Visible to Intel only — GUID: GUID-33117CEC-CF02-48E2-90FD-585C5DF535C0
Abs
AbsBackward
Add
AvgPool
AvgPoolBackward
BatchNormForwardTraining
BatchNormInference
BatchNormTrainingBackward
BiasAdd
BiasAddBackward
Clamp
ClampBackward
Concat
Convolution
ConvolutionBackwardData
ConvolutionBackwardWeights
ConvTranspose
ConvTransposeBackwardData
ConvTransposeBackwardWeights
Dequantize
Divide
DynamicDequantize
DynamicQuantize
Elu
EluBackward
End
Exp
GroupNorm
GELU
GELUBackward
HardSigmoid
HardSigmoidBackward
HardSwish
HardSwishBackward
Interpolate
InterpolateBackward
LayerNorm
LayerNormBackward
LeakyReLU
Log
LogSoftmax
LogSoftmaxBackward
MatMul
Maximum
MaxPool
MaxPoolBackward
Minimum
Mish
MishBackward
Multiply
Pow
PReLU
PReLUBackward
Quantize
Reciprocal
ReduceL1
ReduceL2
ReduceMax
ReduceMean
ReduceMin
ReduceProd
ReduceSum
ReLU
ReLUBackward
Reorder
Round
Select
Sigmoid
SigmoidBackward
SoftMax
SoftMaxBackward
SoftPlus
SoftPlusBackward
Sqrt
SqrtBackward
Square
SquaredDifference
StaticReshape
StaticTranspose
Subtract
Tanh
TanhBackward
TypeCast
Wildcard
enum dnnl_alg_kind_t
enum dnnl_normalization_flags_t
enum dnnl_primitive_kind_t
enum dnnl_prop_kind_t
enum dnnl_query_t
enum dnnl::normalization_flags
enum dnnl::query
struct dnnl_exec_arg_t
struct dnnl_primitive
struct dnnl_primitive_desc
struct dnnl::primitive
struct dnnl::primitive_desc
struct dnnl::primitive_desc_base
Overview
Detailed Documentation
enum dnnl_rnn_direction_t
enum dnnl_rnn_flags_t
enum dnnl::rnn_direction
enum dnnl::rnn_flags
struct dnnl::augru_backward
struct dnnl::augru_forward
struct dnnl::gru_backward
struct dnnl::gru_forward
struct dnnl::lbr_augru_backward
struct dnnl::lbr_augru_forward
struct dnnl::lbr_gru_backward
struct dnnl::lbr_gru_forward
struct dnnl::lstm_backward
struct dnnl::lstm_forward
struct dnnl::rnn_primitive_desc_base
struct dnnl::vanilla_rnn_backward
struct dnnl::vanilla_rnn_forward
Visible to Intel only — GUID: GUID-33117CEC-CF02-48E2-90FD-585C5DF535C0
RNN
Overview
A primitive to compute recurrent neural network layers. More…
// enums
enum dnnl_rnn_direction_t;
enum dnnl_rnn_flags_t;
enum dnnl::rnn_direction;
enum dnnl::rnn_flags;
// structs
struct dnnl::augru_backward;
struct dnnl::augru_forward;
struct dnnl::gru_backward;
struct dnnl::gru_forward;
struct dnnl::lbr_augru_backward;
struct dnnl::lbr_augru_forward;
struct dnnl::lbr_gru_backward;
struct dnnl::lbr_gru_forward;
struct dnnl::lstm_backward;
struct dnnl::lstm_forward;
struct dnnl::rnn_primitive_desc_base;
struct dnnl::vanilla_rnn_backward;
struct dnnl::vanilla_rnn_forward;
// global functions
dnnl_rnn_flags_t dnnl::convert_to_c(rnn_flags flags);
dnnl_rnn_direction_t dnnl::convert_to_c(rnn_direction dir);
dnnl_status_t DNNL_API dnnl_vanilla_rnn_forward_primitive_desc_create(
dnnl_primitive_desc_t* primitive_desc,
dnnl_engine_t engine,
dnnl_prop_kind_t prop_kind,
const dnnl_alg_kind_t activation,
const dnnl_rnn_direction_t 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,
unsigned flags,
float alpha,
float beta,
const_dnnl_primitive_attr_t attr
);
dnnl_status_t DNNL_API dnnl_vanilla_rnn_backward_primitive_desc_create(
dnnl_primitive_desc_t* primitive_desc,
dnnl_engine_t engine,
dnnl_prop_kind_t prop_kind,
const dnnl_alg_kind_t activation,
const dnnl_rnn_direction_t 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,
unsigned flags,
float alpha,
float beta,
const_dnnl_primitive_desc_t hint_fwd_pd,
const_dnnl_primitive_attr_t attr
);
dnnl_status_t DNNL_API dnnl_lstm_forward_primitive_desc_create(
dnnl_primitive_desc_t* primitive_desc,
dnnl_engine_t engine,
dnnl_prop_kind_t prop_kind,
