[Feature] Add FlashAttention3 as a backend for VisionAttention (#5764)

Co-authored-by: othame <chenzhu_912@zju.edu.cn>
Co-authored-by: Mick <mickjagger19@icloud.com>
Co-authored-by: Yi Zhang <1109276519@qq.com>

python/sglang/srt/layers/attention/vision.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
-from functools import lru_cache
+import math
+from functools import lru_cache, wraps
 from typing import Optional, Tuple
 
 import torch
@@ -8,6 +9,13 @@ import torch.nn as nn
 import torch.nn.functional as F
 from einops import rearrange
 
+from sglang.srt.utils import is_cuda
+
+_is_cuda = is_cuda()
+
+if _is_cuda:
+    from sgl_kernel.flash_attn import flash_attn_varlen_func
+
 from sglang.srt.distributed import parallel_state
 from sglang.srt.distributed import utils as dist_utils
 from sglang.srt.layers.attention.triton_ops.prefill_attention import (
@@ -19,166 +27,31 @@ from sglang.srt.layers.linear import (
     RowParallelLinear,
 )
 from sglang.srt.layers.quantization import QuantizationConfig
-from sglang.srt.layers.rotary_embedding import apply_rotary_pos_emb, rotate_half
-from sglang.srt.utils import add_prefix
+from sglang.srt.layers.rotary_embedding import apply_rotary_pos_emb
+from sglang.srt.managers.schedule_batch import global_server_args_dict
+from sglang.srt.utils import add_prefix, logger
+
+ROTARY_EMBED_CLASSES = {
+    "normal": apply_rotary_pos_emb,
+}
 
 
-class VisionAttention(nn.Module):
-    r"""
-        Multi-headed attention without any cache, mostly used for ViT.
+def execute_once(func):
+    has_run = None
+
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        nonlocal has_run
+        if not has_run:
+            func(*args, **kwargs)
+            has_run = True
+
+    return wrapper
 
