Decoding Negative "Warps Per SM" in PyTorch: What's Going On?

Seeing a negative "warps per SM" value during PyTorch inference profiling can be confusing. It suggests something is amiss with your kernel execution. But don't panic, we'll break down what this metric means and why it might be negative, helping you optimize your code.

Understanding Warps Per SM

"Warps per SM" (Streaming Multiprocessor) represents an estimate of the average number of warps actively running on each SM of your GPU during the kernel's execution. Each warp consists of multiple threads (typically 32 on NVIDIA GPUs). The higher the number of warps actively running per SM, the better the GPU's utilization and potentially the higher the throughput.

Why Is "Warps Per SM" Negative?

A negative value for "Warps per SM" is highly unusual and not a standard output. It's almost certainly indicative of a bug or an error in how the profiler is calculating or presenting the data. Here are some potential causes:

• Profiler Bug: The most likely explanation is a bug within the profiling tool itself. There might be an overflow, a data corruption issue, or incorrect interpretation of the raw profiling data.
• Data Type Issue: A variable meant to represent warp count (which should be a positive integer or float) might have inadvertently become a signed type. If some calculation resulted in a value very close to zero, it might be represented negatively due to rounding errors specific to the profiler.
• Kernel Launch Failure: If the kernel launch failed or experienced an unexpected error before the profiler could collect meaningful data, it might, in rare cases, lead to bizarre values. This is an unlikely scenario.

Troubleshooting Negative "Warps Per SM"

Here’s how to proceed when you encounter such error:

1. Verify Profiler Correctness: Try a different profiling tool or an older version of the same tool. If the issue disappears, it strongly suggests a bug in the profiler you were initially using.
2. Check the Kernel Code: Review your CUDA kernel code (or the PyTorch operations that are triggering this kernel) for potential issues like out-of-bounds memory access, race conditions, or other errors could be indirectly affecting the profiler's output.
3. Simplify the Model: If possible, simplify your model or isolate the specific PyTorch operation that triggers this output. This can help narrow down the source of the potential issue.
4. Inspect CUDA Errors: Although it may not directly cause a negative value, ensuring that your CUDA code isn't generating errors during kernel execution is essential.
5. Run On different Hardware: If feasible, try the code on a different GPU or even a different machine. This might help you to discard hardware related reasons.
6. Examine Other Metrics: Focus on other profiling metrics provided by the tool. Are occupancy, shared memory usage, or register usage values reasonable? Discrepancies in other metrics might provide more insights.

Related Long-Tail Terms

• PyTorch GPU profiling
• CUDA kernel debugging
• Interpreting profiler output

By systematically investigating these areas, you'll be well-equipped to identify the root cause of the negative "warps per SM" value and get your PyTorch code running optimally. Remember to carefully document your troubleshooting steps, since this behavior can be dependent on specific hardware or software configurations. The error may very well lie with the PyTorch profiler or the NVIDIA driver.