The execution duration of each stage (including pre/post-processing, model forward, etc.) usually needs to be captured during a complete inference process. Typically, this is done by using `torch.npu.synchronize()` and obtaining CPU timestamps, which increases the performance overhead of host/device synchronization.
**To reduce the performance overhead, we add this feature, using the NPU event timestamp mechanism to observe the device execution time asynchronously.**
## Usage
* Use the environment variable `VLLM_ASCEND_MODEL_EXECUTE_TIME_OBSERVE` to enable this feature.
* Use the non-blocking API `ProfileExecuteDuration().capture_async` to set observation points asynchronously when you need to observe the execution duration.
* Use the blocking API `ProfileExecuteDuration().pop_captured_sync` at an appropriate time to get and print the execution durations of all observed stages.
**We have instrumented the key inference stages (including pre-processing, model forward pass, etc.) for execution duration profiling. Execute the script as follows:**
