config.py

import enum
import json
from dataclasses import dataclass, field, fields
from typing import (TYPE_CHECKING, ClassVar, List, Mapping, Optional, Tuple,
                    Type, Union)

import torch
from transformers import PretrainedConfig

import vllm.envs as envs
from vllm.logger import init_logger
from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
from vllm.model_executor.models import ModelRegistry
from vllm.platforms import current_platform
from vllm.tracing import is_otel_available, otel_import_error_traceback
from vllm.transformers_utils.config import (get_config,
                                            get_hf_image_processor_config,
                                            get_hf_text_config)
from vllm.utils import (STR_NOT_IMPL_ENC_DEC_CUDAGRAPH, GiB_bytes,
                        cuda_device_count_stateless, get_cpu_memory, is_cpu,
                        is_hip, is_neuron, is_openvino, is_xpu,
                        print_warning_once)

if TYPE_CHECKING:
    from ray.util.placement_group import PlacementGroup

    from vllm.executor.executor_base import ExecutorBase
    from vllm.model_executor.model_loader.loader import BaseModelLoader
    from vllm.transformers_utils.tokenizer_group.base_tokenizer_group import (
        BaseTokenizerGroup)

logger = init_logger(__name__)

_EMBEDDING_MODEL_MAX_NUM_BATCHED_TOKENS = 32768
_MULTIMODAL_MODEL_MAX_NUM_BATCHED_TOKENS = 4096

_PP_SUPPORTED_MODELS = [
    "AquilaModel",
    "AquilaForCausalLM",
    "DeepseekV2ForCausalLM",
    "InternLMForCausalLM",
    "JAISLMHeadModel",
    "LlamaForCausalLM",
    "LLaMAForCausalLM",
    "MistralForCausalLM",
    "Phi3ForCausalLM",
    "GPT2LMHeadModel",
    "MixtralForCausalLM",
    "NemotronForCausalLM",
    "Qwen2ForCausalLM",
    "Qwen2MoeForCausalLM",
    "QWenLMHeadModel",
]


class ModelConfig:
    """Configuration for the model.

    Args:
        model: Name or path of the huggingface model to use.
            It is also used as the content for `model_name` tag in metrics 
            output when `served_model_name` is not specified. 
        tokenizer: Name or path of the huggingface tokenizer to use.
        tokenizer_mode: Tokenizer mode. "auto" will use the fast tokenizer if
            available, "slow" will always use the slow tokenizer, and
            "mistral" will always use the tokenizer from `mistral_common`.
        trust_remote_code: Trust remote code (e.g., from HuggingFace) when
            downloading the model and tokenizer.
        dtype: Data type for model weights and activations. The "auto" option
            will use FP16 precision for FP32 and FP16 models, and BF16 precision
            for BF16 models.
        seed: Random seed for reproducibility.
        revision: The specific model version to use. It can be a branch name,
            a tag name, or a commit id. If unspecified, will use the default
            version.
        code_revision: The specific revision to use for the model code on
            Hugging Face Hub. It can be a branch name, a tag name, or a
            commit id. If unspecified, will use the default version.
        rope_scaling: Dictionary containing the scaling configuration for the
            RoPE embeddings. When using this flag, don't update
            `max_position_embeddings` to the expected new maximum.
        tokenizer_revision: The specific tokenizer version to use. It can be a
            branch name, a tag name, or a commit id. If unspecified, will use
            the default version.
        max_model_len: Maximum length of a sequence (including prompt and
            output). If None, will be derived from the model.
        quantization: Quantization method that was used to quantize the model
            weights. If None, we assume the model weights are not quantized.
        quantization_param_path: Path to JSON file containing scaling factors.
            Used to load KV cache scaling factors into the model when KV cache
            type is FP8_E4M3 on ROCm (AMD GPU). In the future these will also
            be used to load activation and weight scaling factors when the
            model dtype is FP8_E4M3 on ROCm.
        enforce_eager: Whether to enforce eager execution. If True, we will
            disable CUDA graph and always execute the model in eager mode.
            If False, we will use CUDA graph and eager execution in hybrid.
            If None, the user did not specify, so default to False -
            except for encoder/decoder models, which currently require
            eager mode.
        max_context_len_to_capture: Maximum context len covered by CUDA graphs.
            When a sequence has context length larger than this, we fall back
            to eager mode (DEPRECATED. Use max_seq_len_to_capture instead).
        max_seq_len_to_capture: Maximum sequence len covered by CUDA graphs.
            When a sequence has context length larger than this, we fall back
            to eager mode
        disable_sliding_window: Whether to disable sliding window. If True,
            we will disable the sliding window functionality of the model.
            If the model does not support sliding window, this argument is
            ignored.
        skip_tokenizer_init: If true, skip initialization of tokenizer and
            detokenizer.
        served_model_name: The model name used in metrics tag `model_name`,
            matches the model name exposed via the APIs. If multiple model 
            names provided, the first name will be used. If not specified, 
            the model name will be the same as `model`.
        limit_mm_per_prompt: Maximum number of data instances per modality 
            per prompt. Only applicable for multimodal models.
    """

    def __init__(
        self,
        model: str,
        tokenizer: str,
        tokenizer_mode: str,
        trust_remote_code: bool,
        dtype: Union[str, torch.dtype],
        seed: int,
        revision: Optional[str] = None,
        code_revision: Optional[str] = None,
        rope_scaling: Optional[dict] = None,
        rope_theta: Optional[float] = None,
        tokenizer_revision: Optional[str] = None,
        max_model_len: Optional[int] = None,
        spec_target_max_model_len: Optional[int] = None,
        quantization: Optional[str] = None,
        quantization_param_path: Optional[str] = None,
        enforce_eager: Optional[bool] = None,
        max_context_len_to_capture: Optional[int] = None,
        max_seq_len_to_capture: Optional[int] = None,
        max_logprobs: int = 20,
        disable_sliding_window: bool = False,
        skip_tokenizer_init: bool = False,
        served_model_name: Optional[Union[str, List[str]]] = None,
        limit_mm_per_prompt: Optional[Mapping[str, int]] = None,
        use_async_output_proc: bool = True,
    ) -> None:
        self.model = model
        self.tokenizer = tokenizer
        self.tokenizer_mode = tokenizer_mode
        self.trust_remote_code = trust_remote_code
        self.seed = seed
        self.revision = revision
        self.code_revision = code_revision
        self.rope_scaling = rope_scaling
        self.rope_theta = rope_theta
        # The tokenizer version is consistent with the model version by default.
        if tokenizer_revision is None:
            self.tokenizer_revision = revision
        else:
            self.tokenizer_revision = tokenizer_revision
        self.quantization = quantization
        self.quantization_param_path = quantization_param_path
        self.enforce_eager = enforce_eager
        if max_context_len_to_capture is not None:
            raise ValueError("`max_context_len_to_capture` is deprecated. "
                             "Use `max_seq_len_to_capture` instead.")
        self.max_seq_len_to_capture = max_seq_len_to_capture
        self.max_logprobs = max_logprobs
        self.disable_sliding_window = disable_sliding_window
        self.skip_tokenizer_init = skip_tokenizer_init

        self.hf_config = get_config(self.model, trust_remote_code, revision,
                                    code_revision, rope_scaling, rope_theta)
        self.hf_text_config = get_hf_text_config(self.hf_config)
        self.hf_image_processor_config = get_hf_image_processor_config(
            self.model, revision)
        self.dtype = _get_and_verify_dtype(self.hf_text_config, dtype)
        self.use_async_output_proc = use_async_output_proc

        # Choose a default enforce_eager value if the user did not specify
        # a value (enforce_eager is None)
        if getattr(self.hf_config, 'is_encoder_decoder', False):
            if self.enforce_eager is None:
                # *Only for encoder/decoder models* and
                # *only if enforce_eager is unset*, override
                # to enforce_eager=True
                #
                # Add a logger message since it is *somewhat* non-intuitive that
                # enforce_eager is True when the user has not specified its
                # value.
                logger.info("Forcing enforce_eager == True because "
                            "enforce_eager setting was unspecified and "
                            "CUDAGraph is not supported with encoder/ "
                            "decoder models.")
                self.enforce_eager = True

            if not self.enforce_eager:
                # Eager mode explicitly disabled by user for an encoder/
                # decoder model; however CUDAGRAPH + encoder/decoder is
                # not currently supported
                raise ValueError(STR_NOT_IMPL_ENC_DEC_CUDAGRAPH)
        elif self.enforce_eager is None:
            # *Only for decoder-only models*, enforce_eager
            # defaults to False if unset. This is intuitive
            # so no logging message needed.
            self.enforce_eager = False

        if (not self.disable_sliding_window
                and self.hf_text_config.model_type == "gemma2"
                and self.hf_text_config.sliding_window is not None):
            print_warning_once(
                "Gemma 2 uses sliding window attention for every odd layer, "
                "which is currently not supported by vLLM. Disabling sliding "
                "window and capping the max length to the sliding window size "
                f"({self.hf_text_config.sliding_window}).")
            self.disable_sliding_window = True

