Create a separate buffer for frozen weights

Oct 13, 2025 by talynt.org 5 views

Support Frozen Weights: Enhancing Fast-LLM Performance

🎯 Goal (What & Why)

Fast-LLM, a cutting-edge language model, has been designed to optimize performance and efficiency. However, a critical issue has been identified that hinders its full potential. Currently, Fast-LLM creates gradient and optimizer state buffers for all parameters, even if they are frozen. This approach not only degrades memory usage but also reduces the speedups gained from freezing weights. Moreover, this limitation is a significant blocker for the implementation of LoRA (Low-Rank Adaptation), a crucial feature that enables efficient fine-tuning of pre-trained models. The primary goal of this project is to address this issue by introducing a separate buffer for frozen weights, thereby enhancing the overall performance and efficiency of Fast-LLM.

🚀 Execution Plan

To achieve the goal of supporting frozen weights, a comprehensive execution plan has been outlined:

Create a separate buffer for frozen weights: This buffer will not store gradients, allowing for efficient storage and processing. The buffer will be stored in training precision, which will require separate restoration when zero-3 is involved.
Add checkpoint support for such buffers: This feature will enable seamless checkpointing and restoration of frozen weights, ensuring that the model's performance is not compromised during training and inference.
Distributed checkpoints: With the introduction of separate buffers for frozen weights, distributed checkpoints will become trivial to implement, allowing for efficient parallelization and scalability.
Imports/Experts: While checkpointing and restoration will be straightforward, imports and experts will require additional work to accommodate the new buffer structure. This will involve modifying the architecture to accommodate the changed weight layout, which will have implications for the architecture/non-architecture split.

📌 Acceptance Criteria (Must-Haves for Completion)

To ensure that the project is completed successfully, the following acceptance criteria must be met:

Things work as described above: The implementation must accurately reflect the execution plan outlined above, ensuring that frozen weights are properly handled and checkpointed.

🛠️ Project Management

To ensure the successful completion of this project, the following tasks must be assigned and tracked:

Assign the project to the Fast-LLM project: This will ensure that the project is properly categorized and prioritized within the Fast-LLM project.
Set the Estimate field (in days) in the GitHub project: This will provide a clear estimate of the time required to complete the project, enabling better planning and resource allocation.
Use the Size field to categorize the PR size (Small/Medium/Large): This will help to categorize the project's size and complexity, enabling better resource allocation and prioritization.
Assign an owner when opening the issue: This will ensure that the project is properly assigned to a team member or owner, who will be responsible for its completion.

Benefits of Supporting Frozen Weights

The implementation of frozen weights will have several benefits, including:

Improved memory usage: By creating a separate buffer for frozen weights, memory usage will be significantly reduced, enabling faster training and inference.
Enhanced speedups: Frozen weights will enable faster training and inference, as the model will not need to compute gradients for these weights.
Efficient checkpointing: With the introduction of separate buffers for frozen weights, checkpointing and restoration will become more efficient, enabling faster training and inference.
Better scalability: The implementation of frozen weights will enable better scalability, as the model will be able to handle larger datasets and more complex tasks.

Challenges and Limitations

While the implementation of frozen weights will have several benefits, there are also some challenges and limitations to consider:

Architecture changes: The introduction of frozen weights will require changes to the model's architecture, which may have implications for the architecture/non-architecture split.
Checkpointing and restoration: While checkpointing and restoration will become more efficient, there may be additional work required to accommodate the new buffer structure.
Imports and experts: The implementation of frozen weights will require additional work to accommodate the new buffer structure, which may have implications for imports and experts.

Conclusion

In conclusion, the implementation of frozen weights is a critical feature that will enhance the performance and efficiency of Fast-LLM. By creating a separate buffer for frozen weights and adding checkpoint support, we can improve memory usage, enhance speedups, and enable better scalability. While there are some challenges and limitations to consider, the benefits of supporting frozen weights make it a worthwhile investment for the Fast-LLM project.
Support Frozen Weights: Q&A

Q: What is the primary goal of this project?

A: The primary goal of this project is to address the issue of creating gradient and optimizer state buffers for all parameters, even if they are frozen, which degrades memory usage and reduces speedups gained from freezing weights.

Q: Why is this issue a blocker for LoRA (Low-Rank Adaptation)?

A: LoRA is a crucial feature that enables efficient fine-tuning of pre-trained models. However, the current implementation of Fast-LLM creates gradient and optimizer state buffers for all parameters, even if they are frozen, which makes it difficult to implement LoRA.

Q: How will the separate buffer for frozen weights be stored?

A: The separate buffer for frozen weights will be stored in training precision, which will require separate restoration when zero-3 is involved.

Q: What is the significance of distributed checkpoints?

A: Distributed checkpoints will become trivial to implement with the introduction of separate buffers for frozen weights, allowing for efficient parallelization and scalability.

Q: What additional work is required for imports and experts?

A: Imports and experts will require additional work to accommodate the new buffer structure, which may have implications for the architecture/non-architecture split.

Q: What are the benefits of supporting frozen weights?

A: The implementation of frozen weights will have several benefits, including improved memory usage, enhanced speedups, efficient checkpointing, and better scalability.

Q: What are the challenges and limitations of supporting frozen weights?

A: While the implementation of frozen weights will have several benefits, there are also some challenges and limitations to consider, including architecture changes, checkpointing and restoration, and imports and experts.

Q: How will the project be managed?

A: The project will be managed through the Fast-LLM project, with tasks assigned and tracked using the GitHub project management tool.

Q: What is the estimated time required to complete the project?

A: The estimated time required to complete the project will be set in the GitHub project management tool, enabling better planning and resource allocation.

Q: How will the project's size and complexity be categorized?

A: The project's size and complexity will be categorized using the Size field in the GitHub project management tool, enabling better resource allocation and prioritization.

Q: Who will be responsible for completing the project?

A: The project will be assigned to a team member or owner, who will be responsible for its completion.

Q: What are the next steps for the project?

A: The next steps for the project will include creating a separate buffer for frozen weights, adding checkpoint support, and implementing distributed checkpoints.

Q: How will the project's progress be tracked?

A: The project's progress will be tracked using the GitHub project management tool, with tasks assigned and tracked, and the project's size and complexity categorized.

Q: What are the expected outcomes of the project?

A: The expected outcomes of the project include improved memory usage, enhanced speedups, efficient checkpointing, and better scalability, as well as the successful implementation of LoRA.