litellm-mirror/litellm/llms/diffusers/fine_tuning/handler.py

from typing import Any, Coroutine, Optional, Union, Dict, List
import logging

try:
    from dataclasses import dataclass
    import torch
    from diffusers import UNet2DConditionModel
    from diffusers.optimization import get_scheduler
    from transformers import CLIPTextModel, CLIPTokenizer
except:
    pass

verbose_logger = logging.getLogger(__name__)


@dataclass
class FineTuningJob:
    id: str
    status: str
    model: str
    created_at: int
    hyperparameters: Dict[str, Any]
    result_files: List[str]


class DiffusersFineTuningAPI:
    """
    Diffusers implementation for fine-tuning stable diffusion models locally
    """

    def __init__(self) -> None:
        self.jobs: Dict[str, FineTuningJob] = {}
        super().__init__()

    async def _train_diffusers_model(
        self,
        training_data: str,
        base_model: str = "stabilityai/stable-diffusion-2",
        output_dir: str = "./fine_tuned_model",
        learning_rate: float = 5e-6,
        train_batch_size: int = 1,
        max_train_steps: int = 500,
        gradient_accumulation_steps: int = 1,
        mixed_precision: str = "fp16",
    ) -> FineTuningJob:
        """Actual training implementation for diffusers"""
        job_id = f"ftjob_{len(self.jobs)+1}"
        job = FineTuningJob(
            id=job_id,
            status="running",
            model=base_model,
            created_at=int(time.time()),
            hyperparameters={
                "learning_rate": learning_rate,
                "batch_size": train_batch_size,
                "steps": max_train_steps,
            },
            result_files=[output_dir],
        )
        self.jobs[job_id] = job

        try:
            # Load models and create pipeline
            tokenizer = CLIPTokenizer.from_pretrained(base_model, subfolder="tokenizer")
            text_encoder = CLIPTextModel.from_pretrained(
                base_model, subfolder="text_encoder"
            )
            unet = UNet2DConditionModel.from_pretrained(base_model, subfolder="unet")

            # Optimizer and scheduler
            optimizer = torch.optim.AdamW(
                unet.parameters(),
                lr=learning_rate,
            )

            lr_scheduler = get_scheduler(
                "linear",
                optimizer=optimizer,
                num_warmup_steps=0,
                num_training_steps=max_train_steps,
            )

            # Training loop would go here
            # This is simplified - actual implementation would need:
            # 1. Data loading from training_data path
            # 2. Proper training loop with forward/backward passes
            # 3. Saving checkpoints

            # Simulate training
            for step in range(max_train_steps):
                if step % 10 == 0:
                    verbose_logger.debug(f"Training step {step}/{max_train_steps}")

            # Save the trained model
            unet.save_pretrained(f"{output_dir}/unet")
            text_encoder.save_pretrained(f"{output_dir}/text_encoder")

            job.status = "succeeded"
            return job

        except Exception as e:
            job.status = "failed"
            verbose_logger.error(f"Training failed: {str(e)}")
            raise

    async def acreate_fine_tuning_job(
        self,
        create_fine_tuning_job_data: dict,
    ) -> FineTuningJob:
        """Create a fine-tuning job asynchronously"""
        return await self._train_diffusers_model(**create_fine_tuning_job_data)

    def create_fine_tuning_job(
        self,
        _is_async: bool,
        create_fine_tuning_job_data: dict,
        **kwargs,
    ) -> Union[FineTuningJob, Coroutine[Any, Any, FineTuningJob]]:
        """Create a fine-tuning job (sync or async)"""
        if _is_async:
            return self.acreate_fine_tuning_job(create_fine_tuning_job_data)
        else:
            # Run async code synchronously
            import asyncio

            return asyncio.run(
                self.acreate_fine_tuning_job(create_fine_tuning_job_data)
            )

    async def alist_fine_tuning_jobs(
        self,
        after: Optional[str] = None,
        limit: Optional[int] = None,
    ):
        """List fine-tuning jobs asynchronously"""
        jobs = list(self.jobs.values())
        if after:
            jobs = [j for j in jobs if j.id > after]
        if limit:
            jobs = jobs[:limit]
        return {"data": jobs}

    def list_fine_tuning_jobs(
        self,
        _is_async: bool,
        after: Optional[str] = None,
        limit: Optional[int] = None,
        **kwargs,
    ):
        """List fine-tuning jobs (sync or async)"""
        if _is_async:
            return self.alist_fine_tuning_jobs(after=after, limit=limit)
        else:
            # Run async code synchronously
            import asyncio

            return asyncio.run(self.alist_fine_tuning_jobs(after=after, limit=limit))

    async def aretrieve_fine_tuning_job(
        self,
        fine_tuning_job_id: str,
    ) -> FineTuningJob:
        """Retrieve a fine-tuning job asynchronously"""
        if fine_tuning_job_id not in self.jobs:
            raise ValueError(f"Job {fine_tuning_job_id} not found")
        return self.jobs[fine_tuning_job_id]

    def retrieve_fine_tuning_job(
        self,
        _is_async: bool,
        fine_tuning_job_id: str,
        **kwargs,
    ):
        """Retrieve a fine-tuning job (sync or async)"""
        if _is_async:
            return self.aretrieve_fine_tuning_job(fine_tuning_job_id)
        else:
            # Run async code synchronously
            import asyncio

            return asyncio.run(self.aretrieve_fine_tuning_job(fine_tuning_job_id))

    async def acancel_fine_tuning_job(
        self,
        fine_tuning_job_id: str,
    ) -> FineTuningJob:
        """Cancel a fine-tuning job asynchronously"""
        if fine_tuning_job_id not in self.jobs:
            raise ValueError(f"Job {fine_tuning_job_id} not found")

        job = self.jobs[fine_tuning_job_id]
        if job.status in ["succeeded", "failed", "cancelled"]:
            raise ValueError(f"Cannot cancel job in status {job.status}")

        job.status = "cancelled"
        return job

    def cancel_fine_tuning_job(
        self,
        _is_async: bool,
        fine_tuning_job_id: str,
        **kwargs,
    ):
        """Cancel a fine-tuning job (sync or async)"""
        if _is_async:
            return self.acancel_fine_tuning_job(fine_tuning_job_id)
        else:
            # Run async code synchronously
            import asyncio

            return asyncio.run(self.acancel_fine_tuning_job(fine_tuning_job_id))