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60 lines
1.8 KiB
Python
60 lines
1.8 KiB
Python
from typing import Dict, Optional
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import torch
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from invokeai.backend.lora.layers.lora_layer_base import LoRALayerBase
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# TODO: find and debug lora/locon with bias
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class LoRALayer(LoRALayerBase):
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# up: torch.Tensor
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# mid: Optional[torch.Tensor]
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# down: torch.Tensor
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def __init__(
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self,
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layer_key: str,
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values: Dict[str, torch.Tensor],
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):
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super().__init__(layer_key, values)
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self.up = values["lora_up.weight"]
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self.down = values["lora_down.weight"]
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self.mid = values.get("lora_mid.weight", None)
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self.rank = self.down.shape[0]
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self.check_keys(
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values,
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{
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"lora_up.weight",
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"lora_down.weight",
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"lora_mid.weight",
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},
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)
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def get_weight(self, orig_weight: torch.Tensor) -> torch.Tensor:
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if self.mid is not None:
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up = self.up.reshape(self.up.shape[0], self.up.shape[1])
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down = self.down.reshape(self.down.shape[0], self.down.shape[1])
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weight = torch.einsum("m n w h, i m, n j -> i j w h", self.mid, up, down)
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else:
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weight = self.up.reshape(self.up.shape[0], -1) @ self.down.reshape(self.down.shape[0], -1)
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return weight
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def calc_size(self) -> int:
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model_size = super().calc_size()
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for val in [self.up, self.mid, self.down]:
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if val is not None:
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model_size += val.nelement() * val.element_size()
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return model_size
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def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None) -> None:
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super().to(device=device, dtype=dtype)
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self.up = self.up.to(device=device, dtype=dtype)
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self.down = self.down.to(device=device, dtype=dtype)
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if self.mid is not None:
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self.mid = self.mid.to(device=device, dtype=dtype)
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