API reference guide#

This document provides information about rocWMMA functions, data types, and other programming constructs.

Synchronous API#

In general, rocWMMA API functions ( load_matrix_sync, store_matrix_sync, mma_sync ) are assumed to be synchronous when used in the context of global memory.

When using these functions in the context of shared memory (e.g. LDS memory), additional explicit workgroup synchronization (synchronize_workgroup) may be required due to the nature of this memory usage.

Supported GPU architectures#

List of supported CDNA architectures (wave64):

gfx908
gfx90a
gfx940
gfx941
gfx942

Note

gfx9 = gfx908, gfx90a, gfx940, gfx941, gfx942

gfx940+ = gfx940, gfx941, gfx942

List of supported RDNA architectures (wave32):

gfx1100
gfx1101
gfx1102

Note

gfx11 = gfx1100, gfx1101, gfx1102

Supported data types#

rocWMMA mixed precision multiply-accumulate operations support the following data type combinations.

Data Types <Ti / To / Tc> = <Input type / Output Type / Compute Type>, where:

Input Type = Matrix A / B
Output Type = Matrix C / D
Compute Type = Math / accumulation type
i8 = 8-bit precision integer
f8 = 8-bit precision floating point
bf8 = 8-bit precision brain floating point
f16 = half-precision floating point
bf16 = half-precision brain floating point
f32 = single-precision floating point
i32 = 32-bit precision integer
xf32 = single-precision tensor floating point
f64 = double-precision floating point

Note

f16 represents equivalent support for both _Float16 and __half types.

Current f8 support is NANOO (optimized) format.

Ti / To / Tc	BlockM	BlockN	BlockK Range* (Powers of 2)	CDNA Support	RDNA Support
bf8 / f32 / f32	16	16	32+	gfx940+	-
bf8 / f32 / f32	32	32	16+	gfx940+	-
f8 / f32 / f32	16	16	32+	gfx940+	-
f8 / f32 / f32	32	32	16+	gfx940+	-
i8 / i32 / i32	16	16	16+	gfx908, gfx90a	gfx11
	16	16	32+	gfx940+	-
	32	32	8+	gfx908, gfx90a	-
	32	32	16+	gfx940+	-
i8 / i8 / i32	16	16	16+	gfx908, gfx90a	gfx11
	16	16	32+	gfx940+	-
	32	32	8+	gfx908, gfx90a	-
	32	32	16+	gfx940+	-
f16 / f32 / f32	16	16	16+	gfx9	gfx11
f16 / f32 / f32	32	32	8+	gfx9	-
f16 / f16 / f32	16	16	16+	gfx9	gfx11
f16 / f16 / f32	32	32	8+	gfx9	-
f16 / f16 / f16**	16	16	16+	gfx9	gfx11
f16 / f16 / f16**	32	32	8+	gfx9	-
bf16 / f32 / f32	16	16	8+	gfx908	-
	16	16	16+	gfx90a, gfx940+	gfx11
	32	32	4+	gfx908	-
	32	32	8+	gfx90a, gfx940+	-
bf16 / bf16 / f32	16	16	8+	gfx908	-
	16	16	16+	gfx90a, gfx940+	gfx11
	32	32	4+	gfx908	-
	32	32	8+	gfx90a, gfx940+	-
bf16 / bf16 / bf16**	16	16	8+	gfx908	-
	16	16	16+	gfx90a, gfx940+	gfx11
	32	32	4+	gfx908	-
	32	32	8+	gfx90a, gfx940+	-
f32 / f32 / f32	16	16	4+	gfx9	-
f32 / f32 / f32	32	32	2+	gfx9	-
xf32 / xf32 / xf32	16	16	8+	gfx940+	-
xf32 / xf32 / xf32	32	32	4+	gfx940+	-
f64 / f64 / f64	16	16	4+	gfx90a, gfx940+	-

Note

* = BlockK range lists the minimum possible value. Other values in the range are powers of 2 larger than the minimum. Practical BlockK values are usually 32 and smaller.

** = CDNA architectures matrix unit accumulation is natively 32-bit precision and is converted to the desired type.

Supported matrix layouts#

(N = col major, T = row major)

LayoutA	LayoutB	Layout C	LayoutD
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

Supported thread block sizes#

rocWMMA generally supports and tests up to 4 wavefronts per threadblock. The X dimension is expected to be a multiple of the wave size and will be scaled as such.

TBlock_X	TBlock_Y
WaveSize	1
WaveSize	2
WaveSize	4
WaveSize*2	1
WaveSize*2	2
WaveSize*4	1

Note

WaveSize (RDNA) = 32

WaveSize (CDNA) = 64

Using rocWMMA API#

This section describes how to use the rocWMMA library API.

rocWMMA datatypes#

matrix_a#

struct matrix_a#: Meta-tag indicating data context is input Matrix A.

matrix_b#

struct matrix_b#: Meta-tag indicating data context is input Matrix B.

accumulator#

struct accumulator#: Meta-tag indicating data context is Accumulator (also used as Matrix C / D).

row_major#

struct row_major#: Meta-tag indicating 2D in-memory data layout as row major.

col_major#

struct col_major#: Meta-tag indicating 2D in-memory data layout as column major.

fragment#

template<typename MatrixT, uint32_t BlockM, uint32_t BlockN, uint32_t BlockK, typename DataT, typename DataLayoutT = void> class fragment#

rocWMMA fragment class. This is the primary object used in block-wise decomposition of the matrix multiply-accumulate (mma) problem space. In general, fragment data is associated with a matrix context (matrix_a, matrix_b or accumulator), a block size (BlockM/N/K), a datatype (e.g. single-precision float, etc.) and an in-memory 2D layout (e.g. row_major or col_major). These fragment properties are used to define how data is handled and stored locally, and to drive API implementations for loading / storing, mma and transforms. Fragment abstractions are designed to promote a simple wavefront programming model, which can accelerate development time. Internal thread-level details are handled by rocWMMA which frees the user to focus on wavefront block-wise decomposition. Written purely in device code, the programmer can use this object in their own device kernels.

Note

Fragments are stored in packed registers, however vector elements have no guaranteed order or locality.

Template Parameters:

MatrixT – fragment context
BlockM/N/K – block dimensions
DataT – datatype
DataLayoutT – in-memory layout as col_major or row_major

Public Types

using IOTraits = typename IOConfig<MatrixT, BlockM, BlockN, BlockK, DataT, DataLayoutT>::IOTraits#: Input / output traits specific to AMDGCN architecture.

Public Functions

inline DataT &operator[](uint32_t index)#

Parameters:: index – Element index
Returns:: Mutable unpacked element accessor at given index

inline DataT const &operator[](uint32_t index) const#

Parameters:: index – Element index
Returns:: Immutable unpacked element accessor at given index

inline Traits::StorageT &operator*()#

Returns:: Mutable packed storage vector accessor

inline Traits::StorageT const &operator*() const#

Returns:: Immutable packed storage vector accessor

Public Members

union rocwmma::fragment::[anonymous] [anonymous]#: Internal data storage views. Compatibility with nvcuda::wmma.

Public Static Functions

static inline constexpr uint32_t height()#

Returns:: The geometric height of fragment

static inline constexpr uint32_t width()#

Returns:: The geometric width of fragment

static inline constexpr uint32_t blockDim()#

Returns:: The leading block dimension (non-K)

static inline constexpr uint32_t kDim()#

Returns:: The k dimension

static inline constexpr uint32_t size()#

Returns:: The size of the unpacked elements vector

struct Traits#

Public Types

using AccessT = VecT<UnpackedElementT, IOTraits::UnpackedSize>#: Unpacked data access view.

using StorageT = VecT<PackedElementT, IOTraits::PackedSize>#: Packed data storage view.

rocWMMA enumeration#

layout_t#

rocWMMA API functions#

template<typename MatrixT, uint32_t BlockM, uint32_t BlockN, uint32_t BlockK, typename DataT, typename DataLayoutT> void rocwmma::fill_fragment(fragment<MatrixT, BlockM, BlockN, BlockK, DataT, DataLayoutT> &frag, DataT value)#

Fills the entire fragment with the desired value.

Parameters:

frag – Fragment of type MatrixT with its associated block sizes, data type and layout
value – Fill value of type DataT

Template Parameters:

Matrix – Fragment context
BlockM/N/K – Block dimensions
DataT – Datatype
DataLayoutT – in-memory layout as col_major or row_major

template<typename MatrixT, uint32_t BlockM, uint32_t BlockN, uint32_t BlockK, typename DataT, typename DataLayoutT> void rocwmma::load_matrix_sync(fragment<MatrixT, BlockM, BlockN, BlockK, DataT, DataLayoutT> &frag, const DataT *data, uint32_t ldm)#

Loads the entire fragment from the data pointer according to its matrix and data layout contexts. Data pointer may point to either local or global memory.

