A container for parameters that extend primitives behavior. More...
Classes | |
| struct | dnnl::post_ops |
| Post-ops. More... | |
| struct | dnnl::primitive_attr |
| Primitive attributes. More... | |
| struct | dnnl_primitive_attr |
| An opaque structure for primitive descriptor attributes. More... | |
| struct | dnnl_post_ops |
| An opaque structure for a chain of post operations. More... | |
Typedefs | |
| typedef struct dnnl_primitive_attr * | dnnl_primitive_attr_t |
| A primitive descriptor attributes handle that controls primitive behavior. More... | |
| typedef const struct dnnl_primitive_attr * | const_dnnl_primitive_attr_t |
| A constant primitive descriptor attributes handle. | |
| typedef struct dnnl_post_ops * | dnnl_post_ops_t |
| A post operation chain handle. | |
| typedef const struct dnnl_post_ops * | const_dnnl_post_ops_t |
| A constant post operation chain handle. | |
Enumerations | |
| enum | dnnl::scratchpad_mode |
| Scratchpad mode. More... | |
| enum | dnnl::prop_kind |
| Propagation kind. More... | |
| enum | dnnl::algorithm |
| Kinds of algorithms. More... | |
| enum | dnnl_scratchpad_mode_t |
| Scratchpad mode. More... | |
Functions | |
| dnnl_status_t DNNL_API | dnnl_primitive_attr_create (dnnl_primitive_attr_t *attr) |
| Creates an empty (default) primitive attributes with all the parameters set to their default values. More... | |
| dnnl_status_t DNNL_API | dnnl_primitive_attr_clone (dnnl_primitive_attr_t *attr, const_dnnl_primitive_attr_t existing_attr) |
| Clones primitive attributes. More... | |
| dnnl_status_t DNNL_API | dnnl_primitive_attr_destroy (dnnl_primitive_attr_t attr) |
| Destroys primitive attributes. More... | |
| dnnl_status_t DNNL_API | dnnl_primitive_attr_get_scratchpad_mode (const_dnnl_primitive_attr_t attr, dnnl_scratchpad_mode_t *mode) |
| Returns the primitive attributes scratchpad mode. More... | |
| dnnl_status_t DNNL_API | dnnl_primitive_attr_set_scratchpad_mode (dnnl_primitive_attr_t attr, dnnl_scratchpad_mode_t mode) |
| Sets primitive attributes scratchpad mode. More... | |
| dnnl_status_t DNNL_API | dnnl_primitive_attr_get_output_scales (const_dnnl_primitive_attr_t attr, dnnl_dim_t *count, int *mask, const float **scales) |
| Returns primitive attributes output scaling factors correspondence mask and values. More... | |
| dnnl_status_t DNNL_API | dnnl_primitive_attr_set_output_scales (dnnl_primitive_attr_t attr, dnnl_dim_t count, int mask, const float *scales) |
| Sets output scaling factors correspondence mask and values. More... | |
| dnnl_status_t DNNL_API | dnnl_primitive_attr_get_scales (dnnl_primitive_attr_t attr, int arg, dnnl_dim_t *count, int *mask, const float **scales) |
| Returns primitive attributes scaling factors correspondence mask and values for a given memory argument. More... | |
| dnnl_status_t DNNL_API | dnnl_primitive_attr_set_scales (dnnl_primitive_attr_t attr, int arg, dnnl_dim_t count, int mask, const float *scales) |
| Sets primitive attributes scaling factors for primitive operations for a given memory argument. More... | |
| dnnl_status_t DNNL_API | dnnl_primitive_attr_get_zero_points (const_dnnl_primitive_attr_t attr, int arg, dnnl_dim_t *count, int *mask, const int32_t **zero_points) |
Returns count, correspondence zero point mask, and a pointer to a constant int32_t array of zero_points for given attr and memory argument (index), previously set by dnnl_primitive_attr_set_zero_points. More... | |
| dnnl_status_t DNNL_API | dnnl_primitive_attr_set_zero_points (dnnl_primitive_attr_t attr, int arg, dnnl_dim_t count, int mask, const int32_t *zero_points) |
| Sets primitive attributes zero points for primitive operations for a given memory argument. More... | |
| dnnl_status_t DNNL_API | dnnl_primitive_attr_get_post_ops (const_dnnl_primitive_attr_t attr, const_dnnl_post_ops_t *post_ops) |
| Returns primitive attributes post-ops. More... | |
| dnnl_status_t DNNL_API | dnnl_primitive_attr_set_post_ops (dnnl_primitive_attr_t attr, const_dnnl_post_ops_t post_ops) |
| Sets primitive attributes post-ops. More... | |
| dnnl_status_t DNNL_API | dnnl_post_ops_create (dnnl_post_ops_t *post_ops) |
| Creates empty post-ops sequence. More... | |
| dnnl_status_t DNNL_API | dnnl_post_ops_destroy (dnnl_post_ops_t post_ops) |
