gemm_bias

oneMKL - Data Parallel C++ Developer Reference

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ID 772045

Date 6/24/2024

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gemm_bias

Computes a matrix-matrix product using general integer matrices with bias.

Description

The gemm_bias routines compute a scalar-matrix-matrix product and add the result to a scalar-matrix product, using general integer matrices with biases/offsets. The operation is defined as:

where:

op(X) is one of op(X) = X, or op(X) = X^T, or op(X) = X^H
alpha and beta are scalars
A_offset is m x k matrix with every element equal to the value ao
B_offset is k x n matrix with every element equal to the value bo
C_offset is m x n matrix defined by the co buffer. See Data Types for more details.
A, B, and C are matrices
op(A) is m x k, op(B) is k x n, and C is m x n

gemm_bias supports the following precisions:

Ta	Tb
`std::uint8_t`	`std::uint8_t`
`std::int8_t`	`std::uint8_t`
`std::uint8_t`	`std::int8_t`
`std::int8_t`	`std::int8_t`

gemm_bias (Buffer Version)

Syntax

namespace oneapi::mkl::blas::column_major {
    void gemm_bias(sycl::queue &queue,
                   oneapi::mkl::transpose transa,
                   oneapi::mkl::transpose transb,
                   oneapi::mkl::offset offsetc,
                   std::int64_t m,
                   std::int64_t n,
                   std::int64_t k,
                   float alpha,
                   sycl::buffer<Ta,1> &a,
                   std::int64_t lda,
                   Ta ao,
                   sycl::buffer<Tb,1> &b,
                   std::int64_t ldb,
                   Tb bo,
                   float beta,
                   sycl::buffer<std::int32_t,1> &c,
                   std::int64_t ldc,
                   sycl::buffer<std::int32_t,1> &co,
                   compute_mode mode = compute_mode::unset)
}

namespace oneapi::mkl::blas::row_major {
    void gemm_bias(sycl::queue &queue,
                   oneapi::mkl::transpose transa,
                   oneapi::mkl::transpose transb,
                   oneapi::mkl::offset offsetc,
                   std::int64_t m,
                   std::int64_t n,
                   std::int64_t k,
                   float alpha,
                   sycl::buffer<Ta,1> &a,
                   std::int64_t lda,
                   Ta ao,
                   sycl::buffer<Tb,1> &b,
                   std::int64_t ldb,
                   Tb bo,
                   float beta,
                   sycl::buffer<std::int32_t,1> &c,
                   std::int64_t ldc,
                   sycl::buffer<std::int32_t,1> &co,
                   compute_mode mode = compute_mode::unset)
}

Input Parameters

queue

The queue where the routine should be executed.

transa

Specifies op(A), the transposition operation applied to matrix A. See Data Types for more details.

transb

Specifies op(B), the transposition operation applied to matrix B. See Data Types for more details.

offsetc

Specifies the form of C_offset used in the matrix multiplication. See Data Types for more details.

m

Number of rows of matrix op(A) and matrix C. Must be at least zero.

n

Number of columns of matrix op(B) and matrix C. Must be at least zero.

k

Number of columns of matrix op(A) and rows of matrix op(B). Must be at least zero.

alpha

Scaling factor for matrix-matrix product.

a

Buffer holding input matrix A. See Matrix Storage for more details.

	`transa` = `transpose::nontrans`	`transa` = `transpose::trans` or `trans` = `transpose::conjtrans`
Column major	`A` is `m` x `k` matrix. Size of array `a` must be at least `lda` * `k`	`A` is `k` x `m` matrix. Size of array `a` must be at least `lda` * `m`
Row major	`A` is `m` x `k` matrix. Size of array `a` must be at least `lda` * `m`	`A` is `k` x `m` matrix. Size of array `a` must be at least `lda` * `k`

lda

Leading dimension of matrix A. Must be positive.

