PyTorch: Max-Autotune Slowdown With Jagged Tensors

Hey guys! Today, we're diving deep into a peculiar performance issue encountered in PyTorch while working with jagged tensors. Specifically, we'll be discussing a scenario where using torch.compile with max-autotune-no-cudagraphs leads to slower performance compared to eager mode. This was observed while testing the jagged_mean kernel from tritonbench. Let's break down the bug, the test cases, and the potential reasons behind this slowdown. So, let's get started and make sure we understand what's going on with jagged tensors and PyTorch compilation.

The Bug: Slower Performance with max-autotune

The core issue is that for certain input shapes of jagged tensors, compiling with max-autotune-no-cudagraphs results in performance degradation rather than improvement. This is unexpected because torch.compile is designed to optimize performance. The slowdown was observed across different implementations of the jagged_mean operation, indicating a more systemic problem rather than an issue with a specific implementation. Understanding this performance discrepancy is crucial for optimizing PyTorch workflows.

Input Shapes Causing Slowdown

The input shapes that triggered this behavior are:

• (B=512, M=64, seqlen=100, sparsity=0.1)
• (B=512, M=64, seqlen=500, sparsity=0.75)

Here, B represents the batch size, M is the feature dimension, seqlen is the maximum sequence length, and sparsity indicates the proportion of empty or zero elements in the tensor. These parameters define the shape and characteristics of the jagged tensor, which is essentially a tensor with variable-length sequences. Analyzing performance across different shapes and sparsity levels helps us identify the conditions under which compilation might be less effective.

Code Snippets and Implementations

To reproduce the bug, several implementations of jagged_mean were tested:

1. Unbind + torch.mean: This implementation involves unbinding the jagged tensor into individual tensors, calculating the mean along a specific dimension using torch.mean, and then concatenating the results. This method is straightforward but might not be the most efficient due to the overhead of unbinding and concatenating.
2. torch.nanmean: This approach uses torch.nanmean in conjunction with torch.ops.aten._jagged_to_padded_dense_forward to handle the jagged tensor. The _jagged_to_padded_dense_forward operation converts the jagged tensor into a padded dense tensor, where missing values are filled with a padding value (NaN in this case). Then, torch.nanmean calculates the mean, ignoring NaN values. This implementation leverages built-in PyTorch functionalities for handling jagged tensors.
3. torch.sum: This method calculates the mean by summing the values in the padded dense tensor and dividing by the sequence lengths. It also uses torch.ops.aten._jagged_to_padded_dense_forward to convert the jagged tensor to a padded format. This approach is similar to the torch.nanmean method but uses summation and division instead of a direct mean calculation. Comparing these implementations allows us to pinpoint whether the slowdown is specific to certain operations or a more general issue with how compiled mode handles jagged tensors.

Benchmarking Methodology

The benchmarking process included a warmup phase (50 runs) followed by a benchmark phase (200 runs). This helps to mitigate the effects of initial overhead and thermal variations on the GPU. The average, standard deviation, and median execution times were recorded to provide a comprehensive performance overview. This rigorous benchmarking approach ensures that the performance measurements are reliable and representative of real-world usage scenarios.

Detailed Results and Analysis

Configuration (512, 64, 100, 0.1)

For the configuration with B=512, M=64, seqlen=100, and sparsity=0.1, the results were as follows:

• Unbind + torch.mean: The compiled version was slightly slower (5.503 ms) than the eager version (5.315 ms), resulting in a speedup of 0.97x.
• torch.nanmean: The compiled version was significantly slower (0.230 ms) than the eager version (0.127 ms), with a speedup of 0.55x.
• torch.sum: Similar to torch.nanmean, the compiled version (0.230 ms) was slower than the eager version (0.103 ms), resulting in a speedup of 0.45x.

These results clearly show that for this configuration, compiling with max-autotune-no-cudagraphs degrades performance for the torch.nanmean and torch.sum implementations. The performance slowdown indicates that the compiled mode might not be effectively optimizing these operations for this particular tensor shape and sparsity.

