Dynamo (torch.compile fullgraph) cannot trace through CuTeDSL internals
(cute.compile, JIT, etc.). The autograd.Function approach was unreliable
with fullgraph mode — Dynamo would still try to trace through it.
Fix: torch.library.custom_op makes Dynamo treat our GEMM as an opaque
black box. No reimplementing the kernel — just route through the existing
runner via a registry pattern:
- Runners registered in global dict with integer IDs
- Custom op takes (tensors, runner_id, shape_hint) -> tensor
- Dynamo calls fake impl for shape inference, never touches the runner
- At execution time, real impl looks up runner and calls _run_impl
Changes:
- New: cutedsl/custom_ops.py (custom op definitions + registry)
- New: tests/test_custom_op.py (local unit tests, no GPU needed)
- Removed: _Nvfp4LinearApply, _MoEApply (autograd.Function classes)
- Updated: nvfp4_linear.py, runner.py, cutedsl.py, nvfp4_cutedsl.py
to use custom ops instead of autograd.Function
- Updated: cutedsl_quant_method.py to use custom op + registry
Major refactor to eliminate all post-load hacks:
- deepseek_v4.py: use upstream model with NVFP4 weight mapper only
(gate_proj→w1, up_proj→w3, down_proj→w2, .self_attn→.attn, .mlp→.ffn)
- Add CuTeDSLMoEExperts as a FusedMoEExpertsModular subclass
that wraps our CuTeDSL runner as a proper vLLM MoE backend
- Register CUTEDSL backend in the NVFP4 oracle
- Use ModelOptNvFp4Config for quantization dispatch (not DeepseekV4FP8Config)
- ModelOptNvFp4LinearMethod handles NVFP4 attention/shared expert projections
- Remove nvfp4_cutedsl.py, cutedsl_quant_method.py, utils.py from Dockerfile
- CuTeDSL runner moved to cutedsl/runner.py for clean imports
- cos_sin_cache float32 fix in deepseek_v4_attention.py
No more monkey-patching, no _convert_nvfp4_post_load, no CuTeDSLNvfp4Method.
