diff --git a/applications/ColossalMoE/train.py b/applications/ColossalMoE/train.py
index c567038ec252..99603282baf3 100644
--- a/applications/ColossalMoE/train.py
+++ b/applications/ColossalMoE/train.py
@@ -238,7 +238,6 @@ def main():
                         lambda x, y: x.loss,
                         optimizer,
                         return_loss=True,
-                        return_outputs=True,
                     )
                     # Backward and optimize
                     if is_pp_last_stage:
diff --git a/colossalai/booster/plugin/hybrid_parallel_plugin.py b/colossalai/booster/plugin/hybrid_parallel_plugin.py
index c37a6b4df72d..f51cb060c356 100644
--- a/colossalai/booster/plugin/hybrid_parallel_plugin.py
+++ b/colossalai/booster/plugin/hybrid_parallel_plugin.py
@@ -1183,6 +1183,9 @@ def execute_pipeline(
     ) -> dict:
         assert self.enable_pipeline_parallelism, "pipeline parallelism is not enabled"
 
+        if return_outputs:
+            warnings.warn("return_outputs may lead to significant extra memory consumption.")
+
         # Create a context for gradient synchronization based on the optimizer type.
         # If it's a HybridParallelZeroOptimizer, use optimizer.no_sync(); otherwise, use model.no_sync().
         # This is to avoid redundant gradient reduction in pipeline parallelism (multiple microbatch values should be reduced once),
diff --git a/colossalai/pipeline/schedule/one_f_one_b.py b/colossalai/pipeline/schedule/one_f_one_b.py
index bf2f01b10e9b..58008b98f24e 100644
--- a/colossalai/pipeline/schedule/one_f_one_b.py
+++ b/colossalai/pipeline/schedule/one_f_one_b.py
@@ -7,7 +7,7 @@
 from torch.utils._pytree import tree_map
 
 from colossalai.accelerator import get_accelerator
-from colossalai.interface import ModelWrapper, OptimizerWrapper
+from colossalai.interface import OptimizerWrapper
 from colossalai.pipeline.p2p import PipelineP2PCommunication, create_send_metadata
 from colossalai.pipeline.stage_manager import PipelineStageManager
 from colossalai.utils import get_current_device
@@ -327,9 +327,7 @@ def run_forward_only(
             self.send_forward(output_obj)
 
         if outputs is not None:
-            if isinstance(model, ModelWrapper):
-                model = model.unwrap()
-            outputs = merge_batch(outputs, getattr(model, "batch_size_dim", 0))
+            outputs = merge_batch(outputs)
         return {"loss": accum_loss, "outputs": outputs}
 
     def run_forward_backward(
@@ -412,9 +410,7 @@ def run_forward_backward(
         assert all(len(v) == 0 for v in input_objs) and all(len(v) == 0 for v in output_objs)
 
         if outputs is not None:
-            if isinstance(model, ModelWrapper):
-                model = model.unwrap()
-            outputs = merge_batch(outputs, getattr(model, "batch_size_dim", 0))
+            outputs = merge_batch(outputs)
         return {"loss": accum_loss, "outputs": outputs}
 
