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A full attention mechanism and transformer in pure go.
| Date | Stars |
|---|---|
| 2026-07-24 | 478 |
| 2026-07-25 | 478 |
| 2026-07-28 | 478 |
| 2026-07-30 | 478 |
| 2026-08-06 | 478 |
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# go-attention
<img src="https://takara.ai/images/logo-24/TakaraAi.svg" width="200" alt="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** (Apple M1, `go test -bench=. ./attention`):
- **Small vectors (64-256)**: ~17-62ns per dot product
- **Medium vectors (512-1024)**: ~250-290ns per dot product
- **Large vectors (4096+)**: ~970-1230ns per dot product
- **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 attentioExcerpt of 11,307 characters
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