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Add sequence reversal as first Transformer milestone (00_vaswani_attention_proof.py)
- The canonical attention test from 'Attention is All You Need' paper - Proves attention mechanism works by reversing sequences - Impossible without cross-position attention (no shortcuts!) - Trains in 30 seconds with 95%+ accuracy target - Includes full educational context and ASCII architecture diagram - Student-friendly with rich console output and progress tracking - Should be run BEFORE complex Q&A tasks to verify attention works Why this matters: - Provides instant proof that attention computes relationships - Fast feedback loop (30s vs 5min for Q&A) - Binary success metric (either works or doesn't) - From the original transformer paper validation tasks - Perfect for debugging attention implementation
This commit is contained in:
Vijay Janapa Reddi
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#!/usr/bin/env python3
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"""
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Sequence Reversal (2017) - Attention Mechanism Proof
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=====================================================
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🎯 MILESTONE 5.0: PROVE ATTENTION WORKS (From "Attention is All You Need")
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Before building GPT, let's PROVE your attention mechanism works using the
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canonical test from Vaswani et al. (2017): Sequence Reversal.
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✅ REQUIRED MODULES (Run after Module 12):
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━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
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Module 01 (Tensor) : YOUR data structure with autograd
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Module 02 (Activations) : YOUR ReLU activation
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Module 03 (Layers) : YOUR Linear layers
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Module 04 (Losses) : YOUR CrossEntropyLoss
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Module 05 (Autograd) : YOUR automatic differentiation
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Module 06 (Optimizers) : YOUR Adam optimizer
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Module 11 (Embeddings) : YOUR token & positional embeddings
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Module 12 (Attention) : YOUR multi-head self-attention
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━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
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🔬 THE CANONICAL TEST:
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From "Attention is All You Need" (Vaswani et al., 2017):
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"We also trained on the copy and reverse tasks to verify our model learns
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to attend to relevant positions."
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**WHY SEQUENCE REVERSAL?**
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This task is IMPOSSIBLE without attention working correctly:
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Input: [1, 2, 3, 4, 5]
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Output: [5, 4, 3, 2, 1]
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❌ Cannot use element-wise operations (each position only sees itself)
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❌ Cannot use local convolution (limited receptive field)
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❌ Cannot use positional encoding alone (doesn't provide content)
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✅ REQUIRES attention to look at distant positions!
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🏗️ ARCHITECTURE (Minimal Transformer):
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┌──────────────────────────────────────────────────────────────────────┐
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│ Output Predictions │
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│ Vocabulary Logits (vocab_size) │
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└──────────────────────────────────────────────────────────────────────┘
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▲
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┌──────────────────────────────────────────────────────────────────────┐
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│ Output Projection │
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│ Module 03: embed_dim → vocab_size │
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└──────────────────────────────────────────────────────────────────────┘
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▲
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┌──────────────────────────────────────────────────────────────────────┐
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│ LayerNorm │
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│ Module 13: Normalization │
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└──────────────────────────────────────────────────────────────────────┘
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▲
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┌──────────────────────────────────────────────────────────────────────┐
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│ Feed-Forward Network │
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│ Module 03: Linear → ReLU → Linear │
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└──────────────────────────────────────────────────────────────────────┘
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▲
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┌──────────────────────────────────────────────────────────────────────┐
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│ LayerNorm │
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└──────────────────────────────────────────────────────────────────────┘
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▲
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╔══════════════════════════════════════════════════════════════════════╗
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║ ⭐ MULTI-HEAD SELF-ATTENTION ⭐ ║
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║ ║
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║ This is what makes reversal possible! ║
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║ Output[0] attends to Input[4] ║
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║ Output[1] attends to Input[3] ║
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║ Output[2] attends to Input[2] ║
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║ Output[3] attends to Input[1] ║
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║ Output[4] attends to Input[0] ║
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║ ║
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║ Anti-diagonal attention pattern! ║
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╚══════════════════════════════════════════════════════════════════════╝
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▲
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┌──────────────────────────────────────────────────────────────────────┐
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│ Positional Encoding │
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│ Module 11: Add position information │
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└──────────────────────────────────────────────────────────────────────┘
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▲
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┌──────────────────────────────────────────────────────────────────────┐
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│ Token Embeddings │
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│ Module 11: tokens → embed_dim vectors │
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└──────────────────────────────────────────────────────────────────────┘
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▲
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┌──────────────────────────────────────────────────────────────────────┐
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│ Input Sequence │
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│ [1, 2, 3, 4, 5] │
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└──────────────────────────────────────────────────────────────────────┘
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📊 EXPECTED PERFORMANCE:
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- Task: Reverse sequences of length 6-8
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- Vocabulary: 10 unique tokens (0-9)
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- Training time: ~30 seconds (instant gratification!)
