mirror of
https://github.com/illian64/llm-translate.git
synced 2026-04-28 11:49:54 +00:00
323458ca27
* Prepare for docker, documentation. * Prepare for docker, documentation. * Prepare for docker, documentation. --------- Co-authored-by: APodoinikov <APodoynikov@detmir.ru>
100 lines
3.5 KiB
Python
100 lines
3.5 KiB
Python
import os
|
|
|
|
import ctranslate2
|
|
import transformers
|
|
from ctranslate2 import Translator
|
|
from tqdm import tqdm
|
|
from transformers import PreTrainedTokenizerBase
|
|
|
|
from app import cuda, params
|
|
from app.app_core import AppCore
|
|
from app.dto import TranslatePluginInitInfo, TranslateStruct
|
|
|
|
plugin_name = os.path.basename(__file__)[:-3]
|
|
|
|
model: Translator
|
|
tokenizer: PreTrainedTokenizerBase
|
|
|
|
|
|
def start(core: AppCore):
|
|
manifest = { # plugin settings
|
|
"name": "Madlab CTranslate2", # name
|
|
"version": "1.0", # version
|
|
|
|
"translate": {
|
|
"madlad_400_ctranslate2": (init, translate) # 1 function - init, 2 - translate
|
|
},
|
|
|
|
"default_options": {
|
|
"model": "models/madlad400-10b-mt-ct2-bfloat16", # key model
|
|
"tokenizer": "models/madlad400-10b-mt-ct2-bfloat16", # transformers.AutoTokenizer
|
|
"compute_type": "bfloat16",
|
|
"cuda": True, # false if you want to run on CPU, true - if on CUDA
|
|
"cuda_device_index": 0, # GPU index (if you have more than one GPU)
|
|
"max_batch_size": 16, # batch processing requests, increase need to more memory
|
|
|
|
"text_split_params": {
|
|
"split_by_sentences_only": True,
|
|
}
|
|
},
|
|
}
|
|
|
|
return manifest
|
|
|
|
|
|
def start_with_options(core: AppCore, manifest:dict):
|
|
params.read_plugin_translate_params(manifest)
|
|
|
|
return manifest
|
|
|
|
|
|
def init(core:AppCore) -> TranslatePluginInitInfo:
|
|
options = core.plugin_options(plugin_name)
|
|
|
|
global model
|
|
global tokenizer
|
|
|
|
model = ctranslate2.Translator(options["model"], compute_type=options["compute_type"],
|
|
device=cuda.get_device(options), device_index=options["cuda_device_index"])
|
|
tokenizer = transformers.AutoTokenizer.from_pretrained(options["tokenizer"])
|
|
|
|
return TranslatePluginInitInfo(plugin_name=plugin_name, model_name=f'{options["model"]}__{options["compute_type"]}')
|
|
|
|
|
|
def translate(core: AppCore, ts: TranslateStruct):
|
|
options = core.plugin_options(plugin_name)
|
|
|
|
# # implementation 1: one part - one batch
|
|
# for part in ts.parts:
|
|
# if not part.text or part.text == "":
|
|
# part.translate = ""
|
|
# else:
|
|
# input_text = "<2" + ts.req.to_lang + ">" + part.text
|
|
# tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(input_text))
|
|
# translate_results = model.translate_batch([tokens])
|
|
# output_tokens = translate_results[0].hypotheses[0]
|
|
# decoded_text = tokenizer.decode(tokenizer.convert_tokens_to_ids(output_tokens))
|
|
# part.translate = decoded_text
|
|
|
|
# implementation 2: all parts - one batch. It's faster, but depends on amount of batches.
|
|
tokens_list = []
|
|
for part in tqdm(ts.parts, unit=params.tp.unit, ascii=params.tp.ascii, desc=params.tp.desc):
|
|
if part.need_to_translate():
|
|
input_text = "<2" + ts.req.to_lang + ">" + part.text
|
|
tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(input_text))
|
|
tokens_list.append(tokens)
|
|
|
|
translate_results = model.translate_batch(
|
|
tokens_list, max_batch_size=options["max_batch_size"], beam_size=1, return_scores=False, disable_unk=False)
|
|
|
|
i = 0
|
|
for part in ts.parts:
|
|
if part.text and part.text != "":
|
|
output_tokens = translate_results[i].hypotheses[0]
|
|
decoded_text = tokenizer.decode(tokenizer.convert_tokens_to_ids(output_tokens))
|
|
part.translate = decoded_text
|
|
i += 1
|
|
else:
|
|
part.translate = ""
|
|
|
|
return ts
|