Data Types {#dev_guide_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](https://en.wikipedia.org/wiki/Single-precision_floating-point_format#IEEE_754_single-precision_binary_floating-point_format:_binary32)
| bf16      | [non-IEEE 16-bit floating-point](https://software.intel.com/content/www/us/en/develop/download/bfloat16-hardware-numerics-definition.html)
| f16       | [IEEE half precision floating-point](https://en.wikipedia.org/wiki/Half-precision_floating-point_format#IEEE_754_half-precision_binary_floating-point_format:_binary16)
| 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:
 * @ref dev_guide_inference_int8
   * @ref dev_guide_attributes_quantization
 * @ref dev_guide_training_bf16

Individual primitives may have additional limitations with respect to data type
by each primitive is included in the corresponding sections of the developer
guide.

## General numerical behavior of the oneDNN library

During a primitive computation, oneDNN can use different datatypes
than those of the inputs/outputs. In particular, oneDNN uses wider
accumulator datatypes (s32 for integral computations, and f32 for
floating-point computations), and converts intermediate results to f32
before applying post-ops. The following formula governs the datatypes
dynamic during a primitive computation:

\f[
\operatorname{convert_{dst\_dt}} ( \operatorname{dst\_zero\_point_{f32}} + \operatorname{postops_{f32}} (\operatorname{oscale_{f32}} * \operatorname{convert_{f32}} (\operatorname{Op}(\operatorname{src_{src\_dt}}), \operatorname{weights_{wei\_dt}}, ...)))
\f]

The `Op` output datatype depends on the datatype of its inputs:
- if `src`, `weights`, ... are floating-point datatype (f32, f16,
  bf16), then the `Op` outputs f32 elements.
- if `src`, `weights`, ... are integral datatypes (s8, u8, s32), then
  the `Op` outputs s32 elements.
- if the primitive allows to mix input datatypes, the `Op` outputs
  datatype will be s32 if its weights are an integral datatype, or f32
  otherwise.

No downconversions are allowed by default, but can be enabled using
the floating-point math controls described in @ref
dev_guide_attributes_fpmath_mode.

### floating-point environment
oneDNN floating-point computation behavior is controlled by the
floating-point environment as defined by the C and C++ standards, in
the fenv.h header. In particular, the floating-point environment can control:
- the rounding mode. It is set to round-to-nearest tie-even by default
  on x64 systems and can be changed using the fesetround() C function.
- the handling of denormal values. Computation on denormals can
  negatively impact performance on x64 systems and are not flushed to
  zero by default.

@note
  Most DNN applications do not require precise computations with denormal
  numbers and flushing these denormals to zero can improve performance.
  On x64 systems, the floating-point environment can be updated to allow
  flushing denormals to zero as follow:
~~~cpp
#include <xmmintrin.h>
_MM_SET_FLUSH_ZERO_MODE(_MM_FLUSH_ZERO_ON);
~~~

## 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 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)
* Intel Advanced Matrix Extensions (Intel AMX)

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 @ref dev_guide_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.

@note 
  The Intel AMX instructions ignore the floating-point environment
  flag and always round to nearest tie-even and flush denormals to
  zero.

### Intel(R) Processor Graphics and Xe Architecture graphics
oneDNN performance optimizations for Intel Processor graphics and
Xe Architecture graphics are specialized based on device microarchitecture (uArch).
The following uArchs have specialized optimizations in the library:
* GEN9 (also covers GEN11)
* Xe-LP (previoulsy known as GEN12LP)
* Xe-HP

The following table indicates the minimal supported uArch for each of the data
types that oneDNN recognizes.
| Data type | Minimal supported uArch
| :---      | :---
| f32       | GEN9
| s8, u8    | Xe-LP
| bf16      | Xe-HP
| f16       | GEN9
