tune_rugpt35_13b.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MGYpq-FbCHvC",
    "outputId": "8ddc45ab-8060-4f25-bbd9-466a822c5ae5"
   },
   "outputs": [],
   "source": [
    "#@title Скачиваем и устанавливаем зависимости\n",
    "\n",
    "from pathlib import Path\n",
    "\n",
    "!git clone https://github.com/IlyaGusev/rulm.git\n",
    "!git clone https://github.com/ggerganov/llama.cpp.git\n",
    "\n",
    "self_instruct_dir = Path('rulm/self_instruct').resolve()\n",
    "\n",
    "!cd {self_instruct_dir} && pip install -r ../requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 126
    },
    "id": "zRDOnr5kCHvC",
    "outputId": "382718c1-6c3c-4574-81d2-94caa8d832a1"
   },
   "outputs": [],
   "source": [
    "#@title Логинимся в WandB\n",
    "\n",
    "import wandb\n",
    "wandb.login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hkCeGj08CHvD",
    "outputId": "b7a025ea-0b87-41a3-9e48-0c62489e0a6b"
   },
   "outputs": [],
   "source": [
    "#@title Генерируем обучающую и валидационную выборку, обрезая её для Colab\n",
    "\n",
    "content_dir = Path('.').resolve()\n",
    "!cd {self_instruct_dir} && python -m src.data_processing.create_chat_set \\\n",
    "    {content_dir / 'train_full.jsonl'} \\\n",
    "    {content_dir / 'val_full.jsonl'}\n",
    "\n",
    "assert (content_dir / 'train_full.jsonl').exists()\n",
    "\n",
    "train_size_limit = 400 #@param {type:\"integer\"}\n",
    "val_size_limit = 200 #@param {type:\"integer\"}\n",
    "\n",
    "!head -n {train_size_limit} {content_dir / 'train_full.jsonl'} > {content_dir / 'train.jsonl'}\n",
    "!head -n {val_size_limit} {content_dir / 'val_full.jsonl'} > {content_dir / 'val.jsonl'}"
   ]
  },
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    },
    "id": "08FYVlDYCHvD",
    "outputId": "0b7b1c5a-c3d4-4f63-b92e-465cf861eae1"
   },
   "outputs": [],
   "source": [
    "#@title Скачиваем базовую модель LLaMa 7B и чиним её конфиг\n",
    "\n",
    "import json\n",
    "from huggingface_hub import snapshot_download\n",
    "\n",
    "model_dir = content_dir / \"ruGPT-3.5-13B\"\n",
    "base_model = \"ai-forever/ruGPT-3.5-13B\" #@param {type:\"string\"}\n",
    "snapshot_download(repo_id=base_model, local_dir=model_dir, ignore_patterns=[\"LICENSE\", \"README.md\", \".gitattributes\"])\n",
    "\n",
    "patch_model_config = True #@param {type:\"boolean\"}\n",
    "\n",
    "if patch_model_config:\n",
    "    replacements = {\n",
    "        \"tokenizer_config.json\": {\n",
    "            \"add_bos_token\": False,\n",
    "            \"add_prefix_space\": False,\n",
    "            \"bos_token\": {\n",
    "                \"__type\": \"AddedToken\",\n",
    "                \"content\": \"<s>\",\n",
    "                \"lstrip\": False,\n",
    "                \"normalized\": True,\n",
    "                \"rstrip\": False,\n",
    "                \"single_word\": False\n",
    "            },\n",
    "            \"clean_up_tokenization_spaces\": True,\n",
    "            \"eos_token\": {\n",
    "                \"__type\": \"AddedToken\",\n",
    "                \"content\": \"</s>\",\n",
    "                \"lstrip\": False,\n",
    "                \"normalized\": True,\n",
    "                \"rstrip\": False,\n",
    "                \"single_word\": False\n",
    "            },\n",
    "            \"errors\": \"replace\",\n",
    "            \"mask_token\": \"<mask>\",\n",
    "            \"model_max_length\": 2048,\n",
    "            \"pad_token\": {\n",
    "                \"__type\": \"AddedToken\",\n",
    "                \"content\": \"<pad>\",\n",
    "                \"lstrip\": False,\n",
    "                \"normalized\": True,\n",
    "                \"rstrip\": False,\n",
    "                \"single_word\": False\n",
    "            },\n",
    "            \"tokenizer_class\": \"GPT2Tokenizer\",\n",
    "            \"unk_token\": {\n",
    "                \"__type\": \"AddedToken\",\n",
    "                \"content\": \"<|endoftext|>\",\n",
    "                \"lstrip\": False,\n",
    "                \"normalized\": True,\n",
    "                \"rstrip\": False,\n",
    "                \"single_word\": False\n",
    "            },\n",
    "            \"padding_side\": \"left\"\n",
    "        },\n",
    "        \"special_tokens_map.json\": {\n",
    "            \"bos_token\": {\n",
