mirror of
https://github.com/MLSysBook/TinyTorch.git
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b2228f4deb
All examples now learning successfully: ✅ Perceptron - 100% accuracy ✅ XOR - Training with validation ✅ MNIST - Deep learning working ✅ CIFAR - Fixed Conv2d weight vs weights issue ✅ TinyGPT - Transformer training Ready for Phase 2: Optimization testing
129 lines
3.5 KiB
Python
129 lines
3.5 KiB
Python
#!/usr/bin/env python3
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"""
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Phase 1: Test all TinyTorch examples to ensure they learn.
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Tests each example and logs results.
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"""
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import subprocess
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import sys
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import time
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from datetime import datetime
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def log(message):
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"""Log with timestamp."""
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timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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print(f"[{timestamp}] {message}")
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sys.stdout.flush()
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def test_example(name, command, success_criteria):
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"""Test an example and return success status."""
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log(f"Testing {name}...")
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log(f"Command: {command}")
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try:
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result = subprocess.run(
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command,
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shell=True,
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capture_output=True,
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text=True,
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timeout=120
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)
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output = result.stdout + result.stderr
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# Check success criteria
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success = all(criterion in output for criterion in success_criteria)
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if success:
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log(f"✅ {name} PASSED - All criteria met")
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else:
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log(f"❌ {name} FAILED - Missing criteria")
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log(f"Output preview: {output[-500:]}")
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return success, output
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except subprocess.TimeoutExpired:
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log(f"⏱️ {name} TIMEOUT - Took too long")
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return False, "TIMEOUT"
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except Exception as e:
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log(f"❌ {name} ERROR - {str(e)}")
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return False, str(e)
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def main():
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"""Test all examples in order of complexity."""
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log("="*60)
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log("PHASE 1: TESTING ALL EXAMPLES FOR LEARNING")
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log("="*60)
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results = []
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# 1. Perceptron (simplest)
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log("\n1. PERCEPTRON (1957)")
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success, output = test_example(
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"Perceptron",
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"python examples/perceptron_1957/rosenblatt_perceptron.py --epochs 100",
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["SUCCESS", "100.0%", "Loss"]
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)
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results.append(("Perceptron", success))
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# 2. XOR (multi-layer)
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log("\n2. XOR (1969)")
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success, output = test_example(
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"XOR",
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"python examples/xor_1969/minsky_xor_problem.py --epochs 200",
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["SUCCESS", "Training Complete", "Val"]
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)
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results.append(("XOR", success))
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# 3. MNIST MLP (deep network)
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log("\n3. MNIST MLP (1986)")
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success, output = test_example(
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"MNIST",
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"python examples/mnist_mlp_1986/train_mlp.py --epochs 2 --batch-size 32",
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["SUCCESS", "Training", "Test"]
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)
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results.append(("MNIST", success))
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# 4. CIFAR CNN (convolutional)
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log("\n4. CIFAR CNN (Modern)")
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success, output = test_example(
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"CIFAR",
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"python examples/cifar_cnn_modern/train_cnn.py --quick-test --epochs 2",
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["SUCCESS", "Forward pass", "CNN"]
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)
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results.append(("CIFAR", success))
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# 5. TinyGPT (transformer)
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log("\n5. TINYGPT (2018)")
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success, output = test_example(
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"TinyGPT",
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"python examples/gpt_2018/train_gpt.py",
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["Success", "transformer", "Loss"]
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)
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results.append(("TinyGPT", success))
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# Summary
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log("\n" + "="*60)
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log("PHASE 1 SUMMARY")
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log("="*60)
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for name, success in results:
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status = "✅ PASS" if success else "❌ FAIL"
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log(f"{name:15} {status}")
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all_passed = all(success for _, success in results)
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if all_passed:
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log("\n🎉 ALL EXAMPLES LEARNING SUCCESSFULLY!")
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log("Ready for Phase 2: Optimization Testing")
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else:
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log("\n⚠️ Some examples need fixing before optimization")
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log("Fix failing examples first")
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return all_passed
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if __name__ == "__main__":
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success = main()
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sys.exit(0 if success else 1)
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