// 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 <string>

#include "paddle/phi/kernels/quantize_linear_kernel.h"

#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/common/type_traits.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/impl/quantize_linear_impl.h"

namespace phi {

template <typename T>
__global__ void KeDequantize(
    const T* in, const T* scale, T max_range, int64_t num, T* out) {
  int64_t idx = threadIdx.x + blockIdx.x * blockDim.x;
  for (int64_t i = idx; i < num; i += blockDim.x * gridDim.x) {
    out[i] = in[i] * scale[0] / max_range;
  }
}

template <typename T>
__global__ void DequantizeOneScaleQuantAxisN(const T* in,
                                             const T* scale,
                                             const T max_range,
                                             const int64_t num,
                                             const int n_scales,
                                             const int quant_stride,
                                             T* out) {
  int64_t idx = blockDim.x * blockIdx.x + threadIdx.x;
  for (int64_t i = idx; i < num; i += blockDim.x * gridDim.x) {
    T s = scale[(i / quant_stride) % n_scales];
    out[i] = in[i] * s / max_range;
  }
}

template <typename T>
struct ChannelDequantizeFunctorV2<phi::GPUContext, T> {
  void operator()(const phi::GPUContext& dev_ctx,
                  const phi::DenseTensor* in,
                  const phi::DenseTensor* scale,
                  T max_range,
                  const int quant_axis,
                  phi::DenseTensor* out) {
    auto in_dims = in->dims();
    const T* in_data = in->data<T>();
    T* out_data = dev_ctx.template Alloc<T>(out, out->numel() * sizeof(T));
    int64_t num = in->numel();
    const T* scale_factor = scale->data<T>();
    int64_t block_size = std::min(
        num, static_cast<int64_t>(dev_ctx.GetMaxThreadsPerBlock() / 4));
    int64_t max_threads =
        dev_ctx.GetMaxPhysicalThreadCount();  // SM * block_per_SM
    const int64_t max_blocks =
        std::max(((max_threads - 1) / block_size + 1), static_cast<int64_t>(1));
    const int64_t grid_size =
        std::min(max_blocks, (num + block_size - 1) / block_size);

    int quant_stride = 1;
    for (int i = quant_axis + 1; i < in_dims.size(); i++) {
      quant_stride *= in_dims[i];
    }

    DequantizeOneScaleQuantAxisN<T>
        <<<grid_size, block_size, 0, dev_ctx.stream()>>>(in_data,
                                                         scale_factor,
                                                         max_range,
                                                         num,
                                                         in_dims[quant_axis],
                                                         quant_stride,
                                                         out_data);
  }
};

template <typename T>
struct DequantizeFunctor<phi::GPUContext, T> {
  void operator()(const phi::GPUContext& dev_ctx,
                  const phi::DenseTensor* in,
                  const phi::DenseTensor* scale,
                  T max_range,
                  phi::DenseTensor* out) {
    const T* in_data = in->data<T>();
    const T* scale_factor = scale->data<T>();
    T* out_data = dev_ctx.template Alloc<T>(out, out->numel() * sizeof(T));

    int64_t num = in->numel();
    int64_t block_size = std::min(
        num, static_cast<int64_t>(dev_ctx.GetMaxThreadsPerBlock() / 4));
    int64_t max_threads =
        dev_ctx.GetMaxPhysicalThreadCount();  // SM * block_per_SM
    const int64_t max_blocks =
        std::max(((max_threads - 1) / block_size + 1), static_cast<int64_t>(1));
    const int64_t grid_size =
        std::min(max_blocks, (num + block_size - 1) / block_size);
    KeDequantize<T><<<grid_size, block_size, 0, dev_ctx.stream()>>>(
        in_data, scale_factor, max_range, num, out_data);
  }
};

template struct DequantizeFunctor<phi::GPUContext, phi::dtype::float16>;
template struct DequantizeFunctor<phi::GPUContext, float>;
template struct DequantizeFunctor<phi::GPUContext, double>;
template struct ChannelDequantizeFunctorV2<phi::GPUContext, float16>;
template struct ChannelDequantizeFunctorV2<phi::GPUContext, float>;
template struct ChannelDequantizeFunctorV2<phi::GPUContext, double>;
}  // namespace phi

PD_REGISTER_KERNEL(dequantize_linear,
                   GPU,
                   ALL_LAYOUT,
                   phi::DeQuantizeLinearKernel,
                   float,
                   int8_t,
                   double,
                   phi::dtype::float16) {
  kernel->OutputAt(0).SetDataType(phi::DataType::UNDEFINED);
}
