testing
Speculative Decoding Performance Validator
Validates attention drift and performance regressions in speculative decoding implementations for production LLM inference.
speculative-decoding inference-optimisation attention-drift
prompt
# Speculative Decoding Performance Validator You are an expert in LLM inference optimisation and speculative decoding algorithms. Validate the performance and stability of speculative decoding implementations. ## Input [Paste your speculative decoding implementation, configuration, or performance metrics here] ## Analysis Framework ### 1. Attention Drift Detection - Analyse attention patterns for stability across speculation windows - Check for attention weight degradation in long sequences - Validate attention consistency between draft and target models - Flag sequences where attention diverges beyond acceptable thresholds ### 2. Performance Regression Analysis - Compare baseline inference latency vs speculative decoding latency - Calculate actual speedup ratios across different sequence lengths - Identify bottlenecks in the draft model prediction pipeline - Validate memory usage patterns for KV cache management ### 3. Production Stability Checks - Test speculation accuracy rates under different workload patterns - Validate graceful fallback to standard decoding when speculation fails - Check for memory leaks during extended inference sessions - Analyse performance under concurrent request scenarios ### 4. Algorithm-Specific Validation For EAGLE 3.1 or similar algorithms: - Verify draft model quality and target model compatibility - Test speculation window size optimisation - Validate acceptance rate thresholds - Check for proper handling of attention layer variations ## Output Format **Stability Assessment**: [STABLE/UNSTABLE/NEEDS_TUNING] **Critical Issues**: - List any attention drift patterns found - Performance regressions beyond 10% of expected speedup - Memory usage anomalies **Performance Metrics**: - Actual vs expected speedup ratios - Speculation acceptance rates - Memory overhead analysis **Recommendations**: - Specific parameter adjustments for stability - Production deployment considerations - Monitoring suggestions for ongoing validation Focus on practical deployment concerns. Provide specific metrics and actionable recommendations for production systems.
Use this validator when deploying speculative decoding in production environments or debugging performance issues with EAGLE, Medusa, or similar algorithms. Works with Claude, GPT-4, and Gemini to analyse implementation code, performance logs, or configuration files.