document_trans.py

from transformers import MBartForConditionalGeneration, MBart50TokenizerFast, AutoModelForSeq2SeqLM, AutoTokenizer, GenerationConfig, pipeline
from abc import ABC, abstractmethod
from typing import Type
import torch
import torch.nn.functional as F
import os

script_dir = os.path.dirname(os.path.abspath(__file__))
parent_dir = os.path.dirname(os.path.dirname(os.path.dirname(script_dir)))

class Model():
    def __init__(self, modelname, selected_lora_model, selected_gpu):
        def get_gpu_index(gpu_info, target_gpu_name):
            """
            从 GPU 信息中获取目标 GPU 的索引
            Args:
                gpu_info (list): 包含 GPU 名称的列表
                target_gpu_name (str): 目标 GPU 的名称
            Returns:
                int: 目标 GPU 的索引，如果未找到则返回 -1
            """
            for i, name in enumerate(gpu_info):
                if target_gpu_name.lower() in name.lower():
                    return i
            return -1
        if selected_gpu != "cpu":
            gpu_count = torch.cuda.device_count()
            gpu_info = [torch.cuda.get_device_name(i) for i in range(gpu_count)]
            selected_gpu_index = get_gpu_index(gpu_info, selected_gpu)
            self.device_name = f"cuda:{selected_gpu_index}"
        else:
            self.device_name = "cpu"
        print("device_name", self.device_name)
        self.model = AutoModelForSeq2SeqLM.from_pretrained(modelname).to(self.device_name)
        self.tokenizer = AutoTokenizer.from_pretrained(modelname)
        # self.translator = pipeline('translation', model=self.original_model, tokenizer=self.tokenizer, src_lang=original_language, tgt_lang=target_language, device=device)
    
