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711 lines
31 KiB
Plaintext
711 lines
31 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "a825ba6b",
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"metadata": {},
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"outputs": [
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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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"\n",
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"===================================BUG REPORT===================================\n",
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"Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
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"For effortless bug reporting copy-paste your error into this form: https://docs.google.com/forms/d/e/1FAIpQLScPB8emS3Thkp66nvqwmjTEgxp8Y9ufuWTzFyr9kJ5AoI47dQ/viewform?usp=sf_link\n",
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"================================================================================\n",
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"CUDA SETUP: CUDA runtime path found: /home/sourab/miniconda3/envs/ml/lib/libcudart.so\n",
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"CUDA SETUP: Highest compute capability among GPUs detected: 7.5\n",
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"CUDA SETUP: Detected CUDA version 117\n",
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"CUDA SETUP: Loading binary /home/sourab/miniconda3/envs/ml/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda117.so...\n"
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]
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}
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],
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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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"from peft import (\n",
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" get_peft_config,\n",
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" get_peft_model,\n",
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" get_peft_model_state_dict,\n",
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" set_peft_model_state_dict,\n",
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" PeftType,\n",
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" PrefixTuningConfig,\n",
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" PromptEncoderConfig,\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, 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": null,
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"id": "2bd7cbb2",
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"metadata": {},
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"outputs": [],
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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.PREFIX_TUNING\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"
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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": "33d9b62e",
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"metadata": {},
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"outputs": [],
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"source": [
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"peft_config = PrefixTuningConfig(task_type=\"SEQ_CLS\", num_virtual_tokens=20)\n",
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"lr = 1e-2"
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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": "152b6177",
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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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"Found cached dataset glue (/home/sourab/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "be1eddbb9a7d4e6dae32fd026e167f96",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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" 0%| | 0/3 [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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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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"Loading cached processed dataset at /home/sourab/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-9fa7887f9eaa03ae.arrow\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "b61574844b6c499b8960fd4d78c5e549",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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" 0%| | 0/1 [00:00<?, ?ba/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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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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"Loading cached processed dataset at /home/sourab/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-7e7eacaa5160936d.arrow\n"
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]
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}
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],
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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\n",
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"\n",
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"datasets = load_dataset(\"glue\", task)\n",
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"metric = evaluate.load(\"glue\", task)\n",
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"\n",
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"\n",
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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=None)\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\")\n",
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"\n",
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"\n",
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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": "code",
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"execution_count": null,
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"id": "f6bc8144",
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"metadata": {},
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"outputs": [],
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"source": [
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"model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, return_dict=True)\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"
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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": "af41c571",
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"metadata": {},
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"outputs": [],
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"source": [
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"optimizer = AdamW(params=model.parameters(), lr=lr)\n",
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"\n",
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"# Instantiate scheduler\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": "code",
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"execution_count": 7,
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"id": "90993c93",
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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 [00:29<00:00, 3.87it/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 0: {'accuracy': 0.7132352941176471, 'f1': 0.7876588021778584}\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': 0.6838235294117647, 'f1': 0.8122270742358079}\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 2: {'accuracy': 0.8088235294117647, 'f1': 0.8717105263157895}\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 3: {'accuracy': 0.7549019607843137, 'f1': 0.8475609756097561}\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 4: {'accuracy': 0.8480392156862745, 'f1': 0.8938356164383561}\n"
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]
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"text": [
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"epoch 5: {'accuracy': 0.8651960784313726, 'f1': 0.9053356282271946}\n"
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]
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},
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"text": [
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"epoch 6: {'accuracy': 0.8700980392156863, 'f1': 0.9065255731922399}\n"
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"text": [
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"epoch 7: {'accuracy': 0.8676470588235294, 'f1': 0.9042553191489361}\n"
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"epoch 8: {'accuracy': 0.875, 'f1': 0.9103690685413005}\n"
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"epoch 9: {'accuracy': 0.8799019607843137, 'f1': 0.913884007029877}\n"
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"epoch 10: {'accuracy': 0.8725490196078431, 'f1': 0.902621722846442}\n"
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"epoch 11: {'accuracy': 0.875, 'f1': 0.9090909090909091}\n"
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"output_type": "stream",
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"output_type": "stream",
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"text": [
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"epoch 12: {'accuracy': 0.8823529411764706, 'f1': 0.9139784946236559}\n"
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"output_type": "stream",
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"text": [
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"epoch 13: {'accuracy': 0.8602941176470589, 'f1': 0.9018932874354562}\n"
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]
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"output_type": "stream",
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"text": [
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"epoch 14: {'accuracy': 0.8700980392156863, 'f1': 0.9075043630017452}\n"
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"output_type": "stream",
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"text": [
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"epoch 15: {'accuracy': 0.875, 'f1': 0.9087656529516995}\n"
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]
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"output_type": "stream",
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"text": [
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"epoch 16: {'accuracy': 0.8578431372549019, 'f1': 0.9003436426116839}\n"
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]
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"epoch 17: {'accuracy': 0.8627450980392157, 'f1': 0.903448275862069}\n"
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"output_type": "stream",
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"epoch 18: {'accuracy': 0.8700980392156863, 'f1': 0.9078260869565218}\n"
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]
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"output_type": "stream",
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"text": [
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"epoch 19: {'accuracy': 0.8774509803921569, 'f1': 0.9125874125874125}\n"
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]
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"output_type": "stream",
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"text": [
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"\n"
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]
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}
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|
],
|
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"source": [
|
|
"model.to(device)\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": "7734299c",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Share adapters on the 🤗 Hub"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"id": "afaf42dd",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"CommitInfo(commit_url='https://huggingface.co/smangrul/roberta-large-peft-prefix-tuning/commit/a00e05a4c9a68e700221784f8e073c2e194637c3', commit_message='Upload model', commit_description='', oid='a00e05a4c9a68e700221784f8e073c2e194637c3', pr_url=None, pr_revision=None, pr_num=None)"
|
|
]
|
|
},
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"model.push_to_hub(\"smangrul/roberta-large-peft-prefix-tuning\", use_auth_token=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "42b20e77",
|
|
"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": 9,
|
|
"id": "868e7580",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "2ce57b4de8ae4f868115733abc2fb883",
|
|
"version_major": 2,
|
|
"version_minor": 0
|
|
},
|
|
"text/plain": [
|
|
"Downloading: 0%| | 0.00/373 [00:00<?, ?B/s]"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Some weights of the model checkpoint at roberta-large were not used when initializing RobertaForSequenceClassification: ['roberta.pooler.dense.bias', 'lm_head.layer_norm.weight', 'lm_head.layer_norm.bias', 'lm_head.dense.weight', 'roberta.pooler.dense.weight', 'lm_head.bias', 'lm_head.decoder.weight', 'lm_head.dense.bias']\n",
|
|
"- 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",
|
|
"- 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",
|
|
"Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-large and are newly initialized: ['classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.dense.weight']\n",
|
|
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "ace158c926a44b31a9b0ea80411bd7a9",
|
|
"version_major": 2,
|
|
"version_minor": 0
|
|
},
|
|
"text/plain": [
|
|
"Downloading: 0%| | 0.00/8.14M [00:00<?, ?B/s]"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"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 [00:06<00:00, 2.04it/s]"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"{'accuracy': 0.8774509803921569, 'f1': 0.9125874125874125}\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"import torch\n",
|
|
"from peft import PeftModel, PeftConfig\n",
|
|
"from transformers import AutoModelForCausalLM, AutoTokenizer\n",
|
|
"\n",
|
|
"peft_model_id = \"smangrul/roberta-large-peft-prefix-tuning\"\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)\n",
|
|
"\n",
|
|
"# Load the Lora model\n",
|
|
"inference_model = PeftModel.from_pretrained(inference_model, peft_model_id)\n",
|
|
"\n",
|
|
"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": "3.10.5 (v3.10.5:f377153967, Jun 6 2022, 12:36:10) [Clang 13.0.0 (clang-1300.0.29.30)]"
|
|
},
|
|
"vscode": {
|
|
"interpreter": {
|
|
"hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
|
|
}
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|