[2/N]Support DeepSeek-R1 w4a8 low latency deepep (#8464)

Co-authored-by: Hank Han <hanhan7630@outlook.com>
Co-authored-by: Shangchuan Huang <2510421000@qq.com>

python/sglang/srt/layers/moe/cutlass_w4a8_moe.py

@@ -11,12 +11,14 @@ from sgl_kernel import (
 )
 
 from sglang.srt.layers.moe.ep_moe.kernels import (
+    deepep_ll_get_cutlass_w4a8_moe_mm_data,
     deepep_permute_triton_kernel,
     deepep_post_reorder_triton_kernel,
     deepep_run_moe_deep_preprocess,
     post_reorder_triton_kernel_for_cutlass_moe,
     pre_reorder_triton_kernel_for_cutlass_moe,
     run_moe_ep_preproess,
+    silu_and_mul_masked_post_per_tensor_quant_fwd,
 )
 
 
@@ -396,3 +398,139 @@ def cutlass_w4a8_moe_deepep_normal(
     )
 
     return output
+
+
+def cutlass_w4a8_moe_deepep_ll(
+    a: torch.Tensor,
+    w1_q: torch.Tensor,
+    w2_q: torch.Tensor,
+    w1_scale: torch.Tensor,
+    w2_scale: torch.Tensor,
+    topk_ids_: torch.Tensor,
+    masked_m: torch.Tensor,
+    a_strides1: torch.Tensor,
+    b_strides1: torch.Tensor,
+    c_strides1: torch.Tensor,
+    a_strides2: torch.Tensor,
+    b_strides2: torch.Tensor,
+    c_strides2: torch.Tensor,
+    s_strides13: torch.Tensor,
+    s_strides2: torch.Tensor,
+    expert_offsets: torch.Tensor,
+    problem_sizes1: torch.Tensor,
+    problem_sizes2: torch.Tensor,
+    a1_scale: Optional[torch.Tensor] = None,
+    a2_scale: Optional[torch.Tensor] = None,
+) -> torch.Tensor:
+    """
+    This function computes a w4a8-quantized Mixture of Experts (MoE) layer
+    using two sets of quantized weights, w1_q and w2_q, and top-k gating
+    mechanism. The matrix multiplications are implemented with CUTLASS
+    grouped gemm.
+
+    Parameters:
+    - a (torch.Tensor): The input tensor to the MoE layer.
+        Shape: [num_local_experts, num_max_dispatch_tokens_per_rank * num_ranks, K]
+    - w1_q (torch.Tensor): The first set of int4-quantized expert weights.
+        Shape: [num_experts, N * 2,  K // 2]
+        (the weights are passed transposed and int4-packed)
+    - w2_q (torch.Tensor): The second set of int4-quantized expert weights.
+        Shape: [num_experts, K, N // 2]
+        (the weights are passed transposed and int4-packed)
+    - w1_scale (torch.Tensor): The fp32 scale to dequantize w1_q.
+        Shape: [num_experts, K // 512, N * 8]
+    - w2_scale (torch.Tensor): The fp32 scale to dequantize w2_q.
+        Shape: [num_experts, N // 512, K * 4]
+    - topk_weights (torch.Tensor): The weights of each token->expert mapping.
+    - a_strides1 (torch.Tensor): The input strides of the first grouped gemm.
+    - b_strides1 (torch.Tensor): The weights strides of the first grouped gemm.
+    - c_strides1 (torch.Tensor): The output strides of the first grouped gemm.
+    - a_strides2 (torch.Tensor): The input strides of the second grouped gemm.
+    - b_strides2 (torch.Tensor): The weights strides of the second grouped gemm.
+    - c_strides2 (torch.Tensor): The output strides of the second grouped gemm.
+    - s_strides13 (torch.Tensor): The input and scale strides of the first grouped gemm.
+    - s_strides2 (torch.Tensor): The scale strides of the second grouped gemm.
+    - a1_scale (Optional[torch.Tensor]): The optional fp32 scale to quantize a.
+        Shape: scalar or [1, K]
+    - a2_scale (Optional[torch.Tensor]): The optional fp32 scale to
+        quantize the intermediate result between the gemms.
+        Shape: scalar or [1, N]
+    - apply_router_weight_on_input (bool): When true, the topk weights are
+        applied directly on the inputs. This is only applicable when topk is 1.
+
+    Returns:
+    - torch.Tensor: The fp8 output tensor after applying the MoE layer.
+    """
+    assert w1_q.dtype == torch.int8
+    assert w2_q.dtype == torch.int8
+    assert a.shape[2] // 2 == w1_q.shape[2], "Hidden size mismatch w1"
+    assert w1_q.shape[2] * 2 == w2_q.shape[1], "Hidden size mismatch w2"
+    assert w1_q.shape[0] == w2_q.shape[0], "Expert number mismatch"
+    assert w1_q.shape[0] == w1_scale.shape[0], "w1 scales expert number mismatch"
+    assert w1_q.shape[0] == w2_scale.shape[0], "w2 scales expert number mismatch"
+
+    assert a_strides1.shape[0] == w1_q.shape[0], "A Strides 1 expert number mismatch"
+    assert b_strides1.shape[0] == w1_q.shape[0], "B Strides 1 expert number mismatch"
+    assert a_strides2.shape[0] == w2_q.shape[0], "A Strides 2 expert number mismatch"
+    assert b_strides2.shape[0] == w2_q.shape[0], "B Strides 2 expert number mismatch"
+    num_experts = w1_q.size(0)
+    m = a.size(1)
+    k = w1_q.size(2) * 2  # w1_q is transposed and packed
+    n = w2_q.size(2) * 2  # w2_q is transposed and packed
+    topk = topk_ids_.size(1)
+
+    device = a.device
+
+    problem_sizes1, problem_sizes2 = deepep_ll_get_cutlass_w4a8_moe_mm_data(
+        masked_m,
+        problem_sizes1,
+        problem_sizes2,
+        num_experts,
+        n,
+        k,
+    )
+
+    gateup_input = torch.empty(a.shape, dtype=torch.float8_e4m3fn, device=device)
+    sgl_per_tensor_quant_fp8(a, gateup_input, a1_scale.float(), True)
+    c1 = torch.empty((num_experts, m, n * 2), device=device, dtype=torch.bfloat16)
+    c2 = torch.empty((num_experts, m, k), device=device, dtype=torch.bfloat16)
+
+    cutlass_w4a8_moe_mm(
+        c1,
+        gateup_input,
+        w1_q,
+        a1_scale.float(),
+        w1_scale,
+        expert_offsets[:-1],
+        problem_sizes1,
+        a_strides1,
+        b_strides1,
+        c_strides1,
+        s_strides13,
+        128,
+        topk,
+    )
+
+    intermediate_q = torch.empty(
+        (num_experts, m, n), device=a.device, dtype=torch.float8_e4m3fn
+    )
+    silu_and_mul_masked_post_per_tensor_quant_fwd(
+        c1, intermediate_q, masked_m, a2_scale
+    )
+    cutlass_w4a8_moe_mm(
+        c2,
+        intermediate_q,
+        w2_q,
+        a2_scale.float(),
+        w2_scale,
+        expert_offsets[:-1],
+        problem_sizes2,
+        a_strides2,
+        b_strides2,
+        c_strides2,
+        s_strides2,
+        128,
+        topk,
+    )
+
+    return c2

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

@@ -1014,3 +1014,197 @@ def zero_experts_compute_triton(
     )
 
     return output
+
+
+@triton.jit
+def compute_problem_sizes_w4a8_kernel(
+    masked_m_ptr,
+    problem_sizes1_ptr,
+    problem_sizes2_ptr,
+    n,
+    k,
+    num_experts,
+    BLOCK_SIZE: tl.constexpr,
+):
+    pid = tl.program_id(axis=0) * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
+    mask = pid < num_experts
+    final_occurrences = tl.load(masked_m_ptr + pid, mask=mask, other=0)
+
+    ps1_idx_0 = pid * 3
+    ps1_idx_1 = ps1_idx_0 + 1
+    ps1_idx_2 = ps1_idx_0 + 2
+
+    ps2_idx_0 = pid * 3
+    ps2_idx_1 = ps2_idx_0 + 1
+    ps2_idx_2 = ps2_idx_0 + 2
+
+    ps1_mask_0 = ps1_idx_0 < num_experts * 3
+    ps1_mask_1 = ps1_idx_1 < num_experts * 3
+    ps1_mask_2 = ps1_idx_2 < num_experts * 3
+    ps2_mask_0 = ps2_idx_0 < num_experts * 3
+    ps2_mask_1 = ps2_idx_1 < num_experts * 3
+    ps2_mask_2 = ps2_idx_2 < num_experts * 3
+
+    tl.store(problem_sizes1_ptr + ps1_idx_0, 2 * n, mask=ps1_mask_0)
+    tl.store(problem_sizes1_ptr + ps1_idx_1, final_occurrences, mask=ps1_mask_1)
+    tl.store(problem_sizes1_ptr + ps1_idx_2, k, mask=ps1_mask_2)
+
+    tl.store(problem_sizes2_ptr + ps2_idx_0, k, mask=ps2_mask_0)
+    tl.store(problem_sizes2_ptr + ps2_idx_1, final_occurrences, mask=ps2_mask_1)
+    tl.store(problem_sizes2_ptr + ps2_idx_2, n, mask=ps2_mask_2)
+
+
+def compute_problem_sizes_w4a8(
+    masked_m, problem_sizes1, problem_sizes2, n, k, num_experts
+):
+    BLOCK_SIZE = 256
+    grid = lambda meta: (triton.cdiv(num_experts, meta["BLOCK_SIZE"]),)
+    compute_problem_sizes_w4a8_kernel[grid](
+        masked_m,
+        problem_sizes1,
+        problem_sizes2,
+        n,
+        k,
+        num_experts,
+        BLOCK_SIZE=BLOCK_SIZE,
+    )
+    return problem_sizes1, problem_sizes2
+
+
+def deepep_ll_get_cutlass_w4a8_moe_mm_data(
+    masked_m,
+    problem_sizes1,
+    problem_sizes2,
+    num_experts,
+    n,
+    k,
+):
+    problem_sizes1, problem_sizes2 = compute_problem_sizes_w4a8(
+        masked_m, problem_sizes1, problem_sizes2, n, k, num_experts
+    )
+    return (
+        problem_sizes1.to(torch.int32),
+        problem_sizes2.to(torch.int32),
+    )
+
+
+@triton.jit
+def _silu_and_mul_post_per_tensor_quant_kernel(
+    input_ptr,
+    stride_input_expert,
+    stride_input_token,
+    stride_input_dim,
+    output_ptr,
+    stride_output_expert,
+    stride_output_token,
+    stride_output_dim,
+    scale_ptr,
+    masked_m_ptr,
+    inner_dim,
+    fp8_max,
+    fp8_min,
+    BLOCK_N: tl.constexpr,
+    NUM_STAGE: tl.constexpr,
+):
+    """
+    Triton kernel: fused SiLU(gate) * up + per-tensor FP8 quantization.
+
+    Shape:
+        input:  [E, T_padded, 2*D]  -> gate: [:,:,D], up: [:,:,D]
+        output: [E, T_padded, D], dtype=float8_e4m3fn
+    """
+    expert_id = tl.program_id(2)
+    block_id_token = tl.program_id(1)
+    block_id_dim = tl.program_id(0)
+
+    num_token_blocks = tl.num_programs(1)
+
+    token_num_cur_expert = tl.load(masked_m_ptr + expert_id)
+
+    scale = 1.0 / tl.load(scale_ptr).to(tl.float32)
