Zijie Tian 2866d4fd88 ✨ feat: add chunk attention CUDA graph test for block sparse attention ...

Validates that pre-allocated CUDA graphs work for chunk-wise attention:
- Each (Q_chunk, K_chunk) pair has its own captured graph
- Zero copy_() during replay - all data pre-filled
- Uses nanovllm's flash_attn_with_lse and merge_attention_outputs

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

2026-01-22 00:57:05 +08:00

..

__init__.py

[WIP] NEED refactor nanovllm mechenism.

2025-12-22 23:52:56 +08:00

modeling_qwen3.py

[refactor] Refactor needle test.

2026-01-03 19:19:37 +08:00

test_chunk_attention_graph.py

✨ feat: add chunk attention CUDA graph test for block sparse attention

2026-01-22 00:57:05 +08:00

test_cudagraph_memory.py

[test] Added test_cudagraph_memory.py.

2026-01-21 03:30:36 +08:00

test_needle_ref.py

[refactor] Refactor needle test.

2026-01-03 19:19:37 +08:00

test_needle.py

[WIP] Before integrate the xattn operator.

2026-01-19 21:19:21 +08:00

test_quest_policy.py

[WIP] move metadata to GPU.

2026-01-06 23:32:32 +08:00

test_ruler.py

♻️ refactor: consolidate RULER test files and document root cause

2026-01-20 23:41:17 +08:00

test_sequential.py

[WIP] Before fix bench_offload.py.

2026-01-06 18:41:08 +08:00

utils.py

[WIP] Before fix bench_offload.py.

2026-01-06 18:41:08 +08:00