// Copyright (c) 2019 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 "lite/kernels/x86/group_norm_compute.h"
#include <immintrin.h>
#include <cmath>
#include "lite/backends/x86/math/avx/group_norm.h"
#include "lite/core/op_registry.h"
#include "lite/core/type_system.h"

namespace paddle {
namespace lite {
namespace kernels {
namespace x86 {

void GroupNormCompute::PrepareForRun() {}

void GroupNormCompute::Run() {
  auto& param = this->Param<param_t>();
  const float* in = param.x->data<float>();
  const float* scale =
      param.scale == nullptr ? nullptr : param.scale->data<float>();
  const float* bias =
      param.bias == nullptr ? nullptr : param.bias->data<float>();
  float* out = param.out->mutable_data<float>();
  float* saved_mean = param.saved_mean->mutable_data<float>();
  float* saved_variance = param.saved_variance->mutable_data<float>();
  float epsilon = param.epsilon;
  int groups = param.groups;

  int n = param.x->dims()[0];
  int c = param.x->dims()[1];
  int height = param.x->dims()[2];
  int width = param.x->dims()[3];

  lite::x86::math::group_norm(in,
                              out,
                              n,
                              c,
                              height,
                              width,
                              epsilon,
                              groups,
                              scale,
                              bias,
                              saved_mean,
                              saved_variance);
}

}  // namespace x86
}  // namespace kernels
}  // namespace lite
}  // namespace paddle

REGISTER_LITE_KERNEL(group_norm,
                     kX86,
                     kFloat,
                     kNCHW,
                     paddle::lite::kernels::x86::GroupNormCompute,
                     def)
    .BindInput("X", {LiteType::GetTensorTy(TARGET(kX86))})
    .BindInput("Scale", {LiteType::GetTensorTy(TARGET(kX86))})
    .BindInput("Bias", {LiteType::GetTensorTy(TARGET(kX86))})
    .BindOutput("Y", {LiteType::GetTensorTy(TARGET(kX86))})
    .BindOutput("Mean", {LiteType::GetTensorTy(TARGET(kX86))})
    .BindOutput("Variance", {LiteType::GetTensorTy(TARGET(kX86))})
    .BindOutput("SavedMean", {LiteType::GetTensorTy(TARGET(kX86))})
    .BindOutput("SavedVariance", {LiteType::GetTensorTy(TARGET(kX86))})
    .Finalize();
