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eef3d65954
* enable 2 test cases on XPU Signed-off-by: YAO Matrix <matrix.yao@intel.com> * Apply style fixes --------- Signed-off-by: YAO Matrix <matrix.yao@intel.com> Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>
45 lines
1.6 KiB
Python
45 lines
1.6 KiB
Python
from diffusers.utils import is_torch_available
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from diffusers.utils.testing_utils import (
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backend_empty_cache,
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backend_max_memory_allocated,
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backend_reset_peak_memory_stats,
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torch_device,
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)
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if is_torch_available():
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import torch
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import torch.nn as nn
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class LoRALayer(nn.Module):
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"""Wraps a linear layer with LoRA-like adapter - Used for testing purposes only
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Taken from
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https://github.com/huggingface/transformers/blob/566302686a71de14125717dea9a6a45b24d42b37/tests/quantization/bnb/test_4bit.py#L62C5-L78C77
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"""
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def __init__(self, module: nn.Module, rank: int):
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super().__init__()
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self.module = module
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self.adapter = nn.Sequential(
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nn.Linear(module.in_features, rank, bias=False),
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nn.Linear(rank, module.out_features, bias=False),
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)
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small_std = (2.0 / (5 * min(module.in_features, module.out_features))) ** 0.5
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nn.init.normal_(self.adapter[0].weight, std=small_std)
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nn.init.zeros_(self.adapter[1].weight)
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self.adapter.to(module.weight.device)
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def forward(self, input, *args, **kwargs):
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return self.module(input, *args, **kwargs) + self.adapter(input)
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@torch.no_grad()
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@torch.inference_mode()
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def get_memory_consumption_stat(model, inputs):
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backend_reset_peak_memory_stats(torch_device)
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backend_empty_cache(torch_device)
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model(**inputs)
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max_mem_allocated = backend_max_memory_allocated(torch_device)
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return max_mem_allocated
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