Related Issues (20)
- [BUG]: llama2 hybrid_parallel or 3d giving None loss when using pp_size > 1 HOT 6
- [DOC]: torch-version HOT 1
- [BUG]: fine train llama-2-7b-hf prepare data set error , `bos_token` and `eos_token` should be the same with `conversation_template.seps`. HOT 2
- [BUG]: No module named 'dropout_layer_norm' HOT 1
- [BUG]: TypeError: LlamaInferenceForwards.llama_causal_lm_forward() got an unexpected keyword argument 'shard_config' HOT 1
- [BUG]: docker build cuda extension error HOT 7
- [BUG]: Shardformer failure with torch 2.3
- [FEATURE]: SP with FlashAttention HOT 1
- [BUG]: Report some errors in test
- [PROPOSAL]: Refactor inference engine by selecting backend during init of modules HOT 1
- [BUG]:Report some errors in test_fx/test_tracer
- Gradients are None after booster.backward HOT 10
- I have searched the existing issues
- [BUG]: Run finetune with the DEMO, get CUDA Out of Memory on a H800 node in hpcaitech cloud instance HOT 4
- [BUG]: FileNotFoundError: [Errno 2] No such file or directory: '/home/sw/anaconda3/envs/colossalai/lib/python3.10/site-packages/colossalai/kernel/extensions/pybind/inference/inference.cpp' HOT 2
- [FEATURE]: LoRA with sharded model
- Use gemini plugin and LowLevelZero to run llama2_7b. In the pulgin in gemini, set the policy to static, shard_param_frac, offload_optim_frac, and offload_param_frac to 0.0, making gemini equal to zero2, and set stage to 2 in LowLevelZero. Using bf16 for training, and comparing the two plugins, we found that the GPU memory usage of gemini is higher than that of LowLevelZero. Why is this? In principle, gemini should save more GPU memory HOT 2
- [FEATURE]: Support Command-R model
- [BUG]: Command-R 8 GPU Pytest failure
- [FEATURE]: Support T5ForTokenClassification
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