peft

IA3.ipynb
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{
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  "cells": [
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    {
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      "cell_type": "code",
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      "execution_count": 2,
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      "id": "a9935ae2",
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      "metadata": {
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        "id": "a9935ae2"
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      },
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      "outputs": [],
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      "source": [
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        "import argparse\n",
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        "import os\n",
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        "\n",
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        "import torch\n",
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        "from torch.optim import AdamW\n",
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        "from torch.utils.data import DataLoader\n",
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        "import peft\n",
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        "\n",
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        "import evaluate\n",
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        "from datasets import load_dataset\n",
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        "from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed\n",
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        "from tqdm import tqdm"
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      ]
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    },
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    {
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      "cell_type": "code",
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      "execution_count": 3,
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      "id": "e3b13308",
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      "metadata": {
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        "id": "e3b13308"
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      },
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      "outputs": [],
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      "source": [
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        "batch_size = 8\n",
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        "model_name_or_path = \"roberta-large\"\n",
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        "task = \"mrpc\"\n",
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        "peft_type = peft.PeftType.IA3\n",
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        "device = \"cuda\"\n",
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        "num_epochs = 12"
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      ]
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    },
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    {
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      "cell_type": "code",
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      "execution_count": 4,
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      "id": "0526f571",
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      "metadata": {
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        "id": "0526f571"
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      },
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      "outputs": [],
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      "source": [
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        "# peft_config = LoraConfig(task_type=\"SEQ_CLS\", inference_mode=False, r=8, lora_alpha=16, lora_dropout=0.1)\n",
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        "peft_config = peft.IA3Config(task_type=\"SEQ_CLS\", inference_mode=False)\n",
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        "lr = 1e-3"
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      ]
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    },
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    {
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      "cell_type": "code",
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      "execution_count": 5,
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      "id": "c2697d07",
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      "metadata": {
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        "colab": {
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          "base_uri": "https://localhost:8080/",
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          "height": 489,
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        {
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        {
380
          "output_type": "stream",
381
          "name": "stdout",
382
          "text": [
383
            "Downloading and preparing dataset glue/mrpc to /root/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad...\n"
384
          ]
385
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396
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            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "e0e903b3ae8044f4a58ecd70825f36ec"
424
            }
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          },
426
          "metadata": {}
427
        },
428
        {
429
          "output_type": "display_data",
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            }
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440
          "metadata": {}
441
        },
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        {
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          "output_type": "display_data",
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            ],
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              "model_id": "9ae4e6a6543e4da6bba06175aeef3ea1"
452
            }
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          },
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          "metadata": {}
455
        },
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        {
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            ],
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            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "786b2430a42942f1aeae253861820dcb"
466
            }
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468
          "metadata": {}
469
        },
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        {
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            ],
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480
            }
481
          },
482
          "metadata": {}
483
        },
484
        {
485
          "output_type": "stream",
486
          "name": "stdout",
487
          "text": [
488
            "Dataset glue downloaded and prepared to /root/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad. Subsequent calls will reuse this data.\n"
489
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490
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501
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          },
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          "metadata": {}
504
        },
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        {
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          "output_type": "display_data",
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          "data": {
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            "text/plain": [
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              "Downloading builder script:   0%|          | 0.00/5.75k [00:00<?, ?B/s]"
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            ],
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            "application/vnd.jupyter.widget-view+json": {
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              "version_major": 2,
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              "model_id": "75ee099566ca46eb8a75475246aaab01"
515
            }
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          "metadata": {}
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            ],
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              "model_id": "a018f7c156584eb3833b6aba3710b3c0"
529
            }
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          },
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          "metadata": {}
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        {
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              "model_id": "f871eeaa54cf451b8a2de64eed90d5b6"
543
            }
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          },
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          "metadata": {}
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        {
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            ],
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            "application/vnd.jupyter.widget-view+json": {
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              "version_major": 2,
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556
              "model_id": "e41888b1dea140d18be25efc6d99d0c3"
557
            }
558
          },
559
          "metadata": {}
560
        }
561
      ],
562
      "source": [
563
        "if any(k in model_name_or_path for k in (\"gpt\", \"opt\", \"bloom\")):\n",
564
        "    padding_side = \"left\"\n",
565
        "else:\n",
566
        "    padding_side = \"right\"\n",
567
        "\n",
568
        "tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, padding_side=padding_side)\n",
569
        "if getattr(tokenizer, \"pad_token_id\") is None:\n",
570
        "    tokenizer.pad_token_id = tokenizer.eos_token_id\n",
571
        "\n",
572
        "datasets = load_dataset(\"glue\", task)\n",
573
        "metric = evaluate.load(\"glue\", task)\n",
574
        "\n",
575
        "\n",
576
        "def tokenize_function(examples):\n",
577
        "    # max_length=None => use the model max length (it's actually the default)\n",
578
        "    outputs = tokenizer(examples[\"sentence1\"], examples[\"sentence2\"], truncation=True, max_length=None)\n",
579
        "    return outputs\n",
580
        "\n",
581
        "\n",
582
        "tokenized_datasets = datasets.map(\n",
583
        "    tokenize_function,\n",
584
        "    batched=True,\n",
585
        "    remove_columns=[\"idx\", \"sentence1\", \"sentence2\"],\n",
586
        ")\n",
587
        "\n",
588
        "# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the\n",
589
        "# transformers library\n",
590
        "tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n",
591
        "\n",
592
        "\n",
593
        "def collate_fn(examples):\n",
594
        "    return tokenizer.pad(examples, padding=\"longest\", return_tensors=\"pt\")\n",
595
        "\n",
596
        "\n",
597
        "# Instantiate dataloaders.\n",
598
        "train_dataloader = DataLoader(tokenized_datasets[\"train\"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size)\n",
599
        "eval_dataloader = DataLoader(\n",
600
        "    tokenized_datasets[\"validation\"], shuffle=False, collate_fn=collate_fn, batch_size=batch_size\n",
601
        ")\n",
602
        "test_dataloader = DataLoader(tokenized_datasets[\"test\"], shuffle=False, collate_fn=collate_fn, batch_size=batch_size)"
603
      ]
604
    },
605
    {
606
      "cell_type": "code",
607
      "execution_count": 6,
608
      "id": "2ed5ac74",
609
      "metadata": {
610
        "colab": {
611
          "base_uri": "https://localhost:8080/",
612
          "height": 1000,
613
          "referenced_widgets": [
614
            "0cecb897c86c4892b94a1990ab08a926",
615
            "b8af0294819e4280ad41fa1c11006adf",
616
            "c7530d63b2f745e799713284abacbd2c",
617
            "12a1e302a69543c5bc0e0a66be008ca0",
618
            "9c372e9e9b20433faed8530ca0f4424c",
619
            "b07f26a21325493cac19113f1aa1ee96",
620
            "fbdf6c544fb54294903524a69384e773",
621
            "6c6f2223243b4a7485aef7fbbfe07668",
622
            "a93509d61ac94628a74bc0f98c0eec06",
623
            "3a8de0eb7db44647a734590b6b351b44",
624
            "64d8affd2e854a1c9043fec7ca8a2796"
625
          ]
626
        },
627
        "id": "2ed5ac74",
628
        "outputId": "18ea15ac-ed8d-4d80-b166-706681ee49ab"
629
      },
630
      "outputs": [
631
        {
632
          "output_type": "display_data",
633
          "data": {
634
            "text/plain": [
635
              "Downloading model.safetensors:   0%|          | 0.00/1.42G [00:00<?, ?B/s]"
636
            ],
637
            "application/vnd.jupyter.widget-view+json": {
638
              "version_major": 2,
639
              "version_minor": 0,
640
              "model_id": "0cecb897c86c4892b94a1990ab08a926"
641
            }
642
          },
643
          "metadata": {}
644
        },
645
        {
646
          "output_type": "stream",
647
          "name": "stderr",
648
          "text": [
649
            "Some weights of the model checkpoint at roberta-large were not used when initializing RobertaForSequenceClassification: ['lm_head.layer_norm.weight', 'lm_head.bias', 'lm_head.dense.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.bias']\n",
650
            "- This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
651
            "- This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
652
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-large and are newly initialized: ['classifier.dense.weight', 'classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.bias']\n",
653
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
654
          ]
655
        },
656
        {
657
          "output_type": "stream",
658
          "name": "stdout",
659
          "text": [
660
            "trainable params: 2,275,332 || all params: 356,585,476 || trainable%: 0.6380888042675075\n"
661
          ]
662
        },
663
        {
664
          "output_type": "execute_result",
665
          "data": {
666
            "text/plain": [
667
              "PeftModelForSequenceClassification(\n",
668
              "  (base_model): IA3Model(\n",
669
              "    (model): RobertaForSequenceClassification(\n",
670
              "      (roberta): RobertaModel(\n",
671
              "        (embeddings): RobertaEmbeddings(\n",
672
              "          (word_embeddings): Embedding(50265, 1024, padding_idx=1)\n",
673
              "          (position_embeddings): Embedding(514, 1024, padding_idx=1)\n",
674
              "          (token_type_embeddings): Embedding(1, 1024)\n",
675
              "          (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
676
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
677
              "        )\n",
678
              "        (encoder): RobertaEncoder(\n",
679
              "          (layer): ModuleList(\n",
680
              "            (0-23): 24 x RobertaLayer(\n",
681
              "              (attention): RobertaAttention(\n",
682
