ablation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "editable": true,
    "id": "ym4Naw1SY5XT",
    "outputId": "cce5fd90-62a8-4746-d645-c5f0b163462c",
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: transformers in /home/idies/miniconda3/envs/py39/lib/python3.9/site-packages (4.46.3)\n",
      "Collecting transformers\n",
      "  Obtaining dependency information for transformers from https://files.pythonhosted.org/packages/d0/a7/7eedcf6a359e1e1eff3bc204ad022485aa5d88c08e1e3e0e0aee8a2e2235/transformers-4.47.0-py3-none-any.whl.metadata\n",
      "  Using cached transformers-4.47.0-py3-none-any.whl.metadata (43 kB)\n",
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      "Collecting tokenizers<0.22,>=0.21 (from transformers)\n",
      "  Obtaining dependency information for tokenizers<0.22,>=0.21 from https://files.pythonhosted.org/packages/22/06/69d7ce374747edaf1695a4f61b83570d91cc8bbfc51ccfecf76f56ab4aac/tokenizers-0.21.0-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n",
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      "Requirement already satisfied: MarkupSafe>=2.0 in /home/idies/miniconda3/envs/py39/lib/python3.9/site-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.1)\n",
      "Requirement already satisfied: mpmath>=0.19 in /home/idies/miniconda3/envs/py39/lib/python3.9/site-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install -U transformers datasets evaluate nltk accelerate trl wandb peft rouge_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "BPa5JXdC1Gpv"
   },
   "outputs": [],
   "source": [
    "update_intervals = [20, 40]\n",
    "num_tokens = [1, 10]\n",
    "memory_len = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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    "id": "6nS1EpEjZSds",
    "outputId": "c2b28107-548e-4bae-b99e-8b8c01efa0dd"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-12-07 03:56:14.131734: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-12-07 03:56:14.796716: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoTokenizer, T5ForConditionalGeneration, T5Config, logging\n",
    "import torch\n",
    "from custom_model import RTModel, CustomTrainer\n",
    "import os\n",
    "import logging as sys_logging\n",
    "\n",
    "sys_logging.captureWarnings(True)\n",
    "logger = sys_logging.getLogger(\"transformers.trainer\")\n",
    "class LogFilter(sys_logging.Filter):\n",
    "    def filter(self, record):\n",
    "        message = record.getMessage()\n",
    "        return \"deprecated\" not in message and \"hang\" not in message\n",
    "logger.addFilter(LogFilter())\n",
    "\n",
    "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"  # Dealing with HF logging bugs\n",
    "\n",
    "model_name = \"google/flan-t5-base\"\n",
    "\n",
    "embed_dim = T5Config.from_pretrained(model_name).d_model\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
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    },
    "id": "hp1ACHlQZVBX",
    "outputId": "3a2c46c7-955c-437e-dca8-74dd09672c0d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max output token number: 257\n",
      "Dataset({\n",
      "    features: ['context', 'statement', 'reasoning', 'depth', 'flag', 'input_ids', 'attention_mask', 'labels'],\n",
      "    num_rows: 1000\n",
      "})\n"
     ]
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "import numpy as np\n",
    "\n",
    "train_dataset = load_dataset(\"Jise/ruletaker\", split=\"train\")\n",
    "test_dataset = load_dataset(\"Jise/ruletaker\", split=\"test\")\n",
    "ood_dataset = load_dataset(\"Jise/ruletaker\", split=\"ood_test\")\n",
    "\n",
    "prompt = \"Based on the facts and rules, first think step by step and give simple reasoning steps citing the rules, and then output whether the assertion is true by true or false.\"\n",
    "\n",
    "temp = tokenizer(train_dataset[\"reasoning\"])\n",
    "print(\"Max output token number:\", max([len(s) for s in temp[\"input_ids\"]]))\n",
    "\n",
    "def preprocess(examples):\n",
    "    inputs = [prompt + \"Assertion:\" + x for x in examples[\"context\"]]\n",
    "    model_inputs = tokenizer(inputs, max_length=511, truncation=True, padding=True, return_tensors=\"pt\")\n",
    "    labels = [x + \"\\nThe answer is \" + y for x, y in zip(examples[\"reasoning\"], examples[\"flag\"])]\n",
    "    labels = tokenizer(text_target=labels, max_length=262, truncation=True, padding=True, return_tensors=\"pt\")\n",
    "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
    "    return model_inputs\n",
    "\n",
    "train_dataset = train_dataset.map(preprocess, batched=True)\n",
    "test_dataset = test_dataset.map(preprocess, batched=True)\n",
    "ood_dataset = ood_dataset.map(preprocess, batched=True)\n",
    "\n",
    "print(train_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
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      "dd8d01a18308456a9a694efd9e2988ae"
     ]
    },
    "id": "LoANhIXeZdL2",
