permute/unpermute kernel for moe optimization (#14568)

Signed-off-by: Caleb_Du <Caleb_Du@zju.edu.cn>

CMakeLists.txt

@@ -15,7 +15,6 @@ project(vllm_extensions LANGUAGES CXX)
 
 # CUDA by default, can be overridden by using -DVLLM_TARGET_DEVICE=... (used by setup.py)
 set(VLLM_TARGET_DEVICE "cuda" CACHE STRING "Target device backend for vLLM")
-
 message(STATUS "Build type: ${CMAKE_BUILD_TYPE}")
 message(STATUS "Target device: ${VLLM_TARGET_DEVICE}")
 
@@ -682,6 +681,17 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   endif()
 endif()
 
+if(VLLM_GPU_LANG STREQUAL "CUDA")
+  set(MOE_PERMUTE_SRC
+      "csrc/moe/permute_unpermute_kernels/moe_permute_unpermute_kernel.cu"
+      "csrc/moe/moe_permute_unpermute_op.cu")
+
+  set_gencode_flags_for_srcs(
+    SRCS "${MARLIN_PERMUTE_SRC}"
+    CUDA_ARCHS "${MOE_PERMUTE_ARCHS}")
+
+  list(APPEND VLLM_MOE_EXT_SRC "${MOE_PERMUTE_SRC}")
+endif()
 message(STATUS "Enabling moe extension.")
 define_gpu_extension_target(
   _moe_C
@@ -690,6 +700,8 @@ define_gpu_extension_target(
   SOURCES ${VLLM_MOE_EXT_SRC}
   COMPILE_FLAGS ${VLLM_GPU_FLAGS}
   ARCHITECTURES ${VLLM_GPU_ARCHES}
+  INCLUDE_DIRECTORIES ${CUTLASS_INCLUDE_DIR}
+  INCLUDE_DIRECTORIES ${CUTLASS_TOOLS_UTIL_INCLUDE_DIR}
   USE_SABI 3
   WITH_SOABI)
 

benchmarks/kernels/benchmark_grouped_gemm_cutlass.py

@@ -90,7 +90,8 @@ def bench_run(results: list[benchmark.Measurement], model: str,
 
     score = torch.randn((m, num_experts), device="cuda", dtype=dtype)
 
-    topk_weights, topk_ids = fused_topk(a, score, topk, renormalize=False)
+    topk_weights, topk_ids, token_expert_indices = fused_topk(
+        a, score, topk, renormalize=False)
 
     def run_triton_moe(a: torch.Tensor, w1: torch.Tensor, w2: torch.Tensor,
                        topk_weights: torch.Tensor, topk_ids: torch.Tensor,

benchmarks/kernels/benchmark_moe.py

@@ -115,8 +115,8 @@ def benchmark_config(config: BenchmarkConfig,
         from vllm.model_executor.layers.fused_moe import override_config
         with override_config(config):
             if use_deep_gemm:
-                topk_weights, topk_ids = fused_topk(x, input_gating, topk,
-                                                    False)
+                topk_weights, topk_ids, token_expert_indices = fused_topk(
+                    x, input_gating, topk, False)
                 return fused_experts(
                     x,
                     w1,

