mirror of
https://github.com/MLSysBook/TinyTorch.git
synced 2026-07-24 14:04:34 -05:00
Vijay Janapa Reddi
8be87d0add
PROBLEM: - nbdev requires #| export directive on EACH cell to export when using # %% markers - Cell markers inside class definitions split classes across multiple cells - Only partial classes were being exported to tinytorch package - Missing matmul, arithmetic operations, and activation classes in exports SOLUTION: 1. Removed # %% cell markers INSIDE class definitions (kept classes as single units) 2. Added #| export to imports cell at top of each module 3. Added #| export before each exportable class definition in all 20 modules 4. Added __call__ method to Sigmoid for functional usage 5. Fixed numpy import (moved to module level from __init__) MODULES FIXED: - 01_tensor: Tensor class with all operations (matmul, arithmetic, shape ops) - 02_activations: Sigmoid, ReLU, Tanh, GELU, Softmax classes - 03_layers: Linear, Dropout classes - 04_losses: MSELoss, CrossEntropyLoss, BinaryCrossEntropyLoss classes - 05_autograd: Function, AddBackward, MulBackward, MatmulBackward, SumBackward - 06_optimizers: Optimizer, SGD, Adam, AdamW classes - 07_training: CosineSchedule, Trainer classes - 08_dataloader: Dataset, TensorDataset, DataLoader classes - 09_spatial: Conv2d, MaxPool2d, AvgPool2d, SimpleCNN classes - 10-20: All exportable classes in remaining modules TESTING: - Test functions use 'if __name__ == "__main__"' guards - Tests run in notebooks but NOT on import - Rosenblatt Perceptron milestone working perfectly RESULT: ✅ All 20 modules export correctly ✅ Perceptron (1957) milestone functional ✅ Clean separation: development (modules/source) vs package (tinytorch)
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In [ ]:
#| default_exp applications.tinygpt
#| exportIn [ ]:
import numpy as np
import time
import json
from pathlib import Path
from typing import Dict, List, Tuple, Optional, Any
import matplotlib.pyplot as plt
# Import all TinyTorch modules (representing 19 modules of work!)
### BEGIN SOLUTION
# Module 01: Tensor foundation
from tinytorch.core.tensor import Tensor
# Module 02: Activations
from tinytorch.core.activations import ReLU, GELU, Sigmoid
# Module 03: Layers
from tinytorch.core.layers import Linear, Sequential, Dropout
# Module 04: Losses
from tinytorch.core.losses import CrossEntropyLoss
# Module 05: Autograd (enhances Tensor)
from tinytorch.core.autograd import Function
# Module 06: Optimizers
from tinytorch.core.optimizers import AdamW, SGD
# Module 07: Training
from tinytorch.core.training import Trainer, CosineSchedule
# Module 08: DataLoader
from tinytorch.data.loader import DataLoader, TensorDataset
# Module 09: Spatial (for potential CNN comparisons)
from tinytorch.core.spatial import Conv2d, MaxPool2d
# Module 10: Tokenization
from tinytorch.text.tokenization import CharTokenizer
# Module 11: Embeddings
from tinytorch.text.embeddings import Embedding, PositionalEncoding
# Module 12: Attention
from tinytorch.core.attention import MultiHeadAttention, scaled_dot_product_attention
# Module 13: Transformers
from tinytorch.models.transformer import GPT, TransformerBlock
# Module 14: KV Caching
from tinytorch.generation.kv_cache import KVCache
# Module 15: Profiling
from tinytorch.profiling.profiler import Profiler
# Module 16: Acceleration
from tinytorch.optimization.acceleration import MixedPrecisionTrainer
# Module 17: Quantization
from tinytorch.optimization.quantization import quantize_model, QuantizedLinear
# Module 18: Compression
from tinytorch.optimization.compression import magnitude_prune, structured_prune
# Module 19: Benchmarking
from tinytorch.benchmarking.benchmark import Benchmark
### END SOLUTION
print("🎉 Successfully imported all 19 TinyTorch modules!")
print("📦 Framework Status: COMPLETE")In [ ]:
class TinyGPT:
"""
Complete GPT implementation integrating all TinyTorch modules.
This class demonstrates how framework components compose into real applications.
Built using modules 01,02,03,11,12,13 as core architecture.
Architecture:
- Token Embeddings (Module 11)
- Positional Encoding (Module 11)
- Transformer Blocks (Module 13)
- Output Linear Layer (Module 03)
- Language Modeling Head (Module 04)
"""
def __init__(self, vocab_size: int, embed_dim: int = 128, num_layers: int = 4,
num_heads: int = 4, max_seq_len: int = 256, dropout: float = 0.1):
"""
Initialize TinyGPT with production-inspired architecture.
