| 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 AutoWrappedModule(torch.nn.Module): |
| def __init__(self, module: torch.nn.Module, offload_dtype, offload_device, onload_dtype, onload_device, computation_dtype, computation_device): |
| 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.state = 0 |
|
|
| def offload(self): |
| if self.state == 1 and (self.offload_dtype != self.onload_dtype or self.offload_device != self.onload_device): |
| self.module.to(dtype=self.offload_dtype, device=self.offload_device) |
| self.state = 0 |
|
|
| def onload(self): |
| if self.state == 0 and (self.offload_dtype != self.onload_dtype or self.offload_device != self.onload_device): |
| self.module.to(dtype=self.onload_dtype, device=self.onload_device) |
| self.state = 1 |
|
|
| def forward(self, *args, **kwargs): |
| if self.onload_dtype == self.computation_dtype and self.onload_device == self.computation_device: |
| module = self.module |
| else: |
| module = copy.deepcopy(self.module).to(dtype=self.computation_dtype, device=self.computation_device) |
| return module(*args, **kwargs) |
| |
|
|
| class AutoWrappedLinear(torch.nn.Linear): |
| def __init__(self, module: torch.nn.Linear, offload_dtype, offload_device, onload_dtype, onload_device, computation_dtype, computation_device): |
| 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.state = 0 |
|
|
| def offload(self): |
| if self.state == 1 and (self.offload_dtype != self.onload_dtype or self.offload_device != self.onload_device): |
| self.to(dtype=self.offload_dtype, device=self.offload_device) |
| self.state = 0 |
|
|
| def onload(self): |
| if self.state == 0 and (self.offload_dtype != self.onload_dtype or self.offload_device != self.onload_device): |
| self.to(dtype=self.onload_dtype, device=self.onload_device) |
| self.state = 1 |
|
|
| def forward(self, x, *args, **kwargs): |
| if self.onload_dtype == self.computation_dtype and self.onload_device == self.computation_device: |
| 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) |
| return torch.nn.functional.linear(x, weight, bias) |
|
|
|
|
| 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): |
| for name, module in model.named_children(): |
| 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_) |
| 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) |
| 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): |
| enable_vram_management_recursively(model, module_map, module_config, max_num_param, overflow_module_config, total_num_param=0) |
| model.vram_management_enabled = True |
|
|
|
|
