diff --git a/python/sglang/srt/hardware_backend/npu/modules/deepseek_v2_attention_mla_npu.py b/python/sglang/srt/hardware_backend/npu/modules/deepseek_v2_attention_mla_npu.py
index 470617585c97..149b4a939249 100644
--- a/python/sglang/srt/hardware_backend/npu/modules/deepseek_v2_attention_mla_npu.py
+++ b/python/sglang/srt/hardware_backend/npu/modules/deepseek_v2_attention_mla_npu.py
@@ -229,19 +229,17 @@ def forward_mla_core_npu(
         k_rope=k_pe,
         **(dict(topk_indices=topk_indices) if topk_indices is not None else {}),
     )
-
     attn_output = attn_output.view(-1, m.num_local_heads, m.kv_lora_rank)
 
     attn_bmm_output = torch.empty(
-        (attn_output.shape[0], m.num_local_heads * m.v_head_dim),
+        (attn_output.shape[0], m.num_local_heads, m.v_head_dim),
         dtype=attn_output.dtype,
         device=attn_output.device,
     )
-    torch.bmm(
-        attn_output.transpose(0, 1),
-        m.w_vc,
-        out=attn_bmm_output.view(-1, m.num_local_heads, m.v_head_dim).transpose(0, 1),
-    )
+    torch.ops.npu.batch_matmul_transpose(attn_output, m.w_vc, attn_bmm_output)
+
+    attn_bmm_output = attn_bmm_output.reshape(-1, m.num_local_heads * m.v_head_dim)
+
     output, _ = m.o_proj(attn_bmm_output)
 
     return output
@@ -358,7 +356,11 @@ def forward_dsa_core_npu(
         device=attn_output.device,
     )
 
-    if not forward_batch.forward_mode.is_decode():
+    if (
+        not forward_batch.forward_mode.is_decode()
+        and not forward_batch.forward_mode.is_draft_extend(include_v2=True)
+        and not forward_batch.forward_mode.is_target_verify()
+    ):
         attn_output = attn_output.transpose(0, 1)
         torch.bmm(
             attn_output,
diff --git a/python/sglang/srt/hardware_backend/npu/quantization/fused_moe_method_npu.py b/python/sglang/srt/hardware_backend/npu/quantization/fused_moe_method_npu.py
index 938314b0f425..275a0bb80314 100644
--- a/python/sglang/srt/hardware_backend/npu/quantization/fused_moe_method_npu.py
+++ b/python/sglang/srt/hardware_backend/npu/quantization/fused_moe_method_npu.py
@@ -569,9 +569,9 @@ def apply_without_routing_weights(
     ):
         hidden_states = torch.ops.npu.npu_grouped_matmul(
             x=[hidden_states],
-            weight=[self.w13_weight],
-            scale=[self.w13_weight_scale],
-            bias=[self.w13_scale_bias],
+            weight=[layer.w13_weight],
+            scale=[layer.w13_weight_scale],
+            bias=[layer.w13_scale_bias],
             per_token_scale=[hidden_states_scale],
             group_list=group_list,
             split_item=2,
@@ -586,9 +586,9 @@ def apply_without_routing_weights(
 
         hidden_states = torch.ops.npu.npu_grouped_matmul(
             x=[hidden_states],
-            weight=[self.w2_weight],
-            scale=[self.w2_weight_scale],
-            bias=[self.w2_scale_bias],
+            weight=[layer.w2_weight],
+            scale=[layer.w2_weight_scale],
+            bias=[layer.w2_scale_bias],
             per_token_scale=[swiglu_out_scale],
             group_list=group_list,
             split_item=2,