
Matrix multiplication using array. More...
Functions  
AFAPI array  matmul (const array &lhs, const array &rhs, const matProp optLhs=AF_MAT_NONE, const matProp optRhs=AF_MAT_NONE) 
Matrix multiply of two arrays. More...  
AFAPI array  matmulNT (const array &lhs, const array &rhs) 
Matrix multiply of two arrays. More...  
AFAPI array  matmulTN (const array &lhs, const array &rhs) 
Matrix multiply of two arrays. More...  
AFAPI array  matmulTT (const array &lhs, const array &rhs) 
Matrix multiply of two arrays. More...  
AFAPI array  matmul (const array &a, const array &b, const array &c) 
Chain 2 matrix multiplications. More...  
AFAPI array  matmul (const array &a, const array &b, const array &c, const array &d) 
Chain 3 matrix multiplications. More...  
AFAPI af_err  af_gemm (af_array *C, const af_mat_prop opA, const af_mat_prop opB, const void *alpha, const af_array A, const af_array B, const void *beta) 
BLAS general matrix multiply (GEMM) of two af_array objects. More...  
AFAPI af_err  af_matmul (af_array *out, const af_array lhs, const af_array rhs, const af_mat_prop optLhs, const af_mat_prop optRhs) 
Matrix multiply of two af_array. More...  
Matrix multiplication using array.
Performs a matrix multiplication on the two input arrays after performing the operations specified in the options. The operations are done while reading the data from memory. This results in no additional memory being used for temporary buffers.
Batched matrix multiplications are supported. Given below are the supported types of batch operations for any given set of two matrices A and B.
Size of Input Matrix A  Size of Input Matrix B  Output Matrix Size 

\( \{ M, K, 1, 1 \} \)  \( \{ K, N, 1, 1 \} \)  \( \{ M, N, 1, 1 \} \) 
\( \{ M, K, b2, b3 \} \)  \( \{ K, N, b2, b3 \} \)  \( \{ M, N, b2, b3 \} \) 
\( \{ M, K, 1, 1 \} \)  \( \{ K, N, b2, b3 \} \)  \( \{ M, N, b2, b3 \} \) 
\( \{ M, K, b2, b3 \} \)  \( \{ K, N, 1, 1 \} \)  \( \{ M, N, b2, b3 \} \) 
where M, K, N are dimensions of the matrix and b2, b3 indicate batch size along the respective dimension.
For the last two entries in the above table, the 2D matrix is broadcasted to match the dimensions of 3D/4D array. This broadcast doesn't involve any additional memory allocations either on host or device.
AFAPI af_err af_gemm  (  af_array *  C, 
const af_mat_prop  opA,  
const af_mat_prop  opB,  
const void *  alpha,  
const af_array  A,  
const af_array  B,  
const void *  beta  
) 
BLAS general matrix multiply (GEMM) of two af_array objects.
This provides a general interface to the BLAS level 3 general matrix multiply (GEMM), which is generally defined as:
\[ C = \alpha * opA(A)opB(B) + \beta * C \]
where \(\alpha\) (alpha
) and \(\beta\) (beta
) are both scalars; \(A\) and \(B\) are the matrix multiply operands; and \(opA\) and \(opB\) are noop (if AF_MAT_NONE
) or transpose (if AF_MAT_TRANS
) operations on \(A\) or \(B\) before the actual GEMM operation. Batched GEMM is supported if at least either \(A\) or \(B\) have more than two dimensions (see af::matmul for more details on broadcasting). However, only one alpha
and one beta
can be used for all of the batched matrix operands.
The af_array that out
points to can be used both as an input and output. An allocation will be performed if you pass a null af_array handle (i.e. af_array c = 0;
). If a valid af_array is passed as \(C\), the operation will be performed on that af_array itself. The C af_array must be the correct type and shape; otherwise, an error will be thrown.
This example demonstrates the usage of the af_gemm function on two matrices. The \(C\) af_array handle is initialized to zero here, so af_gemm will perform an allocation.
The following example shows how you can write to a previously allocated af_array using the af_gemm call. Here we are going to use the af_array s from the previous example and index into the first slice. Only the first slice of the original \(C\) af_array will be modified by this operation.
[in,out]  C  Pointer to the output af_array 
[in]  opA  Operation to perform on A before the multiplication 
[in]  opB  Operation to perform on B before the multiplication 
[in]  alpha  The alpha value; must be the same type as lhs and rhs 
[in]  A  Lefthand side operand 
[in]  B  Righthand side operand 
[in]  beta  The beta value; must be the same type as lhs and rhs 
AFAPI af_err af_matmul  (  af_array *  out, 
const af_array  lhs,  
const af_array  rhs,  
const af_mat_prop  optLhs,  
const af_mat_prop  optRhs  
) 
Matrix multiply of two af_array.
Performs a matrix multiplication on two arrays (lhs, rhs).
[out]  out  Pointer to the output af_array 
[in]  lhs  A 2D matrix af_array object 
[in]  rhs  A 2D matrix af_array object 
[in]  optLhs  Transpose left hand side before the function is performed 
[in]  optRhs  Transpose right hand side before the function is performed 
lhs
and the dense matrix must be rhs
. optLhs
an only be one of AF_MAT_NONE, AF_MAT_TRANS, AF_MAT_CTRANS. optRhs
can only be AF_MAT_NONE. AFAPI array af::matmul  (  const array &  lhs, 
const array &  rhs,  
const matProp  optLhs = AF_MAT_NONE , 