Configuration (512, 64, 500, 0.75)

For the configuration with B=512, M=64, seqlen=500, and sparsity=0.75, the results were:

• Unbind + torch.mean: The compiled version (7.875 ms) was slower than the eager version (5.171 ms), with a speedup of 0.66x.
• torch.nanmean: In contrast to the previous configuration, the compiled version (0.235 ms) was faster than the eager version (0.345 ms), showing a speedup of 1.47x.
• torch.sum: The compiled version (0.236 ms) was slower than the eager version (0.158 ms), resulting in a speedup of 0.67x.

This configuration presents a mixed bag. While torch.nanmean benefited from compilation, the unbind + torch.mean and torch.sum implementations still experienced a slowdown. This variability suggests that the effectiveness of compilation depends on the specific operations and tensor characteristics involved. Understanding the interplay between tensor shapes, sparsity, and operation types is key to optimizing performance.

Summary of Results

The table summarizes the speedup factors (eager time / compiled time) for each implementation and configuration. A speedup less than 1 indicates that the compiled version is slower than the eager version. This comparison highlights the inconsistencies in performance gains achieved through compilation, underscoring the need for a deeper investigation into the underlying causes.

Potential Causes for the Slowdown

Several factors could contribute to the observed slowdown:

1. Overhead of Compilation: The compilation process itself introduces overhead. For small operations or specific tensor shapes, this overhead might outweigh the benefits of optimized code, leading to slower execution times.
2. Suboptimal Kernel Selection: torch.compile uses autotuning to select the best kernel for a given operation and input shape. In some cases, the autotuning process might choose a suboptimal kernel, resulting in degraded performance. It's possible that the selected kernels are not well-optimized for jagged tensors with the tested shapes and sparsity levels.
3. Inefficient Handling of Jagged Tensors: Jagged tensors have variable-length sequences, which can make them challenging to optimize. The compiled mode might not be efficiently handling the variable lengths and padding operations, leading to performance bottlenecks.
4. CUDA Graph Incompatibilities: The max-autotune-no-cudagraphs mode disables CUDA graphs, which can help reduce CPU overhead by capturing and replaying GPU operations. However, certain operations or tensor shapes might benefit more from CUDA graphs, and disabling them could lead to performance degradation. Assessing the impact of CUDA graphs on jagged tensor performance can provide valuable insights.

Steps to Reproduce the Bug

To reproduce the bug, you can use the provided Python script. The script includes functions for generating jagged tensors, implementing jagged_mean using different methods, and benchmarking the performance of eager and compiled versions. By running the script with the specified configurations, you should be able to observe the same slowdown in performance for certain implementations and input shapes.

Required Libraries

Ensure you have the following libraries installed:

• torch
• time
• math
• random
• numpy
• typing

You can install these libraries using pip:

pip install torch numpy

Running the Script

Save the script as a Python file (e.g., jagged_tensor_benchmark.py) and run it from your terminal:

python jagged_tensor_benchmark.py

The script will print the performance results for each implementation and configuration, including the eager time, compiled time, and speedup factor. By examining these results, you can verify the performance issues described in this article.

Conclusion and Next Steps

In conclusion, the performance slowdown observed with torch.compile and max-autotune-no-cudagraphs for specific jagged tensor shapes is a significant issue that warrants further investigation. The mixed results across different implementations and configurations suggest that the effectiveness of compilation depends on various factors, including tensor shape, sparsity, and the specific operations involved. Further research and optimization efforts are needed to ensure that compiled mode consistently delivers performance improvements for jagged tensors.

Future Directions

1. Profiling: Use profiling tools to identify the specific operations and kernels that are causing the slowdown in compiled mode. This can help pinpoint the bottlenecks and guide optimization efforts.
2. Kernel Tuning: Experiment with different kernel implementations and autotuning settings to find the best configuration for jagged tensors. This might involve writing custom kernels or adjusting the autotuning parameters.
3. CUDA Graph Analysis: Evaluate the impact of enabling CUDA graphs on the performance of compiled jagged tensor operations. This can help determine whether CUDA graphs can mitigate the slowdown observed in max-autotune-no-cudagraphs mode.
4. PyTorch Internals: Consult with PyTorch developers and experts to gain insights into the internal workings of torch.compile and how it handles jagged tensors. This can help uncover potential bugs or areas for improvement in the compilation process.

By addressing these issues, we can enhance the performance of PyTorch when working with jagged tensors and ensure that compiled mode provides consistent and substantial speedups. Thanks for joining this deep dive, and stay tuned for more insights into PyTorch optimization!