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import argparse
import datetime
import glob
import inspect
import os
import sys
from inspect import Parameter
from typing import Union
import numpy as np
import pytorch_lightning as pl
import torch
import torchvision
import wandb
from PIL import Image
from matplotlib import pyplot as plt
from natsort import natsorted
from omegaconf import OmegaConf
from packaging import version
from pytorch_lightning import seed_everything
from pytorch_lightning.callbacks import Callback
from pytorch_lightning.loggers import WandbLogger
from pytorch_lightning.trainer import Trainer
from pytorch_lightning.utilities import rank_zero_only
from sgm.util import (
exists,
instantiate_from_config,
isheatmap,
)
MULTINODE_HACKS = True
def default_trainer_args():
argspec = dict(inspect.signature(Trainer.__init__).parameters)
argspec.pop("self")
default_args = {
param: argspec[param].default
for param in argspec
if argspec[param] != Parameter.empty
}
return default_args
def get_parser(**parser_kwargs):
def str2bool(v):
if isinstance(v, bool):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise argparse.ArgumentTypeError("Boolean value expected.")
parser = argparse.ArgumentParser(**parser_kwargs)
parser.add_argument(
"-n",
"--name",
type=str,
const=True,
default="",
nargs="?",
help="postfix for logdir",
)
parser.add_argument(
"--no_date",
type=str2bool,
nargs="?",
const=True,
default=False,
help="if True, skip date generation for logdir and only use naming via opt.base or opt.name (+ opt.postfix, optionally)",
)
parser.add_argument(
"-r",
"--resume",
type=str,
const=True,
default="",
nargs="?",
help="resume from logdir or checkpoint in logdir",
)
parser.add_argument(
"-b",
"--base",
nargs="*",
metavar="base_config.yaml",
help="paths to base configs. Loaded from left-to-right. "
"Parameters can be overwritten or added with command-line options of the form `--key value`.",
default=list(),
)
parser.add_argument(
"-t",
"--train",
type=str2bool,
const=True,
default=True,
nargs="?",
help="train",
)
parser.add_argument(
"--no-test",
type=str2bool,
const=True,
default=False,
nargs="?",
help="disable test",
)
parser.add_argument(
"-p", "--project", help="name of new or path to existing project"
)
parser.add_argument(
"-d",
"--debug",
type=str2bool,
nargs="?",
const=True,
default=False,
help="enable post-mortem debugging",
)
parser.add_argument(
"-s",
"--seed",
type=int,
default=23,
help="seed for seed_everything",
)
parser.add_argument(
"-f",
"--postfix",
type=str,
default="",
help="post-postfix for default name",
)
parser.add_argument(
"--projectname",
type=str,
default="stablediffusion",
)
parser.add_argument(
"-l",
"--logdir",
type=str,
default="logs",
help="directory for logging dat shit",
)
parser.add_argument(
"--scale_lr",
type=str2bool,
nargs="?",
const=True,
default=False,
help="scale base-lr by ngpu * batch_size * n_accumulate",
)
parser.add_argument(
"--legacy_naming",
type=str2bool,
nargs="?",
const=True,
default=False,
help="name run based on config file name if true, else by whole path",
)
parser.add_argument(
"--enable_tf32",
type=str2bool,
nargs="?",
const=True,
default=False,
help="enables the TensorFloat32 format both for matmuls and cuDNN for pytorch 1.12",
)
parser.add_argument(
"--startup",
type=str,
default=None,
help="Startuptime from distributed script",
)
parser.add_argument(
"--wandb",
type=str2bool,
nargs="?",
const=True,
default=False, # TODO: later default to True
help="log to wandb",
)
parser.add_argument(
"--no_base_name",
