| """Inject LoRA into the vendor's REAL training entrypoint. |
| |
| Why: the vendor's own recipe (tasks/vla/train_lingbotvla.py) is full-parameter |
| fine-tune only - grep finds zero LoRA/PEFT wiring anywhere in it. LoRA support |
| DOES exist in the codebase (lingbotvla/utils/lora_utils.py, peft==0.15.2 is |
| even a dependency) but is only called by openroboto CLI's own bundled |
| single-process runner - which uses a DIFFERENT, simplified data pipeline (a |
| decoded JSON episode list) rather than the vendor's real LeRobot dataset |
| loader. Converting our real 1693-episode dataset into that JSON format would |
| mean re-deriving a data pipeline that already works. Patching LoRA into the |
| real entrypoint instead keeps the already-verified real data loading, real |
| model construction, and real depth/video alignment losses, changing only |
| which parameters are trainable. |
| |
| Insertion point: right after the model is built and its param stats are |
| logged, before FSDP wrapping and before the optimizer is constructed. At that |
| point `model` is a plain nn.Module (not yet parallelized), which is exactly |
| what add_lora_to_model expects. |
| |
| Target modules and the r=32/alpha=64 pair are not invented here - they come |
| from openroboto-cli's own train_runner.py, which documents having verified on |
| a real GPU that q_proj/k_proj/v_proj/o_proj matches 396 real tensors (38.9M |
| trainable params), and that the lora_utils.add_lora_to_model signature |
| default ("q,k,v,o,ffn.0,ffn.2") does NOT match this architecture and raises |
| ValueError if used as-is. |
| |
| The optimizer needs no change: split_muon_adamw_params already skips any |
| param with requires_grad=False, so freezing everything but the LoRA adapters |
| automatically means only the adapters get optimized. |
| """ |
| import ast |
|
|
| TRAIN = "/app/lingbot-vla-v2/tasks/vla/train_lingbotvla.py" |
|
|
| LORA_RANK = 32 |
| LORA_ALPHA = 64 |
| LORA_TARGET_MODULES = "q_proj,k_proj,v_proj,o_proj" |
|
|
| anchor = " log_model_param_stats(model)\n\n model_config = model.config" |
| if anchor not in open(TRAIN).read(): |
| raise SystemExit("anchor not found - vendor code changed, patch needs updating") |
|
|
| injection = """ log_model_param_stats(model) |
| |
| from lingbotvla.utils.lora_utils import add_lora_to_model, freeze_parameters |
| _lora_targets = "{targets}".split(",") |
| freeze_parameters(model) |
| add_lora_to_model( |
| model, |
| lora_rank={rank}, |
| lora_alpha={alpha}, |
| lora_target_modules="{targets}", |
| lora_target_modules_support=_lora_targets, |
| ) |
| # add_lora_to_model has no return statement (it mutates `model` in place |
| # via inject_adapter_in_model, which itself modifies its argument |
| # in-place) - capturing a return value here overwrites `model` with None. |
| _n_trainable = sum(p.numel() for p in model.parameters() if p.requires_grad) |
| _n_total = sum(p.numel() for p in model.parameters()) |
| logger.info_rank0( |
| f"LoRA r={rank} alpha={alpha} targets={{_lora_targets}}: " |
| f"trainable {{_n_trainable/1e6:.1f}}M / {{_n_total/1e9:.2f}}B " |
| f"({{100*_n_trainable/_n_total:.3f}}%)" |
| ) |
| if _n_trainable == 0: |
| raise RuntimeError("LoRA injection left nothing trainable - target modules did not match") |
| |
| model_config = model.config""".format(rank=LORA_RANK, alpha=LORA_ALPHA, targets=LORA_TARGET_MODULES) |
|
|
| src = open(TRAIN).read() |
| src = src.replace(anchor, injection, 1) |
| open(TRAIN, "w").write(src) |
|
|
| ast.parse(open(TRAIN).read()) |
| print(f"OK LoRA injection patched in: r={LORA_RANK} alpha={LORA_ALPHA} targets={LORA_TARGET_MODULES}") |
|
|
