forked from phoenix-oss/llama-stack-mirror
API Updates (#73)
* API Keys passed from Client instead of distro configuration * delete distribution registry * Rename the "package" word away * Introduce a "Router" layer for providers Some providers need to be factorized and considered as thin routing layers on top of other providers. Consider two examples: - The inference API should be a routing layer over inference providers, routed using the "model" key - The memory banks API is another instance where various memory bank types will be provided by independent providers (e.g., a vector store is served by Chroma while a keyvalue memory can be served by Redis or PGVector) This commit introduces a generalized routing layer for this purpose. * update `apis_to_serve` * llama_toolchain -> llama_stack * Codemod from llama_toolchain -> llama_stack - added providers/registry - cleaned up api/ subdirectories and moved impls away - restructured api/api.py - from llama_stack.apis.<api> import foo should work now - update imports to do llama_stack.apis.<api> - update many other imports - added __init__, fixed some registry imports - updated registry imports - create_agentic_system -> create_agent - AgenticSystem -> Agent * Moved some stuff out of common/; re-generated OpenAPI spec * llama-toolchain -> llama-stack (hyphens) * add control plane API * add redis adapter + sqlite provider * move core -> distribution * Some more toolchain -> stack changes * small naming shenanigans * Removing custom tool and agent utilities and moving them client side * Move control plane to distribution server for now * Remove control plane from API list * no codeshield dependency randomly plzzzzz * Add "fire" as a dependency * add back event loggers * stack configure fixes * use brave instead of bing in the example client * add init file so it gets packaged * add init files so it gets packaged * Update MANIFEST * bug fix --------- Co-authored-by: Hardik Shah <hjshah@fb.com> Co-authored-by: Xi Yan <xiyan@meta.com> Co-authored-by: Ashwin Bharambe <ashwin@meta.com>
This commit is contained in:
Ashwin Bharambe
GitHub
parent
f294eac5f5
commit
9487ad8294
213 changed files with 1725 additions and 1204 deletions
5
llama_stack/providers/adapters/inference/__init__.py
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5
llama_stack/providers/adapters/inference/__init__.py
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# 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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# 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 .config import FireworksImplConfig
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async def get_adapter_impl(config: FireworksImplConfig, _deps):
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from .fireworks import FireworksInferenceAdapter
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assert isinstance(
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config, FireworksImplConfig
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), f"Unexpected config type: {type(config)}"
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impl = FireworksInferenceAdapter(config)
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await impl.initialize()
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return impl
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20
llama_stack/providers/adapters/inference/fireworks/config.py
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20
llama_stack/providers/adapters/inference/fireworks/config.py
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# 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 llama_models.schema_utils import json_schema_type
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from pydantic import BaseModel, Field
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@json_schema_type
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class FireworksImplConfig(BaseModel):
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url: str = Field(
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default="https://api.fireworks.ai/inference",
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description="The URL for the Fireworks server",
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)
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api_key: str = Field(
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default="",
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description="The Fireworks.ai API Key",
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)
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245
llama_stack/providers/adapters/inference/fireworks/fireworks.py
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245
llama_stack/providers/adapters/inference/fireworks/fireworks.py
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# 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 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 fireworks.client import Fireworks
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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(self, request: CompletionRequest) -> 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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15
llama_stack/providers/adapters/inference/ollama/__init__.py
Normal file
15
llama_stack/providers/adapters/inference/ollama/__init__.py
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# 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 llama_stack.distribution.datatypes import RemoteProviderConfig
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async def get_adapter_impl(config: RemoteProviderConfig, _deps):
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from .ollama import OllamaInferenceAdapter
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impl = OllamaInferenceAdapter(config.url)
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await impl.initialize()
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return impl
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261
llama_stack/providers/adapters/inference/ollama/ollama.py
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261
llama_stack/providers/adapters/inference/ollama/ollama.py
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# 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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import httpx
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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 ollama import AsyncClient
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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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# TODO: Eventually this will move to the llama cli model list command
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# mapping of Model SKUs to ollama models
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OLLAMA_SUPPORTED_SKUS = {
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# "Meta-Llama3.1-8B-Instruct": "llama3.1",
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"Meta-Llama3.1-8B-Instruct": "llama3.1:8b-instruct-fp16",
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"Meta-Llama3.1-70B-Instruct": "llama3.1:70b-instruct-fp16",
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}
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class OllamaInferenceAdapter(Inference):
