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import pandas as pd
import torch
from src.utils.helpers import TRANSFORMER_MODEL, TOKENIZER_MODEL, JSON_DATA, CSV_DATA
from src.models.transformer.seq2seq import BaselineSeq2SeqTransformer
from src.models.transformer.tokenizer import (
TranslationDataset,
PAD_IDX,
SOS_IDX,
EOS_IDX,
)
from src.models.transformer.helpers import generate_square_subsequent_mask
from scripts.preprocess import preprocess_text
SAMPLE_SIZE = 1500
MAX_SEQ_LEN = 128
BEAM_SIZE = 3
MAX_DECODING_LEN = 40
ALPHA = 0.7
def configure_device() -> torch.device:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")
if torch.cuda.is_available():
torch.cuda.empty_cache()
torch.cuda.reset_peak_memory_stats(device)
initial_vram = torch.cuda.memory_allocated(device) / (1024**2)
print(f"Initial GPU Memory Usage: {initial_vram:.2f} MB")
return device
def beam_search_decode(
model: torch.nn.Module,
src_tensor: torch.Tensor,
device: torch.device,
beam_size: int = BEAM_SIZE,
max_len: int = MAX_DECODING_LEN,
alpha: float = ALPHA,
):
model.eval()
src_tensor = src_tensor.to(device)
with torch.no_grad():
src_mask = torch.zeros(
(src_tensor.shape[0], src_tensor.shape[0]), device=device, dtype=torch.bool
)
with torch.amp.autocast("cuda" if torch.cuda.is_available() else "cpu"):
memory = model.encode(src_tensor, src_mask)
beams = [([SOS_IDX], 0.0)]
completed = []
for _ in range(max_len):
candidates = []
for seq, score in beams:
if seq[-1] == EOS_IDX:
completed.append((seq, score))
continue
trg_tensor = torch.tensor(
seq, dtype=torch.long, device=device
).unsqueeze(1)
trg_mask = generate_square_subsequent_mask(trg_tensor.size(0)).to(
device
)
with torch.amp.autocast("cuda" if torch.cuda.is_available() else "cpu"):
out = model.decode(trg_tensor, memory, trg_mask)
logits = model.generator(out[-1])
probs = torch.log_softmax(logits, dim=-1).squeeze(0)
top_probs, top_idxs = torch.topk(probs, beam_size)
for p, idx in zip(top_probs, top_idxs):
idx = idx.item()
p = p.item()
if len(seq) > 2 and idx == seq[-1] and idx == seq[-2]:
continue
candidates.append((seq + [idx], score + p))
beams = sorted(
candidates,
key=lambda x: x[1] / ((len(x[0]) ** alpha) + 1e-6),
reverse=True,
)[:beam_size]
if beams and all(seq[-1] == EOS_IDX for seq, _ in beams):
break
completed.extend(beams)
if not completed:
return beams[0][0]
best_seq = sorted(
completed,
key=lambda x: x[1] / ((len(x[0]) ** alpha) + 1e-6),
reverse=True,
)[0][0]
return best_seq
def translate_sentence(
sentence: str, model: torch.nn.Module, sp, device: torch.device
) -> str:
cleaned_sentence = preprocess_text(sentence.lower())
src_tokens = [SOS_IDX] + sp.encode(str(cleaned_sentence), out_type=int) + [EOS_IDX]
src_tensor = torch.tensor(src_tokens, dtype=torch.long).unsqueeze(1)
predicted_ids = beam_search_decode(model, src_tensor, device, beam_size=BEAM_SIZE)
clean_ids = [idx for idx in predicted_ids if idx not in [SOS_IDX, EOS_IDX, PAD_IDX]]
return sp.decode(clean_ids)
def run_transformer_batch_inference(num_samples: int = SAMPLE_SIZE):
device = configure_device()
print("\nLoading tokenizer and dataset...")
train_dataset = TranslationDataset(
str(JSON_DATA),
spm_model_path=str(TOKENIZER_MODEL),
max_seq_len=MAX_SEQ_LEN,
)
sp = train_dataset.sp
vocab_size = sp.get_piece_size()
model = BaselineSeq2SeqTransformer(
num_encoder_layers=6,
num_decoder_layers=6,
emb_size=512,
nhead=8,
src_vocab_size=vocab_size,
tgt_vocab_size=vocab_size,
dim_feedforward=2048,
dropout=0.3,
).to(device)
model.load_state_dict(torch.load(TRANSFORMER_MODEL, map_location=device))
model.eval()
print("Model loaded successfully!")
print("\nLoading test dataset...")
df = pd.read_csv(CSV_DATA)
if "split" in df.columns:
df = df[df["split"] == "test"]
sample_df = df.sample(n=min(num_samples, len(df)), random_state=42).reset_index(
drop=True
)
print(f"Evaluation samples: {len(sample_df)}")
print("\n" + "=" * 60)
print(f"FINAL INFERENCE PIPELINE ({len(sample_df)} SAMPLES)")
print("=" * 60)
for idx, row in sample_df.iterrows():
source_sentence = str(row.get("source_text", ""))
reference_sentence = str(row.get("target_text", ""))
if not source_sentence:
continue
translation = translate_sentence(source_sentence, model, sp, device)
print(f"\nSample {idx + 1}")
print(f"Source : {source_sentence}")
print(f"Reference : {reference_sentence}")
print(f"Prediction : {translation}")
print("-" * 60)
print("\nInference pipeline completed successfully!")
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Run Transformer inference on the test split"
)
parser.add_argument(
"--samples",
type=int,
default=SAMPLE_SIZE,
help="Number of random samples to translate from the test set",
)
args = parser.parse_args()
run_transformer_batch_inference(num_samples=args.samples)
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