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.
98 lines
3.6 KiB
98 lines
3.6 KiB
import pytest
|
|
import torch
|
|
import torch.distributed as dist
|
|
|
|
import colossalai
|
|
from colossalai.booster import Booster
|
|
from colossalai.booster.plugin import LowLevelZeroPlugin
|
|
from colossalai.booster.plugin.low_level_zero_plugin import LowLevelZeroModel
|
|
from colossalai.moe.manager import MOE_MANAGER
|
|
from colossalai.tensor.moe_tensor.api import is_moe_tensor
|
|
from colossalai.testing import rerun_if_address_is_in_use, spawn
|
|
from tests.test_moe.moe_utils import MoeModel
|
|
|
|
|
|
def run_fwd_bwd(model, data, label, criterion, optimizer, enable_autocast=False):
|
|
model.train()
|
|
with torch.cuda.amp.autocast(enabled=enable_autocast):
|
|
if criterion:
|
|
y = model(data)
|
|
loss = criterion(y, label)
|
|
else:
|
|
loss = model(data, label)
|
|
loss = loss.float()
|
|
|
|
if isinstance(model, LowLevelZeroModel):
|
|
optimizer.backward(loss / 2)
|
|
else:
|
|
loss.backward()
|
|
return y
|
|
|
|
|
|
def run_zero_optim_test(local_rank, world_size, stage=1):
|
|
criterion = torch.nn.CrossEntropyLoss()
|
|
data = torch.randn(16, 4).cuda()
|
|
label = torch.randint(0, 4, (16,)).cuda()
|
|
|
|
MOE_MANAGER.__init__()
|
|
MOE_MANAGER.setup(parallel=None)
|
|
torch_model = MoeModel()
|
|
torch_optimizer = torch.optim.Adam(torch_model.parameters())
|
|
torch_model = torch_model.cuda()
|
|
|
|
MOE_MANAGER.__init__()
|
|
MOE_MANAGER.setup(max_ep_size=2, use_ep_inside=False, parallel="EP")
|
|
zero_model = MoeModel()
|
|
extra_dp_group = MOE_MANAGER.parallel_info_dict[2].dp_group
|
|
ep_rank = dist.get_rank(MOE_MANAGER.parallel_info_dict[2].ep_group)
|
|
ep_size = MOE_MANAGER.parallel_info_dict[2].ep_size
|
|
for zero_param, torch_param in zip(zero_model.parameters(), torch_model.parameters()):
|
|
if is_moe_tensor(zero_param):
|
|
num_expert = torch_param.data.shape[0]
|
|
zero_param.data.copy_(
|
|
torch_param.data[ep_rank * (num_expert // ep_size) : (ep_rank + 1) * (num_expert // ep_size)]
|
|
.detach()
|
|
.clone()
|
|
)
|
|
else:
|
|
zero_param.data.copy_(torch_param.data.detach().clone())
|
|
zero_optimizer = torch.optim.Adam(zero_model.parameters())
|
|
plugin = LowLevelZeroPlugin(stage=stage, precision="fp32")
|
|
plugin.zero_optim_kwargs["moe_extra_dp_process_group"] = extra_dp_group
|
|
booster = Booster(plugin=plugin)
|
|
zero_model, zero_optimizer, _, _, _ = booster.boost(zero_model, zero_optimizer)
|
|
|
|
run_fwd_bwd(torch_model, data, label, criterion, None)
|
|
torch_optimizer.step()
|
|
run_fwd_bwd(zero_model, data, label, criterion, zero_optimizer)
|
|
zero_optimizer.step()
|
|
|
|
for (torch_name, torch_param), (zero_name, zero_param) in zip(
|
|
torch_model.named_parameters(), zero_model.named_parameters()
|
|
):
|
|
if is_moe_tensor(zero_param):
|
|
num_expert = torch_param.data.shape[0]
|
|
torch_param.data = torch_param.data[
|
|
ep_rank * (num_expert // ep_size) : (ep_rank + 1) * (num_expert // ep_size)
|
|
]
|
|
assert torch.allclose(
|
|
torch_param.data, zero_param.data, atol=1e-4
|
|
), f"{torch_name}\ntorch_param {torch_param.data}\nzero_param {zero_param.data}"
|
|
|
|
|
|
def run_dist(rank, world_size, port):
|
|
colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
|
|
run_zero_optim_test(rank, world_size, stage=1)
|
|
run_zero_optim_test(rank, world_size, stage=2)
|
|
|
|
|
|
@pytest.mark.dist
|
|
@pytest.mark.parametrize("world_size", [4])
|
|
@rerun_if_address_is_in_use()
|
|
def test_moe_zero_optim(world_size):
|
|
spawn(run_dist, world_size)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test_moe_zero_optim(world_size=4)
|