train.py

from model import build_transformer
from dataset import BilingualDataset, causal_mask
from config import get_config, get_weights_file_path

import torch
import torch.nn as nn
from torch.utils.data import DataLoader, random_split

import warnings
from tqdm import tqdm
import os
from pathlib import Path
import subprocess
from huggingface_hub import Repository

from datasets import load_dataset
from tokenizers import Tokenizer
from tokenizers.models import WordLevel
from tokenizers.trainers import WordLevelTrainer
from tokenizers.pre_tokenizers import Whitespace

import torchmetrics
from torch.utils.tensorboard import SummaryWriter

REPO_ID = "torinriley/LangGPT"
HF_TOKEN = os.getenv("TOKEN")
REPO_PATH = "/tmp/LangGPT"

def configure_git():
    try:
        subprocess.run(["git", "config", "--global", "--add", "safe.directory", "/app"], check=True)
        print("Configured /app as a safe directory for Git.")
    except subprocess.CalledProcessError as e:
        print(f"Failed to configure /app as a safe directory: {e}")

def initialize_repo():
    if not os.path.exists(REPO_PATH):
        repo = Repository(local_dir=REPO_PATH, clone_from=REPO_ID, use_auth_token=HF_TOKEN)
    else:
        repo = Repository(local_dir=REPO_PATH, use_auth_token=HF_TOKEN)
        repo.git_pull()
    return repo

def greedy_decode(model, source, source_mask, tokenizer_tgt, max_len, device):
    eos_idx = tokenizer_tgt.token_to_id('[EOS]')
    sos_idx = tokenizer_tgt.token_to_id('[SOS]')

    encoder_output = model.encode(source, source_mask)
    decoder_input = torch.empty(1, 1).fill_(sos_idx).type_as(source).to(device)
    while True:
        if decoder_input.size(1) == max_len:
            break

        decoder_mask = causal_mask(decoder_input.size(1)).type_as(source_mask).to(device)
        out = model.decode(encoder_output, source_mask, decoder_input, decoder_mask)
        prob = model.project(out[:, -1])
        _, next_word = torch.max(prob, dim=1)
        decoder_input = torch.cat(
            [decoder_input, torch.empty(1, 1).type_as(source).fill_(next_word.item()).to(device)], dim=1
        )

        if next_word == eos_idx:
            break

    return decoder_input.squeeze(0)

def run_validation(model, validation_ds, tokenizer_src, tokenizer_tgt, max_len, device, print_msg, global_step, writer, num_examples=2):
    model.eval()
    count = 0

    source_texts = []
    expected = []
    predicted = []

    try:
        with os.popen('stty size', 'r') as console:
            _, console_width = console.read().split()
            console_width = int(console_width)
    except:
        console_width = 80

    with torch.no_grad():
        for batch in validation_ds:
            count += 1
            encoder_input = batch["encoder_input"].to(device)
            encoder_mask = batch["encoder_mask"].to(device)

            assert encoder_input.size(0) == 1, "Batch size must be 1 for validation"

            model_out = greedy_decode(model, encoder_input, encoder_mask, tokenizer_src, tokenizer_tgt, max_len, device)

            source_text = batch["src_text"][0]
            target_text = batch["tgt_text"][0]
            model_out_text = tokenizer_tgt.decode(model_out.detach().cpu().numpy())

            source_texts.append(source_text)
            expected.append(target_text)
            predicted.append(model_out_text)
            
            print_msg('-'*console_width)
            print_msg(f"{f'SOURCE: ':>12}{source_text}")
            print_msg(f"{f'TARGET: ':>12}{target_text}")
            print_msg(f"{f'PREDICTED: ':>12}{model_out_text}")

            if count == num_examples:
                print_msg('-'*console_width)
                break
    
    if writer:
        metric = torchmetrics.CharErrorRate()
        cer = metric(predicted, expected)
        writer.add_scalar('validation cer', cer, global_step)
        writer.flush()

        metric = torchmetrics.WordErrorRate()
        wer = metric(predicted, expected)
        writer.add_scalar('validation wer', wer, global_step)
        writer.flush()

        metric = torchmetrics.BLEUScore()
        bleu = metric(predicted, expected)
        writer.add_scalar('validation BLEU', bleu, global_step)
        writer.flush()

def get_all_sentences(ds, lang):
    for item in ds:
        yield item['translation'][lang]

def get_or_build_tokenizer(config, ds, lang):
    tokenizer_path = Path(config['tokenizer_file'].format(lang))
    if not Path.exists(tokenizer_path):
        tokenizer = Tokenizer(WordLevel(unk_token="[UNK]"))
        tokenizer.pre_tokenizer = Whitespace()
        trainer = WordLevelTrainer(special_tokens=["[UNK]", "[PAD]", "[SOS]", "[EOS]"], min_frequency=2)
        tokenizer.train_from_iterator(get_all_sentences(ds, lang), trainer=trainer)
        tokenizer.save(str(tokenizer_path))
    else:
        tokenizer = Tokenizer.from_file(str(tokenizer_path))
    return tokenizer

def get_ds(config):
    ds_raw = load_dataset(f"{config['datasource']}", f"{config['lang_src']}-{config['lang_tgt']}", split='train')

    tokenizer_src = get_or_build_tokenizer(config, ds_raw, config['lang_src'])
    tokenizer_tgt = get_or_build_tokenizer(config, ds_raw, config['lang_tgt'])

    train_ds_size = int(0.9 * len(ds_raw))
    val_ds_size = len(ds_raw) - train_ds_size
    train_ds_raw, val_ds_raw = random_split(ds_raw, [train_ds_size, val_ds_size])