              "                (self): RobertaSelfAttention(\n",
683
              "                  (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
684
              "                  (key): Linear(\n",
685
              "                    in_features=1024, out_features=1024, bias=True\n",
686
              "                    (ia3_l): ParameterDict(  (default): Parameter containing: [torch.FloatTensor of size 1024x1])\n",
687
              "                  )\n",
688
              "                  (value): Linear(\n",
689
              "                    in_features=1024, out_features=1024, bias=True\n",
690
              "                    (ia3_l): ParameterDict(  (default): Parameter containing: [torch.FloatTensor of size 1024x1])\n",
691
              "                  )\n",
692
              "                  (dropout): Dropout(p=0.1, inplace=False)\n",
693
              "                )\n",
694
              "                (output): RobertaSelfOutput(\n",
695
              "                  (dense): Linear(\n",
696
              "                    in_features=1024, out_features=1024, bias=True\n",
697
              "                    (ia3_l): ParameterDict(  (default): Parameter containing: [torch.FloatTensor of size 1x1024])\n",
698
              "                  )\n",
699
              "                  (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
700
              "                  (dropout): Dropout(p=0.1, inplace=False)\n",
701
              "                )\n",
702
              "              )\n",
703
              "              (intermediate): RobertaIntermediate(\n",
704
              "                (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
705
              "                (intermediate_act_fn): GELUActivation()\n",
706
              "              )\n",
707
              "              (output): RobertaOutput(\n",
708
              "                (dense): Linear(\n",
709
              "                  in_features=4096, out_features=1024, bias=True\n",
710
              "                  (ia3_l): ParameterDict(  (default): Parameter containing: [torch.FloatTensor of size 1x4096])\n",
711
              "                )\n",
712
              "                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
713
              "                (dropout): Dropout(p=0.1, inplace=False)\n",
714
              "              )\n",
715
              "            )\n",
716
              "          )\n",
717
              "        )\n",
718
              "      )\n",
719
              "      (classifier): ModulesToSaveWrapper(\n",
720
              "        (original_module): RobertaClassificationHead(\n",
721
              "          (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
722
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
723
              "          (out_proj): Linear(in_features=1024, out_features=2, bias=True)\n",
724
              "        )\n",
725
              "        (modules_to_save): ModuleDict(\n",
726
              "          (default): RobertaClassificationHead(\n",
727
              "            (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
728
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
729
              "            (out_proj): Linear(in_features=1024, out_features=2, bias=True)\n",
730
              "          )\n",
731
              "        )\n",
732
              "      )\n",
733
              "    )\n",
734
              "  )\n",
735
              ")"
736
            ]
737
          },
738
          "metadata": {},
739
          "execution_count": 6
740
        }
741
      ],
742
      "source": [
743
        "model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, return_dict=True)\n",
744
        "model = peft.get_peft_model(model, peft_config)\n",
745
        "model.print_trainable_parameters()\n",
746
        "model"
747
      ]
748
    },
749
    {
750
      "cell_type": "code",
751
      "execution_count": 7,
752
      "id": "0d2d0381",
753
      "metadata": {
754
        "id": "0d2d0381"
755
      },
756
      "outputs": [],
757
      "source": [
758
        "optimizer = AdamW(params=model.parameters(), lr=lr)\n",
759
        "\n",
760
        "# Instantiate scheduler\n",
761
        "lr_scheduler = get_linear_schedule_with_warmup(\n",
762
        "    optimizer=optimizer,\n",
763
        "    num_warmup_steps=0.06 * (len(train_dataloader) * num_epochs),\n",
764
        "    num_training_steps=(len(train_dataloader) * num_epochs),\n",
765
        ")"
766
      ]
767
    },
768
    {
769
      "cell_type": "code",
770
      "execution_count": 8,
771
      "id": "fa0e73be",
772
      "metadata": {
773
        "colab": {
774
          "base_uri": "https://localhost:8080/"
775
        },
776
        "id": "fa0e73be",
777
        "outputId": "bb17c146-8acc-477d-8f9f-65b8be794abb"
778
      },
779
      "outputs": [
780
        {
781
          "output_type": "stream",
782
          "name": "stderr",
783
          "text": [
784
            "  0%|          | 0/459 [00:00<?, ?it/s]You're using a RobertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
785
            "100%|██████████| 459/459 [01:41<00:00,  4.52it/s]\n",
