forked from phoenix-oss/llama-stack-mirror
127 lines
4.2 KiB
Python
127 lines
4.2 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 llama_models.llama3.api.chat_format import ChatFormat
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from llama_models.llama3.api.datatypes import Message
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from llama_models.llama3.api.tokenizer import Tokenizer
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from openai import OpenAI
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from llama_stack.apis.inference import * # noqa: F403
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from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
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from llama_stack.providers.utils.inference.openai_compat import (
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get_sampling_options,
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process_chat_completion_response,
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process_chat_completion_stream_response,
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)
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from llama_stack.providers.utils.inference.prompt_adapter import (
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chat_completion_request_to_prompt,
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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(ModelRegistryHelper, Inference):
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def __init__(self, config: DatabricksImplConfig) -> None:
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ModelRegistryHelper.__init__(
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self, stack_to_provider_models_map=DATABRICKS_SUPPORTED_MODELS
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)
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self.config = config
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self.formatter = ChatFormat(Tokenizer.get_instance())
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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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response_format: Optional[ResponseFormat] = None,
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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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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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response_format: Optional[ResponseFormat] = None,
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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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client = OpenAI(base_url=self.config.url, api_key=self.config.api_token)
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if stream:
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return self._stream_chat_completion(request, client)
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else:
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return await self._nonstream_chat_completion(request, client)
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async def _nonstream_chat_completion(
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self, request: ChatCompletionRequest, client: OpenAI
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) -> ChatCompletionResponse:
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params = self._get_params(request)
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r = client.completions.create(**params)
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return process_chat_completion_response(r, self.formatter)
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async def _stream_chat_completion(
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self, request: ChatCompletionRequest, client: OpenAI
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) -> AsyncGenerator:
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params = self._get_params(request)
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async def _to_async_generator():
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s = client.completions.create(**params)
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for chunk in s:
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yield chunk
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stream = _to_async_generator()
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async for chunk in process_chat_completion_stream_response(
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stream, self.formatter
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):
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yield chunk
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def _get_params(self, request: ChatCompletionRequest) -> dict:
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return {
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"model": self.map_to_provider_model(request.model),
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"prompt": chat_completion_request_to_prompt(request, self.formatter),
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"stream": request.stream,
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**get_sampling_options(request.sampling_params),
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}
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async def embeddings(
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self,
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model: str,
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contents: List[InterleavedTextMedia],
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) -> EmbeddingsResponse:
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raise NotImplementedError()
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