BLAS functions

Functions
mkldnn_status_t MKLDNN_API	mkldnn_sgemm (const char *transa, const char *transb, const int *M, const int *N, const int *K, const float *alpha, const float *A, const int *lda, const float *B, const int *ldb, const float *beta, float *C, const int *ldc)
	SGEMM performs matrix-matrix multiplication operation C := alpha*op( A )*op( B ) + beta*C, where op( X ) is one of op( X ) = X or op( X ) = X**T, alpha and beta are scalars, and A, B and C are matrices, with op( A ) an m by k matrix, op( B ) a k by n matrix and C an m by n matrix. More...
mkldnn_status_t MKLDNN_API	mkldnn_gemm_s8u8s32 (const char *transa, const char *transb, const char *offsetc, const int *M, const int *N, const int *K, const float *alpha, const int8_t *A, const int *lda, const int8_t *ao, const uint8_t *B, const int *ldb, const int8_t *bo, const float *beta, int32_t *c, const int *ldc, const int32_t *co)
	gemm_s8u8s32 and gemm_s8s8s32 perform matrix-matrix multiplication operation and add the result to a scalar-matrix product. More...
mkldnn_status_t MKLDNN_API	mkldnn_gemm_s8s8s32 (const char *transa, const char *transb, const char *offsetc, const int *M, const int *N, const int *K, const float *alpha, const int8_t *A, const int *lda, const int8_t *ao, const int8_t *B, const int *ldb, const int8_t *bo, const float *beta, int32_t *c, const int *ldc, const int32_t *co)

Detailed Description

Function Documentation

mkldnn_sgemm()

mkldnn_status_t MKLDNN_API mkldnn_sgemm	(	const char *	transa,
		const char *	transb,
		const int *	M,
		const int *	N,
		const int *	K,
		const float *	alpha,
		const float *	A,
		const int *	lda,
		const float *	B,
		const int *	ldb,
		const float *	beta,
		float *	C,
		const int *	ldc
	)

SGEMM performs matrix-matrix multiplication operation C := alpha*op( A )*op( B ) + beta*C, where op( X ) is one of op( X ) = X or op( X ) = X**T, alpha and beta are scalars, and A, B and C are matrices, with op( A ) an m by k matrix, op( B ) a k by n matrix and C an m by n matrix.

Note

API is different compared to standard BLAS routine as it returns mkldnn_status_t for error handling. XERBLA is not supported: no error message will be printed in case of incorrect parameters

mkldnn_gemm_s8u8s32()

mkldnn_status_t MKLDNN_API mkldnn_gemm_s8u8s32	(	const char *	transa,
		const char *	transb,
		const char *	offsetc,
		const int *	M,
		const int *	N,
		const int *	K,
		const float *	alpha,
		const int8_t *	A,
		const int *	lda,
		const int8_t *	ao,
		const uint8_t *	B,
		const int *	ldb,
		const int8_t *	bo,
		const float *	beta,
		int32_t *	c,
		const int *	ldc,
		const int32_t *	co
	)

gemm_s8u8s32 and gemm_s8s8s32 perform matrix-matrix multiplication operation and add the result to a scalar-matrix product.

To get the final result, a vector is added to each row or column of the output matrix. 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, A_offset is an m-by-k matrix with every element equal to the value oa, B_offset is an k-by-n matrix with every element equal to the value ob, C_offset is an m-by-n matrix defined by the oc array, size len: if offsetc = F: len must be at least 1 if offsetc = C: len must be at least max(1, m) if offsetc = R: len must be at least max(1, n) alpha and beta are scalars, and A, B and C are matrices, with op( A ) an m-by-k matrix, op( B ) a k-by-n matrix and C an m-by-n matrix.

Note

API is different compared to standard BLAS routine as it returns mkldnn_status_t for error handling. XERBLA is not supported: no error message will be printed in case of incorrect parameters

mkldnn_gemm_s8s8s32()

mkldnn_status_t MKLDNN_API mkldnn_gemm_s8s8s32	(	const char *	transa,
		const char *	transb,
		const char *	offsetc,
		const int *	M,
		const int *	N,
		const int *	K,
		const float *	alpha,
		const int8_t *	A,
		const int *	lda,
		const int8_t *	ao,
		const int8_t *	B,
		const int *	ldb,
		const int8_t *	bo,
		const float *	beta,
		int32_t *	c,
		const int *	ldc,
		const int32_t *	co
	)