Merge commit 'd803bfe2b1fe7f5e219e50ac20d6801a0a58ac75' as 'vendor/ruvector'
This commit is contained in:
ruv
33
vendor/ruvector/crates/ruvector-graph-transformer-wasm/Cargo.toml
vendored
Normal file
33
vendor/ruvector/crates/ruvector-graph-transformer-wasm/Cargo.toml
vendored
Normal file
@@ -0,0 +1,33 @@
|
||||
[package.metadata.wasm-pack.profile.release]
|
||||
wasm-opt = false
|
||||
|
||||
[package]
|
||||
name = "ruvector-graph-transformer-wasm"
|
||||
version.workspace = true
|
||||
edition.workspace = true
|
||||
rust-version.workspace = true
|
||||
license.workspace = true
|
||||
authors.workspace = true
|
||||
repository.workspace = true
|
||||
description = "WASM bindings for ruvector-graph-transformer: proof-gated graph attention in the browser"
|
||||
readme = "README.md"
|
||||
keywords = ["wasm", "graph-transformer", "attention", "verified", "webassembly"]
|
||||
categories = ["wasm", "science", "mathematics"]
|
||||
|
||||
[lib]
|
||||
crate-type = ["cdylib", "rlib"]
|
||||
|
||||
[dependencies]
|
||||
wasm-bindgen = "0.2"
|
||||
serde-wasm-bindgen = "0.6"
|
||||
serde = { workspace = true, features = ["derive"] }
|
||||
serde_json = { workspace = true }
|
||||
js-sys = "0.3"
|
||||
|
||||
[dev-dependencies]
|
||||
wasm-bindgen-test = "0.3"
|
||||
|
||||
[profile.release]
|
||||
opt-level = "s"
|
||||
lto = true
|
||||
codegen-units = 1
|
||||
181
vendor/ruvector/crates/ruvector-graph-transformer-wasm/README.md
vendored
Normal file
181
vendor/ruvector/crates/ruvector-graph-transformer-wasm/README.md
vendored
Normal file
@@ -0,0 +1,181 @@
|
||||
# ruvector-graph-transformer-wasm
|
||||
|
||||
[](https://crates.io/crates/ruvector-graph-transformer-wasm)
|
||||
[](LICENSE)
|
||||
|
||||
**WebAssembly bindings for RuVector Graph Transformer — proof-gated graph attention, verified training, and 8 specialized graph layers running client-side in the browser.**
|
||||
|
||||
Run the full graph transformer in any browser tab — no server, no API calls, no data leaving the device. Every graph mutation is formally verified client-side, so your users get the same mathematical safety guarantees as the Rust version. The WASM binary is size-optimized and loads in milliseconds.
|
||||
|
||||
## Install
|
||||
|
||||
```bash
|
||||
# With wasm-pack (recommended)
|
||||
wasm-pack build crates/ruvector-graph-transformer-wasm --target web
|
||||
|
||||
# Or from npm (when published)
|
||||
npm install ruvector-graph-transformer-wasm
|
||||
```
|
||||
|
||||
## Quick Start
|
||||
|
||||
```javascript
|
||||
import init, { JsGraphTransformer } from "ruvector-graph-transformer-wasm";
|
||||
|
||||
await init();
|
||||
const gt = new JsGraphTransformer();
|
||||
console.log(gt.version()); // "2.0.4"
|
||||
|
||||
// Proof-gated mutation
|
||||
const gate = gt.create_proof_gate(128);
|
||||
const proof = gt.prove_dimension(128, 128);
|
||||
console.log(proof.verified); // true
|
||||
|
||||
// 82-byte attestation for RVF witness chains
|
||||
const attestation = gt.create_attestation(proof.proof_id);
|
||||
console.log(attestation.length); // 82
|
||||
|
||||
// Sublinear attention — O(n log n)
|
||||
const result = gt.sublinear_attention(
|
||||
new Float32Array([0.1, 0.2, 0.3, 0.4]),
|
||||
[{ src: 0, tgt: 1 }, { src: 0, tgt: 2 }],
|
||||
4, 2
|
||||
);
|
||||
|
||||
// Verified training step with certificate
|
||||
const step = gt.verified_training_step(
|
||||
[1.0, 2.0], [0.1, 0.2], 0.01
|
||||
);
|
||||
console.log(step.weights, step.certificate);
|
||||
|
||||
// Physics: symplectic integration
|
||||
const state = gt.hamiltonian_step([1.0, 0.0], [0.0, 1.0], 0.01);
|
||||
console.log(state.energy);
|
||||
|
||||
// Biological: spiking attention
|
||||
const spikes = gt.spiking_attention(
|
||||
[0.5, 1.5, 0.3], [[1], [0, 2], [1]], 1.0
|
||||
);
|
||||
|
||||
// Manifold: mixed-curvature distance
|
||||
const d = gt.product_manifold_distance(
|
||||
[1, 0, 0, 1], [0, 1, 1, 0], [0.0, -1.0]
|
||||
);
|
||||
|
||||
// Temporal: causal masking
|
||||
const scores = gt.causal_attention(
|
||||
[1.0, 0.0],
|
||||
[[1.0, 0.0], [0.0, 1.0]],
|
||||
[1.0, 2.0]
|
||||
);
|
||||
|
||||
// Economic: Nash equilibrium
|
||||
const nash = gt.game_theoretic_attention(
|
||||
[1.0, 0.5, 0.8],
|
||||
[{ src: 0, tgt: 1 }, { src: 1, tgt: 2 }]
|
||||
);
|
||||
console.log(nash.converged);
|
||||
|
||||
// Stats
|
||||
console.log(gt.stats());
|
||||
```
|
||||
|
||||
## API
|
||||
|
||||
### Proof-Gated Operations
|
||||
|
||||
| Method | Returns | Description |
|
||||
|--------|---------|-------------|
|
||||
| `new JsGraphTransformer(config?)` | `JsGraphTransformer` | Create transformer instance |
|
||||
| `version()` | `string` | Crate version |
|
||||
| `create_proof_gate(dim)` | `object` | Create proof gate for dimension |
|
||||
| `prove_dimension(expected, actual)` | `object` | Prove dimension equality |
|
||||
| `create_attestation(proof_id)` | `Uint8Array` | 82-byte proof attestation |
|
||||
| `verify_attestation(bytes)` | `boolean` | Verify attestation from bytes |
|
||||
| `compose_proofs(stages)` | `object` | Type-checked pipeline composition |
|
||||
|
||||
### Sublinear Attention
|
||||
|
||||
| Method | Returns | Description |
|
||||
|--------|---------|-------------|
|
||||
| `sublinear_attention(q, edges, dim, k)` | `object` | Graph-sparse top-k attention |
|
||||
| `ppr_scores(source, adj, alpha)` | `Float64Array` | Personalized PageRank scores |
|
||||
|
||||
### Physics-Informed
|
||||
|
||||
| Method | Returns | Description |
|
||||
|--------|---------|-------------|
|
||||
| `hamiltonian_step(positions, momenta, dt)` | `object` | Symplectic leapfrog step |
|
||||
| `verify_energy_conservation(before, after, tol)` | `object` | Energy conservation proof |
|
||||
|
||||
### Biological
|
||||
|
||||
| Method | Returns | Description |
|
||||
|--------|---------|-------------|
|
||||
| `spiking_attention(spikes, edges, threshold)` | `Float64Array` | Event-driven spiking attention |
|
||||
| `hebbian_update(pre, post, weights, lr)` | `Float64Array` | Hebbian weight update |
|
||||
| `spiking_step(features, adjacency)` | `object` | Full spiking step over feature matrix |
|
||||
|
||||
### Verified Training
|
||||
|
||||
| Method | Returns | Description |
|
||||
|--------|---------|-------------|
|
||||
| `verified_step(weights, gradients, lr)` | `object` | SGD step + proof receipt |
|
||||
| `verified_training_step(features, targets, weights)` | `object` | Training step + certificate |
|
||||
|
||||
### Manifold
|
||||
|
||||
| Method | Returns | Description |
|
||||
|--------|---------|-------------|
|
||||
| `product_manifold_distance(a, b, curvatures)` | `number` | Mixed-curvature distance |
|
||||
| `product_manifold_attention(features, edges)` | `object` | Product manifold attention |
|
||||
|
||||
### Temporal-Causal
|
||||
|
||||
| Method | Returns | Description |
|
||||
|--------|---------|-------------|
|
||||
| `causal_attention(query, keys, timestamps)` | `Float64Array` | Temporally masked attention |
|
||||
| `causal_attention_graph(features, timestamps, edges)` | `Float64Array` | Causal graph attention |
|
||||
| `granger_extract(history, num_nodes, num_steps)` | `object` | Granger causality DAG |
|
||||
|
||||
### Economic
|
||||
|
||||
| Method | Returns | Description |
|
||||
|--------|---------|-------------|
|
||||
| `game_theoretic_attention(features, edges)` | `object` | Nash equilibrium attention |
|
||||
|
||||
### Meta
|
||||
|
||||
| Method | Returns | Description |
|
||||
|--------|---------|-------------|
|
||||
| `stats()` | `object` | Aggregate proof/attestation statistics |
|
||||
| `reset()` | `void` | Reset all internal state |
|
||||
|
||||
## Building
|
||||
|
||||
```bash
|
||||
# Web target (recommended for browsers)
|
||||
wasm-pack build crates/ruvector-graph-transformer-wasm --target web
|
||||
|
||||
# Node.js target
|
||||
wasm-pack build crates/ruvector-graph-transformer-wasm --target nodejs
|
||||
|
||||
# Cargo check
|
||||
cargo check -p ruvector-graph-transformer-wasm
|
||||
```
|
||||
|
||||
## Bundle Size
|
||||
|
||||
The WASM binary is optimized for size with `opt-level = "s"`, LTO, and single codegen unit.
