llm-finetuning
/
11_RLHF_Training_for_CustomDataset_for_AnyModel.ipynb
6600 строк · 242.5 Кб
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5035"flex_flow": null,
5036"grid_area": null,
5037"grid_auto_columns": null,
5038"grid_auto_flow": null,
5039"grid_auto_rows": null,
5040"grid_column": null,
5041"grid_gap": null,
5042"grid_row": null,
5043"grid_template_areas": null,
5044"grid_template_columns": null,
5045"grid_template_rows": null,
5046"height": null,
5047"justify_content": null,
5048"justify_items": null,
5049"left": null,
5050"margin": null,
5051"max_height": null,
5052"max_width": null,
5053"min_height": null,
5054"min_width": null,
5055"object_fit": null,
5056"object_position": null,
5057"order": null,
5058"overflow": null,
5059"overflow_x": null,
5060"overflow_y": null,
5061"padding": null,
5062"right": null,
5063"top": null,
5064"visibility": null,
5065"width": null
5066}
5067},
5068"88a6e00a597d4e7394cdd42386d987a7": {
5069"model_module": "@jupyter-widgets/controls",
5070"model_name": "DescriptionStyleModel",
5071"model_module_version": "1.5.0",
5072"state": {
5073"_model_module": "@jupyter-widgets/controls",
5074"_model_module_version": "1.5.0",
5075"_model_name": "DescriptionStyleModel",
5076"_view_count": null,
5077"_view_module": "@jupyter-widgets/base",
5078"_view_module_version": "1.2.0",
5079"_view_name": "StyleView",
5080"description_width": ""
5081}
5082}
5083}
5084}
5085},
5086"cells": [
5087{
5088"cell_type": "code",
5089"source": [
5090"!pip install transformers trl"
5091],
5092"metadata": {
5093"colab": {
5094"base_uri": "https://localhost:8080/"
5095},
5096"id": "WqgZjNHnQo1p",
5097"outputId": "18ac770e-80de-4b21-8505-5cb163bef5f0"
5098},
5099"execution_count": 1,
5100"outputs": [
5101{
5102"output_type": "stream",
5103"name": "stdout",
5104"text": [
5105"Collecting transformers\n",
5106" Downloading transformers-4.33.0-py3-none-any.whl (7.6 MB)\n",
5107"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m59.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
5108"\u001b[?25hCollecting trl\n",
5109" Downloading trl-0.7.1-py3-none-any.whl (117 kB)\n",
5110"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m118.0/118.0 kB\u001b[0m \u001b[31m16.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
5111"\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.2)\n",
5112"Collecting huggingface-hub<1.0,>=0.15.1 (from transformers)\n",
5113" Downloading huggingface_hub-0.16.4-py3-none-any.whl (268 kB)\n",
5114"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m32.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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5116"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.1)\n",
5117"Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
5118"Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.6.3)\n",
5119"Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
5120"Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers)\n",
5121" Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
5122"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m110.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
5123"\u001b[?25hCollecting safetensors>=0.3.1 (from transformers)\n",
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5125"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m84.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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5127"Requirement already satisfied: torch>=1.4.0 in /usr/local/lib/python3.10/dist-packages (from trl) (2.0.1+cu118)\n",
5128"Collecting accelerate (from trl)\n",
5129" Downloading accelerate-0.22.0-py3-none-any.whl (251 kB)\n",
5130"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m251.2/251.2 kB\u001b[0m \u001b[31m20.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
5131"\u001b[?25hCollecting datasets (from trl)\n",
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5134"\u001b[?25hRequirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.15.1->transformers) (2023.6.0)\n",
5135"Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.15.1->transformers) (4.7.1)\n",
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5139"Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.4.0->trl) (2.0.0)\n",
5140"Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.4.0->trl) (3.27.2)\n",
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5143"Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets->trl) (9.0.0)\n",
5144"Collecting dill<0.3.8,>=0.3.0 (from datasets->trl)\n",
5145" Downloading dill-0.3.7-py3-none-any.whl (115 kB)\n",
5146"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m15.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
5147"\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets->trl) (1.5.3)\n",
5148"Collecting xxhash (from datasets->trl)\n",
5149" Downloading xxhash-3.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
5150"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m25.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
5151"\u001b[?25hCollecting multiprocess (from datasets->trl)\n",
5152" Downloading multiprocess-0.70.15-py310-none-any.whl (134 kB)\n",
5153"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m18.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
5154"\u001b[?25hRequirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets->trl) (3.8.5)\n",
5155"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.2.0)\n",
5156"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
5157"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.4)\n",
5158"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.7.22)\n",
5159"Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->trl) (23.1.0)\n",
5160"Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->trl) (6.0.4)\n",
5161"Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->trl) (4.0.3)\n",
5162"Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->trl) (1.9.2)\n",
5163"Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->trl) (1.4.0)\n",
5164"Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->trl) (1.3.1)\n",
5165"Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.4.0->trl) (2.1.3)\n",
5166"Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets->trl) (2.8.2)\n",
5167"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets->trl) (2023.3)\n",
5168"Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.4.0->trl) (1.3.0)\n",
5169"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets->trl) (1.16.0)\n",
5170"Installing collected packages: tokenizers, safetensors, xxhash, dill, multiprocess, huggingface-hub, transformers, datasets, accelerate, trl\n",
5171"Successfully installed accelerate-0.22.0 datasets-2.14.4 dill-0.3.7 huggingface-hub-0.16.4 multiprocess-0.70.15 safetensors-0.3.3 tokenizers-0.13.3 transformers-4.33.0 trl-0.7.1 xxhash-3.3.0\n"
5172]
5173}
5174]
5175},
5176{
5177"cell_type": "code",
5178"execution_count": 2,
5179"metadata": {
5180"id": "hgj2hw_WQecA"
5181},
5182"outputs": [],
5183"source": [
5184"## Import libraries\n",
5185"import torch\n",
5186"import transformers\n",
5187"from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM, DataCollatorForLanguageModeling\n",
5188"from trl import RewardTrainer, SFTTrainer\n",
5189"from datasets import Dataset\n",
5190"import json\n",
5191"import pandas as pd\n",
5192"from transformers import Trainer, TrainingArguments"
5193]
5194},
5195{
5196"cell_type": "code",
5197"source": [
5198"!wget https://huggingface.co/datasets/CarperAI/openai_summarize_comparisons/resolve/main/data/test-00000-of-00001-0845e2eec675b16a.parquet\n",
5199"# after that rename to test.parquet"
5200],
5201"metadata": {
5202"colab": {
5203"base_uri": "https://localhost:8080/"
5204},
5205"id": "NFgC7kBFRm0P",
5206"outputId": "b23a409d-ff09-4f49-a120-083a0be42d4a"
5207},
5208"execution_count": 3,
5209"outputs": [
5210{
5211"output_type": "stream",
5212"name": "stdout",
5213"text": [
5214"--2023-09-06 12:36:39-- https://huggingface.co/datasets/CarperAI/openai_summarize_comparisons/resolve/main/data/test-00000-of-00001-0845e2eec675b16a.parquet\n",
5215"Resolving huggingface.co (huggingface.co)... 18.172.134.24, 18.172.134.4, 18.172.134.88, ...\n",
5216"Connecting to huggingface.co (huggingface.co)|18.172.134.24|:443... connected.\n",
5217"HTTP request sent, awaiting response... 302 Found\n",
5218"Location: https://cdn-lfs.huggingface.co/repos/02/84/02842250b699bbde4e980e9206f5045df542f9ca374595cc86b8d57189641ff5/a8c5d73113a2c7119faacd44e43959f74e5b59e181f3da57caa918c907ac6620?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27test-00000-of-00001-0845e2eec675b16a.parquet%3B+filename%3D%22test-00000-of-00001-0845e2eec675b16a.parquet%22%3B&Expires=1694262999&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTY5NDI2Mjk5OX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5odWdnaW5nZmFjZS5jby9yZXBvcy8wMi84NC8wMjg0MjI1MGI2OTliYmRlNGU5ODBlOTIwNmY1MDQ1ZGY1NDJmOWNhMzc0NTk1Y2M4NmI4ZDU3MTg5NjQxZmY1L2E4YzVkNzMxMTNhMmM3MTE5ZmFhY2Q0NGU0Mzk1OWY3NGU1YjU5ZTE4MWYzZGE1N2NhYTkxOGM5MDdhYzY2MjA%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qIn1dfQ__&Signature=CLl3zBeYW5jzKbX9MZkryL0p-j8lTtx03ksho0Zwpi5ZeKplDz%7EohUhbXaK5OKCteoEJx%7EDjpluciHcFdC%7EQcfdwgRSuAuT%7ECMaPxAmmzR54Zd0AcIPR02OenGpIbf3scib4X-ZCWgSVAgUlF6F8jfbSq1Yo4eoi4iyJj3m%7El1O0NbWVZ7qm%7E4kXfNzBpaMI1l0pDg-9x6UCrVuto2g1guD3Uzwb%7EahiEwCYIcVGLgxo-SXOFO7-9w4eCZ-KkvwNq%7EgEGeUBmilzf1Wwnu6IVPnygYicEbiAIopHZDcm87wuC3j%7EcMwVKQx9JxrfXf3kP-Q6OqrLDUd2ejN0gInvUQ__&Key-Pair-Id=KVTP0A1DKRTAX [following]\n",
5219"--2023-09-06 12:36:39-- https://cdn-lfs.huggingface.co/repos/02/84/02842250b699bbde4e980e9206f5045df542f9ca374595cc86b8d57189641ff5/a8c5d73113a2c7119faacd44e43959f74e5b59e181f3da57caa918c907ac6620?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27test-00000-of-00001-0845e2eec675b16a.parquet%3B+filename%3D%22test-00000-of-00001-0845e2eec675b16a.parquet%22%3B&Expires=1694262999&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTY5NDI2Mjk5OX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5odWdnaW5nZmFjZS5jby9yZXBvcy8wMi84NC8wMjg0MjI1MGI2OTliYmRlNGU5ODBlOTIwNmY1MDQ1ZGY1NDJmOWNhMzc0NTk1Y2M4NmI4ZDU3MTg5NjQxZmY1L2E4YzVkNzMxMTNhMmM3MTE5ZmFhY2Q0NGU0Mzk1OWY3NGU1YjU5ZTE4MWYzZGE1N2NhYTkxOGM5MDdhYzY2MjA%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qIn1dfQ__&Signature=CLl3zBeYW5jzKbX9MZkryL0p-j8lTtx03ksho0Zwpi5ZeKplDz%7EohUhbXaK5OKCteoEJx%7EDjpluciHcFdC%7EQcfdwgRSuAuT%7ECMaPxAmmzR54Zd0AcIPR02OenGpIbf3scib4X-ZCWgSVAgUlF6F8jfbSq1Yo4eoi4iyJj3m%7El1O0NbWVZ7qm%7E4kXfNzBpaMI1l0pDg-9x6UCrVuto2g1guD3Uzwb%7EahiEwCYIcVGLgxo-SXOFO7-9w4eCZ-KkvwNq%7EgEGeUBmilzf1Wwnu6IVPnygYicEbiAIopHZDcm87wuC3j%7EcMwVKQx9JxrfXf3kP-Q6OqrLDUd2ejN0gInvUQ__&Key-Pair-Id=KVTP0A1DKRTAX\n",
5220"Resolving cdn-lfs.huggingface.co (cdn-lfs.huggingface.co)... 18.154.185.26, 18.154.185.64, 18.154.185.94, ...\n",
5221"Connecting to cdn-lfs.huggingface.co (cdn-lfs.huggingface.co)|18.154.185.26|:443... connected.\n",
5222"HTTP request sent, awaiting response... 200 OK\n",
5223"Length: 20441979 (19M) [binary/octet-stream]\n",
5224"Saving to: ‘test-00000-of-00001-0845e2eec675b16a.parquet’\n",
5225"\n",
5226"test-00000-of-00001 100%[===================>] 19.49M 105MB/s in 0.2s \n",
5227"\n",
5228"2023-09-06 12:36:39 (105 MB/s) - ‘test-00000-of-00001-0845e2eec675b16a.parquet’ saved [20441979/20441979]\n",
5229"\n"
5230]
5231}
5232]
5233},
5234{
5235"cell_type": "markdown",
5236"source": [
5237"### Dataset for Training : https://huggingface.co/datasets/CarperAI/openai_summarize_tldr\n",
5238"\n",
5239"### Dataset for Testing : https://huggingface.co/datasets/CarperAI/openai_summarize_comparisons"
5240],
5241"metadata": {
5242"id": "r81RnxurR-E2"
5243}
5244},
5245{
5246"cell_type": "code",
5247"source": [
5248"Model_name = \"bigcode/tiny_starcoder_py\"\n",
5249"Data_path = \"test.parquet\""
5250],
5251"metadata": {
5252"id": "sY4LnQyVQl5J"
5253},
5254"execution_count": 4,
5255"outputs": []
5256},
5257{
5258"cell_type": "code",
5259"source": [
5260"# Read the file\n",
5261"df = pd.read_parquet(Data_path)\n",
5262"df = df[:10]\n",
5263"raw_dataset = Dataset.from_pandas(df)\n",
5264"raw_dataset"
5265],
5266"metadata": {
5267"colab": {
5268"base_uri": "https://localhost:8080/"
5269},
5270"id": "ReMAX6WySL3s",
5271"outputId": "7dbda935-3fa6-47fd-a5a9-3ce66a006f3c"
5272},
5273"execution_count": 7,
5274"outputs": [
5275{
5276"output_type": "execute_result",
5277"data": {
5278"text/plain": [
5279"Dataset({\n",
5280" features: ['prompt', 'chosen', 'rejected'],\n",
5281" num_rows: 10\n",
5282"})"
5283]
5284},
5285"metadata": {},
5286"execution_count": 7
5287}
5288]
5289},
5290{
5291"cell_type": "markdown",
5292"source": [
5293"### Tokenize the dataset to feed in the model"
5294],
5295"metadata": {
5296"id": "1IPbHllpSeYn"
5297}
5298},
5299{
5300"cell_type": "code",
5301"source": [
5302"tokenizer = AutoTokenizer.from_pretrained(Model_name)\n",
5303"model = AutoModelForCausalLM.from_pretrained(Model_name)"
5304],
5305"metadata": {
5306"colab": {
5307"base_uri": "https://localhost:8080/",
5308"height": 273,
5309"referenced_widgets": [
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5399},
5400"id": "daw09BDBSa44",
5401"outputId": "38e185ab-cfa4-457c-cb2c-aa9f4cf01532"
5402},
5403"execution_count": 8,
5404"outputs": [
5405{
5406"output_type": "display_data",
5407"data": {
5408"text/plain": [
5409"Downloading (…)okenizer_config.json: 0%| | 0.00/677 [00:00<?, ?B/s]"
5410],
5411"application/vnd.jupyter.widget-view+json": {
5412"version_major": 2,
5413"version_minor": 0,
5414"model_id": "49fa119988e94491bd0b0934ab1b04dd"
5415}
5416},
5417"metadata": {}
5418},
5419{
5420"output_type": "display_data",
5421"data": {
5422"text/plain": [
5423"Downloading (…)olve/main/vocab.json: 0%| | 0.00/777k [00:00<?, ?B/s]"
5424],
5425"application/vnd.jupyter.widget-view+json": {
5426"version_major": 2,
5427"version_minor": 0,
5428"model_id": "57f7311ca349467992a24e326a5c8973"
5429}
5430},
5431"metadata": {}
5432},
5433{
5434"output_type": "display_data",
5435"data": {
5436"text/plain": [
5437"Downloading (…)olve/main/merges.txt: 0%| | 0.00/442k [00:00<?, ?B/s]"
5438],
5439"application/vnd.jupyter.widget-view+json": {
5440"version_major": 2,
5441"version_minor": 0,
5442"model_id": "83063ba407ed4a72b7eb808a8991f063"
5443}
5444},
5445"metadata": {}
5446},
5447{
5448"output_type": "display_data",
5449"data": {
5450"text/plain": [
5451"Downloading (…)/main/tokenizer.json: 0%| | 0.00/2.06M [00:00<?, ?B/s]"
5452],
5453"application/vnd.jupyter.widget-view+json": {
5454"version_major": 2,
5455"version_minor": 0,
5456"model_id": "91a1e970c39a45c1a4968936a7ee2b9b"
5457}
5458},
5459"metadata": {}
5460},
5461{
5462"output_type": "display_data",
5463"data": {
5464"text/plain": [
5465"Downloading (…)cial_tokens_map.json: 0%| | 0.00/532 [00:00<?, ?B/s]"
5466],
5467"application/vnd.jupyter.widget-view+json": {
5468"version_major": 2,
5469"version_minor": 0,
5470"model_id": "09ff7ca1f4ae47cead2d35146e5ffc3f"
5471}
5472},
5473"metadata": {}
5474},
5475{
5476"output_type": "display_data",
5477"data": {
5478"text/plain": [
5479"Downloading (…)lve/main/config.json: 0%| | 0.00/1.03k [00:00<?, ?B/s]"
5480],
5481"application/vnd.jupyter.widget-view+json": {
5482"version_major": 2,
5483"version_minor": 0,
5484"model_id": "7c52d3a64462430494f26f614f2cb808"
5485}
5486},
5487"metadata": {}
5488},
5489{
5490"output_type": "display_data",
5491"data": {
5492"text/plain": [
5493"Downloading model.safetensors: 0%| | 0.00/657M [00:00<?, ?B/s]"
5494],
5495"application/vnd.jupyter.widget-view+json": {
5496"version_major": 2,
5497"version_minor": 0,
5498"model_id": "5ef2a9736301486caedd3c739f6999a0"
5499}
5500},
5501"metadata": {}
5502},
5503{
5504"output_type": "display_data",
5505"data": {
5506"text/plain": [
5507"Downloading (…)neration_config.json: 0%| | 0.00/111 [00:00<?, ?B/s]"
5508],
5509"application/vnd.jupyter.widget-view+json": {
5510"version_major": 2,
5511"version_minor": 0,
5512"model_id": "c915bcd052904defa7523185e02deef2"
5513}
5514},
5515"metadata": {}
5516}
5517]
5518},
5519{
5520"cell_type": "markdown",
5521"source": [
5522"### Tokenize the dataset to feed in the model"
5523],
5524"metadata": {
5525"id": "Z15PFpEhS51h"
5526}
5527},
5528{
5529"cell_type": "code",
5530"source": [
5531"tokenizer.add_special_tokens({'pad_token': '[PAD]'})\n",
5532"def formatting_func(examples):\n",
5533" kwargs = {\"padding\": \"max_length\",\n",
5534" \"truncation\": True,\n",
5535" \"max_length\": 256,\n",
5536" \"return_tensors\": \"pt\"\n",
5537" }\n",
5538"\n",
5539" # Prepend the prompt and a line break to the original_response and response-1 fields.\n",
5540" prompt_plus_chosen_response = examples[\"prompt\"] + \"\\n\" + examples[\"chosen\"]\n",
5541" prompt_plus_rejected_response = examples[\"prompt\"] + \"\\n\" + examples[\"rejected\"]\n",
5542"\n",
5543" # Then tokenize these modified fields.\n",
5544" tokens_chosen = tokenizer.encode_plus(prompt_plus_chosen_response, **kwargs)\n",
5545" tokens_rejected = tokenizer.encode_plus(prompt_plus_rejected_response, **kwargs)\n",
5546"\n",
5547" return {\n",
5548" \"input_ids_chosen\": tokens_chosen[\"input_ids\"][0], \"attention_mask_chosen\": tokens_chosen[\"attention_mask\"][0],\n",
5549" \"input_ids_rejected\": tokens_rejected[\"input_ids\"][0], \"attention_mask_rejected\": tokens_rejected[\"attention_mask\"][0]\n",
5550" }"
5551],
5552"metadata": {
5553"id": "gcAtMRzpS2aC"
5554},
5555"execution_count": 9,
5556"outputs": []
5557},
5558{
5559"cell_type": "markdown",
5560"source": [
5561"### Map the function to data"
5562],
5563"metadata": {
5564"id": "qec70IWWVYkU"
5565}
5566},
5567{
5568"cell_type": "code",
5569"source": [
5570"formatted_dataset = raw_dataset.map(formatting_func)\n",
5571"formatted_dataset = formatted_dataset.train_test_split()"
5572],
5573"metadata": {
5574"colab": {
5575"base_uri": "https://localhost:8080/",
5576"height": 49,
5577"referenced_widgets": [
5578"6396b18205ab4daa965b76a29a8e8fda",
5579"df34019ab4154eb38dcce6ed6d204fbc",
5580"fff7525af65f4bed8f1ef699603f3c07",
5581"733550fcf3154e25ba0ace779ceb87a7",
5582"cd99115f95bb4282910caf8e4f03612e",
5583"4561689ef64b40d2a41ccf58815959bd",
5584"0a3eb086e97f459bb3a032ac9ec22dea",
5585"9fce344d959f46a6b28c2f8726c704b4",
5586"92085ca3991f46b5b626f00d665788ed",
5587"be943a11e101479dbbf8f176f3783cd6",
5588"6c6448ae3f764b32b99b2a0e21d85184"
5589]
5590},
5591"id": "b5q-aIpRUCxP",
5592"outputId": "4578f0f4-09c3-45ca-f164-d1ace2c2525d"
5593},
5594"execution_count": 10,
5595"outputs": [
5596{
5597"output_type": "display_data",
5598"data": {
5599"text/plain": [
5600"Map: 0%| | 0/10 [00:00<?, ? examples/s]"
5601],
5602"application/vnd.jupyter.widget-view+json": {
5603"version_major": 2,
5604"version_minor": 0,
5605"model_id": "6396b18205ab4daa965b76a29a8e8fda"
5606}
5607},
5608"metadata": {}
5609}
5610]
5611},
5612{
5613"cell_type": "code",
5614"source": [
5615"model.config"
5616],
5617"metadata": {
5618"colab": {
5619"base_uri": "https://localhost:8080/"
5620},
5621"id": "9ejl2Q_iVXtn",
5622"outputId": "b888e6bc-7fea-4515-bb67-1dc9367fe9cf"
5623},
5624"execution_count": 11,
5625"outputs": [
5626{
5627"output_type": "execute_result",
5628"data": {
5629"text/plain": [
5630"GPTBigCodeConfig {\n",
5631" \"_name_or_path\": \"bigcode/tiny_starcoder_py\",\n",
5632" \"activation_function\": \"gelu_pytorch_tanh\",\n",
5633" \"architectures\": [\n",
5634" \"GPTBigCodeForCausalLM\"\n",
5635" ],\n",
5636" \"attention_softmax_in_fp32\": true,\n",
5637" \"attn_pdrop\": 0.1,\n",
5638" \"bos_token_id\": 0,\n",
5639" \"embd_pdrop\": 0.1,\n",
5640" \"eos_token_id\": 0,\n",
5641" \"inference_runner\": 0,\n",
5642" \"initializer_range\": 0.02,\n",
5643" \"layer_norm_epsilon\": 1e-05,\n",
5644" \"max_batch_size\": null,\n",
5645" \"max_sequence_length\": null,\n",
5646" \"model_type\": \"gpt_bigcode\",\n",
5647" \"multi_query\": true,\n",
5648" \"n_embd\": 768,\n",
5649" \"n_head\": 12,\n",
5650" \"n_inner\": 3072,\n",
5651" \"n_layer\": 20,\n",
5652" \"n_positions\": 8192,\n",
5653" \"pad_key_length\": true,\n",
5654" \"pre_allocate_kv_cache\": false,\n",
5655" \"resid_pdrop\": 0.1,\n",
5656" \"scale_attention_softmax_in_fp32\": true,\n",
5657" \"scale_attn_weights\": true,\n",
5658" \"summary_activation\": null,\n",
5659" \"summary_first_dropout\": 0.1,\n",
5660" \"summary_proj_to_labels\": true,\n",
5661" \"summary_type\": \"cls_index\",\n",
5662" \"summary_use_proj\": true,\n",
5663" \"torch_dtype\": \"float32\",\n",
5664" \"transformers_version\": \"4.33.0\",\n",
5665" \"use_cache\": true,\n",
5666" \"validate_runner_input\": true,\n",
5667" \"vocab_size\": 49152\n",
5668"}"
5669]
5670},
5671"metadata": {},
5672"execution_count": 11
5673}
5674]
5675},
5676{
5677"cell_type": "code",
5678"source": [
5679"### Loading the TRL reward trainer and training the trainer\n",
5680"training_args = TrainingArguments(\n",
5681" output_dir=\"tinystarcoder-rlhf-model\",\n",
5682" num_train_epochs=1,\n",
5683" logging_steps=10,\n",
5684" gradient_accumulation_steps=1,\n",
5685" save_strategy=\"steps\",\n",
5686" evaluation_strategy=\"steps\",\n",
5687" per_device_train_batch_size=2,\n",
5688" per_device_eval_batch_size=1,\n",
5689" eval_accumulation_steps=1,\n",
5690" eval_steps=500,\n",
5691" save_steps=500,\n",
5692" warmup_steps=100,\n",
5693" logging_dir=\"./logs\",\n",
5694" learning_rate=1e-5,\n",
5695" save_total_limit=1,\n",
5696" no_cuda=True\n",
5697" )"
5698],
5699"metadata": {
5700"colab": {
5701"base_uri": "https://localhost:8080/"
5702},
5703"id": "S4UivLiGVgO2",
5704"outputId": "cb75c51d-59b6-4fcf-841d-e0b5c62ee074"
5705},
5706"execution_count": 12,
5707"outputs": [
5708{
5709"output_type": "stream",
5710"name": "stderr",
5711"text": [
5712"/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1257: FutureWarning: using `no_cuda` is deprecated and will be removed in version 5.0 of 🤗 Transformers. Use `use_cpu` instead\n",
5713" warnings.warn(\n"
5714]
5715}
5716]
5717},
5718{
5719"cell_type": "code",
5720"source": [
5721"trainer = RewardTrainer(model=model,\n",
5722" tokenizer=tokenizer,\n",
5723" train_dataset=formatted_dataset['train'],\n",
5724" eval_dataset=formatted_dataset['test'],\n",
5725" args= training_args\n",
5726" )\n",
5727"trainer.train()"
5728],
5729"metadata": {
5730"colab": {
5731"base_uri": "https://localhost:8080/",
5732"height": 252
5733},
5734"id": "P2ooNlBhVim-",
5735"outputId": "259a4a95-1b91-4c2b-cb70-23b64cc267ca"
5736},
5737"execution_count": 13,
5738"outputs": [
5739{
5740"output_type": "stream",
5741"name": "stderr",
5742"text": [
5743"/usr/local/lib/python3.10/dist-packages/trl/trainer/reward_trainer.py:124: UserWarning: When using RewardDataCollatorWithPadding, you should set `max_length` in the RewardTrainer's init it will be set to `512` by default, but you should do it yourself in the future.\n",
5744" warnings.warn(\n",
5745"/usr/local/lib/python3.10/dist-packages/trl/trainer/reward_trainer.py:138: UserWarning: When using RewardDataCollatorWithPadding, you should set `remove_unused_columns=False` in your TrainingArguments we have set it for you, but you should do it yourself in the future.\n",
5746" warnings.warn(\n",
5747"You're using a GPT2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
5748"/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2436: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
5749" warnings.warn(\n",
5750"Could not estimate the number of tokens of the input, floating-point operations will not be computed\n"
5751]
5752},
5753{
5754"output_type": "display_data",
5755"data": {
5756"text/plain": [
5757"<IPython.core.display.HTML object>"
5758],
5759"text/html": [
5760"\n",
5761" <div>\n",
5762" \n",
5763" <progress value='4' max='4' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
5764" [4/4 00:52, Epoch 1/1]\n",
5765" </div>\n",
5766" <table border=\"1\" class=\"dataframe\">\n",
