mirror of
https://github.com/ROCm/ROCm.git
synced 2026-01-09 14:48:06 -05:00
Merge pull request #563 from ROCm/amd/pbhandar/rocm_701_external_to_internal_sync
Sync external develop into internal develop for ROCm 7.0.1
This commit is contained in:
pbhandar-amd
@@ -40,6 +40,7 @@ parameters:
|
||||
- gfortran
|
||||
- libgfortran5
|
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- libopenblas-dev
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||||
- liblapack-dev
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- name: pipModules
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||||
type: object
|
||||
default:
|
||||
@@ -125,10 +126,13 @@ jobs:
|
||||
aggregatePipeline: ${{ parameters.aggregatePipeline }}
|
||||
${{ if parameters.triggerDownstreamJobs }}:
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||||
downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
|
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# NOTE: content between `---` is for transition support between old/new build systems
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# and should be removed once transition is complete.
|
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# -----------------------------
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# Build and install gtest and lapack
|
||||
# $(Pipeline.Workspace)/deps is a temporary folder for the build process
|
||||
# $(Pipeline.Workspace)/s/deps is part of the hipSPARSELt repo
|
||||
- script: mkdir $(Pipeline.Workspace)/deps
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- script: mkdir -p $(Pipeline.Workspace)/deps
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displayName: Create temp folder for external dependencies
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# hipSPARSELt already has a CMake script for external deps, so we can just run that
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# https://github.com/ROCm/hipSPARSELt/blob/develop/deps/CMakeLists.txt
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@@ -144,22 +148,35 @@ jobs:
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- script: sudo make install
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displayName: Install hipSPARSELt external dependencies
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workingDirectory: $(Pipeline.Workspace)/deps
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# -----------------------------
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- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
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parameters:
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os: ${{ job.os }}
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# NOTE: the following options are old build only
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# and can be removed after full transition to new build
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# -DAMDGPU_TARGETS=${{ job.target }}
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# -DCMAKE_Fortran_COMPILER=f95
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# -DTensile_LOGIC=
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# -DTensile_CPU_THREADS=
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# -DTensile_LIBRARY_FORMAT=msgpack
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# -DROCM_PATH=$(Agent.BuildDirectory)/rocm
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# -DBUILD_CLIENTS_TESTS=ON
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# -DBUILD_USE_LOCAL_TENSILE=OFF
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extraBuildFlags: >-
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-DCMAKE_BUILD_TYPE=Release
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-DCMAKE_CXX_COMPILER=$(Agent.BuildDirectory)/rocm/llvm/bin/amdclang++
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-DCMAKE_C_COMPILER=$(Agent.BuildDirectory)/rocm/llvm/bin/amdclang
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-DCMAKE_Fortran_COMPILER=f95
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-DCMAKE_PREFIX_PATH="$(Agent.BuildDirectory)/rocm"
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-DGPU_TARGETS=${{ job.target }}
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-DAMDGPU_TARGETS=${{ job.target }}
|
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-DCMAKE_Fortran_COMPILER=f95
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-DTensile_LOGIC=
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-DTensile_CPU_THREADS=
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||||
-DTensile_LIBRARY_FORMAT=msgpack
|
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-DCMAKE_PREFIX_PATH="$(Agent.BuildDirectory)/rocm"
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-DROCM_PATH=$(Agent.BuildDirectory)/rocm
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-DBUILD_CLIENTS_TESTS=ON
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-DBUILD_USE_LOCAL_TENSILE=OFF
|
||||
-DHIPSPARSELT_ENABLE_FETCH=ON
|
||||
-GNinja
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||||
${{ if ne(parameters.sparseCheckoutDir, '') }}:
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cmakeSourceDir: $(Build.SourcesDirectory)/projects/hipsparselt
|
||||
|
||||
@@ -72,6 +72,7 @@ CU
|
||||
CUDA
|
||||
CUs
|
||||
CXX
|
||||
CX
|
||||
Cavium
|
||||
CentOS
|
||||
ChatGPT
|
||||
@@ -118,6 +119,8 @@ Dependabot
|
||||
Deprecations
|
||||
DevCap
|
||||
DirectX
|
||||
Disaggregated
|
||||
disaggregated
|
||||
Dockerfile
|
||||
Dockerized
|
||||
Doxygen
|
||||
@@ -127,6 +130,7 @@ ENDPGM
|
||||
EPYC
|
||||
ESXi
|
||||
EoS
|
||||
etcd
|
||||
fas
|
||||
FBGEMM
|
||||
FIFOs
|
||||
@@ -178,6 +182,7 @@ GPUs
|
||||
Graphbolt
|
||||
GraphSage
|
||||
GRBM
|
||||
GRE
|
||||
GenAI
|
||||
GenZ
|
||||
GitHub
|
||||
@@ -301,6 +306,7 @@ MirroredStrategy
|
||||
Mixtral
|
||||
MosaicML
|
||||
MoEs
|
||||
Mooncake
|
||||
Mpops
|
||||
Multicore
|
||||
Multithreaded
|
||||
@@ -445,6 +451,7 @@ SKU
|
||||
SKUs
|
||||
SLES
|
||||
SLURM
|
||||
Slurm
|
||||
SMEM
|
||||
SMFMA
|
||||
SMI
|
||||
@@ -615,6 +622,7 @@ coalescable
|
||||
codename
|
||||
collater
|
||||
comgr
|
||||
compat
|
||||
completers
|
||||
composable
|
||||
concretization
|
||||
@@ -776,6 +784,7 @@ lossy
|
||||
macOS
|
||||
matchers
|
||||
maxtext
|
||||
megablocks
|
||||
megatron
|
||||
microarchitecture
|
||||
migraphx
|
||||
@@ -934,6 +943,7 @@ softmax
|
||||
spack
|
||||
spmm
|
||||
src
|
||||
stanford
|
||||
stochastically
|
||||
strided
|
||||
subcommand
|
||||
|
||||
@@ -764,7 +764,7 @@ HIP runtime has the following functional improvements which improves runtime per
|
||||
|
||||
#### Changed
|
||||
|
||||
* VX_RPP extension : Version 3.1.0 release.
|
||||
* VX_RPP extension: Version 3.1.0 release.
|
||||
* Update the parameters and kernel API of Blur, Fog, Jitter, LensCorrection, Rain, Pixelate, Vignette and ResizeCrop wrt tensor kernels replacing the legacy BatchPD API calls in VX_RPP extensions.
|
||||
|
||||
#### Known issues
|
||||
@@ -1144,7 +1144,7 @@ Review the [README](https://github.com/ROCm/rocm_bandwidth_test/blob/amd-mainlin
|
||||
#### Changed
|
||||
|
||||
* Completed migration from legacy [ROCProfiler](https://rocm.docs.amd.com/projects/rocprofiler/en/latest/) to [ROCprofiler-SDK](https://rocm.docs.amd.com/projects/rocprofiler-sdk/en/latest/).
|
||||
* Reorganized the configuration files internally and improved [README/installation](https://github.com/ROCm/rdc/blob/amd-staging/README.md) instructions.
|
||||
* Reorganized the configuration files internally and improved [README/installation](https://github.com/ROCm/rdc/blob/release/rocm-rel-7.0/README.md) instructions.
|
||||
* Updated metrics and monitoring support for the latest AMD data center GPUs.
|
||||
|
||||
#### Optimized
|
||||
@@ -1346,7 +1346,7 @@ The previous default accumulator types could lead to situations in which unexpec
|
||||
- ROCprof Trace Decoder as experimental API:
|
||||
- Requires [ROCprof Trace Decoder plugin](https://github.com/rocm/rocprof-trace-decoder).
|
||||
- Thread trace option in the `rocprofv3` tool under the `--att` parameters:
|
||||
- See [using thread trace with rocprofv3](https://rocm.docs.amd.com/projects/rocprofiler-sdk/en/amd-mainline/how-to/using-thread-trace.html)
|
||||
- See [using thread trace with rocprofv3](https://rocm.docs.amd.com/projects/rocprofiler-sdk/en/docs-7.0.0/how-to/using-thread-trace.html)
|
||||
- Requires [ROCprof Trace Decoder plugin](https://github.com/rocm/rocprof-trace-decoder).
|
||||
- `rocpd` output format documentation:
|
||||
- Requires [ROCprof Trace Decoder plugin](https://github.com/rocm/rocprof-trace-decoder).
|
||||
|
||||
19
RELEASE.md
19
RELEASE.md
@@ -461,8 +461,9 @@ ROCm documentation continues to be updated to provide clearer and more comprehen
|
||||
benchmarking guides have been updated with expanded model coverage and
|
||||
optimized Docker environments. Highlights include:
|
||||
|
||||
* The [Training a model with Primus and Megatron](https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/training/benchmark-docker/primus-megatron.html) benchmarking guide
|
||||
now leverages the unified AMD Primus framework with the Megatron backend. See [Primus: A Lightweight, Unified Training Framework for Large Models on AMD
|
||||
* The [Training a model with Primus and Megatron](https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/training/benchmark-docker/primus-megatron.html)
|
||||
and [Training a model with Primus and PyTorch](https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/training/benchmark-docker/primus-pytorch.html) benchmarking guides
|
||||
now leverage the unified AMD Primus framework with the Megatron and torchtitan backends. See [Primus: A Lightweight, Unified Training Framework for Large Models on AMD
|
||||
GPUs](https://rocm.blogs.amd.com/software-tools-optimization/primus/README.html) for an introduction to Primus.
|
||||
|
||||
* The [Training a model with PyTorch](https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/training/benchmark-docker/pytorch-training.html) benchmarking guide
|
||||
@@ -471,6 +472,9 @@ ROCm documentation continues to be updated to provide clearer and more comprehen
|
||||
* The [Training a model with JAX MaxText](https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/training/benchmark-docker/pytorch-training.html) benchmarking guide
|
||||
now supports [MAD](https://github.com/ROCm/MAD)-integrated benchmarking. The MaxText training environment now uses JAX 0.6.0 or 0.5.0. FP8 quantized training is supported with JAX 0.5.0.
|
||||
|
||||
* The [SGLang distributed inference](https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/inference/benchmark-docker/sglang-distributed.html?model=llama-3.1-8b-instruct) guide
|
||||
provides a recipe to get started with disaggregated prefill/decode inference.
|
||||
|
||||
* The [vLLM inference performance testing](https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/inference/benchmark-docker/vllm.html) documentation
|
||||
now features clearer serving and throughput benchmarking commands -- for improved transparency of model benchmarking configurations. The vLLM inference
|
||||
environment now uses vLLM 0.10.1 and includes improved default configurations.
|
||||
@@ -534,17 +538,16 @@ ROCm documentation continues to be updated to provide clearer and more comprehen
|
||||
|
||||
### User space, driver, and firmware dependent changes
|
||||
|
||||
The software for AMD Datacenter GPU products requires maintaining a hardware
|
||||
and software stack with interdependencies between the GPU and baseboard
|
||||
firmware, AMD GPU drivers, and the ROCm user space software.
|
||||
|
||||
Running GPU software on AMD data center GPUs requires maintaining a coordinated
|
||||
hardware and software stack. This stack has interdependencies between the GPU
|
||||
and baseboard firmware, AMD GPU drivers, and the ROCm user-space software.
|
||||
As of the ROCm 7.0.0 release, these interdependencies are publicly documented.
|
||||
Note that while AMD publishes drivers and ROCm user space, your server or
|
||||
While AMD publishes drivers and ROCm user space components, your server or
|
||||
infrastructure provider publishes the GPU and baseboard firmware by bundling
|
||||
AMD’s firmware releases via AMD’s Platform Level Data Model (PLDM) bundle,
|
||||
which includes Integrated Firmware Image (IFWI).
|
||||
|
||||
GPU and baseboard firmware versioning might differ across GPU families. With the
|
||||
GPU and baseboard firmware versioning might differ across GPU families. Note that with the
|
||||
ROCm 7.0.0 release, the AMD GPU driver (amdgpu) is now versioned separately
|
||||
from ROCm. See [AMD GPU Driver/ROCm packaging separation](#amd-gpu-driver-rocm-packaging-separation).
|
||||
|
||||
|
||||
@@ -29,7 +29,7 @@ additional licenses. Please review individual repositories for more information.
|
||||
| [AMD SMI](https://github.com/ROCm/amdsmi) | [MIT](https://github.com/ROCm/amdsmi/blob/amd-staging/LICENSE) |
|
||||
| [aomp](https://github.com/ROCm/aomp/) | [Apache 2.0](https://github.com/ROCm/aomp/blob/aomp-dev/LICENSE) |
|
||||
| [aomp-extras](https://github.com/ROCm/aomp-extras/) | [MIT](https://github.com/ROCm/aomp-extras/blob/aomp-dev/LICENSE) |
|
||||
| [AQLprofile] | [MIT](https://github.com/ROCm/aqlprofile/blob/amd-staging/LICENSE.md) |
|
||||
| [AQLprofile](https://github.com/rocm/aqlprofile/) | [MIT](https://github.com/ROCm/aqlprofile/blob/amd-staging/LICENSE.md) |
|
||||
| [Code Object Manager (Comgr)](https://github.com/ROCm/llvm-project/tree/amd-staging/amd/comgr) | [The University of Illinois/NCSA](https://github.com/ROCm/llvm-project/blob/amd-staging/amd/comgr/LICENSE.txt) |
|
||||
| [Composable Kernel](https://github.com/ROCm/composable_kernel) | [MIT](https://github.com/ROCm/composable_kernel/blob/develop/LICENSE) |
|
||||
| [half](https://github.com/ROCm/half/) | [MIT](https://github.com/ROCm/half/blob/rocm/LICENSE.txt) |
|
||||
|
||||
@@ -72,7 +72,7 @@ the |docker-icon| icon to view the image on Docker Hub.
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.4.2-py3.12-tf2.18-dev/images/sha256-96754ce2d30f729e19b497279915b5212ba33d5e408e7e5dd3f2304d87e3441e"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
|
||||
|
||||
- `tensorflow-rocm 2.18.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.2/tensorflow_rocm-2.18.1-cp312-cp312-manylinux_2_28_x86_64.whl>`__
|
||||
- `tensorflow-rocm 2.18.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.2/>`__
|
||||
- 24.04
|
||||
- `Python 3.12 <https://www.python.org/downloads/release/python-31210/>`__
|
||||
- `TensorBoard 2.18.0 <https://github.com/tensorflow/tensorboard/tree/2.18.0>`__
|
||||
@@ -81,7 +81,7 @@ the |docker-icon| icon to view the image on Docker Hub.
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.4.2-py3.10-tf2.18-dev/images/sha256-fa741508d383858e86985a9efac85174529127408102558ae2e3a4ac894eea1e"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
|
||||
|
||||
- `tensorflow-rocm 2.18.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.2/tensorflow_rocm-2.18.1-cp310-cp310-manylinux_2_28_x86_64.whl>`__
|
||||
- `tensorflow-rocm 2.18.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.2/>`__
|
||||
- 22.04
|
||||
- `Python 3.10 <https://www.python.org/downloads/release/python-31017/>`__
|
||||
- `TensorBoard 2.18.0 <https://github.com/tensorflow/tensorboard/tree/2.18.0>`__
|
||||
@@ -90,7 +90,7 @@ the |docker-icon| icon to view the image on Docker Hub.
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.4.2-py3.12-tf2.17-dev/images/sha256-3a0aef09f2a8833c2b64b85874dd9449ffc2ad257351857338ff5b706c03a418"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
|
||||
|
||||
- `tensorflow-rocm 2.17.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.2/tensorflow_rocm-2.17.1-cp312-cp312-manylinux_2_28_x86_64.whl>`__
|
||||
- `tensorflow-rocm 2.17.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.2/>`__
|
||||
- 24.04
|
||||
- `Python 3.12 <https://www.python.org/downloads/release/python-31210/>`__
|
||||
- `TensorBoard 2.17.1 <https://github.com/tensorflow/tensorboard/tree/2.17.1>`__
|
||||
@@ -99,7 +99,7 @@ the |docker-icon| icon to view the image on Docker Hub.
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.4.2-py3.10-tf2.17-dev/images/sha256-bc7341a41ebe7ab261aa100732874507c452421ef733e408ac4f05ed453b0bc5"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
|
||||
|
||||
- `tensorflow-rocm 2.17.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.2/tensorflow_rocm-2.17.1-cp310-cp310-manylinux_2_28_x86_64.whl>`__
|
||||
- `tensorflow-rocm 2.17.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.2/>`__
|
||||
- 22.04
|
||||
- `Python 3.10 <https://www.python.org/downloads/release/python-31017/>`__
|
||||
- `TensorBoard 2.17.1 <https://github.com/tensorflow/tensorboard/tree/2.17.1>`__
|
||||
@@ -108,7 +108,7 @@ the |docker-icon| icon to view the image on Docker Hub.
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.4.2-py3.12-tf2.16-dev/images/sha256-4841a8df7c340dab79bf9362dad687797649a00d594e0832eb83ea6880a40d3b"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
|
||||
|
||||
- `tensorflow-rocm 2.16.2 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.2/tensorflow_rocm-2.16.2-cp312-cp312-manylinux_2_28_x86_64.whl>`__
|
||||
- `tensorflow-rocm 2.16.2 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.2/>`__
|
||||
- 24.04
|
||||
- `Python 3.12 <https://www.python.org/downloads/release/python-31210/>`__
|
||||
- `TensorBoard 2.16.2 <https://github.com/tensorflow/tensorboard/tree/2.16.2>`__
|
||||
@@ -117,7 +117,7 @@ the |docker-icon| icon to view the image on Docker Hub.
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.4.2-py3.10-tf2.16-dev/images/sha256-883fa95aba960c58a3e46fceaa18f03ede2c7df89b8e9fd603ab2d47e0852897"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
|
||||
|
||||
- `tensorflow-rocm 2.16.2 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.2/tensorflow_rocm-2.16.2-cp310-cp310-manylinux_2_28_x86_64.whl>`__
|
||||
- `tensorflow-rocm 2.16.2 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.2/>`__
|
||||
- 22.04
|
||||
- `Python 3.10 <https://www.python.org/downloads/release/python-31017/>`__
|
||||
- `TensorBoard 2.16.2 <https://github.com/tensorflow/tensorboard/tree/2.16.2>`__
|
||||
|
||||
@@ -135,9 +135,13 @@ article_pages = [
|
||||
{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.4", "os": ["linux"]},
|
||||
{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.5", "os": ["linux"]},
|
||||
{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.6", "os": ["linux"]},
|
||||
{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.7", "os": ["linux"]},
|
||||
{"file": "how-to/rocm-for-ai/training/benchmark-docker/primus-pytorch", "os": ["linux"]},
|
||||
{"file": "how-to/rocm-for-ai/training/benchmark-docker/pytorch-training", "os": ["linux"]},
|
||||
{"file": "how-to/rocm-for-ai/training/benchmark-docker/jax-maxtext", "os": ["linux"]},
|
||||
{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/jax-maxtext-history", "os": ["linux"]},
|
||||
{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/jax-maxtext-v25.4", "os": ["linux"]},
|
||||
{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/jax-maxtext-v25.5", "os": ["linux"]},
|
||||
{"file": "how-to/rocm-for-ai/training/benchmark-docker/mpt-llm-foundry", "os": ["linux"]},
|
||||
|
||||
{"file": "how-to/rocm-for-ai/fine-tuning/index", "os": ["linux"]},
|
||||
@@ -162,6 +166,8 @@ article_pages = [
|
||||
{"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.9.0.1-20250702", "os": ["linux"]},
|
||||
{"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.9.1-20250702", "os": ["linux"]},
|
||||
{"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.9.1-20250715", "os": ["linux"]},
|
||||
{"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.10.0-20250812", "os": ["linux"]},
|
||||
{"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/sglang-history", "os": ["linux"]},
|
||||
{"file": "how-to/rocm-for-ai/inference/benchmark-docker/pytorch-inference", "os": ["linux"]},
|
||||
{"file": "how-to/rocm-for-ai/inference/deploy-your-model", "os": ["linux"]},
|
||||
|
||||
|
||||
@@ -0,0 +1,32 @@
|
||||
dockers:
|
||||
- pull_tag: lmsysorg/sglang:v0.5.2rc1-rocm700-mi30x
|
||||
docker_hub_url: https://hub.docker.com/layers/lmsysorg/sglang/v0.5.2rc1-rocm700-mi30x/images/sha256-10c4ee502ddba44dd8c13325e6e03868bfe7f43d23d0a44780a8ee8b393f4729
|
||||
components:
|
||||
ROCm: 7.0.0
|
||||
SGLang: v0.5.2rc1
|
||||
pytorch-triton-rocm: 3.4.0+rocm7.0.0.gitf9e5bf54
|
||||
model_groups:
|
||||
- group: Dense models
|
||||
tag: dense-models
|
||||
models:
|
||||
- model: Llama 3.1 8B Instruct
|
||||
model_repo: Llama-3.1-8B-Instruct
|
||||
url: https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct
|
||||
- model: Llama 3.1 405B FP8 KV
|
||||
model_repo: Llama-3.1-405B-Instruct-FP8-KV
|
||||
url: https://huggingface.co/amd/Llama-3.1-405B-Instruct-FP8-KV
|
||||
- model: Llama 3.3 70B FP8 KV
|
||||
model_repo: amd-Llama-3.3-70B-Instruct-FP8-KV
|
||||
url: https://huggingface.co/amd/Llama-3.3-70B-Instruct-FP8-KV
|
||||
- model: Qwen3 32B
|
||||
model_repo: Qwen3-32B
|
||||
url: https://huggingface.co/Qwen/Qwen3-32B
|
||||
- group: Small experts models
|
||||
tag: small-experts-models
|
||||
models:
|
||||
- model: DeepSeek V3
|
||||
model_repo: DeepSeek-V3
|
||||
url: https://huggingface.co/deepseek-ai/DeepSeek-V3
|
||||
- model: Mixtral 8x7B v0.1
|
||||
model_repo: Mixtral-8x7B-v0.1
|
||||
url: https://huggingface.co/mistralai/Mixtral-8x7B-v0.1
|
||||
@@ -0,0 +1,162 @@
|
||||
dockers:
|
||||
- pull_tag: rocm/pytorch-training:v25.7
|
||||
docker_hub_url: https://hub.docker.com/layers/rocm/pytorch-training/v25.7/images/sha256-cc6fd840ab89cb81d926fc29eca6d075aee9875a55a522675a4b9231c9a0a712
|
||||
components:
|
||||
ROCm: 6.4.2
|
||||
PyTorch: 2.8.0a0+gitd06a406
|
||||
Python: 3.10.18
|
||||
Transformer Engine: 2.2.0.dev0+94e53dd8
|
||||
Flash Attention: 3.0.0.post1
|
||||
hipBLASLt: 1.1.0-4b9a52edfc
|
||||
Triton: 3.3.0
|
||||
model_groups:
|
||||
- group: Meta Llama
|
||||
tag: llama
|
||||
models:
|
||||
- model: Llama 4 Scout 17B-16E
|
||||
mad_tag: pyt_train_llama-4-scout-17b-16e
|
||||
model_repo: Llama-4-17B_16E
|
||||
url: https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E
|
||||
precision: BF16
|
||||
training_modes: [finetune_fw, finetune_lora]
|
||||
- model: Llama 3.3 70B
|
||||
mad_tag: pyt_train_llama-3.3-70b
|
||||
model_repo: Llama-3.3-70B
|
||||
url: https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct
|
||||
precision: BF16
|
||||
training_modes: [finetune_fw, finetune_lora, finetune_qlora]
|
||||
- model: Llama 3.2 1B
|
||||
mad_tag: pyt_train_llama-3.2-1b
|
||||
model_repo: Llama-3.2-1B
|
||||
url: https://huggingface.co/meta-llama/Llama-3.2-1B
|
||||
precision: BF16
|
||||
training_modes: [finetune_fw, finetune_lora]
|
||||
- model: Llama 3.2 3B
|
||||
mad_tag: pyt_train_llama-3.2-3b
|
||||
model_repo: Llama-3.2-3B
|
||||
url: https://huggingface.co/meta-llama/Llama-3.2-3B
|
||||
precision: BF16
|
||||
training_modes: [finetune_fw, finetune_lora]
|
||||
- model: Llama 3.2 Vision 11B
|
||||
mad_tag: pyt_train_llama-3.2-vision-11b
|
||||
model_repo: Llama-3.2-Vision-11B
|
||||
url: https://huggingface.co/meta-llama/Llama-3.2-11B-Vision
|
||||
precision: BF16
|
||||
training_modes: [finetune_fw]
|
||||
- model: Llama 3.2 Vision 90B
|
||||
mad_tag: pyt_train_llama-3.2-vision-90b
|
||||
model_repo: Llama-3.2-Vision-90B
|
||||
url: https://huggingface.co/meta-llama/Llama-3.2-90B-Vision
|
||||
precision: BF16
|
||||
training_modes: [finetune_fw]
|
||||
- model: Llama 3.1 8B
|
||||
mad_tag: pyt_train_llama-3.1-8b
|
||||
model_repo: Llama-3.1-8B
|
||||
url: https://huggingface.co/meta-llama/Llama-3.1-8B
|
||||
precision: BF16
|
||||
training_modes: [pretrain, finetune_fw, finetune_lora, HF_pretrain]
|
||||
- model: Llama 3.1 70B
|
||||
mad_tag: pyt_train_llama-3.1-70b
|
||||
model_repo: Llama-3.1-70B
|
||||
url: https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct
|
||||
precision: BF16
|
||||
training_modes: [pretrain, finetune_fw, finetune_lora]
|
||||
- model: Llama 3.1 405B
|
||||
mad_tag: pyt_train_llama-3.1-405b
|
||||
model_repo: Llama-3.1-405B
|
||||
url: https://huggingface.co/meta-llama/Llama-3.1-405B
|
||||
precision: BF16
|
||||
training_modes: [finetune_qlora]
|
||||
- model: Llama 3 8B
|
||||
mad_tag: pyt_train_llama-3-8b
|
||||
model_repo: Llama-3-8B
|
||||
url: https://huggingface.co/meta-llama/Meta-Llama-3-8B
|
||||
precision: BF16
|
||||
training_modes: [finetune_fw, finetune_lora]
|
||||
- model: Llama 3 70B
|
||||
mad_tag: pyt_train_llama-3-70b
|
||||
model_repo: Llama-3-70B
|
||||
url: https://huggingface.co/meta-llama/Meta-Llama-3-70B
|
||||
precision: BF16
|
||||
training_modes: [finetune_fw, finetune_lora]
|
||||
- model: Llama 2 7B
|
||||
mad_tag: pyt_train_llama-2-7b
|
||||
model_repo: Llama-2-7B
|
||||
url: https://github.com/meta-llama/llama-models/tree/main/models/llama2
|
||||
precision: BF16
|
||||
training_modes: [finetune_fw, finetune_lora, finetune_qlora]
|
||||
- model: Llama 2 13B
|
||||
mad_tag: pyt_train_llama-2-13b
|
||||
model_repo: Llama-2-13B
|
||||
url: https://github.com/meta-llama/llama-models/tree/main/models/llama2
|
||||
precision: BF16
|
||||
training_modes: [finetune_fw, finetune_lora]
|
||||
- model: Llama 2 70B
|
||||
mad_tag: pyt_train_llama-2-70b
|
||||
model_repo: Llama-2-70B
|
||||
url: https://github.com/meta-llama/llama-models/tree/main/models/llama2
|
||||
precision: BF16
|
||||
training_modes: [finetune_lora, finetune_qlora]
|
||||
