# 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.
import logging
import math
import os
import random
import re
from datetime import timedelta
from typing import Optional
import hydra
import numpy as np
import omegaconf
import torch
import torch.distributed as dist
from iopath.common.file_io import g_pathmgr
from omegaconf import OmegaConf
def multiply_all(*args):
return np.prod(np.array(args)).item()
def collect_dict_keys(config):
"""This function recursively iterates through a dataset configuration, and collect all the dict_key that are defined"""
val_keys = []
# If the this config points to the collate function, then it has a key
if "_target_" in config and re.match(r".*collate_fn.*", config["_target_"]):
val_keys.append(config["dict_key"])
else:
# Recursively proceed
for v in config.values():
if isinstance(v, type(config)):
val_keys.extend(collect_dict_keys(v))
elif isinstance(v, omegaconf.listconfig.ListConfig):
for item in v:
if isinstance(item, type(config)):
val_keys.extend(collect_dict_keys(item))
return val_keys
class Phase:
TRAIN = "train"
VAL = "val"
def register_omegaconf_resolvers():
OmegaConf.register_new_resolver("get_method", hydra.utils.get_method)
OmegaConf.register_new_resolver("get_class", hydra.utils.get_class)
OmegaConf.register_new_resolver("add", lambda x, y: x + y)
OmegaConf.register_new_resolver("times", multiply_all)
OmegaConf.register_new_resolver("divide", lambda x, y: x / y)
OmegaConf.register_new_resolver("pow", lambda x, y: x**y)
OmegaConf.register_new_resolver("subtract", lambda x, y: x - y)
OmegaConf.register_new_resolver("range", lambda x: list(range(x)))
OmegaConf.register_new_resolver("int", lambda x: int(x))
OmegaConf.register_new_resolver("ceil_int", lambda x: int(math.ceil(x)))
OmegaConf.register_new_resolver("merge", lambda *x: OmegaConf.merge(*x))
def setup_distributed_backend(backend, timeout_mins):
"""
Initialize torch.distributed and set the CUDA device.
Expects environment variables to be set as per
https://pytorch.org/docs/stable/distributed.html#environment-variable-initialization
along with the environ variable "LOCAL_RANK" which is used to set the CUDA device.
"""
# enable TORCH_NCCL_ASYNC_ERROR_HANDLING to ensure dist nccl ops time out after timeout_mins
# of waiting
os.environ["TORCH_NCCL_ASYNC_ERROR_HANDLING"] = "1"
logging.info(f"Setting up torch.distributed with a timeout of {timeout_mins} mins")
dist.init_process_group(backend=backend, timeout=timedelta(minutes=timeout_mins))
return dist.get_rank()
def get_machine_local_and_dist_rank():
"""
Get the distributed and local rank of the current gpu.
"""
local_rank = int(os.environ.get("LOCAL_RANK", None))
distributed_rank = int(os.environ.get("RANK", None))
assert (
local_rank is not None and distributed_rank is not None
), "Please the set the RANK and LOCAL_RANK environment variables."
return local_rank, distributed_rank
def print_cfg(cfg):
"""
Supports printing both Hydra DictConfig and also the AttrDict config
"""
logging.info("Training with config:")
logging.info(OmegaConf.to_yaml(cfg))
def set_seeds(seed_value, max_epochs, dist_rank):
"""
Set the python random, numpy and torch seed for each gpu. Also set the CUDA
seeds if the CUDA is available. This ensures deterministic nature of the training.
"""
# Since in the pytorch sampler, we increment the seed by 1 for every epoch.
seed_value = (seed_value + dist_rank) * max_epochs
logging.info(f"MACHINE SEED: {seed_value}")
random.seed(seed_value)
np.random.seed(seed_value)
torch.manual_seed(seed_value)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed_value)
def makedir(dir_path):
"""
Create the directory if it does not exist.
"""
is_success = False
try:
if not g_pathmgr.exists(dir_path):
g_pathmgr.mkdirs(dir_path)
is_success = True
except BaseException:
logging.info(f"Error creating directory: {dir_path}")
return is_success
def is_dist_avail_and_initialized():
if not dist.is_available():
return False
if not dist.is_initialized():
return False
return True
def get_amp_type(amp_type: Optional[str] = None):
if amp_type is None:
return None
assert amp_type in ["bfloat16", "float16"], "Invalid Amp type."
if amp_type == "bfloat16":
return torch.bfloat16
else:
return torch.float16
def log_env_variables():
env_keys = sorted(list(os.environ.keys()))
st = ""
for k in env_keys:
v = os.environ[k]
st += f"{k}={v}\n"
logging.info("Logging ENV_VARIABLES")
logging.info(st)
class AverageMeter:
"""Computes and stores the average and current value"""
def __init__(self, name, device, fmt=":f"):
self.name = name
self.fmt = fmt
self.device = device
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
self._allow_updates = True
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def __str__(self):
fmtstr = "{name}: {val" + self.fmt + "} ({avg" + self.fmt + "})"
return fmtstr.format(**self.__dict__)
class MemMeter:
"""Computes and stores the current, avg, and max of peak Mem usage per iteration"""
def __init__(self, name, device, fmt=":f"):
self.name = name
self.fmt = fmt
self.device = device
self.reset()
def reset(self):
self.val = 0 # Per iteration max usage
self.avg = 0 # Avg per iteration max usage
self.peak = 0 # Peak usage for lifetime of program
self.sum = 0
self.count = 0
self._allow_updates = True
def update(self, n=1, reset_peak_usage=True):
self.val = torch.cuda.max_memory_allocated() // 1e9
self.sum += self.val * n
self.count += n
self.avg = self.sum / self.count
self.peak = max(self.peak, self.val)
if reset_peak_usage:
torch.cuda.reset_peak_memory_stats()
def __str__(self):
fmtstr = (
"{name}: {val"
+ self.fmt
+ "} ({avg"
+ self.fmt
+ "}/{peak"
+ self.fmt
+ "})"
)
return fmtstr.format(**self.__dict__)
def human_readable_time(time_seconds):
time = int(time_seconds)
minutes, seconds = divmod(time, 60)
hours, minutes = divmod(minutes, 60)
days, hours = divmod(hours, 24)
return f"{days:02}d {hours:02}h {minutes:02}m"
class DurationMeter:
def __init__(self, name, device, fmt=":f"):
self.name = name
self.device = device
self.fmt = fmt
self.val = 0
def reset(self):
self.val = 0
def update(self, val):
self.val = val
def add(self, val):
self.val += val
def __str__(self):
return f"{self.name}: {human_readable_time(self.val)}"
class ProgressMeter:
def __init__(self, num_batches, meters, real_meters, prefix=""):
self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
self.meters = meters
self.real_meters = real_meters
self.prefix = prefix
def display(self, batch, enable_print=False):
entries = [self.prefix + self.batch_fmtstr.format(batch)]
entries += [str(meter) for meter in self.meters]
entries += [
" | ".join(
[
f"{os.path.join(name, subname)}: {val:.4f}"
for subname, val in meter.compute().items()
]
)
for name, meter in self.real_meters.items()
]
logging.info(" | ".join(entries))
if enable_print:
print(" | ".join(entries))
def _get_batch_fmtstr(self, num_batches):
num_digits = len(str(num_batches // 1))
fmt = "{:" + str(num_digits) + "d}"
return "[" + fmt + "/" + fmt.format(num_batches) + "]"
def get_resume_checkpoint(checkpoint_save_dir):
if not g_pathmgr.isdir(checkpoint_save_dir):
return None
ckpt_file = os.path.join(checkpoint_save_dir, "checkpoint.pt")
if not g_pathmgr.isfile(ckpt_file):
return None
return ckpt_file