Primitive Attributes: Post-ops {#dev_guide_attributes_post_ops}
===============================================================

oneDNN implements some basic capabilities of operation fusion using the
**post-ops attributes** API. The operation fusion typically reduces the memory
bandwidth pressure hence leading to higher performance.

*Post-ops* are operations that are appended after a primitive. They are
implemented using the @ref dev_guide_attributes mechanism. If there are
multiple post-ops, they are executed in the order they have been appended.

Currently the following post-ops are supported by the library:
* [Eltwise](@ref dev_guide_attributes_post_ops_eltwise)
* [Sum](@ref dev_guide_attributes_post_ops_sum)
* [Depthwise](@ref dev_guide_attributes_post_ops_depthwise)
* [Binary](@ref dev_guide_attributes_post_ops_binary)
* [PReLu](@ref dev_guide_attributes_post_ops_prelu)

Just like @ref dev_guide_attributes, the post-ops are represented by an opaque
structure (@ref dnnl_post_ops_t in C API and @ref dnnl::post_ops in C++ API)
which is copied once it is attached to the attributes using the C++ @ref
dnnl::primitive_attr::set_post_ops or C @ref dnnl_primitive_attr_set_post_ops
functions. The attributes then must be passed to a primitive descriptor
creation function to take effect. Below is a simple skeleton for the C++ API:

~~~cpp
dnnl::post_ops po; // default empty post-ops
assert(po.len() == 0); // no post-ops attached

po.append_SOMETHING(params); // append some particular post-op
po.append_SOMETHING_ELSE(other_params); // append one more post-op

// (!) Note that the order in which post-ops are appended matters!
assert(po.len() == 2);

dnnl::primitive_attr attr; // default attributes
attr.set_post_ops(po); // attach the post-ops to the attr

// further po changes would not affect attr

primitive::primitive_desc op_pd(engine, params, attr); // create a pd with the attr
~~~

@note
    Different post-ops can be chained together by appending one
    after another. Note that the appending order matters: the sequence of
    the post operations is executed in the order of appearance. The maximum
    number of post operations supported in the library is 32.

@warning
    Different primitives may have different post-ops support. Each primitive
    documentation page contains information about what kind of post operations
    it supports. Moreover, the support might also depend on the actual
    implementation of a primitive. For instance, the library may not support
    post-ops for primitive reference implementations (which are typically very
    slow, so there is no point in doing the actual fusion). Robust code should
    handle errors accordingly. See the
    [section on attributes error handling](@ref dev_guide_attributes_error_handling).

@note
    Post-ops do not change the memory format of the operation destination memory
    object.

The post-op object can be inspected using the @ref dnnl::post_ops::kind()
function that takes an index of the post-op (which must be less than the value
returned by @ref dnnl::post_ops::len()), and returns its kind.

## Supported Post-ops

@anchor dev_guide_attributes_post_ops_eltwise
### Eltwise Post-op

The eltwise post-op enables fusing a primitive with an @ref dev_guide_eltwise
primitive. This is probably one of the most popular kinds of fusion:
an eltwise (typically an activation function) with preceding convolution
or inner product.

The @ref dnnl::primitive::kind of this post-op
is #dnnl::primitive::kind::eltwise.

API:
- C: @ref dnnl_post_ops_append_eltwise
- C++: @ref dnnl::post_ops::append_eltwise

The parameters (C++ API for simplicity):
~~~cpp
void dnnl::post_ops::append_eltwise(
        algorithm alg, float alpha, float beta // same as in eltwise primitive
        );
~~~

The `alg`, `alpha`, and `beta` parameters are the same as in @ref dev_guide_eltwise.

The eltwise post-op replaces:
\f[
    \dst[:] = \operatorname{Op}(...)
\f]

with

\f[
    \dst[:] = \operatorname{eltwise}( \operatorname{Op}(...) )
\f]

The intermediate result of \f$\operatorname{Op}(...)\f$ is not preserved.
Hence, in most cases this kind of fusion cannot be used during training.

@anchor dev_guide_attributes_post_ops_sum
### Sum Post-op

The sum post-op accumulates the result of a primitive with the existing data.
Prior to accumulating the result, the existing value would be shifted by the
zero point and multiplied by scale.

The kind of this post-op is #dnnl::primitive::kind::sum.

This feature might improve performance for cases like residual learning
blocks, where the result of a convolution is accumulated to the previously
computed activations. The scale parameter can be used in
[INT8](@ref dev_guide_attributes_quantization) inference only when the result
and previous activations have different magnitudes. For all other cases the
scale must be `1.0`.

