/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.

   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/activation_op.h"
#include "paddle/fluid/platform/mkldnn_helper.h"

namespace paddle {
namespace operators {

using framework::DataLayout;
using framework::Tensor;
using mkldnn::memory;
using mkldnn::primitive;
using mkldnn::stream;
using platform::GetMKLDNNFormat;
using platform::MKLDNNDeviceContext;
using platform::to_void_cast;

namespace {
std::string gethash(const mkldnn::memory::dims &operand_dims,
                    const mkldnn::algorithm algorithm) {
  auto dim2str = [](const mkldnn::memory::dims &operand_dims) {
    std::string dstr = "";
    for (size_t i = 0; i < operand_dims.size(); ++i) {
      dstr += std::to_string(operand_dims[i]) + "-";
    }
    return dstr;
  };
  return dim2str(operand_dims) + std::to_string(algorithm);
}
}  // namespace

template <typename Functor>
class MKLDNNActivationKernel
    : public framework::OpKernel<typename Functor::ELEMENT_TYPE> {
 public:
  void Compute(const framework::ExecutionContext &ctx) const override {
    const auto *x = ctx.Input<Tensor>("X");
    PADDLE_ENFORCE(x->layout() == DataLayout::kMKLDNN &&
                       x->format() != memory::format::format_undef,
                   "Wrong layout/format set for Input x tensor");

    Functor functor;

    auto attrs = functor.GetAttrs();
    for (auto &attr : attrs) {
      *attr.second = ctx.Attr<float>(attr.first);
    }
    functor(ctx);
  }
};

template <typename Functor>
class MKLDNNActivationGradKernel
    : public framework::OpKernel<typename Functor::ELEMENT_TYPE> {
 public:
  void Compute(const framework::ExecutionContext &ctx) const override {
    const auto *diff_y = ctx.Input<Tensor>(framework::GradVarName("Out"));
    PADDLE_ENFORCE(diff_y->layout() == DataLayout::kMKLDNN &&
                       diff_y->format() != memory::format::format_undef,
                   "Wrong layout/format set for Input OutGrad tensor");

    PADDLE_ENFORCE(
        !ctx.Attr<bool>("is_test"),
        "is_test attribute should be set to False in training phase.");

    Functor functor;

    auto attrs = functor.GetAttrs();
    for (auto &attr : attrs) {
      *attr.second = ctx.Attr<float>(attr.first);
    }
    functor(ctx);
  }
};

template <typename T>
void eltwise_forward(const framework::ExecutionContext &ctx,
                     mkldnn::algorithm algorithm, const T alpha = 0,
                     const T beta = 0) {
  PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
                 "It must use CPUPlace.");
  auto &dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
  const auto &mkldnn_engine = dev_ctx.GetEngine();

  const auto *x = ctx.Input<Tensor>("X");
  auto *y = ctx.Output<Tensor>("Out");

  const T *x_data = x->data<T>();
  T *y_data = y->mutable_data<T>(ctx.GetPlace());

  PADDLE_ENFORCE(
      x->dims().size() == 2 || x->dims().size() == 3 || x->dims().size() == 4,
      "Input dim must be with 2, 3 or 4");

  std::vector<int> src_tz = framework::vectorize2int(x->dims());

  auto src_format =
      src_tz.size() == 2 ? mkldnn::memory::format::nc : x->format();

  const std::string key = gethash(src_tz, algorithm);
  const std::string key_src_data =
      key + ctx.op().Output("Out") + "@eltwise_fwd_src_data";
  const std::string key_src_layout =
      key + ctx.op().Output("Out") + "@eltwise_fwd_src_layout";
  const std::string key_with_layout = key + std::to_string(src_format);
  const std::string key_src_mem = key_with_layout + "@eltwise_fwd_src_mem";
  const std::string key_dst_mem = key_with_layout + "@eltwise_fwd_dst_mem";
  const std::string key_fwd = key_with_layout + "@eltwise_fwd";
  const std::string key_fwd_pd = key_with_layout + "@eltwise_fwd_pd";

  bool is_test = ctx.Attr<bool>("is_test");

  // save input data and layout to be referred in backward path
  auto p_src_data = std::make_shared<const T *>(x_data);
  auto p_src_layout = std::make_shared<memory::format>(src_format);
  if (!is_test) {
    dev_ctx.SetBlob(key_src_data, p_src_data);
    dev_ctx.SetBlob(key_src_layout, p_src_layout);
  }

  auto p_fwd = std::static_pointer_cast<mkldnn::eltwise_forward>(
      dev_ctx.GetBlob(key_fwd));

  std::shared_ptr<memory> dst_memory;

  if (p_fwd == nullptr) {
    // create mkldnn memory for input X
    auto src_md = platform::MKLDNNMemDesc(
        src_tz, platform::MKLDNNGetDataType<T>(), src_format);
    auto src_memory = std::shared_ptr<memory>(
        new memory({src_md, mkldnn_engine}, to_void_cast(x_data)));
    // save src_memory to be referred in backward path
    dev_ctx.SetBlob(key_src_mem, src_memory);

