Initial commit - yes!

llama_toolchain/post_training/api/__init__.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.
+
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described found in the
+# LICENSE file in the root directory of this source tree.
+
+from .datatypes import *  # noqa: F401 F403
+from .endpoints import *  # noqa: F401 F403

llama_toolchain/post_training/api/datatypes.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.
+
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described found in the
+# LICENSE file in the root directory of this source tree.
+
+from enum import Enum
+from typing import List
+
+from pydantic import BaseModel
+
+from strong_typing.schema import json_schema_type
+
+
+class OptimizerType(Enum):
+    adam = "adam"
+    adamw = "adamw"
+    sgd = "sgd"
+
+
+@json_schema_type
+class OptimizerConfig(BaseModel):
+    optimizer_type: OptimizerType
+    lr: float
+    lr_min: float
+    weight_decay: float
+
+
+@json_schema_type
+class TrainingConfig(BaseModel):
+    n_epochs: int
+    batch_size: int
+    shuffle: bool
+    n_iters: int
+
+    enable_activation_checkpointing: bool
+    memory_efficient_fsdp_wrap: bool
+    fsdp_cpu_offload: bool
+
+
+@json_schema_type
+class FinetuningAlgorithm(Enum):
+    full = "full"
+    lora = "lora"
+    qlora = "qlora"
+    dora = "dora"
+
+
+@json_schema_type
+class LoraFinetuningConfig(BaseModel):
+    lora_attn_modules: List[str]
+    apply_lora_to_mlp: bool
+    apply_lora_to_output: bool
+    rank: int
+    alpha: int
+
+
+@json_schema_type
+class QLoraFinetuningConfig(LoraFinetuningConfig):
+    pass
+
+
+@json_schema_type
+class DoraFinetuningConfig(LoraFinetuningConfig):
+    pass
+
+
+@json_schema_type
+class PostTrainingJobLogStream(BaseModel):
+    """Stream of logs from a finetuning job."""
+
+    job_uuid: str
+    log_lines: List[str]
+
+
+@json_schema_type
+class PostTrainingJobStatus(Enum):
+    running = "running"
+    completed = "completed"
+    failed = "failed"
+    scheduled = "scheduled"
+
+
+@json_schema_type
+class RLHFAlgorithm(Enum):
+    dpo = "dpo"
+
+
+@json_schema_type
+class DPOAlignmentConfig(BaseModel):
+    reward_scale: float
+    reward_clip: float
+    epsilon: float
+    gamma: float

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.
+
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described found 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: ...