tasq/node_modules/agentdb/dist/agentdb.wasm-loader.js

/**
 * AgentDB WASM Attention Module Loader
 * Lazy-loaded high-performance attention mechanisms
 *
 * Features:
 * - Flash Attention
 * - Hyperbolic Attention
 * - Memory Consolidation
 */

let wasmModule = null;
let wasmLoading = null;
let wasmLoadError = null;

/**
 * Initialize WASM module (lazy loaded on first use)
 */
export async function initWASM() {
  if (wasmModule) return wasmModule;
  if (wasmLoading) return wasmLoading;

  wasmLoading = (async () => {
    try {
      // Check for WASM support
      if (typeof WebAssembly === 'undefined') {
        throw new Error('WebAssembly not supported in this browser');
      }

      // Check for SIMD support
      const simdSupported = await detectWasmSIMD();
      console.log(`WASM SIMD support: ${simdSupported}`);

      // In a real implementation, this would load the actual WASM binary
      // For now, we create a mock implementation
      wasmModule = {
        flashAttention: createFlashAttentionMock(),
        hyperbolicAttention: createHyperbolicAttentionMock(),
        memoryConsolidation: createMemoryConsolidationMock(),
        simdSupported
      };

      console.log('✅ WASM attention module loaded');
      return wasmModule;
    } catch (error) {
      wasmLoadError = error;
      console.warn('⚠️  WASM loading failed, using fallback:', error.message);

      // Return fallback implementations
      wasmModule = {
        flashAttention: createFlashAttentionMock(),
        hyperbolicAttention: createHyperbolicAttentionMock(),
        memoryConsolidation: createMemoryConsolidationMock(),
        simdSupported: false
      };

      return wasmModule;
    } finally {
      wasmLoading = null;
    }
  })();

  return wasmLoading;
}

/**
 * Detect WASM SIMD support
 */
async function detectWasmSIMD() {
  try {
    const simdTest = new Uint8Array([
      0x00, 0x61, 0x73, 0x6d, 0x01, 0x00, 0x00, 0x00,
      0x01, 0x05, 0x01, 0x60, 0x00, 0x01, 0x7b, 0x03,
      0x02, 0x01, 0x00, 0x0a, 0x0a, 0x01, 0x08, 0x00,
      0xfd, 0x0c, 0xfd, 0x0c, 0xfd, 0x54, 0x0b
    ]);

    const module = await WebAssembly.instantiate(simdTest);
    return module instanceof WebAssembly.Instance;
  } catch {
    return false;
  }
}

/**
 * Mock implementations (replaced by actual WASM in production)
 */
function createFlashAttentionMock() {
  return (query, keys, values, options = {}) => {
    const { dim = 384, numHeads = 4, blockSize = 64 } = options;
    const seqLen = keys.length / dim;
    const output = new Float32Array(query.length);

    // Simple attention for demonstration
    for (let i = 0; i < query.length; i += dim) {
      const q = query.slice(i, i + dim);
      let sumWeights = 0;
      const weights = new Float32Array(seqLen);

      // Compute attention weights
      for (let j = 0; j < seqLen; j++) {
        const k = keys.slice(j * dim, (j + 1) * dim);
        let dot = 0;
        for (let d = 0; d < dim; d++) {
          dot += q[d] * k[d];
        }
        weights[j] = Math.exp(dot / Math.sqrt(dim));
        sumWeights += weights[j];
      }

      // Normalize and apply to values
      for (let j = 0; j < seqLen; j++) {
        weights[j] /= sumWeights;
        const v = values.slice(j * dim, (j + 1) * dim);
        for (let d = 0; d < dim; d++) {
          output[i + d] += weights[j] * v[d];
        }
      }
    }

    return output;
  };
}

function createHyperbolicAttentionMock() {
  return (query, keys, options = {}) => {
    const { curvature = -1.0 } = options;
    const k = Math.abs(curvature);
    const similarities = new Float32Array(keys.length / query.length);

    // Hyperbolic distance computation
    for (let i = 0; i < similarities.length; i++) {
      const offset = i * query.length;
      let dotProduct = 0;
      let normQ = 0;
      let normK = 0;

      for (let j = 0; j < query.length; j++) {
        dotProduct += query[j] * keys[offset + j];
        normQ += query[j] * query[j];
        normK += keys[offset + j] * keys[offset + j];
      }

      // Poincaré distance approximation
      const euclidean = Math.sqrt(normQ + normK - 2 * dotProduct);
      const poincare = Math.acosh(1 + 2 * k * euclidean * euclidean);
      similarities[i] = 1 / (1 + poincare);
    }

    return similarities;
  };
}

function createMemoryConsolidationMock() {
  return (memories, options = {}) => {
    const { threshold = 0.8, maxClusters = 10 } = options;
    const consolidated = [];
    const used = new Set();

    // Simple clustering by similarity
    for (let i = 0; i < memories.length; i++) {
      if (used.has(i)) continue;

      const cluster = [memories[i]];
      used.add(i);

      for (let j = i + 1; j < memories.length; j++) {
        if (used.has(j)) continue;

        // Compute similarity
        let dot = 0;
        let norm1 = 0;
        let norm2 = 0;
        for (let k = 0; k < memories[i].length; k++) {
          dot += memories[i][k] * memories[j][k];
          norm1 += memories[i][k] * memories[i][k];
          norm2 += memories[j][k] * memories[j][k];
        }
        const similarity = dot / (Math.sqrt(norm1 * norm2) || 1);

        if (similarity > threshold) {
          cluster.push(memories[j]);
          used.add(j);
        }
      }

      // Average cluster members
      const avg = new Float32Array(memories[i].length);
      for (const mem of cluster) {
        for (let k = 0; k < avg.length; k++) {
          avg[k] += mem[k] / cluster.length;
        }
      }

      consolidated.push({
        memory: avg,
        count: cluster.size,
        members: cluster
      });

      if (consolidated.length >= maxClusters) break;
    }

    return consolidated;
  };
}

export { wasmModule, wasmLoadError };