mirror of
https://github.com/ROCm/ROCm.git
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remove extra files
This commit is contained in:
Peter Park
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ROCm Version,7.0.2,7.0.1/7.0.0,6.4.3,6.4.2,6.4.1,6.4.0,6.3.3,6.3.2,6.3.1,6.3.0,6.2.4,6.2.2,6.2.1,6.2.0, 6.1.5, 6.1.2, 6.1.1, 6.1.0, 6.0.2, 6.0.0
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:ref:`Operating systems & kernels <OS-kernel-versions>`,Ubuntu 24.04.3,Ubuntu 24.04.3,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2,"Ubuntu 24.04.1, 24.04","Ubuntu 24.04.1, 24.04","Ubuntu 24.04.1, 24.04",Ubuntu 24.04,,,,,,
|
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,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,"Ubuntu 22.04.5, 22.04.4","Ubuntu 22.04.5, 22.04.4","Ubuntu 22.04.5, 22.04.4","Ubuntu 22.04.5, 22.04.4","Ubuntu 22.04.5, 22.04.4, 22.04.3","Ubuntu 22.04.4, 22.04.3","Ubuntu 22.04.4, 22.04.3","Ubuntu 22.04.4, 22.04.3","Ubuntu 22.04.4, 22.04.3, 22.04.2","Ubuntu 22.04.4, 22.04.3, 22.04.2"
|
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,,,,,,,,,,,,,,,"Ubuntu 20.04.6, 20.04.5","Ubuntu 20.04.6, 20.04.5","Ubuntu 20.04.6, 20.04.5","Ubuntu 20.04.6, 20.04.5","Ubuntu 20.04.6, 20.04.5","Ubuntu 20.04.6, 20.04.5"
|
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,"RHEL 10.0 [#rhel-10-702-past-60]_, 9.6 [#rhel-10-702-past-60]_, 9.4 [#rhel-94-702-past-60]_","RHEL 9.6 [#rhel-10-702-past-60], 9.4 [#rhel-94-702-past-60]_","RHEL 9.6, 9.4","RHEL 9.6, 9.4","RHEL 9.6, 9.5, 9.4","RHEL 9.5, 9.4","RHEL 9.5, 9.4","RHEL 9.5, 9.4","RHEL 9.5, 9.4","RHEL 9.5, 9.4","RHEL 9.4, 9.3","RHEL 9.4, 9.3","RHEL 9.4, 9.3","RHEL 9.4, 9.3","RHEL 9.4, 9.3, 9.2","RHEL 9.4, 9.3, 9.2","RHEL 9.4, 9.3, 9.2","RHEL 9.4, 9.3, 9.2","RHEL 9.3, 9.2","RHEL 9.3, 9.2"
|
||||
,RHEL 8.10 [#rhel-700-past-60]_,RHEL 8.10 [#rhel-700-past-60]_,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,"RHEL 8.10, 8.9","RHEL 8.10, 8.9","RHEL 8.10, 8.9","RHEL 8.10, 8.9","RHEL 8.9, 8.8","RHEL 8.9, 8.8","RHEL 8.9, 8.8","RHEL 8.9, 8.8","RHEL 8.9, 8.8","RHEL 8.9, 8.8"
|
||||
,SLES 15 SP7 [#sles-db-700-past-60]_,SLES 15 SP7 [#sles-db-700-past-60]_,"SLES 15 SP7, SP6","SLES 15 SP7, SP6",SLES 15 SP6,SLES 15 SP6,"SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP5, SP4","SLES 15 SP5, SP4","SLES 15 SP5, SP4","SLES 15 SP5, SP4","SLES 15 SP5, SP4","SLES 15 SP5, SP4"
|
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,,,,,,,,,,,,,,,,CentOS 7.9,CentOS 7.9,CentOS 7.9,CentOS 7.9,CentOS 7.9
|
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,"Oracle Linux 10, 9, 8 [#ol-700-mi300x-past-60]_","Oracle Linux 9, 8 [#ol-700-mi300x-past-60]_","Oracle Linux 9, 8 [#mi300x-past-60]_","Oracle Linux 9, 8 [#mi300x-past-60]_","Oracle Linux 9, 8 [#mi300x-past-60]_","Oracle Linux 9, 8 [#mi300x-past-60]_",Oracle Linux 8.10 [#mi300x-past-60]_,Oracle Linux 8.10 [#mi300x-past-60]_,Oracle Linux 8.10 [#mi300x-past-60]_,Oracle Linux 8.10 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,,,
|
||||
,"Debian 13 [#db-mi300x-past-60]_, 12 [#sles-db-700-past-60]_",Debian 12 [#sles-db-700-past-60]_,Debian 12 [#single-node-past-60]_,Debian 12 [#single-node-past-60]_,Debian 12 [#single-node-past-60]_,Debian 12 [#single-node-past-60]_,Debian 12 [#single-node-past-60]_,Debian 12 [#single-node-past-60]_,Debian 12 [#single-node-past-60]_,,,,,,,,,,,
|
||||
,Azure Linux 3.0 [#az-mi300x-past-60]_,Azure Linux 3.0 [#az-mi300x-past-60]_,Azure Linux 3.0 [#az-mi300x-past-60]_,Azure Linux 3.0 [#az-mi300x-past-60]_,Azure Linux 3.0 [#az-mi300x-past-60]_,Azure Linux 3.0 [#az-mi300x-past-60]_,Azure Linux 3.0 [#az-mi300x-630-past-60]_,Azure Linux 3.0 [#az-mi300x-630-past-60]_,,,,,,,,,,,,
|
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,Rocky Linux 9 [#rl-700-past-60]_,Rocky Linux 9 [#rl-700-past-60]_,,,,,,,,,,,,,,,,,,
|
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,.. _architecture-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,
|
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:doc:`Architecture <rocm-install-on-linux:reference/system-requirements>`,CDNA4,CDNA4,,,,,,,,,,,,,,,,,,
|
||||
,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3
|
||||
,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2
|
||||
,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA
|
||||
,RDNA4,RDNA4,RDNA4,RDNA4,RDNA4,,,,,,,,,,,,,,,
|
||||
,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3
|
||||
,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2
|
||||
,.. _gpu-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,
|
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:doc:`GPU / LLVM target <rocm-install-on-linux:reference/system-requirements>`,gfx950 [#mi350x-os-past-60]_,gfx950 [#mi350x-os-past-60]_,,,,,,,,,,,,,,,,,,
|
||||
,gfx1201 [#RDNA-OS-700-past-60]_,gfx1201 [#RDNA-OS-700-past-60]_,gfx1201 [#RDNA-OS-past-60]_,gfx1201 [#RDNA-OS-past-60]_,gfx1201 [#RDNA-OS-past-60]_,,,,,,,,,,,,,,,
|
||||
,gfx1200 [#RDNA-OS-700-past-60]_,gfx1200 [#RDNA-OS-700-past-60]_,gfx1200 [#RDNA-OS-past-60]_,gfx1200 [#RDNA-OS-past-60]_,gfx1200 [#RDNA-OS-past-60]_,,,,,,,,,,,,,,,
|
||||
,gfx1101 [#RDNA-OS-700-past-60]_ [#rd-v710-past-60]_,gfx1101 [#RDNA-OS-700-past-60]_ [#rd-v710-past-60]_,gfx1101 [#RDNA-OS-past-60]_ [#7700XT-OS-past-60]_,gfx1101 [#RDNA-OS-past-60]_ [#7700XT-OS-past-60]_,gfx1101 [#RDNA-OS-past-60]_,,,,,,,,,,,,,,,
|
||||
,gfx1100 [#RDNA-OS-700-past-60]_,gfx1100 [#RDNA-OS-700-past-60]_,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100
|
||||
,gfx1030 [#RDNA-OS-700-past-60]_ [#rd-v620-past-60]_,gfx1030 [#RDNA-OS-700-past-60]_ [#rd-v620-past-60]_,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030
|
||||
,gfx942 [#mi325x-os-past-60]_ [#mi300x-os-past-60]_ [#mi300A-os-past-60]_,gfx942 [#mi325x-os-past-60]_ [#mi300x-os-past-60]_ [#mi300A-os-past-60]_,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942 [#mi300_624-past-60]_,gfx942 [#mi300_622-past-60]_,gfx942 [#mi300_621-past-60]_,gfx942 [#mi300_620-past-60]_, gfx942 [#mi300_612-past-60]_, gfx942 [#mi300_612-past-60]_, gfx942 [#mi300_611-past-60]_, gfx942 [#mi300_610-past-60]_, gfx942 [#mi300_602-past-60]_, gfx942 [#mi300_600-past-60]_
|
||||
,gfx90a [#mi200x-os-past-60]_,gfx90a [#mi200x-os-past-60]_,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a
|
||||
,gfx908 [#mi100-os-past-60]_,gfx908 [#mi100-os-past-60]_,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908
|
||||
,,,,,,,,,,,,,,,,,,,,
|
||||
FRAMEWORK SUPPORT,.. _framework-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,
|
||||
:doc:`PyTorch <../compatibility/ml-compatibility/pytorch-compatibility>`,"2.8, 2.7, 2.6","2.7, 2.6, 2.5","2.6, 2.5, 2.4, 2.3","2.6, 2.5, 2.4, 2.3","2.6, 2.5, 2.4, 2.3","2.6, 2.5, 2.4, 2.3","2.4, 2.3, 2.2, 1.13","2.4, 2.3, 2.2, 1.13","2.4, 2.3, 2.2, 1.13","2.4, 2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13"
|
||||
:doc:`TensorFlow <../compatibility/ml-compatibility/tensorflow-compatibility>`,"2.19.1, 2.18.1, 2.17.1 [#tf-mi350-past-60]_","2.19.1, 2.18.1, 2.17.1 [#tf-mi350-past-60]_","2.18.1, 2.17.1, 2.16.2","2.18.1, 2.17.1, 2.16.2","2.18.1, 2.17.1, 2.16.2","2.18.1, 2.17.1, 2.16.2","2.17.0, 2.16.2, 2.15.1","2.17.0, 2.16.2, 2.15.1","2.17.0, 2.16.2, 2.15.1","2.17.0, 2.16.2, 2.15.1","2.16.1, 2.15.1, 2.14.1","2.16.1, 2.15.1, 2.14.1","2.16.1, 2.15.1, 2.14.1","2.16.1, 2.15.1, 2.14.1","2.15.0, 2.14.0, 2.13.1","2.15.0, 2.14.0, 2.13.1","2.15.0, 2.14.0, 2.13.1","2.15.0, 2.14.0, 2.13.1","2.14.0, 2.13.1, 2.12.1","2.14.0, 2.13.1, 2.12.1"
|
||||
:doc:`JAX <../compatibility/ml-compatibility/jax-compatibility>`,0.6.0,0.6.0,0.4.35,0.4.35,0.4.35,0.4.35,0.4.31,0.4.31,0.4.31,0.4.31,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26
|
||||
:doc:`verl <../compatibility/ml-compatibility/verl-compatibility>` [#verl_compat-past-60]_,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,0.3.0.post0,N/A,N/A,N/A,N/A,N/A,N/A
|
||||
:doc:`Stanford Megatron-LM <../compatibility/ml-compatibility/stanford-megatron-lm-compatibility>` [#stanford-megatron-lm_compat-past-60]_,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,85f95ae,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
|
||||
:doc:`DGL <../compatibility/ml-compatibility/dgl-compatibility>` [#dgl_compat-past-60]_,N/A,N/A,N/A,N/A,N/A,2.4.0,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
|
||||
:doc:`Megablocks <../compatibility/ml-compatibility/megablocks-compatibility>` [#megablocks_compat-past-60]_,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,0.7.0,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
|
||||
:doc:`Taichi <../compatibility/ml-compatibility/taichi-compatibility>` [#taichi_compat-past-60]_,N/A,N/A,N/A,N/A,N/A,N/A,N/A,1.8.0b1,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
|
||||
:doc:`Ray <../compatibility/ml-compatibility/ray-compatibility>` [#ray_compat-past-60]_,N/A,N/A,N/A,N/A,2.48.0.post0,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
|
||||
:doc:`llama.cpp <../compatibility/ml-compatibility/llama-cpp-compatibility>` [#llama-cpp_compat-past-60]_,N/A,b6356,b6356,b6356,b6356,b5997,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
|
||||
:doc:`FlashInfer <../compatibility/ml-compatibility/flashinfer-compatibility>` [#flashinfer_compat-past-60]_,N/A,N/A,N/A,N/A,v0.2.5,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
|
||||
`ONNX Runtime <https://onnxruntime.ai/docs/build/eps.html#amd-migraphx>`_,1.22.0,1.22.0,1.20.0,1.20.0,1.20.0,1.20.0,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.14.1,1.14.1
|
||||
,,,,,,,,,,,,,,,,,,,,
|
||||
,,,,,,,,,,,,,,,,,,,,
|
||||
THIRD PARTY COMMS,.. _thirdpartycomms-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,
|
||||
`UCC <https://github.com/ROCm/ucc>`_,>=1.4.0,>=1.4.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.2.0,>=1.2.0
|
||||
`UCX <https://github.com/ROCm/ucx>`_,>=1.17.0,>=1.17.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.14.1,>=1.14.1,>=1.14.1,>=1.14.1,>=1.14.1,>=1.14.1
|
||||
,,,,,,,,,,,,,,,,,,,,
|
||||
THIRD PARTY ALGORITHM,.. _thirdpartyalgorithm-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,
|
||||
Thrust,2.6.0,2.6.0,2.5.0,2.5.0,2.5.0,2.5.0,2.3.2,2.3.2,2.3.2,2.3.2,2.2.0,2.2.0,2.2.0,2.2.0,2.1.0,2.1.0,2.1.0,2.1.0,2.0.1,2.0.1
|
||||
CUB,2.6.0,2.6.0,2.5.0,2.5.0,2.5.0,2.5.0,2.3.2,2.3.2,2.3.2,2.3.2,2.2.0,2.2.0,2.2.0,2.2.0,2.1.0,2.1.0,2.1.0,2.1.0,2.0.1,2.0.1
|
||||
,,,,,,,,,,,,,,,,,,,,
|
||||
DRIVER & USER SPACE [#kfd_support-past-60]_,.. _kfd-userspace-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,
|
||||
:doc:`AMD GPU Driver <rocm-install-on-linux:reference/user-kernel-space-compat-matrix>`,"30.10.2, 30.10.1 [#driver_patch-past-60]_, 30.10, 6.4.x, 6.3.x","30.10.1 [#driver_patch-past-60]_, 30.10, 6.4.x, 6.3.x, 6.2.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x","6.2.x, 6.1.x, 6.0.x, 5.7.x, 5.6.x","6.2.x, 6.1.x, 6.0.x, 5.7.x, 5.6.x"
|
||||
,,,,,,,,,,,,,,,,,,,,
|
||||
ML & COMPUTER VISION,.. _mllibs-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,
|
||||
:doc:`Composable Kernel <composable_kernel:index>`,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0
|
||||
:doc:`MIGraphX <amdmigraphx:index>`,2.13.0,2.13.0,2.12.0,2.12.0,2.12.0,2.12.0,2.11.0,2.11.0,2.11.0,2.11.0,2.10.0,2.10.0,2.10.0,2.10.0,2.9.0,2.9.0,2.9.0,2.9.0,2.8.0,2.8.0
|
||||
:doc:`MIOpen <miopen:index>`,3.5.0,3.5.0,3.4.0,3.4.0,3.4.0,3.4.0,3.3.0,3.3.0,3.3.0,3.3.0,3.2.0,3.2.0,3.2.0,3.2.0,3.1.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0
|
||||
:doc:`MIVisionX <mivisionx:index>`,3.3.0,3.3.0,3.2.0,3.2.0,3.2.0,3.2.0,3.1.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0,3.0.0,3.0.0,2.5.0,2.5.0,2.5.0,2.5.0,2.5.0,2.5.0
|
||||
:doc:`rocAL <rocal:index>`,2.3.0,2.3.0,2.2.0,2.2.0,2.2.0,2.2.0,2.1.0,2.1.0,2.1.0,2.1.0,2.0.0,2.0.0,2.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0
|
||||
:doc:`rocDecode <rocdecode:index>`,1.0.0,1.0.0,0.10.0,0.10.0,0.10.0,0.10.0,0.8.0,0.8.0,0.8.0,0.8.0,0.6.0,0.6.0,0.6.0,0.6.0,0.6.0,0.6.0,0.5.0,0.5.0,N/A,N/A
|
||||
:doc:`rocJPEG <rocjpeg:index>`,1.1.0,1.1.0,0.8.0,0.8.0,0.8.0,0.8.0,0.6.0,0.6.0,0.6.0,0.6.0,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
|
||||
:doc:`rocPyDecode <rocpydecode:index>`,0.6.0,0.6.0,0.3.1,0.3.1,0.3.1,0.3.1,0.2.0,0.2.0,0.2.0,0.2.0,0.1.0,0.1.0,0.1.0,0.1.0,N/A,N/A,N/A,N/A,N/A,N/A
|
||||
:doc:`RPP <rpp:index>`,2.0.0,2.0.0,1.9.10,1.9.10,1.9.10,1.9.10,1.9.1,1.9.1,1.9.1,1.9.1,1.8.0,1.8.0,1.8.0,1.8.0,1.5.0,1.5.0,1.5.0,1.5.0,1.4.0,1.4.0
|
||||
,,,,,,,,,,,,,,,,,,,,
|
||||
COMMUNICATION,.. _commlibs-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,
|
||||
:doc:`RCCL <rccl:index>`,2.26.6,2.26.6,2.22.3,2.22.3,2.22.3,2.22.3,2.21.5,2.21.5,2.21.5,2.21.5,2.20.5,2.20.5,2.20.5,2.20.5,2.18.6,2.18.6,2.18.6,2.18.6,2.18.3,2.18.3
|
||||
:doc:`rocSHMEM <rocshmem:index>`,3.0.0,3.0.0,2.0.1,2.0.1,2.0.0,2.0.0,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
|
||||
,,,,,,,,,,,,,,,,,,,,
|
||||
MATH LIBS,.. _mathlibs-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,
|
||||
`half <https://github.com/ROCm/half>`_ ,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0
|
||||
:doc:`hipBLAS <hipblas:index>`,3.0.2,3.0.0,2.4.0,2.4.0,2.4.0,2.4.0,2.3.0,2.3.0,2.3.0,2.3.0,2.2.0,2.2.0,2.2.0,2.2.0,2.1.0,2.1.0,2.1.0,2.1.0,2.0.0,2.0.0
|
||||
:doc:`hipBLASLt <hipblaslt:index>`,1.0.0,1.0.0,0.12.1,0.12.1,0.12.1,0.12.0,0.10.0,0.10.0,0.10.0,0.10.0,0.8.0,0.8.0,0.8.0,0.8.0,0.7.0,0.7.0,0.7.0,0.7.0,0.6.0,0.6.0
|
||||
:doc:`hipFFT <hipfft:index>`,1.0.20,1.0.20,1.0.18,1.0.18,1.0.18,1.0.18,1.0.17,1.0.17,1.0.17,1.0.17,1.0.16,1.0.15,1.0.15,1.0.14,1.0.14,1.0.14,1.0.14,1.0.14,1.0.13,1.0.13
|
||||
:doc:`hipfort <hipfort:index>`,0.7.0,0.7.0,0.6.0,0.6.0,0.6.0,0.6.0,0.5.1,0.5.1,0.5.0,0.5.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0
|
||||
:doc:`hipRAND <hiprand:index>`,3.0.0,3.0.0,2.12.0,2.12.0,2.12.0,2.12.0,2.11.1,2.11.1,2.11.1,2.11.0,2.11.1,2.11.0,2.11.0,2.11.0,2.10.16,2.10.16,2.10.16,2.10.16,2.10.16,2.10.16
|
||||
:doc:`hipSOLVER <hipsolver:index>`,3.0.0,3.0.0,2.4.0,2.4.0,2.4.0,2.4.0,2.3.0,2.3.0,2.3.0,2.3.0,2.2.0,2.2.0,2.2.0,2.2.0,2.1.1,2.1.1,2.1.1,2.1.0,2.0.0,2.0.0
|
||||
:doc:`hipSPARSE <hipsparse:index>`,4.0.1,4.0.1,3.2.0,3.2.0,3.2.0,3.2.0,3.1.2,3.1.2,3.1.2,3.1.2,3.1.1,3.1.1,3.1.1,3.1.1,3.0.1,3.0.1,3.0.1,3.0.1,3.0.0,3.0.0
|
||||
:doc:`hipSPARSELt <hipsparselt:index>`,0.2.4,0.2.4,0.2.3,0.2.3,0.2.3,0.2.3,0.2.2,0.2.2,0.2.2,0.2.2,0.2.1,0.2.1,0.2.1,0.2.1,0.2.0,0.2.0,0.1.0,0.1.0,0.1.0,0.1.0
|
||||
:doc:`rocALUTION <rocalution:index>`,4.0.0,4.0.0,3.2.3,3.2.3,3.2.3,3.2.2,3.2.1,3.2.1,3.2.1,3.2.1,3.2.1,3.2.0,3.2.0,3.2.0,3.1.1,3.1.1,3.1.1,3.1.1,3.0.3,3.0.3
|
||||
:doc:`rocBLAS <rocblas:index>`,5.0.2,5.0.0,4.4.1,4.4.1,4.4.0,4.4.0,4.3.0,4.3.0,4.3.0,4.3.0,4.2.4,4.2.1,4.2.1,4.2.0,4.1.2,4.1.2,4.1.0,4.1.0,4.0.0,4.0.0
|
||||
:doc:`rocFFT <rocfft:index>`,1.0.34,1.0.34,1.0.32,1.0.32,1.0.32,1.0.32,1.0.31,1.0.31,1.0.31,1.0.31,1.0.30,1.0.29,1.0.29,1.0.28,1.0.27,1.0.27,1.0.27,1.0.26,1.0.25,1.0.23
|
||||
:doc:`rocRAND <rocrand:index>`,4.0.0,4.0.0,3.3.0,3.3.0,3.3.0,3.3.0,3.2.0,3.2.0,3.2.0,3.2.0,3.1.1,3.1.0,3.1.0,3.1.0,3.0.1,3.0.1,3.0.1,3.0.1,3.0.0,2.10.17
|
||||
:doc:`rocSOLVER <rocsolver:index>`,3.30.1,3.30.0,3.28.2,3.28.2,3.28.0,3.28.0,3.27.0,3.27.0,3.27.0,3.27.0,3.26.2,3.26.0,3.26.0,3.26.0,3.25.0,3.25.0,3.25.0,3.25.0,3.24.0,3.24.0
|
||||
:doc:`rocSPARSE <rocsparse:index>`,4.0.2,4.0.2,3.4.0,3.4.0,3.4.0,3.4.0,3.3.0,3.3.0,3.3.0,3.3.0,3.2.1,3.2.0,3.2.0,3.2.0,3.1.2,3.1.2,3.1.2,3.1.2,3.0.2,3.0.2
|
||||
:doc:`rocWMMA <rocwmma:index>`,2.0.0,2.0.0,1.7.0,1.7.0,1.7.0,1.7.0,1.6.0,1.6.0,1.6.0,1.6.0,1.5.0,1.5.0,1.5.0,1.5.0,1.4.0,1.4.0,1.4.0,1.4.0,1.3.0,1.3.0
|
||||
:doc:`Tensile <tensile:src/index>`,4.44.0,4.44.0,4.43.0,4.43.0,4.43.0,4.43.0,4.42.0,4.42.0,4.42.0,4.42.0,4.41.0,4.41.0,4.41.0,4.41.0,4.40.0,4.40.0,4.40.0,4.40.0,4.39.0,4.39.0
|
||||
,,,,,,,,,,,,,,,,,,,,
|
||||
PRIMITIVES,.. _primitivelibs-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,
|
||||
:doc:`hipCUB <hipcub:index>`,4.0.0,4.0.0,3.4.0,3.4.0,3.4.0,3.4.0,3.3.0,3.3.0,3.3.0,3.3.0,3.2.1,3.2.0,3.2.0,3.2.0,3.1.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0
|
||||
:doc:`hipTensor <hiptensor:index>`,2.0.0,2.0.0,1.5.0,1.5.0,1.5.0,1.5.0,1.4.0,1.4.0,1.4.0,1.4.0,1.3.0,1.3.0,1.3.0,1.3.0,1.2.0,1.2.0,1.2.0,1.2.0,1.1.0,1.1.0
