// Copyright (c) 2022 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/kernels/transpose_kernel.h"
#include "paddle/phi/backends/onednn/onednn_reuse.h"
#include "paddle/phi/core/kernel_registry.h"

namespace phi {

void SetOutMemDescWithLogicalLayoutFusesSupport(
    const OneDNNContext& dev_ctx,
    phi::DenseTensor* out,
    const dnnl::memory::desc& out_md) {
  const auto fused_unsqueeze2_axes =
      dev_ctx.HasDnnAttr("fused_unsqueeze2_axes")
          ? PADDLE_GET_CONST(std::vector<int>,
                             dev_ctx.GetDnnAttr("fused_unsqueeze2_axes"))
          : std::vector<int>();
  const auto fused_reshape2_shape =
      dev_ctx.HasDnnAttr("fused_reshape2_shape")
          ? PADDLE_GET_CONST(std::vector<int>,
                             dev_ctx.GetDnnAttr("fused_reshape2_shape"))
          : std::vector<int>();
  const auto fused_squeeze2_axes =
      dev_ctx.HasDnnAttr("fused_squeeze2_axes")
          ? PADDLE_GET_CONST(std::vector<int>,
                             dev_ctx.GetDnnAttr("fused_squeeze2_axes"))
          : std::vector<int>();

  if (!fused_unsqueeze2_axes.empty()) {
    funcs::SetOutMemDescWithUnsqueeze2FuseSupport(
        fused_unsqueeze2_axes, out, out_md);
  } else if (!fused_reshape2_shape.empty()) {
    funcs::SetOutMemDescWithReshape2FuseSupport(
        fused_reshape2_shape, out, out_md);
  } else if (!fused_squeeze2_axes.empty()) {
    out->set_mem_desc(out_md);
    out->Resize(make_ddim(out_md.dims()));
  } else {
    out->set_mem_desc(out_md);
  }
}

void SetInMemDescWithSqueeze2FuseSupport(
    const std::vector<int> fused_squeeze2_axes,
    DenseTensor* in,
    const dnnl::memory::desc& in_md) {
  const std::set<int64_t> squeeze2_axes_set(fused_squeeze2_axes.begin(),
                                            fused_squeeze2_axes.end());
  const std::vector<int64_t>& x_vec_dims = in_md.dims();
  std::vector<int64_t> squeezed_op_tz(
      x_vec_dims.size() - fused_squeeze2_axes.size(), 0);

  int j = 0;
  for (size_t i = 0; i < x_vec_dims.size(); ++i) {
    if (squeeze2_axes_set.count(i) ||
        squeeze2_axes_set.count(i - x_vec_dims.size())) {
      PADDLE_ENFORCE_EQ(
          x_vec_dims[i],
          1,
          errors::InvalidArgument(
              "Squeeze2 input dim %d should be equal to one, but get %d.",
              i,
              x_vec_dims[i]));
      continue;
    }
    squeezed_op_tz[j++] = x_vec_dims[i];
  }

  in->set_mem_desc(in_md.reshape(squeezed_op_tz));
  in->Resize(make_ddim(squeezed_op_tz));
}

void SetInMemDescWithLogicalLayoutFusesSupport(
    const OneDNNContext& dev_ctx,
    DenseTensor* in,
    const dnnl::memory::desc& in_md) {
  const auto fused_squeeze2_axes =
      dev_ctx.HasDnnAttr("fused_squeeze2_axes")
          ? PADDLE_GET_CONST(std::vector<int>,
                             dev_ctx.GetDnnAttr("fused_squeeze2_axes"))
          : std::vector<int>();
  if (fused_squeeze2_axes.empty()) {
    in->set_mem_desc(in_md);
    in->Resize(make_ddim(in_md.dims()));
  } else {
    SetInMemDescWithSqueeze2FuseSupport(fused_squeeze2_axes, in, in_md);
  }
}

template <typename T, typename Context>
void TransposeKernel(const Context& dev_ctx,
                     const DenseTensor& x,
                     const std::vector<int>& axis,
                     DenseTensor* out) {
  // Here we need to match dims to paddle layout
  // as we are producing non-oneDNN result
  auto x_dims = x.dims();
  if ((x_dims.size() >= 3) &&
      (phi::OneDNNContext::tls().get_cur_paddle_data_layout() ==
       phi::DataLayout::kNHWC)) {
    int axis_size = axis.size();
    std::vector<int> formated_axis = axis;
    std::vector<int> count(axis_size, 0);
    for (int i = 0; i < axis_size; i++) {
      if (axis[i] < 0) {
        formated_axis[i] = axis[i] + axis_size;
      }
    }
    auto dims = phi::vectorize<int>(x_dims);

    std::rotate(dims.begin() + 1, dims.begin() + 2, dims.end());
    x_dims = x_dims.reshape(dims);
    VLOG(3)
        << "Rotating Shape in Transpose from: kMKLDNN to: kNHWC output_shape";

    phi::DDim out_dims(x_dims);
    for (size_t i = 0; i < axis.size(); i++) {
      out_dims[i] = x_dims[formated_axis[i]];
    }
    out->Resize(out_dims);
  }

