InvokeAI/ldm/invoke/model_cache.py

'''
Manage a cache of Stable Diffusion model files for fast switching. 
They are moved between GPU and CPU as necessary. If CPU memory falls
below a preset minimum, the least recently used model will be
cleared and loaded from disk when next needed.
'''

import torch
import os
import io
import time
import gc
import hashlib
import psutil
import transformers
from sys import getrefcount
from omegaconf import OmegaConf
from omegaconf.errors import ConfigAttributeError
from ldm.util import instantiate_from_config

GIGS=2**30
AVG_MODEL_SIZE=2.1*GIGS
DEFAULT_MIN_AVAIL=2*GIGS

class ModelCache(object):
    def __init__(self, config:OmegaConf, device_type:str, precision:str, min_avail_mem=DEFAULT_MIN_AVAIL):
        '''
        Initialize with the path to the models.yaml config file,
        the torch device type, and precision. The optional
        min_avail_mem argument specifies how much unused system
        (CPU) memory to preserve. The cache of models in RAM will
        grow until this value is approached. Default is 2G.
        '''
        # prevent nasty-looking CLIP log message
        transformers.logging.set_verbosity_error()
        self.config = config
        self.precision = precision
        self.device = torch.device(device_type)
        self.min_avail_mem = min_avail_mem
        self.models = {}
        self.stack = []  # this is an LRU FIFO
        self.current_model = None

    def get_model(self, model_name:str):
        '''
        Given a model named identified in models.yaml, return
        the model object. If in RAM will load into GPU VRAM.
        If on disk, will load from there.
        '''
        if model_name not in self.config:
            print(f'** "{model_name}" is not a known model name. Please check your models.yaml file')
            return None

        if self.current_model != model_name:
            self.unload_model(self.current_model)
        
        if model_name in self.models:
            requested_model = self.models[model_name]['model']
            print(f'>> Retrieving model {model_name} from system RAM cache')
            self.models[model_name]['model'] = self._model_from_cpu(requested_model)
            width = self.models[model_name]['width']
            height = self.models[model_name]['height']
            hash = self.models[model_name]['hash']
        else:
            self._check_memory()
            try:
                requested_model, width, height, hash = self._load_model(model_name)
                self.models[model_name] = {}
                self.models[model_name]['model'] = requested_model
                self.models[model_name]['width'] = width
                self.models[model_name]['height'] = height
                self.models[model_name]['hash'] = hash
            except Exception as e:
                print(f'** model {model_name} could not be loaded: {str(e)}')
                print(f'** restoring {self.current_model}')
                return self.get_model(self.current_model)
        
        self.current_model = model_name
        self._push_newest_model(model_name)
        return {
            'model':requested_model,
            'width':width,
            'height':height,
            'hash': hash
        }

    def list_models(self) -> dict:
        '''
        Return a dict of models in the format:
        { model_name1: {'status': ('active'|'cached'|'not loaded'),
                        'description': description,
                       },
          model_name2: { etc }
        '''
        result = {}
        for name in self.config:
            try:
                description = self.config[name].description
            except ConfigAttributeError:
                description = '<no description>'
            if self.current_model == name:
                status = 'active'
            elif name in self.models:
                status = 'cached'
            else:
                status = 'not loaded'
            result[name]={}
            result[name]['status']=status
            result[name]['description']=description
        return result
    
    def print_models(self):
        '''
        Print a table of models, their descriptions, and load status
        '''
        models = self.list_models()
        for name in models:
            line = f'{name:25s} {models[name]["status"]:>10s}  {models[name]["description"]}'
            if models[name]['status'] == 'active':
                print(f'\033[1m{line}\033[0m')
            else:
                print(line)

    def add_model(self, model_name:str, model_attributes:dict, clobber=False) ->str:
        '''
        Update the named model with a dictionary of attributes. Will fail with an
        assertion error if the name already exists. Pass clobber=True to overwrite.
        On a successful update, the config will be changed in memory and a YAML
        string will be returned.
        '''
        omega = self.config
        # check that all the required fields are present
        for field in ('description','weights','height','width','config'):
            assert field in model_attributes, f'required field {field} is missing'

        assert (clobber or model_name not in omega), f'attempt to overwrite existing model definition "{model_name}"'
        config = omega[model_name] if model_name in omega else {}
        for field in model_attributes:
            config[field] = model_attributes[field]

        omega[model_name] = config
        return OmegaConf.to_yaml(omega)
    
    def _check_memory(self):
        avail_memory = psutil.virtual_memory()[1]
        if AVG_MODEL_SIZE + self.min_avail_mem > avail_memory:
            least_recent_model = self._pop_oldest_model()
            if least_recent_model is not None:
                del self.models[least_recent_model]
                gc.collect()

        
    def _load_model(self, model_name:str):
        """Load and initialize the model from configuration variables passed at object creation time"""
        if model_name not in self.config:
            print(f'"{model_name}" is not a known model name. Please check your models.yaml file')
            return None

        mconfig = self.config[model_name]
        config = mconfig.config
        weights = mconfig.weights
        width = mconfig.width
        height = mconfig.height

        print(f'>> Loading {model_name} from {weights}')

        # for usage statistics
        if self._has_cuda():
            torch.cuda.reset_peak_memory_stats()
            torch.cuda.empty_cache()

        tic = time.time()

        # this does the work
        c     = OmegaConf.load(config)
        with open(weights,'rb') as f:
            weight_bytes = f.read()
        model_hash  = self._cached_sha256(weights,weight_bytes)
        pl_sd = torch.load(io.BytesIO(weight_bytes), map_location='cpu')
        del weight_bytes
        sd    = pl_sd['state_dict']
        model = instantiate_from_config(c.model)
        m, u  = model.load_state_dict(sd, strict=False)

        if self.precision == 'float16':
            print('   | Using faster float16 precision')
            model.to(torch.float16)
        else:
            print('   | Using more accurate float32 precision')

        model.to(self.device)
        # model.to doesn't change the cond_stage_model.device used to move the tokenizer output, so set it here
        model.cond_stage_model.device = self.device
        model.eval()

        for m in model.modules():
            if isinstance(m, (torch.nn.Conv2d, torch.nn.ConvTranspose2d)):
                m._orig_padding_mode = m.padding_mode

        # usage statistics
        toc = time.time()
        print(f'>> Model loaded in', '%4.2fs' % (toc - tic))
        if self._has_cuda():
            print(
                '>> Max VRAM used to load the model:',
                '%4.2fG' % (torch.cuda.max_memory_allocated() / 1e9),
                '\n>> Current VRAM usage:'
                '%4.2fG' % (torch.cuda.memory_allocated() / 1e9),
            )
        return model, width, height, model_hash
        
    def unload_model(self, model_name:str):
        if model_name not in self.models:
            return
        print(f'>> Caching model {model_name} in system RAM')
        model = self.models[model_name]['model']
        self.models[model_name]['model'] = self._model_to_cpu(model)
        gc.collect()
        if self._has_cuda():
            torch.cuda.empty_cache()

    def _model_to_cpu(self,model):
        if self.device != 'cpu':
            model.cond_stage_model.device = 'cpu'
            model.first_stage_model.to('cpu')
            model.cond_stage_model.to('cpu') 
            model.model.to('cpu')
            return model.to('cpu')
        else:
            return model

    def _model_from_cpu(self,model):
        if self.device != 'cpu':
            model.to(self.device)
            model.first_stage_model.to(self.device)
            model.cond_stage_model.to(self.device)
            model.cond_stage_model.device = self.device
        return model

    def _pop_oldest_model(self):
        '''
        Remove the first element of the FIFO, which ought
        to be the least recently accessed model. Do not
        pop the last one, because it is in active use!
        '''
        if len(self.stack) > 1:
            return self.stack.pop(0)

    def _push_newest_model(self,model_name:str):
        '''
        Maintain a simple FIFO. First element is always the
        least recent, and last element is always the most recent.
        '''
        try:
            self.stack.remove(model_name)
        except ValueError:
            pass
        self.stack.append(model_name)
        
    def _has_cuda(self):
        return self.device.type == 'cuda'

    def _cached_sha256(self,path,data):
        dirname    = os.path.dirname(path)
        basename   = os.path.basename(path)
        base, _    = os.path.splitext(basename)
        hashpath   = os.path.join(dirname,base+'.sha256')
        if os.path.exists(hashpath) and os.path.getmtime(path) <= os.path.getmtime(hashpath):
            with open(hashpath) as f:
                hash = f.read()
            return hash
        print(f'>> Calculating sha256 hash of weights file')
        tic = time.time()
        sha = hashlib.sha256()
        sha.update(data)
        hash = sha.hexdigest()
        toc = time.time()
        print(f'>> sha256 = {hash}','(%4.2fs)' % (toc - tic))
        with open(hashpath,'w') as f:
            f.write(hash)
        return hash