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chore: remove llama_models.llama3.api imports from providers (#1107)

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There should be a choke-point for llama3.api imports -- this is the
prompt adapter. Creating a ChatFormat() object on demand is inexpensive.
The underlying Tokenizer is a singleton anyway.
This commit is contained in:
Ashwin Bharambe 2025-02-19 19:01:29 -08:00 • committed by GitHub
parent e9b8259cf9
commit cdcbeb005b
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13 changed files with 77 additions and 113 deletions
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14
llama_stack/providers/remote/inference/databricks/databricks.py
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@ -6,8 +6,6 @@
from typing import AsyncGenerator, List, Optional
from llama_models.llama3.api.chat_format import ChatFormat
from llama_models.llama3.api.tokenizer import Tokenizer
from openai import OpenAI
from llama_stack.apis.common.content_types import InterleavedContent
@ -54,12 +52,8 @@ model_aliases = [
class DatabricksInferenceAdapter(ModelRegistryHelper, Inference):
def __init__(self, config: DatabricksImplConfig) -> None:
ModelRegistryHelper.__init__(
self,
model_aliases=model_aliases,
)
ModelRegistryHelper.__init__(self, model_aliases=model_aliases)
self.config = config
self.formatter = ChatFormat(Tokenizer.get_instance())
async def initialize(self) -> None:
return
@ -112,7 +106,7 @@ class DatabricksInferenceAdapter(ModelRegistryHelper, Inference):
) -> ChatCompletionResponse:
params = self._get_params(request)
r = client.completions.create(**params)
return process_chat_completion_response(r, self.formatter, request)
return process_chat_completion_response(r, request)
async def _stream_chat_completion(self, request: ChatCompletionRequest, client: OpenAI) -> AsyncGenerator:
params = self._get_params(request)
@ -123,13 +117,13 @@ class DatabricksInferenceAdapter(ModelRegistryHelper, Inference):
yield chunk
stream = _to_async_generator()
async for chunk in process_chat_completion_stream_response(stream, self.formatter, request):
async for chunk in process_chat_completion_stream_response(stream, request):
yield chunk
def _get_params(self, request: ChatCompletionRequest) -> dict:
return {
"model": request.model,
"prompt": chat_completion_request_to_prompt(request, self.get_llama_model(request.model), self.formatter),
"prompt": chat_completion_request_to_prompt(request, self.get_llama_model(request.model)),
"stream": request.stream,
**get_sampling_options(request.sampling_params),
}