config.py

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

import torch
from transformers import PretrainedConfig

from vllm.logger import init_logger
from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
from vllm.model_executor.models import ModelRegistry
from vllm.transformers_utils.config import get_config, get_hf_text_config
from vllm.utils import get_cpu_memory, is_cpu, is_hip, is_neuron

if TYPE_CHECKING:
    from ray.util.placement_group import PlacementGroup

    from vllm.model_executor.model_loader.loader import BaseModelLoader

logger = init_logger(__name__)

_GB = 1 << 30
_EMBEDDING_MODEL_MAX_NUM_BATCHED_TOKENS = 32768


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, and "slow" will always use the slow tokenizer.
        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.
        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`.
    """

    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,
        tokenizer_revision: Optional[str] = None,
        max_model_len: Optional[int] = None,
        quantization: Optional[str] = None,
        quantization_param_path: Optional[str] = None,
        enforce_eager: bool = False,
        max_context_len_to_capture: Optional[int] = None,
        max_seq_len_to_capture: Optional[int] = None,
        max_logprobs: int = 5,
        disable_sliding_window: bool = False,
        skip_tokenizer_init: bool = False,
        served_model_name: Optional[Union[str, List[str]]] = None,
    ) -> 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.tokenizer_revision = tokenizer_revision
        self.quantization = quantization
        self.quantization_param_path = quantization_param_path
        self.enforce_eager = enforce_eager
        self.max_context_len_to_capture = max_context_len_to_capture
        if self.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
                                       or max_context_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)
        self.hf_text_config = get_hf_text_config(self.hf_config)
        self.dtype = _get_and_verify_dtype(self.hf_text_config, dtype)
        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())
        self.served_model_name = get_served_model_name(model,
                                                       served_model_name)
        if not self.skip_tokenizer_init:
            self._verify_tokenizer_mode()
        self._verify_embedding_mode()
        self._verify_quantization()
        self._verify_cuda_graph()

    def _verify_tokenizer_mode(self) -> None:
        tokenizer_mode = self.tokenizer_mode.lower()
        if tokenizer_mode not in ["auto", "slow"]:
            raise ValueError(
                f"Unknown tokenizer mode: {self.tokenizer_mode}. Must be "
                "either 'auto' or 'slow'.")
        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:
            # SparseML uses a "compression_config" with a "quantization_config".
            compression_cfg = getattr(self.hf_config, "compression_config",
                                      None)
            if compression_cfg is not None:
                quant_cfg = compression_cfg.get("quantization_config", None)

        return quant_cfg

    def _verify_quantization(self) -> None:
        supported_quantization = [*QUANTIZATION_METHODS]
        rocm_supported_quantization = ["gptq", "squeezellm"]
        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 (self.quantization
                    not in ["marlin", "gptq_marlin_24", "gptq_marlin"]):
                logger.warning(
                    "%s quantization is not fully "
                    "optimized yet. The speed can be slower than "
                    "non-quantized models.", self.quantization)

    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_with_parallel_config(
        self,
        parallel_config: "ParallelConfig",
    ) -> None:
        total_num_attention_heads = self.hf_text_config.num_attention_heads
        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}).")

        total_num_hidden_layers = self.hf_text_config.num_hidden_layers
        pipeline_parallel_size = parallel_config.pipeline_parallel_size
        if total_num_hidden_layers % pipeline_parallel_size != 0:
            raise ValueError(
                f"Total number of hidden layers ({total_num_hidden_layers}) "
                "must be divisible by pipeline parallel size "
                f"({pipeline_parallel_size}).")

    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:
        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 in ["dbrx", "mpt"]:
            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:
        return self.hf_text_config.num_attention_heads // \
                    parallel_config.tensor_parallel_size

    def get_num_layers(self, parallel_config: "ParallelConfig") -> int:
        total_num_hidden_layers = self.hf_text_config.num_hidden_layers
        return total_num_hidden_layers // parallel_config.pipeline_parallel_size


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: int,
        cache_dtype: str,
        num_gpu_blocks_override: Optional[int] = None,
        sliding_window: Optional[int] = None,
        enable_prefix_caching: bool = False,
    ) -> None:
        self.block_size = block_size
        self.gpu_memory_utilization = gpu_memory_utilization
        self.swap_space_bytes = swap_space * _GB
        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._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.")
        if self.cache_dtype == "fp8":
            raise NotImplementedError(
                "Prefix caching is not supported for fp8 cache_dtype. "
                "Run with --kv-cache-dtype auto 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 / _GB:.2f} GiB out of "
               f"the {total_cpu_memory / _GB:.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: str
    extra_config: dict

    def __post_init__(self):
        if self.pool_type not in ("ray", ):
            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"


@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.
    """

    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)

    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()

    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[str] = 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 self.distributed_executor_backend != "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:
            from vllm.executor import ray_utils
            ray_found = ray_utils.ray is not None
            self.distributed_executor_backend = "ray" if ray_found else "mp"

        self._verify_args()

    def _verify_args(self) -> None:
        if self.pipeline_parallel_size > 1:
            raise NotImplementedError(
                "Pipeline parallelism is not supported yet.")
        if self.distributed_executor_backend not in ("ray", "mp", None):
            raise ValueError(
                "Unrecognized distributed executor backend. Supported values "
                "are 'ray' or 'mp'.")
        if not self.disable_custom_all_reduce and self.world_size > 1:
            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.")
            elif self.pipeline_parallel_size > 1:
                self.disable_custom_all_reduce = True
                logger.info(
                    "Disabled the custom all-reduce kernel because it is not "
                    "supported with pipeline parallelism.")
        if self.ray_workers_use_nsight and (
                not self.distributed_executor_backend == "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.
    """

    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: Optional[bool] = False,
    ) -> None:
        if max_num_batched_tokens is not None:
            self.max_num_batched_tokens = max_num_batched_tokens
        else:
            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.
                self.max_num_batched_tokens = 512
            elif embedding_mode:
                # For embedding, choose specific value for higher throughput
                self.max_num_batched_tokens = max(
                    max_model_len, _EMBEDDING_MODEL_MAX_NUM_BATCHED_TOKENS)
            else:
                # If max_model_len is too short, use 2048 as the default value
                # for higher throughput.
                self.max_num_batched_tokens = max(max_model_len, 2048)
        if enable_chunked_prefill:
            logger.info("Chunked prefill is enabled (EXPERIMENTAL).")

        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._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.")


class DeviceConfig:

    def __init__(self, device: str = "auto") -> None:
        if device == "auto":
            # Automated device type detection
            if is_neuron():
                self.device_type = "neuron"
            elif is_cpu():
                self.device_type = "cpu"
            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"]:
            self.device = torch.device("cpu")
        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],
        num_speculative_tokens: Optional[int],
        speculative_max_model_len: Optional[int],
        enable_chunked_prefill: bool,
        use_v2_block_manager: bool,
        speculative_disable_by_batch_size: Optional[int],
        ngram_prompt_lookup_max: Optional[int],
        ngram_prompt_lookup_min: Optional[int],
    ) -> 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.
            num_speculative_tokens (Optional[int]): The number of speculative
                tokens, if provided.
            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.

        Returns:
            Optional["SpeculativeConfig"]: An instance of SpeculativeConfig if
                the necessary conditions are met, else None.
        """

        if speculative_model is None and num_speculative_tokens is None:
            return None

        if speculative_model is not None and num_speculative_tokens is None:
            raise ValueError(
                "Expected both speculative_model and "
                "num_speculative_tokens to be provided, but found "
                f"{speculative_model=} and {num_speculative_tokens=}.")

        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=}")

        assert (speculative_model is not None
                and num_speculative_tokens is not None)

        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/quantization/max
        # model len for the draft model. It is not currently supported.
        draft_revision = None
        draft_code_revision = None
        draft_quantization = None

        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,
                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_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))

        return SpeculativeConfig(
            draft_model_config,
            draft_parallel_config,
            num_speculative_tokens,
            speculative_disable_by_batch_size,
            ngram_prompt_lookup_max,
            ngram_prompt_lookup_min,
        )

    @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) -> ParallelConfig:
        """Create a parallel config for use by the draft worker.

        This is mostly a copy of the target parallel config. In the future the
        draft worker can have a different parallel strategy, e.g. TP=1.
        """
        draft_parallel_config = ParallelConfig(
            pipeline_parallel_size=target_parallel_config.
            pipeline_parallel_size,
            tensor_parallel_size=target_parallel_config.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],
    ):
        """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.
        """
        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._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)

    @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):
        # Keep this in sync with csrc/punica/bgmv/bgmv_config.h
        possible_max_ranks = (8, 16, 32, 64)
        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.max_num_batched_tokens > 65528:
            raise ValueError(
                "Due to limitations of the custom LoRA CUDA kernel, "
                "max_num_batched_tokens must be <= 65528 when "
                "LoRA is enabled.")


@dataclass
class VisionLanguageConfig:
    """Configs the input data format and how models should run for
    vision language models."""

    class ImageInputType(enum.Enum):
        """Image input type into the vision language model.

        An image roughly goes through the following transformation:
        Raw image --> pixel values --> image features --> image embeddings.

        The difference between different image input types is where the
        image encoder (pixel values --> image features) is run.
        Different image input types also correspond to different tensor shapes.

        For example, for Llava, PIXEL_VALUES: (1, 3, 336, 336).
        IMAGE_FEATURES: (1, 576, 1024).
        """
        PIXEL_VALUES = enum.auto()
        IMAGE_FEATURES = enum.auto()

    image_input_type: ImageInputType
    # The input id corresponding to image token.
    image_token_id: int
    # Used for running `run_prefill_max_token`.
    # For models that support varying resolution, this corresponds to
    # worst case scenario (biggest supported resolution).
    image_input_shape: tuple
    image_feature_size: int

    @classmethod
    def get_image_input_enum_type(
            cls, value: str) -> "VisionLanguageConfig.ImageInputType":
        """Get the image input type from a string."""
        try:
            return cls.ImageInputType[value.upper()]
        except KeyError as e:
            raise ValueError(f"{value} is not a valid choice. "
                             f"Expecting to choose from "
                             f"{[x.name for x in cls.ImageInputType]}.") from e


_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:
                # Following the common practice, we use float16 for float32
                # models.
                logger.info("Casting torch.float32 to torch.float16.")
                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],
) -> 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

        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: "
            "%d. 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 and rope_scaling["type"] != "su":
        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_scaling["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.")
            pass
        else:
            raise ValueError(
                f"User-specified max_model_len ({max_model_len}) is greater "
                "than the derived max_model_len "
                f"({max_len_key}={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. Make sure the "
                "value is correct and within the model context size.")
    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(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]
    vision_language_config: Optional[VisionLanguageConfig]
    speculative_config: Optional[SpeculativeConfig]
    decoding_config: Optional[DecodingConfig]

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

    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))