Data Types

oneDNN functionality supports a number of numerical data types. IEEE single precision floating point (fp32) is considered to be the golden standard in deep learning applications and is supported in all the library functions. The purpose of low precision data types support is to improve performance of compute intensive operations, such as convolutions, inner product, and recurrent neural network cells in comparison to fp32.

Data type

Description

f32

IEEE single precision floating point

bf16

non-IEEE 16-bit floating point

f16

IEEE half precision floating point

s8/u8

signed/unsigned 8-bit integer

Inference and Training

oneDNN supports training and inference with the following data types:

Usage mode

CPU

GPU

Inference

f32, bf16, s8/u8

f32, bf16, f16, s8/u8

Training

f32, bf16

f32, bf16

Note

Using lower precision arithmetic may require changes in the deep learning model implementation.

See topics for the corresponding data types details:

Individual primitives may have additional limitations with respect to data type support based on the precision requirements. The list of data types supported by each primitive is included in the corresponding sections of the developer guide.

Hardware Limitations

While all the platforms oneDNN supports have hardware acceleration for fp32 arithmetics, that is not the case for other data types. Support for low precision data types may not be available for older platforms. The next sections explain limitations that exist for low precision data types for Intel(R) Architecture processors, Intel Processor Graphics and Xe architecture-based Graphics.

Intel(R) Architecture Processors

oneDNN performance optimizations for Intel Architecture Processors are specialized based on Instruction Set Architecture (ISA). The following ISA have specialized optimizations in the library:

  • Intel Streaming SIMD Extensions 4.1 (Intel SSE4.1)

  • Intel Advanced Vector Extensions (Intel AVX)

  • Intel Advanced Vector Extensions 2 (Intel AVX2)

  • Intel Advanced Vector Extensions 512 (Intel AVX-512)

  • Intel Deep Learning Boost (Intel DL Boost)

The following table indicates the minimal supported ISA for each of the data types that oneDNN recognizes.

Data type

Minimal supported ISA

f32

Intel SSE4.1

s8, u8

Intel AVX2

bf16

Intel DL Boost with bfloat16 support

f16

not supported

Note

See Nuances of int8 Computations in the Developer Guide for additional limitations related to int8 arithmetic.

Note

The library has functional bfloat16 support on processors with Intel AVX-512 Byte and Word Instructions (AVX512BW) support for validation purposes. The performance of bfloat16 primitives on platforms without hardware acceleration for bfloat16 is 3-4x lower in comparison to the same operations on the fp32 data type.

Intel(R) Processor Graphics and Xe architecture-based Graphics

Intel Processor Graphics provides hardware acceleration for fp32 and fp16 arithmetic. Xe architecture-based Graphics additionally provides acceleration for int8 arithmetic (both signed and unsigned). Implementations for the bf16 data type are functional only and do not currently provide performance benefits.

Data type

Support level

f32

optimized

bf16

functional only

f16

optimized

s8, u8

optimized for Xe architecture-based Graphics (code named DG1 and Tiger Lake)