{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5f93b7d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AutoModelForSeq2SeqLM\n",
    "from pet import get_pet_config,get_pet_model, get_pet_model_state_dict\n",
    "import torch\n",
    "from datasets import load_dataset\n",
    "import os\n",
    "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"3\"\n",
    "from transformers import AutoTokenizer\n",
    "from torch.utils.data import DataLoader\n",
    "from transformers import default_data_collator,get_linear_schedule_with_warmup\n",
    "from tqdm import tqdm\n",
    "from datasets import load_dataset\n",
    "\n",
    "device = \"cuda\"\n",
    "model_name_or_path = \"t5-large\"\n",
    "tokenizer_name_or_path = \"t5-large\"\n",
    "\n",
    "config = {\n",
    "    \"pet_type\":\"PREFIX_TUNING\",\n",
    "    \"task_type\":\"SEQ_2_SEQ_LM\",\n",
    "    \"num_virtual_tokens\": 20\n",
    "}\n",
    "checkpoint_name = \"financial_sentiment_analysis_prefix_tuning_v1.pt\"\n",
    "text_column = \"sentence\"\n",
    "label_column = \"text_label\"\n",
    "max_length=128\n",
    "lr = 1e-2\n",
    "num_epochs = 5\n",
    "batch_size=8\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8d0850ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3d73e8a9f76d4a2e918238f8ad0b2a30",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2965839a8e7f48fdafe2d7a909666b25",
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       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainable params: 983040 || all params: 738651136 || trainable%: 0.13308583065659835\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PETModelForSeq2SeqLM(\n",
       "  (base_model): T5ForConditionalGeneration(\n",
       "    (shared): Embedding(32128, 1024)\n",
       "    (encoder): T5Stack(\n",
       "      (embed_tokens): Embedding(32128, 1024)\n",
       "      (block): ModuleList(\n",
       "        (0): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (relative_attention_bias): Embedding(32, 16)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (1): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (2): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (3): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (4): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (5): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (6): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (7): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (8): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (9): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (10): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (11): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (12): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (13): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (14): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (15): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (16): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (17): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (18): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (19): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (20): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (21): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (22): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (23): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (final_layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "      (dropout): Dropout(p=0.1, inplace=False)\n",
       "    )\n",
       "    (decoder): T5Stack(\n",
       "      (embed_tokens): Embedding(32128, 1024)\n",
       "      (block): ModuleList(\n",
       "        (0): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (relative_attention_bias): Embedding(32, 16)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerCrossAttention(\n",
       "              (EncDecAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (2): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (1): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerCrossAttention(\n",
       "              (EncDecAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (2): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (2): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerCrossAttention(\n",
       "              (EncDecAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (2): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (3): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerCrossAttention(\n",
       "              (EncDecAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (2): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (4): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerCrossAttention(\n",
       "              (EncDecAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (2): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (5): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerCrossAttention(\n",
       "              (EncDecAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (2): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (6): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerCrossAttention(\n",
       "              (EncDecAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (2): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (7): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerCrossAttention(\n",
       "              (EncDecAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (2): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (8): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerCrossAttention(\n",
       "              (EncDecAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (2): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (9): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerCrossAttention(\n",
       "              (EncDecAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (2): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (10): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerCrossAttention(\n",
       "              (EncDecAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (2): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (11): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerCrossAttention(\n",
       "              (EncDecAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (2): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (12): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerCrossAttention(\n",
       "              (EncDecAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (2): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (13): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerCrossAttention(\n",