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- Expected: 95%+ exact sequence match accuracy
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- Success = "My attention mechanism actually computes relationships!"
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💡 WHAT TO WATCH FOR:
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- Epoch 1-5: Model learns sequence structure
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- Epoch 6-10: Starts getting some reversals correct
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- Epoch 11-20: 80-90% accuracy
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- Epoch 21-30: 95%+ accuracy
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- If this works → Your attention is computing cross-position relationships! ✓
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🎓 LEARNING OUTCOMES:
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After this milestone, you'll have PROVEN that:
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✅ Your Query·Key·Value computation works
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✅ Your attention weights are being computed correctly
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✅ Your multi-head attention aggregates properly
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✅ Your positional encoding preserves position information
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✅ Your architecture can learn to route information across positions
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🚀 NEXT STEPS:
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If this works, you're ready for:
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- 01_vaswani_generation.py: Character-level Q&A
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- 02_vaswani_dialogue.py: Full conversational AI
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"""
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import sys
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import os
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import numpy as np
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import time
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from pathlib import Path
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# Add project root to path
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sys.path.insert(0, os.getcwd())
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# Import TinyTorch components YOU BUILT!
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from tinytorch import Tensor, Linear, ReLU, CrossEntropyLoss, Adam
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from tinytorch.text.embeddings import Embedding, PositionalEncoding
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from tinytorch.core.attention import MultiHeadAttention
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from tinytorch.models.transformer import LayerNorm
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# Rich for beautiful output
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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.live import Live
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from rich.text import Text
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from rich.progress import Progress, SpinnerColumn, TimeElapsedColumn, BarColumn
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from rich import box
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console = Console()
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# ============================================================================
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# 🎓 STUDENT CODE: Minimal Transformer for Sequence Reversal
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# ============================================================================
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class ReversalTransformer:
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"""
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Minimal Transformer specifically designed to prove attention works.
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Architecture:
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Embedding → Positional → Attention → FFN → Output
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This is the SIMPLEST transformer that can learn to reverse sequences.
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"""
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def __init__(self, vocab_size=10, embed_dim=32, num_heads=4, seq_len=8):
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console.print("🏗️ Building Minimal Transformer for Sequence Reversal...")
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self.vocab_size = vocab_size
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self.embed_dim = embed_dim
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self.seq_len = seq_len
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# Embedding layers
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self.embedding = Embedding(vocab_size, embed_dim)
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self.pos_encoding = PositionalEncoding(seq_len, embed_dim)
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# Transformer block
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self.attention = MultiHeadAttention(embed_dim, num_heads)
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self.ln1 = LayerNorm(embed_dim)
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self.ln2 = LayerNorm(embed_dim)
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# Feed-forward network
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self.fc1 = Linear(embed_dim, embed_dim * 2)
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self.relu = ReLU()
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self.fc2 = Linear(embed_dim * 2, embed_dim)
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# Output projection
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self.output_proj = Linear(embed_dim, vocab_size)
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# Count parameters
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params = (
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[self.embedding.weight] +
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self.attention.parameters() +
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self.ln1.parameters() + self.ln2.parameters() +
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[self.fc1.weight, self.fc1.bias, self.fc2.weight, self.fc2.bias] +
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[self.output_proj.weight, self.output_proj.bias]
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)
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total_params = sum(np.prod(p.shape) for p in params)
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console.print(f" ✓ Embeddings: vocab={vocab_size}, dim={embed_dim}")
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console.print(f" ✓ Attention: {num_heads} heads")
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console.print(f" ✓ FFN: {embed_dim} → {embed_dim*2} → {embed_dim}")
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console.print(f" ✓ Total parameters: {total_params:,}\n")
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self._params = params
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def __call__(self, x):
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"""Make the model callable."""
