concat_mla.cu

#include <ATen/cuda/CUDAContext.h>
#include <ATen/cuda/CUDADataType.h>
#include <cuda_runtime.h>

#include "pytorch_extension_utils.h"

constexpr int NUM_LOCAL_HEADS = 128;
constexpr int QK_NOPE_HEAD_DIM = 128;
constexpr int QK_ROPE_HEAD_DIM = 64;
constexpr int K_HEAD_DIM = QK_NOPE_HEAD_DIM + QK_ROPE_HEAD_DIM;

constexpr int HEAD_CHUNK_SIZE = 16;
constexpr int NUM_HEAD_CHUNKS = NUM_LOCAL_HEADS / HEAD_CHUNK_SIZE;

__forceinline__ __device__ int get_lane_id() {
  int lane_id;
  asm("mov.s32 %0, %laneid;" : "=r"(lane_id));
  return lane_id;
}

int ceil_div(int a, int b) {
  return (a + b - 1) / b;
}

__global__ void concat_mla_k_kernel(
    nv_bfloat16* k,
    nv_bfloat16* k_nope,
    nv_bfloat16* k_rope,
    const int num_tokens,
    const int k_stride_0,
    const int k_stride_1,
    const int k_nope_stride_0,
    const int k_nope_stride_1,
    const int k_rope_stride_0) {
  const int flat_warp_id = (blockIdx.x * blockDim.x + threadIdx.x) / 32;
  const int token_id = flat_warp_id / NUM_HEAD_CHUNKS;
  const int head_chunk_id = flat_warp_id % NUM_HEAD_CHUNKS;
  const int lane_id = get_lane_id();

  if (token_id >= num_tokens) {
    return;
  }

  using KNopeBufType = int2;
  static_assert(sizeof(KNopeBufType) == QK_NOPE_HEAD_DIM * sizeof(k[0]) / 32);
  KNopeBufType k_nope_buf[HEAD_CHUNK_SIZE];

  using KRopeBufType = int;
  static_assert(sizeof(KRopeBufType) == QK_ROPE_HEAD_DIM * sizeof(k[0]) / 32);
  KRopeBufType k_rope_buf;

  {
    const int* base_addr = reinterpret_cast<int*>(k_rope + token_id * k_rope_stride_0);
    k_rope_buf = *(base_addr + lane_id);
  }

#pragma unroll
  for (int i = 0; i < HEAD_CHUNK_SIZE; ++i) {
    const int head_id = head_chunk_id * HEAD_CHUNK_SIZE + i;
    const int2* base_addr = reinterpret_cast<int2*>(k_nope + token_id * k_nope_stride_0 + head_id * k_nope_stride_1);
    k_nope_buf[i] = *(base_addr + lane_id);
  }

#pragma unroll
  for (int i = 0; i < HEAD_CHUNK_SIZE; ++i) {
    const int head_id = head_chunk_id * HEAD_CHUNK_SIZE + i;

    {
      int2* base_addr = reinterpret_cast<int2*>(k + token_id * k_stride_0 + head_id * k_stride_1);
      *(base_addr + lane_id) = k_nope_buf[i];
    }
    {
      int* base_addr = reinterpret_cast<int*>(k + token_id * k_stride_0 + head_id * k_stride_1 + QK_NOPE_HEAD_DIM);
      *(base_addr + lane_id) = k_rope_buf;
    }
  }
}

inline void check_tensor(const at::Tensor& t, int64_t shape0, int64_t shape1, int64_t shape2, c10::ScalarType dtype) {
  TORCH_CHECK_EQ(t.dim(), 3);
  TORCH_CHECK_EQ(t.size(0), shape0);
  TORCH_CHECK_EQ(t.size(1), shape1);
  TORCH_CHECK_EQ(t.size(2), shape2);
  TORCH_CHECK_EQ(t.dtype(), dtype);
  TORCH_CHECK(t.device().is_cuda());
  TORCH_CHECK_EQ(((int64_t)t.data_ptr()) % 16, 0);  // alignment
}

void concat_mla_k(at::Tensor k, at::Tensor k_nope, at::Tensor k_rope) {
  const int num_tokens = k.size(0);

  check_tensor(k, num_tokens, NUM_LOCAL_HEADS, K_HEAD_DIM, at::kBFloat16);
  check_tensor(k_nope, num_tokens, NUM_LOCAL_HEADS, QK_NOPE_HEAD_DIM, at::kBFloat16);
  check_tensor(k_rope, num_tokens, 1, QK_ROPE_HEAD_DIM, at::kBFloat16);
  TORCH_CHECK_EQ(k.stride(2), 1);
  TORCH_CHECK_EQ(k_nope.stride(2), 1);
  TORCH_CHECK_EQ(k_rope.stride(2), 1);

  const auto stream = at::cuda::getCurrentCUDAStream().stream();

  constexpr int num_warps_per_block = 32;
  const int grid_size = ceil_div(num_tokens * NUM_HEAD_CHUNKS, num_warps_per_block);
  const int block_size = num_warps_per_block * 32;

  concat_mla_k_kernel<<<grid_size, block_size, 0, stream>>>(
      reinterpret_cast<nv_bfloat16*>(k.data_ptr()),
      reinterpret_cast<nv_bfloat16*>(k_nope.data_ptr()),
      reinterpret_cast<nv_bfloat16*>(k_rope.data_ptr()),
      num_tokens,
      k.stride(0),
      k.stride(1),
      k_nope.stride(0),
      k_nope.stride(1),
      k_rope.stride(0));
  cudaError_t err = cudaGetLastError();
  TORCH_CHECK(err == cudaSuccess, "CUDA kernel launch failed: ", cudaGetErrorString(err));
}