Update vLLM to use latest version of Run:AI Model Streamer

Signed-off-by: Peter Schuurman <psch@google.com>

Author: pwschuurman

setup.py

@@ -688,8 +688,10 @@ def _read_requirements(filename: str) -> list[str]:
         "bench": ["pandas", "datasets"],
         "tensorizer": ["tensorizer==2.10.1"],
         "fastsafetensors": ["fastsafetensors >= 0.1.10"],
-        "runai":
-        ["runai-model-streamer >= 0.13.3", "runai-model-streamer-s3", "boto3"],
+        "runai": [
+            "runai-model-streamer >= 0.14.0", "runai-model-streamer-gcs",
+            "google-cloud-storage", "runai-model-streamer-s3", "boto3"
+        ],
         "audio": ["librosa", "soundfile",
                   "mistral_common[audio]"],  # Required for audio processing
         "video": [],  # Kept for backwards compatibility

tests/runai_model_streamer_test/test_runai_utils.py

@@ -0,0 +1,39 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import glob
+import os
+import tempfile
+
+import huggingface_hub.constants
+
+from vllm.model_executor.model_loader.weight_utils import (
+    download_weights_from_hf)
+from vllm.transformers_utils.runai_utils import (is_runai_obj_uri,
+                                                 list_safetensors)
+
+
+def test_is_runai_obj_uri():
+    assert is_runai_obj_uri("gs://some-gcs-bucket/path")
+    assert is_runai_obj_uri("s3://some-s3-bucket/path")
+    assert not is_runai_obj_uri("nfs://some-nfs-path")
+
+
+def test_runai_list_safetensors_local():
+    with tempfile.TemporaryDirectory() as tmpdir:
+        huggingface_hub.constants.HF_HUB_OFFLINE = False
+        download_weights_from_hf("openai-community/gpt2",
+                                 allow_patterns=["*.safetensors", "*.json"],
+                                 cache_dir=tmpdir)
+        safetensors = glob.glob(f"{tmpdir}/**/*.safetensors", recursive=True)
+        assert len(safetensors) > 0
+        parentdir = [
+            os.path.dirname(safetensor) for safetensor in safetensors
+        ][0]
+        files = list_safetensors(parentdir)
+        assert len(safetensors) == len(files)
+
+
+if __name__ == "__main__":
+    test_is_runai_obj_uri()
+    test_runai_list_safetensors_local()

vllm/config/__init__.py

@@ -47,8 +47,9 @@
     is_interleaved, maybe_override_with_speculators_target_model,
     try_get_generation_config, try_get_safetensors_metadata,
     try_get_tokenizer_config, uses_mrope)
-from vllm.transformers_utils.s3_utils import S3Model
-from vllm.transformers_utils.utils import is_s3, maybe_model_redirect
+from vllm.transformers_utils.runai_utils import (ObjectStorageModel,
+                                                 is_runai_obj_uri)
+from vllm.transformers_utils.utils import maybe_model_redirect
 from vllm.utils import (DEFAULT_MAX_NUM_BATCHED_TOKENS,
                         STR_DUAL_CHUNK_FLASH_ATTN_VAL, LayerBlockType,
                         LazyLoader, common_broadcastable_dtype, random_uuid)
@@ -598,7 +599,7 @@ def __post_init__(self) -> None:
                 f"'Please instead use `--hf-overrides '{hf_overrides_str}'`")
             warnings.warn(DeprecationWarning(msg), stacklevel=2)
 
-        self.maybe_pull_model_tokenizer_for_s3(self.model, self.tokenizer)
+        self.maybe_pull_model_tokenizer_for_runai(self.model, self.tokenizer)
 
         if self.runner != "draft":
             # If we're not running the draft model, check for speculators config
@@ -840,41 +841,42 @@ def architecture(self) -> str:
         """The architecture vllm actually used."""
         return self._architecture
 
-    def maybe_pull_model_tokenizer_for_s3(self, model: str,
-                                          tokenizer: str) -> None:
-        """Pull model/tokenizer from S3 to temporary directory when needed.
+    def maybe_pull_model_tokenizer_for_runai(self, model: str,
+                                             tokenizer: str) -> None:
+        """Pull model/tokenizer from Object Storage to temporary
+        directory when needed.
 
