Claude
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8e9c22f6c1
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feat: CCCL-derived 3-tier decode dispatch + SM=16 prefill tuning + multi-step scheduling
paged_attn.py:
- Remove use_v1=True hardcode that forced all decode through ixf_F V1
- Wire up paged_attention_v2_triton.py as Tier 2 decode path for seq_len > 8192
- 3-tier dispatch: V1 (short) → Triton V2 (long) → PyTorch (fallback)
- Triton V2 uses CCCL compound-reduce pattern (summary_statistics.cu)
with GQA broadcast (6x KV read reduction for Qwen3.6)
- This is the single highest-impact change: Output TPS is 83% of score
prefix_prefill.py:
- CCCL scan-tuning-informed block sizes for BI-V100 (SM=16, 48KB SMEM)
- BI-V100 path: BLOCK=64 NUM_WARPS=4 (vs BLOCK=128 NUM_WARPS=8 on A100+)
- Matches muh/tuning/tuning_scan.cuh bi100_lookback_4B_o4 pattern
- Fewer warps = less register pressure = higher occupancy on 16 SMs
computility-run.yaml:
- Add --num-scheduler-steps=8: batch 8 decode iterations per Python call
(cuts scheduler overhead ~8x, directly improves Output TPS)
- Add --preemption-mode=recompute (cheaper than swap on BI-V100 HBM)
- Add TRITON_CACHE_DIR for JIT warmup persistence
- Add TRITON_PRINT_AUTOTUNING=0 (use hardcoded CCCL configs, skip autotune)
Competition impact estimate:
- Tier 2 Triton V2 replaces PyTorch fallback for 8K-100K contexts → ~5-10x decode speedup
- Multi-step scheduling → ~20-30% Output TPS improvement
- SM=16 block tuning → ~10-15% Input TPS improvement
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2026-08-03 08:28:38 +00:00 |
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dylanyunlon
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ef6abf3dc7
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[DEPLOY] Complete submission: baseline + all optimizations
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.
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2026-07-30 16:06:20 +00:00 |
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