// Adapt from https://github.com/vllm-project/vllm/blob/v0.7.3/csrc/moe/topk_softmax_kernels.cu
// which is originally adapted from
// https://github.com/NVIDIA/TensorRT-LLM/blob/v0.7.1/cpp/tensorrt_llm/kernels/mixtureOfExperts/moe_kernels.cu
/* Copyright 2025 SGLang Team. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAGuard.h>
#include <torch/all.h>

#ifndef USE_ROCM
#include <cub/cub.cuh>
#include <cub/util_type.cuh>
#include <cuda/functional>
#else
#include <hipcub/hipcub.hpp>
#include <hipcub/util_type.hpp>
#endif

#include "utils.h"

#define MAX(a, b) ((a) > (b) ? (a) : (b))
#define MIN(a, b) ((a) < (b) ? (a) : (b))

// Define reduction operators based on CUDA version
// CUDA 13 (12.9+) deprecated cub::Max/Min in favor of cuda::maximum/minimum
#if CUDA_VERSION >= 12090
using MaxReduceOp = cuda::maximum<>;
using MinReduceOp = cuda::minimum<>;
#else
using MaxReduceOp = cub::Max;
using MinReduceOp = cub::Min;
#endif

/// Aligned array type
template <
    typename T,
    /// Number of elements in the array
    int N,
    /// Alignment requirement in bytes
    int Alignment = sizeof(T) * N>
class alignas(Alignment) AlignedArray {
  T data[N];
};

// ========================== Util functions to convert types ==========================
template <typename T>
__device__ float convert_to_float(T x) {
  if constexpr (std::is_same_v<T, __half>) {
    return __half2float(x);
  } else if constexpr (std::is_same_v<T, __nv_bfloat16>) {
    return __bfloat162float(x);
  } else if constexpr (std::is_same_v<T, float>) {
    return x;
  } else {
    return static_cast<float>(x);
  }
}

// ====================== Softmax things ===============================
// We have our own implementation of softmax here so we can support transposing the output
// in the softmax kernel when we extend this module to support expert-choice routing.
template <typename T, int TPB>
__launch_bounds__(TPB) __global__
    void moeSoftmax(const T* input, const bool* finished, float* output, const int num_cols) {
  using BlockReduce = cub::BlockReduce<float, TPB>;
  __shared__ typename BlockReduce::TempStorage tmpStorage;

  __shared__ float normalizing_factor;
  __shared__ float float_max;

  const int thread_row_offset = blockIdx.x * num_cols;

  float threadData(-FLT_MAX);

  // Don't touch finished rows.
  if ((finished != nullptr) && finished[blockIdx.x]) {
    return;
  }

  for (int ii = threadIdx.x; ii < num_cols; ii += TPB) {
    const int idx = thread_row_offset + ii;
    threadData = max(convert_to_float<T>(input[idx]), threadData);
  }

  const float maxElem = BlockReduce(tmpStorage).Reduce(threadData, MaxReduceOp());

  if (threadIdx.x == 0) {
    float_max = maxElem;
  }
  __syncthreads();

  threadData = 0;

  for (int ii = threadIdx.x; ii < num_cols; ii += TPB) {
    const int idx = thread_row_offset + ii;
    threadData += exp((convert_to_float<T>(input[idx]) - float_max));
  }

  const auto Z = BlockReduce(tmpStorage).Sum(threadData);

  if (threadIdx.x == 0) {
    normalizing_factor = 1.f / Z;
  }
  __syncthreads();

  for (int ii = threadIdx.x; ii < num_cols; ii += TPB) {
    const int idx = thread_row_offset + ii;
    const float val = exp((convert_to_float<T>(input[idx]) - float_max)) * normalizing_factor;
    output[idx] = val;
  }
}

template <int TPB>
__launch_bounds__(TPB) __global__ void moeTopK(
    const float* inputs_after_softmax,
    const bool* finished,
    float* output,
    int* indices,
    const int num_experts,
    const int k,
    const int start_expert,
    const int end_expert,
    const bool renormalize) {
  using cub_kvp = cub::KeyValuePair<int, float>;
  using BlockReduce = cub::BlockReduce<cub_kvp, TPB>;
  __shared__ typename BlockReduce::TempStorage tmpStorage;

  cub_kvp thread_kvp;
  cub::ArgMax arg_max;

