ztensor

README

ztensor

GPU-accelerated tensor, compute engine, and computation graph library for Go.

Part of the 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

go get github.com/zerfoo/ztensor

No CGo required. GPU backends are discovered and loaded at runtime via dlopen/purego.

Quick Start

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.

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{})

Type Safety with Generics

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.

Use Cases

• 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 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 — 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

License

Apache 2.0