495 lines
18 KiB
Python
495 lines
18 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import os
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import re
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import math
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import argparse
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import multiprocessing as mp
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from typing import List, Dict, Any
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import torch
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from tqdm import tqdm
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from datasets import load_dataset, Dataset, concatenate_datasets
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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# =========================
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# Dataset source on Hugging Face Hub and which split to translate.
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DATASET_NAME = "PKU-Alignment/PKU-SafeRLHF-30K"
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SPLIT = "train"
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# Local path to the NLLB model (loaded from disk, not from the Hub).
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NLLB_PATH = "/home/hyrenko/Diploma/models/nllb-200-1.3B"
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# Output directory where the fully translated dataset will be saved.
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OUT_FINAL_DIR = "/home/hyrenko/Diploma/datasets/PKU-SafeRLHF-30K_slk_Latn_SFT_DPO_ONLY"
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# Language codes for NLLB (source and target).
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SRC_LANG = "eng_Latn"
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TGT_LANG = "slk_Latn"
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# Performance-oriented defaults for a 2x RTX Titan 24GB setup.
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# Notes:
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# - MODEL_BATCH_SIZE controls GPU-side translation batching.
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# - MAP_BATCH_SIZE controls CPU/RAM side batching when mapping over a dataset shard.
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# - LONG_THRESHOLD_CHARS splits very long texts to avoid slow/unstable generation.
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# - NUM_BEAMS must be 1 for speed (beam search is expensive).
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# - MAX_NEW_TOKENS caps translation length; reducing it can speed up generation.
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MODEL_BATCH_SIZE = 32 # if OOM: 24 -> 16
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MAP_BATCH_SIZE = 128 # CPU/RAM side
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LONG_THRESHOLD_CHARS = 3500 # higher => fewer slow fallbacks
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NUM_BEAMS = 1 # MUST be 1 for speed
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MAX_NEW_TOKENS = 128 # can lower to 96 for speed
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# =========================
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# Utilities
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# =========================
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# -----------------------------------------------------------------------------
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# normalize_text(x)
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# Purpose:
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# Converts arbitrary dataset values into a clean string:
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# - Handles None values safely.
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# - Removes the Unicode replacement char (<28>, U+FFFD) which can appear in noisy text.
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# - Strips whitespace.
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#
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# Why it exists:
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# Dataset fields may contain None / non-string types / corrupted characters.
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# Translation should always receive a valid string input.
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# -----------------------------------------------------------------------------
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def normalize_text(x: Any) -> str:
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if x is None:
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return ""
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return str(x).replace("\uFFFD", "").strip()
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# -----------------------------------------------------------------------------
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# split_text_safely(text, max_chars)
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# Purpose:
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# Splits long text into smaller chunks to avoid slow generation / memory issues.
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#
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# Strategy:
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# - Normalizes input text.
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# - Splits by paragraph boundaries (2+ newlines).
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# - Ensures each chunk is <= max_chars.
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# - If a paragraph itself is too long, it is cut into fixed-size slices.
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#
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# Output:
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# A list of non-empty chunks; returns [""] for empty input as a safe placeholder.
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# -----------------------------------------------------------------------------
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def split_text_safely(text: str, max_chars: int) -> List[str]:
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text = normalize_text(text)
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if not text:
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return [""]
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chunks: List[str] = []
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# push(piece)
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# Internal helper that:
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# - ignores empty pieces
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# - keeps pieces <= max_chars
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# - slices very long pieces into fixed-size parts
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def push(piece: str):
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piece = piece.strip()
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if not piece:
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return
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if len(piece) <= max_chars:
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chunks.append(piece)
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else:
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for i in range(0, len(piece), max_chars):
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part = piece[i:i + max_chars].strip()
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if part:
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chunks.append(part)
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paras = re.split(r"\n{2,}", text)
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for p in paras:
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p = p.strip()
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if p:
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push(p)
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return chunks if chunks else [""]
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# -----------------------------------------------------------------------------
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# detect_needed_cols_for_sft_dpo(ds)
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# Purpose:
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# Auto-detect which columns should be translated so the output dataset is
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# immediately usable for SFT and/or DPO preparation.
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#
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# Priority:
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# 1) Classic preference schema: prompt + chosen + rejected
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# 2) Pairwise schema: prompt + response_0 + response_1
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# 3) Fallback: prompt + any response-like columns matching a regex pattern
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# 4) Last resort: prompt only
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#
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# Result:
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# A list of columns to translate, minimizing work and storage.
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# -----------------------------------------------------------------------------
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def detect_needed_cols_for_sft_dpo(ds: Dataset) -> List[str]:
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"""
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Auto-detect columns needed for SFT->DPO training.
