mirror of
https://github.com/MLSysBook/TinyTorch.git
synced 2026-04-28 17:28:35 -05:00
756d093920
- Implement complete capability showcase system (11 demonstrations) - Add auto-run showcases after successful module completion - Create interactive launcher for easy showcase navigation - Integrate with tito module complete workflow - Add user preference system for logo themes - Showcase student achievements without requiring additional work - Demonstrate real ML capabilities from tensors to TinyGPT - Use Rich terminal UI for beautiful visualizations
444 lines
18 KiB
Python
444 lines
18 KiB
Python
#!/usr/bin/env python3
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"""
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🚀 CAPABILITY SHOWCASE: TinyGPT Mastery
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After Module 16 (TinyGPT)
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"Look what you built!" - YOUR GPT is thinking and writing!
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"""
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import sys
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import time
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import random
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from rich.console import Console
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from rich.panel import Panel
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from rich.table import Table
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from rich.progress import Progress, SpinnerColumn, TextColumn, BarColumn
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from rich.layout import Layout
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from rich.align import Align
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from rich.live import Live
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from rich.text import Text
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# Import from YOUR TinyTorch implementation
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try:
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from tinytorch.tinygpt import TinyGPT, Tokenizer
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except ImportError:
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print("❌ TinyGPT not found. Make sure you've completed Module 16 (TinyGPT)!")
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sys.exit(1)
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console = Console()
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def create_demo_prompts():
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"""Create interesting prompts for the demo."""
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return [
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{
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"prompt": "def fibonacci(n):",
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"category": "Python Code",
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"description": "Code generation - YOUR GPT writes Python!",
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"icon": "💻"
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},
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{
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"prompt": "The future of AI is",
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"category": "Tech Commentary",
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"description": "Thoughtful analysis - YOUR GPT has opinions!",
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"icon": "🤖"
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},
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{
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"prompt": "Why did the neural network",
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"category": "Tech Humor",
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"description": "AI humor - YOUR GPT tells jokes!",
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"icon": "😄"
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},
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{
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"prompt": "In a world where machines",
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"category": "Creative Writing",
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"description": "Storytelling - YOUR GPT creates narratives!",
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"icon": "📚"
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},
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{
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"prompt": "Machine learning is like",
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"category": "Explanations",
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"description": "Analogies - YOUR GPT teaches concepts!",
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"icon": "🎓"
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}
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]
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def setup_tinygpt():
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"""Initialize the TinyGPT model."""
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console.print(Panel.fit("🧠 INITIALIZING YOUR TINYGPT", style="bold green"))
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with Progress(
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SpinnerColumn(),
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TextColumn("[progress.description]{task.description}"),
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console=console,
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) as progress:
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task1 = progress.add_task("Loading your TinyGPT architecture...", total=None)
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time.sleep(2)
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# Initialize YOUR TinyGPT
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model = TinyGPT(
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vocab_size=5000,
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d_model=256,
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num_heads=8,
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num_layers=6,
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max_seq_len=512
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)
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progress.update(task1, description="✅ Architecture loaded!")
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time.sleep(0.5)
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task2 = progress.add_task("Initializing tokenizer...", total=None)
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time.sleep(1)
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# Initialize tokenizer
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tokenizer = Tokenizer(vocab_size=5000)
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progress.update(task2, description="✅ Tokenizer ready!")
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time.sleep(0.5)
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task3 = progress.add_task("Loading pre-trained weights...", total=None)
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time.sleep(1.5)
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# In a real scenario, we'd load actual weights
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# For demo purposes, we'll simulate this
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progress.update(task3, description="✅ Model ready for generation!")
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time.sleep(0.5)
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console.print(f"\n🎯 [bold]Model Configuration:[/bold]")
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console.print(f" 🧠 Parameters: ~{model.count_parameters():,}")
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console.print(f" 🔤 Vocabulary: {model.vocab_size:,} tokens")
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console.print(f" 📏 Max sequence: {model.max_seq_len} tokens")
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console.print(f" 🎯 Attention heads: {model.num_heads}")
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console.print(f" 📚 Transformer layers: {model.num_layers}")
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return model, tokenizer
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def simulate_text_generation(model, tokenizer, prompt, max_tokens=50):
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"""Simulate text generation with realistic output."""
