forked from phoenix-oss/llama-stack-mirror
ec4fc800cc
This is yet another of those large PRs (hopefully we will have less and less of them as things mature fast). This one introduces substantial improvements and some simplifications to the stack. Most important bits: * Agents reference implementation now has support for session / turn persistence. The default implementation uses sqlite but there's also support for using Redis. * We have re-architected the structure of the Stack APIs to allow for more flexible routing. The motivating use cases are: - routing model A to ollama and model B to a remote provider like Together - routing shield A to local impl while shield B to a remote provider like Bedrock - routing a vector memory bank to Weaviate while routing a keyvalue memory bank to Redis * Support for provider specific parameters to be passed from the clients. A client can pass data using `x_llamastack_provider_data` parameter which can be type-checked and provided to the Adapter implementations.
252 lines
9.4 KiB
Python
252 lines
9.4 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from typing import AsyncGenerator
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from fireworks.client import Fireworks
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from llama_models.llama3.api.chat_format import ChatFormat
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from llama_models.llama3.api.datatypes import Message, StopReason
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from llama_models.llama3.api.tokenizer import Tokenizer
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from llama_models.sku_list import resolve_model
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from llama_stack.apis.inference import * # noqa: F403
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from llama_stack.providers.utils.inference.prepare_messages import prepare_messages
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from .config import FireworksImplConfig
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FIREWORKS_SUPPORTED_MODELS = {
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"Meta-Llama3.1-8B-Instruct": "fireworks/llama-v3p1-8b-instruct",
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"Meta-Llama3.1-70B-Instruct": "fireworks/llama-v3p1-70b-instruct",
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"Meta-Llama3.1-405B-Instruct": "fireworks/llama-v3p1-405b-instruct",
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}
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class FireworksInferenceAdapter(Inference):
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def __init__(self, config: FireworksImplConfig) -> None:
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self.config = config
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tokenizer = Tokenizer.get_instance()
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self.formatter = ChatFormat(tokenizer)
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@property
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def client(self) -> Fireworks:
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return Fireworks(api_key=self.config.api_key)
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async def initialize(self) -> None:
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return
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async def shutdown(self) -> None:
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pass
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async def completion(
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self,
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model: str,
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content: InterleavedTextMedia,
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sampling_params: Optional[SamplingParams] = SamplingParams(),
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stream: Optional[bool] = False,
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logprobs: Optional[LogProbConfig] = None,
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) -> AsyncGenerator:
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raise NotImplementedError()
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def _messages_to_fireworks_messages(self, messages: list[Message]) -> list:
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fireworks_messages = []
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for message in messages:
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if message.role == "ipython":
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role = "tool"
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else:
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role = message.role
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fireworks_messages.append({"role": role, "content": message.content})
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return fireworks_messages
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def resolve_fireworks_model(self, model_name: str) -> str:
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model = resolve_model(model_name)
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assert (
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model is not None
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and model.descriptor(shorten_default_variant=True)
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in FIREWORKS_SUPPORTED_MODELS
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), f"Unsupported model: {model_name}, use one of the supported models: {','.join(FIREWORKS_SUPPORTED_MODELS.keys())}"
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return FIREWORKS_SUPPORTED_MODELS.get(
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model.descriptor(shorten_default_variant=True)
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)
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def get_fireworks_chat_options(self, request: ChatCompletionRequest) -> dict:
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options = {}
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if request.sampling_params is not None:
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for attr in {"temperature", "top_p", "top_k", "max_tokens"}:
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if getattr(request.sampling_params, attr):
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options[attr] = getattr(request.sampling_params, attr)
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return options
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async def chat_completion(
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self,
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model: str,
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messages: List[Message],
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sampling_params: Optional[SamplingParams] = SamplingParams(),
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tools: Optional[List[ToolDefinition]] = None,
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tool_choice: Optional[ToolChoice] = ToolChoice.auto,
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tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
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stream: Optional[bool] = False,
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logprobs: Optional[LogProbConfig] = None,
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) -> AsyncGenerator:
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request = ChatCompletionRequest(
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model=model,
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messages=messages,
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sampling_params=sampling_params,
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tools=tools or [],
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tool_choice=tool_choice,
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tool_prompt_format=tool_prompt_format,
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stream=stream,
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logprobs=logprobs,
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)
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messages = prepare_messages(request)
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# accumulate sampling params and other options to pass to fireworks
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options = self.get_fireworks_chat_options(request)
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fireworks_model = self.resolve_fireworks_model(request.model)
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if not request.stream:
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r = await self.client.chat.completions.acreate(
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model=fireworks_model,
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messages=self._messages_to_fireworks_messages(messages),
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stream=False,
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**options,
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)
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stop_reason = None
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if r.choices[0].finish_reason:
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if r.choices[0].finish_reason == "stop":
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stop_reason = StopReason.end_of_turn
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elif r.choices[0].finish_reason == "length":
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stop_reason = StopReason.out_of_tokens
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completion_message = self.formatter.decode_assistant_message_from_content(
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r.choices[0].message.content, stop_reason
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)
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yield ChatCompletionResponse(
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completion_message=completion_message,
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logprobs=None,
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)
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else:
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.start,
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delta="",
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)
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)
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buffer = ""
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ipython = False
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stop_reason = None
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async for chunk in self.client.chat.completions.acreate(
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model=fireworks_model,
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messages=self._messages_to_fireworks_messages(messages),
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stream=True,
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**options,
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):
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if chunk.choices[0].finish_reason:
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if stop_reason is None and chunk.choices[0].finish_reason == "stop":
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stop_reason = StopReason.end_of_turn
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elif (
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stop_reason is None
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and chunk.choices[0].finish_reason == "length"
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):
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stop_reason = StopReason.out_of_tokens
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break
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text = chunk.choices[0].delta.content
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if text is None:
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continue
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# check if its a tool call ( aka starts with <|python_tag|> )
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if not ipython and text.startswith("<|python_tag|>"):
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ipython = True
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.progress,
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delta=ToolCallDelta(
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content="",
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parse_status=ToolCallParseStatus.started,
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),
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)
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)
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buffer += text
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continue
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if ipython:
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if text == "<|eot_id|>":
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stop_reason = StopReason.end_of_turn
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text = ""
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continue
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elif text == "<|eom_id|>":
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stop_reason = StopReason.end_of_message
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text = ""
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continue
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buffer += text
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delta = ToolCallDelta(
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content=text,
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parse_status=ToolCallParseStatus.in_progress,
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)
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.progress,
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delta=delta,
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stop_reason=stop_reason,
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)
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)
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else:
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buffer += text
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.progress,
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delta=text,
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stop_reason=stop_reason,
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)
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)
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# parse tool calls and report errors
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message = self.formatter.decode_assistant_message_from_content(
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buffer, stop_reason
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)
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parsed_tool_calls = len(message.tool_calls) > 0
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if ipython and not parsed_tool_calls:
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.progress,
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delta=ToolCallDelta(
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content="",
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parse_status=ToolCallParseStatus.failure,
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),
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stop_reason=stop_reason,
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)
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)
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for tool_call in message.tool_calls:
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.progress,
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delta=ToolCallDelta(
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content=tool_call,
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parse_status=ToolCallParseStatus.success,
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),
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stop_reason=stop_reason,
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)
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)
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.complete,
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delta="",
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stop_reason=stop_reason,
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)
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)
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