### What this PR does / why we need it?
This PR introduces a caching mechanism for CPU-based `torch.Generator`
objects in the `_random_sample_310p` function to optimize sampling
performance. It includes unit tests for cache persistence and state
recovery. Feedback highlights a critical bug where keying the cache by
batch index instead of generator ID can break RNG reproducibility during
request re-scheduling, and notes a potential memory leak in the global
cache.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Tested via new unit tests in `tests/ut/_310p/sample/test_sampler_310.py`
verifying cache logic and error handling.
---------
Signed-off-by: csoulnd <daidaicurry@foxmail.com>
### 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.
Signed-off-by: Tflowers-0129 <2906339855@qq.com>