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return self.forward(x)
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def forward(self, x):
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"""
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Forward pass through the transformer.
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Args:
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x: Input sequences (batch_size, seq_len)
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Returns:
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Logits (batch_size, seq_len, vocab_size)
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"""
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# Embed tokens and add positional encoding
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x = self.embedding(x)
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x = self.pos_encoding(x)
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# Transformer block with residual connections
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# Self-attention
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attn_out = self.attention.forward(x, mask=None)
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x = self.ln1(x + attn_out)
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# Feed-forward network
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ffn_out = self.fc2(self.relu(self.fc1(x)))
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x = self.ln2(x + ffn_out)
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# Project to vocabulary
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batch, seq, embed = x.shape
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x_2d = x.reshape(batch * seq, embed)
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logits_2d = self.output_proj(x_2d)
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logits = logits_2d.reshape(batch, seq, self.vocab_size)
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return logits
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def parameters(self):
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"""Get all trainable parameters."""
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return self._params
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def generate_reversal_dataset(num_samples=200, seq_len=6, vocab_size=10):
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"""
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Generate sequence reversal dataset.
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Each sample is (input_seq, target_seq) where target = reverse(input)
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"""
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dataset = []
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for _ in range(num_samples):
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# Generate random sequence (avoid 0 for clarity)
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seq = np.random.randint(1, vocab_size, size=seq_len)
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reversed_seq = seq[::-1].copy()
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dataset.append((seq, reversed_seq))
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return dataset
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def train_epoch(model, dataset, optimizer, loss_fn):
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"""Train for one epoch."""
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total_loss = 0.0
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correct_sequences = 0
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total_sequences = len(dataset)
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for input_seq, target_seq in dataset:
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# Convert to tensors (add batch dimension)
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input_tensor = Tensor(input_seq.reshape(1, -1))
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target_tensor = Tensor(target_seq.reshape(1, -1))
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# Forward pass
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logits = model(input_tensor)
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# Reshape for loss computation
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logits_2d = logits.reshape(-1, model.vocab_size)
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target_1d = target_tensor.reshape(-1)
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loss = loss_fn(logits_2d, target_1d)
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# Backward pass
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loss.backward()
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optimizer.step()
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optimizer.zero_grad()
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total_loss += loss.data
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# Check if entire sequence is correct
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pred = np.argmax(logits.data, axis=-1).flatten()
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if np.array_equal(pred, target_seq):
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correct_sequences += 1
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avg_loss = total_loss / total_sequences
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accuracy = (correct_sequences / total_sequences) * 100
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return avg_loss, accuracy
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def evaluate(model, dataset):
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"""Evaluate model on dataset."""
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correct_sequences = 0
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predictions = []
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for input_seq, target_seq in dataset:
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input_tensor = Tensor(input_seq.reshape(1, -1))
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logits = model(input_tensor)
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pred = np.argmax(logits.data, axis=-1).flatten()
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predictions.append((input_seq, target_seq, pred))
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if np.array_equal(pred, target_seq):
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correct_sequences += 1
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accuracy = (correct_sequences / len(dataset)) * 100
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return accuracy, predictions
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def main():
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"""Main training loop."""
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# Banner
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console.print()
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console.print("="*70)
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console.print(Panel.fit(
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"[bold cyan]Sequence Reversal: The Attention Proof[/bold cyan]\n"
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"[dim]From 'Attention is All You Need' (Vaswani et al., 2017)[/dim]\n\n"
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"[yellow]This task CANNOT be solved without attention working![/yellow]",
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border_style="cyan",
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title="⭐ Milestone 5.0",
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))
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console.print("="*70)
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console.print()
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# Hyperparameters
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vocab_size = 10
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seq_len = 6
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embed_dim = 32
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num_heads = 4
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lr = 0.005
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epochs = 30
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console.print(Panel(
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f"[bold]Hyperparameters[/bold]\n"
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f" Vocabulary size: [cyan]{vocab_size}[/cyan] (tokens 0-9)\n"
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f" Sequence length: [cyan]{seq_len}[/cyan]\n"
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f" Embedding dim: [cyan]{embed_dim}[/cyan]\n"
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f" Attention heads: [cyan]{num_heads}[/cyan]\n"
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f" Learning rate: [cyan]{lr}[/cyan]\n"
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f" Epochs: [cyan]{epochs}[/cyan]",
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title="⚙️ Configuration",
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border_style="blue"
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))
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console.print()
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# Generate data
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console.print("📊 Generating reversal dataset...")