|
||||
:doc:`rocPRIM <rocprim:index>`,4.0.1,4.0.0,3.4.1,3.4.1,3.4.0,3.4.0,3.3.0,3.3.0,3.3.0,3.3.0,3.2.2,3.2.0,3.2.0,3.2.0,3.1.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0
|
||||
:doc:`rocThrust <rocthrust:index>`,4.0.0,4.0.0,3.3.0,3.3.0,3.3.0,3.3.0,3.3.0,3.3.0,3.3.0,3.3.0,3.1.1,3.1.0,3.1.0,3.0.1,3.0.1,3.0.1,3.0.1,3.0.1,3.0.0,3.0.0
|
||||
,,,,,,,,,,,,,,,,,,,,
|
||||
SUPPORT LIBS,,,,,,,,,,,,,,,,,,,,
|
||||
`hipother <https://github.com/ROCm/hipother>`_,7.0.51830,7.0.51830,6.4.43483,6.4.43483,6.4.43483,6.4.43482,6.3.42134,6.3.42134,6.3.42133,6.3.42131,6.2.41134,6.2.41134,6.2.41134,6.2.41133,6.1.40093,6.1.40093,6.1.40092,6.1.40091,6.1.32831,6.1.32830
|
||||
`rocm-core <https://github.com/ROCm/rocm-core>`_,7.0.2,7.0.1/7.0.0,6.4.3,6.4.2,6.4.1,6.4.0,6.3.3,6.3.2,6.3.1,6.3.0,6.2.4,6.2.2,6.2.1,6.2.0,6.1.5,6.1.2,6.1.1,6.1.0,6.0.2,6.0.0
|
||||
`ROCT-Thunk-Interface <https://github.com/ROCm/ROCT-Thunk-Interface>`_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,20240607.5.7,20240607.5.7,20240607.4.05,20240607.1.4246,20240125.5.08,20240125.5.08,20240125.5.08,20240125.3.30,20231016.2.245,20231016.2.245
|
||||
,,,,,,,,,,,,,,,,,,,,
|
||||
SYSTEM MGMT TOOLS,.. _tools-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,
|
||||
:doc:`AMD SMI <amdsmi:index>`,26.0.2,26.0.0,25.5.1,25.5.1,25.4.2,25.3.0,24.7.1,24.7.1,24.7.1,24.7.1,24.6.3,24.6.3,24.6.3,24.6.2,24.5.1,24.5.1,24.5.1,24.4.1,23.4.2,23.4.2
|
||||
:doc:`ROCm Data Center Tool <rdc:index>`,1.1.0,1.1.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0
|
||||
:doc:`rocminfo <rocminfo:index>`,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0
|
||||
:doc:`ROCm SMI <rocm_smi_lib:index>`,7.8.0,7.8.0,7.7.0,7.5.0,7.5.0,7.5.0,7.4.0,7.4.0,7.4.0,7.4.0,7.3.0,7.3.0,7.3.0,7.3.0,7.2.0,7.2.0,7.0.0,7.0.0,6.0.2,6.0.0
|
||||
:doc:`ROCm Validation Suite <rocmvalidationsuite:index>`,1.2.0,1.2.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.0.60204,1.0.60202,1.0.60201,1.0.60200,1.0.60105,1.0.60102,1.0.60101,1.0.60100,1.0.60002,1.0.60000
|
||||
,,,,,,,,,,,,,,,,,,,,
|
||||
PERFORMANCE TOOLS,,,,,,,,,,,,,,,,,,,,
|
||||
:doc:`ROCm Bandwidth Test <rocm_bandwidth_test:index>`,2.6.0,2.6.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0
|
||||
:doc:`ROCm Compute Profiler <rocprofiler-compute:index>`,3.2.3,3.2.3,3.1.1,3.1.1,3.1.0,3.1.0,3.0.0,3.0.0,3.0.0,3.0.0,2.0.1,2.0.1,2.0.1,2.0.1,N/A,N/A,N/A,N/A,N/A,N/A
|
||||
:doc:`ROCm Systems Profiler <rocprofiler-systems:index>`,1.1.1,1.1.0,1.0.2,1.0.2,1.0.1,1.0.0,0.1.2,0.1.1,0.1.0,0.1.0,1.11.2,1.11.2,1.11.2,1.11.2,N/A,N/A,N/A,N/A,N/A,N/A
|
||||
:doc:`ROCProfiler <rocprofiler:index>`,2.0.70002,2.0.70000,2.0.60403,2.0.60402,2.0.60401,2.0.60400,2.0.60303,2.0.60302,2.0.60301,2.0.60300,2.0.60204,2.0.60202,2.0.60201,2.0.60200,2.0.60105,2.0.60102,2.0.60101,2.0.60100,2.0.60002,2.0.60000
|
||||
:doc:`ROCprofiler-SDK <rocprofiler-sdk:index>`,1.0.0,1.0.0,0.6.0,0.6.0,0.6.0,0.6.0,0.5.0,0.5.0,0.5.0,0.5.0,0.4.0,0.4.0,0.4.0,0.4.0,N/A,N/A,N/A,N/A,N/A,N/A
|
||||
:doc:`ROCTracer <roctracer:index>`,4.1.70002,4.1.70000,4.1.60403,4.1.60402,4.1.60401,4.1.60400,4.1.60303,4.1.60302,4.1.60301,4.1.60300,4.1.60204,4.1.60202,4.1.60201,4.1.60200,4.1.60105,4.1.60102,4.1.60101,4.1.60100,4.1.60002,4.1.60000
|
||||
,,,,,,,,,,,,,,,,,,,,
|
||||
DEVELOPMENT TOOLS,,,,,,,,,,,,,,,,,,,,
|
||||
:doc:`HIPIFY <hipify:index>`,20.0.0,20.0.0,19.0.0,19.0.0,19.0.0,19.0.0,18.0.0.25012,18.0.0.25012,18.0.0.24491,18.0.0.24455,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
|
||||
:doc:`ROCm CMake <rocmcmakebuildtools:index>`,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.13.0,0.13.0,0.13.0,0.13.0,0.12.0,0.12.0,0.12.0,0.12.0,0.11.0,0.11.0
|
||||
:doc:`ROCdbgapi <rocdbgapi:index>`,0.77.4,0.77.3,0.77.2,0.77.2,0.77.2,0.77.2,0.77.0,0.77.0,0.77.0,0.77.0,0.76.0,0.76.0,0.76.0,0.76.0,0.71.0,0.71.0,0.71.0,0.71.0,0.71.0,0.71.0
|
||||
:doc:`ROCm Debugger (ROCgdb) <rocgdb:index>`,16.3.0,16.3.0,15.2.0,15.2.0,15.2.0,15.2.0,15.2.0,15.2.0,15.2.0,15.2.0,14.2.0,14.2.0,14.2.0,14.2.0,14.1.0,14.1.0,14.1.0,14.1.0,13.2.0,13.2.0
|
||||
`rocprofiler-register <https://github.com/ROCm/rocprofiler-register>`_,0.5.0,0.5.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.3.0,0.3.0,0.3.0,0.3.0,N/A,N/A
|
||||
:doc:`ROCr Debug Agent <rocr_debug_agent:index>`,2.1.0,2.1.0,2.0.4,2.0.4,2.0.4,2.0.4,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3
|
||||
,,,,,,,,,,,,,,,,,,,,
|
||||
COMPILERS,.. _compilers-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,
|
||||
`clang-ocl <https://github.com/ROCm/clang-ocl>`_,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,0.5.0,0.5.0,0.5.0,0.5.0,0.5.0,0.5.0
|
||||
:doc:`hipCC <hipcc:index>`,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0
|
||||
`Flang <https://github.com/ROCm/flang>`_,20.0.0.25385,20.0.0.25314,19.0.0.25224,19.0.0.25224,19.0.0.25184,19.0.0.25133,18.0.0.25012,18.0.0.25012,18.0.0.24491,18.0.0.24455,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
|
||||
:doc:`llvm-project <llvm-project:index>`,20.0.0.25385,20.0.0.25314,19.0.0.25224,19.0.0.25224,19.0.0.25184,19.0.0.25133,18.0.0.25012,18.0.0.25012,18.0.0.24491,18.0.0.24491,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
|
||||
`OpenMP <https://github.com/ROCm/llvm-project/tree/amd-staging/openmp>`_,20.0.0.25385,20.0.0.25314,19.0.0.25224,19.0.0.25224,19.0.0.25184,19.0.0.25133,18.0.0.25012,18.0.0.25012,18.0.0.24491,18.0.0.24491,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
|
||||
,,,,,,,,,,,,,,,,,,,,
|
||||
RUNTIMES,.. _runtime-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,
|
||||
:doc:`AMD CLR <hip:understand/amd_clr>`,7.0.51831,7.0.51830,6.4.43484,6.4.43484,6.4.43483,6.4.43482,6.3.42134,6.3.42134,6.3.42133,6.3.42131,6.2.41134,6.2.41134,6.2.41134,6.2.41133,6.1.40093,6.1.40093,6.1.40092,6.1.40091,6.1.32831,6.1.32830
|
||||
:doc:`HIP <hip:index>`,7.0.51831,7.0.51830,6.4.43484,6.4.43484,6.4.43483,6.4.43482,6.3.42134,6.3.42134,6.3.42133,6.3.42131,6.2.41134,6.2.41134,6.2.41134,6.2.41133,6.1.40093,6.1.40093,6.1.40092,6.1.40091,6.1.32831,6.1.32830
|
||||
`OpenCL Runtime <https://github.com/ROCm/clr/tree/develop/opencl>`_,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0
|
||||
:doc:`ROCr Runtime <rocr-runtime:index>`,1.18.0,1.18.0,1.15.0,1.15.0,1.15.0,1.15.0,1.14.0,1.14.0,1.14.0,1.14.0,1.14.0,1.14.0,1.14.0,1.13.0,1.13.0,1.13.0,1.13.0,1.13.0,1.12.0,1.12.0
|
||||
|
@@ -1,301 +0,0 @@
|
||||
.. meta::
|
||||
:description: ROCm compatibility matrix
|
||||
:keywords: GPU, architecture, hardware, compatibility, system, requirements, components, libraries
|
||||
|
||||
**************************************************************************************
|
||||
Compatibility matrix
|
||||
**************************************************************************************
|
||||
|
||||
Use this matrix to view the ROCm compatibility and system requirements across successive major and minor releases.
|
||||
|
||||
You can also refer to the :ref:`past versions of ROCm compatibility matrix<past-rocm-compatibility-matrix>`.
|
||||
|
||||
Accelerators and GPUs listed in the following table support compute workloads (no display
|
||||
information or graphics). If you’re using ROCm with AMD Radeon GPUs or Ryzen APUs for graphics
|
||||
workloads, see the `Use ROCm on Radeon and Ryzen
|
||||
<https://rocm.docs.amd.com/projects/radeon-ryzen/en/latest/index.html>`_ to verify
|
||||
compatibility and system requirements.
|
||||
|
||||
.. |br| raw:: html
|
||||
|
||||
<br/>
|
||||
|
||||
.. container:: format-big-table
|
||||
|
||||
.. csv-table::
|
||||
:header: "ROCm Version", "7.0.2", "7.0.1/7.0.0", "6.4.0"
|
||||
:stub-columns: 1
|
||||
|
||||
:ref:`Operating systems & kernels <OS-kernel-versions>`,Ubuntu 24.04.3,Ubuntu 24.04.3,Ubuntu 24.04.2
|
||||
,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5
|
||||
,"RHEL 10.0 [#rhel-10-702]_, 9.6 [#rhel-10-702]_, 9.4 [#rhel-94-702]_","RHEL 9.6 [#rhel-10-702]_, 9.4 [#rhel-94-702]_","RHEL 9.5, 9.4"
|
||||
,RHEL 8.10 [#rhel-700]_,RHEL 8.10 [#rhel-700]_,RHEL 8.10
|
||||
,SLES 15 SP7 [#sles-db-700]_,SLES 15 SP7 [#sles-db-700]_,SLES 15 SP6
|
||||
,"Oracle Linux 10, 9, 8 [#ol-700-mi300x]_","Oracle Linux 9, 8 [#ol-700-mi300x]_","Oracle Linux 9, 8 [#ol-mi300x]_"
|
||||
,"Debian 13 [#db-mi300x]_, 12 [#sles-db-700]_",Debian 12 [#sles-db-700]_,Debian 12 [#single-node]_
|
||||
,Azure Linux 3.0 [#az-mi300x]_,Azure Linux 3.0 [#az-mi300x]_,Azure Linux 3.0 [#az-mi300x]_
|
||||
,Rocky Linux 9 [#rl-700]_,Rocky Linux 9 [#rl-700]_,
|
||||
,.. _architecture-support-compatibility-matrix:,,
|
||||
:doc:`Architecture <rocm-install-on-linux:reference/system-requirements>`,CDNA4,CDNA4,
|
||||
,CDNA3,CDNA3,CDNA3
|
||||
,CDNA2,CDNA2,CDNA2
|
||||
,CDNA,CDNA,CDNA
|
||||
,RDNA4,RDNA4,
|
||||
,RDNA3,RDNA3,RDNA3
|
||||
,RDNA2,RDNA2,RDNA2
|
||||
,.. _gpu-support-compatibility-matrix:,,
|
||||
:doc:`GPU / LLVM target <rocm-install-on-linux:reference/system-requirements>`,gfx950 [#mi350x-os]_,gfx950 [#mi350x-os]_,
|
||||
,gfx1201 [#RDNA-OS-700]_,gfx1201 [#RDNA-OS-700]_,
|
||||
,gfx1200 [#RDNA-OS-700]_,gfx1200 [#RDNA-OS-700]_,
|
||||
,gfx1101 [#RDNA-OS-700]_ [#rd-v710]_,gfx1101 [#RDNA-OS-700]_ [#rd-v710]_,
|
||||
,gfx1100 [#RDNA-OS-700]_,gfx1100 [#RDNA-OS-700]_,gfx1100
|
||||
,gfx1030 [#RDNA-OS-700]_ [#rd-v620]_,gfx1030 [#RDNA-OS-700]_ [#rd-v620]_,gfx1030
|
||||
,gfx942 [#mi325x-os]_ [#mi300x-os]_ [#mi300A-os]_,gfx942 [#mi325x-os]_ [#mi300x-os]_ [#mi300A-os]_,gfx942
|
||||
,gfx90a [#mi200x-os]_,gfx90a [#mi200x-os]_,gfx90a
|
||||
,gfx908 [#mi100-os]_,gfx908 [#mi100-os]_,gfx908
|
||||
,,,
|
||||
FRAMEWORK SUPPORT,.. _framework-support-compatibility-matrix:,,
|
||||
:doc:`PyTorch <../compatibility/ml-compatibility/pytorch-compatibility>`,"2.8, 2.7, 2.6","2.7, 2.6, 2.5","2.6, 2.5, 2.4, 2.3"
|
||||
:doc:`TensorFlow <../compatibility/ml-compatibility/tensorflow-compatibility>`,"2.19.1, 2.18.1, 2.17.1 [#tf-mi350]_","2.19.1, 2.18.1, 2.17.1 [#tf-mi350]_","2.18.1, 2.17.1, 2.16.2"
|
||||
:doc:`JAX <../compatibility/ml-compatibility/jax-compatibility>`,0.6.0,0.6.0,0.4.35
|
||||
:doc:`DGL <../compatibility/ml-compatibility/dgl-compatibility>` [#dgl_compat]_,N/A,N/A,2.4.0
|
||||
:doc:`llama.cpp <../compatibility/ml-compatibility/llama-cpp-compatibility>` [#llama-cpp_compat]_,N/A,b6356,b5997
|
||||
`ONNX Runtime <https://onnxruntime.ai/docs/build/eps.html#amd-migraphx>`_,1.22.0,1.22.0,1.20.0
|
||||
,,,
|
||||
THIRD PARTY COMMS,.. _thirdpartycomms-support-compatibility-matrix:,,
|
||||
`UCC <https://github.com/ROCm/ucc>`_,>=1.4.0,>=1.4.0,>=1.3.0
|
||||
`UCX <https://github.com/ROCm/ucx>`_,>=1.17.0,>=1.17.0,>=1.15.0
|
||||
,,,
|
||||
THIRD PARTY ALGORITHM,.. _thirdpartyalgorithm-support-compatibility-matrix:,,
|
||||
Thrust,2.6.0,2.6.0,2.5.0
|
||||
CUB,2.6.0,2.6.0,2.5.0
|
||||
,,,
|
||||
DRIVER & USER SPACE [#kfd_support]_,.. _kfd-userspace-support-compatibility-matrix:,,
|
||||
:doc:`AMD GPU Driver <rocm-install-on-linux:reference/user-kernel-space-compat-matrix>`,"30.10.2, 30.10.1 [#driver_patch]_, |br| 30.10, 6.4.x, 6.3.x","30.10.1 [#driver_patch]_, 30.10, |br| 6.4.x, 6.3.x, 6.2.x","6.4.x, 6.3.x, 6.2.x, 6.1.x"
|
||||
,,,
|
||||
ML & COMPUTER VISION,.. _mllibs-support-compatibility-matrix:,,
|
||||
:doc:`Composable Kernel <composable_kernel:index>`,1.1.0,1.1.0,1.1.0
|
||||
:doc:`MIGraphX <amdmigraphx:index>`,2.13.0,2.13.0,2.12.0
|
||||
:doc:`MIOpen <miopen:index>`,3.5.0,3.5.0,3.4.0
|
||||
:doc:`MIVisionX <mivisionx:index>`,3.3.0,3.3.0,3.2.0
|
||||
:doc:`rocAL <rocal:index>`,2.3.0,2.3.0,2.2.0
|
||||
:doc:`rocDecode <rocdecode:index>`,1.0.0,1.0.0,0.10.0
|
||||
:doc:`rocJPEG <rocjpeg:index>`,1.1.0,1.1.0,0.8.0
|
||||
:doc:`rocPyDecode <rocpydecode:index>`,0.6.0,0.6.0,0.3.1
|
||||
:doc:`RPP <rpp:index>`,2.0.0,2.0.0,1.9.10
|
||||
,,,
|
||||
COMMUNICATION,.. _commlibs-support-compatibility-matrix:,,
|
||||
:doc:`RCCL <rccl:index>`,2.26.6,2.26.6,2.22.3
|
||||
:doc:`rocSHMEM <rocshmem:index>`,3.0.0,3.0.0,2.0.0
|
||||
,,,
|
||||
MATH LIBS,.. _mathlibs-support-compatibility-matrix:,,
|
||||
`half <https://github.com/ROCm/half>`_ ,1.12.0,1.12.0,1.12.0
|
||||
:doc:`hipBLAS <hipblas:index>`,3.0.2,3.0.0,2.4.0
|
||||
:doc:`hipBLASLt <hipblaslt:index>`,1.0.0,1.0.0,0.12.0
|
||||
:doc:`hipFFT <hipfft:index>`,1.0.20,1.0.20,1.0.18
|
||||
:doc:`hipfort <hipfort:index>`,0.7.0,0.7.0,0.6.0
|
||||
:doc:`hipRAND <hiprand:index>`,3.0.0,3.0.0,2.12.0
|
||||
:doc:`hipSOLVER <hipsolver:index>`,3.0.0,3.0.0,2.4.0
|
||||
:doc:`hipSPARSE <hipsparse:index>`,4.0.1,4.0.1,3.2.0
|
||||
:doc:`hipSPARSELt <hipsparselt:index>`,0.2.4,0.2.4,0.2.3
|
||||
:doc:`rocALUTION <rocalution:index>`,4.0.0,4.0.0,3.2.2
|
||||
:doc:`rocBLAS <rocblas:index>`,5.0.2,5.0.0,4.4.0
|
||||
:doc:`rocFFT <rocfft:index>`,1.0.34,1.0.34,1.0.32
|
||||
:doc:`rocRAND <rocrand:index>`,4.0.0,4.0.0,3.3.0
|
||||
:doc:`rocSOLVER <rocsolver:index>`,3.30.1,3.30.0,3.28.0
|
||||
:doc:`rocSPARSE <rocsparse:index>`,4.0.2,4.0.2,3.4.0
|
||||
:doc:`rocWMMA <rocwmma:index>`,2.0.0,2.0.0,1.7.0
|
||||
:doc:`Tensile <tensile:src/index>`,4.44.0,4.44.0,4.43.0
|
||||
,,,
|
||||
PRIMITIVES,.. _primitivelibs-support-compatibility-matrix:,,
|
||||
:doc:`hipCUB <hipcub:index>`,4.0.0,4.0.0,3.4.0
|
||||
:doc:`hipTensor <hiptensor:index>`,2.0.0,2.0.0,1.5.0
|
||||
:doc:`rocPRIM <rocprim:index>`,4.0.1,4.0.0,3.4.0
|
||||
:doc:`rocThrust <rocthrust:index>`,4.0.0,4.0.0,3.3.0
|
||||
,,,
|
||||
SUPPORT LIBS,,,
|
||||
`hipother <https://github.com/ROCm/hipother>`_,7.0.51830,7.0.51830,6.4.43482
|
||||
`rocm-core <https://github.com/ROCm/rocm-core>`_,7.0.2,7.0.1/7.0.0,6.4.0
|
||||
`ROCT-Thunk-Interface <https://github.com/ROCm/ROCT-Thunk-Interface>`_,N/A [#ROCT-rocr]_,N/A [#ROCT-rocr]_,N/A [#ROCT-rocr]_
|
||||
,,,
|
||||
SYSTEM MGMT TOOLS,.. _tools-support-compatibility-matrix:,,
|
||||
:doc:`AMD SMI <amdsmi:index>`,26.0.2,26.0.0,25.3.0
|
||||
:doc:`ROCm Data Center Tool <rdc:index>`,1.1.0,1.1.0,0.3.0
|
||||
:doc:`rocminfo <rocminfo:index>`,1.0.0,1.0.0,1.0.0
|
||||
:doc:`ROCm SMI <rocm_smi_lib:index>`,7.8.0,7.8.0,7.5.0
|
||||
:doc:`ROCm Validation Suite <rocmvalidationsuite:index>`,1.2.0,1.2.0,1.1.0
|
||||
,,,
|
||||
PERFORMANCE TOOLS,,,
|
||||
:doc:`ROCm Bandwidth Test <rocm_bandwidth_test:index>`,2.6.0,2.6.0,1.4.0
|
||||
:doc:`ROCm Compute Profiler <rocprofiler-compute:index>`,3.2.3,3.2.3,3.1.0
|
||||
:doc:`ROCm Systems Profiler <rocprofiler-systems:index>`,1.1.1,1.1.0,1.0.0
|
||||
:doc:`ROCProfiler <rocprofiler:index>`,2.0.70002,2.0.70000,2.0.60400
|
||||
:doc:`ROCprofiler-SDK <rocprofiler-sdk:index>`,1.0.0,1.0.0,0.6.0
|
||||
:doc:`ROCTracer <roctracer:index>`,4.1.70002,4.1.70000,4.1.60400
|
||||
,,,
|
||||
DEVELOPMENT TOOLS,,,
|
||||
:doc:`HIPIFY <hipify:index>`,20.0.0,20.0.0,19.0.0
|
||||
:doc:`ROCm CMake <rocmcmakebuildtools:index>`,0.14.0,0.14.0,0.14.0
|
||||
:doc:`ROCdbgapi <rocdbgapi:index>`,0.77.4,0.77.3,0.77.2
|
||||
:doc:`ROCm Debugger (ROCgdb) <rocgdb:index>`,16.3.0,16.3.0,15.2.0
|
||||
`rocprofiler-register <https://github.com/ROCm/rocprofiler-register>`_,0.5.0,0.5.0,0.4.0
|
||||
:doc:`ROCr Debug Agent <rocr_debug_agent:index>`,2.1.0,2.1.0,2.0.4
|
||||
,,,
|
||||
COMPILERS,.. _compilers-support-compatibility-matrix:,,
|
||||
`clang-ocl <https://github.com/ROCm/clang-ocl>`_,N/A,N/A,N/A
|
||||
:doc:`hipCC <hipcc:index>`,1.1.1,1.1.1,1.1.1
|
||||
`Flang <https://github.com/ROCm/flang>`_,20.0.0.25385,20.0.0.25314,19.0.0.25133
|
||||
:doc:`llvm-project <llvm-project:index>`,20.0.0.25385,20.0.0.25314,19.0.0.25133
|
||||
`OpenMP <https://github.com/ROCm/llvm-project/tree/amd-staging/openmp>`_,20.0.0.25385,20.0.0.25314,19.0.0.25133
|
||||
,,,
|
||||
RUNTIMES,.. _runtime-support-compatibility-matrix:,,
|
||||
:doc:`AMD CLR <hip:understand/amd_clr>`,7.0.51831,7.0.51830,6.4.43482
|
||||
:doc:`HIP <hip:index>`,7.0.51831,7.0.51830,6.4.43482
|
||||
`OpenCL Runtime <https://github.com/ROCm/clr/tree/develop/opencl>`_,2.0.0,2.0.0,2.0.0
|
||||
:doc:`ROCr Runtime <rocr-runtime:index>`,1.18.0,1.18.0,1.15.0
|
||||
|
||||
.. rubric:: Footnotes
|
||||
|
||||
.. [#rhel-10-702] RHEL 10.0 and RHEL 9.6 are supported on all listed :ref:`supported_GPUs` except AMD Radeon PRO V620 GPU.
|
||||
.. [#rhel-94-702] RHEL 9.4 is supported on all AMD Instinct GPUs listed under :ref:`supported_GPUs`.
|
||||
.. [#rhel-700] RHEL 8.10 is supported only on AMD Instinct MI300X, MI300A, MI250X, MI250, MI210, and MI100 GPUs.
|
||||
.. [#ol-700-mi300x] **For ROCm 7.0.x** - Oracle Linux 10 and 9 are supported only on AMD Instinct MI355X, MI350X, and MI300X GPUs. Oracle Linux 8 is supported only on AMD Instinct MI300X GPU.
|
||||
.. [#ol-mi300x] **Prior ROCm 7.0.0** - Oracle Linux is supported only on AMD Instinct MI300X GPUs.
|
||||
.. [#db-mi300x] **For ROCm 7.0.2** - Debian 13 is supported only on AMD Instinct MI300X GPUs.
|
||||
.. [#sles-db-700] **For ROCm 7.0.x** - SLES 15 SP7 and Debian 12 are supported only on AMD Instinct MI300X, MI300A, MI250X, MI250, and MI210 GPUs.
|
||||
.. [#az-mi300x] Starting ROCm 6.4.0, Azure Linux 3.0 is supported only on AMD Instinct MI300X and AMD Radeon PRO V710 GPUs.
|
||||
.. [#rl-700] Rocky Linux 9 is supported only on AMD Instinct MI300X and MI300A GPUs.
|
||||
.. [#single-node] **Prior to ROCm 7.0.0** - Debian 12 is supported only on AMD Instinct MI300X GPUs for single-node functionality.
|
||||
.. [#mi350x-os] AMD Instinct MI355X (gfx950) and MI350X(gfx950) GPUs are supported only on Ubuntu 24.04.3, Ubuntu 22.04.5, RHEL 10.0, RHEL 9.6, RHEL 9.4, Oracle Linux 10, and Oracle Linux 9.
|
||||
.. [#RDNA-OS-700] **For ROCm 7.0.x** - AMD Radeon PRO AI PRO R9700 (gfx1201), AMD Radeon RX 9070 XT (gfx1201), AMD Radeon RX 9070 GRE (gfx1201), AMD Radeon RX 9070 (gfx1201), AMD Radeon RX 9060 XT (gfx1200), AMD Radeon RX 9060 (gfx1200), AMD Radeon RX 7800 XT (gfx1101), AMD Radeon RX 7700 XT (gfx1101), AMD Radeon PRO W7700 (gfx1101), and AMD Radeon PRO W6800 (gfx1030) are supported only on Ubuntu 24.04.3, Ubuntu 22.04.5, RHEL 10.0, and RHEL 9.6.
|
||||
.. [#rd-v710] **For ROCm 7.0.x** - AMD Radeon PRO V710 (gfx1101) GPUs are supported only on Ubuntu 24.04.3, Ubuntu 22.04.5, RHEL 10.0, RHEL 9.6, and Azure Linux 3.0.
|
||||
.. [#rd-v620] **For ROCm 7.0.x** - AMD Radeon PRO V620 (gfx1030) GPUs are supported only on Ubuntu 24.04.3 and Ubuntu 22.04.5.
|
||||
.. [#mi325x-os] **For ROCm 7.0.x** - AMD Instinct MI325X GPUs (gfx942) are supported only on Ubuntu 24.04.3, Ubuntu 22.04.5, RHEL 9.6, and RHEL 9.4.
|
||||
.. [#mi300x-os] **For ROCm 7.0.x** - AMD Instinct MI300X GPUs (gfx942) are supported on all listed :ref:`supported_distributions`.
|
||||
.. [#mi300A-os] **For ROCm 7.0.x** - AMD Instinct MI300A GPUs (gfx942) are supported only on Ubuntu 24.04.3, Ubuntu 22.04.5, RHEL 10.0, RHEL 9.6, RHEL 9.4, RHEL 8.10, SLES 15 SP7, Debian 12, and Rocky Linux 9.
|
||||
.. [#mi200x-os] **For ROCm 7.0.x** - AMD Instinct MI200 Series GPUs (gfx90a) are supported only on Ubuntu 24.04.3, Ubuntu 22.04.5, RHEL 10.0, RHEL 9.6, RHEL 9.4, RHEL 8.10, SLES 15 SP7, and Debian 12.
|
||||
.. [#mi100-os] **For ROCm 7.0.x** - AMD Instinct MI100 GPUs (gfx908) are supported only on Ubuntu 24.04.3, Ubuntu 22.04.5, RHEL 10.0, RHEL 9.6, RHEL 9.4, and RHEL 8.10.
|
||||
.. [#tf-mi350] TensorFlow 2.17.1 is not supported on AMD Instinct MI350 Series GPUs. Use TensorFlow 2.19.1 or 2.18.1 with MI350 Series GPUs instead.
|
||||
.. [#dgl_compat] DGL is supported only on ROCm 6.4.0.
|
||||
.. [#llama-cpp_compat] llama.cpp is supported only on ROCm 7.0.0 and ROCm 6.4.x.
|
||||
.. [#driver_patch] AMD GPU Driver (amdgpu) 30.10.1 is a quality release that resolves an issue identified in the 30.10 release. There are no other significant changes or feature additions in ROCm 7.0.1 from ROCm 7.0.0. AMD GPU Driver (amdgpu) 30.10.1 is compatible with ROCm 7.0.1 and ROCm 7.0.0.
|
||||
.. [#kfd_support] As of ROCm 6.4.0, forward and backward compatibility between the AMD GPU Driver (amdgpu) and its user space software is provided up to a year apart. For earlier ROCm releases, the compatibility is provided for +/- 2 releases. The supported user space versions on this page were accurate as of the time of initial ROCm release. For the most up-to-date information, see the latest version of this information at `User and AMD GPU Driver support matrix <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/reference/user-kernel-space-compat-matrix.html>`_.
|
||||
.. [#ROCT-rocr] Starting from ROCm 6.3.0, the ROCT Thunk Interface is included as part of the ROCr runtime package.
|
||||
|
||||
|
||||
.. _OS-kernel-versions:
|
||||
|
||||
Operating systems, kernel and Glibc versions
|
||||
*********************************************
|
||||
|
||||
Use this lookup table to confirm which operating system and kernel versions are supported with ROCm.
|
||||
|
||||
.. csv-table::
|
||||
:header: "OS", "Version", "Kernel", "Glibc"
|
||||
:widths: 40, 20, 30, 20
|
||||
:stub-columns: 1
|
||||
|
||||
`Ubuntu <https://ubuntu.com/about/release-cycle#ubuntu-kernel-release-cycle>`_, 24.04.3, "6.8 [GA], 6.14 [HWE]", 2.39
|
||||
,,
|
||||
`Ubuntu <https://ubuntu.com/about/release-cycle#ubuntu-kernel-release-cycle>`_, 24.04.2, "6.8 [GA], 6.11 [HWE]", 2.39
|
||||
,,
|
||||
`Ubuntu <https://ubuntu.com/about/release-cycle#ubuntu-kernel-release-cycle>`_, 22.04.5, "5.15 [GA], 6.8 [HWE]", 2.35
|
||||
,,
|
||||
`Red Hat Enterprise Linux (RHEL 10) <https://access.redhat.com/articles/3078#RHEL9>`_, 10.0, 6.12.0-55, 2.39
|
||||
,,
|
||||
`Red Hat Enterprise Linux (RHEL 9) <https://access.redhat.com/articles/3078#RHEL9>`_, 9.6, 5.14.0-570, 2.34
|
||||
,9.5, 5.14+, 2.34
|
||||
,9.4, 5.14.0-427, 2.34
|
||||
,,
|
||||
`Red Hat Enterprise Linux (RHEL 8) <https://access.redhat.com/articles/3078#RHEL8>`_, 8.10, 4.18.0-553, 2.28
|
||||
,,
|
||||
`SUSE Linux Enterprise Server (SLES) <https://www.suse.com/support/kb/doc/?id=000019587#SLE15SP4>`_, 15 SP7, 6.40-150700.51, 2.38
|
||||
,15 SP6, "6.5.0+, 6.4.0", 2.38
|
||||
,15 SP5, 5.14.21, 2.31
|
||||
,,
|
||||
`Rocky Linux <https://wiki.rockylinux.org/rocky/version/>`_, 9, 5.14.0-570, 2.34
|
||||
,,
|
||||
`Oracle Linux <https://blogs.oracle.com/scoter/post/oracle-linux-and-unbreakable-enterprise-kernel-uek-releases>`_, 10, 6.12.0 (UEK), 2.39
|
||||
,9, 6.12.0 (UEK), 2.34
|
||||
,8, 5.15.0 (UEK), 2.28
|
||||
,,
|
||||
`Debian <https://www.debian.org/download>`_,13, 6.12, 2.35
|
||||
,12, 6.1.0, 2.36
|
||||
,,
|
||||
`Azure Linux <https://techcommunity.microsoft.com/blog/linuxandopensourceblog/azure-linux-3-0-now-in-preview-on-azure-kubernetes-service-v1-31/4287229>`_,3.0, 6.6.92, 2.38
|
||||
,,
|
||||
|
||||
.. note::
|
||||
|
||||
* See `Red Hat Enterprise Linux Release Dates <https://access.redhat.com/articles/3078>`_ to learn about the specific kernel versions supported on Red Hat Enterprise Linux (RHEL).
|
||||
* See `List of SUSE Linux Enterprise Server kernel <https://www.suse.com/support/kb/doc/?id=000019587>`_ to learn about the specific kernel version supported on SUSE Linux Enterprise Server (SLES).
|
||||
..
|
||||
Footnotes and ref anchors in below historical tables should be appended with "-past-60", to differentiate from the
|
||||
footnote references in the above, latest, compatibility matrix. It also allows to easily find & replace.
|
||||
An easy way to work is to download the historical.CSV file, and update open it in excel. Then when content is ready,
|
||||
delete the columns you don't need, to build the current compatibility matrix to use in above table. Find & replace all
|
||||
instances of "-past-60" to make it ready for above table.
|
||||
|
||||
|
||||
.. _past-rocm-compatibility-matrix:
|
||||
|
||||
Past versions of ROCm compatibility matrix
|
||||
***************************************************
|
||||
|
||||
Expand for full historical view of:
|
||||
|
||||
.. dropdown:: ROCm 6.0 - Present
|
||||
|
||||
You can `download the entire .csv <../downloads/compatibility-matrix-historical-6.0.csv>`_ for offline reference.
|
||||
|
||||
.. csv-table::
|
||||
:file: compatibility-matrix-historical-6.0.csv
|
||||
:header-rows: 1
|
||||
:stub-columns: 1
|
||||
|
||||
.. rubric:: Footnotes
|
||||
|
||||
.. [#rhel-10-702-past-60] RHEL 10.0 and RHEL 9.6 are supported on all listed :ref:`supported_GPUs` except AMD Radeon PRO V620 GPU.
|
||||
.. [#rhel-94-702-past-60] RHEL 9.4 is supported on all AMD Instinct GPUs listed under :ref:`supported_GPUs`.
|
||||
.. [#rhel-700-past-60] **For ROCm 7.0.x** - RHEL 8.10 is supported only on AMD Instinct MI300X, MI300A, MI250X, MI250, MI210, and MI100 GPUs.
|
||||
.. [#ol-700-mi300x-past-60] **For ROCm 7.0.x** - Oracle Linux 10 and 9 are supported only on AMD Instinct MI355X, MI350X, and MI300X GPUs. Oracle Linux 8 is supported only on AMD Instinct MI300X GPU.
|
||||
.. [#mi300x-past-60] **Prior ROCm 7.0.0** - Oracle Linux is supported only on AMD Instinct MI300X GPUs.
|
||||
.. [#db-mi300x-past-60] **For ROCm 7.0.2** - Debian 13 is supported only on AMD Instinct MI300X GPUs.
|
||||
.. [#sles-db-700-past-60] **For ROCm 7.0.x** - SLES 15 SP7 and Debian 12 are supported only on AMD Instinct MI300X, MI300A, MI250X, MI250, and MI210 GPUs.
|
||||
.. [#single-node-past-60] **Prior to ROCm 7.0.0** - Debian 12 is supported only on AMD Instinct MI300X GPUs for single-node functionality.
|
||||
.. [#az-mi300x-past-60] Starting from ROCm 6.4.0, Azure Linux 3.0 is supported only on AMD Instinct MI300X and AMD Radeon PRO V710 GPUs.
|
||||
.. [#az-mi300x-630-past-60] **Prior ROCm 6.4.0**- Azure Linux 3.0 is supported only on AMD Instinct MI300X GPUs.
|
||||
.. [#rl-700-past-60] Rocky Linux 9 is supported only on AMD Instinct MI300X and MI300A GPUs.
|
||||
.. [#mi350x-os-past-60] AMD Instinct MI355X (gfx950) and MI350X(gfx950) GPUs are supported only on Ubuntu 24.04.3, Ubuntu 22.04.5, RHEL 9.6, RHEL 9.4, and Oracle Linux 9.
|
||||
.. [#RDNA-OS-700-past-60] **For ROCm 7.0.x** AMD Radeon PRO AI PRO R9700 (gfx1201), AMD Radeon RX 9070 XT (gfx1201), AMD Radeon RX 9070 GRE (gfx1201), AMD Radeon RX 9070 (gfx1201), AMD Radeon RX 9060 XT (gfx1200), AMD Radeon RX 9060 (gfx1200), AMD Radeon RX 7800 XT (gfx1101), AMD Radeon RX 7700 XT (gfx1101), AMD Radeon PRO W7700 (gfx1101), and AMD Radeon PRO W6800 (gfx1030) are supported only on Ubuntu 24.04.3, Ubuntu 22.04.5, RHEL 10.0, RHEL 9.6, RHEL 9.4, Oracle Linux 10, and Oracle Linux 9.
|
||||
.. [#RDNA-OS-past-60] **Prior ROCm 7.0.0** - Radeon AI PRO R9700, Radeon RX 9070 XT (gfx1201), Radeon RX 9060 XT (gfx1200), Radeon PRO W7700 (gfx1101), and Radeon RX 7800 XT (gfx1101) are supported only on Ubuntu 24.04.2, Ubuntu 22.04.5, RHEL 9.6, and RHEL 9.4.
|
||||
.. [#rd-v710-past-60] **For ROCm 7.0.x** - AMD Radeon PRO V710 (gfx1101) is supported only on Ubuntu 24.04.3, Ubuntu 22.04.5, RHEL 10.0, RHEL 9.6, and Azure Linux 3.0.
|
||||
.. [#rd-v620-past-60] **For ROCm 7.0.x** - AMD Radeon PRO V620 (gfx1030) is supported only on Ubuntu 24.04.3 and Ubuntu 22.04.5.
|
||||
.. [#mi325x-os-past-60] **For ROCm 7.0.x** - AMD Instinct MI325X GPU (gfx942) is supported only on Ubuntu 24.04.3, Ubuntu 22.04.5, RHEL 9.6, and RHEL 9.4.
|
||||
.. [#mi300x-os-past-60] **For ROCm 7.0.x** - AMD Instinct MI300X GPU (gfx942) is supported on all listed :ref:`supported_distributions`.
|
||||
.. [#mi300A-os-past-60] **For ROCm 7.0.x** - AMD Instinct MI300A GPU (gfx942) is supported only on Ubuntu 24.04.3, Ubuntu 22.04.5, RHEL 10.0, RHEL 9.6, RHEL 9.4, RHEL 8.10, SLES 15 SP7, Debian 12, and Rocky Linux 9.
|
||||
.. [#mi200x-os-past-60] **For ROCm 7.0.x** - AMD Instinct MI200 Series GPUs (gfx90a) are supported only on Ubuntu 24.04.3, Ubuntu 22.04.5, RHEL 10.0, RHEL 9.6, RHEL 9.4, RHEL 8.10, SLES 15 SP7, and Debian 12.
|
||||
.. [#mi100-os-past-60] **For ROCm 7.0.x** - AMD Instinct MI100 GPU (gfx908) is supported only on Ubuntu 24.04.3, Ubuntu 22.04.5, RHEL 10.0, RHEL 9.6, RHEL 9.4, and RHEL 8.10.
|
||||
.. [#7700XT-OS-past-60] **Prior to ROCm 7.0.0** - Radeon RX 7700 XT (gfx1101) is supported only on Ubuntu 24.04.2 and RHEL 9.6.
|
||||
.. [#mi300_624-past-60] **For ROCm 6.2.4** - MI300X (gfx942) is supported on listed operating systems *except* Ubuntu 22.04.5 [6.8 HWE] and Ubuntu 22.04.4 [6.5 HWE].
|
||||
.. [#mi300_622-past-60] **For ROCm 6.2.2** - MI300X (gfx942) is supported on listed operating systems *except* Ubuntu 22.04.5 [6.8 HWE] and Ubuntu 22.04.4 [6.5 HWE].
|
||||
.. [#mi300_621-past-60] **For ROCm 6.2.1** - MI300X (gfx942) is supported on listed operating systems *except* Ubuntu 22.04.5 [6.8 HWE] and Ubuntu 22.04.4 [6.5 HWE].
|
||||
.. [#mi300_620-past-60] **For ROCm 6.2.0** - MI300X (gfx942) is supported on listed operating systems *except* Ubuntu 22.04.5 [6.8 HWE] and Ubuntu 22.04.4 [6.5 HWE].
|
||||
.. [#mi300_612-past-60] **For ROCm 6.1.2** - MI300A (gfx942) is supported on Ubuntu 22.04.4, RHEL 9.4, RHEL 9.3, RHEL 8.9, and SLES 15 SP5. MI300X (gfx942) is supported only on Ubuntu 22.04.4 and Oracle Linux.
|
||||
.. [#mi300_611-past-60] **For ROCm 6.1.1** - MI300A (gfx942) is supported on Ubuntu 22.04.4, RHEL 9.4, RHEL 9.3, RHEL 8.9, and SLES 15 SP5. MI300X (gfx942) is supported only on Ubuntu 22.04.4 and Oracle Linux.
|
||||
.. [#mi300_610-past-60] **For ROCm 6.1.0** - MI300A (gfx942) is supported on Ubuntu 22.04.4, RHEL 9.4, RHEL 9.3, RHEL 8.9, and SLES 15 SP5. MI300X (gfx942) is supported only on Ubuntu 22.04.4.
|
||||
.. [#mi300_602-past-60] **For ROCm 6.0.2** - MI300A (gfx942) is supported on Ubuntu 22.04.3, RHEL 8.9, and SLES 15 SP5. MI300X (gfx942) is supported only on Ubuntu 22.04.3.
|
||||
.. [#mi300_600-past-60] **For ROCm 6.0.0** - MI300A (gfx942) is supported on Ubuntu 22.04.3, RHEL 8.9, and SLES 15 SP5. MI300X (gfx942) is supported only on Ubuntu 22.04.3.
|
||||
.. [#tf-mi350-past-60] TensorFlow 2.17.1 is not supported on AMD Instinct MI350 Series GPUs. Use TensorFlow 2.19.1 or 2.18.1 with MI350 Series GPUs instead.
|
||||
.. [#verl_compat-past-60] verl is supported only on ROCm 6.2.0.
|
||||
.. [#stanford-megatron-lm_compat-past-60] Stanford Megatron-LM is supported only on ROCm 6.3.0.
|
||||
.. [#dgl_compat-past-60] DGL is supported only on ROCm 6.4.0.
|
||||
.. [#megablocks_compat-past-60] Megablocks is supported only on ROCm 6.3.0.
|
||||
.. [#taichi_compat-past-60] Taichi is supported only on ROCm 6.3.2.
|
||||
.. [#ray_compat-past-60] Ray is supported only on ROCm 6.4.1.
|
||||
.. [#llama-cpp_compat-past-60] llama.cpp is supported only on ROCm 7.0.0 and 6.4.x.
|
||||
.. [#flashinfer_compat-past-60] FlashInfer is supported only on ROCm 6.4.1.
|
||||
.. [#driver_patch-past-60] AMD GPU Driver (amdgpu) 30.10.1 is a quality release that resolves an issue identified in the 30.10 release. There are no other significant changes or feature additions in ROCm 7.0.1 from ROCm 7.0.0. AMD GPU Driver (amdgpu) 30.10.1 is compatible with ROCm 7.0.1 and ROCm 7.0.0.
