[Misc] Add placeholder module (vllm-project#11501)

Signed-off-by: DarkLight1337 <[email protected]>

tests/tensorizer_loader/test_tensorizer.py

@@ -9,7 +9,6 @@
 import pytest
 import torch
 from huggingface_hub import snapshot_download
-from tensorizer import EncryptionParams
 
 from vllm import SamplingParams
 from vllm.engine.arg_utils import EngineArgs
@@ -23,12 +22,18 @@
                                                          serialize_vllm_model,
                                                          tensorize_vllm_model)
 # yapf: enable
-from vllm.utils import import_from_path
+from vllm.utils import PlaceholderModule, import_from_path
 
 from ..conftest import VllmRunner
 from ..utils import VLLM_PATH, RemoteOpenAIServer
 from .conftest import retry_until_skip
 
+try:
+    from tensorizer import EncryptionParams
+except ImportError:
+    tensorizer = PlaceholderModule("tensorizer")  # type: ignore[assignment]
+    EncryptionParams = tensorizer.placeholder_attr("EncryptionParams")
+
 EXAMPLES_PATH = VLLM_PATH / "examples"
 
 prompts = [

vllm/assets/audio.py

@@ -1,11 +1,17 @@
 from dataclasses import dataclass
-from typing import Literal, Tuple
+from typing import Literal
 from urllib.parse import urljoin
 
-import librosa
-import numpy as np
+import numpy.typing as npt
 
-from vllm.assets.base import get_vllm_public_assets, vLLM_S3_BUCKET_URL
+from vllm.utils import PlaceholderModule
+
+from .base import VLLM_S3_BUCKET_URL, get_vllm_public_assets
+
+try:
+    import librosa
+except ImportError:
+    librosa = PlaceholderModule("librosa")  # type: ignore[assignment]
 
 ASSET_DIR = "multimodal_asset"
 
@@ -15,8 +21,7 @@ class AudioAsset:
     name: Literal["winning_call", "mary_had_lamb"]
 
     @property
-    def audio_and_sample_rate(self) -> Tuple[np.ndarray, int]:
-
+    def audio_and_sample_rate(self) -> tuple[npt.NDArray, int]:
         audio_path = get_vllm_public_assets(filename=f"{self.name}.ogg",
                                             s3_prefix=ASSET_DIR)
         y, sr = librosa.load(audio_path, sr=None)
@@ -25,4 +30,4 @@ def audio_and_sample_rate(self) -> Tuple[np.ndarray, int]:
 
     @property
     def url(self) -> str:
-        return urljoin(vLLM_S3_BUCKET_URL, f"{ASSET_DIR}/{self.name}.ogg")
+        return urljoin(VLLM_S3_BUCKET_URL, f"{ASSET_DIR}/{self.name}.ogg")

vllm/assets/base.py

@@ -4,9 +4,8 @@
 
 import vllm.envs as envs
 from vllm.connections import global_http_connection
-from vllm.envs import VLLM_IMAGE_FETCH_TIMEOUT
 
-vLLM_S3_BUCKET_URL = "https://vllm-public-assets.s3.us-west-2.amazonaws.com"
+VLLM_S3_BUCKET_URL = "https://vllm-public-assets.s3.us-west-2.amazonaws.com"
 
 
 def get_cache_dir() -> Path:
@@ -32,8 +31,8 @@ def get_vllm_public_assets(filename: str,
         if s3_prefix is not None:
             filename = s3_prefix + "/" + filename
         global_http_connection.download_file(
-            f"{vLLM_S3_BUCKET_URL}/{filename}",
+            f"{VLLM_S3_BUCKET_URL}/{filename}",
             asset_path,
-            timeout=VLLM_IMAGE_FETCH_TIMEOUT)
+            timeout=envs.VLLM_IMAGE_FETCH_TIMEOUT)
 
     return asset_path

vllm/assets/image.py

@@ -4,7 +4,7 @@
 import torch
 from PIL import Image
 
-from vllm.assets.base import get_vllm_public_assets
+from .base import get_vllm_public_assets
 
