diff --git a/vllm/model_executor/layers/attention.py b/vllm/model_executor/layers/attention.py
index c1259a1b11ea5..b94c82e132583 100644
--- a/vllm/model_executor/layers/attention.py
+++ b/vllm/model_executor/layers/attention.py
@@ -10,9 +10,7 @@ from xformers.ops.fmha.attn_bias import (BlockDiagonalCausalMask,
 from vllm import attention_ops
 from vllm import cache_ops
 from vllm.model_executor.input_metadata import InputMetadata
-from vllm.model_executor.layers.rotary_embedding import (
-    DynamicNTKScalingRotaryEmbedding, LinearScalingRotaryEmbedding,
-    RotaryEmbedding, YaRNScalingRotaryEmbedding)
+from vllm.model_executor.layers.rotary_embedding import get_rope
 
 _SUPPORTED_HEAD_SIZES = [64, 80, 96, 112, 128, 256]
 # Should be the same as PARTITION_SIZE in `paged_attention_v2_launcher`.
@@ -319,36 +317,8 @@ class PagedAttentionWithRoPE(PagedAttention):
                          scale,
                          num_kv_heads,
                          sliding_window=sliding_window)
-        if rope_scaling is None:
-            self.rotary_emb = RotaryEmbedding(head_size, rotary_dim,
-                                              max_position, base,
-                                              is_neox_style)
-        else:
-            scaling_type = rope_scaling["type"]
-            scaling_factor = rope_scaling["factor"]
-            if scaling_type == "linear":
-                self.rotary_emb = LinearScalingRotaryEmbedding(
-                    head_size, rotary_dim, max_position, base, is_neox_style,
-                    scaling_factor)
-            elif scaling_type == "dynamic":
-                self.rotary_emb = DynamicNTKScalingRotaryEmbedding(
-                    head_size, rotary_dim, max_position, base, is_neox_style,
-                    scaling_factor)
-            elif scaling_type == "yarn":
-                original_max_position = rope_scaling[
-                    "original_max_position_embeddings"]
-                assert max_position == original_max_position * scaling_factor
-                extra_kwargs = {
-                    k: v
-                    for k, v in rope_scaling.items()
-                    if k in ("extrapolation_factor", "attn_factor",
-                             "beta_fast", "beta_slow")
-                }
-                self.rotary_emb = YaRNScalingRotaryEmbedding(
-                    head_size, rotary_dim, original_max_position, base,
-                    is_neox_style, scaling_factor, **extra_kwargs)
-            else:
-                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
+        self.rotary_emb = get_rope(head_size, rotary_dim, max_position, base,
+                                   is_neox_style, rope_scaling)
 
     def forward(
         self,
diff --git a/vllm/model_executor/layers/rotary_embedding.py b/vllm/model_executor/layers/rotary_embedding.py
index 2cbd3b584c06e..1b88e9a3b8057 100644
--- a/vllm/model_executor/layers/rotary_embedding.py
+++ b/vllm/model_executor/layers/rotary_embedding.py
@@ -22,7 +22,7 @@
 # limitations under the License.
 """Rotary Positional Embeddings."""
 import math
-from typing import Tuple, Union
+from typing import Any, Dict, Optional, Tuple, Union
 
 import torch
 import torch.nn as nn
@@ -271,3 +271,46 @@ class YaRNScalingRotaryEmbedding(RotaryEmbedding):
         sin = (freqs.sin() * self.mscale)
         cache = torch.cat((cos, sin), dim=-1)
         return cache
+
+
+def get_rope(
+    head_size: int,
+    rotary_dim: int,
+    max_position: int,
+    base: int,
+    is_neox_style: bool,
+    rope_scaling: Optional[Dict[str, Any]],
+) -> RotaryEmbedding:
+    if rope_scaling is None:
+        rotary_emb = RotaryEmbedding(head_size, rotary_dim, max_position, base,
+                                     is_neox_style)
+    else:
+        scaling_type = rope_scaling["type"]
+        scaling_factor = rope_scaling["factor"]
+        if scaling_type == "linear":
+            rotary_emb = LinearScalingRotaryEmbedding(head_size, rotary_dim,
+                                                      max_position, base,
+                                                      is_neox_style,
+                                                      scaling_factor)
+        elif scaling_type == "dynamic":
+            rotary_emb = DynamicNTKScalingRotaryEmbedding(
+                head_size, rotary_dim, max_position, base, is_neox_style,
+                scaling_factor)
+        elif scaling_type == "yarn":
+            original_max_position = rope_scaling[
+                "original_max_position_embeddings"]
+            assert max_position == original_max_position * scaling_factor
+            extra_kwargs = {
+                k: v
+                for k, v in rope_scaling.items()
+                if k in ("extrapolation_factor", "attn_factor", "beta_fast",
+                         "beta_slow")
+            }
+            rotary_emb = YaRNScalingRotaryEmbedding(head_size, rotary_dim,
+                                                    original_max_position,
+                                                    base, is_neox_style,
+                                                    scaling_factor,
+                                                    **extra_kwargs)
+        else:
+            raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
+    return rotary_emb