README

PHP - Oh Really, Tensors?

This extension provides high performance tensor mathematics. It supports a wide range of mathematical operations on tensors, including element-wise computations, reductions, linear algebra functions, and more.

This extension also includes optional integration with Microsoft's ONNX Runtime for efficient model execution.

Features

High-Performance Mathematics: Hardware Acceleration
- CPU (SIMD): WASM, NEON, RISCV64, AVX512, AVX2, SSE4.1, SSE2
- GPU: CUDA
Hybrid Parallelism: Automatic work distribution across CPU/GPU
Comprehensive Type Support: 10 data types with automatic promotion
Memory Efficient: Reference-counted tensors with zero-copy slicing
ONNX Integration: Optional and Direct ONNX model loading and inference

Quick Start

Installation

# Build extension
phpize
./configure --enable-ort
make
sudo make install

Installation with ONNX Runtime

# Fetch onnxruntime from github
wget https://github.com/microsoft/onnxruntime/releases/download/v1.22.0/onnxruntime-linux-x64-1.22.0.tgz

# Install onnxruntime
tar -C /usr/local -xvf onnxruntime-linux-x64-1.22.0.tgz --strip-components 1

# Build and install extension
phpize
./configure --with-ort-onnx=/usr/local --enable-ort
make -j$(nproc)
sudo make install

Note: Use microsoft provided releases of ONNX, not brew or ppa or apt provided releases.

Installation with CUDA

See docs/gpu.md for more information about GPU acceleration.

Post Installation

Add to your php.ini:

extension=ort.so

Basic Usage

use ORT\Tensor\Transient;
use ORT\Math;

// Create tensors
$a = new Transient([1000, 1000], $matrix_data, ORT\Tensor::FLOAT);
$b = new Transient([1000, 1000], $matrix_data, ORT\Tensor::FLOAT);

// High-performance matrix multiplication
$result = Math\matmul($a, $b);

// Element-wise operations
$sum = Math\add($a, $b);
$scaled = Math\multiply($a, 2.5);  // Scalar operations

// Reductions
$total = Math\reduce\tensor\sum($a);
$row_sums = Math\reduce\axis\sum($a, 1);

Performance

SIMD Acceleration: Up to 8x faster with AVX2 vectorization
Multi-Core Scaling: Automatic scaling to available CPU cores
Memory Optimization: Aligned memory allocation for optimal performance

Check your system configuration:

echo "Backend: " . (ORT\Math\backend() ?: "scalar") . "\n";
echo "Cores: " . ORT\Math\cores() . "\n";

API Reference (not exhaustive)

Tensor Types

ORT\Tensor\Transient - Temporary tensors
ORT\Tensor\Persistent - Named, persistent tensors

Mathematical Operations

Binary: add, subtract, multiply, divide, pow, mod
Unary: sin, cos, exp, log, sqrt, abs, neg
Matrix: matmul, dot, transpose
Reduction: sum, mean, min, max, argmax, argmin
Transformation: softmax

ONNX Integration

ORT\Model - Load and manage ONNX models
ORT\Runtime - Execute ONNX model inference

See docs/api.md for more extensive API reference.

Configuration

Environment Variables

ORT_SCALE_CORES - Set thread pool size (default: CPU cores)

Build Options

--enable-ort-backend=type - Enable SIMD optimizations (default: auto)
--with-ort-onnx - Link against ONNX Runtime (default: no)

Backend Types

auto - detect backend type for platform
wasm - use wasm backend
neon - use neon backend
riscv64 - use riscv64 backend
avx2 - use avx2 backend
sse41 - use sse41 backend
sse2 - use sse2 backend
cuda - use cuda backend
none - disable backend

Technical Details

See docs for detailed technical documentation.

Requirements

PHP: 7.4+ or 8.0+
ONNX Runtime: 1.16+ (optional)
Compiler: GCC 4.8+ or Clang 3.8+
CPU: x86_64, aarch64, riscv64, wasm

License

This project is licensed under the PHP License 3.01.

Contributing

Contributions are welcome! Please ensure:

Code follows existing style conventions
SIMD implementations include proper fallbacks
Thread safety is maintained
Tests pass across supported PHP versions

Support

For issues and feature requests, please use the project's issue tracker.

krakjoe / ort

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