 
-    Args:
-        use_qkv_parallel (bool, optional): If True, use QKV-parallel attention.
-        use_context_forward (bool, default to True):
-            if ``True``, a flash_attn style attention will be applied
-            Otherwise, a full-sequence attention will be applied.
-        softmax_in_single_precision (bool, default to False):
-            if ``True``, the softmax will be performed in single-precision
-            Otherwise, it will be performed in half-precision
-
-    """
-
-    def __init__(
-        self,
-        embed_dim: int,
-        num_heads: int,
-        projection_size: int,
-        use_qkv_parallel: bool,
-        quant_config: Optional[QuantizationConfig] = None,
-        dropout: float = 0.0,
-        use_context_forward: bool = True,
-        softmax_in_single_precision: bool = False,
-        flatten_batch: bool = False,
-        prefix: str = "",
-    ):
-        super().__init__()
-        self.use_context_forward = use_context_forward
-        world_size = parallel_state.get_tensor_model_parallel_world_size()
-        self.dropout = dropout
-        self.head_size = embed_dim // num_heads
-        self.hidden_size_per_attention_head = dist_utils.divide(
-            projection_size, num_heads
-        )
-        self.num_attention_heads_per_partition = dist_utils.divide(
-            num_heads, world_size
-        )
-
-        if self.use_context_forward:
-            self.qkv_backend = VisionTritonAttention()
-        else:
-            self.qkv_backend = VisionSdpaAttention(
-                head_size=self.head_size,
-                dropout=dropout,
-                flatten_batch=flatten_batch,
-                softmax_in_single_precision=softmax_in_single_precision,
-            )
-
-        self.use_qkv_parallel = use_qkv_parallel
-        if use_qkv_parallel:
-            self.qkv_proj = QKVParallelLinear(
-                hidden_size=embed_dim,
-                head_size=self.head_size,
-                total_num_heads=num_heads,
-                quant_config=quant_config,
-                prefix=add_prefix("qkv_proj", prefix),
-            )
-        else:
-            self.qkv_proj = ColumnParallelLinear(
-                input_size=embed_dim,
-                output_size=3 * projection_size,
-                quant_config=quant_config,
-                prefix=add_prefix("qkv_proj", prefix),
-            )
-        self.proj = RowParallelLinear(
-            input_size=embed_dim,
-            output_size=embed_dim,
-            quant_config=quant_config,
-            prefix=add_prefix("proj", prefix),
-        )
-
-    def forward(
-        self,
-        x: torch.Tensor,
-        cu_seqlens: Optional[torch.Tensor] = None,
-        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-        r"""
-        Args:
-            x: [b, s, embed_dim]
-            cu_seqlens: [b]
-        Returns:
-             [s, b, head * head_size]
-        """
-        bsz, s, _ = x.shape
-        head = self.num_attention_heads_per_partition
-        if self.use_qkv_parallel:
-            # [b, s, embed_dim] --> [b, s, embed_dim]
-            qkv, _ = self.qkv_proj(x)
-            q, k, v = qkv.chunk(3, dim=-1)
-
-            # [b, s, embed_dim] --> [b * s, head, head_size]
-            q, k, v = [x.reshape(bsz * s, head, -1).contiguous() for x in (q, k, v)]
-        else:
-            # [b, s, embed_dim] --> [s, b, embed_dim]
-            x = rearrange(x, "b s ... -> s b ...")
-            # [s, b, embed_dim] --> [s, b, head * 3 * head_size]
-            qkv, _ = self.qkv_proj(x)
-            # [s, b, head * 3 * head_size] --> [s, b, head, 3 * head_size]
-            new_x_shape = qkv.size()[:-1] + (
-                head,
-                3 * self.hidden_size_per_attention_head,
-            )
-            qkv = qkv.view(*new_x_shape)
-
-            # [s, b, head, 3 * head_size] --> 3 [s, b, head, head_size]
-            q, k, v = dist_utils.split_tensor_along_last_dim(qkv, 3)
-
-            # [s, b, head, head_size] --> [b, s, head, head_size]
-            q, k, v = [
-                rearrange(x, "s b ... -> b s ...").contiguous() for x in (q, k, v)
-            ]
-
-        if position_embeddings is not None:
-            cos, sin = position_embeddings
-            original_shape = q.shape
-            # [total_tokens, head, head_size]
-            q = q.view(-1, head, self.head_size)
-            k = k.view(-1, head, self.head_size)
-
-            q, k = apply_rotary_pos_emb(q, k, cos, sin)
-
-            q = q.view(original_shape)
-            k = k.view(original_shape)
-
-        if self.use_qkv_parallel:
-            pass
-        else:
-            # [b, s, head, head_size] --> [b * s, head, head_size]
-            q, k, v = [rearrange(x, "b s ... -> (b s) ...") for x in [q, k, v]]
-
-        output = self.qkv_backend.forward(q, k, v, bsz, cu_seqlens, attention_mask)
-
-        if self.use_qkv_parallel:
-            # [b * s, h, head_size] --> [b, s, h * head_size]
-            output = rearrange(output, "(b s) ... h d -> b s ... (h d)", b=bsz)
-
-            # [b, s, h * head_size] --> [b, s, h * head_size]
-            output, _ = self.proj(output)
-        else:
-            # [b * s, h, head_size] --> [s, b, h * head_size]
-            context_layer = rearrange(
-                output, "(b s) h d -> s b (h d)", b=bsz, s=s
-            ).contiguous()
-
-            # [s, b, h * head_size] --> [s, b, h * head_size]
-            output, _ = self.proj(context_layer)
-
-            # [s, b, h * head_size] --> [b, s, h * head_size]
-            output = output.view(bsz, s, -1)
-
-        return output
+@execute_once
+def info_once(message: str):
+    logger.info(message)
 
 
 class VisionSdpaAttention(nn.Module):
@@ -189,16 +62,22 @@ class VisionSdpaAttention(nn.Module):
 
     def __init__(
         self,
-        head_size: int,
+        head_dim: int,
+        num_heads: int,
+        num_kv_heads: int,
         dropout: float = 0.0,
         flatten_batch: bool = False,
         softmax_in_single_precision: bool = False,
+        **kwargs,
     ):
         super().__init__()
-        self.head_size = head_size
+        self.head_size = head_dim
+        self.num_heads = num_heads
+        self.num_kv_heads = num_kv_heads
         self.flatten_batch = flatten_batch
         self.softmax_in_single_precision = softmax_in_single_precision
         self.dropout = dropout
+        self.scale = 1.0 / math.sqrt(self.head_size)
 
