vllm.model_executor.model_loader.runai_streamer_loader ¶

class RunaiModelStreamerLoader(BaseModelLoader):
    """
    Model loader that can load safetensors
    files from local FS, S3, GCS, or Azure Blob Storage.
    """

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

        self._is_distributed: bool = False
        if load_config.model_loader_extra_config:
            extra_config = load_config.model_loader_extra_config

            allowed_keys = {"distributed", "concurrency", "memory_limit"}
            if unexpected_keys := set(extra_config) - allowed_keys:
                raise ValueError(
                    "Unexpected extra config keys for runai_streamer: "
                    f"{unexpected_keys}"
                )

            if "distributed" in extra_config:
                distributed = extra_config["distributed"]
                if not isinstance(distributed, bool):
                    raise ValueError(f"distributed must be a bool, got {distributed!r}")
                self._is_distributed = distributed

            # Validate every value before mutating os.environ, so a later
            # invalid key cannot leave an earlier one partially applied.
            env_updates: dict[str, str] = {}
            for key, env_var in (
                ("concurrency", "RUNAI_STREAMER_CONCURRENCY"),
                ("memory_limit", "RUNAI_STREAMER_MEMORY_LIMIT"),
            ):
                if key in extra_config:
                    value = extra_config[key]
                    if (
                        isinstance(value, bool)
                        or not isinstance(value, int)
                        or value <= 0
                    ):
                        raise ValueError(
                            f"{key} must be a positive integer, got {value!r}"
                        )
                    env_updates[env_var] = str(value)
            os.environ.update(env_updates)

            runai_streamer_s3_endpoint = os.getenv("RUNAI_STREAMER_S3_ENDPOINT")
            aws_endpoint_url = os.getenv("AWS_ENDPOINT_URL")
            if runai_streamer_s3_endpoint is None and aws_endpoint_url is not None:
                os.environ["RUNAI_STREAMER_S3_ENDPOINT"] = aws_endpoint_url

    def _prepare_weights(
        self, model_name_or_path: str, revision: str | None
    ) -> list[str]:
        """Prepare weights for the model.

        If the model is not local, it will be downloaded."""

        is_object_storage_path = is_runai_obj_uri(model_name_or_path)
        is_local = os.path.isdir(model_name_or_path)
        safetensors_pattern = "*.safetensors"
        index_file = SAFE_WEIGHTS_INDEX_NAME

        hf_folder = (
            model_name_or_path
            if (is_local or is_object_storage_path)
            else download_weights_from_hf(
                model_name_or_path,
                self.load_config.download_dir,
                [safetensors_pattern],
                revision,
                ignore_patterns=self.load_config.ignore_patterns,
            )
        )
        hf_weights_files = list_safetensors(path=hf_folder)

        if not is_local and not is_object_storage_path:
            download_safetensors_index_file_from_hf(
                model_name_or_path,
                index_file,
                cache_dir=self.load_config.download_dir,
                revision=revision,
            )

        if not hf_weights_files:
            raise RuntimeError(
                f"Cannot find any safetensors model weights with `{model_name_or_path}`"
            )

        return hf_weights_files

    def _get_weights_iterator(
        self, model_or_path: str, revision: str | None
    ) -> Generator[tuple[str, torch.Tensor], None, None]:
        """Get an iterator for the model weights based on the load format."""
        hf_weights_files = self._prepare_weights(model_or_path, revision)
        return runai_safetensors_weights_iterator(
            hf_weights_files, self.load_config.use_tqdm_on_load, self._is_distributed
        )

    def download_model(self, model_config: ModelConfig) -> None:
        """Download model if necessary"""
        self._prepare_weights(model_config.model, model_config.revision)

    def load_weights(self, model: nn.Module, model_config: ModelConfig) -> None:
        """Load weights into a model."""
        model_weights = model_config.model
        if model_weights_override := model_config.model_weights:
            model_weights = model_weights_override
        model.load_weights(
            self._get_weights_iterator(model_weights, model_config.revision)
        )