/* Copyright (c) 2020 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. */

#ifdef PADDLE_WITH_HETERPS
#include <xpu/runtime.h>  // NOLINT
#include <algorithm>
#include <ctime>
#include <memory>
#include <numeric>
#include "paddle/fluid/framework/fleet/ps_gpu_wrapper.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "xpu/kernel/cluster_header.h"  // NOLINT
#include "xpu/kernel/debug.h"           // NOLINT
#include "xpu/kernel/math.h"            // NOLINT
#include "xpu/kernel/simd.h"

namespace paddle {
namespace framework {

__global__ void PullCopy(float* dest, const FeatureValue* src,
                         const long long* len, int hidden, int slot_num,
                         int total_len, unsigned long long* keys) {
  int cid = core_id();
  int ncores = core_num();
  if (cid >= ncores) {
    return;
  }
  int thread_id = ncores * cluster_id() + cid;
  int nthreads = ncores * cluster_num();
  __local__ int64_t local_len[slot_num];
  GM2LM(len, local_len, slot_num * sizeof(int64_t));

  __global_ptr__ unsigned long long* local_keys[slot_num];
  GM2LM(keys, local_keys,
        slot_num * sizeof(__global_ptr__ unsigned long long*));

  __global_ptr__ float* local_dest[slot_num];
  GM2LM(dest, local_dest, slot_num * sizeof(__global_ptr__ float*));

  int read_len = 30;

  for (int i = thread_id; i < slot_num; i += nthreads) {
    // max core local memory = 8KB
    // slot's max memory size = slot_len * sizeof(FeatureValue)
    int slot_len = i ? local_len[i] - local_len[i - 1] : local_len[0];
    // int read_len = min(roundup_div(1024 * 8, sizeof(FeatureValue)),
    // slot_len);
    int dest_len = i ? local_len[i - 1] : 0;
    __local__ FeatureValue local_slot_vals[read_len];
    __local__ float local_dest_vals[read_len * hidden];
    __local__ uint64_t local_slot_keys[read_len];

    // copy read_len (length) of slots' val to LM
    for (int k = 0; k < slot_len; k += read_len) {
      int real_read_len = min(read_len, slot_len - k);
      GM2LM(src + dest_len + k, local_slot_vals,
            real_read_len * sizeof(FeatureValue));
      GM2LM(local_keys[i] + k, local_slot_keys,
            real_read_len * sizeof(uint64_t));
      for (int j = 0; j < real_read_len; j++) {
        if (local_slot_keys[j] == 0) {
          local_dest_vals[j * hidden] = 0;
          local_dest_vals[j * hidden + 1] = 0;
          local_dest_vals[j * hidden + 2] = 0;
        } else {
          local_dest_vals[j * hidden] = local_slot_vals[j].show;
          local_dest_vals[j * hidden + 1] = local_slot_vals[j].clk;
          local_dest_vals[j * hidden + 2] = local_slot_vals[j].lr;
        }

        if (local_slot_vals[j].mf_size == 0 || local_slot_keys[j] == 0) {
          for (int m = 0; m < hidden - 3; m++) {
            local_dest_vals[j * hidden + 3 + m] = 0;
          }
        } else {
          for (int m = 0; m < hidden - 3; m++) {
            local_dest_vals[j * hidden + 3 + m] = local_slot_vals[j].mf[1 + m];
          }
        }
      }
      LM2GM(local_dest_vals, local_dest[i] + k * hidden,
            real_read_len * hidden * sizeof(float));
    }
  }
}

__global__ void CopyKeysKernel(unsigned long long* src_keys,
                               unsigned long long* dest_total_keys,
                               const long long* len, int slot_num,
                               int total_len) {
  int cid = core_id();
  int ncores = core_num();
  if (cid >= ncores) {
    return;
  }
  int thread_id = ncores * cluster_id() + cid;
  int nthreads = ncores * cluster_num();
  __local__ long long local_len[slot_num];
  GM2LM(len, local_len, slot_num * sizeof(long long));

  __global_ptr__ unsigned long long* local_keys[slot_num];
  GM2LM(src_keys, local_keys,
        slot_num * sizeof(__global_ptr__ unsigned long long*));

  for (int i = thread_id; i < slot_num; i += nthreads) {
    // max core local memory = 8KB
    int slot_len = i ? local_len[i] - local_len[i - 1] : local_len[0];
    // int read_len = min(slot_len, 1024);
    int read_len = 100;
    int dest_len = i ? local_len[i - 1] : 0;
    __local__ unsigned long long local_slot_keys[read_len];

    for (int k = 0; k < slot_len; k += read_len) {
      int real_read_len = min(read_len, slot_len - k);
      GM2LM(local_keys[i] + k, local_slot_keys,
            real_read_len * sizeof(unsigned long long));
      LM2GM(local_slot_keys, dest_total_keys + dest_len + k,
            real_read_len * sizeof(unsigned long long));
    }
  }
}

__global__ void PushCopy(FeaturePushValue* dest, float* src, long long* len,
                         int hidden, int slot_num, int total_len, int bs,
                         int* slot_vector) {
  int cid = core_id();
  int ncores = core_num();
  if (cid >= ncores) {
    return;
  }
  int thread_id = ncores * cluster_id() + cid;
  int nthreads = ncores * cluster_num();
  __local__ int64_t local_len[slot_num];
  __local__ int local_slot[slot_num];
  GM2LM(len, local_len, slot_num * sizeof(int64_t));
  GM2LM(slot_vector, local_slot, slot_num * sizeof(int));

  __global_ptr__ float* local_src[slot_num];
  GM2LM(src, local_src, slot_num * sizeof(__global_ptr__ float*));

  for (int i = thread_id; i < slot_num; i += nthreads) {
    int slot_len = i ? local_len[i] - local_len[i - 1] : local_len[0];

    // max core local memory = 8KB
    // slot's max memory size = slot_len * hidden * 8
    // int read_len = min(roundup_div(1024, hidden), slot_len);
    int read_len = 40;
    int dest_len = i ? local_len[i - 1] : 0;
    __local__ float local_slot_grads[read_len * hidden];
    __local__ FeaturePushValue local_dest_grads[read_len];

    // copy read_len(length) of slots' grad to LM
    for (int k = 0; k < slot_len; k += read_len) {
      int real_read_len = min(read_len, slot_len - k);
      GM2LM(local_src[i] + k * hidden, local_slot_grads,
            real_read_len * hidden * sizeof(float));
      // copy from slots' grad to total grad
      for (int j = 0; j < real_read_len; j++) {
        local_dest_grads[j].slot = local_slot[i];
        local_dest_grads[j].show = local_slot_grads[j * hidden];
        local_dest_grads[j].clk = local_slot_grads[j * hidden + 1];
        local_dest_grads[j].lr_g = local_slot_grads[j * hidden + 2] * -1. * bs;
        for (int m = 0; m < hidden - 3; m++) {
          local_dest_grads[j].mf_g[m] =
              local_slot_grads[j * hidden + 3 + m] * -1. * bs;
        }
      }
      LM2GM(local_dest_grads, dest + dest_len + k,
            real_read_len * sizeof(FeaturePushValue));
    }
  }
}

PSGPUWrapper::~PSGPUWrapper() { delete HeterPs_; }

void PSGPUWrapper::CopyForPull(const paddle::platform::Place& place,
                               uint64_t** gpu_keys,
                               const std::vector<float*>& values,
                               const FeatureValue* total_values_gpu,
                               const int64_t* gpu_len, const int slot_num,
                               const int hidden_size,
                               const int64_t total_length) {
  XPUStream stream = nullptr;
  auto dev_ctx = platform::DeviceContextPool::Instance().Get(place);
  stream = static_cast<platform::XPUDeviceContext*>(dev_ctx)
               ->x_context()
               ->xpu_stream;
  // float* buf_value = nullptr;
  // xpu_malloc(reinterpret_cast<void**>(&buf_value),
  //            values.size() * sizeof(float*));
  // float** gpu_values = reinterpret_cast<float**>(&buf_value);
  float* gpu_values = nullptr;
  xpu_malloc(reinterpret_cast<void**>(&gpu_values),
             values.size() * sizeof(float*));
  xpu_memcpy(gpu_values, values.data(), values.size() * sizeof(float*),
             XPU_HOST_TO_DEVICE);

  // unsigned long long** c_keys = (unsigned long long**)gpu_keys;
  unsigned long long* c_keys = reinterpret_cast<unsigned long long*>(gpu_keys);
  const long long* c_len = (const long long*)gpu_len;
  PullCopy<<<2, 64, stream>>>(gpu_values, total_values_gpu, c_len, hidden_size,
                              slot_num, total_length, c_keys);

  xpu_wait(stream);
}

void PSGPUWrapper::CopyKeys(const paddle::platform::Place& place,
                            uint64_t** origin_keys, uint64_t* total_keys,
                            const int64_t* gpu_len, int slot_num,
                            int total_len) {
  XPUStream stream = nullptr;
  auto dev_ctx = platform::DeviceContextPool::Instance().Get(place);
  stream = static_cast<platform::XPUDeviceContext*>(dev_ctx)
               ->x_context()
               ->xpu_stream;
  unsigned long long* o_keys =
      reinterpret_cast<unsigned long long*>(origin_keys);
  unsigned long long* t_keys = (unsigned long long*)total_keys;
  const long long* c_len = (const long long*)gpu_len;
  CopyKeysKernel<<<2, 64, stream>>>(o_keys, t_keys, c_len, slot_num, total_len);
  xpu_wait(stream);
}

void PSGPUWrapper::CopyForPush(const paddle::platform::Place& place,
                               const std::vector<const float*>& grad_values,
                               FeaturePushValue* total_grad_values_gpu,
                               const std::vector<int64_t>& slot_lengths,
                               const int hidden_size,
                               const int64_t total_length,
                               const int batch_size) {
  XPUStream stream = nullptr;
  auto dev_ctx = platform::DeviceContextPool::Instance().Get(place);
  stream = static_cast<platform::XPUDeviceContext*>(dev_ctx)
               ->x_context()
               ->xpu_stream;
  auto slot_lengths_lod = slot_lengths;
  for (size_t i = 1; i < slot_lengths_lod.size(); i++) {
    slot_lengths_lod[i] += slot_lengths_lod[i - 1];
  }

  float* gpu_values = nullptr;
  int64_t* gpu_len = nullptr;
  int* d_slot_vector = nullptr;

  xpu_malloc(reinterpret_cast<void**>(&gpu_values),
             grad_values.size() * sizeof(float*));
  xpu_malloc(reinterpret_cast<void**>(&gpu_len),
             slot_lengths.size() * sizeof(int64_t));
  xpu_malloc(reinterpret_cast<void**>(&d_slot_vector),
             slot_lengths_lod.size() * sizeof(int));

  xpu_memcpy(gpu_values, grad_values.data(),
             grad_values.size() * sizeof(float*), XPU_HOST_TO_DEVICE);
  xpu_memcpy(gpu_len, slot_lengths_lod.data(),
             slot_lengths.size() * sizeof(int64_t), XPU_HOST_TO_DEVICE);
  xpu_memcpy(d_slot_vector, slot_vector_.data(),
             slot_lengths_lod.size() * sizeof(int), XPU_HOST_TO_DEVICE);

  long long* c_len = (long long*)gpu_len;
  PushCopy<<<2, 64, stream>>>(total_grad_values_gpu, gpu_values, c_len,
                              hidden_size, slot_lengths.size(), total_length,
                              batch_size, d_slot_vector);
  xpu_wait(stream);
}

void PSGPUWrapper::SetSparseSGD(float nonclk_coeff, float clk_coeff,
                                float min_bound, float max_bound,
                                float learning_rate, float initial_g2sum,
                                float initial_range) {
  OptimizerConfig optimizer_config;
  optimizer_config.set_sparse_sgd(nonclk_coeff, clk_coeff, min_bound, max_bound,
                                  learning_rate, initial_g2sum, initial_range);
  HeterPs_->set_sparse_sgd(optimizer_config);
}

void PSGPUWrapper::SetEmbedxSGD(float mf_create_thresholds,
                                float mf_learning_rate, float mf_initial_g2sum,
                                float mf_initial_range, float mf_min_bound,
                                float mf_max_bound) {
  OptimizerConfig optimizer_config;
  optimizer_config.set_embedx_sgd(mf_create_thresholds, mf_learning_rate,
                                  mf_initial_g2sum, mf_initial_range,
                                  mf_min_bound, mf_max_bound);
  HeterPs_->set_embedx_sgd(optimizer_config);
}

}  // end namespace framework
}  // end namespace paddle
#endif