5767" <thead>\n",
5768" <tr style=\"text-align: left;\">\n",
5769" <th>Step</th>\n",
5770" <th>Training Loss</th>\n",
5771" <th>Validation Loss</th>\n",
5772" </tr>\n",
5773" </thead>\n",
5774" <tbody>\n",
5775" </tbody>\n",
5776"</table><p>"
5777]
5778},
5779"metadata": {}
5780},
5781{
5782"output_type": "execute_result",
5783"data": {
5784"text/plain": [
5785"TrainOutput(global_step=4, training_loss=0.7674217224121094, metrics={'train_runtime': 101.4459, 'train_samples_per_second': 0.069, 'train_steps_per_second': 0.039, 'total_flos': 0.0, 'train_loss': 0.7674217224121094, 'epoch': 1.0})"
5786]
5787},
5788"metadata": {},
5789"execution_count": 13
5790}
5791]
5792},
5793{
5794"cell_type": "code",
5795"source": [
5796"trainer.evaluate()"
5797],
5798"metadata": {
5799"colab": {
5800"base_uri": "https://localhost:8080/",
5801"height": 231
5802},
5803"id": "ENADz21Gf1XN",
5804"outputId": "eecf2422-7dda-498a-c2d8-479f3aab6b72"
5805},
5806"execution_count": 15,
5807"outputs": [
5808{
5809"output_type": "stream",
5810"name": "stderr",
5811"text": [
5812"/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2436: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
5813" warnings.warn(\n",
5814"/usr/local/lib/python3.10/dist-packages/trl/trainer/reward_trainer.py:214: UserWarning: The use of `x.T` on tensors of dimension other than 2 to reverse their shape is deprecated and it will throw an error in a future release. Consider `x.mT` to transpose batches of matrices or `x.permute(*torch.arange(x.ndim - 1, -1, -1))` to reverse the dimensions of a tensor. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3571.)\n",
5815" logits = torch.stack(logits).mean(dim=2).softmax(dim=0).T\n"
5816]
5817},
5818{
5819"output_type": "display_data",
5820"data": {
5821"text/plain": [
5822"<IPython.core.display.HTML object>"
5823],
5824"text/html": [
5825"\n",
5826" <div>\n",
5827" \n",
5828" <progress value='3' max='3' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
5829" [3/3 00:14]\n",
5830" </div>\n",
5831" "
5832]
5833},
5834"metadata": {}
5835},
5836{
5837"output_type": "execute_result",
5838"data": {
5839"text/plain": [
5840"{'eval_loss': 0.6931466460227966,\n",
5841" 'eval_accuracy': 0.0,\n",
5842" 'eval_runtime': 22.5484,\n",
5843" 'eval_samples_per_second': 0.133,\n",
5844" 'eval_steps_per_second': 0.133,\n",
5845" 'epoch': 1.0}"
5846]
5847},
5848"metadata": {},
5849"execution_count": 15
5850}
5851]
5852},
5853{
5854"cell_type": "code",
5855"source": [
5856"from huggingface_hub import login\n",
5857"login()"
5858],
5859"metadata": {
5860"colab": {
5861"base_uri": "https://localhost:8080/",
5862"height": 145,
5863"referenced_widgets": [
5864"70363dc337a84a4e8d9dbe548bb24e5a",
5865"b50c4b7e474f4b3ca1b626b8390f6d4e",
5866"5b58c1033da8470f82744583306674e4",
5867"85c7e1bc8dcb4ee99baeb9f30cfdd2bb",
5868"2d293c8541264b0cbaeb7219c2d94ef8",
5869"a634b0ccd0a84f53acbb16ede00688b7",
5870"2f2cd4fd194444aeb41ab473ce74f744",
5871"cbcda32fd6694581a3ea49c2abc83232",
5872"f3b324fe716e4abc81fcdd0817387797",
5873"9fbdfbeef5ed4880ae68fcde7d12b617",
5874"b7653907cb634671b17cde0234bdaf18",
5875"9fd5e7396838452da35d5f1240dcfc09",
5876"b6fe4d9105d849da8c69966016b52454",
5877"37b3bc877baa429e99456abc6e47e551",
5878"61e3ec46b6ca4bfaa5cffe684130d13b",
5879"1b1bb860813d45bb88f39b1aaa192e91",
5880"f8a983b16f3644fca1692d8de9f07f0d",
5881"89cc8bd26d154bbd812997f60b0e241d",
5882"6511db857f284380a6d93b9115d14479",
5883"f387c978d4c24221890d90ac8d22e0b6",
5884"79131c98086a4259800d77bd94f81554",
5885"b015f478b8b6453fa0f49c115ff572c2",
5886"2d9d192cfa124aadaf1b3d31bb9dd0ce",
5887"cb52479c2c3844a99388a8e71fd6d0e6",
5888"9d2d72d38d5f43fcbd64b43481982ce8",
5889"7218cccb90284684b43124ee11adb918",
5890"1b93ff678ada4ff5bec69a1bdb36bd5b",
5891"1d5e9cd1f7234b899efbfd3cfb23774c",
5892"a6295ffea3b546e9a9b62552424dc970",
5893"554b5751193b4133992e160b6a938dda",
5894"9edaf08284f24cd6bd37039f565315f2",
5895"1ff9cb3284ad4ffd9eb7ca376e5e3006"
5896]
5897},
5898"id": "ZI4Ex-o7WFHc",
5899"outputId": "4ca5f4f0-44ac-46be-8976-8697f79018cc"
5900},
5901"execution_count": 17,
5902"outputs": [
5903{
5904"output_type": "display_data",
5905"data": {
5906"text/plain": [
5907"VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
5908],
5909"application/vnd.jupyter.widget-view+json": {
5910"version_major": 2,
5911"version_minor": 0,
5912"model_id": "70363dc337a84a4e8d9dbe548bb24e5a"
5913}
5914},
5915"metadata": {}
5916}
5917]
5918},
5919{
5920"cell_type": "code",
5921"source": [
5922"trainer.push_to_hub(\"RLHF model of StarCoder\")"
5923],
5924"metadata": {
5925"colab": {
5926"base_uri": "https://localhost:8080/",
5927"height": 67,
5928"referenced_widgets": [
5929"7511afb8ae6b4947a2445ef0450d449f",
5930"df5badd1ab29425d8ed70a91d618a0e4",
5931"c89e2bd3496a46ed83c1678b2f9d1713",
5932"2fa045000acd438da96717b13d3c5a0c",
5933"a8434eed07314c24935c6ca6a9c4952e",
5934"74551940aad54d309c95ce1d549438e0",
5935"4fe71e691bc04d609baeb07bfe0bd4e1",
5936"cce70368ebaf4d2a83f4bd8fc87b827f",
5937"7b6da5b0aa0744bba4041954252279fa",
5938"e9a71eebb4084b6fb7b2efd8bfc8fa14",
5939"312bca73ede940f280ea3c25206ef3f8"
5940]
5941},
5942"id": "bUrhJDQLWDuu",
5943"outputId": "bb69e358-4c0a-4a0d-9cbc-8bdb35017c9a"
5944},
5945"execution_count": 18,
5946"outputs": [
5947{
5948"output_type": "display_data",
5949"data": {
5950"text/plain": [
5951"pytorch_model.bin: 0%| | 0.00/657M [00:00<?, ?B/s]"
5952],
5953"application/vnd.jupyter.widget-view+json": {
5954"version_major": 2,
5955"version_minor": 0,
5956"model_id": "7511afb8ae6b4947a2445ef0450d449f"
5957}
5958},
5959"metadata": {}
5960},
5961{
5962"output_type": "execute_result",
5963"data": {
5964"text/plain": [
5965"'https://huggingface.co/ashishpatel26/tinystarcoder-rlhf-model/tree/main/'"
5966],
5967"application/vnd.google.colaboratory.intrinsic+json": {
5968"type": "string"
5969}
5970},
5971"metadata": {},
5972"execution_count": 18
5973}
5974]
5975},
5976{
5977"cell_type": "code",
