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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "Vx7KFfeieD7z"
},
"source": [
"# RWKV-v4-RNN-Pile Fine-Tuning\n",
"\n",
"[RWKV](https://github.com/BlinkDL/RWKV-LM) is an RNN with transformer-level performance\n",
"\n",
"\n",
"This notebook aims to streamline fine-tuning RWKV-v4 models"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7JFIiAsrfvJy"
},
"source": [
"\n",
"## Setup"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "g_qFjgYmtSfK"
},
"outputs": [],
"source": [
"#@title Google Drive Options { display-mode: \"form\" }\n",
"save_models_to_drive = True #@param {type:\"boolean\"}\n",
"drive_mount = '/content/drive' #@param {type:\"string\"}\n",
"output_dir = 'rwkv-v4-rnn-pile-tuning' #@param {type:\"string\"}\n",
"tuned_model_name = 'tuned' #@param {type:\"string\"}\n",
"\n",
"import os\n",
"from google.colab import drive\n",
"if save_models_to_drive:\n",
" from google.colab import drive\n",
" drive.mount(drive_mount)\n",
" \n",
"output_path = f\"{drive_mount}/MyDrive/{output_dir}\" if save_models_to_drive else f\"/content/{output_dir}\"\n",
"os.makedirs(f\"{output_path}/{tuned_model_name}\", exist_ok=True)\n",
"os.makedirs(f\"{output_path}/base_models/\", exist_ok=True)\n",
"\n",
"print(f\"Saving models to {output_path}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "eivKJ6FP1_9z",
"outputId": "a687e3ad-8158-492a-da86-4f4ed8804699",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Fri Sep 2 16:11:37 2022 \n",
"+-----------------------------------------------------------------------------+\n",
"| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |\n",
"|-------------------------------+----------------------+----------------------+\n",
"| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
"| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n",
"| | | MIG M. |\n",
"|===============================+======================+======================|\n",
"| 0 Tesla P100-PCIE... Off | 00000000:00:04.0 Off | 0 |\n",
"| N/A 35C P0 28W / 250W | 0MiB / 16280MiB | 0% Default |\n",
"| | | N/A |\n",
"+-------------------------------+----------------------+----------------------+\n",
" \n",
"+-----------------------------------------------------------------------------+\n",
"| Processes: |\n",
"| GPU GI CI PID Type Process name GPU Memory |\n",
"| ID ID Usage |\n",
"|=============================================================================|\n",
"| No running processes found |\n",
"+-----------------------------------------------------------------------------+\n"
]
}
],
"source": [
"!nvidia-smi"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "R4lt0FTegJw9"
},
"outputs": [],
"source": [
"!git clone https://github.com/blinkdl/RWKV-LM\n",
"repo_dir = \"/content/RWKV-LM/RWKV-v4\"\n",
"%cd $repo_dir"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "RDavUrBsgKIV"
},
"outputs": [],
"source": [
"!pip install transformers pytorch-lightning==1.9 deepspeed wandb ninja"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Wt7y7vR6e6U3"
},
"source": [
"## Load Base Model\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "KIgagN-Se3wi"
},
"outputs": [],
"source": [
"#@title Base Model Options\n",
"#@markdown Using any of the listed options will download the checkpoint from huggingface\n",
"\n",
"base_model_name = \"RWKV-4-Pile-169M\" #@param [\"RWKV-4-Pile-1B5\", \"RWKV-4-Pile-430M\", \"RWKV-4-Pile-169M\"]\n",
"base_model_url = f\"https://huggingface.co/BlinkDL/{base_model_name.lower()}\"\n",
"\n",
"# This may take a while\n",
