/* 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/pten/core/dense_tensor.h"

#include "paddle/pten/common/bfloat16.h"
#include "paddle/pten/common/complex.h"
#include "paddle/pten/common/float16.h"

#include "paddle/pten/api/lib/utils/storage.h"
#include "paddle/pten/core/compat/convert_utils.h"

namespace pten {
/* --------------------------- */
/*   From framework::Tensor    */
/* --------------------------- */
DenseTensor::DenseTensor() {
  meta_.dtype = paddle::experimental::DataType::FLOAT32;
  meta_.offset = 0;
}

DenseTensor::DenseTensor(paddle::framework::proto::VarType::Type dtype) {
  meta_.dtype = TransToPtenDataType(dtype);
  meta_.offset = 0;
}

size_t DenseTensor::memory_size() const {
  return holder_ == nullptr ? 0UL : holder_->size() - meta_.offset;
}

void DenseTensor::check_memory_size() const {
  PADDLE_ENFORCE_NOT_NULL(holder_,
                          paddle::platform::errors::PreconditionNotMet(
                              "Tensor holds no memory. "
                              "Call Tensor::mutable_data firstly."));
  PADDLE_ENFORCE_LE(
      numel() * SizeOf(dtype()),
      memory_size(),
      paddle::platform::errors::PreconditionNotMet(
          "Tensor's dimension is out of bound."
          "Tensor's dimension must be equal or less than the size of its "
          "memory."
          "But received Tensor's dimension is d%, memory's size is %d.",
          numel() * SizeOf(dtype()),
          memory_size()));
}

const paddle::platform::Place& DenseTensor::place() const {
  PADDLE_ENFORCE_NOT_NULL(
      holder_,
      paddle::platform::errors::PreconditionNotMet(
          "Tensor not initialized yet when DenseTensor::place() is called."));
  return holder_->place();
}

paddle::framework::proto::VarType::Type DenseTensor::type() const {
  return TransToProtoVarType(meta_.dtype);
}

paddle::framework::proto::VarType::Type DenseTensor::saved_type() const {
  return TransToProtoVarType(meta_.dtype);
}

void DenseTensor::set_layout(const paddle::framework::DataLayout layout) {
  meta_.layout = layout;
}

// Note: When you reset holder, you need to ensure the offset is correct
void DenseTensor::ResetHolder(const std::shared_ptr<pten::Allocation>& holder) {
  if (holder_) {
    // TODO(zyfncg): The change of static_cast<> in check will recover back
    // when SetAllocationForOutputTenosr is deleted.
    // Now the numel() may return -1, and will cast to a very large number when
    // compare with a data with unsigned long type, this will make checking
    // failed, so it's a temporary solution to deal with this problem.
    PADDLE_ENFORCE_LE(
        numel() * static_cast<int64_t>(SizeOf(dtype())) +
            static_cast<int64_t>(meta_.offset),
        static_cast<int64_t>(holder->size()),
        paddle::platform::errors::InvalidArgument(
            "The size of Holder is not enough to store the Tensor."));
  }
  holder_ = holder;
}

void DenseTensor::ResetHolderWithType(
    const std::shared_ptr<pten::Allocation>& holder,
    paddle::framework::proto::VarType::Type type) {
  set_type(type);
  ResetHolder(holder);
}

void DenseTensor::set_type(paddle::framework::proto::VarType::Type type) {
  meta_.dtype = TransToPtenDataType(type);
}

void* DenseTensor::mutable_data(const paddle::platform::Place& place,
                                paddle::framework::proto::VarType::Type type,
                                size_t requested_size) {
  set_type(type);
  PADDLE_ENFORCE_GE(
      numel(),
      0,
      paddle::platform::errors::PreconditionNotMet(
          "The Tensor's element number must be equal or greater than zero. "
          "The Tensor's shape is [",
          dims(),
          "] now"));
  size_t size = numel() * SizeOf(dtype());
  if (requested_size && (requested_size > size)) {
    size = requested_size;
  }

