ColossalAI/tests/test_zero/test_shard_model_v2.py

70 lines
2.6 KiB
Python

#!/usr/bin/env python
# -*- encoding: utf-8 -*-
from functools import partial
import pytest
import torch
import torch.multiprocessing as mp
from common import CONFIG, check_grads_padding, run_fwd_bwd
from torch.nn.parallel import DistributedDataParallel as DDP
import colossalai
from colossalai.testing import parameterize, rerun_if_address_is_in_use
from colossalai.utils import free_port
from colossalai.zero.init_ctx import ZeroInitContext
from colossalai.zero.shard_utils import BucketTensorShardStrategy
from colossalai.zero.sharded_model import ShardedModelV2
from colossalai.zero.sharded_model._utils import cast_tensor_to_fp16
from colossalai.zero.sharded_model.utils import col_model_deepcopy
from tests.components_to_test.registry import non_distributed_component_funcs
@parameterize("enable_autocast", [True])
@parameterize("shard_strategy_class", [BucketTensorShardStrategy])
def run_model_test(enable_autocast, shard_strategy_class):
test_models = ['repeated_computed_layers', 'resnet18', 'bert', 'no_leaf_module']
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, _, _, criterion = get_components_func()
with ZeroInitContext(target_device=torch.device('cuda', torch.cuda.current_device()),
shard_strategy=shard_strategy,
shard_param=True):
zero_model = model_builder(checkpoint=True)
zero_model = ShardedModelV2(zero_model, shard_strategy)
model = model_builder(checkpoint=True).half()
col_model_deepcopy(zero_model, model)
model = model.cuda()
model = DDP(model, device_ids=[torch.cuda.current_device()])
for i, (data, label) in enumerate(train_dataloader):
if i > 5:
break
data, label = cast_tensor_to_fp16(data).cuda(), label.cuda()
run_fwd_bwd(model, data, label, criterion, enable_autocast)
run_fwd_bwd(zero_model, data, label, criterion, enable_autocast)
check_grads_padding(model, zero_model, loose=True)
def run_dist(rank, world_size, port):
colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
run_model_test()
@pytest.mark.dist
@pytest.mark.parametrize("world_size", [1, 2])
@rerun_if_address_is_in_use()
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)