InvokeAI/invokeai/backend/model_management/model_cache.py

"""
Manage a RAM cache of diffusion/transformer models for fast switching.
They are moved between GPU VRAM and CPU RAM as necessary. If the cache
grows larger than a preset maximum, then the least recently used
model will be cleared and (re)loaded from disk when next needed.

The cache returns context manager generators designed to load the
model into the GPU within the context, and unload outside the
context. Use like this:

   cache = ModelCache(max_models_cached=6)
   with cache.get_model('runwayml/stable-diffusion-1-5') as SD1,
          cache.get_model('stabilityai/stable-diffusion-2') as SD2:
       do_something_in_GPU(SD1,SD2)


"""

import contextlib
import gc
import hashlib
import warnings
from collections.abc import Generator
from enum import Enum
from pathlib import Path
from typing import Sequence, Union

import torch
from diffusers import AutoencoderKL, SchedulerMixin, UNet2DConditionModel
from diffusers import logging as diffusers_logging
from diffusers.pipelines.stable_diffusion.safety_checker import \
    StableDiffusionSafetyChecker
from huggingface_hub import HfApi
from picklescan.scanner import scan_file_path
from pydantic import BaseModel
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
from transformers import logging as transformers_logging

from ..globals import global_cache_dir
from ..stable_diffusion import StableDiffusionGeneratorPipeline
from . import load_pipeline_from_original_stable_diffusion_ckpt

MAX_MODELS = 4

# This is the mapping from the stable diffusion submodel dict key to the class
class SDModelType(Enum):
    diffusion_pipeline=StableDiffusionGeneratorPipeline # whole thing
    vae=AutoencoderKL                                   # parts
    text_encoder=CLIPTextModel
    tokenizer=CLIPTokenizer
    unet=UNet2DConditionModel
    scheduler=SchedulerMixin
    safety_checker=StableDiffusionSafetyChecker
    feature_extractor=CLIPFeatureExtractor
        
# The list of model classes we know how to fetch, for typechecking
ModelClass = Union[tuple([x.value for x in SDModelType])]

# Legacy information needed to load a legacy checkpoint file
class LegacyInfo(BaseModel):
    config_file: Path
    vae_file: Path = None

class UnsafeModelException(Exception):
    "Raised when a legacy model file fails the picklescan test"
    pass

class UnscannableModelException(Exception):
    "Raised when picklescan is unable to scan a legacy model file"
    pass

class ModelCache(object):
    def __init__(
            self,
            max_models: int=MAX_MODELS,
            execution_device: torch.device=torch.device('cuda'),
            storage_device: torch.device=torch.device('cpu'),
            precision: torch.dtype=torch.float16,
            sequential_offload: bool=False,
            sha_chunksize: int = 16777216,
    ):
        '''
        :param max_models: Maximum number of models to cache in CPU RAM [4]
        :param execution_device: Torch device to load active model into [torch.device('cuda')]
        :param storage_device: Torch device to save inactive model in [torch.device('cpu')]
        :param precision: Precision for loaded models [torch.float16]
        :param sequential_offload: Conserve VRAM by loading and unloading each stage of the pipeline sequentially
        :param sha_chunksize: Chunksize to use when calculating sha256 model hash
        '''
        self.models: dict = dict()
        self.stack: Sequence = list()
        self.sequential_offload: bool=sequential_offload
        self.precision: torch.dtype=precision
        self.max_models: int=max_models
        self.execution_device: torch.device=execution_device
        self.storage_device: torch.device=storage_device
        self.sha_chunksize=sha_chunksize

    @contextlib.contextmanager
    def get_model(
            self,
            repo_id_or_path: Union[str,Path],
            model_type: SDModelType=SDModelType.diffusion_pipeline,
            subfolder: Path=None,
            submodel: SDModelType=None,
            revision: str=None,
            legacy_info: LegacyInfo=None,
            gpu_load: bool=True,
            )->Generator[ModelClass, None, None]:
        '''
        Load and return a HuggingFace model wrapped in a context manager generator, with RAM caching.
        Use like this:

              cache = ModelCache()
              with cache.get_model('stabilityai/stable-diffusion-2') as SD2:
                   do_something_with_the_model(SD2)

        The model will be locked into GPU VRAM for the duration of the context.
        :param repo_id_or_path: either the HuggingFace repo_id or a Path to a local model
        :param subfolder: name of a subfolder in which the model can be found, e.g. "vae"
        :param submodel: an SDModelType enum indicating the model part to return, e.g. SDModelType.vae
        :param revision: model revision
        :param model_class: class of model to return
        :param gpu_load: load the model into GPU [default True]
        :param legacy_info: a LegacyInfo object containing additional info needed to load a legacy ckpt
        '''
        key = self._model_key( # internal unique identifier for the model
            repo_id_or_path,
            model_type.value,
            revision,
            subfolder
        ) 
        if key in self.models: # cached - move to bottom of stack
            with contextlib.suppress(ValueError):
                self.stack.remove(key)
                self.stack.append(key)
            model = self.models[key]
        else:  # not cached -load
            self._make_cache_room()
            model = self._load_model_from_storage(
                repo_id_or_path=repo_id_or_path,
                model_class=model_type.value,
                subfolder=subfolder,
                revision=revision,
                legacy_info=legacy_info,
            )
            self.stack.append(key)          # add to LRU cache
            self.models[key]=model          # keep copy of model in dict
            
        if submodel:
            model = getattr(model, submodel.name)
            debugging_name = f'{submodel.name} submodel of {repo_id_or_path}'
        else:
            debugging_name = repo_id_or_path
            
        try:
            if gpu_load and hasattr(model,'to'):
                print(f'  | Loading {debugging_name} into GPU')
                model.to(self.execution_device)  # move into GPU
                self._print_cuda_stats()
            yield model
        finally:
            if gpu_load and hasattr(model,'to'):
                print(f'  | Unloading {debugging_name} from GPU')
                model.to(self.storage_device)
                self._print_cuda_stats()

    def model_hash(self,
                   repo_id_or_path: Union[str,Path],
                   revision: str=None)->str:
        '''
        Given the HF repo id or path to a model on disk, returns a unique
        hash. Works for legacy checkpoint files, HF models on disk, and HF repo IDs
        :param repo_id_or_path: repo_id string or Path to model file/directory on disk.
        :param revision: optional revision string (if fetching a HF repo_id)
        '''
        if self.is_legacy_ckpt(repo_id_or_path):
            return self._legacy_model_hash(repo_id_or_path)
        elif Path(repo_id_or_path).is_dir():
            return self._local_model_hash(repo_id_or_path)
        else:
            return self._hf_commit_hash(repo_id_or_path,revision)

    def cache_size(self)->int:
        "Return the current number of models cached."
        return len(self.models)

    @staticmethod
    def _model_key(path,model_class,revision,subfolder)->str:
        return ':'.join([str(path),model_class.__name__,str(revision or ''),str(subfolder or '')])

    def _has_cuda(self)->bool:
        return self.execution_device.type == 'cuda'

    def _print_cuda_stats(self):
        print(
            "  | Current VRAM usage:",
            "%4.2fG" % (torch.cuda.memory_allocated() / 1e9),
        )

    def _make_cache_room(self):
        models_in_ram = len(self.models)
        while models_in_ram >= self.max_models:
            if least_recently_used_key := self.stack.pop(0):
                print(f'  | Maximum cache size reached: cache_size={models_in_ram}; unloading model {least_recently_used_key}')
                del self.models[least_recently_used_key]
            models_in_ram = len(self.models)
        gc.collect()

    @property
    def current_model(self)->ModelClass:
        '''
        Returns current model.
        '''
        return self.models[self._current_model_key]

    @property
    def _current_model_key(self)->str:
        '''
        Returns key of currently loaded model.
        '''
        return self.stack[-1]

    def _load_model_from_storage(
            self,
            repo_id_or_path: Union[str,Path],
            subfolder: Path=None,
            revision: str=None,
            model_class: ModelClass=StableDiffusionGeneratorPipeline,
            legacy_info: LegacyInfo=None,
            )->ModelClass:
        '''
        Load and return a HuggingFace model.
        :param repo_id_or_path: either the HuggingFace repo_id or a Path to a local model
        :param subfolder: name of a subfolder in which the model can be found, e.g. "vae"
        :param revision: model revision
        :param model_class: class of model to return, defaults to StableDiffusionGeneratorPIpeline
        :param legacy_info: a LegacyInfo object containing additional info needed to load a legacy ckpt
        '''
        # silence transformer and diffuser warnings
        with SilenceWarnings():
            if self.is_legacy_ckpt(repo_id_or_path):
                model = self._load_ckpt_from_storage(repo_id_or_path, legacy_info)
            else:
                model = self._load_diffusers_from_storage(
                    repo_id_or_path,
                    subfolder,
                    revision,
                    model_class,
                )
        if self.sequential_offload and isinstance(model,StableDiffusionGeneratorPipeline):
            model.enable_offload_submodels(self.execution_device)
        elif hasattr(model,'to'):
            model.to(self.execution_device)
        return model

    def _load_diffusers_from_storage(
            self,
            repo_id_or_path: Union[str,Path],
            subfolder: Path=None,
            revision: str=None,
            model_class: ModelClass=StableDiffusionGeneratorPipeline,
    )->ModelClass:
        '''
        Load and return a HuggingFace model using from_pretrained().
        :param repo_id_or_path: either the HuggingFace repo_id or a Path to a local model
        :param subfolder: name of a subfolder in which the model can be found, e.g. "vae"
        :param revision: model revision
        :param model_class: class of model to return, defaults to StableDiffusionGeneratorPIpeline
        '''
        return model_class.from_pretrained(
            repo_id_or_path,
            revision=revision,
            subfolder=subfolder or '.',
            cache_dir=global_cache_dir('hub'),
        )

