petals/examples/prompt-tuning-sst2.ipynb

{
 "cells": [
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   "cell_type": "markdown",
   "id": "a07e0f5e",
   "metadata": {},
   "source": [
    "<div>\n",
    "<img src=\"https://camo.githubusercontent.com/473dd9f992924d27457650251786464f72e54121ac6e9210add0f483ca849277/68747470733a2f2f692e696d6775722e636f6d2f3765523750616e2e706e67\" width=\"40%\">  \n",
    "</div>\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)"
   ]
  }
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