Squashed 'vendor/ruvector/' content from commit b64c2172
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
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ruv
359
crates/ruvector-attention-wasm/pkg/ruvector_attention_wasm.d.ts
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359
crates/ruvector-attention-wasm/pkg/ruvector_attention_wasm.d.ts
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/* tslint:disable */
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/* eslint-disable */
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/**
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* Adam optimizer
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*/
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export class WasmAdam {
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free(): void;
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[Symbol.dispose](): void;
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/**
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* Create a new Adam optimizer
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*
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* # Arguments
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* * `param_count` - Number of parameters
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* * `learning_rate` - Learning rate
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*/
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constructor(param_count: number, learning_rate: number);
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/**
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* Reset optimizer state
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*/
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reset(): void;
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/**
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* Perform optimization step
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*
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* # Arguments
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* * `params` - Current parameter values (will be updated in-place)
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* * `gradients` - Gradient values
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*/
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step(params: Float32Array, gradients: Float32Array): void;
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/**
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* Get current learning rate
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*/
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learning_rate: number;
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}
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/**
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* AdamW optimizer (Adam with decoupled weight decay)
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*/
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export class WasmAdamW {
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free(): void;
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[Symbol.dispose](): void;
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/**
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* Create a new AdamW optimizer
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*
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* # Arguments
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* * `param_count` - Number of parameters
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* * `learning_rate` - Learning rate
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* * `weight_decay` - Weight decay coefficient
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*/
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constructor(param_count: number, learning_rate: number, weight_decay: number);
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/**
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* Reset optimizer state
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*/
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reset(): void;
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/**
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* Perform optimization step with weight decay
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*/
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step(params: Float32Array, gradients: Float32Array): void;
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/**
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* Get current learning rate
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*/
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learning_rate: number;
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/**
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* Get weight decay
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*/
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readonly weight_decay: number;
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}
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/**
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* Flash attention mechanism
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*/
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export class WasmFlashAttention {
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free(): void;
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[Symbol.dispose](): void;
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/**
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* Compute flash attention
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*/
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compute(query: Float32Array, keys: any, values: any): Float32Array;
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/**
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* Create a new flash attention instance
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*
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* # Arguments
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* * `dim` - Embedding dimension
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* * `block_size` - Block size for tiling
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*/
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constructor(dim: number, block_size: number);
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}
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/**
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* Hyperbolic attention mechanism
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*/
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export class WasmHyperbolicAttention {
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free(): void;
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[Symbol.dispose](): void;
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/**
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* Compute hyperbolic attention
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*/
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compute(query: Float32Array, keys: any, values: any): Float32Array;
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/**
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* Create a new hyperbolic attention instance
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*
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* # Arguments
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* * `dim` - Embedding dimension
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* * `curvature` - Hyperbolic curvature parameter
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*/
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constructor(dim: number, curvature: number);
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/**
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* Get the curvature
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*/
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readonly curvature: number;
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}
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/**
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* InfoNCE contrastive loss for training
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*/
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export class WasmInfoNCELoss {
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free(): void;
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[Symbol.dispose](): void;
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/**
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* Compute InfoNCE loss
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*
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* # Arguments
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* * `anchor` - Anchor embedding
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* * `positive` - Positive example embedding
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* * `negatives` - Array of negative example embeddings
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*/
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compute(anchor: Float32Array, positive: Float32Array, negatives: any): number;
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/**
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* Create a new InfoNCE loss instance
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*
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* # Arguments
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* * `temperature` - Temperature parameter for softmax
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*/
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constructor(temperature: number);
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}
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/**
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* Learning rate scheduler
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*/
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export class WasmLRScheduler {
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free(): void;
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[Symbol.dispose](): void;
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/**
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* Get learning rate for current step
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*/
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get_lr(): number;
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/**
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* Create a new learning rate scheduler with warmup and cosine decay
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*
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* # Arguments
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* * `initial_lr` - Initial learning rate
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* * `warmup_steps` - Number of warmup steps
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* * `total_steps` - Total training steps
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*/
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constructor(initial_lr: number, warmup_steps: number, total_steps: number);
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/**
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* Reset scheduler
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*/
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reset(): void;
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/**
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* Advance to next step
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*/
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step(): void;
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}
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/**
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* Linear attention (Performer-style)
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*/
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export class WasmLinearAttention {
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free(): void;
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[Symbol.dispose](): void;
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/**
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* Compute linear attention
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*/
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compute(query: Float32Array, keys: any, values: any): Float32Array;
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/**
