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wangxiyuan merged 2 commits into from
Jun 11, 2025
Merged

provide an e2e guide for execute duration profiling #1113

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provide an e2e guide for execute duration profiling
depeng1994 Jun 7, 2025
7fe82f0
Merge branch 'main' of github.com:vllm-project/vllm-ascend into main
depeng1994 Jun 9, 2025
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5 changes: 5 additions & 0 deletions docs/source/developer_guide/evaluation/profile_execute_duration.md
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Expand Up @@ -9,6 +9,11 @@ The execution duration of each stage (including pre/post-processing, model forwa
* 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 execute duration profiling. Execute the script as follows:**
```
VLLM_ASCEND_MODEL_EXECUTE_TIME_OBSERVE=1 python3 vllm-ascend/examples/offline_inference_npu.py
```

## Example Output

```
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4 changes: 2 additions & 2 deletions vllm_ascend/worker/model_runner_v1.py
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Expand Up @@ -1298,8 +1298,8 @@ def execute_model(
for tag, duration in durations.items()
]
captured_name = "Decode" if self.attn_state == AscendAttentionState.DecodeOnly else "Prefill"
print(f"Profile execute duration [{captured_name}]:",
" ".join(dr_str))
logger.info("Profile execute duration [%s]:%s", captured_name,
" ".join(dr_str))

return model_runner_output

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