[Model][Perf] Use cos and sin cache in QwenVL (#28798)

Signed-off-by: gcanlin <canlinguosdu@gmail.com>

vllm/model_executor/layers/rotary_embedding/base.py

@@ -83,6 +83,11 @@ class RotaryEmbeddingBase(CustomOp):
         ):
             self.cos_sin_cache = self.cos_sin_cache.to(query.device, dtype=query.dtype)
 
+    def get_cos_sin(self, seqlen: int) -> tuple[torch.Tensor, torch.Tensor]:
+        cos_sin = self.cos_sin_cache[:seqlen]
+        cos, sin = cos_sin.chunk(2, dim=-1)
+        return cos, sin
+
 
 class RotaryEmbedding(RotaryEmbeddingBase):
     def __init__(

vllm/model_executor/models/glm4_1v.py

@@ -65,6 +65,7 @@ from vllm.model_executor.layers.linear import (
     RowParallelLinear,
 )
 from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.models.module_mapping import MultiModelKeys
 from vllm.multimodal import MULTIMODAL_REGISTRY
@@ -341,7 +342,8 @@ class Glm4vVisionAttention(nn.Module):
         self,
         x: torch.Tensor,
         cu_seqlens: torch.Tensor,
-        rotary_pos_emb: torch.Tensor,
+        rotary_pos_emb_cos: torch.Tensor,
+        rotary_pos_emb_sin: torch.Tensor,
         max_seqlen: int | None = None,  # Only used for Flash Attention
         seqlens: list[int] | None = None,  # Only used for xFormers
     ) -> torch.Tensor:
@@ -353,10 +355,12 @@ class Glm4vVisionAttention(nn.Module):
         batch_size = q.shape[1]
 
         q, k, v = (rearrange(x, "s b ... -> b s ...").contiguous() for x in (q, k, v))
-        if rotary_pos_emb is not None:
+        if rotary_pos_emb_cos is not None and rotary_pos_emb_sin is not None:
             # [2 * b, s, heads, head_dim]
             qk_concat = torch.cat([q, k], dim=0)
-            qk_rotated = apply_rotary_pos_emb_vision(qk_concat, rotary_pos_emb)
+            qk_rotated = apply_rotary_pos_emb_vision(
+                qk_concat, rotary_pos_emb_cos, rotary_pos_emb_sin
+            )
             q, k = torch.chunk(qk_rotated, 2, dim=0)
 
         if self.is_flash_attn_backend:
@@ -454,14 +458,16 @@ class Glm4vVisionBlock(nn.Module):
         self,
         x: torch.Tensor,
         cu_seqlens: torch.Tensor,
-        rotary_pos_emb: torch.Tensor,
+        rotary_pos_emb_cos: torch.Tensor,
+        rotary_pos_emb_sin: torch.Tensor,
         max_seqlen: int | None = None,  # Only used for Flash Attention
         seqlens: list[int] | None = None,  # Only used for xFormers
     ) -> torch.Tensor:
         x_attn = self.attn(
             self.norm1(x),
             cu_seqlens=cu_seqlens,
-            rotary_pos_emb=rotary_pos_emb,
+            rotary_pos_emb_cos=rotary_pos_emb_cos,
+            rotary_pos_emb_sin=rotary_pos_emb_sin,
             max_seqlen=max_seqlen,
             seqlens=seqlens,
         )
@@ -660,44 +666,6 @@ class Glm4vVisionEmbeddings(nn.Module):
         return embeddings
 
 
-class Glm4vVisionRotaryEmbedding(nn.Module):
-    def __init__(self, dim: int, theta: float = 10000.0) -> None:
-        super().__init__()
-        self.dim = dim
-        self.theta = theta
-        inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim))
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self._seq_len_cached = 0
-        self._freqs_cached = None
-
-    def update_freqs_cache(self, seqlen: int) -> None:
-        if seqlen > self._seq_len_cached:
-            seqlen *= 2
-            self._seq_len_cached = seqlen
-            self.inv_freq = 1.0 / (
-                self.theta
-                ** (
-                    torch.arange(
-                        0,
-                        self.dim,
-                        2,
-                        dtype=torch.float,
-                        device=self.inv_freq.device,
-                    )
-                    / self.dim
-                )
-            )
-            seq = torch.arange(
-                seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype
-            )
-            freqs = torch.outer(seq, self.inv_freq)
-            self._freqs_cached = freqs
-
-    def forward(self, seqlen: int) -> torch.Tensor:
-        self.update_freqs_cache(seqlen)
-        return self._freqs_cached[:seqlen]
-
-
 class Glm4vVisionTransformer(nn.Module):
     def __init__(
         self,
@@ -731,7 +699,13 @@ class Glm4vVisionTransformer(nn.Module):
 
