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import argparse
import datetime
import glob
import json
import os
import sys
import time
import random
import numpy as np
import torch
import torchvision
import copy
try:
import lightning.pytorch as pl
except:
import pytorch_lightning as pl
from functools import partial
from omegaconf import OmegaConf
from prefetch_generator import BackgroundGenerator
from torch.utils.data import DataLoader, Dataset
try:
from lightning.pytorch import seed_everything
from lightning.pytorch.callbacks import Callback
from lightning.pytorch.trainer import Trainer
from lightning.pytorch.utilities import rank_zero_info
LIGHTNING_PACK_NAME = "lightning.pytorch."
except:
from pytorch_lightning import seed_everything
from pytorch_lightning.callbacks import Callback
from pytorch_lightning.trainer import Trainer
from pytorch_lightning.utilities import rank_zero_info
LIGHTNING_PACK_NAME = "pytorch_lightning."
from ldm.data.base import Txt2ImgIterableBaseDataset
from ldm.util import instantiate_from_config
import mlperf_logging_utils
import mlperf_logging.mllog.constants as mllog_constants
from mlperf_logging_utils import mllogger
def get_parser(**parser_kwargs):
# A function to create an ArgumentParser object and add arguments to it
def str2bool(v):
# A helper function to parse boolean values from command line arguments
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.")
# Create an ArgumentParser object with specifies kwargs
parser = argparse.ArgumentParser(**parser_kwargs)
# Add vairous command line arguments with their default balues and descriptions
parser.add_argument(
"-n",
"--name",
type=str,
const=True,
default="",
nargs="?",
help="postfix for logdir",
)
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(
"-m",
"--mode",
type=str,
default="train",
choices=["train", "validate"],
help="run mode, train or validation",
)
parser.add_argument(
"-v",
"--validation",
type=str2bool,
const=True,
default=False,
nargs="?",
help="validation",
)
parser.add_argument(
"-p",
"--project",
help="name of new or path to existing project",
)
parser.add_argument(
"-c",
"--ckpt",
type=str,
const=True,
default="",
nargs="?",
help="load pretrained checkpoint from stable AI",
)
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=random.SystemRandom().randint(0, 2**32 - 1),
help="seed for seed_everything",
)
parser.add_argument(
"--fid_threshold",
type=int,
default=90,
help="halt training once this FID validation score or a smaller one is achieved."
"if used with --clip_threshold, both metrics need to reach their targets.",
)
parser.add_argument(
"--clip_threshold",
type=int,
default=0.15,
help="halt training once this CLIP validation score or a higher one is achieved."
"if used with --fid_threshold, both metrics need to reach their targets.",
)
parser.add_argument(
"-f",
"--postfix",
type=str,
default="",
help="post-postfix for default name",
)
parser.add_argument(
"-l",
"--logdir",
type=str,
default="/results",
help="directory for logging dat shit",
)
parser.add_argument(
"--scale_lr",
type=str2bool,
nargs="?",
const=True,
default=True,
help="scale base-lr by ngpu * batch_size * n_accumulate",
)
parser.add_argument(
"--train_log_interval",
type=int,
default=100,
help="Training logging interval"
)
parser.add_argument(
"--validation_log_interval",
type=int,
default=10,
help="Validation logging interval"
)
parser.add_argument(
"--hpus",
type=int,
default=0,
help="number of hpu devices to run",
)
parser.add_argument(
"--use_lazy_mode",
type=lambda x: x.lower() == 'true',
default=True,
help="Run Lazy or Eager Mode on HPU, default: lazy",
)
parser.add_argument(
"--batch_size",
type=int,
default=0,
help="batch size for training",
)
parser.add_argument(
"--log_freq",
type=int,
default=50,
help="frequency of logging loss.",
)
parser.add_argument(
"--autocast", dest="use_autocast",
action="store_true",
help="Use PyTorch autocast on Gaudi"
)
parser.add_argument(
"--warmup", dest="warmup_path",
type=str, default=None, # Set a default value if needed
help="Path to the warmup dataset file"
)
parser.add_argument(
"--async_checkpoint", dest='async_checkpoint',
action='store_true',
help="Enables usage of asynchronous checkpoint saving during training."
)
parser.add_argument(
"--current_validation_iter", dest="current_validation_iter",
type=int, default=1,
help="Id of validation run"
)
parser.add_argument(
"--validation_iters", dest="validation_iters",
type=int, default=5,
help="Number of all validation runs"
)
return parser
# A function that returns the non-default arguments between two objects
def nondefault_trainer_args(opt):
# create an argument parsser
parser = argparse.ArgumentParser()
# parse the empty arguments to obtain the default values
args = parser.parse_args([])
# return all non-default arguments
return sorted(k for k in vars(args) if getattr(opt, k) != getattr(args, k))
class ListEarlyStopping(Callback):
# Early stopping class that accepts a list of metrics and all stopping_thresholds
# must be met before stopping
def __init__(self, monitor_metrics: list = ["validation/fid", "validation/clip"],
mode_metrics: dict = {'validation/fid': 'min', 'validation/clip': 'max'},
stopping_thresholds: dict = {"validation/fid": None, "validation/clip": None},
check_finite: bool = False):
super(ListEarlyStopping, self).__init__()
self.monitor_metrics = monitor_metrics
self.mode_metrics = mode_metrics
self.stopping_thresholds = stopping_thresholds
self.check_finite = check_finite
def check_metrics(self, current_metrics):
should_stop = []
for metric in self.monitor_metrics:
if metric in current_metrics:
current_value = current_metrics[metric]
if self.check_finite and not torch.isfinite(torch.as_tensor(current_value)):
raise ValueError(f"The monitored metric {metric} has become non-finite.")
# Skip metrics without a stopping thresholds
if self.stopping_thresholds[metric] is None:
continue
if self.mode_metrics[metric] == 'min':
should_stop.append(current_value <= self.stopping_thresholds[metric])
if self.mode_metrics[metric] == 'max':
should_stop.append(current_value >= self.stopping_thresholds[metric])
# A minimum of one metric should have been reviewed.
return False if not should_stop else all(should_stop)
def on_validation_end(self, trainer, pl_module):
logs = trainer.callback_metrics
should_stop = self.check_metrics(logs)
if should_stop:
rank_zero_info('Early stopping conditioned have been met. Stopping training.')
trainer.should_stop = True
class SetupCallback(Callback):
# I nitialize the callback with the necessary parameters
def __init__(self, resume, now, logdir, ckptdir, cfgdir, config, lightning_config):
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
# Save a checkpoint if training is interrupted with keyboard interrupt
def on_keyboard_interrupt(self, trainer, pl_module):
if trainer.global_rank == 0:
print("Summoning checkpoint.")
ckpt_path = os.path.join(self.ckptdir, "last.ckpt")
# trainer.save_checkpoint(ckpt_path)
# Create necessary directories and save configuration files before training starts
# def on_pretrain_routine_start(self, trainer, pl_module):
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)
print("Project config")
print(OmegaConf.to_yaml(self.config))
OmegaConf.save(self.config, os.path.join(self.cfgdir, "{}-project.yaml".format(self.now)))
# Save project config and lightning config as YAML files
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)))
# Remove log directory if resuming training and directory already exists
else:
# ModelCheckpoint callback created log directory --- remove it
if 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
# def on_fit_end(self, trainer, pl_module):
# if trainer.global_rank == 0:
# ckpt_path = os.path.join(self.ckptdir, "last.ckpt")
# rank_zero_info(f"Saving final checkpoint in {ckpt_path}.")
# trainer.save_checkpoint(ckpt_path)
class CUDACallback(Callback):
# see https://github.com/SeanNaren/minGPT/blob/master/mingpt/callback.py
def on_train_start(self, trainer, pl_module):
rank_zero_info("Training is starting")
# the method is called at the end of each training epoch
def on_train_end(self, trainer, pl_module):
rank_zero_info("Training is ending")
def on_train_epoch_start(self, trainer, pl_module):
# Reset the memory use counter
torch.cuda.reset_peak_memory_stats(trainer.strategy.root_device.index)
torch.cuda.synchronize(trainer.strategy.root_device.index)
self.start_time = time.time()
def on_train_epoch_end(self, trainer, pl_module):
torch.cuda.synchronize(trainer.strategy.root_device.index)
max_memory = torch.cuda.max_memory_allocated(trainer.strategy.root_device.index) / 2**20
epoch_time = time.time() - self.start_time
try:
max_memory = trainer.strategy.reduce(max_memory)
epoch_time = trainer.strategy.reduce(epoch_time)
rank_zero_info(f"Average Epoch time: {epoch_time:.2f} seconds")
rank_zero_info(f"Average Peak memory {max_memory:.2f}MiB")
except AttributeError:
pass
class HPUCallback(Callback):
def on_train_start(self, trainer, pl_module):
rank_zero_info("Training is starting on HPU")
# the method is called at the end of each training epoch
def on_train_end(self, trainer, pl_module):
rank_zero_info("Training is ending")
def on_train_epoch_start(self, trainer, pl_module):
# Reset the memory use counter
torch.hpu.memory.reset_peak_memory_stats(trainer.strategy.root_device.index)
torch.hpu.synchronize()
self.start_time = time.time()
def on_train_epoch_end(self, trainer, pl_module):
torch.hpu.synchronize()
max_memory = torch.hpu.memory.max_memory_allocated(trainer.strategy.root_device.index) / 2 ** 20
epoch_time = time.time() - self.start_time
try:
max_memory = trainer.strategy.reduce(max_memory)
epoch_time = trainer.strategy.reduce(epoch_time)
rank_zero_info(f"Average Epoch time: {epoch_time:.2f} seconds")
rank_zero_info(f"Average Peak memory {max_memory:.2f} MiB")
except AttributeError:
pass
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
parser = get_parser()
opt, unknown = parser.parse_known_args()
# get the current time to create a new logging directory
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())
# Veirfy the arguments are both specified
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")
if opt.log_freq >= opt.train_log_interval:
raise ValueError("--log_freq should be lesser than --train_log_interval")
# Check if the "resume" option is specified, resume training from the checkpoint if it is true
ckpt = None
if opt.resume:
rank_zero_info("Resuming from {}".format(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])
rank_zero_info("logdir: {}".format(logdir))
ckpt = opt.resume
else:
assert os.path.isdir(opt.resume), opt.resume
logdir = opt.resume.rstrip("/")
ckpt = os.path.join(logdir, "checkpoints", "last.ckpt")
# Finds all ".yaml" configuration files in the log directory and adds them to the list of base configurations
base_configs = sorted(glob.glob(os.path.join(logdir, "configs/*.yaml")))
opt.base = base_configs + opt.base
# Gets the name of the current log directory by splitting the path and taking the last element.
_tmp = logdir.split("/")
nowname = _tmp[-1]
else:
if opt.name:
name = "_" + opt.name
elif opt.base:
rank_zero_info("Using base config {}".format(opt.base))
cfg_fname = os.path.split(opt.base[0])[-1]
cfg_name = os.path.splitext(cfg_fname)[0]
name = "_" + cfg_name
else:
name = ""
nowname = now + name + opt.postfix
logdir = os.path.join(opt.logdir, nowname)
# Sets the checkpoint path of the 'ckpt' option is specified
if opt.ckpt:
ckpt = opt.ckpt
# Create the checkpoint and configuration directories within the log directory.
ckptdir = os.path.join(logdir, "checkpoints")
cfgdir = os.path.join(logdir, "configs")
# Sets the seed for the random number generator to ensure reproducibility
seed_everything(opt.seed)
is_success = False
# Read timestamp for current checkpoint
validation_timestamp = None
if opt.mode == "validate":
if os.path.exists(mllogger.filename):
with open(mllogger.filename, 'r') as mllog_file:
mllogs = mllog_file.readlines()
step = opt.current_validation_iter * 1000
for mllog in mllogs:
mllog = json.loads(mllog.replace(":::MLLOG ", ""))
if mllog["key"] == "checkpoint_saved" and mllog["metadata"]["step_num"] == step:
validation_timestamp = mllog["time_ms"]
break
# Intinalize and save configuratioon using the OmegaConf library.
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())
for k in nondefault_trainer_args(opt):
trainer_config[k] = getattr(opt, k)
if not opt.use_lazy_mode:
os.environ["PT_HPU_LAZY_MODE"] = "2"
if opt.hpus:
trainer_config["devices"] = opt.hpus
if opt.batch_size:
config.data.params.train.params.batch_size = opt.batch_size
if trainer_config["accelerator"] == "hpu":
os.environ["PT_HPU_ENABLE_REFINE_DYNAMIC_SHAPES"] = "0"
os.environ["PT_HPU_POOL_MEM_ACQUIRE_PERC"] = "99"
os.environ["PT_ENABLE_INT64_SUPPORT"] = "true"
# Check whether the accelerator is gpu or hpu
if not trainer_config["accelerator"] == "gpu" and not trainer_config["accelerator"] == "hpu":
del trainer_config["accelerator"]
cpu = True
else:
cpu = False
trainer_opt = argparse.Namespace(**trainer_config)
lightning_config.trainer = trainer_config
# model
use_fp16 = trainer_config.get("precision", 32) == 16
if use_fp16:
config.model["params"].update({"use_fp16": True})
else:
config.model["params"].update({"use_fp16": False})
if ckpt is not None:
# If a checkpoint path is specified in the ckpt variable, the code updates the "ckpt" key in the "params" dictionary of the config.model configuration with the value of ckpt
config.model["params"].update({"ckpt": ckpt})
rank_zero_info("Using ckpt_path = {}".format(config.model["params"]["ckpt"]))
if opt.use_autocast:
config.model.params.use_autocast = opt.use_autocast
config.model.params.hpu = trainer_config["accelerator"] == "hpu"
model = instantiate_from_config(config.model)
if opt.log_freq > 0:
model.log_freq = opt.log_freq
# Configure gradient accumulation
if 'accumulate_grad_batches' in lightning_config.trainer:
accumulate_grad_batches = lightning_config.trainer.accumulate_grad_batches
else:
accumulate_grad_batches = 1
lightning_config.trainer.accumulate_grad_batches = accumulate_grad_batches
# Configure number of GPUs
if not cpu:
ngpu = trainer_config["devices"] * trainer_config["num_nodes"]
else:
ngpu = 1
# Configure batch size
local_batch_size = config.data.params.train.params.batch_size
global_batch_size = local_batch_size*ngpu
# trainer and callbacks
trainer_kwargs = dict()
# config the logger
# Default logger configs to log training metrics during the training process.
# These loggers are specified as targets in the dictionary, along with the configuration settings specific to each logger.
default_logger_cfgs = {
"wandb": {
"target": LIGHTNING_PACK_NAME + "loggers.WandbLogger",
"params": {
"name": nowname,
"save_dir": logdir,
"offline": opt.debug,
"id": nowname,
}
},
"tensorboard": {
"target": LIGHTNING_PACK_NAME + "loggers.TensorBoardLogger",
"params": {
"save_dir": logdir,
"name": "diff_tb",
"log_graph": True
}
}
}
# Set up the logger for TensorBoard
default_logger_cfg = default_logger_cfgs["tensorboard"]
if "logger" in lightning_config:
logger_cfg = lightning_config.logger
else:
logger_cfg = default_logger_cfg
logger_cfg = OmegaConf.merge(default_logger_cfg, logger_cfg)
trainer_kwargs["logger"] = instantiate_from_config(logger_cfg)
# config the strategy, defualt is ddp
if "strategy" in trainer_config:
strategy_cfg = trainer_config["strategy"]
strategy_cfg["target"] = LIGHTNING_PACK_NAME + strategy_cfg["target"]
else:
strategy_cfg = {
"target": LIGHTNING_PACK_NAME + "strategies.DDPStrategy",
"params": {
"find_unused_parameters": False
}
}
if trainer_config["accelerator"] == "hpu":
if trainer_config["devices"] == 1:
from lightning_habana.pytorch.strategies import SingleHPUStrategy
from lightning_habana.pytorch.accelerator import HPUAccelerator
trainer_kwargs["strategy"] = SingleHPUStrategy()
trainer_kwargs["accelerator"] = HPUAccelerator()
elif trainer_config["devices"] > 1:
from lightning_habana.pytorch.strategies import HPUParallelStrategy
from lightning_habana.pytorch.accelerator import HPUAccelerator
parallel_hpus = [torch.device("hpu")] * trainer_config["devices"]
trainer_kwargs["strategy"] = HPUParallelStrategy(parallel_devices=parallel_hpus,
find_unused_parameters=False, gradient_as_bucket_view=True)
trainer_kwargs["accelerator"] = HPUAccelerator()
else:
trainer_kwargs["strategy"] = instantiate_from_config(strategy_cfg)
# Set up various callbacks, including logging, learning rate monitoring, and CUDA management
# add callback which sets up log directory
default_callbacks_cfg = {
"setup_callback": { # callback to set up the training
"target": "main.SetupCallback",
"params": {
"resume": opt.resume, # resume training if applicable
"now": now,
"logdir": logdir, # directory to save the log file
"ckptdir": ckptdir, # directory to save the checkpoint file
"cfgdir": cfgdir, # directory to save the configuration file
"config": config, # configuration dictionary
"lightning_config": lightning_config, # LightningModule configuration
}
},
"learning_rate_logger": { # callback to log learning rate
"target": "lightning.pytorch.callbacks.LearningRateMonitor",
"params": {
"logging_interval": "step", # logging frequency (either 'step' or 'epoch')
}
},
"fid_clip_early_stop_callback" : {
"target": "main.ListEarlyStopping",
"params": {
"stopping_thresholds": {"validation/fid": opt.fid_threshold, "validation/clip": opt.clip_threshold},
"check_finite": True
}
},
}
if trainer_config["accelerator"] == "hpu":
default_callbacks_cfg["hpu_callback"] = {"target": "main.HPUCallback"}
elif trainer_config["accelerator"] == "gpu":
default_callbacks_cfg["cuda_callback"] = {"target": "main.CUDACallback"}
# If the LightningModule configuration has specified callbacks, use those
# Otherwise, create an empty OmegaConf configuration object
if "callbacks" in lightning_config:
callbacks_cfg = lightning_config.callbacks
else:
callbacks_cfg = OmegaConf.create()
# Merge the default callbacks configuration with the specified callbacks configuration, and instantiate the callbacks
callbacks_cfg = OmegaConf.merge(default_callbacks_cfg, callbacks_cfg)
trainer_kwargs["callbacks"] = [instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg]
trainer_kwargs["callbacks"].append(mlperf_logging_utils.MLPerfLoggingCallback(logger=mllogger,
train_log_interval=opt.train_log_interval,
validation_log_interval=opt.validation_log_interval,
validation_iter=opt.current_validation_iter,
validation_timestamp=validation_timestamp,
seed=opt.seed,
gradient_accumulation_steps=accumulate_grad_batches,
global_batch_size=global_batch_size))
# Set up ModelCheckpoint callback to save best models
# modelcheckpoint - use TrainResult/EvalResult(checkpoint_on=metric) to
# specify which metric is used to determine best models
default_modelckpt_cfg = {
"target": LIGHTNING_PACK_NAME + "callbacks.ModelCheckpoint",
"params": {
"dirpath": ckptdir,
"filename": "{epoch:06}-{step:09}",
"verbose": True,
"save_last": True,
}
}
if "modelcheckpoint" in lightning_config:
modelckpt_cfg = lightning_config.modelcheckpoint
else:
modelckpt_cfg = OmegaConf.create()
modelckpt_cfg = OmegaConf.merge(default_modelckpt_cfg, modelckpt_cfg)
trainer_kwargs["callbacks"].append(instantiate_from_config(modelckpt_cfg))
if opt.async_checkpoint:
from lightning.fabric.plugins import TorchCheckpointIO
from lightning.fabric.utilities import move_data_to_device
from concurrent.futures import ThreadPoolExecutor
class CustomCheckpointIO(TorchCheckpointIO):
def save_checkpoint(self, checkpoint, path, storage_options=None):
if getattr(self, '_executor', None) is None:
self._executor = ThreadPoolExecutor(max_workers=1)
def _save_checkpoint(checkpoint, filepath) -> None:
torch.save(checkpoint, filepath)
checkpoint = move_data_to_device(checkpoint, torch.device("cpu"))
# log timestamp of current checkpoint
if "step" in path:
file_name = os.path.basename(path)
step_num = int(file_name.split("step=")[1].split('.')[0])
mllogger.event(key="checkpoint_saved", metadata={mllog_constants.STEP_NUM: step_num})
self._executor.submit(_save_checkpoint, checkpoint, path)
trainer_kwargs["plugins"] = [CustomCheckpointIO()]
# Create a Trainer object with the specified command-line arguments and keyword arguments, and set the log directory
if opt.warmup_path is not None:
trainer_opt_copy = copy.deepcopy(trainer_opt)
trainer_opt_copy.max_steps=100
trainer_opt_copy.max_epochs=1
if "strategy" in trainer_opt_copy:
delattr(trainer_opt_copy, 'strategy')
trainer_warmup = Trainer(**vars(trainer_opt_copy), strategy=copy.deepcopy(trainer_kwargs["strategy"]))
if "logger" in trainer_kwargs:
delattr(trainer_opt, 'logger')
if "strategy" in trainer_kwargs:
delattr(trainer_opt, 'strategy')
if "accelerator" in trainer_kwargs:
delattr(trainer_opt, 'accelerator')
trainer = Trainer(**vars(trainer_opt), **trainer_kwargs)
trainer.logdir = logdir
# Create a data module based on the configuration file
data = instantiate_from_config(config.data)
# Configure learning rate based on the batch size, base learning rate and number of GPUs
# If scale_lr is true, calculate the learning rate based on additional factors
base_lr = config.model.base_learning_rate
if opt.scale_lr:
model.learning_rate = accumulate_grad_batches * ngpu * local_batch_size * base_lr
rank_zero_info(
"Setting learning rate to {:.2e} = {} (accumulate_grad_batches) * {} (num_gpus) * {} (local_batch_size) * {:.2e} (base_lr)"
.format(model.learning_rate, accumulate_grad_batches, ngpu, local_batch_size, base_lr))
else:
model.learning_rate = base_lr
rank_zero_info("++++ NOT USING LR SCALING ++++")
rank_zero_info(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.")
ckpt_path = os.path.join(ckptdir, "last.ckpt")
trainer.save_checkpoint(ckpt_path)
def divein(*args, **kwargs):
if trainer.global_rank == 0:
import pudb
pudb.set_trace()
import signal
# Assign melk to SIGUSR1 signal and divein to SIGUSR2 signal
signal.signal(signal.SIGUSR1, melk)
signal.signal(signal.SIGUSR2, divein)
# Run the training and validation
if opt.mode=="train":
try:
if opt.warmup_path is not None:
training_url = config.data.params.train.params.urls
config.data.params.train.params.urls = opt.warmup_path
# Warmup
trainer_warmup.fit(model, data)
# Actual training
config.data.params.train.params.urls = training_url
trainer.fit(model, data)
except Exception:
melk()
raise
elif opt.mode=="validate":
trainer.validate(model, data)
else:
raise ValueError(f"Unknown mode {opt.mode}")
if opt.mode == "validate":
# Default is True in case thresholds are not defined
fid_success = True
clip_success = True
if opt.fid_threshold is not None:
fid_success = "validation/fid" in trainer.callback_metrics and opt.fid_threshold >= trainer.callback_metrics["validation/fid"].item()
if opt.clip_threshold is not None:
clip_success = "validation/clip" in trainer.callback_metrics and opt.clip_threshold <= trainer.callback_metrics["validation/clip"].item()
is_success = fid_success and clip_success
except Exception:
# If there's an exception, debug it if opt.debug is true and the trainer's global rank is 0
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 the log directory to debug_runs if opt.debug is true and the trainer's global
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 trainer.global_rank == 0:
print(trainer.profiler.summary())
if opt.mode == "validate":
is_last_run = opt.current_validation_iter == opt.validation_iters
if is_success or is_last_run:
mllogger.end(
mllog_constants.RUN_STOP, time_ms=validation_timestamp,
metadata={mllog_constants.STATUS: mllog_constants.SUCCESS if is_success else mllog_constants.ABORTED}
)
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