Revolutionary Approach for Dynamic Cancer Progress with Diet Code

Table of Contents:

1. Introduction
2. Current Auto Schedule Design Challenges
3. Proposal: Diet Code - An Auto Schedule Framework
4. Key Ideas of Diet Code 4.1 Shape Generic Search Space 4.2 Microkernel-Based Cost Model
5. New Interface for Diet Code
6. Evaluation of Diet Code
7. Conclusion
8. Resources
9. FAQ

Automatic Code Generation for Dynamic Cancer Progress with Diet Code

1️⃣ Introduction

In this article, we will dive into the details of a revolutionary approach called Diet Code, which focuses on automatic code generation for dynamic cancer progress. Before we delve into the specifics of Diet Code, let's take a quick review of the current auto schedule design and the challenges it faces.

2️⃣ Current Auto Schedule Design Challenges

The current auto schedule design starts with a user supplying an operator specification and a standard sheet description. However, a key challenge arises when an operator can have infinitely many possible schedules. For example, even a simple transformation like looptailing can lead to infinitely many possible telling candidates. To limit the search space, existing solutions circumscribe the talent candidates to those that are perfect factors and dependent on the loop extent. While this approach has benefits, it becomes extremely challenging to share schedules across different shapes of the same operator. Furthermore, Scheduling dynamic shift workloads also poses difficulties as each Shape instance must be searched individually, resulting in inefficiency.

3️⃣ Proposal: Diet Code - An Auto Schedule Framework

To address the challenges faced by the current auto schedule design, we propose Diet Code, a new auto schedule framework that efficiently supports dynamic chip workloads. Diet Code introduces two key ideas: Shape Generic Search Space and Microkernel-Based Cost Model. These ideas revolutionize the auto schedule process and pave the way for improved performance and efficiency.

4️⃣ Key Ideas of Diet Code

4.1️⃣ Shape Generic Search Space

The shape generic search space in Diet Code constructs a search space composed of microkernels. Each microkernel performs a towel of the entire compute and can be ported to all shapes of the same operator. By sampling the microkernels mostly from hardware constraints instead of shape factors, Diet Code creates a shaped generic search space that enables efficient sharing of schedules across different shapes.

4.2️⃣ Microkernel-Based Cost Model

Diet Code introduces a microkernel-based cost model to accurately predict the performance of each schedule. The existing cost model, trained on one shape, can be inaccurate on other shapes. Diet Code overcomes this limitation by decomposing the cost model into two components: a trainable microkernel cost modeled using XG rules and an analytical Spatial generalization cost modeled using a linear function. This approach ensures that the cost model captures the change in compute support and exhibits predictable trends with respect to shift Dimensions.

5️⃣ New Interface for Diet Code

Diet Code provides a new interface that allows users to define dynamic shape variables and their instances. Users can pass these variables and instances to the workload function, enabling efficient scheduling for dynamic chip workloads. Additionally, users have the option to assign weights to each shape instance, further enhancing the customization and optimization capabilities of Diet Code.

6️⃣ Evaluation of Diet Code

To test the effectiveness of Diet Code, we evaluated it on a hardware platform equipped with a Tesla T4 GPU. We used Diet Code to auto-schedule dense layers extracted from the BERT model, a state-of-the-art language model application. The results showed that Diet Code outperformed the current auto-schedule design and the Cool+Wonder library in terms of performance. Furthermore, Diet Code's auto-scheduling time was significantly reduced compared to existing solutions.

7️⃣ Conclusion

In conclusion, Diet Code is a practical and efficient auto scheduler for dynamic chip workloads. Its innovative approach, incorporating a shape generic search space and microkernel-based cost model, improves performance and reduces the scheduling time. With Diet Code, developers can achieve up to 30% better performance compared to existing solutions. We are currently working on integrating Diet Code as part of the QBM main branch, further solidifying its position as a groundbreaking auto schedule framework.

8️⃣ Resources

• QBM Main Branch: Website Link
• Tesla T4 GPU: Product Link
• BERT Model: Research Paper

9️⃣ FAQ (Frequently Asked Questions)

Q: What is auto schedule? A: Auto schedule, in the context of Diet Code, refers to the automated process of generating code for dynamic cancer progress. It eliminates the need for manual scheduling and optimizes performance.

Q: Can Diet Code be used for other applications apart from cancer progress? A: Yes, Diet Code is a versatile auto schedule framework that can be applied to various domains beyond cancer progress. Its shape generic search space and microkernel-based cost model make it suitable for optimizing performance in dynamic chip workloads.

Q: How does Diet Code improve performance compared to existing auto-schedule designs? A: Diet Code outperforms existing solutions by introducing a shape generic search space, allowing efficient sharing of schedules across different shapes, and by utilizing a microkernel-based cost model that accurately predicts performance. These innovations result in improved performance and reduced scheduling time.

Q: Can Diet Code handle workflows with multiple dynamic axes? A: Yes, Diet Code can handle workflows with multiple dynamic axes. It provides a new interface that supports dynamic shape variables and their instances, enabling efficient scheduling for complex workloads.

Q: Are there any limitations or drawbacks to using Diet Code? A: While Diet Code offers significant performance improvements and efficiency gains, it may require familiarity with the framework and its concepts to harness its full potential. Additionally, as with any technology, there may be specific use cases where alternative solutions might be more suitable.

Note: The FAQ section contains hypothetical questions and answers for illustrative purposes.