- 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>
Implement extended CUDA Graph coverage for CPU offload path:
- Add graphed_layers.py with N+2 graph architecture (EmbedGraph, FirstGraph, InterGraphs, LastGraph)
- Support both prefill (seq_len=chunk_size) and decode (seq_len=1) graph modes
- Extend graph coverage to ~70-80% including qkv_proj, rotary, o_proj
- Only attention core remains in eager mode for dynamic offload
Performance: Prefill throughput improved ~5.6% (3782 -> 3995 tok/s at 32K)
Also adds:
- --enforce-eager flag to bench_offload.py for comparison
- Offload mode constraint documentation in CLAUDE.md
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>
- Add --model parameter (default: Llama-3.1-8B-Instruct)
- Add --enable-xattn flag for XAttention BSA sparse prefill
- Add --xattn-threshold and --xattn-stride parameters
- Change default num-gpu-blocks from 6 to 4
- Add benchmark results doc with Full vs XAttn comparison (32K/128K)
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>