const matProp  optRhs = AF_MAT_NONE 

) 
Matrix multiply of two arrays.
Performs a matrix multiplication on the two input arrays after performing the operations specified in the options. The operations are done while reading the data from memory. This results in no additional memory being used for temporary buffers.
Batched matrix multiplications are supported. Given below are the supported types of batch operations for any given set of two matrices A and B.
Size of Input Matrix A  Size of Input Matrix B  Output Matrix Size 

\( \{ M, K, 1, 1 \} \)  \( \{ K, N, 1, 1 \} \)  \( \{ M, N, 1, 1 \} \) 
\( \{ M, K, b2, b3 \} \)  \( \{ K, N, b2, b3 \} \)  \( \{ M, N, b2, b3 \} \) 
\( \{ M, K, 1, 1 \} \)  \( \{ K, N, b2, b3 \} \)  \( \{ M, N, b2, b3 \} \) 
\( \{ M, K, b2, b3 \} \)  \( \{ K, N, 1, 1 \} \)  \( \{ M, N, b2, b3 \} \) 
where M, K, N are dimensions of the matrix and b2, b3 indicate batch size along the respective dimension.
For the last two entries in the above table, the 2D matrix is broadcasted to match the dimensions of 3D/4D array. This broadcast doesn't involve any additional memory allocations either on host or device.
[in]  lhs  The array object on the left hand side 
[in]  rhs  The array object on the right hand side 
[in]  optLhs  Transpose left hand side before the function is performed 
[in]  optRhs  Transpose right hand side before the function is performed 
lhs
and the dense matrix must be rhs
. optLhs
an only be one of AF_MAT_NONE, AF_MAT_TRANS, AF_MAT_CTRANS. optRhs
can only be AF_MAT_NONE. Chain 2 matrix multiplications.
The matrix multiplications are done in a way to reduce temporary memory
[in]  a  The first array 
[in]  b  The second array 
[in]  c  The third array 
Chain 3 matrix multiplications.
The matrix multiplications are done in a way to reduce temporary memory
[in]  a  The first array 
[in]  b  The second array 
[in]  c  The third array 
[in]  d  The fourth array 
Matrix multiply of two arrays.
Performs a matrix multiplication on the two input arrays after performing the operations specified in the options. The operations are done while reading the data from memory. This results in no additional memory being used for temporary buffers.
Batched matrix multiplications are supported. Given below are the supported types of batch operations for any given set of two matrices A and B.
Size of Input Matrix A  Size of Input Matrix B  Output Matrix Size 

