[Bug] Fix deepseek v4 OOM issue

[yewentao256]

Purpose

On H200

vllm serve deepseek-ai/DeepSeek-V4-Pro   --trust-remote-code   --kv-cache-dtype fp8   --block-size 256   --enable-expert-parallel   --tensor-parallel-size 8   --max-model-len 800000   --gpu-memory-utilization 0.95   --max-num-seqs 512   --max-num-batched-tokens 512   --no-enable-flashinfer-autotune   --compilation-config '{"mode": 0, "cudagraph_mode": "FULL_DECODE_ONLY"}'

Will raise

(Worker_TP0_EP0 pid=1170423) ERROR 06-08 16:38:17 [multiproc_executor.py:888]     self.ffn = DeepseekV4MoE(vllm_config, prefix=f"{prefix}.ffn")
(Worker_TP0_EP0 pid=1170423) ERROR 06-08 16:38:17 [multiproc_executor.py:888]                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(Worker_TP0_EP0 pid=1170423) ERROR 06-08 16:38:17 [multiproc_executor.py:888]   File "/home/yewentao256/vllm-source/vllm/models/deepseek_v4/nvidia/model.py", line 569, in __init__
(Worker_TP0_EP0 pid=1170423) ERROR 06-08 16:38:17 [multiproc_executor.py:888]     self._init_fused_moe_experts(config, quant_config, prefix)
(Worker_TP0_EP0 pid=1170423) ERROR 06-08 16:38:17 [multiproc_executor.py:888]   File "/home/yewentao256/vllm-source/vllm/models/deepseek_v4/nvidia/model.py", line 634, in _init_fused_moe_experts
(Worker_TP0_EP0 pid=1170423) ERROR 06-08 16:38:17 [multiproc_executor.py:888]     self.experts = FusedMoE(
(Worker_TP0_EP0 pid=1170423) ERROR 06-08 16:38:17 [multiproc_executor.py:888]                    ^^^^^^^^^
(Worker_TP0_EP0 pid=1170423) ERROR 06-08 16:38:17 [multiproc_executor.py:888]   File "/home/yewentao256/vllm-source/vllm/model_executor/layers/fused_moe/layer.py", line 336, in FusedMoE
(Worker_TP0_EP0 pid=1170423) ERROR 06-08 16:38:17 [multiproc_executor.py:888]     routed_experts = routed_experts_cls(
(Worker_TP0_EP0 pid=1170423) ERROR 06-08 16:38:17 [multiproc_executor.py:888]                      ^^^^^^^^^^^^^^^^^^^
(Worker_TP0_EP0 pid=1170423) ERROR 06-08 16:38:17 [multiproc_executor.py:888]   File "/home/yewentao256/vllm-source/vllm/model_executor/layers/fused_moe/routed_experts.py", line 165, in __init__
(Worker_TP0_EP0 pid=1170423) ERROR 06-08 16:38:17 [multiproc_executor.py:888]     self.quant_method.create_weights(layer=self, **moe_quant_params)
(Worker_TP0_EP0 pid=1170423) ERROR 06-08 16:38:17 [multiproc_executor.py:888]   File "/home/yewentao256/vllm-source/vllm/model_executor/layers/quantization/fp8.py", line 657, in create_weights
(Worker_TP0_EP0 pid=1170423) ERROR 06-08 16:38:17 [multiproc_executor.py:888]     torch.empty(
(Worker_TP0_EP0 pid=1170423) ERROR 06-08 16:38:17 [multiproc_executor.py:888]   File "/home/yewentao256/.venv/lib/python3.12/site-packages/torch/utils/_device.py", line 116, in __torch_function__
(Worker_TP0_EP0 pid=1170423) ERROR 06-08 16:38:17 [multiproc_executor.py:888]     return func(*args, **kwargs)
(Worker_TP0_EP0 pid=1170423) ERROR 06-08 16:38:17 [multiproc_executor.py:888]            ^^^^^^^^^^^^^^^^^^^^^
(Worker_TP0_EP0 pid=1170423) ERROR 06-08 16:38:17 [multiproc_executor.py:888] torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 1008.00 MiB. GPU 0 has a total capacity of 139.80 GiB of which 979.00 MiB is free. Including non-PyTorch memory, this process has 138.84 GiB memory in use. Of the allocated memory 136.71 GiB is allocated by PyTorch, and 111.48 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)

PR #41184 introduces the issue as the new class is not considered for DSV4

This PR fixes this bug, now

(APIServer pid=2147715) INFO 06-08 18:33:42 [loggers.py:271] Engine 000: Avg prompt throughput: 6.4 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 0.0%
(APIServer pid=2147715) INFO 06-08 18:33:52 [loggers.py:271] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 0.0%

[sfeng33]

LGTM