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dlio_benchmark_test.py
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"""
Copyright (c) 2022, UChicago Argonne, LLC
All Rights Reserved
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.
"""
#!/usr/bin/env python
from hydra import initialize, initialize_config_dir, compose
from omegaconf import OmegaConf
import unittest
import shutil
from mpi4py import MPI
import pathlib
comm = MPI.COMM_WORLD
import pytest
import time
import subprocess
import logging
import os
from dlio_benchmark.utils.config import ConfigArguments
from dlio_benchmark.utils.utility import DLIOMPI
import dlio_benchmark
config_dir=os.path.dirname(dlio_benchmark.__file__)+"/configs/"
logging.basicConfig(
level=logging.INFO,
handlers=[
logging.FileHandler("dlio_benchmark_test.log", mode="a", encoding='utf-8'),
logging.StreamHandler()
], format='[%(levelname)s] %(message)s [%(pathname)s:%(lineno)d]'
# logging's max timestamp resolution is msecs, we will pass in usecs in the message
)
from dlio_benchmark.main import DLIOBenchmark, set_dftracer_initialize, set_dftracer_finalize
import glob
def init():
DLIOMPI.get_instance().initialize()
def finalize():
# DLIOMPI.get_instance().finalize()
pass
def clean(storage_root="./") -> None:
comm.Barrier()
if (comm.rank == 0):
shutil.rmtree(os.path.join(storage_root, "checkpoints"), ignore_errors=True)
shutil.rmtree(os.path.join(storage_root, "data/"), ignore_errors=True)
shutil.rmtree(os.path.join(storage_root, "output"), ignore_errors=True)
comm.Barrier()
def run_benchmark(cfg, storage_root="./", verify=True):
comm.Barrier()
if (comm.rank == 0):
shutil.rmtree(os.path.join(storage_root, "output"), ignore_errors=True)
comm.Barrier()
t0 = time.time()
ConfigArguments.reset()
benchmark = DLIOBenchmark(cfg['workload'])
benchmark.initialize()
benchmark.run()
benchmark.finalize()
t1 = time.time()
if (comm.rank==0):
logging.info("Time for the benchmark: %.10f" %(t1-t0))
if (verify):
assert(len(glob.glob(benchmark.output_folder+"./*_output.json"))==benchmark.comm_size)
return benchmark
@pytest.mark.timeout(60, method="thread")
@pytest.mark.parametrize("fmt, framework", [("png", "tensorflow"), ("npz", "tensorflow"),
("jpeg", "tensorflow"), ("tfrecord", "tensorflow"),
("hdf5", "tensorflow"), ("indexed_binary", "tensorflow"), ("mmap_indexed_binary", "tensorflow")])
def test_gen_data(fmt, framework) -> None:
init()
if (comm.rank == 0):
logging.info("")
logging.info("=" * 80)
logging.info(f" DLIO test for generating {fmt} dataset")
logging.info("=" * 80)
with initialize_config_dir(version_base=None, config_dir=config_dir):
cfg = compose(config_name='config', overrides=[f'++workload.framework={framework}',
f'++workload.reader.data_loader={framework}',
'++workload.workflow.train=False',
'++workload.workflow.generate_data=True',
f"++workload.dataset.format={fmt}",
"++workload.dataset.num_files_train=8",
"++workload.dataset.num_files_eval=8"])
benchmark = run_benchmark(cfg, verify=False)
if benchmark.args.num_subfolders_train <= 1:
train = pathlib.Path(f"{cfg.workload.dataset.data_folder}/train")
train_files = list(train.glob(f"*.{fmt}"))
