IlyaGusev/rulm

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self_instruct/tune_llama_7b.ipynb

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{
 "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_short_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",
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   "outputs": [],
   "source": [
    "#@title Скачиваем базовую модель LLaMA2 7B и чиним её конфиг\n",
    "\n",
    "import json\n",
    "from huggingface_hub import snapshot_download\n",
    "\n",
    "model_dir = content_dir / \"llama2-7b\"\n",
    "base_model = \"TheBloke/Llama-2-7B-fp16\" #@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",
    "            \"tokenizer_class\": \"LlamaTokenizer\",\n",
    "            \"model_max_length\": 4096,\n",
    "            \"padding_side\": \"left\",\n",
    "            \"bos_token\": \"<s>\",\n",
    "            \"eos_token\": \"</s>\",\n",
    "            \"unk_token\": \"<unk>\",\n",
    "            \"clean_up_tokenization_spaces\": False,\n",
    "            \"special_tokens_map_file\": \"special_tokens_map.json\",\n",
    "        },\n",
    "        \"special_tokens_map.json\": {\n",
    "            \"bos_token\": \"<s>\",\n",
    "            \"eos_token\": \"</s>\",\n",
    "            \"pad_token\": \"<unk>\",\n",
    "            \"sep_token\": \"<s>\",\n",
    "            \"unk_token\": \"<unk>\",\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 = self_instruct_dir / 'configs/saiga2_7b.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']['gradient_accumulation_steps'] = 64 #@param {type:\"integer\"}\n",
    "config['max_tokens_count'] = 1024 #@param {type:\"integer\"}\n",
    "config['model_name'] = str(model_dir)\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 = self_instruct_dir / 'configs/saiga2_7b_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.relative_to(self_instruct_dir)} \\\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 инференса обученной модели\n",
    "\n",
    "with (output_dir / 'generation_config.json').open('w') as fp:\n",
    "    json.dump({\n",
    "        \"pad_token_id\": 0,\n",
    "        \"bos_token_id\": 1,\n",
    "        \"eos_token_id\": 2,\n",
    "        \"temperature\": 0.2,\n",
    "        \"top_p\": 0.9,\n",
    "        \"top_k\": 40,\n",
    "        \"do_sample\": True,\n",
    "        \"max_new_tokens\": 1536,\n",
    "        \"repetition_penalty\": 1.1,\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",
    "merged_model_name = 'merged.pt'\n",
    "\n",
    "!cd {self_instruct_dir} && python -m src.convert_to_native \\\n",
    "    {output_dir} \\\n",
    "    {output_dir / merged_model_name} \\\n",
    "    --device=cuda \\\n",
    "    --enable_offloading\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",
    "ggml_f16_model_name = 'model-f16.gguf'\n",
    "\n",
    "!cd llama.cpp && python convert.py \\\n",
    "    {output_dir / merged_model_name} \\\n",
    "    --vocab-dir {output_dir} \\\n",
    "    --outfile {output_dir / ggml_f16_model_name} \\\n",
    "    --outtype f16\n",
    "\n",
    "assert (output_dir / ggml_f16_model_name).exists()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "EihyVABOCHvG"
   },
   "outputs": [],
   "source": [
    "#@title Квантизуем результат в 4 бита\n",
    "\n",
    "quantization_type = \"q4_0\" #@param [\"q4_0\", \"q4_1\"] {allow-input: true}\n",
    "ggml_quantized_model_name = f'model-{quantization_type}.gguf'\n",
    "\n",
    "!cd llama.cpp && make quantize && ./quantize \\\n",
    "    {output_dir / ggml_f16_model_name} \\\n",
    "    {output_dir / ggml_quantized_model_name} \\\n",
    "    {quantization_type}\n",
    "\n",
    "assert (output_dir / 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",
    "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",
    "!cd {self_instruct_dir} && python -m src.infer_saiga_llamacpp \\\n",
    "    --model_name={output_dir / ggml_quantized_model_name} \\\n",
    "    --input_path={content_dir / 'test.jsonl'} \\\n",
    "    --output_path={content_dir / 'test_result.jsonl'} \\\n",
    "    --max_new_tokens={max_new_tokens}\n",
    "\n",
    "assert (content_dir / 'test_result.jsonl').exists()"
   ]
  }
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