dnnl_rnn_direction_t 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,
unsigned flags,
const_dnnl_primitive_attr_t attr
);
dnnl_status_t DNNL_API dnnl_lstm_backward_primitive_desc_create(
dnnl_primitive_desc_t* primitive_desc,
dnnl_engine_t engine,
dnnl_prop_kind_t prop_kind,
dnnl_rnn_direction_t 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,
unsigned flags,
const_dnnl_primitive_desc_t hint_fwd_pd,
const_dnnl_primitive_attr_t attr
);
dnnl_status_t DNNL_API dnnl_gru_forward_primitive_desc_create(
dnnl_primitive_desc_t* primitive_desc,
dnnl_engine_t engine,
dnnl_prop_kind_t prop_kind,
dnnl_rnn_direction_t 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,
unsigned flags,
const_dnnl_primitive_attr_t attr
);
dnnl_status_t DNNL_API dnnl_gru_backward_primitive_desc_create(
dnnl_primitive_desc_t* primitive_desc,
dnnl_engine_t engine,
dnnl_prop_kind_t prop_kind,
dnnl_rnn_direction_t 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,
unsigned flags,
const_dnnl_primitive_desc_t hint_fwd_pd,
const_dnnl_primitive_attr_t attr
);
dnnl_status_t DNNL_API dnnl_lbr_gru_forward_primitive_desc_create(
dnnl_primitive_desc_t* primitive_desc,
dnnl_engine_t engine,
dnnl_prop_kind_t prop_kind,
dnnl_rnn_direction_t 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,
unsigned flags,
const_dnnl_primitive_attr_t attr
);
dnnl_status_t DNNL_API dnnl_lbr_gru_backward_primitive_desc_create(
dnnl_primitive_desc_t* primitive_desc,
dnnl_engine_t engine,
dnnl_prop_kind_t prop_kind,
dnnl_rnn_direction_t 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,
unsigned flags,
const_dnnl_primitive_desc_t hint_fwd_pd,
const_dnnl_primitive_attr_t attr
);
dnnl_status_t DNNL_API dnnl_augru_forward_primitive_desc_create(
dnnl_primitive_desc_t* primitive_desc,
dnnl_engine_t engine,
dnnl_prop_kind_t prop_kind,
dnnl_rnn_direction_t direction,
const_dnnl_memory_desc_t src_layer_desc,
const_dnnl_memory_desc_t src_iter_desc,
const_dnnl_memory_desc_t attention_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,
unsigned flags,
const_dnnl_primitive_attr_t attr
);
dnnl_status_t DNNL_API dnnl_augru_backward_primitive_desc_create(
dnnl_primitive_desc_t* primitive_desc,
dnnl_engine_t engine,
dnnl_prop_kind_t prop_kind,
dnnl_rnn_direction_t direction,
const_dnnl_memory_desc_t src_layer_desc,
const_dnnl_memory_desc_t src_iter_desc,
const_dnnl_memory_desc_t attention_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_attention_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,
unsigned flags,
const_dnnl_primitive_desc_t hint_fwd_pd,
const_dnnl_primitive_attr_t attr
);
dnnl_status_t DNNL_API dnnl_lbr_augru_forward_primitive_desc_create(
dnnl_primitive_desc_t* primitive_desc,
dnnl_engine_t engine,
dnnl_prop_kind_t prop_kind,
dnnl_rnn_direction_t direction,
const_dnnl_memory_desc_t src_layer_desc,
const_dnnl_memory_desc_t src_iter_desc,
const_dnnl_memory_desc_t attention_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,
unsigned flags,
const_dnnl_primitive_attr_t attr
);
dnnl_status_t DNNL_API dnnl_lbr_augru_backward_primitive_desc_create(
dnnl_primitive_desc_t* primitive_desc,
dnnl_engine_t engine,
dnnl_prop_kind_t prop_kind,
dnnl_rnn_direction_t direction,
const_dnnl_memory_desc_t src_layer_desc,
const_dnnl_memory_desc_t src_iter_desc,
const_dnnl_memory_desc_t attention_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_attention_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,
unsigned flags,
const_dnnl_primitive_desc_t hint_fwd_pd,
const_dnnl_primitive_attr_t attr
);Detailed Documentation
A primitive to compute recurrent neural network layers.
