mirror of
https://github.com/ROCm/ROCm.git
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Update PyTorch training benchmark docker doc to 25.7 (#5255)
* Update PyTorch training benchmark docker doc to 25.7 * update .wordlist.txt * update conf.py * update data sheet * fix sphinx warnings
This commit is contained in:
Peter Park
@@ -918,6 +918,7 @@ toolchain
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toolchains
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toolset
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toolsets
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torchtitan
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torchvision
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tqdm
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tracebacks
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@@ -124,11 +124,15 @@ article_pages = [
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{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v25.3", "os": ["linux"]},
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{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v25.4", "os": ["linux"]},
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{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v25.5", "os": ["linux"]},
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{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v25.6", "os": ["linux"]},
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{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-primus-migration-guide", "os": ["linux"]},
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{"file": "how-to/rocm-for-ai/training/benchmark-docker/primus-megatron", "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/previous-versions/pytorch-training-history", "os": ["linux"]},
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{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.3", "os": ["linux"]},
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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/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 +1,120 @@
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unified_docker:
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latest:
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pull_tag: rocm/pytorch-training:v25.6
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docker_hub_url: https://hub.docker.com/r/rocm/pytorch-training/tags
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rocm_version: 6.4.1
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pytorch_version: 2.8.0a0+git7d205b2
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python_version: 3.10.17
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transformer_engine_version: 1.14.0+2f85f5f2
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flash_attention_version: 3.0.0.post1
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hipblaslt_version: 0.15.0-8c6919d
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triton_version: 3.3.0
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model_groups:
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- group: Pre-training
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tag: pre-training
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models:
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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]
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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]
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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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- group: Fine-tuning
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tag: fine-tuning
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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: [finetune_fw, finetune_lora]
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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
|
||||
url: https://huggingface.co/meta-llama/Llama-3.1-70B
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precision: BF16
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training_modes: [finetune_fw, finetune_lora, finetune_qlora]
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- model: Llama 3.1 405B
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mad_tag: pyt_train_llama-3.1-405b
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model_repo: Llama-3.1-405B
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url: https://huggingface.co/meta-llama/Llama-3.1-405B
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precision: BF16
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training_modes: [finetune_qlora, HF_finetune_lora]
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||||
- model: Llama 3 8B
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mad_tag: pyt_train_llama-3-8b
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model_repo: Llama-3-8B
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||||
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]
|
||||
- model: Llama 3 70B
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mad_tag: pyt_train_llama-3-70b
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||||
model_repo: Llama-3-70B
|
||||
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]
|
||||
- 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
|
||||
model_repo: Llama-2-70B
|
||||
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, HF_finetune_lora]
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@@ -1,38 +1,17 @@
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unified_docker:
|
||||
latest:
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||||
pull_tag: rocm/pytorch-training:v25.6
|
||||
docker_hub_url: https://hub.docker.com/r/rocm/pytorch-training/tags
|
||||
rocm_version: 6.4.1
|
||||
pytorch_version: 2.8.0a0+git7d205b2
|
||||
python_version: 3.10.17
|
||||
transformer_engine_version: 1.14.0+2f85f5f2
|
||||
flash_attention_version: 3.0.0.post1
|
||||
hipblaslt_version: 0.15.0-8c6919d
|
||||
triton_version: 3.3.0
|
||||
dockers:
|
||||
- pull_tag: rocm/pytorch-training:v25.7
|
||||
docker_hub_url: https://hub.docker.com/layers/rocm/pytorch-training/v25.7/images/sha256-cc6fd840ab89cb81d926fc29eca6d075aee9875a55a522675a4b9231c9a0a712
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components:
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||||
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
|
||||
hipBLASLt: 1.1.0-4b9a52edfc
|
||||
Triton: 3.3.0
|
||||
model_groups:
|
||||
- group: Pre-training
|
||||
tag: pre-training
|
||||
models:
|
||||
- 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]
|
||||
- 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]
|
||||
- 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]
|
||||
- group: Fine-tuning
|
||||
tag: fine-tuning
|
||||
- group: Meta Llama
|
||||
tag: llama
|
||||
models:
|
||||
- model: Llama 4 Scout 17B-16E
|
||||
mad_tag: pyt_train_llama-4-scout-17b-16e
|
||||
@@ -75,19 +54,19 @@ model_groups:
|
||||
model_repo: Llama-3.1-8B
|
||||
url: https://huggingface.co/meta-llama/Llama-3.1-8B
|
||||
precision: BF16
|
||||
training_modes: [finetune_fw, finetune_lora]
|
||||
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
|
||||
url: https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct
|
||||
precision: BF16
|
||||
training_modes: [finetune_fw, finetune_lora, finetune_qlora]
|
||||
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, HF_finetune_lora]
|
||||
training_modes: [finetune_qlora]
|
||||
- model: Llama 3 8B
|
||||
mad_tag: pyt_train_llama-3-8b
|
||||
model_repo: Llama-3-8B
|
||||
@@ -117,4 +96,67 @@ model_groups:
|
||||
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, HF_finetune_lora]
|
||||
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]
|
||||
|
||||
@@ -23,93 +23,92 @@ The table below summarizes information about ROCm-enabled deep learning framewor
|
||||
- Installation options
