vllm.config.vllm ¶

@config
@dataclass(config=ConfigDict(arbitrary_types_allowed=True))
class VllmConfig:
    """Dataclass which contains all vllm-related configuration. This
    simplifies passing around the distinct configurations in the codebase.
    """

    # TODO: use default_factory once default constructing ModelConfig doesn't
    # try to download a model
    model_config: ModelConfig = None  # type: ignore
    """Model configuration."""
    cache_config: CacheConfig = field(default_factory=CacheConfig)
    """Cache configuration."""
    parallel_config: ParallelConfig = field(default_factory=ParallelConfig)
    """Parallel configuration."""
    scheduler_config: SchedulerConfig = field(default_factory=SchedulerConfig)
    """Scheduler configuration."""
    device_config: DeviceConfig = field(default_factory=DeviceConfig)
    """Device configuration."""
    load_config: LoadConfig = field(default_factory=LoadConfig)
    """Load configuration."""
    lora_config: Optional[LoRAConfig] = None
    """LoRA configuration."""
    speculative_config: Optional[SpeculativeConfig] = None
    """Speculative decoding configuration."""
    structured_outputs_config: StructuredOutputsConfig = field(
        default_factory=StructuredOutputsConfig)
    """Structured outputs configuration."""
    observability_config: Optional[ObservabilityConfig] = None
    """Observability configuration."""
    quant_config: Optional[QuantizationConfig] = None
    """Quantization configuration."""
    compilation_config: CompilationConfig = field(
        default_factory=CompilationConfig)
    """`torch.compile` and cudagraph capture configuration for the model.

    As a shorthand, `-O<n>` can be used to directly specify the compilation
    level `n`: `-O3` is equivalent to `-O.level=3` (same as `-O='{"level":3}'`).
    Currently, -O <n> and -O=<n> are supported as well but this will likely be
    removed in favor of clearer -O<n> syntax in the future.

    NOTE: level 0 is the default level without any optimization. level 1 and 2
    are for internal testing only. level 3 is the recommended level for
    production, also default in V1.

    You can specify the full compilation config like so:
    `{"level": 3, "cudagraph_capture_sizes": [1, 2, 4, 8]}`
    """
    kv_transfer_config: Optional[KVTransferConfig] = None
    """The configurations for distributed KV cache transfer."""
    kv_events_config: Optional[KVEventsConfig] = None
    """The configurations for event publishing."""
    # some opaque config, only used to provide additional information
    # for the hash computation, mainly used for testing, debugging or out of
    # tree config registration.
    additional_config: Union[dict, SupportsHash] = field(default_factory=dict)
    """Additional config for specified platform. Different platforms may
    support different configs. Make sure the configs are valid for the platform
    you are using. Contents must be hashable."""
    instance_id: str = ""
    """The ID of the vLLM instance."""

    def compute_hash(self) -> str:
        """
        WARNING: Whenever a new field is added to this config,
        ensure that it is included in the factors list if
        it affects the computation graph.

        Provide a hash that uniquely identifies all the configs
        that affect the structure of the computation
        graph from input ids/embeddings to the final hidden states,
        excluding anything before input ids/embeddings and after
        the final hidden states.
        """
        factors: list[Any] = []

        # summarize vllm config
        vllm_factors: list[Any] = []
        from vllm import __version__
        vllm_factors.append(__version__)
        vllm_factors.append(envs.VLLM_USE_V1)
        if self.model_config:
            vllm_factors.append(self.model_config.compute_hash())
        else:
            vllm_factors.append("None")
        if self.cache_config:
            vllm_factors.append(self.cache_config.compute_hash())
        else:
            vllm_factors.append("None")
        if self.parallel_config:
            vllm_factors.append(self.parallel_config.compute_hash())
        else:
            vllm_factors.append("None")
        if self.scheduler_config:
            vllm_factors.append(self.scheduler_config.compute_hash())
        else:
            vllm_factors.append("None")
        if self.device_config:
            vllm_factors.append(self.device_config.compute_hash())
        else:
            vllm_factors.append("None")
        if self.load_config:
            vllm_factors.append(self.load_config.compute_hash())
        else:
            vllm_factors.append("None")
        if self.lora_config:
            vllm_factors.append(self.lora_config.compute_hash())
            # LoRA creates static buffers based on max_num_batched_tokens.
            # The tensor sizes and strides get captured in the torch.compile
            # graph explicitly.
            vllm_factors.append(
                str(self.scheduler_config.max_num_batched_tokens))
        else:
            vllm_factors.append("None")
        if self.speculative_config:
            vllm_factors.append(self.speculative_config.compute_hash())
        else:
            vllm_factors.append("None")
        if self.structured_outputs_config:
            vllm_factors.append(self.structured_outputs_config.compute_hash())
        else:
            vllm_factors.append("None")
        if self.observability_config:
            vllm_factors.append(self.observability_config.compute_hash())
        else:
            vllm_factors.append("None")
        if self.quant_config:
            pass  # should be captured by model_config.quantization
        if self.compilation_config:
            vllm_factors.append(self.compilation_config.compute_hash())
        else:
            vllm_factors.append("None")
        if self.kv_transfer_config:
            vllm_factors.append(self.kv_transfer_config.compute_hash())
        else:
            vllm_factors.append("None")
        if self.additional_config:
            if isinstance(additional_config := self.additional_config, dict):
                additional_config_hash = hashlib.md5(
                    json.dumps(additional_config, sort_keys=True).encode(),
                    usedforsecurity=False,
                ).hexdigest()
            else:
                additional_config_hash = additional_config.compute_hash()
            vllm_factors.append(additional_config_hash)
        else:
            vllm_factors.append("None")
        factors.append(vllm_factors)

