Charlie Doern 6494658a10 feat: add finetune_multi_device recipe with fsdp support ... the HF SFTTrainer supports distributed training using FSDP. Add a new recipe, `finetune_multi_device` which supports multi-GPU (cuda) training using FSDP and optionally LoRA. transformers hides _alot_ of their usage of FSDP behind the training args: a6b51e7341/src/transformers/training_args.py (L1535) you need to pass both `fsdp` and `fsdp_config` to get it to work properly. However, it seems many of the `fsdp_config` entries are silently ignored. The key things to get this working were: full_shard offload (cpu offload) transformer_layer_cls_to_wrap (model specific wrapping) cpu_ram_efficient_loading sharding_strategy limit_all_gathers sync_module_states backward_prefetch use_orig_params these can be seen both in `fsdp=` and `fsdp_config=` int he `SFTConfig` call. I have tested this with different model architectures with and without LoRA with success. the user can now toggle `recipe` in their provider config between `single` and `multi` to access the two different recipes. for debugging purposes NCCL logging settings can now be accessed via the provider config as well Signed-off-by: Charlie Doern <cdoern@redhat.com>		2025-06-12 13:33:33 -04:00
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__init__.py	Auto-generate distro yamls + docs (#468)	2024-11-18 14:57:06 -08:00
build.yaml	feat: support postgresql inference store (#2310)	2025-05-29 14:33:09 -07:00
doc_template.md	fix: replace all instances of --yaml-config with --config (#2196)	2025-05-16 14:31:12 -07:00
ollama.py	feat: add huggingface post_training impl (#2132)	2025-05-16 14:41:28 -07:00
run-with-safety.yaml	feat: add finetune_multi_device recipe with fsdp support	2025-06-12 13:33:33 -04:00
run.yaml	feat: add finetune_multi_device recipe with fsdp support	2025-06-12 13:33:33 -04:00