        self.max_model_len = _get_and_verify_max_len(
            hf_config=self.hf_text_config,
            max_model_len=max_model_len,
            disable_sliding_window=self.disable_sliding_window,
            sliding_window_len=self.get_hf_config_sliding_window(),
            spec_target_max_model_len=spec_target_max_model_len)
        self.served_model_name = get_served_model_name(model,
                                                       served_model_name)
        self.multimodal_config = self._init_multimodal_config(
            limit_mm_per_prompt)
        if not self.skip_tokenizer_init:
            self._verify_tokenizer_mode()
        self._verify_embedding_mode()
        self._verify_quantization()
        self._verify_cuda_graph()

    def _init_multimodal_config(
        self, limit_mm_per_prompt: Optional[Mapping[str, int]]
    ) -> Optional["MultiModalConfig"]:
        architectures = getattr(self.hf_config, "architectures", [])
        if any(
                ModelRegistry.is_multimodal_model(arch)
                for arch in architectures):
            return MultiModalConfig(limit_per_prompt=limit_mm_per_prompt or {})
        else:
            if limit_mm_per_prompt:
                raise ValueError(
                    "limit_mm_per_prompt is only supported for multimodal "
                    "models.")
            return None

    def _verify_tokenizer_mode(self) -> None:
        tokenizer_mode = self.tokenizer_mode.lower()
        if tokenizer_mode not in ["auto", "slow", "mistral"]:
            raise ValueError(
                f"Unknown tokenizer mode: {self.tokenizer_mode}. Must be "
                "either 'auto', 'slow' or 'mistral'.")
        self.tokenizer_mode = tokenizer_mode

    def _verify_embedding_mode(self) -> None:
        architectures = getattr(self.hf_config, "architectures", [])
        self.embedding_mode = any(
            ModelRegistry.is_embedding_model(arch) for arch in architectures)

    def _parse_quant_hf_config(self):
        quant_cfg = getattr(self.hf_config, "quantization_config", None)
        if quant_cfg is None:
            # compressed-tensors uses a "compression_config" key
            quant_cfg = getattr(self.hf_config, "compression_config", None)
        return quant_cfg

    def _verify_quantization(self) -> None:
        supported_quantization = [*QUANTIZATION_METHODS]
        rocm_supported_quantization = ["awq", "gptq", "squeezellm", "fp8"]
        optimized_quantization_methods = [
            "fp8", "marlin", "gptq_marlin_24", "gptq_marlin", "awq_marlin",
            "fbgemm_fp8", "compressed_tensors", "compressed-tensors",
            "experts_int8"
        ]
        tpu_supported_quantization = ["tpu_int8"]
        if self.quantization is not None:
            self.quantization = self.quantization.lower()

        # Parse quantization method from the HF model config, if available.
        quant_cfg = self._parse_quant_hf_config()

        if quant_cfg is not None:
            quant_method = quant_cfg.get("quant_method", "").lower()

            # Detect which checkpoint is it
            for _, method in QUANTIZATION_METHODS.items():
                quantization_override = method.override_quantization_method(
                    quant_cfg, self.quantization)
                if quantization_override:
                    quant_method = quantization_override
                    self.quantization = quantization_override
                    break

            # Verify quantization configurations.
            if self.quantization is None:
                self.quantization = quant_method
            elif self.quantization != quant_method:
                raise ValueError(
                    "Quantization method specified in the model config "
                    f"({quant_method}) does not match the quantization "
                    f"method specified in the `quantization` argument "
                    f"({self.quantization}).")

        if self.quantization is not None:
            if self.quantization not in supported_quantization:
                raise ValueError(
                    f"Unknown quantization method: {self.quantization}. Must "
                    f"be one of {supported_quantization}.")
            if is_hip(
            ) and self.quantization not in rocm_supported_quantization:
                raise ValueError(
                    f"{self.quantization} quantization is currently not "
                    f"supported in ROCm.")
            if current_platform.is_tpu(
            ) and self.quantization not in tpu_supported_quantization:
                raise ValueError(
                    f"{self.quantization} quantization is currently not "
                    f"supported in TPU Backend.")
            if self.quantization not in optimized_quantization_methods:
                logger.warning(
                    "%s quantization is not fully "
                    "optimized yet. The speed can be slower than "
                    "non-quantized models.", self.quantization)
            if (self.quantization == "awq" and is_hip()
                    and not envs.VLLM_USE_TRITON_AWQ):
                logger.warning(
                    "Using AWQ quantization with ROCm, but VLLM_USE_TRITON_AWQ"
                    " is not set, enabling VLLM_USE_TRITON_AWQ.")
                envs.VLLM_USE_TRITON_AWQ = True

    def _verify_cuda_graph(self) -> None:
        if self.max_seq_len_to_capture is None:
            self.max_seq_len_to_capture = self.max_model_len
        self.max_seq_len_to_capture = min(self.max_seq_len_to_capture,
                                          self.max_model_len)

    def verify_async_output_proc(self, parallel_config, speculative_config,
                                 device_config) -> None:
        if not self.use_async_output_proc:
            # Nothing to check
            return

        if parallel_config.pipeline_parallel_size > 1:
            logger.warning("Async output processing can not be enabled "
                           "with pipeline parallel")
            self.use_async_output_proc = False
            return

        if device_config.device_type not in ("cuda", "tpu"):
            logger.warning(
                "Async output processing is only supported for CUDA or TPU. "
                "Disabling it for other platforms.")
            self.use_async_output_proc = False
            return

        if envs.VLLM_USE_RAY_SPMD_WORKER:
            logger.warning(
                "Async output processing can not be enabled with ray spmd")
            self.use_async_output_proc = False
            return

        if self.enforce_eager:
            logger.warning(
                "To see benefits of async output processing, enable CUDA "
                "graph. Since, enforce-eager is enabled, async output "
                "processor cannot be used")
            self.use_async_output_proc = not self.enforce_eager
            return

        # Async postprocessor is not necessary with embedding mode
        # since there is no token generation
        if self.embedding_mode:
            self.use_async_output_proc = False

        if speculative_config:
            logger.warning("Async output processing is not supported with"
                           " speculative decoding currently.")
            self.use_async_output_proc = False

    def verify_with_parallel_config(
        self,
        parallel_config: "ParallelConfig",
    ) -> None:
        total_num_attention_heads = getattr(self.hf_text_config,
                                            "num_attention_heads", 0)
        tensor_parallel_size = parallel_config.tensor_parallel_size
        if total_num_attention_heads % tensor_parallel_size != 0:
            raise ValueError(
                f"Total number of attention heads ({total_num_attention_heads})"
                " must be divisible by tensor parallel size "
                f"({tensor_parallel_size}).")

        pipeline_parallel_size = parallel_config.pipeline_parallel_size
        architectures = getattr(self.hf_config, "architectures", [])
        if not all(arch in _PP_SUPPORTED_MODELS
                   for arch in architectures) and pipeline_parallel_size > 1:
            raise NotImplementedError(
                "Pipeline parallelism is only supported for the following "
                f" architectures: {_PP_SUPPORTED_MODELS}.")

        if self.quantization == "bitsandbytes" and (
                parallel_config.tensor_parallel_size > 1
                or parallel_config.pipeline_parallel_size > 1):
            raise ValueError(
                "BitAndBytes quantization with TP or PP is not supported yet.")

        # Remove the constraint after the bitsandbytes issue is fixed:
        # https://github.com/bitsandbytes-foundation/bitsandbytes/issues/1308
        if self.quantization == "bitsandbytes" and self.enforce_eager is False:
            logger.warning("CUDA graph is not supported on BitAndBytes yet, "
                           "fallback to the eager mode.")
            self.enforce_eager = True

        if pipeline_parallel_size > 1 and self.use_async_output_proc:
            logger.warning("Async output processor is not supported with "
                           "pipeline parallelism currently. Disabling it.")
            self.use_async_output_proc = False

    def get_hf_config_sliding_window(self) -> Optional[int]:
        """Get the sliding window size, or None if disabled."""