Parameters:

frag – Fragment of type MatrixT with its associated block sizes, data type and layout
data – Data pointer to global or local memory
ldm – Leading dimension size

Template Parameters:

MatrixT – Fragment context
BlockM/N/K – Block dimensions
DataT – Datatype
DataLayoutT – In-memory layout as col_major or row_major

template<typename MatrixT, uint32_t BlockM, uint32_t BlockN, uint32_t BlockK, typename DataT> void rocwmma::load_matrix_sync(fragment<MatrixT, BlockM, BlockN, BlockK, DataT> &frag, const DataT *data, uint32_t ldm, layout_t layout)#

Loads the entire fragment from the data pointer according to its matrix layout and data layout contexts. Data pointer may point to either local or global memory. This overload provides a run-time ability to choose the data layout of the target fragment.

Parameters:

frag – Fragment of type MatrixT with its associated block sizes, data type and layout
data – Data pointer to global/local memory
ldm – Leading dimension size
layout – Data layout

Template Parameters:

MatrixT – Fragment context
BlockM/N/K – Block dimensions
DataT – Datatype

template<typename MatrixT, uint32_t BlockM, uint32_t BlockN, uint32_t BlockK, typename DataT, typename DataLayoutT> void rocwmma::store_matrix_sync(DataT *data, fragment<MatrixT, BlockM, BlockN, BlockK, DataT, DataLayoutT> const &frag, uint32_t ldm)#

Stores the entire fragment to the data pointer according to its matrix and data layouts. Data pointer may point to either local or global memory.

Parameters:

frag – Fragment of type MatrixT with its associated block sizes, data type and layout
data – Data pointer to global/local memory
ldm – Leading dimension size

Template Parameters:

MatrixT – Fragment context
BlockM/N/K – Block dimensions
DataT – Datatype
DataLayoutT – in-memory layout as col_major or row_major

template<typename MatrixT, uint32_t BlockM, uint32_t BlockN, uint32_t BlockK, typename DataT> void rocwmma::store_matrix_sync(DataT *data, fragment<MatrixT, BlockM, BlockN, BlockK, DataT> const &frag, uint32_t ldm, layout_t layout)#

Stores the entire fragment to the data pointer according to its matrix layout. Data pointer may point to either local or global memory. This overload provides a run-time ability to choose the data layout of the target fragment.

Parameters:

frag – Fragment of type MatrixT with its associated block sizes, data type and layout
data – Data pointer to global/local memory
ldm – Leading dimension size
layout – Data layout

Template Parameters:

MatrixT – Fragment context
BlockM/N/K – Block dimensions
DataT – Datatype

template<uint32_t BlockM, uint32_t BlockN, uint32_t BlockK, typename InputT, typename ComputeT, typename LayoutA, typename LayoutB, typename LayoutC, typename LayoutD> void rocwmma::mma_sync(fragment<accumulator, BlockM, BlockN, BlockK, ComputeT, LayoutD> &d, fragment<matrix_a, BlockM, BlockN, BlockK, InputT, LayoutA> const &a, fragment<matrix_b, BlockM, BlockN, BlockK, InputT, LayoutB> const &b, fragment<accumulator, BlockM, BlockN, BlockK, ComputeT, LayoutC> const &c)#

Performs the Multiply-Accumulate operation on the fragments A, B, C and D (D = A * B + C)

Note

Frag c = d is valid

Parameters:

d – Accumulator output D
a – Input fragment A
b – Input fragment B
c – Input accumulator fragment C

Template Parameters:

BlockM/N/K – block dimensions
InputT – Datatype of input frags A and B
ComputeT – Datatype of accumulator fragment C / D
LayoutA/B/C/D – In-memory layout of frag as col_major or row_major

void rocwmma::synchronize_workgroup()#: Synchronization point for all wavefronts in a workgroup. Guarantees pending reads / writes to LDS are flushed.

rocWMMA cooperative API functions#

template<typename MatrixT, uint32_t BlockM, uint32_t BlockN, uint32_t BlockK, typename DataT, typename DataLayoutT> inline void rocwmma::load_matrix_coop_sync(fragment<MatrixT, BlockM, BlockN, BlockK, DataT, DataLayoutT> &frag, const DataT *data, uint32_t ldm, uint32_t waveIndex, uint32_t waveCount)#

Loads the fragment from memory address cooperatively across wavefronts. Each cooperating wavefront is responsible in loading a portion of the final fragment. This function may be paired with store_matrix_coop_sync to move a single fragment collaboratively between memory locations.

