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

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.

llama_stack/providers/utils/inference/openai_compat.py

@@ -7,7 +7,6 @@ import json
 import logging
 from typing import AsyncGenerator, Dict, List, Optional, Union
 
-from llama_models.llama3.api.chat_format import ChatFormat
 from openai.types.chat import ChatCompletionMessageToolCall
 from pydantic import BaseModel
 
@@ -40,6 +39,7 @@ from llama_stack.models.llama.datatypes import (
 )
 from llama_stack.providers.utils.inference.prompt_adapter import (
     convert_image_content_to_url,
+    decode_assistant_message,
 )
 
 logger = logging.getLogger(__name__)
@@ -149,7 +149,7 @@ def convert_openai_completion_logprobs_stream(text: str, logprobs: Optional[Unio
     return None
 
 
-def process_completion_response(response: OpenAICompatCompletionResponse, formatter: ChatFormat) -> CompletionResponse:
+def process_completion_response(response: OpenAICompatCompletionResponse) -> CompletionResponse:
     choice = response.choices[0]
     # drop suffix <eot_id> if present and return stop reason as end of turn
     if choice.text.endswith("<|eot_id|>"):
@@ -174,16 +174,13 @@ def process_completion_response(response: OpenAICompatCompletionResponse, format
 
 def process_chat_completion_response(
     response: OpenAICompatCompletionResponse,
-    formatter: ChatFormat,
     request: ChatCompletionRequest,
 ) -> ChatCompletionResponse:
     choice = response.choices[0]
 
     # TODO: This does not work well with tool calls for vLLM remote provider
     #   Ref: https://github.com/meta-llama/llama-stack/issues/1058
-    raw_message = formatter.decode_assistant_message_from_content(
-        text_from_choice(choice), get_stop_reason(choice.finish_reason)
-    )
+    raw_message = decode_assistant_message(text_from_choice(choice), get_stop_reason(choice.finish_reason))
 
     # NOTE: If we do not set tools in chat-completion request, we should not
     # expect the ToolCall in the response. Instead, we should return the raw
@@ -217,7 +214,7 @@ def process_chat_completion_response(
 
 
 async def process_completion_stream_response(
-    stream: AsyncGenerator[OpenAICompatCompletionResponse, None], formatter: ChatFormat
+    stream: AsyncGenerator[OpenAICompatCompletionResponse, None],
 ) -> AsyncGenerator:
     stop_reason = None
 
@@ -254,7 +251,6 @@ async def process_completion_stream_response(
 
 async def process_chat_completion_stream_response(
     stream: AsyncGenerator[OpenAICompatCompletionResponse, None],
-    formatter: ChatFormat,
     request: ChatCompletionRequest,
 ) -> AsyncGenerator:
     yield ChatCompletionResponseStreamChunk(
@@ -333,7 +329,7 @@ async def process_chat_completion_stream_response(
             )
 
     # parse tool calls and report errors
-    message = formatter.decode_assistant_message_from_content(buffer, stop_reason)
+    message = decode_assistant_message(buffer, stop_reason)
 
     parsed_tool_calls = len(message.tool_calls) > 0
     if ipython and not parsed_tool_calls:

llama_stack/providers/utils/inference/prompt_adapter.py

@@ -13,7 +13,9 @@ import re
 from typing import List, Optional, Tuple, Union
 
 import httpx
+from llama_models.datatypes import StopReason
 from llama_models.llama3.api.chat_format import ChatFormat
+from llama_models.llama3.api.tokenizer import Tokenizer
 from PIL import Image as PIL_Image
 
 from llama_stack.apis.common.content_types import (
@@ -66,6 +68,11 @@ class CompletionRequestWithRawContent(CompletionRequest):
     content: RawContent
 
 
+def decode_assistant_message(content: str, stop_reason: StopReason) -> RawMessage:
+    formatter = ChatFormat(Tokenizer.get_instance())
+    return formatter.decode_assistant_message_from_content(content, stop_reason)
+
+
 def interleaved_content_as_str(content: InterleavedContent, sep: str = " ") -> str:
     def _process(c) -> str:
         if isinstance(c, str):
@@ -207,20 +214,22 @@ async def convert_image_content_to_url(
         return base64.b64encode(content).decode("utf-8")
 
 
-async def completion_request_to_prompt(request: CompletionRequest, formatter: ChatFormat) -> str:
+async def completion_request_to_prompt(request: CompletionRequest) -> str:
     content = augment_content_with_response_format_prompt(request.response_format, request.content)
     request.content = content
     request = await convert_request_to_raw(request)
+
+    formatter = ChatFormat(tokenizer=Tokenizer.get_instance())
     model_input = formatter.encode_content(request.content)
     return formatter.tokenizer.decode(model_input.tokens)
 
 
-async def completion_request_to_prompt_model_input_info(
-    request: CompletionRequest, formatter: ChatFormat
-) -> Tuple[str, int]:
+async def completion_request_to_prompt_model_input_info(request: CompletionRequest) -> Tuple[str, int]:
     content = augment_content_with_response_format_prompt(request.response_format, request.content)
     request.content = content
     request = await convert_request_to_raw(request)
+
+    formatter = ChatFormat(tokenizer=Tokenizer.get_instance())
     model_input = formatter.encode_content(request.content)
     return (formatter.tokenizer.decode(model_input.tokens), len(model_input.tokens))
 
@@ -237,22 +246,24 @@ def augment_content_with_response_format_prompt(response_format, content):
     return content
 
 
-async def chat_completion_request_to_prompt(
-    request: ChatCompletionRequest, llama_model: str, formatter: ChatFormat
-) -> str:
+async def chat_completion_request_to_prompt(request: ChatCompletionRequest, llama_model: str) -> str:
     messages = chat_completion_request_to_messages(request, llama_model)
     request.messages = messages
     request = await convert_request_to_raw(request)
+
+    formatter = ChatFormat(tokenizer=Tokenizer.get_instance())
     model_input = formatter.encode_dialog_prompt(request.messages)
     return formatter.tokenizer.decode(model_input.tokens)
 
 
 async def chat_completion_request_to_model_input_info(
-    request: ChatCompletionRequest, llama_model: str, formatter: ChatFormat
+    request: ChatCompletionRequest, llama_model: str
 ) -> Tuple[str, int]:
     messages = chat_completion_request_to_messages(request, llama_model)
     request.messages = messages
     request = await convert_request_to_raw(request)
+
+    formatter = ChatFormat(tokenizer=Tokenizer.get_instance())
     model_input = formatter.encode_dialog_prompt(request.messages)
     return (
         formatter.tokenizer.decode(model_input.tokens),