[Feature] Support DeepEP Low Latency (#4767)

Co-authored-by: sleepcoo <sleepcoo@gmail.com>
Co-authored-by: laixinn <xielx@shanghaitech.edu.cn>
Co-authored-by: ch-wan <cwan39@gatech.edu>

docs/backend/server_arguments.md

@@ -91,6 +91,7 @@ Please consult the documentation below to learn more about the parameters you ma
 * `enable_ep_moe`: Enables expert parallelism that distributes the experts onto multiple GPUs for MoE models.
 * `ep_size`: The size of EP. Please shard the model weights with `tp_size=ep_size`, for detailed benchmarking refer to [this PR](https://github.com/sgl-project/sglang/pull/2203). If not set, `ep_size` will be automatically set to `tp_size`.
 * `enable_deepep_moe`: Enables expert parallelism that distributes the experts onto multiple GPUs for DeepSeek-V3 model based on deepseek-ai/DeepEP.
+* `deepep_mode`: Select the mode when enable DeepEP MoE, could be `normal`, `low_latency` or `auto`. Default is `auto`, which means `low_latency` for decode batch and `normal` for prefill batch.
 
 ## Memory and scheduling
 

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

@@ -244,6 +244,148 @@ def silu_and_mul_triton_kernel(
             tl.store(down_input_ptr + offset, silu_mul_output, mask=mask)
 
 
+# copy from https://github.com/ModelTC/lightllm/blob/a000ab69098654df4731f5b12587dd4e7f0a4f41/lightllm/common/fused_moe/moe_silu_and_mul_mix_quant_ep.py
+@triton.jit
+def _silu_and_mul_post_quant_kernel(
+    input_ptr,
+    stride_input_0,
+    stride_input_1,
+    stride_input_2,
+    output_ptr,
+    stride_output_0,
+    stride_output_1,
+    stride_output_2,
+    output_scale_ptr,
+    stride_output_scale_0,
+    stride_output_scale_1,
+    stride_output_scale_2,
+    masked_m_ptr,
+    size_n,
+    fp8_max,
+    fp8_min,
+    BLOCK_N: tl.constexpr,
+    NUM_STAGE: tl.constexpr,
+):
+    expert_id = tl.program_id(2)
+    token_id = tl.program_id(1)
+    hidden_dim_block_index = tl.program_id(0)
+
+    block_num_per_expert = tl.num_programs(1)
+
+    token_num_cur_expert = tl.load(masked_m_ptr + expert_id)
+
+    stride_input_0 = tl.cast(stride_input_0, dtype=tl.int64)
+    stride_output_0 = tl.cast(stride_output_0, dtype=tl.int64)
+    stride_input_1 = tl.cast(stride_input_1, dtype=tl.int64)
+    stride_output_1 = tl.cast(stride_output_1, dtype=tl.int64)
+
+    offs_in_d = hidden_dim_block_index * BLOCK_N + tl.arange(0, BLOCK_N)
+    input_ptr_offs = input_ptr + expert_id * stride_input_0 + offs_in_d
+    output_ptr_offs = output_ptr + expert_id * stride_output_0 + offs_in_d
+    output_scale_offs = (
+        output_scale_ptr
+        + expert_id * stride_output_scale_0
+        + hidden_dim_block_index * stride_output_scale_2
+    )
+
+    for token_index in tl.range(
+        token_id, token_num_cur_expert, block_num_per_expert, num_stages=NUM_STAGE
+    ):
+        gate = tl.load(
+            input_ptr_offs + token_index * stride_input_1,
+            mask=offs_in_d < size_n,
+            other=0.0,
+        ).to(tl.float32)
+        up = tl.load(
+            input_ptr_offs + token_index * stride_input_1 + size_n,
+            mask=offs_in_d < size_n,
+            other=0.0,
+        )
+        gate = gate / (1 + tl.exp(-gate))
+        gate = gate.to(input_ptr.dtype.element_ty)
+        gate_up = up * gate
+        _absmax = tl.maximum(tl.max(tl.abs(gate_up)), 1e-10)
+        output_s = _absmax / fp8_max
+        output_q = tl.clamp(gate_up / output_s, fp8_min, fp8_max).to(
+            output_ptr.dtype.element_ty
+        )
+        tl.store(
+            output_ptr_offs + token_index * stride_output_1,
+            output_q,
+            mask=offs_in_d < size_n,
+        )
+        tl.store(
+            output_scale_offs + token_index * stride_output_scale_1,
+            output_s,
+        )
+
+
+def silu_and_mul_masked_post_quant_fwd(
+    input: torch.Tensor,
+    output: torch.Tensor,
+    output_scale: torch.Tensor,
+    quant_group_size: int,
+    masked_m: torch.Tensor,
+):
+    """
+    input shape [expert_num, token_num_padded, hidden_dim]
+    output shape [expert_num, token_num_padded, hidden_dim // 2], dtype fp8
+    output_scale [expert_num token_num_paddded, hidden_dim // 2 // 128] dtype float32
+    quant_group_size  int,
+    masked_m shape [expert_num],
+    """
+
+    assert input.is_contiguous()
+    assert output.dtype == torch.float8_e4m3fn
+    assert output.is_contiguous()
+    assert len(input.shape) == 3
+    assert input.shape[0] == masked_m.shape[0]
+    assert input.shape[-1] % 2 == 0
+
+    size_n = input.shape[-1] // 2
+    assert size_n % quant_group_size == 0
+
+    expert_num = len(masked_m)
+
+    if expert_num < 4:
+        BLOCK_NUM_PER_EXPERT = 64
+    else:
+        BLOCK_NUM_PER_EXPERT = 32
+
+    BLOCK_N = quant_group_size
+    num_warps = 1
+    NUM_STAGES = 6
+    hidden_dim_split_block_num = triton.cdiv(size_n, BLOCK_N)
+    assert BLOCK_N % quant_group_size == 0
+
+    grid = (
+        hidden_dim_split_block_num,
+        BLOCK_NUM_PER_EXPERT,
+        expert_num,
+    )
+
+    finfo = torch.finfo(torch.float8_e4m3fn)
+    fp8_max = finfo.max
+    fp8_min = -fp8_max
+
+    _silu_and_mul_post_quant_kernel[grid](
+        input,
+        *input.stride(),
+        output,
+        *output.stride(),
+        output_scale,
+        *output_scale.stride(),
+        masked_m,
+        size_n,
+        fp8_max,
+        fp8_min,
+        BLOCK_N=BLOCK_N,
+        NUM_STAGE=NUM_STAGES,
+        num_warps=num_warps,
+    )
+    return
+
+
 @triton.jit
 def tanh(x):
     return 2 * tl.sigmoid(2 * x) - 1

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

@@ -3,12 +3,16 @@ from typing import Callable, List, Optional, Tuple
 
 import torch
 
-# TODO: use deep_gemm masked kernel after low latency dispatch
-# import deep_gemm
-# from deep_gemm import (
-#     get_col_major_tma_aligned_tensor,
-#     m_grouped_gemm_fp8_fp8_bf16_nt_masked,
-# )
+try:
+    from deep_gemm import (
+        get_col_major_tma_aligned_tensor,
+        m_grouped_gemm_fp8_fp8_bf16_nt_masked,
+    )
+
+    use_deep_gemm = True
+except ImportError:
+    use_deep_gemm = False
+
 from torch.nn import Module
 
 from sglang.srt.custom_op import CustomOp
@@ -22,6 +26,7 @@ from sglang.srt.layers.moe.ep_moe.kernels import (
     post_reorder_triton_kernel,
     pre_reorder_triton_kernel,
     run_moe_ep_preproess,
+    silu_and_mul_masked_post_quant_fwd,
     silu_and_mul_triton_kernel,
 )
 from sglang.srt.layers.moe.fused_moe_triton import FusedMoeWeightScaleSupported
@@ -809,6 +814,7 @@ class DeepEPMoE(EPMoE):
         correction_bias: Optional[torch.Tensor] = None,
         custom_routing_function: Optional[Callable] = None,
         activation: str = "silu",
+        deepep_mode: str = "auto",
     ):
         super().__init__(
             num_experts,
@@ -827,21 +833,41 @@ class DeepEPMoE(EPMoE):
             custom_routing_function,
             activation,
         )
+        self.deepep_mode = deepep_mode
+        if self.deepep_mode in ["low_latency", "auto"]:
+            assert use_deep_gemm, f"DeepEP {self.deepep_mode} mode requires deep_gemm"
+        self.w13_weight_fp8 = (
+            self.w13_weight,
+            (
+                self.w13_weight_scale_inv
+                if self.use_block_quant
+                else self.w13_weight_scale
+            ),
+        )
+        self.w2_weight_fp8 = (
+            self.w2_weight,
+            self.w2_weight_scale_inv if self.use_block_quant else self.w2_weight_scale,
+        )
 
     def forward(
         self,
         hidden_states: torch.Tensor,
         reorder_topk_ids: torch.Tensor,
         seg_indptr: torch.Tensor,
+        masked_m: torch.Tensor,
+        expected_m: int,
         forward_mode: ForwardMode,
     ):
-        # Todo: use m_grouped_gemm_fp8_fp8_bf16_nt_masked after low_latency dispatch (decode)
-        if True:  # not forward_mode.is_decode():
+        if self.deepep_mode == "normal" or (
+            self.deepep_mode == "auto" and not forward_mode.is_decode()
+        ):
             return self.forward_normal(hidden_states, reorder_topk_ids, seg_indptr)
+        elif self.deepep_mode == "low_latency" or (
+            self.deepep_mode == "auto" and forward_mode.is_decode()
+        ):
+            return self.forward_deepgemm_masked(hidden_states, masked_m, expected_m)
         else:
-            return self.forward_deepgemm_masked(
-                hidden_states, reorder_topk_ids, seg_indptr
-            )
+            raise ValueError(f"Invalid deepep_mode: {self.deepep_mode}")
 
