mirror of
https://github.com/Wan-Video/Wan2.1.git
synced 2025-11-06 06:44:12 +00:00
143 lines
4.4 KiB
Python
143 lines
4.4 KiB
Python
# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
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import torch
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import numbers
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from peft import LoraConfig
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def get_loraconfig(transformer, rank=128, alpha=128, init_lora_weights="gaussian"):
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target_modules = []
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for name, module in transformer.named_modules():
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if "blocks" in name and "face" not in name and "modulation" not in name and isinstance(module, torch.nn.Linear):
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target_modules.append(name)
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transformer_lora_config = LoraConfig(
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r=rank,
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lora_alpha=alpha,
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init_lora_weights=init_lora_weights,
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target_modules=target_modules,
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)
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return transformer_lora_config
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class TensorList(object):
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def __init__(self, tensors):
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"""
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tensors: a list of torch.Tensor objects. No need to have uniform shape.
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"""
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assert isinstance(tensors, (list, tuple))
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assert all(isinstance(u, torch.Tensor) for u in tensors)
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assert len(set([u.ndim for u in tensors])) == 1
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assert len(set([u.dtype for u in tensors])) == 1
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assert len(set([u.device for u in tensors])) == 1
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self.tensors = tensors
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def to(self, *args, **kwargs):
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return TensorList([u.to(*args, **kwargs) for u in self.tensors])
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def size(self, dim):
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assert dim == 0, 'only support get the 0th size'
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return len(self.tensors)
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def pow(self, *args, **kwargs):
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return TensorList([u.pow(*args, **kwargs) for u in self.tensors])
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def squeeze(self, dim):
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assert dim != 0
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if dim > 0:
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dim -= 1
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return TensorList([u.squeeze(dim) for u in self.tensors])
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def type(self, *args, **kwargs):
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return TensorList([u.type(*args, **kwargs) for u in self.tensors])
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def type_as(self, other):
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assert isinstance(other, (torch.Tensor, TensorList))
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if isinstance(other, torch.Tensor):
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return TensorList([u.type_as(other) for u in self.tensors])
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else:
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return TensorList([u.type(other.dtype) for u in self.tensors])
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@property
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def dtype(self):
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return self.tensors[0].dtype
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@property
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def device(self):
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return self.tensors[0].device
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@property
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def ndim(self):
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return 1 + self.tensors[0].ndim
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def __getitem__(self, index):
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return self.tensors[index]
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def __len__(self):
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return len(self.tensors)
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def __add__(self, other):
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return self._apply(other, lambda u, v: u + v)
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def __radd__(self, other):
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return self._apply(other, lambda u, v: v + u)
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def __sub__(self, other):
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return self._apply(other, lambda u, v: u - v)
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def __rsub__(self, other):
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return self._apply(other, lambda u, v: v - u)
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def __mul__(self, other):
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return self._apply(other, lambda u, v: u * v)
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def __rmul__(self, other):
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return self._apply(other, lambda u, v: v * u)
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def __floordiv__(self, other):
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return self._apply(other, lambda u, v: u // v)
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def __truediv__(self, other):
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return self._apply(other, lambda u, v: u / v)
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def __rfloordiv__(self, other):
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return self._apply(other, lambda u, v: v // u)
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def __rtruediv__(self, other):
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return self._apply(other, lambda u, v: v / u)
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def __pow__(self, other):
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return self._apply(other, lambda u, v: u ** v)
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def __rpow__(self, other):
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return self._apply(other, lambda u, v: v ** u)
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def __neg__(self):
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return TensorList([-u for u in self.tensors])
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def __iter__(self):
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for tensor in self.tensors:
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yield tensor
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def __repr__(self):
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return 'TensorList: \n' + repr(self.tensors)
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def _apply(self, other, op):
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if isinstance(other, (list, tuple, TensorList)) or (
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isinstance(other, torch.Tensor) and (
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other.numel() > 1 or other.ndim > 1
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)
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):
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assert len(other) == len(self.tensors)
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return TensorList([op(u, v) for u, v in zip(self.tensors, other)])
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elif isinstance(other, numbers.Number) or (
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isinstance(other, torch.Tensor) and (
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other.numel() == 1 and other.ndim <= 1
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)
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):
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return TensorList([op(u, other) for u in self.tensors])
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else:
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raise TypeError(
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f'unsupported operand for *: "TensorList" and "{type(other)}"'
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) |