sgl-project--sglang/sgl-kernel/csrc/allreduce/quick_all_reduce_base.h

#pragma once

#include <hip/hip_bf16.h>
#include <hip/hip_fp16.h>
#include <hip/hip_runtime.h>

#include <cstdint>

#define __quickreduce_device_inline__ __device__ __forceinline__
#define __quickreduce_launch_bounds_two_shot__ __launch_bounds__(256, 4)
#define __quickreduce_launch_bounds_one_shot__ __launch_bounds__(512, 4)

namespace quickreduce {

typedef __hip_bfloat16 nv_bfloat16;
typedef __hip_bfloat162 nv_bfloat162;

using int32x2_t = __attribute__((__vector_size__(2 * sizeof(int)))) int;
using int32x4_t = __attribute__((__vector_size__(4 * sizeof(int)))) int;

// Setup acquire-release semantics for vector memory reads (mubuf instruction)
// as per architecture.
#if defined(__gfx942__)
// CDNA3: Scope bits sc0, sc1
#define MUBUF_ACQUIRE 16
#define MUBUF_RELEASE 16
#elif (defined(__gfx908__) || defined(__gfx90a__))
// CDNA1 and CDNA2 - glc bit
#define MUBUF_ACQUIRE 1
#define MUBUF_RELEASE 0
#endif

static constexpr int kNegOne = 0xBC00BC00;  // {-1, -1}, fp16x2_t

// Number of atoms (4xf16x2_t) processed by a single thread
static constexpr int kAtoms = 8;

// We use a workgroup of 256 threads
static constexpr int kBlockSize = 256;
static constexpr int kAtomStride = kBlockSize;

// Size and atom stride of source/destination data that the block will
// process.
// Workgroup scope = Tile = (256 threads x 8 atoms x 16B)
static constexpr int kTileSize = kBlockSize * kAtoms * sizeof(int32x4_t);

// Max number of blocks. 304 CUs on MI300
static constexpr int kMaxNumBlocks = 304 * 4;

// Standard CDNA wavefront size.
static constexpr int kWavefront = 64;

// 256 thread, 4 wavefronts.
static dim3 constexpr kBlockTwoShot = {kWavefront, kBlockSize / kWavefront, 1};

// Number of threads in a group for quantization
// It corresponds to 32 F16 elements in quantization block
static constexpr int kThreadGroupSize = 8;

// Methods
__quickreduce_device_inline__ __host__ unsigned long divceil(unsigned long x, unsigned long y) {
  return ((x + y - 1) / y);
}

union BufferResource {
  __quickreduce_device_inline__ constexpr BufferResource() : config(0x00020000U) {}

  __quickreduce_device_inline__ constexpr BufferResource(void* buffer_address, uint32_t buffer_size)
      : address(buffer_address), range(buffer_size), config(0x00020000U) {}

  int32x4_t descriptor;
  struct {
    void* address;  // 8B, out of which first 48b is address, and 16b is stride
    // (unused)
    uint32_t range;   // Byte range for the buffer resource
    uint32_t config;  // Constant, DFMT=32b
  };
};

__quickreduce_device_inline__ static int32x4_t buffer_load_dwordx4(
    int32x4_t srsrc, int32_t voffset, int32_t soffset, int32_t aux) __asm("llvm.amdgcn.raw.buffer.load.v4i32");

__quickreduce_device_inline__ static void
buffer_store_dwordx4(int32x4_t data, int32x4_t srsrc, int32_t voffset, int32_t soffset, int32_t aux) __asm(
    "llvm.amdgcn.raw.buffer.store.v4i32");

