#pragma once
#include "type_convert.cuh"
#include "dispatch_utils.h"
#include <algorithm>
#include <torch/cuda.h>
#include <c10/cuda/CUDAGuard.h>
#include <ATen/native/cuda/MemoryAccess.cuh>
#include <c10/cuda/CUDAMathCompat.h>
#include <ATen/AccumulateType.h>
#include <THC/THCDeviceUtils.cuh>
#include <cuda.h>
#include <cuda_bf16.h>
#include <cuda_fp16.h>
#include <cuda_runtime.h>
#include <hip/hip_bf16.h>
// #if defined(USE_ROCM)
typedef __hip_bfloat16 nv_bfloat16;
// #endif

#include <iostream>
#include <array>
#include <limits>
#include <map>
#include <unordered_map>
#include <vector>
#ifndef USE_ROCM
  #include <cub/cub.cuh>
#else
  #include <hipcub/hipcub.hpp>
#endif
namespace vllm {
#define CUDACHECK(cmd)                                              \
  do {                                                              \
    cudaError_t e = cmd;                                            \
    if (e != cudaSuccess) {                                         \
      printf("Failed: Cuda error %s:%d '%s'\n", __FILE__, __LINE__, \
             cudaGetErrorString(e));                                \
      exit(EXIT_FAILURE);                                           \
    }                                                               \
  } while (0)

// Maximal number of blocks in allreduce kernel.
constexpr int kMaxBlocks = 128;

// Default number of blocks in allreduce kernel.
#ifndef USE_ROCM
const int defaultBlockLimit = 36;
CUpointer_attribute rangeStartAddrAttr = CU_POINTER_ATTRIBUTE_RANGE_START_ADDR;
#else
const int defaultBlockLimit = 16;
hipPointer_attribute rangeStartAddrAttr =
    HIP_POINTER_ATTRIBUTE_RANGE_START_ADDR;
#endif

// Counter may overflow, but it's fine since unsigned int overflow is
// well-defined behavior.
using FlagType = uint32_t;

// Two sets of peer counters are needed for two syncs: starting and ending an
// operation. The reason is that it's possible for peer GPU block to arrive at
// the second sync point while the current GPU block haven't passed the first
// sync point. Thus, peer GPU may write counter+1 while current GPU is busy
// waiting for counter. We use alternating counter array to avoid this
// possibility.
struct Signal {
  alignas(128) FlagType start[kMaxBlocks][16];
  alignas(128) FlagType end[kMaxBlocks][16];
  alignas(128) FlagType _flag[kMaxBlocks];  // incremental flags for each rank
};

struct __align__(16) RankData {
  const void* ptrs[16];
};

struct __align__(16) RankSignals {
  Signal* signals[16];
};

// like std::array, but aligned
template <typename T, int sz>
struct __align__(alignof(T) * sz) array_t {
  T data[sz];
  using type = T;
  static constexpr int size = sz;
};

// use packed type to maximize memory efficiency
// goal: generate ld.128 and st.128 instructions
template <typename T>
struct packed_t {
  // the (P)acked type for load/store
  using P = array_t<T, 16 / sizeof(T)>;
  // the (A)ccumulator type for reduction
  using A = array_t<float, 16 / sizeof(T)>;
  using F = array_t<int8_t, 16 / sizeof(T)>;
};

#define DINLINE __device__ __forceinline__

// scalar cast functions
DINLINE float upcast_s(half val) { return __half2float(val); }

template <typename T>
DINLINE T downcast_s(float val);
template <>
DINLINE half downcast_s(float val) {
  return __float2half(val);
}

// scalar add functions
// for some reason when compiling with Pytorch, the + operator for half and
// bfloat is disabled so we call the intrinsics directly
DINLINE half& assign_add(half& a, half b) {
  a = __hadd(a, b);
  return a;
}
DINLINE float& assign_add(float& a, float b) { return a += b; }

// #if (__CUDA_ARCH__ >= 800 || !defined(__CUDA_ARCH__))
DINLINE float upcast_s(nv_bfloat16 val) { return __bfloat162float(val); }
template <>
DINLINE nv_bfloat16 downcast_s(float val) {
  return __float2bfloat16(val);
}
DINLINE nv_bfloat16& assign_add(nv_bfloat16& a, nv_bfloat16 b) {
  a = __hadd(a, b);
  return a;
}
// #endif

template <typename T, int N>
DINLINE array_t<T, N>& packed_assign_add(array_t<T, N>& a, array_t<T, N> b) {
#pragma unroll
  for (int i = 0; i < N; i++) {
    assign_add(a.data[i], b.data[i]);
  }
  return a;
}

/**********************************************************/
template <typename P, uint32_t VEC_SIZE>
DINLINE P vec_add(const P& a, const P& b) {
  P sum_tmp;
  #pragma unroll
  for (int i = 0; i < a.size; ++i)
    sum_tmp.data[i] = static_cast<float>(a.data[i]) + static_cast<float>(b.data[i]);
  return sum_tmp;
}

template <typename T, int reducesize=64>
__inline__ __device__ T WarpReduceSum(T val) {
  #pragma unroll
  for (int offset = reducesize / 2; offset > 0; offset >>= 1) {
    val += WARP_SHFL_DOWN(val, offset);
  }
  return val;
}

template <typename T>
DINLINE T BlockReduce(T val, T* shared) {
  const int lid = threadIdx.x % 64;
  const int wid = threadIdx.x / 64;
  const int block_size = blockDim.x;
  const int shared_size = block_size / 64;
  val = WarpReduceSum<T>(val);
  if(block_size==64) return val;
  if (lid == 0 && wid < shared_size) {
    shared[wid] = val;
  }
  __syncthreads();
  val = 0.f;
  if (wid == 0 && lid < shared_size) {
    val= shared[lid];
    val = WarpReduceSum<T, 16>(val);
  }
  return val;
}

template <typename T, typename P, typename A>
DINLINE P fused_add_rms_norm(P const& residual, P const& gamma, int hidden_dim, float eps) {
  static constexpr int VEC_SIZE = 16 / sizeof(T);
  __shared__ float s_val;
  float trstd;
  P norm_out;
  float acc = 0.0f;
  #pragma unroll
  for (int i = 0; i < VEC_SIZE; ++i) {
    float v = static_cast<float>(residual.data[i]);
    acc += v * v;
  }
  __shared__ float r_sum[16];
  acc = BlockReduce(acc, r_sum);
  if (threadIdx.x == 0)
    s_val = rsqrtf(acc / hidden_dim + eps);
  __syncthreads();
  trstd = s_val;
  #pragma unroll
  for (int i = 0; i < VEC_SIZE; ++i) {
    norm_out.data[i] = static_cast<T>(static_cast<float>(residual.data[i]) * trstd * static_cast<float>(gamma.data[i]));
  }
  return norm_out;
}

static inline __device__ int8_t float_to_int8_rn(float x) {
#ifdef USE_ROCM
  static constexpr auto i8_min =
      static_cast<float>(std::numeric_limits<int8_t>::min());
  static constexpr auto i8_max =
      static_cast<float>(std::numeric_limits<int8_t>::max());