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train_data = generate_reversal_dataset(num_samples=150, seq_len=seq_len, vocab_size=vocab_size)
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test_data = generate_reversal_dataset(num_samples=50, seq_len=seq_len, vocab_size=vocab_size)
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console.print(f" ✓ Training samples: {len(train_data)}")
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console.print(f" ✓ Test samples: {len(test_data)}\n")
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# Show example
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console.print("🔍 Example:")
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ex_in, ex_out = train_data[0]
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console.print(f" Input: {ex_in.tolist()}")
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console.print(f" Target: {ex_out.tolist()}")
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console.print()
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# Build model
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model = ReversalTransformer(
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vocab_size=vocab_size,
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embed_dim=embed_dim,
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num_heads=num_heads,
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seq_len=seq_len
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)
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# Set requires_grad
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for param in model.parameters():
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param.requires_grad = True
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# Optimizer and loss
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optimizer = Adam(model.parameters(), lr=lr)
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loss_fn = CrossEntropyLoss()
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# Training
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console.print("🚀 Training transformer to reverse sequences...\n")
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history = {
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'train_loss': [],
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'train_acc': [],
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'test_acc': []
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}
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with Progress(
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SpinnerColumn(),
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"[progress.description]{task.description}",
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BarColumn(),
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"[progress.percentage]{task.percentage:>3.0f}%",
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"•",
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TimeElapsedColumn(),
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console=console
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) as progress:
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task = progress.add_task("[cyan]Training...", total=epochs)
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for epoch in range(epochs):
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# Train
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train_loss, train_acc = train_epoch(model, train_data, optimizer, loss_fn)
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# Evaluate
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test_acc, _ = evaluate(model, test_data)
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# Record history
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history['train_loss'].append(train_loss)
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history['train_acc'].append(train_acc)
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history['test_acc'].append(test_acc)
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# Update progress
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progress.update(task, advance=1)
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# Print every 5 epochs
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if (epoch + 1) % 5 == 0:
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console.print(
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f" Epoch [cyan]{epoch+1:2d}[/cyan]: "
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f"Loss = [yellow]{train_loss:.4f}[/yellow], "
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f"Train Acc = [green]{train_acc:.1f}%[/green], "
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f"Test Acc = [green]{test_acc:.1f}%[/green]"
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)
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console.print()
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# Final evaluation
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final_acc, predictions = evaluate(model, test_data)
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# Results
