Fix the accuracy issues caused by the mrope operator

[zhihaofang1017]

What this PR does / why we need it?

Fix precision and inference length issues with MRoPE operator in multi-image long sequences for Qwen2.5-VL-7B

This PR addresses two critical issues observed with the MRoPE operator:

1. Precision degradation in V1 model outputs
2. Inference length limitations (only generating a few characters) when processing multi-image long sequences
   

Root cause analysis revealed that most CANN operators internally perform contiguous() operations to ensure accessing contiguous data during computations. However, the ”positions“ tensor was missing this crucial step, leading to incorrect memory access and corrupted values during operator calculations.

The fix adds a contiguous() operation on the positions tensor at the Python level, ensuring proper memory layout consistency with other tensor operations.

Does this PR introduce any user-facing change?

no

How was this patch tested?

• vLLM version: v0.9.1
• vLLM main: vllm-project/vllm@83fc60b

Comparing the accuracy of V0 and V1, the error meets the standard

[gemini-code-assist]

Code Review

This pull request addresses a critical accuracy and stability issue with the MRoPE operator on Ascend NPUs. The root cause was correctly identified as a non-contiguous positions tensor being passed to the npu_mrope kernel, leading to incorrect memory access. The fix, which involves adding a .contiguous() call to the positions tensor before the kernel invocation, is direct, correct, and consistent with how other tensor arguments are handled in the same function call. This change effectively resolves the reported problem.