File size: 12,160 Bytes
c6535db | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 | import torch
import torch.nn as nn
from accelerate import init_empty_weights
from .gguf.gguf_utils import GGUFParameter, dequantize_gguf_tensor
@torch.library.custom_op("wanvideo::apply_lora", mutates_args=())
def apply_lora(weight: torch.Tensor, lora_diff_0: torch.Tensor, lora_diff_1: torch.Tensor, lora_diff_2: float, lora_strength: torch.Tensor) -> torch.Tensor:
patch_diff = torch.mm(
lora_diff_0.flatten(start_dim=1),
lora_diff_1.flatten(start_dim=1)
).reshape(weight.shape)
alpha = lora_diff_2 / lora_diff_1.shape[0] if lora_diff_2 != 0.0 else 1.0
scale = lora_strength * alpha
return weight + patch_diff * scale
@apply_lora.register_fake
def _(weight, lora_diff_0, lora_diff_1, lora_diff_2, lora_strength):
# Return weight with same metadata
return weight.clone()
@torch.library.custom_op("wanvideo::apply_single_lora", mutates_args=())
def apply_single_lora(weight: torch.Tensor, lora_diff: torch.Tensor, lora_strength: torch.Tensor) -> torch.Tensor:
return weight + lora_diff * lora_strength
@apply_single_lora.register_fake
def _(weight, lora_diff, lora_strength):
# Return weight with same metadata
return weight.clone()
@torch.library.custom_op("wanvideo::linear_forward", mutates_args=())
def linear_forward(input: torch.Tensor, weight: torch.Tensor, bias: torch.Tensor | None) -> torch.Tensor:
return torch.nn.functional.linear(input, weight, bias)
@linear_forward.register_fake
def _(input, weight, bias):
# Calculate output shape: (..., out_features)
out_features = weight.shape[0]
output_shape = list(input.shape[:-1]) + [out_features]
return input.new_empty(output_shape)
#based on https://github.com/huggingface/diffusers/blob/main/src/diffusers/quantizers/gguf/utils.py
def _replace_linear(model, compute_dtype, state_dict, prefix="", patches=None, scale_weights=None, compile_args=None, modules_to_not_convert=[]):
has_children = list(model.children())
if not has_children:
return
allow_compile = False
for name, module in model.named_children():
if compile_args is not None:
allow_compile = compile_args.get("allow_unmerged_lora_compile", False)
module_prefix = prefix + name + "."
module_prefix = module_prefix.replace("_orig_mod.", "")
_replace_linear(module, compute_dtype, state_dict, module_prefix, patches, scale_weights, compile_args, modules_to_not_convert)
if isinstance(module, nn.Linear) and "loras" not in module_prefix and "dual_controller" not in module_prefix and name not in modules_to_not_convert:
weight_key = module_prefix + "weight"
if weight_key not in state_dict:
continue
in_features = state_dict[weight_key].shape[1]
out_features = state_dict[weight_key].shape[0]
is_gguf = isinstance(state_dict[weight_key], GGUFParameter)
scale_weight = None
if not is_gguf and scale_weights is not None:
scale_key = f"{module_prefix}scale_weight"
scale_weight = scale_weights.get(scale_key)
with init_empty_weights():
model._modules[name] = CustomLinear(
in_features,
out_features,
module.bias is not None,
compute_dtype=compute_dtype,
scale_weight=scale_weight,
allow_compile=allow_compile,
is_gguf=is_gguf
)
model._modules[name].source_cls = type(module)
model._modules[name].requires_grad_(False)
return model
def set_lora_params(module, patches, module_prefix="", device=torch.device("cpu")):
remove_lora_from_module(module)
# Recursively set lora_diffs and lora_strengths for all CustomLinear layers
for name, child in module.named_children():
params = list(child.parameters())
if params:
device = params[0].device
else:
device = torch.device("cpu")
child_prefix = (f"{module_prefix}{name}.")
set_lora_params(child, patches, child_prefix, device)
if isinstance(module, CustomLinear):
key = f"diffusion_model.{module_prefix}weight"
patch = patches.get(key, [])
#print(f"Processing LoRA patches for {key}: {len(patch)} patches found")
if len(patch) == 0:
key = key.replace("_orig_mod.", "")
patch = patches.get(key, [])
#print(f"Processing LoRA patches for {key}: {len(patch)} patches found")
if len(patch) != 0:
lora_diffs = []
for p in patch:
lora_obj = p[1]
if "head" in key:
continue # For now skip LoRA for head layers
elif hasattr(lora_obj, "weights"):
lora_diffs.append(lora_obj.weights)
elif isinstance(lora_obj, tuple) and lora_obj[0] == "diff":
lora_diffs.append(lora_obj[1])
else:
continue
lora_strengths = [p[0] for p in patch]
module.set_lora_diffs(lora_diffs, device=device)
module.set_lora_strengths(lora_strengths, device=device)
module._step.fill_(0) # Initialize step for LoRA scheduling
class CustomLinear(nn.Linear):
def __init__(
self,
in_features,
out_features,
bias=False,
compute_dtype=None,
device=None,
scale_weight=None,
allow_compile=False,
is_gguf=False
) -> None:
super().__init__(in_features, out_features, bias, device)
self.compute_dtype = compute_dtype
self.lora_diffs = []
self.register_buffer("_step", torch.zeros((), dtype=torch.long))
self.scale_weight = scale_weight
self.lora_strengths = []
self.allow_compile = allow_compile