benchmarks/kernels/benchmark_moe_permute_unpermute.py

+# SPDX-License-Identifier: Apache-2.0
+
+import argparse
+from typing import Any, TypedDict
+
+import ray
+import torch
+from transformers import AutoConfig
+
+from vllm.model_executor.layers.fused_moe.deep_gemm_moe import (
+    _moe_permute, _moe_unpermute_and_reduce)
+from vllm.model_executor.layers.fused_moe.fused_moe import *
+from vllm.model_executor.layers.fused_moe.moe_permute_unpermute import *
+from vllm.model_executor.layers.fused_moe.utils import _fp8_quantize
+from vllm.platforms import current_platform
+from vllm.utils import FlexibleArgumentParser
+
+FP8_DTYPE = current_platform.fp8_dtype()
+
+
+class BenchmarkConfig(TypedDict):
+    BLOCK_SIZE_M: int
+    BLOCK_SIZE_N: int
+    BLOCK_SIZE_K: int
+    GROUP_SIZE_M: int
+    num_warps: int
+    num_stages: int
+
+
+def benchmark_permute(num_tokens: int,
+                      num_experts: int,
+                      hidden_size: int,
+                      topk: int,
+                      dtype: torch.dtype,
+                      use_fp8_w8a8: bool,
+                      use_int8_w8a16: bool,
+                      num_iters: int = 100,
+                      use_customized_permute: bool = False) -> float:
+    # init_dtype = torch.float16 if use_fp8_w8a8 else dtype
+    hidden_states = torch.randn(num_tokens, hidden_size, dtype=dtype)
+    # output_hidden_states = torch.empty_like(hidden_states)
+    if use_fp8_w8a8:
+        align_block_size = 128  # deepgemm needs 128 m aligned block
+        qhidden_states, scale = _fp8_quantize(hidden_states, None, None)
+    else:
+        align_block_size = None
+        qhidden_states = hidden_states
+
+    gating_output = torch.randn(num_iters,
+                                num_tokens,
+                                num_experts,
+                                dtype=torch.float32)
+
+    input_gating = torch.randn(num_tokens, num_experts, dtype=torch.float32)
+    topk_weights, topk_ids, token_expert_indices = fused_topk(
+        qhidden_states, input_gating, topk, False)
+
+    def prepare(i: int):
+        input_gating.copy_(gating_output[i])
+
+    def run():
+        if use_customized_permute:
+            (permuted_hidden_states, first_token_off, inv_perm_idx,
+             m_indices) = moe_permute(
+                 qhidden_states,
+                 topk_weights=topk_weights,
+                 topk_ids=topk_ids,
+                 token_expert_indices=token_expert_indices,
+                 topk=topk,
+                 n_expert=num_experts,
+                 n_local_expert=num_experts,
+                 expert_map=None,
+                 align_block_size=align_block_size,
+             )
+        else:
+            (permuted_hidden_states, a1q_scale, sorted_token_ids, expert_ids,
+             inv_perm) = _moe_permute(qhidden_states, None, topk_ids,
+                                      num_experts, None, align_block_size)
+
+    # JIT compilation & warmup
+    run()
+    torch.cuda.synchronize()
+
+    # Capture 10 invocations with CUDA graph
+    graph = torch.cuda.CUDAGraph()
+    with torch.cuda.graph(graph):
+        for _ in range(10):
+            run()
+    torch.cuda.synchronize()
+
+    # Warmup
+    for _ in range(5):
+        graph.replay()
+    torch.cuda.synchronize()
+
+    start_event = torch.cuda.Event(enable_timing=True)
+    end_event = torch.cuda.Event(enable_timing=True)
+
+    latencies: list[float] = []
+    for i in range(num_iters):
+        prepare(i)
+        torch.cuda.synchronize()
+
+        start_event.record()
+        graph.replay()
+        end_event.record()
+        end_event.synchronize()
+        latencies.append(start_event.elapsed_time(end_event))
+    avg = sum(latencies) / (num_iters * 10) * 1000  # us
+    graph.reset()
+    return avg
+
+
+def benchmark_unpermute(num_tokens: int,
+                        num_experts: int,
+                        hidden_size: int,
+                        topk: int,
+                        dtype: torch.dtype,
+                        use_fp8_w8a8: bool,
+                        use_int8_w8a16: bool,
+                        num_iters: int = 100,
+                        use_customized_permute: bool = False) -> float:
+    # init_dtype = torch.float16 if use_fp8_w8a8 else dtype
+    hidden_states = torch.randn(num_tokens, hidden_size, dtype=dtype)
+    output_hidden_states = torch.empty_like(hidden_states)
+    if use_fp8_w8a8:
+        align_block_size = 128  # deepgemm needs 128 m aligned block
+        qhidden_states, scale = _fp8_quantize(hidden_states, None, None)
+    else:
+        align_block_size = None
+        qhidden_states = hidden_states
+
+    input_gating = torch.randn(num_tokens, num_experts, dtype=torch.float32)
+
+    topk_weights, topk_ids, token_expert_indices = fused_topk(
+        qhidden_states, input_gating, topk, False)
+
+    def prepare():
+        if use_customized_permute:
+            (permuted_hidden_states, first_token_off, inv_perm_idx,
+             m_indices) = moe_permute(
+                 qhidden_states,
+                 topk_weights=topk_weights,
+                 topk_ids=topk_ids,
+                 token_expert_indices=token_expert_indices,
+                 topk=topk,
+                 n_expert=num_experts,
+                 n_local_expert=num_experts,
+                 expert_map=None,
+                 align_block_size=align_block_size,
+             )
+            # convert to fp16/bf16 as gemm output
+            return (permuted_hidden_states.to(dtype), first_token_off,
+                    inv_perm_idx, m_indices)
+        else:
+            (permuted_qhidden_states, a1q_scale, sorted_token_ids, expert_ids,
+             inv_perm) = _moe_permute(qhidden_states, None, topk_ids,
+                                      num_experts, None, align_block_size)
+            # convert to fp16/bf16 as gemm output
+            return (permuted_qhidden_states.to(dtype), a1q_scale,
+                    sorted_token_ids, expert_ids, inv_perm)
+
+    def run(input: tuple):
+        if use_customized_permute:
+            (permuted_hidden_states, first_token_off, inv_perm_idx,
+             m_indices) = input
+            moe_unpermute(permuted_hidden_states, topk_weights, topk_ids,
+                          inv_perm_idx, first_token_off, topk, num_experts,
+                          num_experts)
+        else:
+            (permuted_hidden_states, a1q_scale, sorted_token_ids, expert_ids,
+             inv_perm) = input
+            _moe_unpermute_and_reduce(output_hidden_states,
+                                      permuted_hidden_states, inv_perm,
+                                      topk_weights)
+
+    # JIT compilation & warmup
+    input = prepare()
+    run(input)
+    torch.cuda.synchronize()
+
+    # Capture 10 invocations with CUDA graph
+    graph = torch.cuda.CUDAGraph()
+    with torch.cuda.graph(graph):
+        for _ in range(10):
+            run(input)
+    torch.cuda.synchronize()
+
+    # Warmup
+    for _ in range(5):
+        graph.replay()
+    torch.cuda.synchronize()
+
+    start_event = torch.cuda.Event(enable_timing=True)
+    end_event = torch.cuda.Event(enable_timing=True)
+
+    latencies: list[float] = []
+    for i in range(num_iters):
+        torch.cuda.synchronize()
+        start_event.record()
+        graph.replay()
+        end_event.record()
+        end_event.synchronize()
+        latencies.append(start_event.elapsed_time(end_event))
+    avg = sum(latencies) / (num_iters * 10) * 1000  # us
+    graph.reset()
+    return avg
+
+
+@ray.remote(num_gpus=1)
+class BenchmarkWorker:
+
+    def __init__(self, seed: int) -> None:
+        torch.set_default_device("cuda")
+        current_platform.seed_everything(seed)
+        self.seed = seed
+        # Get the device ID to allocate tensors and kernels
+        # on the respective GPU. This is required for Ray to work
+        # correctly with multi-GPU tuning on the ROCm platform.
+        self.device_id = int(ray.get_gpu_ids()[0])
+
+    def benchmark(
+        self,
+        num_tokens: int,
+        num_experts: int,
+        hidden_size: int,
+        topk: int,
+        dtype: torch.dtype,
+        use_fp8_w8a8: bool,
+        use_int8_w8a16: bool,
+        use_customized_permute: bool = False,
+    ) -> tuple[dict[str, int], float]:
+        current_platform.seed_everything(self.seed)
+
+        permute_time = benchmark_permute(
+            num_tokens,
+            num_experts,
+            hidden_size,
+            topk,
+            dtype,
+            use_fp8_w8a8,
+            use_int8_w8a16,
+            num_iters=100,
+            use_customized_permute=use_customized_permute)
+        unpermute_time = benchmark_unpermute(
+            num_tokens,
+            num_experts,
+            hidden_size,
+            topk,
+            dtype,
+            use_fp8_w8a8,
+            use_int8_w8a16,
+            num_iters=100,
+            use_customized_permute=use_customized_permute)
+        return permute_time, unpermute_time
+
+
+def get_weight_block_size_safety(config, default_value=None):
+
+    quantization_config = getattr(config, 'quantization_config', {})
+    if isinstance(quantization_config, dict):
+        return quantization_config.get('weight_block_size', default_value)
+    return default_value
+
+
+def main(args: argparse.Namespace):
+    print(args)
+
+    config = AutoConfig.from_pretrained(
+        args.model, trust_remote_code=args.trust_remote_code)
+    if config.architectures[0] == "DbrxForCausalLM":
+        E = config.ffn_config.moe_num_experts
+        topk = config.ffn_config.moe_top_k
+    elif config.architectures[0] == "JambaForCausalLM":
+        E = config.num_experts
+        topk = config.num_experts_per_tok
+    elif (config.architectures[0] == "DeepseekV3ForCausalLM"
+          or config.architectures[0] == "DeepseekV2ForCausalLM"):
+        E = config.n_routed_experts
+        topk = config.num_experts_per_tok
+    elif config.architectures[0] in [
+            "Qwen2MoeForCausalLM", "Qwen3MoeForCausalLM"
+    ]:
+        E = config.num_experts
+        topk = config.num_experts_per_tok
+
+    else:
+        # Support for llama4
+        config = config.get_text_config()
+        # Default: Mixtral.
+        E = config.num_local_experts
+        topk = config.num_experts_per_tok
+
+    hidden_size = config.hidden_size
+    dtype = torch.float16 if current_platform.is_rocm() else config.torch_dtype
+    use_fp8_w8a8 = args.dtype == "fp8_w8a8"
+    use_int8_w8a16 = args.dtype == "int8_w8a16"
+    use_customized_permute = args.use_customized_permute
+
+    if args.batch_size is None:
+        batch_sizes = [
+            1, 2, 4, 8, 16, 24, 32, 48, 64, 96, 128, 256, 512, 1024, 1536,
+            2048, 3072, 4096
+        ]
+    else:
+        batch_sizes = [args.batch_size]
+
+    ray.init()
+    num_gpus = int(ray.available_resources()["GPU"])
+    workers = [BenchmarkWorker.remote(args.seed) for _ in range(num_gpus)]
+
+    def _distribute(method: str, inputs: list[Any]) -> list[Any]:
+        outputs = []
+        worker_idx = 0
+        for input_args in inputs:
+            worker = workers[worker_idx]
+            worker_method = getattr(worker, method)
+            output = worker_method.remote(*input_args)
+            outputs.append(output)
+            worker_idx = (worker_idx + 1) % num_gpus
+        return ray.get(outputs)
+
+    outputs = _distribute(
+        "benchmark", [(batch_size, E, hidden_size, topk, dtype, use_fp8_w8a8,
+                       use_int8_w8a16, use_customized_permute)
+                      for batch_size in batch_sizes])
+
+    for batch_size, (permute, unpermute) in zip(batch_sizes, outputs):
+        print(f"Batch size: {batch_size}")
+        print(f"Permute time: {permute:.2f} us")
+        print(f"Unpermute time: {unpermute:.2f} us")
+
+
+if __name__ == "__main__":
+    parser = FlexibleArgumentParser()
+    parser.add_argument("--model",
+                        type=str,
+                        default="mistralai/Mixtral-8x7B-Instruct-v0.1")
+    parser.add_argument("--dtype",
+                        type=str,
+                        choices=["auto", "fp8_w8a8", "int8_w8a16"],
+                        default="auto")
+    parser.add_argument("--use-customized-permute", action="store_true")
+    parser.add_argument("--seed", type=int, default=0)
+    parser.add_argument("--batch-size", type=int, required=False)
+    parser.add_argument("--trust-remote-code", action="store_true")
+    args = parser.parse_args()
+
+    main(args)

