How xuefuzhao/openmoe works
OpenMoE is an open-source Mixture-of-Experts language-model project: it releases model checkpoints and supports training, inference, and evaluation. The principal runtime path is a user prompt through the multi-device inference script to a loaded checkpoint and generated text; the sampled MoE layer implements sparse expert routing and distributed expert computation. Evaluation retrieves and normalizes BIG-bench scores before plotting comparisons, while TriQA plotting uses embedded data. Training is documented and has a launcher, but its implementation and wiring are not shown in the sampled source.
Main entry points: inference_on_multi_devices.py Multi-device inference · layer.py Sparse expert layer · result_retrieval_bigbenchlite.py Score retrieval · triqa_plot.py TriQA comparison plot
Components 16
Model execution
- Multi-device inference
script/inference_on_multi_devices.py - Model checkpoint
- Hugging Face Hub
- Tokenizer
script/inference_on_multi_devices.py - Loaded MoE model
script/inference_on_multi_devices.py - Sparse expert layer
analysis/colossalai_replace/layer.py - ColossalAI MoE ops
- Generated text
Evaluation and analysis
- BIG-bench results
- Score retrieval
eval/result_retrieval_bigbenchlite.py - Retrieved score CSV
- BIG-bench plots
eval/plot_bigbench.py - TriQA comparison plot
eval/triqa_plot.py
Training
- Pretraining launcher
script/run_pretrain.sh - Evaluation launcher
script/run_eval.sh
Other components
- User
Connections 14
- User → Multi-device inference: provides prompt
- Multi-device inference → Hugging Face Hub: downloads checkpoint
- Hugging Face Hub → Model checkpoint: returns weights
- Multi-device inference → Model checkpoint: loads weights
- Multi-device inference → Tokenizer: loads tokenizer
- Multi-device inference → Loaded MoE model: dispatches inference
- Loaded MoE model → Sparse expert layer: uses layer
- Sparse expert layer → ColossalAI MoE ops: calls MoE ops
- Multi-device inference → Tokenizer: encodes prompt
- Multi-device inference → Generated text: returns generation
- BIG-bench results → Score retrieval: supplies scores
- Score retrieval → Retrieved score CSV: writes scores
- Retrieved score CSV → BIG-bench plots: reads scores
- TriQA comparison plot → TriQA comparison plot: plots embedded data