Squashed 'vendor/ruvector/' content from commit b64c2172
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
This commit is contained in:
ruv
292
crates/ruvector-learning-wasm/pkg/ruvector_learning_wasm.d.ts
vendored
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292
crates/ruvector-learning-wasm/pkg/ruvector_learning_wasm.d.ts
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/* tslint:disable */
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/* eslint-disable */
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export class WasmMicroLoRA {
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free(): void;
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[Symbol.dispose](): void;
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/**
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* Get delta norm (weight change magnitude)
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*/
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delta_norm(): number;
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/**
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* Adapt with typed array gradient
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*/
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adapt_array(gradient: Float32Array): void;
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/**
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* Get adaptation count
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*/
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adapt_count(): bigint;
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/**
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* Get parameter count
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*/
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param_count(): number;
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/**
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* Forward pass with typed array input (allocates output)
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*/
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forward_array(input: Float32Array): Float32Array;
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/**
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* Get forward pass count
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*/
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forward_count(): bigint;
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/**
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* Get pointer to input buffer for direct memory access
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*/
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get_input_ptr(): number;
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/**
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* Get pointer to output buffer for direct memory access
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*/
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get_output_ptr(): number;
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/**
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* Adapt with improvement reward using input buffer as gradient
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*/
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adapt_with_reward(improvement: number): void;
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/**
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* Get embedding dimension
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*/
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dim(): number;
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/**
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* Create a new MicroLoRA engine
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*
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* @param dim - Embedding dimension (default 256, max 256)
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* @param alpha - Scaling factor (default 0.1)
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* @param learning_rate - Learning rate (default 0.01)
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*/
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constructor(dim?: number | null, alpha?: number | null, learning_rate?: number | null);
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/**
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* Adapt using input buffer as gradient
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*/
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adapt(): void;
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/**
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* Reset the engine
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*/
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reset(): void;
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/**
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* Forward pass using internal buffers (zero-allocation)
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*
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* Write input to get_input_ptr(), call forward(), read from get_output_ptr()
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*/
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forward(): void;
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}
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export class WasmScopedLoRA {
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free(): void;
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[Symbol.dispose](): void;
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/**
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* Get delta norm for operator
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*/
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delta_norm(op_type: number): number;
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/**
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* Get operator scope name
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*/
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static scope_name(op_type: number): string;
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/**
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* Adapt with typed array
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*/
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adapt_array(op_type: number, gradient: Float32Array): void;
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/**
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* Get adapt count for operator
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*/
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adapt_count(op_type: number): bigint;
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/**
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* Reset specific operator adapter
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*/
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reset_scope(op_type: number): void;
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/**
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* Forward pass with typed array
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*/
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forward_array(op_type: number, input: Float32Array): Float32Array;
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/**
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* Get forward count for operator
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*/
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forward_count(op_type: number): bigint;
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/**
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* Get input buffer pointer
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*/
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get_input_ptr(): number;
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/**
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* Get output buffer pointer
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*/
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get_output_ptr(): number;
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/**
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* Adapt with improvement reward
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*/
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adapt_with_reward(op_type: number, improvement: number): void;
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/**
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* Get total adapt count
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*/
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total_adapt_count(): bigint;
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/**
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* Get total forward count
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*/
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total_forward_count(): bigint;
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/**
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* Enable/disable category fallback
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*/
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set_category_fallback(enabled: boolean): void;
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/**
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* Create a new scoped LoRA manager
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*
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* @param dim - Embedding dimension (max 256)
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* @param alpha - Scaling factor (default 0.1)
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* @param learning_rate - Learning rate (default 0.01)
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*/
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constructor(dim?: number | null, alpha?: number | null, learning_rate?: number | null);
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/**
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* Adapt for operator type using input buffer as gradient
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*/
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adapt(op_type: number): void;
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/**
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* Forward pass for operator type (uses internal buffers)
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*
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* @param op_type - Operator type (0-16)
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*/
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forward(op_type: number): void;
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/**
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* Reset all adapters
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*/
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reset_all(): void;
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}
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export class WasmTrajectoryBuffer {
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free(): void;
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[Symbol.dispose](): void;
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/**
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* Get total count
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*/
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total_count(): bigint;
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/**
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* Get success rate
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*/
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success_rate(): number;
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/**
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* Get best attention type
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*/
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best_attention(): number;
