aphrodite-engine/kernels/moe/topk_softmax_kernels.cu

/*
 * Adapted from
 * https://github.com/NVIDIA/TensorRT-LLM/blob/v0.7.1/cpp/tensorrt_llm/kernels/mixtureOfExperts/moe_kernels.cu
 * Copyright (c) 2024, The vLLM team.
 * SPDX-FileCopyrightText: Copyright (c) 1993-2023 NVIDIA CORPORATION &
 * AFFILIATES. All rights reserved. SPDX-License-Identifier: Apache-2.0
 *
 * 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 <torch/all.h>
#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAGuard.h>
#include "../cuda_compat.h"

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

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

namespace aphrodite {
namespace moe {

/// 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 {
  float data[N];
};

// ====================== 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 <int TPB>
__launch_bounds__(TPB) __global__
    void moeSoftmax(const float* 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;

  cub::Sum sum;
  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(static_cast<float>(input[idx]), threadData);
  }

  const float maxElem = BlockReduce(tmpStorage).Reduce(threadData, cub::Max());
  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((static_cast<float>(input[idx]) - float_max));
  }

  const auto Z = BlockReduce(tmpStorage).Reduce(threadData, sum);

  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((static_cast<float>(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, int* source_rows,
                 const int num_experts, const int k, const int start_expert,
                 const int end_expert) {
  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 num_rows = gridDim.x;
  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;
  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);
      source_rows[idx] = k_idx * num_rows + block_row;
    }
    __syncthreads();
  }
}

// ====================== 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 <int VPT, int NUM_EXPERTS, int WARPS_PER_CTA, int BYTES_PER_LDG>
__launch_bounds__(WARPS_PER_CTA* WARP_SIZE) __global__
    void topkGatingSoftmax(const float* input, const bool* finished,
                           float* output, const int num_rows, int* indices,
                           int* source_rows, const int k,
                           const int start_expert, const int end_expert) {
  // 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(float);
  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 float* 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 float* 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<float, ELTS_PER_LDG>;

  // Finally, we pull in the data from global mem
  float row_chunk[VPT];
  AccessType* row_chunk_vec_ptr = reinterpret_cast<AccessType*>(&row_chunk);
  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];
  }

  // 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, APHRODITE_SHFL_XOR_SYNC_WIDTH(
                                     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 += APHRODITE_SHFL_XOR_SYNC_WIDTH(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;

  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 =
          APHRODITE_SHFL_XOR_SYNC_WIDTH(max_val, mask, THREADS_PER_ROW);
      int other_expert =
          APHRODITE_SHFL_XOR_SYNC_WIDTH(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;
      source_rows[idx] = k_idx * num_rows + thread_row;
    }

    // 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;
      }
    }
  }
}

namespace detail {
// Constructs some constants needed to partition the work across threads at
// compile time.
template <int EXPERTS, int BYTES_PER_LDG>
struct TopkConstants {
  static constexpr int ELTS_PER_LDG = BYTES_PER_LDG / sizeof(float);
  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 <int EXPERTS, int WARPS_PER_TB>
void topkGatingSoftmaxLauncherHelper(const float* input, const bool* finished,
                                     float* output, int* indices,
                                     int* source_row, const int num_rows,
                                     const int k, const int start_expert,
                                     const int end_expert,
                                     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(float) * EXPERTS);
  using Constants = detail::TopkConstants<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<VPT, EXPERTS, WARPS_PER_TB, BYTES_PER_LDG>
      <<<num_blocks, block_dim, 0, stream>>>(input, finished, output, num_rows,
                                             indices, source_row, k,
                                             start_expert, end_expert);
}

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

void topkGatingSoftmaxKernelLauncher(
    const float* gating_output, float* topk_weights, int* topk_indicies,
    int* token_expert_indices, float* softmax_workspace, const int num_tokens,
    const int num_experts, const int topk, cudaStream_t stream) {
  static constexpr int WARPS_PER_TB = 4;
  switch (num_experts) {
    case 1:
      LAUNCH_SOFTMAX(1, WARPS_PER_TB);
      break;
    case 2:
      LAUNCH_SOFTMAX(2, WARPS_PER_TB);
      break;
    case 4:
      LAUNCH_SOFTMAX(4, WARPS_PER_TB);
      break;
    case 8:
      LAUNCH_SOFTMAX(8, WARPS_PER_TB);
      break;
    case 16:
      LAUNCH_SOFTMAX(16, WARPS_PER_TB);
      break;
    case 32:
      LAUNCH_SOFTMAX(32, WARPS_PER_TB);
      break;
    case 64:
      LAUNCH_SOFTMAX(64, WARPS_PER_TB);
      break;
    case 128:
      LAUNCH_SOFTMAX(128, WARPS_PER_TB);
      break;
    case 256:
      LAUNCH_SOFTMAX(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<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_indicies,
          token_expert_indices, num_experts, topk, 0, num_experts);
    }
  }
}

}  // namespace moe
}  // namespace aphrodite

void topk_softmax(torch::Tensor& topk_weights,          // [num_tokens, topk]
                  torch::Tensor& topk_indices,          // [num_tokens, topk]
                  torch::Tensor& token_expert_indices,  // [num_tokens, topk]
                  torch::Tensor& gating_output)  // [num_tokens, num_experts]
{
  const int num_experts = gating_output.size(-1);
  const int num_tokens = gating_output.numel() / num_experts;
  const int topk = 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());
  aphrodite::moe::topkGatingSoftmaxKernelLauncher(
      gating_output.data_ptr<float>(), topk_weights.data_ptr<float>(),
      topk_indices.data_ptr<int>(), token_expert_indices.data_ptr<int>(),
      softmax_workspace.data_ptr<float>(), num_tokens, num_experts, topk,
      stream);
}