tensor_impl.h

/* Copyright (c) 2016 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. */

#pragma once
#include "paddle/fluid/memory/memcpy.h"
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/platform/float16.h"

namespace paddle {
namespace framework {

template <typename... T>
struct SizeOfTypeFunctor;

template <typename T>
struct SizeOfTypeFunctor<T> {
  size_t operator()(std::type_index type) const {
    if (typeid(T).hash_code() == type.hash_code()) {
      return sizeof(T);
    } else {
      return 0UL;
    }
  }
};

template <>
struct SizeOfTypeFunctor<> {
  size_t operator()(std::type_index type) const { return 0UL; }
};

template <typename HEAD, typename... TAIL>
struct SizeOfTypeFunctor<HEAD, TAIL...> {
  size_t operator()(std::type_index type) const {
    SizeOfTypeFunctor<HEAD> head;
    size_t head_size = head(type);
    if (head_size != 0) {
      return head_size;
    }
    SizeOfTypeFunctor<TAIL...> tail;
    return tail(type);
  }
};

static inline size_t SizeOfType(std::type_index type) {
  SizeOfTypeFunctor<int, float, double, int16_t, int64_t, bool, size_t,
                    platform::float16>
      functor;
  size_t size = functor(type);
  PADDLE_ENFORCE(size != 0UL, "Cannot get size of type %s", type.name());
  return size;
}

inline void Tensor::check_memory_size() const {
  PADDLE_ENFORCE_NOT_NULL(
      holder_, "Tensor holds no memory. Call Tensor::mutable_data first.");
  PADDLE_ENFORCE_LE(
      numel() * SizeOfType(type()), memory_size(),
      "Tensor's dims_ is out of bound. Call Tensor::mutable_data "
      "first to re-allocate memory.\n"
      "or maybe the required data-type mismatches the data already stored.");
}

inline size_t Tensor::memory_size() const {
  return holder_ == nullptr ? 0UL : holder_->size() - offset_;
}

template <typename T>
inline const T* Tensor::data() const {
  check_memory_size();
  PADDLE_ENFORCE(std::is_same<T, void>::value ||
                     holder_->type().hash_code() == typeid(T).hash_code(),
                 "Tensor holds the wrong type, it holds %s",
                 this->holder_->type().name());

  return reinterpret_cast<const T*>(
      reinterpret_cast<uintptr_t>(holder_->ptr()) + offset_);
}

inline bool Tensor::IsInitialized() const { return holder_ != nullptr; }

template <typename T>
inline T* Tensor::data() {
  check_memory_size();
  PADDLE_ENFORCE(std::is_same<T, void>::value ||
                     holder_->type().hash_code() == typeid(T).hash_code(),
                 "Tensor holds the wrong type, it holds %s",
                 this->holder_->type().name());
  return reinterpret_cast<T*>(reinterpret_cast<uintptr_t>(holder_->ptr()) +
                              offset_);
}

template <typename T>
inline T* Tensor::mutable_data(DDim dims, platform::Place place) {
  static_assert(std::is_pod<T>::value, "T must be POD");
  Resize(dims);
  return mutable_data<T>(place);
}

template <typename T>
inline T* Tensor::mutable_data(platform::Place place) {
  static_assert(std::is_pod<T>::value, "T must be POD");
  return reinterpret_cast<T*>(mutable_data(place, typeid(T)));
}