See also:
RNN in developer guide
Global Functions
dnnl_rnn_flags_t dnnl::convert_to_c(rnn_flags flags)Converts RNN cell flags enum value from C++ API to C API type.
Parameters:
flags |
C++ API RNN cell flags enum value. |
Returns:
Corresponding C API RNN cell flags enum value.
dnnl_rnn_direction_t dnnl::convert_to_c(rnn_direction dir)Converts RNN direction enum value from C++ API to C API type.
Parameters:
dir |
C++ API RNN direction enum value. |
Returns:
Corresponding C API RNN direction enum value.
dnnl_status_t DNNL_API dnnl_vanilla_rnn_forward_primitive_desc_create(
dnnl_primitive_desc_t* primitive_desc,
dnnl_engine_t engine,
dnnl_prop_kind_t prop_kind,
const dnnl_alg_kind_t activation,
const dnnl_rnn_direction_t 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,
unsigned flags,
float alpha,
float beta,
const_dnnl_primitive_attr_t attr
)Creates a primitive descriptor for vanilla RNN forward propagation primitive.
The following arguments may either be NULL or point to a zero memory descriptor:
src_iter_desc,
bias_desc,
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.
Parameters:
primitive_desc |
Output primitive descriptor. |
engine |
Engine to use. |
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 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. |
attr |
Primitive attributes (can be NULL). |
Returns:
dnnl_success on success and a status describing the error otherwise.
dnnl_status_t DNNL_API dnnl_vanilla_rnn_backward_primitive_desc_create(
dnnl_primitive_desc_t* primitive_desc,
dnnl_engine_t engine,
dnnl_prop_kind_t prop_kind,
const dnnl_alg_kind_t activation,
const dnnl_rnn_direction_t 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,
unsigned flags,
float alpha,
float beta,
const_dnnl_primitive_desc_t hint_fwd_pd,
const_dnnl_primitive_attr_t attr
)Creates a primitive descriptor for vanilla RNN backward propagation primitive.
The following arguments may either be NULL or point to a zero memory descriptor:
src_iter_desc together with diff_src_iter_desc,
bias_desc together with diff_bias_desc,
dst_iter_desc together with diff_dst_iter_desc.
This would then indicate that the RNN backward propagation primitive should not use the respective data and should use 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.
Parameters:
primitive_desc |
Output primitive descriptor. |
engine |
Engine to use. |
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 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. |
hint_fwd_pd |
Primitive descriptor for a respective forward propagation primitive. |
attr |
Primitive attributes (can be NULL). |
Returns:
dnnl_success on success and a status describing the error otherwise.
dnnl_status_t DNNL_API dnnl_lstm_forward_primitive_desc_create(
dnnl_primitive_desc_t* primitive_desc,
dnnl_engine_t engine,
dnnl_prop_kind_t prop_kind,
dnnl_rnn_direction_t 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,
unsigned flags,
const_dnnl_primitive_attr_t attr
)Creates a primitive descriptor for an LSTM forward propagation primitive.
The following arguments may either be NULL or point to a zero memory descriptor:
src_iter_desc together with src_iter_c_desc,
weights_peephole_desc,
bias_desc,
dst_iter_desc together with dst_iter_c_desc.
This would then indicate that the LSTM forward propagation primitive should not use them and should default to zero values instead.
The weights_projection_desc could either be NULL or point to a zero memory descriptor. This would then indicate that the LSTM doesn’t have recurrent projection layer.
NOTE:
All memory descriptors can be initialized with dnnl_format_tag_any or with format_kind set to dnnl_format_kind_any.
Parameters:
primitive_desc |
Output primitive descriptor. |
engine |
Engine to use. |
prop_kind |
Propagation kind. Possible values are dnnl_forward_training and dnnl_forward_inference. |
direction |
RNN direction. See 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. |
attr |
Primitive attributes (can be NULL). |
Returns:
dnnl_success on success and a status describing the error otherwise.
dnnl_status_t DNNL_API dnnl_lstm_backward_primitive_desc_create(
dnnl_primitive_desc_t* primitive_desc,
dnnl_engine_t engine,
dnnl_prop_kind_t prop_kind,
dnnl_rnn_direction_t 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,
unsigned flags,
const_dnnl_primitive_desc_t hint_fwd_pd,
const_dnnl_primitive_attr_t attr
)Creates a primitive descriptor for an LSTM backward propagation primitive.
The following arguments may either be NULL or point to a zero memory descriptor:
src_iter_desc together with src_iter_c_desc, diff_src_iter_desc, and diff_src_iter_c_desc,
weights_peephole_desc together with diff_weights_peephole_desc,
bias_desc together with diff_bias_desc,
dst_iter_desc together with dst_iter_c_desc, diff_dst_iter_desc, and diff_dst_iter_c_desc.
This would then indicate that the LSTM backward propagation primitive should not use them and should default to zero values instead.
The weights_projection_desc together with diff_weights_projection_desc could either be NULL or point to a zero memory descriptor. This would then indicate that the LSTM doesn’t have recurrent projection layer.
NOTE:
All memory descriptors can be initialized with dnnl_format_tag_any or with format_kind set to dnnl_format_kind_any.
Parameters:
primitive_desc |
Output primitive descriptor. |
engine |
Engine to use. |
prop_kind |
Propagation kind. Must be dnnl_backward. |
direction |
RNN direction. See 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. |
hint_fwd_pd |
Primitive descriptor for a respective forward propagation primitive. |
attr |
Primitive attributes (can be NULL). |
Returns:
dnnl_success on success and a status describing the error otherwise.
dnnl_status_t DNNL_API dnnl_gru_forward_primitive_desc_create(
dnnl_primitive_desc_t* primitive_desc,
dnnl_engine_t engine,
dnnl_prop_kind_t prop_kind,
dnnl_rnn_direction_t 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,
unsigned flags,
const_dnnl_primitive_attr_t attr
)Creates a primitive descriptor for GRU forward propagation primitive.
The following arguments may either be NULL or point to a zero memory descriptor:
src_iter_desc,
bias_desc,
dst_iter_desc.
This would then indicate that the GRU 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.
Parameters:
primitive_desc |
Output primitive descriptor. |
engine |
Engine to use. |
prop_kind |
Propagation kind. Possible values are dnnl_forward_training and dnnl_forward_inference. |
direction |
RNN direction. See 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. |
attr |
Primitive attributes (can be NULL). |
Returns:
dnnl_success on success and a status describing the error otherwise.
dnnl_status_t DNNL_API dnnl_gru_backward_primitive_desc_create(
dnnl_primitive_desc_t* primitive_desc,
dnnl_engine_t engine,
dnnl_prop_kind_t prop_kind,
dnnl_rnn_direction_t 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,
unsigned flags,
const_dnnl_primitive_desc_t hint_fwd_pd,
const_dnnl_primitive_attr_t attr
)Creates a primitive descriptor for GRU backward propagation primitive.
The following arguments may either be NULL or point to a zero memory descriptor:
src_iter_desc together with diff_src_iter_desc,
bias_desc together with diff_bias_desc,
dst_iter_desc together with diff_dst_iter_desc.
This would then indicate that the GRU backward 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.
Parameters:
primitive_desc |
Output primitive descriptor. |
engine |
Engine to use. |
prop_kind |
Propagation kind. Must be dnnl_backward. |
direction |
RNN direction. See 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. |
hint_fwd_pd |
Primitive descriptor for a respective forward propagation primitive. |
attr |
Primitive attributes (can be NULL). |
Returns:
dnnl_success on success and a status describing the error otherwise.
dnnl_status_t DNNL_API dnnl_lbr_gru_forward_primitive_desc_create(
dnnl_primitive_desc_t* primitive_desc,
dnnl_engine_t engine,
dnnl_prop_kind_t prop_kind,
dnnl_rnn_direction_t 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,
unsigned flags,
const_dnnl_primitive_attr_t attr
)Creates a descriptor for LBR GRU forward propagation primitive.
The following arguments may either be NULL or point to a zero memory descriptor:
src_iter_desc,
bias_desc,
dst_iter_desc.
This would then indicate that the LBR GRU forward propagation primitive should not use them and should default to zero values instead.
Parameters:
primitive_desc |
Output primitive descriptor. |
engine |
Engine to use. |
prop_kind |
Propagation kind. Possible values are dnnl_forward_training and dnnl_forward_inference. |
direction |
RNN direction. See 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. |
attr |
Primitive attributes (can be NULL). |
Returns:
dnnl_success on success and a status describing the error otherwise.
dnnl_status_t DNNL_API dnnl_lbr_gru_backward_primitive_desc_create(
dnnl_primitive_desc_t* primitive_desc,
dnnl_engine_t engine,
dnnl_prop_kind_t prop_kind,
dnnl_rnn_direction_t 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,
unsigned flags,
const_dnnl_primitive_desc_t hint_fwd_pd,
const_dnnl_primitive_attr_t attr
)Creates a primitive descriptor for LBR GRU backward propagation primitive.
The following arguments may either be NULL or point to a zero memory descriptor:
src_iter_desc together with diff_src_iter_desc,
bias_desc together with diff_bias_desc,
dst_iter_desc together with diff_dst_iter_desc.
This would then indicate that the LBR GRU backward 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.
Parameters:
primitive_desc |
Output primitive descriptor. |
engine |
Engine to use. |
prop_kind |
Propagation kind. Must be dnnl_backward. |
direction |
RNN direction. See 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. |
hint_fwd_pd |
Primitive descriptor for a respective forward propagation primitive. |
attr |
Primitive attributes (can be NULL). |
Returns:
dnnl_success on success and a status describing the error otherwise.
dnnl_status_t DNNL_API dnnl_augru_forward_primitive_desc_create(
dnnl_primitive_desc_t* primitive_desc,
dnnl_engine_t engine,
dnnl_prop_kind_t prop_kind,
dnnl_rnn_direction_t direction,
const_dnnl_memory_desc_t src_layer_desc,
const_dnnl_memory_desc_t src_iter_desc,
const_dnnl_memory_desc_t attention_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,
unsigned flags,
const_dnnl_primitive_attr_t attr
)Creates a primitive descriptor for AUGRU forward propagation primitive.
The following arguments may either be NULL or point to a zero memory descriptor:
src_iter_desc,
bias_desc,
dst_iter_desc.
This would then indicate that the AUGRU 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.
