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import torch
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from contextlib import contextmanager
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high_vram = False
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gpu = torch.device('cuda')
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cpu = torch.device('cpu')
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torch.zeros((1, 1)).to(gpu, torch.float32)
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torch.cuda.empty_cache()
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models_in_gpu = []
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@contextmanager
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def movable_bnb_model(m):
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if hasattr(m, 'quantization_method'):
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m.quantization_method_backup = m.quantization_method
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del m.quantization_method
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try:
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yield None
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finally:
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if hasattr(m, 'quantization_method_backup'):
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m.quantization_method = m.quantization_method_backup
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del m.quantization_method_backup
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return
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def load_models_to_gpu(models):
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global models_in_gpu
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if not isinstance(models, (tuple, list)):
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models = [models]
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models_to_remain = [m for m in set(models) if m in models_in_gpu]
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models_to_load = [m for m in set(models) if m not in models_in_gpu]
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models_to_unload = [m for m in set(models_in_gpu) if m not in models_to_remain]
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if not high_vram:
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for m in models_to_unload:
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with movable_bnb_model(m):
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m.to(cpu)
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print('Unload to CPU:', m.__class__.__name__)
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models_in_gpu = models_to_remain
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for m in models_to_load:
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with movable_bnb_model(m):
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m.to(gpu)
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print('Load to GPU:', m.__class__.__name__)
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models_in_gpu = list(set(models_in_gpu + models))
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torch.cuda.empty_cache()
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return
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def unload_all_models(extra_models=None):
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global models_in_gpu
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if extra_models is None:
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extra_models = []
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if not isinstance(extra_models, (tuple, list)):
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extra_models = [extra_models]
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models_in_gpu = list(set(models_in_gpu + extra_models))
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return load_models_to_gpu([])
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