5978"source": [
5979"trainer.save_model(\"tinystarcoder-rlhf-model\")"
5980],
5981"metadata": {
5982"id": "jb3_r6pcVqDx"
5983},
5984"execution_count": 20,
5985"outputs": []
5986},
5987{
5988"cell_type": "markdown",
5989"source": [
5990"### Policy Model"
5991],
5992"metadata": {
5993"id": "pvXbToUJV7Y9"
5994}
5995},
5996{
5997"cell_type": "code",
5998"source": [
5999"help(trainer.push_to_hub)"
6000],
6001"metadata": {
6002"id": "8dfAGf2Ha20p"
6003},
6004"execution_count": null,
6005"outputs": []
6006},
6007{
6008"cell_type": "code",
6009"source": [
6010"## inference the model\n",
6011"rm_model = AutoModelForCausalLM.from_pretrained(\"tinystarcoder-rlhf-model\")\n",
6012"tokenizer = AutoTokenizer.from_pretrained(\"tinystarcoder-rlhf-model\")"
6013],
6014"metadata": {
6015"id": "E_JNgUoqV8cL"
6016},
6017"execution_count": 21,
6018"outputs": []
6019},
6020{
6021"cell_type": "code",
6022"source": [
6023"def get_score(model, tokenizer, prompt, response):\n",
6024"\n",
6025" instructions = tokenizer.encode_plus(prompt,\n",
6026" response,\n",
6027" padding=\"max_length\",\n",
6028" max_length=256,\n",
6029" return_tensors=\"pt\",\n",
6030" truncation=True)\n",
6031" with torch.no_grad():\n",
6032" outputs = model(**instructions)\n",
6033"\n",
6034" logits = outputs[0]\n",
6035"\n",
6036" return logits"
6037],
6038"metadata": {
6039"id": "YyFiSSkkZUSt"
6040},
6041"execution_count": 22,
6042"outputs": []
6043},
6044{
6045"cell_type": "code",
6046"source": [
6047"# usage with prompt\n",
6048"prompt = df.iloc[0][\"prompt\"]\n",
6049"example_prefered_response = df.iloc[0][\"chosen\"]\n",
6050"example_unprefered_response = df.iloc[0][\"rejected\"]"
6051],
6052"metadata": {
6053"id": "BUNv_muAZcrB"
6054},
6055"execution_count": 23,
6056"outputs": []
6057},
6058{
6059"cell_type": "code",
6060"source": [
6061"loss1 = get_score(model, tokenizer, prompt, example_prefered_response)\n",
6062"loss2= get_score(model, tokenizer, prompt, example_unprefered_response)"
6063],
6064"metadata": {
6065"id": "uV6LkWkWZePB"
6066},
6067"execution_count": 24,
6068"outputs": []
6069},
6070{
6071"cell_type": "code",
6072"source": [
6073"from torch import nn\n",
6074"loss = -nn.functional.logsigmoid(loss1 - loss2).mean()"
6075],
6076"metadata": {
6077"id": "0E6NWxhPZgVB"
6078},
6079"execution_count": 25,
6080"outputs": []
6081},
6082{
6083"cell_type": "code",
6084"source": [
6085"tokenizer.decode(torch.max(loss1, axis=-1).indices[0])"
6086],
6087"metadata": {
6088"colab": {
6089"base_uri": "https://localhost:8080/",
6090"height": 87
6091},
6092"id": "IhbtDog6Zh0s",
6093"outputId": "74f7678a-6edc-48ba-804f-16cd63eb4af9"
6094},
6095"execution_count": 26,
6096"outputs": [
6097{
6098"output_type": "execute_result",
6099"data": {
6100"text/plain": [
6101"'_DDIT_\\n \"r/#: Relationship RelationshipRelationship0]0ARCH\\nlsriend\\n2//M]\\n [\\n [\\n the was to the [\\n a friends to\\n\\n: https [lfriend [ me am a aumb. my19 minutes.\\n\\n\"\"\"I updated:** girlfriend was through my Facebook..** I my my friends.**.** my of dayslf.\\n\\n** went dding for my message personirl** I had to makeellpping my my my past** but I was a in\\n\\n** have ali a of ago she tolirt with my girl. and she had my about my.. me few of gir.1viously). was\\'t find that was).\\n\\n** was it for twoirl and the had my Facebook. the she wasand historyirl) was was March,\\n to to find that were flited. I a g.. f.ing on her.\\nirirl wass; I1 girirllfriend is 19 months. through my Facebook.. my permission. I it messages. her.lirty with me coupleirl.\\n found up with me after she through more with\\n'"
6102],
6103"application/vnd.google.colaboratory.intrinsic+json": {
6104"type": "string"
6105}
6106},
6107"metadata": {},
6108"execution_count": 26
6109}
6110]
6111},
6112{
6113"cell_type": "markdown",
6114"source": [
6115"### Policy Model"
6116],
6117"metadata": {
6118"id": "yIjodhd5hP7g"
6119}
6120},
6121{
6122"cell_type": "code",
6123"source": [
6124"import torch\n",
6125"import transformers\n",
6126"from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM, DataCollatorForLanguageModeling\n",
6127"from trl import RewardTrainer, SFTTrainer\n",
6128"from datasets import Dataset\n",
6129"import json\n",
6130"import pandas as pd\n",
6131"from transformers import Trainer, TrainingArguments\n",
6132"from trl import PPOTrainer, PPOConfig, AutoModelForCausalLMWithValueHead, create_reference_model"
6133],
6134"metadata": {
6135"id": "m_7o8be1ZpfJ"
6136},
6137"execution_count": 54,
6138"outputs": []
6139},
6140{
6141"cell_type": "code",
6142"source": [
6143"##model path\n",
6144"MODEL_PATH = \"bigcode/tiny_starcoder_py\"\n",
6145"DATA_PATH = \"test.parquet\""
6146],
6147"metadata": {
6148"id": "7fZZYLbrhRom"
6149},
6150"execution_count": 55,
6151"outputs": []
6152},
6153{
6154"cell_type": "code",
6155"source": [
6156"df = pd.read_parquet(DATA_PATH)\n",
6157"df = df[:1000]\n",
6158"dataset = Dataset.from_pandas(df)\n",
6159"dataset"
6160],
6161"metadata": {
6162"colab": {
6163"base_uri": "https://localhost:8080/"
6164},
6165"id": "6vJxLQvYhU23",
6166"outputId": "85339387-e6f3-441c-f3f8-5322e87005fe"
6167},
6168"execution_count": 56,
6169"outputs": [
6170{
6171"output_type": "execute_result",
6172"data": {
6173"text/plain": [
6174"Dataset({\n",
6175" features: ['prompt', 'chosen', 'rejected'],\n",
6176" num_rows: 1000\n",
6177"})"
6178]
6179},
6180"metadata": {},
6181"execution_count": 56
6182}
6183]
6184},
6185{
6186"cell_type": "code",
6187"source": [
6188"from transformers import AutoTokenizer, pipeline"
6189],
6190"metadata": {
6191"id": "o74znYVghvy5"
6192},
6193"execution_count": 59,
6194"outputs": []
6195},
6196{
6197"cell_type": "code",
6198"source": [
6199"rf_model_path = \"tinystarcoder-rlhf-model\"\n",
6200"starcoder_model = AutoModelForCausalLMWithValueHead.from_pretrained(rf_model_path)\n",
6201"starcoder_model_ref = AutoModelForCausalLMWithValueHead.from_pretrained(rf_model_path)\n",
6202"starcoder_tokenizer = AutoTokenizer.from_pretrained(rf_model_path)\n",
