# Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. from typing import AsyncGenerator from llama_models.llama3.api.chat_format import ChatFormat from llama_models.llama3.api.datatypes import Message, StopReason from llama_models.llama3.api.tokenizer import Tokenizer from llama_models.sku_list import resolve_model from together import Together from llama_stack.apis.inference import * # noqa: F403 from llama_stack.providers.utils.inference.augment_messages import ( augment_messages_for_tools, ) from .config import TogetherImplConfig TOGETHER_SUPPORTED_MODELS = { "Llama3.1-8B-Instruct": "meta-llama/Llama-3.1-8B-Instruct-Turbo", "Llama3.1-70B-Instruct": "meta-llama/Llama-3.1-70B-Instruct-Turbo", "Llama3.1-405B-Instruct": "meta-llama/Llama-3.1-405B-Instruct-Turbo", } class TogetherInferenceAdapter(Inference): def __init__(self, config: TogetherImplConfig) -> None: self.config = config tokenizer = Tokenizer.get_instance() self.formatter = ChatFormat(tokenizer) @property def client(self) -> Together: return Together(api_key=self.config.api_key) async def initialize(self) -> None: return async def shutdown(self) -> None: pass async def completion( self, model: str, content: InterleavedTextMedia, sampling_params: Optional[SamplingParams] = SamplingParams(), stream: Optional[bool] = False, logprobs: Optional[LogProbConfig] = None, ) -> AsyncGenerator: raise NotImplementedError() def _messages_to_together_messages(self, messages: list[Message]) -> list: together_messages = [] for message in messages: if message.role == "ipython": role = "tool" else: role = message.role together_messages.append({"role": role, "content": message.content}) return together_messages def resolve_together_model(self, model_name: str) -> str: model = resolve_model(model_name) assert ( model is not None and model.descriptor(shorten_default_variant=True) in TOGETHER_SUPPORTED_MODELS ), f"Unsupported model: {model_name}, use one of the supported models: {','.join(TOGETHER_SUPPORTED_MODELS.keys())}" return TOGETHER_SUPPORTED_MODELS.get( model.descriptor(shorten_default_variant=True) ) def get_together_chat_options(self, request: ChatCompletionRequest) -> dict: options = {} if request.sampling_params is not None: for attr in {"temperature", "top_p", "top_k", "max_tokens"}: if getattr(request.sampling_params, attr): options[attr] = getattr(request.sampling_params, attr) return options async def chat_completion( self, model: str, messages: List[Message], sampling_params: Optional[SamplingParams] = SamplingParams(), tools: Optional[List[ToolDefinition]] = None, tool_choice: Optional[ToolChoice] = ToolChoice.auto, tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json, stream: Optional[bool] = False, logprobs: Optional[LogProbConfig] = None, ) -> AsyncGenerator: # wrapper request to make it easier to pass around (internal only, not exposed to API) request = ChatCompletionRequest( model=model, messages=messages, sampling_params=sampling_params, tools=tools or [], tool_choice=tool_choice, tool_prompt_format=tool_prompt_format, stream=stream, logprobs=logprobs, ) # accumulate sampling params and other options to pass to together options = self.get_together_chat_options(request) together_model = self.resolve_together_model(request.model) messages = augment_messages_for_tools(request) if not request.stream: # TODO: might need to add back an async here r = self.client.chat.completions.create( model=together_model, messages=self._messages_to_together_messages(messages), stream=False, **options, ) stop_reason = None if r.choices[0].finish_reason: if ( r.choices[0].finish_reason == "stop" or r.choices[0].finish_reason == "eos" ): stop_reason = StopReason.end_of_turn elif r.choices[0].finish_reason == "length": stop_reason = StopReason.out_of_tokens completion_message = self.formatter.decode_assistant_message_from_content( r.choices[0].message.content, stop_reason ) yield ChatCompletionResponse( completion_message=completion_message, logprobs=None, ) else: yield ChatCompletionResponseStreamChunk( event=ChatCompletionResponseEvent( event_type=ChatCompletionResponseEventType.start, delta="", ) ) buffer = "" ipython = False stop_reason = None for chunk in self.client.chat.completions.create( model=together_model, messages=self._messages_to_together_messages(messages), stream=True, **options, ): if chunk.choices[0].finish_reason: if ( stop_reason is None and chunk.choices[0].finish_reason == "stop" ) or ( stop_reason is None and chunk.choices[0].finish_reason == "eos" ): stop_reason = StopReason.end_of_turn elif ( stop_reason is None and chunk.choices[0].finish_reason == "length" ): stop_reason = StopReason.out_of_tokens break text = chunk.choices[0].delta.content if text is None: continue # check if its a tool call ( aka starts with <|python_tag|> ) if not ipython and text.startswith("<|python_tag|>"): ipython = True yield ChatCompletionResponseStreamChunk( event=ChatCompletionResponseEvent( event_type=ChatCompletionResponseEventType.progress, delta=ToolCallDelta( content="", parse_status=ToolCallParseStatus.started, ), ) ) buffer += text continue if ipython: if text == "<|eot_id|>": stop_reason = StopReason.end_of_turn text = "" continue elif text == "<|eom_id|>": stop_reason = StopReason.end_of_message text = "" continue buffer += text delta = ToolCallDelta( content=text, parse_status=ToolCallParseStatus.in_progress, ) yield ChatCompletionResponseStreamChunk( event=ChatCompletionResponseEvent( event_type=ChatCompletionResponseEventType.progress, delta=delta, stop_reason=stop_reason, ) ) else: buffer += text yield ChatCompletionResponseStreamChunk( event=ChatCompletionResponseEvent( event_type=ChatCompletionResponseEventType.progress, delta=text, stop_reason=stop_reason, ) ) # parse tool calls and report errors message = self.formatter.decode_assistant_message_from_content( buffer, stop_reason ) parsed_tool_calls = len(message.tool_calls) > 0 if ipython and not parsed_tool_calls: yield ChatCompletionResponseStreamChunk( event=ChatCompletionResponseEvent( event_type=ChatCompletionResponseEventType.progress, delta=ToolCallDelta( content="", parse_status=ToolCallParseStatus.failure, ), stop_reason=stop_reason, ) ) for tool_call in message.tool_calls: yield ChatCompletionResponseStreamChunk( event=ChatCompletionResponseEvent( event_type=ChatCompletionResponseEventType.progress, delta=ToolCallDelta( content=tool_call, parse_status=ToolCallParseStatus.success, ), stop_reason=stop_reason, ) ) yield ChatCompletionResponseStreamChunk( event=ChatCompletionResponseEvent( event_type=ChatCompletionResponseEventType.complete, delta="", stop_reason=stop_reason, ) )