  PADDLE_ENFORCE_EQ(
      dev_ctx.GetPlace().GetType(),
      AllocationType::CPU,
      errors::PreconditionNotMet("oneDNN Transpose kernel must use CPUPlace"));

  SetInMemDescWithLogicalLayoutFusesSupport(
      dev_ctx, const_cast<DenseTensor*>(&x), x.mem_desc());

  if (axis.size() == 1 || axis.size() == 0) {
    Copy<Context>(dev_ctx, x, x.place(), false, out);
    out->set_mem_desc(x.mem_desc());
    return;
  }

  auto x_vec_dims = vectorize(x.dims());
  auto x_type = funcs::ToOneDNNDataType(x.dtype());

  dnnl::primitive_attr attrs;
  const int32_t mask = 0;
  const auto quantization_scale =
      dev_ctx.HasDnnAttr("scale")
          ? PADDLE_GET_CONST(float, dev_ctx.GetDnnAttr("scale"))
          : 1.0f;
  const auto quantization_shift =
      dev_ctx.HasDnnAttr("shift")
          ? PADDLE_GET_CONST(float, dev_ctx.GetDnnAttr("shift"))
          : 0.0f;
  const auto output_data_type =
      dev_ctx.HasDnnAttr("output_data_type")
          ? PADDLE_GET_CONST(std::string,
                             dev_ctx.GetDnnAttr("output_data_type"))
          : "";
  const bool with_scale = quantization_scale != 1.0f;
  const bool with_shift = quantization_shift != 0.0f;

  if (with_scale) {
    attrs.set_output_scales(mask, {quantization_scale});
  }

  if (with_shift) {
    auto dst = output_data_type == "fp32" ? DNNL_ARG_SRC : DNNL_ARG_DST;
    attrs.set_zero_points(
        dst, mask, {static_cast<int32_t>(quantization_shift)});
  }

  DataType out_dtype;
  if (output_data_type == "bf16") {
    out_dtype = DataType::BFLOAT16;
  } else if (output_data_type == "int8") {
    out_dtype = DataType::INT8;
  } else if (output_data_type == "uint8") {
    out_dtype = DataType::UINT8;
  } else if (output_data_type == "fp32") {
    out_dtype = DataType::FLOAT32;
  } else {
    out_dtype = x.dtype();
  }
  auto out_type = phi::funcs::ToOneDNNDataType(out_dtype);

  funcs::ReorderOneDNNHandler reorder_handler(
      x_vec_dims, x.dtype(), x_type, out_dtype, out_type, dev_ctx.GetEngine());

  auto reorder_src_memory_p = reorder_handler.AcquireSrcMemory(
      x.mem_desc(), funcs::to_void_cast(x.data<T>()));

  auto fake_strides = funcs::FakeTransposeStrides(x_vec_dims, axis);
  auto dst_md = dnnl::memory::desc(x_vec_dims, out_type, fake_strides);
  auto reorder_dst_memory_p =
      reorder_handler.AcquireDstMemory(out, dst_md, dev_ctx.GetPlace());

  auto reorder_p = reorder_handler.AcquireReorder(
      reorder_dst_memory_p, reorder_src_memory_p, attrs);

  auto& astream = OneDNNContext::tls().get_stream();
  reorder_p->execute(astream, *reorder_src_memory_p, *reorder_dst_memory_p);
  astream.wait();

  // it is needed because oneDNN's permute axis understand axes order in
  // different way PaddlePaddle's transpose
  std::vector<int> permute_axis(axis.size());
  for (size_t i = 0; i < axis.size(); ++i) {
    permute_axis[axis[i]] = i;
  }

  SetOutMemDescWithLogicalLayoutFusesSupport(
      dev_ctx,
      out,
      reorder_dst_memory_p->get_desc().permute_axes(
          funcs::TransposeToPermuteAxes(axis)));
}
}  // namespace phi

PD_REGISTER_KERNEL(transpose,
                   OneDNN,
                   ONEDNN,
                   phi::TransposeKernel,
                   float,
                   uint8_t,
                   int8_t,
                   phi::dtype::bfloat16) {}