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🎯 NORTH STAR VISION DOCUMENTED: 'Don't Just Import It, Build It' - Training AI Engineers, not just ML users AI Engineering emerges as a foundational discipline like Computer Engineering, bridging algorithms and systems to build the AI infrastructure of the future. 🧪 ROBUST TESTING FRAMEWORK ESTABLISHED: - Created tests/regression/ for sandbox integrity tests - Implemented test-driven bug prevention workflow - Clear separation: student tests (pedagogical) vs system tests (robustness) - Every bug becomes a test to prevent recurrence ✅ KEY IMPLEMENTATIONS: - NORTH_STAR.md: Vision for AI Engineering discipline - Testing best practices: Focus on robust student sandbox - Git workflow standards: Professional development practices - Regression test suite: Prevent infrastructure issues - Conv->Linear dimension tests (found CNN bug) - Transformer reshaping tests (found GPT bug) 🏗️ SANDBOX INTEGRITY: Students need a solid, predictable environment where they focus on ML concepts, not debugging framework issues. The framework must be invisible. 📚 EDUCATIONAL PHILOSOPHY: TinyTorch isn't just teaching a framework - it's founding the AI Engineering discipline by training engineers who understand how to BUILD ML systems. This establishes the foundation for training the first generation of true AI Engineers who will define this emerging discipline.
380 lines
13 KiB
Python
380 lines
13 KiB
Python
#!/usr/bin/env python3
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"""
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TinyGPT Integration Test - After Module 14
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==========================================
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This test validates that modules 1-14 work together for transformer language models.
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Required modules:
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- Module 01-08: Core MLP and training functionality
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- Module 11: Tokenization for text processing
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- Module 12: Embeddings (token + positional)
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- Module 13: Multi-head self-attention
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- Module 14: Transformer blocks and layer normalization
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This demonstrates the milestone: "Can build transformer language models"
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"""
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import sys
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import os
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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import numpy as np
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from tinytorch.core.tensor import Tensor
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from tinytorch.core.layers import Dense
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from tinytorch.core.activations import ReLU
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# Try to import transformer components
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try:
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from tinytorch.core.embeddings import Embedding, PositionalEncoding
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EMBEDDINGS_AVAILABLE = True
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except ImportError:
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EMBEDDINGS_AVAILABLE = False
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try:
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from tinytorch.core.attention import MultiHeadAttention
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ATTENTION_AVAILABLE = True
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except ImportError:
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ATTENTION_AVAILABLE = False
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try:
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from tinytorch.core.transformers import LayerNorm, TransformerBlock
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TRANSFORMERS_AVAILABLE = True
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except ImportError:
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TRANSFORMERS_AVAILABLE = False
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class SimpleTinyGPT:
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"""Simple GPT-style transformer for language modeling."""
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def __init__(self, vocab_size=1000, embed_dim=128, max_length=50, num_heads=8, num_layers=2):
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self.vocab_size = vocab_size
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self.embed_dim = embed_dim
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self.max_length = max_length
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self.num_heads = num_heads
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# Token representation
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if EMBEDDINGS_AVAILABLE:
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self.embedding = Embedding(vocab_size, embed_dim)
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self.pos_encoding = PositionalEncoding(embed_dim, max_length)
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else:
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# Fallback: simple linear embedding
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self.embedding = Dense(vocab_size, embed_dim)
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# Transformer layers
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if TRANSFORMERS_AVAILABLE and ATTENTION_AVAILABLE:
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self.layers = []
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hidden_dim = embed_dim * 4
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for _ in range(num_layers):
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block = TransformerBlock(embed_dim, num_heads, hidden_dim)
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self.layers.append(block)
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# Output
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self.layer_norm = LayerNorm(embed_dim)
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else:
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# Fallback: simple feedforward layers
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self.layers = [
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Dense(embed_dim, embed_dim * 2),
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ReLU(),
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Dense(embed_dim * 2, embed_dim)
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]
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# Output projection
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self.output_proj = Dense(embed_dim, vocab_size)
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def forward(self, x):
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"""Forward pass."""
