Commit Graph

2 Commits

Author SHA1 Message Date
project6
3342d18bcc fix(critical): remove pip install transformers — was breaking corex kernel loading
ROOT CAUSE FOUND from competitor sub168 docker log comparison:

Sub168 (competitor, works):
  - corex_gdn.py:56] Loaded fused CoreX GDN decode operator ✓
  - corex_moe.py:339] Using CoreX fused MoE prefill operator ✓
  - corex_fa2.py:333] Using CoreX FA2 packed prefill ✓
  - NO NaN warnings, NO MoE fallback
  - max_model_len=256000, gpu_mem=0.95, max_num_seqs=2 (yaml params work)

Sub509 (ours, broken):
  - NaN in prefill GatedDeltaNet layer 0 (frac=0.9998) ✗
  - FusedMoE native kernel failed, falling back to PyTorch ✗
  - NO corex_gdn/corex_moe/corex_fa2 loading logs at all
  - max_model_len=100000, gpu_mem=0.9, max_num_seqs=1 (yaml params ignored)

The pip install transformers==4.55.3 in patch_ops.sh was the likely cause:
it changed dependencies that broke corex kernel loading paths.
Without corex_gdn, GatedDeltaNet falls back to Python → NaN.
Without corex_moe, MoE falls back to PyTorch → 10x slower.

Fix: Remove pip install, use base image's transformers version.
Only register qwen3_5 config files without upgrading the package.
2026-08-07 10:02:09 +00:00
dylanyunlon
ef6abf3dc7 [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.
2026-07-30 16:06:20 +00:00