	`transa` = `transpose::nontrans`	`transa` = `transpose::trans` or `trans` = `transpose::conjtrans`
Column major	Must be at least `m`	Must be at least `k`
Row major	Must be at least `k`	Must be at least `m`

ao

Specifies the scalar offset value for matrix A.

b

Buffer holding input matrix B. See Matrix Storage for more details.

	`transb` = `transpose::nontrans`	`transb` = `transpose::trans` or `trans` = `transpose::conjtrans`
Column major	`B` is `k` x `n` matrix. Size of array `b` must be at least `ldb` * `n`	`B` is `n` x `k` matrix. Size of array `b` must be at least `ldb` * `k`
Row major	`B` is `k` x `n` matrix. Size of array `b` must be at least `ldb` * `k`	`B` is `n` x `k` matrix. Size of array `b` must be at least `ldb` * `n`

ldb

Leading dimension of matrix B. Must be positive.

	`transb` = `transpose::nontrans`	`transb` = `transpose::trans` or `trans` = `transpose::conjtrans`
Column major	Must be at least `k`	Must be at least `n`
Row major	Must be at least `n`	Must be at least `k`

bo

Specifies the scalar offset value for matrix B.

beta

Scaling factor for matrix C.

c

Buffer holding input/output matrix C. See Matrix Storage for more details.

Column major	`C` is `m` x `n` matrix. Size of array `c` must be at least `ldc` * `n`
Row major	`C` is `m` x `n` matrix. Size of array `c` must be at least `ldc` * `m`

ldc

Leading dimension of matrix C. Must be positive.

Column major	Must be at least `m`
Row major	Must be at least `n`

co

Buffer holding the offset values for matrix C.

If offset_type = offset::fix, size of co array must be at least 1.

If offset_type = offset::col, size of co array must be at least max(1,m).

If offset_type = offset::row, size of co array must be at least max(1,n).

See Data Types for more details.

mode

Optional. Compute mode settings. See Compute Modes for more details.

Output Parameters

c: Output buffer overwritten by alpha * (op(A) - A_offset) * (op(B) - B_offset) + beta * C + C_offset.

NOTE:

If beta = 0, matrix C does not need to be initialized before calling gemm_bias.

gemm_bias (USM Version)

Syntax

namespace oneapi::mkl::blas::column_major {
    sycl::event gemm_bias(sycl::queue &queue,
                          oneapi::mkl::transpose transa,
                          oneapi::mkl::transpose transb,
                          oneapi::mkl::offset offsetc,
                          std::int64_t m,
                          std::int64_t n,
                          std::int64_t k,
                          oneapi::mkl::value_or_pointer<float> alpha,
                          const Ta *a,
                          std::int64_t lda,
                          Ta ao,
                          const Tb *b,
                          std::int64_t ldb,
                          Tb bo,
                          oneapi::mkl::value_or_pointer<float> beta,
                          std::int32_t *c,
                          std::int64_t ldc,
                          const std::int32_t *co,
                          compute_mode mode = compute_mode::unset,
                          const std::vector<sycl::event> &dependencies = {})
}

namespace oneapi::mkl::blas::row_major {
    sycl::event gemm_bias(sycl::queue &queue,
                          oneapi::mkl::transpose transa,
                          oneapi::mkl::transpose transb,
                          oneapi::mkl::offset offsetc,
                          std::int64_t m,
                          std::int64_t n,
                          std::int64_t k,
                          oneapi::mkl::value_or_pointer<float> alpha,
                          const Ta *a,
                          std::int64_t lda,
                          Ta ao,
                          const Tb *b,
                          std::int64_t ldb,
                          Tb bo,
                          oneapi::mkl::value_or_pointer<float> beta,
                          std::int32_t *c,
                          std::int64_t ldc,
                          const std::int32_t *co,
                          compute_mode mode = compute_mode::unset,
                          const std::vector<sycl::event> &dependencies = {})
}

Input Parameters

queue

The queue where the routine should be executed.