|
||||
- GitHub
|
||||
|
||||
* - `PyTorch <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/pytorch-compatibility.html>`_
|
||||
* - `PyTorch <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/pytorch-compatibility.html>`__
|
||||
- .. raw:: html
|
||||
|
||||
|
||||
<a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/pytorch-install.html"><i class="fas fa-link fa-lg"></i></a>
|
||||
-
|
||||
- `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/pytorch-install.html#using-a-docker-image-with-pytorch-pre-installed>`_
|
||||
- `Wheels package <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/pytorch-install.html#using-a-wheels-package>`_
|
||||
- `ROCm Base Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/pytorch-install.html#using-the-pytorch-rocm-base-docker-image>`_
|
||||
- `Upstream Docker file <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/pytorch-install.html#using-the-pytorch-upstream-dockerfile>`_
|
||||
- `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/pytorch-install.html#using-a-docker-image-with-pytorch-pre-installed>`__
|
||||
- `Wheels package <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/pytorch-install.html#using-a-wheels-package>`__
|
||||
- `ROCm Base Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/pytorch-install.html#using-the-pytorch-rocm-base-docker-image>`__
|
||||
- `Upstream Docker file <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/pytorch-install.html#using-the-pytorch-upstream-dockerfile>`__
|
||||
- .. raw:: html
|
||||
|
||||
|
||||
<a href="https://github.com/ROCm/pytorch"><i class="fab fa-github fa-lg"></i></a>
|
||||
|
||||
* - `TensorFlow <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/tensorflow-compatibility.html>`_
|
||||
|
||||
* - `TensorFlow <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/tensorflow-compatibility.html>`__
|
||||
- .. raw:: html
|
||||
|
||||
|
||||
<a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/tensorflow-install.html"><i class="fas fa-link fa-lg"></i></a>
|
||||
-
|
||||
- `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/tensorflow-install.html#using-a-docker-image-with-tensorflow-pre-installed>`_
|
||||
- `Wheels package <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/tensorflow-install.html#using-a-wheels-package>`_
|
||||
- `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/tensorflow-install.html#using-a-docker-image-with-tensorflow-pre-installed>`__
|
||||
- `Wheels package <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/tensorflow-install.html#using-a-wheels-package>`__
|
||||
|
||||
- .. raw:: html
|
||||
|
||||
|
||||
<a href="https://github.com/ROCm/tensorflow-upstream"><i class="fab fa-github fa-lg"></i></a>
|
||||
|
||||
* - `JAX <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/jax-compatibility.html>`_
|
||||
* - `JAX <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/jax-compatibility.html>`__
|
||||
- .. raw:: html
|
||||
|
||||
|
||||
<a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/jax-install.html"><i class="fas fa-link fa-lg"></i></a>
|
||||
-
|
||||
- `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/jax-install.html#using-a-prebuilt-docker-image>`_
|
||||
- `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/jax-install.html#using-a-prebuilt-docker-image>`__
|
||||
- .. raw:: html
|
||||
|
||||
|
||||
<a href="https://github.com/ROCm/jax"><i class="fab fa-github fa-lg"></i></a>
|
||||
|
||||
* - `verl <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/verl-compatibility.html>`_
|
||||
|
||||
* - `verl <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/verl-compatibility.html>`__
|
||||
- .. raw:: html
|
||||
|
||||
|
||||
<a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/verl-install.html"><i class="fas fa-link fa-lg"></i></a>
|
||||
-
|
||||
- `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/verl-install.html#use-a-prebuilt-docker-image-with-verl-pre-installed>`_
|
||||
- `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/verl-install.html#use-a-prebuilt-docker-image-with-verl-pre-installed>`__
|
||||
- .. raw:: html
|
||||
|
||||
|
||||
<a href="https://github.com/ROCm/verl"><i class="fab fa-github fa-lg"></i></a>
|
||||
|
||||
* - `Stanford Megatron-LM <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/stanford-megatron-lm-compatibility.html>`_
|
||||
* - `Stanford Megatron-LM <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/stanford-megatron-lm-compatibility.html>`__
|
||||
- .. raw:: html
|
||||
|
||||
|
||||
<a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/stanford-megatron-lm-install.html"><i class="fas fa-link fa-lg"></i></a>
|
||||
-
|
||||
- `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/stanford-megatron-lm-install.html#use-a-prebuilt-docker-image-with-stanford-megatron-lm-pre-installed>`_
|
||||
- `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/stanford-megatron-lm-install.html#use-a-prebuilt-docker-image-with-stanford-megatron-lm-pre-installed>`__
|
||||
- .. raw:: html
|
||||
|
||||
|
||||
<a href="https://github.com/ROCm/Stanford-Megatron-LM"><i class="fab fa-github fa-lg"></i></a>
|
||||
|
||||
* - `DGL <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/dgl-compatibility.html>`_
|
||||
|
||||
* - `DGL <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/dgl-compatibility.html>`__
|
||||
- .. raw:: html
|
||||
|
||||
|
||||
<a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/dgl-install.html"><i class="fas fa-link fa-lg"></i></a>
|
||||
-
|
||||
- `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/dgl-install.html#use-a-prebuilt-docker-image-with-dgl-pre-installed>`_
|
||||
- `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/dgl-install.html#use-a-prebuilt-docker-image-with-dgl-pre-installed>`__
|
||||
- .. raw:: html
|
||||
|
||||
|
||||
<a href="https://github.com/ROCm/dgl"><i class="fab fa-github fa-lg"></i></a>
|
||||
|
||||
* - `Megablocks <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/megablocks-compatibility.html>`_
|
||||
* - `Megablocks <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/megablocks-compatibility.html>`__
|
||||
- .. raw:: html
|
||||
|
||||
|
||||
<a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/megablocks-install.html"><i class="fas fa-link fa-lg"></i></a>
|
||||
-
|
||||
- `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/megablocks-install.html#using-a-prebuilt-docker-image-with-megablocks-pre-installed>`_
|
||||
- `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/megablocks-install.html#using-a-prebuilt-docker-image-with-megablocks-pre-installed>`__
|
||||
- .. raw:: html
|
||||
|
||||
|
||||
<a href="https://github.com/ROCm/megablocks"><i class="fab fa-github fa-lg"></i></a>
|
||||
|
||||
* - `Taichi <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/taichi-compatibility.html>`_
|
||||
|
||||
* - `Taichi <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/taichi-compatibility.html>`__
|
||||
- .. raw:: html
|
||||
|
||||
|
||||
<a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/taichi-install.html"><i class="fas fa-link fa-lg"></i></a>
|
||||
-
|
||||
- `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/taichi-install.html#use-a-prebuilt-docker-image-with-taichi-pre-installed>`_
|
||||
- `Wheels package <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/taichi-install.html#use-a-wheels-package>`_
|
||||
- `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/taichi-install.html#use-a-prebuilt-docker-image-with-taichi-pre-installed>`__
|
||||
- `Wheels package <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/taichi-install.html#use-a-wheels-package>`__
|
||||
|
||||
- .. raw:: html
|
||||
|
||||
<a href="https://github.com/ROCm/taichi"><i class="fab fa-github fa-lg"></i></a>
|
||||
|
||||
<a href="https://github.com/ROCm/taichi"><i class="fab fa-github fa-lg"></i></a>
|
||||
|
||||
Learn how to use your ROCm deep learning environment for training, fine-tuning, inference, and performance optimization
|
||||
through the following guides.
|
||||
@@ -124,10 +123,3 @@ through the following guides.
|
||||
|
||||
* :doc:`Use ROCm for AI inference optimization <rocm-for-ai/inference-optimization/index>`
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -46,7 +46,7 @@ vLLM inference performance testing
|
||||
- {{ unified_docker.hipblaslt_version }}
|
||||
|
||||
With this Docker image, you can quickly test the :ref:`expected
|
||||
inference performance numbers <vllm-benchmark-performance-measurements>` for
|
||||
inference performance numbers <vllm-benchmark-performance-measurements-715>` for
|
||||
MI300X series accelerators.
|
||||
|
||||
What's new
|
||||
@@ -219,7 +219,7 @@ system's configuration.
|
||||
``container_ci-{{model.mad_tag}}``. The latency and throughput reports of the
|
||||
model are collected in the following path: ``~/MAD/reports_{{model.precision}}/``.
|
||||
|
||||
Although the :ref:`available models <vllm-benchmark-available-models>` are preconfigured
|
||||
Although the :ref:`available models <vllm-benchmark-available-models-715>` are preconfigured
|
||||
to collect latency and throughput performance data, you can also change the benchmarking
|
||||
parameters. See the standalone benchmarking tab for more information.
|
||||
|
||||
|
||||
@@ -39,7 +39,7 @@ vLLM inference performance testing
|
||||
- {{ unified_docker.hipblaslt_version }}
|
||||
|
||||
With this Docker image, you can quickly test the :ref:`expected
|
||||
inference performance numbers <vllm-benchmark-performance-measurements>` for
|
||||
inference performance numbers <vllm-benchmark-performance-measurements-812>` for
|
||||
MI300X series accelerators.
|
||||
|
||||
What's new
|
||||
@@ -208,7 +208,7 @@ system's configuration.
|
||||
and ``{{ model.mad_tag }}_serving.csv``.
|
||||
|
||||
Although the :ref:`available models
|
||||
<vllm-benchmark-available-models>` are preconfigured to collect
|
||||
<vllm-benchmark-available-models-812>` are preconfigured to collect
|
||||
offline throughput and online serving performance data, you can
|
||||
also change the benchmarking parameters. See the standalone
|
||||
benchmarking tab for more information.
|
||||
|
||||
@@ -22,9 +22,9 @@ If you’re new to ROCm, refer to the :doc:`ROCm quick start install guide for L
|
||||
<rocm-install-on-linux:install/quick-start>`.
|
||||
|
||||
If you’re using a Radeon GPU for graphics-accelerated applications, refer to the
|
||||
`Radeon installation instructions <https://rocm.docs.amd.com/projects/radeon/en/docs-6.1.3/docs/install/native_linux/install-radeon.html>`_.
|
||||
`Radeon installation instructions <https://rocm.docs.amd.com/projects/radeon/en/latest/docs/install/native_linux/howto_native_linux.html>`_.
|
||||
|
||||
You can install ROCm on :ref:`compatible systems <rocm-install-on-linux:reference/system-requirements>` via your Linux
|
||||
You can install ROCm on :doc:`compatible systems <rocm-install-on-linux:reference/system-requirements>` via your Linux
|
||||
distribution's package manager. See the following documentation resources to get started:
|
||||
|
||||
* :doc:`ROCm installation overview <rocm-install-on-linux:install/install-overview>`
|
||||
|
||||
@@ -18,7 +18,7 @@ Training a model with ROCm Megatron-LM
|
||||
The ROCm Megatron-LM framework is a specialized fork of the robust Megatron-LM, designed to
|
||||
enable efficient training of large-scale language models on AMD GPUs. By leveraging AMD Instinct™ MI300X
|
||||
accelerators, AMD Megatron-LM delivers enhanced scalability, performance, and resource utilization for AI
|
||||
workloads. It is purpose-built to :ref:`support models <amd-megatron-lm-model-support>`
|
||||
workloads. It is purpose-built to :ref:`support models <amd-megatron-lm-model-support-24-12>`
|
||||
like Meta's Llama 2, Llama 3, and Llama 3.1, enabling developers to train next-generation AI models with greater
|
||||
efficiency. See the GitHub repository at `<https://github.com/ROCm/Megatron-LM>`__.
|
||||
|
||||
@@ -67,7 +67,7 @@ Megatron-LM provides the following key features to train large language models e
|
||||
|
||||
- Pre-training
|
||||
|
||||
.. _amd-megatron-lm-model-support:
|
||||
.. _amd-megatron-lm-model-support-24-12:
|
||||
|
||||
The following models are pre-optimized for performance on the AMD Instinct MI300X accelerator.
|
||||
|
||||
|
||||
@@ -67,7 +67,7 @@ Megatron-LM provides the following key features to train large language models e
|
||||
|
||||
- Pre-training
|
||||
|
||||
.. _amd-megatron-lm-model-support:
|
||||
.. _amd-megatron-lm-model-support-25-3:
|
||||
|
||||
The following models are pre-optimized for performance on the AMD Instinct MI300X accelerator.
|
||||
|
||||
@@ -278,7 +278,7 @@ handle a variety of input sequences, including unseen words or domain-specific t
|
||||
.. tab-item:: Llama
|
||||
:sync: llama
|
||||
|
||||
To train any of the Llama 2 models that :ref:`this Docker image supports <amd-megatron-lm-model-support>`, use the ``Llama2Tokenizer``.
|
||||
To train any of the Llama 2 models that :ref:`this Docker image supports <amd-megatron-lm-model-support-25-3>`, use the ``Llama2Tokenizer``.
|
||||
|
||||
To train any of Llama 3 and Llama 3.1 models that this Docker image supports, use the ``HuggingFaceTokenizer``.
|
||||
Set the Hugging Face model link in the ``TOKENIZER_MODEL`` variable.
