# Instruction code heavily inspired by Andreas Köpf # source: https://github.com/andreaskoepf/epfl-megatron/tree/local_changes/ """Processing data for instruction tuning. Example: python instruct/preprocess_instruct_data.py --input=/pure-mlo-scratch/alhernan/data/medmc/medmc-v1.jsonl \ --output_prefix=/pure-mlo-scratch/alhernan/data/medmc/medmc-v1 \ --tokenizer_type=SentencePieceTokenizer \ --vocab_file=/pure-mlo-scratch/llama/tokenizer.model \ --chunk_size=32 --workers=32 \ --vocab_extra_ids_list "[bib_ref],[/bib_ref],[fig_ref],[/fig_ref],[bib],[/bib],[fig],[/fig],[table],[/table],[formula],[/formula],<|im_start|>,<|im_end|>" \ --question_key=input \ --answer_key=output \ --system_key=instruction """ import sys import json import time import itertools from pathlib import Path from typing import Optional from multiprocessing import Pool from argparse import ArgumentParser, Namespace import torch sys.path.append(str(Path(__file__).parent.parent.absolute())) from megatron.tokenizer import build_tokenizer from megatron.tokenizer.tokenizer import AbstractTokenizer from megatron.data.indexed_dataset import make_builder from megatron.data.instruction_dataset import Role class Encoder(object): tokenizer: Optional[AbstractTokenizer] = None def __init__(self, args: Namespace): self.args = args def initializer(self): Encoder.tokenizer = build_tokenizer(self.args) def encode(self, line: str) -> tuple[int, list[int], list[int]]: # get data assert Encoder.tokenizer is not None data = json.loads(line) question = data[self.args.question_key] answer = data[self.args.answer_key] system = None if self.args.system_key is None else data[self.args.system_key] # now format messages if system is not None: system = format_message(system, "system") question = format_message(question, "question") answer = format_message(answer, "answer") # tokenize and get roles tokens = [] roles = [] if system is not None: system = Encoder.tokenizer.tokenize(system) tokens += system roles += [Role.system.value]*len(system) question = Encoder.tokenizer.tokenize(question) tokens += question roles += [Role.prompter.value]*len(question) answer = Encoder.tokenizer.tokenize(answer) tokens += answer roles += [Role.assistant.value]*len(answer) return len(line), tokens, roles @property def special_tokens(self) -> dict: return self.tokenizer._special_tokens class DatasetWriter: def __init__(self, prefix: str, vocab_size: int, dataset_impl: str = "mmap", feature: str = "text"): self.vocab_size = vocab_size self.dataset_impl = dataset_impl self.bin_fname = f"{prefix}-{feature}.bin" self.idx_fname = f"{prefix}-{feature}.idx" self.builder = None def add_item(self, tokens: list[int]): self.builder.add_item(torch.IntTensor(tokens)) def __enter__(self): self.builder = make_builder(self.bin_fname, impl=self.dataset_impl, vocab_size=self.vocab_size) return self def __exit__(self, *_): self.builder.finalize(self.idx_fname) self.builder = None def format_message(message: str, role: str) -> str: return f"<|im_start|>{role}\n{message}<|im_end|>\n" def get_args(): parser = ArgumentParser() group = parser.add_argument_group(title='input data') group.add_argument('--input', type=str, nargs="+", help='Path(s) to input JSON file(s)') group.add_argument('--system_key', help='key to extract system info from json (optional)') group.add_argument('--question_key', default='input', help='key to extract questions from json') group.add_argument('--answer_key', default='output', help='key to extract answers from json') group = parser.add_argument_group(title='tokenizer') group.add_argument('--tokenizer_type', type=str, required=True, choices=['BertWordPieceLowerCase','BertWordPieceCase', 'GPT2BPETokenizer', 'SentencePieceTokenizer', 'FalconTokenizer'], help='What type of tokenizer to use.') group.add_argument('--vocab_file', type=str, default=None, help='Path to the vocab file') group.add_argument('--merge_file', type=str, default=None, help='Path to the BPE merge file (if necessary).') group.add_argument('--lang', type=str, default='english', help='Language to use for NLTK-powered sentence splitting.') group = parser.add_argument_group(title='output data') group.add_argument('--output_prefix', type=Path, required=True, help='Path to binary output file without suffix') group.add_argument('--dataset_impl', type=str, default='mmap', choices=['lazy', 'cached', 'mmap']) group = parser.add_argument_group(title='runtime') group.add_argument('--workers', type=int, required=True, help='Number of worker processes to launch') group.add_argument('--chunk_size', type=int, required=True, help='Chunk size assigned to each worker process') group.add_argument('--log_interval', type=int, default=100, help='Interval between progress updates') group.add_argument('--vocab_extra_ids', type=int, default=0) group.add_argument('--vocab_extra_ids_list', type=str, default=None, help='comma separated list of special vocab ids to add to the tokenizer') group.add_argument("--no_new_tokens", action="store_false", dest="new_tokens", help=("Whether to add special tokens (e.g. CLS, MASK, etc) " "in the sentencepiece tokenizer or not")) args = parser.parse_args() args.keep_empty = False if args.tokenizer_type.lower().startswith('bert'): if not args.split_sentences: print("Bert tokenizer detected, are you sure you don't want to split sentences?") # some default/dummy values for the tokenizer args.rank = 0 args.make_vocab_size_divisible_by = 128 args.tensor_model_parallel_size = 1 return args def main(): args = get_args() startup_start = time.time() encoder = Encoder(args) vocab_size = build_tokenizer(args).vocab_size fs = map(open, args.input) with Pool(args.workers, initializer=encoder.initializer) as pool, \ DatasetWriter(args.output_prefix, vocab_size, args.dataset_impl, "text") as token_writer, \ DatasetWriter(args.output_prefix, 16, args.dataset_impl, "role") as role_writer: f = itertools.chain(*fs) docs = pool.imap(encoder.encode, f, args.chunk_size) startup_end = time.time() proc_start = time.time() total_bytes_processed = 0 print("Time to startup:", startup_end - startup_start) for i, (size, tokens, roles) in enumerate(docs, start=1): total_bytes_processed += size token_writer.add_item(tokens) role_writer.add_item(roles) if i % args.log_interval == 0: elapsed = time.time() - proc_start mbs = total_bytes_processed/1024/1024/elapsed print(f"Processed {i} documents ({i/elapsed} docs/s, {mbs} MB/s).") print("Done! Now finalizing.") for f in fs: f.close() if __name__ == '__main__': main()