feat: remove vllm get_rope (#2964)

python/sglang/srt/layers/rotary_embedding.py

-# Copyright 2023-2024 SGLang Team
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""MRotaryEmbedding"""
+# Adapted from https://raw.githubusercontent.com/vllm-project/vllm/refs/tags/v0.6.6.post1/vllm/model_executor/layers/rotary_embedding.py
+
+"""Rotary Positional Embeddings."""
+import math
 from typing import Any, Dict, List, Optional, Tuple, Union
 
 import torch
-from vllm.model_executor.layers.rotary_embedding import (
-    RotaryEmbedding,
-    _rotate_gptj,
-    _rotate_neox,
-    _yarn_find_correction_range,
-    _yarn_linear_ramp_mask,
-    get_rope,
-    yarn_get_mscale,
-)
-
-
-class MRotaryEmbedding:
-    """Rotary Embedding with Multimodal Sections."""
+import torch.nn as nn
+from vllm.model_executor.custom_op import CustomOp
 
-    @staticmethod
-    def get_input_positions(
-        input_tokens: torch.Tensor,
-        image_grid_thw: Union[List[List[int]], torch.Tensor],
-        vision_start_token_id: int,
-        spatial_merge_size: int,
-        context_len: int = 0,
-    ) -> Tuple[List[List[int]], int]:
-        """Get mrope input positions and delta value."""
 
-        if isinstance(image_grid_thw, torch.Tensor):
-            image_grid_thw = image_grid_thw.tolist()
+def _rotate_neox(x: torch.Tensor) -> torch.Tensor:
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
 
-        vision_start_indices = torch.argwhere(
-            input_tokens == vision_start_token_id
-        ).squeeze(1)
-        image_indices = vision_start_indices + 1
-        image_nums = image_indices.shape[0]
-        llm_pos_ids_list: list = []
 
-        st = 0
-        input_tokens_len = input_tokens.shape[0]
-        for image_index in range(image_nums):
-            ed = image_indices[image_index].item()
-            t, h, w = (
-                image_grid_thw[image_index][0],
-                image_grid_thw[image_index][1],
-                image_grid_thw[image_index][2],
-            )
-            llm_grid_t, llm_grid_h, llm_grid_w = (
-                t,
-                h // spatial_merge_size,
-                w // spatial_merge_size,
+def _rotate_gptj(x: torch.Tensor) -> torch.Tensor:
+    x1 = x[..., ::2]
+    x2 = x[..., 1::2]
+    x = torch.stack((-x2, x1), dim=-1)
+    return x.flatten(-2)
+
+
+def _apply_rotary_emb(
+    x: torch.Tensor,
+    cos: torch.Tensor,
+    sin: torch.Tensor,
+    is_neox_style: bool,
+) -> torch.Tensor:
+    """
+    Args:
+        x: [num_tokens, num_heads, head_size]
+        cos: [num_tokens, head_size // 2]
+        sin: [num_tokens, head_size // 2]
+        is_neox_style: Whether to use the Neox-style or GPT-J-style rotary
+            positional embeddings.
+    """
+    cos = cos.unsqueeze(-2).to(x.dtype)
+    sin = sin.unsqueeze(-2).to(x.dtype)
+    if is_neox_style:
+        x1, x2 = torch.chunk(x, 2, dim=-1)
+    else:
+        x1 = x[..., ::2]
+        x2 = x[..., 1::2]
+    o1 = x1 * cos - x2 * sin
+    o2 = x2 * cos + x1 * sin
+    if is_neox_style:
+        return torch.cat((o1, o2), dim=-1)
+    else:
+        return torch.stack((o1, o2), dim=-1).flatten(-2)
+
+
+@CustomOp.register("rotary_embedding")
+class RotaryEmbedding(CustomOp):
+    """Original rotary positional embedding."""
+
+    def __init__(
+        self,
+        head_size: int,
+        rotary_dim: int,
+        max_position_embeddings: int,
+        base: int,
+        is_neox_style: bool,
+        dtype: torch.dtype,
+    ) -> None:
+        super().__init__()
+        self.head_size = head_size
+        self.rotary_dim = rotary_dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        self.is_neox_style = is_neox_style
+        self.dtype = dtype
+
+        cache = self._compute_cos_sin_cache()
+        cache = cache.to(dtype)
+        self.cos_sin_cache: torch.Tensor
+        self.register_buffer("cos_sin_cache", cache, persistent=False)
+
+    def _compute_inv_freq(self, base: Union[int, float]) -> torch.Tensor:
+        """Compute the inverse frequency."""
+        # NOTE(woosuk): To exactly match the HF implementation, we need to
+        # use CPU to compute the cache and then move it to GPU. However, we
+        # create the cache on GPU for faster initialization. This may cause
+        # a slight numerical difference between the HF implementation and ours.
+        inv_freq = 1.0 / (
+            base
+            ** (
+                torch.arange(0, self.rotary_dim, 2, dtype=torch.float) / self.rotary_dim
             )
-            text_len = ed - st
+        )
+        return inv_freq
 
-            st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
-            llm_pos_ids_list.append(
-                torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx
+    def _compute_cos_sin_cache(self) -> torch.Tensor:
+        """Compute the cos and sin cache."""
+        inv_freq = self._compute_inv_freq(self.base)
+        t = torch.arange(self.max_position_embeddings, 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
+
+    def forward_native(
+        self,
+        positions: torch.Tensor,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        offsets: Optional[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """A PyTorch-native implementation of forward()."""
+        if offsets is not None:
+            positions = positions + offsets
+        positions = positions.flatten()
+        num_tokens = positions.shape[0]
+        cos_sin = self.cos_sin_cache.index_select(0, positions)
+        cos, sin = cos_sin.chunk(2, dim=-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_emb(query_rot, cos, sin, self.is_neox_style)
+        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_emb(key_rot, cos, sin, self.is_neox_style)
+        key = torch.cat((key_rot, key_pass), dim=-1).reshape(key_shape)
+        return query, key
+
+    def forward_cuda(
+        self,
+        positions: torch.Tensor,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        offsets: Optional[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        from vllm import _custom_ops as ops
+
+        self.cos_sin_cache = self.cos_sin_cache.to(query.device, dtype=query.dtype)
+        # ops.rotary_embedding()/batched_rotary_embedding()
+        # are in-place operations that update the query and key tensors.
+        if offsets is not None:
+            ops.batched_rotary_embedding(
+                positions,
+                query,
+                key,
+                self.head_size,
+                self.cos_sin_cache,
+                self.is_neox_style,
+                self.rotary_dim,
+                offsets,
             )
+        else:
+            ops.rotary_embedding(
+                positions,
+                query,
+                key,
+                self.head_size,
+                self.cos_sin_cache,
+                self.is_neox_style,
+            )
+        return query, key
 