csrc/moe/moe_permute_unpermute_op.cu

+#include <c10/core/ScalarType.h>
+#include <torch/all.h>
+#include <ATen/cuda/CUDAContext.h>
+#include "permute_unpermute_kernels/moe_permute_unpermute_kernel.h"
+#include "permute_unpermute_kernels/dispatch.h"
+#include "core/registration.h"
+
+void moe_permute(
+    const torch::Tensor& input,                      // [n_token, hidden]
+    const torch::Tensor& topk_weights,               //[n_token, topk]
+    torch::Tensor& topk_ids,                         // [n_token, topk]
+    const torch::Tensor& token_expert_indicies,      // [n_token, topk]
+    const std::optional<torch::Tensor>& expert_map,  // [n_expert]
+    int64_t n_expert, int64_t n_local_expert, int64_t topk,
+    const std::optional<int64_t>& align_block_size,
+    torch::Tensor&
+        permuted_input,  // [topk * n_token/align_block_size_m, hidden]
+    torch::Tensor& expert_first_token_offset,  // [n_local_expert + 1]
+    torch::Tensor& src_row_id2dst_row_id_map,  // [n_token, topk]
+    torch::Tensor& m_indices) {                // [align_expand_m]
+  TORCH_CHECK(topk_weights.scalar_type() == at::ScalarType::Float,
+              "topk_weights must be float32");
+  TORCH_CHECK(expert_first_token_offset.scalar_type() == at::ScalarType::Long,
+              "expert_first_token_offset must be int64");
+  TORCH_CHECK(topk_ids.scalar_type() == at::ScalarType::Int,
+              "topk_ids must be int32");
+  TORCH_CHECK(token_expert_indicies.scalar_type() == at::ScalarType::Int,
+              "token_expert_indicies must be int32");
+  TORCH_CHECK(src_row_id2dst_row_id_map.scalar_type() == at::ScalarType::Int,
+              "src_row_id2dst_row_id_map must be int32");
+  TORCH_CHECK(expert_first_token_offset.size(0) == n_local_expert + 1,
+              "expert_first_token_offset shape != n_local_expert+1")
+  TORCH_CHECK(
+      src_row_id2dst_row_id_map.sizes() == token_expert_indicies.sizes(),
+      "token_expert_indicies shape must be same as src_row_id2dst_row_id_map");
+  auto n_token = input.sizes()[0];
+  auto n_hidden = input.sizes()[1];
+  auto align_block_size_value =
+      align_block_size.has_value() ? align_block_size.value() : -1;
+  auto stream = at::cuda::getCurrentCUDAStream().stream();
+  const long sorter_size =
+      CubKeyValueSorter::getWorkspaceSize(n_token * topk, n_expert);
+  auto sort_workspace = torch::empty(
+      {sorter_size},
+      torch::dtype(torch::kInt8).device(torch::kCUDA).requires_grad(false));
+  auto permuted_experts_id = torch::empty_like(topk_ids);
+  auto dst_row_id2src_row_id_map = torch::empty_like(src_row_id2dst_row_id_map);
+  auto align_expert_first_token_offset =
+      torch::zeros_like(expert_first_token_offset);
+
+  CubKeyValueSorter sorter{};
+  int64_t* valid_num_ptr = nullptr;
+  // pre-process kernel for expert-parallelism:
+  // no local expert id plus "n_expert" offset for priority to local expert
+  // map local expert id [n, .., n+n_local_expert-1] to [0, n_local_expert -1]
+  // For example, 4 expert with ep_size=2. ep_rank=1 owns global expert id
+  // [2,3] with expert_map[-1, -1, 0, 1], preprocess_topk_id  process topk_ids
+  // and map global expert id [2, 3] to local_expert id [0, 1] and map global
+  // expert id [0, 1] ( not in ep rank=1)  to [4, 5] by plus n_expert. This map
+  // operation is to make local expert high priority in following sort topk_ids
+  // and scan local expert_first_token_offset for each ep rank for next group
+  // gemm.
+  if (expert_map.has_value()) {
+    const int* expert_map_ptr = get_ptr<int>(expert_map.value());
+    valid_num_ptr =
+        get_ptr<int64_t>(expert_first_token_offset) + n_local_expert;
+    preprocessTopkIdLauncher(get_ptr<int>(topk_ids), n_token * topk,
+                             expert_map_ptr, n_expert, stream);
+  }
+  // expert sort topk expert id and scan expert id get expert_first_token_offset
+  sortAndScanExpert(get_ptr<int>(topk_ids), get_ptr<int>(token_expert_indicies),
+                    get_ptr<int>(permuted_experts_id),
+                    get_ptr<int>(dst_row_id2src_row_id_map),
+                    get_ptr<int64_t>(expert_first_token_offset), n_token,
+                    n_expert, n_local_expert, topk, sorter,
+                    get_ptr<int>(sort_workspace), stream);
+
+  // dispatch expandInputRowsKernelLauncher
+  MOE_DISPATCH(input.scalar_type(), [&] {
+    expandInputRowsKernelLauncher<scalar_t>(
+        get_ptr<scalar_t>(input), get_ptr<scalar_t>(permuted_input),
+        get_ptr<float>(topk_weights), get_ptr<int>(permuted_experts_id),
+        get_ptr<int>(dst_row_id2src_row_id_map),
+        get_ptr<int>(src_row_id2dst_row_id_map),
+        get_ptr<int64_t>(expert_first_token_offset), n_token, valid_num_ptr,
+        n_hidden, topk, n_local_expert, align_block_size_value, stream);
+  });
+
+  // get m_indices and update expert_first_token_offset with align block
+  getMIndices(get_ptr<int64_t>(expert_first_token_offset),
+              get_ptr<int64_t>(align_expert_first_token_offset),
+              get_ptr<int>(m_indices), n_local_expert, align_block_size_value,
+              stream);
+  if (align_block_size.has_value()) {
+    // update align_expert_first_token_offset
+    expert_first_token_offset.copy_(align_expert_first_token_offset);
+  }
+}
+
+void moe_unpermute(
+    const torch::Tensor& permuted_hidden_states,     // [n_token * topk, hidden]
+    const torch::Tensor& topk_weights,               //[n_token, topk]
+    const torch::Tensor& topk_ids,                   // [n_token, topk]
+    const torch::Tensor& src_row_id2dst_row_id_map,  // [n_token, topk]
+    const torch::Tensor& expert_first_token_offset,  // [n_local_expert+1]
+    int64_t n_expert, int64_t n_local_expert, int64_t topk,
+    torch::Tensor& hidden_states  // [n_token, hidden]
+) {
+  TORCH_CHECK(src_row_id2dst_row_id_map.sizes() == topk_ids.sizes(),
+              "topk_ids shape must be same as src_row_id2dst_row_id_map");
+  TORCH_CHECK(topk_ids.scalar_type() == at::ScalarType::Int,
+              "topk_ids must be int32");
+  TORCH_CHECK(
+      permuted_hidden_states.scalar_type() == hidden_states.scalar_type(),
+      "topk_ids dtype must be same as src_row_id2dst_row_id_map");
+  auto n_token = hidden_states.size(0);
+  auto n_hidden = hidden_states.size(1);
+  auto stream = at::cuda::getCurrentCUDAStream().stream();
+  const int64_t* valid_ptr =
+      get_ptr<int64_t>(expert_first_token_offset) + n_local_expert;
+  MOE_DISPATCH(hidden_states.scalar_type(), [&] {
+    finalizeMoeRoutingKernelLauncher<scalar_t, scalar_t>(
+        get_ptr<scalar_t>(permuted_hidden_states),
+        get_ptr<scalar_t>(hidden_states), get_ptr<float>(topk_weights),
+        get_ptr<int>(src_row_id2dst_row_id_map), get_ptr<int>(topk_ids),
+        n_token, n_hidden, topk, valid_ptr, stream);
+  });
+}
+
+TORCH_LIBRARY_IMPL_EXPAND(TORCH_EXTENSION_NAME, CUDA, m) {
+  m.impl("moe_permute", &moe_permute);
+  m.impl("moe_unpermute", &moe_unpermute);
+}
\ No newline at end of file

csrc/moe/permute_unpermute_kernels/dispatch.h

+#pragma once
+#include <cuda_fp8.h>
+#define MOE_SWITCH(TYPE, ...)                                     \
+  at::ScalarType _st = ::detail::scalar_type(TYPE);               \
+  switch (_st) {                                                  \
+    __VA_ARGS__                                                   \
+    default:                                                      \
+      TORCH_CHECK(false, "[moe permute]data type dispatch fail!") \
+  }
+
+#define MOE_DISPATCH_CASE(enum_type, ...)                  \
+  case enum_type: {                                        \
+    using scalar_t = ScalarType2CudaType<enum_type>::type; \
+    __VA_ARGS__();                                         \
+    break;                                                 \
+  }
+#define MOE_DISPATCH_FLOAT_CASE(...)                          \
+  MOE_DISPATCH_CASE(at::ScalarType::Float, __VA_ARGS__)       \
+  MOE_DISPATCH_CASE(at::ScalarType::Half, __VA_ARGS__)        \
+  MOE_DISPATCH_CASE(at::ScalarType::BFloat16, __VA_ARGS__)    \
+  MOE_DISPATCH_CASE(at::ScalarType::Float8_e5m2, __VA_ARGS__) \
+  MOE_DISPATCH_CASE(at::ScalarType::Float8_e4m3fn, __VA_ARGS__)
+
+#define MOE_DISPATCH(TYPE, ...) \
+  MOE_SWITCH(TYPE, MOE_DISPATCH_FLOAT_CASE(__VA_ARGS__))
+
+template <at::ScalarType type>
+struct ScalarType2CudaType;
+
+template <>
+struct ScalarType2CudaType<at::ScalarType::Float> {
+  using type = float;
+};
+template <>
+struct ScalarType2CudaType<at::ScalarType::Half> {
+  using type = half;
+};
+template <>
+struct ScalarType2CudaType<at::ScalarType::BFloat16> {
+  using type = __nv_bfloat16;
+};
+
+// #if __CUDA_ARCH__ >= 890
+// fp8
+template <>
+struct ScalarType2CudaType<at::ScalarType::Float8_e5m2> {
+  using type = __nv_fp8_e5m2;
+};
+template <>
+struct ScalarType2CudaType<at::ScalarType::Float8_e4m3fn> {
+  using type = __nv_fp8_e4m3;
+};
+// #endif
\ No newline at end of file