TODO: Build a complete GPT model using TinyTorch components
APPROACH:
1. Create token embeddings (vocab_size × embed_dim)
2. Create positional encoding (max_seq_len × embed_dim)
3. Build transformer layers using TransformerBlock
4. Add output projection layer
5. Calculate and report parameter count
ARCHITECTURE DECISIONS:
- embed_dim=128: Small enough for fast training, large enough for learning
- num_layers=4: Sufficient depth without excessive memory
- num_heads=4: Multi-head attention without head_dim being too small
- max_seq_len=256: Reasonable context length for character-level modeling
EXAMPLE:
>>> model = TinyGPT(vocab_size=50, embed_dim=128, num_layers=4)
>>> print(f"Parameters: {model.count_parameters():,}")
Parameters: 1,234,567
HINTS:
- Use Embedding class for token embeddings
- Use PositionalEncoding for position information
- Stack TransformerBlock instances in a list
- Final Linear layer maps embed_dim → vocab_size
"""
### BEGIN SOLUTION
self.vocab_size = vocab_size
self.embed_dim = embed_dim
self.num_layers = num_layers
self.num_heads = num_heads
self.max_seq_len = max_seq_len
self.dropout = dropout
# Token embeddings: convert token IDs to dense vectors
self.token_embedding = Embedding(vocab_size, embed_dim)
# Positional encoding: add position information
self.positional_encoding = PositionalEncoding(max_seq_len, embed_dim)
# Transformer layers: core processing
self.transformer_blocks = []
for _ in range(num_layers):
block = TransformerBlock(embed_dim, num_heads, mlp_ratio=4.0)
self.transformer_blocks.append(block)
# Output projection: map back to vocabulary
self.output_projection = Linear(embed_dim, vocab_size)
# Dropout for regularization
self.dropout_layer = Dropout(dropout)
# Calculate parameter count for systems analysis
self._param_count = self.count_parameters()
print(f"🏗️ TinyGPT initialized: {self._param_count:,} parameters")
print(f"📐 Architecture: {num_layers}L/{num_heads}H/{embed_dim}D")
print(f"💾 Estimated memory: {self._param_count * 4 / 1024 / 1024:.1f}MB")
### END SOLUTION
def test_unit_tinygpt_init():
"""🔬 Test TinyGPT initialization and parameter counting."""
print("🔬 Unit Test: TinyGPT Initialization...")
# Create a small model for testing
model = TinyGPT(vocab_size=50, embed_dim=64, num_layers=2, num_heads=2, max_seq_len=128)
# Verify architecture components exist
assert hasattr(model, 'token_embedding')
assert hasattr(model, 'positional_encoding')
assert hasattr(model, 'transformer_blocks')
assert hasattr(model, 'output_projection')
assert len(model.transformer_blocks) == 2
# Verify parameter count is reasonable
param_count = model.count_parameters()
assert param_count > 0
assert param_count < 1000000 # Sanity check for small model
print(f"✅ Model created with {param_count:,} parameters")
print("✅ TinyGPT initialization works correctly!")
# Run immediate test
test_unit_tinygpt_init()In [ ]:
def count_parameters(self) -> int:
"""
Count total trainable parameters in the model.
TODO: Implement parameter counting across all components
APPROACH:
1. Get parameters from token embeddings
2. Get parameters from all transformer blocks
3. Get parameters from output projection
4. Sum all parameter counts
5. Return total count
SYSTEMS INSIGHT:
Parameter count directly determines:
- Model memory footprint (params × 4 bytes for float32)
- Training memory (3× params for gradients + optimizer states)
- Inference latency (more params = more compute)
EXAMPLE:
>>> model = TinyGPT(vocab_size=1000, embed_dim=128, num_layers=6)
>>> params = model.count_parameters()
>>> print(f"Memory: {params * 4 / 1024 / 1024:.1f}MB")
Memory: 52.3MB
HINT: Each component has a parameters() method that returns a list
"""
### BEGIN SOLUTION
total_params = 0
# Count embedding parameters
for param in self.token_embedding.parameters():
total_params += np.prod(param.shape)
# Count transformer block parameters
for block in self.transformer_blocks:
for param in block.parameters():
total_params += np.prod(param.shape)
# Count output projection parameters
for param in self.output_projection.parameters():
total_params += np.prod(param.shape)
return total_params
### END SOLUTION
def forward(self, input_ids: Tensor, return_logits: bool = True) -> Tensor:
"""
Forward pass through the complete TinyGPT model.
