mirror of
https://github.com/bigscience-workshop/petals
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320 lines
9.7 KiB
Plaintext
320 lines
9.7 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "a07e0f5e",
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"metadata": {},
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"source": [
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"<div>\n",
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"<img src=\"https://camo.githubusercontent.com/473dd9f992924d27457650251786464f72e54121ac6e9210add0f483ca849277/68747470733a2f2f692e696d6775722e636f6d2f3765523750616e2e706e67\" width=\"40%\"> \n",
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"</div>\n",
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"\n",
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"# Distributed Bloom for Text Classification using Prompt Tuning\n",
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"\n",
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"In this example, we show how to use [prompt tuning](https://aclanthology.org/2021.emnlp-main.243.pdf) to adapt a test 6B version of the [BLOOM](https://huggingface.co/bigscience/bloom) model for a specific downstream task. We will run this model in a decentralized fashion using [Petals](https://github.com/bigscience-workshop/petals). Petals servers will maintain the BLOOM blocks (they are kept unchanged during adaptation), and the gradient descent will learn a few prefix tokens stored on a Petals client.\n",
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"\n",
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"We will adapt the BLOOM model for the classification task using the [SST-2 dataset](https://nlp.stanford.edu/sentiment/). This dataset is a binary classification task, where the goal is to predict whether a sentence is positive or negative. The SST-2 dataset is a subset of the Stanford Sentiment Treebank, and it is available in the [Hugging Face Datasets](https://huggingface.co/datasets) library.\n",
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"\n",
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"To open this notebook in colab: [](https://colab.research.google.com/github/bigscience-workshop/petals/blob/main/examples/prompt-tuning-sst2.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": "a3f8526f",
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"metadata": {},
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"source": [
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"First, we have to prepare all dependencies."
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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": "73bbc648",
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"metadata": {},
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"outputs": [],
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"source": [
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"import subprocess\n",
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"import sys\n",
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"\n",
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"!pip install -r git+https://github.com/bigscience-workshop/petals\n",
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"!pip install datasets wandb\n",
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"\n",
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"IN_COLAB = 'google.colab' in sys.modules\n",
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"if IN_COLAB: # Remove CUDA binaries on CPU-only colabs to not confuse bitsandbytes\n",
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" try:\n",
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" subprocess.check_output([\"nvidia-smi\", \"-L\"])\n",
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" except subprocess.CalledProcessError as e:\n",
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" subprocess.run(\"rm -r /usr/local/cuda/lib64\", shell=True)"
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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": "b4ab6ca7",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import sys\n",
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" \n",
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"import torch\n",
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"import transformers\n",
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"import wandb\n",
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"from datasets import load_dataset, load_metric\n",
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"from tqdm import tqdm\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 transformers import get_scheduler\n",
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"\n",
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"# Import a Petals model\n",
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"from petals.client.remote_model import DistributedBloomForSequenceClassification"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1bf07b5d",
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"metadata": {},
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"source": [
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"Let's set some hyperparameters for 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": null,
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"id": "f04ba4d2",
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"metadata": {},
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"outputs": [],
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"source": [
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"MODEL_NAME = ... # select model you like\n",
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"INITIAL_PEERS = [...] # add your peers adresses here, like \"/ip4/192.168.1.2/tcp/31000/p2p/Qma....\"\n",
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"NUM_PREFIX_TOKENS = 16\n",
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"DEVICE = 'cpu'\n",
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"BATCH_SIZE = 4\n",
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"LR = 1e-2\n",
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"WEIGHT_DECAY = 0.0\n",
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"NUM_SAMPLES = 1000\n",
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"NUM_EPOCHS = 3\n",
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"SEED = 42\n",
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"MODEL_MAX_LENGTH = 64\n",
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"TUNING_MODE = 'ptune' # choose between ['ptune', 'deep_ptune'] "
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]
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},
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{
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"cell_type": "markdown",
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"id": "d38316bd",
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"metadata": {},
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"source": [
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"Prepare tokenizer and distributed model, connect it to servers."
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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": "03c6e53e",
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"metadata": {},
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"outputs": [],
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"source": [
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"tokenizer = transformers.BloomTokenizerFast.from_pretrained(MODEL_NAME)\n",
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"tokenizer.padding_side = 'right'\n",
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"tokenizer.model_max_length = MODEL_MAX_LENGTH\n",
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"model = DistributedBloomForSequenceClassification.from_pretrained(\n",
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" MODEL_NAME, \n",
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" initial_peers=INITIAL_PEERS, \n",
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" pre_seq_len=NUM_PREFIX_TOKENS, \n",
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" tuning_mode=TUNING_MODE\n",
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").to(DEVICE)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "042e3786",
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"metadata": {},
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"source": [
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"Let's prepare the SST-2 dataset. We need just one preprocessing function to tokenize the dataset."
