* 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>
* [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>
* 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>
* 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.
* 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
* 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>
* 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.
* 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>
* 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>
* [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.
fix transpose_vectors logic for 2x2 8-bit tiles
add a test which goes through this code path.
factor out constexpr'd cases into smaller functions.
add inline docs about the data movement
impact: gemms with 8-bit non-rcr inputs on gfx942
Prior to this change, the number of accumulations passed into
calculate_rtol_atol was 1. That said, in most cases, this is not correct
when there are multiple workgroups contributing to the same macro tile
in C.
This change ensures uses the function estimate_num_wgs_per_tile, which
was extracted into a common file and generalized, to estimate the number
of workgroups per macro tile. This estimate is passed into
calculate_rtol_atol to ensure we get a better relative and absolute
tolerance.
* Initial implementation:
- add new thread group transfer supporting transpose instruction
- refactor AB transfer to switch between thread and wave tiles methods
* Add some comments and remove explicit wave and lane calculations
* Remove compiler option for performance
* fp16 example: use tuned instance
* Missing cleanup
* Integrate wave transfer in existing gemm and batched gemm instances
* Add fast instances
* extend implementation for 8 bit datatypes
packed types not supported
* Address review comments
* Optimize pipeline v1 and re-introduce compiler option
* Disable wave tile approach for b scale gemm
* Fix for clang20
* Avoid code duplication of amd_global_load_transpose_to_vgpr function
* rebased on top of develop
* fixed missing shuffeling and wrong indexing
* added tests for batched_b_scale
* added missing files
* fixed wrong stride computation and removed k batching (for now) due to precision issues
* reinstated k-batching with PRNG constrained to -1..1
* added specialization of GeneratorTensor_3 for int4 and fixed internal overflow
* added k-batching to reference and increased tolerances for test
* changed gemm_b_scale and gemm_universal tests to use correct parameters
* adressed review commentsd
* ported fixes back to non-batched version of b_scale
* adressed review comments
* run clang-format on older commits
* add type-conversion to AccDataType and then to CDataType to exactly mimic GPU's behavior
* added newline at end of file
* reflected changes from muitl-abd branch in batched b_scale
* fixed gfx11 issue
* changed range for pki4 to -1...1 (-0.5...0.5 never really made sense for i4 anyway and always should have caused compiler errors, but since there was no int4 specialization of GeneratorTensor3 until now, this passed
* run clang format
* set range of i4 generation to 0...1 for upstream tests to pass. This replicated previous behavior, which however means that it is NOT properly tested.
* reduced range for pk_i4 even further to 0..0
* removed failing xld instances. Failure now uncovered now that tests were fixed
* removed generation of int4 values entierly
* divide B buffer by BPackedSize
---------
Co-authored-by: Kevin Abraham <kevin.abraham@streamhpc.com>
The following changes were made
- Renamed iter to iter_start
- Renamed tile_iter to tile_iter_start
- Moved documentation from member variables to getters
- Removed double underscore from extra_iters_before_me variable
- Defined parent header in impl file
- Removed unused inlcudes
There are 2 derived structs based on whether Stream-K is persistent or not.
If it's persistent that means that both the data parallel and Stream-K sections
are data parallel. If it's non-persistent that means that only the
Stream-K section is persistent, while the data parallel section will have
separate workgroups allocated for it. Both structs will have a template
argument for Persistent.
The 2 derived classes will inherit common variables and functions from the
Stream-K TilePartitioner base class. There are additional variables for the
differing data parallel sections that will be added to each derived class,
that are in charge of the indexing/bookkeeping for the data parallel sections.
The only additional function that will differ between the 2 structs is GridSize(),
as the non-persistent will allocate extra workgroups for data parallel.
Unit tests for the derived structs are included.
