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common.py
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# Copyright (c) 2024, 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:https://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
from abc import ABC, abstractmethod
from deepspeed.accelerator import get_accelerator
import pytest
from _pytest.outcomes import Skipped
from _pytest.fixtures import FixtureLookupError, FixtureFunctionMarker
import random
import train
import torch
import torch.distributed as dist
from torch.multiprocessing import Process
import torch.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
DEEPSPEED_TEST_TIMEOUT = 600
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 set_accelerator_visible():
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 using accelerator specific command instead
if get_accelerator().device_name() == "cuda":
if is_rocm_pytorch():
rocm_smi = subprocess.check_output(["rocm-smi", "--showid"])
gpu_ids = filter(
lambda s: "GPU" in s, rocm_smi.decode("utf-8").strip().split("\n")
)
num_accelerators = len(list(gpu_ids))
else:
nvidia_smi = subprocess.check_output(["nvidia-smi", "--list-gpus"])
num_accelerators = len(nvidia_smi.decode("utf-8").strip().split("\n"))
elif get_accelerator().device_name() == "xpu":
clinfo = subprocess.check_output(["clinfo"])
lines = clinfo.decode("utf-8").strip().split("\n")
num_accelerators = 0
for line in lines:
match = re.search("Device Type.*GPU", line)
if match:
num_accelerators += 1
elif get_accelerator().device_name() == "npu":
npu_smi = subprocess.check_output(["npu-smi", "info", "-l"])
num_accelerators = int(
npu_smi.decode("utf-8").strip().split("\n")[0].split(":")[1].strip()
)
else:
assert get_accelerator().device_name() == "cpu"
cpu_sockets = int(
subprocess.check_output(
'cat /proc/cpuinfo | grep "physical id" | sort -u | wc -l',
shell=True,
)
)
num_accelerators = cpu_sockets
cuda_visible = ",".join(map(str, range(num_accelerators)))
# 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 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 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)
class DistributedExec(ABC):
"""
Base class for distributed execution of functions/methods. Contains common
methods needed for DistributedTest and DistributedFixture.
"""
world_size = 2
backend = get_accelerator().communication_backend_name()
init_distributed = True
set_dist_env = True
requires_cuda_env = True
reuse_dist_env = False
_pool_cache = {}
exec_timeout = DEEPSPEED_TEST_TIMEOUT
@abstractmethod
def run(self):
...
def __call__(self, request=None):
self._fixture_kwargs = self._get_fixture_kwargs(request, self.run)
world_size = self.world_size
if self.requires_cuda_env and not get_accelerator().is_available():
pytest.skip("only supported in accelerator environments.")
if isinstance(world_size, int):
world_size = [world_size]
for procs in world_size:
self._launch_procs(procs)
def _get_fixture_kwargs(self, request, func):
if not request:
return {}
# Grab fixture / parametrize kwargs from pytest request object
fixture_kwargs = {}
params = inspect.getfullargspec(func).args
params.remove("self")
for p in params:
try:
fixture_kwargs[p] = request.getfixturevalue(p)
except FixtureLookupError:
pass # test methods can have kwargs that are not fixtures
return fixture_kwargs
def _launch_procs(self, num_procs):
# Verify we have enough accelerator devices to run this test
if (
get_accelerator().is_available()
and get_accelerator().device_count() < num_procs
):
pytest.skip(
f"Skipping test because not enough GPUs are available: {num_procs} required, {get_accelerator().device_count()} available"
)
mp.set_start_method("spawn", force=True)
# Create process pool or use cached one
master_port = None
if self.reuse_dist_env:
if num_procs not in self._pool_cache:
self._pool_cache[num_procs] = mp.Pool(processes=num_procs)
master_port = get_master_port()
pool = self._pool_cache[num_procs]
else:
pool = mp.Pool(processes=num_procs)
master_port = get_master_port()
# Run the test
args = [(local_rank, num_procs, master_port) for local_rank in range(num_procs)]
skip_msgs_async = pool.starmap_async(self._dist_run, args)
try:
skip_msgs = skip_msgs_async.get(self.exec_timeout)
except mp.TimeoutError:
# Shortcut to exit pytest in the case of a hanged test. This
# usually means an environment error and the rest of tests will
# hang (causing super long unit test runtimes)
pytest.exit("Test hanged, exiting", returncode=0)
# Tear down distributed environment and close process pools
self._close_pool(pool, num_procs)
# If we skipped a test, propagate that to this process
if any(skip_msgs):
assert len(set(skip_msgs)) == 1, "Multiple different skip messages received"
pytest.skip(skip_msgs[0])
def _dist_run(self, local_rank, num_procs, master_port):
skip_msg = ""
if not dist.is_initialized():
"""Initialize deepspeed.comm and execute the user function."""
