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import math |
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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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from .base import LycorisBaseModule |
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from ..functional import tucker_weight_from_conv |
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class GLoRAModule(LycorisBaseModule): |
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name = "glora" |
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support_module = { |
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"linear", |
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"conv1d", |
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"conv2d", |
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"conv3d", |
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} |
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weight_list = [ |
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"a1.weight", |
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"a2.weight", |
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"b1.weight", |
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"b2.weight", |
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"bm.weight", |
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"alpha", |
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] |
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weight_list_det = ["a1.weight"] |
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def __init__( |
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self, |
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lora_name, |
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org_module: nn.Module, |
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multiplier=1.0, |
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lora_dim=4, |
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alpha=1, |
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dropout=0.0, |
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rank_dropout=0.0, |
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module_dropout=0.0, |
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use_tucker=False, |
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use_scalar=False, |
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rank_dropout_scale=False, |
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weight_decompose=False, |
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bypass_mode=None, |
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rs_lora=False, |
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**kwargs, |
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): |
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""" |
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f(x) = WX + WAX + BX, where A and B are low-rank matrices |
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bypass_forward(x) = W(X+A(X)) + B(X) |
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bypass_forward_diff(x) = W(A(X)) + B(X) |
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get_merged_weight() = W + WA + B |
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get_diff_weight() = WA + B |
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""" |
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super().__init__( |
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lora_name, |
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org_module, |
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multiplier, |
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dropout, |
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rank_dropout, |
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module_dropout, |
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rank_dropout_scale, |
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bypass_mode, |
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) |
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if self.module_type not in self.support_module: |
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raise ValueError(f"{self.module_type} is not supported in GLoRA algo.") |
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self.lora_dim = lora_dim |
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self.tucker = False |
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self.rs_lora = rs_lora |
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if self.module_type.startswith("conv"): |
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self.isconv = True |
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in_dim = org_module.in_channels |
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k_size = org_module.kernel_size |
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stride = org_module.stride |
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padding = org_module.padding |
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out_dim = org_module.out_channels |
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use_tucker = use_tucker and all(i == 1 for i in k_size) |
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self.down_op = self.op |
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self.up_op = self.op |
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self.a2 = self.module(in_dim, lora_dim, 1, bias=False) |
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self.a1 = self.module(lora_dim, in_dim, 1, bias=False) |
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if use_tucker and any(i != 1 for i in k_size): |
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self.b2 = self.module(in_dim, lora_dim, 1, bias=False) |
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self.bm = self.module( |
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lora_dim, lora_dim, k_size, stride, padding, bias=False |
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) |
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self.tucker = True |
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else: |
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self.b2 = self.module( |
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in_dim, lora_dim, k_size, stride, padding, bias=False |
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) |
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self.b1 = self.module(lora_dim, out_dim, 1, bias=False) |
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else: |
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self.isconv = False |
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self.down_op = F.linear |
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self.up_op = F.linear |
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in_dim = org_module.in_features |
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out_dim = org_module.out_features |
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self.a2 = nn.Linear(in_dim, lora_dim, bias=False) |
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self.a1 = nn.Linear(lora_dim, in_dim, bias=False) |
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self.b2 = nn.Linear(in_dim, lora_dim, bias=False) |
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self.b1 = nn.Linear(lora_dim, out_dim, bias=False) |
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if type(alpha) == torch.Tensor: |
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alpha = alpha.detach().float().numpy() |
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alpha = lora_dim if alpha is None or alpha == 0 else alpha |
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r_factor = lora_dim |
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if self.rs_lora: |
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r_factor = math.sqrt(r_factor) |
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self.scale = alpha / r_factor |
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self.register_buffer("alpha", torch.tensor(alpha)) |
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if use_scalar: |
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self.scalar = nn.Parameter(torch.tensor(0.0)) |
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else: |
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self.register_buffer("scalar", torch.tensor(1.0), persistent=False) |
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torch.nn.init.kaiming_uniform_(self.a1.weight, a=math.sqrt(5)) |
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torch.nn.init.kaiming_uniform_(self.b1.weight, a=math.sqrt(5)) |
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if use_scalar: |
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torch.nn.init.kaiming_uniform_(self.a2.weight, a=math.sqrt(5)) |
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torch.nn.init.kaiming_uniform_(self.b2.weight, a=math.sqrt(5)) |
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else: |
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torch.nn.init.zeros_(self.a2.weight) |
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torch.nn.init.zeros_(self.b2.weight) |
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@classmethod |
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def make_module_from_state_dict( |
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cls, lora_name, orig_module, a1, a2, b1, b2, bm, alpha |
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): |
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module = cls( |
