[Bugfix] Fix DSV3 kernels breaking _C and _moe_C on unsupported arches

[mgoin]

Purpose

dsv3_fused_a_gemm and dsv3_router_gemm had their ops.impl() in torch_bindings.cpp, creating a hard symbol dependency even when the .cu files are excluded by CMake arch filtering. This causes the entire _C/_moe_C extension to fail to link on architectures like SM121, taking down unrelated ops like topk_softmax.

See #34758 (comment) for reference of failure

Moved impl registration into the source .cu files via TORCH_LIBRARY_IMPL_EXPAND, matching the existing pattern used by marlin, cutlass, and MLA kernels.

Test Plan

Test Result

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[gemini-code-assist]

Code Review

This pull request addresses build failures on unsupported architectures (e.g., SM121) by moving the ops.impl() registrations for DeepSeek V3 kernels from torch_bindings.cpp to their respective source .cu files. This change ensures that the implementation is only registered when the source file is actually compiled, removing hard symbol dependencies that previously caused link errors when CMake arch filtering excluded these files. The approach follows the established pattern in vLLM for architecture-specific kernels. I have identified one critical issue where a required header is missing in one of the modified source files, which would lead to compilation errors on supported platforms.

[gemini-code-assist]

The addition of TORCH_LIBRARY_IMPL_EXPAND at the end of this file requires TORCH_LIBRARY_IMPL, which is defined in <torch/library.h>. While core/registration.h is included, it does not provide the underlying PyTorch macro definitions. Including <torch/all.h> (as seen in csrc/dsv3_fused_a_gemm.cu) or <torch/library.h> is necessary to avoid compilation errors on supported architectures.

#include <cuda_runtime.h>
#include <torch/all.h>

#include "core/registration.h"

[stavinsky]

loads fine
VLLM_USE_FLASHINFER_MOE_FP4=0 vllm serve --host 0.0.0.0 --gpu-memory-utilization 0.4 --load-format fastsafetensors --max-num-seqs 1 --kv-cache-dtype fp8 nvidia/Qwen3-Next-80B-A3B-Instruct-NVFP4

fails on load (i think not related to this pr)

VLLM_USE_FLASHINFER_MOE_FP4=1 vllm serve --host 0.0.0.0 --gpu-memory-utilization 0.4 --load-format fastsafetensors --max-num-seqs 1 --kv-cache-dtype fp8 nvidia/Qwen3-Next-80B-A3B-Instruct-NVFP4

(EngineCore_DP0 pid=111946)   File "/home/dev/dev/vllm_source/vllm/model_executor/layers/quantization/modelopt.py", line 1442, in process_weights_after_loading
(EngineCore_DP0 pid=111946)     assert self.experts_cls is not None

loads, fails on infer (again different story afaik)

vllm serve  --host 0.0.0.0 --gpu-memory-utilization 0.4 --load-format fastsafetensors --max-num-seqs 1 --kv-cache-dtype fp8 nvidia/Qwen3-Next-80B-A3B-Instruct-NVFP4