LoRA Fine-Tuning

LoRA Fine-Tuning#

VeOmni supports LoRA (Low-Rank Adaptation) as a first-class feature of BaseTrainer. LoRA injects trainable low-rank matrices into selected linear layers while freezing the rest of the base model, enabling parameter-efficient fine-tuning with significantly reduced GPU memory.

Installation#

LoRA support requires the peft library. Install it via the lora optional extra:

uv sync --extra gpu --extra lora --group dev

Or with pip:

pip install peft==0.18.0

1. LoRA Config Definition#

LoRA is configured through the model.lora_config field in your YAML:

model:
  lora_config:
    rank: 64          # LoRA rank r
    alpha: 32         # Scaling factor α (effective scale = α / r)
    lora_modules:     # Target linear layer names (module name substrings)
      - q_proj
      - k_proj
      - v_proj
      - o_proj
      - gate_proj
      - up_proj
      - down_proj

Field

Type

Description

rank

int

Rank of the decomposition matrices A and B

alpha

int

LoRA scaling factor; effective scale = alpha / rank

lora_modules

list[str]

Target linear layer name substrings (matched against module FQNs)

lora_adapter

str (optional)

Path to a saved adapter directory to resume from

For resume training, add the lora_adapter key pointing to the saved adapter directory:

model:
  lora_config:
    rank: 64
    alpha: 32
    lora_modules: [q_proj, k_proj, v_proj, o_proj]
    lora_adapter: ./exp/my_run/global_step_500   # HF adapter dir to resume from

2. LoRA Initialization in BaseTrainer#

LoRA wrapping happens in BaseTrainer._setup_lora(), called from _freeze_model_module():

# veomni/trainer/base.py

def _setup_lora(self):
    lora_config = self.args.model.lora_config
    if not bool(lora_config):
        return

    lora_adapter_path = lora_config.get("lora_adapter", None)

    if lora_adapter_path is not None:
        # Resume: read PEFT config from disk; weights loaded later during parallelization
        from peft import PeftModel
        self.model = PeftModel.from_pretrained(
            self.model, lora_adapter_path, is_trainable=True
        )
    else:
        # From scratch: wrap with LoraConfig
        from peft import LoraConfig, get_peft_model
        peft_cfg = LoraConfig(
            r=lora_config["rank"],
            lora_alpha=lora_config["alpha"],
            target_modules=lora_config["lora_modules"],
        )
        self.model = get_peft_model(self.model, peft_cfg)

After wrapping, BaseTrainer._init_callbacks() automatically selects HFLoraCkptCallback instead of HuggingfaceCkptCallback when lora_config is set:

if self.args.model.lora_config:
    self.hf_ckpt_callback = HFLoraCkptCallback(self)
else:
    self.hf_ckpt_callback = HuggingfaceCkptCallback(self)

3. Weight Loading with LoRA#

VeOmni LoRA training uses FSDP2 with init_device: meta. Weight loading goes through build_parallelize_model and then post_process_after_weight_loading in torch_parallelize.py. The LoRA-specific path:

  1. Base-model weights: loaded via rank0_load_and_broadcast_weights or load_model_weights — the standard FSDP2 path, unchanged for LoRA.

  2. Adapter weights (resume only): _build_parallelized_model passes adapter_path to build_parallelize_model, which calls load_lora_model_weights (all-ranks read) or rank0_load_and_broadcast_adapter_weights (rank-0 reads then broadcasts). Both functions remap PEFT keys to model FQNs before dispatching into DTensors.

  3. Adapter weight initialisation from scratch: post_process_after_weight_loading calls _init_lora_parameter for any LoRA parameter not yet filled, invoking reset_lora_parameters to apply kaiming/zero init.

Key difference from base model loading: PEFT saves adapter keys without the adapter-name infix (e.g. lora_A.weight), whereas the live model stores them as lora_A.<adapter_name>.weight. The _remap_adapter_key utility handles this translation.

4. Checkpoint Saving#

DCP checkpoint (training state)#

CheckpointerCallback._save_checkpoint saves the full distributed state (model + optimizer + extra state) via PyTorch DCP. For LoRA training this includes both base-model parameters and adapter parameters; the optimizer state only covers the trainable adapter parameters.