  const int block_row = blockIdx.x;

  const bool row_is_active = finished ? !finished[block_row] : true;
  const int thread_read_offset = blockIdx.x * num_experts;
  float row_sum_for_renormalize = 0;
  for (int k_idx = 0; k_idx < k; ++k_idx) {
    thread_kvp.key = 0;
    thread_kvp.value = -1.f;  // This is OK because inputs are probabilities

    cub_kvp inp_kvp;
    for (int expert = threadIdx.x; expert < num_experts; expert += TPB) {
      const int idx = thread_read_offset + expert;
      inp_kvp.key = expert;
      inp_kvp.value = inputs_after_softmax[idx];

      for (int prior_k = 0; prior_k < k_idx; ++prior_k) {
        const int prior_winning_expert = indices[k * block_row + prior_k];

        if (prior_winning_expert == expert) {
          inp_kvp = thread_kvp;
        }
      }

      thread_kvp = arg_max(inp_kvp, thread_kvp);
    }

    const cub_kvp result_kvp = BlockReduce(tmpStorage).Reduce(thread_kvp, arg_max);
    if (threadIdx.x == 0) {
      // Ignore experts the node isn't responsible for with expert parallelism
      const int expert = result_kvp.key;
      const bool node_uses_expert = expert >= start_expert && expert < end_expert;
      const bool should_process_row = row_is_active && node_uses_expert;

      const int idx = k * block_row + k_idx;
      output[idx] = result_kvp.value;
      indices[idx] = should_process_row ? (expert - start_expert) : num_experts;
      assert(indices[idx] >= 0);
      row_sum_for_renormalize += result_kvp.value;
    }
    __syncthreads();
  }

  if (renormalize && threadIdx.x == 0) {
    float row_sum_for_renormalize_inv = 1.f / row_sum_for_renormalize;
    for (int k_idx = 0; k_idx < k; ++k_idx) {
      const int idx = k * block_row + k_idx;
      output[idx] = output[idx] * row_sum_for_renormalize_inv;
    }
  }
}

// ====================== TopK softmax things ===============================

/*
  A Top-K gating softmax written to exploit when the number of experts in the MoE layers
  are a small power of 2. This allows us to cleanly share the rows among the threads in
  a single warp and eliminate communication between warps (so no need to use shared mem).

  It fuses the softmax, max and argmax into a single kernel.

  Limitations:
  1) This implementation is intended for when the number of experts is a small power of 2.
  2) This implementation assumes k is small, but will work for any k.
*/

template <typename T, int VPT, int NUM_EXPERTS, int WARPS_PER_CTA, int BYTES_PER_LDG>
__launch_bounds__(WARPS_PER_CTA* WARP_SIZE) __global__ void topkGatingSoftmax(
    const T* input,
    const bool* finished,
    float* output,
    const int num_rows,
    int* indices,
    const int k,
    const int start_expert,
    const int end_expert,
    const bool renormalize) {
  // We begin by enforcing compile time assertions and setting up compile time constants.
  static_assert(VPT == (VPT & -VPT), "VPT must be power of 2");
  static_assert(NUM_EXPERTS == (NUM_EXPERTS & -NUM_EXPERTS), "NUM_EXPERTS must be power of 2");
  static_assert(BYTES_PER_LDG == (BYTES_PER_LDG & -BYTES_PER_LDG), "BYTES_PER_LDG must be power of 2");
  static_assert(BYTES_PER_LDG <= 16, "BYTES_PER_LDG must be leq 16");

  // Number of bytes each thread pulls in per load
  static constexpr int ELTS_PER_LDG = BYTES_PER_LDG / sizeof(T);
  static constexpr int ELTS_PER_ROW = NUM_EXPERTS;
  static constexpr int THREADS_PER_ROW = ELTS_PER_ROW / VPT;
  static constexpr int LDG_PER_THREAD = VPT / ELTS_PER_LDG;

  // Restrictions based on previous section.
  static_assert(VPT % ELTS_PER_LDG == 0, "The elements per thread must be a multiple of the elements per ldg");
  static_assert(WARP_SIZE % THREADS_PER_ROW == 0, "The threads per row must cleanly divide the threads per warp");
  static_assert(THREADS_PER_ROW == (THREADS_PER_ROW & -THREADS_PER_ROW), "THREADS_PER_ROW must be power of 2");
  static_assert(THREADS_PER_ROW <= WARP_SIZE, "THREADS_PER_ROW can be at most warp size");

