# (Modifications Copyright(C) [2024] Advanced Micro Devices, Inc. All rights reserved) """ Script for preparing the SFT data for fine-tuning AMD-OLMo model. Modifed from https://github.com/allenai/OLMo/blob/main/scripts/prepare_tulu_data.py """ import logging from argparse import ArgumentParser from functools import partial from pathlib import Path import datasets as ds import numpy as np from rich.progress import track from olmo.tokenizer import Tokenizer from olmo.util import prepare_cli_environment import random from tqdm import tqdm log = logging.getLogger(__name__) def convert_code_feedback_to_tulu_format(dataset, mix=False): log.info("Converting code_feedback ...") y_all = [] for i, sample in enumerate(dataset): y = { "dataset": "code_feedback", "id": "code_feedback_{}".format(i), "messages": sample['messages'] } y_all.append(y) log.info(f"In total {len(y_all)} samples") if mix: return y_all else: new_dataset = ds.Dataset.from_list(y_all) return new_dataset def convert_OpenHermes_to_tulu_format(dataset, mix=False): log.info("Converting OpenHermes ...") role_map = {"human": "user", "gpt": "assistant", "system": "system"} y_all = [] for i, sample in enumerate(dataset): y = { "dataset": "OpenHermes", "id": "OpenHermes_{}".format(i), "messages": [{"role": role_map[mssg["from"]], "content": mssg["value"]} for mssg in sample['conversations']] } y_all.append(y) log.info(f"In total {len(y_all)} samples") if mix: return y_all else: new_dataset = ds.Dataset.from_list(y_all) return new_dataset def convert_WebInstructSub_to_tulu_format(dataset, mix=False): log.info("Converting WebInstructSub ...") y_all = [] for i, sample in tqdm(enumerate(dataset)): y = { "dataset": "WebInstructSub", "id": "WebInstructSub_{}".format(i), "messages": [{"role": "user", "content": sample["question"]}, {"role": "assistant", "content": sample["answer"]}] } y_all.append(y) log.info(f"In total {len(y_all)} samples") if mix: return y_all else: new_dataset = ds.Dataset.from_list(y_all) return new_dataset def main(opts) -> None: tokenizer: Tokenizer if Path(opts.tokenizer).is_file(): tokenizer = Tokenizer.from_file(opts.tokenizer, eos_token_id=opts.eos, pad_token_id=opts.pad) else: tokenizer = Tokenizer.from_pretrained(opts.tokenizer, eos_token_id=opts.eos, pad_token_id=opts.pad) if opts.dataset == "tulu": dataset = ds.load_dataset("allenai/tulu-v2-sft-mixture", split="train") elif opts.dataset == "2nd-phase": datasets = ["code-feedback", "OpenHermes", "WebInstructSub"] combined_datasets = [] for dataset_name in datasets: if dataset_name == "code-feedback": dataset = ds.load_dataset("m-a-p/Code-Feedback", split="train") dataset = convert_code_feedback_to_tulu_format(dataset, mix=True) elif dataset_name == "OpenHermes": dataset = ds.load_dataset("teknium/OpenHermes-2.5", split="train") dataset = convert_OpenHermes_to_tulu_format(dataset, mix=True) elif dataset_name == "WebInstructSub": dataset = ds.load_dataset("TIGER-Lab/WebInstructSub", split="train") dataset = convert_WebInstructSub_to_tulu_format(dataset, mix=True) combined_datasets += dataset random.seed(42) random.shuffle(combined_datasets) log.info(f"In total {len(combined_datasets)} samples") dataset = ds.Dataset.from_list(combined_datasets) log.info("Tokenizing dataset...") dataset = dataset.map( partial(preprocess, tokenizer=tokenizer, max_seq_len=opts.seq_len), batched=False, remove_columns=["dataset", "id", "messages"], num_proc=opts.num_proc, # type: ignore ) log.info("Filtering dataset...") n = len(dataset) # type: ignore dataset = dataset.filter(filter, batched=False, num_proc=opts.num_proc) # type: ignore log.info(f"Filtered out {n - len(dataset):,d} examples") log.info("Counting tokens...") total_tokens = 0 for ex in track(dataset): assert len(ex["input_ids"]) == opts.seq_len # type: ignore total_tokens += len(ex["input_ids"]) # type: ignore log.info(f"Total tokens: {total_tokens:,d}") log.info(f"Saving results to '{opts.output_dir}'...") output_dir = Path(opts.output_dir) output_dir.mkdir(exist_ok=True, parents=True) input_ids_file = np.memmap( str(output_dir / "input_ids.npy"), dtype=np.uint16, mode="w+", shape=(total_tokens,) ) label_mask_file = np.memmap( str(output_dir / "label_mask.npy"), dtype=np.bool_, mode="w+", shape=(total_tokens,) ) offset = 0 for ex in track(dataset): ex_len = len(ex["input_ids"]) # type: ignore input_ids_file[offset : offset + ex_len] = ex["input_ids"] # type: ignore label_mask_file[offset : offset + ex_len] = ex["label_mask"] # type: ignore offset += ex_len input_ids_file.flush() label_mask_file.flush() log.info("Done!") def filter(example): return example["n_labels"] > 0 def preprocess(example, tokenizer: Tokenizer, max_seq_len: int): input_ids = [tokenizer.eos_token_id] label_mask = [False] for msg in example["messages"]: role_tokens = tokenizer.encode(f"<|{msg['role']}|>\n", add_special_tokens=False) label_mask += [False] * len(role_tokens) input_ids += role_tokens if msg["role"] == "assistant": content_tokens = tokenizer.encode( msg["content"].strip() + tokenizer.eos_token + "\n", add_special_tokens=False ) label_mask += [True] * len(content_tokens) # mask out the last '\n' assert content_tokens[-2] == tokenizer.eos_token_id label_mask[-1] = False else: content_tokens = tokenizer.encode(msg["content"].strip() + "\n", add_special_tokens=False) label_mask += [False] * len(content_tokens) input_ids += content_tokens input_ids = input_ids[:max_seq_len] label_mask = label_mask[:max_seq_len] if len(input_ids) < max_seq_len: pad_len = max_seq_len - len(input_ids) input_ids += [tokenizer.pad_token_id] * pad_len label_mask += [False] * pad_len assert len(input_ids) == len(label_mask) n_labels = sum(label_mask) return {"input_ids": input_ids, "label_mask": label_mask, "n_labels": n_labels} def get_parser() -> ArgumentParser: parser = ArgumentParser(description="Prepare Math dataset") parser.add_argument("--output_dir", type=str, help="""Directory to save the results to.""") parser.add_argument( "-t", "--tokenizer", type=str, help="""Tokenizer path or identifier.""", default=Path(__file__).parent / "tokenizers" / "allenai_eleuther-ai-gpt-neox-20b-pii-special.json", ) parser.add_argument("-ds", "--dataset", type=str, help="""Dataset that we are processing. tulu or 2nd-phase""", default="tulu") parser.add_argument("-s", "--seq-len", type=int, help="""Max sequence length.""", default=2048) parser.add_argument("--eos", type=int, help="""EOS token ID.""", default=50279) parser.add_argument("--pad", type=int, help="""PAD token ID.""", default=1) parser.add_argument("-j", "--num-proc", type=int, help="""Number of workers.""", default=8) return parser if __name__ == "__main__": prepare_cli_environment() opts = get_parser().parse_args() main(opts)