Add Tencent HunYuanMoEV1 model support (#7549)

python/sglang/srt/layers/rotary_embedding.py

@@ -890,6 +890,43 @@ class Llama4VisionRotaryEmbedding(RotaryEmbedding):
         return query_out.type_as(query), key_out.type_as(key)
 
 
+class DynamicNTKAlphaRotaryEmbedding(RotaryEmbedding):
+    """RotaryEmbedding extended with Dynamic NTK scaling.
+
+    Credits to the Reddit users /u/bloc97 and /u/emozilla
+    """
+
+    def __init__(
+        self,
+        head_size: int,
+        rotary_dim: int,
+        max_position_embeddings: int,
+        base: int,
+        is_neox_style: bool,
+        scaling_alpha: float,
+        dtype: torch.dtype,
+    ) -> None:
+        self.scaling_alpha = scaling_alpha
+        super().__init__(
+            head_size, rotary_dim, max_position_embeddings, base, is_neox_style, dtype
+        )
+
+    def _compute_cos_sin_cache(self) -> torch.Tensor:
+        max_len = self.max_position_embeddings
+        base = self.base * self.scaling_alpha ** (
+            self.rotary_dim / (self.rotary_dim - 2)
+        )
+
+        inv_freq = self._compute_inv_freq(base)
+        t = torch.arange(max_len, dtype=torch.float)
+
+        freqs = torch.einsum("i,j -> ij", t, inv_freq)
+        cos = freqs.cos()
+        sin = freqs.sin()
+        cache = torch.cat((cos, sin), dim=-1)
+        return cache
+
+
 class MRotaryEmbedding(RotaryEmbedding):
     """Rotary Embedding with Multimodal Sections."""
 
@@ -1234,15 +1271,26 @@ def get_rope(
             )
         elif scaling_type == "dynamic":
             scaling_factor = rope_scaling["factor"]
-            rotary_emb = DynamicNTKScalingRotaryEmbedding(
-                head_size,
-                rotary_dim,
-                max_position,
-                base,
-                is_neox_style,
-                scaling_factor,
-                dtype,
-            )
+            if "alpha" in rope_scaling:
+                rotary_emb = DynamicNTKAlphaRotaryEmbedding(
+                    head_size,
+                    rotary_dim,
+                    max_position,
+                    base,
+                    is_neox_style,
+                    rope_scaling["alpha"],
+                    dtype,
+                )
+            else:
+                rotary_emb = DynamicNTKScalingRotaryEmbedding(
+                    head_size,
+                    rotary_dim,
+                    max_position,
+                    base,
+                    is_neox_style,
+                    scaling_factor,
+                    dtype,
+                )
         elif scaling_type == "yarn":
             scaling_factor = rope_scaling["factor"]
             original_max_position = rope_scaling["original_max_position_embeddings"]