From a3755f07c5f6ea4c1749858ce7782c09a84c1c09 Mon Sep 17 00:00:00 2001 From: Maxime Ellerbach Date: Fri, 24 Jul 2026 13:37:03 +0000 Subject: [PATCH] fix(peft): allow fresh LoRA fine-tuning from a base-model checkpoint --- docs/source/lingbot_va.mdx | 15 +- docs/source/peft_training.mdx | 7 + src/lerobot/configs/train.py | 5 + src/lerobot/policies/factory.py | 48 +++++- .../policies/test_peft_base_model_loading.py | 142 ++++++++++++++++++ 5 files changed, 211 insertions(+), 6 deletions(-) create mode 100644 tests/policies/test_peft_base_model_loading.py diff --git a/docs/source/lingbot_va.mdx b/docs/source/lingbot_va.mdx index d33e90340..6d4ac81c6 100644 --- a/docs/source/lingbot_va.mdx +++ b/docs/source/lingbot_va.mdx @@ -111,17 +111,26 @@ Requirements: - The block-causal masks use PyTorch **flex-attention**, so build the policy with `--policy.attn_mode=flex` for training (the default `torch` SDPA is inference-only). - The full 5B DiT does not fit a single 24–32 GB GPU under AdamW; fine-tune with **LoRA** - (`--policy.use_peft=true`) and/or optimizer offload. `get_optim_params` returns only the - trainable (e.g. adapter) parameters; the VAE + UMT5 text encoder stay frozen. + (`--peft.method_type=LORA`) and/or optimizer offload. `get_optim_params` returns only the + trainable (e.g. adapter) parameters; the VAE + UMT5 text encoder stay frozen. Install the + `lerobot[peft]` extra to enable PEFT support (see the [PEFT training guide](./peft_training)). ```bash lerobot-train \ --policy.path=lerobot/lingbot_va_libero_long --policy.attn_mode=flex \ - --policy.use_peft=true \ + --peft.method_type=LORA --peft.r=32 --peft.lora_alpha=32 \ + --peft.target_modules='transformer\.blocks\.\d+\.attn[12]\.(to_q|to_v)' \ --dataset.repo_id= \ --batch_size=1 --steps=... --output_dir=outputs/train/lingbot_va ``` +Unlike SmolVLA / π₀, LingBot-VA does not ship built-in default LoRA targets, so you must pass +`--peft.target_modules` explicitly. Only `self.transformer` (the dual-stream Wan transformer) is +trainable; the example above adapts the query/value projections of both its self-attention +(`attn1`) and cross-attention (`attn2`) blocks — the standard LoRA target set. Broaden it (e.g. +add `to_k`/`to_out`, or the `ffn` layers) if you need a higher-capacity adapter. Passing +`--peft.method_type` implies PEFT, so `--policy.use_peft=true` is not required. + The dataset must provide camera clips (a temporal window per camera, VAE-encoded to `frame_chunk_size` latent frames) and `frame_chunk_size * action_per_frame` action steps per item. diff --git a/docs/source/peft_training.mdx b/docs/source/peft_training.mdx index d44a3f081..8cc6acd36 100644 --- a/docs/source/peft_training.mdx +++ b/docs/source/peft_training.mdx @@ -62,3 +62,10 @@ to the `--peft.full_training_modules` parameter: The learning rate and the scheduled target learning rate can usually be scaled by a factor of 10 compared to the learning rate used for full fine-tuning (e.g., 1e-4 normal, so 1e-3 using LoRA). + +## Other policies + +The same `--peft.*` flags work for any pre-trained policy. Some policies (SmolVLA, π₀, π₀.₅) ship +built-in default `target_modules`, so `--peft.method_type=LORA` is enough. Others do not, and will +ask you to pass `--peft.target_modules` explicitly — for example LingBot-VA, whose recommended +targets are documented in its [dedicated guide](./lingbot_va#training--fine-tuning). diff --git a/src/lerobot/configs/train.py b/src/lerobot/configs/train.py index e3d354691..0184d36fb 100644 --- a/src/lerobot/configs/train.py +++ b/src/lerobot/configs/train.py @@ -221,6 +221,11 @@ class TrainPipelineConfig(HubMixin): ) active_cfg = self.trainable_config + + # Keep the policy-level `use_peft` flag in sync with the presence of a `--peft.*` config. + if self.peft is not None and self.policy is not None: + self.policy.use_peft = True + if self.rename_map and active_cfg.pretrained_path is None: raise ValueError( "`rename_map` requires a pretrained policy checkpoint. " diff --git a/src/lerobot/policies/factory.py b/src/lerobot/policies/factory.py index 73fd9455f..86c2f8437 100644 --- a/src/lerobot/policies/factory.py +++ b/src/lerobot/policies/factory.py @@ -513,6 +513,37 @@ def make_pre_post_processors( return processors +def _has_peft_adapter_config( + pretrained_path: str, + revision: str | None = None, +) -> bool: + """Return whether ``pretrained_path`` points to an existing PEFT adapter. + + A PEFT adapter checkpoint always ships an ``adapter_config.json``. + A plain base-model checkpoint does not. This distinction lets us tell apart + two very different ``use_peft=True`` scenarios that both set ``pretrained_path``: + + * loading/resuming a previously trained adapter (config lives at ``pretrained_path``) + * starting a *fresh* PEFT fine-tune on top of a base model + + Works for both local directories and Hub repo ids. + """ + import os + + adapter_config_name = "adapter_config.json" + + if os.path.isdir(pretrained_path): + return os.path.isfile(os.path.join(pretrained_path, adapter_config_name)) + + from huggingface_hub import file_exists + from huggingface_hub.errors import HfHubHTTPError + + try: + return file_exists(pretrained_path, adapter_config_name, revision=revision) + except (HfHubHTTPError, OSError): + return False + + def make_policy( cfg: PreTrainedConfig, ds_meta: LeRobotDatasetMetadata | None = None, @@ -607,13 +638,24 @@ def make_policy( "the PEFT config parameters to be set. For training with PEFT, see `lerobot_train.py` on how to do that." ) - if cfg.pretrained_path and not cfg.use_peft: + # When `use_peft=True` and a checkpoint is given, the checkpoint can be one of two things: + # 1. A base model checkpoint (e.g., a pretrained policy) on which we want to start a fresh PEFT fine-tune. + # 2. A PEFT adapter checkpoint (e.g., a previously trained PEFT adapter) + # We distinguish between these two cases + load_existing_adapter = ( + cfg.pretrained_path + and cfg.use_peft + and _has_peft_adapter_config(str(cfg.pretrained_path), cfg.pretrained_revision) + ) + + if cfg.pretrained_path and not load_existing_adapter: # Load a pretrained policy and override the config if needed (for example, if there are inference-time - # hyperparameters that we want to vary). + # hyperparameters that we want to vary). This also covers starting a fresh PEFT fine-tune on top of a + # base model: the base weights are loaded here and `wrap_with_peft` builds the adapter afterwards. kwargs["pretrained_name_or_path"] = cfg.pretrained_path kwargs["revision"] = cfg.pretrained_revision policy = policy_cls.from_pretrained(**kwargs) - elif cfg.pretrained_path and cfg.use_peft: + elif load_existing_adapter: # Load a pretrained PEFT model on top of the policy. The pretrained path points to the folder/repo # of the adapter and the adapter's config contains the path to the base policy. So we need the # adapter config first, then load the correct policy and then apply PEFT. diff --git a/tests/policies/test_peft_base_model_loading.py b/tests/policies/test_peft_base_model_loading.py new file mode 100644 index 000000000..c1e045e85 --- /dev/null +++ b/tests/policies/test_peft_base_model_loading.py @@ -0,0 +1,142 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Regression tests for PEFT loading when the checkpoint is a base model (see issue #3975). + +Starting a fresh LoRA/PEFT fine-tune points ``--policy.path`` at a *base model* (no +``adapter_config.json``) while also setting ``use_peft=True``. This must NOT be mistaken for +loading an existing PEFT adapter. These tests lock in the base-model vs. adapter distinction +made by ``lerobot.policies.factory._has_peft_adapter_config`` and the branch it drives in +``make_policy``. They are pure/fast (no network, no ``peft``), so they run in CI. +""" + +import json +from unittest.mock import MagicMock, patch + +import torch +from huggingface_hub.errors import HfHubHTTPError + +from lerobot.policies.factory import _has_peft_adapter_config + + +def test_local_base_model_dir_has_no_adapter_config(tmp_path): + # A base-model checkpoint directory (only model weights, no adapter config). + (tmp_path / "model.safetensors").write_bytes(b"") + (tmp_path / "config.json").write_text("{}") + assert _has_peft_adapter_config(str(tmp_path)) is False + + +def test_local_adapter_dir_has_adapter_config(tmp_path): + (tmp_path / "adapter_config.json").write_text(json.dumps({"peft_type": "LORA"})) + (tmp_path / "adapter_model.safetensors").write_bytes(b"") + assert _has_peft_adapter_config(str(tmp_path)) is True + + +def test_hub_base_model_repo_has_no_adapter_config(): + with patch("huggingface_hub.file_exists", return_value=False) as mock_exists: + assert _has_peft_adapter_config("lerobot/lingbot_va_base") is False + mock_exists.assert_called_once() + assert mock_exists.call_args.args[1] == "adapter_config.json" + + +def test_hub_adapter_repo_has_adapter_config(): + with patch("huggingface_hub.file_exists", return_value=True): + assert _has_peft_adapter_config("some/adapter-repo", revision="main") is True + + +def test_hub_lookup_error_falls_back_to_base_model(): + # Offline / private / transient Hub errors must not crash; treat as "not an adapter". + with patch( + "huggingface_hub.file_exists", + side_effect=HfHubHTTPError("boom", response=MagicMock()), + ): + assert _has_peft_adapter_config("some/private-repo") is False + with patch("huggingface_hub.file_exists", side_effect=OSError("offline")): + assert _has_peft_adapter_config("some/repo") is False + + +def _make_dummy_policy_cfg(pretrained_path, use_peft): + cfg = MagicMock() + cfg.type = "act" + cfg.device = "cpu" + cfg.pretrained_path = pretrained_path + cfg.pretrained_revision = None + cfg.use_peft = use_peft + cfg.input_features = {} + cfg.output_features = {} + return cfg + + +@patch("lerobot.policies.factory.validate_visual_features_consistency") +@patch("lerobot.policies.factory.env_to_policy_features", return_value={}) +@patch("lerobot.policies.factory.get_policy_class") +def test_make_policy_base_model_with_use_peft_loads_base_not_adapter( + mock_get_cls, _mock_features, _mock_validate +): + """`use_peft=True` on a base model must load the base weights, not a PEFT adapter. + + Before the #3975 fix this went down the ``PeftConfig.from_pretrained`` path and failed + looking for a non-existent ``adapter_config.json``. + """ + from lerobot.policies import factory + + policy_cls = MagicMock() + loaded_policy = torch.nn.Linear(1, 1) # a real nn.Module so make_policy's assert passes + policy_cls.from_pretrained.return_value = loaded_policy + mock_get_cls.return_value = policy_cls + + cfg = _make_dummy_policy_cfg(pretrained_path="lerobot/lingbot_va_base", use_peft=True) + env_cfg = MagicMock() + + with patch.object(factory, "_has_peft_adapter_config", return_value=False) as mock_has_adapter: + policy = factory.make_policy(cfg=cfg, env_cfg=env_cfg) + + mock_has_adapter.assert_called_once() + # Base model is loaded via the normal pretrained path... + policy_cls.from_pretrained.assert_called_once() + assert policy_cls.from_pretrained.call_args.kwargs["pretrained_name_or_path"] == ( + "lerobot/lingbot_va_base" + ) + # ...and PEFT adapter loading is NOT attempted (would need peft + adapter_config.json). + assert policy is loaded_policy + + +@patch("lerobot.policies.factory.validate_visual_features_consistency") +@patch("lerobot.policies.factory.env_to_policy_features", return_value={}) +@patch("lerobot.policies.factory.get_policy_class") +def test_make_policy_existing_adapter_uses_peft_loading(mock_get_cls, _mock_features, _mock_validate): + """A real adapter checkpoint (has ``adapter_config.json``) must go through PEFT loading.""" + from lerobot.policies import factory + + policy_cls = MagicMock() + mock_get_cls.return_value = policy_cls + + cfg = _make_dummy_policy_cfg(pretrained_path="some/adapter-repo", use_peft=True) + env_cfg = MagicMock() + + policy_cls.from_pretrained.return_value = torch.nn.Linear(1, 1) + + fake_peft = MagicMock() + fake_peft_config = MagicMock() + fake_peft_config.base_model_name_or_path = "lerobot/lingbot_va_base" + fake_peft.PeftConfig.from_pretrained.return_value = fake_peft_config + fake_peft.PeftModel.from_pretrained.return_value = torch.nn.Linear(1, 1) + + with ( + patch.object(factory, "_has_peft_adapter_config", return_value=True), + patch.dict("sys.modules", {"peft": fake_peft}), + ): + factory.make_policy(cfg=cfg, env_cfg=env_cfg) + + fake_peft.PeftConfig.from_pretrained.assert_called_once_with("some/adapter-repo") + fake_peft.PeftModel.from_pretrained.assert_called_once()