Precision support#

Applies to Linux and Windows

2024-06-11

9 min read time

Use the following sections to identify data types and HIP types ROCm™ supports.

Integral types#

The signed and unsigned integral types that are supported by ROCm are listed in the following table, together with their corresponding HIP type and a short description.

Type name

HIP type

Description

int8

int8_t, uint8_t

A signed or unsigned 8-bit integer

int16

int16_t, uint16_t

A signed or unsigned 16-bit integer

int32

int32_t, uint32_t

A signed or unsigned 32-bit integer

int64

int64_t, uint64_t

A signed or unsigned 64-bit integer

Floating-point types#

The floating-point types that are supported by ROCm are listed in the following table, together with their corresponding HIP type and a short description.

Supported floating-point types

Type name

HIP type

Description

float8 (E4M3)

-

An 8-bit floating-point number that mostly follows IEEE-754 conventions and S1E4M3 bit layout, as described in 8-bit Numerical Formats for Deep Neural Networks , with expanded range and with no infinity or signed zero. NaN is represented as negative zero.

float8 (E5M2)

-

An 8-bit floating-point number mostly following IEEE-754 conventions and S1E5M2 bit layout, as described in 8-bit Numerical Formats for Deep Neural Networks , with expanded range and with no infinity or signed zero. NaN is represented as negative zero.

float16

half

A 16-bit floating-point number that conforms to the IEEE 754-2008 half-precision storage format.

bfloat16

bfloat16

A shortened 16-bit version of the IEEE 754 single-precision storage format.

tensorfloat32

-

A floating-point number that occupies 32 bits or less of storage, providing improved range compared to half (16-bit) format, at (potentially) greater throughput than single-precision (32-bit) formats.

float32

float

A 32-bit floating-point number that conforms to the IEEE 754 single-precision storage format.

float64

double

A 64-bit floating-point number that conforms to the IEEE 754 double-precision storage format.

Note

  • The float8 and tensorfloat32 types are internal types used in calculations in Matrix Cores and can be stored in any type of the same size.

  • The encodings for FP8 (E5M2) and FP8 (E4M3) that are natively supported by MI300 differ from the FP8 (E5M2) and FP8 (E4M3) encodings used in H100 (FP8 Formats for Deep Learning).

  • In some AMD documents and articles, float8 (E5M2) is referred to as bfloat8.

ROCm support icons#

In the following sections, we use icons to represent the level of support. These icons, described in the following table, are also used on the library data type support pages.

Icon

Definition

Not supported

⚠️

Partial support

Full support

Note

  • Full support means that the type is supported natively or with hardware emulation.

  • Native support means that the operations for that type are implemented in hardware. Types that are not natively supported are emulated with the available hardware. The performance of non-natively supported types can differ from the full instruction throughput rate. For example, 16-bit integer operations can be performed on the 32-bit integer ALUs at full rate; however, 64-bit integer operations might need several instructions on the 32-bit integer ALUs.

  • Any type can be emulated by software, but this page does not cover such cases.

Hardware type support#

AMD GPU hardware support for data types is listed in the following tables.

Compute units support#

The following table lists data type support for compute units.

Type name

int8

int16

int32

int64

MI100

MI200 series

MI300 series

Type name

float8 (E4M3)

float8 (E5M2)

float16

bfloat16

tensorfloat32

float32

float64

MI100

MI200 series

MI300 series

Matrix core support#

The following table lists data type support for AMD GPU matrix cores.

Type name

int8

int16

int32

int64

MI100

MI200 series

MI300 series

Type name

float8 (E4M3)

float8 (E5M2)

float16

bfloat16

tensorfloat32

float32

float64

MI100

MI200 series

MI300 series

Atomic operations support#

The following table lists data type support for atomic operations.

Type name

int8

int16

int32

int64

MI100

MI200 series

MI300 series

Type name

float8 (E4M3)

float8 (E5M2)

float16

bfloat16

tensorfloat32

float32

float64

MI100

MI200 series

MI300 series

Note

For cases that are not natively supported, you can emulate atomic operations using software. Software-emulated atomic operations have high negative performance impact when they frequently access the same memory address.

Data Type support in ROCm Libraries#

ROCm library support for int8, float8 (E4M3), float8 (E5M2), int16, float16, bfloat16, int32, tensorfloat32, float32, int64, and float64 is listed in the following tables.

Libraries input/output type support#

The following tables list ROCm library support for specific input and output data types. For a detailed description, refer to the corresponding library data type support page.

Library input/output data type name

int8

int16

int32

int64

hipSPARSELt (details)

✅/✅

❌/❌

❌/❌

❌/❌

rocRAND (details)

-/✅

-/✅

-/✅

-/✅

hipRAND (details)

-/✅

-/✅

-/✅

-/✅

rocPRIM (details)

✅/✅

✅/✅

✅/✅

✅/✅

hipCUB (details)

✅/✅

✅/✅

✅/✅

✅/✅

rocThrust (details)

✅/✅

✅/✅

✅/✅

✅/✅

Library input/output data type name

float8 (E4M3)

float8 (E5M2)

float16

bfloat16

tensorfloat32

float32

float64

hipSPARSELt (details)

❌/❌

❌/❌

✅/✅

✅/✅

❌/❌

❌/❌

❌/❌

rocRAND (details)

-/❌

-/❌

-/✅

-/❌

-/❌

-/✅

-/✅

hipRAND (details)

-/❌

-/❌

-/✅

-/❌

-/❌

-/✅

-/✅

rocPRIM (details)

❌/❌

❌/❌

✅/✅

✅/✅

❌/❌

✅/✅

✅/✅

hipCUB (details)

❌/❌

❌/❌

✅/✅

✅/✅

❌/❌

✅/✅

✅/✅

rocThrust (details)

❌/❌

❌/❌

⚠️/⚠️

⚠️/⚠️

❌/❌

✅/✅

✅/✅

Libraries internal calculations type support#

The following tables list ROCm library support for specific internal data types. For a detailed description, refer to the corresponding library data type support page.

Library internal data type name

int8

int16

int32

int64

hipSPARSELt (details)

Library internal data type name

float8 (E4M3)

float8 (E5M2)

float16

bfloat16

tensorfloat32

float32

float64

hipSPARSELt (details)