     @staticmethod
     @lru_cache(maxsize=128)
@@ -212,7 +91,7 @@ class VisionSdpaAttention(nn.Module):
             flatten_batch: whether to flatten batch dimension
             cu_seqlens: tuple of cumulative sequence lengths
         Returns:
-            attention mask tensor
+            attention mask tensor of shape [b, 1, s, s] or [1, s, s]
         """
         if flatten_batch:
             mask = torch.zeros([1, s, s], dtype=torch.bool)
@@ -241,7 +120,7 @@ class VisionSdpaAttention(nn.Module):
         flatten_batch: bool = False,
     ) -> Optional[torch.Tensor]:
         r"""
-        Creates a non-causal 4D mask of shape `(b, 1, s, s)` or `(1, 1, s, s)`.
+        Creates a non-causal 4D mask of shape `(b, 1, s, s)` or `(1, s, s)`.
         Args:
             s: sequence length
             cu_seqlens: cumulative sequence lengths tensor. If not, returns an empty mask
@@ -264,6 +143,7 @@ class VisionSdpaAttention(nn.Module):
         bsz: int,
         cu_seqlens: Optional[torch.Tensor] = None,
         attention_mask: Optional[torch.Tensor] = None,
+        **kwargs,
     ) -> torch.Tensor:
         r"""
         Args:
@@ -274,6 +154,8 @@ class VisionSdpaAttention(nn.Module):
         if self.flatten_batch:
             assert bsz == 1, "flatten_batch is True, bsz must be 1"
 
+        assert q.dim() == 3, q.shape
+
         s = q.shape[0] // bsz
 
         # [b, 1, s, s]
@@ -291,10 +173,10 @@ class VisionSdpaAttention(nn.Module):
         q, k, v = [rearrange(x, "(b s) h d -> b h s d", b=bsz) for x in [q, k, v]]
 
         if self.softmax_in_single_precision:
-            scale = self.head_size**-0.5
-            k_transposed = rearrange(k, "b h s d -> b h d s")
-            attn_weights = torch.matmul(q, k_transposed) * scale
-            del k, k_transposed
+            k = rearrange(k, "b h s d -> b h d s")
+            attn_weights = torch.matmul(q, k) * self.scale
+            del k
+            # masking
             attention_mask = (~attention_mask) * torch.finfo(q.dtype).min
             attn_weights = attn_weights + attention_mask
             del attention_mask
@@ -332,6 +214,7 @@ class VisionTritonAttention(nn.Module):
 
     def __init__(
         self,
+        **kwargs,
     ):
         super().__init__()
 
@@ -340,8 +223,8 @@ class VisionTritonAttention(nn.Module):
         q: torch.Tensor,
         k: torch.Tensor,
         v: torch.Tensor,
-        _bsz: int,
         cu_seqlens: Optional[torch.Tensor],
+        **kwargs,
     ) -> torch.Tensor:
         r"""
         Args:
@@ -366,3 +249,247 @@ class VisionTritonAttention(nn.Module):
         )
 