  // We have NUM_EXPERTS elements per row. We specialize for small #experts
  static constexpr int ELTS_PER_WARP = WARP_SIZE * VPT;
  static constexpr int ROWS_PER_WARP = ELTS_PER_WARP / ELTS_PER_ROW;
  static constexpr int ROWS_PER_CTA = WARPS_PER_CTA * ROWS_PER_WARP;

  // Restrictions for previous section.
  static_assert(ELTS_PER_WARP % ELTS_PER_ROW == 0, "The elts per row must cleanly divide the total elt per warp");

  // ===================== From this point, we finally start computing run-time variables. ========================

  // Compute CTA and warp rows. We pack multiple rows into a single warp, and a block contains WARPS_PER_CTA warps.
  // This, each block processes a chunk of rows. We start by computing the start row for each block.
  const int cta_base_row = blockIdx.x * ROWS_PER_CTA;

  // Now, using the base row per thread block, we compute the base row per warp.
  const int warp_base_row = cta_base_row + threadIdx.y * ROWS_PER_WARP;

  // The threads in a warp are split into sub-groups that will work on a row.
  // We compute row offset for each thread sub-group
  const int thread_row_in_warp = threadIdx.x / THREADS_PER_ROW;
  const int thread_row = warp_base_row + thread_row_in_warp;

  // Threads with indices out of bounds should early exit here.
  if (thread_row >= num_rows) {
    return;
  }
  const bool row_is_active = finished ? !finished[thread_row] : true;

  // We finally start setting up the read pointers for each thread. First, each thread jumps to the start of the
  // row it will read.
  const T* thread_row_ptr = input + thread_row * ELTS_PER_ROW;

  // Now, we compute the group each thread belong to in order to determine the first column to start loads.
  const int thread_group_idx = threadIdx.x % THREADS_PER_ROW;
  const int first_elt_read_by_thread = thread_group_idx * ELTS_PER_LDG;
  const T* thread_read_ptr = thread_row_ptr + first_elt_read_by_thread;

  // Determine the pointer type to use to read in the data depending on the BYTES_PER_LDG template param. In theory,
  // this can support all powers of 2 up to 16.
  // NOTE(woosuk): The original implementation uses CUTLASS aligned array here.
  // We defined our own aligned array and use it here to avoid the dependency on CUTLASS.
  using AccessType = AlignedArray<T, ELTS_PER_LDG>;

  // Finally, we pull in the data from global mem
  T row_chunk_temp[VPT];
  AccessType* row_chunk_vec_ptr = reinterpret_cast<AccessType*>(&row_chunk_temp);
  const AccessType* vec_thread_read_ptr = reinterpret_cast<const AccessType*>(thread_read_ptr);
#pragma unroll
  for (int ii = 0; ii < LDG_PER_THREAD; ++ii) {
    row_chunk_vec_ptr[ii] = vec_thread_read_ptr[ii * THREADS_PER_ROW];
  }

  float row_chunk[VPT];
#pragma unroll
  for (int ii = 0; ii < VPT; ++ii) {
    row_chunk[ii] = convert_to_float<T>(row_chunk_temp[ii]);
  }

  // First, we perform a max reduce within the thread. We can do the max in fp16 safely (I think) and just
  // convert to float afterwards for the exp + sum reduction.
  float thread_max = row_chunk[0];
#pragma unroll
  for (int ii = 1; ii < VPT; ++ii) {
    thread_max = max(thread_max, row_chunk[ii]);
  }

// Now, we find the max within the thread group and distribute among the threads. We use a butterfly reduce.
#pragma unroll
  for (int mask = THREADS_PER_ROW / 2; mask > 0; mask /= 2) {
    thread_max = max(thread_max, SGLANG_SHFL_XOR_SYNC_WIDTH(0xffffffff, thread_max, mask, THREADS_PER_ROW));
  }

  // From this point, thread max in all the threads have the max within the row.
  // Now, we subtract the max from each element in the thread and take the exp. We also compute the thread local sum.
  float row_sum = 0;
#pragma unroll
  for (int ii = 0; ii < VPT; ++ii) {
    row_chunk[ii] = expf(row_chunk[ii] - thread_max);
    row_sum += row_chunk[ii];
  }