|
||||
@@ -292,7 +292,7 @@ handle a variety of input sequences, including unseen words or domain-specific t
|
||||
.. tab-item:: DeepSeek V2
|
||||
:sync: deepseek
|
||||
|
||||
To train any of the DeepSeek V2 models that :ref:`this Docker image supports <amd-megatron-lm-model-support>`, use the ``DeepSeekV2Tokenizer``.
|
||||
To train any of the DeepSeek V2 models that :ref:`this Docker image supports <amd-megatron-lm-model-support-25-3>`, use the ``DeepSeekV2Tokenizer``.
|
||||
|
||||
Multi-node training
|
||||
^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
@@ -67,7 +67,7 @@ Megatron-LM provides the following key features to train large language models e
|
||||
|
||||
- Pre-training
|
||||
|
||||
.. _amd-megatron-lm-model-support:
|
||||
.. _amd-megatron-lm-model-support-25-4:
|
||||
|
||||
The following models are pre-optimized for performance on AMD Instinct MI300X series accelerators.
|
||||
|
||||
@@ -291,7 +291,7 @@ or ``${DATA_DIR}/tokenizer_llama2``.
|
||||
.. tab-item:: Llama
|
||||
:sync: llama
|
||||
|
||||
To train any of the Llama 2 models that :ref:`this Docker image supports <amd-megatron-lm-model-support>`, use the ``Llama2Tokenizer``
|
||||
To train any of the Llama 2 models that :ref:`this Docker image supports <amd-megatron-lm-model-support-25-4>`, use the ``Llama2Tokenizer``
|
||||
or the default ``HuggingFaceTokenizer``.
|
||||
|
||||
To train any of Llama 3 and Llama 3.1 models that this Docker image supports, use the ``HuggingFaceTokenizer``.
|
||||
@@ -320,7 +320,7 @@ or ``${DATA_DIR}/tokenizer_llama2``.
|
||||
.. tab-item:: DeepSeek V2
|
||||
:sync: deepseek
|
||||
|
||||
To train any of the DeepSeek V2 models that :ref:`this Docker image supports <amd-megatron-lm-model-support>`, use the ``DeepSeekV2Tokenizer``.
|
||||
To train any of the DeepSeek V2 models that :ref:`this Docker image supports <amd-megatron-lm-model-support-25-4>`, use the ``DeepSeekV2Tokenizer``.
|
||||
|
||||
Multi-node training
|
||||
^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
@@ -16,12 +16,20 @@ previous releases of the ``ROCm/pytorch-training`` Docker image on `Docker Hub <
|
||||
- Components
|
||||
- Resources
|
||||
|
||||
* - v25.7
|
||||
-
|
||||
* ROCm 6.4.2
|
||||
* PyTorch 2.8.0a0+gitd06a406
|
||||
-
|
||||
* :doc:`Documentation <../pytorch-training>`
|
||||
* `Docker Hub <https://hub.docker.com/layers/rocm/pytorch-training/v25.7/images/sha256-cc6fd840ab89cb81d926fc29eca6d075aee9875a55a522675a4b9231c9a0a712>`__
|
||||
|
||||
* - v25.6
|
||||
-
|
||||
* ROCm 6.3.4
|
||||
* PyTorch 2.8.0a0+git7d205b2
|
||||
-
|
||||
* :doc:`Documentation <../pytorch-training>`
|
||||
* :doc:`Documentation <pytorch-training-v25.6>`
|
||||
* `Docker Hub <https://hub.docker.com/layers/rocm/pytorch-training/v25.6/images/sha256-a4cea3c493a4a03d199a3e81960ac071d79a4a7a391aa9866add3b30a7842661>`__
|
||||
|
||||
* - v25.5
|
||||
|
||||
@@ -437,3 +437,8 @@ Once the setup is complete, choose between two options to start benchmarking:
|
||||
|
||||
./pytorch_benchmark_report.sh -t HF_finetune_lora -p BF16 -m Llama-2-70B
|
||||
|
||||
Previous versions
|
||||
=================
|
||||
|
||||
See :doc:`pytorch-training-history` to find documentation for previous releases
|
||||
of the ``ROCm/pytorch-training`` Docker image.
|
||||
|
||||
@@ -0,0 +1,456 @@
|
||||
: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.
|
||||
|
||||
The `PyTorch for ROCm training Docker <https://hub.docker.com/layers/rocm/pytorch-training/v25.6/images/sha256-a4cea3c493a4a03d199a3e81960ac071d79a4a7a391aa9866add3b30a7842661>`_
|
||||
(``rocm/pytorch-training:v25.6``) 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:
|
||||
|
||||
+--------------------------+--------------------------------+
|
||||
| Software component | Version |
|
||||
+==========================+================================+
|
||||
| ROCm | 6.3.4 |
|
||||
+--------------------------+--------------------------------+
|
||||
| PyTorch | 2.8.0a0+git7d205b2 |
|
||||
+--------------------------+--------------------------------+
|
||||
| Python | 3.10.17 |
|
||||
+--------------------------+--------------------------------+
|
||||
| Transformer Engine | 1.14.0+2f85f5f2 |
|
||||
+--------------------------+--------------------------------+
|
||||
| Flash Attention | 3.0.0.post1 |
|
||||
+--------------------------+--------------------------------+
|
||||
| hipBLASLt | 0.15.0-8c6919d |
|
||||
+--------------------------+--------------------------------+
|
||||
| Triton | 3.3.0 |
|
||||
+--------------------------+--------------------------------+
|
||||
|
||||
.. _amd-pytorch-training-model-support-v256:
|
||||
|
||||
Supported models
|
||||
================
|
||||
|
||||
The following models are pre-optimized for performance on the AMD Instinct MI325X and MI300X accelerators.
|
||||
|
||||
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/pytorch-training-v25.6-benchmark-models.yaml
|
||||
|
||||
{% set unified_docker = data.unified_docker.latest %}
|
||||
{% set model_groups = data.model_groups %}
|
||||
|
||||
.. raw:: html
|
||||
|
||||
<div id="vllm-benchmark-ud-params-picker" class="container-fluid">
|
||||
<div class="row">
|
||||
<div class="col-2 me-2 model-param-head">Workload</div>
|
||||
<div class="row col-10">
|
||||
{% for model_group in model_groups %}
|
||||
<div class="col-6 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 mt-1">
|
||||
<div class="col-2 me-2 model-param-head">Model</div>
|
||||
<div class="row col-10">
|
||||
{% for model_group in model_groups %}
|
||||
{% set models = model_group.models %}
|
||||
{% for model in models %}
|
||||
{% if models|length % 3 == 0 %}
|
||||
<div class="col-4 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 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>
|
||||
|
||||
.. note::
|
||||
|
||||
Some models require an external license agreement through a third party (for example, Meta).
|
||||
|
||||
.. _amd-pytorch-training-performance-measurements-v256:
|
||||
|
||||
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 validate configurations and run conditions outside those described.
|
||||
|
||||
Benchmarking
|
||||
============
|
||||
|
||||
Once the setup is complete, choose between two options to start benchmarking:
|
||||
|
||||
.. tab-set::
|
||||
|
||||
.. tab-item:: MAD-integrated benchmarking
|
||||
|
||||
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 }}
|
||||
|
||||
For example, use this command to run the performance benchmark test on the {{ model.model }} model
|
||||
using one GPU 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 }}``, for example. The latency and throughput reports of the
|
||||
model are collected in the following path: ``~/MAD/perf.csv``.
|
||||
|
||||
{% endfor %}
|
||||
{% endfor %}
|
||||
|
||||
.. tab-item:: Standalone benchmarking
|
||||
|
||||
.. rubric:: Download the Docker image and required packages
|
||||
|
||||
Use the following command to pull the Docker image from Docker Hub.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
docker pull {{ unified_docker.pull_tag }}
|
||||
|
||||
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 $HOME/.ssh:/root/.ssh --shm-size 64G --name training_env {{ unified_docker.pull_tag }}
|
||||
|
||||
Use these commands if you exit the ``training_env`` container and need to return to it.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
docker start training_env
|
||||
docker exec -it training_env bash
|
||||
|
||||
In the Docker container, clone the `<https://github.com/ROCm/MAD>`__
|
||||
repository and navigate to the benchmark scripts directory
|
||||
``/workspace/MAD/scripts/pytorch_train``.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
git clone https://github.com/ROCm/MAD
|
||||
cd MAD/scripts/pytorch_train
|
||||
|
||||
.. rubric:: Prepare training datasets and dependencies
|
||||
|
||||
The following benchmarking examples require downloading models and datasets
|
||||
from Hugging Face. To ensure successful access to gated repos, set your
|
||||
``HF_TOKEN``.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
export HF_TOKEN=$your_personal_hugging_face_access_token
|
||||
|
||||
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 %}
|
||||
{% if model_group.tag == "pre-training" and model.mad_tag in ["pyt_train_llama-3.1-8b", "pyt_train_llama-3.1-70b", "pyt_train_flux"] %}
|
||||
|
||||
.. container:: model-doc {{ model.mad_tag }}
|
||||
|
||||
.. rubric:: Pretraining
|
||||
|
||||
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 pretrain -m {{ model.model_repo }} -p $datatype -s $sequence_length
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Name
|
||||
- Options
|
||||
- Description
|
||||
|
||||
{% if model.mad_tag == "pyt_train_llama-3.1-8b" %}
|
||||
* - ``$datatype``
|
||||
- ``BF16`` or ``FP8``
|
||||
- Only Llama 3.1 8B supports FP8 precision.
|
||||
{% else %}
|
||||
* - ``$datatype``
|
||||
- ``BF16``
|
||||
- Only Llama 3.1 8B supports FP8 precision.
|
||||
{% endif %}
|
||||
|
||||
* - ``$sequence_length``
|
||||
- Sequence length for the language model.
|
||||
- Between 2048 and 8192. 8192 by default.
|
||||
|
||||
{% if model.mad_tag == "pyt_train_flux" %}
|
||||
.. container:: model-doc {{ model.mad_tag }}
|
||||
|
||||
.. note::
|
||||
|
||||
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 %}
|
||||
{% endif %}
|
||||
|
||||
{% if model_group.tag == "fine-tuning" %}
|
||||
.. 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.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
./pytorch_benchmark_report.sh -t $training_mode -m {{ model.model_repo }} -p BF16 -s $sequence_length
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Name
|
||||
- Options
|
||||
- Description
|
||||
|
||||
* - ``$training_mode``
|
||||
- ``finetune_fw``
|
||||
- Full weight fine-tuning (BF16 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
|
||||
|
||||
* - ``$datatype``
|
||||
- ``BF16``
|
||||
- All models support BF16.
|
||||
|
||||
* - ``$sequence_length``
|
||||
- Between 2048 and 16384.
|
||||
- Sequence length for the language model.
|
||||
|
||||
.. note::
|
||||
|
||||
{{ model.model }} currently supports the following fine-tuning methods:
|
||||
|
||||
{% for method in model.training_modes %}
|
||||
* ``{{ method }}``
|
||||
{% endfor %}
|
||||
{% if model.training_modes|length < 4 %}
|
||||
|
||||
The upstream `torchtune <https://github.com/pytorch/torchtune>`_ repository
|
||||
does not currently provide YAML configuration files for other combinations of
|
||||
model to fine-tuning method
|
||||
However, you can still configure your own YAML files to enable support for
|
||||
fine-tuning methods not listed here by following existing patterns in the
|
||||
``/workspace/torchtune/recipes/configs`` directory.
|
||||
{% 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>`__.
|
||||
|
||||
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.
|
||||
@@ -9,28 +9,25 @@ Training a model with PyTorch for ROCm
|
||||
PyTorch is an open-source machine learning framework that is widely used for
|
||||
model training with GPU-optimized components for transformer-based models.
|
||||
|
||||
The `PyTorch for ROCm training Docker <https://hub.docker.com/r/rocm/pytorch-training/tags>`_
|
||||
(``rocm/pytorch-training:v25.6``) 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:
|
||||
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/pytorch-training-benchmark-models.yaml
|
||||
|
||||
+--------------------------+--------------------------------+
|
||||
| Software component | Version |
|
||||
+==========================+================================+
|
||||
| ROCm | 6.3.4 |
|
||||
+--------------------------+--------------------------------+
|
||||
| PyTorch | 2.8.0a0+git7d205b2 |
|
||||
+--------------------------+--------------------------------+
|
||||
| Python | 3.10.17 |
|
||||
+--------------------------+--------------------------------+
|
||||
| Transformer Engine | 1.14.0+2f85f5f2 |
|
||||
+--------------------------+--------------------------------+
|
||||
| Flash Attention | 3.0.0.post1 |
|
||||
+--------------------------+--------------------------------+
|
||||
| hipBLASLt | 0.15.0-8c6919d |
|
||||
+--------------------------+--------------------------------+
|
||||
| Triton | 3.3.0 |
|
||||
+--------------------------+--------------------------------+
|
||||
{% 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:
|
||||
|
||||
@@ -38,26 +35,27 @@ 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/pytorch-training-benchmark-models.yaml
|
||||
|
||||
{% set unified_docker = data.unified_docker.latest %}
|
||||
{% 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">
|
||||
<div class="col-2 me-2 model-param-head">Workload</div>
|
||||
<div class="col-2 me-2 model-param-head">Model group</div>
|
||||
<div class="row col-10">
|
||||
{% for model_group in model_groups %}
|
||||
<div class="col-6 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
|
||||
<div class="col-3 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 mt-1">
|
||||
<div class="col-2 me-2 model-param-head">Model</div>
|
||||
<div class="col-2 me-2 model-param-head">Model variant</div>
|
||||
<div class="row col-10">
|
||||
{% for model_group in model_groups %}
|
||||
{% set models = model_group.models %}
|
||||
@@ -73,84 +71,116 @@ The following models are pre-optimized for performance on the AMD Instinct MI325
|
||||
</div>
|
||||
</div>
|
||||
|
||||
.. note::
|
||||
|
||||
Some models require an external license agreement through a third party (for example, Meta).
|
||||
.. _amd-pytorch-training-supported-training-modes:
|
||||
|
||||
.. _amd-pytorch-training-performance-measurements:
|
||||
The following table lists supported training modes per model.
|
||||
|
||||
Performance measurements
|
||||
========================
|
||||
.. dropdown:: Supported training modes
|
||||
|
||||
To evaluate performance, the
|
||||
.. 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:
|
||||
|
||||
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>`_
|
||||
page provides reference throughput and latency measurements for training
|
||||
popular AI models.
|
||||
should not be interpreted as the peak performance achievable by AMD
|
||||
Instinct MI325X and MI300X accelerators or ROCm software.
|
||||
|
||||
.. note::
|
||||
System validation
|
||||
=================
|
||||
|
||||
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.
|
||||
Before running AI workloads, it's important to validate that your AMD hardware is configured
|
||||
correctly and performing optimally.
|
||||
|
||||
System validation
|
||||
=================
|
||||
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.
|
||||
|
||||
Before running AI workloads, it's important to validate that your AMD hardware is configured
|
||||
correctly and performing optimally.
|
||||
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.
|
||||
|
||||
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.
|
||||
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.
|
||||
|
||||
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.
|
||||
Run training
|
||||
============
|
||||
|
||||
This Docker image is optimized for specific model configurations outlined
|
||||
below. Performance can vary for other training workloads, as AMD
|
||||
doesn’t validate configurations and run conditions outside those described.
|
||||
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/pytorch-training-benchmark-models.yaml
|
||||
|
||||
Benchmarking
|
||||
============
|
||||
{% set unified_docker = data.dockers[0] %}
|
||||
{% set model_groups = data.model_groups %}
|
||||
|
||||
Once the setup is complete, choose between two options to start benchmarking:
|
||||
Once the setup is complete, choose between two options to start benchmarking training:
|
||||
|
||||
.. tab-set::
|
||||
|
||||
.. tab-item:: MAD-integrated benchmarking
|
||||
|
||||
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.
|
||||
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
|
||||
.. code-block:: shell
|
||||
|
||||
git clone https://github.com/ROCm/MAD
|
||||
cd MAD
|
||||
pip install -r requirements.txt
|
||||
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 }}
|
||||
|
||||
For example, use this command to run the performance benchmark test on the {{ model.model }} model
|
||||
using one GPU with the {{ model.precision }} data type on the host machine.
|
||||
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
|
||||
.. 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
|
||||
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 }}``, for example. The latency and throughput reports of the
|
||||
model are collected in the following path: ``~/MAD/perf.csv``.
|
||||
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 %}
|
||||
@@ -159,222 +189,213 @@ The following models are pre-optimized for performance on the AMD Instinct MI325
|
||||
|
||||
.. rubric:: Download the Docker image and required packages
|
||||
|
||||
Use the following command to pull the Docker image from Docker Hub.
|
||||
1. Use the following command to pull the Docker image from Docker Hub.
|
||||
|
||||
.. code-block:: shell
|
||||
.. code-block:: shell
|
||||
|
||||
docker pull {{ unified_docker.pull_tag }}
|
||||
docker pull {{ unified_docker.pull_tag }}
|
||||
|
||||
Run the Docker container.
|
||||
2. Run the Docker container.
|
||||
|
||||
.. code-block:: shell
|
||||
.. 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 }}
|
||||
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 $HOME/.ssh:/root/.ssh \
|
||||
--shm-size 64G \
|
||||
--name training_env \
|
||||
{{ unified_docker.pull_tag }}
|
||||
|
||||
Use these commands if you exit the ``training_env`` container and need to return to it.
|
||||
Use these commands if you exit the ``training_env`` container and need to return to it.
|
||||
|
||||
.. code-block:: shell
|
||||
.. code-block:: shell
|
||||
|
||||
docker start training_env
|
||||
docker exec -it training_env bash
|
||||
docker start training_env
|
||||
docker exec -it training_env bash
|
||||
|
||||
In the Docker container, clone the `<https://github.com/ROCm/MAD>`__
|
||||
repository and navigate to the benchmark scripts directory
|
||||
``/workspace/MAD/scripts/pytorch_train``.
|
||||
3. In the Docker container, clone the `<https://github.com/ROCm/MAD>`__
|
||||
repository and navigate to the benchmark scripts directory
|
||||
``/workspace/MAD/scripts/pytorch_train``.
|
||||
|
||||
.. code-block:: shell
|
||||
.. code-block:: shell
|
||||
|
||||
git clone https://github.com/ROCm/MAD
|
||||
cd MAD/scripts/pytorch_train
|
||||
git clone https://github.com/ROCm/MAD
|
||||
cd MAD/scripts/pytorch_train
|
||||
|
||||
.. rubric:: Prepare training datasets and dependencies
|
||||
|
||||
The following benchmarking examples require downloading models and datasets
|
||||
from Hugging Face. To ensure successful access to gated repos, set your
|
||||
``HF_TOKEN``.
|
||||
1. The following benchmarking examples require downloading models and datasets
|
||||
from Hugging Face. To ensure successful access to gated repos, set your
|
||||
``HF_TOKEN``.
|
||||
|
||||
.. code-block:: shell
|
||||
.. code-block:: shell
|
||||
|
||||
export HF_TOKEN=$your_personal_hugging_face_access_token
|
||||
export HF_TOKEN=$your_personal_hugging_face_access_token
|
||||
|
||||
Run the setup script to install libraries and datasets needed for benchmarking.
|
||||
2. Run the setup script to install libraries and datasets needed for benchmarking.
|
||||
|
||||
.. code-block:: shell
|
||||
.. code-block:: shell
|
||||
|
||||
./pytorch_benchmark_setup.sh
|
||||
./pytorch_benchmark_setup.sh
|
||||
|
||||
.. container:: model-doc pyt_train_llama-3.1-8b
|
||||
.. container:: model-doc pyt_train_llama-3.1-8b
|
||||
|
||||
``pytorch_benchmark_setup.sh`` installs the following libraries for Llama 3.1 8B:
|
||||
``pytorch_benchmark_setup.sh`` installs the following libraries for Llama 3.1 8B:
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Library
|
||||
- Reference
|
||||
* - Library
|
||||
- Reference
|
||||
|
||||
* - ``accelerate``
|
||||
- `Hugging Face Accelerate <https://huggingface.co/docs/accelerate/en/index>`_
|
||||
* - ``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
|
||||
* - ``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
|
||||
.. container:: model-doc pyt_train_llama-3.1-70b
|
||||
|
||||
``pytorch_benchmark_setup.sh`` installs the following libraries for Llama 3.1 70B:
|
||||
``pytorch_benchmark_setup.sh`` installs the following libraries for Llama 3.1 70B:
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Library
|
||||
- Reference
|
||||
* - Library
|
||||
- Reference
|
||||
|
||||
* - ``datasets``
|
||||
- `Hugging Face Datasets <https://huggingface.co/docs/datasets/v3.2.0/en/index>`_ 3.2.0
|
||||
* - ``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>`_
|
||||
* - ``torchdata``
|
||||
- `TorchData <https://pytorch.org/data/beta/index.html>`_
|
||||
|
||||
* - ``tomli``
|
||||
- `Tomli <https://pypi.org/project/tomli/>`_
|
||||
* - ``tomli``
|
||||
- `Tomli <https://pypi.org/project/tomli/>`_
|
||||
|
||||
* - ``tiktoken``
|
||||
- `tiktoken <https://github.com/openai/tiktoken>`_
|
||||
* - ``tiktoken``
|
||||
- `tiktoken <https://github.com/openai/tiktoken>`_
|
||||
|
||||
* - ``blobfile``
|
||||
- `blobfile <https://pypi.org/project/blobfile/>`_
|
||||
* - ``blobfile``
|
||||
- `blobfile <https://pypi.org/project/blobfile/>`_
|
||||
|
||||
* - ``tabulate``
|
||||
- `tabulate <https://pypi.org/project/tabulate/>`_
|
||||
* - ``tabulate``
|
||||
- `tabulate <https://pypi.org/project/tabulate/>`_
|
||||
|
||||
* - ``wandb``
|
||||
- `Weights & Biases <https://github.com/wandb/wandb>`_
|
||||
* - ``wandb``
|
||||
- `Weights & Biases <https://github.com/wandb/wandb>`_
|
||||
|
||||
* - ``sentencepiece``
|
||||
- `SentencePiece <https://github.com/google/sentencepiece>`_ 0.2.0
|
||||
* - ``sentencepiece``
|
||||
- `SentencePiece <https://github.com/google/sentencepiece>`_ 0.2.0
|
||||
|
||||
* - ``tensorboard``
|
||||
- `TensorBoard <https://www.tensorflow.org/tensorboard>`_ 2.18.0
|
||||
* - ``tensorboard``
|
||||
- `TensorBoard <https://www.tensorflow.org/tensorboard>`_ 2.18.0
|
||||
|
||||
.. container:: model-doc pyt_train_flux
|
||||
.. container:: model-doc pyt_train_flux
|
||||
|
||||
``pytorch_benchmark_setup.sh`` installs the following libraries for FLUX:
|
||||
``pytorch_benchmark_setup.sh`` installs the following libraries for FLUX:
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Library
|
||||
- Reference
|
||||
* - Library
|
||||
- Reference
|
||||
|
||||
* - ``accelerate``
|
||||
- `Hugging Face Accelerate <https://huggingface.co/docs/accelerate/en/index>`_
|
||||
* - ``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
|
||||
* - ``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
|
||||
* - ``sentencepiece``
|
||||
- `SentencePiece <https://github.com/google/sentencepiece>`_ 0.2.0
|
||||
|
||||
* - ``tensorboard``
|
||||
- `TensorBoard <https://www.tensorflow.org/tensorboard>`_ 2.18.0
|
||||
* - ``tensorboard``
|
||||
- `TensorBoard <https://www.tensorflow.org/tensorboard>`_ 2.18.0
|
||||
|
||||
* - ``csvkit``
|
||||
- `csvkit <https://csvkit.readthedocs.io/en/latest/>`_ 2.0.1
|
||||
* - ``csvkit``
|
||||
- `csvkit <https://csvkit.readthedocs.io/en/latest/>`_ 2.0.1
|
||||
|
||||
* - ``deepspeed``
|
||||
- `DeepSpeed <https://github.com/deepspeedai/DeepSpeed>`_ 0.16.2
|
||||
* - ``deepspeed``
|
||||
- `DeepSpeed <https://github.com/deepspeedai/DeepSpeed>`_ 0.16.2