         norm_layer = partial(RMSNorm, eps=norm_eps)
         head_dim = self.hidden_size // self.num_heads
-        self.rotary_pos_emb = Glm4vVisionRotaryEmbedding(head_dim // 2)
+        self.rotary_pos_emb = get_rope(
+            head_size=head_dim,
+            rotary_dim=head_dim // 2,
+            max_position=8192,
+            base=10000.0,
+            is_neox_style=True,
+        )
         self.blocks = nn.ModuleList(
             [
                 Glm4vVisionBlock(
@@ -789,7 +763,9 @@ class Glm4vVisionTransformer(nn.Module):
     def device(self) -> torch.device:
         return self.patch_embed.proj.weight.device
 
-    def rot_pos_emb(self, grid_thw: torch.Tensor) -> torch.Tensor:
+    def rot_pos_emb(
+        self, grid_thw: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
         pos_ids = []
         for t, h, w in grid_thw:
             hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w)
@@ -817,9 +793,18 @@ class Glm4vVisionTransformer(nn.Module):
             pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1))
         pos_ids = torch.cat(pos_ids, dim=0)
         max_grid_size = grid_thw[:, 1:].max()
-        rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size)
-        rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
-        return rotary_pos_emb, pos_ids
+
+        # Use pre-computed cos_sin_cache from RotaryEmbedding
+        cos, sin = self.rotary_pos_emb.get_cos_sin(max_grid_size)
+
+        cos_h = cos[pos_ids[:, 0]]  # (num_tokens, rotary_dim // 2)
+        cos_w = cos[pos_ids[:, 1]]
+        sin_h = sin[pos_ids[:, 0]]
+        sin_w = sin[pos_ids[:, 1]]
+
+        cos_combined = torch.cat([cos_h, cos_w], dim=-1)
+        sin_combined = torch.cat([sin_h, sin_w], dim=-1)
+        return cos_combined, sin_combined, pos_ids
 
     def compute_attn_mask_seqlen(
         self,
@@ -848,7 +833,9 @@ class Glm4vVisionTransformer(nn.Module):
         x = self.post_conv_layernorm(x)
 
         # compute position embedding
-        rotary_pos_emb, image_type_ids = self.rot_pos_emb(grid_thw)
+        rotary_pos_emb_cos, rotary_pos_emb_sin, image_type_ids = self.rot_pos_emb(
+            grid_thw
+        )
         # compute cu_seqlens
         cu_seqlens = torch.repeat_interleave(
             grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]
@@ -867,7 +854,8 @@ class Glm4vVisionTransformer(nn.Module):
             x = blk(
                 x,
                 cu_seqlens=cu_seqlens,
-                rotary_pos_emb=rotary_pos_emb,
+                rotary_pos_emb_cos=rotary_pos_emb_cos,
+                rotary_pos_emb_sin=rotary_pos_emb_sin,
                 max_seqlen=max_seqlen,
                 seqlens=seqlens,
             )

vllm/model_executor/models/qwen2_5_vl.py

@@ -64,6 +64,7 @@ from vllm.model_executor.layers.linear import (
     RowParallelLinear,
 )
 from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.models.module_mapping import MultiModelKeys
 from vllm.model_executor.models.vision import should_torch_compile_mm_vit
@@ -363,7 +364,8 @@ class Qwen2_5_VisionAttention(nn.Module):
         self,
         x: torch.Tensor,
         cu_seqlens: torch.Tensor,
-        rotary_pos_emb: torch.Tensor,
+        rotary_pos_emb_cos: torch.Tensor,
+        rotary_pos_emb_sin: torch.Tensor,
         max_seqlen: torch.Tensor,  # Only used for Flash Attention
         seqlens: torch.Tensor,  # Only used for xFormers
     ) -> torch.Tensor:
@@ -378,13 +380,15 @@ class Qwen2_5_VisionAttention(nn.Module):
             head=self.num_attention_heads_per_partition,
         )
 
-        if rotary_pos_emb is not None:
+        if rotary_pos_emb_cos is not None and rotary_pos_emb_sin is not None:
             qk, v = qkv[:, :, :2], qkv[:, :, 2]
 
