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https://github.com/huggingface/lerobot.git
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1ff10b935c
Resolves conflicts from 66 commits on the base branch: * pyproject.toml — keep base's transformers>=5.4.0,<5.6.0; add the sentencepiece-dep entry pi052 (FAST action tokenizer) needs. * policies/__init__.py — keep pi052 export; drop the RewardClassifierConfig export that base removed. * policies/factory.py — docstring list resolution (keep pi052; drop reward_classifier, removed by base). * annotations/steerable_pipeline/executor.py — adopt base's renamed _ensure_annotation_metadata_in_info (it already advertises the say tool); drop pi052's older _ensure_tools_in_info call. * configs/train.py — keep pi052's vqa_target_fraction; adopt base's SampleWeightingConfig (legacy RA-BC inline params already covered by the migration shim base added). * scripts/lerobot_train.py — merge pi052's per-policy processor rebuild + dataset_repo_id pass-through with base's active_cfg / is_reward_model_training tightening, and re-route vqa-weighted sampler to active_cfg.drop_n_last_frames. * datasets/language_render.py — adopt base's _select_one + timestamp tolerance (drops pi052's stale _select_latest / per-style sort_key). * tests — adopt base's parametrized per-camera blend + tolerance test; drop pi052 tests that overlap with base's tighter rewrites; keep pi052's flow-only / VQA-blend coverage; add a test_canonical_recipe_loads check on subtask_mem_vqa_speech.yaml. * policies/pi052/processor_pi052.py — import RenderMessagesStep directly from render_messages_processor (base intentionally dropped it from lerobot.processor's re-exports). * uv.lock — regenerated cleanly from base + pi052's pocket-tts / beartype. All 67 touched tests pass (30 pi052 + 37 recipe / language-render / pipeline / render-messages). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
66 lines
2.5 KiB
Python
66 lines
2.5 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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from typing import Any
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from torch.utils.data._utils.collate import default_collate
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from lerobot.datasets.language import LANGUAGE_COLUMNS
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_PYTHON_LIST_KEYS = {"messages", "message_streams", "target_message_indices", *LANGUAGE_COLUMNS}
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def lerobot_collate_fn(batch: list[dict[str, Any] | None]) -> dict[str, Any] | None:
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"""Collate function that preserves Python-list and language fields as lists.
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Drops ``None`` samples (e.g. recipes that yielded no target message), keeps
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rendered-message and language fields as plain Python lists, and delegates
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every other key to PyTorch's ``default_collate``.
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"""
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batch = [sample for sample in batch if sample is not None]
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if not batch:
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return None
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# All-or-nothing per key: a partial-presence batch (e.g. half the samples
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# carry `messages` and half don't) is a real bug in the upstream
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# rendering step — silently filtering would hand downstream consumers a
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# preserved list shorter than the tensor batch. Raise instead so the
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# mismatch surfaces at the boundary.
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preserved: dict[str, list[Any]] = {}
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for key in _PYTHON_LIST_KEYS:
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presence = [key in sample for sample in batch]
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if not any(presence):
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continue
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if not all(presence):
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raise ValueError(
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f"Inconsistent batch: {sum(presence)}/{len(batch)} samples carry {key!r}; "
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f"every sample in a batch must agree."
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)
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preserved[key] = [sample[key] for sample in batch]
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tensorizable = [
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{
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key: value
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for key, value in sample.items()
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if key not in _PYTHON_LIST_KEYS and key not in LANGUAGE_COLUMNS
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}
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for sample in batch
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]
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collated = default_collate(tensorizable)
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collated.update(preserved)
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return collated
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