{ "cells": [ { "cell_type": "markdown", "id": "a07e0f5e", "metadata": {}, "source": [ "
\n", " \n", "
\n", "\n", "# Distributed Bloom for Text Classification using Prompt Tuning\n", "\n", "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", "\n", "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", "\n", "To open this notebook in colab: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/bigscience-workshop/petals/blob/main/examples/prompt-tuning-sst2.ipynb)" ] }, { "cell_type": "markdown", "id": "a3f8526f", "metadata": {}, "source": [ "First, we have to prepare all dependencies." ] }, { "cell_type": "code", "execution_count": null, "id": "73bbc648", "metadata": {}, "outputs": [], "source": [ "import subprocess\n", "import sys\n", "\n", "!pip install -r git+https://github.com/bigscience-workshop/petals\n", "!pip install datasets wandb\n", "\n", "IN_COLAB = 'google.colab' in sys.modules\n", "if IN_COLAB: # Remove CUDA binaries on CPU-only colabs to not confuse bitsandbytes\n", " try:\n", " subprocess.check_output([\"nvidia-smi\", \"-L\"])\n", " except subprocess.CalledProcessError as e:\n", " subprocess.run(\"rm -r /usr/local/cuda/lib64\", shell=True)" ] }, { "cell_type": "code", "execution_count": null, "id": "b4ab6ca7", "metadata": {}, "outputs": [], "source": [ "import os\n", " \n", "import torch\n", "import transformers\n", "import wandb\n", "from datasets import load_dataset, load_metric\n", "from tqdm import tqdm\n", "from torch.optim import AdamW\n", "from torch.utils.data import DataLoader\n", "from transformers import BloomTokenizerFast, get_scheduler\n", "\n", "# Import a Petals model\n", "from petals.client.remote_model import DistributedBloomForSequenceClassification" ] }, { "cell_type": "markdown", "id": "1bf07b5d", "metadata": {}, "source": [ "Let's set some hyperparameters for training:" ] }, { "cell_type": "code", "execution_count": null, "id": "f04ba4d2", "metadata": {}, "outputs": [], "source": [ "MODEL_NAME = ... # select model you like\n", "INITIAL_PEERS = [...] # add your peers adresses here, like \"/ip4/192.168.1.2/tcp/31000/p2p/Qma....\"\n", "NUM_PREFIX_TOKENS = 16\n", "DEVICE = 'cpu'\n", "BATCH_SIZE = 4\n", "LR = 1e-2\n", "WEIGHT_DECAY = 0.0\n", "NUM_SAMPLES = 1000\n", "NUM_EPOCHS = 3\n", "SEED = 42\n", "MODEL_MAX_LENGTH = 64\n", "TUNING_MODE = 'ptune' # choose between ['ptune', 'deep_ptune'] " ] }, { "cell_type": "markdown", "id": "d38316bd", "metadata": {}, "source": [ "Prepare tokenizer and distributed model, connect it to servers." ] }, { "cell_type": "code", "execution_count": null, "id": "03c6e53e", "metadata": {}, "outputs": [], "source": [ "tokenizer = BloomTokenizerFast.from_pretrained(MODEL_NAME)\n", "tokenizer.padding_side = 'right'\n", "tokenizer.model_max_length = MODEL_MAX_LENGTH\n", "model = DistributedBloomForSequenceClassification.from_pretrained(\n", " MODEL_NAME, \n", " initial_peers=INITIAL_PEERS, \n", " pre_seq_len=NUM_PREFIX_TOKENS, \n", " tuning_mode=TUNING_MODE\n", ").to(DEVICE)" ] }, { "cell_type": "markdown", "id": "042e3786", "metadata": {}, "source": [ "Let's prepare the SST-2 dataset. We need just one preprocessing function to tokenize the dataset." ] }, { "cell_type": "code", "execution_count": null, "id": "9c44d516", "metadata": {}, "outputs": [], "source": [ "task = 'sst2'\n", "\n", "dataset = load_dataset(\"glue\", task)\n", "\n", "def preprocess_function(examples):\n", " return tokenizer(examples[\"sentence\"], padding='max_length', truncation=True)\n", "\n", "tokenized_datasets = dataset.map(preprocess_function, batched=True)\n", "tokenized_datasets = tokenized_datasets.remove_columns([\"sentence\", \"idx\", \"attention_mask\"])\n", "tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n", "tokenized_datasets.set_format(\"torch\")\n", "\n", "train_dataset = tokenized_datasets[\"train\"].shuffle(seed=SEED)\n", "valid_dataset = tokenized_datasets[\"validation\"].shuffle(seed=SEED)\n", "\n", "train_dataloader = DataLoader(train_dataset, shuffle=True, batch_size=BATCH_SIZE, drop_last=True)\n", "valid_dataloader = DataLoader(valid_dataset, batch_size=BATCH_SIZE)" ] }, { "cell_type": "markdown", "id": "2a3f3590", "metadata": {}, "source": [ "To check training, we need a metric function. For SST-2 task is accuracy. We will load it from the datasets library." ] }, { "cell_type": "code", "execution_count": null, "id": "1e1812be", "metadata": {}, "outputs": [], "source": [ "metric = load_metric('glue', task)\n", "\n", "def eval_metrics(model, dataloader, device='cpu'):\n", " model.eval()\n", " for batch in dataloader:\n", " batch = {k: v.to(device) for k, v in batch.items()}\n", " \n", " with torch.no_grad():\n", " outputs = model(**batch)\n", "\n", " logits = outputs.logits\n", " predictions = torch.argmax(logits, dim=-1)\n", " metric.add_batch(predictions=predictions, references=batch[\"labels\"])\n", " model.train()\n", " return metric.compute()" ] }, { "cell_type": "markdown", "id": "ef4323fd", "metadata": {}, "source": [ "Before setting up optimizers, check the model parameters that will be trained." ] }, { "cell_type": "code", "execution_count": null, "id": "9cc0ba34", "metadata": {}, "outputs": [], "source": [ "for n, p in model.named_parameters():\n", " if p.requires_grad:\n", " print(n, p.requires_grad, p.device)" ] }, { "cell_type": "markdown", "id": "59cffce7", "metadata": {}, "source": [ "The optimizer will only work on **prompts**, they are only trainable parameters. Let's initialize optimizer and learning rate scheduler." ] }, { "cell_type": "code", "execution_count": null, "id": "ef9bf344", "metadata": {}, "outputs": [], "source": [ "optimizer = AdamW(model.parameters(), lr=LR, weight_decay=WEIGHT_DECAY)\n", "\n", "lr_scheduler = get_scheduler(\n", " name=\"linear\", optimizer=optimizer, num_warmup_steps=0, num_training_steps=len(train_dataloader)\n", ")" ] }, { "cell_type": "markdown", "id": "423c56d5", "metadata": {}, "source": [ "Let's initialize wandb for logging and start the training loop!" ] }, { "cell_type": "code", "execution_count": null, "id": "d9e46807", "metadata": {}, "outputs": [], "source": [ "wandb.init(\n", " project=\"bloom-sst-2\",\n", " config={\n", " \"num_epochs\": NUM_EPOCHS,\n", " \"num_samples\": NUM_SAMPLES,\n", " \"batch_size\": BATCH_SIZE,\n", " \"learning_rate\": LR,\n", " \"weight_decay\": WEIGHT_DECAY,\n", " \"num_prefix_tokens\": NUM_PREFIX_TOKENS,\n", " \"model_name\": MODEL_NAME,\n", " \"seed\": SEED,\n", " }\n", ")\n", "\n", "for epoch in range(NUM_EPOCHS):\n", " for batch in tqdm(train_dataloader):\n", " batch = {k: v.to(DEVICE) for k, v in batch.items()}\n", "\n", " model.train()\n", " outputs = model(**batch)\n", " loss = outputs.loss\n", " loss.backward()\n", "\n", " optimizer.step()\n", " lr_scheduler.step()\n", " optimizer.zero_grad()\n", "\n", " wandb.log({\"Train Loss\": loss})\n", "\n", " accuracy = eval_metrics(model, valid_dataloader, device=DEVICE)\n", " wandb.log({\"Valid Accuracy\": accuracy}, commit=False)" ] }, { "cell_type": "markdown", "id": "51770911", "metadata": {}, "source": [ "Our model have been trained!" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3.8.10 64-bit", "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": "3.6.9" }, "vscode": { "interpreter": { "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" } } }, "nbformat": 4, "nbformat_minor": 5 }