  float dst = std::nearbyint(x);

  dst = (dst < i8_min) ? i8_min : (dst > i8_max) ? i8_max : dst;
  return static_cast<int8_t>(dst);
#else
  // CUDA path
  uint32_t dst;
  asm volatile("cvt.rni.sat.s8.f32 %0, %1;" : "=r"(dst) : "f"(x));
  return reinterpret_cast<const int8_t&>(dst);
#endif
}

template <typename T, int reducesize=64>
__inline__ __device__ T WarpReduceMax(T val) {
  #pragma unroll
  for (int offset = reducesize / 2; offset > 0; offset >>= 1) {
    val = fmaxf(val, WARP_SHFL_DOWN(val, offset));
  }
  return val;
}

template <typename T>
DINLINE T BlockReduceMax_ROW(T val, T* shared) {
  const int lid = threadIdx.x % 64;
  const int wid = threadIdx.x / 64;
  const int block_size = blockDim.x;
  const int shared_size = block_size / 64;
  val = WarpReduceMax<T>(val);
  if(block_size==64) return val;
  if (lid == 0 && wid < shared_size) {
    shared[wid] = val;
  }
  __syncthreads();
  if (wid == 0 && lid<shared_size) {
    val= shared[lid];
    val = WarpReduceMax<T, 16>(val);
  }
  return val;
}

template <typename T, int N>
DINLINE array_t<float, N> upcast(array_t<T, N> val) {
  if constexpr (std::is_same<T, float>::value) {
    return val;
  } else {
    array_t<float, N> out;
#pragma unroll
    for (int i = 0; i < N; i++) {
      out.data[i] = static_cast<float>(val.data[i]);
    }
    return out;
  }
}

template <typename O>
DINLINE O downcast(array_t<float, O::size> val) {
  if constexpr (std::is_same<typename O::type, float>::value) {
    return val;
  } else {
    O out;
#pragma unroll
    for (int i = 0; i < O::size; i++) {
      out.data[i] = static_cast<typename O::type>(val.data[i]);
    }
    return out;
  }
}

#if 0

static DINLINE void st_flag_release(FlagType* flag_addr, FlagType flag) {
  #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 700
  asm volatile("st.release.sys.global.u32 [%1], %0;" ::"r"(flag),
               "l"(flag_addr));
  #else
  asm volatile("membar.sys; st.volatile.global.u32 [%1], %0;" ::"r"(flag),
               "l"(flag_addr));
  #endif
}

static DINLINE FlagType ld_flag_acquire(FlagType* flag_addr) {
  FlagType flag;
  #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 700
  asm volatile("ld.acquire.sys.global.u32 %0, [%1];"
               : "=r"(flag)
               : "l"(flag_addr));
  #else
  asm volatile("ld.volatile.global.u32 %0, [%1]; membar.gl;"
               : "=r"(flag)
               : "l"(flag_addr));
  #endif
  return flag;
}

static DINLINE void st_flag_volatile(FlagType* flag_addr, FlagType flag) {
  asm volatile("st.volatile.global.u32 [%1], %0;" ::"r"(flag), "l"(flag_addr));
}

static DINLINE FlagType ld_flag_volatile(FlagType* flag_addr) {
  FlagType flag;
  asm volatile("ld.volatile.global.u32 %0, [%1];"
               : "=r"(flag)
               : "l"(flag_addr));
  return flag;
}

// This function is meant to be used as the first synchronization in the all
// reduce kernel. Thus, it doesn't need to make any visibility guarantees for
// prior memory accesses. Note: volatile writes will not be reordered against
// other volatile writes.
template <int ngpus>
DINLINE void barrier_at_start(const RankSignals& sg, Signal* self_sg,
                              int rank) {
  uint32_t flag = self_sg->_flag[blockIdx.x] + 1;
  if (threadIdx.x < ngpus) {
    auto peer_counter_ptr = &sg.signals[threadIdx.x]->start[blockIdx.x][rank];
    auto self_counter_ptr = &self_sg->start[blockIdx.x][threadIdx.x];
    // Write the expected counter value to peer and wait for correct value
    // from peer.
    st_flag_volatile(peer_counter_ptr, flag);
    while (ld_flag_volatile(self_counter_ptr) != flag);
  }
  __syncthreads();
  // use one thread to update flag
  if (threadIdx.x == 0) self_sg->_flag[blockIdx.x] = flag;
}

// This function is meant to be used as the second or the final
// synchronization barrier in the all reduce kernel. If it's the final
// synchronization barrier, we don't need to make any visibility guarantees
// for prior memory accesses.
template <int ngpus, bool final_sync = false>
DINLINE void barrier_at_end(const RankSignals& sg, Signal* self_sg, int rank) {
  __syncthreads();
  uint32_t flag = self_sg->_flag[blockIdx.x] + 1;
  if (threadIdx.x < ngpus) {
    auto peer_counter_ptr = &sg.signals[threadIdx.x]->end[blockIdx.x][rank];
    auto self_counter_ptr = &self_sg->end[blockIdx.x][threadIdx.x];
    // Write the expected counter value to peer and wait for correct value from
    // peer.
    if constexpr (!final_sync) {
      st_flag_release(peer_counter_ptr, flag);
      while (ld_flag_acquire(self_counter_ptr) != flag);
    } else {
      st_flag_volatile(peer_counter_ptr, flag);
      while (ld_flag_volatile(self_counter_ptr) != flag);
    }
  }
  if constexpr (!final_sync) __syncthreads();

  // use one thread to update flag
  if (threadIdx.x == 0) self_sg->_flag[blockIdx.x] = flag;
}

#else

template <int ngpus>
DINLINE void barrier_at_start(const RankSignals& sg, Signal* self_sg,
                              int rank) {
  uint32_t flag = self_sg->_flag[blockIdx.x] + 1;  //当前线程块标记+1
  if (threadIdx.x < ngpus) {
    // simultaneously write to the corresponding flag of all ranks.
    // Latency = 1 p2p write
    // __scoped_atomic_store_n(&sg.signals[threadIdx.x]->start[blockIdx.x][rank],
    //                         flag, __ATOMIC_RELAXED, __MEMORY_SCOPE_SYSTEM);
    // 将每个peer GPU对应线程块的本rank flag填入
    __atomic_store_n(&sg.signals[threadIdx.x]->start[blockIdx.x][rank], flag,
      __ATOMIC_RELAXED);
    // wait until we got true from all ranks
    // while (__scoped_atomic_load_n(&self_sg->start[blockIdx.x][threadIdx.x],
    //                               __ATOMIC_RELAXED,
    //                               __MEMORY_SCOPE_DEVICE) < flag);
    //等待对应blockidx.x处理的数据的peer gpu到达
    while (__atomic_load_n(&self_sg->start[blockIdx.x][threadIdx.x],
      __ATOMIC_RELAXED) < flag);
  }
  __syncthreads();
  // use one thread to update flag
  if (threadIdx.x == 0) self_sg->_flag[blockIdx.x] = flag;
}

