torchscript

PyTorch/libtorch compiled to WebAssembly for TypeScript.

This package provides TypeScript bindings for PyTorch tensor operations compiled to WebAssembly using Emscripten. It works in both Node.js and browser environments.

Features

- Tensor creation (ones, zeros, from data)
- Basic tensor operations (add, mul, matmul)
- TorchScript model loading and inference
- Works in Node.js and browsers via WebAssembly

Installation

``bash
npm install torchscript


or


pnpm add torchscript
`

Usage

`typescript
import { loadModule, ones, zeros, tensor, DType } from 'torchscript';

// Initialize the WASM module
await loadModule();

// Create tensors
const a = await ones([2, 3]);
const b = await zeros([2, 3]);
const c = await tensor([1, 2, 3, 4, 5, 6], [2, 3]);

// Tensor operations
const sum = a.add(b);
const product = a.mul(c);

// Matrix multiplication
const x = await ones([2, 3]);
const y = await ones([3, 4]);
const result = x.matmul(y);

// Get tensor data
console.log(result.shape); // [2, 4]
console.log(result.toArray());

// Free tensors when done
a.free();
b.free();
c.free();
sum.free();
product.free();
x.free();
y.free();
result.free();
`

$3

`typescript
import { loadModule, load, tensor } from 'torchscript';

await loadModule();

// Load a TorchScript model
const model = await load('model.pt');

// Create input tensor
const input = await tensor([/ your input data /], [1, 3, 224, 224]);

// Run inference
const output = model.forward(input);

console.log(output.shape);
console.log(output.toArray());

// Clean up
input.free();
output.free();
model.free();
`

Building from Source

$3

- Docker
- Node.js 18+
- pnpm

$3

1. Build the Docker image with Emscripten and PyTorch:
`bash
pnpm wasm:docker-build
`

2. Build the WASM module:
`bash
pnpm wasm:build
`

3. Build the TypeScript wrapper:
`bash
pnpm build:js
`

Or run all steps at once:
`bash
pnpm build
`

Note: The initial Docker build is slow as it compiles PyTorch from source.

API Reference

$3

- loadModule(): Promise - Initialize the WASM module (must be called before other operations)
- version(): Promise - Get PyTorch version
- ones(shape: number[], dtype?: DType): Promise - Create tensor filled with ones
- zeros(shape: number[], dtype?: DType): Promise - Create tensor filled with zeros
- tensor(data: number[] | Float32Array, shape: number[]): Promise - Create tensor from data
- load(filename: string): Promise - Load a TorchScript model

$3

- shape: number[] - Tensor dimensions
- numel: number - Number of elements
- toArray(): Float32Array - Get tensor data
- add(other: Tensor): Tensor - Element-wise addition
- mul(other: Tensor): Tensor - Element-wise multiplication
- matmul(other: Tensor): Tensor - Matrix multiplication
- free(): void - Free tensor memory

$3

- forward(input: Tensor): Tensor - Run model inference
- free(): void - Free module memory

$3

- DType.Float32 - 32-bit floating point
- DType.Float64 - 64-bit floating point
- DType.Int32 - 32-bit integer
- DType.Int64` - 64-bit integer

License

MIT

Credits

Based on:
- libtorch-wasm - PyTorch WASM compilation
- libpg-query-node - Package structure patterns