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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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27
llama_stack/providers/utils/inference/prompt_adapter.py
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@ -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),