loader.py

# ruff: noqa: SIM117
import collections
import copy
import glob
import os
from abc import ABC, abstractmethod
from typing import Any, Dict, Generator, List, Optional, Tuple, Type

import huggingface_hub
import torch
from torch import nn

from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoadFormat,
                         LoRAConfig, ModelConfig, ParallelConfig,
                         SchedulerConfig, VisionLanguageConfig)
from vllm.envs import VLLM_USE_MODELSCOPE
from vllm.logger import init_logger
from vllm.model_executor.layers.quantization.base_config import (
    QuantizationConfig)
from vllm.model_executor.model_loader.tensorizer import (
    TensorizerConfig, is_vllm_tensorized, load_with_tensorizer,
    tensorizer_weights_iterator)
from vllm.model_executor.model_loader.utils import (get_model_architecture,
                                                    set_default_torch_dtype)
from vllm.model_executor.model_loader.weight_utils import (
    download_weights_from_hf, filter_files_not_needed_for_inference,
    get_quant_config, initialize_dummy_weights, np_cache_weights_iterator,
    pt_weights_iterator, safetensors_weights_iterator)
from vllm.model_executor.models.llava import LlavaForConditionalGeneration

_VISION_MODEL_CLASSES = [
    LlavaForConditionalGeneration,
]

logger = init_logger(__name__)


def _get_quantization_config(
        model_config: ModelConfig,
        load_config: LoadConfig) -> Optional[QuantizationConfig]:
    """Get the quantization config."""
    if model_config.quantization is not None:
        quant_config = get_quant_config(model_config, load_config)
        capability = torch.cuda.get_device_capability()
        capability = capability[0] * 10 + capability[1]
        if capability < quant_config.get_min_capability():
            raise ValueError(
                f"The quantization method {model_config.quantization} is not "
                "supported for the current GPU. "
                f"Minimum capability: {quant_config.get_min_capability()}. "
                f"Current capability: {capability}.")
        supported_dtypes = quant_config.get_supported_act_dtypes()
        if model_config.dtype not in supported_dtypes:
            raise ValueError(
                f"{model_config.dtype} is not supported for quantization "
                f"method {model_config.quantization}. Supported dtypes: "
                f"{supported_dtypes}")
        return quant_config
    return None


def _get_model_initialization_kwargs(
        model_class: Type[nn.Module], lora_config: Optional[LoRAConfig],
        vision_language_config: Optional[VisionLanguageConfig]
) -> Dict[str, Any]:
    """Get extra kwargs for model initialization."""
    extra_kwargs = {}
    if hasattr(model_class, "supported_lora_modules"):
        extra_kwargs["lora_config"] = lora_config
    elif lora_config:
        raise ValueError(
            f"Model {model_class.__name__} does not support LoRA, "
            "but LoRA is enabled. Support for this model may "
            "be added in the future. If this is important to you, "
            "please open an issue on github.")
    elif model_class in _VISION_MODEL_CLASSES:
        extra_kwargs["vision_language_config"] = vision_language_config
    return extra_kwargs


def _initialize_model(model_config: ModelConfig, load_config: LoadConfig,
                      lora_config: Optional[LoRAConfig],
                      vision_language_config: Optional[VisionLanguageConfig],
                      cache_config: CacheConfig) -> nn.Module:
    """Initialize a model with the given configurations."""
    model_class = get_model_architecture(model_config)[0]
    quant_config = _get_quantization_config(model_config, load_config)

    return model_class(config=model_config.hf_config,
                       cache_config=cache_config,
                       quant_config=quant_config,
                       **_get_model_initialization_kwargs(
                           model_class, lora_config, vision_language_config))


class BaseModelLoader(ABC):
    """Base class for model loaders."""

    def __init__(self, load_config: LoadConfig):
        self.load_config = load_config

