mirror of
https://github.com/huggingface/lerobot.git
synced 2026-07-10 03:21:54 +00:00
Pepijn
4dfa8cea65
Port the LingBot-VA policy (Wan2.2 dual-stream video+action world model) into LeRobot, following the EO-1 / VLA-JEPA conventions. Covers inference, checkpoint conversion, and predicted-video saving (training is deferred to a follow-up PR). - Vendored Wan transformer/attention/flex/VAE/scheduler modules (key names preserved for near-identity conversion); torch SDPA default, flashattn/flex lazy-guarded. - LingBotVAConfig (registered "lingbot_va") + processor with fixed-quantile action unnormalization; full dual-stream sampling loop with CFG, two flow-matching schedulers and KV cache, mapped onto select_action with observed-keyframe feedback. - convert_lingbot_va_checkpoints.py (libero/robotwin variants): bundles the ~5B transformer, lazy-pulls the frozen VAE+UMT5 from the source repo. - Predicted-video plumbing in lerobot_eval (predicted_frames_callback; opt-in via --policy.save_predicted_video) and ConstantWithWarmupSchedulerConfig. - pyproject: widen diffusers-dep to <0.37, add lingbot_va + imageio-dep extras, add lingbot_va and (missing) eo1 to `all`. - Factory + policies/__init__ wiring, docs page + toctree, and tests. Note: the LIBERO success-rate correctness gate must be validated on a CUDA GPU with the converted checkpoint. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
82 lines
3.1 KiB
Python
82 lines
3.1 KiB
Python
#!/usr/bin/env python
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# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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import torch
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from lerobot.configs.types import FeatureType, PolicyFeature
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from lerobot.policies.lingbot_va.configuration_lingbot_va import LingBotVAConfig
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from lerobot.policies.lingbot_va.processor_lingbot_va import (
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LingBotVAActionUnnormalizeStep,
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make_lingbot_va_pre_post_processors,
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)
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from lerobot.utils.constants import (
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OBS_IMAGES,
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POLICY_POSTPROCESSOR_DEFAULT_NAME,
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POLICY_PREPROCESSOR_DEFAULT_NAME,
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)
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def _make_config() -> LingBotVAConfig:
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cfg = LingBotVAConfig(device="cpu")
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cfg.input_features = {f"{OBS_IMAGES}.image": PolicyFeature(type=FeatureType.VISUAL, shape=(3, 128, 128))}
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cfg.output_features = {}
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cfg.validate_features()
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return cfg
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def test_action_unnormalize_inverts_quantile_norm() -> None:
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q01 = [-1.0, -0.5, 0.0]
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q99 = [1.0, 0.5, 2.0]
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step = LingBotVAActionUnnormalizeStep(action_q01=q01, action_q99=q99)
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# Forward (the policy-side) quantile normalization: (x - q01) / (q99 - q01 + eps) * 2 - 1.
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q01_t = torch.tensor(q01)
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q99_t = torch.tensor(q99)
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raw = torch.tensor([[0.3, 0.1, 1.0]])
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normed = (raw - q01_t) / (q99_t - q01_t + 1e-6) * 2.0 - 1.0
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recovered = step.action(normed)
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assert torch.allclose(recovered, raw, atol=1e-4)
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def test_action_unnormalize_config_roundtrip() -> None:
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step = LingBotVAActionUnnormalizeStep(action_q01=[0.0, 1.0], action_q99=[2.0, 3.0])
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cfg = step.get_config()
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assert cfg == {"action_q01": [0.0, 1.0], "action_q99": [2.0, 3.0]}
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rebuilt = LingBotVAActionUnnormalizeStep(**cfg)
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assert rebuilt.action_q01 == step.action_q01
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assert rebuilt.action_q99 == step.action_q99
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def test_make_pre_post_processors_names_and_steps() -> None:
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cfg = _make_config()
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pre, post = make_lingbot_va_pre_post_processors(cfg, dataset_stats=None)
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assert pre.name == POLICY_PREPROCESSOR_DEFAULT_NAME
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assert post.name == POLICY_POSTPROCESSOR_DEFAULT_NAME
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# The postprocessor must contain the dedicated quantile unnormalize step.
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assert any(isinstance(s, LingBotVAActionUnnormalizeStep) for s in post.steps)
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def test_postprocessor_applies_unnormalization() -> None:
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cfg = _make_config()
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_, post = make_lingbot_va_pre_post_processors(cfg, dataset_stats=None)
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# A normalized action of all -1 should map back to q01.
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normed = torch.full((1, len(cfg.used_action_channel_ids)), -1.0)
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out = post(normed)
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assert torch.allclose(out, torch.tensor(cfg.action_q01).unsqueeze(0), atol=1e-4)
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