⚡ feat: add Phase 5 CUDA Graph optimization for chunked prefill

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>

CLAUDE.md

@@ -93,6 +93,8 @@ PYTHONPATH=/home/zijie/Code/nano-vllm:$PYTHONPATH python tests/test_needle.py
 
 **Files**: `bench.py` (GPU), `bench_offload.py` (CPU offload), `bench_vllm.py` (comparison)
 
+**Offload Mode Constraint**: When using `enable_cpu_offload=True`, only test with context length ≥ 32K. Shorter contexts don't exercise the chunked offload pipeline properly.
+
 **Common Issues**:
 1. `max_num_batched_tokens < max_model_len`: Set equal for long context
 2. CUDA graph dimension mismatch: Ensure `input_len + output_len <= max_model_len`