valid = pathlib.Path(f"{cfg.workload.dataset.data_folder}/valid")
valid_files = list(valid.glob(f"*.{fmt}"))
assert (len(train_files) == cfg.workload.dataset.num_files_train)
assert (len(valid_files) == cfg.workload.dataset.num_files_eval)
else:
train = pathlib.Path(f"{cfg.workload.dataset.data_folder}/train")
train_files = list(train.rglob(f"**/*.{fmt}"))
valid = pathlib.Path(f"{cfg.workload.dataset.data_folder}/valid")
valid_files = list(valid.rglob(f"**/*.{fmt}"))
assert (len(train_files) == cfg.workload.dataset.num_files_train)
assert (len(valid_files) == cfg.workload.dataset.num_files_eval)
clean()
finalize()
@pytest.mark.timeout(60, method="thread")
def test_subset() -> None:
init()
clean()
if comm.rank == 0:
logging.info("")
logging.info("=" * 80)
logging.info(f" DLIO training test for subset")
logging.info("=" * 80)
with initialize_config_dir(version_base=None, config_dir=config_dir):
set_dftracer_finalize(False)
cfg = compose(config_name='config', overrides=['++workload.workflow.train=False', \
'++workload.workflow.generate_data=True'])
benchmark=run_benchmark(cfg, verify=False)
set_dftracer_initialize(False)
cfg = compose(config_name='config', overrides=['++workload.workflow.train=True', \
'++workload.workflow.generate_data=False', \
'++workload.dataset.num_files_train=8', \
'++workload.train.computation_time=0.01'])
benchmark=run_benchmark(cfg, verify=True)
clean()
finalize()
@pytest.mark.timeout(60, method="thread")
@pytest.mark.parametrize("fmt, framework", [("png", "tensorflow"), ("npz", "tensorflow"),
("jpeg", "tensorflow"), ("tfrecord", "tensorflow"),
("hdf5", "tensorflow"), ("indexed_binary", "tensorflow"),
("mmap_indexed_binary", "tensorflow")])
def test_storage_root_gen_data(fmt, framework) -> None:
init()
storage_root = "runs"
clean(storage_root)
if (comm.rank == 0):
logging.info("")
logging.info("=" * 80)
logging.info(f" DLIO test for generating {fmt} dataset")
logging.info("=" * 80)
with initialize_config_dir(version_base=None, config_dir=config_dir):
cfg = compose(config_name='config', overrides=[f'++workload.framework={framework}',
f'++workload.reader.data_loader={framework}',
'++workload.workflow.train=False',
'++workload.workflow.generate_data=True',
f"++workload.storage.storage_root={storage_root}",
f"++workload.dataset.format={fmt}",
"++workload.dataset.num_files_train=16"])
benchmark = run_benchmark(cfg, verify=False)
if benchmark.args.num_subfolders_train <= 1:
assert (
len(glob.glob(
os.path.join(storage_root, cfg.workload.dataset.data_folder, f"train/*.{fmt}"))) ==
cfg.workload.dataset.num_files_train)
assert (
len(glob.glob(
os.path.join(storage_root, cfg.workload.dataset.data_folder, f"valid/*.{fmt}"))) ==
cfg.workload.dataset.num_files_eval)
else:
logging.info(os.path.join(storage_root, cfg.workload.dataset.data_folder, f"train/*/*.{fmt}"))
assert (
len(glob.glob(
os.path.join(storage_root, cfg.workload.dataset.data_folder, f"train/*/*.{fmt}"))) ==
cfg.workload.dataset.num_files_train)
assert (
len(glob.glob(
os.path.join(storage_root, cfg.workload.dataset.data_folder, f"valid/*/*.{fmt}"))) ==
cfg.workload.dataset.num_files_eval)
clean(storage_root)
finalize()
@pytest.mark.timeout(60, method="thread")
def test_iostat_profiling() -> None:
init()
clean()
if (comm.rank == 0):
logging.info("")
logging.info("=" * 80)
logging.info(f" DLIO test for iostat profiling")
logging.info("=" * 80)
with initialize_config_dir(version_base=None, config_dir=config_dir):
cfg = compose(config_name='config', overrides=['++workload.workflow.train=False',
'++workload.workflow.generate_data=True'])
benchmark = run_benchmark(cfg, verify=False)
cfg = compose(config_name='config', overrides=['++workload.workflow.train=True',
'++workload.workflow.generate_data=False',
'workload.train.computation_time=0.01',
'workload.evaluation.eval_time=0.005',
'workload.train.epochs=1',
'workload.workflow.profiling=True',
'workload.profiling.profiler=iostat'])
benchmark = run_benchmark(cfg)
assert (os.path.isfile(benchmark.output_folder + "/iostat.json"))
if (comm.rank == 0):
logging.info("generating output data")
hydra = f"{benchmark.output_folder}/.hydra"
os.makedirs(hydra, exist_ok=True)
yl: str = OmegaConf.to_yaml(cfg)
with open(f"{hydra}/config.yaml", "w") as f:
OmegaConf.save(cfg, f)
with open(f"{hydra}/overrides.yaml", "w") as f:
f.write('[]')
subprocess.run(["ls", "-l", "/dev/null"], capture_output=True)
cmd = f"dlio_postprocessor --output-folder={benchmark.output_folder}"
cmd = cmd.split()
subprocess.run(cmd, capture_output=True, timeout=10)
clean()
finalize()
@pytest.mark.timeout(60, method="thread")
@pytest.mark.parametrize("framework, model_size, optimizers, num_layers, layer_params, zero_stage", [("tensorflow", 1024, [1024, 128], 2, [16], 0),
("pytorch", 1024, [1024, 128], 2, [16], 0),
("tensorflow", 1024, [1024, 128], 2, [16], 3),
("pytorch", 1024, [1024, 128], 2, [16], 3),
("tensorflow", 1024, [128], 1, [16], 0),
("pytorch", 1024, [128], 1, [16], 0)])
def test_checkpoint_epoch(framework, model_size, optimizers, num_layers, layer_params, zero_stage) -> None:
init()
clean()
if comm.rank == 0:
logging.info("")
logging.info("=" * 80)
logging.info(f" DLIO test for checkpointing at the end of epochs")
logging.info("=" * 80)
with initialize_config_dir(version_base=None, config_dir=config_dir):
epochs = 8
epoch_per_ckp = 2
cfg = compose(config_name='config',
overrides=[f'++workload.framework={framework}',
f'++workload.reader.data_loader={framework}',
'++workload.workflow.train=True',
'++workload.workflow.generate_data=True',
'++workload.train.computation_time=0.01',
'++workload.evaluation.eval_time=0.005',
f'++workload.train.epochs={epochs}', '++workload.workflow.checkpoint=True',
f'++workload.checkpoint.epochs_between_checkpoints={epoch_per_ckp}',
f'++workload.model.model_size={model_size}',
f'++workload.model.optimization_groups={optimizers}',
f'++workload.model.num_layers={num_layers}',
f'++workload.model.parallelism.zero_stage={zero_stage}',
f'++workload.model.layer_parameters={layer_params}',
f'++workload.model.parallelism.tensor={comm.size}'])
comm.Barrier()
if comm.rank == 0:
shutil.rmtree("./checkpoints", ignore_errors=True)
os.makedirs("./checkpoints", exist_ok=True)
comm.Barrier()
benchmark = run_benchmark(cfg)
output = pathlib.Path("./checkpoints")
load_bin = list(output.glob(f"*/*"))
n = 0
if len(layer_params) > 0:
n = num_layers
nranks = comm.size
num_model_files = 1
num_optimizer_files = 1
# We are setting num_layer_files to be one because pipeline parallelism is not used.
num_layer_files = 1
files_per_checkpoint = (num_model_files + num_optimizer_files + num_layer_files) * nranks
if framework == "tensorflow":
file_per_ckp = 2
num_check_files = epochs / epoch_per_ckp * (files_per_checkpoint * file_per_ckp + 1)
assert (len(load_bin) == num_check_files), f"files produced are {len(load_bin)} {num_check_files} {load_bin} "
if framework == "pytorch":
num_check_files = epochs / epoch_per_ckp * files_per_checkpoint
assert (len(load_bin) == num_check_files), f"files produced are {len(load_bin)} {num_check_files} {load_bin}"
comm.Barrier()
if comm.rank == 0:
shutil.rmtree("./checkpoints", ignore_errors=True)
comm.Barrier()
clean()
finalize()
@pytest.mark.timeout(60, method="thread")
def test_checkpoint_step() -> None:
init()
clean()
if (comm.rank == 0):
logging.info("")
logging.info("=" * 80)
logging.info(f" DLIO test for checkpointing at the end of steps")
logging.info("=" * 80)
with initialize_config_dir(version_base=None, config_dir=config_dir):
cfg = compose(config_name='config',
overrides=['++workload.workflow.train=True', \
'++workload.workflow.generate_data=True', \
'++workload.train.computation_time=0.01', \
'++workload.evaluation.eval_time=0.005', \
'++workload.train.epochs=8', '++workload.workflow.checkpoint=True', \
'++workload.checkpoint.steps_between_checkpoints=2'])
comm.Barrier()
if comm.rank == 0:
shutil.rmtree("./checkpoints", ignore_errors=True)
os.makedirs("./checkpoints", exist_ok=True)
comm.Barrier()
benchmark = run_benchmark(cfg)
dataset = cfg['workload']['dataset']
nstep = dataset.num_files_train * dataset.num_samples_per_file // cfg['workload']['reader'].batch_size // benchmark.comm_size
ncheckpoints = nstep // 2 * 8
output = pathlib.Path("./checkpoints")
load_bin = list(output.glob(f"*/*"))
assert (len(load_bin) == ncheckpoints)
clean()
finalize()
@pytest.mark.timeout(60, method="thread")
def test_eval() -> None:
init()
clean()
if (comm.rank == 0):
logging.info("")
logging.info("=" * 80)
logging.info(f" DLIO test for evaluation")
logging.info("=" * 80)
with initialize_config_dir(version_base=None, config_dir=config_dir):
cfg = compose(config_name='config',
overrides=['++workload.workflow.train=True', \
'++workload.workflow.generate_data=True', \
'workload.train.computation_time=0.01', \
'workload.evaluation.eval_time=0.005', \
'++workload.train.epochs=4', '++workload.workflow.evaluation=True'])
benchmark = run_benchmark(cfg)
clean()
finalize()
@pytest.mark.timeout(60, method="thread")
@pytest.mark.parametrize("framework, nt", [("tensorflow", 0), ("tensorflow", 1),("tensorflow", 2),
("pytorch", 0), ("pytorch", 1), ("pytorch", 2)])
def test_multi_threads(framework, nt) -> None:
init()
clean()
if (comm.rank == 0):
logging.info("")
logging.info("=" * 80)
logging.info(f" DLIO test for generating multithreading read_threads={nt} {framework} framework")
logging.info("=" * 80)
# with subTest(f"Testing full benchmark for format: {framework}-NT{nt}", nt=nt, framework=framework):
with initialize_config_dir(version_base=None, config_dir=config_dir):
cfg = compose(config_name='config', overrides=['++workload.workflow.train=True',
'++workload.workflow.generate_data=True',
f"++workload.framework={framework}",
f"++workload.reader.data_loader={framework}",
f"++workload.reader.read_threads={nt}",
'workload.train.computation_time=0.01',
'workload.evaluation.eval_time=0.005',
'++workload.train.epochs=1',
'++workload.dataset.num_files_train=8',
'++workload.dataset.num_files_eval=8'])
benchmark = run_benchmark(cfg)
clean()
finalize()
@pytest.mark.timeout(60, method="thread")
