Further cleanup after merge

llama_stack/providers/inline/inference/vllm/openai_utils.py

@@ -7,7 +7,6 @@
 from typing import List, Optional
 
 import vllm
-from llama_models.llama3.api.datatypes import BuiltinTool, ToolDefinition
 
 from llama_stack.apis.inference import (
     ChatCompletionRequest,
@@ -17,6 +16,7 @@ from llama_stack.apis.inference import (
     ToolChoice,
     UserMessage,
 )
+from llama_stack.models.llama.datatypes import BuiltinTool, ToolDefinition
 from llama_stack.providers.utils.inference.openai_compat import (
     convert_message_to_openai_dict,
     get_sampling_options,
@@ -114,7 +114,12 @@ async def llama_stack_chat_completion_to_openai_chat_completion_dict(
     # Llama will try to use built-in tools with no tool catalog, so don't enable
     # tool choice unless at least one tool is enabled.
     converted_tool_choice = "none"
-    if request.tool_choice == ToolChoice.auto and request.tools is not None and len(request.tools) > 0:
+    if (
+        request.tool_config is not None
+        and request.tool_config.tool_choice == ToolChoice.auto
+        and request.tools is not None
+        and len(request.tools) > 0
+    ):
         converted_tool_choice = "auto"
 
     # TODO: Figure out what to do with the tool_prompt_format argument.

llama_stack/providers/inline/inference/vllm/vllm.py

@@ -10,22 +10,11 @@ import re
 import uuid
 from typing import AsyncGenerator, AsyncIterator, Dict, List, Optional, Union
 
-import llama_models.sku_list
-
 # These vLLM modules contain names that overlap with Llama Stack names, so we import
 # fully-qualified names
 import vllm.entrypoints.openai.protocol
 import vllm.sampling_params
 from llama_models.llama3.api.chat_format import ChatFormat
-from llama_models.llama3.api.datatypes import (
-    SamplingParams,
-    StopReason,
-    ToolCall,
-    ToolDefinition,
-    ToolPromptFormat,
-    TopKSamplingStrategy,
-    TopPSamplingStrategy,
-)
 from llama_models.llama3.api.tokenizer import Tokenizer
 from vllm.engine.arg_utils import AsyncEngineArgs
 from vllm.engine.async_llm_engine import AsyncLLMEngine
@@ -54,17 +43,23 @@ from llama_stack.apis.inference import (
     LogProbConfig,
     Message,
     ResponseFormat,
+    SamplingParams,
     TextTruncation,
     TokenLogProbs,
     ToolChoice,
     ToolConfig,
 )
 from llama_stack.apis.models import Model
-from llama_stack.models.llama.llama3.chat_format import ChatFormat
-from llama_stack.models.llama.llama3.tokenizer import Tokenizer
-from llama_stack.models.llama.sku_list import resolve_model
-from llama_stack.providers.datatypes import ModelsProtocolPrivate
-from llama_stack.providers.remote.inference.vllm.vllm import build_model_aliases
+from llama_stack.models.llama import sku_list
+from llama_stack.models.llama.datatypes import (
+    StopReason,
+    ToolCall,
+    ToolDefinition,
+    ToolPromptFormat,
+    TopKSamplingStrategy,
+    TopPSamplingStrategy,
+)
+from llama_stack.providers.remote.inference.vllm.vllm import build_hf_repo_model_entries
 from llama_stack.providers.utils.inference.model_registry import (
     ModelRegistryHelper,
     ModelsProtocolPrivate,
@@ -202,7 +197,7 @@ class VLLMInferenceImpl(Inference, ModelsProtocolPrivate):
         self.config = config
         logger.info(f"Config is: {self.config}")
 
-        self.register_helper = ModelRegistryHelper(build_model_aliases())
+        self.register_helper = ModelRegistryHelper(build_hf_repo_model_entries())
         self.formatter = ChatFormat(Tokenizer.get_instance())
 
         # The following are initialized when paths are bound to this provider
@@ -255,7 +250,7 @@ class VLLMInferenceImpl(Inference, ModelsProtocolPrivate):
         logger.debug(f"In register_model({model})")
 
         # First attempt to interpret the model coordinates as a Llama model name
-        resolved_llama_model = llama_models.sku_list.resolve_model(model.provider_model_id)
+        resolved_llama_model = sku_list.resolve_model(model.provider_model_id)
         if resolved_llama_model is not None:
             # Load from Hugging Face repo into default local cache dir
             model_id_for_vllm = resolved_llama_model.huggingface_repo
@@ -616,7 +611,7 @@ class VLLMInferenceImpl(Inference, ModelsProtocolPrivate):
         formatter = ChatFormat(Tokenizer.get_instance())
 
         # Note that this function call modifies `request` in place.
-        prompt = await chat_completion_request_to_prompt(request, self.resolved_model_id, formatter)
+        prompt = await chat_completion_request_to_prompt(request, self.resolved_model_id)
 
         model_id = list(self.model_ids)[0]  # Any model ID will do here
         completion_response_or_iterator = await self.completion(
@@ -633,7 +628,7 @@ class VLLMInferenceImpl(Inference, ModelsProtocolPrivate):
                 raise TypeError(
                     f"Received unexpected result type {type(completion_response_or_iterator)}for streaming request."
                 )
-            return self._chat_completion_for_meta_llama_streaming(formatter, completion_response_or_iterator)
+            return self._chat_completion_for_meta_llama_streaming(completion_response_or_iterator, request)
 
         # elsif not request.stream:
         if not isinstance(completion_response_or_iterator, CompletionResponse):
@@ -654,7 +649,7 @@ class VLLMInferenceImpl(Inference, ModelsProtocolPrivate):
         )
 
     async def _chat_completion_for_meta_llama_streaming(
-        self, formatter: ChatFormat, results_iterator: AsyncIterator
+        self, results_iterator: AsyncIterator, request: ChatCompletionRequest
     ) -> AsyncIterator:
         """
         Code from :func:`_chat_completion_for_meta_llama()` that needs to be a separate
@@ -686,7 +681,7 @@ class VLLMInferenceImpl(Inference, ModelsProtocolPrivate):
                 )
 
         stream = _generate_and_convert_to_openai_compat()
-        async for chunk in process_chat_completion_stream_response(stream, formatter):
+        async for chunk in process_chat_completion_stream_response(stream, request):
             logger.debug(f"Returning chunk: {chunk}")
             yield chunk