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GPU-accelerated tensor, compute engine, and computation graph library for Go. Zero CGo.
Part of the Zerfoo ML ecosystem.
- 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
go get github.com/zerfoo/ztensorNo CGo required. GPU backends are discovered and loaded at runtime via dlopen/purego.
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 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.
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:
// ROCm (AMD GPUs)
eng, err := compute.NewROCmEngine[float32](numeric.Float32Ops{})
// OpenCL (cross-vendor)
eng, err := compute.NewOpenCLEngine[float32](numeric.Float32Ops{})The tensor.Numeric type constraint ensures compile-time type safety across all supported numeric types:
// 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.
- ML inference engines — ztensor powers the 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 | 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 |
ztensor depends on:
- float16 — IEEE 754 half-precision and BFloat16 arithmetic
- float8 — FP8 E4M3FN arithmetic for quantized inference
ztensor is used by:
- zerfoo — ML inference, training, and serving framework
Apache 2.0