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https://github.com/meta-llama/llama-stack.git
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516e1a3e59
# What does this PR do? Adds the sentence transformer provider and the `all-MiniLM-L6-v2` embedding model to the default models to register in the run.yaml for all providers. ## Test Plan llama stack build --template together --image-type conda llama stack run ~/.llama/distributions/llamastack-together/together-run.yaml
117 lines
4.6 KiB
Python
117 lines
4.6 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 import namedtuple
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from typing import List, Optional
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from llama_models.sku_list import all_registered_models
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from llama_stack.apis.models.models import ModelType
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from llama_stack.providers.datatypes import Model, ModelsProtocolPrivate
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from llama_stack.providers.utils.inference import (
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ALL_HUGGINGFACE_REPOS_TO_MODEL_DESCRIPTOR,
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)
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ModelAlias = namedtuple("ModelAlias", ["provider_model_id", "aliases", "llama_model"])
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def get_huggingface_repo(model_descriptor: str) -> Optional[str]:
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for model in all_registered_models():
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if model.descriptor() == model_descriptor:
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return model.huggingface_repo
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return None
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def build_model_alias(provider_model_id: str, model_descriptor: str) -> ModelAlias:
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return ModelAlias(
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provider_model_id=provider_model_id,
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aliases=[
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get_huggingface_repo(model_descriptor),
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],
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llama_model=model_descriptor,
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)
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def build_model_alias_with_just_provider_model_id(
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provider_model_id: str, model_descriptor: str
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) -> ModelAlias:
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return ModelAlias(
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provider_model_id=provider_model_id,
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aliases=[],
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llama_model=model_descriptor,
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)
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class ModelRegistryHelper(ModelsProtocolPrivate):
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def __init__(self, model_aliases: List[ModelAlias]):
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self.alias_to_provider_id_map = {}
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self.provider_id_to_llama_model_map = {}
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for alias_obj in model_aliases:
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for alias in alias_obj.aliases:
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self.alias_to_provider_id_map[alias] = alias_obj.provider_model_id
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# also add a mapping from provider model id to itself for easy lookup
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self.alias_to_provider_id_map[alias_obj.provider_model_id] = (
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alias_obj.provider_model_id
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)
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# ensure we can go from llama model to provider model id
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self.alias_to_provider_id_map[alias_obj.llama_model] = (
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alias_obj.provider_model_id
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)
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self.provider_id_to_llama_model_map[alias_obj.provider_model_id] = (
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alias_obj.llama_model
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)
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def get_provider_model_id(self, identifier: str) -> str:
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if identifier in self.alias_to_provider_id_map:
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return self.alias_to_provider_id_map[identifier]
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else:
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return None
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def get_llama_model(self, provider_model_id: str) -> str:
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if provider_model_id in self.provider_id_to_llama_model_map:
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return self.provider_id_to_llama_model_map[provider_model_id]
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else:
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return None
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async def register_model(self, model: Model) -> Model:
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if model.model_type == ModelType.embedding:
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# embedding models are always registered by their provider model id and does not need to be mapped to a llama model
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provider_resource_id = model.provider_resource_id
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else:
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provider_resource_id = self.get_provider_model_id(
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model.provider_resource_id
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)
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if provider_resource_id:
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model.provider_resource_id = provider_resource_id
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else:
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if model.metadata.get("llama_model") is None:
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raise ValueError(
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f"Model '{model.provider_resource_id}' is not available and no llama_model was specified in metadata. "
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"Please specify a llama_model in metadata or use a supported model identifier"
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)
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existing_llama_model = self.get_llama_model(model.provider_resource_id)
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if existing_llama_model:
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if existing_llama_model != model.metadata["llama_model"]:
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raise ValueError(
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f"Provider model id '{model.provider_resource_id}' is already registered to a different llama model: '{existing_llama_model}'"
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)
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else:
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if (
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model.metadata["llama_model"]
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not in ALL_HUGGINGFACE_REPOS_TO_MODEL_DESCRIPTOR
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):
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raise ValueError(
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f"Invalid llama_model '{model.metadata['llama_model']}' specified in metadata. "
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f"Must be one of: {', '.join(ALL_HUGGINGFACE_REPOS_TO_MODEL_DESCRIPTOR.keys())}"
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)
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self.provider_id_to_llama_model_map[model.provider_resource_id] = (
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ALL_HUGGINGFACE_REPOS_TO_MODEL_DESCRIPTOR[
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model.metadata["llama_model"]
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]
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)
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return model
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