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#!/usr/bin/env python3
"""
Test script for CPU-optimized training
"""
import torch
import os
import sys
import time
def test_cpu_optimizations():
"""Test CPU optimizations"""
print("=== Testing CPU Optimizations ===")
# Test PyTorch thread settings
print(f"CPU cores: {os.cpu_count()}")
print(f"PyTorch threads: {torch.get_num_threads()}")
print(f"PyTorch interop threads: {torch.get_num_interop_threads()}")
# Test environment variables
print(f"OMP_NUM_THREADS: {os.environ.get('OMP_NUM_THREADS', 'Not set')}")
print(f"MKL_NUM_THREADS: {os.environ.get('MKL_NUM_THREADS', 'Not set')}")
print(f"NUMEXPR_NUM_THREADS: {os.environ.get('NUMEXPR_NUM_THREADS', 'Not set')}")
# Test basic tensor operations
print("\n=== Testing Tensor Operations ===")
# Create test tensors
x = torch.randn(1000, 1000)
y = torch.randn(1000, 1000)
# Test matrix multiplication with simple timing
start_time = time.time()
for _ in range(10):
z = torch.mm(x, y)
end_time = time.time()
avg_time = (end_time - start_time) / 10
print(f"Matrix multiplication (1000x1000): {avg_time:.4f}s average over 10 runs")
# Test model compilation if available
if hasattr(torch, 'compile'):
print("\n=== Testing Model Compilation ===")
# Create a simple model
model = torch.nn.Sequential(
torch.nn.Linear(100, 200),
torch.nn.ReLU(),
torch.nn.Linear(200, 100)
)
try:
compiled_model = torch.compile(model, mode="max-autotune")
print("✓ Model compilation successful")
# Test compiled model
test_input = torch.randn(32, 100)
with torch.no_grad():
output = compiled_model(test_input)
print(f"✓ Compiled model forward pass successful, output shape: {output.shape}")
except Exception as e:
print(f"⚠ Model compilation failed: {e}")
else:
print("⚠ torch.compile not available")
print("\n=== CPU Optimization Test Complete ===")
return True
if __name__ == '__main__':
test_cpu_optimizations()
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