add rope scaling for qwen2

aphrodite/modeling/models/qwen2.py

@@ -88,7 +88,8 @@ class Qwen2Attention(nn.Module):
                  use_sliding_window: bool = False,
                  cache_config: Optional[CacheConfig] = None,
                  quant_config: Optional[QuantizationConfig] = None,
-                 sliding_window: Optional[int] = None) -> None:
+                 sliding_window: Optional[int] = None,
+                 rope_scaling: Optional[Tuple] = None) -> None:
         super().__init__()
         self.hidden_size = hidden_size
         tp_size = get_tensor_model_parallel_world_size()
@@ -132,6 +133,7 @@ class Qwen2Attention(nn.Module):
             rotary_dim=self.head_dim,
             max_position=max_position,
             base=self.rope_theta,
+            rope_scaling=rope_scaling,
         )
         self.attn = Attention(self.num_heads,
                               self.head_dim,
@@ -168,6 +170,7 @@ class Qwen2DecoderLayer(nn.Module):
         self.hidden_size = config.hidden_size
         # Requires transformers > 4.32.0
         rope_theta = getattr(config, "rope_theta", 1000000)
+        rope_scaling = getattr(config, "rope_scaling", None)
         use_sliding_window = (config.use_sliding_window
                               and layer_idx < config.max_window_layers)
         self.self_attn = Qwen2Attention(
@@ -179,7 +182,8 @@ class Qwen2DecoderLayer(nn.Module):
             use_sliding_window=use_sliding_window,
             cache_config=cache_config,
             quant_config=quant_config,
-            sliding_window=config.sliding_window)
+            sliding_window=config.sliding_window,
+            rope_scaling=rope_scaling)
         self.mlp = Qwen2MLP(
             hidden_size=self.hidden_size,
             intermediate_size=config.intermediate_size,