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https://github.com/huggingface/lerobot.git
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beb22afd81
* **#2 — dedupe `_PLACEHOLDER_RE`.** The same regex was compiled in
`recipe.py` and `language_render.py`. Promote to module-level
`PLACEHOLDER_RE` in `recipe.py` (its primary owner — declares
template syntax) and import from `language_render.py`.
* **#3 — centralize language column names.** `io_utils.py` had
hardcoded `{"language_persistent", "language_events"}` literals at
two sites. Replace with `LANGUAGE_COLUMNS` import so a future column
rename can't silently desync.
* **#4 — defensive collate preserved-keys.** `lerobot_collate_fn`
silently filtered language fields from samples that didn't have
them, which would hand downstream consumers a preserved list
shorter than the tensor batch. Now: if any sample carries a key,
every sample in the batch must carry it; otherwise raise a
`ValueError` so the upstream rendering bug surfaces at the boundary.
* **#5 — `_scalar` rejects non-singleton lists.** Previously a zero-
or multi-element list fell through and triggered confusing
`float([])` errors downstream. Now raises `ValueError` with the
actual length.
* **#6 — refactor `_extract_complementary_data`.** Replace 11 lines
of `key = {... if ... else {}}` plus an 11-line splat dict with a
single `_COMPLEMENTARY_KEYS` tuple iterated once.
* **#7 — document `EXTENDED_STYLES`.** Was an empty `set()` with no
comment. Add a docstring explaining it's an intentional extension
point: downstream modules append project-local styles before
`column_for_style` is called.
* **#9 — `tools.mdx` notes the runtime layer is future work.** The
page referenced `src/lerobot/tools/`, `registry.py`, and
`get_tools(meta)` — none exist in this PR. Added a callout at the
start of "How to add your own tool" plus a note on the
implementations paragraph.
* **#10 — tests for YAML round-trip, malformed rows, blend
validation.** `test_recipe.py` grew from 1 case to 12 covering:
blend-or-messages exclusivity, target-turn requirement, blend
emptiness, weight presence/positivity, nested-blend rejection,
`from_dict` with nested blends, `from_yaml` / `load_recipe`
agreement, top-level non-mapping rejection. Added a malformed-row
test for `_normalize_rows` that asserts non-dict entries raise
`TypeError`.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
95 lines
3.7 KiB
Python
95 lines
3.7 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 dataclasses import dataclass
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from typing import Any
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from lerobot.configs import PipelineFeatureType, PolicyFeature
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from lerobot.configs.recipe import TrainingRecipe
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from lerobot.datasets.language import LANGUAGE_EVENTS, LANGUAGE_PERSISTENT
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from lerobot.datasets.language_render import render_sample
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from lerobot.types import EnvTransition, TransitionKey
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from .pipeline import ProcessorStep, ProcessorStepRegistry
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@dataclass
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@ProcessorStepRegistry.register(name="render_messages_processor")
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class RenderMessagesStep(ProcessorStep):
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"""Processor step that turns raw language columns into rendered chat messages.
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Reads ``language_persistent`` and ``language_events`` from the transition's
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complementary data, renders them through ``recipe`` at the sample timestamp,
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and replaces the raw columns with the resulting ``messages`` /
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``message_streams`` / ``target_message_indices`` keys.
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"""
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recipe: TrainingRecipe
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dataset_ctx: Any | None = None
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def __call__(self, transition: EnvTransition) -> EnvTransition | None:
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"""Render messages for a single transition; return ``None`` to drop it."""
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complementary_data = transition.get(TransitionKey.COMPLEMENTARY_DATA) or {}
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persistent = complementary_data.get(LANGUAGE_PERSISTENT) or []
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events = complementary_data.get(LANGUAGE_EVENTS) or []
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if not persistent and not events:
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return transition
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timestamp = complementary_data.get("timestamp")
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if timestamp is None:
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raise KeyError("RenderMessagesStep requires sample timestamp in complementary data.")
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sample_idx = complementary_data.get("index", 0)
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rendered = render_sample(
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recipe=self.recipe,
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persistent=persistent,
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events=events,
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t=_scalar(timestamp),
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sample_idx=int(_scalar(sample_idx)),
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task=complementary_data.get("task"),
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dataset_ctx=self.dataset_ctx,
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)
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if rendered is None:
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return None
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new_transition = transition.copy()
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new_complementary_data = dict(complementary_data)
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new_complementary_data.pop(LANGUAGE_PERSISTENT, None)
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new_complementary_data.pop(LANGUAGE_EVENTS, None)
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new_complementary_data.update(rendered)
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new_transition[TransitionKey.COMPLEMENTARY_DATA] = new_complementary_data
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return new_transition
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def transform_features(
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self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
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) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
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"""Pass features through unchanged; rendering only touches complementary data."""
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return features
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def _scalar(value: Any) -> float | int:
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"""Unwrap a tensor/array/single-element list into a Python scalar."""
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if hasattr(value, "item"):
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return value.item()
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if isinstance(value, list):
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if len(value) != 1:
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raise ValueError(f"Expected a scalar, got list of length {len(value)}: {value!r}")
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return _scalar(value[0])
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return value
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