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# Convert tokens to embeddings
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if EMBEDDINGS_AVAILABLE:
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x = self.embedding(x)
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x = self.pos_encoding(x)
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else:
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# Fallback: convert token indices to one-hot, then embed
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batch_size, seq_len = x.shape
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one_hot = np.zeros((batch_size, seq_len, self.vocab_size))
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for b in range(batch_size):
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for s in range(seq_len):
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token_id = int(x.data[b, s])
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if 0 <= token_id < self.vocab_size:
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one_hot[b, s, token_id] = 1.0
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x = Tensor(one_hot)
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# Apply embedding to each position
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embedded = []
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for s in range(seq_len):
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pos_embed = self.embedding(x[:, s, :]) # (batch, embed_dim)
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embedded.append(pos_embed)
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# Stack to get (batch, seq_len, embed_dim)
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x = Tensor(np.stack([emb.data for emb in embedded], axis=1))
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# Process through transformer layers
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if TRANSFORMERS_AVAILABLE and ATTENTION_AVAILABLE:
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for layer in self.layers:
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x = layer(x)
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x = self.layer_norm(x)
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else:
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# Fallback: process each position through feedforward
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batch_size, seq_len, embed_dim = x.shape
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processed = []
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for s in range(seq_len):
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pos_data = x[:, s, :] # (batch, embed_dim)
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# Apply simple feedforward
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h = self.layers[0](pos_data) # Dense layer
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h = self.layers[1](h) # ReLU
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h = self.layers[2](h) # Dense layer
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processed.append(h.data)
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x = Tensor(np.stack(processed, axis=1))
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# Output projection
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batch_size, seq_len, embed_dim = x.shape
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outputs = []
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for s in range(seq_len):
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pos_output = self.output_proj(x[:, s, :])
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outputs.append(pos_output.data)
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return Tensor(np.stack(outputs, axis=1))
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def __call__(self, x):
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return self.forward(x)
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def test_transformer_components():
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"""Test individual transformer components."""
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print("🧩 Testing Transformer Components...")
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# Test embeddings
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if EMBEDDINGS_AVAILABLE:
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print(" ✓ Testing Embedding layer")
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embed = Embedding(vocab_size=100, embed_dim=32)
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tokens = Tensor(np.array([[1, 2, 3], [4, 5, 6]])) # (batch=2, seq_len=3)
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embedded = embed(tokens)
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assert embedded.shape == (2, 3, 32), f"Expected (2, 3, 32), got {embedded.shape}"
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print(f" Embedding: {tokens.shape} -> {embedded.shape}")
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print(" ✓ Testing Positional Encoding")
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pos_enc = PositionalEncoding(embed_dim=32, max_length=10)
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pos_embedded = pos_enc(embedded)
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assert pos_embedded.shape == embedded.shape, "Positional encoding should preserve shape"
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print(f" Pos encoding: {embedded.shape} -> {pos_embedded.shape}")
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else:
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print(" ⚠️ Embeddings not available - using fallback")
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# Test attention
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if ATTENTION_AVAILABLE:
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print(" ✓ Testing Multi-Head Attention")
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attn = MultiHeadAttention(embed_dim=32, num_heads=4)
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x = Tensor(np.random.randn(2, 5, 32)) # (batch, seq_len, embed_dim)
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attn_out = attn(x)
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assert attn_out.shape == x.shape, f"Attention should preserve shape: {x.shape} -> {attn_out.shape}"
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print(f" Attention: {x.shape} -> {attn_out.shape}")
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else:
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print(" ⚠️ Attention not available - using fallback")
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# Test transformer blocks
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if TRANSFORMERS_AVAILABLE and ATTENTION_AVAILABLE:
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print(" ✓ Testing Transformer Block")
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block = TransformerBlock(embed_dim=32, num_heads=4, hidden_dim=128)
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x = Tensor(np.random.randn(2, 5, 32))
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block_out = block(x)
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assert block_out.shape == x.shape, f"Transformer block should preserve shape"
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print(f" Transformer block: {x.shape} -> {block_out.shape}")
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print(" ✓ Testing Layer Normalization")
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ln = LayerNorm(embed_dim=32)
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ln_out = ln(x)
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assert ln_out.shape == x.shape, "LayerNorm should preserve shape"
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print(f" LayerNorm: {x.shape} -> {ln_out.shape}")
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else:
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print(" ⚠️ Transformer blocks not available - using fallback")
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print("✅ Transformer components tested!")
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return True
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def test_tinygpt_architecture():
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"""Test TinyGPT architecture."""
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print("🤖 Testing TinyGPT Architecture...")
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try:
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# Create small TinyGPT
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model = SimpleTinyGPT(
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vocab_size=100,
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embed_dim=64,
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max_length=10,
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num_heads=4,
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num_layers=2
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)
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# Test input: batch of token sequences
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batch_size, seq_len = 2, 8
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tokens = Tensor(np.random.randint(0, 100, (batch_size, seq_len)))
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print(f" ✓ Created TinyGPT model")
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print(f" Input tokens shape: {tokens.shape}")
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print(f" Vocab size: 100, Embed dim: 64")
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# Forward pass
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outputs = model(tokens)
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print(f" ✓ Forward pass successful")
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print(f" Output shape: {outputs.shape}")
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expected_shape = (batch_size, seq_len, 100) # (batch, seq_len, vocab_size)
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assert outputs.shape == expected_shape, f"Expected {expected_shape}, got {outputs.shape}"
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print("✅ TinyGPT architecture working!")
