from model import build_transformer from dataset import BilingualDataset, causal_mask from config import get_config, get_weights_file_path import torchtext.datasets as datasets import torch import torch.nn as nn from torch.utils.data import Dataset, DataLoader, random_split from torch.optim.lr_scheduler import LambdaLR import warnings from tqdm import tqdm import os from pathlib import Path # Huggingface datasets and tokenizers 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 wandb import torchmetrics 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 step encoder_output = model.encode(source, source_mask) # Initialize the decoder input with the sos token 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 target decoder_mask = causal_mask(decoder_input.size(1)).type_as(source_mask).to(device) # calculate output out = model.decode(encoder_output, source_mask, decoder_input, decoder_mask) # get next token 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, num_examples=2): model.eval() count = 0 source_texts = [] expected = [] predicted = [] try: # get the console window width with os.popen('stty size', 'r') as console: _, console_width = console.read().split() console_width = int(console_width) except: # If we can't get the console width, use 80 as default console_width = 80 with torch.no_grad(): for batch in validation_ds: count += 1 encoder_input = batch["encoder_input"].to(device) # (b, seq_len) encoder_mask = batch["encoder_mask"].to(device) # (b, 1, 1, seq_len) # check that the batch size is 1 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 the source, target and model output 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 # Evaluate the character error rate # Compute the char error rate metric = torchmetrics.CharErrorRate() cer = metric(predicted, expected) wandb.log({'validation/cer': cer, 'global_step': global_step}) # Compute the word error rate metric = torchmetrics.WordErrorRate() wer = metric(predicted, expected) wandb.log({'validation/wer': wer, 'global_step': global_step}) # Compute the BLEU metric metric = torchmetrics.BLEUScore() bleu = metric(predicted, expected) wandb.log({'validation/BLEU': bleu, 'global_step': global_step}) 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): # It only has the train split, so we divide it overselves 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, 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']) # Find the maximum length of each sentence in the source and target sentence 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 train_model(config): # Define the device device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print("Using device:", device) # Make sure the weights folder exists 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) optimizer = torch.optim.Adam(model.parameters(), lr=config['lr'], eps=1e-9) # If the user specified a model to preload before training, load it 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) 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'] del state loss_fn = nn.CrossEntropyLoss(ignore_index=tokenizer_src.token_to_id('[PAD]'), label_smoothing=0.1).to(device) # define our custom x axis metric wandb.define_metric("global_step") # define which metrics will be plotted against it wandb.define_metric("validation/*", step_metric="global_step") wandb.define_metric("train/*", step_metric="global_step") 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) # (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 encoder, decoder and the projection layer 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) # (B, seq_len, d_model) proj_output = model.project(decoder_output) # (B, seq_len, vocab_size) # Compare the output with the label label = batch['label'].to(device) # (B, seq_len) # Compute the loss using a simple cross entropy 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}"}) # Log the loss wandb.log({'train/loss': loss.item(), 'global_step': global_step}) # Backpropagate the loss loss.backward() # Update the weights optimizer.step() optimizer.zero_grad(set_to_none=True) global_step += 1 # Run validation at the end of every epoch run_validation(model, val_dataloader, tokenizer_src, tokenizer_tgt, config['seq_len'], device, lambda msg: batch_iterator.write(msg), global_step) # 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() config['num_epochs'] = 30 config['preload'] = None wandb.init( # set the wandb project where this run will be logged project="pytorch-transformer", # track hyperparameters and run metadata config=config ) train_model(config)