https://github.com/zerfoo/ztensor
GPU-accelerated tensor, compute engine, and computation graph library for Go. CUDA/ROCm/OpenCL backends via purego — zero CGo required.
https://github.com/zerfoo/ztensor
Last synced: about 1 month ago
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GPU-accelerated tensor, compute engine, and computation graph library for Go. CUDA/ROCm/OpenCL backends via purego — zero CGo required.
- Host: GitHub
- URL: https://github.com/zerfoo/ztensor
- Owner: zerfoo
- License: apache-2.0
- Created: 2026-03-16T01:39:01.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2026-06-16T07:42:24.000Z (about 1 month ago)
- Last Synced: 2026-06-16T07:48:47.579Z (about 1 month ago)
- Language: Go
- Size: 1.62 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
# ztensor
[](https://github.com/zerfoo/ztensor/actions/workflows/ci.yml)
[](https://pkg.go.dev/github.com/zerfoo/ztensor)
[](https://opensource.org/licenses/Apache-2.0)
GPU-accelerated tensor, compute engine, and computation graph library for Go. Zero CGo.
Part of the [Zerfoo](https://github.com/zerfoo) ML ecosystem.
## Features
- **Multi-type tensors** with compile-time type safety via Go generics (`float32`, `float64`, `float16`, `bfloat16`, `float8`, integer types)
- **GPU backends** — CUDA (cuBLAS, cuDNN, TensorRT, custom kernels), ROCm (HIP, rocBLAS, MIOpen), and OpenCL (CLBlast), all loaded dynamically via purego (zero CGo)
- **Computation graphs** with fusion passes and CUDA graph capture for optimized inference
- **CPU SIMD** — ARM NEON and x86 AVX2 hand-written assembly for GEMM, RMSNorm, RoPE, SiLU, softmax
- **Memory management** — arena-based GPU memory pools with O(1) per-pass reclamation
- **Quantized storage** — FP8 E4M3/E5M2, FP16, BFloat16 tensor storage with automatic dequantization
## Installation
```bash
go get github.com/zerfoo/ztensor
```
No CGo required. GPU backends are discovered and loaded at runtime via `dlopen`/purego.
## Quick Start
```go
package main
import (
"context"
"fmt"
"github.com/zerfoo/ztensor/compute"
"github.com/zerfoo/ztensor/numeric"
"github.com/zerfoo/ztensor/tensor"
)
func main() {
ctx := context.Background()
// Create a CPU compute engine for float32
eng := compute.NewCPUEngine[float32](numeric.Float32Ops{})
// Create two tensors
a, _ := tensor.New[float32]([]int{2, 3}, []float32{1, 2, 3, 4, 5, 6})
b, _ := tensor.New[float32]([]int{3, 2}, []float32{1, 2, 3, 4, 5, 6})
// Matrix multiplication
c, _ := eng.MatMul(ctx, a, b)
fmt.Println(c.Shape()) // [2, 2]
fmt.Println(c.Data()) // [22 28 49 64]
// Element-wise operations
x, _ := tensor.New[float32]([]int{2, 2}, []float32{1, 2, 3, 4})
y, _ := tensor.New[float32]([]int{2, 2}, []float32{5, 6, 7, 8})
sum, _ := eng.Add(ctx, x, y)
fmt.Println(sum.Data()) // [6 8 10 12]
}
```
## GPU Backend Example
GPU libraries are loaded at runtime via purego — no CGo, no build tags, no linking. If CUDA/ROCm/OpenCL is not available, the engine constructor returns an error and you fall back to CPU.
```go
package main
import (
"context"
"fmt"
"log"
"github.com/zerfoo/ztensor/compute"
"github.com/zerfoo/ztensor/numeric"
"github.com/zerfoo/ztensor/tensor"
)
func main() {
ctx := context.Background()
// Try CUDA first, fall back to CPU
eng, err := compute.NewGPUEngine[float32](numeric.Float32Ops{})
if err != nil {
fmt.Println("CUDA not available, using CPU:", err)
cpuEng := compute.NewCPUEngine[float32](numeric.Float32Ops{})
run(ctx, cpuEng)
return
}
run(ctx, eng)
}
func run(ctx context.Context, eng compute.Engine[float32]) {
a, _ := tensor.New[float32]([]int{2, 3}, []float32{1, 2, 3, 4, 5, 6})
b, _ := tensor.New[float32]([]int{3, 2}, []float32{1, 2, 3, 4, 5, 6})
c, _ := eng.MatMul(ctx, a, b)
fmt.Println(c.Data()) // [22 28 49 64]
}
```
Other GPU backends follow the same pattern:
```go
// ROCm (AMD GPUs)
eng, err := compute.NewROCmEngine[float32](numeric.Float32Ops{})
// OpenCL (cross-vendor)
eng, err := compute.NewOpenCLEngine[float32](numeric.Float32Ops{})
```
## Type Safety with Generics
The `tensor.Numeric` type constraint ensures compile-time type safety across all supported numeric types:
```go
// Works with any Numeric type
func dotProduct[T tensor.Numeric](eng compute.Engine[T], a, b *tensor.TensorNumeric[T]) (*tensor.TensorNumeric[T], error) {
return eng.MatMul(context.Background(), a, b)
}
```
Supported types include `float32`, `float64`, `float16.Float16`, `float16.BFloat16`, `float8.Float8`, and all Go integer types.
## Use Cases
- **ML inference engines** — ztensor powers the [zerfoo](https://github.com/zerfoo/zerfoo) inference runtime for transformer models
- **Scientific computing** — GPU-accelerated linear algebra with automatic backend selection
- **GPU compute from Go** — use CUDA/ROCm/OpenCL from pure Go without CGo or build tags
- **Custom ML operators** — build neural network layers on top of the `compute.Engine` interface
## Package Overview
| Package | Description |
|---------|-------------|
| `tensor/` | Multi-type tensor storage — CPU, GPU, quantized (FP8, FP16, BFloat16) |
| `compute/` | Compute engine interface with CPU, CUDA, ROCm, and OpenCL implementations |
| `graph/` | Computation graph compiler with operator fusion and CUDA graph capture |
| `numeric/` | Type-safe `Arithmetic[T]` interface for all numeric types |
| `device/` | Device abstraction and memory allocators |
| `types/` | Shared type definitions |
| `log/` | Structured logging interface |
| `metrics/` | Performance metrics and profiling |
| `internal/cuda/` | Zero-CGo CUDA runtime bindings via purego, 25+ custom kernels |
| `internal/xblas/` | ARM NEON and x86 AVX2 SIMD assembly (GEMM, RMSNorm, RoPE, SiLU, softmax) |
| `internal/gpuapi/` | GPU Runtime Abstraction Layer — unified adapter for CUDA, ROCm, OpenCL |
| `internal/codegen/` | Megakernel code generator |
## Dependencies
ztensor depends on:
- [float16](https://github.com/zerfoo/float16) — IEEE 754 half-precision and BFloat16 arithmetic
- [float8](https://github.com/zerfoo/float8) — FP8 E4M3FN arithmetic for quantized inference
ztensor is used by:
- [zerfoo](https://github.com/zerfoo/zerfoo) — ML inference, training, and serving framework
## License
Apache 2.0