diff --git a/python/sglang/srt/layers/rotary_embedding.py b/python/sglang/srt/layers/rotary_embedding.py
index 63a6c9a12..56632092e 100644
--- a/python/sglang/srt/layers/rotary_embedding.py
+++ b/python/sglang/srt/layers/rotary_embedding.py
@@ -169,76 +169,6 @@ class RotaryEmbedding(CustomOp):
             )
         return query, key
 
-    def forward_xpu(
-        self,
-        positions: torch.Tensor,
-        query: torch.Tensor,
-        key: torch.Tensor,
-        offsets: Optional[torch.Tensor] = None,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        from vllm._ipex_ops import ipex_ops as ops
-
-        self.cos_sin_cache = self.cos_sin_cache.to(positions.device, dtype=query.dtype)
-        ops.rotary_embedding(
-            positions,
-            query,
-            key,
-            self.head_size,
-            self.cos_sin_cache,
-            self.is_neox_style,
-        )
-        return query, key
-
-    def forward_hpu(
-        self,
-        positions: torch.Tensor,
-        query: torch.Tensor,
-        key: torch.Tensor,
-        offsets: Optional[torch.Tensor] = None,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        from habana_frameworks.torch.hpex.kernels import (
-            RotaryPosEmbeddingMode,
-            apply_rotary_pos_emb,
-        )
-
-        positions = positions.flatten()
-        if offsets is not None:
-            positions = positions + offsets
-        num_tokens = positions.shape[0]
-        cos_sin = self.cos_sin_cache.index_select(0, positions).view(num_tokens, 1, -1)
-        cos, sin = cos_sin.chunk(2, dim=-1)
-        # HPU RoPE kernel requires hidden dimension for cos and sin to be equal
-        # to query hidden dimension, so the original tensors need to be
-        # expanded
-        # GPT-NeoX kernel requires position_ids = None, offset, mode = BLOCKWISE
-        # and expansion of cos/sin tensors via concatenation
-        # GPT-J kernel requires position_ids = None, offset = 0, mode = PAIRWISE
-        # and expansion of cos/sin tensors via repeat_interleave
-        rope_mode: RotaryPosEmbeddingMode
-        if self.is_neox_style:
-            rope_mode = RotaryPosEmbeddingMode.BLOCKWISE
-            cos = torch.cat((cos, cos), dim=-1)
-            sin = torch.cat((sin, sin), dim=-1)
-        else:
-            rope_mode = RotaryPosEmbeddingMode.PAIRWISE
-            sin = torch.repeat_interleave(sin, 2, dim=-1, output_size=cos_sin.shape[-1])
-            cos = torch.repeat_interleave(cos, 2, dim=-1, output_size=cos_sin.shape[-1])
-
-        query_shape = query.shape
-        query = query.view(num_tokens, -1, self.head_size)
-        query_rot = query[..., : self.rotary_dim]
-        query_pass = query[..., self.rotary_dim :]
-        query_rot = apply_rotary_pos_emb(query_rot, cos, sin, None, 0, rope_mode)
-        query = torch.cat((query_rot, query_pass), dim=-1).reshape(query_shape)
-
-        key_shape = key.shape
-        key = key.view(num_tokens, -1, self.head_size)
-        key_rot = key[..., : self.rotary_dim]
-        key_pass = key[..., self.rotary_dim :]
-        key_rot = apply_rotary_pos_emb(key_rot, cos, sin, None, 0, rope_mode)
-        key = torch.cat((key_rot, key_pass), dim=-1).reshape(key_shape)
-        return query, key
-
     def extra_repr(self) -> str:
         s = f"head_size={self.head_size}, rotary_dim={self.rotary_dim}"
         s += f", max_position_embeddings={self.max_position_embeddings}"