dsalt 0.1.14

Dynamic Sparse Attention with Landmark Tokens — High-performance Triton implementation

DSALT: Dynamic Sparse Attention with Landmark Tokens

A high-performance PyTorch library implementing DSALT (Dynamic Sparse Attention with Landmark Tokens)—a memory-efficient sparse attention mechanism for transformers, built with Triton kernels and optimized for distributed training.

Install from PyPI: pip install dsalt
GitHub: dsalt-pytorch
Paper: Zenodo Preprint
Feature Roadmap: See FEATURE.md

🚀 Key Features

• Memory-Efficient Sparse Attention: Triton-accelerated kernels (4–8× memory savings vs. dense attention)
• Adaptive Local Windows: Token-by-token dynamic window sizing that grows with sequence position
• Global Landmark Tokens: Top-k informative tokens selected per head via hybrid energy scoring
• Production-Ready Training: Complete trainer with mixed precision, gradient checkpointing, and validation
• Distributed Training: Full support for DDP and FSDP (model sharding across 2+ GPUs)
• Numerically Verified: CPU/GPU equivalence tests ensure correctness; gradient stability validated

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📋 Table of Contents

1. Installation
2. Quick Start
3. Architecture
4. Training & Generation
5. API Reference
6. Hyperparameter Guide
7. Testing
8. Citation
9. Contributing
10. License

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🛠️ Installation

Requirements

• Python: 3.8+
• PyTorch: 2.0+
• CUDA: 11.0+ (for GPU acceleration; CPU fallback available)
• Triton: 2.0+ (optional; enables GPU kernels; CPU fallback via PyTorch)

From PyPI

pip install dsalt

From PyPI with GPU Acceleration

# Includes Triton for GPU kernels
pip install dsalt

From Source

git clone https://github.com/LeonardoCofone/dsalt-pytorch.git
cd dsalt-pytorch
pip install -e .

Development Setup

pip install -r requirements-dev.txt

---

🚀 Quick Start

1. Minimal Example: Language Model Inference

import torch
from dsalt.model import DSALTLMHeadModel

# Create a DSALT language model
model = DSALTLMHeadModel(
    vocab_size=32000,      # Size of vocabulary
    d_model=1024,          # Hidden dimension
    n_layers=24,           # Depth: 24 transformer blocks
    n_heads=16,            # Multi-head attention heads
    n_min=32,              # Minimum local window
    n_max=512,             # Maximum local window
    k_lmk=64,              # Number of landmark tokens per head
)

# Forward pass (inference)
input_ids = torch.randint(0, 32000, (1, 1024))  # [batch=1, seq_len=1024]
logits = model(input_ids)                        # [1, 1024, 32000]
print(f"Output shape: {logits.shape}")

# With labels: direct loss computation
input_ids = torch.randint(0, 32000, (4, 512))  # [batch=4, seq_len=512]
labels = torch.randint(0, 32000, (4, 512))
outputs = model(input_ids, labels=labels)
loss = outputs.loss
loss.backward()

2. Training: Single GPU

import torch
from torch.utils.data import DataLoader, TensorDataset
from dsalt.model import DSALTLMHeadModel
from dsalt.training import DSALTTrainer

# Prepare dataset
vocab_size = 32000
seq_len = 512
train_dataset = TensorDataset(
    torch.randint(0, vocab_size, (1000, seq_len)),  # 1000 sequences
)
train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True)

# Create model
model = DSALTLMHeadModel(
    vocab_size=vocab_size,
    d_model=768,
    n_layers=12,
    n_heads=12,
    n_min=32,
    n_max=256,
    k_lmk=32,
)

# Train
trainer = DSALTTrainer(
    model=model,
    train_loader=train_loader,
    lr=3e-4,
    total_steps=10000,
    save_dir="checkpoints",
    dtype=torch.bfloat16,  # Mixed precision: BF16
    log_every=50,
)
trainer.train()

2b. Text Generation

import torch
from transformers import GPT2TokenizerFast
from dsalt.model import DSALTLMHeadModel

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2")

# Load trained model
model = DSALTLMHeadModel(
    vocab_size=32000,
    d_model=768,
    n_layers=12,
    n_heads=12,
).to(device)
model.load_state_dict(torch.load("checkpoints/best.pt")["model_state"])

# Generate text
prompt = "Once upon a time"
input_ids = tokenizer(prompt, return_tensors="pt")["input_ids"]

# ✅ Generate with top-k sampling
generated_ids = model.generate(
    input_ids=input_ids,
    max_new_tokens=200,
    temperature=0.8,
    top_k=50,
    device=device,
    tokenizer=tokenizer,
)
print(generated_ids)

3. Training: Multi-GPU with DataParallel (Simple Multi-GPU)

import torch
import torch.nn as nn
from dsalt.model import DSALTLMHeadModel
from dsalt.training import DSALTTrainer

# Create model and wrap with DataParallel for multi-GPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = DSALTLMHeadModel(
    vocab_size=32000,
    d_model=768,
    n_layers=12,
    n_heads=12,
    n_min=32,
    n_max=256,
    k_lmk=32,
).to(device)

# ✅ Wrap with DataParallel — automatically uses all available GPUs
model = nn.DataParallel(model)

trainer = DSALTTrainer(
    model=model,
    train_loader=train_loader,
    val_loader=val_loader,
    lr=3e-4,
    total_steps=100000,
    dtype=torch.bfloat16,
    save_dir="checkpoints",
    device=device,
)
trainer.train()

4. Training: Multi-GPU with FSDP (Fully Sharded Data Parallel)

# Command: torchrun with FSDP enabled
# torchrun --nproc_per_node=2 train.py

import torch
from dsalt.training import DSALTTrainer

trainer = DSALTTrainer(
    model=model,
    train_loader=train_loader,
    val_loader=val_loader,
    lr=3e-4,
    total_steps=100000,
    fsdp=True,              # ← Enable FSDP for 2+ GPU sharding
    dtype=torch.bfloat16,
    save_dir="checkpoints",
)
trainer.train()

---

🏗️ Architecture

Overview

DSALT combines local causal windows (adaptive, growing with position) with global landmark tokens (top-k per head):

┌─ Local Attention ──────┬─ Global Landmarks ────┐
│ Recent N tokens        │ Top-K informative     │
│ (adaptive window)      │ (hybrid energy score) │
└───────────────────────┴──────────────────────┘
         ↓                         ↓
         └─────────────┬───────────┘
                       ↓
            Sparse Attention Output

Key Components

1. DSALTAttention: Multi-head sparse attention module

   • Adaptive window size prediction per token
   • Landmark token selection (no gradient)
   • Sparse kernel computation (Triton or CPU fallback)

2. WindowSizePredictor: Learned dynamic window module

   • Predicts continuous window sizes
   • Enables attention scope to adapt to token importance
   • Regularization: entropy loss on window decisions

3. HybridEnergyScorer (in kernels): Landmark selection

   • Computes energy scores per token (norm-based)
   • Z-score normalization
   • Top-k selection per head
   • Excludes tokens in local window (redundancy-aware)

4. DSALTTransformer: Decoder-only stack

   • Pre-norm RMSNorm for stability
   • SwiGLU feed-forward networks
   • Residual connections and dropout

5. Sparse Attention Kernel (Triton)

   • Fused forward pass: avoids materializing full attention matrix
   • Backward pass: gradient stability for Q, K, V
   • CPU fallback: functional equivalence on devices without GPU

Memory Profile

Dense Attention (standard transformer):

• Attention matrix: O(N²) memory, O(N²) compute

DSALT Sparse Attention:

• Local window: O(w·N) memory (w = adaptive window)
• Landmarks: O(K·N) memory (K = constant landmark count, independent of N)
• Total: O((w+K)·N) ≪ O(N²) for long sequences

---

🎯 Training & Generation

Configuration

trainer = DSALTTrainer(
    model=model,
    train_loader=train_loader,
    val_loader=val_loader,
    
    # Optimization
    lr=3e-4,
    weight_decay=0.1,
    max_grad_norm=1.0,
    grad_accum=2,                  # Gradient accumulation: effective batch 2x
    
    # Schedule
    warmup_steps=500,
    total_steps=100_000,
    
    # Checkpointing & Logging
    save_every=1000,
    log_every=50,
    val_every=500,
    save_dir="checkpoints",
    
    # Precision & Device
    dtype=torch.bfloat16,          # Mixed precision training
    device=torch.device("cuda"),
    
    # Parallelism (choose ONE)
    ddp=False,                     # Standard DDP
    fsdp=True,                     # FSDP: model sharding
    fsdp_cpu_offload=False,        # CPU offload (slow, for very large models)
    
    # Memory Optimization
    gradient_checkpointing=True,   # Gradient checkpointing: saves activation memory
    
    # Regularization
    window_reg_coef=0.01,          # Window entropy regularization
)

trainer.train()

Training with Gradient Accumulation + Distributed

# 2 GPUs, batch=4 per GPU, accumulate 2 steps = effective batch 16
torchrun --nproc_per_node=2 train.py \
    --batch_size 4 \
    --grad_accum 2 \
    --fsdp true \
    --dtype bfloat16

---

📚 API Reference

Core Classes

DSALTLMHeadModel

Language model wrapper for autoregressive training/inference.

model = DSALTLMHeadModel(
    # Required
    vocab_size=32000,           # Size of vocabulary
    d_model=1024,               # Hidden dimension (must be divisible by n_heads)
    n_layers=24,                # Number of transformer blocks
    n_heads=16,                 # Number of attention heads
    
    # Sparse Attention Config
    n_min=32,                   # Minimum local window size (default: 32)
    n_max=512,                  # Maximum local window size (default: 256)
    k_lmk=64,                   # Landmark tokens per head (default: 16)
    alpha=0.6,                  # Initial value for learnable alpha_w per head (default: 0.6)
                                # → alpha_w becomes nn.Parameter and is trained
    
    # Architecture & Regularization
    d_ff=None,                  # Feed-forward hidden dim (None = 4*d_model) (default: None)
    max_seq_len=2048,           # Maximum sequence length (default: 2048)
    dropout=0.0,                # Dropout rate (default: 0.0)
    use_fa2=True,               # Use FlashAttention 2 if available (default: True)
    tie_weights=True,           # Tie embedding & output layer weights (default: True)
)

# Forward: returns (logits, windows) tuple
logits, windows = model(input_ids)
logits.shape  # [batch, seq_len, vocab_size]

# With labels: trainer handles loss internally

DSALTTransformer

Core transformer architecture (without LM head).

transformer = DSALTTransformer(
    d_model=1024,
    n_heads=16,
    n_layers=24,
    n_min=32,
    n_max=512,
    k_lmk=64,
)

# Forward: returns [batch, seq_len, d_model]
x = transformer(input_embeddings)

DSALTAttention

Single multi-head sparse attention layer.

attn = DSALTAttention(
    # Required
    d_model=1024,                       # Hidden dimension
    n_heads=16,                         # Number of heads
    
    # Sparse Attention Config
    n_min=32,                           # Min window size (default: 32)
    n_max=512,                          # Max window size (default: 256)
    k_lmk=64,                           # Landmarks per head (default: 16)
    alpha=0.6,                          # Initial alpha value (default: 0.6)
                                        # → becomes learnable nn.Parameter
    
    # Regularization & Optimization
    dropout=0.0,                        # Attention dropout (default: 0.0)
    use_fa2=True,                       # Use FlashAttention 2 (default: True)
    gradient_checkpointing=False,       # Gradient checkpointing (default: False)
    compile_attention=False,            # torch.compile attention kernel (default: False)
)

# Forward: returns (output, windows) if return_window=True
out, windows = attn(x, return_window=True)
out.shape  # [batch, seq_len, d_model]
windows.shape  # [batch, n_heads, seq_len] - window sizes per position

DSALTTrainer

Training loop with mixed precision, DDP/FSDP, checkpointing.

trainer = DSALTTrainer(
    # Required
    model=model,                            # DSALTLMHeadModel or wrapped (DataParallel/DDP/FSDP)
    train_loader=train_loader,              # Training DataLoader
    
    # Optimization Hyperparameters
    lr=3e-4,                                # Learning rate (default: 3e-4)
    weight_decay=0.1,                       # Weight decay / L2 reg (default: 0.1)
    max_grad_norm=1.0,                      # Gradient clipping norm (default: 1.0)
    grad_accum=1,                           # Gradient accumulation steps (default: 1)
    
    # Schedule Hyperparameters
    warmup_steps=500,                       # LR warmup steps (default: 500)
    total_steps=100_000,                    # Total training steps (default: 10_000)
    
    # Logging & Checkpointing
    log_every=50,                           # Log interval (default: 50)
    val_every=500,                          # Validation interval (default: 500)
    save_every=1000,                        # Checkpoint save interval (default: 1000)
    save_dir="checkpoints",                 # Checkpoint directory (default: "checkpoints")
    
    # Optional: Validation
    val_loader=None,                        # Validation DataLoader (optional)
    
    # Precision & Device
    dtype=torch.bfloat16,                   # Precision: bfloat16, float32, float16 (default: bfloat16)
    device=torch.device("cuda:0"),          # Device (default: auto-detect)
    
    # Multi-GPU Parallelism (choose ONE or NONE)
    ddp=False,                              # Standard DDP (default: False)
    fsdp=False,                             # FSDP model sharding (default: False)
    fsdp_cpu_offload=False,                 # CPU offload params in FSDP (default: False)
    
    # Memory Optimization
    gradient_checkpointing=False,           # Gradient checkpointing (default: False)
    
    # Regularization
    window_reg_coef=0.0,                    # Window entropy regularization coefficient (default: 0.0)
    
    # Advanced: Custom metrics
    compute_metrics_fn=None,                # Custom metrics fn(model, x) → dict (optional)
    
    # Resume from checkpoint
    resume_from=None,                       # Path to checkpoint to resume from (optional)
)

history = trainer.train()  # Blocking call: runs until total_steps
# Returns: dict with keys ['train_loss', 'val_ppl', 'step_time', ...]

Multi-GPU Parallelism Options

Mode	Setup	Use Case	Overhead
Single GPU	device=torch.device("cuda:0")	Small models, development	None
DataParallel	model = nn.DataParallel(model)	Multi-GPU, simple, automatic batching	Medium (batch splits)
DDP	torchrun --nproc_per_node=2 train.py ddp=True	Multi-GPU, distributed, one process per GPU	Low (true parallel)
FSDP	torchrun --nproc_per_node=2 train.py fsdp=True	Large models, sharding across GPUs	Low (true parallel + sharding)

Kernel Functions

dsalt_attention(Q, K, V, window_sizes, landmark_idx)

Low-level sparse attention computation.

from dsalt.kernels import dsalt_attention

# Q, K, V: [batch, n_heads, seq_len, d_head]
# window_sizes: [batch, n_heads, seq_len] int32
# landmark_idx: [batch, n_heads, k_landmarks] int32
# Returns: [batch, n_heads, seq_len, d_head]

out = dsalt_attention(Q, K, V, window_sizes, landmark_idx)

compute_hybrid_energy_scores(X, WV, window_sizes, k, alpha)

Compute landmark scores and select top-k.

from dsalt.kernels import compute_hybrid_energy_scores

# X: [batch, seq_len, d_model]
# WV: [n_heads, d_model, d_head]
# Returns: [batch, n_heads, k]

landmark_idx = compute_hybrid_energy_scores(
    X=hidden_states,
    WV=value_projections,
    window_sizes=window_sizes,
    k=64,
    alpha=torch.tensor([0.6] * n_heads),
)

---

📖 Hyperparameter Guide

For complete documentation of all hyperparameters for each component, see FEATURE.md:

• DSALTLMHeadModel: Model architecture, sparse attention, embedding configuration
• DSALTAttention: Attention-specific parameters, optimization flags
• WindowSizePredictor: Dynamic window learning
• DSALTTransformer: Stack configuration
• DSALTTrainer: Optimization, scheduling, distributed training, precision
• Tuning Guide: Example configs for different hardware (mobile → enterprise)

---

🧪 Testing

Run Full Test Suite

# All tests with coverage
make test-cov

# Or directly with pytest
pytest tests/ -v --cov=dsalt --cov-report=html

# View coverage report
open htmlcov/index.html

Test Modules

• tests/test_sparse_attn.py: Attention kernel CPU/GPU equivalence, backward pass
• tests/test_hybrid_energy.py: Landmark scoring and selection
• tests/test_dsalt_lm.py: Language model wrapper, loss computation
• tests/test_main.py: End-to-end training smoke test

CI/CD

Tests automatically run on:

• Push to any branch
• Pull requests
• Scheduled nightly builds

---

📊 Performance & Benchmarks

Memory Usage (Approximate)

Model: d_model=1024, n_heads=16, n_layers=12, seq_len=1024, batch=4

Attention Type	Memory (GB)	Relative
Dense (Q×K^T)	~3.5	1.0×
FlashAttention 2	~1.8	0.51×
DSALT	~0.6	0.17×

Compute Efficiency

• Forward: ~95% of time spent in Triton kernels (minimal Python overhead)
• Backward: Full gradient support with automatic differentiation
• Mixed Precision (BF16): 1.5–2× speedup vs. FP32 on modern GPUs

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📖 Citation

If DSALT is useful in your research, please cite:

@article{dsalt2024,
  title={Noise Accumulation and Rank Collapse in Dense Self-Attention: DSALT},
  author={Leonardo Cofone},
  journal={Zenodo Preprint},
  year={2026},
  url={https://zenodo.org/records/19312826},
  note={Dynamic Sparse Attention with Landmark Tokens}
}

---

🤝 Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

Areas for contribution:

• Performance tuning (Triton kernel optimization)
• Additional model architectures (encoder, encoder-decoder)
• New training strategies and samplers
• Documentation and tutorials
• Bug reports and fixes

---

📄 License

Licensed under the Apache License 2.0. See LICENSE for details.

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🙏 Acknowledgments

• Triton: GPU kernel framework by OpenAI
• FlashAttention: Inspiration for fused kernel design (Dao et al.)
• PyTorch: Deep learning framework and distributed training infrastructure

---

📞 Support & Questions

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Paper: Zenodo Preprint

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Last Updated: May 2026
Status: ✅ Production-Ready