from typing import Optional, Union, Iterator from functools import partial from datasets import load_dataset from litdata import optimize, TokensLoader from litgpt.tokenizer import Tokenizer from litdata import StreamingDataset def batch_dict_iterator(path: str, name: Optional[str]=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, format: Optional[str]=None) -> Iterator[str]: assert isinstance(format, str) or callable(format) dataset = 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 callable(format): for row in dataset: text = format(row) yield text else: for row in dataset: text = format.format(**row) 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 datasets_configs = [ # # general knowledge # # 3.18 GB, 1,010,500 - paper says that extracted is 6GB *[ {'path': 'JeanKaddour/minipile', 'split': f'train[{i}%:{i + 5}%]', 'format': lambda n: n['text']} for i in range(0, 100, 5) ], {'path': 'JeanKaddour/minipile', 'split': 'validation', 'format': lambda n: n['text']}, {'path': 'JeanKaddour/minipile', 'split': 'test', 'format': lambda n: n['text']}, # # multilingual text # ## 138 MB, 205,568 {'path': 'CohereForAI/aya_dataset', 'format': lambda n: n['inputs']}, {'path': 'CohereForAI/aya_dataset', 'format': lambda n: n['targets']}, [ # 193 MB, 1,141,967 {'path': 'xu-song/cc100-samples', 'name': name, 'split': 'train', 'format': lambda n: n['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', ] ], *[ # ~3 GB, 4,976,850 # {'path': 'saillab/taco-datasets', 'data_dir': name, 'split': 'train', 'format': '{instruction} {input} {output}'} {'path': 'saillab/taco-datasets', 'data_dir': name, 'split': 'train', 'format': lambda n: n['output']} for name in [ # 'multilingual-instruction-tuning-dataset /multilingual-alpaca-52k-gpt-4', 'multilingual-instruction-tuning-dataset /multilinugal-dolly-15k', ] ], # # general knowledge # ## ~17.6 GB, ~6.41M rows # [ # {'path': 'wikimedia/wikipedia', 'name': '20231101.en', 'split': f'train[{i}%:{i + 20}%]', 'format': lambda n: n['text']} # for i in range(0, 100, 20) # ], ## 2.89 GB, 430,000, English September of 2017 # [ # {'path': 'jordiclive/wikipedia-summary-dataset', 'split': f'train[{i}%:{i + 20}%]', 'format': lambda n: n['summary']} # for i in range(0, 100, 20) # ], # 65.1 MB, 7,819 {'path': 'Sketched33/Cities_Wikipedia_Information', 'format': lambda n: n['wikipedia_content']}, # # misc # # 472 KB, 5,034 {'path': 'badrex/llm-emoji-dataset', 'format': '{character} {unicode} {short description} {tags} {LLM description}'}, # # math # ## 2.87 GB, 552,000 - images/text - we use only latex text, top 10% # {'path': 'OleehyO/latex-formulas', 'data_dir': 'cleaned_formulas', 'split': 'train[:10%]', 'format': lambda n: n['latex_formula']}, ## 12.2 MB, 500,000 # {'path': 'fblgit/simple-math', 'revision': 'refs/convert/parquet', 'split': 'train+test', 'format': '{instruction} = {output}'}, ## 125 MB, 1,000,000 # {'path': 'Gusarich/math-expressions-1m', 'revision': 'refs/convert/parquet', 'split': 'train', 'format': '{expression} = {result}'}, ## 3.49 GB, 22,259,474 # [ # {'path': 'AtlasUnified/atlas-math-sets', 'split': f'train[{i}%:{i + 20}%]+validation+test', 'format': '{instruction} . {output}'} # for i in range(0, 100, 20) # ], ## 9.05 GB, 2,583,257 - unsafe # [ # {'path': 'gair-prox/open-web-math-pro', 'split': f'train[{i}%:{i + 20}%]', 'format': lambda n: n['text']} # for i in range(0, 100, 20) # ], # # 12.6 GB, 21,972,791 - we use 1M subset - 639 MB, 1,000,000 # [ # {'path': 'nvidia/OpenMathInstruct-2', 'split': f'train_1M[{i}%:{i + 20}%]', 'format': '{problem} {generated_solution} {expected_answer}'} # for i in range(0, 100, 20) # ], # # stem # ## 1.44 GB, 63,357 # [ # {'path': 'neuralwork/arxiver', 'split': f'train[{i}%:{i + 20}%]', 'format': lambda n: n['markdown']} # for i in range(0, 100, 20) # ], # # code # # [ # # 1.73 GB, 541,041 # {'path': 'bigcode/the-stack-smol-xl', 'data_dir': f'data/{name}', 'format': lambda n: n['content']} # for name in [ # # 'batchfile' - unsafe # # 'powershell' - unsafe # 'ada', 'agda', 'alloy', 'antlr', 'applescript', 'assembly', # 'augeas', 'awk', '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', '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', # ] # ], ## 7.81 GB, ~2,804,025 # [ # {'path': 'rombodawg/code_bagel_hermes-2.5', 'split': f'train[{i}%:{i + 20}%]', 'format': '{input} {output}'} # for i in range(0, 100, 20) # ], ## 6.61 GB, ~2,646,394 # [ # {'path': 'rombodawg/code_bagel', 'split': f'train[{i}%:{i + 20}%]', 'format': '{input} {output}'} # for i in range(0, 100, 20) # ], ] 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=(8193 * 2000), # 8192 + 1 num_workers=32, reorder_files=False, # NOTE: this is only available in newver versions of litdata which current version of litgpt does not use # # This is important to inform LitData that we are encoding contiguous 1D array (tokens). # LitData skips storing metadata for each sample e.g all the tokens are concatenated to form one large tensor. # item_loader=TokensLoader(block_size=8193), ) # # total number of chunks # dataset = StreamingDataset( input_dir='../pretrain-data/', item_loader=TokensLoader(block_size=8193), # 8192 + 1 ) print(len(dataset))