- group: OpenAI
|
||||
tag: openai
|
||||
models:
|
||||
- model: GPT OSS 20B
|
||||
mad_tag: pyt_train_gpt_oss_20b
|
||||
model_repo: GPT-OSS-20B
|
||||
url: https://huggingface.co/openai/gpt-oss-20b
|
||||
precision: BF16
|
||||
training_modes: [HF_finetune_lora]
|
||||
- model: GPT OSS 120B
|
||||
mad_tag: pyt_train_gpt_oss_120b
|
||||
model_repo: GPT-OSS-120B
|
||||
url: https://huggingface.co/openai/gpt-oss-120b
|
||||
precision: BF16
|
||||
training_modes: [HF_finetune_lora]
|
||||
- group: Qwen
|
||||
tag: qwen
|
||||
models:
|
||||
- model: Qwen 3 8B
|
||||
mad_tag: pyt_train_qwen3-8b
|
||||
model_repo: Qwen3-8B
|
||||
url: https://huggingface.co/Qwen/Qwen3-8B
|
||||
precision: BF16
|
||||
training_modes: [finetune_fw, finetune_lora]
|
||||
- model: Qwen 3 32B
|
||||
mad_tag: pyt_train_qwen3-32b
|
||||
model_repo: Qwen3-32
|
||||
url: https://huggingface.co/Qwen/Qwen3-32B
|
||||
precision: BF16
|
||||
training_modes: [finetune_lora]
|
||||
- model: Qwen 2.5 32B
|
||||
mad_tag: pyt_train_qwen2.5-32b
|
||||
model_repo: Qwen2.5-32B
|
||||
url: https://huggingface.co/Qwen/Qwen2.5-32B
|
||||
precision: BF16
|
||||
training_modes: [finetune_lora]
|
||||
- model: Qwen 2.5 72B
|
||||
mad_tag: pyt_train_qwen2.5-72b
|
||||
model_repo: Qwen2.5-72B
|
||||
url: https://huggingface.co/Qwen/Qwen2.5-72B
|
||||
precision: BF16
|
||||
training_modes: [finetune_lora]
|
||||
- model: Qwen 2 1.5B
|
||||
mad_tag: pyt_train_qwen2-1.5b
|
||||
model_repo: Qwen2-1.5B
|
||||
url: https://huggingface.co/Qwen/Qwen2-1.5B
|
||||
precision: BF16
|
||||
training_modes: [finetune_fw, finetune_lora]
|
||||
- model: Qwen 2 7B
|
||||
mad_tag: pyt_train_qwen2-7b
|
||||
model_repo: Qwen2-7B
|
||||
url: https://huggingface.co/Qwen/Qwen2-7B
|
||||
precision: BF16
|
||||
training_modes: [finetune_fw, finetune_lora]
|
||||
- group: Flux
|
||||
tag: flux
|
||||
models:
|
||||
- model: FLUX.1-dev
|
||||
mad_tag: pyt_train_flux
|
||||
model_repo: Flux
|
||||
url: https://huggingface.co/black-forest-labs/FLUX.1-dev
|
||||
precision: BF16
|
||||
training_modes: [pretrain]
|
||||
@@ -0,0 +1,24 @@
|
||||
dockers:
|
||||
- pull_tag: rocm/pytorch-training:v25.8
|
||||
docker_hub_url: https://hub.docker.com/layers/rocm/pytorch-training/v25.8/images/sha256-5082ae01d73fec6972b0d84e5dad78c0926820dcf3c19f301d6c8eb892e573c5
|
||||
components:
|
||||
ROCm: 6.4.3
|
||||
PyTorch: 2.8.0a0+gitd06a406
|
||||
Python: 3.10.18
|
||||
Transformer Engine: 2.2.0.dev0+a1e66aae
|
||||
Flash Attention: 3.0.0.post1
|
||||
hipBLASLt: 1.1.0-d1b517fc7a
|
||||
model_groups:
|
||||
- group: Meta Llama
|
||||
tag: llama
|
||||
models:
|
||||
- model: Llama 3.1 8B
|
||||
mad_tag: primus_pyt_train_llama-3.1-8b
|
||||
model_repo: Llama-3.1-8B
|
||||
url: https://huggingface.co/meta-llama/Llama-3.1-8B
|
||||
precision: BF16
|
||||
- model: Llama 3.1 70B
|
||||
mad_tag: primus_pyt_train_llama-3.1-70b
|
||||
model_repo: Llama-3.1-70B
|
||||
url: https://huggingface.co/meta-llama/Llama-3.1-70B
|
||||
precision: BF16
|
||||
@@ -1,14 +1,13 @@
|
||||
dockers:
|
||||
- pull_tag: rocm/pytorch-training:v25.7
|
||||
docker_hub_url: https://hub.docker.com/layers/rocm/pytorch-training/v25.7/images/sha256-cc6fd840ab89cb81d926fc29eca6d075aee9875a55a522675a4b9231c9a0a712
|
||||
- pull_tag: rocm/pytorch-training:v25.8
|
||||
docker_hub_url: https://hub.docker.com/layers/rocm/pytorch-training/v25.8/images/sha256-5082ae01d73fec6972b0d84e5dad78c0926820dcf3c19f301d6c8eb892e573c5
|
||||
components:
|
||||
ROCm: 6.4.2
|
||||
ROCm: 6.4.3
|
||||
PyTorch: 2.8.0a0+gitd06a406
|
||||
Python: 3.10.18
|
||||
Transformer Engine: 2.2.0.dev0+94e53dd8
|
||||
Transformer Engine: 2.2.0.dev0+a1e66aae
|
||||
Flash Attention: 3.0.0.post1
|
||||
hipBLASLt: 1.1.0-4b9a52edfc
|
||||
Triton: 3.3.0
|
||||
hipBLASLt: 1.1.0-d1b517fc7a
|
||||
model_groups:
|
||||
- group: Meta Llama
|
||||
tag: llama
|
||||
@@ -151,6 +150,15 @@ model_groups:
|
||||
url: https://huggingface.co/Qwen/Qwen2-7B
|
||||
precision: BF16
|
||||
training_modes: [finetune_fw, finetune_lora]
|
||||
- group: Stable Diffusion
|
||||
tag: sd
|
||||
models:
|
||||
- model: Stable Diffusion XL
|
||||
mad_tag: pyt_huggingface_stable_diffusion_xl_2k_lora_finetuning
|
||||
model_repo: SDXL
|
||||
url: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0
|
||||
precision: BF16
|
||||
training_modes: [finetune_lora]
|
||||
- group: Flux
|
||||
tag: flux
|
||||
models:
|
||||
@@ -160,3 +168,11 @@ model_groups:
|
||||
url: https://huggingface.co/black-forest-labs/FLUX.1-dev
|
||||
precision: BF16
|
||||
training_modes: [pretrain]
|
||||
- group: NCF
|
||||
tag: ncf
|
||||
models:
|
||||
- model: NCF
|
||||
mad_tag: pyt_ncf_training
|
||||
model_repo:
|
||||
url: https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Recommendation/NCF
|
||||
precision: FP32
|
||||
|
||||
@@ -16,103 +16,112 @@ previous releases of the ``ROCm/vllm`` Docker image on `Docker Hub <https://hub.
|
||||
- Components
|
||||
- Resources
|
||||
|
||||
* - ``rocm/vllm:rocm6.4.1_vllm_0.10.0_20250812``
|
||||
* - ``rocm/vllm:rocm6.4.1_vllm_0.10.1_20250909``
|
||||
(latest)
|
||||
-
|
||||
-
|
||||
* ROCm 6.4.1
|
||||
* vLLM 0.10.1
|
||||
* PyTorch 2.7.0
|
||||
-
|
||||
* :doc:`Documentation <../vllm>`
|
||||
* `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm6.4.1_vllm_0.10.1_20250909/images/sha256-1113268572e26d59b205792047bea0e61e018e79aeadceba118b7bf23cb3715c>`__
|
||||
|
||||
* - ``rocm/vllm:rocm6.4.1_vllm_0.10.0_20250812``
|
||||
-
|
||||
* ROCm 6.4.1
|
||||
* vLLM 0.10.0
|
||||
* PyTorch 2.7.0
|
||||
-
|
||||
* :doc:`Documentation <../vllm>`
|
||||
-
|
||||
* :doc:`Documentation <vllm-0.10.0-20250812>`
|
||||
* `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm6.4.1_vllm_0.10.0_20250812/images/sha256-4c277ad39af3a8c9feac9b30bf78d439c74d9b4728e788a419d3f1d0c30cacaa>`__
|
||||
|
||||
* - ``rocm/vllm:rocm6.4.1_vllm_0.9.1_20250715``
|
||||
-
|
||||
-
|
||||
* ROCm 6.4.1
|
||||
* vLLM 0.9.1
|
||||
* PyTorch 2.7.0
|
||||
-
|
||||
-
|
||||
* :doc:`Documentation <vllm-0.9.1-20250715>`
|
||||
* `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm6.4.1_vllm_0.9.1_20250715/images/sha256-4a429705fa95a58f6d20aceab43b1b76fa769d57f32d5d28bd3f4e030e2a78ea>`__
|
||||
|
||||
* - ``rocm/vllm:rocm6.4.1_vllm_0.9.1_20250702``
|
||||
-
|
||||
-
|
||||
* ROCm 6.4.1
|
||||
* vLLM 0.9.1
|
||||
* PyTorch 2.7.0
|
||||
-
|
||||
-
|
||||
* :doc:`Documentation <vllm-0.9.1-20250702>`
|
||||
* `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm6.4.1_vllm_0.9.1_20250702/images/sha256-45068a2079cb8df554ed777141bf0c67d6627c470a897256e60c9f262677faab>`__
|
||||
|
||||
* - ``rocm/vllm:rocm6.4.1_vllm_0.9.0.1_20250605``
|
||||
-
|
||||
-
|
||||
* ROCm 6.4.1
|
||||
* vLLM 0.9.0.1
|
||||
* PyTorch 2.7.0
|
||||
-
|
||||
-
|
||||
* :doc:`Documentation <vllm-0.9.0.1-20250605>`
|
||||
* `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm6.4.1_vllm_0.9.0.1_20250605/images/sha256-f48beeb3d72663a93c77211eb45273d564451447c097e060befa713d565fa36c>`__
|
||||
|
||||
* - ``rocm/vllm:rocm6.3.1_vllm_0.8.5_20250521``
|
||||
-
|
||||
-
|
||||
* ROCm 6.3.1
|
||||
* 0.8.5 vLLM (0.8.6.dev)
|
||||
* PyTorch 2.7.0
|
||||
-
|
||||
-
|
||||
* :doc:`Documentation <vllm-0.8.5-20250521>`
|
||||
* `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm6.3.1_vllm_0.8.5_20250521/images/sha256-38410c51af7208897cd8b737c9bdfc126e9bc8952d4aa6b88c85482f03092a11>`__
|
||||
|
||||
* - ``rocm/vllm:rocm6.3.1_vllm_0.8.5_20250513``
|
||||
-
|
||||
-
|
||||
* ROCm 6.3.1
|
||||
* vLLM 0.8.5
|
||||
* PyTorch 2.7.0
|
||||
-
|
||||
-
|
||||
* :doc:`Documentation <vllm-0.8.5-20250513>`
|
||||
* `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm6.3.1_vllm_0.8.5_20250513/images/sha256-5c8b4436dd0464119d9df2b44c745fadf81512f18ffb2f4b5dc235c71ebe26b4>`__
|
||||
|
||||
* - ``rocm/vllm:rocm6.3.1_instinct_vllm0.8.3_20250415``
|
||||
-
|
||||
-
|
||||
* ROCm 6.3.1
|
||||
* vLLM 0.8.3
|
||||
* PyTorch 2.7.0
|
||||
-
|
||||
-
|
||||
* :doc:`Documentation <vllm-0.8.3-20250415>`
|
||||
* `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm6.3.1_instinct_vllm0.8.3_20250415/images/sha256-ad9062dea3483d59dedb17c67f7c49f30eebd6eb37c3fac0a171fb19696cc845>`__
|
||||
|
||||
* - ``rocm/vllm:rocm6.3.1_instinct_vllm0.7.3_20250325``
|
||||
-
|
||||
-
|
||||
* ROCm 6.3.1
|
||||
* vLLM 0.7.3
|
||||
* PyTorch 2.7.0
|
||||
-
|
||||
-
|
||||
* :doc:`Documentation <vllm-0.7.3-20250325>`
|
||||
* `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm6.3.1_instinct_vllm0.7.3_20250325/images/sha256-25245924f61750b19be6dcd8e787e46088a496c1fe17ee9b9e397f3d84d35640>`__
|
||||
|
||||
* - ``rocm/vllm:rocm6.3.1_mi300_ubuntu22.04_py3.12_vllm_0.6.6``
|
||||
-
|
||||
-
|
||||
* ROCm 6.3.1
|
||||
* vLLM 0.6.6
|
||||
* PyTorch 2.7.0
|
||||
-
|
||||
-
|
||||
* :doc:`Documentation <vllm-0.6.6>`
|
||||
* `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm6.3.1_mi300_ubuntu22.04_py3.12_vllm_0.6.6/images/sha256-9a12ef62bbbeb5a4c30a01f702c8e025061f575aa129f291a49fbd02d6b4d6c9>`__
|
||||
|
||||
* - ``rocm/vllm:rocm6.2_mi300_ubuntu20.04_py3.9_vllm_0.6.4``
|
||||
-
|
||||
-
|
||||
* ROCm 6.2.1
|
||||
* vLLM 0.6.4
|
||||
* PyTorch 2.5.0
|
||||
-
|
||||
-
|
||||
* :doc:`Documentation <vllm-0.6.4>`
|
||||
* `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm6.2_mi300_ubuntu20.04_py3.9_vllm_0.6.4/images/sha256-ccbb74cc9e7adecb8f7bdab9555f7ac6fc73adb580836c2a35ca96ff471890d8>`__
|
||||
|
||||
* - ``rocm/vllm:rocm6.2_mi300_ubuntu22.04_py3.9_vllm_7c5fd50``
|
||||
-
|
||||
-
|
||||
* ROCm 6.2.0
|
||||
* vLLM 0.4.3
|
||||
* PyTorch 2.4.0
|
||||
-
|
||||
-
|
||||
* :doc:`Documentation <vllm-0.4.3>`
|
||||
* `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm6.2_mi300_ubuntu22.04_py3.9_vllm_7c5fd50/images/sha256-9e4dd4788a794c3d346d7d0ba452ae5e92d39b8dfac438b2af8efdc7f15d22c0>`__
|
||||
|
||||
|
||||
@@ -0,0 +1,257 @@
|
||||
.. meta::
|
||||
:description: SGLang multi-node disaggregated distributed inference using Mooncake
|
||||
:keywords: model, sglang, mooncake, disagg, disaggregated, distributed, multi-node, docker
|
||||
|
||||
******************************************
|
||||
SGLang distributed inference with Mooncake
|
||||
******************************************
|
||||
|
||||
As LLM inference increasingly demands handling massive models and dynamic workloads, efficient
|
||||
distributed inference becomes essential. Traditional co-located architectures face bottlenecks due
|
||||
to tightly coupled memory and compute resources, which limits scalability and flexibility.
|
||||
Disaggregated inference refers to the process of splitting the inference of LLMs into distinct
|
||||
phases. This architecture, facilitated by libraries like Mooncake, uses high-bandwidth
|
||||
RDMA to transfer the Key-Value (KV) cache between prefill and decode nodes.
|
||||
This allows for independent resource scaling and optimization, resulting in
|
||||
improved efficiency and throughput.
