Deep Neural Network Library (DNNL)  1.2.0
Performance library for Deep Learning
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Primitive Attributes: Post-ops

DNNL 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.

The post-ops change the default behavior of a primitive and hence are implemented through the Primitive Attributes mechanism.

Currently the following post-ops are supported by the library:

Post-ops \ Primitive Convolution Inner Product Batch Normalization
Eltwise Partial Partial Partial
Sum Partial N/A N/A

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

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 of appending 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(params, attr); // create a pd with the attr
Different post-ops can be chained together by appending one after another. Note that the appending order matters: the sequence of the post-ops is executed in the order of appearance.
Different primitives have different capabilities on supporting post-ops. Moreover, the support might also depend on the actual implementation of a primitive. For instance, the library generally doesn't support post-ops for reference primitives (which are typically very slow, so there is no point in doing the actual fusion). So the robust integration should handle errors accordingly. See the section on attributes error handling.

The post-op object can be inspected by dnnl::post_ops::kind() function that takes an index of the post-op (that must be less than the value returned by dnnl::post_ops::len()) and returns it's kind.

Supported Post-ops

Eltwise Post-op

The eltwise post-op enables fusing a primitive with a 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 dnnl::primitive::kind of this post-op is dnnl::primitive::kind::eltwise.


The parameters (C++ API for simplicity):

float scale, // scaling factor (described below)
algorithm alg, float alpha, float beta // same as in Eltwise primitive

The alg, alpha, and beta parameters are the same as in Eltwise.

The Eltwise post-op replaces:

\[ dst(:) = Op(...) \]


\[ dst(:) = scale \cdot Eltwise( Op(...) ) \]

The intermediate result of \(Op(...)\) is not stored. Hence in most of the case this kind of fusion cannot be used with the training.

The \(scale\) factor is supported in INT8 inference only. For other cases the scale must be equal to 1.0.

Sum Post-op

Appends an accumulation (sum) post-op. Prior to accumulating the result, the previous value would be 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 inference only when the result and previous activations have different logical scaling factors.

The sum post-op replaces

\[ dst(:) = Op(...) \]


\[ dst(:) = scale \cdot dst(:) + Op(...) \]

This post-op (as well as all the others) disregards the original layout of the destination; that is, the layout of the original destination is expected to be the same as the layout of the output destination.

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 from the ResNet family.

/* scale = */ 1.f);
/* scale = */ 1.f
/* neg slope = */ 0.f,
/* unused for relu */ 0.f);
convolution_forward::primitive_desc(conv_d, attr, engine);

This will lead to the following primitive behavior:

\[ dst(:) = ReLU(dst(:) + conv(src(:), weights(:)) \]

Tanh -> Sum -> ScaleShift

The hypothetical example to illustrate the sequence of operations applied. We also set all the scales to non-one to as well as use dnnl::primitive_attr::set_output_scales which will be covered in Primitive Attributes: Quantization. Unfortunately (or fortunately) the sequence is not supported by the library and is merely used to illustrate the semantics of post-ops.

/* scale = */ s_tanh,
/* unused for tanh */ 0.f,
/* unused for tanh */ 0.f);
/* scale = */ s_sum);
/* scale = */ s_linear,
/* scale = */ alpha,
/* shift = */ beta);
attr.set_output_scales(0, {s_conv});
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):

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