     def forward_backward_step(
diff --git a/docs/source/en/advanced_tutorials/train_gpt_using_hybrid_parallelism.md b/docs/source/en/advanced_tutorials/train_gpt_using_hybrid_parallelism.md
index e87eafb6eec7..0133dfd86ddf 100644
--- a/docs/source/en/advanced_tutorials/train_gpt_using_hybrid_parallelism.md
+++ b/docs/source/en/advanced_tutorials/train_gpt_using_hybrid_parallelism.md
@@ -178,7 +178,7 @@ def train_epoch(
         for _ in pbar:
             if use_pipeline:
                 outputs = booster.execute_pipeline(
-                    train_dataloader_iter, model, _criterion, optimizer, return_loss=True, return_outputs=True
+                    train_dataloader_iter, model, _criterion, optimizer, return_loss=True
                 )
                 # Backward and optimize
                 if is_pp_last_stage:
diff --git a/docs/source/en/advanced_tutorials/train_vit_with_hybrid_parallelism.md b/docs/source/en/advanced_tutorials/train_vit_with_hybrid_parallelism.md
index 93fed61c34da..dfc2cd596d79 100644
--- a/docs/source/en/advanced_tutorials/train_vit_with_hybrid_parallelism.md
+++ b/docs/source/en/advanced_tutorials/train_vit_with_hybrid_parallelism.md
@@ -231,7 +231,7 @@ def run_forward_backward(
     if isinstance(booster.plugin, HybridParallelPlugin) and booster.plugin.pp_size > 1:
         # run pipeline forward backward when enabling pp in hybrid parallel plugin
         output_dict = booster.execute_pipeline(
-            data_iter, model, criterion, optimizer, return_loss=True, return_outputs=True
+            data_iter, model, criterion, optimizer, return_loss=True
         )
         loss, outputs = output_dict["loss"], output_dict["outputs"]
     else:
diff --git a/docs/source/en/features/pipeline_parallel.md b/docs/source/en/features/pipeline_parallel.md
index 31b20335e529..d6f3cdfafe16 100644
--- a/docs/source/en/features/pipeline_parallel.md
+++ b/docs/source/en/features/pipeline_parallel.md
@@ -198,8 +198,7 @@ def train_epoch(epoch: int, model: nn.Module, optimizer: Optimizer, _criterion:
                                                 model,
                                                 _criterion,
                                                 optimizer,
-                                                return_loss=True,
-                                                return_outputs=True)
+                                                return_loss=True)
             # Backward and optimize
             if is_pp_last_stage:
                 loss = outputs['loss']
diff --git a/docs/source/en/features/shardformer.md b/docs/source/en/features/shardformer.md
index 1e633ebc0f7c..672945ea2336 100644
--- a/docs/source/en/features/shardformer.md
+++ b/docs/source/en/features/shardformer.md
@@ -271,7 +271,7 @@ However, if pipeline parallel is enabled, there are several usages different fro
 3. Do forward and backward passing through calling `Booster.execute_pipeline` method:
     ```python
     outputs = booster.execute_pipeline(
-        train_dataloader_iter, model, _criterion, optimizer, return_loss=True, return_outputs=True
+        train_dataloader_iter, model, _criterion, optimizer, return_loss=True
     )
     ```
     Backward passing has been completed by this method, so there is no need to call `loss.backward()` after executing this method.
diff --git a/docs/source/zh-Hans/advanced_tutorials/train_gpt_using_hybrid_parallelism.md b/docs/source/zh-Hans/advanced_tutorials/train_gpt_using_hybrid_parallelism.md
index ae941b489b90..cf7d191723e1 100644
--- a/docs/source/zh-Hans/advanced_tutorials/train_gpt_using_hybrid_parallelism.md
+++ b/docs/source/zh-Hans/advanced_tutorials/train_gpt_using_hybrid_parallelism.md
@@ -175,7 +175,7 @@ def train_epoch(
         for _ in pbar:
             if use_pipeline:
                 outputs = booster.execute_pipeline(
-                    train_dataloader_iter, model, _criterion, optimizer, return_loss=True, return_outputs=True
+                    train_dataloader_iter, model, _criterion, optimizer, return_loss=True
                 )
                 # Backward and optimize
                 if is_pp_last_stage:
diff --git a/docs/source/zh-Hans/advanced_tutorials/train_vit_with_hybrid_parallelism.md b/docs/source/zh-Hans/advanced_tutorials/train_vit_with_hybrid_parallelism.md
index 3de41601a231..92775bafb22b 100644
--- a/docs/source/zh-Hans/advanced_tutorials/train_vit_with_hybrid_parallelism.md
+++ b/docs/source/zh-Hans/advanced_tutorials/train_vit_with_hybrid_parallelism.md
@@ -234,7 +234,7 @@ def run_forward_backward(
     if isinstance(booster.plugin, HybridParallelPlugin) and booster.plugin.pp_size > 1:
         # run pipeline forward backward when enabling pp in hybrid parallel plugin
         output_dict = booster.execute_pipeline(
-            data_iter, model, criterion, optimizer, return_loss=True, return_outputs=True
+            data_iter, model, criterion, optimizer, return_loss=True
         )
         loss, outputs = output_dict["loss"], output_dict["outputs"]
     else:
diff --git a/docs/source/zh-Hans/features/pipeline_parallel.md b/docs/source/zh-Hans/features/pipeline_parallel.md
index e688020556d8..38e1fbfc56b4 100644
--- a/docs/source/zh-Hans/features/pipeline_parallel.md
+++ b/docs/source/zh-Hans/features/pipeline_parallel.md
@@ -193,8 +193,7 @@ def train_epoch(epoch: int, model: nn.Module, optimizer: Optimizer, _criterion:
                                                 model,
                                                 _criterion,
                                                 optimizer,
-                                                return_loss=True,
-                                                return_outputs=True)
+                                                return_loss=True)
             # Backward and optimize
             if is_pp_last_stage:
                 loss = outputs['loss']
diff --git a/docs/source/zh-Hans/features/shardformer.md b/docs/source/zh-Hans/features/shardformer.md
index 972c48b0c7a2..a7bcbd9f2816 100644
--- a/docs/source/zh-Hans/features/shardformer.md
+++ b/docs/source/zh-Hans/features/shardformer.md