             qk_reshaped = einops.rearrange(
                 qk, "b s two head head_dim -> (two b) s head head_dim", two=2
             )
-            qk_rotated = apply_rotary_pos_emb_vision(qk_reshaped, rotary_pos_emb)
+            qk_rotated = apply_rotary_pos_emb_vision(
+                qk_reshaped, cos=rotary_pos_emb_cos, sin=rotary_pos_emb_sin
+            )
             qk_rotated = qk_rotated.view(
                 2,
                 batch_size,
@@ -434,7 +438,8 @@ class Qwen2_5_VisionAttention(nn.Module):
     dynamic_arg_dims={
         "x": 0,
         "cu_seqlens": 0,
-        "rotary_pos_emb": 0,
+        "rotary_pos_emb_cos": 0,
+        "rotary_pos_emb_sin": 0,
         "seqlens": 0,
     },
     mark_unbacked_dims={"seqlens": 0},
@@ -485,14 +490,16 @@ class Qwen2_5_VisionBlock(nn.Module):
         self,
         x: torch.Tensor,
         cu_seqlens: torch.Tensor,
-        rotary_pos_emb: torch.Tensor,
+        rotary_pos_emb_cos: torch.Tensor,
+        rotary_pos_emb_sin: torch.Tensor,
         max_seqlen: torch.Tensor,  # Only used for Flash Attention
         seqlens: torch.Tensor,  # Only used for xFormers
     ) -> torch.Tensor:
         x_attn = self.attn(
             self.norm1(x),
             cu_seqlens=cu_seqlens,
-            rotary_pos_emb=rotary_pos_emb,
+            rotary_pos_emb_cos=rotary_pos_emb_cos,
+            rotary_pos_emb_sin=rotary_pos_emb_sin,
             max_seqlen=max_seqlen,
             seqlens=seqlens,
         )
@@ -588,42 +595,6 @@ class Qwen2_5_VisionPatchMerger(nn.Module):
         return out
 
 
-class Qwen2_5_VisionRotaryEmbedding(nn.Module):
-    def __init__(self, dim: int, theta: float = 10000.0) -> None:
-        super().__init__()
-        self.dim = dim
-        self.theta = theta
-        inv_freq = 1.0 / (
-            theta ** (torch.arange(0, dim, 2, dtype=torch.float, device="cpu") / dim)
-        )
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self._seq_len_cached = 0
-        self._freqs_cached = None
-
-    def update_freqs_cache(self, seqlen: int) -> None:
-        if seqlen > self._seq_len_cached:
-            seqlen *= 2
-            self._seq_len_cached = seqlen
-            self.inv_freq = 1.0 / (
-                self.theta
-                ** (
-                    torch.arange(
-                        0, self.dim, 2, dtype=torch.float, device=self.inv_freq.device
-                    )
-                    / self.dim
-                )
-            )
-            seq = torch.arange(
-                seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype
-            )
-            freqs = torch.outer(seq, self.inv_freq)
-            self._freqs_cached = freqs
-
-    def forward(self, seqlen: int) -> torch.Tensor:
-        self.update_freqs_cache(seqlen)
-        return self._freqs_cached[:seqlen]
-
-
 class Qwen2_5_VisionTransformer(nn.Module):
     def __init__(
         self,
@@ -666,7 +637,13 @@ class Qwen2_5_VisionTransformer(nn.Module):
 
         norm_layer = partial(RMSNorm, eps=norm_eps)
         head_dim = self.hidden_size // self.num_heads
-        self.rotary_pos_emb = Qwen2_5_VisionRotaryEmbedding(head_dim // 2)
+        self.rotary_pos_emb = get_rope(
+            head_size=head_dim,
+            rotary_dim=head_dim // 2,
+            max_position=8192,
+            base=10000.0,
+            is_neox_style=True,
+        )
 
         use_upstream_fa = False
         self.attn_backend = get_vit_attn_backend(
@@ -757,15 +734,30 @@ class Qwen2_5_VisionTransformer(nn.Module):
         )
         pos_ids = torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1)
         max_size = max(h, w)
-        rotary_pos_emb_full = self.rotary_pos_emb(max_size)
-        rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
-        rotary_pos_emb = rotary_pos_emb.reshape(
-            rotary_pos_emb.shape[0] // self.spatial_merge_unit,
+
+        # Use pre-computed cos_sin_cache from RotaryEmbedding
+        cos, sin = self.rotary_pos_emb.get_cos_sin(max_size)
+
+        cos_h = cos[pos_ids[:, 0]]  # (num_tokens, rotary_dim // 2)
+        cos_w = cos[pos_ids[:, 1]]
+        sin_h = sin[pos_ids[:, 0]]
+        sin_w = sin[pos_ids[:, 1]]
+
+        cos_combined = torch.cat([cos_h, cos_w], dim=-1)
+        sin_combined = torch.cat([sin_h, sin_w], dim=-1)
+
+        cos_combined = cos_combined.reshape(
+            cos_combined.shape[0] // self.spatial_merge_unit,
+            self.spatial_merge_unit,
+            -1,
+        )
+        sin_combined = sin_combined.reshape(
+            sin_combined.shape[0] // self.spatial_merge_unit,
             self.spatial_merge_unit,
             -1,
         )
 
