//   Copyright (c) 2021 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/pten/kernels/flatten_kernel.h"
#include "paddle/pten/backends/all_context.h"
#include "paddle/pten/core/kernel_registry.h"
#include "paddle/pten/infermeta/unary.h"
#include "paddle/pten/kernels/copy_kernel.h"
#include "paddle/pten/kernels/funcs/common_shape.h"

namespace pten {

template <typename T, typename Context>
void Flatten(const Context& dev_ctx,
             const DenseTensor& x,
             int start_axis,
             int stop_axis,
             DenseTensor* out) {
  auto out_dims = out->dims();
  pten::Copy(dev_ctx, x, false, out);
  out->Resize(out_dims);
}

// TODO(yuanrisheng): this kernel is for training and xshape is a Intermediate
// Output Tensor，
// is there a more flexible way to deal with this case?
template <typename T, typename Context>
void FlattenWithXShape(const Context& dev_ctx,
                       const DenseTensor& x,
                       int start_axis,
                       int stop_axis,
                       DenseTensor* out,
                       DenseTensor* xshape) {
  Flatten<T, Context>(dev_ctx, x, start_axis, stop_axis, out);
  funcs::SetXShape(x, xshape);
}

}  // namespace pten

PT_REGISTER_CTX_KERNEL(flatten,
                       CPU,
                       ALL_LAYOUT,
                       pten::Flatten,
                       float,
                       double,
                       uint8_t,
                       int8_t,
                       int,
                       int64_t) {}

PT_REGISTER_CTX_KERNEL(flatten_with_xshape,
                       CPU,
                       ALL_LAYOUT,
                       pten::FlattenWithXShape,
                       float,
                       double,
                       uint8_t,
                       int8_t,
                       int,
                       int64_t) {}

#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
PT_REGISTER_CTX_KERNEL(flatten,
                       GPU,
                       ALL_LAYOUT,
                       pten::Flatten,
                       float,
                       paddle::platform::float16,
                       double,
                       uint8_t,
                       int8_t,
                       int,
                       int64_t) {}

PT_REGISTER_CTX_KERNEL(flatten_with_xshape,
                       GPU,
                       ALL_LAYOUT,
                       pten::FlattenWithXShape,
                       float,
                       paddle::platform::float16,
                       double,
                       uint8_t,
                       int8_t,
                       int,
                       int64_t) {}
#endif

#ifdef PADDLE_WITH_XPU
PT_REGISTER_CTX_KERNEL(flatten,
                       XPU,
                       ALL_LAYOUT,
                       pten::Flatten,
                       float,
                       paddle::platform::float16,
                       double,
                       uint8_t,
                       int8_t,
                       int,
                       int64_t) {}

PT_REGISTER_CTX_KERNEL(flatten_with_xshape,
                       XPU,
                       ALL_LAYOUT,
                       pten::FlattenWithXShape,
                       float,
                       paddle::platform::float16,
                       double,
                       uint8_t,
                       int8_t,
                       int,
                       int64_t) {}
#endif
