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import torch |
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import lyco_helpers |
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import network |
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from modules import devices |
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class ModuleTypeLora(network.ModuleType): |
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def create_module(self, net: network.Network, weights: network.NetworkWeights): |
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if all(x in weights.w for x in ["lora_up.weight", "lora_down.weight"]): |
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return NetworkModuleLora(net, weights) |
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return None |
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class NetworkModuleLora(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.up_model = self.create_module(weights.w, "lora_up.weight") |
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self.down_model = self.create_module(weights.w, "lora_down.weight") |
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self.mid_model = self.create_module(weights.w, "lora_mid.weight", none_ok=True) |
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self.dim = weights.w["lora_down.weight"].shape[0] |
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def create_module(self, weights, key, none_ok=False): |
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weight = weights.get(key) |
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if weight is None and none_ok: |
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return None |
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is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear, torch.nn.MultiheadAttention] |
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is_conv = type(self.sd_module) in [torch.nn.Conv2d] |
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if is_linear: |
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weight = weight.reshape(weight.shape[0], -1) |
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module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False) |
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elif is_conv and key == "lora_down.weight" or key == "dyn_up": |
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if len(weight.shape) == 2: |
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weight = weight.reshape(weight.shape[0], -1, 1, 1) |
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if weight.shape[2] != 1 or weight.shape[3] != 1: |
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module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], self.sd_module.kernel_size, self.sd_module.stride, self.sd_module.padding, bias=False) |
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else: |
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module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False) |
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elif is_conv and key == "lora_mid.weight": |
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module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], self.sd_module.kernel_size, self.sd_module.stride, self.sd_module.padding, bias=False) |
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elif is_conv and key == "lora_up.weight" or key == "dyn_down": |
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module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False) |
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else: |
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raise AssertionError(f'Lora layer {self.network_key} matched a layer with unsupported type: {type(self.sd_module).__name__}') |
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with torch.no_grad(): |
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if weight.shape != module.weight.shape: |
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weight = weight.reshape(module.weight.shape) |
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module.weight.copy_(weight) |
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module.to(device=devices.cpu, dtype=devices.dtype) |
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module.weight.requires_grad_(False) |
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return module |
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def calc_updown(self, orig_weight): |
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up = self.up_model.weight.to(orig_weight.device, dtype=orig_weight.dtype) |
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down = self.down_model.weight.to(orig_weight.device, dtype=orig_weight.dtype) |
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output_shape = [up.size(0), down.size(1)] |
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if self.mid_model is not None: |
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mid = self.mid_model.weight.to(orig_weight.device, dtype=orig_weight.dtype) |
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updown = lyco_helpers.rebuild_cp_decomposition(up, down, mid) |
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output_shape += mid.shape[2:] |
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else: |
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if len(down.shape) == 4: |
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output_shape += down.shape[2:] |
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updown = lyco_helpers.rebuild_conventional(up, down, output_shape, self.network.dyn_dim) |
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return self.finalize_updown(updown, orig_weight, output_shape) |
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def forward(self, x, y): |
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self.up_model.to(device=devices.device) |
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self.down_model.to(device=devices.device) |
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return y + self.up_model(self.down_model(x)) * self.multiplier() * self.calc_scale() |
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