-        return rotary_pos_emb
+        return cos_combined, sin_combined
 
     def get_window_index_thw(self, grid_t, grid_h, grid_w):
         vit_merger_window_size = (
@@ -807,14 +799,19 @@ class Qwen2_5_VisionTransformer(nn.Module):
     @lru_cache(maxsize=1024)  # noqa: B019
     def get_rope_by_thw(self, t, h, w):
         window_index_thw, cu_seqlens_window_thw = self.get_window_index_thw(t, h, w)
-        rotary_pos_emb_thw = self.rotary_pos_emb_thw(t, h, w)
-        rotary_pos_emb_thw = rotary_pos_emb_thw[window_index_thw, :, :]
-        rotary_pos_emb_thw = rotary_pos_emb_thw.flatten(start_dim=0, end_dim=1)
+        cos_thw, sin_thw = self.rotary_pos_emb_thw(t, h, w)
+
+        cos_thw = cos_thw[window_index_thw, :, :]
+        cos_thw = cos_thw.flatten(start_dim=0, end_dim=1)
+        sin_thw = sin_thw[window_index_thw, :, :]
+        sin_thw = sin_thw.flatten(start_dim=0, end_dim=1)
+
         cu_seqlens_thw = torch.repeat_interleave(
             torch.tensor([h * w], dtype=torch.int32), t
         )
         return (
-            rotary_pos_emb_thw,
+            cos_thw,
+            sin_thw,
             window_index_thw,
             cu_seqlens_window_thw,
             cu_seqlens_thw,
@@ -849,7 +846,8 @@ class Qwen2_5_VisionTransformer(nn.Module):
     ) -> torch.Tensor:
         # patchify
         seq_len, _ = x.size()
-        rotary_pos_emb = []
+        rotary_pos_emb_cos = []
+        rotary_pos_emb_sin = []
         window_index: list = []
         cu_window_seqlens: list = [torch.tensor([0], dtype=torch.int32)]
         cu_seqlens: list = []
@@ -865,7 +863,8 @@ class Qwen2_5_VisionTransformer(nn.Module):
             llm_w = w // self.spatial_merge_size
 
             (
-                rotary_pos_emb_thw,
+                cos_thw,
+                sin_thw,
                 window_index_thw,
                 cu_seqlens_window_thw,
                 cu_seqlens_thw,
@@ -878,11 +877,13 @@ class Qwen2_5_VisionTransformer(nn.Module):
             cu_window_seqlens_last = cu_seqlens_window_thw[-1]
             cu_window_seqlens.append(cu_seqlens_window_thw)
 
-            rotary_pos_emb.append(rotary_pos_emb_thw)
+            rotary_pos_emb_cos.append(cos_thw)
+            rotary_pos_emb_sin.append(sin_thw)
 
             cu_seqlens.append(cu_seqlens_thw)
 
-        rotary_pos_emb = torch.cat(rotary_pos_emb)
+        rotary_pos_emb_cos = torch.cat(rotary_pos_emb_cos)
+        rotary_pos_emb_sin = torch.cat(rotary_pos_emb_sin)
         window_index = torch.cat(window_index)
         # compute reverse indices
         reverse_indices = self.invert_permutation(window_index)
@@ -901,7 +902,12 @@ class Qwen2_5_VisionTransformer(nn.Module):
 
         cu_seqlens = cu_seqlens.to(device=self.device, non_blocking=True)
         cu_window_seqlens = cu_window_seqlens.to(device=self.device, non_blocking=True)
-        rotary_pos_emb = rotary_pos_emb.to(device=self.device, non_blocking=True)
+        rotary_pos_emb_cos = rotary_pos_emb_cos.to(
+            device=self.device, non_blocking=True
+        )
+        rotary_pos_emb_sin = rotary_pos_emb_sin.to(
+            device=self.device, non_blocking=True
+        )
         window_index = window_index.to(device=hidden_states.device, non_blocking=True)
         reverse_indices = reverse_indices.to(
             device=hidden_states.device, non_blocking=True
@@ -928,7 +934,8 @@ class Qwen2_5_VisionTransformer(nn.Module):
             hidden_states = blk(
                 hidden_states,
                 cu_seqlens=cu_seqlens_now,
-                rotary_pos_emb=rotary_pos_emb,
+                rotary_pos_emb_cos=rotary_pos_emb_cos,
+                rotary_pos_emb_sin=rotary_pos_emb_sin,
                 max_seqlen=max_seqlen_now,
                 seqlens=seqlens_now,
             )

vllm/model_executor/models/qwen2_vl.py

@@ -32,7 +32,7 @@ from typing import Annotated, Any, Literal, TypeAlias
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from einops import rearrange, repeat
+from einops import rearrange
 from transformers import BatchFeature
 from transformers.models.qwen2_vl import Qwen2VLImageProcessor, Qwen2VLProcessor
 from transformers.models.qwen2_vl.configuration_qwen2_vl import (
@@ -59,7 +59,9 @@ from vllm.model_executor.layers.linear import (
     RowParallelLinear,
 )
 from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.rotary_embedding.common import (
+    apply_rotary_emb_torch,
     dispatch_rotary_emb_function,
 )
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
@@ -275,47 +277,13 @@ class Qwen2VisionMLP(nn.Module):
         return x
 
 
-def rotate_half(x: torch.Tensor, interleaved: bool = False) -> torch.Tensor:
-    if not interleaved:
-        x1, x2 = x.chunk(2, dim=-1)
-        return torch.cat((-x2, x1), dim=-1)
-    else:
-        x1, x2 = x[..., ::2], x[..., 1::2]
-        return rearrange(
-            torch.stack((-x2, x1), dim=-1), "... d two -> ... (d two)", two=2
-        )
-
-
-def apply_rotary_emb_torch(
-    x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor, interleaved: bool = False
+def apply_rotary_pos_emb_vision(
+    t: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor
 ) -> torch.Tensor:
-    """
-    x: (batch_size, seqlen, nheads, headdim)
-    cos, sin: (seqlen, rotary_dim / 2) or (batch_size, seqlen, rotary_dim / 2)
-    """
-    ro_dim = cos.shape[-1] * 2
-    assert ro_dim <= x.shape[-1]
-    cos = repeat(
-        cos, "... d -> ... 1 (2 d)" if not interleaved else "... d -> ... 1 (d 2)"
-    )
-    sin = repeat(
-        sin, "... d -> ... 1 (2 d)" if not interleaved else "... d -> ... 1 (d 2)"
+    rotary_emb_function = dispatch_rotary_emb_function(
+        default=partial(apply_rotary_emb_torch, is_neox_style=True)
     )
-    return torch.cat(
-        [
-            x[..., :ro_dim] * cos + rotate_half(x[..., :ro_dim], interleaved) * sin,
-            x[..., ro_dim:],
-        ],
-        dim=-1,
-    )
-
-
-def apply_rotary_pos_emb_vision(t: torch.Tensor, freqs: torch.Tensor) -> torch.Tensor:
-    rotary_emb_function = dispatch_rotary_emb_function(default=apply_rotary_emb_torch)
-    t_ = t.float()
-    cos = freqs.cos()
-    sin = freqs.sin()
-    output = rotary_emb_function(t_, cos, sin).type_as(t)
+    output = rotary_emb_function(t, cos, sin).type_as(t)
     return output
 
 
@@ -412,7 +380,8 @@ class Qwen2VisionAttention(nn.Module):
         self,
         x: torch.Tensor,
         cu_seqlens: torch.Tensor,
-        rotary_pos_emb: torch.Tensor,
+        rotary_pos_emb_cos: torch.Tensor,
+        rotary_pos_emb_sin: torch.Tensor,
         max_seqlen: int | None = None,  # Only used for Flash Attention
         seqlens: list[int] | None = None,  # Only used for xFormers
     ) -> torch.Tensor:
@@ -424,11 +393,13 @@ class Qwen2VisionAttention(nn.Module):
         batch_size = q.shape[1]
 
         q, k, v = (rearrange(x, "s b ... -> b s ...") for x in (q, k, v))
-        if rotary_pos_emb is not None:
-            # [2 * b, s, heads, head_dim]
-            qk_concat = torch.cat([q, k], dim=0)
-            qk_rotated = apply_rotary_pos_emb_vision(qk_concat, rotary_pos_emb)
-            q, k = torch.chunk(qk_rotated, 2, dim=0)
+
+        # [2 * b, s, heads, head_dim]
+        qk_concat = torch.cat([q, k], dim=0)
+        qk_rotated = apply_rotary_pos_emb_vision(
+            qk_concat, rotary_pos_emb_cos, rotary_pos_emb_sin
+        )
+        q, k = torch.chunk(qk_rotated, 2, dim=0)
 
         if self.is_flash_attn_backend:
             q, k, v = (rearrange(x, "b s ... -> (b s) ...") for x in [q, k, v])
@@ -534,14 +505,16 @@ class Qwen2VisionBlock(nn.Module):
         self,
         x: torch.Tensor,
         cu_seqlens: torch.Tensor,
-        rotary_pos_emb: torch.Tensor,
+        rotary_pos_emb_cos: torch.Tensor,
+        rotary_pos_emb_sin: torch.Tensor,
         max_seqlen: int | None = None,  # Only used for Flash Attention
         seqlens: list[int] | None = None,  # Only used for xFormers
     ) -> torch.Tensor:
         x = x + self.attn(
             self.norm1(x),
             cu_seqlens=cu_seqlens,
-            rotary_pos_emb=rotary_pos_emb,
+            rotary_pos_emb_cos=rotary_pos_emb_cos,
+            rotary_pos_emb_sin=rotary_pos_emb_sin,
             max_seqlen=max_seqlen,
             seqlens=seqlens,
         )
@@ -628,40 +601,6 @@ class Qwen2VisionPatchMerger(nn.Module):
         return out
 
