Resolve merge conflicts

Signed-off-by: Rafael Vasquez <[email protected]>

Dockerfile

@@ -240,9 +240,9 @@ FROM vllm-base AS vllm-openai
 # install additional dependencies for openai api server
 RUN --mount=type=cache,target=/root/.cache/pip \
     if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
-        pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10'; \
+        pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
     else \
-        pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' 'timm==0.9.10'; \
+        pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
     fi
 
 ENV VLLM_USAGE_SOURCE production-docker-image

docs/source/index.md

@@ -85,6 +85,7 @@ serving/distributed_serving
 serving/metrics
 serving/integrations
 serving/tensorizer
+serving/runai_model_streamer
 ```
 
 ```{toctree}

docs/source/serving/runai_model_streamer.md

@@ -0,0 +1,53 @@
+(runai-model-streamer)=
+
+# Loading Models with Run:ai Model Streamer
+
+Run:ai Model Streamer is a library to read tensors in concurrency, while streaming it to GPU memory.
+Further reading can be found in [Run:ai Model Streamer Documentation](https://github.com/run-ai/runai-model-streamer/blob/master/docs/README.md).
+
+vLLM supports loading weights in Safetensors format using the Run:ai Model Streamer.
+You first need to install vLLM RunAI optional dependency:
+
+```console
+$ pip3 install vllm[runai]
+```
+
+To run it as an OpenAI-compatible server, add the `--load-format runai_streamer` flag:
+
+```console
+$ vllm serve /home/meta-llama/Llama-3.2-3B-Instruct --load-format runai_streamer
+```
+
+To run model from AWS S3 object store run:
+
+```console
+$ vllm serve s3://core-llm/Llama-3-8b --load-format runai_streamer
+```
+
+To run model from a S3 compatible object store run:
+
+```console
+$ RUNAI_STREAMER_S3_USE_VIRTUAL_ADDRESSING=0 AWS_EC2_METADATA_DISABLED=true AWS_ENDPOINT_URL=https://storage.googleapis.com vllm serve s3://core-llm/Llama-3-8b --load-format runai_streamer
+```
+
+## Tunable parameters
+
+You can tune parameters using `--model-loader-extra-config`:
+
+You can tune `concurrency` that controls the level of concurrency and number of OS threads reading tensors from the file to the CPU buffer.
+For reading from S3, it will be the number of client instances the host is opening to the S3 server.
+
+> ```console
+> $ vllm serve /home/meta-llama/Llama-3.2-3B-Instruct --load-format runai_streamer --model-loader-extra-config '{"concurrency":16}'
+> ```
+
+You can controls the size of the CPU Memory buffer to which tensors are read from the file, and limit this size.
+You can read further about CPU buffer memory limiting [here](https://github.com/run-ai/runai-model-streamer/blob/master/docs/src/env-vars.md#runai_streamer_memory_limit).
+
+> ```console
+> $ vllm serve /home/meta-llama/Llama-3.2-3B-Instruct --load-format runai_streamer --model-loader-extra-config '{"memory_limit":5368709120}'
+> ```
+
+```{note}
+For further instructions about tunable parameters and additional parameters configurable through environment variables, read the [Environment Variables Documentation](https://github.com/run-ai/runai-model-streamer/blob/master/docs/src/env-vars.md).
+```

setup.py

@@ -630,6 +630,7 @@ def _read_requirements(filename: str) -> List[str]:
     ext_modules=ext_modules,
     extras_require={
         "tensorizer": ["tensorizer>=2.9.0"],
+        "runai": ["runai-model-streamer", "runai-model-streamer-s3", "boto3"],
         "audio": ["librosa", "soundfile"],  # Required for audio processing
         "video": ["decord"]  # Required for video processing
     },

tests/runai_model_streamer/test_runai_model_streamer_loader.py

@@ -0,0 +1,31 @@
+from vllm import SamplingParams
+from vllm.config import LoadConfig, LoadFormat
+from vllm.model_executor.model_loader.loader import (RunaiModelStreamerLoader,
+                                                     get_model_loader)
+
+test_model = "openai-community/gpt2"
+
+prompts = [
+    "Hello, my name is",
+    "The president of the United States is",
+    "The capital of France is",
+    "The future of AI is",
+]
+# Create a sampling params object.
+sampling_params = SamplingParams(temperature=0.8, top_p=0.95, seed=0)
+
+
+def get_runai_model_loader():
+    load_config = LoadConfig(load_format=LoadFormat.RUNAI_STREAMER)
+    return get_model_loader(load_config)
+
+
+def test_get_model_loader_with_runai_flag():
+    model_loader = get_runai_model_loader()
+    assert isinstance(model_loader, RunaiModelStreamerLoader)
+
+
+def test_runai_model_loader_download_files(vllm_runner):
+    with vllm_runner(test_model, load_format=LoadFormat.RUNAI_STREAMER) as llm:
+        deserialized_outputs = llm.generate(prompts, sampling_params)
+        assert deserialized_outputs