| Destroys post-ops. More... | |
| int DNNL_API | dnnl_post_ops_len (const_dnnl_post_ops_t post_ops) |
| Returns the length of post-ops. More... | |
| dnnl_primitive_kind_t DNNL_API | dnnl_post_ops_get_kind (const_dnnl_post_ops_t post_ops, int index) |
| Returns the kind of a post-op entry. More... | |
| dnnl_status_t DNNL_API | dnnl_post_ops_append_sum (dnnl_post_ops_t post_ops, float scale) |
| Appends an accumulation (sum) to post-ops. More... | |
| dnnl_status_t DNNL_API | dnnl_post_ops_append_sum_v2 (dnnl_post_ops_t post_ops, float scale, dnnl_data_type_t data_type) |
| Appends an accumulation v2 (sum) to post-ops. More... | |
| dnnl_status_t DNNL_API | dnnl_post_ops_get_params_sum (const_dnnl_post_ops_t post_ops, int index, float *scale) |
| Returns the parameters of an accumulation (sum) post-op. More... | |
| dnnl_status_t DNNL_API | dnnl_post_ops_get_params_sum_v2 (const_dnnl_post_ops_t post_ops, int index, float *scale, dnnl_data_type_t *data_type) |
| Returns the parameters of an accumulation (sum) post-op with a data type parameter. More... | |
| dnnl_status_t DNNL_API | dnnl_post_ops_append_eltwise (dnnl_post_ops_t post_ops, float scale, dnnl_alg_kind_t alg_kind, float alpha, float beta) |
| Appends an elementwise post-op. More... | |
| dnnl_status_t DNNL_API | dnnl_post_ops_get_params_eltwise (const_dnnl_post_ops_t post_ops, int index, float *scale, dnnl_alg_kind_t *alg_kind, float *alpha, float *beta) |
| Returns the parameters of an elementwise post-op. More... | |
| dnnl_status_t DNNL_API | dnnl_post_ops_append_dw_k3s1p1 (dnnl_post_ops_t post_ops, dnnl_data_type_t weights_data_type, dnnl_data_type_t bias_data_type, dnnl_data_type_t dst_data_type, dnnl_dim_t count, int mask, const float *scales) |
| Appends a depthwise post-op convolution with stride 1. More... | |
| dnnl_status_t DNNL_API | dnnl_post_ops_get_params_dw_k3s1p1 (const_dnnl_post_ops_t post_ops, int index, dnnl_data_type_t *weights_data_type, dnnl_data_type_t *bias_data_type, dnnl_data_type_t *dst_data_type, dnnl_dim_t *count, int *mask, const float **scales) |
| Returns the parameters of an depthwise post-op with stride 1. More... | |
| dnnl_status_t DNNL_API | dnnl_post_ops_append_dw_k3s2p1 (dnnl_post_ops_t post_ops, dnnl_data_type_t weights_data_type, dnnl_data_type_t bias_data_type, dnnl_data_type_t dst_data_type, dnnl_dim_t count, int mask, const float *scales) |
| Appends a depthwise post-op convolution with stride 2. More... | |
| dnnl_status_t DNNL_API | dnnl_post_ops_get_params_dw_k3s2p1 (const_dnnl_post_ops_t post_ops, int index, dnnl_data_type_t *weights_data_type, dnnl_data_type_t *bias_data_type, dnnl_data_type_t *dst_data_type, dnnl_dim_t *count, int *mask, const float **scales) |
| Returns the parameters of an depthwise post-op with stride 2. More... | |
| dnnl_status_t DNNL_API | dnnl_post_ops_append_binary (dnnl_post_ops_t post_ops, dnnl_alg_kind_t alg_kind, const dnnl_memory_desc_t *src1_desc) |
| Appends a binary post-op. More... | |
| dnnl_status_t DNNL_API | dnnl_post_ops_get_params_binary (const_dnnl_post_ops_t post_ops, int index, dnnl_alg_kind_t *alg_kind, const dnnl_memory_desc_t **src1_desc) |
| Returns the parameters of a binary post-op. More... | |
| dnnl_status_t DNNL_API | dnnl_primitive_attr_set_rnn_data_qparams (dnnl_primitive_attr_t attr, const float scale, const float shift) |
| Set quantization scale and shift parameters for RNN data tensors. More... | |
| dnnl_status_t DNNL_API | dnnl_primitive_attr_get_rnn_data_qparams (const_dnnl_primitive_attr_t attr, float *scale, float *shift) |
| Returns the quantization scale and shift parameters for RNN data tensors. More... | |
| dnnl_status_t DNNL_API | dnnl_primitive_attr_set_rnn_weights_qparams (dnnl_primitive_attr_t attr, dnnl_dim_t count, int mask, const float *scales) |
| Sets quantization scaling factors for RNN weights tensors. More... | |
| dnnl_status_t DNNL_API | dnnl_primitive_attr_get_rnn_weights_qparams (const_dnnl_primitive_attr_t attr, dnnl_dim_t *count, int *mask, const float **scales) |
| Returns the quantization scaling factors for RNN weights tensors. More... | |
| dnnl_status_t DNNL_API | dnnl_primitive_attr_set_rnn_weights_projection_qparams (dnnl_primitive_attr_t attr, dnnl_dim_t count, int mask, const float *scales) |
