🧠 StackWise: Modular AI & Diffusion Framework

StackWise is a modular AI research framework for training, evaluating, and scaling both classical and diffusion-inspired Transformer architectures.
It provides a unified stack for encoder, decoder, and depth-as-time models, along with standardized datasets, training curricula, and evaluation harnesses.

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🚀 Key Features

• Unified Architecture: Supports both masked/causal LMs and diffusion-style denoisers.
• Flexible Training Regimes: Left→right (capacity growth) or right→left (reverse-diffusion) curricula.
• Scalable Families: Tiny → XL for encoders (BERT, ModernBERT) and decoders (GPT, LLaMA).
• Compute-Matched Benchmarks: Fair scaling comparison under equal FLOP budgets.
• Modular Integration: Shared configs for datasets, models, and trainers.
• Research Ready: Designed for scaling-law and curriculum-based experiments.

The Ultimate Goal: Train a 70B parameter LLM under 1 H200 GPU comfortably, from scratch.

The Challenge

• Traditional Training: 70B models require at least 8+ H100 GPUs (≈$200K+ hardware) for a decent run
• Memory Bottleneck: Standard training hits GPU memory limits
• Cost Barrier: Most researchers can't access multi-GPU clusters

The Solution: StackWise

• Single GPU Training: Train 70B models on 1 H200 GPU at elongated wall clocks
• Layer-wise Architecture: Progressive training with cached activations
• Bidirectional Learning: More efficient representation learning
• Memory Optimization: 10x+ memory reduction through smart caching

Core Innovation: Depth-as-Time Training

Traditional: [Input] → [Layer 1] → [Layer 2] → ... → [Layer N] → [Output]
StackWise:  [Input] → [Layer 1] → Cache → [Layer 2] → Cache → ... → [Layer N] → [Output]

Read a detailed note on Depth-as-Time viewpoint here

Key Benefits:

• Memory Efficiency: Only one layer active at a time
• Progressive Learning: Each layer learns from previous cached activations
• Bidirectional Attention: Better context understanding during training
• Flexible Inference: Switch between causal (GPT) and bidirectional (BERT) modes
• Unified Training: Single framework for both Encoder and Decoder models
• Mixed Precision: Choose flexible precision formats for frozen trunks and the trainable parts

Training Paradigm

1. Training Phase: Bidirectional attention (BERT-style) for efficient learning
2. Fusion Phase: Progressive model assembly with optional fine-tuning
3. Inference Phase: Causal attention (GPT-style) for autoregressive generation

🏗️ Architecture Components

Read the Block-Stack-Rack nomenclature here, which facilitates training models end-to-end or in progressive manner via different training curricula.

Rack (Complete Model)
├── Stack 1 (4 Blocks)
│   ├── Block 1 (Transformer Layer)
│   ├── Block 2 (Transformer Layer)
│   ├── Block 3 (Transformer Layer)
│   └── Block 4 (Transformer Layer)
├── Stack 2 (4 Blocks)
│   ├── Block 5 (Transformer Layer)
│   ├── Block 6 (Transformer Layer)
│   ├── Block 7 (Transformer Layer)
│   └── Block 8 (Transformer Layer)
└── ... (More Stacks)

Key Innovation: This paradigm supports stack-wise training, where entire stacks can be trained independently, enabling:

• Memory Efficiency: Train one stack at a time
• Progressive Building: Add stacks incrementally
• Flexible Curriculum: Different training strategies per stack

Unified Training Objectives

StackWise unifies Encoder, Decoder, and Diffusion models through a single training framework:

• Masked Language Modeling (MLM): BERT-style bidirectional training
• Causal Language Modeling (CLM): GPT-style autoregressive training
• Diffusion Modeling: Depth-as-Time progressive denoising
• Unified Framework: Switch between MLM, CLM, and diffusion modes seamlessly
• Task Flexibility: Same model architecture for understanding, generation, and diffusion

Progressive Curriculum Learning

StackWise supports two distinct curriculum approaches for building models:

Left-to-Right Curriculum (Capacity Enhancement)

• Focus: Progressive model capacity building
• Approach: Add new stacks to the right
• Benefit: Gradual complexity increase
• Use Case: Traditional model scaling

Right-to-Left Curriculum (Semantic Preservation)

• Focus: Retain learned semantics while improving
• Approach: Add new stacks to the left, freeze rightmost stacks
• Benefit: Preserves learned representations
• Use Case: Incremental model improvement

Advanced Features

• Modern Attention: GQA, MLA, and Kernel-based attention
• Quantization: FP4, FP8, FP16 support for memory efficiency
• QLoRA Integration: Low-rank adapters for efficient fine-tuning
• Progressive Training: Build models incrementally
• Mask-Diffusion: Variable masking (15%-90%) for better learning

🎯 The 70B Model Challenge

Memory Requirements

• Traditional 70B: ~280GB GPU memory (8x H100)
• StackWise 70B: ~35GB GPU memory (1x H200)
• Memory Reduction: 8x improvement through layer-wise training

Training Strategy

# Progressive training for 70B model
config = {
    "model": {
        "d_model": 8192,           # 70B model dimensions
        "n_heads": 64,
        "d_ff": 28672,
        "architecture": {
            "n_stacks": 80,         # 80 stacks
            "blocks_per_stack": 1   # 1 block per stack = 80 layers
        }
    },
    "training": {
        "progressive": {
            "enabled": True,
            "trunk_strategy": "frozen",    # Freeze previous layers
            "new_stack_precision": "fp16", # Memory-efficient training
            "cache_activations": True      # Essential for layer-wise training
        }
    }
}

🚀 Quick Start

1. Setup Environment

# Create and activate virtual environment
uv venv
source .venv/bin/activate

# Install dependencies
uv pip install -e .[advanced]

2. Train a Small Model (Proof of Concept)

# Navigate to examples
cd examples/gpt2_fusion

# Prepare data
python3 data_loader.py --prepare

# Train with layer-wise progressive training
python3 simple_train.py

3. Scale to Large Models

# Navigate to baselines
cd baselines

# Train a medium model
uv run python scripts/train.py model=encoder/bert_family/base

# Train with progressive training
uv run python scripts/train.py --config-name=experiments/bert_reproduction/bert_base_glue

Status: ✅ Complete - All training modules and baselines framework implemented!

🧪 Training Modes

1. Layer-wise Training

• Memory: Ultra-low (single layer at a time)
• Speed: Sequential but memory-efficient
• Use Case: Maximum memory efficiency, debugging

2. Block-wise Training

• Memory: Low (groups of layers)
• Speed: Faster than layer-wise
• Use Case: Balanced efficiency and speed

3. Stack-wise Training ⭐

• Memory: Medium (entire stacks)
• Speed: Fast (stack-level training)
• Use Case: Progressive model building, curriculum learning
• Curriculum Support: Both left-to-right and right-to-left approaches

4. Progressive Training

• Memory: Medium (progressive building)
• Speed: Fast (incremental building)
• Use Case: Large model training, research
• Curriculum Support: Flexible curriculum strategies

5. Fusion Training

• Memory: High (multiple blocks)
• Speed: Variable (depends on frozen/trainable ratio)
• Use Case: Fine-tuning, production

🔬 Research Applications

Memory-Efficient Training

• Single GPU: Train 70B models on 1 H200
• Progressive Building: Add layers incrementally
• Activation Caching: Smart memory management

Unified Training Framework: Encoder, Decoder, and Diffusion

• Single Framework: Train BERT, GPT, and diffusion models
• Flexible Objectives: Switch between MLM, CLM, and diffusion seamlessly
• Task Adaptation: Same architecture for understanding, generation, and diffusion
• Curriculum Learning: Progressive model building strategies
• Depth-as-Time: Revolutionary paradigm where depth equals reverse diffusion time

Curriculum Learning Strategies

Left-to-Right Curriculum (Traditional Scaling)

Stack 1 → Stack 2 → Stack 3 → ... → Stack N

• Approach: Add new stacks to the right
• Focus: Progressive capacity enhancement
• Benefit: Gradual complexity increase
• Use Case: Traditional model scaling

Right-to-Left Curriculum (Semantic Preservation)

Stack N ← Stack N-1 ← ... ← Stack 2 ← Stack 1

• Approach: Add new stacks to the left, freeze rightmost
• Focus: Retain learned semantics while improving
• Benefit: Preserves learned representations
• Use Case: Incremental model improvement

Attention Mechanisms

• Bidirectional Training: Better representation learning
• Modern Attention: GQA, MLA, Kernel-based
• Flexible Inference: Switch between Autogressive next-token and at-once Diffusion

Diffusion Objectives

• Variable Masking: 15%-90% token masking
• Progressive Schedules: Time-as-depth training
• Mask-Diffusion: Token-level diffusion (not embedding noise)

🚀 Getting Started

For Researchers

1. Read the Architecture Guide
2. Try the Progressive Training Example
3. Explore the Baselines Module

For Developers

1. Check the API Reference
2. Read the Configuration Guide
3. Run the Test Suite

For Production

1. Review the Checkpointing Guide
2. Configure for your use case
3. Scale to your target model size

🎯 Baselines Module

The StackWise Baselines module provides a comprehensive benchmarking framework for encoder-decoder model families with Hydra configuration management.

Features

• Reproducible Baselines: BERT, GPT-2, and LLaMA family models
• Hydra Configuration: Hierarchical config management
• Comprehensive Evaluation: GLUE, language modeling, and reasoning tasks
• Experimental Tracking: Automated logging and result analysis
• Multi-run Support: Parameter sweeps and comparisons

Quick Start

# Navigate to baselines
cd baselines

# Train a tiny BERT model
uv run python scripts/train.py model=encoder/bert_family/tiny

# Run a complete experiment
uv run python scripts/train.py --config-name=experiments/bert_reproduction/bert_base_glue

# Learn about Hydra
python examples/hydra_simple_explanation.py

Configuration Examples

# Mix and match components
uv run python scripts/train.py model=encoder/bert_family/base training=depth_time

# Override specific values
uv run python scripts/train.py model=encoder/bert_family/base model.d_model=512

# Run multiple experiments
uv run python scripts/train.py --multirun model=encoder/bert_family/tiny,encoder/bert_family/base

For detailed documentation, see baselines/README.md.

📚 Documentation

• Depth-as-Time Design 🧠 Conceptual Breakthrough! - training paradigm supporting Encoder, Autoregressive Decoder, and Diffusion models
• Architecture Guide - Core architecture concepts
• Progressive Training - Advanced training strategies
• Configuration Guide - Configuration reference
• Checkpointing Guide - Checkpointing reference
• API Reference - API sketch
• Baselines Module - Benchmarking framework

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Diffusion Models for progressive denoising concepts
• Roberta-Diffusion for diffusion training inspirations
• DeepSeek-V2/V3 for MLA formulation
• BERT for bidirectional attention paradigm
• GPT for causal attention paradigm

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Ready to revolutionize transformer training? Start with StackWise and train your first 70B model on a single GPU! 🚀