- Create CuTeDSLNvFp4LinearKernel extending NvFp4LinearKernel base class
- Register it via init_nvfp4_linear_kernel() selection mechanism
(inserted at top of _POSSIBLE_NVFP4_KERNELS, before FlashInfer)
- process_weights_after_loading: uint8→FP4, permute, create CuTeDSL runner
- apply_weights: route through CuTeDSL GEMM
- Update Dockerfile: copy kernel + registration script
- Fix attention: always use forward() for quantized compressor/indexer
layers (dtype check was fragile after kernel swaps weights to dummy BF16)
The upstream deepseek_v4.py has imports that don't exist in the nightly
Docker image (norm_gate_linear, breakable_cudagraph, etc.). Use the
nightly's own files as the base and add only the minimal NVFP4 changes:
- Add _make_deepseek_v4_nvfp4_weights_mapper() for checkpoint key mapping
- Select NVFP4 mapper when quant_config is modelopt_fp4
- cos_sin_cache float32 fix in attention
- Remove utils.py patch (not needed)
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.
Data-dependent expressions (amax().item(), isnan().any().item())
cause Dynamo guard failures even when gated by os.environ.
cudagraph_mode=NONE still uses torch.compile, so these break.
Will need enforce-eager for diagnostics going forward.