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def __init__(self, url: str) -> None:
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self.url = url
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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) -> AsyncClient:
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return AsyncClient(host=self.url)
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async def initialize(self) -> None:
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try:
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await self.client.ps()
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except httpx.ConnectError as e:
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raise RuntimeError(
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"Ollama Server is not running, start it using `ollama serve` in a separate terminal"
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) from e
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async def shutdown(self) -> None:
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pass
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async def completion(self, request: CompletionRequest) -> AsyncGenerator:
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raise NotImplementedError()
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def _messages_to_ollama_messages(self, messages: list[Message]) -> list:
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ollama_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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ollama_messages.append({"role": role, "content": message.content})
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return ollama_messages
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def resolve_ollama_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) in OLLAMA_SUPPORTED_SKUS
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), f"Unsupported model: {model_name}, use one of the supported models: {','.join(OLLAMA_SUPPORTED_SKUS.keys())}"
|
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|
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return OLLAMA_SUPPORTED_SKUS.get(model.descriptor(shorten_default_variant=True))
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|
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def get_ollama_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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if (
|
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request.sampling_params.repetition_penalty is not None
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and request.sampling_params.repetition_penalty != 1.0
|
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):
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options["repeat_penalty"] = request.sampling_params.repetition_penalty
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|
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return options
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|
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async def chat_completion(
|
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self,
|
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model: str,
|
||||
messages: List[Message],
|
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sampling_params: Optional[SamplingParams] = SamplingParams(),
|
||||
tools: Optional[List[ToolDefinition]] = None,
|
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tool_choice: Optional[ToolChoice] = ToolChoice.auto,
|
||||
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:
|
||||
request = ChatCompletionRequest(
|
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model=model,
|
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messages=messages,
|
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sampling_params=sampling_params,
|
||||
tools=tools or [],
|
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tool_choice=tool_choice,
|
||||
tool_prompt_format=tool_prompt_format,
|
||||
stream=stream,
|
||||
logprobs=logprobs,
|
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)
|
||||
|
||||
messages = prepare_messages(request)
|
||||
# accumulate sampling params and other options to pass to ollama
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||||
options = self.get_ollama_chat_options(request)
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||||
ollama_model = self.resolve_ollama_model(request.model)
|
||||
|
||||
res = await self.client.ps()
|
||||
need_model_pull = True
|
||||
for r in res["models"]:
|
||||
if ollama_model == r["model"]:
|
||||
need_model_pull = False
|
||||
break
|
||||
|
||||
if need_model_pull:
|
||||
print(f"Pulling model: {ollama_model}")
|
||||
status = await self.client.pull(ollama_model)
|
||||
assert (
|
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status["status"] == "success"
|
||||
), f"Failed to pull model {self.model} in ollama"
|
||||
|
||||
if not request.stream:
|
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r = await self.client.chat(
|
||||
model=ollama_model,
|
||||
messages=self._messages_to_ollama_messages(messages),
|
||||
stream=False,
|
||||
options=options,
|
||||
)
|
||||
stop_reason = None
|
||||
if r["done"]:
|
||||
if r["done_reason"] == "stop":
|
||||
stop_reason = StopReason.end_of_turn
|
||||
elif r["done_reason"] == "length":
|
||||
stop_reason = StopReason.out_of_tokens
|
||||
|
||||
completion_message = self.formatter.decode_assistant_message_from_content(
|
||||
r["message"]["content"], stop_reason
|
||||
)
|
||||
yield ChatCompletionResponse(
|
||||
completion_message=completion_message,
|
||||
logprobs=None,
|
||||
)
|
||||
else:
|
||||
yield ChatCompletionResponseStreamChunk(
|
||||
event=ChatCompletionResponseEvent(
|
||||
event_type=ChatCompletionResponseEventType.start,
|
||||
delta="",
|
||||
)
|
||||
)
|
||||
stream = await self.client.chat(
|
||||
model=ollama_model,
|
||||
messages=self._messages_to_ollama_messages(messages),
|
||||
stream=True,
|
||||
options=options,
|
||||
)
|
||||
|
||||
buffer = ""
|
||||
ipython = False
|
||||
stop_reason = None
|
||||
|
||||
async for chunk in stream:
|
||||
if chunk["done"]:
|
||||
if stop_reason is None and chunk["done_reason"] == "stop":
|
||||
stop_reason = StopReason.end_of_turn
|
||||
elif stop_reason is None and chunk["done_reason"] == "length":
|
||||
stop_reason = StopReason.out_of_tokens
|
||||
break
|
||||
|
||||
text = chunk["message"]["content"]
|
||||
|
||||
# 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,
|
||||
)
|
||||
)
|
||||
24
llama_stack/providers/adapters/inference/tgi/__init__.py
Normal file
24
llama_stack/providers/adapters/inference/tgi/__init__.py
Normal file
|
|
@ -0,0 +1,24 @@
|
|||
# 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 .config import TGIImplConfig
|
||||
from .tgi import InferenceEndpointAdapter, TGIAdapter
|
||||
|
||||
|
||||
async def get_adapter_impl(config: TGIImplConfig, _deps):
|
||||
assert isinstance(config, TGIImplConfig), f"Unexpected config type: {type(config)}"
|
||||
|
||||
if config.url is not None:
|
||||
impl = TGIAdapter(config)
|
||||
elif config.is_inference_endpoint():
|
||||
impl = InferenceEndpointAdapter(config)
|
||||
else:
|
||||
raise ValueError(
|
||||
"Invalid configuration. Specify either an URL or HF Inference Endpoint details (namespace and endpoint name)."
|
||||
)
|
||||
|
||||
await impl.initialize()
|
||||
return impl
|
||||
29
llama_stack/providers/adapters/inference/tgi/config.py
Normal file
29
llama_stack/providers/adapters/inference/tgi/config.py
Normal file
|
|
@ -0,0 +1,29 @@
|
|||
# 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 Optional
|
||||
|
||||
from llama_models.schema_utils import json_schema_type
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
@json_schema_type
|
||||
class TGIImplConfig(BaseModel):
|
||||
url: Optional[str] = Field(
|
||||
default=None,
|
||||
description="The URL for the local TGI endpoint (e.g., http://localhost:8080)",
|
||||
)
|
||||
api_token: Optional[str] = Field(
|
||||
default=None,
|
||||
description="The HF token for Hugging Face Inference Endpoints (will default to locally saved token if not provided)",
|
||||
)
|
||||
hf_endpoint_name: Optional[str] = Field(
|
||||
default=None,
|
||||
description="The name of the Hugging Face Inference Endpoint : can be either in the format of '{namespace}/{endpoint_name}' (namespace can be the username or organization name) or just '{endpoint_name}' if logged into the same account as the namespace",
|
||||
)
|
||||
|
||||
def is_inference_endpoint(self) -> bool:
|
||||
return self.hf_endpoint_name is not None
|
||||
295
llama_stack/providers/adapters/inference/tgi/tgi.py
Normal file
295
llama_stack/providers/adapters/inference/tgi/tgi.py
Normal file
|
|
@ -0,0 +1,295 @@
|
|||
# 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 Any, AsyncGenerator, Dict
|
||||
|
||||
import requests
|
||||
|
||||
from huggingface_hub import HfApi, InferenceClient
|
||||
from llama_models.llama3.api.chat_format import ChatFormat
|
||||
from llama_models.llama3.api.datatypes import StopReason
|
||||
from llama_models.llama3.api.tokenizer import Tokenizer
|
||||
from llama_stack.apis.inference import * # noqa: F403
|
||||
from llama_stack.providers.utils.inference.prepare_messages import prepare_messages
|
||||
|
||||
from .config import TGIImplConfig
|
||||
|
||||
HF_SUPPORTED_MODELS = {
|
||||
"Meta-Llama3.1-8B-Instruct": "meta-llama/Meta-Llama-3.1-8B-Instruct",
|
||||
"Meta-Llama3.1-70B-Instruct": "meta-llama/Meta-Llama-3.1-70B-Instruct",
|
||||
"Meta-Llama3.1-405B-Instruct": "meta-llama/Meta-Llama-3.1-405B-Instruct",
|
||||
}
|
||||
|
||||
|
||||
class TGIAdapter(Inference):
|
||||
def __init__(self, config: TGIImplConfig) -> None:
|
||||
self.config = config
|
||||
self.tokenizer = Tokenizer.get_instance()
|
||||
self.formatter = ChatFormat(self.tokenizer)
|
||||
|
||||
@property
|
||||
def client(self) -> InferenceClient:
|
||||
return InferenceClient(model=self.config.url, token=self.config.api_token)
|
||||
|
||||
def _get_endpoint_info(self) -> Dict[str, Any]:
|
||||
return {
|
||||
**self.client.get_endpoint_info(),
|
||||
"inference_url": self.config.url,
|
||||
}
|
||||
|
||||
async def initialize(self) -> None:
|
||||
try:
|
||||
info = self._get_endpoint_info()
|
||||
if "model_id" not in info:
|
||||
raise RuntimeError("Missing model_id in model info")
|
||||
if "max_total_tokens" not in info:
|
||||
raise RuntimeError("Missing max_total_tokens in model info")
|
||||
self.max_tokens = info["max_total_tokens"]
|
||||
|
||||
model_id = info["model_id"]
|
||||
model_name = next(
|
||||
(name for name, id in HF_SUPPORTED_MODELS.items() if id == model_id),
|
||||
None,
|
||||
)
|
||||
if model_name is None:
|
||||
raise RuntimeError(
|
||||
f"TGI is serving model: {model_id}, use one of the supported models: {', '.join(HF_SUPPORTED_MODELS.values())}"
|
||||
)
|
||||
self.model_name = model_name
|
||||
self.inference_url = info["inference_url"]
|
||||
except Exception as e:
