File size: 4,633 Bytes
fb11af9 | 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 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 | # Copyright 2025 Bytedance Ltd. and/or its affiliates
#
# 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 typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Union
from dataclasses import dataclass, field
from torch.utils.data._utils.collate import default_collate
import torch
from .data_collator import DataCollator
if TYPE_CHECKING:
from transformers import PreTrainedTokenizer
from .chat_template import ChatTemplate
def split_into_chunks(sequence: Sequence[int], chunk_size: int) -> List[List[int]]:
"""
Splits a long sequence into chunks.
"""
total_len = len(sequence)
chunks = []
for i in range(0, total_len, chunk_size):
chunks.append(sequence[i : i + chunk_size])
return chunks
def process_pretrain_example(
example: Dict[str, Any],
tokenizer: "PreTrainedTokenizer",
max_seq_len: int,
text_keys: Union[str, List[str]] = "content_split",
source_name: Optional[str] = None,
) -> List[Dict[str, "torch.Tensor"]]:
examples = []
if isinstance(text_keys, str):
text_example = example[text_keys]
elif isinstance(text_keys, list):
for key in text_keys:
if key in example:
text_example = example[key]
break
else:
raise ValueError(f"None of the keys {text_keys} are found in the example.")
else:
raise ValueError(f"text_keys must be a string or a list of strings, but got {type(text_keys)}")
tokens = tokenizer.encode(text_example, add_special_tokens=False) + [tokenizer.eos_token_id]
for input_ids in split_into_chunks(tokens, max_seq_len):
examples.append(
{
"input_ids": torch.tensor(input_ids),
"attention_mask": torch.tensor([1] * len(input_ids)),
"labels": torch.tensor(input_ids),
}
)
return examples
def process_sft_example(
example: Dict[str, Any],
chat_template: "ChatTemplate",
max_seq_len: int,
text_keys: Union[str, List[str]] = "messages",
) -> List[Dict[str, "torch.Tensor"]]:
if isinstance(text_keys, str):
text_example = example[text_keys]
elif isinstance(text_keys, list):
for key in text_keys:
if key in example:
text_example = example[key]
break
else:
raise ValueError(f"None of the keys {text_keys} are found in the example.")
else:
raise ValueError(f"text_keys must be a string or a list of strings, but got {type(text_keys)}")
tokenized_example = chat_template.encode_messages(text_example, max_seq_len=max_seq_len)
tokenized_example = {k: torch.tensor(v) for k, v in tokenized_example.items()}
return [tokenized_example]
@dataclass
class VLADataCollatorWithPacking(DataCollator):
"""
Data collator to packing for omni dataset.
Args:
packing_features: features to packing in batch.
concat_features: features to concat in batch.
Example:
>>> from lingbotvla.data import OmniDataCollatorWithPacking
"""
state_features: List = field(
default_factory=lambda: [
"state",
"images",
"img_masks",
"lang_tokens",
"lang_masks",
"action_is_pad",
"actions",
"joint_mask",
"label",
"fast_mask"
],
metadata={"help": "state features with one chunk."},
)
def __call__(self, features: Sequence[Dict[str, "torch.Tensor"]]) -> Dict[str, "torch.Tensor"]:
batch = {}
keys = {key for feature in features for key in feature.keys()}
for input_name in keys:
if input_name in self.state_features:
batch[input_name] = torch.cat(
[feature[input_name].unsqueeze(0) for feature in features if input_name in feature], dim=0
)
else:
batch[input_name] = default_collate(
[feature[input_name] for feature in features if input_name in feature]
)
return batch |