Update types in parallel_utils for meta-refernece-gpu impl

llama_stack/providers/inline/inference/meta_reference/parallel_utils.py

@@ -34,7 +34,10 @@ from pydantic import BaseModel, Field
 from torch.distributed.launcher.api import elastic_launch, LaunchConfig
 from typing_extensions import Annotated
 
-from llama_stack.apis.inference import ChatCompletionRequest, CompletionRequest
+from llama_stack.providers.utils.inference.prompt_adapter import (
+    ChatCompletionRequestWithRawContent,
+    CompletionRequestWithRawContent,
+)
 
 from .generation import TokenResult
 
@@ -79,7 +82,7 @@ class TaskRequest(BaseModel):
     type: Literal[ProcessingMessageName.task_request] = (
         ProcessingMessageName.task_request
     )
-    task: Union[CompletionRequest, ChatCompletionRequest]
+    task: Union[CompletionRequestWithRawContent, ChatCompletionRequestWithRawContent]
 
 
 class TaskResponse(BaseModel):
@@ -264,9 +267,6 @@ def launch_dist_group(
     init_model_cb: Callable,
     **kwargs,
 ) -> None:
-    id = uuid.uuid4().hex
-    dist_url = f"file:///tmp/llama3_{id}_{time.time()}"
-
     with tempfile.TemporaryDirectory() as tmpdir:
         # TODO: track workers and if they terminate, tell parent process about it so cleanup can happen
         launch_config = LaunchConfig(
@@ -315,7 +315,7 @@ def start_model_parallel_process(
     # wait until the model is loaded; rank 0 will send a message to indicate it's ready
 
     request_socket.send(encode_msg(ReadyRequest()))
-    response = request_socket.recv()
+    _response = request_socket.recv()
     log.info("Loaded model...")
 
     return request_socket, process
@@ -349,7 +349,10 @@ class ModelParallelProcessGroup:
         self.started = False
 
     def run_inference(
-        self, req: Union[CompletionRequest, ChatCompletionRequest]
+        self,
+        req: Union[
+            CompletionRequestWithRawContent, ChatCompletionRequestWithRawContent
+        ],
     ) -> Generator:
         assert not self.running, "inference already running"