| Sets quantization scaling factors for RNN projection weights tensors. More... | |
| dnnl_status_t DNNL_API | dnnl_primitive_attr_get_rnn_weights_projection_qparams (const_dnnl_primitive_attr_t attr, dnnl_dim_t *count, int *mask, const float **scales) |
| Returns the quantization scaling factors for RNN projection weights tensors. More... | |
| dnnl_scratchpad_mode_t | dnnl::convert_to_c (scratchpad_mode mode) |
| Converts a scratchpad mode enum value from C++ API to C API type. More... | |
| dnnl_prop_kind_t | dnnl::convert_to_c (prop_kind akind) |
| Converts propagation kind enum value from C++ API to C API type. More... | |
| dnnl_alg_kind_t | dnnl::convert_to_c (algorithm aalgorithm) |
| Converts algorithm kind enum value from C++ API to C API type. More... | |
A container for parameters that extend primitives behavior.
Attributes can also contain Post-ops, which are computations executed after the primitive.
| typedef struct dnnl_primitive_attr* dnnl_primitive_attr_t |
A primitive descriptor attributes handle that controls primitive behavior.
|
strong |
Scratchpad mode.
| Enumerator | |
|---|---|
| library | The library manages the scratchpad allocation according to the policy specified by the When When |
| user | The user manages the scratchpad allocation by querying and providing the scratchpad memory to primitives. This mode is thread-safe as long as the scratchpad buffers are not used concurrently by two primitive executions. |
|
strong |
Propagation kind.
| Enumerator | |
|---|---|
| undef | Undefined propagation kind. |
| forward_training | Forward data propagation (training mode). In this mode, primitives perform computations necessary for subsequent backward propagation. |
| forward_inference | Forward data propagation (inference mode). In this mode, primitives perform only computations that are necessary for inference and omit computations that are necessary only for backward propagation. |
| forward_scoring | Forward data propagation, alias for dnnl::prop_kind::forward_inference. |
| forward | Forward data propagation, alias for dnnl::prop_kind::forward_training. |
| backward | Backward propagation (with respect to all parameters). |
| backward_data | Backward data propagation. |
| backward_weights | Backward weights propagation. |
| backward_bias | Backward bias propagation. |
|
strong |
Kinds of algorithms.
| Enumerator | |
|---|---|
| undef | Undefined algorithm. |
| convolution_auto | Convolution algorithm that is chosen to be either direct or Winograd automatically. |
| convolution_direct | Direct convolution. |
| convolution_winograd | Winograd convolution. |
| deconvolution_direct | Direct deconvolution. |
| deconvolution_winograd | Winograd deconvolution. |
| eltwise_relu | Elementwise: rectified linear unit (ReLU) |
| eltwise_tanh | Elementwise: hyperbolic tangent non-linearity (tanh) |
| eltwise_elu | Elementwise: exponential linear unit (ELU) |
| eltwise_square | Elementwise: square. |
| eltwise_abs | Elementwise: abs. |
| eltwise_sqrt | Elementwise: square root. |
| eltwise_swish | Elementwise: swish ( \(x \cdot sigmoid(a \cdot x)\)) |
| eltwise_linear | Elementwise: linear. |
| eltwise_bounded_relu | Elementwise: bounded_relu. |
| eltwise_soft_relu | Elementwise: soft_relu. |
| eltwise_logistic | Elementwise: logistic. |
| eltwise_exp | Elementwise: exponent. |
| eltwise_gelu | Elementwise: gelu alias for dnnl::algorithm::eltwise_gelu_tanh. |
| eltwise_gelu_tanh | Elementwise: tanh-based gelu. |
| eltwise_gelu_erf | Elementwise: erf-based gelu. |
| eltwise_log | Elementwise: natural logarithm. |
| eltwise_clip | Elementwise: clip. |
| eltwise_pow | Elementwise: pow. |
| eltwise_round | Elementwise: round. |
| eltwise_relu_use_dst_for_bwd | Elementwise: rectified linar unit (ReLU) (dst for backward) |
| eltwise_tanh_use_dst_for_bwd | Elementwise: hyperbolic tangent non-linearity (tanh) (dst for backward) |
| eltwise_elu_use_dst_for_bwd | Elementwise: exponential linear unit (ELU) (dst for backward) |
| eltwise_sqrt_use_dst_for_bwd | Elementwise: square root (dst for backward) |
| eltwise_logistic_use_dst_for_bwd | Elementwise: logistic (dst for backward) |
| eltwise_exp_use_dst_for_bwd | Elementwise: exponent (dst for backward) |
| lrn_across_channels | Local response normalization (LRN) across multiple channels. |