template <int ngpus, bool final_sync = false>
DINLINE void barrier_at_end(const RankSignals& sg, Signal* self_sg, int rank) {
  __syncthreads();
  uint32_t flag = self_sg->_flag[blockIdx.x] + 1;
  if (threadIdx.x < ngpus) {
    // simultaneously write to the corresponding flag of all ranks.
    // Latency = 1 p2p write
    // __scoped_atomic_store_n(&sg.signals[threadIdx.x]->end[blockIdx.x][rank],
    //                         flag,
    //                         final_sync ? __ATOMIC_RELAXED : __ATOMIC_RELEASE,
    //                         __MEMORY_SCOPE_SYSTEM);
    // 告诉其他GPU 本block Reduce完毕
    __atomic_store_n(&sg.signals[threadIdx.x]->end[blockIdx.x][rank], flag,
      final_sync ? __ATOMIC_RELAXED : __ATOMIC_RELEASE);
    // wait until we got true from all ranks
    // while (
    //     __scoped_atomic_load_n(&self_sg->end[blockIdx.x][threadIdx.x],
    //                            final_sync ? __ATOMIC_RELAXED : __ATOMIC_ACQUIRE,
    //                            __MEMORY_SCOPE_DEVICE) < flag);
    // 当前block处理的 hs的其他GPU处理完毕
    while (__atomic_load_n(&self_sg->end[blockIdx.x][threadIdx.x],
                final_sync ? __ATOMIC_RELAXED : __ATOMIC_ACQUIRE) <
    flag);
  }
  if constexpr (!final_sync) __syncthreads();
  // use one thread to update flag
  if (threadIdx.x == 0) self_sg->_flag[blockIdx.x] = flag;
}

template <int ngpus, bool final_sync = false>
DINLINE void barrier_at_end_fuse(const RankSignals& sg, Signal* self_sg, int rank) {
  __syncthreads();
  uint32_t flag = self_sg->_flag[blockIdx.x] + 1;
  if (threadIdx.x < ngpus) {
    // simultaneously write to the corresponding flag of all ranks.
    // Latency = 1 p2p write
    // __scoped_atomic_store_n(&sg.signals[threadIdx.x]->end[blockIdx.x][rank],
    //                         flag,
    //                         final_sync ? __ATOMIC_RELAXED : __ATOMIC_RELEASE,
    //                         __MEMORY_SCOPE_SYSTEM);
    __atomic_store_n(&sg.signals[threadIdx.x]->end[blockIdx.x][rank], flag,
      final_sync ? __ATOMIC_RELAXED : __ATOMIC_RELEASE);
    // wait until we got true from all ranks
    // while (
    //     __scoped_atomic_load_n(&self_sg->end[blockIdx.x][threadIdx.x],
    //                            final_sync ? __ATOMIC_RELAXED : __ATOMIC_ACQUIRE,
    //                            __MEMORY_SCOPE_DEVICE) < flag);
    while (__atomic_load_n(&self_sg->end[blockIdx.x][threadIdx.x],
                final_sync ? __ATOMIC_RELAXED : __ATOMIC_ACQUIRE) <
    flag);
  }
   __syncthreads();
  // use one thread to update flag
  if (threadIdx.x == 0) self_sg->_flag[blockIdx.x] = flag;
}


#endif

template <typename P, int ngpus, typename A>
DINLINE P packed_reduce(const P* ptrs[], int idx) {
  A tmp = upcast(ptrs[0][idx]);
#pragma unroll
  for (int i = 1; i < ngpus; i++) {
    packed_assign_add(tmp, upcast(ptrs[i][idx]));
  }
  return downcast<P>(tmp);
}

template <typename T, int ngpus>
__global__ void __launch_bounds__(512, 1)
    cross_device_reduce_1stage(RankData* _dp, RankSignals sg, Signal* self_sg,
                               T* __restrict__ result, int rank, int size) {
  using P = typename packed_t<T>::P;
  using A = typename packed_t<T>::A;
  // note: we don't reorder the address so the accumulation order is the same
  // for all ranks, ensuring bitwise identical results
  auto dp = *_dp;
  barrier_at_start<ngpus>(sg, self_sg, rank);
  // do the actual reduction
  for (int idx = blockIdx.x * blockDim.x + threadIdx.x; idx < size;
       idx += gridDim.x * blockDim.x) {
    ((P*)result)[idx] = packed_reduce<P, ngpus, A>((const P**)&dp.ptrs[0], idx);
  }
  barrier_at_end<ngpus, true>(sg, self_sg, rank);
}

template <typename P>
DINLINE P* get_tmp_buf(Signal* sg) {
  return (P*)(((Signal*)sg) + 1);
}

template <typename T, int ngpus>
__global__ void __launch_bounds__(1024, 1)
    cross_device_reduce_2stage_fuse_norm(RankData* _dp, RankSignals sg, Signal* self_sg,
                                        T* __restrict__ result, int rank, int size,
                                        int hidden_dim, T* residual_in, T* rms_gamma,
                                        float eps, std::array<int, ngpus> begin_tokens,
                                        std::array<int, ngpus> token_num_per_ranks) {
  static constexpr int VEC_SIZE = 16 / sizeof(T);
  int H_D_word_num = hidden_dim / VEC_SIZE;
  int token_id = blockIdx.x;                      // local token id
  int access_id_in_token = threadIdx.x;           // 当前token内数据部分
  int token_stride = gridDim.x;
  //
  int access_id = token_id * H_D_word_num + access_id_in_token;  // local token id * (token in size) 
  int access_stride = token_stride * H_D_word_num;               // gridDim.x * (token in size)

  using P = typename packed_t<T>::P;
  using A = typename packed_t<T>::A;

  const P* ptrs[ngpus];
  P* tmps[ngpus];
  #pragma unroll
  for (int i = 0; i < ngpus; ++i) {
    int target = (rank + i) % ngpus;   
    ptrs[i] = (const P*)_dp->ptrs[target];
    tmps[i] = get_tmp_buf<P>(sg.signals[target]);
  }

  int start = begin_tokens[rank] * H_D_word_num;
  int part = (begin_tokens[rank] + token_num_per_ranks[rank]) * H_D_word_num;

  auto tmp_out = tmps[0];   // 当前rank的 (meta_data + sizeof(signal))  偏移
  barrier_at_start<ngpus>(sg, self_sg, rank);

  #pragma unroll
  for (int idx = access_id + start; idx < part; idx+=access_stride) {
    tmp_out[idx] = packed_reduce<P, ngpus, A>(ptrs, idx);     
  #pragma unroll
  for (int r = 0; r < ngpus; ++r)
    tmps[r][idx] = tmp_out[idx];   //将当前GPU处理的数据--->其他GPU的对应问题
  }
  barrier_at_end<ngpus>(sg, self_sg, rank);

