Different outputs when upgrading from adapter-transformers with LoRA

[jblamare]

Environment info

• adapters version: 1.0.1
• transformers version: 4.45.2
• Platform: Linux-5.15.0-91-generic-x86_64-with-glibc2.35
• Python version: 3.10.12
• Huggingface_hub version: 0.26.2
• Safetensors version: 0.4.5
• Accelerate version: not installed
• Accelerate config: not found
• PyTorch version (GPU?): 2.5.1+cu124 (True)
• Tensorflow version (GPU?): not installed (NA)
• Flax version (CPU?/GPU?/TPU?): not installed (NA)
• Jax version: not installed
• JaxLib version: not installed
• Using distributed or parallel set-up in script?: No
• Using GPU in script?: Yes
• GPU type: NVIDIA RTX A6000

Information

Model I am using (Bert, XLNet ...): google/flan-t5-small

Language I am using the model on (English, Chinese ...): English

Adapter setup I am using (if any): LoRA

The problem arises when using:

• the official example scripts: (give details below)
• my own modified scripts: (give details below)

The tasks I am working on is:

• an official GLUE/SQUaD task: (give the name)
• my own task or dataset: (give details below)

To reproduce

I have two environments:

• env1

adapter-transformers==3.1.0
torch==1.13.1

• env2

adapters==1.0.1
torch==2.5.1
transformers==4.45.2

I have some input_ids

input_ids = [262, 4, 4815, 10, 8668, 3, 5359, 27415, 5332, 3430, 276, 3577, 20186, 11951, 8472, 11359, 4209, 205, 20931, 23936, 3388, 27447, 8015]

I have a model checkpoint checkpoint.pth which has the T5 weights plus a LoRA adapter and was saved in env1:

with open("checkpoint.pth"), "wb") as f:
    torch.save(model.state_dict(), f)

From there I want to make sure I can load the model, run inference, and get the same outputs in env2. But the outputs are different. I run the following experiments:

1. Create a T5 model, add the empty LoRA adapter, run inference - env1 and env2 get to the same output
2. Create a T5 model, load the non-LoRA weights, run inference - env1 and env2 get to the same output
3. Create a T5 model, add the LoRA adapter, load all the weights, run inference - env1 has the right output but env2 is different.

Here is the code I use (in env1 I just remove import adapters, adapters.init(model), and use adapter_config = transformers.adapters.LoRAConfig(r=8, alpha=16):

import adapters
import torch
import transformers

input_ids = [262, 4, 4815, 10, 8668, 3, 5359, 27415, 5332, 3430, 276, 3577, 20186, 11951, 8472, 11359, 4209, 205, 20931, 23936, 3388, 27447, 8015]
model = transformers.AutoModel.from_pretrained("google/flan-t5-small")
adapters.init(model)
adapter_config = adapters.LoRAConfig(r=8, alpha=16)
model.add_adapter("ct", config=adapter_config)
model.set_active_adapters("ct")
model = model.encoder
checkpoint = torch.load("checkpoint.pth", map_location=torch.device("cpu"))
model.load_state_dict(checkpoint, strict=False)
model = model.eval()
outputs = model(input_ids=torch.IntTensor([input_ids]))

Unfortunately I can't share the model weights. Any thoughts on what might be the reason I get different outputs only if I use LoRA and load my weights?

Expected behavior

Getting the same output in env1 and env2.

[jblamare]

After diving into the codebase, I think I understand the difference. This looks like a bug in the LoRALinear class, but I might be missing something.

• In adapter-transformers, the linear output and the delta are combined with result = lora.com(result, delta_w, scaling=gate). In particular, if lora.use_gating == False then gate is None which means the scaling used is 16/8=2 in my case.
• In adapters, they are combined with scaling=1.0. There is a comment saying "scaling already applied in compose" but I don't think it is? compose will run compose_stack which will run LoRALinear's compose_single which will run LoRA's forward which does not involve any scaling.

Am I missing something?