public class dnnl extends dnnl
| Modifier and Type | Class and Description |
|---|---|
static class |
dnnl.algorithm
Kinds of algorithms.
|
static class |
dnnl.cpu_isa
\copydoc dnnl_cpu_isa_t
|
static class |
dnnl.normalization_flags
\} dnnl_api_attributes
|
static class |
dnnl.prop_kind
Propagation kind.
|
static class |
dnnl.query
\} dnnl_api_rnn
|
static class |
dnnl.rnn_direction
A direction of RNN primitive execution
|
static class |
dnnl.rnn_flags
\} dnnl_api_primitives_common
|
static class |
dnnl.scratchpad_mode
\} dnnl_api_primitives_common
|
static class |
dnnl.status
Status values returned by the library functions.
|
| Modifier and Type | Field and Description |
|---|---|
static int |
dnnl_a
enum dnnl_format_tag_t
|
static int |
dnnl_ab
enum dnnl_format_tag_t
|
static int |
dnnl_abc
enum dnnl_format_tag_t
|
static int |
dnnl_Abc16a
enum dnnl_format_tag_t
|
static int |
dnnl_ABc16a16b
enum dnnl_format_tag_t
|
static int |
dnnl_aBc16b
enum dnnl_format_tag_t
|
static int |
dnnl_ABc16b16a
enum dnnl_format_tag_t
|
static int |
dnnl_ABc16b16a2b
enum dnnl_format_tag_t
|
static int |
dnnl_ABc16b16a4b
enum dnnl_format_tag_t
|
static int |
dnnl_ABc2b8a4b
enum dnnl_format_tag_t
|
static int |
dnnl_ABc32a32b
enum dnnl_format_tag_t
|
static int |
dnnl_aBc32b
enum dnnl_format_tag_t
|
static int |
dnnl_Abc4a
enum dnnl_format_tag_t
|
static int |
dnnl_ABc4a4b
enum dnnl_format_tag_t
|
static int |
dnnl_ABc4a8b8a4b
enum dnnl_format_tag_t
|
static int |
dnnl_aBc4b
enum dnnl_format_tag_t
|
static int |
dnnl_ABc4b16a4b
enum dnnl_format_tag_t
|
static int |
dnnl_ABc4b4a
enum dnnl_format_tag_t
|
static int |
dnnl_ABc8a16b2a
enum dnnl_format_tag_t
|
static int |
dnnl_ABc8a4b
enum dnnl_format_tag_t
|
static int |
dnnl_ABc8a8b
enum dnnl_format_tag_t
|
static int |
dnnl_aBc8b
enum dnnl_format_tag_t
|
static int |
dnnl_ABc8b16a2b
enum dnnl_format_tag_t
|
static int |
dnnl_ABc8b8a
enum dnnl_format_tag_t
|
static int |
dnnl_abcd
enum dnnl_format_tag_t
|
static int |
dnnl_Abcd16a
enum dnnl_format_tag_t
|
static int |
dnnl_ABcd16a16b
enum dnnl_format_tag_t
|
static int |
dnnl_aBcd16b
enum dnnl_format_tag_t
|
static int |
dnnl_ABcd16b16a
enum dnnl_format_tag_t
|
static int |
dnnl_ABcd16b16a2b
enum dnnl_format_tag_t
|
static int |
dnnl_ABcd16b16a4b
enum dnnl_format_tag_t
|
static int |
dnnl_aBCd16b16c
enum dnnl_format_tag_t
|
static int |
dnnl_aBCd16c16b
enum dnnl_format_tag_t
|
static int |
dnnl_aBCd16c16b2c
enum dnnl_format_tag_t
|
static int |
dnnl_aBCd16c16b4c
enum dnnl_format_tag_t
|
static int |
dnnl_ABcd2a8b8a2b
enum dnnl_format_tag_t
|
static int |
dnnl_aBCd2b4c2b
enum dnnl_format_tag_t
|
static int |
dnnl_ABcd2b8a4b
enum dnnl_format_tag_t
|
static int |
dnnl_aBCd2c4b2c
enum dnnl_format_tag_t
|
static int |
dnnl_aBCd2c8b4c
enum dnnl_format_tag_t
|
static int |
dnnl_Abcd32a
enum dnnl_format_tag_t
|
static int |
dnnl_ABcd32a32b
enum dnnl_format_tag_t
|
static int |
dnnl_aBcd32b
enum dnnl_format_tag_t
|
static int |
dnnl_Abcd4a
enum dnnl_format_tag_t
|
static int |
dnnl_ABcd4a4b
enum dnnl_format_tag_t
|
static int |
dnnl_ABcd4a8b8a4b
enum dnnl_format_tag_t
|
static int |
dnnl_aBcd4b
enum dnnl_format_tag_t
|
static int |
dnnl_ABcd4b16a4b
enum dnnl_format_tag_t
|
static int |
dnnl_ABcd4b4a
enum dnnl_format_tag_t
|
static int |
dnnl_aBCd4b4c
enum dnnl_format_tag_t
|
static int |
dnnl_aBCd4b8c2b
enum dnnl_format_tag_t
|
static int |
dnnl_aBCd4b8c8b4c
enum dnnl_format_tag_t
|
static int |
dnnl_aBCd4c16b4c
enum dnnl_format_tag_t
|
static int |
dnnl_aBCd4c4b
enum dnnl_format_tag_t
|
static int |
dnnl_aBCd4c8b2c
enum dnnl_format_tag_t
|
static int |
dnnl_Abcd8a
enum dnnl_format_tag_t
|
static int |
dnnl_ABcd8a16b2a
enum dnnl_format_tag_t
|
static int |
dnnl_ABcd8a4b
enum dnnl_format_tag_t
|
static int |
dnnl_ABcd8a8b
enum dnnl_format_tag_t
|
static int |
dnnl_aBcd8b
enum dnnl_format_tag_t
|
static int |
dnnl_ABcd8b16a2b
enum dnnl_format_tag_t
|
static int |
dnnl_aBCd8b16c2b
enum dnnl_format_tag_t
|
static int |
dnnl_aBCd8b4c
enum dnnl_format_tag_t
|
static int |
dnnl_ABcd8b8a
enum dnnl_format_tag_t
|
static int |
dnnl_aBCd8b8c
enum dnnl_format_tag_t
|
static int |
dnnl_aBCd8c16b2c
enum dnnl_format_tag_t
|
static int |
dnnl_aBCd8c8b
enum dnnl_format_tag_t
|
static int |
dnnl_abcde
enum dnnl_format_tag_t
|
static int |
dnnl_Abcde16a
enum dnnl_format_tag_t
|
static int |
dnnl_ABcde16a16b
enum dnnl_format_tag_t
|
static int |
dnnl_aBcde16b
enum dnnl_format_tag_t
|
static int |
dnnl_ABcde16b16a
enum dnnl_format_tag_t
|
static int |
dnnl_aBCde16b16c
enum dnnl_format_tag_t
|
static int |
dnnl_aBCde16c16b
enum dnnl_format_tag_t
|
static int |
dnnl_aBCde16c16b2c
enum dnnl_format_tag_t
|
static int |
dnnl_aBCde16c16b4c
enum dnnl_format_tag_t
|
static int |
dnnl_aBCde2b4c2b
enum dnnl_format_tag_t
|
static int |
dnnl_ABcde2b8a4b
enum dnnl_format_tag_t
|
static int |
dnnl_aBCde2b8c8b2c
enum dnnl_format_tag_t
|
static int |
dnnl_aBCde2c4b2c
enum dnnl_format_tag_t
|
static int |
dnnl_aBCde2c8b4c
enum dnnl_format_tag_t
|
static int |
dnnl_Abcde32a
enum dnnl_format_tag_t
|
static int |
dnnl_ABcde32a32b
enum dnnl_format_tag_t
|
static int |
dnnl_aBcde32b
enum dnnl_format_tag_t
|
static int |
dnnl_Abcde4a
enum dnnl_format_tag_t
|
static int |
dnnl_ABcde4a4b
enum dnnl_format_tag_t
|
static int |
dnnl_ABcde4a8b8a4b
enum dnnl_format_tag_t
|
static int |
dnnl_aBcde4b
enum dnnl_format_tag_t
|
static int |
dnnl_ABcde4b16a4b
enum dnnl_format_tag_t
|
static int |
dnnl_ABcde4b4a
enum dnnl_format_tag_t
|
static int |
dnnl_aBCde4b4c
enum dnnl_format_tag_t
|
static int |
dnnl_aBCde4b8c2b
enum dnnl_format_tag_t
|
static int |
dnnl_aBCde4b8c8b4c
enum dnnl_format_tag_t
|
static int |
dnnl_aBCde4c16b4c
enum dnnl_format_tag_t
|
static int |
dnnl_aBCde4c4b
enum dnnl_format_tag_t
|
static int |
dnnl_aBCde4c8b2c
enum dnnl_format_tag_t
|
static int |
dnnl_Abcde8a
enum dnnl_format_tag_t
|
static int |
dnnl_ABcde8a16b2a
enum dnnl_format_tag_t
|
static int |
dnnl_ABcde8a4b
enum dnnl_format_tag_t
|
static int |
dnnl_ABcde8a8b
enum dnnl_format_tag_t
|
static int |
dnnl_aBcde8b
enum dnnl_format_tag_t
|
static int |
dnnl_ABcde8b16a2b
enum dnnl_format_tag_t
|
static int |
dnnl_aBCde8b16c2b
enum dnnl_format_tag_t
|
static int |
dnnl_aBCde8b4c
enum dnnl_format_tag_t
|
static int |
dnnl_ABcde8b8a
enum dnnl_format_tag_t
|
static int |
dnnl_aBCde8b8c
enum dnnl_format_tag_t
|
static int |
dnnl_aBCde8c16b2c
enum dnnl_format_tag_t
|
static int |
dnnl_aBCde8c8b
enum dnnl_format_tag_t
|
static int |
dnnl_abcdef
enum dnnl_format_tag_t
|
static int |
dnnl_Abcdef16a
enum dnnl_format_tag_t
|
static int |
dnnl_aBcdef16b
enum dnnl_format_tag_t
|
static int |
dnnl_aBCdef16b16c
enum dnnl_format_tag_t
|
static int |
dnnl_aBCdef16c16b
enum dnnl_format_tag_t
|
static int |
dnnl_aBCdef2b4c2b
enum dnnl_format_tag_t
|
static int |
dnnl_aBCdef2c4b2c
enum dnnl_format_tag_t
|
static int |
dnnl_aBCdef2c8b4c
enum dnnl_format_tag_t
|
static int |
dnnl_Abcdef32a
enum dnnl_format_tag_t
|
static int |
dnnl_aBcdef4b
enum dnnl_format_tag_t
|
static int |
dnnl_aBCdef4b4c
enum dnnl_format_tag_t
|
static int |
dnnl_aBCdef4b8c2b
enum dnnl_format_tag_t
|
static int |
dnnl_aBCdef4b8c8b4c
enum dnnl_format_tag_t
|
static int |
dnnl_aBCdef4c16b4c
enum dnnl_format_tag_t
|
static int |
dnnl_aBCdef4c4b
enum dnnl_format_tag_t
|
static int |
dnnl_aBCdef4c8b2c
enum dnnl_format_tag_t
|
static int |
dnnl_aBCdef8b16c2b
enum dnnl_format_tag_t
|
static int |
dnnl_aBCdef8b4c
enum dnnl_format_tag_t
|
static int |
dnnl_aBCdef8b8c
enum dnnl_format_tag_t
|
static int |
dnnl_aBCdef8c16b2c
enum dnnl_format_tag_t
|
static int |
dnnl_aBCdef8c8b
enum dnnl_format_tag_t
|
static int |
dnnl_abdc
enum dnnl_format_tag_t
|
static int |
dnnl_aBdc16b
enum dnnl_format_tag_t
|
static int |
dnnl_aBdC16b2c
enum dnnl_format_tag_t
|
static int |
dnnl_aBdC16b4c
enum dnnl_format_tag_t
|
static int |
dnnl_aBdc4b
enum dnnl_format_tag_t
|
static int |
dnnl_aBdc8b
enum dnnl_format_tag_t
|
static int |
dnnl_abdec
enum dnnl_format_tag_t
|
static int |
dnnl_aBdec16b
enum dnnl_format_tag_t
|
static int |
dnnl_aBdeC16b2c
enum dnnl_format_tag_t
|
static int |
dnnl_aBdeC16b4c
enum dnnl_format_tag_t
|
static int |
dnnl_aBdec32b
enum dnnl_format_tag_t
|
static int |
dnnl_aBdec4b
enum dnnl_format_tag_t
|
static int |
dnnl_aBdec8b
enum dnnl_format_tag_t
|
static int |
dnnl_aBdefc16b
enum dnnl_format_tag_t
|
static int |
dnnl_aBdefC16b2c
enum dnnl_format_tag_t
|
static int |
dnnl_aBdefc4b
enum dnnl_format_tag_t
|
static int |
dnnl_aBdefc8b
enum dnnl_format_tag_t
|
static int |
dnnl_acb
enum dnnl_format_tag_t
|
static int |
dnnl_Acb16a
enum dnnl_format_tag_t
|
static int |
dnnl_AcB16a2b
enum dnnl_format_tag_t
|
static int |
dnnl_AcB16a4b
enum dnnl_format_tag_t
|
static int |
dnnl_Acb4a
enum dnnl_format_tag_t
|
static int |
dnnl_Acb8a
enum dnnl_format_tag_t
|
static int |
dnnl_aCBd16b16c
enum dnnl_format_tag_t
|
static int |
dnnl_aCBd16c16b
enum dnnl_format_tag_t
|
static int |
dnnl_aCBd4c8b8c4b
enum dnnl_format_tag_t
|
static int |
dnnl_aCBd8b16c2b
enum dnnl_format_tag_t
|
static int |
dnnl_acbde
enum dnnl_format_tag_t
|
static int |
dnnl_aCBde16b16c
enum dnnl_format_tag_t
|
static int |
dnnl_aCBde16c16b
enum dnnl_format_tag_t
|
static int |
dnnl_aCBde4c8b8c4b
enum dnnl_format_tag_t
|
static int |
dnnl_aCBde8b16c2b
enum dnnl_format_tag_t
|
static int |
dnnl_acbdef
enum dnnl_format_tag_t
|
static int |
dnnl_aCBdef16b16c
enum dnnl_format_tag_t
|
static int |
dnnl_aCBdef16c16b
enum dnnl_format_tag_t
|
static int |
dnnl_aCBdef4c8b8c4b
enum dnnl_format_tag_t
|
static int |
dnnl_aCBdef8b16c2b
enum dnnl_format_tag_t
|
static int |
dnnl_acdb
enum dnnl_format_tag_t
|
static int |
dnnl_Acdb16a
enum dnnl_format_tag_t
|
static int |
dnnl_AcdB16a2b
enum dnnl_format_tag_t
|
static int |
dnnl_AcdB16a4b
enum dnnl_format_tag_t
|
static int |
dnnl_Acdb32a
enum dnnl_format_tag_t
|
static int |
dnnl_Acdb4a
enum dnnl_format_tag_t
|
static int |
dnnl_Acdb8a
enum dnnl_format_tag_t
|
static int |
dnnl_acdeb
enum dnnl_format_tag_t
|
static int |
dnnl_Acdeb16a
enum dnnl_format_tag_t
|
static int |
dnnl_AcdeB16a2b
enum dnnl_format_tag_t
|
static int |
dnnl_Acdeb4a
enum dnnl_format_tag_t
|
static int |
dnnl_Acdeb8a
enum dnnl_format_tag_t
|
static int |
dnnl_alg_kind_undef
enum dnnl_alg_kind_t
|
static int |
dnnl_any_engine
enum dnnl_engine_kind_t
|
static int |
DNNL_ARG_ATTR_OUTPUT_SCALES
Output scaling factors provided at execution time.
|
static int |
DNNL_ARG_ATTR_POST_OP_DW
Arguments for fused depthwise convolution.
|
static int |
DNNL_ARG_ATTR_ZERO_POINTS
Zero points provided at execution time.
|
static int |
DNNL_ARG_BIAS
Bias tensor argument.
|
static int |
DNNL_ARG_DIFF_BIAS
Gradient (diff) of the bias tensor argument.
|
static int |
DNNL_ARG_DIFF_DST
A special mnemonic for primitives that have a single diff destination
argument.
|
static int |
DNNL_ARG_DIFF_DST_0
Gradient (diff) of the destination argument #0.
|
static int |
DNNL_ARG_DIFF_DST_1
Gradient (diff) of the destination argument #1.
|
static int |
DNNL_ARG_DIFF_DST_2
Gradient (diff) of the destination argument #2.
|
static int |
DNNL_ARG_DIFF_DST_ITER
A special mnemonic for gradient (diff) of RNN input recurrent hidden state
vector.
|
static int |
DNNL_ARG_DIFF_DST_ITER_C
A special mnemonic for gradient (diff) of RNN input recurrent cell state
vector.
|
static int |
DNNL_ARG_DIFF_DST_LAYER
A special mnemonic for gradient (diff) of RNN output vector.
|
static int |
DNNL_ARG_DIFF_SCALE_SHIFT
A special mnemonic for diff of scale and shift argument of normalization
primitives.
|
static int |
DNNL_ARG_DIFF_SRC
A special mnemonic for primitives that have a single diff source argument.
|
static int |
DNNL_ARG_DIFF_SRC_0
Gradient (diff) of the source argument #0.
|
static int |
DNNL_ARG_DIFF_SRC_1
Gradient (diff) of the source argument #1.
|
static int |
DNNL_ARG_DIFF_SRC_2
Gradient (diff) of the source argument #2.
|
static int |
DNNL_ARG_DIFF_SRC_ITER
A special mnemonic for gradient (diff) of RNN input recurrent hidden state
vector.
|
static int |
DNNL_ARG_DIFF_SRC_ITER_C
A special mnemonic for gradient (diff) of RNN input recurrent cell state
vector.
|
static int |
DNNL_ARG_DIFF_SRC_LAYER
A special mnemonic for gradient (diff) of RNN input vector.
|
static int |
DNNL_ARG_DIFF_WEIGHTS
A special mnemonic for primitives that have a single diff weights
argument.
|
static int |
DNNL_ARG_DIFF_WEIGHTS_0
Gradient (diff) of the weights argument #0.
|
static int |
DNNL_ARG_DIFF_WEIGHTS_1
Gradient (diff) of the weights argument #1.
|
static int |
DNNL_ARG_DIFF_WEIGHTS_2
Gradient (diff) of the weights argument #2.
|
static int |
DNNL_ARG_DIFF_WEIGHTS_3
Gradient (diff) of the weights argument #3.
|
static int |
DNNL_ARG_DIFF_WEIGHTS_ITER
A special mnemonic for diff of RNN weights applied to the recurrent input.
|
static int |
DNNL_ARG_DIFF_WEIGHTS_LAYER
A special mnemonic for diff of RNN weights applied to the layer input.
|
static int |
DNNL_ARG_DIFF_WEIGHTS_PEEPHOLE
A special mnemonic for diff of RNN weights applied to the peephole weights.
|
static int |
DNNL_ARG_DIFF_WEIGHTS_PROJECTION
A special mnemonic for diff of RNN weights applied to the projection
weights.
|
static int |
DNNL_ARG_DST
A special mnemonic for destination argument for primitives that have a
single destination.
|
static int |
DNNL_ARG_DST_0
Destination argument #0.
|
static int |
DNNL_ARG_DST_1
Destination argument #1.
|
static int |
DNNL_ARG_DST_2
Destination argument #2.
|
static int |
DNNL_ARG_DST_ITER
A special mnemonic for RNN input recurrent hidden state vector.
|
static int |
DNNL_ARG_DST_ITER_C
A special mnemonic for LSTM output recurrent cell state vector.
|
static int |
DNNL_ARG_DST_LAYER
A special mnemonic for RNN output vector.
|
static int |
DNNL_ARG_FROM
A special mnemonic for reorder source argument.
|
static int |
DNNL_ARG_MEAN
Mean values tensor argument.
|
static int |
DNNL_ARG_MULTIPLE_DST
Starting index for destination arguments for primitives that produce a
variable number of destination arguments.
|
static int |
DNNL_ARG_MULTIPLE_SRC
Starting index for source arguments for primitives that take a variable
number of source arguments.
|
static int |
DNNL_ARG_SCALE_SHIFT
A special mnemonic for scale and shift argument of normalization
primitives.
|
static int |
DNNL_ARG_SCRATCHPAD
Scratchpad (temporary storage) tensor argument.
|
static int |
DNNL_ARG_SRC
A special mnemonic for source argument for primitives that have a
single source.
|
static int |
DNNL_ARG_SRC_0
Source argument #0.
|
static int |
DNNL_ARG_SRC_1
Source argument #1.
|
static int |
DNNL_ARG_SRC_2
Source argument #2.
|
static int |
DNNL_ARG_SRC_ITER
A special mnemonic for RNN input recurrent hidden state vector.
|
static int |
DNNL_ARG_SRC_ITER_C
A special mnemonic for RNN input recurrent cell state vector.
|
static int |
DNNL_ARG_SRC_LAYER
A special mnemonic for RNN input vector.
|
static int |
DNNL_ARG_TO
A special mnemonic for reorder destination argument.
|
static int |
DNNL_ARG_VARIANCE
Variance values tensor argument.
|
static int |
DNNL_ARG_WEIGHTS
A special mnemonic for primitives that have a single weights
argument.
|
static int |
DNNL_ARG_WEIGHTS_0
Weights argument #0.
|
static int |
DNNL_ARG_WEIGHTS_1
Weights argument #1.
|
static int |
DNNL_ARG_WEIGHTS_2
Weights argument #2.
|
static int |
DNNL_ARG_WEIGHTS_3
Weights argument #3.
|
static int |
DNNL_ARG_WEIGHTS_ITER
A special mnemonic for RNN weights applied to the recurrent input.
|
static int |
DNNL_ARG_WEIGHTS_LAYER
A special mnemonic for RNN weights applied to the layer input.
|
static int |
DNNL_ARG_WEIGHTS_PEEPHOLE
A special mnemonic for RNN weights applied to the peephole weights.
|
static int |
DNNL_ARG_WEIGHTS_PROJECTION
A special mnemonic for RNN weights applied to the projection weights.
|
static int |
DNNL_ARG_WORKSPACE
Workspace tensor argument.
|
static int |
dnnl_ba
enum dnnl_format_tag_t
|
static int |
dnnl_bac
enum dnnl_format_tag_t
|
static int |
dnnl_BAc16a16b
enum dnnl_format_tag_t
|
static int |
dnnl_BAc16b16a
enum dnnl_format_tag_t
|
static int |
dnnl_BAc4b8a8b4a
enum dnnl_format_tag_t
|
static int |
dnnl_BAc8a16b2a
enum dnnl_format_tag_t
|
static int |
dnnl_bacd
enum dnnl_format_tag_t
|
static int |
dnnl_BAcd16a16b
enum dnnl_format_tag_t
|
static int |
dnnl_BAcd16b16a
enum dnnl_format_tag_t
|
static int |
dnnl_BAcd4b8a8b4a
enum dnnl_format_tag_t
|
static int |
dnnl_BAcd8a16b2a
enum dnnl_format_tag_t
|
static int |
dnnl_bacde
enum dnnl_format_tag_t
|
static int |
dnnl_BAcde16a16b
enum dnnl_format_tag_t
|
static int |
dnnl_BAcde16b16a
enum dnnl_format_tag_t
|
static int |
dnnl_BAcde4b8a8b4a
enum dnnl_format_tag_t
|
static int |
dnnl_BAcde8a16b2a
enum dnnl_format_tag_t
|
static int |
dnnl_backward
enum dnnl_prop_kind_t
|
static int |
dnnl_backward_bias
enum dnnl_prop_kind_t
|
static int |
dnnl_backward_data
enum dnnl_prop_kind_t
|
static int |
dnnl_backward_weights
enum dnnl_prop_kind_t
|
static int |
dnnl_batch_normalization
enum dnnl_primitive_kind_t
|
static int |
dnnl_bca
enum dnnl_format_tag_t
|
static int |
dnnl_bcda
enum dnnl_format_tag_t
|
static int |
dnnl_bcdea
enum dnnl_format_tag_t
|
static int |
dnnl_bf16
enum dnnl_data_type_t
|
static int |
dnnl_bidirectional_concat
enum dnnl_rnn_direction_t
|
static int |
dnnl_bidirectional_sum
enum dnnl_rnn_direction_t
|
static int |
dnnl_binary
enum dnnl_primitive_kind_t
|
static int |
dnnl_binary_add
enum dnnl_alg_kind_t
|
static int |
dnnl_binary_max
enum dnnl_alg_kind_t
|
static int |
dnnl_binary_min
enum dnnl_alg_kind_t
|
static int |
dnnl_binary_mul
enum dnnl_alg_kind_t
|
static int |
dnnl_blocked
enum dnnl_format_kind_t
|
static int |
dnnl_cba
enum dnnl_format_tag_t
|
static int |
dnnl_cdba
enum dnnl_format_tag_t
|
static int |
dnnl_cdeba
enum dnnl_format_tag_t
|
static int |
dnnl_chwn
enum dnnl_format_tag_t
|
static int |
dnnl_cn
enum dnnl_format_tag_t
|
static int |
dnnl_concat
enum dnnl_primitive_kind_t
|
static int |
dnnl_convolution
enum dnnl_primitive_kind_t
|
static int |
dnnl_convolution_auto
enum dnnl_alg_kind_t
|
static int |
dnnl_convolution_direct
enum dnnl_alg_kind_t
|
static int |
dnnl_convolution_winograd
enum dnnl_alg_kind_t
|
static int |
dnnl_cpu
enum dnnl_engine_kind_t
|
static int |
dnnl_cpu_isa_all
enum dnnl_cpu_isa_t
|
static int |
dnnl_cpu_isa_avx
enum dnnl_cpu_isa_t
|
static int |
dnnl_cpu_isa_avx2
enum dnnl_cpu_isa_t
|
static int |
dnnl_cpu_isa_avx512_core
enum dnnl_cpu_isa_t
|
static int |
dnnl_cpu_isa_avx512_core_amx
enum dnnl_cpu_isa_t
|
static int |
dnnl_cpu_isa_avx512_core_bf16
enum dnnl_cpu_isa_t
|
static int |
dnnl_cpu_isa_avx512_core_vnni
enum dnnl_cpu_isa_t
|
static int |
dnnl_cpu_isa_avx512_mic
enum dnnl_cpu_isa_t
|
static int |
dnnl_cpu_isa_avx512_mic_4ops
enum dnnl_cpu_isa_t
|
static int |
dnnl_cpu_isa_sse41
enum dnnl_cpu_isa_t
|
static long |
DNNL_CPU_RUNTIME |
static long |
DNNL_CPU_THREADING_RUNTIME
\endcond
|
static int |
dnnl_data_type_undef
enum dnnl_data_type_t
|
static int |
dnnl_dcab
enum dnnl_format_tag_t
|
static int |
dnnl_decab
enum dnnl_format_tag_t
|
static int |
dnnl_deconvolution
enum dnnl_primitive_kind_t
|
static int |
dnnl_deconvolution_direct
enum dnnl_alg_kind_t
|
static int |
dnnl_deconvolution_winograd
enum dnnl_alg_kind_t
|
static int |
dnnl_defcab
enum dnnl_format_tag_t
|
static int |
dnnl_dhwigo
enum dnnl_format_tag_t
|
static int |
dnnl_dhwio
enum dnnl_format_tag_t
|
static int |
dnnl_eltwise
enum dnnl_primitive_kind_t
|
static int |
dnnl_eltwise_abs
enum dnnl_alg_kind_t
|
static int |
dnnl_eltwise_bounded_relu
enum dnnl_alg_kind_t
|
static int |
dnnl_eltwise_clip
enum dnnl_alg_kind_t
|
static int |
dnnl_eltwise_elu
enum dnnl_alg_kind_t
|
static int |
dnnl_eltwise_elu_use_dst_for_bwd
enum dnnl_alg_kind_t
|
static int |
dnnl_eltwise_exp
enum dnnl_alg_kind_t
|
static int |
dnnl_eltwise_exp_use_dst_for_bwd
enum dnnl_alg_kind_t
|
static int |
dnnl_eltwise_gelu
enum dnnl_alg_kind_t
|
static int |
dnnl_eltwise_gelu_erf
enum dnnl_alg_kind_t
|
static int |
dnnl_eltwise_gelu_tanh
enum dnnl_alg_kind_t
|
static int |
dnnl_eltwise_linear
enum dnnl_alg_kind_t
|
static int |
dnnl_eltwise_log
enum dnnl_alg_kind_t
|
static int |
dnnl_eltwise_logistic
enum dnnl_alg_kind_t
|
static int |
dnnl_eltwise_logistic_use_dst_for_bwd
enum dnnl_alg_kind_t
|
static int |
dnnl_eltwise_pow
enum dnnl_alg_kind_t
|
static int |
dnnl_eltwise_relu
enum dnnl_alg_kind_t
|
static int |
dnnl_eltwise_relu_use_dst_for_bwd
enum dnnl_alg_kind_t
|
static int |
dnnl_eltwise_round
enum dnnl_alg_kind_t
|
static int |
dnnl_eltwise_soft_relu
enum dnnl_alg_kind_t
|
static int |
dnnl_eltwise_sqrt
enum dnnl_alg_kind_t
|
static int |
dnnl_eltwise_sqrt_use_dst_for_bwd
enum dnnl_alg_kind_t
|
static int |
dnnl_eltwise_square
enum dnnl_alg_kind_t
|
static int |
dnnl_eltwise_swish
enum dnnl_alg_kind_t
|
static int |
dnnl_eltwise_tanh
enum dnnl_alg_kind_t
|
static int |
dnnl_eltwise_tanh_use_dst_for_bwd
enum dnnl_alg_kind_t
|
static int |
DNNL_ENABLE_EXCEPTIONS
\endcond
|
static int |
dnnl_f16
enum dnnl_data_type_t
|
static int |
dnnl_f32
enum dnnl_data_type_t
|
static int |
dnnl_format_kind_any
enum dnnl_format_kind_t
|
static int |
dnnl_format_kind_rnn_packed
enum dnnl_format_kind_t
|
static int |
dnnl_format_kind_undef
enum dnnl_format_kind_t
|
static int |
dnnl_format_kind_wino
enum dnnl_format_kind_t
|
static int |
dnnl_format_tag_any
enum dnnl_format_tag_t
|
static int |
dnnl_format_tag_last
enum dnnl_format_tag_t
|
static int |
dnnl_format_tag_undef
enum dnnl_format_tag_t
|
static int |
dnnl_forward
enum dnnl_prop_kind_t
|
static int |
dnnl_forward_inference
enum dnnl_prop_kind_t
|
static int |
dnnl_forward_scoring
enum dnnl_prop_kind_t
|
static int |
dnnl_forward_training
enum dnnl_prop_kind_t
|
static int |
dnnl_fuse_norm_relu
enum dnnl_normalization_flags_t
|
static int |
dnnl_gemm
enum dnnl_primitive_kind_t
|
static int |
dnnl_giodhw
enum dnnl_format_tag_t
|
static int |
dnnl_gIOdhw16i16o
enum dnnl_format_tag_t
|
static int |
dnnl_gIOdhw16o16i
enum dnnl_format_tag_t
|
static int |
dnnl_gIOdhw4i8o8i4o
enum dnnl_format_tag_t
|
static int |
dnnl_gIOdhw8o16i2o
enum dnnl_format_tag_t
|
static int |
dnnl_giohw
enum dnnl_format_tag_t
|
static int |
dnnl_gIOhw16i16o
enum dnnl_format_tag_t
|
static int |
dnnl_gIOhw16o16i
enum dnnl_format_tag_t
|
static int |
dnnl_gIOhw4i8o8i4o
enum dnnl_format_tag_t
|
static int |
dnnl_gIOhw8o16i2o
enum dnnl_format_tag_t
|
static int |
dnnl_gIOw16i16o
enum dnnl_format_tag_t
|
static int |
dnnl_gIOw16o16i
enum dnnl_format_tag_t
|
static int |
dnnl_gIOw4i8o8i4o
enum dnnl_format_tag_t
|
static int |
dnnl_gIOw8o16i2o
enum dnnl_format_tag_t
|
static int |
dnnl_gOdhwi16o
enum dnnl_format_tag_t
|
static int |
dnnl_gOdhwI16o2i
enum dnnl_format_tag_t
|
static int |
dnnl_gOdhwi4o
enum dnnl_format_tag_t
|
static int |
dnnl_gOdhwi8o
enum dnnl_format_tag_t
|
static int |
dnnl_gOhwi16o
enum dnnl_format_tag_t
|
static int |
dnnl_gOhwI16o2i
enum dnnl_format_tag_t
|
static int |
dnnl_gOhwI16o4i
enum dnnl_format_tag_t
|
static int |
dnnl_gOhwi32o
enum dnnl_format_tag_t
|
static int |
dnnl_gOhwi4o
enum dnnl_format_tag_t
|
static int |
dnnl_gOhwi8o
enum dnnl_format_tag_t
|
static int |
dnnl_goidhw
enum dnnl_format_tag_t
|
static int |
dnnl_Goidhw16g
enum dnnl_format_tag_t
|
static int |
dnnl_gOIdhw16i16o
enum dnnl_format_tag_t
|
static int |
dnnl_gOidhw16o
enum dnnl_format_tag_t
|
static int |
dnnl_gOIdhw16o16i
enum dnnl_format_tag_t
|
static int |
dnnl_gOIdhw2i4o2i
enum dnnl_format_tag_t
|
static int |
dnnl_gOIdhw2i8o4i
enum dnnl_format_tag_t
|
static int |
dnnl_gOIdhw2o4i2o
enum dnnl_format_tag_t
|
static int |
dnnl_Goidhw32g
enum dnnl_format_tag_t
|
static int |
dnnl_gOIdhw4i16o4i
enum dnnl_format_tag_t
|
static int |
dnnl_gOIdhw4i4o
enum dnnl_format_tag_t
|
static int |
dnnl_gOIdhw4i8o2i
enum dnnl_format_tag_t
|
static int |
dnnl_gOidhw4o
enum dnnl_format_tag_t
|
static int |
dnnl_gOIdhw4o4i
enum dnnl_format_tag_t
|
static int |
dnnl_gOIdhw4o8i2o
enum dnnl_format_tag_t
|
static int |
dnnl_gOIdhw4o8i8o4i
enum dnnl_format_tag_t
|
static int |
dnnl_gOIdhw8i16o2i
enum dnnl_format_tag_t
|
static int |
dnnl_gOIdhw8i8o
enum dnnl_format_tag_t
|
static int |
dnnl_gOIdhw8o16i2o
enum dnnl_format_tag_t
|
static int |
dnnl_gOIdhw8o4i
enum dnnl_format_tag_t
|
static int |
dnnl_gOIdhw8o8i
enum dnnl_format_tag_t
|
static int |
dnnl_goihw
enum dnnl_format_tag_t
|
static int |
dnnl_Goihw16g
enum dnnl_format_tag_t
|
static int |
dnnl_gOIhw16i16o
enum dnnl_format_tag_t
|
static int |
dnnl_gOIhw16i16o2i
enum dnnl_format_tag_t
|
static int |
dnnl_gOIhw16i16o4i
enum dnnl_format_tag_t
|
static int |
dnnl_gOihw16o
enum dnnl_format_tag_t
|
static int |
dnnl_gOIhw16o16i
enum dnnl_format_tag_t
|
static int |
dnnl_gOIhw2i4o2i
enum dnnl_format_tag_t
|
static int |
dnnl_gOIhw2i8o4i
enum dnnl_format_tag_t
|
static int |
dnnl_gOIhw2o4i2o
enum dnnl_format_tag_t
|
static int |
dnnl_gOIhw2o8i8o2i
enum dnnl_format_tag_t
|
static int |
dnnl_Goihw32g
enum dnnl_format_tag_t
|
static int |
dnnl_gOIhw4i16o4i
enum dnnl_format_tag_t
|
static int |
dnnl_gOIhw4i4o
enum dnnl_format_tag_t
|
static int |
dnnl_gOIhw4i8o2i
enum dnnl_format_tag_t
|
static int |
dnnl_gOihw4o
enum dnnl_format_tag_t
|
static int |
dnnl_gOIhw4o4i
enum dnnl_format_tag_t
|
static int |
dnnl_gOIhw4o8i2o
enum dnnl_format_tag_t
|
static int |
dnnl_gOIhw4o8i8o4i
enum dnnl_format_tag_t
|
static int |
dnnl_Goihw8g
enum dnnl_format_tag_t
|
static int |
dnnl_gOIhw8i16o2i
enum dnnl_format_tag_t
|
static int |
dnnl_gOIhw8i8o
enum dnnl_format_tag_t
|
static int |
dnnl_gOIhw8o16i2o
enum dnnl_format_tag_t
|
static int |
dnnl_gOIhw8o4i
enum dnnl_format_tag_t
|
static int |
dnnl_gOIhw8o8i
enum dnnl_format_tag_t
|
static int |
dnnl_goiw
enum dnnl_format_tag_t
|
static int |
dnnl_Goiw16g
enum dnnl_format_tag_t
|
static int |
dnnl_gOIw16i16o
enum dnnl_format_tag_t
|
static int |
dnnl_gOIw16i16o2i
enum dnnl_format_tag_t
|
static int |
dnnl_gOIw16i16o4i
enum dnnl_format_tag_t
|
static int |
dnnl_gOiw16o
enum dnnl_format_tag_t
|
static int |
dnnl_gOIw16o16i
enum dnnl_format_tag_t
|
static int |
dnnl_gOIw2i4o2i
enum dnnl_format_tag_t
|
static int |
dnnl_gOIw2i8o4i
enum dnnl_format_tag_t
|
static int |
dnnl_gOIw2o4i2o
enum dnnl_format_tag_t
|
static int |
dnnl_Goiw32g
enum dnnl_format_tag_t
|
static int |
dnnl_gOIw4i16o4i
enum dnnl_format_tag_t
|
static int |
dnnl_gOIw4i4o
enum dnnl_format_tag_t
|
static int |
dnnl_gOIw4i8o2i
enum dnnl_format_tag_t
|
static int |
dnnl_gOiw4o
enum dnnl_format_tag_t
|
static int |
dnnl_gOIw4o4i
enum dnnl_format_tag_t
|
static int |
dnnl_gOIw4o8i2o
enum dnnl_format_tag_t
|
static int |
dnnl_gOIw4o8i8o4i
enum dnnl_format_tag_t
|
static int |
dnnl_Goiw8g
enum dnnl_format_tag_t
|
static int |
dnnl_gOIw8i16o2i
enum dnnl_format_tag_t
|
static int |
dnnl_gOIw8i8o
enum dnnl_format_tag_t
|
static int |
dnnl_gOIw8o16i2o
enum dnnl_format_tag_t
|
static int |
dnnl_gOIw8o4i
enum dnnl_format_tag_t
|
static int |
dnnl_gOIw8o8i
enum dnnl_format_tag_t
|
static int |
dnnl_gOwi16o
enum dnnl_format_tag_t
|
static int |
dnnl_gOwI16o2i
enum dnnl_format_tag_t
|
static int |
dnnl_gOwI16o4i
enum dnnl_format_tag_t
|
static int |
dnnl_gOwi4o
enum dnnl_format_tag_t
|
static int |
dnnl_gOwi8o
enum dnnl_format_tag_t
|
static int |
dnnl_gpu
enum dnnl_engine_kind_t
|
static long |
DNNL_GPU_RUNTIME |
static int |
dnnl_hwigo
enum dnnl_format_tag_t
|
static int |
dnnl_hwio
enum dnnl_format_tag_t
|
static int |
dnnl_idhwo
enum dnnl_format_tag_t
|
static int |
dnnl_ihwo
enum dnnl_format_tag_t
|
static int |
dnnl_inner_product
enum dnnl_primitive_kind_t
|
static int |
dnnl_invalid_arguments
enum dnnl_status_t
|
static int |
dnnl_io
enum dnnl_format_tag_t
|
static int |
dnnl_iodhw
enum dnnl_format_tag_t
|
static int |
dnnl_IOdhw16i16o
enum dnnl_format_tag_t
|
static int |
dnnl_IOdhw16o16i
enum dnnl_format_tag_t
|
static int |
dnnl_IOdhw4i8o8i4o
enum dnnl_format_tag_t
|
static int |
dnnl_IOdhw8o16i2o
enum dnnl_format_tag_t
|
static int |
dnnl_iohw
enum dnnl_format_tag_t
|
static int |
dnnl_IOhw16i16o
enum dnnl_format_tag_t
|
static int |
dnnl_IOhw16o16i
enum dnnl_format_tag_t
|
static int |
dnnl_IOhw4i8o8i4o
enum dnnl_format_tag_t
|
static int |
dnnl_IOhw8o16i2o
enum dnnl_format_tag_t
|
static int |
dnnl_IOw16i16o
enum dnnl_format_tag_t
|
static int |
dnnl_IOw16o16i
enum dnnl_format_tag_t
|
static int |
dnnl_IOw4i8o8i4o
enum dnnl_format_tag_t
|
static int |
dnnl_IOw8o16i2o
enum dnnl_format_tag_t
|
static int |
dnnl_iterator_ends
enum dnnl_status_t
|
static int |
dnnl_iwo
enum dnnl_format_tag_t
|
static long |
DNNL_JIT_PROFILE_LINUX_JITDUMP
Enable Linux perf integration via jitdump files
|
static long |
DNNL_JIT_PROFILE_LINUX_JITDUMP_USE_TSC
Instruct Linux perf integration via jitdump files to use TSC.
|
static long |
DNNL_JIT_PROFILE_LINUX_PERF
Enable Linux perf integration (both jitdump and perfmap)
|
static long |
DNNL_JIT_PROFILE_LINUX_PERFMAP
Enable Linux perf integration via perfmap files
|
static long |
DNNL_JIT_PROFILE_NONE
Disable profiling completely
|
static long |
DNNL_JIT_PROFILE_VTUNE
Enable VTune Amplifier integration
|
static int |
dnnl_layer_normalization
enum dnnl_primitive_kind_t
|
static int |
dnnl_lbr_gru
enum dnnl_alg_kind_t
|
static int |
dnnl_ldgo
enum dnnl_format_tag_t
|
static int |
dnnl_ldgoi
enum dnnl_format_tag_t
|
static int |
dnnl_ldgoi_p
enum dnnl_rnn_packed_memory_format_t
|
static int |
dnnl_ldigo
enum dnnl_format_tag_t
|
static int |
dnnl_ldigo_p
enum dnnl_rnn_packed_memory_format_t
|
static int |
dnnl_ldio
enum dnnl_format_tag_t
|
static int |
dnnl_ldnc
enum dnnl_format_tag_t
|
static int |
dnnl_ldoi
enum dnnl_format_tag_t
|
static int |
dnnl_logsoftmax
enum dnnl_primitive_kind_t
|
static int |
dnnl_lrn
enum dnnl_primitive_kind_t
|
static int |
dnnl_lrn_across_channels
enum dnnl_alg_kind_t
|
static int |
dnnl_lrn_within_channel
enum dnnl_alg_kind_t
|
static int |
dnnl_matmul
enum dnnl_primitive_kind_t
|
static int |
DNNL_MAX_NDIMS
\} dnnl_api_primitives_common
\} dnnl_api_primitives
|
static int |
dnnl_memory_extra_flag_compensation_conv_s8s8
enum dnnl_memory_extra_flags_t
|
static int |
dnnl_memory_extra_flag_gpu_rnn_u8s8_compensation
enum dnnl_memory_extra_flags_t
|
static int |
dnnl_memory_extra_flag_none
enum dnnl_memory_extra_flags_t
|
static int |
dnnl_memory_extra_flag_scale_adjust
enum dnnl_memory_extra_flags_t
|
static int |
dnnl_nc
enum dnnl_format_tag_t
|
static int |
dnnl_ncdhw
enum dnnl_format_tag_t
|
static int |
dnnl_nCdhw16c
enum dnnl_format_tag_t
|
static int |
dnnl_NCdhw16n16c
enum dnnl_format_tag_t
|
static int |
dnnl_nCdhw32c
enum dnnl_format_tag_t
|
static int |
dnnl_NCdhw32n32c
enum dnnl_format_tag_t
|
static int |
dnnl_nCdhw4c
enum dnnl_format_tag_t
|
static int |
dnnl_nCdhw8c
enum dnnl_format_tag_t
|
static int |
dnnl_nchw
enum dnnl_format_tag_t
|
static int |
dnnl_nChw16c
enum dnnl_format_tag_t
|
static int |
dnnl_NChw16n16c
enum dnnl_format_tag_t
|
static int |
dnnl_nChw32c
enum dnnl_format_tag_t
|
static int |
dnnl_NChw32n32c
enum dnnl_format_tag_t
|
static int |
dnnl_nChw4c
enum dnnl_format_tag_t
|
static int |
dnnl_nChw8c
enum dnnl_format_tag_t
|
static int |
dnnl_ncw
enum dnnl_format_tag_t
|
static int |
dnnl_nCw16c
enum dnnl_format_tag_t
|
static int |
dnnl_NCw16n16c
enum dnnl_format_tag_t
|
static int |
dnnl_nCw32c
enum dnnl_format_tag_t
|
static int |
dnnl_NCw32n32c
enum dnnl_format_tag_t
|
static int |
dnnl_nCw4c
enum dnnl_format_tag_t
|
static int |
dnnl_nCw8c
enum dnnl_format_tag_t
|
static int |
dnnl_ndhwc
enum dnnl_format_tag_t
|
static int |
dnnl_nhwc
enum dnnl_format_tag_t
|
static int |
dnnl_normalization_flags_none
enum dnnl_normalization_flags_t
|
static int |
dnnl_not_required
enum dnnl_status_t
|
static int |
dnnl_nt
enum dnnl_format_tag_t
|
static int |
dnnl_ntc
enum dnnl_format_tag_t
|
static int |
dnnl_nwc
enum dnnl_format_tag_t
|
static int |
dnnl_odhwi
enum dnnl_format_tag_t
|
static int |
dnnl_Odhwi16o
enum dnnl_format_tag_t
|
static int |
dnnl_OdhwI16o2i
enum dnnl_format_tag_t
|
static int |
dnnl_Odhwi4o
enum dnnl_format_tag_t
|
static int |
dnnl_Odhwi8o
enum dnnl_format_tag_t
|
static int |
dnnl_ohwi
enum dnnl_format_tag_t
|
static int |
dnnl_Ohwi16o
enum dnnl_format_tag_t
|
static int |
dnnl_OhwI16o2i
enum dnnl_format_tag_t
|
static int |
dnnl_OhwI16o4i
enum dnnl_format_tag_t
|
static int |
dnnl_Ohwi32o
enum dnnl_format_tag_t
|
static int |
dnnl_Ohwi4o
enum dnnl_format_tag_t
|
static int |
dnnl_Ohwi8o
enum dnnl_format_tag_t
|
static int |
dnnl_oi
enum dnnl_format_tag_t
|
static int |
dnnl_oidhw
enum dnnl_format_tag_t
|
static int |
dnnl_OIdhw16i16o
enum dnnl_format_tag_t
|
static int |
dnnl_Oidhw16o
enum dnnl_format_tag_t
|
static int |
dnnl_OIdhw16o16i
enum dnnl_format_tag_t
|
static int |
dnnl_OIdhw2i8o4i
enum dnnl_format_tag_t
|
static int |
dnnl_OIdhw4i16o4i
enum dnnl_format_tag_t
|
static int |
dnnl_OIdhw4i4o
enum dnnl_format_tag_t
|
static int |
dnnl_Oidhw4o
enum dnnl_format_tag_t
|
static int |
dnnl_OIdhw4o4i
enum dnnl_format_tag_t
|
static int |
dnnl_OIdhw4o8i8o4i
enum dnnl_format_tag_t
|
static int |
dnnl_OIdhw8i16o2i
enum dnnl_format_tag_t
|
static int |
dnnl_OIdhw8i8o
enum dnnl_format_tag_t
|
static int |
dnnl_OIdhw8o16i2o
enum dnnl_format_tag_t
|
static int |
dnnl_OIdhw8o4i
enum dnnl_format_tag_t
|
static int |
dnnl_OIdhw8o8i
enum dnnl_format_tag_t
|
static int |
dnnl_oihw
enum dnnl_format_tag_t
|
static int |
dnnl_OIhw16i16o
enum dnnl_format_tag_t
|
static int |
dnnl_OIhw16i16o2i
enum dnnl_format_tag_t
|
static int |
dnnl_OIhw16i16o4i
enum dnnl_format_tag_t
|
static int |
dnnl_Oihw16o
enum dnnl_format_tag_t
|
static int |
dnnl_OIhw16o16i
enum dnnl_format_tag_t
|
static int |
dnnl_OIhw2i8o4i
enum dnnl_format_tag_t
|
static int |
dnnl_OIhw2o8i8o2i
enum dnnl_format_tag_t
|
static int |
dnnl_OIhw4i16o4i
enum dnnl_format_tag_t
|
static int |
dnnl_OIhw4i4o
enum dnnl_format_tag_t
|
static int |
dnnl_Oihw4o
enum dnnl_format_tag_t
|
static int |
dnnl_OIhw4o4i
enum dnnl_format_tag_t
|
static int |
dnnl_OIhw4o8i8o4i
enum dnnl_format_tag_t
|
static int |
dnnl_OIhw8i16o2i
enum dnnl_format_tag_t
|
static int |
dnnl_OIhw8i8o
enum dnnl_format_tag_t
|
static int |
dnnl_OIhw8o16i2o
enum dnnl_format_tag_t
|
static int |
dnnl_OIhw8o4i
enum dnnl_format_tag_t
|
static int |
dnnl_OIhw8o8i
enum dnnl_format_tag_t
|
static int |
dnnl_oiw
enum dnnl_format_tag_t
|
static int |
dnnl_OIw16i16o
enum dnnl_format_tag_t
|
static int |
dnnl_OIw16i16o2i
enum dnnl_format_tag_t
|
static int |
dnnl_OIw16i16o4i
enum dnnl_format_tag_t
|
static int |
dnnl_Oiw16o
enum dnnl_format_tag_t
|
static int |
dnnl_OIw16o16i
enum dnnl_format_tag_t
|
static int |
dnnl_OIw2i8o4i
enum dnnl_format_tag_t
|
static int |
dnnl_OIw4i16o4i
enum dnnl_format_tag_t
|
static int |
dnnl_OIw4i4o
enum dnnl_format_tag_t
|
static int |
dnnl_Oiw4o
enum dnnl_format_tag_t
|
static int |
dnnl_OIw4o4i
enum dnnl_format_tag_t
|
static int |
dnnl_OIw4o8i8o4i
enum dnnl_format_tag_t
|
static int |
dnnl_OIw8i16o2i
enum dnnl_format_tag_t
|
static int |
dnnl_OIw8i8o
enum dnnl_format_tag_t
|
static int |
dnnl_OIw8o16i2o
enum dnnl_format_tag_t
|
static int |
dnnl_OIw8o4i
enum dnnl_format_tag_t
|
static int |
dnnl_OIw8o8i
enum dnnl_format_tag_t
|
static int |
dnnl_out_of_memory
enum dnnl_status_t
|
static int |
dnnl_owi
enum dnnl_format_tag_t
|
static int |
dnnl_Owi16o
enum dnnl_format_tag_t
|
static int |
dnnl_OwI16o2i
enum dnnl_format_tag_t
|
static int |
dnnl_OwI16o4i
enum dnnl_format_tag_t
|
static int |
dnnl_Owi4o
enum dnnl_format_tag_t
|
static int |
dnnl_Owi8o
enum dnnl_format_tag_t
|
static int |
dnnl_packed_format_undef
enum dnnl_rnn_packed_memory_format_t
|
static int |
dnnl_pooling
enum dnnl_primitive_kind_t
|
static int |
dnnl_pooling_avg
enum dnnl_alg_kind_t
|
static int |
dnnl_pooling_avg_exclude_padding
enum dnnl_alg_kind_t
|
static int |
dnnl_pooling_avg_include_padding
enum dnnl_alg_kind_t
|
static int |
dnnl_pooling_max
enum dnnl_alg_kind_t
|
static int |
dnnl_primitive_kind_max
enum dnnl_primitive_kind_t
|
static int |
dnnl_prop_kind_undef
enum dnnl_prop_kind_t
|
static int |
dnnl_query_batch_normalization_d
enum dnnl_query_t
|
static int |
dnnl_query_binary_d
enum dnnl_query_t
|
static int |
dnnl_query_convolution_d
enum dnnl_query_t
|
static int |
dnnl_query_deconvolution_d
enum dnnl_query_t
|
static int |
dnnl_query_diff_dst_md
enum dnnl_query_t
|
static int |
dnnl_query_diff_src_md
enum dnnl_query_t
|
static int |
dnnl_query_diff_weights_md
enum dnnl_query_t
|
static int |
dnnl_query_dst_md
enum dnnl_query_t
|
static int |
dnnl_query_eltwise_d
enum dnnl_query_t
|
static int |
dnnl_query_engine
enum dnnl_query_t
|
static int |
dnnl_query_exec_arg_md
enum dnnl_query_t
|
static int |
dnnl_query_gemm_d
enum dnnl_query_t
|
static int |
dnnl_query_impl_info_str
enum dnnl_query_t
|
static int |
dnnl_query_inner_product_d
enum dnnl_query_t
|
static int |
dnnl_query_layer_normalization_d
enum dnnl_query_t
|
static int |
dnnl_query_logsoftmax_d
enum dnnl_query_t
|
static int |
dnnl_query_lrn_d
enum dnnl_query_t
|
static int |
dnnl_query_matmul_d
enum dnnl_query_t
|
static int |
dnnl_query_max
enum dnnl_query_t
|
static int |
dnnl_query_memory_consumption_s64
enum dnnl_query_t
|
static int |
dnnl_query_num_of_inputs_s32
enum dnnl_query_t
|
static int |
dnnl_query_num_of_outputs_s32
enum dnnl_query_t
|
static int |
dnnl_query_op_d
enum dnnl_query_t
|
static int |
dnnl_query_pooling_d
enum dnnl_query_t
|
static int |
dnnl_query_primitive_kind
enum dnnl_query_t
|
static int |
dnnl_query_prop_kind
enum dnnl_query_t
|
static int |
dnnl_query_reorder_dst_engine
enum dnnl_query_t
|
static int |
dnnl_query_reorder_src_engine
enum dnnl_query_t
|
static int |
dnnl_query_resampling_d
enum dnnl_query_t
|
static int |
dnnl_query_rnn_d
enum dnnl_query_t
|
static int |
dnnl_query_scratchpad_engine
enum dnnl_query_t
|
static int |
dnnl_query_scratchpad_md
enum dnnl_query_t
|
static int |
dnnl_query_shuffle_d
enum dnnl_query_t
|
static int |
dnnl_query_softmax_d
enum dnnl_query_t
|
static int |
dnnl_query_some_d
enum dnnl_query_t
|
static int |
dnnl_query_some_md
enum dnnl_query_t
|
static int |
dnnl_query_src_md
enum dnnl_query_t
|
static int |
dnnl_query_time_estimate_f64
enum dnnl_query_t
|
static int |
dnnl_query_undef
enum dnnl_query_t
|
static int |
dnnl_query_weights_md
enum dnnl_query_t
|
static int |
dnnl_query_workspace_md
enum dnnl_query_t
|
static int |
dnnl_reorder
enum dnnl_primitive_kind_t
|
static int |
dnnl_resampling
enum dnnl_primitive_kind_t
|
static int |
dnnl_resampling_linear
enum dnnl_alg_kind_t
|
static int |
dnnl_resampling_nearest
enum dnnl_alg_kind_t
|
static int |
dnnl_rnn
enum dnnl_primitive_kind_t
|
static int |
dnnl_rnn_flags_undef
enum dnnl_rnn_flags_t
|
static int |
DNNL_RNN_MAX_N_PARTS
Maximum number of parts of RNN weights tensor that require separate
computation.
|
static long |
DNNL_RUNTIME_DIM_VAL |
static int |
dnnl_runtime_error
enum dnnl_status_t
|
static double |
DNNL_RUNTIME_F32_VAL |
static long |
DNNL_RUNTIME_NONE
\} dnnl_api_stream
|
static long |
DNNL_RUNTIME_OCL
OpenCL runtime
|
static long |
DNNL_RUNTIME_OMP
OpenMP runtime (CPU only)
|
static int |
DNNL_RUNTIME_S32_VAL
\endcond
|
static int |
DNNL_RUNTIME_S32_VAL_REP |
static long |
DNNL_RUNTIME_SEQ
Sequential runtime (CPU only)
|
static long |
DNNL_RUNTIME_SIZE_VAL |
static long |
DNNL_RUNTIME_TBB
TBB runtime (CPU only)
|
static long |
DNNL_RUNTIME_THREADPOOL
Threadpool runtime (CPU only)
|
static int |
dnnl_s32
enum dnnl_data_type_t
|
static int |
dnnl_s8
enum dnnl_data_type_t
|
static int |
dnnl_scratchpad_mode_library
enum dnnl_scratchpad_mode_t
|
static int |
dnnl_scratchpad_mode_user
enum dnnl_scratchpad_mode_t
|
static int |
dnnl_shuffle
enum dnnl_primitive_kind_t
|
static int |
dnnl_softmax
enum dnnl_primitive_kind_t
|
static int |
dnnl_stream_default_flags
enum dnnl_stream_flags_t
|
static int |
dnnl_stream_default_order
enum dnnl_stream_flags_t
|
static int |
dnnl_stream_in_order
enum dnnl_stream_flags_t
|
static int |
dnnl_stream_out_of_order
enum dnnl_stream_flags_t
|
static int |
dnnl_success
enum dnnl_status_t
|
static int |
dnnl_sum
enum dnnl_primitive_kind_t
|
static int |
dnnl_tn
enum dnnl_format_tag_t
|
static int |
dnnl_tnc
enum dnnl_format_tag_t
|
static int |
dnnl_u8
enum dnnl_data_type_t
|
static int |
dnnl_undefined_primitive
enum dnnl_primitive_kind_t
|
static int |
dnnl_unidirectional
enum dnnl_rnn_direction_t
|
static int |
dnnl_unidirectional_left2right
enum dnnl_rnn_direction_t
|
static int |
dnnl_unidirectional_right2left
enum dnnl_rnn_direction_t
|
static int |
dnnl_unimplemented
enum dnnl_status_t
|
static int |
dnnl_use_global_stats
enum dnnl_normalization_flags_t
|
static int |
dnnl_use_scaleshift
enum dnnl_normalization_flags_t
|
static int |
dnnl_vanilla_gru
enum dnnl_alg_kind_t
|
static int |
dnnl_vanilla_lstm
enum dnnl_alg_kind_t
|
static int |
dnnl_vanilla_rnn
enum dnnl_alg_kind_t
|
static String |
DNNL_VERSION_HASH |
static int |
DNNL_VERSION_MAJOR
Major version
|
static int |
DNNL_VERSION_MINOR
Minor version
|
static int |
DNNL_VERSION_PATCH
Patch version
|
static int |
dnnl_wigo
enum dnnl_format_tag_t
|
static int |
dnnl_wino_undef
enum dnnl_wino_memory_format_t
|
static int |
dnnl_wino_wei_aaOBiOo
enum dnnl_wino_memory_format_t
|
static int |
dnnl_wino_wei_aaOio
enum dnnl_wino_memory_format_t
|
static int |
dnnl_wino_wei_aaOIoi
enum dnnl_wino_memory_format_t
|
static int |
dnnl_wino_wei_OBaaIBOIio
enum dnnl_wino_memory_format_t
|
static int |
dnnl_wio
enum dnnl_format_tag_t
|
static int |
dnnl_x
enum dnnl_format_tag_t
|
| Constructor and Description |
|---|
dnnl() |
| Modifier and Type | Method and Description |
|---|---|
static dnnl.normalization_flags |
and(dnnl.normalization_flags lhs,
dnnl.normalization_flags rhs) |
static dnnl.rnn_flags |
and(dnnl.rnn_flags lhs,
dnnl.rnn_flags rhs) |
static int |
and(int lhs,
int rhs) |
static stream.flags |
and(stream.flags lhs,
stream.flags rhs) |
static int[] |
andPut(int[] lhs,
dnnl.normalization_flags rhs) |
static int[] |
andPut(int[] lhs,
dnnl.rnn_flags rhs) |
static int[] |
andPut(int[] lhs,
int rhs) |
static IntBuffer |
andPut(IntBuffer lhs,
dnnl.normalization_flags rhs) |
static IntBuffer |
andPut(IntBuffer lhs,
dnnl.rnn_flags rhs) |
static IntBuffer |
andPut(IntBuffer lhs,
int rhs) |
static IntPointer |
andPut(IntPointer lhs,
dnnl.normalization_flags rhs) |
static IntPointer |
andPut(IntPointer lhs,
dnnl.rnn_flags rhs) |
static IntPointer |
andPut(IntPointer lhs,
int rhs) |
static stream.flags |
andPut(stream.flags lhs,
stream.flags rhs) |
static int |
convert_to_c(dnnl.algorithm aalgorithm)
Converts algorithm kind enum value from C++ API to C API type.
|
static int |
convert_to_c(dnnl.normalization_flags flags)
Converts normalization flags enum value from C++ API to C API type.
|
static int |
convert_to_c(dnnl.prop_kind akind)
Converts propagation kind enum value from C++ API to C API type.
|
static int |
convert_to_c(dnnl.query aquery)
Converts query enum value from C++ API to C API type.
|
static int |
convert_to_c(dnnl.rnn_direction dir)
Converts RNN direction enum value from C++ API to C API type.
|
static int |
convert_to_c(dnnl.rnn_flags flags)
Converts RNN cell flags enum value from C++ API to C API type.
|
static int |
convert_to_c(dnnl.scratchpad_mode mode)
Converts a scratchpad mode enum value from C++ API to C API type.
|
static int |
convert_to_c(engine.kind akind)
Converts engine kind enum value from C++ API to C API type.
|
static int |
convert_to_c(int mode) |
static dnnl_memory_desc_t |
convert_to_c(memory.desc mems)
\} dnnl_api_reorder
|
static int |
convert_to_c(primitive.kind akind)
Converts primitive kind enum value from C++ API to C API type.
|
static int |
dnnl_batch_normalization_backward_desc_init(dnnl_batch_normalization_desc_t bnrm_desc,
int prop_kind,
dnnl_memory_desc_t diff_data_desc,
dnnl_memory_desc_t data_desc,
float epsilon,
int flags)
Initializes a descriptor for a batch normalization backward propagation
primitive.
|
static int |
dnnl_batch_normalization_forward_desc_init(dnnl_batch_normalization_desc_t bnrm_desc,
int prop_kind,
dnnl_memory_desc_t data_desc,
float epsilon,
int flags)
\} dnnl_api_lrn
|
static int |
dnnl_binary_desc_init(dnnl_binary_desc_t binary_desc,
int alg_kind,
dnnl_memory_desc_t src0_desc,
dnnl_memory_desc_t src1_desc,
dnnl_memory_desc_t dst_desc)
\} dnnl_api_sum
|
static int |
dnnl_concat_primitive_desc_create(dnnl_primitive_desc concat_primitive_desc,
dnnl_memory_desc_t dst_desc,
int n,
int concat_dimension,
dnnl_memory_desc_t src_descs,
dnnl_primitive_attr attr,
dnnl_engine engine)
\} dnnl_api_reorder
|
static int |
dnnl_concat_primitive_desc_create(PointerPointer concat_primitive_desc,
dnnl_memory_desc_t dst_desc,
int n,
int concat_dimension,
dnnl_memory_desc_t src_descs,
dnnl_primitive_attr attr,
dnnl_engine engine) |
static int |
dnnl_convolution_backward_data_desc_init(dnnl_convolution_desc_t conv_desc,
int alg_kind,
dnnl_memory_desc_t diff_src_desc,
dnnl_memory_desc_t weights_desc,
dnnl_memory_desc_t diff_dst_desc,
long[] strides,
long[] padding_l,
long[] padding_r) |
static int |
dnnl_convolution_backward_data_desc_init(dnnl_convolution_desc_t conv_desc,
int alg_kind,
dnnl_memory_desc_t diff_src_desc,
dnnl_memory_desc_t weights_desc,
dnnl_memory_desc_t diff_dst_desc,
LongBuffer strides,
LongBuffer padding_l,
LongBuffer padding_r) |
static int |
dnnl_convolution_backward_data_desc_init(dnnl_convolution_desc_t conv_desc,
int alg_kind,
dnnl_memory_desc_t diff_src_desc,
dnnl_memory_desc_t weights_desc,
dnnl_memory_desc_t diff_dst_desc,
LongPointer strides,
LongPointer padding_l,
LongPointer padding_r)
Initializes a descriptor for a convolution backward propagation primitive.
|
static int |
dnnl_convolution_backward_weights_desc_init(dnnl_convolution_desc_t conv_desc,
int alg_kind,
dnnl_memory_desc_t src_desc,
dnnl_memory_desc_t diff_weights_desc,
dnnl_memory_desc_t diff_bias_desc,
dnnl_memory_desc_t diff_dst_desc,
long[] strides,
long[] padding_l,
long[] padding_r) |
static int |
dnnl_convolution_backward_weights_desc_init(dnnl_convolution_desc_t conv_desc,
int alg_kind,
dnnl_memory_desc_t src_desc,
dnnl_memory_desc_t diff_weights_desc,
dnnl_memory_desc_t diff_bias_desc,
dnnl_memory_desc_t diff_dst_desc,
LongBuffer strides,
LongBuffer padding_l,
LongBuffer padding_r) |
static int |
dnnl_convolution_backward_weights_desc_init(dnnl_convolution_desc_t conv_desc,
int alg_kind,
dnnl_memory_desc_t src_desc,
dnnl_memory_desc_t diff_weights_desc,
dnnl_memory_desc_t diff_bias_desc,
dnnl_memory_desc_t diff_dst_desc,
LongPointer strides,
LongPointer padding_l,
LongPointer padding_r)
Initializes a descriptor for a convolution weights gradient primitive.
|
static int |
dnnl_convolution_forward_desc_init(dnnl_convolution_desc_t conv_desc,
int prop_kind,
int alg_kind,
dnnl_memory_desc_t src_desc,
dnnl_memory_desc_t weights_desc,
dnnl_memory_desc_t bias_desc,
dnnl_memory_desc_t dst_desc,
long[] strides,
long[] padding_l,
long[] padding_r) |
static int |
dnnl_convolution_forward_desc_init(dnnl_convolution_desc_t conv_desc,
int prop_kind,
int alg_kind,
dnnl_memory_desc_t src_desc,
dnnl_memory_desc_t weights_desc,
dnnl_memory_desc_t bias_desc,
dnnl_memory_desc_t dst_desc,
LongBuffer strides,
LongBuffer padding_l,
LongBuffer padding_r) |
static int |
dnnl_convolution_forward_desc_init(dnnl_convolution_desc_t conv_desc,
int prop_kind,
int alg_kind,
dnnl_memory_desc_t src_desc,
dnnl_memory_desc_t weights_desc,
dnnl_memory_desc_t bias_desc,
dnnl_memory_desc_t dst_desc,
LongPointer strides,
LongPointer padding_l,
LongPointer padding_r)
\} dnnl_api_binary
|
static int |
dnnl_deconvolution_backward_data_desc_init(dnnl_convolution_desc_t deconv_desc,
int alg_kind,
dnnl_memory_desc_t diff_src_desc,
dnnl_memory_desc_t weights_desc,
dnnl_memory_desc_t diff_dst_desc,
long[] strides,
long[] padding_l,
long[] padding_r) |
static int |
dnnl_deconvolution_backward_data_desc_init(dnnl_convolution_desc_t deconv_desc,
int alg_kind,
dnnl_memory_desc_t diff_src_desc,
dnnl_memory_desc_t weights_desc,
dnnl_memory_desc_t diff_dst_desc,
LongBuffer strides,
LongBuffer padding_l,
LongBuffer padding_r) |
static int |
dnnl_deconvolution_backward_data_desc_init(dnnl_convolution_desc_t deconv_desc,
int alg_kind,
dnnl_memory_desc_t diff_src_desc,
dnnl_memory_desc_t weights_desc,
dnnl_memory_desc_t diff_dst_desc,
LongPointer strides,
LongPointer padding_l,
LongPointer padding_r)
Initializes a descriptor for a deconvolution backward propagation primitive.
|
static int |
dnnl_deconvolution_backward_weights_desc_init(dnnl_convolution_desc_t deconv_desc,
int alg_kind,
dnnl_memory_desc_t src_desc,
dnnl_memory_desc_t diff_weights_desc,
dnnl_memory_desc_t diff_bias_desc,
dnnl_memory_desc_t diff_dst_desc,
long[] strides,
long[] padding_l,
long[] padding_r) |
static int |
dnnl_deconvolution_backward_weights_desc_init(dnnl_convolution_desc_t deconv_desc,
int alg_kind,
dnnl_memory_desc_t src_desc,
dnnl_memory_desc_t diff_weights_desc,
dnnl_memory_desc_t diff_bias_desc,
dnnl_memory_desc_t diff_dst_desc,
LongBuffer strides,
LongBuffer padding_l,
LongBuffer padding_r) |
static int |
dnnl_deconvolution_backward_weights_desc_init(dnnl_convolution_desc_t deconv_desc,
int alg_kind,
dnnl_memory_desc_t src_desc,
dnnl_memory_desc_t diff_weights_desc,
dnnl_memory_desc_t diff_bias_desc,
dnnl_memory_desc_t diff_dst_desc,
LongPointer strides,
LongPointer padding_l,
LongPointer padding_r)
Initializes a descriptor for a deconvolution weights gradient primitive.
|
static int |
dnnl_deconvolution_forward_desc_init(dnnl_convolution_desc_t deconv_desc,
int prop_kind,
int alg_kind,
dnnl_memory_desc_t src_desc,
dnnl_memory_desc_t weights_desc,
dnnl_memory_desc_t bias_desc,
dnnl_memory_desc_t dst_desc,
long[] strides,
long[] padding_l,
long[] padding_r) |
static int |
dnnl_deconvolution_forward_desc_init(dnnl_convolution_desc_t deconv_desc,
int prop_kind,
int alg_kind,
dnnl_memory_desc_t src_desc,
dnnl_memory_desc_t weights_desc,
dnnl_memory_desc_t bias_desc,
dnnl_memory_desc_t dst_desc,
LongBuffer strides,
LongBuffer padding_l,
LongBuffer padding_r) |
static int |
dnnl_deconvolution_forward_desc_init(dnnl_convolution_desc_t deconv_desc,
int prop_kind,
int alg_kind,
dnnl_memory_desc_t src_desc,
dnnl_memory_desc_t weights_desc,
dnnl_memory_desc_t bias_desc,
dnnl_memory_desc_t dst_desc,
LongPointer strides,
LongPointer padding_l,
LongPointer padding_r)
\} dnnl_api_convolution
|
static int |
dnnl_dilated_convolution_backward_data_desc_init(dnnl_convolution_desc_t conv_desc,
int alg_kind,
dnnl_memory_desc_t diff_src_desc,
dnnl_memory_desc_t weights_desc,
dnnl_memory_desc_t diff_dst_desc,
long[] strides,
long[] dilates,
long[] padding_l,
long[] padding_r) |
static int |
dnnl_dilated_convolution_backward_data_desc_init(dnnl_convolution_desc_t conv_desc,
int alg_kind,
dnnl_memory_desc_t diff_src_desc,
dnnl_memory_desc_t weights_desc,
dnnl_memory_desc_t diff_dst_desc,
LongBuffer strides,
LongBuffer dilates,
LongBuffer padding_l,
LongBuffer padding_r) |
static int |
dnnl_dilated_convolution_backward_data_desc_init(dnnl_convolution_desc_t conv_desc,
int alg_kind,
dnnl_memory_desc_t diff_src_desc,
dnnl_memory_desc_t weights_desc,
dnnl_memory_desc_t diff_dst_desc,
LongPointer strides,
LongPointer dilates,
LongPointer padding_l,
LongPointer padding_r)
Initializes a descriptor for a dilated convolution backward propagation
primitive.
|
static int |
dnnl_dilated_convolution_backward_weights_desc_init(dnnl_convolution_desc_t conv_desc,
int alg_kind,
dnnl_memory_desc_t src_desc,
dnnl_memory_desc_t diff_weights_desc,
dnnl_memory_desc_t diff_bias_desc,
dnnl_memory_desc_t diff_dst_desc,
long[] strides,
long[] dilates,
long[] padding_l,
long[] padding_r) |
static int |
dnnl_dilated_convolution_backward_weights_desc_init(dnnl_convolution_desc_t conv_desc,
int alg_kind,
dnnl_memory_desc_t src_desc,
dnnl_memory_desc_t diff_weights_desc,
dnnl_memory_desc_t diff_bias_desc,
dnnl_memory_desc_t diff_dst_desc,
LongBuffer strides,
LongBuffer dilates,
LongBuffer padding_l,
LongBuffer padding_r) |
static int |
dnnl_dilated_convolution_backward_weights_desc_init(dnnl_convolution_desc_t conv_desc,
int alg_kind,
dnnl_memory_desc_t src_desc,
dnnl_memory_desc_t diff_weights_desc,
dnnl_memory_desc_t diff_bias_desc,
dnnl_memory_desc_t diff_dst_desc,
LongPointer strides,
LongPointer dilates,
LongPointer padding_l,
LongPointer padding_r)
Initializes a descriptor for a dilated convolution weights gradient
primitive.
|
static int |
dnnl_dilated_convolution_forward_desc_init(dnnl_convolution_desc_t conv_desc,
int prop_kind,
int alg_kind,
dnnl_memory_desc_t src_desc,
dnnl_memory_desc_t weights_desc,
dnnl_memory_desc_t bias_desc,
dnnl_memory_desc_t dst_desc,
long[] strides,
long[] dilates,
long[] padding_l,
long[] padding_r) |
static int |
dnnl_dilated_convolution_forward_desc_init(dnnl_convolution_desc_t conv_desc,
int prop_kind,
int alg_kind,
dnnl_memory_desc_t src_desc,
dnnl_memory_desc_t weights_desc,
dnnl_memory_desc_t bias_desc,
dnnl_memory_desc_t dst_desc,
LongBuffer strides,
LongBuffer dilates,
LongBuffer padding_l,
LongBuffer padding_r) |
static int |
dnnl_dilated_convolution_forward_desc_init(dnnl_convolution_desc_t conv_desc,
int prop_kind,
int alg_kind,
dnnl_memory_desc_t src_desc,
dnnl_memory_desc_t weights_desc,
dnnl_memory_desc_t bias_desc,
dnnl_memory_desc_t dst_desc,
LongPointer strides,
LongPointer dilates,
LongPointer padding_l,
LongPointer padding_r)
Initializes a descriptor for a dilated convolution forward propagation
primitive.
|
static int |
dnnl_dilated_deconvolution_backward_data_desc_init(dnnl_convolution_desc_t deconv_desc,
int alg_kind,
dnnl_memory_desc_t diff_src_desc,
dnnl_memory_desc_t weights_desc,
dnnl_memory_desc_t diff_dst_desc,
long[] strides,
long[] dilates,
long[] padding_l,
long[] padding_r) |
static int |
dnnl_dilated_deconvolution_backward_data_desc_init(dnnl_convolution_desc_t deconv_desc,
int alg_kind,
dnnl_memory_desc_t diff_src_desc,
dnnl_memory_desc_t weights_desc,
dnnl_memory_desc_t diff_dst_desc,
LongBuffer strides,
LongBuffer dilates,
LongBuffer padding_l,
LongBuffer padding_r) |
static int |
dnnl_dilated_deconvolution_backward_data_desc_init(dnnl_convolution_desc_t deconv_desc,
int alg_kind,
dnnl_memory_desc_t diff_src_desc,
dnnl_memory_desc_t weights_desc,
dnnl_memory_desc_t diff_dst_desc,
LongPointer strides,
LongPointer dilates,
LongPointer padding_l,
LongPointer padding_r)
Initializes a descriptor for a dilated deconvolution backward propagation
primitive.
|
static int |
dnnl_dilated_deconvolution_backward_weights_desc_init(dnnl_convolution_desc_t deconv_desc,
int alg_kind,
dnnl_memory_desc_t src_desc,
dnnl_memory_desc_t diff_weights_desc,
dnnl_memory_desc_t diff_bias_desc,
dnnl_memory_desc_t diff_dst_desc,
long[] strides,
long[] dilates,
long[] padding_l,
long[] padding_r) |
static int |
dnnl_dilated_deconvolution_backward_weights_desc_init(dnnl_convolution_desc_t deconv_desc,
int alg_kind,
dnnl_memory_desc_t src_desc,
dnnl_memory_desc_t diff_weights_desc,
dnnl_memory_desc_t diff_bias_desc,
dnnl_memory_desc_t diff_dst_desc,
LongBuffer strides,
LongBuffer dilates,
LongBuffer padding_l,
LongBuffer padding_r) |
static int |
dnnl_dilated_deconvolution_backward_weights_desc_init(dnnl_convolution_desc_t deconv_desc,
int alg_kind,
dnnl_memory_desc_t src_desc,
dnnl_memory_desc_t diff_weights_desc,
dnnl_memory_desc_t diff_bias_desc,
dnnl_memory_desc_t diff_dst_desc,
LongPointer strides,
LongPointer dilates,
LongPointer padding_l,
LongPointer padding_r)
Initializes a descriptor for a dilated deconvolution weights gradient
primitive.
|
static int |
dnnl_dilated_deconvolution_forward_desc_init(dnnl_convolution_desc_t deconv_desc,
int prop_kind,
int alg_kind,
dnnl_memory_desc_t src_desc,
dnnl_memory_desc_t weights_desc,
dnnl_memory_desc_t bias_desc,
dnnl_memory_desc_t dst_desc,
long[] strides,
long[] dilates,
long[] padding_l,
long[] padding_r) |
static int |
dnnl_dilated_deconvolution_forward_desc_init(dnnl_convolution_desc_t deconv_desc,
int prop_kind,
int alg_kind,
dnnl_memory_desc_t src_desc,
dnnl_memory_desc_t weights_desc,
dnnl_memory_desc_t bias_desc,
dnnl_memory_desc_t dst_desc,
LongBuffer strides,
LongBuffer dilates,
LongBuffer padding_l,
LongBuffer padding_r) |
static int |
dnnl_dilated_deconvolution_forward_desc_init(dnnl_convolution_desc_t deconv_desc,
int prop_kind,
int alg_kind,
dnnl_memory_desc_t src_desc,
dnnl_memory_desc_t weights_desc,
dnnl_memory_desc_t bias_desc,
dnnl_memory_desc_t dst_desc,
LongPointer strides,
LongPointer dilates,
LongPointer padding_l,
LongPointer padding_r)
Initializes a descriptor for a dilated deconvolution forward propagation
primitive.
|
static int |
dnnl_eltwise_backward_desc_init(dnnl_eltwise_desc_t eltwise_desc,
int alg_kind,
dnnl_memory_desc_t diff_data_desc,
dnnl_memory_desc_t data_desc,
float alpha,
float beta)
Initializes a descriptor for eltwise backward propagation primitive.
|
static int |
dnnl_eltwise_forward_desc_init(dnnl_eltwise_desc_t eltwise_desc,
int prop_kind,
int alg_kind,
dnnl_memory_desc_t data_desc,
float alpha,
float beta)
\} dnnl_api_shuffle
|
static int |
dnnl_engine_create(dnnl_engine engine,
int kind,
long index)
Creates an engine.
|
static int |
dnnl_engine_create(PointerPointer engine,
int kind,
long index) |
static int |
dnnl_engine_destroy(dnnl_engine engine)
Destroys an engine.
|
static long |
dnnl_engine_get_count(int kind)
\} dnnl_api_resampling
|
static int |
dnnl_engine_get_kind(dnnl_engine engine,
int[] kind) |
static int |
dnnl_engine_get_kind(dnnl_engine engine,
IntBuffer kind) |
static int |
dnnl_engine_get_kind(dnnl_engine engine,
IntPointer kind)
Returns the kind of an engine.
|
static int |
dnnl_gemm_s8s8s32(byte transa,
byte transb,
byte offsetc,
long M,
long N,
long K,
float alpha,
byte[] A,
long lda,
byte ao,
byte[] B,
long ldb,
byte bo,
float beta,
int[] C,
long ldc,
int[] co) |
static int |
dnnl_gemm_s8s8s32(byte transa,
byte transb,
byte offsetc,
long M,
long N,
long K,
float alpha,
ByteBuffer A,
long lda,
byte ao,
ByteBuffer B,
long ldb,
byte bo,
float beta,
IntBuffer C,
long ldc,
IntBuffer co) |
static int |
dnnl_gemm_s8s8s32(byte transa,
byte transb,
byte offsetc,
long M,
long N,
long K,
float alpha,
BytePointer A,
long lda,
byte ao,
BytePointer B,
long ldb,
byte bo,
float beta,
IntPointer C,
long ldc,
IntPointer co)
Performs integer matrix-matrix multiply on 8-bit signed matrix A, 8-bit
signed matrix B, and 32-bit signed resulting matrix C.
|
static int |
dnnl_gemm_u8s8s32(byte transa,
byte transb,
byte offsetc,
long M,
long N,
long K,
float alpha,
byte[] A,
long lda,
byte ao,
byte[] B,
long ldb,
byte bo,
float beta,
int[] C,
long ldc,
int[] co) |
static int |
dnnl_gemm_u8s8s32(byte transa,
byte transb,
byte offsetc,
long M,
long N,
long K,
float alpha,
ByteBuffer A,
long lda,
byte ao,
ByteBuffer B,
long ldb,
byte bo,
float beta,
IntBuffer C,
long ldc,
IntBuffer co) |
static int |
dnnl_gemm_u8s8s32(byte transa,
byte transb,
byte offsetc,
long M,
long N,
long K,
float alpha,
BytePointer A,
long lda,
byte ao,
BytePointer B,
long ldb,
byte bo,
float beta,
IntPointer C,
long ldc,
IntPointer co)
Performs integer matrix-matrix multiply on 8-bit unsigned matrix A, 8-bit
signed matrix B, and 32-bit signed resulting matrix C.
|
static int |
dnnl_get_effective_cpu_isa()
Gets the maximal ISA the library can dispatch to on the CPU.
