{ "cells": [ { "cell_type": "code", "execution_count": 0, "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": { "byteLimit": 2048000, "rowLimit": 10000 }, "inputWidgets": {}, "nuid": "eb33b19f-1206-41ee-84e2-e6258a12eef7", "showTitle": false, "title": "" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "output_type": "stream", "text": [ "Python 3.11.0rc1\r\n" ] } ], "source": [ "if 'dbutils' in locals():\n", " dbutils.library.restartPython()\n", "\n", "!python --version" ] }, { "cell_type": "code", "execution_count": 0, "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": 0, "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": { "byteLimit": 2048000, "rowLimit": 10000 }, "inputWidgets": {}, "nuid": "6d394937-6c99-4a7c-9d32-7600a280032f", "showTitle": false, "title": "" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "output_type": "stream", "text": [ "workding dir: /Workspace/Users/donghao.huang@mastercard.com/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": 0, "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": { "byteLimit": 2048000, "rowLimit": 10000 }, "inputWidgets": {}, "nuid": "ac667aba-076e-4de6-9984-8f6a67cb09cd", "showTitle": false, "title": "" } }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "True" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "need_to_setup_env = True\n", "need_to_setup_env" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": { "byteLimit": 2048000, "rowLimit": 10000 }, "inputWidgets": {}, "nuid": "72f9cf79-7b0d-4d9e-90a0-1fa5251b947f", "showTitle": false, "title": "" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "output_type": "stream", "text": [ "Writing to /root/.config/pip/pip.conf\n\u001B[43mNote: you may need to restart the kernel using %restart_python or dbutils.library.restartPython() to use updated packages.\u001B[0m\nLooking in indexes: https://artifacts.forge.mastercard.com/artifactory/api/pypi/python/simple\nCollecting tf-keras\n Downloading https://artifacts.forge.mastercard.com/artifactory/api/pypi/python/packages/packages/75/aa/cf09f8956d4f276f655b13674e15d8d6015fd832f9689aa9ff2a515781ab/tf_keras-2.16.0-py3-none-any.whl (1.7 MB)\n\u001B[?25l \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m0.0/1.7 MB\u001B[0m 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uvicorn->llamafactory==0.8.3.dev0) (1.0.1)\r\nCollecting httptools>=0.5.0\r\n Downloading https://artifacts.forge.mastercard.com/artifactory/api/pypi/python/packages/packages/59/23/047a89e66045232fb82c50ae57699e40f70e073ae5ccd53f54e532fbd2a2/httptools-0.6.1-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (318 kB)\r\n\u001B[?25l \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m0.0/318.5 kB\u001B[0m \u001B[31m?\u001B[0m eta \u001B[36m-:--:--\u001B[0m\r\u001B[2K \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m318.5/318.5 kB\u001B[0m \u001B[31m21.8 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\r\n\u001B[?25hCollecting uvloop!=0.15.0,!=0.15.1,>=0.14.0\r\n Downloading https://artifacts.forge.mastercard.com/artifactory/api/pypi/python/packages/packages/86/cc/1829b3f740e4cb1baefff8240a1c6fc8db9e3caac7b93169aec7d4386069/uvloop-0.19.