Implement (chat_)completion for vllm provider

This is the start of an inline inference provider using vllm as a
library.

Issue #142

Working so far:

* `python -m llama_stack.apis.inference.client localhost 5000 --model Llama3.2-1B-Instruct --stream True`
* `python -m llama_stack.apis.inference.client localhost 5000 --model Llama3.2-1B-Instruct --stream False`

Example:

```
$ python -m llama_stack.apis.inference.client localhost 5000 --model Llama3.2-1B-Instruct --stream False
User>hello world, write me a 2 sentence poem about the moon
Assistant>
The moon glows bright in the midnight sky
A beacon of light,
```

I have only tested these models:

* `Llama3.1-8B-Instruct` - across 4 GPUs (tensor_parallel_size = 4)
* `Llama3.2-1B-Instruct` - on a single GPU (tensor_parallel_size = 1)

Signed-off-by: Russell Bryant <[email protected]>

llama_stack/providers/adapters/inference/__init__.py

@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.

llama_stack/providers/adapters/inference/ollama/ollama.py

@@ -63,7 +63,15 @@ async def completion(
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,
     ) -> AsyncGenerator:
-        raise NotImplementedError()
+        messages = [Message(role="user", content=content)]
+        async for result in self.chat_completion(
+            model=model,
+            messages=messages,
+            sampling_params=sampling_params,
+            stream=stream,
+            logprobs=logprobs,
+        ):
+            yield result
 
     def _messages_to_ollama_messages(self, messages: list[Message]) -> list:
         ollama_messages = []

llama_stack/providers/impls/vllm/config.py

@@ -1,5 +1,35 @@
-from pydantic import BaseModel
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
 
+from llama_models.schema_utils import json_schema_type
+from pydantic import BaseModel, Field, field_validator
 
+from llama_stack.providers.utils.inference import supported_inference_models
+
+
+@json_schema_type
 class VLLMConfig(BaseModel):
-    pass
+    """Configuration for the vLLM inference provider."""
+
+    model: str = Field(
+        default="Llama3.1-8B-Instruct",
+        description="Model descriptor from `llama model list`",
+    )
+    tensor_parallel_size: int = Field(
+        default=1,
+        description="Number of tensor parallel replicas (number of GPUs to use).",
+    )
+
+    @field_validator("model")
+    @classmethod
+    def validate_model(cls, model: str) -> str:
+        permitted_models = supported_inference_models()
+        if model not in permitted_models:
+            model_list = "\n\t".join(permitted_models)
+            raise ValueError(
+                f"Unknown model: `{model}`. Choose from [\n\t{model_list}\n]"
+            )
+        return model