786
            "100%|██████████| 51/51 [00:05<00:00,  8.89it/s]\n"
787
          ]
788
        },
789
        {
790
          "output_type": "stream",
791
          "name": "stdout",
792
          "text": [
793
            "epoch 0: {'accuracy': 0.7034313725490197, 'f1': 0.8212703101920238}\n"
794
          ]
795
        },
796
        {
797
          "output_type": "stream",
798
          "name": "stderr",
799
          "text": [
800
            "100%|██████████| 459/459 [01:45<00:00,  4.35it/s]\n",
801
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802
          ]
803
        },
804
        {
805
          "output_type": "stream",
806
          "name": "stdout",
807
          "text": [
808
            "epoch 1: {'accuracy': 0.7794117647058824, 'f1': 0.8432055749128919}\n"
809
          ]
810
        },
811
        {
812
          "output_type": "stream",
813
          "name": "stderr",
814
          "text": [
815
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817
          ]
818
        },
819
        {
820
          "output_type": "stream",
821
          "name": "stdout",
822
          "text": [
823
            "epoch 2: {'accuracy': 0.8406862745098039, 'f1': 0.8794063079777366}\n"
824
          ]
825
        },
826
        {
827
          "output_type": "stream",
828
          "name": "stderr",
829
          "text": [
830
            "100%|██████████| 459/459 [01:49<00:00,  4.20it/s]\n",
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832
          ]
833
        },
834
        {
835
          "output_type": "stream",
836
          "name": "stdout",
837
          "text": [
838
            "epoch 3: {'accuracy': 0.8480392156862745, 'f1': 0.8923611111111109}\n"
839
          ]
840
        },
841
        {
842
          "output_type": "stream",
843
          "name": "stderr",
844
          "text": [
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          ]
848
        },
849
        {
850
          "output_type": "stream",
851
          "name": "stdout",
852
          "text": [
853
            "epoch 4: {'accuracy': 0.8529411764705882, 'f1': 0.894736842105263}\n"
854
          ]
855
        },
856
        {
857
          "output_type": "stream",
858
          "name": "stderr",
859
          "text": [
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        {
865
          "output_type": "stream",
866
          "name": "stdout",
867
          "text": [
868
            "epoch 5: {'accuracy': 0.8700980392156863, 'f1': 0.9090909090909091}\n"
869
          ]
870
        },
871
        {
872
          "output_type": "stream",
873
          "name": "stderr",
874
          "text": [
875
            "100%|██████████| 459/459 [01:49<00:00,  4.20it/s]\n",
876
            "100%|██████████| 51/51 [00:06<00:00,  8.41it/s]\n"
877
          ]
878
        },
879
        {
880
          "output_type": "stream",
881
          "name": "stdout",
882
          "text": [
883
            "epoch 6: {'accuracy': 0.875, 'f1': 0.9090909090909091}\n"
884
          ]
885
        },
886
        {
887
          "output_type": "stream",
888
          "name": "stderr",
889
          "text": [
890
            "100%|██████████| 459/459 [01:49<00:00,  4.21it/s]\n",
891
            "100%|██████████| 51/51 [00:06<00:00,  8.41it/s]\n"
892
          ]
893
        },
894
        {
895
          "output_type": "stream",
896
          "name": "stdout",
897
          "text": [
898
            "epoch 7: {'accuracy': 0.8676470588235294, 'f1': 0.9042553191489361}\n"
899
          ]
900
        },
901
        {
902
          "output_type": "stream",
903
          "name": "stderr",
904
          "text": [
905
            "100%|██████████| 459/459 [01:50<00:00,  4.17it/s]\n",
906
            "100%|██████████| 51/51 [00:06<00:00,  8.45it/s]\n"
907
          ]
908
        },
909
        {
910
          "output_type": "stream",
911
          "name": "stdout",
912
          "text": [
913
            "epoch 8: {'accuracy': 0.8700980392156863, 'f1': 0.9068541300527241}\n"
914
          ]
915
        },
916
        {
917
          "output_type": "stream",
918
          "name": "stderr",
919
          "text": [
920
            "100%|██████████| 459/459 [01:49<00:00,  4.20it/s]\n",
921
            "100%|██████████| 51/51 [00:06<00:00,  8.39it/s]\n"
922
          ]
923
        },
924
        {
925
          "output_type": "stream",
926
          "name": "stdout",
927
          "text": [
928
            "epoch 9: {'accuracy': 0.8676470588235294, 'f1': 0.9028776978417268}\n"
929
          ]
930
        },
931
        {
932
          "output_type": "stream",
933
          "name": "stderr",
934
          "text": [
935
            "100%|██████████| 459/459 [01:49<00:00,  4.20it/s]\n",
936
            "100%|██████████| 51/51 [00:06<00:00,  8.39it/s]\n"
937
          ]
938
        },
939
        {
940
          "output_type": "stream",
941
          "name": "stdout",
942
          "text": [
943
            "epoch 10: {'accuracy': 0.875, 'f1': 0.9100529100529101}\n"
944
          ]
945
        },
946
        {
947
          "output_type": "stream",
948
          "name": "stderr",
949
          "text": [
950
            "100%|██████████| 459/459 [01:49<00:00,  4.18it/s]\n",