TODO: Implement full forward pass integrating all components
APPROACH:
1. Apply token embeddings to convert IDs to vectors
2. Add positional encoding for sequence position information
3. Apply dropout for regularization
4. Pass through each transformer block sequentially
5. Apply final output projection to get logits
ARCHITECTURE FLOW:
input_ids → embeddings → +positional → dropout → transformer_layers → output_proj → logits
EXAMPLE:
>>> model = TinyGPT(vocab_size=100, embed_dim=64)
>>> input_ids = Tensor([[1, 15, 42, 7]]) # Shape: (batch=1, seq_len=4)
>>> logits = model.forward(input_ids)
>>> print(logits.shape)
(1, 4, 100) # (batch, seq_len, vocab_size)
HINTS:
- embeddings + positional should be element-wise addition
- Each transformer block takes and returns same shape
- Final logits shape: (batch_size, seq_len, vocab_size)
"""
### BEGIN SOLUTION
batch_size, seq_len = input_ids.shape
# Step 1: Token embeddings
embeddings = self.token_embedding.forward(input_ids) # (batch, seq_len, embed_dim)
# Step 2: Add positional encoding
positions = self.positional_encoding.forward(embeddings) # Same shape
hidden_states = embeddings + positions
# Step 3: Apply dropout
hidden_states = self.dropout_layer.forward(hidden_states, training=True)
# Step 4: Pass through transformer blocks
for block in self.transformer_blocks:
hidden_states = block.forward(hidden_states)
# Step 5: Output projection to vocabulary
if return_logits:
logits = self.output_projection.forward(hidden_states)
return logits # (batch, seq_len, vocab_size)
else:
return hidden_states # Return final hidden states
### END SOLUTION
def generate(self, prompt_ids: Tensor, max_new_tokens: int = 50,
temperature: float = 1.0, use_cache: bool = True) -> Tensor:
"""
Generate text using autoregressive sampling.
TODO: Implement text generation with KV caching optimization
APPROACH:
1. Initialize KV cache if enabled
2. For each new token position:
a. Get logits for next token
b. Apply temperature scaling
c. Sample from probability distribution
d. Append to sequence
3. Return complete generated sequence
SYSTEMS OPTIMIZATION:
- Without cache: O(n²) complexity (recompute all positions)
- With cache: O(n) complexity (only compute new position)
- Cache memory: O(layers × heads × seq_len × head_dim)
EXAMPLE:
>>> model = TinyGPT(vocab_size=100)
>>> prompt = Tensor([[1, 5, 10]]) # "Hello"
>>> output = model.generate(prompt, max_new_tokens=10)
>>> print(output.shape)
(1, 13) # Original 3 + 10 new tokens
HINTS:
- Use KVCache from Module 14 for efficiency
- Apply softmax with temperature for sampling
- Build sequence iteratively, one token at a time
"""
### BEGIN SOLUTION
batch_size, current_seq_len = prompt_ids.shape
if use_cache and current_seq_len + max_new_tokens <= self.max_seq_len:
# Initialize KV cache for efficient generation
cache = KVCache(
batch_size=batch_size,
max_seq_len=self.max_seq_len,
num_layers=self.num_layers,
num_heads=self.num_heads,
head_dim=self.embed_dim // self.num_heads
)
else:
cache = None
# Start with the prompt
generated_ids = prompt_ids
for step in range(max_new_tokens):
# Get logits for next token prediction
if cache is not None:
# Efficient: only process the last token
current_input = generated_ids[:, -1:] if step > 0 else generated_ids
logits = self.forward_with_cache(current_input, cache, step)
else:
# Standard: process entire sequence each time
logits = self.forward(generated_ids)
# Get logits for the last position (next token prediction)
next_token_logits = logits[:, -1, :] # (batch_size, vocab_size)
# Apply temperature scaling
if temperature != 1.0:
next_token_logits = next_token_logits / temperature
# Sample next token (simple greedy for now)
next_token_id = Tensor(np.argmax(next_token_logits.data, axis=-1, keepdims=True))
# Append to sequence
generated_ids = Tensor(np.concatenate([generated_ids.data, next_token_id.data], axis=1))
# Stop if we hit max sequence length
if generated_ids.shape[1] >= self.max_seq_len:
break
return generated_ids
### END SOLUTION
# Add methods to TinyGPT class
TinyGPT.count_parameters = count_parameters
TinyGPT.forward = forward
TinyGPT.generate = generate
def test_unit_tinygpt_forward():
"""🔬 Test TinyGPT forward pass and generation."""
print("🔬 Unit Test: TinyGPT Forward Pass...")
# Create model and test data
model = TinyGPT(vocab_size=100, embed_dim=64, num_layers=2, num_heads=2)
input_ids = Tensor([[1, 15, 42, 7, 23]]) # Batch size 1, sequence length 5
# Test forward pass
logits = model.forward(input_ids)
# Verify output shape
expected_shape = (1, 5, 100) # (batch, seq_len, vocab_size)
assert logits.shape == expected_shape, f"Expected {expected_shape}, got {logits.shape}"
# Test generation
prompt = Tensor([[1, 15]])
generated = model.generate(prompt, max_new_tokens=5)
# Verify generation extends sequence
assert generated.shape[1] == 7, f"Expected 7 tokens, got {generated.shape[1]}"
assert np.array_equal(generated.data[:, :2], prompt.data), "Prompt should be preserved"
print(f"✅ Forward pass shape: {logits.shape}")
print(f"✅ Generation shape: {generated.shape}")
print("✅ TinyGPT forward and generation work correctly!")
# Run immediate test
test_unit_tinygpt_forward()In [ ]:
class TinyGPTTrainer:
"""
Complete training pipeline integrating optimizers, schedulers, and monitoring.
Uses modules 05 (autograd), 06 (optimizers), 07 (training) for end-to-end training.
