Design Doc: Supporting new Device/Library


Deep learning has a high demand for computing resources. New high-performance devices and computing libraries are appearing very frequently. Deep learning frameworks have to integrate these high-performance devices and computing libraries in a flexible and efficient manner.

On one hand, hardware and computing libraries usually do not have a one-to-one correspondence. For example, Intel CPUs support Eigen and MKL computing libraries while Nvidia GPUs support Eigen and cuDNN computing libraries. We have to implement operator specific kernels for each computing library.

On the other hand, users usually do not want to care about the low-level hardware and computing libraries when writing a neural network configuration. In Fluid, Layer is exposed in Python, and Operator is exposed in C++. Both Layer and Operator are hardware independent.

So, how to support a new Device/Library in Fluid becomes a challenge.

Basic: Integrate A New Device/Library

For a general overview of fluid, please refer to the overview doc.

There are mainly three parts that we have to consider while integrating a new device/library:

  • Place and DeviceContext: indicate the device id and manage hardware resources
  • Memory and Tensor: malloc/free data on certain device
  • Math Functor and OpKernel: implement computing unit on certain devices/libraries

Place and DeviceContext

Please note that device and computing library are not one-to-one corresponding. A device can have a lot of computing libraries and a computing library can also support several devices.


Fluid uses class Place to represent the device memory where data is located. If we add another device, we have to add the corresponding DevicePlace.

        |   CPUPlace
Place --|   CUDAPlace
        |   FPGAPlace

And Place is defined as follows:

typedef boost::variant<CUDAPlace, CPUPlace, FPGAPlace> Place;


Fluid uses class DeviceContext to manage the resources in different libraries, such as CUDA stream in CDUADeviceContext. There are also inheritance relationships between different kinds of DeviceContext.

                /->  CPUDeviceContext   
DeviceContext ---->  CUDADeviceContext  
                \->  FPGADeviceContext

An example of Nvidia GPU is as follows:

  • DeviceContext
class DeviceContext {
  virtual Place GetPlace() const = 0;
  • CUDADeviceContext
class CUDADeviceContext : public DeviceContext {
  Place GetPlace() const override { return place_; }
  CUDAPlace place_;
  cudaStream_t stream_;
  cublasHandle_t cublas_handle_;
  std::unique_ptr<Eigen::GpuDevice> eigen_device_;  // binds with stream_

Memory and Tensor

memory module

Fluid provides the following memory interfaces:

template <typename Place>
void* Alloc(Place place, size_t size);

template <typename Place>
void Free(Place place, void* ptr);

template <typename Place>
size_t Used(Place place);

To implement these interfaces, we have to implement MemoryAllocator for different Devices.


Tensor holds data with some shape in a specific Place.

class Tensor {
  /*! Return a pointer to mutable memory block. */
  template <typename T>
  inline T* data();

   * @brief   Return a pointer to mutable memory block.
   * @note    If not exist, then allocation.
  template <typename T>
  inline T* mutable_data(platform::Place place);

   * @brief     Return a pointer to mutable memory block.
   * @param[in] dims    The dimensions of the memory block.
   * @param[in] place   The place of the memory block.
   * @note      If not exist, then allocation.
  template <typename T>
  inline T* mutable_data(DDim dims, platform::Place place);

  /*! Resize the dimensions of the memory block. */
  inline Tensor& Resize(const DDim& dims);

  /*! Return the dimensions of the memory block. */
  inline const DDim& dims() const;

  /*! holds the memory block if allocated. */
  std::shared_ptr<Placeholder> holder_;

  /*! points to dimensions of memory block. */
  DDim dim_;

Placeholder is used to delay memory allocation; that is, we can first define a tensor, using Resize to configurate its shape, and then call mutuable_data to allocate the actual memory.

paddle::framework::Tensor t;
paddle::platform::CPUPlace place;
// set size first
t.Resize({2, 3});
// allocate memory on CPU later

Math Functor and OpKernel

Fluid implements computing units based on different DeviceContexts. Some computing units are shared between operators. This common part will be put in operators/math directory as basic Functors.

Let’s take MaxOutFunctor as an example:

The interface is defined in the header file.

template <typename DeviceContext, typename T>
class MaxOutFunctor {
  void operator()(const DeviceContext& context, const framework::Tensor& input,
                  framework::Tensor* output, int groups);

CPU implementation is in .cc file

template <typename T>
class MaxOutFunctor<platform::CPUDeviceContext, T> {
  void operator()(const platform::CPUDeviceContext& context,
                  const framework::Tensor& input, framework::Tensor* output,
                  int groups) {

CUDA implementation is in .cu file

template <typename T>
class MaxOutFunctor<platform::CUDADeviceContext, T> {
  void operator()(const platform::CUDADeviceContext& context,
                  const framework::Tensor& input, framework::Tensor* output,
                  int groups) {

We first obtain the computing handle from a concrete DeviceContext and then compute on tensors.

The implementation of OpKernel is similar to math functors, the extra thing we need to do is to register the OpKernel in a global map.

Fluid provides different register interfaces in op_registry.h

Let’s take Crop operator as an example:

In .cc file:

REGISTER_OP_CPU_KERNEL(crop, ops::CropKernel<float>);
    crop_grad, ops::CropGradKernel<paddle::platform::CPUDeviceContext, float>);

In .cu file:

REGISTER_OP_CUDA_KERNEL(crop, ops::CropKernel<float>);
    crop_grad, ops::CropGradKernel<paddle::platform::CUDADeviceContext, float>);

Advanced topics: How to switch between different Device/Library

Generally, we will implement OpKernel for all Device/Library of an Operator. We can easily train a Convolutional Neural Network in GPU. However, some OpKernel is not suitable on a specific Device. For example, crf operator can only run on CPU, whereas most other operators can run on GPU. To achieve high performance in such circumstance, we have to switch between different Device/Library.

For more details, please refer to following docs:

  • operator kernel type doc
  • switch kernel doc