|
||||
.. [#kfd_support-past-60] As of ROCm 6.4.0, forward and backward compatibility between the AMD GPU Driver (amdgpu) and its user space software is provided up to a year apart. For earlier ROCm releases, the compatibility is provided for +/- 2 releases. The supported user space versions on this page were accurate as of the time of initial ROCm release. For the most up-to-date information, see the latest version of this information at `User and AMD GPU Driver support matrix <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/reference/user-kernel-space-compat-matrix.html>`_.
|
||||
.. [#ROCT-rocr-past-60] Starting from ROCm 6.3.0, the ROCT Thunk Interface is included as part of the ROCr runtime package.
|
||||
|
||||
@@ -1,5 +0,0 @@
|
||||
**************************************
|
||||
Deep learning frameworks compatibility
|
||||
**************************************
|
||||
|
||||
Basdflkj; jaksldf;jkasl;d jkl;fdksalsdfhguieqwrasdf .asdf
|
||||
@@ -1,255 +0,0 @@
|
||||
:orphan:
|
||||
|
||||
.. meta::
|
||||
:description: Deep Graph Library (DGL) compatibility
|
||||
:keywords: GPU, DGL compatibility
|
||||
|
||||
.. version-set:: rocm_version latest
|
||||
|
||||
********************************************************************************
|
||||
DGL compatibility
|
||||
********************************************************************************
|
||||
|
||||
Deep Graph Library `(DGL) <https://www.dgl.ai/>`_ is an easy-to-use, high-performance and scalable
|
||||
Python package for deep learning on graphs. DGL is framework agnostic, meaning
|
||||
if a deep graph model is a component in an end-to-end application, the rest of
|
||||
the logic is implemented using PyTorch.
|
||||
|
||||
* ROCm support for DGL is hosted in the `https://github.com/ROCm/dgl <https://github.com/ROCm/dgl>`_ repository.
|
||||
* Due to independent compatibility considerations, this location differs from the `https://github.com/dmlc/dgl <https://github.com/dmlc/dgl>`_ upstream repository.
|
||||
* Use the prebuilt :ref:`Docker images <dgl-docker-compat>` with DGL, PyTorch, and ROCm preinstalled.
|
||||
* See the :doc:`ROCm DGL installation guide <rocm-install-on-linux:install/3rd-party/dgl-install>`
|
||||
to install and get started.
|
||||
|
||||
|
||||
Supported devices
|
||||
================================================================================
|
||||
|
||||
- **Officially Supported**: TF32 with AMD Instinct MI300X (through hipblaslt)
|
||||
- **Partially Supported**: TF32 with AMD Instinct MI250X
|
||||
|
||||
|
||||
.. _dgl-recommendations:
|
||||
|
||||
Use cases and recommendations
|
||||
================================================================================
|
||||
|
||||
DGL can be used for Graph Learning, and building popular graph models like
|
||||
GAT, GCN and GraphSage. Using these we can support a variety of use-cases such as:
|
||||
|
||||
- Recommender systems
|
||||
- Network Optimization and Analysis
|
||||
- 1D (Temporal) and 2D (Image) Classification
|
||||
- Drug Discovery
|
||||
|
||||
Multiple use cases of DGL have been tested and verified.
|
||||
However, a recommended example follows a drug discovery pipeline using the ``SE3Transformer``.
|
||||
Refer to the `AMD ROCm blog <https://rocm.blogs.amd.com/>`_,
|
||||
where you can search for DGL examples and best practices to optimize your training workflows on AMD GPUs.
|
||||
|
||||
Coverage includes:
|
||||
|
||||
- Single-GPU training/inference
|
||||
- Multi-GPU training
|
||||
|
||||
|
||||
.. _dgl-docker-compat:
|
||||
|
||||
Docker image compatibility
|
||||
================================================================================
|
||||
|
||||
.. |docker-icon| raw:: html
|
||||
|
||||
<i class="fab fa-docker"></i>
|
||||
|
||||
AMD validates and publishes `DGL images <https://hub.docker.com/r/rocm/dgl>`_
|
||||
with ROCm and Pytorch backends on Docker Hub. The following Docker image tags and associated
|
||||
inventories were tested on `ROCm 6.4.0 <https://repo.radeon.com/rocm/apt/6.4/>`_.
|
||||
Click the |docker-icon| to view the image on Docker Hub.
|
||||
|
||||
.. list-table:: DGL Docker image components
|
||||
:header-rows: 1
|
||||
:class: docker-image-compatibility
|
||||
|
||||
* - Docker
|
||||
- DGL
|
||||
- PyTorch
|
||||
- Ubuntu
|
||||
- Python
|
||||
|
||||
* - .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/dgl/dgl-2.4_rocm6.4_ubuntu24.04_py3.12_pytorch_release_2.6.0/images/sha256-8ce2c3bcfaa137ab94a75f9e2ea711894748980f57417739138402a542dd5564"><i class="fab fa-docker fa-lg"></i></a>
|
||||
|
||||
- `2.4.0 <https://github.com/dmlc/dgl/releases/tag/v2.4.0>`_
|
||||
- `2.6.0 <https://github.com/ROCm/pytorch/tree/release/2.6>`_
|
||||
- 24.04
|
||||
- `3.12.9 <https://www.python.org/downloads/release/python-3129/>`_
|
||||
|
||||
* - .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/dgl/dgl-2.4_rocm6.4_ubuntu24.04_py3.12_pytorch_release_2.4.1/images/sha256-cf1683283b8eeda867b690229c8091c5bbf1edb9f52e8fb3da437c49a612ebe4"><i class="fab fa-docker fa-lg"></i></a>
|
||||
|
||||
- `2.4.0 <https://github.com/dmlc/dgl/releases/tag/v2.4.0>`_
|
||||
- `2.4.1 <https://github.com/ROCm/pytorch/tree/release/2.4>`_
|
||||
- 24.04
|
||||
- `3.12.9 <https://www.python.org/downloads/release/python-3129/>`_
|
||||
|
||||
|
||||
* - .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/dgl/dgl-2.4_rocm6.4_ubuntu22.04_py3.10_pytorch_release_2.4.1/images/sha256-4834f178c3614e2d09e89e32041db8984c456d45dfd20286e377ca8635686554"><i class="fab fa-docker fa-lg"></i></a>
|
||||
|
||||
- `2.4.0 <https://github.com/dmlc/dgl/releases/tag/v2.4.0>`_
|
||||
- `2.4.1 <https://github.com/ROCm/pytorch/tree/release/2.4>`_
|
||||
- 22.04
|
||||
- `3.10.16 <https://www.python.org/downloads/release/python-31016/>`_
|
||||
|
||||
|
||||
* - .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/dgl/dgl-2.4_rocm6.4_ubuntu22.04_py3.10_pytorch_release_2.3.0/images/sha256-88740a2c8ab4084b42b10c3c6ba984cab33dd3a044f479c6d7618e2b2cb05e69"><i class="fab fa-docker fa-lg"></i></a>
|
||||
|
||||
- `2.4.0 <https://github.com/dmlc/dgl/releases/tag/v2.4.0>`_
|
||||
- `2.3.0 <https://github.com/ROCm/pytorch/tree/release/2.3>`_
|
||||
- 22.04
|
||||
- `3.10.16 <https://www.python.org/downloads/release/python-31016/>`_
|
||||
|
||||
|
||||
Key ROCm libraries for DGL
|
||||
================================================================================
|
||||
|
||||
DGL on ROCm depends on specific libraries that affect its features and performance.
|
||||
Using the DGL Docker container or building it with the provided docker file or a ROCm base image is recommended.
|
||||
If you prefer to build it yourself, ensure the following dependencies are installed:
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - ROCm library
|
||||
- Version
|
||||
- Purpose
|
||||
* - `Composable Kernel <https://github.com/ROCm/composable_kernel>`_
|
||||
- :version-ref:`"Composable Kernel" rocm_version`
|
||||
- Enables faster execution of core operations like matrix multiplication
|
||||
(GEMM), convolutions and transformations.
|
||||
* - `hipBLAS <https://github.com/ROCm/hipBLAS>`_
|
||||
- :version-ref:`hipBLAS rocm_version`
|
||||
- Provides GPU-accelerated Basic Linear Algebra Subprograms (BLAS) for
|
||||
matrix and vector operations.
|
||||
* - `hipBLASLt <https://github.com/ROCm/hipBLASLt>`_
|
||||
- :version-ref:`hipBLASLt rocm_version`
|
||||
- hipBLASLt is an extension of the hipBLAS library, providing additional
|
||||
features like epilogues fused into the matrix multiplication kernel or
|
||||
use of integer tensor cores.
|
||||
* - `hipCUB <https://github.com/ROCm/hipCUB>`_
|
||||
- :version-ref:`hipCUB rocm_version`
|
||||
- Provides a C++ template library for parallel algorithms for reduction,
|
||||
scan, sort and select.
|
||||
* - `hipFFT <https://github.com/ROCm/hipFFT>`_
|
||||
- :version-ref:`hipFFT rocm_version`
|
||||
- Provides GPU-accelerated Fast Fourier Transform (FFT) operations.
|
||||
* - `hipRAND <https://github.com/ROCm/hipRAND>`_
|
||||
- :version-ref:`hipRAND rocm_version`
|
||||
- Provides fast random number generation for GPUs.
|
||||
* - `hipSOLVER <https://github.com/ROCm/hipSOLVER>`_
|
||||
- :version-ref:`hipSOLVER rocm_version`
|
||||
- Provides GPU-accelerated solvers for linear systems, eigenvalues, and
|
||||
singular value decompositions (SVD).
|
||||
* - `hipSPARSE <https://github.com/ROCm/hipSPARSE>`_
|
||||
- :version-ref:`hipSPARSE rocm_version`
|
||||
- Accelerates operations on sparse matrices, such as sparse matrix-vector
|
||||
or matrix-matrix products.
|
||||
* - `hipSPARSELt <https://github.com/ROCm/hipSPARSELt>`_
|
||||
- :version-ref:`hipSPARSELt rocm_version`
|
||||
- Accelerates operations on sparse matrices, such as sparse matrix-vector
|
||||
or matrix-matrix products.
|
||||
* - `hipTensor <https://github.com/ROCm/hipTensor>`_
|
||||
- :version-ref:`hipTensor rocm_version`
|
||||
- Optimizes for high-performance tensor operations, such as contractions.
|
||||
* - `MIOpen <https://github.com/ROCm/MIOpen>`_
|
||||
- :version-ref:`MIOpen rocm_version`
|
||||
- Optimizes deep learning primitives such as convolutions, pooling,
|
||||
normalization, and activation functions.
|
||||
* - `MIGraphX <https://github.com/ROCm/AMDMIGraphX>`_
|
||||
- :version-ref:`MIGraphX rocm_version`
|
||||
- Adds graph-level optimizations, ONNX models and mixed precision support
|
||||
and enable Ahead-of-Time (AOT) Compilation.
|
||||
* - `MIVisionX <https://github.com/ROCm/MIVisionX>`_
|
||||
- :version-ref:`MIVisionX rocm_version`
|
||||
- Optimizes acceleration for computer vision and AI workloads like
|
||||
preprocessing, augmentation, and inferencing.
|
||||
* - `rocAL <https://github.com/ROCm/rocAL>`_
|
||||
- :version-ref:`rocAL rocm_version`
|
||||
- Accelerates the data pipeline by offloading intensive preprocessing and
|
||||
augmentation tasks. rocAL is part of MIVisionX.
|
||||
* - `RCCL <https://github.com/ROCm/rccl>`_
|
||||
- :version-ref:`RCCL rocm_version`
|
||||
- Optimizes for multi-GPU communication for operations like AllReduce and
|
||||
Broadcast.
|
||||
* - `rocDecode <https://github.com/ROCm/rocDecode>`_
|
||||
- :version-ref:`rocDecode rocm_version`
|
||||
- Provides hardware-accelerated data decoding capabilities, particularly
|
||||
for image, video, and other dataset formats.
|
||||
* - `rocJPEG <https://github.com/ROCm/rocJPEG>`_
|
||||
- :version-ref:`rocJPEG rocm_version`
|
||||
- Provides hardware-accelerated JPEG image decoding and encoding.
|
||||
* - `RPP <https://github.com/ROCm/RPP>`_
|
||||
- :version-ref:`RPP rocm_version`
|
||||
- Speeds up data augmentation, transformation, and other preprocessing steps.
|
||||
* - `rocThrust <https://github.com/ROCm/rocThrust>`_
|
||||
- :version-ref:`rocThrust rocm_version`
|
||||
- Provides a C++ template library for parallel algorithms like sorting,
|
||||
reduction, and scanning.
|
||||
* - `rocWMMA <https://github.com/ROCm/rocWMMA>`_
|
||||
- :version-ref:`rocWMMA rocm_version`
|
||||
- Accelerates warp-level matrix-multiply and matrix-accumulate to speed up matrix
|
||||
multiplication (GEMM) and accumulation operations with mixed precision
|
||||
support.
|
||||
|
||||
|
||||
Supported features
|
||||
================================================================================
|
||||
|
||||
Many functions and methods available in DGL Upstream are also supported in DGL ROCm.
|
||||
Instead of listing them all, support is grouped into the following categories to provide a general overview.
|
||||
|
||||
* DGL Base
|
||||
* DGL Backend
|
||||
* DGL Data
|
||||
* DGL Dataloading
|
||||
* DGL DGLGraph
|
||||
* DGL Function
|
||||
* DGL Ops
|
||||
* DGL Sampling
|
||||
* DGL Transforms
|
||||
* DGL Utils
|
||||
* DGL Distributed
|
||||
* DGL Geometry
|
||||
* DGL Mpops
|
||||
* DGL NN
|
||||
* DGL Optim
|
||||
* DGL Sparse
|
||||
|
||||
|
||||
Unsupported features
|
||||
================================================================================
|
||||
|
||||
* Graphbolt
|
||||
* Partial TF32 Support (MI250x only)
|
||||
* Kineto/ ROCTracer integration
|
||||
|
||||
|
||||
Unsupported functions
|
||||
================================================================================
|
||||
|
||||
* ``more_nnz``
|
||||
* ``format``
|
||||
* ``multiprocess_sparse_adam_state_dict``
|
||||
* ``record_stream_ndarray``
|
||||
* ``half_spmm``
|
||||
* ``segment_mm``
|
||||
* ``gather_mm_idx_b``
|
||||
* ``pgexplainer``
|
||||
* ``sample_labors_prob``
|
||||
* ``sample_labors_noprob``
|
||||
@@ -1,107 +0,0 @@
|
||||
:orphan:
|
||||
|
||||
.. meta::
|
||||
:description: FlashInfer deep learning framework compatibility
|
||||
:keywords: GPU, LLM, FlashInfer, compatibility
|
||||
|
||||
.. version-set:: rocm_version latest
|
||||
|
||||
********************************************************************************
|
||||
FlashInfer compatibility
|
||||
********************************************************************************
|
||||
|
||||
`FlashInfer <https://docs.flashinfer.ai/index.html>`__ is a library and kernel generator
|
||||
for Large Language Models (LLMs) that provides high-performance implementation of graphics
|
||||
processing units (GPUs) kernels. FlashInfer focuses on LLM serving and inference, as well
|
||||
as advanced performance across diverse scenarios.
|
||||
|
||||
FlashInfer features highly efficient attention kernels, load-balanced scheduling, and memory-optimized
|
||||
techniques, while supporting customized attention variants. It’s compatible with ``torch.compile``, and
|
||||
offers high-performance LLM-specific operators, with easy integration through PyTorch, and C++ APIs.
|
||||
|
||||
.. note::
|
||||
|
||||
The ROCm port of FlashInfer is under active development, and some features are not yet available.
|
||||
For the latest feature compatibility matrix, refer to the ``README`` of the
|
||||
`https://github.com/ROCm/flashinfer <https://github.com/ROCm/flashinfer>`__ repository.
|
||||
|
||||
Support for the ROCm port of FlashInfer is available as follows:
|
||||
|
||||
- ROCm support for FlashInfer is hosted in the `https://github.com/ROCm/flashinfer
|
||||
<https://github.com/ROCm/flashinfer>`__ repository. This location differs from the
|
||||
`https://github.com/flashinfer-ai/flashinfer <https://github.com/flashinfer-ai/flashinfer>`_
|
||||
upstream repository.
|
||||
|
||||
- To install FlashInfer, use the prebuilt :ref:`Docker image <flashinfer-docker-compat>`,
|
||||
which includes ROCm, FlashInfer, and all required dependencies.
|
||||
|
||||
- See the :doc:`ROCm FlashInfer installation guide <rocm-install-on-linux:install/3rd-party/flashinfer-install>`
|
||||
to install and get started.
|
||||
|
||||
- See the `Installation guide <https://docs.flashinfer.ai/installation.html>`__
|
||||
in the upstream FlashInfer documentation.
|
||||
|
||||
.. note::
|
||||
|
||||
Flashinfer is supported on ROCm 6.4.1.
|
||||
|
||||
Supported devices
|
||||
================================================================================
|
||||
|
||||
**Officially Supported**: AMD Instinct™ MI300X
|
||||
|
||||
|
||||
.. _flashinfer-recommendations:
|
||||
|
||||
Use cases and recommendations
|
||||
================================================================================
|
||||
|
||||
This release of FlashInfer on ROCm provides the decode functionality for LLM inferencing.
|
||||
In the decode phase, tokens are generated sequentially, with the model predicting each new
|
||||
token based on the previously generated tokens and the input context.
|
||||
|
||||
FlashInfer on ROCm brings over upstream features such as load balancing, sparse and dense
|
||||
attention optimizations, and batching support, enabling efficient execution on AMD Instinct™ MI300X GPUs.
|
||||
|
||||
Because large LLMs often require substantial KV caches or long context windows, FlashInfer on ROCm
|
||||
also implements cascade attention from upstream to reduce memory usage.
|
||||
|
||||
For currently supported use cases and recommendations, refer to the `AMD ROCm blog <https://rocm.blogs.amd.com/>`__,
|
||||
where you can search for examples and best practices to optimize your workloads on AMD GPUs.
|
||||
|
||||
.. _flashinfer-docker-compat:
|
||||
|
||||
Docker image compatibility
|
||||
================================================================================
|
||||
|
||||
.. |docker-icon| raw:: html
|
||||
|
||||
<i class="fab fa-docker"></i>
|
||||
|
||||
AMD validates and publishes `ROCm FlashInfer images <https://hub.docker.com/r/rocm/flashinfer/tags>`__
|
||||
with ROCm and Pytorch backends on Docker Hub. The following Docker image tags and associated
|
||||
inventories represent the FlashInfer version from the official Docker Hub.
|
||||
The Docker images have been validated for `ROCm 6.4.1 <https://repo.radeon.com/rocm/apt/6.4.1/>`__.
|
||||
Click |docker-icon| to view the image on Docker Hub.
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
:class: docker-image-compatibility
|
||||
|
||||
* - Docker image
|
||||
- ROCm
|
||||
- FlashInfer
|
||||
- PyTorch
|
||||
- Ubuntu
|
||||
- Python
|
||||
|
||||
* - .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/flashinfer/flashinfer-0.2.5_rocm6.4_ubuntu24.04_py3.12_pytorch2.7/images/sha256-558914838821c88c557fb6d42cfbc1bdb67d79d19759f37c764a9ee801f93313"><i class="fab fa-docker fa-lg"></i> rocm/flashinfer</a>
|
||||
- `6.4.1 <https://repo.radeon.com/rocm/apt/6.4.1/>`__
|
||||
- `v0.2.5 <https://github.com/flashinfer-ai/flashinfer/releases/tag/v0.2.5>`__
|
||||
- `2.7.1 <https://github.com/ROCm/pytorch/releases/tag/v2.7.1>`__
|
||||
- 24.04
|
||||
- `3.12 <https://www.python.org/downloads/release/python-3129/>`__
|
||||
|
||||
|
||||
@@ -1,303 +0,0 @@
|
||||
:orphan:
|
||||
|
||||
.. meta::
|
||||
:description: JAX compatibility
|
||||
:keywords: GPU, JAX compatibility
|
||||
|
||||
.. version-set:: rocm_version latest
|
||||
|
||||
*******************************************************************************
|
||||
JAX compatibility
|
||||
*******************************************************************************
|
||||
|
||||
JAX provides a NumPy-like API, which combines automatic differentiation and the
|
||||
Accelerated Linear Algebra (XLA) compiler to achieve high-performance machine
|
||||
learning at scale.
|
||||
|
||||
JAX uses composable transformations of Python and NumPy through just-in-time
|
||||
(JIT) compilation, automatic vectorization, and parallelization. To learn about
|
||||
JAX, including profiling and optimizations, see the official `JAX documentation
|
||||
<https://jax.readthedocs.io/en/latest/notebooks/quickstart.html>`_.
|
||||
|
||||
ROCm support for JAX is upstreamed, and users can build the official source code
|
||||
with ROCm support:
|
||||
|
||||
- ROCm JAX release:
|
||||
|
||||
- Offers AMD-validated and community :ref:`Docker images <jax-docker-compat>`
|
||||
with ROCm and JAX preinstalled.
|
||||
|
||||
- ROCm JAX repository: `ROCm/rocm-jax <https://github.com/ROCm/rocm-jax>`_
|
||||
|
||||
- See the :doc:`ROCm JAX installation guide <rocm-install-on-linux:install/3rd-party/jax-install>`
|
||||
to get started.
|
||||
|
||||
- Official JAX release:
|
||||
|
||||
- Official JAX repository: `jax-ml/jax <https://github.com/jax-ml/jax>`_
|
||||
|
||||
- See the `AMD GPU (Linux) installation section
|
||||
<https://jax.readthedocs.io/en/latest/installation.html#amd-gpu-linux>`_ in
|
||||
the JAX documentation.
|
||||
|
||||
.. note::
|
||||
|
||||
AMD releases official `ROCm JAX Docker images <https://hub.docker.com/r/rocm/jax>`_
|
||||
quarterly alongside new ROCm releases. These images undergo full AMD testing.
|
||||
`Community ROCm JAX Docker images <https://hub.docker.com/r/rocm/jax-community>`_
|
||||
follow upstream JAX releases and use the latest available ROCm version.
|
||||
|
||||
Use cases and recommendations
|
||||
================================================================================
|
||||
|
||||
* The `nanoGPT in JAX <https://rocm.blogs.amd.com/artificial-intelligence/nanoGPT-JAX/README.html>`_
|
||||
blog explores the implementation and training of a Generative Pre-trained
|
||||
Transformer (GPT) model in JAX, inspired by Andrej Karpathy’s JAX-based
|
||||
nanoGPT. Comparing how essential GPT components—such as self-attention
|
||||
mechanisms and optimizers—are realized in JAX and JAX, also highlights
|
||||
JAX’s unique features.
|
||||
|
||||
* The `Optimize GPT Training: Enabling Mixed Precision Training in JAX using
|
||||
ROCm on AMD GPUs <https://rocm.blogs.amd.com/artificial-intelligence/jax-mixed-precision/README.html>`_
|
||||
blog post provides a comprehensive guide on enhancing the training efficiency
|
||||
of GPT models by implementing mixed precision techniques in JAX, specifically
|
||||
tailored for AMD GPUs utilizing the ROCm platform.
|
||||
|
||||
* The `Supercharging JAX with Triton Kernels on AMD GPUs <https://rocm.blogs.amd.com/artificial-intelligence/jax-triton/README.html>`_
|
||||
blog demonstrates how to develop a custom fused dropout-activation kernel for
|
||||
matrices using Triton, integrate it with JAX, and benchmark its performance
|
||||
using ROCm.
|
||||
|
||||
* The `Distributed fine-tuning with JAX on AMD GPUs <https://rocm.blogs.amd.com/artificial-intelligence/distributed-sft-jax/README.html>`_
|
||||
outlines the process of fine-tuning a Bidirectional Encoder Representations
|
||||
from Transformers (BERT)-based large language model (LLM) using JAX for a text
|
||||
classification task. The blog post discuss techniques for parallelizing the
|
||||
fine-tuning across multiple AMD GPUs and assess the model's performance on a
|
||||
holdout dataset. During the fine-tuning, a BERT-base-cased transformer model
|
||||
and the General Language Understanding Evaluation (GLUE) benchmark dataset was
|
||||
used on a multi-GPU setup.
|
||||
|
||||
* The `MI300X workload optimization guide <https://rocm.docs.amd.com/en/latest/how-to/tuning-guides/mi300x/workload.html>`_
|
||||
provides detailed guidance on optimizing workloads for the AMD Instinct MI300X
|
||||
accelerator using ROCm. The page is aimed at helping users achieve optimal
|
||||
performance for deep learning and other high-performance computing tasks on
|
||||
the MI300X GPU.
|
||||
|
||||
For more use cases and recommendations, see `ROCm JAX blog posts <https://rocm.blogs.amd.com/blog/tag/jax.html>`_.
|
||||
|
||||
.. _jax-docker-compat:
|
||||
|
||||
Docker image compatibility
|
||||
================================================================================
|
||||
|
||||
AMD provides preconfigured Docker images with JAX and the ROCm backend.
|
||||
These images are published on `Docker Hub <https://hub.docker.com/r/rocm/jax>`__ and are the
|
||||
recommended way to get started with deep learning with JAX on ROCm.
|
||||
For ``jax-community`` images, see `rocm/jax-community
|
||||
<https://hub.docker.com/r/rocm/jax-community/tags>`__ on Docker Hub.
|
||||
|
||||
To find the right image tag, see the :ref:`JAX on ROCm installation
|
||||
documentation <rocm-install-on-linux:jax-docker-support>` for a list of
|
||||
available ``rocm/jax`` images.
|
||||
|
||||
.. _key_rocm_libraries:
|
||||
|
||||
Key ROCm libraries for JAX
|
||||
================================================================================
|
||||
|
||||
The following ROCm libraries represent potential targets that could be utilized
|
||||
by JAX on ROCm for various computational tasks. The actual libraries used will
|
||||
depend on the specific implementation and operations performed.
