// Copyright (c) 2024 PaddlePaddle 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 "helper.h"          // NOLINT
#include "../cccl_compat.h"  // CCCL 3.0 compatibility

// #define DEBUG_STEP

__device__ bool speculate_free_and_dispatch_block(const int &qid,
                                                  int *need_block_list,
                                                  const int &need_block_len) {
  bool res = false;
  for (int i = 0; i < need_block_len; i++) {
    if (qid == need_block_list[i]) {
      res = true;
      need_block_list[i] = -1;
      break;
    }
  }
  return res;
}

__global__ void speculate_free_and_dispatch_block(
    bool *stop_flags,
    int *seq_lens_this_time,
    int *seq_lens_decoder,
    int *block_tables,
    int *encoder_block_lens,
    bool *is_block_step,
    int *step_block_list,  // [bsz]
    int *step_len,
    int *recover_block_list,
    int *recover_len,
    int *need_block_list,
    int *need_block_len,
    int *used_list_len,
    int *free_list,
    int *free_list_len,
    int64_t *first_token_ids,
    int *accept_num,
    const int bsz,
    const int block_size,
    const int block_num_per_seq,
    const int max_decoder_block_num,
    const int max_draft_tokens) {
  typedef cub::BlockReduce<cub::KeyValuePair<int, int>, 256> BlockReduce;
  __shared__ typename BlockReduce::TempStorage temp_storage;
  __shared__ bool step_max_block_flag;
  __shared__ int in_need_block_list_len;
  const int tid = threadIdx.x;
  if (tid < bsz) {
    if (tid == 0) {
      step_max_block_flag = false;
      in_need_block_list_len = 0;
    }
    int *block_table_now = block_tables + tid * block_num_per_seq;
    int max_possible_block_idx =
        (seq_lens_decoder[tid] + max_draft_tokens + 1) / block_size;
    if (stop_flags[tid] && !is_block_step[tid]) {
      // 回收block块
      first_token_ids[tid] = -1;
      const int encoder_block_len = encoder_block_lens[tid];
      const int decoder_used_len = used_list_len[tid];
      if (decoder_used_len > 0) {
        const int ori_free_list_len =
            atomicAdd(free_list_len, decoder_used_len);
#ifdef DEBUG_STEP
        printf(
            "free block seq_id: %d, free block num: %d, "
            "encoder_block_len: %d, ori_free_list_len: %d\n",
            tid,
            decoder_used_len,
            encoder_block_len,
            ori_free_list_len);
#endif
        for (int i = 0; i < decoder_used_len; i++) {
          free_list[ori_free_list_len + i] =
              block_table_now[encoder_block_len + i];
          block_table_now[encoder_block_len + i] = -1;
        }
        encoder_block_lens[tid] = 0;
        used_list_len[tid] = 0;
      }
    } else if (seq_lens_this_time[tid] != 0 &&
               max_possible_block_idx < block_num_per_seq &&
               block_table_now[(seq_lens_decoder[tid] + max_draft_tokens + 1) /
                               block_size] == -1) {
      // 统计需要分配block的位置和总数
      const int ori_need_block_len = atomicAdd(need_block_len, 1);
      need_block_list[ori_need_block_len] = tid;
#ifdef DEBUG_STEP
      printf("seq_id: %d need block\n", tid);
#endif
    }
  }
  __syncthreads();

  while (need_block_len[0] > free_list_len[0]) {
#ifdef DEBUG_STEP
    if (tid == 0) {
      printf("need_block_len: %d, free_list_len: %d\n",
             need_block_len[0],
             free_list_len[0]);
    }
#endif
    // 调度block，根据used_list_len从大到小回收block，直到满足need_block_len，已解码到最后一个block的query不参与调度（马上就结束）
    const int used_block_num =
        tid < bsz && !is_block_step[tid] &&
                (step_max_block_flag ||
                 used_list_len[tid] != max_decoder_block_num)
            ? used_list_len[tid]
            : 0;
    cub::KeyValuePair<int, int> kv_pair = {tid, used_block_num};
    kv_pair =
        BlockReduce(temp_storage).Reduce(kv_pair, fd_cub_compat::ArgMax());

    if (tid == 0) {
      if (kv_pair.value == 0) {
        step_max_block_flag = true;
      } else {
        const int encoder_block_len = encoder_block_lens[kv_pair.key];
        // #ifdef DEBUG_STEP
        printf("max_id: %d, max_num: %d, encoder_block_len: %d\n",
               kv_pair.key,
               kv_pair.value,
               encoder_block_len);
        // #endif
        int *block_table_now = block_tables + kv_pair.key * block_num_per_seq;
        for (int i = 0; i < kv_pair.value; i++) {
          free_list[free_list_len[0] + i] =
              block_table_now[encoder_block_len + i];
          block_table_now[encoder_block_len + i] = -1;
        }
        step_block_list[step_len[0]] = kv_pair.key;
        if (speculate_free_and_dispatch_block(
                kv_pair.key,
                need_block_list,
                need_block_len[0] + in_need_block_list_len)) {
          need_block_len[0] -= 1;
          in_need_block_list_len += 1;
        }
        step_len[0] += 1;
        free_list_len[0] += kv_pair.value;
        stop_flags[kv_pair.key] = true;
        is_block_step[kv_pair.key] = true;
        seq_lens_this_time[kv_pair.key] = 0;
        seq_lens_decoder[kv_pair.key] = 0;
        // Note(@wufeisheng): when step, accept num will not be 0 so
        // that next step even if this batch member is stepped, save
        // output still stream output, so accept num should be set to 0
        accept_num[kv_pair.key] = 0;
      }
    }
    __syncthreads();
  }

  // 为需要block的位置分配block，每个位置分配一个block
  if (tid < need_block_len[0] + in_need_block_list_len) {
    const int need_block_id = need_block_list[tid];
    if (need_block_id != -1) {
      if (!stop_flags[need_block_id]) {
        // 如果需要的位置正好是上一步中被释放的位置，不做处理
        used_list_len[need_block_id] += 1;
        const int ori_free_list_len = atomicSub(free_list_len, 1);
        int *block_table_now = block_tables + need_block_id * block_num_per_seq;
#ifdef DEBUG_STEP
        printf("need_block_id %d\n", need_block_id);
        printf("ori_free_list_len %d\n", ori_free_list_len);
        printf("max_draft_tokens %d\n", max_draft_tokens);
        printf("seq_lens_decoder[need_block_id] %d\n",
               seq_lens_decoder[need_block_id]);
        printf("free_list[ori_free_list_len - 1] %d\n",
               free_list[ori_free_list_len - 1]);
#endif
        block_table_now[(seq_lens_decoder[need_block_id] + max_draft_tokens +
                         1) /
                        block_size] = free_list[ori_free_list_len - 1];
      }
      need_block_list[tid] = -1;
    }
  }
  __syncthreads();

  // 计算可以复原的query id
  if (tid == 0) {
    int ori_free_list_len = free_list_len[0];
    int ori_step_len = step_len[0];
    if (ori_step_len > 0) {
      int ori_step_block_id = step_block_list[ori_step_len - 1];
      int tmp_used_len = used_list_len[ori_step_block_id];
      // 比之前调度时多分配一个block，防止马上恢复刚调度的query(比如回收的seq_id在need_block_list中）
      const int max_decoder_block_num_this_seq =
          max_decoder_block_num - encoder_block_lens[ori_step_block_id];
      int used_len = tmp_used_len + 1 < max_decoder_block_num_this_seq
                         ? tmp_used_len + 1
                         : max_decoder_block_num_this_seq;
      if (ori_step_len > 0 && ori_free_list_len >= used_len) {
        // #ifdef DEBUG_STEP
        printf(
            "recover seq_id: %d, free_list_len: %d, used_list_len: "
            "%d\n",
            ori_step_block_id,
            ori_free_list_len,
            used_len);
        // #endif
        recover_block_list[recover_len[0]] = ori_step_block_id;
        is_block_step[ori_step_block_id] = false;
        used_list_len[ori_step_block_id] = used_len;
        ori_free_list_len -= used_len;
        step_block_list[ori_step_len - 1] = -1;
        step_len[0] -= 1;
        recover_len[0] += 1;
        ori_step_len = step_len[0];
        if (ori_step_len > 0) {
          ori_step_block_id = step_block_list[ori_step_len - 1];
          tmp_used_len = used_list_len[ori_step_block_id];
          used_len = tmp_used_len + 1 < max_decoder_block_num_this_seq
                         ? tmp_used_len + 1
                         : max_decoder_block_num_this_seq;
        }
      }
    }
    need_block_len[0] = 0;
  }
}

// 根据上一步计算出的可以复原的query_id进行状态恢复
__global__ void speculate_recover_block(int *recover_block_list,  // [bsz]
                                        int *recover_len,
                                        bool *stop_flags,
                                        int *seq_lens_this_time,
                                        int *ori_seq_lens_encoder,
                                        int *seq_lens_encoder,
                                        int *seq_lens_decoder,
                                        int *block_tables,
                                        int *free_list,
                                        int *free_list_len,
                                        int64_t *input_ids,
                                        int64_t *pre_ids,
                                        int64_t *step_idx,
                                        int *encoder_block_lens,
                                        int *used_list_len,
                                        const int64_t *next_tokens,
                                        const int64_t *first_token_ids,
                                        const int bsz,
                                        const int block_num_per_seq,
                                        const int length,
                                        const int pre_id_length) {
  const int bid = blockIdx.x;
  const int tid = threadIdx.x;
  __shared__ int ori_free_list_len;
  if (bid < recover_len[0]) {
    const int recover_id = recover_block_list[bid];
    const int ori_seq_len_encoder = ori_seq_lens_encoder[recover_id];
    const int step_idx_now = step_idx[recover_id];
    const int seq_len = ori_seq_len_encoder + step_idx_now;
    const int encoder_block_len = encoder_block_lens[recover_id];
    const int decoder_used_len = used_list_len[recover_id];
    int *block_table_now = block_tables + recover_id * block_num_per_seq;
    int64_t *input_ids_now = input_ids + recover_id * length;
    int64_t *pre_ids_now = pre_ids + recover_id * pre_id_length;
    if (tid == 0) {
      seq_lens_this_time[recover_id] = seq_len;
      seq_lens_encoder[recover_id] = seq_len;
      stop_flags[recover_id] = false;
      // input_ids_now[ori_seq_len_encoder + step_idx_now - 1] =
      //     next_tokens[recover_id];  // next tokens
      input_ids_now[0] = first_token_ids[recover_id];  // set first prompt token
      const int ori_free_list_len_tid0 =
          atomicSub(free_list_len, decoder_used_len);
      ori_free_list_len = ori_free_list_len_tid0;
#ifdef DEBUG_STEP
      printf(
          "seq_id: %d, ori_seq_len_encoder: %d, step_idx_now: %d, "
          "seq_len: %d, ori_free_list_len_tid0: %d, "
          "ori_free_list_len: %d\n",
          recover_id,
          ori_seq_len_encoder,
          step_idx_now,
          seq_len,
          ori_free_list_len_tid0,
          ori_free_list_len);
#endif
    }
    __syncthreads();
    // 恢复block table
    for (int i = tid; i < decoder_used_len; i += blockDim.x) {
      block_table_now[encoder_block_len + i] =
          free_list[ori_free_list_len - i - 1];
    }
    // 恢复input_ids
    for (int i = tid; i < step_idx_now; i += blockDim.x) {
      input_ids_now[ori_seq_len_encoder + i] = pre_ids_now[i + 1];
    }
  }

  if (bid == 0 && tid == 0) {
    recover_len[0] = 0;
  }
}

void SpeculateStepPaddle(
    const paddle::Tensor &stop_flags,
    const paddle::Tensor &seq_lens_this_time,
    const paddle::Tensor &ori_seq_lens_encoder,
    const paddle::Tensor &seq_lens_encoder,
    const paddle::Tensor &seq_lens_decoder,
    const paddle::Tensor &block_tables,  // [bsz, block_num_per_seq]
    const paddle::Tensor &encoder_block_lens,
    const paddle::Tensor &is_block_step,
    const paddle::Tensor &step_block_list,
    const paddle::Tensor &step_lens,
    const paddle::Tensor &recover_block_list,
    const paddle::Tensor &recover_lens,
    const paddle::Tensor &need_block_list,
    const paddle::Tensor &need_block_len,
    const paddle::Tensor &used_list_len,
    const paddle::Tensor &free_list,
    const paddle::Tensor &free_list_len,
    const paddle::Tensor &input_ids,
    const paddle::Tensor &pre_ids,
    const paddle::Tensor &step_idx,
    const paddle::Tensor &next_tokens,
    const paddle::Tensor &first_token_ids,
    const paddle::Tensor &accept_num,
    const int block_size,
    const int encoder_decoder_block_num,
    const int max_draft_tokens) {
  auto cu_stream = seq_lens_this_time.stream();
  const int bsz = seq_lens_this_time.shape()[0];
  const int block_num_per_seq = block_tables.shape()[1];
  const int length = input_ids.shape()[1];
  const int pre_id_length = pre_ids.shape()[1];
  constexpr int BlockSize = 256;  // bsz <= 256
  const int max_decoder_block_num = length / block_size;
  // const int max_decoder_block_num = 2048 / block_size -
  // encoder_decoder_block_num;
#ifdef DEBUG_STEP
  printf(
      "bsz: %d, block_num_per_seq: %d, length: %d, max_decoder_block_num: "
      "%d\n",
      bsz,
      block_num_per_seq,
      length,
      max_decoder_block_num);
#endif
  speculate_free_and_dispatch_block<<<1, BlockSize, 0, cu_stream>>>(
      const_cast<bool *>(stop_flags.data<bool>()),
      const_cast<int *>(seq_lens_this_time.data<int>()),
      const_cast<int *>(seq_lens_decoder.data<int>()),
      const_cast<int *>(block_tables.data<int>()),
      const_cast<int *>(encoder_block_lens.data<int>()),
      const_cast<bool *>(is_block_step.data<bool>()),
      const_cast<int *>(step_block_list.data<int>()),
      const_cast<int *>(step_lens.data<int>()),
      const_cast<int *>(recover_block_list.data<int>()),
      const_cast<int *>(recover_lens.data<int>()),
      const_cast<int *>(need_block_list.data<int>()),
      const_cast<int *>(need_block_len.data<int>()),
      const_cast<int *>(used_list_len.data<int>()),
      const_cast<int *>(free_list.data<int>()),
      const_cast<int *>(free_list_len.data<int>()),
      const_cast<int64_t *>(first_token_ids.data<int64_t>()),
      const_cast<int *>(accept_num.data<int>()),
      bsz,
      block_size,
      block_num_per_seq,
      max_decoder_block_num,
      max_draft_tokens);
#ifdef DEBUG_STEP
  cudaDeviceSynchronize();
#endif
  auto cpu_recover_lens = recover_lens.copy_to(paddle::CPUPlace(), false);
  const int grid_size = cpu_recover_lens.data<int>()[0];
#ifdef DEBUG_STEP
  printf("grid_size2 %d\n", grid_size);
#endif
  if (grid_size > 0) {
    speculate_recover_block<<<grid_size, BlockSize, 0, cu_stream>>>(
        const_cast<int *>(recover_block_list.data<int>()),
        const_cast<int *>(recover_lens.data<int>()),
        const_cast<bool *>(stop_flags.data<bool>()),
        const_cast<int *>(seq_lens_this_time.data<int>()),
        const_cast<int *>(ori_seq_lens_encoder.data<int>()),
        const_cast<int *>(seq_lens_encoder.data<int>()),
        const_cast<int *>(seq_lens_decoder.data<int>()),
        const_cast<int *>(block_tables.data<int>()),
        const_cast<int *>(free_list.data<int>()),
        const_cast<int *>(free_list_len.data<int>()),
        const_cast<int64_t *>(input_ids.data<int64_t>()),
        const_cast<int64_t *>(pre_ids.data<int64_t>()),
        const_cast<int64_t *>(step_idx.data<int64_t>()),
        const_cast<int *>(encoder_block_lens.data<int>()),
        const_cast<int *>(used_list_len.data<int>()),
        next_tokens.data<int64_t>(),
        first_token_ids.data<int64_t>(),
        bsz,
        block_num_per_seq,
        length,
        pre_id_length);
#ifdef DEBUG_STEP
    cudaDeviceSynchronize();
#endif
  }
}

PD_BUILD_STATIC_OP(speculate_step_paddle)
    .Inputs({"stop_flags",
             "seq_lens_this_time",
             "ori_seq_lens_encoder",
             "seq_lens_encoder",
             "seq_lens_decoder",
             "block_tables",
             "encoder_block_lens",
             "is_block_step",
             "step_block_list",
             "step_lens",
             "recover_block_list",
             "recover_lens",
             "need_block_list",
             "need_block_len",
             "used_list_len",
             "free_list",
             "free_list_len",
             "input_ids",
             "pre_ids",
             "step_idx",
             "next_tokens",
             "first_token_ids",
             "accept_num"})
    .Attrs({"block_size: int",
            "encoder_decoder_block_num: int",
            "max_draft_tokens: int"})
    .Outputs({"stop_flags_out",
              "seq_lens_this_time_out",
              "seq_lens_encoder_out",
              "seq_lens_decoder_out",
              "block_tables_out",
              "encoder_block_lens_out",
              "is_block_step_out",
              "step_block_list_out",
              "step_lens_out",
              "recover_block_list_out",
              "recover_lens_out",
              "need_block_list_out",
              "need_block_len_out",
              "used_list_len_out",
              "free_list_out",
              "free_list_len_out",
              "input_ids_out",
              "first_token_ids_out"})
    .SetInplaceMap({{"stop_flags", "stop_flags_out"},
                    {"seq_lens_this_time", "seq_lens_this_time_out"},
                    {"seq_lens_encoder", "seq_lens_encoder_out"},
                    {"seq_lens_decoder", "seq_lens_decoder_out"},
                    {"block_tables", "block_tables_out"},
                    {"encoder_block_lens", "encoder_block_lens_out"},
                    {"is_block_step", "is_block_step_out"},
                    {"step_block_list", "step_block_list_out"},
                    {"step_lens", "step_lens_out"},
                    {"recover_block_list", "recover_block_list_out"},
                    {"recover_lens", "recover_lens_out"},
                    {"need_block_list", "need_block_list_out"},
                    {"need_block_len", "need_block_len_out"},
                    {"used_list_len", "used_list_len_out"},
                    {"free_list", "free_list_out"},
                    {"free_list_len", "free_list_len_out"},
                    {"input_ids", "input_ids_out"},
                    {"first_token_ids", "first_token_ids_out"}})
    .SetKernelFn(PD_KERNEL(SpeculateStepPaddle));