     def forward_normal(
         self,
@@ -958,89 +984,66 @@ class DeepEPMoE(EPMoE):
 
     def forward_deepgemm_masked(
         self,
-        hidden_states: torch.Tensor,
-        reorder_topk_ids: torch.Tensor,
-        seg_indptr: torch.Tensor,
+        hidden_states_fp8: Tuple[torch.Tensor, torch.Tensor],
+        masked_m: torch.Tensor,
+        expected_m: int,
     ):
         assert self.quant_method is not None
         assert self.activation == "silu"
-
-        if self.activation_scheme == "dynamic" and not self.use_block_quant:
-            max_value = (
-                torch.max(hidden_states)
-                .repeat(self.num_experts_per_partition)
-                .to(torch.float32)
-            )
-            self.w13_input_scale = max_value / torch.finfo(self.fp8_dtype).max
+        assert (
+            hidden_states_fp8[0].size(0) % 4 == 0
+        ), f"TMA alignment error: {hidden_states_fp8[0].size(0)}"
 
         # GroupGemm-0
+        num_groups, m, k = hidden_states_fp8[0].size()
+        n = self.w13_weight.size(1)
+        expected_m = min(expected_m, m)
         gateup_output = torch.empty(
-            hidden_states.shape[0],
-            self.w13_weight.shape[1],
-            device=hidden_states.device,
-            dtype=hidden_states.dtype,
+            (num_groups, m, n), device=hidden_states_fp8[0].device, dtype=torch.bfloat16
+        )
+        m_grouped_gemm_fp8_fp8_bf16_nt_masked(
+            hidden_states_fp8, self.w13_weight_fp8, gateup_output, masked_m, expected_m
         )
-        if hidden_states.shape[0] > 0:
-            # Transpose earlier so that the testing will not trigger transposing kernels
-            hidden_states = (
-                hidden_states[0],
-                get_col_major_tma_aligned_tensor(hidden_states[1]),
-            )
-            """
-            gateup_output = deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_masked(
-                hidden_states, self.w13_weight, out, masked_m, expected_m
-            )
-            """
 
         # Act
         down_input = torch.empty(
-            gateup_output.shape[0],
-            gateup_output.shape[1] // 2,
-            device=gateup_output.device,
-            dtype=(
-                self.fp8_dtype
-                if (self.use_fp8_w8a8 and not self.use_block_quant)
-                else hidden_states.dtype
+            (
+                gateup_output.shape[0],
+                gateup_output.shape[1],
+                gateup_output.shape[2] // 2,
             ),
+            device=gateup_output.device,
+            dtype=self.fp8_dtype,
         )
-        if self.w2_input_scale is None and not self.use_block_quant:
-            self.w2_input_scale = torch.ones(
-                self.num_experts_per_partition,
-                dtype=torch.float32,
-                device=hidden_states.device,
-            )
-
-        if self.activation == "silu":
-            silu_and_mul_triton_kernel[(gateup_output.shape[0],)](
-                gateup_output,
-                down_input,
+        scale_block_size = 128
+        down_input_scale = torch.empty(
+            (
+                gateup_output.shape[0],
                 gateup_output.shape[1],
-                reorder_topk_ids,
-                self.w2_input_scale,
-                0,
-                self.num_experts_per_partition - 1,
-                BLOCK_SIZE=512,
-            )
-        else:
-            raise ValueError(f"Unsupported activation: {self.activation=}")
+                gateup_output.shape[2] // 2 // scale_block_size,
+            ),
+            device=gateup_output.device,
+            dtype=torch.float32,
+        )
+        silu_and_mul_masked_post_quant_fwd(
+            gateup_output,
+            down_input,
+            down_input_scale,
+            scale_block_size,
+            masked_m,
+        )
 
         # GroupGemm-1
+        n = self.w2_weight.size(1)
+        down_input_fp8 = (
+            down_input,
+            get_col_major_tma_aligned_tensor(down_input_scale),
+        )
         down_output = torch.empty(
-            down_input.shape[0],
-            self.w2_weight.shape[1],
-            device=hidden_states.device,
-            dtype=hidden_states.dtype,
+            (num_groups, m, n), device=down_input.device, dtype=torch.bfloat16
+        )
+        m_grouped_gemm_fp8_fp8_bf16_nt_masked(
+            down_input_fp8, self.w2_weight_fp8, down_output, masked_m, expected_m
         )
-        if down_input.shape[0] > 0:
-            # Transpose earlier so that the testing will not trigger transposing kernels
-            down_input = (
-                down_input[0],
-                get_col_major_tma_aligned_tensor(down_input[1]),
-            )
-            """
-            down_output = deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_masked(
-                down_input, self.w2_weight, out, masked_m, expected_m
-            )
-            """
 
