2 Commits

Author	SHA1	Message	Date
project6-dev	d86b39d1ae	refactor(corex): rewrite 3 dlopen modules to use real ixformer::infer dispatch chain ... corex_moe.py: - Tier 0: ix_bridge.fused_moe_forward (all 7 ixformer::infer steps in C++) - Tier 1: ix_bridge step-by-step (topk→gen_idx→expand→gemm→silu→gemm→combine) - Tier 2: Python topk + ixf_F.silu_and_mul + torch.matmul expert loop corex_gdn.py: - Gate clamping [-5, 0] (decay only) from real machine logs - State clamping ±100 prevents inf propagation corex_fa2.py: - Tier 0: ix_bridge C++ paged_attention/flash_attn - Tier 1: ixformer.contrib.vllm_flash_attn Python - Tier 2: ixf_F.vllm_single_query_cached_kv_attention (V1) All modules now use: ix_full_bridge.cpp → ixformer::infer → libixattn.so Matches comp 168 actual dispatch chain from docker log.	2026-08-10 06:16:39 +00:00
project6-dev	1be9449883	feat(EX): corex_gdn + corex_moe — dlopen dispatch chain from comp 168 log analysis ... From 2d5232c5 docker log analysis: 07-23 (168's docker): corex_gdn.py + corex_moe.py → full fused kernels 08-07 (our docker): missing both → NaN GDN + PyTorch MoE fallback corex_gdn.py: GDN fused kernel dispatch - FlashQLA .so loading (gdn_forward.cu pre-compiled) - PyTorch chunked delta rule with fp32 accum + clamp (no NaN) - Decode single-step recurrent with state clamping corex_moe.py: MoE fused pipeline - topk_softmax: replaces MISSING ixf_F.vllm_moe_topk_softmax - Per-expert GEMM via torch.matmul (cublas under the hood) - ixformer.silu_and_mul for activation when available DLOPEN_DISPATCH_CHAIN.md: complete .so loading chain map deploy_corex_modules.sh: wire into VLLM/model_executor/models/	2026-08-10 03:37:15 +00:00