@@ -264,7 +264,7 @@ elif args.plugin == "hybrid_parallel":
 3. 通过调用`Booster.execute_pipeline` 方法来执行前向和后向传递:
     ```python
     outputs = booster.execute_pipeline(
-        train_dataloader_iter, model, _criterion, optimizer, return_loss=True, return_outputs=True
+        train_dataloader_iter, model, _criterion, optimizer, return_loss=True
     )
     ```
     该方法会自动执行后向传递，所以在执行该方法后不需要再调用 `loss.backward()`方法。
diff --git a/examples/images/vit/vit_benchmark.py b/examples/images/vit/vit_benchmark.py
index 0780173241aa..32b1ec803aec 100644
--- a/examples/images/vit/vit_benchmark.py
+++ b/examples/images/vit/vit_benchmark.py
@@ -120,7 +120,7 @@ def criterion(outputs, inputs):
                 # run pipeline forward backward
                 batch = iter([batch])
                 outputs = booster.execute_pipeline(
-                    batch, model, criterion, optimizer, return_loss=True, return_outputs=True
+                    batch, model, criterion, optimizer, return_loss=True
                 )
             else:
                 outputs = model(**batch)
diff --git a/examples/language/bert/finetune.py b/examples/language/bert/finetune.py
index 0b1e77ffff06..bd6c393a7ddc 100644
--- a/examples/language/bert/finetune.py
+++ b/examples/language/bert/finetune.py
@@ -148,7 +148,7 @@ def train_epoch(
         for _ in pbar:
             if use_pipeline:
                 outputs = booster.execute_pipeline(
-                    train_dataloader_iter, model, _criterion, optimizer, return_loss=True, return_outputs=True
+                    train_dataloader_iter, model, _criterion, optimizer, return_loss=True
                 )
                 # Backward and optimize
                 if is_pp_last_device:
diff --git a/examples/language/gpt/hybridparallelism/finetune.py b/examples/language/gpt/hybridparallelism/finetune.py
index eb56ee530a0a..888f47aaaab0 100644
--- a/examples/language/gpt/hybridparallelism/finetune.py
+++ b/examples/language/gpt/hybridparallelism/finetune.py
@@ -145,7 +145,7 @@ def train_epoch(
         for _ in pbar:
             if use_pipeline:
                 outputs = booster.execute_pipeline(
-                    train_dataloader_iter, model, _criterion, optimizer, return_loss=True, return_outputs=True
+                    train_dataloader_iter, model, _criterion, optimizer, return_loss=True
                 )
                 # Backward and optimize
                 if is_pp_last_stage:
diff --git a/examples/language/llama2/finetune.py b/examples/language/llama2/finetune.py
index 3dbd0cf357b4..122186c30a58 100644
--- a/examples/language/llama2/finetune.py
+++ b/examples/language/llama2/finetune.py
@@ -271,7 +271,7 @@ def main():
             for step in pbar:
                 if use_pipeline:
                     outputs = booster.execute_pipeline(
-                        dataloader_iter, model, _criterion, optimizer, return_loss=True, return_outputs=True
+                        dataloader_iter, model, _criterion, optimizer, return_loss=True
                     )
                     loss = outputs["loss"]
                 else:
diff --git a/examples/language/llama2/pretrain.py b/examples/language/llama2/pretrain.py
index fe7d958307e9..7b5805b801a8 100644
--- a/examples/language/llama2/pretrain.py
+++ b/examples/language/llama2/pretrain.py
@@ -185,7 +185,7 @@ def main():
             microbatch_size=1,
             enable_jit_fused=False,
             zero_stage=0,
-            precision="fp32",
+            precision=args.mixed_precision,
             initial_scale=1,
         )
     else:
@@ -286,7 +286,7 @@ def main():
             for step in pbar:
                 if use_pipeline:
                     outputs = booster.execute_pipeline(
-                        dataloader_iter, model, _criterion, optimizer, return_loss=True, return_outputs=True
+                        dataloader_iter, model, _criterion, optimizer, return_loss=True
                     )
                     loss = outputs["loss"]
                 else:
diff --git a/examples/language/openmoe/benchmark/benchmark_cai.py b/examples/language/openmoe/benchmark/benchmark_cai.py
index 770c500d86bf..a6d5f8bf2c0e 100644
--- a/examples/language/openmoe/benchmark/benchmark_cai.py
+++ b/examples/language/openmoe/benchmark/benchmark_cai.py
@@ -270,7 +270,6 @@ def main():
                     lambda x, y: x.loss,
                     optimizer,
                     return_loss=True,
-                    return_outputs=True,
                 )
                 # Backward and optimize
                 if is_pp_last_stage:
diff --git a/examples/language/openmoe/train.py b/examples/language/openmoe/train.py
index 89c4d5420994..f3267b7c6a68 100644
--- a/examples/language/openmoe/train.py
+++ b/examples/language/openmoe/train.py
@@ -340,7 +340,6 @@ def main():
                         lambda x, y: x.loss,
                         optimizer,
                         return_loss=True,
-                        return_outputs=True,
                     )
                     # Backward and optimize
                     if is_pp_last_stage:
diff --git a/examples/language/opt/opt_train_demo.py b/examples/language/opt/opt_train_demo.py
index fddbc1b408e7..82dff1920fde 100644
--- a/examples/language/opt/opt_train_demo.py
+++ b/examples/language/opt/opt_train_demo.py
@@ -42,7 +42,7 @@ def train_epoch(epoch, model, optimizer, _criterion, lr_scheduler, dataloader, b
         for _ in pbar:
             if use_pipeline:
                 outputs = booster.execute_pipeline(
-                    dataloader, model, _criterion, optimizer, return_loss=True, return_outputs=True
+                    dataloader, model, _criterion, optimizer, return_loss=True
                 )
                 # Backward and optimize
                 if is_pp_last_stage:
diff --git a/tests/test_booster/test_plugin/test_3d_plugin.py b/tests/test_booster/test_plugin/test_3d_plugin.py
index 38361d803c49..61558c003c91 100644
--- a/tests/test_booster/test_plugin/test_3d_plugin.py
+++ b/tests/test_booster/test_plugin/test_3d_plugin.py
@@ -74,7 +74,7 @@ def _criterion(outputs, inputs):
             loss = criterion(outputs[output_key])
             return loss
 
