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