BLAS functions

Intel® oneAPI Deep Neural Network Developer Guide and Reference

Download PDF

ID 768875

Date 3/31/2025

Version

Public

A newer version of this document is available. Customers should click here to go to the newest version.

Visible to Intel only — GUID: GUID-E28100F9-D175-43BF-B701-48CB24EF41D4

View Details

BLAS functions

Overview

A subset of Basic Linear Algebra (BLAS) functions that perform matrix-matrix multiplication. More…


// global functions

status dnnl::sgemm(
    char transa,
    char transb,
    dnnl_dim_t M,
    dnnl_dim_t N,
    dnnl_dim_t K,
    float alpha,
    const float* A,
    dnnl_dim_t lda,
    const float* B,
    dnnl_dim_t ldb,
    float beta,
    float* C,
    dnnl_dim_t ldc
    );

status dnnl::gemm_u8s8s32(
    char transa,
    char transb,
    char offsetc,
    dnnl_dim_t M,
    dnnl_dim_t N,
    dnnl_dim_t K,
    float alpha,
    const uint8_t* A,
    dnnl_dim_t lda,
    uint8_t ao,
    const int8_t* B,
    dnnl_dim_t ldb,
    int8_t bo,
    float beta,
    int32_t* C,
    dnnl_dim_t ldc,
    const int32_t* co
    );

status dnnl::gemm_s8s8s32(
    char transa,
    char transb,
    char offsetc,
    dnnl_dim_t M,
    dnnl_dim_t N,
    dnnl_dim_t K,
    float alpha,
    const int8_t* A,
    dnnl_dim_t lda,
    int8_t ao,
    const int8_t* B,
    dnnl_dim_t ldb,
    int8_t bo,
    float beta,
    int32_t* C,
    dnnl_dim_t ldc,
    const int32_t* co
    );

dnnl_status_t DNNL_API dnnl_sgemm(
    char transa,
    char transb,
    dnnl_dim_t M,
    dnnl_dim_t N,
    dnnl_dim_t K,
    float alpha,
    const float* A,
    dnnl_dim_t lda,
    const float* B,
    dnnl_dim_t ldb,
    float beta,
    float* C,
    dnnl_dim_t ldc
    );

dnnl_status_t DNNL_API dnnl_gemm_u8s8s32(
    char transa,
    char transb,
    char offsetc,
    dnnl_dim_t M,
    dnnl_dim_t N,
    dnnl_dim_t K,
    float alpha,
    const uint8_t* A,
    dnnl_dim_t lda,
    uint8_t ao,
    const int8_t* B,
    dnnl_dim_t ldb,
    int8_t bo,
    float beta,
    int32_t* C,
    dnnl_dim_t ldc,
    const int32_t* co
    );

dnnl_status_t DNNL_API dnnl_gemm_s8s8s32(
    char transa,
    char transb,
    char offsetc,
    dnnl_dim_t M,
    dnnl_dim_t N,
    dnnl_dim_t K,
    float alpha,
    const int8_t* A,
    dnnl_dim_t lda,
    int8_t ao,
    const int8_t* B,
    dnnl_dim_t ldb,
    int8_t bo,
    float beta,
    int32_t* C,
    dnnl_dim_t ldc,
    const int32_t* co
    );

Detailed Documentation

A subset of Basic Linear Algebra (BLAS) functions that perform matrix-matrix multiplication.

Global Functions


status dnnl::sgemm(
    char transa,
    char transb,
    dnnl_dim_t M,
    dnnl_dim_t N,
    dnnl_dim_t K,
    float alpha,
    const float* A,
    dnnl_dim_t lda,
    const float* B,
    dnnl_dim_t ldb,
    float beta,
    float* C,
    dnnl_dim_t ldc
    )

Performs single-precision matrix-matrix multiply.

The operation is defined as:

C := alpha * op( A ) * op( B ) + beta * C

where

op( X ) = X or op( X ) = X**T,
alpha and beta are scalars, and
A, B, and C are matrices:
- op( A ) is an MxK matrix,
- op( B ) is an KxN matrix,
- C is an MxN matrix.

The matrices are assumed to be stored in row-major order (the elements in each of the matrix rows are contiguous in memory).

NOTE:

This API does not support XERBLA. Instead, unlike the standard BLAS functions, this one returns a dnnl_status_t value to allow error handling.

Parameters:

transa	Transposition flag for matrix A: ‘N’ or ‘n’ means A is not transposed, and ‘T’ or ‘t’ means that A is transposed.
transb	Transposition flag for matrix B: ‘N’ or ‘n’ means B is not transposed, and ‘T’ or ‘t’ means that B is transposed.
M	The M dimension.
N	The N dimension.
K	The K dimension.
alpha	The alpha parameter that is used to scale the product of matrices A and B.
A	A pointer to the A matrix data.
lda	The leading dimension for the matrix A.
B	A pointer to the B matrix data.
ldb	The leading dimension for the matrix B.
beta	The beta parameter that is used to scale the matrix C.
C	A pointer to the C matrix data.
ldc	The leading dimension for the matrix C.