         return output
+
+
+class VisionFlash3Attention(nn.Module):
+    def __init__(
+        self,
+        **kwargs,
+    ):
+        if not _is_cuda:
+            raise Exception("VisionFlash3Attention is only available for cuda")
+        super().__init__()
+
+    def forward(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        cu_seqlens: Optional[torch.Tensor],
+        attention_mask: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        r"""
+        Args:
+            cu_seqlens: [b]
+        Returns:
+             [b * s, h, head_size]
+        """
+        cu_seqlens = cu_seqlens.to(dtype=torch.int32).cuda()
+        seq_lens = cu_seqlens[1:] - cu_seqlens[:-1]
+        max_seqlen = seq_lens.max().item()
+        output = flash_attn_varlen_func(
+            q,
+            k,
+            v,
+            cu_seqlens_q=cu_seqlens,
+            cu_seqlens_k=cu_seqlens,
+            max_seqlen_q=max_seqlen,
+            max_seqlen_k=max_seqlen,
+        )
+
+        return output
+
+
+QKV_BACKEND_IMPL = {
+    "triton_attn": VisionTritonAttention,
+    "sdpa": VisionSdpaAttention,
+    "fa3": VisionFlash3Attention,
+}
+
+
+class VisionAttention(nn.Module):
+    r"""
+        Multi-headed attention without any cache, mostly used for multimodal transformers.
+
+
+    Args:
+        use_qkv_parallel (bool, optional): If True, use QKV-parallel attention.
+        softmax_in_single_precision (bool, default to False):
+            if ``True``, the softmax will be performed in single-precision
+            Otherwise, it will be performed in half-precision
+
+    """
+
+    def __init__(
+        self,
+        embed_dim: int,
+        num_heads: int,
+        projection_size: int,
+        use_qkv_parallel: bool,
+        qkv_backend: Optional[str] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        dropout: float = 0.0,
+        softmax_in_single_precision: bool = False,
+        flatten_batch: bool = False,
+        prefix: str = "",
+        proj_bias: bool = True,
+        **kwargs,
+    ):
+        super().__init__()
+        world_size = parallel_state.get_tensor_model_parallel_world_size()
+        self.dropout = dropout
+        self.head_size = embed_dim // num_heads
+        self.hidden_size_per_attention_head = dist_utils.divide(
+            projection_size, num_heads
+        )
+        self.num_attention_heads_per_partition = dist_utils.divide(
+            num_heads, world_size
+        )
+        self.num_attention_kv_heads_per_partition = dist_utils.divide(
+            num_heads, world_size
+        )
+
+        self.q_size = self.num_attention_heads_per_partition * self.head_size
+        self.kv_size = self.num_attention_kv_heads_per_partition * self.head_size
+
+        if global_server_args_dict["mm_attention_backend"] is None:
+            if qkv_backend is None:
+                qkv_backend = "sdpa"
+            info_once(f"Multimodal attention backend not set. Use {qkv_backend}.")
+        else:
+            qkv_backend = global_server_args_dict["mm_attention_backend"]
+
+        info_once(f"Using {qkv_backend} as multimodal attention backend.")
+
+        self.qkv_backend = QKV_BACKEND_IMPL[qkv_backend](
+            head_dim=self.head_size,
+            num_heads=self.num_attention_heads_per_partition,
+            num_kv_heads=self.num_attention_kv_heads_per_partition,
+            dropout=dropout,
+            flatten_batch=flatten_batch,
+            softmax_in_single_precision=softmax_in_single_precision,
+        )
+
+        self.use_qkv_parallel = use_qkv_parallel
+        if use_qkv_parallel:
+            self.qkv_proj = QKVParallelLinear(
+                hidden_size=embed_dim,
+                head_size=self.head_size,
+                total_num_heads=num_heads,
+                total_num_kv_heads=num_heads,
+                quant_config=quant_config,
+                prefix=add_prefix("qkv_proj", prefix),
+            )
+        else:
+            self.qkv_proj = ColumnParallelLinear(
+                input_size=embed_dim,
+                output_size=3 * projection_size,
+                quant_config=quant_config,
+                prefix=add_prefix("qkv_proj", prefix),
+            )
+        self.proj = RowParallelLinear(
+            input_size=embed_dim,
+            output_size=embed_dim,
+            bias=proj_bias,
+            quant_config=quant_config,
+            prefix=add_prefix("proj", prefix),
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        cu_seqlens: Optional[torch.Tensor] = None,
+        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        r"""
+        Args:
+            x: [b, s, embed_dim]
+            cu_seqlens: [b]
+        Returns:
+             [s, b, head * head_size]
+        """
+        if x.dim() == 2:
+            x = x.unsqueeze(0)
+        assert x.dim() == 3, x.shape
+        bsz, s, _ = x.shape
+        head = self.num_attention_heads_per_partition
+        kv_head = self.num_attention_kv_heads_per_partition
+        if self.use_qkv_parallel:
+            # [b, s, embed_dim] --> [b, s, embed_dim]
+            qkv, _ = self.qkv_proj(x)
+
+            q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+
+            # [b, s, embed_dim] --> [b * s, head, head_size]
+            q = q.reshape(bsz * s, head, -1).contiguous()
+            k = k.reshape(bsz * s, kv_head, -1).contiguous()
+            v = v.reshape(bsz * s, kv_head, -1).contiguous()
+        else:
+            # [b, s, embed_dim] --> [s, b, embed_dim]
+            x = rearrange(x, "b s ... -> s b ...")
+            # [s, b, embed_dim] --> [s, b, head * 3 * head_size]
+            qkv, _ = self.qkv_proj(x)
+
+            # [s, b, head * 3 * head_size] --> [s, b, head, 3 * head_size]
+            new_x_shape = qkv.size()[:-1] + (
+                head,
+                3 * self.hidden_size_per_attention_head,
+            )
+            qkv = qkv.view(*new_x_shape)
+
+            # [s, b, head, 3 * head_size] --> 3 [s, b, head, head_size]
+            q, k, v = dist_utils.split_tensor_along_last_dim(qkv, 3)
+            # [s, b, head, head_size] --> [b, s, head, head_size]
+            q, k, v = [
+                rearrange(x, "s b ... -> b s ...").contiguous() for x in (q, k, v)
+            ]
+
+        if position_embeddings is not None:
+            cos, sin = position_embeddings
+            original_shape = q.shape
+            # [total_tokens, head, head_size]
+            q = q.view(-1, head, self.head_size)
+            k = k.view(-1, head, self.head_size)
+
+            q, k = apply_rotary_pos_emb(q, k, cos, sin)
+
+            q = q.view(original_shape)
+            k = k.view(original_shape)
+
+        if q.dim() == 4:
+            # [b, s, head, head_size] --> [b * s, head, head_size]
+            q = rearrange(q, "b s ... -> (b s) ...")
+        if k.dim() == 4:
+            # [b, s, head, head_size] --> [b * s, head, head_size]
+            k = rearrange(k, "b s ... -> (b s) ...")
+        if v.dim() == 4:
+            # [b, s, head, head_size] --> [b * s, head, head_size]
+            v = rearrange(v, "b s ... -> (b s) ...")
+
+        assert q.dim() == 3, q.dim()
+        assert k.dim() == 3, k.dim()
+        assert v.dim() == 3, v.dim()
+
+        output = self.qkv_backend.forward(
+            q=q,
+            k=k,
+            v=v,
+            bsz=bsz,
+            cu_seqlens=cu_seqlens,
+            attention_mask=attention_mask,
+        )
+
+        assert output.dim() == 3, output.shape
+
+        if self.use_qkv_parallel:
+            # [b * s, h, head_size] --> [b, s, h * head_size]
+            output = rearrange(output, "(b s) ... h d -> b s ... (h d)", b=bsz)
+
+            # [b, s, h * head_size] --> [b, s, h * head_size]
+            output, _ = self.proj(output)
+        else:
+            # [b * s, h, head_size] --> [s, b, h * head_size]
+            context_layer = rearrange(
+                output, "(b s) h d -> s b (h d)", b=bsz, s=s
+            ).contiguous()
+
+            # [s, b, h * head_size] --> [s, b, h * head_size]
+            output, _ = self.proj(context_layer)
+
+            # [s, b, h * head_size] --> [b, s, h * head_size]
+            output = output.view(bsz, s, -1)
+
+        return output

