Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN)
0.17.4
Performance library for Deep Learning
|
A primitive to compute common recurrent layer. More...
Functions | |
mkldnn_status_t MKLDNN_API | mkldnn_rnn_cell_desc_init (mkldnn_rnn_cell_desc_t *rnn_cell_desc, mkldnn_alg_kind_t kind, mkldnn_alg_kind_t f, unsigned int flags, float alpha, float clipping) |
Initializes a recurrent cell descriptor rnn_cell_desc using rnn_cell_desc , kind (possible values are mkldnn_vanilla_rnn, mkldnn_vanilla_lstm, mkldnn_vanilla_gru, mkldnn_gru_linear_before_reset), f (possible values are mkldnn_eltwise_relu, mkldnn_eltwise_tanh), flags , alpha , and clipping . More... | |
int MKLDNN_API | mkldnn_rnn_cell_get_gates_count (const mkldnn_rnn_cell_desc_t *rnn_cell_desc) |
Returns the number of gates of a particular rnn_cell_desc . More... | |
int MKLDNN_API | mkldnn_rnn_cell_get_states_count (const mkldnn_rnn_cell_desc_t *rnn_cell_desc) |
Returns the number of states of a particular rnn_cell_desc . More... | |
mkldnn_status_t MKLDNN_API | mkldnn_rnn_forward_desc_init (mkldnn_rnn_desc_t *rnn_desc, mkldnn_prop_kind_t prop_kind, const mkldnn_rnn_cell_desc_t *rnn_cell_desc, const mkldnn_rnn_direction_t direction, const mkldnn_memory_desc_t *src_layer_desc, const mkldnn_memory_desc_t *src_iter_desc, const mkldnn_memory_desc_t *weights_layer_desc, const mkldnn_memory_desc_t *weights_iter_desc, const mkldnn_memory_desc_t *bias_desc, const mkldnn_memory_desc_t *dst_layer_desc, const mkldnn_memory_desc_t *dst_iter_desc) |
Initializes a rnn descriptor rnn_desc for forward propagation using prop_kind , rnn_cell_desc , direction , and memory descriptors. More... | |
mkldnn_status_t MKLDNN_API | mkldnn_rnn_backward_desc_init (mkldnn_rnn_desc_t *rnn_desc, mkldnn_prop_kind_t prop_kind, const mkldnn_rnn_cell_desc_t *rnn_cell_desc, const mkldnn_rnn_direction_t direction, const mkldnn_memory_desc_t *src_layer_desc, const mkldnn_memory_desc_t *src_iter_desc, const mkldnn_memory_desc_t *weights_layer_desc, const mkldnn_memory_desc_t *weights_iter_desc, const mkldnn_memory_desc_t *bias_desc, const mkldnn_memory_desc_t *dst_layer_desc, const mkldnn_memory_desc_t *dst_iter_desc, const mkldnn_memory_desc_t *diff_src_layer_desc, const mkldnn_memory_desc_t *diff_src_iter_desc, const mkldnn_memory_desc_t *diff_weights_layer_desc, const mkldnn_memory_desc_t *diff_weights_iter_desc, const mkldnn_memory_desc_t *diff_bias_desc, const mkldnn_memory_desc_t *diff_dst_layer, const mkldnn_memory_desc_t *diff_dst_iter_desc) |
Initializes a rnn descriptor rnn_desc for backward propagation using prop_kind , rnn_cell_desc , direction , and memory descriptors. More... | |
A primitive to compute common recurrent layer.
mkldnn_status_t MKLDNN_API mkldnn_rnn_cell_desc_init | ( | mkldnn_rnn_cell_desc_t * | rnn_cell_desc, |
mkldnn_alg_kind_t | kind, | ||
mkldnn_alg_kind_t | f, | ||
unsigned int | flags, | ||
float | alpha, | ||
float | clipping | ||
) |
Initializes a recurrent cell descriptor rnn_cell_desc
using rnn_cell_desc
, kind
(possible values are mkldnn_vanilla_rnn, mkldnn_vanilla_lstm, mkldnn_vanilla_gru, mkldnn_gru_linear_before_reset), f
(possible values are mkldnn_eltwise_relu, mkldnn_eltwise_tanh), flags
, alpha
, and clipping
.