+
+    stride_input_expert = tl.cast(stride_input_expert, tl.int32)
+    stride_output_expert = tl.cast(stride_output_expert, tl.int32)
+    stride_input_token = tl.cast(stride_input_token, tl.int32)
+    stride_output_token = tl.cast(stride_output_token, tl.int32)
+
+    offset_d = block_id_dim * BLOCK_N + tl.arange(0, BLOCK_N)
+    mask_d = offset_d < inner_dim
+
+    # base pointers for current expert and dim block
+    input_base_offs = input_ptr + expert_id * stride_input_expert + offset_d
+    output_base_offs = output_ptr + expert_id * stride_output_expert + offset_d
+
+    for token_idx in tl.range(
+        block_id_token, token_num_cur_expert, num_token_blocks, num_stages=NUM_STAGE
+    ):
+        gate_ptr = input_base_offs + token_idx * stride_input_token
+        up_ptr = gate_ptr + inner_dim
+        gate = tl.load(gate_ptr, mask=mask_d, other=0.0).to(tl.float32)
+        up = tl.load(up_ptr, mask=mask_d, other=0.0).to(tl.float32)
+
+        # SiLU: x * sigmoid(x)
+        gate = gate / (1 + tl.exp(-gate))
+        gate = gate.to(input_ptr.dtype.element_ty)
+        gate_up = up * gate
+
+        scaled = gate_up * scale
+        output_q = tl.clamp(scaled, fp8_min, fp8_max).to(output_ptr.dtype.element_ty)
+        out_ptr = output_base_offs + token_idx * stride_output_token
+        tl.store(out_ptr, output_q, mask=mask_d)
+
+
+def silu_and_mul_masked_post_per_tensor_quant_fwd(
+    input: torch.Tensor,
+    output: torch.Tensor,
+    masked_m: torch.Tensor,
+    scale: torch.Tensor,
+) -> torch.Tensor:
+    """
+    Fused SiLU + Mul + Per-Tensor Quantization to FP8.
+
+    Args:
+        input: [expert_num, token_num_padded, 2 * inner_dim]
+        output: [expert_num, token_num_padded, inner_dim], dtype=torch.float8_e4m3fn
+        masked_m: [expert_num], actual token count for each expert
+        scale: [1] or [expert_num], quantization scale (per-tensor or per-expert)
+
+    Returns:
+        output tensor
+    """
+    assert input.is_contiguous()
+    assert output.is_contiguous()
+    assert output.dtype == torch.float8_e4m3fn
+    assert input.ndim == 3
+    assert input.shape[0] == masked_m.shape[0]
+    assert input.shape[-1] % 2 == 0
+    assert scale.numel() == 1 or scale.shape[0] == input.shape[0]
+
+    expert_num = input.shape[0]
+    #  3584
+    inner_dim = input.shape[-1] // 2
+
+    BLOCK_N = 256
+    BLOCK_M = 64 if expert_num < 4 else 32
+    NUM_STAGES = 3
+    hidden_dim_split_block_num = triton.cdiv(inner_dim, BLOCK_N)
+
+    grid = (hidden_dim_split_block_num, BLOCK_M, expert_num)
+    finfo = torch.finfo(torch.float8_e4m3fn)
+    fp8_max = finfo.max
+    fp8_min = -fp8_max
+
+    _silu_and_mul_post_per_tensor_quant_kernel[grid](
+        input,
+        *input.stride(),
+        output,
+        *output.stride(),
+        scale,
+        masked_m,
+        inner_dim,
+        fp8_max,
+        fp8_min,
+        BLOCK_N=BLOCK_N,
+        NUM_STAGE=NUM_STAGES,
+    )
+    return output

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

@@ -100,6 +100,7 @@ class DeepEPMoE(FusedMoE):
             self.use_fp8_w8a8 = False
             self.use_block_quant = False
         else:
+            self.use_w4afp8 = False
             self.use_fp8_w8a8 = False
             self.use_block_quant = False
             self.use_w4afp8 = False
@@ -199,6 +200,8 @@ class DeepEPMoE(FusedMoE):
                 return self.forward_flashinfer_cutedsl(
                     dispatch_output, down_gemm_overlap_args=down_gemm_overlap_args
                 )
+            elif self.use_w4afp8:
+                return self.forward_cutlass_w4afp8_masked(dispatch_output)
             assert deep_gemm_wrapper.ENABLE_JIT_DEEPGEMM and self.use_fp8_w8a8
             assert down_gemm_overlap_args is None
             return self.forward_deepgemm_masked(dispatch_output)
@@ -514,6 +517,20 @@ class DeepEPMoE(FusedMoE):
 
         return down_output
 
+    def forward_cutlass_w4afp8_masked(
+        self,
+        dispatch_output: DeepEPNormalOutput,
+    ):
+        assert self.moe_runner_config.activation == "silu"
+        assert isinstance(self.quant_method, W4AFp8MoEMethod)
+        assert get_bool_env_var(
+            "SGLANG_DEEPEP_BF16_DISPATCH"
+        ), "W4AFP8 does not support FP8 dispatch; please set SGLANG_DEEPEP_BF16_DISPATCH=1."
+        return self.quant_method.apply_deepep_ll(
+            layer=self,
+            dispatch_output=dispatch_output,
+        )
+
     def forward_npu(
         self,
         dispatch_output: Union[DeepEPNormalOutput, DeepEPLLOutput],

python/sglang/srt/layers/quantization/w4afp8.py

@@ -23,6 +23,7 @@ if TYPE_CHECKING:
     from sglang.srt.layers.moe.ep_moe.layer import DeepEPMoE
     from sglang.srt.layers.moe.token_dispatcher import (
         CombineInput,
+        DeepEPLLOutput,
         DeepEPNormalOutput,
         StandardDispatchOutput,
     )
@@ -328,6 +329,41 @@ class W4AFp8MoEMethod(FusedMoEMethodBase):
             output *= self.moe_runner_config.routed_scaling_factor
         return StandardCombineInput(hidden_states=output)
 
+    def apply_deepep_ll(
+        self,
+        layer: DeepEPMoE,
+        dispatch_output: DeepEPLLOutput,
+    ) -> torch.Tensor:
+
+        from sglang.srt.layers.moe.cutlass_w4a8_moe import cutlass_w4a8_moe_deepep_ll
+
+        hidden_states, _, topk_ids, _, masked_m, _ = dispatch_output
+
+        output = cutlass_w4a8_moe_deepep_ll(
+            hidden_states,
+            layer.w13_weight,
+            layer.w2_weight,
+            layer.w13_weight_scale_inv,
+            layer.w2_weight_scale_inv,
+            topk_ids,
+            masked_m,
+            layer.quant_method.a_strides1,
+            layer.quant_method.b_strides1,
+            layer.quant_method.c_strides1,
+            layer.quant_method.a_strides2,
+            layer.quant_method.b_strides2,
+            layer.quant_method.c_strides2,
+            layer.quant_method.s_strides13,
+            layer.quant_method.s_strides2,
+            layer.quant_method.expert_offsets,
+            layer.quant_method.problem_sizes1,
+            layer.quant_method.problem_sizes2,
+            layer.w13_input_scale,
+            layer.w2_input_scale,
+        )
+
+        return output
+
     def apply_deepep_normal(
         self,
         layer: DeepEPMoE,

sgl-kernel/csrc/moe/cutlass_moe/w4a8/w4a8_get_group_starts.cuh

@@ -34,6 +34,40 @@ __global__ void int4_fp8_get_group_gemm_starts(
   b_scales_offsets[expert_id] = b_scales_base_as_int + (per_out_ch ? expert_id * n * k / 128 : expert_id);
 }
 
+template <typename ElementA, typename ElementB, typename ElementC, typename ElementAccumulator>
+__global__ void int4_fp8_get_group_gemm_starts_3d(
+    ElementA** a_offsets,
+    ElementB** b_offsets,
+    ElementC** out_offsets,
+    ElementAccumulator** a_scales_offsets,
+    cutlass::bfloat16_t** b_scales_offsets,
+    ElementA* a_base_as_int,
+    ElementB* b_base_as_int,
+    ElementC* out_base_as_int,
+    ElementAccumulator* a_scales_base_as_int,
+    cutlass::bfloat16_t* b_scales_base_as_int,
+    int64_t n,
+    int64_t m,
+    int64_t k,
+    bool per_act_token,
+    bool per_out_ch,
+    int num_experts) {
+  int expert_id = blockIdx.x * blockDim.x + threadIdx.x;
+  if (expert_id >= num_experts) return;
+
+  int64_t a_offset = expert_id * m * k;
+  int64_t b_offset = expert_id * k * n / 2;
+  int64_t out_offset = expert_id * m * n;
+  int64_t a_scales_offset = 0;
+  int64_t b_scales_offset = per_out_ch ? expert_id * n * 4 * k / 512 : expert_id;
+
+  a_offsets[expert_id] = a_base_as_int + a_offset;
+  b_offsets[expert_id] = b_base_as_int + b_offset;
+  out_offsets[expert_id] = out_base_as_int + out_offset;
+  a_scales_offsets[expert_id] = a_scales_base_as_int + a_scales_offset;
+  b_scales_offsets[expert_id] = b_scales_base_as_int + b_scales_offset;
+}
+
 #define __CALL_W4A8_GET_STARTS_KERNEL(TENSOR_C_TYPE, C_TYPE)                              \
   else if (out_tensors.dtype() == TENSOR_C_TYPE) {                                        \
     int4_fp8_get_group_gemm_starts<cutlass::float_e4m3_t, cutlass::int8_t, C_TYPE, float> \
@@ -55,6 +89,28 @@ __global__ void int4_fp8_get_group_gemm_starts(
             per_out_ch);                                                                  \
   }
 