// Now, we perform the sum reduce within each thread group. Similar to the max reduce, we use a bufferfly pattern.
#pragma unroll
  for (int mask = THREADS_PER_ROW / 2; mask > 0; mask /= 2) {
    row_sum += SGLANG_SHFL_XOR_SYNC_WIDTH(0xffffffff, row_sum, mask, THREADS_PER_ROW);
  }

  // From this point, all threads have the max and the sum for their rows in the thread_max and thread_sum variables
  // respectively. Finally, we can scale the rows for the softmax. Technically, for top-k gating we don't need to
  // compute the entire softmax row. We can likely look at the maxes and only compute for the top-k values in the row.
  // However, this kernel will likely not be a bottle neck and it seems better to closer match torch and find the
  // argmax after computing the softmax.
  const float reciprocal_row_sum = 1.f / row_sum;

#pragma unroll
  for (int ii = 0; ii < VPT; ++ii) {
    row_chunk[ii] = row_chunk[ii] * reciprocal_row_sum;
  }

  // Now, softmax_res contains the softmax of the row chunk. Now, I want to find the topk elements in each row, along
  // with the max index.
  int start_col = first_elt_read_by_thread;
  static constexpr int COLS_PER_GROUP_LDG = ELTS_PER_LDG * THREADS_PER_ROW;

  float row_sum_for_renormalize = 0;

  for (int k_idx = 0; k_idx < k; ++k_idx) {
    // First, each thread does the local argmax
    float max_val = row_chunk[0];
    int expert = start_col;
#pragma unroll
    for (int ldg = 0, col = start_col; ldg < LDG_PER_THREAD; ++ldg, col += COLS_PER_GROUP_LDG) {
#pragma unroll
      for (int ii = 0; ii < ELTS_PER_LDG; ++ii) {
        float val = row_chunk[ldg * ELTS_PER_LDG + ii];

        // No check on the experts here since columns with the smallest index are processed first and only
        // updated if > (not >=)
        if (val > max_val) {
          max_val = val;
          expert = col + ii;
        }
      }
    }

// Now, we perform the argmax reduce. We use the butterfly pattern so threads reach consensus about the max.
// This will be useful for K > 1 so that the threads can agree on "who" had the max value. That thread can
// then blank out their max with -inf and the warp can run more iterations...
#pragma unroll
    for (int mask = THREADS_PER_ROW / 2; mask > 0; mask /= 2) {
      float other_max = SGLANG_SHFL_XOR_SYNC_WIDTH(0xffffffff, max_val, mask, THREADS_PER_ROW);
      int other_expert = SGLANG_SHFL_XOR_SYNC_WIDTH(0xffffffff, expert, mask, THREADS_PER_ROW);

      // We want lower indices to "win" in every thread so we break ties this way
      if (other_max > max_val || (other_max == max_val && other_expert < expert)) {
        max_val = other_max;
        expert = other_expert;
      }
    }

    // Write the max for this k iteration to global memory.
    if (thread_group_idx == 0) {
      // Add a guard to ignore experts not included by this node
      const bool node_uses_expert = expert >= start_expert && expert < end_expert;
      const bool should_process_row = row_is_active && node_uses_expert;

      // The lead thread from each sub-group will write out the final results to global memory. (This will be a
      // single) thread per row of the input/output matrices.
      const int idx = k * thread_row + k_idx;
      output[idx] = max_val;
      indices[idx] = should_process_row ? (expert - start_expert) : NUM_EXPERTS;
      row_sum_for_renormalize += max_val;
    }

    // Finally, we clear the value in the thread with the current max if there is another iteration to run.
    if (k_idx + 1 < k) {
      const int ldg_group_for_expert = expert / COLS_PER_GROUP_LDG;
      const int thread_to_clear_in_group = (expert / ELTS_PER_LDG) % THREADS_PER_ROW;

      // Only the thread in the group which produced the max will reset the "winning" value to -inf.
      if (thread_group_idx == thread_to_clear_in_group) {
        const int offset_for_expert = expert % ELTS_PER_LDG;
        // Safe to set to any negative value since row_chunk values must be between 0 and 1.
        row_chunk[ldg_group_for_expert * ELTS_PER_LDG + offset_for_expert] = -10000.f;
      }
    }
  }

  // Fuse renormalization of topk_weights into this kernel
  if (renormalize && thread_group_idx == 0) {
    float row_sum_for_renormalize_inv = 1.f / row_sum_for_renormalize;
#pragma unroll
    for (int k_idx = 0; k_idx < k; ++k_idx) {
      const int idx = k * thread_row + k_idx;
      output[idx] = output[idx] * row_sum_for_renormalize_inv;
    }
  }
}

namespace detail {
// Constructs some constants needed to partition the work across threads at compile time.
template <typename T, int EXPERTS, int BYTES_PER_LDG>
struct TopkConstants {
  static constexpr int ELTS_PER_LDG = BYTES_PER_LDG / sizeof(T);
  static_assert(EXPERTS / (ELTS_PER_LDG * WARP_SIZE) == 0 || EXPERTS % (ELTS_PER_LDG * WARP_SIZE) == 0, "");
  static constexpr int VECs_PER_THREAD = MAX(1, EXPERTS / (ELTS_PER_LDG * WARP_SIZE));
  static constexpr int VPT = VECs_PER_THREAD * ELTS_PER_LDG;
  static constexpr int THREADS_PER_ROW = EXPERTS / VPT;
  static constexpr int ROWS_PER_WARP = WARP_SIZE / THREADS_PER_ROW;
};
}  // namespace detail

template <typename T, int EXPERTS, int WARPS_PER_TB>
void topkGatingSoftmaxLauncherHelper(
    const T* input,
    const bool* finished,
    float* output,
    int* indices,
    const int num_rows,
    const int k,
    const int start_expert,
    const int end_expert,
    const bool renormalize,
    cudaStream_t stream) {
  static constexpr std::size_t MAX_BYTES_PER_LDG = 16;

  static constexpr int BYTES_PER_LDG = MIN(MAX_BYTES_PER_LDG, sizeof(T) * EXPERTS);
  using Constants = detail::TopkConstants<T, EXPERTS, BYTES_PER_LDG>;
  static constexpr int VPT = Constants::VPT;
  static constexpr int ROWS_PER_WARP = Constants::ROWS_PER_WARP;
  const int num_warps = (num_rows + ROWS_PER_WARP - 1) / ROWS_PER_WARP;
  const int num_blocks = (num_warps + WARPS_PER_TB - 1) / WARPS_PER_TB;

  dim3 block_dim(WARP_SIZE, WARPS_PER_TB);
  topkGatingSoftmax<T, VPT, EXPERTS, WARPS_PER_TB, BYTES_PER_LDG><<<num_blocks, block_dim, 0, stream>>>(
      input, finished, output, num_rows, indices, k, start_expert, end_expert, renormalize);
}

#define LAUNCH_SOFTMAX(TYPE, NUM_EXPERTS, WARPS_PER_TB)             \
  topkGatingSoftmaxLauncherHelper<TYPE, NUM_EXPERTS, WARPS_PER_TB>( \
      gating_output, nullptr, topk_weights, topk_indices, num_tokens, topk, 0, num_experts, renormalize, stream);

template <typename T>
void topkGatingSoftmaxKernelLauncher(
    const T* gating_output,
    float* topk_weights,
    int* topk_indices,
    float* softmax_workspace,
    const int num_tokens,
    const int num_experts,
    const int topk,
    const bool renormalize,
    cudaStream_t stream) {
  static constexpr int WARPS_PER_TB = 4;
  switch (num_experts) {
    case 1:
      LAUNCH_SOFTMAX(T, 1, WARPS_PER_TB);
      break;
    case 2:
      LAUNCH_SOFTMAX(T, 2, WARPS_PER_TB);
      break;
    case 4:
      LAUNCH_SOFTMAX(T, 4, WARPS_PER_TB);
      break;
    case 8:
      LAUNCH_SOFTMAX(T, 8, WARPS_PER_TB);
      break;
    case 16:
      LAUNCH_SOFTMAX(T, 16, WARPS_PER_TB);
      break;
    case 32:
      LAUNCH_SOFTMAX(T, 32, WARPS_PER_TB);
      break;
    case 64:
      LAUNCH_SOFTMAX(T, 64, WARPS_PER_TB);
      break;
    case 128:
      LAUNCH_SOFTMAX(T, 128, WARPS_PER_TB);
      break;
    case 256:
      LAUNCH_SOFTMAX(T, 256, WARPS_PER_TB);
      break;
    default: {
      TORCH_CHECK(
          softmax_workspace != nullptr,
          "softmax_workspace must be provided for num_experts that are not a power of 2.");
      static constexpr int TPB = 256;
      moeSoftmax<T, TPB><<<num_tokens, TPB, 0, stream>>>(gating_output, nullptr, softmax_workspace, num_experts);
      moeTopK<TPB><<<num_tokens, TPB, 0, stream>>>(
          softmax_workspace, nullptr, topk_weights, topk_indices, num_experts, topk, 0, num_experts, renormalize);
    }
  }
}