-            t_index = (
-                torch.arange(llm_grid_t)
-                .view(-1, 1)
-                .expand(-1, llm_grid_h * llm_grid_w)
-                .flatten()
+    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()/batched_rotary_embedding()
+        # are in-place operations that update the query and key tensors.
+        if offsets is not None:
+            ops.batched_rotary_embedding(
+                positions,
+                query,
+                key,
+                self.head_size,
+                self.cos_sin_cache,
+                self.is_neox_style,
+                self.rotary_dim,
+                offsets,
             )
-            h_index = (
-                torch.arange(llm_grid_h)
-                .view(1, -1, 1)
-                .expand(llm_grid_t, -1, llm_grid_w)
-                .flatten()
+        else:
+            ops.rotary_embedding(
+                positions,
+                query,
+                key,
+                self.head_size,
+                self.cos_sin_cache,
+                self.is_neox_style,
             )
-            w_index = (
-                torch.arange(llm_grid_w)
-                .view(1, 1, -1)
-                .expand(llm_grid_t, llm_grid_h, -1)
-                .flatten()
+        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}"
+        s += f", base={self.base}, is_neox_style={self.is_neox_style}"
+        return s
+
+
+class LinearScalingRotaryEmbedding(RotaryEmbedding):
+    """RotaryEmbedding extended with linear scaling.
+
+    It supports multiple scaling factors. Since multiple LoRA adapters may have
+    different scaling factors, we need multiple cos/sin caches. In this way,
+    instead of running rotary embedding kernel per lora, we can run multiple
+    lora in a batched way.
+
+    In addition to that, we also keep the cos/sin cache for the scaling factor
+    of 1 (default) at all times.
+
+    Exemplary for two scaling factors x=1, y and z with embeddings
+    [[x11, x12, ... x1m], ..., [xn1, xn2, ..., xnm]] and
+    [[y11, y12, ... y1o], ..., [yn1, yn2, ..., yno]], and
+    [[z11, z12, ... z1p], ..., [zn1, zn2, ..., znp]],
+
+    we construct the cos/sin cache as follows:
+    [[x11, x12, ... x1m, y11, y12, ... y1o, z11, z12, ... z1p],
+        ...
+     [xn1, xn2, ... xnm, yn1, yn2, ... yno, zn1, zn2, ... znp]]
+
+    We then use offsets to index into the cos/sin cache for
+    the respective scaling factors.
+
+    The offset to cache can be accessed via `scaling_factor_to_offset` API.
+
+    Credits to the Reddit user /u/kaiokendev
+    """
+
+    def __init__(
+        self,
+        head_size: int,
+        rotary_dim: int,
+        max_position_embeddings: int,
+        base: int,
+        is_neox_style: bool,
+        scaling_factors: Union[List[float], float],
+        dtype: torch.dtype,
+    ) -> None:
+        if isinstance(scaling_factors, float):
+            scaling_factors = [scaling_factors]
+        self.scaling_factors: List[float] = scaling_factors  # noqa
+        super().__init__(
+            head_size, rotary_dim, max_position_embeddings, base, is_neox_style, dtype
+        )
+        # Lazy initialized.
+        self._scaling_factor_to_offset: Dict[float, int]
+
+    def _compute_cos_sin_cache(self) -> torch.Tensor:
+        inv_freq = self._compute_inv_freq(self.base)
+        cache_list: List[torch.Tensor] = []
+        # offsets to the next cache in a tensor.
+        # Each offset corresponds to the same index in scaling_factors.
+        offsets: List[int] = []
+        for scaling_factor in self.scaling_factors:
+            # NOTE(woosuk): self.max_position_embeddings is the original
+            # maximum length before applying the rope scaling.
+            # Thus, the maximum length after applying the rope scaling is
+            # self.max_position_embeddings * self.scaling_factor.
+            max_len = self.max_position_embeddings * scaling_factor
+            t = torch.arange(max_len, dtype=torch.float)
+            t = t / scaling_factor
+
+            freqs = torch.einsum("i,j -> ij", t, inv_freq)
+            cos = freqs.cos()
+            sin = freqs.sin()
+            cache = torch.cat((cos, sin), dim=-1)
+            if not cache_list:
+                offset = 0
+            else:
+                last_offset = offsets[-1]
+                next_max_len = cache_list[-1].shape[0]
+                offset = last_offset + next_max_len
+            offsets.append(offset)
+            cache_list.append(cache)
+        self._scaling_factor_to_offset = {
+            float(scaling_factor): offsets[i]
+            for i, scaling_factor in enumerate(self.scaling_factors)
+        }
+        assert len(self.scaling_factors) == len(offsets)
+        return torch.cat(cache_list, dim=0)
+
+    @property
+    def scaling_factor_to_offset(self) -> Dict[float, int]:
+        return self._scaling_factor_to_offset
+
+
+class DynamicNTKScalingRotaryEmbedding(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_factor: float,
+        dtype: torch.dtype,
+    ) -> None:
+        self.scaling_factor = scaling_factor
+        super().__init__(
+            head_size, rotary_dim, max_position_embeddings, base, is_neox_style, dtype
+        )
+