HF LoRA adapter (inference artifact)#

HFLoraCkptCallback._save_checkpoint calls save_lora_adapter_with_dcp (veomni/utils/save_safetensor_utils.py), which:

  1. Extracts adapter-only tensors via get_peft_model_state_dict.

  2. Saves them with dcp.save in parallel to a temporary DCP directory.

  3. Consolidates on rank 0 into adapter_model.bin and adapter_config.json.

  4. Removes the temporary DCP directory.

Output structure for each checkpoint:

<output_dir>/
├── checkpoints/
│   └── global_step_N/          ← DCP checkpoint (resume training)
│       ├── __0_0.distcp
│       └── .metadata
└── global_step_N/              ← HF adapter (inference / resume)
    ├── adapter_config.json
    └── adapter_model.bin

5. Training Examples#

5.1 Wan2.1-I2V-1.3B LoRA (DiT, FSDP2)#

Config: configs/dit/wan2.1_I2V_1.3B_lora.yaml

model:
  lora_config:
    rank: 128
    alpha: 64
    lora_modules:
      - to_q
      - to_k
      - to_v
      - to_out.0
      - ffn.net.0.proj
      - ffn.net.2

train:
  init_device: meta
  accelerator:
    fsdp_config:
      fsdp_mode: fsdp2

Launch (8 GPUs, SP=2):

SP_SIZE=2
NPROC_PER_NODE=8

bash train.sh tasks/train_dit.py configs/dit/wan2.1_I2V_1.3B_lora.yaml \
    --model.model_path           ./Wan2.1-T2V-1.3B-Diffusers/transformer \
    --model.condition_model_path ./Wan2.1-T2V-1.3B-Diffusers \
    --data.train_path            ./my_dataset_offline \
    --data.source_name           my_dataset \
    --train.training_task        offline_training \
    --train.global_batch_size    8 \
    --train.micro_batch_size     1 \
    --train.accelerator.ulysses_size ${SP_SIZE} \
    --train.checkpoint.output_dir ./exp/wan_lora \
    --train.checkpoint.save_hf_weights true \
    --train.checkpoint.save_epochs 5 \
    --train.checkpoint.load_path auto \
    --train.num_train_epochs 30

See Wan2.1-I2V-1.3B Training Guide for the complete dataset preparation and inference workflow.

5.2 Qwen3-0.6B LoRA (LLM, FSDP2)#

Config: configs/text/qwen3_lora.yaml

model:
  model_path: Qwen3-0.6B-Base
  ops_implementation:
    attn_implementation: flash_attention_2
    cross_entropy_loss_implementation: eager
    rms_norm_implementation: eager
    swiglu_mlp_implementation: eager
    rotary_pos_emb_implementation: eager
  lora_config:
    rank: 64
    alpha: 32
    lora_modules:
      - q_proj
      - k_proj
      - v_proj
      - o_proj
      - gate_proj
      - up_proj
      - down_proj

train:
  init_device: meta          # required for FSDP2
  accelerator:
    fsdp_config:
      fsdp_mode: fsdp2

Launch (8 GPUs, SP=2):

SP_SIZE=2
NPROC_PER_NODE=8

bash train.sh tasks/train_text.py configs/text/qwen3_lora.yaml \
    --model.model_path /path/to/Qwen3-0.6B-Base \
    --data.train_path  /path/to/tulu-3-sft-mixture/data \
    --train.global_batch_size 8 \
    --train.micro_batch_size  1 \
    --train.num_train_epochs  3 \
    --train.checkpoint.output_dir ./exp/qwen3_lora \
    --train.checkpoint.save_hf_weights true \
    --train.checkpoint.load_path auto \
    --train.wandb.enable true

To resume from a saved adapter:

bash train.sh tasks/train_text.py configs/text/qwen3_lora.yaml \
    --model.model_path /path/to/Qwen3-0.6B-Base \
    --data.train_path  /path/to/tulu-3-sft-mixture/data \
    --train.checkpoint.output_dir ./exp/qwen3_lora \
    --train.checkpoint.load_path auto          # auto-picks latest DCP checkpoint

6. Testing#

The test suite is under tests/lora/ and verifies save/load correctness using a two-layer toy Qwen3 model:

torchrun --nproc_per_node=4 tests/lora/test_lora_trainer_saveload.py \
    tests/lora/qwen3_toy_lora.yaml

What the test verifies:

  1. Train 3 steps with LoRA on a dummy dataset (FSDP2, meta device).

  2. After step 1: snapshot LoRA weights and save DCP checkpoint.

  3. Continue training (steps 2–3 mutate adapter weights).

  4. Reload the step-1 checkpoint; assert every LoRA tensor is bit-identical to the snapshot.