void topk_softmax(
    torch::Tensor& topk_weights,  // [num_tokens, topk]
    torch::Tensor& topk_indices,  // [num_tokens, topk]
    torch::Tensor& gating_output,
    const bool renormalize)  // [num_tokens, num_experts]
{
  // Check data type
  TORCH_CHECK(
      gating_output.scalar_type() == at::ScalarType::Float || gating_output.scalar_type() == at::ScalarType::Half ||
          gating_output.scalar_type() == at::ScalarType::BFloat16,
      "gating_output must be float32, float16, or bfloat16");

  // Check dimensions
  TORCH_CHECK(gating_output.dim() == 2, "gating_output must be 2D tensor [num_tokens, num_experts]");
  TORCH_CHECK(topk_weights.dim() == 2, "topk_weights must be 2D tensor [num_tokens, topk]");
  TORCH_CHECK(topk_indices.dim() == 2, "topk_indices must be 2D tensor [num_tokens, topk]");

  // Check shapes
  TORCH_CHECK(
      gating_output.size(0) == topk_weights.size(0),
      "First dimension of topk_weights must match num_tokens in gating_output");
  TORCH_CHECK(
      gating_output.size(0) == topk_indices.size(0),
      "First dimension of topk_indices must match num_tokens in gating_output");
  TORCH_CHECK(
      topk_weights.size(-1) == topk_indices.size(-1),
      "Second dimension of topk_indices must match topk in topk_weights");
  TORCH_CHECK(topk_weights.size(-1) <= gating_output.size(-1), "topk must be less than or equal to num_experts");

  const int num_experts = static_cast<int>(gating_output.size(-1));
  const int num_tokens = static_cast<int>(gating_output.size(0));
  const int topk = static_cast<int>(topk_weights.size(-1));

  const bool is_pow_2 = (num_experts != 0) && ((num_experts & (num_experts - 1)) == 0);
  const bool needs_workspace = !is_pow_2 || num_experts > 256;
  const int64_t workspace_size = needs_workspace ? num_tokens * num_experts : 0;

  const at::cuda::OptionalCUDAGuard device_guard(device_of(gating_output));
  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
  torch::Tensor softmax_workspace =
      torch::empty({workspace_size}, gating_output.options().dtype(at::ScalarType::Float));

  const at::ScalarType dtype = gating_output.scalar_type();
  if (dtype == at::ScalarType::Float) {
    topkGatingSoftmaxKernelLauncher<float>(
        gating_output.data_ptr<float>(),
        topk_weights.data_ptr<float>(),
        topk_indices.data_ptr<int>(),
        softmax_workspace.data_ptr<float>(),
        num_tokens,
        num_experts,
        topk,
        renormalize,
        stream);
  } else if (dtype == at::ScalarType::Half) {
    topkGatingSoftmaxKernelLauncher<__half>(
        reinterpret_cast<const __half*>(gating_output.data_ptr<at::Half>()),
        topk_weights.data_ptr<float>(),
        topk_indices.data_ptr<int>(),
        softmax_workspace.data_ptr<float>(),
        num_tokens,
        num_experts,
        topk,
        renormalize,
        stream);
  } else if (dtype == at::ScalarType::BFloat16) {
    topkGatingSoftmaxKernelLauncher<__nv_bfloat16>(
        reinterpret_cast<const __nv_bfloat16*>(gating_output.data_ptr<at::BFloat16>()),
        topk_weights.data_ptr<float>(),
        topk_indices.data_ptr<int>(),
        softmax_workspace.data_ptr<float>(),
        num_tokens,
        num_experts,
        topk,
        renormalize,
        stream);
  } else {
    TORCH_CHECK(false, "Unsupported gating_output dtype: ", dtype);
  }
}
