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{
"cells": [
{
"cell_type": "markdown",
"id": "d36e1e93-ae93-4a4e-93c6-68fd868d2882",
"metadata": {},
"source": [
"# Using VB-LoRA for sequence classification"
]
},
{
"cell_type": "markdown",
"id": "ddfc0610-55f6-4343-a950-125ccf0f45ac",
"metadata": {},
"source": [
"In this example, we fine-tune Roberta on a sequence classification task using VB-LoRA.\n",
"\n",
"This notebook is adapted from `examples/sequence_classification/VeRA.ipynb`."
]
},
{
"cell_type": "markdown",
"id": "45addd81-d4f3-4dfd-960d-3920d347f0a6",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a9935ae2",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from torch.optim import AdamW\n",
"from torch.utils.data import DataLoader\n",
"from peft import (\n",
" get_peft_model,\n",
" VBLoRAConfig,\n",
" PeftType,\n",
")\n",
"\n",
"import evaluate\n",
"from datasets import load_dataset\n",
"from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup\n",
"from tqdm import tqdm"
]
},
{
"cell_type": "markdown",
"id": "62c959bf-7cc2-49e0-b97e-4c10ec3b9bf3",
"metadata": {},
"source": [
"## Parameters"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e3b13308",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<torch._C.Generator at 0x7f4fc7c3c750>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"batch_size = 32\n",
"model_name_or_path = \"roberta-large\"\n",
"task = \"mrpc\"\n",
"peft_type = PeftType.VBLORA\n",
"device = torch.accelerator.current_accelerator().type if hasattr(torch, \"accelerator\") else \"cuda\"\n",
"num_epochs = 20\n",
"rank = 4\n",
"max_length = 128\n",
"num_vectors = 90\n",
"vector_length = 256\n",
"torch.manual_seed(0)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "0526f571",
"metadata": {},
"outputs": [],
"source": [
"peft_config = VBLoRAConfig(\n",
" task_type=\"SEQ_CLS\", \n",
" r=rank,\n",
" topk=2,\n",
" target_modules=['key', 'value', 'query', 'output.dense', 'intermediate.dense'],\n",
" num_vectors=num_vectors,\n",
" vector_length=vector_length,\n",
" 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",
" vblora_dropout=0.,\n",
")\n",
"head_lr = 4e-3\n",
"vector_bank_lr = 1e-3\n",
"logits_lr = 1e-2"
]
},
{
"cell_type": "markdown",
"id": "c075c5d2-a457-4f37-a7f1-94fd0d277972",
"metadata": {},
"source": [
"## Loading data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "7bb52cb4-d1c3-4b04-8bf0-f39ca88af139",
"metadata": {},
"outputs": [],
"source": [
"if any(k in model_name_or_path for k in (\"gpt\", \"opt\", \"bloom\")):\n",
" padding_side = \"left\"\n",
"else:\n",
" padding_side = \"right\"\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, padding_side=padding_side)\n",
"if getattr(tokenizer, \"pad_token_id\") is None:\n",
" tokenizer.pad_token_id = tokenizer.eos_token_id"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "e69c5e1f-d27b-4264-a41e-fc9b99d025e6",
"metadata": {},
"outputs": [],
"source": [
"datasets = load_dataset(\"glue\", task)\n",
"metric = evaluate.load(\"glue\", task)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "0209f778-c93b-40eb-a4e0-24c25db03980",
"metadata": {},
"outputs": [],
"source": [
"def tokenize_function(examples):\n",
" # max_length=None => use the model max length (it's actually the default)\n",
" outputs = tokenizer(examples[\"sentence1\"], examples[\"sentence2\"], truncation=True, max_length=max_length)\n",
" return outputs\n",
"\n",
"\n",
"tokenized_datasets = datasets.map(\n",
" tokenize_function,\n",
" batched=True,\n",
" remove_columns=[\"idx\", \"sentence1\", \"sentence2\"],\n",
")\n",
"\n",
"# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the\n",
"# transformers library\n",
"tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "7453954e-982c-46f0-b09c-589776e6d6cb",
"metadata": {},
"outputs": [],
"source": [
"def collate_fn(examples):\n",
" return tokenizer.pad(examples, padding=\"longest\", return_tensors=\"pt\")\n",
"\n",
"\n",
"# Instantiate dataloaders.\n",
"train_dataloader = DataLoader(tokenized_datasets[\"train\"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size)\n",
