.. meta::
  :description: rocFFT documentation and API reference library
  :keywords: rocFFT, ROCm, API, documentation

.. _working-with-rocfft:

********************************************************************
Working with rocFFT
********************************************************************

This topic describes how to use rocFFT, including how to structure the workflow, set up and clean up the library,
and use plans, buffers, and batches.

Workflow
========

To compute an FFT with rocFFT, first create a plan. A plan is a handle to an internal data structure that
holds the details about the transform. After creating the plan, execute it with the specified data buffers
using a separate API call. The execution step can be repeated
with the same plan on different input/output buffers
as needed. The plan is destroyed when it is no longer needed.

To perform a transform, follow these steps:

#. Initialize the library by calling :cpp:func:`rocfft_setup()`.
#. Create a plan for each distinct type of FFT that is required:

   *  If the plan specification is simple, call :cpp:func:`rocfft_plan_create` and specify the value of the fundamental parameters.
   *  If the plan includes more details, first create a plan description using :cpp:func:`rocfft_plan_description_create`. Call additional APIs, such
      as :cpp:func:`rocfft_plan_description_set_data_layout`, to specify the plan details. Then call :cpp:func:`rocfft_plan_create`,
      passing the description handle to it along with the other details.

#. Optionally, allocate a work buffer for the plan:

   *  Call :cpp:func:`rocfft_plan_get_work_buffer_size` to check the size of the work buffer required by the plan.
   *  If a non-zero size is required:

      * Create an execution info object using :cpp:func:`rocfft_execution_info_create`.
      * Allocate a buffer using ``hipMalloc`` and pass the allocated buffer to :cpp:func:`rocfft_execution_info_set_work_buffer`.
   *  If the plan uses multiple HIP devices for input or output, :ref:`repeat the above steps<multi_device_work_mem>` for each HIP device.

#. Execute the plan:

   * Use the execution API :cpp:func:`rocfft_execute` to execute the actual computation on the data buffers specified.
   * Pass the extra execution information such as work buffers and compute streams to :cpp:func:`rocfft_execute` in the :cpp:type:`rocfft_execution_info` object.
   * :cpp:func:`rocfft_execute` can be called repeatedly as needed for different data with the same plan.
   * If the plan requires a work buffer but one wasn't provided, :cpp:func:`rocfft_execute` automatically allocates a work buffer and frees it when execution is finished.

#. If a work buffer was allocated:

   * Call ``hipFree`` to free the work buffer.
   * Call :cpp:func:`rocfft_execution_info_destroy` to destroy the execution info object.

#. Destroy the plan by calling :cpp:func:`rocfft_plan_destroy`.
#. Terminate the library by calling :cpp:func:`rocfft_cleanup()`.


Example
-------

.. code-block:: c

   #include <iostream>
   #include <vector>
   #include "hip/hip_runtime_api.h"
   #include "hip/hip_vector_types.h"
   #include "rocfft/rocfft.h"
   
   int main()
   {
           // rocFFT gpu compute
           // ========================================
  
           rocfft_setup();

           size_t N = 16;
           size_t Nbytes = N * sizeof(float2);
   
           // Create HIP device buffer
           float2 *x;
           hipMalloc(&x, Nbytes);

           // Initialize data
           std::vector<float2> cx(N);
           for (size_t i = 0; i < N; i++)
           {
                   cx[i].x = 1;
                   cx[i].y = -1;
           }

           //  Copy data to device
           hipMemcpy(x, cx.data(), Nbytes, hipMemcpyHostToDevice);

           // Create rocFFT plan
           rocfft_plan plan = nullptr;
           size_t length = N;
           rocfft_plan_create(&plan, rocfft_placement_inplace,
                rocfft_transform_type_complex_forward, rocfft_precision_single,
                1, &length, 1, nullptr);

           // Check if the plan requires a work buffer
           size_t work_buf_size = 0;
           // This is a single-device plan that uses the current HIP device,
           // so any work buffer would be on that device.
           rocfft_plan_get_work_buffer_size(plan, &work_buf_size);
           void* work_buf = nullptr;
           rocfft_execution_info info = nullptr;
           if(work_buf_size)
           {
                   rocfft_execution_info_create(&info);
                   hipMalloc(&work_buf, work_buf_size);
                   rocfft_execution_info_set_work_buffer(info, work_buf, work_buf_size);
           }
   
           // Execute plan
           rocfft_execute(plan, (void**) &x, nullptr, info);
   
           // Wait for execution to finish
           hipDeviceSynchronize();