  //debug --- 验证reduce结果
  // for (int r = 0; r < ngpus; ++r) {
  //   int cm_access_id = access_id + begin_tokens[r] * H_D_word_num;
  //   int cm_token_id = token_id + begin_tokens[r];
  //   int cm_token_access = (begin_tokens[r] + token_num_per_ranks[r]) * H_D_word_num;
  //   for (int idx = cm_access_id; idx < cm_token_access; idx += access_stride)
  //   ((P*)result)[idx] = tmp_out[idx];
  // }
  P m_residual_val, m_gamm_val;
  m_gamm_val = ((P*)rms_gamma)[access_id_in_token];
  #pragma unroll
  for (int r = 0; r < ngpus; ++r) { 
    int cm_access_id = access_id + begin_tokens[r] * H_D_word_num;
    int cm_token_id = token_id + begin_tokens[r];
    int cm_tot_access = (begin_tokens[r] + token_num_per_ranks[r]) * H_D_word_num;
    for (int idx = cm_access_id; idx < cm_tot_access; idx += access_stride) {
      P sum_val;
      sum_val = tmp_out[idx];
      m_residual_val =((P*)residual_in)[idx];
      sum_val = vec_add<P, VEC_SIZE>(sum_val, m_residual_val);

      sum_val = fused_add_rms_norm<T, P, A>(sum_val, m_gamm_val, hidden_dim, eps);
      ((P*)result)[idx] = sum_val;
    }
  }
}

template <typename T, typename T_out, int ngpus, bool isResidual=true, bool update_input=false>
__global__ void __launch_bounds__(1024, 1)
    cross_device_reduce_1stage_norm_quant(RankData* _dp, RankSignals sg, Signal* self_sg,
                                          T_out* __restrict__ result, int rank, int size,
                                          int hidden_dim, T* residual_in, T* rms_gamma,
                                          float* __restrict__ scales, float eps,
                                          T* __restrict__ norm_res) {
  // static constexpr int VEC_SIZE = 16 / sizeof(T);
  static constexpr int VEC_SIZE = packed_t<T>::P::size;
  int H_D_word_num = hidden_dim / VEC_SIZE;
  int token_id = blockIdx.x; 
  int access_id_in_token = threadIdx.x; 
  int token_stride = gridDim.x;
  int access_id = token_id * H_D_word_num + access_id_in_token;
  int access_stride = token_stride * H_D_word_num; 

  using P = typename packed_t<T>::P;
  using A = typename packed_t<T>::A;
  using F = typename packed_t<T>::F;

  P m_residual_val, m_gamm_val;
  m_gamm_val = reinterpret_cast<P*>(rms_gamma)[access_id_in_token];
  auto dp = *_dp;
  P sum_val;
  barrier_at_start<ngpus>(sg, self_sg, rank);
  sum_val = packed_reduce<P, ngpus, A>((const P**)&dp.ptrs[0], access_id);
  barrier_at_end<ngpus, true>(sg, self_sg, rank);

  if constexpr(isResidual) {
    m_residual_val = reinterpret_cast<P*>(residual_in)[access_id];
    sum_val = vec_add<P, VEC_SIZE>(m_residual_val, sum_val);
    ((P*)residual_in)[access_id] = sum_val;
  }

  __shared__ float s_val;
  P norm_out;
  float acc = 0.f;
  #pragma unroll
  for (int i = 0; i < VEC_SIZE; ++i) {
    float v = static_cast<float>(sum_val.data[i]);
    acc += v * v;
  }
  __shared__ float r_sum[16];
  acc = BlockReduce<float>(acc, r_sum);
  if (threadIdx.x == 0)
    s_val = rsqrt(acc / hidden_dim + eps);
  __syncthreads();

  float block_absmax_val_maybe = 0.f;
  #pragma unroll
  for (int i = 0; i < VEC_SIZE; ++i) {
    norm_out.data[i] = static_cast<float>(sum_val.data[i]) * s_val * static_cast<float>(m_gamm_val.data[i]);
    block_absmax_val_maybe = fmaxf(block_absmax_val_maybe, fabs(norm_out.data[i]));
  }

  block_absmax_val_maybe = BlockReduceMax_ROW(block_absmax_val_maybe,r_sum);
  //
  __shared__ float s_token_scale;
  float scale = 0.0f;
  if (threadIdx.x == 0) {
    scale = block_absmax_val_maybe;
    s_token_scale = scale;
  }
  __syncthreads();
  float inv_s = (s_token_scale == 0.f) ? 0.f : 127.f / s_token_scale;
  F out_vec;
  #pragma unroll
  for (int i = 0; i < VEC_SIZE; ++i)
    out_vec.data[i] = float_to_int8_rn(norm_out.data[i] * inv_s);
  constexpr float qmax = 127.0f;
  constexpr float min_scale = 1.19209e-07f;
  ((F*)result)[access_id] = out_vec;
  if constexpr (update_input) 
    ((P*)norm_res)[access_id] = norm_out;
  if (threadIdx.x == 0)
    scales[blockIdx.x] = fmaxf(scale/qmax, min_scale);
}

template <typename T, typename T_out, int ngpus, bool isResidual=true, bool update_input=false>
__global__ void __launch_bounds__(1024, 1)
    cross_device_reduce_2stage_fuse_norm_quant(RankData* _dp, RankSignals sg, Signal* self_sg,
                                              T_out* __restrict__ result, int rank, int size,
                                              int hidden_dim, T* residual_in, T* rms_gamma,
                                              float* __restrict__ scales, float eps, 
                                              T* __restrict__ norm_res,
                                              std::array<int, ngpus> begin_tokens,
                                              std::array<int, ngpus> token_num_per_ranks) {
  static constexpr int VEC_SIZE = 16 / sizeof(T);
  int H_D_word_num = hidden_dim / VEC_SIZE;
  int token_id = blockIdx.x;                      // local token id
  int access_id_in_token = threadIdx.x;           // 当前token内数据部分
  int token_stride = gridDim.x;
  //
  int access_id = token_id * H_D_word_num + access_id_in_token;  // local token id * (token in size) 
  int access_stride = token_stride * H_D_word_num;               // gridDim.x * (token in size)

  using P = typename packed_t<T>::P;
  using A = typename packed_t<T>::A;
  using F = typename packed_t<T>::F;

  const P* ptrs[ngpus];
  P* tmps[ngpus];
  #pragma unroll
  for (int i = 0; i < ngpus; ++i) {
    int target = (rank + i) % ngpus;   
    ptrs[i] = (const P*)_dp->ptrs[target];
    tmps[i] = get_tmp_buf<P>(sg.signals[target]);
  }

  int start = begin_tokens[rank] * H_D_word_num;
  int part = (begin_tokens[rank] + token_num_per_ranks[rank]) * H_D_word_num;

  auto tmp_out = tmps[0];   // 当前rank的 (meta_data + sizeof(signal))  偏移
  auto input = ptrs[0];
  barrier_at_start<ngpus>(sg, self_sg, rank);

  #pragma unroll
  for (int idx = access_id + start; idx < part; idx+=access_stride) {
    tmp_out[idx] = packed_reduce<P, ngpus, A>(ptrs, idx);     
  #pragma unroll
  for (int r = 0; r < ngpus; ++r)
    tmps[r][idx] = tmp_out[idx];   //将当前GPU处理的数据--->其他GPU的对应问题
  }
  barrier_at_end<ngpus>(sg, self_sg, rank);

