Initial commit

llama_toolchain/post_training/api/endpoints.py

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+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from datetime import datetime
+
+from typing import Any, Dict, List, Optional, Protocol
+
+from pydantic import BaseModel, Field
+
+from pyopenapi import webmethod
+from strong_typing.schema import json_schema_type
+
+from llama_models.llama3_1.api.datatypes import *  # noqa: F403
+from llama_toolchain.dataset.api.datatypes import *  # noqa: F403
+from llama_toolchain.common.training_types import *  # noqa: F403
+from .datatypes import *  # noqa: F403
+
+
+@json_schema_type
+class PostTrainingSFTRequest(BaseModel):
+    """Request to finetune a model."""
+
+    job_uuid: str
+
+    model: PretrainedModel
+    dataset: TrainEvalDataset
+    validation_dataset: TrainEvalDataset
+
+    algorithm: FinetuningAlgorithm
+    algorithm_config: Union[
+        LoraFinetuningConfig, QLoraFinetuningConfig, DoraFinetuningConfig
+    ]
+
+    optimizer_config: OptimizerConfig
+    training_config: TrainingConfig
+
+    # TODO: define these
+    hyperparam_search_config: Dict[str, Any]
+    logger_config: Dict[str, Any]
+
+
+@json_schema_type
+class PostTrainingRLHFRequest(BaseModel):
+    """Request to finetune a model."""
+
+    job_uuid: str
+
+    finetuned_model: URL
+
+    dataset: TrainEvalDataset
+    validation_dataset: TrainEvalDataset
+
+    algorithm: RLHFAlgorithm
+    algorithm_config: Union[DPOAlignmentConfig]
+
+    optimizer_config: OptimizerConfig
+    training_config: TrainingConfig
+
+    # TODO: define these
+    hyperparam_search_config: Dict[str, Any]
+    logger_config: Dict[str, Any]
+
+
+class PostTrainingJob(BaseModel):
+
+    job_uuid: str
+
+
+@json_schema_type
+class PostTrainingJobStatusResponse(BaseModel):
+    """Status of a finetuning job."""
+
+    job_uuid: str
+    status: PostTrainingJobStatus
+
+    scheduled_at: Optional[datetime] = None
+    started_at: Optional[datetime] = None
+    completed_at: Optional[datetime] = None
+
+    resources_allocated: Optional[Dict[str, Any]] = None
+
+    checkpoints: List[Checkpoint] = Field(default_factory=list)
+
+
+@json_schema_type
+class PostTrainingJobArtifactsResponse(BaseModel):
+    """Artifacts of a finetuning job."""
+
+    job_uuid: str
+    checkpoints: List[Checkpoint] = Field(default_factory=list)
+
+    # TODO(ashwin): metrics, evals
+
+
+class PostTraining(Protocol):
+    @webmethod(route="/post_training/supervised_fine_tune")
+    def post_supervised_fine_tune(
+        self,
+        request: PostTrainingSFTRequest,
+    ) -> PostTrainingJob: ...
+
+    @webmethod(route="/post_training/preference_optimize")
+    def post_preference_optimize(
+        self,
+        request: PostTrainingRLHFRequest,
+    ) -> PostTrainingJob: ...
+
+    @webmethod(route="/post_training/jobs")
+    def get_training_jobs(self) -> List[PostTrainingJob]: ...
+
+    # sends SSE stream of logs
+    @webmethod(route="/post_training/job/logs")
+    def get_training_job_logstream(self, job_uuid: str) -> PostTrainingJobLogStream: ...
+
+    @webmethod(route="/post_training/job/status")
+    def get_training_job_status(
+        self, job_uuid: str
+    ) -> PostTrainingJobStatusResponse: ...
+
+    @webmethod(route="/post_training/job/cancel")
+    def cancel_training_job(self, job_uuid: str) -> None: ...
+
+    @webmethod(route="/post_training/job/artifacts")
+    def get_training_job_artifacts(
+        self, job_uuid: str
+    ) -> PostTrainingJobArtifactsResponse: ...