|
static int |
dnnl_get_primitive_cache_capacity(int[] _capacity) |
static int |
dnnl_get_primitive_cache_capacity(IntBuffer _capacity) |
static int |
dnnl_get_primitive_cache_capacity(IntPointer _capacity)
\} dnnl_api_stream
|
static int |
dnnl_gru_backward_desc_init(dnnl_rnn_desc_t rnn_desc,
int prop_kind,
int direction,
dnnl_memory_desc_t src_layer_desc,
dnnl_memory_desc_t src_iter_desc,
dnnl_memory_desc_t weights_layer_desc,
dnnl_memory_desc_t weights_iter_desc,
dnnl_memory_desc_t bias_desc,
dnnl_memory_desc_t dst_layer_desc,
dnnl_memory_desc_t dst_iter_desc,
dnnl_memory_desc_t diff_src_layer_desc,
dnnl_memory_desc_t diff_src_iter_desc,
dnnl_memory_desc_t diff_weights_layer_desc,
dnnl_memory_desc_t diff_weights_iter_desc,
dnnl_memory_desc_t diff_bias_desc,
dnnl_memory_desc_t diff_dst_layer_desc,
dnnl_memory_desc_t diff_dst_iter_desc,
int flags)
Initializes a descriptor for GRU backward propagation primitive.
|
static int |
dnnl_gru_forward_desc_init(dnnl_rnn_desc_t rnn_desc,
int prop_kind,
int direction,
dnnl_memory_desc_t src_layer_desc,
dnnl_memory_desc_t src_iter_desc,
dnnl_memory_desc_t weights_layer_desc,
dnnl_memory_desc_t weights_iter_desc,
dnnl_memory_desc_t bias_desc,
dnnl_memory_desc_t dst_layer_desc,
dnnl_memory_desc_t dst_iter_desc,
int flags)
Initializes a descriptor for GRU forward propagation primitive.
|
static int |
dnnl_inner_product_backward_data_desc_init(dnnl_inner_product_desc_t ip_desc,
dnnl_memory_desc_t diff_src_desc,
dnnl_memory_desc_t weights_desc,
dnnl_memory_desc_t diff_dst_desc)
Initializes descriptor for inner product backward propagation.
|
static int |
dnnl_inner_product_backward_weights_desc_init(dnnl_inner_product_desc_t ip_desc,
dnnl_memory_desc_t src_desc,
dnnl_memory_desc_t diff_weights_desc,
dnnl_memory_desc_t diff_bias_desc,
dnnl_memory_desc_t diff_dst_desc)
Initializes descriptor for inner product weights gradient primitive.
|
static int |
dnnl_inner_product_forward_desc_init(dnnl_inner_product_desc_t ip_desc,
int prop_kind,
dnnl_memory_desc_t src_desc,
dnnl_memory_desc_t weights_desc,
dnnl_memory_desc_t bias_desc,
dnnl_memory_desc_t dst_desc)
\} dnnl_api_layer_normalization
|
static int |
dnnl_layer_normalization_backward_desc_init(dnnl_layer_normalization_desc_t lnrm_desc,
int prop_kind,
dnnl_memory_desc_t diff_data_desc,
dnnl_memory_desc_t data_desc,
dnnl_memory_desc_t stat_desc,
float epsilon,
int flags)
Initializes a descriptor for a layer normalization backward propagation
primitive.
|
static int |
dnnl_layer_normalization_forward_desc_init(dnnl_layer_normalization_desc_t lnrm_desc,
int prop_kind,
dnnl_memory_desc_t data_desc,
dnnl_memory_desc_t stat_desc,
float epsilon,
int flags)
\} dnnl_api_batch_normalization
|
static int |
dnnl_lbr_gru_backward_desc_init(dnnl_rnn_desc_t rnn_desc,
int prop_kind,
int direction,
dnnl_memory_desc_t src_layer_desc,
dnnl_memory_desc_t src_iter_desc,
dnnl_memory_desc_t weights_layer_desc,
dnnl_memory_desc_t weights_iter_desc,
dnnl_memory_desc_t bias_desc,
dnnl_memory_desc_t dst_layer_desc,
dnnl_memory_desc_t dst_iter_desc,
dnnl_memory_desc_t diff_src_layer_desc,
dnnl_memory_desc_t diff_src_iter_desc,
dnnl_memory_desc_t diff_weights_layer_desc,
dnnl_memory_desc_t diff_weights_iter_desc,
dnnl_memory_desc_t diff_bias_desc,
dnnl_memory_desc_t diff_dst_layer_desc,
dnnl_memory_desc_t diff_dst_iter_desc,
int flags)
Initializes a descriptor for LBR GRU backward propagation primitive.
|
static int |
dnnl_lbr_gru_forward_desc_init(dnnl_rnn_desc_t rnn_desc,
int prop_kind,
int direction,
dnnl_memory_desc_t src_layer_desc,
dnnl_memory_desc_t src_iter_desc,
dnnl_memory_desc_t weights_layer_desc,
dnnl_memory_desc_t weights_iter_desc,
dnnl_memory_desc_t bias_desc,
dnnl_memory_desc_t dst_layer_desc,
dnnl_memory_desc_t dst_iter_desc,
int flags)
Initializes a descriptor for LBR GRU forward propagation primitive.
|
static int |
dnnl_logsoftmax_backward_desc_init(dnnl_softmax_desc_t logsoftmax_desc,
dnnl_memory_desc_t diff_data_desc,
dnnl_memory_desc_t data_desc,
int logsoftmax_axis)
Initializes a descriptor for logsoftmax backward propagation primitive.
|
static int |
dnnl_logsoftmax_forward_desc_init(dnnl_softmax_desc_t logsoftmax_desc,
int prop_kind,
dnnl_memory_desc_t data_desc,
int logsoftmax_axis)
\} dnnl_api_softmax
|
static int |
dnnl_lrn_backward_desc_init(dnnl_lrn_desc_t lrn_desc,
int alg_kind,
dnnl_memory_desc_t diff_data_desc,
dnnl_memory_desc_t data_desc,
long local_size,
float alpha,
float beta,
float k)
Initializes a descriptor for LRN backward propagation primitive.
|
static int |
dnnl_lrn_forward_desc_init(dnnl_lrn_desc_t lrn_desc,
int prop_kind,
int alg_kind,
dnnl_memory_desc_t data_desc,
long local_size,
float alpha,
float beta,
float k)
\} dnnl_api_pooling
|
static int |
dnnl_lstm_backward_desc_init_v2(dnnl_rnn_desc_t rnn_desc,
int prop_kind,
int direction,
dnnl_memory_desc_t src_layer_desc,
dnnl_memory_desc_t src_iter_desc,
dnnl_memory_desc_t src_iter_c_desc,
dnnl_memory_desc_t weights_layer_desc,
dnnl_memory_desc_t weights_iter_desc,
dnnl_memory_desc_t weights_peephole_desc,
dnnl_memory_desc_t bias_desc,
dnnl_memory_desc_t dst_layer_desc,
dnnl_memory_desc_t dst_iter_desc,
dnnl_memory_desc_t dst_iter_c_desc,
dnnl_memory_desc_t diff_src_layer_desc,
dnnl_memory_desc_t diff_src_iter_desc,
dnnl_memory_desc_t diff_src_iter_c_desc,
dnnl_memory_desc_t diff_weights_layer_desc,
dnnl_memory_desc_t diff_weights_iter_desc,
dnnl_memory_desc_t diff_weights_peephole_desc,
dnnl_memory_desc_t diff_bias_desc,
dnnl_memory_desc_t diff_dst_layer_desc,
dnnl_memory_desc_t diff_dst_iter_desc,
dnnl_memory_desc_t diff_dst_iter_c_desc,
int flags)
Initializes a descriptor for an LSTM (with or without peephole) backward
propagation primitive.
|
static int |
dnnl_lstm_backward_desc_init_v3(dnnl_rnn_desc_t rnn_desc,
int prop_kind,
int direction,
dnnl_memory_desc_t src_layer_desc,
dnnl_memory_desc_t src_iter_desc,
dnnl_memory_desc_t src_iter_c_desc,
dnnl_memory_desc_t weights_layer_desc,
dnnl_memory_desc_t weights_iter_desc,
dnnl_memory_desc_t weights_peephole_desc,
dnnl_memory_desc_t weights_projection_desc,
dnnl_memory_desc_t bias_desc,
dnnl_memory_desc_t dst_layer_desc,
dnnl_memory_desc_t dst_iter_desc,
dnnl_memory_desc_t dst_iter_c_desc,
dnnl_memory_desc_t diff_src_layer_desc,
dnnl_memory_desc_t diff_src_iter_desc,
dnnl_memory_desc_t diff_src_iter_c_desc,
dnnl_memory_desc_t diff_weights_layer_desc,
dnnl_memory_desc_t diff_weights_iter_desc,
dnnl_memory_desc_t diff_weights_peephole_desc,
dnnl_memory_desc_t diff_weights_projection_desc,
dnnl_memory_desc_t diff_bias_desc,
dnnl_memory_desc_t diff_dst_layer_desc,
dnnl_memory_desc_t diff_dst_iter_desc,
dnnl_memory_desc_t diff_dst_iter_c_desc,
int flags)
Initializes a descriptor for an LSTM (with or without peephole and with or
with out recurrent projection layer) backward propagation primitive.
|
static int |
dnnl_lstm_backward_desc_init(dnnl_rnn_desc_t rnn_desc,
int prop_kind,
int direction,
dnnl_memory_desc_t src_layer_desc,
dnnl_memory_desc_t src_iter_desc,
dnnl_memory_desc_t src_iter_c_desc,
dnnl_memory_desc_t weights_layer_desc,
dnnl_memory_desc_t weights_iter_desc,
dnnl_memory_desc_t bias_desc,
dnnl_memory_desc_t dst_layer_desc,
dnnl_memory_desc_t dst_iter_desc,
dnnl_memory_desc_t dst_iter_c_desc,
dnnl_memory_desc_t diff_src_layer_desc,
dnnl_memory_desc_t diff_src_iter_desc,
dnnl_memory_desc_t diff_src_iter_c_desc,
dnnl_memory_desc_t diff_weights_layer_desc,
dnnl_memory_desc_t diff_weights_iter_desc,
dnnl_memory_desc_t diff_bias_desc,
dnnl_memory_desc_t diff_dst_layer_desc,
dnnl_memory_desc_t diff_dst_iter_desc,
dnnl_memory_desc_t diff_dst_iter_c_desc,
int flags)
Initializes a descriptor for an LSTM backward propagation primitive.
|
static int |
dnnl_lstm_forward_desc_init_v2(dnnl_rnn_desc_t rnn_desc,
int prop_kind,
int direction,
dnnl_memory_desc_t src_layer_desc,
dnnl_memory_desc_t src_iter_desc,
dnnl_memory_desc_t src_iter_c_desc,
dnnl_memory_desc_t weights_layer_desc,
dnnl_memory_desc_t weights_iter_desc,
dnnl_memory_desc_t weights_peephole_desc,
dnnl_memory_desc_t bias_desc,
dnnl_memory_desc_t dst_layer_desc,
dnnl_memory_desc_t dst_iter_desc,
dnnl_memory_desc_t dst_iter_c_desc,
int flags)
Initializes a descriptor for an LSTM (with or without peephole) forward
propagation primitive.
|
static int |
dnnl_lstm_forward_desc_init_v3(dnnl_rnn_desc_t rnn_desc,
int prop_kind,
int direction,
dnnl_memory_desc_t src_layer_desc,
dnnl_memory_desc_t src_iter_desc,
dnnl_memory_desc_t src_iter_c_desc,
dnnl_memory_desc_t weights_layer_desc,
dnnl_memory_desc_t weights_iter_desc,
dnnl_memory_desc_t weights_peephole_desc,
dnnl_memory_desc_t weights_projection_desc,
dnnl_memory_desc_t bias_desc,
dnnl_memory_desc_t dst_layer_desc,
dnnl_memory_desc_t dst_iter_desc,
dnnl_memory_desc_t dst_iter_c_desc,
int flags)
Initializes a descriptor for an LSTM (with or without peephole and with
or without recurrent projection layer) forward propagation primitive.
|
static int |
dnnl_lstm_forward_desc_init(dnnl_rnn_desc_t rnn_desc,
int prop_kind,
int direction,
dnnl_memory_desc_t src_layer_desc,
dnnl_memory_desc_t src_iter_desc,
dnnl_memory_desc_t src_iter_c_desc,
dnnl_memory_desc_t weights_layer_desc,
dnnl_memory_desc_t weights_iter_desc,
dnnl_memory_desc_t bias_desc,
dnnl_memory_desc_t dst_layer_desc,
dnnl_memory_desc_t dst_iter_desc,
dnnl_memory_desc_t dst_iter_c_desc,
int flags)
Initializes a descriptor for LSTM forward propagation primitive.
|
static int |
dnnl_matmul_desc_init(dnnl_matmul_desc_t matmul_desc,
dnnl_memory_desc_t src_desc,
dnnl_memory_desc_t weights_desc,
dnnl_memory_desc_t bias_desc,
dnnl_memory_desc_t dst_desc)
\} dnnl_api_rnn
|
static int |
dnnl_memory_create(dnnl_memory memory,
dnnl_memory_desc_t memory_desc,
dnnl_engine engine,
Pointer handle)
Creates a memory object.
|
static int |
dnnl_memory_create(PointerPointer memory,
dnnl_memory_desc_t memory_desc,
dnnl_engine engine,
Pointer handle) |
static int |
dnnl_memory_desc_equal(dnnl_memory_desc_t lhs,
dnnl_memory_desc_t rhs)
Compares two memory descriptors.
|
static long |
dnnl_memory_desc_get_size(dnnl_memory_desc_t memory_desc)
Returns the size of a memory descriptor.
|
static int |
dnnl_memory_desc_init_by_strides(dnnl_memory_desc_t memory_desc,
int ndims,
long[] dims,
int data_type,
long[] strides) |
static int |
dnnl_memory_desc_init_by_strides(dnnl_memory_desc_t memory_desc,
int ndims,
LongBuffer dims,
int data_type,
LongBuffer strides) |
static int |
dnnl_memory_desc_init_by_strides(dnnl_memory_desc_t memory_desc,
int ndims,
LongPointer dims,
int data_type,
LongPointer strides)
\} dnnl_api_attributes
|
static int |
dnnl_memory_desc_init_by_tag(dnnl_memory_desc_t memory_desc,
int ndims,
long[] dims,
int data_type,
int tag) |
static int |
dnnl_memory_desc_init_by_tag(dnnl_memory_desc_t memory_desc,
int ndims,
LongBuffer dims,
int data_type,
int tag) |
static int |
dnnl_memory_desc_init_by_tag(dnnl_memory_desc_t memory_desc,
int ndims,
LongPointer dims,
int data_type,
int tag)
Initializes a memory descriptor using dimensions and memory format tag.
|
static int |
dnnl_memory_desc_init_submemory(dnnl_memory_desc_t memory_desc,
dnnl_memory_desc_t parent_memory_desc,
long[] dims,
long[] offsets) |
static int |
dnnl_memory_desc_init_submemory(dnnl_memory_desc_t memory_desc,
dnnl_memory_desc_t parent_memory_desc,
LongBuffer dims,
LongBuffer offsets) |
static int |
dnnl_memory_desc_init_submemory(dnnl_memory_desc_t memory_desc,
dnnl_memory_desc_t parent_memory_desc,
LongPointer dims,
LongPointer offsets) |
static int |
dnnl_memory_desc_permute_axes(dnnl_memory_desc_t out_memory_desc,
dnnl_memory_desc_t in_memory_desc,
int[] permutation) |
static int |
dnnl_memory_desc_permute_axes(dnnl_memory_desc_t out_memory_desc,
dnnl_memory_desc_t in_memory_desc,
IntBuffer permutation) |
static int |
dnnl_memory_desc_permute_axes(dnnl_memory_desc_t out_memory_desc,
dnnl_memory_desc_t in_memory_desc,
IntPointer permutation)
Initializes a memory descriptor by permuting axes in an existing one.
|
static int |
dnnl_memory_desc_reshape(dnnl_memory_desc_t out_memory_desc,
dnnl_memory_desc_t in_memory_desc,
int ndims,
long[] dims) |
static int |
dnnl_memory_desc_reshape(dnnl_memory_desc_t out_memory_desc,
dnnl_memory_desc_t in_memory_desc,
int ndims,
LongBuffer dims) |
static int |
dnnl_memory_desc_reshape(dnnl_memory_desc_t out_memory_desc,
dnnl_memory_desc_t in_memory_desc,
int ndims,
LongPointer dims)
Initializes a memory descriptor by reshaping an existing one.
|
static int |
dnnl_memory_destroy(dnnl_memory memory)
Destroys a memory object.
|
static int |
dnnl_memory_get_data_handle(dnnl_memory memory,
Pointer handle) |
static int |
dnnl_memory_get_data_handle(dnnl_memory memory,
PointerPointer handle)
Returns memory object's data handle.
|
static int |
dnnl_memory_get_engine(dnnl_memory memory,
dnnl_engine engine)
Returns the engine of a memory object.
|
static int |
dnnl_memory_get_engine(dnnl_memory memory,
PointerPointer engine) |
static int |
dnnl_memory_get_memory_desc(dnnl_memory memory,
dnnl_memory_desc_t memory_desc) |
static int |
dnnl_memory_get_memory_desc(dnnl_memory memory,
PointerPointer memory_desc)
Returns the memory descriptor for a memory object.
|
static int |
dnnl_memory_map_data(dnnl_memory memory,
Pointer mapped_ptr) |
static int |
dnnl_memory_map_data(dnnl_memory memory,
PointerPointer mapped_ptr)
Maps a memory object and returns a host-side pointer to a memory buffer
with a copy of its contents.
|
static int |
dnnl_memory_set_data_handle_v2(dnnl_memory memory,
Pointer handle,
dnnl_stream stream)
Sets the underlying memory buffer.
|
static int |
dnnl_memory_set_data_handle(dnnl_memory memory,
Pointer handle)
Sets the underlying memory buffer.
|
static int |
dnnl_memory_unmap_data(dnnl_memory memory,
Pointer mapped_ptr)
Unmaps a memory object and writes back any changes made to the previously
mapped memory buffer.
|
static int |
dnnl_pooling_backward_desc_init(dnnl_pooling_desc_t pool_desc,
int alg_kind,
dnnl_memory_desc_t diff_src_desc,
dnnl_memory_desc_t diff_dst_desc,
long[] strides,
long[] kernel,
long[] padding_l,
long[] padding_r) |
static int |
dnnl_pooling_backward_desc_init(dnnl_pooling_desc_t pool_desc,
int alg_kind,
dnnl_memory_desc_t diff_src_desc,
dnnl_memory_desc_t diff_dst_desc,
LongBuffer strides,
LongBuffer kernel,
LongBuffer padding_l,
LongBuffer padding_r) |
static int |
dnnl_pooling_backward_desc_init(dnnl_pooling_desc_t pool_desc,
int alg_kind,
dnnl_memory_desc_t diff_src_desc,
dnnl_memory_desc_t diff_dst_desc,
LongPointer strides,
LongPointer kernel,
LongPointer padding_l,
LongPointer padding_r)
Initializes a descriptor for pooling backward propagation primitive.
|
static int |
dnnl_pooling_forward_desc_init(dnnl_pooling_desc_t pool_desc,
int prop_kind,
int alg_kind,
dnnl_memory_desc_t src_desc,
dnnl_memory_desc_t dst_desc,
long[] strides,
long[] kernel,
long[] padding_l,
long[] padding_r) |
static int |
dnnl_pooling_forward_desc_init(dnnl_pooling_desc_t pool_desc,
int prop_kind,
int alg_kind,
dnnl_memory_desc_t src_desc,
dnnl_memory_desc_t dst_desc,
LongBuffer strides,
LongBuffer kernel,
LongBuffer padding_l,
LongBuffer padding_r) |
static int |
dnnl_pooling_forward_desc_init(dnnl_pooling_desc_t pool_desc,
int prop_kind,
int alg_kind,
dnnl_memory_desc_t src_desc,
dnnl_memory_desc_t dst_desc,
LongPointer strides,
LongPointer kernel,
LongPointer padding_l,
LongPointer padding_r)
\} dnnl_api_logsoftmax
|
static int |
dnnl_post_ops_append_dw_k3s1p1(dnnl_post_ops post_ops,
int weights_data_type,
int bias_data_type,
int dst_data_type,
long count,
int mask,
float[] scales) |
static int |
dnnl_post_ops_append_dw_k3s1p1(dnnl_post_ops post_ops,
int weights_data_type,
int bias_data_type,
int dst_data_type,
long count,
int mask,
FloatBuffer scales) |
static int |
dnnl_post_ops_append_dw_k3s1p1(dnnl_post_ops post_ops,
int weights_data_type,
int bias_data_type,
int dst_data_type,
long count,
int mask,
FloatPointer scales)
Appends a depthwise post-op convolution with stride 1.
|
static int |
dnnl_post_ops_append_dw_k3s2p1(dnnl_post_ops post_ops,
int weights_data_type,
int bias_data_type,
int dst_data_type,
long count,
int mask,
float[] scales) |
static int |
dnnl_post_ops_append_dw_k3s2p1(dnnl_post_ops post_ops,
int weights_data_type,
int bias_data_type,
int dst_data_type,
long count,
int mask,
FloatBuffer scales) |
static int |
dnnl_post_ops_append_dw_k3s2p1(dnnl_post_ops post_ops,
int weights_data_type,
int bias_data_type,
int dst_data_type,
long count,
int mask,
FloatPointer scales)
Appends a depthwise post-op convolution with stride 2.
|
static int |
dnnl_post_ops_append_eltwise(dnnl_post_ops post_ops,
float scale,
int alg_kind,
float alpha,
float beta)
Appends an elementwise post-op.
|
static int |
dnnl_post_ops_append_sum_v2(dnnl_post_ops post_ops,
float scale,
int data_type)
Appends an accumulation v2 (sum) to post-ops.
|
static int |
dnnl_post_ops_append_sum(dnnl_post_ops post_ops,
float scale)
Appends an accumulation (sum) to post-ops.
|
static int |
dnnl_post_ops_create(dnnl_post_ops post_ops)
Creates empty post-ops sequence.
|
static int |
dnnl_post_ops_create(PointerPointer post_ops) |
static int |
dnnl_post_ops_destroy(dnnl_post_ops post_ops)
Destroys post-ops.
|
static int |
dnnl_post_ops_get_kind(dnnl_post_ops post_ops,
int index)
Returns the kind of a post-op entry.
|
static int |
dnnl_post_ops_get_params_dw_k3s1p1(dnnl_post_ops post_ops,
int index,
int[] weights_data_type,
int[] bias_data_type,
int[] dst_data_type,
long[] count,
int[] mask,
float[] scales) |
static int |
dnnl_post_ops_get_params_dw_k3s1p1(dnnl_post_ops post_ops,
int index,
IntBuffer weights_data_type,
IntBuffer bias_data_type,
IntBuffer dst_data_type,
LongBuffer count,
IntBuffer mask,
FloatBuffer scales) |
static int |
dnnl_post_ops_get_params_dw_k3s1p1(dnnl_post_ops post_ops,
int index,
IntPointer weights_data_type,
IntPointer bias_data_type,
IntPointer dst_data_type,
LongPointer count,
IntPointer mask,
FloatPointer scales) |
static int |
dnnl_post_ops_get_params_dw_k3s1p1(dnnl_post_ops post_ops,
int index,
IntPointer weights_data_type,
IntPointer bias_data_type,
IntPointer dst_data_type,
LongPointer count,
IntPointer mask,
PointerPointer scales)
Returns the parameters of an depthwise post-op with stride 1.
|
static int |
dnnl_post_ops_get_params_dw_k3s2p1(dnnl_post_ops post_ops,
int index,
int[] weights_data_type,
int[] bias_data_type,
int[] dst_data_type,
long[] count,
int[] mask,
float[] scales) |
static int |
dnnl_post_ops_get_params_dw_k3s2p1(dnnl_post_ops post_ops,
int index,
IntBuffer weights_data_type,
IntBuffer bias_data_type,
IntBuffer dst_data_type,
LongBuffer count,
IntBuffer mask,
FloatBuffer scales) |
static int |
dnnl_post_ops_get_params_dw_k3s2p1(dnnl_post_ops post_ops,
int index,
IntPointer weights_data_type,
IntPointer bias_data_type,
IntPointer dst_data_type,
LongPointer count,
IntPointer mask,
FloatPointer scales) |
static int |
dnnl_post_ops_get_params_dw_k3s2p1(dnnl_post_ops post_ops,
int index,
IntPointer weights_data_type,
IntPointer bias_data_type,
IntPointer dst_data_type,
LongPointer count,
IntPointer mask,
PointerPointer scales)
Returns the parameters of an depthwise post-op with stride 2.
|
static int |
dnnl_post_ops_get_params_eltwise(dnnl_post_ops post_ops,
int index,
float[] scale,
int[] alg_kind,
float[] alpha,
float[] beta) |
static int |
dnnl_post_ops_get_params_eltwise(dnnl_post_ops post_ops,
int index,
FloatBuffer scale,
IntBuffer alg_kind,
FloatBuffer alpha,
FloatBuffer beta) |
static int |
dnnl_post_ops_get_params_eltwise(dnnl_post_ops post_ops,
int index,
FloatPointer scale,
IntPointer alg_kind,
FloatPointer alpha,
FloatPointer beta)
Returns the parameters of an elementwise post-up.
|
static int |
dnnl_post_ops_get_params_sum_v2(dnnl_post_ops post_ops,
int index,
float[] scale,
int[] data_type) |
static int |
dnnl_post_ops_get_params_sum_v2(dnnl_post_ops post_ops,
int index,
FloatBuffer scale,
IntBuffer data_type) |
static int |
dnnl_post_ops_get_params_sum_v2(dnnl_post_ops post_ops,
int index,
FloatPointer scale,
IntPointer data_type)
Returns the parameters of an accumulation (sum) post-op with
a data type parameter.
|
static int |
dnnl_post_ops_get_params_sum(dnnl_post_ops post_ops,
int index,
float[] scale) |
static int |
dnnl_post_ops_get_params_sum(dnnl_post_ops post_ops,
int index,
FloatBuffer scale) |
static int |
dnnl_post_ops_get_params_sum(dnnl_post_ops post_ops,
int index,
FloatPointer scale)
Returns the parameters of an accumulation (sum) post-op.
|
static int |
dnnl_post_ops_len(dnnl_post_ops post_ops)
Returns the length of post-ops.
|
static int |
dnnl_primitive_attr_clone(dnnl_primitive_attr attr,
dnnl_primitive_attr existing_attr)
Clones primitive attributes.
|
static int |
dnnl_primitive_attr_clone(PointerPointer attr,
dnnl_primitive_attr existing_attr) |
static int |
dnnl_primitive_attr_create(dnnl_primitive_attr attr)
\} dnnl_api_primitives_common
|
static int |
dnnl_primitive_attr_create(PointerPointer attr) |
static int |
dnnl_primitive_attr_destroy(dnnl_primitive_attr attr)
Destroys primitive attributes.
|
static int |
dnnl_primitive_attr_get_output_scales(dnnl_primitive_attr attr,
long[] count,
int[] mask,
float[] scales) |
static int |
dnnl_primitive_attr_get_output_scales(dnnl_primitive_attr attr,
LongBuffer count,
IntBuffer mask,
FloatBuffer scales) |
static int |
dnnl_primitive_attr_get_output_scales(dnnl_primitive_attr attr,
LongPointer count,
IntPointer mask,
FloatPointer scales) |
static int |
dnnl_primitive_attr_get_output_scales(dnnl_primitive_attr attr,
LongPointer count,
IntPointer mask,
PointerPointer scales)
Returns primitive attributes output scaling factors correspondence mask
and values.
|
static int |
dnnl_primitive_attr_get_post_ops(dnnl_primitive_attr attr,
dnnl_post_ops post_ops)
Returns primitive attributes post-ops.
|
static int |
dnnl_primitive_attr_get_post_ops(dnnl_primitive_attr attr,
PointerPointer post_ops) |
static int |
dnnl_primitive_attr_get_scales(dnnl_primitive_attr attr,
int arg,
long[] count,
int[] mask,
float[] scales) |
static int |
dnnl_primitive_attr_get_scales(dnnl_primitive_attr attr,
int arg,
LongBuffer count,
IntBuffer mask,
FloatBuffer scales) |
static int |
dnnl_primitive_attr_get_scales(dnnl_primitive_attr attr,
int arg,
LongPointer count,
IntPointer mask,
FloatPointer scales) |
static int |
dnnl_primitive_attr_get_scales(dnnl_primitive_attr attr,
int arg,
LongPointer count,
IntPointer mask,
PointerPointer scales)
Returns primitive attributes scaling factors correspondence mask and values
for a given memory argument.
|
static int |
dnnl_primitive_attr_get_scratchpad_mode(dnnl_primitive_attr attr,
int[] mode) |
static int |
dnnl_primitive_attr_get_scratchpad_mode(dnnl_primitive_attr attr,
IntBuffer mode) |
static int |
dnnl_primitive_attr_get_scratchpad_mode(dnnl_primitive_attr attr,
IntPointer mode)
Returns the primitive attributes scratchpad mode.
|
static int |
dnnl_primitive_attr_get_zero_points(dnnl_primitive_attr attr,
int arg,
long[] count,
int[] mask,
int[] zero_points) |
static int |
dnnl_primitive_attr_get_zero_points(dnnl_primitive_attr attr,
int arg,
LongBuffer count,
IntBuffer mask,
IntBuffer zero_points) |
static int |
dnnl_primitive_attr_get_zero_points(dnnl_primitive_attr attr,
int arg,
LongPointer count,
IntPointer mask,
IntPointer zero_points) |
static int |
dnnl_primitive_attr_get_zero_points(dnnl_primitive_attr attr,
int arg,
LongPointer count,
IntPointer mask,
PointerPointer zero_points)
Returns \p count, correspondence zero point \p mask, and a pointer to a
constant int32_t array of \p zero_points for given \p attr and memory
argument (index), previously set by dnnl_primitive_attr_set_zero_points.
|
static int |
dnnl_primitive_attr_set_output_scales(dnnl_primitive_attr attr,
long count,
int mask,
float[] scales) |
static int |
dnnl_primitive_attr_set_output_scales(dnnl_primitive_attr attr,
long count,
int mask,
FloatBuffer scales) |
static int |
dnnl_primitive_attr_set_output_scales(dnnl_primitive_attr attr,
long count,
int mask,
FloatPointer scales)
Sets output scaling factors correspondence mask and values.
|
static int |
dnnl_primitive_attr_set_post_ops(dnnl_primitive_attr attr,
dnnl_post_ops post_ops)
Sets primitive attributes post-ops.
|
static int |
dnnl_primitive_attr_set_rnn_data_qparams(dnnl_primitive_attr attr,
float scale,
float shift)
\} dnnl_api_inner_product
|
static int |
dnnl_primitive_attr_set_rnn_weights_qparams(dnnl_primitive_attr attr,
long count,
int mask,
float[] scales) |
static int |
dnnl_primitive_attr_set_rnn_weights_qparams(dnnl_primitive_attr attr,
long count,
int mask,
FloatBuffer scales) |
static int |
dnnl_primitive_attr_set_rnn_weights_qparams(dnnl_primitive_attr attr,
long count,
int mask,
FloatPointer scales)
Sets quantization scaling factors for RNN weights tensors.
|
static int |
dnnl_primitive_attr_set_scales(dnnl_primitive_attr attr,
int arg,
long count,
int mask,
float[] scales) |
static int |
dnnl_primitive_attr_set_scales(dnnl_primitive_attr attr,
int arg,
long count,
int mask,
FloatBuffer scales) |
static int |
dnnl_primitive_attr_set_scales(dnnl_primitive_attr attr,
int arg,
long count,
int mask,
FloatPointer scales)
Sets primitive attributes scaling factors for primitive operations for a
given memory argument.
|
static int |
dnnl_primitive_attr_set_scratchpad_mode(dnnl_primitive_attr attr,
int mode)
Sets primitive attributes scratchpad mode.
|
static int |
dnnl_primitive_attr_set_zero_points(dnnl_primitive_attr attr,
int arg,
long count,
int mask,
int[] zero_points) |
static int |
dnnl_primitive_attr_set_zero_points(dnnl_primitive_attr attr,
int arg,
long count,
int mask,
IntBuffer zero_points) |
static int |
dnnl_primitive_attr_set_zero_points(dnnl_primitive_attr attr,
int arg,
long count,
int mask,
IntPointer zero_points)
Sets primitive attributes zero points for primitive operations for a given
memory argument.
|
static int |
dnnl_primitive_create(dnnl_primitive primitive,
dnnl_primitive_desc primitive_desc)
Creates a primitive.
|
static int |
dnnl_primitive_create(PointerPointer primitive,
dnnl_primitive_desc primitive_desc) |
static int |
dnnl_primitive_desc_clone(dnnl_primitive_desc primitive_desc,
dnnl_primitive_desc existing_primitive_desc)
Clones a primitive descriptor.
|
static int |
dnnl_primitive_desc_clone(PointerPointer primitive_desc,
dnnl_primitive_desc existing_primitive_desc) |
static int |
dnnl_primitive_desc_create(dnnl_primitive_desc primitive_desc,
const_dnnl_op_desc_t op_desc,
dnnl_primitive_attr attr,
dnnl_engine engine,
dnnl_primitive_desc hint_forward_primitive_desc)
Creates a primitive descriptor.
|
static int |
dnnl_primitive_desc_create(PointerPointer primitive_desc,
const_dnnl_op_desc_t op_desc,
dnnl_primitive_attr attr,
dnnl_engine engine,
dnnl_primitive_desc hint_forward_primitive_desc) |
static int |
dnnl_primitive_desc_destroy(dnnl_primitive_desc primitive_desc)
Destroys a primitive descriptor.
|
static int |
dnnl_primitive_desc_get_attr(dnnl_primitive_desc primitive_desc,
dnnl_primitive_attr attr)
Returns a constant reference to the attributes of a primitive descriptor.
|
static int |
dnnl_primitive_desc_get_attr(dnnl_primitive_desc primitive_desc,
PointerPointer attr) |
static int |
dnnl_primitive_desc_iterator_create(dnnl_primitive_desc_iterator iterator,
const_dnnl_op_desc_t op_desc,
dnnl_primitive_attr attr,
dnnl_engine engine,
dnnl_primitive_desc hint_forward_primitive_desc)
\addtogroup dnnl_api
\{
|
static int |
dnnl_primitive_desc_iterator_create(PointerPointer iterator,
const_dnnl_op_desc_t op_desc,
dnnl_primitive_attr attr,
dnnl_engine engine,
dnnl_primitive_desc hint_forward_primitive_desc) |
static int |
dnnl_primitive_desc_iterator_destroy(dnnl_primitive_desc_iterator iterator)
Destroys a primitive descriptor iterator.
|
static dnnl_primitive_desc |
dnnl_primitive_desc_iterator_fetch(dnnl_primitive_desc_iterator iterator)
Fetches the current primitive descriptor from a primitive descriptor
iterator.
|
static int |
dnnl_primitive_desc_iterator_next(dnnl_primitive_desc_iterator iterator)
Advances the primitive descriptor iterator to point to the next available
implementation.
|
static dnnl_memory_desc_t |
dnnl_primitive_desc_query_md(dnnl_primitive_desc primitive_desc,
int what,
int index)
Queries primitive descriptor for a memory descriptor.
|
static int |
dnnl_primitive_desc_query_s32(dnnl_primitive_desc primitive_desc,
int what,
int index)
Queries primitive descriptor for a signed 32bit int.
|
static int |
dnnl_primitive_desc_query(dnnl_primitive_desc primitive_desc,
int what,
int index,
Pointer result)
Queries a primitive descriptor for various pieces of information.
|
static int |
dnnl_primitive_destroy(dnnl_primitive primitive)
Destroys a primitive.
|
static int |
dnnl_primitive_execute(dnnl_primitive primitive,
dnnl_stream stream,
int nargs,
dnnl_exec_arg_t args)
Executes a primitive.
|
static int |
dnnl_primitive_get_primitive_desc(dnnl_primitive primitive,
dnnl_primitive_desc primitive_desc)
Retrieves a constant reference to the primitive descriptor of a given
primitive.
|
static int |
dnnl_primitive_get_primitive_desc(dnnl_primitive primitive,
PointerPointer primitive_desc) |
static int |
dnnl_reorder_primitive_desc_create(dnnl_primitive_desc reorder_primitive_desc,
dnnl_memory_desc_t src_desc,
dnnl_engine src_engine,
dnnl_memory_desc_t dst_desc,
dnnl_engine dst_engine,
dnnl_primitive_attr attr)
\} dnnl_api_memory
|
static int |
dnnl_reorder_primitive_desc_create(PointerPointer reorder_primitive_desc,
dnnl_memory_desc_t src_desc,
dnnl_engine src_engine,
dnnl_memory_desc_t dst_desc,
dnnl_engine dst_engine,
dnnl_primitive_attr attr) |
static int |
dnnl_resampling_backward_desc_init(dnnl_resampling_desc_t resampling_desc,
int alg_kind,
float[] factors,
dnnl_memory_desc_t diff_src_desc,
dnnl_memory_desc_t diff_dst_desc) |
static int |
dnnl_resampling_backward_desc_init(dnnl_resampling_desc_t resampling_desc,
int alg_kind,
FloatBuffer factors,
dnnl_memory_desc_t diff_src_desc,
dnnl_memory_desc_t diff_dst_desc) |
static int |
dnnl_resampling_backward_desc_init(dnnl_resampling_desc_t resampling_desc,
int alg_kind,
FloatPointer factors,
dnnl_memory_desc_t diff_src_desc,
dnnl_memory_desc_t diff_dst_desc)
Initializes a descriptor for resampling backward propagation primitive.
|
static int |
dnnl_resampling_forward_desc_init(dnnl_resampling_desc_t resampling_desc,
int prop_kind,
int alg_kind,
float[] factors,
dnnl_memory_desc_t src_desc,
dnnl_memory_desc_t dst_desc) |
static int |
dnnl_resampling_forward_desc_init(dnnl_resampling_desc_t resampling_desc,
int prop_kind,
int alg_kind,
FloatBuffer factors,
dnnl_memory_desc_t src_desc,
dnnl_memory_desc_t dst_desc) |
static int |
dnnl_resampling_forward_desc_init(dnnl_resampling_desc_t resampling_desc,
int prop_kind,
int alg_kind,
FloatPointer factors,
dnnl_memory_desc_t src_desc,
dnnl_memory_desc_t dst_desc)
\} dnnl_api_matmul
|
static long |
DNNL_RUNTIME_DIM_VAL()
A wildcard value for dimensions that are unknown at a primitive creation
time.
|
static double |
DNNL_RUNTIME_F32_VAL()
\endcond
|
static int |
DNNL_RUNTIME_S32_VAL_REP()
\cond DO_NOT_DOCUMENT_THIS
|
static long |
DNNL_RUNTIME_SIZE_VAL()
A
size_t counterpart of the DNNL_RUNTIME_DIM_VAL. |
static int |
dnnl_set_jit_dump(int enable)
Configures dumping of JIT-generated code.
|
static int |
dnnl_set_jit_profiling_flags(int flags)
Sets library profiling flags.
|
static int |
dnnl_set_jit_profiling_jitdumpdir(BytePointer dir)
Sets JIT dump output path.
|
static int |
dnnl_set_jit_profiling_jitdumpdir(String dir) |
static int |
dnnl_set_max_cpu_isa(int isa)
Sets the maximal ISA the library can dispatch to on the CPU.
|
static int |
dnnl_set_primitive_cache_capacity(int _capacity)
Sets a number of primitives that can be held in the primitive cache
at a time.
|
static int |
dnnl_set_verbose(int level)
\} dnnl_api_primitive_cache
|
static int |
dnnl_sgemm(byte transa,
byte transb,
long M,
long N,
long K,
float alpha,
float[] A,
long lda,
float[] B,
long ldb,
float beta,
float[] C,
long ldc) |
static int |
dnnl_sgemm(byte transa,
byte transb,
long M,
long N,
long K,
float alpha,
FloatBuffer A,
long lda,
FloatBuffer B,
long ldb,
float beta,
FloatBuffer C,
long ldc) |
static int |
dnnl_sgemm(byte transa,
byte transb,
long M,
long N,
long K,
float alpha,
FloatPointer A,
long lda,
FloatPointer B,
long ldb,
float beta,
FloatPointer C,
long ldc)
\} dnnl_api_service
|
static int |
dnnl_shuffle_backward_desc_init(dnnl_shuffle_desc_t shuffle_desc,
dnnl_memory_desc_t diff_data_desc,
int axis,
long group_size)
Initializes a descriptor for shuffle backward propagation primitive.
|
static int |
dnnl_shuffle_forward_desc_init(dnnl_shuffle_desc_t shuffle_desc,
int prop_kind,
dnnl_memory_desc_t data_desc,
int axis,
long group_size)
\} dnnl_api_deconvolution
|
static int |
dnnl_softmax_backward_desc_init(dnnl_softmax_desc_t softmax_desc,
dnnl_memory_desc_t diff_data_desc,
dnnl_memory_desc_t data_desc,
int softmax_axis)
Initializes a descriptor for softmax backward propagation primitive.
