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# 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() | |