type=str2bool,
nargs="?",
const=True,
default=False, # TODO: later default to True
help="log to wandb",
)
if version.parse(torch.__version__) >= version.parse("2.0.0"):
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help="single checkpoint file to resume from",
)
default_args = default_trainer_args()
for key in default_args:
parser.add_argument("--" + key, default=default_args[key])
return parser
def get_checkpoint_name(logdir):
ckpt = os.path.join(logdir, "checkpoints", "last**.ckpt")
ckpt = natsorted(glob.glob(ckpt))
print('available "last" checkpoints:')
print(ckpt)
if len(ckpt) > 1:
print("got most recent checkpoint")
ckpt = sorted(ckpt, key=lambda x: os.path.getmtime(x))[-1]
print(f"Most recent ckpt is {ckpt}")
with open(os.path.join(logdir, "most_recent_ckpt.txt"), "w") as f:
f.write(ckpt + "\n")
try:
version = int(ckpt.split("/")[-1].split("-v")[-1].split(".")[0])
except Exception as e:
print("version confusion but not bad")
print(e)
version = 1
# version = last_version + 1
else:
# in this case, we only have one "last.ckpt"
ckpt = ckpt[0]
version = 1
melk_ckpt_name = f"last-v{version}.ckpt"
print(f"Current melk ckpt name: {melk_ckpt_name}")
return ckpt, melk_ckpt_name
class SetupCallback(Callback):
def __init__(
self,
resume,
now,
logdir,
ckptdir,
cfgdir,
config,
lightning_config,
debug,
ckpt_name=None,
):
super().__init__()
self.resume = resume
self.now = now
self.logdir = logdir
self.ckptdir = ckptdir
self.cfgdir = cfgdir
self.config = config
self.lightning_config = lightning_config
self.debug = debug
self.ckpt_name = ckpt_name
def on_exception(self, trainer: pl.Trainer, pl_module, exception):
if not self.debug and trainer.global_rank == 0:
print("Summoning checkpoint.")
if self.ckpt_name is None:
ckpt_path = os.path.join(self.ckptdir, "last.ckpt")
else:
ckpt_path = os.path.join(self.ckptdir, self.ckpt_name)
trainer.save_checkpoint(ckpt_path)
def on_fit_start(self, trainer, pl_module):
if trainer.global_rank == 0:
# Create logdirs and save configs
os.makedirs(self.logdir, exist_ok=True)
os.makedirs(self.ckptdir, exist_ok=True)
os.makedirs(self.cfgdir, exist_ok=True)
if "callbacks" in self.lightning_config:
if (
"metrics_over_trainsteps_checkpoint"
in self.lightning_config["callbacks"]
):
os.makedirs(
os.path.join(self.ckptdir, "trainstep_checkpoints"),
exist_ok=True,
)
print("Project config")
print(OmegaConf.to_yaml(self.config))
if MULTINODE_HACKS:
import time
time.sleep(5)
OmegaConf.save(
self.config,
os.path.join(self.cfgdir, "{}-project.yaml".format(self.now)),
)
print("Lightning config")
print(OmegaConf.to_yaml(self.lightning_config))
OmegaConf.save(
OmegaConf.create({"lightning": self.lightning_config}),
os.path.join(self.cfgdir, "{}-lightning.yaml".format(self.now)),
)
else:
# ModelCheckpoint callback created log directory --- remove it
if not MULTINODE_HACKS and not self.resume and os.path.exists(self.logdir):
dst, name = os.path.split(self.logdir)
dst = os.path.join(dst, "child_runs", name)
os.makedirs(os.path.split(dst)[0], exist_ok=True)
try:
os.rename(self.logdir, dst)
except FileNotFoundError:
pass
class ImageLogger(Callback):
def __init__(
self,
batch_frequency,
max_images,
clamp=True,
increase_log_steps=True,
rescale=True,
disabled=False,
log_on_batch_idx=False,
log_first_step=False,
log_images_kwargs=None,
log_before_first_step=False,
enable_autocast=True,
):
super().__init__()
self.enable_autocast = enable_autocast
self.rescale = rescale
self.batch_freq = batch_frequency
self.max_images = max_images
self.log_steps = [2**n for n in range(int(np.log2(self.batch_freq)) + 1)]
