40.4. Scaling GNN Inference (Inductive Serving)

Status: Draft Version: 1.0.0 Tags: #GNN, #Inference, #Rust, #ONNX, #Distillation Author: MLOps Team

The Inference Latency Crisis
Inductive vs Transductive Serving
Strategy 1: Neighbor Caching
Strategy 2: Knowledge Distillation (GNN -> MLP)
Rust Implementation: ONNX GNN Server
Infrastructure: The “Feature Prefetcher” Sidecar
Case Study: Pinterest’s PinSage Inference
Troubleshooting: Production Incidents
Future Trends: Serverless GNNs
MLOps Interview Questions
Glossary
Summary Checklist

Prerequisites

Before diving into this chapter, ensure you have the following installed:

Rust: 1.70+ (ort crate for ONNX Runtime)
Python: PyTorch (to export model).
Redis: For feature lookup.

In standard ML (e.g., Computer Vision), interference is $O(1)$. Input Image -> ResNet -> Output. In GNNs, inference is $O(D^L)$. Input Node -> Fetch Neighbors -> Fetch Neighbors of Neighbors -> Aggregate -> Output.

The Math of Slowness:

Layers $L=2$. Neighbors $K=20$.
Total feature vectors to fetch: $1 + 20 + 400 = 421$.
Redis Latency: 0.5ms.
Total IO Time: $421 \times 0.5 \text{ms} = 210 \text{ms}$.
Conclusion: You cannot do real-time GNN inference with naive neighbor fetching.

Inductive vs Transductive Serving

1. Transductive (Pre-computed Embeddings)

If the graph is static, we just run the GNN offline (Batch Job) for ALL nodes.

Save embeddings to Redis: Map<NodeID, Vector>.
Serving: GET user:123.
Pros: 1ms latency.
Cons: Can’t handle new users (Cold Start).

2. Inductive (Real-Time Computation)

We run the GNN logic on-the-fly.

Pros: Handles dynamic features and new nodes.
Cons: The Neighbor Execution problem described above.

The Hybrid Approach: Pre-compute embeddings for old nodes. Run Inductive GNN only for new nodes updates.

Strategy 1: Neighbor Caching

Most queries follow a power law. 1% of nodes (Hubs/Celebrities) appear in 90% of neighbor lists. We can cache their aggregated embeddings.

$$ h_v^{(l)} = \text{AGG}({h_u^{(l-1)} \forall u \in N(v)}) $$

If node $v$ is popular, we cache $h_v^{(l)}$. When node $z$ needs $v$ as a neighbor, we don’t fetch $v$’s neighbors. We just fetch the cached $h_v^{(l)}$.

Strategy 2: Knowledge Distillation (GNN -> MLP)

The “Cold Start” problem requires GNNs (to use topology). The “Latency” problem requires MLPs (Matrix Multiply only).

Solution: GLP (Graph-less Prediction)

Teacher: Deep GCN (Offline, Accurate, Slow).
Student: Simple MLP (Online, Fast).
Training: Minimize $KL(Student(X), Teacher(A, X))$.

The Student learns to hallucinate the structural information solely from the node features $X$.

Inference: $O(1)$. 0 Neighbor lookups.
Accuracy: Typically 95% of Teacher.

Rust Implementation: ONNX GNN Server

We assume we must run the full GNN (Inductive). We optimize the compute using ONNX Runtime in Rust. The key here is efficient Tensor handling and async I/0 for neighbor fetches.