        # Some models, like Qwen2 and Qwen1.5, use `use_sliding_window` in
        # addition to sliding window size. We check if that field is present
        # and if it's False, return None.
        if (hasattr(self.hf_text_config, "use_sliding_window")
                and not self.hf_text_config.use_sliding_window):
            return None
        return getattr(self.hf_text_config, "sliding_window", None)

    def get_sliding_window(self) -> Optional[int]:
        """Get the sliding window size, or None if disabled.
        """
        # If user disables sliding window, return None.
        if self.disable_sliding_window:
            return None
        # Otherwise get the value from the hf config.
        return self.get_hf_config_sliding_window()

    def get_vocab_size(self) -> int:
        return self.hf_text_config.vocab_size

    def get_hidden_size(self) -> int:
        return self.hf_text_config.hidden_size

    def get_head_size(self) -> int:
        # TODO remove hard code
        if hasattr(self.hf_text_config, "model_type"
                   ) and self.hf_text_config.model_type == 'deepseek_v2':
            # FlashAttention supports only head_size 32, 64, 128, 256,
            # we need to pad head_size 192 to 256
            return 256
        if hasattr(self.hf_text_config, "head_dim"):
            return self.hf_text_config.head_dim
        # FIXME(woosuk): This may not be true for all models.
        return (self.hf_text_config.hidden_size //
                self.hf_text_config.num_attention_heads)

    def get_total_num_kv_heads(self) -> int:
        """Returns the total number of KV heads."""
        # For GPTBigCode & Falcon:
        # NOTE: for falcon, when new_decoder_architecture is True, the
        # multi_query flag is ignored and we use n_head_kv for the number of
        # KV heads.
        falcon_model_types = ["falcon", "RefinedWeb", "RefinedWebModel"]
        new_decoder_arch_falcon = (
            self.hf_config.model_type in falcon_model_types
            and getattr(self.hf_config, "new_decoder_architecture", False))
        if not new_decoder_arch_falcon and getattr(self.hf_text_config,
                                                   "multi_query", False):
            # Multi-query attention, only one KV head.
            # Currently, tensor parallelism is not supported in this case.
            return 1

        # For DBRX and MPT
        if self.hf_config.model_type == "mpt":
            if "kv_n_heads" in self.hf_config.attn_config:
                return self.hf_config.attn_config["kv_n_heads"]
            return self.hf_config.num_attention_heads
        if self.hf_config.model_type == "dbrx":
            return getattr(self.hf_config.attn_config, "kv_n_heads",
                           self.hf_config.num_attention_heads)

        attributes = [
            # For Falcon:
            "n_head_kv",
            "num_kv_heads",
            # For LLaMA-2:
            "num_key_value_heads",
            # For ChatGLM:
            "multi_query_group_num",
        ]
        for attr in attributes:
            num_kv_heads = getattr(self.hf_text_config, attr, None)
            if num_kv_heads is not None:
                return num_kv_heads

        # For non-grouped-query attention models, the number of KV heads is
        # equal to the number of attention heads.
        return self.hf_text_config.num_attention_heads

    def get_num_kv_heads(self, parallel_config: "ParallelConfig") -> int:
        """Returns the number of KV heads per GPU."""
        total_num_kv_heads = self.get_total_num_kv_heads()
        # If tensor parallelism is used, we divide the number of KV heads by
        # the tensor parallel size. We will replicate the KV heads in the
        # case where the number of KV heads is smaller than the tensor
        # parallel size so each GPU has at least one KV head.
        return max(1,
                   total_num_kv_heads // parallel_config.tensor_parallel_size)

    def get_num_attention_heads(self,
                                parallel_config: "ParallelConfig") -> int:
        num_heads = getattr(self.hf_text_config, "num_attention_heads", 0)
        return num_heads // parallel_config.tensor_parallel_size

    def get_num_layers(self, parallel_config: "ParallelConfig") -> int:
        from vllm.distributed.utils import get_pp_indices
        total_num_hidden_layers = getattr(self.hf_text_config,
                                          "num_hidden_layers", 0)
        pp_rank = parallel_config.rank // parallel_config.tensor_parallel_size
        pp_size = parallel_config.pipeline_parallel_size
        start, end = get_pp_indices(total_num_hidden_layers, pp_rank, pp_size)
        return end - start

    def contains_seqlen_agnostic_layers(
            self, parallel_config: "ParallelConfig") -> bool:
        """True for Mamba/SSM models (Jamba)"""
        return self._get_num_seqlen_agnostic_layers(parallel_config) > 0

    def get_layers_block_type(self,
                              parallel_config: "ParallelConfig") -> List[str]:
        num_layers = self.get_num_layers(parallel_config)
        # Transformers supports layers_block_type @property
        return getattr(self.hf_config, "layers_block_type",
                       ["attention"] * num_layers)

    def get_num_attention_layers(self,
                                 parallel_config: "ParallelConfig") -> int:
        return len([
            t for t in self.get_layers_block_type(parallel_config)
            if t == "attention"
        ])

    def _get_num_seqlen_agnostic_layers(
            self, parallel_config: "ParallelConfig") -> int:
        return len([
            t for t in self.get_layers_block_type(parallel_config)
            if t != "attention"
        ])

    def get_multimodal_config(self) -> "MultiModalConfig":
        """
        Get the multimodal configuration of the model.

        Raises:
            ValueError: If the model is not multimodal.
        """
        if self.multimodal_config is None:
            raise ValueError("The model is not multimodal.")

        return self.multimodal_config

    @property
    def is_encoder_decoder_model(self) -> bool:
        """Extract the HF encoder/decoder model flag."""
        return getattr(self.hf_config, "is_encoder_decoder", False)

    @property
    def is_embedding_model(self) -> bool:
        """Extract the embedding model flag."""
        return self.embedding_mode

    @property
    def is_multimodal_model(self) -> bool:
        return self.multimodal_config is not None


class CacheConfig:
    """Configuration for the KV cache.

    Args:
        block_size: Size of a cache block in number of tokens.
        gpu_memory_utilization: Fraction of GPU memory to use for the
            vLLM execution.
        swap_space: Size of the CPU swap space per GPU (in GiB).
        cache_dtype: Data type for kv cache storage.
        num_gpu_blocks_override: Number of GPU blocks to use. This overrides the
            profiled num_gpu_blocks if specified. Does nothing if None.
    """

    def __init__(
        self,
        block_size: int,
        gpu_memory_utilization: float,
        swap_space: float,
        cache_dtype: str,
        num_gpu_blocks_override: Optional[int] = None,
        sliding_window: Optional[int] = None,
        enable_prefix_caching: bool = False,
        cpu_offload_gb: float = 0,
    ) -> None:
        self.block_size = block_size
        self.gpu_memory_utilization = gpu_memory_utilization
        self.swap_space_bytes = swap_space * GiB_bytes
        self.num_gpu_blocks_override = num_gpu_blocks_override
        self.cache_dtype = cache_dtype
        self.sliding_window = sliding_window
        self.enable_prefix_caching = enable_prefix_caching
        self.cpu_offload_gb = cpu_offload_gb
        self._verify_args()
        self._verify_cache_dtype()
        self._verify_prefix_caching()

        # Will be set after profiling.
        self.num_gpu_blocks = None
        self.num_cpu_blocks = None

    def metrics_info(self):
        # convert cache_config to dict(key: str, value: str) for prometheus
        # metrics info
        return {key: str(value) for key, value in self.__dict__.items()}

    def _verify_args(self) -> None:
        if self.gpu_memory_utilization > 1.0:
            raise ValueError(
                "GPU memory utilization must be less than 1.0. Got "
                f"{self.gpu_memory_utilization}.")

    def _verify_cache_dtype(self) -> None:
        if self.cache_dtype == "auto":
            pass
        elif self.cache_dtype in ("fp8", "fp8_e4m3", "fp8_e5m2"):
            logger.info(
                "Using fp8 data type to store kv cache. It reduces the GPU "
                "memory footprint and boosts the performance. "
                "Meanwhile, it may cause accuracy drop without a proper "
                "scaling factor")
        else:
            raise ValueError(f"Unknown kv cache dtype: {self.cache_dtype}")

    def _verify_prefix_caching(self) -> None:
        if not self.enable_prefix_caching:
            return

        if self.sliding_window is not None:
            raise NotImplementedError(
                "Prefix caching is not supported with sliding window. "
                "Run with --disable-sliding-window to use prefix caching.")

    def verify_with_parallel_config(
        self,
        parallel_config: "ParallelConfig",
    ) -> None:
        total_cpu_memory = get_cpu_memory()
        # FIXME(woosuk): Here, it is assumed that the GPUs in a tensor parallel
        # group are in the same node. However, the GPUs may span multiple nodes.
        num_gpus_per_node = parallel_config.tensor_parallel_size
        cpu_memory_usage = self.swap_space_bytes * num_gpus_per_node

        msg = (f"{cpu_memory_usage / GiB_bytes:.2f} GiB out of the "
               f"{total_cpu_memory / GiB_bytes:.2f} GiB total CPU memory "
               "is allocated for the swap space.")
        if cpu_memory_usage > 0.7 * total_cpu_memory:
            raise ValueError("Too large swap space. " + msg)
        elif cpu_memory_usage > 0.4 * total_cpu_memory:
            logger.warning("Possibly too large swap space. %s", msg)


@dataclass
class TokenizerPoolConfig:
    """Configuration for the tokenizer pool.

    Args:
        pool_size: Number of tokenizer workers in the pool.
        pool_type: Type of the pool.
        extra_config: Additional config for the pool.
            The way the config will be used depends on the
            pool type.
    """
    pool_size: int
    pool_type: Union[str, Type["BaseTokenizerGroup"]]
    extra_config: dict

    def __post_init__(self):
        if self.pool_type not in ("ray", ) and not isinstance(
                self.pool_type, type):
            raise ValueError(f"Unknown pool type: {self.pool_type}")
        if not isinstance(self.extra_config, dict):
            raise ValueError("extra_config must be a dictionary.")

