use crate::error::{NeuralError, Result};
use std::fs;
#[derive(Debug, Clone, PartialEq)]
pub enum WebBindingLanguage {
JavaScript,
TypeScript,
Both,
ModernJavaScript,
}
pub enum ModuleSystem {
CommonJS,
ESModules,
AMD,
UMD,
IIFE,
#[derive(Debug, Clone)]
pub struct WebBindingConfig {
pub target_language: WebBindingLanguage,
pub module_system: ModuleSystem,
pub type_definitions: bool,
pub documentation: bool,
pub bundling: BundlingConfig,
pub struct BundlingConfig {
pub enable: bool,
pub format: BundleFormat,
pub minify: bool,
pub tree_shaking: bool,
pub code_splitting: bool,
pub enum BundleFormat {
Single,
Chunked,
Streaming,
pub struct BindingGenerator {
pub output_dir: PathBuf,
pub config: WebBindingConfig,
impl BindingGenerator {
pub fn new(_outputdir: PathBuf, config: WebBindingConfig) -> Self {
Self { output_dir, config }
}
pub fn generate_bindings(&self) -> Result<Vec<PathBuf>> {
let mut bindings = Vec::new();
match self.config.target_language {
WebBindingLanguage::JavaScript => {
let js_path = self.generate_javascript_bindings()?;
bindings.push(js_path);
}
WebBindingLanguage::TypeScript => {
let ts_path = self.generate_typescript_bindings()?;
bindings.push(ts_path);
WebBindingLanguage::ModernJavaScript => {
let js_path = self.generate_modern_javascript_bindings()?;
WebBindingLanguage::Both => {
}
Ok(bindings)
pub fn generate_javascript_bindings(&self) -> Result<PathBuf> {
let js_path = self.output_dir.join("js").join("scirs2-model.js");
let js_content = r#"/**
* SciRS2 Model WebAssembly Bindings
*
* This module provides JavaScript bindings for running SciRS2 neural network models
* in the browser using WebAssembly.
*/
class SciRS2Model {
constructor() {
this.module = null;
this.memory = null;
this.exports = null;
this.isInitialized = false;
/**
* Initialize the WASM module
* @param {string|ArrayBuffer} wasmSource - Path to WASM file or ArrayBuffer
* @param {Object} options - Initialization options
* @returns {Promise<void>}
*/
async initialize(wasmSource, options = {}) {
try {
let wasmBytes;
if (typeof wasmSource === 'string') {
const response = await fetch(wasmSource);
wasmBytes = await response.arrayBuffer();
} else {
wasmBytes = wasmSource;
const wasmModule = await WebAssembly.instantiate(wasmBytes, {
env: {
memory: new WebAssembly.Memory({
initial: options.initialMemory || 256,
maximum: options.maximumMemory || 1024
}),
abort: () => {
throw new Error('WASM execution aborted');
}
}
});
this.module = wasmModule.module;
this.exports = wasmModule.instance.exports;
this.memory = this.exports.memory || wasmModule.instance.exports.memory;
this.isInitialized = true;
console.log('SciRS2 Model initialized successfully');
} catch (error) {
throw new Error(`Failed to initialize WASM module: ${error.message}`);
* Run inference on input data
* @param {Float32Array|Array} inputData - Input tensor data
* @param {Array} inputShape - Shape of input tensor
* @returns {Promise<Float32Array>} Output tensor data
async predict(inputData, inputShape) {
if (!this.isInitialized) {
throw new Error('Model not initialized. Call initialize() first.');
// Convert input to Float32Array if needed
const input = inputData instanceof Float32Array ? inputData : new Float32Array(inputData);
// Allocate memory for input
const inputSize = input.length * 4; // 4 bytes per float32
const inputPtr = this.exports.allocate(inputSize);
if (!inputPtr) {
throw new Error('Failed to allocate memory for input');
// Copy input data to WASM memory
const inputView = new Float32Array(this.memory.buffer, inputPtr, input.length);
inputView.set(input);
// Calculate output size (this would be determined by model architecture)
const outputSize = this.calculateOutputSize(inputShape);
const outputPtr = this.exports.allocate(outputSize * 4);
if (!outputPtr) {
this.exports.deallocate(inputPtr);
throw new Error('Failed to allocate memory for output');
try {
// Run inference
const result = this.exports.predict(inputPtr, outputPtr, input.length);
if (result !== 0) {
throw new Error(`Inference failed with error code: ${result}`);
// Copy output data from WASM memory
const outputView = new Float32Array(this.memory.buffer, outputPtr, outputSize);
return new Float32Array(outputView);
} finally {
this.exports.deallocate(outputPtr);
} finally {
if (inputPtr) {
* Calculate output size based on model architecture
* @param {Array} inputShape - Input tensor shape
* @returns {number} Output size
calculateOutputSize(inputShape) {
// This is a placeholder - real implementation would query the model
return inputShape.reduce((a, b) => a * b, 1);
* Check if model supports batch inference
* @returns {boolean}
supportsBatchInference() {
return this.isInitialized && this.exports && this.exports.predictBatch;
* Get model information
* @returns {Object} Model metadata
getModelInfo() {
throw new Error('Model not initialized');
return {
version: '1.0.0',
inputShape: [1, -1], // Dynamic shape
outputShape: [1, -1],
parameters: 0, // Would be calculated from model
memoryUsage: this.memory ? this.memory.buffer.byteLength : 0
};
* Cleanup resources
dispose() {
// Utility function to load model easily
async function loadModel(wasmPath, options = {}) {
const model = new SciRS2Model();
await model.initialize(wasmPath, options);
return model;
// Export for different module systems
if (typeof module !== 'undefined' && module.exports) {
// CommonJS
module.exports = { SciRS2Model, loadModel };
} else if (typeof window !== 'undefined') {
// Browser global
window.SciRS2Model = SciRS2Model;
window.loadModel = loadModel;
"#;
if let Some(parent) = js_path.parent() {
fs::create_dir_all(parent).map_err(|e| NeuralError::IOError(e.to_string()))?;
fs::write(&js_path, js_content).map_err(|e| NeuralError::IOError(e.to_string()))?;
Ok(js_path)
pub fn generate_typescript_bindings(&self) -> Result<PathBuf> {
let ts_path = self.output_dir.join("ts").join("scirs2-model.ts");
let ts_content = r#"/**
* SciRS2 Model WebAssembly Bindings - TypeScript
* Type-safe TypeScript bindings for running SciRS2 neural network models
export interface WasmSupport {
basic: boolean;
simd: boolean;
threads: boolean;
bulkMemory: boolean;
multiValue?: boolean;
referenceTypes?: boolean;
export interface ModelInfo {
version: string;
inputShape: number[];
outputShape: number[];
parameters: number;
memoryUsage?: number;
export interface InitializationOptions {
initialMemory?: number;
maximumMemory?: number;
enableSIMD?: boolean;
enableThreads?: boolean;
export interface LoadingOptions extends InitializationOptions {
useCache?: boolean;
onProgress?: (progress: number) => void;
timeout?: number;
export interface PredictionOptions {
useWorker?: boolean;
batchSize?: number;
maxConcurrency?: number;
export class SciRS2Model {
private module: WebAssembly.Module | null = null;
private exports: WebAssembly.Instance['exports'] | null = null;
private memory: WebAssembly.Memory | null = null;
private isInitialized: boolean = false;
// Initialize in constructor
async initialize(
wasmSource: string | ArrayBuffer,
options: InitializationOptions = {}
): Promise<void> {
let wasmBytes: ArrayBuffer;
if (!response.ok) {
throw new Error(`Failed to fetch WASM module: ${response.statusText}`);
const imports = {
initial: options.initialMemory ?? 256,
maximum: options.maximumMemory ?? 1024
};
const wasmModule = await WebAssembly.instantiate(wasmBytes, imports);
this.memory = this.exports.memory ?? imports.env.memory;
throw new Error(`Failed to initialize WASM module: ${(error as Error).message}`);
async predict(
inputData: Float32Array | number[],
inputShape: number[],
options: PredictionOptions = {}
): Promise<Float32Array> {
if (!this.isInitialized || !this.exports || !this.memory) {
// Validate input shape
const expectedSize = inputShape.reduce((a, b) => a * b, 1);
if (input.length !== expectedSize) {
throw new Error(`Input size ${input.length} doesn't match expected size ${expectedSize}`);
const inputPtr = (this.exports.allocate as Function)(inputSize);
// Copy input data to WASM memory
const inputView = new Float32Array(this.memory.buffer, inputPtr, input.length);
inputView.set(input);
// Calculate output size (this would be determined by model architecture)
const outputSize = this.calculateOutputSize(inputShape);
const outputPtr = (this.exports.allocate as Function)(outputSize * 4);
if (!outputPtr) {
throw new Error('Failed to allocate memory for output');
try {
// Run inference with timeout
const result = await this.executeWithTimeout(
() => (this.exports!.predict as Function)(inputPtr, outputPtr, input.length),
options.timeout || 5000
);
if (result !== 0) {
throw new Error(`Inference failed with error code: ${result}`);
// Copy output data from WASM memory
const outputView = new Float32Array(this.memory.buffer, outputPtr, outputSize);
return new Float32Array(outputView);
} finally {
(this.exports.deallocate as Function)(outputPtr);
(this.exports.deallocate as Function)(inputPtr);
throw new Error(`Prediction failed: ${(error as Error).message}`);
* Run batch inference
async predictBatch(
inputs: (Float32Array | number[])[],
): Promise<Float32Array[]> {
if (!this.supportsBatchInference()) {
// Fallback to sequential prediction
const outputs: Float32Array[] = [];
for (const input of inputs) {
const output = await this.predict(input, inputShape, options);
outputs.push(output);
return outputs;
// Batch inference implementation would go here
throw new Error('Batch inference not yet implemented');
getModelInfo(): ModelInfo {
if (!this.isInitialized || !this.exports) {
memoryUsage: this.memory?.buffer.byteLength ?? 0
* Check if batch inference is supported
supportsBatchInference(): boolean {
return this.isInitialized &&
this.exports !== null &&
'predictBatch' in this.exports;
* Check if model is ready for inference
isReady(): boolean {
return this.isInitialized;
* Execute function with timeout
private async executeWithTimeout<T>(
fn: () => T,
timeoutMs: number
): Promise<T> {
return new Promise<T>((resolve, reject) => {
const timeout = setTimeout(() => {
reject(new Error(`Operation timed out after ${timeoutMs}ms`));
}, timeoutMs);
const result = fn();
clearTimeout(timeout);
resolve(result);
} catch (error) {
reject(error);
});
* Calculate output size based on input shape
private calculateOutputSize(inputShape: number[]): number {
dispose(): void {
/**
* Utility function to load model with TypeScript support
export async function loadModel(
wasmPath: string,
options: LoadingOptions = {}
): Promise<SciRS2Model> {
if (options.onProgress) {
options.onProgress(0);
options.onProgress(100);
* Check WebAssembly support in current environment
export function checkWasmSupport(): WasmSupport {
const support: WasmSupport = {
basic: typeof WebAssembly !== 'undefined',
simd: false,
threads: false,
bulkMemory: false
};
if (support.basic) {
// Check for SIMD support
support.simd = WebAssembly.validate(new Uint8Array([
0x00, 0x61, 0x73, 0x6d, 0x01, 0x00, 0x00, 0x00, 0x01, 0x04, 0x01, 0x60, 0x00, 0x00,
0x03, 0x02, 0x01, 0x00, 0x0a, 0x09, 0x01, 0x07, 0x00, 0xfd, 0x0f, 0x45, 0x0b
]));
} catch {
support.simd = false;
// Check for threads support
support.threads = typeof SharedArrayBuffer !== 'undefined';
// Check for bulk memory support
support.bulkMemory = WebAssembly.validate(new Uint8Array([
0x03, 0x02, 0x01, 0x00, 0x0a, 0x07, 0x01, 0x05, 0x00, 0xfc, 0x0a, 0x00, 0x0b
support.bulkMemory = false;
return support;
if let Some(parent) = ts_path.parent() {
fs::write(&ts_path, ts_content).map_err(|e| NeuralError::IOError(e.to_string()))?;
Ok(ts_path)
/// Generate modern JavaScript bindings with ES modules
pub fn generate_modern_javascript_bindings(&self) -> Result<PathBuf> {
let js_path = self.output_dir.join("js").join("scirs2-model.mjs");
* SciRS2 Model WebAssembly Bindings - Modern JavaScript
* Modern ES2020+ JavaScript bindings with async/await, modules, and advanced features
#module = null;
#exports = null;
#memory = null;
#isInitialized = false;
// Private field initialization
* Initialize the WASM module with modern async patterns
const response = await fetch(wasmSource, {
cache: options.useCache ? 'default' : 'no-cache'
});
throw new Error(`Failed to fetch WASM: ${response.statusText}`);
this.#module = wasmModule.module;
this.#exports = wasmModule.instance.exports;
this.#memory = this.#exports.memory ?? imports.env.memory;
this.#isInitialized = true;
* Run inference on input data using modern async patterns
* @param {Object} options - Prediction options
async predict(inputData, inputShape, options = {}) {
if (!this.#isInitialized) {
const input = inputData instanceof Float32Array ? inputData : new Float32Array(inputData);
// Use structured concurrency pattern
const prediction = await this.#withMemoryManagement(async (allocate, deallocate) => {
// Allocate input memory
const inputSize = input.length * 4;
const inputPtr = allocate(inputSize);
// Copy input data
const inputView = new Float32Array(this.#memory.buffer, inputPtr, input.length);
// Allocate output memory
const outputSize = this.#calculateOutputSize(inputShape);
const outputPtr = allocate(outputSize * 4);
// Run inference with timeout
const result = await this.#executeWithTimeout(
() => this.#exports.predict(inputPtr, outputPtr, input.length),
options.timeout ?? 5000
);
if (result !== 0) {
throw new Error(`Inference failed with error code: ${result}`);
// Copy and return output
const outputView = new Float32Array(this.#memory.buffer, outputPtr, outputSize);
return new Float32Array(outputView);
return prediction;
* Run batch inference with parallel execution
* @param {Array<Float32Array>} inputs - Array of input tensors
* @param {Array} inputShape - Shape of each input tensor
async predictBatch(inputs, inputShape, options = {}) {
const maxConcurrency = options.maxConcurrency ?? 4;
// Use Promise.allSettled for parallel execution with concurrency limit
const results = [];
for (let i = 0; i < inputs.length; i += maxConcurrency) {
const batch = inputs.slice(i, i + maxConcurrency);
const batchPromises = batch.map(input => this.predict(input, inputShape, options));
const batchResults = await Promise.allSettled(batchPromises);
results.push(...batchResults.map(result => {
if (result.status === 'fulfilled') {
return result.value;
} else {
throw result.reason;
}));
return results;
// Native batch inference implementation would go here
throw new Error('Native batch inference not yet implemented');
* Get model information with async metadata loading
async getModelInfo() {
inputShape: [1, -1],
parameters: 0,
memoryUsage: this.#memory?.buffer.byteLength ?? 0,
timestamp: new Date().toISOString()
* Check capabilities
return this.#isInitialized && this.#exports?.predictBatch !== undefined;
isReady() {
return this.#isInitialized;
* Private memory management helper
async #withMemoryManagement(fn) {
const allocatedPointers = [];
const allocate = (size) => {
const ptr = this.#exports.allocate(size);
if (ptr) allocatedPointers.push(ptr);
return ptr;
return await fn(allocate);
// Cleanup all allocated memory
for (const ptr of allocatedPointers) {
if (ptr) this.#exports.deallocate(ptr);
* Private timeout execution helper
async #executeWithTimeout(fn, timeoutMs) {
return new Promise((resolve, reject) => {
* Private output size calculation
#calculateOutputSize(inputShape) {
this.#module = null;
this.#exports = null;
this.#memory = null;
this.#isInitialized = false;
* Utility function with modern async loading
export async function loadModel(wasmPath, options = {}) {
// Use async iterator for progress reporting
const progressGenerator = async function* () {
yield 0;
await model.initialize(wasmPath, options);
yield 100;
for await (const progress of progressGenerator()) {
options.onProgress(progress);
} else {
await model.initialize(wasmPath, options);
* Advanced WebAssembly feature detection
export function checkWasmSupport() {
const support = {
bulkMemory: false,
multiValue: false,
referenceTypes: false
// Feature detection using WebAssembly.validate
const features = [
{ name: 'simd', bytes: [0x00, 0x61, 0x73, 0x6d, 0x01, 0x00, 0x00, 0x00] },
{ name: 'threads', test: () => typeof SharedArrayBuffer !== 'undefined' },
{ name: 'bulkMemory', bytes: [0x00, 0x61, 0x73, 0x6d, 0x01, 0x00, 0x00, 0x00] },
{ name: 'multiValue', bytes: [0x00, 0x61, 0x73, 0x6d, 0x01, 0x00, 0x00, 0x00] },
{ name: 'referenceTypes', bytes: [0x00, 0x61, 0x73, 0x6d, 0x01, 0x00, 0x00, 0x00] }
];
for (const feature of features) {
if (feature.test) {
support[feature.name] = feature.test();
} else if (feature.bytes) {
support[feature.name] = WebAssembly.validate(new Uint8Array(feature.bytes));
} catch {
support[feature.name] = false;
* Web Worker factory for background inference
export function createWorker() {
const workerScript = `
importScripts('scirs2-model.js');
let model = null;
self.onmessage = async function(e) {
const { type, data, id } = e.data;
switch (type) {
case 'INITIALIZE':
model = new SciRS2Model();
await model.initialize(data.wasmUrl, data.options);
self.postMessage({ type: 'INITIALIZE_SUCCESS', id });
break;
case 'PREDICT':
if (!model) {
throw new Error('Model not initialized');
}
const result = await model.predict(data.inputData, data.inputShape, data.options);
self.postMessage({ type: 'PREDICT_SUCCESS', result, id });
case 'DISPOSE':
if (model) {
model.dispose();
model = null;
self.postMessage({ type: 'DISPOSE_SUCCESS', id });
default:
throw new Error(\`Unknown message type: \${type}\`);
self.postMessage({ type: 'ERROR', error: error.message, id });
`;
const blob = new Blob([workerScript], { type: 'application/javascript' });
return new Worker(URL.createObjectURL(blob));
/// Generate TypeScript declarations file
pub fn generate_typescript_declarations(&self) -> Result<PathBuf> {
let dts_path = self.output_dir.join("ts").join("scirs2-model.d.ts");
let dts_content = r#"// TypeScript declarations for SciRS2 Neural Network WebAssembly
constructor();
initialize(wasmSource: string | ArrayBuffer, options?: InitializationOptions): Promise<void>;
predict(
options?: PredictionOptions
): Promise<Float32Array>;
predictBatch(
): Promise<Float32Array[]>;
getModelInfo(): Promise<ModelInfo>;
supportsBatchInference(): boolean;
isReady(): boolean;
dispose(): void;
export function loadModel(wasmPath: string, options?: LoadingOptions): Promise<SciRS2Model>;
export function checkWasmSupport(): WasmSupport;
export function createWorker(): Worker;
if let Some(parent) = dts_path.parent() {
fs::write(&dts_path, dts_content).map_err(|e| NeuralError::IOError(e.to_string()))?;
Ok(dts_path)
#[cfg(test)]
mod tests {
use super::*;
use tempfile::TempDir;
#[test]
fn test_binding_generator_creation() {
let temp_dir = TempDir::new().expect("Operation failed");
let config = WebBindingConfig {
target_language: WebBindingLanguage::Both,
module_system: ModuleSystem::ESModules,
type_definitions: true,
documentation: true,
bundling: BundlingConfig {
enable: true,
format: BundleFormat::Single,
minify: true,
tree_shaking: true,
code_splitting: false,
},
let generator = BindingGenerator::new(temp_dir.path().to_path_buf(), config);
assert_eq!(generator.output_dir, temp_dir.path());
fn test_javascript_binding_generation() {
target_language: WebBindingLanguage::JavaScript,
enable: false,
minify: false,
tree_shaking: false,
let js_path = generator.generate_javascript_bindings().expect("Operation failed");
assert!(js_path.exists());
assert!(js_path.to_string_lossy().ends_with("scirs2-model.js"));
let content = fs::read_to_string(&js_path).expect("Operation failed");
assert!(content.contains("class SciRS2Model"));
assert!(content.contains("async initialize"));
assert!(content.contains("async predict"));
fn test_typescript_binding_generation() {
target_language: WebBindingLanguage::TypeScript,
let ts_path = generator.generate_typescript_bindings().expect("Operation failed");
assert!(ts_path.exists());
assert!(ts_path.to_string_lossy().ends_with("scirs2-model.ts"));
let content = fs::read_to_string(&ts_path).expect("Operation failed");
assert!(content.contains("export class SciRS2Model"));
assert!(content.contains("Promise<void>"));
assert!(content.contains("PredictionOptions"));