+    def _compute_cos_sin_cache(self) -> torch.Tensor:
+        # NOTE(woosuk): self.max_position_embeddings is the original
+        # maximum length before applying the rope scaling.
+        # Thus, the maximum length after applying the rope scaling is
+        # self.max_position_embeddings * self.scaling_factor.
+        max_len = self.max_position_embeddings * self.scaling_factor
+        base = self.base * (
+            (self.scaling_factor * max_len / self.max_position_embeddings)
+            - (self.scaling_factor - 1)
+        ) ** (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
+
+
+# Inverse dim formula to find dim based on number of rotations
+def _yarn_find_correction_dim(
+    num_rotations: int,
+    dim: int,
+    base: float = 10000,
+    max_position_embeddings: int = 2048,
+) -> float:
+    return (dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi))) / (
+        2 * math.log(base)
+    )
+
+
+# Find dim range bounds based on rotations
+def _yarn_find_correction_range(
+    low_rot: int,
+    high_rot: int,
+    dim: int,
+    base: float = 10000,
+    max_position_embeddings: int = 2048,
+) -> Tuple[int, int]:
+    low = math.floor(
+        _yarn_find_correction_dim(low_rot, dim, base, max_position_embeddings)
+    )
+    high = math.ceil(
+        _yarn_find_correction_dim(high_rot, dim, base, max_position_embeddings)
+    )
+    return max(low, 0), min(high, dim - 1)  # Clamp values just in case
+
+
+def _yarn_linear_ramp_mask(
+    low: float, high: float, dim: int, dtype: torch.dtype
+) -> torch.Tensor:
+    if low == high:
+        high += 0.001  # Prevent singularity
+
+    linear_func = (torch.arange(dim, dtype=dtype) - low) / (high - low)
+    ramp_func = torch.clamp(linear_func, 0, 1)
+    return ramp_func
+
+
+def _yarn_get_mscale(scale: float = 1) -> float:
+    if scale <= 1:
+        return 1.0
+    return 0.1 * math.log(scale) + 1.0
+
+
+class YaRNScalingRotaryEmbedding(RotaryEmbedding):
+    """RotaryEmbedding extended with YaRN method.
+
+    Credits to Peng et al. github.com/jquesnelle/yarn
+    """
+
+    def __init__(
+        self,
+        head_size: int,
+        rotary_dim: int,
+        max_position_embeddings: int,
+        base: int,
+        is_neox_style: bool,
+        scaling_factor: float,
+        dtype: torch.dtype,
+        *,
+        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
+        # Get n-d magnitude scaling corrected for interpolation
+        self.mscale = float(_yarn_get_mscale(self.scaling_factor) * attn_factor)
+        super().__init__(
+            head_size, rotary_dim, max_position_embeddings, base, is_neox_style, dtype
+        )
+
+    def _compute_inv_freq(self, scaling_factor: float) -> torch.Tensor:
+        pos_freqs = self.base ** (
+            torch.arange(0, self.rotary_dim, 2, dtype=torch.float) / self.rotary_dim
+        )
+        inv_freq_extrapolation = 1.0 / pos_freqs
+        inv_freq_interpolation = 1.0 / (scaling_factor * pos_freqs)
+
+        low, high = _yarn_find_correction_range(
+            self.beta_fast,
+            self.beta_slow,
+            self.rotary_dim,
+            self.base,
+            self.max_position_embeddings,
+        )
+        # Get n-d rotational scaling corrected for extrapolation
+        inv_freq_mask = (
+            1
+            - _yarn_linear_ramp_mask(low, high, self.rotary_dim // 2, dtype=torch.float)
+        ) * self.extrapolation_factor
+        inv_freq = (
+            inv_freq_interpolation * (1 - inv_freq_mask)
+            + inv_freq_extrapolation * inv_freq_mask
+        )
+        return inv_freq
+
+    def _compute_cos_sin_cache(self) -> torch.Tensor:
+        inv_freq = self._compute_inv_freq(self.scaling_factor)
+        t = torch.arange(
+            self.max_position_embeddings * self.scaling_factor, dtype=torch.float32
+        )
+        freqs = torch.einsum("i,j -> ij", t, inv_freq)
+        cos = freqs.cos() * self.mscale
+        sin = freqs.sin() * self.mscale
+        cache = torch.cat((cos, sin), dim=-1)
+        return cache
+
+
+class Phi3LongRoPEScaledRotaryEmbedding(nn.Module):
+    """Phi3 family of models scaled rotary embedding.
+
+    Based on the original RotaryEmbedding implementation.
+    """
+
+    def __init__(
+        self,
+        head_size: int,
+        rotary_dim: int,
+        max_position_embeddings: int,
+        original_max_position_embeddings: int,
+        base: int,
+        is_neox_style: bool,
+        dtype: torch.dtype,
+        short_factor: List[float],
+        long_factor: List[float],
+        short_mscale: Optional[float] = None,
+        long_mscale: Optional[float] = None,
+    ):
+        super().__init__()
+
+        if rotary_dim != head_size:
+            raise ValueError(
+                f"`Phi3LongRoPEScaledRotaryEmbedding` does not support \
+                    rotary_dim != head_size ({rotary_dim}!={head_size})."
             )
-            llm_pos_ids_list.append(
-                torch.stack([t_index, h_index, w_index]) + text_len + st_idx
+        if is_neox_style is False:
+            raise ValueError(
+                "`Phi3LongRoPEScaledRotaryEmbedding` only supports neox_style."
             )
-            st = ed + llm_grid_t * llm_grid_h * llm_grid_w
 
