forked from phoenix-oss/llama-stack-mirror
2232bfa8b5
* RFC-0001-The-Llama-Stack * Add OpenAPI generation utility, update SPEC to reflect latest types * First cut at an observability API * llama3_1 -> llama3 --------- Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
127 lines
3.3 KiB
Python
127 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 datetime import datetime
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from typing import Any, Dict, List, Optional, Protocol
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from llama_models.schema_utils import json_schema_type, webmethod
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from pydantic import BaseModel, Field
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from llama_models.llama3.api.datatypes import * # noqa: F403
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from llama_toolchain.dataset.api.datatypes import * # noqa: F403
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from llama_toolchain.common.training_types import * # noqa: F403
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from .datatypes import * # noqa: F403
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@json_schema_type
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class PostTrainingSFTRequest(BaseModel):
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"""Request to finetune a model."""
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job_uuid: str
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model: str
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dataset: TrainEvalDataset
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validation_dataset: TrainEvalDataset
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algorithm: FinetuningAlgorithm
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algorithm_config: Union[
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LoraFinetuningConfig, QLoraFinetuningConfig, DoraFinetuningConfig
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]
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optimizer_config: OptimizerConfig
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training_config: TrainingConfig
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# TODO: define these
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hyperparam_search_config: Dict[str, Any]
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logger_config: Dict[str, Any]
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@json_schema_type
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class PostTrainingRLHFRequest(BaseModel):
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"""Request to finetune a model."""
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job_uuid: str
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finetuned_model: URL
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dataset: TrainEvalDataset
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validation_dataset: TrainEvalDataset
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algorithm: RLHFAlgorithm
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algorithm_config: Union[DPOAlignmentConfig]
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optimizer_config: OptimizerConfig
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training_config: TrainingConfig
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# TODO: define these
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hyperparam_search_config: Dict[str, Any]
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logger_config: Dict[str, Any]
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class PostTrainingJob(BaseModel):
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job_uuid: str
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@json_schema_type
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class PostTrainingJobStatusResponse(BaseModel):
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"""Status of a finetuning job."""
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job_uuid: str
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status: PostTrainingJobStatus
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scheduled_at: Optional[datetime] = None
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started_at: Optional[datetime] = None
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completed_at: Optional[datetime] = None
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resources_allocated: Optional[Dict[str, Any]] = None
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checkpoints: List[Checkpoint] = Field(default_factory=list)
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@json_schema_type
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class PostTrainingJobArtifactsResponse(BaseModel):
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"""Artifacts of a finetuning job."""
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job_uuid: str
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checkpoints: List[Checkpoint] = Field(default_factory=list)
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# TODO(ashwin): metrics, evals
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class PostTraining(Protocol):
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@webmethod(route="/post_training/supervised_fine_tune")
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def supervised_fine_tune(
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self,
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request: PostTrainingSFTRequest,
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) -> PostTrainingJob: ...
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@webmethod(route="/post_training/preference_optimize")
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def preference_optimize(
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self,
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request: PostTrainingRLHFRequest,
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) -> PostTrainingJob: ...
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@webmethod(route="/post_training/jobs")
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def get_training_jobs(self) -> List[PostTrainingJob]: ...
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# sends SSE stream of logs
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@webmethod(route="/post_training/job/logs")
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def get_training_job_logstream(self, job_uuid: str) -> PostTrainingJobLogStream: ...
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@webmethod(route="/post_training/job/status")
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def get_training_job_status(
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self, job_uuid: str
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) -> PostTrainingJobStatusResponse: ...
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@webmethod(route="/post_training/job/cancel")
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def cancel_training_job(self, job_uuid: str) -> None: ...
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@webmethod(route="/post_training/job/artifacts")
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def get_training_job_artifacts(
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self, job_uuid: str
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) -> PostTrainingJobArtifactsResponse: ...
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