Isekai-Qwen/recipes/finetune/deepspeed/finetune_lora_multi_gpu.ipynb

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    "# LoRA Fine-Tuning Qwen-Chat Large Language Model (Multiple GPUs)\n",
    "\n",
    "Tongyi Qianwen is a large language model developed by Alibaba Cloud based on the Transformer architecture, trained on an extensive set of pre-training data. The pre-training data is diverse and covers a wide range, including a large amount of internet text, specialized books, code, etc. In addition, an AI assistant called Qwen-Chat has been created based on the pre-trained model using alignment mechanism.\n",
    "\n",
    "This notebook uses Qwen-1.8B-Chat as an example to introduce how to LoRA fine-tune the Qianwen model using Deepspeed.\n",
    "\n",
    "## Environment Requirements\n",
    "\n",
    "Please refer to **requirements.txt** to install the required dependencies.\n",
    "\n",
    "## Preparation\n",
    "\n",
    "### Download Qwen-1.8B-Chat\n",
    "\n",
    "First, download the model files. You can choose to download directly from ModelScope."
   ]
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   "source": [
    "from modelscope.hub.snapshot_download import snapshot_download\n",
    "model_dir = snapshot_download('Qwen/Qwen-1_8B-Chat', cache_dir='.', revision='master')"
   ]
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    "### Download Example Training Data\n",
    "\n",
    "Download the data required for training; here, we provide a tiny dataset as an example. It is sampled from [Belle](https://github.com/LianjiaTech/BELLE).\n",
    "\n",
    "Disclaimer: the dataset can be only used for the research purpose."
   ]
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   "source": [
    "!wget https://atp-modelzoo-sh.oss-cn-shanghai.aliyuncs.com/release/tutorials/qwen_recipes/Belle_sampled_qwen.json"
   ]
  },
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    "You can also refer to this format to prepare the dataset. Below is a simple example list with 1 sample:\n",
    "\n",
    "```json\n",
    "[\n",
    "  {\n",
    "    \"id\": \"identity_0\",\n",
    "    \"conversations\": [\n",
    "      {\n",
    "        \"from\": \"user\",\n",
    "        \"value\": \"你好\"\n",
    "      },\n",
    "      {\n",
    "        \"from\": \"assistant\",\n",
    "        \"value\": \"我是一个语言模型，我叫通义千问。\"\n",
    "      }\n",
    "    ]\n",
    "  }\n",
    "]\n",
    "```\n",
    "\n",
    "You can also use multi-turn conversations as the training set. Here is a simple example:\n",
    "\n",
    "```json\n",
    "[\n",
    "  {\n",
    "    \"id\": \"identity_0\",\n",
    "    \"conversations\": [\n",
    "      {\n",
    "        \"from\": \"user\",\n",
    "        \"value\": \"你好，能告诉我遛狗的最佳时间吗？\"\n",
    "      },\n",
    "      {\n",
    "        \"from\": \"assistant\",\n",
    "        \"value\": \"当地最佳遛狗时间因地域差异而异，请问您所在的城市是哪里？\"\n",
    "      },\n",
    "      {\n",
    "        \"from\": \"user\",\n",
    "        \"value\": \"我在纽约市。\"\n",
    "      },\n",
    "      {\n",
    "        \"from\": \"assistant\",\n",
    "        \"value\": \"纽约市的遛狗最佳时间通常在早晨6点至8点和晚上8点至10点之间，因为这些时间段气温较低，遛狗更加舒适。但具体时间还需根据气候、气温和季节变化而定。\"\n",
    "      }\n",
    "    ]\n",
    "  }\n",
    "]\n",
    "```\n",
    "\n",
    "## Fine-Tune the Model\n",
    "\n",
    "You can directly run the prepared training script to fine-tune the model. **nproc_per_node** refers to the number of GPUs used fro training."
   ]
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   "source": [
    "!torchrun --nproc_per_node 2 --nnodes 1 --node_rank 0 --master_addr localhost --master_port 6601 ../../finetune.py \\\n",
    "    --model_name_or_path \"Qwen/Qwen-1_8B-Chat/\" \\\n",
    "    --data_path \"Belle_sampled_qwen.json\" \\\n",
    "    --bf16 True \\\n",
    "    --output_dir \"output_qwen\" \\\n",
    "    --num_train_epochs 5 \\\n",
    "    --per_device_train_batch_size 1 \\\n",
    "    --per_device_eval_batch_size 1 \\\n",
    "    --gradient_accumulation_steps 16 \\\n",
    "    --evaluation_strategy \"no\" \\\n",
    "    --save_strategy \"steps\" \\\n",
    "    --save_steps 1000 \\\n",
    "    --save_total_limit 10 \\\n",
    "    --learning_rate 1e-5 \\\n",
    "    --weight_decay 0.1 \\\n",
    "    --adam_beta2 0.95 \\\n",
    "    --warmup_ratio 0.01 \\\n",
    "    --lr_scheduler_type \"cosine\" \\\n",
    "    --logging_steps 1 \\\n",
    "    --report_to \"none\" \\\n",
    "    --model_max_length 512 \\\n",
    "    --gradient_checkpointing True \\\n",
    "    --lazy_preprocess True \\\n",
    "    --deepspeed \"../../finetune/ds_config_zero2.json\" \\\n",
    "    --use_lora"
   ]
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   "source": [
    "## Merge Weights\n",
    "\n",
    "The training of both LoRA and Q-LoRA only saves the adapter parameters. You can load the fine-tuned model and merge weights as shown below:"
   ]
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   "source": [
    "from transformers import AutoModelForCausalLM\n",
    "from peft import PeftModel\n",
    "import torch\n",
    "\n",
    "model = AutoModelForCausalLM.from_pretrained(\"Qwen/Qwen-1_8B-Chat/\", torch_dtype=torch.float16, device_map=\"auto\", trust_remote_code=True)\n",
    "model = PeftModel.from_pretrained(model, \"output_qwen/\")\n",
    "merged_model = model.merge_and_unload()\n",
    "merged_model.save_pretrained(\"output_qwen_merged\", max_shard_size=\"2048MB\", safe_serialization=True)"
   ]
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   "source": [
    "The tokenizer files are not saved in the new directory in this step. You can copy the tokenizer files or use the following code:"
   ]
  },
  {
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   "source": [
    "from transformers import AutoTokenizer\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(\n",
    "    \"Qwen/Qwen-1_8B-Chat/\",\n",
    "    trust_remote_code=True\n",
    ")\n",
    "\n",
    "tokenizer.save_pretrained(\"output_qwen_merged\")"
   ]
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   "source": [
    "## Test the Model\n",
    "\n",
    "After merging the weights, we can test the model as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
    "from transformers.generation import GenerationConfig\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"output_qwen_merged\", trust_remote_code=True)\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    \"output_qwen_merged\",\n",
    "    device_map=\"auto\",\n",
    "    trust_remote_code=True\n",
    ").eval()\n",
    "\n",
    "response, history = model.chat(tokenizer, \"你好\", history=None)\n",
    "print(response)"
   ]
  }
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