Add span metrics for model_forward, scheduler and sampler time - #21
Add span metrics for model_forward, scheduler and sampler time#21sfc-gh-mkeralapura wants to merge 6 commits into
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… sampling time + get elapsed event time after sampler
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| if seq_group.metrics.scheduler_time is not None: | ||
| seq_group.metrics.scheduler_time += scheduler_time | ||
| else: | ||
| seq_group.metrics.scheduler_time = scheduler_time |
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is it possible to count time being swapped out separately? I think counting scheduler time for a non-running request is a bit hard to interpret, because we'd normally want to compare it with model forward time and execute time to understand the overhead, and those are only collected for running requests.
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For now, counted scheduler time only over the running ones.
For a future change, we can track a different metric of swapped_out_time for each request.
| sampling_metadata=model_input.sampling_metadata, | ||
| ) | ||
| if self.observability_config.collect_model_forward_time: | ||
| torch.cuda.synchronize() |
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it looks like there's a Event.synchronize() which seems a better fit than this global synchronize() https://pytorch.org/docs/stable/generated/torch.cuda.Event.html
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Done. Thanks for the tip!
| intermediate_tensors = IntermediateTensors( | ||
| get_pp_group().recv_tensor_dict()) | ||
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2 participants
See an example trace log here
s3://ml-dev-sfc-or-dev-misc1-k8s/vllm_bench/spanlogs/mkeralapura_llama-3-70b-0ffc9601_vllm-engine_0.span_trace_log to maheshkm/mkeralapura_llama-3-70b-0ffc9601_vllm-engine_0.span_trace_log
An entry there will look like
Span #0
Trace ID : e3e70682c2094cac629f6fbed82c07cd
Parent ID :
ID : 0a5d2f346baa9455
Name : llm_request
Kind : Server
Start time : 2024-08-02 18:18:39.590171648 +0000 UTC
End time : 2024-08-02 18:18:43.366565895 +0000 UTC
Status code : Unset
Status message :
Attributes:
-> gen_ai.response.model: Str(/data-fast/s3/ml-dev-sfc-or-dev-misc1-k8s/yak/hf_models/meta-llama/Meta-Llama-3-70B)
-> gen_ai.request.id: Str(fcdae8c235f84b618c24b656f3ad4024)
-> gen_ai.request.temperature: Double(0)
-> gen_ai.request.top_p: Double(1)
-> gen_ai.request.max_tokens: Int(256)
-> gen_ai.request.best_of: Int(1)
-> gen_ai.request.n: Int(1)
-> gen_ai.usage.num_sequences: Int(1)
-> gen_ai.usage.prompt_tokens: Int(2106)
-> gen_ai.usage.completion_tokens: Int(256)
-> gen_ai.latency.time_in_queue: Double(0.012117147445678711)
-> gen_ai.latency.time_to_first_token: Double(0.2345738410949707)
-> gen_ai.latency.e2e: Double(3.775825023651123)
-> gen_ai.latency.time_in_scheduler: Double(0.017550230026245117)
-> gen_ai.latency.time_in_model_forward: Double(3.151565277099609)
-> gen_ai.latency.time_in_model_execute: Double(3.6468167304992676)
{"kind": "exporter", "data_type": "traces", "name": "debug"}
Had to put the model_forward time behind a flag since I could not get it working without the synchronize.
The synchronize does not seem to add a delay though. For example the llama3 example benchmark run 9 seconds faster (27s vs 267). Overall, our expectation is that there should be no operations pending by the time the sampler is finished. Maybe if there is no token or in some other corner case there is something pending and elapsed_time() crashes with Cuda runtime error: device is busy.
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