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import argparse
import contextlib
import importlib
import logging
import os
import sys
import time
import traceback
import pytorch_lightning as pl
import torch
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import LearningRateMonitor, ModelCheckpoint
from pytorch_lightning.loggers import CSVLogger, TensorBoardLogger
from pytorch_lightning.utilities.rank_zero import rank_zero_only
import craftsman
from craftsman.systems.base import BaseSystem
from craftsman.utils.callbacks import (
CodeSnapshotCallback,
ConfigSnapshotCallback,
CustomProgressBar,
ProgressCallback,
)
from craftsman.utils.config import ExperimentConfig, load_config
from craftsman.utils.misc import get_rank
from craftsman.utils.typing import Optional
class ColoredFilter(logging.Filter):
"""
A logging filter to add color to certain log levels.
"""
RESET = "\033[0m"
RED = "\033[31m"
GREEN = "\033[32m"
YELLOW = "\033[33m"
BLUE = "\033[34m"
MAGENTA = "\033[35m"
CYAN = "\033[36m"
COLORS = {
"WARNING": YELLOW,
"INFO": GREEN,
"DEBUG": BLUE,
"CRITICAL": MAGENTA,
"ERROR": RED,
}
RESET = "\x1b[0m"
def __init__(self):
super().__init__()
def filter(self, record):
if record.levelname in self.COLORS:
color_start = self.COLORS[record.levelname]
record.levelname = f"{color_start}[{record.levelname}]"
record.msg = f"{record.msg}{self.RESET}"
return True
def load_custom_module(module_path):
module_name = os.path.basename(module_path)
if os.path.isfile(module_path):
sp = os.path.splitext(module_path)
module_name = sp[0]
try:
if os.path.isfile(module_path):
module_spec = importlib.util.spec_from_file_location(
module_name, module_path
)
else:
module_spec = importlib.util.spec_from_file_location(
module_name, os.path.join(module_path, "__init__.py")
)
module = importlib.util.module_from_spec(module_spec)
sys.modules[module_name] = module
module_spec.loader.exec_module(module)
return True
except Exception as e:
print(traceback.format_exc())
print(f"Cannot import {module_path} module for custom nodes:", e)
return False
def load_custom_modules():
node_paths = ["custom"]
node_import_times = []
if not os.path.exists("node_paths"):
return
for custom_node_path in node_paths:
possible_modules = os.listdir(custom_node_path)
if "__pycache__" in possible_modules:
possible_modules.remove("__pycache__")
for possible_module in possible_modules:
module_path = os.path.join(custom_node_path, possible_module)
if (
os.path.isfile(module_path)
and os.path.splitext(module_path)[1] != ".py"
):
continue
if module_path.endswith(".disabled"):
continue
time_before = time.perf_counter()
success = load_custom_module(module_path)
node_import_times.append(
(time.perf_counter() - time_before, module_path, success)
)
if len(node_import_times) > 0:
print("\nImport times for custom modules:")
for n in sorted(node_import_times):
if n[2]:
import_message = ""
else:
import_message = " (IMPORT FAILED)"
print("{:6.1f} seconds{}:".format(n[0], import_message), n[1])
print()
def main(args, extras) -> None:
# set CUDA_VISIBLE_DEVICES if needed, then import pytorch-lightning
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
env_gpus_str = os.environ.get("CUDA_VISIBLE_DEVICES", None)
env_gpus = list(env_gpus_str.split(",")) if env_gpus_str else []
selected_gpus = [0]
torch.set_float32_matmul_precision("high")