    @classmethod
    def is_legacy_ckpt(cls, repo_id_or_path: Union[str,Path])->bool:
        '''
        Return true if the indicated path is a legacy checkpoint
        :param repo_id_or_path: either the HuggingFace repo_id or a Path to a local model
        '''
        path = Path(repo_id_or_path)
        return path.is_file() and path.suffix in [".ckpt",".safetensors"]

    @classmethod
    def scan_model(cls, model_name, checkpoint):
        """
        Apply picklescanner to the indicated checkpoint and issue a warning
        and option to exit if an infected file is identified.
        """
        # scan model
        print(f"  | Scanning Model: {model_name}")
        scan_result = scan_file_path(checkpoint)
        if scan_result.infected_files != 0:
            if scan_result.infected_files == 1:
                raise UnsafeModelException("The legacy model you are trying to load may contain malware. Aborting.")
            else:
                raise UnscannableModelException("InvokeAI was unable to scan the legacy model you requested. Aborting")
        else:
            print("  | Model scanned ok")
            
    def _load_ckpt_from_storage(self,
                                ckpt_path: Union[str,Path],
                                legacy_info:LegacyInfo)->StableDiffusionGeneratorPipeline:
        '''
        Load a legacy checkpoint, convert it, and return a StableDiffusionGeneratorPipeline.
        :param ckpt_path: string or Path pointing to the weights file (.ckpt or .safetensors)
        :param legacy_info: LegacyInfo object containing paths to legacy config file and alternate vae if required
        '''
        assert legacy_info is not None
        pipeline = load_pipeline_from_original_stable_diffusion_ckpt(
            checkpoint_path=ckpt_path,
            original_config_file=legacy_info.config_file,
            vae_path=legacy_info.vae_file,
            return_generator_pipeline=True,
            precision=self.precision,
        )
        return pipeline

    def _legacy_model_hash(self, checkpoint_path: Union[str,Path])->str:
        sha = hashlib.sha256()
        path = Path(checkpoint_path)
        assert path.is_file()

        hashpath = path.parent / f"{path.name}.sha256"
        if hashpath.exists() and path.stat().st_mtime <= hashpath.stat().st_mtime:
            with open(hashpath) as f:
                hash = f.read()
            return hash
        
        print(f'  | computing hash of model {path.name}')
        with open(path, "rb") as f:
            while chunk := f.read(self.sha_chunksize):
                sha.update(chunk)
        hash = sha.hexdigest()
                
        with open(hashpath, "w") as f:
            f.write(hash)
        return hash
        
    def _local_model_hash(self, model_path: Union[str,Path])->str:
        sha = hashlib.sha256()
        path = Path(model_path)
        
        hashpath = path / "checksum.sha256"
        if hashpath.exists() and path.stat().st_mtime <= hashpath.stat().st_mtime:
            with open(hashpath) as f:
                hash = f.read()
            return hash
        
        print(f'  | computing hash of model {path.name}')
        for file in list(path.rglob("*.ckpt")) \
            + list(path.rglob("*.safetensors")) \
            + list(path.rglob("*.pth")):
            with open(file, "rb") as f:
                while chunk := f.read(self.sha_chunksize):
                    sha.update(chunk)
        hash = sha.hexdigest()
        with open(hashpath, "w") as f:
            f.write(hash)
        return hash
    
    def _hf_commit_hash(self, repo_id: str, revision: str='main')->str:
        api = HfApi()
        info = api.list_repo_refs(
            repo_id=repo_id,
            repo_type='model',
        )
        desired_revisions = [branch for branch in info.branches if branch.name==revision]
        if not desired_revisions:
            raise KeyError(f"Revision '{revision}' not found in {repo_id}")
        return desired_revisions[0].target_commit

class SilenceWarnings(object):
    def __init__(self):
        self.transformers_verbosity = transformers_logging.get_verbosity()
        self.diffusers_verbosity = diffusers_logging.get_verbosity()
        
    def __enter__(self):
        transformers_logging.set_verbosity_error()
        diffusers_logging.set_verbosity_error()
        warnings.simplefilter('ignore')

    def __exit__(self,type,value,traceback):
        transformers_logging.set_verbosity(self.transformers_verbosity)
        diffusers_logging.set_verbosity(self.diffusers_verbosity)
        warnings.simplefilter('default')