Returns:

dnnl_success / dnnl::status::success on success and a status describing the error otherwise.


status dnnl::gemm_u8s8s32(
    char transa,
    char transb,
    char offsetc,
    dnnl_dim_t M,
    dnnl_dim_t N,
    dnnl_dim_t K,
    float alpha,
    const uint8_t* A,
    dnnl_dim_t lda,
    uint8_t ao,
    const int8_t* B,
    dnnl_dim_t ldb,
    int8_t bo,
    float beta,
    int32_t* C,
    dnnl_dim_t ldc,
    const int32_t* co
    )

Performs integer matrix-matrix multiply on 8-bit unsigned matrix A, 8-bit signed matrix B, and 32-bit signed resulting matrix C.

The operation is defined as:

C := alpha * (op(A) - A_offset) * (op(B) - B_offset) + beta * C + C_offset

where

op( X ) = X or op( X ) = X**T,
alpha and beta are scalars, and
A, B, and C are matrices:
- op( A ) is an MxK matrix,
- op( B ) is an KxN matrix,
- C is an MxN matrix.
A_offset is an MxK matrix with every element equal the ao value,
B_offset is an KxN matrix with every element equal the bo value,
C_offset is an MxN matrix which is defined by the co array of size len :
- if offsetc = F : the len must be at least 1,
- if offsetc = C : the len must be at least max(1, m),
- if offsetc = R : the len must be at least max(1, n),

The matrices are assumed to be stored in row-major order (the elements in each of the matrix rows are contiguous in memory).

NOTE:

This API does not support XERBLA. Instead, unlike the standard BLAS functions, this one returns a dnnl_status_t value to allow error handling.

WARNING:

On some architectures saturation may happen during intermediate computations, which would lead to unexpected results. For more details, refer to Nuances of int8 Computations.

Parameters:

transa	Transposition flag for matrix A: ‘N’ or ‘n’ means A is not transposed, and ‘T’ or ‘t’ means that A is transposed.
transb	Transposition flag for matrix B: ‘N’ or ‘n’ means B is not transposed, and ‘T’ or ‘t’ means that B is transposed.
offsetc	Flag specifying how offsets should be applied to matrix C: ‘F’ means that the same offset will be applied to each element of the matrix C, ‘C’ means that individual offset will be applied to each element within each column, ‘R’ means that individual offset will be applied to each element within each row.
M	The M dimension.
N	The N dimension.
K	The K dimension.
alpha	The alpha parameter that is used to scale the product of matrices A and B.
A	A pointer to the A matrix data.
lda	The leading dimension for the matrix A.
ao	The offset value for the matrix A.
B	A pointer to the B matrix data.
ldb	The leading dimension for the matrix B.
bo	The offset value for the matrix B.
beta	The beta parameter that is used to scale the matrix C.
C	A pointer to the C matrix data.
ldc	The leading dimension for the matrix C.
co	An array of offset values for the matrix C. The number of elements in the array depends on the value of `offsetc`.

Returns:

dnnl_success / dnnl::status::success on success and a status describing the error otherwise.


status dnnl::gemm_s8s8s32(
    char transa,
    char transb,
    char offsetc,
    dnnl_dim_t M,
    dnnl_dim_t N,
    dnnl_dim_t K,
    float alpha,
    const int8_t* A,
    dnnl_dim_t lda,
    int8_t ao,
    const int8_t* B,
    dnnl_dim_t ldb,
    int8_t bo,
    float beta,
    int32_t* C,
    dnnl_dim_t ldc,
    const int32_t* co
    )

Performs integer matrix-matrix multiply on 8-bit signed matrix A, 8-bit signed matrix B, and 32-bit signed resulting matrix C.

The operation is defined as:

C := alpha * (op(A) - A_offset) * (op(B) - B_offset) + beta * C + C_offset

where

op( X ) = X or op( X ) = X**T,
alpha and beta are scalars, and
A, B, and C are matrices:
- op( A ) is an MxK matrix,
- op( B ) is an KxN matrix,
- C is an MxN matrix.
A_offset is an MxK matrix with every element equal the ao value,
B_offset is an KxN matrix with every element equal the bo value,
C_offset is an MxN matrix which is defined by the co array of size len :
- if offsetc = F : the len must be at least 1,
- if offsetc = C : the len must be at least max(1, m),
- if offsetc = R : the len must be at least max(1, n),

The matrices are assumed to be stored in row-major order (the elements in each of the matrix rows are contiguous in memory).