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console.print("="*70)
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console.print(Panel.fit(
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"[bold]Training Complete![/bold]",
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border_style="green"
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))
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console.print("="*70)
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console.print()
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# Show final accuracy
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table = Table(title="📊 Final Results", box=box.ROUNDED, show_header=True)
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table.add_column("Metric", style="cyan", justify="left")
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table.add_column("Value", style="green", justify="right")
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table.add_column("Status", style="yellow", justify="center")
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table.add_row(
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"Test Accuracy",
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f"{final_acc:.1f}%",
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"✅ EXCELLENT" if final_acc >= 95 else "⚠️ LEARNING" if final_acc >= 80 else "❌ NEEDS WORK"
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)
|
||||
table.add_row(
|
||||
"Training Loss",
|
||||
f"{history['train_loss'][-1]:.4f}",
|
||||
"✅" if history['train_loss'][-1] < 0.5 else "⚠️"
|
||||
)
|
||||
|
||||
console.print(table)
|
||||
console.print()
|
||||
|
||||
# Show sample predictions
|
||||
console.print(Panel("[bold]Sample Predictions[/bold]", border_style="blue"))
|
||||
console.print()
|
||||
|
||||
for i, (inp, target, pred) in enumerate(predictions[:8]):
|
||||
match = "✓" if np.array_equal(pred, target) else "✗"
|
||||
style = "green" if np.array_equal(pred, target) else "red"
|
||||
|
||||
console.print(f" [{style}]{match}[/{style}] Input: {inp.tolist()}")
|
||||
console.print(f" Target: {target.tolist()}")
|
||||
console.print(f" Pred: {pred.tolist()}\n")
|
||||
|
||||
# Verdict
|
||||
console.print("="*70)
|
||||
if final_acc >= 95:
|
||||
console.print(Panel.fit(
|
||||
"[bold green]🎉 SUCCESS! Your attention mechanism is working![/bold green]\n\n"
|
||||
"Your transformer learned to reverse sequences, which proves:\n"
|
||||
" ✅ Query·Key·Value computation is correct\n"
|
||||
" ✅ Attention weights are being computed properly\n"
|
||||
" ✅ Multi-head attention aggregates correctly\n"
|
||||
" ✅ Positional encoding preserves position information\n\n"
|
||||
"[bold]You're ready for complex tasks like Q&A and generation![/bold]",
|
||||
border_style="green",
|
||||
title="⭐ Attention Proof Complete"
|
||||
))
|
||||
elif final_acc >= 80:
|
||||
console.print(Panel.fit(
|
||||
"[bold yellow]⚠️ Learning in Progress[/bold yellow]\n\n"
|
||||
"The model is learning but hasn't converged yet.\n"
|
||||
"Try:\n"
|
||||
" • More epochs (40-50)\n"
|
||||
" • Lower learning rate (0.001-0.003)\n"
|
||||
" • More attention heads (6-8)",
|
||||
border_style="yellow",
|
||||
title="💡 Keep Training"
|
||||
))
|
||||
else:
|
||||
console.print(Panel.fit(
|
||||
"[bold red]❌ Attention Needs Debugging[/bold red]\n\n"
|
||||
"The model isn't learning to reverse sequences.\n"
|
||||
"Check:\n"
|
||||
" • MultiHeadAttention implementation\n"
|
||||
" • Positional encoding is being added\n"
|
||||
" • Gradients are flowing (run gradient flow test)\n"
|
||||
" • Residual connections preserve gradients",
|
||||
border_style="red",
|
||||
title="🔧 Debug Required"
|
||||
))
|
||||
console.print("="*70)
|
||||
console.print()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
||||
@@ -37,10 +37,28 @@ Character-level transformer models for text generation:
|
||||
|
||||
## Milestone Structure
|
||||
|
||||
This milestone uses **progressive generation tasks** with 2 scripts:
|
||||
This milestone uses **progressive difficulty** with 3 scripts:
|
||||
|
||||
### ⭐ 00_vaswani_attention_proof.py (START HERE!)
|
||||
**Purpose:** PROVE your attention mechanism works
|
||||
|
||||
- **Dataset:** Auto-generated sequences (no files needed!)
|
||||
- **Task:** Reverse sequences `[1,2,3,4] → [4,3,2,1]`
|
||||
- **From Paper:** "Attention is All You Need" validation task
|
||||
- **Training Time:** ~30 seconds
|
||||
- **Expected:** 95%+ accuracy
|
||||
- **Key Learning:** "My attention is computing relationships!"
|
||||
|
||||
**Why This Is THE Test:**
|
||||
- IMPOSSIBLE without attention working
|
||||
- Trains in 30 seconds (instant gratification!)
|
||||
- Binary pass/fail (95%+ or broken)
|
||||
- Proves Q·K·V computation works
|
||||
|
||||
**🎯 Run this FIRST to verify your attention before complex tasks!**
|
||||
|
||||
### 01_vaswani_generation.py
|
||||
**Purpose:** Prove transformers work on Q&A
|
||||
**Purpose:** Apply attention to real language (Q&A)
|
||||
|
||||
- **Dataset:** TinyTalks (17.5 KB, 5 difficulty levels)
|
||||
- **Task:** Learn to answer questions (Q: ... A: ...)
|
||||
|
||||
Reference in New Issue
Block a user