-        if st < input_tokens_len:
-            st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
-            text_len = input_tokens_len - st
-            llm_pos_ids_list.append(
-                torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx
+        self.head_size = head_size
+        self.max_position_embeddings = max_position_embeddings
+        self.original_max_position_embeddings = original_max_position_embeddings
+        self.base = base
+        self.short_factor = short_factor
+        self.long_factor = long_factor
+
+        scale = self.max_position_embeddings / self.original_max_position_embeddings
+        if scale <= 1.0:
+            scaling_factor = 1.0
+        else:
+            scaling_factor = math.sqrt(
+                1 + math.log(scale) / math.log(self.original_max_position_embeddings)
             )
+        if short_mscale is None:
+            short_mscale = scaling_factor
+        if long_mscale is None:
+            long_mscale = scaling_factor
 
-        llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
-        llm_positions = llm_positions[:, context_len:]
-        mrope_position_delta = (llm_positions.max() + 1 - input_tokens_len).item()
-        return llm_positions.tolist(), mrope_position_delta
+        self.short_mscale = short_mscale
+        self.long_mscale = long_mscale
 
-    @staticmethod
-    def get_next_input_positions(
-        mrope_position_delta: int,
-        context_len: int,
-        seq_len: int,
-    ) -> List[List[int]]:
-        return [
-            list(
-                range(
-                    context_len + mrope_position_delta, seq_len + mrope_position_delta
+        short_cache = self._compute_cos_sin_cache(
+            original_max_position_embeddings, short_factor, short_mscale
+        )
+        short_cache = short_cache.to(dtype)
+        self.register_buffer("short_cos_sin_cache", short_cache, persistent=False)
+
+        long_cache = self._compute_cos_sin_cache(
+            max_position_embeddings, long_factor, long_mscale
+        )
+        long_cache = long_cache.to(dtype)
+        self.register_buffer("long_cos_sin_cache", long_cache, persistent=False)
+
+        long_short_cache = torch.cat(
+            [self.short_cos_sin_cache, self.long_cos_sin_cache], dim=0
+        )
+        self.register_buffer(
+            "long_short_cos_sin_cache", long_short_cache, persistent=False
+        )
+
+    def _compute_inv_freq(self, rescale_factors: List[float]) -> torch.Tensor:
+        rescale_factors = torch.tensor(rescale_factors, dtype=torch.float32)
+        inv_freq = 1.0 / (
+            rescale_factors
+            * (
+                self.base
+                ** (
+                    torch.arange(0, self.head_size, 2, dtype=torch.float)
+                    / self.head_size
                 )
             )
-            for _ in range(3)
-        ]
+        )
+        return inv_freq
+
+    def _compute_cos_sin_cache(
+        self,
+        max_position_embeddings: int,
+        rescale_factors: List[float],
+        mscale: float,
+    ) -> torch.Tensor:
+        inv_freq = self._compute_inv_freq(rescale_factors)
+        t = torch.arange(max_position_embeddings, dtype=torch.float)
+        freqs = torch.einsum("i,j -> ij", t, inv_freq)
+        cos = freqs.cos() * mscale
+        sin = freqs.sin() * mscale
+        cache = torch.cat((cos, sin), dim=-1)
+        return cache
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        offsets: Optional[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        query = query.view(*query.shape[:-1], -1, self.head_size)
+        key = key.view(*key.shape[:-1], -1, self.head_size)
+
+        k = self.original_max_position_embeddings
+        long_prompt_offset = (
+            torch.any(positions > k).float() * torch.full_like(positions, k)
+        ).long()
+        idx = (
+            torch.add(positions, long_prompt_offset)
+            if long_prompt_offset is not None
+            else positions
+        )
+        self.long_short_cos_sin_cache: torch.Tensor = self.long_short_cos_sin_cache.to(
+            idx.device
+        )
+        idx = torch.add(idx, offsets) if offsets is not None else idx
+        cos_sin = torch.index_select(self.long_short_cos_sin_cache, 0, idx)
+
+        cos, sin = cos_sin.chunk(2, dim=-1)
+        cos = cos.repeat(1, 2).unsqueeze(-2)
+        sin = sin.repeat(1, 2).unsqueeze(-2)
+
+        query = query * cos + _rotate_neox(query) * sin
+        key = key * cos + _rotate_neox(key) * sin
+
+        return query.flatten(-2), key.flatten(-2)
+
+
+def yarn_get_mscale(scale: float = 1, mscale: float = 1) -> float:
+    if scale <= 1:
+        return 1.0
+    return 0.1 * mscale * math.log(scale) + 1.0
 
 
-# TODO: in the DeepseekScalingRotaryEmbedding class defined in vllm,
-# the device has been hard-coded to "cuda" in these two places:
-# https://github.com/vllm-project/vllm/blob/8a1f938e6f02052df0f4953c149410605a2d56d8/vllm/model_executor/layers/rotary_embedding.py#L646
-# https://github.com/vllm-project/vllm/blob/8a1f938e6f02052df0f4953c149410605a2d56d8/vllm/model_executor/layers/rotary_embedding.py#L665
-# We port the related code to this file to make it compatible with the CPU version.
-# We will add an optimized rotary embedding kernel for CPU and will remove the ported code then.
 class DeepseekScalingRotaryEmbedding(RotaryEmbedding):
     """RotaryEmbedding extended with YaRN method.
 
@@ -149,7 +664,6 @@ class DeepseekScalingRotaryEmbedding(RotaryEmbedding):
         beta_slow: int = 1,
         mscale: float = 1,
         mscale_all_dim: float = 0,
-        device: Optional[str] = None,
     ) -> None:
         self.scaling_factor = scaling_factor
         self.extrapolation_factor = extrapolation_factor
@@ -162,14 +676,13 @@ class DeepseekScalingRotaryEmbedding(RotaryEmbedding):
             / yarn_get_mscale(self.scaling_factor, float(mscale_all_dim))
             * attn_factor
         )
-        self.device = device
         super().__init__(
             head_size, rotary_dim, max_position_embeddings, base, is_neox_style, dtype
         )
 
     def _compute_inv_freq(self, scaling_factor: float) -> torch.Tensor:
         pos_freqs = self.base ** (
-            torch.arange(0, self.rotary_dim, 2, dtype=torch.float, device=self.device)
+            torch.arange(0, self.rotary_dim, 2, dtype=torch.float, device="cuda")
             / self.rotary_dim
         )
         inv_freq_extrapolation = 1.0 / pos_freqs
@@ -197,7 +710,7 @@ class DeepseekScalingRotaryEmbedding(RotaryEmbedding):
         inv_freq = self._compute_inv_freq(self.scaling_factor)
         t = torch.arange(
             self.max_position_embeddings * self.scaling_factor,
-            device=self.device,
+            device="cuda",
             dtype=torch.float32,
         )
         freqs = torch.einsum("i,j -> ij", t, inv_freq)
@@ -248,10 +761,249 @@ class DeepseekScalingRotaryEmbedding(RotaryEmbedding):
         return query, key
 
