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config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "yonyou-sg/nllb-200-distilled-1.3B",
+  "_name_or_path": "facebook/nllb-200-distilled-1.3B",
   "activation_dropout": 0.0,
   "activation_function": "relu",
   "architectures": [
@@ -28,7 +28,7 @@
   "pad_token_id": 1,
   "scale_embedding": true,
   "torch_dtype": "float32",
-  "transformers_version": "4.40.0",
+  "transformers_version": "4.35.2",
   "use_cache": true,
   "vocab_size": 256206
 }

model.py

@@ -0,0 +1,234 @@
+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
+from modules.file import ExcelFileWriter
+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]
+        def process_gpu_translate_result(temp_outputs):
+            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)
+            excel_writer = ExcelFileWriter()
+            excel_writer.write_text(os.path.join(parent_dir,r"temp/empty.xlsx"), outputs, 'A', 1, len(outputs))
+        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:
+            print("length of inputs: ",len(inputs))
+            batch_size = min(len(inputs), int(max_batch_size))
+            batches = [inputs[i:i + batch_size] for i in range(0, len(inputs), batch_size)]
+            print("length of batches size: ", len(batches))
+            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)
+                if (index + 1) * max_batch_size // 1000 - index  * max_batch_size // 1000 == 1:
+                    print("Already processed number: ", len(temp_outputs))
+                    process_gpu_translate_result(temp_outputs)
+            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