Parameters:
primitive_desc |
Output primitive descriptor. |
engine |
Engine to use. |
prop_kind |
Propagation kind. Possible values are dnnl_forward_training and dnnl_forward_inference. |
direction |
RNN direction. See 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. |
attention_desc |
Memory descriptor for the attention 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. |
attr |
Primitive attributes (can be NULL). |
Returns:
dnnl_success on success and a status describing the error otherwise.
dnnl_status_t DNNL_API dnnl_augru_backward_primitive_desc_create(
dnnl_primitive_desc_t* primitive_desc,
dnnl_engine_t engine,
dnnl_prop_kind_t prop_kind,
dnnl_rnn_direction_t direction,
const_dnnl_memory_desc_t src_layer_desc,
const_dnnl_memory_desc_t src_iter_desc,
const_dnnl_memory_desc_t attention_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_attention_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,
unsigned flags,
const_dnnl_primitive_desc_t hint_fwd_pd,
const_dnnl_primitive_attr_t attr
)Creates a primitive descriptor for AUGRU backward propagation primitive.
The following arguments may either be NULL or point to a zero memory descriptor:
src_iter_desc together with diff_src_iter_desc,
bias_desc together with diff_bias_desc,
dst_iter_desc together with diff_dst_iter_desc.
This would then indicate that the AUGRU backward 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.
Parameters:
primitive_desc |
Output primitive descriptor. |
engine |
Engine to use. |
prop_kind |
Propagation kind. Must be dnnl_backward. |
direction |
RNN direction. See 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. |
attention_desc |
Memory descriptor for the attention 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_attention_desc |
Memory descriptor for the diff of attention 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. |
hint_fwd_pd |
Primitive descriptor for a respective forward propagation primitive. |
attr |
Primitive attributes (can be NULL). |
Returns:
dnnl_success on success and a status describing the error otherwise.
dnnl_status_t DNNL_API dnnl_lbr_augru_forward_primitive_desc_create(
dnnl_primitive_desc_t* primitive_desc,
dnnl_engine_t engine,
dnnl_prop_kind_t prop_kind,
dnnl_rnn_direction_t direction,
const_dnnl_memory_desc_t src_layer_desc,
const_dnnl_memory_desc_t src_iter_desc,
const_dnnl_memory_desc_t attention_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,
unsigned flags,
const_dnnl_primitive_attr_t attr
)Creates a primitive descriptor for LBR AUGRU forward propagation primitive.
The following arguments may either be NULL or point to a zero memory descriptor:
src_iter_desc,
bias_desc,
dst_iter_desc.
This would then indicate that the LBR AUGRU forward propagation primitive should not use them and should default to zero values instead.
Parameters:
primitive_desc |
Output primitive descriptor. |
engine |
Engine to use. |
prop_kind |
Propagation kind. Possible values are dnnl_forward_training and dnnl_forward_inference. |
direction |
RNN direction. See 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. |
attention_desc |
Memory descriptor for the attention 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. |
attr |
Primitive attributes (can be NULL). |
Returns:
dnnl_success on success and a status describing the error otherwise.
dnnl_status_t DNNL_API dnnl_lbr_augru_backward_primitive_desc_create(
dnnl_primitive_desc_t* primitive_desc,
dnnl_engine_t engine,
dnnl_prop_kind_t prop_kind,
dnnl_rnn_direction_t direction,
const_dnnl_memory_desc_t src_layer_desc,
const_dnnl_memory_desc_t src_iter_desc,
const_dnnl_memory_desc_t attention_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_attention_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,
unsigned flags,
const_dnnl_primitive_desc_t hint_fwd_pd,
const_dnnl_primitive_attr_t attr
)Creates a primitive descriptor for LBR AUGRU backward propagation primitive.
The following arguments may either be NULL or point to a zero memory descriptor:
src_iter_desc together with diff_src_iter_desc,
bias_desc together with diff_bias_desc,
dst_iter_desc together with diff_dst_iter_desc.
This would then indicate that the LBR AUGRU backward 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.
Parameters:
primitive_desc |
Output primitive descriptor. |
engine |
Engine to use. |
prop_kind |
Propagation kind. Must be dnnl_backward. |
direction |
RNN direction. See 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. |
attention_desc |
Memory descriptor for the attention 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_attention_desc |
Memory descriptor for the diff of attention 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. |
hint_fwd_pd |
Primitive descriptor for a respective forward propagation primitive. |
attr |
Primitive attributes (can be NULL). |
Returns:
dnnl_success on success and a status describing the error otherwise.