 
-class Qwen2VisionRotaryEmbedding(nn.Module):
-    def __init__(self, dim: int, theta: float = 10000.0) -> None:
-        super().__init__()
-        self.dim = dim
-        self.theta = theta
-        inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim))
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self._seq_len_cached = 0
-        self._freqs_cached = None
-
-    def update_freqs_cache(self, seqlen: int) -> None:
-        if seqlen > self._seq_len_cached:
-            seqlen *= 2
-            self._seq_len_cached = seqlen
-            self.inv_freq = 1.0 / (
-                self.theta
-                ** (
-                    torch.arange(
-                        0, self.dim, 2, dtype=torch.float, device=self.inv_freq.device
-                    )
-                    / self.dim
-                )
-            )
-            seq = torch.arange(
-                seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype
-            )
-            freqs = torch.outer(seq, self.inv_freq)
-            self._freqs_cached = freqs
-
-    def forward(self, seqlen: int) -> torch.Tensor:
-        self.update_freqs_cache(seqlen)
-        return self._freqs_cached[:seqlen]
-
-
 class Qwen2VisionTransformer(nn.Module):
     def __init__(
         self,
@@ -700,7 +639,13 @@ class Qwen2VisionTransformer(nn.Module):
 
         norm_layer = partial(nn.LayerNorm, eps=norm_eps)
         head_dim = embed_dim // num_heads
-        self.rotary_pos_emb = Qwen2VisionRotaryEmbedding(head_dim // 2)
+        self.rotary_pos_emb = get_rope(
+            head_size=head_dim,
+            rotary_dim=head_dim // 2,
+            max_position=8192,
+            base=10000.0,
+            is_neox_style=True,
+        )
 
         self.blocks = nn.ModuleList(
             [
@@ -744,7 +689,9 @@ class Qwen2VisionTransformer(nn.Module):
     def device(self) -> torch.device:
         return self.patch_embed.proj.weight.device
 
-    def rot_pos_emb(self, grid_thw: list[list[int]]) -> torch.Tensor:
+    def rot_pos_emb(
+        self, grid_thw: list[list[int]]
+    ) -> tuple[torch.Tensor, torch.Tensor]:
         pos_ids = []
         max_grid_size = 0
         for t, h, w in grid_thw:
@@ -773,9 +720,18 @@ class Qwen2VisionTransformer(nn.Module):
             pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1))
             max_grid_size = max(max_grid_size, h, w)
         pos_ids = torch.cat(pos_ids, dim=0)
-        rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size)
-        rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
-        return rotary_pos_emb
+
+        # Use pre-computed cos_sin_cache from RotaryEmbedding
+        cos, sin = self.rotary_pos_emb.get_cos_sin(max_grid_size)
+
+        cos_h = cos[pos_ids[:, 0]]  # (num_tokens, rotary_dim // 2)
+        cos_w = cos[pos_ids[:, 1]]
+        sin_h = sin[pos_ids[:, 0]]
+        sin_w = sin[pos_ids[:, 1]]
+
+        cos_combined = torch.cat([cos_h, cos_w], dim=-1)
+        sin_combined = torch.cat([sin_h, sin_w], dim=-1)
+        return cos_combined, sin_combined
 
     def compute_attn_mask_seqlen(
         self, cu_seqlens: torch.Tensor
@@ -806,7 +762,7 @@ class Qwen2VisionTransformer(nn.Module):
             grid_thw_list = grid_thw.tolist()
 
         # compute position embedding
-        rotary_pos_emb = self.rot_pos_emb(grid_thw_list)
+        rotary_pos_emb_cos, rotary_pos_emb_sin = self.rot_pos_emb(grid_thw_list)
 
