[EP] Add cuda kernel for moe_ep_post_reorder (#6837)

Co-authored-by: luoyuan.luo <luoyuan.luo@antgroup.com>

sgl-kernel/benchmark/bench_moe_ep_post_reorder.py

@@ -0,0 +1,92 @@
+import torch
+import triton
+from sgl_kernel import ep_moe_post_reorder
+
+from sglang.srt.layers.moe.ep_moe.kernels import post_reorder_triton_kernel
+
+batch_sizes = [64, 128, 256, 512, 640, 768, 1024, 2048, 4096]
+configs = [(bs,) for bs in batch_sizes]
+
+
+@triton.testing.perf_report(
+    triton.testing.Benchmark(
+        x_names=["batch_size"],
+        x_vals=[list(_) for _ in configs],
+        line_arg="provider",
+        line_vals=["cuda", "triton"],
+        line_names=["CUDA Kernel", "Triton Kernel"],
+        styles=[("green", "-"), ("orange", "-")],
+        ylabel="us",
+        plot_name="ep-moe-post-reorder-performance",
+        args={},
+    )
+)
+def benchmark(batch_size, provider):
+    dtype = torch.bfloat16
+    device = torch.device("cuda")
+    hidden_size, topk, start_expert_id, end_expert_id, block_size = 4096, 8, 0, 255, 512
+
+    def alloc_tensors():
+        down_output = torch.randn(
+            batch_size * topk, hidden_size, dtype=dtype, device=device
+        )
+        output = torch.zeros(batch_size, hidden_size, dtype=dtype, device=device)
+        src2dst = torch.randint(
+            0, batch_size * topk, (batch_size, topk), dtype=torch.int32, device=device
+        )
+        topk_ids = torch.randint(
+            start_expert_id,
+            end_expert_id + 1,
+            (batch_size, topk),
+            dtype=torch.int32,
+            device=device,
+        )
+        topk_weights = torch.rand(batch_size, topk, dtype=dtype, device=device)
+        return down_output, output, src2dst, topk_ids, topk_weights
+
+    quantiles = [0.5, 0.2, 0.8]
+
+    if provider == "cuda":
+        d_out, out, s2d, tk_ids, tk_weights = alloc_tensors()
+
+        def run_cuda():
+            ep_moe_post_reorder(
+                d_out,
+                out,
+                s2d,
+                tk_ids,
+                tk_weights,
+                start_expert_id,
+                end_expert_id,
+                topk,
+            )
+
+        ms, min_ms, max_ms = triton.testing.do_bench(run_cuda, quantiles=quantiles)
+
+    elif provider == "triton":
+        d_out, out, s2d, tk_ids, tk_weights = alloc_tensors()
+
+        def run_triton():
+            post_reorder_triton_kernel[(batch_size,)](
+                d_out.view(-1),
+                out.view(-1),
+                s2d.view(-1),
+                tk_ids.view(-1),
+                tk_weights.view(-1),
+                start_expert_id,
+                end_expert_id,
+                topk,
+                hidden_size,
+                block_size,
+            )
+
+        ms, min_ms, max_ms = triton.testing.do_bench(run_triton, quantiles=quantiles)
+
+    else:
+        raise ValueError(f"Unknown provider: {provider}")
+
+    return 1000 * ms, 1000 * max_ms, 1000 * min_ms
+
+
+if __name__ == "__main__":
+    benchmark.run(print_data=True)

