// 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/backends/onednn/onednn_reuse.h"
#include "paddle/phi/core/kernel_registry.h"

namespace phi {
namespace fusion {

template <typename T, dnnl::algorithm BINARY_OP>
void FusedElementwiseKernel(const OneDNNContext& dev_ctx,
                            const DenseTensor& x,
                            const DenseTensor& y,
                            const int axis,
                            const std::string& fuse_activation,
                            const float fuse_alpha,
                            const float fuse_beta,
                            const float fused_output_scale,
                            const std::vector<int>& fused_unsqueeze2_axes,
                            const float scale_x,
                            const float scale_y,
                            const float scale_out,
                            DenseTensor* out) {
  const auto& onednn_engine = dev_ctx.GetEngine();

  dnnl::post_ops post_operations;
  funcs::AppendActivation(
      dev_ctx, post_operations, 1.0f, fuse_activation, fuse_alpha, fuse_beta);
  if (fused_output_scale != 1.0) {
    post_operations.append_eltwise(
        1.0, dnnl::algorithm::eltwise_linear, fused_output_scale, 0.0f);
  }

  auto* non_const_x = &x;
  auto* non_const_y = &y;

  funcs::BinaryOneDNNHandler<T> handler(BINARY_OP,
                                        axis,
                                        onednn_engine,
                                        dev_ctx.GetPlace(),
                                        non_const_x,
                                        non_const_y,
                                        out,
                                        scale_x,
                                        scale_y,
                                        scale_out,
                                        true,
                                        post_operations);

  // oneDNN's binary is optimized for broadcasting y into x, so in other case
  // we have to swap tensors to achieve optimal performance
  if (x.numel() < y.numel()) {
    std::swap(non_const_x, non_const_y);
  }

  const auto src_x_memory = handler.AcquireSrcMemory(non_const_x);
  const auto src_y_memory = handler.AcquireSecondSrcMemory(non_const_y);
  // For Inplace src and dst should be the same memory object.
  // So x should share buffer with z. But UT mechanics is testing inplace
  // execution for this op not checking that x can be bradcasted to match in
  // shape y tensor.
  // This is wrong as when x is to be broadcasted then z(out) will match the
  // shape of y which is bigger than x. Hence if x is smaller in shape than z
  // and they share a buffer (of shape x) then this buffer is not big enough
  // to hold result of elementwise operation.
  const bool reuse_x_memory = non_const_x->numel() == out->numel() &&
                              non_const_x->IsSharedBufferWith(*out);
  std::shared_ptr<dnnl::memory> dst_memory;

  if (reuse_x_memory) {
    dst_memory = src_x_memory;
    // NOTE(chenfeiyu): when the output reuses memory from other tensor rather
    // than allocate its own, it's still need to take care of its data type.
    // Unfortunately, paddle's operator only infers the output' shape, but not
    // the data type. Alloc<T> takes care of allocation and data type
    // normally, but if the memory is already allocated and there is no need
    // to re-allocate, it just set the data type. So this it added there to
    // get the right data type.
    dev_ctx.template Alloc<T>(out);
  } else {
    dst_memory = handler.AcquireDstMemory(out);
  }

  const auto binary_prim = handler.AcquireForwardPrimitive();

  auto& astream = OneDNNContext::tls().get_stream();

  const std::unordered_map<int, dnnl::memory> args = {
      {DNNL_ARG_SRC_0, *src_x_memory},
      {DNNL_ARG_SRC_1, *src_y_memory},
      {DNNL_ARG_DST, *dst_memory}};

  binary_prim->execute(astream, args);
  astream.wait();

  auto out_md = dst_memory->get_desc();

  if (handler.use_broadcasting_hack) {
    auto dims = out_md.dims();
    dims.insert(dims.begin(), non_const_x->dims()[0]);
    dims[1] /= dims[0];
    out_md = out_md.reshape(dims);
  }

  if (fused_unsqueeze2_axes.empty()) {
    out->set_mem_desc(out_md);
  } else {
    funcs::SetOutMemDescWithUnsqueeze2FuseSupport(
        fused_unsqueeze2_axes, out, out_md);
  }
}

#define DEFINE_ONEDNN_ELEMENTWISE_KERNEL(name, algorithm)          \
  template <typename T, typename Context>                          \
  void name##Kernel(const Context& dev_ctx,                        \
                    const DenseTensor& x,                          \
                    const DenseTensor& y,                          \
                    const int axis,                                \
                    const std::string& fuse_activation,            \
                    const float fuse_alpha,                        \
                    const float fuse_beta,                         \
                    const float fused_output_scale,                \
                    const std::vector<int>& fused_unsqueeze2_axes, \
                    const float scale_x,                           \
                    const float scale_y,                           \
                    const float scale_out,                         \
                    DenseTensor* out) {                            \
    FusedElementwiseKernel<T, algorithm>(dev_ctx,                  \
                                         x,                        \
                                         y,                        \
                                         axis,                     \
                                         fuse_activation,          \
                                         fuse_alpha,               \
                                         fuse_beta,                \
                                         fused_output_scale,       \
                                         fused_unsqueeze2_axes,    \
                                         scale_x,                  \
                                         scale_y,                  \
                                         scale_out,                \
                                         out);                     \
  }

DEFINE_ONEDNN_ELEMENTWISE_KERNEL(FusedMultiply, dnnl::algorithm::binary_mul)
DEFINE_ONEDNN_ELEMENTWISE_KERNEL(FusedDivide, dnnl::algorithm::binary_div)

}  // namespace fusion
}  // namespace phi

PD_REGISTER_KERNEL(fused_elementwise_mul,
                   OneDNN,
                   ONEDNN,
                   phi::fusion::FusedMultiplyKernel,
                   float,
                   phi::dtype::bfloat16,
                   int8_t,
                   uint8_t) {}

PD_REGISTER_KERNEL(fused_elementwise_div,
                   OneDNN,
                   ONEDNN,
                   phi::fusion::FusedDivideKernel,
                   float,
                   phi::dtype::bfloat16,
                   int8_t,
                   uint8_t) {}