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Priority:
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1) prompt + chosen + rejected (classic preference schema)
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2) prompt + response_0 + response_1 (pairwise schema)
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3) fallback: prompt + any reasonable response fields
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"""
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cols = set(ds.column_names)
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# classic preference format
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if {"prompt", "chosen", "rejected"}.issubset(cols):
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return ["prompt", "chosen", "rejected"]
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# common pairwise format
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if {"prompt", "response_0", "response_1"}.issubset(cols):
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return ["prompt", "response_0", "response_1"]
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# fallback: translate prompt + any text response columns that exist
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candidates = []
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for c in ds.column_names:
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if c == "prompt":
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continue
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if re.match(r"^(chosen|rejected|response_\d+|answer_\d+|completion_\d+)$", c):
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candidates.append(c)
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if "prompt" in cols and candidates:
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return ["prompt"] + sorted(candidates)
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# last resort: translate only prompt (still useful for later)
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if "prompt" in cols:
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return ["prompt"]
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raise RuntimeError("Could not detect a 'prompt' column; cannot proceed.")
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# -----------------------------------------------------------------------------
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# nllb_translate_batch(tokenizer, model, texts)
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# Purpose:
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# Performs batched translation for a list of short texts via NLLB.
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#
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# Details:
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# - Sets tokenizer.src_lang each time to ensure correct source language.
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# - Uses forced_bos_token_id to force decoding in the target language.
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# - Tokenizes to max_length=1024 (encoder-side), pads and truncates as needed.
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# - Uses model.generate() with max_new_tokens, num_beams, caching for speed.
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# - Returns stripped decoded strings.
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#
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# Important:
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# - @torch.inference_mode disables gradients for speed and lower memory.
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# -----------------------------------------------------------------------------
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@torch.inference_mode()
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def nllb_translate_batch(tokenizer, model, texts: List[str]) -> List[str]:
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tokenizer.src_lang = SRC_LANG
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forced_bos = tokenizer.convert_tokens_to_ids(TGT_LANG)
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inputs = tokenizer(
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texts,
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return_tensors="pt",
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padding=True,
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truncation=True,
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max_length=1024,
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)
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inputs = {k: v.to(model.device) for k, v in inputs.items()}
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gen = model.generate(
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**inputs,
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forced_bos_token_id=forced_bos,
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max_new_tokens=MAX_NEW_TOKENS,
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num_beams=NUM_BEAMS,
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do_sample=False,
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use_cache=True,
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)
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out = tokenizer.batch_decode(gen, skip_special_tokens=True)
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return [o.strip() for o in out]
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# -----------------------------------------------------------------------------
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# translate_long_text(tokenizer, model, text, sub_batch)
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# Purpose:
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# Safe fallback path for very long texts that may be slow or unstable if
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# translated in one shot.
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#
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# Strategy:
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# - Split the text into <= LONG_THRESHOLD_CHARS chunks (paragraph-aware).
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# - Translate chunks in sub-batches to control VRAM use.
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# - Join translated chunks with blank lines to preserve paragraph separation.
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# -----------------------------------------------------------------------------
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def translate_long_text(tokenizer, model, text: str, sub_batch: int) -> str:
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text = normalize_text(text)
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if not text:
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return ""
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chunks = split_text_safely(text, max_chars=LONG_THRESHOLD_CHARS)
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out_chunks: List[str] = []
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for i in range(0, len(chunks), sub_batch):
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out_chunks.extend(nllb_translate_batch(tokenizer, model, chunks[i:i + sub_batch]))
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return "\n\n".join([c for c in out_chunks if c])
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# -----------------------------------------------------------------------------
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# _safe_mkdir(p)
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# Purpose:
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# Convenience helper to create directories idempotently.
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# -----------------------------------------------------------------------------
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def _safe_mkdir(p: str):
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os.makedirs(p, exist_ok=True)
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# =========================
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# Worker (one GPU)
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# =========================
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# -----------------------------------------------------------------------------
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# worker_translate(...)
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# Purpose:
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# Runs translation on a shard of the dataset using a specific GPU.
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#
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# Key ideas:
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# - Uses CUDA_VISIBLE_DEVICES to bind this worker process to a single GPU.
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# - Loads the full dataset, then selects only rows belonging to this shard via:
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# index % total_shards == shard_id
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# This guarantees a complete partition with no missing rows across workers.
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# - Translates only the required columns (cols_to_translate).
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# - Uses a two-path translation:
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# * fast path for short texts (batched GPU translation)
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# * slow fallback for very long texts (split + smaller sub-batches)
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# - Saves this shard’s translated dataset to tmp_dir/shard_XX for later merging.
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# -----------------------------------------------------------------------------
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def worker_translate(
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shard_id: int,
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gpu_id: int,
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total_shards: int,
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tmp_dir: str,
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cols_to_translate: List[str],
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total_len: int,
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):
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# Bind this process to a specific GPU.
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os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id)
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# Global inference optimizations (no gradients).
