// Copyright (c) 2022 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 "paddle/phi/kernels/adamw_kernel.h"

#include <vector>

#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/operators/jit/kernels.h"
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/common/float16.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/adam_kernel.h"
#include "paddle/phi/kernels/funcs/adam_functors.h"

namespace phi {

template <typename T, typename Context>
void AdamwDenseKernel(const Context& dev_ctx,
                      const DenseTensor& param,
                      const DenseTensor& grad,
                      const DenseTensor& learning_rate,
                      const DenseTensor& moment1,
                      const DenseTensor& moment2,
                      const DenseTensor& beta1_pow,
                      const DenseTensor& beta2_pow,
                      const paddle::optional<DenseTensor>& master_param,
                      const paddle::optional<DenseTensor>& skip_update,
                      const Scalar& beta1,
                      const Scalar& beta2,
                      const Scalar& epsilon,
                      float lr_ratio,
                      float coeff,
                      bool with_decay,
                      bool lazy_mode,
                      int64_t min_row_size_to_use_multithread,
                      bool multi_precision,
                      bool use_global_beta_pow,
                      DenseTensor* param_out,
                      DenseTensor* moment1_out,
                      DenseTensor* moment2_out,
                      DenseTensor* beta1_pow_out,
                      DenseTensor* beta2_pow_out,
                      DenseTensor* master_param_outs) {
  bool skip_update_ = false;
  if (skip_update.is_initialized()) {
    PADDLE_ENFORCE_EQ(
        skip_update->numel(),
        1,
        errors::InvalidArgument("Input(SkipUpdate) size must be 1, but get %d",
                                skip_update->numel()));
    std::vector<bool> skip_update_vec;
    paddle::framework::TensorToVector(*skip_update, dev_ctx, &skip_update_vec);
    skip_update_ = skip_update_vec[0];
  }
  VLOG(3) << "Skip update" << skip_update_;

  if (skip_update_ || !with_decay) {
    AdamDenseKernel<T, Context>(dev_ctx,
                                param,
                                grad,
                                learning_rate,
                                moment1,
                                moment2,
                                beta1_pow,
                                beta2_pow,
                                master_param,
                                skip_update,
                                beta1,
                                beta2,
                                epsilon,
                                lazy_mode,
                                min_row_size_to_use_multithread,
                                multi_precision,
                                use_global_beta_pow,
                                param_out,
                                moment1_out,
                                moment2_out,
                                beta1_pow_out,
                                beta2_pow_out,
                                master_param_outs);
    return;
  }

  T beta1_ = beta1.to<T>();
  T beta2_ = beta2.to<T>();
  T epsilon_ = epsilon.to<T>();
  T coeff_ = static_cast<T>(coeff);
  T lr_ratio_ = static_cast<T>(lr_ratio);

  VLOG(3) << "beta1_pow.numel() : " << beta1_pow.numel();
  VLOG(3) << "beta2_pow.numel() : " << beta2_pow.numel();
  VLOG(3) << "param.numel(): " << param.numel();

  PADDLE_ENFORCE_EQ(
      beta1_pow_out->numel(),
      1,
      errors::InvalidArgument("beta1 pow output size should be 1, but received "
                              "value is:%d.",
                              beta1_pow_out->numel()));

  PADDLE_ENFORCE_EQ(
      beta2_pow_out->numel(),
      1,
      errors::InvalidArgument("beta2 pow output size should be 1, but received "
                              "value is:%d.",
                              beta2_pow_out->numel()));

  T beta1_p = beta1_pow.data<T>()[0];
  T beta2_p = beta2_pow.data<T>()[0];

  if (!use_global_beta_pow) {
    dev_ctx.template Alloc<T>(beta1_pow_out)[0] = beta1_ * beta1_p;
    dev_ctx.template Alloc<T>(beta2_pow_out)[0] = beta2_ * beta2_p;
  }

  T* param_out_ptr = dev_ctx.template Alloc<T>(param_out);
  T* mom1_out_ptr = dev_ctx.template Alloc<T>(moment1_out);
  T* mom2_out_ptr = dev_ctx.template Alloc<T>(moment2_out);
  T old_lr = learning_rate.data<T>()[0];
  T learning_rate_ =
      learning_rate.data<T>()[0] * (sqrt(1 - beta2_p) / (1 - beta1_p));
  T eps = epsilon_ * sqrt(1 - beta2_p);

  int64_t numel = param.numel();

  const T* param_ptr = param.data<T>();
  const T* mom1_ptr = moment1.data<T>();
  const T* mom2_ptr = moment2.data<T>();
  const T* grad_ptr = grad.data<T>();

  auto adamw =
      paddle::operators::jit::KernelFuncs<paddle::operators::jit::AdamWTuple<T>,
                                          phi::CPUPlace>::Cache()
          .At(1);

  static constexpr int64_t chunk_size = 512;

#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for
#endif
  for (int64_t i = 0; i < numel / chunk_size; ++i) {
    const int64_t offset = i * chunk_size;
    adamw(beta1_,
          beta2_,
          -learning_rate_,
          eps,
          old_lr,
          lr_ratio_,
          coeff_,
          chunk_size,
          grad_ptr + offset,
          mom1_ptr + offset,
          mom2_ptr + offset,
          param_ptr + offset,
          mom1_out_ptr + offset,
          mom2_out_ptr + offset,
          param_out_ptr + offset);
  }

  if (numel % chunk_size != 0) {
    const int64_t offset = (numel / chunk_size) * chunk_size;
    const int64_t tail_numel = numel % chunk_size;
    adamw(beta1_,
          beta2_,
          -learning_rate_,
          eps,
          old_lr,
          lr_ratio_,
          coeff_,
          tail_numel,
          grad_ptr + offset,
          mom1_ptr + offset,
          mom2_ptr + offset,
          param_ptr + offset,
          mom1_out_ptr + offset,
          mom2_out_ptr + offset,
          param_out_ptr + offset);
  }
}

}  // namespace phi

PD_REGISTER_KERNEL(
    adamw, CPU, ALL_LAYOUT, phi::AdamwDenseKernel, float, double) {}
