Softmax {#dev_guide_softmax}
============================

>
> [API Reference](@ref dnnl_api_softmax)
>

## General

The softmax primitive performs forward or backward softmax or logsoftmax
operation along a particular axis on data with arbitrary dimensions. All other
axes are treated as independent (batch).

### Forward

In general form, the operation is defined by the following formulas (the
variable names follow the standard @ref dev_guide_conventions).

Softmax:

\f[
    \dst(\overline{ou}, c, \overline{in}) =
        \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})}
        }
\f]

Logsoftmax:

\f[
    \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)
\f]

Above

- \f$c\f$ is the axis over which the operation is computed on,
- \f$\overline{ou}\f$ is the outermost index (to the left of the axis),
- \f$\overline{in}\f$ is the innermost index (to the right of the axis), and
- \f$\nu\f$ is used to produce numerically stable results and defined as:

\f[
    \nu(\overline{ou}, \overline{in}) =
        \max\limits_{ic}
        \src(\overline{ou}, ic, \overline{in})
\f]

#### Difference Between Forward Training and Forward Inference

There is no difference between the #dnnl_forward_training
and #dnnl_forward_inference propagation kinds.

### Backward

The backward propagation computes \f$\diffsrc(ou, c, in)\f$, based on
\f$\diffdst(ou, c, in)\f$ and \f$\dst(ou, c, in)\f$.

## 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 |
| ---                    | ---                      |
| \src                   | DNNL_ARG_SRC             |
| \dst                   | DNNL_ARG_DST             |
| \diffsrc               | DNNL_ARG_DIFF_SRC        |
| \diffdst               | DNNL_ARG_DIFF_DST        |

## Implementation Details

### General Notes

1. 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. This support is
   limited to cases when data types of `src`/`dst` or `diff_src`/`diff_dst` are
   identical.

### Post-ops and Attributes

Attributes enable you to modify the behavior of the softmax primitive.
The following attributes are supported by the softmax primitive:

| Propagation | Type      | Operation                                                    | Description                                        | Restrictions                      |
| :--         | :--       | :--                                                          | :--                                                | :--                               |
| forward     | attribute | [Output scale](@ref dnnl::primitive_attr::set_output_scales) | Scales the result of softmax by given scale factor | int8 softmax only, zero mask only |

### Data Type Support

The softmax primitive supports the following combinations of data types:

| Propagation        | Source            | Destination |
| :--                | :--               | :--         |
| forward / backward | f32, bf16         | f32, bf16   |
| forward            | f16               | f16         |
| forward            | f32, bf16, u8, s8 | u8, s8      |
| forward            | u8, s8            | f32, bf16   |

### Data Representation

#### Source, Destination, and Their Gradients

The softmax primitive works with arbitrary data tensors. There is no special
meaning associated with any logical dimensions. However, the softmax axis is
typically referred to as channels (hence in formulas we use \f$c\f$).


## Implementation Limitations

1. No primitive specific limitations. Refer to @ref dev_guide_data_types for
   limitations related to data types support.

## Performance Tips

1. Use in-place operations whenever possible.

2. Currently the softmax primitive is optimized for the cases where
   the dimension of the softmax axis is physically dense. For instance:
   - Optimized: 2D case, tensor \f$A \times B\f$,
                softmax axis 1 (B), format tag #dnnl_ab
   - Optimized: 4D case, tensor \f$A \times B \times C \times D\f$,
                softmax axis 3 (D), format tag #dnnl_abcd
   - Optimized: 4D case, tensor \f$A \times B \times C \times D\f$,
                softmax axis 1 (B), format tag #dnnl_abcd, and
                \f$C = D = 1\f$
   - Optimized: 4D case, tensor \f$A \times B \times C \times D\f$,
                softmax axis 1 (B), format tag #dnnl_acdb or #dnnl_aBcd16b, and
                \f$C \cdot D \ne 1\f$
   - Non-optimized: 2D case, tensor \f$A \times B\f$,
                    softmax axis 0 (A), format tag #dnnl_ab,
                    and \f$B \ne 1\f$
   - Non-optimized: 2D case, tensor \f$A \times B\f$,
                    softmax axis 1 (B), format tag #dnnl_ba,
                    and \f$A \ne 1\f$
   - Non-optimized: 4D case, tensor \f$A \times B \times C \times D\f$,
                    softmax axis 2 (C), format tag #dnnl_acdb, and
                    and \f$D \cdot B \ne 1\f$

## Example

[Softmax Primitive Example](@ref softmax_example_cpp)

@copydetails softmax_example_cpp_short
