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https://github.com/meta-llama/llama-stack.git
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94b0592240
Adds type stubs and fixes mypy errors for better type coverage. Changes: - Added type_checking dependency group with type stubs (torchtune, trl, etc.) - Added lm-format-enforcer to pre-commit hook - Created HFAutoModel Protocol for type-safe HuggingFace model handling - Added mypy.overrides for untyped libraries (torchtune, fairscale, etc.) - Fixed type issues in post-training providers, databricks, and api_recorder Note: ~1,200 errors remain in excluded files (see pyproject.toml exclude list). --------- Co-authored-by: Claude <noreply@anthropic.com>
106 lines
4.7 KiB
Python
106 lines
4.7 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 collections.abc import Iterable
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from typing import Any, cast
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from together import AsyncTogether # type: ignore[import-untyped]
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from together.constants import BASE_URL # type: ignore[import-untyped]
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from llama_stack.apis.inference import (
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OpenAIEmbeddingsRequestWithExtraBody,
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OpenAIEmbeddingsResponse,
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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
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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.openai_mixin import OpenAIMixin
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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, NeedsRequestProviderData):
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config: TogetherImplConfig
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embedding_model_metadata: dict[str, dict[str, int]] = {
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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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_model_cache: dict[str, Model] = {}
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provider_data_api_key_field: str = "together_api_key"
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def get_base_url(self):
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return BASE_URL
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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.auth_credential.get_secret_value() if self.config.auth_credential 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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async def list_provider_model_ids(self) -> Iterable[str]:
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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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return [m.id for m in await self._get_client().models.list()]
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async def openai_embeddings(
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self,
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params: OpenAIEmbeddingsRequestWithExtraBody,
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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 params.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 params.dimensions is not None:
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raise ValueError("Together's embeddings endpoint does not support dimensions param.")
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# Cast encoding_format to match OpenAI SDK's expected Literal type
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response = await self.client.embeddings.create(
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model=await self._get_provider_model_id(params.model),
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input=params.input,
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encoding_format=cast(Any, params.encoding_format),
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)
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response.model = (
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params.model
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) # 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 {params.model} did not return usage information, substituting -1s."
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)
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# Cast to allow monkey-patching the response object
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response.usage = cast(Any, OpenAIEmbeddingUsage(prompt_tokens=-1, total_tokens=-1))
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# Together's CreateEmbeddingResponse is compatible with OpenAIEmbeddingsResponse after monkey-patching
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return cast(OpenAIEmbeddingsResponse, response)
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