Production Compilation Pipeline and Failure Modes

The Production Compilation Pipeline
A robust pipeline: export from training framework → validate numerical accuracy → compile to target format → benchmark latency → deploy behind A/B test. Store both source model and compiled artifacts. Include compilation config (precision, optimization flags) in version control. Automate rebuilds when dependencies change.
Silent Numerical Divergence
The most dangerous failure: compiled model produces different outputs than source but still "works." Causes: operation reordering changes floating-point accumulation order; fused kernels use different algorithms; INT8 calibration on unrepresentative data. Symptoms: accuracy drops 1-3% in production but passes unit tests. Prevention: compare outputs on 1000+ diverse inputs; use maximum absolute difference thresholds (1e-5 for FP32, 1e-2 for INT8).
Operator Coverage Gaps
Every compiler supports a different operator set. A model using custom ops, recent PyTorch additions, or uncommon operations may fail to compile or fall back to slow generic implementations. Before choosing a compiler, audit your model"s operations against the compiler"s supported ops list. Custom operators require writing compiler plugins or replacing with supported alternatives.
Dynamic Shape Handling
Most compilers optimize for fixed input shapes. Variable batch sizes or sequence lengths require either: compiling multiple shape variants and switching at runtime; specifying shape ranges during compilation (TensorRT); or accepting suboptimal performance on dynamic workloads. Compilation time multiplies with shape variants; a model supporting 5 batch sizes takes 5x longer to compile.
✅ Validation: Run A/B test comparing compiled vs source model in production. Monitor prediction distribution, not just accuracy metrics.

💡 Key Takeaways

✓Pipeline: export → validate numerically → compile → benchmark → A/B test; version both source and artifacts

✓Silent divergence: compiled model passes tests but drops 1-3% accuracy from operation reordering or fusion

✓Use 1000+ diverse inputs with max absolute difference thresholds (1e-5 FP32, 1e-2 INT8) for validation

✓Audit operator coverage before choosing compiler; unsupported ops fail or fall back to slow implementations

✓Dynamic shapes multiply compilation time; TensorRT supports shape ranges, others need multiple variants

📌 Interview Tips

1Describe the silent divergence problem and how to detect it - shows production debugging experience

2Mention operator coverage auditing as first step when evaluating compilers

3Recommend A/B testing compiled models and monitoring prediction distribution, not just accuracy

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