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    • zhanqiuhu's avatar
      [Metrics] Add labeled prompt token metrics for P/D disaggregation (#33290) · 4403e3ed
      zhanqiuhu authored
      
      
      Add labeled Prometheus metrics to distinguish where prompt tokens come
      from in P/D disaggregated deployments.
      
      In P/D disaggregation, decode instances receive KV cache from prefill instances.
      Currently, decode reports inflated prompt throughput because it counts all
      prompt tokens as "computed", even though most were transferred.
      
      This PR adds labeled metrics so users can understand actual compute work vs
      transferred work:
      
      vllm:prompt_tokens_by_source_total{source="local_compute"}        # Tokens prefilled locally
      vllm:prompt_tokens_by_source_total{source="external_kv_transfer"} # Tokens received via KV transfer  
      vllm:prompt_tokens_by_source_total{source="local_cache_hit"}      # Tokens from local prefix cache
      vllm:prompt_tokens_cached_total                                    # Total cached (local + external, -1 when all 
      Signed-off-by: default avatarZhanqiu Hu <zh338@cornell.edu>
      4403e3ed
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    • afeldman-nm's avatar
      [V1] Logprobs and prompt logprobs support (#9880) · 0630d453
      afeldman-nm authored
      
      
      This PR is adding support for sample logprobs & prompt logprobs to vLLM v1.
      
      New behavior:
      
      - During model execution, model runner computes sample logprobs (if user-provided logprobs setting is not None) and prompt logprobs (if user-provided prompt_logprobs setting is not None). For both sample and prompt logprobs, the engine core returns 3 vectors: token ids, token logprob values, token ranks. Ranks reflect tokens' 1-indexed positions in the vocabulary vector after sorting the vocabulary by log probability in descending order.
      - In scheduler.update_from_output(), sample and prompt logprobs are incorporated into the EngineCoreOutput data structure which is transferred to the engine client. If multiprocessing is enabled, then sample and prompt logprobs will be (de)serialized when the EngineCoreOutput data structure is (de)serialized.
      - During output processing, the LogprobsProcessor transforms the triplet of token ids, token logprobs values, and token ranks into the OpenAI-compatible List[Dict[token id,Logprob]] format (for sample and prompt logprobs respectively.)
      - Each Logprob instance (whether sample- or prompt-) consists of a token's log-probability, rank, and detokenized string representation. Note that logprob detokenization is handled by the LogprobsProcessor not the detokenizer.
      Signed-off-by: default avatarAndrew Feldman <afeldman@neuralmagic.com>
      Signed-off-by: default avatarNick Hill <nhill@redhat.com>
      Signed-off-by: default avatarrshaw@neuralmagic.com <rshaw@neuralmagic.com>
      Co-authored-by: default avatarrshaw@neuralmagic.com <rshaw@neuralmagic.com>
      Co-authored-by: default avatarNick Hill <nhill@redhat.com>
      0630d453