Update rope_scaling to rope_parameters in preparation for Transformers v5 (#28542)

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

vllm/model_executor/models/openpangu.py

@@ -77,6 +77,7 @@ from vllm.model_executor.models.utils import (
     sequence_parallel_chunk,
 )
 from vllm.sequence import IntermediateTensors
+from vllm.transformers_utils.config import set_default_rope_theta
 
 
 def check_ffn_act_fn(act_fn: str):
@@ -259,7 +260,6 @@ class OpenPanguMLAAttention(nn.Module):
         v_head_dim: int,
         q_lora_rank: int | None,
         kv_lora_rank: int,
-        rope_theta: float = 10000,
         max_position_embeddings: int = 8192,
         cache_config: CacheConfig | None = None,
         quant_config: QuantizationConfig | None = None,
@@ -274,8 +274,6 @@ class OpenPanguMLAAttention(nn.Module):
         self.v_head_dim = v_head_dim
         self.q_lora_rank = q_lora_rank
         self.kv_lora_rank = kv_lora_rank
-        self.rope_theta = rope_theta
-
         self.tp_size = get_tensor_model_parallel_world_size()
         if num_heads % self.tp_size != 0:
             raise ValueError(
@@ -339,7 +337,9 @@ class OpenPanguMLAAttention(nn.Module):
         )
 
         # TODO: remove hard coding
-        rope_scaling = {
+        set_default_rope_theta(config, default_theta=10000)
+        rope_parameters = {
+            "rope_theta": config.rope_parameters["rope_theta"],
             "beta_fast": 32,
             "beta_slow": 1,
             "factor": 1,
@@ -353,8 +353,7 @@ class OpenPanguMLAAttention(nn.Module):
             qk_rope_head_dim,
             rotary_dim=qk_rope_head_dim,
             max_position=max_position_embeddings,
-            base=rope_theta,
-            rope_scaling=rope_scaling,
+            rope_parameters=rope_parameters,
             is_neox_style=False,
         )
 
@@ -407,8 +406,6 @@ class OpenPanguEmbeddedAttention(nn.Module):
         hidden_size: int,
         num_heads: int,
         num_kv_heads: int,
-        rope_theta: float = 10000,
-        rope_scaling: dict[str, Any] | None = None,
         max_position_embeddings: int = 8192,
         quant_config: QuantizationConfig | None = None,
         bias: bool = False,
@@ -454,7 +451,6 @@ class OpenPanguEmbeddedAttention(nn.Module):
         self.q_size = self.num_heads * self.head_dim
         self.kv_size = self.num_kv_heads * self.head_dim
         self.scaling = self.head_dim**-0.5
-        self.rope_theta = rope_theta
         self.max_position_embeddings = max_position_embeddings
 
         self.qkv_proj = QKVParallelLinear(
@@ -475,9 +471,7 @@ class OpenPanguEmbeddedAttention(nn.Module):
             prefix=f"{prefix}.o_proj",
         )
 
-        self._init_rotary_emb(
-            config, rope_scaling=rope_scaling, quant_config=quant_config
-        )
+        self._init_rotary_emb(config, quant_config=quant_config)
 
         if hasattr(config, "interleaved_sliding_window"):
             interleaved_sliding_window = config.interleaved_sliding_window
@@ -521,7 +515,6 @@ class OpenPanguEmbeddedAttention(nn.Module):
     def _init_rotary_emb(
         self,
         config: PretrainedConfig,
-        rope_scaling: dict[str, Any] | None,
         quant_config: QuantizationConfig | None,
     ) -> None:
         is_neox_style = True
@@ -533,8 +526,7 @@ class OpenPanguEmbeddedAttention(nn.Module):
             self.head_dim,
             rotary_dim=self.head_dim,
             max_position=self.max_position_embeddings,
-            base=self.rope_theta,
-            rope_scaling=rope_scaling,
+            rope_parameters=config.rope_parameters,
             is_neox_style=is_neox_style,
         )
 
@@ -555,7 +547,6 @@ class OpenPanguDecoderLayer(nn.Module):
         parallel_config = vllm_config.parallel_config
 
         self.hidden_size = config.hidden_size
-        rope_theta = getattr(config, "rope_theta", 10000)
         max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
 
         layer_idx = int(prefix.split(sep=".")[-1])
@@ -579,7 +570,6 @@ class OpenPanguDecoderLayer(nn.Module):
                     config.q_lora_rank if hasattr(config, "q_lora_rank") else None
                 ),
                 kv_lora_rank=config.kv_lora_rank,
-                rope_theta=rope_theta,
                 max_position_embeddings=max_position_embeddings,
                 cache_config=cache_config,
                 quant_config=quant_config,
@@ -607,8 +597,6 @@ class OpenPanguDecoderLayer(nn.Module):
                 num_kv_heads=getattr(
                     config, "num_key_value_heads", config.num_attention_heads
                 ),
-                rope_theta=rope_theta,
-                rope_scaling=getattr(config, "rope_scaling", None),
                 max_position_embeddings=max_position_embeddings,
                 quant_config=quant_config,
                 bias=attention_bias,