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from __future__ import annotations |
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from dataclasses import dataclass |
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from typing import Generator |
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import numpy as np |
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from sentence_transformers import SentenceTransformer |
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from tqdm import tqdm |
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import torch |
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import argparse |
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from pathlib import Path |
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from datasets import load_dataset |
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parser = argparse.ArgumentParser(description="Convert datasets to embeddings") |
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parser.add_argument( |
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"-t", |
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"--target", |
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type=str, |
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required=True, |
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choices=["data", "chunked"], |
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help="target dataset, data or chunked", |
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) |
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parser.add_argument( |
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"-d", |
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"--debug", |
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action="store_true", |
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help="debug mode, use small dataset", |
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) |
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parser.add_argument( |
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"-m", |
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"--model_name", |
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type=str, |
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required=True, |
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help="huggingface model name", |
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) |
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parser.add_argument( |
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"-i", |
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"--input_prefix", |
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type=str, |
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required=False, |
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default="", |
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help="input prefix", |
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) |
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parser.add_argument( |
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"-l", |
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"--max_seq_length", |
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type=int, |
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required=False, |
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default=512, |
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help="max sequence length", |
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) |
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parser.add_argument( |
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"-o", |
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"--output_name", |
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type=str, |
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required=True, |
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help="output dir", |
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) |
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args = parser.parse_args() |
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@dataclass |
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class EmbConfig: |
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model_name: str |
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input_prefix: str |
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max_seq_length: int |
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args = parser.parse_args() |
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target_local_ds = args.target |
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EMB_CONFIG = EmbConfig( |
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model_name=args.model_name, |
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input_prefix=args.input_prefix, |
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max_seq_length=args.max_seq_length, |
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) |
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embs_dir = f"embs{'_debug' if args.debug else ''}" |
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output_embs_path = Path("/".join([embs_dir, args.output_name, target_local_ds])) |
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output_embs_path.mkdir(parents=True, exist_ok=True) |
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print("output path:", output_embs_path) |
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MODEL = SentenceTransformer(EMB_CONFIG.model_name) |
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MODEL.max_seq_length = EMB_CONFIG.max_seq_length |
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def to_embs(texts: list[str], group_size=1024) -> Generator[np.ndarray, None, None]: |
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group = [] |
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for text in texts: |
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group.append(text) |
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if len(group) == group_size: |
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embeddings = MODEL.encode( |
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group, |
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normalize_embeddings=True, |
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show_progress_bar=False, |
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) |
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yield embeddings |
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group = [] |
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if len(group) > 0: |
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embeddings = MODEL.encode( |
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group, normalize_embeddings=True, show_progress_bar=False |
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) |
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yield embeddings |
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def _to_data_text( |
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data, prefix=EMB_CONFIG.input_prefix, max_len=int(EMB_CONFIG.max_seq_length * 1.5) |
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): |
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return (prefix + data["title"] + "\n" + data["text"])[0:max_len] |
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def _to_chunk_text( |
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data, prefix=EMB_CONFIG.input_prefix, max_len=int(EMB_CONFIG.max_seq_length * 1.5) |
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): |
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return (prefix + data["title"] + "\n" + data["overlap_text"] + data["text"])[ |
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:max_len |
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] |
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def ds_to_embs( |
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ds, |
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text_fn, |
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group_size: int, |
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): |
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texts = [] |
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total = len(ds) |
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pbar = tqdm(total=total) |
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for i in range(0, total, group_size): |
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texts = [] |
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for data in ds.select(range(i, min(i + group_size, total))): |
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data: dict = data |
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text = text_fn(data) |
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texts.append(text) |
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embs = [] |
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for group_embs in to_embs(texts): |
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embs.append(group_embs) |
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pbar.update(len(group_embs)) |
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embs = np.concatenate(embs) |
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yield embs, i, pbar |
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if torch.cuda.is_available(): |
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print("use cuda") |
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MODEL.to("cuda") |
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else: |
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print("!! Warning: use cpu") |
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ds = load_dataset(args.target)["train"] |
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to_text = _to_data_text if args.target == "data" else _to_chunk_text |
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if args.debug: |
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print("debug mode") |
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ds = ds.select(range(19998)) |
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print("small dataset len:", len(ds)) |
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group_size = 10000 |
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else: |
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print("dataset len:", len(ds)) |
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group_size = 100_000 |
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for embs, idx, pbar in ds_to_embs(ds, to_text, group_size=group_size): |
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filename = f"{idx}.npz" |
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filepath = output_embs_path / filename |
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pbar.desc = f"saving...: {str(filepath)}" |
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np.savez_compressed(filepath, embs=embs.astype(np.float16)) |
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pbar.desc = "" |
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