    "outputId": "97648298-7f2f-4b88-b449-3f7fd0338fb8"
   },
   "outputs": [],
   "source": [
    "from transformers import Seq2SeqTrainingArguments, Seq2SeqTrainer, DataCollatorForSeq2Seq\n",
    "import requests\n",
    "import pickle\n",
    "import torch\n",
    "import wandb\n",
    "import nltk\n",
    "import evaluate\n",
    "\n",
    "nltk.download(\"punkt\", quiet=True)\n",
    "nltk.download(\"punkt_tab\", quiet=True)\n",
    "metric = evaluate.load(\"rouge\")\n",
    "\n",
    "with open(\"TOKENS.pkl\", \"rb\") as f:\n",
    "    TOKENS = pickle.load(f)\n",
    "\n",
    "HF_TOKEN = TOKENS[\"HF_TOKEN\"]\n",
    "\n",
    "def compute_metrics(eval_preds):\n",
    "    preds, labels = eval_preds\n",
    "\n",
    "    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
    "    preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n",
    "    labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
    "\n",
    "    decoded_preds = [\"\\n\".join(nltk.sent_tokenize(pred.strip())) for pred in preds]\n",
    "    decoded_labels = [\"\\n\".join(nltk.sent_tokenize(label.strip())) for label in labels]\n",
    "\n",
    "    result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)\n",
    "\n",
    "    result[\"acc\"] = np.mean([(\"true\" in label.lower() and \"true\" in pred.lower()) or (\"false\" in label.lower() and \"false\" in pred.lower()) for pred, label in zip(preds, labels)])\n",
    "\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Detected kernel version 4.18.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='625' max='625' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [625/625 14:33, Epoch 5/5]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Passing a tuple of `past_key_values` is deprecated and will be removed in Transformers v4.48.0. You should pass an instance of `EncoderDecoderCache` instead, e.g. `past_key_values=EncoderDecoderCache.from_legacy_cache(past_key_values)`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Results for update interval: 20\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='40' max='16' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [16/16 20:10]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_test_loss': 61.051517486572266, 'eval_test_rouge1': 0.07391676186889429, 'eval_test_rouge2': 0.040734904287047596, 'eval_test_rougeL': 0.06712375123187533, 'eval_test_rougeLsum': 0.0728435298485233, 'eval_test_acc': 0.0, 'eval_test_runtime': 508.515, 'eval_test_samples_per_second': 0.492, 'eval_test_steps_per_second': 0.031, 'epoch': 5.0, 'eval_ood_loss': 42.98136901855469, 'eval_ood_rouge1': 0.10203888697244479, 'eval_ood_rouge2': 0.05689134788080519, 'eval_ood_rougeL': 0.09227450903251991, 'eval_ood_rougeLsum': 0.10094993481445658, 'eval_ood_acc': 0.0, 'eval_ood_runtime': 740.5145, 'eval_ood_samples_per_second': 0.506, 'eval_ood_steps_per_second': 0.032}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:py.warnings:/home/idies/workspace/Temporary/JiseShen/scratch/custom_model.py:21: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `CustomTrainer.__init__`. Use `processing_class` instead.\n",
      "  super().__init__(*args, **kwargs)\n",
      "\n",
      "WARNING:accelerate.utils.other:Detected kernel version 4.18.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='625' max='625' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [625/625 14:38, Epoch 5/5]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Results for update interval: 40\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='40' max='16' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [16/16 20:09]\n",
       "    </div>\n",
       "    "
      ],
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_test_loss': 32.86512756347656, 'eval_test_rouge1': 0.14756606049117407, 'eval_test_rouge2': 0.09454600518784967, 'eval_test_rougeL': 0.13247346467543225, 'eval_test_rougeLsum': 0.1389711238588532, 'eval_test_acc': 0.036, 'eval_test_runtime': 508.5258, 'eval_test_samples_per_second': 0.492, 'eval_test_steps_per_second': 0.031, 'epoch': 5.0, 'eval_ood_loss': 28.138641357421875, 'eval_ood_rouge1': 0.17954083862827658, 'eval_ood_rouge2': 0.10817440215581078, 'eval_ood_rougeL': 0.15745894128450233, 'eval_ood_rougeLsum': 0.1658277861664561, 'eval_ood_acc': 0.016, 'eval_ood_runtime': 738.8661, 'eval_ood_samples_per_second': 0.508, 'eval_ood_steps_per_second': 0.032}\n"
     ]
    }
   ],
   "source": [
    "for update_interval in update_intervals:\n",
    "\n",
    "    num_token = 5\n",
    "\n",
    "    model = RTModel.from_pretrained(model_name, num_token=num_token)\n",
    "    model.config.max_length = 256\n",
    "\n",
    "    data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model)\n",
    "\n",
    "    sft_training_args = Seq2SeqTrainingArguments(\n",
    "        output_dir=f\"./flan-t5-rt-ruletaker-{num_token}-{update_interval}\",\n",
    "        eval_strategy=\"no\",\n",
    "        save_strategy=\"no\",\n",
    "        save_total_limit=1,\n",
    "        per_device_train_batch_size=2,\n",