|
static int |
dnnl_softmax_forward_desc_init(dnnl_softmax_desc_t softmax_desc,
int prop_kind,
dnnl_memory_desc_t data_desc,
int softmax_axis)
\} dnnl_api_eltwise
|
static int |
dnnl_stream_attr_create(dnnl_stream_attr attr,
int kind)
\} dnnl_api_engine
|
static int |
dnnl_stream_attr_create(PointerPointer attr,
int kind) |
static int |
dnnl_stream_attr_destroy(dnnl_stream_attr attr)
Destroys execution stream attributes.
|
static int |
dnnl_stream_create_v2(dnnl_stream stream,
dnnl_engine engine,
int flags,
dnnl_stream_attr attr)
Creates an execution stream.
|
static int |
dnnl_stream_create_v2(PointerPointer stream,
dnnl_engine engine,
int flags,
dnnl_stream_attr attr) |
static int |
dnnl_stream_create(dnnl_stream stream,
dnnl_engine engine,
int flags)
Creates an execution stream.
|
static int |
dnnl_stream_create(PointerPointer stream,
dnnl_engine engine,
int flags) |
static int |
dnnl_stream_destroy(dnnl_stream stream)
Destroys an execution stream.
|
static int |
dnnl_stream_wait(dnnl_stream stream)
Waits for all primitives in the execution stream to finish computations.
|
static int |
dnnl_sum_primitive_desc_create(dnnl_primitive_desc sum_primitive_desc,
dnnl_memory_desc_t dst_desc,
int n,
float[] scales,
dnnl_memory_desc_t src_descs,
dnnl_primitive_attr attr,
dnnl_engine engine) |
static int |
dnnl_sum_primitive_desc_create(dnnl_primitive_desc sum_primitive_desc,
dnnl_memory_desc_t dst_desc,
int n,
FloatBuffer scales,
dnnl_memory_desc_t src_descs,
dnnl_primitive_attr attr,
dnnl_engine engine) |
static int |
dnnl_sum_primitive_desc_create(dnnl_primitive_desc sum_primitive_desc,
dnnl_memory_desc_t dst_desc,
int n,
FloatPointer scales,
dnnl_memory_desc_t src_descs,
dnnl_primitive_attr attr,
dnnl_engine engine)
\} dnnl_api_concat
|
static int |
dnnl_sum_primitive_desc_create(PointerPointer sum_primitive_desc,
dnnl_memory_desc_t dst_desc,
int n,
float[] scales,
dnnl_memory_desc_t src_descs,
dnnl_primitive_attr attr,
dnnl_engine engine) |
static int |
dnnl_sum_primitive_desc_create(PointerPointer sum_primitive_desc,
dnnl_memory_desc_t dst_desc,
int n,
FloatBuffer scales,
dnnl_memory_desc_t src_descs,
dnnl_primitive_attr attr,
dnnl_engine engine) |
static int |
dnnl_sum_primitive_desc_create(PointerPointer sum_primitive_desc,
dnnl_memory_desc_t dst_desc,
int n,
FloatPointer scales,
dnnl_memory_desc_t src_descs,
dnnl_primitive_attr attr,
dnnl_engine engine) |
static int |
dnnl_vanilla_rnn_backward_desc_init(dnnl_rnn_desc_t rnn_desc,
int prop_kind,
int activation,
int direction,
dnnl_memory_desc_t src_layer_desc,
dnnl_memory_desc_t src_iter_desc,
dnnl_memory_desc_t weights_layer_desc,
dnnl_memory_desc_t weights_iter_desc,
dnnl_memory_desc_t bias_desc,
dnnl_memory_desc_t dst_layer_desc,
dnnl_memory_desc_t dst_iter_desc,
dnnl_memory_desc_t diff_src_layer_desc,
dnnl_memory_desc_t diff_src_iter_desc,
dnnl_memory_desc_t diff_weights_layer_desc,
dnnl_memory_desc_t diff_weights_iter_desc,
dnnl_memory_desc_t diff_bias_desc,
dnnl_memory_desc_t diff_dst_layer_desc,
dnnl_memory_desc_t diff_dst_iter_desc,
int flags,
float alpha,
float beta)
Initializes a descriptor for vanilla RNN backward propagation primitive.
|
static int |
dnnl_vanilla_rnn_forward_desc_init(dnnl_rnn_desc_t rnn_desc,
int prop_kind,
int activation,
int direction,
dnnl_memory_desc_t src_layer_desc,
dnnl_memory_desc_t src_iter_desc,
dnnl_memory_desc_t weights_layer_desc,
dnnl_memory_desc_t weights_iter_desc,
dnnl_memory_desc_t bias_desc,
dnnl_memory_desc_t dst_layer_desc,
dnnl_memory_desc_t dst_iter_desc,
int flags,
float alpha,
float beta)
\} dnnl_api_attributes
|
static String |
DNNL_VERSION_HASH()
Git commit hash
|
static dnnl_version_t |
dnnl_version()
Returns library version information.
|
static boolean |
equals(int a,
memory.data_type b) |
static boolean |
equals(int a,
memory.format_tag b) |
static boolean |
equals(memory.data_type a,
int b) |
static boolean |
equals(memory.format_tag a,
int b) |
static dnnl.status |
gemm_s8s8s32(byte transa,
byte transb,
byte offsetc,
long M,
long N,
long K,
float alpha,
byte[] A,
long lda,
byte ao,
byte[] B,
long ldb,
byte bo,
float beta,
int[] C,
long ldc,
int[] co) |
static int |
gemm_s8s8s32(byte transa,
byte transb,
byte offsetc,
long M,
long N,
long K,
float alpha,
ByteBuffer A,
long lda,
byte ao,
ByteBuffer B,
long ldb,
byte bo,
float beta,
IntBuffer C,
long ldc,
IntBuffer co) |
static dnnl.status |
gemm_s8s8s32(byte transa,
byte transb,
byte offsetc,
long M,
long N,
long K,
float alpha,
BytePointer A,
long lda,
byte ao,
BytePointer B,
long ldb,
byte bo,
float beta,
IntPointer C,
long ldc,
IntPointer co)
\copydoc dnnl_gemm_s8s8s32()
|
static dnnl.status |
gemm_u8s8s32(byte transa,
byte transb,
byte offsetc,
long M,
long N,
long K,
float alpha,
byte[] A,
long lda,
byte ao,
byte[] B,
long ldb,
byte bo,
float beta,
int[] C,
long ldc,
int[] co) |
static int |
gemm_u8s8s32(byte transa,
byte transb,
byte offsetc,
long M,
long N,
long K,
float alpha,
ByteBuffer A,
long lda,
byte ao,
ByteBuffer B,
long ldb,
byte bo,
float beta,
IntBuffer C,
long ldc,
IntBuffer co) |
static dnnl.status |
gemm_u8s8s32(byte transa,
byte transb,
byte offsetc,
long M,
long N,
long K,
float alpha,
BytePointer A,
long lda,
byte ao,
BytePointer B,
long ldb,
byte bo,
float beta,
IntPointer C,
long ldc,
IntPointer co)
\copydoc dnnl_gemm_u8s8s32()
|
static dnnl.cpu_isa |
get_effective_cpu_isa()
\copydoc dnnl_get_effective_cpu_isa()
|
static int |
get_primitive_cache_capacity()
\} dnnl_api_service
|
static dnnl.normalization_flags |
not(dnnl.normalization_flags rhs) |
static dnnl.rnn_flags |
not(dnnl.rnn_flags rhs) |
static int |
not(int rhs) |
static stream.flags |
not(stream.flags rhs) |
static boolean |
notEquals(int a,
memory.data_type b) |
static boolean |
notEquals(int a,
memory.format_tag b) |
static boolean |
notEquals(memory.data_type a,
int b) |
static boolean |
notEquals(memory.format_tag a,
int b) |
static dnnl.normalization_flags |
or(dnnl.normalization_flags lhs,
dnnl.normalization_flags rhs) |
static dnnl.rnn_flags |
or(dnnl.rnn_flags lhs,
dnnl.rnn_flags rhs) |
static int |
or(int lhs,
int rhs) |
static stream.flags |
or(stream.flags lhs,
stream.flags rhs)
\} dnnl_api_engine
|
static int[] |
orPut(int[] lhs,
dnnl.normalization_flags rhs) |
static int[] |
orPut(int[] lhs,
dnnl.rnn_flags rhs) |
static int[] |
orPut(int[] lhs,
int rhs) |
static IntBuffer |
orPut(IntBuffer lhs,
dnnl.normalization_flags rhs) |
static IntBuffer |
orPut(IntBuffer lhs,
dnnl.rnn_flags rhs) |
static IntBuffer |
orPut(IntBuffer lhs,
int rhs) |
static IntPointer |
orPut(IntPointer lhs,
dnnl.normalization_flags rhs) |
static IntPointer |
orPut(IntPointer lhs,
dnnl.rnn_flags rhs) |
static IntPointer |
orPut(IntPointer lhs,
int rhs) |
static stream.flags |
orPut(stream.flags lhs,
stream.flags rhs) |
static dnnl.status |
set_jit_dump(int enable)
\copydoc dnnl_set_jit_dump()
|
static dnnl.status |
set_jit_profiling_flags(int flags)
\copydoc dnnl_set_jit_profiling_flags()
|
static dnnl.status |
set_jit_profiling_jitdumpdir(BytePointer dir)
\copydoc dnnl_set_jit_profiling_jitdumpdir()
|
static int |
set_jit_profiling_jitdumpdir(String dir) |
static dnnl.status |
set_max_cpu_isa(dnnl.cpu_isa isa)
\copydoc dnnl_set_max_cpu_isa()
|
static int |
set_max_cpu_isa(int isa) |
static void |
set_primitive_cache_capacity(int _capacity)
\copydoc dnnl_set_primitive_cache_capacity(int capacity)
|
static dnnl.status |
set_verbose(int level)
\copydoc dnnl_set_verbose()
|
static dnnl.status |
sgemm(byte transa,
byte transb,
long M,
long N,
long K,
float alpha,
float[] A,
long lda,
float[] B,
long ldb,
float beta,
float[] C,
long ldc) |
static int |
sgemm(byte transa,
byte transb,
long M,
long N,
long K,
float alpha,
FloatBuffer A,
long lda,
FloatBuffer B,
long ldb,
float beta,
FloatBuffer C,
long ldc) |
static dnnl.status |
sgemm(byte transa,
byte transb,
long M,
long N,
long K,
float alpha,
FloatPointer A,
long lda,
FloatPointer B,
long ldb,
float beta,
FloatPointer C,
long ldc)
\} dnnl_api_primitive_cache
|
static dnnl_version_t |
version()
\copydoc dnnl_version()
|
static dnnl.normalization_flags |
xor(dnnl.normalization_flags lhs,
dnnl.normalization_flags rhs) |
static dnnl.rnn_flags |
xor(dnnl.rnn_flags lhs,
dnnl.rnn_flags rhs) |
static int |
xor(int lhs,
int rhs) |
static stream.flags |
xor(stream.flags lhs,
stream.flags rhs) |
static int[] |
xorPut(int[] lhs,
dnnl.normalization_flags rhs) |
static int[] |
xorPut(int[] lhs,
dnnl.rnn_flags rhs) |
static int[] |
xorPut(int[] lhs,
int rhs) |
static IntBuffer |
xorPut(IntBuffer lhs,
dnnl.normalization_flags rhs) |
static IntBuffer |
xorPut(IntBuffer lhs,
dnnl.rnn_flags rhs) |
static IntBuffer |
xorPut(IntBuffer lhs,
int rhs) |
static IntPointer |
xorPut(IntPointer lhs,
dnnl.normalization_flags rhs) |
static IntPointer |
xorPut(IntPointer lhs,
dnnl.rnn_flags rhs) |
static IntPointer |
xorPut(IntPointer lhs,
int rhs) |
static stream.flags |
xorPut(stream.flags lhs,
stream.flags rhs) |
public static final int dnnl_success
public static final int dnnl_out_of_memory
public static final int dnnl_invalid_arguments
public static final int dnnl_unimplemented
public static final int dnnl_iterator_ends
public static final int dnnl_runtime_error
public static final int dnnl_not_required
public static final int dnnl_data_type_undef
public static final int dnnl_f16
public static final int dnnl_bf16
public static final int dnnl_f32
public static final int dnnl_s32
public static final int dnnl_s8
public static final int dnnl_u8
public static final int dnnl_format_kind_undef
public static final int dnnl_format_kind_any
public static final int dnnl_blocked
public static final int dnnl_format_kind_wino
public static final int dnnl_format_kind_rnn_packed
public static final int dnnl_format_tag_undef
public static final int dnnl_format_tag_any
public static final int dnnl_a
public static final int dnnl_ab
public static final int dnnl_abc
public static final int dnnl_abcd
public static final int dnnl_abcde
public static final int dnnl_abcdef
public static final int dnnl_abdc
public static final int dnnl_abdec
public static final int dnnl_acb
public static final int dnnl_acbde
public static final int dnnl_acbdef
public static final int dnnl_acdb
public static final int dnnl_acdeb
public static final int dnnl_ba
public static final int dnnl_bac
public static final int dnnl_bacd
public static final int dnnl_bacde
public static final int dnnl_bca
public static final int dnnl_bcda
public static final int dnnl_bcdea
public static final int dnnl_cba
public static final int dnnl_cdba
public static final int dnnl_dcab
public static final int dnnl_cdeba
public static final int dnnl_decab
public static final int dnnl_defcab
public static final int dnnl_Abc16a
public static final int dnnl_ABc16a16b
public static final int dnnl_ABc32a32b
public static final int dnnl_ABc4a4b
public static final int dnnl_aBc16b
public static final int dnnl_ABc16b16a
public static final int dnnl_Abc4a
public static final int dnnl_aBc32b
public static final int dnnl_aBc4b
public static final int dnnl_ABc4b16a4b
public static final int dnnl_ABc2b8a4b
public static final int dnnl_ABc16b16a4b
public static final int dnnl_ABc16b16a2b
public static final int dnnl_ABc4b4a
public static final int dnnl_ABc8a16b2a
public static final int dnnl_ABc8a8b
public static final int dnnl_ABc8a4b
public static final int dnnl_aBc8b
public static final int dnnl_ABc8b16a2b
public static final int dnnl_BAc8a16b2a
public static final int dnnl_ABc8b8a
public static final int dnnl_Abcd16a
public static final int dnnl_Abcd8a
public static final int dnnl_ABcd16a16b
public static final int dnnl_Abcd32a
public static final int dnnl_ABcd32a32b
public static final int dnnl_aBcd16b
public static final int dnnl_ABcd16b16a
public static final int dnnl_aBCd16b16c
public static final int dnnl_aBCd16c16b
public static final int dnnl_Abcd4a
public static final int dnnl_aBcd32b
public static final int dnnl_aBcd4b
public static final int dnnl_ABcd4b16a4b
public static final int dnnl_ABcd16b16a4b
public static final int dnnl_ABcd16b16a2b
public static final int dnnl_ABcd4b4a
public static final int dnnl_ABcd4a4b
public static final int dnnl_aBCd2c4b2c
public static final int dnnl_aBCd4b8c2b
public static final int dnnl_aBCd4c16b4c
public static final int dnnl_aBCd2c8b4c
public static final int dnnl_aBCd16c16b4c
public static final int dnnl_aBCd16c16b2c
public static final int dnnl_aBCd4c4b
public static final int dnnl_aBCd4b4c
public static final int dnnl_ABcd8a16b2a
public static final int dnnl_ABcd2b8a4b
public static final int dnnl_ABcd8a8b
public static final int dnnl_ABcd8a4b
public static final int dnnl_aBcd8b
public static final int dnnl_aBCd4c8b2c
public static final int dnnl_ABcd8b16a2b
public static final int dnnl_aBCd8b16c2b
public static final int dnnl_BAcd8a16b2a
public static final int dnnl_ABcd8b8a
public static final int dnnl_aBCd8b8c
public static final int dnnl_aBCd8b4c
public static final int dnnl_aBCd8c16b2c
public static final int dnnl_ABcde8a16b2a
public static final int dnnl_aCBd8b16c2b
public static final int dnnl_aBCd8c8b
public static final int dnnl_Abcde16a
public static final int dnnl_Abcde32a
public static final int dnnl_ABcde16a16b
public static final int dnnl_BAcde8a16b2a
public static final int dnnl_aBCd2b4c2b
public static final int dnnl_ABcde4b16a4b
public static final int dnnl_ABcde2b8a4b
public static final int dnnl_aBcde16b
public static final int dnnl_ABcde16b16a
public static final int dnnl_aBCde16b16c
public static final int dnnl_aBCde16c16b
public static final int dnnl_aBCde2c8b4c
public static final int dnnl_Abcde4a
public static final int dnnl_aBcde32b
public static final int dnnl_aBcde4b
public static final int dnnl_ABcde4b4a
public static final int dnnl_ABcde4a4b
public static final int dnnl_aBCde4b4c
public static final int dnnl_aBCde2c4b2c
public static final int dnnl_aBCde4b8c2b
public static final int dnnl_aBCde4c16b4c
public static final int dnnl_aBCde16c16b4c
public static final int dnnl_aBCde16c16b2c
public static final int dnnl_aBCde4c4b
public static final int dnnl_Abcde8a
public static final int dnnl_ABcde8a8b
public static final int dnnl_ABcde8a4b
public static final int dnnl_BAcde16b16a
public static final int dnnl_aBcde8b
public static final int dnnl_ABcde8b16a2b
public static final int dnnl_aBCde8b16c2b
public static final int dnnl_aBCde4c8b2c
public static final int dnnl_aCBde8b16c2b
public static final int dnnl_ABcde8b8a
public static final int dnnl_ABcde32a32b
public static final int dnnl_aBCde8b8c
public static final int dnnl_aBCde8b4c
public static final int dnnl_ABc4a8b8a4b
public static final int dnnl_ABcd4a8b8a4b
public static final int dnnl_ABcde4a8b8a4b
public static final int dnnl_BAc4b8a8b4a
public static final int dnnl_BAcd4b8a8b4a
public static final int dnnl_BAcde4b8a8b4a
public static final int dnnl_ABcd2a8b8a2b
public static final int dnnl_aBCd4b8c8b4c
public static final int dnnl_aBCde4b8c8b4c
public static final int dnnl_aBCde2b8c8b2c
public static final int dnnl_aBCde8c16b2c
public static final int dnnl_aBCde8c8b
public static final int dnnl_aBCde2b4c2b
public static final int dnnl_aBcdef16b
public static final int dnnl_aBCdef16b16c
public static final int dnnl_aBCdef16c16b
public static final int dnnl_aBCdef4c16b4c
public static final int dnnl_aBCdef2c8b4c
public static final int dnnl_aBCdef4c8b2c
public static final int dnnl_aBCdef2b4c2b
public static final int dnnl_aBcdef4b
public static final int dnnl_aBCdef4c4b
public static final int dnnl_aBCdef4b4c
public static final int dnnl_aBCdef2c4b2c
public static final int dnnl_aBCdef4b8c2b
public static final int dnnl_aBCdef8b8c
public static final int dnnl_aBCdef8b4c
public static final int dnnl_aBCdef8c16b2c
public static final int dnnl_aBCdef4b8c8b4c
public static final int dnnl_aBCdef8b16c2b
public static final int dnnl_aCBdef8b16c2b
public static final int dnnl_aBCdef8c8b
public static final int dnnl_aBdc16b
public static final int dnnl_aBdC16b2c
public static final int dnnl_aBdC16b4c
public static final int dnnl_aBdc4b
public static final int dnnl_aBdc8b
public static final int dnnl_aBdec16b
public static final int dnnl_aBdeC16b2c
public static final int dnnl_aBdeC16b4c
public static final int dnnl_aBdec32b
public static final int dnnl_aBdec4b
public static final int dnnl_aBdec8b
public static final int dnnl_aBdefc16b
public static final int dnnl_aBdefC16b2c
public static final int dnnl_aCBdef16c16b
public static final int dnnl_aBdefc4b
public static final int dnnl_aBdefc8b
public static final int dnnl_Abcdef16a
public static final int dnnl_Abcdef32a
public static final int dnnl_Acb16a
public static final int dnnl_AcB16a2b
public static final int dnnl_AcB16a4b
public static final int dnnl_Acb4a
public static final int dnnl_Acb8a
public static final int dnnl_aCBd16b16c
public static final int dnnl_aCBd16c16b
public static final int dnnl_aCBde16b16c
public static final int dnnl_aCBde16c16b
public static final int dnnl_Acdb16a
public static final int dnnl_AcdB16a2b
public static final int dnnl_AcdB16a4b
public static final int dnnl_Acdb32a
public static final int dnnl_Acdb4a
public static final int dnnl_Acdb8a
public static final int dnnl_Acdeb16a
public static final int dnnl_AcdeB16a2b
public static final int dnnl_Acdeb4a
public static final int dnnl_Acdeb8a
public static final int dnnl_BAc16a16b
public static final int dnnl_BAc16b16a
public static final int dnnl_BAcd16a16b
public static final int dnnl_BAcd16b16a
public static final int dnnl_aCBd4c8b8c4b
public static final int dnnl_aCBde4c8b8c4b
public static final int dnnl_aCBdef4c8b8c4b
public static final int dnnl_BAcde16a16b
public static final int dnnl_aCBdef16b16c
public static final int dnnl_format_tag_last
public static final int dnnl_x
public static final int dnnl_nc
public static final int dnnl_cn
public static final int dnnl_tn
public static final int dnnl_nt
public static final int dnnl_ncw
public static final int dnnl_nwc
public static final int dnnl_nchw
public static final int dnnl_nhwc
public static final int dnnl_chwn
public static final int dnnl_ncdhw
public static final int dnnl_ndhwc
public static final int dnnl_oi
public static final int dnnl_io
public static final int dnnl_oiw
public static final int dnnl_owi
public static final int dnnl_wio
public static final int dnnl_iwo
public static final int dnnl_oihw
public static final int dnnl_hwio
public static final int dnnl_ohwi
public static final int dnnl_ihwo
public static final int dnnl_iohw
public static final int dnnl_oidhw
public static final int dnnl_iodhw
public static final int dnnl_dhwio
public static final int dnnl_odhwi
public static final int dnnl_idhwo
public static final int dnnl_goiw
public static final int dnnl_wigo
public static final int dnnl_goihw
public static final int dnnl_hwigo
public static final int dnnl_giohw
public static final int dnnl_goidhw
public static final int dnnl_giodhw
public static final int dnnl_dhwigo
public static final int dnnl_tnc
public static final int dnnl_ntc
public static final int dnnl_ldnc
public static final int dnnl_ldigo
public static final int dnnl_ldgoi
public static final int dnnl_ldio
public static final int dnnl_ldoi
public static final int dnnl_ldgo
public static final int dnnl_nCdhw32c
public static final int dnnl_nCdhw16c
public static final int dnnl_nCdhw4c
public static final int dnnl_nCdhw8c
public static final int dnnl_nChw32c
public static final int dnnl_nChw16c
public static final int dnnl_nChw4c
public static final int dnnl_nChw8c
public static final int dnnl_nCw32c
public static final int dnnl_nCw16c
public static final int dnnl_nCw4c
public static final int dnnl_nCw8c
public static final int dnnl_NCw16n16c
public static final int dnnl_NCdhw16n16c
public static final int dnnl_NChw16n16c
public static final int dnnl_NCw32n32c
public static final int dnnl_NChw32n32c
public static final int dnnl_NCdhw32n32c
public static final int dnnl_IOw16o16i
public static final int dnnl_IOw16i16o
public static final int dnnl_OIw16i16o
public static final int dnnl_OIw16o16i
public static final int dnnl_Oiw16o
public static final int dnnl_OIw4i16o4i
public static final int dnnl_OIw2i8o4i
public static final int dnnl_OIw16i16o4i
public static final int dnnl_OIw16i16o2i
public static final int dnnl_OIw4i4o
public static final int dnnl_OIw4o4i
public static final int dnnl_Oiw4o
public static final int dnnl_OIw8i16o2i
public static final int dnnl_OIw8i8o
public static final int dnnl_OIw8o16i2o
public static final int dnnl_IOw8o16i2o
public static final int dnnl_OIw8o8i
public static final int dnnl_OIw8o4i
public static final int dnnl_Owi16o
public static final int dnnl_OwI16o2i
public static final int dnnl_OwI16o4i
public static final int dnnl_Owi4o
public static final int dnnl_Owi8o
public static final int dnnl_IOhw16i16o
public static final int dnnl_IOhw16o16i
public static final int dnnl_Ohwi16o
public static final int dnnl_OhwI16o2i
public static final int dnnl_OhwI16o4i
public static final int dnnl_Ohwi32o
public static final int dnnl_Ohwi4o
public static final int dnnl_Ohwi8o
public static final int dnnl_OIhw16i16o
public static final int dnnl_OIhw16o16i
public static final int dnnl_Oihw16o
public static final int dnnl_OIhw4i16o4i
public static final int dnnl_OIhw16i16o4i
public static final int dnnl_OIhw16i16o2i
public static final int dnnl_OIhw4i4o
public static final int dnnl_OIhw4o4i
public static final int dnnl_Oihw4o
public static final int dnnl_OIhw8i16o2i
public static final int dnnl_OIhw8i8o
public static final int dnnl_OIhw8o16i2o
public static final int dnnl_OIhw2i8o4i
public static final int dnnl_IOhw8o16i2o
public static final int dnnl_OIhw8o8i
public static final int dnnl_OIhw8o4i
public static final int dnnl_Odhwi16o
public static final int dnnl_OdhwI16o2i
public static final int dnnl_Odhwi4o
public static final int dnnl_Odhwi8o
public static final int dnnl_OIdhw16i16o
public static final int dnnl_OIdhw16o16i
public static final int dnnl_Oidhw16o
public static final int dnnl_OIdhw4i4o
public static final int dnnl_OIdhw4o4i
public static final int dnnl_Oidhw4o
public static final int dnnl_OIdhw8i16o2i
public static final int dnnl_OIdhw8i8o
public static final int dnnl_OIdhw8o16i2o
public static final int dnnl_IOdhw8o16i2o
public static final int dnnl_OIdhw4i16o4i
public static final int dnnl_OIdhw2i8o4i
public static final int dnnl_OIdhw8o8i
public static final int dnnl_OIdhw8o4i
public static final int dnnl_IOdhw16i16o
public static final int dnnl_OIdhw4o8i8o4i
public static final int dnnl_IOdhw16o16i
public static final int dnnl_Goiw16g
public static final int dnnl_Goiw8g
public static final int dnnl_gIOw16o16i
public static final int dnnl_gIOw16i16o
public static final int dnnl_gOIw16i16o
public static final int dnnl_gOIw16o16i
public static final int dnnl_gOiw16o
public static final int dnnl_gOIw4i16o4i
public static final int dnnl_gOIw2i8o4i
public static final int dnnl_gOIw16i16o4i
public static final int dnnl_gOIw16i16o2i
public static final int dnnl_gOIw4i4o
public static final int dnnl_gOIw4o4i
public static final int dnnl_gOiw4o
public static final int dnnl_gOIw8i16o2i
public static final int dnnl_gOIw8i8o
public static final int dnnl_gOIw8o16i2o
public static final int dnnl_gIOw8o16i2o
public static final int dnnl_gOIw8o8i
public static final int dnnl_gOIw8o4i
public static final int dnnl_gOwi16o
public static final int dnnl_gOwI16o2i
public static final int dnnl_gOwI16o4i
public static final int dnnl_gOwi4o
public static final int dnnl_gOwi8o
public static final int dnnl_Goiw32g
public static final int dnnl_gOIw2i4o2i
public static final int dnnl_gOIw2o4i2o
public static final int dnnl_gOIw4i8o2i
public static final int dnnl_gOIw4o8i2o
public static final int dnnl_gIOhw16i16o
public static final int dnnl_gIOhw16o16i
public static final int dnnl_gOhwi16o
public static final int dnnl_gOhwI16o2i
public static final int dnnl_gOhwI16o4i
public static final int dnnl_gOhwi32o
public static final int dnnl_gOhwi4o
public static final int dnnl_gOhwi8o
public static final int dnnl_Goihw16g
public static final int dnnl_gOIhw16i16o
public static final int dnnl_gOIhw16o16i
public static final int dnnl_gOihw16o
public static final int dnnl_gOIhw2i8o4i
public static final int dnnl_gOIhw4i16o4i
public static final int dnnl_gOIhw16i16o4i
public static final int dnnl_gOIhw16i16o2i
public static final int dnnl_gOIhw4i4o
public static final int dnnl_gOIhw4o4i
public static final int dnnl_gOihw4o
public static final int dnnl_Goihw8g
public static final int dnnl_gOIhw8i16o2i
public static final int dnnl_gOIhw8i8o
public static final int dnnl_gOIhw8o16i2o
public static final int dnnl_gIOhw8o16i2o
public static final int dnnl_gOIhw8o8i
public static final int dnnl_gOIhw8o4i
public static final int dnnl_Goihw32g
public static final int dnnl_OIw4o8i8o4i
public static final int dnnl_OIhw4o8i8o4i
public static final int dnnl_IOw4i8o8i4o
public static final int dnnl_IOhw4i8o8i4o
public static final int dnnl_IOdhw4i8o8i4o
public static final int dnnl_OIhw2o8i8o2i
public static final int dnnl_gOIw4o8i8o4i
public static final int dnnl_gOIhw4o8i8o4i
public static final int dnnl_gOIdhw4o8i8o4i
public static final int dnnl_gIOw4i8o8i4o
public static final int dnnl_gIOhw4i8o8i4o
public static final int dnnl_gIOdhw4i8o8i4o
public static final int dnnl_gOIhw2o8i8o2i
public static final int dnnl_gOIhw2i4o2i
public static final int dnnl_gOIhw2o4i2o
public static final int dnnl_gOIhw4i8o2i
public static final int dnnl_gOIhw4o8i2o
public static final int dnnl_gIOdhw16i16o
public static final int dnnl_gIOdhw16o16i
public static final int dnnl_gOdhwi16o
public static final int dnnl_gOdhwI16o2i
public static final int dnnl_gOdhwi4o
public static final int dnnl_gOdhwi8o
public static final int dnnl_gOIdhw16i16o
public static final int dnnl_gOIdhw4i16o4i
public static final int dnnl_gOIdhw2i8o4i
public static final int dnnl_gOIdhw16o16i
public static final int dnnl_gOidhw16o
public static final int dnnl_gOIdhw4i4o
public static final int dnnl_gOIdhw4o4i
public static final int dnnl_gOidhw4o
public static final int dnnl_gOIdhw8i16o2i
public static final int dnnl_gOIdhw8i8o
public static final int dnnl_gOIdhw8o16i2o
public static final int dnnl_gIOdhw8o16i2o
public static final int dnnl_gOIdhw8o8i
public static final int dnnl_gOIdhw8o4i
public static final int dnnl_Goidhw16g
public static final int dnnl_Goidhw32g
public static final int dnnl_gOIdhw2i4o2i
public static final int dnnl_gOIdhw4i8o2i
public static final int dnnl_gOIdhw2o4i2o
public static final int dnnl_gOIdhw4o8i2o
public static final int dnnl_prop_kind_undef
public static final int dnnl_forward_training
public static final int dnnl_forward_inference
public static final int dnnl_forward_scoring
public static final int dnnl_forward
public static final int dnnl_backward
public static final int dnnl_backward_data
public static final int dnnl_backward_weights
public static final int dnnl_backward_bias
public static final int dnnl_undefined_primitive
public static final int dnnl_reorder
public static final int dnnl_shuffle
public static final int dnnl_concat
public static final int dnnl_sum
public static final int dnnl_convolution
public static final int dnnl_deconvolution
public static final int dnnl_eltwise
public static final int dnnl_softmax
public static final int dnnl_pooling
public static final int dnnl_lrn
public static final int dnnl_batch_normalization
public static final int dnnl_layer_normalization
public static final int dnnl_inner_product
public static final int dnnl_rnn
public static final int dnnl_gemm
public static final int dnnl_binary
public static final int dnnl_logsoftmax
public static final int dnnl_matmul
public static final int dnnl_resampling
public static final int dnnl_primitive_kind_max
public static final int dnnl_alg_kind_undef
public static final int dnnl_convolution_direct
public static final int dnnl_convolution_winograd
public static final int dnnl_convolution_auto
public static final int dnnl_deconvolution_direct
public static final int dnnl_deconvolution_winograd
public static final int dnnl_eltwise_relu
public static final int dnnl_eltwise_tanh
public static final int dnnl_eltwise_elu
public static final int dnnl_eltwise_square
public static final int dnnl_eltwise_abs
public static final int dnnl_eltwise_sqrt
public static final int dnnl_eltwise_linear
public static final int dnnl_eltwise_bounded_relu
public static final int dnnl_eltwise_soft_relu
public static final int dnnl_eltwise_logistic
public static final int dnnl_eltwise_exp
public static final int dnnl_eltwise_gelu_tanh
public static final int dnnl_eltwise_gelu
public static final int dnnl_eltwise_swish
public static final int dnnl_eltwise_log
public static final int dnnl_eltwise_clip
public static final int dnnl_eltwise_pow
public static final int dnnl_eltwise_gelu_erf
public static final int dnnl_eltwise_round
public static final int dnnl_eltwise_relu_use_dst_for_bwd
public static final int dnnl_eltwise_tanh_use_dst_for_bwd
public static final int dnnl_eltwise_elu_use_dst_for_bwd
public static final int dnnl_eltwise_sqrt_use_dst_for_bwd
public static final int dnnl_eltwise_logistic_use_dst_for_bwd
public static final int dnnl_eltwise_exp_use_dst_for_bwd
public static final int dnnl_pooling_max
public static final int dnnl_pooling_avg_include_padding
public static final int dnnl_pooling_avg_exclude_padding
public static final int dnnl_pooling_avg
public static final int dnnl_lrn_across_channels
public static final int dnnl_lrn_within_channel
public static final int dnnl_vanilla_rnn
public static final int dnnl_vanilla_lstm
public static final int dnnl_vanilla_gru
public static final int dnnl_lbr_gru
public static final int dnnl_binary_add
public static final int dnnl_binary_mul
public static final int dnnl_binary_max
public static final int dnnl_binary_min
public static final int dnnl_resampling_nearest
public static final int dnnl_resampling_linear
public static final int dnnl_normalization_flags_none
public static final int dnnl_use_global_stats
public static final int dnnl_use_scaleshift
public static final int dnnl_fuse_norm_relu
public static final int DNNL_MAX_NDIMS
\addtogroup dnnl_api_memory \{
Maximum number of dimensions a tensor can have. Only restricts the amount of space used for the tensor description. Individual computational primitives may support only tensors of certain dimensions.
public static final long DNNL_RUNTIME_DIM_VAL
public static final long DNNL_RUNTIME_SIZE_VAL
public static final double DNNL_RUNTIME_F32_VAL
public static final int DNNL_RUNTIME_S32_VAL_REP
public static final int DNNL_RUNTIME_S32_VAL
A wildcard value for int32_t values that are unknown at a primitive creation time.
public static final int dnnl_wino_undef
public static final int dnnl_wino_wei_aaOIoi
public static final int dnnl_wino_wei_aaOio
public static final int dnnl_wino_wei_aaOBiOo
public static final int dnnl_wino_wei_OBaaIBOIio
public static final int dnnl_packed_format_undef
public static final int dnnl_ldigo_p
public static final int dnnl_ldgoi_p
public static final int DNNL_RNN_MAX_N_PARTS
public static final int dnnl_memory_extra_flag_none
public static final int dnnl_memory_extra_flag_compensation_conv_s8s8
public static final int dnnl_memory_extra_flag_scale_adjust
public static final int dnnl_memory_extra_flag_gpu_rnn_u8s8_compensation
public static final int dnnl_rnn_flags_undef
public static final int dnnl_unidirectional_left2right
public static final int dnnl_unidirectional_right2left
public static final int dnnl_bidirectional_concat
public static final int dnnl_bidirectional_sum
public static final int dnnl_unidirectional
public static final int dnnl_any_engine
public static final int dnnl_cpu
public static final int dnnl_gpu
public static final int dnnl_scratchpad_mode_library
public static final int dnnl_scratchpad_mode_user
public static final int DNNL_ARG_SRC_0
public static final int DNNL_ARG_SRC
public static final int DNNL_ARG_SRC_LAYER
public static final int DNNL_ARG_FROM
public static final int DNNL_ARG_SRC_1
public static final int DNNL_ARG_SRC_ITER
public static final int DNNL_ARG_SRC_2
public static final int DNNL_ARG_SRC_ITER_C
public static final int DNNL_ARG_DST_0
public static final int DNNL_ARG_DST
public static final int DNNL_ARG_TO
public static final int DNNL_ARG_DST_LAYER
public static final int DNNL_ARG_DST_1
public static final int DNNL_ARG_DST_ITER
public static final int DNNL_ARG_DST_2
public static final int DNNL_ARG_DST_ITER_C
public static final int DNNL_ARG_WEIGHTS_0
public static final int DNNL_ARG_WEIGHTS
public static final int DNNL_ARG_SCALE_SHIFT
public static final int DNNL_ARG_WEIGHTS_LAYER
public static final int DNNL_ARG_WEIGHTS_1
public static final int DNNL_ARG_WEIGHTS_ITER
public static final int DNNL_ARG_WEIGHTS_2
public static final int DNNL_ARG_WEIGHTS_PEEPHOLE
public static final int DNNL_ARG_WEIGHTS_3
public static final int DNNL_ARG_WEIGHTS_PROJECTION
public static final int DNNL_ARG_BIAS
public static final int DNNL_ARG_MEAN
public static final int DNNL_ARG_VARIANCE
public static final int DNNL_ARG_WORKSPACE
public static final int DNNL_ARG_SCRATCHPAD
public static final int DNNL_ARG_DIFF_SRC_0
public static final int DNNL_ARG_DIFF_SRC
public static final int DNNL_ARG_DIFF_SRC_LAYER
public static final int DNNL_ARG_DIFF_SRC_1
public static final int DNNL_ARG_DIFF_SRC_ITER
public static final int DNNL_ARG_DIFF_SRC_2
public static final int DNNL_ARG_DIFF_SRC_ITER_C
public static final int DNNL_ARG_DIFF_DST_0
public static final int DNNL_ARG_DIFF_DST
public static final int DNNL_ARG_DIFF_DST_LAYER
public static final int DNNL_ARG_DIFF_DST_1
public static final int DNNL_ARG_DIFF_DST_ITER
public static final int DNNL_ARG_DIFF_DST_2
public static final int DNNL_ARG_DIFF_DST_ITER_C
public static final int DNNL_ARG_DIFF_WEIGHTS_0
public static final int DNNL_ARG_DIFF_WEIGHTS
public static final int DNNL_ARG_DIFF_SCALE_SHIFT
public static final int DNNL_ARG_DIFF_WEIGHTS_LAYER
public static final int DNNL_ARG_DIFF_WEIGHTS_1
public static final int DNNL_ARG_DIFF_WEIGHTS_ITER
public static final int DNNL_ARG_DIFF_WEIGHTS_2
public static final int DNNL_ARG_DIFF_WEIGHTS_PEEPHOLE
public static final int DNNL_ARG_DIFF_WEIGHTS_3
public static final int DNNL_ARG_DIFF_WEIGHTS_PROJECTION
public static final int DNNL_ARG_DIFF_BIAS
public static final int DNNL_ARG_ATTR_OUTPUT_SCALES
public static final int DNNL_ARG_MULTIPLE_SRC
public static final int DNNL_ARG_MULTIPLE_DST
public static final int DNNL_ARG_ATTR_ZERO_POINTS
public static final int DNNL_ARG_ATTR_POST_OP_DW
public static final int dnnl_query_undef
public static final int dnnl_query_engine
public static final int dnnl_query_primitive_kind
public static final int dnnl_query_num_of_inputs_s32
public static final int dnnl_query_num_of_outputs_s32
public static final int dnnl_query_time_estimate_f64
public static final int dnnl_query_memory_consumption_s64
public static final int dnnl_query_scratchpad_engine
public static final int dnnl_query_impl_info_str
public static final int dnnl_query_reorder_src_engine
public static final int dnnl_query_reorder_dst_engine
public static final int dnnl_query_prop_kind
public static final int dnnl_query_some_d
public static final int dnnl_query_op_d
public static final int dnnl_query_convolution_d
public static final int dnnl_query_deconvolution_d
public static final int dnnl_query_shuffle_d
public static final int dnnl_query_eltwise_d
public static final int dnnl_query_softmax_d
public static final int dnnl_query_pooling_d
public static final int dnnl_query_lrn_d
public static final int dnnl_query_batch_normalization_d
public static final int dnnl_query_layer_normalization_d
public static final int dnnl_query_inner_product_d
public static final int dnnl_query_rnn_d
public static final int dnnl_query_gemm_d
public static final int dnnl_query_binary_d
public static final int dnnl_query_logsoftmax_d
public static final int dnnl_query_matmul_d
public static final int dnnl_query_resampling_d
public static final int dnnl_query_some_md
public static final int dnnl_query_src_md
public static final int dnnl_query_diff_src_md
public static final int dnnl_query_weights_md
public static final int dnnl_query_diff_weights_md
public static final int dnnl_query_dst_md
public static final int dnnl_query_diff_dst_md
public static final int dnnl_query_workspace_md
public static final int dnnl_query_scratchpad_md
public static final int dnnl_query_exec_arg_md
public static final int dnnl_query_max
public static final int dnnl_stream_default_order
public static final int dnnl_stream_in_order
public static final int dnnl_stream_out_of_order
public static final int dnnl_stream_default_flags
public static final long DNNL_RUNTIME_NONE
\addtogroup dnnl_api_service \{
No runtime (disabled)
public static final long DNNL_RUNTIME_SEQ
public static final long DNNL_RUNTIME_OMP
public static final long DNNL_RUNTIME_TBB
public static final long DNNL_RUNTIME_THREADPOOL
public static final long DNNL_RUNTIME_OCL
public static final long DNNL_JIT_PROFILE_NONE
public static final long DNNL_JIT_PROFILE_VTUNE
public static final long DNNL_JIT_PROFILE_LINUX_PERFMAP
public static final long DNNL_JIT_PROFILE_LINUX_JITDUMP
public static final long DNNL_JIT_PROFILE_LINUX_JITDUMP_USE_TSC
public static final long DNNL_JIT_PROFILE_LINUX_PERF
public static final int dnnl_cpu_isa_all
public static final int dnnl_cpu_isa_sse41
public static final int dnnl_cpu_isa_avx
public static final int dnnl_cpu_isa_avx2
public static final int dnnl_cpu_isa_avx512_mic
public static final int dnnl_cpu_isa_avx512_mic_4ops
public static final int dnnl_cpu_isa_avx512_core
public static final int dnnl_cpu_isa_avx512_core_vnni
public static final int dnnl_cpu_isa_avx512_core_bf16
public static final int dnnl_cpu_isa_avx512_core_amx
public static final long DNNL_CPU_THREADING_RUNTIME
public static final long DNNL_CPU_RUNTIME
public static final long DNNL_GPU_RUNTIME
public static final int DNNL_VERSION_MAJOR
public static final int DNNL_VERSION_MINOR
public static final int DNNL_VERSION_PATCH
public static final String DNNL_VERSION_HASH
public static final int DNNL_ENABLE_EXCEPTIONS
@MemberGetter public static long DNNL_RUNTIME_DIM_VAL()
@MemberGetter public static long DNNL_RUNTIME_SIZE_VAL()
size_t counterpart of the DNNL_RUNTIME_DIM_VAL.
