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@ecamartins ecamartins commented Sep 16, 2025

Proposed changes

NOTE: This PR is left as a draft as it depends on PR #2781. Once PR #2781 is merged, I will update this PR and mark it as ready to review

Recently, an initial implementation of Stream-K has been added to CK Tile (see PR #2781) with a gtest suite covering initial cases for fp16 and bf16. Thus, this PR aims to add a minimal example for Stream-K in example/ck_tile. Specifically, the example allows users to run Stream-K with either b16 or fp16 with options to configure number of Stream-K blocks, reduction strategy, etc. At this time, only the atomic reduction strategy is supported (see PR #2781 for the guard in IsSupportedArgument for details).

There are plans to add more gtests cases to the Stream-K unit tests, which will test for variants, including more pipelines. These examples will be expanded once more unit tests are in place.

Checklist

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  • I have added tests relevant to the introduced functionality, and the unit tests are passing locally
  • I have added the test to REGRESSION_TESTS list defined at the top of CMakeLists.txt in tests/CMakeLists.txt, IF the test takes more than 30 seconds to run.
  • I have added inline documentation which enables the maintainers with understanding the motivation
  • I have removed the stale documentation which is no longer relevant after this pull request
  • (If this change is user-facing) I have added release notes which provide the end users with a brief summary of the improvement from this pull request
  • I have run clang-format on all changed files
  • Any dependent changes have been merged

ecamartins and others added 10 commits September 16, 2025 14:31
…::MakeGemmTensorViews function

Prior to this change, the splitk_batch_offset parameter of
MakeGemmTensorViews had type SplitKBatchOffset. But, the only member
variable of the SplitKBatchOffset class used in the MakeGemmTensorViews
function was splitted_k (an int32_t). The splitted_k value was used as
part of defining the dimensions of the tensor view. That said, for
Stream K, we do not need to use the SplitKBatchOffset class since we are
not using Split K. Thus, this commit changes the splitk_batch_offset
parameter to a int32_t called k_size. This will avoid the constraint of
requiring a caller of MakeGemmTensorViews to use the SplitKBatchOffset
class while still providing the same functionality. Calls to
UniversalGemmKernel::MakeGemmTensorViews have been updated accordingly.
Stream K cannot simply use UniversalGemmKernel's RunGemm for the
following reasons:

1. The UniversalGemmKernel::RunGemm function computes num_loop based on
   a static function of the TilePartitioner. That said, for Stream K,
num_loop must be computed using a member function (namely
GetCurrentIterLength from PR #2708).
2. The UniversalGemmKernel::RunGemm function requires the use of a
   SplitKBatchOffset object which is not used for Stream K since we are
not using Split K.

Thus, this change adds a RunGemm function in the StreamKKernel class.
These changes do the following:
- Ensure offsets along the M and N dimensions are multiplied by
  MPerblock or NPerBlock, respectively. This ensures tile window origins
are at the correct locations.
- Fix bug in the tile partitioner's GetTileIdxWithOffset. Now, we apply
  divmod to the given references to ensure correct values are available
to the caller.
- Added documentation in the Stream-K operator()
These changes add an initial gtest suite for the CK Tile Stream-K
kernel. Currently, due to bugs in the StreamKTilePartitioner (which will
be handled in a future PR), there are validation issues for certain
cases which may differ on different architectures. Thus, we opted to run
cases that are only fully data-parallel (skipping others). A guard was
added to Stream-K's IsSupportedArgument method to ensure that callers
are aware of this constraint. Additionally, to ensure testing
reproducibility, options for setting the number of CUs and occupancy
were added to MakeKernelArgs.
In Stream-K, the num_loop value varies per WG and per iteration of a
Stream-K loop. So instead, we use the version of the GemmPipeline's
operator() function that takes in has_hot_loop and tail_num. This is
similar to what is done in Grouped GEMM.
Prior to this change, WGs travelled backwards through their assigned
macro tiles in the C tensor. For instance, if WG0 is responsible for C
tiles 0 and 1, it would first visit tile 1 then tile 0. This means that
the iter_end decrements in each iteration of the stream-K while loop.

Since we are working with unsigned integers, the subtraction operation
may not be safe. Thus, this change makes is such that WGs travel forward
so that their iter_start is incremented and their iter_end remains
fixed.

Additionally, we added a guard against WGs that are neither sk_blocks
nor dp_blocks to ensure such WGs do not participate in the GEMM.

Together, these changes make is such that the algorithm is correct when
sk_blocks is greater than zero.
This instance involves >=3 WGs contributing to each macro tile in C. Due
to the use of atomics, this is resulting in precision errors. These
errors will not persist once the reduction strategy is implemented. We
will re-enable this test then.
Addition of initial CK Tile Stream-K example for bf16 and fp16. These
examples are minimal. As more functionality and gtests are added for
Stream-K (coming in future PRs), these examples will be expanded.
@ecamartins ecamartins self-assigned this Sep 16, 2025
@ecamartins ecamartins closed this Sep 16, 2025
@ecamartins ecamartins deleted the emimarti/ck_tile/streamk_example branch September 29, 2025 22:25
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3 participants