    // create primitive descriptor for activation forward and save it
    auto mkldnn_forward_prop_kind = is_test
                                        ? mkldnn::prop_kind::forward_inference
                                        : mkldnn::prop_kind::forward_training;
    auto forward_desc = mkldnn::eltwise_forward::desc(
        mkldnn_forward_prop_kind, algorithm,
        src_memory->get_primitive_desc().desc(), alpha, beta);
    auto forward_pd = std::make_shared<mkldnn::eltwise_forward::primitive_desc>(
        forward_desc, mkldnn_engine);

    // save prim desc into global device context to be referred in backward path
    if (!is_test) dev_ctx.SetBlob(key_fwd_pd, forward_pd);

    // create mkldnn memory for output y
    dst_memory =
        std::make_shared<memory>(forward_pd->dst_primitive_desc(), y_data);

    dev_ctx.SetBlob(key_dst_mem, dst_memory);

    // create activation primitive
    p_fwd = std::make_shared<mkldnn::eltwise_forward>(*forward_pd, *src_memory,
                                                      *dst_memory);
    dev_ctx.SetBlob(key_fwd, p_fwd);
  } else {
    // primitives already exist
    auto src_memory =
        std::static_pointer_cast<mkldnn::memory>(dev_ctx.GetBlob(key_src_mem));
    PADDLE_ENFORCE(src_memory != nullptr,
                   "Fail to find eltwise src_memory in device context.");
    dst_memory =
        std::static_pointer_cast<mkldnn::memory>(dev_ctx.GetBlob(key_dst_mem));
    PADDLE_ENFORCE(dst_memory != nullptr,
                   "Fail to find eltwise dst_memory in device context.");

    src_memory->set_data_handle(platform::to_void_cast(x_data));
    dst_memory->set_data_handle(y_data);
  }

  // push primitive to stream and wait until it's executed
  std::vector<primitive> pipeline;
  pipeline.push_back(*p_fwd);
  stream(stream::kind::eager).submit(pipeline).wait();

  y->set_layout(DataLayout::kMKLDNN);
  y->set_format(GetMKLDNNFormat(*dst_memory));
}

template <typename T>
void eltwise_grad(const framework::ExecutionContext &ctx,
                  mkldnn::algorithm algorithm, const T alpha = 0,
                  const T beta = 0) {
  auto &dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
  const auto &mkldnn_engine = dev_ctx.GetEngine();

  const auto *diff_y = ctx.Input<Tensor>(framework::GradVarName("Out"));
  auto *diff_x = ctx.Output<Tensor>(framework::GradVarName("X"));

  const T *diff_y_data = diff_y->data<T>();
  T *diff_x_data = diff_x->mutable_data<T>(ctx.GetPlace());

  std::vector<int> diff_dst_tz = framework::vectorize2int(diff_y->dims());

  auto diff_y_format =
      diff_dst_tz.size() == 2 ? mkldnn::memory::format::nc : diff_y->format();

  const std::string key = gethash(diff_dst_tz, algorithm);
  const std::string key_src_data =
      key + ctx.op().Input("Out") + "@eltwise_fwd_src_data";
  const std::string key_src_layout =
      key + ctx.op().Input("Out") + "@eltwise_fwd_src_layout";
  const auto p_src_layout =
      std::static_pointer_cast<memory::format>(dev_ctx.GetBlob(key_src_layout));
  const std::string key_src_mem =
      key + std::to_string(*p_src_layout) + "@eltwise_fwd_src_mem";
  const std::string key_fwd_pd =
      key + std::to_string(*p_src_layout) + "@eltwise_fwd_pd";
  const std::string key_with_layouts =
      key + std::to_string(*p_src_layout) + "-" + std::to_string(diff_y_format);
  const std::string key_diff_src_mem =
      key_with_layouts + "@eltwise_diff_src_mem";
  const std::string key_diff_dst_mem =
      key_with_layouts + "@eltwise_diff_dst_mem";
  const std::string key_grad = key_with_layouts + "@eltwise_grad";

  const auto p_src_data =
      std::static_pointer_cast<T *>(dev_ctx.GetBlob(key_src_data));

  auto src_memory =
      std::static_pointer_cast<mkldnn::memory>(dev_ctx.GetBlob(key_src_mem));
  PADDLE_ENFORCE(src_memory != nullptr,
                 "Fail to find src_memory in device context");
  src_memory->set_data_handle(*p_src_data.get());

  std::shared_ptr<memory> diff_src_memory;

  auto p_grad = std::static_pointer_cast<mkldnn::eltwise_backward>(
      dev_ctx.GetBlob(key_grad));

  if (p_grad == nullptr) {
    // create mkldnn memory for input diff_y
    auto diff_dst_md = platform::MKLDNNMemDesc(
        diff_dst_tz, platform::MKLDNNGetDataType<T>(), diff_y_format);
    auto diff_dst_memory = std::shared_ptr<memory>(
        new memory({diff_dst_md, mkldnn_engine}, to_void_cast(diff_y_data)));
    dev_ctx.SetBlob(key_diff_dst_mem, diff_dst_memory);