| lrn_within_channel | LRN within a single channel. |
| pooling_max | Max pooling. |
| pooling_avg | Average pooling exclude padding, alias for dnnl::algorithm::pooling_avg_include_padding. |
| pooling_avg_include_padding | Average pooling include padding. |
| pooling_avg_exclude_padding | Average pooling exclude padding. |
| vanilla_rnn | RNN cell. |
| vanilla_lstm | LSTM cell. |
| vanilla_gru | GRU cell. |
| lbr_gru | GRU cell with linear before reset. Differs from the vanilla GRU in how the new memory gate is calculated: \(c_t = tanh(W_c*x_t + b_{c_x} + r_t*(U_c*h_{t-1}+b_{c_h})) \) LRB GRU expects 4 bias tensors on input: \([b_{u}, b_{r}, b_{c_x}, b_{c_h}]\) |
| binary_add | Binary add. |
| binary_mul | Binary mul. |
| binary_max | Binary max. |
| binary_min | Binary min. |
| binary_div | Binary div. |
| binary_sub | Binary sub. |
| resampling_nearest | Nearest Neighbor resampling method. |
| resampling_linear | Linear (Bilinear, Trilinear) resampling method. |
| reduction_max | Reduction using max operation. |
| reduction_min | Reduction using min operation. |
| reduction_sum | Reduction using sum operation. |
| reduction_mul | Reduction using mul operation. |
| reduction_mean | Reduction using mean operation. |
| reduction_norm_lp_max | Reduction using norm_lp_max operation. |
| reduction_norm_lp_sum | Reduction using norm_lp_sum operation. |
| reduction_norm_lp_power_p_max | Reduction using norm_lp_power_p_max operation. |
| reduction_norm_lp_power_p_sum | Reduction using norm_lp_power_p_sum operation. |
Scratchpad mode.
| Enumerator | |
|---|---|
| dnnl_scratchpad_mode_library | The library manages the scratchpad allocation according to the policy specified by the When When |
| dnnl_scratchpad_mode_user | The user manages the scratchpad allocation by querying and providing the scratchpad memory to primitives. This mode is thread-safe as long as the scratchpad buffers are not used concurrently by two primitive executions. |
| dnnl_status_t DNNL_API dnnl_primitive_attr_create | ( | dnnl_primitive_attr_t * | attr | ) |
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. |
| dnnl_status_t DNNL_API dnnl_primitive_attr_clone | ( | dnnl_primitive_attr_t * | attr, |
| const_dnnl_primitive_attr_t | existing_attr | ||
| ) |
Clones primitive attributes.
| attr | Output primitive attributes. |
| existing_attr | Primitive attributes to clone. |
| dnnl_status_t DNNL_API dnnl_primitive_attr_destroy | ( | dnnl_primitive_attr_t | attr | ) |
Destroys primitive attributes.
| attr | Primitive attributes to destroy. |
| dnnl_status_t DNNL_API dnnl_primitive_attr_get_scratchpad_mode | ( | const_dnnl_primitive_attr_t | attr, |
| dnnl_scratchpad_mode_t * | mode | ||
| ) |
Returns the primitive attributes scratchpad mode.
| attr | Primitive attributes. |
| mode | Output scratchpad mode. |
| dnnl_status_t DNNL_API dnnl_primitive_attr_set_scratchpad_mode | ( | dnnl_primitive_attr_t | attr, |
| dnnl_scratchpad_mode_t | mode | ||
| ) |
Sets primitive attributes scratchpad mode.
| attr | Primitive attributes. |
| mode | Scratchpad mode. The possible values are: dnnl_scratchpad_mode_library (default) and dnnl_scratchpad_mode_user. |
| dnnl_status_t DNNL_API dnnl_primitive_attr_get_output_scales | ( | const_dnnl_primitive_attr_t | attr, |
| dnnl_dim_t * | count, | ||
| int * | mask, | ||
| const float ** | scales | ||
| ) |
Returns primitive attributes output scaling factors correspondence mask and values.
scales array is an internal part of the primitive attributes attr, so it is an error to modify or destroy the scales array.scales array is the same as that of the primitive attributes attr to which it belongs, so it is an error to use scales after attr is destroyed.| attr | Primitive attributes. |
| count | Output length of the array of scaling factors scales. |
| mask | Output scaling factors correspondence mask that defines the correspondence between the output tensor dimensions and the 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. |
| dnnl_status_t DNNL_API dnnl_primitive_attr_set_output_scales | ( | dnnl_primitive_attr_t | attr, |
| dnnl_dim_t | count, | ||
| int | mask, | ||
| const float * | scales | ||
| ) |
Sets output scaling factors correspondence mask and values.
Example usage:
| attr | Primitive attributes. |
| count | Length of the array of scaling factors scales. |
| mask | Scaling factors correspondence mask that defines the correspondence between the output tensor dimensions and the 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 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. |
| dnnl_status_t DNNL_API dnnl_primitive_attr_get_scales | ( | dnnl_primitive_attr_t | attr, |
| int | arg, | ||
| dnnl_dim_t * | count, | ||
| int * | mask, | ||
| const float ** | scales | ||
| ) |
Returns primitive attributes scaling factors correspondence mask and values for a given memory argument.
scales array is an internal part of the primitive attributes attr, so it is an error to modify or destroy the scales array.scales array is the same as that of the primitive attributes attr to which it belongs, so it is an error to use scales after attr is destroyed.| attr | Primitive attributes. |
| arg | Parameter argument index as passed to the dnnl_primitive_execute() call. |
| count | Output length of the array of scaling factors scales. |
| mask | Output scaling factors correspondence mask that defines the correspondence between the output tensor dimensions and the 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. |
| dnnl_status_t DNNL_API dnnl_primitive_attr_set_scales | ( | dnnl_primitive_attr_t | attr, |
| int | arg, | ||
| dnnl_dim_t | count, | ||
| int | mask, | ||
| const float * | scales | ||
| ) |
Sets primitive attributes scaling factors for primitive operations for a given memory argument.
| attr | Primitive attributes. |
| arg | Parameter argument index as passed to the dnnl_primitive_execute() call. |
| count | Length of the array of scaling factors scales. |
| mask | Scaling factors correspondence mask that defines the correspondence between the tensor dimensions and the 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 count scales and the following equality must hold: \[count = \prod\limits_{d \in mask} output.dims[d].\] |
| dnnl_status_t DNNL_API dnnl_primitive_attr_get_zero_points | ( | const_dnnl_primitive_attr_t | attr, |
| int | arg, | ||
| dnnl_dim_t * | count, | ||
| int * | mask, | ||
| const int32_t ** | zero_points | ||
| ) |
Returns count, correspondence zero point mask, and a pointer to a constant int32_t array of zero_points for given attr and memory argument (index), previously set by dnnl_primitive_attr_set_zero_points.
zero_points array is an internal part of the primitive attributes attr, so it is an error to modify or destroy the zero_points array.zero_points array is the same as that of the primitive attributes attr to which it belongs, so it is an error to use zero_points after attr is destroyed.| attr | Primitive attributes. |
| arg | Parameter argument index as passed to the dnnl_primitive_execute() call. |
| count | Output length of the array of zero points zero_points. |
| mask | Output zero points correspondence mask that defines the correspondence between the output tensor dimensions and the 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. |
| dnnl_status_t DNNL_API dnnl_primitive_attr_set_zero_points | ( | dnnl_primitive_attr_t | attr, |
| int | arg, | ||
| dnnl_dim_t | count, | ||
| int | mask, | ||
| const int32_t * | zero_points | ||
| ) |
Sets primitive attributes zero points for primitive operations for a given memory argument.
| attr | Primitive attributes. |
| arg | Parameter argument index as passed to the dnnl_primitive_execute() call. |
| count | Length of the array of zero points zero_points. |
| mask | Zero point correspondence mask that defines the correspondence between the tensor dimensions and the 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 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_status_t DNNL_API dnnl_primitive_attr_get_post_ops | ( | const_dnnl_primitive_attr_t | attr, |
| const_dnnl_post_ops_t * | post_ops | ||
| ) |
Returns primitive attributes post-ops.
post_ops points to the internal attr field, so it is an error to modify or destroy them. The lifetime of post_ops is the same as that of the attr it belongs to, so it is an error to use post_ops after attr has been destroyed.| attr | Primitive attributes. |
| post_ops | Output post-ops. |
| dnnl_status_t DNNL_API dnnl_primitive_attr_set_post_ops | ( | dnnl_primitive_attr_t | attr, |
| const_dnnl_post_ops_t | post_ops | ||
| ) |
Sets primitive attributes post-ops.
| attr | Primitive attributes. |
| post_ops | Post-ops to set. |
| dnnl_status_t DNNL_API dnnl_post_ops_create | ( | dnnl_post_ops_t * | post_ops | ) |
Creates empty post-ops sequence.