 
+class Llama3RotaryEmbedding(RotaryEmbedding):
+
+    def __init__(
+        self,
+        head_size: int,
+        rotary_dim: int,
+        max_position_embeddings: int,
+        base: int,
+        is_neox_style: bool,
+        dtype: torch.dtype,
+        scaling_factor: float,
+        low_freq_factor: float,
+        high_freq_factor: float,
+        orig_max_position: int,
+    ) -> None:
+        self.scaling_factor = scaling_factor
+        self.low_freq_factor = low_freq_factor
+        self.high_freq_factor = high_freq_factor
+        self.orig_max_position = orig_max_position
+        super().__init__(
+            head_size, rotary_dim, max_position_embeddings, base, is_neox_style, dtype
+        )
+
+    def _compute_inv_freq(self, base: Union[int, float]) -> torch.Tensor:
+        inv_freqs = super()._compute_inv_freq(base)
+        low_freq_wavelen = self.orig_max_position / self.low_freq_factor
+        high_freq_wavelen = self.orig_max_position / self.high_freq_factor
+
+        wave_len = 2 * math.pi / inv_freqs
+        if self.low_freq_factor != self.high_freq_factor:
+            smooth = (self.orig_max_position / wave_len - self.low_freq_factor) / (
+                self.high_freq_factor - self.low_freq_factor
+            )
+        else:
+            smooth = 0
+        new_freqs = torch.where(
+            wave_len < high_freq_wavelen,
+            inv_freqs,
+            torch.where(
+                wave_len > low_freq_wavelen,
+                inv_freqs / self.scaling_factor,
+                (1 - smooth) * inv_freqs / self.scaling_factor + smooth * inv_freqs,
+            ),
+        )
+        return new_freqs
+
+
+class MRotaryEmbedding(RotaryEmbedding):
+    """Rotary Embedding with Multimodal Sections."""
+
+    def __init__(
+        self,
+        head_size: int,
+        rotary_dim: int,
+        max_position_embeddings: int,
+        base: int,
+        is_neox_style: bool,
+        dtype: torch.dtype,
+        mrope_section: Optional[List[int]] = None,
+    ) -> None:
+        super().__init__(
+            head_size, rotary_dim, max_position_embeddings, base, is_neox_style, dtype
+        )
+
+        self.mrope_section = mrope_section
+        if self.mrope_section:
+            assert sum(self.mrope_section) == rotary_dim // 2
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        query: torch.Tensor,
+        key: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """PyTorch-native implementation equivalent to forward().
+
+        Args:
+            positions:
+                [num_tokens,] (text only) or
+                [3, num_tokens] (T/H/W positions with multimodal inputs)
+            query: [num_tokens, num_heads * head_size]
+            key: [num_tokens, num_kv_heads * head_size]
+        """
+        assert positions.ndim == 1 or positions.ndim == 2
+
+        num_tokens = positions.shape[-1]
+        cos_sin = self.cos_sin_cache[positions]
+        cos, sin = cos_sin.chunk(2, dim=-1)
+        if positions.ndim == 2:
+            assert self.mrope_section
+
+            cos = torch.cat(
+                [m[i] for i, m in enumerate(cos.split(self.mrope_section, dim=-1))],
+                dim=-1,
+            )
+            sin = torch.cat(
+                [m[i] for i, m in enumerate(sin.split(self.mrope_section, dim=-1))],
+                dim=-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_emb(query_rot, cos, sin, self.is_neox_style)
+        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_emb(key_rot, cos, sin, self.is_neox_style)
+        key = torch.cat((key_rot, key_pass), dim=-1).reshape(key_shape)
+        return query, key
+
+    @staticmethod
+    def get_input_positions(
+        input_tokens: List[int],
+        image_grid_thw: Union[List[List[int]], torch.Tensor],
+        video_grid_thw: Union[List[List[int]], torch.Tensor],
+        image_token_id: int,
+        video_token_id: int,
+        vision_start_token_id: int,
+        vision_end_token_id: int,
+        spatial_merge_size: int,
+        context_len: int = 0,
+        seq_len: Optional[int] = None,
+    ) -> Tuple[List[List[int]], int]:
+        """Get mrope input positions and delta value."""
+
+        if isinstance(image_grid_thw, torch.Tensor):
+            image_grid_thw = image_grid_thw.tolist()
+        if isinstance(video_grid_thw, torch.Tensor):
+            video_grid_thw = video_grid_thw.tolist()
+
+        input_tokens_tensor = torch.tensor(input_tokens)
+        vision_start_indices = torch.argwhere(
+            input_tokens_tensor == vision_start_token_id
+        ).squeeze(1)
+        vision_tokens = input_tokens_tensor[vision_start_indices + 1]
+        image_nums = (vision_tokens == image_token_id).sum()
+        video_nums = (vision_tokens == video_token_id).sum()
+        llm_pos_ids_list: list = []
+
+        st = 0
+        remain_images, remain_videos = image_nums, video_nums
+
+        image_index, video_index = 0, 0
+        for _ in range(image_nums + video_nums):
+            if image_token_id in input_tokens and remain_images > 0:
+                ed_image = input_tokens.index(image_token_id, st)
+            else:
+                ed_image = len(input_tokens) + 1
+            if video_token_id in input_tokens and remain_videos > 0:
+                ed_video = input_tokens.index(video_token_id, st)
+            else:
+                ed_video = len(input_tokens) + 1
+            if ed_image < ed_video:
+                t, h, w = (
+                    image_grid_thw[image_index][0],
+                    image_grid_thw[image_index][1],
+                    image_grid_thw[image_index][2],
+                )
+                image_index += 1
+                remain_images -= 1
+                ed = ed_image
+            else:
+                t, h, w = (
+                    video_grid_thw[video_index][0],
+                    video_grid_thw[video_index][1],
+                    video_grid_thw[video_index][2],
+                )
+                video_index += 1
+                remain_videos -= 1
+                ed = ed_video
+            llm_grid_t, llm_grid_h, llm_grid_w = (
+                t,
+                h // spatial_merge_size,
+                w // spatial_merge_size,
+            )
+            text_len = ed - st
+
+            st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+            llm_pos_ids_list.append(
+                torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx
+            )
+
+            t_index = (
+                torch.arange(llm_grid_t)
+                .view(-1, 1)
+                .expand(-1, llm_grid_h * llm_grid_w)
+                .flatten()
+            )
+            h_index = (
+                torch.arange(llm_grid_h)
+                .view(1, -1, 1)
+                .expand(llm_grid_t, -1, llm_grid_w)
+                .flatten()
+            )
+            w_index = (
+                torch.arange(llm_grid_w)
+                .view(1, 1, -1)
+                .expand(llm_grid_t, llm_grid_h, -1)
+                .flatten()
+            )
+            llm_pos_ids_list.append(
+                torch.stack([t_index, h_index, w_index]) + text_len + st_idx
+            )
+            st = ed + llm_grid_t * llm_grid_h * llm_grid_w
+
+        if st < len(input_tokens):
+            st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+            text_len = len(input_tokens) - st
+            llm_pos_ids_list.append(
+                torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx
+            )
+
+        llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
+        mrope_position_delta = (llm_positions.max() + 1 - len(input_tokens)).item()
+        llm_positions = llm_positions[:, context_len:seq_len]
+
+        return llm_positions.tolist(), mrope_position_delta
+
+    @staticmethod
+    def get_next_input_positions(
+        mrope_position_delta: int,
+        context_len: int,
+        seq_len: int,
+    ) -> List[List[int]]:
+        return [
+            list(
+                range(
+                    context_len + mrope_position_delta, seq_len + mrope_position_delta
+                )
+            )
+            for _ in range(3)
+        ]
+
+
 _ROPE_DICT: Dict[Tuple, RotaryEmbedding] = {}
 
