// Adapted from
// https://github.com/sgl-project/sglang/tree/main/sgl-kernel/csrc/cpu

// clang-format off

#pragma once
#include "common.h"
#include "blas_gemm.h"

#if defined(__AVX512F__) && defined(__AVX512BF16__) && defined(__AMX_BF16__)
#define CPU_CAPABILITY_AVX512
#endif

// 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;
}

constexpr bool brgemm_supported() {
#if defined(CPU_CAPABILITY_AVX512)
  return true;
#else
  return false;
#endif
}

// 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 brgemm_supported() && M > 4;
}
template <>
inline bool can_use_brgemm<at::Half>(int M) {
  return brgemm_supported();
}
// this requires PyTorch 2.7 or above
template <>
inline bool can_use_brgemm<int8_t>(int M) {
  return brgemm_supported() && M > 4;
}

template <>
inline bool can_use_brgemm<uint8_t>(int M) {
  return brgemm_supported() && M > 4;
}

template <>
inline bool can_use_brgemm<at::Float8_e4m3fn>(int M) {
  return brgemm_supported() && M > 4;
}

// 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);
}

// uint8: mxfp4 or int4
template <>
inline int64_t get_row_size<uint8_t>(int64_t K) {
  return K >> 1;
}

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

enum class CPUAcTMethod : int { silu_and_mul = 0, swiglu = 1 };

constexpr bool operator==(CPUAcTMethod a, int b) {
  return static_cast<int>(a) == b;
}

constexpr bool operator==(int a, CPUAcTMethod b) {
  return a == static_cast<int>(b);
}

enum class CPUQuantMethod : int64_t { BF16 = 0, INT8_W8A8 = 1, FP8_W8A16 = 2, INT4_W4A8 = 3, MXFP4 = 4 };

constexpr bool operator==(CPUQuantMethod a, int64_t b) {
  return static_cast<int64_t>(a) == b;
}

constexpr bool operator==(int64_t a, CPUQuantMethod b) {
  return a == static_cast<int64_t>(b);
}

enum class CPUQuantAlgo : int64_t { AWQ = 0, GPTQ = 1 };

constexpr bool operator==(CPUQuantAlgo a, int64_t b) {
  return static_cast<int64_t>(a) == b;
}

constexpr bool operator==(int64_t a, CPUQuantAlgo b) {
  return a == static_cast<int64_t>(b);
}

inline int64_t get_4bit_block_k_size(int64_t group_size) {
  return group_size > 128 ? 128 : group_size;
}

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

// pack scale to blocked format for mxfp4
at::Tensor convert_scale_packed(at::Tensor& scale);

// pack weight to vnni format for int4
std::tuple<at::Tensor, at::Tensor, at::Tensor>
convert_weight_packed_scale_zp(at::Tensor qweight, at::Tensor qzeros, at::Tensor scales);

// 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);

// moe implementations for fp8 w8a16 and mxfp4
template <typename scalar_t, typename packed_t, typename param_t, bool is_mxfp4>
void fused_experts_fp_kernel_impl(
    scalar_t* __restrict__ output,
    scalar_t* __restrict__ ic0,
    scalar_t* __restrict__ ic1,
    scalar_t* __restrict__ ic2,
    scalar_t* __restrict__ A_tmp,
    scalar_t* __restrict__ B_tmp,
    float* __restrict__ C_tmp,
    const scalar_t* __restrict__ input,
    const packed_t* __restrict__ packed_w1,
    const packed_t* __restrict__ packed_w2,
    const float* __restrict__ w1_bias,
    const float* __restrict__ w2_bias,
    const param_t* __restrict__ w1s,
    const param_t* __restrict__ w2s,
    int64_t block_size_N,
    int64_t block_size_K,
    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,
    float alpha,
    float limit,
    CPUAcTMethod act_func,
    bool with_bias);