         return down_output

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

@@ -76,8 +76,7 @@ def get_buffer_low_latency(
         assert num_experts % group.size() == 0
         _buffer_low_latency = Buffer(
             group,
-            0,
-            num_rdma_bytes,
+            num_rdma_bytes=num_rdma_bytes,
             low_latency_mode=True,
             num_qps_per_rank=num_experts // group.size(),
         )
@@ -95,62 +94,63 @@ class DeepEPDispatcher:
         group: torch.distributed.ProcessGroup,
         router_topk: int,
         permute_fusion: bool = False,
-        capacity_factor: float = None,
         num_experts: int = None,
         num_local_experts: int = None,
         hidden_size: int = None,
         params_dtype: torch.dtype = None,
+        deepep_mode: str = "auto",
         async_finish: bool = False,
+        return_recv_hook: bool = False,
     ):
+        if not use_deepep:
+            raise ImportError(
+                "DeepEP is not installed. Please install DeepEP package from "
+                "https://github.com/deepseek-ai/deepep."
+            )
+
         self.group = group
         self.router_topk = router_topk
-        self.capacity_factor = capacity_factor
         self.permute_fusion = permute_fusion
         self.num_experts = num_experts
         self.num_local_experts = num_local_experts
         self.hidden_size = hidden_size
-        self.recv_expert_count = None
         self.params_dtype = params_dtype
         self.params_bytes = 2
-        # Metadata
-        self.token_indices = None
-        self.token_probs = None
-        # Handle used for combine operation
-        self.handle = None
-        self.async_finish = async_finish
 
-        # `num_max_dispatch_tokens_per_rank` (the actual batch size in the decoding engine) should be less than 256
-        # https://github.com/deepseek-ai/DeepEP?tab=readme-ov-file#example-use-in-inference-decoding
-        self.num_max_dispatch_tokens_per_rank = 128
+        self.deepep_mode = deepep_mode
+        self.handle = None
 
-        if not use_deepep:
-            raise ImportError(
-                "DeepEP is not installed. Please install DeepEP package from "
-                "https://github.com/deepseek-ai/deepep."
+        if self.deepep_mode in ["normal", "auto"]:  # for normal / auto mode
+            self.buffer_normal = get_buffer_normal(
+                self.group, self.hidden_size * self.params_bytes
             )
-        self.buffer_normal = get_buffer_normal(
-            self.group, self.hidden_size * self.params_bytes
-        )
-        self.buffer_low_latency = None
-        # Todo: enable low latency dispatch
-        """
-        self.buffer_low_latency = get_buffer_low_latency(
-            self.group,
-            self.num_max_dispatch_tokens_per_rank,
-            self.hidden_size * self.params_bytes,
-            self.num_experts,
-        )
-        """
+            self.async_finish = async_finish
+            self.src2dst = None
+        if self.deepep_mode in ["low_latency", "auto"]:  # for low_latency / auto mode
+            """
+            num_max_dispatch_tokens_per_rank: the actual batch size in the decoding engine should be less than 256
+            https://github.com/deepseek-ai/DeepEP?tab=readme-ov-file#example-use-in-inference-decoding
+            """
+            # TODO(ch-wan): allow users to set this value
+            self.num_max_dispatch_tokens_per_rank = 128
+            self.buffer_low_latency = get_buffer_low_latency(
+                self.group,
+                self.num_max_dispatch_tokens_per_rank,
+                self.hidden_size,
+                self.num_experts,
+            )
+            self.return_recv_hook = return_recv_hook
 
     def deepep_permute(
         self,
-        hidden_states,
-        fp8_dtype=None,
-        use_fp8_w8a8=False,
-        use_block_quant=False,
+        hidden_states: torch.Tensor,
+        topk_idx: torch.Tensor,
+        fp8_dtype: Optional[torch.dtype] = None,
+        use_fp8_w8a8: bool = False,
+        use_block_quant: bool = False,
     ):
-        reorder_topk_ids, src2dst, seg_indptr = deepep_run_moe_deep_preprocess(
-            self.topk_idx, self.num_experts
+        reorder_topk_ids, self.src2dst, seg_indptr = deepep_run_moe_deep_preprocess(
+            topk_idx, self.num_experts
         )
         num_total_tokens = reorder_topk_ids.numel()
         gateup_input = torch.empty(
@@ -166,14 +166,13 @@ class DeepEPDispatcher:
         deepep_permute_triton_kernel[(hidden_states.shape[0],)](
             hidden_states,
             gateup_input,
-            src2dst,
-            self.topk_idx,
+            self.src2dst,
+            topk_idx,
             None,
             self.router_topk,
             hidden_states.shape[1],
             BLOCK_SIZE=512,
         )
-        self.src2dst = src2dst
         return reorder_topk_ids, seg_indptr, gateup_input
 