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - ROCm library
|
||||
- Version
|
||||
- Purpose
|
||||
* - `hipBLAS <https://github.com/ROCm/hipBLAS>`_
|
||||
- :version-ref:`hipBLAS rocm_version`
|
||||
- Provides GPU-accelerated Basic Linear Algebra Subprograms (BLAS) for
|
||||
matrix and vector operations.
|
||||
* - `hipBLASLt <https://github.com/ROCm/hipBLASLt>`_
|
||||
- :version-ref:`hipBLASLt rocm_version`
|
||||
- hipBLASLt is an extension of hipBLAS, providing additional
|
||||
features like epilogues fused into the matrix multiplication kernel or
|
||||
use of integer tensor cores.
|
||||
* - `hipCUB <https://github.com/ROCm/hipCUB>`_
|
||||
- :version-ref:`hipCUB rocm_version`
|
||||
- Provides a C++ template library for parallel algorithms for reduction,
|
||||
scan, sort and select.
|
||||
* - `hipFFT <https://github.com/ROCm/hipFFT>`_
|
||||
- :version-ref:`hipFFT rocm_version`
|
||||
- Provides GPU-accelerated Fast Fourier Transform (FFT) operations.
|
||||
* - `hipRAND <https://github.com/ROCm/hipRAND>`_
|
||||
- :version-ref:`hipRAND rocm_version`
|
||||
- Provides fast random number generation for GPUs.
|
||||
* - `hipSOLVER <https://github.com/ROCm/hipSOLVER>`_
|
||||
- :version-ref:`hipSOLVER rocm_version`
|
||||
- Provides GPU-accelerated solvers for linear systems, eigenvalues, and
|
||||
singular value decompositions (SVD).
|
||||
* - `hipSPARSE <https://github.com/ROCm/hipSPARSE>`_
|
||||
- :version-ref:`hipSPARSE rocm_version`
|
||||
- Accelerates operations on sparse matrices, such as sparse matrix-vector
|
||||
or matrix-matrix products.
|
||||
* - `hipSPARSELt <https://github.com/ROCm/hipSPARSELt>`_
|
||||
- :version-ref:`hipSPARSELt rocm_version`
|
||||
- Accelerates operations on sparse matrices, such as sparse matrix-vector
|
||||
or matrix-matrix products.
|
||||
* - `MIOpen <https://github.com/ROCm/MIOpen>`_
|
||||
- :version-ref:`MIOpen rocm_version`
|
||||
- Optimized for deep learning primitives such as convolutions, pooling,
|
||||
normalization, and activation functions.
|
||||
* - `RCCL <https://github.com/ROCm/rccl>`_
|
||||
- :version-ref:`RCCL rocm_version`
|
||||
- Optimized for multi-GPU communication for operations like all-reduce,
|
||||
broadcast, and scatter.
|
||||
* - `rocThrust <https://github.com/ROCm/rocThrust>`_
|
||||
- :version-ref:`rocThrust rocm_version`
|
||||
- Provides a C++ template library for parallel algorithms like sorting,
|
||||
reduction, and scanning.
|
||||
|
||||
.. note::
|
||||
|
||||
This table shows ROCm libraries that could potentially be utilized by JAX. Not
|
||||
all libraries may be used in every configuration, and the actual library usage
|
||||
will depend on the specific operations and implementation details.
|
||||
|
||||
Supported data types and modules
|
||||
===============================================================================
|
||||
|
||||
The following tables lists the supported public JAX API data types and modules.
|
||||
|
||||
Supported data types
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
ROCm supports all the JAX data types of `jax.dtypes <https://docs.jax.dev/en/latest/jax.dtypes.html>`_
|
||||
module, `jax.numpy.dtype <https://docs.jax.dev/en/latest/_autosummary/jax.numpy.dtype.html>`_
|
||||
and `default_dtype <https://docs.jax.dev/en/latest/default_dtypes.html>`_ .
|
||||
The ROCm supported data types in JAX are collected in the following table.
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Data type
|
||||
- Description
|
||||
|
||||
* - ``bfloat16``
|
||||
- 16-bit bfloat (brain floating point).
|
||||
|
||||
* - ``bool``
|
||||
- Boolean.
|
||||
|
||||
* - ``complex128``
|
||||
- 128-bit complex.
|
||||
|
||||
* - ``complex64``
|
||||
- 64-bit complex.
|
||||
|
||||
* - ``float16``
|
||||
- 16-bit (half precision) floating-point.
|
||||
|
||||
* - ``float32``
|
||||
- 32-bit (single precision) floating-point.
|
||||
|
||||
* - ``float64``
|
||||
- 64-bit (double precision) floating-point.
|
||||
|
||||
* - ``half``
|
||||
- 16-bit (half precision) floating-point.
|
||||
|
||||
* - ``int16``
|
||||
- Signed 16-bit integer.
|
||||
|
||||
* - ``int32``
|
||||
- Signed 32-bit integer.
|
||||
|
||||
* - ``int64``
|
||||
- Signed 64-bit integer.
|
||||
|
||||
* - ``int8``
|
||||
- Signed 8-bit integer.
|
||||
|
||||
* - ``uint16``
|
||||
- Unsigned 16-bit (word) integer.
|
||||
|
||||
* - ``uint32``
|
||||
- Unsigned 32-bit (dword) integer.
|
||||
|
||||
* - ``uint64``
|
||||
- Unsigned 64-bit (qword) integer.
|
||||
|
||||
* - ``uint8``
|
||||
- Unsigned 8-bit (byte) integer.
|
||||
|
||||
.. note::
|
||||
|
||||
JAX data type support is effected by the :ref:`key_rocm_libraries` and it's
|
||||
collected on :doc:`ROCm data types and precision support <rocm:reference/precision-support>`
|
||||
page.
|
||||
|
||||
Supported modules
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
For a complete and up-to-date list of JAX public modules (for example, ``jax.numpy``,
|
||||
``jax.scipy``, ``jax.lax``), their descriptions, and usage, please refer directly to the
|
||||
`official JAX API documentation <https://jax.readthedocs.io/en/latest/jax.html>`_.
|
||||
|
||||
.. note::
|
||||
|
||||
Since version 0.1.56, JAX has full support for ROCm, and the
|
||||
:ref:`Known issues and important notes <jax_comp_known_issues>` section
|
||||
contains details about limitations specific to the ROCm backend. The list of
|
||||
JAX API modules are maintained by the JAX project and is subject to change.
|
||||
Refer to the official Jax documentation for the most up-to-date information.
|
||||
|
||||
Key features and enhancements for ROCm 7.0
|
||||
===============================================================================
|
||||
|
||||
- Upgraded XLA backend: Integrates a newer XLA version, enabling better
|
||||
optimizations, broader operator support, and potential performance gains.
|
||||
|
||||
- RNN support: Native RNN support (including LSTMs via ``jax.experimental.rnn``)
|
||||
now available on ROCm, aiding sequence model development.
|
||||
|
||||
- Comprehensive linear algebra capabilities: Offers robust ``jax.linalg``
|
||||
operations, essential for scientific and machine learning tasks.
|
||||
|
||||
- Expanded AMD GPU architecture support: Provides ongoing support for gfx1101
|
||||
GPUs and introduces support for gfx950 and gfx12xx GPUs.
|
||||
|
||||
- Mixed FP8 precision support: Enables ``lax.dot_general`` operations with mixed FP8
|
||||
types, offering pathways for memory and compute efficiency.
|
||||
|
||||
- Streamlined PyPi packaging: Provides reliable PyPi wheels for JAX on ROCm,
|
||||
simplifying the installation process.
|
||||
|
||||
- Pallas experimental kernel development: Continued Pallas framework
|
||||
enhancements for custom GPU kernels, including new intrinsics (specific
|
||||
kernel behaviors under review).
|
||||
|
||||
- Improved build system and CI: Enhanced ROCm build system and CI for greater
|
||||
reliability and maintainability.
|
||||
|
||||
- Enhanced distributed computing setup: Improved JAX setup in multi-GPU
|
||||
distributed environments.
|
||||
|
||||
.. _jax_comp_known_issues:
|
||||
|
||||
Known issues and notes for ROCm 7.0
|
||||
===============================================================================
|
||||
|
||||
- ``nn.dot_product_attention``: Certain configurations of ``jax.nn.dot_product_attention``
|
||||
may cause segmentation faults, though the majority of use cases work correctly.
|
||||
|
||||
- SVD with dynamic shapes: SVD on inputs with dynamic/symbolic shapes might result in an error.
|
||||
SVD with static shapes is unaffected.
|
||||
|
||||
- QR decomposition with symbolic shapes: QR decomposition operations may fail when using
|
||||
symbolic/dynamic shapes in shape polymorphic contexts.
|
||||
|
||||
- Pallas kernels: Specific advanced Pallas kernels may exhibit variations in
|
||||
numerical output or resource usage. These are actively reviewed as part of
|
||||
Pallas's experimental development.
|
||||
@@ -1,275 +0,0 @@
|
||||
:orphan:
|
||||
|
||||
.. meta::
|
||||
:description: llama.cpp deep learning framework compatibility
|
||||
:keywords: GPU, GGML, llama.cpp compatibility
|
||||
|
||||
.. version-set:: rocm_version latest
|
||||
|
||||
********************************************************************************
|
||||
llama.cpp compatibility
|
||||
********************************************************************************
|
||||
|
||||
`llama.cpp <https://github.com/ggml-org/llama.cpp>`__ is an open-source framework
|
||||
for Large Language Model (LLM) inference that runs on both central processing units
|
||||
(CPUs) and graphics processing units (GPUs). It is written in plain C/C++, providing
|
||||
a simple, dependency-free setup.
|
||||
|
||||
The framework supports multiple quantization options, from 1.5-bit to 8-bit integers,
|
||||
to accelerate inference and reduce memory usage. Originally built as a CPU-first library,
|
||||
llama.cpp is easy to integrate with other programming environments and is widely
|
||||
adopted across diverse platforms, including consumer devices.
|
||||
|
||||
ROCm support for llama.cpp is upstreamed, and you can build the official source code
|
||||
with ROCm support:
|
||||
|
||||
- ROCm support for llama.cpp is hosted in the official `https://github.com/ROCm/llama.cpp
|
||||
<https://github.com/ROCm/llama.cpp>`_ repository.
|
||||
|
||||
- Due to independent compatibility considerations, this location differs from the
|
||||
`https://github.com/ggml-org/llama.cpp <https://github.com/ggml-org/llama.cpp>`_ upstream repository.
|
||||
|
||||
- To install llama.cpp, use the prebuilt :ref:`Docker image <llama-cpp-docker-compat>`,
|
||||
which includes ROCm, llama.cpp, and all required dependencies.
|
||||
|
||||
- See the :doc:`ROCm llama.cpp installation guide <rocm-install-on-linux:install/3rd-party/llama-cpp-install>`
|
||||
to install and get started.
|
||||
|
||||
- See the `Installation guide <https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md#hip>`__
|
||||
in the upstream llama.cpp documentation.
|
||||
|
||||
.. note::
|
||||
|
||||
llama.cpp is supported on ROCm 7.0.0 and ROCm 6.4.x.
|
||||
|
||||
Supported devices
|
||||
================================================================================
|
||||
|
||||
**Officially Supported**: AMD Instinct™ MI300X, MI325X, MI210
|
||||
|
||||
|
||||
Use cases and recommendations
|
||||
================================================================================
|
||||
|
||||
llama.cpp can be applied in a variety of scenarios, particularly when you need to meet one or more of the following requirements:
|
||||
|
||||
- Plain C/C++ implementation with no external dependencies
|
||||
- Support for 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory usage
|
||||
- Custom HIP (Heterogeneous-compute Interface for Portability) kernels for running large language models (LLMs) on AMD GPUs (graphics processing units)
|
||||
- CPU (central processing unit) + GPU (graphics processing unit) hybrid inference for partially accelerating models larger than the total available VRAM (video random-access memory)
|
||||
|
||||
llama.cpp is also used in a range of real-world applications, including:
|
||||
|
||||
- Games such as `Lucy's Labyrinth <https://github.com/MorganRO8/Lucys_Labyrinth>`__:
|
||||
A simple maze game where AI-controlled agents attempt to trick the player.
|
||||
- Tools such as `Styled Lines <https://marketplace.unity.com/packages/tools/ai-ml-integration/style-text-webgl-ios-stand-alone-llm-llama-cpp-wrapper-292902>`__:
|
||||
A proprietary, asynchronous inference wrapper for Unity3D game development, including pre-built mobile and web platform wrappers and a model example.
|
||||
- Various other AI applications use llama.cpp as their inference engine;
|
||||
for a detailed list, see the `user interfaces (UIs) section <https://github.com/ggml-org/llama.cpp?tab=readme-ov-file#description>`__.
|
||||
|
||||
For more use cases and recommendations, refer to the `AMD ROCm blog <https://rocm.blogs.amd.com/>`__,
|
||||
where you can search for llama.cpp examples and best practices to optimize your workloads on AMD GPUs.
|
||||
|
||||
- The `Llama.cpp Meets Instinct: A New Era of Open-Source AI Acceleration <https://rocm.blogs.amd.com/ecosystems-and-partners/llama-cpp/README.html>`__
|
||||
blog post outlines how the open-source llama.cpp framework enables efficient LLM inference—including interactive inference with ``llama-cli``,
|
||||
server deployment with ``llama-server``, GGUF model preparation and quantization, performance benchmarking, and optimizations tailored for
|
||||
AMD Instinct GPUs within the ROCm ecosystem.
|
||||
|
||||
.. _llama-cpp-docker-compat:
|
||||
|
||||
Docker image compatibility
|
||||
================================================================================
|
||||
|
||||
.. |docker-icon| raw:: html
|
||||
|
||||
<i class="fab fa-docker"></i>
|
||||
|
||||
AMD validates and publishes `ROCm llama.cpp Docker images <https://hub.docker.com/r/rocm/llama.cpp/tags>`__
|
||||
with ROCm backends on Docker Hub. The following Docker image tags and associated
|
||||
inventories represent the available llama.cpp versions from the official Docker Hub.
|
||||
Click |docker-icon| to view the image on Docker Hub.
|
||||
|
||||
.. important::
|
||||
|
||||
Tag endings of ``_full``, ``_server``, and ``_light`` serve different purposes for entrypoints as follows:
|
||||
|
||||
- Full: This image includes both the main executable file and the tools to convert ``LLaMA`` models into ``ggml`` and convert into 4-bit quantization.
|
||||
- Server: This image only includes the server executable file.
|
||||
- Light: This image only includes the main executable file.
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
:class: docker-image-compatibility
|
||||
|
||||
* - Full Docker
|
||||
- Server Docker
|
||||
- Light Docker
|
||||
- llama.cpp
|
||||
- ROCm
|
||||
- Ubuntu
|
||||
|
||||
* - .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm7.0.0_ubuntu24.04_full/images/sha256-a2ecd635eaa65bb289a9041330128677f3ae88bee6fee0597424b17e38d4903c"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
|
||||
- .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm7.0.0_ubuntu24.04_server/images/sha256-cb46b47df415addb5ceb6e6fdf0be70bf9d7f6863bbe6e10c2441ecb84246d52"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
|
||||
- .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm7.0.0_ubuntu24.04_light/images/sha256-8f8536eec4b05c0ff1c022f9fc6c527ad1c89e6c1ca0906e4d39e4de73edbde9"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
|
||||
- `b6356 <https://github.com/ROCm/llama.cpp/tree/release/b6356>`__
|
||||
- `7.0.0 <https://repo.radeon.com/rocm/apt/7.0/>`__
|
||||
- 24.04
|
||||
|
||||
* - .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm7.0.0_ubuntu22.04_full/images/sha256-f36de2a3b03ae53e81c85422cb3780368c9891e1ac7884b04403a921fe2ea45d"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
|
||||
- .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm7.0.0_ubuntu22.04_server/images/sha256-df15e8ab11a6837cd3736644fec1e047465d49e37d610ab0b79df000371327df"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
|
||||
- .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm7.0.0_ubuntu22.04_light/images/sha256-4ea2d5bb7964f0ee3ea9b30ba7f343edd6ddfab1b1037669ca7eafad2e3c2bd7"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
|
||||
- `b6356 <https://github.com/ROCm/llama.cpp/tree/release/b6356>`__
|
||||
- `7.0.0 <https://repo.radeon.com/rocm/apt/7.0/>`__
|
||||
- 22.04
|
||||
|
||||
* - .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.3_ubuntu24.04_full/images/sha256-5960fc850024a8a76451f9eaadd89b7e59981ae9f393b407310c1ddf18892577"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
|
||||
- .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.3_ubuntu24.04_server/images/sha256-1b79775d9f546065a6aaf9ca426e1dd4ed4de0b8f6ee83687758cc05af6538e6"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
|
||||
- .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.3_ubuntu24.04_light/images/sha256-8f863c4c2857ae42bebd64e4f1a0a1e7cc3ec4503f243e32b4a4dcad070ec361"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
|
||||
- `b6356 <https://github.com/ROCm/llama.cpp/tree/release/b6356>`__
|
||||
- `6.4.3 <https://repo.radeon.com/rocm/apt/6.4.3/>`__
|
||||
- 24.04
|
||||
|
||||
* - .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.3_ubuntu22.04_full/images/sha256-888879b3ee208f9247076d7984524b8d1701ac72611689e89854a1588bec9867"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
|
||||
- .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.3_ubuntu22.04_server/images/sha256-90e4ff99a66743e33fd00728cd71a768588e5f5ef355aaa196669fe65ac70672"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
|
||||
- .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.3_ubuntu22.04_light/images/sha256-bd447a049939cb99054f8fbf3f2352870fe906a75e2dc3339c845c08b9c53f9b"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
|
||||
- `b6356 <https://github.com/ROCm/llama.cpp/tree/release/b6356>`__
|
||||
- `6.4.3 <https://repo.radeon.com/rocm/apt/6.4.3/>`__
|
||||
- 22.04
|
||||
|
||||
|
||||
* - .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.2_ubuntu24.04_full/images/sha256-5b3a1bc4889c1fcade434b937fbf9cc1c22ff7dc0317c130339b0c9238bc88c4"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
|
||||
- .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.2_ubuntu24.04_server/images/sha256-5228ff99d0f627a9032d668f4381b2e80dc1e301adc3e0821f26d8354b175271"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
|
||||
- .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.2_ubuntu24.04_light/images/sha256-b12723b332a826a89b7252dddf868cbe4d1a869562fc4aa4032f59e1a683b968"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
|
||||
- `b6356 <https://github.com/ROCm/llama.cpp/tree/release/b6356>`__
|
||||
- `6.4.2 <https://repo.radeon.com/rocm/apt/6.4.2/>`__
|
||||
- 24.04
|
||||
|
||||
* - .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.2_ubuntu22.04_full/images/sha256-cd6e21a6a73f59b35dd5309b09dd77654a94d783bf13a55c14eb8dbf8e9c2615"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
|
||||
- .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.2_ubuntu22.04_server/images/sha256-c2b4689ab2c47e6626e8fea22d7a63eb03d47c0fde9f5ef8c9f158d15c423e58"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
|
||||
- .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.2_ubuntu22.04_light/images/sha256-1acc28f29ed87db9cbda629cb29e1989b8219884afe05f9105522be929e94da4"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
|
||||
- `b6356 <https://github.com/ROCm/llama.cpp/tree/release/b6356>`__
|
||||
- `6.4.2 <https://repo.radeon.com/rocm/apt/6.4.2/>`__
|
||||
- 22.04
|
||||
|
||||
|
||||
* - .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.1_ubuntu24.04_full/images/sha256-2f8ae8a44510d96d52dea6cb398b224f7edeb7802df7ec488c6f63d206b3cdc9"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
|
||||
- .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.1_ubuntu24.04_server/images/sha256-fece497ff9f4a28b12f645de52766941da8ead8471aa1ea84b61d4b4568e51f2"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
|
||||
- .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.1_ubuntu24.04_light/images/sha256-3e14352fa6f8c6128b23cf9342531c20dbfb522550b626e09d83b260a1947022"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
|
||||
- `b6356 <https://github.com/ROCm/llama.cpp/tree/release/b6356>`__
|
||||
- `6.4.1 <https://repo.radeon.com/rocm/apt/6.4.1/>`__
|
||||
- 24.04
|
||||
|
||||
* - .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.1_ubuntu22.04_full/images/sha256-80763062ef0bec15038c35fd01267f1fc99a5dd171d4b48583cc668b15efad69"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
|
||||
- .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.1_ubuntu22.04_server/images/sha256-db2a6c957555ed83b819bbc54aea884a93192da0fb512dae63d32e0dc4e8ab8f"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
|
||||
- .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.1_ubuntu22.04_light/images/sha256-c6dbb07cc655fb079d5216e4b77451cb64a9daa0585d23b6fb8b32cb22021197"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
|
||||
- `b6356 <https://github.com/ROCm/llama.cpp/tree/release/b6356>`__
|
||||
- `6.4.1 <https://repo.radeon.com/rocm/apt/6.4.1/>`__
|
||||
- 22.04
|
||||
|
||||
* - .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b5997_rocm6.4.0_ubuntu24.04_full/images/sha256-f78f6c81ab2f8e957469415fe2370a1334fe969c381d1fe46050c85effaee9d5"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
|
||||
- .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b5997_rocm6.4.0_ubuntu24.04_server/images/sha256-275ad9e18f292c26a00a2de840c37917e98737a88a3520bdc35fd3fc5c9a6a9b"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
|
||||
- .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b5997_rocm6.4.0_ubuntu24.04_light/images/sha256-cc324e6faeedf0e400011f07b49d2dc41a16bae257b2b7befa0f4e2e97231320"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
|
||||
- `b5997 <https://github.com/ROCm/llama.cpp/tree/release/b5997>`__
|
||||
- `6.4.0 <https://repo.radeon.com/rocm/apt/6.4/>`__
|
||||
- 24.04
|
||||
|
||||
|
||||
Key ROCm libraries for llama.cpp
|
||||
================================================================================
|
||||
|
||||
llama.cpp functionality on ROCm is determined by its underlying library
|
||||
dependencies. These ROCm components affect the capabilities, performance, and
|
||||
feature set available to developers. Ensure you have the required libraries for
|
||||
your corresponding ROCm version.
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - ROCm library
|
||||
- ROCm 7.0.0 version
|
||||
- ROCm 6.4.x version
|
||||
- Purpose
|
||||
- Usage
|
||||
* - `hipBLAS <https://github.com/ROCm/hipBLAS>`__
|
||||
- 3.0.0
|
||||
- 2.4.0
|
||||
- Provides GPU-accelerated Basic Linear Algebra Subprograms (BLAS) for
|
||||
matrix and vector operations.
|
||||
- Supports operations such as matrix multiplication, matrix-vector
|
||||
products, and tensor contractions. Utilized in both dense and batched
|
||||
linear algebra operations.
|
||||
* - `hipBLASLt <https://github.com/ROCm/hipBLASLt>`__
|
||||
- 1.0.0
|
||||
- 0.12.0
|
||||
- hipBLASLt is an extension of the hipBLAS library, providing additional
|
||||
features like epilogues fused into the matrix multiplication kernel or
|
||||
use of integer tensor cores.
|
||||
- By setting the flag ``ROCBLAS_USE_HIPBLASLT``, you can dispatch hipblasLt
|
||||
kernels where possible.
|
||||
* - `rocWMMA <https://github.com/ROCm/rocWMMA>`__
|
||||
- 2.0.0
|
||||
- 1.7.0
|
||||
- Accelerates warp-level matrix-multiply and matrix-accumulate to speed up matrix
|
||||
multiplication (GEMM) and accumulation operations with mixed precision
|
||||
support.
|
||||
- Can be used to enhance the flash attention performance on AMD compute, by enabling
|
||||
the flag during compile time.
|
||||
|
||||
Previous versions
|
||||
===============================================================================
|
||||
See :doc:`rocm-install-on-linux:install/3rd-party/previous-versions/llama-cpp-history` to find documentation for previous releases
|
||||
of the ``ROCm/llama.cpp`` Docker image.
|
||||
@@ -1,93 +0,0 @@
|
||||
:orphan:
|
||||
|
||||
.. meta::
|
||||
:description: Megablocks compatibility
|
||||
:keywords: GPU, megablocks, compatibility
|
||||
|
||||
.. version-set:: rocm_version latest
|
||||
|
||||
********************************************************************************
|
||||
Megablocks compatibility
|
||||
********************************************************************************
|
||||
|
||||
Megablocks is a light-weight library for mixture-of-experts (MoE) training.
|
||||
The core of the system is efficient "dropless-MoE" and standard MoE layers.
|
||||
Megablocks is integrated with `https://github.com/stanford-futuredata/Megatron-LM <https://github.com/stanford-futuredata/Megatron-LM>`_,
|
||||
where data and pipeline parallel training of MoEs is supported.
|
||||
|
||||
* ROCm support for Megablocks is hosted in the official `https://github.com/ROCm/megablocks <https://github.com/ROCm/megablocks>`_ repository.
|
||||
* Due to independent compatibility considerations, this location differs from the `https://github.com/stanford-futuredata/Megatron-LM <https://github.com/stanford-futuredata/Megatron-LM>`_ upstream repository.
|
||||
* Use the prebuilt :ref:`Docker image <megablocks-docker-compat>` with ROCm, PyTorch, and Megablocks preinstalled.
|
||||
* See the :doc:`ROCm Megablocks installation guide <rocm-install-on-linux:install/3rd-party/megablocks-install>` to install and get started.
|
||||
|
||||
.. note::
|
||||
|
||||
Megablocks is supported on ROCm 6.3.0.
|
||||
|
||||
Supported devices
|
||||
================================================================================
|
||||
|
||||
- **Officially Supported**: AMD Instinct MI300X
|
||||
- **Partially Supported** (functionality or performance limitations): AMD Instinct MI250X, MI210
|
||||
|
||||
Supported models and features
|
||||
================================================================================
|
||||
|
||||
This section summarizes the Megablocks features supported by ROCm.
|
||||
|
||||
* Distributed Pre-training
|
||||
* Activation Checkpointing and Recomputation
|
||||
* Distributed Optimizer
|
||||
* Mixture-of-Experts
|
||||
* dropless-Mixture-of-Experts
|
||||
|
||||
|
||||
.. _megablocks-recommendations:
|
||||
|
||||
Use cases and recommendations
|
||||
================================================================================
|
||||
|
||||
The `ROCm Megablocks blog posts <https://rocm.blogs.amd.com/artificial-intelligence/megablocks/README.html>`_
|
||||
guide how to leverage the ROCm platform for pre-training using the Megablocks framework.
|
||||
It features how to pre-process datasets and how to begin pre-training on AMD GPUs through:
|
||||
|
||||
* Single-GPU pre-training
|
||||
* Multi-GPU pre-training
|
||||
|
||||
|
||||
.. _megablocks-docker-compat:
|
||||
|
||||
Docker image compatibility
|
||||
================================================================================
|
||||
|
||||
.. |docker-icon| raw:: html
|
||||
|
||||
<i class="fab fa-docker"></i>
|
||||
|
||||
AMD validates and publishes `ROCm Megablocks images <https://hub.docker.com/r/rocm/megablocks/tags>`_
|
||||
with ROCm and Pytorch backends on Docker Hub. The following Docker image tags and associated
|
||||
inventories represent the latest Megatron-LM version from the official Docker Hub.
|
||||
The Docker images have been validated for `ROCm 6.3.0 <https://repo.radeon.com/rocm/apt/6.3/>`_.
|
||||
Click |docker-icon| to view the image on Docker Hub.
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
:class: docker-image-compatibility
|
||||
|
||||
* - Docker image
|
||||
- ROCm
|
||||
- Megablocks
|
||||
- PyTorch
|
||||
- Ubuntu
|
||||
- Python
|
||||
|
||||
* - .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/megablocks/megablocks-0.7.0_rocm6.3.0_ubuntu24.04_py3.12_pytorch2.4.0/images/sha256-372ff89b96599019b8f5f9db469c84add2529b713456781fa62eb9a148659ab4"><i class="fab fa-docker fa-lg"></i> rocm/megablocks</a>
|
||||
- `6.3.0 <https://repo.radeon.com/rocm/apt/6.3/>`_
|
||||
- `0.7.0 <https://github.com/databricks/megablocks/releases/tag/v0.7.0>`_
|
||||
- `2.4.0 <https://github.com/ROCm/pytorch/tree/release/2.4>`_
|
||||
- 24.04
|
||||
- `3.12.9 <https://www.python.org/downloads/release/python-3129/>`_
|
||||
|
||||
|
||||
@@ -1,477 +0,0 @@
|
||||
:orphan:
|
||||
|
||||
.. meta::
|
||||
:description: PyTorch compatibility
|
||||
:keywords: GPU, PyTorch compatibility
|
||||
|
||||
.. version-set:: rocm_version latest
|
||||
|
||||
********************************************************************************
|
||||
PyTorch compatibility
|
||||
********************************************************************************
|
||||
|
||||
`PyTorch <https://pytorch.org/>`__ is an open-source tensor library designed for
|
||||
deep learning. PyTorch on ROCm provides mixed-precision and large-scale training
|
||||
using `MIOpen <https://github.com/ROCm/MIOpen>`__ and
|
||||
`RCCL <https://github.com/ROCm/rccl>`__ libraries.
|
||||
|
||||
ROCm support for PyTorch is upstreamed into the official PyTorch repository. Due
|
||||
to independent compatibility considerations, this results in two distinct
|
||||
release cycles for PyTorch on ROCm:
|
||||
|
||||
- ROCm PyTorch release:
|
||||
|
||||
- Provides the latest version of ROCm but might not necessarily support the
|
||||
latest stable PyTorch version.
|
||||
|
||||
- Offers :ref:`Docker images <pytorch-docker-compat>` with ROCm and PyTorch
|
||||
preinstalled.
|
||||
|
||||
- ROCm PyTorch repository: `<https://github.com/ROCm/pytorch>`__
|
||||
|
||||
- See the :doc:`ROCm PyTorch installation guide <rocm-install-on-linux:install/3rd-party/pytorch-install>`
|
||||
to get started.
|
||||
|
||||
- Official PyTorch release:
|
||||
|
||||
- Provides the latest stable version of PyTorch but might not necessarily
|
||||
support the latest ROCm version.
|
||||
|
||||
- Official PyTorch repository: `<https://github.com/pytorch/pytorch>`__
|
||||
|
||||
- See the `Nightly and latest stable version installation guide <https://pytorch.org/get-started/locally/>`__
|
||||
or `Previous versions <https://pytorch.org/get-started/previous-versions/>`__
|
||||
to get started.
|
||||
|
||||
PyTorch includes tooling that generates HIP source code from the CUDA backend.
|
||||
This approach allows PyTorch to support ROCm without requiring manual code
|
||||
modifications. For more information, see :doc:`HIPIFY <hipify:index>`.
|
||||
|
||||
ROCm development is aligned with the stable release of PyTorch, while upstream
|
||||
PyTorch testing uses the stable release of ROCm to maintain consistency.
|
||||
|
||||
.. _pytorch-recommendations:
|
||||
|
||||
Use cases and recommendations
|
||||
================================================================================
|
||||
|
||||
* :doc:`Using ROCm for AI: training a model </how-to/rocm-for-ai/training/benchmark-docker/pytorch-training>`
|
||||
guides how to leverage the ROCm platform for training AI models. It covers the
|
||||
steps, tools, and best practices for optimizing training workflows on AMD GPUs
|
||||
using PyTorch features.
|
||||
|
||||
* :doc:`Single-GPU fine-tuning and inference </how-to/rocm-for-ai/fine-tuning/single-gpu-fine-tuning-and-inference>`
|
||||
describes and demonstrates how to use the ROCm platform for the fine-tuning
|
||||
and inference of machine learning models, particularly large language models
|
||||
(LLMs), on systems with a single GPU. This topic provides a detailed guide for
|
||||
setting up, optimizing, and executing fine-tuning and inference workflows in
|
||||
such environments.
|
||||
|
||||
* :doc:`Multi-GPU fine-tuning and inference optimization </how-to/rocm-for-ai/fine-tuning/multi-gpu-fine-tuning-and-inference>`
|
||||
describes and demonstrates the fine-tuning and inference of machine learning
|
||||
models on systems with multiple GPUs.
|
||||
|
||||
* The :doc:`Instinct MI300X workload optimization guide </how-to/rocm-for-ai/inference-optimization/workload>`
|
||||
provides detailed guidance on optimizing workloads for the AMD Instinct MI300X
|
||||
accelerator using ROCm. This guide helps users achieve optimal performance for
|
||||
deep learning and other high-performance computing tasks on the MI300X
|
||||
accelerator.
|
||||
|
||||
* The :doc:`Inception with PyTorch documentation </conceptual/ai-pytorch-inception>`
|
||||
describes how PyTorch integrates with ROCm for AI workloads It outlines the
|
||||
use of PyTorch on the ROCm platform and focuses on efficiently leveraging AMD
|
||||
GPU hardware for training and inference tasks in AI applications.
|
||||
|
||||
For more use cases and recommendations, see `ROCm PyTorch blog posts <https://rocm.blogs.amd.com/blog/tag/pytorch.html>`__.
|
||||
|
||||
.. _pytorch-docker-compat:
|
||||
|
||||
Docker image compatibility
|
||||
================================================================================
|
||||
|
||||
AMD provides preconfigured Docker images with PyTorch and the ROCm backend.
|
||||
These images are published on `Docker Hub <https://hub.docker.com/r/rocm/pytorch>`__ and are the
|
||||
recommended way to get started with deep learning with PyTorch on ROCm.
|
||||
|
||||
To find the right image tag, see the :ref:`PyTorch on ROCm installation
|
||||
documentation <rocm-install-on-linux:pytorch-docker-support>` for a list of
|
||||
available ``rocm/pytorch`` images.
|
||||
|
||||
Key ROCm libraries for PyTorch
|
||||
================================================================================
|
||||
|
||||
PyTorch functionality on ROCm is determined by its underlying library
|
||||
dependencies. These ROCm components affect the capabilities, performance, and
|
||||
feature set available to developers.
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - ROCm library
|
||||
- Version
|
||||
- Purpose
|
||||
- Used in
|
||||
* - `Composable Kernel <https://github.com/ROCm/composable_kernel>`__
|
||||
- :version-ref:`"Composable Kernel" rocm_version`
|
||||
- Enables faster execution of core operations like matrix multiplication
|
||||
(GEMM), convolutions and transformations.
|
||||
- Speeds up ``torch.permute``, ``torch.view``, ``torch.matmul``,
|
||||
``torch.mm``, ``torch.bmm``, ``torch.nn.Conv2d``, ``torch.nn.Conv3d``
|
||||
and ``torch.nn.MultiheadAttention``.
|
||||
* - `hipBLAS <https://github.com/ROCm/hipBLAS>`__
|
||||
- :version-ref:`hipBLAS rocm_version`
|
||||
- Provides GPU-accelerated Basic Linear Algebra Subprograms (BLAS) for
|
||||
matrix and vector operations.
|
||||
- Supports operations such as matrix multiplication, matrix-vector
|
||||
products, and tensor contractions. Utilized in both dense and batched
|
||||
linear algebra operations.
|
||||
* - `hipBLASLt <https://github.com/ROCm/hipBLASLt>`__
|
||||
- :version-ref:`hipBLASLt rocm_version`
|
||||
- hipBLASLt is an extension of the hipBLAS library, providing additional
|
||||
features like epilogues fused into the matrix multiplication kernel or
|
||||
use of integer tensor cores.
|
||||
- Accelerates operations such as ``torch.matmul``, ``torch.mm``, and the
|
||||
matrix multiplications used in convolutional and linear layers.
|
||||
* - `hipCUB <https://github.com/ROCm/hipCUB>`__
|
||||
- :version-ref:`hipCUB rocm_version`
|
||||
- Provides a C++ template library for parallel algorithms for reduction,
|
||||
scan, sort and select.
|
||||
- Supports operations such as ``torch.sum``, ``torch.cumsum``,
|
||||
``torch.sort`` irregular shapes often involve scanning, sorting, and
|
||||
filtering, which hipCUB handles efficiently.
|
||||
* - `hipFFT <https://github.com/ROCm/hipFFT>`__
|
||||
- :version-ref:`hipFFT rocm_version`
|
||||
- Provides GPU-accelerated Fast Fourier Transform (FFT) operations.
|
||||
- Used in functions like the ``torch.fft`` module.
|
||||
* - `hipRAND <https://github.com/ROCm/hipRAND>`__
|
||||
- :version-ref:`hipRAND rocm_version`
|
||||
- Provides fast random number generation for GPUs.
|
||||
- The ``torch.rand``, ``torch.randn``, and stochastic layers like
|
||||
``torch.nn.Dropout`` rely on hipRAND.
|
||||
* - `hipSOLVER <https://github.com/ROCm/hipSOLVER>`__
|
||||
- :version-ref:`hipSOLVER rocm_version`
|
||||
- Provides GPU-accelerated solvers for linear systems, eigenvalues, and
|
||||
singular value decompositions (SVD).
|
||||
- Supports functions like ``torch.linalg.solve``,
|
||||
``torch.linalg.eig``, and ``torch.linalg.svd``.
|
||||
* - `hipSPARSE <https://github.com/ROCm/hipSPARSE>`__
|
||||
- :version-ref:`hipSPARSE rocm_version`
|
||||
- Accelerates operations on sparse matrices, such as sparse matrix-vector
|
||||
or matrix-matrix products.
|
||||
- Sparse tensor operations ``torch.sparse``.
|
||||
* - `hipSPARSELt <https://github.com/ROCm/hipSPARSELt>`__
|
||||
- :version-ref:`hipSPARSELt rocm_version`
|
||||
- Accelerates operations on sparse matrices, such as sparse matrix-vector
|
||||
or matrix-matrix products.
|
||||
- Sparse tensor operations ``torch.sparse``.
|
||||
* - `hipTensor <https://github.com/ROCm/hipTensor>`__
|
||||
- :version-ref:`hipTensor rocm_version`
|
||||
- Optimizes for high-performance tensor operations, such as contractions.
|
||||
- Accelerates tensor algebra, especially in deep learning and scientific
|
||||
computing.
|
||||
* - `MIOpen <https://github.com/ROCm/MIOpen>`__
|
||||
- :version-ref:`MIOpen rocm_version`
|
||||
- Optimizes deep learning primitives such as convolutions, pooling,
|
||||
normalization, and activation functions.
|
||||
- Speeds up convolutional neural networks (CNNs), recurrent neural
|
||||
networks (RNNs), and other layers. Used in operations like
|
||||
``torch.nn.Conv2d``, ``torch.nn.ReLU``, and ``torch.nn.LSTM``.
|
||||
* - `MIGraphX <https://github.com/ROCm/AMDMIGraphX>`__
|
||||
- :version-ref:`MIGraphX rocm_version`
|
||||
- Adds graph-level optimizations, ONNX models and mixed precision support
|
||||
and enable Ahead-of-Time (AOT) Compilation.
|
||||
- Speeds up inference models and executes ONNX models for
|
||||
compatibility with other frameworks.
|
||||
``torch.nn.Conv2d``, ``torch.nn.ReLU``, and ``torch.nn.LSTM``.
|
||||
* - `MIVisionX <https://github.com/ROCm/MIVisionX>`__
|
||||
- :version-ref:`MIVisionX rocm_version`
|
||||
- Optimizes acceleration for computer vision and AI workloads like
|
||||
preprocessing, augmentation, and inferencing.
|
||||
- Faster data preprocessing and augmentation pipelines for datasets like
|
||||
ImageNet or COCO and easy to integrate into PyTorch's ``torch.utils.data``
|
||||
and ``torchvision`` workflows.
|
||||
* - `rocAL <https://github.com/ROCm/rocAL>`__
|
||||
- :version-ref:`rocAL rocm_version`
|
||||
- Accelerates the data pipeline by offloading intensive preprocessing and
|
||||
augmentation tasks. rocAL is part of MIVisionX.
|
||||
- Easy to integrate into PyTorch's ``torch.utils.data`` and
|
||||
``torchvision`` data load workloads.
|
||||
* - `RCCL <https://github.com/ROCm/rccl>`__
|
||||
- :version-ref:`RCCL rocm_version`
|
||||
- Optimizes for multi-GPU communication for operations like AllReduce and
|
||||
Broadcast.
|
||||
- Distributed data parallel training (``torch.nn.parallel.DistributedDataParallel``).
|
||||
Handles communication in multi-GPU setups.
|
||||
* - `rocDecode <https://github.com/ROCm/rocDecode>`__
|
||||
- :version-ref:`rocDecode rocm_version`
|
||||
- Provides hardware-accelerated data decoding capabilities, particularly
|
||||
for image, video, and other dataset formats.
|
||||
- Can be integrated in ``torch.utils.data``, ``torchvision.transforms``
|
||||
and ``torch.distributed``.
|
||||
* - `rocJPEG <https://github.com/ROCm/rocJPEG>`__
|
||||
- :version-ref:`rocJPEG rocm_version`
|
||||
- Provides hardware-accelerated JPEG image decoding and encoding.
|
||||
- GPU accelerated ``torchvision.io.decode_jpeg`` and
|
||||
``torchvision.io.encode_jpeg`` and can be integrated in
|
||||
``torch.utils.data`` and ``torchvision``.
|
||||
* - `RPP <https://github.com/ROCm/RPP>`__
|
||||
- :version-ref:`RPP rocm_version`
|
||||
- Speeds up data augmentation, transformation, and other preprocessing steps.
|
||||
- Easy to integrate into PyTorch's ``torch.utils.data`` and
|
||||
``torchvision`` data load workloads to speed up data processing.
|
||||
* - `rocThrust <https://github.com/ROCm/rocThrust>`__
|
||||
- :version-ref:`rocThrust rocm_version`
|
||||
- Provides a C++ template library for parallel algorithms like sorting,
|
||||
reduction, and scanning.
|
||||
- Utilized in backend operations for tensor computations requiring
|
||||
parallel processing.
|
||||
* - `rocWMMA <https://github.com/ROCm/rocWMMA>`__
|
||||
- :version-ref:`rocWMMA rocm_version`
|
||||
- Accelerates warp-level matrix-multiply and matrix-accumulate to speed up matrix
|
||||
multiplication (GEMM) and accumulation operations with mixed precision
|
||||
support.
|
||||
- Linear layers (``torch.nn.Linear``), convolutional layers
|
||||
(``torch.nn.Conv2d``), attention layers, general tensor operations that
|
||||
involve matrix products, such as ``torch.matmul``, ``torch.bmm``, and
|
||||
more.
|
||||
|
||||
Supported modules and data types
|
||||
================================================================================
|
||||
|
||||
The following section outlines the supported data types, modules, and domain
|
||||
libraries available in PyTorch on ROCm.
|
||||
|
||||
Supported data types
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
The tensor data type is specified using the ``dtype`` attribute or argument.
|
||||
PyTorch supports many data types for different use cases.
|
||||
|
||||
The following table lists `torch.Tensor <https://pytorch.org/docs/stable/tensors.html>`__
|
||||
single data types:
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Data type
|
||||
- Description
|
||||
* - ``torch.float8_e4m3fn``
|
||||
- 8-bit floating point, e4m3
|
||||
* - ``torch.float8_e5m2``
|
||||
- 8-bit floating point, e5m2
|
||||
* - ``torch.float16`` or ``torch.half``
|
||||
- 16-bit floating point
|
||||
* - ``torch.bfloat16``
|
||||
- 16-bit floating point
|
||||
* - ``torch.float32`` or ``torch.float``
|
||||
- 32-bit floating point
|
||||
* - ``torch.float64`` or ``torch.double``
|
||||
- 64-bit floating point
|
||||
* - ``torch.complex32`` or ``torch.chalf``
|
||||
- 32-bit complex numbers
|
||||
* - ``torch.complex64`` or ``torch.cfloat``
|
||||
- 64-bit complex numbers
|
||||
* - ``torch.complex128`` or ``torch.cdouble``
|
||||
- 128-bit complex numbers
|
||||
* - ``torch.uint8``
|
||||
- 8-bit integer (unsigned)
|
||||
* - ``torch.uint16``
|
||||
- 16-bit integer (unsigned);
|
||||
Not natively supported in ROCm
|
||||
* - ``torch.uint32``
|
||||
- 32-bit integer (unsigned);
|
||||
Not natively supported in ROCm
|
||||
* - ``torch.uint64``
|
||||
- 64-bit integer (unsigned);
|
||||
Not natively supported in ROCm
|
||||
* - ``torch.int8``
|
||||
- 8-bit integer (signed)
|
||||
* - ``torch.int16`` or ``torch.short``
|
||||
- 16-bit integer (signed)
|
||||
* - ``torch.int32`` or ``torch.int``
|
||||
- 32-bit integer (signed)
|
||||
* - ``torch.int64`` or ``torch.long``
|
||||
- 64-bit integer (signed)
|
||||
* - ``torch.bool``
|
||||
- Boolean
|
||||
* - ``torch.quint8``
|
||||
- Quantized 8-bit integer (unsigned)
|
||||
* - ``torch.qint8``
|
||||
- Quantized 8-bit integer (signed)
|
||||
* - ``torch.qint32``
|
||||
- Quantized 32-bit integer (signed)
|
||||
* - ``torch.quint4x2``
|
||||
- Quantized 4-bit integer (unsigned)
|
||||
|
||||
.. note::
|
||||
|
||||
Unsigned types, except ``uint8``, have limited support in eager mode. They
|
||||
primarily exist to assist usage with ``torch.compile``.
|
||||
|
||||
See :doc:`ROCm precision support <rocm:reference/precision-support>` for the
|
||||
native hardware support of data types.
|
||||
|
||||
Supported modules
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
For a complete and up-to-date list of PyTorch core modules (for example., ``torch``,
|
||||
``torch.nn``, ``torch.cuda``, ``torch.backends.cuda`` and
|
||||
``torch.backends.cudnn``), their descriptions, and usage, please refer directly
|
||||
to the `official PyTorch documentation <https://pytorch.org/docs/stable/index.html>`_.
|
||||
|
||||
Core PyTorch functionality on ROCm includes tensor operations, neural network
|
||||
layers, automatic differentiation, distributed training, mixed-precision
|
||||
training, compilation features, and domain-specific libraries for audio, vision,
|
||||
text processing, and more.
|
||||
|
||||
Supported domain libraries
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
PyTorch offers specialized `domain libraries <https://pytorch.org/domains/>`_ with
|
||||
GPU acceleration that build on its core features to support specific application
|
||||
areas. The table below lists the PyTorch domain libraries that are compatible
|
||||
with ROCm.
