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import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader, random_split
from torch.utils.tensorboard import SummaryWriter
from model import TransformerModel
from dataset import BilingualDataset, casual_mask
from configuration import Get_configuration, Get_weights_file_path, latest_weights_file_path
from datasets import load_dataset
from tokenizers import Tokenizer
from tokenizers.models import WordLevel
from tokenizers.pre_tokenizers import Whitespace
from tokenizers.trainers import WordLevelTrainer
from pathlib import Path
import warnings
from tqdm import tqdm
import os
def greedy_search(model, source, source_mask, source_tokenizer, target_tokenizer, max_len, device):
sos_idx = target_tokenizer.token_to_id('[SOS]')
eos_idx = target_tokenizer.token_to_id('[EOS]')
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 = casual_mask(decoder_input.size(1)).type_as(source_mask).to(device)
out = model.decode(encoder_output, source_mask, decoder_input, decoder_mask)
# get next token (get the token with the maximum probabilty)
prob = model.linear(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, source_tokenizer, target_tokenizer, max_len, device, print_msg, global_step, writer, num_examples=2):
model.eval()
count = 0
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_input_mask"].to(device)
assert encoder_input.size(0) == 1, "Batch size must be 1 for validation"
model_out = greedy_search(model, encoder_input, encoder_mask, source_tokenizer, target_tokenizer, max_len, device)
source_text = batch["target_text"][0]
target_text = batch["target_text"][0]
model_out_text = target_tokenizer.decode(model_out.detach().cpu().numpy())
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:
break
def Get_All_Sentences(dataset, language):
for lang in dataset:
yield lang['translation'][language]
def Build_Tokenizer(configuration, dataset, language):
tokenizer_path = Path(configuration['tokenizer_file'].format(language))
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(dataset, language), trainer=trainer)
tokenizer.save(str(tokenizer_path))
else:
tokenizer = Tokenizer.from_file(str(tokenizer_path))
return tokenizer
def Get_dataset(configuration):
dataset_Raw = load_dataset(f"{configuration['datasource']}", f"{configuration['source_language']}-{configuration['target_language']}", split="train")
source_tokenizer = Build_Tokenizer(configuration, dataset_Raw, configuration['source_language'])
target_tokenizer = Build_Tokenizer(configuration, dataset_Raw, configuration['target_language'])
train_dataset_Size = int(0.9 * len(dataset_Raw))
validation_dataset_Size = len(dataset_Raw) - train_dataset_Size
train_dataset_Raw, validation_dataset_Raw = random_split(dataset_Raw, [train_dataset_Size, validation_dataset_Size])
train_dataset = BilingualDataset(train_dataset_Raw, source_tokenizer, target_tokenizer, configuration['source_language'], configuration['target_language'], configuration['sequence_length'])
validation_dataset = BilingualDataset(validation_dataset_Raw, source_tokenizer, target_tokenizer, configuration['source_language'], configuration['target_language'], configuration['sequence_length'])
maximum_source_sequence_length = 0
maximum_target_sequence_length = 0
for item in dataset_Raw:
source_id = source_tokenizer.encode(item['translation'][configuration['source_language']]).ids
target_id = target_tokenizer.encode(item['translation'][configuration['target_language']]).ids
maximum_source_sequence_length = max(maximum_source_sequence_length, len(source_id))
maximum_target_sequence_length = max(maximum_target_sequence_length, len(target_id))
print(f"maximum_source_sequence_length : {maximum_source_sequence_length}")
print(f"maximum_target_sequence_length: {maximum_target_sequence_length}")
train_dataLoader = DataLoader(train_dataset, batch_size= configuration['batch_size'], shuffle=True)
validation_dataLoader = DataLoader(validation_dataset, batch_size= 1, shuffle=True)
return train_dataLoader, validation_dataLoader, source_tokenizer, target_tokenizer
def Get_model(configuration, source_vocab_size, target_vocab_size):
model = TransformerModel(source_vocab_size, target_vocab_size, configuration['sequence_length'], configuration['sequence_length'], configuration['d_model'])
return model
def train_model(configuration):
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)
Path(f"{configuration['datasource']}_{configuration['model_folder']}").mkdir(parents=True, exist_ok=True)
train_dataLoader, validation_dataLoader, source_tokenizer, target_tokenizer = Get_dataset(configuration)
model = Get_model(configuration, source_tokenizer.get_vocab_size(), target_tokenizer.get_vocab_size()).to(device)
writer = SummaryWriter(configuration['experiment_name'])
optimizer = torch.optim.Adam(model.parameters(), lr=configuration['lr'], eps=1e-9)
initial_epoch = 0
global_step = 0
preload = configuration['preload']
model_filename = latest_weights_file_path(configuration) if preload == 'latest' else Get_weights_file_path(configuration, 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=source_tokenizer.token_to_id('[PAD]'), label_smoothing=0.1).to(device)
for epoch in range(initial_epoch, configuration['num_epochs']):
torch.cuda.empty_cache()
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)
decoder_input = batch['decoder_input'].to(device)
encoder_mask = batch['encoder_input_mask'].to(device)
decoder_mask = batch['encoder_input_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.linear(decoder_output)
Target = batch['Target'].to(device)
loss = loss_fn(proj_output.view(-1, target_tokenizer.get_vocab_size()), Target.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)
# run_validation(model, validation_dataLoader, source_tokenizer, target_tokenizer, configuration['sequence_length'], device, lambda msg: batch_iterator.write(msg), global_step, writer)
global_step += 1
model_filename = Get_weights_file_path(configuration, 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")
configuration = Get_configuration()
train_model(configuration) |