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add databricks provider (#83)
* add databricks provider * update provider and test
This commit is contained in:
Prithu Dasgupta
GitHub
parent
f73e247ba1
commit
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5 changed files with 316 additions and 0 deletions
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name: local-databricks
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distribution_spec:
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description: Use Databricks for running LLM inference
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providers:
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inference: remote::databricks
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memory: meta-reference
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safety: meta-reference
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agents: meta-reference
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telemetry: meta-reference
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image_type: conda
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@ -0,0 +1,16 @@
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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 DatabricksImplConfig
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from .databricks import DatabricksInferenceAdapter
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async def get_adapter_impl(config: DatabricksImplConfig, _deps):
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assert isinstance(
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config, DatabricksImplConfig
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), f"Unexpected config type: {type(config)}"
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impl = DatabricksInferenceAdapter(config)
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await impl.initialize()
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return impl
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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 Optional
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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 DatabricksImplConfig(BaseModel):
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url: str = Field(
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default=None,
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description="The URL for the Databricks model serving endpoint",
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)
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api_token: str = Field(
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default=None,
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description="The Databricks API token",
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)
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@ -0,0 +1,257 @@
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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 openai import OpenAI
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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.augment_messages import (
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augment_messages_for_tools,
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)
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from .config import DatabricksImplConfig
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DATABRICKS_SUPPORTED_MODELS = {
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"Llama3.1-70B-Instruct": "databricks-meta-llama-3-1-70b-instruct",
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"Llama3.1-405B-Instruct": "databricks-meta-llama-3-1-405b-instruct",
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}
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class DatabricksInferenceAdapter(Inference):
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def __init__(self, config: DatabricksImplConfig) -> 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) -> OpenAI:
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return OpenAI(
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base_url=self.config.url,
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api_key=self.config.api_token
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)
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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 validate_routing_keys(self, routing_keys: list[str]) -> None:
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# these are the model names the Llama Stack will use to route requests to this provider
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# perform validation here if necessary
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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_databricks_messages(self, messages: list[Message]) -> list:
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databricks_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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databricks_messages.append({"role": role, "content": message.content})
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return databricks_messages
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def resolve_databricks_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 DATABRICKS_SUPPORTED_MODELS
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), f"Unsupported model: {model_name}, use one of the supported models: {','.join(DATABRICKS_SUPPORTED_MODELS.keys())}"
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return DATABRICKS_SUPPORTED_MODELS.get(
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model.descriptor(shorten_default_variant=True)
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)
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def get_databricks_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 = augment_messages_for_tools(request)
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options = self.get_databricks_chat_options(request)
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databricks_model = self.resolve_databricks_model(request.model)
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if not request.stream:
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r = self.client.chat.completions.create(
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model=databricks_model,
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messages=self._messages_to_databricks_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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for chunk in self.client.chat.completions.create(
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model=databricks_model,
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messages=self._messages_to_databricks_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 (
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stop_reason is None
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and chunk.choices[0].finish_reason == "stop"
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):
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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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@ -104,6 +104,17 @@ def available_providers() -> List[ProviderSpec]:
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config_class="llama_stack.providers.adapters.inference.bedrock.BedrockConfig",
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),
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),
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remote_provider_spec(
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api=Api.inference,
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adapter=AdapterSpec(
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adapter_type="databricks",
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pip_packages=[
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"openai",
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],
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module="llama_stack.providers.adapters.inference.databricks",
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config_class="llama_stack.providers.adapters.inference.databricks.DatabricksImplConfig",
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),
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),
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InlineProviderSpec(
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api=Api.inference,
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provider_type="vllm",
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