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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 |