project_6

Files

dylan ddcfbad431 feat: pybind wrapper for CUTLASS batched GEMM → MoE decode path

Based on verified result (issue #68):
  CUTLASS Cu10 TensorOp batched: 2.462ms (8 experts, 1 launch)
  vs 8× torch.matmul: 4.6ms (8 launches)
  vs Python F.linear loop: 10.36ms

New files:
  ex_engine/xllm_kernels/cuda/bindings/corex_batched_gemm_bind.cpp
    pybind11 wrapper: batched_gemm_fp16() + moe_decode_fused()
  ex_engine/xllm_kernels/cuda/corex_batched_gemm_kernel.cu
    CUTLASS GemmBatched<half> kernel (from cat_files/batched_gemm.cu)
  qwen3_6_scripts/build_corex_batched_gemm.sh
    Build script for BI-V100 (ivcore10)

Modified:
  qwen3_6_scripts/qwen3_5.py
    import corex_batched_gemm + _USE_COREX_BATCHED_GEMM flag
    Tier 1.5 in MoE decode: after corex_direct_routed, before corex_gather

Build on device: bash qwen3_6_scripts/build_corex_batched_gemm.sh
Output: prebuilt/corex-3.2.3-ivcore10/corex_batched_gemm.so

2026-08-15 11:54:26 +00:00

csrc

fix: use F.silu in test script for old corex torch

2026-08-15 05:22:31 +00:00

fla_kernels

feat: import CUDA kernels from xllm/CCCL/FLA upstream repos

2026-08-14 07:48:52 +00:00

include

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

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

python

feat: ix_ops integration layer — wire ix_full_bridge.so into vllm hot path