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Pepijn
e275ea3960
* feat(policies): add LingBot-VA autoregressive video-action world model 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. * feat(lingbot_va): RoboTwin eef-pose eval, single-file model, Hub checkpoints Make the LingBot-VA port runnable on both LIBERO and RoboTwin and clean up the package to LeRobot conventions. - Consolidate all vendored Wan2.2 model code (transformer, attention, VAE helpers, flow-matching scheduler, grid utils, flex-attention) into a single modeling_lingbot_va.py; remove the separate wan_*/schedulers modules. - Move the fixed action (un)normalization quantiles out of the config and into the post-processor (LIBERO 7-DoF + RoboTwin 16-d eef); remove the conversion script in favour of ready-to-use LeRobot-format checkpoints on the Hub. - Fixes found via on-sim validation: undo LIBERO's 180-degree image flip (image_hflip), encode obs as a multi-frame streaming-VAE clip, reset the streaming VAE cache between episodes, run the transformer in config.dtype, lazy-load frozen VAE/UMT5 by subfolder with the text encoder on CPU. - RoboTwin: add an end-effector-pose action mode to RoboTwinEnv (16-d per-arm xyz+quat+gripper deltas composed onto the initial eef pose, executed via CuRobo IK) and the robotwin_tshape latent layout (full-res head + half-res wrists via a second streaming VAE) with the upstream RoboTwin action quantiles + camera mapping. - Predicted-video saving works for both benchmarks; docs + tests updated. * feat(lingbot_va): implement training / fine-tuning (flow-matching loss) - Implement LingBotVAPolicy.forward(): dual-stream flow-matching training loss (latent + action, timestep-weighted, action-masked) ported from upstream train.py; VAE-encodes camera clips, UMT5-encodes the task, noises both streams, runs the block-causal flex-attention training pass (forward_train). - training_loss_from_streams() core + _build_training_streams() data prep (action scatter into the 30-d space, multi-frame VAE encode incl. robotwin_tshape). - get_optim_params returns only trainable transformer params (LoRA/PEFT friendly); VAE/UMT5 stay frozen. Training needs attn_mode='flex'. - Add a tiny-config single-training-step test (forward->loss->backward->AdamW) and a Training/fine-tuning section in the docs. * fix(lingbot_va): CI quality gate + fast-test collection - Add tests/policies/lingbot_va/__init__.py so the test files don't clash by basename with tests/policies/vla_jepa/* under pytest's default import mode (fast-test collection error). - Fix vendored typos flagged by the typos hook (pach_scale->patch_scale, total_tolen-> total_token_len, stablized->stabilized) and a mypy union-attr in RoboTwinEnv._read_eef_pose. - Apply Prettier formatting to docs/source/lingbot_va.mdx. * docs(lingbot_va): document EEF action-channel schema + camera order * Update lingbot_va.mdx Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com> * Update pyproject.toml Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com> * Update pyproject.toml Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com> * refactor(lingbot_va): drop hardcoded action quantiles; source from checkpoint The LIBERO/RoboTwin action (un)normalization quantiles were hardcoded as module constants in processor_lingbot_va.py. They are already serialized into each checkpoint's policy_postprocessor.json (via LingBotVAActionUnnormalizeStep.get_config) and restored on load by PolicyProcessorPipeline.from_pretrained, so the constants are dead at eval/load time for the released checkpoints (verified: libero_long/robotwin/base all carry their quantiles on the Hub). - Remove LIBERO_ACTION_Q01/Q99, ROBOTWIN_ACTION_Q01/Q99 and _default_action_quantiles. - make_lingbot_va_pre_post_processors now defaults a fresh (unconverted) build to a neutral [-1, 1] mapping (identity rescale); real per-benchmark stats come from the saved checkpoint (or postprocessor_overrides), analogous to dataset-stats normalization. - Update the config doc comment to point at the checkpoint as the source of truth. - Tests: replace the LIBERO-default assertion with a neutral-default check, and add a save_pretrained/from_pretrained round-trip guard for the quantile serialization. * docs(lingbot_va): trim verbose comments - configuration_lingbot_va.py: condense multi-line field comments to one-liners (keep the ── section headers). - processor_lingbot_va.py: shorten the action-quantile explanation block. - modeling_lingbot_va.py: drop the bare "# ----" separator rules, keeping the one-line section headers. No code changes. * docs(lingbot_va): trim provenance comments; default wan path to base repo - configuration_lingbot_va.py: drop the "──" decorations and the "(from transformer/config.json)" note; default wan_pretrained_path to robbyant/lingbot-va-base (has the frozen vae/text_encoder/tokenizer subfolders). - modeling_lingbot_va.py: remove the vendored-code banner and the "(upstream wan_va/...)" section-header provenance/dash decorations; condense the transformer-dtype comment to one line. No code changes. * refactor(lingbot_va): use built-in UnnormalizerProcessorStep for actions Replace the bespoke LingBotVAActionUnnormalizeStep with the standard UnnormalizerProcessorStep in QUANTILES mode, which computes the identical (action + 1) / 2 * (q99 - q01) + q01 mapping. The per-channel q01/q99 are stored as the step's saved state (a safetensors file) and restored on load; a fresh build has no action stats so the step is an identity passthrough. The 3 Hub checkpoints (lerobot/lingbot_va_{libero_long,robotwin,base}) have been re-uploaded with the new post-processor (policy_postprocessor.json + *_unnormalizer_processor.safetensors); reloading from the Hub round-trips q01/q99. - processor_lingbot_va.py: drop the custom step + registry; build the post-processor with UnnormalizerProcessorStep (explicit ACTION->QUANTILES norm_map so the preprocessor / training path is unchanged). - tests: assert the built-in step is used, identity-when-no-stats, correct quantile unnormalization, and a save_pretrained/from_pretrained stats round-trip. * docs(lingbot_va): point checkpoint paths at the lerobot org The LeRobot-format checkpoints moved from pepijn223/* to lerobot/* (libero_long, robotwin, base). Update the eval/train --policy.path examples accordingly. * docs(lingbot_va): condense processor normalization comments * fix(lingbot-va): align RoboTwin evaluation (#3784) Thank you for the RoboTwin fix, and alignment! * applying fixes * updating uv lock and linting * adjusting test to match expected values * cleaning up deps * cleaning up top level imports, styling, and deps guards * cleanup * moving wan utils and loading utils to `utils.py` * removing ftfy by replicating the prompt_clean function without it (we don't expect to have weird chars given in the prompt anyway) * removing unused function * guarding for scipy dep, renaming test to avoid collision * adding back accelerate for peak memory usage optim + justifying robotwin description dep --------- Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com> Co-authored-by: pepijn223 <pepijn223@hf.co> Co-authored-by: Gangwei XU <gwxu@hust.edu.cn> Co-authored-by: Maxime Ellerbach <maxime.ellerbach@huggingface.co>
165 lines
2.6 KiB
Plaintext
165 lines
2.6 KiB
Plaintext
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