-    if not dist.is_available():
-        return False
-    if not dist.is_initialized():
-        return False
-    return True
-
-
-def get_world_size():
-    if not is_dist_avail_and_initialized():
-        return 1
-    return dist.get_world_size()
-
-
-def get_rank():
-    if not is_dist_avail_and_initialized():
-        return 0
-    return dist.get_rank()
-
-
-def is_main_process():
-    return get_rank() == 0
-
-
-def init_distributed_mode(args):
-    if "RANK" in os.environ and "WORLD_SIZE" in os.environ:
-        args["rank"] = int(os.environ["RANK"])
-        args["world_size"] = int(os.environ["WORLD_SIZE"])
-        args["gpu"] = int(os.environ["LOCAL_RANK"])
-    elif "SLURM_PROCID" in os.environ:
-        args["rank"] = int(os.environ["SLURM_PROCID"])
-        args["gpu"] = args["rank"] % torch.cuda.device_count()
-    else:
-        print("Not using distributed mode")
-        args["distributed"] = False
-        return
-
-    args["distributed"] = True
-
-    torch.cuda.set_device(args["gpu"])
-    args["dist_backend"] = "nccl"
-    print(
-        "| distributed init (rank {}): {}".format(args["rank"], args["dist_url"]),
-        flush=True,
-    )
-    torch.distributed.init_process_group(
-        backend=args["dist_backend"],
-        init_method=args["dist_url"],
-        world_size=args["world_size"],
-        rank=args["rank"],
-    )
-    torch.distributed.barrier()
-    setup_for_distributed(args["rank"] == 0)
-
-
-def save_result(result, directory, file_name):
-    rank_path = os.path.join(directory, "{}_rank_{}.json".format(file_name, get_rank()))
-    main_path = os.path.join(directory, "{}.json".format(file_name))
-    json.dump(result, open(rank_path, "w"))
-
-    if is_dist_avail_and_initialized():
-        dist.barrier()
-
-    if is_main_process():
-        result = []
-        for rank in range(get_world_size()):
-            rank_path = os.path.join(
-                directory, "{}_rank_{}.json".format(file_name, rank)
-            )
-            rank_res = json.load(open(rank_path, "r"))
-            result += rank_res
-        json.dump(result, open(main_path, "w"))
-
-    if is_dist_avail_and_initialized():
-        dist.barrier()
-
-
-def add_weight_decay(model: nn.Module, weight_decay: float) -> None:
-    decay = []
-    no_decay = []
-    for name, param in model.named_parameters():
-        if not param.requires_grad:
-            continue  # skip weight_decay for momentum models
-        if len(param.shape) == 1 or name.endswith(".bias"):
-            no_decay.append(param)
-        else:
-            decay.append(param)
-    return [
-        {"params": no_decay, "weight_decay": 0.0},
-        {"params": decay, "weight_decay": weight_decay},
-    ]