  P m_residual_val, m_gamm_val;
  m_gamm_val = reinterpret_cast<P*>(rms_gamma)[access_id_in_token];
  #pragma unroll
  for (int r = 0; r < ngpus; ++r) { 
    int cm_access_id = access_id + begin_tokens[r] * H_D_word_num;
    int cm_token_id = token_id + begin_tokens[r];
    int cm_tot_access = (begin_tokens[r] + token_num_per_ranks[r]) * H_D_word_num;
    for (int idx = cm_access_id, tidx = cm_token_id; idx < cm_tot_access; 
              idx += access_stride, tidx += token_stride) {
      P sum_val;
      sum_val = tmp_out[idx];
      if constexpr (isResidual) {
        m_residual_val = reinterpret_cast<P*>(residual_in)[idx];
        sum_val = vec_add<P, VEC_SIZE>(sum_val, m_residual_val);
        ((P*)residual_in)[idx] = sum_val;
      }
      __shared__ float s_val;
      P norm_out;
      float acc = 0.0f;
      #pragma unroll
      for (int i = 0; i < VEC_SIZE; ++i) {
        float v = static_cast<float>(sum_val.data[i]);
        acc += v * v;
      }
      __shared__ float r_sum[16];
      acc = BlockReduce(acc, r_sum);
      if (threadIdx.x == 0)
        s_val = rsqrtf(acc / hidden_dim + eps);
      __syncthreads();

      float block_absmax_val_maybe = 0.f;
      #pragma unroll
      for (int i = 0; i < VEC_SIZE; ++i) {
        norm_out.data[i] = static_cast<T>(static_cast<float>(sum_val.data[i]) * s_val * static_cast<float>(m_gamm_val.data[i]));
        block_absmax_val_maybe = fmaxf(block_absmax_val_maybe, fabs(norm_out.data[i]));
      }

      block_absmax_val_maybe = BlockReduceMax_ROW(block_absmax_val_maybe, r_sum);
      __shared__ float s_token_scale;
      float scale = 0.0f;
      if (threadIdx.x == 0) {
        scale = block_absmax_val_maybe;
        s_token_scale = scale;
      }
      __syncthreads();
      float inv_s = (s_token_scale == 0.f) ? 0.f : 127.f / s_token_scale;
      
      F out_vec;
      #pragma unroll
      for (int i = 0; i < VEC_SIZE; ++i)
        out_vec.data[i] = float_to_int8_rn(norm_out.data[i] * inv_s);

      constexpr float qmax = 127.0f;
      constexpr float min_scale = 1.19209e-07f;
      ((F*)result)[idx] = out_vec;
      if constexpr (update_input)
        ((P*)norm_res)[idx] = norm_out;
      if (threadIdx.x == 0)
        scales[tidx] = fmaxf(scale/qmax, min_scale);
    }
  }
}

template <typename T, int ngpus>
__global__ void __launch_bounds__(512, 1)
    cross_device_reduce_2stage(RankData* _dp, RankSignals sg, Signal* self_sg,
                               T* __restrict__ result, int rank, int size) {
  int tid = blockIdx.x * blockDim.x + threadIdx.x;
  int stride = gridDim.x * blockDim.x;
  using P = typename packed_t<T>::P;
  using A = typename packed_t<T>::A;
  int part = size / ngpus;
  int start = rank * part;
  int end = rank == ngpus - 1 ? size : start + part;
  int largest_part = part + size % ngpus;
  const P* ptrs[ngpus];
  P* tmps[ngpus];
#pragma unroll
  for (int i = 0; i < ngpus; i++) {
    int target = (rank + i) % ngpus;
    ptrs[i] = (const P*)_dp->ptrs[target];
    tmps[i] = get_tmp_buf<P>(sg.signals[target]);
  }
  auto tmp_out = tmps[0];
  barrier_at_start<ngpus>(sg, self_sg, rank);

  // stage 1: reduce scatter
  for (int idx = start + tid; idx < end; idx += stride) {
    tmp_out[idx - start] = packed_reduce<P, ngpus, A>(ptrs, idx);
  }
  barrier_at_end<ngpus>(sg, self_sg, rank);

  // stage 2: allgather. Note: it's important to match the tid between
  // the two stages, because visibility across devices is only guaranteed
  // between threads that have the same tid. If thread i computes the sum of
  // start + i in the first stage, then thread i also gathers start + i from
  // all ranks.

  for (int idx = tid; idx < largest_part; idx += stride) {
#pragma unroll
    for (int i = 0; i < ngpus; i++) {
      int gather_from_rank = ((rank + i) % ngpus);
      if (gather_from_rank == ngpus - 1 || idx < part) {
        int dst_idx = gather_from_rank * part + idx;
        ((P*)result)[dst_idx] = tmps[i][idx];
      }
    }
  }
}

template <typename T, int ngpus>
__global__ void __launch_bounds__(512, 1)
    cross_device_reduce_1stage_pcie(RankData* _dp, RankSignals sg, Signal* self_sg,
                               T* __restrict__ result, int rank, int size,
                               uint32_t** curr_hdp_reg, int world_size) {

  using P = typename packed_t<T>::P;
  using A = typename packed_t<T>::A;
  // note: we don't reorder the address so the accumulation order is the same
  // for all ranks, ensuring bitwise identical results
  auto dp = *_dp;
  if (threadIdx.x == 1) {
    for(int i = 0; i < world_size; i++) {
      __atomic_store_n(curr_hdp_reg[i], 0x1, __ATOMIC_RELAXED);
    }
  }
  barrier_at_start<ngpus>(sg, self_sg, rank);
  // do the actual reduction
  for (int idx = blockIdx.x * blockDim.x + threadIdx.x; idx < size;
       idx += gridDim.x * blockDim.x) {
    ((P*)result)[idx] = packed_reduce<P, ngpus, A>((const P**)&dp.ptrs[0], idx);
  }
  barrier_at_end<ngpus, true>(sg, self_sg, rank);
}

template <typename T, int ngpus>
__global__ void __launch_bounds__(512, 1)
    cross_device_reduce_2stage_pcie(RankData* _dp, RankSignals sg, Signal* self_sg,
                               T* __restrict__ result, int rank, int size,
                               uint32_t** curr_hdp_reg, int world_size) {
  int tid = blockIdx.x * blockDim.x + threadIdx.x;
  int stride = gridDim.x * blockDim.x;
  using P = typename packed_t<T>::P;
  using A = typename packed_t<T>::A;
  int part = size / ngpus;
  int start = rank * part;
  int end = rank == ngpus - 1 ? size : start + part;
  int largest_part = part + size % ngpus;
  const P* ptrs[ngpus];
  P* tmps[ngpus];
  if (threadIdx.x == 1) {
    for(int i = 0; i < world_size; i++) {
      __atomic_store_n(curr_hdp_reg[i], 0x1, __ATOMIC_RELAXED);
    }
  }
#pragma unroll
  for (int i = 0; i < ngpus; i++) {
    int target = (rank + i) % ngpus;
    ptrs[i] = (const P*)_dp->ptrs[target];
    tmps[i] = get_tmp_buf<P>(sg.signals[target]);
  }
  auto tmp_out = tmps[0];
  barrier_at_start<ngpus>(sg, self_sg, rank);

