Commit Graph

4 Commits

Author SHA1 Message Date
7c882fe2e0 fix: correct weight quantization for CuTeDSL kernel
Weight K dimension (hidden) must be the packed dimension, not N.
Block scales computed along K dim. FP4 packing along K.
2026-05-16 02:58:55 +00:00
ca28f1335d refactor: copy CuTeDSL kernel into repo with local imports
Copied from CUTLASS examples (no more runtime dependency on
/root/cutlass/examples/). Fixed all imports to use cutedsl.kernel.*
instead of blackwell.kernel.*.

Structure:
  cutedsl/__init__.py
  cutedsl/kernel/__init__.py
  cutedsl/kernel/moe/  (the MoE scaled grouped GEMM)
  cutedsl/kernel/blockscaled_gemm/  (dense blockscaled GEMM)

test_cutedsl.py updated to import from our local copy.
2026-05-16 02:57:54 +00:00
a3aa2d201e fix: clarify import path setup for CuTeDSL 2026-05-16 02:55:25 +00:00
f951d284e7 test: add CuTeDSL NVFP4 GEMM test using reference ScaledGroupedGemmKernel
Tests the NVIDIA reference kernel with our quantization pipeline:
1. Quantize BF16 → NVFP4 (our stage_activation logic)
2. Pad and swizzle scale factors (to_blocked)
3. Run ScaledGroupedGemmKernel (2Dx3D scenario)
4. Compare against BF16 matmul reference

Also adds cutedsl/moe.py module for the future pipeline integration.
2026-05-16 02:55:04 +00:00