Working with rocFFT

Workflow

In order to compute an FFT with rocFFT, a plan has to be created first. A plan is a handle to an internal data structure that holds the details about the transform that the user wishes to compute. After the plan is created, it can be executed (a separate API call) with the specified data buffers. The execution step can be repeated any number of times with the same plan on different input/output buffers as needed. And when the plan is no longer needed, it gets destroyed.

To do a transform,

1. Initialize the library by calling rocfft_setup().

2. Create a plan, for each distinct type of FFT needed:

   • To create a plan, do either of the following

     • If the plan specification is simple, call rocfft_plan_create() and specify the value of the fundamental parameters.

     • If the plan has more details, first a plan description is created with rocfft_plan_description_create(), and additional APIs such as rocfft_plan_description_set_data_layout() are called to specify plan details. And then, rocfft_plan_create() is called with the description handle passed to it along with other details.

   • Optionally, allocate a work buffer for the plan:

     • Call rocfft_plan_get_work_buffer_size() to check the size of work buffer required by the plan.

     • If a non-zero size is required:

       • Create an execution info object with rocfft_execution_info_create().

       • Allocate a buffer using hipMalloc() and pass the allocated buffer to rocfft_execution_info_set_work_buffer().

3. Execute the plan:

   • The execution API rocfft_execute() is used to do the actual computation on the data buffers specified.

   • Extra execution information such as work buffers and compute streams are passed to rocfft_execute() in the rocfft_execution_info object.

   • rocfft_execute() can be called repeatedly as needed for different data, with the same plan.

   • If the plan requires a work buffer but none was provided, rocfft_execute() will automatically allocate a work buffer and free it when execution is finished.

4. If a work buffer was allocated:

   • Call hipFree() to free the work buffer.

   • Call rocfft_execution_info_destroy() to destroy the execution info object.

5. Destroy the plan by calling rocfft_plan_destroy().

6. Terminate the library by calling rocfft_cleanup().

Example

#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;
        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, before any of the library APIs are called, the function rocfft_setup() has to be called. Similarly, the function rocfft_cleanup() has to be called at the end of the program. These APIs ensure resources are properly allocated and freed.

Plans

A plan is the collection of (almost) all the parameters needed to specify an FFT computation. A rocFFT plan includes the following information:

• Type of transform (complex or real)

• Dimension of the transform (1D, 2D, or 3D)

• Length or extent of data in each dimension

• Number of datasets that are transformed (batch size)

• Floating-point precision of the data

• In-place or not in-place transform

• Format (array type) of the input/output buffer

• Layout of data in the input/output buffer

• Scaling factor to apply to the output of the transform

The 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

The input/output buffers that hold the data for the transform must be allocated, initialized and specified to the library by the user. For larger transforms, temporary work buffers may be needed. Because the library tries to minimize its own allocation of memory regions on the device, it expects the user to manage work buffers. The size of the buffer needed can be queried using rocfft_plan_get_work_buffer_size() and after their allocation can be passed to the library by rocfft_execution_info_set_work_buffer(). The samples in the source repository show how to use these.

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:

  1. Planar format - where the real and imaginary components are kept in 2 separate arrays:

     • Buffer1: RRRRR...

     • Buffer2: IIIII...

  2. Interleaved format - where 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 forward FFT that results in complex data (Hermitian).

  • Complex data (Hermitian) being subject to backward FFT that results in real data.

Note

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

The library provides the rocfft_transform_type and rocfft_array_type enums to specify transform and array types, respectively.

Batches

The efficiency of the library is improved by utilizing transforms in batches. Sending as much data as possible in a single transform call leverages the parallel compute capabilities of devices (GPU devices in particular), and minimizes the penalty of control transfer. It is best to think of a device as a high-throughput, high-latency device. Using a networking analogy as an example, this approach is similar to having a massively high-bandwidth pipe with very high ping response times. If the client is ready to send data to the device for compute, it should be sent in as few API calls as possible, and this can be done by batching. rocFFT plans have a parameter number_of_transforms (this value is also referred to as batch size in various places in the document) in rocfft_plan_create() to describe the number of transforms being requested. All 1D, 2D, and 3D transforms can be batched.

Result placement

The API supports both in-place and not in-place transforms via the rocfft_result_placement enum. With in-place transforms, only input buffers are provided to the execution API, and 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 may overwrite input buffers on real inverse (complex-to-real) transforms, even if they are requested to not be in-place. rocFFT is able to optimize the FFT better by doing this.

Strides and Distances

Strides and distances enable users to specify custom layout of data using 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 that exists between corresponding elements of successive FFT data instances (primitives) in a batch. Distance is measured in units of the memory type; complex data measures in complex units, and real data measures 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; 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 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, such that each logical FFT instance is not overlapping with any other; if not valid, undefined results may occur. A simple example would be to perform a 1D length 4096 on each row of an array of 1024 rows x 4096 columns of values stored in a column-major array, such as a FORTRAN program might provide. (This would be equivalent to a C or C++ program that has an array of 4096 rows x 1024 columns stored in a row-major manner, on which you want to perform a 1D length 4096 transform on each column.) In this case, specify the strides as [1024] and distance as 1.

