- Add record_comm_density() call in select_blocks to track CPU block selection
- Add get_per_layer_comm_density() method for detailed analysis
- Update print_summary() to show both densities and H2D savings ratio
- Set DensityObserver mode (offload/gpu_only) in test_ruler.py
- Update get_summary() to return both density types
Key insight: Comm density can be 100% even when compute density is ~37%
because sparse BSA blocks are distributed across all CPU blocks.
Since CPU block granularity is 32x coarser (4096 vs 128 tokens),
any() aggregation across heads/Q-blocks results in all CPU blocks being needed.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Added analysis documentation for xattn density alignment.
- Refactored ModelRunner to pre-allocate policy metadata buffers regardless of CPU offload configuration.
- Updated FullAttentionPolicy and SparsePolicy to accept query and key tensors for block selection.
- Enhanced QuestPolicy to utilize query tensor for block selection and improved handling of selected blocks.
- Expanded XAttentionBSAPolicy to support chunked prefill and improved attention score computation with historical and current chunk handling.
- Introduced DensityObserver to track compute and communication density for sparse attention layers.
- Updated attention layer to ensure block selection is always called, improving robustness in first chunk scenarios.
- Added tests for attention kernel behavior with enhanced input patterns.
- Add DensityObserver class to track per-layer density statistics
- Integrate DensityObserver into compute_prefill for GPU-only mode
- Fix stride parameter not being passed to xattn_estimate
- Add density statistics output to test_ruler.py for XATTN_BSA
- Add comprehensive density benchmark documentation
Key changes:
- nanovllm/utils/density_observer.py: New Observer for density tracking
- xattn_bsa.py: Add stride param to xattn_estimate, integrate DensityObserver
- test_ruler.py: Enable DensityObserver and print summary for XATTN_BSA
- docs/xattn_density_benchmark.md: Benchmark results for 4K-32K contexts
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