-        booster.execute_pipeline(data_iter, model, _criterion, optimizer, return_loss=True, return_outputs=False)
+        booster.execute_pipeline(data_iter, model, _criterion, optimizer, return_loss=True)
         optimizer.step()
 
     except Exception as e:
diff --git a/tests/test_checkpoint_io/test_hybrid_parallel_plugin_checkpoint_io.py b/tests/test_checkpoint_io/test_hybrid_parallel_plugin_checkpoint_io.py
index b5cb31715aed..557666a804e3 100644
--- a/tests/test_checkpoint_io/test_hybrid_parallel_plugin_checkpoint_io.py
+++ b/tests/test_checkpoint_io/test_hybrid_parallel_plugin_checkpoint_io.py
@@ -75,7 +75,7 @@ def _preprocess_data(data):
     model.train()
     if booster.plugin.stage_manager is not None:
         booster.execute_pipeline(
-            _preprocess_data(data), model, _criterion, optimizer, return_loss=True, return_outputs=False
+            _preprocess_data(data), model, _criterion, optimizer, return_loss=True
         )
     else:
         output = model(**_preprocess_data(data))
@@ -109,7 +109,7 @@ def _preprocess_data(data):
     data_for_origin = data_gen_fn()
     if booster.plugin.stage_manager is not None:
         booster.execute_pipeline(
-            _preprocess_data(data_for_shard), model, _criterion, optimizer, return_loss=True, return_outputs=False
+            _preprocess_data(data_for_shard), model, _criterion, optimizer, return_loss=True
         )
         booster.execute_pipeline(
             _preprocess_data(data_for_origin),
@@ -117,7 +117,6 @@ def _preprocess_data(data):
             _criterion,
             new_optimizer,
             return_loss=True,
-            return_outputs=False,
         )
     else:
         old_model_loss = criterion(model(**_preprocess_data(data_for_shard)))
diff --git a/tests/test_moe/test_moe_checkpoint.py b/tests/test_moe/test_moe_checkpoint.py
index d6dad2d7fb41..10e63592ac07 100644
--- a/tests/test_moe/test_moe_checkpoint.py
+++ b/tests/test_moe/test_moe_checkpoint.py
@@ -49,7 +49,6 @@ def run_fwd_bwd(
             lambda x, y: x.loss,
             optimizer,
             return_loss=True,
-            return_outputs=True,
         )
         # Backward and optimize
         if is_pp_last_stage:
diff --git a/tests/test_pipeline/test_schedule/test_interleaved.py b/tests/test_pipeline/test_schedule/test_interleaved.py
index 0e81818eb239..7aa4640553ca 100644
--- a/tests/test_pipeline/test_schedule/test_interleaved.py
+++ b/tests/test_pipeline/test_schedule/test_interleaved.py
@@ -104,7 +104,7 @@ def criterion(x, *args, **kwargs):
     torch_loss.backward()
 
