// Copyright (c) 2023 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/group_norm_kernel.h"

#include <algorithm>
#include <array>
#include <numeric>
#include <string>

#include "paddle/common/layout.h"
#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/full_kernel.h"

namespace phi {

template <typename T, typename Context>
void GroupNormKernel(const Context& dev_ctx,
                     const DenseTensor& x,
                     const optional<DenseTensor>& scale,
                     const optional<DenseTensor>& bias,
                     double epsilon,
                     int groups,
                     const std::string& data_layout_str,
                     DenseTensor* y,
                     DenseTensor* mean,
                     DenseTensor* var) {
  if (y && y->numel() == 0) {
    dev_ctx.template Alloc<T>(y);
    if (mean) {
      Full<T, Context>(dev_ctx, mean->dims(), 0, mean);
    }
    if (var) {
      Full<T, Context>(dev_ctx, var->dims(), 0, var);
    }
    return;
  }
  using XPUType = typename XPUTypeTrait<T>::Type;

  const DataLayout data_layout = StringToDataLayout(data_layout_str);
  const auto scale_ptr = scale.get_ptr();
  const auto bias_ptr = bias.get_ptr();

  const auto x_dims = vectorize<int64_t>(x.dims());
  const int64_t N = x_dims[0];
  const bool channel_first =
      data_layout == DataLayout::NCHW || data_layout == DataLayout::NCDHW;
  const int64_t C = (channel_first ? x_dims[1] : x_dims[x_dims.size() - 1]);
  const int64_t L =
      (channel_first ? std::accumulate(x_dims.begin() + 2,
                                       x_dims.end(),
                                       1,
                                       std::multiplies<int64_t>())
                     : std::accumulate(x_dims.begin() + 1,
                                       x_dims.end() - 1,
                                       1,
                                       std::multiplies<int64_t>()));

  dev_ctx.template Alloc<T>(y);
  dev_ctx.template Alloc<float>(mean);
  dev_ctx.template Alloc<float>(var);

  auto* x_data = x.data<T>();
  auto* y_data = y->data<T>();
  auto* mean_data = mean->data<float>();
  auto* var_data = var->data<float>();

  xpu::ctx_guard RAII_GUARD(dev_ctx.x_context());
  const float* scale_data = nullptr;
  if (scale_ptr) {
    if (std::is_same<T, float>::value) {
      scale_data = scale_ptr->data<float>();
    } else {
      float* scale_fp32 = RAII_GUARD.alloc_l3_or_gm<float>(scale_ptr->numel());
      int r = xpu::cast<XPUType, float>(
          dev_ctx.x_context(),
          reinterpret_cast<const XPUType*>(scale_ptr->data<T>()),
          scale_fp32,
          scale_ptr->numel());
      PADDLE_ENFORCE_XDNN_SUCCESS(r, "cast");
      scale_data = scale_fp32;
    }
  }

  const float* bias_data = nullptr;
  if (bias_ptr) {
    if (std::is_same<T, float>::value) {
      bias_data = bias_ptr->data<float>();
    } else {
      float* bias_fp32 = RAII_GUARD.alloc_l3_or_gm<float>(bias_ptr->numel());
      int r = xpu::cast<XPUType, float>(
          dev_ctx.x_context(),
          reinterpret_cast<const XPUType*>(bias_ptr->data<T>()),
          bias_fp32,
          bias_ptr->numel());
      PADDLE_ENFORCE_XDNN_SUCCESS(r, "cast");
      bias_data = bias_fp32;
    }
  }

  int r = xpu::group_norm<XPUType>(dev_ctx.x_context(),
                                   reinterpret_cast<const XPUType*>(x_data),
                                   reinterpret_cast<XPUType*>(y_data),
                                   N,
                                   C,
                                   L,
                                   1,
                                   groups,
                                   epsilon,
                                   scale_data,
                                   bias_data,
                                   mean_data,
                                   var_data,
                                   channel_first,  // is_nchw
                                   false);         // is_rstd
  PADDLE_ENFORCE_XDNN_SUCCESS(r, "group_norm");
}

}  // namespace phi

PD_REGISTER_KERNEL(group_norm,
                   XPU,
                   ALL_LAYOUT,
                   phi::GroupNormKernel,
                   float,
                   phi::float16,
                   phi::bfloat16) {}