{ "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": 7, "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": 7, "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": 8, "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/mgtv-results_nv4080_p2.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": [ "Thu Jul 11 13:53:26 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 P8 3W / 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 9.31 ms, sys: 0 ns, total: 9.31 ms\n", "Wall time: 553 ms\n" ] } ], "source": [ "%%time\n", "!python --version\n", "!pip show flash-attn" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "loading /home/inflaton/code/projects/courses/logical-reasoning/llm_toolkit/logical_reasoning_utils.py\n" ] } ], "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_p2.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: P2.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": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "loading existing data from: llama-factory/data/alpaca_mgtv_p2.json\n", "--------------------------------------------------\n", "instruction: 你是一个情景猜谜游戏的主持人。游戏规则如下：\n", "\n", "1. 参与者会得到一个谜面，谜面会描述一个简单又难以理解的事件。\n", "2. 主持人知道谜底，谜底是谜面的答案。\n", "3. 参与者可以询问任何封闭式问题来找寻事件的真相。\n", "4. 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。各回答的判断标准如下：\n", " - 若谜面和谜底能找到问题的答案，回答：是或者不是\n", " - 若谜面和谜底不能直接或者间接推断出问题的答案，回答：不重要\n", " - 若参与者提问不是一个封闭式问题或者问题难以理解，回答：问法错误\n", " - 若参与者提问基本还原了谜底真相，回答：回答正确\n", "5. 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", "\n", "请严格按照这些规则回答参与者提出的问题。\n", "\n", "**谜面:** 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", "**谜底:** 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": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Current Directory:\n", "/home/inflaton/code/projects/courses/logical-reasoning/llama-factory\n", "config/internlm2_5_7b_lora_sft_4bit_p2.yaml:\n", " {\n", " \"model_name_or_path\": \"internlm/internlm2_5-7b-chat-1m\",\n", " \"stage\": \"sft\",\n", " \"do_train\": true,\n", " \"finetuning_type\": \"lora\",\n", " \"lora_target\": \"all\",\n", " \"quantization_bit\": 4,\n", " \"loraplus_lr_ratio\": 16.0,\n", " \"upcast_layernorm\": true,\n", " \"dataset\": \"alpaca_mgtv_p2\",\n", " \"template\": \"chatml\",\n", " \"cutoff_len\": 1024,\n", " \"max_samples\": 5000,\n", " \"overwrite_cache\": true,\n", " \"preprocessing_num_workers\": 16,\n", " \"output_dir\": \"saves/internlm2_5_7b/lora/sft_p2\",\n", " \"logging_steps\": 100,\n", " \"save_steps\": 562,\n", " \"plot_loss\": true,\n", " \"overwrite_output_dir\": true,\n", " \"per_device_train_batch_size\": 1,\n", " \"gradient_accumulation_steps\": 8,\n", " \"learning_rate\": 0.0001,\n", " \"num_train_epochs\": 6.0,\n", " \"lr_scheduler_type\": \"cosine\",\n", " \"warmup_ratio\": 0.1,\n", " \"bf16\": true,\n", " \"ddp_timeout\": 180000000,\n", " \"val_size\": 0.1,\n", " \"per_device_eval_batch_size\": 1,\n", " \"eval_strategy\": \"steps\",\n", " \"eval_steps\": 562,\n", " \"report_to\": \"none\",\n", " \"run_name\": \"internlm2_5_7b\"\n", "}\n", "07/11/2024 13:53:37 - INFO - llamafactory.hparams.parser - Process rank: 0, device: cuda:0, n_gpu: 1, distributed training: False, compute dtype: torch.bfloat16\n", "[INFO|tokenization_utils_base.py:2161] 2024-07-11 13:53:43,438 >> loading file ./tokenizer.model from cache at /home/inflaton/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat-1m/snapshots/8d1a709a04d71440ef3df6ebbe204672f411c8b6/./tokenizer.model\n", "[INFO|tokenization_utils_base.py:2161] 2024-07-11 13:53:43,438 >> loading file added_tokens.json from cache at None\n", "[INFO|tokenization_utils_base.py:2161] 2024-07-11 13:53:43,438 >> loading file special_tokens_map.json from