Provide the basic code

.gitignore

@@ -158,3 +158,11 @@ cython_debug/
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
+
+# IDEs
+.vscode/
+
+# ASPEN
+__pycache__/
+*.egg-info/
+*.egg

README.md

@@ -1,4 +1,5 @@
 # ASPEN: Efficient Multi-LoRA Fine Tuning with Shared-Based Model
+[![Python application](https://github.com/TUDB-Labs/multi-lora-fine-tune/actions/workflows/python-app.yml/badge.svg)](https://github.com/TUDB-Labs/multi-lora-fine-tune/actions/workflows/python-app.yml)
 
 This repository provides tools for fine-tuning large language models (LLMs) using the LoRA or QLoRA methods more efficiently. It provides the framework to support multiple LoRA/qLoRA models fine tunning at the same time. By reusing the shared frozen-based model, we provide the framework to reduce GPU Memory usage for multiple fine-tuning models.  
 
@@ -41,14 +42,11 @@ Submit a pull request with a detailed explanation of your changes.
 ```bibtex
 @article{multi-lora,
   title={Aspen: Efficient Finetuning of Multiple Lora and QLora},
-  author={zhenmao ye*, dengchen li*, tingfen lan, zhaoyi liu, jie zuo, lei duan, mingjie tang},
+  author={zhengmao ye*, dengchun li*, tingfeng lan, zhaoyi liu, jie zuo, lei duan, mingjie tang},
   journal={arXiv preprint arXiv:xxxx},
   year={2023}
 }
 ```
 
 ## License
 This project is licensed under the Apache 2.0 License - see the LICENSE file for details
-
-
-

aspen/__init__.py

@@ -0,0 +1,26 @@
+from aspen.utils import convert_hf_to_pth
+from aspen.tokenizer import Tokenizer
+from aspen.model import LlamaModel, Linear, RMSNorm
+from aspen.modelargs import TokenizerArgs, LlamaModelArgs, MultiLoraBatchData, LoraBatchDataConfig
+from aspen.dataset import DataSet
+from aspen.modelloader import load_llama_7b_weight, load_llama_tf_weight
+from aspen.modelloader import load_alpaca_lora_7b_weight, load_random_lora_7b_weight
+from aspen.modelloader import save_lora_model
+
+__all__ = [
+    "Tokenizer",
+    "LlamaModel",
+    "Linear",
+    "RMSNorm",
+    "TokenizerArgs",
+    "LlamaModelArgs",
+    "MultiLoraBatchData",
+    "LoraBatchDataConfig",
+    "DataSet",
+    "convert_hf_to_pth",
+    "load_llama_7b_weight",
+    "load_llama_tf_weight",
+    "load_alpaca_lora_7b_weight",
+    "load_random_lora_7b_weight",
+    "save_lora_model"
+]

aspen/dataset.py

@@ -0,0 +1,146 @@
+import math
+import json
+import random
+from aspen import MultiLoraBatchData, LoraBatchDataConfig, Tokenizer
+from typing import Dict, List, Tuple
+
+
+class DataSet():
+    config_ = None
+    # Dict[lora_name, <prompt, token>]
+    lora_token_data_: Dict[str, List[Tuple[str, List[int]]]] = None
+
+    lora_num_epochs_: Dict[str, int] = {}
+    lora_cnt_epochs_: Dict[str, int] = {}
+    lora_start_idx_: Dict[str, int] = {}
+    lora_batch_size_: Dict[str, int] = {}
+
+    def __get_lora_text_data(self) -> Dict[str, List[str]]:
+        lora_text_data = {}
+        for lora_config in self.config_["lora"]:
+            lora_name = lora_config["name"]
+            data_path = lora_config["data"]
+            lora_text_data[lora_name] = []
+            self.lora_cnt_epochs_[lora_name] = 0
+            self.lora_start_idx_[lora_name] = 0
+            self.lora_batch_size_[lora_name] = lora_config["batch_size"]
+            self.lora_num_epochs_[lora_name] = lora_config["num_epochs"]
+
+            with open(data_path, 'r', encoding='utf8') as fp:
+                for raw_data in json.load(fp):
+                    raw_data_input = raw_data["input"]
+                    raw_data_output = raw_data["output"]
+                    raw_data_instruction = raw_data["instruction"]
+                    text_data = ""
+                    if raw_data_input is None or len(raw_data_input) <= 1:
+                        text_data = lora_config["prompt_no_input"].replace(
+                            "{output}", raw_data_output).replace("{instruction}", raw_data_instruction)
+                    else:
+                        text_data = lora_config["prompt_input"].replace(
+                            "{output}", raw_data_output).replace("{instruction}", raw_data_instruction).replace("{input}", raw_data_input)
+                    lora_text_data[lora_name].append(text_data)
+
+        return lora_text_data
+
+    def __init__(self, config: Dict[str, str], tokenizer: Tokenizer):
+        self.config_ = config
+        self.tokenizer_: Tokenizer = tokenizer
+
+        print("to load text data from file.")
+        lora_text_data = self.__get_lora_text_data()
+        print("load text data from file done.")
+
+        # Dict[lora_name, <prompt, token>]
+        self.lora_token_data_: Dict[str, List[Tuple[str, List[int]]]] = {}
+
+        print("to encode text data to tokens")
+        for lora_name in lora_text_data:
+            self.lora_token_data_[lora_name] = []
+
+            for idx, text in enumerate(lora_text_data[lora_name]):
+                tokens = tokenizer.encode(text, bos=True, eos=True)
+                if len(tokens) > config["cutoff_len"]:
+                    tokens = tokens[:config["cutoff_len"]]
+                self.lora_token_data_[lora_name].append((text, tokens))
+                if idx % 10000 == 0:
+                    print(
+                        f"encode text data: {idx}/{len(lora_text_data[lora_name])}")
+            # group by length
+            if self.config_["group_by_length"]:
+                self.lora_token_data_[lora_name].sort(
+                    key=lambda x: len(x[1]), reverse=True)
+            else:
+                random.shuffle(self.lora_token_data_[lora_name])
+        print("encode text data to tokens done.")
+
+    def check_done(self) -> bool:
+        for lora_name in self.lora_token_data_:
+            if self.lora_cnt_epochs_[lora_name] < self.lora_num_epochs_[lora_name]:
+                return False
+        return True
+
+    def get_batch_data(self) -> MultiLoraBatchData:
+        prompts_list: List[str] = []
+        batch_tokens_list: List[List[int]] = []
+
+        prompts_batch_config_list: List[LoraBatchDataConfig] = []
+
+        tokens_without_pad_len_list: List[int] = []
+
+        max_token_len = 0
+
+        batch_start_idx = 0
+
+        for lora_name in self.lora_token_data_:
+            if self.lora_cnt_epochs_[lora_name] >= self.lora_num_epochs_[lora_name]:
+                continue
+            start_idx = self.lora_start_idx_[lora_name]
+            end_idx = start_idx + self.lora_batch_size_[lora_name]
+            prompt_and_tokens_list = self.lora_token_data_[
+                lora_name][start_idx:end_idx]
+
+            for pt in prompt_and_tokens_list:
+                prompt, token = pt
+                prompts_list.append(prompt)
+                batch_tokens_list.append(token.copy())
+
+                max_token_len = max(max_token_len, len(token))
+                tokens_without_pad_len_list.append(len(token))
+
+            lora_config = LoraBatchDataConfig(adapter_name_=lora_name, batch_start_idx_=batch_start_idx,
+                                              batch_end_idx_=batch_start_idx + len(prompt_and_tokens_list))
+            batch_start_idx += len(prompt_and_tokens_list)
+            prompts_batch_config_list.append(lora_config)
+
+            self.lora_start_idx_[lora_name] += self.lora_batch_size_[lora_name]
+            if self.lora_start_idx_[lora_name] >= len(self.lora_token_data_[lora_name]):
+                self.lora_start_idx_[lora_name] = 0
+                self.lora_cnt_epochs_[lora_name] += 1
+
+            print(f"{lora_name} train data:")
+            print(
+                f"    epoch: {self.lora_cnt_epochs_[lora_name] + 1} / {self.lora_num_epochs_[lora_name]}")
+            print(
+                f"    step : {self.lora_start_idx_[lora_name]} / {len(self.lora_token_data_[lora_name])}")
+            print(
+                f"         : {(self.lora_cnt_epochs_[lora_name] * len(self.lora_token_data_[lora_name]) + self.lora_start_idx_[lora_name]) * 100 / (self.lora_num_epochs_[lora_name] * len(self.lora_token_data_[lora_name]))}%")
+
+        # align batch data
+        max_token_len = math.ceil(max_token_len / 8) * 8
+
+        for tokens in batch_tokens_list:
+            while len(tokens) < max_token_len:
+                if self.config_["expand_right"]:
+                    tokens.append(self.tokenizer_.pad_id_)
+                else:
+                    tokens.insert(0, self.tokenizer_.pad_id_)
+
+        print(
+            f"batch data size: {max_token_len} * {len(batch_tokens_list)}")
+
+        return MultiLoraBatchData(prompts_=prompts_list,
+                                  lora_batch_data_config_=prompts_batch_config_list,
+                                  batch_seq_len_=max_token_len,
+                                  expand_right_=self.config_["expand_right"],
+                                  batch_tokens_=batch_tokens_list,
+                                  tokens_len_without_pad_=tokens_without_pad_len_list)