           // Clean up work buffer
           if(work_buf_size)
           {
                   hipFree(work_buf);
                   rocfft_execution_info_destroy(info);
           }

           // Destroy plan
           rocfft_plan_destroy(plan);
   
           // Copy result back to host
           std::vector<float2> y(N);
           hipMemcpy(y.data(), x, Nbytes, hipMemcpyDeviceToHost);
   
           // Print results
           for (size_t i = 0; i < N; i++)
           {
                   std::cout << y[i].x << ", " << y[i].y << std::endl;
           }
   
           // Free device buffer
           hipFree(x);
   
           rocfft_cleanup();

           return 0;
   }

Library setup and cleanup
=========================

At the beginning of the program, the function :cpp:func:`rocfft_setup` must be called before any of the library APIs. Similarly,
the function :cpp:func:`rocfft_cleanup` must be called at the end of the program.
These APIs properly allocate and free the resources.

Plans
=====

A plan is a collection of most of the parameters needed to specify an FFT computation. A rocFFT plan includes the
following information:

* The type of transform (complex or real)
* The dimension of the transform (1D, 2D, or 3D)
* The length or extent of data in each dimension
* The number of datasets that are transformed (batch size)
* The floating-point precision of the data
* Whether the transform is in-place or not in-place
* The format (array type) of the input/output buffer
* The layout of data in the input/output buffer 
* The scaling factor to apply to the output of the transform

A rocFFT plan does not include the following parameters:

* The handles to the input and output data buffers.
* The handle to a temporary work buffer (if needed).
* Other information to control execution on the device.

These parameters are specified when the plan is executed.

Data
====

You must allocate, initialize, and specify the input/output buffers that hold the data for the transform.
For larger transforms, temporary work buffers might be needed. Because the library tries to minimize its own allocation of
memory regions on the device, it expects you to manage the work buffers. The size of the buffer that is needed can be queried using
:cpp:func:`rocfft_plan_get_work_buffer_size`. After allocation, it can be passed to the library using
:cpp:func:`rocfft_execution_info_set_work_buffer`. The `GitHub repository <https://github.com/ROCm/rocm-libraries/tree/develop/projects/rocfft/clients/samples>`_
provide some samples and examples.

The :cpp:func:`rocfft_plan_get_work_buffer_size` and
:cpp:func:`rocfft_execution_info_set_work_buffer` functions query and
set work buffers for the current HIP device.  By default, rocFFT
plans run on the HIP device that is current when
:cpp:func:`rocfft_plan_create` is called, and work buffers may only
be required on the same device.  Any supplied work buffer for a device must be accessible 

.. _multi_device_work_mem:

If the plan uses multiple HIP devices by decomposing the input or
output into :ref:`fields<input_output_fields>`, work memory may be
required on any of the devices used by the input or output fields as
well as the current HIP device when the plan was created.  In this
case, the steps to query and set work buffers need to be repeated for
each HIP device, as illustrated by the following code fragment:

.. code-block:: c++

  rocfft_plan plan = nullptr;
  rocfft_execution_info info = nullptr;

  // For brevity, error checking and the steps to create the plan and
  // execution info have been omitted

  // Start on device 0
  hipSetDevice(0);
  
  // Get the number of HIP devices
  int ndevices = 0;
  hipGetDeviceCount(&ndevices);

  // Loop over each device, checking work memory requirements for each one
  std::vector<void*> workbufs(ndevices);
  for(int device = 0; device < ndevices; ++device)
  {
      hipSetDevice(device);
      size_t worksize = 0;
      // Get work memory size required for the current device
      rocfft_plan_get_work_buffer_size(plan, &worksize);
      if(worksize)
      {
          // Allocate and set the work buffer for the current device
          hipMalloc(&workbufs[device], worksize);
          rocfft_execution_info_set_work_buffer(info, workbufs[device], worksize);
      }
  }

  // Return to device 0
  hipSetDevice(0);
  
  // Execution of the plan with the execution info would happen here

  // Free the work buffers that were allocated
  for(int device = 0; device < ndevices; ++device)
  {
      if(workbufs[device])
      {
          hipSetDevice(device);
          hipFree(workbufs[device]);
      }
  }

  // Return to device 0
  hipSetDevice(0);

Transform and array types 
=========================

There are two main types of FFTs in the library:

* **Complex FFT**: Transformation of complex data (forward or backward). The library supports the following two
  array types to store complex numbers:

  *  Planar format: The real and imaginary components are kept in two separate arrays:

     * Buffer 1: ``RRRRR...`` 
     * Buffer 2: ``IIIII...``

  *  Interleaved format: The real and imaginary components are stored as contiguous pairs in the same array: 

     * Buffer: ``RIRIRIRIRIRI...``
  
* **Real FFT**: Transformation of real data. For transforms involving real data, there are two possibilities:

  * Real data being subject to a forward FFT that results in complex data (Hermitian).
  * Complex data (Hermitian) being subject to a backward FFT that results in real data.