csrc/moe/permute_unpermute_kernels/moe_permute_unpermute_kernel.cu

+
+#include "moe_permute_unpermute_kernel.h"
+
+// CubKeyValueSorter definition begin
+CubKeyValueSorter::CubKeyValueSorter()
+    : num_experts_(0), num_bits_(sizeof(int) * 8) {}
+
+int CubKeyValueSorter::expertsToBits(int num_experts) {
+  // Max value we represent is V = num_experts + (num_experts - 1) = 2 *
+  // num_experts - 1 The maximum number of bits is therefore floor(log2(V)) + 1
+  return static_cast<int>(log2(2 * num_experts - 1)) + 1;
+}
+
+CubKeyValueSorter::CubKeyValueSorter(int const num_experts)
+    : num_experts_(num_experts), num_bits_(expertsToBits(num_experts)) {}
+
+void CubKeyValueSorter::updateNumExperts(int const num_experts) {
+  num_experts_ = num_experts;
+  num_bits_ = expertsToBits(num_experts);
+}
+
+size_t CubKeyValueSorter::getWorkspaceSize(size_t const num_key_value_pairs,
+                                           int const num_experts) {
+  int num_bits = expertsToBits(num_experts);
+  size_t required_storage = 0;
+  int* null_int = nullptr;
+  cub::DeviceRadixSort::SortPairs(nullptr, required_storage, null_int, null_int,
+                                  null_int, null_int, num_key_value_pairs, 0,
+                                  num_bits);
+
+  //   when num_key_value_pairs, num_experts, num_bits, required_storage = 64,
+  //   4, 3, 0 The required_storage seems to vary between 0 and 1 for the same
+  //   inputs
+  if (required_storage == 0) {
+    required_storage = 1;
+  }
+  return required_storage;
+}
+
+void CubKeyValueSorter::run(void* workspace, size_t const workspace_size,
+                            int const* keys_in, int* keys_out,
+                            int const* values_in, int* values_out,
+                            size_t const num_key_value_pairs,
+                            cudaStream_t stream) {
+  size_t expected_ws_size = getWorkspaceSize(num_key_value_pairs, num_experts_);
+  size_t actual_ws_size = workspace_size;
+
+  TORCH_CHECK(expected_ws_size <= workspace_size,
+              "[CubKeyValueSorter::run] The allocated workspace is too small "
+              "to run this problem.");
+  cub::DeviceRadixSort::SortPairs(workspace, actual_ws_size, keys_in, keys_out,
+                                  values_in, values_out, num_key_value_pairs, 0,
+                                  num_bits_, stream);
+}
+// CubKeyValueSorter definition end
+
+static inline size_t pad_to_multiple_of_16(size_t const& input) {
+  static constexpr int ALIGNMENT = 16;
+  return ALIGNMENT * ((input + ALIGNMENT - 1) / ALIGNMENT);
+}
+template <class T>
+__device__ inline int64_t findTotalEltsLessThanTarget(T const* sorted_indices,
+                                                      int64_t const arr_length,
+                                                      T const target) {
+  int64_t low = 0, high = arr_length - 1, target_location = -1;
+  while (low <= high) {
+    int64_t mid = (low + high) / 2;
+
+    if (sorted_indices[mid] >= target) {
+      high = mid - 1;
+    } else {
+      low = mid + 1;
+      target_location = mid;
+    }
+  }
+  return target_location + 1;
+}
+
+// Calculates the start offset of the tokens for a given expert. The last
+// element is the total number of valid tokens
+__global__ void computeExpertFirstTokenOffsetKernel(
+    int const* sorted_experts, int64_t const sorted_experts_len,
+    int const num_experts, int64_t* expert_first_token_offset) {
+  // First, compute the global tid. We only need 1 thread per expert.
+  int const expert = blockIdx.x * blockDim.x + threadIdx.x;
+
+  // Note that expert goes [0, num_experts] (inclusive) because we want a count
+  // for the total number of active tokens at the end of the scan.
+  if (expert >= num_experts + 1) {
+    return;
+  }
+  expert_first_token_offset[expert] =
+      findTotalEltsLessThanTarget(sorted_experts, sorted_experts_len, expert);
+}
+
+void computeExpertFirstTokenOffset(int const* sorted_indices,
+                                   int const total_indices,
+                                   int const num_experts,
+                                   int64_t* expert_first_token_offset,
+                                   cudaStream_t stream) {
+  int const num_entries = num_experts + 1;
+  int const threads = std::min(1024, num_entries);
+  int const blocks = (num_entries + threads - 1) / threads;
+
+  computeExpertFirstTokenOffsetKernel<<<blocks, threads, 0, stream>>>(
+      sorted_indices, total_indices, num_experts, expert_first_token_offset);
+}
+
+void sortAndScanExpert(int* expert_for_source_row, const int* source_rows,
+                       int* permuted_experts, int* permuted_rows,
+                       int64_t* expert_first_token_offset, int num_rows,
+                       int num_experts, int num_experts_per_node, int k,
+                       CubKeyValueSorter& sorter, void* sorter_ws,
+                       cudaStream_t stream) {
+  int64_t const expanded_num_rows = static_cast<int64_t>(k) * num_rows;
+  // We need to use the full num_experts because that is the sentinel value used
+  // by topk for disabled experts
+  sorter.updateNumExperts(num_experts);
+  size_t const sorter_ws_size_bytes = pad_to_multiple_of_16(
+      sorter.getWorkspaceSize(expanded_num_rows, num_experts));
+  sorter.run((void*)sorter_ws, sorter_ws_size_bytes, expert_for_source_row,
+             permuted_experts, source_rows, permuted_rows, expanded_num_rows,
+             stream);
+  computeExpertFirstTokenOffset(permuted_experts, expanded_num_rows,
+                                num_experts_per_node, expert_first_token_offset,
+                                stream);
+}
+
+__global__ void preprocessTopkIdKernel(int* topk_id_ptr, int size,
+                                       const int* expert_map_ptr,
+                                       int num_experts) {
+  auto tidx = threadIdx.x;
+  auto bidx = blockIdx.x;
+  auto lidx = tidx & 31;
+  auto widx = tidx >> 5;
+  auto warp_count = (blockDim.x + 31) >> 5;
+  auto offset = bidx * blockDim.x;
+  auto bound = min(offset + blockDim.x, size);
+  extern __shared__ int smem_expert_map[];
+  // store expert_map in smem
+  for (int i = tidx; i < num_experts; i += blockDim.x) {
+    smem_expert_map[i] = expert_map_ptr[i];
+  }
+  __syncthreads();
+
+  // query global expert id in expert map.
+  // if global expert id = -1 in exert map, plus n_expert
+  // else set global expert id = exert map[global expert id]
+  if (offset + tidx < bound) {
+    auto topk_id = topk_id_ptr[offset + tidx];
+    auto local_expert_idx = smem_expert_map[topk_id];
+    if (local_expert_idx == -1) {
+      topk_id += num_experts;
+    } else {
+      topk_id = local_expert_idx;
+    }
+    __syncwarp();
+    topk_id_ptr[offset + tidx] = topk_id;
+  }
+}
+void preprocessTopkIdLauncher(int* topk_id_ptr, int size,
+                              const int* expert_map_ptr, int num_experts,
+                              cudaStream_t stream) {
+  int block = std::min(size, 1024);
+  int grid = (size + block - 1) / block;
+  int smem_size = (num_experts) * sizeof(int);
+  preprocessTopkIdKernel<<<grid, block, smem_size, stream>>>(
+      topk_id_ptr, size, expert_map_ptr, num_experts);
+}
+
+template <bool ALIGN_BLOCK_SIZE>
+__global__ void getMIndicesKernel(int64_t* expert_first_token_offset,
+                                  int64_t* align_expert_first_token_offset,
+                                  int* m_indices, const int num_local_expert,
+                                  const int align_block_size) {
+  int eidx = blockIdx.x;
+  int tidx = threadIdx.x;
+  extern __shared__ int64_t smem_expert_first_token_offset[];
+  for (int i = tidx; i <= num_local_expert; i += blockDim.x) {
+    smem_expert_first_token_offset[tidx] = __ldg(expert_first_token_offset + i);
+  }
+  __syncthreads();
+  auto last_token_offset = smem_expert_first_token_offset[eidx + 1];
+  auto first_token_offset = smem_expert_first_token_offset[eidx];
+  int n_token_in_expert = last_token_offset - first_token_offset;
+
+  if constexpr (ALIGN_BLOCK_SIZE) {
+    n_token_in_expert = (n_token_in_expert + align_block_size - 1) /
+                        align_block_size * align_block_size;
+    // round up to ALIGN_BLOCK_SIZE
+    int64_t accumulate_align_offset = 0;
+    for (int i = 1; i <= eidx + 1; i++) {
+      int n_token = smem_expert_first_token_offset[i] -
+                    smem_expert_first_token_offset[i - 1];
+      accumulate_align_offset =
+          accumulate_align_offset + (n_token + align_block_size - 1) /
+                                        align_block_size * align_block_size;
+      if (i == eidx) {
+        first_token_offset = accumulate_align_offset;
+      }
+      // last block store align_expert_first_token_offset
+      if (eidx == num_local_expert - 1 && threadIdx.x == 0) {
+        align_expert_first_token_offset[i] = accumulate_align_offset;
+      }
+    }
+  }
+  for (int idx = tidx; idx < n_token_in_expert; idx += blockDim.x) {
+    // update m_indice with expert id
+    m_indices[first_token_offset + idx] = eidx;
+  }
+}
+
+void getMIndices(int64_t* expert_first_token_offset,
+                 int64_t* align_expert_first_token_offset, int* m_indices,
+                 int num_local_expert, const int align_block_size,
+                 cudaStream_t stream) {
+  int block = 256;
+  int grid = num_local_expert;
+  int smem_size = sizeof(int64_t) * (num_local_expert + 1);
+  if (align_block_size == -1) {
+    getMIndicesKernel<false><<<grid, block, smem_size, stream>>>(
+        expert_first_token_offset, align_expert_first_token_offset, m_indices,
+        num_local_expert, align_block_size);
+  } else {
+    getMIndicesKernel<true><<<grid, block, smem_size, stream>>>(
+        expert_first_token_offset, align_expert_first_token_offset, m_indices,
+        num_local_expert, align_block_size);
+  }
+}
\ No newline at end of file