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.5 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(pyproject.toml) ... \u001B[?25l-\b \b\\\b \b|\b \b/\b \b-\b \bdone\r\n\u001B[?25h Created wheel for llamafactory: filename=llamafactory-0.8.3.dev0-0.editable-py3-none-any.whl size=20606 sha256=86369c868814853c5caddc693688c8614012a22eee900a2a118ac3729fbca627\r\n Stored in directory: /tmp/pip-ephem-wheel-cache-dxf90sjv/wheels/fd/43/11/15706c0f3370423ac02e35a1ec8d8366771c0d4b983302c272\r\n Building wheel for ffmpy (setup.py) ... \u001B[?25l-\b \b\\\b \bdone\r\n\u001B[?25h Created wheel for ffmpy: filename=ffmpy-0.3.2-py3-none-any.whl size=5583 sha256=9920ffb5547ad3559ce7783441e83263c159384c4a8499db8e4350f1559f4d47\r\n Stored in directory: /root/.cache/pip/wheels/ed/55/90/17b538ceab811732884e7b3d395db681473c4c623433bdc6a4\r\nSuccessfully built fire llamafactory ffmpy\r\nInstalling collected packages: pydub, ffmpy, websockets, uvloop, uvicorn, urllib3, tzdata, toolz, tomlkit, shtab, shellingham, semantic-version, ruff, python-multipart, pydantic-core, httptools, fire, docstring-parser, dnspython, annotated-types, aiofiles, watchfiles, starlette, pydantic, pandas, email_validator, tyro, typer, sse-starlette, huggingface-hub, altair, tokenizers, gradio-client, fastapi-cli, bitsandbytes, transformers, fastapi, trl, peft, gradio, llamafactory\r\n Attempting uninstall: urllib3\r\n Found existing installation: urllib3 1.26.16\r\n Not uninstalling urllib3 at /databricks/python3/lib/python3.11/site-packages, outside environment /local_disk0/.ephemeral_nfs/envs/pythonEnv-4f12d295-94c6-4b4f-aa70-006a091913dc\r\n Can't uninstall 'urllib3'. No files were found to uninstall.\r\n Attempting uninstall: tzdata\r\n Found existing installation: tzdata 2022.1\r\n Not uninstalling tzdata at /databricks/python3/lib/python3.11/site-packages, outside environment /local_disk0/.ephemeral_nfs/envs/pythonEnv-4f12d295-94c6-4b4f-aa70-006a091913dc\r\n Can't uninstall 'tzdata'. No files were found to uninstall.\r\n Attempting uninstall: pydantic\r\n Found existing installation: pydantic 1.10.6\r\n Not uninstalling pydantic at /databricks/python3/lib/python3.11/site-packages, outside environment /local_disk0/.ephemeral_nfs/envs/pythonEnv-4f12d295-94c6-4b4f-aa70-006a091913dc\r\n Can't uninstall 'pydantic'. No files were found to uninstall.\r\n Attempting uninstall: pandas\r\n Found existing installation: pandas 1.5.3\r\n Not uninstalling pandas at /databricks/python3/lib/python3.11/site-packages, outside environment /local_disk0/.ephemeral_nfs/envs/pythonEnv-4f12d295-94c6-4b4f-aa70-006a091913dc\r\n Can't uninstall 'pandas'. No files were found to uninstall.\r\n Attempting uninstall: typer\r\n Found existing installation: typer 0.9.4\r\n Not uninstalling typer at /databricks/python3/lib/python3.11/site-packages, outside environment /local_disk0/.ephemeral_nfs/envs/pythonEnv-4f12d295-94c6-4b4f-aa70-006a091913dc\r\n Can't uninstall 'typer'. No files were found to uninstall.\r\n Attempting uninstall: huggingface-hub\r\n Found existing installation: huggingface-hub 0.20.2\r\n Not uninstalling huggingface-hub at /databricks/python3/lib/python3.11/site-packages, outside environment /local_disk0/.ephemeral_nfs/envs/pythonEnv-4f12d295-94c6-4b4f-aa70-006a091913dc\r\n Can't uninstall 'huggingface-hub'. No files were found to uninstall.\r\n Attempting uninstall: tokenizers\r\n Found existing installation: tokenizers 0.15.0\r\n Not uninstalling tokenizers at /databricks/python3/lib/python3.11/site-packages, outside environment /local_disk0/.ephemeral_nfs/envs/pythonEnv-4f12d295-94c6-4b4f-aa70-006a091913dc\r\n Can't uninstall 'tokenizers'. No files were found to uninstall.\r\n Attempting uninstall: transformers\r\n Found existing installation: transformers 4.39.2\r\n Not uninstalling transformers at /databricks/python3/lib/python3.11/site-packages, outside environment /local_disk0/.ephemeral_nfs/envs/pythonEnv-4f12d295-94c6-4b4f-aa70-006a091913dc\r\n Can't uninstall 'transformers'. No files were found to uninstall.