if not increase_log_steps:
self.log_steps = [self.batch_freq]
self.clamp = clamp
self.disabled = disabled
self.log_on_batch_idx = log_on_batch_idx
self.log_images_kwargs = log_images_kwargs if log_images_kwargs else {}
self.log_first_step = log_first_step
self.log_before_first_step = log_before_first_step
@rank_zero_only
def log_local(
self,
save_dir,
split,
images,
global_step,
current_epoch,
batch_idx,
pl_module: Union[None, pl.LightningModule] = None,
):
root = os.path.join(save_dir, "images", split)
for k in images:
if isheatmap(images[k]):
fig, ax = plt.subplots()
ax = ax.matshow(
images[k].cpu().numpy(), cmap="hot", interpolation="lanczos"
)
plt.colorbar(ax)
plt.axis("off")
filename = "{}_gs-{:06}_e-{:06}_b-{:06}.png".format(
k, global_step, current_epoch, batch_idx
)
os.makedirs(root, exist_ok=True)
path = os.path.join(root, filename)
plt.savefig(path)
plt.close()
# TODO: support wandb
else:
grid = torchvision.utils.make_grid(images[k], nrow=4)
if self.rescale:
grid = (grid + 1.0) / 2.0 # -1,1 -> 0,1; c,h,w
grid = grid.transpose(0, 1).transpose(1, 2).squeeze(-1)
grid = grid.numpy()
grid = (grid * 255).astype(np.uint8)
filename = "{}_gs-{:06}_e-{:06}_b-{:06}.png".format(
k, global_step, current_epoch, batch_idx
)
path = os.path.join(root, filename)
os.makedirs(os.path.split(path)[0], exist_ok=True)
img = Image.fromarray(grid)
img.save(path)
if exists(pl_module):
assert isinstance(
pl_module.logger, WandbLogger
), "logger_log_image only supports WandbLogger currently"
pl_module.logger.log_image(
key=f"{split}/{k}",
images=[
img,
],
step=pl_module.global_step,
)
@rank_zero_only
def log_img(self, pl_module, batch, batch_idx, split="train"):
check_idx = batch_idx if self.log_on_batch_idx else pl_module.global_step
if (
self.check_frequency(check_idx)
and hasattr(pl_module, "log_images") # batch_idx % self.batch_freq == 0
and callable(pl_module.log_images)
and
# batch_idx > 5 and
self.max_images > 0
):
logger = type(pl_module.logger)
is_train = pl_module.training
if is_train:
pl_module.eval()
gpu_autocast_kwargs = {
"enabled": self.enable_autocast, # torch.is_autocast_enabled(),
"dtype": torch.get_autocast_gpu_dtype(),
"cache_enabled": torch.is_autocast_cache_enabled(),
}
with torch.no_grad(), torch.cuda.amp.autocast(**gpu_autocast_kwargs):
images = pl_module.log_images(
batch, split=split, **self.log_images_kwargs
)
for k in images:
N = min(images[k].shape[0], self.max_images)
if not isheatmap(images[k]):
images[k] = images[k][:N]
if isinstance(images[k], torch.Tensor):
images[k] = images[k].detach().float().cpu()
if self.clamp and not isheatmap(images[k]):
images[k] = torch.clamp(images[k], -1.0, 1.0)
self.log_local(
pl_module.logger.save_dir,
split,
images,
pl_module.global_step,
pl_module.current_epoch,
batch_idx,
pl_module=pl_module
if isinstance(pl_module.logger, WandbLogger)
else None,
)
if is_train:
pl_module.train()
def check_frequency(self, check_idx):
if ((check_idx % self.batch_freq) == 0 or (check_idx in self.log_steps)) and (
check_idx > 0 or self.log_first_step
):
try:
self.log_steps.pop(0)
except IndexError as e:
print(e)
pass
return True
return False
@rank_zero_only
def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
if not self.disabled and (pl_module.global_step > 0 or self.log_first_step):
self.log_img(pl_module, batch, batch_idx, split="train")
@rank_zero_only
def on_train_batch_start(self, trainer, pl_module, batch, batch_idx):
if self.log_before_first_step and pl_module.global_step == 0:
print(f"{self.__class__.__name__}: logging before training")
self.log_img(pl_module, batch, batch_idx, split="train")
@rank_zero_only
def on_validation_batch_end(
self, trainer, pl_module, outputs, batch, batch_idx, *args, **kwargs
):
if not self.disabled and pl_module.global_step > 0:
self.log_img(pl_module, batch, batch_idx, split="val")
if hasattr(pl_module, "calibrate_grad_norm"):
if (
pl_module.calibrate_grad_norm and batch_idx % 25 == 0
) and batch_idx > 0:
self.log_gradients(trainer, pl_module, batch_idx=batch_idx)
@rank_zero_only
def init_wandb(save_dir, opt, config, group_name, name_str):
print(f"setting WANDB_DIR to {save_dir}")
os.makedirs(save_dir, exist_ok=True)
os.environ["WANDB_DIR"] = save_dir
if opt.debug:
wandb.init(project=opt.projectname, mode="offline", group=group_name)
else:
wandb.init(
project=opt.projectname,
config=config,
settings=wandb.Settings(code_dir="./sgm"),
group=group_name,
name=name_str,
)
if __name__ == "__main__":
# custom parser to specify config files, train, test and debug mode,
# postfix, resume.
# `--key value` arguments are interpreted as arguments to the trainer.
# `nested.key=value` arguments are interpreted as config parameters.
# configs are merged from left-to-right followed by command line parameters.
# model:
# base_learning_rate: float
# target: path to lightning module
# params:
# key: value
# data:
# target: main.DataModuleFromConfig
# params:
# batch_size: int
# wrap: bool
# train:
# target: path to train dataset
# params:
# key: value
# validation:
# target: path to validation dataset
# params:
# key: value
# test:
# target: path to test dataset
# params:
# key: value
# lightning: (optional, has sane defaults and can be specified on cmdline)
# trainer:
# additional arguments to trainer
# logger:
# logger to instantiate
# modelcheckpoint:
# modelcheckpoint to instantiate
# callbacks:
# callback1:
# target: importpath
# params:
# key: value
now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
# add cwd for convenience and to make classes in this file available when
# running as `python main.py`
# (in particular `main.DataModuleFromConfig`)
sys.path.append(os.getcwd())
parser = get_parser()
opt, unknown = parser.parse_known_args()
if opt.name and opt.resume:
raise ValueError(
"-n/--name and -r/--resume cannot be specified both."
"If you want to resume training in a new log folder, "
"use -n/--name in combination with --resume_from_checkpoint"
)
melk_ckpt_name = None
name = None
if opt.resume:
if not os.path.exists(opt.resume):
raise ValueError("Cannot find {}".format(opt.resume))
if os.path.isfile(opt.resume):
paths = opt.resume.split("/")
# idx = len(paths)-paths[::-1].index("logs")+1
# logdir = "/".join(paths[:idx])
logdir = "/".join(paths[:-2])
ckpt = opt.resume
_, melk_ckpt_name = get_checkpoint_name(logdir)
else:
assert os.path.isdir(opt.resume), opt.resume
logdir = opt.resume.rstrip("/")
ckpt, melk_ckpt_name = get_checkpoint_name(logdir)
print("#" * 100)
print(f'Resuming from checkpoint "{ckpt}"')
print("#" * 100)
opt.resume_from_checkpoint = ckpt
base_configs = sorted(glob.glob(os.path.join(logdir, "configs/*.yaml")))
opt.base = base_configs + opt.base
_tmp = logdir.split("/")
nowname = _tmp[-1]
else:
if opt.name:
name = "_" + opt.name
elif opt.base:
if opt.no_base_name:
name = ""
else:
if opt.legacy_naming:
cfg_fname = os.path.split(opt.base[0])[-1]
cfg_name = os.path.splitext(cfg_fname)[0]
else:
assert "configs" in os.path.split(opt.base[0])[0], os.path.split(
opt.base[0]
)[0]
cfg_path = os.path.split(opt.base[0])[0].split(os.sep)[