// shared expert implementation 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);

template <typename scalar_t>
void fused_experts_int4_w4a8_kernel_impl(
    scalar_t* __restrict__ output,
    scalar_t* __restrict__ ic0,
    scalar_t* __restrict__ ic1,
    scalar_t* __restrict__ ic2,
    uint8_t* __restrict__ A_tmp,
    uint8_t* __restrict__ Aq_tmp,
    float* __restrict__ As_tmp,
    int32_t* __restrict__ Azp_tmp,
    float* __restrict__ C_tmp,
    int8_t* __restrict__ dqB_tmp,
    const scalar_t* __restrict__ input,
    const uint8_t* __restrict__ packed_w1,
    const uint8_t* __restrict__ packed_w2,
    const int8_t* __restrict__ w1z,
    const int8_t* __restrict__ w2z,
    const float* __restrict__ w1s,
    const float* __restrict__ w2s,
    int group_size,
    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);

template <typename scalar_t>
void shared_expert_fp8_kernel_impl(
    scalar_t* __restrict__ output,
    scalar_t* __restrict__ ic0,
    scalar_t* __restrict__ ic1,
    scalar_t* __restrict__ B_tmp,
    float* __restrict__ C_tmp,
    const scalar_t* __restrict__ input,
    const at::Float8_e4m3fn* __restrict__ packed_w1,
    const at::Float8_e4m3fn* __restrict__ packed_w2,
    const float* __restrict__ w1s,
    const float* __restrict__ w2s,
    int64_t block_size_N,
    int64_t block_size_K,
    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);

// block quantization
template <typename scalar_t>
void tinygemm_kernel(
    const scalar_t* __restrict__ A,
    const at::Float8_e4m3fn* __restrict__ B,
    scalar_t* __restrict__ C,
    scalar_t* __restrict__ Btmp,
    float* __restrict__ Ctmp,
    const float* __restrict__ Bbias,
    const float* __restrict__ scale,
    int64_t M,
    int64_t N,
    int64_t K,
    int64_t lda,
    int64_t ldb,
    int64_t ldc,
    bool brg,
    int64_t block_size_K,
    bool do_unpack = true);

// per tensor quantization
template <typename scalar_t>
void tinygemm_kernel(
    const scalar_t* __restrict__ A,
    const at::Float8_e4m3fn* __restrict__ B,
    scalar_t* __restrict__ C,
    scalar_t* __restrict__ Btmp,
    float* __restrict__ Ctmp,
    float scale,
    int64_t M,
    int64_t N,
    int64_t K,
    int64_t lda,
    int64_t ldb,
    int64_t ldc,
    bool brg);

// mxfp4
template <typename scalar_t>
void tinygemm_kernel(
    const scalar_t* __restrict__ A,
    const uint8_t* __restrict__ B,
    scalar_t* __restrict__ C,
    scalar_t* __restrict__ Btmp,
    float* __restrict__ Ctmp,
    const float* __restrict__ Bbias,
    const uint8_t* __restrict__ scale,
    int64_t M,
    int64_t N,
    int64_t K,
    int64_t lda,
    int64_t ldb,
    int64_t ldc,
    bool brg,
    int64_t block_size_K,
    bool do_unpack = true);

template <typename scalar_t>
void tinygemm_kernel(
    scalar_t* C,
    float* C_temp,
    const uint8_t* A,
    const float* scales_a,
    const int32_t* qzeros_a,
    const uint8_t* B,
    const float* scales_b,
    const int8_t* qzeros_b,
    const int32_t* compensation,
    int8_t* dqB_tmp,
    int64_t M,
    int64_t K,
    int64_t lda,
    int64_t ldc_f,
    int64_t ldc_s,
    bool store_out,
    bool use_brgemm);

// mxfp4
template <typename scalar_t>
void tinygemm_kernel(
    const scalar_t* __restrict__ A,
    const uint8_t* __restrict__ B,
    scalar_t* __restrict__ C,
    scalar_t* __restrict__ Btmp,
    float* __restrict__ Ctmp,
    const uint8_t* __restrict__ scale,
    int64_t M,
    int64_t N,
    int64_t K,
    int64_t lda,
    int64_t ldb,
    int64_t ldc,
    bool brg,
    int64_t block_size_K,
    bool do_unpack = true);
