[NPU] support model skywork-reward-gemma2-2-27B-v0.2 (#16947)

Co-authored-by: cy <chenyang08056032@163.com>

python/sglang/srt/hardware_backend/npu/attention/ascend_backend.py

@@ -225,6 +225,11 @@ class AscendAttnBackend(AttentionBackend):
             self.q_head_dim = self.qk_rope_head_dim + self.qk_nope_head_dim
         else:
             self.use_alibi = getattr(model_runner.model_config, "use_alibi", False)
+            if (
+                "Gemma2ForSequenceClassification"
+                in model_runner.model_config.hf_config.architectures
+            ):
+                self.use_native_sdpa = True
         self.native_attn = AscendTorchNativeAttnBackend()
         self.graph_metadata = {}
         self.max_context_len = model_runner.model_config.context_len
@@ -821,10 +826,12 @@ class AscendAttnBackend(AttentionBackend):
 
                 # there are some accuracy issues in cross attention scene to use torch_npu._npu_flash_attention_qlens
                 # forward_batch.encoder_lens is not None in cross attention scend, we add native attn to solve accuracy issues
+                # Model skywork-reward-gemma2-2-27B also suffers from precision anomalies, thus the torch native backend becomes beneficial approach.
                 if (
                     layer.qk_head_dim <= 128
                     and causal
                     and forward_batch.encoder_lens is None
+                    and not getattr(self, "use_native_sdpa", False)
                 ):
                     if not self.use_alibi:
                         query = q.reshape(-1, layer.tp_q_head_num * layer.qk_head_dim)