A high-performance CUDA implementation of the Titans neural memory architecture from "Titans: Learning to Memorize at Test Time" (Behrouz et al., 2024).

Titans introduces a novel neural memory module that learns at test time via gradient descent. This framework provides optimized CUDA implementations using:

• CUDA Kernels — Custom kernels for memory operations
• CUB — Block/warp-level primitives for reductions
• Thrust — High-level parallel algorithms
• cuBLAS — Optimized matrix operations

Titans Memory Module
├── Memory: MLP that updates at test time
├── Surprise: Gradient + momentum signal
├── Forgetting: Weight decay for memory management
└── Variants: MAC, MAG, MAL

S_t = η · S_{t-1} - θ · ∇ℓ(M_{t-1}; x_t)    # Surprise (momentum + gradient)
M_t = (1 - α) · M_{t-1} + S_t                # Memory update with forgetting

Where:

• M = Neural memory (MLP weights)
• S = Surprise signal (accumulated gradients)
• η = Momentum coefficient
• θ = Learning rate
• α = Forgetting rate (weight decay)

titans-cuda/
├── include/
│   ├── titans/
│   │   ├── memory.cuh          # Neural memory module
│   │   ├── surprise.cuh        # Surprise computation
│   │   ├── projections.cuh     # Key-value projections
│   │   ├── variants.cuh        # MAC, MAG, MAL variants
│   │   └── utils.cuh           # Utilities
│   └── common/
│       ├── cuda_utils.cuh      # Error checking, timing
│       └── tensor.cuh          # Simple tensor wrapper
├── src/
│   ├── memory.cu               # Memory module implementation
│   ├── surprise.cu             # Surprise kernels
│   ├── projections.cu          # Projection kernels
│   └── variants.cu             # Variant implementations
├── kernels/
│   ├── naive/                  # Baseline implementations
│   ├── optimized/              # Optimized versions
│   └── experimental/           # Cutting-edge optimizations
├── tests/
│   ├── test_memory.cu          # Memory module tests
│   ├── test_surprise.cu        # Surprise computation tests
│   └── benchmarks.cu           # Performance benchmarks
├── examples/
│   ├── simple_memory.cu        # Basic usage example
│   ├── sequence_modeling.cu    # Sequence task example
│   └── benchmark_vs_pytorch.py # Compare with PyTorch
├── python/
│   └── titans_cuda/            # Python bindings (optional)
├── CMakeLists.txt
└── README.md

• CUDA Toolkit 12.x
• CMake 3.20+
• C++17 compiler
• (Optional) PyTorch for Python bindings

mkdir build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
make -j$(nproc)

cd build
ctest --verbose

./benchmarks --all

• Basic tensor operations
• MLP forward pass
• Associative memory loss
• Gradient computation
• Memory update with momentum

• Fused forward + backward kernel
• Shared memory for MLP weights
• CUB block reductions
• Vectorized memory access
• Multi-stream execution

• MAC (Memory as Context)
• MAG (Memory as Gate)
• MAL (Memory as Layer)

• Chunked parallel training
• Tensor Core support (FP16/BF16)
• Multi-GPU support
• Python/PyTorch bindings

#include <titans/memory.cuh>

int main() {
    // Create memory module
    titans::NeuralMemory memory(
        /*dim=*/256,
        /*depth=*/2,
        /*hidden_mult=*/4.0f
    );
    
    // Create update config
    titans::UpdateConfig config{
        .lr = 0.01f,
        .momentum = 0.9f,
        .forgetting = 0.01f
    };
    
    // Process sequence
    for (int t = 0; t < seq_len; t++) {
        // Project input to key-value
        auto [key, value] = projections.forward(input[t]);
        
        // Query memory
        auto output = memory.forward(key);
        
        // Update memory (test-time learning!)
        memory.update(key, value, config);
    }
    
    return 0;
}

Target performance vs PyTorch baseline:

Instead of separate forward and backward passes, compute both in one kernel to avoid memory round-trips.

For small memory MLPs, keep weights in shared memory during the update step.

Use CUB's warp-level primitives for efficient gradient accumulation across threads.

Use float4 loads/stores for coalesced memory access patterns.

Process sequences in chunks for better parallelism, following the TTT paper's approach.

• Titans: Learning to Memorize at Test Time (Behrouz et al., 2024)
• Learning to (Learn at Test Time) (Sun et al., 2024) - TTT
• It's All Connected (Behrouz et al., 2025) - Miras Framework

MIT

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