"eval_dataloader = DataLoader(\n",
" tokenized_datasets[\"validation\"], shuffle=False, collate_fn=collate_fn, batch_size=batch_size\n",
")"
]
},
{
"cell_type": "markdown",
"id": "f3b9b2e8-f415-4d0f-9fb4-436f1a3585ea",
"metadata": {},
"source": [
"## Preparing the VB-LoRA model"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "2ed5ac74",
"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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"trainable params: 1,696,770 || all params: 357,058,564 || trainable%: 0.4752\n",
"VB-LoRA params to-be-saved (float32-equivalent): 33,408 || total params to-be-saved: 1,085,058\n"
]
}
],
"source": [
"model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, return_dict=True, max_length=None)\n",
"model = get_peft_model(model, peft_config)\n",
"model.print_trainable_parameters()\n",
"model.print_savable_parameters()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "0d2d0381",
"metadata": {},
"outputs": [],
"source": [
"\n",
"from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS\n",
"from transformers.trainer_pt_utils import get_parameter_names\n",
"\n",
"decay_parameters = get_parameter_names(model, ALL_LAYERNORM_LAYERS)\n",
"decay_parameters = [name for name in decay_parameters if \"bias\" not in name]\n",
"vector_bank_parameters = [name for name, _ in model.named_parameters() if \"vector_bank\" in name]\n",
"logits_parameters = [name for name, _ in model.named_parameters() if \"logits\" in name ]\n",
"\n",
"optimizer_grouped_parameters = [\n",
" {\n",
" \"params\": [p for n, p in model.named_parameters() if n in decay_parameters and \\\n",
" n not in logits_parameters and n not in vector_bank_parameters],\n",
" \"weight_decay\": 0.1,\n",
" \"lr\": head_lr,\n",
" },\n",
" {\n",
" \"params\": [p for n, p in model.named_parameters() if n not in decay_parameters and \\\n",
" n not in logits_parameters and n not in vector_bank_parameters],\n",
" \"weight_decay\": 0.0,\n",
" \"lr\": head_lr,\n",
" },\n",
" {\n",
" \"params\": [p for n, p in model.named_parameters() if n in vector_bank_parameters],\n",
" \"lr\": vector_bank_lr,\n",
" \"weight_decay\": 0.0,\n",
" },\n",
" {\n",
" \"params\": [p for n, p in model.named_parameters() if n in logits_parameters],\n",
" \"lr\": logits_lr,\n",
" \"weight_decay\": 0.0,\n",
" },\n",
"]\n",
"\n",
"optimizer = AdamW(optimizer_grouped_parameters)\n",
"lr_scheduler = get_linear_schedule_with_warmup(\n",
" optimizer=optimizer,\n",
" num_warmup_steps=0.06 * (len(train_dataloader) * num_epochs),\n",
" num_training_steps=(len(train_dataloader) * num_epochs),\n",
")"
]
},
{
"cell_type": "markdown",
"id": "c0dd5aa8-977b-4ac0-8b96-884b17bcdd00",
"metadata": {},
"source": [
"## Training"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "fa0e73be",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 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",
"100%|██████████| 115/115 [00:34<00:00, 3.33it/s]\n",
"100%|██████████| 13/13 [00:01<00:00, 7.84it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch 0: {'accuracy': 0.6691176470588235, 'f1': 0.786053882725832}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 115/115 [00:34<00:00, 3.37it/s]\n",
"100%|██████████| 13/13 [00:01<00:00, 7.83it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch 1: {'accuracy': 0.5833333333333334, 'f1': 0.6136363636363636}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 115/115 [00:34<00:00, 3.34it/s]\n",
"100%|██████████| 13/13 [00:01<00:00, 7.82it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch 2: {'accuracy': 0.7107843137254902, 'f1': 0.8238805970149253}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 115/115 [00:34<00:00, 3.34it/s]\n",
"100%|██████████| 13/13 [00:01<00:00, 7.80it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch 3: {'accuracy': 0.8284313725490197, 'f1': 0.8833333333333333}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 115/115 [00:34<00:00, 3.34it/s]\n",