    def generate(self, inputs, original_language, target_languages, max_batch_size):
        def language_mapping(original_language):
            d = {
                "Achinese (Arabic script)": "ace_Arab",
                "Achinese (Latin script)": "ace_Latn",
                "Mesopotamian Arabic": "acm_Arab",
                "Ta'izzi-Adeni Arabic": "acq_Arab",
                "Tunisian Arabic": "aeb_Arab",
                "Afrikaans": "afr_Latn",
                "South Levantine Arabic": "ajp_Arab",
                "Akan": "aka_Latn",
                "Amharic": "amh_Ethi",
                "North Levantine Arabic": "apc_Arab",
                "Standard Arabic": "arb_Arab",
                "Najdi Arabic": "ars_Arab",
                "Moroccan Arabic": "ary_Arab",
                "Egyptian Arabic": "arz_Arab",
                "Assamese": "asm_Beng",
                "Asturian": "ast_Latn",
                "Awadhi": "awa_Deva",
                "Central Aymara": "ayr_Latn",
                "South Azerbaijani": "azb_Arab",
                "North Azerbaijani": "azj_Latn",
                "Bashkir": "bak_Cyrl",
                "Bambara": "bam_Latn",
                "Balinese": "ban_Latn",
                "Belarusian": "bel_Cyrl",
                "Bemba": "bem_Latn",
                "Bengali": "ben_Beng",
                "Bhojpuri": "bho_Deva",
                "Banjar (Arabic script)": "bjn_Arab",
                "Banjar (Latin script)": "bjn_Latn",
                "Tibetan": "bod_Tibt",
                "Bosnian": "bos_Latn",
                "Buginese": "bug_Latn",
                "Bulgarian": "bul_Cyrl",
                "Catalan": "cat_Latn",
                "Cebuano": "ceb_Latn",
                "Czech": "ces_Latn",
                "Chokwe": "cjk_Latn",
                "Central Kurdish": "ckb_Arab",
                "Crimean Tatar": "crh_Latn",
                "Welsh": "cym_Latn",
                "Danish": "dan_Latn",
                "German": "deu_Latn",
                "Dinka": "dik_Latn",
                "Jula": "dyu_Latn",
                "Dzongkha": "dzo_Tibt",
                "Greek": "ell_Grek",
                "English": "eng_Latn",
                "Esperanto": "epo_Latn",
                "Estonian": "est_Latn",
                "Basque": "eus_Latn",
                "Ewe": "ewe_Latn",
                "Faroese": "fao_Latn",
                "Persian": "pes_Arab",
                "Fijian": "fij_Latn",
                "Finnish": "fin_Latn",
                "Fon": "fon_Latn",
                "French": "fra_Latn",
                "Friulian": "fur_Latn",
                "Nigerian Fulfulde": "fuv_Latn",
                "Scottish Gaelic": "gla_Latn",
                "Irish": "gle_Latn",
                "Galician": "glg_Latn",
                "Guarani": "grn_Latn",
                "Gujarati": "guj_Gujr",
                "Haitian Creole": "hat_Latn",
                "Hausa": "hau_Latn",
                "Hebrew": "heb_Hebr",
                "Hindi": "hin_Deva",
                "Chhattisgarhi": "hne_Deva",
                "Croatian": "hrv_Latn",
                "Hungarian": "hun_Latn",
                "Armenian": "hye_Armn",
                "Igbo": "ibo_Latn",
                "Iloko": "ilo_Latn",
                "Indonesian": "ind_Latn",
                "Icelandic": "isl_Latn",
                "Italian": "ita_Latn",
                "Javanese": "jav_Latn",
                "Japanese": "jpn_Jpan",
                "Kabyle": "kab_Latn",
                "Kachin": "kac_Latn",
                "Arabic": "ar_AR",
                "Chinese": "zho_Hans", 
                "Spanish": "spa_Latn",
                "Dutch": "nld_Latn", 
                "Kazakh": "kaz_Cyrl", 
                "Korean": "kor_Hang", 
                "Lithuanian": "lit_Latn",
                "Malayalam": "mal_Mlym", 
                "Marathi": "mar_Deva", 
                "Nepali": "ne_NP", 
                "Polish": "pol_Latn", 
                "Portuguese": "por_Latn", 
                "Russian": "rus_Cyrl", 
                "Sinhala": "sin_Sinh",
                "Tamil": "tam_Taml", 
                "Turkish": "tur_Latn", 
                "Ukrainian": "ukr_Cyrl", 
                "Urdu": "urd_Arab", 
                "Vietnamese": "vie_Latn", 
                "Thai":"tha_Thai"
            }
            return d[original_language]
        self.tokenizer.src_lang = language_mapping(original_language)
        if self.device_name == "cpu":
            # Tokenize input
            input_ids = self.tokenizer(inputs, return_tensors="pt", padding=True, max_length=128).to(self.device_name)
            output = []
            for target_language in target_languages:
                # Get language code for the target language
                target_lang_code = self.tokenizer.lang_code_to_id[language_mapping(target_language)]
                # Generate translation
                generated_tokens = self.model.generate(
                    **input_ids,
                    forced_bos_token_id=target_lang_code,
                    max_length=128
                )
                generated_translation = self.tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
                # Append result to output
                output.append({
                    "target_language": target_language,
                    "generated_translation": generated_translation,
                })
            outputs = []
            length = len(output[0]["generated_translation"])
            for i in range(length):
                temp = []
                for trans in output:
                    temp.append({
                        "target_language": trans["target_language"],
                        "generated_translation": trans['generated_translation'][i],
                    })
                outputs.append(temp)
            return outputs
        else:
            # 最大批量大小 = 可用 GPU 内存字节数 / 4 / （张量大小 + 可训练参数）
            # max_batch_size = 10
            # Ensure batch size is within model limits:
            batch_size = min(len(inputs), int(max_batch_size))
            batches = [inputs[i:i + batch_size] for i in range(0, len(inputs), batch_size)]
            temp_outputs = []
            processed_num = 0
            for index, batch in enumerate(batches):
                # Tokenize input
                input_ids = self.tokenizer(batch, return_tensors="pt", padding=True).to(self.device_name)
                temp = []
                for target_language in target_languages:
                    target_lang_code = self.tokenizer.lang_code_to_id[language_mapping(target_language)]
                    generated_tokens = self.model.generate(
                        **input_ids,
                        forced_bos_token_id=target_lang_code,
                    )
                    generated_translation = self.tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
                    # Append result to output
                    temp.append({
                        "target_language": target_language,
                        "generated_translation": generated_translation,
                    })
                input_ids.to('cpu')
                del input_ids
                temp_outputs.append(temp)
                processed_num += len(batch)
            outputs = []
            for temp_output in temp_outputs:
                length = len(temp_output[0]["generated_translation"])
                for i in range(length):
                    temp = []
                    for trans in temp_output:
                        temp.append({
                            "target_language": trans["target_language"],
                            "generated_translation": trans['generated_translation'][i],
                        })
                    outputs.append(temp)
            return outputs