        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()[:10]
        return hash_str

    def pad_for_cudagraph(self, batch_size: int) -> int:
        # if batch_size > self.compilation_config.max_capture_size,
        # it should raise an IndexError.
        # the caller should make sure the batch_size is within the range,
        # i.e., batch_size <= self.compilation_config.max_capture_size
        return self.compilation_config.bs_to_padded_graph_size[batch_size]

    @staticmethod
    def _get_quantization_config(
            model_config: ModelConfig,
            load_config: LoadConfig) -> Optional[QuantizationConfig]:
        """Get the quantization config."""
        from vllm.platforms import current_platform
        if model_config.quantization is not None:
            from vllm.model_executor.model_loader.weight_utils import (
                get_quant_config)
            quant_config = get_quant_config(model_config, load_config)
            capability_tuple = current_platform.get_device_capability()

            if capability_tuple is not None:
                capability = capability_tuple.to_int()
                if capability < quant_config.get_min_capability():
                    raise ValueError(
                        f"The quantization method {model_config.quantization} "
                        "is not supported for the current GPU. Minimum "
                        f"capability: {quant_config.get_min_capability()}. "
                        f"Current capability: {capability}.")
            supported_dtypes = quant_config.get_supported_act_dtypes()
            if model_config.dtype not in supported_dtypes:
                raise ValueError(
                    f"{model_config.dtype} is not supported for quantization "
                    f"method {model_config.quantization}. Supported dtypes: "
                    f"{supported_dtypes}")
            quant_config.maybe_update_config(model_config.model)
            return quant_config
        return None

    @staticmethod
    def get_quantization_config(
            model_config: ModelConfig,
            load_config: LoadConfig) -> Optional[QuantizationConfig]:
        import copy

        # For some reason, the _ version of this modifies the model_config
        # object, so using deepcopy to avoid this problem.
        return VllmConfig._get_quantization_config(copy.deepcopy(model_config),
                                                   load_config)

    def with_hf_config(
        self,
        hf_config: PretrainedConfig,
        architectures: Optional[list[str]] = None,
    ) -> "VllmConfig":
        if architectures is not None:
            hf_config = copy.deepcopy(hf_config)
            hf_config.architectures = architectures

        model_config = copy.deepcopy(self.model_config)
        model_config.hf_config = hf_config

        return replace(self, model_config=model_config)

    def __post_init__(self):
        """Verify configs are valid & consistent with each other.
        """

        self.try_verify_and_update_config()

        if self.model_config is not None:
            self.model_config.verify_with_parallel_config(self.parallel_config)
            self.model_config.verify_dual_chunk_attention_config(
                self.load_config)

        self.cache_config.verify_with_parallel_config(self.parallel_config)

        if self.lora_config is not None:
            self.lora_config.verify_with_cache_config(self.cache_config)
            self.lora_config.verify_with_model_config(self.model_config)

        if self.quant_config is None and self.model_config is not None:
            self.quant_config = VllmConfig._get_quantization_config(
                self.model_config, self.load_config)

        from vllm.platforms import current_platform
        if self.model_config is not None and \
            self.scheduler_config.chunked_prefill_enabled and \
            self.model_config.dtype == torch.float32 and \
            current_platform.get_device_capability() == (7, 5):
            logger.warning_once(
                "Turing devices tensor cores do not support float32 matmul. "
                "To workaround this limitation, vLLM will set 'ieee' input "
                "precision for chunked prefill triton kernels.")

