[Feat] QWen-1M context support[2/2]: Update block sparse attention backend (#5949)

python/sglang/srt/configs/model_config.py

@@ -27,6 +27,7 @@ from sglang.srt.hf_transformers_utils import (
     get_context_length,
     get_generation_config,
     get_hf_text_config,
+    get_sparse_attention_config,
 )
 from sglang.srt.layers.quantization import QUANTIZATION_METHODS
 from sglang.srt.server_args import ServerArgs
@@ -270,6 +271,9 @@ class ModelConfig:
         # Verify quantization
         self._verify_quantization()
 
+        # Verify dual-chunk attention config
+        self._verify_dual_chunk_attention_config()
+
         # Cache attributes
         self.hf_eos_token_id = self.get_hf_eos_token_id()
 
@@ -297,6 +301,13 @@ class ModelConfig:
             **kwargs,
         )
 
+    def get_total_num_attention_heads(self) -> int:
+        return self.num_attention_heads
+
+    def get_num_attention_heads(self, tensor_parallel_size) -> int:
+        total_num_attention_heads = self.num_attention_heads
+        return max(1, total_num_attention_heads // tensor_parallel_size)
+
     # adapted from https://github.com/vllm-project/vllm/blob/main/vllm/config.py#L289
     def get_total_num_kv_heads(self) -> int:
         """Returns the total number of KV heads."""
@@ -484,6 +495,23 @@ class ModelConfig:
                     self.quantization,
                 )
 
+    def _verify_dual_chunk_attention_config(self) -> None:
+        if hasattr(self.hf_config, "dual_chunk_attention_config"):
+            # Try loading the sparse attention config
+            sparse_attn_config = get_sparse_attention_config(self.model_path)
+            if not sparse_attn_config:
+                return
+            self.hf_config.dual_chunk_attention_config["sparse_attention_config"] = (
+                sparse_attn_config
+            )
+            if (
+                "sparse_attention_enabled"
+                not in self.hf_config.dual_chunk_attention_config
+            ):
+                self.hf_config.dual_chunk_attention_config[
+                    "sparse_attention_enabled"
+                ] = True
+
     def get_hf_eos_token_id(self) -> Optional[Set[int]]:
         eos_ids = getattr(self.hf_config, "eos_token_id", None)
         if eos_ids is not None:

python/sglang/srt/disaggregation/decode_schedule_batch_mixin.py

@@ -76,6 +76,9 @@ class ScheduleBatchDisaggregationDecodeMixin:
             req_pool_indices, dtype=torch.int64, device=self.device
         )
         self.seq_lens = torch.tensor(seq_lens, dtype=torch.int64, device=self.device)
+        self.orig_seq_lens = torch.tensor(
+            seq_lens, dtype=torch.int32, device=self.device
+        )
         self.out_cache_loc = out_cache_loc
         self.seq_lens_sum = sum(seq_lens)
 

python/sglang/srt/hf_transformers_utils.py

@@ -14,10 +14,11 @@
 """Utilities for Huggingface Transformers."""
 
 import contextlib
+import json
 import os
 import warnings
 from pathlib import Path
-from typing import Dict, Optional, Type, Union
+from typing import Any, Dict, Optional, Type, Union
 
 import torch
 from huggingface_hub import snapshot_download
@@ -62,11 +63,17 @@ for name, cls in _CONFIG_REGISTRY.items():
         AutoConfig.register(name, cls)
 
 
-def download_from_hf(model_path: str):
+def download_from_hf(
+    model_path: str,
+    allow_patterns: Optional[Union[str, list]] = None,
+):
     if os.path.exists(model_path):
         return model_path
 
-    return snapshot_download(model_path, allow_patterns=["*.json", "*.bin", "*.model"])
+    if not allow_patterns:
+        allow_patterns = ["*.json", "*.bin", "*.model"]
+
+    return snapshot_download(model_path, allow_patterns=allow_patterns)
 
 
 def get_hf_text_config(config: PretrainedConfig):
@@ -171,6 +178,26 @@ def get_generation_config(
         return None
 