Project Structure

gnn-serving/
├── Cargo.toml
└── src/
    └── main.rs

Cargo.toml:

[package]
name = "gnn-serving"
version = "0.1.0"
edition = "2021"

[dependencies]
ort = "1.16" # ONNX Runtime bindings
ndarray = "0.15"
tokio = { version = "1", features = ["full"] }
axum = "0.6"
serde = { version = "1.0", features = ["derive"] }
serde_json = "1.0"
redis = "0.23"

src/main.rs:

//! High-Performance GNN Inference Server.
//! Uses ONNX Runtime for model execution.
//! Demonstrates Zero-Copy tensor creation from Vec<f32>.

use axum::{extract::Json, routing::post, Router};
use ndarray::{Array2, Axis};
use ort::{Environment, SessionBuilder, Value};
use serde::Deserialize;
use std::sync::Arc;

#[derive(Deserialize)]
struct InferenceRequest {
    target_node: i64,
    // In real app, we might accept raw features or fetch them from Redis
    neighbor_features: Vec<Vec<f32>>, 
}

/// Global Application State sharing the ONNX Session
struct AppState {
    model: ort::Session,
}

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // 1. Initialize ONNX Runtime Environment
    // We enable graph optimizations (Constant Folding, etc.)
    let environment = Arc::new(Environment::builder()
        .with_name("gnn_inference")
        .build()?);
        
    // 2. Load the Model
    // GraphSAGE model exported to ONNX format
    let model = SessionBuilder::new(&environment)?
        .with_optimization_level(ort::GraphOptimizationLevel::Level3)?
        .with_model_from_file("graph_sage_v1.onnx")?;

    let state = Arc::new(AppState { model });

    // 3. Start high-performance HTTP Server
    let app = Router::new()
        .route("/predict", post(handle_predict))
        .with_state(state);

    println!("GNN Inference Server running on 0.0.0.0:3000");
    axum::Server::bind(&"0.0.0.0:3000".parse()?)
        .serve(app.into_make_service())
        .await?;
    
    Ok(())
}

/// Handle prediction request.
/// Input: JSON with features.
/// Output: JSON with Embedding Vector.
async fn handle_predict(
    axum::extract::State(state): axum::extract::State<Arc<AppState>>,
    Json(payload): Json<InferenceRequest>,
) -> Json<serde_json::Value> {
    
    // Safety check: ensure features exist
    if payload.neighbor_features.is_empty() {
        return Json(serde_json::json!({ "error": "No features provided" }));
    }

    // Convert Vec<Vec<f32>> to Tensor (Batch, NumNeighbors, FeatDim)
    // Flatten logic is CPU intensive for large batches; assume client sends flat array in prod
    let num_neighbors = payload.neighbor_features.len();
    let dim = payload.neighbor_features[0].len();
    let shape = (1, num_neighbors, dim); // Batch size 1
    
    let flat_data: Vec<f32> = payload.neighbor_features.into_iter().flatten().collect();
    let input_tensor = Array2::from_shape_vec(shape, flat_data).unwrap();
    
    // Run Inference
    // We wrap the input array in an ONNX Value
    let inputs = vec![Value::from_array(state.model.allocator(), &input_tensor).unwrap()];
    
    // Execute the graph
    let outputs = state.model.run(inputs).unwrap();
    
    // Parse Output
    // Extract the first output tensor (Embedding)
    let embedding: Vec<f32> = outputs[0]
        .try_extract()
        .unwrap()
        .view()
        .to_slice()
        .unwrap()
        .to_vec();
    
    Json(serde_json::json!({ "embedding": embedding }))
}

Infrastructure: The “Feature Prefetcher” Sidecar

Latency mainly comes from Redis Round-Trips. If we request 100 neighbors, doing 100 Redis GETs is suicide. Redis MGET is better, but large payloads clog the network.

Architecture:

Pod A (GNN Service): CPU intensive.
Pod B (Sidecar Prefetcher): C++ Sidecar connected to local NVMe Cache + Redis.
Protocol: Shared Memory (Apache Arrow Plasma).

The GNN service writes TargetNodeID to Shared Memory. The Sidecar wakes up, fetches all 2-hop neighbors (using its local graph index), MGETs features, writes Tensor to Shared Memory. GNN Service reads Tensor. Zero Copy.