+#define __CALL_W4A8_GET_STARTS_KERNEL_3D(TENSOR_C_TYPE, C_TYPE)                              \
+  else if (out_tensors.dtype() == TENSOR_C_TYPE) {                                           \
+    int4_fp8_get_group_gemm_starts_3d<cutlass::float_e4m3_t, cutlass::int8_t, C_TYPE, float> \
+        <<<1, num_experts, 0, stream>>>(                                                     \
+            static_cast<cutlass::float_e4m3_t**>(a_ptrs.data_ptr()),                         \
+            static_cast<cutlass::int8_t**>(b_ptrs.data_ptr()),                               \
+            static_cast<C_TYPE**>(out_ptrs.data_ptr()),                                      \
+            static_cast<float**>(a_scales_ptrs.data_ptr()),                                  \
+            static_cast<cutlass::bfloat16_t**>(b_scales_ptrs.data_ptr()),                    \
+            static_cast<cutlass::float_e4m3_t*>(a_tensors.data_ptr()),                       \
+            static_cast<cutlass::int8_t*>(b_tensors.data_ptr()),                             \
+            static_cast<C_TYPE*>(out_tensors.data_ptr()),                                    \
+            static_cast<float*>(a_scales.data_ptr()),                                        \
+            static_cast<cutlass::bfloat16_t*>(b_scales.data_ptr()),                          \
+            out_tensors.size(2),                                                             \
+            a_tensors.size(1),                                                               \
+            a_tensors.size(2),                                                               \
+            per_act_token,                                                                   \
+            per_out_ch,                                                                      \
+            num_experts);                                                                    \
+  }
+
 namespace {
 
 void run_int4_fp8_get_group_gemm_starts(
@@ -80,12 +136,22 @@ void run_int4_fp8_get_group_gemm_starts(
 
   auto stream = at::cuda::getCurrentCUDAStream(expert_offsets.device().index());
 
-  if (false) {
-  }
-  __CALL_W4A8_GET_STARTS_KERNEL(torch::kBFloat16, cutlass::bfloat16_t)
-  __CALL_W4A8_GET_STARTS_KERNEL(torch::kFloat16, half)
-  else {
-    TORCH_CHECK(false, "Invalid output type (must be float16 or bfloat16)");
+  if (a_tensors.dim() == 3) {
+    if (false) {
+    }
+    __CALL_W4A8_GET_STARTS_KERNEL_3D(torch::kBFloat16, cutlass::bfloat16_t)
+    __CALL_W4A8_GET_STARTS_KERNEL_3D(torch::kFloat16, half)
+    else {
+      TORCH_CHECK(false, "Invalid output type (must be float16 or bfloat16)");
+    }
+  } else {
+    if (false) {
+    }
+    __CALL_W4A8_GET_STARTS_KERNEL(torch::kBFloat16, cutlass::bfloat16_t)
+    __CALL_W4A8_GET_STARTS_KERNEL(torch::kFloat16, half)
+    else {
+      TORCH_CHECK(false, "Invalid output type (must be float16 or bfloat16)");
+    }
   }
 }
 

sgl-kernel/csrc/moe/cutlass_moe/w4a8/w4a8_grouped_mm_c3x.cuh

@@ -174,7 +174,7 @@ void cutlass_w4a8_group_gemm_caller(
   bool per_out_ch = b_scales.numel() != num_experts;
 
