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from typing import Optional, Union, Callable, Iterator
from collections.abc import Collection
from functools import partial
from datasets import load_dataset
from litdata import optimize, TokensLoader
from litgpt.tokenizer import Tokenizer
from litdata import StreamingDataset
from cognition_dataset import self_cognition_messages
def batch_dict_iterator(path: Optional[str]=None,
name: Optional[str]=None,
data: Optional[Collection]=None,
data_dir: Optional[str]=None,
data_files: Optional[str]=None,
keep_in_memory: bool=False,
revision: Optional[str]=None,
split: str='train',
num_proc: Optional[int]=None,
field: Optional[str]=None,
transform: Optional[Callable]=None) -> Iterator[str]:
assert isinstance(format, str) or callable(format)
if path and not data:
data = load_dataset(path=path,
name=name,
data_dir=data_dir,
data_files=data_files,
keep_in_memory=keep_in_memory,
revision=revision,
split=split,
trust_remote_code=True,
num_proc=num_proc)
if data and field:
data = data[field]
if transform:
data = [transform(n) for n in data]
for n in data:
text: list[str] | str = []
for m in n:
fm = f'<im_start>{m["role"]}\n{m["content"]}<im_end>'
text.append(fm)
text = '\n'.join(text)
yield text
def batch_iterator(dataset_config: Union[list, dict]):
if isinstance(dataset_config, dict):
for text in batch_dict_iterator(**dataset_config):
yield text
elif isinstance(dataset_config, list):
for dc in dataset_config:
for text in batch_dict_iterator(**dc):
yield text
else:
raise ValueError('')
def tokenize_fn(dataset_config: Union[dict, list], tokenizer: Optional[Tokenizer]=None):
assert isinstance(dataset_config, (dict, list))
for text in batch_iterator(dataset_config):
text_ids = tokenizer.encode(text, bos=False, eos=True)
yield text_ids
roles_map = {
'system': 'system',
'user': 'user',
'human': 'user',
'assistant': 'assistant',
'gpt': 'assistant',
'AI': 'assistant',
}
datasets_configs = [
#
# cognition
#
{'path': None, 'field': None, 'data': self_cognition_messages, 'transform': lambda r: [
{'role': 'user', 'content': r['instruction']},
{'role': 'assistant', 'content': r['output']},
]},
#
# general instructs
#
# arcee-ai/The-Tome - 4.58 GB, 1,752,473
# - arcee-ai/infini-instruct-top-500k (BAAI/Infinity-Instruct)
# - TIGER-Lab/WebInstructSub (top-500k) - IGNORE
# - jondurbin/airoboros-3.2
# - gardner/glaive-function-calling-v2-sharegpt
# - arcee-ai/reasoning-sharegpt (SkunkworksAI/reasoning-0.01)
# - arcee-ai/self-instruct-sharegpt (bigcode/self-oss-instruct-sc2-exec-filter-50k)
# - cognitivecomputations/ultrainteract_trajectories_sharegpt
# - cognitivecomputations/SystemChat-2.0
# - arcee-ai/qwen2-72b-magpie-en
[
{'path': 'arcee-ai/The-Tome', 'split': f'train[{i}%:{i + 20}%]', 'field': 'conversations', 'transform': lambda msgs: [
{'role': roles_map[m['from']], 'content': m['value']}
for m in msgs
]}
for i in range(0, 100, 20)
],