os.path.split(opt.base[0])[0].split(os.sep).index("configs")
+ 1 :
] # cut away the first one (we assert all configs are in "configs")
cfg_name = os.path.splitext(os.path.split(opt.base[0])[-1])[0]
cfg_name = "-".join(cfg_path) + f"-{cfg_name}"
name = "_" + cfg_name
else:
name = ""
if not opt.no_date:
nowname = now + name + opt.postfix
else:
nowname = name + opt.postfix
if nowname.startswith("_"):
nowname = nowname[1:]
logdir = os.path.join(opt.logdir, nowname)
print(f"LOGDIR: {logdir}")
ckptdir = os.path.join(logdir, "checkpoints")
cfgdir = os.path.join(logdir, "configs")
seed_everything(opt.seed, workers=True)
# move before model init, in case a torch.compile(...) is called somewhere
if opt.enable_tf32:
# pt_version = version.parse(torch.__version__)
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
print(f"Enabling TF32 for PyTorch {torch.__version__}")
else:
print(f"Using default TF32 settings for PyTorch {torch.__version__}:")
print(
f"torch.backends.cuda.matmul.allow_tf32={torch.backends.cuda.matmul.allow_tf32}"
)
print(f"torch.backends.cudnn.allow_tf32={torch.backends.cudnn.allow_tf32}")
try:
# init and save configs
configs = [OmegaConf.load(cfg) for cfg in opt.base]
cli = OmegaConf.from_dotlist(unknown)
config = OmegaConf.merge(*configs, cli)
lightning_config = config.pop("lightning", OmegaConf.create())
# merge trainer cli with config
trainer_config = lightning_config.get("trainer", OmegaConf.create())
# default to gpu
trainer_config["accelerator"] = "gpu"
#
standard_args = default_trainer_args()
for k in standard_args:
if getattr(opt, k) != standard_args[k]:
trainer_config[k] = getattr(opt, k)
ckpt_resume_path = opt.resume_from_checkpoint
if not "devices" in trainer_config and trainer_config["accelerator"] != "gpu":
del trainer_config["accelerator"]
cpu = True
else:
gpuinfo = trainer_config["devices"]
print(f"Running on GPUs {gpuinfo}")
cpu = False
trainer_opt = argparse.Namespace(**trainer_config)
lightning_config.trainer = trainer_config
# model
model = instantiate_from_config(config.model)
# trainer and callbacks
trainer_kwargs = dict()
# default logger configs
default_logger_cfgs = {
"wandb": {
"target": "pytorch_lightning.loggers.WandbLogger",
"params": {
"name": nowname,
# "save_dir": logdir,
"offline": opt.debug,
"id": nowname,
"project": opt.projectname,
"log_model": False,
# "dir": logdir,
},
},
"csv": {
"target": "pytorch_lightning.loggers.CSVLogger",
"params": {
"name": "testtube", # hack for sbord fanatics
"save_dir": logdir,
},
},
}
default_logger_cfg = default_logger_cfgs["wandb" if opt.wandb else "csv"]
if opt.wandb:
# TODO change once leaving "swiffer" config directory
try:
group_name = nowname.split(now)[-1].split("-")[1]
except:
group_name = nowname
default_logger_cfg["params"]["group"] = group_name
init_wandb(
os.path.join(os.getcwd(), logdir),
opt=opt,
group_name=group_name,
config=config,
name_str=nowname,
)
if "logger" in lightning_config:
logger_cfg = lightning_config.logger
else:
logger_cfg = OmegaConf.create()
logger_cfg = OmegaConf.merge(default_logger_cfg, logger_cfg)
trainer_kwargs["logger"] = instantiate_from_config(logger_cfg)
# modelcheckpoint - use TrainResult/EvalResult(checkpoint_on=metric) to
# specify which metric is used to determine best models
default_modelckpt_cfg = {
"target": "pytorch_lightning.callbacks.ModelCheckpoint",
"params": {
"dirpath": ckptdir,
"filename": "{epoch:06}",
"verbose": True,
"save_last": True,
},
}
if hasattr(model, "monitor"):
print(f"Monitoring {model.monitor} as checkpoint metric.")