 
-def get_rope_cpu(
+def get_rope(
     head_size: int,
     rotary_dim: int,
     max_position: int,
@@ -260,7 +1012,6 @@ def get_rope_cpu(
     rope_scaling: Optional[Dict[str, Any]] = None,
     dtype: Optional[torch.dtype] = None,
     partial_rotary_factor: float = 1.0,
-    device: Optional[str] = None,
 ) -> RotaryEmbedding:
     if dtype is None:
         dtype = torch.get_default_dtype()
@@ -286,75 +1037,140 @@ def get_rope_cpu(
     if key in _ROPE_DICT:
         return _ROPE_DICT[key]
 
-    assert rope_scaling is not None
-    scaling_type = rope_scaling["rope_type"]
-    assert (
-        scaling_type == "deepseek_yarn"
-    ), "Only deepseek_yarn is supported for CPU for now"
-
-    scaling_factor = rope_scaling["factor"]
-    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",
-            "mscale",
-            "mscale_all_dim",
+    if rope_scaling is None:
+        rotary_emb = RotaryEmbedding(
+            head_size, rotary_dim, max_position, base, is_neox_style, dtype
         )
-    }
-    extra_kwargs["device"] = device
-    rotary_emb = DeepseekScalingRotaryEmbedding(
-        head_size,
-        rotary_dim,
-        original_max_position,
-        base,
-        is_neox_style,
-        scaling_factor,
-        dtype,
-        **extra_kwargs,
-    )
+    else:
+        scaling_type = rope_scaling["rope_type"]
 
+        if scaling_type == "llama3":
+            scaling_factor = rope_scaling["factor"]
+            low_freq_factor = rope_scaling["low_freq_factor"]
+            high_freq_factor = rope_scaling["high_freq_factor"]
+            original_max_position = rope_scaling["original_max_position_embeddings"]
+            rotary_emb = Llama3RotaryEmbedding(
+                head_size,
+                rotary_dim,
+                max_position,
+                base,
+                is_neox_style,
+                dtype,
+                scaling_factor,
+                low_freq_factor,
+                high_freq_factor,
+                original_max_position,
+            )
+        elif scaling_type == "default":
+            if "mrope_section" in rope_scaling:
+                rotary_emb = MRotaryEmbedding(
+                    head_size,
+                    rotary_dim,
+                    max_position,
+                    base,
+                    is_neox_style,
+                    dtype,
+                    mrope_section=rope_scaling["mrope_section"],
+                )
+            else:
+                rotary_emb = RotaryEmbedding(
+                    head_size,
+                    rotary_dim,
+                    max_position,
+                    base,
+                    is_neox_style,
+                    dtype,
+                )
+        elif scaling_type == "linear":
+            scaling_factor = rope_scaling["factor"]
+            rotary_emb = LinearScalingRotaryEmbedding(
+                head_size,
+                rotary_dim,
+                max_position,
+                base,
+                is_neox_style,
+                scaling_factor,
+                dtype,
+            )
+        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,
+            )
+        elif scaling_type == "yarn":
+            scaling_factor = rope_scaling["factor"]
+            original_max_position = rope_scaling["original_max_position_embeddings"]
+            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,
+                dtype,
+                **extra_kwargs,
+            )
+        elif scaling_type == "deepseek_yarn":
+            scaling_factor = rope_scaling["factor"]
+            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",
+                    "mscale",
+                    "mscale_all_dim",
+                )
+            }
+            rotary_emb = DeepseekScalingRotaryEmbedding(
+                head_size,
+                rotary_dim,
+                original_max_position,
+                base,
+                is_neox_style,
+                scaling_factor,
+                dtype,
+                **extra_kwargs,
+            )
+        elif scaling_type == "longrope":
+            short_factor = rope_scaling["short_factor"]
+            long_factor = rope_scaling["long_factor"]
+            original_max_position = rope_scaling["original_max_position_embeddings"]
+            extra_kwargs = {
+                k: v
+                for k, v in rope_scaling.items()
+                if k in ("short_mscale", "long_mscale")
+            }
+            rotary_emb = Phi3LongRoPEScaledRotaryEmbedding(
+                head_size,
+                rotary_dim,
+                max_position,
+                original_max_position,
+                base,
+                is_neox_style,
+                dtype,
+                short_factor,
+                long_factor,
+                **extra_kwargs,
+            )
+        else:
+            raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
     _ROPE_DICT[key] = rotary_emb
     return rotary_emb
-
-
-def get_rope_wrapper(
-    head_size: int,
-    rotary_dim: int,
-    max_position: int,
-    base: int,
-    is_neox_style: bool = True,
-    rope_scaling: Optional[Dict[str, Any]] = None,
-    dtype: Optional[torch.dtype] = None,
-    partial_rotary_factor: float = 1.0,
-    device: Optional[str] = None,
-):
-    if device != "cpu":
-        return get_rope(
-            head_size,
-            rotary_dim,
-            max_position,
-            base,
-            is_neox_style,
-            rope_scaling,
-            dtype,
-            partial_rotary_factor,
-        )
-
-    return get_rope_cpu(
-        head_size,
-        rotary_dim,
-        max_position,
-        base,
-        is_neox_style,
-        rope_scaling,
-        dtype,
-        partial_rotary_factor,
-        device,
-    )

python/sglang/srt/models/baichuan.py

@@ -24,7 +24,6 @@ from typing import Iterable, Optional, Tuple
 import torch
 from torch import nn
 from transformers import PretrainedConfig
-from vllm.model_executor.layers.rotary_embedding import get_rope
 
 from sglang.srt.distributed import (
     get_tensor_model_parallel_rank,
@@ -40,6 +39,7 @@ from sglang.srt.layers.linear import (
 from sglang.srt.layers.logits_processor import LogitsProcessor
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.layers.rotary_embedding import get_rope
 from sglang.srt.layers.vocab_parallel_embedding import (
     ParallelLMHead,
     VocabParallelEmbedding,

python/sglang/srt/models/chatglm.py

@@ -21,7 +21,6 @@ from typing import Iterable, Optional, Tuple
 import torch
 from torch import nn
 from torch.nn import LayerNorm
-from vllm.model_executor.layers.rotary_embedding import get_rope
 
 from sglang.srt.configs import ChatGLMConfig
 from sglang.srt.distributed import get_tensor_model_parallel_world_size
@@ -35,6 +34,7 @@ from sglang.srt.layers.linear import (
 from sglang.srt.layers.logits_processor import LogitsProcessor
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.layers.rotary_embedding import get_rope
 from sglang.srt.layers.vocab_parallel_embedding import (
     ParallelLMHead,
     VocabParallelEmbedding,