tests/runai_model_streamer/test_weight_utils.py

@@ -0,0 +1,39 @@
+import glob
+import tempfile
+
+import huggingface_hub.constants
+import torch
+
+from vllm.model_executor.model_loader.weight_utils import (
+    download_weights_from_hf, runai_safetensors_weights_iterator,
+    safetensors_weights_iterator)
+
+
+def test_runai_model_loader():
+    with tempfile.TemporaryDirectory() as tmpdir:
+        huggingface_hub.constants.HF_HUB_OFFLINE = False
+        download_weights_from_hf("openai-community/gpt2",
+                                 allow_patterns=["*.safetensors"],
+                                 cache_dir=tmpdir)
+        safetensors = glob.glob(f"{tmpdir}/**/*.safetensors", recursive=True)
+        assert len(safetensors) > 0
+
+        runai_model_streamer_tensors = {}
+        hf_safetensors_tensors = {}
+
+        for name, tensor in runai_safetensors_weights_iterator(safetensors):
+            runai_model_streamer_tensors[name] = tensor
+
+        for name, tensor in safetensors_weights_iterator(safetensors):
+            hf_safetensors_tensors[name] = tensor
+
+        assert len(runai_model_streamer_tensors) == len(hf_safetensors_tensors)
+
+        for name, runai_tensor in runai_model_streamer_tensors.items():
+            assert runai_tensor.dtype == hf_safetensors_tensors[name].dtype
+            assert runai_tensor.shape == hf_safetensors_tensors[name].shape
+            assert torch.all(runai_tensor.eq(hf_safetensors_tensors[name]))
+
+
+if __name__ == "__main__":
+    test_runai_model_loader()

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.utils import is_s3
 from vllm.utils import (GiB_bytes, LayerBlockType, cuda_device_count_stateless,
                         get_cpu_memory, print_warning_once, random_uuid,
                         resolve_obj_by_qualname)
@@ -256,6 +257,8 @@ def __init__(self,
                    f"'Please instead use `--hf-overrides '{hf_override!r}'`")
             warnings.warn(DeprecationWarning(msg), stacklevel=2)
 
+        self.maybe_pull_model_tokenizer_for_s3(model, tokenizer)
+
         # The tokenizer version is consistent with the model version by default.
         if tokenizer_revision is None:
             self.tokenizer_revision = revision
@@ -357,6 +360,39 @@ def __init__(self,
         self._verify_cuda_graph()
         self._verify_bnb_config()
 
+    def maybe_pull_model_tokenizer_for_s3(self, model: str,
+                                          tokenizer: str) -> None:
+        """
+        Pull the model config or tokenizer to a temporary 
+        directory in case of S3.
+
+        Args:
+            model: The model name or path.
+            tokenizer: The tokenizer name or path.
+
+        """
+        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"])
+                self.model_weights = self.model
+                self.model = self.s3_model.dir
+
+            if is_s3(tokenizer):
+                self.s3_tokenizer = S3Model()
+                self.s3_tokenizer.pull_files(
+                    model, ignore_pattern=["*.pt", "*.safetensors", "*.bin"])
+                self.tokenizer = self.s3_tokenizer.dir
+
     def _init_multimodal_config(
         self, limit_mm_per_prompt: Optional[Mapping[str, int]]
     ) -> Optional["MultiModalConfig"]:
@@ -1099,6 +1135,7 @@ class LoadFormat(str, enum.Enum):
     GGUF = "gguf"
     BITSANDBYTES = "bitsandbytes"
     MISTRAL = "mistral"
+    RUNAI_STREAMER = "runai_streamer"
 
 
 @dataclass

vllm/engine/arg_utils.py

@@ -316,6 +316,8 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
             '* "tensorizer" will load the weights using tensorizer from '
             'CoreWeave. See the Tensorize vLLM Model script in the Examples '
             'section for more information.\n'
+            '* "runai_streamer" will load the Safetensors weights using Run:ai'
+            'Model Streamer \n'
             '* "bitsandbytes" will load the weights using bitsandbytes '
             'quantization.\n')
         parser.add_argument(

vllm/model_executor/model_loader/loader.py

@@ -45,9 +45,10 @@
     filter_duplicate_safetensors_files, filter_files_not_needed_for_inference,
     get_gguf_extra_tensor_names, gguf_quant_weights_iterator,
     initialize_dummy_weights, np_cache_weights_iterator, pt_weights_iterator,
-    safetensors_weights_iterator)
+    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.utils import is_s3
 from vllm.utils import is_pin_memory_available
 
 
@@ -1234,6 +1235,118 @@ def load_model(self, vllm_config: VllmConfig) -> nn.Module:
         return model
 
 
+class RunaiModelStreamerLoader(BaseModelLoader):
+    """
+        Model loader that can load safetensors 
+        files from local FS or S3 bucket.
+    """
+
+    def __init__(self, load_config: LoadConfig):
+        super().__init__(load_config)
+        if load_config.model_loader_extra_config:
+            extra_config = load_config.model_loader_extra_config
+
+            if ("concurrency" in extra_config
+                    and isinstance(extra_config.get("concurrency"), int)):
+                os.environ["RUNAI_STREAMER_CONCURRENCY"] = str(
+                    extra_config.get("concurrency"))
+
+            if ("memory_limit" in extra_config
+                    and isinstance(extra_config.get("memory_limit"), int)):
+                os.environ["RUNAI_STREAMER_MEMORY_LIMIT"] = str(
+                    extra_config.get("memory_limit"))
+
+            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: Optional[str]) -> List[str]:
+        """Prepare weights for the model.
+
+        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
+
+        hf_folder = (model_name_or_path if
+                     (is_local or is_s3_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:
+            download_safetensors_index_file_from_hf(
+                model_name_or_path, index_file, self.load_config.download_dir,
+                revision)
+
+        if not hf_weights_files:
+            raise RuntimeError(
+                f"Cannot find any safetensors model weights with "
+                f"`{model_name_or_path}`")
+
+        return hf_weights_files
+
+    def _get_weights_iterator(
+            self, model_or_path: str,
+            revision: str) -> 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)
+
+    def download_model(self, model_config: ModelConfig) -> None:
+        """Download model if necessary"""
+        self._prepare_weights(model_config.model, model_config.revision)
+
+    def load_model(self, vllm_config: VllmConfig) -> nn.Module:
+        """Perform streaming of the model to destination"""
+        device_config = vllm_config.device_config
+        model_config = vllm_config.model_config
+
+        target_device = torch.device(device_config.device)
+        with set_default_torch_dtype(model_config.dtype):
+            with target_device:
+                model = _initialize_model(vllm_config=vllm_config)
+
+            model_weights = model_config.model
+            if hasattr(model_config, "model_weights"):
+                model_weights = model_config.model_weights
+            model.load_weights(
+                self._get_weights_iterator(model_weights,
+                                           model_config.revision))
+
+            for _, module in model.named_modules():
+                quant_method = getattr(module, "quant_method", None)
+                if quant_method is not None:
+                    with device_loading_context(module, target_device):
+                        quant_method.process_weights_after_loading(module)
+        return model.eval()
+
+
 def get_model_loader(load_config: LoadConfig) -> BaseModelLoader:
     """Get a model loader based on the load format."""
 
@@ -1255,4 +1368,7 @@ def get_model_loader(load_config: LoadConfig) -> BaseModelLoader:
     if load_config.load_format == LoadFormat.GGUF:
         return GGUFModelLoader(load_config)
 
+    if load_config.load_format == LoadFormat.RUNAI_STREAMER:
+        return RunaiModelStreamerLoader(load_config)
+
     return DefaultModelLoader(load_config)

vllm/model_executor/model_loader/weight_utils.py

@@ -410,6 +410,30 @@ def safetensors_weights_iterator(
                 yield name, param
 
 
+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:
+        for st_file in tqdm(
+                hf_weights_files,
+                desc="Loading safetensors using Runai Model Streamer",
+                disable=not enable_tqdm,
+                bar_format=_BAR_FORMAT,
+        ):
+            streamer.stream_file(st_file)
+            yield from streamer.get_tensors()
+
+
 def pt_weights_iterator(
     hf_weights_files: List[str]
 ) -> Generator[Tuple[str, torch.Tensor], None, None]: