/* Copyright (c) 2016 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/fluid/operators/fake_dequantize_op.h"

namespace paddle {
namespace operators {

template <typename T>
__global__ void KeDequantize(const T* in, const T* scale, T max_range, int num,
                             T* out) {
  const int idx = threadIdx.x + blockIdx.x * blockDim.x;
  if (idx < num) {
    out[idx] = in[idx] * scale[0] / max_range;
  }
}

template <typename T>
struct DequantizeFunctor<platform::CUDADeviceContext, T> {
  void operator()(const platform::CUDADeviceContext& dev_ctx,
                  const framework::Tensor* in, const framework::Tensor* scale,
                  T max_range, framework::Tensor* out) {
    const T* in_data = in->data<T>();
    const T* scale_factor = scale->data<T>();
    T* out_data = out->mutable_data<T>(dev_ctx.GetPlace());

    int num = in->numel();
    int block = 512;
    int grid = (num + block - 1) / block;

    KeDequantize<T><<<grid, block, 0, dev_ctx.stream()>>>(
        in_data, scale_factor, max_range, num, out_data);
  }
};

template <typename T>
__global__ void DequantizeOneScaleQuantAxis0(const T* in, const T* scale,
                                             T max_range, int num, int channel,
                                             T* out) {
  int tid = threadIdx.x;
  int channel_size = num / channel;
  const T* in_c = in + blockIdx.x * channel_size;
  T* out_c = out + blockIdx.x * channel_size;
  for (int i = tid; i < channel_size; i += blockDim.x) {
    out_c[i] = in_c[i] * scale[blockIdx.x] / max_range;
  }
}

template <typename T>
__global__ void DequantizeOneScaleQuantAxis1(const T* in, const T* scale,
                                             T max_range, const int num,
                                             const int cin, const int cout,
                                             T* out) {
  int bid = blockIdx.x;
  T s = scale[bid % cout];

  int wh_size = num / (cin * cout);
  const T* in_current = in + bid * wh_size;
  T* out_current = out + bid * wh_size;

  for (int i = threadIdx.x; i < wh_size; i += blockDim.x) {
    out_current[i] = in_current[i] * s / max_range;
  }
}

template <typename T>
__global__ void DequantizeTwoScale(const T* in, const T* scale_one,
                                   const T* scale_two, T max_range, int num,
                                   int iter_size, int channel, T* out) {
  int tid = threadIdx.x;
  int channel_size = num / (iter_size * channel);
  int scale_index = blockIdx.x % channel;
  const T* in_c = in + blockIdx.x * channel_size;
  T* out_c = out + blockIdx.x * channel_size;
  for (int i = tid; i < channel_size; i += blockDim.x) {
    out_c[i] = in_c[i] * scale_one[scale_index] * scale_two[0] / max_range;
  }
}

template <typename T>
struct ChannelDequantizeFunctor<platform::CUDADeviceContext, T> {
  void operator()(const platform::CUDADeviceContext& dev_ctx,
                  const framework::Tensor* in, const framework::Tensor** scales,
                  const int scale_num, T max_range, const int quant_axis,
                  const int x_num_col_dims, framework::Tensor* out) {
    auto in_dims = in->dims();
    const T* in_data = in->data<T>();
    T* out_data = out->mutable_data<T>(dev_ctx.GetPlace());
    if (scale_num == 1) {
      int num = in->numel();
      const T* scale_factor = scales[0]->data<T>();
      if (quant_axis == 0) {
        int grid = in_dims[0];
        int block = 1024;
        DequantizeOneScaleQuantAxis0<T><<<grid, block, 0, dev_ctx.stream()>>>(
            in_data, scale_factor, max_range, num, in_dims[0], out_data);
      } else if (quant_axis == 1) {
        // Dequantize weight of Cin * Cout * W * H
        int grid = in_dims[0] * in_dims[1];
        int block = 1024;
        DequantizeOneScaleQuantAxis1<T><<<grid, block, 0, dev_ctx.stream()>>>(
            in_data, scale_factor, max_range, num, in_dims[0], in_dims[1],
            out_data);
      }
    } else if (scale_num == 2) {
      // Not need to consider quant_axis
      int num = in->numel();
      int iter_size = 1;
      for (int i = 0; i < x_num_col_dims; i++) {
        iter_size *= in->dims()[i];
      }
      int channel = in->dims()[x_num_col_dims];
      const T* scale_one = scales[0]->data<T>();
      const T* scale_two = scales[1]->data<T>();
      int block = 1024;
      int grid = iter_size * channel;
      DequantizeTwoScale<T><<<grid, block, 0, dev_ctx.stream()>>>(
          in_data, scale_one, scale_two, max_range, num, iter_size, channel,
          out_data);
    }
  }
};

template struct DequantizeFunctor<platform::CUDADeviceContext, float>;
template struct DequantizeFunctor<platform::CUDADeviceContext, double>;
template struct ChannelDequantizeFunctor<platform::CUDADeviceContext, float>;
template struct ChannelDequantizeFunctor<platform::CUDADeviceContext, double>;

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
using CUDA = paddle::platform::CUDADeviceContext;
REGISTER_OP_CUDA_KERNEL(fake_dequantize_max_abs,
                        ops::FakeDequantizeMaxAbsKernel<CUDA, float>,
                        ops::FakeDequantizeMaxAbsKernel<CUDA, double>);
REGISTER_OP_CUDA_KERNEL(
    fake_channel_wise_dequantize_max_abs,
    ops::FakeChannelWiseDequantizeMaxAbsKernel<CUDA, float>,
    ops::FakeChannelWiseDequantizeMaxAbsKernel<CUDA, double>);