.. note::

   Real backward FFTs require Hermitian-symmetric input data, which would naturally happen in the output of a
   real forward FFT. rocFFT produces undefined results if
   this requirement is not met.

The library provides the :cpp:enum:`rocfft_transform_type` and
:cpp:enum:`rocfft_array_type` enumerations to specify transform and array
types, respectively.

Batches
=======

The efficiency of the library is improved by batching the transforms. Sending as much data as possible in a single
transform call leverages the parallel compute capabilities of the GPU devices and minimizes the control transfer
penalty. It's best to think of the GPU as a high-throughput, high-latency device, similar to a
high-bandwidth networking pipe with high ping response times. If the client
is ready to send data to the device to compute, it should send it using as few API calls as possible,
which can be accomplished by batching.
rocFFT plans can use the ``number_of_transforms`` parameter (also referred to as the batch size)
in :cpp:func:`rocfft_plan_create` to describe the number of transforms being requested.
All 1D, 2D, and 3D transforms can be batched.

.. _resultplacement:

Result placement
================

The API supports both in-place and not in-place transforms through the :cpp:enum:`rocfft_result_placement` enumeration.
With in-place transforms, only the input buffers are provided to the
execution API. The resulting data is written to the same buffer, overwriting the input data.
With not in-place transforms, distinct
output buffers are provided, and the results are written into the output buffer.

.. note::

   rocFFT can often overwrite the input buffers on real inverse (complex-to-real) transforms,
   even if they are requested as not in-place. By doing this, rocFFT is better able to optimize the FFT.

.. _stridesdistances:

Strides and distances
=====================

Strides and distances enable users to specify a custom layout for the data using :cpp:func:`rocfft_plan_description_set_data_layout`.

For 1D data, if ``strides[0] == strideX == 1``, successive elements in the first dimension (dimension index ``0``) are stored
contiguously in memory. If ``strideX`` is a value greater than ``1``, gaps in memory exist between each element of the vector.
For multidimensional cases, if ``strides[1] == strideY == LenX`` for 2D data and ``strides[2] == strideZ == LenX * LenY`` for 3D data,
no gaps exist in memory between each element, and all vectors are stored tightly packed in memory. Here, ``LenX``, ``LenY``, and ``LenZ`` denote the
transform lengths ``lengths[0]``, ``lengths[1]``, and ``lengths[2]``, respectively, which are used to set up the plan.

Distance is the stride between corresponding elements of successive FFT data instances (primitives) in a batch.
Distance is measured in units of the memory type.
Complex data is measured in complex units and real data in real units. For tightly packed data,
the distance between FFT primitives is the size of the FFT primitive,
such that ``dist == LenX`` for 1D data, ``dist == LenX * LenY`` for 2D data, and ``dist == LenX * LenY * LenZ`` for
3D data. It is possible to set the distance of a plan to be less than the size
of the FFT vector, which is typically ``1`` when doing column (strided) access on packed data.

When computing
a batch of 1D FFT vectors, if ``distance == 1`` and ``strideX == length(vector)``,
it means the data for each logical FFT is read along columns, in this case, along the batch. You must
verify that the distance and strides are valid and confirm that each logical
FFT instance does not overlap with any other in the output data. If this is not the case, undefined results
can occur. Overlapping on input data is generally allowed. 

A simple example of a column data access pattern would be a 1D transform of length 4096 on
each row of an array of 1024 rows by 4096 columns of values stored in a column-major array, 
for example, from a Fortran program. (This would be equivalent to a C or C++ program that
has an array of 4096 rows by 1024 columns stored in row-major format, where you
execute a 1D transform of length 4096 on each column.) In this case, specify the strides as ``1024`` and the distance as ``1``.

Overwriting non-contiguous buffers
==================================

rocFFT guarantees that both the reading of FFT input and the writing of FFT output respects the
specified strides.  However, temporary results can be written to these buffers
contiguously, which might be unexpected if the strides are designed to avoid certain memory locations completely
for reading and writing.

For example, a 1D FFT of length :math:`N` with an input and output stride of 2 only transforms the
even-indexed elements in the input and output buffers. But if temporary data needs to be written to
the buffers, the odd-indexed elements might be overwritten.