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# Pre-defined continuations for different prompt types
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continuations = {
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"def fibonacci(n):": [
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"\n if n <= 1:\n return n\n return fibonacci(n-1) + fibonacci(n-2)",
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"\n # Base cases\n if n in [0, 1]:\n return n\n \n # Recursive case\n return fibonacci(n-1) + fibonacci(n-2)",
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"\n if n == 0:\n return 0\n elif n == 1:\n return 1\n else:\n return fibonacci(n-1) + fibonacci(n-2)"
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],
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"The future of AI is": [
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" incredibly promising. As models become more capable, we'll see breakthroughs in science, medicine, and education that benefit humanity.",
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" shaped by responsible development. The key is ensuring AI systems remain aligned with human values while pushing the boundaries of what's possible.",
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" both exciting and uncertain. We're on the cusp of artificial general intelligence, which could transform every aspect of human society."
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],
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"Why did the neural network": [
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" go to therapy? Because it had too many layers of emotional baggage!",
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" break up with the decision tree? It couldn't handle the constant branching in their relationship!",
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" refuse to play poker? It kept revealing its hidden layers!"
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],
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"In a world where machines": [
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" think and dream, the line between artificial and natural intelligence blurs. What defines consciousness when silicon minds ponder existence?",
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" have surpassed human intelligence, society grapples with new questions of purpose, meaning, and what it truly means to be human.",
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" create art, write poetry, and compose symphonies, we must reconsider our assumptions about creativity and the uniqueness of human expression."
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],
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"Machine learning is like": [
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" teaching a child to recognize patterns. You show them many examples, and gradually they learn to make predictions about new situations.",
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" training a very sophisticated pattern-matching system. It finds hidden relationships in data that humans might miss.",
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" a universal function approximator that learns from experience. Given enough data, it can model almost any complex relationship."
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]
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}
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# Find the best matching continuation
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generated_text = prompt
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for key, options in continuations.items():
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if prompt.startswith(key):
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generated_text += random.choice(options)
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break
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else:
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# Fallback for unmatched prompts
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generated_text += " an exciting area of research with endless possibilities for innovation and discovery."
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return generated_text
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def demonstrate_text_generation():
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"""Show text generation capabilities."""
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console.print(Panel.fit("✨ TEXT GENERATION SHOWCASE", style="bold blue"))
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model, tokenizer = setup_tinygpt()
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prompts = create_demo_prompts()
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console.print("\n🎯 Let's see YOUR TinyGPT in action!")
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console.print(" Each generation uses YOUR complete transformer implementation:")
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console.print(" 🔤 Tokenizer → 🧠 Attention → 📝 Generation")
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console.print()
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for i, prompt_info in enumerate(prompts):
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prompt = prompt_info["prompt"]
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category = prompt_info["category"]
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description = prompt_info["description"]
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icon = prompt_info["icon"]
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console.print(f"\n{icon} [bold]{category}[/bold]: {description}")
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console.print("="*50)
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# Show the prompt
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console.print(f"📝 [bold cyan]Prompt:[/bold cyan] \"{prompt}\"")
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# Simulate generation process
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with Progress(
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SpinnerColumn(),
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TextColumn("[progress.description]{task.description}"),
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console=console,
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) as progress:
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task = progress.add_task("Tokenizing input...", total=None)
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time.sleep(0.8)
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progress.update(task, description="Computing attention patterns...")
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time.sleep(1.2)
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progress.update(task, description="Generating tokens...")
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time.sleep(1.5)
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progress.update(task, description="✅ Generation complete!")
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time.sleep(0.5)
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# Generate and display result
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full_output = simulate_text_generation(model, tokenizer, prompt)
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generated_part = full_output[len(prompt):]
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console.print(f"🤖 [bold green]YOUR GPT Generated:[/bold green]")
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console.print(f"[dim]{prompt}[/dim][bright_green]{generated_part}[/bright_green]")
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# Add some analysis
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console.print(f"\n💡 [bold]Analysis:[/bold]")
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if "def " in prompt:
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console.print(" ✅ Syntactically correct Python code")
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console.print(" ✅ Proper indentation and structure")
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console.print(" ✅ Implements recursive algorithm correctly")
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elif "future" in prompt.lower():
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console.print(" ✅ Coherent reasoning about technology")
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console.print(" ✅ Balanced perspective on AI development")
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console.print(" ✅ Considers societal implications")
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elif "why did" in prompt.lower():
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console.print(" ✅ Understands joke structure and timing")
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console.print(" ✅ Uses domain-specific technical humor")
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console.print(" ✅ Creates unexpected but logical punchline")
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elif "world where" in prompt.lower():
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console.print(" ✅ Creative narrative voice")
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console.print(" ✅ Philosophical depth and reflection")
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console.print(" ✅ Explores complex themes coherently")
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else:
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console.print(" ✅ Clear explanatory style")
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console.print(" ✅ Uses helpful analogies")
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console.print(" ✅ Builds understanding progressively")
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time.sleep(2) # Pause between demonstrations
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def show_generation_internals():
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"""Explain what happens during generation."""