"""
def __init__(self, model: TinyGPT, tokenizer: CharTokenizer,
learning_rate: float = 3e-4, weight_decay: float = 0.01):
"""
Initialize trainer with model and optimization components.
TODO: Set up complete training infrastructure
APPROACH:
1. Store model and tokenizer references
2. Initialize AdamW optimizer (standard for transformers)
3. Initialize loss function (CrossEntropyLoss for language modeling)
4. Set up learning rate scheduler (cosine schedule)
5. Initialize training metrics tracking
PRODUCTION CHOICES:
- AdamW: Better generalization than Adam (weight decay)
- learning_rate=3e-4: Standard for small transformers
- Cosine schedule: Smooth learning rate decay
- CrossEntropy: Standard for classification/language modeling
EXAMPLE:
>>> model = TinyGPT(vocab_size=100)
>>> tokenizer = CharTokenizer(['a', 'b', 'c'])
>>> trainer = TinyGPTTrainer(model, tokenizer)
>>> print("Trainer ready for training")
Trainer ready for training
HINTS:
- Get all model parameters with model.parameters()
- Use AdamW with weight_decay for better generalization
- CrossEntropyLoss handles the language modeling objective
"""
### BEGIN SOLUTION
self.model = model
self.tokenizer = tokenizer
# Collect all trainable parameters
all_params = []
all_params.extend(model.token_embedding.parameters())
for block in model.transformer_blocks:
all_params.extend(block.parameters())
all_params.extend(model.output_projection.parameters())
# Initialize optimizer (AdamW for transformers)
self.optimizer = AdamW(
params=all_params,
lr=learning_rate,
weight_decay=weight_decay,
betas=(0.9, 0.95) # Standard for language models
)
# Loss function for next token prediction
self.loss_fn = CrossEntropyLoss()
# Learning rate scheduler
self.scheduler = CosineSchedule(
optimizer=self.optimizer,
max_epochs=100, # Will adjust based on actual training
min_lr=learning_rate * 0.1
)
# Training metrics
self.training_history = {
'losses': [],
'perplexities': [],
'learning_rates': [],
'epoch': 0
}
print(f"🚀 Trainer initialized:")
print(f" Optimizer: AdamW (lr={learning_rate}, wd={weight_decay})")
print(f" Parameters: {len(all_params):,} tensors")
print(f" Loss: CrossEntropyLoss")
### END SOLUTION
def prepare_batch(self, text_batch: List[str], max_length: int = 128) -> Tuple[Tensor, Tensor]:
"""
Convert text batch to input/target tensors for language modeling.
TODO: Implement text-to-tensor conversion with proper targets
APPROACH:
1. Tokenize each text in the batch
2. Pad/truncate to consistent length
3. Create input_ids (text) and target_ids (text shifted by 1)
4. Convert to Tensor format
LANGUAGE MODELING OBJECTIVE:
- Input: [token1, token2, token3, token4]
- Target: [token2, token3, token4, token5]
- Model predicts next token at each position
EXAMPLE:
>>> trainer = TinyGPTTrainer(model, tokenizer)
>>> texts = ["hello world", "ai is fun"]
>>> inputs, targets = trainer.prepare_batch(texts)
>>> print(inputs.shape, targets.shape)
(2, 128) (2, 128)
HINTS:
- Use tokenizer.encode() for text → token conversion
- Pad shorter sequences with tokenizer pad token
- Target sequence is input sequence shifted right by 1
"""
### BEGIN SOLUTION
batch_size = len(text_batch)
# Tokenize all texts
tokenized_batch = []
for text in text_batch:
tokens = self.tokenizer.encode(text)
# Truncate or pad to max_length
if len(tokens) > max_length:
tokens = tokens[:max_length]
else:
# Pad with special token (use 0 as pad)
tokens.extend([0] * (max_length - len(tokens)))
tokenized_batch.append(tokens)
# Convert to numpy then Tensor
input_ids = Tensor(np.array(tokenized_batch)) # (batch_size, seq_len)
# Create targets (shifted input for next token prediction)
target_ids = Tensor(np.roll(input_ids.data, -1, axis=1)) # Shift left by 1
return input_ids, target_ids
### END SOLUTION
def train_step(self, input_ids: Tensor, target_ids: Tensor) -> float:
"""
Single training step with forward, backward, and optimization.
TODO: Implement complete training step
APPROACH:
1. Zero gradients from previous step
2. Forward pass to get logits
3. Compute loss between logits and targets
4. Backward pass to compute gradients
5. Optimizer step to update parameters
6. Return loss value for monitoring
MEMORY MANAGEMENT:
During training, memory usage = 3× model size:
- 1× for parameters
- 1× for gradients
- 1× for optimizer states (Adam moments)
EXAMPLE:
>>> loss = trainer.train_step(input_ids, target_ids)
>>> print(f"Training loss: {loss:.4f}")
Training loss: 2.3456
HINTS:
- Always zero_grad() before forward pass
- Loss should be computed on flattened logits and targets
- Call backward() on the loss tensor
"""
### BEGIN SOLUTION
# Zero gradients from previous step
self.optimizer.zero_grad()
# Forward pass
logits = self.model.forward(input_ids) # (batch, seq_len, vocab_size)
# Reshape for loss computation
batch_size, seq_len, vocab_size = logits.shape
logits_flat = logits.reshape(batch_size * seq_len, vocab_size)
targets_flat = target_ids.reshape(batch_size * seq_len)
# Compute loss
loss = self.loss_fn.forward(logits_flat, targets_flat)
# Backward pass
loss.backward()
# Optimizer step
self.optimizer.step()
# Return scalar loss for monitoring
return float(loss.data.item() if hasattr(loss.data, 'item') else loss.data)
### END SOLUTION
def test_unit_training_pipeline():
"""🔬 Test training pipeline components."""
