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788 lines
22 KiB
Plaintext
788 lines
22 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "d36e1e93-ae93-4a4e-93c6-68fd868d2882",
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"metadata": {},
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"source": [
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"# Using VB-LoRA for sequence classification"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ddfc0610-55f6-4343-a950-125ccf0f45ac",
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"metadata": {},
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"source": [
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"In this example, we fine-tune Roberta on a sequence classification task using VB-LoRA.\n",
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"\n",
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"This notebook is adapted from `examples/sequence_classification/VeRA.ipynb`."
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]
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},
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{
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"cell_type": "markdown",
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"id": "45addd81-d4f3-4dfd-960d-3920d347f0a6",
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"metadata": {},
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"source": [
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"## Imports"
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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": null,
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"id": "a9935ae2",
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"metadata": {},
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"outputs": [],
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"source": [
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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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"from peft import (\n",
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" get_peft_model,\n",
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" VBLoRAConfig,\n",
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" PeftType,\n",
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")\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\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": "markdown",
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"id": "62c959bf-7cc2-49e0-b97e-4c10ec3b9bf3",
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"metadata": {},
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"source": [
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"## Parameters"
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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": null,
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"id": "e3b13308",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<torch._C.Generator at 0x7f4fc7c3c750>"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"batch_size = 32\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 = PeftType.VBLORA\n",
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"device = torch.accelerator.current_accelerator().type if hasattr(torch, \"accelerator\") else \"cuda\"\n",
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"num_epochs = 20\n",
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"rank = 4\n",
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"max_length = 128\n",
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"num_vectors = 90\n",
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"vector_length = 256\n",
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"torch.manual_seed(0)"
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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": "0526f571",
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"metadata": {},
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"outputs": [],
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"source": [
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"peft_config = VBLoRAConfig(\n",
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" task_type=\"SEQ_CLS\", \n",
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" r=rank,\n",
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" topk=2,\n",
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" target_modules=['key', 'value', 'query', 'output.dense', 'intermediate.dense'],\n",
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" num_vectors=num_vectors,\n",
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" vector_length=vector_length,\n",
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" save_only_topk_weights=True, # Set to True to reduce storage space. Note that the saved parameters cannot be used to resume training from checkpoints.\n",
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" vblora_dropout=0.,\n",
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")\n",
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"head_lr = 4e-3\n",
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"vector_bank_lr = 1e-3\n",
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"logits_lr = 1e-2"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c075c5d2-a457-4f37-a7f1-94fd0d277972",
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"metadata": {},
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"source": [
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"## Loading data"
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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": "7bb52cb4-d1c3-4b04-8bf0-f39ca88af139",
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"metadata": {},
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"outputs": [],
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"source": [
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"if any(k in model_name_or_path for k in (\"gpt\", \"opt\", \"bloom\")):\n",
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" padding_side = \"left\"\n",
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"else:\n",
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" padding_side = \"right\"\n",
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"\n",
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"tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, padding_side=padding_side)\n",
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"if getattr(tokenizer, \"pad_token_id\") is None:\n",
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" tokenizer.pad_token_id = tokenizer.eos_token_id"
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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": "e69c5e1f-d27b-4264-a41e-fc9b99d025e6",
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"metadata": {},
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"outputs": [],
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"source": [
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"datasets = load_dataset(\"glue\", task)\n",
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"metric = evaluate.load(\"glue\", task)"
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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": 6,
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"id": "0209f778-c93b-40eb-a4e0-24c25db03980",
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"metadata": {},
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"outputs": [],
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"source": [
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"def tokenize_function(examples):\n",
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" # max_length=None => use the model max length (it's actually the default)\n",
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" outputs = tokenizer(examples[\"sentence1\"], examples[\"sentence2\"], truncation=True, max_length=max_length)\n",
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" return outputs\n",
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"\n",
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"\n",
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"tokenized_datasets = datasets.map(\n",
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" tokenize_function,\n",
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" batched=True,\n",
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" remove_columns=[\"idx\", \"sentence1\", \"sentence2\"],\n",
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")\n",
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"\n",
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"# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the\n",
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"# transformers library\n",
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"tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")"
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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": 7,
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"id": "7453954e-982c-46f0-b09c-589776e6d6cb",
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"metadata": {},
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"outputs": [],
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"source": [
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"def collate_fn(examples):\n",
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" return tokenizer.pad(examples, padding=\"longest\", return_tensors=\"pt\")\n",
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"\n",
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"\n",
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"# Instantiate dataloaders.\n",
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"train_dataloader = DataLoader(tokenized_datasets[\"train\"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size)\n",
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"eval_dataloader = DataLoader(\n",
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" tokenized_datasets[\"validation\"], shuffle=False, collate_fn=collate_fn, batch_size=batch_size\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f3b9b2e8-f415-4d0f-9fb4-436f1a3585ea",
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"metadata": {},
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"source": [
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"## Preparing the VB-LoRA model"
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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": 8,
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"id": "2ed5ac74",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"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",
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"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"trainable params: 1,696,770 || all params: 357,058,564 || trainable%: 0.4752\n",
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"VB-LoRA params to-be-saved (float32-equivalent): 33,408 || total params to-be-saved: 1,085,058\n"
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]
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}
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],
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"source": [
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"model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, return_dict=True, max_length=None)\n",
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"model = get_peft_model(model, peft_config)\n",
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"model.print_trainable_parameters()\n",
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"model.print_savable_parameters()"
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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": 9,
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"id": "0d2d0381",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS\n",
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"from transformers.trainer_pt_utils import get_parameter_names\n",
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"\n",
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"decay_parameters = get_parameter_names(model, ALL_LAYERNORM_LAYERS)\n",
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"decay_parameters = [name for name in decay_parameters if \"bias\" not in name]\n",
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"vector_bank_parameters = [name for name, _ in model.named_parameters() if \"vector_bank\" in name]\n",
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"logits_parameters = [name for name, _ in model.named_parameters() if \"logits\" in name ]\n",
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"\n",
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"optimizer_grouped_parameters = [\n",
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" {\n",
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" \"params\": [p for n, p in model.named_parameters() if n in decay_parameters and \\\n",
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" n not in logits_parameters and n not in vector_bank_parameters],\n",
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" \"weight_decay\": 0.1,\n",
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" \"lr\": head_lr,\n",
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" },\n",
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" {\n",
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" \"params\": [p for n, p in model.named_parameters() if n not in decay_parameters and \\\n",
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" n not in logits_parameters and n not in vector_bank_parameters],\n",
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" \"weight_decay\": 0.0,\n",
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" \"lr\": head_lr,\n",
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" },\n",
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" {\n",
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" \"params\": [p for n, p in model.named_parameters() if n in vector_bank_parameters],\n",
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" \"lr\": vector_bank_lr,\n",
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" \"weight_decay\": 0.0,\n",
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" },\n",
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" {\n",
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" \"params\": [p for n, p in model.named_parameters() if n in logits_parameters],\n",
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" \"lr\": logits_lr,\n",
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" \"weight_decay\": 0.0,\n",
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" },\n",
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"]\n",
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"\n",
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"optimizer = AdamW(optimizer_grouped_parameters)\n",
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"lr_scheduler = get_linear_schedule_with_warmup(\n",
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" optimizer=optimizer,\n",
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" num_warmup_steps=0.06 * (len(train_dataloader) * num_epochs),\n",
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" num_training_steps=(len(train_dataloader) * num_epochs),\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c0dd5aa8-977b-4ac0-8b96-884b17bcdd00",
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"metadata": {},
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"source": [
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"## Training"
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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": 10,
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"id": "fa0e73be",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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" 0%| | 0/115 [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",
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"100%|██████████| 115/115 [00:34<00:00, 3.33it/s]\n",
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"100%|██████████| 13/13 [00:01<00:00, 7.84it/s]\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"epoch 0: {'accuracy': 0.6691176470588235, 'f1': 0.786053882725832}\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100%|██████████| 115/115 [00:34<00:00, 3.37it/s]\n",
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"100%|██████████| 13/13 [00:01<00:00, 7.83it/s]\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"epoch 1: {'accuracy': 0.5833333333333334, 'f1': 0.6136363636363636}\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100%|██████████| 115/115 [00:34<00:00, 3.34it/s]\n",
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"epoch 2: {'accuracy': 0.7107843137254902, 'f1': 0.8238805970149253}\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100%|██████████| 115/115 [00:34<00:00, 3.34it/s]\n",
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]
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},
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"epoch 3: {'accuracy': 0.8284313725490197, 'f1': 0.8833333333333333}\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100%|██████████| 115/115 [00:34<00:00, 3.34it/s]\n",
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"100%|██████████| 13/13 [00:01<00:00, 7.79it/s]\n"
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},
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"epoch 4: {'accuracy': 0.8480392156862745, 'f1': 0.8847583643122676}\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"100%|██████████| 115/115 [00:34<00:00, 3.30it/s]\n",
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]
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},
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"epoch 5: {'accuracy': 0.8676470588235294, 'f1': 0.898876404494382}\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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},
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"epoch 6: {'accuracy': 0.8602941176470589, 'f1': 0.9035532994923858}\n"
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]
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},
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{
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"output_type": "stream",
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},
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"epoch 7: {'accuracy': 0.8774509803921569, 'f1': 0.911660777385159}\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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},
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"epoch 8: {'accuracy': 0.8872549019607843, 'f1': 0.9172661870503597}\n"
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]
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},
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{
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"name": "stderr",
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"epoch 9: {'accuracy': 0.875, 'f1': 0.9113043478260869}\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"epoch 10: {'accuracy': 0.8823529411764706, 'f1': 0.9166666666666666}\n"
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]
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},
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"output_type": "stream",
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"output_type": "stream",
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"text": [
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"epoch 11: {'accuracy': 0.8970588235294118, 'f1': 0.9252669039145908}\n"
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]
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},
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"output_type": "stream",
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"text": [
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"epoch 12: {'accuracy': 0.8946078431372549, 'f1': 0.9246935201401051}\n"
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]
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},
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"output_type": "stream",
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"text": [
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"epoch 13: {'accuracy': 0.9068627450980392, 'f1': 0.9316546762589928}\n"
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]
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},
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"output_type": "stream",
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"text": [
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"epoch 14: {'accuracy': 0.8946078431372549, 'f1': 0.9225225225225225}\n"
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]
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},
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"output_type": "stream",
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"text": [
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"epoch 15: {'accuracy': 0.8995098039215687, 'f1': 0.926391382405745}\n"
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]
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},
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"output_type": "stream",
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"text": [
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"epoch 16: {'accuracy': 0.9068627450980392, 'f1': 0.9316546762589928}\n"
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]
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},
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"output_type": "stream",
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"epoch 17: {'accuracy': 0.8921568627450981, 'f1': 0.9217081850533808}\n"
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"output_type": "stream",
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"text": [
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"epoch 18: {'accuracy': 0.8995098039215687, 'f1': 0.9266547406082289}\n"
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]
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},
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"output_type": "stream",
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"text": [
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"epoch 19: {'accuracy': 0.9044117647058824, 'f1': 0.9297297297297298}\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"model.to(device)\n",
|
|
"\n",
|
|
"for epoch in range(num_epochs):\n",
|
|
" model.train()\n",
|
|
" for step, batch in enumerate(tqdm(train_dataloader)):\n",
|
|
" batch.to(device)\n",
|
|
" outputs = model(**batch)\n",
|
|
" loss = outputs.loss\n",
|
|
" loss.backward()\n",
|
|
" optimizer.step()\n",
|
|
" lr_scheduler.step()\n",
|
|
" optimizer.zero_grad()\n",
|
|
"\n",
|
|
" model.eval()\n",
|
|
" for step, batch in enumerate(tqdm(eval_dataloader)):\n",
|
|
" batch.to(device)\n",
|
|
" with torch.no_grad():\n",
|
|
" outputs = model(**batch)\n",
|
|
" predictions = outputs.logits.argmax(dim=-1)\n",
|
|
" predictions, references = predictions, batch[\"labels\"]\n",
|
|
" metric.add_batch(\n",
|
|
" predictions=predictions,\n",
|
|
" references=references,\n",
|
|
" )\n",
|
|
"\n",
|
|
" eval_metric = metric.compute()\n",
|
|
" print(f\"epoch {epoch}:\", eval_metric)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "f2b2caca",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Share adapters on the 🤗 Hub"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"id": "7b23af6f-cf6e-486f-9d10-0eada95b631f",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"account_id = ... # your Hugging Face Hub account ID"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "990b3c93",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"model.push_to_hub(f\"{account_id}/roberta-large-peft-vblora\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "9d140b26",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Load adapters from the Hub\n",
|
|
"\n",
|
|
"You can also directly load adapters from the Hub using the commands below:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"id": "c283e028-b349-46b0-a20e-cde0ee5fbd7b",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import torch\n",
|
|
"from peft import PeftModel, PeftConfig\n",
|
|
"from transformers import AutoTokenizer"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"id": "320b10a0-4ea8-4786-9f3c-4670019c6b18",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"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",
|
|
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"peft_model_id = f\"{account_id}/roberta-large-peft-vblora\"\n",
|
|
"config = PeftConfig.from_pretrained(peft_model_id)\n",
|
|
"inference_model = AutoModelForSequenceClassification.from_pretrained(config.base_model_name_or_path)\n",
|
|
"tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"id": "b3a94049-bc01-4f2e-8cf9-66daf24a4402",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Load the model\n",
|
|
"inference_model = PeftModel.from_pretrained(inference_model, peft_model_id)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"id": "bd919fef-4e9a-4dc5-a957-7b879cfc5d38",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" 0%| | 0/13 [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",
|
|
"100%|██████████| 13/13 [00:01<00:00, 7.81it/s]"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"{'accuracy': 0.9044117647058824, 'f1': 0.9297297297297298}\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"inference_model.to(device)\n",
|
|
"inference_model.eval()\n",
|
|
"for step, batch in enumerate(tqdm(eval_dataloader)):\n",
|
|
" batch.to(device)\n",
|
|
" with torch.no_grad():\n",
|
|
" outputs = inference_model(**batch)\n",
|
|
" predictions = outputs.logits.argmax(dim=-1)\n",
|
|
" predictions, references = predictions, batch[\"labels\"]\n",
|
|
" metric.add_batch(\n",
|
|
" predictions=predictions,\n",
|
|
" references=references,\n",
|
|
" )\n",
|
|
"\n",
|
|
"eval_metric = metric.compute()\n",
|
|
"print(eval_metric)"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.10.14"
|
|
},
|
|
"vscode": {
|
|
"interpreter": {
|
|
"hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
|
|
}
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|