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docs(PyTorch training v25.8): Add Primus and update PyTorch training benchmark docs (#5331)
* pyt: update previous versions list update conf.py * pyt: update yaml and rst update update toc * update headings and anchors * pyt: update doc * update docker hub urls
This commit is contained in:
Peter Park
@@ -135,6 +135,9 @@ article_pages = [
|
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{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.4", "os": ["linux"]},
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{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.5", "os": ["linux"]},
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{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.6", "os": ["linux"]},
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{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.7", "os": ["linux"]},
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{"file": "how-to/rocm-for-ai/training/benchmark-docker/primus-pytorch", "os": ["linux"]},
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{"file": "how-to/rocm-for-ai/training/benchmark-docker/pytorch-training", "os": ["linux"]},
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{"file": "how-to/rocm-for-ai/training/benchmark-docker/jax-maxtext", "os": ["linux"]},
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{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/jax-maxtext-history", "os": ["linux"]},
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{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/jax-maxtext-v25.4", "os": ["linux"]},
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@@ -0,0 +1,162 @@
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dockers:
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- pull_tag: rocm/pytorch-training:v25.7
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||||
docker_hub_url: https://hub.docker.com/layers/rocm/pytorch-training/v25.7/images/sha256-cc6fd840ab89cb81d926fc29eca6d075aee9875a55a522675a4b9231c9a0a712
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||||
components:
|
||||
ROCm: 6.4.2
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||||
PyTorch: 2.8.0a0+gitd06a406
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Python: 3.10.18
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Transformer Engine: 2.2.0.dev0+94e53dd8
|
||||
Flash Attention: 3.0.0.post1
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||||
hipBLASLt: 1.1.0-4b9a52edfc
|
||||
Triton: 3.3.0
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model_groups:
|
||||
- group: Meta Llama
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tag: llama
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models:
|
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- model: Llama 4 Scout 17B-16E
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mad_tag: pyt_train_llama-4-scout-17b-16e
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model_repo: Llama-4-17B_16E
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url: https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E
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precision: BF16
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training_modes: [finetune_fw, finetune_lora]
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- model: Llama 3.3 70B
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mad_tag: pyt_train_llama-3.3-70b
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model_repo: Llama-3.3-70B
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url: https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct
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precision: BF16
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training_modes: [finetune_fw, finetune_lora, finetune_qlora]
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- model: Llama 3.2 1B
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mad_tag: pyt_train_llama-3.2-1b
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model_repo: Llama-3.2-1B
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url: https://huggingface.co/meta-llama/Llama-3.2-1B
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precision: BF16
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training_modes: [finetune_fw, finetune_lora]
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- model: Llama 3.2 3B
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mad_tag: pyt_train_llama-3.2-3b
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model_repo: Llama-3.2-3B
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url: https://huggingface.co/meta-llama/Llama-3.2-3B
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precision: BF16
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training_modes: [finetune_fw, finetune_lora]
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- model: Llama 3.2 Vision 11B
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mad_tag: pyt_train_llama-3.2-vision-11b
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model_repo: Llama-3.2-Vision-11B
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url: https://huggingface.co/meta-llama/Llama-3.2-11B-Vision
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precision: BF16
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training_modes: [finetune_fw]
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- model: Llama 3.2 Vision 90B
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mad_tag: pyt_train_llama-3.2-vision-90b
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model_repo: Llama-3.2-Vision-90B
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url: https://huggingface.co/meta-llama/Llama-3.2-90B-Vision
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precision: BF16
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training_modes: [finetune_fw]
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- model: Llama 3.1 8B
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mad_tag: pyt_train_llama-3.1-8b
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model_repo: Llama-3.1-8B
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url: https://huggingface.co/meta-llama/Llama-3.1-8B
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precision: BF16
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training_modes: [pretrain, finetune_fw, finetune_lora, HF_pretrain]
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- model: Llama 3.1 70B
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mad_tag: pyt_train_llama-3.1-70b
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model_repo: Llama-3.1-70B
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url: https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct
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precision: BF16
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training_modes: [pretrain, finetune_fw, finetune_lora]
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- model: Llama 3.1 405B
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mad_tag: pyt_train_llama-3.1-405b
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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
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mad_tag: pyt_train_llama-3-8b
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||||
model_repo: Llama-3-8B
|
||||
url: https://huggingface.co/meta-llama/Meta-Llama-3-8B
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precision: BF16
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training_modes: [finetune_fw, finetune_lora]
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- model: Llama 3 70B
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mad_tag: pyt_train_llama-3-70b
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model_repo: Llama-3-70B
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||||
url: https://huggingface.co/meta-llama/Meta-Llama-3-70B
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precision: BF16
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training_modes: [finetune_fw, finetune_lora]
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- model: Llama 2 7B
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mad_tag: pyt_train_llama-2-7b
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model_repo: Llama-2-7B
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url: https://github.com/meta-llama/llama-models/tree/main/models/llama2
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precision: BF16
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training_modes: [finetune_fw, finetune_lora, finetune_qlora]
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- model: Llama 2 13B
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mad_tag: pyt_train_llama-2-13b
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model_repo: Llama-2-13B
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url: https://github.com/meta-llama/llama-models/tree/main/models/llama2
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precision: BF16
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training_modes: [finetune_fw, finetune_lora]
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- model: Llama 2 70B
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||||
mad_tag: pyt_train_llama-2-70b
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model_repo: Llama-2-70B
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url: https://github.com/meta-llama/llama-models/tree/main/models/llama2
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precision: BF16
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training_modes: [finetune_lora, finetune_qlora]
|
||||
- group: OpenAI
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tag: openai
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models:
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- model: GPT OSS 20B
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mad_tag: pyt_train_gpt_oss_20b
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model_repo: GPT-OSS-20B
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url: https://huggingface.co/openai/gpt-oss-20b
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precision: BF16
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training_modes: [HF_finetune_lora]
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- model: GPT OSS 120B
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mad_tag: pyt_train_gpt_oss_120b
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model_repo: GPT-OSS-120B
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url: https://huggingface.co/openai/gpt-oss-120b
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precision: BF16
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||||
training_modes: [HF_finetune_lora]
|
||||
- group: Qwen
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||||
tag: qwen
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||||
models:
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||||
- model: Qwen 3 8B
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||||
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]
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||||
- model: Qwen 3 32B
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||||
mad_tag: pyt_train_qwen3-32b
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||||
model_repo: Qwen3-32
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||||
url: https://huggingface.co/Qwen/Qwen3-32B
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||||
precision: BF16
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||||
training_modes: [finetune_lora]
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||||
- model: Qwen 2.5 32B
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||||
mad_tag: pyt_train_qwen2.5-32b
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||||
model_repo: Qwen2.5-32B
|
||||
url: https://huggingface.co/Qwen/Qwen2.5-32B
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precision: BF16
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training_modes: [finetune_lora]
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- model: Qwen 2.5 72B
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mad_tag: pyt_train_qwen2.5-72b
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||||
model_repo: Qwen2.5-72B
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||||
url: https://huggingface.co/Qwen/Qwen2.5-72B
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precision: BF16
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training_modes: [finetune_lora]
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- model: Qwen 2 1.5B
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mad_tag: pyt_train_qwen2-1.5b
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||||
model_repo: Qwen2-1.5B
|
||||
url: https://huggingface.co/Qwen/Qwen2-1.5B
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precision: BF16
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||||
training_modes: [finetune_fw, finetune_lora]
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||||
- model: Qwen 2 7B
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||||
mad_tag: pyt_train_qwen2-7b
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||||
model_repo: Qwen2-7B
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||||
url: https://huggingface.co/Qwen/Qwen2-7B
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precision: BF16
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||||
training_modes: [finetune_fw, finetune_lora]
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||||
- group: Flux
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||||
tag: flux
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||||
models:
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||||
- model: FLUX.1-dev
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mad_tag: pyt_train_flux
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||||
model_repo: Flux
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||||
url: https://huggingface.co/black-forest-labs/FLUX.1-dev
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precision: BF16
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||||
training_modes: [pretrain]
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@@ -0,0 +1,24 @@
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dockers:
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||||
- pull_tag: rocm/pytorch-training:v25.8
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docker_hub_url: https://hub.docker.com/layers/rocm/pytorch-training/v25.8/images/sha256-5082ae01d73fec6972b0d84e5dad78c0926820dcf3c19f301d6c8eb892e573c5
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||||
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
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||||
tag: llama
|
||||
models:
|
||||
- model: Llama 3.1 8B
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||||
mad_tag: primus_pyt_train_llama-3.1-8b
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||||
model_repo: Llama-3.1-8B
|
||||
url: https://huggingface.co/meta-llama/Llama-3.1-8B
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precision: BF16
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||||
- model: Llama 3.1 70B
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||||
mad_tag: primus_pyt_train_llama-3.1-70b
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||||
model_repo: Llama-3.1-70B
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||||
url: https://huggingface.co/meta-llama/Llama-3.1-70B
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precision: BF16
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@@ -1,14 +1,13 @@
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dockers:
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||||
- pull_tag: rocm/pytorch-training:v25.7
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||||
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
|
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model_groups:
|
||||
- group: Meta Llama
|
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tag: llama
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@@ -3,7 +3,7 @@
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:keywords: ROCm, AI, LLM, train, jax, torch, Llama, flux, tutorial, docker
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******************************************
|
||||
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
|
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|
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@@ -5,15 +5,13 @@
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:keywords: ROCm, AI, LLM, train, Megatron-LM, megatron, Llama, tutorial, docker, torch
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||||
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||||
******************************************
|
||||
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 \
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||||
SEQ_LEN=4096 \
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||||
PAD_LEN=4096 \
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||||
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.
|
||||
@@ -1,9 +1,11 @@
|
||||
: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
|
||||
**************************************
|
||||
|
||||
PyTorch is an open-source machine learning framework that is widely used for
|
||||
@@ -388,7 +390,7 @@ 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
|
||||
|
||||
@@ -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
|
||||
|
||||
Reference in New Issue
Block a user