Claude
eb57eb7d1c
clean: remove 444 .pyc files + libcccl_allocator.so from git tracking
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These cause docker build failures on competition platform.
.gitignore and .dockerignore already exclude them.
2026-08-14 02:20:26 +00:00
Claude
34b3a4a617
[BASE] block_table.py: CCCL dispatch_select_if alias_temporaries batch allocation
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Source: cccl_upstream/cub/cub/device/dispatch/dispatch_select_if.cuh
Target: vllm/core/block/block_table.py
CCCL system design applied:
- dispatch_select_if alias_temporaries: compute all allocation sizes
upfront, pack into single blob, then init all at once
- streaming_context_t.advance(): batch state changes instead of
mutating mid-iteration
- Applied to ensure_num_empty_slots: Phase 1 batch-allocate all
new blocks, Phase 2 batch-append to BlockList
- Separates allocation planning from execution, preventing
prev_block chain corruption during multi-block allocation
2026-08-06 04:22:02 +00:00
Claude
dd59ec95c2
[ENGINE] prefix_caching_block: CCCL DeviceCopy::Batched 3-phase swap_in/swap_out
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Source: cccl_upstream/cub/test/catch2_test_device_copy_env.cu
Target: vllm/core/block/prefix_caching_block.py
CCCL system design applied:
- DeviceCopy::Batched separates index_to_ptr (offset collection),
get_size (range sizing), and kernel launch (execution) into 3 phases
- Applied to swap_in: Phase 1 classify, Phase 2 batch-allocate,
Phase 3 batch-assign block_ids
- Applied to swap_out: Phase 1 collect, Phase 2 batch-free
- Prevents evictor state corruption from interleaved alloc+assign
Also applied to paged_attn.py:
- V1/V2 dispatch: CCCL dispatch_reduce.cuh tile-capacity decision
replaces hardcoded max_seq_len<=8192
- Added BI-V100 GridEvenShare constants from grid_even_share.cuh
2026-08-06 04:12:19 +00:00
dylanyunlon
ef6abf3dc7
[DEPLOY] Complete submission: baseline + all optimizations
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Adds ALL files needed for Dockerfile build:
- qwen3_6_scripts/ (baseline patches + our optimizations)
- vllm/ (full vllm package)
- paged_attention_v2_pytorch.py (V2 with single-bmm optimization)
- Dockerfile + computility-run.yaml
Our optimizations vs baseline:
1. paged_attn.py: pre-gathered context KV (eliminates 194 gather calls),
Triton try/fallback, V2 heuristic, threshold 32K→64K
2. paged_attention_v2_pytorch.py: fills NotImplementedError,
single-bmm Phase 1 (195 launches → 3)
3. patch_enable_triton.py: HAS_TRITON=True with safety fallback
4. patch_triton_tuning.py: BLOCK=64, NUM_WARPS=4 for BI-V100
5. computility-run.yaml: gpu-memory-utilization 0.9→0.95,
max-num-batched-tokens 8192→16384
This repo can now be submitted to dev.modelhub.org.cn as-is.
2026-07-30 16:06:20 +00:00