print("🔬 Unit Test: Training Pipeline...")
# Create small model and trainer
model = TinyGPT(vocab_size=50, embed_dim=32, num_layers=2, num_heads=2)
tokenizer = CharTokenizer(['a', 'b', 'c', 'd', 'e', ' '])
trainer = TinyGPTTrainer(model, tokenizer, learning_rate=1e-3)
# Test batch preparation
texts = ["hello", "world"]
input_ids, target_ids = trainer.prepare_batch(texts, max_length=8)
assert input_ids.shape == (2, 8), f"Expected (2, 8), got {input_ids.shape}"
assert target_ids.shape == (2, 8), f"Expected (2, 8), got {target_ids.shape}"
# Test training step
initial_loss = trainer.train_step(input_ids, target_ids)
assert initial_loss > 0, "Loss should be positive"
# Second step should work (gradients computed and applied)
second_loss = trainer.train_step(input_ids, target_ids)
assert second_loss > 0, "Second loss should also be positive"
print(f"✅ Batch preparation shape: {input_ids.shape}")
print(f"✅ Initial loss: {initial_loss:.4f}")
print(f"✅ Second loss: {second_loss:.4f}")
print("✅ Training pipeline works correctly!")
# Run immediate test
test_unit_training_pipeline()In [ ]:
def analyze_tinygpt_memory_scaling():
"""📊 Analyze how TinyGPT memory usage scales with model size."""
print("📊 Analyzing TinyGPT Memory Scaling...")
configs = [
{"embed_dim": 64, "num_layers": 2, "name": "Tiny"},
{"embed_dim": 128, "num_layers": 4, "name": "Small"},
{"embed_dim": 256, "num_layers": 6, "name": "Base"},
{"embed_dim": 512, "num_layers": 8, "name": "Large"}
]
results = []
for config in configs:
model = TinyGPT(
vocab_size=1000,
embed_dim=config["embed_dim"],
num_layers=config["num_layers"],
num_heads=config["embed_dim"] // 32, # Maintain reasonable head_dim
max_seq_len=256
)
# Use Module 15 profiler
profiler = Profiler()
param_count = profiler.count_parameters(model)
# Calculate memory footprint
inference_memory = param_count * 4 / (1024 * 1024) # MB
training_memory = inference_memory * 3 # Parameters + gradients + optimizer
results.append({
"name": config["name"],
"params": param_count,
"inference_mb": inference_memory,
"training_mb": training_memory,
"embed_dim": config["embed_dim"],
"layers": config["num_layers"]
})
print(f"{config['name']}: {param_count:,} params, "
f"Inference: {inference_memory:.1f}MB, Training: {training_memory:.1f}MB")
# Analyze scaling trends
print("\n💡 Memory Scaling Insights:")
tiny_params = results[0]["params"]
large_params = results[-1]["params"]
scaling_factor = large_params / tiny_params
print(f" Parameter growth: {scaling_factor:.1f}× from Tiny to Large")
print(f" Training memory range: {results[0]['training_mb']:.1f}MB → {results[-1]['training_mb']:.1f}MB")
return results
def analyze_optimization_impact():
"""📊 Analyze the impact of quantization and pruning on model performance."""
print("📊 Analyzing Optimization Techniques Impact...")
# Create base model
model = TinyGPT(vocab_size=100, embed_dim=128, num_layers=4, num_heads=4)
profiler = Profiler()
# Baseline measurements
base_params = profiler.count_parameters(model)
base_memory = base_params * 4 / (1024 * 1024)
print(f"📐 Baseline Model:")
print(f" Parameters: {base_params:,}")
print(f" Memory: {base_memory:.1f}MB")
# Simulate quantization impact (Module 17)
print(f"\n🔧 After INT8 Quantization:")
quantized_memory = base_memory / 4 # INT8 = 1 byte vs FP32 = 4 bytes
print(f" Memory: {quantized_memory:.1f}MB ({quantized_memory/base_memory:.1%} of original)")
print(f" Memory saved: {base_memory - quantized_memory:.1f}MB")
# Simulate pruning impact (Module 18)
sparsity_levels = [0.5, 0.7, 0.9]
print(f"\n✂️ Pruning Analysis:")
for sparsity in sparsity_levels:
effective_params = base_params * (1 - sparsity)
memory_reduction = base_memory * sparsity
print(f" {sparsity:.0%} sparsity: {effective_params:,} active params, "
f"{memory_reduction:.1f}MB saved")
# Combined optimization
print(f"\n🚀 Combined Optimization (90% pruning + INT8):")
combined_memory = base_memory * 0.1 / 4 # 10% params × 1/4 size
print(f" Memory: {combined_memory:.1f}MB ({combined_memory/base_memory:.1%} of original)")
print(f" Total reduction: {base_memory/combined_memory:.1f}× smaller")
def analyze_training_performance():
"""📊 Analyze training vs inference performance characteristics."""
print("📊 Analyzing Training vs Inference Performance...")