NOTE:

This API does not support XERBLA. Instead, unlike the standard BLAS functions, this one returns a dnnl_status_t value to allow error handling.

WARNING:

On some architectures saturation may happen during intermediate computations, which would lead to unexpected results. For more details, refer to Nuances of int8 Computations.

Parameters:

transa	Transposition flag for matrix A: ‘N’ or ‘n’ means A is not transposed, and ‘T’ or ‘t’ means that A is transposed.
transb	Transposition flag for matrix B: ‘N’ or ‘n’ means B is not transposed, and ‘T’ or ‘t’ means that B is transposed.
offsetc	Flag specifying how offsets should be applied to matrix C: ‘F’ means that the same offset will be applied to each element of the matrix C, ‘C’ means that individual offset will be applied to each element within each column, ‘R’ means that individual offset will be applied to each element within each row.
M	The M dimension.
N	The N dimension.
K	The K dimension.
alpha	The alpha parameter that is used to scale the product of matrices A and B.
A	A pointer to the A matrix data.
lda	The leading dimension for the matrix A.
ao	The offset value for the matrix A.
B	A pointer to the B matrix data.
ldb	The leading dimension for the matrix B.
bo	The offset value for the matrix B.
beta	The beta parameter that is used to scale the matrix C.
C	A pointer to the C matrix data.
ldc	The leading dimension for the matrix C.
co	An array of offset values for the matrix C. The number of elements in the array depends on the value of `offsetc`.

Returns:

dnnl_success / dnnl::status::success on success and a status describing the error otherwise.


dnnl_status_t DNNL_API dnnl_sgemm(
    char transa,
    char transb,
    dnnl_dim_t M,
    dnnl_dim_t N,
    dnnl_dim_t K,
    float alpha,
    const float* A,
    dnnl_dim_t lda,
    const float* B,
    dnnl_dim_t ldb,
    float beta,
    float* C,
    dnnl_dim_t ldc
    )

Performs single-precision matrix-matrix multiply.

The operation is defined as:

C := alpha * op( A ) * op( B ) + beta * C

where

op( X ) = X or op( X ) = X**T,
alpha and beta are scalars, and
A, B, and C are matrices:
- op( A ) is an MxK matrix,
- op( B ) is an KxN matrix,
- C is an MxN matrix.

The matrices are assumed to be stored in row-major order (the elements in each of the matrix rows are contiguous in memory).

NOTE:

This API does not support XERBLA. Instead, unlike the standard BLAS functions, this one returns a dnnl_status_t value to allow error handling.

Parameters:

transa	Transposition flag for matrix A: ‘N’ or ‘n’ means A is not transposed, and ‘T’ or ‘t’ means that A is transposed.
transb	Transposition flag for matrix B: ‘N’ or ‘n’ means B is not transposed, and ‘T’ or ‘t’ means that B is transposed.
M	The M dimension.
N	The N dimension.
K	The K dimension.
alpha	The alpha parameter that is used to scale the product of matrices A and B.
A	A pointer to the A matrix data.
lda	The leading dimension for the matrix A.
B	A pointer to the B matrix data.
ldb	The leading dimension for the matrix B.
beta	The beta parameter that is used to scale the matrix C.
C	A pointer to the C matrix data.
ldc	The leading dimension for the matrix C.

Returns:

dnnl_success / dnnl::status::success on success and a status describing the error otherwise.


dnnl_status_t DNNL_API dnnl_gemm_u8s8s32(
    char transa,
    char transb,
    char offsetc,
    dnnl_dim_t M,
    dnnl_dim_t N,
    dnnl_dim_t K,
    float alpha,
    const uint8_t* A,
    dnnl_dim_t lda,
    uint8_t ao,
    const int8_t* B,
    dnnl_dim_t ldb,
    int8_t bo,
    float beta,
    int32_t* C,
    dnnl_dim_t ldc,
    const int32_t* co
    )

Performs integer matrix-matrix multiply on 8-bit unsigned matrix A, 8-bit signed matrix B, and 32-bit signed resulting matrix C.

The operation is defined as:

C := alpha * (op(A) - A_offset) * (op(B) - B_offset) + beta * C + C_offset

where

op( X ) = X or op( X ) = X**T,
alpha and beta are scalars, and
A, B, and C are matrices:
- op( A ) is an MxK matrix,
- op( B ) is an KxN matrix,
- C is an MxN matrix.
A_offset is an MxK matrix with every element equal the ao value,
B_offset is an KxN matrix with every element equal the bo value,
C_offset is an MxN matrix which is defined by the co array of size len :
- if offsetc = F : the len must be at least 1,
- if offsetc = C : the len must be at least max(1, m),
- if offsetc = R : the len must be at least max(1, n),

The matrices are assumed to be stored in row-major order (the elements in each of the matrix rows are contiguous in memory).

