add gemma3

vllm-v0.6.2/vllm/model_executor/models/gemma3.py

@@ -237,7 +237,13 @@ class Gemma3Attention(nn.Module):
         k = self.k_norm(k)
         k = k.flatten(-2, -1)
 
-        q, k = self.rotary_emb(positions, q, k)
+        # MLU rotary_emb expects a single concatenated tensor, not
+        # separate q and k (forward_mlu signature differs from forward_native).
+        qk = torch.cat([q, k], dim=-1)
+        self.rotary_emb(positions,
+                        qk.view(-1, self.num_heads + self.num_kv_heads,
+                                self.head_dim))
+        q, k = qk.split([self.q_size, self.kv_size], dim=-1)
         attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
         output, _ = self.o_proj(attn_output)
         return output