         Args:
             model: Model name or path
             tokenizer: Tokenizer name or path
         """
-        if not (is_s3(model) or is_s3(tokenizer)):
+        if not (is_runai_obj_uri(model) or is_runai_obj_uri(tokenizer)):
             return
 
-        if is_s3(model):
-            s3_model = S3Model()
-            s3_model.pull_files(model,
-                                allow_pattern=["*.model", "*.py", "*.json"])
+        if is_runai_obj_uri(model):
+            object_storage_model = ObjectStorageModel()
+            object_storage_model.pull_files(
+                model, allow_pattern=["*.model", "*.py", "*.json"])
             self.model_weights = model
-            self.model = s3_model.dir
+            self.model = object_storage_model.dir
 
             # If tokenizer is same as model, download to same directory
             if model == tokenizer:
-                s3_model.pull_files(model,
-                                    ignore_pattern=[
-                                        "*.pt", "*.safetensors", "*.bin",
-                                        "*.tensors"
-                                    ])
-                self.tokenizer = s3_model.dir
+                object_storage_model.pull_files(model,
+                                                ignore_pattern=[
+                                                    "*.pt", "*.safetensors",
+                                                    "*.bin", "*.tensors"
+                                                ])
+                self.tokenizer = object_storage_model.dir
                 return
 
         # Only download tokenizer if needed and not already handled
-        if is_s3(tokenizer):
-            s3_tokenizer = S3Model()
-            s3_tokenizer.pull_files(
+        if is_runai_obj_uri(tokenizer):
+            object_storage_tokenizer = ObjectStorageModel()
+            object_storage_tokenizer.pull_files(
                 model,
                 ignore_pattern=["*.pt", "*.safetensors", "*.bin", "*.tensors"])
-            self.tokenizer = s3_tokenizer.dir
+            self.tokenizer = object_storage_tokenizer.dir
 
     def _init_multimodal_config(self) -> Optional["MultiModalConfig"]:
         if self._model_info.supports_multimodal:

vllm/model_executor/model_loader/runai_streamer_loader.py

@@ -1,7 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 # ruff: noqa: SIM117
-import glob
 import os
 from collections.abc import Generator
 from typing import Optional
@@ -15,8 +14,8 @@
 from vllm.model_executor.model_loader.weight_utils import (
     download_safetensors_index_file_from_hf, download_weights_from_hf,
     runai_safetensors_weights_iterator)
-from vllm.transformers_utils.s3_utils import glob as s3_glob
-from vllm.transformers_utils.utils import is_s3
+from vllm.transformers_utils.runai_utils import (is_runai_obj_uri,
+                                                 list_safetensors)
 
 
 class RunaiModelStreamerLoader(BaseModelLoader):
@@ -53,27 +52,22 @@ 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)
+        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_s3_path) else download_weights_from_hf(
+        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,
                      ))
-        if is_s3_path:
-            hf_weights_files = s3_glob(path=hf_folder,
-                                       allow_pattern=[safetensors_pattern])
-        else:
-            hf_weights_files = glob.glob(
-                os.path.join(hf_folder, safetensors_pattern))
-
-        if not is_local and not is_s3_path:
+        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, self.load_config.download_dir,
                 revision)

vllm/transformers_utils/runai_utils.py

@@ -0,0 +1,99 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import os
+import shutil
+import signal
+import tempfile
+from typing import Optional
+
+from vllm.logger import init_logger
+from vllm.utils import PlaceholderModule
+
+logger = init_logger(__name__)
+
+SUPPORTED_SCHEMES = ['s3://', 'gs://']
+
+try:
+    from runai_model_streamer import list_safetensors as runai_list_safetensors
+    from runai_model_streamer import pull_files as runai_pull_files
+except (ImportError, OSError):
+    # see https://github.com/run-ai/runai-model-streamer/issues/26
+    # OSError will be raised on arm64 platform
+    runai_model_streamer = PlaceholderModule(
+        "runai_model_streamer")  # type: ignore[assignment]
+    runai_pull_files = runai_model_streamer.placeholder_attr("pull_files")
+    runai_list_safetensors = runai_model_streamer.placeholder_attr(
+        "list_safetensors")
+
+
+def list_safetensors(path: str = "") -> list[str]:
+    """
+    List full file names from object path and filter by allow pattern.
+
+    Args:
+        path: The object storage path to list from.
+        allow_pattern: A list of patterns of which files to pull.
+
+    Returns:
+        list[str]: List of full object storage paths allowed by the pattern
+    """
+    return runai_list_safetensors(path)
+
+
+def is_runai_obj_uri(model_or_path: str) -> bool:
+    return model_or_path.lower().startswith(tuple(SUPPORTED_SCHEMES))
+
+
+class ObjectStorageModel:
+    """
+    A class representing an ObjectStorage model mirrored into a
+    temporary directory.
+
+    Attributes:
+        dir: The temporary created directory.
+
+    Methods:
+        pull_files(): Pull model from object storage to the temporary
+        directory.
+    """
+
+    def __init__(self) -> None:
+        for sig in (signal.SIGINT, signal.SIGTERM):
+            existing_handler = signal.getsignal(sig)
+            signal.signal(sig, self._close_by_signal(existing_handler))
+
+        self.dir = tempfile.mkdtemp()
+
+    def __del__(self):
+        self._close()
+
+    def _close(self) -> None:
+        if os.path.exists(self.dir):
+            shutil.rmtree(self.dir)
+
+    def _close_by_signal(self, existing_handler=None):
+
+        def new_handler(signum, frame):
+            self._close()
+            if existing_handler:
+                existing_handler(signum, frame)
+
+        return new_handler
+
+    def pull_files(self,
+                   model_path: str = "",
+                   allow_pattern: Optional[list[str]] = None,
+                   ignore_pattern: Optional[list[str]] = None) -> None:
+        """
+        Pull files from object storage into the temporary directory.
+
+        Args:
+            model_path: The object storage path of the model.
+            allow_pattern: A list of patterns of which files to pull.
+            ignore_pattern: A list of patterns of which files not to pull.
+
+        """
+        if not model_path.endswith("/"):
+            model_path = model_path + "/"
+        runai_pull_files(model_path, self.dir, allow_pattern, ignore_pattern)

vllm/transformers_utils/s3_utils.py

@@ -2,11 +2,6 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import fnmatch
-import os
-import shutil
-import signal
-import tempfile
-from pathlib import Path
 from typing import Optional
 
 from vllm.utils import PlaceholderModule
@@ -93,70 +88,3 @@ def list_files(
         paths = _filter_ignore(paths, ignore_pattern)
 
     return bucket_name, prefix, paths
-
-
-class S3Model:
-    """
-    A class representing a S3 model mirrored into a temporary directory.
-
-    Attributes:
-        s3: S3 client.
-        dir: The temporary created directory.
-
-    Methods:
-        pull_files(): Pull model from S3 to the temporary directory.
-    """
-
-    def __init__(self) -> None:
-        self.s3 = boto3.client('s3')
-        for sig in (signal.SIGINT, signal.SIGTERM):
-            existing_handler = signal.getsignal(sig)
-            signal.signal(sig, self._close_by_signal(existing_handler))
-
-        self.dir = tempfile.mkdtemp()
-
-    def __del__(self):
-        self._close()
-
-    def _close(self) -> None:
-        if os.path.exists(self.dir):
-            shutil.rmtree(self.dir)
-
-    def _close_by_signal(self, existing_handler=None):
-
-        def new_handler(signum, frame):
-            self._close()
-            if existing_handler:
-                existing_handler(signum, frame)
-
-        return new_handler
-
-    def pull_files(self,
-                   s3_model_path: str = "",
-                   allow_pattern: Optional[list[str]] = None,
-                   ignore_pattern: Optional[list[str]] = None) -> None:
-        """
-        Pull files from S3 storage into the temporary directory.
-
-        Args:
-            s3_model_path: The S3 path of the model.
-            allow_pattern: A list of patterns of which files to pull.
-            ignore_pattern: A list of patterns of which files not to pull.
-
-        """
-        if not s3_model_path.endswith("/"):
-            s3_model_path = s3_model_path + "/"
-
-        bucket_name, base_dir, files = list_files(self.s3, s3_model_path,
-                                                  allow_pattern,
-                                                  ignore_pattern)
-        if len(files) == 0:
-            return
-
-        for file in files:
-            destination_file = os.path.join(
-                self.dir,
-                file.removeprefix(base_dir).lstrip("/"))
-            local_dir = Path(destination_file).parent
-            os.makedirs(local_dir, exist_ok=True)
-            self.s3.download_file(bucket_name, file, destination_file)