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
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via [Happy](https://happy.engineering)
Co-Authored-By: Claude <noreply@anthropic.com>
Co-Authored-By: Happy <yesreply@happy.engineering>
- Remove cross-layer pipeline from OffloadEngine (saves ~1GB GPU memory for long sequences)
- Delete layer_k/v_buffer_a/b double buffers
- Remove start_decode_pipeline, get_decode_layer_kv, end_decode_pipeline methods
- Remove pipeline state tracking variables
- Simplify decode to use ring buffer pipeline only (more efficient for long sequences)
- Rename compute_chunked_attention → compute_chunked_prefill for clarity
- Add mandatory needle test requirements: --enable-offload --input-len 32768
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Import os and socket modules
- Add _find_free_port() function for automatic port detection
- Use NANOVLLM_DIST_PORT env var if set, otherwise auto-assign
- Enables running multiple model instances without port conflicts
Co-Authored-By: Claude <noreply@anthropic.com>
Combines two performance optimization features:
- perf_opt-1: Cross-layer pipeline for decode (double-buffered layer cache)
- perf_opt-2: Per-layer prefill buffer for async offload
Both features are complementary and improve CPU offload performance.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
