[BugFix][310p] Fix post-sampling not working in graph mode on 310P

[Tflowers-0129]

What this PR does / why we need it?

Enabling temperature in post-processing on 310P devices can cause the service to stall and eventually hang. We first traced the issue to a timeout where the temperature-related div operator was waiting for results from a sub-stream. After investigating the preceding operators, we finally identified the root cause as the q.exponential_() operator, which is not well supported on 310P and triggers an internal issue in the add kernel.

Does this PR introduce any user-facing change?

NA

How was this patch tested?

This patch was thoroughly tested locally（accuracy-dataset test and stress test）. It is not easy to design a proper unit test for this case, and I appreciate your understanding.

• vLLM version: v0.18.0
• vLLM main: vllm-project/vllm@14acf42

[gemini-code-assist]

Summary of Changes

Hello, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request addresses a critical stability issue where 310P devices would hang during post-sampling due to an incompatible exponential operator kernel. By offloading the exponential distribution generation to the CPU and synchronizing streams appropriately, the service can now perform temperature-based sampling reliably without triggering internal NPU kernel errors.

Highlights

• Bug Fix: Resolved a service hang issue on 310P devices caused by the unsupported q.exponential_() operator in the temperature-based post-sampling process.
• Implementation: Introduced AscendSampler310 to handle exponential generation on the CPU, bypassing the problematic NPU kernel.
• Integration: Updated NPUModelRunner310 to utilize the new sampler and ensure compatibility with existing rejection sampling logic.

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[github-actions]

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[gemini-code-assist]

Code Review

Suggested PR Title:

[310P][Sampler][Feature] Implement AscendSampler310 and RejectionSampler support

Suggested PR Summary:

### What this PR does / why we need it?
This pull request introduces the `AscendSampler310` and `AscendTopKTopPSampler310` classes to support sampling operations specifically for the Ascend 310P hardware. It also integrates `RejectionSampler` into the `NPUModelRunner310`. 

Feedback was provided regarding inefficient memory management and potential device mismatches in `sampler.py`. Specifically, the `_generate_exponential_q` function should be updated to handle device placement more robustly, and tensors should be initialized directly on the CPU to avoid unnecessary NPU allocations and blocking transfers when performing CPU-based exponential sampling.

### Does this PR introduce _any_ user-facing change?
No.

### How was this patch tested?
CI passed with existing tests.