NOTE:

This API does not support XERBLA. Instead, unlike the standard BLAS functions, this one returns a dnnl_status_t value to allow error handling.

WARNING:

On some architectures saturation may happen during intermediate computations, which would lead to unexpected results. For more details, refer to Nuances of int8 Computations.

Parameters:

transa	Transposition flag for matrix A: ‘N’ or ‘n’ means A is not transposed, and ‘T’ or ‘t’ means that A is transposed.
transb	Transposition flag for matrix B: ‘N’ or ‘n’ means B is not transposed, and ‘T’ or ‘t’ means that B is transposed.
offsetc	Flag specifying how offsets should be applied to matrix C: ‘F’ means that the same offset will be applied to each element of the matrix C, ‘C’ means that individual offset will be applied to each element within each column, ‘R’ means that individual offset will be applied to each element within each row.
M	The M dimension.
N	The N dimension.
K	The K dimension.
alpha	The alpha parameter that is used to scale the product of matrices A and B.
A	A pointer to the A matrix data.
lda	The leading dimension for the matrix A.
ao	The offset value for the matrix A.
B	A pointer to the B matrix data.
ldb	The leading dimension for the matrix B.
bo	The offset value for the matrix B.
beta	The beta parameter that is used to scale the matrix C.
C	A pointer to the C matrix data.
ldc	The leading dimension for the matrix C.
co	An array of offset values for the matrix C. The number of elements in the array depends on the value of `offsetc`.

Returns:

dnnl_success / dnnl::status::success on success and a status describing the error otherwise.


dnnl_status_t DNNL_API dnnl_gemm_s8s8s32(
    char transa,
    char transb,
    char offsetc,
    dnnl_dim_t M,
    dnnl_dim_t N,
    dnnl_dim_t K,
    float alpha,
    const int8_t* A,
    dnnl_dim_t lda,
    int8_t ao,
    const int8_t* B,
    dnnl_dim_t ldb,
    int8_t bo,
    float beta,
    int32_t* C,
    dnnl_dim_t ldc,
    const int32_t* co
    )

Performs integer matrix-matrix multiply on 8-bit signed matrix A, 8-bit signed matrix B, and 32-bit signed resulting matrix C.

The operation is defined as:

C := alpha * (op(A) - A_offset) * (op(B) - B_offset) + beta * C + C_offset

where

op( X ) = X or op( X ) = X**T,
alpha and beta are scalars, and
A, B, and C are matrices:
- op( A ) is an MxK matrix,
- op( B ) is an KxN matrix,
- C is an MxN matrix.
A_offset is an MxK matrix with every element equal the ao value,
B_offset is an KxN matrix with every element equal the bo value,
C_offset is an MxN matrix which is defined by the co array of size len :
- if offsetc = F : the len must be at least 1,
- if offsetc = C : the len must be at least max(1, m),
- if offsetc = R : the len must be at least max(1, n),

The matrices are assumed to be stored in row-major order (the elements in each of the matrix rows are contiguous in memory).

NOTE:

This API does not support XERBLA. Instead, unlike the standard BLAS functions, this one returns a dnnl_status_t value to allow error handling.

WARNING:

On some architectures saturation may happen during intermediate computations, which would lead to unexpected results. For more details, refer to Nuances of int8 Computations.

Parameters:

transa	Transposition flag for matrix A: ‘N’ or ‘n’ means A is not transposed, and ‘T’ or ‘t’ means that A is transposed.
transb	Transposition flag for matrix B: ‘N’ or ‘n’ means B is not transposed, and ‘T’ or ‘t’ means that B is transposed.
offsetc	Flag specifying how offsets should be applied to matrix C: ‘F’ means that the same offset will be applied to each element of the matrix C, ‘C’ means that individual offset will be applied to each element within each column, ‘R’ means that individual offset will be applied to each element within each row.
M	The M dimension.
N	The N dimension.
K	The K dimension.
alpha	The alpha parameter that is used to scale the product of matrices A and B.
A	A pointer to the A matrix data.
lda	The leading dimension for the matrix A.
ao	The offset value for the matrix A.
B	A pointer to the B matrix data.
ldb	The leading dimension for the matrix B.
bo	The offset value for the matrix B.
beta	The beta parameter that is used to scale the matrix C.
C	A pointer to the C matrix data.
ldc	The leading dimension for the matrix C.
co	An array of offset values for the matrix C. The number of elements in the array depends on the value of `offsetc`.

Returns:

dnnl_success / dnnl::status::success on success and a status describing the error otherwise.

Select Your Language

Using Intel.com Search

Quick Links

Recent Searches

Advanced Search

Only search in

Intel® oneAPI Deep Neural Network Developer Guide and Reference

BLAS functions

Overview

Detailed Documentation