8e9c22f6c1
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
57 lines
1.9 KiB
YAML
57 lines
1.9 KiB
YAML
concurrency: 1
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command:
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- python3
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- -m
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- vllm.entrypoints.openai.api_server
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- --model
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- /model
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- --served-model-name
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- llm
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- --max-model-len
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- '100000'
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- --gpu-memory-utilization
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- '0.9'
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- --trust-remote-code
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- -tp
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- '4'
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- --max-num-seqs
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- '8'
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- --disable-log-requests
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- --disable-frontend-multiprocessing
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- --max-num-batched-tokens
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- '8192'
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- --enable-chunked-prefill
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- --max-seq-len-to-capture
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- '32768'
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- --enable-auto-tool-choice
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- --tool-call-parser
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- qwen3_coder
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- --reasoning-parser
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- qwen3
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- --enable-prefix-caching
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# CCCL-derived optimizations:
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# Multi-step scheduling reduces Python dispatch overhead per decode iteration.
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# With max-num-seqs=8 and 4 GPUs, each step processes 8 tokens across 4 devices.
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# num-scheduler-steps=8 batches 8 decode iterations before returning to Python,
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# cutting scheduler overhead by ~8x. This directly improves Output TPS (83% weight).
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- --num-scheduler-steps
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- '8'
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# Recompute is cheaper than swap on BI-V100 (limited HBM bandwidth for swap).
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# When a sequence is preempted, recomputing the prefix is faster than
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# swapping KV blocks to/from CPU memory over PCIe.
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- --preemption-mode
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- recompute
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env:
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- name: VLLM_ENGINE_ITERATION_TIMEOUT_S
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value: 3600
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# Cache Triton JIT compilations across restarts.
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# Competition platform rebuilds the container each run — prewarmed cache
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# saves 30-60s of first-request latency.
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- name: TRITON_CACHE_DIR
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value: /tmp/triton_cache
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# Disable Triton autotuning at runtime (use hardcoded CCCL-derived configs).
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# Autotuning wastes 5-10s per kernel on first call and the BI-V100 optimal
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# configs are already baked into prefix_prefill.py and paged_attention_v2_triton.py.
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- name: TRITON_PRINT_AUTOTUNING
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value: '0'
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