65 lines
2.5 KiB
Python
65 lines
2.5 KiB
Python
import torch
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import lyco_helpers
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import network
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class ModuleTypeLokr(network.ModuleType):
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def create_module(self, net: network.Network, weights: network.NetworkWeights):
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has_1 = "lokr_w1" in weights.w or ("lokr_w1a" in weights.w and "lokr_w1b" in weights.w)
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has_2 = "lokr_w2" in weights.w or ("lokr_w2a" in weights.w and "lokr_w2b" in weights.w)
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if has_1 and has_2:
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return NetworkModuleLokr(net, weights)
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return None
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def make_kron(orig_shape, w1, w2):
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if len(w2.shape) == 4:
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w1 = w1.unsqueeze(2).unsqueeze(2)
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w2 = w2.contiguous()
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return torch.kron(w1, w2).reshape(orig_shape)
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class NetworkModuleLokr(network.NetworkModule):
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def __init__(self, net: network.Network, weights: network.NetworkWeights):
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super().__init__(net, weights)
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self.w1 = weights.w.get("lokr_w1")
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self.w1a = weights.w.get("lokr_w1_a")
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self.w1b = weights.w.get("lokr_w1_b")
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self.dim = self.w1b.shape[0] if self.w1b else self.dim
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self.w2 = weights.w.get("lokr_w2")
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self.w2a = weights.w.get("lokr_w2_a")
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self.w2b = weights.w.get("lokr_w2_b")
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self.dim = self.w2b.shape[0] if self.w2b else self.dim
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self.t2 = weights.w.get("lokr_t2")
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def calc_updown(self, orig_weight):
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if self.w1 is not None:
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w1 = self.w1.to(orig_weight.device, dtype=orig_weight.dtype)
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else:
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w1a = self.w1a.to(orig_weight.device, dtype=orig_weight.dtype)
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w1b = self.w1b.to(orig_weight.device, dtype=orig_weight.dtype)
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w1 = w1a @ w1b
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if self.w2 is not None:
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w2 = self.w2.to(orig_weight.device, dtype=orig_weight.dtype)
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elif self.t2 is None:
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w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype)
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w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype)
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w2 = w2a @ w2b
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else:
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t2 = self.t2.to(orig_weight.device, dtype=orig_weight.dtype)
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w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype)
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w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype)
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w2 = lyco_helpers.make_weight_cp(t2, w2a, w2b)
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output_shape = [w1.size(0) * w2.size(0), w1.size(1) * w2.size(1)]
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if len(orig_weight.shape) == 4:
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output_shape = orig_weight.shape
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updown = make_kron(output_shape, w1, w2)
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return self.finalize_updown(updown, orig_weight, output_shape)
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