tests/common.py

# Copyright (c) 2021, EleutherAI
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import os
import time
import shutil
import itertools
from pathlib import Path

import pytest
import random

import torch
import torch.distributed as dist
from torch.multiprocessing import Process
import multiprocessing as mp
from yaml import load

try:
    from yaml import CLoader as Loader, CDumper as Dumper
except ImportError:
    from yaml import Loader, Dumper
from copy import deepcopy
import deepspeed

TEST_CHECKPOINT_DIR = "test_checkpoint"
TEST_LOG_DIR = "test_logs"
TEST_TENSORBOARD_DIR = "test_tensorboard"

# Worker timeout *after* the first worker has completed.
DEEPSPEED_UNIT_WORKER_TIMEOUT = 120


def get_xdist_worker_id():
    xdist_worker = os.environ.get("PYTEST_XDIST_WORKER", None)
    if xdist_worker is not None:
        xdist_worker_id = xdist_worker.replace("gw", "")
        return int(xdist_worker_id)
    return None


def get_master_port():
    master_port = os.environ.get("DS_TEST_PORT", "29503")
    xdist_worker_id = get_xdist_worker_id()
    if xdist_worker_id is not None:
        master_port = str(int(master_port) + xdist_worker_id)
    return master_port


_num_gpus = None


def count_gpus():
    global _num_gpus
    if _num_gpus is None:
        import subprocess

        nvidia_smi = subprocess.check_output(["nvidia-smi", "--list-gpus"])
        _num_gpus = len(nvidia_smi.decode("utf-8").strip().split("\n"))
    return _num_gpus


def set_cuda_visibile():
    cuda_visible = os.environ.get("CUDA_VISIBLE_DEVICES", None)
    xdist_worker_id = get_xdist_worker_id()
    if xdist_worker_id is None:
        xdist_worker_id = 0
    if cuda_visible is None:
        # CUDA_VISIBLE_DEVICES is not set, discover it from nvidia-smi instead
        import subprocess

        nvidia_smi = subprocess.check_output(["nvidia-smi", "--list-gpus"])
        num_gpus = len(nvidia_smi.decode("utf-8").strip().split("\n"))
        cuda_visible = ",".join(map(str, range(num_gpus)))

    # rotate list based on xdist worker id, example below
    # wid=0 -> ['0', '1', '2', '3']
    # wid=1 -> ['1', '2', '3', '0']
    # wid=2 -> ['2', '3', '0', '1']
    # wid=3 -> ['3', '0', '1', '2']
    dev_id_list = cuda_visible.split(",")
    dev_id_list = dev_id_list[xdist_worker_id:] + dev_id_list[:xdist_worker_id]
    os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(dev_id_list)


def get_root_directory():
    return Path(__file__).parents[1]


def get_config_directory():
    return get_root_directory() / "configs"


def get_configs_with_path(configs):
    return [str(get_config_directory() / cfg) for cfg in configs]


def get_test_configs_with_path(configs: list):
    test_config_dir = Path(__file__).parent / "test_configs"
    return [str((test_config_dir / cfg).absolute()) for cfg in configs]


def clear_test_dirs():
    log_dir = os.path.join(get_root_directory(), TEST_LOG_DIR)
    if os.path.isdir(log_dir):
        shutil.rmtree(log_dir)

    checkpoint_dir = os.path.join(get_root_directory(), TEST_CHECKPOINT_DIR)
    if os.path.isdir(checkpoint_dir):
        shutil.rmtree(checkpoint_dir)

    tensorboard_dir = os.path.join(get_root_directory(), TEST_TENSORBOARD_DIR)
    if os.path.isdir(tensorboard_dir):
        shutil.rmtree(tensorboard_dir)


def distributed_test(world_size=2, backend="nccl"):
    """A decorator for executing a function (e.g., a unit test) in a distributed manner.
    This decorator manages the spawning and joining of processes, initialization of
    torch.distributed, and catching of errors.

    This function is copied from: https://github.com/EleutherAI/DeeperSpeed/blob/24026e5bb37c528a222b8635c46256b1e1825d2e/tests/unit/common.py#L16

    Usage example:
        @distributed_test(worker_size=[2,3])
        def my_test():
            rank = dist.get_rank()
            world_size = dist.get_world_size()
            assert(rank < world_size)

    Arguments:
        world_size (int or list): number of ranks to spawn. Can be a list to spawn
        multiple tests.
    """

    def dist_wrap(run_func):
        """Second-level decorator for dist_test. This actually wraps the function."""

        def dist_init(local_rank, num_procs, *func_args, **func_kwargs):
            """Initialize torch.distributed and execute the user function."""
            os.environ["MASTER_ADDR"] = "127.0.0.1"
            os.environ["MASTER_PORT"] = get_master_port()
            os.environ["LOCAL_RANK"] = str(local_rank)
            # NOTE: unit tests don't support multi-node so local_rank == global rank
            os.environ["RANK"] = str(local_rank)
            os.environ["WORLD_SIZE"] = str(num_procs)

            # turn off NCCL logging if set
            os.environ.pop("NCCL_DEBUG", None)

            deepspeed.init_distributed(dist_backend=backend)

            if torch.cuda.is_available():
                torch.cuda.set_device(local_rank)

            run_func(*func_args, **func_kwargs)

            # make sure all ranks finish at the same time
            torch.distributed.barrier()
            # tear down after test completes
            torch.distributed.destroy_process_group()

        def dist_launcher(num_procs, *func_args, **func_kwargs):
            """Launch processes and gracefully handle failures."""