The full load is split into work items (default = waveCount). Work items are assigned in round robin fashion to waves in the range of [0, waveCount). The current wave index determines the order of the current wave in the collaboration pool. Work items are consumed in order by waves [0, waveCount) until there are no more work items and the operation is completed.

Note

Individual wavefronts only load a smaller portion of the full data that they are responsible for.

Parameters:

frag – Fragment of type MatrixT with its associated block sizes, data type and layout
data – Data pointer to global/local memory
ldm – Leading dimension size
waveIndex – Index assignment of current wave in collaboration
waveCount – Number of waves assigned for collaboration

Template Parameters:

MatrixT – fragment context
BlockM/N/K – block dimensions
DataT – data type
DataLayoutT – in-memory layout as col_major or row_major

template<typename MatrixT, uint32_t BlockM, uint32_t BlockN, uint32_t BlockK, typename DataT, typename DataLayoutT> void rocwmma::load_matrix_coop_sync(fragment<MatrixT, BlockM, BlockN, BlockK, DataT, DataLayoutT> &frag, const DataT *data, uint32_t ldm)#

The full load is split into work items (current waveCount). Work items are assigned in round robin fashion to waves in the range of [0, waveCount). The current wave index determines the order of the current wave in the collaboration pool. Work items are consumed in order by waves [0, waveCount) until there are no more work items and the operation is completed.

Note

Individual wavefronts only load a smaller portion of the full data that they are responsible for.

Parameters:

frag – Fragment of type MatrixT with its associated block sizes, data type and layout
data – Data pointer to global/local memory
ldm – Leading dimension size

Template Parameters:

MatrixT – fragment context
BlockM/N/K – block dimensions
DataT – data type
DataLayoutT – in-memory layout as col_major or row_major

template<uint32_t WaveCount, typename MatrixT, uint32_t BlockM, uint32_t BlockN, uint32_t BlockK, typename DataT, typename DataLayoutT> void rocwmma::load_matrix_coop_sync(fragment<MatrixT, BlockM, BlockN, BlockK, DataT, DataLayoutT> &frag, const DataT *data, uint32_t ldm, uint32_t waveIndex)#

This flavor of cooperative load includes WaveCount as a template parameter that may be used to optimize during compile time, and is preferred over providing this value as runtime function argument.

The full load is split into work items (WaveCount). Work items are assigned in round robin fashion to waves in the range of [0, waveCount). The current wave index determines the order of the current wave in the collaboration pool. Work items are consumed in order by waves [0, waveCount) until there are no more work items and the operation is completed.

Note

Individual wavefronts only load a smaller portion of the full data that they are responsible for.

Parameters:

frag – Fragment of type MatrixT with its associated block sizes, data type and layout
data – Data pointer to global/local memory
ldm – Leading dimension size
waveIndex – Index assignment of current wave in collaboration

Template Parameters:

uint32_t – WaveCount
MatrixT – fragment context
BlockM/N/K – block dimensions
DataT – data type
DataLayoutT – in-memory layout as col_major or row_major

template<typename MatrixT, uint32_t BlockM, uint32_t BlockN, uint32_t BlockK, typename DataT, typename DataLayoutT> void rocwmma::store_matrix_coop_sync(DataT *data, fragment<MatrixT, BlockM, BlockN, BlockK, DataT, DataLayoutT> const &frag, uint32_t ldm, uint32_t waveIndex, uint32_t waveCount)#

Cooperative Store Matrix - Stores the entire fragment to data address cooperatively across waves. Each cooperative wave is responsible in storing a portion of the final fragment.

The full store is split into work items (default = waveCount). Work items are assigned in round robin fashion to waves in the range of [0, waveCount). The current wave index determines the order of the current wave in the collaboration pool. Work items are consumed in order by waves [0, waveCount) until there are no more work items and the operation is completed.

Note

The full fragment data is not required to be cohesive for individual waves as they only store a piece of the data. This function may be paired with load_matrix_coop_sync to move a single fragment collaboratively between memory locations.

