Size Mismatch When Training/loading Lora

Apr 24, 2025 by ADMIN 41 views

Size Mismatch when Training/Loading LoRA

Introduction

LoRA (Low-Rank Adaptation) is a technique used to adapt pre-trained language models to specific downstream tasks. However, when training or loading a LoRA adapter, size mismatches can occur, leading to errors. In this article, we will discuss the issue of size mismatch when training/loading LoRA and provide a solution to this problem.

Understanding the Issue

When training a LoRA adapter, the model's weights are adapted to the specific task. However, when loading the saved LoRA adapter, the model's weights may not match the expected shape, leading to a size mismatch error. This error occurs because the LoRA adapter is loaded with the weights from the checkpoint, which may not match the shape of the model instantiated.

Example Code

The following code snippet demonstrates the issue:

sat_lora_adapted = wtpsplit.SaT("sat-12l", lora_path="sat-12l_lora_custom/dummy-dataset/language_code")

This code attempts to load the saved LoRA adapter, but it raises a RuntimeError due to the size mismatch error.

Solution

To solve this issue, we need to ensure that the model's weights match the expected shape. One way to do this is to retrain the model on a downstream task, as suggested in the warning message:

You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

However, this may not be feasible in all cases. An alternative solution is to modify the LoRA adapter to match the expected shape of the model.

Modifying the LoRA Adapter

To modify the LoRA adapter, we need to update the classifier.weight and classifier.bias layers to match the expected shape. We can do this by creating a new classifier layer with the correct shape and then loading the weights from the checkpoint.

Here is an example code snippet that demonstrates how to modify the LoRA adapter:

import torch
from transformers import SubwordXLMForTokenClassification

# Load the pre-trained model
model = SubwordXLMForTokenClassification.from_pretrained("segment-any-text/sat-12l")

# Create a new classifier layer with the correct shape
new_classifier = torch.nn.Linear(127, 768)

# Load the weights from the checkpoint
new_classifier.load_state_dict(torch.load("sat-12l_lora_custom/dummy-dataset/language_code", map_location="cpu")["classifier.weight"])

# Update the model's classifier layer
model.classifier = new_classifier

This code creates a new classifier layer with the correct shape and loads the weights from the checkpoint. We then update the model's classifier layer to match the new layer.

Conclusion

In conclusion, size mismatches can occur when training or loading a LoRA adapter. To solve this issue, we need to ensure that the model's weights match the expected shape. We can do this by retraining the model on a downstream task or modifying the LoRA adapter to match the expected shape. By following the steps outlined in this article, we can successfully train and load a LoRA adapter.

Additional Note

As mentioned in the original issue, it would be helpful to release the LoRA weights for -SM or provide instructions on how to produce these ourselves. This would enable users to adapt pre-trained language models to specific downstream tasks more easily.

Release of LoRA Weights for -SM Models

To release the LoRA weights for -SM models, we would need to generate the weights for each -SM model and make them available for download. This would require significant computational resources and expertise in language model adaptation.

However, we can provide instructions on how to produce the LoRA weights for -SM models ourselves. This would involve using a library such as transformers to load the pre-trained -SM model and then adapting it to the specific downstream task using LoRA.

Here is an example code snippet that demonstrates how to produce the LoRA weights for an -SM model:

import torch
from transformers import SubwordXLMForTokenClassification

# Load the pre-trained -SM model
model = SubwordXLMForTokenClassification.from_pretrained("segment-any-text/sat-12l")

# Adapt the model to the specific downstream task using LoRA
lora_adapter = torch.nn.Linear(111, 768)
lora_adapter.load_state_dict(torch.load("sat-12l_lora_custom/dummy-dataset/language_code", map_location="cpu")["classifier.weight"])

# Update the model's classifier layer
model.classifier = lora_adapter

This code loads the pre-trained -SM model and adapts it to the specific downstream task using LoRA. We then update the model's classifier layer to match the new layer.

By following the steps outlined in this article, we can successfully produce the LoRA weights for -SM models and adapt pre-trained language models to specific downstream tasks.
Q&A: Size Mismatch when Training/Loading LoRA

Q: What is LoRA and why is it used?

A: LoRA (Low-Rank Adaptation) is a technique used to adapt pre-trained language models to specific downstream tasks. It is used to fine-tune the model's weights to the specific task, improving its performance.

Q: What is the issue with size mismatch when training/loading LoRA?

A: When training a LoRA adapter, the model's weights are adapted to the specific task. However, when loading the saved LoRA adapter, the model's weights may not match the expected shape, leading to a size mismatch error.

Q: How can I solve the size mismatch issue?

A: To solve the size mismatch issue, you can retrain the model on a downstream task, or modify the LoRA adapter to match the expected shape of the model.

Q: How do I modify the LoRA adapter to match the expected shape?

A: To modify the LoRA adapter, you need to update the classifier.weight and classifier.bias layers to match the expected shape. You can do this by creating a new classifier layer with the correct shape and then loading the weights from the checkpoint.

Q: How do I create a new classifier layer with the correct shape?

A: You can create a new classifier layer with the correct shape using the torch.nn.Linear function. For example:

new_classifier = torch.nn.Linear(127, 768)

Q: How do I load the weights from the checkpoint?

A: You can load the weights from the checkpoint using the torch.load function. For example:

new_classifier.load_state_dict(torch.load("sat-12l_lora_custom/dummy-dataset/language_code", map_location="cpu")["classifier.weight"])

Q: How do I update the model's classifier layer?

A: You can update the model's classifier layer by assigning the new classifier layer to the model's classifier attribute. For example:

model.classifier = new_classifier

Q: Can I release the LoRA weights for -SM models?

A: Yes, we can release the LoRA weights for -SM models. However, this would require generating the weights for each -SM model and making them available for download.

Q: How can I produce the LoRA weights for -SM models myself?

A: You can produce the LoRA weights for -SM models yourself by using a library such as transformers to load the pre-trained -SM model and then adapting it to the specific downstream task using LoRA.

Q: How do I adapt the pre-trained -SM model to the specific downstream task using LoRA?

A: You can adapt the pre-trained -SM model to the specific downstream task using LoRA by creating a new classifier layer with the correct shape and then loading the weights from the checkpoint. For example:

lora_adapter = torch.nn.Linear(111, 768)
lora_adapter.load_state_dict(torch.load("sat-12l_lora_custom/dummy-dataset/language_code", map_location="cpu")["classifier.weight"])
model.classifier = lora_adapter

Q: What are the benefits of usingRA?

A: The benefits of using LoRA include improved performance on specific downstream tasks, reduced computational requirements, and easier adaptation of pre-trained language models to new tasks.

Q: What are the limitations of using LoRA?

A: The limitations of using LoRA include the need for careful adaptation of the model to the specific task, potential overfitting to the training data, and the requirement for significant computational resources to generate the LoRA weights.