sgl-kernel/csrc/common_extension.cc

@@ -174,9 +174,13 @@ TORCH_LIBRARY_FRAGMENT(sgl_kernel, m) {
       "(Tensor[])");
   m.impl("moe_fused_gate", torch::kCUDA, &moe_fused_gate);
   m.def(
-      "ep_moe_pre_reorder(Tensor input_ptr, Tensor gateup_input_ptr, Tensor src2dst_ptr, Tensor topk_ids_ptr, Tensor "
-      "a1_scales_ptr, int start_expert_id, int end_expert_id, int topk, bool use_per_token_if_dynamic) -> ()");
+      "ep_moe_pre_reorder(Tensor input, Tensor gateup_input, Tensor src2dst, Tensor topk_ids, Tensor "
+      "a1_scales, int start_expert_id, int end_expert_id, int topk, bool use_per_token_if_dynamic) -> ()");
   m.impl("ep_moe_pre_reorder", torch::kCUDA, &ep_moe_pre_reorder);
+  m.def(
+      "ep_moe_post_reorder(Tensor down_output, Tensor output, Tensor src2dst, Tensor topk_ids, Tensor "
+      "topk_weights, int start_expert_id, int end_expert_id, int topk) -> ()");
+  m.impl("ep_moe_post_reorder", torch::kCUDA, &ep_moe_post_reorder);
   m.def(
       "fp8_blockwise_scaled_grouped_mm(Tensor output, Tensor a_ptrs, Tensor b_ptrs, Tensor out_ptrs, Tensor "
       "a_scales_ptrs, Tensor b_scales_ptrs, Tensor a, Tensor b, Tensor scales_a, Tensor scales_b, Tensor "

sgl-kernel/csrc/moe/ep_moe_reorder_kernel.cu

@@ -67,6 +67,57 @@ __global__ void ep_pre_reorder_cuda_kernel(
   }
 }
 
+template <typename scalar_t>
+__global__ void ep_post_reorder_cuda_kernel(
+    const scalar_t* __restrict__ down_output_ptr,
+    scalar_t* __restrict__ output_ptr,
+    const int* __restrict__ src2dst_ptr,
+    const int* __restrict__ topk_ids_ptr,
+    const scalar_t* __restrict__ topk_weights_ptr,
+    int start_expert_id,
+    int end_expert_id,
+    int topk,
+    int hidden_size) {
+  const int token_idx = blockIdx.x;
+  const int tid = threadIdx.x;
+
+  const int* token_src2dst = src2dst_ptr + token_idx * topk;
+  const int* token_topk_ids = topk_ids_ptr + token_idx * topk;
+  const scalar_t* token_topk_weights = topk_weights_ptr + token_idx * topk;
+
+  scalar_t* dst_ptr = output_ptr + static_cast<int64_t>(token_idx) * hidden_size;
+
+  constexpr uint32_t vec_size = 16 / sizeof(scalar_t);
+  using vec_t = flashinfer::vec_t<scalar_t, vec_size>;
+
+  const int vec_iters = hidden_size / vec_size;
+  for (int idx = tid; idx < vec_iters; idx += blockDim.x) {
+    float acc[vec_size] = {0};
+
+    for (int k = 0; k < topk; ++k) {
+      const int expert_id = token_topk_ids[k];
+      if (expert_id < start_expert_id || expert_id > end_expert_id) continue;
+      const int src_row = token_src2dst[k];
+      const scalar_t* src_ptr = down_output_ptr + static_cast<int64_t>(src_row) * hidden_size;
+      const float weight = static_cast<float>(token_topk_weights[k]);
+
+      vec_t src_vec;
+      src_vec.cast_load(src_ptr + idx * vec_size);
+
+#pragma unroll
+      for (uint32_t i = 0; i < vec_size; ++i) {
+        acc[i] += static_cast<float>(src_vec[i]) * weight;
+      }
+    }
+    vec_t out_vec;
+#pragma unroll
+    for (uint32_t i = 0; i < vec_size; ++i)
+      out_vec[i] = static_cast<scalar_t>(acc[i]);
+
+    out_vec.cast_store(dst_ptr + idx * vec_size);
+  }
+}
+
 void ep_moe_pre_reorder(
     torch::Tensor input,
     torch::Tensor gateup_input,
@@ -77,8 +128,8 @@ void ep_moe_pre_reorder(
     int64_t end_expert_id,
     int64_t topk,
     bool use_per_token_if_dynamic) {
-  int total_blocks = input.size(0);
-  int block_size = 512;
+  const int total_blocks = input.size(0);
+  const int block_size = 512;
   dim3 grid(total_blocks);
   dim3 block(block_size);
   int hidden_size = input.size(1);
@@ -98,3 +149,33 @@ void ep_moe_pre_reorder(
     return true;
   });
 }
+
+void ep_moe_post_reorder(
+    torch::Tensor down_output,
+    torch::Tensor output,
+    torch::Tensor src2dst,
+    torch::Tensor topk_ids,
+    torch::Tensor topk_weights,
+    int64_t start_expert_id,
+    int64_t end_expert_id,
+    int64_t topk) {
+  const int total_tokens = output.size(0);
+  const int block_size = 512;
+  dim3 grid(total_tokens);
+  dim3 block(block_size);
+  const int hidden_size = output.size(1);
+
+  DISPATCH_PYTORCH_DTYPE_TO_CTYPE_FLOAT_FP16(down_output.scalar_type(), scalar_t, [&] {
+    ep_post_reorder_cuda_kernel<scalar_t><<<grid, block>>>(
+        static_cast<scalar_t*>(down_output.data_ptr()),
+        static_cast<scalar_t*>(output.data_ptr()),
+        src2dst.data_ptr<int>(),
+        topk_ids.data_ptr<int>(),
+        static_cast<scalar_t*>(topk_weights.data_ptr()),
+        static_cast<int>(start_expert_id),
+        static_cast<int>(end_expert_id),
+        static_cast<int>(topk),
+        hidden_size);
+    return true;
+  });
+}

