.. SPDX-FileCopyrightText: 2019-2020 Intel Corporation .. .. SPDX-License-Identifier: CC-BY-4.0 .. default-domain:: cpp .. include:: /elements/oneDNN/source/replacements.inc.rst ########## LogSoftmax ########## The logsoftmax primitive performs softmax along a particular axis on data with arbitrary dimensions followed by the logarithm function. All other axes are treated as independent (batch). In general form, the operation is defined by the following formulas. Variable names follow the standard :ref:conventions-label. ******* Forward ******* The second form is used as more numerically stable: .. math:: \begin{array}{rcl} \dst(\overline{ou}, c, \overline{in}) & = & \ln\left({\frac { e^{\src(\overline{ou}, c, \overline{in}) - \nu(\overline{ou}, \overline{in})} } { \sum\limits_{ic} e^{\src(\overline{ou}, ic, \overline{in}) - \nu(\overline{ou}, \overline{in})} }}\right) \\ & = & \left(\src(\overline{ou}, c, \overline{in}) - \nu(\overline{ou}, \overline{in})\right) - \ln\left( \sum\limits_{ic} e^{\src(\overline{ou}, ic, \overline{in}) - \nu(\overline{ou}, \overline{in})} \right), \end{array} where - :math:c axis over which the logsoftmax computation is computed on, - :math:\overline{ou} is the outermost index (to the left of logsoftmax axis), - :math:\overline{in} is the innermost index (to the right of logsoftmax axis), and - :math:\nu is used to produce more accurate results and defined as: .. math:: \nu(\overline{ou}, \overline{in}) = \max\limits_{ic} \src(\overline{ou}, ic, \overline{in}) Difference Between Forward Training and Forward Inference ========================================================= There is no difference between the |forward_training| and |forward_inference| propagation kinds. ******** Backward ******** The backward propagation computes :math:\diffsrc(ou, c, in), based on :math:\diffdst(ou, c, in) and :math:\dst(ou, c, in). ******************* Execution Arguments ******************* When executed, the inputs and outputs should be mapped to an execution argument index as specified by the following table. ====================== ======================== Primitive input/output Execution argument index ====================== ======================== :math:\src |DNNL_ARG_SRC| :math:\dst |DNNL_ARG_DST| :math:\diffsrc |DNNL_ARG_DIFF_SRC| :math:\diffdst |DNNL_ARG_DIFF_DST| ====================== ======================== ***************** Operation Details ***************** Both forward and backward propagation support in-place operations, meaning that src can be used as input and output for forward propagation, and diff_dst can be used as input and output for backward propagation. In case of in-place operation, the original data will be overwritten. *********************** Post-ops and Attributes *********************** The logsoftmax primitive does not support any post-ops or attributes. ***************** Data Type Support ***************** The logsoftmax primitive supports the following combinations of data types. .. note:: Here we abbreviate data types names for readability. For example, |_f32| is abbreviated to |f32|. ================== ==================== Propagation Source / Destination ================== ==================== forward / backward |bf16|, |f32| ================== ==================== ******************* Data Representation ******************* Source, Destination, and Their Gradients ======================================== The logsoftmax primitive works with arbitrary data tensors. There is no special meaning associated with any logical dimensions. However, the logsoftmax axis is typically referred to as channels (hence in formulas we use :math:c). *** API *** .. doxygenstruct:: dnnl::logsoftmax_forward :project: oneDNN :members: .. doxygenstruct:: dnnl::logsoftmax_backward :project: oneDNN :members: .. vim: ts=3 sw=3 et spell spelllang=en