diff --git "a/competition/05r2_InternLM_NV4080.ipynb" "b/competition/05r2_InternLM_NV4080.ipynb" new file mode 100644--- /dev/null +++ "b/competition/05r2_InternLM_NV4080.ipynb" @@ -0,0 +1,4271 @@ +{ + "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/llm-finetuning\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/llm-finetuning\n" + ] + } + ], + "source": [ + "os.chdir(workding_dir)\n", + "sys.path.append(workding_dir)\n", + "print(\"workding dir:\", workding_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "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/llm-finetuning/.env\n" + ] + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 15, + "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": 16, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "f1597656-8042-4878-9d3b-9ebfb8dd86dc", + "showTitle": false, + "title": "" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "('unsloth/Qwen2-0.5B-Instruct',\n", + " True,\n", + " None,\n", + " None,\n", + " 2048,\n", + " 6,\n", + " None,\n", + " 'datasets/mgtv',\n", + " 'results/mgtv-results_nv4080_r2.csv',\n", + " None,\n", + " None)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "\n", + "model_name = os.getenv(\"MODEL_NAME\")\n", + "token = os.getenv(\"HF_TOKEN\") or None\n", + "load_in_4bit = os.getenv(\"LOAD_IN_4BIT\") == \"true\"\n", + "local_model = os.getenv(\"LOCAL_MODEL\")\n", + "hub_model = os.getenv(\"HUB_MODEL\")\n", + "num_train_epochs = int(os.getenv(\"NUM_TRAIN_EPOCHS\") or 0)\n", + "data_path = os.getenv(\"LOGICAL_REASONING_DATA_PATH\")\n", + "results_path = os.getenv(\"LOGICAL_REASONING_RESULTS_PATH\")\n", + "\n", + "max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!\n", + "dtype = (\n", + " None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n", + ")\n", + "\n", + "model_name, load_in_4bit, local_model, hub_model, max_seq_length, num_train_epochs, dtype, data_path, results_path, os.getenv(\"DISABLE_MLFLOW_INTEGRATION\"), os.getenv(\"WANDB_DISABLED\")" + ] + }, + { + "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": [ + "Sun Jul 7 09:03:20 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 54C P0 29W / 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 5.67 ms, sys: 11.7 ms, total: 17.3 ms\n", + "Wall time: 779 ms\n" + ] + } + ], + "source": [ + "%%time\n", + "!python --version\n", + "!pip show flash-attn" + ] + }, + { + "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, num_train_epochs, adapter_path_base, 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(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": 19, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "0894c329-05c5-4fe8-85ff-404defe8528a", + "showTitle": false, + "title": "" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0\n", + "loading env vars from: /home/inflaton/code/projects/courses/llm-finetuning/.env\n", + "Adding /home/inflaton/code/projects/courses/llm-finetuning to sys.path\n", + "loading /home/inflaton/code/projects/courses/llm-finetuning/llm_toolkit/logical_reasoning_utils.py\n", + "internlm/internlm2_5-7b-chat-1m None False datasets/mgtv results/mgtv-results_nv4080_r2.csv\n", + "(1) GPU = NVIDIA GeForce RTX 4080 Laptop GPU. Max memory = 11.994 GB.\n", + "0.0 GB of memory reserved.\n", + "loading model: internlm/internlm2_5-7b-chat-1m\n", + "Loading checkpoint shards: 100%|██████████████████| 8/8 [03:55<00:00, 29.38s/it]\n", + "WARNING:root:Some parameters are on the meta device device because they were offloaded to the cpu.\n", + "(2) GPU = NVIDIA GeForce RTX 4080 Laptop GPU. Max memory = 11.994 GB.\n", + "8.834 GB of memory reserved.\n", + "loading train/test data files\n", + "DatasetDict({\n", + " train: Dataset({\n", + " features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n", + " num_rows: 25000\n", + " })\n", + " test: Dataset({\n", + " features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n", + " num_rows: 3000\n", + " })\n", + "})\n", + " text ... prompt\n", + "0 甄加索是自杀吗 ... <|im_start|>system\\nYou are an expert in lo...\n", + "\n", + "[1 rows x 8 columns]\n", + "Evaluating model: internlm/internlm2_5-7b-chat-1m\n", + " 0%| | 0/3000 [00:00\n", + "--------\n", + "step 2: 不是\n", + "--------\n", + "step 3: 不是\n", + "100%|█████████████████████████████████████| 3000/3000 [1:27:06<00:00, 1.74s/it]\n", + "(3) GPU = NVIDIA GeForce RTX 4080 Laptop GPU. Max memory = 11.994 GB.\n", + "9.9 GB of memory reserved.\n", + " text ... internlm/internlm2_5-7b-chat-1m\n", + "0 甄加索是自杀吗 ... 不是\n", + "\n", + "[1 rows x 6 columns]\n", + "{'accuracy': 0.7586666666666667, 'incorrect_ids': [2, 9, 11, 14, 15, 16, 18, 19, 29, 31, 36, 48, 52, 53, 59, 61, 66, 67, 75, 76, 78, 80, 81, 82, 83, 84, 88, 91, 93, 94, 96, 99, 103, 104, 105, 106, 113, 115, 117, 120, 121, 123, 126, 128, 129, 136, 141, 143, 149, 150, 153, 155, 161, 164, 173, 190, 191, 193, 199, 200, 201, 202, 218, 224, 226, 227, 228, 230, 234, 236, 237, 238, 245, 248, 250, 252, 259, 260, 261, 264, 269, 275, 276, 278, 279, 283, 284, 290, 293, 295, 299, 308, 311, 314, 318, 319, 320, 324, 325, 328, 334, 335, 337, 338, 339, 343, 346, 349, 356, 361, 368, 370, 375, 377, 383, 384, 386, 389, 397, 409, 410, 425, 429, 430, 432, 438, 445, 447, 450, 452, 453, 454, 456, 457, 458, 464, 465, 469, 470, 471, 472, 478, 481, 486, 490, 492, 495, 497, 501, 502, 503, 506, 507, 510, 515, 516, 517, 518, 519, 520, 533, 536, 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