6203"\n",
6204"tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, padding_side='left')\n",
6205"tokenizer.pad_token = tokenizer.eos_token"
6206],
6207"metadata": {
6208"id": "NCrrq1WEjWYm"
6209},
6210"execution_count": 62,
6211"outputs": []
6212},
6213{
6214"cell_type": "code",
6215"source": [
6216"txt_in_len = 5\n",
6217"txt_out_len = 32\n",
6218"seed = 1\n",
6219"\n",
6220"dataset = dataset.map(\n",
6221" lambda x: {\"input_ids\": tokenizer.encode(\" \" + x[\"chosen\"], return_tensors=\"pt\", truncation=True, padding=\"max_length\", max_length=32)[0]},\n",
6222" batched=False,\n",
6223")\n",
6224"dataset = dataset.map(lambda x: {\"query\": tokenizer.decode(x[\"input_ids\"])}, batched=False)\n",
6225"dataset = dataset[:20480]\n",
6226"from datasets import Dataset\n",
6227"\n",
6228"dataset = Dataset.from_dict(dataset)\n",
6229"dataset.set_format(\"pytorch\")"
6230],
6231"metadata": {
6232"colab": {
6233"base_uri": "https://localhost:8080/",
6234"height": 81,
6235"referenced_widgets": [
6236"1a97f217b8974c71979590b3921142f4",
6237"8cf22559c709440da8afa8284f5721c8",
6238"e7e2120d9a6d46cc9198d3ec6227d731",
6239"f51b130d5bee4a3f9856d786feeb49dd",
6240"4ec54d6d87b54e589350489b434035fe",
6241"f49f4dc7622a421f8b51e5dbf42b9897",
6242"c34cf7b8aae94de9bbae518c124f11be",
6243"6cad2953fe054449b1c3c57dc12cb2da",
6244"63b5f2cc7f4e4dd889e3e0ce1218abb3",
6245"117f42f3c9664da481bf04bbbc3b5021",
6246"a922b8c9424b4576824246bff7023f89",
6247"d60942cf3d974c71bd1f9d5984e0580c",
6248"d4d7add22a154279943428d496ac05ca",
6249"42d9369bf5bc4f538f925e0fd0e3048f",
6250"bbe71558dbf0434a9334aba784e547ac",
6251"5d677887f4074e288eb95c7dba354680",
6252"aa6374e9313348bda63dfd8bad97481a",
6253"c8c318424b2b4ba0b788d7a15217ddbe",
6254"8614f95764c0409da8dd82ec9ad474f1",
6255"9820beb8b5d44759b81e6ca97ff7b250",
6256"07b8e67462694703a1d9dbed9f9d5c1b",
6257"88a6e00a597d4e7394cdd42386d987a7"
6258]
6259},
6260"id": "WDPj8gqqjdBY",
6261"outputId": "a405d2a3-6a76-4781-cc0d-1ca70a4dd688"
6262},
6263"execution_count": 63,
6264"outputs": [
6265{
6266"output_type": "display_data",
6267"data": {
6268"text/plain": [
6269"Map: 0%| | 0/1000 [00:00<?, ? examples/s]"
6270],
6271"application/vnd.jupyter.widget-view+json": {
6272"version_major": 2,
6273"version_minor": 0,
6274"model_id": "1a97f217b8974c71979590b3921142f4"
6275}
6276},
6277"metadata": {}
6278},
6279{
6280"output_type": "display_data",
6281"data": {
6282"text/plain": [
6283"Map: 0%| | 0/1000 [00:00<?, ? examples/s]"
6284],
6285"application/vnd.jupyter.widget-view+json": {
6286"version_major": 2,
6287"version_minor": 0,
6288"model_id": "d60942cf3d974c71bd1f9d5984e0580c"
6289}
6290},
6291"metadata": {}
6292}
6293]
6294},
6295{
6296"cell_type": "code",
6297"source": [
6298"dataset"
6299],
6300"metadata": {
6301"colab": {
6302"base_uri": "https://localhost:8080/"
6303},
6304"id": "jkQnoXkwj96E",
6305"outputId": "136d7e64-9d45-4552-97d9-31cbe6a40e85"
6306},
6307"execution_count": 64,
6308"outputs": [
6309{
6310"output_type": "execute_result",
6311"data": {
6312"text/plain": [
6313"Dataset({\n",
6314" features: ['review', 'chosen', 'rejected', 'input_ids', 'query'],\n",
6315" num_rows: 1000\n",
6316"})"
6317]
6318},
6319"metadata": {},
6320"execution_count": 64
6321}
6322]
6323},
6324{
6325"cell_type": "code",
6326"source": [
6327"sentiment_pipe_kwargs = {\"top_k\": None, \"function_to_apply\": \"none\"}\n",
6328"\n",
6329"config = PPOConfig(\n",
6330" model_name=MODEL_PATH, steps=51200, learning_rate=1.41e-5, remove_unused_columns=True\n",
6331")\n",
6332"\n",
6333"txt_in_len = 5\n",
6334"txt_out_len = 20\n",
6335"seed = 1"
6336],
6337"metadata": {
6338"id": "f9F3y_XDkAvj"
6339},
6340"execution_count": 98,
6341"outputs": []
6342},
6343{
6344"cell_type": "code",
6345"source": [
6346"import torch\n",
6347"optimizer = torch.optim.SGD(starcoder_model.parameters(), lr=config.learning_rate)\n",
6348"ppo_trainer = PPOTrainer(config, starcoder_model, starcoder_model, starcoder_tokenizer, dataset=dataset, data_collator=collator, optimizer=optimizer)"
6349],
6350"metadata": {
6351"id": "bJYnA8hZkTEK"
6352},
6353"execution_count": 99,
6354"outputs": []
6355},
6356{
6357"cell_type": "code",
6358"source": [
6359"# for i in ppo_trainer.dataloader:\n",
6360"# print(i)"
6361],
6362"metadata": {
6363"id": "i_RWvbQDkgsx"
6364},
6365"execution_count": 100,
6366"outputs": []
6367},
6368{
6369"cell_type": "code",
6370"source": [
6371"ctrl_str = [\"[negative]\", \"[positive]\"]\n",
6372"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\") # this should be handled by accelerate\n",
6373"ctrl_tokens = dict((s, starcoder_tokenizer.encode(s, return_tensors=\"pt\").squeeze().to(device)) for s in ctrl_str)"
6374],
6375"metadata": {
6376"id": "YFs55MmokaWm"
6377},
6378"execution_count": 101,
6379"outputs": []
6380},
6381{
6382"cell_type": "code",
6383"source": [
6384"def pos_logit_to_reward(logit, task):\n",
6385" \"\"\"\n",
6386" Take the positive sentiment logit and scale it for the task.\n",
6387" task [negative]: reward = -logit\n",
6388" task [neutral]: reward = -2*abs(logit)+4\n",
6389" task [positive]: reward = logit\n",
6390" \"\"\"\n",
6391" for i in range(len(logit)):\n",
6392" if task[i] == \"[negative]\":\n",
6393" logit[i] = -logit[i]\n",
6394" elif task[i] == \"[positive]\":\n",
6395" pass\n",
6396" else:\n",
6397" raise ValueError(\"task has to be in [0, 1, 2]!\")\n",
6398" return logit"
6399],
6400"metadata": {
6401"id": "g6EMveqTkuNW"
6402},
6403"execution_count": 102,
6404"outputs": []
6405},
6406{
6407"cell_type": "code",
6408"source": [
6409"pos_logit_to_reward(torch.Tensor([4, 4]), ctrl_str)"
6410],
6411"metadata": {
6412"colab": {
6413"base_uri": "https://localhost:8080/"
6414},
6415"id": "7INpXsiskue5",
6416"outputId": "fc0be95d-5b3a-4bb2-8b29-45bb91f66016"
6417},
6418"execution_count": 103,
6419"outputs": [
6420{
6421"output_type": "execute_result",
6422"data": {
6423"text/plain": [
6424"tensor([-4., 4.])"