    "        per_device_eval_batch_size=16,\n",
    "        gradient_accumulation_steps=4,\n",
    "        gradient_checkpointing=True,\n",
    "        label_smoothing_factor=1e-5,\n",
    "        learning_rate=1e-4,\n",
    "        num_train_epochs=5,\n",
    "        logging_strategy=\"no\",\n",
    "        weight_decay=0.01,\n",
    "        bf16=True,\n",
    "        predict_with_generate=True,\n",
    "        generation_max_length=256,\n",
    "        push_to_hub=False,\n",
    "        report_to=\"none\",\n",
    "        save_safetensors=False,\n",
    "    )\n",
    "\n",
    "    rt_trainer = CustomTrainer(\n",
    "        update_interval=update_interval,\n",
    "        memory_len=memory_len,\n",
    "        model=model,\n",
    "        args=sft_training_args,\n",
    "        train_dataset=train_dataset,\n",
    "        eval_dataset={\"test\": test_dataset, \"ood\": ood_dataset},\n",
    "        tokenizer=tokenizer,\n",
    "        data_collator=data_collator,\n",
    "        compute_metrics=compute_metrics,\n",
    "    )\n",
    "\n",
    "    rt_trainer.train()\n",
    "    print(f\"Test Results for update interval: {update_interval}\")\n",
    "    print(rt_trainer.evaluate())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "editable": true,
    "id": "4AmUFu9HU8P-",
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Detected kernel version 4.18.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='625' max='625' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [625/625 22:36, Epoch 5/5]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Passing a tuple of `past_key_values` is deprecated and will be removed in Transformers v4.48.0. You should pass an instance of `EncoderDecoderCache` instead, e.g. `past_key_values=EncoderDecoderCache.from_legacy_cache(past_key_values)`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Results for num token: 1\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='40' max='16' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [16/16 19:39]\n",
       "    </div>\n",
       "    "
      ],
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_test_loss': 39.05982208251953, 'eval_test_rouge1': 0.22105174131491598, 'eval_test_rouge2': 0.15474633104325503, 'eval_test_rougeL': 0.20263790745498275, 'eval_test_rougeLsum': 0.2139468104566816, 'eval_test_acc': 0.244, 'eval_test_runtime': 495.6717, 'eval_test_samples_per_second': 0.504, 'eval_test_steps_per_second': 0.032, 'epoch': 5.0, 'eval_ood_loss': 48.232879638671875, 'eval_ood_rouge1': 0.22399039439266244, 'eval_ood_rouge2': 0.1538141368139594, 'eval_ood_rougeL': 0.19464367854394313, 'eval_ood_rougeLsum': 0.21754566709298906, 'eval_ood_acc': 0.31466666666666665, 'eval_ood_runtime': 720.6752, 'eval_ood_samples_per_second': 0.52, 'eval_ood_steps_per_second': 0.033}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:py.warnings:/home/idies/workspace/Temporary/JiseShen/scratch/custom_model.py:21: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `CustomTrainer.__init__`. Use `processing_class` instead.\n",
      "  super().__init__(*args, **kwargs)\n",
      "\n",
      "WARNING:accelerate.utils.other:Detected kernel version 4.18.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='297' max='625' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [297/625 10:47 < 12:00, 0.46 it/s, Epoch 2.37/5]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[6], line 44\u001b[0m\n\u001b[1;32m     10\u001b[0m sft_training_args \u001b[38;5;241m=\u001b[39m Seq2SeqTrainingArguments(\n\u001b[1;32m     11\u001b[0m     output_dir\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m./flan-t5-rt-ruletaker-\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnum_token\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m-\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mupdate_interval\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     12\u001b[0m     eval_strategy\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mno\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     29\u001b[0m     save_safetensors\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m     30\u001b[0m )\n\u001b[1;32m     32\u001b[0m rt_trainer \u001b[38;5;241m=\u001b[39m CustomTrainer(\n\u001b[1;32m     33\u001b[0m     update_interval\u001b[38;5;241m=\u001b[39mupdate_interval,\n\u001b[1;32m     34\u001b[0m     memory_len\u001b[38;5;241m=\u001b[39mmemory_len,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     41\u001b[0m     compute_metrics\u001b[38;5;241m=\u001b[39mcompute_metrics,\n\u001b[1;32m     42\u001b[0m )\n\u001b[0;32m---> 44\u001b[0m \u001b[43mrt_trainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     45\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTest Results for num token: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnum_token\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     46\u001b[0m \u001b[38;5;28mprint\u001b[39m(rt_trainer\u001b[38;5;241m.\u001b[39mevaluate())\n",
      "File \u001b[0;32m~/miniconda3/envs/py39/lib/python3.9/site-packages/transformers/trainer.py:2123\u001b[0m, in \u001b[0;36mTrainer.train\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m   2121\u001b[0m         hf_hub_utils\u001b[38;5;241m.\u001b[39menable_progress_bars()\n\u001b[1;32m   2122\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2123\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43minner_training_loop\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2124\u001b[0m \u001b[43m        \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2125\u001b[0m \u001b[43m        \u001b[49m\u001b[43mresume_from_checkpoint\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresume_from_checkpoint\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2126\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtrial\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrial\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2127\u001b[0m \u001b[43m        \u001b[49m\u001b[43mignore_keys_for_eval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_keys_for_eval\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2128\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/py39/lib/python3.9/site-packages/transformers/trainer.py:2481\u001b[0m, in \u001b[0;36mTrainer._inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m   2475\u001b[0m context \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m   2476\u001b[0m     functools\u001b[38;5;241m.\u001b[39mpartial(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maccelerator\u001b[38;5;241m.\u001b[39mno_sync, model\u001b[38;5;241m=\u001b[39mmodel)\n\u001b[1;32m   2477\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(batch_samples) \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m   2478\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m contextlib\u001b[38;5;241m.\u001b[39mnullcontext\n\u001b[1;32m   2479\u001b[0m )\n\u001b[1;32m   2480\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m context():\n\u001b[0;32m-> 2481\u001b[0m     tr_loss_step \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtraining_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_items_in_batch\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2483\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m   2484\u001b[0m     args\u001b[38;5;241m.\u001b[39mlogging_nan_inf_filter\n\u001b[1;32m   2485\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_torch_xla_available()\n\u001b[1;32m   2486\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m (torch\u001b[38;5;241m.\u001b[39misnan(tr_loss_step) \u001b[38;5;129;01mor\u001b[39;00m torch\u001b[38;5;241m.\u001b[39misinf(tr_loss_step))\n\u001b[1;32m   2487\u001b[0m ):\n\u001b[1;32m   2488\u001b[0m     \u001b[38;5;66;03m# if loss is nan or inf simply add the average of previous logged losses\u001b[39;00m\n\u001b[1;32m   2489\u001b[0m     tr_loss \u001b[38;5;241m=\u001b[39m tr_loss \u001b[38;5;241m+\u001b[39m tr_loss \u001b[38;5;241m/\u001b[39m (\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mglobal_step \u001b[38;5;241m-\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_globalstep_last_logged)\n",
      "File \u001b[0;32m~/miniconda3/envs/py39/lib/python3.9/site-packages/transformers/trainer.py:3579\u001b[0m, in \u001b[0;36mTrainer.training_step\u001b[0;34m(self, model, inputs, num_items_in_batch)\u001b[0m\n\u001b[1;32m   3576\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m loss_mb\u001b[38;5;241m.\u001b[39mreduce_mean()\u001b[38;5;241m.\u001b[39mdetach()\u001b[38;5;241m.\u001b[39mto(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\u001b[38;5;241m.\u001b[39mdevice)\n\u001b[1;32m   3578\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcompute_loss_context_manager():\n\u001b[0;32m-> 3579\u001b[0m     loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute_loss\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_items_in_batch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_items_in_batch\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3581\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m inputs\n\u001b[1;32m   3582\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m   3583\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\u001b[38;5;241m.\u001b[39mtorch_empty_cache_steps \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   3584\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mglobal_step \u001b[38;5;241m%\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\u001b[38;5;241m.\u001b[39mtorch_empty_cache_steps \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m   3585\u001b[0m ):\n",