 
+# Qwen-1M related
+def get_sparse_attention_config(
+    model: str,
+    sparse_attention_config_filename: str = "sparse_attention_config.json",
+) -> Dict[str, Any]:
+    is_local = os.path.isdir(model)
+    if not is_local:
+        # Download the config files.
+        model = download_from_hf(model, allow_patterns=["*.json"])
+
+    config_file = os.path.join(model, sparse_attention_config_filename)
+    if not os.path.exists(config_file):
+        return {}
+
+    # Load the sparse attention config.
+    with open(config_file) as f:
+        config = json.load(f)
+    return config
+
+
 # Models don't use the same configuration key for determining the maximum
 # context length.  Store them here so we can sanely check them.
 # NOTE: The ordering here is important. Some models have two of these and we

python/sglang/srt/layers/rotary_embedding.py

@@ -1172,6 +1172,202 @@ class MRotaryEmbedding(RotaryEmbedding):
         )
 
 
+class DualChunkRotaryEmbedding(CustomOp):
+    """Rotary positional embedding for Dual Chunk Attention."""
+
+    def __init__(
+        self,
+        head_size: int,
+        rotary_dim: int,
+        max_position_embeddings: int,
+        base: int,
+        is_neox_style: bool,
+        dtype: torch.dtype,
+        chunk_size: int,
+        local_size: int,
+    ) -> 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.chunk_size = chunk_size
+        self.local_size = local_size
+        self.dtype = dtype
+        self.device = torch.device(f"cuda:{torch.cuda.current_device()}")
+        (q_cache, qc_cache, k_cache, qc_no_clamp_cache, q_inter_cache) = (
+            self._compute_cos_sin_cache()
+        )
+
+        self.register_buffer("cos_sin_q_cache", q_cache, persistent=False)
+        self.register_buffer("cos_sin_qc_cache", qc_cache, persistent=False)
+        self.register_buffer("cos_sin_k_cache", k_cache, persistent=False)
+        self.register_buffer(
+            "cos_sin_qc_no_clamp_cache", qc_no_clamp_cache, persistent=False
+        )
+        self.register_buffer("cos_sin_q_inter_cache", q_inter_cache, persistent=False)
+
+    def _compute_inv_freq(self, base: Union[int, float]) -> torch.Tensor:
+        """Compute the inverse frequency."""
+        # NOTE(woosuk): The HF implementation uses `torch.arange(...).float()`.
+        # However, we use `torch.arange(..., dtype=torch.float)` instead to
+        # avoid numerical issues with large base values (e.g., 10000000).
+        # This may cause a slight numerical difference between the HF
+        # implementation and ours.
+        # 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
+            )
+        )
+        return inv_freq
+
+    def _compute_cos_sin_cache(self) -> torch.Tensor:
+        """Compute the cos and sin cache."""
+        inv_freq = self._compute_inv_freq(self.base)
+        chunk_len = self.chunk_size - self.local_size
+        q_t = torch.arange(chunk_len, dtype=torch.float)
+        qc_t = (torch.arange(chunk_len, dtype=torch.float) + chunk_len).clamp(
+            max=self.chunk_size
+        )
+        k_t = torch.arange(self.max_position_embeddings, dtype=torch.float) % chunk_len
+
+        # count from chunk_len, no clamp(self.chunk_size) restriction
+        qc_no_clamp_t = torch.arange(chunk_len, dtype=torch.float) + chunk_len
+        # count from self.chunk_size for q_inter's rope
+        q_inter_t = torch.arange(chunk_len, dtype=torch.float) + self.chunk_size
+
+        q_freqs = torch.outer(q_t, inv_freq)
+        qc_freqs = torch.outer(qc_t, inv_freq)
+        k_freqs = torch.outer(k_t, inv_freq)
+        qc_no_clamp_freqs = torch.outer(qc_no_clamp_t, inv_freq)
+        q_inter_freqs = torch.outer(q_inter_t, inv_freq)
+
+        q_cos = q_freqs.cos()
+        q_sin = q_freqs.sin()
+        qc_cos = qc_freqs.cos()
+        qc_sin = qc_freqs.sin()
+        k_cos = k_freqs.cos()
+        k_sin = k_freqs.sin()
+
+        qc_no_clamp_cos = qc_no_clamp_freqs.cos()
+        qc_no_clamp_sin = qc_no_clamp_freqs.sin()
+        q_inter_cos = q_inter_freqs.cos()
+        q_inter_sin = q_inter_freqs.sin()
+
+        q_cache = torch.cat((q_cos, q_sin), dim=-1).to(
+            dtype=self.dtype, device=self.device
+        )
+        qc_cache = torch.cat((qc_cos, qc_sin), dim=-1).to(
+            dtype=self.dtype, device=self.device
+        )
+        k_cache = torch.cat((k_cos, k_sin), dim=-1).to(
+            dtype=self.dtype, device=self.device
+        )
+        qc_no_clamp_cache = torch.cat((qc_no_clamp_cos, qc_no_clamp_sin), dim=-1).to(
+            dtype=self.dtype, device=self.device
+        )
+        q_inter_cache = torch.cat((q_inter_cos, q_inter_sin), dim=-1).to(
+            dtype=self.dtype, device=self.device
+        )
+        return q_cache, qc_cache, k_cache, qc_no_clamp_cache, q_inter_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)
+        query_rot = query[..., : self.rotary_dim]
+        key_rot = key[..., : self.rotary_dim]
+        if self.rotary_dim < self.head_size:
+            query_pass = query[..., self.rotary_dim :]
+            key_pass = key[..., self.rotary_dim :]
+        else:
+            query_pass = None
+            key_pass = None
+
+        positions_with_offsets = (
+            torch.add(positions, offsets) if offsets is not None else positions
+        )
+        key = self._apply_rotary_embedding(
+            self.cos_sin_k_cache[positions_with_offsets], key_rot, key_pass
+        )
+        chunk_len = self.chunk_size - self.local_size
+        query = self._apply_rotary_embedding(
+            self.cos_sin_q_cache[positions_with_offsets % chunk_len],
+            query_rot,
+            query_pass,
+        )
+        query_succ = self._apply_rotary_embedding(
+            self.cos_sin_qc_cache[positions_with_offsets % chunk_len],
+            query_rot,
+            query_pass,
+        )
+        query_inter = self._apply_rotary_embedding(
+            self.cos_sin_qc_cache[chunk_len - 1].repeat(positions.shape[0], 1),
+            query_rot,
+            query_pass,
+        )
+        query_succ_critical = self._apply_rotary_embedding(
+            self.cos_sin_qc_no_clamp_cache[positions_with_offsets % chunk_len],
+            query_rot,
+            query_pass,
+        )
+        query_inter_critical = self._apply_rotary_embedding(
+            self.cos_sin_q_inter_cache[positions_with_offsets % chunk_len],
+            query_rot,
+            query_pass,
+        )
+
+        # merge query into one tensor to simplify the interfaces
+        query = torch.cat(
+            (
+                query,
+                query_succ,
+                query_inter,
+                query_succ_critical,
+                query_inter_critical,
+            ),
+            dim=-1,
+        )
+        return query, key
+
+    def _apply_rotary_embedding(self, cos_sin, hidden_rot, hidden_pass):
+        cos, sin = cos_sin.chunk(2, dim=-1)
+        if self.is_neox_style:
+            # NOTE(woosuk): Here we assume that the positions tensor has the
+            # shape [batch_size, seq_len].
+            cos = cos.repeat(1, 1, 2).unsqueeze(-2)
+            sin = sin.repeat(1, 1, 2).unsqueeze(-2)
+        else:
+            cos = cos.repeat_interleave(2, dim=-1).unsqueeze(-2)
+            sin = sin.repeat_interleave(2, dim=-1).unsqueeze(-2)
+        rotate_fn = _rotate_neox if self.is_neox_style else _rotate_gptj
+        hidden_rot = hidden_rot * cos + rotate_fn(hidden_rot) * sin
+
+        if self.rotary_dim < self.head_size:
+            hidden = torch.cat((hidden_rot, hidden_pass), dim=-1)
+        else:
+            hidden = hidden_rot
+        return hidden.flatten(-2).squeeze(0)
+
+    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}"
+        s += f", chunk_size={self.chunk_size}, local_size={self.local_size}"
+        return s
+
+
 _ROPE_DICT: Dict[Tuple, RotaryEmbedding] = {}
 
 
@@ -1184,6 +1380,7 @@ def get_rope(
     rope_scaling: Optional[Dict[str, Any]] = None,
     dtype: Optional[torch.dtype] = None,
     partial_rotary_factor: float = 1.0,
+    dual_chunk_attention_config: Optional[Dict[str, Any]] = None,
 ) -> RotaryEmbedding:
     if dtype is None:
         dtype = torch.get_default_dtype()
@@ -1195,6 +1392,17 @@ def get_rope(
         rope_scaling_args = tuple(rope_scaling_tuple.items())
     else:
         rope_scaling_args = None
+
+    if dual_chunk_attention_config is not None:
+        dual_chunk_attention_tuple = {
+            k: tuple(v) if isinstance(v, list) else v
+            for k, v in dual_chunk_attention_config.items()
+            if k != "sparse_attention_config"
+        }
+        dual_chunk_attention_args = tuple(dual_chunk_attention_tuple.items())
+    else:
+        dual_chunk_attention_args = None
+
     if partial_rotary_factor < 1.0:
         rotary_dim = int(rotary_dim * partial_rotary_factor)
     key = (
@@ -1204,12 +1412,28 @@ def get_rope(
         base,
         is_neox_style,
         rope_scaling_args,
+        dual_chunk_attention_args,
         dtype,
     )
     if key in _ROPE_DICT:
         return _ROPE_DICT[key]
 
-    if rope_scaling is None:
+    if dual_chunk_attention_config is not None:
+        extra_kwargs = {
+            k: v
+            for k, v in dual_chunk_attention_config.items()
+            if k in ("chunk_size", "local_size")
+        }
+        rotary_emb = DualChunkRotaryEmbedding(
+            head_size,
+            rotary_dim,
+            max_position,
+            base,
+            is_neox_style,
+            dtype,
+            **extra_kwargs,
+        )
+    elif rope_scaling is None:
         rotary_emb = RotaryEmbedding(
             head_size, rotary_dim, max_position, base, is_neox_style, dtype
         )

python/sglang/srt/managers/schedule_batch.py

@@ -846,6 +846,8 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
 
     # The sum of all sequence lengths
     seq_lens_sum: int = None
+    # The original sequence lengths, Qwen-1M related
+    orig_seq_lens: torch.Tensor = None  # shape: [b], int32
 
     # For DP attention
     global_num_tokens: Optional[List[int]] = None
@@ -1131,6 +1133,7 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
         input_ids = [r.fill_ids[len(r.prefix_indices) :] for r in reqs]
         extend_num_tokens = sum(len(ids) for ids in input_ids)
         seq_lens = [len(r.fill_ids) for r in reqs]
+        orig_seq_lens = [max(len(r.fill_ids), len(r.origin_input_ids)) for r in reqs]
         prefix_lens = [len(r.prefix_indices) for r in reqs]
         extend_lens = [r.extend_input_len for r in reqs]
 
