diff --git a/src/twinkle/model/megatron/megatron.py b/src/twinkle/model/megatron/megatron.py
index dcece0ff..cdd3b655 100644
--- a/src/twinkle/model/megatron/megatron.py
+++ b/src/twinkle/model/megatron/megatron.py
@@ -155,17 +155,6 @@ def _should_bind_device_id_for_process_group(self, backend: str) -> bool:
         # PG so Megatron's later Gloo DP groups stay decoupled on NPU.
         return backend == 'nccl'
 
-    @staticmethod
-    def _drop_npu_causal_4d_mask(batch, unwrapped_model):
-        """On NPU, drop the generic 4D dense mask so MindSpeed can build
-        its own compressed causal mask for FlashAttention."""
-        if Platform.device_prefix() != 'npu':
-            return
-        attention_mask = batch.get('attention_mask')
-        if (isinstance(attention_mask, torch.Tensor) and attention_mask.dim() == 4
-                and getattr(unwrapped_model.config, 'attention_mask_type', None) == 'causal'):
-            batch['attention_mask'] = None
-
     def _construct_default_optimizer_group(self):
         return MegatronOptimizerGroup(
             loss_instance=CrossEntropyLoss(reduction='sum'),
@@ -315,7 +304,13 @@ def forward_backward(self,
         if micro_batch_size is None:
             # Compatible with DPO
             micro_batch_size = min(2, len(inputs))
-        inputs = processor(inputs, micro_batch_size=micro_batch_size, variable_seq_lengths=self.variable_seq_lengths)
+        unwrapped_model = self.strategy.unwrap_model(self.model)[0]
+        inputs = processor(
+            inputs,
+            micro_batch_size=micro_batch_size,
+            variable_seq_lengths=self.variable_seq_lengths,
+            attention_mask_type=getattr(unwrapped_model.config, 'attention_mask_type', None),
+        )
 
         # Get parallelism settings for sequence padding and splitting
         cp_size = self.device_mesh.cp_world_size
@@ -379,7 +374,6 @@ def forward_step_func(data_iterator, model):
             batch = next(data_iterator)
             labels = batch.pop('labels', None)
             unwrapped_model = self.strategy.unwrap_model([model])[0]
-            self._drop_npu_causal_4d_mask(batch, unwrapped_model)
             if disable_lora and isinstance(unwrapped_model, PeftModel):
                 with unwrapped_model.disable_adapter():
                     output_tensor = model(**batch)
diff --git a/src/twinkle/processor/base.py b/src/twinkle/processor/base.py
index eb182bf5..5906aa13 100644
--- a/src/twinkle/processor/base.py
+++ b/src/twinkle/processor/base.py
@@ -65,6 +65,7 @@ def __init__(self,
             self.collate_fn,
             self.to_transformers_dict,
             self.add_extra_padding_free_args,
+            self.drop_causal_4d_mask,
             self.split_cp,
             self.prepare_outputs,
         ]
@@ -236,6 +237,20 @@ def add_extra_padding_free_args(self, inputs: List[InputFeature], **kwargs) -> L
                 _inp['packed_seq_params'] = self._get_packed_seq_params(_inp['position_ids'])
         return inputs
 
+    def drop_causal_4d_mask(self, inputs: List[InputFeature], **kwargs) -> List[InputFeature]:
+        """On NPU, drop the generic 4D dense mask so MindSpeed can build
+        its own compressed causal mask for FlashAttention."""
+        if Platform.device_prefix() != 'npu':
+            return inputs
+        attention_mask_type = kwargs.get('attention_mask_type')
+        if attention_mask_type != 'causal':
+            return inputs
+        for _inp in inputs:
+            attention_mask = _inp.get('attention_mask')
+            if isinstance(attention_mask, torch.Tensor) and attention_mask.dim() == 4:
+                _inp['attention_mask'] = None
+        return inputs
+
     @staticmethod
     def _pad_sequence(sequences, padding_value, padding_side):
         if padding_side == 'right':