python/sglang/srt/models/commandr.py

@@ -44,7 +44,6 @@ import torch.utils.checkpoint
 from torch import nn
 from torch.nn.parameter import Parameter
 from transformers import PretrainedConfig
-from vllm.model_executor.layers.rotary_embedding import get_rope
 
 from sglang.srt.distributed import (
     get_tensor_model_parallel_rank,
@@ -59,6 +58,7 @@ from sglang.srt.layers.linear import (
 from sglang.srt.layers.logits_processor import LogitsProcessor
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.layers.rotary_embedding import get_rope
 from sglang.srt.layers.vocab_parallel_embedding import VocabParallelEmbedding
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 from sglang.srt.model_loader.weight_utils import default_weight_loader

python/sglang/srt/models/dbrx.py

@@ -19,7 +19,6 @@ from typing import Iterable, Optional, Tuple
 
 import torch
 import torch.nn as nn
-from vllm.model_executor.layers.rotary_embedding import get_rope
 
 from sglang.srt.configs import DbrxConfig
 from sglang.srt.distributed import (
@@ -36,6 +35,7 @@ from sglang.srt.layers.logits_processor import LogitsProcessor
 from sglang.srt.layers.moe.fused_moe_triton import fused_moe
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.layers.rotary_embedding import get_rope
 from sglang.srt.layers.vocab_parallel_embedding import (
     DEFAULT_VOCAB_PADDING_SIZE,
     ParallelLMHead,

python/sglang/srt/models/deepseek.py

@@ -21,7 +21,6 @@ from typing import Any, Dict, Iterable, Optional, Tuple
 import torch
 from torch import nn
 from transformers import PretrainedConfig
-from vllm.model_executor.layers.rotary_embedding import get_rope
 
 from sglang.srt.distributed import (
     get_tensor_model_parallel_rank,
@@ -40,6 +39,7 @@ from sglang.srt.layers.logits_processor import LogitsProcessor
 from sglang.srt.layers.moe.fused_moe_triton import fused_moe
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.layers.rotary_embedding import get_rope
 from sglang.srt.layers.vocab_parallel_embedding import (
     ParallelLMHead,
     VocabParallelEmbedding,

python/sglang/srt/models/deepseek_v2.py

@@ -23,7 +23,6 @@ import torch.nn.functional as F
 from torch import nn
 from transformers import PretrainedConfig
 from vllm import _custom_ops as ops
-from vllm.model_executor.layers.rotary_embedding import get_rope
 
 from sglang.srt.distributed import (
     get_tensor_model_parallel_rank,
@@ -49,7 +48,7 @@ from sglang.srt.layers.quantization.fp8_utils import (
     normalize_e4m3fn_to_e4m3fnuz,
 )
 from sglang.srt.layers.radix_attention import RadixAttention
-from sglang.srt.layers.rotary_embedding import get_rope_wrapper
+from sglang.srt.layers.rotary_embedding import get_rope
 from sglang.srt.layers.vocab_parallel_embedding import (
     ParallelLMHead,
     VocabParallelEmbedding,
@@ -272,7 +271,7 @@ class DeepseekV2Attention(nn.Module):
             quant_config=quant_config,
         )
         rope_scaling["rope_type"] = "deepseek_yarn"
-        self.rotary_emb = get_rope_wrapper(
+        self.rotary_emb = get_rope(
             qk_rope_head_dim,
             rotary_dim=qk_rope_head_dim,
             max_position=max_position_embeddings,

python/sglang/srt/models/exaone.py

@@ -20,7 +20,6 @@ from typing import Any, Dict, Iterable, Optional, Tuple
 
 import torch
 from torch import nn
-from vllm.model_executor.layers.rotary_embedding import get_rope
 
 from sglang.srt.distributed import get_tensor_model_parallel_world_size
 from sglang.srt.layers.activation import SiluAndMul
@@ -33,6 +32,7 @@ from sglang.srt.layers.linear import (
 from sglang.srt.layers.logits_processor import LogitsProcessor, LogitsProcessorOutput
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.layers.rotary_embedding import get_rope
 from sglang.srt.layers.vocab_parallel_embedding import (
     ParallelLMHead,
     VocabParallelEmbedding,

python/sglang/srt/models/gemma.py

@@ -21,7 +21,6 @@ from typing import Iterable, Optional, Tuple
 import torch
 from torch import nn
 from transformers import PretrainedConfig
-from vllm.model_executor.layers.rotary_embedding import get_rope
 
 from sglang.srt.distributed import get_tensor_model_parallel_world_size
 from sglang.srt.layers.activation import GeluAndMul
@@ -34,6 +33,7 @@ from sglang.srt.layers.linear import (
 from sglang.srt.layers.logits_processor import LogitsProcessor
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.layers.rotary_embedding import get_rope
 from sglang.srt.layers.vocab_parallel_embedding import VocabParallelEmbedding
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 from sglang.srt.model_loader.weight_utils import default_weight_loader

python/sglang/srt/models/gemma2.py

@@ -15,12 +15,11 @@
 # Adapted from:
 # https://github.com/vllm-project/vllm/blob/56b325e977435af744f8b3dca7af0ca209663558/vllm/model_executor/models/gemma2.py
 
-from typing import Iterable, Optional, Set, Tuple, Union
+from typing import Iterable, Optional, Set, Tuple
 
 import torch
 from torch import nn
 from transformers import PretrainedConfig
-from vllm.model_executor.layers.rotary_embedding import get_rope
 
 from sglang.srt.distributed import get_tensor_model_parallel_world_size
 from sglang.srt.layers.activation import GeluAndMul
@@ -33,6 +32,7 @@ from sglang.srt.layers.linear import (
 from sglang.srt.layers.logits_processor import LogitsProcessor
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.layers.rotary_embedding import get_rope
 from sglang.srt.layers.vocab_parallel_embedding import VocabParallelEmbedding
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 from sglang.srt.model_loader.weight_utils import default_weight_loader
@@ -45,10 +45,6 @@ def get_attention_sliding_window_size(config):
     return config.sliding_window - 1
 
