local_segment/training/train.py

# 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 os
import random
import sys
import traceback
from argparse import ArgumentParser

import submitit
import torch

from hydra import compose, initialize_config_module
from hydra.utils import instantiate

from iopath.common.file_io import g_pathmgr
from omegaconf import OmegaConf

from training.utils.train_utils import makedir, register_omegaconf_resolvers

os.environ["HYDRA_FULL_ERROR"] = "1"


def single_proc_run(local_rank, main_port, cfg, world_size):
    """Single GPU process"""
    os.environ["MASTER_ADDR"] = "localhost"
    os.environ["MASTER_PORT"] = str(main_port)
    os.environ["RANK"] = str(local_rank)
    os.environ["LOCAL_RANK"] = str(local_rank)
    os.environ["WORLD_SIZE"] = str(world_size)
    try:
        register_omegaconf_resolvers()
    except Exception as e:
        logging.info(e)

    trainer = instantiate(cfg.trainer, _recursive_=False)
    trainer.run()


def single_node_runner(cfg, main_port: int):
    assert cfg.launcher.num_nodes == 1
    num_proc = cfg.launcher.gpus_per_node
    torch.multiprocessing.set_start_method(
        "spawn"
    )  # CUDA runtime does not support `fork`
    if num_proc == 1:
        # directly call single_proc so we can easily set breakpoints
        # mp.spawn does not let us set breakpoints
        single_proc_run(local_rank=0, main_port=main_port, cfg=cfg, world_size=num_proc)
    else:
        mp_runner = torch.multiprocessing.start_processes
        args = (main_port, cfg, num_proc)
        # Note: using "fork" below, "spawn" causes time and error regressions. Using
        # spawn changes the default multiprocessing context to spawn, which doesn't
        # interact well with the dataloaders (likely due to the use of OpenCV).
        mp_runner(single_proc_run, args=args, nprocs=num_proc, start_method="spawn")


def format_exception(e: Exception, limit=20):
    traceback_str = "".join(traceback.format_tb(e.__traceback__, limit=limit))
    return f"{type(e).__name__}: {e}\nTraceback:\n{traceback_str}"


class SubmititRunner(submitit.helpers.Checkpointable):
    """A callable which is passed to submitit to launch the jobs."""

    def __init__(self, port, cfg):
        self.cfg = cfg
        self.port = port
        self.has_setup = False

    def run_trainer(self):
        job_env = submitit.JobEnvironment()
        # Need to add this again so the hydra.job.set_env PYTHONPATH
        # is also set when launching jobs.
        add_pythonpath_to_sys_path()
        os.environ["MASTER_ADDR"] = job_env.hostnames[0]
        os.environ["MASTER_PORT"] = str(self.port)
        os.environ["RANK"] = str(job_env.global_rank)
        os.environ["LOCAL_RANK"] = str(job_env.local_rank)
        os.environ["WORLD_SIZE"] = str(job_env.num_tasks)

        register_omegaconf_resolvers()
        cfg_resolved = OmegaConf.to_container(self.cfg, resolve=False)
        cfg_resolved = OmegaConf.create(cfg_resolved)

        trainer = instantiate(cfg_resolved.trainer, _recursive_=False)
        trainer.run()

    def __call__(self):
        job_env = submitit.JobEnvironment()
        self.setup_job_info(job_env.job_id, job_env.global_rank)
        try:
            self.run_trainer()
        except Exception as e:
            # Log the exception. Then raise it again (as what SubmititRunner currently does).
            message = format_exception(e)
            logging.error(message)
            raise e

    def setup_job_info(self, job_id, rank):
        """Set up slurm job info"""
        self.job_info = {
            "job_id": job_id,
            "rank": rank,
            "cluster": self.cfg.get("cluster", None),
            "experiment_log_dir": self.cfg.launcher.experiment_log_dir,
        }

        self.has_setup = True


def add_pythonpath_to_sys_path():
    if "PYTHONPATH" not in os.environ or not os.environ["PYTHONPATH"]:
        return
    sys.path = os.environ["PYTHONPATH"].split(":") + sys.path


def main(args) -> None:
    cfg = compose(config_name=args.config)
    if cfg.launcher.experiment_log_dir is None:
        cfg.launcher.experiment_log_dir = os.path.join(
            os.getcwd(), "sam2_logs", args.config
        )
    print("###################### Train App Config ####################")
    print(OmegaConf.to_yaml(cfg))
    print("############################################################")

    add_pythonpath_to_sys_path()
    makedir(cfg.launcher.experiment_log_dir)
    with g_pathmgr.open(
        os.path.join(cfg.launcher.experiment_log_dir, "config.yaml"), "w"
    ) as f:
        f.write(OmegaConf.to_yaml(cfg))

    cfg_resolved = OmegaConf.to_container(cfg, resolve=False)
    cfg_resolved = OmegaConf.create(cfg_resolved)

    with g_pathmgr.open(
        os.path.join(cfg.launcher.experiment_log_dir, "config_resolved.yaml"), "w"
    ) as f:
        f.write(OmegaConf.to_yaml(cfg_resolved, resolve=True))