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Library
|
||||
- Description
|
||||
|
||||
* - `torchaudio <https://docs.pytorch.org/audio/stable/index.html>`_
|
||||
- Audio and signal processing library for PyTorch. Provides utilities for
|
||||
audio I/O, signal and data processing functions, datasets, model
|
||||
implementations, and application components for audio and speech
|
||||
processing tasks.
|
||||
|
||||
**Note:** To ensure GPU-acceleration with ``torchaudio.transforms``,
|
||||
you need to explicitly move audio data (waveform tensor) to GPU using
|
||||
``.to('cuda')``.
|
||||
|
||||
* - `torchtune <https://docs.pytorch.org/torchtune/stable/index.html>`_
|
||||
- PyTorch-native library designed for fine-tuning large language models
|
||||
(LLMs). Provides supports the full fine-tuning workflow and offers
|
||||
compatibility with popular production inference systems.
|
||||
|
||||
**Note:** Only official release exists.
|
||||
|
||||
* - `torchvision <https://docs.pytorch.org/vision/stable/index.html>`_
|
||||
- Computer vision library that is part of the PyTorch project. Provides
|
||||
popular datasets, model architectures, and common image transformations
|
||||
for computer vision applications.
|
||||
|
||||
* - `torchtext <https://docs.pytorch.org/text/stable/index.html>`_
|
||||
- Text processing library for PyTorch. Provides data processing utilities
|
||||
and popular datasets for natural language processing, including
|
||||
tokenization, vocabulary management, and text embeddings.
|
||||
|
||||
**Note:** ``torchtext`` does not implement ROCm-specific kernels.
|
||||
ROCm acceleration is provided through the underlying PyTorch framework
|
||||
and ROCm library integration. Only official release exists.
|
||||
|
||||
* - `torchdata <https://meta-pytorch.org/data/beta/index.html#torchdata>`_
|
||||
- Beta library of common modular data loading primitives for easily
|
||||
constructing flexible and performant data pipelines, with features still
|
||||
in prototype stage.
|
||||
|
||||
* - `torchrec <https://docs.pytorch.org/torchrec/>`_
|
||||
- PyTorch domain library for common sparsity and parallelism primitives
|
||||
needed for large-scale recommender systems, enabling authors to train
|
||||
models with large embedding tables shared across many GPUs.
|
||||
|
||||
**Note:** ``torchrec`` does not implement ROCm-specific kernels. ROCm
|
||||
acceleration is provided through the underlying PyTorch framework and
|
||||
ROCm library integration.
|
||||
|
||||
* - `torchserve <https://docs.pytorch.org/serve/>`_
|
||||
- Performant, flexible and easy-to-use tool for serving PyTorch models in
|
||||
production, providing features for model management, batch processing,
|
||||
and scalable deployment.
|
||||
|
||||
**Note:** `torchserve <https://docs.pytorch.org/serve/>`_ is no longer
|
||||
actively maintained. Last official release is sent out with PyTorch 2.4.
|
||||
|
||||
* - `torchrl <https://docs.pytorch.org/rl/stable/index.html>`_
|
||||
- Open-source, Python-first Reinforcement Learning library for PyTorch
|
||||
with a focus on high modularity and good runtime performance, providing
|
||||
low and high-level RL abstractions and reusable functionals for cost
|
||||
functions, returns, and data processing.
|
||||
|
||||
**Note:** Only official release exists.
|
||||
|
||||
* - `tensordict <https://docs.pytorch.org/tensordict/stable/index.html>`_
|
||||
- Dictionary-like class that simplifies operations on batches of tensors,
|
||||
enhancing code readability, compactness, and modularity by abstracting
|
||||
tailored operations and reducing errors through automatic operation
|
||||
dispatching.
|
||||
|
||||
**Note:** Only official release exists.
|
||||
|
||||
Key features and enhancements for PyTorch 2.7 with ROCm 7.0
|
||||
================================================================================
|
||||
|
||||
- Enhanced TunableOp framework: Introduces ``tensorfloat32`` support for
|
||||
TunableOp operations, improved offline tuning for ScaledGEMM operations,
|
||||
submatrix offline tuning capabilities, and better logging for BLAS operations
|
||||
without bias vectors.
|
||||
|
||||
- Expanded GPU architecture support: Provides optimized support for newer GPU
|
||||
architectures, including gfx1200 and gfx1201 with preferred hipBLASLt backend
|
||||
selection, along with improvements for gfx950 and gfx1100 series GPUs.
|
||||
|
||||
- Advanced Triton Integration: AOTriton 0.10b introduces official support for
|
||||
gfx950 and gfx1201, along with experimental support for gfx1101, gfx1151,
|
||||
gfx1150, and gfx1200.
|
||||
|
||||
- Improved element-wise kernel performance: Delivers enhanced vectorized
|
||||
element-wise kernels with better support for heterogeneous tensor types and
|
||||
optimized input vectorization for tensors with mixed data types.
|
||||
|
||||
- MIOpen deep learning optimizations: Enables NHWC BatchNorm by default on
|
||||
ROCm 7.0+, provides ``maxpool`` forward and backward performance improvements
|
||||
targeting ResNet scenarios, and includes updated launch configurations for
|
||||
better performance.
|
||||
|
||||
- Enhanced memory and tensor operations: Features fixes for in-place ``aten``
|
||||
sum operations with specialized templated kernels, improved 3D tensor
|
||||
performance with NHWC format, and better handling of memory-bound matrix
|
||||
multiplication operations.
|
||||
|
||||
- Robust testing and quality improvements: Includes comprehensive test suite
|
||||
updates with improved tolerance handling for Navi3x architectures, generalized
|
||||
ROCm-specific test conditions, and enhanced unit test coverage for Flash
|
||||
Attention and Memory Efficient operations.
|
||||
|
||||
- Build system and infrastructure improvements: Provides updated CentOS Stream 9
|
||||
support, improved Docker configuration, migration to public MAGMA repository,
|
||||
and enhanced QA automation scripts for PyTorch unit testing.
|
||||
|
||||
- Composable Kernel (CK) updates: Features updated CK submodule integration with
|
||||
the latest optimizations and performance improvements for core mathematical
|
||||
operations.
|
||||
|
||||
- Development and debugging enhancements: Includes improved source handling for
|
||||
dynamic compilation, better error handling for atomic operations, and enhanced
|
||||
state checking for trace operations.
|
||||
|
||||
- Integrate APEX fused layer normalization, which can have positive impact on
|
||||
text-to-video models.
|
||||
|
||||
- Integrate APEX distributed fused LAMB and distributed fused ADAM, which can
|
||||
have positive impact on BERT-L and Llama2-SFT.
|
||||
|
||||
- FlashAttention v3 has been integrated for AMD GPUs.
|
||||
|
||||
- `Pytorch C++ extensions <https://pytorch.org/tutorials/advanced/cpp_extension.html>`_
|
||||
provide a mechanism for compiling custom operations that can be used during
|
||||
network training or inference. For AMD platforms, ``amdclang++`` has been
|
||||
validated as the supported compiler for building these extensions.
|
||||
|
||||
Known issues and notes for PyTorch 2.7 with ROCm 7.0
|
||||
================================================================================
|
||||
|
||||
- The ``matmul.allow_fp16_reduced_precision_reduction`` and
|
||||
``matmul.allow_bf16_reduced_precision_reduction`` options under
|
||||
``torch.backends.cuda`` are not supported. As a result,
|
||||
reduced-precision reductions using FP16 or BF16 accumulation types are not
|
||||
available.
|
||||
@@ -1,111 +0,0 @@
|
||||
:orphan:
|
||||
|
||||
.. meta::
|
||||
:description: Ray deep learning framework compatibility
|
||||
:keywords: GPU, Ray compatibility
|
||||
|
||||
.. version-set:: rocm_version latest
|
||||
|
||||
*******************************************************************************
|
||||
Ray compatibility
|
||||
*******************************************************************************
|
||||
|
||||
Ray is a unified framework for scaling AI and Python applications from your laptop
|
||||
to a full cluster, without changing your code. Ray consists of `a core distributed
|
||||
runtime <https://docs.ray.io/en/latest/ray-core/walkthrough.html>`_ and a set of
|
||||
`AI libraries <https://docs.ray.io/en/latest/ray-air/getting-started.html>`_ for
|
||||
simplifying machine learning computations.
|
||||
|
||||
Ray is a general-purpose framework that runs many types of workloads efficiently.
|
||||
Any Python application can be scaled with Ray, without extra infrastructure.
|
||||
|
||||
ROCm support for Ray is upstreamed, and you can build the official source code
|
||||
with ROCm support:
|
||||
|
||||
- ROCm support for Ray is hosted in the official `https://github.com/ROCm/ray
|
||||
<https://github.com/ROCm/ray>`_ repository.
|
||||
|
||||
- Due to independent compatibility considerations, this location differs from the
|
||||
`https://github.com/ray-project/ray <https://github.com/ray-project/ray>`_ upstream repository.
|
||||
|
||||
- To install Ray, use the prebuilt :ref:`Docker image <ray-docker-compat>`
|
||||
which includes ROCm, Ray, and all required dependencies.
|
||||
|
||||
- See the :doc:`ROCm Ray installation guide <rocm-install-on-linux:install/3rd-party/ray-install>`
|
||||
for instructions to get started.
|
||||
|
||||
- See the `Installation section <https://docs.ray.io/en/latest/ray-overview/installation.html>`_
|
||||
in the upstream Ray documentation.
|
||||
|
||||
- The Docker image provided is based on the upstream Ray `Daily Release (Nightly) wheels <https://docs.ray.io/en/latest/ray-overview/installation.html#daily-releases-nightlies>`__
|
||||
corresponding to commit `005c372 <https://github.com/ray-project/ray/commit/005c372262e050d5745f475e22e64305fa07f8b8>`__.
|
||||
|
||||
.. note::
|
||||
|
||||
Ray is supported on ROCm 6.4.1.
|
||||
|
||||
Supported devices
|
||||
================================================================================
|
||||
|
||||
**Officially Supported**: AMD Instinct™ MI300X, MI210
|
||||
|
||||
|
||||
Use cases and recommendations
|
||||
================================================================================
|
||||
|
||||
* The `Reinforcement Learning from Human Feedback on AMD GPUs with verl and ROCm
|
||||
Integration <https://rocm.blogs.amd.com/artificial-intelligence/verl-large-scale/README.html>`__
|
||||
blog provides an overview of Volcano Engine Reinforcement Learning (verl)
|
||||
for large language models (LLMs) and discusses its benefits in large-scale
|
||||
reinforcement learning from human feedback (RLHF). It uses Ray as part of a
|
||||
hybrid orchestration engine to schedule and coordinate training and inference
|
||||
tasks in parallel, enabling optimized resource utilization and potential overlap
|
||||
between these phases. This dynamic resource allocation strategy significantly
|
||||
improves overall system efficiency. The blog presents verl’s performance results,
|
||||
focusing on throughput and convergence accuracy achieved on AMD Instinct™ MI300X
|
||||
GPUs. Follow this guide to get started with verl on AMD Instinct GPUs and
|
||||
accelerate your RLHF training with ROCm-optimized performance.
|
||||
|
||||
* The `Exploring Use Cases for Scalable AI: Implementing Ray with ROCm Support for Efficient ML Workflows
|
||||
<https://rocm.blogs.amd.com/artificial-intelligence/rocm-ray/README.html>`__
|
||||
blog post describes key use cases such as training and inference for large language models (LLMs),
|
||||
model serving, hyperparameter tuning, reinforcement learning, and the orchestration of large-scale
|
||||
workloads using Ray in the ROCm environment.
|
||||
|
||||
For more use cases and recommendations, see the AMD GPU tabs in the `Accelerator Support
|
||||
topic <https://docs.ray.io/en/latest/ray-core/scheduling/accelerators.html#accelerator-support>`__
|
||||
of the Ray core documentation and refer to the `AMD ROCm blog <https://rocm.blogs.amd.com/>`__,
|
||||
where you can search for Ray examples and best practices to optimize your workloads on AMD GPUs.
|
||||
|
||||
.. _ray-docker-compat:
|
||||
|
||||
Docker image compatibility
|
||||
================================================================================
|
||||
|
||||
.. |docker-icon| raw:: html
|
||||
|
||||
<i class="fab fa-docker"></i>
|
||||
|
||||
AMD validates and publishes ready-made `ROCm Ray Docker images <https://hub.docker.com/r/rocm/ray/tags>`__
|
||||
with ROCm backends on Docker Hub. The following Docker image tags and
|
||||
associated inventories represent the latest Ray version from the official Docker Hub and are validated for
|
||||
`ROCm 6.4.1 <https://repo.radeon.com/rocm/apt/6.4.1/>`_. Click the |docker-icon|
|
||||
icon to view the image on Docker Hub.
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
:class: docker-image-compatibility
|
||||
|
||||
* - Docker image
|
||||
- Ray
|
||||
- Pytorch
|
||||
- Ubuntu
|
||||
- Python
|
||||
|
||||
* - .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/ray/ray-2.48.0.post0_rocm6.4.1_ubuntu24.04_py3.12_pytorch2.6.0/images/sha256-0d166fe6bdced38338c78eedfb96eff92655fb797da3478a62dd636365133cc0"><i class="fab fa-docker fa-lg"></i> rocm/ray</a>
|
||||
- `2.48.0.post0 <https://github.com/ROCm/ray/tree/release/2.48.0.post0>`_
|
||||
- 2.6.0+git684f6f2
|
||||
- 24.04
|
||||
- `3.12.10 <https://www.python.org/downloads/release/python-31210/>`_
|
||||
@@ -1,100 +0,0 @@
|
||||
:orphan:
|
||||
|
||||
.. meta::
|
||||
:description: Stanford Megatron-LM compatibility
|
||||
:keywords: Stanford, Megatron-LM, compatibility
|
||||
|
||||
.. version-set:: rocm_version latest
|
||||
|
||||
********************************************************************************
|
||||
Stanford Megatron-LM compatibility
|
||||
********************************************************************************
|
||||
|
||||
Stanford Megatron-LM is a large-scale language model training framework developed by NVIDIA `https://github.com/NVIDIA/Megatron-LM <https://github.com/NVIDIA/Megatron-LM>`_. It is
|
||||
designed to train massive transformer-based language models efficiently by model and data parallelism.
|
||||
|
||||
* ROCm support for Stanford Megatron-LM is hosted in the official `https://github.com/ROCm/Stanford-Megatron-LM <https://github.com/ROCm/Stanford-Megatron-LM>`_ repository.
|
||||
* Due to independent compatibility considerations, this location differs from the `https://github.com/stanford-futuredata/Megatron-LM <https://github.com/stanford-futuredata/Megatron-LM>`_ upstream repository.
|
||||
* Use the prebuilt :ref:`Docker image <megatron-lm-docker-compat>` with ROCm, PyTorch, and Megatron-LM preinstalled.
|
||||
* See the :doc:`ROCm Stanford Megatron-LM installation guide <rocm-install-on-linux:install/3rd-party/stanford-megatron-lm-install>` to install and get started.
|
||||
|
||||
.. note::
|
||||
|
||||
Stanford Megatron-LM is supported on ROCm 6.3.0.
|
||||
|
||||
|
||||
Supported Devices
|
||||
================================================================================
|
||||
|
||||
- **Officially Supported**: AMD Instinct MI300X
|
||||
- **Partially Supported** (functionality or performance limitations): AMD Instinct MI250X, MI210
|
||||
|
||||
|
||||
Supported models and features
|
||||
================================================================================
|
||||
|
||||
This section details models & features that are supported by the ROCm version on Stanford Megatron-LM.
|
||||
|
||||
Models:
|
||||
|
||||
* Bert
|
||||
* GPT
|
||||
* T5
|
||||
* ICT
|
||||
|
||||
Features:
|
||||
|
||||
* Distributed Pre-training
|
||||
* Activation Checkpointing and Recomputation
|
||||
* Distributed Optimizer
|
||||
* Mixture-of-Experts
|
||||
|
||||
.. _megatron-lm-recommendations:
|
||||
|
||||
Use cases and recommendations
|
||||
================================================================================
|
||||
|
||||
See the `Efficient MoE training on AMD ROCm: How-to use Megablocks on AMD GPUs blog <https://rocm.blogs.amd.com/artificial-intelligence/megablocks/README.html>`_ post
|
||||
to leverage the ROCm platform for pre-training by using the Stanford Megatron-LM framework of pre-processing datasets on AMD GPUs.
|
||||
Coverage includes:
|
||||
|
||||
* Single-GPU pre-training
|
||||
* Multi-GPU pre-training
|
||||
|
||||
|
||||
.. _megatron-lm-docker-compat:
|
||||
|
||||
Docker image compatibility
|
||||
================================================================================
|
||||
|
||||
.. |docker-icon| raw:: html
|
||||
|
||||
<i class="fab fa-docker"></i>
|
||||
|
||||
AMD validates and publishes `Stanford Megatron-LM images <https://hub.docker.com/r/rocm/megatron-lm>`_
|
||||
with ROCm and Pytorch backends on Docker Hub. The following Docker image tags and associated
|
||||
inventories represent the latest Megatron-LM version from the official Docker Hub.
|
||||
The Docker images have been validated for `ROCm 6.3.0 <https://repo.radeon.com/rocm/apt/6.3/>`_.
|
||||
Click |docker-icon| to view the image on Docker Hub.
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
:class: docker-image-compatibility
|
||||
|
||||
* - Docker image
|
||||
- Stanford Megatron-LM
|
||||
- PyTorch
|
||||
- Ubuntu
|
||||
- Python
|
||||
|
||||
* - .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/stanford-megatron-lm/stanford-megatron-lm85f95ae_rocm6.3.0_ubuntu24.04_py3.12_pytorch2.4.0/images/sha256-070556f078be10888a1421a2cb4f48c29f28b02bfeddae02588d1f7fc02a96a6"><i class="fab fa-docker fa-lg"></i></a>
|
||||
|
||||
- `85f95ae <https://github.com/stanford-futuredata/Megatron-LM/commit/85f95aef3b648075fe6f291c86714fdcbd9cd1f5>`_
|
||||
- `2.4.0 <https://github.com/ROCm/pytorch/tree/release/2.4>`_
|
||||
- 24.04
|
||||
- `3.12.9 <https://www.python.org/downloads/release/python-3129/>`_
|
||||
|
||||
|
||||
|
||||
@@ -1,76 +0,0 @@
|
||||
:orphan:
|
||||
|
||||
.. meta::
|
||||
:description: Taichi compatibility
|
||||
:keywords: GPU, Taichi compatibility
|
||||
|
||||
.. version-set:: rocm_version latest
|
||||
|
||||
*******************************************************************************
|
||||
Taichi compatibility
|
||||
*******************************************************************************
|
||||
|
||||
`Taichi <https://www.taichi-lang.org/>`_ is an open-source, imperative, and parallel
|
||||
programming language designed for high-performance numerical computation.
|
||||
Embedded in Python, it leverages just-in-time (JIT) compilation frameworks such as LLVM to accelerate
|
||||
compute-intensive Python code by compiling it to native GPU or CPU instructions.
|
||||
|
||||
Taichi is widely used across various domains, including real-time physical simulation,
|
||||
numerical computing, augmented reality, artificial intelligence, computer vision, robotics,
|
||||
visual effects in film and gaming, and general-purpose computing.
|
||||
|
||||
* ROCm support for Taichi is hosted in the official `https://github.com/ROCm/taichi <https://github.com/ROCm/taichi>`_ repository.
|
||||
* Due to independent compatibility considerations, this location differs from the `https://github.com/taichi-dev <https://github.com/taichi-dev>`_ upstream repository.
|
||||
* Use the prebuilt :ref:`Docker image <taichi-docker-compat>` with ROCm, PyTorch, and Taichi preinstalled.
|
||||
* See the :doc:`ROCm Taichi installation guide <rocm-install-on-linux:install/3rd-party/taichi-install>` to install and get started.
|
||||
|
||||
.. note::
|
||||
|
||||
Taichi is supported on ROCm 6.3.2.
|
||||
|
||||
Supported devices and features
|
||||
===============================================================================
|
||||
There is support through the ROCm software stack for all Taichi GPU features on AMD Instinct MI250X and MI210X series GPUs with the exception of Taichi’s GPU rendering system, CGUI.
|
||||
AMD Instinct MI300X series GPUs will be supported by November.
|
||||
|
||||
.. _taichi-recommendations:
|
||||
|
||||
Use cases and recommendations
|
||||
================================================================================
|
||||
To fully leverage Taichi's performance capabilities in compute-intensive tasks, it is best to adhere to specific coding patterns and utilize Taichi decorators.
|
||||
A collection of example use cases is available in the `https://github.com/ROCm/taichi_examples <https://github.com/ROCm/taichi_examples>`_ repository,
|
||||
providing practical insights and foundational knowledge for working with the Taichi programming language.
|
||||
You can also refer to the `AMD ROCm blog <https://rocm.blogs.amd.com/>`_ to search for Taichi examples and best practices to optimize your workflows on AMD GPUs.
|
||||
|
||||
.. _taichi-docker-compat:
|
||||
|
||||
Docker image compatibility
|
||||
================================================================================
|
||||
|
||||
.. |docker-icon| raw:: html
|
||||
|
||||
<i class="fab fa-docker"></i>
|
||||
|
||||
AMD validates and publishes ready-made `ROCm Taichi Docker images <https://hub.docker.com/r/rocm/taichi/tags>`_
|
||||
with ROCm backends on Docker Hub. The following Docker image tags and associated inventories
|
||||
represent the latest Taichi version from the official Docker Hub.
|
||||
The Docker images have been validated for `ROCm 6.3.2 <https://rocm.docs.amd.com/en/docs-6.3.2/about/release-notes.html>`_.
|
||||
Click |docker-icon| to view the image on Docker Hub.
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
:class: docker-image-compatibility
|
||||
|
||||
* - Docker image
|
||||
- ROCm
|
||||
- Taichi
|
||||
- Ubuntu
|
||||
- Python
|
||||
|
||||
* - .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/taichi/taichi-1.8.0b1_rocm6.3.2_ubuntu22.04_py3.10.12/images/sha256-e016964a751e6a92199032d23e70fa3a564fff8555afe85cd718f8aa63f11fc6"><i class="fab fa-docker fa-lg"></i> rocm/taichi</a>
|
||||
- `6.3.2 <https://repo.radeon.com/rocm/apt/6.3.2/>`_
|
||||
- `1.8.0b1 <https://github.com/taichi-dev/taichi>`_
|
||||
- 22.04
|
||||
- `3.10.12 <https://www.python.org/downloads/release/python-31012/>`_
|
||||
@@ -1,439 +0,0 @@
|
||||
:orphan:
|
||||
|
||||
.. meta::
|
||||
:description: TensorFlow compatibility
|
||||
:keywords: GPU, TensorFlow compatibility
|
||||
|
||||
.. version-set:: rocm_version latest
|
||||
|
||||
*******************************************************************************
|
||||
TensorFlow compatibility
|
||||
*******************************************************************************
|
||||
|
||||
`TensorFlow <https://www.tensorflow.org/>`__ is an open-source library for
|
||||
solving machine learning, deep learning, and AI problems. It can solve many
|
||||
problems across different sectors and industries but primarily focuses on
|
||||
neural network training and inference. It is one of the most popular and
|
||||
in-demand frameworks and is very active in open-source contribution and
|
||||
development.
|
||||
|
||||
The `official TensorFlow repository <http://github.com/tensorflow/tensorflow>`__
|
||||
includes full ROCm support. AMD maintains a TensorFlow `ROCm repository
|
||||
<http://github.com/rocm/tensorflow-upstream>`__ in order to quickly add bug
|
||||
fixes, updates, and support for the latest ROCM versions.
|
||||
|
||||
- ROCm TensorFlow release:
|
||||
|
||||
- Offers :ref:`Docker images <tensorflow-docker-compat>` with
|
||||
ROCm and TensorFlow pre-installed.
|
||||
|
||||
- ROCm TensorFlow repository: `<https://github.com/ROCm/tensorflow-upstream>`__
|
||||
|
||||
- See the :doc:`ROCm TensorFlow installation guide <rocm-install-on-linux:install/3rd-party/tensorflow-install>`
|
||||
to get started.
|
||||
|
||||
- Official TensorFlow release:
|
||||
|
||||
- Official TensorFlow repository: `<https://github.com/tensorflow/tensorflow>`__
|
||||
|
||||
- See the `TensorFlow API versions <https://www.tensorflow.org/versions>`__ list.
|
||||
|
||||
.. note::
|
||||
|
||||
The official TensorFlow documentation does not cover ROCm support. Use the
|
||||
ROCm documentation for installation instructions for Tensorflow on ROCm.
|
||||
See :doc:`rocm-install-on-linux:install/3rd-party/tensorflow-install`.
|
||||
|
||||
.. _tensorflow-docker-compat:
|
||||
|
||||
Docker image compatibility
|
||||
================================================================================
|
||||
|
||||
AMD provides preconfigured Docker images with TensorFlow and the ROCm backend.
|
||||
These images are published on `Docker Hub <https://hub.docker.com/r/rocm/tensorflow>`__ and are the
|
||||
recommended way to get started with deep learning with TensorFlow on ROCm.
|
||||
|
||||
To find the right image tag, see the :ref:`TensorFlow on ROCm installation
|
||||
documentation <rocm-install-on-linux:tensorflow-docker-support>` for a list of
|
||||
available ``rocm/tensorflow`` images.
|
||||
|
||||
|
||||
Critical ROCm libraries for TensorFlow
|
||||
===============================================================================
|
||||
|
||||
TensorFlow depends on multiple components and the supported features of those
|
||||
components can affect the TensorFlow ROCm supported feature set. The versions
|
||||
in the following table refer to the first TensorFlow version where the ROCm
|
||||
library was introduced as a dependency. The versions described
|
||||
are available in ROCm :version:`rocm_version`.
|
||||
|
||||
.. list-table::
|
||||
:widths: 25, 10, 35, 30
|
||||
:header-rows: 1
|
||||
|
||||
* - ROCm library
|
||||
- Version
|
||||
- Purpose
|
||||
- Used in
|
||||
* - `hipBLAS <https://github.com/ROCm/hipBLAS>`__
|
||||
- :version-ref:`hipBLAS rocm_version`
|
||||
- Provides GPU-accelerated Basic Linear Algebra Subprograms (BLAS) for
|
||||
matrix and vector operations.
|
||||
- Accelerates operations like ``tf.matmul``, ``tf.linalg.matmul``, and
|
||||
other matrix multiplications commonly used in neural network layers.
|
||||
* - `hipBLASLt <https://github.com/ROCm/hipBLASLt>`__
|
||||
- :version-ref:`hipBLASLt rocm_version`
|
||||
- Extends hipBLAS with additional optimizations like fused kernels and
|
||||
integer tensor cores.
|
||||
- Optimizes matrix multiplications and linear algebra operations used in
|
||||
layers like dense, convolutional, and RNNs in TensorFlow.
|
||||
* - `hipCUB <https://github.com/ROCm/hipCUB>`__
|
||||
- :version-ref:`hipCUB rocm_version`
|
||||
- Provides a C++ template library for parallel algorithms for reduction,
|
||||
scan, sort and select.
|
||||
- Supports operations like ``tf.reduce_sum``, ``tf.cumsum``, ``tf.sort``
|
||||
and other tensor operations in TensorFlow, especially those involving
|
||||
scanning, sorting, and filtering.
|
||||
* - `hipFFT <https://github.com/ROCm/hipFFT>`__
|
||||
- :version-ref:`hipFFT rocm_version`
|
||||
- Accelerates Fast Fourier Transforms (FFT) for signal processing tasks.
|
||||
- Used for operations like signal processing, image filtering, and
|
||||
certain types of neural networks requiring FFT-based transformations.
|
||||
* - `hipSOLVER <https://github.com/ROCm/hipSOLVER>`__
|
||||
- :version-ref:`hipSOLVER rocm_version`
|
||||
- Provides GPU-accelerated direct linear solvers for dense and sparse
|
||||
systems.
|
||||
- Optimizes linear algebra functions such as solving systems of linear
|
||||
equations, often used in optimization and training tasks.
|
||||
* - `hipSPARSE <https://github.com/ROCm/hipSPARSE>`__
|
||||
- :version-ref:`hipSPARSE rocm_version`
|
||||
- Optimizes sparse matrix operations for efficient computations on sparse
|
||||
data.
|
||||
- Accelerates sparse matrix operations in models with sparse weight
|
||||
matrices or activations, commonly used in neural networks.
|
||||
* - `MIOpen <https://github.com/ROCm/MIOpen>`__
|
||||
- :version-ref:`MIOpen rocm_version`
|
||||
- Provides optimized deep learning primitives such as convolutions,
|
||||
pooling,
|
||||
normalization, and activation functions.
|
||||
- Speeds up convolutional neural networks (CNNs) and other layers. Used
|
||||
in TensorFlow for layers like ``tf.nn.conv2d``, ``tf.nn.relu``, and
|
||||
``tf.nn.lstm_cell``.
|
||||
* - `RCCL <https://github.com/ROCm/rccl>`__
|
||||
- :version-ref:`RCCL rocm_version`
|
||||
- Optimizes for multi-GPU communication for operations like AllReduce and
|
||||
Broadcast.
|
||||
- Distributed data parallel training (``tf.distribute.MirroredStrategy``).
|
||||
Handles communication in multi-GPU setups.
|
||||
* - `rocThrust <https://github.com/ROCm/rocThrust>`__
|
||||
- :version-ref:`rocThrust rocm_version`
|
||||
- Provides a C++ template library for parallel algorithms like sorting,
|
||||
reduction, and scanning.
|
||||
- Reduction operations like ``tf.reduce_sum``, ``tf.cumsum`` for computing
|
||||
the cumulative sum of elements along a given axis or ``tf.unique`` to
|
||||
finds unique elements in a tensor can use rocThrust.
|
||||
|
||||
Supported and unsupported features
|
||||
===============================================================================
|
||||
|
||||
The following section maps supported data types and GPU-accelerated TensorFlow
|
||||
features to their minimum supported ROCm and TensorFlow versions.
|
||||
|
||||
Data types
|
||||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
The data type of a tensor is specified using the ``dtype`` attribute or
|
||||
argument, and TensorFlow supports a wide range of data types for different use
|
||||
cases.
|
||||
|
||||
The basic, single data types of `tf.dtypes <https://www.tensorflow.org/api_docs/python/tf/dtypes>`__
|
||||
are as follows:
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Data type
|
||||
- Description
|
||||
- Since TensorFlow
|
||||
- Since ROCm
|
||||
* - ``bfloat16``
|
||||
- 16-bit bfloat (brain floating point).
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``bool``
|
||||
- Boolean.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``complex128``
|
||||
- 128-bit complex.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``complex64``
|
||||
- 64-bit complex.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``double``
|
||||
- 64-bit (double precision) floating-point.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``float16``
|
||||
- 16-bit (half precision) floating-point.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``float32``
|
||||
- 32-bit (single precision) floating-point.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``float64``
|
||||
- 64-bit (double precision) floating-point.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``half``
|
||||
- 16-bit (half precision) floating-point.
|
||||
- 2.0.0
|
||||
- 2.0
|
||||
* - ``int16``
|
||||
- Signed 16-bit integer.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``int32``
|
||||
- Signed 32-bit integer.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``int64``
|
||||
- Signed 64-bit integer.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``int8``
|
||||
- Signed 8-bit integer.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``qint16``
|
||||
- Signed quantized 16-bit integer.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``qint32``
|
||||
- Signed quantized 32-bit integer.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``qint8``
|
||||
- Signed quantized 8-bit integer.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``quint16``
|
||||
- Unsigned quantized 16-bit integer.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``quint8``
|
||||
- Unsigned quantized 8-bit integer.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``resource``
|
||||
- Handle to a mutable, dynamically allocated resource.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``string``
|
||||
- Variable-length string, represented as byte array.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``uint16``
|
||||
- Unsigned 16-bit (word) integer.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``uint32``
|
||||
- Unsigned 32-bit (dword) integer.
|
||||
- 1.5.0
|
||||
- 1.7
|
||||
* - ``uint64``
|
||||
- Unsigned 64-bit (qword) integer.
|
||||
- 1.5.0
|
||||
- 1.7
|
||||
* - ``uint8``
|
||||
- Unsigned 8-bit (byte) integer.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``variant``
|
||||
- Data of arbitrary type (known at runtime).
|
||||
- 1.4.0
|
||||
- 1.7
|
||||
|
||||
Features
|
||||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
This table provides an overview of key features in TensorFlow and their
|
||||
availability in ROCm.
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Module
|
||||
- Description
|
||||
- Since TensorFlow
|
||||
- Since ROCm
|
||||
* - ``tf.linalg`` (Linear Algebra)
|
||||
- Operations for matrix and tensor computations, such as
|
||||
``tf.linalg.matmul`` (matrix multiplication), ``tf.linalg.inv``
|
||||
(matrix inversion) and ``tf.linalg.cholesky`` (Cholesky decomposition).
|
||||
These leverage GPUs for high-performance linear algebra operations.
|
||||
- 1.4
|
||||
- 1.8.2
|
||||
* - ``tf.nn`` (Neural Network Operations)
|
||||
- GPU-accelerated building blocks for deep learning models, such as 2D
|
||||
convolutions with ``tf.nn.conv2d``, max pooling operations with
|
||||
``tf.nn.max_pool``, activation functions like ``tf.nn.relu`` or softmax
|
||||
for output layers with ``tf.nn.softmax``.
|
||||
- 1.0
|
||||
- 1.8.2
|
||||
* - ``tf.image`` (Image Processing)
|
||||
- GPU-accelerated functions for image preprocessing and augmentations,
|
||||
such as resize images with ``tf.image.resize``, flip images horizontally
|
||||
with ``tf.image.flip_left_right`` and adjust image brightness randomly
|
||||
with ``tf.image.random_brightness``.
|
||||
- 1.1
|
||||
- 1.8.2
|
||||
* - ``tf.keras`` (High-Level API)
|
||||
- GPU acceleration for Keras layers and models, including dense layers
|
||||
(``tf.keras.layers.Dense``), convolutional layers
|
||||
(``tf.keras.layers.Conv2D``) and recurrent layers
|
||||
(``tf.keras.layers.LSTM``).
|
||||
- 1.4
|
||||
- 1.8.2
|
||||
* - ``tf.math`` (Mathematical Operations)
|
||||
- GPU-accelerated mathematical operations, such as sum across dimensions
|
||||
with ``tf.math.reduce_sum``, elementwise exponentiation with
|
||||
``tf.math.exp`` and sigmoid activation (``tf.math.sigmoid``).
|
||||
- 1.5
|
||||
- 1.8.2
|
||||
* - ``tf.signal`` (Signal Processing)
|
||||
- Functions for spectral analysis and signal transformations.
|
||||
- 1.13
|
||||
- 2.1
|
||||
* - ``tf.data`` (Data Input Pipeline)
|
||||
- GPU-accelerated data preprocessing for efficient input pipelines,
|
||||
Prefetching with ``tf.data.experimental.AUTOTUNE``. GPU-enabled
|
||||
transformations like map and batch.
|
||||
- 1.4
|
||||
- 1.8.2
|
||||
* - ``tf.distribute`` (Distributed Training)
|
||||
- Enabling to scale computations across multiple devices on a single
|
||||
machine or across multiple machines.
|
||||
- 1.13
|
||||
- 2.1
|
||||
* - ``tf.random`` (Random Number Generation)
|
||||
- GPU-accelerated random number generation
|
||||
- 1.12
|
||||
- 1.9.2
|
||||
* - ``tf.TensorArray`` (Dynamic Array Operations)
|
||||
- Enables dynamic tensor manipulation on GPUs.
|
||||
- 1.0
|
||||
- 1.8.2
|
||||
* - ``tf.sparse`` (Sparse Tensor Operations)
|
||||
- GPU-accelerated sparse matrix manipulations.
|
||||
- 1.9
|
||||
- 1.9.0
|
||||
* - ``tf.experimental.numpy``
|
||||
- GPU-accelerated NumPy-like API for numerical computations.
|
||||
- 2.4
|
||||
- 4.1.1
|
||||
* - ``tf.RaggedTensor``
|
||||
- Handling of variable-length sequences and ragged tensors with GPU
|
||||
support.
|
||||
- 1.13
|
||||
- 2.1
|
||||
* - ``tf.function`` with XLA (Accelerated Linear Algebra)
|
||||
- Enable GPU-accelerated functions in optimization.
|
||||
- 1.14
|
||||
- 2.4
|
||||
* - ``tf.quantization``
|
||||
- Quantized operations for inference, accelerated on GPUs.
|
||||
- 1.12
|
||||
- 1.9.2
|
||||
|
||||
Distributed library features
|
||||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
Enables developers to scale computations across multiple devices on a single machine or
|
||||
across multiple machines.
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Feature
|
||||
- Description
|
||||
- Since TensorFlow
|
||||
- Since ROCm
|
||||
* - ``MultiWorkerMirroredStrategy``
|
||||
- Synchronous training across multiple workers using mirrored variables.
|
||||
- 2.0
|
||||
- 3.0
|
||||
* - ``MirroredStrategy``
|
||||
- Synchronous training across multiple GPUs on one machine.
|
||||
- 1.5
|
||||
- 2.5
|
||||
* - ``TPUStrategy``
|
||||
- Efficiently trains models on Google TPUs.
|
||||
- 1.9
|
||||
- ❌
|
||||
* - ``ParameterServerStrategy``
|
||||
- Asynchronous training using parameter servers for variable management.
|
||||
- 2.1
|
||||
- 4.0
|
||||
* - ``CentralStorageStrategy``
|
||||
- Keeps variables on a single device and performs computation on multiple
|
||||
devices.
|
||||
- 2.3
|
||||
- 4.1
|
||||
* - ``CollectiveAllReduceStrategy``
|
||||
- Synchronous training across multiple devices and hosts.
|
||||
- 1.14
|
||||
- 3.5
|
||||
* - Distribution Strategies API
|
||||
- High-level API to simplify distributed training configuration and
|
||||
execution.
|
||||
- 1.10
|
||||
- 3.0
|
||||
|
||||
Unsupported TensorFlow features
|
||||
===============================================================================
|
||||
|
||||
The following are GPU-accelerated TensorFlow features not currently supported by
|
||||
ROCm.
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Feature
|
||||
- Description
|
||||
- Since TensorFlow
|
||||
* - Mixed Precision with TF32
|
||||
- Mixed precision with TF32 is used for matrix multiplications,
|
||||
convolutions, and other linear algebra operations, particularly in
|
||||
deep learning workloads like CNNs and transformers.
|
||||
- 2.4
|
||||
* - ``tf.distribute.TPUStrategy``
|
||||
- Efficiently trains models on Google TPUs.
|
||||
- 1.9
|
||||
|
||||
Use cases and recommendations
|
||||
===============================================================================
|
||||
|
||||
* The `Training a Neural Collaborative Filtering (NCF) Recommender on an AMD
|
||||
GPU <https://rocm.blogs.amd.com/artificial-intelligence/ncf/README.html>`__
|
||||
blog post discusses training an NCF recommender system using TensorFlow. It
|
||||
explains how NCF improves traditional collaborative filtering methods by
|
||||
leveraging neural networks to model non-linear user-item interactions. The
|
||||
post outlines the implementation using the recommenders library, focusing on
|
||||
the use of implicit data (for example, user interactions like viewing or
|
||||
purchasing) and how it addresses challenges like the lack of negative values.
|
||||
|
||||
* The `Creating a PyTorch/TensorFlow code environment on AMD GPUs
|
||||
<https://rocm.blogs.amd.com/software-tools-optimization/pytorch-tensorflow-env/README.html>`__
|
||||
blog post provides instructions for creating a machine learning environment
|
||||
for PyTorch and TensorFlow on AMD GPUs using ROCm. It covers steps like
|
||||
installing the libraries, cloning code repositories, installing dependencies,
|
||||
and troubleshooting potential issues with CUDA-based code. Additionally, it
|
||||
explains how to HIPify code (port CUDA code to HIP) and manage Docker images
|
||||
for a better experience on AMD GPUs. This guide aims to help data scientists
|
||||
and ML practitioners adapt their code for AMD GPUs.
|
||||
|
||||
For more use cases and recommendations, see the `ROCm Tensorflow blog posts <https://rocm.blogs.amd.com/blog/tag/tensorflow.html>`__.
|
||||
@@ -1,86 +0,0 @@
|
||||
:orphan:
|
||||
|
||||
.. meta::
|
||||
:description: verl compatibility
|
||||
:keywords: GPU, verl compatibility
|
||||
|
||||
.. version-set:: rocm_version latest
|
||||
|
||||
*******************************************************************************
|
||||
verl compatibility
|
||||
*******************************************************************************
|
||||
|
||||
Volcano Engine Reinforcement Learning for LLMs (verl) is a reinforcement learning framework designed for large language models (LLMs).
|
||||
verl offers a scalable, open-source fine-tuning solution optimized for AMD Instinct GPUs with full ROCm support.
|
||||
|
||||
* See the `verl documentation <https://verl.readthedocs.io/en/latest/>`_ for more information about verl.
|
||||
* The official verl GitHub repository is `https://github.com/volcengine/verl <https://github.com/volcengine/verl>`_.
|
||||
* Use the AMD-validated :ref:`Docker images <verl-docker-compat>` with ROCm and verl preinstalled.
|
||||
* See the :doc:`ROCm verl installation guide <rocm-install-on-linux:install/3rd-party/verl-install>` to install and get started.
|
||||
|
||||
.. note::
|
||||
|
||||
verl is supported on ROCm 6.2.0.
|
||||
|
||||
.. _verl-recommendations:
|
||||
|
||||
Use cases and recommendations
|
||||
================================================================================
|
||||
|
||||
The benefits of verl in large-scale reinforcement learning from human feedback (RLHF) are discussed in the `Reinforcement Learning from Human Feedback on AMD GPUs with verl and ROCm Integration <https://rocm.blogs.amd.com/artificial-intelligence/verl-large-scale/README.html>`_ blog.
|
||||
|
||||
.. _verl-supported_features:
|
||||
|
||||
Supported features
|
||||
===============================================================================
|
||||
|
||||
The following table shows verl on ROCm support for GPU-accelerated modules.
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Module
|
||||
- Description
|
||||
- verl version
|
||||
- ROCm version
|
||||
* - ``FSDP``
|
||||
- Training engine
|
||||
- 0.3.0.post0
|
||||
- 6.2.0
|
||||
* - ``vllm``
|
||||
- Inference engine
|
||||
- 0.3.0.post0
|
||||
- 6.2.0
|
||||
|
||||
.. _verl-docker-compat:
|
||||
|
||||
Docker image compatibility
|
||||
================================================================================
|
||||
|
||||
.. |docker-icon| raw:: html
|
||||
|
||||
<i class="fab fa-docker"></i>
|
||||
|
||||
AMD validates and publishes ready-made `ROCm verl Docker images <https://hub.docker.com/r/rocm/verl/tags>`_
|
||||
with ROCm backends on Docker Hub. The following Docker image tags and associated inventories represent the available verl versions from the official Docker Hub.
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Docker image
|
||||
- ROCm
|
||||
- verl
|
||||
- Ubuntu
|
||||
- Pytorch
|
||||
- Python
|
||||
- vllm
|
||||
|
||||
* - .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/verl/verl-0.3.0.post0_rocm6.2_vllm0.6.3/images/sha256-cbe423803fd7850448b22444176bee06f4dcf22cd3c94c27732752d3a39b04b2"><i class="fab fa-docker fa-lg"></i> rocm/verl</a>
|
||||
- `6.2.0 <https://repo.radeon.com/rocm/apt/6.2/>`_
|
||||
- `0.3.0post0 <https://github.com/volcengine/verl/releases/tag/v0.3.0.post0>`_
|
||||
- 20.04
|
||||
- `2.5.0 <https://github.com/ROCm/pytorch/tree/release/2.5>`_
|
||||
- `3.9.19 <https://www.python.org/downloads/release/python-3919/>`_
|
||||
- `0.6.3 <https://github.com/vllm-project/vllm/releases/tag/v0.6.3>`_
|
||||
Reference in New Issue
Block a user