/*
Copyright 2020 The OneFlow Authors. All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
#include "hip/hip_runtime.h"
#include "oneflow/core/framework/framework.h"
#include "oneflow/core/device/nccl_util.h"
#include "oneflow/core/job/eager_nccl_comm_manager.h"
#include "oneflow/core/job/parallel_desc.h"
#include "oneflow/core/ep/rocm/cuda_stream.h"
#include "oneflow/user/kernels/gather_kernel_util.h"
#include "oneflow/user/kernels/unsorted_segment_sum_kernel_util.h"
#include "oneflow/core/hip/atomic.hip.h"
#include "oneflow/core/embedding/hash_functions.hip.h"
#include "oneflow/core/hip/elementwise.hip.h"
#include "oneflow/core/ep/include/primitive/copy_nd.h"
#include "oneflow/core/hip/atomic.hip.h"

namespace oneflow {

namespace {

template<typename K>
struct TableEntry {
  K key;
  uint32_t value;
};

template<typename K, typename V, typename IDX, typename HASH>
__global__ void HashTableUniqueAndPartitionPairs(const uint32_t table_capacity,
                                                 const uint32_t num_keys, int32_t num_partition,
                                                 IDX* unique_counts, TableEntry<K>* table,
                                                 const K* keys, const V* values,
                                                 K* partitioned_unique_keys,
                                                 V* partitioned_unique_values, IDX* reverse_index,
                                                 bool need_process_values) {
  CUDA_1D_KERNEL_LOOP_T(uint32_t, i, num_keys) {
    IDX r_index_plus_one = 0;
    const K key = keys[i];
    size_t key_hash = HASH()(key);
    uint32_t partition_id = key_hash % num_partition;
    IDX* unique_count = unique_counts + partition_id;
    K* unique_keys = partitioned_unique_keys + partition_id * num_keys;
    uint32_t pos = key_hash % table_capacity;
    const K key_hi = (key | 0x1);
    const K key_lo = (key & 0x1);
    uint32_t counter = 0;
    while (r_index_plus_one == 0) {
      bool prob_next = false;
      K* key_ptr = &table[pos].key;
      volatile uint32_t* table_value_ptr = &table[pos].value;
      const K old_key = cuda::atomic::CAS(key_ptr, 0, key_hi);
      if (old_key == 0) {
        IDX unique_pos = cuda::atomic::Add(unique_count, 1);
        r_index_plus_one = unique_pos + 1;
        unique_keys[unique_pos] = key;
        if (need_process_values) {
          partitioned_unique_values[partition_id * num_keys + unique_pos] = values[i];
        }
        *table_value_ptr = ((r_index_plus_one << 1U) | key_lo);
      } else if (old_key == key_hi) {
        const uint32_t value = *table_value_ptr;
        if (value == 0) {
          // do nothing
        } else if ((value & 0x1) == key_lo) {
          r_index_plus_one = (value >> 1U);
        } else {
          prob_next = true;
        }
      } else {
        prob_next = true;
      }
      if (prob_next) {
        pos += 1;
        counter += 1;
        if (pos >= table_capacity) { pos -= table_capacity; }
        if (counter >= table_capacity) { asm volatile("s_trap 0;"); }
      }
    }
    reverse_index[i] = partition_id * num_keys + r_index_plus_one - 1;
  }
}

template<typename U>
__global__ void GenerateTableIds(int32_t elem_cnt, int32_t num_tables, U* table_ids) {
  CUDA_1D_KERNEL_LOOP(i, elem_cnt) { table_ids[i] = i % num_tables; }
}

template<typename K, typename V, typename IDX, typename HASH>
void UniqueAndPartition(hipStream_t cuda_stream, int64_t num_ids, size_t capacity,
                        int64_t num_partition, const K* ids, const V* table_ids,
                        IDX* num_partitioned_unique_ids_ptr, K* partitioned_unique_ids,
                        V* partitioned_unique_table_ids, IDX* inverse_unique_partition_indices,
                        void* workspace_ptr, size_t workspace_bytes, bool need_process_table_ids) {
  size_t table_capacity_bytes = capacity * sizeof(TableEntry<K>);
  CHECK_GE(workspace_bytes, table_capacity_bytes);
  OF_CUDA_CHECK(hipMemsetAsync(workspace_ptr, 0, table_capacity_bytes, cuda_stream));
  OF_CUDA_CHECK(
      hipMemsetAsync(num_partitioned_unique_ids_ptr, 0, num_partition * sizeof(IDX), cuda_stream));
  hipLaunchKernelGGL(HIP_KERNEL_NAME(HashTableUniqueAndPartitionPairs<K, V, IDX, HASH>), BlocksNum4ThreadsNum(num_ids), kCudaThreadsNumPerBlock, 0, cuda_stream, 
          capacity, num_ids, num_partition, num_partitioned_unique_ids_ptr,
          reinterpret_cast<TableEntry<K>*>(workspace_ptr), ids, table_ids, partitioned_unique_ids,
          partitioned_unique_table_ids, inverse_unique_partition_indices, need_process_table_ids);
}

template<typename T>
void ShuffleData(hipStream_t cuda_stream, ncclComm_t comm, DataType data_type,
                 const std::vector<int64_t>& send_offsets,
                 const std::vector<int64_t>& send_elem_cnt, const T* send_data,
                 const std::vector<int64_t>& recv_offsets,
                 const std::vector<int64_t>& recv_elem_cnt, T* recv_data) {
  ncclDataType_t nccl_data_type = GetNcclDataType(data_type);
  const int64_t parallel_num = send_offsets.size();
  OF_NCCL_CHECK(ncclGroupStart());
  for (int64_t i = 0; i < parallel_num; ++i) {
    OF_NCCL_CHECK(ncclSend(send_data + send_offsets.at(i), send_elem_cnt.at(i), nccl_data_type, i,
                           comm, cuda_stream));
    OF_NCCL_CHECK(ncclRecv(recv_data + recv_offsets.at(i), recv_elem_cnt.at(i), nccl_data_type, i,
                           comm, cuda_stream));
  }
  OF_NCCL_CHECK(ncclGroupEnd());
}

template<typename IDX>
void MakeShuffleParams(const IDX* host_num_unique_matrix, const int64_t num_ids,
                       const int64_t row_size, int64_t parallel_id, int64_t parallel_num,
                       std::vector<int64_t>* scatter_offset_vec,
                       std::vector<int64_t>* scatter_elem_cnt_vec,
                       std::vector<int64_t>* gather_offset_vec,
                       std::vector<int64_t>* gather_elem_cnt_vec) {
  scatter_offset_vec->resize(parallel_num);
  scatter_elem_cnt_vec->resize(parallel_num);
  gather_offset_vec->resize(parallel_num);
  gather_elem_cnt_vec->resize(parallel_num);
  int64_t gather_offset = 0;
  for (int64_t i = 0; i < parallel_num; ++i) {
    const int64_t scatter_elem_cnt =
        host_num_unique_matrix[parallel_id * parallel_num + i] * row_size;
    const int64_t gather_elem_cnt =
        host_num_unique_matrix[i * parallel_num + parallel_id] * row_size;
    scatter_offset_vec->at(i) = i * num_ids * row_size;
    scatter_elem_cnt_vec->at(i) = scatter_elem_cnt;
    gather_offset_vec->at(i) = gather_offset;
    gather_elem_cnt_vec->at(i) = gather_elem_cnt;
    gather_offset += gather_elem_cnt;
  }
}

template<typename K, typename U, typename IDX>
void ShuffleIdsAndTableIds(hipStream_t cuda_stream, ncclComm_t comm, int64_t parallel_id,
                           int64_t parallel_num, int64_t num_ids, DataType ids_data_type,
                           DataType table_ids_data_type, IDX* host_num_unique_matrix,
                           K* partitioned_unique_ids, U* partitioned_unique_table_ids,
                           K* received_ids, U* received_table_ids, int64_t* received_elem_cnt,
                           bool need_process_table_ids) {
  std::vector<int64_t> send_offsets;
  std::vector<int64_t> send_elem_cnt;
  std::vector<int64_t> recv_offsets;
  std::vector<int64_t> recv_elem_cnt;
  MakeShuffleParams(host_num_unique_matrix, num_ids, 1, parallel_id, parallel_num, &send_offsets,
                    &send_elem_cnt, &recv_offsets, &recv_elem_cnt);
  ShuffleData(cuda_stream, comm, ids_data_type, send_offsets, send_elem_cnt, partitioned_unique_ids,
              recv_offsets, recv_elem_cnt, received_ids);
  *received_elem_cnt = recv_offsets.at(parallel_num - 1) + recv_elem_cnt.at(parallel_num - 1);
  if (need_process_table_ids) {
    ShuffleData(cuda_stream, comm, table_ids_data_type, send_offsets, send_elem_cnt,
                partitioned_unique_table_ids, recv_offsets, recv_elem_cnt, received_table_ids);
  }
}

enum class IdShuffleBufferType {
  kNumPartitionedUnique = 0,
  kPartitionedUniqueIds,
  kReceivedIds,
  kTableIds,
  kPartitionedUniqueTableIds,
  kReceivedTableIds,
  kWorkspace,
  kMaxType
};

template<typename K, typename U, typename IDX>
class IdShuffleTmpBufferManager final {
 public:
  OF_DISALLOW_COPY_AND_MOVE(IdShuffleTmpBufferManager);
  IdShuffleTmpBufferManager(void* ptr, const int64_t num_ids, const int64_t parallel_num,
                            bool need_table_ids, bool need_process_table_ids)
      : offset_(0),
        offsets_(static_cast<size_t>(IdShuffleBufferType::kMaxType), -1),
        sizes_(static_cast<size_t>(IdShuffleBufferType::kMaxType)),
        ptr_(ptr) {
    const int64_t num_table_ids = need_process_table_ids ? num_ids : 0;
    const size_t table_ids_bytes = need_table_ids ? num_ids * sizeof(U) : 0;
    AllocBuffer(IdShuffleBufferType::kNumPartitionedUnique, parallel_num * sizeof(IDX));
    size_t partitioned_ids_bytes = parallel_num * num_ids * sizeof(K);
    AllocBuffer(IdShuffleBufferType::kPartitionedUniqueIds, partitioned_ids_bytes);
    AllocBuffer(IdShuffleBufferType::kReceivedIds, partitioned_ids_bytes);
    AllocBuffer(IdShuffleBufferType::kTableIds, table_ids_bytes);
    size_t partitioned_table_ids_bytes = parallel_num * num_table_ids * sizeof(U);
    AllocBuffer(IdShuffleBufferType::kPartitionedUniqueTableIds, partitioned_table_ids_bytes);
    AllocBuffer(IdShuffleBufferType::kReceivedTableIds, partitioned_table_ids_bytes);
    const size_t hash_table_capacity = parallel_num * num_ids;
    AllocBuffer(IdShuffleBufferType::kWorkspace, hash_table_capacity * sizeof(TableEntry<K>));
  }

  template<typename T = void>
  T* Ptr(IdShuffleBufferType type) {
    CHECK(ptr_ != nullptr);
    int64_t offset = offsets_.at(static_cast<size_t>(type));
    CHECK_NE(offset, -1);
    return reinterpret_cast<T*>(reinterpret_cast<char*>(ptr_) + offset);
  }

  int64_t Size(IdShuffleBufferType type) { return sizes_.at(static_cast<size_t>(type)); }

