TileLang Autotune Cache Bug With Input Shapes
Introduction
This article addresses a critical bug in the Autotune cache implementation within TileLang. Currently, the cache reuses results across different input shapes, leading to suboptimal configurations and performance. This article will delve into the specifics of the issue, explain why it occurs, and propose a solution to ensure accurate and efficient autotuning. The current implementation of Autotune cache utilizes a hash of the program and configurations as the key. However, this approach overlooks a crucial factor: the input shapes of the data being processed. This can lead to significant performance degradation, especially in scenarios involving varying problem sizes. At its core, the problem lies in the fact that the Autotune cache key doesn't account for the dimensions of the input tensors. This means that when autotuning a GEMM (General Matrix Multiplication) operation with, say, dimensions (4096, 4096, 4096), the results are cached. Subsequently, when autotuning a GEMM with different dimensions, such as (4096, 8192, 8192), the autotuner incorrectly uses the cached results from the previous shape. This reuse of cached results leads to the selection of suboptimal configurations because the ideal tiling and optimization strategies can vary greatly depending on the matrix dimensions. For instance, a configuration that works well for smaller matrices might not be the most efficient for larger ones, and vice versa. The absence of input shape information in the cache key effectively defeats the purpose of autotuning, which is to find the best configuration for a specific problem size. By using cached results from different shapes, the autotuner fails to explore the configuration space that is most appropriate for the current input, resulting in a significant performance bottleneck.
Problem Description
Consider a scenario where you first autotune a GEMM operation with dimensions (4096, 4096, 4096). The autotuner explores various configurations and stores the best-performing one in the cache. Now, if you autotune another GEMM operation with dimensions (4096, 8192, 8192), the current implementation of Autotune will directly use the cached result from the previous shape because the cache key doesn't differentiate between these two different problem sizes. This results in sub-optimal configs and results for the second GEMM operation. The core issue is that the problem size, represented by (M, N, K) in the case of GEMM, is not included in the cache key. This omission means that the autotuner is essentially treating different problem sizes as the same, leading to the reuse of inappropriate configurations. To illustrate further, imagine you have found an optimal tiling strategy for a 4096x4096 matrix multiplication. This strategy might involve dividing the matrices into specific block sizes that fit well within the cache hierarchy and take advantage of SIMD (Single Instruction, Multiple Data) instructions. However, when you double the dimensions to 8192x8192, the same tiling strategy might no longer be optimal. The larger matrices could lead to cache thrashing, increased memory access latency, and other performance bottlenecks that the original tiling strategy didn't account for. The current Autotune cache, by not considering the (M, N, K) values, fails to capture these crucial differences. It blindly applies the cached configuration, which might be far from the best choice for the new problem size. This not only leads to reduced performance but also undermines the primary goal of autotuning: to automatically discover the most efficient configuration for a given workload. In essence, the bug causes the autotuner to become less adaptive and responsive to changes in input size, resulting in a significant loss of potential performance gains. Therefore, addressing this issue is paramount to ensuring the robustness and efficiency of TileLang's autotuning capabilities.
Reproducible Example
To reproduce this issue, you can use the provided example code:
python3 examples/gemm/example_gemm_autotune.py
python3 examples/gemm/example_gemm_autotune.py --n 8192 --k 8192
This code snippet first runs the example_gemm_autotune.py script with the default GEMM size. Subsequently, it runs the same script with modified dimensions (--n 8192 --k 8192). By running these two commands in sequence, you can observe the autotuner incorrectly applying the cached results from the first run to the second, leading to suboptimal performance. The example_gemm_autotune.py script likely performs a matrix multiplication operation, and the autotuning process aims to find the most efficient configuration (e.g., tiling sizes, loop order) for this operation. When you run the script the first time, the autotuner explores different configurations and caches the best one it finds. However, the current bug means that this cached configuration is tied only to the program and its inherent structure, not to the specific dimensions of the matrices being multiplied. When you run the script a second time with larger matrices, the autotuner should ideally re-explore the configuration space to find the optimal settings for this new problem size. But because the cache key doesn't include the dimensions, the autotuner mistakenly believes that the cached configuration from the first run is still the best choice. This leads to the use of a configuration that is likely not well-suited for the larger matrices, resulting in a significant performance penalty. To truly observe the impact of this bug, you would need to benchmark the performance of the GEMM operation in both scenarios: first with the incorrect cached configuration and then with a properly autotuned configuration for the larger matrices. The difference in execution time and resource utilization would clearly demonstrate the performance degradation caused by the incorrect cache reuse. By providing this simple and reproducible example, it becomes easy for developers and users to verify the existence of the bug and to assess the effectiveness of any proposed fixes. This hands-on approach is essential for ensuring that the autotuning system in TileLang operates correctly and delivers the performance benefits it is designed to provide.
