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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
Entry point for dora to launch solvers for running training loops.
See more info on how to use dora: https://github.com/facebookresearch/dora
"""
import logging
import multiprocessing
import os
from pathlib import Path
import sys
import typing as tp
from dora import git_save, hydra_main, XP
import flashy
import hydra
import omegaconf
from .environment import AudioCraftEnvironment
from .utils.cluster import get_slurm_parameters
logger = logging.getLogger(__name__)
def resolve_config_dset_paths(cfg):
"""Enable Dora to load manifest from git clone repository."""
# manifest files for the different splits
for key, value in cfg.datasource.items():
if isinstance(value, str):
cfg.datasource[key] = git_save.to_absolute_path(value)
def get_solver(cfg):
from . import solvers
# Convert batch size to batch size for each GPU
assert cfg.dataset.batch_size % flashy.distrib.world_size() == 0
cfg.dataset.batch_size //= flashy.distrib.world_size()
for split in ['train', 'valid', 'evaluate', 'generate']:
if hasattr(cfg.dataset, split) and hasattr(cfg.dataset[split], 'batch_size'):
assert cfg.dataset[split].batch_size % flashy.distrib.world_size() == 0
cfg.dataset[split].batch_size //= flashy.distrib.world_size()
resolve_config_dset_paths(cfg)
solver = solvers.get_solver(cfg)
return solver
def get_solver_from_xp(xp: XP, override_cfg: tp.Optional[tp.Union[dict, omegaconf.DictConfig]] = None,
restore: bool = True, load_best: bool = True,
ignore_state_keys: tp.List[str] = [], disable_fsdp: bool = True):
"""Given a XP, return the Solver object.
Args:
xp (XP): Dora experiment for which to retrieve the solver.
override_cfg (dict or None): If not None, should be a dict used to
override some values in the config of `xp`. This will not impact
the XP signature or folder. The format is different
than the one used in Dora grids, nested keys should actually be nested dicts,
not flattened, e.g. `{'optim': {'batch_size': 32}}`.
restore (bool): If `True` (the default), restore state from the last checkpoint.
load_best (bool): If `True` (the default), load the best state from the checkpoint.
ignore_state_keys (list[str]): List of sources to ignore when loading the state, e.g. `optimizer`.
disable_fsdp (bool): if True, disables FSDP entirely. This will
also automatically skip loading the EMA. For solver specific
state sources, like the optimizer, you might want to
use along `ignore_state_keys=['optimizer']`. Must be used with `load_best=True`.
"""
logger.info(f"Loading solver from XP {xp.sig}. "
f"Overrides used: {xp.argv}")
cfg = xp.cfg
if override_cfg is not None:
cfg = omegaconf.OmegaConf.merge(cfg, omegaconf.DictConfig(override_cfg))
if disable_fsdp and cfg.fsdp.use:
cfg.fsdp.use = False
assert load_best is True
# ignoring some keys that were FSDP sharded like model, ema, and best_state.
# fsdp_best_state will be used in that case. When using a specific solver,
# one is responsible for adding the relevant keys, e.g. 'optimizer'.
# We could make something to automatically register those inside the solver, but that
# seem overkill at this point.
ignore_state_keys = ignore_state_keys + ['model', 'ema', 'best_state']
try:
with xp.enter():
solver = get_solver(cfg)
if restore:
solver.restore(load_best=load_best, ignore_state_keys=ignore_state_keys)
return solver
finally:
hydra.core.global_hydra.GlobalHydra.instance().clear()
def get_solver_from_sig(sig: str, *args, **kwargs):
"""Return Solver object from Dora signature, i.e. to play with it from a notebook.
See `get_solver_from_xp` for more information.
"""
xp = main.get_xp_from_sig(sig)
return get_solver_from_xp(xp, *args, **kwargs)
def init_seed_and_system(cfg):
import numpy as np
import torch
import random
from audiocraft.modules.transformer import set_efficient_attention_backend
multiprocessing.set_start_method(cfg.mp_start_method)
logger.debug('Setting mp start method to %s', cfg.mp_start_method)
random.seed(cfg.seed)
np.random.seed(cfg.seed)
# torch also initialize cuda seed if available
torch.manual_seed(cfg.seed)
torch.set_num_threads(cfg.num_threads)
os.environ['MKL_NUM_THREADS'] = str(cfg.num_threads)
os.environ['OMP_NUM_THREADS'] = str(cfg.num_threads)
logger.debug('Setting num threads to %d', cfg.num_threads)
set_efficient_attention_backend(cfg.efficient_attention_backend)
logger.debug('Setting efficient attention backend to %s', cfg.efficient_attention_backend)
if 'SLURM_JOB_ID' in os.environ:
tmpdir = Path('/scratch/slurm_tmpdir/' + os.environ['SLURM_JOB_ID'])
if tmpdir.exists():
logger.info("Changing tmpdir to %s", tmpdir)
os.environ['TMPDIR'] = str(tmpdir)
@hydra_main(config_path='../config', config_name='config', version_base='1.1')
def main(cfg):
init_seed_and_system(cfg)
# Setup logging both to XP specific folder, and to stderr.
log_name = '%s.log.{rank}' % cfg.execute_only if cfg.execute_only else 'solver.log.{rank}'
flashy.setup_logging(level=str(cfg.logging.level).upper(), log_name=log_name)
# Initialize distributed training, no need to specify anything when using Dora.
flashy.distrib.init()
solver = get_solver(cfg)
if cfg.show:
solver.show()
return
if cfg.execute_only:
assert cfg.execute_inplace or cfg.continue_from is not None, \
"Please explicitly specify the checkpoint to continue from with continue_from=<sig_or_path> " + \
"when running with execute_only or set execute_inplace to True."
solver.restore(replay_metrics=False) # load checkpoint
solver.run_one_stage(cfg.execute_only)
return
return solver.run()
main.dora.dir = AudioCraftEnvironment.get_dora_dir()
main._base_cfg.slurm = get_slurm_parameters(main._base_cfg.slurm)
if main.dora.shared is not None and not os.access(main.dora.shared, os.R_OK):
print("No read permission on dora.shared folder, ignoring it.", file=sys.stderr)
main.dora.shared = None
if __name__ == '__main__':
main()