# import ldm.modules.encoders.modules # import open_clip # import torch # import transformers.utils.hub # # from modules import shared # # # class ReplaceHelper: # def __init__(self): # self.replaced = [] # # def replace(self, obj, field, func): # original = getattr(obj, field, None) # if original is None: # return None # # self.replaced.append((obj, field, original)) # setattr(obj, field, func) # # return original # # def restore(self): # for obj, field, original in self.replaced: # setattr(obj, field, original) # # self.replaced.clear() # # # class DisableInitialization(ReplaceHelper): # """ # When an object of this class enters a `with` block, it starts: # - preventing torch's layer initialization functions from working # - changes CLIP and OpenCLIP to not download model weights # - changes CLIP to not make requests to check if there is a new version of a file you already have # # When it leaves the block, it reverts everything to how it was before. # # Use it like this: # ``` # with DisableInitialization(): # do_things() # ``` # """ # # def __init__(self, disable_clip=True): # super().__init__() # self.disable_clip = disable_clip # # def replace(self, obj, field, func): # original = getattr(obj, field, None) # if original is None: # return None # # self.replaced.append((obj, field, original)) # setattr(obj, field, func) # # return original # # def __enter__(self): # def do_nothing(*args, **kwargs): # pass # # def create_model_and_transforms_without_pretrained(*args, pretrained=None, **kwargs): # return self.create_model_and_transforms(*args, pretrained=None, **kwargs) # # def CLIPTextModel_from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs): # res = self.CLIPTextModel_from_pretrained(None, *model_args, config=pretrained_model_name_or_path, state_dict={}, **kwargs) # res.name_or_path = pretrained_model_name_or_path # return res # # def transformers_modeling_utils_load_pretrained_model(*args, **kwargs): # args = args[0:3] + ('/', ) + args[4:] # resolved_archive_file; must set it to something to prevent what seems to be a bug # return self.transformers_modeling_utils_load_pretrained_model(*args, **kwargs) # # def transformers_utils_hub_get_file_from_cache(original, url, *args, **kwargs): # # # this file is always 404, prevent making request # if url == 'https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/added_tokens.json' or url == 'openai/clip-vit-large-patch14' and args[0] == 'added_tokens.json': # return None # # try: # res = original(url, *args, local_files_only=True, **kwargs) # if res is None: # res = original(url, *args, local_files_only=False, **kwargs) # return res # except Exception: # return original(url, *args, local_files_only=False, **kwargs) # # def transformers_utils_hub_get_from_cache(url, *args, local_files_only=False, **kwargs): # return transformers_utils_hub_get_file_from_cache(self.transformers_utils_hub_get_from_cache, url, *args, **kwargs) # # def transformers_tokenization_utils_base_cached_file(url, *args, local_files_only=False, **kwargs): # return transformers_utils_hub_get_file_from_cache(self.transformers_tokenization_utils_base_cached_file, url, *args, **kwargs) # # def transformers_configuration_utils_cached_file(url, *args, local_files_only=False, **kwargs): # return transformers_utils_hub_get_file_from_cache(self.transformers_configuration_utils_cached_file, url, *args, **kwargs) # # self.replace(torch.nn.init, 'kaiming_uniform_', do_nothing) # self.replace(torch.nn.init, '_no_grad_normal_', do_nothing) # self.replace(torch.nn.init, '_no_grad_uniform_', do_nothing) # # if self.disable_clip: # self.create_model_and_transforms = self.replace(open_clip, 'create_model_and_transforms', create_model_and_transforms_without_pretrained) # self.CLIPTextModel_from_pretrained = self.replace(ldm.modules.encoders.modules.CLIPTextModel, 'from_pretrained', CLIPTextModel_from_pretrained) # self.transformers_modeling_utils_load_pretrained_model = self.replace(transformers.modeling_utils.PreTrainedModel, '_load_pretrained_model', transformers_modeling_utils_load_pretrained_model) # self.transformers_tokenization_utils_base_cached_file = self.replace(transformers.tokenization_utils_base, 'cached_file', transformers_tokenization_utils_base_cached_file) # self.transformers_configuration_utils_cached_file = self.replace(transformers.configuration_utils, 'cached_file', transformers_configuration_utils_cached_file) # self.transformers_utils_hub_get_from_cache = self.replace(transformers.utils.hub, 'get_from_cache', transformers_utils_hub_get_from_cache) # # def __exit__(self, exc_type, exc_val, exc_tb): # self.restore() # # # class InitializeOnMeta(ReplaceHelper): # """ # Context manager that causes all parameters for linear/conv2d/mha layers to be allocated on meta device, # which results in those parameters having no values and taking no memory. model.to() will be broken and # will need to be repaired by using LoadStateDictOnMeta below when loading params from state dict. # # Usage: # ``` # with sd_disable_initialization.InitializeOnMeta(): # sd_model = instantiate_from_config(sd_config.model) # ``` # """ # # def __enter__(self): # if shared.cmd_opts.disable_model_loading_ram_optimization: # return # # def set_device(x): # x["device"] = "meta" # return x # # linear_init = self.replace(torch.nn.Linear, '__init__', lambda *args, **kwargs: linear_init(*args, **set_device(kwargs))) # conv2d_init = self.replace(torch.nn.Conv2d, '__init__', lambda *args, **kwargs: conv2d_init(*args, **set_device(kwargs))) # mha_init = self.replace(torch.nn.MultiheadAttention, '__init__', lambda *args, **kwargs: mha_init(*args, **set_device(kwargs))) # self.replace(torch.nn.Module, 'to', lambda *args, **kwargs: None) # # def __exit__(self, exc_type, exc_val, exc_tb): # self.restore() # # # class LoadStateDictOnMeta(ReplaceHelper): # """ # Context manager that allows to read parameters from state_dict into a model that has some of its parameters in the meta device. # As those parameters are read from state_dict, they will be deleted from it, so by the end state_dict will be mostly empty, to save memory. # Meant to be used together with InitializeOnMeta above. # # Usage: # ``` # with sd_disable_initialization.LoadStateDictOnMeta(state_dict): # model.load_state_dict(state_dict, strict=False) # ``` # """ # # def __init__(self, state_dict, device, weight_dtype_conversion=None): # super().__init__() # self.state_dict = state_dict # self.device = device # self.weight_dtype_conversion = weight_dtype_conversion or {} # self.default_dtype = self.weight_dtype_conversion.get('') # # def get_weight_dtype(self, key): # key_first_term, _ = key.split('.', 1) # return self.weight_dtype_conversion.get(key_first_term, self.default_dtype) # # def __enter__(self): # if shared.cmd_opts.disable_model_loading_ram_optimization: # return # # sd = self.state_dict # device = self.device # # def load_from_state_dict(original, module, state_dict, prefix, *args, **kwargs): # used_param_keys = [] # # for name, param in module._parameters.items(): # if param is None: # continue # # key = prefix + name # sd_param = sd.pop(key, None) # if sd_param is not None: # state_dict[key] = sd_param.to(dtype=self.get_weight_dtype(key)) # used_param_keys.append(key) # # if param.is_meta: # dtype = sd_param.dtype if sd_param is not None else param.dtype # module._parameters[name] = torch.nn.parameter.Parameter(torch.zeros_like(param, device=device, dtype=dtype), requires_grad=param.requires_grad) # # for name in module._buffers: # key = prefix + name # # sd_param = sd.pop(key, None) # if sd_param is not None: # state_dict[key] = sd_param # used_param_keys.append(key) # # original(module, state_dict, prefix, *args, **kwargs) # # for key in used_param_keys: # state_dict.pop(key, None) # # def load_state_dict(original, module, state_dict, strict=True): # """torch makes a lot of copies of the dictionary with weights, so just deleting entries from state_dict does not help # because the same values are stored in multiple copies of the dict. The trick used here is to give torch a dict with # all weights on meta device, i.e. deleted, and then it doesn't matter how many copies torch makes. # # In _load_from_state_dict, the correct weight will be obtained from a single dict with the right weights (sd). # # The dangerous thing about this is if _load_from_state_dict is not called, (if some exotic module overloads # the function and does not call the original) the state dict will just fail to load because weights # would be on the meta device. # """ # # if state_dict is sd: # state_dict = {k: v.to(device="meta", dtype=v.dtype) for k, v in state_dict.items()} # # original(module, state_dict, strict=strict) # # module_load_state_dict = self.replace(torch.nn.Module, 'load_state_dict', lambda *args, **kwargs: load_state_dict(module_load_state_dict, *args, **kwargs)) # module_load_from_state_dict = self.replace(torch.nn.Module, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(module_load_from_state_dict, *args, **kwargs)) # linear_load_from_state_dict = self.replace(torch.nn.Linear, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(linear_load_from_state_dict, *args, **kwargs)) # conv2d_load_from_state_dict = self.replace(torch.nn.Conv2d, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(conv2d_load_from_state_dict, *args, **kwargs)) # mha_load_from_state_dict = self.replace(torch.nn.MultiheadAttention, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(mha_load_from_state_dict, *args, **kwargs)) # layer_norm_load_from_state_dict = self.replace(torch.nn.LayerNorm, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(layer_norm_load_from_state_dict, *args, **kwargs)) # group_norm_load_from_state_dict = self.replace(torch.nn.GroupNorm, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(group_norm_load_from_state_dict, *args, **kwargs)) # # def __exit__(self, exc_type, exc_val, exc_tb): # self.restore()