train.py

import torch
import torch.nn as nn

from torch.utils.data import Dataset, DataLoader, random_split


from config import get_weights_file_path, get_config

import warnings
from dataset import BilingualDataset, causal_mask
from model import build_transformer

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

from torch.utils.tensorboard import SummaryWriter
from pathlib import Path

from tqdm import tqdm

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

    # Precompute the encoder output and reuse it for every token we get from the encoder
    encoder_output = model.encode(source, source_mask)
    # Initialize the decoder input with
    decoder_input = torch.empty(1,1).fill_(sos_idx).type_as(source).to(device)
    while True:
        if decoder_input.size(1) == max_len:
            break

        # Build mask for the target (decoder input)
        decoder_mask = causal_mask(decoder_input.size(1)).type_as(source_mask).to(device)

        # Calculate the output of the decoder
        out = model.decode(encoder_output, source_mask, decoder_input, decoder_mask)

        # Get the next token
        prob = model.project(out[:,-1])

        # Select the token with the max probability (because it is a greedy search)
        _, 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_state,writer,num_examples=2):
    model.eval()
    count = 0



    # Size of the control window (just use a default value)
    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())



            # Print to the console
            print_msg('-'*console_width)
            print_msg(f'SOURCE: {source_text}')
            print_msg(f'TARGET: {target_text}')
            print_msg(f'MODEL: {model_out_text}')

            if count == num_examples:
                break


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):
        # Most code taken from: https://huggingface.co/docs/tokenizers/quicktour
        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('opus_books', f'{config["lang_src"]}-{config["lang_tgt"]}', split='train')

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

    # Keep 90% for training and 10% for validation
    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['d_model'])
    return model

def train_model(config):
    # Define the device
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    print(f'Using device: {device}')

    Path(config['model_folder']).mkdir(parents=True,exist_ok=True)

    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)

    # Tensorboard
    writer = SummaryWriter(config['experiment_name'])

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

    initial_epoch = 0
    global_step = 0
    if config['preload']:
        model_filename = get_weights_file_path(config, config['preload'])
        print(f"Preloading model {model_filename}")
        state = torch.load(model_filename)
        initial_epoch = state['epoch']
        optimizer.load_state_dict(state['optimizer'])
        global_step = state['global_step']

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

    for epoch in range(initial_epoch, config['epochs']):

        batch_iterator = tqdm(train_dataloader, desc=f'Processing epoch {epoch:02d}')
        for batch in batch_iterator:
            model.train()

            encoder_input = batch['encoder_input'].to(device) # (B, Seq_len)
            decoder_input = batch['decoder_input'].to(device) # (B, Seq_len)
            encoder_mask = batch['encoder_mask'].to(device) # (B, 1 , 1, Seq_len)
            decoder_mask = batch['decoder_mask'].to(device) # (B, 1, Seq_len, Seq_len)

            # Run the tensors through the transformer
            encoder_output = model.encode(encoder_input, encoder_mask) # (B, Seq_len, d_model)
            decoder_output = model.decode(encoder_output, encoder_mask, decoder_input, decoder_mask)
            proj_output = model.project(decoder_output) # (B, Seq_len, tgt_vocab_size)

            label = batch['label'].to(device) # (B, Seq_len)

            # (B, Seq_len, tgt_vocab_size) --> (B * Seq_len, tgt_vocab_size)
            loss = loss_fn(proj_output.view(-1, tokenizer_tgt.get_vocab_size()), label.view(-1))
            batch_iterator.set_postfix({f"loss": f"{loss.item():6.3f}"})

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

            # Back propagate the loss
            optimizer.step()
            optimizer.zero_grad()


            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)

    # Save the model at the end of every epoch
    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)

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