However, rocFFT is guaranteed to respect the size of buffers. In the above example, the
input/output buffers are :math:`2N` elements long, even if only :math:`N` even-indexed
elements are being transformed. No more than :math:`2N` elements of temporary data are written
to the buffers during the transform.

These policies apply to both input and output buffers, because not in-place transforms might overwrite input data.
See :ref:`resultplacement` for more information.

.. _input_output_fields:

Input and output fields
=======================

By default, the rocFFT inputs and outputs are on the same device and the layouts of
each are described using a set of strides passed to
:cpp:func:`rocfft_plan_description_set_data_layout`.

rocFFT optionally allows for inputs and outputs to be described as **fields**,
each of which is decomposed into multiple **bricks**. Each brick can
reside on a different device and have its own layout parameters.

.. note::

   The rocFFT APIs for declaring fields and bricks are currently experimental and
   subject to change in future releases. To submit feedback, questions, and comments
   about these interfaces, use the `rocm-libraries issue tracker
   <https://github.com/ROCm/rocm-libraries/issues>`_.

The workflow for using fields is as follows:

#. Allocate a :cpp:type:`rocfft_field` struct by calling :cpp:func:`rocfft_field_create`.

#. Add one or more bricks to the field:

   #. Allocate a :cpp:type:`rocfft_brick` by calling
      :cpp:func:`rocfft_brick_create`. Define the brick dimensions
      in terms of the lower and upper coordinates in the field's
      index space.

      Note that the lower coordinate is inclusive (contained within the brick) and
      the upper coordinate is exclusive (the first index past the end of the brick).

      Specify the device on which the brick resides, along
      with the strides of the brick in device memory.

      The coordinates and strides provided here include the batch dimensions unless
      the batch is ``1``.

   #. Add the brick to the field by calling :cpp:func:`rocfft_field_add_brick`.

   #. Deallocate the brick by calling :cpp:func:`rocfft_brick_destroy`.

#. Set the field as an input or output for the transform by calling either
   :cpp:func:`rocfft_plan_description_add_infield` or
   :cpp:func:`rocfft_plan_description_add_outfield` on a plan description that
   has already been allocated. The plan description must then be provided to
   :cpp:func:`rocfft_plan_create`.

   The offsets, strides, and distances specified by
   :cpp:func:`rocfft_plan_description_set_data_layout` for input or output are
   ignored when a field is set for the corresponding input or output.

   If the same field layout is used for both input and output, the same
   :cpp:type:`rocfft_field` struct can be passed to both
   :cpp:func:`rocfft_plan_description_add_infield` and
   :cpp:func:`rocfft_plan_description_add_outfield`.

   For in-place transforms, only call
   :cpp:func:`rocfft_plan_description_add_infield`. Do not call
   :cpp:func:`rocfft_plan_description_add_outfield`.

#. Deallocate the field by calling :cpp:func:`rocfft_field_destroy`.

#. Create the plan by calling :cpp:func:`rocfft_plan_create`. Pass the plan
   description that has already been allocated.

#. Execute the plan by calling :cpp:func:`rocfft_execute`. This function takes
   arrays of pointers for input and output. If fields have been set for input
   or output, then the arrays must contain pointers to each brick in the input
   or output.

   The pointers must be provided in the same order in which the bricks were
   added to the field (using calls to :cpp:func:`rocfft_field_add_brick`) and
   must point to memory on the device that was specified at that time.

.. important::

   For in-place transforms, pass a non-empty array of input pointers and
   an empty array of output pointers.

Transforms of real data
=======================

See :doc:`Real data <./real-data>` for more information.

Reproducibility of results
==========================

The results of an FFT computation generated by rocFFT are bitwise reproducible.
For example, deterministic behavior is expected between runs and every run generates the exact same
results. Bitwise reproducibility is achieved provided the following attributes
are kept constant between runs:

* FFT parameters
* rocFFT library version
* GPU model

A valid FFT plan is a requirement for reproducibility. In particular, the
:ref:`rules for overlapping of FFT data <stridesdistances>` must be followed.

Result scaling
==============

The output of a forward or backward FFT often needs to be multiplied
by a scaling factor before the data can be passed to the next step of
a computation. While you can launch a separate GPU
kernel to do this work, rocFFT provides a
:cpp:func:`rocfft_plan_description_set_scale_factor` function to more
efficiently combine this scaling multiplication with the FFT work.

The scaling factor is set as part of the plan description before plan creation.

Loading and storing callbacks
=============================

See :doc:`Loading and storing callbacks <./load-store-callbacks>` for more information.

Runtime compilation
===================

See :doc:`Runtime compilation <./runtime-compilation>` for more information.