@@ -1147,6 +1150,9 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
         seq_lens_tensor = torch.tensor(seq_lens, dtype=torch.int64).to(
             self.device, non_blocking=True
         )
+        orig_seq_lens_tensor = torch.tensor(orig_seq_lens, dtype=torch.int32).to(
+            self.device, non_blocking=True
+        )
         prefix_lens_tensor = torch.tensor(
             prefix_lens, dtype=torch.int64, device=self.device
         )
@@ -1260,6 +1266,7 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
         self.input_ids = input_ids_tensor
         self.req_pool_indices = req_pool_indices_tensor
         self.seq_lens = seq_lens_tensor
+        self.orig_seq_lens = orig_seq_lens_tensor
         self.out_cache_loc = out_cache_loc
         self.input_embeds = (
             torch.tensor(input_embeds).to(self.device, non_blocking=True)
@@ -1507,6 +1514,7 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
         self.forward_mode = ForwardMode.IDLE
         self.input_ids = torch.empty(0, dtype=torch.int64, device=self.device)
         self.seq_lens = torch.empty(0, dtype=torch.int64, device=self.device)
+        self.orig_seq_lens = torch.empty(0, dtype=torch.int32, device=self.device)
         self.out_cache_loc = torch.empty(0, dtype=torch.int64, device=self.device)
         self.req_pool_indices = torch.empty(0, dtype=torch.int32, device=self.device)
         self.seq_lens_sum = 0
@@ -1561,9 +1569,11 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
         if self.enable_overlap:
             # Do not use in-place operations in the overlap mode
             self.seq_lens = self.seq_lens + 1
+            self.orig_seq_lens = self.orig_seq_lens + 1
         else:
             # A faster in-place version
             self.seq_lens.add_(1)
+            self.orig_seq_lens.add_(1)
         self.seq_lens_sum += bs
 
         # free memory
@@ -1627,6 +1637,7 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
             self.multimodal_inputs = [self.multimodal_inputs[i] for i in keep_indices]
         self.req_pool_indices = self.req_pool_indices[keep_indices_device]
         self.seq_lens = self.seq_lens[keep_indices_device]
+        self.orig_seq_lens = self.orig_seq_lens[keep_indices_device]
         self.out_cache_loc = None
         self.seq_lens_sum = self.seq_lens.sum().item()
         self.output_ids = self.output_ids[keep_indices_device]
@@ -1659,6 +1670,7 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
             [self.req_pool_indices, other.req_pool_indices]
         )
         self.seq_lens = torch.cat([self.seq_lens, other.seq_lens])
+        self.orig_seq_lens = torch.cat([self.orig_seq_lens, other.orig_seq_lens])
         self.out_cache_loc = None
         self.seq_lens_sum += other.seq_lens_sum
         if self.output_ids is not None:
@@ -1733,6 +1745,7 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
             input_ids=self.input_ids,
             req_pool_indices=self.req_pool_indices,
             seq_lens=self.seq_lens,
+            orig_seq_lens=self.orig_seq_lens,
             out_cache_loc=self.out_cache_loc,
             seq_lens_cpu=seq_lens_cpu,
             seq_lens_sum=self.seq_lens_sum,
@@ -1900,6 +1913,9 @@ class ModelWorkerBatch:
     # Sampling info
     sampling_info: SamplingBatchInfo
 
+    # The original sequence lengths, Qwen-1M related
+    orig_seq_lens: Optional[torch.Tensor] = None
+
     # The input Embeds
     input_embeds: Optional[torch.Tensor] = None
 

python/sglang/srt/model_executor/cuda_graph_runner.py

@@ -589,6 +589,7 @@ class CudaGraphRunner:
             req_pool_indices=req_pool_indices,
             seq_lens=seq_lens,
             next_token_logits_buffer=next_token_logits_buffer,
+            orig_seq_lens=seq_lens,
             req_to_token_pool=self.model_runner.req_to_token_pool,
             token_to_kv_pool=self.model_runner.token_to_kv_pool,
             attn_backend=self.model_runner.attn_backend,

python/sglang/srt/model_executor/forward_batch_info.py

@@ -180,6 +180,9 @@ class ForwardBatch:
     # The sum of all sequence lengths
     seq_lens_sum: int
 
