Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN)  0.95.0
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Introduction to Low-Precision 8-bit Integer Computations

$\dagger$ Disclaimer: MKLDNN Int8 primitives are a work in progress and not all definitions and configurations have been implemented or included in the documentation. Moreover, the example included in this documentation relies on int8 primitives which use the MKL binary dependency and is limited to MKLDNN built with the MKL binary.


To push higher performance during inference computations, recent work has focused on computing at a lower precision (i.e. shrinking the size of data for activations and weights) to achieve higher throughput. Eight-bit computations (referred to as int8) offer improved performance over higher precision types -because it allows packing more data into a single instruction, at the cost of reduced but acceptable accuracy.

Int8 Workflow

Quantization Process

To operate with int8 data types from a higher precision format (e.g. 32-bit floating point), data must first be quantized. The quantization process converts a given input into a lower-precision format. The precision and accuracy factors are determined by the scaling factors.


The scale is usually obtained from sampling the dataset of previous executions in the original format (e.g. the activations and weights from training in fp32) and is formulated as:

The purpose is to establish the range of values used in the computation where selecting a proper scaling factor prevents over or underflows when computing the lower precision results.

Quantization Factor

The next step is to calculate the quantization factor for converting the values into the corresponding int8 range. This is also known as the scale or scaling factor applied to the original high-precision values and is calculated as:

The low-precision values, known as the quantized activation, weights, and bias values are calculated as:

When the destination value (e.g. from a convolution) is stored as a signed 32-bit integer, the result is bound to the same quantization scaling factors:

Where the approximated value is due to the rounded values.

Inversely, the dequantized value is calculated as:

Quantization Example

To show how the int8 parameters are obtained, suppose we first start off with a set of arbitrary high-precision input and output values. These values come from sampling a previously executed training run and are in their original 32-bit floating point format as:

The scaling factors are:

Finally, the quantized input values for the 8-bit operation are calculated as:

These arrays are the new inputs for the int8 net.

MKLDNN Support for low-precision int8 Primitives

MKLDNN supports low-precision computations for inference through the int8 primitives. Int8 primitives are ordinary MKLDNN primitives which have their input and output parameters configured to 8-bit types. Int8 primitives are optimized for high performance, one example is the use of specialized 512-bit wide low-precision instructions available through the Advanced Vector Extensions 512 (AVX512) for Intel Skylake Server Systems. Currently, the $\dagger$supported primitives are:

MKLDNN Attributes

MKLDNN primitive behaviour may be extended for additional functionalities involving output data transformation. These additional features are configured via primitive attributes. The primitive attributes definition is an opaque structure for passing extra parameters to a primitive descriptor. These parameters include Scaling Factor and Fused Post-Operations (PostOps). All operation primitives support the attributes structure, however, not all configurations are implemented and result in failed primitive creation.

The scaling factor, as previously described, is known prior to the inference operation where the values are calculated from a set of formulas. In MKLDNN, the scaling factor is applied to the output of a primitive. Moreover, to perform input transformations (e.g. source, bias and weights), MKLDNN performs quantizing and dequantizing of data for int8 through the Reorder Primitive.

MKLDNN has 2 formats for defining the output scaling factor, depending on the configuration set by the scaling mask, the output is either scaled uniformly across all the dimensions (mask = 0) or a set of scaling values are applied to specific dimension(s), as explanation below:

Note: Mask is always applied to the logical dimension; this is independent of the dimension format that the primitive might select. The dimensions in MKLDNN are defined as follows:

Fused Post-Operations (PostOps) allow chaining operations during the primitive computation. Note that the resulting output value from PostOps is always affected by the scaling factor. The supported operations are:

The list of supported eltwise operations for int8 is currently limited to ReLU. For instance, PostOps may only configure a convolution with accumulation followed by eltwise (relu).

Simple_Net.cpp Example using 8-bit and PostOps Computations

The MKLDNN repository contains an example called simple_int8_net.cpp that executes a Convolution with ReLU from the AlexNet topology using int8 computations. This example extends the simple_net.cpp source focusing on the creation and execution of int8 primitives using PostOps and Scaling Factors to obtain similar results.

