* Add indexing support to pooling operator
- Add IndexDataType template parameter to pooling problem and kernel
definitions
- Enable pooling kernel to output indices of selected elements during
max/absmax pooling
- Add overloaded operators for Max and AbsMax that track when values
change using bool changed parameter
- Support optional index buffer allocation and management in device
memory
- Modify BlockReduce2d classes to handle index tensors alongside value
tensors
- Add separate shared memory allocation for index data in cross-warp
reductions
- Create validate_pool_indices function to verify index correctness
- Modify pool3d.cpp example to demonstrate index output functionality
- Add tests for index output
* fixes
* Refactor BlockReduce2D functions to get rid auxiliary private types.
* comment resolutions and some changes to block_reduce2d
- index reference implementation improved
- reduce_operator.hpp cleanedup
- updated the block_reduce2d.hpp to have index calculation for
BlockReduce2dLinearCrossWarpSync as well
* conditionally used variable declaration improvement
- the conditionally used vairbales are used only when indexing is
enabled. To inform the compiler that they may be unused and declare them
with least size possible. This may allow it to be optimized compared to
the previous declarations
* comment resolutions
* lexical ordering of the indicies
- introduced accumulate methods that handle the intermediate steps if
needed to order the indexes
* add reduce_operator_accumulate.hpp to core.hpp
---------
Co-authored-by: Adam Osewski <Adam.Osewski@amd.com>
[ROCm/composable_kernel commit: 3052d7c9e6]
* [CK_TILE] fmha: Add query padding support to backward pass
Introduces support for query sequence padding (q_padding) in the FMHA backward pass kernels.
- Passing `seqlen_q_ptr` to the backward kernels to distinguish logical from physical sequence lengths.
- Updating `OGradDotO`, `ConvertQGrad`, and `DQDKDV` kernels to respect logical lengths and handle zero-length sequences.
- Aligning LSE indexing in the forward kernel with the padded layout for consistency.
- Adding a new GTest suite (`test_fmha_bwd_kernel_padding.cpp`) with comprehensive tests for various padding scenarios, including zero-length
sequences and deterministic mode.
* fix clang format
* Adapt fmha_bwd_runner.cpp to new q, kv sequence padding
Add backward q/kv sequence padding unit tests.
* [CK_TILE] fmha: Unify sequence length and padding handling
Refactor the handling of sequence lengths and padding in the
FMHA forward and backward kernels to provide a more unified and flexible
interface.
- Replaced `seqstart_padded_*_ptr` with a more robust system that uses
`seqstart_*_ptr` for physical sequence lengths and introduces
`seqlen_*_ptr` and `cu_seqlen_*_ptr` for logical (unpadded) lengths.
- Established a clear order of precedence for determining sequence
length: cumulative lengths (`cu_seqlen_*_ptr`) take priority,
followed by per-sequence lengths (`seqlen_*_ptr`), and finally
physical lengths derived from `seqstart_*_ptr`.
- Clarified the distinction between "group mode" and "batch mode" and
how sequence lengths are handled in each case.
- Renamed `cu_seqlen_kv_ptr` to `cu_seqlen_k_ptr` for consistency.
- Updated comments and documentation to reflect the new argument
structure and usage.
---------
Co-authored-by: illsilin_amdeng <Illia.Silin@amd.com>
[ROCm/composable_kernel commit: 7c6430eca0]
This pull requests adds some initial "factory tests" - these check that the instances which are used in MIOpen are actually present in CK. The main reason for this is documentation and sanity checking. Its likely that these tests get outdated fast, so we'll have to maintain them, but fortunately this is quite straight forward and shouldn't take a lot of time once they are in place.
[ROCm/composable_kernel commit: 6f58d6e457]
* refactor package generation, add dedicated switch
* allow building packages not only on gfx9
* enable last stage to post packages
* stash packages from different arch into separate stashes
* build packages daily automatically
[ROCm/composable_kernel commit: 155d63f4fe]
* update test cases
* format codes
* use GTEST_FAIL
* add bquant to grouped_gemm
* fix a bug in test_grouped_gemm_util
* skip test when use wmma on grouped_quant kernel
* add tensorwise quant in grouped gemm
* fix example issue
* update test cases
* format codes
* fix a bug in test_grouped_gemm_util
* tests(quant_grouped_gemm): add unit tests to cover bquant in grouped_gemm
* Update test/ck_tile/grouped_gemm_quant/test_grouped_gemm_util_quant.hpp
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Update example/ck_tile/17_grouped_gemm/quant_grouped_gemm.hpp
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* feat: add bf8 support
* chore: remove unnecessary decltype usage
* chore: add default quant_mode to function signature as fallback
* fix: pass correct runtime pipeline params in grouped_gemm bquant kernel
Calculate has_hot_loop, num_loop, and tail_number on device side for each
GEMM problem instead of using default values. This fixes incorrect results
when different problems in the group have different K dimensions.
* chore: set default quant mode in function signature
* test: add additional test cases to cover edge case of no hotloop
* chore: clang formatting
---------
Co-authored-by: kyle-256 <Kyle.Zhao@amd.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
[ROCm/composable_kernel commit: 4368fd9f57]
* Refactor instance_traits_util and add unit tests tests
* Address reviewer comments.
Just adds some TODOs to indicate deprecated layouts in our reflection. Our strategy is to leave the reflection code broad (covering deprecated features), but keep the builder concepts narrow. Once we've removed deprecated features from all instances, we can remove them from reflection.
Also add a comment to the cmake to explain the unit test target test_conv_builder.
* Addressed more reviewer comments.
* Remove duplicate PassThrough::name
Accidentally added this field to the end of the struct, too. The `name` field should be a the start of the struct for consistency.
[ROCm/composable_kernel commit: 54746e9329]
* change base docker image for aiter
* do not add group irc to aiter docker
* add user and group jenkins
* pip install ninja
* update permissions for /home/jenkins
[ROCm/composable_kernel commit: e02b1e7caf]
* Addition of streamk fp8 example for CK Tile
* Adding in bf8 streamk example in CK Tile
* Refactoring fp8/bf8 unit tests
Refactored the unit tests for fp8/bf8 to utilize the test harness.
Implemented smoke tests with layouts: CCR, CRR, RCR, RRR for fp8/bf8.
The tests are using 128x128x32 for the tile configuration, as other
configurations revealed implementation gaps that are currently being
documented.
[ROCm/composable_kernel commit: 715395bc86]
* Partial Progress : CK Tile Engine GEMM
* Partial Progress : CK Tile Engine GEMM
* Partial Progress : Working GEMM Code
* Partial Progress : Working GEMM Code
* Changinf jenkins to remove preshuffle
* Partial Progress : CK TILE ENGINE GEMM Debugging
* Partial Progress : Removing changes that are not GEMM
* Partial Progress : Validation of full block size in GEMM
* Changes in Jenkins to run only fp16 and bf16
* Addressing Review Comments
* Partial Progress : Addressing CI issues
* Partial Progress - Runing GEMM for fp16,bf16 and rcr
* Clang
* Adding fp8 and bf8
* Adding fp8 and bf8
* Adding additional architrcture
* Limited datatypes and layouts
* Adding k_block_per_cu in test config
* Changes to faling CI errors
* Changes to faling CI errors
* Validation for GEMM
* Adding Layout support
* Adding Validations
* Adding layout in jenkins
* Update on Jenkins
* Distribution validation for GEMM
* Resolving merge conflicts
* Solving merge conflicts
[ROCm/composable_kernel commit: 7fc0a38e90]
* Adding support for TiledPermuteN
* Adding test
* moving shuffle functions to common place
* resolving commit hook
* fix formatting
[ROCm/composable_kernel commit: b11f53a484]
* Implement argument passing to element-wise functions for fwd convolution
* Add files for fwd + bias + clamp example
* Implement Bias
* Implement Clamp
* Elementwise function composition
* Composition unit test
* Implement fwd + bias + clamp example
* Simplify argument passing and composition
* elfunc -> bias_and_clamp
* Rename function to specify example
* Move element-wise function instantiation to kernel
* Make bias a runtime tensor
* No ugly namespace aliasing
* Initialize element-wise function on host
* Remove function initialization helper, simplify Compose initialization
* Remove unintended LSP compatibility patch
* Clean up includes and unused code
* Switch names in cshuffle epilogue
* Move CDElementwise to conv traits
* Re-add required include
* Initialize bias in same way as other tensors
* Better type specification for ds pointer
* Disable 1D convolution
* Add warning for non-group-constant bias
[ROCm/composable_kernel commit: 5c1974065e]
* Persistent Stream-K Kernel Implementation
This change implements an operator() function in the
reboot::StreamKKernel class that is enabled when the Persistent flag is
set to true. In this case, the data-parallel portion and the Stream-K
portion of the kernel are fully persistent.
The changes were made in the reboot namespace. A future PR will remove
the old Stream-K kernel class and remove the reboot namespace.
* Unit Tests for Persistent Stream-K Kernel
This change contains the inital test suite for the Persitent Stream-K
Kernel. The files contain "reboot" in the name; a future PR will remove
tests for the old Stream-K Kernel and remove the "reboot" naming.
A future commit will add tests for the non-persistent kernel.
Also added estimate_num_wgs_per_tile to the StreamKTilePartitionerBase
class. This allows us to estimate the number of accumulations done per
macro tile in C to use during validation when computing relative and
absolute tolerance.
* Adding implementation for the Non-Persistent Stream-K kernel
This code is adding the operator() function for the Non-Persistent Stream-K
kernel. Persistency of the kernel is determined through a template argument.
The Non-Persistent kernel will allocate additional workgroups for the data
parallel section, leading to a different structure for processing the data
parallel and Stream-K sections.
There has been an addition to the TilePartitioner to get access to the whether
Persistent has been set to true or false in the StreamKKernel.
* Adding in the tests for the Non-Persistent Stream-K kernel
* Refactor Stream-K Reboot Unit Tests
This commit makes the following changes:
- Update test cases to determine M, N, and K based on the number of CUs.
This ensures that each test case is one of Edge Case, SK Only, DP
Only, or DP + 2 Tile SK regardless of the architecture.
- Since the DP + 2 Tile SK test case takes long to run, this change
moves this case into a separate .inc file and labels it as an extended
test.
- Since the extended test takes > 30 seconds to run, this test is added
to the list of regression tests.
* Fix spelling errors in comments for test cases
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Changes based on review
Removed const volatile for typenames
Set up alias for is_tuple_t
Naming changes for clarity: GemmCommon -> BaseGemm
Moved std::enable_if_t out of template parameters and changed to a return type for operator()
Added constructor for StreamKKernelArgs to clarify UniversalGemm inheritance
---------
Co-authored-by: Emily Martins <emily.martins@amd.com>
Co-authored-by: Christopher Millette <63608002+cgmillette@users.noreply.github.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
[ROCm/composable_kernel commit: 054fdb765c]
Developers who use cline on the code base need to ignore .cline* directories like .cline_storage and .clinerules. Using a wildcard to ignore any other cline-related directories.
[ROCm/composable_kernel commit: 0b68423015]
We need to check all the architectures for build errors. This missing tf32 type came up as a build failure when I compiled for different instinct architectures.
[ROCm/composable_kernel commit: 6d709dac41]
* Add name member to unary elementwise ops.
* Update elementwise_op_name to check for name attribute.
* Require that the layout is derived from BaseTensorLayout struct.
[ROCm/composable_kernel commit: f53d857b25]
Adds new testing functionality: an inline diff for string comparison.
Example usage:
EXPECT_THAT("Actual string", ck_tile::test::StringEqWithDiff("Expected string"));
Failure message:
Value of: "Actual string"
Expected: "Expected string"
Actual: "Actual string" (of type char [14]),
Diff: "[Expe|A]ct[ed|ual] string"
The inline-diff function uses the Wagner-Fischer algorithm to find the minimum edit distance and generate diff markers, which has O(N^2) complexity. It has optional color codes that are enabled with the matcher.
[ROCm/composable_kernel commit: e576992dca]
* Fixing check for changed files
* Testing CI skip behavior
* Testing CI Trigger
This should skip CI
---------
Co-authored-by: Illia Silin <98187287+illsilin@users.noreply.github.com>
[ROCm/composable_kernel commit: 775b96ea6a]
* add tensorwise quant in grouped gemm
* fix example issue
* update test cases
* format codes
* clang format
* use GTEST_FAIL
* fix a bug in test_grouped_gemm_util
* skip test when use wmma on grouped_quant kernel
* change cmake
* change code based on comments
---------
Co-authored-by: ThomasNing <thomas.ning@amd.com>
[ROCm/composable_kernel commit: 3c12a02827]
* Disable c++20-compat warnings when building old CK in C++20 mode
Turns out that this creates some warnings for no good reason.
* ck-builder: add missing layouts and element-wise op names
For layouts, we can directly use the ::name attribute, which should
cover all layouts. For element-wise ops, I just added the ones which
are currently missing when compiling CK with -DMIOPEN_REQ_LIBS_ONLY.
[ROCm/composable_kernel commit: d0364641ed]
* Conv bwd splitN support
* Adjust splitting calculations to lengths format
* Prepare indexing for future splitK support
[ROCm/composable_kernel commit: cbd1279ae6]
* Introduce tree reduction for BlockReduce2dCrossWarpSync
* Rename original impl to BlockReduce2dLinearCrossWarpSync
* Replace warp_size with get_warp_size()
---------
Co-authored-by: Illia Silin <98187287+illsilin@users.noreply.github.com>
[ROCm/composable_kernel commit: 5a27a97391]
* [CK_BILDER] Add compile-time reflection for a convolution instance
Introduce InstanceTraits template metaprogramming framework to enable runtime introspection of device kernel template parameters without requiring implementation knowledge. This reflection system extracts configuration details (block sizes, data types, layouts, tuning parameters) directly from kernel specializations through template
pattern matching. In particular, the GetInstanceString method returns a string that uniquely idenitfies the kernel, by explicitly serializing all template paramter values.
This provides critical functionality for MIOpen integration, since the existing GetTypeString method is ambiguous, and only captures some of the template paramters.
The implementation uses a two-level design: a primary InstanceTraits template declaration in instance_traits.hpp serves as the interface, while kernel-specific specializations (e.g., for DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle_V3) provide the actual extraction logic. This separation allows the reflection system to scale to additional kernel types without modifying the core interface.
Key architectural decisions:
- Forward-declare device kernels in instance_traits.hpp to avoid circular dependencies, since device implementation headers will include the reflection headers
- Use compile-time constants and type aliases to expose kernel parameters, enabling zero-overhead introspection
- Provide a templated instance_string() function that generates human-readable kernel configuration strings by serializing all template parameters in order, useful for debugging and kernel identification
- Guard reflection integration with preprocessor definition CK_EXPERIMENTAL_BUILDER to keep it opt-in until the API stabilizes
- Add GetInstanceString() virtual method to BaseOperator, allowing runtime polymorphic access to compile-time kernel information
This infrastructure also enables upcoming higher-level semantic reflection abstractions (like ConvTraits) to query kernel configurations programmatically.
Includes unit tests validating both the trait extraction accuracy and the string generation format.
[ROCm/composable_kernel commit: 37dff024c1]
