{ "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 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 BLOOM 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 use this notebook in Colab:\n", "\n", "1. Follow this link: [![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)\n", "2. Go to **Runtime** -> **Change runtime type** and select the GPU accelerator." ] }, { "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": [ "%pip install -q petals datasets wandb scikit-learn" ] }, { "cell_type": "code", "execution_count": null, "id": "b4ab6ca7", "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\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", "from petals 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": [ "# Choose a model you'd like to prompt-tune. We recommend starting with\n", "# the smaller 7.1B version of BLOOM (bigscience/bloom-7b1-petals) for faster prototyping.\n", "# Once your code is ready, you can switch to full-scale\n", "# 176B-parameter BLOOM (bigscience/bloom-petals) or BLOOMZ (bigscience/bloomz-petals).\n", "MODEL_NAME = \"bigscience/bloom-7b1-petals\"\n", "\n", "# Choose a prompt-tuning mode ('ptune' or 'deep_ptune').\n", "# The latter fine-tunes separate prefixes for each transformer block,\n", "# so prompt-tuning will take more time but yield better results.\n", "# See this paper for details of how it works: https://arxiv.org/pdf/2110.07602.pdf\n", "TUNING_MODE = 'ptune'\n", "\n", "NUM_PREFIX_TOKENS = 16\n", "DEVICE = 'cuda'\n", "BATCH_SIZE = 16\n", "LR = 1e-2\n", "WEIGHT_DECAY = 0.0\n", "NUM_EPOCHS = 3\n", "SEED = 42\n", "MODEL_MAX_LENGTH = 64" ] }, { "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", " 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", " \"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!" ] }, { "cell_type": "markdown", "id": "1bbf014f", "metadata": {}, "source": [ "## Beyond soft-prompt tuning\n", "\n", "Let's try to tune model using adapters in the middle of the model." ] }, { "cell_type": "code", "execution_count": null, "id": "3bea4391", "metadata": {}, "outputs": [], "source": [ "class BloomBasedClassifier(nn.Module):\n", " def __init__(\n", " self,\n", " model,\n", " intermediate_size: int = 32,\n", " num_classes: int = 2,\n", " adapter_layer_position: int = 6,\n", " head_layer_position: int = 10\n", " ):\n", " super().__init__()\n", " self.distributed_layers = model.transformer.h\n", "\n", " self.hidden_size = model.config.hidden_size\n", " self.dtype = model.config.torch_dtype\n", " self.intermediate_size = intermediate_size\n", " self.num_classes = num_classes\n", " self.adapter_layer_position = adapter_layer_position\n", " self.head_layer_position = head_layer_position\n", " \n", " self.word_embeddings = model.transformer.word_embeddings\n", " self.adapter = nn.Sequential(\n", " nn.Linear(self.hidden_size, self.intermediate_size),\n", " nn.Linear(self.intermediate_size, self.hidden_size),\n", " ).to(self.dtype)\n", " self.head = nn.Sequential(\n", " nn.LayerNorm(self.hidden_size),\n", " nn.Linear(self.hidden_size, self.num_classes),\n", " ).to(self.dtype)\n", " \n", " def forward(self, embeddings):\n", " before_layers = self.distributed_layers[0:self.adapter_layer_position]\n", " after_layers = self.distributed_layers[self.adapter_layer_position:self.head_layer_position]\n", " \n", " hidden_states = before_layers(embeddings)\n", " hidden_states = self.adapter(hidden_states)\n", " hidden_states = after_layers(hidden_states)\n", " pooled_states = torch.mean(hidden_states, dim=1)\n", " return self.head(pooled_states)" ] }, { "cell_type": "markdown", "id": "15299620", "metadata": {}, "source": [ "Clear model and device memory." ] }, { "cell_type": "code", "execution_count": null, "id": "aa27b168", "metadata": {}, "outputs": [], "source": [ "del model, optimizer, lr_scheduler\n", "torch.cuda.empty_cache()" ] }, { "cell_type": "markdown", "id": "5406390f", "metadata": {}, "source": [ "Create new model with adapters." ] }, { "cell_type": "code", "execution_count": null, "id": "a251db80", "metadata": {}, "outputs": [], "source": [ "INTERMEDIATE_SIZE = 32\n", "ADAPTER_LAYER_POSITION = 6\n", "HEAD_LAYER_POSITION = 10" ] }, { "cell_type": "code", "execution_count": null, "id": "3578df3a", "metadata": {}, "outputs": [], "source": [ "cls_model = BloomBasedClassifier(\n", " DistributedBloomForSequenceClassification.from_pretrained(MODEL_NAME),\n", " intermediate_size=INTERMEDIATE_SIZE,\n", " adapter_layer_position=ADAPTER_LAYER_POSITION,\n", " head_layer_position=HEAD_LAYER_POSITION,\n", ").to(DEVICE)\n", "cls_optimizer = AdamW(cls_model.parameters(), lr=LR, weight_decay=WEIGHT_DECAY)\n", "cls_criterion = nn.CrossEntropyLoss()\n", "\n", "lr_scheduler = get_scheduler(\n", " name=\"linear\", optimizer=cls_optimizer, num_warmup_steps=0, num_training_steps=len(train_dataloader)\n", ")" ] }, { "cell_type": "markdown", "id": "a40468b9", "metadata": {}, "source": [ "And start training our new adapted model." ] }, { "cell_type": "code", "execution_count": null, "id": "ed051a5d", "metadata": {}, "outputs": [], "source": [ "wandb.init(\n", " project=\"bloom_based_cls-sst-2\",\n", " config={\n", " \"num_epochs\": NUM_EPOCHS,\n", " \"batch_size\": BATCH_SIZE,\n", " \"learning_rate\": LR,\n", " \"weight_decay\": WEIGHT_DECAY,\n", " \"model_name\": MODEL_NAME,\n", " \"seed\": SEED,\n", " \"intermediate_size\": INTERMEDIATE_SIZE,\n", " \"adapter_layer_position\": ADAPTER_LAYER_POSITION,\n", " \"head_layer_position\": HEAD_LAYER_POSITION,\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", " cls_model.train()\n", " with torch.no_grad():\n", " embeddings_output = cls_model.word_embeddings(batch[\"input_ids\"])\n", " outputs = cls_model(embeddings_output)\n", " loss = cls_criterion(outputs, batch[\"labels\"])\n", " loss.backward()\n", "\n", " cls_optimizer.step()\n", " lr_scheduler.step()\n", " cls_optimizer.zero_grad()\n", "\n", " wandb.log({\"Train Loss\": loss})\n", "\n", " accuracy = eval_metrics(cls_model, valid_dataloader, device=DEVICE)\n", " wandb.log({\"Valid Accuracy\": accuracy}, commit=False)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.8.8" }, "vscode": { "interpreter": { "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" } } }, "nbformat": 4, "nbformat_minor": 5 }