diff --git a/src/llamafactory/model/model_utils/sdpa_npu_redirect.py b/src/llamafactory/model/model_utils/sdpa_npu_redirect.py
deleted file mode 100644
index 7275ad5d..00000000
--- a/src/llamafactory/model/model_utils/sdpa_npu_redirect.py
+++ /dev/null
@@ -1,131 +0,0 @@
-# Copyright 2025 the LlamaFactory team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import logging
-import math
-import os
-from typing import Optional
-
-import torch
-import torch.nn.functional as F
-from transformers.utils import is_torch_npu_available
-
-
-logger = logging.getLogger(__name__)
-
-_ORIG_SDPA = F.scaled_dot_product_attention
-
-
-def _to_bool_4d_mask(
-    attn_mask: Optional[torch.Tensor], q_len: int, kv_len: int, device: torch.device
-) -> Optional[torch.Tensor]:
-    """Normalize additive/other Hugging Face masks into a boolean mask of shape [B, 1, Q, K] (True = masked)."""
-    if attn_mask is None:
-        return None
-    if attn_mask.dtype != torch.bool:
-        attn_mask = attn_mask < 0  # additive -inf -> True
-    if attn_mask.dim() == 4:
-        return attn_mask[..., :q_len, :kv_len].contiguous()
-    if attn_mask.dim() == 3:
-        return attn_mask[:, None, :q_len, :kv_len].contiguous()
-    if attn_mask.dim() == 2:
-        return attn_mask[:, None, None, :kv_len].expand(-1, 1, q_len, -1).contiguous()
-    return attn_mask.to(device)
-
-
-def _merge_causal_mask(
-    attn_mask: Optional[torch.Tensor], is_causal: bool, L: int, S: int, device: torch.device
-) -> Optional[torch.Tensor]:
-    """Merge `is_causal` into the boolean/additive attention mask (True = masked)."""
-    if not is_causal or L != S:
-        return attn_mask
-    causal_bool = torch.ones((1, 1, L, L), dtype=torch.bool, device=device).triu(1)
-    if attn_mask is None:
-        return causal_bool
-    if attn_mask.dtype != torch.bool:
-        attn_mask = attn_mask < 0
-    if attn_mask.dim() == 2:
-        attn_mask = attn_mask[:, None, None, :L].expand(-1, 1, L, -1).contiguous()
-    elif attn_mask.dim() == 3:
-        attn_mask = attn_mask[:, None, :L, :L].contiguous()
-    return attn_mask | causal_bool
-
-
-def _sdpa_npu_redirect(
-    q: torch.Tensor,
-    k: torch.Tensor,
-    v: torch.Tensor,
-    attn_mask: Optional[torch.Tensor] = None,
-    dropout_p: float = 0.0,
-    is_causal: bool = False,
-    scale: Optional[float] = None,
-):
-    """A drop-in replacement for `F.scaled_dot_product_attention`.
-
-    Automatically falls back to the native SDPA when conditions are not met.
-    The NPU-fused path is only enabled when q/k/v have shape (B, N, S, D); otherwise, it falls back.
-    """
-    # Fall back if the feature is disabled or the conditions are not satisfied.
-    if os.environ.get("NPU_FA_DISABLE", "0") == "1":
-        return _ORIG_SDPA(q, k, v, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, scale=scale)
-
-    npu_ok = is_torch_npu_available() and (q.device.type == "npu")
-    dtype_ok = q.dtype in (torch.float16, torch.bfloat16)
-    shape_ok = q.dim() == 4 and k.dim() == 4 and v.dim() == 4  # 期望 BNSD
-    if not (npu_ok and dtype_ok and shape_ok):
-        return _ORIG_SDPA(q, k, v, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, scale=scale)
-
-    L, S = q.size(-2), k.size(-2)
-    merged_mask = _merge_causal_mask(attn_mask, is_causal, L, S, q.device)
-    mask_bool = _to_bool_4d_mask(merged_mask, q_len=L, kv_len=S, device=q.device)
-
-    head_dim = q.size(-1)
-    sc = (1.0 / math.sqrt(head_dim)) if (scale is None) else scale
-
-    train_mode = torch.is_grad_enabled() and (dropout_p > 0)
-    keep_prob = 1.0 - (dropout_p if train_mode else 0.0)
-
-    try:
-        import torch_npu
-
-        out = torch_npu.npu_fusion_attention(
-            q.contiguous(),
-            k.contiguous(),
-            v.contiguous(),
-            head_num=q.size(-3),  # N
-            input_layout="BNSD",  # (B, N, S, D)
-            pse=None,
-            atten_mask=mask_bool,  # True = masked
-            scale=sc,
-            pre_tockens=2147483647,
-            next_tockens=2147483647,
-            keep_prob=keep_prob,
-            sync=False,
-            inner_precise=0,
-        )[0]
-        return out
-    except Exception as e:
-        if os.environ.get("NPU_FA_VERBOSE", "0") == "1":
-            logger.warning(f"[sdpa_npu_redirect] npu_fusion_attention failed: {e}; fallback to SDPA.")
-        return _ORIG_SDPA(q, k, v, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, scale=scale)
-
-
-def apply_sdpa_npu_redirect(verbose: bool = True):
-    """Install the redirection by pointing `F.scaled_dot_product_attention` to our implementation."""
-    if getattr(F.scaled_dot_product_attention, "__wrapped_by_npu__", False):
-        return
-    F.scaled_dot_product_attention = _sdpa_npu_redirect
-    setattr(F.scaled_dot_product_attention, "__wrapped_by_npu__", True)
-    if verbose:
-        logger.info("[sdpa_npu_redirect] SDPA has been redirected to Ascend npu_fusion_attention when available.")
diff --git a/src/llamafactory/model/patcher.py b/src/llamafactory/model/patcher.py
index d4681f5a..cbf9aea3 100644
--- a/src/llamafactory/model/patcher.py
+++ b/src/llamafactory/model/patcher.py
@@ -193,23 +193,6 @@ def patch_model(
     if not model_args.use_unsloth:
         print_attn_implementation(model.config)
 
-    # ======== NPU fused attention redirect: SDPA -> torch_npu.npu_fusion_attention ========
-    # Place after all structural modifications and before DeepSpeed/Trainer initialization;
-    # does not modify any Module/_parameters, safe for ZeRO-3 + offload.
-    try:
-        import os
-
-        import torch
-
-        if hasattr(torch, "npu") and torch.npu.is_available() and os.environ.get("NPU_FA_DISABLE", "0") != "1":
-            from .model_utils.sdpa_npu_redirect import apply_sdpa_npu_redirect
-
-            apply_sdpa_npu_redirect(verbose=not model_args.use_unsloth)
-            logger.info_rank0("[sdpa_npu_redirect] Enabled: SDPA will use Ascend npu_fusion_attention when available.")
-    except Exception as e:
-        logger.warning_rank0(f"[sdpa_npu_redirect] Failed to enable redirect, will keep native SDPA. Reason: {e}")
-    # =====================================================================================
-
     try:
         model.add_model_tags(["llama-factory"])
     except Exception: