project_6

Files

Claude ab42fc1fd7 feat: hgemm_blocktiling.cu — FP16 GEMM kernel for MoE expert dispatch on BI-V100

Adapted from siboehm/SGEMM_CUDA kernel 6 (vectorize + A transpose)
and wangzyon/NVIDIA_SGEMM_PRACTICE kernel 6 (mysgemm_v6).

Key design decisions:
- FP16 data with FP32 accumulation (avoid precision loss)
- No WARPSIZE dependency (safe for BI-V100 warp_size=64)
- Boundary checks for non-aligned M/N/K (MoE expert token counts vary)
- BM=128 BN=128 BK=8 TM=8 TN=8 (256 threads, fits BI-V100 128KB smem)
- A transpose in shared memory for coalesced reads

Two entry points:
1. hgemm(A, B) — standalone FP16 GEMM
2. moe_expert_gemm(input, weights, expert_counts) — MoE prefill path
   loops over experts with variable token counts

For decode (M=1), use cublasHgemmStridedBatched (confirmed working).

Upstream refs: upstream_ref/sgemm_cuda/6_kernel_vectorize.cuh
              upstream_ref/nvidia_sgemm_practice/kernel_6.cuh

2026-08-14 16:22:00 +00:00

csrc

feat: moe_expert_gemm.cpp — C++ loop over experts via ixformer_linear (replaces Python for-loop)

2026-08-14 12:09:50 +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

fix(build): match project_7 proven Dockerfile — remove ENV lines + .dockerignore