+    # The original sequence length without being chunked. Qwen-1M related.
+    orig_seq_lens: Optional[torch.Tensor] = None
+
     # Optional seq_lens on cpu
     seq_lens_cpu: Optional[torch.Tensor] = None
 
@@ -321,6 +324,7 @@ class ForwardBatch:
             encoder_out_cache_loc=batch.encoder_out_cache_loc,
             seq_lens_sum=batch.seq_lens_sum,
             seq_lens_cpu=batch.seq_lens_cpu,
+            orig_seq_lens=batch.orig_seq_lens,
             return_logprob=batch.return_logprob,
             top_logprobs_nums=batch.top_logprobs_nums,
             token_ids_logprobs=batch.token_ids_logprobs,

python/sglang/srt/model_executor/model_runner.py

@@ -1467,6 +1467,12 @@ class ModelRunner:
 
             logger.info(f"Intel AMX attention backend is enabled.")
             return IntelAMXAttnBackend(self)
+        elif self.server_args.attention_backend == "dual_chunk_flash_attn":
+            from sglang.srt.layers.attention.dual_chunk_flashattention_backend import (
+                DualChunkFlashAttentionBackend,
+            )
+
+            return DualChunkFlashAttentionBackend(self)
         else:
             raise ValueError(f"Invalid attention backend: {backend_str}")
 

python/sglang/srt/models/qwen2.py

@@ -107,6 +107,7 @@ class Qwen2Attention(nn.Module):
         rope_scaling: Optional[Dict[str, Any]] = None,
         max_position_embeddings: int = 32768,
         quant_config: Optional[QuantizationConfig] = None,
+        dual_chunk_attention_config: Optional[dict[str, Any]] = None,
         prefix: str = "",
     ) -> None:
         super().__init__()
@@ -158,6 +159,7 @@ class Qwen2Attention(nn.Module):
             max_position=max_position_embeddings,
             base=rope_theta,
             rope_scaling=rope_scaling,
+            dual_chunk_attention_config=dual_chunk_attention_config,
         )
         self.attn = RadixAttention(
             self.num_heads,
@@ -198,6 +200,9 @@ class Qwen2DecoderLayer(nn.Module):
         rope_scaling = getattr(config, "rope_scaling", None)
         max_position_embeddings = getattr(config, "max_position_embeddings", 32768)
         head_dim = getattr(config, "head_dim", None)
+        dual_chunk_attention_config = getattr(
+            config, "dual_chunk_attention_config", None
+        )
         self.self_attn = Qwen2Attention(
             hidden_size=self.hidden_size,
             num_heads=config.num_attention_heads,
@@ -208,6 +213,7 @@ class Qwen2DecoderLayer(nn.Module):
             rope_scaling=rope_scaling,
             max_position_embeddings=max_position_embeddings,
             quant_config=quant_config,
+            dual_chunk_attention_config=dual_chunk_attention_config,
             prefix=add_prefix("self_attn", prefix),
         )
         self.mlp = Qwen2MLP(