# Create model for analysis
model = TinyGPT(vocab_size=1000, embed_dim=256, num_layers=6, num_heads=8)
profiler = Profiler()
# Simulate batch processing at different sizes
batch_sizes = [1, 4, 16, 32]
seq_len = 128
print(f"📈 Batch Size Impact (seq_len={seq_len}):")
for batch_size in batch_sizes:
# Calculate memory for batch
input_memory = batch_size * seq_len * 4 / (1024 * 1024) # Input tokens
activation_memory = input_memory * model.num_layers * 2 # Rough estimate
total_memory = model._param_count * 4 / (1024 * 1024) + activation_memory
# Estimate throughput (tokens/second)
# Rough approximation based on batch efficiency
base_throughput = 100 # tokens/second for batch_size=1
efficiency = min(batch_size, 16) / 16 # Efficiency plateaus at batch_size=16
throughput = base_throughput * batch_size * efficiency
print(f" Batch {batch_size:2d}: {total_memory:6.1f}MB memory, "
f"{throughput:5.0f} tokens/sec")
print("\n💡 Performance Insights:")
print(" Memory scales linearly with batch size")
print(" Throughput improves with batching (better GPU utilization)")
print(" Sweet spot: batch_size=16-32 for most GPUs")
# Run all analyses
memory_results = analyze_tinygpt_memory_scaling()
analyze_optimization_impact()
analyze_training_performance()In [ ]:
class CompleteTinyGPTPipeline:
"""
End-to-end ML pipeline demonstrating integration of all 19 modules.
Pipeline stages:
1. Data preparation (Module 10: Tokenization)
2. Model creation (Modules 01-04, 11-13: Architecture)
3. Training setup (Modules 05-07: Optimization)
4. Training loop (Module 08: DataLoader)
5. Optimization (Modules 17-18: Quantization, Pruning)
6. Evaluation (Module 19: Benchmarking)
7. Generation (Module 14: KV Caching)
"""
def __init__(self, vocab_size: int = 100, embed_dim: int = 128,
num_layers: int = 4, num_heads: int = 4):
"""Initialize complete pipeline with model architecture."""
### BEGIN SOLUTION
self.vocab_size = vocab_size
self.embed_dim = embed_dim
self.num_layers = num_layers
self.num_heads = num_heads
# Stage 1: Initialize tokenizer (Module 10)
self.tokenizer = CharTokenizer([chr(i) for i in range(32, 127)]) # Printable ASCII
# Stage 2: Create model (Modules 01-04, 11-13)
self.model = TinyGPT(
vocab_size=vocab_size,
embed_dim=embed_dim,
num_layers=num_layers,
num_heads=num_heads,
max_seq_len=256
)
# Stage 3: Setup training (Modules 05-07)
self.trainer = TinyGPTTrainer(self.model, self.tokenizer, learning_rate=3e-4)
# Stage 4: Initialize profiler and benchmark (Modules 15, 19)
self.profiler = Profiler()
self.benchmark = Benchmark([self.model], [], ["perplexity", "latency"])
# Pipeline state
self.is_trained = False
self.training_history = []
print("🏗️ Complete TinyGPT Pipeline Initialized")
print(f" Model: {self.model.count_parameters():,} parameters")
print(f" Memory: {self.model.count_parameters() * 4 / 1024 / 1024:.1f}MB")
### END SOLUTION
def prepare_training_data(self, text_corpus: List[str], batch_size: int = 8) -> DataLoader:
"""
Prepare training data using DataLoader (Module 08).
TODO: Create DataLoader for training text data
APPROACH:
1. Tokenize all texts in corpus
2. Create input/target pairs for language modeling
3. Package into TensorDataset
4. Create DataLoader with batching and shuffling
EXAMPLE:
>>> pipeline = CompleteTinyGPTPipeline()
>>> corpus = ["hello world", "ai is amazing"]
>>> dataloader = pipeline.prepare_training_data(corpus, batch_size=2)
>>> print(f"Batches: {len(dataloader)}")
Batches: 1
"""
### BEGIN SOLUTION
# Tokenize and prepare training pairs
input_sequences = []
target_sequences = []
for text in text_corpus:
tokens = self.tokenizer.encode(text)
if len(tokens) < 2:
continue # Skip very short texts
# Create sliding window of input/target pairs
for i in range(len(tokens) - 1):
input_seq = tokens[:i+1]
target_seq = tokens[i+1]
# Pad input to consistent length
max_len = 32 # Reasonable context window
if len(input_seq) > max_len:
input_seq = input_seq[-max_len:]
else:
input_seq = [0] * (max_len - len(input_seq)) + input_seq
input_sequences.append(input_seq)
target_sequences.append(target_seq)
# Convert to tensors
inputs = Tensor(np.array(input_sequences))
targets = Tensor(np.array(target_sequences))
# Create dataset and dataloader
dataset = TensorDataset(inputs, targets)
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
print(f"📚 Training data prepared: {len(dataset)} examples, {len(dataloader)} batches")
return dataloader
### END SOLUTION
def train(self, dataloader: DataLoader, epochs: int = 10) -> Dict[str, List[float]]:
"""
Complete training loop with monitoring.
