iGavroche-rocm-ninodes/RULES.md

ROCM Ninodes Development Rules

Testing Rules

Mandatory Testing

• All Changes: Every code change must have corresponding tests
• Performance Regression: No change can regress performance by >5%
• Coverage: Critical paths must have >90% test coverage
• Validation: All tests must pass before merging

Test Categories

1. Unit Tests: Individual node functionality
2. Performance Tests: Timing and memory usage
3. Integration Tests: Full workflow execution
4. Regression Tests: Prevent performance degradation

Test Execution

• Standalone: Tests must run without ComfyUI when possible
• Data-Driven: Use captured pickle data for consistent testing
• Automated: CI/CD pipeline integration required
• Documentation: All test failures must be documented

Benchmarking Rules

Baseline Requirements

• Before Optimization: Measure baseline performance first
• A/B Testing: Compare before/after for every change
• Multiple Runs: Minimum 3 runs for statistical significance
• Documentation: Record all benchmark results

Performance Targets

• Flux 1024x1024: ≥78% improvement over baseline
• WAN 320x320 17-frame: ≥5.6% improvement over baseline
• VAE Decode: <10s for any supported resolution
• Checkpoint Load: <30s for Flux models

Benchmark Process

1. Capture Data: Use ROCM_NINODES_DEBUG=1 to capture test data
2. Run Baseline: Measure current performance
3. Implement Change: Make optimization
4. Run Comparison: Measure new performance
5. Validate: Ensure improvement meets targets
6. Document: Record results and methodology

Architecture Constraints

gfx1151 Specific

• Precision: fp32 preferred over fp16 for stability
• Tile Sizes: 768-1024 optimal range
• TF32: Disabled (not beneficial on gfx1151)
• Memory: Conservative allocation (1.5x modifier for attention)
• Batch Size: Conservative to prevent OOM

Memory Management

• Allocation: Conservative approach to prevent OOM
• Cleanup: Regular torch.cuda.empty_cache() calls
• Monitoring: Track memory usage in all operations
• Limits: Respect 128GB unified RAM constraint

Data Formats

• Video: 5D tensor input [B, C, T, H, W] → 4D output [B*T, H, W, C]
• Images: Standard 4D tensor [B, H, W, C]
• Latents: ComfyUI format {"samples": tensor}
• Precision: Maintain dtype consistency throughout pipeline

Vision and Goals

Performance Targets

• Flux Workflow: 78% improvement (≤110s total time)
• WAN Workflow: 5.6% improvement (≤93s sampling time)
• VAE Decode: <10s for any resolution
• Memory Usage: <80% of available RAM

Quality Standards

• Reliability: Zero crashes during normal operation
• Consistency: Reproducible results across runs
• Compatibility: Works with standard ComfyUI workflows
• Maintainability: Clean, documented, testable code

User Experience

• Ease of Use: Drop-in replacement for standard nodes
• Performance: Noticeable speedup without configuration
• Debugging: Clear error messages and logging
• Documentation: Comprehensive guides and examples

Best Practices

Code Quality

• Style: Follow PEP 8 and project conventions
• Documentation: Docstrings for all public methods
• Type Hints: Use type annotations where possible
• Error Handling: Comprehensive exception handling

Performance

• Profiling: Profile before and after optimizations
• Memory: Monitor memory usage in all operations
• Caching: Use appropriate caching strategies
• Batching: Optimize batch sizes for gfx1151

Testing

• Isolation: Tests should not depend on external state
• Data: Use captured real data for testing
• Coverage: Test edge cases and error conditions
• Automation: Integrate with CI/CD pipeline

Debugging

• Logging: Use appropriate log levels
• Debug Mode: ROCM_NINODES_DEBUG=1 for data capture
• Error Messages: Clear, actionable error messages
• Documentation: Document known issues and workarounds

Research Process

Methodology

1. Measure First: Establish baseline performance
2. Optimize Second: Implement targeted improvements
3. Validate Third: Ensure improvements are real and stable

Optimization Strategy

• Targeted: Focus on identified bottlenecks
• Incremental: Small, testable changes
• Measurable: Quantify all improvements
• Reversible: Changes must be easily revertible

Documentation

• Process: Document optimization methodology
• Results: Record all performance measurements
• Decisions: Explain optimization choices
• Lessons: Capture learnings for future work

Quality Gates

Pre-commit

• Linting: Code must pass all linting checks
• Tests: All tests must pass
• Performance: No performance regression
• Documentation: Updated documentation required

Pre-merge

• Integration: Full integration test suite passes
• Performance: Meets performance targets
• Compatibility: Works with existing workflows
• Review: Code review approval required

Release

• Stability: No known critical issues
• Performance: Meets all performance targets
• Documentation: Complete user documentation
• Testing: Full test suite passes

Error Handling

Error Categories

1. Critical: System crashes or data corruption
2. Warning: Performance degradation or unexpected behavior
3. Info: Normal operation with additional information

Response Strategy

• Critical: Immediate fix required, no release until resolved
• Warning: Fix in next release cycle
• Info: Document and monitor

Logging

• Levels: Use appropriate log levels
• Context: Include relevant context in error messages
• Stack Traces: Include full stack traces for debugging
• User Action: Provide actionable next steps

Memory Management

Allocation Strategy

• Conservative: Prefer under-allocation to over-allocation
• Monitoring: Track memory usage in all operations
• Cleanup: Explicit cleanup after operations
• Limits: Respect system memory constraints

Optimization

• Reuse: Reuse memory where possible
• Batching: Optimize batch sizes for memory efficiency
• Tiling: Use tiled processing for large operations
• Streaming: Process data in streams when possible

Performance Monitoring

Metrics

• Timing: Measure execution time for all operations
• Memory: Track memory usage and allocation
• Throughput: Measure data processing rates
• Quality: Validate output correctness

Tools

• Profiling: Use PyTorch profiler for detailed analysis
• Monitoring: Real-time performance monitoring
• Logging: Structured logging for analysis
• Visualization: Performance dashboards and reports