diff --git a/vllm/attention/ops/vit_attn_wrappers.py b/vllm/attention/ops/vit_attn_wrappers.py
index 6cefe7441668..06a9f7cd8226 100644
--- a/vllm/attention/ops/vit_attn_wrappers.py
+++ b/vllm/attention/ops/vit_attn_wrappers.py
@@ -14,6 +14,7 @@ To use these ops, you must have a recent version of PyTorch installed (>= 2.4.0)
 
 import einops
 import torch
+import torch.nn.functional as F
 
 from vllm.utils.torch_utils import direct_register_custom_op
 
@@ -123,3 +124,55 @@ def vit_flash_attn_wrapper(
     return torch.ops.vllm.flash_attn_maxseqlen_wrapper(
         q, k, v, cu_seqlens, max_seqlen, batch_size, is_rocm_aiter, use_upstream_fa
     )
+
+
+# TODO: Once we have a torch 2.10, we can use tensor slices
+# so we won't need to wrap this in custom ops
+def torch_sdpa_wrapper(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    cu_seqlens: torch.Tensor,
+) -> torch.Tensor:
+    outputs = []
+    for i in range(1, len(cu_seqlens)):
+        start_idx = cu_seqlens[i - 1]
+        end_idx = cu_seqlens[i]
+        q_i = q[:, start_idx:end_idx]
+        k_i = k[:, start_idx:end_idx]
+        v_i = v[:, start_idx:end_idx]
+        q_i, k_i, v_i = (
+            einops.rearrange(x, "b s h d -> b h s d") for x in [q_i, k_i, v_i]
+        )
+        output_i = F.scaled_dot_product_attention(q_i, k_i, v_i, dropout_p=0.0)
+        output_i = einops.rearrange(output_i, "b h s d -> b s h d ")
+        outputs.append(output_i)
+    context_layer = torch.cat(outputs, dim=1)
+    context_layer = einops.rearrange(context_layer, "b s h d -> s b (h d)").contiguous()
+    return context_layer
+
+
+def torch_sdpa_wrapper_fake(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    cu_seqlens: torch.Tensor,
+) -> torch.Tensor:
+    b, s, h, d = q.shape
+    return torch.empty((s, b, h * d), dtype=q.dtype, device=q.device)
+
+
+direct_register_custom_op(
+    op_name="torch_sdpa_wrapper",
+    op_func=torch_sdpa_wrapper,
+    fake_impl=torch_sdpa_wrapper_fake,
+)
+
+
+def vit_torch_sdpa_wrapper(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    cu_seqlens: torch.Tensor,
+) -> torch.Tensor:
+    return torch.ops.vllm.torch_sdpa_wrapper(q, k, v, cu_seqlens)
diff --git a/vllm/model_executor/models/qwen2_5_vl.py b/vllm/model_executor/models/qwen2_5_vl.py
index 3585783e4ccc..2b04608dfd03 100644
--- a/vllm/model_executor/models/qwen2_5_vl.py
+++ b/vllm/model_executor/models/qwen2_5_vl.py
@@ -46,6 +46,7 @@ from vllm.attention.backends.registry import _Backend
 from vllm.attention.layer import maybe_get_vit_flash_attn_backend
 from vllm.attention.ops.vit_attn_wrappers import (
     vit_flash_attn_wrapper,
+    vit_torch_sdpa_wrapper,
     vit_xformers_attn_wrapper,
 )
 from vllm.compilation.decorators import support_torch_compile
@@ -442,23 +443,12 @@ class Qwen2_5_VisionAttention(nn.Module):
                 q = q.contiguous()
                 k = k.contiguous()
                 v = v.contiguous()
-            outputs = []
-            for i in range(1, len(cu_seqlens)):
-                start_idx = cu_seqlens[i - 1]
-                end_idx = cu_seqlens[i]
-                q_i = q[:, start_idx:end_idx]
-                k_i = k[:, start_idx:end_idx]
-                v_i = v[:, start_idx:end_idx]
-                q_i, k_i, v_i = (
-                    einops.rearrange(x, "b s h d -> b h s d") for x in [q_i, k_i, v_i]
-                )
-                output_i = F.scaled_dot_product_attention(q_i, k_i, v_i, dropout_p=0.0)
-                output_i = einops.rearrange(output_i, "b h s d -> b s h d ")
-                outputs.append(output_i)
-            context_layer = torch.cat(outputs, dim=1)
-            context_layer = einops.rearrange(
-                context_layer, "b s h d -> s b (h d)"
-            ).contiguous()
+            context_layer = vit_torch_sdpa_wrapper(
+                q,
+                k,
+                v,
+                cu_seqlens,
+            )
         elif self.attn_backend == _Backend.XFORMERS:
             context_layer = vit_xformers_attn_wrapper(q, k, v, seqlens)
 
@@ -466,17 +456,15 @@ class Qwen2_5_VisionAttention(nn.Module):
         return output
 
 
-# (FIXME): Enable this after dynamic slicing is fixed
-# See https://github.com/vllm-project/vllm/pull/27760
-# @support_torch_compile(
-#     dynamic_arg_dims={
-#         "x": 0,
-#         "cu_seqlens": 0,
-#         "rotary_pos_emb": 0,
-#         "seqlens": 0,
-#     },
-#     mark_unbacked_dims={"seqlens": 0},
-# )
+@support_torch_compile(
+    dynamic_arg_dims={
+        "x": 0,
+        "cu_seqlens": 0,
+        "rotary_pos_emb": 0,
+        "seqlens": 0,
+    },
+    mark_unbacked_dims={"seqlens": 0},
+)
 class Qwen2_5_VisionBlock(nn.Module):
     def __init__(
         self,