mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-04 13:20:12 +06:00
fa84ae26d6
This is the result of:
$ black --line-length 119 examples templates transformers utils hubconf.py setup.py
There's a lot of fairly long lines in the project. As a consequence, I'm
picking the longest widely accepted line length, 119 characters.
This is also Thomas' preference, because it allows for explicit variable
names, to make the code easier to understand.
132 lines
4.2 KiB
Python
132 lines
4.2 KiB
Python
# coding=utf-8
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# Copyright 2019-present, the HuggingFace Inc. team and Facebook, Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Utils to train DistilBERT
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adapted in part from Facebook, Inc XLM model (https://github.com/facebookresearch/XLM)
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"""
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import git
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import json
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import os
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import socket
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import torch
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import numpy as np
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import logging
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logging.basicConfig(
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format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s",
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datefmt="%m/%d/%Y %H:%M:%S",
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level=logging.INFO,
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)
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logger = logging.getLogger(__name__)
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def git_log(folder_path: str):
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"""
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Log commit info.
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"""
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repo = git.Repo(search_parent_directories=True)
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repo_infos = {
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"repo_id": str(repo),
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"repo_sha": str(repo.head.object.hexsha),
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"repo_branch": str(repo.active_branch),
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}
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with open(os.path.join(folder_path, "git_log.json"), "w") as f:
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json.dump(repo_infos, f, indent=4)
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def init_gpu_params(params):
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"""
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Handle single and multi-GPU / multi-node.
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"""
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if params.n_gpu <= 0:
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params.local_rank = 0
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params.master_port = -1
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params.is_master = True
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params.multi_gpu = False
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return
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assert torch.cuda.is_available()
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logger.info("Initializing GPUs")
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if params.n_gpu > 1:
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assert params.local_rank != -1
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params.world_size = int(os.environ["WORLD_SIZE"])
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params.n_gpu_per_node = int(os.environ["N_GPU_NODE"])
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params.global_rank = int(os.environ["RANK"])
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# number of nodes / node ID
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params.n_nodes = params.world_size // params.n_gpu_per_node
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params.node_id = params.global_rank // params.n_gpu_per_node
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params.multi_gpu = True
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assert params.n_nodes == int(os.environ["N_NODES"])
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assert params.node_id == int(os.environ["NODE_RANK"])
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# local job (single GPU)
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else:
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assert params.local_rank == -1
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params.n_nodes = 1
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params.node_id = 0
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params.local_rank = 0
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params.global_rank = 0
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params.world_size = 1
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params.n_gpu_per_node = 1
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params.multi_gpu = False
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# sanity checks
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assert params.n_nodes >= 1
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assert 0 <= params.node_id < params.n_nodes
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assert 0 <= params.local_rank <= params.global_rank < params.world_size
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assert params.world_size == params.n_nodes * params.n_gpu_per_node
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# define whether this is the master process / if we are in multi-node distributed mode
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params.is_master = params.node_id == 0 and params.local_rank == 0
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params.multi_node = params.n_nodes > 1
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# summary
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PREFIX = f"--- Global rank: {params.global_rank} - "
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logger.info(PREFIX + "Number of nodes: %i" % params.n_nodes)
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logger.info(PREFIX + "Node ID : %i" % params.node_id)
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logger.info(PREFIX + "Local rank : %i" % params.local_rank)
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logger.info(PREFIX + "World size : %i" % params.world_size)
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logger.info(PREFIX + "GPUs per node : %i" % params.n_gpu_per_node)
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logger.info(PREFIX + "Master : %s" % str(params.is_master))
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logger.info(PREFIX + "Multi-node : %s" % str(params.multi_node))
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logger.info(PREFIX + "Multi-GPU : %s" % str(params.multi_gpu))
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logger.info(PREFIX + "Hostname : %s" % socket.gethostname())
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# set GPU device
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torch.cuda.set_device(params.local_rank)
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# initialize multi-GPU
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if params.multi_gpu:
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logger.info("Initializing PyTorch distributed")
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torch.distributed.init_process_group(
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init_method="env://", backend="nccl",
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)
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def set_seed(args):
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"""
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Set the random seed.
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"""
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np.random.seed(args.seed)
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torch.manual_seed(args.seed)
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if args.n_gpu > 0:
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torch.cuda.manual_seed_all(args.seed)
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