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[raindaywhu]

What this PR does / why we need it?

Does this PR introduce any user-facing change?

How was this patch tested?

• vLLM version: v0.12.0
• vLLM main: vllm-project/vllm@ad32e3e

[github-actions]

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[gemini-code-assist]

Code Review

This pull request introduces a mix_placement feature for Mixture-of-Experts models, primarily targeting DeepseekV2/V3 on Ascend hardware. The changes involve modifications to configuration, expert selection logic, and weight loading patches. While the overall direction seems correct for enabling this new functionality, I've identified a critical bug in the expert group calculation within moe_mlp.py that will lead to incorrect behavior. I've also pointed out a high-severity maintainability issue related to a hardcoded magic number in the expert selection logic. Please address these points to ensure correctness and code quality.

[gemini-code-assist]

There is a bug in the calculation of new_group. When group_list is a cumulative sum of token counts, this logic incorrectly calculates the token count for the first group. For example, if group_list is [10, 25, 30], the token counts per group are [10, 15, 5]. The current code produces [15, 15, 5], using the second group's count for the first group. Additionally, this will raise an IndexError if group_list has fewer than two elements.

The correct approach is to combine the first element of group_list with the differences of the rest of the list.

Suggested change

	group_diff = torch.diff(group_list)
	new_group = torch.cat([group_diff[0].unsqueeze(0), group_diff], dim=0)
	group_diff = torch.diff(group_list)
	new_group = torch.cat([group_list[:1], group_diff])

[gemini-code-assist]

The value 0.4 is hardcoded as the weight for the padded shared expert. This is a magic number which harms readability and maintainability. It should be defined as a named constant with a descriptive name at the top of the file (e.g., SHARED_EXPERT_DEFAULT_WEIGHT), or passed as a parameter if it's meant to be configurable.