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Commit 526a3180 authored by Frank Lee's avatar Frank Lee
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[unit test] Refactored test cases with component func (#339) 

* refactored test with component func

* fixed bug
parent de464504
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Showing with 163 additions and 435 deletions
tests/components_to_test/nested_model.py
View file @ 526a3180
......@@ -43,7 +43,7 @@ class DummyDataLoader(DummyDataGenerator):
@non_distributed_component_funcs.register(name='nested_model')
def get_training_components():
def model_builder(checkpoint):
def model_builder(checkpoint=True):
return NestedNet(checkpoint)
trainloader = DummyDataLoader()
......
tests/components_to_test/utils/dummy_data_generator.py
View file @ 526a3180
......@@ -3,12 +3,23 @@ from abc import ABC, abstractmethod
class DummyDataGenerator(ABC):
def __init__(self, length=10):
self.length = length
@abstractmethod
def generate(self):
pass
def __iter__(self):
self.step = 0
return self
def __next__(self):
return self.generate()
if self.step < self.length:
self.step += 1
return self.generate()
else:
raise StopIteration
def __len__(self):
return self.length
tests/test_trainer/test_trainer_with_non_pipe_schedule.py
View file @ 526a3180
import os
from functools import partial
from pathlib import Path
import colossalai
import pytest
import torch
import torch.multiprocessing as mp
import torch.nn as nn
from colossalai.amp.amp_type import AMP_TYPE
from colossalai.core import global_context as gpc
from colossalai.logging import get_dist_logger
from colossalai.trainer import Trainer
from colossalai.utils import MultiTimer, free_port, get_dataloader
from torch.optim import Adam
from torchvision import transforms
from torchvision.datasets import CIFAR10
from torchvision.models import resnet18
from colossalai.utils import MultiTimer, free_port
from tests.components_to_test.registry import non_distributed_component_funcs
BATCH_SIZE = 16
IMG_SIZE = 32
......@@ -29,60 +22,32 @@ CONFIG = dict(
def run_trainer_no_pipeline(rank, world_size, port):
colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
# build model
model = resnet18(num_classes=10)
# build dataloaders
train_dataset = CIFAR10(root=Path(os.environ['DATA']),
download=True,
transform=transforms.Compose([
transforms.Resize(size=(IMG_SIZE, IMG_SIZE)),
transforms.ToTensor(),
transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
]))
test_dataset = CIFAR10(root=Path(os.environ['DATA']),
train=False,
download=True,
transform=transforms.Compose([
transforms.Resize(size=(IMG_SIZE, IMG_SIZE)),
transforms.ToTensor(),
transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
]))
train_dataloader = get_dataloader(dataset=train_dataset,
shuffle=True,
batch_size=BATCH_SIZE,
pin_memory=True,
drop_last=True)
test_dataloader = get_dataloader(dataset=test_dataset, batch_size=BATCH_SIZE, pin_memory=True, drop_last=True)
# build optimizer
optimizer = Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()
engine, train_dataloader, *args = colossalai.initialize(model=model,
optimizer=optimizer,
criterion=criterion,
train_dataloader=train_dataloader)
logger = get_dist_logger()
logger.info("engine is built", ranks=[0])
timer = MultiTimer()
trainer = Trainer(engine=engine, logger=logger, timer=timer)
logger.info("trainer is built", ranks=[0])
logger.info("start training", ranks=[0])
trainer.fit(train_dataloader=train_dataloader,
test_dataloader=test_dataloader,
epochs=NUM_EPOCHS,
max_steps=100,
display_progress=True,
test_interval=5)
gpc.destroy()
torch.cuda.empty_cache()
test_models = ['repeated_computed_layers', 'resnet18', 'nested_model']
for name in test_models:
get_components_func = non_distributed_component_funcs.get_callable(name)
model_builder, train_dataloader, test_dataloader, optimizer_builder, criterion = get_components_func()
model = model_builder()
optimizer = optimizer_builder(model)
engine, train_dataloader, *_ = colossalai.initialize(model=model,
optimizer=optimizer,
criterion=criterion,
train_dataloader=train_dataloader)
logger = get_dist_logger()
logger.info("engine is built", ranks=[0])
timer = MultiTimer()
trainer = Trainer(engine=engine, logger=logger, timer=timer)
logger.info("trainer is built", ranks=[0])
logger.info("start training", ranks=[0])
trainer.fit(train_dataloader=train_dataloader,
test_dataloader=test_dataloader,
epochs=NUM_EPOCHS,
max_steps=5,
display_progress=True,
test_interval=5)
torch.cuda.empty_cache()
@pytest.mark.dist
......
tests/test_utils/test_zero_gradient_clippling.py
View file @ 526a3180
......@@ -20,6 +20,7 @@ from torch.nn.utils import clip_grad_norm_
class Enumerator:
def __init__(self, arg_names: List[str], arg_values: List[tuple]) -> None:
self.arg_names = arg_names
self.enums = Enumerator.all_enumerate(arg_values)
......@@ -49,11 +50,12 @@ class Enumerator:
def checkpoint_wrapper(module, enable=True):
if enable:
module.forward = partial(checkpoint, module.forward)
module.forward = partial(checkpoint, module.forward, False)
return module
class Net(nn.Module):
def __init__(self, checkpoint=False) -> None:
super().__init__()
self.fc1 = nn.Linear(5, 5)
......@@ -61,13 +63,7 @@ class Net(nn.Module):
self.fc3 = nn.Linear(5, 1)
if checkpoint:
self.fc1 = checkpoint_wrapper(self.fc1)
self.layers = [
self.fc1,
self.fc2,
self.fc1,
self.fc2,
self.fc3
]
self.layers = [self.fc1, self.fc2, self.fc1, self.fc2, self.fc3]
def forward(self, x):
for layer in self.layers:
......@@ -158,12 +154,7 @@ def check_config(checkpoint=False, fp16=False, offload=False, norm_type=2.0):
def run_dist(rank, world_size, port):
disable_existing_loggers()
colossalai.launch(config={},
rank=rank,
world_size=world_size,
host='localhost',
port=port,
backend='nccl')
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
args = ['checkpoint', 'fp16', 'offload', 'norm_type']
arg_values = [(False, True), (False, True), (False, True), (1.0, 2.0, float('inf'))]
......@@ -176,7 +167,7 @@ def run_dist(rank, world_size, port):
check_config()
@ pytest.mark.dist
@pytest.mark.dist
def test_zero_clip_grad():
world_size = 4
run_func = partial(run_dist, world_size=world_size, port=free_port())
......
tests/test_zero_data_parallel/common.py
View file @ 526a3180
......@@ -43,23 +43,6 @@ def checkpoint_wrapper(module, enable=True):
return module
class Net(nn.Module):
def __init__(self, checkpoint=False) -> None:
super().__init__()
self.fc1 = nn.Linear(5, 5)
self.fc2 = nn.Linear(5, 5)
self.fc3 = nn.Linear(5, 1)
if checkpoint:
self.fc1 = checkpoint_wrapper(self.fc1)
self.layers = [self.fc1, self.fc2, self.fc1, self.fc2, self.fc3]
def forward(self, x):
for layer in self.layers:
x = layer(x)
return x
def allclose(tensor_a: torch.Tensor, tensor_b: torch.Tensor, loose=False) -> bool:
if loose:
return torch.allclose(tensor_a, tensor_b, atol=1e-2, rtol=1e-3)
......
tests/test_zero_data_parallel/test_shard_param.py
View file @ 526a3180
......@@ -13,7 +13,8 @@ from colossalai.utils import free_port
from colossalai.zero.shard_utils import TensorShardStrategy
from colossalai.zero.sharded_param import ShardedParam, ShardedTensor
from colossalai.zero.sharded_param.sharded_param import ShardedParamV2
from tests.test_zero_data_parallel.common import CONFIG, Net, allclose
from tests.test_zero_data_parallel.common import CONFIG, allclose
from tests.components_to_test.registry import non_distributed_component_funcs
def _run_shard_tensor(rank, world_size, port):
......@@ -68,21 +69,22 @@ def _run_test_shard_param(rank, world_size, port):
print(param_ref.data)
logger = get_dist_logger()
model = Net()
# add an attribute as ca_attr to hijack the access to param.data
for _, param in model.named_parameters():
numel_ref = (param.numel() + world_size - 1) // world_size
param.ca_attr = ShardedParam(param)
param.ca_attr.shard()
param_data = param.ca_attr.payload(torch.device('cpu'))
assert (numel_ref == param_data.numel())
for _, param in model.named_parameters():
param.ca_attr.gather()
param_data = param.ca_attr.payload(torch.device('cpu'))
disable_existing_loggers([logger])
for get_components_func in non_distributed_component_funcs:
model_builder, *_ = get_components_func()
model = model_builder(checkpoint=True)
# add an attribute as col_attr to hijack the access to param.data
for _, param in model.named_parameters():
numel_ref = (param.numel() + world_size - 1) // world_size
param.col_attr = ShardedParam(param)
param.col_attr.shard()
param_data = param.col_attr.payload(torch.device('cpu'))
assert (numel_ref == param_data.numel())
for _, param in model.named_parameters():
param.col_attr.gather()
param_data = param.col_attr.payload(torch.device('cpu'))
disable_existing_loggers([logger])
@pytest.mark.dist
......
tests/test_zero_data_parallel/test_sharded_optim.py
View file @ 526a3180
......@@ -3,19 +3,13 @@ import colossalai
import copy
import pytest
import torch.multiprocessing as mp
import torch.nn as nn
from colossalai.zero import ShardedOptimizer
from torch.nn.parallel import DistributedDataParallel as DDP
from colossalai.utils import free_port
from functools import partial
def check_equal(a, b):
"""
This function checks if two tensors are equal within tolerance
"""
assert torch.allclose(a.float(), b.float(), rtol=1e-4, atol=1e-3), f'a = {a}, b = {b}'
from common import allclose
from tests.components_to_test.registry import non_distributed_component_funcs
def check_completely_equal(a, b):
......@@ -36,61 +30,56 @@ def check_sharded_param_consistency():
pg: partition gradients and optimizer states
"""
# create layers
oss_linear1 = nn.Linear(128, 256)
oss_linear2 = nn.Linear(256, 512)
# create model
oss_model = nn.Sequential(oss_linear1, oss_linear2)
pg_model = copy.deepcopy(oss_model)
oss_model = oss_model.cuda().half()
pg_model = pg_model.cuda().half()
# create optimizer
oss_optimizer = torch.optim.Adam(oss_model.parameters(), lr=0.001)
pg_optimizer = torch.optim.Adam(pg_model.parameters(), lr=0.001)
oss_optimizer = ShardedOptimizer(oss_optimizer, overlap_communication=True, initial_scale=1, clip_grad_norm=0.0)
pg_optimizer = ShardedOptimizer(pg_optimizer,
overlap_communication=True,
partition_grad=True,
initial_scale=1,
clip_grad_norm=0.0)
# create
input_data = torch.rand(32, 128).cuda().half()
# forward
oss_output = oss_model(input_data)
pg_output = pg_model(input_data)
check_completely_equal(oss_output, pg_output)
# backward
oss_optimizer.backward(oss_output.mean().float())
pg_optimizer.backward(pg_output.mean().float())
# check grad
# as this param is small, the backward reduction
# will not be fired
oss_linear1_grad = oss_model[0].weight.grad
oss_linear2_grad = oss_model[1].weight.grad
pg_linear1_grad = pg_model[0].weight.grad
pg_linear2_grad = pg_model[1].weight.grad
check_completely_equal(oss_linear1_grad, pg_linear1_grad)
check_completely_equal(oss_linear2_grad, pg_linear2_grad)
# step
oss_optimizer.sync_grad()
pg_optimizer.sync_grad()
# step
oss_optimizer.step()
pg_optimizer.step()
# check updated param
check_completely_equal(oss_model[0].weight, pg_model[0].weight)
check_completely_equal(oss_model[1].weight, pg_model[1].weight)
test_models = ['repeated_computed_layers', 'resnet18', 'nested_model']
for name in test_models:
get_components_func = non_distributed_component_funcs.get_callable(name)
model_builder, train_dataloader, *_ = get_components_func()
# create model
oss_model = model_builder(checkpoint=True).cuda().half()
pg_model = copy.deepcopy(oss_model)
# create optimizer
oss_optimizer = torch.optim.Adam(oss_model.parameters(), lr=0.001)
pg_optimizer = torch.optim.Adam(pg_model.parameters(), lr=0.001)
oss_optimizer = ShardedOptimizer(oss_optimizer, overlap_communication=True, initial_scale=1, clip_grad_norm=0.0)
pg_optimizer = ShardedOptimizer(pg_optimizer,
overlap_communication=True,
partition_grad=True,
initial_scale=1,
clip_grad_norm=0.0)
# create
data, label = next(iter(train_dataloader))
input_data = data.cuda().half()
# forward
oss_output = oss_model(input_data)
pg_output = pg_model(input_data)
check_completely_equal(oss_output, pg_output)
# backward
oss_optimizer.backward(oss_output.mean().float())
pg_optimizer.backward(pg_output.mean().float())
# check grad
# as this param is small, the backward reduction
# will not be fired
for oss_param, pg_param in zip(oss_model.parameters(), pg_model.parameters()):
check_completely_equal(oss_param.grad, pg_param.grad)
# step
oss_optimizer.sync_grad()
pg_optimizer.sync_grad()
# step
oss_optimizer.step()
pg_optimizer.step()
# check updated param
for oss_param, pg_param in zip(oss_model.parameters(), pg_model.parameters()):
check_completely_equal(oss_param, pg_param)
def check_sharded_optim_against_torch_ddp():
......@@ -103,61 +92,62 @@ def check_sharded_optim_against_torch_ddp():
differences in model output and updated parameters are within tolerance.
"""
# create layer
zero_linear1 = nn.Linear(128, 256)
zero_linear2 = nn.Linear(256, 512)
test_models = ['repeated_computed_layers', 'resnet18', 'nested_model']
# create model
zero_model = nn.Sequential(zero_linear1, zero_linear2)
torch_model = copy.deepcopy(zero_model)
for name in test_models:
get_components_func = non_distributed_component_funcs.get_callable(name)
model_builder, train_dataloader, *_ = get_components_func()
zero_model = zero_model.cuda().half()
torch_model = DDP(torch_model.cuda())
# create model
zero_model = model_builder(checkpoint=True).cuda()
torch_model = copy.deepcopy(zero_model)
# create optimizer
zero_optimizer = torch.optim.Adam(zero_model.parameters(), lr=0.001)
zero_model = zero_model.half()
torch_model = DDP(torch_model.cuda())
# we only test stage 1 here
# in `check_sharded_param_consistency.py`, we will test whether
# level 1 and 2 will produce exactly the same results
zero_optimizer = ShardedOptimizer(zero_optimizer, overlap_communication=True, initial_scale=1, clip_grad_norm=0.0)
# create optimizer
zero_optimizer = torch.optim.Adam(zero_model.parameters(), lr=0.001)
torch_optimizer = torch.optim.Adam(torch_model.parameters(), lr=0.001)
# we only test stage 1 here
# in `check_sharded_param_consistency.py`, we will test whether
# level 1 and 2 will produce exactly the same results
zero_optimizer = ShardedOptimizer(zero_optimizer,
overlap_communication=True,
initial_scale=1,
clip_grad_norm=0.0)
torch_optimizer = torch.optim.Adam(torch_model.parameters(), lr=0.001)
# create
input_data = torch.rand(32, 128).cuda()
# create
input_data, _ = next(iter(train_dataloader))
input_data = input_data.cuda()
# zero-dp forward
zero_output = zero_model(input_data.half())
# zero-dp forward
zero_output = zero_model(input_data.half())
# torch-ddp forward
torch_output = torch_model(input_data)
check_equal(zero_output, torch_output)
# torch-ddp forward
torch_output = torch_model(input_data)
allclose(zero_output, torch_output.half())
# zero-dp backward
zero_optimizer.backward(zero_output.mean().float())
# zero-dp backward
zero_optimizer.backward(zero_output.mean().float())
# torch-ddp backward
torch_output.mean().backward()
# torch-ddp backward
torch_output.mean().backward()
# check grad
zero_linear1_grad = zero_model[0].weight.grad
zero_linear2_grad = zero_model[1].weight.grad
torch_linear1_grad = torch_model.module[0].weight.grad
torch_linear2_grad = torch_model.module[1].weight.grad
check_equal(zero_linear1_grad, torch_linear1_grad)
check_equal(zero_linear2_grad, torch_linear2_grad)
# check grad
for oss_param, torch_param in zip(zero_model.parameters(), torch_model.parameters()):
allclose(oss_param.grad, torch_param.grad.half())
# zero-dp step
zero_optimizer.sync_grad()
zero_optimizer.step()
# zero-dp step
zero_optimizer.sync_grad()
zero_optimizer.step()
# torch ddp step
torch_optimizer.step()
# torch ddp step
torch_optimizer.step()
# check updated param
check_equal(zero_model[0].weight, torch_model.module[0].weight)
check_equal(zero_model[1].weight, torch_model.module[1].weight)
# check updated param
for oss_param, torch_param in zip(zero_model.parameters(), torch_model.parameters()):
allclose(oss_param, torch_param.half())
def run_dist(rank, world_size, port):
......
tests/test_zero_data_parallel/test_sharded_optim_with_sync_bn.py
View file @ 526a3180
......@@ -15,6 +15,8 @@ import torch.distributed as dist
def run_dist(rank, world_size, port):
# this test only runs on resnet18
# as this model has sync batch normalization
# need to configure cudnn deterministic so that
# randomness of convolution layers will be disabled
colossalai.launch(config=dict(zero=dict(level=2, partition_grad=True),
......
tests/test_zero_data_parallel/test_state_dict.py
View file @ 526a3180
......@@ -22,8 +22,8 @@ def run_dist(rank, world_size, port):
test_models = ['repeated_computed_layers', 'resnet18']
for model_name in test_models:
get_components_func = non_distributed_component_funcs.get_callable(model_name)
model, train_dataloader, test_dataloader, optimizer, criterion = get_components_func()
model = model()
model_builder, train_dataloader, test_dataloader, optimizer, criterion = get_components_func()
model = model_builder()
shard_strategy = TensorShardStrategy()
model = model.half().cuda()
zero_model = ShardedModelV2(deepcopy(model), shard_strategy)
......
tests/test_zero_data_parallel/test_zero_dev_3.py deleted 100644 → 0
View file @ de464504
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
import copy
from functools import partial
import colossalai
import pytest
import torch
import torch.multiprocessing as mp
import torch.nn as nn
from colossalai.logging import disable_existing_loggers
from colossalai.utils import checkpoint, free_port
from colossalai.zero.sharded_model import ShardedModel
from common import Net, check_grads, check_params, check_params
def checkpoint_wrapper(module, enable=True):
if enable:
module.forward = partial(checkpoint, module.forward)
return module
class Net(nn.Module):
def __init__(self, checkpoint=False) -> None:
super().__init__()
self.fc1 = nn.Linear(5, 5)
self.fc2 = nn.Linear(5, 5)
self.fc3 = nn.Linear(5, 1)
if checkpoint:
self.fc1 = checkpoint_wrapper(self.fc1)
self.layers = [
self.fc1,
self.fc2,
self.fc1,
self.fc2,
self.fc3
]
def forward(self, x):
for layer in self.layers:
x = layer(x)
return x
def run_step(model, optimizer, x, enable_autocast=False):
model.train()
optimizer.zero_grad()
with torch.cuda.amp.autocast(enabled=enable_autocast):
y = model(x)
loss = y.sum()
loss = loss.float()
loss.backward()
optimizer.step()
def decode_booleans(intval, bits):
res = []
for bit in range(bits):
mask = 1 << bit
res.append((intval & mask) == mask)
return res
def check_config(checkpoint=False, fp16=False, offload=False):
model = Net(checkpoint=checkpoint).cuda()
zero_model = copy.deepcopy(model)
offload_config = {}
if offload:
offload_config['device'] = 'cpu'
zero_model = zero_model.cpu()
zero_model = ShardedModel(zero_model, mixed_precision=fp16, offload_config=offload_config)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
zero_optimizer = torch.optim.Adam(zero_model.parameters(), lr=1e-3)
for _ in range(5):
x = torch.rand(2, 5).cuda()
run_step(model, optimizer, x, enable_autocast=fp16)
run_step(zero_model, zero_optimizer, x, enable_autocast=fp16)
check_grads(model, zero_model)
check_params(model, zero_model)
for _ in range(5):
x = torch.rand(2, 5).cuda()
run_step(model, optimizer, x, enable_autocast=False)
run_step(zero_model, zero_optimizer, x, enable_autocast=False)
check_grads(model, zero_model, loose=True)
check_params(model, zero_model, loose=True)
def run_dist(rank, world_size, port):
disable_existing_loggers()
colossalai.launch(config={},
rank=rank,
world_size=world_size,
host='localhost',
port=port,
backend='nccl')
args = ['checkpoint', 'fp16', 'offload']
def pack_args(i):
booleans = decode_booleans(i, len(args))
return {arg: booleans[idx] for idx, arg in enumerate(args)}
for j in range(2 ** len(args)):
kwargs = pack_args(j)
print(kwargs)
check_config(**kwargs)
@pytest.mark.dist
def test_zero_level_3():
world_size = 1
run_func = partial(run_dist, world_size=world_size, port=free_port())
mp.spawn(run_func, nprocs=world_size)
if __name__ == '__main__':
test_zero_level_3()
tests/test_zero_data_parallel/test_zero_dev_3_mp4.py deleted 100644 → 0
View file @ de464504
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
import copy
from functools import partial
import colossalai
import pytest
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
import torch.nn as nn
from colossalai.logging import disable_existing_loggers
from colossalai.utils import checkpoint, free_port
from colossalai.zero.sharded_model import ShardedModel
from torch.nn.parallel import DistributedDataParallel as DDP
from common import Net, check_grads_padding, check_params_padding
def run_step(model, optimizer, x, enable_autocast=False):
model.train()
optimizer.zero_grad()
with torch.cuda.amp.autocast(enabled=enable_autocast):
y = model(x)
loss = y.sum()
loss = loss.float()
loss.backward()
optimizer.step()
def decode_booleans(intval, bits):
res = []
for bit in range(bits):
mask = 1 << bit
res.append((intval & mask) == mask)
return res
def check_config(checkpoint=False, fp16=False, offload=False):
model = Net(checkpoint=checkpoint).cuda()
zero_model = copy.deepcopy(model)
ddp_model = DDP(model)
offload_config = {}
if offload:
offload_config['device'] = 'cpu'
zero_model = zero_model.cpu()
zero_model = ShardedModel(zero_model, mixed_precision=fp16, offload_config=offload_config)
optimizer = torch.optim.Adam(ddp_model.parameters(), lr=1e-3)
zero_optimizer = torch.optim.Adam(zero_model.parameters(), lr=1e-3)
for _ in range(5):
x = torch.rand(2, 5).cuda()
run_step(ddp_model, optimizer, x, enable_autocast=fp16)
run_step(zero_model, zero_optimizer, x, enable_autocast=fp16)
check_grads_padding(ddp_model, zero_model)
check_params_padding(ddp_model, zero_model)
for _ in range(5):
x = torch.rand(2, 5).cuda()
run_step(ddp_model, optimizer, x, enable_autocast=False)
run_step(zero_model, zero_optimizer, x, enable_autocast=False)
check_grads_padding(ddp_model, zero_model, loose=True)
check_params_padding(ddp_model, zero_model, loose=True)
def run_dist(rank, world_size, port):
disable_existing_loggers()
colossalai.launch(config={},
rank=rank,
world_size=world_size,
host='localhost',
port=port,
backend='nccl')
args = ['checkpoint', 'fp16', 'offload']
def pack_args(i):
booleans = decode_booleans(i, len(args))
return {arg: booleans[idx] for idx, arg in enumerate(args)}
for j in range(2 ** len(args)):
kwargs = pack_args(j)
if dist.get_rank() == 0:
print(kwargs)
check_config(**kwargs)
@pytest.mark.dist
def test_zero_level_3():
world_size = 4
run_func = partial(run_dist, world_size=world_size, port=free_port())
mp.spawn(run_func, nprocs=world_size)
if __name__ == '__main__':
test_zero_level_3()
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