[BUG] Possible Race Condition In Sm90_gemm_array_tma_warpspecialized_cooperative

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BUG: Possible Race Condition in sm90_gemm_array_tma_warpspecialized_cooperative

In the context of the 57_hopper_grouped_gemm-like kernel, a rare issue has been observed where a 128x32 patch of outputs differs when running the kernel twice on the same inputs. This article delves into the possible cause of this issue, which is suspected to be a race condition in the sm90_gemm_array_tma_warpspecialized_cooperative function.

The symptoms of this issue are as follows:

  • When running the 57_hopper_grouped_gemm-like kernel twice on the same inputs, a 128x32 patch of outputs are different.
  • This issue is very rare and only occurs when the kernel is run twice on the same inputs.

The hypothesis is that there is a possible race condition in the sm90_gemm_array_tma_warpspecialized_cooperative function when performing tensormaps update in store-related warps (Consumer0/Consumer1). The issue is suspected to be caused by the lack of explicit waiting on tma_store finish.

The analysis of the code reveals that there is no explicit waiting on tma_store finish in the producer warp. Instead, always_wait = true is set in epi_store_pipeline_params, and producer_acquire is called in every store(...) call, which results in a tma_store_wait<UnacquiredStages>. However, UnacquiredStages is not zero, so there may be at least one tma_store "in flight" when tensormaps are changed.

The possible cause of this issue is a race condition in the sm90_gemm_array_tma_warpspecialized_cooperative function. When tensormaps are updated, there is no explicit waiting on tma_store finish, which can lead to a situation where multiple tma_store operations are in flight simultaneously. This can cause the tensormaps to be updated incorrectly, resulting in a 128x32 patch of outputs being different.

The fix for this issue is to add a tma_store_wait<0> before tensormaps_perform_update. This ensures that there is no tma_store operation in flight when tensormaps are updated, which should prevent the race condition from occurring.

There is another issue in the epilogue-load related tensormaps_update, where the condition to collective_epilogue.load is work_tile_info.is_valid() && curr_batch != next_work_tile_info.L_idx;, but when actually updating tensormaps, the condition is also work_tile_info.is_valid() && did_batch_change. However, work_tile_info is changed, but the implication next_work_tile_info.is_valid() => work_tile_info.is_valid() should hold, so this is not a problem.

In conclusion, the possible cause of the issue in the 57_hopper_grouped_gemm-like kernel is a race condition in the sm90_gemm_array_tma_warpspecialized_cooperative function. The fix for this issue is to add a tma_store_wait<0> before tensormaps_perform_update. This should prevent the race condition from occurring and ensure that the kernel runs correctly.

Based on the analysis, the following recommendations are made:

  • Add a tma_store_wait<0> before tensormaps_perform_update to prevent the race condition from occurring.
  • Review the code to ensure that there are no other potential issues that could cause the kernel to run incorrectly.
  • Consider adding additional synchronization mechanisms to ensure that the kernel runs correctly in all scenarios.

Future work should focus on further investigating the cause of the issue and ensuring that the kernel runs correctly in all scenarios. This may involve additional testing and analysis to identify any other potential issues.

The code review should focus on the following areas:

  • Review the sm90_gemm_array_tma_warpspecialized_cooperative function to ensure that it is correct and free of bugs.
  • Review the tma_store_wait function to ensure that it is correct and free of bugs.
  • Review the tensormaps_perform_update function to ensure that it is correct and free of bugs.

The testing should focus on the following areas:

  • Test the kernel with different inputs to ensure that it runs correctly in all scenarios.
  • Test the kernel with different synchronization mechanisms to ensure that it runs correctly in all scenarios.
  • Test the kernel with different error handling mechanisms to ensure that it runs correctly in all scenarios.

In conclusion, the possible cause of the issue in the 57_hopper_grouped_gemm-like kernel is a race condition in the sm90_gemm_array_tma_warpspecialized_cooperative function. The fix for this issue is to add a tma_store_wait<0> before tensormaps_perform_update. This should prevent the race condition from occurring and ensure that the kernel runs correctly.
Q&A: Possible Race Condition in sm90_gemm_array_tma_warpspecialized_cooperative

A: The possible cause of the issue in the 57_hopper_grouped_gemm-like kernel is a race condition in the sm90_gemm_array_tma_warpspecialized_cooperative function. When tensormaps are updated, there is no explicit waiting on tma_store finish, which can lead to a situation where multiple tma_store operations are in flight simultaneously.

A: The fix for this issue is to add a tma_store_wait<0> before tensormaps_perform_update. This ensures that there is no tma_store operation in flight when tensormaps are updated, which should prevent the race condition from occurring.

A: The tma_store_wait<0> is necessary because it ensures that there is no tma_store operation in flight when tensormaps are updated. This prevents the race condition from occurring and ensures that the kernel runs correctly.

A: The implication of the next_work_tile_info.is_valid() => work_tile_info.is_valid() condition is that if next_work_tile_info is valid, then work_tile_info is also valid. This is because work_tile_info is changed, but the implication should hold.

A: No, the issue in the epilogue-load related tensormaps_update is not a problem. The condition to collective_epilogue.load is work_tile_info.is_valid() && curr_batch != next_work_tile_info.L_idx;, but when actually updating tensormaps, the condition is also work_tile_info.is_valid() && did_batch_change. However, work_tile_info is changed, but the implication next_work_tile_info.is_valid() => work_tile_info.is_valid() should hold.

A: The recommendations for fixing this issue are:

  • Add a tma_store_wait<0> before tensormaps_perform_update to prevent the race condition from occurring.
  • Review the code to ensure that there are no other potential issues that could cause the kernel to run incorrectly.
  • Consider adding additional synchronization mechanisms to ensure that the kernel runs correctly in all scenarios.

A: The future work for this issue is to further investigate the cause of the issue and ensure that the kernel runs correctly in all scenarios. This may involve additional testing and analysis to identify any other potential issues.

A: The code review for this issue should focus on the following areas:

  • Review the sm90_gemm_array_tma_warpspecialized_cooperative function to ensure that it is correct and free of bugs.
  • Review the tma_store_wait function to ensure that it is correct and free of bugs.
  • Review the tensormaps_perform_update function to ensure that it is correct and free of bugs.

A: The testing for this issue should focus on the following areas:

  • Test the kernel with different inputs to ensure that it runs correctly in all scenarios.
  • Test the kernel with different synchronization mechanisms to ensure that it runs correctly in all scenarios.
  • Test the kernel with different error handling mechanisms to ensure that it runs correctly in all scenarios.

In conclusion, the possible cause of the issue in the 57_hopper_grouped_gemm-like kernel is a race condition in the sm90_gemm_array_tma_warpspecialized_cooperative function. The fix for this issue is to add a tma_store_wait<0> before tensormaps_perform_update. This should prevent the race condition from occurring and ensure that the kernel runs correctly.