|
||||
|
||||
## Related Packages
|
||||
|
||||
| Package | Description |
|
||||
|---------|-------------|
|
||||
| [`ruvector-graph-transformer`](../ruvector-graph-transformer) | Core Rust crate (186 tests) |
|
||||
| [`@ruvector/graph-transformer`](../ruvector-graph-transformer-node) | Node.js NAPI-RS bindings |
|
||||
| [`ruvector-verified-wasm`](../ruvector-verified-wasm) | Formal verification WASM bindings |
|
||||
|
||||
## License
|
||||
|
||||
MIT
|
||||
181
vendor/ruvector/crates/ruvector-graph-transformer-wasm/pkg/README.md
vendored
Normal file
181
vendor/ruvector/crates/ruvector-graph-transformer-wasm/pkg/README.md
vendored
Normal file
@@ -0,0 +1,181 @@
|
||||
# ruvector-graph-transformer-wasm
|
||||
|
||||
[](https://crates.io/crates/ruvector-graph-transformer-wasm)
|
||||
[](LICENSE)
|
||||
|
||||
**WebAssembly bindings for RuVector Graph Transformer — proof-gated graph attention, verified training, and 8 specialized graph layers running client-side in the browser.**
|
||||
|
||||
Run the full graph transformer in any browser tab — no server, no API calls, no data leaving the device. Every graph mutation is formally verified client-side, so your users get the same mathematical safety guarantees as the Rust version. The WASM binary is size-optimized and loads in milliseconds.
|
||||
|
||||
## Install
|
||||
|
||||
```bash
|
||||
# With wasm-pack (recommended)
|
||||
wasm-pack build crates/ruvector-graph-transformer-wasm --target web
|
||||
|
||||
# Or from npm (when published)
|
||||
npm install ruvector-graph-transformer-wasm
|
||||
```
|
||||
|
||||
## Quick Start
|
||||
|
||||
```javascript
|
||||
import init, { JsGraphTransformer } from "ruvector-graph-transformer-wasm";
|
||||
|
||||
await init();
|
||||
const gt = new JsGraphTransformer();
|
||||
console.log(gt.version()); // "2.0.4"
|
||||
|
||||
// Proof-gated mutation
|
||||
const gate = gt.create_proof_gate(128);
|
||||
const proof = gt.prove_dimension(128, 128);
|
||||
console.log(proof.verified); // true
|
||||
|
||||
// 82-byte attestation for RVF witness chains
|
||||
const attestation = gt.create_attestation(proof.proof_id);
|
||||
console.log(attestation.length); // 82
|
||||
|
||||
// Sublinear attention — O(n log n)
|
||||
const result = gt.sublinear_attention(
|
||||
new Float32Array([0.1, 0.2, 0.3, 0.4]),
|
||||
[{ src: 0, tgt: 1 }, { src: 0, tgt: 2 }],
|
||||
4, 2
|
||||
);
|
||||
|
||||
// Verified training step with certificate
|
||||
const step = gt.verified_training_step(
|
||||
[1.0, 2.0], [0.1, 0.2], 0.01
|
||||
);
|
||||
console.log(step.weights, step.certificate);
|
||||
|
||||
// Physics: symplectic integration
|
||||
const state = gt.hamiltonian_step([1.0, 0.0], [0.0, 1.0], 0.01);
|
||||
console.log(state.energy);
|
||||
|
||||
// Biological: spiking attention
|
||||
const spikes = gt.spiking_attention(
|
||||
[0.5, 1.5, 0.3], [[1], [0, 2], [1]], 1.0
|
||||
);
|
||||
|
||||
// Manifold: mixed-curvature distance
|
||||
const d = gt.product_manifold_distance(
|
||||
[1, 0, 0, 1], [0, 1, 1, 0], [0.0, -1.0]
|
||||
);
|
||||
|
||||
// Temporal: causal masking
|
||||
const scores = gt.causal_attention(
|
||||
[1.0, 0.0],
|
||||
[[1.0, 0.0], [0.0, 1.0]],
|
||||
[1.0, 2.0]
|
||||
);
|
||||
|
||||
// Economic: Nash equilibrium
|
||||
const nash = gt.game_theoretic_attention(
|
||||
[1.0, 0.5, 0.8],
|
||||
[{ src: 0, tgt: 1 }, { src: 1, tgt: 2 }]
|
||||
);
|
||||
console.log(nash.converged);
|
||||
|
||||
// Stats
|
||||
console.log(gt.stats());
|
||||
```
|
||||
|
||||
## API
|
||||
|
||||
### Proof-Gated Operations
|
||||
|
||||
| Method | Returns | Description |
|
||||
|--------|---------|-------------|
|
||||
| `new JsGraphTransformer(config?)` | `JsGraphTransformer` | Create transformer instance |
|
||||
| `version()` | `string` | Crate version |
|
||||
| `create_proof_gate(dim)` | `object` | Create proof gate for dimension |
|
||||
| `prove_dimension(expected, actual)` | `object` | Prove dimension equality |
|
||||
| `create_attestation(proof_id)` | `Uint8Array` | 82-byte proof attestation |
|
||||
| `verify_attestation(bytes)` | `boolean` | Verify attestation from bytes |
|
||||
| `compose_proofs(stages)` | `object` | Type-checked pipeline composition |
|
||||
|
||||
### Sublinear Attention
|
||||
|
||||
| Method | Returns | Description |
|
||||
|--------|---------|-------------|
|
||||
| `sublinear_attention(q, edges, dim, k)` | `object` | Graph-sparse top-k attention |
|
||||
| `ppr_scores(source, adj, alpha)` | `Float64Array` | Personalized PageRank scores |
|
||||
|
||||
### Physics-Informed
|
||||
|
||||
| Method | Returns | Description |
|
||||
|--------|---------|-------------|
|
||||
| `hamiltonian_step(positions, momenta, dt)` | `object` | Symplectic leapfrog step |
|
||||
| `verify_energy_conservation(before, after, tol)` | `object` | Energy conservation proof |
|
||||
|
||||
### Biological
|
||||
|
||||
| Method | Returns | Description |
|
||||
|--------|---------|-------------|
|
||||
| `spiking_attention(spikes, edges, threshold)` | `Float64Array` | Event-driven spiking attention |
|
||||
| `hebbian_update(pre, post, weights, lr)` | `Float64Array` | Hebbian weight update |
|
||||
| `spiking_step(features, adjacency)` | `object` | Full spiking step over feature matrix |
|
||||
|
||||
### Verified Training
|
||||
|
||||
| Method | Returns | Description |
|
||||
|--------|---------|-------------|
|
||||
| `verified_step(weights, gradients, lr)` | `object` | SGD step + proof receipt |
|
||||
| `verified_training_step(features, targets, weights)` | `object` | Training step + certificate |
|
||||
|
||||
### Manifold
|
||||
|
||||
| Method | Returns | Description |
|
||||
|--------|---------|-------------|
|
||||
| `product_manifold_distance(a, b, curvatures)` | `number` | Mixed-curvature distance |
|
||||
