DeepLearningExamples/PyTorch/LanguageModeling/BERT/utils.py

# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import torch
import torch.distributed as dist

from pathlib import Path


def get_rank():
    if not dist.is_available():
        return 0
    if not dist.is_initialized():
        return 0
    return dist.get_rank()


def get_world_size():
    if not dist.is_available():
        return 1
    if not dist.is_initialized():
        return 1
    return dist.get_world_size()


def is_main_process():
    return get_rank() == 0


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


def format_step(step):
    if isinstance(step, str):
        return step
    s = ""
    if len(step) > 0:
        s += "Training Epoch: {} ".format(step[0])
    if len(step) > 1:
        s += "Training Iteration: {} ".format(step[1])
    if len(step) > 2:
        s += "Validation Iteration: {} ".format(step[2])
    return s


def mkdir(path):
    Path(path).mkdir(parents=True, exist_ok=True)


def mkdir_by_main_process(path):
    if is_main_process():
        mkdir(path)
    barrier()