"100%|██████████| 13/13 [00:01<00:00, 7.79it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch 4: {'accuracy': 0.8480392156862745, 'f1': 0.8847583643122676}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 115/115 [00:34<00:00, 3.30it/s]\n",
"100%|██████████| 13/13 [00:01<00:00, 7.78it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch 5: {'accuracy': 0.8676470588235294, 'f1': 0.898876404494382}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 115/115 [00:34<00:00, 3.31it/s]\n",
"100%|██████████| 13/13 [00:01<00:00, 7.76it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch 6: {'accuracy': 0.8602941176470589, 'f1': 0.9035532994923858}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 115/115 [00:34<00:00, 3.32it/s]\n",
"100%|██████████| 13/13 [00:01<00:00, 7.76it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch 7: {'accuracy': 0.8774509803921569, 'f1': 0.911660777385159}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 115/115 [00:34<00:00, 3.33it/s]\n",
"100%|██████████| 13/13 [00:01<00:00, 7.79it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch 8: {'accuracy': 0.8872549019607843, 'f1': 0.9172661870503597}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 115/115 [00:34<00:00, 3.32it/s]\n",
"100%|██████████| 13/13 [00:01<00:00, 7.78it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch 9: {'accuracy': 0.875, 'f1': 0.9113043478260869}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 115/115 [00:34<00:00, 3.32it/s]\n",
"100%|██████████| 13/13 [00:01<00:00, 7.76it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch 10: {'accuracy': 0.8823529411764706, 'f1': 0.9166666666666666}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 115/115 [00:34<00:00, 3.33it/s]\n",
"100%|██████████| 13/13 [00:01<00:00, 7.76it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch 11: {'accuracy': 0.8970588235294118, 'f1': 0.9252669039145908}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 115/115 [00:34<00:00, 3.32it/s]\n",
"100%|██████████| 13/13 [00:01<00:00, 7.75it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch 12: {'accuracy': 0.8946078431372549, 'f1': 0.9246935201401051}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 115/115 [00:34<00:00, 3.33it/s]\n",
"100%|██████████| 13/13 [00:01<00:00, 7.76it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch 13: {'accuracy': 0.9068627450980392, 'f1': 0.9316546762589928}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 115/115 [00:34<00:00, 3.33it/s]\n",
"100%|██████████| 13/13 [00:01<00:00, 7.76it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch 14: {'accuracy': 0.8946078431372549, 'f1': 0.9225225225225225}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 115/115 [00:34<00:00, 3.33it/s]\n",
"100%|██████████| 13/13 [00:01<00:00, 7.76it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch 15: {'accuracy': 0.8995098039215687, 'f1': 0.926391382405745}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 115/115 [00:34<00:00, 3.30it/s]\n",
"100%|██████████| 13/13 [00:01<00:00, 7.76it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch 16: {'accuracy': 0.9068627450980392, 'f1': 0.9316546762589928}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
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"100%|██████████| 13/13 [00:01<00:00, 7.77it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch 17: {'accuracy': 0.8921568627450981, 'f1': 0.9217081850533808}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 115/115 [00:34<00:00, 3.33it/s]\n",
"100%|██████████| 13/13 [00:01<00:00, 7.77it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch 18: {'accuracy': 0.8995098039215687, 'f1': 0.9266547406082289}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 115/115 [00:34<00:00, 3.33it/s]\n",
"100%|██████████| 13/13 [00:01<00:00, 7.77it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch 19: {'accuracy': 0.9044117647058824, 'f1': 0.9297297297297298}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\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
}
|