python/sglang/srt/models/qwen2_moe.py

@@ -210,6 +210,7 @@ class Qwen2MoeAttention(nn.Module):
         max_position_embeddings: int = 8192,
         qkv_bias: int = True,
         quant_config: Optional[QuantizationConfig] = None,
+        dual_chunk_attention_config: Optional[dict[str, Any]] = None,
         prefix: str = "",
     ) -> None:
         super().__init__()
@@ -267,6 +268,7 @@ class Qwen2MoeAttention(nn.Module):
             max_position=max_position_embeddings,
             base=rope_theta,
             rope_scaling=rope_scaling,
+            dual_chunk_attention_config=dual_chunk_attention_config,
         )
         self.attn = RadixAttention(
             self.num_heads,
@@ -308,6 +310,9 @@ class Qwen2MoeDecoderLayer(nn.Module):
         rope_scaling = getattr(config, "rope_scaling", None)
         max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
         qkv_bias = getattr(config, "qkv_bias", True)
+        dual_chunk_attention_config = getattr(
+            config, "dual_chunk_attention_config", None
+        )
         self.self_attn = Qwen2MoeAttention(
             hidden_size=self.hidden_size,
             num_heads=config.num_attention_heads,
@@ -317,6 +322,7 @@ class Qwen2MoeDecoderLayer(nn.Module):
             rope_scaling=rope_scaling,
             max_position_embeddings=max_position_embeddings,
             quant_config=quant_config,
+            dual_chunk_attention_config=dual_chunk_attention_config,
             qkv_bias=qkv_bias,
             prefix=add_prefix("self_attn", prefix),
         )

python/sglang/srt/models/qwen3_moe.py

@@ -295,6 +295,7 @@ class Qwen3MoeAttention(nn.Module):
         attention_bias: bool = False,
         quant_config: Optional[QuantizationConfig] = None,
         prefix: str = "",
+        dual_chunk_attention_config: Optional[dict[str, Any]] = None,
         alt_stream: Optional[torch.cuda.Stream] = None,
     ) -> None:
         super().__init__()
@@ -353,6 +354,7 @@ class Qwen3MoeAttention(nn.Module):
             max_position=max_position_embeddings,
             base=rope_theta,
             rope_scaling=rope_scaling,
+            dual_chunk_attention_config=dual_chunk_attention_config,
         )
         self.attn = RadixAttention(
             self.num_heads,
@@ -458,6 +460,9 @@ class Qwen3MoeDecoderLayer(nn.Module):
         )
         rms_norm_eps = config.rms_norm_eps
         attention_bias = config.attention_bias
+        dual_chunk_attention_config = getattr(
+            config, "dual_chunk_attention_config", None
+        )
         self.self_attn = Qwen3MoeAttention(
             hidden_size=self.hidden_size,
             num_heads=config.num_attention_heads,
@@ -471,6 +476,7 @@ class Qwen3MoeDecoderLayer(nn.Module):
             attention_bias=attention_bias,
             quant_config=quant_config,
             prefix=add_prefix("self_attn", prefix),
+            dual_chunk_attention_config=dual_chunk_attention_config,
             alt_stream=alt_stream,
         )
 

python/sglang/srt/server_args.py

@@ -502,6 +502,20 @@ class ServerArgs:
                 # use bf16 for mxfp4 triton kernels
                 self.dtype = "bfloat16"
 
+        if self.attention_backend == "dual_chunk_flash_attn":
+            logger.warning(
+                "Mixed chunk is disabled because of using dual chunk flash attention backend"
+            )
+            logger.warning(
+                "Radix cache is disabled because of using dual chunk flash attention backend"
+            )
+            logger.warning(
+                "Cuda graph is disabled because of using dual chunk flash attention backend"
+            )
+            self.enable_mixed_chunk = False
+            self.disable_cuda_graph = True
+            self.disable_radix_cache = True
+
         # Set page size
         if self.page_size is None:
             self.page_size = 1
@@ -1337,6 +1351,7 @@ class ServerArgs:
                 "triton",
                 "trtllm_mla",
                 "trtllm_mha",
+                "dual_chunk_flash_attn",
             ],
             default=ServerArgs.attention_backend,
             help="Choose the kernels for attention layers.",

python/sglang/srt/two_batch_overlap.py

@@ -661,6 +661,7 @@ class TboForwardBatchPreparer:
             "padded_static_len",
             "mrope_positions",  # only used by qwen2-vl, thus not care
             "split_index",  # for split prefill
+            "orig_seq_lens",  # only used by qwen-1m, thus not care
         ]:
             output_dict[key] = getattr(batch, key)
         if not batch.forward_mode.is_target_verify():