Add fine tune scripts

finetune/configs/ds_config_zero2.json

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+{
+    "train_batch_size": "auto",
+    "train_micro_batch_size_per_gpu": "auto",
+    "gradient_accumulation_steps": "auto",
+    "gradient_clipping": 1.0,
+    "fp16": {
+        "enabled": "auto",
+        "loss_scale": 0,
+        "loss_scale_window": 1000,
+        "initial_scale_power": 16,
+        "hysteresis": 2,
+        "min_loss_scale": 1
+    },
+    "bf16": {
+        "enabled": "auto"
+    },
+    "zero_optimization": {
+        "stage": 2,
+        "overlap_comm": true,
+        "reduce_scatter": true,
+        "contiguous_gradients": true
+    },
+    "wall_clock_breakdown": false,
+    "flops_profiler": {
+        "enabled": false,
+        "profile_step": 1,
+        "module_depth": -1,
+        "top_modules": 1,
+        "detailed": true,
+        "output_file": null
+    }
+}

finetune/configs/ds_config_zero2_offload.json

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+{
+    "train_batch_size": "auto",
+    "train_micro_batch_size_per_gpu": "auto",
+    "gradient_accumulation_steps": "auto",
+    "gradient_clipping": 1.0,
+    "fp16": {
+        "enabled": "auto",
+        "loss_scale": 0,
+        "loss_scale_window": 1000,
+        "initial_scale_power": 16,
+        "hysteresis": 2,
+        "min_loss_scale": 1
+    },
+    "bf16": {
+        "enabled": "auto"
+    },
+    "zero_optimization": {
+        "stage": 2,
+        "overlap_comm": true,
+        "reduce_scatter": true,
+        "contiguous_gradients": true,
+        "offload_optimizer": {
+            "device": "cpu"
+        },
+        "offload_param": {
+            "device": "cpu"
+        }
+    },
+    "wall_clock_breakdown": false,
+    "flops_profiler": {
+        "enabled": false,
+        "profile_step": 1,
+        "module_depth": -1,
+        "top_modules": 1,
+        "detailed": true,
+        "output_file": null
+    }
+}

finetune/configs/ds_config_zero3.json

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+{
+    "train_batch_size": "auto",
+    "train_micro_batch_size_per_gpu": "auto",
+    "gradient_accumulation_steps": "auto",
+    "gradient_clipping": 1.0,
+    "bf16": {
+        "enabled": "auto"
+    },
+    "zero_optimization": {
+        "stage": 3,
+        "overlap_comm": true,
+        "stage3_gather_16bit_weights_on_model_save": true
+    },
+    "flops_profiler": {
+        "enabled": false,
+        "profile_step": 1,
+        "module_depth": -1,
+        "top_modules": 1,
+        "detailed": true,
+        "output_file": null
+    }
+}

