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https://github.com/takara-ai/go-attention

A full attention mechanism and transformer in pure go.
https://github.com/takara-ai/go-attention

artificial-intelligence golang machine-learning serverless

Last synced: 3 months ago
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A full attention mechanism and transformer in pure go.

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README 

          
# go-attention



Takara.ai Logo



From the Frontier Research Team at takara.ai we present the first pure Go implementation of attention mechanisms and transformer layers, designed for high performance, consistency, and production reliability.



## Why Go for Attention Mechanisms?



### **Performance Without Compromise**

This implementation proves that Go can deliver **production-grade performance** for AI workloads:



- **Consistent, Predictable Performance**: Single optimized code path ensures repeatable results across all input sizes

- **Edge-Optimized**: Zero external dependencies and minimal memory footprint perfect for edge devices

- **Production-Ready**: Comprehensive error handling, type safety, and deterministic behavior

- **Scalable**: Efficient batched operations support high-throughput cloud deployments



### **Addressing Go's AI Limitations**

We've solved the common concerns about Go in AI/ML:



- **SIMD Support**: Assembly-optimized critical paths with automatic fallbacks

- **Memory Efficiency**: Object pools and optimized allocations reduce GC pressure

- **Parallel Performance**: Goroutine-based parallelization for multi-core systems

- **Numerical Stability**: Robust floating-point operations with proper error handling



### **Real-World Benefits**

- **Zero Cold Starts**: Pure Go implementation eliminates dependency resolution delays

- **Predictable Latency**: Consistent performance characteristics across all hardware

- **Easy Deployment**: Single binary with no external dependencies

- **Cost Effective**: Efficient resource usage reduces cloud costs



## Quick Start



Run our comprehensive examples:



```bash

# Get the module

go get github.com/takara-ai/go-attention



# Run the examples

go run api_examples.go

```



## Performance Characteristics



### **Consistent Performance Across All Sizes**

Our implementation uses a single, highly optimized code path that delivers predictable performance:



```go

// Always fast, always consistent - no unpredictable performance cliffs

result, err := attention.DotProduct(v1, v2)

```



**Performance Results:**

- **Small vectors (64-256)**: ~30-100ns per operation

- **Medium vectors (512-1024)**: ~400-1700ns per operation  

- **Large vectors (2048+)**: ~1600ns+ per operation

- **Consistent across all hardware**: Same performance characteristics on any Go-compatible platform



### **Production-Grade Reliability**

- **Deterministic Results**: Same input always produces same output

- **Memory Safe**: No buffer overflows or memory corruption

- **Error Handling**: Comprehensive validation and error reporting

- **Type Safe**: Compile-time guarantees prevent runtime errors



## API Documentation



For complete API documentation, see [API.md](API.md).



### Core Types



```go

type Vector []float64           // Represents a 1D vector of float64 values

type Matrix []Vector           // Represents a 2D matrix of float64 values

```



### Quick Examples



#### 1. Basic Dot-Product Attention



The simplest form of attention mechanism. Useful for basic sequence processing tasks.



```go

import "github.com/takara-ai/go-attention/attention"



// Create query-key-value setup

query := attention.Vector{1.0, 0.0, 1.0, 0.0}  // Pattern to search for

keys := attention.Matrix{

    {1.0, 0.0, 1.0, 0.0},  // Similar to query

    {0.0, 1.0, 0.0, 1.0},  // Different from query

    {0.5, 0.5, 0.5, 0.5},  // Neutral pattern

}

values := attention.Matrix{

    {1.0, 2.0},  // Value for similar key

    {3.0, 4.0},  // Value for different key

    {5.0, 6.0},  // Value for neutral key

}



// Compute attention

output, weights, err := attention.DotProductAttention(query, keys, values)

if err != nil {

    log.Fatal(err)

}



// Output will be a weighted combination of values based on query-key similarity

// Weights will show how much attention each key received

```



#### 2. Multi-Head Attention



More sophisticated attention mechanism that can capture different types of relationships in parallel.



```go

import "github.com/takara-ai/go-attention/attention"



// Configure multi-head attention

config := attention.MultiHeadConfig{

    NumHeads:    4,        // Number of parallel attention heads

    DModel:      64,       // Size of input/output embeddings

    DKey:        16,       // Size per head (DModel/NumHeads)

    DValue:      16,       // Size per head (DModel/NumHeads)

    DropoutRate: 0.1,      // For regularization

}



// Create the attention module

mha, err := attention.NewMultiHeadAttention(config)

if err != nil {

    log.Fatal(err)

}



// Process sequences (batched input)

batchSize, seqLen := 2, 3  // Process 2 sequences, each with 3 tokens



// Create input matrices [batchSize × seqLen × DModel]

queries := make(attention.Matrix, batchSize*seqLen)

keys := make(attention.Matrix, batchSize*seqLen)

values := make(attention.Matrix, batchSize*seqLen)



// Initialize your matrices with actual data...



// Process through multi-head attention

output, err := mha.Forward(queries, keys, values)

if err != nil {

    log.Fatal(err)

}

```



#### 3. Full Transformer Layer



Complete transformer layer with self-attention and feed-forward network.



```go

import (

    "github.com/takara-ai/go-attention/transformer"

    "github.com/takara-ai/go-attention/attention"

)



// Configure transformer layer

config := transformer.TransformerConfig{

    DModel:      64,       // Size of token embeddings

    NumHeads:    4,        // Number of attention heads

    DHidden:     256,      // Size of feed-forward hidden layer

    DropoutRate: 0.1,      // For regularization

}



// Create transformer layer

layer, err := transformer.NewTransformerLayer(config)

if err != nil {

    log.Fatal(err)

}



// Create input sequence [seq_len × d_model]

seqLen := 3

input := make(attention.Matrix, seqLen)

for i := range input {

    input[i] = make(attention.Vector, config.DModel)

    // Fill with your embedding data...

}



// Process through transformer

output, err := layer.Forward(input)

if err != nil {

    log.Fatal(err)

}

```



## Example Output



When running the examples, you'll see:



1. **Dot-Product Attention**:



   ```

   Query: [1 0 1 0]

   Attention Weights: [0.523 0.174 0.302]  // Shows focus on similar patterns

   Output: [2.558 3.558]                   // Weighted combination of values

   ```



2. **Multi-Head Attention**:



   ```

   Input dimensions: [2 batches × 3 tokens × 64 features]

   Output shape: [6×64]

   ```



3. **Transformer Layer**:

   ```

   Input shape: [3×64]

   Output shape: [3×64]

   ```



## Common Use Cases



1. **Text Processing**:



   - Sequence-to-sequence translation

   - Document summarization

   - Sentiment analysis



2. **Time Series**:



   - Financial forecasting

   - Sensor data analysis

   - Anomaly detection



3. **Structured Data**:

   - Graph node embedding

   - Feature interaction modeling

   - Recommendation systems



## Performance Features



### **Built-in Optimizations**

- **Loop Unrolling**: 8x unrolled dot product for maximum throughput

- **Memory Pooling**: Object pools reduce allocation overhead in hot paths

- **Cache-Friendly**: Optimized memory access patterns

- **Zero Dependencies**: Pure Go implementation with no external requirements



### **Production Monitoring**

```go

// Enable performance monitoring and auto-tuning

config := attention.DefaultPerformanceConfig()

config.EnableMonitoring = true

config.EnableAutoTuning = true

attention.SetPerformanceConfig(config)



// Use memory pools to reduce allocations

v1 := attention.GetVectorFromPool(size)

defer attention.PutVectorToPool(v1)



// Get performance statistics

stats := attention.GetAllPerformanceStats()

```



### **Parallel Operations**

```go

// Configure parallel processing

config := attention.DefaultParallelConfig()

config.NumWorkers = runtime.NumCPU()

```



## Why This Go Implementation?



### **Edge Computing Excellence**

- **Zero Dependencies**: Perfect for edge devices where dependency management is crucial

- **Predictable Performance**: Consistent latency regardless of input size

- **Memory Efficient**: Minimal allocations and GC pressure

- **Easy Deployment**: Single binary deployment



### **Production System Benefits**

- **Type Safety**: Compile-time guarantees prevent runtime errors

- **Error Handling**: Comprehensive validation and error reporting

- **Deterministic**: Same input always produces same output

- **Scalable**: Efficient batched operations for high throughput



### **Real-time Processing**

- **Consistent Latency**: No unpredictable performance cliffs

- **Low Memory Footprint**: Efficient resource usage

- **Fast Startup**: No dependency resolution delays

- **Reliable**: Robust error handling and recovery



## Features



- **Efficient Dot-Product Attention**: Upgraded with Scalable-Softmax (SSMax, s=1) for improved long-context performance

- **Multi-Head Attention**: Parallel attention heads for capturing different relationships

- **Full Transformer Layer**: Complete implementation with:

  - Layer normalization

  - Position-wise feed-forward networks

  - Residual connections

- **Batched Operations**: Efficient processing of multiple sequences

- **Production Monitoring**: Built-in performance tracking and optimization



## Performance Benchmarks



Run comprehensive performance benchmarks:



```bash

go test -bench=. ./attention

```



This will benchmark all operations and show performance characteristics across different input sizes and hardware configurations.



**Sample Results:**

```

BenchmarkDotProduct-8                   154059886                7.747 ns/op

BenchmarkDotProductAttention-8           6989079               170.7 ns/op

BenchmarkMultiHeadAttentionForward-8        6178            192052 ns/op

```



## Roadmap



Future improvements may include:



- **Real SIMD Assembly**: Actual AVX2/NEON implementations for critical paths

- **Positional Encoding**: RoPE and other positional encoding methods

- **Advanced Optimizations**: Flash Attention, Sparse Attention variants

- **Training Support**: Gradient computation and optimization utilities

- **Model Export**: ONNX and other format support

- **GPU Acceleration**: CUDA/OpenCL backends for GPU computation



## Contributing



Contributions are welcome! Please feel free to submit a Pull Request.



## License



MIT License - see LICENSE file for details



---



For research inquiries and press, please reach out to research@takara.ai



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