cache at /home/inflaton/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat-1m/snapshots/8d1a709a04d71440ef3df6ebbe204672f411c8b6/special_tokens_map.json\n", "[INFO|tokenization_utils_base.py:2161] 2024-07-11 13:53:43,438 >> loading file tokenizer_config.json from cache at /home/inflaton/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat-1m/snapshots/8d1a709a04d71440ef3df6ebbe204672f411c8b6/tokenizer_config.json\n", "[INFO|tokenization_utils_base.py:2161] 2024-07-11 13:53:43,438 >> loading file tokenizer.json from cache at None\n", "07/11/2024 13:53:44 - INFO - llamafactory.data.template - Replace eos token: <|im_end|>\n", "07/11/2024 13:53:44 - INFO - llamafactory.data.template - Add <|im_start|> to stop words.\n", "07/11/2024 13:53:44 - INFO - llamafactory.data.loader - Loading dataset alpaca_mgtv_p2.json...\n", "Generating train split: 25000 examples [00:00, 43158.06 examples/s]\n", "Converting format of dataset (num_proc=16): 100%|█| 5000/5000 [00:00<00:00, 1422\n", "Running tokenizer on dataset (num_proc=16): 100%|█| 5000/5000 [00:01<00:00, 3141\n", "input_ids:\n", "[92543, 1008, 364, 60403, 68625, 77794, 62591, 63352, 68309, 69323, 60687, 60364, 60355, 68309, 69776, 68411, 60387, 402, 312, 281, 262, 69102, 60497, 60382, 89428, 63352, 60388, 60353, 63352, 60388, 60382, 69401, 68252, 87114, 70436, 68865, 82168, 60355, 364, 314, 281, 262, 74243, 68290, 63352, 60930, 60353, 63352, 60930, 60357, 63352, 68421, 69059, 60355, 364, 308, 281, 262, 69102, 60497, 68251, 73477, 68574, 74004, 60550, 68287, 89214, 61683, 88840, 73687, 60355, 364, 319, 281, 262, 68390, 68772, 68287, 60353, 74243, 60530, 68420, 74740, 68855, 68544, 72719, 68423, 68538, 60387, 60357, 60359, 68278, 60359, 82568, 60359, 68855, 69077, 60359, 60593, 60408, 69583, 60355, 60684, 68855, 60354, 69844, 68559, 68411, 60387, 364, 393, 285, 262, 61369, 63352, 81953, 63352, 60930, 91085, 70670, 69059, 60353, 68855, 60387, 60357, 68319, 68278, 364, 393, 285, 262, 61369, 63352, 81953, 63352, 60930, 68336, 68376, 68319, 80078, 60876, 61015, 60389, 70670, 69059, 60353, 68855, 60387, 82568, 364, 393, 285, 262, 61369, 69102, 60497, 73912, 79865, 74004, 60550, 68287, 68319, 68287, 70436, 68865, 60353, 68855, 60387, 60593, 60408, 69583, 364, 393, 285, 262, 61369, 69102, 60497, 73912, 68406, 71940, 60362, 63352, 60930, 73687, 60353, 68855, 60387, 68855, 69077, 364, 317, 281, 262, 68855, 60366, 68336, 68535, 68574, 69344, 68347, 60353, 71452, 81256, 68423, 68322, 78818, 60666, 60355, 69192, 60353, 73263, 60581, 60419, 68278, 60420, 81256, 60397, 60419, 60358, 60420, 60355, 402, 60836, 86910, 68374, 69776, 68855, 69102, 60497, 74743, 68287, 60355, 402, 465, 63352, 60388, 334, 465, 262, 60361, 63840, 60396, 78165, 60353, 68935, 79406, 70952, 60387, 69731, 71150, 88982, 82620, 60353, 71150, 61329, 60425, 60649, 68935, 69410, 71150, 60382, 60358, 62273, 60458, 61217, 60353, 71479, 60400, 72593, 69380, 79594, 90209, 60355, 60836, 75326, 71150, 82066, 79202, 68540, 60355, 402, 465, 63352, 60930, 334, 465, 262, 73687, 69607, 60510, 70226, 60372, 62650, 60354, 61044, 61066, 69045, 60355, 71389, 61044, 61066, 89463, 60353, 61002, 60510, 70226, 73027, 70134, 60544, 61422, 60355, 68310, 74907, 60361, 71150, 88982, 82620, 68980, 60355, 69104, 60353, 71062, 61976, 60364, 60353, 70134, 60361, 72325, 60463, 68294, 60612, 70623, 60366, 60877, 60668, 60355, 74726, 60354, 61044, 61066, 68394, 70367, 60447, 69126, 70134, 60353, 69731, 68549, 60530, 69410, 71150, 61882, 60825, 60353, 70395, 70134, 60354, 62296, 60463, 60353, 72069, 86407, 68304, 63024, 60880, 60355, 68597, 68891, 73936, 60362, 69372, 60353, 71093, 72276, 60425, 68252, 82569, 70952, 60355, 402, 465, 69102, 60497, 74743, 68287, 334, 465, 262, 61882, 68279, 60548, 60780, 61076, 364, 92542, 364, 92543, 525, 11353, 364, 68278, 92542]\n", "inputs:\n", "<|im_start|>user\n", "你是一个情景猜谜游戏的主持人。