mirror of https://github.com/hpcaitech/ColossalAI
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
115 lines
4.0 KiB
115 lines
4.0 KiB
from functools import partial
|
|
|
|
import colossalai
|
|
import pytest
|
|
import torch
|
|
import torch.multiprocessing as mp
|
|
import torch.nn as nn
|
|
from colossalai.nn import CheckpointModule
|
|
from colossalai.logging import get_dist_logger
|
|
from colossalai.testing import parameterize
|
|
from colossalai.utils import free_port
|
|
from colossalai.context import MOE_CONTEXT
|
|
from colossalai.nn.layer import MoeModule
|
|
from colossalai.zero.init_ctx import ZeroInitContext
|
|
from colossalai.zero.shard_utils import (BucketTensorShardStrategy, TensorShardStrategy)
|
|
|
|
from colossalai.testing import rerun_if_address_is_in_use
|
|
from colossalai.utils import get_current_device
|
|
from tests.test_zero.common import CONFIG
|
|
|
|
|
|
class MoeModel(nn.Module):
|
|
|
|
def __init__(self, checkpoint: bool = False):
|
|
|
|
class TestSubModule(CheckpointModule):
|
|
|
|
def __init__(self):
|
|
super().__init__(checkpoint)
|
|
expert_cls = nn.Linear
|
|
expert_args_dict = dict(in_features=16, out_features=16)
|
|
self.moe = MoeModule(dim_model=16,
|
|
num_experts=8,
|
|
use_residual=True,
|
|
expert_cls=expert_cls,
|
|
**expert_args_dict)
|
|
self.proj = nn.Linear(16, 4)
|
|
|
|
def _forward(self, x):
|
|
x, y = self.moe(x)
|
|
x = self.proj(x)
|
|
return x, y
|
|
|
|
super().__init__()
|
|
self.test_embed = nn.Linear(4, 16)
|
|
self.test_transform = TestSubModule()
|
|
|
|
def forward(self, x):
|
|
MOE_CONTEXT.reset_loss()
|
|
|
|
x = self.test_embed(x)
|
|
x, y = self.test_transform(x)
|
|
|
|
MOE_CONTEXT.add_loss(y)
|
|
return x
|
|
|
|
|
|
@parameterize("init_device_type", ['cpu', 'cuda'])
|
|
@parameterize("shard_strategy_class", [TensorShardStrategy, BucketTensorShardStrategy])
|
|
def run_moe_zero_init(init_device_type, shard_strategy_class):
|
|
logger = get_dist_logger("test_moe_zero_init")
|
|
|
|
if init_device_type == 'cuda':
|
|
init_device = get_current_device()
|
|
elif init_device_type == 'cpu':
|
|
init_device = torch.device("cpu")
|
|
else:
|
|
raise NotImplementedError("Unknown device found.")
|
|
|
|
model_numel_tensor = torch.zeros(1, dtype=torch.int)
|
|
with ZeroInitContext(target_device=init_device,
|
|
shard_strategy=shard_strategy_class(),
|
|
shard_param=True,
|
|
model_numel_tensor=model_numel_tensor):
|
|
model = MoeModel(checkpoint=True)
|
|
|
|
for name, param in model.named_parameters():
|
|
assert hasattr(param, 'colo_attr')
|
|
|
|
# the parameters in moe experts and its gate should not be sharded
|
|
if ('experts' in name) or ('gate' in name) or ('residual_combine' in name):
|
|
assert not param.colo_attr.sharded_data_tensor.is_sharded, "`{}` parameter has problem".format(name)
|
|
else:
|
|
assert param.colo_attr.sharded_data_tensor.is_sharded
|
|
|
|
# the parameters in moe experts is not replicated
|
|
if 'experts' in name:
|
|
assert not param.colo_attr.is_replicated
|
|
else:
|
|
assert param.colo_attr.is_replicated
|
|
|
|
if param.colo_attr.param_is_sharded:
|
|
assert param.colo_attr.data_payload.device.type == init_device.type, \
|
|
f'{param.colo_attr.data_payload.device.type} vs. {init_device.type}'
|
|
else:
|
|
assert param.colo_attr.data_payload.device.type == 'cuda'
|
|
|
|
|
|
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_moe_zero_init()
|
|
|
|
|
|
@pytest.mark.dist
|
|
@pytest.mark.parametrize("world_size", [2, 4])
|
|
@rerun_if_address_is_in_use()
|
|
def test_moe_zero_init(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_init(world_size=2)
|