mirror of
https://github.com/MLSysBook/TinyTorch.git
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1611af0b78
Implements comprehensive demo system showing AI capabilities unlocked by each module export: - 8 progressive demos from tensor math to language generation - Complete tito demo CLI integration with capability matrix - Real AI demonstrations including XOR solving, computer vision, attention mechanisms - Educational explanations connecting implementations to production ML systems Repository reorganization: - demos/ directory with all demo files and comprehensive README - docs/ organized by category (development, nbgrader, user guides) - scripts/ for utility and testing scripts - Clean root directory with only essential files Students can now run 'tito demo' after each module export to see their framework's growing intelligence through hands-on demonstrations.
83 lines
2.5 KiB
Python
83 lines
2.5 KiB
Python
#!/usr/bin/env python3
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"""
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Test TinyGPT package demo to see if text generation works
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"""
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import sys
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import time
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import tinytorch.tinygpt as tgpt
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def test_tinygpt_demo():
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"""Test if TinyGPT can generate text as a packaged demo"""
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print("🤖 TinyGPT Package Demo Test")
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print("=" * 50)
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# Simple Shakespeare text for testing
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text = """To be, or not to be, that is the question:
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Whether 'tis nobler in the mind to suffer
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The slings and arrows of outrageous fortune,
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Or to take arms against a sea of troubles
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And by opposing end them."""
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print(f"📚 Training text: {len(text)} characters")
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try:
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# Create tokenizer
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print("\n🔤 Creating tokenizer...")
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tokenizer = tgpt.CharTokenizer(vocab_size=50)
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tokenizer.fit(text)
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vocab_size = tokenizer.get_vocab_size()
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print(f" Vocabulary size: {vocab_size}")
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# Create model
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print("\n🧠 Creating TinyGPT model...")
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model = tgpt.TinyGPT(
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vocab_size=vocab_size,
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d_model=64,
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num_heads=4,
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num_layers=2,
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d_ff=256,
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max_length=128,
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dropout=0.1
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)
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print(f" Model parameters: {model.count_parameters():,}")
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# Create trainer
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print("\n🎓 Creating trainer...")
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trainer = tgpt.LanguageModelTrainer(model, tokenizer)
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# Test generation BEFORE training (should be random)
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print("\n📝 Pre-training generation test:")
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prompt = "To be"
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generated = trainer.generate_text(prompt, max_length=20, temperature=1.0)
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print(f" '{prompt}' → '{generated}'")
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# Quick training test
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print("\n🚀 Quick training test (1 epoch)...")
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history = trainer.fit(
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text=text,
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epochs=1,
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seq_length=16,
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batch_size=2,
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val_split=0.2,
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verbose=True
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)
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# Test generation AFTER training
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print("\n📝 Post-training generation test:")
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for temp in [0.3, 0.7, 1.0]:
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generated = trainer.generate_text(prompt, max_length=30, temperature=temp)
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print(f" '{prompt}' (T={temp}) → '{generated}'")
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print("\n✅ TinyGPT package demo successful!")
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return True
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except Exception as e:
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print(f"\n❌ Demo failed: {e}")
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import traceback
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traceback.print_exc()
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return False
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if __name__ == "__main__":
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success = test_tinygpt_demo()
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sys.exit(0 if success else 1) |