diff --git a/colossalai/initialize.py b/colossalai/initialize.py
index e907efdde..f3719dcb4 100644
--- a/colossalai/initialize.py
+++ b/colossalai/initialize.py
@@ -15,26 +15,25 @@ from torch.optim.lr_scheduler import _LRScheduler
 from torch.optim.optimizer import Optimizer
 from torch.utils.data import DataLoader
 
-from colossalai.core import global_context as gpc
-from colossalai.context.moe_context import MOE_CONTEXT
-
-from colossalai.logging import get_dist_logger
-
-from colossalai.engine.schedule import NonPipelineSchedule, PipelineSchedule, InterleavedPipelineSchedule, get_tensor_shape
-from colossalai.engine import Engine
-from colossalai.gemini.ophooks import BaseOpHook
-
-from colossalai.utils import (get_current_device, is_using_ddp, is_using_pp, is_using_sequence, sync_model_param)
-from colossalai.utils.moe import sync_moe_model_param
-
 from colossalai.amp import AMP_TYPE, convert_to_amp
 from colossalai.amp.naive_amp import NaiveAMPModel
 from colossalai.builder.builder import build_gradient_handler
 from colossalai.context import Config, ConfigException, ParallelMode
+from colossalai.context.moe_context import MOE_CONTEXT
+from colossalai.core import global_context as gpc
+from colossalai.engine import Engine
 from colossalai.engine.gradient_accumulation import accumulate_gradient
-
+from colossalai.engine.schedule import (
+    InterleavedPipelineSchedule,
+    NonPipelineSchedule,
+    PipelineSchedule,
+    get_tensor_shape,
+)
+from colossalai.gemini.ophooks import BaseOpHook
+from colossalai.logging import get_dist_logger
 from colossalai.nn.optimizer.colossalai_optimizer import ColossalaiOptimizer
-
+from colossalai.utils import get_current_device, is_using_ddp, is_using_pp, is_using_sequence, sync_model_param
+from colossalai.utils.moe import sync_moe_model_param
 from colossalai.zero import convert_to_zero_v2
 from colossalai.zero.sharded_optim.sharded_optim_v2 import ShardedOptimizerV2
 
@@ -301,9 +300,9 @@ def initialize(model: nn.Module,
             model = model().to(get_current_device())
 
         # optimizer maybe a optimizer_cls
-        logger.warning("Initializing an non ZeRO model with optimizer class")
         if isinstance(optimizer, Callable):
             optimizer = optimizer(model.parameters())
+            logger.warning("Initializing an non ZeRO model with optimizer class")
 
     if not use_zero:
         if is_using_sequence():