87 lines
3.9 KiB
Markdown
87 lines
3.9 KiB
Markdown
# muh Pipeline Status — Ground Truth
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**Last verified**: 2026-08-07T01:45:31Z by automated analysis
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## Architecture Summary
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```
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CCCL policy_selector(compute_capability) → ReducePolicy{threads, items, vec, algo, load_mod}
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↕ mirrors
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muh policy_selector(hardware_capability) → same struct types, BI-V100 values
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↕ gen_patch.py extracts bi100_* values
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vllm patch_ops.sh → full-file Python replacements with tuning values baked in
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```
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## Injection Reality
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### What gen_patch.py THINKS (csrc/*.cu — DEAD)
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```
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tuning_reduce.cuh → csrc/attention/attention_kernels.cu NUM_THREADS ← NO .cu SOURCE
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tuning_scan.cuh → csrc/attention/paged_attention_v1.cu SCAN_BLOCK_SIZE ← NO .cu SOURCE
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tuning_topk.cuh → csrc/sampling/sampling_kernels.cu SAMPLING_BLOCK_SIZE ← NO .cu SOURCE
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```
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### What ACTUALLY happens (Python runtime — ALIVE)
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```
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_custom_ops.py → SMEM 49152 (was 32768) ← DEPLOYED ✓
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paged_attn.py → _PARTITION_SIZE=512 ← DEPLOYED ✓ (V2 partition, NOT CTA tile)
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xformers.py → _Q_CHUNK=256, sdpa_fallback ← DEPLOYED ✓
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sampler.py → torch.topk fast path ← DEPLOYED ✓
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prefix_prefill.py → Triton BLOCK_M/N/warps ← DEPLOYED ✓ (but Triton not available)
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computility-run.yaml → vllm server args ← DEPLOYED ✓
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```
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### The Gap
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muh C++ headers define precise per-type-per-op tuning values (14 reduce structs, 22 scan structs).
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But the vllm engine on BI-V100 runs ixformer .so (precompiled, not tunable) + Python fallbacks.
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The C++ headers' values cannot be injected into the precompiled .so.
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They CAN inform:
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1. Python fallback implementations (paged_attn.py, xformers.py) — tile sizes, chunk sizes
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2. Triton JIT configs — if Triton were available (it's not on BI-V100 base image)
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3. Future EngineX releases that expose tuning knobs
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## Asset Inventory
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| Asset | Count | Status |
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|-------|-------|--------|
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| CCCL tuning headers (upstream) | 27 | Complete |
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| muh BI-V100 headers | 27 | Complete (14 reduce + 22 scan + others) |
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| muh schema YAMLs | 27 | Complete |
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| CUB benchmarks | 91 | Synced to NVIDIA/cccl main |
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| CUB tests | 243 | Complete |
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| CUB examples | 18 | Complete |
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| Thrust examples | 60 | Complete |
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| Deployed patches | 15 files | Via patch_ops.sh full replacement |
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| bench_bi100.py search spaces | 5 algos | Defined, needs BI-V100 hardware to run |
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## Tool Chain Status
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| Tool | Input | Output | Status |
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|------|-------|--------|--------|
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| parse.py | baseline.muh | JSON config | ✓ Working |
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| gen_patch.py | tuning_*.cuh | Patch report | ⚠ Reports structs but generates 0 patches (injection mapping mismatch) |
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| gen_yaml.py | baseline.muh | computility-run.yaml | ✓ Working |
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| bench_bi100.py | algo+dtype | CCCL-format speedup data | Needs BI-V100 hardware |
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| patch_ops.sh | qwen3_6_scripts/ | Docker vllm patches | ✓ Working |
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| muh_dispatch.py | hw+dtype+head_dim | AttentionConfig | ✓ Working (needs torch) |
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| scale_mem_bound | (threads, items, type_size) | (items, threads) | ✓ CCCL parity verified |
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## Critical Numbers
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| Metric | Competition Threshold | Current Status |
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|--------|----------------------|----------------|
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| Functional tests | 50+ pass | 13 items In Progress (all FEA) |
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| Effect deviation | ≤ ±4% | Untested (needs hardware) |
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| Token throughput weighted | ≥ 8000 | Untested |
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| Output TPS weight | 83% (×16.796) | Reduce/scan/topk optimization focus |
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| SMEM limit | 49152 bytes | All 36 scan+reduce structs verified ✓ |
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| SM count | 16 (confirmed) | All headers updated |
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## Next Actions (Ranked by Competition Impact)
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1. **Run bench_bi100.py on BI-V100** → get real speedup data for reduce/scan/topk
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2. **Backfill speedup data to muh headers** → replace TBD/theoretical values
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3. **Optimize Python fallback tile sizes** → paged_attn.py, xformers.py Q_CHUNK
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4. **Tune computility-run.yaml** → max-num-seqs, max-batched-tokens, gpu-mem-util
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5. **Enable prefix caching benchmark** → cached_tokens > 0 for repeat prompts
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