     def dispatch(
@@ -182,54 +181,64 @@ class DeepEPDispatcher:
         topk_idx: torch.Tensor,
         topk_weights: torch.Tensor,
         num_experts: int,
-        forward_mode: ForwardMode,
         num_max_dispatch_tokens_per_rank: int = 128,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        forward_mode: ForwardMode = None,
+    ) -> Tuple:
         topk_idx = topk_idx.to(torch.int64)
-        # Todo: enable low latency dispatch
-        if True:  # not forward_mode.is_decode():
+        reorder_topk_ids = torch.empty(
+            (0,), device=hidden_states.device, dtype=torch.int64
+        )
+        seg_indptr = torch.zeros(
+            (num_experts + 1,), device=hidden_states.device, dtype=torch.int64
+        )
+        masked_m = torch.empty(
+            (self.num_local_experts,), device=hidden_states.device, dtype=torch.int64
+        )
+        expected_m = 0
+
+        if self.deepep_mode == "normal" or (
+            self.deepep_mode == "auto" and not forward_mode.is_decode()
+        ):
             (
                 hidden_states,
                 topk_idx,
                 topk_weights,
-                num_recv_tokens_per_expert_list,
-                handle,
                 event,
             ) = self.dispatch_normal(hidden_states, topk_idx, topk_weights, num_experts)
-            self.tokens_per_expert = torch.tensor(
-                num_recv_tokens_per_expert_list,
-                device=hidden_states.device,
-                dtype=torch.int64,
-            )
-        else:
-            hidden_states, recv_expert_count, handle, event, hook = (
-                self.dispatch_low_latency(
-                    hidden_states,
-                    topk_idx,
-                    num_max_dispatch_tokens_per_rank,
-                    num_experts,
+            event.current_stream_wait() if self.async_finish else ()
+            if hidden_states.shape[0] > 0:
+                reorder_topk_ids, seg_indptr, hidden_states = self.deepep_permute(
+                    hidden_states, topk_idx, fp8_dtype=hidden_states.dtype
                 )
+        elif self.deepep_mode == "low_latency" or (
+            self.deepep_mode == "auto" and forward_mode.is_decode()
+        ):
+            expected_m = (
+                hidden_states.shape[0]
+                * self.buffer_low_latency.group_size
+                * topk_idx.shape[1]
+                + num_experts
+            ) // num_experts
+            hidden_states, masked_m, event, hook = self.dispatch_low_latency(
+                hidden_states,
+                topk_idx,
+                num_max_dispatch_tokens_per_rank,
+                num_experts,
+                use_fp8=True,
             )
-            self.recv_expert_count = recv_expert_count
-
-        if self.async_finish:
-            event.current_stream_wait()
-
-        self.handle = handle
-        self.topk_idx = topk_idx
-        self.topk_weights = topk_weights
-        if hidden_states.shape[0] > 0:
-            reorder_topk_ids, seg_indptr, hidden_states = self.deepep_permute(
-                hidden_states, fp8_dtype=hidden_states.dtype
-            )
+            hook() if self.return_recv_hook else event.current_stream_wait()
         else:
-            reorder_topk_ids = torch.empty(
-                (0,), device=hidden_states.device, dtype=torch.int64
-            )
-            seg_indptr = torch.zeros(
-                (num_experts + 1,), device=hidden_states.device, dtype=torch.int64
-            )
-        return hidden_states, reorder_topk_ids, seg_indptr
+            raise ValueError(f"Invalid deepep_mode: {self.deepep_mode}")
+
+        return (
+            hidden_states,
+            topk_idx,
+            topk_weights,
+            reorder_topk_ids,
+            seg_indptr,
+            masked_m,
+            expected_m,
+        )
 
     def dispatch_normal(
         self,
@@ -254,12 +263,15 @@ class DeepEPDispatcher:
             allocate_on_comm_stream=previous_event is not None,
         )
 
+        # FIXME: `handle` should be transmitted with tokens from dispatch to combine.
+        # However, doing this would incur an unknown synchronization error, but keeping
+        # `handle` as a member variable works.
         (
             recv_x,
             recv_topk_idx,
             recv_topk_weights,
-            num_recv_tokens_per_expert_list,
-            handle,
+            _,  # num_recv_tokens_per_expert_list
+            self.handle,
             event,
         ) = self.buffer_normal.dispatch(
             x,
@@ -278,8 +290,6 @@ class DeepEPDispatcher:
             recv_x,
             recv_topk_idx,
             recv_topk_weights,
-            num_recv_tokens_per_expert_list,
-            handle,
             event,
         )
 
@@ -289,18 +299,19 @@ class DeepEPDispatcher:
         topk_idx: torch.Tensor,
         num_max_dispatch_tokens_per_rank: int,
         num_experts: int,
+        use_fp8: bool = False,
     ):
         """
-        # For H20, there will be an CUDA error: DeepEP/csrc/kernels/internode_ll.cu:337 'too many blocks in cooperative launch'
-        # Please please make sure to change DeepEP code in internode_ll.cu dispatch / combine first and then reinstall!
+        # For H20, there will be an CUDA error: DeepEP/csrc/kernels/internode_ll.cu:337 'too many blocks in cooperative launch'.
+        # Please make sure to change DeepEP code in internode_ll.cu dispatch / combine as below first and then reinstall.
         # More details refer: https://github.com/deepseek-ai/DeepEP/issues/15#issuecomment-2709715782
-        +
+
         diff --git a/csrc/kernels/internode_ll.cu b/csrc/kernels/internode_ll.cu
-        index f60e933..cddaabf 100644
+        index 76ae2e2..8ecd08f 100644
         --- a/csrc/kernels/internode_ll.cu
         +++ b/csrc/kernels/internode_ll.cu
-        @@ -307,14 +307,14 @@ void dispatch(void* packed_recv_x, float* packed_recv_x_scales,
-                    int num_topk, int num_experts, int rank, int num_ranks,
+        @@ -310,8 +310,8 @@ void dispatch(void* packed_recv_x, float* packed_recv_x_scales,
+                    int num_topk, int num_experts, int rank, int num_ranks, bool use_fp8,
                     void* workspace, cudaStream_t stream, int phases) {
             constexpr int kNumMaxTopK = 9;
         -    constexpr int kNumWarpsPerGroup = 10;
@@ -308,16 +319,9 @@ class DeepEPDispatcher:
         +    constexpr int kNumWarpsPerGroup = 8;
         +    constexpr int kNumWarpGroups = 4;
             EP_STATIC_ASSERT(kNumMaxTopK + 1 <= kNumWarpGroups * kNumWarpsPerGroup, "Too many top-k selections");
-        +
+
             const auto num_warps = kNumWarpGroups * kNumWarpsPerGroup;
-            const auto num_sms = cell_div(num_experts, kNumWarpGroups);
-            EP_HOST_ASSERT(num_topk <= kNumMaxTopK);
-        -    EP_HOST_ASSERT(cell_div(static_cast<int>(hidden * 2 / sizeof(int4)), 32 * (num_warps - 1)) <= 2);
-        +    // EP_HOST_ASSERT(cell_div(static_cast<int>(hidden * 2 / sizeof(int4)), 32 * (num_warps - 1)) <= 2);
-        +
-            // Workspace checks
-            auto atomic_counter_per_expert = reinterpret_cast<int*>(workspace);
-        @@ -505,8 +505,8 @@ void combine(void* combined_x,
+        @@ -501,8 +501,8 @@ void combine(void* combined_x,
                     int num_combined_tokens, int hidden, int num_max_dispatch_tokens_per_rank,
                     int num_topk, int num_experts, int rank, int num_ranks,
                     void* workspace, cudaStream_t stream, int phases) {
@@ -326,28 +330,33 @@ class DeepEPDispatcher:
         +    constexpr int kNumWarpsPerGroup = 8;
         +    constexpr int kNumWarpGroups = 4;
             constexpr int kNumMaxTopk = 9;
-        +
+
             const auto num_warps = kNumWarpGroups * kNumWarpsPerGroup;
         """
 
-        recv_hidden_states, recv_expert_count, handle, event, hook = (
+        packed_recv_hidden, packed_recv_count, self.handle, event, hook = (
             self.buffer_low_latency.low_latency_dispatch(
                 hidden_states,
                 topk_idx,
                 num_max_dispatch_tokens_per_rank,
                 num_experts,
-                async_finish=self.async_finish,
-                return_recv_hook=False,  # True for double-batch overlapping, need call hook()
+                use_fp8=use_fp8,
+                async_finish=not self.return_recv_hook,
+                return_recv_hook=self.return_recv_hook,
             )
         )
-        # hook()
-        return recv_hidden_states, recv_expert_count, handle, event, hook
+        return packed_recv_hidden, packed_recv_count, event, hook
 
     def combine(
-        self, hidden_states: torch.Tensor, forward_mode: ForwardMode
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
-        # Todo: enable low latency combine
-        if True:  # not forward_mode.is_decode():
+        self,
+        hidden_states: torch.Tensor,
+        topk_idx: torch.Tensor,
+        topk_weights: torch.Tensor,
+        forward_mode: ForwardMode,
+    ) -> torch.Tensor:
+        if self.deepep_mode == "normal" or (
+            self.deepep_mode == "auto" and not forward_mode.is_decode()
+        ):
             if hidden_states.shape[0] > 0:
                 num_tokens = self.src2dst.shape[0] // self.router_topk
                 output = torch.empty(
@@ -359,8 +368,8 @@ class DeepEPDispatcher:
                     hidden_states,
                     output,
                     self.src2dst,
-                    self.topk_idx,
-                    self.topk_weights,
+                    topk_idx,
+                    topk_weights,
                     self.router_topk,
                     hidden_states.shape[1],
                     BLOCK_SIZE=512,
@@ -371,24 +380,30 @@ class DeepEPDispatcher:
                     device=hidden_states.device,
                     dtype=hidden_states.dtype,
                 )
-            hidden_states, event = self.combine_normal(output, self.handle)
-        else:
+            hidden_states, event = self.combine_normal(
+                output,
+            )
+            event.current_stream_wait() if self.async_finish else ()
+        elif self.deepep_mode == "low_latency" or (
+            self.deepep_mode == "auto" and forward_mode.is_decode()
+        ):
             hidden_states, event, hook = self.combine_low_latency(
-                hidden_states, self.topk_idx, self.topk_weights, self.handle
+                hidden_states,
+                topk_idx,
+                topk_weights,
             )
+            hook() if self.return_recv_hook else event.current_stream_wait()
+        else:
+            raise ValueError(f"Invalid deepep_mode: {self.deepep_mode}")
 
