95 lines
5.8 KiB
Python
95 lines
5.8 KiB
Python
import ldm.modules.encoders.modules
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import open_clip
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import torch
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import transformers.utils.hub
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class DisableInitialization:
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"""
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When an object of this class enters a `with` block, it starts:
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- preventing torch's layer initialization functions from working
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- changes CLIP and OpenCLIP to not download model weights
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- changes CLIP to not make requests to check if there is a new version of a file you already have
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When it leaves the block, it reverts everything to how it was before.
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Use it like this:
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```
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with DisableInitialization():
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do_things()
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```
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"""
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def __enter__(self):
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def do_nothing(*args, **kwargs):
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pass
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def create_model_and_transforms_without_pretrained(*args, pretrained=None, **kwargs):
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return self.create_model_and_transforms(*args, pretrained=None, **kwargs)
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def CLIPTextModel_from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs):
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return self.CLIPTextModel_from_pretrained(None, *model_args, config=pretrained_model_name_or_path, state_dict={}, **kwargs)
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def transformers_modeling_utils_load_pretrained_model(*args, **kwargs):
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args = args[0:3] + ('/', ) + args[4:] # resolved_archive_file; must set it to something to prevent what seems to be a bug
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return self.transformers_modeling_utils_load_pretrained_model(*args, **kwargs)
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def transformers_utils_hub_get_file_from_cache(original, url, *args, **kwargs):
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# this file is always 404, prevent making request
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if url == 'https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/added_tokens.json':
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raise transformers.utils.hub.EntryNotFoundError
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try:
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return original(url, *args, local_files_only=True, **kwargs)
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except Exception as e:
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return original(url, *args, local_files_only=False, **kwargs)
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def transformers_utils_hub_get_from_cache(url, *args, local_files_only=False, **kwargs):
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return transformers_utils_hub_get_file_from_cache(self.transformers_utils_hub_get_from_cache, url, *args, **kwargs)
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def transformers_tokenization_utils_base_cached_file(url, *args, local_files_only=False, **kwargs):
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return transformers_utils_hub_get_file_from_cache(self.transformers_tokenization_utils_base_cached_file, url, *args, **kwargs)
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def transformers_configuration_utils_cached_file(url, *args, local_files_only=False, **kwargs):
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return transformers_utils_hub_get_file_from_cache(self.transformers_configuration_utils_cached_file, url, *args, **kwargs)
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self.init_kaiming_uniform = torch.nn.init.kaiming_uniform_
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self.init_no_grad_normal = torch.nn.init._no_grad_normal_
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self.init_no_grad_uniform_ = torch.nn.init._no_grad_uniform_
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self.create_model_and_transforms = open_clip.create_model_and_transforms
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self.CLIPTextModel_from_pretrained = ldm.modules.encoders.modules.CLIPTextModel.from_pretrained
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self.transformers_modeling_utils_load_pretrained_model = getattr(transformers.modeling_utils.PreTrainedModel, '_load_pretrained_model', None)
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self.transformers_tokenization_utils_base_cached_file = getattr(transformers.tokenization_utils_base, 'cached_file', None)
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self.transformers_configuration_utils_cached_file = getattr(transformers.configuration_utils, 'cached_file', None)
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self.transformers_utils_hub_get_from_cache = getattr(transformers.utils.hub, 'get_from_cache', None)
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torch.nn.init.kaiming_uniform_ = do_nothing
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torch.nn.init._no_grad_normal_ = do_nothing
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torch.nn.init._no_grad_uniform_ = do_nothing
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open_clip.create_model_and_transforms = create_model_and_transforms_without_pretrained
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ldm.modules.encoders.modules.CLIPTextModel.from_pretrained = CLIPTextModel_from_pretrained
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if self.transformers_modeling_utils_load_pretrained_model is not None:
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transformers.modeling_utils.PreTrainedModel._load_pretrained_model = transformers_modeling_utils_load_pretrained_model
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if self.transformers_tokenization_utils_base_cached_file is not None:
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transformers.tokenization_utils_base.cached_file = transformers_tokenization_utils_base_cached_file
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if self.transformers_configuration_utils_cached_file is not None:
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transformers.configuration_utils.cached_file = transformers_configuration_utils_cached_file
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if self.transformers_utils_hub_get_from_cache is not None:
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transformers.utils.hub.get_from_cache = transformers_utils_hub_get_from_cache
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def __exit__(self, exc_type, exc_val, exc_tb):
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torch.nn.init.kaiming_uniform_ = self.init_kaiming_uniform
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torch.nn.init._no_grad_normal_ = self.init_no_grad_normal
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torch.nn.init._no_grad_uniform_ = self.init_no_grad_uniform_
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open_clip.create_model_and_transforms = self.create_model_and_transforms
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ldm.modules.encoders.modules.CLIPTextModel.from_pretrained = self.CLIPTextModel_from_pretrained
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if self.transformers_modeling_utils_load_pretrained_model is not None:
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transformers.modeling_utils.PreTrainedModel._load_pretrained_model = self.transformers_modeling_utils_load_pretrained_model
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if self.transformers_tokenization_utils_base_cached_file is not None:
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transformers.utils.hub.cached_file = self.transformers_tokenization_utils_base_cached_file
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if self.transformers_configuration_utils_cached_file is not None:
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transformers.utils.hub.cached_file = self.transformers_configuration_utils_cached_file
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if self.transformers_utils_hub_get_from_cache is not None:
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transformers.utils.hub.get_from_cache = self.transformers_utils_hub_get_from_cache
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