3 Commits

Author	SHA1	Message	Date
biondizzle	00746c2d2b	Fix module path: move loader code from __init__.py to loader.py ... quantize.py and others import from dsv4.kernels.cuda.loader — the module must be a separate file, not just __init__.py.	2026-06-01 21:18:29 +00:00
biondizzle	e0607c9e2f	P0: Add fused_amax_quantize.cu kernel + CUDA module loader with compile-once caching ... - fused_amax_quantize.cu: Single kernel launch computes amax → gsa → NVFP4 quantize Zero CPU-GPU syncs. gsa written to GPU buffer for downstream GEMM global_scale_a. - dsv4/kernels/cuda/__init__.py: Module loader that compiles .cu once and caches. Eliminates JIT recompilation overhead (was ~100ms per call, ~500x per token). - P1 audit corrected: layer-pipe at batch=1 is wrong, but single-GPU doesn't fit (800GB weights vs 192GB HBM). Correct fix is EP=8 for MoE + TP/replicate for dense.	2026-06-01 21:02:03 +00:00
biondizzle	3fb3c925af	Restructure: cutedsl/ -> dsv4/ with proper layering ... - Split bridge.py -> ops/quantize.py, ops/layouts.py, ops/gemm_runner.py - Renamed classes: CuTeDSLNvfp4Linear -> Nvfp4Linear, etc. - Moved kernel code to dsv4/kernels/ (gemm, attention, compressor, decode, cuda) - Moved PyTorch bridges to dsv4/ops/ - Moved nn.Module layers to dsv4layers/ - Moved reference implementations to dsv4/reference/ - Moved vendored CUTLASS code to vendored/ - Archived ~190 debug tests to tests/archive/ - Kept ~15 canonical tests in tests/unit/ - Updated all import paths - Added stubs for future components (model/, cache/, loader/) - Updated pyproject.toml: dsv4-inference package name	2026-05-21 17:30:44 +00:00