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import torch, copy
from models.utils import init_weights_on_device
def cast_to(weight, dtype, device):
r = torch.empty_like(weight, dtype=dtype, device=device)
r.copy_(weight)
return r
class AutoTorchModule(torch.nn.Module):
def __init__(self):
super().__init__()
def check_free_vram(self):
gpu_mem_state = torch.cuda.mem_get_info(self.computation_device)
used_memory = (gpu_mem_state[1] - gpu_mem_state[0]) / (1024**3)
return used_memory < self.vram_limit
def offload(self):
if self.state != 0:
self.to(dtype=self.offload_dtype, device=self.offload_device)
self.state = 0
def onload(self):
if self.state != 1:
self.to(dtype=self.onload_dtype, device=self.onload_device)
self.state = 1
def keep(self):
if self.state != 2:
self.to(dtype=self.computation_dtype, device=self.computation_device)
self.state = 2
class AutoWrappedModule(AutoTorchModule):
def __init__(
self,
module: torch.nn.Module,
offload_dtype,
offload_device,
onload_dtype,
onload_device,
computation_dtype,
computation_device,
vram_limit,
**kwargs,
):
super().__init__()
self.module = module.to(dtype=offload_dtype, device=offload_device)
self.offload_dtype = offload_dtype
self.offload_device = offload_device
self.onload_dtype = onload_dtype
self.onload_device = onload_device
self.computation_dtype = computation_dtype
self.computation_device = computation_device
self.vram_limit = vram_limit
self.state = 0
def forward(self, *args, **kwargs):
if self.state == 2:
module = self.module
else:
if (
self.onload_dtype == self.computation_dtype
and self.onload_device == self.computation_device
):
module = self.module
elif self.vram_limit is not None and self.check_free_vram():
self.keep()
module = self.module
else:
module = copy.deepcopy(self.module).to(
dtype=self.computation_dtype, device=self.computation_device
)
return module(*args, **kwargs)
class WanAutoCastLayerNorm(torch.nn.LayerNorm, AutoTorchModule):
def __init__(
self,
module: torch.nn.LayerNorm,
offload_dtype,
offload_device,
onload_dtype,
onload_device,
computation_dtype,
computation_device,
vram_limit,
**kwargs,
):
with init_weights_on_device(device=torch.device("meta")):
super().__init__(
module.normalized_shape,
eps=module.eps,
elementwise_affine=module.elementwise_affine,
bias=module.bias is not None,
dtype=offload_dtype,
device=offload_device,
)
self.weight = module.weight
self.bias = module.bias
self.offload_dtype = offload_dtype
self.offload_device = offload_device
self.onload_dtype = onload_dtype
self.onload_device = onload_device
self.computation_dtype = computation_dtype
self.computation_device = computation_device
self.vram_limit = vram_limit
self.state = 0
def forward(self, x, *args, **kwargs):
if self.state == 2:
weight, bias = self.weight, self.bias
else:
if (
self.onload_dtype == self.computation_dtype
and self.onload_device == self.computation_device
):
weight, bias = self.weight, self.bias
elif self.vram_limit is not None and self.check_free_vram():
self.keep()
weight, bias = self.weight, self.bias
else:
weight = (
None
if self.weight is None
else cast_to(
self.weight, self.computation_dtype, self.computation_device
)
)
bias = (
None
if self.bias is None
else cast_to(
self.bias, self.computation_dtype, self.computation_device
)
)
with torch.amp.autocast(device_type=x.device.type):
x = torch.nn.functional.layer_norm(
x.float(), self.normalized_shape, weight, bias, self.eps
).type_as(x)
return x
class AutoWrappedLinear(torch.nn.Linear, AutoTorchModule):
def __init__(
self,
module: torch.nn.Linear,
offload_dtype,
offload_device,
onload_dtype,
onload_device,
computation_dtype,
computation_device,
vram_limit,
name="",
**kwargs,
):
with init_weights_on_device(device=torch.device("meta")):
super().__init__(
in_features=module.in_features,
out_features=module.out_features,
bias=module.bias is not None,
dtype=offload_dtype,
device=offload_device,
)
self.weight = module.weight
self.bias = module.bias
self.offload_dtype = offload_dtype
self.offload_device = offload_device
self.onload_dtype = onload_dtype
self.onload_device = onload_device
self.computation_dtype = computation_dtype
self.computation_device = computation_device
self.vram_limit = vram_limit
self.state = 0
self.name = name
self.lora_A_weights = []
self.lora_B_weights = []
self.lora_merger = None
def forward(self, x, *args, **kwargs):
if self.state == 2:
weight, bias = self.weight, self.bias
else:
if (
self.onload_dtype == self.computation_dtype
and self.onload_device == self.computation_device
):
weight, bias = self.weight, self.bias
elif self.vram_limit is not None and self.check_free_vram():
self.keep()
weight, bias = self.weight, self.bias
else:
weight = cast_to(
self.weight, self.computation_dtype, self.computation_device
)
bias = (
None
if self.bias is None
else cast_to(
self.bias, self.computation_dtype, self.computation_device
)
)
out = torch.nn.functional.linear(x, weight, bias)
if len(self.lora_A_weights) == 0:
# No LoRA
return out
elif self.lora_merger is None:
# Native LoRA inference
for lora_A, lora_B in zip(self.lora_A_weights, self.lora_B_weights):
out = out + x @ lora_A.T @ lora_B.T
else:
# LoRA fusion
lora_output = []
for lora_A, lora_B in zip(self.lora_A_weights, self.lora_B_weights):
lora_output.append(x @ lora_A.T @ lora_B.T)
lora_output = torch.stack(lora_output)
out = self.lora_merger(out, lora_output)
return out
def enable_vram_management_recursively(
model: torch.nn.Module,
module_map: dict,
module_config: dict,
max_num_param=None,
overflow_module_config: dict = None,
total_num_param=0,
vram_limit=None,
name_prefix="",
):
for name, module in model.named_children():
layer_name = name if name_prefix == "" else name_prefix + "." + name
for source_module, target_module in module_map.items():
if isinstance(module, source_module):
num_param = sum(p.numel() for p in module.parameters())
if (
max_num_param is not None
and total_num_param + num_param > max_num_param
):
module_config_ = overflow_module_config
else:
module_config_ = module_config
module_ = target_module(
module, **module_config_, vram_limit=vram_limit, name=layer_name
)
setattr(model, name, module_)
total_num_param += num_param
break
else:
total_num_param = enable_vram_management_recursively(
module,
module_map,
module_config,
max_num_param,
overflow_module_config,
total_num_param,
vram_limit=vram_limit,
name_prefix=layer_name,
)
return total_num_param
def enable_vram_management(
model: torch.nn.Module,
module_map: dict,
module_config: dict,
max_num_param=None,
overflow_module_config: dict = None,
vram_limit=None,
):
enable_vram_management_recursively(
model,
module_map,
module_config,
max_num_param,
overflow_module_config,
total_num_param=0,
vram_limit=vram_limit,
)
model.vram_management_enabled = True
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