| `product_manifold_attention(features, edges)` | `object` | Product manifold attention |
|
||||
|
||||
### Temporal-Causal
|
||||
|
||||
| Method | Returns | Description |
|
||||
|--------|---------|-------------|
|
||||
| `causal_attention(query, keys, timestamps)` | `Float64Array` | Temporally masked attention |
|
||||
| `causal_attention_graph(features, timestamps, edges)` | `Float64Array` | Causal graph attention |
|
||||
| `granger_extract(history, num_nodes, num_steps)` | `object` | Granger causality DAG |
|
||||
|
||||
### Economic
|
||||
|
||||
| Method | Returns | Description |
|
||||
|--------|---------|-------------|
|
||||
| `game_theoretic_attention(features, edges)` | `object` | Nash equilibrium attention |
|
||||
|
||||
### Meta
|
||||
|
||||
| Method | Returns | Description |
|
||||
|--------|---------|-------------|
|
||||
| `stats()` | `object` | Aggregate proof/attestation statistics |
|
||||
| `reset()` | `void` | Reset all internal state |
|
||||
|
||||
## Building
|
||||
|
||||
```bash
|
||||
# Web target (recommended for browsers)
|
||||
wasm-pack build crates/ruvector-graph-transformer-wasm --target web
|
||||
|
||||
# Node.js target
|
||||
wasm-pack build crates/ruvector-graph-transformer-wasm --target nodejs
|
||||
|
||||
# Cargo check
|
||||
cargo check -p ruvector-graph-transformer-wasm
|
||||
```
|
||||
|
||||
## Bundle Size
|
||||
|
||||
The WASM binary is optimized for size with `opt-level = "s"`, LTO, and single codegen unit.
|
||||
|
||||
## Related Packages
|
||||
|
||||
| Package | Description |
|
||||
|---------|-------------|
|
||||
| [`ruvector-graph-transformer`](../ruvector-graph-transformer) | Core Rust crate (186 tests) |
|
||||
| [`@ruvector/graph-transformer`](../ruvector-graph-transformer-node) | Node.js NAPI-RS bindings |
|
||||
| [`ruvector-verified-wasm`](../ruvector-verified-wasm) | Formal verification WASM bindings |
|
||||
|
||||
## License
|
||||
|
||||
MIT
|
||||
31
vendor/ruvector/crates/ruvector-graph-transformer-wasm/pkg/package.json
vendored
Normal file
31
vendor/ruvector/crates/ruvector-graph-transformer-wasm/pkg/package.json
vendored
Normal file
@@ -0,0 +1,31 @@
|
||||
{
|
||||
"name": "ruvector-graph-transformer-wasm",
|
||||
"type": "module",
|
||||
"collaborators": [
|
||||
"Ruvector Team"
|
||||
],
|
||||
"description": "WASM bindings for ruvector-graph-transformer: proof-gated graph attention in the browser",
|
||||
"version": "2.0.5",
|
||||
"license": "MIT",
|
||||
"repository": {
|
||||
"type": "git",
|
||||
"url": "https://github.com/ruvnet/ruvector"
|
||||
},
|
||||
"files": [
|
||||
"ruvector_graph_transformer_wasm_bg.wasm",
|
||||
"ruvector_graph_transformer_wasm.js",
|
||||
"ruvector_graph_transformer_wasm.d.ts"
|
||||
],
|
||||
"main": "ruvector_graph_transformer_wasm.js",
|
||||
"types": "ruvector_graph_transformer_wasm.d.ts",
|
||||
"sideEffects": [
|
||||
"./snippets/*"
|
||||
],
|
||||
"keywords": [
|
||||
"wasm",
|
||||
"graph-transformer",
|
||||
"attention",
|
||||
"verified",
|
||||
"webassembly"
|
||||
]
|
||||
}
|
||||
225
vendor/ruvector/crates/ruvector-graph-transformer-wasm/pkg/ruvector_graph_transformer_wasm.d.ts
vendored
Normal file
225
vendor/ruvector/crates/ruvector-graph-transformer-wasm/pkg/ruvector_graph_transformer_wasm.d.ts
vendored
Normal file
@@ -0,0 +1,225 @@
|
||||
/* tslint:disable */
|
||||
/* eslint-disable */
|
||||
|
||||
/**
|
||||
* Graph transformer for the browser.
|
||||
*
|
||||
* Wraps the core `CoreGraphTransformer` and exposes proof-gated, sublinear,
|
||||
* physics, biological, verified-training, manifold, temporal, and economic
|
||||
* operations via wasm_bindgen.
|
||||
*/
|
||||
export class JsGraphTransformer {
|
||||
free(): void;
|
||||
[Symbol.dispose](): void;
|
||||
/**
|
||||
* Causal attention with temporal ordering over graph edges.
|
||||
*
|
||||
* `features` is a Float64Array, `timestamps` is a Float64Array,
|
||||
* `edges` is `[{ src, tgt }, ...]`.
|
||||
* Returns attention-weighted output features.
|
||||
*/
|
||||
causal_attention(features: any, timestamps: any, edges: any): any;
|
||||
/**
|
||||
* Compose a chain of pipeline stages, verifying type compatibility.
|
||||
*
|
||||
* `stages` is a JS array of `{ name, input_type_id, output_type_id }`.
|
||||
* Returns a composed proof with the overall input/output types.
|
||||
*/
|
||||
compose_proofs(stages: any): any;
|
||||
/**
|
||||
* Create a proof attestation for a given proof ID.
|
||||
*
|
||||
* Returns the attestation as a byte buffer (82 bytes).
|
||||
*/
|
||||
create_attestation(proof_id: number): Uint8Array;
|
||||
/**
|
||||
* Create a proof gate for the given embedding dimension.
|
||||
*
|
||||
* Returns a serialized `ProofGate` object.
|
||||
*/
|
||||
create_proof_gate(dim: number): any;
|
||||
/**
|
||||
* Game-theoretic attention: computes Nash equilibrium allocations.
|
||||
*
|
||||
* `features` is a Float64Array, `edges` is `[{ src, tgt }, ...]`.
|
||||
* Returns `{ allocations, utilities, nash_gap, converged }`.
|
||||
*/
|
||||
game_theoretic_attention(features: any, edges: any): any;
|
||||
/**
|
||||
* Extract Granger causality DAG from attention history.
|
||||
*
|
||||
* `attention_history` is a flat Float64Array (T x N row-major).
|
||||
* Returns `{ edges: [{ source, target, f_statistic, is_causal }], num_nodes }`.
|
||||
*/
|
||||
granger_extract(attention_history: any, num_nodes: number, num_steps: number): any;
|
||||
/**
|
||||
* Symplectic integrator step (leapfrog / Stormer-Verlet).
|
||||
*
|
||||
* `positions` and `momenta` are Float64Arrays, `edges` is
|
||||
* `[{ src, tgt }, ...]`. Returns `{ positions, momenta, energy,
|
||||
* energy_conserved }`.
|
||||
*/
|
||||
hamiltonian_step(positions: any, momenta: any, edges: any): any;
|
||||
/**
|
||||
* Hebbian weight update.
|
||||
*
|
||||
* `pre`, `post`, `weights` are Float64Arrays. Returns updated weights.
|
||||
*/
|
||||
hebbian_update(pre: any, post: any, weights: any): any;
|
||||
/**
|
||||
* Create a new graph transformer.
|
||||
*
|
||||
* `config` is an optional JS object (reserved for future use).
|
||||
*/
|
||||
constructor(config: any);
|
||||
/**
|
||||
* Compute personalized PageRank scores from a source node.
|
||||
*
|
||||
* Returns array of PPR scores, one per node.
|
||||
*/
|
||||
ppr_scores(source: number, adjacency: any, alpha: number): any;
|
||||
/**
|
||||
* Product manifold attention with mixed curvatures.
|
||||
*
|
||||
* `features` is a Float64Array, `edges` is `[{ src, tgt }, ...]`.
|
||||
* Optional `curvatures` (defaults to `[0.0, -1.0]`).
|
||||
* Returns `{ output, curvatures, distances }`.
|
||||
*/
|
||||
product_manifold_attention(features: any, edges: any): any;
|
||||
/**
|
||||
* Product manifold distance between two points.
|
||||
*
|
||||
* `a` and `b` are Float64Arrays, `curvatures` is `[number, ...]`.
|
||||
*/
|
||||
product_manifold_distance(a: any, b: any, curvatures: any): number;
|
||||
/**
|
||||
* Prove that two dimensions are equal.
|
||||
*
|
||||
* Returns `{ proof_id, expected, actual, verified }`.
|
||||
*/
|
||||
prove_dimension(expected: number, actual: number): any;
|
||||
/**
|
||||
* Reset all internal state (caches, counters, gates).
|
||||
*/
|
||||
reset(): void;
|
||||
/**
|
||||
* Spiking neural attention step over 2D features with adjacency.
|
||||
*
|
||||
* `features` is `[[f64, ...], ...]`, `adjacency` is a flat row-major
|
||||
* Float64Array (n x n). Returns `{ features, spikes, weights }`.
|
||||
*/
|
||||
spiking_step(features: any, adjacency: any): any;
|
||||
/**
|
||||
* Return transformer statistics.
|
||||
*
|
||||
* Returns `{ proofs_constructed, proofs_verified, cache_hits,
|
||||
* cache_misses, attention_ops, physics_ops, bio_ops, training_steps }`.
|
||||
*/
|
||||
stats(): any;
|
||||
/**
|
||||
* Sublinear graph attention using personalized PageRank sparsification.
|
||||
*
|
||||
* `query` is a Float64Array, `edges` is `[[u32, ...], ...]`.
|
||||
* Returns `{ scores, top_k_indices, sparsity_ratio }`.
|
||||
*/
|
||||
sublinear_attention(query: any, edges: any, dim: number, k: number): any;
|
||||
/**
|
||||
* A single verified SGD step (raw weights + gradients).
|
||||
*
|
||||
* Returns `{ weights, proof_id, loss_before, loss_after, gradient_norm }`.
|
||||
*/
|
||||
verified_step(weights: any, gradients: any, lr: number): any;
|
||||
/**
|
||||
* Verified training step with features, targets, and weights.
|
||||
*
|
||||
* `features`, `targets`, `weights` are Float64Arrays.
|
||||
* Returns `{ weights, certificate_id, loss, loss_monotonic,
|
||||
* lipschitz_satisfied }`.
|
||||
*/
|
||||
verified_training_step(features: any, targets: any, weights: any): any;
|
||||
/**
|
||||
* Verify an attestation from its byte representation.
|
||||
*
|
||||
* Returns `true` if the attestation is structurally valid.
|
||||
*/
|
||||
verify_attestation(bytes: Uint8Array): boolean;
|
||||
/**
|
||||
* Verify energy conservation between two states.
|
||||
*
|
||||
* Returns `{ conserved, delta, relative_error }`.
|
||||
*/
|
||||
verify_energy_conservation(before: number, after: number, tolerance: number): any;
|
||||
/**
|
||||
* Get the library version string.
|
||||
*/
|
||||
version(): string;
|
||||
}
|
||||
|
||||
/**
|
||||
* Called automatically when the WASM module is loaded.
|
||||
*/
|
||||
export function init(): void;
|
||||
|
||||
/**
|
||||
* Return the crate version.
|
||||
*/
|
||||
export function version(): string;
|
||||
|
||||
export type InitInput = RequestInfo | URL | Response | BufferSource | WebAssembly.Module;
|
||||
|
||||
export interface InitOutput {
|
||||
readonly memory: WebAssembly.Memory;
|
||||
readonly __wbg_jsgraphtransformer_free: (a: number, b: number) => void;
|
||||
readonly init: () => void;
|
||||
readonly jsgraphtransformer_causal_attention: (a: number, b: number, c: number, d: number, e: number) => void;
|
||||
readonly jsgraphtransformer_compose_proofs: (a: number, b: number, c: number) => void;
|
||||
readonly jsgraphtransformer_create_attestation: (a: number, b: number, c: number) => void;
|
||||
readonly jsgraphtransformer_create_proof_gate: (a: number, b: number, c: number) => void;
|
||||
readonly jsgraphtransformer_game_theoretic_attention: (a: number, b: number, c: number, d: number) => void;
|
||||
readonly jsgraphtransformer_granger_extract: (a: number, b: number, c: number, d: number, e: number) => void;
|
||||
readonly jsgraphtransformer_hamiltonian_step: (a: number, b: number, c: number, d: number, e: number) => void;
|
||||
readonly jsgraphtransformer_hebbian_update: (a: number, b: number, c: number, d: number, e: number) => void;
|
||||
readonly jsgraphtransformer_new: (a: number, b: number) => void;
|
||||
readonly jsgraphtransformer_ppr_scores: (a: number, b: number, c: number, d: number, e: number) => void;
|
||||
readonly jsgraphtransformer_product_manifold_attention: (a: number, b: number, c: number, d: number) => void;
|
||||
readonly jsgraphtransformer_product_manifold_distance: (a: number, b: number, c: number, d: number, e: number) => void;
|
||||
readonly jsgraphtransformer_prove_dimension: (a: number, b: number, c: number, d: number) => void;
|
||||
readonly jsgraphtransformer_reset: (a: number) => void;
|
||||
readonly jsgraphtransformer_spiking_step: (a: number, b: number, c: number, d: number) => void;
|
||||
readonly jsgraphtransformer_stats: (a: number, b: number) => void;
|
||||
readonly jsgraphtransformer_sublinear_attention: (a: number, b: number, c: number, d: number, e: number, f: number) => void;
|
||||
readonly jsgraphtransformer_verified_step: (a: number, b: number, c: number, d: number, e: number) => void;
|
||||
readonly jsgraphtransformer_verified_training_step: (a: number, b: number, c: number, d: number, e: number) => void;
|
||||
readonly jsgraphtransformer_verify_attestation: (a: number, b: number, c: number) => number;
|
||||
readonly jsgraphtransformer_verify_energy_conservation: (a: number, b: number, c: number, d: number, e: number) => void;
|
||||
readonly jsgraphtransformer_version: (a: number, b: number) => void;
|
||||
readonly version: (a: number) => void;
|
||||
readonly __wbindgen_export: (a: number, b: number) => number;
|
||||
readonly __wbindgen_export2: (a: number, b: number, c: number, d: number) => number;
|
||||
readonly __wbindgen_export3: (a: number) => void;
|
||||
readonly __wbindgen_add_to_stack_pointer: (a: number) => number;
|
||||
readonly __wbindgen_export4: (a: number, b: number, c: number) => void;
|
||||
readonly __wbindgen_start: () => void;
|
||||
}
|
||||
|
||||
export type SyncInitInput = BufferSource | WebAssembly.Module;
|
||||
|
||||
/**
|
||||
* Instantiates the given `module`, which can either be bytes or
|
||||
* a precompiled `WebAssembly.Module`.
|
||||
*
|
||||
* @param {{ module: SyncInitInput }} module - Passing `SyncInitInput` directly is deprecated.
|
||||
*
|
||||
* @returns {InitOutput}
|
||||
*/
|
||||
export function initSync(module: { module: SyncInitInput } | SyncInitInput): InitOutput;
|
||||
|
||||
/**
|
||||
* If `module_or_path` is {RequestInfo} or {URL}, makes a request and
|
||||
* for everything else, calls `WebAssembly.instantiate` directly.
|
||||
*
|
||||
* @param {{ module_or_path: InitInput | Promise<InitInput> }} module_or_path - Passing `InitInput` directly is deprecated.
|
||||
*
|
||||
* @returns {Promise<InitOutput>}
|
||||
*/
|
||||
export default function __wbg_init (module_or_path?: { module_or_path: InitInput | Promise<InitInput> } | InitInput | Promise<InitInput>): Promise<InitOutput>;
|
||||
1040
vendor/ruvector/crates/ruvector-graph-transformer-wasm/pkg/ruvector_graph_transformer_wasm.js
vendored
Normal file
1040
vendor/ruvector/crates/ruvector-graph-transformer-wasm/pkg/ruvector_graph_transformer_wasm.js
vendored
Normal file
File diff suppressed because it is too large
Load Diff
BIN
vendor/ruvector/crates/ruvector-graph-transformer-wasm/pkg/ruvector_graph_transformer_wasm_bg.wasm
vendored
Normal file
BIN
vendor/ruvector/crates/ruvector-graph-transformer-wasm/pkg/ruvector_graph_transformer_wasm_bg.wasm
vendored
Normal file
Binary file not shown.
@@ -0,0 +1,34 @@
|
||||
/* tslint:disable */
|
||||
/* eslint-disable */
|
||||
export const memory: WebAssembly.Memory;
|
||||
export const __wbg_jsgraphtransformer_free: (a: number, b: number) => void;
|
||||
export const init: () => void;
|
||||
export const jsgraphtransformer_causal_attention: (a: number, b: number, c: number, d: number, e: number) => void;
|
||||
export const jsgraphtransformer_compose_proofs: (a: number, b: number, c: number) => void;
|
||||
export const jsgraphtransformer_create_attestation: (a: number, b: number, c: number) => void;
|
||||
export const jsgraphtransformer_create_proof_gate: (a: number, b: number, c: number) => void;
|
||||
export const jsgraphtransformer_game_theoretic_attention: (a: number, b: number, c: number, d: number) => void;
|
||||
export const jsgraphtransformer_granger_extract: (a: number, b: number, c: number, d: number, e: number) => void;
|
||||
export const jsgraphtransformer_hamiltonian_step: (a: number, b: number, c: number, d: number, e: number) => void;
|
||||
export const jsgraphtransformer_hebbian_update: (a: number, b: number, c: number, d: number, e: number) => void;
|
||||
export const jsgraphtransformer_new: (a: number, b: number) => void;
|
||||
export const jsgraphtransformer_ppr_scores: (a: number, b: number, c: number, d: number, e: number) => void;
|
||||
export const jsgraphtransformer_product_manifold_attention: (a: number, b: number, c: number, d: number) => void;
|
||||
export const jsgraphtransformer_product_manifold_distance: (a: number, b: number, c: number, d: number, e: number) => void;
|
||||
export const jsgraphtransformer_prove_dimension: (a: number, b: number, c: number, d: number) => void;
|
||||
export const jsgraphtransformer_reset: (a: number) => void;
|
||||
export const jsgraphtransformer_spiking_step: (a: number, b: number, c: number, d: number) => void;
|
||||
export const jsgraphtransformer_stats: (a: number, b: number) => void;
|
||||
export const jsgraphtransformer_sublinear_attention: (a: number, b: number, c: number, d: number, e: number, f: number) => void;
|
||||
export const jsgraphtransformer_verified_step: (a: number, b: number, c: number, d: number, e: number) => void;
|
||||
export const jsgraphtransformer_verified_training_step: (a: number, b: number, c: number, d: number, e: number) => void;
|
||||
export const jsgraphtransformer_verify_attestation: (a: number, b: number, c: number) => number;
|
||||
export const jsgraphtransformer_verify_energy_conservation: (a: number, b: number, c: number, d: number, e: number) => void;
|
||||
export const jsgraphtransformer_version: (a: number, b: number) => void;
|
||||
export const version: (a: number) => void;
|
||||
export const __wbindgen_export: (a: number, b: number) => number;
|
||||
export const __wbindgen_export2: (a: number, b: number, c: number, d: number) => number;
|
||||
export const __wbindgen_export3: (a: number) => void;
|
||||
export const __wbindgen_add_to_stack_pointer: (a: number) => number;
|
||||
export const __wbindgen_export4: (a: number, b: number, c: number) => void;
|
||||
export const __wbindgen_start: () => void;
|
||||
460
vendor/ruvector/crates/ruvector-graph-transformer-wasm/src/lib.rs
vendored
Normal file
460
vendor/ruvector/crates/ruvector-graph-transformer-wasm/src/lib.rs
vendored
Normal file
@@ -0,0 +1,460 @@
|
||||
//! WASM bindings for `ruvector-graph-transformer`: proof-gated graph attention
|
||||
//! in the browser.
|
||||
//!
|
||||
//! # Quick Start (JavaScript)
|
||||
//!
|
||||
//! ```js
|
||||
//! import init, { JsGraphTransformer } from "ruvector-graph-transformer-wasm";
|
||||
//!
|
||||
//! await init();
|
||||
//! const gt = new JsGraphTransformer();
|
||||
//!
|
||||
//! // Create a proof gate and prove dimensions
|
||||
//! const gate = gt.createProofGate(128);
|
||||
//! const proof = gt.proveDimension(128, 128);
|
||||
//!
|
||||
//! // Sublinear attention
|
||||
//! const result = gt.sublinearAttention(
|
||||
//! new Float64Array([0.1, 0.2]),
|
||||
//! [[1, 2], [0, 2], [0, 1]],
|
||||
//! 2, 2,
|
||||
//! );
|
||||
//!
|
||||
//! // Physics: Hamiltonian step with graph edges
|
||||
//! const state = gt.hamiltonianStep([1.0, 0.0], [0.0, 1.0], [{ src: 0, tgt: 1 }]);
|
||||
//!
|
||||
//! // Biological: spiking step
|
||||
//! const spikes = gt.spikingStep([[0.8, 0.6], [0.1, 0.2]], [0, 0.5, 0.3, 0]);
|
||||
//!
|
||||
//! // Temporal: causal attention
|
||||
//! const attn = gt.causalAttention([1.0, 0.0], [1.0, 2.0, 3.0], [{ src: 0, tgt: 1 }]);
|
||||
//!
|
||||
//! // Manifold: product manifold attention
|
||||
//! const manifold = gt.productManifoldAttention([1.0, 0.5], [{ src: 0, tgt: 1 }]);
|
||||
//!
|
||||
//! // Verified training
|
||||
//! const training = gt.verifiedTrainingStep([1.0, 2.0], [0.5, 1.0], [0.5, 0.5]);
|
||||
//!
|
||||
//! // Economic: game-theoretic attention
|
||||
//! const eqm = gt.gameTheoreticAttention([1.0, 0.5, 0.8], [{ src: 0, tgt: 1 }]);
|
||||
//!
|
||||
//! // Stats
|
||||
//! console.log(gt.stats());
|
||||
//! ```
|
||||
|
||||
mod transformer;
|
||||
mod utils;
|
||||
|
||||
use transformer::{CoreGraphTransformer, Edge, PipelineStage as CorePipelineStage};
|
||||
use wasm_bindgen::prelude::*;
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Module init
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Called automatically when the WASM module is loaded.
|
||||
#[wasm_bindgen(start)]
|
||||
pub fn init() {
|
||||
utils::set_panic_hook();
|
||||
}
|
||||
|
||||
/// Return the crate version.
|
||||
#[wasm_bindgen]
|
||||
pub fn version() -> String {
|
||||
env!("CARGO_PKG_VERSION").to_string()
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// JsGraphTransformer -- main entry point
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Graph transformer for the browser.
|
||||
///
|
||||
/// Wraps the core `CoreGraphTransformer` and exposes proof-gated, sublinear,
|
||||
/// physics, biological, verified-training, manifold, temporal, and economic
|
||||
/// operations via wasm_bindgen.
|
||||
#[wasm_bindgen]
|
||||
pub struct JsGraphTransformer {
|
||||
inner: CoreGraphTransformer,
|
||||
}
|
||||
|
||||
#[wasm_bindgen]
|
||||
impl JsGraphTransformer {
|
||||
/// Create a new graph transformer.
|
||||
///
|
||||
/// `config` is an optional JS object (reserved for future use).
|
||||
#[wasm_bindgen(constructor)]
|
||||
pub fn new(config: JsValue) -> Result<JsGraphTransformer, JsError> {
|
||||
let _ = config; // reserved for future configuration
|
||||
Ok(Self {
|
||||
inner: CoreGraphTransformer::new(),
|
||||
})
|
||||
}
|
||||
|
||||
/// Get the library version string.
|
||||
#[wasm_bindgen]
|
||||
pub fn version(&self) -> String {
|
||||
self.inner.version()
|
||||
}
|
||||
|
||||
// ===================================================================
|
||||
// Proof-Gated Operations
|
||||
// ===================================================================
|
||||
|
||||
/// Create a proof gate for the given embedding dimension.
|
||||
///
|
||||
/// Returns a serialized `ProofGate` object.
|
||||
pub fn create_proof_gate(&mut self, dim: u32) -> Result<JsValue, JsError> {
|
||||
let gate = self.inner.create_proof_gate(dim);
|
||||
serde_wasm_bindgen::to_value(&gate).map_err(|e| JsError::new(&e.to_string()))
|
||||
}
|
||||
|
||||
/// Prove that two dimensions are equal.
|
||||
///
|
||||
/// Returns `{ proof_id, expected, actual, verified }`.
|
||||
pub fn prove_dimension(&mut self, expected: u32, actual: u32) -> Result<JsValue, JsError> {
|
||||
let result = self
|
||||
.inner
|
||||
.prove_dimension(expected, actual)
|
||||
.map_err(|e| JsError::new(&format!("{e}")))?;
|
||||
serde_wasm_bindgen::to_value(&result).map_err(|e| JsError::new(&e.to_string()))
|
||||
}
|
||||
|
||||
/// Create a proof attestation for a given proof ID.
|
||||
///
|
||||
/// Returns the attestation as a byte buffer (82 bytes).
|
||||
pub fn create_attestation(&self, proof_id: u32) -> Result<Vec<u8>, JsError> {
|
||||
let att = self.inner.create_attestation(proof_id);
|
||||
Ok(att.to_bytes())
|
||||
}
|
||||
|
||||
/// Verify an attestation from its byte representation.
|
||||
///
|
||||
/// Returns `true` if the attestation is structurally valid.
|
||||
pub fn verify_attestation(&self, bytes: &[u8]) -> bool {
|
||||
self.inner.verify_attestation(bytes)
|
||||
}
|
||||
|
||||
/// Compose a chain of pipeline stages, verifying type compatibility.
|
||||
///
|
||||
/// `stages` is a JS array of `{ name, input_type_id, output_type_id }`.
|
||||
/// Returns a composed proof with the overall input/output types.
|
||||
pub fn compose_proofs(&mut self, stages: JsValue) -> Result<JsValue, JsError> {
|
||||
let rust_stages: Vec<CorePipelineStage> = serde_wasm_bindgen::from_value(stages)
|
||||
.map_err(|e| JsError::new(&format!("invalid stages: {e}")))?;
|
||||
let result = self
|
||||
.inner
|
||||
.compose_proofs(&rust_stages)
|
||||
.map_err(|e| JsError::new(&format!("{e}")))?;
|
||||
serde_wasm_bindgen::to_value(&result).map_err(|e| JsError::new(&e.to_string()))
|
||||
}
|
||||
|
||||
// ===================================================================
|
||||
// Sublinear Attention
|
||||
// ===================================================================
|
||||
|
||||
/// Sublinear graph attention using personalized PageRank sparsification.
|
||||
///
|
||||
/// `query` is a Float64Array, `edges` is `[[u32, ...], ...]`.
|
||||
/// Returns `{ scores, top_k_indices, sparsity_ratio }`.
|
||||
pub fn sublinear_attention(
|
||||
&mut self,
|
||||
query: JsValue,
|
||||
edges: JsValue,
|
||||
dim: u32,
|
||||
k: u32,
|
||||
) -> Result<JsValue, JsError> {
|
||||
let q: Vec<f64> = serde_wasm_bindgen::from_value(query)
|
||||
.map_err(|e| JsError::new(&format!("invalid query: {e}")))?;
|
||||
let ed: Vec<Vec<u32>> = serde_wasm_bindgen::from_value(edges)
|
||||
.map_err(|e| JsError::new(&format!("invalid edges: {e}")))?;
|
||||
let result = self
|
||||
.inner
|
||||
.sublinear_attention(&q, &ed, dim, k)
|
||||
.map_err(|e| JsError::new(&format!("{e}")))?;
|
||||
serde_wasm_bindgen::to_value(&result).map_err(|e| JsError::new(&e.to_string()))
|
||||
}
|
||||
|
||||
/// Compute personalized PageRank scores from a source node.
|
||||
///
|
||||
/// Returns array of PPR scores, one per node.
|
||||
pub fn ppr_scores(
|
||||
&mut self,
|
||||
source: u32,
|
||||
adjacency: JsValue,
|
||||
alpha: f64,
|
||||
) -> Result<JsValue, JsError> {
|
||||
let adj: Vec<Vec<u32>> = serde_wasm_bindgen::from_value(adjacency)
|
||||
.map_err(|e| JsError::new(&format!("invalid adjacency: {e}")))?;
|
||||
let scores = self.inner.ppr_scores(source, &adj, alpha);
|
||||
serde_wasm_bindgen::to_value(&scores).map_err(|e| JsError::new(&e.to_string()))
|
||||
}
|
||||
|
||||
// ===================================================================
|
||||
// Physics-Informed Layers
|
||||
// ===================================================================
|
||||
|
||||
/// Symplectic integrator step (leapfrog / Stormer-Verlet).
|
||||
///
|
||||
/// `positions` and `momenta` are Float64Arrays, `edges` is
|
||||
/// `[{ src, tgt }, ...]`. Returns `{ positions, momenta, energy,
|
||||
/// energy_conserved }`.
|
||||
pub fn hamiltonian_step(
|
||||
&mut self,
|
||||
positions: JsValue,
|
||||
momenta: JsValue,
|
||||
edges: JsValue,
|
||||
) -> Result<JsValue, JsError> {
|
||||
let pos: Vec<f64> = serde_wasm_bindgen::from_value(positions)
|
||||
.map_err(|e| JsError::new(&format!("invalid positions: {e}")))?;
|
||||
let mom: Vec<f64> = serde_wasm_bindgen::from_value(momenta)
|
||||
.map_err(|e| JsError::new(&format!("invalid momenta: {e}")))?;
|
||||
let ed: Vec<Edge> = serde_wasm_bindgen::from_value(edges)
|
||||
.map_err(|e| JsError::new(&format!("invalid edges: {e}")))?;
|
||||
let result = self
|
||||
.inner
|
||||
.hamiltonian_step_graph(&pos, &mom, &ed, 0.01)
|
||||
.map_err(|e| JsError::new(&format!("{e}")))?;
|
||||
serde_wasm_bindgen::to_value(&result).map_err(|e| JsError::new(&e.to_string()))
|
||||
}
|
||||
|
||||
/// Verify energy conservation between two states.
|
||||
///
|
||||
/// Returns `{ conserved, delta, relative_error }`.
|
||||
pub fn verify_energy_conservation(
|
||||
&self,
|
||||
before: f64,
|
||||
after: f64,
|
||||
tolerance: f64,
|
||||
) -> Result<JsValue, JsError> {
|
||||
let v = self
|
||||
.inner
|
||||
.verify_energy_conservation(before, after, tolerance);
|
||||
serde_wasm_bindgen::to_value(&v).map_err(|e| JsError::new(&e.to_string()))
|
||||
}
|
||||
|
||||
// ===================================================================
|
||||
// Biological-Inspired
|
||||
// ===================================================================
|
||||
|
||||
/// Spiking neural attention step over 2D features with adjacency.
|
||||
///
|
||||
/// `features` is `[[f64, ...], ...]`, `adjacency` is a flat row-major
|
||||
/// Float64Array (n x n). Returns `{ features, spikes, weights }`.
|
||||
pub fn spiking_step(
|
||||
&mut self,
|
||||
features: JsValue,
|
||||
adjacency: JsValue,
|
||||
) -> Result<JsValue, JsError> {
|
||||
let feats: Vec<Vec<f64>> = serde_wasm_bindgen::from_value(features)
|
||||
.map_err(|e| JsError::new(&format!("invalid features: {e}")))?;
|
||||
let adj: Vec<f64> = serde_wasm_bindgen::from_value(adjacency)
|
||||
.map_err(|e| JsError::new(&format!("invalid adjacency: {e}")))?;
|
||||
let result = self.inner.spiking_step(&feats, &adj, 1.0);
|
||||
serde_wasm_bindgen::to_value(&result).map_err(|e| JsError::new(&e.to_string()))
|
||||
}
|
||||
|
||||
/// Hebbian weight update.
|
||||
///
|
||||
/// `pre`, `post`, `weights` are Float64Arrays. Returns updated weights.
|
||||
pub fn hebbian_update(
|
||||
&mut self,
|
||||
pre: JsValue,
|
||||
post: JsValue,
|
||||
weights: JsValue,
|
||||
) -> Result<JsValue, JsError> {
|
||||
let pre_v: Vec<f64> = serde_wasm_bindgen::from_value(pre)
|
||||
.map_err(|e| JsError::new(&format!("invalid pre: {e}")))?;
|
||||
let post_v: Vec<f64> = serde_wasm_bindgen::from_value(post)
|
||||
.map_err(|e| JsError::new(&format!("invalid post: {e}")))?;
|
||||
let w: Vec<f64> = serde_wasm_bindgen::from_value(weights)
|
||||
.map_err(|e| JsError::new(&format!("invalid weights: {e}")))?;
|
||||
let result = self.inner.hebbian_update(&pre_v, &post_v, &w, 0.01);
|
||||
serde_wasm_bindgen::to_value(&result).map_err(|e| JsError::new(&e.to_string()))
|
||||
}
|
||||
|
||||
// ===================================================================
|
||||
// Temporal
|
||||
// ===================================================================
|
||||
|
||||
/// Causal attention with temporal ordering over graph edges.
|
||||
///
|
||||
/// `features` is a Float64Array, `timestamps` is a Float64Array,
|
||||
/// `edges` is `[{ src, tgt }, ...]`.
|
||||
/// Returns attention-weighted output features.
|
||||
pub fn causal_attention(
|
||||
&mut self,
|
||||
features: JsValue,
|
||||
timestamps: JsValue,
|
||||
edges: JsValue,
|
||||
) -> Result<JsValue, JsError> {
|
||||
let feats: Vec<f64> = serde_wasm_bindgen::from_value(features)
|
||||
.map_err(|e| JsError::new(&format!("invalid features: {e}")))?;
|
||||
let ts: Vec<f64> = serde_wasm_bindgen::from_value(timestamps)
|
||||
.map_err(|e| JsError::new(&format!("invalid timestamps: {e}")))?;
|
||||
let ed: Vec<Edge> = serde_wasm_bindgen::from_value(edges)
|
||||
.map_err(|e| JsError::new(&format!("invalid edges: {e}")))?;
|
||||
let result = self.inner.causal_attention_graph(&feats, &ts, &ed);
|
||||
serde_wasm_bindgen::to_value(&result).map_err(|e| JsError::new(&e.to_string()))
|
||||
}
|
||||
|
||||
/// Extract Granger causality DAG from attention history.
|
||||
///
|
||||
/// `attention_history` is a flat Float64Array (T x N row-major).
|
||||
/// Returns `{ edges: [{ source, target, f_statistic, is_causal }], num_nodes }`.
|
||||
pub fn granger_extract(
|
||||
&mut self,
|
||||
attention_history: JsValue,
|
||||
num_nodes: u32,
|
||||
num_steps: u32,
|
||||
) -> Result<JsValue, JsError> {
|
||||
let hist: Vec<f64> = serde_wasm_bindgen::from_value(attention_history)
|
||||
.map_err(|e| JsError::new(&format!("invalid attention_history: {e}")))?;
|
||||
let dag = self.inner.granger_extract(&hist, num_nodes, num_steps);
|
||||
serde_wasm_bindgen::to_value(&dag).map_err(|e| JsError::new(&e.to_string()))
|
||||
}
|
||||
|
||||
// ===================================================================
|
||||
// Manifold
|
||||
// ===================================================================
|
||||
|
||||
/// Product manifold attention with mixed curvatures.
|
||||
///
|
||||
/// `features` is a Float64Array, `edges` is `[{ src, tgt }, ...]`.
|
||||
/// Optional `curvatures` (defaults to `[0.0, -1.0]`).
|
||||
/// Returns `{ output, curvatures, distances }`.
|
||||
pub fn product_manifold_attention(
|
||||
&mut self,
|
||||
features: JsValue,
|
||||
edges: JsValue,
|
||||
) -> Result<JsValue, JsError> {
|
||||
let feats: Vec<f64> = serde_wasm_bindgen::from_value(features)
|
||||
.map_err(|e| JsError::new(&format!("invalid features: {e}")))?;
|
||||
let ed: Vec<Edge> = serde_wasm_bindgen::from_value(edges)
|
||||
.map_err(|e| JsError::new(&format!("invalid edges: {e}")))?;
|
||||
let curvatures = vec![0.0, -1.0]; // default mixed curvatures
|
||||
let result = self
|
||||
.inner
|
||||
.product_manifold_attention(&feats, &ed, &curvatures);
|
||||
serde_wasm_bindgen::to_value(&result).map_err(|e| JsError::new(&e.to_string()))
|
||||
}
|
||||
|
||||
/// Product manifold distance between two points.
|
||||
///
|
||||
/// `a` and `b` are Float64Arrays, `curvatures` is `[number, ...]`.
|
||||
pub fn product_manifold_distance(
|
||||
&self,
|
||||
a: JsValue,
|
||||
b: JsValue,
|
||||
curvatures: JsValue,
|
||||
) -> Result<f64, JsError> {
|
||||
let av: Vec<f64> = serde_wasm_bindgen::from_value(a)
|
||||
.map_err(|e| JsError::new(&format!("invalid a: {e}")))?;
|
||||
let bv: Vec<f64> = serde_wasm_bindgen::from_value(b)
|
||||
.map_err(|e| JsError::new(&format!("invalid b: {e}")))?;
|
||||
let cv: Vec<f64> = serde_wasm_bindgen::from_value(curvatures)
|
||||
.map_err(|e| JsError::new(&format!("invalid curvatures: {e}")))?;
|
||||
Ok(self.inner.product_manifold_distance(&av, &bv, &cv))
|
||||
}
|
||||
|
||||
// ===================================================================
|
||||
// Verified Training
|
||||
// ===================================================================
|
||||
|
||||
/// Verified training step with features, targets, and weights.
|
||||
///
|
||||
/// `features`, `targets`, `weights` are Float64Arrays.
|
||||
/// Returns `{ weights, certificate_id, loss, loss_monotonic,
|
||||
/// lipschitz_satisfied }`.
|
||||
pub fn verified_training_step(
|
||||
&mut self,
|
||||
features: JsValue,
|
||||
targets: JsValue,
|
||||
weights: JsValue,
|
||||
) -> Result<JsValue, JsError> {
|
||||
let f: Vec<f64> = serde_wasm_bindgen::from_value(features)
|
||||
.map_err(|e| JsError::new(&format!("invalid features: {e}")))?;
|
||||
let t: Vec<f64> = serde_wasm_bindgen::from_value(targets)
|
||||
.map_err(|e| JsError::new(&format!("invalid targets: {e}")))?;
|
||||
let w: Vec<f64> = serde_wasm_bindgen::from_value(weights)
|
||||
.map_err(|e| JsError::new(&format!("invalid weights: {e}")))?;
|
||||
let result = self
|
||||
.inner
|
||||
.verified_training_step(&f, &t, &w, 0.001)
|
||||
.map_err(|e| JsError::new(&format!("{e}")))?;
|
||||
serde_wasm_bindgen::to_value(&result).map_err(|e| JsError::new(&e.to_string()))
|
||||
}
|
||||
|
||||
/// A single verified SGD step (raw weights + gradients).
|
||||
///
|
||||
/// Returns `{ weights, proof_id, loss_before, loss_after, gradient_norm }`.
|
||||
pub fn verified_step(
|
||||
&mut self,
|
||||
weights: JsValue,
|
||||
gradients: JsValue,
|
||||
lr: f64,
|
||||
) -> Result<JsValue, JsError> {
|
||||
let w: Vec<f64> = serde_wasm_bindgen::from_value(weights)
|
||||
.map_err(|e| JsError::new(&format!("invalid weights: {e}")))?;
|
||||
let g: Vec<f64> = serde_wasm_bindgen::from_value(gradients)
|
||||
.map_err(|e| JsError::new(&format!("invalid gradients: {e}")))?;
|
||||
let result = self
|
||||
.inner
|
||||
.verified_step(&w, &g, lr)
|
||||
.map_err(|e| JsError::new(&format!("{e}")))?;
|
||||
serde_wasm_bindgen::to_value(&result).map_err(|e| JsError::new(&e.to_string()))
|
||||
}
|
||||
|
||||
// ===================================================================
|
||||
// Economic / Game-Theoretic
|
||||
// ===================================================================
|
||||
|
||||
/// Game-theoretic attention: computes Nash equilibrium allocations.
|
||||
///
|
||||
/// `features` is a Float64Array, `edges` is `[{ src, tgt }, ...]`.
|
||||
/// Returns `{ allocations, utilities, nash_gap, converged }`.
|
||||
pub fn game_theoretic_attention(
|
||||
&mut self,
|
||||
features: JsValue,
|
||||
edges: JsValue,
|
||||
) -> Result<JsValue, JsError> {
|
||||
let feats: Vec<f64> = serde_wasm_bindgen::from_value(features)
|
||||
.map_err(|e| JsError::new(&format!("invalid features: {e}")))?;
|
||||
let ed: Vec<Edge> = serde_wasm_bindgen::from_value(edges)
|
||||
.map_err(|e| JsError::new(&format!("invalid edges: {e}")))?;
|
||||
let result = self.inner.game_theoretic_attention(&feats, &ed);
|
||||
serde_wasm_bindgen::to_value(&result).map_err(|e| JsError::new(&e.to_string()))
|
||||
}
|
||||
|
||||
// ===================================================================
|
||||
// Stats & Reset
|
||||
// ===================================================================
|
||||
|
||||
/// Return transformer statistics.
|
||||
///
|
||||
/// Returns `{ proofs_constructed, proofs_verified, cache_hits,
|
||||
/// cache_misses, attention_ops, physics_ops, bio_ops, training_steps }`.
|
||||
pub fn stats(&self) -> Result<JsValue, JsError> {
|
||||
let s = self.inner.stats();
|
||||
serde_wasm_bindgen::to_value(&s).map_err(|e| JsError::new(&e.to_string()))
|
||||
}
|
||||
|
||||
/// Reset all internal state (caches, counters, gates).
|
||||
pub fn reset(&mut self) {
|
||||
self.inner.reset();
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Tests
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn test_version_nonempty() {
|
||||
assert!(!version().is_empty());
|
||||
}
|
||||
}
|
||||
1440
vendor/ruvector/crates/ruvector-graph-transformer-wasm/src/transformer.rs
vendored
Normal file
1440
vendor/ruvector/crates/ruvector-graph-transformer-wasm/src/transformer.rs
vendored
Normal file
File diff suppressed because it is too large
Load Diff
9
vendor/ruvector/crates/ruvector-graph-transformer-wasm/src/utils.rs
vendored
Normal file
9
vendor/ruvector/crates/ruvector-graph-transformer-wasm/src/utils.rs
vendored
Normal file
@@ -0,0 +1,9 @@
|
||||
//! WASM utility helpers.
|
||||
|
||||
/// Set panic hook for better panic messages in the browser.
|
||||
///
|
||||
/// No-op; add `console_error_panic_hook` as an optional dependency for
|
||||
/// improved browser diagnostics.
|
||||
pub fn set_panic_hook() {
|
||||
// Intentional no-op. In production, wire up console_error_panic_hook.
|
||||
}
|
||||
60
vendor/ruvector/crates/ruvector-graph-transformer-wasm/tests/web.rs
vendored
Normal file
60
vendor/ruvector/crates/ruvector-graph-transformer-wasm/tests/web.rs
vendored
Normal file
@@ -0,0 +1,60 @@
|
||||
//! WASM integration tests (run with wasm-pack test --headless --chrome).
|
||||
|
||||
#![cfg(target_arch = "wasm32")]
|
||||
|
||||
use wasm_bindgen::JsValue;
|
||||
use wasm_bindgen_test::*;
|
||||
|
||||
wasm_bindgen_test_configure!(run_in_browser);
|
||||
|
||||
#[wasm_bindgen_test]
|
||||
fn test_version() {
|
||||
let v = ruvector_graph_transformer_wasm::version();
|
||||
assert!(!v.is_empty());
|
||||
}
|
||||
|
||||
#[wasm_bindgen_test]
|
||||
fn test_proof_gate_roundtrip() {
|
||||
let mut gt = ruvector_graph_transformer_wasm::JsGraphTransformer::new(JsValue::NULL)
|
||||
.expect("default config should work");
|
||||
|
||||
// Create gate
|
||||
let gate = gt.create_proof_gate(64).expect("create_proof_gate");
|
||||
|
||||
// Prove with some data
|
||||
let data: Vec<f32> = vec![0.5; 64];
|
||||
let att = gt.prove_and_mutate(gate, &data).expect("prove_and_mutate");
|
||||
|
||||
assert!(!att.is_undefined());
|
||||
assert!(!att.is_null());
|
||||
}
|
||||
|
||||
#[wasm_bindgen_test]
|
||||
fn test_sublinear_attention() {
|
||||
let gt = ruvector_graph_transformer_wasm::JsGraphTransformer::new(JsValue::NULL)
|
||||
.expect("default config");
|
||||
|
||||
let query: Vec<f32> = vec![0.1; 8];
|
||||
let edges = serde_wasm_bindgen::to_value(&vec![
|
||||
serde_json::json!({"src": 0, "tgt": 1}),
|
||||
serde_json::json!({"src": 0, "tgt": 2}),
|
||||
serde_json::json!({"src": 1, "tgt": 3}),
|
||||
])
|
||||
.unwrap();
|
||||
|
||||
let scores = gt
|
||||
.sublinear_attention(&query, edges, 8, 2)
|
||||
.expect("sublinear_attention");
|
||||
|
||||
assert_eq!(scores.len(), 2);
|
||||
}
|
||||
|
||||
#[wasm_bindgen_test]
|
||||
fn test_stats() {
|
||||
let gt = ruvector_graph_transformer_wasm::JsGraphTransformer::new(JsValue::NULL)
|
||||
.expect("default config");
|
||||
|
||||
let stats = gt.stats().expect("stats");
|
||||
assert!(!stats.is_undefined());
|
||||
assert!(!stats.is_null());
|
||||
}
|
||||
Reference in New Issue
Block a user