perf: adaptive parallel contexts for embed + rerank, fix VRAM waste
Holistic overhaul of context management: 1. Parallel embedding contexts: embedBatch now splits work across multiple EmbeddingContexts (same pattern as reranking). Each context is ~143 MB. Benchmarked 6x speedup on 20 texts with 4 contexts vs 1. 2. Rerank context size: was using auto (40960 tokens = 11.6 GB per context!). Reranking chunks are ~800 tokens max, so 1024 is plenty. Now 711 MB per context — 16x less VRAM. 4 contexts went from 46 GB to 2.8 GB. 3. Adaptive parallelism via computeParallelism(): checks available VRAM and allocates at most 25% of free VRAM for contexts, capped at 8. Falls back to 1 on CPU (no benefit from multiple contexts with node-llama-cpp's withLock serialization). Gracefully handles allocation failures — uses however many contexts succeeded. VRAM budget per operation: - Embed: N × 143 MB (nomic-embed, 2048 ctx) - Rerank: N × 711 MB (Qwen3-Reranker-0.6B, 1024 ctx) - Generate: ~1.1 GB (qmd-expansion-1.7B, fresh ctx per call) Works across: - Large GPU boxes (4x A6000, 190 GB): allocates up to 8 contexts - Consumer GPUs (16 GB): 2-4 contexts fit comfortably - Apple Metal (8-16 GB unified): 1-4 contexts depending on memory - CPU-only: single context (parallelism has no benefit)
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Tobi Lütke
parent
0a0e1e6f29
commit
4ac95b5e26
@ -221,10 +221,15 @@ describe("LlamaCpp Integration", () => {
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const successCount = allResults.filter(r => r !== null).length;
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expect(successCount).toBe(10);
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// THE KEY ASSERTION: Only 1 context should be created, not 5
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// Without the fix, contextCreateCount would be 5 (one per concurrent embedBatch call)
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console.log(`Context creation count: ${contextCreateCount} (expected: 1)`);
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expect(contextCreateCount).toBe(1);
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// THE KEY ASSERTION: Contexts should be created once (by ensureEmbedContexts),
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// not duplicated per concurrent embedBatch call. The exact count depends on
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// available VRAM (computeParallelism), but should not be 5 (one per call).
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// Without the fix, contextCreateCount would be 5× the intended count (one set per concurrent call).
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// With the promise guard, contexts are created exactly once regardless of concurrent callers.
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// The count depends on VRAM (computeParallelism), but should be ≤ 8 (the cap).
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console.log(`Context creation count: ${contextCreateCount} (expected: ≤ 8, not 5× duplicated)`);
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expect(contextCreateCount).toBeGreaterThanOrEqual(1);
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expect(contextCreateCount).toBeLessThanOrEqual(8);
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await freshLlm.dispose();
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}, 60000);
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160
src/llm.ts
160
src/llm.ts
@ -354,7 +354,7 @@ const DEFAULT_INACTIVITY_TIMEOUT_MS = 5 * 60 * 1000;
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export class LlamaCpp implements LLM {
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private llama: Llama | null = null;
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private embedModel: LlamaModel | null = null;
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private embedContext: LlamaEmbeddingContext | null = null;
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private embedContexts: LlamaEmbeddingContext[] = [];
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private generateModel: LlamaModel | null = null;
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private rerankModel: LlamaModel | null = null;
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private rerankContexts: Awaited<ReturnType<LlamaModel["createRankingContext"]>>[] = [];
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@ -366,7 +366,6 @@ export class LlamaCpp implements LLM {
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// Ensure we don't load the same model/context concurrently (which can allocate duplicate VRAM).
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private embedModelLoadPromise: Promise<LlamaModel> | null = null;
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private embedContextCreatePromise: Promise<LlamaEmbeddingContext> | null = null;
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private generateModelLoadPromise: Promise<LlamaModel> | null = null;
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private rerankModelLoadPromise: Promise<LlamaModel> | null = null;
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@ -423,7 +422,7 @@ export class LlamaCpp implements LLM {
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* Check if any contexts are currently loaded (and therefore worth unloading on inactivity).