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lora_name, |
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orig_module, |
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1, |
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a2.size(0), |
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float(alpha), |
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use_tucker=bm is not None, |
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) |
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module.a1.weight.data.copy_(a1) |
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module.a2.weight.data.copy_(a2) |
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module.b1.weight.data.copy_(b1) |
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module.b2.weight.data.copy_(b2) |
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if bm is not None: |
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module.bm.weight.data.copy_(bm) |
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return module |
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def custom_state_dict(self): |
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destination = {} |
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destination["alpha"] = self.alpha |
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destination["a1.weight"] = self.a1.weight |
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destination["a2.weight"] = self.a2.weight * self.scalar |
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destination["b1.weight"] = self.b1.weight |
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destination["b2.weight"] = self.b2.weight * self.scalar |
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if self.tucker: |
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destination["bm.weight"] = self.bm.weight |
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return destination |
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def load_weight_hook(self, module: nn.Module, incompatible_keys): |
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missing_keys = incompatible_keys.missing_keys |
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for key in missing_keys: |
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if "scalar" in key: |
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del missing_keys[missing_keys.index(key)] |
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if isinstance(self.scalar, nn.Parameter): |
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self.scalar.data.copy_(torch.ones_like(self.scalar)) |
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elif getattr(self, "scalar", None) is not None: |
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self.scalar.copy_(torch.ones_like(self.scalar)) |
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else: |
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self.register_buffer( |
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"scalar", torch.ones_like(self.scalar), persistent=False |
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) |
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def make_weight(self, device=None): |
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wa1 = self.a1.weight.view(self.a1.weight.size(0), -1) |
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wa2 = self.a2.weight.view(self.a2.weight.size(0), -1) |
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orig = self.org_weight |
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if self.tucker: |
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wb = tucker_weight_from_conv(self.b1.weight, self.b2.weight, self.bm.weight) |
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else: |
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wb1 = self.b1.weight.view(self.b1.weight.size(0), -1) |
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wb2 = self.b2.weight.view(self.b2.weight.size(0), -1) |
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wb = wb1 @ wb2 |
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wb = wb.view(*orig.shape) |
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if orig.dim() > 2: |
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w_wa1 = torch.einsum("o i ..., i j -> o j ...", orig, wa1) |
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w_wa2 = torch.einsum("o i ..., i j -> o j ...", w_wa1, wa2) |
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else: |
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w_wa2 = (orig @ wa1) @ wa2 |
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return (wb + w_wa2) * self.scale * self.scalar |
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def get_diff_weight(self, multiplier=1.0, shape=None, device=None): |
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weight = self.make_weight(device) * multiplier |
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if shape is not None: |
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weight = weight.view(shape) |
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return weight, None |
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def get_merged_weight(self, multiplier=1, shape=None, device=None): |
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diff_w, _ = self.get_diff_weight(multiplier, shape, device) |
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return self.org_weight + diff_w, None |
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def _bypass_forward(self, x, scale=1, diff=False): |
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scale = self.scale * scale |
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ax_mid = self.a2(x) * scale |
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bx_mid = self.b2(x) * scale |
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if self.rank_dropout and self.training: |
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drop_a = ( |
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torch.rand(self.lora_dim, device=ax_mid.device) < self.rank_dropout |
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).to(ax_mid.dtype) |
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drop_b = ( |
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torch.rand(self.lora_dim, device=bx_mid.device) < self.rank_dropout |
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).to(bx_mid.dtype) |
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if self.rank_dropout_scale: |
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drop_a /= drop_a.mean() |
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drop_b /= drop_b.mean() |
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if (dims := len(x.shape)) == 4: |
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drop_a = drop_a.view(1, -1, 1, 1) |
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drop_b = drop_b.view(1, -1, 1, 1) |
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else: |
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drop_a = drop_a.view(*[1] * (dims - 1), -1) |
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drop_b = drop_b.view(*[1] * (dims - 1), -1) |
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ax_mid = ax_mid * drop_a |
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bx_mid = bx_mid * drop_b |
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return ( |
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self.org_forward( |
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(0 if diff else x) + self.drop(self.a1(ax_mid)) * self.scale |
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) |
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+ self.drop(self.b1(bx_mid)) * self.scale |
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) |
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def bypass_forward_diff(self, x, scale=1): |
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return self._bypass_forward(x, scale=scale, diff=True) |
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def bypass_forward(self, x, scale=1): |
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return self._bypass_forward(x, scale=scale, diff=False) |
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def forward(self, x, *args, **kwargs): |
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if self.module_dropout and self.training: |
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if torch.rand(1) < self.module_dropout: |
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return self.org_forward(x) |
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if self.bypass_mode: |
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return self.bypass_forward(x, self.multiplier) |
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else: |
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weight = ( |
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self.org_module[0].weight.data.to(self.dtype) |
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+ self.get_diff_weight(multiplier=self.multiplier)[0] |
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) |
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bias = ( |
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None |
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if self.org_module[0].bias is None |
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else self.org_module[0].bias.data |
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) |
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return self.op(x, weight, bias, **self.kw_dict) |
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