\r\n\u001B[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\npetastorm 0.12.1 requires pyspark>=2.1.0, which is not installed.\r\nydata-profiling 4.5.1 requires pandas!=1.4.0,<2.1,>1.1, but you have pandas 2.2.2 which is incompatible.\r\nydata-profiling 4.5.1 requires pydantic<2,>=1.8.1, but you have pydantic 2.8.2 which is incompatible.\r\nydata-profiling 4.5.1 requires seaborn<0.13,>=0.10.1, but you have seaborn 0.13.2 which is incompatible.\r\nweasel 0.3.4 requires typer<0.10.0,>=0.3.0, but you have typer 0.12.3 which is incompatible.\r\nspacy 3.7.2 requires typer<0.10.0,>=0.3.0, but you have typer 0.12.3 which is incompatible.\r\nlangchain 0.1.13 requires langchain-core<0.2.0,>=0.1.33, but you have langchain-core 0.2.11 which is incompatible.\r\nlangchain-community 0.0.34 requires langchain-core<0.2.0,>=0.1.45, but you have langchain-core 0.2.11 which is incompatible.\r\ngoogle-auth 2.21.0 requires urllib3<2.0, but you have urllib3 2.2.2 which is incompatible.\r\nbotocore 1.34.39 requires urllib3<2.1,>=1.25.4; python_version >= \"3.10\", but you have urllib3 2.2.2 which is incompatible.\u001B[0m\u001B[31m\r\n\u001B[0mSuccessfully installed aiofiles-23.2.1 altair-5.3.0 annotated-types-0.7.0 bitsandbytes-0.43.1 dnspython-2.6.1 docstring-parser-0.16 email_validator-2.2.0 fastapi-0.111.0 fastapi-cli-0.0.4 ffmpy-0.3.2 fire-0.6.0 gradio-4.37.2 gradio-client-1.0.2 httptools-0.6.1 huggingface-hub-0.23.4 llamafactory-0.8.3.dev0 pandas-2.2.2 peft-0.11.1 pydantic-2.8.2 pydantic-core-2.20.1 pydub-0.25.1 python-multipart-0.0.9 ruff-0.5.1 semantic-version-2.10.0 shellingham-1.5.4 shtab-1.7.1 sse-starlette-2.1.2 starlette-0.37.2 tokenizers-0.19.1 tomlkit-0.12.0 toolz-0.12.1 transformers-4.42.3 trl-0.9.4 typer-0.12.3 tyro-0.8.5 tzdata-2024.1 urllib3-2.2.2 uvicorn-0.30.1 uvloop-0.19.0 watchfiles-0.22.0 websockets-11.0.3\r\n\r\n\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.0.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.1.1\u001B[0m\r\n\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\r\nLooking in indexes: https://artifacts.forge.mastercard.com/artifactory/api/pypi/python/simple\r\nRequirement already satisfied: flash-attn in /databricks/python3/lib/python3.11/site-packages (2.5.6)\r\nCollecting flash-attn\r\n Downloading https://artifacts.forge.mastercard.com/artifactory/api/pypi/python/packages/packages/14/cd/53cbd9bd45e9bf1002f0c4de488e549501d085f4318c906807a0e50164cd/flash_attn-2.5.9.post1.tar.gz (2.6 MB)\r\n\u001B[?25l \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m0.0/2.6 MB\u001B[0m \u001B[31m?\u001B[0m eta \u001B[36m-:--:--\u001B[0m\r\u001B[2K \u001B[91m━━━━━━━━━━━━━\u001B[0m\u001B[90m╺\u001B[0m\u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m0.8/2.6 MB\u001B[0m \u001B[31m25.8 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(12.1.105)\r\nRequirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /databricks/python3/lib/python3.11/site-packages (from torch->flash-attn) (12.1.105)\r\nRequirement already satisfied: nvidia-nccl-cu12==2.20.5 in /local_disk0/.ephemeral_nfs/envs/pythonEnv-4f12d295-94c6-4b4f-aa70-006a091913dc/lib/python3.11/site-packages (from torch->flash-attn) (2.20.5)\r\nRequirement already satisfied: filelock in /databricks/python3/lib/python3.11/site-packages (from torch->flash-attn) (3.9.0)\r\nRequirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /databricks/python3/lib/python3.11/site-packages (from torch->flash-attn) (11.0.2.54)\r\nRequirement already satisfied: triton==2.3.0 in /local_disk0/.ephemeral_nfs/envs/pythonEnv-4f12d295-94c6-4b4f-aa70-006a091913dc/lib/python3.11/site-packages (from torch->flash-attn) (2.3.0)\r\nRequirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /databricks/python3/lib/python3.11/site-packages (from torch->flash-attn) 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/databricks/python3/lib/python3.11/site-packages (from sympy->torch->flash-attn) (1.3.0)\r\nBuilding wheels for collected packages: flash-attn\r\n Building wheel for flash-attn (setup.py) ... \u001B[?25l-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \bdone\r\n\u001B[?25h Created wheel for flash-attn: filename=flash_attn-2.5.9.post1-cp311-cp311-linux_x86_64.whl size=120006504 sha256=1fa47bf3860e5fc91274ed1950319126fbe56455d77d98790d2d5bc4ea7314d8\r\n Stored in directory: /root/.cache/pip/wheels/db/5a/cb/16589a3850b0e8b66ad3b4a53a92533584def3d68c867e27e8\r\nSuccessfully built flash-attn\r\nInstalling collected packages: flash-attn\r\n Attempting uninstall: flash-attn\r\n Found existing installation: flash-attn 2.5.6\r\n Not uninstalling flash-attn at /databricks/python3/lib/python3.11/site-packages, outside environment /local_disk0/.ephemeral_nfs/envs/pythonEnv-4f12d295-94c6-4b4f-aa70-006a091913dc\r\n Can't uninstall 'flash-attn'. No files were found to uninstall.\r\nSuccessfully installed flash-attn-2.5.9.post1\r\n\r\n\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.0.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.1.1\u001B[0m\r\n\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\r\n" ] } ], "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": 0, "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": { "byteLimit": 2048000, "rowLimit": 10000 }, "inputWidgets": {}, "nuid": "c06c61fd-4c6f-4099-bd3b-46188ab835d7", "showTitle": false, "title": "" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "output_type": "stream", "text": [ "workding dir: /Workspace/Users/donghao.huang@mastercard.com/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": 0, "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": { "byteLimit": 2048000, "rowLimit": 10000 }, "inputWidgets": {}, "nuid": "9f67ec60-2f24-411c-84eb-0dd664b44775", "showTitle": false, "title": "" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "output_type": "stream", "text": [ "loading env vars from: /Workspace/Users/donghao.huang@mastercard.com/llm-finetuning/.env\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "True" ] }, "execution_count": 6, "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": 0, "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": { "byteLimit": 2048000, "rowLimit": 10000 }, "inputWidgets": {}, "nuid": "f1597656-8042-4878-9d3b-9ebfb8dd86dc", "showTitle": false, "title": "" } }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "('unsloth/Qwen2-1.5B-Instruct',\n", " True,\n", " None,\n", " None,\n", " 2048,\n", " 10,\n", " None,\n", " 'datasets/mgtv/',\n", " 'results/mgtv-results_bf16.csv',\n", " 'TRUE',\n", " 'true')" ] }, "execution_count": 7, "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": 0, "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": { "byteLimit": 2048000, "rowLimit": 10000 }, "inputWidgets": {}, "nuid": "e3ab54ba-7b6d-4817-bf2e-c5d711508b58", "showTitle": false, "title": "" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "output_type": "stream", "text": [ "Sun Jul 7 00:36:17 2024 \r\n+---------------------------------------------------------------------------------------+\r\n| NVIDIA-SMI 535.54.03 Driver Version: 535.54.03 CUDA Version: 12.2 |\r\n|-----------------------------------------+----------------------+----------------------+\r\n| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\r\n| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\r\n| | | MIG M. |\r\n|=========================================+======================+======================|\r\n| 0 Tesla T4 Off | 00000001:00:00.0 Off | 0 |\r\n| N/A 31C P8 9W / 70W | 2MiB / 15360MiB | 0% Default |\r\n| | | N/A |\r\n+-----------------------------------------+----------------------+----------------------+\r\n \r\n+---------------------------------------------------------------------------------------+\r\n| Processes: |\r\n| GPU GI CI PID Type Process name GPU Memory |\r\n| ID ID Usage |\r\n|=======================================================================================|\r\n| No running processes found |\r\n+---------------------------------------------------------------------------------------+\r\n" ] } ], "source": [ "!nvidia-smi" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": { "byteLimit": 2048000, "rowLimit": 10000 }, "inputWidgets": {}, "nuid": "b2a43943-9324-4839-9a47-cfa72de2244b", "showTitle": false, "title": "" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "output_type": "stream", "text": [ "Python 3.11.0rc1\r\nName: flash-attn\nVersion: 2.5.9.post1\nSummary: Flash Attention: Fast and Memory-Efficient Exact Attention\nHome-page: https://github.com/Dao-AILab/flash-attention\nAuthor: Tri Dao\nAuthor-email: trid@cs.stanford.edu\nLicense: \nLocation: /local_disk0/.ephemeral_nfs/envs/pythonEnv-4f12d295-94c6-4b4f-aa70-006a091913dc/lib/python3.11/site-packages\nRequires: einops, torch\nRequired-by: \nCPU times: user 11.2 ms, sys: 5.22 ms, total: 16.4 ms\nWall time: 4.34 s\n" ] } ], "source": [ "%%time\n", "!python --version\n", "!pip show flash-attn" ] }, { "cell_type": "code", "execution_count": 0, "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": 0, "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": { "byteLimit": 2048000, "rowLimit": 10000 }, "inputWidgets": {}, "nuid": "0894c329-05c5-4fe8-85ff-404defe8528a", "showTitle": false, "title": "" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "output_type": "stream", "text": [ "Epoch 0\nloading env vars from: /Workspace/Users/donghao.huang@mastercard.com/llm-finetuning/.env\r\nAdding /Workspace/Users/donghao.huang@mastercard.com/llm-finetuning to sys.path\r\n2024-07-07 01:15:04.481346: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n[2024-07-07 01:15:13,808] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect)\r\nloading /Workspace/Users/donghao.huang@mastercard.com/llm-finetuning/llm_toolkit/logical_reasoning_utils.py\r\ninternlm/internlm2_5-7b-chat-1m None False datasets/mgtv/ results/mgtv-results_bf16.csv\r\n(1) GPU = Tesla T4. Max memory = 14.581 GB.\r\n0.002 GB of memory reserved.\r\nloading model: internlm/internlm2_5-7b-chat-1m\r\n\rLoading checkpoint shards: 0%| | 0/8 [00:00<|im_start|>system\\nYou are an expert in lo...\r\n\r\n[1 rows x 8 columns]\r\nEvaluating model: internlm/internlm2_5-7b-chat-1m\r\n\r 0%| | 0/3000 [00:00\r\n--------\r\nstep 2: 不是\r\n--------\r\nstep 3: 不是\r\n\r 0%| | 1/3000 [00:06<5:45:00, 6.90s/it]\r 0%| | 2/3000 [00:13<5:28:44, 6.58s/it]\r 0%| | 3/3000 [00:19<5:24:10, 6.49s/it]\r 0%| | 4/3000 [00:26<5:22:12, 6.45s/it]\r 0%| | 5/3000 [00:32<5:21:53, 6.45s/it]\r 0%| | 6/3000 [00:38<5:22:08, 6.46s/it]\r 0%| | 7/3000 [00:45<5:22:44, 6.47s/it]\r 0%| | 8/3000 [00:51<5:23:37, 6.49s/it]\r 0%| | 9/3000 [00:58<5:24:26, 6.51s/it]\r 0%|▏ | 10/3000 [01:05<5:25:28, 6.53s/it]\r 0%|▏ | 11/3000 [01:11<5:26:08, 6.55s/it]\r 0%|▏ | 12/3000 [01:18<5:26:58, 6.57s/it]\r 0%|▏ | 13/3000 [01:24<5:28:13, 6.59s/it]\r 0%|▏ | 14/3000 [01:31<5:29:06, 6.61s/it]\r 0%|▏ | 15/3000 [01:38<5:29:30, 6.62s/it]\r 1%|▏ | 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