    @classmethod
    def create_config(
        cls, tokenizer_pool_size: int, tokenizer_pool_type: str,
        tokenizer_pool_extra_config: Optional[Union[str, dict]]
    ) -> Optional["TokenizerPoolConfig"]:
        """Create a TokenizerPoolConfig from the given parameters.

        If tokenizer_pool_size is 0, return None.

        Args:
            tokenizer_pool_size: Number of tokenizer workers in the pool.
            tokenizer_pool_type: Type of the pool.
            tokenizer_pool_extra_config: Additional config for the pool.
                The way the config will be used depends on the
                pool type. This can be a JSON string (will be parsed).
        """
        if tokenizer_pool_size:
            if isinstance(tokenizer_pool_extra_config, str):
                tokenizer_pool_extra_config_parsed = json.loads(
                    tokenizer_pool_extra_config)
            else:
                tokenizer_pool_extra_config_parsed = (
                    tokenizer_pool_extra_config or {})
            tokenizer_pool_config = cls(tokenizer_pool_size,
                                        tokenizer_pool_type,
                                        tokenizer_pool_extra_config_parsed)
        else:
            tokenizer_pool_config = None
        return tokenizer_pool_config


class LoadFormat(str, enum.Enum):
    AUTO = "auto"
    PT = "pt"
    SAFETENSORS = "safetensors"
    NPCACHE = "npcache"
    DUMMY = "dummy"
    TENSORIZER = "tensorizer"
    SHARDED_STATE = "sharded_state"
    GGUF = "gguf"
    BITSANDBYTES = "bitsandbytes"


@dataclass
class LoadConfig:
    """
        download_dir: Directory to download and load the weights, default to the
            default cache directory of huggingface.
        load_format: The format of the model weights to load:
            "auto" will try to load the weights in the safetensors format and
                fall back to the pytorch bin format if safetensors format is
                not available.
            "pt" will load the weights in the pytorch bin format.
            "safetensors" will load the weights in the safetensors format.
            "npcache" will load the weights in pytorch format and store
                a numpy cache to speed up the loading.
            "dummy" will initialize the weights with random values, which is
                mainly for profiling.
            "tensorizer" will use CoreWeave's tensorizer library for
                fast weight loading.
            "bitsandbytes" will load nf4 type weights.
        ignore_patterns: The list of patterns to ignore when loading the model.
            Default to "original/**/*" to avoid repeated loading of llama's 
            checkpoints.
            
    """

    load_format: Union[str, LoadFormat, "BaseModelLoader"] = LoadFormat.AUTO
    download_dir: Optional[str] = None
    model_loader_extra_config: Optional[Union[str, dict]] = field(
        default_factory=dict)
    ignore_patterns: Optional[Union[List[str], str]] = None

    def __post_init__(self):
        model_loader_extra_config = self.model_loader_extra_config or {}
        if isinstance(model_loader_extra_config, str):
            self.model_loader_extra_config = json.loads(
                model_loader_extra_config)
        self._verify_load_format()

        if self.ignore_patterns is not None and len(self.ignore_patterns) > 0:
            logger.info(
                "Ignoring the following patterns when downloading weights: %s",
                self.ignore_patterns)
        else:
            self.ignore_patterns = ["original/**/*"]

    def _verify_load_format(self) -> None:
        if not isinstance(self.load_format, str):
            return

        load_format = self.load_format.lower()
        self.load_format = LoadFormat(load_format)

        rocm_not_supported_load_format: List[str] = []
        if is_hip() and load_format in rocm_not_supported_load_format:
            rocm_supported_load_format = [
                f for f in LoadFormat.__members__
                if (f not in rocm_not_supported_load_format)
            ]
            raise ValueError(
                f"load format '{load_format}' is not supported in ROCm. "
                f"Supported load formats are "
                f"{rocm_supported_load_format}")


class ParallelConfig:
    """Configuration for the distributed execution.

    Args:
        pipeline_parallel_size: Number of pipeline parallel groups.
        tensor_parallel_size: Number of tensor parallel groups.
        worker_use_ray: Deprecated, use distributed_executor_backend instead.
        max_parallel_loading_workers: Maximum number of multiple batches
            when load model sequentially. To avoid RAM OOM when using tensor
            parallel and large models.
        disable_custom_all_reduce: Disable the custom all-reduce kernel and
            fall back to NCCL.
        tokenizer_pool_config: Config for the tokenizer pool.
            If None, will use synchronous tokenization.
        ray_workers_use_nsight: Whether to profile Ray workers with nsight, see
            https://docs.ray.io/en/latest/ray-observability/user-guides/profiling.html#profiling-nsight-profiler.
        placement_group: ray distributed model workers placement group.
        distributed_executor_backend: Backend to use for distributed model
            workers, either "ray" or "mp" (multiprocessing). If either
            pipeline_parallel_size or tensor_parallel_size is greater than 1,
            will default to "ray" if Ray is installed or "mp" otherwise.
    """

    def __init__(
        self,
        pipeline_parallel_size: int,
        tensor_parallel_size: int,
        worker_use_ray: Optional[bool] = None,
        max_parallel_loading_workers: Optional[int] = None,
        disable_custom_all_reduce: bool = False,
        tokenizer_pool_config: Optional[TokenizerPoolConfig] = None,
        ray_workers_use_nsight: bool = False,
        placement_group: Optional["PlacementGroup"] = None,
        distributed_executor_backend: Optional[Union[
            str, Type["ExecutorBase"]]] = None,
    ) -> None:
        self.pipeline_parallel_size = pipeline_parallel_size
        self.tensor_parallel_size = tensor_parallel_size
        self.distributed_executor_backend = distributed_executor_backend
        self.max_parallel_loading_workers = max_parallel_loading_workers
        self.disable_custom_all_reduce = disable_custom_all_reduce
        self.tokenizer_pool_config = tokenizer_pool_config
        self.ray_workers_use_nsight = ray_workers_use_nsight
        self.placement_group = placement_group
        self.world_size = pipeline_parallel_size * self.tensor_parallel_size

        if worker_use_ray:
            if self.distributed_executor_backend is None:
                self.distributed_executor_backend = "ray"
            elif not self.use_ray:
                raise ValueError(f"worker-use-ray can't be used with "
                                 f"distributed executor backend "
                                 f"'{self.distributed_executor_backend}'.")

        if self.distributed_executor_backend is None and self.world_size > 1:
            # We use multiprocessing by default if world_size fits on the
            # current node and we aren't in a ray placement group.

            from vllm.executor import ray_utils
            backend = "mp"
            ray_found = ray_utils.ray_is_available()
            if cuda_device_count_stateless() < self.world_size:
                if not ray_found:
                    raise ValueError("Unable to load Ray which is "
                                     "required for multi-node inference, "
                                     "please install Ray with `pip install "
                                     "ray`.") from ray_utils.ray_import_err
                backend = "ray"
            elif ray_found:
                if self.placement_group:
                    backend = "ray"
                else:
                    from ray import is_initialized as ray_is_initialized
                    if ray_is_initialized():
                        from ray.util import get_current_placement_group
                        if get_current_placement_group():
                            backend = "ray"
            self.distributed_executor_backend = backend
            logger.info("Defaulting to use %s for distributed inference",
                        backend)

        self._verify_args()
        self.rank: int = 0

    @property
    def use_ray(self) -> bool:
        return self.distributed_executor_backend == "ray" or (
            isinstance(self.distributed_executor_backend, type)
            and self.distributed_executor_backend.uses_ray)

    def _verify_args(self) -> None:
        # Lazy import to avoid circular import
        from vllm.executor.executor_base import ExecutorBase

        if self.distributed_executor_backend not in (
                "ray", "mp", None) and not (isinstance(
                    self.distributed_executor_backend, type) and issubclass(
                        self.distributed_executor_backend, ExecutorBase)):
            raise ValueError(
                "Unrecognized distributed executor backend "
                f"{self.distributed_executor_backend}. Supported "
                "values are 'ray', 'mp' or custom ExecutorBase subclass.")
        if self.use_ray:
            from vllm.executor import ray_utils
            ray_utils.assert_ray_available()
        if is_hip():
            self.disable_custom_all_reduce = True
            logger.info(
                "Disabled the custom all-reduce kernel because it is not "
                "supported on AMD GPUs.")
        if self.ray_workers_use_nsight and not self.use_ray:
            raise ValueError("Unable to use nsight profiling unless workers "
                             "run with Ray.")


class SchedulerConfig:
    """Scheduler configuration.

    Args:
        max_num_batched_tokens: Maximum number of tokens to be processed in
            a single iteration.
        max_num_seqs: Maximum number of sequences to be processed in a single
            iteration.
        max_model_len: Maximum length of a sequence (including prompt
            and generated text).
        use_v2_block_manager: Whether to use the BlockSpaceManagerV2 or not.
        num_lookahead_slots: The number of slots to allocate per sequence per
            step, beyond the known token ids. This is used in speculative
            decoding to store KV activations of tokens which may or may not be
            accepted.
        delay_factor: Apply a delay (of delay factor multiplied by previous
            prompt latency) before scheduling next prompt.
        enable_chunked_prefill: If True, prefill requests can be chunked based
            on the remaining max_num_batched_tokens.
        embedding_mode: Whether the running model is for embedding.
        preemption_mode: Whether to perform preemption by swapping or 
            recomputation. If not specified, we determine the mode as follows:
            We use recomputation by default since it incurs lower overhead than
            swapping. However, when the sequence group has multiple sequences
            (e.g., beam search), recomputation is not currently supported. In
            such a case, we use swapping instead.
        send_delta_data: Private API. If used, scheduler sends delta data to
            workers instead of an entire data. It should be enabled only
            when SPMD worker architecture is enabled. I.e.,
            VLLM_USE_RAY_SPMD_WORKER=1

    """

    def __init__(self,
                 max_num_batched_tokens: Optional[int],
                 max_num_seqs: int,
                 max_model_len: int,
                 use_v2_block_manager: bool = False,
                 num_lookahead_slots: int = 0,
                 delay_factor: float = 0.0,
                 enable_chunked_prefill: bool = False,
                 embedding_mode: bool = False,
                 is_multimodal_model: bool = False,
                 preemption_mode: Optional[str] = None,
                 num_scheduler_steps: int = 1,
                 send_delta_data: bool = False) -> None:
        if max_num_batched_tokens is None:
            if enable_chunked_prefill:
                # It is the values that have the best balance between ITL
                # and TTFT on A100. Note it is not optimized for throughput.
                max_num_batched_tokens = 512
            else:
                # If max_model_len is too short, use 2048 as the default value
                # for higher throughput.
                max_num_batched_tokens = max(max_model_len, 2048)

            if embedding_mode:
                # For embedding, choose specific value for higher throughput
                max_num_batched_tokens = max(
                    max_num_batched_tokens,
                    _EMBEDDING_MODEL_MAX_NUM_BATCHED_TOKENS,
                )
            if is_multimodal_model:
                # The value needs to be at least the number of multimodal tokens
                max_num_batched_tokens = max(
                    max_num_batched_tokens,
                    _MULTIMODAL_MODEL_MAX_NUM_BATCHED_TOKENS,
                )

        self.max_num_batched_tokens = max_num_batched_tokens

        if enable_chunked_prefill:
            logger.info(
                "Chunked prefill is enabled with max_num_batched_tokens=%d.",
                self.max_num_batched_tokens)

        self.max_num_seqs = max_num_seqs
        self.max_model_len = max_model_len
        self.use_v2_block_manager = use_v2_block_manager
        self.num_lookahead_slots = num_lookahead_slots
        self.delay_factor = delay_factor
        self.chunked_prefill_enabled = enable_chunked_prefill
        self.embedding_mode = embedding_mode
        self.preemption_mode = preemption_mode
        self.num_scheduler_steps = num_scheduler_steps
        self.send_delta_data = send_delta_data
        self._verify_args()

    def _verify_args(self) -> None:
        if (self.max_num_batched_tokens < self.max_model_len
                and not self.chunked_prefill_enabled):
            raise ValueError(
                f"max_num_batched_tokens ({self.max_num_batched_tokens}) is "
                f"smaller than max_model_len ({self.max_model_len}). "
                "This effectively limits the maximum sequence length to "
                "max_num_batched_tokens and makes vLLM reject longer "
                "sequences. Please increase max_num_batched_tokens or "
                "decrease max_model_len.")

        if self.max_num_batched_tokens < self.max_num_seqs:
            raise ValueError(
                f"max_num_batched_tokens ({self.max_num_batched_tokens}) must "
                "be greater than or equal to max_num_seqs "
                f"({self.max_num_seqs}).")

        if self.num_lookahead_slots < 0:
            raise ValueError(
                "num_lookahead_slots "
                f"({self.num_lookahead_slots}) must be greater than or "
                "equal to 0.")

        if self.num_scheduler_steps < 1:
            raise ValueError(
                "num_scheduler_steps "
                f"({self.num_scheduler_steps}) must be greater than or "
                "equal to 1.")

    @property
    def is_multi_step(self) -> bool:
        return self.num_scheduler_steps > 1


class DeviceConfig:
    device: Optional[torch.device]

    def __init__(self, device: str = "auto") -> None:
        if device == "auto":
            # Automated device type detection
            if is_neuron():
                self.device_type = "neuron"
            elif is_openvino():
                self.device_type = "openvino"
            elif current_platform.is_tpu():
                self.device_type = "tpu"
            elif is_cpu():
                self.device_type = "cpu"
            elif is_xpu():
                self.device_type = "xpu"
            else:
                # We don't call torch.cuda.is_available() here to
                # avoid initializing CUDA before workers are forked
                self.device_type = "cuda"
        else:
            # Device type is assigned explicitly
            self.device_type = device

        # Some device types require processing inputs on CPU
        if self.device_type in ["neuron", "openvino"]:
            self.device = torch.device("cpu")
        elif self.device_type in ["tpu"]:
            self.device = None
        else:
            # Set device with device type
            self.device = torch.device(self.device_type)


class SpeculativeConfig:
    """Configuration for speculative decoding.

    The configuration is currently specialized to draft-model speculative
    decoding with top-1 proposals.
    """

    @staticmethod
    def maybe_create_spec_config(
        target_model_config: ModelConfig,
        target_parallel_config: ParallelConfig,
        target_dtype: str,
        speculative_model: Optional[str],
        speculative_model_quantization: Optional[str],
        speculative_draft_tensor_parallel_size: Optional[int],
        num_speculative_tokens: Optional[int],
        speculative_max_model_len: Optional[int],
        enable_chunked_prefill: bool,
        use_v2_block_manager: bool,
        disable_log_stats: bool,
        speculative_disable_by_batch_size: Optional[int],
        ngram_prompt_lookup_max: Optional[int],
        ngram_prompt_lookup_min: Optional[int],
        draft_token_acceptance_method: str,
        typical_acceptance_sampler_posterior_threshold: Optional[float],
        typical_acceptance_sampler_posterior_alpha: Optional[float],
        disable_logprobs: Optional[bool],
    ) -> Optional["SpeculativeConfig"]:
        """Create a SpeculativeConfig if possible, else return None.

        This function attempts to create a SpeculativeConfig object based on the
        provided parameters. If the necessary conditions are met, it returns an
        instance of SpeculativeConfig. Otherwise, it returns None.

        Args:
            target_model_config (ModelConfig): The configuration of the target
                model.
            target_parallel_config (ParallelConfig): The parallel configuration
                for the target model.
            target_dtype (str): The data type used for the target model.
            speculative_model (Optional[str]): The name of the speculative
                model, if provided.
            speculative_model_quantization (Optional[str]): Quantization method
                that was used to quantize the speculative model weights. If
                None, we assume the model weights are not quantized.
            speculative_draft_tensor_parallel_size (Optional[int]): The degree
                of the tensor parallelism for the draft model.
            num_speculative_tokens (Optional[int]): The number of speculative
                tokens, if provided. Will default to the number in the draft
                model config if present, otherwise is required.
            speculative_max_model_len (Optional[int]): The maximum model len of
                the speculative model. Used when testing the ability to skip
                speculation for some sequences.
            enable_chunked_prefill (bool): Whether vLLM is configured to use
                chunked prefill or not. Used for raising an error since its not
                yet compatible with spec decode.
            use_v2_block_manager (bool): Whether vLLM is configured to use the
                v2 block manager or not. Used for raising an error since the v2
                block manager is required with spec decode.
            speculative_disable_by_batch_size (Optional[int]): Disable
                speculative decoding for new incoming requests when the number
                of enqueue requests  is larger than this value, if provided.
            ngram_prompt_lookup_max (Optional[int]): Max size of ngram token
                window, if provided.
            ngram_prompt_lookup_min (Optional[int]): Min size of ngram token
                window, if provided.
            draft_token_acceptance_method (str): The method to use for
                accepting draft tokens. This can take two possible
                values 'rejection_sampler' and 'typical_acceptance_sampler'
                for RejectionSampler and TypicalAcceptanceSampler
                respectively.
            typical_acceptance_sampler_posterior_threshold (Optional[float]):
                A threshold value that sets a lower bound on the posterior
                probability of a token in the target model for it to be
                accepted. This threshold is used only when we use the 
                TypicalAcceptanceSampler for token acceptance.
            typical_acceptance_sampler_posterior_alpha (Optional[float]):
                A scaling factor for the entropy-based threshold in the
                TypicalAcceptanceSampler.
            disable_logprobs (Optional[bool]): If set to True, token log
                probabilities are not returned during speculative decoding.
                If set to False, token log probabilities are returned
                according to the log probability settings in SamplingParams.
                If not specified, it defaults to True.
    
        Returns:
            Optional["SpeculativeConfig"]: An instance of SpeculativeConfig if
                the necessary conditions are met, else None.
        """

        if speculative_model is None:
            if num_speculative_tokens is not None:
                raise ValueError("num_speculative_tokens was provided without "
                                 "speculative_model.")
            return None

        if (speculative_disable_by_batch_size is not None
                and speculative_disable_by_batch_size < 2):
            raise ValueError("Expect the batch size threshold of disabling "
                             "speculative decoding is > 1, but got "
                             f"{speculative_disable_by_batch_size=}")

        if enable_chunked_prefill:
            raise ValueError(
                "Speculative decoding and chunked prefill are "
                f"currently mutually exclusive ({enable_chunked_prefill=}).")

        if not use_v2_block_manager:
            raise ValueError(
                "Speculative decoding requires usage of the V2 "
                "block manager. Enable it with --use-v2-block-manager.")

        # TODO: The user should be able to specify revision/max model len
        # for the draft model. It is not currently supported.
        draft_revision = None
        draft_code_revision = None
        draft_quantization = speculative_model_quantization

        if speculative_model == "[ngram]":
            if ngram_prompt_lookup_min is None:
                ngram_prompt_lookup_min = 1
            if ngram_prompt_lookup_max is None or ngram_prompt_lookup_max < 1:
                raise ValueError(f"{ngram_prompt_lookup_max=} must be > 0")
            if ngram_prompt_lookup_min < 1:
                raise ValueError(f"{ngram_prompt_lookup_min=} must be > 0")
            if ngram_prompt_lookup_min > ngram_prompt_lookup_max:
                raise ValueError(f"{ngram_prompt_lookup_min=} cannot be "
                                 f"larger than {ngram_prompt_lookup_max=}")

            # TODO: current we still need extract vocab_size from target model
            # config, in future, we may try refactor it out, and set
            # draft related config as None here.
            draft_model_config = target_model_config
            draft_parallel_config = target_parallel_config
        else:
            ngram_prompt_lookup_max = 0
            ngram_prompt_lookup_min = 0
            draft_model_config = ModelConfig(
                model=speculative_model,
                tokenizer=target_model_config.tokenizer,
                tokenizer_mode=target_model_config.tokenizer_mode,
                trust_remote_code=target_model_config.trust_remote_code,
                dtype=target_model_config.dtype,
                seed=target_model_config.seed,
                revision=draft_revision,
                code_revision=draft_code_revision,
                tokenizer_revision=target_model_config.tokenizer_revision,
                max_model_len=None,
                spec_target_max_model_len=target_model_config.max_model_len,
                quantization=draft_quantization,
                enforce_eager=target_model_config.enforce_eager,
                max_seq_len_to_capture=target_model_config.
                max_seq_len_to_capture,
                max_logprobs=target_model_config.max_logprobs,
            )

            draft_hf_config = draft_model_config.hf_config

            if (num_speculative_tokens is not None
                    and hasattr(draft_hf_config, "num_lookahead_tokens")):
                draft_hf_config.num_lookahead_tokens = num_speculative_tokens

            n_predict = getattr(draft_hf_config, "n_predict", None)
            if n_predict is not None:
                if num_speculative_tokens is None:
                    # Default to max value defined in draft model config.
                    num_speculative_tokens = n_predict
                elif num_speculative_tokens > n_predict:
                    # Verify provided value doesn't exceed the maximum
                    # supported by the draft model.
                    raise ValueError(
                        "This speculative model supports a maximum of "
                        f"num_speculative_tokens={n_predict}, but "
                        f"{num_speculative_tokens=} was provided.")

            draft_model_config.max_model_len = (
                SpeculativeConfig._maybe_override_draft_max_model_len(
                    speculative_max_model_len,
                    draft_model_config.max_model_len,
                    target_model_config.max_model_len,
                ))

            draft_parallel_config = (
                SpeculativeConfig.create_draft_parallel_config(
                    target_parallel_config,
                    speculative_draft_tensor_parallel_size, draft_hf_config))

        if num_speculative_tokens is None:
            raise ValueError(
                "num_speculative_tokens must be provided with "
                "speculative_model unless the draft model config contains an "
                "n_predict parameter.")

        if typical_acceptance_sampler_posterior_threshold is None:
            typical_acceptance_sampler_posterior_threshold = 0.09
        if typical_acceptance_sampler_posterior_alpha is None:
            typical_acceptance_sampler_posterior_alpha = 0.3
        if disable_logprobs is None:
            disable_logprobs = True

        return SpeculativeConfig(
            draft_model_config,
            draft_parallel_config,
            num_speculative_tokens,
            speculative_disable_by_batch_size,
            ngram_prompt_lookup_max,
            ngram_prompt_lookup_min,
            draft_token_acceptance_method=draft_token_acceptance_method,
            typical_acceptance_sampler_posterior_threshold=\
                typical_acceptance_sampler_posterior_threshold,
            typical_acceptance_sampler_posterior_alpha=\
                typical_acceptance_sampler_posterior_alpha,
            disable_logprobs=disable_logprobs,
            disable_log_stats=disable_log_stats,
        )

    @staticmethod
    def _maybe_override_draft_max_model_len(
        speculative_max_model_len: Optional[int],
        draft_max_model_len: int,
        target_max_model_len: int,
    ) -> int:
        """Determine the max sequence len for the draft model. This is usually
        the draft_max_model_len, but may be the target_max_model_len if it is
        less than the draft_max_model_len, or may be speculative_max_model_len
        if it is specified.

        This is necessary so that sequences do not exceed the capacity of the
        draft model or the target model.

        speculative_max_model_len is mainly used for testing that sequences can
        skip speculation.
        """

        if speculative_max_model_len is not None:

            if speculative_max_model_len > draft_max_model_len:
                raise ValueError(f"{speculative_max_model_len=} cannot be "
                                 f"larger than {draft_max_model_len=}")

            if speculative_max_model_len > target_max_model_len:
                raise ValueError(f"{speculative_max_model_len=} cannot be "
                                 f"larger than {target_max_model_len=}")

            return speculative_max_model_len

        return min(
            draft_max_model_len,
            target_max_model_len,
        )

    @staticmethod
    def create_draft_parallel_config(
        target_parallel_config: ParallelConfig,
        speculative_draft_tensor_parallel_size: Optional[int],
        draft_hf_config: PretrainedConfig,
    ) -> ParallelConfig:
        """Create a parallel config for use by the draft worker.

        This is mostly a copy of the target parallel config, except the tp_size.
        """
        if speculative_draft_tensor_parallel_size is None:
            if draft_hf_config.model_type == "mlp_speculator":
                speculative_draft_tensor_parallel_size = 1
                if target_parallel_config.tensor_parallel_size > 1:
                    logger.warning(
                        "MLPSpeculator cannot currently be run with tp>1; "
                        "setting speculative_draft_tensor_parallel_size=1")
            else:
                speculative_draft_tensor_parallel_size = \
                    target_parallel_config.tensor_parallel_size
        elif speculative_draft_tensor_parallel_size != 1:
            # TODO(wooyeon): allow tp values larger than 1
            raise ValueError(
                f"{speculative_draft_tensor_parallel_size=} cannot be "
                f"other value than 1")

        draft_parallel_config = ParallelConfig(
            pipeline_parallel_size=target_parallel_config.
            pipeline_parallel_size,
            tensor_parallel_size=speculative_draft_tensor_parallel_size,
            distributed_executor_backend=target_parallel_config.
            distributed_executor_backend,
            max_parallel_loading_workers=target_parallel_config.
            max_parallel_loading_workers,
            disable_custom_all_reduce=target_parallel_config.
            disable_custom_all_reduce,
            tokenizer_pool_config=target_parallel_config.tokenizer_pool_config,
            ray_workers_use_nsight=target_parallel_config.
            ray_workers_use_nsight,
            placement_group=target_parallel_config.placement_group,
        )

        return draft_parallel_config

    def __init__(
        self,
        draft_model_config: ModelConfig,
        draft_parallel_config: ParallelConfig,
        num_speculative_tokens: int,
        speculative_disable_by_batch_size: Optional[int],
        ngram_prompt_lookup_max: Optional[int],
        ngram_prompt_lookup_min: Optional[int],
        draft_token_acceptance_method: str,
        typical_acceptance_sampler_posterior_threshold: float,
        typical_acceptance_sampler_posterior_alpha: float,
        disable_logprobs: bool,
        disable_log_stats: bool,
    ):
        """Create a SpeculativeConfig object.

        Args:
            draft_model_config: ModelConfig for the draft model.
            draft_parallel_config: ParallelConfig for the draft model.
            num_speculative_tokens: The number of tokens to sample from the
                draft model before scoring with the target model.
            speculative_disable_by_batch_size: Disable speculative
                decoding for new incoming requests when the number of
                enqueue requests is larger than this value.
            ngram_prompt_lookup_max: Max size of ngram token window.
            ngram_prompt_lookup_min: Min size of ngram token window.
            draft_token_acceptance_method (str): The method to use for
                accepting draft tokens. This can take two possible
                values 'rejection_sampler' and 'typical_acceptance_sampler'
                for RejectionSampler and TypicalAcceptanceSampler
                respectively.
            typical_acceptance_sampler_posterior_threshold (Optional[float]):
                A threshold value that sets a lower bound on the posterior
                probability of a token in the target model for it to be
                accepted. This threshold is used only when we use the 
                TypicalAcceptanceSampler for token acceptance.
            typical_acceptance_sampler_posterior_alpha (Optional[float]):
                A scaling factor for the entropy-based threshold in the
                TypicalAcceptanceSampler.
            disable_logprobs: If set to True, token log probabilities will not
                be returned even if requested by sampling parameters. This 
                reduces latency by skipping logprob calculation in proposal
                sampling, target sampling, and after accepted tokens are
                determined. If set to False, log probabilities will be
                returned.
            disable_log_stats: Whether to disable periodic printing of stage
                times in speculative decoding.
        """
        self.draft_model_config = draft_model_config
        self.draft_parallel_config = draft_parallel_config
        self.num_speculative_tokens = num_speculative_tokens
        self.speculative_disable_by_batch_size = \
            speculative_disable_by_batch_size
        self.ngram_prompt_lookup_max = ngram_prompt_lookup_max or 0
        self.ngram_prompt_lookup_min = ngram_prompt_lookup_min or 0
        self.draft_token_acceptance_method = draft_token_acceptance_method
        self.typical_acceptance_sampler_posterior_threshold = \
            typical_acceptance_sampler_posterior_threshold
        self.typical_acceptance_sampler_posterior_alpha = \
            typical_acceptance_sampler_posterior_alpha
        self.disable_logprobs = disable_logprobs
        self.disable_log_stats = disable_log_stats

        self._verify_args()

    def _verify_args(self) -> None:
        if self.num_speculative_tokens <= 0:
            raise ValueError("Expected num_speculative_tokens to be greater "
                             f"than zero ({self.num_speculative_tokens}).")

        if self.draft_model_config:
            self.draft_model_config.verify_with_parallel_config(
                self.draft_parallel_config)
            # Validate and set draft token acceptance related settings.

        if (self.draft_token_acceptance_method is None):
            raise ValueError("draft_token_acceptance_method is not set. "
                             "Expected values are rejection_sampler or "
                             "typical_acceptance_sampler.")

        if (self.draft_token_acceptance_method != 'rejection_sampler'
                and self.draft_token_acceptance_method !=
                'typical_acceptance_sampler'):
            raise ValueError(
                "Expected draft_token_acceptance_method to be either "
                "rejection_sampler or typical_acceptance_sampler. Instead it "
                f"is {self.draft_token_acceptance_method}")

        if (self.typical_acceptance_sampler_posterior_threshold < 0
                or self.typical_acceptance_sampler_posterior_alpha < 0):
            raise ValueError(
                "Expected typical_acceptance_sampler_posterior_threshold "
                "and typical_acceptance_sampler_posterior_alpha to be > 0. "
                "Instead found "
                f"typical_acceptance_sampler_posterior_threshold = "
                f"{self.typical_acceptance_sampler_posterior_threshold} and "
                f"typical_acceptance_sampler_posterior_alpha = "
                f"{self.typical_acceptance_sampler_posterior_alpha}")

    @property
    def num_lookahead_slots(self) -> int:
        """The number of additional slots the scheduler should allocate per
        step, in addition to the slots allocated for each known token.

        This is equal to the number of speculative tokens, as each speculative
        token must be scored.
        """
        return self.num_speculative_tokens

    def __repr__(self) -> str:
        if self.ngram_prompt_lookup_max > 0:
            draft_model = "[ngram]"
        else:
            draft_model = self.draft_model_config.model
        num_spec_tokens = self.num_speculative_tokens
        return f"SpeculativeConfig({draft_model=}, {num_spec_tokens=})"


@dataclass
class LoRAConfig:
    max_lora_rank: int
    max_loras: int
    fully_sharded_loras: bool = False
    max_cpu_loras: Optional[int] = None
    lora_dtype: Optional[torch.dtype] = None
    lora_extra_vocab_size: int = 256
    # This is a constant.
    lora_vocab_padding_size: ClassVar[int] = 256
    long_lora_scaling_factors: Optional[Tuple[float]] = None

    def __post_init__(self):
        # Setting the maximum rank to 256 should be able to satisfy the vast
        # majority of applications.
        possible_max_ranks = (8, 16, 32, 64, 128, 256)
        possible_lora_extra_vocab_size = (0, 256, 512)
        if self.max_lora_rank not in possible_max_ranks:
            raise ValueError(
                f"max_lora_rank ({self.max_lora_rank}) must be one of "
                f"{possible_max_ranks}.")
        if self.lora_extra_vocab_size not in possible_lora_extra_vocab_size:
            raise ValueError(
                f"lora_extra_vocab_size ({self.lora_extra_vocab_size}) "
                f"must be one of {possible_lora_extra_vocab_size}.")
        if self.max_loras < 1:
            raise ValueError(f"max_loras ({self.max_loras}) must be >= 1.")
        if self.max_cpu_loras is None:
            self.max_cpu_loras = self.max_loras
        elif self.max_cpu_loras < self.max_loras:
            raise ValueError(
                f"max_cpu_loras ({self.max_cpu_loras}) must be >= "
                f"max_loras ({self.max_loras})")

    def verify_with_model_config(self, model_config: ModelConfig):
        if self.lora_dtype in (None, "auto"):
            self.lora_dtype = model_config.dtype
        elif isinstance(self.lora_dtype, str):
            self.lora_dtype = getattr(torch, self.lora_dtype)
        if model_config.quantization and model_config.quantization not in [
                "awq", "gptq"
        ]:
            # TODO support marlin and squeezellm
            logger.warning("%s quantization is not tested with LoRA yet.",
                           model_config.quantization)

    def verify_with_scheduler_config(self, scheduler_config: SchedulerConfig):
        if scheduler_config.chunked_prefill_enabled:
            raise ValueError("LoRA is not supported with chunked prefill yet.")


@dataclass
class PromptAdapterConfig:
    max_prompt_adapters: int
    max_prompt_adapter_token: int
    max_cpu_prompt_adapters: Optional[int] = None
    prompt_adapter_dtype: Optional[torch.dtype] = None

    def __post_init__(self):
        library_name = 'peft'
        try:
            __import__(library_name)
        except ImportError as e:
            raise ImportError(
                f"'{library_name}' is not installed for prompt adapter support."
                f"Please install it using 'pip install {library_name}'."
            ) from e

        if self.max_prompt_adapters < 1:
            raise ValueError(f"max_prompt_adapters "
                             f"({self.max_prompt_adapters}) must be >= 1.")
        if self.max_prompt_adapter_token == 0:
            raise ValueError("max_prompt_adapter_token must be set.")
        if self.max_cpu_prompt_adapters is None:
            self.max_cpu_prompt_adapters = self.max_prompt_adapters

    def verify_with_model_config(self, model_config: ModelConfig):
        if self.prompt_adapter_dtype in (None, "auto"):
            self.prompt_adapter_dtype = model_config.dtype
        elif isinstance(self.prompt_adapter_dtype, str):
            self.prompt_adapter_dtype = getattr(torch,
                                                self.prompt_adapter_dtype)


@dataclass
class MultiModalConfig:
    """Controls the behavior of multimodal models."""

    limit_per_prompt: Mapping[str, int] = field(default_factory=dict)
    """
    The maximum number of multi-modal input instances allowed per prompt
    for each :class:`~vllm.multimodal.MultiModalPlugin`.
    """

    # TODO: Add configs to init vision tower or not.


_STR_DTYPE_TO_TORCH_DTYPE = {
    "half": torch.float16,
    "float16": torch.float16,
    "float": torch.float32,
    "float32": torch.float32,
    "bfloat16": torch.bfloat16,
}

_ROCM_NOT_SUPPORTED_DTYPE: List[str] = []  #


def _get_and_verify_dtype(
    config: PretrainedConfig,
    dtype: Union[str, torch.dtype],
) -> torch.dtype:
    # NOTE: getattr(config, "torch_dtype", torch.float32) is not correct
    # because config.torch_dtype can be None.
    config_dtype = getattr(config, "torch_dtype", None)
    if config_dtype is None:
        config_dtype = torch.float32

    if isinstance(dtype, str):
        dtype = dtype.lower()
        if dtype == "auto":
            if config_dtype == torch.float32:
                if config.model_type == "gemma2":
                    logger.info(
                        "For Gemma 2, we downcast float32 to bfloat16 instead "
                        "of float16 by default. Please specify `dtype` if you "
                        "want to use float16.")
                    torch_dtype = torch.bfloat16
                else:
                    # Following the common practice, we use float16 for float32
                    # models.
                    torch_dtype = torch.float16
            else:
                torch_dtype = config_dtype
        else:
            if dtype not in _STR_DTYPE_TO_TORCH_DTYPE:
                raise ValueError(f"Unknown dtype: {dtype}")
            torch_dtype = _STR_DTYPE_TO_TORCH_DTYPE[dtype]
    elif isinstance(dtype, torch.dtype):
        torch_dtype = dtype
    else:
        raise ValueError(f"Unknown dtype: {dtype}")

    # Verify the dtype.
    if torch_dtype != config_dtype:
        if torch_dtype == torch.float32:
            # Upcasting to float32 is allowed.
            logger.info("Upcasting %s to %s.", config_dtype, torch_dtype)
            pass
        elif config_dtype == torch.float32:
            # Downcasting from float32 to float16 or bfloat16 is allowed.
            logger.info("Downcasting %s to %s.", config_dtype, torch_dtype)
            pass
        else:
            # Casting between float16 and bfloat16 is allowed with a warning.
            logger.warning("Casting %s to %s.", config_dtype, torch_dtype)

    return torch_dtype


def _get_and_verify_max_len(
    hf_config: PretrainedConfig,
    max_model_len: Optional[int],
    disable_sliding_window: bool,
    sliding_window_len: Optional[int],
    spec_target_max_model_len: Optional[int] = None,
) -> int:
    """Get and verify the model's maximum length."""
    derived_max_model_len = float("inf")
    possible_keys = [
        # OPT
        "max_position_embeddings",
        # GPT-2
        "n_positions",
        # MPT
        "max_seq_len",
        # ChatGLM2
        "seq_length",
        # Command-R
        "model_max_length",
        # Others
        "max_sequence_length",
        "max_seq_length",
        "seq_len",
    ]
    # Choose the smallest "max_length" from the possible keys.
    max_len_key = None
    for key in possible_keys:
        max_len = getattr(hf_config, key, None)
        if max_len is not None:
            max_len_key = key if max_len < derived_max_model_len \
                else max_len_key
            derived_max_model_len = min(derived_max_model_len, max_len)

    # If sliding window is manually disabled, max_length should be less
    # than the sliding window length in the model config.
    if disable_sliding_window and sliding_window_len is not None:
        max_len_key = "sliding_window" \
            if sliding_window_len < derived_max_model_len else max_len_key
        derived_max_model_len = min(derived_max_model_len, sliding_window_len)

    # If none of the keys were found in the config, use a default and
    # log a warning.
    if derived_max_model_len == float("inf"):
        if max_model_len is not None:
            # If max_model_len is specified, we use it.
            return max_model_len

        if spec_target_max_model_len is not None:
            # If this is a speculative draft model, we use the max model len
            # from the target model.
            return spec_target_max_model_len

        default_max_len = 2048
        logger.warning(
            "The model's config.json does not contain any of the following "
            "keys to determine the original maximum length of the model: "
            "%s. Assuming the model's maximum length is %d.", possible_keys,
            default_max_len)
        derived_max_model_len = default_max_len

    rope_scaling = getattr(hf_config, "rope_scaling", None)
    if rope_scaling is not None:
        if "type" in rope_scaling:
            rope_type = rope_scaling["type"]
        elif "rope_type" in rope_scaling:
            rope_type = rope_scaling["rope_type"]
        else:
            raise ValueError(
                "rope_scaling must have a 'type' or 'rope_type' key.")

        # The correct one should be "longrope", kept "su" here
        # to be backward compatible
        if rope_type not in ("su", "longrope", "llama3"):
            if disable_sliding_window:
                # TODO(robertgshaw): Find a model that supports rope_scaling
                # with sliding window to see if this case should be allowed.
                raise NotImplementedError(
                    "Disabling sliding window is not supported for models "
                    "with rope_scaling. Please raise an issue so we can "
                    "investigate.")

            assert "factor" in rope_scaling
            scaling_factor = rope_scaling["factor"]
            if rope_type == "yarn":
                derived_max_model_len = rope_scaling[
                    "original_max_position_embeddings"]
            derived_max_model_len *= scaling_factor

    # If the user specified a max length, make sure it is smaller than the
    # derived length from the HF model config.
    if max_model_len is None:
        max_model_len = int(derived_max_model_len)
    elif max_model_len > derived_max_model_len:
        # Some models might have a separate key for specifying model_max_length
        # that will be bigger than derived_max_model_len. We compare user input
        # with model_max_length and allow this override when it's smaller.
        model_max_length = getattr(hf_config, "model_max_length", None)
        if model_max_length is not None and max_model_len <= model_max_length:
            if disable_sliding_window:
                # TODO(robertgshaw): Find a model that has model_max_length
                # with sliding window to see if this case should be allowed.
                raise NotImplementedError(
                    "Disabling sliding window is not supported for models "
                    "model_max_length in the config. Please raise an issue "
                    "so we can investigate.")
        else:
            msg = (
                f"User-specified max_model_len ({max_model_len}) is greater "
                f"than the derived max_model_len ({max_len_key}="
                f"{derived_max_model_len} or model_max_length="
                f"{model_max_length} in model's config.json). This may lead "
                "to incorrect model outputs or CUDA errors.")
            if envs.VLLM_ALLOW_LONG_MAX_MODEL_LEN:
                logger.warning(
                    "%s Make sure the value is correct and within the "
                    "model context size.", msg)
            else:
                raise ValueError(
                    f"{msg} To allow overriding this maximum, set "
                    "the env var VLLM_ALLOW_LONG_MAX_MODEL_LEN=1")
    return int(max_model_len)


def get_served_model_name(model: str,
                          served_model_name: Optional[Union[str, List[str]]]):
    """
    If the input is a non-empty list, the first model_name in 
    `served_model_name` is taken. 
    If the input is a non-empty string, it is used directly. 
    For cases where the input is either an empty string or an 
    empty list, the fallback is to use `self.model`.
    """
    if not served_model_name:
        return model
    if isinstance(served_model_name, list):
        return served_model_name[0]
    return served_model_name


@dataclass
class DecodingConfig:
    """Dataclass which contains the decoding strategy of the engine"""

    # Which guided decoding algo to use. 'outlines' / 'lm-format-enforcer'
    guided_decoding_backend: str = 'outlines'

    def __post_init__(self):
        valid_guided_backends = ['outlines', 'lm-format-enforcer']
        backend = self.guided_decoding_backend
        if backend not in valid_guided_backends:
            raise ValueError(f"Invalid guided_decoding_backend '{backend},"
                             f"must be one of {valid_guided_backends}")


@dataclass
class ObservabilityConfig:
    """Configuration for observability."""
    otlp_traces_endpoint: Optional[str] = None

    # Collecting detailed timing information for each request can be expensive.

    # If set, collects the model forward time for the request.
    collect_model_forward_time: bool = False

    # If set, collects the model execute time for the request.
    collect_model_execute_time: bool = False

    def __post_init__(self):
        if not is_otel_available() and self.otlp_traces_endpoint is not None:
            raise ValueError(
                "OpenTelemetry is not available. Unable to configure "
                "'otlp_traces_endpoint'. Ensure OpenTelemetry packages are "
                f"installed. Original error:\n{otel_import_error_traceback}")

        if ((self.collect_model_forward_time
             or self.collect_model_execute_time)
                and self.otlp_traces_endpoint is None):
            raise ValueError(
                "collect_model_forward_time or collect_model_execute_time "
                "requires --otlp-traces-endpoint to be set.")


@dataclass(frozen=True)
class EngineConfig:
    """Dataclass which contains all engine-related configuration. This
    simplifies passing around the distinct configurations in the codebase.
    """

    model_config: ModelConfig
    cache_config: CacheConfig
    parallel_config: ParallelConfig
    scheduler_config: SchedulerConfig
    device_config: DeviceConfig
    load_config: LoadConfig
    lora_config: Optional[LoRAConfig]
    speculative_config: Optional[SpeculativeConfig]
    decoding_config: Optional[DecodingConfig]
    observability_config: Optional[ObservabilityConfig]
    prompt_adapter_config: Optional[PromptAdapterConfig]

    def __post_init__(self):
        """Verify configs are valid & consistent with each other.
        """
        self.model_config.verify_async_output_proc(self.parallel_config,
                                                   self.speculative_config,
                                                   self.device_config)
        self.model_config.verify_with_parallel_config(self.parallel_config)
        self.cache_config.verify_with_parallel_config(self.parallel_config)

        if self.lora_config:
            self.lora_config.verify_with_model_config(self.model_config)
            self.lora_config.verify_with_scheduler_config(
                self.scheduler_config)
        if self.prompt_adapter_config:
            self.prompt_adapter_config.verify_with_model_config(
                self.model_config)

    def to_dict(self):
        """Return the configs as a dictionary, for use in **kwargs.
        """
        return dict(
            (field.name, getattr(self, field.name)) for field in fields(self))