@pytest.mark.parametrize("nt, context", [(0, None), (1, "fork"), (2, "spawn"), (2, "forkserver")])
def test_pytorch_multiprocessing_context(nt, context) -> None:
init()
clean()
if (comm.rank == 0):
logging.info("")
logging.info("=" * 80)
logging.info(f" DLIO test for pytorch multiprocessing_context={context} read_threads={nt}")
logging.info("=" * 80)
# with subTest(f"Testing full benchmark for format: {framework}-NT{nt}", nt=nt, framework=pytorch):
with initialize_config_dir(version_base=None, config_dir=config_dir):
cfg = compose(config_name='config', overrides=['++workload.workflow.train=True',
'++workload.workflow.generate_data=True',
f"++workload.framework=pytorch",
f"++workload.reader.data_loader=pytorch",
f"++workload.reader.read_threads={nt}",
f"++workload.reader.multiprocessing_context={context}",
'workload.train.computation_time=0.01',
'workload.evaluation.eval_time=0.005',
'++workload.train.epochs=1',
'++workload.dataset.num_files_train=8',
'++workload.dataset.num_files_eval=8'])
benchmark = run_benchmark(cfg)
clean()
finalize()
@pytest.mark.timeout(60, method="thread")
@pytest.mark.parametrize("fmt, framework, dataloader, is_even", [("png", "tensorflow","tensorflow", True), ("npz", "tensorflow","tensorflow", True),
("jpeg", "tensorflow","tensorflow", True), ("tfrecord", "tensorflow","tensorflow", True),
("hdf5", "tensorflow","tensorflow", True), ("csv", "tensorflow","tensorflow", True),
("indexed_binary", "tensorflow","tensorflow", True), ("mmap_indexed_binary", "tensorflow","tensorflow", True),
("png", "pytorch", "pytorch", True), ("npz", "pytorch", "pytorch", True),
("jpeg", "pytorch", "pytorch", True), ("hdf5", "pytorch", "pytorch", True),
("csv", "pytorch", "pytorch", True), ("indexed_binary", "pytorch", "pytorch", True),
("mmap_indexed_binary", "pytorch", "pytorch", True),
("png", "tensorflow", "dali", True), ("npz", "tensorflow", "dali", True),
("jpeg", "tensorflow", "dali", True), ("hdf5", "tensorflow", "dali", True),
("csv", "tensorflow", "dali", True), ("indexed_binary", "tensorflow", "dali", True),
("mmap_indexed_binary", "tensorflow", "dali", True),
("png", "pytorch", "dali", True), ("npz", "pytorch", "dali", True),
("jpeg", "pytorch", "dali", True), ("hdf5", "pytorch", "dali", True),
("csv", "pytorch", "dali", True), ("indexed_binary", "pytorch", "dali", True),
("mmap_indexed_binary", "pytorch", "dali", True),
("png", "tensorflow","tensorflow", False), ("npz", "tensorflow","tensorflow", False),
("jpeg", "tensorflow","tensorflow", False), ("tfrecord", "tensorflow","tensorflow", False),
("hdf5", "tensorflow","tensorflow", False), ("csv", "tensorflow","tensorflow", False),
("indexed_binary", "tensorflow","tensorflow", False), ("mmap_indexed_binary", "tensorflow","tensorflow", False),
("png", "pytorch", "pytorch", False), ("npz", "pytorch", "pytorch", False),
("jpeg", "pytorch", "pytorch", False), ("hdf5", "pytorch", "pytorch", False),
("csv", "pytorch", "pytorch", False), ("indexed_binary", "pytorch", "pytorch", False),
("mmap_indexed_binary", "pytorch", "pytorch", False),
("png", "tensorflow", "dali", False), ("npz", "tensorflow", "dali", False),
("jpeg", "tensorflow", "dali", False), ("hdf5", "tensorflow", "dali", False),
("csv", "tensorflow", "dali", False), ("indexed_binary", "tensorflow", "dali", False),
("mmap_indexed_binary", "tensorflow", "dali", False),
("png", "pytorch", "dali", False), ("npz", "pytorch", "dali", False),
("jpeg", "pytorch", "dali", False), ("hdf5", "pytorch", "dali", False),
("csv", "pytorch", "dali", False), ("indexed_binary", "pytorch", "dali", False),
("mmap_indexed_binary", "pytorch", "dali", False),
])
def test_train(fmt, framework, dataloader, is_even) -> None:
init()
clean()
if is_even:
num_files = 16
else:
num_files = 17
if comm.rank == 0:
logging.info("")
logging.info("=" * 80)
logging.info(f" DLIO training test: Generating data for {fmt} format")
logging.info("=" * 80)
with initialize_config_dir(version_base=None, config_dir=config_dir):
cfg = compose(config_name='config', overrides=['++workload.workflow.train=True',
'++workload.workflow.generate_data=True',
f"++workload.framework={framework}", \
f"++workload.reader.data_loader={dataloader}", \
f"++workload.dataset.format={fmt}",
'workload.train.computation_time=0.01', \
'workload.evaluation.eval_time=0.005', \
'++workload.train.epochs=1', \
f'++workload.dataset.num_files_train={num_files}', \
'++workload.reader.read_threads=1'])
benchmark = run_benchmark(cfg)
#clean()
finalize()
@pytest.mark.timeout(60, method="thread")
@pytest.mark.parametrize("fmt, framework", [("png", "tensorflow"), ("npz", "tensorflow"),
("jpeg", "tensorflow"), ("tfrecord", "tensorflow"),
("hdf5", "tensorflow"), ("csv", "tensorflow"),
("indexed_binary", "tensorflow"), ("mmap_indexed_binary", "tensorflow"),
("png", "pytorch"), ("npz", "pytorch"),
("jpeg", "pytorch"), ("hdf5", "pytorch"),
("csv", "pytorch"), ("indexed_binary", "pytorch"),
("mmap_indexed_binary", "pytorch"),
])
def test_custom_storage_root_train(fmt, framework) -> None:
init()
storage_root = "root_dir"
clean(storage_root)
if (comm.rank == 0):
logging.info("")
logging.info("=" * 80)
logging.info(f" DLIO training test for {fmt} format in {framework} framework")
logging.info("=" * 80)
with initialize_config_dir(version_base=None, config_dir=config_dir):
cfg = compose(config_name='config', overrides=['++workload.workflow.train=True', \
'++workload.workflow.generate_data=True', \
f"++workload.framework={framework}", \
f"++workload.reader.data_loader={framework}", \
f"++workload.dataset.format={fmt}",
f"++workload.storage.storage_root={storage_root}", \
'workload.train.computation_time=0.01', \
'workload.evaluation.eval_time=0.005', \
'++workload.train.epochs=1', \
'++workload.dataset.num_files_train=16', \
'++workload.reader.read_threads=1'])
benchmark = run_benchmark(cfg)
clean(storage_root)
finalize()
compute_time_distributions = {
"uniform": {"type": "uniform", "min": 1.0, "max": 2.0},
"normal": {"type": "normal", "mean": 1.0, "stdev": 1.0},
"gamma": {"type": "gamma", "shape": 1.0, "scale": 1.0},
"exp": {"type": "exponential", "scale": 1.0},
"poisson": {"type": "poisson", "lam": 1.0},
"normal_v2": {"mean": 1.0}, # mean, dist: normal
"normal_v3": {"mean": 1.0, "stdev": 1.0}, # mean, stdev, dist: normal
"normal_v4": 2.0, # mean, dist: normal
}
@pytest.mark.timeout(60, method="thread")
@pytest.mark.parametrize("dist", list(compute_time_distributions.keys()))
def test_computation_time_distribution(request, dist) -> None:
init()
clean()
compute_time_overrides = []
dist_val = compute_time_distributions[dist]
if isinstance(dist_val, dict):
for key, value in dist_val.items():
compute_time_overrides.append(f"++workload.train.computation_time.{key}={value}")
else:
compute_time_overrides.append(f"++workload.train.computation_time={dist_val}")
if (comm.rank == 0):
logging.info("")
logging.info("=" * 80)
logging.info(f" DLIO test for computation time distribution")
logging.info("=" * 80)
with initialize_config_dir(version_base=None, config_dir=config_dir):
if request.config.is_dftracer_initialized:
set_dftracer_initialize(False)
else:
set_dftracer_finalize(False)
cfg = compose(config_name='config',
overrides=['++workload.workflow.train=True', \
'++workload.workflow.generate_data=True', \
'++workload.train.epochs=4'] + compute_time_overrides)
benchmark = run_benchmark(cfg)
if not request.config.is_dftracer_initialized:
request.config.is_dftracer_initialized = True
clean()
finalize()
if __name__ == '__main__':
unittest.main()