inline void* Tensor::mutable_data(platform::Place place, std::type_index type) {
  if (holder_ != nullptr) {
    holder_->set_type(type);
  }
  PADDLE_ENFORCE_GE(numel(), 0,
                    "When calling this method, the Tensor's numel must be "
                    "equal or larger than zero. "
                    "Please check Tensor::Resize has been called first.");
  int64_t size = numel() * SizeOfType(type);
  /* some versions of boost::variant don't have operator!= */
  if (holder_ == nullptr || !(holder_->place() == place) ||
      holder_->size() < size + offset_) {
    if (platform::is_cpu_place(place)) {
      holder_.reset(new PlaceholderImpl<platform::CPUPlace>(
          boost::get<platform::CPUPlace>(place), size, type));
    } else if (platform::is_gpu_place(place) ||
               platform::is_cuda_pinned_place(place)) {
#ifndef PADDLE_WITH_CUDA
      PADDLE_THROW(
          "CUDAPlace or CUDAPinnedPlace is not supported in CPU-only mode.");
    }
#else
      if (platform::is_gpu_place(place)) {
        holder_.reset(new PlaceholderImpl<platform::CUDAPlace>(
            boost::get<platform::CUDAPlace>(place), size, type));
      } else if (platform::is_cuda_pinned_place(place)) {
        holder_.reset(new PlaceholderImpl<platform::CUDAPinnedPlace>(
            boost::get<platform::CUDAPinnedPlace>(place), size, type));
      }
    }
#endif
    offset_ = 0;
  }
  return reinterpret_cast<void*>(reinterpret_cast<uintptr_t>(holder_->ptr()) +
                                 offset_);
}

inline void* Tensor::mutable_data(platform::Place place) {
  PADDLE_ENFORCE(this->holder_ != nullptr,
                 "Cannot invoke mutable data if current hold nothing.");
  return mutable_data(place, holder_->type());
}

inline Tensor& Tensor::ShareDataWith(const Tensor& src) {
  src.check_memory_size();
  *this = src;
  return *this;
}

inline Tensor& Tensor::ShareDataWith(Tensor* src, size_t offset) {
  // NOTE: data size is determined by current tensor shape and data type
  src->check_memory_size();
  PADDLE_ENFORCE_EQ(src->type(), this->type(),
                    "tensor data type must be the same when sharing data");
  auto place = src->place();
  auto type = src->type();
  size_t size = src->numel() * SizeOfType(src->type());
  auto* ref = static_cast<uint8_t*>(src->mutable_data(place)) + offset;
  if (platform::is_cpu_place(place)) {
    holder_.reset(new SharedPlaceholderImpl<platform::CPUPlace>(
        boost::get<platform::CPUPlace>(place), ref, size, type));
  } else if (platform::is_gpu_place(place) ||
             platform::is_cuda_pinned_place(place)) {
#ifndef PADDLE_WITH_CUDA
    PADDLE_THROW(
        "CUDAPlace or CUDAPinnedPlace is not supported in CPU-only mode.");
  }
#else
    if (platform::is_gpu_place(place)) {
      holder_.reset(new SharedPlaceholderImpl<platform::CUDAPlace>(
          boost::get<platform::CUDAPlace>(place), ref, size, type));
    } else if (platform::is_cuda_pinned_place(place)) {
      holder_.reset(new SharedPlaceholderImpl<platform::CUDAPinnedPlace>(
          boost::get<platform::CUDAPinnedPlace>(place), ref, size, type));
    }
  }
#endif
  return *this;
}

inline Tensor Tensor::Slice(int begin_idx, int end_idx) const {
  check_memory_size();
  PADDLE_ENFORCE_GE(begin_idx, 0,
                    "The start row index must be greater than 0.");
  PADDLE_ENFORCE_LE(end_idx, dims_[0], "The end row index is out of bound.");
  PADDLE_ENFORCE_LT(
      begin_idx, end_idx,
      "The start row index must be lesser than the end row index.");

  if (dims_[0] == 1) {
    return *this;
  } else {
    size_t base = numel() / dims_[0];
    Tensor dst;
    dst.holder_ = holder_;
    dst.set_layout(layout_);
    DDim dst_dims = dims_;
    dst_dims[0] = end_idx - begin_idx;
    dst.Resize(dst_dims);
    dst.offset_ = offset_ + begin_idx * base * SizeOfType(type());
    return dst;
  }
}

inline Tensor& Tensor::Resize(const DDim& dims) {
  dims_ = dims;
  return *this;
}

inline const DDim& Tensor::dims() const { return dims_; }

inline int64_t Tensor::numel() const { return product(dims_); }

inline Tensor ReshapeToMatrix(const Tensor& src, int num_col_dims) {
  Tensor res;
  res.ShareDataWith(src);
  res.Resize(flatten_to_2d(src.dims(), num_col_dims));
  return res;
}

}  // namespace framework
}  // namespace paddle