python/sglang/srt/model_executor/model_runner.py

@@ -173,6 +173,7 @@ class ModelRunner:
                 "speculative_accept_threshold_single": server_args.speculative_accept_threshold_single,
                 "speculative_accept_threshold_acc": server_args.speculative_accept_threshold_acc,
                 "use_mla_backend": self.use_mla_backend,
+                "mm_attention_backend": server_args.mm_attention_backend,
             }
         )
 

python/sglang/srt/models/clip.py

@@ -151,20 +151,20 @@ class CLIPEncoderLayer(nn.Module):
         self.layer_norm1 = norm_layer(config.hidden_size)
         self.layer_norm2 = norm_layer(config.hidden_size)
         if attn_implementation == "sdpa":
-            use_context_forward = False
+            qkv_backend = "sdpa"
             softmax_in_single_precision = False
         elif attn_implementation == "flash_attention_2":
+            qkv_backend = "triton_attn"
             softmax_in_single_precision = False
-            use_context_forward = True
         elif attn_implementation == "eager":
+            qkv_backend = "sdpa"
             softmax_in_single_precision = True
-            use_context_forward = False
         self.self_attn = VisionAttention(
             embed_dim=config.hidden_size,
             num_heads=config.num_attention_heads,
             projection_size=config.hidden_size,
             use_qkv_parallel=True,
-            use_context_forward=use_context_forward,
+            qkv_backend=qkv_backend,
             softmax_in_single_precision=softmax_in_single_precision,
             flatten_batch=True,
             quant_config=quant_config,

python/sglang/srt/models/deepseek_janus_pro.py

@@ -532,7 +532,7 @@ class VisionTransformerBlock(nn.Module):
             num_heads=num_heads,
             projection_size=dim,
             use_qkv_parallel=True,
-            use_context_forward=False,
+            qkv_backend="sdpa",
             softmax_in_single_precision=False,
             dropout=attn_drop,
         )

python/sglang/srt/models/gemma3_mm.py

@@ -281,7 +281,7 @@ class Gemma3ForConditionalGeneration(PreTrainedModel):
         pixel_values = torch.stack(
             flatten_nested_list([item.pixel_values for item in items]), dim=0
         )
-        pixel_values = pixel_values.to("cuda")
+        pixel_values = pixel_values.to(device=self.vision_tower.device)
         pixel_values = pixel_values.to(dtype=self.language_model.dtype())
 