    @abstractmethod
    def load_model(self, *, model_config: ModelConfig,
                   device_config: DeviceConfig,
                   lora_config: Optional[LoRAConfig],
                   vision_language_config: Optional[VisionLanguageConfig],
                   parallel_config: ParallelConfig,
                   scheduler_config: SchedulerConfig,
                   cache_config: CacheConfig) -> nn.Module:
        """Load a model with the given configurations."""
        ...


class DefaultModelLoader(BaseModelLoader):
    """Model loader that can load different file types from disk."""

    def __init__(self, load_config: LoadConfig):
        super().__init__(load_config)
        if load_config.model_loader_extra_config:
            raise ValueError(f"Model loader extra config is not supported for "
                             f"load format {load_config.load_format}")

    def _maybe_download_from_modelscope(
            self, model: str, revision: Optional[str]) -> Optional[str]:
        """Download model from ModelScope hub if VLLM_USE_MODELSCOPE is True.
        
        Returns the path to the downloaded model, or None if the model is not
        downloaded from ModelScope."""
        if VLLM_USE_MODELSCOPE:
            # download model from ModelScope hub,
            # lazy import so that modelscope is not required for normal use.
            # pylint: disable=C.
            from modelscope.hub.snapshot_download import snapshot_download

            if not os.path.exists(model):
                model_path = snapshot_download(
                    model_id=model,
                    cache_dir=self.load_config.download_dir,
                    local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,
                    revision=revision,
                )
            else:
                model_path = model
            return model_path
        return None

    def _prepare_weights(self, model_name_or_path: str,
                         revision: Optional[str],
                         fall_back_to_pt: bool) -> Tuple[str, List[str], bool]:
        """Prepare weights for the model.

        If the model is not local, it will be downloaded."""
        model_name_or_path = self._maybe_download_from_modelscope(
            model_name_or_path, revision) or model_name_or_path

        is_local = os.path.isdir(model_name_or_path)
        load_format = self.load_config.load_format
        use_safetensors = False
        # Some quantized models use .pt files for storing the weights.
        if load_format == LoadFormat.AUTO:
            allow_patterns = ["*.safetensors", "*.bin"]
        elif load_format == LoadFormat.SAFETENSORS:
            use_safetensors = True
            allow_patterns = ["*.safetensors"]
        elif load_format == LoadFormat.PT:
            allow_patterns = ["*.pt"]
        elif load_format == LoadFormat.NPCACHE:
            allow_patterns = ["*.bin"]
        else:
            raise ValueError(f"Unknown load_format: {load_format}")

        if fall_back_to_pt:
            allow_patterns += ["*.pt"]

        if not is_local:
            hf_folder = download_weights_from_hf(model_name_or_path,
                                                 self.load_config.download_dir,
                                                 allow_patterns, revision)
        else:
            hf_folder = model_name_or_path

        hf_weights_files: List[str] = []
        for pattern in allow_patterns:
            hf_weights_files += glob.glob(os.path.join(hf_folder, pattern))
            if len(hf_weights_files) > 0:
                if pattern == "*.safetensors":
                    use_safetensors = True
                break

        if not use_safetensors:
            hf_weights_files = filter_files_not_needed_for_inference(
                hf_weights_files)

        if len(hf_weights_files) == 0:
            raise RuntimeError(
                f"Cannot find any model weights with `{model_name_or_path}`")

        return hf_folder, hf_weights_files, use_safetensors

    def _get_weights_iterator(
        self, model_name_or_path: str, revision: Optional[str],
        fall_back_to_pt: bool
    ) -> Generator[Tuple[str, torch.Tensor], None, None]:
        """Get an iterator for the model weights based on the load format."""
        hf_folder, hf_weights_files, use_safetensors = self._prepare_weights(
            model_name_or_path, revision, fall_back_to_pt)
        if self.load_config.load_format == LoadFormat.NPCACHE:
            # Currently np_cache only support *.bin checkpoints
            assert use_safetensors is False
            return np_cache_weights_iterator(model_name_or_path,
                                             self.load_config.download_dir,
                                             hf_folder, hf_weights_files)
        if use_safetensors:
            return safetensors_weights_iterator(hf_weights_files)
        return pt_weights_iterator(hf_weights_files)

    def load_model(self, *, model_config: ModelConfig,
                   device_config: DeviceConfig,
                   lora_config: Optional[LoRAConfig],
                   vision_language_config: Optional[VisionLanguageConfig],
                   parallel_config: ParallelConfig,
                   scheduler_config: SchedulerConfig,
                   cache_config: CacheConfig) -> nn.Module:
        with set_default_torch_dtype(model_config.dtype):
            with torch.device(device_config.device):
                model = _initialize_model(model_config, self.load_config,
                                          lora_config, vision_language_config,
                                          cache_config)
            model.load_weights(
                self._get_weights_iterator(model_config.model,
                                           model_config.revision,
                                           fall_back_to_pt=getattr(
                                               model,
                                               "fall_back_to_pt_during_load",
                                               True)), )
            for _, module in model.named_modules():
                quant_method = getattr(module, "quant_method", None)
                if quant_method is not None:
                    quant_method.process_weights_after_loading(module)
                # FIXME: Remove this after Mixtral is updated
                # to use quant_method.
                if hasattr(module, "process_weights_after_loading"):
                    module.process_weights_after_loading()
        return model.eval()


class DummyModelLoader(BaseModelLoader):
    """Model loader that will set model weights to random values."""

    def __init__(self, load_config: LoadConfig):
        super().__init__(load_config)
        if load_config.model_loader_extra_config:
            raise ValueError(f"Model loader extra config is not supported for "
                             f"load format {load_config.load_format}")

    def load_model(self, *, model_config: ModelConfig,
                   device_config: DeviceConfig,
                   lora_config: Optional[LoRAConfig],
                   vision_language_config: Optional[VisionLanguageConfig],
                   parallel_config: ParallelConfig,
                   scheduler_config: SchedulerConfig,
                   cache_config: CacheConfig) -> nn.Module:
        with set_default_torch_dtype(model_config.dtype):
            with torch.device(device_config.device):
                model = _initialize_model(model_config, self.load_config,
                                          lora_config, vision_language_config,
                                          cache_config)
            # NOTE(woosuk): For accurate performance evaluation, we assign
            # random values to the weights.
            initialize_dummy_weights(model)
        return model.eval()


class TensorizerLoader(BaseModelLoader):
    """Model loader using CoreWeave's tensorizer library."""

    def __init__(self, load_config: LoadConfig):
        super().__init__(load_config)
        if isinstance(load_config.model_loader_extra_config, TensorizerConfig):
            self.tensorizer_config = load_config.model_loader_extra_config
        else:
            self.tensorizer_config = TensorizerConfig(
                **load_config.model_loader_extra_config)

    def _verify_config(self, model_config: ModelConfig,
                       parallel_config: ParallelConfig):
        self.tensorizer_config.verify_with_model_config(model_config)
        self.tensorizer_config.verify_with_parallel_config(parallel_config)

    def _get_weights_iterator(
            self) -> Generator[Tuple[str, torch.Tensor], None, None]:
        tensorizer_args = self.tensorizer_config._construct_tensorizer_args()
        return tensorizer_weights_iterator(tensorizer_args)

    def _load_model_serialized_cpu(
        self,
        model_config: ModelConfig,
        device_config: DeviceConfig,
        lora_config: Optional[LoRAConfig],
        vision_language_config: Optional[VisionLanguageConfig],
        cache_config: CacheConfig,
    ) -> nn.Module:
        """Load a serialized model with tensorizer to the CPU.

        This is only necessary when the model isn't vLLM-tensorized (see
        examples/tensorize_vllm_model.py) This should still be faster than
        default HuggingFace loading, but will be slower than loading a
        vLLM-tensorized model.
        """
        with set_default_torch_dtype(model_config.dtype):
            with torch.device(device_config.device):
                model = _initialize_model(model_config, self.load_config,
                                          lora_config, vision_language_config,
                                          cache_config)

            model.load_weights(self._get_weights_iterator())
        return model.eval()

    def _load_model_serialized(
        self,
        model_config: ModelConfig,
        device_config: DeviceConfig,
        lora_config: Optional[LoRAConfig],
        vision_language_config: Optional[VisionLanguageConfig],
        cache_config: CacheConfig,
    ) -> nn.Module:
        """Load a serialized model with tensorizer.

        Expects a vLLM-tensorized model. See the
        examples/tensorize_vllm_model.py example script
        for serializing vLLM models."""
        with set_default_torch_dtype(model_config.dtype):
            with torch.device(device_config.device):
                model_class = get_model_architecture(model_config)[0]
                quant_config = _get_quantization_config(
                    model_config, self.load_config)
                extra_kwargs = _get_model_initialization_kwargs(
                    model_class, lora_config, vision_language_config)
                extra_kwargs["quant_config"] = quant_config
                extra_kwargs["cache_config"] = cache_config

                tensorizer_config = copy.copy(self.tensorizer_config)
                tensorizer_config.model_class = model_class
                tensorizer_config.hf_config = model_config.hf_config
                tensorizer_config.dtype = model_config.dtype

                model = load_with_tensorizer(tensorizer_config, **extra_kwargs)
        return model.eval()

    def load_model(self, *, model_config: ModelConfig,
                   device_config: DeviceConfig,
                   lora_config: Optional[LoRAConfig],
                   vision_language_config: Optional[VisionLanguageConfig],
                   parallel_config: ParallelConfig,
                   scheduler_config: SchedulerConfig,
                   cache_config: CacheConfig) -> nn.Module:
        self._verify_config(model_config, parallel_config)

        if is_vllm_tensorized(self.tensorizer_config):
            return self._load_model_serialized(model_config, device_config,
                                               lora_config,
                                               vision_language_config,
                                               cache_config)
        return self._load_model_serialized_cpu(model_config, device_config,
                                               lora_config,
                                               vision_language_config,
                                               cache_config)


class ShardedStateLoader(BaseModelLoader):
    """
    Model loader that directly loads each worker's model state dict, which
    enables a fast load path for large tensor-parallel models where each worker
    only needs to read its own shard rather than the entire checkpoint. See
    `examples/save_sharded_states.py` for creating a sharded checkpoint.
    """

    DEFAULT_PATTERN = "model-rank-{rank}-part-{part}.safetensors"

    def __init__(self, load_config: LoadConfig):
        super().__init__(load_config)
        extra_config = ({} if load_config.model_loader_extra_config is None
                        else load_config.model_loader_extra_config.copy())
        self.pattern = extra_config.pop("pattern", self.DEFAULT_PATTERN)
        if extra_config:
            raise ValueError(f"Unexpected extra config keys for load format "
                             f"{load_config.load_format}: "
                             f"{load_config.model_loader_extra_config.keys()}")

    @staticmethod
    def _filter_subtensors(
            tensors: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
        """
        Filter out all tensors that share the same memory or a subset of the
        memory of another tensor.
        """
        same_storage_groups = collections.defaultdict(list)
        for key, tensor in tensors.items():
            if tensor.numel():
                ptr = tensor.untyped_storage().data_ptr()
                same_storage_groups[tensor.device, ptr].append((key, tensor))

        def get_end_ptr(tensor: torch.Tensor) -> int:
            return tensor.view(-1)[-1].data_ptr() + tensor.element_size()

        result = {}
        for group in same_storage_groups.values():
            for k, t in group:
                a, b = t.data_ptr(), get_end_ptr(t)
                for k2, t2 in group:
                    if not t2.is_contiguous():
                        continue
                    a2, b2 = t2.data_ptr(), get_end_ptr(t2)
                    if a < a2 or b2 < b:
                        continue
                    if a2 < a or b < b2 or not t.is_contiguous():
                        break  # t2 covers strictly more memory than t.
                    if k2 < k:
                        # Same tensors, keep the one with the smaller key.
                        break
                else:
                    result[k] = t
        return result

    def load_model(self, *, model_config: ModelConfig,
                   device_config: DeviceConfig,
                   lora_config: Optional[LoRAConfig],
                   vision_language_config: Optional[VisionLanguageConfig],
                   parallel_config: ParallelConfig,
                   scheduler_config: SchedulerConfig,
                   cache_config: CacheConfig) -> nn.Module:
        from safetensors.torch import safe_open

        from vllm.distributed import get_tensor_model_parallel_rank
        with set_default_torch_dtype(model_config.dtype):
            with torch.device(device_config.device):
                model = _initialize_model(model_config, self.load_config,
                                          lora_config, vision_language_config,
                                          cache_config)
            rank = get_tensor_model_parallel_rank()
            pattern = os.path.join(
                model_config.model,
                self.pattern.format(rank=rank, part="*"),
            )
            filepaths = glob.glob(pattern)
            if not filepaths:
                # TODO: support un-sharded checkpoints too
                raise ValueError(
                    f"Could not find checkpoint files '{pattern}', only "
                    f"pre-sharded checkpoints are currently supported!")
            state_dict = self._filter_subtensors(model.state_dict())
            for path in filepaths:
                with safe_open(path, framework="pt") as f:
                    for key in f.keys():  # noqa: SIM118
                        tensor = f.get_tensor(key)
                        # If loading with LoRA enabled, additional padding may
                        # be added to certain parameters. We only load into a
                        # narrowed view of the parameter data.
                        param_data = state_dict[key].data
                        param_shape = state_dict[key].shape
                        for dim, size in enumerate(tensor.shape):
                            if size < param_shape[dim]:
                                param_data = param_data.narrow(dim, 0, size)
                        if tensor.shape != param_shape:
                            logger.warning(
                                "loading tensor of shape %s into "
                                "parameter '%s' of shape %s", tensor.shape,
                                key, param_shape)
                        param_data.copy_(tensor)
                        state_dict.pop(key)
            if state_dict:
                raise ValueError(
                    f"Missing keys {tuple(state_dict)} in loaded state!")
        return model.eval()

    @staticmethod
    def save_model(
        model: torch.nn.Module,
        path: str,
        pattern: Optional[str] = None,
        max_size: Optional[int] = None,
    ) -> None:
        from safetensors.torch import save_file

        from vllm.distributed import get_tensor_model_parallel_rank
        if pattern is None:
            pattern = ShardedStateLoader.DEFAULT_PATTERN
        rank = get_tensor_model_parallel_rank()
        part_idx = 0
        total_size = 0
        state_dict = ShardedStateLoader._filter_subtensors(model.state_dict())
        state_dict_part: Dict[str, torch.Tensor] = {}
        for key, tensor in state_dict.items():
            param_size = tensor.nelement() * tensor.element_size()
            if max_size is not None and total_size + param_size > max_size:
                filename = pattern.format(rank=rank, part=part_idx)
                save_file(
                    state_dict_part,
                    os.path.join(path, filename),
                )
                part_idx += 1
                total_size = 0
                state_dict_part = {}
            state_dict_part[key] = tensor
            total_size += param_size
        if len(state_dict_part) > 0:
            filename = pattern.format(rank=rank, part=part_idx)
            save_file(
                state_dict_part,
                os.path.join(path, filename),
            )


def get_model_loader(load_config: LoadConfig) -> BaseModelLoader:
    """Get a model loader based on the load format."""

    if isinstance(load_config.load_format, type):
        return load_config.load_format(load_config)

    if load_config.load_format == LoadFormat.DUMMY:
        return DummyModelLoader(load_config)

    if load_config.load_format == LoadFormat.TENSORIZER:
        return TensorizerLoader(load_config)

    if load_config.load_format == LoadFormat.SHARDED_STATE:
        return ShardedStateLoader(load_config)

    return DefaultModelLoader(load_config)