diff --git "a/competition/08_InterLM_finetuning.ipynb" "b/competition/08_InterLM_finetuning.ipynb" new file mode 100644--- /dev/null +++ "b/competition/08_InterLM_finetuning.ipynb" @@ -0,0 +1,4274 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "eb33b19f-1206-41ee-84e2-e6258a12eef7", + "showTitle": false, + "title": "" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 3.11.9\n" + ] + } + ], + "source": [ + "if 'dbutils' in locals():\n", + " dbutils.library.restartPython()\n", + "\n", + "!python --version" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "0ea8b46b-839b-445b-8043-ccdf4e920ace", + "showTitle": false, + "title": "" + } + }, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "6d394937-6c99-4a7c-9d32-7600a280032f", + "showTitle": false, + "title": "" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "workding dir: /home/inflaton/code/projects/courses/logical-reasoning\n" + ] + } + ], + "source": [ + "import os\n", + "import sys\n", + "from pathlib import Path\n", + "\n", + "workding_dir = str(Path.cwd().parent)\n", + "os.chdir(workding_dir)\n", + "sys.path.append(workding_dir)\n", + "print(\"workding dir:\", workding_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "ac667aba-076e-4de6-9984-8f6a67cb09cd", + "showTitle": false, + "title": "" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "need_to_setup_env = False\n", + "need_to_setup_env" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "72f9cf79-7b0d-4d9e-90a0-1fa5251b947f", + "showTitle": false, + "title": "" + } + }, + "outputs": [], + "source": [ + "if need_to_setup_env:\n", + " %pip config set global.index-url https://artifacts.forge.mastercard.com/artifactory/api/pypi/python/simple\n", + " %pip install tf-keras\n", + " %pip install -q --upgrade accelerate einops xformers torchvision\n", + " %pip install -r requirements.txt\n", + " !cd ../LLaMA-Factory && pip install -e .[torch,bitsandbytes] && FLASH_ATTENTION_FORCE_BUILD=TRUE pip install --upgrade flash-attn" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "c06c61fd-4c6f-4099-bd3b-46188ab835d7", + "showTitle": false, + "title": "" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "workding dir: /home/inflaton/code/projects/courses/logical-reasoning\n" + ] + } + ], + "source": [ + "os.chdir(workding_dir)\n", + "sys.path.append(workding_dir)\n", + "print(\"workding dir:\", workding_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "9f67ec60-2f24-411c-84eb-0dd664b44775", + "showTitle": false, + "title": "" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading env vars from: /home/inflaton/code/projects/courses/logical-reasoning/.env\n" + ] + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from dotenv import find_dotenv, load_dotenv\n", + "\n", + "found_dotenv = find_dotenv(\".env\")\n", + "\n", + "if len(found_dotenv) == 0:\n", + " found_dotenv = find_dotenv(\".env.example\")\n", + "print(f\"loading env vars from: {found_dotenv}\")\n", + "load_dotenv(found_dotenv, override=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "f1597656-8042-4878-9d3b-9ebfb8dd86dc", + "showTitle": false, + "title": "" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "internlm/internlm2_5-7b-chat-1m None True datasets/mgtv results/results/mgtv-results_temp.csv\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "model_name = os.getenv(\"MODEL_NAME\")\n", + "adapter_name_or_path = os.getenv(\"ADAPTER_NAME_OR_PATH\")\n", + "load_in_4bit = os.getenv(\"LOAD_IN_4BIT\") == \"true\"\n", + "data_path = os.getenv(\"LOGICAL_REASONING_DATA_PATH\")\n", + "results_path = os.getenv(\"LOGICAL_REASONING_RESULTS_PATH\")\n", + "use_english_datasets = os.getenv(\"USE_ENGLISH_DATASETS\") == \"true\"\n", + "\n", + "print(model_name, adapter_name_or_path, load_in_4bit, data_path, results_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "e3ab54ba-7b6d-4817-bf2e-c5d711508b58", + "showTitle": false, + "title": "" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tue Jul 9 20:54:47 2024 \n", + "+---------------------------------------------------------------------------------------+\n", + "| NVIDIA-SMI 545.23.07 Driver Version: 546.12 CUDA Version: 12.3 |\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 NVIDIA GeForce RTX 4080 ... On | 00000000:01:00.0 Off | N/A |\n", + "| N/A 52C P0 28W / 150W | 0MiB / 12282MiB | 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": 10, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "b2a43943-9324-4839-9a47-cfa72de2244b", + "showTitle": false, + "title": "" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 3.11.9\n", + "\u001b[33mWARNING: Package(s) not found: flash-attn\u001b[0m\u001b[33m\n", + "\u001b[0mCPU times: user 7.81 ms, sys: 1.3 ms, total: 9.11 ms\n", + "Wall time: 495 ms\n" + ] + } + ], + "source": [ + "%%time\n", + "!python --version\n", + "!pip show flash-attn" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pandas as pd\n", + "from llm_toolkit.logical_reasoning_utils import *\n", + "from llm_toolkit.llm_utils import *\n", + "\n", + "def load_data(alpaca_data_path=\"llama-factory/data/alpaca_mgtv_p1.json\"):\n", + " if os.path.exists(alpaca_data_path):\n", + " print(\"loading existing data from:\", alpaca_data_path)\n", + " data = pd.read_json(alpaca_data_path, orient=\"records\", lines=False)\n", + " return data\n", + "\n", + " print(\"loading new data from:\", alpaca_data_path)\n", + " datasets = load_logical_reasoning_dataset(\n", + " data_path, chinese_prompt=not use_english_datasets\n", + " )\n", + " df_train = datasets[\"train\"].to_pandas()\n", + " df_train[\"instruction\"] = df_train.apply(\n", + " lambda x: P1.format(x[\"puzzle\"], x[\"truth\"], x[\"text\"]), axis=1\n", + " )\n", + "\n", + " df_alpaca = pd.DataFrame(\n", + " {\"instruction\": [\"\"] * len(df_train), \"input\": [\"\"] * len(df_train)}\n", + " )\n", + " df_alpaca[\"instruction\"] = df_train[\"instruction\"]\n", + " df_alpaca[\"output\"] = df_train[\"label\"]\n", + " df_alpaca.to_json(alpaca_data_path, orient=\"records\", lines=False, indent=2)\n", + " return df_alpaca" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading existing data from: llama-factory/data/alpaca_mgtv_p1.json\n", + "--------------------------------------------------\n", + "instruction: 你是一个逻辑游戏的主持人。游戏规则如下：\n", + "\n", + "1. 参与者会得到一个谜题。\n", + "2. 参与者可以通过提问来获取线索，尝试解开谜题。\n", + "3. 对于每个问题，主持人将根据实际情况回答以下五个选项之一：是、不是、不重要、回答正确、问法错误。\n", + "4. 回答中不能添加任何其它信息，也不能省略选项中的任何一个字。例如，不可以把“不是”省略成“不”。\n", + "5. 参与者需要根据回答来推理，并最终找出谜题的正确答案。\n", + "\n", + "请严格按照这些规则回答参与者提出的问题。\n", + "\n", + "谜题: 在甄家村里，有一个古老的传说：每年南瓜丰收的季节，南瓜田里总有一个最大的南瓜会不翼而飞，村民们对此现象困惑不解。请找出南瓜失踪背后的原因。\n", + "\n", + "实际情况: 真相原来与一位年迈的农夫有关。这位农夫年轻时，曾与一位美丽的姑娘相恋。他们约定在南瓜丰收的季节结婚。然而，命运弄人，姑娘在婚礼前的一场意外中离世。悲伤的农夫为了纪念心爱的姑娘，每年都会将最大的南瓜偷走，放到姑娘的墓前，以此寄托自己的哀思。这一行为延续了多年，成为了乡村里一个神秘的传说。\n", + "\n", + "参与者提出的问题: 偷的人信神吗\n", + "\n", + "--------------------------------------------------\n", + "input: \n", + "--------------------------------------------------\n", + "output: 不是\n" + ] + } + ], + "source": [ + "df_alpaca = load_data()\n", + "print_row_details(df_alpaca)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "\n", + "!./scripts/tune-lf.sh config/internlm2_5_7b_lora_sft.yaml" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "4f51fafe-5527-49a2-8ccf-a0192e69493a", + "showTitle": false, + "title": "" + } + }, + "outputs": [], + "source": [ + "def evaluate_model_all_epochs(model_name, adapter_path_base, num_train_epochs, start_epoch=0, load_in_4bit=True, num_of_entries=-1):\n", + " os.environ[\"MODEL_NAME\"] = model_name \n", + " os.environ[\"LOAD_IN_4BIT\"] = \"true\" if load_in_4bit else \"false\"\n", + " for i in range(start_epoch, num_train_epochs + 1):\n", + " print(f\"Epoch {i}\")\n", + " if i == 0:\n", + " os.unsetenv(\"ADAPTER_NAME_OR_PATH\")\n", + " else:\n", + " adapter_path = f\"{adapter_path_base}/checkpoint-{560 * i}\"\n", + " os.environ[\"ADAPTER_NAME_OR_PATH\"] = adapter_path\n", + " \n", + " !python llm_toolkit/eval_logical_reasoning.py {num_of_entries}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "\n", + "evaluate_model_all_epochs(\"internlm/internlm2_5-7b-chat-1m\", \"saves/internlm2_5_7b/lora/sft\", 3, start_epoch=1, load_in_4bit=True, num_of_entries=10)" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "application/vnd.databricks.v1+notebook": { + "dashboards": [], + "environmentMetadata": null, + "language": "python", + "notebookMetadata": { + "mostRecentlyExecutedCommandWithImplicitDF": { + "commandId": -1, + "dataframes": [ + "_sqldf" + ] + }, + "pythonIndentUnit": 4 + }, + "notebookName": "10_eval-lf-medium-py3.11", + "widgets": {} + }, + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": 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