"!git lfs clone $base_model_url\n",
"\n",
"from glob import glob\n",
"base_model_path = glob(f\"{base_model_name.lower()}/{base_model_name}*.pth\")[0]\n",
"\n",
"print(f\"Using {base_model_path} as base\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hCOPnLelfJgP"
},
"source": [
"## Generate Training Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "wW5OmlXmvaIU",
"cellView": "form"
},
"outputs": [],
"source": [
"#@title Training Data Options\n",
"#@markdown `input_file` should be the path to a single file that contains the text you want to fine-tune with.\n",
"#@markdown Either upload a file to this notebook instance or reference a file in your Google drive.\n",
"\n",
"import numpy as np\n",
"from transformers import PreTrainedTokenizerFast\n",
"\n",
"tokenizer = PreTrainedTokenizerFast(tokenizer_file=f'{repo_dir}/20B_tokenizer.json')\n",
"\n",
"input_file = \"/content/drive/MyDrive/training.txt\" #@param {type:\"string\"}\n",
"output_file = 'train.npy'\n",
"\n",
"print(f'Tokenizing {input_file} (VERY slow. please wait)')\n",
"\n",
"data_raw = open(input_file, encoding=\"utf-8\").read()\n",
"print(f'Raw length = {len(data_raw)}')\n",
"\n",
"data_code = tokenizer.encode(data_raw)\n",
"print(f'Tokenized length = {len(data_code)}')\n",
"\n",
"out = np.array(data_code, dtype='uint16')\n",
"np.save(output_file, out, allow_pickle=False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "I4lz-3maeIwY"
},
"source": [
"## Training"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "fuCw5_ASwMud"
},
"outputs": [],
"source": [
"#@title Training Options { display-mode: \"form\" }\n",
"from shutil import copy\n",
"import os\n",
"\n",
"def training_options():\n",
" EXPRESS_PILE_MODE = True\n",
" EXPRESS_PILE_MODEL_NAME = base_model_path.split(\".\")[0]\n",
" EXPRESS_PILE_MODEL_TYPE = base_model_name\n",
" n_epoch = 100 #@param {type:\"integer\"}\n",
" epoch_save_frequency = 25 #@param {type:\"integer\"}\n",
" batch_size = 11#@param {type:\"integer\"} \n",
" ctx_len = 384 #@param {type:\"integer\"}\n",
" epoch_save_path = f\"{output_path}/{tuned_model_name}\"\n",
" return locals()\n",
"\n",
"def model_options():\n",
" T_MAX = 384 #@param {type:\"integer\"}\n",
" return locals()\n",
"\n",
"def env_vars():\n",
" RWKV_FLOAT_MODE = 'fp16' #@param ['fp16', 'bf16', 'bf32'] {type:\"string\"}\n",
" RWKV_DEEPSPEED = '0' #@param ['0', '1'] {type:\"string\"}\n",
" return {f\"os.environ['{key}']\": value for key, value in locals().items()}\n",
"\n",
"def replace_lines(file_name, to_replace):\n",
" with open(file_name, 'r') as f:\n",
" lines = f.readlines()\n",
" with open(f'{file_name}.tmp', 'w') as f:\n",
" for line in lines:\n",
" key = line.split(\" =\")[0]\n",
" if key.strip() in to_replace:\n",
" value = to_replace[key.strip()]\n",
" if isinstance(value, str):\n",
" f.write(f'{key} = \"{value}\"\\n')\n",
" else:\n",
" f.write(f'{key} = {value}\\n')\n",
" else:\n",
" f.write(line)\n",
" copy(f'{file_name}.tmp', file_name)\n",
" os.remove(f'{file_name}.tmp')\n",
"\n",
"values = training_options()\n",
"values.update(env_vars())\n",
"replace_lines('train.py', values)\n",
"replace_lines('src/model.py', model_options())"
]
},
{
"cell_type": "code",
"source": [
"!python train.py "
],
"metadata": {
"id": "0ZSF8U-nzylI"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "pcDci4O7xJiZ"
},
"execution_count": null,
"outputs": []
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"name": "RWKV-v4-RNN-Pile Fine-Tuning",
"provenance": [],
"toc_visible": true
},
"gpuClass": "standard",
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
} |