     pp_ret = schedule.forward_backward_step(
-        sharded_model, iter(input_list), criterion, pp_optimizer, return_loss=True, return_outputs=True
+        sharded_model, iter(input_list), criterion, pp_optimizer, return_loss=True
     )
 
     # check loss
@@ -134,7 +134,7 @@ def criterion(x, *args, **kwargs):
         torch_loss = criterion(torch_output)
 
         pp_ret = schedule.forward_backward_step(
-            sharded_model, iter(input_list), criterion, pp_optimizer, return_loss=True, return_outputs=True
+            sharded_model, iter(input_list), criterion, pp_optimizer, return_loss=True
         )
         if stage_manager.is_last_stage(ignore_chunk=True):
             assert torch.allclose(torch_loss, pp_ret["loss"])
diff --git a/tests/test_pipeline/test_schedule/test_oneF_oneB.py b/tests/test_pipeline/test_schedule/test_oneF_oneB.py
index a08dc6d277d0..e1a679890c8d 100644
--- a/tests/test_pipeline/test_schedule/test_oneF_oneB.py
+++ b/tests/test_pipeline/test_schedule/test_oneF_oneB.py
@@ -100,7 +100,7 @@ def custom_fwd(self, x):
     torch_loss = criterion(torch_output)
     torch_loss.backward()
     pp_ret = schedule.forward_backward_step(
-        sharded_model, iter(input_list), criterion, pp_optimizer, return_loss=True, return_outputs=True
+        sharded_model, iter(input_list), criterion, pp_optimizer, return_loss=True
     )
 
     # check loss
@@ -130,7 +130,7 @@ def custom_fwd(self, x):
         torch_loss = criterion(torch_output)
 
         pp_ret = schedule.forward_backward_step(
-            sharded_model, iter(input_list), criterion, pp_optimizer, return_loss=True, return_outputs=True
+            sharded_model, iter(input_list), criterion, pp_optimizer, return_loss=True
         )
         if stage_manager.is_last_stage():
             assert torch.allclose(torch_loss, pp_ret["loss"])