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*/
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private hasLoadedContexts(): boolean {
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return !!(this.embedContext || this.rerankContexts.length > 0);
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return !!(this.embedContexts.length > 0 || this.rerankContexts.length > 0);
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}
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/**
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@ -445,10 +444,10 @@ export class LlamaCpp implements LLM {
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}
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// Dispose contexts first
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if (this.embedContext) {
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await this.embedContext.dispose();
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this.embedContext = null;
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for (const ctx of this.embedContexts) {
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await ctx.dispose();
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}
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this.embedContexts = [];
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for (const ctx of this.rerankContexts) {
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await ctx.dispose();
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}
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@ -557,34 +556,69 @@ export class LlamaCpp implements LLM {
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}
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/**
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* Load embedding context (lazy). Context can be disposed and recreated without reloading the model.
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* Compute how many parallel contexts to create based on available VRAM.
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* Conservative: uses at most 25% of free VRAM for contexts, capped at 8.
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*/
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private async computeParallelism(perContextMB: number): Promise<number> {
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const llama = await this.ensureLlama();
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if (!llama.gpu) return 1; // CPU: no benefit from multiple contexts
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try {
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const vram = await llama.getVramState();
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const freeMB = vram.free / (1024 * 1024);
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// Use at most 25% of free VRAM, min 1, max 8
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const maxByVram = Math.floor((freeMB * 0.25) / perContextMB);
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return Math.max(1, Math.min(8, maxByVram));
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} catch {
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return 2; // Conservative fallback
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}
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}
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/**
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* Load embedding contexts (lazy). Creates multiple for parallel embedding.
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* Uses promise guard to prevent concurrent context creation race condition.
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*/
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private async ensureEmbedContext(): Promise<LlamaEmbeddingContext> {
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if (!this.embedContext) {
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// If context creation is already in progress, wait for it
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if (this.embedContextCreatePromise) {
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return await this.embedContextCreatePromise;
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}
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private embedContextsCreatePromise: Promise<LlamaEmbeddingContext[]> | null = null;
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// Start context creation and store promise so concurrent calls wait
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this.embedContextCreatePromise = (async () => {
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const model = await this.ensureEmbedModel();
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const context = await model.createEmbeddingContext();
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this.embedContext = context;
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return context;
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})();
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try {
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const context = await this.embedContextCreatePromise;
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this.touchActivity();
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return context;
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} finally {
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this.embedContextCreatePromise = null;
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}
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private async ensureEmbedContexts(): Promise<LlamaEmbeddingContext[]> {
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if (this.embedContexts.length > 0) {
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this.touchActivity();
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return this.embedContexts;
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}
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this.touchActivity();
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return this.embedContext;
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if (this.embedContextsCreatePromise) {
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return await this.embedContextsCreatePromise;
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}
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this.embedContextsCreatePromise = (async () => {
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const model = await this.ensureEmbedModel();
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// Embed contexts are ~143 MB each (nomic-embed 2048 ctx)
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const n = await this.computeParallelism(150);
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for (let i = 0; i < n; i++) {
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try {
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this.embedContexts.push(await model.createEmbeddingContext());
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} catch {
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if (this.embedContexts.length === 0) throw new Error("Failed to create any embedding context");
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break;
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}
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}
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this.touchActivity();
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return this.embedContexts;
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})();
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try {
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return await this.embedContextsCreatePromise;
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} finally {
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this.embedContextsCreatePromise = null;
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}
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}
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/**
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* Get a single embed context (for single-embed calls). Uses first from pool.
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*/
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private async ensureEmbedContext(): Promise<LlamaEmbeddingContext> {
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const contexts = await this.ensureEmbedContexts();
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return contexts[0]!;
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}
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/**
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@ -648,21 +682,24 @@ export class LlamaCpp implements LLM {
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/**
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* Load rerank contexts (lazy). Creates multiple contexts for parallel ranking.