__quickreduce_device_inline__ static void set_fp16_ovfl(bool const value) {
#if defined(__gfx942__)
  if (value) {
    asm volatile("s_setreg_imm32_b32 0xdc1, 1;" ::);
  } else {
    asm volatile("s_setreg_imm32_b32 0xdc1, 0;" ::);
  }
#endif
}
union bf162_int_union {
  int i;
  nv_bfloat162 bf2;
};

template <typename T>
__quickreduce_device_inline__ void packed_assign_add(int32x4_t* A, int32x4_t* B);

template <>
__quickreduce_device_inline__ void packed_assign_add<half>(int32x4_t* A, int32x4_t* B) {
  int32x4_t& tR_fragment = A[0];
  int32x4_t& tA_fragment = B[0];

  asm volatile("v_pk_add_f16 %0, %1, %2" : "=v"(tR_fragment[0]) : "v"(tR_fragment[0]), "v"(tA_fragment[0]));
  asm volatile("v_pk_add_f16 %0, %1, %2" : "=v"(tR_fragment[1]) : "v"(tR_fragment[1]), "v"(tA_fragment[1]));
  asm volatile("v_pk_add_f16 %0, %1, %2" : "=v"(tR_fragment[2]) : "v"(tR_fragment[2]), "v"(tA_fragment[2]));
  asm volatile("v_pk_add_f16 %0, %1, %2" : "=v"(tR_fragment[3]) : "v"(tR_fragment[3]), "v"(tA_fragment[3]));
}

template <>
__quickreduce_device_inline__ void packed_assign_add<nv_bfloat16>(int32x4_t* A, int32x4_t* B) {
  nv_bfloat162* tA = reinterpret_cast<nv_bfloat162*>(A);
  nv_bfloat162* tB = reinterpret_cast<nv_bfloat162*>(B);
#pragma unroll
  for (int i = 0; i < 4; i++) {
    tA[i] = __hadd2(tA[i], tB[i]);
  }
}

template <typename T>
__quickreduce_device_inline__ int packed_max(int a, int b);

template <>
__quickreduce_device_inline__ int packed_max<half>(int a, int b) {
  int result;
  asm volatile("v_pk_max_f16 %0, %1, %2" : "=v"(result) : "v"(a), "v"(b));
  return result;
}

template <>
__quickreduce_device_inline__ int packed_max<nv_bfloat16>(int a, int b) {
  bf162_int_union A, B, R;
  A.i = a;
  B.i = b;
  R.bf2 = __hmax2(A.bf2, B.bf2);
  return R.i;
}

template <typename T>
__quickreduce_device_inline__ int packed_min(int a, int b);

template <>
__quickreduce_device_inline__ int packed_min<half>(int a, int b) {
  int result;
  asm volatile("v_pk_min_f16 %0, %1, %2" : "=v"(result) : "v"(a), "v"(b));
  return result;
}

template <>
__quickreduce_device_inline__ int packed_min<nv_bfloat16>(int a, int b) {
  bf162_int_union A, B, R;
  A.i = a;
  B.i = b;
  R.bf2 = __hmin2(A.bf2, B.bf2);
  return R.i;
}

template <typename T>
__quickreduce_device_inline__ int packed_abs_max(int a, int b);

template <>
__quickreduce_device_inline__ int packed_abs_max<half>(int a, int b) {
  half2 wmaxh2 = __builtin_bit_cast(half2, a);
  half2 wminh2 = __builtin_bit_cast(half2, b);
  half2 wblockmaxh2;

  wblockmaxh2.x = __hgt(__habs(wmaxh2.x), __habs(wminh2.x)) ? wmaxh2.x : wminh2.x;
  wblockmaxh2.y = __hgt(__habs(wmaxh2.y), __habs(wminh2.y)) ? wmaxh2.y : wminh2.y;
  return __builtin_bit_cast(int, wblockmaxh2);
}

template <>
__quickreduce_device_inline__ int packed_abs_max<nv_bfloat16>(int a, int b) {
  bf162_int_union A, B, R;
  A.i = a;
  B.i = b;
  R.bf2.x = __hgt(__habs(A.bf2.x), __habs(B.bf2.x)) ? A.bf2.x : B.bf2.x;
  R.bf2.y = __hgt(__habs(A.bf2.y), __habs(B.bf2.y)) ? A.bf2.y : B.bf2.y;
  return R.i;
}

template <typename T>
__quickreduce_device_inline__ int packed_add(int a, int b);

template <>
__quickreduce_device_inline__ int packed_add<half>(int a, int b) {
  int result;
  asm volatile("v_pk_add_f16 %0, %1, %2" : "=v"(result) : "v"(a), "v"(b));
  return result;
}

template <>
__quickreduce_device_inline__ int packed_add<nv_bfloat16>(int a, int b) {
  bf162_int_union A, B, R;
  A.i = a;
  B.i = b;
  R.bf2 = __hadd2(A.bf2, B.bf2);
  return R.i;
}

template <>
__quickreduce_device_inline__ int packed_add<int16_t>(int a, int b) {
  int result;
  asm volatile("v_pk_add_i16 %0, %1, %2" : "=v"(result) : "v"(a), "v"(b));
  return result;
}

template <typename T>
__quickreduce_device_inline__ int packed_sub(int a, int b);

template <>
__quickreduce_device_inline__ int packed_sub<half>(int a, int b) {
  int result;