# Always rely on CUDA_VISIBLE_DEVICES if specific GPU ID(s) are specified.
# As far as Pytorch Lightning is concerned, we always use all available GPUs
# (possibly filtered by CUDA_VISIBLE_DEVICES).
devices = -1
if len(env_gpus) > 0:
n_gpus = len(env_gpus)
else:
selected_gpus = list(args.gpu.split(","))
n_gpus = len(selected_gpus)
print(f"Using {n_gpus} GPUs: {selected_gpus}")
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
if args.typecheck:
from jaxtyping import install_import_hook
install_import_hook("craftsman", "typeguard.typechecked")
logger = logging.getLogger("pytorch_lightning")
if args.verbose:
logger.setLevel(logging.DEBUG)
for handler in logger.handlers:
if handler.stream == sys.stderr: # type: ignore
if not args.gradio:
handler.setFormatter(logging.Formatter("%(levelname)s %(message)s"))
handler.addFilter(ColoredFilter())
else:
handler.setFormatter(logging.Formatter("[%(levelname)s] %(message)s"))
load_custom_modules()
# parse YAML config to OmegaConf
cfg: ExperimentConfig
cfg = load_config(args.config, cli_args=extras, n_gpus=n_gpus)
# set a different seed for each device
pl.seed_everything(cfg.seed + get_rank(), workers=True)
dm = craftsman.find(cfg.data_type)(cfg.data)
system: BaseSystem = craftsman.find(cfg.system_type)(
cfg.system, resumed=cfg.resume is not None
)
system.set_save_dir(os.path.join(cfg.trial_dir, "save"))
if args.gradio:
fh = logging.FileHandler(os.path.join(cfg.trial_dir, "logs"))
fh.setLevel(logging.INFO)
if args.verbose:
fh.setLevel(logging.DEBUG)
fh.setFormatter(logging.Formatter("[%(levelname)s] %(message)s"))
logger.addHandler(fh)
callbacks = []
if args.train:
callbacks += [
ModelCheckpoint(
dirpath=os.path.join(cfg.trial_dir, "ckpts"), **cfg.checkpoint
),
LearningRateMonitor(logging_interval="step"),
CodeSnapshotCallback(
os.path.join(cfg.trial_dir, "code"), use_version=False
),
ConfigSnapshotCallback(
args.config,
cfg,
os.path.join(cfg.trial_dir, "configs"),
use_version=False,
),
]
if args.gradio:
callbacks += [
ProgressCallback(save_path=os.path.join(cfg.trial_dir, "progress"))
]
else:
callbacks += [CustomProgressBar(refresh_rate=1)]
def write_to_text(file, lines):
with open(file, "w") as f:
for line in lines:
f.write(line + "\n")
loggers = []
if args.train:
# make tensorboard logging dir to suppress warning
rank_zero_only(
lambda: os.makedirs(os.path.join(cfg.trial_dir, "tb_logs"), exist_ok=True)
)()
loggers += [
TensorBoardLogger(cfg.trial_dir, name="tb_logs"),
CSVLogger(cfg.trial_dir, name="csv_logs"),
] + system.get_loggers()
rank_zero_only(
lambda: write_to_text(
os.path.join(cfg.trial_dir, "cmd.txt"),
["python " + " ".join(sys.argv), str(args)],
)
)()
trainer = Trainer(
callbacks=callbacks,
logger=loggers,
inference_mode=False,
accelerator="gpu",
devices=devices,
# profiler="advanced",
**cfg.trainer,
)
def set_system_status(system: BaseSystem, ckpt_path: Optional[str]):
if ckpt_path is None:
return
ckpt = torch.load(ckpt_path, map_location="cpu")
system.set_resume_status(ckpt["epoch"], ckpt["global_step"])
if args.train:
trainer.fit(system, datamodule=dm, ckpt_path=cfg.resume)
trainer.test(system, datamodule=dm)
if args.gradio:
# also export assets if in gradio mode
trainer.predict(system, datamodule=dm)
elif args.validate:
# manually set epoch and global_step as they cannot be automatically resumed
set_system_status(system, cfg.resume)
trainer.validate(system, datamodule=dm, ckpt_path=cfg.resume)
elif args.test:
# manually set epoch and global_step as they cannot be automatically resumed
set_system_status(system, cfg.resume)
trainer.test(system, datamodule=dm, ckpt_path=cfg.resume)
elif args.export:
set_system_status(system, cfg.resume)
trainer.predict(system, datamodule=dm, ckpt_path=cfg.resume)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--config", required=True, help="path to config file")
parser.add_argument(
"--gpu",
default="0",
help="GPU(s) to be used. 0 means use the 1st available GPU. "
"1,2 means use the 2nd and 3rd available GPU. "
"If CUDA_VISIBLE_DEVICES is set before calling `launch.py`, "
"this argument is ignored and all available GPUs are always used.",
)
group = parser.add_mutually_exclusive_group(required=True)
group.add_argument("--train", action="store_true")
group.add_argument("--validate", action="store_true")
group.add_argument("--test", action="store_true")
group.add_argument("--export", action="store_true")
parser.add_argument(
"--gradio", action="store_true", help="if true, run in gradio mode"
)
parser.add_argument(
"--verbose", action="store_true", help="if true, set logging level to DEBUG"
)
parser.add_argument(
"--typecheck",
action="store_true",
help="whether to enable dynamic type checking",
)
args, extras = parser.parse_known_args()
if args.gradio:
with contextlib.redirect_stdout(sys.stderr):
main(args, extras)
else:
main(args, extras)
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