[Reland] perf: optimize qwen-vl with symm mem allreduce (#11457)

Co-authored-by: luoyuan.luo <luoyuan.luo@antgroup.com>

python/sglang/srt/distributed/device_communicators/all_reduce_utils.py

@@ -3,13 +3,13 @@ MiB = 1024 * 1024
 SYMM_MEM_ALL_REDUCE_MAX_SIZES = {
     9: {
         2: 64 * MiB,  # 64 MB
-        4: 32 * MiB,  # 32 MB
-        6: 64 * MiB,  # 64 MB
-        8: 64 * MiB,  # 64 MB
+        4: 64 * MiB,  # 64 MB
+        6: 128 * MiB,  # 128 MB
+        8: 128 * MiB,  # 128 MB
     },
     10: {
         2: 64 * MiB,  # 64 MB
-        4: 32 * MiB,  # 32 MB
+        4: 64 * MiB,  # 64 MB
         6: 128 * MiB,  # 128 MB
         8: 128 * MiB,  # 128 MB
     },

python/sglang/srt/distributed/parallel_state.py

@@ -615,8 +615,11 @@ class GroupCoordinator:
 
     def _all_reduce_in_place(self, input_: torch.Tensor) -> None:
         pynccl_comm = self.pynccl_comm
+        symm_mem_comm = self.symm_mem_comm
         if pynccl_comm is not None and not pynccl_comm.disabled:
             pynccl_comm.all_reduce(input_)
+        elif symm_mem_comm is not None and not symm_mem_comm.disabled:
+            symm_mem_comm.all_reduce(input_)
         else:
             torch.distributed.all_reduce(input_, group=self.device_group)
 

python/sglang/srt/layers/rotary_embedding.py

@@ -1008,6 +1008,17 @@ class DynamicNTKAlphaRotaryEmbedding(RotaryEmbedding):
         return cache
 
 
+def apply_interleaved_rope(x: torch.Tensor, mrope_section: list[int]) -> torch.Tensor:
+    """Apply interleaved MRoPE to 3D rotary embeddings.
+    Reorganizes frequency layout from chunked [TTT...HHH...WWW] to
+    interleaved [THTHWHTHW...TT], preserving frequency continuity.
+    """
+    x_t = x[0].clone()
+    x_t[..., 1 : mrope_section[1] * 3 : 3] = x[1, ..., 1 : mrope_section[1] * 3 : 3]
+    x_t[..., 2 : mrope_section[2] * 3 : 3] = x[2, ..., 2 : mrope_section[2] * 3 : 3]
+    return x_t
+
+
 class MRotaryEmbedding(RotaryEmbedding):
     """Rotary Embedding with Multimodal Sections."""
 
@@ -1020,12 +1031,14 @@ class MRotaryEmbedding(RotaryEmbedding):
         is_neox_style: bool,
         dtype: torch.dtype,
         mrope_section: Optional[List[int]] = None,
+        mrope_interleaved: bool = False,
     ) -> None:
         super().__init__(
             head_size, rotary_dim, max_position_embeddings, base, is_neox_style, dtype
         )
 
         self.mrope_section = mrope_section
+        self.mrope_interleaved = mrope_interleaved
         if self.mrope_section:
             expected_sum = rotary_dim // 2
             actual_sum = sum(self.mrope_section)
@@ -1086,7 +1099,10 @@ class MRotaryEmbedding(RotaryEmbedding):
         cos, sin = cos_sin.chunk(2, dim=-1)
         if positions.ndim == 2:
             assert self.mrope_section
-
+            if self.mrope_interleaved:
+                cos = apply_interleaved_rope(cos, self.mrope_section)
+                sin = apply_interleaved_rope(sin, self.mrope_section)
+            else:
                 cos = torch.cat(
                     [m[i] for i, m in enumerate(cos.split(self.mrope_section, dim=-1))],
                     dim=-1,
@@ -1768,6 +1784,7 @@ def get_rope(
                     is_neox_style,
                     dtype,
                     mrope_section=rope_scaling["mrope_section"],
+                    mrope_interleaved=rope_scaling.get("mrope_interleaved", False),
                 )
             else:
                 rotary_emb = RotaryEmbedding(