csrc/moe/permute_unpermute_kernels/moe_permute_unpermute_kernel.h

+#pragma once
+// reference from tensorrt_llm moe kernel implementation archive in
+// https://github.com/BBuf/tensorrt-llm-moe/tree/master
+
+#include <c10/core/ScalarType.h>
+#include <torch/all.h>
+#include "dispatch.h"
+#include <cub/cub.cuh>
+#include <cub/device/device_radix_sort.cuh>
+#include <cub/util_type.cuh>
+#include "cutlass/numeric_size.h"
+#include "cutlass/array.h"
+
+template <typename T>
+inline T* get_ptr(torch::Tensor& t) {
+  return reinterpret_cast<T*>(t.data_ptr());
+}
+
+template <typename T>
+inline const T* get_ptr(const torch::Tensor& t) {
+  return reinterpret_cast<const T*>(t.data_ptr());
+}
+
+class CubKeyValueSorter {
+ public:
+  CubKeyValueSorter();
+
+  CubKeyValueSorter(int const num_experts);
+
+  void updateNumExperts(int const num_experts);
+
+  static size_t getWorkspaceSize(size_t const num_key_value_pairs,
+                                 int const num_experts);
+
+  void run(void* workspace, size_t const workspace_size, int const* keys_in,
+           int* keys_out, int const* values_in, int* values_out,
+           size_t const num_key_value_pairs, cudaStream_t stream);
+
+ private:
+  static int expertsToBits(int experts);
+  int num_experts_;
+  int num_bits_;
+};
+
+void computeExpertFirstTokenOffset(int const* sorted_indices,
+                                   int const total_indices,
+                                   int const num_experts,
+                                   int64_t* expert_first_token_offset,
+                                   cudaStream_t stream);
+
+void sortAndScanExpert(int* expert_for_source_row, const int* source_rows,
+                       int* permuted_experts, int* permuted_rows,
+                       int64_t* expert_first_token_offset, int num_rows,
+                       int num_experts, int num_experts_per_node, int k,
+                       CubKeyValueSorter& sorter, void* sorter_ws,
+                       cudaStream_t stream);
+
+template <typename T>
+void expandInputRowsKernelLauncher(
+    T const* unpermuted_input, T* permuted_output,
+    const float* unpermuted_scales, int* sorted_experts,
+    int const* expanded_dest_row_to_expanded_source_row,
+    int* expanded_source_row_to_expanded_dest_row,
+    int64_t* expert_first_token_offset, int64_t const num_rows,
+    int64_t const* num_valid_tokens_ptr, int64_t const cols, int const k,
+    int num_local_experts, const int& align_block_size, cudaStream_t stream);
+
+// Final kernel to unpermute and scale
+// This kernel unpermutes the original data, does the k-way reduction and
+// performs the final skip connection.
+template <typename T, typename OutputType, bool CHECK_SKIPPED>
+__global__ void finalizeMoeRoutingKernel(
+    T const* expanded_permuted_rows, OutputType* reduced_unpermuted_output,
+    float const* scales, int const* expanded_source_row_to_expanded_dest_row,
+    int const* expert_for_source_row, int64_t const orig_cols, int64_t const k,
+    int64_t const* num_valid_ptr);
+
+template <class T, class OutputType>
+void finalizeMoeRoutingKernelLauncher(
+    T const* expanded_permuted_rows, OutputType* reduced_unpermuted_output,
+    float const* scales, int const* expanded_source_row_to_expanded_dest_row,
+    int const* expert_for_source_row, int64_t const num_rows,
+    int64_t const cols, int64_t const k, int64_t const* num_valid_ptr,
+    cudaStream_t stream);
+
+void preprocessTopkIdLauncher(int* topk_id_ptr, int size,
+                              const int* expert_map_ptr, int num_experts,
+                              cudaStream_t stream);
+
+void getMIndices(int64_t* expert_first_token_offset,
+                 int64_t* align_expert_first_token_offset, int* m_indices,
+                 int num_local_expert, const int align_block_size,
+                 cudaStream_t stream);
+
+#include "moe_permute_unpermute_kernel.inl"

csrc/moe/permute_unpermute_kernels/moe_permute_unpermute_kernel.inl

+#pragma once
+
+template <typename T, bool CHECK_SKIPPED, bool ALIGN_BLOCK_SIZE>
+__global__ void expandInputRowsKernel(
+    T const* unpermuted_input, T* permuted_output,
+    const float* unpermuted_scales, int* sorted_experts,
+    int const* expanded_dest_row_to_expanded_source_row,
+    int* expanded_source_row_to_expanded_dest_row,
+    int64_t* expert_first_token_offset, int64_t const num_rows,
+    int64_t const* num_dest_rows, int64_t const cols, int64_t k,
+    int num_local_experts, int align_block_size) {
+  // Reverse permutation map.
+  // I do this so that later, we can use the source -> dest map to do the k-way
+  // reduction and unpermuting. I need the reverse map for that reduction to
+  // allow each threadblock to do 1 k-way reduce without atomics later in MoE. 1
+  // thread block will be responsible for all k summations.
+  int64_t expanded_dest_row = blockIdx.x;
+  int64_t const expanded_source_row =
+      expanded_dest_row_to_expanded_source_row[expanded_dest_row];
+  int expert_id = sorted_experts[expanded_dest_row];
+
+  extern __shared__ int64_t smem_expert_first_token_offset[];
+  int64_t align_expanded_row_accumulate = 0;
+  if constexpr (ALIGN_BLOCK_SIZE) {
+    // load g2s
+    for (int idx = threadIdx.x; idx < num_local_experts + 1;
+         idx += blockDim.x) {
+      smem_expert_first_token_offset[idx] =
+          __ldg(expert_first_token_offset + idx);
+    }
+    __syncthreads();
+    int lane_idx = threadIdx.x & 31;
+
+    if (lane_idx == 0) {
+      // set token_offset_in_expert = 0 if this expert is not local expert
+      int token_offset_in_expert =
+          expert_id >= num_local_experts
+              ? 0
+              : expanded_dest_row - smem_expert_first_token_offset[expert_id];
+      int64_t accumulate_align_offset = 0;
+#pragma unroll 1
+      for (int eidx = 1; eidx <= min(expert_id, num_local_experts); eidx++) {
+        auto n_token_in_expert = smem_expert_first_token_offset[eidx] -
+                                 smem_expert_first_token_offset[eidx - 1];
+        accumulate_align_offset += (n_token_in_expert + align_block_size - 1) /
+                                   align_block_size * align_block_size;
+      }
+      expanded_dest_row = accumulate_align_offset + token_offset_in_expert;
+    }
+    // lane0 shuffle broadcast align_expanded_dest_row
+    expanded_dest_row = __shfl_sync(0xffffffff, expanded_dest_row, 0);
+  }
+
+  if (threadIdx.x == 0) {
+    assert(expanded_dest_row <= INT32_MAX);
+    expanded_source_row_to_expanded_dest_row[expanded_source_row] =
+        static_cast<int>(expanded_dest_row);
+  }
+
+  if (!CHECK_SKIPPED || blockIdx.x < *num_dest_rows) {
+    // Load 128-bits per thread
+    constexpr int64_t ELEM_PER_THREAD = 128 / cutlass::sizeof_bits<T>::value;
+    using DataElem = cutlass::Array<T, ELEM_PER_THREAD>;
+
+    // Duplicate and permute rows
+    int64_t const source_k_rank = expanded_source_row / num_rows;
+    int64_t const source_row = expanded_source_row % num_rows;
+
+    auto const* source_row_ptr =
+        reinterpret_cast<DataElem const*>(unpermuted_input + source_row * cols);
+    auto* dest_row_ptr =
+        reinterpret_cast<DataElem*>(permuted_output + expanded_dest_row * cols);
+
+    int64_t const start_offset = threadIdx.x;
+    int64_t const stride = blockDim.x;
+    int64_t const num_elems_in_col = cols / ELEM_PER_THREAD;
+
+    for (int elem_index = start_offset; elem_index < num_elems_in_col;
+         elem_index += stride) {
+      dest_row_ptr[elem_index] = source_row_ptr[elem_index];
+    }
+  }
+}
+
+template <typename T>
+void expandInputRowsKernelLauncher(
+    T const* unpermuted_input, T* permuted_output,
+    const float* unpermuted_scales, int* sorted_experts,
+    int const* expanded_dest_row_to_expanded_source_row,
+    int* expanded_source_row_to_expanded_dest_row,
+    int64_t* expert_first_token_offset, int64_t const num_rows,
+    int64_t const* num_valid_tokens_ptr, int64_t const cols, int const k,
+    int num_local_experts, const int& align_block_size, cudaStream_t stream) {
+  int64_t const blocks = num_rows * k;
+  int64_t const threads = 256;
+  using FuncPtr = decltype(&expandInputRowsKernel<T, true, true>);
+  FuncPtr func_map[2][2] = {
+      {&expandInputRowsKernel<T, false, false>,
+       &expandInputRowsKernel<T, false, true>},
+      {&expandInputRowsKernel<T, true, false>,
+       &expandInputRowsKernel<T, true, true>},
+  };
+  bool is_check_skip = num_valid_tokens_ptr != nullptr;
+  bool is_align_block_size = align_block_size != -1;
+  auto func = func_map[is_check_skip][is_align_block_size];
+
+  int64_t smem_size = sizeof(int64_t) * (num_local_experts + 1);
+
+  func<<<blocks, threads, smem_size, stream>>>(
+      unpermuted_input, permuted_output, unpermuted_scales, sorted_experts,
+      expanded_dest_row_to_expanded_source_row,
+      expanded_source_row_to_expanded_dest_row, expert_first_token_offset,
+      num_rows, num_valid_tokens_ptr, cols, k, num_local_experts,
+      align_block_size);
+}
+
+template <class T, class U>
+__host__ __device__ constexpr static U arrayConvert(T const& input) {
+  using Type = typename U::Element;
+  static_assert(T::kElements == U::kElements);
+  U u;
+#pragma unroll
+  for (int i = 0; i < U::kElements; i++) {
+    u[i] = static_cast<Type>(input[i]);
+  }
+  return u;
+}
+
+template <typename T, typename OutputType, bool CHECK_SKIPPED>
+__global__ void finalizeMoeRoutingKernel(
+    T const* expanded_permuted_rows, OutputType* reduced_unpermuted_output,
+    float const* scales, int const* expanded_source_row_to_expanded_dest_row,
+    int const* expert_for_source_row, int64_t const orig_cols, int64_t const k,
+    int64_t const* num_valid_ptr) {
+  assert(orig_cols % 4 == 0);
+  int64_t const original_row = blockIdx.x;
+  int64_t const num_rows = gridDim.x;
+  auto const offset = original_row * orig_cols;
+  OutputType* reduced_row_ptr = reduced_unpermuted_output + offset;
+  int64_t const num_valid = *num_valid_ptr;
+
+  // Load 128-bits per thread, according to the smallest data type we read/write
+  constexpr int64_t FINALIZE_ELEM_PER_THREAD =
+      128 / std::min(cutlass::sizeof_bits<OutputType>::value,
+                     cutlass::sizeof_bits<T>::value);
+
+  int64_t const start_offset = threadIdx.x;
+  int64_t const stride = blockDim.x;
+  int64_t const num_elems_in_col = orig_cols / FINALIZE_ELEM_PER_THREAD;
+
+  using InputElem = cutlass::Array<T, FINALIZE_ELEM_PER_THREAD>;
+  using OutputElem = cutlass::Array<OutputType, FINALIZE_ELEM_PER_THREAD>;
+  using ComputeElem = cutlass::Array<float, FINALIZE_ELEM_PER_THREAD>;
+  auto const* expanded_permuted_rows_v =
+      reinterpret_cast<InputElem const*>(expanded_permuted_rows);
+  auto* reduced_row_ptr_v = reinterpret_cast<OutputElem*>(reduced_row_ptr);
+
+#pragma unroll
+  for (int elem_index = start_offset; elem_index < num_elems_in_col;
+       elem_index += stride) {
+    ComputeElem thread_output;
+    thread_output.fill(0);
+    float row_rescale{0.f};
+    for (int k_idx = 0; k_idx < k; ++k_idx) {
+      int64_t const expanded_original_row = original_row + k_idx * num_rows;
+      int64_t const expanded_permuted_row =
+          expanded_source_row_to_expanded_dest_row[expanded_original_row];
+
+      int64_t const k_offset = original_row * k + k_idx;
+      float const row_scale = scales[k_offset];
+
+      // Check after row_rescale has accumulated
+      if (CHECK_SKIPPED && expanded_permuted_row >= num_valid) {
+        continue;
+      }
+
+      auto const* expanded_permuted_rows_row_ptr =
+          expanded_permuted_rows_v + expanded_permuted_row * num_elems_in_col;
+
+      int64_t const expert_idx = expert_for_source_row[k_offset];
+
+      ComputeElem expert_result = arrayConvert<InputElem, ComputeElem>(
+          expanded_permuted_rows_row_ptr[elem_index]);
+      thread_output = thread_output + row_scale * (expert_result);
+    }
+
+    OutputElem output_elem =
+        arrayConvert<ComputeElem, OutputElem>(thread_output);
+    reduced_row_ptr_v[elem_index] = output_elem;
+  }
+}
+
+template <class T, class OutputType>
+void finalizeMoeRoutingKernelLauncher(
+    T const* expanded_permuted_rows, OutputType* reduced_unpermuted_output,
+    float const* scales, int const* expanded_source_row_to_expanded_dest_row,
+    int const* expert_for_source_row, int64_t const num_rows,
+    int64_t const cols, int64_t const k, int64_t const* num_valid_ptr,
+    cudaStream_t stream) {
+  int64_t const blocks = num_rows;
+  int64_t const threads = 256;
+  bool const check_finished = num_valid_ptr != nullptr;
+  using FuncPtr = decltype(&finalizeMoeRoutingKernel<T, OutputType, false>);
+  FuncPtr func_map[2] = {&finalizeMoeRoutingKernel<T, OutputType, false>,
+                         &finalizeMoeRoutingKernel<T, OutputType, true>};
+  auto* const kernel = func_map[check_finished];
+  kernel<<<blocks, threads, 0, stream>>>(
+      expanded_permuted_rows, reduced_unpermuted_output, scales,
+      expanded_source_row_to_expanded_dest_row, expert_for_source_row, cols, k,
+      num_valid_ptr);
+}