default_modelckpt_cfg["params"]["monitor"] = model.monitor
default_modelckpt_cfg["params"]["save_top_k"] = 3
if "modelcheckpoint" in lightning_config:
modelckpt_cfg = lightning_config.modelcheckpoint
else:
modelckpt_cfg = OmegaConf.create()
modelckpt_cfg = OmegaConf.merge(default_modelckpt_cfg, modelckpt_cfg)
print(f"Merged modelckpt-cfg: \n{modelckpt_cfg}")
# https://pytorch-lightning.readthedocs.io/en/stable/extensions/strategy.html
# default to ddp if not further specified
default_strategy_config = {"target": "pytorch_lightning.strategies.DDPStrategy"}
if "strategy" in lightning_config:
strategy_cfg = lightning_config.strategy
else:
strategy_cfg = OmegaConf.create()
default_strategy_config["params"] = {
"find_unused_parameters": False,
# "static_graph": True,
# "ddp_comm_hook": default.fp16_compress_hook # TODO: experiment with this, also for DDPSharded
}
strategy_cfg = OmegaConf.merge(default_strategy_config, strategy_cfg)
print(
f"strategy config: \n ++++++++++++++ \n {strategy_cfg} \n ++++++++++++++ "
)
trainer_kwargs["strategy"] = instantiate_from_config(strategy_cfg)
# add callback which sets up log directory
default_callbacks_cfg = {
"setup_callback": {
"target": "main.SetupCallback",
"params": {
"resume": opt.resume,
"now": now,
"logdir": logdir,
"ckptdir": ckptdir,
"cfgdir": cfgdir,
"config": config,
"lightning_config": lightning_config,
"debug": opt.debug,
"ckpt_name": melk_ckpt_name,
},
},
"image_logger": {
"target": "main.ImageLogger",
"params": {"batch_frequency": 1000, "max_images": 4, "clamp": True},
},
"learning_rate_logger": {
"target": "pytorch_lightning.callbacks.LearningRateMonitor",
"params": {
"logging_interval": "step",
# "log_momentum": True
},
},
}
if version.parse(pl.__version__) >= version.parse("1.4.0"):
default_callbacks_cfg.update({"checkpoint_callback": modelckpt_cfg})
if "callbacks" in lightning_config:
callbacks_cfg = lightning_config.callbacks
else:
callbacks_cfg = OmegaConf.create()
if "metrics_over_trainsteps_checkpoint" in callbacks_cfg:
print(
"Caution: Saving checkpoints every n train steps without deleting. This might require some free space."
)
default_metrics_over_trainsteps_ckpt_dict = {
"metrics_over_trainsteps_checkpoint": {
"target": "pytorch_lightning.callbacks.ModelCheckpoint",
"params": {
"dirpath": os.path.join(ckptdir, "trainstep_checkpoints"),
"filename": "{epoch:06}-{step:09}",
"verbose": True,
"save_top_k": -1,
"every_n_train_steps": 10000,
"save_weights_only": True,
},
}
}
default_callbacks_cfg.update(default_metrics_over_trainsteps_ckpt_dict)
callbacks_cfg = OmegaConf.merge(default_callbacks_cfg, callbacks_cfg)
if "ignore_keys_callback" in callbacks_cfg and ckpt_resume_path is not None:
callbacks_cfg.ignore_keys_callback.params["ckpt_path"] = ckpt_resume_path
elif "ignore_keys_callback" in callbacks_cfg:
del callbacks_cfg["ignore_keys_callback"]
trainer_kwargs["callbacks"] = [
instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg
]
if not "plugins" in trainer_kwargs:
trainer_kwargs["plugins"] = list()
# cmd line trainer args (which are in trainer_opt) have always priority over config-trainer-args (which are in trainer_kwargs)
trainer_opt = vars(trainer_opt)
trainer_kwargs = {
key: val for key, val in trainer_kwargs.items() if key not in trainer_opt
}
trainer = Trainer(**trainer_opt, **trainer_kwargs)
trainer.logdir = logdir ###
# data
data = instantiate_from_config(config.data)
