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https://github.com/meta-llama/llama-stack.git
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71caa271ad
there are likely scenarios where admins of a stack only want to allow clients to fine-tune certain models, register certain models to be fine-tuned. etc introduce the post_training router and post_training_models as the associated type. A different model type needs to be used for inference vs post_training due to the structure of the router currently. Signed-off-by: Charlie Doern <cdoern@redhat.com>
101 lines
3.3 KiB
Python
101 lines
3.3 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 Any
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from llama_stack.apis.models import Model
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from llama_stack.apis.post_training import (
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AlgorithmConfig,
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DPOAlignmentConfig,
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ListPostTrainingJobsResponse,
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PostTraining,
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PostTrainingJob,
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PostTrainingJobArtifactsResponse,
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PostTrainingJobStatusResponse,
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TrainingConfig,
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)
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from llama_stack.log import get_logger
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from llama_stack.providers.datatypes import RoutingTable
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logger = get_logger(name=__name__, category="core")
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class PostTrainingRouter(PostTraining):
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"""Routes to an provider based on the model"""
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async def initialize(self) -> None:
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pass
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def __init__(
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self,
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routing_table: RoutingTable,
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) -> None:
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logger.debug("Initializing InferenceRouter")
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self.routing_table = routing_table
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async def supervised_fine_tune(
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self,
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job_uuid: str,
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training_config: TrainingConfig,
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hyperparam_search_config: dict[str, Any],
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logger_config: dict[str, Any],
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model: str,
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checkpoint_dir: str | None = None,
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algorithm_config: AlgorithmConfig | None = None,
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) -> PostTrainingJob:
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provider = self.routing_table.get_provider_impl(model)
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params = dict(
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job_uuid=job_uuid,
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training_config=training_config,
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hyperparam_search_config=hyperparam_search_config,
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logger_config=logger_config,
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model=model,
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checkpoint_dir=checkpoint_dir,
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algorithm_config=algorithm_config,
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)
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return provider.supervised_fine_tune(**params)
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async def register_model(self, model: Model) -> Model:
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try:
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# get static list of models
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model = await self.register_helper.register_model(model)
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except ValueError:
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# if model is NOT in the list, its probably ok, but warn the user.
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#
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logger.warning(
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f"Model {model.identifier} is not in the model registry for this provider, there might be unexpected issues."
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)
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if model.provider_resource_id is None:
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raise ValueError("Model provider_resource_id cannot be None")
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provider_resource_id = self.register_helper.get_provider_model_id(model.provider_resource_id)
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if provider_resource_id is None:
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provider_resource_id = model.provider_resource_id
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model.provider_resource_id = provider_resource_id
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return model
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async def preference_optimize(
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self,
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job_uuid: str,
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finetuned_model: str,
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algorithm_config: DPOAlignmentConfig,
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training_config: TrainingConfig,
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hyperparam_search_config: dict[str, Any],
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logger_config: dict[str, Any],
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) -> PostTrainingJob:
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pass
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async def get_training_jobs(self) -> ListPostTrainingJobsResponse:
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pass
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async def get_training_job_status(self, job_uuid: str) -> PostTrainingJobStatusResponse | None:
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pass
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async def cancel_training_job(self, job_uuid: str) -> None:
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pass
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async def get_training_job_artifacts(self, job_uuid: str) -> PostTrainingJobArtifactsResponse | None:
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pass
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