self.is_gguf = is_gguf
if not allow_compile:
self._apply_lora_impl = self._apply_lora_custom_op
self._apply_single_lora_impl = self._apply_single_lora_custom_op
self._linear_forward_impl = self._linear_forward_custom_op
else:
self._apply_lora_impl = self._apply_lora_direct
self._apply_single_lora_impl = self._apply_single_lora_direct
self._linear_forward_impl = self._linear_forward_direct
# Direct implementations (no custom ops)
def _apply_lora_direct(self, weight, lora_diff_0, lora_diff_1, lora_diff_2, lora_strength):
patch_diff = torch.mm(
lora_diff_0.flatten(start_dim=1),
lora_diff_1.flatten(start_dim=1)
).reshape(weight.shape) + 0
alpha = lora_diff_2 / lora_diff_1.shape[0] if lora_diff_2 != 0.0 else 1.0
scale = lora_strength * alpha
return weight + patch_diff * scale
def _apply_single_lora_direct(self, weight, lora_diff, lora_strength):
return weight + lora_diff * lora_strength
def _linear_forward_direct(self, input, weight, bias):
return torch.nn.functional.linear(input, weight, bias)
# Custom op implementations
def _apply_lora_custom_op(self, weight, lora_diff_0, lora_diff_1, lora_diff_2, lora_strength):
return torch.ops.wanvideo.apply_lora(weight, lora_diff_0, lora_diff_1,
float(lora_diff_2) if lora_diff_2 is not None else 0.0, lora_strength
)
def _apply_single_lora_custom_op(self, weight, lora_diff, lora_strength):
return torch.ops.wanvideo.apply_single_lora(weight, lora_diff, lora_strength)
def _linear_forward_custom_op(self, input, weight, bias):
return torch.ops.wanvideo.linear_forward(input, weight, bias)
def set_lora_diffs(self, lora_diffs, device=torch.device("cpu")):
self.lora_diffs = []
for i, diff in enumerate(lora_diffs):
if len(diff) > 1:
self.register_buffer(f"lora_diff_{i}_0", diff[0].to(device, self.compute_dtype))
self.register_buffer(f"lora_diff_{i}_1", diff[1].to(device, self.compute_dtype))
setattr(self, f"lora_diff_{i}_2", diff[2])
self.lora_diffs.append((f"lora_diff_{i}_0", f"lora_diff_{i}_1", f"lora_diff_{i}_2"))
else:
self.register_buffer(f"lora_diff_{i}_0", diff[0].to(device, self.compute_dtype))
self.lora_diffs.append(f"lora_diff_{i}_0")
def set_lora_strengths(self, lora_strengths, device=torch.device("cpu")):
self._lora_strength_tensors = []
self._lora_strength_is_scheduled = []
self._step = self._step.to(device)
for i, strength in enumerate(lora_strengths):
if isinstance(strength, list):
tensor = torch.tensor(strength, dtype=self.compute_dtype, device=device)
self.register_buffer(f"_lora_strength_{i}", tensor)
self._lora_strength_is_scheduled.append(True)
else:
tensor = torch.tensor([strength], dtype=self.compute_dtype, device=device)
self.register_buffer(f"_lora_strength_{i}", tensor)
self._lora_strength_is_scheduled.append(False)
def _get_lora_strength(self, idx):
strength_tensor = getattr(self, f"_lora_strength_{idx}")
if self._lora_strength_is_scheduled[idx]:
return strength_tensor.index_select(0, self._step).squeeze(0)
return strength_tensor[0]
def _get_weight_with_lora(self, weight):
"""Apply LoRA using custom ops to avoid graph breaks"""
if not hasattr(self, "lora_diff_0_0"):
return weight
for idx, lora_diff_names in enumerate(self.lora_diffs):
lora_strength = self._get_lora_strength(idx)
if isinstance(lora_diff_names, tuple):
lora_diff_0 = getattr(self, lora_diff_names[0])
lora_diff_1 = getattr(self, lora_diff_names[1])
lora_diff_2 = getattr(self, lora_diff_names[2])
weight = self._apply_lora_impl(
weight, lora_diff_0, lora_diff_1,
float(lora_diff_2) if lora_diff_2 is not None else 0.0, lora_strength
)
else:
lora_diff = getattr(self, lora_diff_names)
weight = self._apply_single_lora_impl(weight, lora_diff, lora_strength)
return weight
def _prepare_weight(self, input):
"""Prepare weight tensor - handles both regular and GGUF weights"""
if self.is_gguf:
weight = dequantize_gguf_tensor(self.weight).to(self.compute_dtype)
else:
weight = self.weight.to(input)
return weight
def forward(self, input):
weight = self._prepare_weight(input)
if self.bias is not None:
bias = self.bias.to(input if not self.is_gguf else self.compute_dtype)
else:
bias = None
# Only apply scale_weight for non-GGUF models
if not self.is_gguf and self.scale_weight is not None:
if weight.numel() < input.numel():
weight = weight * self.scale_weight
else:
input = input * self.scale_weight
weight = self._get_weight_with_lora(weight)
out = self._linear_forward_impl(input, weight, bias)
del weight, input, bias
return out
def update_lora_step(module, step):
for name, submodule in module.named_modules():
if isinstance(submodule, CustomLinear) and hasattr(submodule, "_step"):
submodule._step.fill_(step)
def remove_lora_from_module(module):
for name, submodule in module.named_modules():
if hasattr(submodule, "lora_diffs"):
for i in range(len(submodule.lora_diffs)):
if hasattr(submodule, f"lora_diff_{i}_0"):
delattr(submodule, f"lora_diff_{i}_0")
if hasattr(submodule, f"lora_diff_{i}_1"):
delattr(submodule, f"lora_diff_{i}_1")
if hasattr(submodule, f"lora_diff_{i}_2"):
delattr(submodule, f"lora_diff_{i}_2")
|