finetune/finetune.py

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+# -*- coding: utf-8 -*-
+import json
+import time
+from typing import Dict, Optional
+from datetime import datetime
+from dataclasses import dataclass, field
+
+import torch
+from torch.utils.data import Dataset
+
+import transformers
+from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer
+
+
+@dataclass
+class ModelArguments:
+    model_name_or_path: Optional[str] = field(default="baichuan-inc/Baichuan2-7B-Base")
+
+
+@dataclass
+class DataArguments:
+    train_data_path: str = field(
+        default="data/AdvertiseGenChatML/train.json",
+        metadata={"help": "Path to the training data."},
+    )
+    eval_data_path: str = field(
+        default="data/AdvertiseGenChatML/dev.json",
+        metadata={"help": "Path to the test data."},
+    )
+
+
+@dataclass
+class TrainingArguments(transformers.TrainingArguments):
+    cache_dir: Optional[str] = field(default=None)
+    optim: str = field(default="adamw_torch")
+    model_max_length: int = field(
+        default=512,
+        metadata={
+            "help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."
+        },
+    )
+    use_lora: bool = field(default=False)
+
+
+class SupervisedDataset(Dataset):
+    """Dataset for supervised fine-tuning."""
+
+    def __init__(
+        self,
+        data_path,
+        tokenizer,
+        model_max_length=4096,
+        user_tokens=[1786, 4194, 95388],
+        assistant_tokens=[1786, 10850, 95388],
+    ):
+        super(SupervisedDataset, self).__init__()
+        self.data = json.load(open(data_path))
+        self.tokenizer = tokenizer
+        self.model_max_length = model_max_length
+        self.user_tokens = user_tokens
+        self.assistant_tokens = assistant_tokens
+        self.ignore_index = -100
+        item = self.preprocessing(self.data[0])
+        print("input:", self.tokenizer.decode(item["input_ids"]))
+        labels = []
+        for id_ in item["label_ids"]:
+            if id_ == -100:
+                continue
+
+            labels.append(id_)
+        print("label:", self.tokenizer.decode(labels))
+
+    def __len__(self):
+        return len(self.data)
+
+    def preprocessing(self, example):
+        input_ids = [self.tokenizer.bos_token_id]
+        label_ids = [self.tokenizer.bos_token_id]
+
+        for message in example["messages"]:
+            role = message["role"]
+            content = message["content"]
+            content_ids = self.tokenizer.encode(content, add_special_tokens=False)
+
+            if role == "user":
+                input_ids += self.user_tokens + content_ids
+                label_ids += (
+                    [self.ignore_index] * len(self.user_tokens)
+                    + [self.tokenizer.eos_token_id]
+                    + [self.ignore_index] * len(content_ids)
+                )
+            else:
+                input_ids += self.assistant_tokens + content_ids
+                label_ids += [self.ignore_index] * len(
+                    self.assistant_tokens
+                ) + content_ids
+        input_ids.append(self.tokenizer.eos_token_id)
+        label_ids.append(self.tokenizer.eos_token_id)
+        input_ids = input_ids[: self.model_max_length]
+        label_ids = label_ids[: self.model_max_length]
+        # input_ids += [self.tokenizer.eos_token_id] * (len(label_ids) - len(input_ids))
+        input_ids += [self.tokenizer.eos_token_id] * (
+            self.model_max_length - len(input_ids)
+        )
+        label_ids += [self.ignore_index] * (self.model_max_length - len(label_ids))
+        input_ids = torch.LongTensor(input_ids)
+        label_ids = torch.LongTensor(label_ids)
+        # print(f"len input_ids: {len(input_ids)}, len label_ids: {len(label_ids)}")
+        attention_mask = input_ids.ne(self.tokenizer.eos_token_id)
+        return {
+            "input_ids": input_ids,
+            "label_ids": label_ids,
+            "attention_mask": attention_mask,
+        }
+
+    def __getitem__(self, idx) -> Dict[str, torch.Tensor]:
+        return self.preprocessing(self.data[idx])
+
+
+def load_model_and_tokenizer(
+    model_path: str,
+    max_length: int = 4096,
+    use_lora: bool = True,
+    bf16: bool = False,
+    fp16: bool = False,
+):
+    """load model and tokenizer"""
+    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+    tokenizer.pad_token = tokenizer.eos_token
+
+    assert not (bf16 and fp16), "bf16 or fp16, not both"
+    if bf16:
+        dtype = torch.bfloat16
+    elif fp16:
+        dtype = torch.float16
+    else:
+        dtype = torch.float32
+    model = AutoModelForCausalLM.from_pretrained(
+        model_path,
+        torch_dtype=dtype,
+        trust_remote_code=True,
+    )
+    if use_lora:
+        from peft import LoraConfig, TaskType, get_peft_model
+
+        lora_config = LoraConfig(
+            init_lora_weights="gaussian",
+            task_type=TaskType.CAUSAL_LM,
+            target_modules=["q_proj", "v_proj"],
+            r=8,
+            lora_alpha=32,
+            lora_dropout=0.1,
+            inference_mode=False,
+        )
+        model = get_peft_model(model, lora_config)
+        # trainable params: 2,949,120 || all params: 3,010,652,928 || trainable%: 0.09795616002669305
+        model.print_trainable_parameters()
+        model.enable_input_require_grads()
+
+    return model, tokenizer
+
+
+if __name__ == "__main__":
+    model_path = "/mnt/data/user/tc_agi/yh/models/MiniCPM"
+    max_length = 512
+    parser = transformers.HfArgumentParser(
+        (ModelArguments, DataArguments, TrainingArguments)
+    )
+    model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+    model, tokenizer = load_model_and_tokenizer(
+        model_path=model_args.model_name_or_path,
+        max_length=training_args.model_max_length,
+        use_lora=training_args.use_lora,
+    )
+
+    train_dataset = SupervisedDataset(
+        data_path=data_args.train_data_path,
+        tokenizer=tokenizer,
+        model_max_length=training_args.model_max_length,
+    )
+    eval_dataset = SupervisedDataset(
+        data_path=data_args.eval_data_path,
+        tokenizer=tokenizer,
+        model_max_length=training_args.model_max_length,
+    )
+
+    formatted_time = datetime.now().strftime("%Y%m%d%H%M%S")
+
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=train_dataset,
+        eval_dataset=eval_dataset,
+        tokenizer=tokenizer,
+        # compute_metrics=compute_metrics,
+    )
+
+    trainer.train()