|
||||
|
||||
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/sglang-distributed-benchmark-models.yaml
|
||||
|
||||
{% set docker = data.dockers[0] %}
|
||||
|
||||
`SGLang <https://docs.sglang.ai>`__ is a high-performance inference and
|
||||
serving engine for large language models (LLMs) and vision models. The
|
||||
ROCm-enabled `SGLang base Docker image <{{ docker.docker_hub_url }}>`__
|
||||
bundles SGLang with PyTorch, which is optimized for AMD Instinct MI300X series
|
||||
accelerators. It includes the following software components:
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Software component
|
||||
- Version
|
||||
|
||||
{% for component_name, component_version in docker.components.items() %}
|
||||
* - {{ component_name }}
|
||||
- {{ component_version }}
|
||||
{% endfor %}
|
||||
|
||||
The following guides on setting up and running SGLang and Mooncake for disaggregated
|
||||
distributed inference on a Slurm cluster using AMD Instinct MI300X series accelerators backed by
|
||||
Mellanox CX-7 NICs.
|
||||
|
||||
Prerequisites
|
||||
=============
|
||||
|
||||
Before starting, ensure you have:
|
||||
|
||||
* A Slurm cluster with at least three nodes: one for the proxy, one for prefill (``xP``), and one for decode (``yD``).
|
||||
|
||||
``Nodes -> xP + yD + 1``
|
||||
|
||||
* A Dockerized environment with SGLang, Mooncake, etcd, and NIC drivers built in. See :ref:`sglang-disagg-inf-build-docker-image` for instructions.
|
||||
|
||||
* A shared filesystem for storing models, scripts, and logs (cluster-specific).
|
||||
|
||||
Supported models
|
||||
================
|
||||
|
||||
The following models are supported for SGLang disaggregated prefill/decode
|
||||
inference. Some instructions, commands, and recommendations in this
|
||||
documentation might vary by selected model.
|
||||
|
||||
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/sglang-distributed-benchmark-models.yaml
|
||||
|
||||
{% set model_groups = data.model_groups %}
|
||||
.. raw:: html
|
||||
|
||||
<div id="vllm-benchmark-ud-params-picker" class="container-fluid">
|
||||
<div class="row gx-0">
|
||||
<div class="col-2 me-1 px-2 model-param-head">Model type</div>
|
||||
<div class="row col-10 pe-0">
|
||||
{% for model_group in model_groups %}
|
||||
<div class="col-6 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
|
||||
{% endfor %}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="row gx-0 pt-1">
|
||||
<div class="col-2 me-1 px-2 model-param-head">Model</div>
|
||||
<div class="row col-10 pe-0">
|
||||
{% for model_group in model_groups %}
|
||||
{% set models = model_group.models %}
|
||||
{% for model in models %}
|
||||
{% if models|length % 3 == 0 %}
|
||||
<div class="col-4 px-2 model-param" data-param-k="model" data-param-v="{{ model.model_repo | lower }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
|
||||
{% else %}
|
||||
<div class="col-6 px-2 model-param" data-param-k="model" data-param-v="{{ model.model_repo | lower }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
|
||||
{% endif %}
|
||||
{% endfor %}
|
||||
{% endfor %}
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{% for model_group in model_groups %}
|
||||
{% for model in model_group.models %}
|
||||
|
||||
.. container:: model-doc {{ model.model_repo }}
|
||||
|
||||
.. note::
|
||||
|
||||
See the `{{ model.model }} model card on Hugging Face <{{ model.url }}>`__ to learn more about this model.
|
||||
Some models require access authorization prior to use through an external license agreement with a third party.
|
||||
|
||||
{% endfor %}
|
||||
{% endfor %}
|
||||
|
||||
.. _sglang-disagg-inf-build-docker-image:
|
||||
|
||||
Build the Docker image
|
||||
----------------------
|
||||
|
||||
Get the Dockerfile located in
|
||||
`<https://github.com/ROCm/MAD/blob/develop/docker/sglang_dissag_inference.ubuntu.amd.Dockerfile>`__.
|
||||
It uses `lmsysorg/sglang:v0.5.2rc1-rocm700-mi30x
|
||||
<https://hub.docker.com/layers/lmsysorg/sglang/v0.4.9.post1-rocm630/images/sha256-2f6b1748e4bcc70717875a7da76c87795fd8aa46a9646e08d38aa7232fc78538>`__
|
||||
as the base Docker image and installs the necessary components for Mooncake, etcd, and Mellanox network
|
||||
drivers.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
git clone https://github.com/ROCm/MAD.git
|
||||
cd MAD/docker
|
||||
docker build \
|
||||
-t sglang_disagg_pd_image \
|
||||
-f sglang_disagg_inference.ubuntu.amd.Dockerfile .
|
||||
|
||||
Benchmarking
|
||||
============
|
||||
|
||||
The `<https://github.com/ROCm/MAD/tree/develop/scripts/sglang_dissag>`__
|
||||
repository contains scripts to launch SGLang inference with prefill/decode
|
||||
disaggregation via Mooncake for supported models.
|
||||
|
||||
* `scripts/sglang_dissag/run_xPyD_models.slurm <https://github.com/ROCm/MAD/blob/develop/scripts/sglang_disagg/run_xPyD_models.slurm>`__
|
||||
-- the main Slurm batch script to launch Docker containers on all nodes using ``sbatch`` or ``salloc``.
|
||||
|
||||
* `scripts/sglang_dissag/sglang_disagg_server.sh <https://github.com/ROCm/MAD/blob/develop/scripts/sglang_disagg/sglang_disagg_server.sh>`__
|
||||
-- the entrypoint script that runs inside each container to start the correct service -- proxy, prefill, or decode.
|
||||
|
||||
* `scripts/sglang_dissag/benchmark_xPyD.sh <https://github.com/ROCm/MAD/blob/develop/scripts/sglang_disagg/benchmark_xPyD.sh>`__
|
||||
-- the benchmark script to run the GSM8K accuracy benchmark and the SGLang benchmarking tool for performance measurement.
|
||||
|
||||
* `scripts/sglang_dissag/benchmark_parser.py <https://github.com/ROCm/MAD/blob/develop/scripts/sglang_disagg/benchmark_parser.py>`__
|
||||
-- the log parser script to be run on the concurrency benchmark log file to generate tabulated data.
|
||||
|
||||
Launch the service
|
||||
------------------
|
||||
|
||||
The service is deployed using a Slurm batch script that orchestrates the containers across the
|
||||
allocated nodes.
|
||||
|
||||
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/sglang-distributed-benchmark-models.yaml
|
||||
|
||||
{% set model_groups = data.model_groups %}
|
||||
{% for model_group in model_groups %}
|
||||
{% for model in model_group.models %}
|
||||
|
||||
.. container:: model-doc {{ model.model_repo }}
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
# Clone the MAD repo if you haven't already and
|
||||
# navigate to the scripts directory
|
||||
git clone https://github.com/ROCm/MAD.git
|
||||
cd MAD/scripts/sglang_disagg/
|
||||
|
||||
# Slurm sbatch run command
|
||||
export DOCKER_IMAGE_NAME=sglang_disagg_pd_image
|
||||
export xP=<num_prefill_nodes>
|
||||
export yD=<num_decode_nodes>
|
||||
export MODEL_NAME={{ model.model_repo }}
|
||||
# num_nodes = xP + yD + 1
|
||||
sbatch -N <num_nodes> -n <num_nodes> --nodelist=<Nodes> run_xPyD_models.slurm
|
||||
|
||||
{% endfor %}
|
||||
{% endfor %}
|
||||
|
||||
Post-run logs and testing
|
||||
-------------------------
|
||||
|
||||
Logs are stored in your shared filesystem in the directory specified by the ``LOG_PATH`` variable in the Slurm script.
|
||||
A new directory named after the Slurm job ID is created for each run.
|
||||
|
||||
Inside that directory, you can access various logs:
|
||||
|
||||
* ``pd_sglang_bench_serving.sh_NODE<...>.log`` -- the main log for each server node.
|
||||
|
||||
* ``etcd_NODE<...>.log`` -- logs for etcd services.
|
||||
|
||||
* ``prefill_NODE<...>.log`` -- logs for the prefill services.
|
||||
|
||||
* ``decode_NODE<...>.log`` -- logs for the decode services.
|
||||
|
||||
Use the benchmark parser script for concurrency logs to tabulate different data.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
python3 benchmark_parser.py <log_path/benchmark_XXX_CONCURRENCY.log>
|
||||
|
||||
To verify the service is responsive, you can try sending a ``curl`` request to test the launched
|
||||
server from the Docker container on the proxy node. For example:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
curl -X POST http://127.0.0.1:30000/generate \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{ "text": "Let me tell you a story ", "sampling_params": { "temperature": 0.3 } }'
|
||||
|
||||
Known issues
|
||||
============
|
||||
|
||||
When running larger models, such as DeepSeek-V3 and Llama-3.1-405B-Instruct-FP8-KV, at
|
||||
higher concurrency levels (512+), the following error might occur:
|
||||
|
||||
.. code-block:: shell-session
|
||||
|
||||
<TransferEncodingError: 400, message:
|
||||
Not enough data to satisfy transfer length header.
|
||||
|
||||
The above exception was the direct cause of the following exception:
|
||||
|
||||
Traceback (most recent call last):
|
||||
...
|
||||
|
||||
This leads to dropping requests and lower throughput.
|
||||
|
||||
Further reading
|
||||
===============
|
||||
|
||||
- To learn about Mooncake, see `Welcome to Mooncake <https://kvcache-ai.github.io/Mooncake/>`__.
|
||||
|
||||
- To learn more about the options for latency and throughput benchmark scripts,
|
||||
see `<https://github.com/sgl-project/sglang/tree/main/benchmark/blog_v0_2>`__.
|
||||
|
||||
- See the base upstream Docker image on `Docker Hub <https://hub.docker.com/layers/lmsysorg/sglang/v0.5.2rc1-rocm700-mi30x/images/sha256-10c4ee502ddba44dd8c13325e6e03868bfe7f43d23d0a44780a8ee8b393f4729>`__.
|
||||
|
||||
- To learn more about system settings and management practices to configure your system for
|
||||
MI300X series accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`__.
|
||||
|
||||
- For application performance optimization strategies for HPC and AI workloads,
|
||||
including inference with vLLM, see :doc:`/how-to/rocm-for-ai/inference-optimization/workload`.
|
||||
|
||||
- To learn how to run community models from Hugging Face on AMD GPUs, see
|
||||
:doc:`Running models from Hugging Face </how-to/rocm-for-ai/inference/hugging-face-models>`.
|
||||
|
||||
- To learn how to fine-tune LLMs and optimize inference, see
|
||||
:doc:`Fine-tuning LLMs and inference optimization </how-to/rocm-for-ai/fine-tuning/fine-tuning-and-inference>`.
|
||||
|
||||
- For a list of other ready-made Docker images for AI with ROCm, see
|
||||
`AMD Infinity Hub <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`_.
|
||||
|
||||
Previous versions
|
||||
=================
|
||||
|
||||
See :doc:`previous-versions/sglang-history` to find documentation for previous releases
|
||||
of SGLang inference performance testing.
|
||||
@@ -1,6 +1,5 @@
|
||||
.. meta::
|
||||
:description: Learn how to validate LLM inference performance on MI300X accelerators using AMD MAD and the
|
||||
ROCm vLLM Docker image.
|
||||
:description: Learn how to validate LLM inference performance on MI300X accelerators using AMD MAD and the ROCm vLLM Docker image.
|
||||
:keywords: model, MAD, automation, dashboarding, validate
|
||||
|
||||
**********************************
|
||||
|
||||
@@ -3,7 +3,7 @@
|
||||
:keywords: ROCm, AI, LLM, train, jax, torch, Llama, flux, tutorial, docker
|
||||
|
||||
******************************************
|
||||
Training a model with JAX MaxText for ROCm
|
||||
Training a model with JAX MaxText on ROCm
|
||||
******************************************
|
||||
|
||||
MaxText is a high-performance, open-source framework built on the Google JAX
|
||||
@@ -406,8 +406,6 @@ benchmark results:
|
||||
Further reading
|
||||
===============
|
||||
|
||||
- See the ROCm/maxtext benchmarking README at `<https://github.com/ROCm/maxtext/blob/main/benchmarks/gpu-rocm/readme.md>`__.
|
||||
|
||||
- To learn more about MAD and the ``madengine`` CLI, see the `MAD usage guide <https://github.com/ROCm/MAD?tab=readme-ov-file#usage-guide>`__.
|
||||
|
||||
- To learn more about system settings and management practices to configure your system for
|
||||
|
||||
@@ -5,15 +5,13 @@
|
||||
:keywords: ROCm, AI, LLM, train, Megatron-LM, megatron, Llama, tutorial, docker, torch
|
||||
|
||||
******************************************
|
||||
Training a model with Megatron-LM for ROCm
|
||||
Training a model with Megatron-LM on ROCm
|
||||
******************************************
|
||||
|
||||
.. caution::
|
||||
|
||||
The ROCm Megatron-LM framework now has limited support with this Docker
|
||||
environment; it now focuses on Primus with Megatron-Core. See :doc:`primus-megatron`.
|
||||
|
||||
To learn how to migrate your existing workloads to Primus with Megatron-Core,
|
||||
Primus with Megatron supersedes this ROCm Megatron-LM training workflow.
|
||||
To learn how to migrate workloads from Megatron-LM to Primus with Megatron,
|
||||
see :doc:`previous-versions/megatron-lm-primus-migration-guide`.
|
||||
|
||||
The `Megatron-LM framework for ROCm <https://github.com/ROCm/Megatron-LM>`_ is
|
||||
@@ -807,9 +805,16 @@ Single node training
|
||||
AC=none \
|
||||
SEQ_LEN=4096 \
|
||||
PAD_LEN=4096 \
|
||||
TRAIN_ITERS=50 \
|
||||
TRAIN_ITERS=20 \
|
||||
bash examples/deepseek_v2/train_deepseekv2.sh
|
||||
|
||||
.. note::
|
||||
|
||||
Note that DeepSeek-V2-Lite is experiencing instability due to GPU memory access fault
|
||||
for large iterations.
|
||||
For stability, it's recommended to use Primus for this workload.
|
||||
See :doc:`primus-megatron`.