int MKLDNN_API mkldnn_rnn_cell_get_gates_count | ( | const mkldnn_rnn_cell_desc_t * | rnn_cell_desc | ) |
Returns the number of gates of a particular rnn_cell_desc
.
int MKLDNN_API mkldnn_rnn_cell_get_states_count | ( | const mkldnn_rnn_cell_desc_t * | rnn_cell_desc | ) |
Returns the number of states of a particular rnn_cell_desc
.
mkldnn_status_t MKLDNN_API mkldnn_rnn_forward_desc_init | ( | mkldnn_rnn_desc_t * | rnn_desc, |
mkldnn_prop_kind_t | prop_kind, | ||
const mkldnn_rnn_cell_desc_t * | rnn_cell_desc, | ||
const mkldnn_rnn_direction_t | direction, | ||
const mkldnn_memory_desc_t * | src_layer_desc, | ||
const mkldnn_memory_desc_t * | src_iter_desc, | ||
const mkldnn_memory_desc_t * | weights_layer_desc, | ||
const mkldnn_memory_desc_t * | weights_iter_desc, | ||
const mkldnn_memory_desc_t * | bias_desc, | ||
const mkldnn_memory_desc_t * | dst_layer_desc, | ||
const mkldnn_memory_desc_t * | dst_iter_desc | ||
) |
Initializes a rnn descriptor rnn_desc
for forward propagation using prop_kind
, rnn_cell_desc
, direction
, and memory descriptors.
prop_kind
equals mkldnn_forward_training, you need to query a workspace memory descriptor before creating the primitive.src_iter_desc
, bias_desc
, and dst_iter_desc
are allowed to be either NULL or point to a zero memory descriptor that would indicate RNN primitive should not use them.
src_iter_desc
are allowed to be initialized with mkldnn_any value of format_kind
.Order of inputs:
Order of outputs:
prop_kind
equals mkldnn_forward_training mkldnn_status_t MKLDNN_API mkldnn_rnn_backward_desc_init | ( | mkldnn_rnn_desc_t * | rnn_desc, |
mkldnn_prop_kind_t | prop_kind, | ||
const mkldnn_rnn_cell_desc_t * | rnn_cell_desc, | ||
const mkldnn_rnn_direction_t | direction, | ||
const mkldnn_memory_desc_t * | src_layer_desc, | ||
const mkldnn_memory_desc_t * | src_iter_desc, | ||
const mkldnn_memory_desc_t * | weights_layer_desc, | ||
const mkldnn_memory_desc_t * | weights_iter_desc, | ||
const mkldnn_memory_desc_t * | bias_desc, | ||
const mkldnn_memory_desc_t * | dst_layer_desc, | ||
const mkldnn_memory_desc_t * | dst_iter_desc, | ||
const mkldnn_memory_desc_t * | diff_src_layer_desc, | ||
const mkldnn_memory_desc_t * | diff_src_iter_desc, | ||
const mkldnn_memory_desc_t * | diff_weights_layer_desc, | ||
const mkldnn_memory_desc_t * | diff_weights_iter_desc, | ||
const mkldnn_memory_desc_t * | diff_bias_desc, | ||
const mkldnn_memory_desc_t * | diff_dst_layer, | ||
const mkldnn_memory_desc_t * | diff_dst_iter_desc | ||
) |
Initializes a rnn descriptor rnn_desc
for backward propagation using prop_kind
, rnn_cell_desc
, direction
, and memory descriptors.
format_kind
.src_iter_desc
(simultaneously with diff_src_iter_desc
), bias_desc
(simultaneously with diff_bias_desc
), and dst_iter_desc
(simultaneously with diff_src_iter_desc
) are allowed to be either NULL or point to a zero memory descriptor that would indicate RNN primitive should not use them.
Order of inputs:
Order of outputs: