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#!/usr/bin/env python3
"""
Test script to verify training optimizations work correctly
"""
import torch
import os
import sys
from pathlib import Path
def test_mixed_precision():
"""Test mixed precision training setup"""
print("Testing Mixed Precision Training...")
try:
from torch.cuda.amp import GradScaler, autocast
# Test scaler creation
scaler = GradScaler(enabled=True)
print("✓ GradScaler created successfully")
# Test autocast context
with autocast():
x = torch.randn(10, 10)
y = torch.randn(10, 10)
z = x @ y
print("✓ Autocast context works")
return True
except Exception as e:
print(f"✗ Mixed precision test failed: {e}")
return False
def test_optimized_dataset():
"""Test optimized dataset functionality"""
print("\nTesting Optimized Dataset...")
try:
from morphological_dataset import MorphologicalDataset, build_vocabulary
# Create dummy data files
os.makedirs("test_data", exist_ok=True)
with open("test_data/test.src", "w") as f:
f.write("hello world\n")
f.write("test sequence\n")
with open("test_data/test.tgt", "w") as f:
f.write("hola mundo\n")
f.write("secuencia prueba\n")
# Test vocabulary building
src_vocab = build_vocabulary(["test_data/test.src"])
tgt_vocab = build_vocabulary(["test_data/test.tgt"])
print("✓ Vocabulary building works")
# Test dataset creation
dataset = MorphologicalDataset("test_data/test.src", "test_data/test.tgt",
src_vocab, tgt_vocab, max_length=10)
print("✓ Dataset creation works")
# Test data loading
item = dataset[0]
print(f"✓ Dataset item shape: {len(item)}")
# Cleanup
import shutil
shutil.rmtree("test_data")
return True
except Exception as e:
print(f"✗ Dataset test failed: {e}")
return False
def test_optimized_dataloader():
"""Test optimized DataLoader configuration"""
print("\nTesting Optimized DataLoader...")
try:
from torch.utils.data import DataLoader
from morphological_dataset import MorphologicalDataset, build_vocabulary, collate_fn
# Create test dataset
os.makedirs("test_data", exist_ok=True)
with open("test_data/test.src", "w") as f:
f.write("hello world\n")
f.write("test sequence\n")
with open("test_data/test.tgt", "w") as f:
f.write("hola mundo\n")
f.write("secuencia prueba\n")
src_vocab = build_vocabulary(["test_data/test.src"])
tgt_vocab = build_vocabulary(["test_data/test.tgt"])
dataset = MorphologicalDataset("test_data/test.src", "test_data/test.tgt",
src_vocab, tgt_vocab, max_length=10)
# Test optimized DataLoader
dataloader = DataLoader(
dataset,
batch_size=2,
shuffle=True,
collate_fn=lambda batch: collate_fn(batch, src_vocab, tgt_vocab, 10),
num_workers=0, # Use 0 for testing
pin_memory=False, # Disable for testing
persistent_workers=False, # Disable for testing
drop_last=True
)
# Test iteration
for batch in dataloader:
src, src_mask, tgt, tgt_mask = batch
print(f"✓ Batch shapes - src: {src.shape}, tgt: {tgt.shape}")
break
# Cleanup
import shutil
shutil.rmtree("test_data")
return True
except Exception as e:
print(f"✗ DataLoader test failed: {e}")
return False
def test_cuda_optimizations():
"""Test CUDA optimizations"""
print("\nTesting CUDA Optimizations...")
if not torch.cuda.is_available():
print("⚠ CUDA not available, skipping CUDA tests")
return True
try:
# Test CUDA optimizations
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.deterministic = False
print("✓ CUDA optimizations enabled")
# Test non-blocking transfers
x = torch.randn(100, 100)
y = x.cuda(non_blocking=True)
print("✓ Non-blocking CUDA transfer works")
return True
except Exception as e:
print(f"✗ CUDA test failed: {e}")
return False
def test_model_creation():
"""Test model creation with optimizations"""
print("\nTesting Model Creation...")
try:
from transformer import TagTransformer
# Test model creation
model = TagTransformer(
src_vocab_size=1000,
trg_vocab_size=1000,
embed_dim=256,
nb_heads=4,
src_hid_size=1024,
src_nb_layers=2,
trg_hid_size=1024,
trg_nb_layers=2,
dropout_p=0.1,
tie_trg_embed=True,
label_smooth=0.1,
nb_attr=5,
src_c2i={},
trg_c2i={},
attr_c2i={}
)
print("✓ Model creation works")
# Test parameter count
param_count = model.count_nb_params()
print(f"✓ Model has {param_count:,} parameters")
return True
except Exception as e:
print(f"✗ Model test failed: {e}")
return False
def run_all_tests():
"""Run all optimization tests"""
print("=== Testing Training Optimizations ===\n")
tests = [
test_mixed_precision,
test_optimized_dataset,
test_optimized_dataloader,
test_cuda_optimizations,
test_model_creation
]
passed = 0
total = len(tests)
for test in tests:
try:
if test():
passed += 1
except Exception as e:
print(f"✗ Test {test.__name__} failed with exception: {e}")
print(f"\n=== Test Results ===")
print(f"Passed: {passed}/{total}")
if passed == total:
print("🎉 All tests passed! Optimizations are working correctly.")
return True
else:
print("❌ Some tests failed. Check the errors above.")
return False
if __name__ == '__main__':
success = run_all_tests()
sys.exit(0 if success else 1)
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