  size_t TotalBufferSize() const { return offset_; }

 private:
  void AllocBuffer(IdShuffleBufferType type, size_t size) {
    const size_t type_id = static_cast<size_t>(type);
    CHECK_EQ(offsets_.at(type_id), -1);
    offsets_.at(type_id) = offset_;
    sizes_.at(type_id) = size;
    offset_ += GetCudaAlignedSize(size);
  }
  size_t offset_;
  std::vector<int64_t> offsets_;
  std::vector<int64_t> sizes_;
  void* ptr_;
};

template<typename IDX>
class DataShuffleKernelState final : public user_op::OpKernelState {
 public:
  explicit DataShuffleKernelState(user_op::KernelInitContext* ctx)
      : device_index_(-1),
        stream_name_(EagerNcclCommMgr::kDefaultStreamName),
        parallel_desc_(ctx->parallel_desc()) {
    OF_CUDA_CHECK(hipGetDevice(&device_index_));
    if (ctx->op_conf().has_stream_name_hint()) { stream_name_ = ctx->op_conf().stream_name_hint(); }
    OF_CUDA_CHECK(hipMallocHost(
        reinterpret_cast<void **>(&host_num_unique_matrix_),
        parallel_desc_.parallel_num() * parallel_desc_.parallel_num() * sizeof(IDX)));
  }
  ~DataShuffleKernelState() {
    CudaCurrentDeviceGuard guard(device_index_);
    OF_CUDA_CHECK(hipHostFree(host_num_unique_matrix_));
  }

  ncclComm_t comm() { return GetOrCreate().comm; }

  IDX* HostNumUniqueMatrix() { return host_num_unique_matrix_; }

 private:
  struct Comm {
    Comm(ncclComm_t comm) : comm(comm) {}
    ncclComm_t comm;
  };

  const Comm& GetOrCreate() {
    if (!comm_) { Init(); }
    return *comm_;
  }

  void Init() {
    std::set<std::pair<int64_t, int64_t>> device_set;
    for (int64_t parallel_id = 0; parallel_id < parallel_desc_.parallel_num(); ++parallel_id) {
      int64_t machine_id = CHECK_JUST(parallel_desc_.MachineId4ParallelId(parallel_id));
      int64_t device_id = CHECK_JUST(parallel_desc_.DeviceId4ParallelId(parallel_id));
      device_set.emplace(std::make_pair(machine_id, device_id));
    }
    EagerNcclCommMgr* comm_mgr = CHECK_NOTNULL(Singleton<EagerNcclCommMgr>::Get());
    ncclComm_t comm;
    comm = comm_mgr->GetCommForDeviceAndStreamName(device_set, stream_name_);
    comm_.reset(new Comm(comm));
  }

  int device_index_;
  bool has_independent_stream_;
  std::string stream_name_;
  ParallelDesc parallel_desc_;
  std::unique_ptr<Comm> comm_;
  IDX* host_num_unique_matrix_;
};

}  // namespace

template<typename K, typename U, typename IDX>
class IdShuffleKernel final : public user_op::OpKernel {
 public:
  IdShuffleKernel() = default;
  ~IdShuffleKernel() override = default;

  std::shared_ptr<user_op::OpKernelState> CreateOpKernelState(
      user_op::KernelInitContext* ctx) const override {
    return std::make_shared<DataShuffleKernelState<IDX>>(ctx);
  }

 private:
  using user_op::OpKernel::Compute;
  void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state,
               const user_op::OpKernelCache*) const override {
    auto* kernel_state = dynamic_cast<DataShuffleKernelState<IDX>*>(state);
    CHECK(kernel_state != nullptr);
    const user_op::Tensor* ids = ctx->Tensor4ArgNameAndIndex("ids", 0);
    user_op::Tensor* num_unique_matrix = ctx->Tensor4ArgNameAndIndex("num_unique_matrix", 0);
    user_op::Tensor* inverse_unique_partition_indices =
        ctx->Tensor4ArgNameAndIndex("inverse_unique_partition_indices", 0);
    user_op::Tensor* cur_rank_num_unique = ctx->Tensor4ArgNameAndIndex("cur_rank_num_unique", 0);
    user_op::Tensor* cur_rank_unique_ids = ctx->Tensor4ArgNameAndIndex("cur_rank_unique_ids", 0);
    user_op::Tensor* cur_rank_unique_table_ids =
        ctx->Tensor4ArgNameAndIndex("cur_rank_unique_table_ids", 0);
    user_op::Tensor* cur_rank_inverse_indices =
        ctx->Tensor4ArgNameAndIndex("cur_rank_inverse_indices", 0);
    user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0);
    const int32_t num_tables = ctx->Attr<int32_t>("num_tables");
    const bool has_table_ids = ctx->has_input("table_ids", 0);
    const bool need_gen_table_ids = (!has_table_ids && num_tables > 1);
    const bool need_process_table_ids = (has_table_ids || num_tables > 1);
    const int64_t num_ids = ids->shape_view().elem_cnt();
    const int64_t parallel_num = ctx->parallel_ctx().parallel_num();
    const int64_t parallel_id = ctx->parallel_ctx().parallel_id();
    hipStream_t cuda_stream = ctx->stream()->As<ep::CudaStream>()->cuda_stream();
    IdShuffleTmpBufferManager<K, U, IDX> buffer_manager(
        tmp_buffer->mut_dptr(), num_ids, parallel_num, need_gen_table_ids, need_process_table_ids);
    CHECK_GE(tmp_buffer->shape_view().elem_cnt(), buffer_manager.TotalBufferSize());

    const U* table_ids_ptr;
    if (has_table_ids) {
      const user_op::Tensor* table_ids = ctx->Tensor4ArgNameAndIndex("table_ids", 0);
      table_ids_ptr = reinterpret_cast<const U*>(table_ids->dptr());
    } else if (need_gen_table_ids) {
      hipLaunchKernelGGL(GenerateTableIds, BlocksNum4ThreadsNum(num_ids), kCudaThreadsNumPerBlock, 0, cuda_stream, 
          num_ids, num_tables, buffer_manager.template Ptr<U>(IdShuffleBufferType::kTableIds));
      table_ids_ptr = buffer_manager.template Ptr<U>(IdShuffleBufferType::kTableIds);
    } else {
      table_ids_ptr = nullptr;
    }
    IDX* num_partitioned_unique =
        buffer_manager.template Ptr<IDX>(IdShuffleBufferType::kNumPartitionedUnique);
    K* partitioned_unique_ids =
        buffer_manager.template Ptr<K>(IdShuffleBufferType::kPartitionedUniqueIds);
    U* partitioned_unique_table_ids =
        buffer_manager.template Ptr<U>(IdShuffleBufferType::kPartitionedUniqueTableIds);
    IDX* num_unique_matrix_ptr = reinterpret_cast<IDX*>(num_unique_matrix->mut_dptr());
    size_t hash_table_capacity = parallel_num * num_ids;
    void* workspace_ptr = buffer_manager.Ptr(IdShuffleBufferType::kWorkspace);
    size_t workspace_size = buffer_manager.Size(IdShuffleBufferType::kWorkspace);
    UniqueAndPartition<K, U, IDX, embedding::ShardingHash>(
        cuda_stream, num_ids, hash_table_capacity, parallel_num,
        reinterpret_cast<const K*>(ids->dptr()), table_ids_ptr, num_partitioned_unique,
        partitioned_unique_ids, partitioned_unique_table_ids,
        reinterpret_cast<IDX*>(inverse_unique_partition_indices->mut_dptr()), workspace_ptr,
        workspace_size, need_process_table_ids);
    ncclComm_t comm = kernel_state->comm();
    OF_NCCL_CHECK(ncclAllGather(num_partitioned_unique, num_unique_matrix_ptr, parallel_num,
                                GetNcclDataType(num_unique_matrix->data_type()), comm,
                                cuda_stream));
    IDX* host_num_unique_matrix = kernel_state->HostNumUniqueMatrix();
    OF_CUDA_CHECK(hipMemcpyAsync(host_num_unique_matrix, num_unique_matrix_ptr,
                                  parallel_num * parallel_num * sizeof(IDX), hipMemcpyDefault,
                                  cuda_stream));
    CHECK_JUST(ctx->stream()->Sync());

    K* received_ids = buffer_manager.template Ptr<K>(IdShuffleBufferType::kReceivedIds);
    U* received_table_ids = buffer_manager.template Ptr<U>(IdShuffleBufferType::kReceivedTableIds);
    int64_t received_elem_cnt = 0;
    ShuffleIdsAndTableIds(cuda_stream, comm, parallel_id, parallel_num, num_ids, ids->data_type(),
                          cur_rank_unique_table_ids->data_type(), host_num_unique_matrix,
                          partitioned_unique_ids, partitioned_unique_table_ids, received_ids,
                          received_table_ids, &received_elem_cnt, need_process_table_ids);
    UniqueAndPartition<K, U, IDX, embedding::LocalUniqueHash>(
        cuda_stream, received_elem_cnt, hash_table_capacity, 1, received_ids, received_table_ids,
        reinterpret_cast<IDX*>(cur_rank_num_unique->mut_dptr()),
        reinterpret_cast<K*>(cur_rank_unique_ids->mut_dptr()),
        reinterpret_cast<U*>(cur_rank_unique_table_ids->mut_dptr()),
        reinterpret_cast<IDX*>(cur_rank_inverse_indices->mut_dptr()), workspace_ptr, workspace_size,
        need_process_table_ids);
    if (!need_process_table_ids) {
      OF_CUDA_CHECK(hipMemsetAsync(cur_rank_unique_table_ids->mut_dptr(), 0,
                                    received_elem_cnt * sizeof(U), cuda_stream));
    }
  }
  bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; }
};

#define ID_DATA_TYPE_SEQ                            \
  OF_PP_MAKE_TUPLE_SEQ(uint32_t, DataType::kUInt32) \
  OF_PP_MAKE_TUPLE_SEQ(uint64_t, DataType::kUInt64) \
  OF_PP_MAKE_TUPLE_SEQ(int32_t, DataType::kInt32)   \
  OF_PP_MAKE_TUPLE_SEQ(int64_t, DataType::kInt64)

#define TABLE_ID_DATA_TYPE_SEQ                      \
  OF_PP_MAKE_TUPLE_SEQ(uint8_t, DataType::kUInt8)   \
  OF_PP_MAKE_TUPLE_SEQ(uint32_t, DataType::kUInt32) \
  OF_PP_MAKE_TUPLE_SEQ(uint64_t, DataType::kUInt64) \
  OF_PP_MAKE_TUPLE_SEQ(int8_t, DataType::kInt8)     \
  OF_PP_MAKE_TUPLE_SEQ(int32_t, DataType::kInt32)   \
  OF_PP_MAKE_TUPLE_SEQ(int64_t, DataType::kInt64)

#define IDX_DATA_TYPE_SEQ                           \
  OF_PP_MAKE_TUPLE_SEQ(uint32_t, DataType::kUInt32) \
  OF_PP_MAKE_TUPLE_SEQ(int32_t, DataType::kInt32)