if self.set_dist_env:
os.environ["MASTER_ADDR"] = "127.0.0.1"
os.environ["MASTER_PORT"] = str(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)
# In case of multiprocess launching LOCAL_SIZE should be same as WORLD_SIZE
# DeepSpeed single node launcher would also set LOCAL_SIZE accordingly
os.environ["LOCAL_SIZE"] = str(num_procs)
os.environ["WORLD_SIZE"] = str(num_procs)
# turn off NCCL logging if set
os.environ.pop("NCCL_DEBUG", None)
if get_accelerator().is_available():
set_accelerator_visible()
if get_accelerator().is_available():
get_accelerator().set_device(local_rank)
if self.init_distributed:
deepspeed.init_distributed(dist_backend=self.backend)
dist.barrier()
try:
self.run(**self._fixture_kwargs)
except BaseException as e:
if isinstance(e, Skipped):
skip_msg = e.msg
else:
raise e
return skip_msg
def _dist_destroy(self):
if (dist is not None) and dist.is_initialized():
dist.barrier()
dist.destroy_process_group()
def _close_pool(self, pool, num_procs, force=False):
if force or not self.reuse_dist_env:
msg = pool.starmap(self._dist_destroy, [() for _ in range(num_procs)])
pool.close()
pool.join()
class DistributedFixture(DistributedExec):
"""
Implementation that extends @pytest.fixture to allow for distributed execution.
This is primarily meant to be used when a test requires executing two pieces of
code with different world sizes.
There are 2 parameters that can be modified:
- world_size: int = 2 -- the number of processes to launch
- backend: Literal['nccl','mpi','gloo'] = 'nccl' -- which backend to use
Features:
- able to call pytest.skip() inside fixture
- can be reused by multiple tests
- can accept other fixtures as input
Limitations:
- cannot use @pytest.mark.parametrize
- world_size cannot be modified after definition and only one world_size value is accepted
- any fixtures used must also be used in the test that uses this fixture (see example below)
- return values cannot be returned. Passing values to a DistributedTest
object can be achieved using class_tmpdir and writing to file (see example below)
Usage:
- must implement a run(self, ...) method
- fixture can be used by making the class name input to a test function
Example:
@pytest.fixture(params=[10,20])
def regular_pytest_fixture(request):
return request.param
class distributed_fixture_example(DistributedFixture):
world_size = 4
def run(self, regular_pytest_fixture, class_tmpdir):
assert int(os.environ["WORLD_SIZE"]) == self.world_size
local_rank = os.environ["LOCAL_RANK"]
print(f"Rank {local_rank} with value {regular_pytest_fixture}")
with open(os.path.join(class_tmpdir, f"{local_rank}.txt"), "w") as f:
f.write(f"{local_rank},{regular_pytest_fixture}")
class TestExample(DistributedTest):
world_size = 1
def test(self, distributed_fixture_example, regular_pytest_fixture, class_tmpdir):
assert int(os.environ["WORLD_SIZE"]) == self.world_size
for rank in range(4):
with open(os.path.join(class_tmpdir, f"{rank}.txt"), "r") as f:
assert f.read() == f"{rank},{regular_pytest_fixture}"
"""
is_dist_fixture = True
# These values are just placeholders so that pytest recognizes this as a fixture
_pytestfixturefunction = FixtureFunctionMarker(scope="function", params=None)
__name__ = ""
def __init__(self):
assert isinstance(
self.world_size, int
), "Only one world size is allowed for distributed fixtures"
self.__name__ = type(self).__name__
_pytestfixturefunction = FixtureFunctionMarker(
scope="function", params=None, name=self.__name__
)
class DistributedTest(DistributedExec):
"""
Implementation for running pytest with distributed execution.
There are 2 parameters that can be modified:
- world_size: Union[int,List[int]] = 2 -- the number of processes to launch
- backend: Literal['nccl','mpi','gloo'] = 'nccl' -- which backend to use
Features:
- able to call pytest.skip() inside tests
- works with pytest fixtures, parametrize, mark, etc.
- can contain multiple tests (each of which can be parametrized separately)
- class methods can be fixtures (usable by tests in this class only)
- world_size can be changed for individual tests using @pytest.mark.world_size(world_size)
- class_tmpdir is a fixture that can be used to get a tmpdir shared among
all tests (including DistributedFixture)
Usage:
- class name must start with "Test"
- must implement one or more test*(self, ...) methods
Example:
@pytest.fixture(params=[10,20])
def val1(request):
return request.param
@pytest.mark.fast
@pytest.mark.parametrize("val2", [30,40])
class TestExample(DistributedTest):
world_size = 2
@pytest.fixture(params=[50,60])
def val3(self, request):
return request.param
def test_1(self, val1, val2, str1="hello world"):
assert int(os.environ["WORLD_SIZE"]) == self.world_size
assert all(val1, val2, str1)
@pytest.mark.world_size(1)
@pytest.mark.parametrize("val4", [70,80])
def test_2(self, val1, val2, val3, val4):
assert int(os.environ["WORLD_SIZE"]) == 1
assert all(val1, val2, val3, val4)
"""
def __init__(self):
self.is_dist_test = True
# Temporary directory that is shared among test methods in a class
@pytest.fixture(autouse=True, scope="class")
def class_tmpdir(self, tmpdir_factory):
fn = tmpdir_factory.mktemp(self.__class__.__name__)
return fn
def run(self, **fixture_kwargs):
self._current_test(**fixture_kwargs)
def __call__(self, request):
self._current_test = self._get_current_test_func(request)
self._fixture_kwargs = self._get_fixture_kwargs(request, self._current_test)
if self.requires_cuda_env and not get_accelerator().is_available():
pytest.skip("only supported in accelerator environments.")
# Catch world_size override pytest mark
for mark in getattr(request.function, "pytestmark", []):
if mark.name == "world_size":
world_size = mark.args[0]
break
else:
world_size = self.world_size
if isinstance(world_size, int):
world_size = [world_size]
for procs in world_size:
self._launch_procs(procs)
time.sleep(0.5)
def _get_current_test_func(self, request):
# DistributedTest subclasses may have multiple test methods
func_name = request.function.__name__
return getattr(self, func_name)
def get_test_path(filename):
curr_path = Path(__file__).parent
return str(curr_path.joinpath(filename))
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 simulate_deepy_env(monkeypatch, input_args):
from megatron.neox_arguments import NeoXArgs
monkeypatch.setenv("WORLD_SIZE", "1")
monkeypatch.setenv("RANK", "0")
neox_args = NeoXArgs.consume_deepy_args(input_args)
deepspeed_main_args = neox_args.get_deepspeed_main_args()
return deepspeed_main_args
def save_random_model(input_args, model_dir, train_iters=0):
# Save randomly initialised model
train_args = {
"do_train": False,
"train_iters": train_iters,
"save": model_dir,
"extra_save_iters": [train_iters],
}
train.main(input_args=input_args, overwrite_values=train_args)
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 model_setup_simple(deepspeed_main_args, overwrite_values, iteration=None):
from megatron.neox_arguments import NeoXArgs
from megatron import initialize_megatron
from megatron.training import setup_model_and_optimizer
neox_args = NeoXArgs.consume_neox_args(
input_args=deepspeed_main_args, overwrite_values=overwrite_values
)
neox_args.configure_distributed_args()
neox_args.build_tokenizer()
initialize_megatron(neox_args=neox_args)
model, optimizer, lr_scheduler = setup_model_and_optimizer(
neox_args=neox_args, use_cache=False
)
return model, optimizer, lr_scheduler, neox_args
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("tests/config/test_setup.yml", "r") as f:
BASE_CONFIG = load(f, Loader=Loader)
print(f"Base Config:\n{BASE_CONFIG}")