951
            "100%|██████████| 51/51 [00:06<00:00,  8.41it/s]"
952
          ]
953
        },
954
        {
955
          "output_type": "stream",
956
          "name": "stdout",
957
          "text": [
958
            "epoch 11: {'accuracy': 0.8651960784313726, 'f1': 0.9012567324955117}\n"
959
          ]
960
        },
961
        {
962
          "output_type": "stream",
963
          "name": "stderr",
964
          "text": [
965
            "\n"
966
          ]
967
        }
968
      ],
969
      "source": [
970
        "model.to(device)\n",
971
        "for epoch in range(num_epochs):\n",
972
        "    model.train()\n",
973
        "    for step, batch in enumerate(tqdm(train_dataloader)):\n",
974
        "        batch.to(device)\n",
975
        "        outputs = model(**batch)\n",
976
        "        loss = outputs.loss\n",
977
        "        loss.backward()\n",
978
        "        optimizer.step()\n",
979
        "        lr_scheduler.step()\n",
980
        "        optimizer.zero_grad()\n",
981
        "\n",
982
        "    model.eval()\n",
983
        "    for step, batch in enumerate(tqdm(eval_dataloader)):\n",
984
        "        batch.to(device)\n",
985
        "        with torch.no_grad():\n",
986
        "            outputs = model(**batch)\n",
987
        "        predictions = outputs.logits.argmax(dim=-1)\n",
988
        "        predictions, references = predictions, batch[\"labels\"]\n",
989
        "        metric.add_batch(\n",
990
        "            predictions=predictions,\n",
991
        "            references=references,\n",
992
        "        )\n",
993
        "\n",
994
        "    eval_metric = metric.compute()\n",
995
        "    print(f\"epoch {epoch}:\", eval_metric)"
996
      ]
997
    },
998
    {
999
      "cell_type": "markdown",
1000
      "id": "f2b2caca",
1001
      "metadata": {
1002
        "id": "f2b2caca"
1003
      },
1004
      "source": [
1005
        "## Share adapters on the 🤗 Hub"
1006
      ]
1007
    },
1008
    {
1009
      "cell_type": "code",
1010
      "execution_count": null,
1011
      "id": "990b3c93",
1012
      "metadata": {
1013
        "colab": {
1014
          "referenced_widgets": [
1015
            "ad8979af959541b2a5a67f389884f057",
1016
            "1c020005cfe04de6bdd404ef41e0dfef"
1017
          ]
1018
        },
1019
        "id": "990b3c93",
1020
        "outputId": "0e2f13c2-0285-4f47-ab9a-d3aefea7ee05"
1021
      },
1022
      "outputs": [
1023
        {
1024
          "data": {
1025
            "application/vnd.jupyter.widget-view+json": {
1026
              "model_id": "ad8979af959541b2a5a67f389884f057",
1027
              "version_major": 2,
1028
              "version_minor": 0
1029
            },
1030
            "text/plain": [
1031
              "Upload 1 LFS files:   0%|          | 0/1 [00:00<?, ?it/s]"
1032
            ]
1033
          },
1034
          "metadata": {},
1035
          "output_type": "display_data"
1036
        },
1037
        {
1038
          "data": {
1039
            "application/vnd.jupyter.widget-view+json": {
1040
              "model_id": "1c020005cfe04de6bdd404ef41e0dfef",
1041
              "version_major": 2,
1042
              "version_minor": 0
1043
            },
1044
            "text/plain": [
1045
              "adapter_model.bin:   0%|          | 0.00/4.93M [00:00<?, ?B/s]"
1046
            ]
1047
          },
1048
          "metadata": {},
1049
          "output_type": "display_data"
1050
        },
1051
        {
1052
          "data": {
1053
            "text/plain": [
1054
              "CommitInfo(commit_url='https://huggingface.co/SumanthRH/roberta-large-peft-ia3/commit/9968de770e53ecd24e8e689a10144b7f55059a75', commit_message='Upload model', commit_description='', oid='9968de770e53ecd24e8e689a10144b7f55059a75', pr_url='https://huggingface.co/SumanthRH/roberta-large-peft-ia3/discussions/3', pr_revision='refs/pr/3', pr_num=3)"
1055
            ]
1056
          },
1057
          "execution_count": 25,
1058
          "metadata": {},
1059
          "output_type": "execute_result"
1060
        }
1061
      ],
1062
      "source": [
1063
        "model.push_to_hub(\"SumanthRH/roberta-large-peft-ia3\", use_auth_token=True)"
1064
      ]
1065
    },
1066
    {
1067
      "cell_type": "markdown",
1068
      "id": "9d140b26",
1069
      "metadata": {
1070
        "id": "9d140b26"
1071
      },
1072
      "source": [
1073
        "## Load adapters from the Hub\n",
1074
        "\n",
1075
        "You can also directly load adapters from the Hub using the commands below:"
1076
      ]
1077
    },
1078
    {
1079
      "cell_type": "code",
1080
      "execution_count": null,
1081
      "id": "4d55c87d",
1082
      "metadata": {
1083
        "colab": {
1084
          "referenced_widgets": [
1085
            "fd320d7b5d9a43df8abf044897e25cc7",
1086
            "0022338db24d43bba27d0e72773855e0"
1087
          ]
1088
        },
1089
        "id": "4d55c87d",
1090
        "outputId": "e6e232d7-22a3-4c85-c72e-90d8275c9762"
1091
      },
1092
      "outputs": [
1093
        {
1094
          "data": {
1095
            "application/vnd.jupyter.widget-view+json": {
1096