transa

Specifies op(A), the transposition operation applied to matrix A. See Data Types for more details.

transb

Specifies op(B), the transposition operation applied to matrix B. See Data Types for more details.

offsetc

Specifies the form of C_offset used in the matrix multiplication. See Data Types for more details.

m

Number of rows of matrix op(A) and matrix C. Must be at least zero.

n

Number of columns of matrix op(B) and matrix C. Must be at least zero.

k

Number of columns of matrix op(A) and rows of matrix op(B). Must be at least zero.

alpha

Scaling factor for matrix-matrix product. See Scalar Arguments for more information on the value_or_pointer data type.

a

Pointer to input matrix A. See Matrix Storage for more details.

	`transa` = `transpose::nontrans`	`transa` = `transpose::trans` or `trans` = `transpose::conjtrans`
Column major	`A` is `m` x `k` matrix. Size of array `a` must be at least `lda` * `k`	`A` is `k` x `m` matrix. Size of array `a` must be at least `lda` * `m`
Row major	`A` is `m` x `k` matrix. Size of array `a` must be at least `lda` * `m`	`A` is `k` x `m` matrix. Size of array `a` must be at least `lda` * `k`

lda

Leading dimension of matrix A. Must be positive.

	`transa` = `transpose::nontrans`	`transa` = `transpose::trans` or `trans` = `transpose::conjtrans`
Column major	Must be at least `m`	Must be at least `k`
Row major	Must be at least `k`	Must be at least `m`

ao

Specifies the scalar offset value for matrix A.

b

Pointer to input matrix B. See Matrix Storage for more details.

	`transb` = `transpose::nontrans`	`transb` = `transpose::trans` or `trans` = `transpose::conjtrans`
Column major	`B` is `k` x `n` matrix. Size of array `b` must be at least `ldb` * `n`	`B` is `n` x `k` matrix. Size of array `b` must be at least `ldb` * `k`
Row major	`B` is `k` x `n` matrix. Size of array `b` must be at least `ldb` * `k`	`B` is `n` x `k` matrix. Size of array `b` must be at least `ldb` * `n`

ldb

Leading dimension of matrix B. Must be positive.

	`transb` = `transpose::nontrans`	`transb` = `transpose::trans` or `trans` = `transpose::conjtrans`
Column major	Must be at least `k`	Must be at least `n`
Row major	Must be at least `n`	Must be at least `k`

bo

Specifies the scalar offset value for matrix B.

beta

Scaling factor for matrix C. See Scalar Arguments for more information on the value_or_pointer data type.

c

Pointer to input/output matrix C. See Matrix Storage for more details.

Column major	`C` is `m` x `n` matrix. Size of array `c` must be at least `ldc` * `n`
Row major	`C` is `m` x `n` matrix. Size of array `c` must be at least `ldc` * `m`

ldc

Leading dimension of matrix C. Must be positive.

Column major	Must be at least `m`
Row major	Must be at least `n`

co

Pointer to array holding offset values for matrix C.

If offset_type = offset::fix, size of co array must be at least 1.

If offset_type = offset::col, size of co array must be at least max(1,m).

If offset_type = offset::row, size of co array must be at least max(1,n).

See Data Types for more details.

mode

Optional. Compute mode settings. See Compute Modes for more details.

dependencies

Optional. List of events to wait for before starting computation, if any. If omitted, defaults to no dependencies.

mode and dependencies may be omitted independently; it is not necessary to specify mode in order to provide dependencies.

Output Parameters

c: Pointer to output matrix C overwritten by alpha * (op(A) - A_offset) * (op(B) - B_offset) + beta * C + C_offset.

NOTE:

If beta = 0, matrix C does not need to be initialized before calling gemm_bias.

Return Values

Output event to wait on to ensure computation is complete.

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oneMKL - Data Parallel C++ Developer Reference

gemm_bias

Description

gemm_bias (Buffer Version)

gemm_bias (USM Version)