\( \{ M, K, 1, 1 \} \)  \( \{ K, N, 1, 1 \} \)  \( \{ M, N, 1, 1 \} \) 
\( \{ M, K, b2, b3 \} \)  \( \{ K, N, b2, b3 \} \)  \( \{ M, N, b2, b3 \} \) 
\( \{ M, K, 1, 1 \} \)  \( \{ K, N, b2, b3 \} \)  \( \{ M, N, b2, b3 \} \) 
\( \{ M, K, b2, b3 \} \)  \( \{ K, N, 1, 1 \} \)  \( \{ M, N, b2, b3 \} \) 
where M, K, N are dimensions of the matrix and b2, b3 indicate batch size along the respective dimension.
For the last two entries in the above table, the 2D matrix is broadcasted to match the dimensions of 3D/4D array. This broadcast doesn't involve any additional memory allocations either on host or device.
[in]  lhs  The array object on the left hand side 
[in]  rhs  The array object on the right hand side 
lhs
, transpose(rhs
)Matrix multiply of two arrays.
Performs a matrix multiplication on the two input arrays after performing the operations specified in the options. The operations are done while reading the data from memory. This results in no additional memory being used for temporary buffers.
Batched matrix multiplications are supported. Given below are the supported types of batch operations for any given set of two matrices A and B.
Size of Input Matrix A  Size of Input Matrix B  Output Matrix Size 

\( \{ M, K, 1, 1 \} \)  \( \{ K, N, 1, 1 \} \)  \( \{ M, N, 1, 1 \} \) 
\( \{ M, K, b2, b3 \} \)  \( \{ K, N, b2, b3 \} \)  \( \{ M, N, b2, b3 \} \) 
\( \{ M, K, 1, 1 \} \)  \( \{ K, N, b2, b3 \} \)  \( \{ M, N, b2, b3 \} \) 
\( \{ M, K, b2, b3 \} \)  \( \{ K, N, 1, 1 \} \)  \( \{ M, N, b2, b3 \} \) 
where M, K, N are dimensions of the matrix and b2, b3 indicate batch size along the respective dimension.
For the last two entries in the above table, the 2D matrix is broadcasted to match the dimensions of 3D/4D array. This broadcast doesn't involve any additional memory allocations either on host or device.
[in]  lhs  The array object on the left hand side 
[in]  rhs  The array object on the right hand side 
lhs
), rhs
Matrix multiply of two arrays.
Performs a matrix multiplication on the two input arrays after performing the operations specified in the options. The operations are done while reading the data from memory. This results in no additional memory being used for temporary buffers.
Batched matrix multiplications are supported. Given below are the supported types of batch operations for any given set of two matrices A and B.
Size of Input Matrix A  Size of Input Matrix B  Output Matrix Size 

\( \{ M, K, 1, 1 \} \)  \( \{ K, N, 1, 1 \} \)  \( \{ M, N, 1, 1 \} \) 
\( \{ M, K, b2, b3 \} \)  \( \{ K, N, b2, b3 \} \)  \( \{ M, N, b2, b3 \} \) 
\( \{ M, K, 1, 1 \} \)  \( \{ K, N, b2, b3 \} \)  \( \{ M, N, b2, b3 \} \) 
\( \{ M, K, b2, b3 \} \)  \( \{ K, N, 1, 1 \} \)  \( \{ M, N, b2, b3 \} \) 
where M, K, N are dimensions of the matrix and b2, b3 indicate batch size along the respective dimension.
For the last two entries in the above table, the 2D matrix is broadcasted to match the dimensions of 3D/4D array. This broadcast doesn't involve any additional memory allocations either on host or device.
[in]  lhs  The array object on the left hand side 
[in]  rhs  The array object on the right hand side 
lhs
), transpose(rhs
)