              "model_id": "fd320d7b5d9a43df8abf044897e25cc7",
1097
              "version_major": 2,
1098
              "version_minor": 0
1099
            },
1100
            "text/plain": [
1101
              "Downloading (…)/adapter_config.json:   0%|          | 0.00/345 [00:00<?, ?B/s]"
1102
            ]
1103
          },
1104
          "metadata": {},
1105
          "output_type": "display_data"
1106
        },
1107
        {
1108
          "name": "stderr",
1109
          "output_type": "stream",
1110
          "text": [
1111
            "Some weights of the model checkpoint at roberta-large were not used when initializing RobertaForSequenceClassification: ['lm_head.decoder.weight', 'roberta.pooler.dense.bias', 'roberta.pooler.dense.weight', 'lm_head.dense.weight', 'lm_head.layer_norm.bias', 'lm_head.layer_norm.weight', 'lm_head.dense.bias', 'lm_head.bias']\n",
1112
            "- This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
1113
            "- This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
1114
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-large and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n",
1115
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
1116
          ]
1117
        },
1118
        {
1119
          "data": {
1120
            "application/vnd.jupyter.widget-view+json": {
1121
              "model_id": "0022338db24d43bba27d0e72773855e0",
1122
              "version_major": 2,
1123
              "version_minor": 0
1124
            },
1125
            "text/plain": [
1126
              "Downloading adapter_model.bin:   0%|          | 0.00/4.93M [00:00<?, ?B/s]"
1127
            ]
1128
          },
1129
          "metadata": {},
1130
          "output_type": "display_data"
1131
        },
1132
        {
1133
          "name": "stderr",
1134
          "output_type": "stream",
1135
          "text": [
1136
            "  0%|                                                                                                                                                                  | 0/51 [00:00<?, ?it/s]You're using a RobertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
1137
            "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 51/51 [00:04<00:00, 10.57it/s]"
1138
          ]
1139
        },
1140
        {
1141
          "name": "stdout",
1142
          "output_type": "stream",
1143
          "text": [
1144
            "{'accuracy': 0.8774509803921569, 'f1': 0.9116607773851589}\n"
1145
          ]
1146
        },
1147
        {
1148
          "name": "stderr",
1149
          "output_type": "stream",
1150
          "text": [
1151
            "\n"
1152
          ]
1153
        }
1154
      ],
1155
      "source": [
1156
        "import torch\n",
1157
        "from peft import PeftModel, PeftConfig\n",
1158
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
1159
        "\n",
1160
        "peft_model_id = \"SumanthRH/roberta-large-peft-ia3\"\n",
1161
        "config = PeftConfig.from_pretrained(peft_model_id)\n",
1162
        "inference_model = AutoModelForSequenceClassification.from_pretrained(config.base_model_name_or_path)\n",
1163
        "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
1164
        "\n",
1165
        "# Load the Lora model\n",
1166
        "inference_model = PeftModel.from_pretrained(inference_model, peft_model_id)\n",
1167
        "\n",
1168
        "inference_model.to(device)\n",
1169
        "inference_model.eval()\n",
1170
        "for step, batch in enumerate(tqdm(eval_dataloader)):\n",
1171
        "    batch.to(device)\n",
1172
        "    with torch.no_grad():\n",
1173
        "        outputs = inference_model(**batch)\n",
1174
        "    predictions = outputs.logits.argmax(dim=-1)\n",
1175
        "    predictions, references = predictions, batch[\"labels\"]\n",
1176
        "    metric.add_batch(\n",
1177
        "        predictions=predictions,\n",
1178
        "        references=references,\n",
1179
        "    )\n",
1180
        "\n",
1181
        "eval_metric = metric.compute()\n",
1182
        "print(eval_metric)"
1183
      ]
1184
    },
1185
    {
1186
      "cell_type": "code",
1187
      "execution_count": null,
1188
      "id": "27c43da1",
1189
      "metadata": {
1190
        "id": "27c43da1"
1191
      },
1192
      "outputs": [],
1193
      "source": []
1194
    }
1195
  ],
1196
  "metadata": {
1197
    "kernelspec": {
1198
      "display_name": "Python 3",
1199
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1200
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1201
    "language_info": {
1202
      "codemirror_mode": {
1203
        "name": "ipython",
1204
        "version": 3
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1206
      "file_extension": ".py",
1207
      "mimetype": "text/x-python",
1208
      "name": "python",
1209
      "nbconvert_exporter": "python",
1210
      "pygments_lexer": "ipython3",
1211
      "version": "3.10.9"
1212
    },
1213
    "vscode": {
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1215
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1216
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1220
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