GLM5.2 int8: 'AMXMoEWrapper' object has no attribute 'submit_write_weight_scale_to_buffer'

Reminder

• I have read the above rules and searched the existing issues.

System Info

System: GPU: 4xA100/80 CPU: 2xAMD EPYC 9254, Platform: Linux 7.0.11-arch1-1 KMD Version: 610.43.02 CUDA UMD Version: 13.3 Python: 3.11 in conda venv: CUDA 12.8
Packages: kt-kernel 0.6.4
sglang-kt 0.6.3.post1 (sglang-kt)

Reproduction

• prepare conda venv (install compilers, libraries matching CUDA 12.8, nvcc for 12.8, compile ktransformers, set env vars to compile with venv compilers and link w venv libraries)
• download GLM 5.2 FP16
• convert CPU weights: python convert_cpu_weights.py --input-path '/home/user/LLMs/weights/GLM-5.2-FP16' --input-type fp16 --output '/home/user/LLMs/weights/GLM-5.2-int8-CPU' --quant-method int8 --cpuinfer-threads 48 --threadpool-count 8 --no-merge-safetensor
• use original BF16 GPU weights (because conversion fails, but it is different topic)
• launch:

python -m sglang.launch_server \
  --model-path '/home/user/LLMs/weights/GLM-5.2-FP16' \
  --kt-weight-path '/home/user/LLMs/weights/GLM-5.2-int8-CPU' \
  --kt-cpuinfer 48 \
  --kt-threadpool-count 8 \
  --kt-num-gpu-experts 30 \
  --kt-method AMXINT8 \
  --kt-gpu-prefill-token-threshold 512 \
  --kt-enable-dynamic-expert-update \
  --kt-expert-placement-strategy uniform \
  --kt-max-deferred-experts-per-token 1 \
  --chunked-prefill-size 16384 \
  --enable-p2p-check \
  --enable-mixed-chunk \
  --sleep-on-idle \
  --watchdog-timeout 3000 \
  --tp-size 4 \
  --cuda-graph-max-bs 4 \
  --trust-remote-code \
  --mem-fraction-static 0.95 \
  --kv-cache-dtype auto \
  --max-total-tokens 192000 \
  --max-running-requests 4 \
  --attention-backend triton \
  --fp8-gemm-backend auto \
  --tool-call-parser glm47 \
  --reasoning-parser glm45 \
  --served-model-name GLM5.2i \
  --host 127.0.0.1 \
  --port 1027 --dtype auto

-result: model running -send a request -result: crash + long traceback (attached)

log.log

Others

flashinfer is 0.6.3

GLM5.2 w BF16 - works ~ 6.6 tk/s