20 lines
992 B
Markdown
20 lines
992 B
Markdown
MoE Backends in Axolotl
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Axolotl supports selecting a Mixture-of-Experts (MoE) compute backend via the training config (YAML):
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- Set `moe_backend: auto|hf_triton|torch_grouped|naive`
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Behavior
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- auto (default): prefers PyTorch 2.8+ grouped GEMM, then Hugging Face kernels hub, otherwise naive.
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- hf_triton: uses the Hugging Face kernels hub (kernels-community/triton_kernels) when available.
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- torch_grouped: targets PyTorch 2.8+ grouped GEMM.
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- naive: keeps the reference per-expert loop.
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Notes
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- Current implementation wires the backend selector and routes Mixtral MoE through it. The hf_triton path is initially a stub: it uses kernels hub for routing but still falls back to per-expert computation until grouped GEMM is fully integrated.
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- No changes to training scripts are required; selection happens inside the model forward. The `AXOLOTL_MOE_BACKEND` environment variable is no longer used.
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Example
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moe_backend: hf_triton
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accelerate launch -m axolotl.cli.train path/to/config.yaml
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