mirror of https://github.com/hpcaitech/ColossalAI
76 lines
3.0 KiB
Python
76 lines
3.0 KiB
Python
from functools import partial
|
|
|
|
import colossalai
|
|
import pytest
|
|
import torch
|
|
import torch.multiprocessing as mp
|
|
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, TensorShardStrategy)
|
|
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
|
|
from colossalai.engine.gradient_handler import MoeGradientHandler
|
|
from colossalai.context import MOE_CONTEXT
|
|
from colossalai.testing import assert_equal_in_group
|
|
|
|
from tests.test_zero.common import CONFIG, check_grads_padding, run_fwd_bwd
|
|
from tests.test_moe.test_moe_zero_init import MoeModel
|
|
|
|
|
|
@parameterize("enable_autocast", [False])
|
|
@parameterize("shard_strategy_class", [TensorShardStrategy, BucketTensorShardStrategy])
|
|
def run_model_test(enable_autocast, shard_strategy_class):
|
|
shard_strategy = shard_strategy_class()
|
|
|
|
get_components_func = non_distributed_component_funcs.get_callable('no_leaf_module')
|
|
_, 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 = MoeModel(checkpoint=True)
|
|
zero_model = ShardedModelV2(zero_model, shard_strategy)
|
|
|
|
# check whether parameters are identical in ddp
|
|
for name, p in zero_model.named_parameters():
|
|
if not p.colo_attr.param_is_sharded and p.colo_attr.is_replicated:
|
|
assert_equal_in_group(p.colo_attr.data_payload)
|
|
|
|
model = MoeModel(checkpoint=True).half()
|
|
col_model_deepcopy(zero_model, model)
|
|
model = model.cuda()
|
|
grad_handler = MoeGradientHandler(model)
|
|
|
|
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)
|
|
grad_handler.handle_gradient()
|
|
|
|
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')
|
|
MOE_CONTEXT.setup(seed=42)
|
|
MOE_CONTEXT.reset_loss()
|
|
run_model_test()
|
|
|
|
|
|
@pytest.mark.dist
|
|
@pytest.mark.parametrize("world_size", [2])
|
|
@rerun_if_address_is_in_use()
|
|
def test_moe_zero_model(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_moe_zero_model(world_size=2)
|