sgl-kernel/include/sgl_kernel_ops.h

@@ -264,6 +264,16 @@ void ep_moe_pre_reorder(
     int64_t topk,
     bool use_per_token_if_dynamic);
 
+void ep_moe_post_reorder(
+    torch::Tensor down_output,
+    torch::Tensor output,
+    torch::Tensor src2dst,
+    torch::Tensor topk_ids,
+    torch::Tensor topk_weights,
+    int64_t start_expert_id,
+    int64_t end_expert_id,
+    int64_t topk);
+
 void shuffle_rows(const torch::Tensor& input_tensor, const torch::Tensor& dst2src_map, torch::Tensor& output_tensor);
 
 void cutlass_fp4_group_mm(

sgl-kernel/python/sgl_kernel/__init__.py

@@ -49,6 +49,7 @@ from sgl_kernel.gemm import (
 from sgl_kernel.grammar import apply_token_bitmask_inplace_cuda
 from sgl_kernel.moe import (
     cutlass_fp4_group_mm,
+    ep_moe_post_reorder,
     ep_moe_pre_reorder,
     fp8_blockwise_scaled_grouped_mm,
     moe_align_block_size,

sgl-kernel/python/sgl_kernel/moe.py

@@ -88,6 +88,28 @@ def ep_moe_pre_reorder(
     )
 
 
+def ep_moe_post_reorder(
+    down_output,
+    output,
+    src2dst,
+    topk_ids,
+    topk_weights,
+    start_expert_id,
+    end_expert_id,
+    topk,
+):
+    return torch.ops.sgl_kernel.ep_moe_post_reorder.default(
+        down_output,
+        output,
+        src2dst,
+        topk_ids,
+        topk_weights,
+        start_expert_id,
+        end_expert_id,
+        topk,
+    )
+
+
 def fp8_blockwise_scaled_grouped_mm(
     output,
     a_ptrs,