|
||||
|
||||
.. container:: model-doc pyt_megatron_lm_train_mixtral-8x7b
|
||||
|
||||
To run training on a single node for Mixtral 8x7B (MoE with expert parallel),
|
||||
|
||||
@@ -3,7 +3,7 @@
|
||||
:keywords: ROCm, AI, LLM, train, PyTorch, torch, Llama, flux, tutorial, docker
|
||||
|
||||
******************************************
|
||||
Training MPT-30B with LLM Foundry and ROCm
|
||||
Training MPT-30B with LLM Foundry on ROCm
|
||||
******************************************
|
||||
|
||||
MPT-30B is a 30-billion parameter decoder-style transformer-based model from
|
||||
|
||||
@@ -4,7 +4,7 @@
|
||||
PyTorch training performance testing version history
|
||||
****************************************************
|
||||
|
||||
This table lists previous versions of the ROCm Megatron-LM training Docker image for
|
||||
This table lists previous versions of the ROCm PyTorch training Docker image for
|
||||
inference performance testing. For detailed information about available models
|
||||
for benchmarking, see the version-specific documentation. You can find tagged
|
||||
previous releases of the ``ROCm/pytorch-training`` Docker image on `Docker Hub <https://hub.docker.com/r/rocm/pytorch-training/tags>`_.
|
||||
@@ -16,12 +16,21 @@ previous releases of the ``ROCm/pytorch-training`` Docker image on `Docker Hub <
|
||||
- Components
|
||||
- Resources
|
||||
|
||||
* - v25.8 (latest)
|
||||
-
|
||||
* ROCm 6.4.3
|
||||
* PyTorch 2.8.0a0+gitd06a406
|
||||
-
|
||||
* :doc:`Primus PyTorch Training documentation <../primus-pytorch>`
|
||||
* :doc:`PyTorch training (legacy) documentation <../pytorch-training>`
|
||||
* `Docker Hub <https://hub.docker.com/r/rocm/pytorch-training/tags>`__
|
||||
|
||||
* - v25.7
|
||||
-
|
||||
* ROCm 6.4.2
|
||||
* PyTorch 2.8.0a0+gitd06a406
|
||||
-
|
||||
* :doc:`Documentation <../pytorch-training>`
|
||||
* :doc:`Documentation <pytorch-training-v25.7>`
|
||||
* `Docker Hub <https://hub.docker.com/layers/rocm/pytorch-training/v25.7/images/sha256-cc6fd840ab89cb81d926fc29eca6d075aee9875a55a522675a4b9231c9a0a712>`__
|
||||
|
||||
* - v25.6
|
||||
|
||||
@@ -0,0 +1,567 @@
|
||||
:orphan:
|
||||
|
||||
.. meta::
|
||||
:description: How to train a model using PyTorch for ROCm.
|
||||
:keywords: ROCm, AI, LLM, train, PyTorch, torch, Llama, flux, tutorial, docker
|
||||
|
||||
**************************************
|
||||
Training a model with PyTorch for ROCm
|
||||
**************************************
|
||||
|
||||
.. caution::
|
||||
|
||||
This documentation does not reflect the latest version of ROCm vLLM
|
||||
performance benchmark documentation. See :doc:`../pytorch-training` for the latest version.
|
||||
|
||||
PyTorch is an open-source machine learning framework that is widely used for
|
||||
model training with GPU-optimized components for transformer-based models.
|
||||
|
||||
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/pytorch-training-v25.7-benchmark-models.yaml
|
||||
|
||||
{% set dockers = data.dockers %}
|
||||
{% set docker = dockers[0] %}
|
||||
The `PyTorch for ROCm training Docker <{{ docker.docker_hub_url }}>`__
|
||||
(``{{ docker.pull_tag }}``) image provides a prebuilt optimized environment for fine-tuning and pretraining a
|
||||
model on AMD Instinct MI325X and MI300X accelerators. It includes the following software components to accelerate
|
||||
training workloads:
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Software component
|
||||
- Version
|
||||
|
||||
{% for component_name, component_version in docker.components.items() %}
|
||||
* - {{ component_name }}
|
||||
- {{ component_version }}
|
||||
{% endfor %}
|
||||
|
||||
.. _amd-pytorch-training-model-support-v257:
|
||||
|
||||
Supported models
|
||||
================
|
||||
|
||||
The following models are pre-optimized for performance on the AMD Instinct MI325X and MI300X accelerators.
|
||||
Some instructions, commands, and training recommendations in this documentation might
|
||||
vary by model -- select one to get started.
|
||||
|
||||
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/pytorch-training-v25.7-benchmark-models.yaml
|
||||
|
||||
{% set unified_docker = data.dockers[0] %}
|
||||
{% set model_groups = data.model_groups %}
|
||||
.. raw:: html
|
||||
|
||||
<div id="vllm-benchmark-ud-params-picker" class="container-fluid">
|
||||
<div class="row gx-0">
|
||||
<div class="col-2 me-1 px-2 model-param-head">Model</div>
|
||||
<div class="row col-10 pe-0">
|
||||
{% for model_group in model_groups %}
|
||||
<div class="col-3 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
|
||||
{% endfor %}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="row gx-0 pt-1">
|
||||
<div class="col-2 me-1 px-2 model-param-head">Variant</div>
|
||||
<div class="row col-10 pe-0">
|
||||
{% for model_group in model_groups %}
|
||||
{% set models = model_group.models %}
|
||||
{% for model in models %}
|
||||
{% if models|length % 3 == 0 %}
|
||||
<div class="col-4 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
|
||||
{% else %}
|
||||
<div class="col-6 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
|
||||
{% endif %}
|
||||
{% endfor %}
|
||||
{% endfor %}
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
|
||||
.. _amd-pytorch-training-supported-training-modes-v257:
|
||||
|
||||
The following table lists supported training modes per model.
|
||||
|
||||
.. dropdown:: Supported training modes
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Model
|
||||
- Supported training modes
|
||||
|
||||
{% for model_group in model_groups %}
|
||||
{% set models = model_group.models %}
|
||||
{% for model in models %}
|
||||
* - {{ model.model }}
|
||||
- ``{{ model.training_modes | join('``, ``') }}``
|
||||
|
||||
{% endfor %}
|
||||
{% endfor %}
|
||||
|
||||
.. note::
|
||||
|
||||
Some model and fine-tuning combinations are not listed. This is
|
||||
because the `upstream torchtune repository <https://github.com/pytorch/torchtune>`__
|
||||
doesn't provide default YAML configurations for them.
|
||||
For advanced usage, you can create a custom configuration to enable
|
||||
unlisted fine-tuning methods by using an existing file in the
|
||||
``/workspace/torchtune/recipes/configs`` directory as a template.
|
||||
|
||||
.. _amd-pytorch-training-performance-measurements-v257:
|
||||
|
||||
Performance measurements
|
||||
========================
|
||||
|
||||
To evaluate performance, the
|
||||
`Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html#tabs-a8deaeb413-item-21cea50186-tab>`_
|
||||
page provides reference throughput and latency measurements for training
|
||||
popular AI models.
|
||||
|
||||
.. note::
|
||||
|
||||
The performance data presented in
|
||||
`Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html#tabs-a8deaeb413-item-21cea50186-tab>`_
|
||||
should not be interpreted as the peak performance achievable by AMD
|
||||
Instinct MI325X and MI300X accelerators or ROCm software.
|
||||
|
||||
System validation
|
||||
=================
|
||||
|
||||
Before running AI workloads, it's important to validate that your AMD hardware is configured
|
||||
correctly and performing optimally.
|
||||
|
||||
If you have already validated your system settings, including aspects like NUMA auto-balancing, you
|
||||
can skip this step. Otherwise, complete the procedures in the :ref:`System validation and
|
||||
optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
|
||||
before starting training.
|
||||
|
||||
To test for optimal performance, consult the recommended :ref:`System health benchmarks
|
||||
<rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
|
||||
system's configuration.
|
||||
|
||||
This Docker image is optimized for specific model configurations outlined
|
||||
below. Performance can vary for other training workloads, as AMD
|
||||
doesn’t test configurations and run conditions outside those described.
|
||||
|
||||
Run training
|
||||
============
|
||||
|
||||
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/pytorch-training-v25.7-benchmark-models.yaml
|
||||
|
||||
{% set unified_docker = data.dockers[0] %}
|
||||
{% set model_groups = data.model_groups %}
|
||||
|
||||
Once the setup is complete, choose between two options to start benchmarking training:
|
||||
|
||||
.. tab-set::
|
||||
|
||||
.. tab-item:: MAD-integrated benchmarking
|
||||
|
||||
1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
|
||||
directory and install the required packages on the host machine.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
git clone https://github.com/ROCm/MAD
|
||||
cd MAD
|
||||
pip install -r requirements.txt
|
||||
|
||||
{% for model_group in model_groups %}
|
||||
{% for model in model_group.models %}
|
||||
|
||||
.. container:: model-doc {{ model.mad_tag }}
|
||||
|
||||
2. For example, use this command to run the performance benchmark test on the {{ model.model }} model
|
||||
using one node with the {{ model.precision }} data type on the host machine.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
|
||||
madengine run \
|
||||
--tags {{ model.mad_tag }} \
|
||||
--keep-model-dir \
|
||||
--live-output \
|
||||
--timeout 28800
|
||||
|
||||
MAD launches a Docker container with the name
|
||||
``container_ci-{{ model.mad_tag }}``. The latency and throughput reports of the
|
||||
model are collected in ``~/MAD/perf.csv``.
|
||||
|
||||
{% endfor %}
|
||||
{% endfor %}
|
||||
|
||||
.. tab-item:: Standalone benchmarking
|
||||
|
||||
.. rubric:: Download the Docker image and required packages
|
||||
|
||||
1. Use the following command to pull the Docker image from Docker Hub.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
docker pull {{ unified_docker.pull_tag }}
|
||||
|
||||
2. Run the Docker container.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
docker run -it \
|
||||
--device /dev/dri \
|
||||
--device /dev/kfd \
|
||||
--network host \
|
||||
--ipc host \
|
||||
--group-add video \
|
||||
--cap-add SYS_PTRACE \
|
||||
--security-opt seccomp=unconfined \
|
||||
--privileged \
|
||||
-v $HOME:$HOME \
|
||||
-v $HOME/.ssh:/root/.ssh \
|
||||
--shm-size 64G \
|
||||
--name training_env \
|
||||
{{ unified_docker.pull_tag }}
|
||||
|
||||
Use these commands if you exit the ``training_env`` container and need to return to it.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
docker start training_env
|
||||
docker exec -it training_env bash
|
||||
|
||||
3. In the Docker container, clone the `<https://github.com/ROCm/MAD>`__
|
||||
repository and navigate to the benchmark scripts directory
|
||||
``/workspace/MAD/scripts/pytorch_train``.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
git clone https://github.com/ROCm/MAD
|
||||
cd MAD/scripts/pytorch_train
|
||||
|
||||
.. rubric:: Prepare training datasets and dependencies
|
||||
|
||||
1. The following benchmarking examples require downloading models and datasets
|
||||
from Hugging Face. To ensure successful access to gated repos, set your
|
||||
``HF_TOKEN``.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
export HF_TOKEN=$your_personal_hugging_face_access_token
|
||||
|
||||
2. Run the setup script to install libraries and datasets needed for benchmarking.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
./pytorch_benchmark_setup.sh
|
||||
|
||||
.. container:: model-doc pyt_train_llama-3.1-8b
|
||||
|
||||
``pytorch_benchmark_setup.sh`` installs the following libraries for Llama 3.1 8B:
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Library
|
||||
- Reference
|
||||
|
||||
* - ``accelerate``
|
||||
- `Hugging Face Accelerate <https://huggingface.co/docs/accelerate/en/index>`_
|
||||
|
||||
* - ``datasets``
|
||||
- `Hugging Face Datasets <https://huggingface.co/docs/datasets/v3.2.0/en/index>`_ 3.2.0
|
||||
|
||||
.. container:: model-doc pyt_train_llama-3.1-70b
|
||||
|
||||
``pytorch_benchmark_setup.sh`` installs the following libraries for Llama 3.1 70B:
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Library
|
||||
- Reference
|
||||
|
||||
* - ``datasets``
|
||||
- `Hugging Face Datasets <https://huggingface.co/docs/datasets/v3.2.0/en/index>`_ 3.2.0
|
||||
|
||||
* - ``torchdata``
|
||||
- `TorchData <https://pytorch.org/data/beta/index.html>`_
|
||||
|
||||
* - ``tomli``
|
||||
- `Tomli <https://pypi.org/project/tomli/>`_
|
||||
|
||||
* - ``tiktoken``
|
||||
- `tiktoken <https://github.com/openai/tiktoken>`_
|
||||
|
||||
* - ``blobfile``
|
||||
- `blobfile <https://pypi.org/project/blobfile/>`_
|
||||
|
||||
* - ``tabulate``
|
||||
- `tabulate <https://pypi.org/project/tabulate/>`_
|
||||
|
||||
* - ``wandb``
|
||||
- `Weights & Biases <https://github.com/wandb/wandb>`_
|
||||
|
||||
* - ``sentencepiece``
|
||||
- `SentencePiece <https://github.com/google/sentencepiece>`_ 0.2.0
|
||||
|
||||
* - ``tensorboard``
|
||||
- `TensorBoard <https://www.tensorflow.org/tensorboard>`_ 2.18.0
|
||||
|
||||
.. container:: model-doc pyt_train_flux
|
||||
|
||||
``pytorch_benchmark_setup.sh`` installs the following libraries for FLUX:
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Library
|
||||
- Reference
|
||||
|
||||
* - ``accelerate``
|
||||
- `Hugging Face Accelerate <https://huggingface.co/docs/accelerate/en/index>`_
|
||||
|
||||
* - ``datasets``
|
||||
- `Hugging Face Datasets <https://huggingface.co/docs/datasets/v3.2.0/en/index>`_ 3.2.0
|
||||
|
||||
* - ``sentencepiece``
|
||||
- `SentencePiece <https://github.com/google/sentencepiece>`_ 0.2.0
|
||||
|
||||
* - ``tensorboard``
|
||||
- `TensorBoard <https://www.tensorflow.org/tensorboard>`_ 2.18.0
|
||||
|
||||
* - ``csvkit``
|
||||
- `csvkit <https://csvkit.readthedocs.io/en/latest/>`_ 2.0.1
|
||||
|
||||
* - ``deepspeed``
|
||||
- `DeepSpeed <https://github.com/deepspeedai/DeepSpeed>`_ 0.16.2
|
||||
|
||||
* - ``diffusers``
|
||||
- `Hugging Face Diffusers <https://huggingface.co/docs/diffusers/en/index>`_ 0.31.0
|
||||
|
||||
* - ``GitPython``
|
||||
- `GitPython <https://github.com/gitpython-developers/GitPython>`_ 3.1.44
|
||||
|
||||
* - ``opencv-python-headless``
|
||||
- `opencv-python-headless <https://pypi.org/project/opencv-python-headless/>`_ 4.10.0.84
|
||||
|
||||
* - ``peft``
|
||||
- `PEFT <https://huggingface.co/docs/peft/en/index>`_ 0.14.0
|
||||
|
||||
* - ``protobuf``
|
||||
- `Protocol Buffers <https://github.com/protocolbuffers/protobuf>`_ 5.29.2
|
||||
|
||||
* - ``pytest``
|
||||
- `PyTest <https://docs.pytest.org/en/stable/>`_ 8.3.4
|
||||
|
||||
* - ``python-dotenv``
|
||||
- `python-dotenv <https://pypi.org/project/python-dotenv/>`_ 1.0.1
|
||||
|
||||
* - ``seaborn``
|
||||
- `Seaborn <https://seaborn.pydata.org/>`_ 0.13.2
|
||||
|
||||
* - ``transformers``
|
||||
- `Transformers <https://huggingface.co/docs/transformers/en/index>`_ 4.47.0
|
||||
|
||||
``pytorch_benchmark_setup.sh`` downloads the following datasets from Hugging Face:
|
||||
|
||||
* `bghira/pseudo-camera-10k <https://huggingface.co/datasets/bghira/pseudo-camera-10k>`_
|
||||
|
||||
{% for model_group in model_groups %}
|
||||
{% for model in model_group.models %}
|
||||
{% set training_modes = model.training_modes %}
|
||||
{% set training_mode_descs = {
|
||||
"pretrain": "Benchmark pre-training.",
|
||||
"HF_pretrain": "Llama 3.1 8B pre-training with FP8 precision."
|
||||
} %}
|
||||
{% set available_modes = training_modes | select("in", ["pretrain", "HF_pretrain"]) | list %}
|
||||
{% if available_modes %}
|
||||
|
||||
.. container:: model-doc {{ model.mad_tag }}
|
||||
|
||||
.. rubric:: Pre-training
|
||||
|
||||
To start the pre-training benchmark, use the following command with the
|
||||
appropriate options. See the following list of options and their descriptions.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
./pytorch_benchmark_report.sh -t {% if available_modes | length == 1 %}{{ available_modes[0] }}{% else %}$training_mode{% endif %} \
|
||||
-m {{ model.model_repo }} \
|
||||
-p $datatype \
|
||||
-s $sequence_length
|
||||
|
||||
{% if model.mad_tag == "pyt_train_flux" %}
|
||||
.. container:: model-doc {{ model.mad_tag }}
|
||||
|
||||
.. note::
|
||||
|
||||
Currently, FLUX models are not supported out-of-the-box on {{ unified_docker.pull_tag }}.
|
||||
To use FLUX, refer to the previous version of the ``pytorch-training`` Docker: :doc:`pytorch-training-v25.6`
|
||||
|
||||
Occasionally, downloading the Flux dataset might fail. In the event of this
|
||||
error, manually download it from Hugging Face at
|
||||
`black-forest-labs/FLUX.1-dev <https://huggingface.co/black-forest-labs/FLUX.1-dev>`_
|
||||
and save it to `/workspace/FluxBenchmark`. This ensures that the test script can access
|
||||
the required dataset.
|
||||
{% endif %}
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Name
|
||||
- Options
|
||||
- Description
|
||||
|
||||
{% for mode in available_modes %}
|
||||
* - {% if loop.first %}``$training_mode``{% endif %}
|
||||
- ``{{ mode }}``
|
||||
- {{ training_mode_descs[mode] }}
|
||||
{% endfor %}
|
||||
|
||||
* - ``$datatype``
|
||||
- ``BF16``{% if model.mad_tag == "pyt_train_llama-3.1-8b" %} or ``FP8``{% endif %}
|
||||
- Only Llama 3.1 8B supports FP8 precision.
|
||||
|
||||
* - ``$sequence_length``
|
||||
- Sequence length for the language model.
|
||||
- Between 2048 and 8192. 8192 by default.
|
||||
{% endif %}
|
||||
|
||||
{% set training_mode_descs = {
|
||||
"finetune_fw": "Full weight fine-tuning (BF16 and FP8 supported).",
|
||||
"finetune_lora": "LoRA fine-tuning (BF16 supported).",
|
||||
"finetune_qlora": "QLoRA fine-tuning (BF16 supported).",
|
||||
"HF_finetune_lora": "LoRA fine-tuning with Hugging Face PEFT.",
|
||||
} %}
|
||||
{% set available_modes = training_modes | select("in", ["finetune_fw", "finetune_lora", "finetune_qlora", "HF_finetune_lora"]) | list %}
|
||||
{% if available_modes %}
|
||||
.. container:: model-doc {{ model.mad_tag }}
|
||||
|
||||
.. rubric:: Fine-tuning
|
||||
|
||||
To start the fine-tuning benchmark, use the following command with the
|
||||
appropriate options. See the following list of options and their descriptions.
|
||||
See :ref:`supported training modes <amd-pytorch-training-supported-training-modes-v257>`.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
./pytorch_benchmark_report.sh -t $training_mode \
|
||||
-m {{ model.model_repo }} \
|
||||
-p $datatype \
|
||||
-s $sequence_length
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Name
|
||||
- Options
|
||||
- Description
|
||||
|
||||
{% for mode in available_modes %}
|
||||
* - {% if loop.first %}``$training_mode``{% endif %}
|
||||
- ``{{ mode }}``
|
||||
- {{ training_mode_descs[mode] }}
|
||||
{% endfor %}
|
||||
|
||||
* - ``$datatype``
|
||||
- ``BF16``{% if "finetune_fw" in available_modes %} or ``FP8``{% endif %}
|
||||
- All models support BF16.{% if "finetune_fw" in available_modes %} FP8 is only available for full weight fine-tuning.{% endif %}
|
||||
|
||||
* - ``$sequence_length``
|
||||
- Between 2048 and 16384.
|
||||
- Sequence length for the language model.
|
||||
|
||||
{% if model.mad_tag in ["pyt_train_llama3.2-vision-11b", "pyt_train_llama-3.2-vision-90b"] %}
|
||||
.. note::
|
||||
|
||||
For LoRA and QLoRA support with vision models (Llama 3.2 11B and 90B),
|
||||
use the following torchtune commit for compatibility:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
git checkout 48192e23188b1fc524dd6d127725ceb2348e7f0e
|
||||
|
||||
{% elif model.mad_tag in ["pyt_train_llama-2-7b", "pyt_train_llama-2-13b", "pyt_train_llama-2-70b"] %}
|
||||
.. note::
|
||||
|
||||
You might encounter the following error with Llama 2: ``ValueError: seq_len (16384) of
|
||||
input tensor should be smaller than max_seq_len (4096)``.
|
||||
This error indicates that an input sequence is longer than the model's maximum context window.
|
||||
|
||||
Ensure your tokenized input does not exceed the model's ``max_seq_len`` (4096
|
||||
tokens in this case). You can resolve this by truncating the input or splitting
|
||||
it into smaller chunks before passing it to the model.
|
||||
|
||||
Note on reproducibility: The results in this guide are based on
|
||||
commit ``b4c98ac`` from the upstream
|
||||
`<https://github.com/pytorch/torchtune>`__ repository. For the
|
||||
latest updates, you can use the main branch.
|
||||
|
||||
{% endif %}
|
||||
{% endif %}
|
||||
{% endfor %}
|
||||
{% endfor %}
|
||||
|
||||
.. rubric:: Benchmarking examples
|
||||
|
||||
For examples of benchmarking commands, see `<https://github.com/ROCm/MAD/tree/develop/benchmark/pytorch_train#benchmarking-examples>`__.
|
||||
|
||||
Multi-node training
|
||||
-------------------
|
||||
|
||||
Pre-training
|
||||
~~~~~~~~~~~~
|
||||
|
||||
Multi-node training with torchtitan is supported. The provided SLURM script is pre-configured for Llama 3 70B.
|
||||
|
||||
To launch the training job on a SLURM cluster for Llama 3 70B, run the following commands from the MAD repository.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
# In the MAD repository
|
||||
cd scripts/pytorch_train
|
||||
sbatch run_slurm_train.sh
|
||||
|
||||
Fine-tuning
|
||||
~~~~~~~~~~~
|
||||
|
||||
Multi-node training with torchtune is supported. The provided SLURM script is pre-configured for Llama 3.3 70B.
|
||||
|
||||
To launch the training job on a SLURM cluster for Llama 3.3 70B, run the following commands from the MAD repository.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
huggingface-cli login # Get access to HF Llama model space
|
||||
huggingface-cli download meta-llama/Llama-3.3-70B-Instruct --local-dir ./models/Llama-3.3-70B-Instruct # Download the Llama 3.3 model locally
|
||||
# In the MAD repository
|
||||
cd scripts/pytorch_train
|
||||
sbatch Torchtune_Multinode.sh
|
||||
|
||||
.. note::
|
||||
|
||||
Information regarding benchmark setup:
|
||||
|
||||
* By default, Llama 3.3 70B is fine-tuned using ``alpaca_dataset``.
|
||||
* You can adjust the torchtune `YAML configuration file
|
||||
<https://github.com/pytorch/torchtune/blob/main/recipes/configs/llama3_3/70B_full_multinode.yaml>`__
|
||||
if you're using a different model.
|
||||
* The number of nodes and other parameters can be tuned in the SLURM script ``Torchtune_Multinode.sh``.
|
||||
* Set the ``mounting_paths`` inside the SLURM script.
|
||||
|
||||
Once the run is finished, you can find the log files in the ``result_torchtune/`` directory.
|
||||
|
||||
Further reading
|
||||
===============
|
||||
|
||||
- To learn more about MAD and the ``madengine`` CLI, see the `MAD usage guide <https://github.com/ROCm/MAD?tab=readme-ov-file#usage-guide>`__.
|
||||
|
||||
- To learn more about system settings and management practices to configure your system for
|
||||
AMD Instinct MI300X series accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_.
|
||||
|
||||
- For a list of other ready-made Docker images for AI with ROCm, see
|
||||
`AMD Infinity Hub <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`_.
|
||||
|
||||
Previous versions
|
||||
=================
|
||||
|
||||
See :doc:`pytorch-training-history` to find documentation for previous releases
|
||||
of the ``ROCm/pytorch-training`` Docker image.
|
||||
@@ -0,0 +1,305 @@
|
||||
.. meta::
|
||||
:description: How to train a model using PyTorch for ROCm.
|
||||
:keywords: ROCm, AI, LLM, train, PyTorch, torch, Llama, flux, tutorial, docker
|
||||
|
||||
****************************************
|
||||
Training a model with Primus and PyTorch
|
||||
****************************************
|
||||
|
||||
`Primus <https://github.com/AMD-AGI/Primus>`__ is a unified and flexible
|
||||
LLM training framework designed to streamline training. It streamlines LLM
|
||||
training on AMD Instinct accelerators using a modular, reproducible configuration paradigm.
|
||||
Primus now supports the PyTorch torchtitan backend.
|
||||
|
||||
.. note::
|
||||
|
||||
Primus with the PyTorch torchtitan backend is intended to supersede the :doc:`ROCm PyTorch training <pytorch-training>` workflow.
|
||||
See :doc:`pytorch-training` to see steps to run workloads without Primus.
|
||||
|
||||
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-pytorch-benchmark-models.yaml
|
||||
|
||||
{% set dockers = data.dockers %}
|
||||
{% set docker = dockers[0] %}
|
||||
For ease of use, AMD provides a ready-to-use Docker image -- ``{{
|
||||
docker.pull_tag }}`` -- for MI300X series accelerators containing essential
|
||||
components for Primus and PyTorch training with
|
||||
Primus Turbo optimizations.
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Software component
|
||||
- Version
|
||||
|
||||
{% for component_name, component_version in docker.components.items() %}
|
||||
* - {{ component_name }}
|
||||
- {{ component_version }}
|
||||
{% endfor %}
|
||||
|
||||
.. _amd-primus-pytorch-model-support-v258:
|
||||
|
||||
Supported models
|
||||
================
|
||||
|
||||
The following models are pre-optimized for performance on the AMD Instinct MI325X and MI300X accelerators.
|
||||
Some instructions, commands, and training recommendations in this documentation might
|
||||
vary by model -- select one to get started.
|
||||
|
||||
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-pytorch-benchmark-models.yaml
|
||||
|
||||
{% set unified_docker = data.dockers[0] %}
|
||||
{% set model_groups = data.model_groups %}
|
||||
.. raw:: html
|
||||
|
||||
<div id="vllm-benchmark-ud-params-picker" class="container-fluid">
|
||||
<div class="row gx-0" style="display: none;">
|
||||
<div class="col-2 me-1 px-2 model-param-head">Model</div>
|
||||
<div class="row col-10 pe-0">
|
||||
{% for model_group in model_groups %}
|
||||
<div class="col-3 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
|
||||
{% endfor %}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="row gx-0 pt-1">
|
||||
<div class="col-2 me-1 px-2 model-param-head">Model</div>
|
||||
<div class="row col-10 pe-0">
|
||||
{% for model_group in model_groups %}
|
||||
{% set models = model_group.models %}
|
||||
{% for model in models %}
|
||||
{% if models|length % 3 == 0 %}
|
||||
<div class="col-4 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
|
||||
{% else %}
|
||||
<div class="col-6 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
|
||||
{% endif %}
|
||||
{% endfor %}
|
||||
{% endfor %}
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
.. seealso::
|
||||
|
||||
For additional workloads, including Llama 3.3, Llama 3.2, Llama 2, GPT OSS, Qwen, and Flux models,
|
||||
see the documentation :doc:`pytorch-training` (without Primus)
|
||||
|
||||
.. _amd-primus-pytorch-performance-measurements-v258:
|
||||
|
||||
System validation
|
||||
=================
|
||||
|
||||
Before running AI workloads, it's important to validate that your AMD hardware is configured
|
||||
correctly and performing optimally.
|
||||
|
||||
If you have already validated your system settings, including aspects like NUMA auto-balancing, you
|
||||
can skip this step. Otherwise, complete the procedures in the :ref:`System validation and
|
||||
optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
|
||||
before starting training.
|
||||
|
||||
To test for optimal performance, consult the recommended :ref:`System health benchmarks
|
||||
<rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
|
||||
system's configuration.
|
||||
|
||||
This Docker image is optimized for specific model configurations outlined
|
||||
below. Performance can vary for other training workloads, as AMD
|
||||
doesn’t test configurations and run conditions outside those described.
|
||||
|
||||
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-pytorch-benchmark-models.yaml
|
||||
|
||||
{% set unified_docker = data.dockers[0] %}
|
||||
|
||||
Pull the Docker image
|
||||
=====================
|
||||
|
||||
Use the following command to pull the `Docker image <{{ unified_docker.docker_hub_url }}>`_ from Docker Hub.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
docker pull {{ unified_docker.pull_tag }}
|
||||
|
||||
Run training
|
||||
============
|
||||
|
||||
{% set model_groups = data.model_groups %}
|
||||
|
||||
Once the setup is complete, choose between the following two workflows to start benchmarking training.
|
||||
For fine-tuning workloads and multi-node training examples, see :doc:`pytorch-training` (without Primus).
|
||||
|
||||
.. tab-set::
|
||||
|
||||
.. tab-item:: MAD-integrated benchmarking
|
||||
|
||||
{% for model_group in model_groups %}
|
||||
{% for model in model_group.models %}
|
||||
|
||||
.. container:: model-doc {{ model.mad_tag }}
|
||||
|
||||
The following run command is tailored to {{ model.model }}.
|
||||
See :ref:`amd-primus-pytorch-model-support-v258` to switch to another available model.
|
||||
|
||||
1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
|
||||
directory and install the required packages on the host machine.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
git clone https://github.com/ROCm/MAD
|
||||
cd MAD
|
||||
pip install -r requirements.txt
|
||||
|
||||
2. For example, use this command to run the performance benchmark test on the {{ model.model }} model
|
||||
using one node with the {{ model.precision }} data type on the host machine.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
|
||||
madengine run \
|
||||
--tags {{ model.mad_tag }} \
|
||||
--keep-model-dir \
|
||||
--live-output \
|
||||
--timeout 28800
|
||||
|
||||
MAD launches a Docker container with the name
|
||||
``container_ci-{{ model.mad_tag }}``. The latency and throughput reports of the
|
||||
model are collected in ``~/MAD/perf.csv``.
|
||||
|
||||
{% endfor %}
|
||||
{% endfor %}
|
||||
|
||||
.. tab-item:: Standalone benchmarking
|
||||
|
||||
{% for model_group in model_groups %}
|
||||
{% for model in model_group.models %}
|
||||
|
||||
.. container:: model-doc {{ model.mad_tag }}
|
||||
|
||||
The following run commands are tailored to {{ model.model }}.
|
||||
See :ref:`amd-primus-pytorch-model-support-v258` to switch to another available model.
|
||||
|
||||
.. rubric:: Download the Docker image and required packages
|
||||
|
||||
1. Use the following command to pull the Docker image from Docker Hub.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
docker pull {{ unified_docker.pull_tag }}
|
||||
|
||||
2. Run the Docker container.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
docker run -it \
|
||||
--device /dev/dri \
|
||||
--device /dev/kfd \
|
||||
--network host \
|
||||
--ipc host \
|
||||
--group-add video \
|
||||
--cap-add SYS_PTRACE \
|
||||
--security-opt seccomp=unconfined \
|
||||
--privileged \
|
||||
-v $HOME:$HOME \
|
||||
-v $HOME/.ssh:/root/.ssh \
|
||||
--shm-size 64G \
|
||||
--name training_env \
|
||||
{{ unified_docker.pull_tag }}
|
||||
|
||||
Use these commands if you exit the ``training_env`` container and need to return to it.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
docker start training_env
|
||||
docker exec -it training_env bash
|
||||
|
||||
3. In the Docker container, clone the `<https://github.com/ROCm/MAD>`__
|
||||
repository and navigate to the benchmark scripts directory
|
||||
``/workspace/MAD/scripts/pytorch_train``.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
git clone https://github.com/ROCm/MAD
|
||||
cd MAD/scripts/pytorch_train
|
||||
|
||||
.. rubric:: Prepare training datasets and dependencies
|
||||
|
||||
1. The following benchmarking examples require downloading models and datasets
|
||||
from Hugging Face. To ensure successful access to gated repos, set your
|
||||
``HF_TOKEN``.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
export HF_TOKEN=$your_personal_hugging_face_access_token
|
||||
|
||||
2. Run the setup script to install libraries and datasets needed for benchmarking.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
./pytorch_benchmark_setup.sh
|
||||
|
||||
.. rubric:: Pretraining
|
||||
|
||||
To start the pretraining benchmark, use the following command with the
|
||||
appropriate options. See the following list of options and their descriptions.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
./pytorch_benchmark_report.sh -t pretrain \
|
||||
-m {{ model.model_repo }} \
|
||||
-p $datatype \
|
||||
-s $sequence_length
|
||||
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Name
|
||||
- Options
|
||||
- Description
|
||||
|
||||
{% for mode in available_modes %}
|
||||
* - {% if loop.first %}``$training_mode``{% endif %}
|
||||
- ``{{ mode }}``
|
||||
- {{ training_mode_descs[mode] }}
|
||||
{% endfor %}
|
||||
|
||||
* - ``$datatype``
|
||||
- ``BF16``{% if model.mad_tag == "primus_pyt_train_llama-3.1-8b" %} or ``FP8``{% endif %}
|
||||
- Currently, only Llama 3.1 8B supports FP8 precision.
|
||||
|
||||
* - ``$sequence_length``
|
||||
- Sequence length for the language model.
|
||||
- Between 2048 and 8192. 8192 by default.
|
||||
|
||||
.. rubric:: Benchmarking examples
|
||||
|
||||
Use the following command to run train {{ model.model }} with BF16 precision using Primus torchtitan.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
./pytorch_benchmark_report.sh -m {{ model.model_repo }}
|
||||
|
||||
To train {{ model.model }} with FP8 precision, use the following command.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
./pytorch_benchmark_report.sh -m {{ model.model_repo }} -p FP8
|
||||
{% endfor %}
|
||||
{% endfor %}
|
||||
|
||||
Further reading
|
||||
===============
|
||||
|
||||
- For an introduction to Primus, see `Primus: A Lightweight, Unified Training
|
||||
Framework for Large Models on AMD GPUs <https://rocm.blogs.amd.com/software-tools-optimization/primus/README.html>`__.
|
||||
|
||||
- To learn more about MAD and the ``madengine`` CLI, see the `MAD usage guide <https://github.com/ROCm/MAD?tab=readme-ov-file#usage-guide>`__.
|
||||
|
||||
- To learn more about system settings and management practices to configure your system for
|
||||
AMD Instinct MI300X series accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_.
|
||||
|
||||
- For a list of other ready-made Docker images for AI with ROCm, see
|
||||
`AMD Infinity Hub <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`_.
|
||||
|
||||
Previous versions
|
||||
=================
|
||||
|
||||
See :doc:`previous-versions/pytorch-training-history` to find documentation for previous releases
|
||||
of the ``ROCm/pytorch-training`` Docker image.
|
||||
@@ -1,11 +1,18 @@
|
||||
:orphan:
|
||||
|
||||
.. meta::
|
||||
:description: How to train a model using PyTorch for ROCm.
|
||||
:keywords: ROCm, AI, LLM, train, PyTorch, torch, Llama, flux, tutorial, docker
|
||||
|
||||
**************************************
|
||||
Training a model with PyTorch for ROCm
|
||||
Training a model with PyTorch on ROCm
|
||||
**************************************
|
||||
|
||||
.. note::
|
||||
|
||||
Primus with the PyTorch torchtitan backend is intended to supersede the :doc:`ROCm PyTorch training <pytorch-training>` workflow.
|
||||
See :doc:`primus-pytorch` for details.
|
||||
|
||||
PyTorch is an open-source machine learning framework that is widely used for
|
||||
model training with GPU-optimized components for transformer-based models.
|
||||
|
||||
@@ -49,7 +56,7 @@ vary by model -- select one to get started.
|
||||
<div class="col-2 me-1 px-2 model-param-head">Model</div>
|
||||
<div class="row col-10 pe-0">
|
||||
{% for model_group in model_groups %}
|
||||
<div class="col-3 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
|
||||
<div class="col-4 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
|
||||
{% endfor %}
|
||||
</div>
|
||||
</div>
|
||||
@@ -87,9 +94,11 @@ vary by model -- select one to get started.
|
||||
{% for model_group in model_groups %}
|
||||
{% set models = model_group.models %}
|
||||
{% for model in models %}
|
||||
{% if model.training_modes %}
|
||||
* - {{ model.model }}
|
||||
- ``{{ model.training_modes | join('``, ``') }}``
|
||||
|
||||
{% endif %}
|
||||
{% endfor %}
|
||||
{% endfor %}
|
||||
|
||||
@@ -152,20 +161,23 @@ Run training
|
||||
|
||||
.. tab-item:: MAD-integrated benchmarking
|
||||
|
||||
1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
|
||||
directory and install the required packages on the host machine.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
git clone https://github.com/ROCm/MAD
|
||||
cd MAD
|
||||
pip install -r requirements.txt
|
||||
|
||||
{% for model_group in model_groups %}
|
||||
{% for model in model_group.models %}
|
||||
|
||||
.. container:: model-doc {{ model.mad_tag }}
|
||||
|
||||
The following run command is tailored to {{ model.model }}.
|
||||
See :ref:`amd-pytorch-training-model-support` to switch to another available model.
|
||||
|
||||
1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
|
||||
directory and install the required packages on the host machine.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
git clone https://github.com/ROCm/MAD
|
||||
cd MAD
|
||||
pip install -r requirements.txt
|
||||
|
||||
2. For example, use this command to run the performance benchmark test on the {{ model.model }} model
|
||||
using one node with the {{ model.precision }} data type on the host machine.
|
||||
|
||||
@@ -187,6 +199,17 @@ Run training
|
||||
|
||||
.. tab-item:: Standalone benchmarking
|
||||
|
||||
{% for model_group in model_groups %}
|
||||
{% for model in model_group.models %}
|
||||
|
||||
.. container:: model-doc {{ model.mad_tag }}
|
||||
|
||||
The following commands are tailored to {{ model.model }}.
|
||||
See :ref:`amd-pytorch-training-model-support` to switch to another available model.
|
||||
|
||||
{% endfor %}
|
||||
{% endfor %}
|
||||
|
||||
.. rubric:: Download the Docker image and required packages
|
||||
|
||||
1. Use the following command to pull the Docker image from Docker Hub.
|
||||
@@ -388,7 +411,7 @@ Run training
|
||||
.. note::
|
||||
|
||||
Currently, FLUX models are not supported out-of-the-box on {{ unified_docker.pull_tag }}.
