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import gc |
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from datasets import load_dataset |
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from litdata import optimize, TokensLoader |
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from litgpt.tokenizer import Tokenizer |
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from functools import partial |
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def batch_iterator(name=None): |
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if name in (None, 'xu-song/cc100-samples'): |
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dataset = ( |
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load_dataset(name, lang, split='train') |
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for lang 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'] |
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) |
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for d in dataset: |
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for row in d['text']: |
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yield row |
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del dataset |
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gc.collect() |
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if name in (None, 'bigcode/the-stack-smol-xs'): |
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dataset = ( |
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load_dataset(name, lang, split='train', trust_remote_code=True) |
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for lang in [ |
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'ada', 'agda', 'alloy', 'antlr', 'applescript', 'assembly', 'augeas', 'awk', 'batchfile', 'bison', 'bluespec', 'c', |
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'c++', 'c-sharp', 'clojure', 'cmake', 'coffeescript', 'common-lisp', 'css', 'cuda', 'dart', 'dockerfile', 'elixir', |
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'elm', 'emacs-lisp','erlang', 'f-sharp', 'fortran', 'glsl', 'go', 'groovy', 'haskell','html', 'idris', 'isabelle', 'java', |
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'java-server-pages', 'javascript', 'julia', 'kotlin', 'lean', 'literate-agda', 'literate-coffeescript', 'literate-haskell', |
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'lua', 'makefile', 'maple', 'markdown', 'mathematica', 'matlab', 'ocaml', 'pascal', 'perl', 'php', 'powershell', 'prolog', |
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'protocol-buffer', 'python', 'r', 'racket', 'restructuredtext', 'rmarkdown', 'ruby', 'rust', 'sas', 'scala', 'scheme', |
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'shell', 'smalltalk', 'solidity', 'sparql', 'sql', 'stan', 'standard-ml', 'stata', 'systemverilog', 'tcl', 'tcsh', 'tex', |
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'thrift', 'typescript', 'verilog', 'vhdl', 'visual-basic', 'xslt', 'yacc', 'zig' |
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] |
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) |
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for d in dataset: |
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for row in d: |
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yield row['content'] |
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del dataset |
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gc.collect() |
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if name in (None, 'nampdn-ai/tiny-textbooks'): |
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dataset = load_dataset(name, split='train') |
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for row in dataset: |
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yield row['textbook'] |
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del dataset |
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gc.collect() |
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if name in (None, 'm-a-p/CodeFeedback-Filtered-Instruction'): |
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dataset = load_dataset(name, split='train') |
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for row in dataset: |
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yield row['query'] + '\n' + row['answer'] |
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del dataset |
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gc.collect() |
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if name in (None, 'nampdn-ai/tiny-codes'): |
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dataset = load_dataset(name, split='train') |
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for row in dataset: |
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yield row['prompt'] + '\n' + row['response'] |
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del dataset |
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gc.collect() |
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if name in (None, 'ajibawa-2023/Maths-College'): |
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dataset = load_dataset(name, split='train') |
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for row in dataset: |
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yield row['instruction'] + '\n' + row['output'] |
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del dataset |
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gc.collect() |
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if name in (None, 'microsoft/orca-math-word-problems-200k'): |
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dataset = load_dataset(name, split='train') |
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for row in dataset: |
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yield row['question'] + '\n' + row['answer'] |
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del dataset |
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gc.collect() |
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if name in (None, 'datatab/orca_math_world_problem_200k_serbian'): |
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dataset = load_dataset(name, split='train') |
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for row in dataset: |
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yield row['question_translated_srb'] + '\n' + row['answer_translated_srb'] |
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del dataset |
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gc.collect() |
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if name in (None, 'badrex/llm-emoji-dataset'): |
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dataset = load_dataset(name, split='train') |
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for row in dataset: |
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yield f'{row["character"]}\n{row["unicode"]}\n{row["short description"]}\n{row["tags"]}\n{row["LLM description"]}' |
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del dataset |
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gc.collect() |
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alpaca_datasets_names = [ |
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'saillab/alpaca-english-cleaned', |
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'saillab/alpaca-serbian-cleaned', |
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'saillab/alpaca-croatian-cleaned', |
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'saillab/alpaca-bosnian-cleaned', |
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'saillab/alpaca-macedonian-cleaned', |
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'saillab/alpaca-slovenian-cleaned', |
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] |
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if name in (None, *alpaca_datasets_names): |
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for split in ['train', 'test']: |
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dataset = load_dataset(name, split=split) |
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for row in dataset: |
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if row['input'] in (None, '', 'nan'): |
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yield row['instruction'] + '\n' + row['output'] |
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else: |
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yield row['instruction'] + '\n' + row['input'] + '\n' + row['output'] |
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del dataset |
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gc.collect() |
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def tokenize_fn(dataset_name, tokenizer=None): |
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for text in batch_iterator(dataset_name): |
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text_ids = tokenizer.encode(text, bos=False, eos=True) |
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yield text_ids |
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datasets_names = [ |
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'xu-song/cc100-samples', |
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'bigcode/the-stack-smol-xs', |
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'nampdn-ai/tiny-textbooks', |
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'm-a-p/CodeFeedback-Filtered-Instruction', |
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'nampdn-ai/tiny-codes', |
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'ajibawa-2023/Maths-College', |
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'microsoft/orca-math-word-problems-200k', |
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'datatab/orca_math_world_problem_200k_serbian', |
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'badrex/llm-emoji-dataset', |
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'saillab/alpaca-english-cleaned', |
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'saillab/alpaca-serbian-cleaned', |
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'saillab/alpaca-croatian-cleaned', |
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'saillab/alpaca-bosnian-cleaned', |
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'saillab/alpaca-macedonian-cleaned', |
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'saillab/alpaca-slovenian-cleaned', |
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] |
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outputs = optimize( |
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fn=partial(tokenize_fn, tokenizer=Tokenizer('..')), |
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inputs=datasets_names, |
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output_dir='../data/', |
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chunk_size=(8193 * 2003), |
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num_workers=16, |
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) |
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