Files
project_6/computility-run.yaml
project6-dev f28223c9da perf: native ixformer decode (v1 ≤32K, v2 >32K) + flash_attn_varlen prefill
Replaces all Python PyTorch fallback attention with native ixformer kernels:

Decode path:
- ≤32K: paged_attention_v1 (5D KV layout, x=8) — verified on real BI-V100
- >32K: paged_attention_v2 (5D→4D permute) — verified 65K+ on real BI-V100
- Removes _forward_decode_pytorch Python fallback entirely

Prefill path (profiling):
- _run_sdpa_fallback now uses ixformer.flash_attn_varlen_func
- head_dim=256 verified correct (diff<0.004) and 1.7x faster than PyTorch
- Falls back to Q-tiling pure-math if ixformer unavailable

Also includes: MoE kernel integration, GDN C++ kernels, diagnostic scripts,
xllm upstream layer/kernel references, .dockerignore cleanup.

All changes verified on real BI-V100 hardware (single card).
2026-08-13 07:04:21 +00:00

52 lines
1.2 KiB
YAML

concurrency: 1
command:
- python3
- -m
- vllm.entrypoints.openai.api_server
- --model
- /model
- --served-model-name
- llm
- --max-model-len
- '80000'
- --gpu-memory-utilization
- '0.90'
- --trust-remote-code
- -tp
- '4'
- --max-num-seqs
- '2'
- --disable-log-requests
- --disable-frontend-multiprocessing
- --max-num-batched-tokens
- '8192'
- --enable-chunked-prefill
- --max-seq-len-to-capture
- '32768'
- --enable-auto-tool-choice
- --tool-call-parser
- qwen3_coder
- --reasoning-parser
- qwen3
- --enable-prefix-caching
- --enforce-eager
- --dtype
- half
env:
- name: VLLM_ENGINE_ITERATION_TIMEOUT_S
value: 3600
- name: BI100_MOE_COREX_DIRECT_ROUTED
value: 1
- name: BI100_GDN_COREX_PACKED_DECODE
value: 1
- name: BI100_HYBRID_KV_ACCOUNTING
value: full_attention
- name: BI100_GDN_CACHE_POLICY
value: admission64
- name: BI100_GDN_RESTORE_MODE
value: hybrid64
- name: BI100_MOE_COREX_TOPK_SOFTMAX
value: '1'
- name: PYTORCH_CUDA_ALLOC_CONF
value: expandable_segments:True