  1. Initially, the example configures the tensors according to the dimensions in Conv3 of AlexNet.
    memory::dims conv_src_tz = { batch, 256, 13, 13 };
    memory::dims conv_weights_tz = { 384, 256, 3, 3 };
    memory::dims conv_bias_tz = { 384 };
    memory::dims conv_dst_tz = { batch, 384, 13, 13 };
    memory::dims conv_strides = { 1, 1 };
    auto conv_padding = { 1, 1 };
  2. Next, the example configures the scales used to quantize fp32 data into int8. For this example, the scaling value is chosen as an arbitrary number, although in a realistic scenario, it should be calculated from a set of precomputed values as previously mentioned.
    /* Set Scaling mode for int8 quantizing */
    const std::vector<float> src_scales = { 1.8 };
    const std::vector<float> weight_scales = { 2 };
    const std::vector<float> bias_scales = { 1 };
    const std::vector<float> dst_scales = { 0.55 };
    /* assign halves of vector with arbitrary values */
    std::vector<float> conv_scales(384);
    const int scales_half = 384 / 2;
    std::fill(conv_scales.begin(), conv_scales.begin() + scales_half, 0.3);
    std::fill(conv_scales.begin() + scales_half, conv_scales.end(), 0.8);
    The source, weights, bias and destination datasets use the single-scale format with mask set to ‘0’, while the output from the convolution (conv_scales) will use the array format where mask = 2 corresponding to the output dimension.
    const int src_mask = 0;
    const int weight_mask = 0;
    const int bias_mask = 0;
    const int dst_mask = 0;
    const int conv_mask = 2; // 1 << output_channel_dim
  3. Create the memory primitives for user data (source, weights and bias). The user data will be in its original 32-bit floating point format.
    auto user_src_memory = memory(
    { { { conv_src_tz }, memory::data_type::f32, memory::format::nchw },
    cpu_engine },;
    /* ... */
  4. Create a memory descriptor for each convolution parameter. The convolution data uses 8-bit integer values, so the memory descriptors are configured as:

    • 8-bit unsigned (u8) for source and destination.
    • 8-bit signed (s8) for bias and weights.

    Note: the destination type is chosen as unsigned because the convolution applies a ReLU operation where data results ≥ 0.

    auto conv_src_md = memory::desc(
    { conv_src_tz }, memory::data_type::u8, memory::format::any);
    /* ... */
    auto conv_dst_md = memory::desc(
    { conv_dst_tz }, memory::data_type::u8, memory::format::any);
  5. Create a convolution descriptor passing the int8 memory descriptors as parameters.
    auto conv_desc = convolution_forward::desc(prop_kind::forward,
    convolution_direct, conv_src_md, conv_weights_md, conv_bias_md,
    conv_dst_md, conv_strides, conv_padding, conv_padding);
  6. Configuring int8-specific parameters in an int8 primitive is done via the Attributes Primitive. Create an attributes object for the convolution and configure it accordingly.

    primitive_attr conv_attr;
    /* Specify the scales array and corresponding mask */
    conv_attr.set_output_scales(conv_mask, conv_scales);

    The ReLU layer from Alexnet is executed through the PostOps feature. Create a PostOps object and configure it to execute an eltwise relu operation.

    const float ops_scale = 1.;
    const float negative_slope = 0.;
    post_ops ops;
    ops.append_eltwise(ops_scale, algorithm::eltwise_relu, negative_slope, 0.);
  7. Create a primitive descriptor using the convolution descriptor and passing along the int8 attributes in the constructor. The primitive descriptor for the convolution will contain the specific memory formats for the computation.
    auto conv_prim_desc = convolution_forward::primitive_desc(
    conv_desc, conv_attr, cpu_engine);
  8. Create a memory primitive for each of the convolution’s data input parameters (source, bias, weights and destination). Using the convolution primitive descriptor as the creation parameter allows MKLDNN to configure the memory formats for the convolution.
    auto conv_src_memory = memory(conv_prim_desc.src_primitive_desc());
    Scaling parameters are passed to the reorder primitive via the attributes primitive.
    primitive_attr src_attr;
    src_attr.set_output_scales(src_mask, src_scales);
  9. User memory must be transformed into convolution-friendly memory (for int8 and memory format). A reorder layer performs the data transformation from fp32 (the original user data) into int8 format (the data used for the convolution). In addition, the reorder will transform the user data into the required memory format (as explained in the simple_net example).
    auto src_reorder_pd
    = reorder::primitive_desc(user_src_memory.get_primitive_desc(),
    conv_src_memory.get_primitive_desc(), src_attr);
    net.push_back(reorder(src_reorder_pd, user_src_memory, conv_src_memory));
    /* ... */
  10. Create the convolution primitive and add it to the net. The int8 example computes the same Convolution +ReLU layers from AlexNet simple-net.cpp using the int8 and PostOps approach. Although performance is not measured here, if it were, in practice, it would require less computation time to achieve similar results.
    net.push_back(convolution_forward(conv_prim_desc, conv_src_memory,
    conv_weights_memory, conv_bias_memory, conv_dst_memory));
  11. Finally, dst memory may be dequantized from int8 into the original fp32 format. Create a memory primitive for the user data in the original 32-bit floating point format and apply a reorder to transform the computation output data.
    auto user_dst_memory = memory(
    { { { conv_dst_tz }, memory::data_type::f32, memory::format::nchw },
    cpu_engine },;
    primitive_attr dst_attr;
    dst_attr.set_output_scales(dst_mask, dst_scales);
    auto dst_reorder_pd
    = reorder::primitive_desc(conv_dst_memory.get_primitive_desc(),
    user_dst_memory.get_primitive_desc(), dst_attr);

The diagram to summarize this example is as follows:

Diagram depicting simple_net_int8 data flow

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