  /* some versions of boost::variant don't have operator!= */
  if (holder_ == nullptr || !(holder_->place() == place) ||
      holder_->size() < size + meta_.offset) {
    holder_.reset();
    holder_ = paddle::memory::AllocShared(place, size);
    meta_.offset = 0;
  }
  return reinterpret_cast<void*>(reinterpret_cast<uintptr_t>(holder_->ptr()) +
                                 meta_.offset);
}

void* DenseTensor::mutable_data(const paddle::platform::Place& place,
                                size_t requested_size) {
  return mutable_data(place, type(), requested_size);
}

void* DenseTensor::mutable_data(const paddle::platform::Place& place,
                                paddle::framework::proto::VarType::Type type,
                                const pten::Stream& stream) {
  set_type(type);
  PADDLE_ENFORCE_GE(
      numel(),
      0,
      paddle::platform::errors::PreconditionNotMet(
          "The Tensor's element number must be equal or greater than zero. "
          "The Tensor's shape is [",
          dims(),
          "] now"));
  size_t size = numel() * SizeOf(dtype());

  /* some versions of boost::variant don't have operator!= */
  if (holder_ == nullptr || !(holder_->place() == place) ||
      holder_->size() < size + meta_.offset ||
      !(paddle::platform::is_gpu_place(place) &&
        paddle::memory::InSameStream(holder_, stream))) {
    holder_.reset();
    holder_ = paddle::memory::AllocShared(place, size, stream);
    meta_.offset = 0;
  }
  return reinterpret_cast<void*>(reinterpret_cast<uintptr_t>(holder_->ptr()) +
                                 meta_.offset);
}

/* @jim19930609: The following "mutable_data" only supports specific dtypes
   defined in OpProto. This part need another clean up once the data type across
   Fluid
   and Pten get unified.
   */
template <typename T>
inline T* DenseTensor::mutable_data(const DDim& dims,
                                    const paddle::platform::Place& place,
                                    size_t requested_size) {
  static_assert(std::is_pod<T>::value, "T must be POD");
  meta_.dims = dims;
  return mutable_data<T>(place, requested_size);
}

template <typename T>
inline T* DenseTensor::mutable_data(const paddle::platform::Place& place,
                                    size_t requested_size) {
  static_assert(std::is_pod<T>::value, "T must be POD");
  return reinterpret_cast<T*>(mutable_data(
      place, paddle::framework::DataTypeTrait<T>::DataType(), requested_size));
}

void DenseTensor::ShareBufferWith(const DenseTensor& tensor) {
  holder_ = tensor.holder_;
  meta_.offset = tensor.meta().offset;
  meta_.dtype = tensor.dtype();
}

#define LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(dtype) \
  template dtype* DenseTensor::mutable_data(         \
      const DDim& dims,                              \
      const paddle::platform::Place& place,          \
      size_t requested_size);                        \
  template dtype* DenseTensor::mutable_data(         \
      const paddle::platform::Place& place, size_t requested_size);

LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(bool)
LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(int8_t)
LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(uint8_t)
LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(int16_t)
LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(int32_t)
LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(int64_t)
LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(float)
LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(double)
LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(::paddle::platform::bfloat16)
LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(::paddle::platform::float16)
LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(::paddle::experimental::complex64)
LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(::paddle::experimental::complex128)

#undef LEGACY_DATA_MEMBER_FUNC_INSTANTIATION

/* ------------------------------ */
/*   From framework::LoDTensor    */
/* ------------------------------ */

DenseTensor::DenseTensor(intrusive_ptr<Storage> storage,
                         const DenseTensorMeta& meta)
    : meta_(meta), holder_(storage->move_data_shared()) {}

DenseTensor::DenseTensor(intrusive_ptr<Storage> storage, DenseTensorMeta&& meta)
    : meta_(std::move(meta)), holder_(storage->move_data_shared()) {}

DenseTensor::DenseTensor(const LoD& lod) : DenseTensor() { meta_.lod = lod; }

void DenseTensor::set_lod(const LoD& lod) { meta_.lod = lod; }

LoD* DenseTensor::mutable_lod() { return &meta_.lod; }

std::pair<size_t, size_t> DenseTensor::lod_element(size_t level,
                                                   size_t elem) const {
  PADDLE_ENFORCE_LT(
      level,
      NumLevels(),
      paddle::platform::errors::InvalidArgument(
          "The input level of LoD is invalid, it should be less than LoD "
          "size. The input level is %zu, the LoD size is %zu.",
          level,
          NumLevels()));