 VLM_IMAGES_DIR = "vision_model_images"
 
@@ -15,7 +15,6 @@ class ImageAsset:
 
     @property
     def pil_image(self) -> Image.Image:
-
         image_path = get_vllm_public_assets(filename=f"{self.name}.jpg",
                                             s3_prefix=VLM_IMAGES_DIR)
         return Image.open(image_path)

vllm/assets/video.py

@@ -2,13 +2,13 @@
 from functools import lru_cache
 from typing import List, Literal
 
+import cv2
 import numpy as np
 import numpy.typing as npt
 from huggingface_hub import hf_hub_download
 from PIL import Image
 
-from vllm.multimodal.video import (sample_frames_from_video,
-                                   try_import_video_packages)
+from vllm.multimodal.video import sample_frames_from_video
 
 from .base import get_cache_dir
 
@@ -19,7 +19,7 @@ def download_video_asset(filename: str) -> str:
     Download and open an image from huggingface
     repo: raushan-testing-hf/videos-test
     """
-    video_directory = get_cache_dir() / "video-eample-data"
+    video_directory = get_cache_dir() / "video-example-data"
     video_directory.mkdir(parents=True, exist_ok=True)
 
     video_path = video_directory / filename
@@ -35,8 +35,6 @@ def download_video_asset(filename: str) -> str:
 
 
 def video_to_ndarrays(path: str, num_frames: int = -1) -> npt.NDArray:
-    cv2, _ = try_import_video_packages()
-
     cap = cv2.VideoCapture(path)
     if not cap.isOpened():
         raise ValueError(f"Could not open video file {path}")
@@ -59,7 +57,6 @@ def video_to_ndarrays(path: str, num_frames: int = -1) -> npt.NDArray:
 
 def video_to_pil_images_list(path: str,
                              num_frames: int = -1) -> List[Image.Image]:
-    cv2, _ = try_import_video_packages()
     frames = video_to_ndarrays(path, num_frames)
     return [
         Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))

vllm/config.py

@@ -29,6 +29,7 @@
     get_hf_text_config, get_pooling_config,
     get_sentence_transformer_tokenizer_config, is_encoder_decoder,
     try_get_generation_config, uses_mrope)
+from vllm.transformers_utils.s3_utils import S3Model
 from vllm.transformers_utils.utils import is_s3
 from vllm.utils import (GiB_bytes, LayerBlockType, cuda_device_count_stateless,
                         get_cpu_memory, print_warning_once, random_uuid,
@@ -372,15 +373,6 @@ def maybe_pull_model_tokenizer_for_s3(self, model: str,
 
         """
         if is_s3(model) or is_s3(tokenizer):
-            try:
-                from vllm.transformers_utils.s3_utils import S3Model
-            except ImportError as err:
-                raise ImportError(
-                    "Please install Run:ai optional dependency "
-                    "to use the S3 capabilities. "
-                    "You can install it with: pip install vllm[runai]"
-                ) from err
-
             if is_s3(model):
                 self.s3_model = S3Model()
                 self.s3_model.pull_files(model, allow_pattern=["*config.json"])

vllm/model_executor/model_loader/loader.py

@@ -48,6 +48,7 @@
     runai_safetensors_weights_iterator, safetensors_weights_iterator)
 from vllm.model_executor.utils import set_weight_attrs
 from vllm.platforms import current_platform
+from vllm.transformers_utils.s3_utils import glob as s3_glob
 from vllm.transformers_utils.utils import is_s3
 from vllm.utils import is_pin_memory_available
 
