rocWMMA

rocWMMA is a C++ library for accelerating mixed-precision matrix multiply-accumulate (MMA) operations leveraging AMD GPU hardware. rocWMMA makes it easier to break down MMA problems into fragments and distribute block-wise MMA operations in parallel across GPU wavefronts. Our API consists of a header library, that you can use to compile MMA acceleration directly into GPU kernel device code. This can benefit from compiler optimization in the generation of kernel assembly, and doesn't incur additional overhead costs of linking to external runtime libraries or having to launch separate kernels.

The rocWMMA API is designed from a wave perspective, where block-wise API calls are processed by GPU threads coordinated together as a group, or a wave. Importantly, individual threads may only access a portion of a fragment, and there is no guaranteed layout or locality of this data. Thread access to fragment data is analogous to the vector register access of individual threads. Fragments and block-wise operations can therefore be thought of as a wave of grouped data buffers and operations.

Thread blocks that contain multiple waves have the added benefit of resource sharing and cooperation. For rocWMMA, this is especially useful for moving data that may be shared across multiple waves. The rocWMMA Coop API facilitates cooperation between multiple waves when loading and storing opaque data fragments. The order and locality of fragment data contribution per wave is not guaranteed, and can be thought of as a distributed wave. Typically, waves cooperate in moving data from global memory to shared memory (LDS), loading their own full fragment copies from LDS.

Memory addresses are treated as 1D arrays. The rocWMMA API can opaquely handle moving data between both global and shared memory address locations. The library code includes utilities for mapping 2D grid and matrix coordinates to 1D array coordinates, and supports either row-major or column-major data layouts. Block-wise dimensions (BlockM,N,K) familiar to block-wise general matrix multiply (GEMM) product algorithms are supported directly through the availability of matrix instructions for the target architecture. Likewise, you can vary mixed-precision data types for input, output, and accumulation fragments (as listed in the supported configurations section).

rocWMMA is a header library that includes test and sample projects to validate and demonstrate API use. GEMM is used as primary validation given the heavy precedent for the library. However, we are expanding our portfolio to demonstrate more diverse rocWMMA uses.

Requirements

You can use rocWMMA with the following hardware:

• AMD CDNA class GPU featuring matrix core support: gfx908, gfx90a as 'gfx9'
• AMD RDNA3 class GPU featuring AI acceleration support: gfx1100, gfx1101, gfx1102 as 'gfx11'

Double precision FP64 data type support requires gfx90a

rocWMMA software requirements include:

• ROCm stack minimum version 5.4
• rocm-cmake minimum version 0.8.0 for ROCm 5.3
• C++ 17
• CMake >=3.6
• OpenMP

Optional:

• rocBLAS minimum version 2.46.0 for ROCm 5.4 (for rocBLAS validation and benchmarks)
• Doxygen (for building documentation)

Documentation

To build our documentation locally, use the following commands.

cd docs

pip3 install -r sphinx/requirements.txt

python3 -m sphinx -T -E -b html -d _build/doctrees -D language=en . _build/html

Currently supported configurations

• Wave Size: Wave32 (gfx11), Wave64 (gfx9)
• Matrix Layout <LayoutA, LayoutB, Layout C, LayoutD> (N = col major, T = row major)

<N, N, N, N>, <N, N, T, T>

<N, T, N, N>, <N, T, T, T>

<T, N, N, N>, <T, N, T, T>

<T, T, N, N>, <T, T, T, T>

• Thread Block Sizes <TBlockX, TBlockY>

TBlockX must be a multiple of WaveSize

For GEMM, rocWMMA focuses on thread blocks of up to 4 waves for optimal resource usage and occupancy. Larger thread block sizes are possible but are not officially supported.

