|
|
| import torch
|
| import numbers
|
| from peft import LoraConfig
|
|
|
|
|
| def get_loraconfig(transformer, rank=128, alpha=128, init_lora_weights="gaussian"):
|
| target_modules = []
|
| for name, module in transformer.named_modules():
|
| if "blocks" in name and "face" not in name and "modulation" not in name and isinstance(module, torch.nn.Linear):
|
| target_modules.append(name)
|
|
|
| transformer_lora_config = LoraConfig(
|
| r=rank,
|
| lora_alpha=alpha,
|
| init_lora_weights=init_lora_weights,
|
| target_modules=target_modules,
|
| )
|
| return transformer_lora_config
|
|
|
|
|
|
|
| class TensorList(object):
|
|
|
| def __init__(self, tensors):
|
| """
|
| tensors: a list of torch.Tensor objects. No need to have uniform shape.
|
| """
|
| assert isinstance(tensors, (list, tuple))
|
| assert all(isinstance(u, torch.Tensor) for u in tensors)
|
| assert len(set([u.ndim for u in tensors])) == 1
|
| assert len(set([u.dtype for u in tensors])) == 1
|
| assert len(set([u.device for u in tensors])) == 1
|
| self.tensors = tensors
|
|
|
| def to(self, *args, **kwargs):
|
| return TensorList([u.to(*args, **kwargs) for u in self.tensors])
|
|
|
| def size(self, dim):
|
| assert dim == 0, 'only support get the 0th size'
|
| return len(self.tensors)
|
|
|
| def pow(self, *args, **kwargs):
|
| return TensorList([u.pow(*args, **kwargs) for u in self.tensors])
|
|
|
| def squeeze(self, dim):
|
| assert dim != 0
|
| if dim > 0:
|
| dim -= 1
|
| return TensorList([u.squeeze(dim) for u in self.tensors])
|
|
|
| def type(self, *args, **kwargs):
|
| return TensorList([u.type(*args, **kwargs) for u in self.tensors])
|
|
|
| def type_as(self, other):
|
| assert isinstance(other, (torch.Tensor, TensorList))
|
| if isinstance(other, torch.Tensor):
|
| return TensorList([u.type_as(other) for u in self.tensors])
|
| else:
|
| return TensorList([u.type(other.dtype) for u in self.tensors])
|
|
|
| @property
|
| def dtype(self):
|
| return self.tensors[0].dtype
|
|
|
| @property
|
| def device(self):
|
| return self.tensors[0].device
|
|
|
| @property
|
| def ndim(self):
|
| return 1 + self.tensors[0].ndim
|
|
|
| def __getitem__(self, index):
|
| return self.tensors[index]
|
|
|
| def __len__(self):
|
| return len(self.tensors)
|
|
|
| def __add__(self, other):
|
| return self._apply(other, lambda u, v: u + v)
|
|
|
| def __radd__(self, other):
|
| return self._apply(other, lambda u, v: v + u)
|
|
|
| def __sub__(self, other):
|
| return self._apply(other, lambda u, v: u - v)
|
|
|
| def __rsub__(self, other):
|
| return self._apply(other, lambda u, v: v - u)
|
|
|
| def __mul__(self, other):
|
| return self._apply(other, lambda u, v: u * v)
|
|
|
| def __rmul__(self, other):
|
| return self._apply(other, lambda u, v: v * u)
|
|
|
| def __floordiv__(self, other):
|
| return self._apply(other, lambda u, v: u // v)
|
|
|
| def __truediv__(self, other):
|
| return self._apply(other, lambda u, v: u / v)
|
|
|
| def __rfloordiv__(self, other):
|
| return self._apply(other, lambda u, v: v // u)
|
|
|
| def __rtruediv__(self, other):
|
| return self._apply(other, lambda u, v: v / u)
|
|
|
| def __pow__(self, other):
|
| return self._apply(other, lambda u, v: u ** v)
|
|
|
| def __rpow__(self, other):
|
| return self._apply(other, lambda u, v: v ** u)
|
|
|
| def __neg__(self):
|
| return TensorList([-u for u in self.tensors])
|
|
|
| def __iter__(self):
|
| for tensor in self.tensors:
|
| yield tensor
|
|
|
| def __repr__(self):
|
| return 'TensorList: \n' + repr(self.tensors)
|
|
|
| def _apply(self, other, op):
|
| if isinstance(other, (list, tuple, TensorList)) or (
|
| isinstance(other, torch.Tensor) and (
|
| other.numel() > 1 or other.ndim > 1
|
| )
|
| ):
|
| assert len(other) == len(self.tensors)
|
| return TensorList([op(u, v) for u, v in zip(self.tensors, other)])
|
| elif isinstance(other, numbers.Number) or (
|
| isinstance(other, torch.Tensor) and (
|
| other.numel() == 1 and other.ndim <= 1
|
| )
|
| ):
|
| return TensorList([op(u, other) for u in self.tensors])
|
| else:
|
| raise TypeError(
|
| f'unsupported operand for *: "TensorList" and "{type(other)}"'
|
| ) |
