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vllm.entrypoints.logger

logger module-attribute 

logger = init_logger(__name__)

RequestLogger

Source code in vllm/entrypoints/logger.py 
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class RequestLogger:

    def __init__(self, *, max_log_len: Optional[int]) -> None:
        self.max_log_len = max_log_len

    def log_inputs(
        self,
        request_id: str,
        prompt: Optional[str],
        prompt_token_ids: Optional[list[int]],
        prompt_embeds: Optional[torch.Tensor],
        params: Optional[Union[SamplingParams, PoolingParams,
                               BeamSearchParams]],
        lora_request: Optional[LoRARequest],
    ) -> None:
        max_log_len = self.max_log_len
        if max_log_len is not None:
            if prompt is not None:
                prompt = prompt[:max_log_len]

            if prompt_token_ids is not None:
                prompt_token_ids = prompt_token_ids[:max_log_len]

        logger.info(
            "Received request %s: prompt: %r, "
            "params: %s, prompt_token_ids: %s, "
            "prompt_embeds shape: %s, "
            "lora_request: %s.", request_id, prompt, params, prompt_token_ids,
            prompt_embeds.shape if prompt_embeds is not None else None,
            lora_request)

    def log_outputs(
        self,
        request_id: str,
        outputs: str,
        output_token_ids: Optional[Sequence[int]],
        finish_reason: Optional[str] = None,
        is_streaming: bool = False,
        delta: bool = False,
    ) -> None:
        max_log_len = self.max_log_len
        if max_log_len is not None:
            if outputs is not None:
                outputs = outputs[:max_log_len]

            if output_token_ids is not None:
                # Convert to list and apply truncation
                output_token_ids = list(output_token_ids)[:max_log_len]

        stream_info = ""
        if is_streaming:
            stream_info = (" (streaming delta)"
                           if delta else " (streaming complete)")

        logger.info(
            "Generated response %s%s: output: %r, "
            "output_token_ids: %s, finish_reason: %s",
            request_id,
            stream_info,
            outputs,
            output_token_ids,
            finish_reason,
        )

max_log_len instance-attribute 

max_log_len = max_log_len

__init__ 

__init__(*, max_log_len: Optional[int]) -> None
Source code in vllm/entrypoints/logger.py 
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def __init__(self, *, max_log_len: Optional[int]) -> None:
    self.max_log_len = max_log_len

log_inputs 

log_inputs(
    request_id: str,
    prompt: Optional[str],
    prompt_token_ids: Optional[list[int]],
    prompt_embeds: Optional[Tensor],
    params: Optional[
        Union[
            SamplingParams, PoolingParams, BeamSearchParams
        ]
    ],
    lora_request: Optional[LoRARequest],
) -> None
Source code in vllm/entrypoints/logger.py 
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def log_inputs(
    self,
    request_id: str,
    prompt: Optional[str],
    prompt_token_ids: Optional[list[int]],
    prompt_embeds: Optional[torch.Tensor],
    params: Optional[Union[SamplingParams, PoolingParams,
                           BeamSearchParams]],
    lora_request: Optional[LoRARequest],
) -> None:
    max_log_len = self.max_log_len
    if max_log_len is not None:
        if prompt is not None:
            prompt = prompt[:max_log_len]

        if prompt_token_ids is not None:
            prompt_token_ids = prompt_token_ids[:max_log_len]

    logger.info(
        "Received request %s: prompt: %r, "
        "params: %s, prompt_token_ids: %s, "
        "prompt_embeds shape: %s, "
        "lora_request: %s.", request_id, prompt, params, prompt_token_ids,
        prompt_embeds.shape if prompt_embeds is not None else None,
        lora_request)

log_outputs 

log_outputs(
    request_id: str,
    outputs: str,
    output_token_ids: Optional[Sequence[int]],
    finish_reason: Optional[str] = None,
    is_streaming: bool = False,
    delta: bool = False,
) -> None
Source code in vllm/entrypoints/logger.py 
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def log_outputs(
    self,
    request_id: str,
    outputs: str,
    output_token_ids: Optional[Sequence[int]],
    finish_reason: Optional[str] = None,
    is_streaming: bool = False,
    delta: bool = False,
) -> None:
    max_log_len = self.max_log_len
    if max_log_len is not None:
        if outputs is not None:
            outputs = outputs[:max_log_len]

        if output_token_ids is not None:
            # Convert to list and apply truncation
            output_token_ids = list(output_token_ids)[:max_log_len]

    stream_info = ""
    if is_streaming:
        stream_info = (" (streaming delta)"
                       if delta else " (streaming complete)")

    logger.info(
        "Generated response %s%s: output: %r, "
        "output_token_ids: %s, finish_reason: %s",
        request_id,
        stream_info,
        outputs,
        output_token_ids,
        finish_reason,
    )