    "                \"content\": \"<s>\",\n",
    "                \"lstrip\": False,\n",
    "                \"normalized\": True,\n",
    "                \"rstrip\": False,\n",
    "                \"single_word\": False\n",
    "            },\n",
    "            \"eos_token\": {\n",
    "                \"content\": \"</s>\",\n",
    "                \"lstrip\": False,\n",
    "                \"normalized\": True,\n",
    "                \"rstrip\": False,\n",
    "                \"single_word\": False\n",
    "            },\n",
    "            \"mask_token\": \"<mask>\",\n",
    "            \"pad_token\": {\n",
    "                \"content\": \"<pad>\",\n",
    "                \"lstrip\": False,\n",
    "                \"normalized\": True,\n",
    "                \"rstrip\": False,\n",
    "                \"single_word\": False\n",
    "            },\n",
    "            \"sep_token\": \"<s>\",\n",
    "            \"unk_token\": {\n",
    "                \"content\": \"<|endoftext|>\",\n",
    "                \"lstrip\": False,\n",
    "                \"normalized\": True,\n",
    "                \"rstrip\": False,\n",
    "                \"single_word\": False\n",
    "            }\n",
    "        },\n",
    "        \"generation_config.json\": {\n",
    "            \"_from_model_config\": True,\n",
    "            \"bos_token_id\": 2,\n",
    "            \"eos_token_id\": 3,\n",
    "            \"pad_token_id\": 0,\n",
    "            \"temperature\": 0.2,\n",
    "            \"top_p\": 0.9,\n",
    "            \"top_k\": 30,\n",
    "            \"do_sample\": True,\n",
    "            \"max_new_tokens\": 1536,\n",
    "            \"num_beams\": 1,\n",
    "            \"repetition_penalty\": 1.15,\n",
    "            \"no_repeat_ngram_size\": 15\n",
    "        },\n",
    "    }\n",
    "\n",
    "    print('Patching model config...')\n",
    "    for filename, new_content in replacements.items():\n",
    "        print(f'{filename}:')\n",
    "        with (model_dir / filename).open() as fp:\n",
    "            old_content = json.load(fp)\n",
    "            print(f'    Original content: {old_content}')\n",
    "            if old_content == new_content:\n",
    "                print('    Already patched, skipping')\n",
    "        print(f'    Updated content:  {new_content}')\n",
    "        with (model_dir / filename).open('w') as fp:\n",
    "            json.dump(new_content, fp, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ZMmK8a6_CHvE"
   },
   "outputs": [],
   "source": [
    "#@title Уменьшаем размер батча и лимит токенов, чтобы поместиться в Colab, и длительность обучения для демки\n",
    "\n",
    "original_config_path = content_dir / 'configs/rugpt35_13b.json'\n",
    "\n",
    "with original_config_path.open('r') as fp:\n",
    "    config = json.load(fp)\n",
    "\n",
    "# Colab adjustments\n",
    "config['trainer']['per_device_train_batch_size'] = 2 #@param {type:\"integer\"}\n",
    "config['trainer']['per_device_eval_batch_size'] = 1 #@param {type:\"integer\"}\n",
    "config['trainer']['gradient_accumulation_steps'] = 128 #@param {type:\"integer\"}\n",
    "config['trainer']['eval_steps'] = 50 #@param {type:\"integer\"}\n",
    "config['trainer']['save_steps'] = 50 #@param {type:\"integer\"}\n",
    "config['max_tokens_count'] = 1000 #@param {type:\"integer\"}\n",
    "#config['model_name'] = str(model_dir)\n",
    "config['templates_path'] = str(content_dir / 'internal_prompts/rugpt35.json')\n",
    "config['load_in_8bit'] = True #@param {type:\"bool\"}\n",
    "config['load_in_4bit'] = False #@param {type:\"bool\"}\n",
    "\n",
    "# Demo adjustments\n",
    "config['trainer']['eval_steps'] = 2 #@param {type:\"integer\"}\n",
    "config['trainer']['logging_steps'] = 1 #@param {type:\"integer\"}\n",
    "config['trainer']['num_train_epochs'] = 1 #@param {type:\"integer\"}\n",
    "\n",
    "config_path = content_dir / 'configs/rugpt35_13b_colab.json'\n",
    "\n",
    "with config_path.open('w') as fp:\n",
    "    json.dump(config, fp, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "d30GN0aECHvE",
    "outputId": "9b7c2157-bd94-4374-d0f6-e0d463046758"
   },
   "outputs": [],
   "source": [
    "#@title Запускаем обучение!\n",
    "\n",
    "output_dir = content_dir / 'output'\n",
    "\n",
    "!cd {self_instruct_dir} && python3 -m src.train \\\n",