|
||||
import traceback
|
||||
|
||||
traceback.print_exc()
|
||||
raise RuntimeError(f"Error initializing TGIAdapter: {e}") from e
|
||||
|
||||
async def shutdown(self) -> None:
|
||||
pass
|
||||
|
||||
async def completion(self, request: CompletionRequest) -> AsyncGenerator:
|
||||
raise NotImplementedError()
|
||||
|
||||
def get_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:
|
||||
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,
|
||||
)
|
||||
|
||||
messages = prepare_messages(request)
|
||||
model_input = self.formatter.encode_dialog_prompt(messages)
|
||||
prompt = self.tokenizer.decode(model_input.tokens)
|
||||
|
||||
input_tokens = len(model_input.tokens)
|
||||
max_new_tokens = min(
|
||||
request.sampling_params.max_tokens or (self.max_tokens - input_tokens),
|
||||
self.max_tokens - input_tokens - 1,
|
||||
)
|
||||
|
||||
print(f"Calculated max_new_tokens: {max_new_tokens}")
|
||||
|
||||
assert (
|
||||
request.model == self.model_name
|
||||
), f"Model mismatch, expected {self.model_name}, got {request.model}"
|
||||
|
||||
options = self.get_chat_options(request)
|
||||
if not request.stream:
|
||||
response = self.client.text_generation(
|
||||
prompt=prompt,
|
||||
stream=False,
|
||||
details=True,
|
||||
max_new_tokens=max_new_tokens,
|
||||
stop_sequences=["<|eom_id|>", "<|eot_id|>"],
|
||||
**options,
|
||||
)
|
||||
stop_reason = None
|
||||
if response.details.finish_reason:
|
||||
if response.details.finish_reason == "stop":
|
||||
stop_reason = StopReason.end_of_turn
|
||||
elif response.details.finish_reason == "length":
|
||||
stop_reason = StopReason.out_of_tokens
|
||||
|
||||
completion_message = self.formatter.decode_assistant_message_from_content(
|
||||
response.generated_text,
|
||||
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
|
||||
tokens = []
|
||||
|
||||
for response in self.client.text_generation(
|
||||
prompt=prompt,
|
||||
stream=True,
|
||||
details=True,
|
||||
max_new_tokens=max_new_tokens,
|
||||
stop_sequences=["<|eom_id|>", "<|eot_id|>"],
|
||||
**options,
|
||||
):
|
||||
token_result = response.token
|
||||
|
||||
buffer += token_result.text
|
||||
tokens.append(token_result.id)
|
||||
|
||||
if not ipython and buffer.startswith("<|python_tag|>"):
|
||||
ipython = True
|
||||
yield ChatCompletionResponseStreamChunk(
|
||||
event=ChatCompletionResponseEvent(
|
||||
event_type=ChatCompletionResponseEventType.progress,
|
||||
delta=ToolCallDelta(
|
||||
content="",
|
||||
parse_status=ToolCallParseStatus.started,
|
||||
),
|
||||
)
|
||||
)
|
||||
buffer = buffer[len("<|python_tag|>") :]
|
||||
continue
|
||||
|
||||
if token_result.text == "<|eot_id|>":
|
||||
stop_reason = StopReason.end_of_turn
|
||||
text = ""
|
||||
elif token_result.text == "<|eom_id|>":
|
||||
stop_reason = StopReason.end_of_message
|
||||
text = ""
|
||||
else:
|
||||
text = token_result.text
|
||||
|
||||
if ipython:
|
||||
delta = ToolCallDelta(
|
||||
content=text,
|
||||
parse_status=ToolCallParseStatus.in_progress,
|
||||
)
|
||||
else:
|
||||
delta = text
|
||||
|
||||
if stop_reason is None:
|
||||
yield ChatCompletionResponseStreamChunk(
|
||||
event=ChatCompletionResponseEvent(
|
||||
event_type=ChatCompletionResponseEventType.progress,
|
||||
delta=delta,
|
||||
stop_reason=stop_reason,
|
||||
)
|
||||
)
|
||||
|
||||
if stop_reason is None:
|
||||
stop_reason = StopReason.out_of_tokens
|
||||
|
||||
# parse tool calls and report errors
|
||||
message = self.formatter.decode_assistant_message(tokens, 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,
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
class InferenceEndpointAdapter(TGIAdapter):