6425]
6426},
6427"metadata": {},
6428"execution_count": 103
6429}
6430]
6431},
6432{
6433"cell_type": "code",
6434"source": [
6435"generation_kwargs = {\n",
6436" \"min_length\": -1,\n",
6437" \"top_k\": 0.0,\n",
6438" \"top_p\": 1.0,\n",
6439" \"do_sample\": True,\n",
6440" \"pad_token_id\": starcoder_tokenizer.eos_token_id,\n",
6441" \"max_new_tokens\": 32,\n",
6442" \"eos_token_id\": -1,\n",
6443"}"
6444],
6445"metadata": {
6446"id": "Y-oelQ_qkv0D"
6447},
6448"execution_count": 104,
6449"outputs": []
6450},
6451{
6452"cell_type": "code",
6453"source": [
6454"def get_score(model, tokenizer, responses):\n",
6455" positive_logist = []\n",
6456" for i in responses:\n",
6457" instructions = tokenizer.encode_plus(\n",
6458" i,\n",
6459" padding=\"max_length\",\n",
6460" max_length=32,\n",
6461" return_tensors=\"pt\")\n",
6462" with torch.no_grad():\n",
6463" outputs = model(**instructions)\n",
6464"\n",
6465" logits = outputs[0].mean()\n",
6466" positive_logist.append(logits)\n",
6467"\n",
6468" return positive_logist\n",
6469""
6470],
6471"metadata": {
6472"id": "hs9DCR5Lk0Ut"
6473},
6474"execution_count": 105,
6475"outputs": []
6476},
6477{
6478"cell_type": "code",
6479"source": [
6480"# responses =[\"ashish is a goo\", \"heelow how are you\", \"__IT_\\nr/\\n: r RelationshipRelationship]]0]\\nlsriend\\n2//M]\\n [ [ a\\n the was to the [. a friends to\\n\\n:\\n [lfriend [ me have a aried in his19 minutes.\\n\\nWhat Modified:** girlfriend was through the Facebook.. I my my friends.**** my of lf**\\n\\n** was d1ing for my few personirl** I had for findoolpping my my the future** but I was that in\\n\\n** have ali of to she tolirt my me girl. and she found my about my.. me few of gir.1viously). was\\'t find her was).\\n\\n** was it about my twoirl and the had Facebook. the and she gand historyirl) was in April,\\n to, find, were flirted. I a messages.. f.ing on her.\\n girlM\\n; I1 girirllfriend and the19 months. to my Facebook.. my permission. she her messages. my.lirty with my fewirl.\\n found her with me. I through more with\\n\"]\n",
6481"# get_score(starcoder_model, tokenizer, responses)"
6482],
6483"metadata": {
6484"id": "PV0W3vzzlAK7"
6485},
6486"execution_count": 113,
6487"outputs": []
6488},
6489{
6490"cell_type": "code",
6491"source": [
6492"# from random import choices\n",
6493"# from tqdm import tqdm\n",
6494"# import time\n",
6495"# import numpy as np\n",
6496"\n",
6497"# for epoch in range(1):\n",
6498"# for batch in tqdm(ppo_trainer.dataloader):\n",
6499"# (logs, game_data,) = (\n",
6500"# dict(),\n",
6501"# dict(),\n",
6502"# )\n",
6503"\n",
6504"# print(ctrl_str)\n",
6505"# #### prepend a random control token\n",
6506"# task_list = choices(ctrl_str, k=config.batch_size)\n",
6507"# game_data[\"query\"] = [t + q for t, q in zip(task_list, batch[\"query\"])]\n",
6508"# query_tensors = [torch.cat((ctrl_tokens[t], input_ids)) for t, input_ids in zip(task_list, batch[\"input_ids\"])]\n",
6509"\n",
6510"# #### get response from gpt2\n",
6511"# response_tensors = []\n",
6512"# for query in query_tensors:\n",
6513"# response = ppo_trainer.generate(query, **generation_kwargs)\n",
6514"# response_tensors.append(response.squeeze()[-txt_out_len:])\n",
6515"# # print(response_tensors)\n",
6516"# game_data[\"response\"] = [starcoder_tokenizer.decode(r.squeeze()) for r in response_tensors]\n",
6517"\n",
6518"# #### sentiment analysis\n",
6519"# texts = [q + r for q, r in zip(batch[\"query\"], game_data[\"response\"])]\n",
6520"# logits = get_score(starcoder_model,starcoder_tokenizer, texts)\n",
6521"# rewards = pos_logit_to_reward(logits, task_list)\n",
6522"\n",
6523"# #### Run PPO training\n",
6524"# t = time.time()\n",
6525"# stats = ppo_trainer.step(query_tensors, response_tensors, rewards)\n",
6526"\n",
6527"# for cs in ctrl_str:\n",
6528"# key = \"env/reward_\" + cs.strip(\"[]\")\n",
6529"# stats[key] = np.mean([r.cpu().numpy() for r, t in zip(rewards, task_list) if t == cs])\n",
6530"# ppo_trainer.log_stats(stats, game_data, rewards)"
6531],
6532"metadata": {
6533"id": "WTiDklXBlT52"
6534},
6535"execution_count": 115,
6536"outputs": []
6537},
6538{
6539"cell_type": "code",
6540"source": [
6541"###saving the model\n",
6542"starcoder_model.save_pretrained(\"StarCoderTinyrhlfmodel/\")\n",
6543"starcoder_tokenizer.save_pretrained(\"StarCoderTinyrhlfmodel/\")"
6544],
6545"metadata": {
6546"colab": {
6547"base_uri": "https://localhost:8080/"
6548},
6549"id": "MNFD6wvSlWux",
6550"outputId": "7cda4399-b238-4336-a5aa-480613ece097"
6551},
6552"execution_count": 109,
6553"outputs": [
6554{
6555"output_type": "execute_result",
6556"data": {
6557"text/plain": [
6558"('StarCoderTinyrhlfmodel/tokenizer_config.json',\n",
6559" 'StarCoderTinyrhlfmodel/special_tokens_map.json',\n",
6560" 'StarCoderTinyrhlfmodel/vocab.json',\n",
6561" 'StarCoderTinyrhlfmodel/merges.txt',\n",
6562" 'StarCoderTinyrhlfmodel/added_tokens.json',\n",
6563" 'StarCoderTinyrhlfmodel/tokenizer.json')"
6564]
6565},
6566"metadata": {},
6567"execution_count": 109
6568}
6569]
6570},
6571{
6572"cell_type": "code",
6573"source": [
6574"from transformers import pipeline, set_seed\n",
6575"model_path = \"StarCoderTinyrhlfmodel/\"\n",
6576"set_seed(42)\n",
6577"pipe = pipeline(\"text-generation\",model=model_path, tokenizer=model_path, max_length=30, num_return_sequences=5)"
6578],
6579"metadata": {
6580"colab": {
6581"base_uri": "https://localhost:8080/"
6582},
6583"id": "f9KWg8IWoQqN",
6584"outputId": "b175814c-5206-488a-fc35-da972e3719cb"
6585},
6586"execution_count": 110,
6587"outputs": [
6588{
6589"output_type": "stream",
6590"name": "stderr",
6591"text": [
6592"Some weights of the model checkpoint at StarCoderTinyrhlfmodel/ were not used when initializing GPTBigCodeForCausalLM: ['v_head.summary.weight', 'v_head.summary.bias']\n",
6593"- This IS expected if you are initializing GPTBigCodeForCausalLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
6594"- This IS NOT expected if you are initializing GPTBigCodeForCausalLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
6595]
6596}
6597]
6598}
6599]
6600}