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# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
"""Megatron global variables."""
import os
import sys
from collections import defaultdict
from megatron import dist_signal_handler
from megatron.tokenizer import build_tokenizer
from .microbatches import build_num_microbatches_calculator
from .timers import Timers
_GLOBAL_ARGS = None
_GLOBAL_NUM_MICROBATCHES_CALCULATOR = None
_GLOBAL_TOKENIZER = None
_GLOBAL_TENSORBOARD_WRITER = None
_GLOBAL_ADLR_AUTORESUME = None
_GLOBAL_TIMERS = None
_GLOBAL_SIGNAL_HANDLER = None
_GLOBAL_COUNTERS = None
def get_args():
"""Return arguments."""
_ensure_var_is_initialized(_GLOBAL_ARGS, 'args')
return _GLOBAL_ARGS
def get_num_microbatches():
return _GLOBAL_NUM_MICROBATCHES_CALCULATOR.get()
def get_current_global_batch_size():
return _GLOBAL_NUM_MICROBATCHES_CALCULATOR.get_current_global_batch_size()
def update_num_microbatches(consumed_samples, consistency_check=True):
_GLOBAL_NUM_MICROBATCHES_CALCULATOR.update(consumed_samples,
consistency_check)
def get_tokenizer():
"""Return tokenizer."""
_ensure_var_is_initialized(_GLOBAL_TOKENIZER, 'tokenizer')
return _GLOBAL_TOKENIZER
def get_tensorboard_writer():
"""Return our wrapped tensorboard/wandb writer. It can be None so no need
to check if it is initialized."""
return _GLOBAL_TENSORBOARD_WRITER
def get_adlr_autoresume():
"""ADLR autoresume object. It can be None so no need
to check if it is initialized."""
return _GLOBAL_ADLR_AUTORESUME
def get_timers():
"""Return timers."""
_ensure_var_is_initialized(_GLOBAL_TIMERS, 'timers')
return _GLOBAL_TIMERS
def get_counters():
"""Return counters."""
_ensure_var_is_initialized(_GLOBAL_COUNTERS, 'counters')
return _GLOBAL_COUNTERS
def get_signal_handler():
_ensure_var_is_initialized(_GLOBAL_SIGNAL_HANDLER, 'signal handler')
return _GLOBAL_SIGNAL_HANDLER
def _set_signal_handler():
global _GLOBAL_SIGNAL_HANDLER
_ensure_var_is_not_initialized(_GLOBAL_SIGNAL_HANDLER, 'signal handler')
_GLOBAL_SIGNAL_HANDLER = dist_signal_handler.DistributedSignalHandler().__enter__()
def _set_args(args):
global _GLOBAL_ARGS
_GLOBAL_ARGS = args
def set_global_variables(args):
"""Set args, tokenizer, tensorboard_writer, adlr_autoresume, and timers."""
assert args is not None
_ensure_var_is_not_initialized(_GLOBAL_ARGS, 'args')
_set_args(args)
_build_num_microbatches_calculator(args)
if args.vocab_file or args.tokenizer_type in ["FalconTokenizer", "LlamaTokenizer", "GPT2BPETokenizer"]:
_ = _build_tokenizer(args)
_set_tensorboard_writer(args)
_set_adlr_autoresume(args)
_set_timers(args)
_set_counters(args)
if args.exit_signal_handler:
_set_signal_handler()
def _build_num_microbatches_calculator(args):
global _GLOBAL_NUM_MICROBATCHES_CALCULATOR
_ensure_var_is_not_initialized(_GLOBAL_NUM_MICROBATCHES_CALCULATOR,
'num microbatches calculator')
_GLOBAL_NUM_MICROBATCHES_CALCULATOR = build_num_microbatches_calculator(args)
def _build_tokenizer(args):
"""Initialize tokenizer."""
global _GLOBAL_TOKENIZER
_ensure_var_is_not_initialized(_GLOBAL_TOKENIZER, 'tokenizer')
_GLOBAL_TOKENIZER = build_tokenizer(args)
return _GLOBAL_TOKENIZER
def rebuild_tokenizer(args):
global _GLOBAL_TOKENIZER
_GLOBAL_TOKENIZER = None
return _build_tokenizer(args)
def _set_tensorboard_writer(args):
"""Set our wrapped tensorboard/wandb writer."""
global _GLOBAL_TENSORBOARD_WRITER
_ensure_var_is_not_initialized(_GLOBAL_TENSORBOARD_WRITER,
'tensorboard writer')
if getattr(args,"wandb_logger",False):
"""
if this arg is set to True, we check the other wandb relevant arguments and
return a shim which exposes the wandb logging via a tensorboard-y API
"""
if args.rank == (args.world_size - 1):
try:
from megatron.wandb_logger import WandBConfig,WandbTBShim
cfg=WandBConfig.from_args(args)
shim=WandbTBShim(cfg)
print('> setting wandb ...')
_GLOBAL_TENSORBOARD_WRITER=shim
except ModuleNotFoundError:
print('WARNING: WanDB writing requested but is not '
'available, '
'no WandB logs will be written.', flush=True)
else:
if hasattr(args, 'tensorboard_dir') and \
args.tensorboard_dir and args.rank == (args.world_size - 1):
try:
from torch.utils.tensorboard import SummaryWriter
print('> setting tensorboard ...')
_GLOBAL_TENSORBOARD_WRITER = SummaryWriter(
log_dir=args.tensorboard_dir,
max_queue=args.tensorboard_queue_size)
except ModuleNotFoundError:
print('WARNING: TensorBoard writing requested but is not '
'available (are you using PyTorch 1.1.0 or later?), '
'no TensorBoard logs will be written.', flush=True)
def _set_adlr_autoresume(args):
"""Initialize ADLR autoresume."""
global _GLOBAL_ADLR_AUTORESUME
_ensure_var_is_not_initialized(_GLOBAL_ADLR_AUTORESUME, 'adlr autoresume')
if args.adlr_autoresume:
if args.rank == 0:
print('enabling autoresume ...', flush=True)
sys.path.append(os.environ.get('SUBMIT_SCRIPTS', '.'))
try:
from userlib.auto_resume import AutoResume
except BaseException:
print('ADLR autoresume is not available, exiting ...')
sys.exit()
_GLOBAL_ADLR_AUTORESUME = AutoResume
def _set_timers(args):
"""Initialize timers."""
global _GLOBAL_TIMERS
_ensure_var_is_not_initialized(_GLOBAL_TIMERS, 'timers')
_GLOBAL_TIMERS = Timers(args.timing_log_level, args.timing_log_option)
def _set_counters(args):
global _GLOBAL_COUNTERS
_ensure_var_is_not_initialized(_GLOBAL_COUNTERS, 'counters')
_GLOBAL_COUNTERS = defaultdict(int)
def _ensure_var_is_initialized(var, name):
"""Make sure the input variable is not None."""
assert var is not None, '{} is not initialized.'.format(name)
def _ensure_var_is_not_initialized(var, name):
"""Make sure the input variable is not None."""
assert var is None, '{} is already initialized.'.format(name)
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