tensor.cc

/* 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/phi/api/include/tensor.h"

#include <memory>
#include <utility>
#include <vector>

#include "glog/logging.h"

#include "paddle/phi/api/lib/utils/allocator.h"
#include "paddle/phi/backends/gpu/gpu_info.h"
#include "paddle/phi/core/ddim.h"
#include "paddle/phi/core/dense_tensor.h"
#include "paddle/phi/core/enforce.h"
#include "paddle/phi/core/selected_rows.h"
#include "paddle/phi/core/sparse_coo_tensor.h"
#include "paddle/phi/core/sparse_csr_tensor.h"
#include "paddle/phi/core/tensor_base.h"
#include "paddle/phi/core/tensor_meta.h"
#include "paddle/phi/core/tensor_utils.h"

#include "paddle/fluid/platform/stream/cuda_stream.h"

namespace paddle {
namespace experimental {
namespace detail {
static Place GetCorrectPlaceByPlaceType(const Place &place_type) {
  auto alloc_type = place_type.GetType();
  switch (alloc_type) {
    case AllocationType::CPU:
      return place_type;
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
    case AllocationType::GPU:
      return phi::Place(AllocationType::GPU,
                        phi::backends::gpu::GetCurrentDeviceId());
#endif
    default:
      PADDLE_THROW(phi::errors::Unavailable(
          "The PlaceType is a legacy design, only supports CPU and GPU, "
          "and will not support other place types in the future."));
  }
}
}  // namespace detail

/////// Tensor Methods ////////

/* Part 1: Construction and destruction methods */

Tensor::Tensor(std::shared_ptr<phi::TensorBase> tensor_impl)
    : impl_(std::move(tensor_impl)) {
  PADDLE_ENFORCE_NOT_NULL(
      impl_,
      phi::errors::InvalidArgument("TensorImpl with nullptr is not supported"));
}

Tensor::Tensor(const Place &place) {
  LOG(WARNING) << "The Tensor(place) constructor is deprecated since version "
                  "2.3, and will be removed in version 2.4! Please use "
                  "`paddle::empty/full` method to create a new "
                  "Tensor instead. "
                  "Reason: A legal tensor cannot be constructed only based on "
                  "the `place`, and datatype, shape, layout, etc. is also "
                  "required.";
  DefaultAllocator alloc(detail::GetCorrectPlaceByPlaceType(place));
  impl_ = std::move(std::make_shared<phi::DenseTensor>(
      &alloc,
      std::move(phi::DenseTensorMeta(
          phi::DataType::FLOAT32, phi::make_ddim({}), phi::DataLayout::NCHW))));
}

Tensor::Tensor(const Place &place, const std::vector<int64_t> &shape) {
  LOG(WARNING) << "The Tensor(place, shape) constructor is deprecated since "
                  "version 2.3, and will be removed in version 2.4! Please use "
                  "`paddle::empty/full` method to create a new "
                  "Tensor instead. "
                  "Reason: A legal tensor cannot be constructed only based on "
                  "the `place` and `shape`, and datatype, layout, etc. is also "
                  "required.";
  DefaultAllocator alloc(detail::GetCorrectPlaceByPlaceType(place));
  impl_ = std::move(std::make_shared<phi::DenseTensor>(
      &alloc,
      std::move(phi::DenseTensorMeta(phi::DataType::FLOAT32,
                                     phi::make_ddim({shape}),
                                     phi::DataLayout::NCHW))));
}

Tensor::Tensor(std::shared_ptr<phi::TensorBase> tensor_impl,
               const std::string &name)
    : impl_(std::move(tensor_impl)), name_(std::move(name)) {}

/* Part 2: Dimension, DataType and DataLayout methods */

int64_t Tensor::numel() const { return impl_->numel(); }

int64_t Tensor::size() const { return impl_->numel(); }

phi::DDim Tensor::dims() const { return impl_->dims(); }

std::vector<int64_t> Tensor::shape() const {
  auto dims = impl_->dims();
  if (dims.size() == 1 && dims.at(0) == 0) {
    return {};
  }
  return phi::vectorize<int64_t>(dims);
}

void Tensor::reshape(const std::vector<int64_t> &shape) {
  LOG(WARNING) << "The function of resetting the shape of the uninitialized "
                  "Tensor of the `reshape` method is deprecated since version "
                  "2.3, and will be removed in version 2.4, please use "
                  "`paddle::empty/full` method to create a new Tensor "
                  "instead. "
                  "reason: `reshape` means changing the tensor shape without "
                  "touching underlying data, this requires the total size of "
                  "the tensor to remain constant.";
  if (is_dense_tensor()) {
    static_cast<phi::DenseTensor *>(impl_.get())->Resize(phi::make_ddim(shape));
  } else {
    PADDLE_THROW(phi::errors::Unimplemented(
        "Only support reshape operation on DenseTensor now."));
  }
}

DataType Tensor::dtype() const { return impl_->dtype(); }

DataType Tensor::type() const { return impl_->dtype(); }

DataLayout Tensor::layout() const { return impl_->layout(); }

bool Tensor::is_dense_tensor() const {
  return phi::DenseTensor::classof(impl_.get());
}
bool Tensor::is_selected_rows() const {
  return phi::SelectedRows::classof(impl_.get());
}
bool Tensor::is_sparse_coo_tensor() const {
  return phi::SparseCooTensor::classof(impl_.get());
}
bool Tensor::is_sparse_csr_tensor() const {
  return phi::SparseCsrTensor::classof(impl_.get());
}
/* Part 3: Device and Backend methods */

Place Tensor::place() const {
  PADDLE_ENFORCE_NOT_NULL(
      impl_,
      phi::errors::PermissionDenied(
          "Null pointer error, the impl_ of Tensor should not be "
          "Null when calling Tensor::place()."));
  return impl_->place();
}

bool Tensor::is_cpu() const { return paddle::platform::is_cpu_place(place()); }

bool Tensor::is_gpu() const { return paddle::platform::is_gpu_place(place()); }

bool Tensor::is_gpu_pinned() const {
  return paddle::platform::is_cuda_pinned_place(place());
}

/* Part 4: Data Access methods */

template <typename T>
T *Tensor::mutable_data() {
  LOG(WARNING) << "Allocating memory through `mutable_data` method is "
                  "deprecated since version 2.3, and `mutable_data` method "
                  "will be removed in version 2.4! Please use "
                  "`paddle::empty/full` method to create a new "
                  "Tensor with allocated memory, and use data<T>() method "
                  "to get the memory pointer of tensor instead. "
                  "Reason: When calling `mutable_data` to allocate memory, "
                  "the place, datatype, and data layout of tensor may be in "
                  "an illegal state.";
  if (is_dense_tensor()) {
    return static_cast<phi::DenseTensor *>(impl_.get())
        ->mutable_data<T>(place());
  }
  return nullptr;
}

template PADDLE_API float *Tensor::mutable_data<float>();
template PADDLE_API double *Tensor::mutable_data<double>();
template PADDLE_API int64_t *Tensor::mutable_data<int64_t>();
template PADDLE_API int32_t *Tensor::mutable_data<int32_t>();
template PADDLE_API uint8_t *Tensor::mutable_data<uint8_t>();
template PADDLE_API int8_t *Tensor::mutable_data<int8_t>();
template PADDLE_API int16_t *Tensor::mutable_data<int16_t>();
template PADDLE_API bool *Tensor::mutable_data<bool>();
template PADDLE_API phi::dtype::complex<float>
    *Tensor::mutable_data<phi::dtype::complex<float>>();
template PADDLE_API phi::dtype::complex<double>
    *Tensor::mutable_data<phi::dtype::complex<double>>();
template PADDLE_API phi::dtype::float16 *
Tensor::mutable_data<phi::dtype::float16>();

template <typename T>
T *Tensor::mutable_data(const Place &place) {
  LOG(WARNING) << "Allocating memory through `mutable_data` method is "
                  "deprecated since version 2.3, and `mutable_data` method "
                  "will be removed in version 2.4! Please use "
                  "`paddle::empty/full` method to create a new "
                  "Tensor with allocated memory, and use data<T>() method "
                  "to get the memory pointer of tensor instead. "
                  "Reason: When calling `mutable_data` to allocate memory, "
                  "the datatype, and data layout of tensor may be in "
                  "an illegal state.";
  if (is_dense_tensor()) {
    return static_cast<phi::DenseTensor *>(impl_.get())->mutable_data<T>(place);
  }
  return nullptr;
}

template PADDLE_API float *Tensor::mutable_data<float>(const Place &place);
template PADDLE_API double *Tensor::mutable_data<double>(const Place &place);
template PADDLE_API int64_t *Tensor::mutable_data<int64_t>(const Place &place);
template PADDLE_API int32_t *Tensor::mutable_data<int32_t>(const Place &place);
template PADDLE_API uint8_t *Tensor::mutable_data<uint8_t>(const Place &place);
template PADDLE_API int8_t *Tensor::mutable_data<int8_t>(const Place &place);
template PADDLE_API int16_t *Tensor::mutable_data<int16_t>(const Place &place);
template PADDLE_API bool *Tensor::mutable_data<bool>(const Place &place);
template PADDLE_API phi::dtype::complex<float>
    *Tensor::mutable_data<phi::dtype::complex<float>>(const Place &place);
template PADDLE_API phi::dtype::complex<double>
    *Tensor::mutable_data<phi::dtype::complex<double>>(const Place &place);
template PADDLE_API phi::dtype::float16 *
Tensor::mutable_data<phi::dtype::float16>(const Place &place);

template <typename T>
const T *Tensor::data() const {
  if (is_dense_tensor()) {
    return static_cast<phi::DenseTensor *>(impl_.get())->data<T>();
  } else if (is_selected_rows()) {
    return static_cast<phi::SelectedRows *>(impl_.get())->value().data<T>();
  }
  return nullptr;
}

template PADDLE_API const float *Tensor::data<float>() const;
template PADDLE_API const double *Tensor::data<double>() const;
template PADDLE_API const int64_t *Tensor::data<int64_t>() const;
template PADDLE_API const int32_t *Tensor::data<int32_t>() const;
template PADDLE_API const uint8_t *Tensor::data<uint8_t>() const;
template PADDLE_API const int8_t *Tensor::data<int8_t>() const;
template PADDLE_API const int16_t *Tensor::data<int16_t>() const;
template PADDLE_API const bool *Tensor::data<bool>() const;
template PADDLE_API const phi::dtype::complex<float>
    *Tensor::data<phi::dtype::complex<float>>() const;
template PADDLE_API const phi::dtype::complex<double>
    *Tensor::data<phi::dtype::complex<double>>() const;
template PADDLE_API const phi::dtype::float16 *
Tensor::data<phi::dtype::float16>() const;
template PADDLE_API const phi::dtype::bfloat16 *
Tensor::data<phi::dtype::bfloat16>() const;

template <typename T>
T *Tensor::data() {
  if (is_dense_tensor()) {
    return static_cast<phi::DenseTensor *>(impl_.get())->data<T>();
  } else if (is_selected_rows()) {
    return static_cast<phi::SelectedRows *>(impl_.get())
        ->mutable_value()
        ->data<T>();
  }
  return nullptr;
}

template PADDLE_API float *Tensor::data<float>();
template PADDLE_API double *Tensor::data<double>();
template PADDLE_API int64_t *Tensor::data<int64_t>();
template PADDLE_API int32_t *Tensor::data<int32_t>();
template PADDLE_API uint8_t *Tensor::data<uint8_t>();
template PADDLE_API int8_t *Tensor::data<int8_t>();
template PADDLE_API int16_t *Tensor::data<int16_t>();
template PADDLE_API bool *Tensor::data<bool>();
template PADDLE_API phi::dtype::complex<float>
    *Tensor::data<phi::dtype::complex<float>>();
template PADDLE_API phi::dtype::complex<double>
    *Tensor::data<phi::dtype::complex<double>>();
template PADDLE_API phi::dtype::float16 *Tensor::data<phi::dtype::float16>();