|
||||
To use FLUX, refer to the previous version of the ``pytorch-training`` Docker: :doc:`previous-versions/pytorch-training-v25.6`
|
||||
To use FLUX, refer to ``rocm/pytorch-training`` Docker: :doc:`previous-versions/pytorch-training-v25.6`
|
||||
|
||||
Occasionally, downloading the Flux dataset might fail. In the event of this
|
||||
error, manually download it from Hugging Face at
|
||||
|
||||
@@ -12,14 +12,14 @@ subtrees:
|
||||
- file: compatibility/compatibility-matrix.rst
|
||||
title: Compatibility matrix
|
||||
entries:
|
||||
- url: https://rocm.docs.amd.com/projects/install-on-linux-internal/en/latest/reference/system-requirements.html
|
||||
- url: https://rocm.docs.amd.com/projects/install-on-linux/en/latest/reference/system-requirements.html
|
||||
title: Linux system requirements
|
||||
- url: https://rocm.docs.amd.com/projects/install-on-windows/en/${branch}/reference/system-requirements.html
|
||||
title: Windows system requirements
|
||||
|
||||
- caption: Install
|
||||
entries:
|
||||
- url: https://rocm.docs.amd.com/projects/install-on-linux-internal/en/latest/
|
||||
- url: https://rocm.docs.amd.com/projects/install-on-linux/en/${branch}/
|
||||
title: ROCm on Linux
|
||||
- url: https://rocm.docs.amd.com/projects/install-on-windows/en/latest/
|
||||
title: HIP SDK on Windows
|
||||
@@ -67,9 +67,9 @@ subtrees:
|
||||
subtrees:
|
||||
- entries:
|
||||
- file: how-to/rocm-for-ai/training/benchmark-docker/primus-megatron.rst
|
||||
title: Train a model with Primus and Megatron-Core
|
||||
- file: how-to/rocm-for-ai/training/benchmark-docker/pytorch-training.rst
|
||||
title: Train a model with PyTorch
|
||||
title: Train a model with Primus and Megatron-LM
|
||||
- file: how-to/rocm-for-ai/training/benchmark-docker/primus-pytorch.rst
|
||||
title: Train a model with Primus and PyTorch
|
||||
- file: how-to/rocm-for-ai/training/benchmark-docker/jax-maxtext.rst
|
||||
title: Train a model with JAX MaxText
|
||||
- file: how-to/rocm-for-ai/training/benchmark-docker/mpt-llm-foundry
|
||||
@@ -106,6 +106,8 @@ subtrees:
|
||||
title: PyTorch inference performance testing
|
||||
- file: how-to/rocm-for-ai/inference/benchmark-docker/sglang.rst
|
||||
title: SGLang inference performance testing
|
||||
- file: how-to/rocm-for-ai/inference/benchmark-docker/sglang-distributed.rst
|
||||
title: SGLang distributed inference with Mooncake
|
||||
- file: how-to/rocm-for-ai/inference/deploy-your-model.rst
|
||||
title: Deploy your model
|
||||
|
||||
|
||||
@@ -60,7 +60,7 @@ div[data-param-k="model-group"][data-param-state="disabled"] {
|
||||
.model-param-head {
|
||||
background-color: var(--compat-head-color);
|
||||
padding: 0.15rem 0.15rem 0.15rem 0.67rem;
|
||||
border-right: solid 4px var(--compat-accent-color);
|
||||
border-right: solid 3px var(--compat-accent-color);
|
||||
font-weight: 600;
|
||||
}
|
||||
|
||||
|
||||
@@ -1,80 +0,0 @@
|
||||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<manifest>
|
||||
<remote name="gerritgit" fetch="ssh://gerritgit/" review="gerrit-git.amd.com"/>
|
||||
<remote name="lightning-ghemu" fetch="ssh://github-emu/AMD-Lightning-Internal"/>
|
||||
<remote name="rocm" fetch="https://github.com/ROCm"/>
|
||||
<remote name="rocm-ghemu" fetch="ssh://github-emu/AMD-ROCm-Internal"/>
|
||||
|
||||
<default remote="gerritgit" revision="release/rocm-rel-7.0" sync-j="4" sync-c="true"/>
|
||||
|
||||
<project name="AMDMIGraphX" remote="rocm" revision="11b59f590b017cbe9b1cf4c08071ecab373f0015" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="mathlibs"/>
|
||||
<project name="MIVisionX" remote="rocm" revision="c64df3c2197d937c460be25ffa8f8c4504149291" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="mathlibs"/>
|
||||
<project name="OpenCL-CLHPP" remote="rocm-ghemu" revision="96be921927f5225ec419d1d56741d952d98fc387" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="stage1"/>
|
||||
<project name="OpenCL-Headers" remote="rocm-ghemu" revision="3ef425f2054170d6de921f2c272869c153d4689e" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="stage1"/>
|
||||
<project name="ROCR-Runtime" remote="rocm-ghemu" revision="737ba1dcdfa980949e21eaaf03225c4e1e5c7a0b" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="stage1"/>
|
||||
<project name="ROCdbgapi" remote="rocm-ghemu" revision="d51db839e5168422cbd01b172c5f68dc0b153ccb" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="stage1"/>
|
||||
<project name="ROCgdb" remote="rocm-ghemu" revision="4ea5c0df6b6befb8c1a55d120b04d3b3673ac4ee" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="stage1"/>
|
||||
<project name="ROCmValidationSuite" remote="rocm" revision="22fc8f4d29c40f248b36cea778dc0198907ac6e0" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="mathlibs"/>
|
||||
<project name="TransferBench" remote="rocm" revision="2d0ecaaed647b6ba754fa2593b7bc2c40b8095a8" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="mathlibs"/>
|
||||
<project name="amdsmi" remote="rocm-ghemu" revision="ff168a2f8c607f7c451de2bcc81d0b2dde772cc7" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="stage1"/>
|
||||
<project name="aomp" path="openmp-extras/aomp" remote="lightning-ghemu" revision="39f46a52047e353b6b077d34ce09df77e6c5efcd" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="llvmdeps,stage1"/>
|
||||
<project name="aomp-extras" path="openmp-extras/aomp-extras" remote="lightning-ghemu" revision="b781ffecbdd296888abee337fdf7e7cd3ee2e336" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="llvmdeps,stage1"/>
|
||||
<project name="aqlprofile" remote="rocm-ghemu" revision="5e99084425053ad6c7a076842128fc926f48dccb" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="stage1"/>
|
||||
<project name="build-infra" path="ROCm" remote="rocm-ghemu" revision="9eff340603b54ef6996959af64ab4e95e287e7ca" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="infra"/>
|
||||
<project name="clr" remote="rocm-ghemu" revision="a7d87f827681b172e3fb8a0c32f9431a0597fede" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="stage1"/>
|
||||
<project name="composable_kernel" remote="rocm" revision="b8893b933963e86b76fa3fa088ededc4504119f9" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="mathlibs"/>
|
||||
<project name="compute/ec/prototype" path="build" revision="d71a2766e11e057e5c698caea8fc4ebc0f72cb3e" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="infra,stage1"/>
|
||||
<project name="flang" path="openmp-extras/flang" remote="lightning-ghemu" revision="01f4a3088ad8048bf474d03ad5f65964f49ee0dc" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="llvmdeps,stage1"/>
|
||||
<project name="half" remote="rocm" revision="207ee58595a64b5c4a70df221f1e6e704b807811" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="mathlibs"/>
|
||||
<project name="hip" remote="rocm-ghemu" revision="2a5f7e80383825585442fcd0dc9fe3a84d28c87f" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="stage1"/>
|
||||
<project name="hip-tests" remote="rocm-ghemu" revision="2dbdbafe05e9c0d84d7c97e151aee23f07bdff60" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="stage1"/>
|
||||
<project name="hipFFT" remote="rocm" revision="c6be513032ae64cc7a89443c9de1c255050124f7" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="mathlibs"/>
|
||||
<project name="hipSOLVER" remote="rocm" revision="f4fddfc89865d8e7cbdbcb2e38719fea52f2ed3a" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="mathlibs"/>
|
||||
<project name="hipTensor" remote="rocm" revision="ff1e8d59253c44b5f72d1b733d8ff2a27e87405e" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="mathlibs"/>
|
||||
<project name="hipfort" remote="rocm" revision="23c8f1f7a832bb0c31569cdb842aa133b3e6191c" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="mathlibs"/>
|
||||
<project name="hipify" path="HIPIFY" remote="lightning-ghemu" revision="5ac718c3d71b8a3f78adadd0327e4415083075a2" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="llvmdeps,stage1"/>
|
||||
<project name="hipother" remote="rocm-ghemu" revision="dd2934dc89d8cf9f8b0dce1fdd5af6c9cc67f754" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="stage1"/>
|
||||
<project name="jenkins-utils" remote="rocm-ghemu" revision="c0ce7cbf3d3f04f3d96d6a3de5b80191b0d7daea" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="infra"/>
|
||||
<project name="llvm-project" remote="lightning-ghemu" revision="82aed4e69d70bef3c89c38a2ee85c8c41294dfc9" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="llvmdeps,stage1"/>
|
||||
<project name="rccl" remote="rocm" revision="47b1b79ba29a0efa5414b71759aa413239aba958" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="mathlibs"/>
|
||||
<project name="rdc" remote="rocm-ghemu" revision="49933423b0b4132aca8c1dc15d04d6622c93867c" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="mathlibs"/>
|
||||
<project name="rocAL" remote="rocm" revision="3c8ef63ae85032f1be3cf488258a13e9931861b7" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="mathlibs"/>
|
||||
<project name="rocALUTION" remote="rocm" revision="3554cfdce2ecfc743fd49344a0ce16811731ce50" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="mathlibs"/>
|
||||
<project name="rocDecode" remote="rocm" revision="eb487c3204b5fa36fd9f1c8dc0ee148524a4ecde" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="mathlibs"/>
|
||||
<project name="rocFFT" remote="rocm" revision="374509ca074f8f2e4db8de5f68198a0a0e143734" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="mathlibs"/>
|
||||
<project name="rocJPEG" remote="rocm" revision="d54ce56c5a7d6c4fea998c6a862f76e54fc576e1" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="mathlibs"/>
|
||||
<project name="rocPyDecode" remote="rocm" revision="b5a5722b46adb2e949e9abc3bb7fa72d5736a0ad" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="mathlibs"/>
|
||||
<project name="rocSHMEM" remote="rocm" revision="ecc474db503f314c4c1559925c5b8ff2a5b7aa3b" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="mathlibs"/>
|
||||
<project name="rocSOLVER" remote="rocm" revision="534bd69687c3e25d5cc18a87908acd744e6eaba4" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="mathlibs"/>
|
||||
<project name="rocWMMA" remote="rocm" revision="2445fb2fe5235fbe2a8257f1bbbe7ed52f7774eb" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="mathlibs"/>
|
||||
<project name="rocm-cmake" remote="rocm" revision="2c6dcc0d5acd6e1c81c76b32238613d82fd0b92e" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="mathlibs"/>
|
||||
<project name="rocm-core" remote="rocm-ghemu" revision="942821090f1a81d7ad55f910dffdba93c2cdca8a" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="infra,stage1"/>
|
||||
<project name="rocm-examples" remote="rocm" revision="de57ee2f77597a73e93d6413162d54e3c41ca812" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="stage1"/>
|
||||
<project name="rocm-libraries" remote="rocm" revision="ce2f7eba97c33377760eab5c9cd6e17db1e8e5bb" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="mathlibs">
|
||||
<linkfile src="projects/hipcub" dest="hipCUB"/>
|
||||
<linkfile src="projects/rocprim" dest="rocPRIM"/>
|
||||
<linkfile src="projects/hiprand" dest="hipRAND"/>
|
||||
<linkfile src="projects/rocrand" dest="rocRAND"/>
|
||||
<linkfile src="projects/rocthrust" dest="rocThrust"/>
|
||||
<linkfile src="projects/hipblas-common" dest="hipBLAS-common"/>
|
||||
<linkfile src="projects/hipblaslt" dest="hipBLASLt"/>
|
||||
<linkfile src="projects/rocblas" dest="rocBLAS"/>
|
||||
<linkfile src="projects/hipsparselt" dest="hipSPARSELt"/>
|
||||
<linkfile src="projects/rocsparse" dest="rocSPARSE"/>
|
||||
<linkfile src="projects/hipsparse" dest="hipSPARSE"/>
|
||||
<linkfile src="projects/hipblas" dest="hipBLAS"/>
|
||||
<linkfile src="projects/miopen" dest="MIOpen"/>
|
||||
</project>
|
||||
<project name="rocm_bandwidth_test" remote="rocm-ghemu" revision="49a72abaaaadb9934d6bcc96ac70663d7cca02f3" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="stage1"/>
|
||||
<project name="rocm_smi_lib" remote="rocm-ghemu" revision="88b905064b04d8bd674b5662b48005fd90d0fe2f" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="stage1"/>
|
||||
<project name="rocminfo" remote="rocm-ghemu" revision="aec5d6fa53152668207d523ac481cc1ec9fb7298" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="stage1"/>
|
||||
<project name="rocprofiler" remote="rocm-ghemu" revision="93e0c4a17c062119f14ef54745956446098d894f" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="stage1"/>
|
||||
<project name="rocprofiler-compute" remote="rocm" revision="b934f199d43ca092ea2a91c757bd88789dacabc4" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="stage1"/>
|
||||
<project name="rocprofiler-register-internal" path="rocprofiler-register" remote="rocm-ghemu" revision="80fcffeb5aaa1033d250f1d8e14fa9e387fc783b" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="stage1"/>
|
||||
<project name="rocprofiler-sdk-internal" path="rocprofiler-sdk" remote="rocm-ghemu" revision="30e239d1b7bb74df8d255f01956e4631780723d8" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="stage1"/>
|
||||
<project name="rocprofiler-systems" remote="rocm" revision="7598407b24d1f9bfd96b8c384793a3a3edf49d00" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="stage1"/>
|
||||
<project name="rocr_debug_agent" remote="rocm-ghemu" revision="7db477f3c14f3fa3111f05575bed927ab3be6d62" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="stage1"/>
|
||||
<project name="roctracer" remote="rocm-ghemu" revision="dd745ed9c731cf1c67a182a4ce41ce30afbfb8ca" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="stage1"/>
|
||||
<project name="rpp" remote="rocm" revision="5f94a2acd51ff294f7f6548b814485f68f83e553" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="mathlibs"/>
|
||||
<project name="spirv-llvm-translator" path="llvm-project/llvm/projects/SPIRV-LLVM-Translator" remote="lightning-ghemu" revision="8a4d73af8cd0f6f4212cdcdc440223915c81e765" upstream="release/rocm-rel-7.0" dest-branch="release/rocm-rel-7.0" groups="llvmdeps,stage1"/>
|
||||
</manifest>
|
||||
70
tools/rocm-build/rocm-7.0.0.xml
Normal file
70
tools/rocm-build/rocm-7.0.0.xml
Normal file
@@ -0,0 +1,70 @@
|
||||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<manifest>
|
||||
<remote name="rocm-org" fetch="https://github.com/ROCm/" />
|
||||
<default revision="refs/tags/rocm-7.0.0"
|
||||
remote="rocm-org"
|
||||
sync-c="true"
|
||||
sync-j="4" />
|
||||
<!--list of projects for ROCm-->
|
||||
<project name="ROCm" revision="roc-7.0.x" />
|
||||
<project name="ROCK-Kernel-Driver" />
|
||||
<project name="ROCR-Runtime" />
|
||||
<project name="amdsmi" />
|
||||
<project name="aqlprofile" />
|
||||
<project name="rdc" />
|
||||
<project name="rocm_bandwidth_test" />
|
||||
<project name="rocm_smi_lib" />
|
||||
<project name="rocm-core" />
|
||||
<project name="rocm-examples" />
|
||||
<project name="rocminfo" />
|
||||
<project name="rocprofiler" />
|
||||
<project name="rocprofiler-register" />
|
||||
<project name="rocprofiler-sdk" />
|
||||
<project name="rocprofiler-compute" />
|
||||
<project name="rocprofiler-systems" />
|
||||
<project name="roctracer" />
|
||||
<!--HIP Projects-->
|
||||
<project name="hip" />
|
||||
<project name="hip-tests" />
|
||||
<project name="HIPIFY" />
|
||||
<project name="clr" />
|
||||
<project name="hipother" />
|
||||
<!-- The following projects are all associated with the AMDGPU LLVM compiler -->
|
||||
<project name="half" />
|
||||
<project name="llvm-project" />
|
||||
<project name="spirv-llvm-translator" />
|
||||
<!-- gdb projects -->
|
||||
<project name="ROCdbgapi" />
|
||||
<project name="ROCgdb" />
|
||||
<project name="rocr_debug_agent" />
|
||||
<!-- ROCm Libraries -->
|
||||
<project groups="mathlibs" name="AMDMIGraphX" />
|
||||
<project groups="mathlibs" name="MIVisionX" />
|
||||
<project groups="mathlibs" name="ROCmValidationSuite" />
|
||||
<project groups="mathlibs" name="composable_kernel" />
|
||||
<project groups="mathlibs" name="hipSOLVER" />
|
||||
<project groups="mathlibs" name="hipTensor" />
|
||||
<project groups="mathlibs" name="hipfort" />
|
||||
<project groups="mathlibs" name="rccl" />
|
||||
<project groups="mathlibs" name="rocAL" />
|
||||
<project groups="mathlibs" name="rocALUTION" />
|
||||
<project groups="mathlibs" name="rocDecode" />
|
||||
<project groups="mathlibs" name="rocJPEG" />
|
||||
<!-- The following components have been migrated to rocm-libraries:
|
||||
hipBLAS-common hipBLAS hipBLASLt hipCUB
|
||||
hipFFT hipRAND hipSPARSE hipSPARSELt
|
||||
MIOpen rocBLAS rocFFT rocPRIM rocRAND
|
||||
rocSPARSE rocThrust Tensile -->
|
||||
<project groups="mathlibs" name="rocm-libraries" />
|
||||
<project groups="mathlibs" name="rocPyDecode" />
|
||||
<project groups="mathlibs" name="rocSHMEM" />
|
||||
<project groups="mathlibs" name="rocSOLVER" />
|
||||
<project groups="mathlibs" name="rocWMMA" />
|
||||
<project groups="mathlibs" name="rocm-cmake" />
|
||||
<project groups="mathlibs" name="rpp" />
|
||||
<project groups="mathlibs" name="TransferBench" />
|
||||
<!-- Projects for OpenMP-Extras -->
|
||||
<project name="aomp" path="openmp-extras/aomp" />
|
||||
<project name="aomp-extras" path="openmp-extras/aomp-extras" />
|
||||
<project name="flang" path="openmp-extras/flang" />
|
||||
</manifest>
|
||||
Reference in New Issue
Block a user