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wifi-densepose/vendor/ruvector/npm/packages/ruvllm/src/simd.d.ts

90 lines
2.1 KiB
TypeScript

/**
* SIMD Operations for vector computations
*
* Uses native SIMD instructions (AVX2/AVX512/SSE4.1/NEON) when available,
* falls back to JavaScript implementations otherwise.
*/
/**
* SIMD Operations class
*
* Provides hardware-accelerated vector operations when native module is available.
*
* @example
* ```typescript
* import { SimdOps } from '@ruvector/ruvllm';
*
* const simd = new SimdOps();
*
* // Compute dot product
* const result = simd.dotProduct([1, 2, 3], [4, 5, 6]);
* console.log(result); // 32
*
* // Check capabilities
* console.log(simd.capabilities()); // ['AVX2', 'FMA']
* ```
*/
export declare class SimdOps {
private native;
constructor();
/**
* Compute dot product of two vectors
*/
dotProduct(a: number[], b: number[]): number;
/**
* Compute cosine similarity between two vectors
*/
cosineSimilarity(a: number[], b: number[]): number;
/**
* Compute L2 (Euclidean) distance between two vectors
*/
l2Distance(a: number[], b: number[]): number;
/**
* Matrix-vector multiplication
*/
matvec(matrix: number[][], vector: number[]): number[];
/**
* Softmax activation function
*/
softmax(input: number[]): number[];
/**
* Element-wise addition
*/
add(a: number[], b: number[]): number[];
/**
* Element-wise multiplication
*/
mul(a: number[], b: number[]): number[];
/**
* Scale vector by scalar
*/
scale(a: number[], scalar: number): number[];
/**
* Normalize vector to unit length
*/
normalize(a: number[]): number[];
/**
* ReLU activation
*/
relu(input: number[]): number[];
/**
* GELU activation (approximate)
*/
gelu(input: number[]): number[];
/**
* Sigmoid activation
*/
sigmoid(input: number[]): number[];
/**
* Layer normalization
*/
layerNorm(input: number[], eps?: number): number[];
/**
* Check if native SIMD is available
*/
isNative(): boolean;
/**
* Get available SIMD capabilities
*/
capabilities(): string[];
}
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