mirror of https://github.com/hpcaitech/ColossalAI
73 lines
2.9 KiB
Python
73 lines
2.9 KiB
Python
from functools import partial
|
|
|
|
import pytest
|
|
import torch
|
|
import torch.multiprocessing as mp
|
|
from common import CONFIG
|
|
|
|
import colossalai
|
|
from colossalai.nn.optimizer import HybridAdam
|
|
from colossalai.testing import parameterize, rerun_if_address_is_in_use
|
|
from colossalai.utils import free_port
|
|
from colossalai.utils.cuda import get_current_device
|
|
from colossalai.zero.legacy.init_ctx import ZeroInitContext
|
|
from colossalai.zero.legacy.shard_utils import BucketTensorShardStrategy
|
|
from colossalai.zero.legacy.sharded_model import ShardedModelV2
|
|
from colossalai.zero.legacy.sharded_optim import ShardedOptimizerV2
|
|
from colossalai.zero.low_level._utils import has_inf_or_nan
|
|
from tests.components_to_test.registry import non_distributed_component_funcs
|
|
from tests.test_zero.test_sharded_optim_v2 import _run_step
|
|
|
|
|
|
@parameterize("cpu_offload", [True, False])
|
|
@parameterize("shard_strategy_class", [BucketTensorShardStrategy])
|
|
@parameterize("gpu_margin_mem_ratio", [0.0, 0.7])
|
|
def _run_test_found_inf(cpu_offload, shard_strategy_class, gpu_margin_mem_ratio):
|
|
test_models = ['repeated_computed_layers']
|
|
shard_strategy = shard_strategy_class()
|
|
|
|
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()
|
|
|
|
with ZeroInitContext(target_device=torch.device(f'cpu:0') if cpu_offload else get_current_device(),
|
|
shard_strategy=shard_strategy,
|
|
shard_param=True):
|
|
zero_model = model_builder(checkpoint=True)
|
|
zero_model = ShardedModelV2(
|
|
zero_model,
|
|
shard_strategy,
|
|
tensor_placement_policy='cpu' if cpu_offload else 'cuda',
|
|
reuse_fp16_shard=True,
|
|
)
|
|
|
|
sharded_optim = HybridAdam(zero_model.parameters(), lr=1e-3)
|
|
sharded_optim = ShardedOptimizerV2(zero_model, sharded_optim, gpu_margin_mem_ratio=gpu_margin_mem_ratio)
|
|
|
|
for i, (data, label) in enumerate(train_dataloader):
|
|
if i > 1:
|
|
break
|
|
assert zero_model.overflow_counter == 0
|
|
data, label = data.cuda(), label.cuda()
|
|
_run_step(zero_model, sharded_optim, data, label, criterion, False)
|
|
for param in zero_model.parameters():
|
|
assert not has_inf_or_nan(param.colo_attr.data_payload)
|
|
|
|
|
|
def _run_dist(rank, world_size, port):
|
|
colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
|
|
_run_test_found_inf()
|
|
|
|
|
|
# use_cpuadam = True can be used with cpu_offload = False
|
|
@pytest.mark.dist
|
|
@pytest.mark.parametrize("world_size", [1, 2])
|
|
@rerun_if_address_is_in_use()
|
|
def test_found_inf(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_found_inf(world_size=2)
|