mirror of https://github.com/MLSysBook/TinyTorch.git synced 2026-03-12 03:03:37 -05:00

Files

Vijay Janapa Reddi 57ba9692f8 CLEANUP: Remove temporary files and add comprehensive documentation

Removed unnecessary files:
• Backup files (.bak, _backup.py, _clean.py) - 6 files removed
• Debug scripts (debug_*.py) - 4 files removed
• Temporary test files (test_cnn_*, test_conv2d_*, test_fixed_*) - 21 files removed
• Test result files (tinymlperf_results/) - 31 JSON files removed
• Python cache files (__pycache__/) and log files

Added valuable documentation:
• Comprehensive readability assessment reports (_reviews/ directory)
• Module structure clarification and quality reports
• Tutorial scorecard template for ongoing assessment
• MODULE_OVERVIEW.md with complete project structure

Retained essential files:
• Core milestone tests (test_complete_solution.py, test_tinygpt_milestone.py)
• Compression benchmark results (compression_benchmark_results.png)
• All production modules and core framework files

Result: Clean, organized codebase ready for production deployment with
comprehensive documentation for ongoing quality assurance.

2025-09-26 11:27:25 -04:00

5.3 KiB

Raw Blame History

TinyTorch Module Overview

Complete Module Listing (20 Core Modules)

Part I: Neural Network Foundations (Modules 1-8)

Goal: Build and train neural networks from scratch

Module	Name	Purpose	Key Components
01	Setup	Development environment configuration	CLI tools, testing framework, environment validation
02	Tensor	N-dimensional arrays with basic operations	Tensor class, broadcasting, element-wise operations
03	Activations	Non-linear activation functions	ReLU, Sigmoid, Tanh, Softmax
04	Layers	Neural network building blocks	Linear, Module base class, Sequential, Flatten
05	Losses	Loss functions for optimization	MSE, CrossEntropy, Binary CrossEntropy
06	Autograd	Automatic differentiation engine	Computational graph, backward pass, gradient tracking
07	Optimizers	Optimization algorithms	SGD, Adam, learning rate scheduling
08	Training	Complete training loops	Training pipeline, validation, checkpointing

Milestone: After Module 8, students can train CNNs on MNIST/CIFAR-10

Part II: Computer Vision (Modules 9-10)

Goal: Build convolutional neural networks for image classification

Module	Name	Purpose	Key Components
09	Spatial	Convolutional operations	Conv2d, MaxPool2d, spatial transformations
10	DataLoader	Efficient data pipelines	Batching, shuffling, data augmentation, CIFAR-10 support

Milestone: Achieve 55%+ accuracy on CIFAR-10 with CNNs

Part III: Language Models (Modules 11-14)

Goal: Build transformer-based language models

Module	Name	Purpose	Key Components
11	Tokenization	Text processing for NLP	BPE tokenizer, vocabulary building, encoding/decoding
12	Embeddings	Dense representations of tokens	Token embeddings, positional encoding, embedding layers
13	Attention	Self-attention mechanisms	Scaled dot-product, multi-head attention, causal masks
14	Transformers	Complete transformer architecture	Transformer blocks, GPT-style models, generation

Milestone: Build TinyGPT capable of text generation

Part IV: Systems Optimization (Modules 15-20)

Goal: Optimize ML systems for production deployment

Module	Name	Purpose	Key Components
15	Profiling	Performance analysis tools	Memory profiling, computational bottlenecks, visualization
16	Acceleration	Hardware optimization	Vectorization, SIMD operations, GPU kernels basics
17	Quantization	Reduced precision computing	INT8 quantization, QAT, post-training quantization
18	Compression	Model size reduction	Weight pruning, knowledge distillation, compression ratios
19	Caching	Inference optimization	KV-cache for transformers, memory management
20	Benchmarking	Performance measurement	Latency, throughput, memory usage, scaling analysis

Milestone: Deploy optimized models with 10x+ inference speedup

Learning Progression

Foundation Path (Modules 1-8)

Setup → Tensor → Activations → Layers → Losses → Autograd → Optimizers → Training

Outcome: Complete understanding of neural network fundamentals

Vision Path (Modules 9-10)

Spatial → DataLoader → CNN Training

Outcome: Build and train state-of-the-art CNNs

Language Path (Modules 11-14)

Tokenization → Embeddings → Attention → Transformers

Outcome: Implement GPT-style language models

Systems Path (Modules 15-20)

Profiling → Acceleration → Quantization → Compression → Caching → Benchmarking

Outcome: Optimize models for production deployment

Key Design Principles

No Forward Dependencies: Each module only depends on previous modules
Immediate Testing: Every implementation is tested right after coding
Systems Focus: Memory, performance, and scaling analysis in every module
Production Context: Compare with PyTorch/TensorFlow implementations
KISS Principle: Keep implementations simple and understandable

Module Structure

Every module follows this consistent structure:

Learning objectives
Mathematical foundations
Implementation with immediate tests
Systems analysis (memory/performance)
Production context
Integration testing
ML systems thinking questions
Module summary

Testing Strategy

Unit Tests: In each module's if __name__ == "__main__" block
Integration Tests: In tests/ directory
Checkpoint Tests: Capability validation after module completion
Performance Tests: Memory and speed benchmarking

Current Status

✅ All 20 modules have complete implementations
✅ All modules have test coverage
✅ Module 01 (Setup) is exemplary and serves as template
⚠️ Modules 02-05 need refactoring to remove forward dependencies
⚠️ Some modules need better systems analysis sections

Next Steps

Critical: Remove autograd from modules 02-05
Important: Enhance systems analysis in all modules
Nice to have: Add more production context examples
Future: Add advanced topics (distributed training, model serving)

5.3 KiB Raw Blame History