       "              (EncDecAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (2): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (14): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerCrossAttention(\n",
       "              (EncDecAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (2): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (15): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerCrossAttention(\n",
       "              (EncDecAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (2): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (16): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerCrossAttention(\n",
       "              (EncDecAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (2): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (17): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerCrossAttention(\n",
       "              (EncDecAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (2): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (18): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerCrossAttention(\n",
       "              (EncDecAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (2): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (19): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerCrossAttention(\n",
       "              (EncDecAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (2): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (20): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerCrossAttention(\n",
       "              (EncDecAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (2): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (21): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerCrossAttention(\n",
       "              (EncDecAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (2): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (22): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerCrossAttention(\n",
       "              (EncDecAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (2): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (23): T5Block(\n",
       "          (layer): ModuleList(\n",
       "            (0): T5LayerSelfAttention(\n",
       "              (SelfAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (1): T5LayerCrossAttention(\n",
       "              (EncDecAttention): T5Attention(\n",
       "                (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "                (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (2): T5LayerFF(\n",
       "              (DenseReluDense): T5DenseActDense(\n",
       "                (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
       "                (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "                (act): ReLU()\n",
       "              )\n",
       "              (layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (final_layer_norm): FusedRMSNorm(torch.Size([1024]), eps=1e-06, elementwise_affine=True)\n",
       "      (dropout): Dropout(p=0.1, inplace=False)\n",
       "    )\n",
       "    (lm_head): Linear(in_features=1024, out_features=32128, bias=False)\n",
       "  )\n",
       "  (word_embeddings): Embedding(32128, 1024)\n",
       "  (prompt_encoder): PrefixEncoder(\n",
       "    (embedding): Embedding(20, 49152)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating model\n",
    "pet_config = get_pet_config(config)\n",
    "\n",
    "model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path)\n",
    "model = get_pet_model(model, pet_config)\n",
    "model.print_trainable_parameters()\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4ee2babf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sourab/miniconda3/envs/ml/lib/python3.10/site-packages/huggingface_hub/utils/_deprecation.py:97: FutureWarning: Deprecated argument(s) used in 'dataset_info': token. Will not be supported from version '0.12'.\n",
      "  warnings.warn(message, FutureWarning)\n",
      "Found cached dataset financial_phrasebank (/home/sourab/.cache/huggingface/datasets/financial_phrasebank/sentences_allagree/1.0.0/550bde12e6c30e2674da973a55f57edde5181d53f5a5a34c1531c53f93b7e141)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e3f8b8faca0a4112b2c3499faee9544b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "935c8aebde284a5784348588e0bb013a",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e3487cd55f6847588492bf7fa51348ca",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'sentence': 'ADPnews - Feb 5 , 2010 - Finnish real estate investor Sponda Oyj HEL : SDA1V said today that it slipped to a net loss of EUR 81.5 million USD 11.8 m in 2009 from a profit of EUR 29.3 million in 2008 .',\n",
       " 'label': 0,\n",
       " 'text_label': 'negative'}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loading dataset\n",
    "dataset = load_dataset(\"financial_phrasebank\", 'sentences_allagree')\n",
    "dataset = dataset[\"train\"].train_test_split(test_size=0.1)\n",
    "dataset[\"validation\"] = dataset[\"test\"]\n",
    "del(dataset[\"test\"])\n",
    "\n",
    "classes = dataset[\"train\"].features[\"label\"].names\n",
    "dataset = dataset.map(\n",
    "    lambda x: {\"text_label\": [classes[label] for label in x[\"label\"]]},\n",
    "    batched=True,\n",
    "    num_proc=1,\n",
    "    \n",
    ")\n",
    "\n",
    "dataset[\"train\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "adf9608c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2ce088f4437d4e2c80c267332a5b84e5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/792k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4e5f69b61f194220b39336e48edd2f9e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sourab/transformers/src/transformers/models/t5/tokenization_t5_fast.py:156: FutureWarning: This tokenizer was incorrectly instantiated with a model max length of 512 which will be corrected in Transformers v5.\n",
      "For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`.\n",
      "- Be aware that you SHOULD NOT rely on t5-large automatically truncating your input to 512 when padding/encoding.\n",
      "- If you want to encode/pad to sequences longer than 512 you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding.\n",
      "- To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "230c5631891e4ea8ac7a1b39f315a4f0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b581e5677d2a45459ceb725534ed0891",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Running tokenizer on dataset:   0%|          | 0/1 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data preprocessing\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)\n",
    "def preprocess_function(examples):\n",
    "    inputs = examples[text_column]\n",
    "    targets = examples[label_column]\n",
    "    model_inputs = tokenizer(inputs, max_length=max_length, padding=\"max_length\", truncation=True, return_tensors=\"pt\")\n",
    "    labels = tokenizer(targets, max_length=2, padding=\"max_length\", truncation=True, return_tensors=\"pt\")\n",
    "    labels = labels[\"input_ids\"]\n",
    "    labels[labels==tokenizer.pad_token_id] = -100\n",
    "    model_inputs[\"labels\"] = labels\n",
    "    return model_inputs\n",
    "\n",
    "processed_datasets = dataset.map(\n",
    "            preprocess_function,\n",
    "            batched=True,\n",
    "            num_proc=1,\n",
    "            remove_columns=dataset[\"train\"].column_names,\n",
    "            load_from_cache_file=False,\n",
    "            desc=\"Running tokenizer on dataset\",\n",
    "        )\n",
    "\n",
    "train_dataset = processed_datasets[\"train\"]\n",
    "eval_dataset = processed_datasets[\"validation\"]\n",
    "\n",
    "train_dataloader = DataLoader(\n",
    "        train_dataset, shuffle=True, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True\n",
    "    )\n",
    "eval_dataloader = DataLoader(eval_dataset, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True)\n",
    "\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f733a3c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# optimizer and lr scheduler\n",
    "optimizer = torch.optim.AdamW(model.parameters(), lr=lr)\n",
    "lr_scheduler = get_linear_schedule_with_warmup(\n",
    "    optimizer=optimizer,\n",
    "    num_warmup_steps=0,\n",
    "    num_training_steps=(len(train_dataloader) * num_epochs),\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6b3a4090",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch=0: train_ppl=tensor(2697769., device='cuda:0') train_epoch_loss=tensor(14.8079, device='cuda:0') eval_ppl=tensor(1.0089, device='cuda:0') eval_epoch_loss=tensor(0.0089, device='cuda:0')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch=1: train_ppl=tensor(2.9475, device='cuda:0') train_epoch_loss=tensor(1.0809, device='cuda:0') eval_ppl=tensor(1.0072, device='cuda:0') eval_epoch_loss=tensor(0.0072, device='cuda:0')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████| 255/255 [00:20<00:00, 12.71it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch=2: train_ppl=tensor(2.0588, device='cuda:0') train_epoch_loss=tensor(0.7221, device='cuda:0') eval_ppl=tensor(1.0055, device='cuda:0') eval_epoch_loss=tensor(0.0054, device='cuda:0')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████| 255/255 [00:20<00:00, 12.70it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch=3: train_ppl=tensor(1.7939, device='cuda:0') train_epoch_loss=tensor(0.5844, device='cuda:0') eval_ppl=tensor(1.0063, device='cuda:0') eval_epoch_loss=tensor(0.0063, device='cuda:0')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████| 255/255 [00:19<00:00, 13.01it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch=4: train_ppl=tensor(1.7740, device='cuda:0') train_epoch_loss=tensor(0.5732, device='cuda:0') eval_ppl=tensor(1.0062, device='cuda:0') eval_epoch_loss=tensor(0.0061, device='cuda:0')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# training and evaluation\n",
    "model = model.to(device)\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    model.train()\n",
    "    total_loss = 0\n",
    "    for step, batch in enumerate(tqdm(train_dataloader)):\n",
    "        batch = {k: v.to(device) for k, v in batch.items()}\n",
    "        outputs = model(**batch)\n",
    "        loss = outputs.loss\n",
    "        total_loss += loss.detach().float()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        lr_scheduler.step()\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "    model.eval()\n",
    "    eval_loss = 0\n",
    "    eval_preds = []\n",
    "    for step, batch in enumerate(tqdm(eval_dataloader)):\n",
    "        batch = {k: v.to(device) for k, v in batch.items()}\n",
    "        with torch.no_grad():\n",
    "            outputs = model(**batch)\n",
    "        loss = outputs.loss\n",
    "        eval_loss += loss.detach().float()\n",
    "        eval_preds.extend(tokenizer.batch_decode(torch.argmax(outputs.logits, -1).detach().cpu().numpy(), skip_special_tokens=True))\n",
    "\n",
    "    eval_epoch_loss = eval_loss/len(train_dataloader)\n",
    "    eval_ppl = torch.exp(eval_epoch_loss)\n",
    "    train_epoch_loss = total_loss/len(eval_dataloader)\n",
    "    train_ppl = torch.exp(train_epoch_loss)\n",
    "    print(f\"{epoch=}: {train_ppl=} {train_epoch_loss=} {eval_ppl=} {eval_epoch_loss=}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6cafa67b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy=96.47577092511013 % on the evaluation dataset\n",
      "eval_preds[:10]=['neutral', 'neutral', 'neutral', 'negative', 'neutral', 'neutral', 'neutral', 'neutral', 'positive', 'positive']\n",
      "dataset['validation']['text_label'][:10]=['neutral', 'neutral', 'neutral', 'negative', 'neutral', 'neutral', 'neutral', 'neutral', 'positive', 'positive']\n"
     ]
    }
   ],
   "source": [
    "# print accuracy\n",
    "correct =0\n",
    "total = 0\n",
    "for pred,true in zip(eval_preds, dataset[\"validation\"][\"text_label\"]):\n",
    "    if pred.strip()==true.strip():\n",
    "        correct+=1\n",
    "    total+=1  \n",
    "accuracy = correct/total*100\n",
    "print(f\"{accuracy=} % on the evaluation dataset\")\n",
    "print(f\"{eval_preds[:10]=}\")\n",
    "print(f\"{dataset['validation']['text_label'][:10]=}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a8de6005",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'prompt_embeddings': tensor([[-0.3165, -0.8389,  0.3262,  ..., -1.5049, -1.6963,  0.3444],\n",
      "        [-1.8359,  1.1936,  1.0483,  ...,  0.6197, -0.4452,  0.5844],\n",
      "        [-0.6027,  0.3246, -1.5601,  ..., -0.3645,  0.2329,  0.3402],\n",
      "        ...,\n",
      "        [-1.9525, -0.5035,  0.8474,  ...,  0.4793, -0.0789, -0.9305],\n",
      "        [-1.9741,  0.5242, -2.0594,  ..., -0.7970, -0.4889,  2.7323],\n",
      "        [ 0.9355, -0.2714,  0.4610,  ...,  0.2692, -1.5801, -1.6405]])}\n"
     ]
    }
   ],
   "source": [
    "# saving model\n",
    "state_dict = get_pet_model_state_dict(model)\n",
    "torch.save(state_dict, checkpoint_name)\n",
    "print(state_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bd20cd4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3,8M\tfinancial_sentiment_analysis_prefix_tuning_v1.pt\r\n"
     ]
    }
   ],
   "source": [
    "!du -h $checkpoint_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76c2fc29",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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