csrc/moe/torch_bindings.cpp

@@ -53,7 +53,29 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, m) {
       "int size_m, int size_n, int size_k,"
       "bool is_full_k, bool use_atomic_add,"
       "bool use_fp32_reduce, bool is_zp_float) -> Tensor");
+  m.def(
+      "marlin_gemm_moe(Tensor! a, Tensor! b_q_weights, Tensor! sorted_ids, "
+      "Tensor! topk_weights, Tensor! topk_ids, Tensor! b_scales, Tensor! "
+      "b_zeros, Tensor! g_idx, Tensor! perm, Tensor! workspace, "
+      "int b_q_type, SymInt size_m, "
+      "SymInt size_n, SymInt size_k, bool is_k_full, int num_experts, int "
+      "topk, "
+      "int moe_block_size, bool replicate_input, bool apply_weights)"
+      " -> Tensor");
+
+  m.def(
+      "moe_permute(Tensor input, Tensor topk_weight, Tensor! topk_ids,"
+      "Tensor token_expert_indicies, Tensor? expert_map, int n_expert,"
+      "int n_local_expert,"
+      "int topk, int? align_block_size,Tensor! permuted_input, Tensor! "
+      "expert_first_token_offset, Tensor! src_row_id2dst_row_id_map, Tensor! "
+      "m_indices)->()");
 
+  m.def(
+      "moe_unpermute(Tensor permuted_hidden_states, Tensor topk_weights,"
+      "Tensor topk_ids,Tensor src_row_id2dst_row_id_map, Tensor "
+      "expert_first_token_offset, int n_expert, int n_local_expert,int "
+      "topk, Tensor! hidden_states)->()");
   // conditionally compiled so impl registration is in source file
 
 #endif

tests/kernels/moe/test_moe.py

@@ -420,7 +420,8 @@ def test_fused_marlin_moe(
 
     score = torch.randn((m, e), device="cuda", dtype=dtype)
 
-    topk_weights, topk_ids = fused_topk(a, score, topk, False)
+    topk_weights, topk_ids, token_expert_indices = fused_topk(
+        a, score, topk, False)
 
     torch_output = torch_moe(a, w_ref1, w_ref2, score, topk, e_map)
 

