wifi-densepose/crates/ruvector-learning-wasm/pkg/ruvector_learning_wasm.d.ts

/* tslint:disable */
/* eslint-disable */

export class WasmMicroLoRA {
  free(): void;
  [Symbol.dispose](): void;
  /**
   * Get delta norm (weight change magnitude)
   */
  delta_norm(): number;
  /**
   * Adapt with typed array gradient
   */
  adapt_array(gradient: Float32Array): void;
  /**
   * Get adaptation count
   */
  adapt_count(): bigint;
  /**
   * Get parameter count
   */
  param_count(): number;
  /**
   * Forward pass with typed array input (allocates output)
   */
  forward_array(input: Float32Array): Float32Array;
  /**
   * Get forward pass count
   */
  forward_count(): bigint;
  /**
   * Get pointer to input buffer for direct memory access
   */
  get_input_ptr(): number;
  /**
   * Get pointer to output buffer for direct memory access
   */
  get_output_ptr(): number;
  /**
   * Adapt with improvement reward using input buffer as gradient
   */
  adapt_with_reward(improvement: number): void;
  /**
   * Get embedding dimension
   */
  dim(): number;
  /**
   * Create a new MicroLoRA engine
   *
   * @param dim - Embedding dimension (default 256, max 256)
   * @param alpha - Scaling factor (default 0.1)
   * @param learning_rate - Learning rate (default 0.01)
   */
  constructor(dim?: number | null, alpha?: number | null, learning_rate?: number | null);
  /**
   * Adapt using input buffer as gradient
   */
  adapt(): void;
  /**
   * Reset the engine
   */
  reset(): void;
  /**
   * Forward pass using internal buffers (zero-allocation)
   *
   * Write input to get_input_ptr(), call forward(), read from get_output_ptr()
   */
  forward(): void;
}

export class WasmScopedLoRA {
  free(): void;
  [Symbol.dispose](): void;
  /**
   * Get delta norm for operator
   */
  delta_norm(op_type: number): number;
  /**
   * Get operator scope name
   */
  static scope_name(op_type: number): string;
  /**
   * Adapt with typed array
   */
  adapt_array(op_type: number, gradient: Float32Array): void;
  /**
   * Get adapt count for operator
   */
  adapt_count(op_type: number): bigint;
  /**
   * Reset specific operator adapter
   */
  reset_scope(op_type: number): void;
  /**
   * Forward pass with typed array
   */
  forward_array(op_type: number, input: Float32Array): Float32Array;
  /**
   * Get forward count for operator
   */
  forward_count(op_type: number): bigint;
  /**
   * Get input buffer pointer
   */
  get_input_ptr(): number;
  /**
   * Get output buffer pointer
   */
  get_output_ptr(): number;
  /**
   * Adapt with improvement reward
   */
  adapt_with_reward(op_type: number, improvement: number): void;
  /**
   * Get total adapt count
   */
  total_adapt_count(): bigint;
  /**
   * Get total forward count
   */
  total_forward_count(): bigint;
  /**
   * Enable/disable category fallback
   */
  set_category_fallback(enabled: boolean): void;
  /**
   * Create a new scoped LoRA manager
   *
   * @param dim - Embedding dimension (max 256)
   * @param alpha - Scaling factor (default 0.1)
   * @param learning_rate - Learning rate (default 0.01)
   */
  constructor(dim?: number | null, alpha?: number | null, learning_rate?: number | null);
  /**
   * Adapt for operator type using input buffer as gradient
   */
  adapt(op_type: number): void;
  /**
   * Forward pass for operator type (uses internal buffers)
   *
   * @param op_type - Operator type (0-16)
   */
  forward(op_type: number): void;
  /**
   * Reset all adapters
   */
  reset_all(): void;
}

export class WasmTrajectoryBuffer {
  free(): void;
  [Symbol.dispose](): void;
  /**
   * Get total count
   */
  total_count(): bigint;
  /**
   * Get success rate
   */
  success_rate(): number;
  /**
   * Get best attention type
   */
  best_attention(): number;
  /**
   * Get best improvement
   */
  best_improvement(): number;
  /**
   * Get mean improvement
   */
  mean_improvement(): number;
  /**
   * Get trajectory count for operator
   */
  count_by_operator(op_type: number): number;
  /**
   * Get high quality trajectory count
   */
  high_quality_count(threshold: number): number;
  /**
   * Get buffer length
   */
  len(): number;
  /**
   * Create a new trajectory buffer
   *
   * @param capacity - Maximum number of trajectories to store
   * @param embedding_dim - Dimension of embeddings (default 256)
   */
  constructor(capacity?: number | null, embedding_dim?: number | null);
  /**
   * Reset buffer
   */
  reset(): void;
  /**
   * Record a trajectory
   *
   * @param embedding - Embedding vector (Float32Array)
   * @param op_type - Operator type (0-16)
   * @param attention_type - Attention mechanism used
   * @param execution_ms - Actual execution time
   * @param baseline_ms - Baseline execution time
   */
  record(embedding: Float32Array, op_type: number, attention_type: number, execution_ms: number, baseline_ms: number): void;
  /**
   * Check if empty
   */
  is_empty(): boolean;
  /**
   * Get variance
   */
  variance(): number;
}

export type InitInput = RequestInfo | URL | Response | BufferSource | WebAssembly.Module;

export interface InitOutput {
  readonly memory: WebAssembly.Memory;
  readonly __wbg_wasmmicrolora_free: (a: number, b: number) => void;
  readonly __wbg_wasmscopedlora_free: (a: number, b: number) => void;
  readonly __wbg_wasmtrajectorybuffer_free: (a: number, b: number) => void;
  readonly wasmmicrolora_adapt: (a: number) => void;
  readonly wasmmicrolora_adapt_array: (a: number, b: number, c: number) => void;
  readonly wasmmicrolora_adapt_count: (a: number) => bigint;
  readonly wasmmicrolora_adapt_with_reward: (a: number, b: number) => void;
  readonly wasmmicrolora_delta_norm: (a: number) => number;
  readonly wasmmicrolora_dim: (a: number) => number;
  readonly wasmmicrolora_forward: (a: number) => void;
  readonly wasmmicrolora_forward_array: (a: number, b: number, c: number, d: number) => void;
  readonly wasmmicrolora_forward_count: (a: number) => bigint;
  readonly wasmmicrolora_get_input_ptr: (a: number) => number;
  readonly wasmmicrolora_get_output_ptr: (a: number) => number;
  readonly wasmmicrolora_new: (a: number, b: number, c: number) => number;
  readonly wasmmicrolora_param_count: (a: number) => number;
  readonly wasmmicrolora_reset: (a: number) => void;
  readonly wasmscopedlora_adapt: (a: number, b: number) => void;
  readonly wasmscopedlora_adapt_array: (a: number, b: number, c: number, d: number) => void;
  readonly wasmscopedlora_adapt_count: (a: number, b: number) => bigint;
  readonly wasmscopedlora_adapt_with_reward: (a: number, b: number, c: number) => void;
  readonly wasmscopedlora_delta_norm: (a: number, b: number) => number;
  readonly wasmscopedlora_forward: (a: number, b: number) => void;
  readonly wasmscopedlora_forward_array: (a: number, b: number, c: number, d: number, e: number) => void;
  readonly wasmscopedlora_forward_count: (a: number, b: number) => bigint;
  readonly wasmscopedlora_get_input_ptr: (a: number) => number;
  readonly wasmscopedlora_get_output_ptr: (a: number) => number;
  readonly wasmscopedlora_new: (a: number, b: number, c: number) => number;
  readonly wasmscopedlora_reset_all: (a: number) => void;
  readonly wasmscopedlora_reset_scope: (a: number, b: number) => void;
  readonly wasmscopedlora_scope_name: (a: number, b: number) => void;
  readonly wasmscopedlora_set_category_fallback: (a: number, b: number) => void;
  readonly wasmscopedlora_total_adapt_count: (a: number) => bigint;
  readonly wasmscopedlora_total_forward_count: (a: number) => bigint;
  readonly wasmtrajectorybuffer_best_attention: (a: number) => number;
  readonly wasmtrajectorybuffer_best_improvement: (a: number) => number;
  readonly wasmtrajectorybuffer_count_by_operator: (a: number, b: number) => number;
  readonly wasmtrajectorybuffer_high_quality_count: (a: number, b: number) => number;
  readonly wasmtrajectorybuffer_is_empty: (a: number) => number;
  readonly wasmtrajectorybuffer_len: (a: number) => number;
  readonly wasmtrajectorybuffer_mean_improvement: (a: number) => number;
  readonly wasmtrajectorybuffer_new: (a: number, b: number) => number;
  readonly wasmtrajectorybuffer_record: (a: number, b: number, c: number, d: number, e: number, f: number, g: number) => void;
  readonly wasmtrajectorybuffer_reset: (a: number) => void;
  readonly wasmtrajectorybuffer_success_rate: (a: number) => number;
  readonly wasmtrajectorybuffer_total_count: (a: number) => bigint;
  readonly wasmtrajectorybuffer_variance: (a: number) => number;
  readonly __wbindgen_export: (a: number, b: number) => number;
  readonly __wbindgen_add_to_stack_pointer: (a: number) => number;
  readonly __wbindgen_export2: (a: number, b: number, c: number) => 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>;