WS = Wave Size

<WS, 1>, <WS, 2>, <WS, 4>

<WS * 2, 1>, <WS * 2, 2>

<WS * 4, 1>

• Data Types <Ti / To / Tc> = <Input type / Output Type / Compute Type>

  Input Type = Matrix A/B

  Output Type = Matrix C/D

  Compute Type = math / accumulation type

gfx11 only supports BlockM/N = 16

Ti / To / Tc	BlockM	BlockN	BlockK Range (powers of 2)	Notes
i8 / i32 / i32	16	16	[16, 256]
	32	32	[8, 128]
i8 / i8 / i32	16	16	[16, 256]
	32	32	[8, 128]
f16 / f32 / f32	16	16	[16, 256]
	32	32	[8, 128]
f16 / f16 / f32	16	16	[16, 256]
	32	32	[8, 128]
f16 / f16 / f16*	16	16	[16, 256]	*= CDNA native f32 accumulation downcasted to fp16
	32	32	[8, 128]
__half / f32 / f32	16	16	[16, 256]
	32	32	[8, 128]
__half / __half / f32	16	16	[16, 256]
	32	32	[8, 128]
__half / __half / __half*	16	16	[16, 256]	*= CDNA native f32 accumulation downcasted to __half
	32	32	[8, 128]
bf16 / f32 / f32	16	16	[8, 256]
	32	32	[4, 128]
bf16 / bf16 / f32	16	16	[8, 256]
	32	32	[4, 128]
bf16 / bf16 / bf16*	16	16	[8, 256]	*= CDNA native f32 accumulation downcasted to bf16
	32	32	[4, 128]
f32 / f32 / f32*	16	16	[4, 256]	*= Supported only on gfx9
	32	32	[2, 128]
f64 / f64 / f64*	16	16	[4, 256]	*= Supported only on gfx90a +

API functions

fill_fragment

Broadcasts a desired value to all elements in the fragment.

load_matrix_sync / store_matrix_sync

Loads data from memory according to the matrix layout.

• Matrix A layout: Loads and stores matrix columns in the K direction (Matrix A = M x K, fragA = BlockM x BlockK)
• Matrix B layout: Loads and stores matrix rows in the K direction (Matrix B = K x N, fragB = BlockK x BlockN)
• Matrix C layout: Loads and stores matrix rows in a vector width of 4 (Matrix C = M x N, fragAcc = BlockM x BlockN)

Fragments are stored in packed registers in optimal load and store patterns. In-register elements have no guaranteed order, which optimizes loading and storing efficiency.

mma_sync

The MMA operation is performed on fragment data. The outer product of Fragment A elements with Fragment B elements is added to the accumulator fragment.

synchronize_workgroup

Flow control for synchronization across multiple wavefronts in a workgroup. It also ensures the synchronization of shared and global memory accesses across wavefronts.

load_matrix_coop_sync / store_matrix_coop_sync

Loads data from memory according to the matrix layout. Splits operation among wave members of a workgroup.

• Matrix A: Layout loads and stores matrix columns in the K direction (Matrix A = M x K, fragA = BlockM x BlockK)
• Matrix B: Layout loads and stores matrix rows in the K direction (Matrix B = K x N, fragB = BlockK x BlockN)
• Matrix C: Layout loads and stores matrix rows in vector width of 4 (Matrix C = M x N, fragAcc = BlockM x BlockN)

Each contributing wave handles partial operation data, so split parameters should be consistent across subsequent operations. For example, cooperatively moving data from global to shared memory should use the same split parameters for the global load and subsequent local store.

Contributing to the code

1. Create and track a rocWMMA fork.

2. Clone your fork:

   git clone -b develop https://github.com/<your_fork>/rocWMMA.git .
   .githooks/install
   git checkout -b <new_branch>
   ...
   git add <new_work>
   git commit -m "What was changed"
   git push origin <new_branch>
   ...

3. Create a pull request to the ROCmSoftwarePlatform/rocWMMA develop branch.

4. Await CI and approval feedback.

5. Once approved, merge.

You must install GitHooks because there are triggers for Clang formatting in commits.

Build with CMake

Project options

Option	Description	Default value
AMDGPU_TARGETS	Build code for specific GPU target(s)	gfx908:xnack-;gfx90a:xnack-;gfx90a:xnack+;gfx1100;gfx1101;gfx1102
ROCWMMA_BUILD_TESTS	Build Tests	ON
ROCWMMA_BUILD_SAMPLES	Build Samples	ON
ROCWMMA_BUILD_DOCS	Build doxygen documentation from code	OFF
ROCWMMA_BUILD_ASSEMBLY	Generate assembly files	OFF
ROCWMMA_BUILD_VALIDATION_TESTS	Build validation tests	ON (requires ROCWMMA_BUILD_TESTS=ON)
ROCWMMA_BUILD_BENCHMARK_TESTS	Build benchmark tests	OFF (requires ROCWMMA_BUILD_TESTS=ON)
ROCWMMA_BUILD_EXTENDED_TESTS	Build extended testing coverage	OFF (requires ROCWMMA_BUILD_TESTS=ON)
ROCWMMA_VALIDATE_WITH_ROCBLAS	Use rocBLAS for validation tests	ON (requires ROCWMMA_BUILD_VALIDATION_TESTS=ON)
ROCWMMA_BENCHMARK_WITH_ROCBLAS	Include rocBLAS benchmarking data	OFF (requires ROCWMMA_BUILD_BENCHMARK_TESTS=ON)

Example configurations

By default, the project is configured in release mode and is linked against rocBLAS for validating results. Here are some configuration examples:

Configuration	Command
Basic	CC=hipcc CXX=hipcc cmake -B<build_dir> .
Targeting gfx908	CC=hipcc CXX=hipcc cmake -B<build_dir> . -DAMDGPU_TARGETS=gfx908:xnack-
Debug build	CC=hipcc CXX=hipcc cmake -B<build_dir> . -DCMAKE_BUILD_TYPE=Debug
Build without rocBLAS (default on)	CC=hipcc CXX=hipcc cmake -B<build_dir> . -DROCWMMA_VALIDATE_WITH_ROCBLAS=OFF -DROCWMMA_BENCHMARK_WITH_ROCBLAS=OFF

After configuration, build with cmake --build <build_dir> -- -j<nproc>

The build time for all projects can take several minutes.

Tips to reduce tests compile time

• Target a specific GPU (e.g., gfx908:xnack-)

• Use lots of threads (e.g., -j64)

• Select ROCWMMA_BUILD_ASSEMBLY=OFF

• Select ROCWMMA_BUILD_DOCS=OFF

• Select ROCWMMA_BUILD_EXTENDED_TESTS=OFF

• Specify ROCWMMA_BUILD_VALIDATION_TESTS or ROCWMMA_BUILD_BENCHMARK_TESTS as ON, and the other as OFF

• For investigating particular kernels, build the ad hoc test with the parameters you are interested in

• Manually build specific tests:

  cd <build_dir>
  make <target_name> -j64

  Where <target_name> is one of the following:

  <target_name>	Description
  rocwmma_unit_tests	Build all rocWMMA unit tests
  rocwmma_gemm_tests_validate	Build all GEMM validation tests
  rocwmma_gemm_tests_bench	Build all GEMM benchmark tests
  rocwmma_dlrm_tests_validate	Build all deep learning recommendation model (DLRM) validation tests
  rocwmma_dlrm_tests_bench	Build all DLRM benchmark tests
  rocwmma_samples	Build all rocWMMA samples
  Individual target name (contamination_test, simple_sgemm, etcetera)	Build individual rocWMMA test or sample

• Manually reduce tests or test case coverage

Run unit tests

rocWMMA library features are showcased, validated, and benchmarked if applicable in GoogleTest applications.

With CTest enabled, you can run the entire test suite by running CTest in the build folder:

cd <build_dir>
ctest --output-on-failure

Otherwise, individual tests can be run as follows:

Contamination test

Unit tests for loading and storing APIs to verify that data boundaries are not crossed and pristine data remain untouched.

Run the validation:

<build_dir>/test/unit/contamination_test

Cross-lane ops test

Unit tests for vector cross-lane operations.

Run the validation:

<build_dir>/test/unit/cross_lane_ops_test

Fill fragment test

Tests the rocwmma::fill_fragment API function for all supported configurations. Tests broadcasting of a desired value to all elements in the fragment.

Run the validation:

<build_dir>/test/unit/fill_fragment_test

I/O shape test

Unit test for I/O shape meta-data generation on host machine

Run the validation:

<build_dir>/test/unit/io_shape_test

I/O traits test

Unit test for I/O traits metadata generation on host machine.

Run the validation:

<build_dir>/test/unit/io_traits_test

Layout test

Unit tests for the internal collect and scatter matrix element to register mapping transforms.

Run the validation:

<build_dir>/test/unit/layout_test

Load and store matrix sync test

Tests the rocwmma::load_matrix_sync and rocwmma::store_matrix_sync API functions for all supported configurations. Tests proper emplacement of data during loads and stores.

Run the validation:

<build_dir>/test/unit/load_store_matrix_sync_test
<build_dir>/test/unit/load_store_matrix_coop_sync_test

Map util test

Unit tests for the utility class used to calculate transforms and offsets between grid, matrix, and data coordinate systems.

Run the validation:

<build_dir>/test/unit/map_util_test

Vector iterator test

Unit tests for internal vector iteration and navigation during access and storage.

Run the validation:

<build_dir>/test/unit/vector_iterator_test

Vector test

Unit tests for internal vector storage.

Run the validation:

<build_dir>/test/unit/vector_test

GEMM tests

Implements a GEMM blocking algorithm using rocWMMA for all supported parametric configurations. Validates on a CPU algorithm, or rocBLAS if available. Validation runs are performed on a reduced subset of matrix sizes. Benchmark runs are performed on a larger set of matrix sizes. Extended tests provide comprehensive parameter coverage and larger matrix sizes.

GEMM kernel naming nomenclature

As of rocWMMA v0.8, we've introduced GEMM kernel naming nomenclature to allow compact representation and quick identification of kernel implementation features. The rocWMMA GEMM test library includes kernels that support the following features:

PGR# - Prefetch Global Read lookup stages. PGR0 = no global read prefetch. PGR1 = 1 stage global read prefetch.
LB# - Lds buffer count. LB0 = no lds usage, LB2 = 2 Lds buffers used for swap.
MP# - MFMA instruction priority. MP0 = default MFMA instruction priority of 0. MP1 = raise MFMA instruction priority to 1.
MB - Multiple output blocks targeted per wave
SB - Single output block target per wave
NC - Non-Cooperative load / store
CP - Cooperative load / store
BLK - Cooperative load / store per block tile
WV - Cooperative load / store per wave tile
WG - Cooperative load / store per macro tile

• gemm_PGR0_LB0_MP0_SB_NC: The simplest blocked GEMM example, which targets one output block of matrix multiplication per wave. No prefetch, no lDs usage, default MFMA prioritization, single block output and non-collaborative.

• gemm_PGR0_LB0_MP0_MB_NC: Implements a multi-block GEMM where each wave is responsible for a BlocksX x BlocksY grid of output blocks. No prefetch, no lDs usage, default MFMA prioritization, multiple blocks output, and non-collaborative.

• gemm_PGR1_LB2_MP0_MB_CP_BLK: Implements a multi-block GEMM where each wave is responsible for a BlocksX x BlocksY grid of output blocks. This kernel leverages shared memory to implement a data prefetching pipeline and collaborates with other waves to improve performance. Implements single stage prefetch, double lDs buffer, default MFMA prioritization, multiple blocks output, and is block-tile collaborative in global read and local write.

• gemm_PGR1_LB2_MP0_MB_CP_WV: Implements a multi-block GEMM where each wave is responsible for a BlocksX x BlocksY grid of output blocks. This kernel leverages shared memory to implement a data prefetching pipeline and collaborates with other waves to improve performance. Implements single stage prefetch, double lDs buffer, default MFMA prioritization, multiple blocks output, and is wave-tile collaborative in global read and local write.

• gemm_PGR1_LB2_MP0_MB_CP_WG: Implements a multi-block GEMM where each wave is responsible for a BlocksX x BlocksY grid of output blocks. This kernel leverages shared memory to implement a data prefetching pipeline and collaborates with other waves to improve performance. Implements single stage prefetch, double lDs buffer, default MFMA prioritization, multiple blocks output and is macro-tile collaborative in global read and local write.

• Ad Hoc Test: An executable that focuses on a specific set of kernel parameters. This is used as a quick mock-up of a situational investigation of a particular GEMM kernel.

Validation tests are postfixed with -validate. Benchmark tests are postfixed with -bench.

Run the validation tests:

<build_dir>/test/gemm/gemm_PGR0_LB0_MP0_SB_NC-validate
<build_dir>/test/gemm/gemm_PGR0_LB0_MP0_MB_NC-validate
<build_dir>/test/gemm/gemm_PGR1_LB2_MP0_MB_CP_BLK-validate
<build_dir>/test/gemm/gemm_PGR1_LB2_MP0_MB_CP_WV-validate
<build_dir>/test/gemm/gemm_PGR1_LB2_MP0_MB_CP_WG-validate

Run the benchmark only:

Note that benchmark runs can take several hours to complete.

<build_dir>/test/gemm/gemm_PGR0_LB0_MP0_SB_NC-bench
<build_dir>/test/gemm/gemm_PGR0_LB0_MP0_MB_NC-bench
<build_dir>/test/gemm/gemm_PGR1_LB2_MP0_MB_CP_BLK-bench
<build_dir>/test/gemm/gemm_PGR1_LB2_MP0_MB_CP_WV-bench
<build_dir>/test/gemm/gemm_PGR1_LB2_MP0_MB_CP_WG-bench

Run the ad hoc test:

<build_dir>/test/gemm/gemm_PGR0_LB0_MP0_SB_NC_ad_hoc-validate
<build_dir>/test/gemm/gemm_PGR0_LB0_MP0_MB_NC_ad_hoc-validate
<build_dir>/test/gemm/gemm_PGR1_LB2_MP0_MB_CP_ad_hoc-validate

<build_dir>/test/gemm/gemm_PGR0_LB0_MP0_SB_NC_ad_hoc-bench
<build_dir>/test/gemm/gemm_PGR0_LB0_MP0_MB_NC_ad_hoc-bench
<build_dir>/test/gemm/gemm_PGR1_LB2_MP0_MB_CP_ad_hoc-bench

GEMM test logging arguments

Compact	Verbose	Description
-os <output_file>.csv	--output_stream <output_file>.csv	stream GEMM testing output to CSV file
	--omit	omits certain outputs : 1 = SKIPPED tests 2 - FAILED tests 4 - PASSED tests 8 - All non-gtest output

Tips to reduce run time

• Use GoogleTest filters, target specific test names:

<test_exe> --gtest_filter=*name_filter*

• Manually adjust the test cases coverage
• Use ad hoc tests to focus in on specific parameters
• Select ROCWMMA_BUILD_EXTENDED_TESTS=OFF

Samples

These are standalone, practical use cases for the rocWMMA API. They have minimal dependencies and represent a targeted application with a fixed set of parameters.

GEMM

Use MMA to demonstrate rocWMMA API usage in the context of wave-level GEMM computation, in both simplified and optimized versions.

Simple GEMM

Simple GEMM algorithm demonstration without LDS memory usage and no transpose.

simple_sgemm calculates D = Alpha * A x B + Beta * C with fp32 inputs and output.

simple_dgemm calculates D = Alpha * A x B + Beta * C with fp64 inputs and output.

simple_hgemm calculates D = Alpha * A x B + Beta * C with fp16 inputs and output.

Includes a simple CPU validation and benchmark.

Run a simple gemm sample:

<build_dir>/samples/simple_sgemm
<build_dir>/samples/simple_dgemm
<build_dir>/samples/simple_hgemm

Peformant SGEMM

To implement and measure performance of Matrix Multiply-Accumulate(D = Alpha * A x B + Beta * C) with user-defined configurations on a GPU.

It contains the best performant version of the multi-block GEMM algorithm with LDS memory, macro-tile collaboration, data reuse, and optimized pipeline, configured with the finest parameters for larger sizes (1K and greater).

perf_sgemm calculates D = Alpha * A x B + Beta * C with fp32 inputs and output.

perf_dgemm calculates D = Alpha * A x B + Beta * C with fp64 inputs and output.

perf_hgemm calculates D = Alpha * A x B + Beta * C with fp16 inputs and output.

Includes a simple CPU validation and benchmark.

Run a perf gemm sample:

<build_dir>/samples/perf_sgemm
<build_dir>/samples/perf_dgemm
<build_dir>/samples/perf_hgemm

GEMV

SGEMV

Simple MMA with a vector demonstration, without LDS or transpose.

Calculates Y = alpha * (A) * X + beta * Y with fp32 inputs and output.

Includes a simple CPU validation and benchmark.

A = Matrix of size m * k (col-major)

X = Vector of size k * 1 (col-major)

Y = accumulator of size m * 1 (col-major)

Run the sgemv sample:

<build_dir>/samples/simple_sgemv

DGEMV

Simple MMA with a vector demonstration, without LDS or transpose.

Calculates Y = alpha * (A) * X + beta * Y with fp64 inputs and output.

Includes a simple CPU validation and benchmark.

A = Matrix of size m * k (row-major)

X = Vector of size k * 1 (col-major)

Y = accumulator of size m * 1 (row-major)

Run the dgemv sample:

<build_dir>/samples/simple_dgemv

Simple deep learning recommendation model

Simple deep learning recommendation model (DLRM) for machine learning. Implements both forward and backward passes on fp16 inputs and outputs.

Includes a simple CPU validation and benchmark.

Run the simple_dlrm sample:

<build_dir>/samples/simple_dlrm

hipRTC support

The HIP runtime compilation (hipRTC) environment allows simultaneous compilation, loading, and running of device code on AMD GPUs. The rocWMMA library is compatible with hipRTC, so you can leverage it for runtime-generated kernels. A simple GEMM sample is included to demonstrate compatibility. For more information, refer to the HIP API reference.

The `rocwmma::bfloat16_t` data type is not currently supported in hipRTC.

hipRTC GEMM sample

A simple GEMM algorithm demonstrating runtime compilation (hipRTC) compatibility. Calculates D = Alpha * A x B + Beta * C with mixed-precision fp16 inputs and fp32 output. Includes code compilation, module loading, and kernel launch with the hipRTC API, with simple CPU validation and benchmarking.

Run the hipRTC_gemm sample:

<build_dir>/samples/hipRTC_gemm