      "File \u001b[0;32m~/workspace/Temporary/JiseShen/scratch/custom_model.py:72\u001b[0m, in \u001b[0;36mCustomTrainer.compute_loss\u001b[0;34m(self, model, inputs, return_outputs, num_items_in_batch)\u001b[0m\n\u001b[1;32m     70\u001b[0m         reasoning_token \u001b[38;5;241m=\u001b[39m reasoning_token\u001b[38;5;241m.\u001b[39mdetach()\n\u001b[1;32m     71\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m total_loss \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maccelerator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtotal_loss\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     73\u001b[0m     fake_loss \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m total_loss\u001b[38;5;241m.\u001b[39mdetach()\n\u001b[1;32m     75\u001b[0m fake_loss \u001b[38;5;241m=\u001b[39m fake_loss\u001b[38;5;241m.\u001b[39mclone()\u001b[38;5;241m.\u001b[39mdetach()\u001b[38;5;241m.\u001b[39mrequires_grad_(\u001b[38;5;28;01mTrue\u001b[39;00m) \u001b[38;5;241m/\u001b[39m (idx \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m)\n",
      "File \u001b[0;32m~/miniconda3/envs/py39/lib/python3.9/site-packages/accelerate/accelerator.py:2248\u001b[0m, in \u001b[0;36mAccelerator.backward\u001b[0;34m(self, loss, **kwargs)\u001b[0m\n\u001b[1;32m   2246\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlomo_backward(loss, learning_rate)\n\u001b[1;32m   2247\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2248\u001b[0m     \u001b[43mloss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/py39/lib/python3.9/site-packages/torch/_tensor.py:492\u001b[0m, in \u001b[0;36mTensor.backward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    482\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    483\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[1;32m    484\u001b[0m         Tensor\u001b[38;5;241m.\u001b[39mbackward,\n\u001b[1;32m    485\u001b[0m         (\u001b[38;5;28mself\u001b[39m,),\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    490\u001b[0m         inputs\u001b[38;5;241m=\u001b[39minputs,\n\u001b[1;32m    491\u001b[0m     )\n\u001b[0;32m--> 492\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    493\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\n\u001b[1;32m    494\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/py39/lib/python3.9/site-packages/torch/autograd/__init__.py:251\u001b[0m, in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m    246\u001b[0m     retain_graph \u001b[38;5;241m=\u001b[39m create_graph\n\u001b[1;32m    248\u001b[0m \u001b[38;5;66;03m# The reason we repeat the same comment below is that\u001b[39;00m\n\u001b[1;32m    249\u001b[0m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[1;32m    250\u001b[0m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[0;32m--> 251\u001b[0m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[1;32m    252\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    253\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrad_tensors_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    254\u001b[0m \u001b[43m    \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    255\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    256\u001b[0m \u001b[43m    \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    257\u001b[0m \u001b[43m    \u001b[49m\u001b[43mallow_unreachable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    258\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    259\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for num_token in num_tokens:\n",
    "\n",
    "    update_interval = 10\n",
    "\n",
    "    model = RTModel.from_pretrained(model_name, num_token=num_token)\n",
    "    model.config.max_length = 256\n",
    "\n",
    "    data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model)\n",
    "\n",
    "    sft_training_args = Seq2SeqTrainingArguments(\n",
    "        output_dir=f\"./flan-t5-rt-ruletaker-{num_token}-{update_interval}\",\n",
    "        eval_strategy=\"no\",\n",
    "        save_strategy=\"no\",\n",
    "        save_total_limit=1,\n",
    "        per_device_train_batch_size=2,\n",
    "        per_device_eval_batch_size=16,\n",
    "        gradient_accumulation_steps=4,\n",
    "        gradient_checkpointing=True,\n",
    "        label_smoothing_factor=1e-5,\n",
    "        learning_rate=1e-4,\n",
    "        num_train_epochs=5,\n",
    "        logging_strategy=\"no\",\n",
    "        weight_decay=0.01,\n",
    "        bf16=True,\n",
    "        predict_with_generate=True,\n",
    "        generation_max_length=256,\n",
    "        push_to_hub=False,\n",
    "        report_to=\"none\",\n",
    "        save_safetensors=False,\n",
    "    )\n",
    "\n",
    "    rt_trainer = CustomTrainer(\n",
    "        update_interval=update_interval,\n",
    "        memory_len=memory_len,\n",
    "        model=model,\n",
    "        args=sft_training_args,\n",
    "        train_dataset=train_dataset,\n",
    "        eval_dataset={\"test\": test_dataset, \"ood\": ood_dataset},\n",
    "        tokenizer=tokenizer,\n",
    "        data_collator=data_collator,\n",
    "        compute_metrics=compute_metrics,\n",
    "    )\n",
    "\n",
    "    rt_trainer.train()\n",
    "    print(f\"Test Results for num token: {num_token}\")\n",
    "    print(rt_trainer.evaluate())"
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