         # compute cu_seqlens
         cu_seqlens = torch.repeat_interleave(
@@ -824,7 +780,8 @@ class Qwen2VisionTransformer(nn.Module):
             x = blk(
                 x,
                 cu_seqlens=cu_seqlens,
-                rotary_pos_emb=rotary_pos_emb,
+                rotary_pos_emb_cos=rotary_pos_emb_cos,
+                rotary_pos_emb_sin=rotary_pos_emb_sin,
                 max_seqlen=max_seqlen,
                 seqlens=seqlens,
             )

vllm/model_executor/models/qwen3_omni_moe_thinker.py

@@ -60,6 +60,7 @@ from vllm.model_executor.layers.linear import (
 )
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.models.qwen2_audio import Qwen2AudioProcessingInfo
@@ -90,7 +91,6 @@ from .qwen2_5_omni_thinker import (
 )
 from .qwen2_5_vl import (
     Qwen2_5_VisionAttention,
-    Qwen2_5_VisionRotaryEmbedding,
     Qwen2_5_VLProcessingInfo,
 )
 from .qwen3_moe import Qwen3MoeForCausalLM, Qwen3MoeModel
@@ -221,14 +221,16 @@ class Qwen3_VisionBlock(nn.Module):
         self,
         x: torch.Tensor,
         cu_seqlens: torch.Tensor,
-        rotary_pos_emb: torch.Tensor,
+        rotary_pos_emb_cos: torch.Tensor,
+        rotary_pos_emb_sin: torch.Tensor,
         max_seqlen: torch.Tensor,  # Only used for Flash Attention
         seqlens: torch.Tensor,  # Only used for xFormers
     ) -> torch.Tensor:
         x = x + self.attn(
             self.norm1(x),
             cu_seqlens=cu_seqlens,
-            rotary_pos_emb=rotary_pos_emb,
+            rotary_pos_emb_cos=rotary_pos_emb_cos,
+            rotary_pos_emb_sin=rotary_pos_emb_sin,
             max_seqlen=max_seqlen,
             seqlens=seqlens,
         )
@@ -332,7 +334,13 @@ class Qwen3Omni_VisionTransformer(nn.Module):
 
         norm_layer = partial(nn.LayerNorm, eps=norm_eps)
         head_dim = self.hidden_size // self.num_heads
-        self.rotary_pos_emb = Qwen2_5_VisionRotaryEmbedding(head_dim // 2)
+        self.rotary_pos_emb = get_rope(
+            head_size=head_dim,
+            rotary_dim=head_dim // 2,
+            max_position=8192,
+            base=10000.0,
+            is_neox_style=True,
+        )
 
         self.blocks = nn.ModuleList(
             [
@@ -416,9 +424,19 @@ class Qwen3Omni_VisionTransformer(nn.Module):
             pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1))
         pos_ids = torch.cat(pos_ids, dim=0)
         max_grid_size = grid_thw[:, 1:].max()
-        rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size)
-        rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
-        return rotary_pos_emb
+
+        # Use pre-computed cos_sin_cache from RotaryEmbedding
+        cos, sin = self.rotary_pos_emb.get_cos_sin(max_grid_size)
+
+        cos_h = cos[pos_ids[:, 0]]  # (num_tokens, rotary_dim // 2)
+        cos_w = cos[pos_ids[:, 1]]
+        sin_h = sin[pos_ids[:, 0]]
+        sin_w = sin[pos_ids[:, 1]]
+
+        cos_combined = torch.cat([cos_h, cos_w], dim=-1)
+        sin_combined = torch.cat([sin_h, sin_w], dim=-1)
+
+        return cos_combined, sin_combined
 
     def fast_pos_embed_interpolate(self, grid_thw: list[list[int]]) -> torch.Tensor:
         num_grid_per_side = self.num_grid_per_side
@@ -508,7 +526,7 @@ class Qwen3Omni_VisionTransformer(nn.Module):
         if self.apply_vit_abs_pos_embed:
             pos_embeds = self.fast_pos_embed_interpolate(grid_thw)
             hidden_states = hidden_states + pos_embeds
-        rotary_pos_emb = self.rot_pos_emb(grid_thw)
+        rotary_pos_emb_cos, rotary_pos_emb_sin = self.rot_pos_emb(grid_thw)
 
         cu_seqlens = torch.repeat_interleave(
             grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]
@@ -519,7 +537,8 @@ class Qwen3Omni_VisionTransformer(nn.Module):
         cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0)
 
         hidden_states = hidden_states.unsqueeze(1)
-        rotary_pos_emb = rotary_pos_emb.to(hidden_states.device)
+        rotary_pos_emb_cos = rotary_pos_emb_cos.to(hidden_states.device)
+        rotary_pos_emb_sin = rotary_pos_emb_sin.to(hidden_states.device)
         max_seqlen, seqlens = self.compute_attn_mask_seqlen(cu_seqlens)
 
         hidden_states_list = []
@@ -529,7 +548,8 @@ class Qwen3Omni_VisionTransformer(nn.Module):
             hidden_states = blk(
                 hidden_states,
                 cu_seqlens=cu_seqlens,
-                rotary_pos_emb=rotary_pos_emb,
+                rotary_pos_emb_cos=rotary_pos_emb_cos,
+                rotary_pos_emb_sin=rotary_pos_emb_sin,
                 max_seqlen=max_seqlen,
                 seqlens=seqlens,
             )

