[Feature] Add DeepEPv2 (ElasticBuffer) MoE A2A backend (#35634)

Co-authored-by: menyu <menyu@nvidia.com>
Co-authored-by: Jinyan Chen <93358689+liz-badada@users.noreply.github.com>
Co-authored-by: Han Yu <helloyu0903@gmail.com>
Co-authored-by: Cheng Wan <cheng.wan@radixark.ai>

python/sglang/kernels/ops/moe/ep_moe_kernels.py

@@ -1221,6 +1221,142 @@ def ep_scatter(
     return
 
 
+@triton.jit
+def _fwd_kernel_ep_scatter_psum_init(
+    psum_num_recv_tokens_per_expert,
+    expert_start_loc,
+    m_indices,
+    BLOCK_E: tl.constexpr,
+):
+    cur_expert = tl.program_id(0)
+    cur_end = tl.load(psum_num_recv_tokens_per_expert + cur_expert)
+    cur_start = tl.load(
+        psum_num_recv_tokens_per_expert + cur_expert - 1,
+        mask=cur_expert > 0,
+        other=0,
+    )
+    cur_token_num = cur_end - cur_start
+    tl.store(expert_start_loc + cur_expert, cur_start)
+
+    off_expert = tl.arange(0, BLOCK_E)
+    for start_m in tl.range(0, cur_token_num, BLOCK_E, num_stages=4):
+        # Mask the tail because this expert is packed against the next one.
+        idx = cur_start + start_m + off_expert
+        tl.store(m_indices + idx, cur_expert, mask=idx < cur_end)
+
+
+@torch.no_grad()
+def ep_scatter_from_psum(
+    recv_x: torch.Tensor,
+    recv_x_scale: torch.Tensor,
+    recv_topk: torch.Tensor,
+    psum_num_recv_tokens_per_expert: torch.Tensor,
+    expert_start_loc: torch.Tensor,
+    output_tensor: torch.Tensor,
+    output_tensor_scale: torch.Tensor,
+    m_indices: torch.Tensor,
+    output_index: torch.Tensor,
+    scale_ue8m0: bool = False,
+):
+    BLOCK_E = 128
+    BLOCK_D = 128
+    num_warps = 8
+    num_experts = psum_num_recv_tokens_per_expert.shape[0]
+    hidden_size = recv_x.shape[1]
+    scale_hidden_size = hidden_size // BLOCK_D
+    if scale_ue8m0:
+        scale_hidden_size = ceil_div(scale_hidden_size, 4)
+
+    assert m_indices.shape[0] % BLOCK_E == 0
+    is_fp8 = recv_x_scale is not None and recv_x.dtype != torch.bfloat16
+    if is_fp8:
+        assert recv_x_scale.dtype == output_tensor_scale.dtype
+        assert (
+            recv_x_scale.shape[1] == output_tensor_scale.shape[1] == scale_hidden_size
+        )
+
+    _fwd_kernel_ep_scatter_psum_init[(num_experts,)](
+        psum_num_recv_tokens_per_expert,
+        expert_start_loc,
+        m_indices,
+        num_warps=num_warps,
+        BLOCK_E=BLOCK_E,
+    )
+
+    grid = min(recv_topk.shape[0], 1024 * 8)
+    _fwd_kernel_ep_scatter_2[(grid,)](
+        recv_topk.shape[0],
+        expert_start_loc,
+        recv_x,
+        recv_x.stride(0),
+        recv_x.stride(1),
+        recv_x_scale,
+        recv_x_scale.stride(0) if is_fp8 else 0,
+        recv_x_scale.stride(1) if is_fp8 else 0,
+        recv_topk,
+        recv_topk.stride(0),
+        recv_topk.stride(1),
+        output_tensor,
+        output_tensor.stride(0),
+        output_tensor.stride(1),
+        output_tensor_scale,
+        output_tensor_scale.stride(0) if is_fp8 else 0,
+        output_tensor_scale.stride(1) if is_fp8 else 0,
+        output_index,
+        output_index.stride(0),
+        output_index.stride(1),
+        topk_num=recv_topk.shape[1],
+        num_warps=num_warps,
+        HIDDEN_SIZE=hidden_size,
+        HIDDEN_SIZE_PAD=triton.next_power_of_2(hidden_size),
+        SCALE_HIDDEN_SIZE=scale_hidden_size,
+        SCALE_HIDDEN_SIZE_PAD=triton.next_power_of_2(scale_hidden_size),
+        ATOMIC_ADD_SEM=None if not _is_musa else "relaxed",
+        IS_FP8=is_fp8,
+    )
+    return
+
+
+@triton.jit
+def _fwd_kernel_ep_expand_m_indices_init(
+    psum_num_recv_tokens_per_expert,
+    m_indices,
+    BLOCK_E: tl.constexpr,
+):
+    cur_expert = tl.program_id(0)
+    cur_end = tl.load(psum_num_recv_tokens_per_expert + cur_expert)
+    prev_end = tl.load(
+        psum_num_recv_tokens_per_expert + cur_expert - 1,
+        mask=cur_expert > 0,
+        other=0,
+    )
+    cur_start = ((prev_end + BLOCK_E - 1) // BLOCK_E) * BLOCK_E
+    aligned_end = ((cur_end + BLOCK_E - 1) // BLOCK_E) * BLOCK_E
+
+    off_expert = tl.arange(0, BLOCK_E)
+    for start_m in tl.range(0, aligned_end - cur_start, BLOCK_E, num_stages=4):
+        idx = cur_start + start_m + off_expert
+        tl.store(m_indices + idx, cur_expert, mask=idx < aligned_end)
+
+
+@torch.no_grad()
+def ep_expand_init_m_indices_from_psum(
+    psum_num_recv_tokens_per_expert: torch.Tensor,
+    m_indices: torch.Tensor,
+):
+    BLOCK_E = 128
+    num_warps = 8
+    num_experts = psum_num_recv_tokens_per_expert.shape[0]
+    assert m_indices.shape[0] % BLOCK_E == 0
+    _fwd_kernel_ep_expand_m_indices_init[(num_experts,)](
+        psum_num_recv_tokens_per_expert,
+        m_indices,
+        num_warps=num_warps,
+        BLOCK_E=BLOCK_E,
+    )
+    return
+
+
 @triton.jit
 def _fwd_kernel_ep_gather(
     total_token_num,
@@ -2021,6 +2157,233 @@ def fp8_per_token_to_per_tensor_quant_triton(
     )
 
 
+# Expanded psum starts each expert at align(psum[e-1]); contiguous psum already
+# includes the alignment padding.
+
+_DEEPEP_V2_REPACK_WORKERS_PER_EXPERT = 64
+
+
+# Scale strides support row-major FP32 and packed column-major UE8M0.
+@triton.jit
+def _fwd_kernel_expand_to_masked_slab(
+    psum_ptr,
+    recv_x_ptr,
+    recv_x_stride0,
+    recv_x_scale_ptr,
+    recv_x_scale_stride0,
+    recv_x_scale_stride1,
+    output_tensor_ptr,
+    output_tensor_stride0,
+    output_tensor_scale_ptr,
+    masked_m_ptr,
+    overflow_ptr,
+    MAX_M: tl.constexpr,
+    ALIGN: tl.constexpr,
+    HIDDEN: tl.constexpr,
+    HIDDEN_PAD: tl.constexpr,
+    SCALE_HIDDEN: tl.constexpr,
+    SCALE_HIDDEN_PAD: tl.constexpr,
+    IS_FP8: tl.constexpr,
+    CHECK_OVERFLOW: tl.constexpr,
+    NUM_WORKERS: tl.constexpr,
+):
+    # A fixed worker grid makes conservative max_m values graph-safe.
+    e = tl.program_id(0)
+    worker = tl.program_id(1)
+    prev_end = tl.load(psum_ptr + e - 1, mask=e > 0, other=0)
+    start = ((prev_end + ALIGN - 1) // ALIGN) * ALIGN
+    end = tl.load(psum_ptr + e)
+    raw_count = end - start
+    count = tl.minimum(raw_count, MAX_M)
+    if worker == 0:
+        tl.store(masked_m_ptr + e, count)
+        if CHECK_OVERFLOW:
+            # Graph capture omits this host-visible overflow flag.
+            ovf = tl.arange(0, 1)
+            tl.store(overflow_ptr + ovf, 1, mask=raw_count > MAX_M)
+    off = tl.arange(0, HIDDEN_PAD)
+    mask = off < HIDDEN
+    off_s = tl.arange(0, SCALE_HIDDEN_PAD)
+    mask_s = off_s < SCALE_HIDDEN
+    for j in tl.range(worker, count, NUM_WORKERS):
+        src = (start + j).to(tl.int64)
+        dst = (e * MAX_M + j).to(tl.int64)
+        v = tl.load(recv_x_ptr + src * recv_x_stride0 + off, mask=mask)
+        tl.store(output_tensor_ptr + dst * output_tensor_stride0 + off, v, mask=mask)
+        if IS_FP8:
+            vs = tl.load(
+                recv_x_scale_ptr
+                + src * recv_x_scale_stride0
+                + off_s * recv_x_scale_stride1,
+                mask=mask_s,
+            )
+            # Write physical [E, SCALE_HIDDEN, MAX_M] for an mn-major view.
+            tl.store(
+                output_tensor_scale_ptr + e * SCALE_HIDDEN * MAX_M + off_s * MAX_M + j,
+                vs,
+                mask=mask_s,
+            )
+
+
+@torch.no_grad()
+def expand_to_masked_slab(
+    recv_x: torch.Tensor,
+    recv_x_scale,
+    psum_num_recv_tokens_per_expert: torch.Tensor,
+    num_local_experts: int,
+    max_m: int,
+    expert_alignment: int,
+):
+    """expanded [total, hidden] -> ([E_local, max_m, hidden], [E_local, max_m, sh] or None, masked_m[E_local])."""
+    hidden = recv_x.shape[1]
+    is_fp8 = recv_x_scale is not None and recv_x.dtype != torch.bfloat16
+    output_tensor = torch.empty(
+        (num_local_experts * max_m, hidden), device=recv_x.device, dtype=recv_x.dtype
+    )
+    masked_m = torch.empty(
+        (num_local_experts,), device=recv_x.device, dtype=torch.int32
+    )
+    check_overflow = not torch.cuda.is_current_stream_capturing()
+    # Dummy pointer under capture; CHECK_OVERFLOW compiles out every store to it.
+    overflow = (
+        torch.zeros((1,), device=recv_x.device, dtype=torch.int32)
+        if check_overflow
+        else masked_m
+    )
+    if is_fp8:
+        sh = recv_x_scale.shape[1]
+        # Store [E, sh, max_m] so the returned transpose is mn-major.
+        output_tensor_scale = torch.empty(
+            (num_local_experts * sh, max_m),
+            device=recv_x.device,
+            dtype=recv_x_scale.dtype,
+        )
+        scale_arg = recv_x_scale
+        scale_s0 = recv_x_scale.stride(0)
+        scale_s1 = recv_x_scale.stride(1)
+    else:
+        sh = 1
+        output_tensor_scale = None
+        scale_arg = recv_x
+        scale_s0 = 0
+        scale_s1 = 0
+    num_workers = min(max_m, _DEEPEP_V2_REPACK_WORKERS_PER_EXPERT)
+    _fwd_kernel_expand_to_masked_slab[(num_local_experts, num_workers)](
+        psum_num_recv_tokens_per_expert,
+        recv_x,
+        recv_x.stride(0),
+        scale_arg,
+        scale_s0,
+        scale_s1,
+        output_tensor,
+        output_tensor.stride(0),
+        output_tensor_scale if is_fp8 else scale_arg,
+        masked_m,
+        overflow,
+        MAX_M=max_m,
+        ALIGN=expert_alignment,
+        HIDDEN=hidden,
+        HIDDEN_PAD=triton.next_power_of_2(hidden),
+        SCALE_HIDDEN=sh,
+        SCALE_HIDDEN_PAD=triton.next_power_of_2(sh),
+        IS_FP8=is_fp8,
+        CHECK_OVERFLOW=check_overflow,
+        NUM_WORKERS=num_workers,
+        num_warps=4,
+    )
+    # Capture relies on max_m = cap * ep_group_size; eager also checks counts.
+    if check_overflow and int(overflow.item()) != 0:
+        raise RuntimeError(
+            f"DeepEP v2 masked slab overflow: an expert received more than max_m="
+            f"{max_m} tokens; increase "
+            f"SGLANG_DEEPEP_V2_NUM_MAX_DISPATCH_TOKENS_PER_RANK."
+        )
+    output_tensor = output_tensor.view(num_local_experts, max_m, hidden)
+    if is_fp8:
+        output_tensor_scale = output_tensor_scale.view(
+            num_local_experts, sh, max_m
+        ).transpose(1, 2)
+    return output_tensor, output_tensor_scale, masked_m
+
+
+@triton.jit
+def _fwd_kernel_masked_slab_to_expand(
+    psum_ptr,
+    input_tensor_ptr,
+    input_tensor_stride0,
+    output_tensor_ptr,
+    output_tensor_stride0,
+    weight_ptr,
+    MAX_M: tl.constexpr,
+    ALIGN: tl.constexpr,
+    HIDDEN: tl.constexpr,
+    HIDDEN_PAD: tl.constexpr,
+    HAS_W: tl.constexpr,
+    NUM_WORKERS: tl.constexpr,
+):
+    e = tl.program_id(0)
+    worker = tl.program_id(1)
+    prev_end = tl.load(psum_ptr + e - 1, mask=e > 0, other=0)
+    start = ((prev_end + ALIGN - 1) // ALIGN) * ALIGN
+    end = tl.load(psum_ptr + e)
+    count = end - start
+    count = tl.minimum(count, MAX_M)
+    off = tl.arange(0, HIDDEN_PAD)
+    mask = off < HIDDEN
+    for j in tl.range(worker, count, NUM_WORKERS):
+        src = (e * MAX_M + j).to(tl.int64)
+        dst = (start + j).to(tl.int64)
+        v = tl.load(input_tensor_ptr + src * input_tensor_stride0 + off, mask=mask)
+        if HAS_W:
+            w = tl.load(weight_ptr + dst)
+            v = (v.to(tl.float32) * w).to(v.dtype)
+        tl.store(output_tensor_ptr + dst * output_tensor_stride0 + off, v, mask=mask)
+
+
+@torch.no_grad()
+def masked_slab_to_expand(
+    input_tensor: torch.Tensor,
+    psum_num_recv_tokens_per_expert: torch.Tensor,
+    total_expanded_tokens: int,
+    expert_alignment: int,
+    topk_weights=None,
+):
+    """Convert masked-GEMM output to expanded order.
+
+    Only real rows are written; combine ignores uninitialized padding through the
+    handle. Optional top-k weights are fused into the copy over real rows only.
+    """
+    num_local_experts, max_m, hidden = input_tensor.shape
+    output_tensor = torch.empty(
+        (total_expanded_tokens, hidden),
+        device=input_tensor.device,
+        dtype=input_tensor.dtype,
+    )
+    input_tensor2d = input_tensor.view(num_local_experts * max_m, hidden)
+    has_w = topk_weights is not None
+    if has_w:
+        weight_arg = topk_weights.reshape(-1).to(torch.float32).contiguous()
+    else:
+        weight_arg = input_tensor2d
+    num_workers = min(max_m, _DEEPEP_V2_REPACK_WORKERS_PER_EXPERT)
+    _fwd_kernel_masked_slab_to_expand[(num_local_experts, num_workers)](
+        psum_num_recv_tokens_per_expert,
+        input_tensor2d,
+        input_tensor2d.stride(0),
+        output_tensor,
+        output_tensor.stride(0),
+        weight_arg,
+        MAX_M=max_m,
+        ALIGN=expert_alignment,
+        HIDDEN=hidden,
+        HIDDEN_PAD=triton.next_power_of_2(hidden),
+        HAS_W=has_w,
+        NUM_WORKERS=num_workers,
+        num_warps=4,
+    )
+    return output_tensor
+
+
 def _moe_permute_rows(
     inputs: torch.Tensor,
     topk_ids: torch.Tensor,

python/sglang/srt/arg_groups/overrides.py

@@ -2806,7 +2806,7 @@ def _moe_runner_fusion_disable(view: Any) -> dict:
 def _a2a_fusion_adjustments(view: Any) -> dict:
     """A2A-backend-driven shared-experts fusion adjustments, declared at the
     legacy write slots in _handle_a2a_moe: Waterfill requires the
-    fusion enabled; FlashInfer A2A requires it disabled."""
+    fusion enabled; FlashInfer and DeepEP v2 A2A require it disabled."""
     if view.moe_a2a_backend in ("deepep", "megamoe") and view.enable_waterfill:
         if view.disable_shared_experts_fusion:
             logger.warning(
@@ -2819,6 +2819,9 @@ def _a2a_fusion_adjustments(view: Any) -> dict:
             "Flashinfer MoE A2A is enabled. --disable-shared-experts-fusion is automatically set."
         )
         return {"disable_shared_experts_fusion": True}
+    if view.moe_a2a_backend == "deepep_v2":
+        # Fused shared experts are not validated with DeepEP v2.
+        return {"disable_shared_experts_fusion": True}
     return {}
 
 
@@ -2827,6 +2830,7 @@ _A2A_EP_SPANNING_BACKENDS = frozenset(
     {
         "megamoe",
         "deepep",
+        "deepep_v2",
         "mooncake",
         "nixl",
         "ascend_fuseep",

python/sglang/srt/environ.py

@@ -1043,6 +1043,10 @@ class Envs:
     # read by several call sites; do not use in new code.
     SGLANG_DEEPEP_BF16_DISPATCH = EnvBool(False)
     SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK = EnvInt(128)
+    # Per-rank buffer capacity, not a model token limit.
+    SGLANG_DEEPEP_V2_NUM_MAX_DISPATCH_TOKENS_PER_RANK = EnvInt(128)
+    # 0 lets ElasticBuffer select its theoretical communication SM/QP counts.
+    SGLANG_DEEPEP_V2_NUM_SMS = EnvInt(0)
     SGLANG_DEEPEP_LL_COMBINE_SEND_NUM_SMS = EnvInt(32)
     SGLANG_BLACKWELL_OVERLAP_SHARED_EXPERTS_OUTSIDE_SBO = EnvBool(False)
     SGLANG_ENABLE_QWEN_DEEPEP_SHARED_OVERLAP = EnvBool(True)

python/sglang/srt/layers/moe/ep_moe/layer.py

@@ -103,7 +103,9 @@ class DeepEPMoE(FusedMoE):
             and quant_config is not None
             and quant_config.get_name() == "humming"
         )
-        if is_humming:
+        if get_moe_a2a_backend().is_deepep_v2():
+            self.deprecate_flag = True
+        elif is_humming:
             self.deprecate_flag = True
         elif _use_aiter:
             self.deprecate_flag = True
@@ -354,6 +356,7 @@ def get_moe_impl_class(quant_config: Optional[QuantizationConfig]):
     if (
         get_moe_a2a_backend().is_mori()
         or get_moe_a2a_backend().is_deepep()
+        or get_moe_a2a_backend().is_deepep_v2()
         or get_moe_a2a_backend().is_mooncake()
         or get_moe_a2a_backend().is_nixl()
         or get_moe_a2a_backend().is_pplx()

python/sglang/srt/layers/moe/fused_moe_triton/layer.py

@@ -38,6 +38,7 @@ from sglang.srt.layers.moe.token_dispatcher.ascend_tp import (
     AscendTPDispatcher,
 )
 from sglang.srt.layers.moe.token_dispatcher.base import BaseDispatcher
+from sglang.srt.layers.moe.token_dispatcher.deepep_v2 import DeepEPv2Dispatcher
 from sglang.srt.layers.moe.token_dispatcher.flashinfer import FlashinferDispatcher
 from sglang.srt.layers.moe.token_dispatcher.standard import (
     StandardDispatcher,
@@ -189,6 +190,15 @@ def create_moe_dispatcher(moe_runner_config: MoeRunnerConfig) -> BaseDispatcher:
             async_finish=True,
             return_recv_hook=True,
         )
+    elif a2a_backend.is_deepep_v2():
+        return DeepEPv2Dispatcher(
+            group=get_tp_group().device_group,
+            router_topk=moe_runner_config.top_k,
+            num_experts=moe_runner_config.num_experts,
+            num_local_experts=moe_runner_config.num_local_experts,
+            hidden_size=moe_runner_config.hidden_size,
+            params_dtype=moe_runner_config.params_dtype,
+        )
     elif a2a_backend.is_flashinfer():
         return FlashinferDispatcher(
             group=get_tp_group().device_group,
@@ -226,6 +236,34 @@ def _validate_hpc_ops_quant_method(quant_method) -> None:
         )
 
 
+def _validate_deepep_v2_quant_method(quant_method) -> None:
+    """Validate the FP8 contract consumed by the DeepEP v2 adapter."""
+    if not get_moe_a2a_backend().is_deepep_v2():
+        return
+
+    config = (
+        quant_method.quant_config if isinstance(quant_method, Fp8MoEMethod) else None
+    )
+    reason = None
+    if not isinstance(quant_method, Fp8MoEMethod):
+        reason = f"selected {type(quant_method).__name__}"
+    elif quant_method.use_mxfp8:
+        reason = "selected MXFP8 weights"
+    elif quant_method.is_fp4_expert:
+        reason = "selected FP4 experts"
+    elif list(quant_method.weight_block_size or []) != [128, 128]:
+        reason = f"has weight_block_size={quant_method.weight_block_size}"
+    elif config.activation_scheme != "dynamic":
+        reason = f"has activation_scheme={config.activation_scheme!r}"
+
+    if reason is not None:
+        raise ValueError(
+            "--moe-a2a-backend deepep_v2 requires 128x128 blockwise FP8 "
+            f"experts with dynamic activation scaling, but this layer {reason}. "
+            "Use a compatible checkpoint or --moe-a2a-backend deepep."
+        )
+
+
 class FusedMoE(torch.nn.Module):
     """FusedMoE layer for MoE models.
 
@@ -407,6 +445,7 @@ class FusedMoE(torch.nn.Module):
                     self.use_deep_gemm,
                 )
         _validate_hpc_ops_quant_method(self.quant_method)
+        _validate_deepep_v2_quant_method(self.quant_method)
         self.supports_deferred_finalize = (
             envs.SGLANG_ENABLE_MOE_DEFERRED_FINALIZE.get()
             and get_moe_runner_backend().is_flashinfer_trtllm()

python/sglang/srt/layers/moe/moe_runner/deep_gemm.py

@@ -49,6 +49,10 @@ if TYPE_CHECKING:
         DeepEPNormalCombineInput,
         DeepEPNormalDispatchOutput,
     )
+    from sglang.srt.layers.moe.token_dispatcher.deepep_v2 import (
+        DeepEPv2CombineInput,
+        DeepEPv2DispatchOutput,
+    )
     from sglang.srt.layers.moe.token_dispatcher.standard import (
         StandardCombineInput,
         StandardDispatchOutput,
@@ -206,6 +210,7 @@ class DeepGemmRunnerInput(RunnerInput):
     masked_m: Optional[torch.Tensor] = None
     expected_m: Optional[int] = None
     m_indices: Optional[torch.Tensor] = None
+    hidden_states_scale_tma_aligned: bool = False
 
     @property
     def runner_backend(self) -> MoeRunnerBackend:
@@ -321,7 +326,10 @@ class DeepGemmRunnerCore(MoeRunnerCore):
             device=hidden_states_device,
             dtype=torch.bfloat16,
         )
-        if deep_gemm_wrapper.DEEPGEMM_NEED_TMA_ALIGNED_SCALES:
+        if (
+            deep_gemm_wrapper.DEEPGEMM_NEED_TMA_ALIGNED_SCALES
+            and not runner_input.hidden_states_scale_tma_aligned
+        ):
             hidden_states_scale = tma_align_input_scale(hidden_states_scale)
 
         deep_gemm_wrapper.grouped_gemm_nt_f8f8bf16_contig(
@@ -1419,3 +1427,186 @@ def _apply_swiglu_limit(
     out = torch.cat([gate, up], dim=-1)
     assert out.shape == (num_tokens, hidden_size_x2)
     return out
+
+
+@register_pre_permute("deepep_v2", "deep_gemm")
+def pre_permute_deepep_v2_to_deep_gemm(
+    dispatch_output: DeepEPv2DispatchOutput,
+    quant_info: DeepGemmMoeQuantInfo,
+    runner_config: MoeRunnerConfig,
+    running_state: dict,
+) -> DeepGemmRunnerInput:
+    from sglang.kernels.ops.moe.ep_moe_kernels import (
+        ep_expand_init_m_indices_from_psum,
+        ep_scatter_from_psum,
+    )
+
+    hidden_states = dispatch_output.hidden_states
+    hidden_states_scale = dispatch_output.hidden_states_scale
+    topk_ids = dispatch_output.topk_ids
+    topk_weights = dispatch_output.topk_weights
+    psum_num_recv_tokens_per_expert = dispatch_output.psum_num_recv_tokens_per_expert
+    is_expanded = dispatch_output.is_expanded
+    hidden_states_scale_tma_aligned = dispatch_output.hidden_states_scale_tma_aligned
+    deepep_v2_use_masked = dispatch_output.use_masked_gemm
+    deepep_v2_expected_m = dispatch_output.expected_m
+    deepep_v2_masked_max_m = dispatch_output.masked_max_m
+    deepep_v2_total_expanded = dispatch_output.total_expanded
+    deepep_v2_expert_alignment = dispatch_output.expert_alignment
+    if hidden_states_scale is None:
+        raise RuntimeError(
+            "DeepEP v2 -> DeepGEMM requires FP8 dispatch output with activation "
+            "scales, but the dispatch output carried none."
+        )
+    assert runner_config.activation == "silu"
+
+    if is_expanded:
+        if psum_num_recv_tokens_per_expert is None:
+            raise RuntimeError(
+                "DeepEP v2 requires the native expert prefix sums from the "
+                "ElasticBuffer dispatch handle."
+            )
+        all_tokens = hidden_states.shape[0]
+        running_state["all_tokens"] = all_tokens
+        running_state["hidden_states_shape"] = hidden_states.shape
+        running_state["hidden_states_device"] = hidden_states.device
+        running_state["hidden_states_dtype"] = hidden_states.dtype
+        running_state["topk_ids"] = None
+        running_state["topk_weights"] = topk_weights
+        running_state["deepep_v2_expanded"] = True
+
+        if deepep_v2_use_masked:
+            # masked_m bounds each expert independently of buffer capacity.
+            from sglang.kernels.ops.moe.ep_moe_kernels import expand_to_masked_slab
+
+            num_local_experts = psum_num_recv_tokens_per_expert.shape[0]
+            input_tensor, input_tensor_scale, masked_m = expand_to_masked_slab(
+                hidden_states,
+                hidden_states_scale,
+                psum_num_recv_tokens_per_expert,
+                num_local_experts,
+                deepep_v2_masked_max_m,
+                deepep_v2_expert_alignment,
+            )
+            running_state["deepep_v2_masked"] = True
+            running_state["deepep_v2_psum"] = psum_num_recv_tokens_per_expert
+            running_state["deepep_v2_total_expanded"] = deepep_v2_total_expanded
+            running_state["deepep_v2_expert_alignment"] = deepep_v2_expert_alignment
+            return DeepGemmRunnerInput(
+                hidden_states=input_tensor,
+                hidden_states_scale=input_tensor_scale,
+                use_masked_gemm=True,
+                masked_m=masked_m,
+                expected_m=deepep_v2_expected_m,
+            )
+
+        # Mark aligned expert rows and leave the unused receive tail at -1.
+        m_indices = torch.full(
+            (all_tokens,), -1, device=hidden_states.device, dtype=torch.int32
+        )
+        ep_expand_init_m_indices_from_psum(psum_num_recv_tokens_per_expert, m_indices)
+        return DeepGemmRunnerInput(
+            hidden_states=hidden_states,
+            hidden_states_scale=hidden_states_scale,
+            use_masked_gemm=False,
+            m_indices=m_indices,
+            hidden_states_scale_tma_aligned=hidden_states_scale_tma_aligned,
+        )
+
+    all_tokens = int(psum_num_recv_tokens_per_expert[-1].item())
+    K = hidden_states.shape[1]
+    running_state["all_tokens"] = all_tokens
+    running_state["hidden_states_shape"] = hidden_states.shape
+    running_state["hidden_states_device"] = hidden_states.device
+    running_state["hidden_states_dtype"] = hidden_states.dtype
+    running_state["topk_ids"] = topk_ids
+    running_state["topk_weights"] = topk_weights
+
+    input_tensor = torch.empty(
+        (all_tokens, K), device=hidden_states.device, dtype=hidden_states.dtype
+    )
+    if deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0:
+        # Packed UE8M0 scales require zero padding lanes.
+        input_tensor_scale = torch.zeros(
+            (ceil_div(K // 128, 4), all_tokens),
+            device=hidden_states.device,
+            dtype=torch.int,
+        ).transpose(0, 1)
+    else:
+        input_tensor_scale = torch.empty(
+            (all_tokens, K // 128), device=hidden_states.device, dtype=torch.float32
+        )
+    m_indices = torch.empty(all_tokens, device=hidden_states.device, dtype=torch.int32)
+    output_index = torch.empty_like(topk_ids)
+    # Contiguous psum already includes the 128-row expert alignment.
+    expert_start_loc = torch.empty_like(psum_num_recv_tokens_per_expert)
+    ep_scatter_from_psum(
+        hidden_states,
+        hidden_states_scale,
+        topk_ids,
+        psum_num_recv_tokens_per_expert,
+        expert_start_loc,
+        input_tensor,
+        input_tensor_scale,
+        m_indices,
+        output_index,
+        scale_ue8m0=deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0,
+    )
+    dispose_tensor(hidden_states)
+    dispose_tensor(hidden_states_scale)
+    running_state["output_index"] = output_index
+
+    return DeepGemmRunnerInput(
+        hidden_states=input_tensor,
+        hidden_states_scale=input_tensor_scale,
+        use_masked_gemm=False,
+        m_indices=m_indices,
+    )
+
+
+@register_post_permute("deep_gemm", "deepep_v2")
+def post_permute_deep_gemm_to_deepep_v2(
+    runner_output: DeepGemmRunnerOutput,
+    quant_info: DeepGemmMoeQuantInfo,
+    runner_config: MoeRunnerConfig,
+    running_state: dict,
+) -> DeepEPv2CombineInput:
+    from sglang.kernels.ops.moe.ep_moe_kernels import ep_gather
+    from sglang.srt.layers.moe.token_dispatcher.deepep_v2 import DeepEPv2CombineInput
+
+    if running_state.get("deepep_v2_expanded", False):
+        hidden_states = runner_output.hidden_states
+        topk_weights = running_state["topk_weights"]
+        if running_state.get("deepep_v2_masked", False):
+            # Expanded combine does not consume top-k weights.
+            from sglang.kernels.ops.moe.ep_moe_kernels import masked_slab_to_expand
+
+            hidden_states = masked_slab_to_expand(
+                hidden_states,
+                running_state["deepep_v2_psum"],
+                running_state["deepep_v2_total_expanded"],
+                running_state["deepep_v2_expert_alignment"],
+                topk_weights=topk_weights,
+            )
+            return DeepEPv2CombineInput(hidden_states, None)
+        if topk_weights is not None:
+            # Expanded combine does not consume top-k weights.
+            hidden_states = hidden_states * topk_weights.to(
+                hidden_states.dtype
+            ).unsqueeze(-1)
+        return DeepEPv2CombineInput(hidden_states, None)
+
+    hidden_states = runner_output.hidden_states
+    topk_ids = running_state["topk_ids"]
+    topk_weights = running_state["topk_weights"]
+    output_index = running_state["output_index"]
+    gather_out = torch.empty(
+        running_state["hidden_states_shape"],
+        device=running_state["hidden_states_device"],
+        dtype=torch.bfloat16,
+    )
+    ep_gather(hidden_states, topk_ids, topk_weights, output_index, gather_out)
+    return DeepEPv2CombineInput(
+        hidden_states=gather_out,
+        topk_weights=topk_weights,
+    )

python/sglang/srt/layers/moe/moe_runner/runner.py

@@ -50,6 +50,15 @@ class MoeRunner:
                 "--moe-runner-backend hpc_ops for this model."
             )
 
+        if get_moe_a2a_backend().is_deepep_v2() and not runner_backend.is_deep_gemm():
+            raise ValueError(
+                "--moe-a2a-backend deepep_v2 requires the deep_gemm MoE runner, "
+                f"but this MoE layer's quantization method selected the "
+                f"'{runner_backend.value}' runner. deepep_v2 dispatches FP8 "
+                "activations plus scales, which only deep_gemm consumes; use an "
+                "FP8 blockwise-quantized checkpoint, or --moe-a2a-backend deepep."
+            )
+
         self.fused_func = None
 
         if runner_backend.is_triton():

python/sglang/srt/layers/moe/token_dispatcher/__init__.py

@@ -21,6 +21,11 @@ from sglang.srt.layers.moe.token_dispatcher.deepep import (
     DeepEPNormalCombineInput,
     DeepEPNormalDispatchOutput,
 )
+from sglang.srt.layers.moe.token_dispatcher.deepep_v2 import (
+    DeepEPv2CombineInput,
+    DeepEPv2Dispatcher,
+    DeepEPv2DispatchOutput,
+)
 from sglang.srt.layers.moe.token_dispatcher.flashinfer import (
     FlashinferDispatcher,
     FlashinferDispatchOutput,
@@ -72,6 +77,9 @@ __all__ = [
     "MoriEPLLDispatchOutput",
     "MoriEPLLCombineInput",
     "MoriEPDispatcher",
+    "DeepEPv2Dispatcher",
+    "DeepEPv2DispatchOutput",
+    "DeepEPv2CombineInput",
     "NixlEPCombineInput",
     "NixlEPDispatchOutput",
     "NixlEPDispatcher",

python/sglang/srt/layers/moe/token_dispatcher/base.py

@@ -27,6 +27,8 @@ if TYPE_CHECKING:
         DeepEPLLDispatchOutput,
         DeepEPNormalCombineInput,
         DeepEPNormalDispatchOutput,
+        DeepEPv2CombineInput,
+        DeepEPv2DispatchOutput,
         FlashinferCombineInput,
         FlashinferDispatchOutput,
         StandardCombineInput,
@@ -165,6 +167,12 @@ class DispatchOutputChecker:
     ) -> TypeGuard[FlashinferDispatchOutput]:
         return dispatch_output.format.is_flashinfer()
 
+    @staticmethod
+    def format_is_deepep_v2(
+        dispatch_output: DispatchOutput,
+    ) -> TypeGuard[DeepEPv2DispatchOutput]:
+        return dispatch_output.format.is_deepep_v2()
+
 
 class DispatchOutputFormat(Enum):
 
@@ -172,6 +180,7 @@ class DispatchOutputFormat(Enum):
     DEEPEP_NORMAL = "deepep_normal"
     DEEPEP_LL = "deepep_ll"
     FLASHINFER = "flashinfer"
+    DEEPEP_V2 = "deepep_v2"
     ASCEND_TP = "ascend_tp"
 
     def is_standard(self) -> bool:
@@ -195,6 +204,9 @@ class DispatchOutputFormat(Enum):
     def is_flashinfer(self) -> bool:
         return self == DispatchOutputFormat.FLASHINFER
 
+    def is_deepep_v2(self) -> bool:
+        return self == DispatchOutputFormat.DEEPEP_V2
+
 
 @runtime_checkable
 class DispatchOutput(Protocol):
@@ -249,12 +261,19 @@ class CombineInputChecker:
     ) -> TypeGuard[FlashinferCombineInput]:
         return combine_input.format == CombineInputFormat.FLASHINFER
 
+    @staticmethod
+    def format_is_deepep_v2(
+        combine_input: CombineInput,
+    ) -> TypeGuard[DeepEPv2CombineInput]:
+        return combine_input.format == CombineInputFormat.DEEPEP_V2
+
 
 class CombineInputFormat(Enum):
     STANDARD = "standard"
     DEEPEP_NORMAL = "deepep_normal"
     DEEPEP_LL = "deepep_ll"
     FLASHINFER = "flashinfer"
+    DEEPEP_V2 = "deepep_v2"
     ASCEND_TP = "ascend_tp"
 
 

python/sglang/srt/layers/moe/token_dispatcher/deepep_v2.py

@@ -0,0 +1,460 @@
+from __future__ import annotations
+
+import logging
+import os
+from typing import NamedTuple, Optional
+
+import torch
+import torch.distributed as dist
+
+from sglang.srt.environ import envs
+from sglang.srt.layers.dp_attention import get_is_extend_in_batch
+from sglang.srt.layers.moe.token_dispatcher.base import (
+    BaseDispatcher,
+    CombineInput,
+    CombineInputFormat,
+    DispatchOutput,
+    DispatchOutputFormat,
+)
+from sglang.srt.layers.moe.topk import TopKOutput
+from sglang.srt.layers.moe.utils import (
+    DeepEPv2Fp8ScaleFormat,
+    get_deepep_v2_fp8_scale_format,
+)
+
+logger = logging.getLogger(__name__)
+
+_SCALE_BLOCK_SIZE = 128
+# Must match DeepGEMM's contiguous expert alignment.
+_EXPERT_ALIGNMENT = 128
+_deepep_v2_import_error: Optional[BaseException] = None
+_fp8_quant_import_error: Optional[BaseException] = None
+sglang_per_token_group_quant_fp8 = None
+
+try:
+    from deep_ep import ElasticBuffer
+
+    use_deepep_v2 = True
+except (ImportError, OSError) as exc:
+    use_deepep_v2 = False
+    _deepep_v2_import_error = exc
+
+if use_deepep_v2:
+    try:
+        from sglang.kernels.ops.quantization.fp8_kernel import (
+            sglang_per_token_group_quant_fp8,
+        )
+    except (ImportError, OSError) as exc:
+        _fp8_quant_import_error = exc
+
+
+class DeepEPv2DispatchOutput(NamedTuple):
+    hidden_states: torch.Tensor
+    hidden_states_scale: Optional[torch.Tensor]
+    topk_ids: Optional[torch.Tensor]
+    topk_weights: torch.Tensor
+    psum_num_recv_tokens_per_expert: Optional[torch.Tensor] = None
+    is_expanded: bool = False
+    hidden_states_scale_tma_aligned: bool = False
+    use_masked_gemm: bool = False
+    expected_m: int = 0
+    masked_max_m: int = 0
+    total_expanded: int = 0
+    expert_alignment: int = 128
+
+    @property
+    def format(self) -> DispatchOutputFormat:
+        return DispatchOutputFormat.DEEPEP_V2
+
+
+class DeepEPv2CombineInput(NamedTuple):
+    hidden_states: torch.Tensor
+    topk_weights: Optional[torch.Tensor]
+
+    @property
+    def format(self) -> CombineInputFormat:
+        return CombineInputFormat.DEEPEP_V2
+
+
+assert isinstance(DeepEPv2DispatchOutput, DispatchOutput)
+assert isinstance(DeepEPv2CombineInput, CombineInput)
+
+
+def _raise_deepep_v2_import_error() -> None:
+    detail = (
+        f" Original import error: {_deepep_v2_import_error}"
+        if _deepep_v2_import_error is not None
+        else ""
+    )
+    raise ImportError(
+        "DeepEP v2 (ElasticBuffer) is not available. Install DeepEP v2 from "
+        "https://github.com/deepseek-ai/DeepEP." + detail
+    )
+
+
+def _ensure_deepep_v2_available() -> None:
+    if not use_deepep_v2:
+        _raise_deepep_v2_import_error()
+
+
+def _ensure_fp8_quant_available() -> None:
+    _ensure_deepep_v2_available()
+    if sglang_per_token_group_quant_fp8 is None:
+        detail = (
+            f" Original import error: {_fp8_quant_import_error}"
+            if _fp8_quant_import_error is not None
+            else ""
+        )
+        raise ImportError(
+            "DeepEP v2 FP8 dispatch requires the SGLang FP8 quantization kernel."
+            + detail
+        )
+
+
+def _get_allow_hybrid_mode() -> bool:
+    from sglang.srt.runtime_context import get_exec
+
+    return get_exec().moe.deepep_v2_mode == "hybrid"
+
+
+def _quantize_for_deepep_v2_dispatch(
+    hidden_states: torch.Tensor, scale_format: DeepEPv2Fp8ScaleFormat
+):
+    _ensure_fp8_quant_available()
+    return sglang_per_token_group_quant_fp8(
+        hidden_states,
+        _SCALE_BLOCK_SIZE,
+        column_major_scales=scale_format.tma_aligned,
+        scale_tma_aligned=scale_format.tma_aligned,
+        scale_ue8m0=scale_format.ue8m0,
+    )
+
+
+class DeepEPv2Buffer:
+    """Facade for the process-wide ElasticBuffer stored in runtime resources."""
+
+    _STATE_KEY = "deepep_v2_ep_state"
+
+    @classmethod
+    def _state(cls):
+        from types import SimpleNamespace
+
+        from sglang.srt.runtime_context import get_resources
+
+        buffers = get_resources().buffers
+        state = buffers.get(cls._STATE_KEY)
+        if state is None:
+            state = SimpleNamespace(buffer=None, key=None)
+            buffers[cls._STATE_KEY] = state
+        return state
+
+    @classmethod
+    def get_buffer(
+        cls,
+        group: dist.ProcessGroup,
+        hidden_size: int,
+        router_topk: int,
+        num_max_dispatch_tokens_per_rank: int,
+        use_fp8_dispatch: bool,
+        allow_hybrid_mode: Optional[bool] = None,
+    ) -> ElasticBuffer:
+        _ensure_deepep_v2_available()
+
+        if allow_hybrid_mode is None:
+            allow_hybrid_mode = _get_allow_hybrid_mode()
+        state = cls._state()
+        # A key change rebuilds ElasticBuffer collectively on every rank.
+        key = (
+            group,
+            hidden_size,
+            router_topk,
+            num_max_dispatch_tokens_per_rank,
+            use_fp8_dispatch,
+            allow_hybrid_mode,
+            dist.get_world_size(group),
+        )
+        if state.buffer is not None and state.key == key:
+            return state.buffer
+
+        # Native explicit teardown is unavailable unless explicitly_destroy=True.
+        cls.destroy()
+
+        # Communicator reuse requires a device-bound process group.
+        os.environ.setdefault("EP_REUSE_NCCL_COMM", "0")
+        buffer = ElasticBuffer(
+            group,
+            num_max_tokens_per_rank=num_max_dispatch_tokens_per_rank,
+            hidden=hidden_size,
+            num_topk=router_topk,
+            use_fp8_dispatch=use_fp8_dispatch,
+            allow_hybrid_mode=allow_hybrid_mode,
+            sl_idx=0,
+            prefer_overlap_with_compute=False,
+        )
+        # Publish only after collective construction succeeds.
+        state.buffer = buffer
+        state.key = key
+        logger.info(
+            "Initialized DeepEP v2 ElasticBuffer: world_size=%s hidden_size=%s "
+            "num_topk=%s max_dispatch_tokens_per_rank=%s use_fp8_dispatch=%s "
+            "allow_hybrid_mode=%s num_bytes=%s",
+            dist.get_world_size(group),
+            hidden_size,
+            router_topk,
+            num_max_dispatch_tokens_per_rank,
+            use_fp8_dispatch,
+            allow_hybrid_mode,
+            buffer.num_bytes,
+        )
+        return buffer
+
+    @classmethod
+    def destroy(cls) -> None:
+        state = cls._state()
+        state.buffer = None
+        state.key = None
+
+
+class _DeepEPv2Impl:
+    def __init__(
+        self,
+        group: dist.ProcessGroup,
+        router_topk: int,
+        num_experts: int,
+        num_local_experts: int,
+        hidden_size: int,
+        scale_format: DeepEPv2Fp8ScaleFormat,
+        num_max_dispatch_tokens_per_rank: int,
+    ):
+        self.group = group
+        self.router_topk = router_topk
+        self.num_experts = num_experts
+        self.num_local_experts = num_local_experts
+        self.hidden_size = hidden_size
+        self.scale_format = scale_format
+        self.num_max_dispatch_tokens_per_rank = num_max_dispatch_tokens_per_rank
+        self.rank = dist.get_rank(group)
+        self._handle = None
+        self._pad_empty_combine = False
+
+    def _destroy_handle(self) -> None:
+        self._handle = None
+
+    def _get_buffer(self) -> ElasticBuffer:
+        return DeepEPv2Buffer.get_buffer(
+            self.group,
+            self.hidden_size,
+            self.router_topk,
+            self.num_max_dispatch_tokens_per_rank,
+            True,
+        )
+
+    def _validate_common(
+        self, hidden_states: torch.Tensor, topk_ids: torch.Tensor
+    ) -> None:
+        if hidden_states.shape[0] > self.num_max_dispatch_tokens_per_rank:
+            raise ValueError(
+                f"DeepEP v2 dispatch input exceeds the per-rank buffer capacity "
+                f"{self.num_max_dispatch_tokens_per_rank}, got {hidden_states.shape[0]}. "
+                "Increase SGLANG_DEEPEP_V2_NUM_MAX_DISPATCH_TOKENS_PER_RANK or "
+                "lower the active prefill/decode batch limit."
+            )
+        if hidden_states.shape[1] != self.hidden_size:
+            raise ValueError(
+                f"DeepEP v2 hidden size mismatch: expected {self.hidden_size}, "
+                f"got {hidden_states.shape[1]}"
+            )
+        if self.hidden_size % _SCALE_BLOCK_SIZE != 0:
+            raise ValueError(
+                "DeepEP v2 FP8 dispatch requires hidden_size multiple of "
+                f"{_SCALE_BLOCK_SIZE}, got {self.hidden_size}"
+            )
+        if topk_ids.shape[1] != self.router_topk:
+            raise ValueError(
+                f"DeepEP v2 topk mismatch: expected {self.router_topk}, "
+                f"got {topk_ids.shape[1]}"
+            )
+
+    def dispatch(
+        self, hidden_states: torch.Tensor, topk_output: TopKOutput
+    ) -> DeepEPv2DispatchOutput:
+        if self._handle is not None:
+            raise RuntimeError(
+                "DeepEP v2 dispatch called while the previous dispatch handle is "
+                "still unconsumed (missing combine)"
+            )
+        _ensure_deepep_v2_available()
+        topk_weights = topk_output.topk_weights
+        topk_ids = topk_output.topk_ids.to(torch.int64)
+        self._validate_common(hidden_states, topk_ids)
+        # Decode uses expanded/masked layout; extend uses contiguous in both modes.
+        use_expand_layout = not get_is_extend_in_batch()
+        use_masked = use_expand_layout
+
+        # CPU-synced dispatch needs a dummy token to notify from an idle rank.
+        self._pad_empty_combine = (not use_masked) and hidden_states.shape[0] == 0
+        if self._pad_empty_combine:
+            hidden_states = hidden_states.new_zeros((1, hidden_states.shape[-1]))
+            # Dummy routes need distinct expert ids; zero weights null the result.
+            topk_ids = torch.arange(
+                topk_ids.shape[-1], dtype=topk_ids.dtype, device=topk_ids.device
+            ).unsqueeze(0)
+            topk_weights = topk_weights.new_zeros((1, topk_weights.shape[-1]))
+
+        _ensure_fp8_quant_available()
+        if use_masked:
+            _ue8m0 = self.scale_format.ue8m0
+            dispatch_x = sglang_per_token_group_quant_fp8(
+                hidden_states,
+                _SCALE_BLOCK_SIZE,
+                column_major_scales=_ue8m0,
+                scale_tma_aligned=_ue8m0,
+                scale_ue8m0=_ue8m0,
+            )
+            use_tma_aligned_col_major_sf = _ue8m0
+        else:
+            dispatch_x = _quantize_for_deepep_v2_dispatch(
+                hidden_states, self.scale_format
+            )
+            use_tma_aligned_col_major_sf = self.scale_format.tma_aligned
+
+        # This collective argument must not depend on a rank-local batch.
+        num_max_tokens = self.num_max_dispatch_tokens_per_rank
+        # Masked dispatch stays asynchronous for CUDA graph capture.
+        do_cpu_sync_val = True
+        if use_masked:
+            do_cpu_sync_val = False
+
+        buffer = self._get_buffer()
+        recv_x, recv_topk_idx, recv_topk_weights, handle, event = buffer.dispatch(
+            dispatch_x,
+            topk_idx=topk_ids,
+            topk_weights=topk_weights,
+            num_experts=self.num_experts,
+            num_max_tokens_per_rank=num_max_tokens,
+            expert_alignment=_EXPERT_ALIGNMENT,
+            num_sms=envs.SGLANG_DEEPEP_V2_NUM_SMS.get(),
+            use_tma_aligned_col_major_sf=use_tma_aligned_col_major_sf,
+            do_cpu_sync=do_cpu_sync_val,
+            do_expand=use_expand_layout,
+        )
+        self._handle = handle
+        local_tokens = hidden_states.shape[0]
+        if event.event is not None:
+            event.current_stream_wait()
+
+        if isinstance(recv_x, tuple):
+            recv_hidden_states, recv_hidden_states_scale = recv_x
+        else:
+            recv_hidden_states = recv_x
+            recv_hidden_states_scale = None
+
+        if use_expand_layout:
+            # Expanded combine uses handle metadata instead of recv_topk_idx.
+            local_topk_ids = None
+        else:
+            num_recv_tokens = int(
+                handle.psum_num_recv_tokens_per_scaleup_rank[-1].item()
+            )
+            recv_topk_idx = recv_topk_idx[:num_recv_tokens]
+            recv_topk_weights = recv_topk_weights[:num_recv_tokens]
+            recv_hidden_states = recv_hidden_states[:num_recv_tokens]
+            if recv_hidden_states_scale is not None:
+                recv_hidden_states_scale = recv_hidden_states_scale[:num_recv_tokens]
+
+            local_topk_ids = recv_topk_idx
+
+        expected_m = 0
+        masked_max_m = 0
+        total_expanded = 0
+        if use_masked:
+            # expected_m is only a schedule hint; masked_m is the actual bound.
+            ep_group_size = max(1, self.num_experts // self.num_local_experts)
+            expected_m = max(
+                1,
+                (local_tokens * ep_group_size * self.router_topk + self.num_experts)
+                // self.num_experts,
+            )
+            # Account for the worst case where every rank targets one local expert.
+            masked_max_m = self.num_max_dispatch_tokens_per_rank * ep_group_size
+            total_expanded = recv_hidden_states.shape[0]
+
+        return DeepEPv2DispatchOutput(
+            recv_hidden_states,
+            recv_hidden_states_scale,
+            local_topk_ids,
+            recv_topk_weights,
+            handle.psum_num_recv_tokens_per_expert,
+            use_expand_layout,
+            use_tma_aligned_col_major_sf,
+            use_masked,
+            expected_m,
+            masked_max_m,
+            total_expanded,
+            _EXPERT_ALIGNMENT,
+        )
+
+    def combine(self, combine_input: DeepEPv2CombineInput) -> torch.Tensor:
+        if self._handle is None:
+            raise RuntimeError(
+                "DeepEP v2 combine called without a valid dispatch handle"
+            )
+        # Release the single-use handle even when combine fails.
+        try:
+            buffer = self._get_buffer()
+            combined_x, _, event = buffer.combine(
+                combine_input.hidden_states,
+                handle=self._handle,
+                topk_weights=combine_input.topk_weights,
+            )
+            if event.event is not None:
+                event.current_stream_wait()
+            if self._pad_empty_combine:
+                combined_x = combined_x[:0]
+            return combined_x
+        finally:
+            self._pad_empty_combine = False
+            self._destroy_handle()
+
+
+class DeepEPv2Dispatcher(BaseDispatcher):
+    def __init__(
+        self,
+        group: dist.ProcessGroup,
+        router_topk: int,
+        num_experts: int,
+        num_local_experts: int,
+        hidden_size: int,
+        params_dtype: torch.dtype,
+    ):
+        super().__init__()
+        if params_dtype != torch.bfloat16:
+            raise NotImplementedError(
+                "DeepEP v2 dispatch adapter currently expects BF16 model activations, "
+                f"got {params_dtype}"
+            )
+        scale_format = get_deepep_v2_fp8_scale_format()
+        self.num_max_dispatch_tokens_per_rank = (
+            envs.SGLANG_DEEPEP_V2_NUM_MAX_DISPATCH_TOKENS_PER_RANK.get()
+        )
+        self._impl = _DeepEPv2Impl(
+            group=group,
+            router_topk=router_topk,
+            num_experts=num_experts,
+            num_local_experts=num_local_experts,
+            hidden_size=hidden_size,
+            scale_format=scale_format,
+            num_max_dispatch_tokens_per_rank=self.num_max_dispatch_tokens_per_rank,
+        )
+
+    def dispatch(
+        self, hidden_states: torch.Tensor, topk_output: TopKOutput
+    ) -> DispatchOutput:
+        return self._impl.dispatch(hidden_states, topk_output)
+
+    def combine(self, combine_input: CombineInput) -> torch.Tensor:
+        if combine_input.format != CombineInputFormat.DEEPEP_V2:
+            raise TypeError(
+                f"Expected DeepEP v2 combine input, got {combine_input.format}"
+            )
+        return self._impl.combine(combine_input)

python/sglang/srt/layers/moe/utils.py

@@ -4,6 +4,7 @@ import logging
 import os
 from contextlib import contextmanager
 from enum import Enum, IntEnum
+from typing import NamedTuple
 
 import torch
 
@@ -40,6 +41,7 @@ class MoeA2ABackend(Enum):
     ASCEND_TP = "ascend_tp"
     FLASHINFER = "flashinfer"
     MEGAMOE = "megamoe"
+    DEEPEP_V2 = "deepep_v2"
     PPLX = "pplx"
     CUSTOMIZED = "customized"
 
@@ -79,6 +81,9 @@ class MoeA2ABackend(Enum):
     def is_megamoe(self):
         return self == MoeA2ABackend.MEGAMOE
 
+    def is_deepep_v2(self):
+        return self == MoeA2ABackend.DEEPEP_V2
+
     def is_pplx(self):
         return self == MoeA2ABackend.PPLX
 
@@ -178,6 +183,13 @@ class MoeRunnerBackend(Enum):
         return self == MoeRunnerBackend.AITER
 
 
+class DeepEPv2Fp8ScaleFormat(NamedTuple):
+    """DeepGEMM FP8 activation-scale layout expected from DeepEP v2."""
+
+    tma_aligned: bool
+    ue8m0: bool
+
+
 class DeepEPMode(Enum):
 
     NORMAL = "normal"
@@ -311,6 +323,19 @@ def get_ascend_dispatcher_output_dtype(dispatcher):
     return DispatcherOutputDtype.BF16
 
 
+def get_deepep_v2_fp8_scale_format() -> DeepEPv2Fp8ScaleFormat:
+    """Resolve the FP8 scale layout DeepEP v2 must pre-quantize into."""
+    from sglang.srt.layers import deep_gemm_wrapper
+
+    return DeepEPv2Fp8ScaleFormat(
+        tma_aligned=(
+            deep_gemm_wrapper.DEEPGEMM_NEED_TMA_ALIGNED_SCALES
+            or deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0
+        ),
+        ue8m0=deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0,
+    )
+
+
 def initialize_moe_config():
     """Seed the MoE runtime flags from the published configuration.
 
@@ -502,9 +527,15 @@ def is_sbo_enabled() -> bool:
 
 
 def is_deepep_class_backend() -> bool:
-    """Check if the MoE backend is DeepEP-family (DeepEP, Mooncake, Mori, or PPLX)."""
+    """Return whether A2A combine occurs inside a DeepEP-family dispatcher."""
     b = get_moe_a2a_backend()
-    return b.is_deepep() or b.is_mooncake() or b.is_mori() or b.is_pplx()
+    return (
+        b.is_deepep()
+        or b.is_deepep_v2()
+        or b.is_mooncake()
+        or b.is_mori()
+        or b.is_pplx()
+    )
 
 
 def uses_per_rank_fused_shared_slots() -> bool:

python/sglang/srt/models/deepseek_v2.py

@@ -744,6 +744,7 @@ class DeepseekV2MoE(nn.Module):
                 or get_moe_a2a_backend().is_ascend_fuseep()
                 or get_moe_a2a_backend().is_flashinfer()
                 or get_moe_a2a_backend().is_megamoe()
+                or get_moe_a2a_backend().is_deepep_v2()
                 or should_use_flashinfer_cutlass_moe_fp4_allgather()
                 or envs.SGLANG_SHARED_EXPERT_TP1.get()
             )
@@ -833,6 +834,7 @@ class DeepseekV2MoE(nn.Module):
             or get_moe_a2a_backend().is_nixl()
             or get_moe_a2a_backend().is_mori()
             or get_moe_a2a_backend().is_ascend_fuseep()
+            or get_moe_a2a_backend().is_deepep_v2()
         ):
             # TODO: we will support tp < ep in the future
             self.ep_size = get_parallel().moe_ep_size
@@ -855,6 +857,7 @@ class DeepseekV2MoE(nn.Module):
             or get_moe_a2a_backend().is_mori()
             or get_moe_a2a_backend().is_ascend_fuseep()
             or get_moe_a2a_backend().is_flashinfer()
+            or get_moe_a2a_backend().is_deepep_v2()
         )
         self._fuse_shared_experts_inside_sbo = SboFlags.fuse_shared_experts_inside_sbo()
         # SGLANG_OPT_MOE_QUANT_ONCE eligibility, resolved lazily on first
@@ -2757,10 +2760,11 @@ class DeepseekV2Model(nn.Module):
                 )
             )
         self.layers_to_capture = []
-        if get_moe_a2a_backend().is_deepep() or get_moe_a2a_backend().is_mooncake():
+        self.enable_a2a_moe = (
-            self.enable_a2a_moe = True
+            get_moe_a2a_backend().is_deepep()
-        else:
+            or get_moe_a2a_backend().is_mooncake()
-            self.enable_a2a_moe = False
+            or get_moe_a2a_backend().is_deepep_v2()
+        )
 
         # llama_4_scaling: for supporting Mistral-Large-3 model
         self.llama_4_scaling_config = getattr(config, "llama_4_scaling", None)

python/sglang/srt/server_args.py

@@ -303,10 +303,20 @@ MOE_A2A_BACKEND_CHOICES = [
     "ascend_fuseep",
     "flashinfer",
     "megamoe",
+    "deepep_v2",
     "pplx",
     "ascend_tp",
 ]
 
+# These architectures take the A2A MoE path and skip post-expert all-reduce.
+_DEEPEP_V2_VALIDATED_ARCHITECTURES = frozenset(
+    {
+        "DeepseekV3ForCausalLM",
+        "DeepseekV4ForCausalLM",
+        "Qwen3MoeForCausalLM",
+    }
+)
+
 MXFP8_MOE_RUNNER_BACKEND_CHOICES = [
     "cutlass",
     "deep_gemm",
@@ -2443,6 +2453,8 @@ class ServerArgs:
             "ascend_fuseep",
             "flashinfer",
             "megamoe",
+            "deepep_v2",
+            "ascend_tp",
             "pplx",
         ],
         Arg(
@@ -2459,6 +2471,15 @@ class ServerArgs:
         "--moe-a2a-backend megamoe.",
         NS("exec.moe"),
     ] = False
+    deepep_v2_mode: A[
+        Literal["direct", "hybrid"],
+        "DeepEP v2 ElasticBuffer communication topology, fixed at server init: "
+        "`direct` (single-node NVLink) or `hybrid` (multi-node scale-out). "
+        "Layout/grouped-GEMM and the decode CUDA graph are chosen per batch by "
+        "inference phase, independent of this knob; not equivalent to DeepEP v1 "
+        "normal/low_latency.",
+        NS("exec.moe"),
+    ] = "direct"
     moe_runner_backend: A[
         str,
         Arg(
@@ -4020,6 +4041,10 @@ class ServerArgs:
         # time; last declarations of the resolution, mirroring that order.
         self._handle_model_capability_adjustments()
 
+        # Validate after all batch-size declarations are visible.
+        self._validate_deepep_v2_speculative_draft()
+        self._validate_deepep_v2_dispatch_token_budget()
+
         self._resolution_finished = True
 
     def _handle_return_hidden_states_mode(self):
@@ -7415,6 +7440,93 @@ class ServerArgs:
                 f"(e.g. --max-prefill-tokens) to <= {max_cutedsl_tokens}."
             )
 
+    def _validate_deepep_v2_dispatch_token_budget(self) -> None:
+        """Check the configured prefill and decode-graph buffer bounds."""
+        view = resolved_view(self)
+        if view.moe_a2a_backend != "deepep_v2":
+            return
+
+        capacity = envs.SGLANG_DEEPEP_V2_NUM_MAX_DISPATCH_TOKENS_PER_RANK.get()
+        if view.disaggregation_mode != "decode":
+            prefill_tokens = self.max_prefill_buffer_tokens() or (
+                view.max_prefill_tokens or 0
+            )
+            if prefill_tokens > capacity:
+                raise ValueError(
+                    "DeepEP v2 per-rank prefill budget exceeds "
+                    "SGLANG_DEEPEP_V2_NUM_MAX_DISPATCH_TOKENS_PER_RANK: "
+                    f"required={prefill_tokens}, capacity={capacity}. Raise the "
+                    "environment value or lower --chunked-prefill-size/"
+                    "--max-prefill-tokens."
+                )
+
+        if view.disaggregation_mode == "prefill":
+            return
+        decode_config = getattr(view.cuda_graph_config, "decode", None)
+        if decode_config is None or decode_config.backend == Backend.DISABLED:
+            return
+
+        graph_bs = decode_config.max_bs or 0
+        if view.max_running_requests is not None:
+            attn_dp_size = view.dp_size if view.enable_dp_attention else 1
+            per_rank_pool_bs = max(1, view.max_running_requests // attn_dp_size)
+            graph_bs = min(graph_bs, per_rank_pool_bs)
+        tokens_per_req = (
+            self.max_speculative_num_draft_tokens or 1
+            if view.speculative_algorithm
+            else 1
+        )
+        graph_tokens = graph_bs * tokens_per_req
+        if graph_tokens > capacity:
+            raise ValueError(
+                "DeepEP v2 per-rank decode CUDA graph exceeds "
+                "SGLANG_DEEPEP_V2_NUM_MAX_DISPATCH_TOKENS_PER_RANK: "
+                f"required={graph_tokens}, capacity={capacity} "
+                f"(requests={graph_bs}, tokens/request={tokens_per_req}). Raise "
+                "the environment value or lower --cuda-graph-max-bs."
+            )
+
+    def _validate_deepep_v2_model_architecture(self) -> None:
+        """Allow DeepEP v2 only where its model workflow is validated."""
+        if (
+            parse_connector_type(resolved_view(self).model_path)
+            == ConnectorType.INSTANCE
+        ):
+            raise ValueError(
+                "DeepEP v2 MoE cannot validate a model loaded through an instance "
+                "connector. Load it from a model path or use "
+                "--moe-a2a-backend deepep."
+            )
+
+        architectures = (
+            getattr(self.get_model_config().hf_config, "architectures", None) or []
+        )
+
+        architecture = architectures[0] if architectures else None
+        if architecture not in _DEEPEP_V2_VALIDATED_ARCHITECTURES:
+            raise ValueError(
+                f"DeepEP v2 MoE is not validated for {architecture!r}; supported "
+                f"architectures are {sorted(_DEEPEP_V2_VALIDATED_ARCHITECTURES)}. "
+                "Other model workflows may require an all-reduce after A2A "
+                "combine. Use --moe-a2a-backend deepep."
+            )
+
+    def _validate_deepep_v2_speculative_draft(self) -> None:
+        """Reject an explicit or inherited DeepEP v2 draft backend."""
+        view = resolved_view(self)
+        draft_backend = view.speculative_moe_a2a_backend
+        if draft_backend is None and view.speculative_algorithm:
+            from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
+
+            algorithm = SpeculativeAlgorithm.from_string(view.speculative_algorithm)
+            if not algorithm.is_ngram():
+                draft_backend = view.moe_a2a_backend
+        if draft_backend == "deepep_v2":
+            raise ValueError(
+                "DeepEP v2 MoE is not validated as a speculative draft backend. "
+                "Select another --speculative-moe-a2a-backend."
+            )
+
     def _handle_a2a_moe(self):
         # The backend overrides and the ep_size=tp_size adjustments moved to
         # the resolution pipeline (arg_groups/overrides.py:
@@ -7466,6 +7578,60 @@ class ServerArgs:
                 cfg.cuda_graph_config.decode.backend = Backend.DISABLED
                 cfg.cuda_graph_config.prefill.backend = Backend.DISABLED
 
+        if a2a_backend == "deepep_v2":
+            self._validate_deepep_v2_model_architecture()
+            if resolved_view(self).enable_deterministic_inference:
+                raise ValueError(
+                    "DeepEP v2 does not forward deterministic=True to "
+                    "ElasticBuffer, so deterministic sorting remains disabled. "
+                    "Disable --enable-deterministic-inference or use "
+                    "--moe-a2a-backend deepep."
+                )
+            # ElasticBuffer requires CUMEM, but not NVLS or its preallocation.
+            os.environ.setdefault("NCCL_CUMEM_ENABLE", "1")
+            # Respect model-level runner declarations before resolving auto.
+            resolved_runner = resolved_view(self).moe_runner_backend
+            if resolved_runner == "auto":
+                self._declare("_handle_a2a_moe", moe_runner_backend="deep_gemm")
+                logger.warning(
+                    "DeepEP v2 MoE: resolved --moe-runner-backend auto -> deep_gemm."
+                )
+            elif resolved_runner != "deep_gemm":
+                raise ValueError(
+                    "DeepEP v2 MoE currently supports only "
+                    f"--moe-runner-backend deep_gemm. Got {resolved_runner!r}. "
+                    "Add a runner adapter before enabling DeepEP v2 with other "
+                    "MoE runners."
+                )
+            if cfg.enable_two_batch_overlap or cfg.enable_single_batch_overlap:
+                raise ValueError(
+                    "DeepEP v2 MoE has not implemented the TBO/SBO overlap hooks yet. "
+                    "Disable --enable-two-batch-overlap and "
+                    "--enable-single-batch-overlap when using --moe-a2a-backend deepep_v2."
+                )
+            if cfg.enforce_shared_experts_fusion:
+                raise ValueError(
+                    "DeepEP v2 MoE has not validated fused shared experts yet. "
+                    "Remove --enforce-shared-experts-fusion when using "
+                    "--moe-a2a-backend deepep_v2."
+                )
+            # Prefill reads host counts and is not graph-capturable.
+            cfg.cuda_graph_config.prefill.backend = Backend.DISABLED
+            logger.warning(
+                f"DeepEP v2 MoE is enabled. The expert parallel size is adjusted to be the same as the tensor parallel size[{cfg.tp_size}]."
+            )
+            logger.warning(
+                "DeepEP v2 MoE is using deepep_v2_mode=%s. This controls "
+                "ElasticBuffer direct/hybrid mode and is independent from "
+                "--deepep-mode normal/low_latency. DeepEP v2 MoE enables the "
+                "decode CUDA graph on the masked decode path (any comm mode) "
+                "and disables shared expert fusion. "
+                "SGLANG_DEEPEP_V2_NUM_MAX_DISPATCH_TOKENS_PER_RANK is a "
+                "per-rank communication buffer capacity, not a model limit; "
+                "increase it for large prefill/chunked-prefill workloads.",
+                cfg.deepep_v2_mode,
+            )
+
         # The resolving view, not the field: `_a2a_backend_overrides` may have
         # moved this already (waterfill forces `deepep`).
         a2a_now = resolved_view(self).moe_a2a_backend

python/sglang/srt/state_capturer/routed_experts.py

@@ -20,6 +20,12 @@ from sglang.srt.runtime_context import (
 from sglang.srt.state_capturer.base import BaseTopkCapturer
 
 
+def _is_scattered_a2a_backend() -> bool:
+    """Return whether routed tokens are scattered across attention-TP ranks."""
+    backend = get_moe_a2a_backend()
+    return backend.is_deepep() or backend.is_deepep_v2()
+
+
 class RoutedExpertsCapturer(BaseTopkCapturer):
     """Capturer for routed experts with host buffer.
 
@@ -84,11 +90,8 @@ class RoutedExpertsCapturer(BaseTopkCapturer):
             device_topk_size=topk_size + num_fused_shared_experts,
         )
 
-        # DeepEP a2a path: each attn-TP rank only sees its scattered slice of
+        # Rebuild the full token batch before routed-expert readback.
-        # topk_ids. All-gather across attn-TP at capture time so device_cache
+        if _is_scattered_a2a_backend():
-        # holds the full batch and the existing _get_local_slice / D2H sync
-        # paths work unchanged. Pre-allocate the gather target.
-        if get_moe_a2a_backend().is_deepep():
             attn_tp_size = (
                 get_parallel().attn_tp_size if is_dp_attention_enabled() else 1
             )
@@ -102,7 +105,7 @@ class RoutedExpertsCapturer(BaseTopkCapturer):
             )
 
     def capture(self, layer_id: int, topk_indices: torch.Tensor):
-        if get_moe_a2a_backend().is_deepep():
+        if _is_scattered_a2a_backend():
             local_topk = topk_indices
             topk_indices = self.gather_buffer[
                 : local_topk.size(0) * get_parallel().attn_tp_size
@@ -116,10 +119,8 @@ class RoutedExpertsCapturer(BaseTopkCapturer):
         can_run_graph: bool,
         cuda_graph_batch: Optional[int],
     ) -> torch.Tensor:
-        # Under DeepEP, capture() already attn_tp_all_gathered into the head of
+        # Gathered rows start at buffer offset zero on every DP rank.
-        # the per-rank buffer, so the local DP rank's data lives at [0:N_local]
+        if is_dp_attention_enabled() and not _is_scattered_a2a_backend():
-        # rather than at the global [start_pos:end_pos] offset.
-        if is_dp_attention_enabled() and not get_moe_a2a_backend().is_deepep():
             # GPU->CPU sync would break overlap; operate on CPU directly.
             local_start_pos, local_num_tokens = get_dp_local_slice_cpu(
                 forward_batch, can_run_graph, cuda_graph_batch

test/registered/ep/test_routed_experts_dp_readback.py

@@ -0,0 +1,194 @@
+"""DP>1 routed-expert readback parity for DeepEP-family A2A backends."""
+
+import concurrent.futures
+import json
+import os
+import unittest
+
+import numpy as np
+import pybase64
+import requests
+import torch
+
+from sglang.srt.utils import kill_process_tree
+from sglang.test.ci.ci_register import register_cuda_ci
+from sglang.test.test_utils import (
+    DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+    DEFAULT_URL_FOR_TEST,
+    CustomTestCase,
+    popen_launch_server,
+)
+
+register_cuda_ci(est_time=900, stage="base-c", runner_config="4-gpu-h100")
+
+_MODEL = os.environ.get("SGLANG_ROUTED_EXPERTS_TEST_MODEL", "deepseek-ai/DeepSeek-V3")
+_NUM_EXPERTS = 24
+_NUM_LAYERS = 1
+_TOPK = 8
+
+_DUMMY_WEIGHT_ENV = {
+    "SGLANG_ENABLE_ASYNC_ASSERT": "0",
+    "SGLANG_SANITIZE_NAN_LOGITS": "1",
+    "SGLANG_CUDA_COREDUMP": "0",
+    "CUDA_ENABLE_COREDUMP_ON_EXCEPTION": "0",
+    "SGLANG_CUDA_COREDUMP_BEFORE_CRASH": "0",
+}
+
+
+def _deep_ep_has(attr: str) -> bool:
+    try:
+        import deep_ep  # noqa: F401
+    except ImportError:
+        return False
+    return hasattr(deep_ep, attr)
+
+
+def _deep_ep_nccl_compatible() -> bool:
+    try:
+        version = torch.cuda.nccl.version()
+    except (AttributeError, RuntimeError):
+        return False
+    return version is not None and version >= (2, 30, 7)
+
+
+class _ReadbackMixin:
+    backend_args: list
+
+    @classmethod
+    def setUpClass(cls):
+        cls.base_url = DEFAULT_URL_FOR_TEST
+        other_args = [
+            "--trust-remote-code",
+            "--load-format",
+            "dummy",
+            "--json-model-override-args",
+            json.dumps(
+                {
+                    "num_hidden_layers": _NUM_LAYERS,
+                    "first_k_dense_replace": 0,
+                    "n_routed_experts": _NUM_EXPERTS,
+                }
+            ),
+            "--tp",
+            "2",
+            "--dp",
+            "2",
+            "--ep",
+            "2",
+            "--enable-dp-attention",
+            "--enable-return-routed-experts",
+            "--disable-cuda-graph",
+            "--disable-radix-cache",
+            # Keep the startup budget within the test's 256-token buffer.
+            "--chunked-prefill-size",
+            "256",
+            "--mem-fraction-static",
+            "0.5",
+            *cls.backend_args,
+        ]
+        cls.process = popen_launch_server(
+            _MODEL,
+            cls.base_url,
+            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+            other_args=other_args,
+            env={
+                **os.environ,
+                **_DUMMY_WEIGHT_ENV,
+                "SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK": "256",
+                "SGLANG_DEEPEP_V2_NUM_MAX_DISPATCH_TOKENS_PER_RANK": "256",
+            },
+        )
+
+    @classmethod
+    def tearDownClass(cls):
+        if getattr(cls, "process", None):
+            kill_process_tree(cls.process.pid)
+
+    def _one_request(self, i: int):
+        resp = requests.post(
+            self.base_url + "/generate",
+            json={
+                "text": f"{self._WORDS[i]} is item number {i}. Describe it in detail.",
+                "sampling_params": {"max_new_tokens": 24, "temperature": 0},
+                "return_routed_experts": True,
+            },
+            timeout=300,
+        )
+        self.assertEqual(resp.status_code, 200)
+        meta = resp.json()["meta_info"]
+        self.assertIn("routed_experts", meta)
+        arr = np.frombuffer(pybase64.b64decode(meta["routed_experts"]), dtype=np.int32)
+        self.assertEqual(
+            arr.size % (_NUM_LAYERS * _TOPK),
+            0,
+            f"req{i}: payload size {arr.size} not a multiple of layers*topk",
+        )
+        rows = arr.reshape(-1, _NUM_LAYERS, _TOPK)
+        self.assertGreater(rows.shape[0], 0)
+        self.assertTrue(
+            bool(((rows >= 0) & (rows < _NUM_EXPERTS)).all()),
+            f"req{i}: expert id out of range [{rows.min()}, {rows.max()}]",
+        )
+        return rows
+
+    _WORDS = ["Alpha", "Bravo", "Charlie", "Delta", "Echo", "Foxtrot"]
+    _N_REQ = 6
+
+    def test_dp2_readback(self):
+        solo = [self._one_request(i) for i in range(self._N_REQ)]
+
+        with concurrent.futures.ThreadPoolExecutor(max_workers=self._N_REQ) as ex:
+            conc = list(ex.map(self._one_request, range(self._N_REQ)))
+
+        for i in range(self._N_REQ):
+            a, b = solo[i], conc[i]
+            n = min(a.shape[0], b.shape[0])
+            total = match = 0
+            for t in range(n):
+                for layer in range(_NUM_LAYERS):
+                    total += 1
+                    if set(a[t, layer].tolist()) == set(b[t, layer].tolist()):
+                        match += 1
+            frac = match / max(1, total)
+            self.assertGreaterEqual(
+                frac,
+                0.9,
+                f"req{i}: only {frac:.1%} of per-token expert sets match the "
+                "solo baseline — the capturer is reading rows that belong to "
+                "other tokens (DeepEP-class backend misclassification)",
+            )
+
+
+@unittest.skipUnless(_deep_ep_has("Buffer"), "DeepEP (v1 Buffer) not installed")
+class TestRoutedExpertsReadbackDeepEP(_ReadbackMixin, CustomTestCase):
+    backend_args = [
+        "--moe-a2a-backend",
+        "deepep",
+        "--deepep-mode",
+        "low_latency",
+        "--deepep-dispatcher-output-dtype",
+        "fp8",
+        "--moe-runner-backend",
+        "deep_gemm",
+    ]
+
+
+@unittest.skipUnless(
+    _deep_ep_has("ElasticBuffer"), "DeepEP v2 (ElasticBuffer) not installed"
+)
+@unittest.skipUnless(
+    _deep_ep_nccl_compatible(), "DeepEP v2 requires NCCL runtime >= 2.30.7"
+)
+class TestRoutedExpertsReadbackDeepEPv2(_ReadbackMixin, CustomTestCase):
+    backend_args = [
+        "--moe-a2a-backend",
+        "deepep_v2",
+        "--deepep-v2-mode",
+        "direct",
+        "--moe-runner-backend",
+        "deep_gemm",
+    ]
+
+
+if __name__ == "__main__":
+    unittest.main()

test/registered/unit/layers/moe/test_deepep_v2_buffer_lifecycle.py

@@ -0,0 +1,154 @@
+"""CPU-only tests for the DeepEP v2 ElasticBuffer ownership facade."""
+
+import unittest
+from unittest.mock import patch
+
+import torch
+
+from sglang.srt.layers.moe.token_dispatcher import deepep_v2
+from sglang.srt.runtime_context import get_resources, reset_context
+from sglang.test.ci.ci_register import register_cpu_ci
+from sglang.test.test_utils import CustomTestCase
+
+register_cpu_ci(est_time=3, suite="base-a-test-cpu")
+
+
+class _FakeGroup:
+    pass
+
+
+class _FakeBuffer:
+    instances = []
+
+    def __init__(self, *args, **kwargs):
+        self.args = args
+        self.kwargs = kwargs
+        self.num_bytes = 1 << 20
+        type(self).instances.append(self)
+
+
+class TestDeepEPv2BufferLifecycle(CustomTestCase):
+    def setUp(self):
+        reset_context()
+        _FakeBuffer.instances = []
+        self._patches = [
+            patch.object(deepep_v2, "use_deepep_v2", True),
+            patch.object(deepep_v2, "ElasticBuffer", _FakeBuffer, create=True),
+            patch.object(deepep_v2.dist, "get_world_size", return_value=8),
+        ]
+        for item in self._patches:
+            item.start()
+
+    def tearDown(self):
+        reset_context()
+        for item in reversed(self._patches):
+            item.stop()
+
+    def _get(self, group=None, **overrides):
+        kwargs = {
+            "group": group or _FakeGroup(),
+            "hidden_size": 4096,
+            "router_topk": 8,
+            "num_max_dispatch_tokens_per_rank": 128,
+            "use_fp8_dispatch": True,
+            "allow_hybrid_mode": False,
+        }
+        kwargs.update(overrides)
+        return deepep_v2.DeepEPv2Buffer.get_buffer(**kwargs)
+
+    def test_same_key_reuses_buffer(self):
+        group = _FakeGroup()
+        first = self._get(group)
+        second = self._get(group)
+        self.assertIs(first, second)
+        self.assertEqual(len(_FakeBuffer.instances), 1)
+
+    def test_constructor_inputs_participate_in_key(self):
+        group = _FakeGroup()
+        first = self._get(group)
+        second = self._get(group, num_max_dispatch_tokens_per_rank=256)
+        third = self._get(
+            group,
+            num_max_dispatch_tokens_per_rank=256,
+            allow_hybrid_mode=True,
+        )
+        self.assertIsNot(first, second)
+        self.assertIsNot(second, third)
+        self.assertEqual(len(_FakeBuffer.instances), 3)
+
+    def test_key_keeps_process_group_object(self):
+        group = _FakeGroup()
+        self._get(group)
+        state = get_resources().buffers[deepep_v2.DeepEPv2Buffer._STATE_KEY]
+        self.assertIs(state.key[0], group)
+
+    def test_distinct_process_group_rebuilds(self):
+        first = self._get(_FakeGroup())
+        second = self._get(_FakeGroup())
+        self.assertIsNot(first, second)
+        self.assertEqual(len(_FakeBuffer.instances), 2)
+
+    def test_state_lives_in_runtime_resources(self):
+        self._get()
+        self.assertIn(
+            deepep_v2.DeepEPv2Buffer._STATE_KEY,
+            get_resources().buffers,
+        )
+
+    def test_reset_context_drops_state_and_rebuilds(self):
+        group = _FakeGroup()
+        self._get(group)
+        reset_context()
+        self.assertNotIn(
+            deepep_v2.DeepEPv2Buffer._STATE_KEY,
+            get_resources().buffers,
+        )
+        self._get(group)
+        self.assertEqual(len(_FakeBuffer.instances), 2)
+
+    def test_failed_constructor_is_not_published(self):
+        class _FailingBuffer:
+            def __init__(self, *args, **kwargs):
+                raise RuntimeError("construct failed")
+
+        with patch.object(deepep_v2, "ElasticBuffer", _FailingBuffer):
+            with self.assertRaisesRegex(RuntimeError, "construct failed"):
+                self._get()
+
+        state = get_resources().buffers[deepep_v2.DeepEPv2Buffer._STATE_KEY]
+        self.assertIsNone(state.buffer)
+        self.assertIsNone(state.key)
+        self._get()
+        self.assertEqual(len(_FakeBuffer.instances), 1)
+
+    def test_destroy_clears_facade_state(self):
+        group = _FakeGroup()
+        first = self._get(group)
+        deepep_v2.DeepEPv2Buffer.destroy()
+        state = get_resources().buffers[deepep_v2.DeepEPv2Buffer._STATE_KEY]
+        self.assertIsNone(state.buffer)
+        self.assertIsNone(state.key)
+        second = self._get(group)
+        self.assertIsNot(first, second)
+
+    def test_unavailable_deepep_fails_before_state_creation(self):
+        with patch.object(deepep_v2, "use_deepep_v2", False):
+            with self.assertRaisesRegex(ImportError, "github.com/deepseek-ai/DeepEP"):
+                self._get()
+        self.assertNotIn(
+            deepep_v2.DeepEPv2Buffer._STATE_KEY,
+            get_resources().buffers,
+        )
+
+    def test_dispatch_capacity_guard_uses_actual_input_rows(self):
+        impl = object.__new__(deepep_v2._DeepEPv2Impl)
+        impl.num_max_dispatch_tokens_per_rank = 4
+        impl.hidden_size = 128
+        impl.router_topk = 2
+        impl._validate_common(torch.empty(4, 128), torch.zeros(4, 2))
+        with self.assertRaisesRegex(ValueError, "per-rank buffer capacity"):
+            impl._validate_common(torch.empty(5, 128), torch.zeros(5, 2))
+
+
+if __name__ == "__main__":
+    unittest.main()

test/registered/unit/layers/moe/test_deepep_v2_masked_slab.py

@@ -0,0 +1,244 @@
+"""Tests for the DeepEP v2 expanded/masked repack kernels."""
+
+import unittest
+
+import torch
+
+from sglang.kernels.ops.moe.ep_moe_kernels import (
+    expand_to_masked_slab,
+    masked_slab_to_expand,
+)
+from sglang.test.ci.ci_register import register_cuda_ci
+from sglang.test.test_utils import CustomTestCase
+
+register_cuda_ci(est_time=10, stage="base-b", runner_config="1-gpu-large")
+
+DEVICE = "cuda"
+
+
+def _build_layout(counts, align, hidden, dtype, with_scale=False, scale_hidden=4):
+    """Build synthetic expanded-layout buffers for per-expert counts."""
+    starts, psum = [], []
+    prev_end = 0
+    for c in counts:
+        start = ((prev_end + align - 1) // align) * align
+        end = start + c
+        starts.append(start)
+        psum.append(end)
+        prev_end = end
+    total = max(((prev_end + align - 1) // align) * align, 1)
+
+    # Vary rows and columns to expose broadcast or stride errors.
+    base = torch.zeros((total, hidden), dtype=torch.float32, device=DEVICE)
+    col_gain = 1.0 + (torch.arange(hidden, device=DEVICE) % 2).float()
+    for s, c in zip(starts, counts):
+        for j in range(c):
+            base[s + j] = float((s + j) % 200 + 1) * col_gain
+    recv_x = base.to(dtype)
+
+    scale = None
+    if with_scale:
+        scale = torch.zeros((total, scale_hidden), dtype=torch.float32, device=DEVICE)
+        # Vary scale columns to expose pack-dimension stride errors.
+        col = torch.arange(scale_hidden, dtype=torch.float32, device=DEVICE)
+        for s, c in zip(starts, counts):
+            for j in range(c):
+                scale[s + j] = float((s + j) % 50 + 1) * 0.5 + col
+
+    psum_t = torch.tensor(psum, dtype=torch.int32, device=DEVICE)
+    return recv_x, scale, psum_t, starts, total
+
+
+def _real_rows(starts, counts):
+    rows = []
+    for s, c in zip(starts, counts):
+        rows.extend(range(s, s + c))
+    return rows
+
+
+class TestDeepEPv2MaskedSlab(CustomTestCase):
+    ALIGN = 16
+    HIDDEN = 8
+    MAX_M = 32
+
+    def _check_expand_roundtrip(self, counts, dtype, with_scale, topk=False):
+        recv_x, scale, psum, starts, total = _build_layout(
+            counts, self.ALIGN, self.HIDDEN, dtype, with_scale=with_scale
+        )
+        E = len(counts)
+        masked_x, masked_x_scale, masked_m = expand_to_masked_slab(
+            recv_x, scale, psum, E, self.MAX_M, self.ALIGN
+        )
+
+        self.assertEqual(masked_m.tolist(), list(counts))
+        self.assertEqual(tuple(masked_x.shape), (E, self.MAX_M, self.HIDDEN))
+
+        for e, (s, c) in enumerate(zip(starts, counts)):
+            for j in range(c):
+                torch.testing.assert_close(
+                    masked_x[e, j].float(), recv_x[s + j].float()
+                )
+                if with_scale:
+                    torch.testing.assert_close(
+                        masked_x_scale[e, j].float(), scale[s + j].float()
+                    )
+
+        weights = None
+        if topk:
+            weights = torch.zeros(total, dtype=torch.float32, device=DEVICE)
+            for r in _real_rows(starts, counts):
+                weights[r] = 0.25 + (r % 7) * 0.1
+        out = masked_slab_to_expand(
+            masked_x, psum, total, self.ALIGN, topk_weights=weights
+        )
+        self.assertEqual(tuple(out.shape), (total, self.HIDDEN))
+        for e, (s, c) in enumerate(zip(starts, counts)):
+            for j in range(c):
+                expected = masked_x[e, j].float()
+                if topk:
+                    expected = (expected * weights[s + j]).to(masked_x.dtype).float()
+                torch.testing.assert_close(out[s + j].float(), expected)
+
+    def test_roundtrip_bf16(self):
+        self._check_expand_roundtrip([3, 0, 5, 1], torch.bfloat16, with_scale=False)
+
+    def test_roundtrip_bf16_with_topk_weight(self):
+        self._check_expand_roundtrip(
+            [2, 4, 0, 7], torch.bfloat16, with_scale=False, topk=True
+        )
+
+    def test_roundtrip_fp8_with_scale(self):
+        self._check_expand_roundtrip([3, 1, 6, 2], torch.float8_e4m3fn, with_scale=True)
+
+    def test_empty_experts(self):
+        self._check_expand_roundtrip([0, 0, 0, 0], torch.bfloat16, with_scale=False)
+
+    def test_single_hot_expert(self):
+        self._check_expand_roundtrip(
+            [0, self.MAX_M, 0, 0], torch.bfloat16, with_scale=False, topk=True
+        )
+
+    def test_count_at_max_m_boundary(self):
+        self._check_expand_roundtrip(
+            [self.MAX_M, 1, self.MAX_M], torch.bfloat16, with_scale=False
+        )
+
+    def test_overflow_fails_fast(self):
+        counts = [self.MAX_M + 1, 2]
+        recv_x, scale, psum, starts, total = _build_layout(
+            counts, self.ALIGN, self.HIDDEN, torch.bfloat16
+        )
+        with self.assertRaises(RuntimeError):
+            expand_to_masked_slab(
+                recv_x, None, psum, len(counts), self.MAX_M, self.ALIGN
+            )
+
+    def _production_packed_ue8m0_layout(self, counts):
+        """Build expanded rows with the production packed UE8M0 quantizer."""
+        from sglang.kernels.ops.quantization.fp8_kernel import (
+            sglang_per_token_group_quant_fp8,
+        )
+
+        # hidden=1024 ensures the packed scale has multiple columns.
+        hidden = 1024
+        raw, _, psum, starts, total = _build_layout(
+            counts, self.ALIGN, hidden, torch.bfloat16
+        )
+        recv_x, recv_x_scale = sglang_per_token_group_quant_fp8(
+            raw,
+            128,
+            column_major_scales=True,
+            scale_tma_aligned=True,
+            scale_ue8m0=True,
+        )
+        self.assertEqual(recv_x_scale.dtype, torch.int32)
+        self.assertGreater(recv_x_scale.shape[1], 1, "pack dim must be indexed")
+        self.assertNotEqual(recv_x_scale.stride(1), 1)
+        return recv_x, recv_x_scale, psum, starts, total, hidden
+
+    def test_fp8_packed_ue8m0_scale_from_production_quantizer(self):
+        counts = [3, 1, 6, 2]
+        recv_x, recv_x_scale, psum, starts, _, hidden = (
+            self._production_packed_ue8m0_layout(counts)
+        )
+        E = len(counts)
+        masked_x, masked_x_scale, masked_m = expand_to_masked_slab(
+            recv_x, recv_x_scale, psum, E, self.MAX_M, self.ALIGN
+        )
+        self.assertEqual(masked_m.tolist(), list(counts))
+        self.assertEqual(tuple(masked_x.shape), (E, self.MAX_M, hidden))
+        for e, (s, c) in enumerate(zip(starts, counts)):
+            for j in range(c):
+                torch.testing.assert_close(
+                    masked_x[e, j].float(), recv_x[s + j].float()
+                )
+                torch.testing.assert_close(masked_x_scale[e, j], recv_x_scale[s + j])
+
+    def test_expand_under_cuda_graph_capture(self):
+        # Exercise replay with the production packed scale layout.
+        counts = [3, 1, 6, 2]
+        recv_x, recv_x_scale, psum, starts, _, _ = self._production_packed_ue8m0_layout(
+            counts
+        )
+        E = len(counts)
+        warm = torch.cuda.Stream()
+        warm.wait_stream(torch.cuda.current_stream())
+        with torch.cuda.stream(warm):
+            expand_to_masked_slab(recv_x, recv_x_scale, psum, E, self.MAX_M, self.ALIGN)
+        torch.cuda.current_stream().wait_stream(warm)
+        graph = torch.cuda.CUDAGraph()
+        with torch.cuda.graph(graph):
+            masked_x, masked_x_scale, masked_m = expand_to_masked_slab(
+                recv_x, recv_x_scale, psum, E, self.MAX_M, self.ALIGN
+            )
+        graph.replay()
+        torch.cuda.synchronize()
+        self.assertEqual(masked_m.tolist(), list(counts))
+        for e, (s, c) in enumerate(zip(starts, counts)):
+            for j in range(c):
+                torch.testing.assert_close(
+                    masked_x[e, j].float(), recv_x[s + j].float()
+                )
+                torch.testing.assert_close(masked_x_scale[e, j], recv_x_scale[s + j])
+
+
+class TestDeepEPv2HandleLifecycle(CustomTestCase):
+    """CPU-only dispatch/combine handle guards."""
+
+    @staticmethod
+    def _bare_impl():
+        from sglang.srt.layers.moe.token_dispatcher.deepep_v2 import _DeepEPv2Impl
+
+        impl = object.__new__(_DeepEPv2Impl)
+        impl._handle = None
+        impl._pad_empty_combine = False
+        return impl
+
+    def test_combine_without_dispatch_raises(self):
+        impl = self._bare_impl()
+        with self.assertRaisesRegex(RuntimeError, "without a valid dispatch handle"):
+            impl.combine(None)
+
+    def test_dispatch_with_unconsumed_handle_raises(self):
+        impl = self._bare_impl()
+        impl._handle = object()
+        with self.assertRaisesRegex(RuntimeError, "unconsumed"):
+            impl.dispatch(None, None)
+
+    def test_handle_cleared_when_combine_fails(self):
+        impl = self._bare_impl()
+        impl._handle = object()
+        impl._pad_empty_combine = True
+
+        def _boom():
+            raise RuntimeError("boom")
+
+        impl._get_buffer = _boom
+        with self.assertRaisesRegex(RuntimeError, "boom"):
+            impl.combine(None)
+        self.assertIsNone(impl._handle)
+        self.assertFalse(impl._pad_empty_combine)
+
+
+if __name__ == "__main__":
+    unittest.main()

test/registered/unit/layers/moe/test_hpc_ops_runner_guard.py

@@ -1,16 +1,13 @@
-"""The hpc_ops MoE runner backend makes the standard dispatcher keep global
+"""Startup guards for MoE runner and dispatcher quantization contracts."""
-expert ids, so a quant method that silently falls back to another runner
-(e.g. an unquantized MoE) would misroute tokens under EP>1. MoeRunner must
-reject that combination loudly at startup.
-"""
 
 import sys
+from types import SimpleNamespace
 
 import pytest
 
 from sglang.srt.layers.moe.moe_runner.base import MoeRunnerConfig
 from sglang.srt.layers.moe.moe_runner.runner import MoeRunner
-from sglang.srt.layers.moe.utils import MoeRunnerBackend
+from sglang.srt.layers.moe.utils import MoeA2ABackend, MoeRunnerBackend
 from sglang.srt.runtime_context import get_flags
 from sglang.test.ci.ci_register import register_cpu_ci
 
@@ -18,27 +15,27 @@ register_cpu_ci(est_time=6, suite="base-c-test-cpu")
 
 
 @pytest.fixture
-def _runner_backend_flag():
+def _moe_flags():
     moe = get_flags().moe
-    saved = moe.runner_backend
+    saved = (moe.runner_backend, moe.a2a_backend)
     yield moe
-    moe.runner_backend = saved
+    moe.runner_backend, moe.a2a_backend = saved
 
 
-def test_non_hpc_runner_rejected_when_hpc_ops_requested(_runner_backend_flag):
+def test_non_hpc_runner_rejected_when_hpc_ops_requested(_moe_flags):
-    _runner_backend_flag.runner_backend = MoeRunnerBackend.HPC_OPS
+    _moe_flags.runner_backend = MoeRunnerBackend.HPC_OPS
     with pytest.raises(ValueError, match="hpc_ops"):
         MoeRunner(MoeRunnerBackend.TRITON, MoeRunnerConfig())
 
 
-def test_triton_runner_allowed_without_hpc_ops(_runner_backend_flag):
+def test_triton_runner_allowed_without_hpc_ops(_moe_flags):
-    _runner_backend_flag.runner_backend = MoeRunnerBackend.TRITON
+    _moe_flags.runner_backend = MoeRunnerBackend.TRITON
     runner = MoeRunner(MoeRunnerBackend.TRITON, MoeRunnerConfig())
     assert runner.runner_core is not None
 
 
 def test_direct_kernel_quant_method_rejected_when_hpc_ops_requested(
-    _runner_backend_flag,
+    _moe_flags,
 ):
     # W4AFp8MoEMethod never constructs a MoeRunner (apply() calls its kernel
     # directly), so it bypasses the MoeRunner-level guard; the layer-level
@@ -49,15 +46,95 @@ def test_direct_kernel_quant_method_rejected_when_hpc_ops_requested(
     from sglang.srt.layers.quantization.fp8 import Fp8MoEMethod
     from sglang.srt.layers.quantization.w4afp8 import W4AFp8MoEMethod
 
-    _runner_backend_flag.runner_backend = MoeRunnerBackend.HPC_OPS
+    _moe_flags.runner_backend = MoeRunnerBackend.HPC_OPS
     with pytest.raises(ValueError, match="hpc_ops"):
         _validate_hpc_ops_quant_method(object.__new__(W4AFp8MoEMethod))
     # The FP8 method (the one the hpc_ops runner supports) passes.
     _validate_hpc_ops_quant_method(object.__new__(Fp8MoEMethod))
     # Without hpc_ops requested, any quant method passes.
-    _runner_backend_flag.runner_backend = MoeRunnerBackend.TRITON
+    _moe_flags.runner_backend = MoeRunnerBackend.TRITON
     _validate_hpc_ops_quant_method(object.__new__(W4AFp8MoEMethod))
 
 
+def _fp8_method(**overrides):
+    from sglang.srt.layers.quantization.fp8 import Fp8MoEMethod
+
+    values = {
+        "activation_scheme": "dynamic",
+        "weight_block_size": (128, 128),
+        "use_mxfp8": False,
+        "is_fp4_expert": False,
+    }
+    values.update(overrides)
+    method = object.__new__(Fp8MoEMethod)
+    method.quant_config = SimpleNamespace(
+        activation_scheme=values["activation_scheme"],
+    )
+    method.weight_block_size = values["weight_block_size"]
+    method.use_mxfp8 = values["use_mxfp8"]
+    method.is_fp4_expert = values["is_fp4_expert"]
+    return method
+
+
+def test_deepep_v2_quant_contract_accepts_blockwise_fp8(_moe_flags):
+    from sglang.srt.layers.moe.fused_moe_triton.layer import (
+        _validate_deepep_v2_quant_method,
+    )
+
+    _moe_flags.a2a_backend = MoeA2ABackend.DEEPEP_V2
+    _validate_deepep_v2_quant_method(_fp8_method(weight_block_size=[128, 128]))
+
+
+@pytest.mark.parametrize(
+    ("overrides", "expected"),
+    [
+        ({"activation_scheme": "static"}, "activation_scheme"),
+        ({"weight_block_size": None}, "weight_block_size"),
+        ({"weight_block_size": (1, 32), "use_mxfp8": True}, "MXFP8"),
+        ({"is_fp4_expert": True}, "FP4 experts"),
+    ],
+)
+def test_deepep_v2_quant_contract_rejects_incompatible_fp8(
+    _moe_flags, overrides, expected
+):
+    from sglang.srt.layers.moe.fused_moe_triton.layer import (
+        _validate_deepep_v2_quant_method,
+    )
+
+    _moe_flags.a2a_backend = MoeA2ABackend.DEEPEP_V2
+    with pytest.raises(ValueError, match=expected):
+        _validate_deepep_v2_quant_method(_fp8_method(**overrides))
+
+
+def test_deepep_v2_quant_contract_rejects_incompatible_methods(_moe_flags):
+    from sglang.srt.layers.moe.fused_moe_triton.layer import (
+        _validate_deepep_v2_quant_method,
+    )
+    from sglang.srt.layers.quantization.unquant import UnquantizedFusedMoEMethod
+    from sglang.srt.layers.quantization.w4afp8 import W4AFp8MoEMethod
+
+    _moe_flags.a2a_backend = MoeA2ABackend.DEEPEP_V2
+    for method_type in (UnquantizedFusedMoEMethod, W4AFp8MoEMethod):
+        with pytest.raises(ValueError, match=method_type.__name__):
+            _validate_deepep_v2_quant_method(object.__new__(method_type))
+
+
+def test_deepep_v2_quant_contract_does_not_affect_other_backends(_moe_flags):
+    from sglang.srt.layers.moe.fused_moe_triton.layer import (
+        _validate_deepep_v2_quant_method,
+    )
+    from sglang.srt.layers.quantization.unquant import UnquantizedFusedMoEMethod
+
+    _moe_flags.a2a_backend = MoeA2ABackend.DEEPEP
+    _validate_deepep_v2_quant_method(object.__new__(UnquantizedFusedMoEMethod))
+
+
+def test_deepep_v2_runner_backstop(_moe_flags):
+    _moe_flags.a2a_backend = MoeA2ABackend.DEEPEP_V2
+    with pytest.raises(ValueError, match="deep_gemm"):
+        MoeRunner(MoeRunnerBackend.TRITON, MoeRunnerConfig())
+    assert MoeRunner(MoeRunnerBackend.DEEP_GEMM, MoeRunnerConfig()).runner_core
+
+
 if __name__ == "__main__":
     sys.exit(pytest.main([__file__, "-v"]))

test/registered/unit/server_args/test_server_args.py

@@ -2106,6 +2106,328 @@ class TestSamplingBackendTokenOracleEnvGate(CustomTestCase):
         self.assertEqual(parsed.sampling_backend, "token_oracle")
 
 
+class TestDeepEPv2Args(CustomTestCase):
+    """DeepEP v2 server-argument resolution and validation."""
+
+    def _args(self, **overrides):
+        server_args = ServerArgs(model_path="dummy", moe_a2a_backend="deepep_v2")
+        server_args.model_config = SimpleNamespace(
+            hf_config=SimpleNamespace(architectures=["DeepseekV4ForCausalLM"])
+        )
+        # The dummy path does not initialize phase configs.
+        server_args.cuda_graph_config = CudaGraphConfig(
+            decode=PhaseConfig(backend=Backend.FULL, max_bs=512),
+            prefill=PhaseConfig(backend=Backend.FULL, max_bs=512),
+        )
+        server_args._resolved_overrides = []
+        valid = {f.name for f in dataclasses.fields(ServerArgs)}
+        for key, value in overrides.items():
+            # Reject stale field names before setattr silently accepts them.
+            assert key in valid, f"{key} is not a ServerArgs field"
+            setattr(server_args, key, value)
+        return server_args
+
+    def test_validated_architectures_allowed(self):
+        for architecture in (
+            "DeepseekV3ForCausalLM",
+            "DeepseekV4ForCausalLM",
+            "Qwen3MoeForCausalLM",
+        ):
+            args = self._args(moe_runner_backend="deep_gemm")
+            args.model_config.hf_config.architectures = [architecture]
+            args._handle_a2a_moe()
+
+    def test_unvalidated_and_missing_architectures_rejected(self):
+        for architectures in (
+            ["Qwen2MoeForCausalLM"],
+            ["Qwen3_5MoeForCausalLM"],
+            [],
+            None,
+        ):
+            args = self._args(moe_runner_backend="deep_gemm")
+            args.model_config.hf_config.architectures = architectures
+            with self.assertRaisesRegex(ValueError, "not validated"):
+                args._handle_a2a_moe()
+
+    def test_instance_connector_rejected(self):
+        args = self._args(
+            model_path="instance://worker/model",
+            moe_runner_backend="deep_gemm",
+        )
+        with self.assertRaisesRegex(ValueError, "instance connector"):
+            args._handle_a2a_moe()
+
+    def test_deterministic_inference_rejected(self):
+        args = self._args(
+            moe_runner_backend="deep_gemm",
+            enable_deterministic_inference=True,
+        )
+        with self.assertRaisesRegex(ValueError, "deterministic sorting"):
+            args._handle_a2a_moe()
+
+    def test_rl_on_policy_deterministic_inference_rejected(self):
+        args = self._args(
+            moe_runner_backend="deep_gemm",
+            rl_on_policy_target="fsdp",
+        )
+        args.model_config.hf_config.architectures = ["Qwen3MoeForCausalLM"]
+        with (
+            envs.SGLANG_VLM_CACHE_SIZE_MB.override(envs.SGLANG_VLM_CACHE_SIZE_MB.get()),
+            envs.SGLANG_ENABLE_DETERMINISTIC_INFERENCE.override(
+                envs.SGLANG_ENABLE_DETERMINISTIC_INFERENCE.get()
+            ),
+        ):
+            args._handle_deterministic_inference()
+        with self.assertRaisesRegex(ValueError, "deterministic sorting"):
+            args._handle_a2a_moe()
+
+    def test_deterministic_inference_does_not_affect_legacy_deepep(self):
+        args = self._args(
+            moe_a2a_backend="deepep",
+            moe_runner_backend="deep_gemm",
+            enable_deterministic_inference=True,
+        )
+        args._handle_a2a_moe()
+
+    def test_runner_restored_by_declaration_fails_fast(self):
+        # Validate the declaration-resolved runner rather than the raw field.
+        args = self._args(moe_runner_backend="auto")
+        args._resolved_overrides = [
+            ("test_mxfp8", {"moe_runner_backend": "flashinfer_trtllm"})
+        ]
+        with self.assertRaises(ValueError):
+            args._handle_a2a_moe()
+
+    def test_declarations_resolve_ep_size_and_fusion(self):
+        from sglang.srt.arg_groups.overrides import resolved_view
+
+        args = self._args(moe_runner_backend="auto", tp_size=2)
+        args._handle_a2a_moe()
+        self.assertEqual(resolved_view(args).ep_size, args.tp_size)
+        self.assertTrue(resolved_view(args).disable_shared_experts_fusion)
+
+    def test_auto_runner_defaults_to_deep_gemm(self):
+        from sglang.srt.arg_groups.overrides import resolved_view
+
+        args = self._args(moe_runner_backend="auto")
+        args._handle_a2a_moe()
+        self.assertEqual(resolved_view(args).moe_runner_backend, "deep_gemm")
+
+    def test_unsupported_runner_rejected(self):
+        args = self._args(moe_runner_backend="flashinfer_trtllm")
+        with self.assertRaises(ValueError):
+            args._handle_a2a_moe()
+
+    def test_triton_runner_rejected(self):
+        args = self._args(moe_runner_backend="triton")
+        with self.assertRaises(ValueError):
+            args._handle_a2a_moe()
+
+    def test_decode_graph_stays_enabled_in_both_comm_modes(self):
+        for mode in ("direct", "hybrid"):
+            args = self._args(moe_runner_backend="deep_gemm", deepep_v2_mode=mode)
+            args._handle_a2a_moe()
+            self.assertEqual(args.cuda_graph_config.decode.backend, Backend.FULL)
+            self.assertEqual(args.cuda_graph_config.prefill.backend, Backend.DISABLED)
+
+    def test_two_batch_overlap_rejected(self):
+        args = self._args(moe_runner_backend="deep_gemm", enable_two_batch_overlap=True)
+        with self.assertRaises(ValueError):
+            args._handle_a2a_moe()
+
+    def test_speculative_draft_backend_rejected(self):
+        for main_backend in ("none", "deepep", "deepep_v2"):
+            args = self._args(
+                moe_a2a_backend=main_backend,
+                moe_runner_backend="deep_gemm",
+                speculative_moe_a2a_backend="deepep_v2",
+            )
+            with self.assertRaisesRegex(ValueError, "speculative draft backend"):
+                args._validate_deepep_v2_speculative_draft()
+
+    def test_inherited_speculative_draft_backend_rejected(self):
+        args = self._args(
+            moe_runner_backend="deep_gemm",
+            speculative_algorithm="EAGLE",
+        )
+        with self.assertRaisesRegex(ValueError, "speculative draft backend"):
+            args._validate_deepep_v2_speculative_draft()
+
+    def test_ngram_does_not_inherit_a_draft_backend(self):
+        args = self._args(
+            moe_runner_backend="deep_gemm",
+            speculative_algorithm="NGRAM",
+        )
+        args._validate_deepep_v2_speculative_draft()
+
+    def test_explicit_legacy_speculative_backend_allowed(self):
+        args = self._args(
+            moe_runner_backend="deep_gemm",
+            speculative_algorithm="EAGLE",
+            speculative_moe_a2a_backend="deepep",
+        )
+        args._validate_deepep_v2_speculative_draft()
+
+    def test_resolved_legacy_speculative_backend_allowed(self):
+        args = self._args(
+            moe_runner_backend="deep_gemm",
+            speculative_algorithm="EAGLE",
+        )
+        args._resolved_overrides = [
+            (
+                "test_speculative_backend",
+                {"speculative_moe_a2a_backend": "deepep"},
+            )
+        ]
+        args._validate_deepep_v2_speculative_draft()
+
+    def test_prefill_chunk_exceeding_cap_rejected(self):
+        args = self._args(moe_runner_backend="deep_gemm", chunked_prefill_size=2048)
+        with envs.SGLANG_DEEPEP_V2_NUM_MAX_DISPATCH_TOKENS_PER_RANK.override(1024):
+            with self.assertRaisesRegex(ValueError, "NUM_MAX_DISPATCH_TOKENS_PER_RANK"):
+                args._validate_deepep_v2_dispatch_token_budget()
+
+    def test_prefill_chunk_at_cap_boundary_accepted(self):
+        args = self._args(moe_runner_backend="deep_gemm", chunked_prefill_size=1024)
+        with envs.SGLANG_DEEPEP_V2_NUM_MAX_DISPATCH_TOKENS_PER_RANK.override(1024):
+            args._validate_deepep_v2_dispatch_token_budget()
+
+    def test_dynamic_chunking_probe_is_included(self):
+        args = self._args(
+            chunked_prefill_size=1024,
+            max_prefill_tokens=1024,
+            enable_dynamic_chunking=True,
+            pp_size=2,
+            disaggregation_mode="prefill",
+        )
+        with envs.SGLANG_DEEPEP_V2_NUM_MAX_DISPATCH_TOKENS_PER_RANK.override(1024):
+            with self.assertRaisesRegex(ValueError, "required=1280"):
+                args._validate_deepep_v2_dispatch_token_budget()
+
+    def test_disabled_chunking_uses_max_prefill_tokens(self):
+        for disabled in (None, 0, -1):
+            args = self._args(
+                chunked_prefill_size=disabled,
+                max_prefill_tokens=1024,
+                disaggregation_mode="prefill",
+            )
+            with envs.SGLANG_DEEPEP_V2_NUM_MAX_DISPATCH_TOKENS_PER_RANK.override(128):
+                with self.assertRaisesRegex(ValueError, "required=1024"):
+                    args._validate_deepep_v2_dispatch_token_budget()
+
+    def test_decode_role_skips_prefill_capacity(self):
+        args = self._args(
+            chunked_prefill_size=4096,
+            disaggregation_mode="decode",
+            max_running_requests=32,
+            dp_size=1,
+        )
+        with envs.SGLANG_DEEPEP_V2_NUM_MAX_DISPATCH_TOKENS_PER_RANK.override(128):
+            args._validate_deepep_v2_dispatch_token_budget()
+
+    def test_decode_graph_capacity_boundaries(self):
+        for max_bs, raises in ((128, False), (129, True)):
+            args = self._args(
+                disaggregation_mode="decode",
+                max_running_requests=None,
+            )
+            args.cuda_graph_config.decode.max_bs = max_bs
+            with envs.SGLANG_DEEPEP_V2_NUM_MAX_DISPATCH_TOKENS_PER_RANK.override(128):
+                if raises:
+                    with self.assertRaisesRegex(ValueError, "decode CUDA graph"):
+                        args._validate_deepep_v2_dispatch_token_budget()
+                else:
+                    args._validate_deepep_v2_dispatch_token_budget()
+
+    def test_dp_attention_divides_max_running_requests_per_rank(self):
+        args = self._args(
+            disaggregation_mode="decode",
+            max_running_requests=256,
+            tp_size=8,
+            dp_size=8,
+            enable_dp_attention=True,
+        )
+        with envs.SGLANG_DEEPEP_V2_NUM_MAX_DISPATCH_TOKENS_PER_RANK.override(128):
+            args._validate_deepep_v2_dispatch_token_budget()
+
+    def test_tp_only_max_running_requests_is_not_divided(self):
+        args = self._args(
+            disaggregation_mode="decode",
+            max_running_requests=256,
+            tp_size=8,
+            dp_size=1,
+            enable_dp_attention=False,
+        )
+        with envs.SGLANG_DEEPEP_V2_NUM_MAX_DISPATCH_TOKENS_PER_RANK.override(128):
+            with self.assertRaisesRegex(ValueError, "decode CUDA graph"):
+                args._validate_deepep_v2_dispatch_token_budget()
+
+    def test_memory_derived_eager_pool_remains_runtime_validated(self):
+        args = self._args(
+            disaggregation_mode="decode",
+            max_running_requests=None,
+        )
+        args.cuda_graph_config.decode.backend = Backend.DISABLED
+        with envs.SGLANG_DEEPEP_V2_NUM_MAX_DISPATCH_TOKENS_PER_RANK.override(1):
+            args._validate_deepep_v2_dispatch_token_budget()
+
+    def test_speculative_decode_width_is_included(self):
+        args = self._args(
+            disaggregation_mode="decode",
+            speculative_algorithm="EAGLE",
+            speculative_num_draft_tokens=8,
+            max_running_requests=256,
+            dp_size=8,
+            enable_dp_attention=True,
+        )
+        with envs.SGLANG_DEEPEP_V2_NUM_MAX_DISPATCH_TOKENS_PER_RANK.override(128):
+            with self.assertRaisesRegex(ValueError, "tokens/request=8"):
+                args._validate_deepep_v2_dispatch_token_budget()
+
+    def test_adaptive_speculative_uses_widest_candidate(self):
+        args = self._args(
+            disaggregation_mode="decode",
+            speculative_algorithm="EAGLE",
+            speculative_num_draft_tokens=4,
+            speculative_adaptive=True,
+            max_running_requests=128,
+            dp_size=8,
+            enable_dp_attention=True,
+        )
+        with patch.object(
+            ServerArgs,
+            "max_speculative_num_draft_tokens",
+            new=property(lambda _self: 16),
+        ):
+            with envs.SGLANG_DEEPEP_V2_NUM_MAX_DISPATCH_TOKENS_PER_RANK.override(128):
+                with self.assertRaisesRegex(ValueError, "tokens/request=16"):
+                    args._validate_deepep_v2_dispatch_token_budget()
+
+    def test_prefill_role_skips_decode_capacity(self):
+        args = self._args(
+            disaggregation_mode="prefill",
+            chunked_prefill_size=64,
+            max_running_requests=8192,
+        )
+        with envs.SGLANG_DEEPEP_V2_NUM_MAX_DISPATCH_TOKENS_PER_RANK.override(128):
+            args._validate_deepep_v2_dispatch_token_budget()
+
+    def test_other_backend_skips_capacity_validation(self):
+        args = self._args(
+            moe_a2a_backend="deepep",
+            chunked_prefill_size=4096,
+            max_running_requests=4096,
+        )
+        with envs.SGLANG_DEEPEP_V2_NUM_MAX_DISPATCH_TOKENS_PER_RANK.override(1):
+            args._validate_deepep_v2_dispatch_token_budget()
+
+    def test_capacity_validation_uses_resolved_backend(self):
+        args = self._args(chunked_prefill_size=4096)
+        args._resolved_overrides = [("test", {"moe_a2a_backend": "deepep"})]
+        with envs.SGLANG_DEEPEP_V2_NUM_MAX_DISPATCH_TOKENS_PER_RANK.override(1):
+            args._validate_deepep_v2_dispatch_token_budget()
+
+
 class TestHandleCrashDumpEnv(CustomTestCase):
     _COREDUMP_ENV_KEYS = (
         "CUDA_ENABLE_COREDUMP_ON_EXCEPTION",

test/registered/unit/state_capturer/test_routed_experts_scattered_a2a.py

@@ -0,0 +1,86 @@
+"""DeepEP-family backend recognition in RoutedExpertsCapturer."""
+
+import unittest
+from types import SimpleNamespace
+from unittest import mock
+
+import torch
+
+from sglang.srt.layers.moe.utils import MoeA2ABackend
+from sglang.srt.state_capturer import routed_experts as re_mod
+from sglang.test.ci.ci_register import register_cpu_ci
+from sglang.test.test_utils import CustomTestCase
+
+register_cpu_ci(est_time=5, suite="base-a-test-cpu")
+
+
+class TestScatteredA2ABackendHelper(CustomTestCase):
+    def test_classification(self):
+        expected = {
+            "deepep": True,
+            "deepep_v2": True,
+            "none": False,
+            "mooncake": False,
+        }
+        for value, exp in expected.items():
+            with mock.patch.object(
+                re_mod, "get_moe_a2a_backend", return_value=MoeA2ABackend(value)
+            ):
+                self.assertEqual(
+                    re_mod._is_scattered_a2a_backend(), exp, f"backend={value}"
+                )
+
+
+class TestGetLocalSliceBackendBranch(CustomTestCase):
+    T, L, K = 16, 3, 4
+
+    def _capturer(self):
+        cap = object.__new__(re_mod.RoutedExpertsCapturer)
+        buf = torch.arange(self.T * self.L * self.K, dtype=torch.int32).reshape(
+            self.T, self.L, self.K
+        )
+        cap.device_cache = SimpleNamespace(buffer=buf)
+        cap.topk_size = self.K
+        return cap, buf
+
+    def _slice(self, cap, n_local):
+        fb = SimpleNamespace(out_cache_loc=torch.empty(n_local))
+        return cap._get_local_slice(fb, can_run_graph=False, cuda_graph_batch=None)
+
+    def test_deepep_v2_reads_buffer_head(self):
+        cap, buf = self._capturer()
+        with mock.patch.object(
+            re_mod, "is_dp_attention_enabled", return_value=True
+        ), mock.patch.object(
+            re_mod, "get_moe_a2a_backend", return_value=MoeA2ABackend("deepep_v2")
+        ):
+            out = self._slice(cap, n_local=5)
+        self.assertTrue(torch.equal(out, buf[0:5, :, : self.K]))
+
+    def test_deepep_v2_matches_deepep(self):
+        cap, _ = self._capturer()
+        outs = []
+        for backend in ("deepep", "deepep_v2"):
+            with mock.patch.object(
+                re_mod, "is_dp_attention_enabled", return_value=True
+            ), mock.patch.object(
+                re_mod, "get_moe_a2a_backend", return_value=MoeA2ABackend(backend)
+            ):
+                outs.append(self._slice(cap, n_local=7))
+        self.assertTrue(torch.equal(outs[0], outs[1]))
+
+    def test_tp_moe_reads_global_offset(self):
+        cap, buf = self._capturer()
+        with mock.patch.object(
+            re_mod, "is_dp_attention_enabled", return_value=True
+        ), mock.patch.object(
+            re_mod, "get_moe_a2a_backend", return_value=MoeA2ABackend("none")
+        ), mock.patch.object(
+            re_mod, "get_dp_local_slice_cpu", return_value=(6, 4)
+        ):
+            out = self._slice(cap, n_local=999)
+        self.assertTrue(torch.equal(out, buf[6:10, :, : self.K]))
+
+
+if __name__ == "__main__":
+    unittest.main()