   // Check inputs
-  TORCH_CHECK(a_tensors.dim() == 2, "A tensor must be 2D");
+  TORCH_CHECK(a_tensors.dim() == 2 or a_tensors.dim() == 3, "A tensor must be 2D/3D");
   TORCH_CHECK(b_tensors.dim() == 3, "B tensor must be 3D [E, N, K/2]");
   TORCH_CHECK(b_scales.dim() == 3, "Scale tensor must be 3D [E, K//512, N*4]");
   TORCH_CHECK(a_scales.dim() == 1, "A Scale tensor must be 1D [1]");
@@ -186,7 +186,9 @@ void cutlass_w4a8_group_gemm_caller(
   TORCH_CHECK(problem_sizes.size(1) == 3, "problem_sizes must have 3 columns (N, M, K)");
   TORCH_CHECK(b_tensors.size(0) == num_experts, "B tensor first dimension must match number of groups");
   TORCH_CHECK(b_scales.size(0) == num_experts, "Scale tensor first dimension must match number of groups");
-  TORCH_CHECK(b_tensors.size(2) * 2 == a_tensors.size(1), "B tensor K/2 dimension must match A tensor K dimension");
+  TORCH_CHECK(
+      b_tensors.size(2) * 2 == a_tensors.size(1) or b_tensors.size(2) * 2 == a_tensors.size(2),
+      "B tensor K/2 dimension must match A tensor K dimension");
 
   // Check tensor types
   TORCH_CHECK(a_tensors.scalar_type() == torch::kFloat8_e4m3fn, "A tensor must be fp8 (float_e4m3_t) type");

test/srt/quant/test_w4a8_deepseek_v3.py

@@ -1,3 +1,4 @@
+import os
 import unittest
 from types import SimpleNamespace
 
@@ -173,5 +174,73 @@ class TestDeepseekV3W4Afp8DeepepNormal(CustomTestCase):
         self.assertGreater(metrics["accuracy"], 0.92)
 
 
+class TestDeepseekV3W4Afp8DeepepAutoMtp(CustomTestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.model = try_cached_model(DEFAULT_DEEPSEEK_W4AFP8_MODEL_FOR_TEST)
+        cls.base_url = DEFAULT_URL_FOR_TEST
+        other_args = [
+            "--tp",
+            "8",
+            "--trust-remote-code",
+            "--ep-size",
+            "8",
+            "--cuda-graph-bs",
+            "256",
+            "--disable-radix-cache",
+            "--moe-a2a-backend",
+            "deepep",
+            "--deepep-mode",
+            "auto",
+            "--dp",
+            "8",
+            "--enable-dp-attention",
+            "--moe-runner-backend",
+            "cutlass",
+            "--speculative-algorithm",
+            "EAGLE",
+            "--speculative-num-steps",
+            "3",
+            "--speculative-eagle-topk",
+            "1",
+            "--speculative-num-draft-tokens",
+            "4",
+        ]
+        if not is_in_amd_ci():
+            other_args += ["--mem-frac", "0.7"]
+        cls.process = popen_launch_server(
+            cls.model,
+            cls.base_url,
+            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+            other_args=other_args,
+            env={
+                **os.environ,
+                "SGLANG_DEEPEP_BF16_DISPATCH": "1",
+                "SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK": "256",
+            },
+        )
+
+    @classmethod
+    def tearDownClass(cls):
+        kill_process_tree(cls.process.pid)
+
+    def test_gsm8k(
+        self,
+    ):
+        args = SimpleNamespace(
+            num_shots=5,
+            data_path=None,
+            num_questions=200,
+            max_new_tokens=512,
+            parallel=128,
+            host="http://127.0.0.1",
+            port=int(self.base_url.split(":")[-1]),
+        )
+        metrics = run_eval_few_shot_gsm8k(args)
+        print(f"Eval accuracy of GSM8K: {metrics=}")
+
+        self.assertGreater(metrics["accuracy"], 0.92)
+
+
 if __name__ == "__main__":
     unittest.main()

test/srt/run_suite.py

@@ -172,7 +172,7 @@ suites = {
         TestFile("test_disaggregation_hybrid_attention.py", 200),
     ],
     "per-commit-8-gpu-h20": [
-        TestFile("quant/test_w4a8_deepseek_v3.py", 371),
+        TestFile("quant/test_w4a8_deepseek_v3.py", 520),
         TestFile("test_disaggregation_different_tp.py", 600),
         TestFile("test_disaggregation_pp.py", 140),
     ],