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>

llama_stack/templates/ollama/run-with-safety.yaml

@@ -91,6 +91,7 @@ providers:
       checkpoint_format: huggingface
       distributed_backend: null
       device: cpu
+      recipe: single
   tool_runtime:
   - provider_id: brave-search
     provider_type: remote::brave-search

llama_stack/templates/ollama/run.yaml

@@ -89,6 +89,7 @@ providers:
       checkpoint_format: huggingface
       distributed_backend: null
       device: cpu
+      recipe: single
   tool_runtime:
   - provider_id: brave-search
     provider_type: remote::brave-search