Source code for catalyst.utils.scripts

# flake8: noqa
# @TODO: code formatting issue for 20.07 release
from typing import Callable
from importlib.util import module_from_spec, spec_from_file_location
import os
import pathlib
import shutil
import subprocess
import sys
import warnings

import torch
import torch.distributed

from catalyst.utils.distributed import (
    get_distributed_env,
    get_distributed_params,
)
from catalyst.utils.misc import get_utcnow_time


def import_module(expdir: pathlib.Path):
    """
    @TODO: Docs. Contribution is welcome
    """
    # @TODO: better PYTHONPATH handling
    if not isinstance(expdir, pathlib.Path):
        expdir = pathlib.Path(expdir)
    sys.path.insert(0, str(expdir.absolute()))
    sys.path.insert(0, os.path.dirname(str(expdir.absolute())))
    s = spec_from_file_location(
        expdir.name,
        str(expdir.absolute() / "__init__.py"),
        submodule_search_locations=[expdir.absolute()],
    )
    m = module_from_spec(s)
    s.loader.exec_module(m)
    sys.modules[expdir.name] = m
    return m


def _tricky_dir_copy(dir_from, dir_to):
    os.makedirs(dir_to, exist_ok=True)
    shutil.rmtree(dir_to)
    shutil.copytree(dir_from, dir_to)


def dump_code(expdir, logdir):
    """
    @TODO: Docs. Contribution is welcome
    """
    expdir = expdir[:-1] if expdir.endswith("/") else expdir
    new_src_dir = "code"

    # @TODO: hardcoded
    old_pro_dir = os.path.dirname(os.path.abspath(__file__)) + "/../"
    new_pro_dir = os.path.join(logdir, new_src_dir, "catalyst")
    _tricky_dir_copy(old_pro_dir, new_pro_dir)

    old_expdir = os.path.abspath(expdir)
    new_expdir = os.path.basename(old_expdir)
    new_expdir = os.path.join(logdir, new_src_dir, new_expdir)
    _tricky_dir_copy(old_expdir, new_expdir)


def dump_python_files(src, dst):
    """
    @TODO: Docs. Contribution is welcome
    """
    py_files = list(src.glob("*.py"))
    ipynb_files = list(src.glob("*.ipynb"))

    py_files += ipynb_files
    py_files = list(set(py_files))
    for py_file in py_files:
        shutil.copy2(f"{str(py_file.absolute())}", f"{dst}/{py_file.name}")


def import_experiment_and_runner(expdir: pathlib.Path):
    """
    @TODO: Docs. Contribution is welcome
    """
    if not isinstance(expdir, pathlib.Path):
        expdir = pathlib.Path(expdir)
    m = import_module(expdir)
    runner_fn = m.Runner
    if hasattr(m, "Experiment"):
        experiment_fn = m.Experiment
    else:
        experiment_fn = None
    return experiment_fn, runner_fn


def dump_base_experiment_code(src: pathlib.Path, dst: pathlib.Path):
    """
    @TODO: Docs. Contribution is welcome
    """
    utcnow = get_utcnow_time()
    dst = dst.joinpath("code")
    dst = dst.joinpath(f"code-{utcnow}") if dst.exists() else dst
    os.makedirs(dst, exist_ok=True)
    dump_python_files(src, dst)


def distributed_cmd_run(
    worker_fn: Callable, distributed: bool = True, *args, **kwargs
) -> None:
    """
    Distributed run

    Args:
        worker_fn (Callable): worker fn to run in distributed mode
        distributed (bool): distributed flag
        args: additional parameters for worker_fn
        kwargs: additional key-value parameters for worker_fn
    """
    distributed_params = get_distributed_params()
    local_rank = distributed_params["local_rank"]
    world_size = distributed_params["world_size"]

    if distributed and torch.distributed.is_initialized():
        warnings.warn(
            "Looks like you are trying to call distributed setup twice, "
            "switching to normal run for correct distributed training."
        )

    if (
        not distributed
        or torch.distributed.is_initialized()
        or world_size <= 1
    ):
        worker_fn(*args, **kwargs)
    elif local_rank is not None:
        torch.cuda.set_device(int(local_rank))

        torch.distributed.init_process_group(
            backend="nccl", init_method="env://"
        )
        worker_fn(*args, **kwargs)
    else:
        workers = []
        try:
            for local_rank in range(torch.cuda.device_count()):
                rank = distributed_params["start_rank"] + local_rank
                env = get_distributed_env(local_rank, rank, world_size)
                cmd = [sys.executable] + sys.argv.copy()
                workers.append(subprocess.Popen(cmd, env=env))
            for worker in workers:
                worker.wait()
        finally:
            for worker in workers:
                worker.kill()


__all__ = [
    "import_module",
    "dump_code",
    "dump_python_files",
    "import_experiment_and_runner",
    "dump_base_experiment_code",
    "distributed_cmd_run",
]