- 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>
Add support for GLM-4 model architecture with the following changes:
- Add glm4.py with ChatGLMForCausalLM, GLM4Model, GLM4Attention, GLM4MLP
- Add GLM4RotaryEmbedding with interleaved partial rotation (rotary_dim = head_dim // 2)
- Add apply_rotary_emb_interleaved function for GLM-4 style RoPE
- Add GLM-4 weight name conversion and loading in loader.py
- Add GLM-4 chat template conversion in test_ruler.py
- Add trust_remote_code=True for GLM-4 config loading
Key GLM-4 specific adaptations:
- QKV bias enabled (add_qkv_bias: true)
- RoPE with rope_ratio scaling (base = 10000 * rope_ratio)
- Interleaved RoPE (pairs adjacent elements, not first/second half)
- Partial rotation (only half of head_dim is rotated)
- Uses multi_query_group_num instead of num_key_value_heads
- Uses kv_channels instead of head_dim
- Uses ffn_hidden_size instead of intermediate_size
Tested with RULER niah_single_1 (5 samples): 100% accuracy
Both GPU-only and CPU offload modes verified
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Refactor Observer into base class with common enable/disable/reset interface
- Create InferenceObserver subclass for TTFT/TPOT metrics
- Fix TTFT calculation timing: compute after prefill completes instead of
at decode start (fixes max_tokens=1 returning TTFT=0)
- Integrate InferenceObserver into bench.py and bench_offload.py for
accurate internal timing metrics vs external wall-clock time
- Add get_summary() and print_summary() methods for structured output
Generated with [Claude Code](https://claude.ai/code)
via [Happy](https://happy.engineering)
Co-Authored-By: Claude <noreply@anthropic.com>
Co-Authored-By: Happy <yesreply@happy.engineering>
