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from typing import Optional
from functools import partial
from datasets import load_dataset
from litdata import optimize, TokensLoader
from litgpt.tokenizer import Tokenizer
def batch_iterator(path: str,
name: Optional[str]=None,
data_dir: Optional[str]=None,
data_files: Optional[str]=None,
revision: Optional[str]=None,
split: str='train',
format: Optional[str]=None):
assert format is not None
dataset = load_dataset(path=path,
name=name,
data_dir=data_dir,
data_files=data_files,
revision=revision,
split=split,
trust_remote_code=True)
for row in dataset:
text = format.format(**row)
yield text
def tokenize_fn(datasets_config, tokenizer=None):
for text in batch_iterator(**datasets_config):
text_ids = tokenizer.encode(text, bos=False, eos=True)
yield text_ids
datasets_configs = [
{'path': 'yahma/alpaca-cleaned', 'format': '{instruction} {input} {output}'},
{'path': 'gbharti/wealth-alpaca_lora', 'format': '{instruction} {input} {output}'},
*[
{'path': 'saillab/taco-datasets', 'data_dir': data_dir, 'split': 'train[:10%]', 'format': '{instruction} {input} {output}'}
for data_dir in [
'multilingual-instruction-tuning-dataset /multilingual-alpaca-52k-gpt-4',
'multilingual-instruction-tuning-dataset /multilinugal-dolly-15k',
]
],
*[
{'path': 'xu-song/cc100-samples', 'name': name, 'split': 'train[:10%]', 'format': '{text}'}
for name in [
'am', 'ar', 'as', 'az', 'be', 'bg', 'bn', 'bn_rom', 'br',
'bs', 'ca', 'cs', 'cy', 'da', 'de', 'el', 'en', 'eo', 'es',
'et', 'eu', 'fa', 'ff', 'fi', 'fr', 'fy', 'ga', 'gd', 'gl',
'gn', 'gu', 'ha', 'he', 'hi', 'hi_rom', 'hr', 'ht', 'hu',
'hy', 'id', 'ig', 'is', 'it', 'ja', 'jv', 'ka', 'kk', 'km',
'kn', 'ko', 'ku', 'ky', 'la', 'lg', 'li', 'ln', 'lo', 'lt',
'lv', 'mg', 'mk', 'ml', 'mn', 'mr', 'ms', 'my', 'my_zaw',
'ne', 'nl', 'no', 'ns', 'om', 'or', 'pa', 'pl', 'ps', 'pt',
'qu', 'rm', 'ro', 'ru', 'sa', 'si', 'sc', 'sd', 'sk', 'sl',
'so', 'sq', 'sr', 'ss', 'su', 'sv', 'sw', 'ta', 'ta_rom',
'te', 'te_rom', 'th', 'tl', 'tn', 'tr', 'ug', 'uk', 'ur',
'ur_rom', 'uz', 'vi', 'wo', 'xh', 'yi', 'yo',
'zh-Hans', 'zh-Hant', 'zu',
]
],
{'path': 'ontocord/fineweb-permissive-multilingual-2m', 'split': 'train[:5%]', 'format': '{text}'},
{'path': 'MuskumPillerum/General-Knowledge', 'format': '{Question} {Answer}'},
{'path': 'yirenc/general_knowledge_boolean', 'split': 'train+validation', 'format': '{question}? {answer}. {passage}'},
{'path': 'nampdn-ai/tiny-textbooks', 'split': 'train+test', 'format': '{textbook}'},
{'path': 'nampdn-ai/tiny-codes', 'split': 'train[:5%]', 'format': '{prompt} {response}'},
*[
{'path': 'bigcode/the-stack-smol-xs', 'name': name, 'format': '{content}'}
for name in [
'ada', 'agda', 'alloy', 'antlr', 'applescript', 'assembly',
'augeas', 'awk', 'batchfile', 'bison', 'bluespec', 'c',
'c++', 'c-sharp', 'clojure', 'cmake', 'coffeescript', 'common-lisp',
'css', 'cuda', 'dart', 'dockerfile', 'elixir',
'elm', 'emacs-lisp','erlang', 'f-sharp', 'fortran', 'glsl', 'go',
'groovy', 'haskell','html', 'idris', 'isabelle', 'java',
'java-server-pages', 'javascript', 'julia', 'kotlin', 'lean',
'literate-agda', 'literate-coffeescript', 'literate-haskell',
'lua', 'makefile', 'maple', 'markdown', 'mathematica', 'matlab',
'ocaml', 'pascal', 'perl', 'php', 'powershell', 'prolog',
'protocol-buffer', 'python', 'r', 'racket', 'restructuredtext',
'rmarkdown', 'ruby', 'rust', 'sas', 'scala', 'scheme',
'shell', 'smalltalk', 'solidity', 'sparql', 'sql', 'stan',
'standard-ml', 'stata', 'systemverilog', 'tcl', 'tcsh', 'tex',
'thrift', 'typescript', 'verilog', 'vhdl', 'visual-basic', 'xslt',
'yacc', 'zig',
]
],
{'path': 'm-a-p/CodeFeedback-Filtered-Instruction', 'split': 'train', 'format': '{query} {answer}'},
{'path': 'jtatman/python-code-dataset-500k', 'format': '{instruction} {output}'},
{'path': 'iamtarun/python_code_instructions_18k_alpaca', 'format': '{instruction} {input} {output}'},
{'path': 'HuggingFaceH4/CodeAlpaca_20K', 'split': 'train+test', 'format': '{prompt} {completion}'},
{'path': 'gair-prox/open-web-math-pro', 'split': 'train[:5%]', 'format': '{text}'},
{'path': 'rvv-karma/Math-QA', 'split': 'train+val+test', 'format': '{question} {answer}'},
{'path': 'ajibawa-2023/Maths-College', 'split': 'train[:10%]', 'format': '{instruction} {output}'},
{'path': 'microsoft/orca-math-word-problems-200k', 'format': '{question} {answer}'},
{'path': 'fblgit/simple-math', 'revision': 'refs/convert/parquet', 'split': 'train+test', 'format': '{instruction} = {output}'},
{'path': 'SkunkworksAI/reasoning-0.01', 'format': '{instruction} {reasoning} {output}'},
{'path': 'badrex/llm-emoji-dataset', 'format': '{character} {unicode} {short description} {tags} {LLM description}'},
]
outputs = optimize(
fn=partial(tokenize_fn, tokenizer=Tokenizer('..')),
inputs=datasets_configs,
output_dir='../pretrain-data/',
# Number of tokens to store by chunks. This is roughly 64MB of tokens per chunk.
chunk_size=(2049 * 8012),
num_workers=32,
)
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