Low Precision {#dev_guide_graph_low_precision}
==============================================

oneDNN Graph provides low precision support with int8 (signed/unsigned 8-bit
integer), bf16 and f16 data types. oneDNN Graph API expects the computation
graph is converted to low precision representation, the data's precision and
quantization parameters are specified explicitly. oneDNN Graph API
implementation will strictly respect the numeric precision of the computation.

@anchor dev_guide_graph_int8_quantization_model
## INT8

oneDNN Graph API provides below two operations to support quantized model with
static quantization:

- [Dequantize](@ref dev_guide_op_dequantize)

- [Quantize](@ref dev_guide_op_quantize)

Dequantize operation takes integer tensor with its associated scale and zero
point and returns f32 tensor. Quantize operation takes f32 tensor, scale, zero
point, and returns integer tensor. The scale and zero point are single dimension
tensors, which could contain one value for the per-tensor quantization case or
multiple values for the per-channel quantization case. The integer tensor could
be represented in unsigned int8 or signed int8 data type. Zero point could be
zero for symmetric quantization scheme, and a non-zero value for asymmetric
quantization scheme.

Dequantize and Quantize operation should be inserted manually in the graph as
part of quantization process before passing to oneDNN Graph. oneDNN Graph honors
the data type passed via logical tensor and faithfully follows the numeric
semantics. For example, if the graph has a Quantize operation followed by a
Dequantize operation with exact same scale and zero point, oneDNN Graph
implementation should not eliminate them since that implicitly changes the
numeric precision.

oneDNN Graph partitioning API may return a partition containing Dequantize,
Quantize, and Convolution operations in-between. It is not necessary to
recognize the subgraph pattern explicitly and convert to fused operation.
Depending on oneDNN Graph implementation capability, the partition may include
more or fewer operations.

@img{int8_programming.jpg,Figure 1: Overview of int8 programming model.,80%,}

@anchor dev_guide_graph_mixed_precision_model
## BF16/F16

oneDNN Graph provides [TypeCast](@ref dev_guide_op_typecast) operation, which
can convert a f32 tensor to bf16 or f16, and vice versa. It is used to support
auto mixed precision mechanism in popular deep learning frameworks. All oneDNN
Graph operations support bf16 and f16 data types.

A TypeCast operation performing down conversion should be inserted clearly to
indicate the use of low numeric precision. oneDNN Graph implementation fully
honors the API-specified numeric precision.

@img{bf16_programming.jpg,Figure 2: Overview of bf16 programming model.,80%,}