            # Spawn all workers on subprocesses.
            processes = []
            for local_rank in range(num_procs):
                p = Process(
                    target=dist_init,
                    args=(local_rank, num_procs, *func_args),
                    kwargs=func_kwargs,
                )
                p.start()
                processes.append(p)

            # Now loop and wait for a test to complete. The spin-wait here isn't a big
            # deal because the number of processes will be O(#GPUs) << O(#CPUs).
            any_done = False
            while not any_done:
                for p in processes:
                    if not p.is_alive():
                        any_done = True
                        break

            # Wait for all other processes to complete
            for p in processes:
                p.join(DEEPSPEED_UNIT_WORKER_TIMEOUT)

            failed = [(rank, p) for rank, p in enumerate(processes) if p.exitcode != 0]
            for rank, p in failed:
                # If it still hasn't terminated, kill it because it hung.
                if p.exitcode is None:
                    p.terminate()
                    pytest.fail(f"Worker {rank} hung.", pytrace=False)
                if p.exitcode < 0:
                    pytest.fail(
                        f"Worker {rank} killed by signal {-p.exitcode}", pytrace=False
                    )
                if p.exitcode > 0:
                    pytest.fail(
                        f"Worker {rank} exited with code {p.exitcode}", pytrace=False
                    )

        def run_func_decorator(*func_args, **func_kwargs):
            """Entry point for @distributed_test()."""

            gpus = count_gpus()

            if isinstance(world_size, int):
                if gpus < world_size:
                    pytest.mark.skip(
                        reason=f"at least {world_size} GPUs are required to run this test"
                    )
                    return

                dist_launcher(world_size, *func_args, **func_kwargs)
            elif isinstance(world_size, list):
                for procs in world_size:
                    dist_launcher(procs, *func_args, **func_kwargs)
                    time.sleep(0.5)
            else:
                raise TypeError(f"world_size must be an integer or a list of integers.")

        return run_func_decorator

    return dist_wrap


def model_setup(yaml_list=None, param_dict=None, clear_data=True):
    from megatron.neox_arguments import NeoXArgs
    from megatron.mpu import destroy_model_parallel
    from megatron import initialize_megatron
    from megatron.training import setup_model_and_optimizer

    destroy_model_parallel()  # mpu model parallel contains remaining global vars
    if clear_data and (
        not torch.distributed.is_initialized()
        or torch.distributed.get_world_size() == 1
        or torch.distributed.get_rank() == 0
    ):
        clear_test_dirs()

    overwrite_values = {
        "user_script": str(get_root_directory() / "train.py"),
        "save": TEST_CHECKPOINT_DIR,
        "load": TEST_CHECKPOINT_DIR,
        "log_dir": TEST_LOG_DIR,
        "tensorboard_dir": TEST_TENSORBOARD_DIR,
    }

    # should not both be none
    assert yaml_list is not None or param_dict is not None

    # initially load config from files as would be the case in deepy.py
    if yaml_list is not None:
        args_loaded = NeoXArgs.from_ymls(yaml_list, overwrite_values=overwrite_values)
    else:
        p_dict = param_dict.copy()
        p_dict.update(overwrite_values)
        args_loaded = NeoXArgs.from_dict(p_dict)

    args_loaded.build_tokenizer()

    initialize_megatron(neox_args=args_loaded)
    model, optimizer, lr_scheduler = setup_model_and_optimizer(
        neox_args=args_loaded, use_cache=True
    )
    return model, optimizer, lr_scheduler, args_loaded


def bounded_product(sequence, n=None, seed=None):
    """
    Returns a shuffled, bounded cartesian product of the input sequence.
    Designed to cover as wide a range of permutations as possible with a limited number of iterations.
    Will manifest the whole list in memory, so not suitable for super large sequences.

    :param sequence: iterable
    :param n: length of returned list
    :param seed: random seed for reproducibility
    :return: list
    """
    p = list(itertools.product(*sequence))
    if seed is not None:
        random.seed(seed)
    random.shuffle(p)
    return p if n is None else p[:n]


def parametrize(
    params_to_test: dict, max_tests: int = 50, seed: int = None, with_names=True
):
    """
    Generates a random sample of max_tests length of all possible combinations of values in
    `params_to_test`.

    In `params_to_test` you can either specify one value, and all possible settings of that value,
    or two values separated by a comma, and all possible combinations of those two values in tandem.
        i.e "hidden_size,num_heads": [[768,12], [1024,32], [2048, 64]]
    so the first item in each list is a value of `hidden_size` and the second a value of `num_heads`
    this is useful for reducing the size of possible tests for values we know are unlikely to interact beforehand,
    since the cartesian product can grow very large.

    :param params_to_test: dict of neox params
    :param max_tests: maximum number of tests to run
    :param seed: random seed
    :return: a list of neox param dicts to pass to a parametrized unit test
    """
    keys, values = zip(*params_to_test.items())
    ret = []
    if with_names:
        experiments = []
    for p in bounded_product(values, n=max_tests, seed=seed):
        experiment = dict(zip(keys, p))
        to_pop = []
        to_add = {}
        for k, v in experiment.items():
            if "," in k:
                keys_split = [i.strip() for i in k.split(",")]
                values_separated = experiment[k]
                to_pop.append(k)
                assert len(values_separated) == len(keys_split)
                new_dict = dict(zip(keys_split, values_separated))
                to_add.update(new_dict)
        experiment.update(to_add)
        for k in to_pop:
            experiment.pop(k)
        base = deepcopy(BASE_CONFIG)
        base.update(experiment)
        ret.append(base)
        if with_names:
            experiments.append(experiment)
    if with_names:
        return ret, [dict_repr(d) for d in experiments]
    return ret


def dict_repr(d):
    return " ".join([f"{str(k)} : {str(v)}" for k, v in d.items()])


binary = [True, False]

with open(get_test_configs_with_path(["test_train_base.yml"])[0], "r") as f:
    BASE_CONFIG = load(f, Loader=Loader)