project_6

Author SHA1 Message Date

Author	SHA1	Message	Date
Claude	f87689a4ef	fix(CRITICAL): engine death on image request + stop overwriting base corex modules Root cause from latest docker build log: ValueError: You set image=0 in --limit-mm-per-prompt, but found 1 items → Engine background task crashes → AsyncEngineDeadError → all subsequent 503 Fixes: 1. computility-run.yaml: add --limit-mm-per-prompt image=1 Prevents multimodal ValueError from killing the engine process. 2. patch_ops.sh: DON'T overwrite base image's corex_gdn.py/corex_moe.py Comp 168 log proves base image's corex modules work with libcorex_gdn.so. Our overwrite broke CoreXGDN.__init__ (unexpected kwarg 'num_v_heads'). Only deploy ours if base has NO corex modules at all. Also deploy corex_fa2.py if base lacks it. 3. qwen3_5.py: try multiple CoreXGDN init signatures Base image CoreXGDN may accept different kwargs than ours. Try kwargs form first, fall back to positional. 4. corex_gdn.py: accept both calling conventions in __init__ Future-proof for when we DO need to deploy ours. 5. Copied upstream_ref headers: ilu_layer_fused_moe.h, ilu_layer_attention.h Last 2 missing ILU files from xllm. All 14/14 now present.	2026-08-10 09:12:05 +00:00
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

Claude

f87689a4ef

fix(CRITICAL): engine death on image request + stop overwriting base corex modules

Root cause from latest docker build log:
  ValueError: You set image=0 in --limit-mm-per-prompt, but found 1 items
  → Engine background task crashes → AsyncEngineDeadError → all subsequent 503

Fixes:
1. computility-run.yaml: add --limit-mm-per-prompt image=1
   Prevents multimodal ValueError from killing the engine process.

2. patch_ops.sh: DON'T overwrite base image's corex_gdn.py/corex_moe.py
   Comp 168 log proves base image's corex modules work with libcorex_gdn.so.
   Our overwrite broke CoreXGDN.__init__ (unexpected kwarg 'num_v_heads').
   Only deploy ours if base has NO corex modules at all.
   Also deploy corex_fa2.py if base lacks it.

3. qwen3_5.py: try multiple CoreXGDN init signatures
   Base image CoreXGDN may accept different kwargs than ours.
   Try kwargs form first, fall back to positional.

4. corex_gdn.py: accept both calling conventions in __init__
   Future-proof for when we DO need to deploy ours.

5. Copied upstream_ref headers: ilu_layer_fused_moe.h, ilu_layer_attention.h
   Last 2 missing ILU files from xllm. All 14/14 now present.

2026-08-10 09:12:05 +00:00

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

3 Commits