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/**
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* Get best improvement
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*/
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best_improvement(): number;
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/**
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* Get mean improvement
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*/
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mean_improvement(): number;
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/**
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* Get trajectory count for operator
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*/
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count_by_operator(op_type: number): number;
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/**
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* Get high quality trajectory count
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*/
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high_quality_count(threshold: number): number;
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/**
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* Get buffer length
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*/
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len(): number;
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/**
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* Create a new trajectory buffer
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*
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* @param capacity - Maximum number of trajectories to store
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* @param embedding_dim - Dimension of embeddings (default 256)
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*/
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constructor(capacity?: number | null, embedding_dim?: number | null);
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/**
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* Reset buffer
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*/
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reset(): void;
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/**
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* Record a trajectory
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*
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* @param embedding - Embedding vector (Float32Array)
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* @param op_type - Operator type (0-16)
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* @param attention_type - Attention mechanism used
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* @param execution_ms - Actual execution time
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* @param baseline_ms - Baseline execution time
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*/
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record(embedding: Float32Array, op_type: number, attention_type: number, execution_ms: number, baseline_ms: number): void;
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/**
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* Check if empty
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*/
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is_empty(): boolean;
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/**
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* Get variance
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*/
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variance(): number;
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}
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export type InitInput = RequestInfo | URL | Response | BufferSource | WebAssembly.Module;
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export interface InitOutput {
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readonly memory: WebAssembly.Memory;
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readonly __wbg_wasmmicrolora_free: (a: number, b: number) => void;
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readonly __wbg_wasmscopedlora_free: (a: number, b: number) => void;
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readonly __wbg_wasmtrajectorybuffer_free: (a: number, b: number) => void;
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readonly wasmmicrolora_adapt: (a: number) => void;
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readonly wasmmicrolora_adapt_array: (a: number, b: number, c: number) => void;
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readonly wasmmicrolora_adapt_count: (a: number) => bigint;
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readonly wasmmicrolora_adapt_with_reward: (a: number, b: number) => void;
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readonly wasmmicrolora_delta_norm: (a: number) => number;
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readonly wasmmicrolora_dim: (a: number) => number;
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readonly wasmmicrolora_forward: (a: number) => void;
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readonly wasmmicrolora_forward_array: (a: number, b: number, c: number, d: number) => void;
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readonly wasmmicrolora_forward_count: (a: number) => bigint;
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readonly wasmmicrolora_get_input_ptr: (a: number) => number;
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readonly wasmmicrolora_get_output_ptr: (a: number) => number;
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readonly wasmmicrolora_new: (a: number, b: number, c: number) => number;
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readonly wasmmicrolora_param_count: (a: number) => number;
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readonly wasmmicrolora_reset: (a: number) => void;
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readonly wasmscopedlora_adapt: (a: number, b: number) => void;
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readonly wasmscopedlora_adapt_array: (a: number, b: number, c: number, d: number) => void;
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readonly wasmscopedlora_adapt_count: (a: number, b: number) => bigint;
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readonly wasmscopedlora_adapt_with_reward: (a: number, b: number, c: number) => void;
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readonly wasmscopedlora_delta_norm: (a: number, b: number) => number;
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readonly wasmscopedlora_forward: (a: number, b: number) => void;
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readonly wasmscopedlora_forward_array: (a: number, b: number, c: number, d: number, e: number) => void;
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readonly wasmscopedlora_forward_count: (a: number, b: number) => bigint;
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readonly wasmscopedlora_get_input_ptr: (a: number) => number;
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readonly wasmscopedlora_get_output_ptr: (a: number) => number;
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readonly wasmscopedlora_new: (a: number, b: number, c: number) => number;
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readonly wasmscopedlora_reset_all: (a: number) => void;
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readonly wasmscopedlora_reset_scope: (a: number, b: number) => void;
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readonly wasmscopedlora_scope_name: (a: number, b: number) => void;
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readonly wasmscopedlora_set_category_fallback: (a: number, b: number) => void;
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readonly wasmscopedlora_total_adapt_count: (a: number) => bigint;
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readonly wasmscopedlora_total_forward_count: (a: number) => bigint;
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readonly wasmtrajectorybuffer_best_attention: (a: number) => number;
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readonly wasmtrajectorybuffer_best_improvement: (a: number) => number;
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readonly wasmtrajectorybuffer_count_by_operator: (a: number, b: number) => number;
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readonly wasmtrajectorybuffer_high_quality_count: (a: number, b: number) => number;
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readonly wasmtrajectorybuffer_is_empty: (a: number) => number;
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readonly wasmtrajectorybuffer_len: (a: number) => number;
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readonly wasmtrajectorybuffer_mean_improvement: (a: number) => number;
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readonly wasmtrajectorybuffer_new: (a: number, b: number) => number;
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readonly wasmtrajectorybuffer_record: (a: number, b: number, c: number, d: number, e: number, f: number, g: number) => void;
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readonly wasmtrajectorybuffer_reset: (a: number) => void;
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readonly wasmtrajectorybuffer_success_rate: (a: number) => number;
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readonly wasmtrajectorybuffer_total_count: (a: number) => bigint;
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readonly wasmtrajectorybuffer_variance: (a: number) => number;
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readonly __wbindgen_export: (a: number, b: number) => number;
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readonly __wbindgen_add_to_stack_pointer: (a: number) => number;
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readonly __wbindgen_export2: (a: number, b: number, c: number) => void;
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}
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export type SyncInitInput = BufferSource | WebAssembly.Module;
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/**
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* Instantiates the given `module`, which can either be bytes or
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* a precompiled `WebAssembly.Module`.
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*
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* @param {{ module: SyncInitInput }} module - Passing `SyncInitInput` directly is deprecated.
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*
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* @returns {InitOutput}
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*/
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export function initSync(module: { module: SyncInitInput } | SyncInitInput): InitOutput;
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/**
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* If `module_or_path` is {RequestInfo} or {URL}, makes a request and
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* for everything else, calls `WebAssembly.instantiate` directly.
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*
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* @param {{ module_or_path: InitInput | Promise<InitInput> }} module_or_path - Passing `InitInput` directly is deprecated.
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*
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* @returns {Promise<InitOutput>}
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*/
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export default function __wbg_init (module_or_path?: { module_or_path: InitInput | Promise<InitInput> } | InitInput | Promise<InitInput>): Promise<InitOutput>;
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