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torch.set_grad_enabled(False)
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torch.backends.cudnn.benchmark = True
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device = "cuda" if torch.cuda.is_available() else "cpu"
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dtype = torch.float16 if device == "cuda" else torch.float32
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print(f"\n[WORKER {shard_id}] GPU={gpu_id} device={device} dtype={dtype}")
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print(f"[WORKER {shard_id}] Translating columns: {cols_to_translate}")
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# Load the dataset split (each worker loads it independently).
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ds = load_dataset(DATASET_NAME, split=SPLIT)
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n = len(ds)
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if n != total_len:
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print(f"[WORKER {shard_id}] WARN: dataset length mismatch: got {n}, expected {total_len}")
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# Strict sharding by original index to avoid missing rows and keep deterministic coverage.
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indices = [i for i in range(n) if (i % total_shards) == shard_id]
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ds_shard = ds.select(indices)
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shard_len = len(ds_shard)
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# Load tokenizer + model from local path (NLLB).
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tokenizer = AutoTokenizer.from_pretrained(NLLB_PATH, use_fast=True)
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model = AutoModelForSeq2SeqLM.from_pretrained(
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NLLB_PATH,
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torch_dtype=dtype,
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low_cpu_mem_usage=True,
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).to(device)
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model.eval()
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# Number of CPU-side map batches (used only for progress bar granularity).
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total_map_batches = math.ceil(shard_len / MAP_BATCH_SIZE)
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# map_fn(batch)
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# Purpose:
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# Translates the requested columns for a single CPU-side batch.
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#
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# Workflow per column:
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# 1) Normalize all values to strings.
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# 2) Split into short_texts and long_texts by LONG_THRESHOLD_CHARS.
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# 3) Translate short texts with fast batched translation:
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# - internal sub-batches of MODEL_BATCH_SIZE
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# 4) Translate long texts individually through translate_long_text().
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# 5) Return a dict: {col_name: translated_list} for each requested column.
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def map_fn(batch: Dict[str, List[Any]]) -> Dict[str, List[str]]:
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out: Dict[str, List[str]] = {}
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for col in cols_to_translate:
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vals = [normalize_text(v) for v in batch[col]]
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short_texts: List[str] = []
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short_pos: List[int] = []
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long_texts: List[str] = []
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long_pos: List[int] = []
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# Partition examples by length so most data goes through the fast path.
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for j, t in enumerate(vals):
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if (not t) or (len(t) <= LONG_THRESHOLD_CHARS):
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short_pos.append(j)
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short_texts.append(t)
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else:
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long_pos.append(j)
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long_texts.append(t)
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translated = [""] * len(vals)
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# Fast batched translate for short texts (GPU-optimized).
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if short_texts:
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tr_short: List[str] = []
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for k in range(0, len(short_texts), MODEL_BATCH_SIZE):
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tr_short.extend(nllb_translate_batch(tokenizer, model, short_texts[k:k + MODEL_BATCH_SIZE]))
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for pos, tr in zip(short_pos, tr_short):
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translated[pos] = tr
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# Slow fallback for very long texts (split + smaller batches).
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for pos, t in zip(long_pos, long_texts):
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translated[pos] = translate_long_text(tokenizer, model, t, sub_batch=max(1, MODEL_BATCH_SIZE // 2))
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out[col] = translated
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return out
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# Manual loop (instead of a single ds_shard.map) to show a stable progress bar.
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# Each loop translates a slice [start:end] of the shard.
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parts: List[Dataset] = []
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with tqdm(total=total_map_batches, desc=f"GPU{gpu_id} shard{shard_id}", ncols=100) as pbar:
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for start in range(0, shard_len, MAP_BATCH_SIZE):
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end = min(shard_len, start + MAP_BATCH_SIZE)
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part = ds_shard.select(range(start, end)).map(
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map_fn,
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batched=True,
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batch_size=end - start,
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desc=None,
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)
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parts.append(part)
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pbar.update(1)
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# Concatenate translated parts into a single shard dataset.
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ds_tr = concatenate_datasets(parts)
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# Save this shard to disk for later merge.
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shard_path = os.path.join(tmp_dir, f"shard_{shard_id:02d}")
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_safe_mkdir(shard_path)
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ds_tr.save_to_disk(shard_path)
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print(f"[WORKER {shard_id}] Saved -> {shard_path}")
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# =========================
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# Main (one-click)
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# =========================
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# -----------------------------------------------------------------------------
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# main()
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# Purpose:
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# One-click launcher for 2-GPU translation + shard merge.
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#
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# Steps:
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# 1) Parse CLI args:
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# --resume: skip workers for shards that already exist and only merge.
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# 2) Validate:
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# - NLLB model path exists
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# - at least 2 GPUs are available (script expects exactly 2 workers here)
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# 3) Load dataset metadata (length + schema) and determine which columns to translate.
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# 4) Spawn 2 worker processes (GPU 0 and GPU 1) using spawn start method.
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# 5) After workers finish, merge shard datasets and restore original row order.
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# 6) Save the final merged dataset to OUT_FINAL_DIR and verify row count.
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# -----------------------------------------------------------------------------
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("--resume", action="store_true", help="Skip existing shards and only merge.")
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args = ap.parse_args()
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# Ensure the local NLLB path is valid before starting.
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if not os.path.isdir(NLLB_PATH):
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raise SystemExit(f"[ERROR] NLLB model path not found: {NLLB_PATH}")
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# This script is designed for a 2-GPU setup (two workers).
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if torch.cuda.device_count() < 2:
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raise SystemExit(f"[ERROR] Need 2 GPUs; found: {torch.cuda.device_count()}")
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# Prepare output directories.
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_safe_mkdir(OUT_FINAL_DIR)
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tmp_dir = os.path.join(OUT_FINAL_DIR, "_tmp_shards")
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_safe_mkdir(tmp_dir)
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# Load dataset once in the main process to:
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# - get total length n
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# - auto-detect which columns are necessary for SFT/DPO
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print("[INFO] Loading dataset metadata...")
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ds = load_dataset(DATASET_NAME, split=SPLIT)
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n = len(ds)
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cols_to_translate = detect_needed_cols_for_sft_dpo(ds)
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print(f"[INFO] Dataset: {DATASET_NAME} split={SPLIT}")
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print(f"[INFO] Total rows: {n}")
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print(f"[INFO] Translating ONLY needed cols for SFT->DPO: {cols_to_translate}")
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print(f"[INFO] Output: {OUT_FINAL_DIR}")
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print(f"[INFO] Params: MODEL_BATCH_SIZE={MODEL_BATCH_SIZE}, MAP_BATCH_SIZE={MAP_BATCH_SIZE}, "
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f"LONG_THRESHOLD_CHARS={LONG_THRESHOLD_CHARS}, NUM_BEAMS={NUM_BEAMS}, MAX_NEW_TOKENS={MAX_NEW_TOKENS}")
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# Two workers => two shards, pinned to GPU 0 and GPU 1.
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gpus = [0, 1]
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total_shards = 2
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# Use "spawn" for CUDA safety in multiprocessing.
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mp.set_start_method("spawn", force=True)
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procs = []
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# Start workers unless resume mode skips an existing shard.
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for shard_id, gpu_id in enumerate(gpus):
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shard_path = os.path.join(tmp_dir, f"shard_{shard_id:02d}")
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if args.resume and os.path.isdir(shard_path):
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print(f"[INFO] Resume: shard exists, skipping worker {shard_id} -> {shard_path}")
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continue
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p = mp.Process(
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target=worker_translate,
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args=(shard_id, gpu_id, total_shards, tmp_dir, cols_to_translate, n),
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)
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p.start()
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procs.append(p)
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# Wait for workers and fail fast if any worker exits with a non-zero code.
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for p in procs:
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p.join()
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if p.exitcode != 0:
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raise SystemExit("[ERROR] A worker failed. See logs above.")
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# Merge step:
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# - load each shard dataset from disk
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# - concatenate them
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# - restore original ordering (because sharding interleaves indices)
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print("\n[INFO] Merging shards...")
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shard_dirs = [os.path.join(tmp_dir, f"shard_{i:02d}") for i in range(total_shards)]
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for sd in shard_dirs:
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if not os.path.isdir(sd):
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raise SystemExit(f"[ERROR] Missing shard directory: {sd}")
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shards = [Dataset.load_from_disk(sd) for sd in shard_dirs]
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merged = concatenate_datasets(shards)
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# Restore original order:
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||
# Each shard contains indices satisfying (i % total_shards == shard_id).
|
||
merged_indices = []
|
||
for shard_id in range(total_shards):
|
||
merged_indices.extend([i for i in range(n) if (i % total_shards) == shard_id])
|
||
|
||
# Add an ordering column, sort by it, then remove it.
|
||
merged = merged.add_column("orig_index", merged_indices)
|
||
merged = merged.sort("orig_index").remove_columns(["orig_index"])
|
||
|
||
# Safety check: ensure no missing rows after merge.
|
||
if len(merged) != n:
|
||
raise SystemExit(f"[ERROR] Length mismatch after merge: merged={len(merged)} expected={n}")
|
||
|
||
# Save the final translated dataset.
|
||
print("[INFO] Saving final dataset...")
|
||
merged.save_to_disk(OUT_FINAL_DIR)
|
||
|
||
print(f"[OK] Done. Saved FULL translated dataset to: {OUT_FINAL_DIR}")
|
||
print("[OK] Verified: no missing rows.")
|
||
print(f"[OK] Translated columns: {cols_to_translate}")
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|