Parameters:

data – Data pointer to global/local memory
frag – Fragment of type MatrixT with its associated block sizes, data type and layout
ldm – Leading dimension size
waveIndex – Index assignment of current wave in collaboration
waveCount – Number of waves assigned for collaboration

Template Parameters:

MatrixT – fragment context
BlockM/N/K – block dimensions
DataT – data type
DataLayoutT – in-memory layout as col_major or row_major

Cooperative Store Matrix - Stores the entire fragment to data address cooperatively across waves. Each cooperative wave is responsible in storing a portion of the final fragment.

The full store is split into work items (current waveCount). Work items are assigned in round robin fashion to waves in the range of [0, waveCount). The current wave index determines the order of the current wave in the collaboration pool. Work items are consumed in order by waves [0, waveCount) until there are no more work items and the operation is completed.

Note

Parameters:

data – Data pointer to global/local memory
frag – Fragment of type MatrixT with its associated block sizes, data type and layout
ldm – Leading dimension size

Template Parameters:

MatrixT – fragment context
BlockM/N/K – block dimensions
DataT – data type
DataLayoutT – in-memory layout as col_major or row_major

template<uint32_t WaveCount, typename MatrixT, uint32_t BlockM, uint32_t BlockN, uint32_t BlockK, typename DataT, typename DataLayoutT> void rocwmma::store_matrix_coop_sync(DataT *data, fragment<MatrixT, BlockM, BlockN, BlockK, DataT, DataLayoutT> const &frag, uint32_t ldm, uint32_t waveIndex)#

Cooperative Store Matrix - Stores the entire fragment to data address cooperatively across waves. Each cooperative wave is responsible in storing a portion of the final fragment.

This flavor of cooperative store includes WaveCount as a template parameter that may be used to optimize during compile time, and is preferred over providing this value as runtime function argument.

The full store is split into work items (WaveCount). Work items are assigned in round robin fashion to waves in the range of [0, waveCount). The current wave index determines the order of the current wave in the collaboration pool. Work items are consumed in order by waves [0, waveCount) until there are no more work items and the operation is completed.

Note

Parameters:

data – Data pointer to global/local memory
frag – Fragment of type MatrixT with its associated block sizes, data type and layout
ldm – Leading dimension size
waveIndex – Index assignment of current wave in collaboration

Template Parameters:

WaveCount – Number of waves participating
MatrixT – fragment context
BlockM/N/K – block dimensions
DataT – data type
DataLayoutT – in-memory layout as col_major or row_major

rocWMMA transforms API functions#

template<typename FragT> static inline T rocwmma::applyTranspose(FragT &&frag)#

Applies the transpose transform the input fragment. Transpose is defined as orthogonal matrix and data layout. E.g. T(fragment<matrix_a, BlockM, BlockN, BlockK, DataT, row_major>) = fragment<matrix_b, BlockN, BlockM, BlockK, DataT, col_major>

Parameters:: frag – Fragment of type MatrixT with its associated block sizes, data type and layout
Template Parameters:: FragT – The incoming fragment type
Returns:: Transposed (orthogonal) fragment

template<typename DataLayoutT, uint32_t WaveCount = 1, typename FragT> static inline T rocwmma::applyDataLayout(FragT &&frag)#

Transforms the input fragment to have the desired data layout.

Parameters:

frag – Fragment of type MatrixT with its associated block sizes, data type and layout

Template Parameters:

DataLayoutT – The desired fragment data layout to apply
WaveCount – The number of cooperative waves for cooperative fragments (defaults to 1, or non-cooperative)
FragT – The incoming fragment type

Returns:

Fragment with transformed data layout

Sample programs#

See a sample code for calling rocWMMA functions load_matrix_sync, store_matrix_sync, fill_fragment, and mma_sync here. For more such sample programs, refer to the Samples directory.

API reference guide

Contents

API reference guide#

Synchronous API#

Supported GPU architectures#

Supported data types#

Supported matrix layouts#

Supported thread block sizes#

Using rocWMMA API#

rocWMMA datatypes#

matrix_a#

matrix_b#

accumulator#

row_major#

col_major#

fragment#

rocWMMA enumeration#

layout_t#

rocWMMA API functions#

rocWMMA cooperative API functions#

rocWMMA transforms API functions#

Sample programs#