gemm.h

#pragma once

#include <ATen/native/CPUBlas.h>

// amx-bf16
#define TILE_M 16
#define TILE_N 16
#define TILE_K 32

// block size for AMX gemm
constexpr int block_size_m() {
  return 2 * TILE_M;
}
constexpr int block_size_n() {
  return 2 * TILE_N;
}

// define threshold using brgemm (intel AMX)
template <typename T>
inline bool can_use_brgemm(int M);
template <>
inline bool can_use_brgemm<at::BFloat16>(int M) {
  return M > 4;
}
template <>
inline bool can_use_brgemm<at::Half>(int M) {
  return true;
}
// TODO: add u8s8 brgemm, this requires PyTorch 2.7
template <>
inline bool can_use_brgemm<int8_t>(int M) {
  return false;
}

// work around compiler internal error
#define BLOCK_K 128  // 4 * TILE_K

// adjust leading dimension size for K
template <typename T>
inline int64_t get_row_size(int64_t K) {
  return K;
}

template <>
inline int64_t get_row_size<int8_t>(int64_t K) {
  return K + sizeof(int32_t);
}

inline int64_t get_row_size(int64_t K, bool use_int8_w8a8) {
  return use_int8_w8a8 ? K + sizeof(int32_t) : K;
}

// pack weight to vnni format
at::Tensor convert_weight_packed(at::Tensor& weight);

// moe implementations for int8 w8a8
template <typename scalar_t>
void fused_experts_int8_kernel_impl(
    scalar_t* __restrict__ output,
    scalar_t* __restrict__ ic1,
    scalar_t* __restrict__ ic2,
    uint8_t* __restrict__ A_tmp,
    float* __restrict__ C_tmp,
    uint8_t* __restrict__ Aq_tmp,
    float* __restrict__ As_tmp,
    const scalar_t* __restrict__ input,
    const int8_t* __restrict__ packed_w1,
    const int8_t* __restrict__ packed_w2,
    const float* __restrict__ w1s,
    const float* __restrict__ w2s,
    const float* __restrict__ topk_weights,
    const int32_t* __restrict__ sorted_ids,
    const int32_t* __restrict__ expert_ids,
    const int32_t* __restrict__ offsets,
    int64_t M,
    int64_t N,
    int64_t K,
    int64_t E,
    int64_t topk,
    int64_t num_tokens_post_pad);

// shared expert implememntation for int8 w8a8
template <typename scalar_t>
void shared_expert_int8_kernel_impl(
    scalar_t* __restrict__ output,
    scalar_t* __restrict__ ic1,
    float* __restrict__ C_tmp,
    uint8_t* __restrict__ Aq_tmp,
    float* __restrict__ As_tmp,
    const scalar_t* __restrict__ input,
    const int8_t* __restrict__ packed_w1,
    const int8_t* __restrict__ packed_w2,
    const float* __restrict__ w1s,
    const float* __restrict__ w2s,
    const scalar_t* __restrict__ fused_experts_out,
    float routed_scaling_factor,
    int64_t M,
    int64_t N,
    int64_t K);

// tinygemm interface
template <typename scalar_t>
void tinygemm_kernel(
    const scalar_t* __restrict__ A,
    const scalar_t* __restrict__ B,
    scalar_t* __restrict__ C,
    float* __restrict__ Ctmp,
    int64_t M,
    int64_t N,
    int64_t K,
    int64_t lda,
    int64_t ldb,
    int64_t ldc,
    bool brg);

template <typename scalar_t>
void tinygemm_kernel(
    const uint8_t* __restrict__ A,
    const int8_t* __restrict__ B,
    scalar_t* __restrict__ C,
    int32_t* __restrict__ Ctmp,
    const float* __restrict__ As,
    const float* __restrict__ Bs,
    int64_t M,
    int64_t N,
    int64_t K,
    int64_t lda,
    int64_t ldb,
    int64_t ldc,
    bool brg);