nanochat/nanochat/common.py
2025-11-07 16:44:26 -05:00

241 lines
9.1 KiB
Python

"""
Common utilities for nanochat.
"""
import os
import re
import logging
import urllib.request
import torch
import torch.distributed as dist
from filelock import FileLock
class ColoredFormatter(logging.Formatter):
"""Custom formatter that adds colors to log messages."""
# ANSI color codes
COLORS = {
'DEBUG': '\033[36m', # Cyan
'INFO': '\033[32m', # Green
'WARNING': '\033[33m', # Yellow
'ERROR': '\033[31m', # Red
'CRITICAL': '\033[35m', # Magenta
}
RESET = '\033[0m'
BOLD = '\033[1m'
def format(self, record):
# Add color to the level name
levelname = record.levelname
if levelname in self.COLORS:
record.levelname = f"{self.COLORS[levelname]}{self.BOLD}{levelname}{self.RESET}"
# Format the message
message = super().format(record)
# Add color to specific parts of the message
if levelname == 'INFO':
# Highlight numbers and percentages
message = re.sub(r'(\d+\.?\d*\s*(?:GB|MB|%|docs))', rf'{self.BOLD}\1{self.RESET}', message)
message = re.sub(r'(Shard \d+)', rf'{self.COLORS["INFO"]}{self.BOLD}\1{self.RESET}', message)
return message
def setup_default_logging():
handler = logging.StreamHandler()
handler.setLevel(logging.INFO)
handler.setFormatter(ColoredFormatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
logging.basicConfig(
level=logging.INFO,
handlers=[handler]
)
def setup_file_logger(logger_name, filename, level=logging.DEBUG, formatter=None):
clean_name = os.path.basename(filename)
if clean_name != filename or not clean_name:
raise ValueError(f"Invalid log filename provided: {filename}")
if not clean_name.endswith(".log"):
clean_name += ".log"
logs_dir = get_logs_dir()
path = os.path.join(logs_dir, clean_name)
handler = logging.FileHandler(path, mode="w")
handler.setLevel(level)
handler.setFormatter(
formatter
or ColoredFormatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
)
logger = logging.getLogger(logger_name)
logger.addHandler(handler)
return path
setup_default_logging()
logger = logging.getLogger(__name__)
def get_base_dir():
# co-locate nanochat intermediates with other cached data in ~/.cache (by default)
if os.environ.get("NANOCHAT_BASE_DIR"):
nanochat_dir = os.environ.get("NANOCHAT_BASE_DIR")
else:
home_dir = os.path.expanduser("~")
cache_dir = os.path.join(home_dir, ".cache")
nanochat_dir = os.path.join(cache_dir, "nanochat")
os.makedirs(nanochat_dir, exist_ok=True)
return nanochat_dir
def get_project_root():
# locates the project root by walking upward from this file
_PROJECT_MARKERS = ('.git', 'uv.lock')
curr = os.path.dirname(os.path.abspath(__file__))
while True:
if any(os.path.exists(os.path.join(curr, m)) for m in _PROJECT_MARKERS):
return curr
parent = os.path.dirname(curr)
if parent == curr: # reached filesystem root
return None
curr = parent
def get_logs_dir():
"""
Resolves the directory where log files should be written.
- if $LOG_DIR is set, use that.
- else if, project root is detected, use <project_root>/logs.
- else, fall back to <get_base_dir()>/logs
"""
env = os.environ.get("LOG_DIR")
if env:
path = os.path.abspath(env)
os.makedirs(path, exist_ok=True)
return path
root = get_project_root()
if not root:
root = get_base_dir()
logs = os.path.join(root, 'logs')
os.makedirs(logs, exist_ok=True)
return logs
def download_file_with_lock(url, filename, postprocess_fn=None):
"""
Downloads a file from a URL to a local path in the base directory.
Uses a lock file to prevent concurrent downloads among multiple ranks.
"""
base_dir = get_base_dir()
file_path = os.path.join(base_dir, filename)
lock_path = file_path + ".lock"
if os.path.exists(file_path):
return file_path
with FileLock(lock_path):
# Only a single rank can acquire this lock
# All other ranks block until it is released
# Recheck after acquiring lock
if os.path.exists(file_path):
return file_path
# Download the content as bytes
print(f"Downloading {url}...")
with urllib.request.urlopen(url) as response:
content = response.read() # bytes
# Write to local file
with open(file_path, 'wb') as f:
f.write(content)
print(f"Downloaded to {file_path}")
# Run the postprocess function if provided
if postprocess_fn is not None:
postprocess_fn(file_path)
return file_path
def print0(s="",**kwargs):
ddp_rank = int(os.environ.get('RANK', 0))
if ddp_rank == 0:
print(s, **kwargs)
def print_banner():
# Cool DOS Rebel font ASCII banner made with https://manytools.org/hacker-tools/ascii-banner/
banner = """
█████ █████
░░███ ░░███
████████ ██████ ████████ ██████ ██████ ░███████ ██████ ███████
░░███░░███ ░░░░░███ ░░███░░███ ███░░███ ███░░███ ░███░░███ ░░░░░███░░░███░
░███ ░███ ███████ ░███ ░███ ░███ ░███░███ ░░░ ░███ ░███ ███████ ░███
░███ ░███ ███░░███ ░███ ░███ ░███ ░███░███ ███ ░███ ░███ ███░░███ ░███ ███
████ █████░░████████ ████ █████░░██████ ░░██████ ████ █████░░███████ ░░█████
░░░░ ░░░░░ ░░░░░░░░ ░░░░ ░░░░░ ░░░░░░ ░░░░░░ ░░░░ ░░░░░ ░░░░░░░░ ░░░░░
"""
print0(banner)
def is_ddp():
# TODO is there a proper way
return int(os.environ.get('RANK', -1)) != -1
def get_dist_info():
if is_ddp():
assert all(var in os.environ for var in ['RANK', 'LOCAL_RANK', 'WORLD_SIZE'])
ddp_rank = int(os.environ['RANK'])
ddp_local_rank = int(os.environ['LOCAL_RANK'])
ddp_world_size = int(os.environ['WORLD_SIZE'])
return True, ddp_rank, ddp_local_rank, ddp_world_size
else:
return False, 0, 0, 1
def autodetect_device_type():
# prefer to use CUDA if available, otherwise use MPS, otherwise fallback on CPU
if torch.cuda.is_available():
device_type = "cuda"
elif torch.backends.mps.is_available():
device_type = "mps"
else:
device_type = "cpu"
print0(f"Autodetected device type: {device_type}")
return device_type
def compute_init(device_type="cuda"): # cuda|cpu|mps
"""Basic initialization that we keep doing over and over, so make common."""
assert device_type in ["cuda", "mps", "cpu"], "Invalid device type atm"
if device_type == "cuda":
assert torch.cuda.is_available(), "Your PyTorch installation is not configured for CUDA but device_type is 'cuda'"
if device_type == "mps":
assert torch.backends.mps.is_available(), "Your PyTorch installation is not configured for MPS but device_type is 'mps'"
# Reproducibility
torch.manual_seed(42)
if device_type == "cuda":
torch.cuda.manual_seed(42)
# skipping full reproducibility for now, possibly investigate slowdown later
# torch.use_deterministic_algorithms(True)
# Precision
if device_type == "cuda":
torch.set_float32_matmul_precision("high") # uses tf32 instead of fp32 for matmuls
# Distributed setup: Distributed Data Parallel (DDP), optional, and requires CUDA
ddp, ddp_rank, ddp_local_rank, ddp_world_size = get_dist_info()
if ddp and device_type == "cuda":
device = torch.device("cuda", ddp_local_rank)
torch.cuda.set_device(device) # make "cuda" default to this device
dist.init_process_group(backend="nccl", device_id=device)
dist.barrier()
else:
device = torch.device(device_type) # mps|cpu
if ddp_rank == 0:
logger.info(f"Distributed world size: {ddp_world_size}")
return ddp, ddp_rank, ddp_local_rank, ddp_world_size, device
def compute_cleanup():
"""Companion function to compute_init, to clean things up before script exit"""
if is_ddp():
dist.destroy_process_group()
class DummyWandb:
"""Useful if we wish to not use wandb but have all the same signatures"""
def __init__(self):
pass
def log(self, *args, **kwargs):
pass
def finish(self):
pass