Visible to Intel only — GUID: GUID-BF796335-46CD-4306-B8F3-63D0ADF4394C
Abs
AbsBackward
Add
AvgPool
AvgPoolBackward
BatchNormForwardTraining
BatchNormInference
BatchNormTrainingBackward
BiasAdd
BiasAddBackward
Clamp
ClampBackward
Concat
Convolution
ConvolutionBackwardData
ConvolutionBackwardWeights
ConvTranspose
ConvTransposeBackwardData
ConvTransposeBackwardWeights
Dequantize
Divide
DynamicDequantize
DynamicQuantize
Elu
EluBackward
End
Exp
GELU
GELUBackward
HardSwish
HardSwishBackward
Interpolate
InterpolateBackward
LayerNorm
LayerNormBackward
LeakyReLU
Log
LogSoftmax
LogSoftmaxBackward
MatMul
Maximum
MaxPool
MaxPoolBackward
Minimum
Mish
MishBackward
Multiply
PReLU
PReLUBackward
Quantize
Reciprocal
ReduceL1
ReduceL2
ReduceMax
ReduceMean
ReduceMin
ReduceProd
ReduceSum
ReLU
ReLUBackward
Reorder
Round
Sigmoid
SigmoidBackward
Softmax
SoftmaxBackward
SoftPlus
SoftPlusBackward
Sqrt
SqrtBackward
Square
SquaredDifference
StaticReshape
StaticTranspose
Subtract
Tanh
TanhBackward
TypeCast
Wildcard
enum dnnl_alg_kind_t
enum dnnl_normalization_flags_t
enum dnnl_primitive_kind_t
enum dnnl_prop_kind_t
enum dnnl_query_t
enum dnnl::normalization_flags
enum dnnl::query
struct dnnl_exec_arg_t
struct dnnl_primitive
struct dnnl_primitive_desc
struct dnnl::primitive
struct dnnl::primitive_desc
struct dnnl::primitive_desc_base
enum dnnl_rnn_direction_t
enum dnnl_rnn_flags_t
enum dnnl::rnn_direction
enum dnnl::rnn_flags
struct dnnl::augru_backward
struct dnnl::augru_forward
struct dnnl::gru_backward
struct dnnl::gru_forward
struct dnnl::lbr_augru_backward
struct dnnl::lbr_augru_forward
struct dnnl::lbr_gru_backward
struct dnnl::lbr_gru_forward
struct dnnl::lstm_backward
struct dnnl::lstm_forward
struct dnnl::rnn_primitive_desc_base
struct dnnl::vanilla_rnn_backward
struct dnnl::vanilla_rnn_forward
Visible to Intel only — GUID: GUID-BF796335-46CD-4306-B8F3-63D0ADF4394C
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:
|
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:
|
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:
|
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:
|
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.