For instance, this value is returned by dnnl_memory_desc_get_size() if
either of the dimensions or strides equal to #DNNL_RUNTIME_DIM_VAL.@MemberGetter public static double DNNL_RUNTIME_F32_VAL()
A wildcard value for floating point values that are unknown at a primitive creation time.
@MemberGetter public static int DNNL_RUNTIME_S32_VAL_REP()
@MemberGetter public static String DNNL_VERSION_HASH()
@Cast(value="dnnl_status_t") public static int dnnl_primitive_desc_iterator_create(@ByPtrPtr dnnl_primitive_desc_iterator iterator, const_dnnl_op_desc_t op_desc, @Const dnnl_primitive_attr attr, dnnl_engine engine, @Const dnnl_primitive_desc hint_forward_primitive_desc)
\addtogroup dnnl_api_primitives \{
\addtogroup dnnl_api_primitives_common \{
Creates a primitive descriptor iterator.
iterator - Output primitive descriptor iterator.op_desc - Operation descriptor.attr - Primitive attributes (can be NULL).engine - Engine to use.hint_forward_primitive_desc - For backward propagation: primitive
descriptor for a respective forward propagation primitive. Pass NULL
for forward propagation.@Cast(value="dnnl_status_t") public static int dnnl_primitive_desc_iterator_create(@Cast(value="dnnl_primitive_desc_iterator_t*") PointerPointer iterator, const_dnnl_op_desc_t op_desc, @Const dnnl_primitive_attr attr, dnnl_engine engine, @Const dnnl_primitive_desc hint_forward_primitive_desc)
@Cast(value="dnnl_status_t") public static int dnnl_primitive_desc_iterator_next(dnnl_primitive_desc_iterator iterator)
iterator - A primitive descriptor iterator to advance.public static dnnl_primitive_desc dnnl_primitive_desc_iterator_fetch(@Const dnnl_primitive_desc_iterator iterator)
iterator - A primitive descriptor iterator.@Cast(value="dnnl_status_t") public static int dnnl_primitive_desc_iterator_destroy(dnnl_primitive_desc_iterator iterator)
iterator - Primitive descriptor iterator to destroy.@Cast(value="dnnl_status_t") public static int dnnl_primitive_desc_create(@ByPtrPtr dnnl_primitive_desc primitive_desc, const_dnnl_op_desc_t op_desc, @Const dnnl_primitive_attr attr, dnnl_engine engine, @Const dnnl_primitive_desc hint_forward_primitive_desc)
primitive_desc - Output primitive descriptor.op_desc - Operation descriptor.attr - Primitive attributes (can be NULL).engine - Engine to use.hint_forward_primitive_desc - For backward propagation: primitive
descriptor for a respective forward propagation primitive. Pass NULL
for forward propagation.@Cast(value="dnnl_status_t") public static int dnnl_primitive_desc_create(@Cast(value="dnnl_primitive_desc_t*") PointerPointer primitive_desc, const_dnnl_op_desc_t op_desc, @Const dnnl_primitive_attr attr, dnnl_engine engine, @Const dnnl_primitive_desc hint_forward_primitive_desc)
@Cast(value="dnnl_status_t") public static int dnnl_primitive_desc_clone(@ByPtrPtr dnnl_primitive_desc primitive_desc, @Const dnnl_primitive_desc existing_primitive_desc)
primitive_desc - Output primitive descriptor.existing_primitive_desc - Primitive descriptor to clone.@Cast(value="dnnl_status_t") public static int dnnl_primitive_desc_clone(@Cast(value="dnnl_primitive_desc_t*") PointerPointer primitive_desc, @Const dnnl_primitive_desc existing_primitive_desc)
@Cast(value="dnnl_status_t") public static int dnnl_primitive_desc_get_attr(@Const dnnl_primitive_desc primitive_desc, @Const @ByPtrPtr dnnl_primitive_attr attr)
primitive_desc - Primitive descriptor.attr - Output primitive attributes.@Cast(value="dnnl_status_t") public static int dnnl_primitive_desc_get_attr(@Const dnnl_primitive_desc primitive_desc, @Cast(value="const_dnnl_primitive_attr_t*") PointerPointer attr)
@Cast(value="dnnl_status_t") public static int dnnl_primitive_desc_destroy(dnnl_primitive_desc primitive_desc)
primitive_desc - Primitive descriptor to destroy.@Cast(value="dnnl_status_t") public static int dnnl_primitive_desc_query(@Const dnnl_primitive_desc primitive_desc, @Cast(value="dnnl_query_t") int what, int index, Pointer result)
primitive_desc - Primitive descriptor.what - Parameter to query.index - Index of the parameter to query for.result - Output result. The type depends on the query. For example,
it must be a \c dnnl_memory_desc_t* if querying for a memory
descriptor.for more options@Const public static dnnl_memory_desc_t dnnl_primitive_desc_query_md(@Const dnnl_primitive_desc primitive_desc, @Cast(value="dnnl_query_t") int what, int index)
primitive_desc - Primitive descriptor.what - Kind of memory descriptor parameter to query for.index - Index of the parameter to query.public static int dnnl_primitive_desc_query_s32(@Const dnnl_primitive_desc primitive_desc, @Cast(value="dnnl_query_t") int what, int index)
primitive_desc - Primitive descriptor.what - Kind of the value to query for.index - Index of the parameter to query.@Cast(value="dnnl_status_t") public static int dnnl_primitive_create(@ByPtrPtr dnnl_primitive primitive, @Const dnnl_primitive_desc primitive_desc)
primitive - Output primitive.primitive_desc - Primitive descriptor used to create the primitive.@Cast(value="dnnl_status_t") public static int dnnl_primitive_create(@Cast(value="dnnl_primitive_t*") PointerPointer primitive, @Const dnnl_primitive_desc primitive_desc)
@Cast(value="dnnl_status_t") public static int dnnl_primitive_execute(@Const dnnl_primitive primitive, dnnl_stream stream, int nargs, @Const dnnl_exec_arg_t args)
primitive - Primitive to execute.stream - Stream to use.nargs - Number of arguments.args - Array of arguments. Each argument is an
DNNL_ARG_*
values such as DNNL_ARG_SRC. Unless runtime shapes are used (see
#DNNL_RUNTIME_DIM_VAL), the memory object must have the same memory
descriptor as that returned by
#dnnl_primitive_desc_query_md(#dnnl_query_exec_arg_md, index).@Cast(value="dnnl_status_t") public static int dnnl_primitive_get_primitive_desc(@Const dnnl_primitive primitive, @Const @ByPtrPtr dnnl_primitive_desc primitive_desc)
primitive - Primitive to query for the primitive descriptor.primitive_desc - Output primitive descriptor.@Cast(value="dnnl_status_t") public static int dnnl_primitive_get_primitive_desc(@Const dnnl_primitive primitive, @Cast(value="const_dnnl_primitive_desc_t*") PointerPointer primitive_desc)
@Cast(value="dnnl_status_t") public static int dnnl_primitive_destroy(dnnl_primitive primitive)
primitive - The primitive to destroy.@Cast(value="dnnl_status_t") public static int dnnl_primitive_attr_create(@ByPtrPtr dnnl_primitive_attr attr)
\addtogroup dnnl_api_attributes \{
Creates an empty (default) primitive attributes with all the parameters set to their default values. Empty attributes are implied whenever the respective argument is NULL.
attr - Output primitive attributes.@Cast(value="dnnl_status_t") public static int dnnl_primitive_attr_create(@Cast(value="dnnl_primitive_attr_t*") PointerPointer attr)
@Cast(value="dnnl_status_t") public static int dnnl_primitive_attr_clone(@ByPtrPtr dnnl_primitive_attr attr, @Const dnnl_primitive_attr existing_attr)
attr - Output primitive attributes.existing_attr - Primitive attributes to clone.@Cast(value="dnnl_status_t") public static int dnnl_primitive_attr_clone(@Cast(value="dnnl_primitive_attr_t*") PointerPointer attr, @Const dnnl_primitive_attr existing_attr)
@Cast(value="dnnl_status_t") public static int dnnl_primitive_attr_destroy(dnnl_primitive_attr attr)
attr - Primitive attributes to destroy.@Cast(value="dnnl_status_t") public static int dnnl_primitive_attr_get_scratchpad_mode(@Const dnnl_primitive_attr attr, @Cast(value="dnnl_scratchpad_mode_t*") IntPointer mode)
attr - Primitive attributes.mode - Output scratchpad mode.@Cast(value="dnnl_status_t") public static int dnnl_primitive_attr_get_scratchpad_mode(@Const dnnl_primitive_attr attr, @Cast(value="dnnl_scratchpad_mode_t*") IntBuffer mode)
@Cast(value="dnnl_status_t") public static int dnnl_primitive_attr_get_scratchpad_mode(@Const dnnl_primitive_attr attr, @Cast(value="dnnl_scratchpad_mode_t*") int[] mode)
@Cast(value="dnnl_status_t") public static int dnnl_primitive_attr_set_scratchpad_mode(dnnl_primitive_attr attr, @Cast(value="dnnl_scratchpad_mode_t") int mode)
attr - Primitive attributes.mode - Scratchpad mode. The possible values are:
#dnnl_scratchpad_mode_library (default) and
#dnnl_scratchpad_mode_user.@Cast(value="dnnl_status_t") public static int dnnl_primitive_attr_get_output_scales(@Const dnnl_primitive_attr attr, @Cast(value="dnnl_dim_t*") LongPointer count, IntPointer mask, @Cast(value="const float**") PointerPointer scales)
attr - Primitive attributes.count - Output length of the array of scaling factors \p scales.mask - Output scaling factors correspondence mask that defines the
correspondence between the output tensor dimensions and the \p scales
vector. The set i-th bit indicates that a dedicated output scaling
factor is used for each index along that dimension. The mask value of
0 implies a common output scaling factor for the whole output tensor.scales - Output pointer to a constant array of scaling factors.@Cast(value="dnnl_status_t") public static int dnnl_primitive_attr_get_output_scales(@Const dnnl_primitive_attr attr, @Cast(value="dnnl_dim_t*") LongPointer count, IntPointer mask, @Const @ByPtrPtr FloatPointer scales)
@Cast(value="dnnl_status_t") public static int dnnl_primitive_attr_get_output_scales(@Const dnnl_primitive_attr attr, @Cast(value="dnnl_dim_t*") LongBuffer count, IntBuffer mask, @Const @ByPtrPtr FloatBuffer scales)
@Cast(value="dnnl_status_t") public static int dnnl_primitive_attr_get_output_scales(@Const dnnl_primitive_attr attr, @Cast(value="dnnl_dim_t*") long[] count, int[] mask, @Const @ByPtrPtr float[] scales)
@Cast(value="dnnl_status_t") public static int dnnl_primitive_attr_set_output_scales(dnnl_primitive_attr attr, @Cast(value="dnnl_dim_t") long count, int mask, @Const FloatPointer scales)
int mb = 32, oc = 32, oh = 14, ow = 14; // convolution output params
float scales[oc] = { ... }; // unique output scales per output channel
int oc_dim = 1; // mb_dim = 0, channel_dim = 1, height_dim = 2, ...
dnnl_convolution_desc_t conv_d; // create a convolution descriptor
dnnl_primitive_attr_t attr;
dnnl_primitive_attr_create(&attr); // create primitive attributes
dnnl_primitive_attr_set_output_scales(attr, oc, 1 << oc_dim, scales);
dnnl_primitive_desc_t conv_pd;
dnnl_primitive_desc_create(&conv_pd, &conv_d, attr, engine, NULL);
attr - Primitive attributes.count - Length of the array of scaling factors \p scales.mask - Scaling factors correspondence mask that defines the
correspondence between the output tensor dimensions and the \p scales
array. The set i-th bit indicates that a dedicated output scaling
factor is used for each index along that dimension. The mask value of
0 implies a common output scaling factor for the whole output tensor.scales - Array of output scaling factors. If the output scaling
factors are known at the time of this call, this array must contain \p
count values and the following equality must hold:
\[count = \prod\limits_{d \in mask} output.dims[d].\]
Violations can only be detected when the attributes are used to create
a primitive descriptor.
If the output scaling factors are not known at the time of the call,
this array must contain a single #DNNL_RUNTIME_F32_VAL value and the
output scaling factors must be passed at execution time as an argument
with index #DNNL_ARG_ATTR_OUTPUT_SCALES.@Cast(value="dnnl_status_t") public static int dnnl_primitive_attr_set_output_scales(dnnl_primitive_attr attr, @Cast(value="dnnl_dim_t") long count, int mask, @Const FloatBuffer scales)
@Cast(value="dnnl_status_t") public static int dnnl_primitive_attr_set_output_scales(dnnl_primitive_attr attr, @Cast(value="dnnl_dim_t") long count, int mask, @Const float[] scales)
@Cast(value="dnnl_status_t") public static int dnnl_primitive_attr_get_scales(dnnl_primitive_attr attr, int arg, @Cast(value="dnnl_dim_t*") LongPointer count, IntPointer mask, @Cast(value="const float**") PointerPointer scales)
attr - Primitive attributes.arg - Parameter argument index as passed to the
dnnl_primitive_execute() call.count - Output length of the array of scaling factors \p scales.mask - Output scaling factors correspondence mask that defines the
correspondence between the output tensor dimensions and the \p
scales array. The set i-th bit indicates that a dedicated output scaling
factor is used for each index along that dimension. The mask value of 0
implies a common scaling factor for the whole output tensor.scales - Output pointer to a constant array of float scaling factors.@Cast(value="dnnl_status_t") public static int dnnl_primitive_attr_get_scales(dnnl_primitive_attr attr, int arg, @Cast(value="dnnl_dim_t*") LongPointer count, IntPointer mask, @Const @ByPtrPtr FloatPointer scales)
@Cast(value="dnnl_status_t") public static int dnnl_primitive_attr_get_scales(dnnl_primitive_attr attr, int arg, @Cast(value="dnnl_dim_t*") LongBuffer count, IntBuffer mask, @Const @ByPtrPtr FloatBuffer scales)
@Cast(value="dnnl_status_t") public static int dnnl_primitive_attr_get_scales(dnnl_primitive_attr attr, int arg, @Cast(value="dnnl_dim_t*") long[] count, int[] mask, @Const @ByPtrPtr float[] scales)
@Cast(value="dnnl_status_t") public static int dnnl_primitive_attr_set_scales(dnnl_primitive_attr attr, int arg, @Cast(value="dnnl_dim_t") long count, int mask, @Const FloatPointer scales)
attr - Primitive attributes.arg - Parameter argument index as passed to the
dnnl_primitive_execute() call.count - Length of the array of scaling factors \p scales.mask - Scaling factors correspondence mask that defines the
correspondence between the tensor dimensions and the \p scales array.
The set i-th bit indicates that a dedicated scaling factor is used for
each index along that dimension. Set the mask to 0 to use a common
scaling factor for the whole output tensor.scales - Constant array of float scaling factors. This array must
contain \p count scales and the following equality must hold:
\[count = \prod\limits_{d \in mask} output.dims[d].\]dnnl_primitive_attr_set_output_scales@Cast(value="dnnl_status_t") public static int dnnl_primitive_attr_set_scales(dnnl_primitive_attr attr, int arg, @Cast(value="dnnl_dim_t") long count, int mask, @Const FloatBuffer scales)
@Cast(value="dnnl_status_t") public static int dnnl_primitive_attr_set_scales(dnnl_primitive_attr attr, int arg, @Cast(value="dnnl_dim_t") long count, int mask, @Const float[] scales)
@Cast(value="dnnl_status_t") public static int dnnl_primitive_attr_get_zero_points(@Const dnnl_primitive_attr attr, int arg, @Cast(value="dnnl_dim_t*") LongPointer count, IntPointer mask, @Cast(value="const int32_t**") PointerPointer zero_points)
attr - Primitive attributes.arg - Parameter argument index as passed to the
dnnl_primitive_execute() call.count - Output length of the array of zero points \p zero_points.mask - Output zero points correspondence mask that defines the
correspondence between the output tensor dimensions and the \p
zero_points array. The set i-th bit indicates that a dedicated output
zero point is used for each index along that dimension. The mask
value of 0 implies a common zero point for the whole output tensor.zero_points - Output pointer to a constant array of int32_t zero
points.@Cast(value="dnnl_status_t") public static int dnnl_primitive_attr_get_zero_points(@Const dnnl_primitive_attr attr, int arg, @Cast(value="dnnl_dim_t*") LongPointer count, IntPointer mask, @Const @ByPtrPtr IntPointer zero_points)
@Cast(value="dnnl_status_t") public static int dnnl_primitive_attr_get_zero_points(@Const dnnl_primitive_attr attr, int arg, @Cast(value="dnnl_dim_t*") LongBuffer count, IntBuffer mask, @Const @ByPtrPtr IntBuffer zero_points)
@Cast(value="dnnl_status_t") public static int dnnl_primitive_attr_get_zero_points(@Const dnnl_primitive_attr attr, int arg, @Cast(value="dnnl_dim_t*") long[] count, int[] mask, @Const @ByPtrPtr int[] zero_points)
@Cast(value="dnnl_status_t") public static int dnnl_primitive_attr_set_zero_points(dnnl_primitive_attr attr, int arg, @Cast(value="dnnl_dim_t") long count, int mask, @Const IntPointer zero_points)
attr - Primitive attributes.arg - Parameter argument index as passed to the
dnnl_primitive_execute() call.count - Length of the array of zero points \p zero_points.mask - Zero point correspondence mask that defines the
correspondence between the tensor dimensions and the \p
zero_points array. The set i-th bit indicates that a dedicated
zero point is used for each index along that dimension. Set the
mask to 0 to use a common zero point for the whole output tensor.zero_points - Constant array of int32_t zero points. If the zero
points are known at the time of this call, this array must contain \p
count zero points and the following equality must hold:
\[count = \prod\limits_{d \in mask} output.dims[d].\]
If the zero points are not known at the time of the call, this array
must contain a single #DNNL_RUNTIME_S32_VAL and the zero points must
be passed at execution time as an argument with index
#DNNL_ARG_ATTR_ZERO_POINTS.dnnl_primitive_attr_set_output_scales@Cast(value="dnnl_status_t") public static int dnnl_primitive_attr_set_zero_points(dnnl_primitive_attr attr, int arg, @Cast(value="dnnl_dim_t") long count, int mask, @Const IntBuffer zero_points)
@Cast(value="dnnl_status_t") public static int dnnl_primitive_attr_set_zero_points(dnnl_primitive_attr attr, int arg, @Cast(value="dnnl_dim_t") long count, int mask, @Const int[] zero_points)
@Cast(value="dnnl_status_t") public static int dnnl_primitive_attr_get_post_ops(@Const dnnl_primitive_attr attr, @Const @ByPtrPtr dnnl_post_ops post_ops)
attr - Primitive attributes.post_ops - Output post-ops.@Cast(value="dnnl_status_t") public static int dnnl_primitive_attr_get_post_ops(@Const dnnl_primitive_attr attr, @Cast(value="const_dnnl_post_ops_t*") PointerPointer post_ops)
@Cast(value="dnnl_status_t") public static int dnnl_primitive_attr_set_post_ops(dnnl_primitive_attr attr, @Const dnnl_post_ops post_ops)
attr - Primitive attributes.post_ops - Post-ops to set.@Cast(value="dnnl_status_t") public static int dnnl_post_ops_create(@ByPtrPtr dnnl_post_ops post_ops)
post_ops - Output post-ops.@Cast(value="dnnl_status_t") public static int dnnl_post_ops_create(@Cast(value="dnnl_post_ops_t*") PointerPointer post_ops)
@Cast(value="dnnl_status_t") public static int dnnl_post_ops_destroy(dnnl_post_ops post_ops)
post_ops - Post-ops to destroy.public static int dnnl_post_ops_len(@Const dnnl_post_ops post_ops)
post_ops - Post-ops.@Cast(value="dnnl_primitive_kind_t") public static int dnnl_post_ops_get_kind(@Const dnnl_post_ops post_ops, int index)
post_ops - Post-ops.index - Post-op entry index.@Cast(value="dnnl_status_t") public static int dnnl_post_ops_append_sum(dnnl_post_ops post_ops, float scale)
post_ops - Post-ops.scale - Accumulation scaling factor.@Cast(value="dnnl_status_t") public static int dnnl_post_ops_append_sum_v2(dnnl_post_ops post_ops, float scale, @Cast(value="dnnl_data_type_t") int data_type)
post_ops - Post-ops.scale - Accumulation scaling factor.data_type - Accumulation data_type.@Cast(value="dnnl_status_t") public static int dnnl_post_ops_get_params_sum(@Const dnnl_post_ops post_ops, int index, FloatPointer scale)
post_ops - Post-ops.index - Index of the sum post-op.scale - Output accumulation scaling factor.@Cast(value="dnnl_status_t") public static int dnnl_post_ops_get_params_sum(@Const dnnl_post_ops post_ops, int index, FloatBuffer scale)
@Cast(value="dnnl_status_t") public static int dnnl_post_ops_get_params_sum(@Const dnnl_post_ops post_ops, int index, float[] scale)
@Cast(value="dnnl_status_t") public static int dnnl_post_ops_get_params_sum_v2(@Const dnnl_post_ops post_ops, int index, FloatPointer scale, @Cast(value="dnnl_data_type_t*") IntPointer data_type)
post_ops - Post-ops.index - Index of the sum post-op.scale - Output accumulation scaling factor.data_type - Data type for accumulation.@Cast(value="dnnl_status_t") public static int dnnl_post_ops_get_params_sum_v2(@Const dnnl_post_ops post_ops, int index, FloatBuffer scale, @Cast(value="dnnl_data_type_t*") IntBuffer data_type)
@Cast(value="dnnl_status_t") public static int dnnl_post_ops_get_params_sum_v2(@Const dnnl_post_ops post_ops, int index, float[] scale, @Cast(value="dnnl_data_type_t*") int[] data_type)
@Cast(value="dnnl_status_t") public static int dnnl_post_ops_append_eltwise(dnnl_post_ops post_ops, float scale, @Cast(value="dnnl_alg_kind_t") int alg_kind, float alpha, float beta)
post_ops - Post-ops.scale - Scaling factor.alg_kind - Elementwise algorithm for the post-op.alpha - Alpha parameter for the elementwise algorithm.beta - Beta parameter for the elementwise algorithm.@Cast(value="dnnl_status_t") public static int dnnl_post_ops_get_params_eltwise(@Const dnnl_post_ops post_ops, int index, FloatPointer scale, @Cast(value="dnnl_alg_kind_t*") IntPointer alg_kind, FloatPointer alpha, FloatPointer beta)
post_ops - Post-ops.index - Index of the elementwise post-op.scale - Output scaling factor.alg_kind - Output elementwise algorithm kind.alpha - Output alpha parameter for the elementwise algorithm.beta - Output beta parameter for the elementwise algorithm.@Cast(value="dnnl_status_t") public static int dnnl_post_ops_get_params_eltwise(@Const dnnl_post_ops post_ops, int index, FloatBuffer scale, @Cast(value="dnnl_alg_kind_t*") IntBuffer alg_kind, FloatBuffer alpha, FloatBuffer beta)
@Cast(value="dnnl_status_t") public static int dnnl_post_ops_get_params_eltwise(@Const dnnl_post_ops post_ops, int index, float[] scale, @Cast(value="dnnl_alg_kind_t*") int[] alg_kind, float[] alpha, float[] beta)
@Cast(value="dnnl_status_t") public static int dnnl_post_ops_append_dw_k3s1p1(dnnl_post_ops post_ops, @Cast(value="dnnl_data_type_t") int weights_data_type, @Cast(value="dnnl_data_type_t") int bias_data_type, @Cast(value="dnnl_data_type_t") int dst_data_type, @Cast(value="dnnl_dim_t") long count, int mask, @Const FloatPointer scales)
post_ops - Post-ops.weights_data_type - Weights data type of depthwise post-opbias_data_type - Bias data type of depthwise post-opdst_data_type - Output data type of depthwise post-opcount - Output length of the array of scaling factors \p scales.mask - Output scaling factors correspondence mask that defines the
correspondence between the output tensor dimensions and the \p
scales array. The set i-th bit indicates that a dedicated output scaling
factor is used for each index along that dimension. The mask value of 0
implies a common scaling factor for the whole output tensor.scales - Output pointer to a constant array of float scaling factors.@Cast(value="dnnl_status_t") public static int dnnl_post_ops_append_dw_k3s1p1(dnnl_post_ops post_ops, @Cast(value="dnnl_data_type_t") int weights_data_type, @Cast(value="dnnl_data_type_t") int bias_data_type, @Cast(value="dnnl_data_type_t") int dst_data_type, @Cast(value="dnnl_dim_t") long count, int mask, @Const FloatBuffer scales)
@Cast(value="dnnl_status_t") public static int dnnl_post_ops_append_dw_k3s1p1(dnnl_post_ops post_ops, @Cast(value="dnnl_data_type_t") int weights_data_type, @Cast(value="dnnl_data_type_t") int bias_data_type, @Cast(value="dnnl_data_type_t") int dst_data_type, @Cast(value="dnnl_dim_t") long count, int mask, @Const float[] scales)
@Cast(value="dnnl_status_t") public static int dnnl_post_ops_get_params_dw_k3s1p1(@Const dnnl_post_ops post_ops, int index, @Cast(value="dnnl_data_type_t*") IntPointer weights_data_type, @Cast(value="dnnl_data_type_t*") IntPointer bias_data_type, @Cast(value="dnnl_data_type_t*") IntPointer dst_data_type, @Cast(value="dnnl_dim_t*") LongPointer count, IntPointer mask, @Cast(value="const float**") PointerPointer scales)
post_ops - Post-ops.index - Index of the elementwise post-op.weights_data_type - Weights data type of depthwise post-opbias_data_type - Bias data type of depthwise post-opdst_data_type - Output data type of depthwise post-opcount - Output length of the array of scaling factors \p scales.mask - Output scaling factors correspondence mask that defines the
correspondence between the output tensor dimensions and the \p
scales array. The set i-th bit indicates that a dedicated output scaling
factor is used for each index along that dimension. The mask value of 0
implies a common scaling factor for the whole output tensor.scales - Output pointer to a constant array of float scaling factors.@Cast(value="dnnl_status_t") public static int dnnl_post_ops_get_params_dw_k3s1p1(@Const dnnl_post_ops post_ops, int index, @Cast(value="dnnl_data_type_t*") IntPointer weights_data_type, @Cast(value="dnnl_data_type_t*") IntPointer bias_data_type, @Cast(value="dnnl_data_type_t*") IntPointer dst_data_type, @Cast(value="dnnl_dim_t*") LongPointer count, IntPointer mask, @Const @ByPtrPtr FloatPointer scales)
@Cast(value="dnnl_status_t") public static int dnnl_post_ops_get_params_dw_k3s1p1(@Const dnnl_post_ops post_ops, int index, @Cast(value="dnnl_data_type_t*") IntBuffer weights_data_type, @Cast(value="dnnl_data_type_t*") IntBuffer bias_data_type, @Cast(value="dnnl_data_type_t*") IntBuffer dst_data_type, @Cast(value="dnnl_dim_t*") LongBuffer count, IntBuffer mask, @Const @ByPtrPtr FloatBuffer scales)
@Cast(value="dnnl_status_t") public static int dnnl_post_ops_get_params_dw_k3s1p1(@Const dnnl_post_ops post_ops, int index, @Cast(value="dnnl_data_type_t*") int[] weights_data_type, @Cast(value="dnnl_data_type_t*") int[] bias_data_type, @Cast(value="dnnl_data_type_t*") int[] dst_data_type, @Cast(value="dnnl_dim_t*") long[] count, int[] mask, @Const @ByPtrPtr float[] scales)
@Cast(value="dnnl_status_t") public static int dnnl_post_ops_append_dw_k3s2p1(dnnl_post_ops post_ops, @Cast(value="dnnl_data_type_t") int weights_data_type, @Cast(value="dnnl_data_type_t") int bias_data_type, @Cast(value="dnnl_data_type_t") int dst_data_type, @Cast(value="dnnl_dim_t") long count, int mask, @Const FloatPointer scales)
post_ops - Post-ops.weights_data_type - Weights data type of depthwise post-opbias_data_type - Bias data type of depthwise post-opdst_data_type - Output data type of depthwise post-opcount - Output length of the array of scaling factors \p scales.mask - Output scaling factors correspondence mask that defines the
correspondence between the output tensor dimensions and the \p
scales array. The set i-th bit indicates that a dedicated output scaling
factor is used for each index along that dimension. The mask value of 0
implies a common scaling factor for the whole output tensor.scales - Output pointer to a constant array of float scaling factors.@Cast(value="dnnl_status_t") public static int dnnl_post_ops_append_dw_k3s2p1(dnnl_post_ops post_ops, @Cast(value="dnnl_data_type_t") int weights_data_type, @Cast(value="dnnl_data_type_t") int bias_data_type, @Cast(value="dnnl_data_type_t") int dst_data_type, @Cast(value="dnnl_dim_t") long count, int mask, @Const FloatBuffer scales)
@Cast(value="dnnl_status_t") public static int dnnl_post_ops_append_dw_k3s2p1(dnnl_post_ops post_ops, @Cast(value="dnnl_data_type_t") int weights_data_type, @Cast(value="dnnl_data_type_t") int bias_data_type, @Cast(value="dnnl_data_type_t") int dst_data_type, @Cast(value="dnnl_dim_t") long count, int mask, @Const float[] scales)
@Cast(value="dnnl_status_t") public static int dnnl_post_ops_get_params_dw_k3s2p1(@Const dnnl_post_ops post_ops, int index, @Cast(value="dnnl_data_type_t*") IntPointer weights_data_type, @Cast(value="dnnl_data_type_t*") IntPointer bias_data_type, @Cast(value="dnnl_data_type_t*") IntPointer dst_data_type, @Cast(value="dnnl_dim_t*") LongPointer count, IntPointer mask, @Cast(value="const float**") PointerPointer scales)
post_ops - Post-ops.index - Index of the elementwise post-op.weights_data_type - Weights data type of depthwise post-opbias_data_type - Bias data type of depthwise post-opdst_data_type - Output data type of depthwise post-opcount - Output length of the array of scaling factors \p scales.mask - Output scaling factors correspondence mask that defines the
correspondence between the output tensor dimensions and the \p
scales array. The set i-th bit indicates that a dedicated output scaling
factor is used for each index along that dimension. The mask value of 0
implies a common scaling factor for the whole output tensor.scales - Output pointer to a constant array of float scaling factors.@Cast(value="dnnl_status_t") public static int dnnl_post_ops_get_params_dw_k3s2p1(@Const dnnl_post_ops post_ops, int index, @Cast(value="dnnl_data_type_t*") IntPointer weights_data_type, @Cast(value="dnnl_data_type_t*") IntPointer bias_data_type, @Cast(value="dnnl_data_type_t*") IntPointer dst_data_type, @Cast(value="dnnl_dim_t*") LongPointer count, IntPointer mask, @Const @ByPtrPtr FloatPointer scales)
@Cast(value="dnnl_status_t") public static int dnnl_post_ops_get_params_dw_k3s2p1(@Const dnnl_post_ops post_ops, int index, @Cast(value="dnnl_data_type_t*") IntBuffer weights_data_type, @Cast(value="dnnl_data_type_t*") IntBuffer bias_data_type, @Cast(value="dnnl_data_type_t*") IntBuffer dst_data_type, @Cast(value="dnnl_dim_t*") LongBuffer count, IntBuffer mask, @Const @ByPtrPtr FloatBuffer scales)
@Cast(value="dnnl_status_t") public static int dnnl_post_ops_get_params_dw_k3s2p1(@Const dnnl_post_ops post_ops, int index, @Cast(value="dnnl_data_type_t*") int[] weights_data_type, @Cast(value="dnnl_data_type_t*") int[] bias_data_type, @Cast(value="dnnl_data_type_t*") int[] dst_data_type, @Cast(value="dnnl_dim_t*") long[] count, int[] mask, @Const @ByPtrPtr float[] scales)
@Cast(value="dnnl_status_t") public static int dnnl_memory_desc_init_by_strides(dnnl_memory_desc_t memory_desc, int ndims, @Cast(value="const int64_t*") LongPointer dims, @Cast(value="dnnl_data_type_t") int data_type, @Cast(value="const int64_t*") LongPointer strides)
\} dnnl_api_primitives
\addtogroup dnnl_api_memory \{
Initializes a memory descriptor using dimensions and strides.
\note
As always, the logical order of dimensions corresponds to the abc...
format tag, and the physical meaning of the dimensions depends on both
the primitive that consumes the memory and the context of that
consumption.
memory_desc - Output memory descriptor.ndims - Number of dimensionsdims - Array of dimensions.data_type - Elements data type.strides - Strides in each dimension.@Cast(value="dnnl_status_t") public static int dnnl_memory_desc_init_by_strides(dnnl_memory_desc_t memory_desc, int ndims, @Cast(value="const int64_t*") LongBuffer dims, @Cast(value="dnnl_data_type_t") int data_type, @Cast(value="const int64_t*") LongBuffer strides)
@Cast(value="dnnl_status_t") public static int dnnl_memory_desc_init_by_strides(dnnl_memory_desc_t memory_desc, int ndims, @Cast(value="const int64_t*") long[] dims, @Cast(value="dnnl_data_type_t") int data_type, @Cast(value="const int64_t*") long[] strides)
@Cast(value="dnnl_status_t") public static int dnnl_memory_desc_init_by_tag(dnnl_memory_desc_t memory_desc, int ndims, @Cast(value="const int64_t*") LongPointer dims, @Cast(value="dnnl_data_type_t") int data_type, @Cast(value="dnnl_format_tag_t") int tag)
abc...
format tag, and the physical meaning of the dimensions depends on both
the primitive that consumes the memory and the context of that
consumption.memory_desc - Output memory descriptor.ndims - Number of dimensionsdims - Array of dimensions.data_type - Elements data type.tag - Memory format tag. Can be #dnnl_format_tag_any which would
allow a primitive to chose the final memory format. In this case the
format_kind field of the memory descriptor would be set to
#dnnl_format_kind_any.@Cast(value="dnnl_status_t") public static int dnnl_memory_desc_init_by_tag(dnnl_memory_desc_t memory_desc, int ndims, @Cast(value="const int64_t*") LongBuffer dims, @Cast(value="dnnl_data_type_t") int data_type, @Cast(value="dnnl_format_tag_t") int tag)
@Cast(value="dnnl_status_t") public static int dnnl_memory_desc_init_by_tag(dnnl_memory_desc_t memory_desc, int ndims, @Cast(value="const int64_t*") long[] dims, @Cast(value="dnnl_data_type_t") int data_type, @Cast(value="dnnl_format_tag_t") int tag)
@Cast(value="dnnl_status_t") public static int dnnl_memory_desc_init_submemory(dnnl_memory_desc_t memory_desc, @Const dnnl_memory_desc_t parent_memory_desc, @Cast(value="const int64_t*") LongPointer dims, @Cast(value="const int64_t*") LongPointer offsets)
memory_desc - Output memory descriptor.
/** @param parent_memory_desc An existing memory descriptor.
/** @param dims Sizes of the region.
/** @param offsets Offsets to the region from the encompassing
/** memory object in each dimension
/** @return #dnnl_success on success and a status describing the error
/** otherwise.@Cast(value="dnnl_status_t") public static int dnnl_memory_desc_init_submemory(dnnl_memory_desc_t memory_desc, @Const dnnl_memory_desc_t parent_memory_desc, @Cast(value="const int64_t*") LongBuffer dims, @Cast(value="const int64_t*") LongBuffer offsets)
@Cast(value="dnnl_status_t") public static int dnnl_memory_desc_init_submemory(dnnl_memory_desc_t memory_desc, @Const dnnl_memory_desc_t parent_memory_desc, @Cast(value="const int64_t*") long[] dims, @Cast(value="const int64_t*") long[] offsets)
@Cast(value="dnnl_status_t") public static int dnnl_memory_desc_reshape(dnnl_memory_desc_t out_memory_desc, @Const dnnl_memory_desc_t in_memory_desc, int ndims, @Cast(value="const int64_t*") LongPointer dims)
1. This is always possible.
2. Remove a dimension of size 1. This is possible only if the dimension
has no padding (i.e. padded_dims[dim] == dims[dim] && dims[dim] == 1).
3. Split a dimension into multiple ones. This is possible only if the size
of the dimension is exactly equal to the product of the split ones and
the dimension does not have padding (i.e.
padded_dims[dim] = dims[dim]).
4. Joining multiple consecutive dimensions into a single one. As in the
cases above, this requires that the dimensions do not have padding and
that the memory format is such that in physical memory these dimensions
are dense and have the same order as their logical counterparts. This
also assumes that these dimensions are not blocked.
- Here, dense means:
stride for dim[i] == (stride for dim[i + 1]) * dim[i + 1];
- And same order means:
i < j if and only if stride for dim[j] <= stride for dim[i].
\warning
Some combinations of physical memory layout and/or offsets or
dimensions may result in a failure to make a reshape.out_memory_desc - Output memory descriptor.in_memory_desc - An existing memory descriptor. Must have format_kind
set to #dnnl_blocked or #dnnl_format_kind_any.ndims - Number of dimensions for the output memory descriptor.dims - Dimensions for the output memory descriptor.@Cast(value="dnnl_status_t") public static int dnnl_memory_desc_reshape(dnnl_memory_desc_t out_memory_desc, @Const dnnl_memory_desc_t in_memory_desc, int ndims, @Cast(value="const int64_t*") LongBuffer dims)
@Cast(value="dnnl_status_t") public static int dnnl_memory_desc_reshape(dnnl_memory_desc_t out_memory_desc, @Const dnnl_memory_desc_t in_memory_desc, int ndims, @Cast(value="const int64_t*") long[] dims)
@Cast(value="dnnl_status_t") public static int dnnl_memory_desc_permute_axes(dnnl_memory_desc_t out_memory_desc, @Const dnnl_memory_desc_t in_memory_desc, @Const IntPointer permutation)
for (i: 0 .. in_memory_desc->ndims)
out_memory_desc->dims[permutation[i]] = in_memory_desc->dims[i];
Example:
dnnl_memory_desc_t in_md, out_md, expect_out_md;
const int permutation[] = {1, 0}; // swap the first and the second axes
dnnl_dims_t in_dims = {2, 3}, out_dims = {3, 2};
dnnl_format_tag_t in_tag = dnnl_ab, out_tag = dnnl_ba;
dnnl_memory_desc_init_by_tag(
&in_md, 2, in_dims, data_type, in_tag);
dnnl_memory_desc_init_by_tag(
&expect_out_md, 2, out_dims, data_type, out_tag);
dnnl_memory_desc_permute_axes(&out_md, in_md, permutation);
assert(dnnl_memory_desc_equal(&out_md, &expect_out_md));
out_memory_desc - Output memory descriptor.in_memory_desc - An existing memory descriptor. Must have format_kind
set to #dnnl_blocked or #dnnl_format_kind_any.permutation - Axes permutation (of size in_memory_desc->ndims).@Cast(value="dnnl_status_t") public static int dnnl_memory_desc_permute_axes(dnnl_memory_desc_t out_memory_desc, @Const dnnl_memory_desc_t in_memory_desc, @Const IntBuffer permutation)
@Cast(value="dnnl_status_t") public static int dnnl_memory_desc_permute_axes(dnnl_memory_desc_t out_memory_desc, @Const dnnl_memory_desc_t in_memory_desc, @Const int[] permutation)
public static int dnnl_memory_desc_equal(@Const dnnl_memory_desc_t lhs, @Const dnnl_memory_desc_t rhs)
lhs - Left-hand side of the comparison.rhs - Right-hand side of the comparison.@Cast(value="size_t") public static long dnnl_memory_desc_get_size(@Const dnnl_memory_desc_t memory_desc)
memory_desc - Memory descriptor.@Cast(value="dnnl_status_t") public static int dnnl_memory_create(@ByPtrPtr dnnl_memory memory, @Const dnnl_memory_desc_t memory_desc, dnnl_engine engine, Pointer handle)
memory - Output memory object.memory_desc - Memory descriptor.engine - Engine to use.handle - Handle of the memory buffer to use as an underlying storage.
