Update base for Update on "[2/N] Support lora checkpoint on partial save and multi-source load"

 ### Summary

  - **`merge_adapter` config**: Replace `save_format: str` with `merge_adapter: bool` on
  `LoRAConverter.Config`. When `True`, adapters are folded into base weights (`base + alpha/rank * B @ A`) at
  end of training. When `False` (default), adapter weights are saved separately — use
  `checkpoint.last_save_in_hf=True` to save in HuggingFace PEFT format.

  - **`finalize()` lifecycle on ModelConverter protocol**: Add an end-of-training hook called before the last
  checkpoint save. `ModelConvertersContainer` runs finalize in reverse converter order (LoRA merge before
  quantization CONVERT). A `converter_finalize_fn` closure is attached to each model part during `convert()`
  so the checkpoint system can invoke it. All existing converters (Float8Linear, Float8GroupedMM, MXFP8) get
  no-op `finalize()` implementations.

  - **Checkpoint integration**: `ModelWrapper` gains converter-aware methods (`state_dict_to_save`,
  `export_state_dict`, `base_state_dict`, `has_converter_keys`, `converter_save_last_fn`,
  `converter_load_additional_fn`) to support adapter-only checkpointing. `dcp_load` accepts `checkpoint_ids:
  list[str]` for loading from multiple sources (base model + adapter weights). A new `additional_load_paths`
  config field enables multi-source loading. The PEFT save path in `_save_last_step` is gated on
  `last_save_in_hf`.

  - **LoRA PEFT save/load**: `_make_peft_save_fn()` writes `adapter_model.safetensors` + `adapter_config.json`
   with HF PEFT key naming. `_make_peft_load_fn()` loads and remaps keys back. `remap_lora_keys_to_hf()` /
  `remap_lora_keys_from_hf()` handle the bidirectional key translation.

  - **Configs**: Add `llama3_8b_lora` (rank=128, alpha=32, `last_save_in_hf=True`). Update
  `llama3_debugmodel_lora` with checkpoint settings for proper resumption.

  ### Test plan

  - [ ] `pytest tests/unit_tests/test_checkpoint.py -x` — new `TestModelWrapperConverterKeys` tests (strict vs
   partial planner)
  - [ ] `pytest tests/unit_tests/test_model_converter.py -x` — new `test_lora_key_remap_roundtrip`
  - [ ] `torchrun --nproc_per_node=4 -m torchtitan.train --module llama3 --config llama3_debugmodel_lora` —
  verify LoRA training runs end-to-end
  - [ ] `torchrun --nproc_per_node=4 -m torchtitan.train --module llama3 --config llama3_8b_lora` — verify 8B
  LoRA config with PEFT save


* #2484

[ghstack-poisoned]

Author: mori360