tests/kernels/moe/test_moe_permute_unpermute.py

+# SPDX-License-Identifier: Apache-2.0
+"""Tests for the MOE permute/unpermute kernel
+
+Run `pytest tests/kernels/test_moe_permute_unpermute.py`.
+"""
+
+from typing import Optional
+
+import numpy as np
+import pytest
+import torch
+
+from vllm.model_executor.layers.fused_moe.fused_moe import fused_topk
+from vllm.model_executor.layers.fused_moe.layer import determine_expert_map
+from vllm.model_executor.layers.fused_moe.moe_permute_unpermute import (
+    moe_permute, moe_unpermute)
+from vllm.platforms import current_platform
+
+NUM_EXPERTS = [16, 64]
+TOP_KS = [2, 4, 6, 8]
+EP_SIZE = [1, 4, 16]
+current_platform.seed_everything(0)
+
+
+def torch_permute(hidden_states: torch.Tensor,
+                  topk_ids: torch.Tensor,
+                  token_expert_indices: torch.Tensor,
+                  topk: int,
+                  n_expert: int,
+                  n_local_expert: int,
+                  start_expert: int,
+                  expert_map: Optional[torch.Tensor] = None,
+                  align_block_size: Optional[int] = None,
+                  fill_invalid_expert: int = -1) -> list[torch.Tensor]:
+    n_token, n_hidden = hidden_states.shape[0], hidden_states.shape[1]
+    if expert_map is not None:
+        is_local_expert = (expert_map[topk_ids] != -1)
+        not_local_expert = (expert_map[topk_ids] == -1)
+        topk_ids = is_local_expert * (
+            topk_ids - start_expert) + not_local_expert * (topk_ids + n_expert)
+
+    sorted_topk_ids, sorted_indices = torch.sort(topk_ids.flatten(),
+                                                 stable=True)
+    dst_row_id2src_row_id_map = token_expert_indices.flatten()[sorted_indices]
+
+    expert_first_token_offset = torch.zeros(n_local_expert + 1,
+                                            dtype=torch.int64,
+                                            device="cuda")
+    idx = 0
+    for i in range(0, n_local_expert):
+        cnt = 0
+        while idx < sorted_topk_ids.numel() and sorted_topk_ids[idx] == i:
+            cnt += 1
+            idx += 1
+        expert_first_token_offset[i + 1] = expert_first_token_offset[i] + cnt
+
+    _, src2dst_idx = torch.sort(dst_row_id2src_row_id_map)
+    valid_row_idx = []
+    if align_block_size is None:
+
+        permuted_hidden_states = hidden_states[dst_row_id2src_row_id_map %
+                                               n_token, ...]
+        permuted_row_size = permuted_hidden_states.shape[0]
+        m_indices = torch.empty(permuted_row_size,
+                                device="cuda",
+                                dtype=torch.int32).fill_(fill_invalid_expert)
+        for i in range(1, n_local_expert + 1):
+            first_token_offset = expert_first_token_offset[i - 1]
+            last_token_offset = expert_first_token_offset[i]
+            m_indices[first_token_offset:last_token_offset] = i - 1
+        src_row_id2dst_row_id_map = torch.arange(
+            0, n_token * topk, device="cuda",
+            dtype=torch.int32)[src2dst_idx].reshape((n_token, topk))
+        valid_row_idx += [i for i in range(expert_first_token_offset[-1])]
+        return [
+            permuted_hidden_states, expert_first_token_offset,
+            src_row_id2dst_row_id_map, m_indices, valid_row_idx
+        ]
+    else:
+        permuted_row_size = (topk * n_token + n_expert *
+                             (align_block_size - 1) + align_block_size -
+                             1) // align_block_size * align_block_size
+        permuted_hidden_states = torch.empty((permuted_row_size, n_hidden),
+                                             device="cuda",
+                                             dtype=hidden_states.dtype)
+        align_src_row_id2dst_row_id = torch.empty(n_token * topk,
+                                                  device="cuda",
+                                                  dtype=torch.int32)
+        align_expert_first_token_offset = torch.zeros_like(
+            expert_first_token_offset)
+        m_indices = torch.empty(permuted_row_size,
+                                device="cuda",
+                                dtype=torch.int32).fill_(fill_invalid_expert)
+        # get align_permuted_hidden_states,
+        # valid row_idx and align_expert_first_token_offset
+        for i in range(1, n_local_expert + 1):
+            first_token_offset = expert_first_token_offset[i - 1]
+            last_token_offset = expert_first_token_offset[i]
+            n_token_in_expert = last_token_offset - first_token_offset
+            align_expert_first_token_offset[
+                i] = align_expert_first_token_offset[
+                    i - 1] + (n_token_in_expert + align_block_size -
+                              1) // align_block_size * align_block_size
+            align_first_token_offset = align_expert_first_token_offset[i - 1]
+            align_last_token_offset = align_expert_first_token_offset[i]
+            dst_row_id2src_row_id_in_expert = dst_row_id2src_row_id_map[
+                first_token_offset:first_token_offset +
+                n_token_in_expert] % n_token
+            # store token in current expert with align_first_token_offset
+            permuted_hidden_states[align_first_token_offset:\
+                                   align_first_token_offset+n_token_in_expert,\
+                                      ...] = hidden_states[\
+                                       dst_row_id2src_row_id_in_expert, ...]
+            # set current expert m_indices
+            m_indices[align_first_token_offset:align_last_token_offset] = i - 1
+            valid_row_idx += [
+                i for i in range(align_first_token_offset,
+                                 align_first_token_offset + n_token_in_expert)
+            ]
+        # get align_src_row_id2dst_row_id
+        for i in range(n_token * topk):
+            eid = sorted_topk_ids[i]
+            if (eid >= n_local_expert):
+                # check token not in local expert
+                align_src_row_id2dst_row_id[
+                    i] = align_expert_first_token_offset[-1]
+                continue
+            first_token_offset = expert_first_token_offset[eid]
+            align_first_token_offset = align_expert_first_token_offset[eid]
+            token_offset = i - first_token_offset
+            align_src_row_id2dst_row_id[
+                i] = align_first_token_offset + token_offset
+        align_src_row_id2dst_row_id = align_src_row_id2dst_row_id[\
+            src2dst_idx].reshape((n_token, topk))
+        return [
+            permuted_hidden_states, align_expert_first_token_offset,
+            align_src_row_id2dst_row_id, m_indices, valid_row_idx
+        ]
+
+
+def torch_unpermute(permuted_hidden_states: torch.Tensor,
+                    topk_weights: torch.Tensor, topk_ids: torch.Tensor,
+                    token_expert_indices: torch.Tensor,
+                    src_row_id2dst_row_id_map: torch.Tensor,
+                    valid_row_idx: torch.Tensor, topk: int,
+                    n_expert: int) -> torch.Tensor:
+    # ignore invalid row
+    mask = torch.zeros(permuted_hidden_states.shape[0],
+                       dtype=bool,
+                       device="cuda")
+    mask[valid_row_idx] = True
+    permuted_hidden_states[~mask] = 0
+    idx = src_row_id2dst_row_id_map.flatten()[
+        token_expert_indices.flatten()].reshape(token_expert_indices.shape)
+    output = permuted_hidden_states[idx, ...] * topk_weights[..., None]
+    output = output.sum(dim=1).to(permuted_hidden_states.dtype)
+    return output
+
+
+@pytest.mark.parametrize("n_token", [1, 33, 64, 222, 1024, 2048, 3000, 5000])
+@pytest.mark.parametrize("n_hidden", [2048, 4096, 7168])
+@pytest.mark.parametrize("n_expert", NUM_EXPERTS)
+@pytest.mark.parametrize("topk", TOP_KS)
+@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
+@pytest.mark.parametrize("ep_size", EP_SIZE)
+@pytest.mark.parametrize("align_block_size", [None, 128])
+def test_moe_permute_unpermute(n_token: int, n_hidden: int, topk: int,
+                               n_expert: int, ep_size: int, dtype: torch.dtype,
+                               align_block_size: Optional[int]):
+    fill_invalid_expert = 0
+    ep_rank = np.random.randint(0, ep_size)
+    expert_map = None
+    n_local_expert = n_expert
+    if (ep_size != 1):
+        n_local_expert, expert_map = determine_expert_map(
+            ep_size, ep_rank, n_expert)
+        expert_map = expert_map.cuda()
+    start_expert = n_local_expert * ep_rank
+    current_platform.seed_everything(0)
+    hidden_states = torch.randn((n_token, n_hidden), device="cuda").to(dtype)
+    gating_output = torch.randn((n_token, n_expert), device="cuda").to(dtype)
+    topk_weights, topk_ids, token_expert_indices = fused_topk(
+        hidden_states, gating_output, topk, False)
+    gold0, gold1, gold2, gold3, valid_row_idx = torch_permute(
+        hidden_states,
+        topk_ids,
+        token_expert_indices,
+        topk,
+        n_expert,
+        n_local_expert,
+        start_expert,
+        expert_map=expert_map,
+        align_block_size=align_block_size,
+        fill_invalid_expert=fill_invalid_expert)
+
+    result0, result1, result2, result3 = moe_permute(
+        hidden_states, topk_weights, topk_ids, token_expert_indices, topk,
+        n_expert, n_local_expert, expert_map, align_block_size,
+        fill_invalid_expert)
+
+    # check expert_first_token_offset
+    torch.testing.assert_close(gold1, result1, atol=0, rtol=0)
+    # check src_row_id2dst_row_id_map
+    torch.testing.assert_close(gold2, result2, atol=0, rtol=0)
+    # check mindice
+    torch.testing.assert_close(gold3, result3, atol=0, rtol=0)
+    # check permuted_hidden_states, only valid token
+    torch.testing.assert_close(gold0[valid_row_idx],
+                               result0[valid_row_idx],
+                               atol=0,
+                               rtol=0)
+
+    # add a random tensor to simulate group gemm
+    result0 = 0.5 * result0 + torch.randn_like(result0)
+
+    result4 = moe_unpermute(result0, topk_weights, topk_ids, result2, result1,
+                            topk, n_expert, n_local_expert)
+    gold4 = torch_unpermute(result0, topk_weights, topk_ids,
+                            token_expert_indices, result2, valid_row_idx, topk,
+                            n_local_expert)
+
+    # check unpermuted hidden
+    torch.testing.assert_close(result4, gold4, atol=2e-2, rtol=0)

tests/kernels/quantization/test_awq_marlin.py

@@ -84,7 +84,8 @@ def test_fused_marlin_moe_awq(
 
     score = torch.randn((m, e), device="cuda", dtype=dtype)
 
-    topk_weights, topk_ids = fused_topk(a, score, topk, False)
+    topk_weights, topk_ids, token_expert_indices = fused_topk(
+        a, score, topk, False)
     marlin_output = torch.ops.vllm.fused_marlin_moe(
         a,
         qweight1,

tests/kernels/quantization/test_block_fp8.py

@@ -338,7 +338,8 @@ def deep_gemm_w8a8_block_fp8_moe(M, K, a, w1, w2, w1_s, w2_s, score, topk,
     M, K = a.shape
     N = w2.shape[-1]
 
-    topk_weight, topk_ids = fused_topk(a, score.float(), topk, False)
+    topk_weight, topk_ids, token_expert_indices = fused_topk(
+        a, score.float(), topk, False)
 
     block_m = deep_gemm.get_m_alignment_for_contiguous_layout()
 
@@ -435,7 +436,8 @@ def test_w8a8_block_fp8_deep_gemm_fused_moe(M, N, K, E, topk, seed):
             ref_out = torch_w8a8_block_fp8_moe(a, w1, w2, w1_s, w2_s, score,
                                                topk, block_size)
 
-        topk_weights, topk_ids = fused_topk(a, score.float(), topk, False)
+        topk_weights, topk_ids, token_expert_indices = fused_topk(
+            a, score.float(), topk, False)
 
         out = deep_gemm_moe_fp8(a, w1, w2, w1_s, w2_s, topk_weights, topk_ids)
 

vllm/model_executor/layers/fused_moe/fused_marlin_moe.py

@@ -71,8 +71,8 @@ def single_marlin_moe(
     E = w.shape[0]
     N = w.shape[2] // (num_bits // 2)
 
-    topk_weights, topk_ids = fused_topk(hidden_states, gating_output, topk,
-                                        renormalize)
+    topk_weights, topk_ids, token_expert_indices = fused_topk(
+        hidden_states, gating_output, topk, renormalize)
 
     # This might not be an optimal config for a single MMM
     get_config_func = functools.partial(try_get_optimal_moe_config,

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -854,7 +854,7 @@ def fused_topk(
     gating_output: torch.Tensor,
     topk: int,
     renormalize: bool,
-) -> Tuple[torch.Tensor, torch.Tensor]:
+) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
     assert hidden_states.shape[0] == gating_output.shape[0], (
         "Number of tokens mismatch")
 
@@ -868,20 +868,19 @@ def fused_topk(
                            topk,
                            dtype=torch.int32,
                            device=hidden_states.device)
-    token_expert_indicies = torch.empty(M,
-                                        topk,
-                                        dtype=torch.int32,
-                                        device=hidden_states.device)
+    token_expert_indices = torch.empty(M,
+                                       topk,
+                                       dtype=torch.int32,
+                                       device=hidden_states.device)
 
     gating_output_float = gating_output.float()  # TODO(woosuk): Optimize this.
 
     topk_func = dispatch_topk_func()
     topk_weights, topk_ids = topk_func(topk_weights, topk_ids,
-                                       token_expert_indicies,
+                                       token_expert_indices,
                                        gating_output_float, renormalize)
 
-    del token_expert_indicies  # Not used. Will be used in the future.
-    return topk_weights, topk_ids
+    return topk_weights, topk_ids, token_expert_indices
 
 
 # This is used by the Deepseek-V2 and Deepseek-V3 model
@@ -1510,8 +1509,8 @@ def fused_moe(
                                               topk, renormalize,
                                               num_expert_group, topk_group)
     elif custom_routing_function is None:
-        topk_weights, topk_ids = fused_topk(hidden_states, gating_output, topk,
-                                            renormalize)
+        topk_weights, topk_ids, token_expert_indices = fused_topk(
+            hidden_states, gating_output, topk, renormalize)
     else:
         topk_weights, topk_ids = custom_routing_function(
             hidden_states, gating_output, topk, renormalize)

vllm/model_executor/layers/fused_moe/layer.py

@@ -801,10 +801,11 @@ class FusedMoE(torch.nn.Module):
                 scoring_func=scoring_func,
                 e_score_correction_bias=e_score_correction_bias)
         elif custom_routing_function is None:
-            topk_weights, topk_ids = fused_topk(hidden_states=hidden_states,
-                                                gating_output=router_logits,
-                                                topk=top_k,
-                                                renormalize=renormalize)
+            topk_weights, topk_ids, token_expert_indices = fused_topk(
+                hidden_states=hidden_states,
+                gating_output=router_logits,
+                topk=top_k,
+                renormalize=renormalize)
         else:
             topk_weights, topk_ids = custom_routing_function(
                 hidden_states=hidden_states,

vllm/model_executor/layers/fused_moe/moe_permute_unpermute.py

+# SPDX-License-Identifier: Apache-2.0
+from typing import Optional, Tuple
+
+import torch
+
+
+def moe_permute(
+    hidden_states: torch.Tensor,
+    topk_weights: torch.Tensor,
+    topk_ids: torch.Tensor,
+    token_expert_indices: torch.Tensor,
+    topk: int,
+    n_expert: int,
+    n_local_expert: int,
+    expert_map: Optional[torch.Tensor] = None,
+    align_block_size: Optional[int] = None,
+    fill_invalid_expert: int = -1
+) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+    """
+    This function expands and permutes activation to gather uncontinuous tokens 
+      for each expert.
+    Parameters:
+    - hidden_states (torch.Tensor): The input tensor to the MoE layer.    
+    - topk_weights (torch.Tensor): topk expert route weight for each token.
+    - topk_ids (torch.Tensor): topk expert route id for each token.
+    - token_expert_indices (torch.Tensor): indice for expanded hidden.
+    - topk (int): The number of top-k experts to select.
+    - n_expert (int): The number of expert.
+    - n_local_expert (int): The number of expert in current EP rank.
+    - expert_map (Optional[torch.Tensor]):  A tensor mapping expert indices 
+        from the global expert space to the local expert space of the expert 
+        parallel shard.
+    - align_block_size (Optional[int]): align group gemm block size for deepgemm
+    - fill_invalid_expert(int): fill expert id in m_indices for invalid expert 
+      to workaround DeepGemm unsupported -1 in m_indices
+    Returns:
+    - permuted_hidden_states (torch.Tensor): permuted activation.
+    - expert_first_token_offset (torch.Tensor): offset of the first token
+       of each expert for standard grouped gemm. if enable 'align_block_size'
+       expert_first_token_offset will align up to 'align_block_size'.
+    - src_row_id2dst_row_id_map (torch.Tensor): idx map for moe_unpermute.
+    - m_indices: m_indices for grouped gemm in deepgemm,`m_indices[i]` records 
+    the group which the j-th row of the LHS belong to.`
+    """
+    n_token, n_hidden = hidden_states.shape
+    assert (n_hidden * hidden_states.element_size()
+            ) % 16 == 0, "permue kernel need hidden dim align to 16B"
+    permuted_row_size = n_token * topk
+    if align_block_size is not None:
+        permuted_row_size = (permuted_row_size + n_expert *
+                             (align_block_size - 1) + align_block_size -
+                             1) // align_block_size * align_block_size
+
+    permuted_hidden_states = torch.empty(
+        (permuted_row_size, n_hidden),
+        dtype=hidden_states.dtype,
+        device=hidden_states.device,
+    )
+    m_indices = torch.full((permuted_row_size, ),
+                           fill_invalid_expert,
+                           dtype=torch.int32,
+                           device=hidden_states.device)
+    expert_first_token_offset = torch.empty(n_local_expert + 1,
+                                            dtype=torch.int64,
+                                            device=hidden_states.device)
+    src_row_id2dst_row_id_map = torch.empty((n_token, topk),
+                                            dtype=torch.int32,
+                                            device=hidden_states.device)
+    torch.ops._moe_C.moe_permute(hidden_states, topk_weights, topk_ids,
+                                 token_expert_indices, expert_map, n_expert,
+                                 n_local_expert, topk, align_block_size,
+                                 permuted_hidden_states,
+                                 expert_first_token_offset,
+                                 src_row_id2dst_row_id_map, m_indices)
+    return (permuted_hidden_states, expert_first_token_offset,
+            src_row_id2dst_row_id_map, m_indices)
+
+
+def moe_unpermute(
+    permuted_hidden_states: torch.Tensor,
+    topk_weights: torch.Tensor,
+    topk_ids: torch.Tensor,
+    src_row_id2dst_row_id_map: torch.Tensor,
+    expert_first_token_offset: torch.Tensor,
+    topk: int,
+    n_expert: int,
+    n_local_expert: int,
+) -> torch.Tensor:
+    """
+    This function expands and permutes activation to gathering uncontinuous 
+      tokens for each expert.
+    Parameters:
+    - permuted_hidden_states (torch.Tensor): permuted activation.
+    - topk_weights (torch.Tensor): topk expert route weight for each token.
+    - topk_ids (torch.Tensor): topk expert route id for each token.
+    - expert_first_token_offset (torch.Tensor): offset of the first token
+       of each expert for grouped gemm.
+    - topk (int): The number of top-k experts to select.
+    - n_expert (int): The number of expert.
+    - n_local_expert (int): The number of expert in current EP rank.
+    Returns:
+    - hidden_states (torch.Tensor): The reduced and unpermuted activation 
+      tensor.  
+    """
+    n_token, n_hidden = topk_weights.shape[0], permuted_hidden_states.shape[-1]
+    assert (n_hidden * permuted_hidden_states.element_size()
+            ) % 16 == 0, "unpermue kernel need hidden dim align to 16B"
+    hidden_states = torch.empty((n_token, n_hidden),
+                                dtype=permuted_hidden_states.dtype,
+                                device=permuted_hidden_states.device)
+
+    torch.ops._moe_C.moe_unpermute(permuted_hidden_states, topk_weights,
+                                   topk_ids, src_row_id2dst_row_id_map,
+                                   expert_first_token_offset, n_expert,
+                                   n_local_expert, topk, hidden_states)
+    return hidden_states

vllm/model_executor/models/arctic.py

@@ -175,10 +175,8 @@ class ArcticMoE(nn.Module):
         # router_logits: (num_tokens, n_experts)
         router_logits, _ = self.gate(hidden_states)
         do_normalize = self.top_k > 1
-        topk_weights, topk_ids = fused_topk(hidden_states,
-                                            router_logits,
-                                            self.top_k,
-                                            renormalize=do_normalize)
+        topk_weights, topk_ids, token_expert_indices = fused_topk(
+            hidden_states, router_logits, self.top_k, renormalize=do_normalize)
         # topk_ids: (num_tokens, k)
         if self.is_quant:
             if 2 * num_tokens <= self.num_experts: