132eb740f0
* wip for dbrx finetuning * add fastcore for parallel loading of sharded weights * fix dtype for load, use PartialState instead of accelerator to init process group, remove redundant wandb callback * update to use v2 of the converted model * more fixes for dbrx loras * make sure to enable fsdp activation checkpointing * fix support for 8bit loras too for dbrx * apply z3 leaf moe fix for DBRX with deepspeed * don't raise value error since child module searches could fail and be ok * revert a previous change to fix fsdp * update mistral/mistral qlora+fsdp yamls * fix qlora+fsdp quant storage type * more edge cases for qlora-fsdp * fixes for fsdp+qlora w optimizer in 8bit * add bigstral z3 config and make sure to use full_state_dict for fsdp
27 lines
1.1 KiB
Markdown
27 lines
1.1 KiB
Markdown
# DBRX MoE
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Currently, for LoRA, only the `q_proj`, `k_proj`, `v_proj` `out_proj` and `layer` Linear layers are trainable.
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We are using the "converted" base models based on [this issue](https://huggingface.co/databricks/dbrx-instruct/discussions/10)
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where the Experts are fused as an `nn.Parameter` rather than a `nn.Linear` layer. However, the implementation
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is still a bit buggy and attempting to train a LoRA adapter over those `w1`, `w2` and `v1` layers
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results in the trainer hanging.
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### FSDP
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We've tested using the [`LnL-AI/dbrx-base-converted-v2`](https://huggingface.co/LnL-AI/dbrx-base-converted-v2) model as the base model for FSDP.
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The high memory usage seen w/ FSDP is due to FSDP not supporting 8bit optimizers.
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- 16-bit LoRA w/ FSDP
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- ✅ w/o CPU Offload - 8x80GB uses ~80GiB/gpu
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- ❌ w/ CPU Offload - `paged_adamw_8bit` optimizer errors from being on cpu
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- ✅ 8-bit LoRA w/ FSDP
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- ❌ 4-bit QLoRA w/ FSDP - errors w/: `Error an illegal memory access was encountered at line 90 in file /src/csrc/ops.cu`
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- ✅ bf16 full finetune w/ FSDP, freezing all but first 8 layers (8x80GB uses ~78GiB/gpu)
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### Deepspeed
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WIP
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