# rombodawg/Everything_Instruct_Multilingual - 2.48 GB, 5,808,694
# Science:
# antiven0m/physical-reasoning-dpoScience
# LawalAfeez/science-dataset
# Social media:
# Kyle1668/AG-Tweets
# euclaise/reddit-instruct-curated
# General Knowledge:
# NousResearch/CharacterCodex_Characters
# jstet/quotes-500k_Famous_Quotes
# FronkonGames/steam-games-dataset_Video_Games
# totuta_youtube_subs_howto100M_HowTo
# Multi-lingual:
# Amani27/massive_translation_dataset
# udmurtNLP/udmurt-russian-english-labse
# grosenthal/latin_english
# msarmi9/korean-english-multitarget-ted-talks-task
# HaiderSultanArc/MT-Urdu-English_Translate
# Garsa3112/ChineseEnglishTranslationDataset
# Cooking:
# andrewsiah/se_cooking_preference_sft
# Hieu-Phamkaggle/food_recipes
# Writing:
# shahules786/PoetryFoundationData
# euclaise/writingprompts
# qwedsacf/ivypanda-essaysEssay
# Medicine:
# keivalya/MedQuad-MedicalQnADataset
# nuvocare/MSD
# History:
# ambrosfitz10k/history_data_v4
# Law:
# dzunggg/legal-qa-v1
# Role-Play:
# roleplay4/fun_CoupleRP
# Undi95andrijdavid/roleplay-conversation-sharegpt
# News:
# RealTimeData/bbc_news_alltime
# Coding: (rombodawg/code_bagel)
# layoric/tiny-codes-alpaca
# glaiveai/glaive-code-assistant-v3
# ajibawa-2023/Code-290k-ShareGPT
# chargoddard/commitpack-ft-instruct-rated
# iamtarun/code_instructions_120k_alpaca
# ise-uiuc/Magicoder-Evol-Instruct-110K
# cognitivecomputations/dolphin-coder
# nickrosh/Evol-Instruct-Code-80k-v1
# coseal/CodeUltraFeedback_binarized
# CyberNative/Code_Vulnerability_Security_DPO
# Math: (rombodawg/code_bagel)
# TIGER-Lab/MathInstruct
# Function calling: (rombodawg/code_bagel)
# glaiveai/glaive-function-calling-v2
# General Instruct: (rombodawg/OpenHermes-2.5-Uncensored)
# teknium/OpenHermes-2.5
[
{'path': 'rombodawg/Everything_Instruct_Multilingual', 'split': f'train[{i}%:{i + 20}%]', 'transform': lambda r: [
{'role': 'system', 'content': r['instruction']},
{'role': 'user', 'content': r['input']},
{'role': 'assistant', 'content': r['output']},
]}
for i in range(0, 100, 20)
],
#
# math
#
# 6.07 GB, 11,402,286
[
{'path': 'ai2-adapt-dev/openmath-2-math', 'split': f'train[{i}%:{i + 10}%]', 'field': 'messages'}
for i in range(0, 100, 10)
],
#
# tool/function calling
#
# 65.7 MB, 11,578
{'path': 'NousResearch/hermes-function-calling-v1', 'field': 'conversations', 'transform': lambda msgs: [
{'role': roles_map[m['from']], 'content': m['value']}
for m in msgs
]},
#
# agent
#
# 1.51 GB, 485,874
[
{'path': 'arcee-ai/agent-data', 'split': f'train[{i}%:{i + 20}%]', 'field': 'conversations', 'transform': lambda msgs: [
{'role': roles_map[m['from']], 'content': m['value']}
for m in msgs
]}
for i in range(0, 100, 20)
],
#
# general reasoning
#
[
# 10.8 MB, 15,770
# {'path': 'AtlasUnified/Atlas-Reasoning', 'data_files': 'reasoning.csv', 'format': '{Prompt} {Step-by-step reasoning} {Solution}'},
{'path': 'AtlasUnified/Atlas-Reasoning', 'data_files': 'reasoning.csv', 'transform': lambda r: [
{'role': 'user', 'content': r['Prompt']},
{'role': 'assistant', 'content': r['Step-by-step reasoning'] + '\n' + r['Solution']},
]},
],
#
# math reasoning
#
[
# 8.99 MB, 6,914
# {'path': 'thesven/gsm8k-reasoning', 'format': '{question} {generation} {answer} {short_answer}'},
{'path': 'thesven/gsm8k-reasoning', 'transform': lambda r: [
{'role': 'user', 'content': r['question']},
{'role': 'assistant', 'content': r['generation'] + '\n' + r['answer'] + '\n' + r['short_answer']},
]},
# 1.79 MB, 3,963
# {'path': 'AlgorithmicResearchGroup/math_reasoning_autoformalization_track', 'format': '{informal_statement} {informal_proof} {formal_proof}'},
{'path': 'AlgorithmicResearchGroup/math_reasoning_autoformalization_track', 'transform': lambda r: [
{'role': 'user', 'content': r['informal_statement']},
{'role': 'assistant', 'content': r['informal_proof'] + '\n' + r['formal_proof']},
]},
# 307 MB, 19,944
# {'path': 'KingNish/reasoning-base-20k', 'format': '{user} {reasoning} {assistant}'},
{'path': 'KingNish/reasoning-base-20k', 'transform': lambda r: [
{'role': 'user', 'content': r['user']},
{'role': 'assistant', 'content': r['reasoning'] + '\n' + r['assistant']},
]},
# 9.45 MB, 10,000
# {'path': 'Aarushhh/math-reasoning-10k', 'format': '{problem} {plan} {solution}'},
{'path': 'Aarushhh/math-reasoning-10k', 'transform': lambda r: [
{'role': 'user', 'content': r['problem']},
{'role': 'assistant', 'content': r['plan'] + '\n' + r['solution']},
]},
],
#
# code reasoning
#
[
# 56.4 MB, 29,857
# {'path': 'SkunkworksAI/reasoning-0.01', 'format': '{instruction} {reasoning} {output}'},
{'path': 'SkunkworksAI/reasoning-0.01', 'transform': lambda r: [
{'role': 'user', 'content': r['instruction']},
{'role': 'assistant', 'content': r['reasoning'] + '\n' + r['output']},
]},
# 368 MB, 150,000
# {'path': 'Magpie-Align/Magpie-Reasoning-150K', 'format': '{instruction} {response}'},
{'path': 'Magpie-Align/Magpie-Reasoning-150K', 'transform': lambda r: [
{'role': 'user', 'content': r['instruction']},
{'role': 'assistant', 'content': r['response']},
]},
],
#
# reflection
#
[
# 4.17 MB, 1,000
{'path': 'dvilasuero/reflection-v1-gpt-4o-judge', 'transform': lambda r: [
{'role': 'system', 'content': r['system']},
{'role': 'user', 'content': r['prompt']},
{'role': 'assistant', 'content': r['response']},
]},
# 12.4 MB, 3,000
{'path': 'dvilasuero/reflection-v1-openai-o-mini-judge', 'transform': lambda r: [
{'role': 'system', 'content': r['system']},
{'role': 'user', 'content': r['prompt']},
{'role': 'assistant', 'content': r['response']},
]},
# 70.8 MB, 36,549
{'path': 'dvilasuero/reflection-v1-final-dedup', 'transform': lambda r: [
{'role': 'system', 'content': r['system']},
{'role': 'user', 'content': r['prompt']},
{'role': 'assistant', 'content': r['response']},
]},
# 30.6 MB, 25,391
{'path': 'flozi00/reflection-qwen2.5-72b-260924', 'transform': lambda r: [
r['system'][0],
{'role': 'user', 'content': r['input']},
{'role': 'assistant', 'content': r['reflection'] + '\n' + r['output']},
]},
# 26.8 MB, 23,164
{'path': 'gretelai/synthetic-gsm8k-reflection-405b', 'split': 'train+test', 'transform': lambda r: [
{'role': 'user', 'content': r['question']},
{'role': 'assistant', 'content': r['answer_with_tags']},
]},
],
]
outputs = optimize(
fn=partial(tokenize_fn, tokenizer=Tokenizer('..')),
inputs=datasets_configs,
output_dir='../contrain-data/',
# Number of tokens to store by chunks. This is roughly 64MB of tokens per chunk.
chunk_size=(1024 * 16000),
num_workers=32,
)
#
# total number of chunks
#
dataset = StreamingDataset(
input_dir='../contrain-data/',
item_loader=TokensLoader(block_size=1024),
)
print(len(dataset))
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