| | 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 |
| |
|