Proposed Solution
To address this issue, it is crucial to separate the compile cache and the Autotune result cache. The compile cache should continue to cache the compiled code based on the program and configurations. However, the Autotune result cache needs to be more specific. The key for the Autotune result cache should include the problem size, such as (M, N, K) for GEMM, in addition to the program and configurations. This separation ensures that the autotuner only reuses cached results when the input shapes are identical, leading to more accurate and efficient tuning. Separating the compile cache from the autotuning cache is a critical step towards resolving the incorrect cache reuse issue in TileLang. The compile cache's primary function is to store the compiled code generated from the TileLang program and its configurations. This cache is essential for speeding up the compilation process, especially when the same program with the same configurations is run multiple times. By caching the compiled code, TileLang avoids the overhead of recompilation, which can be a time-consuming process. The key for the compile cache typically includes the program's source code and the specific configurations used during compilation. This ensures that only identical programs and configurations result in a cache hit, guaranteeing the correctness of the compiled code being reused. On the other hand, the autotuning cache's purpose is to store the performance results of different configurations for a given program and input size. This cache helps the autotuner to quickly identify the best-performing configurations without having to re-evaluate them every time. However, as we've seen, the current implementation of the autotuning cache incorrectly reuses results across different input sizes, leading to suboptimal performance. To fix this, the autotuning cache needs to be more discerning about when to reuse cached results. The proposed solution involves modifying the cache key to include the input size, such as the (M, N, K) dimensions for a GEMM operation. This ensures that the autotuner only reuses cached results when the input size is exactly the same as the one for which the results were originally obtained. In addition to the input size, the cache key should continue to include the program and configurations. This ensures that the autotuner considers all relevant factors when deciding whether to reuse a cached result. By separating the compile cache and the autotuning cache and by making the autotuning cache key more specific, we can ensure that TileLang's autotuning system operates correctly and efficiently. This will lead to significant performance improvements, especially in scenarios where the input size varies. The benefits of this separation extend beyond just correctness; it also improves the overall maintainability and clarity of the autotuning system. By clearly delineating the responsibilities of each cache, it becomes easier to reason about and debug the autotuning process.
Expected Behavior
With the proposed solution, the Autotune cache will accurately store and retrieve results based on the input shapes. This will ensure that the autotuner explores the configuration space appropriate for each specific problem size, leading to optimal performance. The expected behavior after implementing the proposed solution is that the Autotune cache will no longer reuse results across different input shapes. This means that when the autotuner encounters a new input size, it will treat it as a distinct problem and explore the configuration space specifically for that size. This will ensure that the autotuner finds the best-performing configuration for each input size, leading to optimal performance in all scenarios. To understand the expected behavior in more detail, let's consider the GEMM example again. Previously, when autotuning GEMM with dimensions (4096, 4096, 4096) followed by GEMM with (4096, 8192, 8192), the autotuner would incorrectly reuse the cached results from the first run for the second run. After implementing the solution, the autotuner will recognize that the input sizes are different and will perform a separate autotuning process for each size. This means that for the (4096, 8192, 8192) GEMM, the autotuner will explore different tiling strategies, loop orders, and other configuration parameters to find the best settings for these specific dimensions. The results of this autotuning process will then be stored in the cache, keyed by the input size (4096, 8192, 8192). When the same GEMM operation with the same dimensions is encountered again in the future, the autotuner will be able to retrieve the cached results directly, avoiding the need for re-autotuning. This will significantly speed up the execution of the GEMM operation in subsequent runs. In addition to improved performance, the expected behavior also includes increased accuracy in the autotuning process. By considering the input size as part of the cache key, the autotuner will be able to make more informed decisions about which configurations are likely to perform well. This will lead to a more efficient exploration of the configuration space and a higher likelihood of finding the truly optimal configuration for a given problem. Overall, the expected behavior is a more robust, accurate, and efficient autotuning system that can adapt to different input sizes and consistently deliver optimal performance. This will make TileLang a more powerful and versatile tool for a wide range of applications.
Conclusion
The described bug in TileLang's Autotune cache can lead to suboptimal performance due to the reuse of cached results across different input shapes. By separating the compile cache from the Autotune result cache and including the problem size in the Autotune cache key, this issue can be effectively resolved. This will ensure that TileLang's autotuning capabilities are accurate and efficient, leading to better performance across various workloads. To further enhance your understanding of autotuning and performance optimization, consider exploring resources on LLVM, a widely used compiler infrastructure project, which offers various tools and techniques for program optimization.