- A pointer to the user-allocated buffer. In this case the library
doesn't own the buffer.
- The DNNL_MEMORY_ALLOCATE special value. Instructs the library to
allocate the buffer for the memory object. In this case the library
owns the buffer.
- DNNL_MEMORY_NONE to create dnnl_memory without an underlying buffer.dnnl_memory_set_data_handle()@Cast(value="dnnl_status_t") public static int dnnl_memory_create(@Cast(value="dnnl_memory_t*") PointerPointer memory, @Const dnnl_memory_desc_t memory_desc, dnnl_engine engine, Pointer handle)
@Cast(value="dnnl_status_t") public static int dnnl_memory_get_memory_desc(@Const dnnl_memory memory, @Cast(value="const dnnl_memory_desc_t**") PointerPointer memory_desc)
memory - Memory object.memory_desc - Output memory descriptor (a copy).@Cast(value="dnnl_status_t") public static int dnnl_memory_get_memory_desc(@Const dnnl_memory memory, @Const @ByPtrPtr dnnl_memory_desc_t memory_desc)
@Cast(value="dnnl_status_t") public static int dnnl_memory_get_engine(@Const dnnl_memory memory, @ByPtrPtr dnnl_engine engine)
memory - Memory object.engine - Output engine on which the memory is located.@Cast(value="dnnl_status_t") public static int dnnl_memory_get_engine(@Const dnnl_memory memory, @Cast(value="dnnl_engine_t*") PointerPointer engine)
@Cast(value="dnnl_status_t") public static int dnnl_memory_map_data(@Const dnnl_memory memory, @Cast(value="void**") PointerPointer mapped_ptr)
memory - Memory object.mapped_ptr - Output pointer to the mapped buffer.@Cast(value="dnnl_status_t") public static int dnnl_memory_map_data(@Const dnnl_memory memory, @Cast(value="void**") @ByPtrPtr Pointer mapped_ptr)
@Cast(value="dnnl_status_t") public static int dnnl_memory_unmap_data(@Const dnnl_memory memory, Pointer mapped_ptr)
memory - Memory object.mapped_ptr - Pointer to the mapped buffer that must have been
obtained using the dnnl_memory_map_data() function.@Cast(value="dnnl_status_t") public static int dnnl_memory_get_data_handle(@Const dnnl_memory memory, @Cast(value="void**") PointerPointer handle)
memory - Memory object.handle - Output data handle. For the CPU engine, the data handle is a
pointer to the actual data. For OpenCL it is a cl_mem.@Cast(value="dnnl_status_t") public static int dnnl_memory_get_data_handle(@Const dnnl_memory memory, @Cast(value="void**") @ByPtrPtr Pointer handle)
@Cast(value="dnnl_status_t") public static int dnnl_memory_set_data_handle(dnnl_memory memory, Pointer handle)
memory - Memory object.handle - Data handle. For the CPU engine, the data handle is a
pointer to the actual data. For OpenCL it is a cl_mem.@Cast(value="dnnl_status_t") public static int dnnl_memory_set_data_handle_v2(dnnl_memory memory, Pointer handle, dnnl_stream stream)
memory - Memory object.handle - Data handle. For the CPU engine, the data handle is a
pointer to the actual data. For OpenCL it is a cl_mem.stream - Stream to use to execute padding in.@Cast(value="dnnl_status_t") public static int dnnl_memory_destroy(dnnl_memory memory)
memory - Memory object to destroy.@Cast(value="dnnl_status_t") public static int dnnl_reorder_primitive_desc_create(@ByPtrPtr dnnl_primitive_desc reorder_primitive_desc, @Const dnnl_memory_desc_t src_desc, dnnl_engine src_engine, @Const dnnl_memory_desc_t dst_desc, dnnl_engine dst_engine, @Const dnnl_primitive_attr attr)
\addtogroup dnnl_api_primitives \{
\addtogroup dnnl_api_reorder \{
Creates a primitive descriptor for a reorder primitive.
reorder_primitive_desc - Output primitive descriptor.src_desc - Source memory descriptor.src_engine - Engine on which the source memory object will be
located.dst_desc - Destination memory descriptor.dst_engine - Engine on which the destination memory object
will be located.attr - Primitive attributes to use (can be NULL).@Cast(value="dnnl_status_t") public static int dnnl_reorder_primitive_desc_create(@Cast(value="dnnl_primitive_desc_t*") PointerPointer reorder_primitive_desc, @Const dnnl_memory_desc_t src_desc, dnnl_engine src_engine, @Const dnnl_memory_desc_t dst_desc, dnnl_engine dst_engine, @Const dnnl_primitive_attr attr)
@Cast(value="dnnl_status_t") public static int dnnl_concat_primitive_desc_create(@ByPtrPtr dnnl_primitive_desc concat_primitive_desc, @Const dnnl_memory_desc_t dst_desc, int n, int concat_dimension, @Const dnnl_memory_desc_t src_descs, @Const dnnl_primitive_attr attr, dnnl_engine engine)
\addtogroup dnnl_api_concat \{
Creates a primitive descriptor for an out-of-place concatenation primitive.
concat_primitive_desc - Output primitive descriptor.dst_desc - Destination memory descriptor.n - Number of source parameters.concat_dimension - Source tensors will be concatenated over
dimension with this index. Note that order of dimensions does
not depend on memory format.src_descs - Array of source memory descriptors with \p n elements.attr - Primitive attributes to use (can be NULL).engine - Engine to use.@Cast(value="dnnl_status_t") public static int dnnl_concat_primitive_desc_create(@Cast(value="dnnl_primitive_desc_t*") PointerPointer concat_primitive_desc, @Const dnnl_memory_desc_t dst_desc, int n, int concat_dimension, @Const dnnl_memory_desc_t src_descs, @Const dnnl_primitive_attr attr, dnnl_engine engine)
@Cast(value="dnnl_status_t") public static int dnnl_sum_primitive_desc_create(@ByPtrPtr dnnl_primitive_desc sum_primitive_desc, @Const dnnl_memory_desc_t dst_desc, int n, @Const FloatPointer scales, @Const dnnl_memory_desc_t src_descs, @Const dnnl_primitive_attr attr, dnnl_engine engine)
\addtogroup dnnl_api_sum \{
Creates a primitive descriptor for an (out-of-place) sum primitive.
sum_primitive_desc - Output primitive descriptor.dst_desc - Destination memory descriptor.n - Number of source parameters.scales - Vector of scales to multiply data in each source
memory by.src_descs - Array of source memory descriptors having \p n elements.attr - Primitive attributes to use (can be NULL).engine - Engine to use.@Cast(value="dnnl_status_t") public static int dnnl_sum_primitive_desc_create(@Cast(value="dnnl_primitive_desc_t*") PointerPointer sum_primitive_desc, @Const dnnl_memory_desc_t dst_desc, int n, @Const FloatBuffer scales, @Const dnnl_memory_desc_t src_descs, @Const dnnl_primitive_attr attr, dnnl_engine engine)
@Cast(value="dnnl_status_t") public static int dnnl_sum_primitive_desc_create(@ByPtrPtr dnnl_primitive_desc sum_primitive_desc, @Const dnnl_memory_desc_t dst_desc, int n, @Const float[] scales, @Const dnnl_memory_desc_t src_descs, @Const dnnl_primitive_attr attr, dnnl_engine engine)
@Cast(value="dnnl_status_t") public static int dnnl_sum_primitive_desc_create(@Cast(value="dnnl_primitive_desc_t*") PointerPointer sum_primitive_desc, @Const dnnl_memory_desc_t dst_desc, int n, @Const FloatPointer scales, @Const dnnl_memory_desc_t src_descs, @Const dnnl_primitive_attr attr, dnnl_engine engine)
@Cast(value="dnnl_status_t") public static int dnnl_sum_primitive_desc_create(@ByPtrPtr dnnl_primitive_desc sum_primitive_desc, @Const dnnl_memory_desc_t dst_desc, int n, @Const FloatBuffer scales, @Const dnnl_memory_desc_t src_descs, @Const dnnl_primitive_attr attr, dnnl_engine engine)
@Cast(value="dnnl_status_t") public static int dnnl_sum_primitive_desc_create(@Cast(value="dnnl_primitive_desc_t*") PointerPointer sum_primitive_desc, @Const dnnl_memory_desc_t dst_desc, int n, @Const float[] scales, @Const dnnl_memory_desc_t src_descs, @Const dnnl_primitive_attr attr, dnnl_engine engine)
@Cast(value="dnnl_status_t") public static int dnnl_binary_desc_init(dnnl_binary_desc_t binary_desc, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t src0_desc, @Const dnnl_memory_desc_t src1_desc, @Const dnnl_memory_desc_t dst_desc)
\addtogroup dnnl_api_binary \{
Initializes a descriptor for a binary primitive. \note Memory descriptor \p dst_desc is allowed to be initialized with #dnnl_format_tag_any or with format_kind set to #dnnl_format_kind_any. \note Both memory descriptors must have the same number of dimensions. Element broadcasting is supported for memory descriptor \p src1_desc and are applied to \ src1_desc dimensions that have size equal to 1.
binary_desc - Output descriptor for a binary primitive.alg_kind - Algorithm kind. Valid values are #dnnl_binary_add,
#dnnl_binary_mul, #dnnl_binary_max and #dnnl_binary_min.src0_desc - Source 0 memory descriptor.src1_desc - Source 1 memory descriptor.dst_desc - Destination memory descriptor.@Cast(value="dnnl_status_t") public static int dnnl_convolution_forward_desc_init(dnnl_convolution_desc_t conv_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t src_desc, @Const dnnl_memory_desc_t weights_desc, @Const dnnl_memory_desc_t bias_desc, @Const dnnl_memory_desc_t dst_desc, @Cast(value="const int64_t*") LongPointer strides, @Cast(value="const int64_t*") LongPointer padding_l, @Cast(value="const int64_t*") LongPointer padding_r)
\addtogroup dnnl_api_convolution \{
Initializes a descriptor for a convolution forward propagation primitive. \note Memory descriptors can be initialized with #dnnl_format_tag_any or with format_kind set to #dnnl_format_kind_any. Arrays \p strides, \p padding_l, and \p padding_r contain values for spatial dimensions only and hence must have the same number of elements as there are spatial dimensions. The order of values is the same as in the tensor: depth (for 3D tensors), height (for 3D and 2D tensors), and width.
conv_desc - Output descriptor for a convolution primitive.prop_kind - Propagation kind. Possible values are
#dnnl_forward_training and #dnnl_forward_inference.alg_kind - Convolution algorithm. Possible values are
#dnnl_convolution_direct, #dnnl_convolution_winograd,
#dnnl_convolution_auto.src_desc - Source memory descriptor.weights_desc - Weights memory descriptor.bias_desc - Bias memory descriptor. Passing NULL, a zero memory
descriptor, or a memory descriptor with format_kind set to
#dnnl_format_kind_undef disables the bias term.dst_desc - Destination memory descriptor.strides - Array of strides for spatial dimension.padding_l - Array of padding values for low indices for each spatial
dimension ([[front,] top,] left).padding_r - Array of padding values for high indices for each spatial
dimension ([[back,] bottom,] right). Can be NULL in which case
padding is assumed to be symmetrical.@Cast(value="dnnl_status_t") public static int dnnl_convolution_forward_desc_init(dnnl_convolution_desc_t conv_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t src_desc, @Const dnnl_memory_desc_t weights_desc, @Const dnnl_memory_desc_t bias_desc, @Const dnnl_memory_desc_t dst_desc, @Cast(value="const int64_t*") LongBuffer strides, @Cast(value="const int64_t*") LongBuffer padding_l, @Cast(value="const int64_t*") LongBuffer padding_r)
@Cast(value="dnnl_status_t") public static int dnnl_convolution_forward_desc_init(dnnl_convolution_desc_t conv_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t src_desc, @Const dnnl_memory_desc_t weights_desc, @Const dnnl_memory_desc_t bias_desc, @Const dnnl_memory_desc_t dst_desc, @Cast(value="const int64_t*") long[] strides, @Cast(value="const int64_t*") long[] padding_l, @Cast(value="const int64_t*") long[] padding_r)
@Cast(value="dnnl_status_t") public static int dnnl_dilated_convolution_forward_desc_init(dnnl_convolution_desc_t conv_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t src_desc, @Const dnnl_memory_desc_t weights_desc, @Const dnnl_memory_desc_t bias_desc, @Const dnnl_memory_desc_t dst_desc, @Cast(value="const int64_t*") LongPointer strides, @Cast(value="const int64_t*") LongPointer dilates, @Cast(value="const int64_t*") LongPointer padding_l, @Cast(value="const int64_t*") LongPointer padding_r)
conv_desc - Output descriptor for a convolution primitive.prop_kind - Propagation kind. Possible values are
#dnnl_forward_training and #dnnl_forward_inference.alg_kind - Convolution algorithm. Possible values are
#dnnl_convolution_direct, #dnnl_convolution_winograd,
#dnnl_convolution_auto.src_desc - Source memory descriptor.weights_desc - Weights memory descriptor.bias_desc - Bias memory descriptor. Passing NULL, a zero memory
descriptor, or a memory descriptor with format_kind set to
#dnnl_format_kind_undef disables the bias term.dst_desc - Destination memory descriptor.strides - Array of strides for spatial dimension.dilates - Array of dilations for spatial dimension. A zero value
means no dilation in the corresponding dimension.padding_l - Array of padding values for low indices for each spatial
dimension ([[front,] top,] left).padding_r - Array of padding values for high indices for each spatial
dimension ([[back,] bottom,] right). Can be NULL in which case
padding is considered to be symmetrical.@Cast(value="dnnl_status_t") public static int dnnl_dilated_convolution_forward_desc_init(dnnl_convolution_desc_t conv_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t src_desc, @Const dnnl_memory_desc_t weights_desc, @Const dnnl_memory_desc_t bias_desc, @Const dnnl_memory_desc_t dst_desc, @Cast(value="const int64_t*") LongBuffer strides, @Cast(value="const int64_t*") LongBuffer dilates, @Cast(value="const int64_t*") LongBuffer padding_l, @Cast(value="const int64_t*") LongBuffer padding_r)
@Cast(value="dnnl_status_t") public static int dnnl_dilated_convolution_forward_desc_init(dnnl_convolution_desc_t conv_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t src_desc, @Const dnnl_memory_desc_t weights_desc, @Const dnnl_memory_desc_t bias_desc, @Const dnnl_memory_desc_t dst_desc, @Cast(value="const int64_t*") long[] strides, @Cast(value="const int64_t*") long[] dilates, @Cast(value="const int64_t*") long[] padding_l, @Cast(value="const int64_t*") long[] padding_r)
@Cast(value="dnnl_status_t") public static int dnnl_convolution_backward_data_desc_init(dnnl_convolution_desc_t conv_desc, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t diff_src_desc, @Const dnnl_memory_desc_t weights_desc, @Const dnnl_memory_desc_t diff_dst_desc, @Cast(value="const int64_t*") LongPointer strides, @Cast(value="const int64_t*") LongPointer padding_l, @Cast(value="const int64_t*") LongPointer padding_r)
conv_desc - Output descriptor for a convolution primitive.alg_kind - Convolution algorithm. Possible values are
#dnnl_convolution_direct, #dnnl_convolution_winograd,
#dnnl_convolution_auto.diff_src_desc - Diff source memory descriptor.weights_desc - Weights memory descriptor.diff_dst_desc - Diff destination memory descriptor.strides - Array of strides for spatial dimension.padding_l - Array of padding values for low indices for each spatial
dimension ([[front,] top,] left).padding_r - Array of padding values for high indices for each spatial
dimension ([[back,] bottom,] right). Can be NULL in which case
padding is assumed to be symmetrical.@Cast(value="dnnl_status_t") public static int dnnl_convolution_backward_data_desc_init(dnnl_convolution_desc_t conv_desc, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t diff_src_desc, @Const dnnl_memory_desc_t weights_desc, @Const dnnl_memory_desc_t diff_dst_desc, @Cast(value="const int64_t*") LongBuffer strides, @Cast(value="const int64_t*") LongBuffer padding_l, @Cast(value="const int64_t*") LongBuffer padding_r)
@Cast(value="dnnl_status_t") public static int dnnl_convolution_backward_data_desc_init(dnnl_convolution_desc_t conv_desc, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t diff_src_desc, @Const dnnl_memory_desc_t weights_desc, @Const dnnl_memory_desc_t diff_dst_desc, @Cast(value="const int64_t*") long[] strides, @Cast(value="const int64_t*") long[] padding_l, @Cast(value="const int64_t*") long[] padding_r)
@Cast(value="dnnl_status_t") public static int dnnl_dilated_convolution_backward_data_desc_init(dnnl_convolution_desc_t conv_desc, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t diff_src_desc, @Const dnnl_memory_desc_t weights_desc, @Const dnnl_memory_desc_t diff_dst_desc, @Cast(value="const int64_t*") LongPointer strides, @Cast(value="const int64_t*") LongPointer dilates, @Cast(value="const int64_t*") LongPointer padding_l, @Cast(value="const int64_t*") LongPointer padding_r)
conv_desc - Output descriptor for a convolution primitive.alg_kind - Convolution algorithm. Possible values are
#dnnl_convolution_direct, #dnnl_convolution_winograd,
#dnnl_convolution_auto.diff_src_desc - Diff source memory descriptor.weights_desc - Weights memory descriptor.diff_dst_desc - Diff destination memory descriptor.strides - Array of strides for spatial dimension.dilates - Array of dilations for spatial dimension. A zero value
means no dilation in the corresponding dimension.padding_l - Array of padding values for low indices for each spatial
dimension ([[front,] top,] left).padding_r - Array of padding values for high indices for each spatial
dimension ([[back,] bottom,] right). Can be NULL in which case
padding is considered to be symmetrical.@Cast(value="dnnl_status_t") public static int dnnl_dilated_convolution_backward_data_desc_init(dnnl_convolution_desc_t conv_desc, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t diff_src_desc, @Const dnnl_memory_desc_t weights_desc, @Const dnnl_memory_desc_t diff_dst_desc, @Cast(value="const int64_t*") LongBuffer strides, @Cast(value="const int64_t*") LongBuffer dilates, @Cast(value="const int64_t*") LongBuffer padding_l, @Cast(value="const int64_t*") LongBuffer padding_r)
@Cast(value="dnnl_status_t") public static int dnnl_dilated_convolution_backward_data_desc_init(dnnl_convolution_desc_t conv_desc, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t diff_src_desc, @Const dnnl_memory_desc_t weights_desc, @Const dnnl_memory_desc_t diff_dst_desc, @Cast(value="const int64_t*") long[] strides, @Cast(value="const int64_t*") long[] dilates, @Cast(value="const int64_t*") long[] padding_l, @Cast(value="const int64_t*") long[] padding_r)
@Cast(value="dnnl_status_t") public static int dnnl_convolution_backward_weights_desc_init(dnnl_convolution_desc_t conv_desc, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t src_desc, @Const dnnl_memory_desc_t diff_weights_desc, @Const dnnl_memory_desc_t diff_bias_desc, @Const dnnl_memory_desc_t diff_dst_desc, @Cast(value="const int64_t*") LongPointer strides, @Cast(value="const int64_t*") LongPointer padding_l, @Cast(value="const int64_t*") LongPointer padding_r)
conv_desc - Output descriptor for a convolution primitive.alg_kind - Convolution algorithm. Possible values are
#dnnl_convolution_direct, #dnnl_convolution_winograd,
#dnnl_convolution_auto.src_desc - Source memory descriptor.diff_weights_desc - Diff weights memory descriptor.diff_bias_desc - Diff bias memory descriptor. Passing NULL, a zero
memory descriptor, or a memory descriptor with format_kind set to
#dnnl_format_kind_undef disables the bias term.diff_dst_desc - Diff destination memory descriptor.strides - Array of strides for spatial dimension.padding_l - Array of padding values for low indices for each spatial
dimension ([[front,] top,] left).padding_r - Array of padding values for high indices for each spatial
dimension ([[back,] bottom,] right). Can be NULL in which case
padding is considered to be symmetrical.@Cast(value="dnnl_status_t") public static int dnnl_convolution_backward_weights_desc_init(dnnl_convolution_desc_t conv_desc, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t src_desc, @Const dnnl_memory_desc_t diff_weights_desc, @Const dnnl_memory_desc_t diff_bias_desc, @Const dnnl_memory_desc_t diff_dst_desc, @Cast(value="const int64_t*") LongBuffer strides, @Cast(value="const int64_t*") LongBuffer padding_l, @Cast(value="const int64_t*") LongBuffer padding_r)
@Cast(value="dnnl_status_t") public static int dnnl_convolution_backward_weights_desc_init(dnnl_convolution_desc_t conv_desc, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t src_desc, @Const dnnl_memory_desc_t diff_weights_desc, @Const dnnl_memory_desc_t diff_bias_desc, @Const dnnl_memory_desc_t diff_dst_desc, @Cast(value="const int64_t*") long[] strides, @Cast(value="const int64_t*") long[] padding_l, @Cast(value="const int64_t*") long[] padding_r)
@Cast(value="dnnl_status_t") public static int dnnl_dilated_convolution_backward_weights_desc_init(dnnl_convolution_desc_t conv_desc, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t src_desc, @Const dnnl_memory_desc_t diff_weights_desc, @Const dnnl_memory_desc_t diff_bias_desc, @Const dnnl_memory_desc_t diff_dst_desc, @Cast(value="const int64_t*") LongPointer strides, @Cast(value="const int64_t*") LongPointer dilates, @Cast(value="const int64_t*") LongPointer padding_l, @Cast(value="const int64_t*") LongPointer padding_r)
conv_desc - Output descriptor for a convolution primitive.alg_kind - Convolution algorithm. Possible values are
#dnnl_convolution_direct, #dnnl_convolution_winograd,
#dnnl_convolution_auto.src_desc - Source memory descriptor.diff_weights_desc - Diff weights memory descriptor.diff_bias_desc - Diff bias memory descriptor. Passing NULL, a zero
memory descriptor, or a memory descriptor with format_kind set to
#dnnl_format_kind_undef disables the bias term.diff_dst_desc - Diff destination memory descriptor.strides - Array of strides for spatial dimension.dilates - Array of dilations for spatial dimension. A zero value
means no dilation in the corresponding dimension.padding_l - Array of padding values for low indices for each spatial
dimension ([[front,] top,] left).padding_r - Array of padding values for high indices for each spatial
dimension ([[back,] bottom,] right). Can be NULL in which case
padding is considered to be symmetrical.@Cast(value="dnnl_status_t") public static int dnnl_dilated_convolution_backward_weights_desc_init(dnnl_convolution_desc_t conv_desc, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t src_desc, @Const dnnl_memory_desc_t diff_weights_desc, @Const dnnl_memory_desc_t diff_bias_desc, @Const dnnl_memory_desc_t diff_dst_desc, @Cast(value="const int64_t*") LongBuffer strides, @Cast(value="const int64_t*") LongBuffer dilates, @Cast(value="const int64_t*") LongBuffer padding_l, @Cast(value="const int64_t*") LongBuffer padding_r)
@Cast(value="dnnl_status_t") public static int dnnl_dilated_convolution_backward_weights_desc_init(dnnl_convolution_desc_t conv_desc, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t src_desc, @Const dnnl_memory_desc_t diff_weights_desc, @Const dnnl_memory_desc_t diff_bias_desc, @Const dnnl_memory_desc_t diff_dst_desc, @Cast(value="const int64_t*") long[] strides, @Cast(value="const int64_t*") long[] dilates, @Cast(value="const int64_t*") long[] padding_l, @Cast(value="const int64_t*") long[] padding_r)
@Cast(value="dnnl_status_t") public static int dnnl_deconvolution_forward_desc_init(@Cast(value="dnnl_deconvolution_desc_t*") dnnl_convolution_desc_t deconv_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t src_desc, @Const dnnl_memory_desc_t weights_desc, @Const dnnl_memory_desc_t bias_desc, @Const dnnl_memory_desc_t dst_desc, @Cast(value="const int64_t*") LongPointer strides, @Cast(value="const int64_t*") LongPointer padding_l, @Cast(value="const int64_t*") LongPointer padding_r)
\addtogroup dnnl_api_deconvolution \{
Initializes a descriptor for a deconvolution forward propagation primitive. \note Memory descriptors can be initialized with #dnnl_format_tag_any or with format_kind set to #dnnl_format_kind_any. Arrays \p strides, \p padding_l, and \p padding_r contain values for spatial dimensions only and hence must have the same number of elements as there are spatial dimensions. The order of values is the same as in the tensor: depth (for 3D tensors), height (for 3D and 2D tensors), and width.
deconv_desc - Output descriptor for a deconvolution primitive.prop_kind - Propagation kind. Possible values are
#dnnl_forward_training and #dnnl_forward_inference.alg_kind - Deconvolution algorithm. Possible values are
#dnnl_deconvolution_direct, #dnnl_deconvolution_winograd.src_desc - Source memory descriptor.weights_desc - Weights memory descriptor.bias_desc - Bias memory descriptor. Passing NULL, a zero memory
descriptor, or a memory descriptor with format_kind set to
#dnnl_format_kind_undef disables the bias term.dst_desc - Destination memory descriptor.strides - Array of strides for spatial dimension.padding_l - Array of padding values for low indices for each spatial
dimension ([[front,] top,] left).padding_r - Array of padding values for high indices for each spatial
dimension ([[back,] bottom,] right). Can be NULL in which case
padding is considered to be symmetrical.@Cast(value="dnnl_status_t") public static int dnnl_deconvolution_forward_desc_init(@Cast(value="dnnl_deconvolution_desc_t*") dnnl_convolution_desc_t deconv_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t src_desc, @Const dnnl_memory_desc_t weights_desc, @Const dnnl_memory_desc_t bias_desc, @Const dnnl_memory_desc_t dst_desc, @Cast(value="const int64_t*") LongBuffer strides, @Cast(value="const int64_t*") LongBuffer padding_l, @Cast(value="const int64_t*") LongBuffer padding_r)
@Cast(value="dnnl_status_t") public static int dnnl_deconvolution_forward_desc_init(@Cast(value="dnnl_deconvolution_desc_t*") dnnl_convolution_desc_t deconv_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t src_desc, @Const dnnl_memory_desc_t weights_desc, @Const dnnl_memory_desc_t bias_desc, @Const dnnl_memory_desc_t dst_desc, @Cast(value="const int64_t*") long[] strides, @Cast(value="const int64_t*") long[] padding_l, @Cast(value="const int64_t*") long[] padding_r)
@Cast(value="dnnl_status_t") public static int dnnl_dilated_deconvolution_forward_desc_init(@Cast(value="dnnl_deconvolution_desc_t*") dnnl_convolution_desc_t deconv_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t src_desc, @Const dnnl_memory_desc_t weights_desc, @Const dnnl_memory_desc_t bias_desc, @Const dnnl_memory_desc_t dst_desc, @Cast(value="const int64_t*") LongPointer strides, @Cast(value="const int64_t*") LongPointer dilates, @Cast(value="const int64_t*") LongPointer padding_l, @Cast(value="const int64_t*") LongPointer padding_r)
deconv_desc - Output descriptor for a deconvolution primitive.prop_kind - Propagation kind. Possible values are
#dnnl_forward_training and #dnnl_forward_inference.alg_kind - Deconvolution algorithm. Possible values are
#dnnl_deconvolution_direct, #dnnl_deconvolution_winograd.src_desc - Source memory descriptor.weights_desc - Weights memory descriptor.bias_desc - Bias memory descriptor. Passing NULL, a zero memory
descriptor, or a memory descriptor with format_kind set to
#dnnl_format_kind_undef disables the bias term.dst_desc - Destination memory descriptor.strides - Array of strides for spatial dimension.dilates - Array of dilations for spatial dimension. A zero value
means no dilation in the corresponding dimension.padding_l - Array of padding values for low indices for each spatial
dimension ([[front,] top,] left).padding_r - Array of padding values for high indices for each spatial
dimension ([[back,] bottom,] right). Can be NULL in which case
padding is considered to be symmetrical.@Cast(value="dnnl_status_t") public static int dnnl_dilated_deconvolution_forward_desc_init(@Cast(value="dnnl_deconvolution_desc_t*") dnnl_convolution_desc_t deconv_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t src_desc, @Const dnnl_memory_desc_t weights_desc, @Const dnnl_memory_desc_t bias_desc, @Const dnnl_memory_desc_t dst_desc, @Cast(value="const int64_t*") LongBuffer strides, @Cast(value="const int64_t*") LongBuffer dilates, @Cast(value="const int64_t*") LongBuffer padding_l, @Cast(value="const int64_t*") LongBuffer padding_r)
@Cast(value="dnnl_status_t") public static int dnnl_dilated_deconvolution_forward_desc_init(@Cast(value="dnnl_deconvolution_desc_t*") dnnl_convolution_desc_t deconv_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t src_desc, @Const dnnl_memory_desc_t weights_desc, @Const dnnl_memory_desc_t bias_desc, @Const dnnl_memory_desc_t dst_desc, @Cast(value="const int64_t*") long[] strides, @Cast(value="const int64_t*") long[] dilates, @Cast(value="const int64_t*") long[] padding_l, @Cast(value="const int64_t*") long[] padding_r)
@Cast(value="dnnl_status_t") public static int dnnl_deconvolution_backward_data_desc_init(@Cast(value="dnnl_deconvolution_desc_t*") dnnl_convolution_desc_t deconv_desc, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t diff_src_desc, @Const dnnl_memory_desc_t weights_desc, @Const dnnl_memory_desc_t diff_dst_desc, @Cast(value="const int64_t*") LongPointer strides, @Cast(value="const int64_t*") LongPointer padding_l, @Cast(value="const int64_t*") LongPointer padding_r)
deconv_desc - Output descriptor for a deconvolution primitive.alg_kind - Deconvolution algorithm. Possible values are
#dnnl_deconvolution_direct, #dnnl_deconvolution_winograd.diff_src_desc - Diff source memory descriptor.weights_desc - Weights memory descriptor.diff_dst_desc - Diff destination memory descriptor.strides - Array of strides for spatial dimension.padding_l - Array of padding values for low indices for each spatial
dimension ([[front,] top,] left).padding_r - Array of padding values for high indices for each spatial
dimension ([[back,] bottom,] right). Can be NULL in which case
padding is considered to be symmetrical.@Cast(value="dnnl_status_t") public static int dnnl_deconvolution_backward_data_desc_init(@Cast(value="dnnl_deconvolution_desc_t*") dnnl_convolution_desc_t deconv_desc, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t diff_src_desc, @Const dnnl_memory_desc_t weights_desc, @Const dnnl_memory_desc_t diff_dst_desc, @Cast(value="const int64_t*") LongBuffer strides, @Cast(value="const int64_t*") LongBuffer padding_l, @Cast(value="const int64_t*") LongBuffer padding_r)
@Cast(value="dnnl_status_t") public static int dnnl_deconvolution_backward_data_desc_init(@Cast(value="dnnl_deconvolution_desc_t*") dnnl_convolution_desc_t deconv_desc, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t diff_src_desc, @Const dnnl_memory_desc_t weights_desc, @Const dnnl_memory_desc_t diff_dst_desc, @Cast(value="const int64_t*") long[] strides, @Cast(value="const int64_t*") long[] padding_l, @Cast(value="const int64_t*") long[] padding_r)
@Cast(value="dnnl_status_t") public static int dnnl_dilated_deconvolution_backward_data_desc_init(@Cast(value="dnnl_deconvolution_desc_t*") dnnl_convolution_desc_t deconv_desc, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t diff_src_desc, @Const dnnl_memory_desc_t weights_desc, @Const dnnl_memory_desc_t diff_dst_desc, @Cast(value="const int64_t*") LongPointer strides, @Cast(value="const int64_t*") LongPointer dilates, @Cast(value="const int64_t*") LongPointer padding_l, @Cast(value="const int64_t*") LongPointer padding_r)
deconv_desc - Output descriptor for a deconvolution primitive.alg_kind - Deconvolution algorithm. Possible values are
#dnnl_deconvolution_direct, #dnnl_deconvolution_winograd.diff_src_desc - Diff source memory descriptor.weights_desc - Weights memory descriptor.diff_dst_desc - Diff destination memory descriptor.strides - Array of strides for spatial dimension.dilates - Array of dilations for spatial dimension. A zero value
means no dilation in the corresponding dimension.padding_l - Array of padding values for low indices for each spatial
dimension ([[front,] top,] left).padding_r - Array of padding values for high indices for each spatial
dimension ([[back,] bottom,] right). Can be NULL in which case
padding is considered to be symmetrical.@Cast(value="dnnl_status_t") public static int dnnl_dilated_deconvolution_backward_data_desc_init(@Cast(value="dnnl_deconvolution_desc_t*") dnnl_convolution_desc_t deconv_desc, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t diff_src_desc, @Const dnnl_memory_desc_t weights_desc, @Const dnnl_memory_desc_t diff_dst_desc, @Cast(value="const int64_t*") LongBuffer strides, @Cast(value="const int64_t*") LongBuffer dilates, @Cast(value="const int64_t*") LongBuffer padding_l, @Cast(value="const int64_t*") LongBuffer padding_r)
@Cast(value="dnnl_status_t") public static int dnnl_dilated_deconvolution_backward_data_desc_init(@Cast(value="dnnl_deconvolution_desc_t*") dnnl_convolution_desc_t deconv_desc, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t diff_src_desc, @Const dnnl_memory_desc_t weights_desc, @Const dnnl_memory_desc_t diff_dst_desc, @Cast(value="const int64_t*") long[] strides, @Cast(value="const int64_t*") long[] dilates, @Cast(value="const int64_t*") long[] padding_l, @Cast(value="const int64_t*") long[] padding_r)
@Cast(value="dnnl_status_t") public static int dnnl_deconvolution_backward_weights_desc_init(@Cast(value="dnnl_deconvolution_desc_t*") dnnl_convolution_desc_t deconv_desc, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t src_desc, @Const dnnl_memory_desc_t diff_weights_desc, @Const dnnl_memory_desc_t diff_bias_desc, @Const dnnl_memory_desc_t diff_dst_desc, @Cast(value="const int64_t*") LongPointer strides, @Cast(value="const int64_t*") LongPointer padding_l, @Cast(value="const int64_t*") LongPointer padding_r)
deconv_desc - Output descriptor for a deconvolution primitive.alg_kind - Deconvolution algorithm. Possible values are
#dnnl_deconvolution_direct, #dnnl_deconvolution_winograd.src_desc - Source memory descriptor.diff_weights_desc - Diff weights memory descriptor.diff_bias_desc - Diff bias memory descriptor. Passing NULL, a zero
memory descriptor, or a memory descriptor with format_kind set to
#dnnl_format_kind_undef disables the bias term.diff_dst_desc - Diff destination memory descriptor.strides - Array of strides for spatial dimension.padding_l - Array of padding values for low indices for each spatial
dimension ([[front,] top,] left).padding_r - Array of padding values for high indices for each spatial
dimension ([[back,] bottom,] right). Can be NULL in which case
padding is considered to be symmetrical.@Cast(value="dnnl_status_t") public static int dnnl_deconvolution_backward_weights_desc_init(@Cast(value="dnnl_deconvolution_desc_t*") dnnl_convolution_desc_t deconv_desc, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t src_desc, @Const dnnl_memory_desc_t diff_weights_desc, @Const dnnl_memory_desc_t diff_bias_desc, @Const dnnl_memory_desc_t diff_dst_desc, @Cast(value="const int64_t*") LongBuffer strides, @Cast(value="const int64_t*") LongBuffer padding_l, @Cast(value="const int64_t*") LongBuffer padding_r)
@Cast(value="dnnl_status_t") public static int dnnl_deconvolution_backward_weights_desc_init(@Cast(value="dnnl_deconvolution_desc_t*") dnnl_convolution_desc_t deconv_desc, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t src_desc, @Const dnnl_memory_desc_t diff_weights_desc, @Const dnnl_memory_desc_t diff_bias_desc, @Const dnnl_memory_desc_t diff_dst_desc, @Cast(value="const int64_t*") long[] strides, @Cast(value="const int64_t*") long[] padding_l, @Cast(value="const int64_t*") long[] padding_r)
@Cast(value="dnnl_status_t") public static int dnnl_dilated_deconvolution_backward_weights_desc_init(@Cast(value="dnnl_deconvolution_desc_t*") dnnl_convolution_desc_t deconv_desc, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t src_desc, @Const dnnl_memory_desc_t diff_weights_desc, @Const dnnl_memory_desc_t diff_bias_desc, @Const dnnl_memory_desc_t diff_dst_desc, @Cast(value="const int64_t*") LongPointer strides, @Cast(value="const int64_t*") LongPointer dilates, @Cast(value="const int64_t*") LongPointer padding_l, @Cast(value="const int64_t*") LongPointer padding_r)
deconv_desc - Output descriptor for a deconvolution primitive.alg_kind - Deconvolution algorithm. Possible values are
#dnnl_deconvolution_direct, #dnnl_deconvolution_winograd.src_desc - Source memory descriptor.diff_weights_desc - Diff weights memory descriptor.diff_bias_desc - Diff bias memory descriptor. Passing NULL, a zero
memory descriptor, or a memory descriptor with format_kind set to
#dnnl_format_kind_undef disables the bias term.diff_dst_desc - Diff destination memory descriptor.strides - Array of strides for spatial dimension.dilates - Array of dilations for spatial dimension. A zero value
means no dilation in the corresponding dimension.padding_l - Array of padding values for low indices for each spatial
dimension ([[front,] top,] left).padding_r - Array of padding values for high indices for each spatial
dimension ([[back,] bottom,] right). Can be NULL in which case
padding is considered to be symmetrical.@Cast(value="dnnl_status_t") public static int dnnl_dilated_deconvolution_backward_weights_desc_init(@Cast(value="dnnl_deconvolution_desc_t*") dnnl_convolution_desc_t deconv_desc, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t src_desc, @Const dnnl_memory_desc_t diff_weights_desc, @Const dnnl_memory_desc_t diff_bias_desc, @Const dnnl_memory_desc_t diff_dst_desc, @Cast(value="const int64_t*") LongBuffer strides, @Cast(value="const int64_t*") LongBuffer dilates, @Cast(value="const int64_t*") LongBuffer padding_l, @Cast(value="const int64_t*") LongBuffer padding_r)
@Cast(value="dnnl_status_t") public static int dnnl_dilated_deconvolution_backward_weights_desc_init(@Cast(value="dnnl_deconvolution_desc_t*") dnnl_convolution_desc_t deconv_desc, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t src_desc, @Const dnnl_memory_desc_t diff_weights_desc, @Const dnnl_memory_desc_t diff_bias_desc, @Const dnnl_memory_desc_t diff_dst_desc, @Cast(value="const int64_t*") long[] strides, @Cast(value="const int64_t*") long[] dilates, @Cast(value="const int64_t*") long[] padding_l, @Cast(value="const int64_t*") long[] padding_r)
@Cast(value="dnnl_status_t") public static int dnnl_shuffle_forward_desc_init(dnnl_shuffle_desc_t shuffle_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Const dnnl_memory_desc_t data_desc, int axis, @Cast(value="dnnl_dim_t") long group_size)
\addtogroup dnnl_api_shuffle \{
Initializes a descriptor for shuffle forward propagation primitive.
shuffle_desc - Output descriptor for a shuffle primitive.prop_kind - Propagation kind. Possible values are
#dnnl_forward_training and #dnnl_forward_inference.data_desc - Source and destination memory descriptor.axis - The axis along which the data is shuffled.group_size - Shuffle group size.@Cast(value="dnnl_status_t") public static int dnnl_shuffle_backward_desc_init(dnnl_shuffle_desc_t shuffle_desc, @Const dnnl_memory_desc_t diff_data_desc, int axis, @Cast(value="dnnl_dim_t") long group_size)
shuffle_desc - Output descriptor for a shuffle primitive.diff_data_desc - Diff source and diff destination memory descriptor.axis - The axis along which the data is shuffled.group_size - Shuffle group size.@Cast(value="dnnl_status_t") public static int dnnl_eltwise_forward_desc_init(dnnl_eltwise_desc_t eltwise_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t data_desc, float alpha, float beta)
\addtogroup dnnl_api_eltwise \{
Initializes a descriptor for eltwise forward propagation primitive.
eltwise_desc - Output descriptor for an eltwise primitive.prop_kind - Propagation kind. Possible values are
#dnnl_forward_training and #dnnl_forward_inference.alg_kind - Elementwise algorithm kind.data_desc - Source and destination memory descriptor.alpha - The alpha parameter for the elementwise operation. Specific
meaning depends on the algorithm.beta - The beta parameter for the elementwise operation. Specific
meaning depends on the algorithm.@Cast(value="dnnl_status_t") public static int dnnl_eltwise_backward_desc_init(dnnl_eltwise_desc_t eltwise_desc, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t diff_data_desc, @Const dnnl_memory_desc_t data_desc, float alpha, float beta)
eltwise_desc - Output descriptor for an eltwise primitive.alg_kind - Elementwise algorithm kind.diff_data_desc - Diff source and diff destination memory descriptors.data_desc - Source and destination memory descriptor.alpha - The alpha parameter for the elementwise operation. Specific
meaning depends on the algorithm.beta - The beta parameter for the elementwise operation. Specific
meaning depends on the algorithm.@Cast(value="dnnl_status_t") public static int dnnl_softmax_forward_desc_init(dnnl_softmax_desc_t softmax_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Const dnnl_memory_desc_t data_desc, int softmax_axis)
\addtogroup dnnl_api_softmax \{
Initializes a descriptor for softmax forward propagation primitive.
softmax_desc - Output descriptor for a softmax primitive.prop_kind - Propagation kind. Possible values are
#dnnl_forward_training and #dnnl_forward_inference.data_desc - Source and destination memory descriptor.softmax_axis - Axis over which softmax is computed.@Cast(value="dnnl_status_t") public static int dnnl_softmax_backward_desc_init(dnnl_softmax_desc_t softmax_desc, @Const dnnl_memory_desc_t diff_data_desc, @Const dnnl_memory_desc_t data_desc, int softmax_axis)
softmax_desc - Output descriptor for a softmax primitive.diff_data_desc - Diff source and diff destination memory descriptors.data_desc - Destination memory descriptor.softmax_axis - Axis over which softmax is computed.@Cast(value="dnnl_status_t") public static int dnnl_logsoftmax_forward_desc_init(@Cast(value="dnnl_logsoftmax_desc_t*") dnnl_softmax_desc_t logsoftmax_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Const dnnl_memory_desc_t data_desc, int logsoftmax_axis)
\addtogroup dnnl_api_logsoftmax \{
Initializes a descriptor for logsoftmax forward propagation primitive.
logsoftmax_desc - Output descriptor for a logsoftmax primitive.prop_kind - Propagation kind. Possible values are
#dnnl_forward_training and #dnnl_forward_inference.data_desc - Source and destination memory descriptor.logsoftmax_axis - Axis over which logsoftmax is computed.@Cast(value="dnnl_status_t") public static int dnnl_logsoftmax_backward_desc_init(@Cast(value="dnnl_logsoftmax_desc_t*") dnnl_softmax_desc_t logsoftmax_desc, @Const dnnl_memory_desc_t diff_data_desc, @Const dnnl_memory_desc_t data_desc, int logsoftmax_axis)
logsoftmax_desc - Output descriptor for a logsoftmax primitive.diff_data_desc - Diff source and diff destination memory descriptors.data_desc - Destination memory descriptor.logsoftmax_axis - Axis over which softmax is computed.@Cast(value="dnnl_status_t") public static int dnnl_pooling_forward_desc_init(dnnl_pooling_desc_t pool_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t src_desc, @Const dnnl_memory_desc_t dst_desc, @Cast(value="const int64_t*") LongPointer strides, @Cast(value="const int64_t*") LongPointer kernel, @Cast(value="const int64_t*") LongPointer padding_l, @Cast(value="const int64_t*") LongPointer padding_r)
\addtogroup dnnl_api_pooling \{
Initializes a descriptor for pooling forward propagation primitive. Arrays \p strides, \p kernel, \p padding_l, and \p padding_r contain values for spatial dimensions only and hence must have the same number of elements as there are spatial dimensions. The order of values is the same as in the tensor: depth (for 3D tensors), height (for 3D and 2D tensors), and width.
pool_desc - Output descriptor for a pooling primitive.prop_kind - Propagation kind. Possible values are
#dnnl_forward_training and #dnnl_forward_inference.alg_kind - Pooling algorithm kind: either #dnnl_pooling_max,
#dnnl_pooling_avg_include_padding, or #dnnl_pooling_avg (same as
#dnnl_pooling_avg_exclude_padding).src_desc - Source memory descriptor.dst_desc - Destination memory descriptor.strides - Array of strides for spatial dimension.kernel - Array of kernel spatial dimensions.padding_l - Array of padding values for low indices for each spatial
dimension ([[front,] top,] left).padding_r - Array of padding values for high indices for each spatial
dimension ([[back,] bottom,] right). Can be NULL in which case
padding is considered to be symmetrical.@Cast(value="dnnl_status_t") public static int dnnl_pooling_forward_desc_init(dnnl_pooling_desc_t pool_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t src_desc, @Const dnnl_memory_desc_t dst_desc, @Cast(value="const int64_t*") LongBuffer strides, @Cast(value="const int64_t*") LongBuffer kernel, @Cast(value="const int64_t*") LongBuffer padding_l, @Cast(value="const int64_t*") LongBuffer padding_r)
@Cast(value="dnnl_status_t") public static int dnnl_pooling_forward_desc_init(dnnl_pooling_desc_t pool_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t src_desc, @Const dnnl_memory_desc_t dst_desc, @Cast(value="const int64_t*") long[] strides, @Cast(value="const int64_t*") long[] kernel, @Cast(value="const int64_t*") long[] padding_l, @Cast(value="const int64_t*") long[] padding_r)
@Cast(value="dnnl_status_t") public static int dnnl_pooling_backward_desc_init(dnnl_pooling_desc_t pool_desc, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t diff_src_desc, @Const dnnl_memory_desc_t diff_dst_desc, @Cast(value="const int64_t*") LongPointer strides, @Cast(value="const int64_t*") LongPointer kernel, @Cast(value="const int64_t*") LongPointer padding_l, @Cast(value="const int64_t*") LongPointer padding_r)
pool_desc - Output descriptor for a pooling primitive.alg_kind - Pooling algorithm kind: either #dnnl_pooling_max,
#dnnl_pooling_avg_include_padding, or #dnnl_pooling_avg (same as
#dnnl_pooling_avg_exclude_padding).diff_src_desc - Diff source memory descriptor.diff_dst_desc - Diff destination memory descriptor.strides - Array of strides for spatial dimension.kernel - Array of kernel spatial dimensions.padding_l - Array of padding values for low indices for each spatial
dimension ([[front,] top,] left).padding_r - Array of padding values for high indices for each spatial
dimension ([[back,] bottom,] right). Can be NULL in which case
padding is considered to be symmetrical.@Cast(value="dnnl_status_t") public static int dnnl_pooling_backward_desc_init(dnnl_pooling_desc_t pool_desc, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t diff_src_desc, @Const dnnl_memory_desc_t diff_dst_desc, @Cast(value="const int64_t*") LongBuffer strides, @Cast(value="const int64_t*") LongBuffer kernel, @Cast(value="const int64_t*") LongBuffer padding_l, @Cast(value="const int64_t*") LongBuffer padding_r)
@Cast(value="dnnl_status_t") public static int dnnl_pooling_backward_desc_init(dnnl_pooling_desc_t pool_desc, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t diff_src_desc, @Const dnnl_memory_desc_t diff_dst_desc, @Cast(value="const int64_t*") long[] strides, @Cast(value="const int64_t*") long[] kernel, @Cast(value="const int64_t*") long[] padding_l, @Cast(value="const int64_t*") long[] padding_r)
@Cast(value="dnnl_status_t") public static int dnnl_lrn_forward_desc_init(dnnl_lrn_desc_t lrn_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t data_desc, @Cast(value="dnnl_dim_t") long local_size, float alpha, float beta, float k)
\addtogroup dnnl_api_lrn \{
Initializes a descriptor for LRN forward propagation primitive.
lrn_desc - Output descriptor for a LRN primitive.prop_kind - Propagation kind. Possible values are
#dnnl_forward_training and #dnnl_forward_inference.alg_kind - LRN algorithm kind: either #dnnl_lrn_across_channels or
#dnnl_lrn_within_channel.data_desc - Source and destination memory descriptor.local_size - Regularization local size.alpha - The alpha regularization parameter.beta - The beta regularization parameter.k - The k regularization parameter.@Cast(value="dnnl_status_t") public static int dnnl_lrn_backward_desc_init(dnnl_lrn_desc_t lrn_desc, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const dnnl_memory_desc_t diff_data_desc, @Const dnnl_memory_desc_t data_desc, @Cast(value="dnnl_dim_t") long local_size, float alpha, float beta, float k)
lrn_desc - Output descriptor for a LRN primitive.alg_kind - LRN algorithm kind: either #dnnl_lrn_across_channels or
#dnnl_lrn_within_channel.diff_data_desc - Diff source and diff destination memory descriptor.data_desc - Source memory descriptor.local_size - Regularization local size.alpha - The alpha regularization parameter.beta - The beta regularization parameter.k - The k regularization parameter.@Cast(value="dnnl_status_t") public static int dnnl_batch_normalization_forward_desc_init(dnnl_batch_normalization_desc_t bnrm_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Const dnnl_memory_desc_t data_desc, float epsilon, @Cast(value="unsigned") int flags)
\addtogroup dnnl_api_batch_normalization \{
Initializes a descriptor for a batch normalization forward propagation primitive. \note In-place operation is supported: the dst can refer to the same memory as the src.
bnrm_desc - Output descriptor for batch normalization primitive.prop_kind - Propagation kind. Possible values are
#dnnl_forward_training and #dnnl_forward_inference.data_desc - Source and destination memory descriptor.epsilon - Batch normalization epsilon parameter.flags - Batch normalization flags (\ref dnnl_normalization_flags_t).@Cast(value="dnnl_status_t") public static int dnnl_batch_normalization_backward_desc_init(dnnl_batch_normalization_desc_t bnrm_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Const dnnl_memory_desc_t diff_data_desc, @Const dnnl_memory_desc_t data_desc, float epsilon, @Cast(value="unsigned") int flags)
bnrm_desc - Output descriptor for batch normalization primitive.prop_kind - Propagation kind. Possible values are
#dnnl_backward_data and #dnnl_backward (diffs for all parameters are
computed in this case).diff_data_desc - Diff source and diff destination memory descriptor.data_desc - Source memory descriptor.epsilon - Batch normalization epsilon parameter.flags - Batch normalization flags (\ref dnnl_normalization_flags_t).@Cast(value="dnnl_status_t") public static int dnnl_layer_normalization_forward_desc_init(dnnl_layer_normalization_desc_t lnrm_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Const dnnl_memory_desc_t data_desc, @Const dnnl_memory_desc_t stat_desc, float epsilon, @Cast(value="unsigned") int flags)
\addtogroup dnnl_api_layer_normalization \{
Initializes a descriptor for layer normalization forward propagation primitive. \note In-place operation is supported: the dst can refer to the same memory as the src.
lnrm_desc - Output descriptor for layer normalization primitive.prop_kind - Propagation kind. Possible values are
#dnnl_forward_training and #dnnl_forward_inference.data_desc - Source and destination memory descriptor.stat_desc - Memory descriptor for mean and variance. If this
parameter is NULL, a zero memory descriptor, or a memory descriptor
with format_kind set to #dnnl_format_kind_undef, then the memory
descriptor for stats is derived from \p data_desc by removing the last
dimension.epsilon - Layer normalization epsilon parameter.flags - Layer normalization flags (\ref dnnl_normalization_flags_t).@Cast(value="dnnl_status_t") public static int dnnl_layer_normalization_backward_desc_init(dnnl_layer_normalization_desc_t lnrm_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Const dnnl_memory_desc_t diff_data_desc, @Const dnnl_memory_desc_t data_desc, @Const dnnl_memory_desc_t stat_desc, float epsilon, @Cast(value="unsigned") int flags)
lnrm_desc - Output descriptor for layer normalization primitive.prop_kind - Propagation kind. Possible values are
#dnnl_backward_data and #dnnl_backward (diffs for all parameters are
computed in this case).diff_data_desc - Diff source and diff destination memory descriptor.data_desc - Source memory descriptor.stat_desc - Memory descriptor for mean and variance. If this
parameter is NULL, a zero memory descriptor, or a memory descriptor
with format_kind set to #dnnl_format_kind_undef, then the memory
descriptor for stats is derived from \p data_desc by removing the last
dimension.epsilon - Layer normalization epsilon parameter.flags - Layer normalization flags (\ref dnnl_normalization_flags_t).@Cast(value="dnnl_status_t") public static int dnnl_inner_product_forward_desc_init(dnnl_inner_product_desc_t ip_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Const dnnl_memory_desc_t src_desc, @Const dnnl_memory_desc_t weights_desc, @Const dnnl_memory_desc_t bias_desc, @Const dnnl_memory_desc_t dst_desc)
\addtogroup dnnl_api_inner_product \{
Initializes descriptor for inner product forward propagation. \note Memory descriptors can be initialized with #dnnl_format_tag_any or with format_kind set to #dnnl_format_kind_any.
ip_desc - Output descriptor for inner product primitive.prop_kind - Propagation kind. Possible values are
#dnnl_forward_training and #dnnl_forward_inference.src_desc - Source memory descriptor.weights_desc - Weights memory descriptor.bias_desc - Bias memory descriptor. Passing NULL, a zero memory
descriptor, or a memory descriptor with format_kind set to
#dnnl_format_kind_undef disables the bias term.dst_desc - Destination memory descriptor.@Cast(value="dnnl_status_t") public static int dnnl_inner_product_backward_data_desc_init(dnnl_inner_product_desc_t ip_desc, @Const dnnl_memory_desc_t diff_src_desc, @Const dnnl_memory_desc_t weights_desc, @Const dnnl_memory_desc_t diff_dst_desc)
ip_desc - Output descriptor for inner product primitive.diff_src_desc - Diff source memory descriptor.weights_desc - Weights memory descriptor.diff_dst_desc - Diff destination memory descriptor.@Cast(value="dnnl_status_t") public static int dnnl_inner_product_backward_weights_desc_init(dnnl_inner_product_desc_t ip_desc, @Const dnnl_memory_desc_t src_desc, @Const dnnl_memory_desc_t diff_weights_desc, @Const dnnl_memory_desc_t diff_bias_desc, @Const dnnl_memory_desc_t diff_dst_desc)
ip_desc - Output descriptor for inner product primitive.src_desc - Source memory descriptor.diff_weights_desc - Diff weights memory descriptor.diff_bias_desc - Diff bias memory descriptor. Passing NULL, a zero
memory descriptor, or a memory descriptor with format_kind set to
#dnnl_format_kind_undef disables the bias term.diff_dst_desc - Diff destination memory descriptor.@Cast(value="dnnl_status_t") public static int dnnl_primitive_attr_set_rnn_data_qparams(dnnl_primitive_attr attr, float scale, float shift)
\addtogroup dnnl_api_attributes \{
Set quantization scale and shift parameters for RNN data tensors.
For performance reasons, the low-precision configuration of the RNN
primitives expects input activations to have the unsigned 8-bit integer
data type. The scale and shift parameters are used to quantize
floating-point data to unsigned integer and must be passed to the RNN
primitive using attributes.
The quantization formula is scale * (data + shift).
\note
Quantization scale and shift are common for src_layer, src_iter,
dst_iter, and dst_layer.
Example usage:
// RNN parameters
int l = 2, t = 2, mb = 32, sic = 32, slc = 32, dic = 32, dlc = 32;
// Activations quantization parameters
float scale = ..., shift = ..;
dnnl_primitive_attr_t rnn_attr;
// Create default attributes
dnnl_primitive_attr_create(&rnn_attr);
// Set scale and shift for int8 quantization of activation
dnnl_primitive_attr_set_rnn_data_qparams(rnn_attr, scale, shift);
// Create and configure rnn op_desc
dnnl_rnn_desc_t rnn_d;
dnnl_primitive_desc_t rnn_pd;
dnnl_primitive_desc_create(&rnn_pd, &rnn_d, attr, engine, NULL);
attr - Primitive attributes.scale - The value to scale the data by.shift - The value to shift the data by.@Cast(value="dnnl_status_t") public static int dnnl_primitive_attr_set_rnn_weights_qparams(dnnl_primitive_attr attr, @Cast(value="dnnl_dim_t") long count, int mask, @Const FloatPointer scales)
attr - Primitive attributes.count - Number of elements in the \p scales array.mask - Scaling factors correspondence mask that defines the
correspondence between the output tensor dimensions and the \p
scales vector. The set i-th bit indicates that a dedicated scaling
factor should be used for each index along that dimension. Set the
mask to 0 to use a common scaling factor for the whole output
tensor.scales - Array of output scaling factors that must contain \p count
values and the following equality must hold:
\[count = \prod\limits_{d \in mask} weights.dims[d].\]
Violations can only be detected when the attributes are used to create
a primitive descriptor.@Cast(value="dnnl_status_t") public static int dnnl_primitive_attr_set_rnn_weights_qparams(dnnl_primitive_attr attr, @Cast(value="dnnl_dim_t") long count, int mask, @Const FloatBuffer scales)
@Cast(value="dnnl_status_t") public static int dnnl_primitive_attr_set_rnn_weights_qparams(dnnl_primitive_attr attr, @Cast(value="dnnl_dim_t") long count, int mask, @Const float[] scales)
@Cast(value="dnnl_status_t") public static int dnnl_vanilla_rnn_forward_desc_init(dnnl_rnn_desc_t rnn_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Cast(value="const dnnl_alg_kind_t") int activation, @Cast(value="const dnnl_rnn_direction_t") int direction, @Const dnnl_memory_desc_t src_layer_desc, @Const dnnl_memory_desc_t src_iter_desc, @Const dnnl_memory_desc_t weights_layer_desc, @Const dnnl_memory_desc_t weights_iter_desc, @Const dnnl_memory_desc_t bias_desc, @Const dnnl_memory_desc_t dst_layer_desc, @Const dnnl_memory_desc_t dst_iter_desc, @Cast(value="unsigned") int flags, float alpha, float beta)
\addtogroup dnnl_api_rnn \{
Initializes a descriptor for vanilla RNN forward propagation primitive. The following arguments may either be \c NULL or point to a zero memory descriptor: - \p src_iter_desc, - \p bias_desc, - \p dst_iter_desc. This would then indicate that the RNN forward propagation primitive should not use them and should default to zero values instead. \note All memory descriptors can be initialized with #dnnl_format_tag_any or with format_kind set to #dnnl_format_kind_any.
rnn_desc - Output descriptor for vanilla RNN primitive.prop_kind - Propagation kind. Possible values are
#dnnl_forward_training and #dnnl_forward_inference.activation - Activation kind. Possible values are #dnnl_eltwise_relu,
#dnnl_eltwise_tanh or #dnnl_eltwise_logistic.direction - RNN direction. See \ref dnnl_rnn_direction_t for more
info.src_layer_desc - Memory descriptor for the input vector.src_iter_desc - Memory descriptor for the input recurrent hidden
state vector.weights_layer_desc - Memory descriptor for the weights applied to the
layer input.weights_iter_desc - Memory descriptor for the weights applied to the
recurrent input.bias_desc - Bias memory descriptor.dst_layer_desc - Memory descriptor for the output vector.dst_iter_desc - Memory descriptor for the output recurrent hidden
state vector.flags - Unused.alpha - Negative slope if activation is #dnnl_eltwise_relu.beta - Unused.@Cast(value="dnnl_status_t") public static int dnnl_vanilla_rnn_backward_desc_init(dnnl_rnn_desc_t rnn_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Cast(value="const dnnl_alg_kind_t") int activation, @Cast(value="const dnnl_rnn_direction_t") int direction, @Const dnnl_memory_desc_t src_layer_desc, @Const dnnl_memory_desc_t src_iter_desc, @Const dnnl_memory_desc_t weights_layer_desc, @Const dnnl_memory_desc_t weights_iter_desc, @Const dnnl_memory_desc_t bias_desc, @Const dnnl_memory_desc_t dst_layer_desc, @Const dnnl_memory_desc_t dst_iter_desc, @Const dnnl_memory_desc_t diff_src_layer_desc, @Const dnnl_memory_desc_t diff_src_iter_desc, @Const dnnl_memory_desc_t diff_weights_layer_desc, @Const dnnl_memory_desc_t diff_weights_iter_desc, @Const dnnl_memory_desc_t diff_bias_desc, @Const dnnl_memory_desc_t diff_dst_layer_desc, @Const dnnl_memory_desc_t diff_dst_iter_desc, @Cast(value="unsigned") int flags, float alpha, float beta)
rnn_desc - Output descriptor for vanilla RNN primitive.prop_kind - Propagation kind. Must be #dnnl_backward.activation - Activation kind. Possible values are #dnnl_eltwise_relu,
#dnnl_eltwise_tanh or #dnnl_eltwise_logistic.direction - RNN direction. See \ref dnnl_rnn_direction_t for more
info.src_layer_desc - Memory descriptor for the input vector.src_iter_desc - Memory descriptor for the input recurrent hidden
state vector.weights_layer_desc - Memory descriptor for the weights applied to the
layer input.weights_iter_desc - Memory descriptor for the weights applied to the
recurrent input.bias_desc - Bias memory descriptor.dst_layer_desc - Memory descriptor for the output vector.dst_iter_desc - Memory descriptor for the output recurrent hidden
state vector.diff_src_layer_desc - Memory descriptor for the diff of input vector.diff_src_iter_desc - Memory descriptor for the diff of input recurrent
hidden state vector.diff_weights_layer_desc - Memory descriptor for the diff of weights
applied to the layer input.diff_weights_iter_desc - Memory descriptor for the diff of weights
applied to the recurrent input.diff_bias_desc - Diff bias memory descriptor.diff_dst_layer_desc - Memory descriptor for the diff of output
vector.diff_dst_iter_desc - Memory descriptor for the diff of output
recurrent hidden state vector.flags - Unused.alpha - Negative slope if activation is #dnnl_eltwise_relu.beta - Unused.@Cast(value="dnnl_status_t") public static int dnnl_lstm_forward_desc_init(dnnl_rnn_desc_t rnn_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Cast(value="dnnl_rnn_direction_t") int direction, @Const dnnl_memory_desc_t src_layer_desc, @Const dnnl_memory_desc_t src_iter_desc, @Const dnnl_memory_desc_t src_iter_c_desc, @Const dnnl_memory_desc_t weights_layer_desc, @Const dnnl_memory_desc_t weights_iter_desc, @Const dnnl_memory_desc_t bias_desc, @Const dnnl_memory_desc_t dst_layer_desc, @Const dnnl_memory_desc_t dst_iter_desc, @Const dnnl_memory_desc_t dst_iter_c_desc, @Cast(value="unsigned") int flags)
rnn_desc - Output descriptor for LSTM primitive.prop_kind - Propagation kind. Possible values are
#dnnl_forward_training and #dnnl_forward_inference.direction - RNN direction. See \ref dnnl_rnn_direction_t for more
info.src_layer_desc - Memory descriptor for the input vector.src_iter_desc - Memory descriptor for the input recurrent hidden
state vector.src_iter_c_desc - Memory descriptor for the input recurrent cell
state vector.weights_layer_desc - Memory descriptor for the weights applied to the
layer input.weights_iter_desc - Memory descriptor for the weights applied to the
recurrent input.bias_desc - Bias memory descriptor.dst_layer_desc - Memory descriptor for the output vector.dst_iter_desc - Memory descriptor for the output recurrent hidden
state vector.dst_iter_c_desc - Memory descriptor for the output recurrent cell
state vector.flags - Unused.to initialize forward LSTM with and
without peephole,
to initialize forward LSTM with and
without peephole / recurrent projection layer@Cast(value="dnnl_status_t") public static int dnnl_lstm_forward_desc_init_v2(dnnl_rnn_desc_t rnn_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Cast(value="dnnl_rnn_direction_t") int direction, @Const dnnl_memory_desc_t src_layer_desc, @Const dnnl_memory_desc_t src_iter_desc, @Const dnnl_memory_desc_t src_iter_c_desc, @Const dnnl_memory_desc_t weights_layer_desc, @Const dnnl_memory_desc_t weights_iter_desc, @Const dnnl_memory_desc_t weights_peephole_desc, @Const dnnl_memory_desc_t bias_desc, @Const dnnl_memory_desc_t dst_layer_desc, @Const dnnl_memory_desc_t dst_iter_desc, @Const dnnl_memory_desc_t dst_iter_c_desc, @Cast(value="unsigned") int flags)
rnn_desc - Output descriptor for LSTM primitive.prop_kind - Propagation kind. Possible values are
#dnnl_forward_training and #dnnl_forward_inference.direction - RNN direction. See \ref dnnl_rnn_direction_t for more
info.src_layer_desc - Memory descriptor for the input vector.src_iter_desc - Memory descriptor for the input recurrent hidden
state vector.src_iter_c_desc - Memory descriptor for the input recurrent cell
state vector.weights_layer_desc - Memory descriptor for the weights applied to the
layer input.weights_iter_desc - Memory descriptor for the weights applied to the
recurrent input.weights_peephole_desc - Memory descriptor for the weights applied to
the cell states (according to the Peephole LSTM formula).bias_desc - Bias memory descriptor.dst_layer_desc - Memory descriptor for the output vector.dst_iter_desc - Memory descriptor for the output recurrent hidden
state vector.dst_iter_c_desc - Memory descriptor for the output recurrent cell
state vector.flags - Unused.to initialize forward LSTM with and
without peephole / recurrent projection layer@Cast(value="dnnl_status_t") public static int dnnl_lstm_forward_desc_init_v3(dnnl_rnn_desc_t rnn_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Cast(value="dnnl_rnn_direction_t") int direction, @Const dnnl_memory_desc_t src_layer_desc, @Const dnnl_memory_desc_t src_iter_desc, @Const dnnl_memory_desc_t src_iter_c_desc, @Const dnnl_memory_desc_t weights_layer_desc, @Const dnnl_memory_desc_t weights_iter_desc, @Const dnnl_memory_desc_t weights_peephole_desc, @Const dnnl_memory_desc_t weights_projection_desc, @Const dnnl_memory_desc_t bias_desc, @Const dnnl_memory_desc_t dst_layer_desc, @Const dnnl_memory_desc_t dst_iter_desc, @Const dnnl_memory_desc_t dst_iter_c_desc, @Cast(value="unsigned") int flags)
rnn_desc - Output descriptor for LSTM primitive.prop_kind - Propagation kind. Possible values are
#dnnl_forward_training and #dnnl_forward_inference.direction - RNN direction. See \ref dnnl_rnn_direction_t for more
info.src_layer_desc - Memory descriptor for the input vector.src_iter_desc - Memory descriptor for the input recurrent hidden
state vector.src_iter_c_desc - Memory descriptor for the input recurrent cell
state vector.weights_layer_desc - Memory descriptor for the weights applied to the
layer input.weights_iter_desc - Memory descriptor for the weights applied to the
recurrent input.weights_peephole_desc - Memory descriptor for the weights applied to
the cell states (according to the Peephole LSTM formula).weights_projection_desc - Memory descriptor for the weights applied to
the hidden states to get the recurrent projection (according to the
Projection LSTM formula).bias_desc - Bias memory descriptor.dst_layer_desc - Memory descriptor for the output vector.dst_iter_desc - Memory descriptor for the output recurrent hidden
state vector.dst_iter_c_desc - Memory descriptor for the output recurrent cell
state vector.flags - Unused.@Cast(value="dnnl_status_t") public static int dnnl_lstm_backward_desc_init(dnnl_rnn_desc_t rnn_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Cast(value="dnnl_rnn_direction_t") int direction, @Const dnnl_memory_desc_t src_layer_desc, @Const dnnl_memory_desc_t src_iter_desc, @Const dnnl_memory_desc_t src_iter_c_desc, @Const dnnl_memory_desc_t weights_layer_desc, @Const dnnl_memory_desc_t weights_iter_desc, @Const dnnl_memory_desc_t bias_desc, @Const dnnl_memory_desc_t dst_layer_desc, @Const dnnl_memory_desc_t dst_iter_desc, @Const dnnl_memory_desc_t dst_iter_c_desc, @Const dnnl_memory_desc_t diff_src_layer_desc, @Const dnnl_memory_desc_t diff_src_iter_desc, @Const dnnl_memory_desc_t diff_src_iter_c_desc, @Const dnnl_memory_desc_t diff_weights_layer_desc, @Const dnnl_memory_desc_t diff_weights_iter_desc, @Const dnnl_memory_desc_t diff_bias_desc, @Const dnnl_memory_desc_t diff_dst_layer_desc, @Const dnnl_memory_desc_t diff_dst_iter_desc, @Const dnnl_memory_desc_t diff_dst_iter_c_desc, @Cast(value="unsigned") int flags)
rnn_desc - Output descriptor for LSTM primitive.prop_kind - Propagation kind. Must be #dnnl_backward.direction - RNN direction. See \ref dnnl_rnn_direction_t for more
info.src_layer_desc - Memory descriptor for the input vector.src_iter_desc - Memory descriptor for the input recurrent hidden
state vector.src_iter_c_desc - Memory descriptor for the input recurrent cell
state vector.weights_layer_desc - Memory descriptor for the weights applied to the
layer input.weights_iter_desc - Memory descriptor for the weights applied to the
recurrent input.bias_desc - Bias memory descriptor.dst_layer_desc - Memory descriptor for the output vector.dst_iter_desc - Memory descriptor for the output recurrent hidden
state vector.dst_iter_c_desc - Memory descriptor for the output recurrent cell
state vector.diff_src_layer_desc - Memory descriptor for the diff of input vector.diff_src_iter_desc - Memory descriptor for the diff of input recurrent
hidden state vector.diff_src_iter_c_desc - Memory descriptor for the diff of input
recurrent cell state vector.diff_weights_layer_desc - Memory descriptor for the diff of weights
applied to the layer input.diff_weights_iter_desc - Memory descriptor for the diff of weights
applied to the recurrent input.diff_bias_desc - Diff bias memory descriptor.diff_dst_layer_desc - Memory descriptor for the diff of output
vector.diff_dst_iter_desc - Memory descriptor for the diff of output
recurrent hidden state vector.diff_dst_iter_c_desc - Memory descriptor for the diff of output
recurrent cell state vector.flags - Unused.to initialize backward LSTM with and
without peephole,
to initialize backward LSTM with and
without peephole / recurrent projection layer@Cast(value="dnnl_status_t") public static int dnnl_lstm_backward_desc_init_v2(dnnl_rnn_desc_t rnn_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Cast(value="dnnl_rnn_direction_t") int direction, @Const dnnl_memory_desc_t src_layer_desc, @Const dnnl_memory_desc_t src_iter_desc, @Const dnnl_memory_desc_t src_iter_c_desc, @Const dnnl_memory_desc_t weights_layer_desc, @Const dnnl_memory_desc_t weights_iter_desc, @Const dnnl_memory_desc_t weights_peephole_desc, @Const dnnl_memory_desc_t bias_desc, @Const dnnl_memory_desc_t dst_layer_desc, @Const dnnl_memory_desc_t dst_iter_desc, @Const dnnl_memory_desc_t dst_iter_c_desc, @Const dnnl_memory_desc_t diff_src_layer_desc, @Const dnnl_memory_desc_t diff_src_iter_desc, @Const dnnl_memory_desc_t diff_src_iter_c_desc, @Const dnnl_memory_desc_t diff_weights_layer_desc, @Const dnnl_memory_desc_t diff_weights_iter_desc, @Const dnnl_memory_desc_t diff_weights_peephole_desc, @Const dnnl_memory_desc_t diff_bias_desc, @Const dnnl_memory_desc_t diff_dst_layer_desc, @Const dnnl_memory_desc_t diff_dst_iter_desc, @Const dnnl_memory_desc_t diff_dst_iter_c_desc, @Cast(value="unsigned") int flags)
rnn_desc - Output descriptor for LSTM primitive.prop_kind - Propagation kind. Must be #dnnl_backward.direction - RNN direction. See \ref dnnl_rnn_direction_t for more
info.src_layer_desc - Memory descriptor for the input vector.src_iter_desc - Memory descriptor for the input recurrent hidden
state vector.src_iter_c_desc - Memory descriptor for the input recurrent cell
state vector.weights_layer_desc - Memory descriptor for the weights applied to the
layer input.weights_iter_desc - Memory descriptor for the weights applied to the
recurrent input.weights_peephole_desc - Memory descriptor for the weights applied to
the cell states (according to the Peephole LSTM formula).bias_desc - Bias memory descriptor.dst_layer_desc - Memory descriptor for the output vector.dst_iter_desc - Memory descriptor for the output recurrent hidden
state vector.dst_iter_c_desc - Memory descriptor for the output recurrent cell
state vector.diff_src_layer_desc - Memory descriptor for the diff of input vector.diff_src_iter_desc - Memory descriptor for the diff of input recurrent
hidden state vector.diff_src_iter_c_desc - Memory descriptor for the diff of input
recurrent cell state vector.diff_weights_layer_desc - Memory descriptor for the diff of weights
applied to the layer input.diff_weights_iter_desc - Memory descriptor for the diff of weights
applied to the recurrent input.diff_weights_peephole_desc - Memory descriptor for the diff of weights
applied to the cell states (according to the Peephole LSTM formula).diff_bias_desc - Diff bias memory descriptor.diff_dst_layer_desc - Memory descriptor for the diff of output
vector.diff_dst_iter_desc - Memory descriptor for the diff of output
recurrent hidden state vector.diff_dst_iter_c_desc - Memory descriptor for the diff of output
recurrent cell state vector.flags - Unused.to initialize backward LSTM with and
without peephole / recurrent projection layer@Cast(value="dnnl_status_t") public static int dnnl_lstm_backward_desc_init_v3(dnnl_rnn_desc_t rnn_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Cast(value="dnnl_rnn_direction_t") int direction, @Const dnnl_memory_desc_t src_layer_desc, @Const dnnl_memory_desc_t src_iter_desc, @Const dnnl_memory_desc_t src_iter_c_desc, @Const dnnl_memory_desc_t weights_layer_desc, @Const dnnl_memory_desc_t weights_iter_desc, @Const dnnl_memory_desc_t weights_peephole_desc, @Const dnnl_memory_desc_t weights_projection_desc, @Const dnnl_memory_desc_t bias_desc, @Const dnnl_memory_desc_t dst_layer_desc, @Const dnnl_memory_desc_t dst_iter_desc, @Const dnnl_memory_desc_t dst_iter_c_desc, @Const dnnl_memory_desc_t diff_src_layer_desc, @Const dnnl_memory_desc_t diff_src_iter_desc, @Const dnnl_memory_desc_t diff_src_iter_c_desc, @Const dnnl_memory_desc_t diff_weights_layer_desc, @Const dnnl_memory_desc_t diff_weights_iter_desc, @Const dnnl_memory_desc_t diff_weights_peephole_desc, @Const dnnl_memory_desc_t diff_weights_projection_desc, @Const dnnl_memory_desc_t diff_bias_desc, @Const dnnl_memory_desc_t diff_dst_layer_desc, @Const dnnl_memory_desc_t diff_dst_iter_desc, @Const dnnl_memory_desc_t diff_dst_iter_c_desc, @Cast(value="unsigned") int flags)
rnn_desc - Output descriptor for LSTM primitive.prop_kind - Propagation kind. Must be #dnnl_backward.direction - RNN direction. See \ref dnnl_rnn_direction_t for more
info.src_layer_desc - Memory descriptor for the input vector.src_iter_desc - Memory descriptor for the input recurrent hidden
state vector.src_iter_c_desc - Memory descriptor for the input recurrent cell
state vector.weights_layer_desc - Memory descriptor for the weights applied to the
layer input.weights_iter_desc - Memory descriptor for the weights applied to the
recurrent input.weights_peephole_desc - Memory descriptor for the weights applied to
the cell states (according to the Peephole LSTM formula).weights_projection_desc - Memory descriptor for the weights applied to
the hidden states to get the recurrent projection (according to the
Projection LSTM formula).bias_desc - Bias memory descriptor.dst_layer_desc - Memory descriptor for the output vector.dst_iter_desc - Memory descriptor for the output recurrent hidden
state vector.dst_iter_c_desc - Memory descriptor for the output recurrent cell
state vector.diff_src_layer_desc - Memory descriptor for the diff of input vector.diff_src_iter_desc - Memory descriptor for the diff of input recurrent
hidden state vector.diff_src_iter_c_desc - Memory descriptor for the diff of input
recurrent cell state vector.diff_weights_layer_desc - Memory descriptor for the diff of weights
applied to the layer input.diff_weights_iter_desc - Memory descriptor for the diff of weights
applied to the recurrent input.diff_weights_peephole_desc - Memory descriptor for the diff of weights
applied to the cell states (according to the Peephole LSTM formula).diff_weights_projection_desc - Memory descriptor for the diff of
weights applied to the hidden states to get the recurrent projection
(according to the Projection LSTM formula).diff_bias_desc - Diff bias memory descriptor.diff_dst_layer_desc - Memory descriptor for the diff of output
vector.diff_dst_iter_desc - Memory descriptor for the diff of output
recurrent hidden state vector.diff_dst_iter_c_desc - Memory descriptor for the diff of output
recurrent cell state vector.flags - Unused.@Cast(value="dnnl_status_t") public static int dnnl_gru_forward_desc_init(dnnl_rnn_desc_t rnn_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Cast(value="dnnl_rnn_direction_t") int direction, @Const dnnl_memory_desc_t src_layer_desc, @Const dnnl_memory_desc_t src_iter_desc, @Const dnnl_memory_desc_t weights_layer_desc, @Const dnnl_memory_desc_t weights_iter_desc, @Const dnnl_memory_desc_t bias_desc, @Const dnnl_memory_desc_t dst_layer_desc, @Const dnnl_memory_desc_t dst_iter_desc, @Cast(value="unsigned") int flags)
rnn_desc - Output descriptor for GRU primitive.prop_kind - Propagation kind. Possible values are
#dnnl_forward_training and #dnnl_forward_inference.direction - RNN direction. See \ref dnnl_rnn_direction_t for more
info.src_layer_desc - Memory descriptor for the input vector.src_iter_desc - Memory descriptor for the input recurrent hidden
state vector.weights_layer_desc - Memory descriptor for the weights applied to the
layer input.weights_iter_desc - Memory descriptor for the weights applied to the
recurrent input.bias_desc - Bias memory descriptor.dst_layer_desc - Memory descriptor for the output vector.dst_iter_desc - Memory descriptor for the output recurrent hidden
state vector.flags - Unused.@Cast(value="dnnl_status_t") public static int dnnl_gru_backward_desc_init(dnnl_rnn_desc_t rnn_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Cast(value="dnnl_rnn_direction_t") int direction, @Const dnnl_memory_desc_t src_layer_desc, @Const dnnl_memory_desc_t src_iter_desc, @Const dnnl_memory_desc_t weights_layer_desc, @Const dnnl_memory_desc_t weights_iter_desc, @Const dnnl_memory_desc_t bias_desc, @Const dnnl_memory_desc_t dst_layer_desc, @Const dnnl_memory_desc_t dst_iter_desc, @Const dnnl_memory_desc_t diff_src_layer_desc, @Const dnnl_memory_desc_t diff_src_iter_desc, @Const dnnl_memory_desc_t diff_weights_layer_desc, @Const dnnl_memory_desc_t diff_weights_iter_desc, @Const dnnl_memory_desc_t diff_bias_desc, @Const dnnl_memory_desc_t diff_dst_layer_desc, @Const dnnl_memory_desc_t diff_dst_iter_desc, @Cast(value="unsigned") int flags)
rnn_desc - Output descriptor for GRU primitive.prop_kind - Propagation kind. Must be #dnnl_backward.direction - RNN direction. See \ref dnnl_rnn_direction_t for more
info.src_layer_desc - Memory descriptor for the input vector.src_iter_desc - Memory descriptor for the input recurrent hidden
state vector.weights_layer_desc - Memory descriptor for the weights applied to the
layer input.weights_iter_desc - Memory descriptor for the weights applied to the
recurrent input.bias_desc - Bias memory descriptor.dst_layer_desc - Memory descriptor for the output vector.dst_iter_desc - Memory descriptor for the output recurrent hidden
state vector.diff_src_layer_desc - Memory descriptor for the diff of input vector.diff_src_iter_desc - Memory descriptor for the diff of input recurrent
hidden state vector.diff_weights_layer_desc - Memory descriptor for the diff of weights
applied to the layer input.diff_weights_iter_desc - Memory descriptor for the diff of weights
applied to the recurrent input.diff_bias_desc - Diff bias memory descriptor.diff_dst_layer_desc - Memory descriptor for the diff of output
vector.diff_dst_iter_desc - Memory descriptor for the diff of output
recurrent hidden state vector.flags - Unused.@Cast(value="dnnl_status_t") public static int dnnl_lbr_gru_forward_desc_init(dnnl_rnn_desc_t rnn_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Cast(value="dnnl_rnn_direction_t") int direction, @Const dnnl_memory_desc_t src_layer_desc, @Const dnnl_memory_desc_t src_iter_desc, @Const dnnl_memory_desc_t weights_layer_desc, @Const dnnl_memory_desc_t weights_iter_desc, @Const dnnl_memory_desc_t bias_desc, @Const dnnl_memory_desc_t dst_layer_desc, @Const dnnl_memory_desc_t dst_iter_desc, @Cast(value="unsigned") int flags)
rnn_desc - Output descriptor for LBR GRU primitive.prop_kind - Propagation kind. Possible values are
#dnnl_forward_training and #dnnl_forward_inference.direction - RNN direction. See \ref dnnl_rnn_direction_t for more
info.src_layer_desc - Memory descriptor for the input vector.src_iter_desc - Memory descriptor for the input recurrent hidden
state vector.weights_layer_desc - Memory descriptor for the weights applied to the
layer input.weights_iter_desc - Memory descriptor for the weights applied to the
recurrent input.bias_desc - Bias memory descriptor.dst_layer_desc - Memory descriptor for the output vector.dst_iter_desc - Memory descriptor for the output recurrent hidden
state vector.flags - Unused.@Cast(value="dnnl_status_t") public static int dnnl_lbr_gru_backward_desc_init(dnnl_rnn_desc_t rnn_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Cast(value="dnnl_rnn_direction_t") int direction, @Const dnnl_memory_desc_t src_layer_desc, @Const dnnl_memory_desc_t src_iter_desc, @Const dnnl_memory_desc_t weights_layer_desc, @Const dnnl_memory_desc_t weights_iter_desc, @Const dnnl_memory_desc_t bias_desc, @Const dnnl_memory_desc_t dst_layer_desc, @Const dnnl_memory_desc_t dst_iter_desc, @Const dnnl_memory_desc_t diff_src_layer_desc, @Const dnnl_memory_desc_t diff_src_iter_desc, @Const dnnl_memory_desc_t diff_weights_layer_desc, @Const dnnl_memory_desc_t diff_weights_iter_desc, @Const dnnl_memory_desc_t diff_bias_desc, @Const dnnl_memory_desc_t diff_dst_layer_desc, @Const dnnl_memory_desc_t diff_dst_iter_desc, @Cast(value="unsigned") int flags)
rnn_desc - Output descriptor for LBR GRU primitive.prop_kind - Propagation kind. Must be #dnnl_backward.direction - RNN direction. See \ref dnnl_rnn_direction_t for more
info.src_layer_desc - Memory descriptor for the input vector.src_iter_desc - Memory descriptor for the input recurrent hidden
state vector.weights_layer_desc - Memory descriptor for the weights applied to the
layer input.weights_iter_desc - Memory descriptor for the weights applied to the
recurrent input.bias_desc - Bias memory descriptor.dst_layer_desc - Memory descriptor for the output vector.dst_iter_desc - Memory descriptor for the output recurrent hidden
state vector.diff_src_layer_desc - Memory descriptor for the diff of input vector.diff_src_iter_desc - Memory descriptor for the diff of input recurrent
hidden state vector.diff_weights_layer_desc - Memory descriptor for the diff of weights
applied to the layer input.diff_weights_iter_desc - Memory descriptor for the diff of weights
applied to the recurrent input.diff_bias_desc - Diff bias memory descriptor.diff_dst_layer_desc - Memory descriptor for the diff of output
vector.diff_dst_iter_desc - Memory descriptor for the diff of output
recurrent hidden state vector.flags - Unused.@Cast(value="dnnl_status_t") public static int dnnl_matmul_desc_init(dnnl_matmul_desc_t matmul_desc, @Const dnnl_memory_desc_t src_desc, @Const dnnl_memory_desc_t weights_desc, @Const dnnl_memory_desc_t bias_desc, @Const dnnl_memory_desc_t dst_desc)
\addtogroup dnnl_api_matmul \{
Initializes a matrix multiplication descriptor.
