File size: 2,496 Bytes
0d80452 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | #!/usr/bin/env python
# Copyright 2026 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.
from __future__ import annotations
from typing import Any
from torch.utils.data._utils.collate import default_collate
from lerobot.datasets.language import LANGUAGE_COLUMNS
_PYTHON_LIST_KEYS = {"messages", "message_streams", "target_message_indices"}
def lerobot_collate_fn(batch: list[dict[str, Any] | None]) -> dict[str, Any] | None:
"""Collate function that preserves Python-list and language fields as lists.
Drops ``None`` samples (e.g. recipes that yielded no target message), keeps
rendered-message and language fields as plain Python lists, and delegates
every other key to PyTorch's ``default_collate``.
"""
batch = [sample for sample in batch if sample is not None]
if not batch:
return None
# All-or-nothing per key: a partial-presence batch (e.g. half the samples
# carry `messages` and half don't) is a real bug in the upstream
# rendering step — silently filtering would hand downstream consumers a
# preserved list shorter than the tensor batch. Raise instead so the
# mismatch surfaces at the boundary.
preserved: dict[str, list[Any]] = {}
for key in _PYTHON_LIST_KEYS:
presence = [key in sample for sample in batch]
if not any(presence):
continue
if not all(presence):
raise ValueError(
f"Inconsistent batch: {sum(presence)}/{len(batch)} samples carry {key!r}; "
f"every sample in a batch must agree."
)
preserved[key] = [sample[key] for sample in batch]
tensorizable = [
{
key: value
for key, value in sample.items()
if key not in _PYTHON_LIST_KEYS and key not in LANGUAGE_COLUMNS
}
for sample in batch
]
collated = default_collate(tensorizable)
collated.update(preserved)
return collated
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