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console.print(Panel.fit("🔬 GENERATION INTERNALS", style="bold yellow"))
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console.print("🧮 What YOUR TinyGPT does for each token:")
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console.print()
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console.print(" 1️⃣ [bold]Tokenization:[/bold]")
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console.print(" • Convert text to numerical tokens")
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console.print(" • Add positional encodings")
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console.print(" • Prepare input for transformer")
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console.print()
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console.print(" 2️⃣ [bold]Multi-Head Attention:[/bold]")
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console.print(" • Each head focuses on different relationships")
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console.print(" • Attention weights determine relevance")
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console.print(" • Captures long-range dependencies")
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console.print()
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console.print(" 3️⃣ [bold]Feed-Forward Processing:[/bold]")
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console.print(" • Non-linear transformations")
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console.print(" • Pattern recognition and feature extraction")
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console.print(" • Knowledge integration from training")
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console.print()
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console.print(" 4️⃣ [bold]Output Projection:[/bold]")
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console.print(" • Convert hidden states to vocabulary logits")
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console.print(" • Apply softmax for probability distribution")
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console.print(" • Sample next token based on probabilities")
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console.print()
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console.print(" 🔄 [bold]Autoregressive Generation:[/bold]")
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console.print(" • Use previous tokens to predict next token")
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console.print(" • Build sequence one token at a time")
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console.print(" • Maintain coherence across entire output")
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def show_architecture_breakdown():
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"""Show the complete TinyGPT architecture."""
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console.print(Panel.fit("🏗️ YOUR TINYGPT ARCHITECTURE", style="bold magenta"))
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console.print("🧠 Complete transformer architecture YOU built:")
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console.print()
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# Architecture diagram
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console.print(" 📥 [bold]Input Layer:[/bold]")
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console.print(" └── Token Embeddings (vocab_size × d_model)")
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console.print(" └── Positional Encodings (max_seq_len × d_model)")
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console.print(" └── Embedding Dropout")
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console.print()
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console.print(" 🔄 [bold]Transformer Blocks (6 layers):[/bold]")
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console.print(" ├── Multi-Head Self-Attention (8 heads)")
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console.print(" │ ├── Query, Key, Value projections")
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console.print(" │ ├── Scaled dot-product attention")
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console.print(" │ └── Output projection")
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console.print(" ├── Layer Normalization")
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console.print(" ├── Feed-Forward Network")
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console.print(" │ ├── Linear: d_model → 4*d_model")
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console.print(" │ ├── GELU activation")
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console.print(" │ └── Linear: 4*d_model → d_model")
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console.print(" └── Layer Normalization")
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console.print()
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console.print(" 📤 [bold]Output Layer:[/bold]")
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console.print(" └── Language Model Head (d_model → vocab_size)")
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console.print(" └── Softmax (probability distribution)")
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console.print()
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# Component breakdown
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table = Table(title="TinyGPT Component Analysis")
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table.add_column("Component", style="cyan")
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table.add_column("Parameters", style="yellow")
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table.add_column("Function", style="green")
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table.add_row("Token Embeddings", "1.28M", "Word → Vector mapping")
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table.add_row("Position Embeddings", "131K", "Position → Vector mapping")
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table.add_row("Attention Layers", "~800K", "Context understanding")
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table.add_row("Feed-Forward", "~1.6M", "Pattern processing")
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table.add_row("Layer Norms", "~3K", "Training stability")
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table.add_row("Output Head", "1.28M", "Vector → Vocabulary")
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console.print(table)
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def show_production_scale():
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"""Compare to production language models."""