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* Each context has its own sequence, so they can evaluate independently.
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*
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* Uses contextSize 1024 instead of auto (40960) — reranking chunks are ~800
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* tokens max, so 1024 is plenty. This drops VRAM from 11.6 GB to 711 MB per context.
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*/
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private static readonly RERANK_PARALLEL_CONTEXTS = 4;
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private static readonly RERANK_CONTEXT_SIZE = 1024;
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private async ensureRerankContexts(): Promise<Awaited<ReturnType<LlamaModel["createRankingContext"]>>[]> {
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if (this.rerankContexts.length === 0) {
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const model = await this.ensureRerankModel();
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const n = LlamaCpp.RERANK_PARALLEL_CONTEXTS;
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// Create contexts sequentially to avoid VRAM allocation races
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// Rerank contexts are ~711 MB each at contextSize 1024
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const n = await this.computeParallelism(750);
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for (let i = 0; i < n; i++) {
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try {
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this.rerankContexts.push(await model.createRankingContext());
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this.rerankContexts.push(await model.createRankingContext({
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contextSize: LlamaCpp.RERANK_CONTEXT_SIZE,
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}));
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} catch {
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// VRAM exhausted — use however many we got
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if (this.rerankContexts.length === 0) {
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// Must have at least one
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throw new Error("Failed to create any rerank context");
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}
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break;
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@ -741,26 +778,51 @@ export class LlamaCpp implements LLM {
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if (texts.length === 0) return [];
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try {
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const context = await this.ensureEmbedContext();
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const contexts = await this.ensureEmbedContexts();
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const n = contexts.length;
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// node-llama-cpp handles batching internally when we make parallel requests
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const embeddings = await Promise.all(
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texts.map(async (text) => {
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if (n === 1) {
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// Single context: sequential (no point splitting)
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const context = contexts[0]!;
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const embeddings = [];
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for (const text of texts) {
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try {
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const embedding = await context.getEmbeddingFor(text);
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this.touchActivity(); // Keep-alive during slow batches
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return {
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embedding: Array.from(embedding.vector),
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model: this.embedModelUri,
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};
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this.touchActivity();
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embeddings.push({ embedding: Array.from(embedding.vector), model: this.embedModelUri });
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} catch (err) {
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console.error("Embedding error for text:", err);
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return null;
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embeddings.push(null);
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}
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}
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return embeddings;
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}
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// Multiple contexts: split texts across contexts for parallel evaluation
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const chunkSize = Math.ceil(texts.length / n);
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const chunks = Array.from({ length: n }, (_, i) =>
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texts.slice(i * chunkSize, (i + 1) * chunkSize)
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);
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const chunkResults = await Promise.all(
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chunks.map(async (chunk, i) => {
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const ctx = contexts[i]!;
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const results: (EmbeddingResult | null)[] = [];
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for (const text of chunk) {
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try {
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const embedding = await ctx.getEmbeddingFor(text);
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this.touchActivity();
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results.push({ embedding: Array.from(embedding.vector), model: this.embedModelUri });
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} catch (err) {
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console.error("Embedding error for text:", err);
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results.push(null);
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}
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}
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return results;
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})
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);
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return embeddings;
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return chunkResults.flat();
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} catch (error) {
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console.error("Batch embedding error:", error);
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return texts.map(() => null);
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@ -1015,7 +1077,7 @@ export class LlamaCpp implements LLM {
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}
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// Clear references
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this.embedContext = null;
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this.embedContexts = [];
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this.rerankContexts = [];
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this.embedModel = null;
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this.generateModel = null;
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@ -1024,7 +1086,7 @@ export class LlamaCpp implements LLM {
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// Clear any in-flight load/create promises
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this.embedModelLoadPromise = null;
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this.embedContextCreatePromise = null;
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this.embedContextsCreatePromise = null;
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this.generateModelLoadPromise = null;
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this.rerankModelLoadPromise = null;
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}
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