-        if self.async_finish:
-            event.current_stream_wait()
-
-        self.handle = None
         return hidden_states
 
-    def combine_normal(self, x: torch.Tensor, handle: Tuple):
+    def combine_normal(self, x: torch.Tensor):
         previous_event = Buffer.capture() if self.async_finish else None
 
         combined_x, _, event = self.buffer_normal.combine(
             x,
-            handle,
+            self.handle,
             async_finish=self.async_finish,
             previous_event=previous_event,
             allocate_on_comm_stream=previous_event is not None,
@@ -400,17 +415,15 @@ class DeepEPDispatcher:
         hidden_states: torch.Tensor,
         topk_idx: torch.Tensor,
         topk_weights: torch.Tensor,
-        handle: Tuple,
     ):
-        combined_hidden_states, event_overlap, hook = (
+        combined_hidden_states, event, hook = (
             self.buffer_low_latency.low_latency_combine(
                 hidden_states,
                 topk_idx,
                 topk_weights,
-                handle,
-                async_finish=self.async_finish,
-                return_recv_hook=False,  # True for double-batch overlapping, need call hook()
+                self.handle,
+                async_finish=not self.return_recv_hook,
+                return_recv_hook=self.return_recv_hook,
             )
         )
-        # hook()
-        return combined_hidden_states, event_overlap, hook
+        return combined_hidden_states, event, hook

python/sglang/srt/managers/schedule_batch.py

@@ -72,6 +72,7 @@ global_server_args_dict = {
     "enable_dp_attention": ServerArgs.enable_dp_attention,
     "enable_ep_moe": ServerArgs.enable_ep_moe,
     "enable_deepep_moe": ServerArgs.enable_deepep_moe,
+    "deepep_mode": ServerArgs.deepep_mode,
     "device": ServerArgs.device,
     "speculative_accept_threshold_single": ServerArgs.speculative_accept_threshold_single,
     "speculative_accept_threshold_acc": ServerArgs.speculative_accept_threshold_acc,

python/sglang/srt/model_executor/model_runner.py

@@ -147,6 +147,7 @@ class ModelRunner:
                 "enable_dp_attention": server_args.enable_dp_attention,
                 "enable_ep_moe": server_args.enable_ep_moe,
                 "enable_deepep_moe": server_args.enable_deepep_moe,
+                "deepep_mode": server_args.deepep_mode,
                 "device": server_args.device,
                 "speculative_accept_threshold_single": server_args.speculative_accept_threshold_single,
                 "speculative_accept_threshold_acc": server_args.speculative_accept_threshold_acc,
@@ -272,7 +273,7 @@ class ModelRunner:
                 server_args.disable_radix_cache = True
 
         if server_args.enable_deepep_moe:
-            logger.info("DeepEP is turned on.")
+            logger.info(f"DeepEP is turned on. DeepEP mode: {server_args.deepep_mode}")
 
     def init_torch_distributed(self):
         logger.info("Init torch distributed begin.")

python/sglang/srt/models/deepseek_v2.py

@@ -188,19 +188,35 @@ class DeepseekV2MoE(nn.Module):
             if global_server_args_dict["enable_deepep_moe"]
             else (EPMoE if global_server_args_dict["enable_ep_moe"] else FusedMoE)
         )
-        self.experts = MoEImpl(
-            num_experts=config.n_routed_experts,
-            top_k=config.num_experts_per_tok,
-            hidden_size=config.hidden_size,
-            intermediate_size=config.moe_intermediate_size,
-            renormalize=config.norm_topk_prob,
-            quant_config=quant_config,
-            use_grouped_topk=True,
-            num_expert_group=config.n_group,
-            topk_group=config.topk_group,
-            correction_bias=self.gate.e_score_correction_bias,
-            prefix=add_prefix("experts", prefix),
-        )
+        if not global_server_args_dict["enable_deepep_moe"]:
+            self.experts = MoEImpl(
+                num_experts=config.n_routed_experts,
+                top_k=config.num_experts_per_tok,
+                hidden_size=config.hidden_size,
+                intermediate_size=config.moe_intermediate_size,
+                renormalize=config.norm_topk_prob,
+                quant_config=quant_config,
+                use_grouped_topk=True,
+                num_expert_group=config.n_group,
+                topk_group=config.topk_group,
+                correction_bias=self.gate.e_score_correction_bias,
+                prefix=add_prefix("experts", prefix),
+            )
+        else:
+            self.experts = MoEImpl(
+                num_experts=config.n_routed_experts,
+                top_k=config.num_experts_per_tok,
+                hidden_size=config.hidden_size,
+                intermediate_size=config.moe_intermediate_size,
+                renormalize=config.norm_topk_prob,
+                quant_config=quant_config,
+                use_grouped_topk=True,
+                num_expert_group=config.n_group,
+                topk_group=config.topk_group,
+                correction_bias=self.gate.e_score_correction_bias,
+                prefix=add_prefix("experts", prefix),
+                deepep_mode=global_server_args_dict["deepep_mode"],
+            )
 