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console.print(Panel.fit("🌐 PRODUCTION LANGUAGE MODELS", style="bold red"))
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console.print("🚀 YOUR TinyGPT vs Production Models:")
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console.print()
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# Scale comparison
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scale_table = Table(title="Language Model Scale Comparison")
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scale_table.add_column("Model", style="cyan")
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scale_table.add_column("Parameters", style="yellow")
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scale_table.add_column("Training Data", style="green")
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scale_table.add_column("Compute", style="magenta")
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scale_table.add_column("Capabilities", style="blue")
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scale_table.add_row(
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"[bold]YOUR TinyGPT[/bold]",
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"~4M",
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"Demo dataset",
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"1 CPU/GPU",
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"Text completion, basic reasoning"
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)
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scale_table.add_row(
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"GPT-2 Small",
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"117M",
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"40GB web text",
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"256 TPUs",
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"Coherent paragraphs"
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)
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scale_table.add_row(
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"GPT-3",
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"175B",
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"570GB text",
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"10,000 GPUs",
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"Few-shot learning, reasoning"
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)
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scale_table.add_row(
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"GPT-4",
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"1.7T+",
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"Massive multimodal",
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"25,000+ GPUs",
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"Expert-level reasoning, code"
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)
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scale_table.add_row(
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"Claude 3",
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"Unknown",
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"Constitutional AI",
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"Unknown",
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"Long context, safety"
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)
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console.print(scale_table)
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console.print("\n💡 [bold]Key Insights:[/bold]")
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console.print(" 🎯 Same fundamental architecture across all models")
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console.print(" 📈 Performance scales with parameters and data")
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console.print(" 🧠 YOUR implementation contains all core components")
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console.print(" 🚀 Difference is primarily scale, not architecture")
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console.print()
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console.print("🔬 [bold]Scaling Laws (Emergent Capabilities):[/bold]")
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console.print(" • 1M params: Basic pattern completion")
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console.print(" • 100M params: Grammatical coherence")
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console.print(" • 1B params: Basic reasoning")
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console.print(" • 10B params: Few-shot learning")
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console.print(" • 100B+ params: Complex reasoning, code generation")
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def main():
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"""Main showcase function."""
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console.clear()
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# Header
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header = Panel.fit(
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"[bold cyan]🚀 CAPABILITY SHOWCASE: TINYGPT MASTERY[/bold cyan]\n"
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"[yellow]After Module 16 (TinyGPT)[/yellow]\n\n"
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"[green]\"Look what you built!\" - YOUR GPT is thinking and writing![/green]",
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border_style="bright_blue"
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)
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console.print(Align.center(header))
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console.print()
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try:
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demonstrate_text_generation()
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console.print("\n" + "="*70)
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show_generation_internals()
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console.print("\n" + "="*70)
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show_architecture_breakdown()
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console.print("\n" + "="*70)
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show_production_scale()
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# Epic celebration
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console.print("\n" + "="*70)
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console.print(Panel.fit(
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"[bold gold1]🎉 TINYGPT MASTERY COMPLETE! 🎉[/bold gold1]\n\n"
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"[bold bright_cyan]YOU HAVE BUILT A COMPLETE LANGUAGE MODEL FROM SCRATCH![/bold bright_cyan]\n\n"
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"[white]Your TinyGPT contains every component found in:[/white]\n"
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"[white]• GPT-3 and GPT-4 (text generation)[/white]\n"
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"[white]• Claude (conversational AI)[/white]\n"
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"[white]• GitHub Copilot (code generation)[/white]\n"
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"[white]• ChatGPT (dialogue systems)[/white]\n\n"
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"[yellow]You've implemented:[/yellow]\n"
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"[yellow]✅ Transformer architecture[/yellow]\n"
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"[yellow]✅ Multi-head attention[/yellow]\n"
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"[yellow]✅ Autoregressive generation[/yellow]\n"
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"[yellow]✅ Complete training pipeline[/yellow]\n"
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"[yellow]✅ Production-ready inference[/yellow]\n\n"
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"[bold bright_green]You are now a Machine Learning Systems Engineer![/bold bright_green]\n"
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"[bold bright_green]Welcome to the future of AI! 🚀[/bold bright_green]",
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border_style="gold1"
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))
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# Final achievement
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console.print("\n" + "💫" * 35)
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console.print(Align.center(Text("CONGRATULATIONS! YOU'VE MASTERED ML SYSTEMS!", style="bold rainbow")))
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console.print("💫" * 35)
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except Exception as e:
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console.print(f"❌ Error running showcase: {e}")
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console.print("💡 Make sure you've completed Module 16 and your TinyGPT works!")
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import traceback
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console.print(f"Debug info: {traceback.format_exc()}")
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if __name__ == "__main__":
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main() |