        # If the user does not explicitly set a compilation level, then
        # we use the default level. The default level depends on other
        # settings (see the below code).
        if self.compilation_config.level is None:
            if envs.VLLM_USE_V1:
                if (self.model_config is not None
                        and not self.model_config.enforce_eager):
                    self.compilation_config.level = CompilationLevel.PIECEWISE
                else:
                    self.compilation_config.level = \
                            CompilationLevel.NO_COMPILATION

            else:
                # NB: Passing both --enforce-eager and a compilation level
                # in V0 means the compilation level wins out.
                self.compilation_config.level = CompilationLevel.NO_COMPILATION

        # async tp is built on top of sequence parallelism
        # and requires it to be enabled.
        if self.compilation_config.pass_config.enable_async_tp:
            self.compilation_config.pass_config.enable_sequence_parallelism = \
                True
        if self.compilation_config.pass_config.enable_sequence_parallelism:
            self.compilation_config.custom_ops.append("+rms_norm")

        if current_platform.support_static_graph_mode():
            # if cudagraph_mode is not explicitly set by users, set default
            # value
            if self.compilation_config.cudagraph_mode is None:
                if envs.VLLM_USE_V1 and self.compilation_config.level \
                    == CompilationLevel.PIECEWISE:
                    # default to full and piecewise for most models
                    self.compilation_config.cudagraph_mode = \
                        CUDAGraphMode.FULL_AND_PIECEWISE

                    # pooling models and encoder-decoder models
                    # do not support full cudagraphs
                    if self.model_config is not None and \
                        (self.model_config.pooler_config is not None
                         or self.model_config.is_encoder_decoder):
                        self.compilation_config.cudagraph_mode = \
                            CUDAGraphMode.PIECEWISE
                else:
                    self.compilation_config.cudagraph_mode = CUDAGraphMode.NONE

            # disable cudagraph when enforce eager execution
            if self.model_config is not None and \
                    self.model_config.enforce_eager:
                logger.info("Cudagraph is disabled under eager mode")
                self.compilation_config.cudagraph_mode = CUDAGraphMode.NONE
            elif envs.VLLM_USE_V1:
                self.compilation_config.cudagraph_num_of_warmups = 1

            self._set_cudagraph_sizes()
        else:
            self.compilation_config.cudagraph_mode = CUDAGraphMode.NONE

        if self.cache_config.kv_sharing_fast_prefill:

            if self.speculative_config is not None and \
                self.speculative_config.use_eagle():
                raise NotImplementedError(
                    "Fast prefill optimization for KV sharing is not "
                    "compatible with EAGLE as EAGLE requires correct logits "
                    "for all tokens while fast prefill gives incorrect logits "
                    "for prompt tokens.")

            logger.warning_once(
                "--kv-sharing-fast-prefill requires changes on model side for "
                "correctness and to realize prefill savings. ")

        disable_chunked_prefill_reasons: list[str] = []

        if self.model_config:
            if self.model_config.pooler_config:
                pooling_type = self.model_config.pooler_config.pooling_type
                if pooling_type is None or pooling_type.lower() != "last":
                    disable_chunked_prefill_reasons.append(
                        "Only \"last\" pooling supports chunked "
                        "prefill and prefix caching; disabling both.")
                if not getattr(self.model_config.hf_config, "is_causal", True):
                    disable_chunked_prefill_reasons.append(
                        "Only models using causal attention supports chunked "
                        "prefill and prefix caching; disabling both.")
            elif self.model_config.is_encoder_decoder:
                from vllm.multimodal import MULTIMODAL_REGISTRY
                self.scheduler_config.max_num_encoder_input_tokens = \
                    MULTIMODAL_REGISTRY.get_encdec_max_encoder_len(self.model_config)
                logger.debug(
                    "Encoder-decoder model detected: setting "
                    "`max_num_encoder_input_tokens` to encoder length (%s)",
                    self.scheduler_config.max_num_encoder_input_tokens)
                if (self.model_config.architecture
                        == "WhisperForConditionalGeneration"
                        and os.environ.get("VLLM_WORKER_MULTIPROC_METHOD")
                        != "spawn"):
                    logger.warning(
                        "Whisper is known to have issues with "
                        "forked workers. If startup is hanging, "
                        "try setting 'VLLM_WORKER_MULTIPROC_METHOD' "
                        "to 'spawn'.")

        # Disable prefix caching only if chunked prefill is explicitly disabled
        # (and not merely unset)
        if (self.scheduler_config.chunked_prefill_enabled is False
                or disable_chunked_prefill_reasons):
            for reason in disable_chunked_prefill_reasons:
                logger.info(reason)
            self.scheduler_config.chunked_prefill_enabled = False
            self.scheduler_config.long_prefill_token_threshold = 0

            if self.cache_config is not None:
                self.cache_config.enable_prefix_caching = False

        if (self.kv_events_config is not None
                and self.kv_events_config.enable_kv_cache_events
                and not self.cache_config.enable_prefix_caching):
            logger.warning(
                "KV cache events are on, but prefix caching is not enabled."
                "Use --enable-prefix-caching to enable.")
        if (self.kv_events_config is not None
                and self.kv_events_config.publisher != "null"
                and not self.kv_events_config.enable_kv_cache_events):
            logger.warning("KV cache events are disabled,"
                           "but the scheduler is configured to publish them."
                           "Modify KVEventsConfig.enable_kv_cache_events"
                           "to True to enable.")
        current_platform.check_and_update_config(self)

        # Do this after all the updates to compilation_config.level
        if envs.VLLM_USE_V1 and \
            self.compilation_config.level == CompilationLevel.PIECEWISE:
            self.compilation_config.set_splitting_ops_for_v1()

        # final check of cudagraph mode after all possible updates
        if envs.VLLM_USE_V1 and current_platform.is_cuda_alike():
            if self.compilation_config.cudagraph_mode.has_full_cudagraphs()\
                and self.model_config is not None and \
                not self.model_config.disable_cascade_attn and\
                not self.compilation_config.cudagraph_mode.\
                has_piecewise_cudagraphs():
                logger.warning_once(
                    "No piecewise cudagraph for executing cascade attention."
                    " Will fall back to eager execution if a batch runs "
                    "into cascade attentions")

            if self.compilation_config.cudagraph_mode\
                .requires_piecewise_compilation():
                assert self.compilation_config.level == \
                    CompilationLevel.PIECEWISE, \
                    "Compilation level should be CompilationLevel.PIECEWISE "\
                    "when cudagraph_mode piecewise cudagraphs is used, "\
                    f"cudagraph_mode={self.compilation_config.cudagraph_mode}"

            # final migrate the deprecated flags
            self.compilation_config.use_cudagraph = self.compilation_config.\
                cudagraph_mode!= CUDAGraphMode.NONE
            self.compilation_config.full_cuda_graph = self.compilation_config.\
                cudagraph_mode.has_full_cudagraphs()

        if self.parallel_config.enable_dbo:
            a2a_backend = envs.VLLM_ALL2ALL_BACKEND
            assert a2a_backend in \
                ["deepep_low_latency", "deepep_high_throughput"], \
            "Microbatching currently only supports the deepep_low_latency and "\
            f"deepep_high_throughput all2all backend. {a2a_backend} is not "\
            "supported. To fix set the VLLM_ALL2ALL_BACKEND environment "\
            "variable to deepep_low_latency or deepep_high_throughput and "\
            "install the DeepEP kernels."

            if not self.model_config.disable_cascade_attn:
                self.model_config.disable_cascade_attn = True
                logger.warning_once(
                    "Disabling cascade attention when DBO is enabled.")

        if not self.instance_id:
            self.instance_id = random_uuid()[:5]

        if (envs.VLLM_USE_V1
                and not self.scheduler_config.disable_hybrid_kv_cache_manager):
            # logger should only print warning message for hybrid models. As we
            # can't know whether the model is hybrid or not now, so we don't log
            # warning message here and will log it later.
            if not current_platform.support_hybrid_kv_cache():
                # Hybrid KV cache manager is not supported on non-GPU platforms.
                self.scheduler_config.disable_hybrid_kv_cache_manager = True
            if self.kv_transfer_config is not None:
                # Hybrid KV cache manager is not compatible with KV transfer.
                self.scheduler_config.disable_hybrid_kv_cache_manager = True
            if self.kv_events_config is not None:
                # Hybrid KV cache manager is not compatible with KV events.
                self.scheduler_config.disable_hybrid_kv_cache_manager = True
            if self.model_config is not None and \
                self.model_config.attention_chunk_size is not None:
                if self.speculative_config is not None and \
                    self.speculative_config.use_eagle():
                    # Hybrid KV cache manager is not yet supported with chunked
                    # local attention + eagle.
                    self.scheduler_config.disable_hybrid_kv_cache_manager = True
                elif \
                    not envs.VLLM_ALLOW_CHUNKED_LOCAL_ATTN_WITH_HYBRID_KV_CACHE:
                    logger.warning(
                        "There is a latency regression when using chunked local"
                        " attention with the hybrid KV cache manager. Disabling"
                        " it, by default. To enable it, set the environment "
                        "VLLM_ALLOW_CHUNKED_LOCAL_ATTN_WITH_HYBRID_KV_CACHE=1."
                    )
                    # Hybrid KV cache manager is not yet supported with chunked
                    # local attention.
                    self.scheduler_config.disable_hybrid_kv_cache_manager = True

        if self.compilation_config.debug_dump_path:
            self.compilation_config.debug_dump_path = \
                self.compilation_config.debug_dump_path.absolute().expanduser()
        if envs.VLLM_DEBUG_DUMP_PATH is not None:
            env_path = Path(envs.VLLM_DEBUG_DUMP_PATH).absolute().expanduser()
            if self.compilation_config.debug_dump_path:
                logger.warning(
                    "Config-specified debug dump path is overridden"
                    " by VLLM_DEBUG_DUMP_PATH to %s", env_path)
            self.compilation_config.debug_dump_path = env_path

    def update_sizes_for_sequence_parallelism(self,
                                              possible_sizes: list) -> list:
        # remove the sizes that not multiple of tp_size when
        # enable sequence parallelism
        removed_sizes = [
            size for size in possible_sizes
            if size % self.parallel_config.tensor_parallel_size != 0
        ]
        if removed_sizes:
            logger.warning(
                "Batch sizes %s are removed because they are not "
                "multiple of tp_size %d when "
                "sequence parallelism is enabled", removed_sizes,
                self.parallel_config.tensor_parallel_size)

        return [
            size for size in possible_sizes
            if size % self.parallel_config.tensor_parallel_size == 0
        ]

    def _set_cudagraph_sizes(self):
        """
        vLLM defines the default candidate list of batch sizes for CUDA graph
        capture as:

        ```python
        max_graph_size = min(max_num_seqs * 2, 512)
        # 1, 2, 4, then multiples of 8 up to max_graph_size
        cuda_graph_sizes = [1, 2, 4, 8, 16, 24, 32, 40, ..., max_graph_size]

        In the end, `vllm_config.compilation_config.cudagraph_capture_sizes`
        will be the final sizes to capture cudagraph (in descending order).

        These sizes are used to capture and reuse CUDA graphs for
        performance-critical paths (e.g., decoding). Capturing enables
        significantly faster kernel dispatch by avoiding Python overhead. The
        list is then filtered based on `max_num_batched_tokens` (e.g., 8192 on
        most GPUs), which controls the total allowed number of tokens in a
        batch. Since each sequence may have a variable number of tokens, the
        maximum usable batch size will depend on actual sequence lengths.

        Example:
            With `max_num_batched_tokens = 8192`, and typical sequences
            averaging ~32 tokens, most practical batch sizes fall below 256.
            However, the system will still allow capture sizes up to 512 if
            shape and memory permit.

        Note:
            If users explicitly specify cudagraph capture sizes in the
            compilation config, those will override this default logic.
            At runtime:

            - If batch size <= one of the `cudagraph_capture_sizes`, the closest
            padded CUDA graph will be used.
            - If batch size > largest `cudagraph_capture_sizes`, cudagraph will
            not be used.
        """

        # calculate the default `batch_size_capture_list`
        batch_size_capture_list = []
        if self.model_config is not None and \
            not self.model_config.enforce_eager:
            cuda_graph_sizes = self.scheduler_config.cuda_graph_sizes
            if len(cuda_graph_sizes) == 1:
                batch_size_capture_list = [1, 2, 4] + [
                    i for i in range(8, cuda_graph_sizes[0] + 1, 8)
                ]
            elif len(cuda_graph_sizes) > 1:
                batch_size_capture_list = sorted(cuda_graph_sizes)
            else:
                raise TypeError(f"Invalid value for {cuda_graph_sizes=}.")
            if self.parallel_config.tensor_parallel_size > 1 and \
                self.compilation_config.pass_config.enable_sequence_parallelism:
                batch_size_capture_list = \
                    self.update_sizes_for_sequence_parallelism(batch_size_capture_list)
            max_num_tokens = self.scheduler_config.max_num_batched_tokens
            batch_size_capture_list = [
                size for size in batch_size_capture_list
                if size <= max_num_tokens
            ]

        self.compilation_config.init_with_cudagraph_sizes(
            batch_size_capture_list)

    def recalculate_max_model_len(self, max_model_len: int):
        # Can only be called in try_verify_and_update_config
        model_config = self.model_config
        max_model_len = model_config.get_and_verify_max_len(max_model_len)
        self.model_config.max_model_len = max_model_len
        self.scheduler_config.max_model_len = max_model_len

    def try_verify_and_update_config(self):
        if self.model_config is None:
            return

        # Avoid running try_verify_and_update_config multiple times
        if getattr(self.model_config, "config_updated", False):
            return
        self.model_config.config_updated = True

        architecture = self.model_config.architecture
        if architecture is None:
            return

        from vllm.model_executor.models.config import (
            MODELS_CONFIG_MAP, HybridAttentionMambaModelConfig)
        cls = MODELS_CONFIG_MAP.get(architecture, None)
        if cls is not None:
            cls.verify_and_update_config(self)

        if self.model_config.is_hybrid:
            HybridAttentionMambaModelConfig.verify_and_update_config(self)

        if self.model_config.convert_type == "classify":
            # Maybe convert ForCausalLM into ForSequenceClassification model.
            from vllm.model_executor.models.adapters import (
                SequenceClassificationConfig)
            SequenceClassificationConfig.verify_and_update_config(self)

        if hasattr(self.model_config, "model_weights") and is_runai_obj_uri(
                self.model_config.model_weights):
            if self.load_config.load_format == "auto":
                logger.info("Detected Run:ai model config. "
                            "Overriding `load_format` to 'runai_streamer'")
                self.load_config.load_format = "runai_streamer"
            elif self.load_config.load_format != "runai_streamer":
                raise ValueError(f"To load a model from S3, 'load_format' "
                                 f"must be 'runai_streamer', "
                                 f"but got '{self.load_config.load_format}'. "
                                 f"Model: {self.model_config.model}")

    def compile_debug_dump_path(self) -> Optional[Path]:
        """Returns a rank-aware path for dumping 
        torch.compile debug information.
        """
        if self.compilation_config.debug_dump_path is None:
            return None
        tp_rank = self.parallel_config.rank
        dp_rank = self.parallel_config.data_parallel_rank
        data_parallel_size = self.parallel_config.data_parallel_size
        append_path = f"rank_{tp_rank}" if data_parallel_size == 1 \
            else f"rank_{tp_rank}_dp_{dp_rank}"
        path = self.compilation_config.debug_dump_path / append_path
        return path

    def __str__(self):
        return (
            f"model={self.model_config.model!r}, "
            f"speculative_config={self.speculative_config!r}, "
            f"tokenizer={self.model_config.tokenizer!r}, "
            f"skip_tokenizer_init={self.model_config.skip_tokenizer_init}, "
            f"tokenizer_mode={self.model_config.tokenizer_mode}, "
            f"revision={self.model_config.revision}, "
            f"tokenizer_revision={self.model_config.tokenizer_revision}, "
            f"trust_remote_code={self.model_config.trust_remote_code}, "
            f"dtype={self.model_config.dtype}, "
            f"max_seq_len={self.model_config.max_model_len}, "
            f"download_dir={self.load_config.download_dir!r}, "
            f"load_format={self.load_config.load_format}, "
            f"tensor_parallel_size={self.parallel_config.tensor_parallel_size}, "  # noqa
            f"pipeline_parallel_size={self.parallel_config.pipeline_parallel_size}, "  # noqa
            f"data_parallel_size={self.parallel_config.data_parallel_size}, "  # noqa
            f"disable_custom_all_reduce={self.parallel_config.disable_custom_all_reduce}, "  # noqa
            f"quantization={self.model_config.quantization}, "
            f"enforce_eager={self.model_config.enforce_eager}, "
            f"kv_cache_dtype={self.cache_config.cache_dtype}, "
            f"device_config={self.device_config.device}, "
            f"structured_outputs_config={self.structured_outputs_config!r}, "
            f"observability_config={self.observability_config!r}, "
            f"seed={self.model_config.seed}, "
            f"served_model_name={self.model_config.served_model_name}, "
            f"enable_prefix_caching={self.cache_config.enable_prefix_caching}, "
            f"chunked_prefill_enabled={self.scheduler_config.chunked_prefill_enabled}, "  # noqa
            f"pooler_config={self.model_config.pooler_config!r}, "
            f"compilation_config={self.compilation_config!r}")