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import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
from torchmetrics.text import BLEUScore, SacreBLEUScore
from tqdm.auto import tqdm
import config
from src import model, utils
TGT_VOCAB_SIZE: int = config.VOCAB_SIZE
def train_one_epoch(
model: model.Transformer,
dataloader: DataLoader,
optimizer: torch.optim.Optimizer,
criterion: nn.Module,
scheduler: torch.optim.lr_scheduler.LambdaLR,
device: torch.device,
logger=None,
) -> float:
"""
Runs a single training epoch.
Args:
model: The Transformer model.
dataloader: The training DataLoader.
optimizer: The optimizer.
criterion: The loss function (e.g., CrossEntropyLoss).
device: The device to run on (e.g., 'cuda').
Returns:
The average training loss for the epoch.
"""
# Set model to training mode
# This enables dropout, etc.
model.train()
total_loss = 0.0
# Use tqdm for a progress bar
progress_bar = tqdm(dataloader, desc="Training", leave=False)
batch_idx: int = 0
for batch in progress_bar:
batch_idx += 1
# 1. Move batch to device (GPU)
# We define a helper for this
batch_gpu = {
k: v.to(device) for k, v in batch.items() if isinstance(v, torch.Tensor)
}
# 2. Zero gradients before forward pass
optimizer.zero_grad()
# 3. Forward pass
# Get inputs for the model (as defined in Transformer.forward)
logits = model(
src=batch_gpu["src_ids"],
tgt=batch_gpu["tgt_input_ids"],
src_mask=batch_gpu["src_mask"],
tgt_mask=batch_gpu["tgt_mask"],
) # Shape: (B, T_tgt, vocab_size)
# 4. Calculate loss
# CrossEntropyLoss expects (N, C) and (N,)
# We must reshape logits and labels
# Logits: (B, T_tgt, C) -> (B * T_tgt, C)
# Labels: (B, T_tgt) -> (B * T_tgt)
loss = criterion(logits.view(-1, TGT_VOCAB_SIZE), batch_gpu["labels"].view(-1))
# 5. Backward pass (compute gradients)
loss.backward()
# 6. Gradient Clipping (from paper)
# Helps prevent exploding gradients. '1.0' is a common value.
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
# 7. Update weights
optimizer.step()
# 8. Update learning rate scheduler if used
scheduler.step()
# 9. Update stats
total_loss += loss.item()
progress_bar.set_postfix(loss=loss.item())
# 10. Log metrics
if logger and batch_idx % 100 == 0:
logger.log(
{
"train/batch_loss": loss.item(),
"train/learning_rate": optimizer.param_groups[0]["lr"],
}
)
# Return average loss for the epoch
return total_loss / len(dataloader)
def validate_one_epoch(
model: model.Transformer,
dataloader: DataLoader,
criterion: nn.Module,
device: torch.device,
) -> float:
"""
Runs a single validation epoch.
Args:
model: The Transformer model.
dataloader: The validation DataLoader.
criterion: The loss function (e.g., CrossEntropyLoss).
device: The device to run on (e.g., 'cuda').
Returns:
The average validation loss for the epoch.
"""
# Set model to evaluation mode
# This disables dropout.
model.eval()
total_loss = 0.0
# Use tqdm for a progress bar
progress_bar = tqdm(dataloader, desc="Validating", leave=False)
# Disable gradient computation
# This saves VRAM and speeds up inference.
with torch.no_grad():
for batch in progress_bar:
# 1. Move batch to device (GPU)
batch_gpu = {
k: v.to(device) for k, v in batch.items() if isinstance(v, torch.Tensor)
}
# 2. Forward pass
logits = model(
src=batch_gpu["src_ids"],
tgt=batch_gpu["tgt_input_ids"],
src_mask=batch_gpu["src_mask"],
tgt_mask=batch_gpu["tgt_mask"],
) # Shape: (B, T_tgt, vocab_size)
# 3. Calculate loss
# (Use the same reshaping as in training for consistency)
loss = criterion(
logits.view(-1, TGT_VOCAB_SIZE), batch_gpu["labels"].view(-1)
)
# 4. Update stats
total_loss += loss.item()
progress_bar.set_postfix(loss=loss.item())
# Return average loss for the epoch
return total_loss / len(dataloader)
def evaluate_model(
model: model.Transformer,
dataloader: DataLoader,
tokenizer: PreTrainedTokenizerFast,
device: torch.device,
table=None,
) -> tuple[float, float]:
"""
Runs final evaluation on the test set using Beam Search
and calculates the SacreBLEU score.
"""
print("\n--- Starting Evaluation (BLEU + SacreBLEU) ---")
# Set model to evaluation mode
# This disables dropout.
model.eval()
all_predicted_strings = []
all_expected_strings = []
# --- No gradients needed ---
with torch.no_grad():
for batch in tqdm(dataloader, desc="Evaluating"):
batch_gpu = {
k: v.to(device) for k, v in batch.items() if isinstance(v, torch.Tensor)
}
src_ids = batch_gpu["src_ids"]
src_mask = batch_gpu["src_mask"]
expected_ids = batch_gpu["labels"] # (B, T_tgt) [on GPU]
B = src_ids.size(0)
# --- Handle 2D Expected IDs) ---
batch_expected_strings = []
# Convert 2D GPU Tensor -> 2D CPU List
expected_id_lists = expected_ids.cpu().tolist()
# Now we iterate over the CPU list
for id_list in expected_id_lists:
# id_list is a 1D Python list (e.g., [70, 950, 7, 3])
# This call is now safe
token_list = tokenizer.convert_ids_to_tokens(id_list)
batch_expected_strings.append(
utils.filter_and_detokenize(token_list, skip_special=True)
)
# --- Generate (decode) one sentence at a time ---
batch_predicted_strings = []
for i in tqdm(range(B), desc="Decoding Batch", leave=False):
src_sentence = src_ids[i].unsqueeze(0)
src_sentence_mask = src_mask[i].unsqueeze(0)
# (predicted_ids is 1D Tensor [T_out] on GPU)
predicted_ids = utils.greedy_decode_sentence(
model,
src_sentence,
src_sentence_mask,
max_len=config.MAX_SEQ_LEN,
sos_token_id=config.SOS_TOKEN_ID,
eos_token_id=config.EOS_TOKEN_ID,
device=device,
)
# Convert 1D GPU Tensor -> 1D CPU List
predicted_id_list = predicted_ids.cpu().tolist()
# This call is now safe
predicted_token_list = tokenizer.convert_ids_to_tokens(
predicted_id_list
)
decoded_str = utils.filter_and_detokenize(
predicted_token_list, skip_special=True
)
batch_predicted_strings.append(decoded_str)
# --- Store strings for final metric calculation ---
all_predicted_strings.extend(batch_predicted_strings)
all_expected_strings.extend([[s] for s in batch_expected_strings])
bleu_metric = BLEUScore(n_gram=4, smooth=True).to(config.DEVICE)
sacrebleu_metric = SacreBLEUScore(
n_gram=4, smooth=True, tokenize="intl", lowercase=False
).to(config.DEVICE)
# --- 5. Calculate final score ---
print("\nCalculating final BLEU score...")
final_bleu = bleu_metric(all_predicted_strings, all_expected_strings)
# print(f"\n========================================")
# print(f"🎉 FINAL BLEU SCORE (Evaluation Set): {final_bleu.item() * 100:.4f}%")
# print(f"========================================")
print("\nCalculating final SacreBLEU score...")
final_sacrebleu = sacrebleu_metric(all_predicted_strings, all_expected_strings)
# print(f"\n========================================")
# print(
# f"🎉 FINAL SacreBLEU SCORE (Evaluation Set): {final_sacrebleu.item() * 100:.4f}%"
# )
# print(f"========================================")
# --- Show some examples ---
print("\n--- Translation Examples (Pred vs Exp) ---")
for i in range(min(5, len(all_predicted_strings))):
print(f" PRED: {all_predicted_strings[i]}")
print(f" EXP: {all_expected_strings[i][0]}")
print(" ---")
table.add_data(all_expected_strings[i][0], all_predicted_strings[i])
return final_bleu.item() * 100, final_sacrebleu.item() * 100
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