- CuTeDSLNvfp4Method: custom quant method that creates CuTeDSL runners
during process_weights_after_loading, then swaps to CuTeDSLNvfp4LinearMethod
for forward dispatch
- Attention projections (fused_wqa_wkv, wq_b, wo_b) now route through
CuTeDSLNvfp4Linear (cosine 0.992-0.996 vs BF16 reference)
- Shared expert now uses CuTeDSLSharedExpertRunner (cosine 0.992 vs BF16)
with monkey-patched forward for fused L1+SiLU+L2 pipeline
- Deleted all BF16 dequant code (_dequant_nvfp4_to_bf16, _post_quant_fix,
input_scale fixes)
- Deleted _post_quant_fix hook from utils.py
- Fixed SwiGLU clamp: gate clamped BEFORE SiLU (matching SiluAndMulWithClamp)
- Cleaned up all debug prints
- Updated Dockerfile with new kernel files
Instead of fragile inline Dockerfile patching, just copy a modified
utils.py (with _post_quant_fix call) into the image, same pattern
as deepseek_v4.py and deepseek_v4_attention.py patches.
Forward pre-hook approach didn't work — torch.compile and model
wrappers bypass hooks. Instead, patch vLLM's utils.py to call
model._post_quant_fix() at the end of process_weights_after_loading.
This guarantees the fix runs AFTER quant methods set up their attrs.
Dockerfile now patches:
model_loader/utils.py → calls model._post_quant_fix() if it exists
DeepseekV4ForCausalLM._post_quant_fix() dequantizes attention
NVFP4 weights to BF16 and replaces quant_method.