matmul_desc - Output descriptor for matmul primitive.src_desc - Source memory descriptor (matrix A)weights_desc - Weights memory descriptor (matrix B)bias_desc - Bias memory descriptor. Passing NULL, a zero memory
descriptor, or a memory descriptor with format_kind set to
#dnnl_format_kind_undef disables the bias term.dst_desc - Destination memory descriptor (matrix C).@Cast(value="dnnl_status_t") public static int dnnl_resampling_forward_desc_init(dnnl_resampling_desc_t resampling_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const FloatPointer factors, @Const dnnl_memory_desc_t src_desc, @Const dnnl_memory_desc_t dst_desc)
\addtogroup dnnl_api_resampling Resampling \{
Initializes a descriptor for a resampling forward propagation primitive. \note Destination memory descriptor is allowed to be initialized with #dnnl_format_tag_any or with format_kind set to #dnnl_format_kind_any.
resampling_desc - Output descriptor for a resampling primitive.prop_kind - Propagation kind. Possible values are
#dnnl_forward_training and #dnnl_forward_inference.alg_kind - resampling algorithm kind: either #dnnl_resampling_nearest,
or #dnnl_resampling_linear.factors - Array of scaling factors for spatial dimension.src_desc - Source memory descriptor.dst_desc - Destination memory descriptor.@Cast(value="dnnl_status_t") public static int dnnl_resampling_forward_desc_init(dnnl_resampling_desc_t resampling_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const FloatBuffer factors, @Const dnnl_memory_desc_t src_desc, @Const dnnl_memory_desc_t dst_desc)
@Cast(value="dnnl_status_t") public static int dnnl_resampling_forward_desc_init(dnnl_resampling_desc_t resampling_desc, @Cast(value="dnnl_prop_kind_t") int prop_kind, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const float[] factors, @Const dnnl_memory_desc_t src_desc, @Const dnnl_memory_desc_t dst_desc)
@Cast(value="dnnl_status_t") public static int dnnl_resampling_backward_desc_init(dnnl_resampling_desc_t resampling_desc, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const FloatPointer factors, @Const dnnl_memory_desc_t diff_src_desc, @Const dnnl_memory_desc_t diff_dst_desc)
resampling_desc - Output descriptor for a resampling primitive.alg_kind - resamplinging algorithm kind: either
#dnnl_resampling_nearest, or #dnnl_resampling_linear.diff_src_desc - Diff source memory descriptor.diff_dst_desc - Diff destination memory descriptor.factors - Array of scaling factors for spatial dimension.@Cast(value="dnnl_status_t") public static int dnnl_resampling_backward_desc_init(dnnl_resampling_desc_t resampling_desc, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const FloatBuffer factors, @Const dnnl_memory_desc_t diff_src_desc, @Const dnnl_memory_desc_t diff_dst_desc)
@Cast(value="dnnl_status_t") public static int dnnl_resampling_backward_desc_init(dnnl_resampling_desc_t resampling_desc, @Cast(value="dnnl_alg_kind_t") int alg_kind, @Const float[] factors, @Const dnnl_memory_desc_t diff_src_desc, @Const dnnl_memory_desc_t diff_dst_desc)
@Cast(value="size_t") public static long dnnl_engine_get_count(@Cast(value="dnnl_engine_kind_t") int kind)
\} dnnl_api_primitives
\addtogroup dnnl_api_engine \{
Returns the number of engines of a particular kind.
kind - Kind of engines to count.@Cast(value="dnnl_status_t") public static int dnnl_engine_create(@ByPtrPtr dnnl_engine engine, @Cast(value="dnnl_engine_kind_t") int kind, @Cast(value="size_t") long index)
engine - Output engine.kind - Engine kind.index - Engine index that should be between 0 and the count of
engines of the requested kind.@Cast(value="dnnl_status_t") public static int dnnl_engine_create(@Cast(value="dnnl_engine_t*") PointerPointer engine, @Cast(value="dnnl_engine_kind_t") int kind, @Cast(value="size_t") long index)
@Cast(value="dnnl_status_t") public static int dnnl_engine_get_kind(dnnl_engine engine, @Cast(value="dnnl_engine_kind_t*") IntPointer kind)
engine - Engine to query.kind - Output engine kind.@Cast(value="dnnl_status_t") public static int dnnl_engine_get_kind(dnnl_engine engine, @Cast(value="dnnl_engine_kind_t*") IntBuffer kind)
@Cast(value="dnnl_status_t") public static int dnnl_engine_get_kind(dnnl_engine engine, @Cast(value="dnnl_engine_kind_t*") int[] kind)
@Cast(value="dnnl_status_t") public static int dnnl_engine_destroy(dnnl_engine engine)
engine - Engine to destroy.@Cast(value="dnnl_status_t") public static int dnnl_stream_attr_create(@ByPtrPtr dnnl_stream_attr attr, @Cast(value="dnnl_engine_kind_t") int kind)
\addtogroup dnnl_api_stream \{
Creates execution stream attributes for a stream that runs on an engine of a particular kind.
attr - Output execution stream attributes.kind - Target engine kind.@Cast(value="dnnl_status_t") public static int dnnl_stream_attr_create(@Cast(value="dnnl_stream_attr_t*") PointerPointer attr, @Cast(value="dnnl_engine_kind_t") int kind)
@Cast(value="dnnl_status_t") public static int dnnl_stream_attr_destroy(dnnl_stream_attr attr)
attr - Execution stream attributes to destroy.@Cast(value="dnnl_status_t") public static int dnnl_stream_create(@ByPtrPtr dnnl_stream stream, dnnl_engine engine, @Cast(value="unsigned") int flags)
stream - Output execution stream.engine - Engine to create the execution stream on.flags - Stream behavior flags (@see dnnl_stream_flags_t).@Cast(value="dnnl_status_t") public static int dnnl_stream_create(@Cast(value="dnnl_stream_t*") PointerPointer stream, dnnl_engine engine, @Cast(value="unsigned") int flags)
@Cast(value="dnnl_status_t") public static int dnnl_stream_create_v2(@ByPtrPtr dnnl_stream stream, dnnl_engine engine, @Cast(value="unsigned") int flags, @Const dnnl_stream_attr attr)
stream - Output execution stream.engine - Engine to create the execution stream on.flags - Stream behavior flags (@see dnnl_stream_flags_t).attr - Stream attributes.@Cast(value="dnnl_status_t") public static int dnnl_stream_create_v2(@Cast(value="dnnl_stream_t*") PointerPointer stream, dnnl_engine engine, @Cast(value="unsigned") int flags, @Const dnnl_stream_attr attr)
@Cast(value="dnnl_status_t") public static int dnnl_stream_wait(dnnl_stream stream)
stream - Execution stream.@Cast(value="dnnl_status_t") public static int dnnl_stream_destroy(dnnl_stream stream)
stream - Execution stream to destroy.@Cast(value="dnnl_status_t") public static int dnnl_get_primitive_cache_capacity(IntPointer _capacity)
\addtogroup dnnl_api_primitive_cache \{
Returns the number of primitives that can be held in the primitive cache at the same time.
capacity - Primitive cache capacity to query. Concurrently
accessing \p capacity is safe.@Cast(value="dnnl_status_t") public static int dnnl_get_primitive_cache_capacity(IntBuffer _capacity)
@Cast(value="dnnl_status_t") public static int dnnl_get_primitive_cache_capacity(int[] _capacity)
@Cast(value="dnnl_status_t") public static int dnnl_set_primitive_cache_capacity(int _capacity)
capacity - Primitive cache capacity to set. If a new \p capacity is
less than a number of primitives that the primitive cache already has
then the excess entries will be evicted. Setting the \p capacity to 0
clears the primitive cache and disables it. Concurrently modifying
\p capacity is safe.@Cast(value="dnnl_status_t") public static int dnnl_set_verbose(int level)
\addtogroup dnnl_api_service \{
Configures verbose output to stdout. \note Enabling verbose output affects performance. This setting overrides the DNNL_VERBOSE environment variable.
level - Verbosity level:
- 0: no verbose output (default),
- 1: primitive information at execution,
- 2: primitive information at creation and execution.@Cast(value="dnnl_status_t") public static int dnnl_set_jit_dump(int enable)
enable - Flag value. Set to 0 to disable and set to 1 to enable.@Const public static dnnl_version_t dnnl_version()
@Cast(value="dnnl_status_t") public static int dnnl_set_jit_profiling_flags(@Cast(value="unsigned") int flags)
flags - Profiling flags that can contain the following bits:
- \ref DNNL_JIT_PROFILE_VTUNE -- integration with VTune Amplifier
(on by default)
- \ref DNNL_JIT_PROFILE_LINUX_JITDUMP -- produce Linux-specific
jit-pid.dump output (off by default). The location of the output
is controlled via JITDUMPDIR environment variable or via
dnnl_set_jit_profiling_jitdumpdir() function.
- \ref DNNL_JIT_PROFILE_LINUX_PERFMAP -- produce Linux-specific
perf-pid.map output (off by default). The output is always placed
into /tmp.
Passing \ref DNNL_JIT_PROFILE_NONE disables profiling completely.dev_guide_profilers@Cast(value="dnnl_status_t") public static int dnnl_set_jit_profiling_jitdumpdir(@Cast(value="const char*") BytePointer dir)
dir - JIT dump output path.dev_guide_profilers
\note
This setting overrides JITDUMPDIR environment variable. If
JITDUMPDIR is not set, and this function is never called, the path
defaults to HOME. Passing NULL reverts the value to default.
\note
The directory is accessed only when the first JIT kernel is being
created. JIT profiling will be disabled in case of any errors
accessing or creating this directory.@Cast(value="dnnl_status_t") public static int dnnl_set_jit_profiling_jitdumpdir(String dir)
@Cast(value="dnnl_status_t") public static int dnnl_set_max_cpu_isa(@Cast(value="dnnl_cpu_isa_t") int isa)
DNNL_MAX_CPU_ISA=AVX2.
\note
The ISAs are only partially ordered:
- SSE41 < AVX < AVX2,
- AVX2 < AVX512_MIC < AVX512_MIC_4OPS,
- AVX2 < AVX512_CORE < AVX512_CORE_VNNI < AVX512_CORE_BF16
< AVX512_CORE_AMX.isa - Maximal ISA the library should dispatch to. Pass
#dnnl_cpu_isa_all/#dnnl::cpu_isa::all to remove ISA restrictions
(except for ISAs with initial support in the library).dev_guide_cpu_dispatcher_control for more details@Cast(value="dnnl_cpu_isa_t") public static int dnnl_get_effective_cpu_isa()
dev_guide_cpu_dispatcher_control for more details@Cast(value="dnnl_status_t") public static int dnnl_sgemm(@Cast(value="char") byte transa, @Cast(value="char") byte transb, @Cast(value="dnnl_dim_t") long M, @Cast(value="dnnl_dim_t") long N, @Cast(value="dnnl_dim_t") long K, float alpha, @Const FloatPointer A, @Cast(value="dnnl_dim_t") long lda, @Const FloatPointer B, @Cast(value="dnnl_dim_t") long ldb, float beta, FloatPointer C, @Cast(value="dnnl_dim_t") long ldc)
\addtogroup dnnl_api_blas \{
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.
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.@Cast(value="dnnl_status_t") public static int dnnl_sgemm(@Cast(value="char") byte transa, @Cast(value="char") byte transb, @Cast(value="dnnl_dim_t") long M, @Cast(value="dnnl_dim_t") long N, @Cast(value="dnnl_dim_t") long K, float alpha, @Const FloatBuffer A, @Cast(value="dnnl_dim_t") long lda, @Const FloatBuffer B, @Cast(value="dnnl_dim_t") long ldb, float beta, FloatBuffer C, @Cast(value="dnnl_dim_t") long ldc)
@Cast(value="dnnl_status_t") public static int dnnl_sgemm(@Cast(value="char") byte transa, @Cast(value="char") byte transb, @Cast(value="dnnl_dim_t") long M, @Cast(value="dnnl_dim_t") long N, @Cast(value="dnnl_dim_t") long K, float alpha, @Const float[] A, @Cast(value="dnnl_dim_t") long lda, @Const float[] B, @Cast(value="dnnl_dim_t") long ldb, float beta, float[] C, @Cast(value="dnnl_dim_t") long ldc)
@Cast(value="dnnl_status_t") public static int dnnl_gemm_u8s8s32(@Cast(value="char") byte transa, @Cast(value="char") byte transb, @Cast(value="char") byte offsetc, @Cast(value="dnnl_dim_t") long M, @Cast(value="dnnl_dim_t") long N, @Cast(value="dnnl_dim_t") long K, float alpha, @Cast(value="const uint8_t*") BytePointer A, @Cast(value="dnnl_dim_t") long lda, @Cast(value="uint8_t") byte ao, @Const BytePointer B, @Cast(value="dnnl_dim_t") long ldb, byte bo, float beta, IntPointer C, @Cast(value="dnnl_dim_t") long ldc, @Const IntPointer co)
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 \ref dev_guide_int8_computations.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 \p offsetc.@Cast(value="dnnl_status_t") public static int dnnl_gemm_u8s8s32(@Cast(value="char") byte transa, @Cast(value="char") byte transb, @Cast(value="char") byte offsetc, @Cast(value="dnnl_dim_t") long M, @Cast(value="dnnl_dim_t") long N, @Cast(value="dnnl_dim_t") long K, float alpha, @Cast(value="const uint8_t*") ByteBuffer A, @Cast(value="dnnl_dim_t") long lda, @Cast(value="uint8_t") byte ao, @Const ByteBuffer B, @Cast(value="dnnl_dim_t") long ldb, byte bo, float beta, IntBuffer C, @Cast(value="dnnl_dim_t") long ldc, @Const IntBuffer co)
@Cast(value="dnnl_status_t") public static int dnnl_gemm_u8s8s32(@Cast(value="char") byte transa, @Cast(value="char") byte transb, @Cast(value="char") byte offsetc, @Cast(value="dnnl_dim_t") long M, @Cast(value="dnnl_dim_t") long N, @Cast(value="dnnl_dim_t") long K, float alpha, @Cast(value="const uint8_t*") byte[] A, @Cast(value="dnnl_dim_t") long lda, @Cast(value="uint8_t") byte ao, @Const byte[] B, @Cast(value="dnnl_dim_t") long ldb, byte bo, float beta, int[] C, @Cast(value="dnnl_dim_t") long ldc, @Const int[] co)
@Cast(value="dnnl_status_t") public static int dnnl_gemm_s8s8s32(@Cast(value="char") byte transa, @Cast(value="char") byte transb, @Cast(value="char") byte offsetc, @Cast(value="dnnl_dim_t") long M, @Cast(value="dnnl_dim_t") long N, @Cast(value="dnnl_dim_t") long K, float alpha, @Const BytePointer A, @Cast(value="dnnl_dim_t") long lda, byte ao, @Const BytePointer B, @Cast(value="dnnl_dim_t") long ldb, byte bo, float beta, IntPointer C, @Cast(value="dnnl_dim_t") long ldc, @Const IntPointer co)
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 \ref dev_guide_int8_computations.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 \p offsetc.@Cast(value="dnnl_status_t") public static int dnnl_gemm_s8s8s32(@Cast(value="char") byte transa, @Cast(value="char") byte transb, @Cast(value="char") byte offsetc, @Cast(value="dnnl_dim_t") long M, @Cast(value="dnnl_dim_t") long N, @Cast(value="dnnl_dim_t") long K, float alpha, @Const ByteBuffer A, @Cast(value="dnnl_dim_t") long lda, byte ao, @Const ByteBuffer B, @Cast(value="dnnl_dim_t") long ldb, byte bo, float beta, IntBuffer C, @Cast(value="dnnl_dim_t") long ldc, @Const IntBuffer co)
@Cast(value="dnnl_status_t") public static int dnnl_gemm_s8s8s32(@Cast(value="char") byte transa, @Cast(value="char") byte transb, @Cast(value="char") byte offsetc, @Cast(value="dnnl_dim_t") long M, @Cast(value="dnnl_dim_t") long N, @Cast(value="dnnl_dim_t") long K, float alpha, @Const byte[] A, @Cast(value="dnnl_dim_t") long lda, byte ao, @Const byte[] B, @Cast(value="dnnl_dim_t") long ldb, byte bo, float beta, int[] C, @Cast(value="dnnl_dim_t") long ldc, @Const int[] co)
@Namespace(value="dnnl") @Cast(value="dnnl_primitive_kind_t") public static int convert_to_c(primitive.kind akind)
akind - C++ API primitive kind enum value.@Namespace(value="dnnl") @Cast(value="dnnl_scratchpad_mode_t") public static int convert_to_c(dnnl.scratchpad_mode mode)
mode - C++ API scratchpad mode enum value.@Namespace(value="dnnl") @Cast(value="dnnl_scratchpad_mode_t") public static int convert_to_c(@Cast(value="dnnl::scratchpad_mode") int mode)
@Namespace(value="dnnl") @Cast(value="dnnl_prop_kind_t") public static int convert_to_c(dnnl.prop_kind akind)
akind - C++ API propagation kind enum value.@Namespace(value="dnnl") @Cast(value="dnnl_alg_kind_t") public static int convert_to_c(dnnl.algorithm aalgorithm)
aalgorithm - C++ API algorithm kind enum value.@Namespace(value="dnnl") @Cast(value="dnnl_normalization_flags_t") public static int convert_to_c(dnnl.normalization_flags flags)
flags - C++ API normalization flags enum value.@Namespace(value="dnnl") @Cast(value="dnnl_rnn_flags_t") public static int convert_to_c(dnnl.rnn_flags flags)
flags - C++ API RNN cell flags enum value.@Namespace(value="dnnl") @Name(value="operator |") public static dnnl.normalization_flags or(dnnl.normalization_flags lhs, dnnl.normalization_flags rhs)
@Namespace(value="dnnl") @Name(value="operator |") @Cast(value="dnnl::normalization_flags") public static int or(@Cast(value="dnnl::normalization_flags") int lhs, @Cast(value="dnnl::normalization_flags") int rhs)
@Namespace(value="dnnl") @Name(value="operator &") public static dnnl.normalization_flags and(dnnl.normalization_flags lhs, dnnl.normalization_flags rhs)
@Namespace(value="dnnl") @Name(value="operator &") @Cast(value="dnnl::normalization_flags") public static int and(@Cast(value="dnnl::normalization_flags") int lhs, @Cast(value="dnnl::normalization_flags") int rhs)
@Namespace(value="dnnl") @Name(value="operator ^") public static dnnl.normalization_flags xor(dnnl.normalization_flags lhs, dnnl.normalization_flags rhs)
@Namespace(value="dnnl") @Name(value="operator ^") @Cast(value="dnnl::normalization_flags") public static int xor(@Cast(value="dnnl::normalization_flags") int lhs, @Cast(value="dnnl::normalization_flags") int rhs)
@Namespace(value="dnnl") @ByRef @Name(value="operator |=") @Cast(value="dnnl::normalization_flags*") public static IntPointer orPut(@ByRef @Cast(value="dnnl::normalization_flags*") IntPointer lhs, dnnl.normalization_flags rhs)
@Namespace(value="dnnl") @ByRef @Name(value="operator |=") @Cast(value="dnnl::normalization_flags*") public static IntBuffer orPut(@ByRef @Cast(value="dnnl::normalization_flags*") IntBuffer lhs, @Cast(value="dnnl::normalization_flags") int rhs)
@Namespace(value="dnnl") @ByRef @Name(value="operator |=") @Cast(value="dnnl::normalization_flags*") public static int[] orPut(@ByRef @Cast(value="dnnl::normalization_flags*") int[] lhs, dnnl.normalization_flags rhs)
@Namespace(value="dnnl") @ByRef @Name(value="operator |=") @Cast(value="dnnl::normalization_flags*") public static IntPointer orPut(@ByRef @Cast(value="dnnl::normalization_flags*") IntPointer lhs, @Cast(value="dnnl::normalization_flags") int rhs)
@Namespace(value="dnnl") @ByRef @Name(value="operator |=") @Cast(value="dnnl::normalization_flags*") public static IntBuffer orPut(@ByRef @Cast(value="dnnl::normalization_flags*") IntBuffer lhs, dnnl.normalization_flags rhs)
@Namespace(value="dnnl") @ByRef @Name(value="operator |=") @Cast(value="dnnl::normalization_flags*") public static int[] orPut(@ByRef @Cast(value="dnnl::normalization_flags*") int[] lhs, @Cast(value="dnnl::normalization_flags") int rhs)
@Namespace(value="dnnl") @ByRef @Name(value="operator &=") @Cast(value="dnnl::normalization_flags*") public static IntPointer andPut(@ByRef @Cast(value="dnnl::normalization_flags*") IntPointer lhs, dnnl.normalization_flags rhs)
@Namespace(value="dnnl") @ByRef @Name(value="operator &=") @Cast(value="dnnl::normalization_flags*") public static IntBuffer andPut(@ByRef @Cast(value="dnnl::normalization_flags*") IntBuffer lhs, @Cast(value="dnnl::normalization_flags") int rhs)
@Namespace(value="dnnl") @ByRef @Name(value="operator &=") @Cast(value="dnnl::normalization_flags*") public static int[] andPut(@ByRef @Cast(value="dnnl::normalization_flags*") int[] lhs, dnnl.normalization_flags rhs)
@Namespace(value="dnnl") @ByRef @Name(value="operator &=") @Cast(value="dnnl::normalization_flags*") public static IntPointer andPut(@ByRef @Cast(value="dnnl::normalization_flags*") IntPointer lhs, @Cast(value="dnnl::normalization_flags") int rhs)
@Namespace(value="dnnl") @ByRef @Name(value="operator &=") @Cast(value="dnnl::normalization_flags*") public static IntBuffer andPut(@ByRef @Cast(value="dnnl::normalization_flags*") IntBuffer lhs, dnnl.normalization_flags rhs)
@Namespace(value="dnnl") @ByRef @Name(value="operator &=") @Cast(value="dnnl::normalization_flags*") public static int[] andPut(@ByRef @Cast(value="dnnl::normalization_flags*") int[] lhs, @Cast(value="dnnl::normalization_flags") int rhs)
@Namespace(value="dnnl") @ByRef @Name(value="operator ^=") @Cast(value="dnnl::normalization_flags*") public static IntPointer xorPut(@ByRef @Cast(value="dnnl::normalization_flags*") IntPointer lhs, dnnl.normalization_flags rhs)
@Namespace(value="dnnl") @ByRef @Name(value="operator ^=") @Cast(value="dnnl::normalization_flags*") public static IntBuffer xorPut(@ByRef @Cast(value="dnnl::normalization_flags*") IntBuffer lhs, @Cast(value="dnnl::normalization_flags") int rhs)
@Namespace(value="dnnl") @ByRef @Name(value="operator ^=") @Cast(value="dnnl::normalization_flags*") public static int[] xorPut(@ByRef @Cast(value="dnnl::normalization_flags*") int[] lhs, dnnl.normalization_flags rhs)
@Namespace(value="dnnl") @ByRef @Name(value="operator ^=") @Cast(value="dnnl::normalization_flags*") public static IntPointer xorPut(@ByRef @Cast(value="dnnl::normalization_flags*") IntPointer lhs, @Cast(value="dnnl::normalization_flags") int rhs)
@Namespace(value="dnnl") @ByRef @Name(value="operator ^=") @Cast(value="dnnl::normalization_flags*") public static IntBuffer xorPut(@ByRef @Cast(value="dnnl::normalization_flags*") IntBuffer lhs, dnnl.normalization_flags rhs)
@Namespace(value="dnnl") @ByRef @Name(value="operator ^=") @Cast(value="dnnl::normalization_flags*") public static int[] xorPut(@ByRef @Cast(value="dnnl::normalization_flags*") int[] lhs, @Cast(value="dnnl::normalization_flags") int rhs)
@Namespace(value="dnnl") @Name(value="operator ~") public static dnnl.normalization_flags not(dnnl.normalization_flags rhs)
@Namespace(value="dnnl") @Name(value="operator ~") @Cast(value="dnnl::normalization_flags") public static int not(@Cast(value="dnnl::normalization_flags") int rhs)
@Namespace(value="dnnl") @Name(value="operator |") public static dnnl.rnn_flags or(dnnl.rnn_flags lhs, dnnl.rnn_flags rhs)
@Namespace(value="dnnl") @Name(value="operator &") public static dnnl.rnn_flags and(dnnl.rnn_flags lhs, dnnl.rnn_flags rhs)
@Namespace(value="dnnl") @Name(value="operator ^") public static dnnl.rnn_flags xor(dnnl.rnn_flags lhs, dnnl.rnn_flags rhs)
@Namespace(value="dnnl") @ByRef @Name(value="operator |=") @Cast(value="dnnl::rnn_flags*") public static IntPointer orPut(@ByRef @Cast(value="dnnl::rnn_flags*") IntPointer lhs, dnnl.rnn_flags rhs)
@Namespace(value="dnnl") @ByRef @Name(value="operator |=") @Cast(value="dnnl::rnn_flags*") public static int[] orPut(@ByRef @Cast(value="dnnl::rnn_flags*") int[] lhs, dnnl.rnn_flags rhs)
@Namespace(value="dnnl") @ByRef @Name(value="operator |=") @Cast(value="dnnl::rnn_flags*") public static IntBuffer orPut(@ByRef @Cast(value="dnnl::rnn_flags*") IntBuffer lhs, dnnl.rnn_flags rhs)
@Namespace(value="dnnl") @ByRef @Name(value="operator &=") @Cast(value="dnnl::rnn_flags*") public static IntPointer andPut(@ByRef @Cast(value="dnnl::rnn_flags*") IntPointer lhs, dnnl.rnn_flags rhs)
@Namespace(value="dnnl") @ByRef @Name(value="operator &=") @Cast(value="dnnl::rnn_flags*") public static int[] andPut(@ByRef @Cast(value="dnnl::rnn_flags*") int[] lhs, dnnl.rnn_flags rhs)
@Namespace(value="dnnl") @ByRef @Name(value="operator &=") @Cast(value="dnnl::rnn_flags*") public static IntBuffer andPut(@ByRef @Cast(value="dnnl::rnn_flags*") IntBuffer lhs, dnnl.rnn_flags rhs)
@Namespace(value="dnnl") @ByRef @Name(value="operator ^=") @Cast(value="dnnl::rnn_flags*") public static IntPointer xorPut(@ByRef @Cast(value="dnnl::rnn_flags*") IntPointer lhs, dnnl.rnn_flags rhs)
@Namespace(value="dnnl") @ByRef @Name(value="operator ^=") @Cast(value="dnnl::rnn_flags*") public static int[] xorPut(@ByRef @Cast(value="dnnl::rnn_flags*") int[] lhs, dnnl.rnn_flags rhs)
@Namespace(value="dnnl") @ByRef @Name(value="operator ^=") @Cast(value="dnnl::rnn_flags*") public static IntBuffer xorPut(@ByRef @Cast(value="dnnl::rnn_flags*") IntBuffer lhs, dnnl.rnn_flags rhs)
@Namespace(value="dnnl") @Name(value="operator ~") public static dnnl.rnn_flags not(dnnl.rnn_flags rhs)
@Namespace(value="dnnl") @Cast(value="dnnl_rnn_direction_t") public static int convert_to_c(dnnl.rnn_direction dir)
dir - C++ API RNN direction enum value.@Namespace(value="dnnl") @Cast(value="dnnl_query_t") public static int convert_to_c(dnnl.query aquery)
aquery - C++ API query enum value.@Namespace(value="dnnl") @Cast(value="dnnl_engine_kind_t") public static int convert_to_c(engine.kind akind)
akind - C++ API engine kind enum value.@Namespace(value="dnnl") @Name(value="operator |") public static stream.flags or(stream.flags lhs, stream.flags rhs)
\addtogroup dnnl_api_stream Stream An encapsulation of execution context tied to a particular engine.
dev_guide_basic_concepts
\{
\cond DO_NOT_DOCUMENT_THIS
@Namespace(value="dnnl") @Name(value="operator &") public static stream.flags and(stream.flags lhs, stream.flags rhs)
@Namespace(value="dnnl") @Name(value="operator ^") public static stream.flags xor(stream.flags lhs, stream.flags rhs)
@Namespace(value="dnnl") @Name(value="operator |=") public static stream.flags orPut(stream.flags lhs, stream.flags rhs)
@Namespace(value="dnnl") @Name(value="operator &=") public static stream.flags andPut(stream.flags lhs, stream.flags rhs)
@Namespace(value="dnnl") @Name(value="operator ^=") public static stream.flags xorPut(stream.flags lhs, stream.flags rhs)
@Namespace(value="dnnl") @Name(value="operator ~") public static stream.flags not(stream.flags rhs)
@Namespace(value="dnnl") @Cast(value="bool") @Name(value="operator ==") public static boolean equals(@Cast(value="dnnl_data_type_t") int a, memory.data_type b)
@Namespace(value="dnnl") @Cast(value="bool") @Name(value="operator !=") public static boolean notEquals(@Cast(value="dnnl_data_type_t") int a, memory.data_type b)
@Namespace(value="dnnl") @Cast(value="bool") @Name(value="operator ==") public static boolean equals(memory.data_type a, @Cast(value="dnnl_data_type_t") int b)
@Namespace(value="dnnl") @Cast(value="bool") @Name(value="operator !=") public static boolean notEquals(memory.data_type a, @Cast(value="dnnl_data_type_t") int b)
@Namespace(value="dnnl") @Cast(value="bool") @Name(value="operator ==") public static boolean equals(@Cast(value="dnnl_format_tag_t") int a, memory.format_tag b)
@Namespace(value="dnnl") @Cast(value="bool") @Name(value="operator !=") public static boolean notEquals(@Cast(value="dnnl_format_tag_t") int a, memory.format_tag b)
@Namespace(value="dnnl") @Cast(value="bool") @Name(value="operator ==") public static boolean equals(memory.format_tag a, @Cast(value="dnnl_format_tag_t") int b)
@Namespace(value="dnnl") @Cast(value="bool") @Name(value="operator !=") public static boolean notEquals(memory.format_tag a, @Cast(value="dnnl_format_tag_t") int b)
@Namespace(value="dnnl") @StdVector public static dnnl_memory_desc_t convert_to_c(@StdVector memory.desc mems)
\addtogroup dnnl_api_concat Concat A primitive to concatenate data by arbitrary dimension.
dev_guide_concat in developer guide
\{
\cond DO_NOT_DOCUMENT_THIS
@Namespace(value="dnnl") public static dnnl.status set_verbose(int level)
@Namespace(value="dnnl") @Cast(value="const dnnl::version_t*") public static dnnl_version_t version()
@Namespace(value="dnnl") public static dnnl.status set_jit_dump(int enable)
@Namespace(value="dnnl") public static dnnl.status set_jit_profiling_flags(@Cast(value="unsigned") int flags)
@Namespace(value="dnnl") public static dnnl.status set_jit_profiling_jitdumpdir(@StdString BytePointer dir)
@Namespace(value="dnnl") @Cast(value="dnnl::status") public static int set_jit_profiling_jitdumpdir(@StdString String dir)
@Namespace(value="dnnl") public static dnnl.status set_max_cpu_isa(dnnl.cpu_isa isa)
@Namespace(value="dnnl") @Cast(value="dnnl::status") public static int set_max_cpu_isa(@Cast(value="dnnl::cpu_isa") int isa)
@Namespace(value="dnnl") public static dnnl.cpu_isa get_effective_cpu_isa()
@Namespace(value="dnnl") public static int get_primitive_cache_capacity()
\addtogroup dnnl_api_primitive_cache Primitive Cache A set of functions that provide primitive cache control. \{
Returns the number of primitives that can be held in the primitive cache at the same time.
@Namespace(value="dnnl") public static void set_primitive_cache_capacity(int _capacity)
@Namespace(value="dnnl") public static dnnl.status sgemm(@Cast(value="char") byte transa, @Cast(value="char") byte transb, @Cast(value="dnnl_dim_t") long M, @Cast(value="dnnl_dim_t") long N, @Cast(value="dnnl_dim_t") long K, float alpha, @Const FloatPointer A, @Cast(value="dnnl_dim_t") long lda, @Const FloatPointer B, @Cast(value="dnnl_dim_t") long ldb, float beta, FloatPointer C, @Cast(value="dnnl_dim_t") long ldc)
\addtogroup dnnl_api_blas BLAS functions A subset of Basic Linear Algebra (BLAS) functions that perform matrix-matrix multiplication. \{
\copydoc dnnl_sgemm()
@Namespace(value="dnnl") @Cast(value="dnnl::status") public static int sgemm(@Cast(value="char") byte transa, @Cast(value="char") byte transb, @Cast(value="dnnl_dim_t") long M, @Cast(value="dnnl_dim_t") long N, @Cast(value="dnnl_dim_t") long K, float alpha, @Const FloatBuffer A, @Cast(value="dnnl_dim_t") long lda, @Const FloatBuffer B, @Cast(value="dnnl_dim_t") long ldb, float beta, FloatBuffer C, @Cast(value="dnnl_dim_t") long ldc)
@Namespace(value="dnnl") public static dnnl.status sgemm(@Cast(value="char") byte transa, @Cast(value="char") byte transb, @Cast(value="dnnl_dim_t") long M, @Cast(value="dnnl_dim_t") long N, @Cast(value="dnnl_dim_t") long K, float alpha, @Const float[] A, @Cast(value="dnnl_dim_t") long lda, @Const float[] B, @Cast(value="dnnl_dim_t") long ldb, float beta, float[] C, @Cast(value="dnnl_dim_t") long ldc)
@Namespace(value="dnnl") public static dnnl.status gemm_u8s8s32(@Cast(value="char") byte transa, @Cast(value="char") byte transb, @Cast(value="char") byte offsetc, @Cast(value="dnnl_dim_t") long M, @Cast(value="dnnl_dim_t") long N, @Cast(value="dnnl_dim_t") long K, float alpha, @Cast(value="const uint8_t*") BytePointer A, @Cast(value="dnnl_dim_t") long lda, @Cast(value="uint8_t") byte ao, @Const BytePointer B, @Cast(value="dnnl_dim_t") long ldb, byte bo, float beta, IntPointer C, @Cast(value="dnnl_dim_t") long ldc, @Const IntPointer co)
@Namespace(value="dnnl") @Cast(value="dnnl::status") public static int gemm_u8s8s32(@Cast(value="char") byte transa, @Cast(value="char") byte transb, @Cast(value="char") byte offsetc, @Cast(value="dnnl_dim_t") long M, @Cast(value="dnnl_dim_t") long N, @Cast(value="dnnl_dim_t") long K, float alpha, @Cast(value="const uint8_t*") ByteBuffer A, @Cast(value="dnnl_dim_t") long lda, @Cast(value="uint8_t") byte ao, @Const ByteBuffer B, @Cast(value="dnnl_dim_t") long ldb, byte bo, float beta, IntBuffer C, @Cast(value="dnnl_dim_t") long ldc, @Const IntBuffer co)
@Namespace(value="dnnl") public static dnnl.status gemm_u8s8s32(@Cast(value="char") byte transa, @Cast(value="char") byte transb, @Cast(value="char") byte offsetc, @Cast(value="dnnl_dim_t") long M, @Cast(value="dnnl_dim_t") long N, @Cast(value="dnnl_dim_t") long K, float alpha, @Cast(value="const uint8_t*") byte[] A, @Cast(value="dnnl_dim_t") long lda, @Cast(value="uint8_t") byte ao, @Const byte[] B, @Cast(value="dnnl_dim_t") long ldb, byte bo, float beta, int[] C, @Cast(value="dnnl_dim_t") long ldc, @Const int[] co)
@Namespace(value="dnnl") public static dnnl.status gemm_s8s8s32(@Cast(value="char") byte transa, @Cast(value="char") byte transb, @Cast(value="char") byte offsetc, @Cast(value="dnnl_dim_t") long M, @Cast(value="dnnl_dim_t") long N, @Cast(value="dnnl_dim_t") long K, float alpha, @Const BytePointer A, @Cast(value="dnnl_dim_t") long lda, byte ao, @Const BytePointer B, @Cast(value="dnnl_dim_t") long ldb, byte bo, float beta, IntPointer C, @Cast(value="dnnl_dim_t") long ldc, @Const IntPointer co)
@Namespace(value="dnnl") @Cast(value="dnnl::status") public static int gemm_s8s8s32(@Cast(value="char") byte transa, @Cast(value="char") byte transb, @Cast(value="char") byte offsetc, @Cast(value="dnnl_dim_t") long M, @Cast(value="dnnl_dim_t") long N, @Cast(value="dnnl_dim_t") long K, float alpha, @Const ByteBuffer A, @Cast(value="dnnl_dim_t") long lda, byte ao, @Const ByteBuffer B, @Cast(value="dnnl_dim_t") long ldb, byte bo, float beta, IntBuffer C, @Cast(value="dnnl_dim_t") long ldc, @Const IntBuffer co)
@Namespace(value="dnnl") public static dnnl.status gemm_s8s8s32(@Cast(value="char") byte transa, @Cast(value="char") byte transb, @Cast(value="char") byte offsetc, @Cast(value="dnnl_dim_t") long M, @Cast(value="dnnl_dim_t") long N, @Cast(value="dnnl_dim_t") long K, float alpha, @Const byte[] A, @Cast(value="dnnl_dim_t") long lda, byte ao, @Const byte[] B, @Cast(value="dnnl_dim_t") long ldb, byte bo, float beta, int[] C, @Cast(value="dnnl_dim_t") long ldc, @Const int[] co)
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