#define REGISTER_CUDA_ID_SHUFFLE_KERNEL(k_dtype_pair, table_id_dtype_pair, idx_dtype_pair)        \
  REGISTER_USER_KERNEL("id_shuffle")                                                              \
      .SetCreateFn<                                                                               \
          IdShuffleKernel<OF_PP_PAIR_FIRST(k_dtype_pair), OF_PP_PAIR_FIRST(table_id_dtype_pair),  \
                          OF_PP_PAIR_FIRST(idx_dtype_pair)>>()                                    \
      .SetIsMatchedHob(                                                                           \
          (user_op::HobDeviceType() == DeviceType::kCUDA)                                         \
          && (user_op::HobDataType("ids", 0) == OF_PP_PAIR_SECOND(k_dtype_pair))                  \
          && (user_op::HobDataType("cur_rank_unique_table_ids", 0)                                \
              == OF_PP_PAIR_SECOND(table_id_dtype_pair))                                          \
          && (user_op::HobDataType("num_unique_matrix", 0) == OF_PP_PAIR_SECOND(idx_dtype_pair))) \
      .SetInferTmpSizeFn([](user_op::InferContext* ctx) {                                         \
        const user_op::TensorDesc& ids = ctx->InputTensorDesc("ids", 0);                          \
        const bool has_table_ids = ctx->has_input("table_ids", 0);                                \
        const int32_t num_tables = ctx->Attr<int32_t>("num_tables");                              \
        const bool need_gen_table_ids = (!has_table_ids && num_tables > 1);                       \
        const bool need_process_table_ids = (has_table_ids || num_tables > 1);                    \
        IdShuffleTmpBufferManager<OF_PP_PAIR_FIRST(k_dtype_pair),                                 \
                                  OF_PP_PAIR_FIRST(table_id_dtype_pair),                          \
                                  OF_PP_PAIR_FIRST(idx_dtype_pair)>                               \
            buffer_manager(nullptr, ids.shape().elem_cnt(), ctx->parallel_desc().parallel_num(),  \
                           need_gen_table_ids, need_process_table_ids);                           \
        return buffer_manager.TotalBufferSize();                                                  \
      });

OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CUDA_ID_SHUFFLE_KERNEL, ID_DATA_TYPE_SEQ,
                                 TABLE_ID_DATA_TYPE_SEQ, IDX_DATA_TYPE_SEQ)

template<typename T, typename IDX>
void ShuffleEmbeddings(hipStream_t cuda_stream, ncclComm_t comm, int64_t parallel_id,
                       int64_t parallel_num, int64_t num_ids, int64_t embedding_size,
                       DataType data_type, IDX* host_num_unique_matrix,
                       T* reverse_unique_cur_rank_embeddings, T* received_embeddings) {
  std::vector<int64_t> send_offsets;
  std::vector<int64_t> send_elem_cnt;
  std::vector<int64_t> recv_offsets;
  std::vector<int64_t> recv_elem_cnt;
  MakeShuffleParams(host_num_unique_matrix, num_ids, embedding_size, parallel_id, parallel_num,
                    &recv_offsets, &recv_elem_cnt, &send_offsets, &send_elem_cnt);
  ShuffleData(cuda_stream, comm, data_type, send_offsets, send_elem_cnt,
              reverse_unique_cur_rank_embeddings, recv_offsets, recv_elem_cnt, received_embeddings);
}

// Quantized Version.
template<typename T, typename IDX>
void ShuffleEmbeddings(hipStream_t cuda_stream, ncclComm_t comm, int64_t parallel_id,
                       int64_t parallel_num, int64_t num_ids, int64_t embedding_size,
                       DataType data_type, IDX* host_num_unique_matrix,
                       int8_t* reverse_unique_cur_rank_embeddings, int8_t* received_embeddings,
                       T* reverse_cur_rank_quantize_factor, T* recv_quantize_factor) {
  std::vector<int64_t> send_offsets;
  std::vector<int64_t> send_elem_cnt;
  std::vector<int64_t> recv_offsets;
  std::vector<int64_t> recv_elem_cnt;
  // shuffle quantized_embedding
  MakeShuffleParams(host_num_unique_matrix, num_ids, embedding_size, parallel_id, parallel_num,
                    &recv_offsets, &recv_elem_cnt, &send_offsets, &send_elem_cnt);
  ShuffleData(cuda_stream, comm, DataType::kInt8, send_offsets, send_elem_cnt,
              reverse_unique_cur_rank_embeddings, recv_offsets, recv_elem_cnt, received_embeddings);
  // shuffle quantize_factor
  MakeShuffleParams(host_num_unique_matrix, num_ids, /*embedding_size=*/1, parallel_id,
                    parallel_num, &recv_offsets, &recv_elem_cnt, &send_offsets, &send_elem_cnt);
  ShuffleData(cuda_stream, comm, data_type, send_offsets, send_elem_cnt,
              reverse_cur_rank_quantize_factor, recv_offsets, recv_elem_cnt, recv_quantize_factor);
}

__device__ float RoundHalfAwayFromZero(const float x) {
  float abs_val = abs(x);
  float floor_val = floor(abs_val + static_cast<float>(0.5));
  return copysignf(floor_val, x);
}

// warp reduce version.
constexpr int32_t kWarpSize = 32;
constexpr int32_t kMaxColSize = 1024;

template<typename T, int thread_group_width = kWarpSize>
__inline__ __device__ T WarpMaxAllReduce(T val) {
  for (int32_t lane_mask = thread_group_width / 2; lane_mask > 0; lane_mask /= 2) {
    // val = max(val, __shfl_xor_sync(0xffffffff, val, lane_mask, thread_group_width));
    val = max(val, __shfl_xor(val, lane_mask, thread_group_width));
  }
  return val;
}

inline hipError_t GetWarpImplNumBlocks(int64_t block_size, int64_t max_blocks, int64_t waves,
                                        int* num_blocks) {
  int dev;
  {
    hipError_t err = hipGetDevice(&dev);
    if (err != hipSuccess) { return err; }
  }
  int sm_count;
  {
    hipError_t err = hipDeviceGetAttribute(&sm_count, hipDeviceAttributeMultiprocessorCount, dev);
    if (err != hipSuccess) { return err; }
  }
  int tpm;
  {
    hipError_t err = hipDeviceGetAttribute(&tpm, hipDeviceAttributeMaxThreadsPerMultiProcessor, dev);
    if (err != hipSuccess) { return err; }
  }
  *num_blocks =
      std::max<int>(1, std::min<int64_t>(max_blocks, sm_count * tpm / block_size * waves));
  return hipSuccess;
}

template<typename T, typename ComputeType, int pack_size, int cols_per_thread,
         int thread_group_width, int rows_per_access, bool padding>
__global__ void QuantizeWarpImplKernel(const T* src, int8_t* dst, T* quantize_factor,
                                       const int64_t rows, const int64_t cols) {
  static_assert(cols_per_thread % pack_size == 0, "");
  static_assert(thread_group_width <= kWarpSize, "");
  static_assert(kWarpSize % thread_group_width == 0, "");
  constexpr int num_packs = cols_per_thread / pack_size;
  assert(cols <= cols_per_thread * thread_group_width);
  ComputeType buf[rows_per_access][cols_per_thread];
  const int global_thread_group_id = blockIdx.x * blockDim.y + threadIdx.y;
  const int num_global_thread_group = gridDim.x * blockDim.y;
  const int lane_id = threadIdx.x;
  const int64_t step = num_global_thread_group * rows_per_access;
  using LoadType = cuda::elementwise::PackType<T, pack_size>;
  using LoadPack = cuda::elementwise::Pack<T, pack_size>;
  using StoreType = cuda::elementwise::PackType<int8_t, pack_size>;
  using StorePack = cuda::elementwise::Pack<int8_t, pack_size>;

  for (int64_t row = global_thread_group_id * rows_per_access; row < rows; row += step) {
    ComputeType thread_abs_max[rows_per_access];
#pragma unroll
    for (int row_id = 0; row_id < rows_per_access; row_id++) {
      ComputeType* row_buf = buf[row_id];
      thread_abs_max[row_id] = 0.0;
#pragma unroll
      for (int pack_id = 0; pack_id < num_packs; pack_id++) {
        const int pack_offset = pack_id * pack_size;
        const int col = (pack_id * thread_group_width + lane_id) * pack_size;
        LoadPack load_pack;
        if (!padding || col < cols) {
          const int64_t load_offset = ((row + row_id) * cols + col) / pack_size;
          load_pack.storage = *(reinterpret_cast<const LoadType*>(src) + load_offset);
#pragma unroll
          for (int i = 0; i < pack_size; i++) {
            row_buf[pack_offset + i] = static_cast<ComputeType>(load_pack.elem[i]);
            thread_abs_max[row_id] = max(thread_abs_max[row_id], abs(row_buf[pack_offset + i]));
          }
        } else {
#pragma unroll
          for (int i = 0; i < pack_size; i++) { row_buf[pack_offset + i] = 0.0; }
        }
      }
    }
    ComputeType warp_max[rows_per_access];
#pragma unroll
    for (int row_id = 0; row_id < rows_per_access; row_id++) {
      warp_max[row_id] = WarpMaxAllReduce<ComputeType, thread_group_width>(thread_abs_max[row_id]);
      if (threadIdx.x == 0) { quantize_factor[row + row_id] = static_cast<T>(warp_max[row_id]); }
      ComputeType* row_buf = buf[row_id];
      ComputeType quantize_factor_val = static_cast<ComputeType>(127.0) / warp_max[row_id];
#pragma unroll
      for (int col = 0; col < cols_per_thread; col++) {
        row_buf[col] = RoundHalfAwayFromZero(row_buf[col] * quantize_factor_val);
      }
#pragma unroll
      for (int pack_id = 0; pack_id < num_packs; pack_id++) {
        const int pack_offset = pack_id * pack_size;
        const int col = (pack_id * thread_group_width + lane_id) * pack_size;
        StorePack store_pack;
        if (!padding || col < cols) {
          const int64_t store_offset = ((row + row_id) * cols + col) / pack_size;
          for (int i = 0; i < pack_size; i++) {
            store_pack.elem[i] = static_cast<int8_t>(row_buf[pack_id * pack_size + i]);
          }
          *(reinterpret_cast<StoreType*>(dst) + store_offset) = store_pack.storage;
        }
      }
    }
  }
}

template<typename T, typename ComputeType, int pack_size, int cols_per_thread,
         int thread_group_width, int rows_per_access, bool padding>
inline hipError_t LaunchQuantizeWarpImpl(hipStream_t stream, const T* src, int8_t* dst,
                                          T* quantize_factor, const int64_t rows,
                                          const int64_t cols) {
  constexpr int block_size = 128;
  constexpr int waves = 32;
  static_assert(block_size % thread_group_width == 0, "");
  constexpr int thread_groups_per_block = block_size / thread_group_width;
  dim3 block_dim(thread_group_width, thread_groups_per_block);
  const int64_t num_blocks =
      (rows / rows_per_access + thread_groups_per_block - 1) / thread_groups_per_block;
  int grid_dim_x = 0;

  hipError_t err = GetWarpImplNumBlocks(block_size, num_blocks, waves, &grid_dim_x);
  if (err != hipSuccess) { return err; }