游戏规则如下：\n", "\n", "1. 参与者会得到一个谜面，谜面会描述一个简单又难以理解的事件。\n", "2. 主持人知道谜底，谜底是谜面的答案。\n", "3. 参与者可以询问任何封闭式问题来找寻事件的真相。\n", "4. 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。各回答的判断标准如下：\n", " - 若谜面和谜底能找到问题的答案，回答：是或者不是\n", " - 若谜面和谜底不能直接或者间接推断出问题的答案，回答：不重要\n", " - 若参与者提问不是一个封闭式问题或者问题难以理解，回答：问法错误\n", " - 若参与者提问基本还原了谜底真相，回答：回答正确\n", "5. 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", "\n", "请严格按照这些规则回答参与者提出的问题。\n", "\n", "**谜面:** 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", "**谜底:** 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", "<|im_end|>\n", "<|im_start|>assistant\n", "不是<|im_end|>\n", "label_ids:\n", "[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 68278, 92542]\n", "labels:\n", "不是<|im_end|>\n", "[INFO|configuration_utils.py:733] 2024-07-11 13:53:56,968 >> loading configuration file config.json from cache at /home/inflaton/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat-1m/snapshots/8d1a709a04d71440ef3df6ebbe204672f411c8b6/config.json\n", "[INFO|configuration_utils.py:733] 2024-07-11 13:54:00,093 >> loading configuration file config.json from cache at /home/inflaton/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat-1m/snapshots/8d1a709a04d71440ef3df6ebbe204672f411c8b6/config.json\n", "[INFO|configuration_utils.py:800] 2024-07-11 13:54:00,094 >> Model config InternLM2Config {\n", " \"_name_or_path\": \"internlm/internlm2_5-7b-chat-1m\",\n", " \"architectures\": [\n", " \"InternLM2ForCausalLM\"\n", " ],\n", " \"attn_implementation\": \"eager\",\n", " \"auto_map\": {\n", " \"AutoConfig\": \"internlm/internlm2_5-7b-chat-1m--configuration_internlm2.InternLM2Config\",\n", " \"AutoModel\": \"internlm/internlm2_5-7b-chat-1m--modeling_internlm2.InternLM2ForCausalLM\",\n", " \"AutoModelForCausalLM\": \"internlm/internlm2_5-7b-chat-1m--modeling_internlm2.InternLM2ForCausalLM\"\n", " },\n", " \"bias\": false,\n", " \"bos_token_id\": 1,\n", " \"eos_token_id\": 2,\n", " \"hidden_act\": \"silu\",\n", " \"hidden_size\": 4096,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 14336,\n", " \"max_position_embeddings\": 262144,\n", " \"model_type\": \"internlm2\",\n", " \"num_attention_heads\": 32,\n", " \"num_hidden_layers\": 32,\n", " \"num_key_value_heads\": 8,\n", " \"pad_token_id\": 2,\n", " \"pretraining_tp\": 1,\n", " \"rms_norm_eps\": 1e-05,\n", " \"rope_scaling\": {\n", " \"factor\": 2.5,\n", " \"type\": \"dynamic\"\n", " },\n", " \"rope_theta\": 50000000,\n", " \"tie_word_embeddings\": false,\n", " \"torch_dtype\": \"bfloat16\",\n", " \"transformers_version\": \"4.42.3\",\n", " \"use_cache\": true,\n", " \"vocab_size\": 92544\n", "}\n", "\n", "07/11/2024 13:54:00 - INFO - llamafactory.model.model_utils.quantization - Quantizing model to 4 bit with bitsandbytes.\n", "[INFO|modeling_utils.py:3556] 2024-07-11 13:54:02,069 >> loading weights file model.safetensors from cache at /home/inflaton/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat-1m/snapshots/8d1a709a04d71440ef3df6ebbe204672f411c8b6/model.safetensors.index.json\n", "[INFO|modeling_utils.py:1531] 2024-07-11 13:54:02,217 >> Instantiating InternLM2ForCausalLM model under default dtype torch.bfloat16.\n", "[INFO|configuration_utils.py:1000] 2024-07-11 13:54:02,217 >> Generate config GenerationConfig {\n", " \"bos_token_id\": 1,\n", " \"eos_token_id\": 2,\n", " \"pad_token_id\": 2\n", "}\n", "\n", "Loading checkpoint shards: 100%|██████████████████| 8/8 [11:33<00:00, 86.64s/it]\n", "[INFO|modeling_utils.py:4364] 2024-07-11 14:05:35,470 >> All model checkpoint weights were used when initializing InternLM2ForCausalLM.\n", "\n", "[INFO|modeling_utils.py:4372] 2024-07-11 14:05:35,470 >> All the weights of InternLM2ForCausalLM were initialized from the model checkpoint at internlm/internlm2_5-7b-chat-1m.\n", "If your task is similar to the task the model of the checkpoint was trained on, you can already use InternLM2ForCausalLM for predictions without further training.\n", "[INFO|configuration_utils.py:955] 2024-07-11 14:05:36,035 >> loading configuration file generation_config.json from cache at /home/inflaton/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat-1m/snapshots/8d1a709a04d71440ef3df6ebbe204672f411c8b6/generation_config.json\n", "[INFO|configuration_utils.py:1000] 2024-07-11 14:05:36,035 >> Generate config GenerationConfig {\n", " \"bos_token_id\": 1,\n", " \"eos_token_id\": [\n", " 2,\n", " 92542\n", " ],\n", " \"pad_token_id\": 2\n", "}\n", "\n", "07/11/2024 14:05:36 - INFO - llamafactory.model.model_utils.checkpointing - Upcasting layernorm weights in float32.\n", "07/11/2024 14:05:36 - INFO - llamafactory.model.model_utils.checkpointing - Gradient checkpointing enabled.\n", "07/11/2024 14:05:36 - INFO - llamafactory.model.model_utils.attention - Using vanilla attention implementation.\n", "07/11/2024 14:05:36 - INFO - llamafactory.model.adapter - Upcasting trainable params to float32.\n", "07/11/2024 14:05:36 - INFO - llamafactory.model.adapter - Fine-tuning method: LoRA\n", "07/11/2024 14:05:36 - INFO - llamafactory.model.model_utils.misc - Found linear modules: w1,wqkv,w3,wo,w2\n", "07/11/2024 14:05:36 - INFO - llamafactory.model.loader - trainable params: 18,874,368 || all params: 7,756,582,912 || trainable%: 0.2433\n", "[INFO|trainer.py:642] 2024-07-11 14:05:36,681 >> Using auto half precision backend\n", "07/11/2024 14:05:36 - INFO - llamafactory.train.trainer_utils - Using LoRA+ optimizer with loraplus lr ratio 16.00.\n", "[INFO|trainer.py:2128] 2024-07-11 14:05:36,907 >> ***** Running training *****\n", "[INFO|trainer.py:2129] 2024-07-11 14:05:36,907 >> Num examples = 4,500\n", "[INFO|trainer.py:2130] 2024-07-11 14:05:36,907 >> Num Epochs = 6\n", "[INFO|trainer.py:2131] 2024-07-11 14:05:36,907 >> Instantaneous batch size per device = 1\n", "[INFO|trainer.py:2134] 2024-07-11 14:05:36,907 >> Total train batch size (w. parallel, distributed & accumulation) = 8\n", "[INFO|trainer.py:2135] 2024-07-11 14:05:36,907 >> Gradient Accumulation steps = 8\n", "[INFO|trainer.py:2136] 2024-07-11 14:05:36,907 >> Total optimization steps = 3,372\n", "[INFO|trainer.py:2137] 2024-07-11 14:05:36,909 >> Number of trainable parameters = 18,874,368\n", "{'loss': 0.4566, 'grad_norm': 2.716310501098633, 'learning_rate': 2.958579881656805e-05, 'epoch': 0.18}\n", "{'loss': 0.3644, 'grad_norm': 4.124796390533447, 'learning_rate': 5.91715976331361e-05, 'epoch': 0.36}\n", "{'loss': 0.3789, 'grad_norm': 2.9282803535461426, 'learning_rate': 8.875739644970414e-05, 'epoch': 0.53}\n", "{'loss': 0.37, 'grad_norm': 2.6953532695770264, 'learning_rate': 9.989699867437137e-05, 'epoch': 0.71}\n", "{'loss': 0.3485, 'grad_norm': 6.001204013824463, 'learning_rate': 9.92981892269398e-05, 'epoch': 0.89}\n", " 17%|█████▊ | 562/3372 [2:40:33<13:23:16, 17.15s/it][INFO|trainer.py:3788] 2024-07-11 16:46:10,114 >> \n", "***** Running Evaluation *****\n", "[INFO|trainer.py:3790] 2024-07-11 16:46:10,115 >> Num examples = 500\n", "[INFO|trainer.py:3793] 2024-07-11 16:46:10,115 >> Batch size = 1\n", "\n", " 0%| | 0/500 [00:00> Saving model checkpoint to saves/internlm2_5_7b/lora/sft_p2/checkpoint-562\n", "[INFO|configuration_utils.py:733] 2024-07-11 16:52:57,877 >> loading configuration file config.json from cache at /home/inflaton/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat-1m/snapshots/8d1a709a04d71440ef3df6ebbe204672f411c8b6/config.json\n", "[INFO|configuration_utils.py:800] 2024-07-11 16:52:57,878 >> Model config InternLM2Config {\n", " \"architectures\": [\n", " \"InternLM2ForCausalLM\"\n", " ],\n", " \"attn_implementation\": \"eager\",\n", " \"auto_map\": {\n", " \"AutoConfig\": \"internlm/internlm2_5-7b-chat-1m--configuration_internlm2.InternLM2Config\",\n", " \"AutoModel\": \"internlm/internlm2_5-7b-chat-1m--modeling_internlm2.InternLM2ForCausalLM\",\n", " \"AutoModelForCausalLM\": \"internlm/internlm2_5-7b-chat-1m--modeling_internlm2.InternLM2ForCausalLM\"\n", " },\n", " \"bias\": false,\n", " \"bos_token_id\": 1,\n", " \"eos_token_id\": 2,\n", " \"hidden_act\": \"silu\",\n", " \"hidden_size\": 4096,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 14336,\n", " \"max_position_embeddings\": 262144,\n", " \"model_type\": \"internlm2\",\n", " \"num_attention_heads\": 32,\n", " \"num_hidden_layers\": 32,\n", " \"num_key_value_heads\": 8,\n", " \"pad_token_id\": 2,\n", " \"pretraining_tp\": 1,\n", " \"rms_norm_eps\": 1e-05,\n", " \"rope_scaling\": {\n", " \"factor\": 2.5,\n", " \"type\": \"dynamic\"\n", " },\n", " \"rope_theta\": 50000000,\n", " \"tie_word_embeddings\": false,\n", " \"torch_dtype\": \"bfloat16\",\n", " \"transformers_version\": \"4.42.3\",\n", " \"use_cache\": true,\n", " \"vocab_size\": 92544\n", "}\n", "\n", "[INFO|tokenization_utils_base.py:2574] 2024-07-11 16:52:58,318 >> tokenizer config file saved in saves/internlm2_5_7b/lora/sft_p2/checkpoint-562/tokenizer_config.json\n", "[INFO|tokenization_utils_base.py:2583] 2024-07-11 16:52:58,318 >> Special tokens file saved in saves/internlm2_5_7b/lora/sft_p2/checkpoint-562/special_tokens_map.json\n", "{'loss': 0.3563, 'grad_norm': 5.8297953605651855, 'learning_rate': 9.817128546774103e-05, 'epoch': 1.07}\n", " 18%|██████▍ | 619/3372 [3:03:42<13:13:23, 17.29s/it]^C\n", "Traceback (most recent call last):\n", " File \"/home/inflaton/miniconda3/envs/llama-factory/bin/llamafactory-cli\", line 8, in \n", " sys.exit(main())\n", " ^^^^^^\n", " File \"/home/inflaton/code/projects/courses/LLaMA-Factory/src/llamafactory/cli.py\", line 111, in main\n", " run_exp()\n", " File \"/home/inflaton/code/projects/courses/LLaMA-Factory/src/llamafactory/train/tuner.py\", line 50, in run_exp\n", " run_sft(model_args, data_args, training_args, finetuning_args, generating_args, callbacks)\n", " File \"/home/inflaton/code/projects/courses/LLaMA-Factory/src/llamafactory/train/sft/workflow.py\", line 88, in run_sft\n", " train_result = trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"/home/inflaton/miniconda3/envs/llama-factory/lib/python3.11/site-packages/transformers/trainer.py\", line 1932, in train\n", " return inner_training_loop(\n", " ^^^^^^^^^^^^^^^^^^^^\n", " File \"/home/inflaton/miniconda3/envs/llama-factory/lib/python3.11/site-packages/transformers/trainer.py\", line 2268, in _inner_training_loop\n", " tr_loss_step = self.training_step(model, inputs)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"/home/inflaton/miniconda3/envs/llama-factory/lib/python3.11/site-packages/transformers/trainer.py\", line 3307, in training_step\n", " loss = self.compute_loss(model, inputs)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"/home/inflaton/miniconda3/envs/llama-factory/lib/python3.11/site-packages/transformers/trainer.py\", line 3338, in compute_loss\n", " outputs = model(**inputs)\n", " ^^^^^^^^^^^^^^^\n", " File \"/home/inflaton/miniconda3/envs/llama-factory/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1532, in _wrapped_call_impl\n", " return self._call_impl(*args, **kwargs)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"/home/inflaton/miniconda3/envs/llama-factory/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1541, in _call_impl\n", " return forward_call(*args, **kwargs)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"/home/inflaton/miniconda3/envs/llama-factory/lib/python3.11/site-packages/accelerate/utils/operations.py\", line 822, in forward\n", " return model_forward(*args, **kwargs)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"/home/inflaton/miniconda3/envs/llama-factory/lib/python3.11/site-packages/accelerate/utils/operations.py\", line 810, in __call__\n", " return convert_to_fp32(self.model_forward(*args, **kwargs))\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"/home/inflaton/miniconda3/envs/llama-factory/lib/python3.11/site-packages/torch/amp/autocast_mode.py\", line 16, in decorate_autocast\n", " return func(*args, **kwargs)\n", " ^^^^^^^^^^^^^^^^^^^^^\n", " File \"/home/inflaton/miniconda3/envs/llama-factory/lib/python3.11/site-packages/peft/peft_model.py\", line 1430, in forward\n", " return self.base_model(\n", " ^^^^^^^^^^^^^^^^\n", " File \"/home/inflaton/miniconda3/envs/llama-factory/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1532, in _wrapped_call_impl\n", " return self._call_impl(*args, **kwargs)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"/home/inflaton/miniconda3/envs/llama-factory/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1541, in _call_impl\n", " return forward_call(*args, **kwargs)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"/home/inflaton/miniconda3/envs/llama-factory/lib/python3.11/site-packages/peft/tuners/tuners_utils.py\", line 179, in forward\n", " return self.model.forward(*args, **kwargs)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"/home/inflaton/miniconda3/envs/llama-factory/lib/python3.11/site-packages/accelerate/hooks.py\", line 166, in new_forward\n", " output = module._old_forward(*args, **kwargs)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"/home/inflaton/.cache/huggingface/modules/transformers_modules/internlm/internlm2_5-7b-chat-1m/8d1a709a04d71440ef3df6ebbe204672f411c8b6/modeling_internlm2.py\", line 1204, in forward\n", " outputs = self.model(\n", " ^^^^^^^^^^^\n", " File \"/home/inflaton/miniconda3/envs/llama-factory/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1532, in _wrapped_call_impl\n", " return self._call_impl(*args, **kwargs)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"/home/inflaton/miniconda3/envs/llama-factory/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1541, in _call_impl\n", " return forward_call(*args, **kwargs)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"/home/inflaton/miniconda3/envs/llama-factory/lib/python3.11/site-packages/accelerate/hooks.py\", line 166, in new_forward\n", " output = module._old_forward(*args, **kwargs)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"/home/inflaton/.cache/huggingface/modules/transformers_modules/internlm/internlm2_5-7b-chat-1m/8d1a709a04d71440ef3df6ebbe204672f411c8b6/modeling_internlm2.py\", line 993, in forward\n", " layer_outputs = self._gradient_checkpointing_func(\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"/home/inflaton/code/projects/courses/LLaMA-Factory/src/llamafactory/model/model_utils/checkpointing.py\", line 65, in custom_gradient_checkpointing_func\n", " return gradient_checkpointing_func(func, *args, **kwargs)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"/home/inflaton/miniconda3/envs/llama-factory/lib/python3.11/site-packages/torch/_compile.py\", line 24, in inner\n", " return torch._dynamo.disable(fn, recursive)(*args, **kwargs)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"/home/inflaton/miniconda3/envs/llama-factory/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py\", line 451, in _fn\n", " return fn(*args, **kwargs)\n", " ^^^^^^^^^^^^^^^^^^^\n", " File \"/home/inflaton/miniconda3/envs/llama-factory/lib/python3.11/site-packages/torch/_dynamo/external_utils.py\", line 36, in inner\n", " return fn(*args, **kwargs)\n", " ^^^^^^^^^^^^^^^^^^^\n", " File \"/home/inflaton/miniconda3/envs/llama-factory/lib/python3.11/site-packages/torch/utils/checkpoint.py\", line 487, in checkpoint\n", " return CheckpointFunction.apply(function, preserve, *args)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"/home/inflaton/miniconda3/envs/llama-factory/lib/python3.11/site-packages/torch/autograd/function.py\", line 598, in apply\n", " return super().apply(*args, **kwargs) # type: ignore[misc]\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"/home/inflaton/miniconda3/envs/llama-factory/lib/python3.11/site-packages/torch/utils/checkpoint.py\", line 262, in forward\n", " outputs = run_function(*args)\n", " ^^^^^^^^^^^^^^^^^^^\n", " File \"/home/inflaton/miniconda3/envs/llama-factory/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1532, in _wrapped_call_impl\n", " return self._call_impl(*args, **kwargs)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"/home/inflaton/miniconda3/envs/llama-factory/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1541, in _call_impl\n", " return forward_call(*args, **kwargs)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"/home/inflaton/miniconda3/envs/llama-factory/lib/python3.11/site-packages/accelerate/hooks.py\", line 166, in new_forward\n", " output = module._old_forward(*args, **kwargs)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"/home/inflaton/.cache/huggingface/modules/transformers_modules/internlm/internlm2_5-7b-chat-1m/8d1a709a04d71440ef3df6ebbe204672f411c8b6/modeling_internlm2.py\", line 752, in forward\n", " hidden_states = self.feed_forward(hidden_states)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"/home/inflaton/miniconda3/envs/llama-factory/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1532, in _wrapped_call_impl\n", " return self._call_impl(*args, **kwargs)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"/home/inflaton/miniconda3/envs/llama-factory/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1541, in _call_impl\n", " return forward_call(*args, **kwargs)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"/home/inflaton/miniconda3/envs/llama-factory/lib/python3.11/site-packages/accelerate/hooks.py\", line 166, in new_forward\n", " output = module._old_forward(*args, **kwargs)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"/home/inflaton/.cache/huggingface/modules/transformers_modules/internlm/internlm2_5-7b-chat-1m/8d1a709a04d71440ef3df6ebbe204672f411c8b6/modeling_internlm2.py\", line 206, in forward\n", " down_proj = self.w2(self.act_fn(self.w1(x)) * self.w3(x))\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"/home/inflaton/miniconda3/envs/llama-factory/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1532, in _wrapped_call_impl\n", " return self._call_impl(*args, **kwargs)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File 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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-{562 * 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\", \"llama-factory/saves/internlm2_5_7b/lora/sft\", 6, start_epoch=0, load_in_4bit=True, num_of_entries=-1)" ] } ], "metadata": { "accelerator": "GPU", "application/vnd.databricks.v1+notebook": { "dashboards": [], "environmentMetadata": null, "language": "python", "notebookMetadata": { 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