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return True
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except Exception as e:
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print(f"❌ TinyGPT architecture test 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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def test_language_modeling():
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"""Test language modeling capability."""
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print("📝 Testing Language Modeling...")
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try:
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# Create very small model for quick test
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model = SimpleTinyGPT(
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vocab_size=20,
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embed_dim=16,
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max_length=5,
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num_heads=2,
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num_layers=1
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)
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# Create simple sequence
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tokens = Tensor(np.array([[1, 2, 3, 4]])) # Single sequence
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print(f" ✓ Created small model for language modeling")
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print(f" Input sequence: {tokens.shape}")
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# Get predictions
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logits = model(tokens)
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print(f" ✓ Generated predictions")
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print(f" Logits shape: {logits.shape}")
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print(f" Each position predicts next token from vocab of size 20")
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# Check logits are reasonable
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assert logits.shape == (1, 4, 20), f"Expected (1, 4, 20), got {logits.shape}"
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# Test that different positions give different predictions (model is learning positional info)
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pos0_logits = logits.data[0, 0, :] # First position
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pos1_logits = logits.data[0, 1, :] # Second position
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# They should be different (not identical)
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diff = np.sum(np.abs(pos0_logits - pos1_logits))
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if diff > 0.001:
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print(f" ✓ Different positions give different predictions (diff: {diff:.4f})")
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else:
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print(f" ⚠️ Positions give similar predictions (diff: {diff:.4f})")
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print("✅ Language modeling capability tested!")
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return True
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except Exception as e:
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print(f"❌ Language modeling test 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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def test_text_generation_potential():
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"""Test potential for text generation."""
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print("✍️ Testing Text Generation Potential...")
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try:
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model = SimpleTinyGPT(vocab_size=10, embed_dim=8, max_length=3, num_heads=2, num_layers=1)
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# Start with a single token
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start_token = Tensor(np.array([[5]])) # Start with token 5
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print(f" ✓ Testing autoregressive generation")
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print(f" Start token: {start_token.data}")
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# Generate next token prediction
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logits = model(start_token)
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print(f" ✓ Generated logits shape: {logits.shape}")
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# Get most likely next token
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next_token_logits = logits.data[0, 0, :] # First (and only) position
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next_token = np.argmax(next_token_logits)
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print(f" ✓ Predicted next token: {next_token}")
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print(f" (In real generation, this would be added to sequence)")
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# Test with longer sequence
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longer_seq = Tensor(np.array([[5, int(next_token)]]))
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longer_logits = model(longer_seq)
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print(f" ✓ Processed longer sequence: {longer_seq.shape} -> {longer_logits.shape}")
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print("✅ Text generation potential demonstrated!")
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return True
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except Exception as e:
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print(f"❌ Text generation test 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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def run_tinygpt_integration_test():
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"""Run complete TinyGPT integration test."""
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print("=" * 60)
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print("🔥 TINYGPT INTEGRATION TEST - Modules 1-14")
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print("=" * 60)
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print()
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# Component availability summary
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components = [
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("Embeddings", EMBEDDINGS_AVAILABLE),
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("Attention", ATTENTION_AVAILABLE),
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("Transformers", TRANSFORMERS_AVAILABLE)
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]
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print("📋 Component Availability:")
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for name, available in components:
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status = "✅ Available" if available else "⚠️ Using fallback"
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print(f" {name}: {status}")
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print()
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success = True
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tests = [
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test_transformer_components,
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test_tinygpt_architecture,
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test_language_modeling,
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test_text_generation_potential
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]
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for test in tests:
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try:
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if not test():
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success = False
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print()
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except Exception as e:
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print(f"❌ Test failed with error: {e}")
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import traceback
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traceback.print_exc()
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success = False
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print()
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if success:
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print("🎉 TINYGPT INTEGRATION TEST PASSED!")
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print()
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print("✅ Milestone Achieved: Can build transformer language models")
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print(" • Transformer architecture handles sequential data")
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print(" • Language modeling predictions generated")
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print(" • Text generation potential demonstrated")
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print(" • End-to-end NLP pipeline functional")
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print()
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print("🏆 CONGRATULATIONS: All core ML capabilities working!")
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else:
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print("❌ TINYGPT INTEGRATION TEST FAILED!")
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print(" Check transformer modules before proceeding")
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print("=" * 60)
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return success
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if __name__ == "__main__":
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run_tinygpt_integration_test() |