  // stage 1: reduce scatter
  for (int idx = start + tid; idx < end; idx += stride) {
    tmp_out[idx - start] = packed_reduce<P, ngpus, A>(ptrs, idx);
  }
  barrier_at_end<ngpus>(sg, self_sg, rank);

  // stage 2: allgather. Note: it's important to match the tid between
  // the two stages, because visibility across devices is only guaranteed
  // between threads that have the same tid. If thread i computes the sum of
  // start + i in the first stage, then thread i also gathers start + i from
  // all ranks.

  for (int idx = tid; idx < largest_part; idx += stride) {
#pragma unroll
    for (int i = 0; i < ngpus; i++) {
      int gather_from_rank = ((rank + i) % ngpus);
      if (gather_from_rank == ngpus - 1 || idx < part) {
        int dst_idx = gather_from_rank * part + idx;
        ((P*)result)[dst_idx] = tmps[i][idx];
      }
    }
  }
}

using IPC_KEY = std::array<uint8_t, sizeof(cudaIpcMemHandle_t)>;
static_assert(sizeof(IPC_KEY) == sizeof(cudaIpcMemHandle_t));
static_assert(alignof(IPC_KEY) == alignof(cudaIpcMemHandle_t));

class CustomAllreduce {
 public:
  int rank_;
  int world_size_;
  // Full NVLink or xGMI connection between GPUs.
  bool fully_connected_;

  RankSignals sg_;
  // Stores a map from a pointer to its peer pointers from all ranks.
  std::unordered_map<void*, RankData*> buffers_;
  Signal* self_sg_;

  // Stores rank data from all ranks. This is mainly for cuda graph purposes.
  // For cuda graph to work, all kernel arguments must be fixed during graph
  // capture time. However, the peer pointers are not known during graph
  // capture time. Therefore, during capture, we increment the rank data
  // pointer and use that as the argument to the kernel. The kernel arguments
  // are stored in graph_unreg_buffers_. The actual peer pointers will be
  // filled in at the memory pointed to by the pointers in
  // graph_unreg_buffers_ when the IPC handles are exchanged between ranks.
  //
  // The overall process looks like this:
  // 1. Graph capture.
  // 2. Each rank obtains the IPC handles for each addresses used during cuda
  // graph capture using get_graph_buffer_ipc_meta.
  // 3. (In Python) all gather the IPC handles.
  // 4. Obtain the peer pointers by opening the IPC handles, and store them in
  // the rank data array at corresponding positions.
  RankData *d_rank_data_base_, *d_rank_data_end_;
  std::vector<void*> graph_unreg_buffers_;
  // a map from IPC handles to opened IPC pointers
  std::map<IPC_KEY, char*> ipc_handles_;

  uint32_t** dev_curr_hdp_reg;
  /**
   * Signals are an array of ipc-enabled buffers from all ranks.
   * For each of the buffer, the layout is as follows:
   * | -- sizeof(Signal) -- | ------ a few MB ----- |
   * The first section is for allreduce synchronization, and the second
   * section is for storing the intermediate results required by some
   * allreduce algos.
   *
   * Note: this class does not own any device memory. Any required buffers
   * are passed in from the constructor.
   */
  CustomAllreduce(Signal** signals, void* rank_data, size_t rank_data_sz,
                  int rank, int world_size, bool fully_connected = true)
      : rank_(rank),
        world_size_(world_size),
        fully_connected_(fully_connected),
        self_sg_(signals[rank]),
        d_rank_data_base_(reinterpret_cast<RankData*>(rank_data)),
        d_rank_data_end_(d_rank_data_base_ + rank_data_sz / sizeof(RankData)) {
    for (int i = 0; i < world_size_; i++) {
      sg_.signals[i] = signals[i];
    }
    if (!fully_connected) {
      cudaMalloc((void**)&dev_curr_hdp_reg, world_size_ * sizeof(uint32_t*));
      for (int i = 0; i < world_size_; ++i) {
        hipDeviceGetAttribute((int*)&dev_curr_hdp_reg[i], hipDeviceAttributeHdpMemFlushCntl, i);
      }
    }
  }

  char* open_ipc_handle(const void* ipc_handle) {
    auto [it, new_handle] =
        ipc_handles_.insert({*((IPC_KEY*)ipc_handle), nullptr});
    if (new_handle) {
      char* ipc_ptr;
      CUDACHECK(cudaIpcOpenMemHandle((void**)&ipc_ptr,
                                     *((const cudaIpcMemHandle_t*)ipc_handle),
                                     cudaIpcMemLazyEnablePeerAccess));
      it->second = ipc_ptr;
    }
    return it->second;
  }

  std::pair<std::string, std::vector<int64_t>> get_graph_buffer_ipc_meta() {
    auto num_buffers = graph_unreg_buffers_.size();
    auto handle_sz = sizeof(cudaIpcMemHandle_t);
    std::string handles(handle_sz * num_buffers, static_cast<char>(0));
    std::vector<int64_t> offsets(num_buffers);
    for (int i = 0; i < num_buffers; i++) {
      auto ptr = graph_unreg_buffers_[i];
      void* base_ptr;
      // note: must share the base address of each allocation, or we get wrong
      // address
      if (cuPointerGetAttribute(&base_ptr, rangeStartAddrAttr,
                                (CUdeviceptr)ptr) != CUDA_SUCCESS)
        throw std::runtime_error("failed to get pointer attr");
      CUDACHECK(cudaIpcGetMemHandle(
          (cudaIpcMemHandle_t*)&handles[i * handle_sz], base_ptr));
      offsets[i] = ((char*)ptr) - ((char*)base_ptr);
    }
    return std::make_pair(handles, offsets);
  }

  void check_rank_data_capacity(size_t num = 1) {
    if (d_rank_data_base_ + num > d_rank_data_end_)
      throw std::runtime_error(
          "Rank data buffer is overflowed by " +
          std::to_string(d_rank_data_base_ + num - d_rank_data_end_));
  }

  /**
   * Register already-shared IPC pointers.
   */
  void register_buffer(void** ptrs) {
    check_rank_data_capacity();
    RankData data;
    for (int i = 0; i < world_size_; i++) {
      data.ptrs[i] = ptrs[i];
    }
    auto d_data = d_rank_data_base_++;
    CUDACHECK(
        cudaMemcpy(d_data, &data, sizeof(RankData), cudaMemcpyHostToDevice));
    buffers_[ptrs[rank_]] = d_data;
  }

  // Note: when registering graph buffers, we intentionally choose to not
  // deduplicate the addresses. That means if the allocator reuses some
  // addresses, they will be registered again. This is to account for the
  // remote possibility of different allocation patterns between ranks. For
  // example, rank 1 may get the same input address for the second allreduce,
  // but rank 2 got a different address. IPC handles have internal reference
  // counting mechanism so overhead should be small.
  void register_graph_buffers(
      const std::vector<std::string>& handles,
      const std::vector<std::vector<int64_t>>& offsets) {
    auto num_buffers = graph_unreg_buffers_.size();
    check_rank_data_capacity(num_buffers);
    std::vector<RankData> rank_data(num_buffers);
    for (int i = 0; i < num_buffers; i++) {
      auto self_ptr = graph_unreg_buffers_[i];
      auto& rd = rank_data[i];
      for (int j = 0; j < world_size_; j++) {
        if (j != rank_) {
          char* handle =
              open_ipc_handle(&handles[j][i * sizeof(cudaIpcMemHandle_t)]);
          handle += offsets[j][i];
          rd.ptrs[j] = handle;
        } else {
          rd.ptrs[j] = self_ptr;
        }
      }
    }
    CUDACHECK(cudaMemcpy(d_rank_data_base_, rank_data.data(),
                         sizeof(RankData) * num_buffers,
                         cudaMemcpyHostToDevice));
    d_rank_data_base_ += num_buffers;
    graph_unreg_buffers_.clear();
  }

  template <typename T>
  void allreduce_pcie(cudaStream_t stream, T* input, T* output, int size,
                 int threads = 512, int block_limit = defaultBlockLimit) {
    auto d = packed_t<T>::P::size;
    if (size % d != 0)
      throw std::runtime_error(
          "custom allreduce currently requires input length to be multiple "
          "of " +
          std::to_string(d));
    if (block_limit > kMaxBlocks)
      throw std::runtime_error("max supported block limit is " +
                               std::to_string(kMaxBlocks) + ". Got " +
                               std::to_string(block_limit));

    RankData* ptrs;
    cudaStreamCaptureStatus status;
    CUDACHECK(cudaStreamIsCapturing(stream, &status));
    if (status == cudaStreamCaptureStatusActive) {
      ptrs = d_rank_data_base_ + graph_unreg_buffers_.size();
      graph_unreg_buffers_.push_back(input);
    } else {
      auto it = buffers_.find(input);
      if (it == buffers_.end())
        throw std::runtime_error(
            "buffer address " +
            std::to_string(reinterpret_cast<uint64_t>(input)) +
            " is not registered!");
      ptrs = it->second;
    }

    size /= d;
    auto bytes = size * sizeof(typename packed_t<T>::P);
    int blocks = std::min(block_limit, (size + threads - 1) / threads);
#define KL(ngpus, name)                                                       \
  name<T, ngpus><<<blocks, threads, 0, stream>>>(ptrs, sg_, self_sg_, output, \
                                                  rank_, size, dev_curr_hdp_reg, world_size_) ;

#define REDUCE_CASE(ngpus)                            \
  case ngpus: {                                       \
    if (world_size_ == 2) {                           \
      KL(ngpus, cross_device_reduce_1stage_pcie);     \
    } else {                                          \
      if ((world_size_ <= 4 && bytes < 128 * 8192) || \
          (world_size_ <= 8 && bytes < 8 * 8192)) {  \
        KL(ngpus, cross_device_reduce_1stage_pcie);   \
      } else {                                        \
        KL(ngpus, cross_device_reduce_2stage_pcie);   \
      }                                               \
    }                                                 \
    break;                                            \
  }

    switch (world_size_) {
      REDUCE_CASE(2)
      REDUCE_CASE(4)
      REDUCE_CASE(6)
      REDUCE_CASE(8)
      REDUCE_CASE(16)
      default:
        throw std::runtime_error(
            "custom allreduce only supports num gpus in (2,4,6,8,16). Actual "
            "num "
            "gpus = " +
            std::to_string(world_size_));
    }
#undef REDUCE_CASE
#undef KL
  }

  /**
   * Performs allreduce, assuming input has already been registered.
   *
   * Block and grid default configs are results after careful grid search.
   * Using 36 blocks give the best or close to the best runtime on the devices
   * I tried: A100, A10, A30, T4, V100. You'll notice that NCCL kernels also
   * only take a small amount of SMs. Not quite sure the underlying reason,
   * but my guess is that too many SMs will cause contention on NVLink bus.
   */

  template <typename T>
  void allreduce_fuse_norm(cudaStream_t stream, T* input, T* output, int size,
                          int token_num, int hidden_dim, T* residual, T* rms_weight,
                          double eps, int threads = 512, int block_limit = defaultBlockLimit) {
    auto d = packed_t<T>::P::size;
    if (hidden_dim % d != 0)
        throw std::runtime_error(
            "custom allreduce currently requires input length to be multiple "
            "of " +
            std::to_string(d));
    if (block_limit > kMaxBlocks)
        throw std::runtime_error("max supported block limit is " +
                                std::to_string(kMaxBlocks) + ". Got " +
                                std::to_string(block_limit));

    RankData* ptrs;
    cudaStreamCaptureStatus status;
    CUDACHECK(cudaStreamIsCapturing(stream, &status));
    if (status == cudaStreamCaptureStatusActive) {
      ptrs = d_rank_data_base_ + graph_unreg_buffers_.size();
      graph_unreg_buffers_.push_back(input);
    } else {
      auto it = buffers_.find(input);
      if (it == buffers_.end())
        throw std::runtime_error(
            "buffer address " +
            std::to_string(reinterpret_cast<uint64_t>(input)) +
            " is not registered!");
      ptrs = it->second;
    }

    int block_num = token_num;

#define KL(ngpus, name)                                                 \
    std::array<int, ngpus> begin_tokens, token_num_per_ranks;           \
    int remaining_token = token_num % ngpus;                            \
    int token_num_per_rank = token_num / ngpus;                         \
    block_num = token_num_per_rank;                                     \
    if (remaining_token)                                                \
      block_num++;                                                      \
    for (int i = 0; i < ngpus; ++i) {                                   \
      begin_tokens[i] = i * token_num_per_rank + (remaining_token > i ? i : remaining_token);     \
      token_num_per_ranks[i] = token_num_per_rank + (remaining_token > i ? 1 : 0);                \
    }                                                                                             \
    int thread_per_token = hidden_dim / d;                                                        \
    int grid_size = std::min(kMaxBlocks, block_num);                                              \
    int threads_in_block = thread_per_token;                                                      \
  name<T, ngpus><<<grid_size, threads_in_block, 0, stream>>>(ptrs, sg_, self_sg_, output, \
                                                 rank_, size, hidden_dim, residual, \
                                                 rms_weight, eps, begin_tokens, token_num_per_ranks); 
#define REDUCE_CASE(ngpus)                            \
  case ngpus: {                                       \
    if (world_size_ == 2) {                           \
      KL(ngpus, cross_device_reduce_2stage_fuse_norm);          \
    } else if (fully_connected_) {                    \
      if ((world_size_ <= 4) || \
          (world_size_ <= 8 )) { \
        KL(ngpus, cross_device_reduce_2stage_fuse_norm);        \
      } else {                                        \
        KL(ngpus, cross_device_reduce_2stage_fuse_norm);        \
      }                                               \
    }                                                 \
    break;                                            \
  }

    switch (world_size_) {
      REDUCE_CASE(2)
      REDUCE_CASE(4)
      REDUCE_CASE(6)
      REDUCE_CASE(8)
      default:
        throw std::runtime_error(
            "custom allreduce only supports num gpus in (2,4,6,8). Actual "
            "num "
            "gpus = " +
            std::to_string(world_size_));
    }
#undef REDUCE_CASE
#undef KL
  } 

 template<typename scalar_in_t, typename scalar_out_t, bool isResidual=true, bool update_input=false>
  void allreduce_fuse_norm_quant(cudaStream_t stream, scalar_in_t* input, scalar_out_t* output, int size,
                                 int token_num, int hidden_dim, scalar_in_t* residual, scalar_in_t* rms_weight,
                                 scalar_in_t* norm_out,
                                 double eps, float* scales, int threads = 512, int block_limit = defaultBlockLimit) {
    auto d = packed_t<scalar_in_t>::P::size;
    if (hidden_dim % d != 0)
        throw std::runtime_error(
            "custom allreduce currently requires input length to be multiple "
            "of " +
            std::to_string(d));
    if (block_limit > kMaxBlocks)
        throw std::runtime_error("max supported block limit is " +
                                std::to_string(kMaxBlocks) + ". Got " +
                                std::to_string(block_limit));

    RankData* ptrs;
    cudaStreamCaptureStatus status;
    CUDACHECK(cudaStreamIsCapturing(stream, &status));
    if (status == cudaStreamCaptureStatusActive) {
      ptrs = d_rank_data_base_ + graph_unreg_buffers_.size();
      graph_unreg_buffers_.push_back(input);
    } else {
      auto it = buffers_.find(input);
      if (it == buffers_.end())
        throw std::runtime_error(
            "buffer address " +
            std::to_string(reinterpret_cast<uint64_t>(input)) +
            " is not registered!");
      ptrs = it->second;
    }

    int block_num = token_num;
    int thread_per_token = hidden_dim / d;  
    auto bytes = (size / d) * sizeof(typename packed_t<scalar_in_t>::P);
#define KL1(ngpus, name)                                                 \
    std::array<int, ngpus> begin_tokens, token_num_per_ranks;           \
    int remaining_token = token_num % ngpus;                            \
    int token_num_per_rank = token_num / ngpus;                         \
    block_num = token_num_per_rank;                                     \
    if (remaining_token)                                                \
      block_num++;                                                      \
    for (int i = 0; i < ngpus; ++i) {                                   \
      begin_tokens[i] = i * token_num_per_rank + (remaining_token > i ? i : remaining_token);     \
      token_num_per_ranks[i] = token_num_per_rank + (remaining_token > i ? 1 : 0);                \
    }                                                                                             \                                                      
    int grid_size = std::min(kMaxBlocks, block_num);                                              \
    int threads_in_block = thread_per_token;                                                      \
  name<scalar_in_t, scalar_out_t, ngpus, isResidual, update_input><<<block_num, threads_in_block, 0, stream>>>(ptrs, sg_,  \
                                                self_sg_, output,  rank_, size, hidden_dim, residual,        \
                                                 rms_weight, scales, eps, norm_out, begin_tokens, token_num_per_ranks); 
#define KL(ngpus, name)                                              \
    name<scalar_in_t, scalar_out_t, ngpus, isResidual, update_input><<<block_num, thread_per_token, 0, stream>>>(ptrs, sg_, \
                                                self_sg_, output, rank_, size, hidden_dim, residual, rms_weight,             \
                                                scales, eps, norm_out);                                
#define REDUCE_CASE(ngpus)                            \
  case ngpus: {                                       \
    if (world_size_ == 2) {                           \
      KL(ngpus, cross_device_reduce_1stage_norm_quant);          \
    } else if (fully_connected_) {                    \
      if ((world_size_ <= 4 && bytes < 1024 * 1024) || \
          (world_size_ <= 8 && bytes < 512 * 1024)) { \
        KL(ngpus, cross_device_reduce_1stage_norm_quant);        \
      } else {                                        \
        KL1(ngpus, cross_device_reduce_2stage_fuse_norm_quant);        \
      }                                               \
    }                                                 \
    break;                                            \
  }

    switch (world_size_) {
      REDUCE_CASE(2)
      REDUCE_CASE(4)
      REDUCE_CASE(6)
      REDUCE_CASE(8)
      default:
        throw std::runtime_error(
            "custom allreduce only supports num gpus in (2,4,6,8). Actual "
            "num "
            "gpus = " +
            std::to_string(world_size_));
    }
#undef REDUCE_CASE
#undef KL
  }




  template <typename T>
  void allreduce(cudaStream_t stream, T* input, T* output, int size,
                 int threads = 512, int block_limit = defaultBlockLimit) {
    auto d = packed_t<T>::P::size;
    if (size % d != 0)
      throw std::runtime_error(
          "custom allreduce currently requires input length to be multiple "
          "of " +
          std::to_string(d));
    if (block_limit > kMaxBlocks)
      throw std::runtime_error("max supported block limit is " +
                               std::to_string(kMaxBlocks) + ". Got " +
                               std::to_string(block_limit));

    RankData* ptrs;
    cudaStreamCaptureStatus status;
    CUDACHECK(cudaStreamIsCapturing(stream, &status));
    if (status == cudaStreamCaptureStatusActive) {
      ptrs = d_rank_data_base_ + graph_unreg_buffers_.size();
      graph_unreg_buffers_.push_back(input);
    } else {
      auto it = buffers_.find(input);
      if (it == buffers_.end())
        throw std::runtime_error(
            "buffer address " +
            std::to_string(reinterpret_cast<uint64_t>(input)) +
            " is not registered!");
      ptrs = it->second;
    }

    size /= d;
    auto bytes = size * sizeof(typename packed_t<T>::P);
    int blocks = std::min(block_limit, (size + threads - 1) / threads);
#define KL(ngpus, name)                                                       \
  name<T, ngpus><<<blocks, threads, 0, stream>>>(ptrs, sg_, self_sg_, output, \
                                                 rank_, size);
#define REDUCE_CASE(ngpus)                            \
  case ngpus: {                                       \
    if (world_size_ == 2) {                           \
      KL(ngpus, cross_device_reduce_1stage);          \
    } else if (fully_connected_) {                    \
      if ((world_size_ <= 4 && bytes < 512 * 1024) || \
          (world_size_ <= 8 && bytes < 256 * 1024)) { \
        KL(ngpus, cross_device_reduce_1stage);        \
      } else {                                        \
        KL(ngpus, cross_device_reduce_2stage);        \
      }                                               \
    }                                                 \
    break;                                            \
  }

    switch (world_size_) {
      REDUCE_CASE(2)
      REDUCE_CASE(4)
      REDUCE_CASE(6)
      REDUCE_CASE(8)
      default:
        throw std::runtime_error(
            "custom allreduce only supports num gpus in (2,4,6,8). Actual "
            "num "
            "gpus = " +
            std::to_string(world_size_));
    }
#undef REDUCE_CASE
#undef KL
  }

  ~CustomAllreduce() {
    for (auto [_, ptr] : ipc_handles_) {
      CUDACHECK(cudaIpcCloseMemHandle(ptr));
    }
    cudaFree(dev_curr_hdp_reg);
  }
};

/**
 * To inspect PTX/SASS, copy paste this header file to compiler explorer and
 add a template instantiation:
 * template void vllm::CustomAllreduce::allreduce<half>(cudaStream_t, half *,
 half *, int, int, int);
*/
}  // namespace vllm