# NOTE according to https://pytorch-lightning.readthedocs.io/en/latest/datamodules.html
# calling these ourselves should not be necessary but it is.
# lightning still takes care of proper multiprocessing though
data.prepare_data()
# data.setup()
print("#### Data #####")
try:
for k in data.datasets:
print(
f"{k}, {data.datasets[k].__class__.__name__}, {len(data.datasets[k])}"
)
except:
print("datasets not yet initialized.")
# configure learning rate
if "batch_size" in config.data.params:
bs, base_lr = config.data.params.batch_size, config.model.base_learning_rate
else:
bs, base_lr = (
config.data.params.train.loader.batch_size,
config.model.base_learning_rate,
)
if not cpu:
ngpu = len(lightning_config.trainer.devices.strip(",").split(","))
else:
ngpu = 1
if "accumulate_grad_batches" in lightning_config.trainer:
accumulate_grad_batches = lightning_config.trainer.accumulate_grad_batches
else:
accumulate_grad_batches = 1
print(f"accumulate_grad_batches = {accumulate_grad_batches}")
lightning_config.trainer.accumulate_grad_batches = accumulate_grad_batches
if opt.scale_lr:
model.learning_rate = accumulate_grad_batches * ngpu * bs * base_lr
print(
"Setting learning rate to {:.2e} = {} (accumulate_grad_batches) * {} (num_gpus) * {} (batchsize) * {:.2e} (base_lr)".format(
model.learning_rate, accumulate_grad_batches, ngpu, bs, base_lr
)
)
else:
model.learning_rate = base_lr
print("++++ NOT USING LR SCALING ++++")
print(f"Setting learning rate to {model.learning_rate:.2e}")
# allow checkpointing via USR1
def melk(*args, **kwargs):
# run all checkpoint hooks
if trainer.global_rank == 0:
print("Summoning checkpoint.")
if melk_ckpt_name is None:
ckpt_path = os.path.join(ckptdir, "last.ckpt")
else:
ckpt_path = os.path.join(ckptdir, melk_ckpt_name)
trainer.save_checkpoint(ckpt_path)
def divein(*args, **kwargs):
if trainer.global_rank == 0:
import pudb
pudb.set_trace()
import signal
signal.signal(signal.SIGUSR1, melk)
signal.signal(signal.SIGUSR2, divein)
# run
if opt.train:
try:
trainer.fit(model, data, ckpt_path=ckpt_resume_path)
except Exception:
if not opt.debug:
melk()
raise
if not opt.no_test and not trainer.interrupted:
trainer.test(model, data)
except RuntimeError as err:
if MULTINODE_HACKS:
import requests
import datetime
import os
import socket
device = os.environ.get("CUDA_VISIBLE_DEVICES", "?")
hostname = socket.gethostname()
ts = datetime.datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S")
resp = requests.get("http://169.254.169.254/latest/meta-data/instance-id")
print(
f"ERROR at {ts} on {hostname}/{resp.text} (CUDA_VISIBLE_DEVICES={device}): {type(err).__name__}: {err}",
flush=True,
)
raise err
except Exception:
if opt.debug and trainer.global_rank == 0:
try:
import pudb as debugger
except ImportError:
import pdb as debugger
debugger.post_mortem()
raise
finally:
# move newly created debug project to debug_runs
if opt.debug and not opt.resume and trainer.global_rank == 0:
dst, name = os.path.split(logdir)
dst = os.path.join(dst, "debug_runs", name)
os.makedirs(os.path.split(dst)[0], exist_ok=True)
os.rename(logdir, dst)
if opt.wandb:
wandb.finish()
# if trainer.global_rank == 0:
# print(trainer.profiler.summary())
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