project_6

Author SHA1 Message Date

Author	SHA1	Message	Date
project6-dev	5efb0fcc35	feat(EX): corex_fa2.py — third dlopen module from comp 168 AST chain Log analysis from dockerrizhi.txt (07-23 Sub168 run) reveals THREE corex modules, not two: 1. corex_gdn.py — GatedDeltaNet fused kernel (already implemented) 2. corex_moe.py — MoE routing + expert GEMM (already implemented) 3. corex_fa2.py — FlashAttention2 dispatch (NEW) corex_fa2.py handles 32/36 attention layers with three modes: :333 → FA2 packed prefill (B=2 Hq=4 Hkv=1 D=256 max_q=2048) :507 → FA2 paged chunked prefill (B=1 max_q=17 cache_blocks=2) :225 → FA2 paged decode (B=1 max_k=45455 partition=256) Wraps ixformer.contrib.vllm_flash_attn + ixf_F.vllm_single_query_cached_kv_attention. These .so files EXIST in the base image (libixattn.so). Also: wired corex_fa2 import into qwen3_5.py + deploy script.	2026-08-10 04:01:21 +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

project6-dev

5efb0fcc35

feat(EX): corex_fa2.py — third dlopen module from comp 168 AST chain

Log analysis from dockerrizhi.txt (07-23 Sub168 run) reveals THREE
corex modules, not two:

  1. corex_gdn.py — GatedDeltaNet fused kernel (already implemented)
  2. corex_moe.py — MoE routing + expert GEMM (already implemented)
  3. corex_fa2.py — FlashAttention2 dispatch (NEW)

corex_fa2.py handles 32/36 attention layers with three modes:
  :333 → FA2 packed prefill (B=2 Hq=4 Hkv=1 D=256 max_q=2048)
  :507 → FA2 paged chunked prefill (B=1 max_q=17 cache_blocks=2)
  :225 → FA2 paged decode (B=1 max_k=45455 partition=256)

Wraps ixformer.contrib.vllm_flash_attn + ixf_F.vllm_single_query_cached_kv_attention.
These .so files EXIST in the base image (libixattn.so).

Also: wired corex_fa2 import into qwen3_5.py + deploy script.

2026-08-10 04:01:21 +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

2 Commits