  PADDLE_ENFORCE_LT(elem,
                    NumElements(level),
                    paddle::platform::errors::InvalidArgument(
                        "The input element of LoD is invalid, it should be "
                        "less than the number of elements in its level."
                        "The input element is %zu, the number of elements in "
                        "its level is %zu.",
                        elem,
                        NumElements(level)));

  return std::make_pair((meta_.lod)[level][elem], (meta_.lod)[level][elem + 1]);
}

size_t DenseTensor::NumLevels() const { return meta_.lod.size(); }

size_t DenseTensor::NumElements(size_t level) const {
  PADDLE_ENFORCE_LT(
      level,
      NumLevels(),
      paddle::platform::errors::InvalidArgument(
          "The input level of LoD is invalid, it should be less than LoD "
          "size. The input level is %zu, the LoD size is %zu.",
          level,
          NumLevels()));

  // the last offset is the end of last element
  return (meta_.lod)[level].size() - 1;
}

DenseTensor& DenseTensor::Resize(const DDim& dims) {
  meta_.dims = dims;
  return *this;
}

DenseTensor DenseTensor::Slice(int64_t begin_idx, int64_t end_idx) const {
  check_memory_size();
  PADDLE_ENFORCE_GE(begin_idx,
                    0,
                    paddle::platform::errors::OutOfRange(
                        "The start row index must be greater than 0."
                        "But received the start index is d%.",
                        begin_idx));
  PADDLE_ENFORCE_LE(end_idx,
                    meta_.dims[0],
                    paddle::platform::errors::OutOfRange(
                        "The end row index is out of bound."));
  PADDLE_ENFORCE_LT(
      begin_idx,
      end_idx,
      paddle::platform::errors::InvalidArgument(
          "The start row index must be less than the end row index."
          "But received the start index = %d, the end index = %d.",
          begin_idx,
          end_idx));

  if (meta_.dims[0] == 1) {
    return *this;
  } else {
    size_t base = numel() / meta_.dims[0];
    DenseTensor dst;
    dst.holder_ = holder_;
    dst.set_layout(meta_.layout);
    dst.meta_.dtype = meta_.dtype;
    DDim dst_dims = meta_.dims;
    dst_dims[0] = end_idx - begin_idx;
    dst.Resize(dst_dims);
    dst.meta_.offset = meta_.offset + begin_idx * base * SizeOf(dtype());
    return dst;
  }
}

std::vector<DenseTensor> DenseTensor::Split(int64_t split_size,
                                            int64_t axis) const {
  check_memory_size();

  PADDLE_ENFORCE_GE(meta_.dims.size(),
                    0,
                    paddle::platform::errors::OutOfRange(
                        "split expects at least a 1-dimensional tensor"));

  PADDLE_ENFORCE_GE(
      split_size,
      0,
      paddle::platform::errors::OutOfRange(
          "split expects split_size be non-negative, but got split_size is %d",
          split_size));

  int64_t numel_size = meta_.dims[axis];

  int64_t num_splits = 1;
  if (split_size != 0) {
    num_splits =
        std::max<int64_t>((numel_size + split_size - 1) / split_size, 1);
  }

  std::vector<DenseTensor> splits(num_splits);
  int64_t last_split_size = split_size - (split_size * num_splits - numel_size);

  for (int64_t i = 0; i < num_splits; ++i) {
    int64_t length = i < num_splits - 1 ? split_size : last_split_size;
    splits[i] = Slice(i * split_size, i * split_size + length);
  }
  return splits;
}

std::vector<DenseTensor> DenseTensor::Chunk(int64_t chunks,
                                            int64_t axis) const {
  check_memory_size();
  PADDLE_ENFORCE_GE(meta_.dims.size(),
                    0,
                    paddle::platform::errors::OutOfRange(
                        "split expects at least a 1-dimensional tensor"));
  PADDLE_ENFORCE_GE(
      chunks,
      0,
      paddle::platform::errors::OutOfRange(
          "chunks expects to be greater than 0, but got chunks is %d", chunks));

  int64_t numel_size = meta_.dims[axis];
  int64_t split_size = (numel_size + chunks - 1) / chunks;
  return Split(split_size, axis);
}

DenseTensor& DenseTensor::ShareDataWith(const DenseTensor& src) {
  src.check_memory_size();
  // Preserve LoD
  auto lod = meta_.lod;
  *this = src;
  meta_.lod = lod;
  return *this;
}

DenseTensor& DenseTensor::ShareInplaceVersionCounterWith(
    const DenseTensor& src) {
  PADDLE_ENFORCE_NOT_NULL(
      inplace_version_counter_,
      paddle::platform::errors::PreconditionNotMet(
          "Tensor does not hold inplace_version_counter_."));

  inplace_version_counter_ = src.inplace_version_counter_;
  return *this;
}
}  // namespace pten