|
||||
def __init__(self, config: TGIImplConfig) -> None:
|
||||
super().__init__(config)
|
||||
self.config.url = self._construct_endpoint_url()
|
||||
|
||||
def _construct_endpoint_url(self) -> str:
|
||||
hf_endpoint_name = self.config.hf_endpoint_name
|
||||
assert hf_endpoint_name.count("/") <= 1, (
|
||||
"Endpoint name must be in the format of 'namespace/endpoint_name' "
|
||||
"or 'endpoint_name'"
|
||||
)
|
||||
if "/" not in hf_endpoint_name:
|
||||
hf_namespace: str = self.get_namespace()
|
||||
endpoint_path = f"{hf_namespace}/{hf_endpoint_name}"
|
||||
else:
|
||||
endpoint_path = hf_endpoint_name
|
||||
return f"https://api.endpoints.huggingface.cloud/v2/endpoint/{endpoint_path}"
|
||||
|
||||
def get_namespace(self) -> str:
|
||||
return HfApi().whoami()["name"]
|
||||
|
||||
@property
|
||||
def client(self) -> InferenceClient:
|
||||
return InferenceClient(model=self.inference_url, token=self.config.api_token)
|
||||
|
||||
def _get_endpoint_info(self) -> Dict[str, Any]:
|
||||
headers = {
|
||||
"accept": "application/json",
|
||||
"authorization": f"Bearer {self.config.api_token}",
|
||||
}
|
||||
response = requests.get(self.config.url, headers=headers)
|
||||
response.raise_for_status()
|
||||
endpoint_info = response.json()
|
||||
return {
|
||||
"inference_url": endpoint_info["status"]["url"],
|
||||
"model_id": endpoint_info["model"]["repository"],
|
||||
"max_total_tokens": int(
|
||||
endpoint_info["model"]["image"]["custom"]["env"]["MAX_TOTAL_TOKENS"]
|
||||
),
|
||||
}
|
||||
|
||||
async def initialize(self) -> None:
|
||||
await super().initialize()
|
||||
|
|
@ -0,0 +1,18 @@
|
|||
# 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 .config import TogetherImplConfig
|
||||
|
||||
|
||||
async def get_adapter_impl(config: TogetherImplConfig, _deps):
|
||||
from .together import TogetherInferenceAdapter
|
||||
|
||||
assert isinstance(
|
||||
config, TogetherImplConfig
|
||||
), f"Unexpected config type: {type(config)}"
|
||||
impl = TogetherInferenceAdapter(config)
|
||||
await impl.initialize()
|
||||
return impl
|
||||
20
llama_stack/providers/adapters/inference/together/config.py
Normal file
20
llama_stack/providers/adapters/inference/together/config.py
Normal file
|
|
@ -0,0 +1,20 @@
|
|||
# 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 llama_models.schema_utils import json_schema_type
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
@json_schema_type
|
||||
class TogetherImplConfig(BaseModel):
|
||||
url: str = Field(
|
||||
default="https://api.together.xyz/v1",
|
||||
description="The URL for the Together AI server",
|
||||
)
|
||||
api_key: str = Field(
|
||||
default="",
|
||||
description="The Together AI API Key",
|
||||
)
|
||||
252
llama_stack/providers/adapters/inference/together/together.py
Normal file
252
llama_stack/providers/adapters/inference/together/together.py
Normal file
|
|
@ -0,0 +1,252 @@
|
|||
# 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.prepare_messages import prepare_messages
|
||||
|
||||
from .config import TogetherImplConfig
|
||||
|
||||
TOGETHER_SUPPORTED_MODELS = {
|
||||
"Meta-Llama3.1-8B-Instruct": "meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo",
|
||||
"Meta-Llama3.1-70B-Instruct": "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo",
|
||||
"Meta-Llama3.1-405B-Instruct": "meta-llama/Meta-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, request: CompletionRequest) -> 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 = prepare_messages(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,
|
||||
)
|
||||
)
|
||||
Loading…
Add table
Add a link
Reference in a new issue