         if config.n_shared_experts is not None:
             intermediate_size = config.moe_intermediate_size * config.n_shared_experts
@@ -227,6 +243,8 @@ class DeepseekV2MoE(nn.Module):
                 )
 
         if global_server_args_dict["enable_deepep_moe"]:
+            # TODO: we will support tp < ep in the future
+            self.ep_size = get_tensor_model_parallel_world_size()
             self.num_experts = config.n_routed_experts
             self.top_k = config.num_experts_per_tok
             self.renormalize = config.norm_topk_prob
@@ -246,7 +264,9 @@ class DeepseekV2MoE(nn.Module):
                 num_local_experts=config.n_routed_experts // self.tp_size,
                 hidden_size=config.hidden_size,
                 params_dtype=config.torch_dtype,
+                deepep_mode=global_server_args_dict["deepep_mode"],
                 async_finish=True,  # TODO
+                return_recv_hook=True,
             )
 
     def forward(
@@ -301,28 +321,39 @@ class DeepseekV2MoE(nn.Module):
                 num_expert_group=self.num_expert_group,
                 correction_bias=self.correction_bias,
             )
-        if self.tp_size > 1:
-            recv_hidden_states, reorder_topk_ids, seg_indptr = (
-                self.deepep_dispatcher.dispatch(
-                    hidden_states,
-                    topk_idx,
-                    topk_weights,
-                    self.num_experts,
-                    forward_mode,
-                )
+        if self.ep_size > 1:
+            (
+                hidden_states,
+                topk_idx,
+                topk_weights,
+                reorder_topk_ids,
+                seg_indptr,
+                masked_m,
+                expected_m,
+            ) = self.deepep_dispatcher.dispatch(
+                hidden_states,
+                topk_idx,
+                topk_weights,
+                self.num_experts,
+                forward_mode=forward_mode,
             )
         final_hidden_states = (
             self.experts(
-                hidden_states=recv_hidden_states,
+                hidden_states=hidden_states,
                 reorder_topk_ids=reorder_topk_ids,
                 seg_indptr=seg_indptr,
+                masked_m=masked_m,
+                expected_m=expected_m,
                 forward_mode=forward_mode,
             )
             * self.routed_scaling_factor
         )
-        if self.tp_size > 1:
+        if self.ep_size > 1:
             final_hidden_states = self.deepep_dispatcher.combine(
-                final_hidden_states, forward_mode
+                final_hidden_states,
+                topk_idx,
+                topk_weights,
+                forward_mode,
             )
         if shared_output is not None:
             final_hidden_states = final_hidden_states + shared_output

python/sglang/srt/server_args.py

@@ -161,6 +161,7 @@ class ServerArgs:
     enable_dp_attention: bool = False
     enable_ep_moe: bool = False
     enable_deepep_moe: bool = False
+    deepep_mode: Optional[str] = "auto"
     enable_torch_compile: bool = False
     torch_compile_max_bs: int = 32
     cuda_graph_max_bs: Optional[int] = None
@@ -285,6 +286,13 @@ class ServerArgs:
         if self.grammar_backend is None:
             self.grammar_backend = "xgrammar"
 
+        # Expert parallelism
+        if self.enable_ep_moe:
+            self.ep_size = self.tp_size
+            logger.info(
+                f"EP MoE is enabled. The expert parallel size is adjusted to be the same as the tensor parallel size[{self.tp_size}]."
+            )
+
         # Data parallelism attention
         if self.enable_dp_attention:
             self.schedule_conservativeness = self.schedule_conservativeness * 0.3
@@ -300,6 +308,10 @@ class ServerArgs:
         self.enable_sp_layernorm = False
         # DeepEP MoE
         if self.enable_deepep_moe:
+            if self.deepep_mode == "auto":
+                assert (
+                    not self.enable_dp_attention
+                ), "DeepEP MoE `auto` mode is not supported with DP Attention."
             self.ep_size = self.tp_size
             self.enable_sp_layernorm = (
                 self.dp_size < self.tp_size if self.enable_dp_attention else True
@@ -1082,6 +1094,12 @@ class ServerArgs:
             action="store_true",
             help="Enabling DeepEP MoE implementation for EP MoE.",
         )
+        parser.add_argument(
+            "--deepep-mode",
+            type=str,
+            choices=["normal", "low_latency", "auto"],
+            help="Select the mode when enable DeepEP MoE, could be `normal`, `low_latency` or `auto`. Default is `auto`, which means `low_latency` for decode batch and `normal` for prefill batch.",
+        )
 
         # Server warmups
         parser.add_argument(