// TODO(chenweihang): replace slice impl by API
Tensor Tensor::slice(int64_t begin_idx, int64_t end_idx) const {
  if (is_dense_tensor()) {
    return Tensor(std::make_shared<phi::DenseTensor>(
        std::move(phi::DenseTensorUtils::Slice(
            *(static_cast<phi::DenseTensor *>(impl_.get())),
            begin_idx,
            end_idx))));
  } else {
    PADDLE_THROW(phi::errors::Unimplemented(
        "Only support slice operation on DenseTensor now."));
  }
}

const std::shared_ptr<phi::TensorBase> &Tensor::impl() const { return impl_; }

void Tensor::set_impl(const std::shared_ptr<phi::TensorBase> &impl) {
  impl_ = impl;
}

void Tensor::set_impl(std::shared_ptr<phi::TensorBase> &&impl) {
  impl_ = std::move(impl);
}

#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
gpuStream_t Tensor::stream() const {
  return platform::stream::get_current_stream(-1)->raw_stream();
}
#endif

/* Part 5: Status utils methods */

bool Tensor::defined() const { return impl_ != nullptr; }

bool Tensor::initialized() const { return defined() && impl_->initialized(); }

bool Tensor::is_initialized() const {
  LOG(WARNING) << "The `is_initialized` method is deprecated since version "
                  "2.3, and will be removed in version 2.4! "
                  "Please use `initialized` method instead.";
  return defined() && impl_->initialized();
}

void Tensor::reset() { impl_.reset(); }

/* Part 6: Operator overloading */

Tensor &Tensor::operator=(const Tensor &x) & {
  impl_ = x.impl_;
  autograd_meta_ = x.autograd_meta_;
  name_ = x.name_;
  return *this;
}

Tensor &Tensor::operator=(Tensor &&x) & {
  impl_ = std::move(x.impl_);
  autograd_meta_ = std::move(x.autograd_meta_);
  name_ = std::move(x.name_);
  return *this;
}

AbstractAutogradMeta *Tensor::get_autograd_meta() const {
  return autograd_meta_.get();
}

const std::shared_ptr<AbstractAutogradMeta> &Tensor::mutable_autograd_meta()
    const {
  return autograd_meta_;
}

void Tensor::set_autograd_meta(
    std::shared_ptr<AbstractAutogradMeta> autograd_meta) {
  autograd_meta_ = std::move(autograd_meta);
}

void Tensor::bump_inplace_version() {
  if (is_dense_tensor()) {
    auto &inplace_version_counter =
        static_cast<phi::DenseTensor *>(impl_.get())->InplaceVersionCounter();
    inplace_version_counter.Bump();
  } else {
    PADDLE_THROW(phi::errors::Unimplemented(
        "bump_inplace_version is only supported on DenseTensor now."));
  }
}

uint32_t Tensor::current_inplace_version() {
  if (is_dense_tensor()) {
    auto &inplace_version_counter =
        static_cast<phi::DenseTensor *>(impl_.get())->InplaceVersionCounter();
    return inplace_version_counter.CurrentVersion();
  } else {
    PADDLE_THROW(phi::errors::Unimplemented(
        "current_inplace_version is only supported on DenseTensor now."));
  }
  return 0;
}

void Tensor::reset_inplace_version(bool set_to_zero) {
  if (set_to_zero) {
    if (is_dense_tensor()) {
      auto &inplace_version_counter =
          static_cast<phi::DenseTensor *>(impl_.get())->InplaceVersionCounter();
      inplace_version_counter.SetInplaceVersionToZero();
    }
  }
}

}  // namespace experimental
}  // namespace paddle