finetune/lora_finetune.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# MiniCPM-2B 参数高效微调（LoRA）消费级单卡示例\n",
+    "\n",
+    "本 notebook 是一个使用 `AdvertiseGen` 数据集对 MiniCPM-2B 进行 LoRA 微调，使其具备专业的广告生成能力的代码示例。\n",
+    "\n",
+    "## 硬件需求\n",
+    "- 显存：24GB\n",
+    "- 显卡架构：安培架构（推荐）\n",
+    "- 内存：16GB"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. 准备数据集\n",
+    "\n",
+    "下载 AdvertiseGen 数据集\n",
+    "- [Google Drive](https://drive.google.com/file/d/13_vf0xRTQsyneRKdD1bZIr93vBGOczrk/view?usp=sharing)\n",
+    "- [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/f/b3f119a008264b1cabd1/?dl=1)\n",
+    "\n",
+    "下载后的数据集格式为 `.tar.gz` 的压缩格式，接下来的操作中，假设该压缩包被置于 `finetune/data/`。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 校验文件完整性\n",
+    "!md5sum data/AdvertiseGen.tar.gz "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 解压数据集\n",
+    "!tar xvf data/AdvertiseGen.tar.gz "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 转换为 ChatML 格式\n",
+    "import os\n",
+    "import shutil\n",
+    "import json\n",
+    "\n",
+    "input_dir = \"data/AdvertiseGen\"\n",
+    "output_dir = \"data/AdvertiseGenChatML\"\n",
+    "if os.path.exists(output_dir):\n",
+    "    shutil.rmtree(output_dir)\n",
+    "os.makedirs(output_dir, exist_ok=True)\n",
+    "\n",
+    "for fn in [\"train.json\", \"dev.json\"]:\n",
+    "    data_out_list = []\n",
+    "    with open(os.path.join(input_dir, fn), \"r\") as f, open(os.path.join(output_dir, fn), \"w\") as fo:\n",
+    "        for line in f:\n",
+    "            if len(line.strip()) > 0:\n",
+    "                data = json.loads(line)\n",
+    "                data_out = {\n",
+    "                    \"messages\": [\n",
+    "                        {\n",
+    "                            \"role\": \"user\",\n",
+    "                            \"content\": data[\"content\"],\n",
+    "                        },\n",
+    "                        {\n",
+    "                            \"role\": \"assistant\",\n",
+    "                            \"content\": data[\"summary\"],\n",
+    "                        },\n",
+    "                    ]\n",
+    "                }\n",
+    "                data_out_list.append(data_out)\n",
+    "        json.dump(data_out_list, fo, ensure_ascii=False, indent=4)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. 使用 LoRA 进行微调\n",
+    "\n",
+    "命令行一键运行"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!bash lora_finetune.sh"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

finetune/lora_finetune.sh

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+formatted_time=$(date +"%Y%m%d%H%M%S")
+echo $formatted_time
+
+CUDA_VISIBLE_DEVICES=0 python finetune.py \
+    --model_name_or_path <your_model_name_or_path> \
+    --output_dir output/AdvertiseGenLoRA/$formatted_time/ \
+    --train_data_path data/AdvertiseGenChatML/train.json \
+    --eval_data_path data/AdvertiseGenChatML/dev.json \
+    --learning_rate 1e-3 --per_device_train_batch_size 1 \
+    --per_device_eval_batch_size 32 --fp16\
+    --gradient_accumulation_steps 8 --warmup_steps 100 \
+    --max_steps 3000 --weight_decay 0.01 \
+    --evaluation_strategy steps --eval_steps 500 \
+    --save_strategy steps --save_steps 500 \
+    --use_lora true --seed 42 \
+    --log_level info --logging_strategy steps --logging_steps 10

finetune/lora_finetune_ds.sh

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+formatted_time=$(date +"%Y%m%d%H%M%S")
+echo $formatted_time
+
+
+deepspeed --include localhost:0,1 finetune.py \
+    --model_name_or_path <your_model_name_or_path> \
+    --output_dir output/AdvertiseGenLoRA/$formatted_time/ \
+    --train_data_path data/AdvertiseGenChatML/train.json \
+    --eval_data_path data/AdvertiseGenChatML/dev.json \
+    --learning_rate 1e-3 --per_device_train_batch_size 1 \
+    --per_device_eval_batch_size 1 --fp16 --use_lora \
+    --gradient_accumulation_steps 8 --warmup_steps 100 \
+    --max_steps 3000 --weight_decay 0.01 \
+    --evaluation_strategy steps --eval_steps 500 \
+    --save_strategy steps --save_steps 500 --seed 42 \
+    --log_level info --logging_strategy steps --logging_steps 10 \
+    --deepspeed configs/ds_config_zero2_offload.json

finetune/requirements.txt

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+# for finetune
+jieba>=0.42.1
+ruamel_yaml>=0.18.5
+rouge_chinese>=1.0.3
+jupyter>=1.0.0
+datasets>=2.16.1
+peft>=0.7.1
+deepspeed>=0.13.1
+flash_attn>=2.5.1

finetune/sft_finetune.sh

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+formatted_time=$(date +"%Y%m%d%H%M%S")
+echo $formatted_time
+
+
+deepspeed --include localhost:1,2 finetune.py \
+    --model_name_or_path <your_model_name_or_path> \
+    --output_dir output/AdvertiseGenLoRA/$formatted_time/ \
+    --train_data_path data/AdvertiseGenChatML/train.json \
+    --eval_data_path data/AdvertiseGenChatML/dev.json \
+    --learning_rate 1e-3 --per_device_train_batch_size 1 \
+    --per_device_eval_batch_size 32 --fp16 \
+    --gradient_accumulation_steps 8 --warmup_steps 100 \
+    --max_steps 3000 --weight_decay 0.01 \
+    --evaluation_strategy steps --eval_steps 500 \
+    --save_strategy steps --save_steps 500 --seed 42 \
+    --log_level info --logging_strategy steps --logging_steps 10 \
+    --deepspeed configs/ds_config_zero2.json