Case Study: Pinterest’s PinSage Inference

Pinterest has 3 billion pins.

MapReduce: Generating embeddings for all pins takes days.
Incremental: They only recompute embeddings for pins that had new interactions.
Serving: They use “HITS” (Hierarchical Interest Training Strategy).
- Top 100k categories are cached in RAM.
- Long tail pins are fetched from SSD-backed key-value store.
- GNN is only run Inductively for new pins uploaded in the last hour.

Troubleshooting: Production Incidents

Scenario 1: The “Super Node” spike (Thundering Herd)

Symptom: p99 latency jumps to 2 seconds.
Cause: A user interacted with “Justin Bieber” (User with 10M edges). The GNN tried to aggregate 10M neighbors.
Fix: Hard Cap on neighbor sampling. Never fetch more than 20 neighbors. Use random sampling if > 20.

Scenario 2: GC Pauses

Symptom: Python/Java services freezing.
Cause: Creating millions of small objects (Feature Vectors) for every request.
Fix: Object Pooling or use Rust (Deterministic destruction).

Scenario 3: ONNX Version Mismatch

Symptom: InvalidGraph error on startup.
Cause: Model exported with Opset 15, Runtime supports Opset 12.
Fix: Pin the opset_version in torch.onnx.export.

Future Trends: Serverless GNNs

Running heavy GNN pods 24/7 is expensive if traffic is bursty. New frameworks (like AWS Lambda + EFS) allow loading the Graph Index on EFS (Network Storage) and spinning up 1000 lambdas to handle a traffic spike.

Challenge: Cold Start (loading libraries).
Solution: Rust Lambdas (10ms cold start) + Arrow Zero-Copy from EFS.

MLOps Interview Questions

Q: When should you use GNN -> MLP Distillation? A: Almost always for consumer recommendation systems. The latency cost of neighbor fetching ($O(D^L)$) is rarely worth the marginal accuracy gain over a well-distilled MLP ($O(1)$) in real-time path.
Q: How do you handle “Feature Drift” in GNNs? A: If node features change (User gets older), the cached embedding becomes stale. You need a TTL (Time to Live) on the Redis cache, typically matched to the user’s session length.
Q: What is “Graph Quantization”? A: Storing the graph structure using Compressed Integers (VarInt) and edge weights as int8. Reduces memory usage by 70%, allowing larger graphs to fit in GPU/CPU Cache.
Q: Explain “Request Batching” for GNNs. A: Instead of processing 1 user per request, wait 5ms to accumulate 10 users.
- Process union of neighbors.
- De-duplicate fetches (User A and User B both follow Node C; fetch C only once).
Q: Why is ONNX better than Pickle for GNNs? A: Pickle is Python-specific and slow. ONNX graph allows fusion of operators (e.g. MatMul + Relu) and running on non-Python runtimes (Rust/C++) for lower overhead.

Glossary

Inductive: Capability to generate embeddings for previously unseen nodes.
Distillation: Training a small model (Student) to mimic a large model (Teacher).
Sidecar: A helper process running in the same container/pod.
ONNX: Open Neural Network Exchange format.
Zero-Copy: Moving data between processes without CPU copy instructions (using pointers).

Summary Checklist

Distillation: Attempt to train an MLP Student. If accuracy is within 2%, deploy the MLP, not the GNN.
Timeout: Set strict timeouts on Neighbor Fetching (e.g. 20ms). If timeout, use Mean Embedding of 0-hop.
Cap Neighbors: Enforce max_degree=20 in the online sampler.
Format: Use ONNX for deployment. Don’t serve PyTorch directly in high-load setups.
Testing: Load Test with “Super Nodes” to ensure the system doesn’t crash on high-degree queries.
Caching: Implement a 2-Layer Cache: Local RAM (L1) -> Redis (L2) -> Feature Store (L3).
Monitoring: Track Neighbor_Fetch_Count per request. If it grows, your sampling depth is too high.

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