|
|
|
from functools import partial
|
|
|
|
|
|
|
|
import colossalai
|
|
|
|
import pytest
|
|
|
|
import torch.multiprocessing as mp
|
|
|
|
from colossalai.amp import AMP_TYPE
|
|
|
|
from colossalai.context import Config
|
|
|
|
from colossalai.core import global_context as gpc
|
|
|
|
from colossalai.utils import free_port
|
|
|
|
from tests.components_to_test.registry import non_distributed_component_funcs
|
|
|
|
|
|
|
|
CONFIG = dict(parallel=dict(pipeline=dict(size=1), tensor=dict(size=1, mode=None)),
|
|
|
|
fp16=dict(mode=None),
|
|
|
|
clip_grad_norm=1.0)
|
|
|
|
|
|
|
|
|
|
|
|
def run_train():
|
|
|
|
test_models = ['repeated_computed_layers', 'resnet18', 'repeated_computed_layers']
|
|
|
|
# FIXME: test bert
|
|
|
|
for model_name in test_models:
|
|
|
|
get_components_func = non_distributed_component_funcs.get_callable(model_name)
|
|
|
|
model_builder, train_dataloader, _, optimizer_class, criterion = get_components_func()
|
|
|
|
|
|
|
|
model = model_builder(checkpoint=False)
|
|
|
|
engine, train_dataloader, *args = colossalai.initialize(model=model,
|
|
|
|
optimizer=optimizer_class(model.parameters(), lr=1e-3),
|
|
|
|
criterion=criterion,
|
|
|
|
train_dataloader=train_dataloader)
|
|
|
|
|
|
|
|
try:
|
|
|
|
engine.train()
|
|
|
|
for data, label in train_dataloader:
|
|
|
|
engine.zero_grad()
|
|
|
|
data = data.cuda()
|
|
|
|
label = label.cuda()
|
|
|
|
if criterion:
|
|
|
|
output = engine(data)
|
|
|
|
loss = engine.criterion(output, label)
|
|
|
|
else:
|
|
|
|
loss = engine(data, label)
|
|
|
|
engine.backward(loss)
|
|
|
|
engine.step()
|
|
|
|
break
|
|
|
|
except IndexError:
|
|
|
|
# if using apex amp, NetWithRepeatedlyComputedLayers will raise an index out of range issue
|
|
|
|
# the following check fails in apex
|
|
|
|
# if cached_x.grad_fn.next_functions[1][0].variable is not x:
|
|
|
|
continue
|
|
|
|
|
|
|
|
|
|
|
|
def run_with_no_amp():
|
|
|
|
run_train()
|
|
|
|
|
|
|
|
|
|
|
|
def run_with_torch_amp():
|
|
|
|
# hack config
|
|
|
|
CONFIG['fp16']['mode'] = AMP_TYPE.TORCH
|
|
|
|
gpc._config = Config(CONFIG)
|
|
|
|
run_train()
|
|
|
|
|
|
|
|
|
|
|
|
def run_with_apex_amp():
|
|
|
|
# hack config
|
|
|
|
CONFIG['fp16']['mode'] = AMP_TYPE.APEX
|
|
|
|
gpc._config = Config(CONFIG)
|
|
|
|
run_train()
|
|
|
|
|
|
|
|
|
|
|
|
def run_with_naive_amp():
|
|
|
|
# hack config
|
|
|
|
CONFIG['fp16']['mode'] = AMP_TYPE.NAIVE
|
|
|
|
gpc._config = Config(CONFIG)
|
|
|
|
run_train()
|
|
|
|
|
|
|
|
|
|
|
|
def run_engine(rank, world_size, port):
|
|
|
|
# init dist env
|
|
|
|
colossalai.launch(config=dict(), rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
|
|
|
|
run_with_no_amp()
|
|
|
|
run_with_torch_amp()
|
|
|
|
run_with_apex_amp()
|
|
|
|
run_with_naive_amp()
|
|
|
|
|
|
|
|
|
|
|
|
@pytest.mark.dist
|
|
|
|
def test_engine():
|
|
|
|
world_size = 2
|
|
|
|
run_func = partial(run_engine, world_size=world_size, port=free_port())
|
|
|
|
mp.spawn(run_func, nprocs=world_size)
|
|
|
|
|
|
|
|
|
|
|
|
if __name__ == '__main__':
|
|
|
|
test_engine()
|