| post_ops | Output post-ops. |
| dnnl_status_t DNNL_API dnnl_post_ops_destroy | ( | dnnl_post_ops_t | post_ops | ) |
Destroys post-ops.
| post_ops | Post-ops to destroy. |
| int DNNL_API dnnl_post_ops_len | ( | const_dnnl_post_ops_t | post_ops | ) |
Returns the length of post-ops.
| post_ops | Post-ops. |
| dnnl_primitive_kind_t DNNL_API dnnl_post_ops_get_kind | ( | const_dnnl_post_ops_t | post_ops, |
| int | index | ||
| ) |
Returns the kind of a post-op entry.
| post_ops | Post-ops. |
| index | Post-op entry index. |
| dnnl_status_t DNNL_API dnnl_post_ops_append_sum | ( | dnnl_post_ops_t | post_ops, |
| float | scale | ||
| ) |
Appends an accumulation (sum) to post-ops.
Prior to accumulating the result, the previous value is multiplied by a scale.
The kind of this post-op is dnnl_sum.
This feature may improve performance for cases like residual learning blocks, where the result of convolution is accumulated to the previously computed activations. The parameter scale may be used for the integer-based computations when the result and previous activations have different logical scaling factors.
In the simplest case when the accumulation is the only post-op, the computations would be:
dst[:] <- scale * dst[:] + op(...) // instead of dst[:] <- op(...)
| post_ops | Post-ops. |
| scale | Accumulation scaling factor. |
| dnnl_status_t DNNL_API dnnl_post_ops_append_sum_v2 | ( | dnnl_post_ops_t | post_ops, |
| float | scale, | ||
| dnnl_data_type_t | data_type | ||
| ) |
Appends an accumulation v2 (sum) to post-ops.
Prior to accumulating the result, the previous value is multiplied by a scale.
The kind of this post-op is dnnl_sum.
This feature may improve performance for cases like residual learning blocks, where the result of convolution is accumulated to the previously computed activations. The parameter scale may be used for the integer-based computations when the result and previous activations have different logical scaling factors.
In the simplest case when the accumulation is the only post-op, the computations would be:
dst[:] <- scale * dst[:] + op(...) // instead of dst[:] <- op(...)
If data_type is specified, original dst tensor will be reinterpreted as a tensor with provided data type. Since it is reinterpretation, data_type and dst data type should have same size. As a result, computations would be:
dst[:] <- scale * as_data_type(dst[:]) + op(...)
// instead of dst[:] <- op(...)
| post_ops | Post-ops. |
| scale | Accumulation scaling factor. |
| data_type | Accumulation data_type. |
| dnnl_status_t DNNL_API dnnl_post_ops_get_params_sum | ( | const_dnnl_post_ops_t | post_ops, |
| int | index, | ||
| float * | scale | ||
| ) |
Returns the parameters of an accumulation (sum) post-op.
| post_ops | Post-ops. |
| index | Index of the sum post-op. |
| scale | Output accumulation scaling factor. |
index does not refer to a sum post-op. | dnnl_status_t DNNL_API dnnl_post_ops_get_params_sum_v2 | ( | const_dnnl_post_ops_t | post_ops, |
| int | index, | ||
| float * | scale, | ||
| dnnl_data_type_t * | data_type | ||
| ) |
Returns the parameters of an accumulation (sum) post-op with a data type parameter.
| post_ops | Post-ops. |
| index | Index of the sum post-op. |
| scale | Output accumulation scaling factor. |
| data_type | Data type for accumulation. |
| dnnl_status_t DNNL_API dnnl_post_ops_append_eltwise | ( | dnnl_post_ops_t | post_ops, |
| float | scale, | ||
| dnnl_alg_kind_t | alg_kind, | ||
| float | alpha, | ||
| float | beta | ||
| ) |
Appends an elementwise post-op.
The kind of this post operation is dnnl_eltwise.
In the simplest case when the elementwise is the only post operation, the computations would be:
dst[:] <- scale * eltwise_op (op(...)) // instead of dst[:] <- op(...)
where eltwise_op is configured with the given parameters.
| 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. |
| dnnl_status_t DNNL_API dnnl_post_ops_get_params_eltwise | ( | const_dnnl_post_ops_t | post_ops, |
| int | index, | ||
| float * | scale, | ||
| dnnl_alg_kind_t * | alg_kind, | ||
| float * | alpha, | ||
| float * | beta | ||
| ) |
Returns the parameters of an elementwise post-op.
| 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. |
index does not refer to an elementwise post-op. | dnnl_status_t DNNL_API dnnl_post_ops_append_dw_k3s1p1 | ( | dnnl_post_ops_t | post_ops, |
| dnnl_data_type_t | weights_data_type, | ||
| dnnl_data_type_t | bias_data_type, | ||
| dnnl_data_type_t | dst_data_type, | ||
| dnnl_dim_t | count, | ||
| int | mask, | ||
| const float * | scales | ||
| ) |
Appends a depthwise post-op convolution with stride 1.
This post-op can only be fused with a 2D 1x1 convolution (convolution with weights spatial dimension equal to 1 i.e., kh=kw=1).
The kind of this post-op is dnnl_convolution.
The number of outputs for primitive remain same as before. The output size remain same as the original primitive due to stride=1.
The Post-op can be defined as:
dst[:] <- scales * (conv_dw(conv_1x1))
See dev_guide_attributes_post_ops_depthwise and dev_guide_attributes_post_ops_depthwise_fusion for more info.
| post_ops | Post-ops. |
| weights_data_type | Weights data type of depthwise post-op |
| bias_data_type | Bias data type of depthwise post-op |
| dst_data_type | Output data type of depthwise post-op |
| count | Output length of the array of scaling factors scales. |
| mask | Output scaling factors correspondence mask that defines the correspondence between the output tensor dimensions and the 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. |
| dnnl_status_t DNNL_API dnnl_post_ops_get_params_dw_k3s1p1 | ( | const_dnnl_post_ops_t | post_ops, |
| int | index, | ||
| dnnl_data_type_t * | weights_data_type, | ||
| dnnl_data_type_t * | bias_data_type, | ||
| dnnl_data_type_t * | dst_data_type, | ||
| dnnl_dim_t * | count, | ||
| int * | mask, | ||
| const float ** | scales | ||
| ) |
Returns the parameters of an depthwise post-op with stride 1.
| post_ops | Post-ops. |
| index | Index of the elementwise post-op. |
| weights_data_type | Weights data type of depthwise post-op |
| bias_data_type | Bias data type of depthwise post-op |
| dst_data_type | Output data type of depthwise post-op |
| count | Output length of the array of scaling factors scales. |
| mask | Output scaling factors correspondence mask that defines the correspondence between the output tensor dimensions and the 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. |
| dnnl_status_t DNNL_API dnnl_post_ops_append_dw_k3s2p1 | ( | dnnl_post_ops_t | post_ops, |
| dnnl_data_type_t | weights_data_type, | ||
| dnnl_data_type_t | bias_data_type, | ||
| dnnl_data_type_t | dst_data_type, | ||
| dnnl_dim_t | count, | ||
| int | mask, | ||
| const float * | scales | ||
| ) |
Appends a depthwise post-op convolution with stride 2.
This post-op can only be fused with a 2D 1x1 convolution (convolution with weights spatial dimension equal to 1 i.e., kh=kw=1).
The kind of this post-op is dnnl_convolution.
The number of outputs for primitive remain same as before. The output spatial size can be derived as below:
output_height = ceil(output_height_1x1_convolution, stride) output_width = ceil(output_width_1x1_convolution, stride)
The Post-op can be defined as:
dst[:] <- scales * (conv_dw(conv_1x1))
See dev_guide_attributes_post_ops_depthwise and dev_guide_attributes_post_ops_depthwise_fusion for more info.
| post_ops | Post-ops. |
| weights_data_type | Weights data type of depthwise post-op |
| bias_data_type | Bias data type of depthwise post-op |
| dst_data_type | Output data type of depthwise post-op |
| count | Output length of the array of scaling factors scales. |
| mask | Output scaling factors correspondence mask that defines the correspondence between the output tensor dimensions and the 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. |
| dnnl_status_t DNNL_API dnnl_post_ops_get_params_dw_k3s2p1 | ( | const_dnnl_post_ops_t | post_ops, |
| int | index, | ||
| dnnl_data_type_t * | weights_data_type, | ||
| dnnl_data_type_t * | bias_data_type, | ||
| dnnl_data_type_t * | dst_data_type, | ||
| dnnl_dim_t * | count, | ||
| int * | mask, | ||
| const float ** | scales | ||
| ) |
Returns the parameters of an depthwise post-op with stride 2.
| post_ops | Post-ops. |
| index | Index of the elementwise post-op. |
| weights_data_type | Weights data type of depthwise post-op |
| bias_data_type | Bias data type of depthwise post-op |
| dst_data_type | Output data type of depthwise post-op |
| count | Output length of the array of scaling factors scales. |
| mask | Output scaling factors correspondence mask that defines the correspondence between the output tensor dimensions and the 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. |
| dnnl_status_t DNNL_API dnnl_post_ops_append_binary | ( | dnnl_post_ops_t | post_ops, |
| dnnl_alg_kind_t | alg_kind, | ||
| const dnnl_memory_desc_t * | src1_desc | ||
| ) |
Appends a binary post-op.
The kind of this post operation is dnnl_binary.
In the simplest case when the binary is the only post operation, the computations would be:
dst[:] <- binary_op (dst[:], another_input[:])
where binary_op is configured with the given parameters. binary_op supports broadcast semantics for a second operand.
| post_ops | Post-ops. |
| alg_kind | Binary algorithm for the post-op. |
| src1_desc | Memory descriptor of a second operand. |
| dnnl_status_t DNNL_API dnnl_post_ops_get_params_binary | ( | const_dnnl_post_ops_t | post_ops, |
| int | index, | ||
| dnnl_alg_kind_t * | alg_kind, | ||
| const dnnl_memory_desc_t ** | src1_desc | ||
| ) |
Returns the parameters of a binary post-op.
| post_ops | Post-ops. |
| index | Index of the binary post-op. |
| alg_kind | Output binary algorithm kind. |
| src1_desc | Output memory descriptor of a second operand. |
index does not refer to a binary post-op. | dnnl_status_t DNNL_API dnnl_primitive_attr_set_rnn_data_qparams | ( | dnnl_primitive_attr_t | attr, |
| const float | scale, | ||
| const float | shift | ||
| ) |
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.
Example usage:
| attr | Primitive attributes. |
| scale | The value to scale the data by. |
| shift | The value to shift the data by. |
| dnnl_status_t DNNL_API dnnl_primitive_attr_get_rnn_data_qparams | ( | const_dnnl_primitive_attr_t | attr, |
| float * | scale, | ||
| float * | shift | ||
| ) |
Returns the quantization scale and shift parameters for RNN data tensors.
| attr | Primitive attributes. |
| scale | The value to scale the data by. |
| shift | The value to shift the data by. |
| dnnl_status_t DNNL_API dnnl_primitive_attr_set_rnn_weights_qparams | ( | dnnl_primitive_attr_t | attr, |
| dnnl_dim_t | count, | ||
| int | mask, | ||
| const float * | scales | ||
| ) |
Sets quantization scaling factors for RNN weights tensors.
The low-precision configuration of the RNN primitives expects input weights to use the signed 8-bit integer data type. The scaling factors are used to quantize floating-point data to signed integer and must be passed to RNN primitives using attributes.
| attr | Primitive attributes. |
| count | Number of elements in the scales array. |
| mask | Scaling factors correspondence mask that defines the correspondence between the output tensor dimensions and the 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 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. |
| dnnl_status_t DNNL_API dnnl_primitive_attr_get_rnn_weights_qparams | ( | const_dnnl_primitive_attr_t | attr, |
| dnnl_dim_t * | count, | ||
| int * | mask, | ||
| const float ** | scales | ||
| ) |
Returns the quantization scaling factors for RNN weights tensors.
| attr | Primitive attributes. |
| count | Number of elements in the scales array. |
| mask | Scaling factors correspondence mask that defines the correspondence between the output tensor dimensions and the 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 contain count values and the following equality must hold: \[count = \prod\limits_{d \in mask} weights.dims[d].\] |
| dnnl_status_t DNNL_API dnnl_primitive_attr_set_rnn_weights_projection_qparams | ( | dnnl_primitive_attr_t | attr, |
| dnnl_dim_t | count, | ||
| int | mask, | ||
| const float * | scales | ||
| ) |
Sets quantization scaling factors for RNN projection weights tensors.
The low-precision configuration of the RNN primitives expects input weights to use the signed 8-bit integer data type. The scaling factors are used to quantize floating-point data to signed integer and must be passed to RNN primitives using attributes.
| attr | Primitive attributes. |
| count | Number of elements in the scales array. |
| mask | Scaling factors correspondence mask that defines the correspondence between the output tensor dimensions and the 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 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. |
| dnnl_status_t DNNL_API dnnl_primitive_attr_get_rnn_weights_projection_qparams | ( | const_dnnl_primitive_attr_t | attr, |
| dnnl_dim_t * | count, | ||
| int * | mask, | ||
| const float ** | scales | ||
| ) |
Returns the quantization scaling factors for RNN projection weights tensors.
| attr | Primitive attributes. |
| count | Number of elements in the scales array. |
| mask | Scaling factors correspondence mask that defines the correspondence between the output tensor dimensions and the 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 contain count values and the following equality must hold: \[count = \prod\limits_{d \in mask} weights.dims[d].\] |
|
inline |
Converts a scratchpad mode enum value from C++ API to C API type.
| mode | C++ API scratchpad mode enum value. |
|
inline |
Converts propagation kind enum value from C++ API to C API type.
| akind | C++ API propagation kind enum value. |
|
inline |
Converts algorithm kind enum value from C++ API to C API type.
| aalgorithm | C++ API algorithm kind enum value. |