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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": "9c44d516",
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"metadata": {},
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"outputs": [],
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"source": [
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"task = 'sst2'\n",
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"\n",
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"dataset = load_dataset(\"glue\", task)\n",
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"\n",
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"def preprocess_function(examples):\n",
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" return tokenizer(examples[\"sentence\"], padding='max_length', truncation=True)\n",
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"\n",
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"tokenized_datasets = dataset.map(preprocess_function, batched=True)\n",
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"tokenized_datasets = tokenized_datasets.remove_columns([\"sentence\", \"idx\", \"attention_mask\"])\n",
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"tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n",
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"tokenized_datasets.set_format(\"torch\")\n",
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"\n",
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"train_dataset = tokenized_datasets[\"train\"].shuffle(seed=SEED)\n",
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"valid_dataset = tokenized_datasets[\"validation\"].shuffle(seed=SEED)\n",
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"\n",
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"train_dataloader = DataLoader(train_dataset, shuffle=True, batch_size=BATCH_SIZE, drop_last=True)\n",
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"valid_dataloader = DataLoader(valid_dataset, batch_size=BATCH_SIZE)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2a3f3590",
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"metadata": {},
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"source": [
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"To check training, we need a metric function. For SST-2 task is accuracy. We will load it from the datasets library."
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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": "1e1812be",
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"metadata": {},
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"outputs": [],
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"source": [
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"metric = load_metric('glue', task)\n",
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"\n",
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"def eval_metrics(model, dataloader, device='cpu'):\n",
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" model.eval()\n",
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" for batch in dataloader:\n",
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" batch = {k: v.to(device) for k, v in batch.items()}\n",
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" \n",
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" with torch.no_grad():\n",
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" outputs = model(**batch)\n",
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"\n",
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" logits = outputs.logits\n",
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" predictions = torch.argmax(logits, dim=-1)\n",
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" metric.add_batch(predictions=predictions, references=batch[\"labels\"])\n",
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" model.train()\n",
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" return metric.compute()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ef4323fd",
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"metadata": {},
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"source": [
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"Before setting up optimizers, check the model parameters that will be trained."
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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": "9cc0ba34",
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"metadata": {},
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"outputs": [],
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"source": [
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"for n, p in model.named_parameters():\n",
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" if p.requires_grad:\n",
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" print(n, p.requires_grad, p.device)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "59cffce7",
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"metadata": {},
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"source": [
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"The optimizer will only work on **prompts**, they are only trainable parameters. Let's initialize optimizer and learning rate scheduler."
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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": "ef9bf344",
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"metadata": {},
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"outputs": [],
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"source": [
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"optimizer = AdamW(model.parameters(), lr=LR, weight_decay=WEIGHT_DECAY)\n",
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"\n",
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"lr_scheduler = get_scheduler(\n",
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" name=\"linear\", optimizer=optimizer, num_warmup_steps=0, num_training_steps=len(train_dataloader)\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": "423c56d5",
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"metadata": {},
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"source": [
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"Let's initialize wandb for logging and start the training loop!"
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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": "d9e46807",
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"metadata": {},
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"outputs": [],
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"source": [
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"wandb.init(\n",
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" project=\"bloom-sst-2\",\n",
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" config={\n",
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" \"num_epochs\": NUM_EPOCHS,\n",
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" \"num_samples\": NUM_SAMPLES,\n",
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" \"batch_size\": BATCH_SIZE,\n",
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" \"learning_rate\": LR,\n",
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" \"weight_decay\": WEIGHT_DECAY,\n",
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" \"num_prefix_tokens\": NUM_PREFIX_TOKENS,\n",
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" \"model_name\": MODEL_NAME,\n",
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" \"seed\": SEED,\n",
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" }\n",
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")\n",
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"\n",
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"for epoch in range(NUM_EPOCHS):\n",
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" for batch in tqdm(train_dataloader):\n",
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" batch = {k: v.to(DEVICE) for k, v in batch.items()}\n",
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"\n",
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" model.train()\n",
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" outputs = model(**batch)\n",
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" loss = outputs.loss\n",
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" loss.backward()\n",
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"\n",
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" optimizer.step()\n",
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" lr_scheduler.step()\n",
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" optimizer.zero_grad()\n",
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"\n",
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" wandb.log({\"Train Loss\": loss})\n",
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"\n",
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" accuracy = eval_metrics(model, valid_dataloader, device=DEVICE)\n",
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" wandb.log({\"Valid Accuracy\": accuracy}, commit=False)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "51770911",
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"metadata": {},
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"source": [
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"Our model have been trained!"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.8.10 64-bit",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.9"
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},
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"vscode": {
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"interpreter": {
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"hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
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"nbformat": 4,
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"nbformat_minor": 5
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