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#!/usr/bin/env python3
"""
Test script to verify mixed precision configuration
"""
import torch
import sys
import os
# Add project root to path
project_root = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0, project_root)
def test_mixed_precision():
"""Test mixed precision configuration"""
print("Testing mixed precision configuration...")
# Test 1: Check CUDA availability
print(f"CUDA available: {torch.cuda.is_available()}")
if torch.cuda.is_available():
print(f"CUDA device count: {torch.cuda.device_count()}")
print(f"CUDA device capability: {torch.cuda.get_device_capability()}")
print(f"Current device: {torch.cuda.current_device()}")
# Test 2: Test model loading with different dtypes
try:
from src.model import SmolLM3Model
from config.train_smollm3_h100_lightweight import SmolLM3ConfigH100Lightweight
config = SmolLM3ConfigH100Lightweight()
print(f"Config fp16: {config.fp16}")
print(f"Config bf16: {config.bf16}")
# Test model loading
model = SmolLM3Model(
model_name="HuggingFaceTB/SmolLM3-3B",
max_seq_length=4096,
config=config
)
print(f"Model dtype: {model.torch_dtype}")
print(f"Model device map: {model.device_map}")
print("✅ Model loading successful!")
# Test training arguments
training_args = model.get_training_arguments("/tmp/test")
print(f"Training args fp16: {training_args.fp16}")
print(f"Training args bf16: {training_args.bf16}")
print("✅ Training arguments created successfully!")
except Exception as e:
print(f"❌ Error: {e}")
return False
return True
if __name__ == "__main__":
success = test_mixed_precision()
if success:
print("\n🎉 Mixed precision test passed!")
else:
print("\n❌ Mixed precision test failed!")
sys.exit(1) |