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