Overwriting non-contiguous buffers

rocFFT guarantees that both the reading of FFT input and the writing of FFT output will respect the specified strides. However, temporary results can potentially be written to these buffers contiguously, which may be unexpected if the strides would avoid certain memory locations completely for reading and writing.

For example, a 1D FFT of length \(N\) with input and output stride of 2 is transforming only even-indexed elements in the input and output buffers. But if temporary data needs to be written to the buffers, odd-indexed elements may be overwritten.

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

These policies apply to both input and output buffers, because not in-place transforms may overwrite input data.

Input and Output Fields

By default, rocFFT inputs and outputs are on the same device, and the layouts of each are described using a set of strides passed to 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, where each brick can reside on a different device and have its own layout parameters.

Note

The rocFFT APIs to declare fields and bricks are currently experimental and subject to change in future releases. We welcome feedback and questions about these interfaces. Please open issues on the rocFFT issue tracker with questions and comments.

The workflow for fields is as follows:

1. Allocate a rocfft_field struct by calling rocfft_field_create().

2. Add one or more bricks to the field:

   1. Allocate a rocfft_brick by calling rocfft_brick_create(). The brick’s dimensions are defined in terms of 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 (first index past the end of the brick).

      The device on which the brick resides is also specified at this time, along with the strides of the brick in device memory.

      All coordinates and strides provided here also include batch dimensions.

   2. Add the brick to the field by calling rocfft_field_add_brick().

   3. Deallocate the brick by calling rocfft_brick_destroy().

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

   Offsets, strides, and distances as specified by 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 rocfft_field struct may be passed to both rocfft_plan_description_add_infield() and rocfft_plan_description_add_outfield().

   For in-place transforms, only call rocfft_plan_description_add_infield() and do not call rocfft_plan_description_add_outfield().

4. Deallocate the field by calling rocfft_field_destroy().

5. Create the plan by calling rocfft_plan_create(). Pass the plan description that has already been allocated.

6. Execute the plan by calling 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 (via calls to rocfft_field_add_brick()), and must point to memory on the device that was specified at that time.

   For in-place transforms, only pass input pointers and do not pass output pointers.

Transforms of real data

• Real data
  • Supported array type combinations
  • Setting strides
  • Examples

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 users of rocFFT can launch a separate GPU kernel to do this work, rocFFT provides a rocfft_plan_description_set_scale_factor() function to more efficiently combine this scaling multiplication with the FFT work.

The scaling factor is set on the plan description prior to plan creation.

Load and Store Callbacks

rocFFT includes experimental functionality to call user-defined device functions when loading input from global memory at the start of a transform, or when storing output to global memory at the end of a transform.

These user-defined callback functions may be optionally supplied to the library using rocfft_execution_info_set_load_callback() and rocfft_execution_info_set_store_callback().

Device functions supplied as callbacks must load and store element data types that are appropriate for the transform being performed.

Transform type	Load element type	Store element type
Complex-to-complex, half-precision	_Float16_2	_Float16_2
Complex-to-complex, single-precision	float2	float2
Complex-to-complex, double-precision	double2	double2
Real-to-complex, single-precision	float	float2
Real-to-complex, half-precision	_Float16	_Float16_2
Real-to-complex, double-precision	double	double2
Complex-to-real, half-precision	_Float16_2	_Float16
Complex-to-real, single-precision	float2	float
Complex-to-real, double-precision	double2	double

The callback function signatures must match the specifications below.

T load_callback(T* buffer, size_t offset, void* callback_data, void* shared_memory);
void store_callback(T* buffer, size_t offset, T element, void* callback_data, void* shared_memory);

The parameters for the functions are defined as:

• T: The data type of each element being loaded or stored from the input or output.

• buffer: Pointer to the input (for load callbacks) or output (for store callbacks) in device memory that was passed to rocfft_execute().

• offset: The offset of the location being read from or written to. This counts in elements, from the buffer pointer.

• element: For store callbacks only, the element to be stored.

• callback_data: A pointer value accepted by rocfft_execution_info_set_load_callback() and rocfft_execution_info_set_store_callback() which is passed through to the callback function.

• shared_memory: A pointer to an amount of shared memory requested when the callback is set. Currently, shared memory is not supported and this parameter is always null.

Callback functions are called exactly once for each element being loaded or stored in a transform. Note that multiple kernels may be launched to decompose a transform, which means that separate kernels may call the load and store callbacks for a transform if both are specified.

Currently, callbacks functions are only supported for transforms that do not use planar format for input or output.

Runtime compilation

rocFFT includes many kernels for common FFT problems. Some plans may require additional kernels aside from what is built in to the library. In these cases, rocFFT will compile optimized kernels for the plan when the plan is created.

Compiled kernels are stored in memory by default and will be reused if they are required again for plans in the same process.

If the ROCFFT_RTC_CACHE_PATH environment variable is set to a writable file location, rocFFT will write compiled kernels to this location. rocFFT will read kernels from this location for plans in other processes that need runtime-compiled kernels. rocFFT will create the specified file if it does not already exist.