The sum post-op replaces
\f[
    \dst[:] = \operatorname{Op}(...)
\f]

with

\f[
    \dst[:] = scale \cdot (\dst[:] - zero\_point) + \operatorname{Op}(...)
\f]

If the data type parameter is specified, the original destination tensor will be
reinterpreted as a tensor with the provided data type. Because it is a
reinterpretation, data_type and the destination data type must have the same size. As a
result, the computation will be:

\f[
    \dst(:) = scale \cdot (\operatorname{as\_data\_type}(\dst[:]) - zero\_point) + \operatorname{Op}(...)
\f]

@note
* **GPU**
    * Currently only a u8/s8 data type parameter is supported.
    * Zero point is not supported.

@anchor dev_guide_attributes_post_ops_depthwise
### Depthwise Post-op

Appends a Depthwise convolution as a post-op. This post-op can only be fused
with 1x1 convolution as generally seen in models (like MobileNet_v1) that use a
stack of Separable convolutions: Depthwise convolution followed by 1x1
convolution. The stack of these Separable convolutions (like in MobileNet_v1)
provide an opportunity to fuse 1x1-Convolution with bandwidth-limited Depthwise
convolution.

The @ref dnnl::primitive::kind of this post-op
is #dnnl::primitive::kind::convolution.

API:
- C: @ref dnnl_post_ops_append_dw
- C++: @ref dnnl::post_ops::append_dw

For better readability, below we assume a 2D convolution and use the following
notations:
- `conv_1x1` Convolution with weights spatial=1 i.e., `kh` = `kw` = 1.
- `conv_dw` Depthwise convolution with weights spatial=3 i.e., `kh` = `kw` = 3,
  `g` = `oc` = `ic` and `pad_l` = `pad_r` = {1, 1}.

The Depthwise post-op replaces

\f[
    dst[:] = Conv_{1x1}(...)
\f]

with

\f[
    dst[:] = Conv_{dw}(Conv_{1x1}(...))
\f]


The final output dimensions of the after post-op is defined as

\f[
    dst_{conv_dw} = \{ n, oc_{1x1}, \operatorname{ceil}(oh_{conv_{1x1}}/stride),
     \operatorname{ceil}(ow_{conv_{1x1}}/stride) \}
\f]

where `oh_conv_1x1`, `ow_conv_1x1` are height and width of conv_1x1 destination.

![Fusion](images/img_depthwise_fusion.jpg)

Supported data types

| conv 1x1 output data type        | depthwise post-op output data type | depthwise post-op weights data type | depthwise post-op bias data type
| :--                              | :--                                | :--                                 | :--
| u8, s8                           | u8, s8, s32, f32                   | s8                                  | f32, s32
| f32                              | f32                                | f32                                 | f32
| bf16                             | bf16, f32                          | bf16                                | f32, bf16
| f16                              | f16, f32                           | f16                                 | f32, f16

@note
  * Though it is called a post-operation type, it does not follow the
    post-operation convention which implies an application of operation in
    f32 data type.

  * Currently only supported for 2D 1x1 convolution.

  * Sum or another depthwise post-ops cannot be a part of post-op chain.

  * The `dst_1x1`, `wei_dw` and `dst_dw` are assumed to be #dnnl_format_tag_any.

  * Operation descriptor for base 1x1 convolution requires spatial dimensions of
    destination memory descriptor to coincide with source spatial dimensions. It
    is important for cases when depthwise post-op stride is not equal to `1`.
    In this case, the queried destination descriptor after fusion **will not**
    coincide with the one passed to base convolution. It means that if
    intermediate object is utilized in other places in user application, its
    lifetime has to be handled by user separately since the library does not
    provide a mechanism to query an intermediate output of base convolution.

  * Currently, f16 support for depthwise fusion is only through reference fusion
    implementation. Thus, performance gain is not expected for this data type.

@anchor dev_guide_attributes_post_ops_binary
### Binary Post-op

The binary post-op enables fusing a primitive with a @ref dev_guide_binary
primitive.

The @ref dnnl::primitive::kind of this post-op is
#dnnl::primitive::kind::binary.

API:
- C: @ref dnnl_post_ops_append_binary
- C++: @ref dnnl::post_ops::append_binary

The parameters (C++ API for simplicity):
~~~cpp
void dnnl::post_ops::append_binary(
        algorithm alg, // binary algorithm to apply
        const memory::desc &src1 // memory descriptor for a second memory operand
        );
~~~

The `alg` and `src1` parameters are the same as in @ref dev_guide_binary.

