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# 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 os
import re
from typing import List, Literal, Optional
from datasets import DatasetDict, concatenate_datasets, load_dataset, load_from_disk
from datasets.builder import DatasetGenerationError
import numpy as np
np.random.seed(100)
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")):
# prompt_messages = example["chosen"][:-1]
# # Now we extract the final turn to define chosen/rejected responses
# chosen_messages = example["chosen"]
# rejected_messages = example["rejected"]
# example["text_prompt"] = tokenizer.apply_chat_template(prompt_messages, tokenize=False)
# example["text_chosen"] = tokenizer.apply_chat_template(chosen_messages, tokenize=False).replace(example["text_prompt"], "")
# example["text_rejected"] = tokenizer.apply_chat_template(rejected_messages, tokenize=False).replace(example["text_prompt"], "")
example["text_prompt"] = example['prompt']
# Use the tokenizer's actual EOS token instead of hardcoded "<|end|>"
# so this works for Qwen ("<|im_end|>"), Llama ("<|eot_id|>"), Phi ("<|end|>"), etc.
_eos = tokenizer.eos_token if (tokenizer is not None and tokenizer.eos_token) else "<|end|>"
example["text_chosen"] = example['chosen'] + _eos
example["text_rejected"] = example['rejected'] + _eos
else:
raise ValueError(
f"Could not format example as dialogue for `dpo` task! Require `[chosen, rejected]` keys but found {list(example.keys())}"
)
else:
raise ValueError(
f"Task {task} not supported, please ensure that the provided task is one of {['sft', 'generation', 'rm', 'dpo']}"
)
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 and testing/validation 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 and testing/validation 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 not os.path.isdir(ds):
# Try first if dataset on a Hub repo
dataset = load_dataset(ds, split=split)
else:
# If not, check local dataset
dataset = load_from_disk(os.path.join(ds, split))
if "train" in split:
raw_train_datasets.append(dataset)
elif "test" in split:
raw_val_datasets.append(dataset)
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):
# randomly select a subset of the training data
inds = np.random.permutation(len(dataset))
select_inds = inds[:int(frac * len(dataset))]
train_subset = dataset.select(select_inds)
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
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