Update torch-npu version to 2.7.1 (#3896)

### What this PR does / why we need it?
Upgrade torch-npu to the official release version 2.7.1


- vLLM version: v0.11.0
- vLLM main:
83f478bb19

---------

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>

vllm_ascend/utils.py

@@ -551,8 +551,7 @@ def register_ascend_customop(vllm_config: Optional[VllmConfig] = None):
     from vllm_ascend.ops.activation import AscendQuickGELU, AscendSiluAndMul
     from vllm_ascend.ops.fused_moe.fused_moe import (AscendFusedMoE,
                                                      AscendSharedFusedMoE)
-    from vllm_ascend.ops.layernorm import (AscendGemmaRMSNorm,
-                                           AscendQuantRMSNorm, AscendRMSNorm)
+    from vllm_ascend.ops.layernorm import AscendGemmaRMSNorm, AscendRMSNorm
     from vllm_ascend.ops.linear import (AscendColumnParallelLinear,
                                         AscendMergedColumnParallelLinear,
                                         AscendQKVParallelLinear,
@@ -586,12 +585,6 @@ def register_ascend_customop(vllm_config: Optional[VllmConfig] = None):
         "FusedMoE": AscendFusedMoE,
         "SharedFusedMoE": AscendSharedFusedMoE,
     }
-
-    if vllm_config is not None and \
-        vllm_config.quant_config is not None and \
-        any("norm.bias" in name for name in vllm_config.quant_config.quant_description.keys()) and \
-            not version_check():
-        REGISTERED_ASCEND_OPS["RMSNorm"] = AscendQuantRMSNorm
     mla_to_register = "MultiHeadLatentAttention" if vllm_version_is(
         "0.11.0") else "MultiHeadLatentAttentionWrapper"
     if vllm_config and vllm_config.model_config and vllm_config.model_config.use_mla:
@@ -791,21 +784,6 @@ def is_hierarchical_communication_enabled():
             and os.getenv("HCCL_INTRA_PCIE_ENABLE", "") == "1")
 
 
-@functools.cache
-def version_check():
-    """check if torch_npu version >= dev20250919"""
-    import re  # noqa
-    torch_npu_version = torch_npu.version.__version__
-    date_pattern = r'dev(\d{8})'
-
-    match = re.search(date_pattern, torch_npu_version)
-    if match:
-        full_date = match.group(1)
-        if full_date >= "20250919":
-            return True
-    return False
-
-
 def has_layer_idx(model_instance: torch.nn.Module) -> bool:
     if model_instance is None:
         return False