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d266c59c2a
# What does this PR do? remove unused chat_completion implementations vllm features ported - - requires max_tokens be set, use config value - set tool_choice to none if no tools provided ## Test Plan ci
206 lines
8.1 KiB
Python
206 lines
8.1 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 openai import AsyncOpenAI
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from together import AsyncTogether
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from together.constants import BASE_URL
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from llama_stack.apis.inference import (
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ChatCompletionRequest,
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Inference,
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LogProbConfig,
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OpenAIEmbeddingsResponse,
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ResponseFormat,
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ResponseFormatType,
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SamplingParams,
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)
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from llama_stack.apis.inference.inference import OpenAIEmbeddingUsage
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from llama_stack.apis.models import Model, ModelType
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from llama_stack.core.request_headers import NeedsRequestProviderData
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from llama_stack.log import get_logger
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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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convert_message_to_openai_dict,
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get_sampling_options,
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)
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from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
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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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request_has_media,
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)
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from .config import TogetherImplConfig
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logger = get_logger(name=__name__, category="inference::together")
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class TogetherInferenceAdapter(OpenAIMixin, Inference, NeedsRequestProviderData):
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embedding_model_metadata = {
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"togethercomputer/m2-bert-80M-32k-retrieval": {"embedding_dimension": 768, "context_length": 32768},
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"BAAI/bge-large-en-v1.5": {"embedding_dimension": 1024, "context_length": 512},
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"BAAI/bge-base-en-v1.5": {"embedding_dimension": 768, "context_length": 512},
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"Alibaba-NLP/gte-modernbert-base": {"embedding_dimension": 768, "context_length": 8192},
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"intfloat/multilingual-e5-large-instruct": {"embedding_dimension": 1024, "context_length": 512},
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}
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def __init__(self, config: TogetherImplConfig) -> None:
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ModelRegistryHelper.__init__(self)
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self.config = config
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self.allowed_models = config.allowed_models
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self._model_cache: dict[str, Model] = {}
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def get_api_key(self):
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return self.config.api_key.get_secret_value()
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def get_base_url(self):
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return BASE_URL
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async def initialize(self) -> None:
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pass
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async def shutdown(self) -> None:
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pass
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def _get_client(self) -> AsyncTogether:
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together_api_key = None
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config_api_key = self.config.api_key.get_secret_value() if self.config.api_key else None
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if config_api_key:
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together_api_key = config_api_key
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else:
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provider_data = self.get_request_provider_data()
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if provider_data is None or not provider_data.together_api_key:
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raise ValueError(
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'Pass Together API Key in the header X-LlamaStack-Provider-Data as { "together_api_key": <your api key>}'
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)
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together_api_key = provider_data.together_api_key
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return AsyncTogether(api_key=together_api_key)
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def _get_openai_client(self) -> AsyncOpenAI:
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together_client = self._get_client().client
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return AsyncOpenAI(
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base_url=together_client.base_url,
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api_key=together_client.api_key,
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)
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def _build_options(
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self,
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sampling_params: SamplingParams | None,
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logprobs: LogProbConfig | None,
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fmt: ResponseFormat,
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) -> dict:
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options = get_sampling_options(sampling_params)
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if fmt:
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if fmt.type == ResponseFormatType.json_schema.value:
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options["response_format"] = {
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"type": "json_object",
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"schema": fmt.json_schema,
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}
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elif fmt.type == ResponseFormatType.grammar.value:
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raise NotImplementedError("Grammar response format not supported yet")
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else:
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raise ValueError(f"Unknown response format {fmt.type}")
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if logprobs and logprobs.top_k:
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if logprobs.top_k != 1:
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raise ValueError(
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f"Unsupported value: Together only supports logprobs top_k=1. {logprobs.top_k} was provided",
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)
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options["logprobs"] = 1
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return options
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async def _get_params(self, request: ChatCompletionRequest) -> dict:
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input_dict = {}
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media_present = request_has_media(request)
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llama_model = self.get_llama_model(request.model)
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if media_present or not llama_model:
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input_dict["messages"] = [await convert_message_to_openai_dict(m) for m in request.messages]
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else:
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input_dict["prompt"] = await chat_completion_request_to_prompt(request, llama_model)
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params = {
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"model": request.model,
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**input_dict,
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"stream": request.stream,
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**self._build_options(request.sampling_params, request.logprobs, request.response_format),
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}
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logger.debug(f"params to together: {params}")
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return params
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async def list_models(self) -> list[Model] | None:
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self._model_cache = {}
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# Together's /v1/models is not compatible with OpenAI's /v1/models. Together support ticket #13355 -> will not fix, use Together's own client
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for m in await self._get_client().models.list():
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if m.type == "embedding":
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if m.id not in self.embedding_model_metadata:
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logger.warning(f"Unknown embedding dimension for model {m.id}, skipping.")
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continue
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metadata = self.embedding_model_metadata[m.id]
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self._model_cache[m.id] = Model(
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provider_id=self.__provider_id__,
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provider_resource_id=m.id,
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identifier=m.id,
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model_type=ModelType.embedding,
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metadata=metadata,
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)
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else:
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self._model_cache[m.id] = Model(
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provider_id=self.__provider_id__,
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provider_resource_id=m.id,
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identifier=m.id,
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model_type=ModelType.llm,
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)
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return self._model_cache.values()
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async def should_refresh_models(self) -> bool:
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return True
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async def check_model_availability(self, model):
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return model in self._model_cache
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async def openai_embeddings(
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self,
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model: str,
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input: str | list[str],
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encoding_format: str | None = "float",
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dimensions: int | None = None,
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user: str | None = None,
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) -> OpenAIEmbeddingsResponse:
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"""
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Together's OpenAI-compatible embeddings endpoint is not compatible with
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the standard OpenAI embeddings endpoint.
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The endpoint -
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- not all models return usage information
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- does not support user param, returns 400 Unrecognized request arguments supplied: user
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- does not support dimensions param, returns 400 Unrecognized request arguments supplied: dimensions
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"""
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# Together support ticket #13332 -> will not fix
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if user is not None:
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raise ValueError("Together's embeddings endpoint does not support user param.")
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# Together support ticket #13333 -> escalated
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if dimensions is not None:
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raise ValueError("Together's embeddings endpoint does not support dimensions param.")
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response = await self.client.embeddings.create(
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model=await self._get_provider_model_id(model),
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input=input,
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encoding_format=encoding_format,
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)
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response.model = model # return the user the same model id they provided, avoid exposing the provider model id
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# Together support ticket #13330 -> escalated
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# - togethercomputer/m2-bert-80M-32k-retrieval *does not* return usage information
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if not hasattr(response, "usage") or response.usage is None:
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logger.warning(
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f"Together's embedding endpoint for {model} did not return usage information, substituting -1s."
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)
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response.usage = OpenAIEmbeddingUsage(prompt_tokens=-1, total_tokens=-1)
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return response
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