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chore: remove deprecated inference.chat_completion implementations (#3654)

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# What does this PR do?

remove unused chat_completion implementations

vllm features ported -
 - requires max_tokens be set, use config value
 - set tool_choice to none if no tools provided


## Test Plan

ci
This commit is contained in:
Matthew Farrellee 2025-10-03 07:55:34 -04:00 • committed by GitHub
parent 4dfbe46954
commit d266c59c2a
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18 changed files with 193 additions and 1410 deletions
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85
llama_stack/providers/remote/inference/passthrough/passthrough.py
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@ -4,33 +4,22 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from collections.abc import AsyncGenerator, AsyncIterator
from collections.abc import AsyncIterator
from typing import Any
from llama_stack_client import AsyncLlamaStackClient
from llama_stack.apis.inference import (
ChatCompletionResponse,
ChatCompletionResponseStreamChunk,
CompletionMessage,
Inference,
LogProbConfig,
Message,
OpenAIChatCompletion,
OpenAIChatCompletionChunk,
OpenAICompletion,
OpenAIEmbeddingsResponse,
OpenAIMessageParam,
OpenAIResponseFormatParam,
ResponseFormat,
SamplingParams,
ToolChoice,
ToolConfig,
ToolDefinition,
ToolPromptFormat,
)
from llama_stack.apis.models import Model
from llama_stack.core.library_client import convert_pydantic_to_json_value, convert_to_pydantic
from llama_stack.core.library_client import convert_pydantic_to_json_value
from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
from llama_stack.providers.utils.inference.openai_compat import prepare_openai_completion_params
@ -85,76 +74,6 @@ class PassthroughInferenceAdapter(Inference):
provider_data=provider_data,
)
async def chat_completion(
self,
model_id: str,
messages: list[Message],
sampling_params: SamplingParams | None = None,
tools: list[ToolDefinition] | None = None,
tool_choice: ToolChoice | None = ToolChoice.auto,
tool_prompt_format: ToolPromptFormat | None = None,
response_format: ResponseFormat | None = None,
stream: bool | None = False,
logprobs: LogProbConfig | None = None,
tool_config: ToolConfig | None = None,
) -> AsyncGenerator:
if sampling_params is None:
sampling_params = SamplingParams()
model = await self.model_store.get_model(model_id)
# TODO: revisit this remove tool_calls from messages logic
for message in messages:
if hasattr(message, "tool_calls"):
message.tool_calls = None
request_params = {
"model_id": model.provider_resource_id,
"messages": messages,
"sampling_params": sampling_params,
"tools": tools,
"tool_choice": tool_choice,
"tool_prompt_format": tool_prompt_format,
"response_format": response_format,
"stream": stream,
"logprobs": logprobs,
}
# only pass through the not None params
request_params = {key: value for key, value in request_params.items() if value is not None}
# cast everything to json dict
json_params = self.cast_value_to_json_dict(request_params)
if stream:
return self._stream_chat_completion(json_params)
else:
return await self._nonstream_chat_completion(json_params)
async def _nonstream_chat_completion(self, json_params: dict[str, Any]) -> ChatCompletionResponse:
client = self._get_client()
response = await client.inference.chat_completion(**json_params)
return ChatCompletionResponse(
completion_message=CompletionMessage(
content=response.completion_message.content.text,
stop_reason=response.completion_message.stop_reason,
tool_calls=response.completion_message.tool_calls,
),
logprobs=response.logprobs,
)
async def _stream_chat_completion(self, json_params: dict[str, Any]) -> AsyncGenerator:
client = self._get_client()
stream_response = await client.inference.chat_completion(**json_params)
async for chunk in stream_response:
chunk = chunk.to_dict()
# temporary hack to remove the metrics from the response
chunk["metrics"] = []
chunk = convert_to_pydantic(ChatCompletionResponseStreamChunk, chunk)
yield chunk
async def openai_embeddings(
self,
model: str,