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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dylanyunlon
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9b21a13119
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[MUH] Bootstrap muh toolchain — extract/parse/gen_yaml/gen_patch + baseline.muh
Pipeline:
1. extract.py: Parses all 26 CCCL tuning_*.cuh → 26 YAML schemas in muh/schema/
2. parse.py: .muh file parser with extends-inheritance + schema validation
3. gen_yaml.py: .muh → computility-run.yaml (verified: matches competition reference)
4. gen_patch.py: .muh → vllm kernel unified diff patches (6 algorithm mappings)
5. baseline.muh: Competition reference config, all tuning values pending BI-V100 benchmarks
Schemas extracted:
26 algorithms, 8-19 params each, SM75/80/90/100 reference tunings
Priority mapping: reduce→attention, topk→sampling, scan→paged_attention,
transform→activations, batch_memcpy→KV_cache, for→RoPE
Tested: extract→parse→validate→gen_yaml→gen_patch full pipeline passes
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2026-07-30 10:39:06 +00:00 |
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