ColossalAI/tests/test_moe/test_moe_zero_model.py

78 lines
3.1 KiB
Python

from functools import partial
import colossalai
import pytest
import torch
import torch.multiprocessing as mp
from colossalai.nn import MoeLoss
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, _, optimizer_class, _ = get_components_func()
criterion = MoeLoss(aux_weight=0.01, loss_fn=torch.nn.CrossEntropyLoss)
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)
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)