sgl-kernel/tests/test_ep_moe_post_reorder_kernel.py

@@ -0,0 +1,163 @@
+import itertools
+
+import pytest
+import torch
+from sgl_kernel import ep_moe_post_reorder
+
+from sglang.srt.layers.moe.ep_moe.kernels import post_reorder_triton_kernel
+
+
+def create_test_tensors(
+    batch_size: int,
+    hidden_size: int,
+    topk: int,
+    start_expert_id: int,
+    end_expert_id: int,
+    dtype: torch.dtype,
+    device: torch.device,
+):
+    down_output = torch.randn(
+        batch_size * topk, hidden_size, dtype=dtype, device=device
+    )
+
+    # Ensure src2dst has no duplicate destinations to avoid race conditions
+    total_tokens = batch_size * topk
+    dst_indices = torch.randperm(total_tokens, device=device, dtype=torch.int32)
+    src2dst = dst_indices.view(batch_size, topk)
+
+    topk_ids = torch.randint(
+        start_expert_id,
+        end_expert_id + 1,
+        (batch_size, topk),
+        dtype=torch.int32,
+        device=device,
+    )
+
+    topk_weights = torch.rand(batch_size, topk, dtype=dtype, device=device)
+
+    return down_output, src2dst, topk_ids, topk_weights
+
+
+def run_cuda_kernel(
+    down_output: torch.Tensor,
+    output: torch.Tensor,
+    src2dst: torch.Tensor,
+    topk_ids: torch.Tensor,
+    topk_weights: torch.Tensor,
+    start_expert_id: int,
+    end_expert_id: int,
+    topk: int,
+):
+    ep_moe_post_reorder(
+        down_output,
+        output,
+        src2dst,
+        topk_ids,
+        topk_weights,
+        start_expert_id,
+        end_expert_id,
+        topk,
+    )
+    return output
+
+
+def run_triton_kernel(
+    down_output: torch.Tensor,
+    output: torch.Tensor,
+    src2dst: torch.Tensor,
+    topk_ids: torch.Tensor,
+    topk_weights: torch.Tensor,
+    start_expert_id: int,
+    end_expert_id: int,
+    topk: int,
+    hidden_size: int,
+):
+    batch_size = down_output.size(0)
+    block_size = 512
+
+    post_reorder_triton_kernel[(batch_size,)](
+        down_output,
+        output,
+        src2dst,
+        topk_ids,
+        topk_weights,
+        start_expert_id,
+        end_expert_id,
+        topk,
+        hidden_size,
+        block_size,
+    )
+    return output
+
+
+def assert_close(a, b):
+    a32, b32 = a.float(), b.float()
+    if a.dtype is torch.float16:
+        torch.testing.assert_close(a32, b32, rtol=1e-5, atol=1e-2)
+    elif a.dtype is torch.bfloat16:
+        torch.testing.assert_close(a32, b32, rtol=1e-4, atol=1e-1)
+    else:
+        torch.testing.assert_close(a32, b32, rtol=1e-5, atol=1e-5)
+
+
+@pytest.mark.parametrize(
+    "batch_size,hidden_size,topk",
+    list(itertools.product([32, 64], [128, 256, 512], [2, 4, 8])),
+)
+@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16, torch.float32])
+def test_ep_moe_post_reorder_vs_triton(
+    batch_size: int,
+    hidden_size: int,
+    topk: int,
+    dtype: torch.dtype,
+):
+    device = torch.device("cuda")
+    start_expert_id = 0
+    end_expert_id = 15
+
+    (
+        down_output,
+        src2dst,
+        topk_ids,
+        topk_weights,
+    ) = create_test_tensors(
+        batch_size,
+        hidden_size,
+        topk,
+        start_expert_id,
+        end_expert_id,
+        dtype,
+        device,
+    )
+
+    output_cuda = torch.empty(batch_size, hidden_size, dtype=dtype, device=device)
+    output_triton = torch.empty(batch_size, hidden_size, dtype=dtype, device=device)
+
+    cuda_output = run_cuda_kernel(
+        down_output,
+        output_cuda,
+        src2dst,
+        topk_ids,
+        topk_weights,
+        start_expert_id,
+        end_expert_id,
+        topk,
+    )
+
+    triton_output = run_triton_kernel(
+        down_output,
+        output_triton,
+        src2dst,
+        topk_ids,
+        topk_weights,
+        start_expert_id,
+        end_expert_id,
+        topk,
+        hidden_size,
+    )
+
+    assert_close(cuda_output, triton_output)
+
+
+if __name__ == "__main__":
+    pytest.main([__file__])