         vision_outputs = self.vision_tower(pixel_values=pixel_values).last_hidden_state

python/sglang/srt/models/minicpmv.py

@@ -197,7 +197,7 @@ class Idefics2EncoderLayer(nn.Module):
             use_qkv_parallel=True,
             quant_config=quant_config,
             dropout=config.attention_dropout,
-            use_context_forward=False,
+            qkv_backend="sdpa",
             softmax_in_single_precision=True,
             flatten_batch=False,
             prefix=add_prefix("self_attn", prefix),

python/sglang/srt/models/mllama.py

@@ -203,7 +203,7 @@ class MllamaVisionEncoderLayer(nn.Module):
             use_qkv_parallel=True,
             quant_config=quant_config,
             dropout=0.0,
-            use_context_forward=False,
+            qkv_backend="sdpa",
             softmax_in_single_precision=False,
             flatten_batch=False,
             prefix=add_prefix("self_attn", prefix),

python/sglang/srt/models/qwen2_5_vl.py

@@ -125,16 +125,20 @@ class Qwen2_5_VisionBlock(nn.Module):
         self.norm1 = Qwen2RMSNorm(dim, eps=1e-6)
         self.norm2 = Qwen2RMSNorm(dim, eps=1e-6)
         if attn_implementation == "sdpa":
-            use_context_forward = False
             softmax_in_single_precision = False
+            qkv_backend = "sdpa"
             flatten_batch = True
         elif attn_implementation == "flash_attention_2":
             softmax_in_single_precision = False
-            use_context_forward = True
+            qkv_backend = "triton_attn"
             flatten_batch = True
         elif attn_implementation == "eager":
             softmax_in_single_precision = True
-            use_context_forward = False
+            qkv_backend = "sdpa"
+            flatten_batch = True
+        elif attn_implementation == "flash_attention_3":
+            softmax_in_single_precision = False
+            qkv_backend = "fa3"
             flatten_batch = True
 
         self.attn = VisionAttention(
@@ -142,7 +146,7 @@ class Qwen2_5_VisionBlock(nn.Module):
             num_heads=num_heads,
             projection_size=dim,
             use_qkv_parallel=True,
-            use_context_forward=use_context_forward,
+            qkv_backend=qkv_backend,
             softmax_in_single_precision=softmax_in_single_precision,
             flatten_batch=flatten_batch,
             quant_config=quant_config,

python/sglang/srt/models/qwen2_vl.py

@@ -139,21 +139,21 @@ class Qwen2VisionBlock(nn.Module):
         self.norm2 = norm_layer(dim)
         mlp_hidden_dim = int(dim * mlp_ratio)
         if attn_implementation == "sdpa":
-            use_context_forward = False
+            qkv_backend = "sdpa"
             softmax_in_single_precision = False
         elif attn_implementation == "flash_attention_2":
+            qkv_backend = "triton_attn"
             softmax_in_single_precision = False
-            use_context_forward = True
         elif attn_implementation == "eager":
+            qkv_backend = "sdpa"
             softmax_in_single_precision = True
-            use_context_forward = False
 
         self.attn = VisionAttention(
             embed_dim=dim,
             num_heads=num_heads,
             projection_size=dim,
             use_qkv_parallel=True,
-            use_context_forward=use_context_forward,
+            qkv_backend=qkv_backend,
             softmax_in_single_precision=softmax_in_single_precision,
             flatten_batch=True,
             quant_config=quant_config,

python/sglang/srt/server_args.py

@@ -187,6 +187,7 @@ class ServerArgs:
     n_share_experts_fusion: int = 0
     disable_chunked_prefix_cache: bool = False
     disable_fast_image_processor: bool = False
+    mm_attention_backend: Optional[str] = None
 
     # Debug tensor dumps
     debug_tensor_dump_output_folder: Optional[str] = None
@@ -1265,6 +1266,14 @@ class ServerArgs:
             help="The URL of the PD disaggregation load balancer. If set, the prefill/decode server will register with the load balancer.",
         )
 
+        parser.add_argument(
+            "--mm-attention-backend",
+            type=str,
+            choices=["sdpa", "fa3", "triton_attn"],
+            default=ServerArgs.mm_attention_backend,
+            help="Set multimodal attention backend.",
+        )
+
     @classmethod
     def from_cli_args(cls, args: argparse.Namespace):
         args.tp_size = args.tensor_parallel_size