diff --git a/colossalai/auto_parallel/meta_profiler/meta_registry/activation.py b/colossalai/auto_parallel/meta_profiler/meta_registry/activation.py
index 909232e61..774457f7d 100644
--- a/colossalai/auto_parallel/meta_profiler/meta_registry/activation.py
+++ b/colossalai/auto_parallel/meta_profiler/meta_registry/activation.py
@@ -30,7 +30,7 @@ def relu_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, Lis
 
     input_tensor = args[0].data
     output_tensor = next(filter(lambda x: x.type == OperationDataType.OUTPUT, args)).data
-    inplace = kwargs.get("inplace", False)
+    is_inplace = kwargs.get("inplace", False)
 
     # construct input args for forward
     fwd_in_args = [input_tensor]
@@ -51,7 +51,7 @@ def relu_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, Lis
     # NOTE: the inplace ReLU don't have forward memory cost
     # NOTE: currently in SPMD solver we always believe that there will be a new tensor created in forward
     fwd_memory_cost = MemoryCost(
-        activation=activation_size(input_tensor) if inplace else activation_size([output_tensor, input_tensor]),
+        activation=activation_size(input_tensor) if is_inplace else activation_size([output_tensor, input_tensor]),
         parameter=0,
         temp=0,
         buffer=0)
diff --git a/colossalai/auto_parallel/meta_profiler/meta_registry/pooling.py b/colossalai/auto_parallel/meta_profiler/meta_registry/pooling.py
index 3ecabb6dc..79780c92e 100644
--- a/colossalai/auto_parallel/meta_profiler/meta_registry/pooling.py
+++ b/colossalai/auto_parallel/meta_profiler/meta_registry/pooling.py
@@ -14,6 +14,7 @@ __all__ = ["avgpool_meta_info", "maxpool_meta_info"]
 @meta_register.register(torch.nn.AdaptiveAvgPool1d)
 @meta_register.register(torch.nn.AdaptiveAvgPool2d)
 @meta_register.register(torch.nn.AdaptiveAvgPool3d)
+@meta_register.register(torch.flatten)
 def avgpool_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, List[torch.Tensor]]:
     """Meta info for AdaptiveAvgPool
     The aten graph of AdaptiveAvgPool is
@@ -32,6 +33,7 @@ def avgpool_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem,
 
     input_tensor = args[0].data
     output_tensor = next(filter(lambda x: x.type == OperationDataType.OUTPUT, args)).data
+    is_inplace = kwargs.get("inplace", False)
 
     # construct forward args for flop mapping
     fwd_in_args = [input_tensor]
@@ -51,8 +53,8 @@ def avgpool_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem,
     compute_cost = TrainCycleItem(fwd=fwd_compute_cost, bwd=bwd_compute_cost, total=fwd_compute_cost + bwd_compute_cost)
 
     # calculate memory cost
-    fwd_mem_cost = MemoryCost(activation=activation_size(output_tensor))
-    bwd_mem_cost = MemoryCost(activation=activation_size(input_tensor))
+    fwd_mem_cost = MemoryCost() if is_inplace else MemoryCost(activation=activation_size(output_tensor))
+    bwd_mem_cost = MemoryCost() if is_inplace else MemoryCost(activation=activation_size(input_tensor))
 
     # total cost
     total_mem_cost = MemoryCost(activation=fwd_mem_cost.activation + bwd_mem_cost.activation)