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Add prompt tuning example on Personachat dataset (#69)
This commit is contained in:
Artem Chumachenko
GitHub
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examples/prompt-tuning-personachat.ipynb
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307
examples/prompt-tuning-personachat.ipynb
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{
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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 Generation 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 chatbot task using the [Personachat](https://huggingface.co/datasets/bavard/personachat_truecased) dataset. For a given dialogue context, the model has to provide a relevant answer."
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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": "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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"sys.path.insert(0, \"..\")\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\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 src.client.remote_model import DistributedBloomForCausalLM"
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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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"SEED = 42\n",
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"MODEL_MAX_LENGTH = 256\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 = DistributedBloomForCausalLM.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 Personachat dataset. We need two mapping functions, one to concatenate history and candidate answers, and another for tokenization."
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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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"dataset = load_dataset(\"bavard/personachat_truecased\")\n",
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"\n",
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"\n",
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"def chunking(examples):\n",
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" inputs = [\n",
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" \"\\n-----\\n\".join(history) + \"\\n-----\\n\" + candidate\n",
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" for history, candidates in zip(examples[\"history\"], examples[\"candidates\"])\n",
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" for candidate in candidates\n",
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" ]\n",
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" return {\"chunks\": inputs}\n",
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"\n",
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"\n",
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"def tokenize(examples):\n",
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" outputs = {\n",
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" \"input_ids\": tokenizer(examples[\"chunks\"], padding='max_length', truncation=True)[\"input_ids\"]\n",
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" }\n",
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" outputs[\"labels\"] = outputs[\"input_ids\"]\n",
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" return outputs\n",
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"\n",
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"\n",
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"tokenized_datasets = (\n",
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" dataset\n",
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" .map(chunking, batched=True, remove_columns=dataset[\"train\"].column_names)\n",
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" .map(tokenize, batched=True, remove_columns=[\"chunks\"])\n",
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")\n",
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"\n",
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"\n",
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"tokenized_datasets.set_format(\"torch\")\n",
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"train_dataset = tokenized_datasets[\"train\"].shuffle(seed=SEED)\n",
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"train_dataloader = DataLoader(\n",
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" train_dataset.select(list(range(NUM_SAMPLES))),\n",
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" shuffle=True,\n",
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" batch_size=BATCH_SIZE,\n",
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" drop_last=True,\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": "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-personachat\",\n",
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" config={\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 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})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0f36cb80",
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"metadata": {},
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"source": [
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"Try to talk with the trained model! Submit an empty input to stop the execution.\n",
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"\n",
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"\n",
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"__Note__: In this example, we the whole dialogue as a prefix when generating each new replica. In the future, we will support a faster \"interactive\" dialogue mode, so generating a new replica will be able to reuse inference caches from the previous replica."
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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": "720181b7",
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"metadata": {},
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"outputs": [],
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"source": [
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"MAX_TOKENS = 16\n",
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"TOP_K = 100\n",
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"TEMPERATURE = 0.6\n",
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"dialog = \"\"\n",
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"\n",
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"while True:\n",
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" user_phrase = input()\n",
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" if len(user_phrase) == 0:\n",
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" break\n",
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" dialog += f\"{user_phrase}\\n-----\\n\"\n",
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" inputs = tokenizer([dialog], return_tensors='pt')['input_ids']\n",
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" outputs = model.generate(\n",
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" inputs,\n",
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" temperature=TEMPERATURE,\n",
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" do_sample=True,\n",
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" top_k=TOP_K,\n",
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" eos_token_id=tokenizer.eos_token_id,\n",
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" max_new_tokens=MAX_TOKENS,\n",
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" )\n",
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" bloom_answer = tokenizer.batch_decode(outputs)[0]\n",
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" bloom_answer = bloom_answer[len(dialog):].split(\"\\n\")[0]\n",
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" print(bloom_answer)\n",
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" dialog += f\"{bloom_answer}\\n-----\\n\""
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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 (ipykernel)",
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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": "3.8.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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