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dist_util_torch.py
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dist_util_torch.py
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# coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team and Facebook, Inc.
#
# 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.
""" Utils to train DistilBERT
adapted in part from Facebook, Inc XLM model (https://github.com/facebookresearch/XLM)
"""
import json
import logging
import os
import socket
# import git
import numpy as np
import torch
import logging
class FileLogger:
def __init__(self, output_dir, is_master=False, is_rank0=False):
self.output_dir = output_dir
# Log to console if rank 0, Log to console and file if master
if not is_rank0: self.logger = NoOp()
else: self.logger = self.get_logger(output_dir, log_to_file=is_master)
def get_logger(self, output_dir, log_to_file=True):
logger = logging.getLogger('imagenet_training')
logger.setLevel(logging.DEBUG)
formatter = logging.Formatter('%(message)s')
if log_to_file:
vlog = logging.FileHandler(output_dir+'/verbose.log')
vlog.setLevel(logging.INFO)
vlog.setFormatter(formatter)
logger.addHandler(vlog)
eventlog = logging.FileHandler(output_dir+'/event.log')
eventlog.setLevel(logging.WARN)
eventlog.setFormatter(formatter)
logger.addHandler(eventlog)
time_formatter = logging.Formatter('%(asctime)s - %(filename)s:%(lineno)d - %(message)s')
debuglog = logging.FileHandler(output_dir+'/debug.log')
debuglog.setLevel(logging.DEBUG)
debuglog.setFormatter(time_formatter)
logger.addHandler(debuglog)
console = logging.StreamHandler()
console.setFormatter(formatter)
console.setLevel(logging.DEBUG)
logger.addHandler(console)
return logger
def console(self, *args):
self.logger.debug(*args)
def event(self, *args):
self.logger.warn(*args)
def info(self, *args):
self.logger.info(*args)
# no_op method/object that accept every signature
class NoOp:
def __getattr__(self, *args):
def no_op(*args, **kwargs): pass
return no_op
# logging.basicConfig(
# format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s",
# datefmt="%m/%d/%Y %H:%M:%S",
# level=logging.INFO,
# )
# logger = logging.getLogger(__name__)
# def git_log(folder_path: str):
# """
# Log commit info.
# """
# repo = git.Repo(search_parent_directories=True)
# repo_infos = {
# "repo_id": str(repo),
# "repo_sha": str(repo.head.object.hexsha),
# "repo_branch": str(repo.active_branch),
# }
# with open(os.path.join(folder_path, "git_log.json"), "w") as f:
# json.dump(repo_infos, f, indent=4)
def init_gpu_params(params):
"""
Handle single and multi-GPU / multi-node.
"""
if params.n_gpu <= 1:
params.local_rank = 0
params.master_port = -1
params.is_master = True
params.multi_gpu = False
return
assert torch.cuda.is_available()
print("Initializing GPUs")
if params.n_gpu > 1:
assert params.local_rank != -1
params.world_size = int(os.environ["WORLD_SIZE"])
params.n_gpu_per_node = int(os.environ["N_GPU_NODE"])
params.global_rank = int(os.environ["RANK"])
# number of nodes / node ID
params.n_nodes = params.world_size // params.n_gpu_per_node
params.node_id = params.global_rank // params.n_gpu_per_node
params.multi_gpu = True
assert params.n_nodes == int(os.environ["N_NODES"])
assert params.node_id == int(os.environ["NODE_RANK"])
# local job (single GPU)
else:
assert params.local_rank == -1
params.n_nodes = 1
params.node_id = 0
params.local_rank = 0
params.global_rank = 0
params.world_size = 1
params.n_gpu_per_node = 1
params.multi_gpu = False
# sanity checks
assert params.n_nodes >= 1
assert 0 <= params.node_id < params.n_nodes
assert 0 <= params.local_rank <= params.global_rank < params.world_size
assert params.world_size == params.n_nodes * params.n_gpu_per_node
# define whether this is the master process / if we are in multi-node distributed mode
params.is_master = params.node_id == 0 and params.local_rank == 0
params.multi_node = params.n_nodes > 1
# summary
PREFIX = f"--- Global rank: {params.global_rank} - "
print(PREFIX + "Number of nodes: %i" % params.n_nodes)
print(PREFIX + "Node ID : %i" % params.node_id)
print(PREFIX + "Local rank : %i" % params.local_rank)
print(PREFIX + "World size : %i" % params.world_size)
print(PREFIX + "GPUs per node : %i" % params.n_gpu_per_node)
print(PREFIX + "Master : %s" % str(params.is_master))
print(PREFIX + "Multi-node : %s" % str(params.multi_node))
print(PREFIX + "Multi-GPU : %s" % str(params.multi_gpu))
print(PREFIX + "Hostname : %s" % socket.gethostname())
# set GPU device
torch.cuda.set_device(params.local_rank)
# initialize multi-GPU
if params.multi_gpu:
print("Initializing PyTorch distributed")
torch.distributed.init_process_group(
init_method="env://", backend="nccl",
)
def set_seed(args):
"""
Set the random seed.
"""
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)