    "    --config-file {config_path} \\\n",
    "    --train-file {content_dir / 'train.jsonl'} \\\n",
    "    --val-file {content_dir / 'val.jsonl'} \\\n",
    "    --output-dir {output_dir}\n",
    "\n",
    "assert (output_dir / 'adapter_config.json').exists()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "I8cDrk7cCHvF"
   },
   "outputs": [],
   "source": [
    "#@title Исправляем конфиг обученной модели\n",
    "\n",
    "with (output_dir / 'generation_config.json').open('w') as fp:\n",
    "    json.dump({\n",
    "        \"bos_token_id\": 2,\n",
    "        \"eos_token_id\": 3,\n",
    "        \"pad_token_id\": 0,\n",
    "        \"temperature\": 0.2,\n",
    "        \"top_p\": 0.9,\n",
    "        \"top_k\": 30,\n",
    "        \"do_sample\": True,\n",
    "        \"max_new_tokens\": 1536,\n",
    "        \"num_beams\": 1,\n",
    "        \"repetition_penalty\": 1.15,\n",
    "        \"no_repeat_ngram_size\": 15,\n",
    "    }, fp, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0ZU8pFRwCHvF",
    "outputId": "872709ee-9bb6-480f-cc71-55a0eeaff312"
   },
   "outputs": [],
   "source": [
    "#@title Склеиваем вместе обученные адаптеры с базовой моделью, сохраняем результат в формат PyTorch\n",
    "\n",
    "from convert_to_native import convert_to_native\n",
    "merged_model_name = 'pytorch_model.bin'\n",
    "\n",
    "convert_to_native(\n",
    "    model_name=str(output_dir),\n",
    "    output_path=str(output_dir / merged_model_name),\n",
    "    device='cpu',\n",
    "    enable_offloading=True\n",
    ")\n",
    "\n",
    "assert (output_dir / merged_model_name).exists()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yqmBVyq_CHvF",
    "outputId": "62ce6442-7bc2-4fcb-c6a1-7e901c29b073"
   },
   "outputs": [],
   "source": [
    "#@title Конвертируем склеенную модель в 16-битный формат GGML (llama.cpp)\n",
    "\n",
    "from llm_rs.convert import AutoConverter\n",
    "\n",
    "ggml_f16_model_name = 'ruGPT-3.5-13B-lora-f16.bin'\n",
    "input_ggml = content_dir / 'ruGPT-3.5-13B-lora'\n",
    "output_ggml = content_dir / 'output_ggml'\n",
    "\n",
    "converted_model = AutoConverter.convert(input_ggml, output_ggml)\n",
    "\n",
    "assert (output_ggml / ggml_f16_model_name).exists()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "EihyVABOCHvG"
   },
   "outputs": [],
   "source": [
    "#@title Квантизуем результат в 4 бита\n",
    "\n",
    "from llm_rs import AutoQuantizer, QuantizationType, ContainerType\n",
    "\n",
    "AutoQuantizer.quantize(converted_model, quantization=QuantizationType.Q4_0, container=ContainerType.GGML)\n",
    "# AutoQuantizer.quantize(converted_model, quantization=QuantizationType.Q4_0, container=ContainerType.GGJT)\n",
    "\n",
    "# AutoQuantizer.quantize(converted_model, quantization=QuantizationType.Q4_1, container=ContainerType.GGML)\n",
    "# AutoQuantizer.quantize(converted_model, quantization=QuantizationType.Q5_0, container=ContainerType.GGML)\n",
    "# AutoQuantizer.quantize(converted_model, quantization=QuantizationType.Q5_1, container=ContainerType.GGML)\n",
    "# AutoQuantizer.quantize(converted_model, quantization=QuantizationType.Q8_0, container=ContainerType.GGML)\n",
    "\n",
    "ggml_quantized_model_name = 'ruGPT-3.5-13B-lora-q4_0.bin'\n",
    "assert (output_ggml / ggml_quantized_model_name).exists()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "0QPyQ3eACHvG"
   },
   "outputs": [],
   "source": [
    "#@title Прогоняем полученную модель на нескольких диалогах из валидационной выборки\n",
    "\n",
    "from llm_rs import AutoModel\n",
    "\n",
    "num_test_samples = 3 #@param {type:\"integer\"}\n",
    "max_new_tokens = 20 #@param {type:\"integer\"}\n",
    "\n",
    "!head -n {num_test_samples} {content_dir / 'val.jsonl'} > {content_dir / 'test.jsonl'}\n",
    "\n",
    "model = AutoModel.from_pretrained(output_ggml, model_file=ggml_quantized_model_name)\n",
    "\n",
    "with open(content_dir / 'test.jsonl', 'r') as f, open(content_dir / 'test_result.jsonl', 'w') as out_f:\n",
    "    for line in f:\n",
    "        data = json.loads(line)\n",
    "        input_text = data['messages'][0]['content']\n",
    "        output = model.generate(input_text).text\n",
    "        print(output)\n",
    "\n",
    "assert (content_dir / 'test_result.jsonl').exists()"
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