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https://github.com/lllyasviel/stable-diffusion-webui-forge.git
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72 lines
2.1 KiB
Python
72 lines
2.1 KiB
Python
import gguf
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import torch
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quants_mapping = {
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gguf.GGMLQuantizationType.Q2_K: gguf.Q2_K,
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gguf.GGMLQuantizationType.Q3_K: gguf.Q3_K,
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gguf.GGMLQuantizationType.Q4_0: gguf.Q4_0,
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gguf.GGMLQuantizationType.Q4_K: gguf.Q4_K,
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gguf.GGMLQuantizationType.Q4_1: gguf.Q4_1,
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gguf.GGMLQuantizationType.Q5_0: gguf.Q5_0,
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gguf.GGMLQuantizationType.Q5_1: gguf.Q5_1,
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gguf.GGMLQuantizationType.Q5_K: gguf.Q5_K,
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gguf.GGMLQuantizationType.Q6_K: gguf.Q6_K,
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gguf.GGMLQuantizationType.Q8_0: gguf.Q8_0,
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gguf.GGMLQuantizationType.BF16: gguf.BF16,
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}
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class ParameterGGUF(torch.nn.Parameter):
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def __init__(self, tensor=None, requires_grad=False, no_init=False):
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super().__init__()
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if no_init:
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return
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self.gguf_cls = quants_mapping.get(tensor.tensor_type, None)
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self.real_shape = torch.Size(reversed(list(tensor.shape)))
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self.computation_dtype = torch.float16
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self.baked = False
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return
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@property
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def shape(self):
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return self.real_shape
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def __new__(cls, tensor=None, requires_grad=False, no_init=False):
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return super().__new__(cls, torch.tensor(tensor.data), requires_grad=requires_grad)
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def dequantize_as_pytorch_parameter(self):
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if self.gguf_cls is not None:
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self.gguf_cls.bake(self)
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return torch.nn.Parameter(dequantize_tensor(self), requires_grad=False)
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def copy_with_data(self, data):
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new = ParameterGGUF(data, no_init=True)
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new.gguf_cls = self.gguf_cls
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new.real_shape = self.real_shape
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new.computation_dtype = self.computation_dtype
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new.baked = self.baked
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return new
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def to(self, *args, **kwargs):
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return self.copy_with_data(self.data.to(*args, **kwargs))
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def pin_memory(self, device=None):
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return self.copy_with_data(torch.Tensor.pin_memory(self, device=device))
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def dequantize_tensor(tensor):
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if tensor is None:
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return None
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if not hasattr(tensor, 'gguf_cls'):
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return tensor
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gguf_cls = tensor.gguf_cls
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if gguf_cls is None:
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return tensor
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return gguf_cls.dequantize_pytorch(tensor)
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