  QuantizeWarpImplKernel<T, ComputeType, pack_size, cols_per_thread, thread_group_width,
                         rows_per_access, padding>
      <<<grid_dim_x, block_dim, 0, stream>>>(src, dst, quantize_factor, rows, cols);
  return hipPeekAtLastError();
}

template<typename T, typename ComputeType, int pack_size, int cols_per_thread,
         int thread_group_width, int rows_per_access>
inline hipError_t DispatchQuantizeWarpImplPadding(hipStream_t stream, const T* src, int8_t* dst,
                                                   T* quantize_factor, const int64_t rows,
                                                   const int64_t cols) {
  if (cols == cols_per_thread * thread_group_width) {
    return LaunchQuantizeWarpImpl<T, ComputeType, pack_size, cols_per_thread, thread_group_width,
                                  rows_per_access, false>(stream, src, dst, quantize_factor, rows,
                                                          cols);
  } else {
    return LaunchQuantizeWarpImpl<T, ComputeType, pack_size, cols_per_thread, thread_group_width,
                                  rows_per_access, true>(stream, src, dst, quantize_factor, rows,
                                                         cols);
  }
}

template<typename T, typename ComputeType, int pack_size>
typename std::enable_if<pack_size == 1, hipError_t>::type DispatchQuantizeWarpImplCols(
    hipStream_t stream, const T* src, int8_t* dst, T* quantize_factor, const int64_t rows,
    const int64_t cols) {
  if (cols <= 0) { return hipErrorInvalidValue; }
#define DEFINE_ONE_ELIF(thread_group_width)                                                       \
  else if (cols <= (thread_group_width)*pack_size) {                                              \
    if (rows % 2 == 0) {                                                                          \
      return DispatchQuantizeWarpImplPadding<T, ComputeType, pack_size, pack_size,                \
                                             thread_group_width, 2>(stream, src, dst,             \
                                                                    quantize_factor, rows, cols); \
    } else {                                                                                      \
      return DispatchQuantizeWarpImplPadding<T, ComputeType, pack_size, pack_size,                \
                                             thread_group_width, 1>(stream, src, dst,             \
                                                                    quantize_factor, rows, cols); \
    }                                                                                             \
  }
  DEFINE_ONE_ELIF(1)
  DEFINE_ONE_ELIF(2)
  DEFINE_ONE_ELIF(4)
  DEFINE_ONE_ELIF(8)
  DEFINE_ONE_ELIF(16)
  DEFINE_ONE_ELIF(32)
#undef DEFINE_ONE_ELIF
#define DEFINE_ONE_ELIF(col)                                                              \
  else if (cols <= (col)*kWarpSize) {                                                     \
    return DispatchQuantizeWarpImplPadding<T, ComputeType, pack_size, col, kWarpSize, 1>( \
        stream, src, dst, quantize_factor, rows, cols);                                   \
  }
  DEFINE_ONE_ELIF(2)
  DEFINE_ONE_ELIF(3)
  DEFINE_ONE_ELIF(4)
  DEFINE_ONE_ELIF(5)
  DEFINE_ONE_ELIF(6)
  DEFINE_ONE_ELIF(7)
  DEFINE_ONE_ELIF(8)
  DEFINE_ONE_ELIF(9)
  DEFINE_ONE_ELIF(10)
  DEFINE_ONE_ELIF(11)
  DEFINE_ONE_ELIF(12)
  DEFINE_ONE_ELIF(13)
  DEFINE_ONE_ELIF(14)
  DEFINE_ONE_ELIF(15)
  DEFINE_ONE_ELIF(16)
  DEFINE_ONE_ELIF(17)
  DEFINE_ONE_ELIF(18)
  DEFINE_ONE_ELIF(19)
  DEFINE_ONE_ELIF(20)
  DEFINE_ONE_ELIF(21)
  DEFINE_ONE_ELIF(22)
  DEFINE_ONE_ELIF(23)
  DEFINE_ONE_ELIF(24)
  DEFINE_ONE_ELIF(25)
  DEFINE_ONE_ELIF(26)
  DEFINE_ONE_ELIF(27)
  DEFINE_ONE_ELIF(28)
  DEFINE_ONE_ELIF(29)
  DEFINE_ONE_ELIF(30)
  DEFINE_ONE_ELIF(31)
  DEFINE_ONE_ELIF(32)
#undef DEFINE_ONE_ELIF
  else {
    return hipErrorInvalidValue;
  }
}

template<typename T, typename ComputeType, int pack_size>
typename std::enable_if<pack_size == 2, hipError_t>::type DispatchQuantizeWarpImplCols(
    hipStream_t stream, const T* src, int8_t* dst, T* quantize_factor, const int64_t rows,
    const int64_t cols) {
  if (cols <= 0) { return hipErrorInvalidValue; }
#define DEFINE_ONE_ELIF(thread_group_width)                                                       \
  else if (cols <= (thread_group_width)*pack_size) {                                              \
    if (rows % 2 == 0) {                                                                          \
      return DispatchQuantizeWarpImplPadding<T, ComputeType, pack_size, pack_size,                \
                                             thread_group_width, 2>(stream, src, dst,             \
                                                                    quantize_factor, rows, cols); \
    } else {                                                                                      \
      return DispatchQuantizeWarpImplPadding<T, ComputeType, pack_size, pack_size,                \
                                             thread_group_width, 1>(stream, src, dst,             \
                                                                    quantize_factor, rows, cols); \
    }                                                                                             \
  }
  DEFINE_ONE_ELIF(1)
  DEFINE_ONE_ELIF(2)
  DEFINE_ONE_ELIF(4)
  DEFINE_ONE_ELIF(8)
  DEFINE_ONE_ELIF(16)
  DEFINE_ONE_ELIF(32)
#undef DEFINE_ONE_ELIF
#define DEFINE_ONE_ELIF(col)                                                              \
  else if (cols <= (col)*kWarpSize) {                                                     \
    return DispatchQuantizeWarpImplPadding<T, ComputeType, pack_size, col, kWarpSize, 1>( \
        stream, src, dst, quantize_factor, rows, cols);                                   \
  }
  DEFINE_ONE_ELIF(4)
  DEFINE_ONE_ELIF(6)
  DEFINE_ONE_ELIF(8)
  DEFINE_ONE_ELIF(10)
  DEFINE_ONE_ELIF(12)
  DEFINE_ONE_ELIF(14)
  DEFINE_ONE_ELIF(16)
  DEFINE_ONE_ELIF(18)
  DEFINE_ONE_ELIF(20)
  DEFINE_ONE_ELIF(22)
  DEFINE_ONE_ELIF(24)
  DEFINE_ONE_ELIF(26)
  DEFINE_ONE_ELIF(28)
  DEFINE_ONE_ELIF(30)
  DEFINE_ONE_ELIF(32)
#undef DEFINE_ONE_ELIF
  else {
    return hipErrorInvalidValue;
  }
}

template<typename T, typename ComputeType>
struct DispatchQuantizeWarpImplPackSize {
  hipError_t operator()(hipStream_t stream, const T* src, int8_t* dst, T* quantize_factor,
                         const int64_t rows, const int64_t cols) {
    if (cols % 2 == 0) {
      return DispatchQuantizeWarpImplCols<T, ComputeType, 2>(stream, src, dst, quantize_factor,
                                                             rows, cols);
    } else {
      return DispatchQuantizeWarpImplCols<T, ComputeType, 1>(stream, src, dst, quantize_factor,
                                                             rows, cols);
    }
  }
};

template<typename T, typename ComputeType, typename IDX, int pack_size>
__global__ void DequantizeKernel(const int8_t* x, T* quantize_factor, T* out, IDX col_size,
                                 IDX elem_cnt);

template<typename T, typename ComputeType, typename IDX, int pack_size>
__global__ void DequantizeKernel(const int8_t* x, T* quantize_factor, T* out, IDX col_size,
                                 IDX elem_cnt) {
  IDX global_thread_id = blockIdx.x * blockDim.x + threadIdx.x;

  for (int index = global_thread_id * pack_size; index < elem_cnt;
       index += gridDim.x * blockDim.x * pack_size) {
    IDX quantize_factor_idx = index / col_size;
    ComputeType quantize_factor_val = static_cast<ComputeType>(quantize_factor[quantize_factor_idx])
                                      / static_cast<ComputeType>(127.0);
    using LoadPackType = cuda::elementwise::PackType<int8_t, pack_size>;
    using LoadPack = cuda::elementwise::Pack<int8_t, pack_size>;
    using StorePackType = cuda::elementwise::PackType<T, pack_size>;
    using StorePack = cuda::elementwise::Pack<T, pack_size>;
    LoadPack load_pack{};
    StorePack store_pack{};
    load_pack.storage = *(reinterpret_cast<const LoadPackType*>(x) + index / pack_size);
#pragma unroll
    for (int i = 0; i < pack_size; i++) {
      store_pack.elem[i] =
          static_cast<T>(static_cast<ComputeType>(load_pack.elem[i]) * quantize_factor_val);
    }
    *(reinterpret_cast<StorePackType*>(out) + index / pack_size) = store_pack.storage;
  }
}

template<typename T, typename ComputeType, typename IDX, int pack_size>
hipError_t DispatchDequantizeKernelPackSize(hipStream_t stream, const int8_t* src,
                                             T* quantize_factor, T* dst, const int64_t col_size,
                                             const int64_t elem_cnt) {
  const int64_t pack_num = elem_cnt / pack_size;
  int grid_size = 0;
  hipError_t err = cuda::elementwise::GetNumBlocks(pack_num, &grid_size);
  if (err != hipSuccess) { return err; }
  hipLaunchKernelGGL(HIP_KERNEL_NAME(DequantizeKernel<T, ComputeType, IDX, pack_size>), grid_size, cuda::elementwise::kBlockSize, 0, stream, src, quantize_factor, dst, col_size,
                                                                elem_cnt);
  return hipSuccess;
}

template<typename T, typename ComputeType, typename IDX>
inline hipError_t LaunchDequantizeKernel(hipStream_t stream, const int8_t* src,
                                          T* quantize_factor, T* dst, const int64_t col_size,
                                          const int64_t elem_cnt) {
  constexpr int quantized_src_pack_size = cuda::elementwise::PackSize<int8_t>();
  constexpr int dst_pack_size = cuda::elementwise::PackSize<T>();
  int launch_pack_size = std::min(quantized_src_pack_size, dst_pack_size);
  if (launch_pack_size == 8 && col_size % 8 == 0) {
    hipError_t err = DispatchDequantizeKernelPackSize<T, ComputeType, IDX, 8>(
        stream, src, quantize_factor, dst, col_size, elem_cnt);
    if (err != hipSuccess) { return err; }
  } else if (launch_pack_size == 4 && col_size % 4 == 0) {
    hipError_t err = DispatchDequantizeKernelPackSize<T, ComputeType, IDX, 4>(
        stream, src, quantize_factor, dst, col_size, elem_cnt);
    if (err != hipSuccess) { return err; }
  } else if (launch_pack_size == 2 && col_size % 2 == 0) {
    hipError_t err = DispatchDequantizeKernelPackSize<T, ComputeType, IDX, 2>(
        stream, src, quantize_factor, dst, col_size, elem_cnt);
    if (err != hipSuccess) { return err; }
  } else {
    hipError_t err = DispatchDequantizeKernelPackSize<T, ComputeType, IDX, 1>(
        stream, src, quantize_factor, dst, col_size, elem_cnt);
    if (err != hipSuccess) { return err; }
  }
  return hipPeekAtLastError();
}

template<typename T>
struct DefaultComputeType {
  using type = T;
};

template<>
struct DefaultComputeType<half> {
  using type = float;
};

template<typename T, typename IDX>
class EmbeddingShuffleKernel final : public user_op::OpKernel {
 public:
  EmbeddingShuffleKernel() = default;
  ~EmbeddingShuffleKernel() override = default;

  std::shared_ptr<user_op::OpKernelState> CreateOpKernelState(
      user_op::KernelInitContext* ctx) const override {
    return std::make_shared<DataShuffleKernelState<IDX>>(ctx);
  }

 private:
  using user_op::OpKernel::Compute;
  void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state,
               const user_op::OpKernelCache*) const override {
    auto* kernel_state = dynamic_cast<DataShuffleKernelState<IDX>*>(state);
    CHECK(kernel_state != nullptr);
    const user_op::Tensor* cur_rank_embeddings =
        ctx->Tensor4ArgNameAndIndex("cur_rank_embeddings", 0);
    const user_op::Tensor* num_unique_matrix = ctx->Tensor4ArgNameAndIndex("num_unique_matrix", 0);
    const user_op::Tensor* cur_rank_inverse_indices =
        ctx->Tensor4ArgNameAndIndex("cur_rank_inverse_indices", 0);
    const user_op::Tensor* inverse_unique_partition_indices =
        ctx->Tensor4ArgNameAndIndex("inverse_unique_partition_indices", 0);
    user_op::Tensor* embeddings = ctx->Tensor4ArgNameAndIndex("embeddings", 0);
    user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0);
    ncclComm_t comm = kernel_state->comm();
    using ComputeType = typename DefaultComputeType<T>::type;
    const int64_t embedding_size = cur_rank_embeddings->shape_view().At(1);
    IDX* host_num_unique_matrix = kernel_state->HostNumUniqueMatrix();
    DataType data_type = cur_rank_embeddings->data_type();
    const int64_t num_ids = inverse_unique_partition_indices->shape_view().elem_cnt();
    const int64_t parallel_num = ctx->parallel_ctx().parallel_num();
    const int64_t parallel_id = ctx->parallel_ctx().parallel_id();
    bool enable_quantized_comm_env_var =
        ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_ENABLE_QUANTIZED_COMM", false);
    bool enable_quantized_comm = enable_quantized_comm_env_var && (embedding_size < kMaxColSize);
    if (enable_quantized_comm_env_var && !enable_quantized_comm) {
      LOG(WARNING) << "Only envrionment variable ONEFLOW_ONE_EMBEDDING_ENABLE_QUANTIZED_COMM=1 and "
                      "embedding_size less equal than 1024 can use quantized communication. ";
    }
    hipStream_t cuda_stream = ctx->stream()->As<ep::CudaStream>()->cuda_stream();
    OF_CUDA_CHECK(hipMemcpyAsync(
        host_num_unique_matrix, reinterpret_cast<const IDX*>(num_unique_matrix->dptr()),
        parallel_num * parallel_num * sizeof(IDX), hipMemcpyDefault, cuda_stream));
    CHECK_JUST(ctx->stream()->Sync());
    int64_t cur_rank_num_ids = 0;
    for (int64_t i = 0; i < parallel_num; ++i) {
      cur_rank_num_ids += host_num_unique_matrix[i * parallel_num + parallel_id];
    }
    size_t full_elem_cnt = parallel_num * num_ids * embedding_size;
    CHECK_EQ(full_elem_cnt, cur_rank_embeddings->shape_view().elem_cnt());
    if (!enable_quantized_comm) {
      size_t reverse_unique_cur_rank_embeddings_size =
          GetCudaAlignedSize(full_elem_cnt * sizeof(T));
      size_t received_embeddings_size = reverse_unique_cur_rank_embeddings_size;

      CHECK_GE(tmp_buffer->shape_view().elem_cnt(),
               reverse_unique_cur_rank_embeddings_size + received_embeddings_size);

      T* reverse_unique_cur_rank_embeddings = reinterpret_cast<T*>(tmp_buffer->mut_dptr());
      T* received_embeddings = reinterpret_cast<T*>(tmp_buffer->mut_dptr<char>()
                                                    + reverse_unique_cur_rank_embeddings_size);
      // reverse cur_rank unique
      GatherKernelUtilImpl<DeviceType::kCUDA, T, IDX>::Forward(
          ctx->stream(), reinterpret_cast<const IDX*>(cur_rank_inverse_indices->dptr()),
          cur_rank_num_ids, cur_rank_embeddings->dptr<T>(),
          Shape({1, cur_rank_embeddings->shape_view().elem_cnt() / embedding_size, embedding_size}),
          reverse_unique_cur_rank_embeddings, 0);

      ShuffleEmbeddings(cuda_stream, comm, parallel_id, parallel_num, num_ids, embedding_size,
                        data_type, host_num_unique_matrix, reverse_unique_cur_rank_embeddings,
                        received_embeddings);

      // reverse unique_partition
      GatherKernelUtilImpl<DeviceType::kCUDA, T, IDX>::Forward(
          ctx->stream(), reinterpret_cast<const IDX*>(inverse_unique_partition_indices->dptr()),
          inverse_unique_partition_indices->shape_view().elem_cnt(), received_embeddings,
          Shape({1, parallel_num * num_ids, embedding_size}), embeddings->mut_dptr<T>(), 0);
    } else {
      size_t reverse_unique_cur_rank_embeddings_size =
          GetCudaAlignedSize(full_elem_cnt * sizeof(int8_t));
      size_t received_embeddings_size = reverse_unique_cur_rank_embeddings_size;
      size_t quantize_cur_rank_embeddings_size = reverse_unique_cur_rank_embeddings_size;
      size_t reverse_recv_quantize_cur_rank_embeddings_size =
          reverse_unique_cur_rank_embeddings_size;
      size_t cur_rank_quantize_factor_size =
          GetCudaAlignedSize(cur_rank_embeddings->shape_view().At(0) * sizeof(T));
      size_t reverse_cur_rank_quantize_factor_size = cur_rank_quantize_factor_size;
      size_t recv_quantize_factor_size = cur_rank_quantize_factor_size;
      size_t reverse_recv_quantize_factor_size = cur_rank_quantize_factor_size;
      CHECK_GE(tmp_buffer->shape_view().elem_cnt(),
               reverse_unique_cur_rank_embeddings_size + received_embeddings_size
                   + quantize_cur_rank_embeddings_size
                   + reverse_recv_quantize_cur_rank_embeddings_size + cur_rank_quantize_factor_size
                   + reverse_cur_rank_quantize_factor_size + recv_quantize_factor_size
                   + reverse_recv_quantize_factor_size);
      int8_t* reverse_unique_cur_rank_embeddings =
          reinterpret_cast<int8_t*>(tmp_buffer->mut_dptr());
      int8_t* received_embeddings = reinterpret_cast<int8_t*>(
          tmp_buffer->mut_dptr<char>() + reverse_unique_cur_rank_embeddings_size);
      int8_t* quantize_cur_rank_embeddings = reinterpret_cast<int8_t*>(
          tmp_buffer->mut_dptr<char>() + reverse_unique_cur_rank_embeddings_size
          + received_embeddings_size);
      int8_t* reverse_recv_quantize_cur_rank_embeddings = reinterpret_cast<int8_t*>(
          tmp_buffer->mut_dptr<char>() + reverse_unique_cur_rank_embeddings_size
          + received_embeddings_size + quantize_cur_rank_embeddings_size);
      T* cur_rank_quantize_factor = reinterpret_cast<T*>(
          tmp_buffer->mut_dptr<char>() + reverse_unique_cur_rank_embeddings_size
          + received_embeddings_size + quantize_cur_rank_embeddings_size
          + reverse_recv_quantize_cur_rank_embeddings_size);
      T* reverse_cur_rank_quantize_factor = reinterpret_cast<T*>(
          tmp_buffer->mut_dptr<char>() + reverse_unique_cur_rank_embeddings_size
          + received_embeddings_size + quantize_cur_rank_embeddings_size
          + reverse_recv_quantize_cur_rank_embeddings_size + cur_rank_quantize_factor_size);
      T* recv_quantize_factor = reinterpret_cast<T*>(
          tmp_buffer->mut_dptr<char>() + reverse_unique_cur_rank_embeddings_size
          + received_embeddings_size + quantize_cur_rank_embeddings_size
          + reverse_recv_quantize_cur_rank_embeddings_size + cur_rank_quantize_factor_size
          + reverse_cur_rank_quantize_factor_size);
      T* reverse_recv_quantize_factor = reinterpret_cast<T*>(
          tmp_buffer->mut_dptr<char>() + reverse_unique_cur_rank_embeddings_size
          + received_embeddings_size + quantize_cur_rank_embeddings_size
          + reverse_recv_quantize_cur_rank_embeddings_size + cur_rank_quantize_factor_size
          + reverse_cur_rank_quantize_factor_size + recv_quantize_factor_size);
      DispatchQuantizeWarpImplPackSize<T, ComputeType>()(
          cuda_stream, cur_rank_embeddings->dptr<T>(), quantize_cur_rank_embeddings,
          cur_rank_quantize_factor, cur_rank_num_ids, embedding_size);
      // reverse cur_rank embedding unique
      GatherKernelUtilImpl<DeviceType::kCUDA, int8_t, IDX>::Forward(
          ctx->stream(), reinterpret_cast<const IDX*>(cur_rank_inverse_indices->dptr()),
          cur_rank_num_ids, quantize_cur_rank_embeddings,
          Shape({1, cur_rank_embeddings->shape_view().elem_cnt() / embedding_size, embedding_size}),
          reverse_unique_cur_rank_embeddings, 0);

      // reverse cur_rank quantize factor unique
      GatherKernelUtilImpl<DeviceType::kCUDA, T, IDX>::Forward(
          ctx->stream(), reinterpret_cast<const IDX*>(cur_rank_inverse_indices->dptr()),
          cur_rank_num_ids, cur_rank_quantize_factor,
          Shape({1, cur_rank_embeddings->shape_view().elem_cnt() / embedding_size, 1}),
          reverse_cur_rank_quantize_factor, 0);

      ShuffleEmbeddings(cuda_stream, comm, parallel_id, parallel_num, num_ids, embedding_size,
                        data_type, host_num_unique_matrix, reverse_unique_cur_rank_embeddings,
                        received_embeddings, reverse_cur_rank_quantize_factor,
                        recv_quantize_factor);

      // reverse unique_partition
      GatherKernelUtilImpl<DeviceType::kCUDA, int8_t, IDX>::Forward(
          ctx->stream(), reinterpret_cast<const IDX*>(inverse_unique_partition_indices->dptr()),
          inverse_unique_partition_indices->shape_view().elem_cnt(), received_embeddings,
          Shape({1, parallel_num * num_ids, embedding_size}),
          reverse_recv_quantize_cur_rank_embeddings, 0);

      GatherKernelUtilImpl<DeviceType::kCUDA, T, IDX>::Forward(
          ctx->stream(), reinterpret_cast<const IDX*>(inverse_unique_partition_indices->dptr()),
          inverse_unique_partition_indices->shape_view().elem_cnt(), recv_quantize_factor,
          Shape({1, parallel_num * num_ids, 1}), reverse_recv_quantize_factor, 0);

      int32_t dequantize_row_size = inverse_unique_partition_indices->shape_view().elem_cnt();
      IDX dequantize_elem_cnt = dequantize_row_size * embedding_size;
      OF_CUDA_CHECK((LaunchDequantizeKernel<T, ComputeType, IDX>(
          cuda_stream, reverse_recv_quantize_cur_rank_embeddings, reverse_recv_quantize_factor,
          embeddings->mut_dptr<T>(), embedding_size, dequantize_elem_cnt)));
    }
  }
  bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; }
};

#define REGISTER_CUDA_EMBEDDING_SHUFFLE_KERNEL(t_dtype_pair, idx_dtype_pair)                      \
  REGISTER_USER_KERNEL("embedding_shuffle")                                                       \
      .SetCreateFn<EmbeddingShuffleKernel<OF_PP_PAIR_FIRST(t_dtype_pair),                         \
                                          OF_PP_PAIR_FIRST(idx_dtype_pair)>>()                    \
      .SetIsMatchedHob(                                                                           \
          (user_op::HobDeviceType() == DeviceType::kCUDA)                                         \
          && (user_op::HobDataType("cur_rank_embeddings", 0) == OF_PP_PAIR_SECOND(t_dtype_pair))  \
          && (user_op::HobDataType("num_unique_matrix", 0) == OF_PP_PAIR_SECOND(idx_dtype_pair))) \
      .SetInferTmpSizeFn([](user_op::InferContext* ctx) {                                         \
        const user_op::TensorDesc& cur_rank_embeddings =                                          \
            ctx->InputTensorDesc("cur_rank_embeddings", 0);                                       \
        bool enable_quantized_comm =                                                              \
            ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_ENABLE_QUANTIZED_COMM", false)             \
            && (cur_rank_embeddings.shape().At(1) < kMaxColSize);                                 \
        size_t tmp_size = 0;                                                                      \
        if (!enable_quantized_comm) {                                                             \
          size_t reverse_cur_rank_embeddings_size = GetCudaAlignedSize(                           \
              cur_rank_embeddings.shape().elem_cnt() * sizeof(OF_PP_PAIR_FIRST(t_dtype_pair)));   \
          size_t recv_unique_embeddings_size = reverse_cur_rank_embeddings_size;                  \
          tmp_size = reverse_cur_rank_embeddings_size + recv_unique_embeddings_size;              \
        } else {                                                                                  \
          size_t total_elem_cnt = cur_rank_embeddings.shape().elem_cnt();                         \
          size_t reverse_cur_rank_embeddings_size =                                               \
              GetCudaAlignedSize(total_elem_cnt * sizeof(int8_t));                                \
          size_t recv_unique_embeddings = reverse_cur_rank_embeddings_size;                       \
          size_t quantize_cur_rank_embeddings_size = reverse_cur_rank_embeddings_size;            \
          size_t reverse_recv_quantize_cur_rank_embeddings_size =                                 \
              reverse_cur_rank_embeddings_size;                                                   \
          size_t cur_rank_quantize_factor_size = GetCudaAlignedSize(                              \
              cur_rank_embeddings.shape().At(0) * sizeof(OF_PP_PAIR_FIRST(t_dtype_pair)));        \
          size_t reverse_cur_rank_quantize_factor_size = cur_rank_quantize_factor_size;           \
          size_t recv_quantize_factor_size = cur_rank_quantize_factor_size;                       \
          size_t reverse_recv_quantize_factor_size = cur_rank_quantize_factor_size;               \
          tmp_size = reverse_cur_rank_embeddings_size + recv_unique_embeddings                    \
                     + quantize_cur_rank_embeddings_size                                          \
                     + reverse_recv_quantize_cur_rank_embeddings_size                             \
                     + cur_rank_quantize_factor_size + reverse_cur_rank_quantize_factor_size      \
                     + recv_quantize_factor_size + reverse_recv_quantize_factor_size;             \
        }                                                                                         \
        return tmp_size;                                                                          \
      });

OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CUDA_EMBEDDING_SHUFFLE_KERNEL,
                                 FLOATING_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ, IDX_DATA_TYPE_SEQ)
                                //  FLOATING_DATA_TYPE_SEQ, IDX_DATA_TYPE_SEQ)
                                 

template<typename T, typename IDX>
void ShuffleEmbeddingsGrad(hipStream_t cuda_stream, ncclComm_t comm, int64_t parallel_id,
                           int64_t parallel_num, int64_t num_ids, int64_t embedding_size,
                           DataType data_type, IDX* host_num_unique_matrix,
                           T* unique_partition_embedding_grad, T* received_embeddings_grad) {
  std::vector<int64_t> send_offsets;
  std::vector<int64_t> send_elem_cnt;
  std::vector<int64_t> recv_offsets;
  std::vector<int64_t> recv_elem_cnt;
  MakeShuffleParams(host_num_unique_matrix, num_ids, embedding_size, parallel_id, parallel_num,
                    &send_offsets, &send_elem_cnt, &recv_offsets, &recv_elem_cnt);
  ShuffleData(cuda_stream, comm, data_type, send_offsets, send_elem_cnt,
              unique_partition_embedding_grad, recv_offsets, recv_elem_cnt,
              received_embeddings_grad);
}

// Quantize Version.
template<typename T, typename IDX>
void ShuffleEmbeddingsGrad(hipStream_t cuda_stream, ncclComm_t comm, int64_t parallel_id,
                           int64_t parallel_num, int64_t num_ids, int64_t embedding_size,
                           DataType data_type, IDX* host_num_unique_matrix,
                           int8_t* unique_partition_embedding_grad,
                           int8_t* received_embeddings_grad, T* cur_rank_quantize_factor,
                           T* received_cur_rank_quantize_factor) {
  std::vector<int64_t> send_offsets;
  std::vector<int64_t> send_elem_cnt;
  std::vector<int64_t> recv_offsets;
  std::vector<int64_t> recv_elem_cnt;
  // Shuffle Embedding Grad.
  MakeShuffleParams(host_num_unique_matrix, num_ids, embedding_size, parallel_id, parallel_num,
                    &send_offsets, &send_elem_cnt, &recv_offsets, &recv_elem_cnt);
  ShuffleData(cuda_stream, comm, DataType::kInt8, send_offsets, send_elem_cnt,
              unique_partition_embedding_grad, recv_offsets, recv_elem_cnt,
              received_embeddings_grad);
  // Shuffle Quantize factor.
  MakeShuffleParams(host_num_unique_matrix, num_ids, /*embedding_size=*/1, parallel_id,
                    parallel_num, &send_offsets, &send_elem_cnt, &recv_offsets, &recv_elem_cnt);
  ShuffleData(cuda_stream, comm, data_type, send_offsets, send_elem_cnt, cur_rank_quantize_factor,
              recv_offsets, recv_elem_cnt, received_cur_rank_quantize_factor);
}

template<typename K, typename IDX>
__global__ void UnsortedSegmentHalfGpu(const IDX in_h2_elem_cnt, const IDX h2_inner_dim_size,
                                       const IDX inner_dim_size, const half* data,
                                       const K* segment_ids, const IDX num_segments,
                                       half2* out_h2) {
  CUDA_1D_KERNEL_LOOP_T(IDX, i, in_h2_elem_cnt) {
    const IDX segment_id_idx = i / h2_inner_dim_size;
    const IDX h2_inner_idx = i - segment_id_idx * h2_inner_dim_size;
    const IDX inner_idx_0 = 2 * h2_inner_idx;
    const IDX inner_idx_1 = inner_idx_0 + 1;
    const half* data_row = data + segment_id_idx * inner_dim_size;
    half2 val;
    val.data.x = data_row[inner_idx_0];
    val.data.y = (inner_idx_1 >= inner_dim_size) ? static_cast<half>(0) : data_row[inner_idx_1];
    const IDX idx = segment_ids[segment_id_idx];
    const IDX out_h2_offset = idx * h2_inner_dim_size + h2_inner_idx;
    cuda::atomic::Add(out_h2 + out_h2_offset, val);
  }
}

template<typename T, typename K>
struct UnsortedSegmentSumPad {
  void operator()(ep::Stream* stream, const K* segment_ids, const T* data, int64_t num_segment_ids,
                  int64_t num_segments, int64_t inner_dim_size, int64_t padded_inner_dim_size,
                  T* out) const {
    UNIMPLEMENTED();
  }
};

template<typename K>
struct UnsortedSegmentSumPad<half, K> {
  void operator()(ep::Stream* stream, const K* segment_ids, const half* data,
                  int64_t num_segment_ids, int64_t num_segments, int64_t inner_dim_size,
                  int64_t padded_inner_dim_size, half* out) const {
    const int64_t data_elem_cnt = num_segment_ids * inner_dim_size;
    const int64_t out_elem_cnt = num_segments * padded_inner_dim_size;
    CHECK_EQ(padded_inner_dim_size % 2, 0);
    CHECK_EQ(inner_dim_size + 1, padded_inner_dim_size);
    const int64_t h2_inner_dim_size = padded_inner_dim_size / 2;
    const int64_t in_h2_elem_cnt = num_segment_ids * h2_inner_dim_size;
    if (std::max(data_elem_cnt, out_elem_cnt) < GetMaxVal<int32_t>() / 2) {
      UnsortedSegmentHalfGpu<K, int32_t>
          <<<BlocksNum4ThreadsNum(in_h2_elem_cnt), kCudaThreadsNumPerBlock, 0,
             stream->As<ep::CudaStream>()->cuda_stream()>>>(
              in_h2_elem_cnt, h2_inner_dim_size, inner_dim_size, data, segment_ids, num_segments,
              reinterpret_cast<half2*>(out));
    } else {
      UnsortedSegmentHalfGpu<K, int64_t>
          <<<BlocksNum4ThreadsNum(in_h2_elem_cnt), kCudaThreadsNumPerBlock, 0,
             stream->As<ep::CudaStream>()->cuda_stream()>>>(
              in_h2_elem_cnt, h2_inner_dim_size, inner_dim_size, data, segment_ids, num_segments,
              reinterpret_cast<half2*>(out));
    }
  }
};

template<typename T, typename K>
void UnsortedSegmentSum(ep::Stream* stream, const K* segment_ids, const T* data,
                        int64_t num_segment_ids, int64_t num_segments, int64_t inner_dim_size,
                        int64_t padded_inner_dim_size, T* out) {
  if (inner_dim_size == padded_inner_dim_size) {
    UnsortedSegmentSumKernelUtil<DeviceType::kCUDA, T, K, T>::UnsortedSegmentSum(
        stream, segment_ids, data, num_segment_ids, num_segments, 1, inner_dim_size, 0, out);
  } else {
    CHECK_EQ(inner_dim_size + 1, padded_inner_dim_size);
    UnsortedSegmentSumPad<T, K>()(stream, segment_ids, data, num_segment_ids, num_segments,
                                  inner_dim_size, padded_inner_dim_size, out);
  }
}

template<typename T, typename IDX>
void UniquePartitionEmbeddingGrad(ep::Stream* stream, int64_t parallel_id, int64_t parallel_num,
                                  int64_t num_ids, int64_t embedding_size,
                                  int64_t padded_embedding_size, const IDX* host_num_unique_matrix,
                                  const T* embedding_grad,
                                  const IDX* inverse_unique_partition_indices,
                                  T* unique_partition_embedding_grad) {
  for (int64_t i = 0; i < parallel_num; ++i) {
    const int64_t offset = i * num_ids * padded_embedding_size;
    const int64_t valid_value_size =
        host_num_unique_matrix[parallel_id * parallel_num + i] * padded_embedding_size * sizeof(T);
    OF_CUDA_CHECK(hipMemsetAsync(unique_partition_embedding_grad + offset, 0, valid_value_size,
                                  stream->As<ep::CudaStream>()->cuda_stream()));
  }
  UnsortedSegmentSum<T, IDX>(stream, inverse_unique_partition_indices, embedding_grad, num_ids,
                             parallel_num * num_ids, embedding_size, padded_embedding_size,
                             unique_partition_embedding_grad);
}

template<typename T, typename IDX>
void UniqueCurRankEmbeddingGrad(ep::Stream* stream, DataType data_type, int64_t cur_rank_num_ids,
                                int64_t embedding_size, int64_t padded_embedding_size,
                                const T* cur_rank_embedding_grad,
                                const IDX* cur_rank_inverse_indices,
                                T* cur_rank_unique_embedding_grad, T* tmp_buffer) {
  T* unsorted_segment_sum_out =
      (embedding_size == padded_embedding_size) ? cur_rank_unique_embedding_grad : tmp_buffer;
  OF_CUDA_CHECK(hipMemsetAsync(unsorted_segment_sum_out, 0,
                                cur_rank_num_ids * padded_embedding_size * sizeof(T),
                                stream->As<ep::CudaStream>()->cuda_stream()));
  UnsortedSegmentSum<T, IDX>(stream, cur_rank_inverse_indices, cur_rank_embedding_grad,
                             cur_rank_num_ids, cur_rank_num_ids, padded_embedding_size,
                             padded_embedding_size, unsorted_segment_sum_out);
  if (embedding_size != padded_embedding_size) {
    std::unique_ptr<ep::primitive::CopyNd> primitive =
        ep::primitive::NewPrimitive<ep::primitive::CopyNdFactory>(DeviceType::kCUDA, 2);
    DimVector dst_shape = {cur_rank_num_ids, embedding_size};
    DimVector dst_pos_vec = {0, 0};
    DimVector src_shape = {cur_rank_num_ids, padded_embedding_size};
    DimVector src_pos_vec = {0, 0};
    DimVector extent_vec = {cur_rank_num_ids, embedding_size};
    primitive->Launch(stream, data_type, 2, cur_rank_unique_embedding_grad, dst_shape.data(),
                      dst_pos_vec.data(), unsorted_segment_sum_out, src_shape.data(),
                      src_pos_vec.data(), extent_vec.data());
  }
}

int64_t GetPaddedEmbeddingSize(DataType data_type, int64_t embedding_size) {
  if (data_type == DataType::kFloat16 && embedding_size % 2 != 0) {
    return embedding_size + 1;
  } else {
    return embedding_size;
  }
}

template<typename T, typename IDX>
class EmbeddingGradientShuffleKernel final : public user_op::OpKernel {
 public:
  EmbeddingGradientShuffleKernel() = default;
  ~EmbeddingGradientShuffleKernel() override = default;

  std::shared_ptr<user_op::OpKernelState> CreateOpKernelState(
      user_op::KernelInitContext* ctx) const override {
    return std::make_shared<DataShuffleKernelState<IDX>>(ctx);
  }

 private:
  using user_op::OpKernel::Compute;
  void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state,
               const user_op::OpKernelCache*) const override {
    auto* kernel_state = dynamic_cast<DataShuffleKernelState<IDX>*>(state);
    CHECK(kernel_state != nullptr);
    const user_op::Tensor* embedding_grad = ctx->Tensor4ArgNameAndIndex("embedding_grad", 0);

    const user_op::Tensor* num_unique_matrix = ctx->Tensor4ArgNameAndIndex("num_unique_matrix", 0);
    const user_op::Tensor* cur_rank_inverse_indices =
        ctx->Tensor4ArgNameAndIndex("cur_rank_inverse_indices", 0);
    const user_op::Tensor* inverse_unique_partition_indices =
        ctx->Tensor4ArgNameAndIndex("inverse_unique_partition_indices", 0);
    user_op::Tensor* cur_rank_unique_embedding_grad =
        ctx->Tensor4ArgNameAndIndex("cur_rank_unique_embedding_grad", 0);
    const int64_t embedding_size = cur_rank_unique_embedding_grad->shape_view().At(1);
    IDX* host_num_unique_matrix = kernel_state->HostNumUniqueMatrix();
    DataType data_type = embedding_grad->data_type();
    const int64_t num_ids = inverse_unique_partition_indices->shape_view().elem_cnt();
    const int64_t parallel_num = ctx->parallel_ctx().parallel_num();
    const int64_t parallel_id = ctx->parallel_ctx().parallel_id();
    const int64_t padded_embedding_size = GetPaddedEmbeddingSize(data_type, embedding_size);
    user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0);
    ncclComm_t comm = kernel_state->comm();
    using ComputeType = typename DefaultComputeType<T>::type;
    bool enable_quantized_comm_env_var =
        ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_ENABLE_QUANTIZED_COMM", false);
    bool enable_quantized_comm =
        enable_quantized_comm_env_var && (padded_embedding_size < kMaxColSize);
    if (enable_quantized_comm_env_var && !enable_quantized_comm) {
      LOG(WARNING) << "Only envrionment variable ONEFLOW_ONE_EMBEDDING_ENABLE_QUANTIZED_COMM=1 and "
                      "embedding_size less equal than 1024 can use quantized communication. ";
    }
    hipStream_t cuda_stream = ctx->stream()->As<ep::CudaStream>()->cuda_stream();
    OF_CUDA_CHECK(hipMemcpyAsync(host_num_unique_matrix, num_unique_matrix->dptr(),
                                  parallel_num * parallel_num * sizeof(IDX), hipMemcpyDefault,
                                  cuda_stream));
    CHECK_JUST(ctx->stream()->Sync());

    int64_t cur_rank_num_ids = 0;
    for (int64_t i = 0; i < parallel_num; ++i) {
      cur_rank_num_ids += host_num_unique_matrix[i * parallel_num + parallel_id];
    }
    size_t full_num_ids = parallel_num * num_ids;
    size_t full_elem_cnt = full_num_ids * padded_embedding_size;
    size_t unique_partition_embedding_grad_size = GetCudaAlignedSize(full_elem_cnt * sizeof(T));

    if (!enable_quantized_comm) {
      size_t received_embedding_grad_size = unique_partition_embedding_grad_size;
      T* unique_partition_embedding_grad = reinterpret_cast<T*>(tmp_buffer->mut_dptr());
      T* received_embedding_grad =
          reinterpret_cast<T*>(tmp_buffer->mut_dptr<char>() + unique_partition_embedding_grad_size);
      CHECK_GE(tmp_buffer->shape_view().elem_cnt(),
               unique_partition_embedding_grad_size + received_embedding_grad_size);

      UniquePartitionEmbeddingGrad(
          ctx->stream(), parallel_id, parallel_num, num_ids, embedding_size, padded_embedding_size,
          host_num_unique_matrix, embedding_grad->dptr<T>(),
          reinterpret_cast<const IDX*>(inverse_unique_partition_indices->dptr()),
          unique_partition_embedding_grad);

      ShuffleEmbeddingsGrad(cuda_stream, comm, parallel_id, parallel_num, num_ids,
                            padded_embedding_size, data_type, host_num_unique_matrix,
                            unique_partition_embedding_grad, received_embedding_grad);

      // use unique_partition_embedding_grad as UniqueCurRankEmbeddingGrad buffer.
      T* buffer_ptr = unique_partition_embedding_grad;
      UniqueCurRankEmbeddingGrad(ctx->stream(), data_type, cur_rank_num_ids, embedding_size,
                                 padded_embedding_size, received_embedding_grad,
                                 reinterpret_cast<const IDX*>(cur_rank_inverse_indices->dptr()),
                                 cur_rank_unique_embedding_grad->mut_dptr<T>(), buffer_ptr);
    } else {
      size_t received_embedding_grad_size = GetCudaAlignedSize(full_elem_cnt * sizeof(int8_t));
      size_t quantize_cur_rank_embedding_grad_size = received_embedding_grad_size;
      size_t cur_rank_quantize_factor_size = GetCudaAlignedSize(full_num_ids * sizeof(T));
      size_t received_cur_rank_quantize_factor_size = cur_rank_quantize_factor_size;
      size_t dequantize_cur_rank_embedding_grad_size =
          GetCudaAlignedSize(full_elem_cnt * sizeof(T));
      CHECK_GE(tmp_buffer->shape_view().elem_cnt(),
               unique_partition_embedding_grad_size + received_embedding_grad_size
                   + quantize_cur_rank_embedding_grad_size + cur_rank_quantize_factor_size
                   + received_cur_rank_quantize_factor_size
                   + dequantize_cur_rank_embedding_grad_size);
      T* unique_partition_embedding_grad = reinterpret_cast<T*>(tmp_buffer->mut_dptr());
      int8_t* received_embedding_grad = reinterpret_cast<int8_t*>(
          tmp_buffer->mut_dptr<char>() + unique_partition_embedding_grad_size);

      int8_t* quantize_cur_rank_embedding_grad = reinterpret_cast<int8_t*>(
          tmp_buffer->mut_dptr<char>() + unique_partition_embedding_grad_size
          + received_embedding_grad_size);
      T* cur_rank_quantize_factor = reinterpret_cast<T*>(
          tmp_buffer->mut_dptr<char>() + unique_partition_embedding_grad_size
          + received_embedding_grad_size + quantize_cur_rank_embedding_grad_size);
      T* received_cur_rank_quantize_factor = reinterpret_cast<T*>(
          tmp_buffer->mut_dptr<char>() + unique_partition_embedding_grad_size
          + received_embedding_grad_size + quantize_cur_rank_embedding_grad_size
          + cur_rank_quantize_factor_size);
      T* dequantize_cur_rank_embedding_grad = reinterpret_cast<T*>(
          tmp_buffer->mut_dptr<char>() + unique_partition_embedding_grad_size
          + received_embedding_grad_size + quantize_cur_rank_embedding_grad_size
          + cur_rank_quantize_factor_size + received_cur_rank_quantize_factor_size);

      UniquePartitionEmbeddingGrad(
          ctx->stream(), parallel_id, parallel_num, num_ids, embedding_size, padded_embedding_size,
          host_num_unique_matrix, embedding_grad->dptr<T>(),
          reinterpret_cast<const IDX*>(inverse_unique_partition_indices->dptr()),
          unique_partition_embedding_grad);

      // Quantize.
      for (int64_t i = 0; i < parallel_num; ++i) {
        const int64_t embedding_grad_offset = i * num_ids * padded_embedding_size;
        const int64_t quantize_factor_offset = i * num_ids;
        const int64_t valid_row_size = host_num_unique_matrix[parallel_id * parallel_num + i];
        DispatchQuantizeWarpImplPackSize<T, ComputeType>()(
            cuda_stream, unique_partition_embedding_grad + embedding_grad_offset,
            quantize_cur_rank_embedding_grad + embedding_grad_offset,
            cur_rank_quantize_factor + quantize_factor_offset, valid_row_size,
            padded_embedding_size);
      }

      ShuffleEmbeddingsGrad(cuda_stream, comm, parallel_id, parallel_num, num_ids,
                            padded_embedding_size, data_type, host_num_unique_matrix,
                            quantize_cur_rank_embedding_grad, received_embedding_grad,
                            cur_rank_quantize_factor, received_cur_rank_quantize_factor);

      int64_t dequantize_cur_rank_num = 0;
      for (int64_t i = 0; i < parallel_num; ++i) {
        /*
        Host num unique matrix:
                |  Partition0  |  Partition1  |
        | Rank0 |      2       |       4      |
        | Rank1 |      3       |       3      |
        After ShuffleEmbeddingGrads, each rank will exchange partition.
        For example:
        Rank0 will have (matrix[rank0][part0] + matrix[rank1][part0]) grad tensor.
        Rank1 will have (matrix[rank0][part1] + matrix[rank1][part1]) grad tensor.
        */
        dequantize_cur_rank_num += host_num_unique_matrix[i * parallel_num + parallel_id];
      }
      IDX dequantize_elem_cnt = dequantize_cur_rank_num * padded_embedding_size;
      OF_CUDA_CHECK((LaunchDequantizeKernel<T, ComputeType, IDX>(
          cuda_stream, received_embedding_grad, received_cur_rank_quantize_factor,
          dequantize_cur_rank_embedding_grad, padded_embedding_size, dequantize_elem_cnt)));
      // use unique_partition_embedding_grad as UniqueCurRankEmbeddingGrad buffer.
      T* buffer_ptr = unique_partition_embedding_grad;
      UniqueCurRankEmbeddingGrad(ctx->stream(), data_type, cur_rank_num_ids, embedding_size,
                                 padded_embedding_size, dequantize_cur_rank_embedding_grad,
                                 reinterpret_cast<const IDX*>(cur_rank_inverse_indices->dptr()),
                                 cur_rank_unique_embedding_grad->mut_dptr<T>(), buffer_ptr);
    }
  }
  bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; }
};

#define REGISTER_CUDA_EMBEDDING_GRADIENT_SHUFFLE_KERNEL(t_dtype_pair, idx_dtype_pair)             \
  REGISTER_USER_KERNEL("embedding_gradient_shuffle")                                              \
      .SetCreateFn<EmbeddingGradientShuffleKernel<OF_PP_PAIR_FIRST(t_dtype_pair),                 \
                                                  OF_PP_PAIR_FIRST(idx_dtype_pair)>>()            \
      .SetIsMatchedHob(                                                                           \
          (user_op::HobDeviceType() == DeviceType::kCUDA)                                         \
          && (user_op::HobDataType("embedding_grad", 0) == OF_PP_PAIR_SECOND(t_dtype_pair))       \
          && (user_op::HobDataType("num_unique_matrix", 0) == OF_PP_PAIR_SECOND(idx_dtype_pair))) \
      .SetInferTmpSizeFn([](user_op::InferContext* ctx) {                                         \
        const user_op::TensorDesc& cur_rank_unique_embedding_grad =                               \
            ctx->InputTensorDesc("cur_rank_unique_embedding_grad", 0);                            \
        size_t cur_rank_embedding_grad_num = cur_rank_unique_embedding_grad.shape().At(0);        \
        size_t embedding_size = cur_rank_unique_embedding_grad.shape().At(1);                     \
        size_t padded_embedding_size =                                                            \
            GetPaddedEmbeddingSize(cur_rank_unique_embedding_grad.data_type(), embedding_size);   \
        size_t cur_rank_embedding_grad_elem_cnt =                                                 \
            cur_rank_embedding_grad_num * padded_embedding_size;                                  \
        bool enable_quantized_comm =                                                              \
            ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_ENABLE_QUANTIZED_COMM", false)             \
            && (padded_embedding_size < kMaxColSize);                                             \
        size_t tmp_size = 0;                                                                      \
        if (!enable_quantized_comm) {                                                             \
          size_t cur_rank_embedding_grad_size = GetCudaAlignedSize(                               \
              cur_rank_embedding_grad_elem_cnt * sizeof(OF_PP_PAIR_FIRST(t_dtype_pair)));         \
          tmp_size = 2 * cur_rank_embedding_grad_size;                                            \
        } else {                                                                                  \
          size_t unique_partition_embedding_grad_size = GetCudaAlignedSize(                       \
              cur_rank_embedding_grad_elem_cnt * sizeof(OF_PP_PAIR_FIRST(t_dtype_pair)));         \
          size_t received_embedding_grad_size =                                                   \
              GetCudaAlignedSize(cur_rank_embedding_grad_elem_cnt * sizeof(int8_t));              \
          size_t quantize_cur_rank_embedding_grad_size = received_embedding_grad_size;            \
          size_t cur_rank_quantize_factor_size = GetCudaAlignedSize(                              \
              cur_rank_embedding_grad_num * sizeof(OF_PP_PAIR_FIRST(t_dtype_pair)));              \
          size_t received_cur_rank_quantize_factor_size = cur_rank_quantize_factor_size;          \
          size_t dequantize_cur_rank_embedding_grad_size = unique_partition_embedding_grad_size;  \
          tmp_size = unique_partition_embedding_grad_size + received_embedding_grad_size          \
                     + quantize_cur_rank_embedding_grad_size + cur_rank_quantize_factor_size      \
                     + received_cur_rank_quantize_factor_size                                     \
                     + dequantize_cur_rank_embedding_grad_size;                                   \
        }                                                                                         \
        return tmp_size;                                                                          \
      });

OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CUDA_EMBEDDING_GRADIENT_SHUFFLE_KERNEL,
                                 FLOATING_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ, IDX_DATA_TYPE_SEQ)
                                //  FLOATING_DATA_TYPE_SEQ, IDX_DATA_TYPE_SEQ)

template<typename K, typename V, typename IDX>
class UniqueKeyValuePairKernel final : public user_op::OpKernel {
 public:
  UniqueKeyValuePairKernel() = default;
  ~UniqueKeyValuePairKernel() override = default;

 private:
  using user_op::OpKernel::Compute;

  void Compute(user_op::KernelComputeContext* ctx) const override {
    const user_op::Tensor* keys = ctx->Tensor4ArgNameAndIndex("keys", 0);
    user_op::Tensor* num_unique = ctx->Tensor4ArgNameAndIndex("num_unique", 0);
    user_op::Tensor* unique_keys = ctx->Tensor4ArgNameAndIndex("unique_keys", 0);
    user_op::Tensor* unique_values = ctx->Tensor4ArgNameAndIndex("unique_values", 0);
    user_op::Tensor* inverse_indices = ctx->Tensor4ArgNameAndIndex("inverse_indices", 0);
    user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0);
    const int32_t num_tables = ctx->Attr<int32_t>("num_tables");
    const bool has_values = ctx->has_input("values", 0);
    const bool need_values_buffer = (!has_values && num_tables > 1);
    size_t values_buffer_bytes =
        need_values_buffer ? GetCudaAlignedSize(keys->shape_view().elem_cnt() * sizeof(V)) : 0;
    const int64_t num_keys = keys->shape_view().elem_cnt();
    const int64_t hash_capacity = num_keys;
    const size_t workspace_bytes = GetCudaAlignedSize(hash_capacity * sizeof(TableEntry<K>));
    CHECK_LE(values_buffer_bytes + workspace_bytes, tmp_buffer->shape_view().elem_cnt());
    hipStream_t cuda_stream = ctx->stream()->As<ep::CudaStream>()->cuda_stream();
    const V* values_ptr;
    if (has_values) {
      const user_op::Tensor* values = ctx->Tensor4ArgNameAndIndex("values", 0);
      values_ptr = reinterpret_cast<const V*>(values->dptr());
    } else if (need_values_buffer) {
      V* values_buffer_ptr = reinterpret_cast<V*>(tmp_buffer->mut_dptr());
      hipLaunchKernelGGL(GenerateTableIds, BlocksNum4ThreadsNum(num_keys), kCudaThreadsNumPerBlock, 0, cuda_stream, 
          num_keys, num_tables, values_buffer_ptr);
      values_ptr = values_buffer_ptr;
    } else {
      values_ptr = nullptr;
    }
    const bool need_process_table_ids = (has_values || num_tables > 1);
    TableEntry<K>* workspace_ptr =
        reinterpret_cast<TableEntry<K>*>(tmp_buffer->mut_dptr<char>() + values_buffer_bytes);
    UniqueAndPartition<K, V, IDX, embedding::GlobalUniqueHash>(
        cuda_stream, num_keys, hash_capacity, 1, reinterpret_cast<const K*>(keys->dptr()),
        values_ptr, reinterpret_cast<IDX*>(num_unique->mut_dptr()),
        reinterpret_cast<K*>(unique_keys->mut_dptr()),
        reinterpret_cast<V*>(unique_values->mut_dptr()),
        reinterpret_cast<IDX*>(inverse_indices->mut_dptr()), workspace_ptr, workspace_bytes,
        need_process_table_ids);
  }
  bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; }
};

#define REGISTER_CUDA_UNIQUE_KEY_VALUE_PAIR_KERNEL(k_dtype_pair, value_dtype_pair, idx_dtype_pair) \
  REGISTER_USER_KERNEL("unique_key_value_pair")                                                    \
      .SetCreateFn<UniqueKeyValuePairKernel<OF_PP_PAIR_FIRST(k_dtype_pair),                        \
                                            OF_PP_PAIR_FIRST(value_dtype_pair),                    \
                                            OF_PP_PAIR_FIRST(idx_dtype_pair)>>()                   \
      .SetIsMatchedHob(                                                                            \
          (user_op::HobDeviceType() == DeviceType::kCUDA)                                          \
          && (user_op::HobDataType("keys", 0) == OF_PP_PAIR_SECOND(k_dtype_pair))                  \
          && (user_op::HobDataType("inverse_indices", 0) == OF_PP_PAIR_SECOND(idx_dtype_pair))     \
          && (user_op::HobDataType("unique_values", 0) == OF_PP_PAIR_SECOND(value_dtype_pair)))    \
      .SetInferTmpSizeFn([](user_op::InferContext* ctx) {                                          \
        const user_op::TensorDesc& keys = ctx->InputTensorDesc("keys", 0);                         \
        const int64_t num_keys = keys.shape().elem_cnt();                                          \
        const int64_t hash_capacity = num_keys;                                                    \
        const size_t workspace_bytes = GetCudaAlignedSize(                                         \
            hash_capacity * sizeof(TableEntry<OF_PP_PAIR_FIRST(k_dtype_pair)>));                   \
        const int32_t num_tables = ctx->Attr<int32_t>("num_tables");                               \
        const bool has_values = ctx->has_input("values", 0);                                       \
        const bool need_values_buffer = (!has_values && num_tables > 1);                           \
        size_t values_buffer_bytes =                                                               \
            need_values_buffer                                                                     \
                ? GetCudaAlignedSize(num_keys * sizeof(OF_PP_PAIR_FIRST(value_dtype_pair)))        \
                : 0;                                                                               \
        return workspace_bytes + values_buffer_bytes;                                              \
      });

OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CUDA_UNIQUE_KEY_VALUE_PAIR_KERNEL, ID_DATA_TYPE_SEQ,
                                 ID_DATA_TYPE_SEQ, IDX_DATA_TYPE_SEQ)

REGISTER_USER_KERNEL_UNIFIED_NCCL_COMM_INIT("id_shuffle");
REGISTER_USER_KERNEL_UNIFIED_NCCL_COMM_INIT("embedding_shuffle");
REGISTER_USER_KERNEL_UNIFIED_NCCL_COMM_INIT("embedding_gradient_shuffle");

}  // namespace oneflow