@@ -1269,16 +1270,6 @@ def _prepare_weights(self, model_name_or_path: str,
 
         If the model is not local, it will be downloaded."""
         is_s3_path = is_s3(model_name_or_path)
-        if is_s3_path:
-            try:
-                from vllm.transformers_utils.s3_utils import glob as s3_glob
-            except ImportError as err:
-                raise ImportError(
-                    "Please install Run:ai optional dependency "
-                    "to use the S3 capabilities. "
-                    "You can install it with: pip install vllm[runai]"
-                ) from err
-
         is_local = os.path.isdir(model_name_or_path)
         safetensors_pattern = "*.safetensors"
         index_file = SAFE_WEIGHTS_INDEX_NAME

vllm/model_executor/model_loader/tensorizer.py

@@ -19,9 +19,7 @@
 from vllm.logger import init_logger
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     VocabParallelEmbedding)
-from vllm.utils import FlexibleArgumentParser
-
-tensorizer_error_msg = None
+from vllm.utils import FlexibleArgumentParser, PlaceholderModule
 
 try:
     from tensorizer import (DecryptionParams, EncryptionParams,
@@ -34,8 +32,19 @@
         open_stream,
         mode=mode,
     ) for mode in ("rb", "wb+"))
-except ImportError as e:
-    tensorizer_error_msg = str(e)
+except ImportError:
+    tensorizer = PlaceholderModule("tensorizer")
+    DecryptionParams = tensorizer.placeholder_attr("DecryptionParams")
+    EncryptionParams = tensorizer.placeholder_attr("EncryptionParams")
+    TensorDeserializer = tensorizer.placeholder_attr("TensorDeserializer")
+    TensorSerializer = tensorizer.placeholder_attr("TensorSerializer")
+    open_stream = tensorizer.placeholder_attr("stream_io.open_stream")
+    convert_bytes = tensorizer.placeholder_attr("utils.convert_bytes")
+    get_mem_usage = tensorizer.placeholder_attr("utils.get_mem_usage")
+    no_init_or_tensor = tensorizer.placeholder_attr("utils.no_init_or_tensor")
+
+    _read_stream = tensorizer.placeholder_attr("_read_stream")
+    _write_stream = tensorizer.placeholder_attr("_write_stream")
 
 __all__ = [
     'EncryptionParams', 'DecryptionParams', 'TensorDeserializer',
@@ -267,12 +276,6 @@ class TensorizerAgent:
     """
 
     def __init__(self, tensorizer_config: TensorizerConfig, vllm_config):
-        if tensorizer_error_msg is not None:
-            raise ImportError(
-                "Tensorizer is not installed. Please install tensorizer "
-                "to use this feature with `pip install vllm[tensorizer]`. "
-                "Error message: {}".format(tensorizer_error_msg))
-
         self.tensorizer_config = tensorizer_config
         self.tensorizer_args = (
             self.tensorizer_config._construct_tensorizer_args())

vllm/model_executor/model_loader/weight_utils.py

@@ -25,7 +25,15 @@
                                                      get_quantization_config)
 from vllm.model_executor.layers.quantization.schema import QuantParamSchema
 from vllm.platforms import current_platform
-from vllm.utils import print_warning_once
+from vllm.utils import PlaceholderModule, print_warning_once
+
+try:
+    from runai_model_streamer import SafetensorsStreamer
+except ImportError:
+    runai_model_streamer = PlaceholderModule(
+        "runai_model_streamer")  # type: ignore[assignment]
+    SafetensorsStreamer = runai_model_streamer.placeholder_attr(
+        "SafetensorsStreamer")
 
 logger = init_logger(__name__)
 
@@ -414,13 +422,6 @@ def runai_safetensors_weights_iterator(
     hf_weights_files: List[str]
 ) -> Generator[Tuple[str, torch.Tensor], None, None]:
     """Iterate over the weights in the model safetensor files."""
-    try:
-        from runai_model_streamer import SafetensorsStreamer
-    except ImportError as err:
-        raise ImportError(
-            "Please install Run:ai optional dependency."
-            "You can install it with: pip install vllm[runai]") from err
-
     enable_tqdm = not torch.distributed.is_initialized(
     ) or torch.distributed.get_rank() == 0
     with SafetensorsStreamer() as streamer:

vllm/multimodal/audio.py

@@ -1,13 +1,17 @@
-from typing import Any
-
 import numpy as np
 import numpy.typing as npt
 
 from vllm.inputs.registry import InputContext
+from vllm.utils import PlaceholderModule
 
 from .base import MultiModalPlugin
 from .inputs import AudioItem, MultiModalData, MultiModalKwargs
 
+try:
+    import librosa
+except ImportError:
+    librosa = PlaceholderModule("librosa")  # type: ignore[assignment]
+
 
 class AudioPlugin(MultiModalPlugin):
     """Plugin for audio data."""
@@ -28,26 +32,10 @@ def _default_max_multimodal_tokens(self, ctx: InputContext) -> int:
             "There is no default maximum multimodal tokens")
 
 
-def try_import_audio_packages() -> tuple[Any, Any]:
-    try:
-        import librosa
-        import soundfile
-    except ImportError as exc:
-        raise ImportError(
-            "Please install vllm[audio] for audio support.") from exc
-    return librosa, soundfile
-
-
 def resample_audio(
     audio: npt.NDArray[np.floating],
     *,
     orig_sr: float,
     target_sr: float,
 ) -> npt.NDArray[np.floating]:
-    try:
-        import librosa
-    except ImportError as exc:
-        msg = "Please install vllm[audio] for audio support."
-        raise ImportError(msg) from exc
-
     return librosa.resample(audio, orig_sr=orig_sr, target_sr=target_sr)

vllm/multimodal/utils.py

@@ -13,10 +13,24 @@
 from vllm.connections import global_http_connection
 from vllm.logger import init_logger
 from vllm.transformers_utils.tokenizer import AnyTokenizer, get_tokenizer
+from vllm.utils import PlaceholderModule
 
-from .audio import try_import_audio_packages
 from .inputs import MultiModalDataDict, PlaceholderRange
-from .video import try_import_video_packages
+
+try:
+    import decord
+except ImportError:
+    decord = PlaceholderModule("decord")  # type: ignore[assignment]
+
+try:
+    import librosa
+except ImportError:
+    librosa = PlaceholderModule("librosa")  # type: ignore[assignment]
+
+try:
+    import soundfile
+except ImportError:
+    soundfile = PlaceholderModule("soundfile")  # type: ignore[assignment]
 
 logger = init_logger(__name__)
 
@@ -128,8 +142,6 @@ async def async_fetch_image(image_url: str,
 
 
 def _load_video_from_bytes(b: bytes, num_frames: int = 32) -> npt.NDArray:
-    _, decord = try_import_video_packages()
-
     video_path = BytesIO(b)
     vr = decord.VideoReader(video_path, num_threads=1)
     total_frame_num = len(vr)
@@ -204,8 +216,6 @@ def fetch_audio(audio_url: str) -> Tuple[np.ndarray, Union[int, float]]:
     """
     Load audio from a URL.
     """
-    librosa, _ = try_import_audio_packages()
-
     if audio_url.startswith("http"):
         audio_bytes = global_http_connection.get_bytes(
             audio_url,
@@ -226,8 +236,6 @@ async def async_fetch_audio(
     """
     Asynchronously fetch audio from a URL.
     """
-    librosa, _ = try_import_audio_packages()
-
     if audio_url.startswith("http"):
         audio_bytes = await global_http_connection.async_get_bytes(
             audio_url,
@@ -286,8 +294,6 @@ def encode_audio_base64(
     sampling_rate: int,
 ) -> str:
     """Encode audio as base64."""
-    _, soundfile = try_import_audio_packages()
-
     buffered = BytesIO()
     soundfile.write(buffered, audio, sampling_rate, format="WAV")