  // MI300 lacks packed fp16 sub instruction. So we do -1 * min + max
  asm volatile("v_pk_fma_f16 %0, %1, %2 %3" : "=v"(result) : "v"(kNegOne), "v"(b), "v"(a));
  return result;
}

template <>
__quickreduce_device_inline__ int packed_sub<nv_bfloat16>(int a, int b) {
  bf162_int_union A, B, R;
  A.i = a;
  B.i = b;
  R.bf2 = __hsub2(A.bf2, B.bf2);
  return R.i;
}

template <typename T>
__quickreduce_device_inline__ int packed_mul(int a, int b);

template <>
__quickreduce_device_inline__ int packed_mul<half>(int a, int b) {
  int result;
  asm volatile("v_pk_mul_f16 %0, %1, %2" : "=v"(result) : "v"(a), "v"(b));
  return result;
}

template <>
__quickreduce_device_inline__ int packed_mul<nv_bfloat16>(int a, int b) {
  nv_bfloat162* tA = reinterpret_cast<nv_bfloat162*>(&a);
  nv_bfloat162* tB = reinterpret_cast<nv_bfloat162*>(&b);
  nv_bfloat162 tR = __hmul2(*tA, *tB);
  return *(reinterpret_cast<int*>(&tR));
}

template <typename T>
__quickreduce_device_inline__ int packed_rcp(int a);

template <>
__quickreduce_device_inline__ int packed_rcp<half>(int a) {
  return __builtin_bit_cast(int, h2rcp(__builtin_bit_cast(half2, a)));
}

template <>
__quickreduce_device_inline__ int packed_rcp<nv_bfloat16>(int a) {
  bf162_int_union A, R;
  A.i = a;
  R.bf2 = h2rcp(A.bf2);
  return R.i;
}

// changes dtype
__quickreduce_device_inline__ float T2float_cast(half a) {
  return __half2float(a);
}

__quickreduce_device_inline__ float T2float_cast(nv_bfloat16 a) {
  return __bfloat162float(a);
}

template <typename T>
__quickreduce_device_inline__ int group_abs_max(int32x4_t atom) {
  const int group_leader = (threadIdx.x / kThreadGroupSize) * kThreadGroupSize;

  int wmax, wmin, wblockmax;
  int a, b;
  a = packed_max<T>(atom[0], atom[1]);
  b = packed_max<T>(atom[2], atom[3]);

  wmax = packed_max<T>(a, b);

  a = packed_min<T>(atom[0], atom[1]);
  b = packed_min<T>(atom[2], atom[3]);

  wmin = packed_min<T>(a, b);

  // Reduce the max among a group of threads
  // Note: This is basically 2 blocks of values setup as the
  // upper/lower halves of the f16x2_t
  for (int i = 1; i < kThreadGroupSize; i <<= 1) {
    int x = __shfl_down(wmax, i);
    wmax = packed_max<T>(wmax, x);

    int y = __shfl_down(wmin, i);
    wmin = packed_min<T>(wmin, y);
  }
  wblockmax = packed_abs_max<T>(wmax, wmin);
  // Share with the cohort
  wblockmax = __shfl(wblockmax, group_leader);
  return wblockmax;
}

__quickreduce_device_inline__ void set_sync_flag(uint32_t* flag_ptr, uint32_t flag) {
  __atomic_store_n(flag_ptr, flag, __ATOMIC_RELEASE);
}

__quickreduce_device_inline__ void wait_sync_flag(uint32_t* flag_ptr, uint32_t flag) {
  while (__atomic_load_n(flag_ptr, __ATOMIC_RELAXED) != flag) {
  }
}

}  // namespace quickreduce