TODO: Implement full training with progress tracking
APPROACH:
1. Loop through epochs
2. For each batch: forward, backward, optimize
3. Track loss and perplexity
4. Update learning rate schedule
5. Return training history
EXAMPLE:
>>> history = pipeline.train(dataloader, epochs=5)
>>> print(f"Final loss: {history['losses'][-1]:.4f}")
Final loss: 1.2345
"""
### BEGIN SOLUTION
history = {'losses': [], 'perplexities': [], 'epochs': []}
print(f"🚀 Starting training for {epochs} epochs...")
for epoch in range(epochs):
epoch_losses = []
for batch_idx, (inputs, targets) in enumerate(dataloader):
# Training step
loss = self.trainer.train_step(inputs, targets)
epoch_losses.append(loss)
# Log progress
if batch_idx % 10 == 0:
perplexity = np.exp(loss)
print(f" Epoch {epoch+1}/{epochs}, Batch {batch_idx}: "
f"Loss={loss:.4f}, PPL={perplexity:.2f}")
# Epoch summary
avg_loss = np.mean(epoch_losses)
avg_perplexity = np.exp(avg_loss)
history['losses'].append(avg_loss)
history['perplexities'].append(avg_perplexity)
history['epochs'].append(epoch + 1)
# Update learning rate
self.trainer.scheduler.step()
print(f"✅ Epoch {epoch+1} complete: Loss={avg_loss:.4f}, PPL={avg_perplexity:.2f}")
self.is_trained = True
self.training_history = history
print(f"🎉 Training complete! Final perplexity: {history['perplexities'][-1]:.2f}")
return history
### END SOLUTION
def optimize_model(self, quantize: bool = True, prune_sparsity: float = 0.0):
"""
Apply optimization techniques (Modules 17-18).
TODO: Apply quantization and pruning optimizations
APPROACH:
1. Optionally apply quantization to reduce precision
2. Optionally apply pruning to remove weights
3. Measure size reduction
4. Validate model still works
EXAMPLE:
>>> pipeline.optimize_model(quantize=True, prune_sparsity=0.5)
Model optimized: 75% size reduction
"""
### BEGIN SOLUTION
original_params = self.model.count_parameters()
original_memory = original_params * 4 / (1024 * 1024)
optimizations_applied = []
if quantize:
# Apply quantization (simulated)
# In real implementation, would use quantize_model()
quantized_memory = original_memory / 4 # INT8 vs FP32
optimizations_applied.append(f"INT8 quantization (4× memory reduction)")
print(" Applied INT8 quantization")
if prune_sparsity > 0:
# Apply pruning (simulated)
# In real implementation, would use magnitude_prune()
remaining_weights = 1 - prune_sparsity
optimizations_applied.append(f"{prune_sparsity:.0%} pruning ({remaining_weights:.0%} weights remain)")
print(f" Applied {prune_sparsity:.0%} magnitude pruning")
# Calculate final size
size_reduction = 1.0
if quantize:
size_reduction *= 0.25 # 4× smaller
if prune_sparsity > 0:
size_reduction *= (1 - prune_sparsity)
final_memory = original_memory * size_reduction
reduction_factor = original_memory / final_memory
print(f"🔧 Model optimization complete:")
print(f" Original: {original_memory:.1f}MB")
print(f" Optimized: {final_memory:.1f}MB")
print(f" Reduction: {reduction_factor:.1f}× smaller")
print(f" Applied: {', '.join(optimizations_applied)}")
### END SOLUTION
def generate_text(self, prompt: str, max_tokens: int = 50) -> str:
"""
Generate text using the trained model.
TODO: Implement text generation with proper encoding/decoding
APPROACH:
1. Encode prompt to token IDs
2. Use model.generate() for autoregressive generation
3. Decode generated tokens back to text
4. Return generated text
EXAMPLE:
>>> text = pipeline.generate_text("Hello", max_tokens=10)
>>> print(f"Generated: {text}")
Generated: Hello world this is AI
"""
### BEGIN SOLUTION
if not self.is_trained:
print("⚠️ Model not trained yet. Generating with random weights.")
# Encode prompt
prompt_tokens = self.tokenizer.encode(prompt)
prompt_tensor = Tensor([prompt_tokens])
# Generate tokens
generated_tokens = self.model.generate(
prompt_tensor,
max_new_tokens=max_tokens,
temperature=0.8,
use_cache=True
)
# Decode to text
all_tokens = generated_tokens.data[0].tolist()
generated_text = self.tokenizer.decode(all_tokens)
return generated_text
### END SOLUTION
def test_unit_complete_pipeline():
"""🔬 Test complete pipeline integration."""
print("🔬 Unit Test: Complete Pipeline Integration...")
# Create pipeline
pipeline = CompleteTinyGPTPipeline(vocab_size=50, embed_dim=32, num_layers=2)
# Test data preparation
corpus = ["hello world", "ai is fun", "machine learning"]
dataloader = pipeline.prepare_training_data(corpus, batch_size=2)
assert len(dataloader) > 0, "DataLoader should have batches"
# Test training (minimal)
history = pipeline.train(dataloader, epochs=1)
assert 'losses' in history, "History should contain losses"
assert len(history['losses']) == 1, "Should have one epoch of losses"
# Test optimization
pipeline.optimize_model(quantize=True, prune_sparsity=0.5)
# Test generation
generated = pipeline.generate_text("hello", max_tokens=5)
assert isinstance(generated, str), "Generated output should be string"
assert len(generated) > 0, "Generated text should not be empty"
print(f"✅ Pipeline stages completed successfully")
print(f"✅ Training history: {len(history['losses'])} epochs")
print(f"✅ Generated text: '{generated[:20]}...'")
print("✅ Complete pipeline integration works!")
# Run immediate test
test_unit_complete_pipeline()In [ ]:
def test_module():
"""
Comprehensive test of entire capstone module functionality.
This final test runs before module summary to ensure:
- TinyGPT architecture works correctly
- Training pipeline integrates properly
- Optimization techniques can be applied
- Text generation produces output
- All systems analysis functions execute
- Complete pipeline demonstrates end-to-end functionality
"""
print("🧪 RUNNING MODULE INTEGRATION TEST")
print("=" * 60)
# Test 1: TinyGPT Architecture
print("🔬 Testing TinyGPT architecture...")
test_unit_tinygpt_init()
test_unit_tinygpt_forward()
# Test 2: Training Pipeline
print("\n🔬 Testing training pipeline...")
test_unit_training_pipeline()
# Test 3: Complete Pipeline
print("\n🔬 Testing complete pipeline...")
test_unit_complete_pipeline()
# Test 4: Systems Analysis
print("\n🔬 Testing systems analysis...")
# Create model for final validation
print("🔬 Final integration test...")
model = TinyGPT(vocab_size=100, embed_dim=64, num_layers=2, num_heads=2)
# Verify core functionality
assert hasattr(model, 'count_parameters'), "Model should have parameter counting"
assert hasattr(model, 'forward'), "Model should have forward method"
assert hasattr(model, 'generate'), "Model should have generation method"
# Test parameter counting
param_count = model.count_parameters()
assert param_count > 0, "Model should have parameters"
# Test forward pass
test_input = Tensor([[1, 2, 3, 4, 5]])
output = model.forward(test_input)
assert output.shape == (1, 5, 100), f"Expected (1, 5, 100), got {output.shape}"
# Test generation
generated = model.generate(test_input, max_new_tokens=3)
assert generated.shape[1] == 8, f"Expected 8 tokens, got {generated.shape[1]}"
print("\n" + "=" * 60)
print("🎉 ALL CAPSTONE TESTS PASSED!")
print("🚀 TinyGPT system fully functional!")
print("✅ All 19 modules successfully integrated!")
print("🎯 Ready for real-world deployment!")
print("\nRun: tito module complete 20")
# Call the comprehensive test
test_module()In [ ]:
if __name__ == "__main__":
print("🚀 Running TinyGPT Capstone module...")
# Run the comprehensive test
test_module()
# Demo the complete system
print("\n" + "=" * 60)
print("🎭 CAPSTONE DEMONSTRATION")
print("=" * 60)
# Create a demo pipeline
print("🏗️ Creating demonstration pipeline...")
demo_pipeline = CompleteTinyGPTPipeline(
vocab_size=100,
embed_dim=128,
num_layers=4,
num_heads=4
)
# Show parameter breakdown
print(f"\n📊 Model Architecture Summary:")
print(f" Parameters: {demo_pipeline.model.count_parameters():,}")
print(f" Layers: {demo_pipeline.num_layers}")
print(f" Heads: {demo_pipeline.num_heads}")
print(f" Embedding dimension: {demo_pipeline.embed_dim}")
# Demonstrate text generation (with untrained model)
print(f"\n🎭 Demonstration Generation (untrained model):")
sample_text = demo_pipeline.generate_text("Hello", max_tokens=10)
print(f" Input: 'Hello'")
print(f" Output: '{sample_text}'")
print(f" Note: Random output expected (model not trained)")
print("\n✅ Capstone demonstration complete!")
print("🎯 TinyGPT represents the culmination of 19 modules of ML systems learning!")