pavelgpt_7b_128k.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",
    "!git clone https://github.com/IlyaGusev/rulm.git\n",
    "!git clone https://github.com/ggerganov/llama.cpp.git\n",
    "!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",
    "\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",
    "from pathlib import Path\n",
    "\n",
    "self_instruct_dir = Path('rulm/self_instruct').resolve()\n",
    "content_dir = Path('.').resolve()\n",
    "#!cd {self_instruct_dir} && python -m src.data_processing.create_chat_set {content_dir / 'train_full.jsonl'} {content_dir / 'val_full.jsonl'}\n",
    "\n",
    "#!python -m data_processing.create_instruct_set {content_dir / 'train_full.jsonl'} {content_dir / 'val_full.jsonl'}\n",
    "!python -m data_processing.create_instruct_set_v2 {content_dir / 'train_full.jsonl'} {content_dir / 'val_full.jsonl'}\n",
    "#!python -m data_processing.create_chat_set {content_dir / 'train_full.jsonl'} {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",
    "import shutil\n",
    "from huggingface_hub import snapshot_download\n",
    "from pathlib import Path\n",
    "\n",
    "output_dir = content_dir / 'pavelgpt_7b_128k'\n",
    "Path(output_dir).mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "model_dir = content_dir / \"Yarn-Mistral-7b-128k\"\n",
    "Path(model_dir).mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "base_model = \"NousResearch/Yarn-Mistral-7b-128k\"  #@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)\n",
    "\n",
    "with (output_dir / 'generation_config.json').open('w') as fp:\n",
    "    json.dump({\n",
    "        \"pad_token_id\": 0,\n",
    "        \"unk_token_id\": 0,\n",
    "        \"bos_token_id\": 1,\n",
    "        \"eos_token_id\": 2,\n",
    "        \"temperature\": 0.2,\n",
    "        \"top_p\": 0.2,\n",
    "        \"top_k\": 20,\n",
    "        \"do_sample\": True,\n",
    "        \"max_new_tokens\": 4096,\n",
    "        \"repetition_penalty\": 1.2,\n",
    "        \"no_repeat_ngram_size\": 15,\n",
    "    }, fp, indent=4)\n",
    "\n",
    "shutil.copyfile(str(model_dir / 'config.json'), str(output_dir / 'config.json'))"
   ]
  },
  {
   "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/mistral_7b_128k.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'] = 1\n",
    "config['trainer']['per_device_eval_batch_size'] = 1\n",
    "config['trainer']['gradient_accumulation_steps'] = 128\n",
    "config['max_tokens_count'] = 4096  #@param {type:\"integer\"}\n",
    "config['model_name'] = str(model_dir)\n",
    "config[\"use_flash_attention_2\"] = True\n",
    "config[\"load_in_8bit\"] = False\n",
    "config[\"load_in_4bit\"] = True\n",
    "\n",
    "# Demo adjustments\n",
    "config['trainer']['eval_steps'] = 100  #@param {type:\"integer\"}\n",
    "config['trainer']['save_steps'] = 100  #@param {type:\"integer\"}\n",
    "config['trainer']['logging_steps'] = 1  #@param {type:\"integer\"}\n",
    "config['trainer']['num_train_epochs'] = 1  #@param {type:\"integer\"}\n",
    "config['templates_path'] = str(content_dir / 'internal_prompts/chat.json')\n",
    "\n",
    "config_path = content_dir / 'configs/mistral_7b_128k_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",
    "!cd {self_instruct_dir} && python3 -m src.train --config-file { config_path } --train-file { content_dir / 'train_full.jsonl' } --val-file { content_dir / 'val_full.jsonl' } --output-dir { output_dir }\n",
    "\n",
    "assert (output_dir / 'adapter_config.json').exists()"
   ]
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# Merge LoRA to base model\n",
    "\n",
    "import torch\n",
    "from transformers import AutoModelForCausalLM\n",
    "from peft import PeftConfig, PeftModel\n",
    "\n",
    "from pathlib import Path\n",
    "content_dir = Path('.').resolve()\n",
    "output_dir = content_dir / 'pavelgpt_7b_128k'\n",
    "\n",
    "device = 'cpu'\n",
    "merged_path = output_dir / 'pytorch_model.bin'\n",
    "\n",
    "# Load Peft config\n",
    "peft_config = PeftConfig.from_pretrained(output_dir)\n",
    "\n",
    "# Load base model\n",
    "original_model = AutoModelForCausalLM.from_pretrained(\n",
    "    peft_config.base_model_name_or_path,\n",
    "    torch_dtype=torch.float16,\n",
    "    device_map={'': device},\n",
    ")\n",
    "\n",
    "# Load config\n",
    "lora_model = PeftModel.from_pretrained(\n",
    "    original_model,\n",
    "    output_dir,\n",
    "    device_map={'': device},\n",
    "    torch_dtype=torch.float16\n",
    ")\n",
    "lora_model = lora_model.merge_and_unload()\n",
    "lora_model.train(False)\n",
    "\n",
    "# Save to a single file\n",
    "lora_model_sd = lora_model.state_dict()\n",
    "torch.save(lora_model_sd, f'{merged_path}')"
   ],
   "metadata": {
    "collapsed": false
   },
   "execution_count": null
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# Convert LoRA adapter to GGUF\n",
    "\n",
    "#!pip install -r llama.cpp/requirements.txt\n",
    "#!python llama.cpp/convert-lora-to-ggml.py { str(output_dir) }\n",
    "!python llama.cpp/convert.py --ctx 131072 --outtype f16 { str(output_dir) }"
   ],
   "metadata": {
    "collapsed": false
   },
   "execution_count": null
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "!llama.cpp/bin/quantize { str(output_dir / 'ggml-model-f16.gguf') } Q4_0\n",
    "!llama.cpp/bin/quantize { str(output_dir / 'ggml-model-f16.gguf') } Q4_1\n",
    "!llama.cpp/bin/quantize { str(output_dir / 'ggml-model-f16.gguf') } Q4_K_S\n",
    "!llama.cpp/bin/quantize { str(output_dir / 'ggml-model-f16.gguf') } Q4_K_M\n",
    "!llama.cpp/bin/quantize { str(output_dir / 'ggml-model-f16.gguf') } Q2_K\n",
    "!llama.cpp/bin/quantize { str(output_dir / 'ggml-model-f16.gguf') } Q3_K_M"
   ],
   "metadata": {
    "collapsed": false
   },
   "execution_count": null
  }
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