Spaces:
Sleeping
Sleeping
File size: 8,383 Bytes
8d1ee8d |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 |
# coding=utf-8
# coding=utf-8
# Copyright 2023 The HuggingFace 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.
import re
from typing import List, Literal, Optional
from datasets import DatasetDict, concatenate_datasets, load_dataset
from .configs import DataArguments
DEFAULT_CHAT_TEMPLATE = "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n' + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}"
def apply_chat_template(
example, tokenizer, task: Literal["sft", "generation", "rm", "dpo"] = "sft", assistant_prefix="<|assistant|>\n"
):
def _strip_prefix(s, pattern):
# Use re.escape to escape any special characters in the pattern
return re.sub(f"^{re.escape(pattern)}", "", s)
if task in ["sft", "generation"]:
messages = example["messages"]
# We add an empty system message if there is none
if messages[0]["role"] != "system":
messages.insert(0, {"role": "system", "content": ""})
example["text"] = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True if task == "generation" else False
)
elif task == "rm":
if all(k in example.keys() for k in ("chosen", "rejected")):
chosen_messages = example["chosen"]
rejected_messages = example["rejected"]
# We add an empty system message if there is none
if chosen_messages[0]["role"] != "system":
chosen_messages.insert(0, {"role": "system", "content": ""})
if rejected_messages[0]["role"] != "system":
rejected_messages.insert(0, {"role": "system", "content": ""})
example["text_chosen"] = tokenizer.apply_chat_template(chosen_messages, tokenize=False)
example["text_rejected"] = tokenizer.apply_chat_template(rejected_messages, tokenize=False)
else:
raise ValueError(
f"Could not format example as dialogue for `rm` task! Require `[chosen, rejected]` keys but found {list(example.keys())}"
)
elif task == "dpo":
if all(k in example.keys() for k in ("chosen", "rejected")):
# Compared to reward modeling, we filter out the prompt, so the text is everything after the last assistant token
prompt_messages = [[msg for msg in example["chosen"] if msg["role"] == "user"][0]]
# Insert system message
if example["chosen"][0]["role"] != "system":
prompt_messages.insert(0, {"role": "system", "content": ""})
else:
prompt_messages.insert(0, example["chosen"][0])
# TODO: handle case where chosen/rejected also have system messages
chosen_messages = example["chosen"][1:]
rejected_messages = example["rejected"][1:]
example["text_chosen"] = tokenizer.apply_chat_template(chosen_messages, tokenize=False)
example["text_rejected"] = tokenizer.apply_chat_template(rejected_messages, tokenize=False)
example["text_prompt"] = tokenizer.apply_chat_template(
prompt_messages, tokenize=False, add_generation_prompt=True
)
example["text_chosen"] = _strip_prefix(example["text_chosen"], assistant_prefix)
example["text_rejected"] = _strip_prefix(example["text_rejected"], assistant_prefix)
else:
raise ValueError(
f"Could not format example as dialogue for `dpo` task! Require `[chosen, rejected]` keys but found {list(example.keys())}"
)
return example
def get_datasets(
data_config: DataArguments | dict,
splits: List[str] = ["train", "test"],
shuffle: bool = True,
) -> DatasetDict:
"""
Loads one or more datasets with varying training set proportions.
Args:
data_config (`DataArguments` or `dict`):
Dataset configuration and split proportions.
splits (`List[str]`, *optional*, defaults to `['train', 'test']`):
Dataset splits to load and mix. Assumes the splits exist in all datasets and have a `train_` or `test_` prefix.
shuffle (`bool`, *optional*, defaults to `True`):
Whether to shuffle the training data.
Returns
[`DatasetDict`]: The dataset dictionary containing the loaded datasets.
"""
if type(data_config) is DataArguments:
# Structure of the config to read the datasets and their mix
# datasets_mixer:
# - 'dataset1': 0.5
# - 'dataset2': 0.3
# - 'dataset3': 0.2
dataset_mixer = data_config.dataset_mixer
elif type(data_config) is dict:
# Structure of the input is:
# dataset_mixer = {
# "dataset1": 0.5,
# "dataset1": 0.3,
# "dataset1": 0.2,
# }
dataset_mixer = data_config
else:
raise ValueError(f"Data config {data_config} not recognized.")
raw_datasets = mix_datasets(dataset_mixer, splits=splits, shuffle=shuffle)
return raw_datasets
def mix_datasets(dataset_mixer: dict, splits: Optional[List[str]] = None, shuffle=True) -> DatasetDict:
"""
Loads and mixes datasets according to proportions specified in `dataset_mixer`.
Args:
dataset_mixer (`dict`):
Dictionary containing the dataset names and their training proportions. By default, all test proportions are 1.
splits (Optional[List[str]], *optional*, defaults to `None`):
Dataset splits to load and mix. Assumes the splits exist in all datasets and have a `train_` or `test_` prefix.
shuffle (`bool`, *optional*, defaults to `True`):
Whether to shuffle the training data.
"""
raw_datasets = DatasetDict()
raw_train_datasets = []
raw_val_datasets = []
fracs = []
for ds, frac in dataset_mixer.items():
fracs.append(frac)
for split in splits:
if "train" in split:
raw_train_datasets.append(
load_dataset(
ds,
split=split,
)
)
elif "test" in split:
raw_val_datasets.append(
load_dataset(
ds,
split=split,
)
)
else:
raise ValueError(f"Split type {split} not recognized as one of test or train.")
if any(frac < 0 for frac in fracs):
raise ValueError("Dataset fractions cannot be negative.")
if len(raw_train_datasets) > 0:
train_subsets = []
for dataset, frac in zip(raw_train_datasets, fracs):
train_subset = dataset.select(range(int(frac * len(dataset))))
train_subsets.append(train_subset)
if shuffle:
raw_datasets["train"] = concatenate_datasets(train_subsets).shuffle(seed=42)
else:
raw_datasets["train"] = concatenate_datasets(train_subsets)
# No subsampling for test datasets to enable fair comparison across models
if len(raw_val_datasets) > 0:
if shuffle:
raw_datasets["test"] = concatenate_datasets(raw_val_datasets).shuffle(seed=42)
else:
raw_datasets["test"] = concatenate_datasets(raw_val_datasets)
if len(raw_datasets) == 0:
raise ValueError(
f"Dataset {dataset_mixer} not recognized with split {split}. Check the dataset has been correctly formatted."
)
return raw_datasets
|