    train_ds = BilingualDataset(train_ds_raw, tokenizer_src, tokenizer_tgt, config['lang_src'], config['lang_tgt'], config['seq_len'])
    val_ds = BilingualDataset(val_ds_raw, tokenizer_src, tokenizer_tgt, config['lang_src'], config['lang_tgt'], config['seq_len'])

    max_len_src = 0
    max_len_tgt = 0

    for item in ds_raw:
        src_ids = tokenizer_src.encode(item['translation'][config['lang_src']]).ids
        tgt_ids = tokenizer_tgt.encode(item['translation'][config['lang_tgt']]).ids
        max_len_src = max(max_len_src, len(src_ids))
        max_len_tgt = max(max_len_tgt, len(tgt_ids))

    print(f'Max length of source sentence: {max_len_src}')
    print(f'Max length of target sentence: {max_len_tgt}')
    
    train_dataloader = DataLoader(train_ds, batch_size=config['batch_size'], shuffle=True)
    val_dataloader = DataLoader(val_ds, batch_size=1, shuffle=True)

    return train_dataloader, val_dataloader, tokenizer_src, tokenizer_tgt

def get_model(config, vocab_src_len, vocab_tgt_len):
    model = build_transformer(vocab_src_len, vocab_tgt_len, config["seq_len"], config['seq_len'], d_model=config['d_model'])
    return model

def latest_weights_file_path(config):
    model_folder = Path(f"{config['datasource']}_{config['model_folder']}")
    model_files = list(model_folder.glob("*.pt"))
    if not model_files:
        return None
    latest_file = max(model_files, key=os.path.getctime)
    return latest_file

def train_model(config):
    device = "cuda" if torch.cuda.is_available() else "mps" if torch.has_mps or torch.backends.mps.is_available() else "cpu"
    print("Using device:", device)
    if device == 'cuda':
        print(f"Device name: {torch.cuda.get_device_name(device.index)}")
        print(f"Device memory: {torch.cuda.get_device_properties(device.index).total_memory / 1024 ** 3} GB")
    elif device == 'mps':
        print(f"Device name: <mps>")
    else:
        print("NOTE: If you have a GPU, consider using it for training.")
        print("      On a Windows machine with NVidia GPU, check this video: https://www.youtube.com/watch?v=GMSjDTU8Zlc")
        print("      On a Mac machine, run: pip3 install --pre torch torchvision torchaudio torchtext --index-url https://download.pytorch.org/whl/nightly/cpu")

    preload = config.get('preload', False)
    
    model_filename = get_weights_file_path(config, preload) if preload else None
    train_dataloader, val_dataloader, tokenizer_src, tokenizer_tgt = get_ds(config)
    model = get_model(config, tokenizer_src.get_vocab_size(), tokenizer_tgt.get_vocab_size()).to(device)
    writer = SummaryWriter(config['experiment_name'])

    optimizer = torch.optim.Adam(model.parameters(), lr=config['lr'], eps=1e-9)

    initial_epoch = 0
    global_step = 0
    preload = config['preload']
    model_filename = latest_weights_file_path(config) if preload == 'latest' else get_weights_file_path(config, preload) if preload else None
    if model_filename:
        print(f'Preloading model {model_filename}')
        state = torch.load(model_filename)
        model.load_state_dict(state['model_state_dict'])
        initial_epoch = state['epoch'] + 1
        optimizer.load_state_dict(state['optimizer_state_dict'])
        global_step = state['global_step']
    else:
        print('No model to preload, starting from scratch')

    loss_fn = nn.CrossEntropyLoss(ignore_index=tokenizer_src.token_to_id('[PAD]'), label_smoothing=0.1).to(device)

    configure_git()
    repo = initialize_repo()

    for epoch in range(initial_epoch, config['num_epochs']):
        torch.cuda.empty_cache()
        model.train()
        batch_iterator = tqdm(train_dataloader, desc=f"Processing Epoch {epoch:02d}")
        for batch in batch_iterator:

            encoder_input = batch['encoder_input'].to(device)
            decoder_input = batch['decoder_input'].to(device)
            encoder_mask = batch['encoder_mask'].to(device)
            decoder_mask = batch['decoder_mask'].to(device)

            encoder_output = model.encode(encoder_input, encoder_mask)
            decoder_output = model.decode(encoder_output, encoder_mask, decoder_input, decoder_mask)
            proj_output = model.project(decoder_output)

            label = batch['label'].to(device)

            loss = loss_fn(proj_output.view(-1, tokenizer_tgt.get_vocab_size()), label.view(-1))
            batch_iterator.set_postfix({"loss": f"{loss.item():6.3f}"})

            writer.add_scalar('train loss', loss.item(), global_step)
            writer.flush()

            loss.backward()
            optimizer.step()
            optimizer.zero_grad(set_to_none=True)

            global_step += 1

        run_validation(model, val_dataloader, tokenizer_src, tokenizer_tgt, config['seq_len'], device, lambda msg: batch_iterator.write(msg), global_step, writer)

        model_filename = get_weights_file_path(config, f"{epoch:02d}")
        torch.save({
            'epoch': epoch,
            'model_state_dict': model.state_dict(),
            'optimizer_state_dict': optimizer.state_dict(),
            'global_step': global_step
        }, model_filename)

        hf_checkpoint_path = Path(REPO_PATH) / model_filename.name
        model_filename.rename(hf_checkpoint_path)
        repo.push_to_hub(commit_message=f"Add checkpoint for epoch {epoch}")
        print(f"Checkpoint successfully uploaded to Hugging Face Hub: {hf_checkpoint_path}")

if __name__ == '__main__':
    warnings.filterwarnings("ignore")
    config = get_config()
    train_model(config)