 
-# FIXME: temporary solution, remove after next vllm release
-from vllm.model_executor.layers.rotary_embedding import RotaryEmbedding
-
-
 class Gemma2MLP(nn.Module):
     def __init__(
         self,

python/sglang/srt/models/gpt2.py

@@ -25,8 +25,6 @@ from transformers import GPT2Config
 
 from sglang.srt.distributed.parallel_state import get_tensor_model_parallel_world_size
 from sglang.srt.layers.activation import get_act_fn
-
-# from sglang.srt.layers.activation import get_act_fn
 from sglang.srt.layers.linear import (
     ColumnParallelLinear,
     QKVParallelLinear,

python/sglang/srt/models/granite.py

@@ -22,7 +22,6 @@ from typing import Any, Dict, Iterable, Optional, Tuple
 import torch
 from torch import nn
 from transformers import GraniteConfig
-from vllm.model_executor.layers.rotary_embedding import get_rope
 
 from sglang.srt.distributed import get_tensor_model_parallel_world_size
 from sglang.srt.layers.activation import SiluAndMul
@@ -36,6 +35,7 @@ from sglang.srt.layers.logits_processor import LogitsProcessor, LogitsProcessorO
 from sglang.srt.layers.pooler import Pooler, PoolingType
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.layers.rotary_embedding import get_rope
 from sglang.srt.layers.vocab_parallel_embedding import (
     ParallelLMHead,
     VocabParallelEmbedding,

python/sglang/srt/models/grok.py

@@ -22,7 +22,6 @@ import torch
 import torch.nn.functional as F
 from torch import nn
 from transformers import PretrainedConfig
-from vllm.model_executor.layers.rotary_embedding import get_rope
 
 from sglang.srt.distributed import (
     get_tensor_model_parallel_rank,
@@ -40,6 +39,7 @@ from sglang.srt.layers.logits_processor import LogitsProcessor
 from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.layers.rotary_embedding import get_rope
 from sglang.srt.layers.vocab_parallel_embedding import (
     ParallelLMHead,
     VocabParallelEmbedding,

python/sglang/srt/models/internlm2.py

@@ -19,7 +19,6 @@ from typing import Any, Dict, Iterable, Optional, Tuple
 import torch
 from torch import nn
 from transformers import PretrainedConfig
-from vllm.model_executor.layers.rotary_embedding import get_rope
 
 from sglang.srt.distributed import get_tensor_model_parallel_world_size
 from sglang.srt.layers.activation import SiluAndMul
@@ -32,6 +31,7 @@ from sglang.srt.layers.linear import (
 from sglang.srt.layers.logits_processor import LogitsProcessor
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.layers.rotary_embedding import get_rope
 from sglang.srt.layers.vocab_parallel_embedding import (
     ParallelLMHead,
     VocabParallelEmbedding,

python/sglang/srt/models/llama.py

@@ -22,7 +22,6 @@ from typing import Any, Dict, Iterable, Optional, Tuple
 import torch
 from torch import nn
 from transformers import LlamaConfig
-from vllm.model_executor.layers.rotary_embedding import get_rope
 
 from sglang.srt.distributed import (
     get_tensor_model_parallel_rank,
@@ -39,6 +38,7 @@ from sglang.srt.layers.logits_processor import LogitsProcessor, LogitsProcessorO
 from sglang.srt.layers.pooler import Pooler, PoolingType
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.layers.rotary_embedding import get_rope
 from sglang.srt.layers.vocab_parallel_embedding import (
     ParallelLMHead,
     VocabParallelEmbedding,

python/sglang/srt/models/minicpm.py

@@ -18,7 +18,6 @@ from typing import Any, Dict, Iterable, Optional, Tuple
 
 import torch
 from torch import nn
-from vllm.model_executor.layers.rotary_embedding import get_rope
 
 from sglang.srt.distributed import get_tensor_model_parallel_world_size
 from sglang.srt.layers.activation import SiluAndMul
@@ -31,6 +30,7 @@ from sglang.srt.layers.linear import (
 from sglang.srt.layers.logits_processor import LogitsProcessor
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.layers.rotary_embedding import get_rope
 from sglang.srt.layers.vocab_parallel_embedding import (
     ParallelLMHead,
     VocabParallelEmbedding,

python/sglang/srt/models/minicpm3.py

@@ -19,7 +19,6 @@ from typing import Any, Dict, Iterable, Optional, Tuple
 import torch
 from torch import nn
 from transformers import PretrainedConfig
-from vllm.model_executor.layers.rotary_embedding import get_rope
 
 from sglang.srt.distributed import get_tensor_model_parallel_world_size
 from sglang.srt.layers.activation import SiluAndMul
@@ -33,6 +32,7 @@ from sglang.srt.layers.linear import (
 from sglang.srt.layers.logits_processor import LogitsProcessor
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.layers.rotary_embedding import get_rope
 from sglang.srt.layers.vocab_parallel_embedding import (
     ParallelLMHead,
     VocabParallelEmbedding,

python/sglang/srt/models/mixtral.py

@@ -21,7 +21,6 @@ from typing import Iterable, Optional, Tuple
 import torch
 from torch import nn
 from transformers import MixtralConfig
-from vllm.model_executor.layers.rotary_embedding import get_rope
 
 from sglang.srt.distributed import (
     get_tensor_model_parallel_world_size,
@@ -38,6 +37,7 @@ from sglang.srt.layers.moe.ep_moe.layer import EPMoE
 from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.layers.rotary_embedding import get_rope
 from sglang.srt.layers.vocab_parallel_embedding import (
     ParallelLMHead,
     VocabParallelEmbedding,

python/sglang/srt/models/mixtral_quant.py

@@ -23,7 +23,6 @@ import torch
 import torch.nn.functional as F
 from torch import nn
 from transformers import MixtralConfig
-from vllm.model_executor.layers.rotary_embedding import get_rope
 
 from sglang.srt.distributed import (
     get_tensor_model_parallel_rank,
@@ -39,6 +38,7 @@ from sglang.srt.layers.linear import (
 from sglang.srt.layers.logits_processor import LogitsProcessor
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.layers.rotary_embedding import get_rope
 from sglang.srt.layers.vocab_parallel_embedding import (
     ParallelLMHead,
     VocabParallelEmbedding,

python/sglang/srt/models/olmo.py

@@ -20,7 +20,6 @@ from typing import Iterable, Optional, Tuple
 import torch
 from torch import nn
 from transformers import OlmoConfig
-from vllm.model_executor.layers.rotary_embedding import get_rope
 
 from sglang.srt.distributed import get_tensor_model_parallel_world_size
 from sglang.srt.layers.activation import SiluAndMul
@@ -32,6 +31,7 @@ from sglang.srt.layers.linear import (
 from sglang.srt.layers.logits_processor import LogitsProcessor
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.layers.rotary_embedding import get_rope
 from sglang.srt.layers.vocab_parallel_embedding import (
     ParallelLMHead,
     VocabParallelEmbedding,