The binary post-op replaces:
\f[
    \dst[:] = \operatorname{Op}(...)
\f]

with

\f[
    \dst[:] = \operatorname{binary}(\operatorname{Op}(...), Source\_1[:])
\f]

The intermediate result of \f$\operatorname{Op}(...)\f$ is not preserved.
Hence, in most cases this kind of fusion cannot be used during training.

Currently the following scenarios are optimized:
* Per tensor broadcast, when \f$Source\_1\f$ is represented as a one-element
  tensor, i.e. {1, 1, 1, 1} for 2D spatial \f$\operatorname{Op}(...)\f$.
* Per channels (i.e. dimension 1) broadcast, when a `dim[1]` value of
  \f$Source\_1\f$ coincides with a `dim[1]` value of
  \f$\operatorname{Op}(...)\f$, i.e. {1, C, 1, 1} for 2D spatial
  \f$\operatorname{Op}(...)\f$.
* Per element broadcast, when \f$Source\_1\f$ coincides with
  \f$\operatorname{Op}(...)\f$. In this case user may create `src1` memory
  descriptor with `format_tag::any` or set a specific tag. However, in later
  case if tags mismatch with \f$\operatorname{Op}(...)\f$, it would result in
  suboptimal performance. In case of using `format_tag::any`, a primitive
  descriptor of the operation will initialize a memory descriptor for binary
  post-operation which format may be queried from attributes using
  `dnnl::post_ops::get_params_binary(...)` function call.

@anchor dev_guide_attributes_post_ops_prelu
### Prelu Post-op

The prelu post-op enables fusing a primitive with a @ref dev_guide_prelu
primitive.

The @ref dnnl::primitive::kind of this post-op is
#dnnl::primitive::kind::prelu.

API:
- C: @ref dnnl_post_ops_append_prelu
- C++: @ref dnnl::post_ops::append_prelu


The parameters (C++ API for simplicity):

~~~cpp
void dnnl::post_ops::append_prelu(
    int mask /*mask describing prelu weights broadcast.*/);
~~~

The prelu post-op replaces:
\f[
    \dst[:] = \operatorname{Op}(...)
\f]

with

\f[
    \dst[:] = \operatorname{prelu}(\operatorname{Op}(...), weights[:])
\f]

Assumptions:
- the weights tensor is passed in runtime using
DNNL_ARG_ATTR_MULTIPLE_POST_OP(index) | DNNL_ARG_WEIGHTS mechanism, where index
is the sequence number of the prelu in post-operations chain;
- only fp32 weights tensor data type is supported;
- only plain layout (a, ab, acb, acdb, acdeb) is supported for weights tensor;
- mask defines the correspondence between the output tensor dimensions and
  the prelu weights tensor. The set i-th bit indicates that a dedicated weights
  value is used for each index along that dimension. Mask 0 value means common
  (scalar) weights value for the whole output tensor.
- the order of dimensions does not depend on how elements are laid out in memory.
For example:
    * for a 2D CNN activations tensor the order is always (n, c)
    * for a 4D CNN activations tensor the order is always (n, c, h, w)

## Examples of Chained Post-ops

Different post-ops can be chained together by appending one after another.
Note that the order matters: the post-ops are executed in the order they have
been appended.

Let's consider some examples.

### Sum -> ReLU

This pattern is pretty common for the CNN topologies of the ResNet family.

~~~cpp
dnnl::post_ops po;
po.append_sum();
po.append_eltwise(
        /* alg kind  = */ dnnl::algorithm::eltwise_relu,
        /* neg slope = */ 0.f,
        /* unused for relu */ 0.f);

dnnl::primitive_attr attr;
attr.set_post_ops(po);

convolution_forward::primitive_desc(conv_d, attr, engine);
~~~

This will lead to the following primitive behavior:

\f[
    \dst[:] = \operatorname{ReLU}(\dst[:] + \operatorname{conv}(\src[:], \weights[:])
\f]


@anchor dev_guide_attributes_post_ops_with_scales
### Tanh -> Sum -> ScaleShift

This is a hypothetical example that illustrates the sequence of operations
applied.  We also set all the scales to values other than 1.0 and use @ref
dnnl::primitive_attr::set_scales_mask which will be covered in @ref
dev_guide_attributes_quantization.

~~~cpp
dnnl::post_ops po;
po.append_eltwise(
        /* alg kind  = */ dnnl::algorithm::eltwise_tanh,
        /* unused for tanh */ 0.f,
        /* unused for tanh */ 0.f);
po.append_sum();
po.append_eltwise(
        /* alg kind     = */ dnnl::algorithm::eltwise_linear,
        /* linear scale = */ alpha,
        /* linear shift = */ beta);

dnnl::primitive_attr attr;
attr.set_scales_mask(DNNL_ARG_SRC, 0);
attr.set_scales_mask(DNNL_ARG_WEIGHTS, 0);
attr.set_scales_mask(DNNL_ARG_DST, 0);
attr.set_post_ops(po);

convolution_forward::primitive_desc(conv_d, attr, engine);
~~~

This will lead to the following primitive behavior (for better readability
the tensors are designated by their names only; i.e., `[:]` is omitted):

\f[
    \dst
        =
        s_{linear} \cdot
        (
            \alpha \cdot
            (
                s_{sum} \cdot \dst
                +
                s_{tanh} \cdot \tanh
                (
                    s_{conv} \cdot \operatorname{conv}(\src, \weights)
                )
            )
            + \beta
        )
\f]


@anchor dev_guide_attributes_post_ops_depthwise_fusion
### Relu -> Depthwise -> Relu

An example of fusing depthwise convolution with 1x1 convolution in MobileNet.

~~~cpp
dnnl::post_ops po;

po.append_eltwise(
        /* alg kind  = */ dnnl::algorithm::eltwise_relu,
        /* neg slope = */ 0.f,
        /* unused for relu */ 0.f);

po.append_dw(
        /* depthwise weights data type = */ dnnl::memory::data_type::s8,
        /* depthwise bias data type (undef implies no bias) = */ dnnl::memory::data_type::undef,
        /* depthwise destination data type = */ dnnl::memory::data_type::u8,
        /* kernel size of fused depthwise convolution = */ kernel,
        /* stride size of fused depthwise convolution = */ stride,
        /* padding size of fused depthwise convolution = */ padding)

po.append_eltwise(
        /* alg kind  = */ dnnl::algorithm::eltwise_relu,
        /* neg slope = */ 0.f,
        /* unused for relu */ 0.f);

dnnl::primitive_attr attr;
attr.set_scales_mask(DNNL_ARG_DST, 0);
attr.set_scales_mask(DNNL_ARG_ATTR_POST_OP_DW | DNNL_ARG_DST, 0);
attr.set_post_ops(po);

auto cpd = convolution_forward::primitive_desc(conv_1x1, attr, engine);
auto dw_weight_md = cpd.query(query::exec_arg_md,
                DNNL_ARG_ATTR_POST_OP_DW | DNNL_ARG_WEIGHTS);
auto dw_bias_md = cpd.query(query::exec_arg_md,
                DNNL_ARG_ATTR_POST_OP_DW | DNNL_ARG_BIAS);

~~~

This will lead to the following primitive behaviour:

\f[
    dst
        =
        ReLU_{depthwise}
        (
            scales_{depthwise} \cdot
            (
                conv_{depthwise}
                (
                    ReLU_{1x1}
                    (
                        scales_{conv_{1x1}} \cdot
                        (
                            conv_{1x1}()
                        )
                    )
                )
            )
        )
\f]

@anchor dev_guide_attributes_post_ops_binary_fusion
### Binary

An example of fusing convolution with binary post-op with per channel addition.

~~~cpp
dnnl::memory::desc conv_dst_md {MB, C, H, W}; /* 2D conv destination memory desc */

dnnl::post_ops po;

/* Append eltwise post-op prior the binary post-op */
po.append_eltwise(
        /* alg kind  = */ dnnl::algorithm::eltwise_relu,
        /* neg slope = */ 0.f,
        /* unused for relu */ 0.f);

/* Note that `C` coincides with the one from `conv_dst_md`. Also note that only
 * supported memory format for src1 memory is `nchw` (or `abcd`) format. */
po.append_binary(
        /* alg kind = */ dnnl::algorithm::binary_add,
        /* src1_md = */ dnnl::memory::desc(
                {1, C, 1, 1},
                dnnl::memory::data_type::f32,
                dnnl::memory::format_tag::abcd));

dnnl::primitive_attr attr;
attr.set_post_ops(po);

auto cpd = convolution_forward::primitive_desc(conv, attr, engine);

/* To set memory argument for binary post-op, the following should take place: */
std::unordered_map<int, memory> args;

args.insert(DNNL_ARG_SRC, conv_src_memory);
...
int binary_post_op_position = 1; /* hard coded here, but may be queried */
args.insert(
        DNNL_ARG_ATTR_MULTIPLE_POST_OP(binary_post_op_position) | DNNL_ARG_SRC_1, /* note parentheses around index */
        binary_post_op_src1_memory);
~~~