|
||||
|
||||
* - ``diffusers``
|
||||
- `Hugging Face Diffusers <https://huggingface.co/docs/diffusers/en/index>`_ 0.31.0
|
||||
* - ``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
|
||||
* - ``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
|
||||
* - ``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
|
||||
* - ``peft``
|
||||
- `PEFT <https://huggingface.co/docs/peft/en/index>`_ 0.14.0
|
||||
|
||||
* - ``protobuf``
|
||||
- `Protocol Buffers <https://github.com/protocolbuffers/protobuf>`_ 5.29.2
|
||||
* - ``protobuf``
|
||||
- `Protocol Buffers <https://github.com/protocolbuffers/protobuf>`_ 5.29.2
|
||||
|
||||
* - ``pytest``
|
||||
- `PyTest <https://docs.pytest.org/en/stable/>`_ 8.3.4
|
||||
* - ``pytest``
|
||||
- `PyTest <https://docs.pytest.org/en/stable/>`_ 8.3.4
|
||||
|
||||
* - ``python-dotenv``
|
||||
- `python-dotenv <https://pypi.org/project/python-dotenv/>`_ 1.0.1
|
||||
* - ``python-dotenv``
|
||||
- `python-dotenv <https://pypi.org/project/python-dotenv/>`_ 1.0.1
|
||||
|
||||
* - ``seaborn``
|
||||
- `Seaborn <https://seaborn.pydata.org/>`_ 0.13.2
|
||||
* - ``seaborn``
|
||||
- `Seaborn <https://seaborn.pydata.org/>`_ 0.13.2
|
||||
|
||||
* - ``transformers``
|
||||
- `Transformers <https://huggingface.co/docs/transformers/en/index>`_ 4.47.0
|
||||
* - ``transformers``
|
||||
- `Transformers <https://huggingface.co/docs/transformers/en/index>`_ 4.47.0
|
||||
|
||||
``pytorch_benchmark_setup.sh`` downloads the following datasets from Hugging Face:
|
||||
``pytorch_benchmark_setup.sh`` downloads the following datasets from Hugging Face:
|
||||
|
||||
* `bghira/pseudo-camera-10k <https://huggingface.co/datasets/bghira/pseudo-camera-10k>`_
|
||||
* `bghira/pseudo-camera-10k <https://huggingface.co/datasets/bghira/pseudo-camera-10k>`_
|
||||
|
||||
{% for model_group in model_groups %}
|
||||
{% for model in model_group.models %}
|
||||
{% if model_group.tag == "pre-training" and model.mad_tag in ["pyt_train_llama-3.1-8b", "pyt_train_llama-3.1-70b", "pyt_train_flux"] %}
|
||||
{% 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:: Pretraining
|
||||
.. 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 pretrain -m {{ model.model_repo }} -p $datatype -s $sequence_length
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Name
|
||||
- Options
|
||||
- Description
|
||||
|
||||
{% if model.mad_tag == "pyt_train_llama-3.1-8b" %}
|
||||
* - ``$datatype``
|
||||
- ``BF16`` or ``FP8``
|
||||
- Only Llama 3.1 8B supports FP8 precision.
|
||||
{% else %}
|
||||
* - ``$datatype``
|
||||
- ``BF16``
|
||||
- Only Llama 3.1 8B supports FP8 precision.
|
||||
{% endif %}
|
||||
|
||||
* - ``$sequence_length``
|
||||
- Sequence length for the language model.
|
||||
- Between 2048 and 8192. 8192 by default.
|
||||
./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:`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
|
||||
`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 %}
|
||||
{% endif %}
|
||||
|
||||
{% if model_group.tag == "fine-tuning" %}
|
||||
.. 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.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
./pytorch_benchmark_report.sh -t $training_mode -m {{ model.model_repo }} -p BF16 -s $sequence_length
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
@@ -383,53 +404,143 @@ The following models are pre-optimized for performance on the AMD Instinct MI325
|
||||
- Options
|
||||
- Description
|
||||
|
||||
* - ``$training_mode``
|
||||
- ``finetune_fw``
|
||||
- Full weight fine-tuning (BF16 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
|
||||
{% for mode in available_modes %}
|
||||
* - {% if loop.first %}``$training_mode``{% endif %}
|
||||
- ``{{ mode }}``
|
||||
- {{ training_mode_descs[mode] }}
|
||||
{% endfor %}
|
||||
|
||||
* - ``$datatype``
|
||||
- ``BF16``
|
||||
- All models support BF16.
|
||||
- ``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>`.
|
||||
|
||||
.. 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::
|
||||
|
||||
{{ model.model }} currently supports the following fine-tuning methods:
|
||||
For LoRA and QLoRA support with vision models (Llama 3.2 11B and 90B),
|
||||
use the following torchtune commit for compatibility:
|
||||
|
||||
{% for method in model.training_modes %}
|
||||
* ``{{ method }}``
|
||||
{% endfor %}
|
||||
{% if model.training_modes|length < 4 %}
|
||||
.. 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.
|
||||
|
||||
The upstream `torchtune <https://github.com/pytorch/torchtune>`_ repository
|
||||
does not currently provide YAML configuration files for other combinations of
|
||||
model to fine-tuning method
|
||||
However, you can still configure your own YAML files to enable support for
|
||||
fine-tuning methods not listed here by following existing patterns in the
|
||||
``/workspace/torchtune/recipes/configs`` directory.
|
||||
{% endif %}
|
||||
{% endif %}
|
||||
{% endfor %}
|
||||
{% endfor %}
|
||||
|
||||
.. rubric:: Benchmarking examples
|
||||
.. rubric:: Benchmarking examples
|
||||
|
||||
For examples of benchmarking commands, see `<https://github.com/ROCm/MAD/tree/develop/benchmark/pytorch_train#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
|
||||
===============
|
||||
|
||||
Reference in New Issue
Block a user