petals/examples/prompt-tuning-sst2.ipynb
2022-11-30 04:28:31 +00:00

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{
"cells": [
{
"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 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",
"import sys\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 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 = transformers.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!"
]
}
],
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