vllm/model_executor/models/qwen3_vl.py

@@ -63,6 +63,7 @@ from vllm.model_executor.layers.linear import (
 )
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.models.module_mapping import MultiModelKeys
@@ -95,7 +96,6 @@ from .interfaces import (
 )
 from .qwen2_5_vl import (
     Qwen2_5_VisionAttention,
-    Qwen2_5_VisionRotaryEmbedding,
     Qwen2_5_VLImageEmbeddingInputs,
     Qwen2_5_VLImageInputs,
     Qwen2_5_VLImagePixelInputs,
@@ -232,14 +232,16 @@ class Qwen3_VisionBlock(nn.Module):
         self,
         x: torch.Tensor,
         cu_seqlens: torch.Tensor,
-        rotary_pos_emb: torch.Tensor,
+        rotary_pos_emb_cos: torch.Tensor,
+        rotary_pos_emb_sin: torch.Tensor,
         max_seqlen: torch.Tensor,  # Only used for Flash Attention
         seqlens: torch.Tensor,  # Only used for xFormers
     ) -> torch.Tensor:
         x = x + self.attn(
             self.norm1(x),
             cu_seqlens=cu_seqlens,
-            rotary_pos_emb=rotary_pos_emb,
+            rotary_pos_emb_cos=rotary_pos_emb_cos,
+            rotary_pos_emb_sin=rotary_pos_emb_sin,
             max_seqlen=max_seqlen,
             seqlens=seqlens,
         )
@@ -339,7 +341,13 @@ class Qwen3_VisionTransformer(nn.Module):
 
         norm_layer = partial(nn.LayerNorm, eps=norm_eps)
         head_dim = self.hidden_size // self.num_heads
-        self.rotary_pos_emb = Qwen2_5_VisionRotaryEmbedding(head_dim // 2)
+        self.rotary_pos_emb = get_rope(
+            head_size=head_dim,
+            rotary_dim=head_dim // 2,
+            max_position=8192,
+            base=10000.0,
+            is_neox_style=True,
+        )
 
         self.merger = Qwen3_VisionPatchMerger(
             d_model=vision_config.out_hidden_size,
@@ -452,9 +460,19 @@ class Qwen3_VisionTransformer(nn.Module):
             for t, h, w in grid_thw
         ]
         pos_ids = torch.cat(pos_ids, dim=0)
-        rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size)
-        rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
-        return rotary_pos_emb
+
+        # Use pre-computed cos_sin_cache from RotaryEmbedding
+        cos, sin = self.rotary_pos_emb.get_cos_sin(max_grid_size)
+
+        cos_h = cos[pos_ids[:, 0]]  # (num_tokens, rotary_dim // 2)
+        cos_w = cos[pos_ids[:, 1]]
+        sin_h = sin[pos_ids[:, 0]]
+        sin_w = sin[pos_ids[:, 1]]
+
+        cos_combined = torch.cat([cos_h, cos_w], dim=-1)
+        sin_combined = torch.cat([sin_h, sin_w], dim=-1)
+
+        return cos_combined, sin_combined
 
     def fast_pos_embed_interpolate(self, grid_thw: list[list[int]]) -> torch.Tensor:
         num_grid_per_side = self.num_grid_per_side
@@ -547,8 +565,13 @@ class Qwen3_VisionTransformer(nn.Module):
 
         pos_embeds = self.fast_pos_embed_interpolate(grid_thw_list)
         hidden_states = hidden_states + pos_embeds
-        rotary_pos_emb = self.rot_pos_emb(grid_thw_list)
-        rotary_pos_emb = rotary_pos_emb.to(hidden_states.device, non_blocking=True)
+        rotary_pos_emb_cos, rotary_pos_emb_sin = self.rot_pos_emb(grid_thw_list)
+        rotary_pos_emb_cos = rotary_pos_emb_cos.to(
+            hidden_states.device, non_blocking=True
+        )
+        rotary_pos_emb_sin = rotary_pos_emb_sin.to(
+            hidden_states.device, non_blocking=True
+        )
 
         cu_seqlens = torch.repeat_interleave(
             grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]
@@ -564,7 +587,8 @@ class Qwen3_VisionTransformer(nn.Module):
             hidden_states = blk(
                 hidden_states,
                 cu_seqlens=cu_seqlens,
-                rotary_pos_emb=rotary_pos_emb,
+                rotary_pos_emb_cos=rotary_pos_emb_cos,
+                rotary_pos_emb_sin=rotary_pos_emb_sin,
                 max_seqlen=max_seqlen,
                 seqlens=seqlens,
             )