    submitit_conf = cfg.get("submitit", None)
    assert submitit_conf is not None, "Missing submitit config"

    submitit_dir = cfg.launcher.experiment_log_dir
    submitit_dir = os.path.join(submitit_dir, "submitit_logs")
    # Priotrize cmd line args
    cfg.launcher.gpus_per_node = (
        args.num_gpus if args.num_gpus is not None else cfg.launcher.gpus_per_node
    )
    cfg.launcher.num_nodes = (
        args.num_nodes if args.num_nodes is not None else cfg.launcher.num_nodes
    )
    submitit_conf.use_cluster = (
        args.use_cluster if args.use_cluster is not None else submitit_conf.use_cluster
    )
    if submitit_conf.use_cluster:
        executor = submitit.AutoExecutor(folder=submitit_dir)
        submitit_conf.partition = (
            args.partition
            if args.partition is not None
            else submitit_conf.get("partition", None)
        )
        submitit_conf.account = (
            args.account
            if args.account is not None
            else submitit_conf.get("account", None)
        )
        submitit_conf.qos = (
            args.qos if args.qos is not None else submitit_conf.get("qos", None)
        )
        job_kwargs = {
            "timeout_min": 60 * submitit_conf.timeout_hour,
            "name": (
                submitit_conf.name if hasattr(submitit_conf, "name") else args.config
            ),
            "slurm_partition": submitit_conf.partition,
            "gpus_per_node": cfg.launcher.gpus_per_node,
            "tasks_per_node": cfg.launcher.gpus_per_node,  # one task per GPU
            "cpus_per_task": submitit_conf.cpus_per_task,
            "nodes": cfg.launcher.num_nodes,
            "slurm_additional_parameters": {
                "exclude": " ".join(submitit_conf.get("exclude_nodes", [])),
            },
        }
        if "include_nodes" in submitit_conf:
            assert (
                len(submitit_conf["include_nodes"]) >= cfg.launcher.num_nodes
            ), "Not enough nodes"
            job_kwargs["slurm_additional_parameters"]["nodelist"] = " ".join(
                submitit_conf["include_nodes"]
            )
        if submitit_conf.account is not None:
            job_kwargs["slurm_additional_parameters"]["account"] = submitit_conf.account
        if submitit_conf.qos is not None:
            job_kwargs["slurm_additional_parameters"]["qos"] = submitit_conf.qos

        if submitit_conf.get("mem_gb", None) is not None:
            job_kwargs["mem_gb"] = submitit_conf.mem_gb
        elif submitit_conf.get("mem", None) is not None:
            job_kwargs["slurm_mem"] = submitit_conf.mem

        if submitit_conf.get("constraints", None) is not None:
            job_kwargs["slurm_constraint"] = submitit_conf.constraints

        if submitit_conf.get("comment", None) is not None:
            job_kwargs["slurm_comment"] = submitit_conf.comment

        # Supports only cpu-bind option within srun_args. New options can be added here
        if submitit_conf.get("srun_args", None) is not None:
            job_kwargs["slurm_srun_args"] = []
            if submitit_conf.srun_args.get("cpu_bind", None) is not None:
                job_kwargs["slurm_srun_args"].extend(
                    ["--cpu-bind", submitit_conf.srun_args.cpu_bind]
                )

        print("###################### SLURM Config ####################")
        print(job_kwargs)
        print("##########################################")
        executor.update_parameters(**job_kwargs)

        main_port = random.randint(
            submitit_conf.port_range[0], submitit_conf.port_range[1]
        )
        runner = SubmititRunner(main_port, cfg)
        job = executor.submit(runner)
        print(f"Submitit Job ID: {job.job_id}")
        runner.setup_job_info(job.job_id, rank=0)
    else:
        cfg.launcher.num_nodes = 1
        main_port = random.randint(
            submitit_conf.port_range[0], submitit_conf.port_range[1]
        )
        single_node_runner(cfg, main_port)


if __name__ == "__main__":

    initialize_config_module("sam2", version_base="1.2")
    parser = ArgumentParser()
    parser.add_argument(
        "-c",
        "--config",
        required=True,
        type=str,
        help="path to config file (e.g. configs/sam2.1_training/sam2.1_hiera_b+_MOSE_finetune.yaml)",
    )
    parser.add_argument(
        "--use-cluster",
        type=int,
        default=None,
        help="whether to launch on a cluster, 0: run locally, 1: run on a cluster",
    )
    parser.add_argument("--partition", type=str, default=None, help="SLURM partition")
    parser.add_argument("--account", type=str, default=None, help="SLURM account")
    parser.add_argument("--qos", type=str, default=None, help="SLURM qos")
    parser.add_argument(
        "--num-gpus", type=int, default=None, help="number of GPUS per node"
    )
    parser.add_argument("--num-nodes", type=int, default=None, help="Number of nodes")
    args = parser.parse_args()
    args.use_cluster = bool(args.use_cluster) if args.use_cluster is not None else None
    register_omegaconf_resolvers()
    main(args)