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[test] optimized zero data parallel test (#452)

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This commit is contained in:
Frank Lee
2022-03-18 11:35:54 +08:00
committed by GitHub
parent cfcc8271f3
commit f27d801a13
7 changed files with 81 additions and 141 deletions
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30
tests/test_zero_data_parallel/test_shard_model_v2.py
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@@ -20,12 +20,13 @@ from tests.components_to_test.registry import non_distributed_component_funcs
from torch.nn.parallel import DistributedDataParallel as DDP
from common import CONFIG, check_grads_padding, run_fwd_bwd
from colossalai.testing import parameterize
def run_dist(rank, world_size, port, use_zero_init_ctx, enable_autocast, shard_strategy):
colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
logger = get_dist_logger()
logger.set_level('DEBUG')
@parameterize("enable_autocast", [True])
@parameterize("use_zero_init_ctx", [True])
@parameterize("shard_strategy", [TensorShardStrategy, BucketTensorShardStrategy])
def run_model_test(enable_autocast, use_zero_init_ctx, shard_strategy, logger):
test_models = ['repeated_computed_layers', 'resnet18', 'bert']
shard_strategy = shard_strategy()
for model_name in test_models:
@@ -66,20 +67,19 @@ def run_dist(rank, world_size, port, use_zero_init_ctx, enable_autocast, shard_s
# logger.debug('model cuda ', zero_model._memstats_collector._model_data_cuda)
def run_dist(rank, world_size, port):
colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
logger = get_dist_logger()
logger.set_level('DEBUG')
run_model_test(logger=logger)
@pytest.mark.dist
@pytest.mark.parametrize("world_size", [1, 2])
@pytest.mark.parametrize("enable_autocast", [True])
@pytest.mark.parametrize("use_zero_init_ctx", [True])
@pytest.mark.parametrize("shard_strategy", [TensorShardStrategy, BucketTensorShardStrategy])
def test_shard_model_v2(world_size, use_zero_init_ctx, enable_autocast, shard_strategy):
run_func = partial(run_dist,
world_size=world_size,
port=free_port(),
use_zero_init_ctx=use_zero_init_ctx,
enable_autocast=enable_autocast,
shard_strategy=shard_strategy)
def test_shard_model_v2(world_size):
run_func = partial(run_dist, world_size=world_size, port=free_port())
mp.spawn(run_func, nprocs=world_size)
if __name__ == '__main__':
test_shard_model_v2(world_size=2, use_zero_init_ctx=True, enable_autocast=True, shard_strategy=TensorShardStrategy)
test_shard_model_v2(world_size=2)