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* Create a new linear attention instance
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*
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* # Arguments
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* * `dim` - Embedding dimension
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* * `num_features` - Number of random features
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*/
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constructor(dim: number, num_features: number);
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}
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/**
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* Local-global attention mechanism
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*/
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export class WasmLocalGlobalAttention {
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free(): void;
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[Symbol.dispose](): void;
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/**
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* Compute local-global attention
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*/
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compute(query: Float32Array, keys: any, values: any): Float32Array;
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/**
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* Create a new local-global attention instance
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*
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* # Arguments
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* * `dim` - Embedding dimension
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* * `local_window` - Size of local attention window
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* * `global_tokens` - Number of global attention tokens
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*/
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constructor(dim: number, local_window: number, global_tokens: number);
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}
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/**
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* Mixture of Experts (MoE) attention
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*/
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export class WasmMoEAttention {
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free(): void;
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[Symbol.dispose](): void;
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/**
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* Compute MoE attention
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*/
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compute(query: Float32Array, keys: any, values: any): Float32Array;
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/**
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* Create a new MoE attention instance
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*
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* # Arguments
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* * `dim` - Embedding dimension
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* * `num_experts` - Number of expert attention mechanisms
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* * `top_k` - Number of experts to use per query
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*/
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constructor(dim: number, num_experts: number, top_k: number);
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}
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/**
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* Multi-head attention mechanism
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*/
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export class WasmMultiHeadAttention {
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free(): void;
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[Symbol.dispose](): void;
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/**
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* Compute multi-head attention
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*/
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compute(query: Float32Array, keys: any, values: any): Float32Array;
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/**
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* Create a new multi-head attention instance
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*
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* # Arguments
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* * `dim` - Embedding dimension
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* * `num_heads` - Number of attention heads
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*/
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constructor(dim: number, num_heads: number);
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/**
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* Get the dimension
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*/
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readonly dim: number;
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/**
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* Get the number of heads
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*/
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readonly num_heads: number;
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}
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/**
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* SGD optimizer with momentum
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*/
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export class WasmSGD {
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free(): void;
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[Symbol.dispose](): void;
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/**
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* Create a new SGD optimizer
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*
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* # Arguments
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* * `param_count` - Number of parameters
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* * `learning_rate` - Learning rate
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* * `momentum` - Momentum coefficient (default: 0)
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*/
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constructor(param_count: number, learning_rate: number, momentum?: number | null);
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/**
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* Reset optimizer state
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*/
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reset(): void;
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/**
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* Perform optimization step
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*/
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step(params: Float32Array, gradients: Float32Array): void;
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/**
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* Get current learning rate
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*/
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learning_rate: number;
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}
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/**
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* Compute attention weights from scores
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*/
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export function attention_weights(scores: Float32Array, temperature?: number | null): void;
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/**
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* Get information about available attention mechanisms
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*/
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export function available_mechanisms(): any;
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/**
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* Batch normalize vectors
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*/
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export function batch_normalize(vectors: any, epsilon?: number | null): Float32Array;
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/**
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* Compute cosine similarity between two vectors
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*/
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export function cosine_similarity(a: Float32Array, b: Float32Array): number;
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/**
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* Initialize the WASM module with panic hook
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*/
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export function init(): void;
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/**
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* Compute L2 norm of a vector
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*/
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export function l2_norm(vec: Float32Array): number;
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/**
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* Log a message to the browser console
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*/
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export function log(message: string): void;
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/**
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* Log an error to the browser console
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*/
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export function log_error(message: string): void;
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/**
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* Normalize a vector to unit length
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*/
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export function normalize(vec: Float32Array): void;
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/**
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* Compute pairwise distances between vectors
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*/
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export function pairwise_distances(vectors: any): Float32Array;
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/**
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* Generate random orthogonal matrix (for initialization)
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*/
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export function random_orthogonal_matrix(dim: number): Float32Array;
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/**
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* Compute scaled dot-product attention
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*
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* # Arguments
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* * `query` - Query vector as Float32Array
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* * `keys` - Array of key vectors
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* * `values` - Array of value vectors
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* * `scale` - Optional scaling factor (defaults to 1/sqrt(dim))
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*/
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export function scaled_dot_attention(query: Float32Array, keys: any, values: any, scale?: number | null): Float32Array;
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/**
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* Compute softmax of a vector
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*/
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export function softmax(vec: Float32Array): void;
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/**
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* Get the version of the ruvector-attention-wasm crate
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*/
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export function version(): string;
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