    // retrieve eltwise primitive desc from device context
    auto forward_pd =
        std::static_pointer_cast<mkldnn::eltwise_forward::primitive_desc>(
            dev_ctx.GetBlob(key_fwd_pd));
    PADDLE_ENFORCE(forward_pd != nullptr,
                   "Fail to find eltwise_fwd_pd in device context");

    // ceate primitive descriptor for activation backward
    auto backward_desc = mkldnn::eltwise_backward::desc(
        algorithm, diff_dst_memory->get_primitive_desc().desc(),
        src_memory->get_primitive_desc().desc(), alpha, beta);
    auto backward_pd = mkldnn::eltwise_backward::primitive_desc(
        backward_desc, mkldnn_engine, *forward_pd);

    // create mkldnn memory for output diff_src
    diff_src_memory = std::make_shared<memory>(
        backward_pd.diff_src_primitive_desc(), diff_x_data);
    dev_ctx.SetBlob(key_diff_src_mem, diff_src_memory);

    // create activation backward primitive
    p_grad = std::make_shared<mkldnn::eltwise_backward>(
        backward_pd, *src_memory, *diff_dst_memory, *diff_src_memory);
    dev_ctx.SetBlob(key_grad, p_grad);
  } else {
    // primitives already exist
    diff_src_memory = std::static_pointer_cast<mkldnn::memory>(
        dev_ctx.GetBlob(key_diff_src_mem));
    auto diff_dst_memory = std::static_pointer_cast<mkldnn::memory>(
        dev_ctx.GetBlob(key_diff_dst_mem));

    diff_src_memory->set_data_handle(
        platform::to_void_reinterpret_cast(diff_x_data));
    diff_dst_memory->set_data_handle(
        platform::to_void_reinterpret_cast(diff_y_data));
  }

  // push primitive to stream and wait until it's executed
  std::vector<primitive> pipeline;
  pipeline.push_back(*p_grad);
  stream(stream::kind::eager).submit(pipeline).wait();

  diff_x->set_layout(DataLayout::kMKLDNN);
  diff_x->set_format(GetMKLDNNFormat(*diff_src_memory));
}

template <typename T, mkldnn::algorithm algorithm>
struct MKLDNNActivationFunc : public BaseActivationFunctor<T> {
  void operator()(const framework::ExecutionContext &ctx) const {
    eltwise_forward<T>(ctx, algorithm);
  }
};

template <typename T, mkldnn::algorithm algorithm>
struct MKLDNNActivationGradFunc : public BaseActivationFunctor<T> {
  void operator()(const framework::ExecutionContext &ctx) const {
    eltwise_grad<T>(ctx, algorithm);
  }
};

template <typename T>
using ReluMKLDNNFunctor =
    MKLDNNActivationFunc<T, mkldnn::algorithm::eltwise_relu>;

template <typename T>
using TanhMKLDNNFunctor =
    MKLDNNActivationFunc<T, mkldnn::algorithm::eltwise_tanh>;

template <typename T>
using SqrtMKLDNNFunctor =
    MKLDNNActivationFunc<T, mkldnn::algorithm::eltwise_sqrt>;

template <typename T>
using AbsMKLDNNFunctor =
    MKLDNNActivationFunc<T, mkldnn::algorithm::eltwise_abs>;

template <typename T>
using ReluMKLDNNGradFunctor =
    MKLDNNActivationGradFunc<T, mkldnn::algorithm::eltwise_relu>;

template <typename T>
using TanhMKLDNNGradFunctor =
    MKLDNNActivationGradFunc<T, mkldnn::algorithm::eltwise_tanh>;

template <typename T>
using SqrtMKLDNNGradFunctor =
    MKLDNNActivationGradFunc<T, mkldnn::algorithm::eltwise_sqrt>;

template <typename T>
using AbsMKLDNNGradFunctor =
    MKLDNNActivationGradFunc<T, mkldnn::algorithm::eltwise_abs>;
}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;

#define REGISTER_ACTIVATION_MKLDNN_KERNEL(act_type, functor, grad_functor) \
  REGISTER_OP_KERNEL(act_type, MKLDNN, ::paddle::platform::CPUPlace,       \
                     ops::MKLDNNActivationKernel<ops::functor<float>>);    \
  REGISTER_OP_KERNEL(                                                      \
      act_type##_grad, MKLDNN, ::paddle::platform::CPUPlace,               \
      ops::MKLDNNActivationGradKernel<ops::grad_functor<float>>);

#define FOR_EACH_MKLDNN_KERNEL_FUNCTOR(__macro)            \
  __macro(relu, ReluMKLDNNFunctor, ReluMKLDNNGradFunctor); \
  __macro(tanh, TanhMKLDNNFunctor, TanhMKLDNNGradFunctor); \
  __macro(sqrt, SqrtMKLDNNFunctor, SqrtMKLDNNGradFunctor); \
  __macro(abs, AbsMKLDNNFunctor, AbsMKLDNNGradFunctor);

FOR_EACH_MKLDNN_KERNEL_FUNCTOR(REGISTER_ACTIVATION_MKLDNN_KERNEL);