config.py

from typing import Optional

import torch
from transformers import PretrainedConfig

from vllm.logger import init_logger
from vllm.transformers_utils.config import get_config
from vllm.utils import get_cpu_memory

logger = init_logger(__name__)

_GB = 1 << 30


class ModelConfig:
    """Configuration for the model.

    Args:
        model: Name or path of the huggingface model to use.
        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.
        download_dir: Directory to download and load the weights, default to the
            default cache directory of huggingface.
        use_np_weights: Save a numpy copy of model weights for faster loading.
            This can increase the disk usage by up to 2x.
        use_dummy_weights: Use dummy values for model weights (for profiling).
        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.
    """

    def __init__(
        self,
        model: str,
        tokenizer: str,
        tokenizer_mode: str,
        trust_remote_code: bool,
        download_dir: Optional[str],
        use_np_weights: bool,
        use_dummy_weights: bool,
        dtype: str,
        seed: int,
    ) -> None:
        self.model = model
        self.tokenizer = tokenizer
        self.tokenizer_mode = tokenizer_mode
        self.trust_remote_code = trust_remote_code
        self.download_dir = download_dir
        self.use_np_weights = use_np_weights
        self.use_dummy_weights = use_dummy_weights
        self.seed = seed

        self.hf_config = get_config(model, trust_remote_code)
        self.dtype = _get_and_verify_dtype(self.hf_config, dtype)
        self._verify_tokenizer_mode()

    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_with_parallel_config(
        self,
        parallel_config: "ParallelConfig",
    ) -> None:
        total_num_attention_heads = self.hf_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_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_hidden_size(self) -> int:
        return self.hf_config.hidden_size

    def get_head_size(self) -> int:
        # FIXME(woosuk): This may not be true for all models.
        return self.hf_config.hidden_size // self.hf_config.num_attention_heads

    def get_num_heads(self, parallel_config: "ParallelConfig") -> int:
        # 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.
        new_decoder_arch_falcon = (
            self.hf_config.model_type == "falcon"
            and getattr(self.hf_config, "new_decoder_architecture", False))
        if not new_decoder_arch_falcon and getattr(self.hf_config,
                                                   "multi_query", False):
            # Multi-query attention, only one KV head.
            return 1
        # For Falcon:
        if getattr(self.hf_config, "n_head_kv", None) is not None:
            return (self.hf_config.n_head_kv //
                    parallel_config.tensor_parallel_size)
        # For LLaMA-2:
        if getattr(self.hf_config, "num_key_value_heads", None) is not None:
            return (self.hf_config.num_key_value_heads //
                    parallel_config.tensor_parallel_size)
        total_num_attention_heads = self.hf_config.num_attention_heads
        return total_num_attention_heads // parallel_config.tensor_parallel_size

    def get_max_model_len(self) -> int:
        max_model_len = float("inf")
        possible_keys = [
            # OPT
            "max_position_embeddings",
            # GPT-2
            "n_positions",
            # MPT
            "max_seq_len",
            # Others
            "max_sequence_length",
            "max_seq_length",
            "seq_len",
        ]
        for key in possible_keys:
            max_len_key = getattr(self.hf_config, key, None)
            if max_len_key is not None:
                max_model_len = min(max_model_len, max_len_key)
        return max_model_len

    def get_num_layers(self, parallel_config: "ParallelConfig") -> int:
        total_num_hidden_layers = self.hf_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).
    """

    def __init__(
        self,
        block_size: int,
        gpu_memory_utilization: float,
        swap_space: int,
    ) -> None:
        self.block_size = block_size
        self.gpu_memory_utilization = gpu_memory_utilization
        self.swap_space_bytes = swap_space * _GB
        self._verify_args()

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

    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_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. " + msg)


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: Whether to use Ray for model workers. Will be set to
            True if either pipeline_parallel_size or tensor_parallel_size is
            greater than 1.
    """

    def __init__(
        self,
        pipeline_parallel_size: int,
        tensor_parallel_size: int,
        worker_use_ray: bool,
    ) -> None:
        self.pipeline_parallel_size = pipeline_parallel_size
        self.tensor_parallel_size = tensor_parallel_size
        self.worker_use_ray = worker_use_ray

        self.world_size = pipeline_parallel_size * tensor_parallel_size
        if self.world_size > 1:
            self.worker_use_ray = True
        self._verify_args()

    def _verify_args(self) -> None:
        if self.pipeline_parallel_size > 1:
            raise NotImplementedError(
                "Pipeline parallelism is not supported yet.")


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).
    """

    def __init__(self, max_num_batched_tokens: int, max_num_seqs: int,
                 max_model_len: int) -> None:
        self.max_num_batched_tokens = max_num_batched_tokens
        self.max_num_seqs = max_num_seqs
        self.max_model_len = max_model_len


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


def _get_and_verify_dtype(
    config: PretrainedConfig,
    dtype: str,
) -> 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

    dtype = dtype.lower()
    if dtype == "auto":
        if config_dtype == torch.float32:
            # 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]

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

    # Check if the GPU supports the dtype.
    if torch_dtype == torch.bfloat16:
        compute_capability = torch.cuda.get_device_capability()
        if compute_capability[0] < 8:
            gpu_name = torch.cuda.get_device_name()
            raise ValueError(
                "Bfloat16 is only supported on GPUs with compute capability "
                f"of at least 8.0. Your {gpu_name} GPU has compute capability "
                f"{compute_capability[0]}.{compute_capability[1]}.")
    return torch_dtype