| 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, |
| gguf.GGMLQuantizationType.BF16: gguf.BF16, |
| } |
|
|
|
|
| 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) |
|
|
