[Bugfix] Fix broken deepseek fp8 TP weights loading

[Isotr0py]

Purpose

• Fix [Core] Allow disabling TP sharding for parallel Linear layer #23024 (comment)
• Manually update parameter's tp_size and tp_rank through self.update_param_tp_status()
  (I just realized that __post_init__ is never called for nn.Module because it's not a dataclass. My bad. 😅)

Also cc @minosfuture, can you check if this PR also fixed the issue on your side? Thanks!

Test Plan

vllm serve Isotr0py/DeepSeek-V3-0324-tiny -tp 2 --enforce-eager --trust-remote-code

Test Result

Verified on dummy tiny Deepseek-v3 FP8 checkpoint with 1 MLA layer.

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[gemini-code-assist]

Code Review

This pull request aims to fix an issue with loading DeepSeek FP8 weights with tensor parallelism. The changes involve renaming __post_init__ to update_param_tp_status in LinearBase and calling it explicitly in ColumnParallelLinear and RowParallelLinear, which is a correct approach since nn.Module doesn't support __post_init__. Additionally, the logic for retrieving tp_size in Fp8LinearMethod is updated to respect layer-specific tensor parallelism settings.

My main feedback is a potential omission in update_param_tp_status. While the PR description mentions updating both tp_size and tp_rank, the implementation only seems to update tp_rank. This could lead to issues if tp_size is required by the parameter loading logic. I've added a specific comment with a suggestion to address this.