| from diffusers.utils import is_torch_available |
|
|
| from ..testing_utils import ( |
| backend_empty_cache, |
| backend_max_memory_allocated, |
| backend_reset_peak_memory_stats, |
| torch_device, |
| ) |
|
|
|
|
| if is_torch_available(): |
| import torch |
| import torch.nn as nn |
|
|
| class LoRALayer(nn.Module): |
| """Wraps a linear layer with LoRA-like adapter - Used for testing purposes only |
| |
| Taken from |
| https://github.com/huggingface/transformers/blob/566302686a71de14125717dea9a6a45b24d42b37/tests/quantization/bnb/test_4bit.py#L62C5-L78C77 |
| """ |
|
|
| def __init__(self, module: nn.Module, rank: int): |
| super().__init__() |
| self.module = module |
| self.adapter = nn.Sequential( |
| nn.Linear(module.in_features, rank, bias=False), |
| nn.Linear(rank, module.out_features, bias=False), |
| ) |
| small_std = (2.0 / (5 * min(module.in_features, module.out_features))) ** 0.5 |
| nn.init.normal_(self.adapter[0].weight, std=small_std) |
| nn.init.zeros_(self.adapter[1].weight) |
| self.adapter.to(module.weight.device) |
|
|
| def forward(self, input, *args, **kwargs): |
| return self.module(input, *args, **kwargs) + self.adapter(input) |
|
|
| @torch.no_grad() |
| @torch.inference_mode() |
| def get_memory_consumption_stat(model, inputs): |
| backend_reset_peak_memory_stats(torch_device) |
| backend_empty_cache(torch_device) |
|
|
| model(**inputs) |
| max_mem_allocated = backend_max_memory_allocated(torch_device) |
| return max_mem_allocated |
|
|
