[feat] Support MRoPE + YaRN (vllm-project#25384)

Signed-off-by: liuye.hj <liuye.hj@alibaba-inc.com>
Co-authored-by: liuye.hj <liuye.hj@alibaba-inc.com>
Signed-off-by: xuebwang-amd <xuebwang@amd.com>

Author: 2 people

vllm/model_executor/layers/rotary_embedding/__init__.py

@@ -153,11 +153,23 @@ def get_rope(
                 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, dtype,
-                                                    **extra_kwargs)
+            if "mrope_section" in rope_scaling:
+                rotary_emb = MRotaryEmbedding(
+                    head_size,
+                    rotary_dim,
+                    original_max_position,
+                    base,
+                    is_neox_style,
+                    dtype,
+                    mrope_section=rope_scaling["mrope_section"],
+                    mrope_interleaved=rope_scaling.get("mrope_interleaved",
+                                                       False),
+                    scaling_factor=scaling_factor,
+                    **extra_kwargs)
+            else:
+                rotary_emb = YaRNScalingRotaryEmbedding(
+                    head_size, rotary_dim, original_max_position, base,
+                    is_neox_style, scaling_factor, dtype, **extra_kwargs)
         elif scaling_type == "deepseek_yarn":
             scaling_factor = rope_scaling["factor"]
             original_max_position = rope_scaling[

vllm/model_executor/layers/rotary_embedding/mrope.py

@@ -12,6 +12,7 @@
 
 from .base import RotaryEmbedding
 from .common import apply_rotary_emb_dispatch
+from .yarn_scaling_rope import YaRNScalingRotaryEmbedding, yarn_get_mscale
 
 
 @triton.jit
@@ -213,7 +214,27 @@ def __init__(
         dtype: torch.dtype,
         mrope_section: Optional[list[int]] = None,
         mrope_interleaved: bool = False,
+        # YaRN parameters.
+        *,
+        scaling_factor: Optional[float] = None,
+        extrapolation_factor: float = 1,
+        attn_factor: float = 1,
+        beta_fast: int = 32,
+        beta_slow: int = 1,
     ) -> None:
+
+        self.scaling_factor = scaling_factor
+        self.extrapolation_factor = extrapolation_factor
+        self.attn_factor = attn_factor
+        self.beta_fast = beta_fast
+        self.beta_slow = beta_slow
+        if self.scaling_factor is not None:
+            # Get n-d magnitude scaling corrected for interpolation
+            self.mscale = float(
+                yarn_get_mscale(self.scaling_factor) * attn_factor)
+        else:
+            self.mscale = 1.0
+
         # In Qwen2.5-VL, the maximum index value is related to the duration of
         # the input video. We enlarge max_position_embeddings to 4 times to get
         # a larger the cos and sin cache.
@@ -226,6 +247,16 @@ def __init__(
         if self.mrope_section:
             assert sum(self.mrope_section) == rotary_dim // 2
 
+    def _compute_inv_freq(self, base: float) -> torch.Tensor:
+        if self.scaling_factor is None:
+            return super()._compute_inv_freq(base)
+        return YaRNScalingRotaryEmbedding._compute_inv_freq(self, base)
+
+    def _compute_cos_sin_cache(self) -> torch.Tensor:
+        if self.scaling_factor is None:
+            return super()._compute_cos_sin_cache()
+        return YaRNScalingRotaryEmbedding._compute_cos_sin_cache(self)
+
     def forward_native(
         self,
         positions: torch.Tensor,