* Implement grouped gemm fastgelu for RDNA4
* chore: some cleanup and minor inconsistencies in grouped gemm profiler
* chore: clarified logic and reporting of supported instance warnings
Our concept-base conversions are fragile and too complex. We want to refactor to straightforward functions
for each intance trace class template. This change adds unit test coverage to make that refactoring safer.
Refactor function amd_buffer_load_invalid_element_return_zero to avoid
the inefficient ASM code generated by compiler.
Compiler generates suboptimal assembly for ternary operator, causing excessive VGPR usage
Tested compilers:
- Rocm 7.0.1
- Rocm 7.1.1
Co-authored-by: Thomas Ning <Thomas.Ning@amd.com>
* update grouped_gemm blockwise kernel
* update config
* update kernel
* update examples
* remove test code for now
* sync test files with origin/develop
* update example
* fix code lint
* fix code-lint
* update test code
* run clang format
* run pre-commit
* update api
* ck-builder: explicitly delete forward declarations
Before, these functions were seen as a forward declaration for an existing function.
If no actual implementation overload could be found, these would be selected and
a linker error or warning would be generated. By marking these functions as explicitly
deleted, they incorrect invocations are generated as compile error instead.
* ck-builder: ckt::run plumbing for reference conv
This implements the ckt::run plumbing for the reference convolution
implementation and sets up the first complete end-to-end test.
* ck-builder: make validation system check for all-zeros
When both the actual and reference output are both all zero bits,
there is probably something wrong in the test framework.
* ck-builder: proper implementation+tests for TensorDescriptor::is_packed
* ck-builder: fix typos
* [CK_TILE] unify double and single lds implementation (#108)
Unify LDS buffer management API for single and double buffering modes
This change consolidates the Local Data Store (LDS) buffer management by:
Merging single and double LDS buffer APIs into a unified interface
Implementing ping-pong address calculation in pipeline when double LDS is enabled
Computing pong buffer addresses dynamically using base address offsets
---------
Co-authored-by: joye <joye@amd.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* update wp_pipeline
* fix a c++17 issue
* update for ci errors
* fix ci issues
* include a header to fix ci errors
* fix some rebase issues
* update with rebase
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
This pull request builds on #3267 by proving the "validation" infrastructure, the means to compare a set of `Outputs`.
The design of the validation infrastructure is relatively straight forward:
- Each SIGNATURE should come with a `validate()` implementation, which should be implemented in a similar way that the other functions/types from `testing.hpp` are implemented.
- `validate()` returns a `ValidationReport`, which is a structure that keeps all relevant information about comparing the tensors from two `Outputs`. Note that crucially, `validate()` should not do any reporting by itself. Rather, glue logic should be implemented by the user to turn `ValidationReport` into a relevant error message.
- You can see this clue code for CK-Builder itself in `testing_utils.hpp`, its `MatchesReference()`. This functionality is relatively barebones right now, it will be expanded upon in a different PR to keep the scope of this one down.
The comparison is done on the GPU (using an atomic for now), to keep tests relatively quick. Some notable items from this PR:
- To help compare the tensors and with writing tests, I've written a generic function `tensor_foreach` which invokes a callback on every element of a tensor.
- For that it was useful that the `TensorDescriptor` has a rank which is known at compile-time, so I've changed the implementation of `TensorDescriptor` for that. I felt like it was a better approach than keeping it dynamic, for multiple reasons:
- This is C++ and we should use static typing where possible and useful. This way, we don't have to implement runtime assertions about the tensor rank.
- We know already know the rank of tensors statically, as it can be derived from the SIGNATURE.
- It simpifies the implementation of `tensor_foreach` and other comparison code.
- There are a lot of new tests for validating the validation implementation, validating validation validation tests (Only 3 recursive levels though...). For a few of those functions, I felt like it would be useful to expose them to the user.
- Doc comments everywhere.
* add page_block_size parameter
* add is_sglang_layout to parameters
* add kv_offset_array_transform to batch async for page size 16
* add kv_last_page_lens to kernel
* change kv layout to [num_total_pages, page_block_size, hdim]
* format
* - enable codegen of batch_prefill kernels
- create new problem struct BlockFmhaBatchPrefillPipelineProblem for
batch prefill kernels
- generate different page sizes of batch prefill kernels (1, 16)
* 1. fix wrong calculation of page id in kv_offset_array_transform in gfx950
2. support page size 1024
* fix python format
* change kv cache layout to [num_blocks, num_kv_heads, head_size/x,
block_size, x] and [num_blocks, num_kv_heads, block_size/X, head_size, X]
* 1. Introduced `kVectorSize` in BlockFmhaBatchPrefillPipelineProblem instead of using hardcode values
2. Makes batch prefill kernel traits structures inherent from fmha fwd
traits
3. Add some static check for Page size, vector size, hdim, ..., etc.
* [Refactor] Replace is_sglang_layout with Enums for KV cache configuration
Refactored `fmha_batch_prefill` to use `BlockAttentionKVCacheMemoryLayoutEnum` (VECTORIZED/LINEAR) and `BlockAttentionKVCacheLookupTableEnum` (SGLANG_1D/VLLM_2D) instead of a single
boolean.
**Changes:**
* Added Enum definitions in `block_attention_kvcache_layout_enum.hpp`.
* Updated Kernel, Pipeline, and Traits to template on these Enums.
* Implemented `kv_offset_array_transform` logic based on `kKVMemoryLayout`.
* Refactored `PageBlockTableKargs` to adapt to `kKVLookupTable`.
* Updated CodeGen scripts to support new parameters.
This decouples memory layout from the paging mechanism, enabling flexible KV cache configurations.
* 1. remove batch prefill pipeline with sk_pad=false
2. correct some comments
3. add static assert to make sure v offsets is in same page within a tile.
* fix vgpr spill count
* remove unnecessary t2s functions
* add fp8 support for receipt 200 and 600 in fmha_bath_prefill.py
* support linear kv cache layout
* Remove block_table_ptr from fwd_batch_prefill_args. Instead, reuse
kv_page_indices as a pointer of the lookup table.
* 1. merge multiple transforms into single transform.
2. add static check to make sure vlayout is row-major.
* move FmhaFwdCommonKargs::seqlen_k_ptr to VllmPageTableKargs.
* update changelog
---------
Co-authored-by: ltqin <letaoqin@amd.com>
Co-authored-by: PoYen, Chen <PoYen.Chen@amd.com>
* initial poc
* factor out common parts in operator()
* cv4
* rest of the universal gemm pipelines
* fix test
* remove boilerplate from tile engine
* fix example
* fix example
* format
* fix tests build for gemm
* remove base pipeline codegen from gemm instance builder
* unify v3 logic with the rest of universal gemm pipelines
* fix build for multi abd test
* fix test gemm multi d
* fix build for weight preshuffle
* fix grouped gemm test
* fix grouped gemm multi d test
* fix grouped gemm preshuffle
* fix grouped gemm example except for quant
* fix gemm preshuffle
* fix splitk 2 stage example
* fix batched gemm example
* fix multid example
* fix multiabd example
* fix batched gemm test
* fixup
* fix examples build
* fix grouped gemm test build
* fix smoke builder
* hacky poc
* fix tile engine
* kill the lambda
* maybe fix test build
* more fixes
* clang-format
* save temp
* clang-format
* mostly fix examples
* clang-format
* remove dead code
* more cleanup
* fix fmha bwd build (default epilogue set/add appears to be broken)
* fix default epilogue tests but not correctness
* clang-format
* fix bquant
* clang-format
* cleanup dead code
* rearrange make windows for readability
* restore changes to IsSupportedArgument
* fix smoke-builder
* clang-format
* fixup rename class
* build fixes
* clang-format
* fix builder
* fixup
* remove set from builder tests
* fix test
* clang-format
* re-refactor the kernels
* clang-format
* fix header license
* remove memory operation from conv bwd test
* clang-format
* clang-format example,include
* clang-format test
* build fixes
* clang-format
* solve compilation error
* fix the CI
* solve compilation error
* clang format
* solve merge conflict
* solve merge conflict
* solve the gfx11 error
* solve test error
* moar build fixes
* remove AtomicAddRequiresKBatchGreaterThanOne test since the property is removed from the kernel scope
---------
Co-authored-by: Thomas Ning <Thomas.Ning@amd.com>
Previously, the code used unsigned long for literals and format specifiers to represent 64-bit unsigned values. While this worked on Linux, it caused compatibility issues on Windows.
The C++ standard does not guarantee that long is 64 bits. On LP64 systems (e.g., Linux), long maps to 64-bit values, but on LLP64 systems (e.g., Windows), long maps to 32-bit values. This discrepancy led to incorrect behavior when assuming unsigned long was always 64-bit.
This commit updates all relevant literals and format specifiers to explicitly use 64-bit unsigned types, ensuring consistent behavior across platforms.
Remove the dependency from device_tensor_generator.hpp and fix a typo from a previous force push. The changes replace standard library math functions with their ck::math equivalents and define PI as a local constant instead of computing it using std::acos.
Key changes:
* Removed #include header dependency
* Replaced std::acos(-1.0) with hardcoded PI constant 3.141592653f
* Replaced std::sqrt, std::cos, and std::sin with ck::math equivalents
Added instance traits for the following bwd weight conv algorithms
DeviceGroupedConvBwdWeight_Xdl_CShuffleV3
DeviceGroupedConvBwdWeight_Wmma_CShuffleV3
DeviceGroupedConvBwdWeight_Wmma_CShuffle
DeviceGroupedConvBwdWeight_TwoStage_Xdl_CShuffle
DeviceGroupedConvBwdWeight_TwoStage_Wmma_CShuffleV3
DeviceGroupedConvBwdWeight_DL
DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle
DeviceGroupedConvBwdWeightMultipleD_Wmma_CShuffleV3
Added also unit tests for instance traits of those bwd weigth algorithms that are currently exposed by the narrow CK build for MIOpen.
---------
Co-authored-by: Ville Pietilä <>
## Proposed changes
This source file won't build for gfx1101 on Windows. It builds successfully on other gfx110X architectures, and also builds successfully on gfx1101 on Linux.
This is the compile error:
```
[composable_kernel] FAILED: library/src/tensor_operation_instance/gpu/grouped_conv3d_bwd_weight_bilinear/CMakeFiles/device_grouped_conv3d_bwd_weight_bilinear_instance.dir/wmma/device_grouped_conv3d_bwd_weight_wmma_bilinear_ndhwgc_gkzyxc_ndhwgk_f16_instance.cpp.obj
[composable_kernel] ccache B:\build\core\clr\dist\lib\llvm\bin\clang++.exe -DCK_ENABLE_BF16 -DCK_ENABLE_BF8 -DCK_ENABLE_FP16 -DCK_ENABLE_FP32 -DCK_ENABLE_FP64 -DCK_ENABLE_FP8 -DCK_ENABLE_INT8 -DCK_TILE_USE_WMMA=1 -DCK_TIME_KERNEL=1 -DCK_USE_WMMA -DCK_USE_XDL -DDPP_KERNELS -DLLVM_MAIN_REVISION=524190 -DUSE_PROF_API=1 -D__HIP_PLATFORM_AMD__=1 -D__HIP_PLATFORM_HCC__=1 -IC:/home/runner/_work/TheRock/TheRock/ml-libs/composable_kernel/library/include -IC:/home/runner/_work/TheRock/TheRock/ml-libs/composable_kernel/include -IB:/build/ml-libs/composable_kernel/build/include -IB:/build/base/half/stage/include -isystem B:/build/core/clr/dist/include -DWIN32 -DWIN32_LEAN_AND_MEAN -D_CRT_SECURE_NO_WARNINGS -DNOMINMAX -fms-extensions -fms-compatibility -D_ENABLE_EXTENDED_ALIGNED_STORAGE -Wno-documentation-unknown-command -Wno-documentation-pedantic -Wno-unused-command-line-argument -Wno-explicit-specialization-storage-class -Wno-ignored-attributes -Wno-unknown-attributes -Wno-duplicate-decl-specifier --hip-path=B:/build/core/clr/dist --hip-device-lib-path=B:/build/core/clr/dist/lib/llvm/amdgcn/bitcode -O3 -DNDEBUG -D_DLL -D_MT -Xclang --dependent-lib=msvcrt -std=c++20 -Wall -Wextra -Wcomment -Wendif-labels -Wformat -Winit-self -Wreturn-type -Wsequence-point -Wswitch -Wtrigraphs -Wundef -Wuninitialized -Wunreachable-code -Wunused -Wno-reserved-identifier -Wno-option-ignored -Wsign-compare -Wno-extra-semi-stmt -Wno-unused-template -Wno-missing-field-initializers -Wno-error=deprecated-declarations -Wall -Wextra -Wcomment -Wendif-labels -Wformat -Winit-self -Wreturn-type -Wsequence-point -Wswitch -Wtrigraphs -Wundef -Wuninitialized -Wunreachable-code -Wunused -Wno-reserved-identifier -Wno-option-ignored -Wsign-compare -Wno-extra-semi-stmt -Wno-unused-template -Weverything -Wno-c++98-compat -Wno-c++98-compat-pedantic -Wno-conversion -Wno-double-promotion -Wno-exit-time-destructors -Wno-extra-semi -Wno-float-conversion -Wno-gnu-anonymous-struct -Wno-gnu-zero-variadic-macro-arguments -Wno-missing-prototypes -Wno-nested-anon-types -Wno-padded -Wno-return-std-move-in-c++11 -Wno-shorten-64-to-32 -Wno-sign-conversion -Wno-unknown-warning-option -Wno-unused-command-line-argument -Wno-weak-vtables -Wno-covered-switch-default -Wno-unsafe-buffer-usage -Wno-unused-lambda-capture -Wno-nvcc-compat -Wno-c++20-compat -Wno-bit-int-extension -Wno-pass-failed -Wno-switch-default -Wno-unique-object-duplication -Wno-nrvo -Werror -Weverything -fcolor-diagnostics -x hip --offload-arch=gfx1100 --offload-arch=gfx1101 --offload-arch=gfx1102 --offload-arch=gfx1103 --offload-arch=gfx1100 --offload-arch=gfx1101 --offload-arch=gfx1102 --offload-arch=gfx1103 -MD -MT library/src/tensor_operation_instance/gpu/grouped_conv3d_bwd_weight_bilinear/CMakeFiles/device_grouped_conv3d_bwd_weight_bilinear_instance.dir/wmma/device_grouped_conv3d_bwd_weight_wmma_bilinear_ndhwgc_gkzyxc_ndhwgk_f16_instance.cpp.obj -MF library\src\tensor_operation_instance\gpu\grouped_conv3d_bwd_weight_bilinear\CMakeFiles\device_grouped_conv3d_bwd_weight_bilinear_instance.dir\wmma\device_grouped_conv3d_bwd_weight_wmma_bilinear_ndhwgc_gkzyxc_ndhwgk_f16_instance.cpp.obj.d -o library/src/tensor_operation_instance/gpu/grouped_conv3d_bwd_weight_bilinear/CMakeFiles/device_grouped_conv3d_bwd_weight_bilinear_instance.dir/wmma/device_grouped_conv3d_bwd_weight_wmma_bilinear_ndhwgc_gkzyxc_ndhwgk_f16_instance.cpp.obj -c C:/home/runner/_work/TheRock/TheRock/ml-libs/composable_kernel/library/src/tensor_operation_instance/gpu/grouped_conv3d_bwd_weight_bilinear/wmma/device_grouped_conv3d_bwd_weight_wmma_bilinear_ndhwgc_gkzyxc_ndhwgk_f16_instance.cpp
[composable_kernel] error: Illegal instruction detected: Operand has incorrect register class.
[composable_kernel] V_CMP_NE_U32_e32 0, $src_private_base, implicit-def $vcc, implicit $exec
[composable_kernel] 1 error generated when compiling for gfx1101.
```
This appears to be a compiler bug and we'll follow up to get a proper fix landed, but for the purposes of landing some work to enable gfx1151 support in TheRock we'd like to disable building of these kernels on this architecture temporarily.
## Checklist
Please put an `x` into the boxes that apply. You can also fill these out after creating the PR. If you're not sure, please don't hesitate to ask.
- [X] I have added tests relevant to the introduced functionality, and the unit tests are passing locally
- [X] 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.
- [x] I have added inline documentation which enables the maintainers with understanding the motivation
- [X] I have removed the stale documentation which is no longer relevant after this pull request
- [X] (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
- [X] I have run `clang-format` on all changed files
- [X] Any dependent changes have been merged
* Added device level implementation for bwd_data_wmma_v3.
* Added first instance of bwd_data_wmma_v3(f16).
* Add support for bwd data in gridwise implementation
Some changes are general for convolution and some are specific for bwd
data. We need to generalize them once we have fwd, bwd data and bwd
weight
* Initial device implementation of bwd data
* Remove unused template parameters in device impl
* Add one instance for different layout
initial check of device implementation
* Add tests for splitk and for different layouts
* Appended more instances to wmma_v3_f16.
* Added conv_2d bf16 wmma_v3 instances.
* Added conv_3d_bf16 wmma_v3_instances.
* Added conv_3d_f16_wmma_v3_instances.
* Added SplitN test cases for wmma.
* Conv3d_bwd_data_scale_wmma_v3 instances.
* Conv3d_bwd_data_bilinear_wmma_v3_instances
* Renaming the device level instances file to common name , since it is defined for different DataTypes.
* Renaming the instances and fixing typo
* Added the test cases to regression test list
* NCHW support for wmma_v3
* Examples for bf16 and f16 bwd_data_wmma_v3
* Added transpose conditons for device impl
* fixing bugs
* Added the gemm_args array implmentation
* WIP debug conv bwd
* fix splitk
* Grouped gemm fix
* Update CmakeLists with EOF
* Added more instances for tests
* Fixed the run time error in examples and removed 3d conv examples.
* Fixed a typo.
* Updated CmakeLists to removed the 3d convultion deleted files
* Added print error statements for unsupoorted argument
* Added the merge conflict related changes
* Fixed compilation error
* Fixed the InstanceFactory duplication error.
* Removed the print statements and added logs to Arg function
* All the merge conflict related errors resolved
* Added d_tensor tests.
* Added the missing example types of wmm_v3
* Merge error fix
* Corrected the instance name
* Reverted the bias relu change
* Revereted the transpose load local change
* Updated the regression test list with bwd_data_scale
* Revert "Revereted the transpose load local change"
This reverts commit 0b7281edb2bf008e407006690a00621174d9d19b.
* Revert "Merge error fix"
This reverts commit f3c85daa474b1b83d10c8a3ce077354e71d91a2b.
* Reverting the local change
* Added merge error fix
* Build error fix due to merge conflicts
* Added bias_relu example for wmma_v3
* Modified the main method in dtensor tests
* Updated the dtensor tests to pick all the shapes
* Updated the dtensor test shapes.
* Updated the mem operations in tests.
* Added reference func
* Fixed typos in device impl
* Added new header file and modified the include file for 3d tests
* Renamed the test file and added reference func call.
* clang format fix
* Added ignore params
* Modified device impl and tests
* Removed debug print statements and updated dtensor test shapes
* Fixing merge conflicts
* Fixing more merge conflicts
* Fixed copyrights
* Updated the tuned instances to bilinear and scale.
* Adding tuned instances to vanilla wmma_v3
* Removed all unused instances and modified test layouts.
* Cleaned up all instances , reverted back fwd fp16 instances and updated tuned fp16 instances.
* Fix clang format
* Updated tuned f16/-genric instances
* Formatting the instances file
* Fixed copyrights and clang issues
* Nonsense commit to force git to force
* Removed the transpose instances
* Added verified genric instances
* Fixing namespace errors
* Added todo for failing shapes
* Formatting instance file
* Fix instance list formatting
* Removing unnecessary formats
* Renamed the common file
* Unification of xdl and wmma bwd_data tests
* Updated Cmake
* Added all layout types and deleted code.
* Updated Cmake to add the condition to all tests.
---------
Co-authored-by: Enrico Degregori <enrico@streamhpc.com>
Co-authored-by: Anton Gorenko <anton@streamhpc.com>
Co-authored-by: kiefer <kiefer.van.teutem@streamhpc.com>
* [CK_BUILDER] Integrate GPU reference as ConvAlgorithm
Add GPU reference as a ConvAlgorithm specialization, enabling:
- Unified Builder API for reference and optimized kernels
- Future ckProfiler integration for validation
- First step toward numerical validation in Builder tests
Changes:
- Add ConvAlgorithmSpecialization::REFERENCE enum
- Add ConvAlgorithm_Reference struct
- Add IsReferenceAlgorithm concept
- Create 3 reference factories (Forward, BwdData, BwdWeight)
- Wire into conv_dispatcher
- Add proof-of-concept test (passing)
Test result: Can instantiate reference through Builder API
* Add GPU reference execution tests
- Reference kernel executes through Builder (459ms)
- Both reference and optimized can instantiate
- Tests passing
Next: Implement utilities for comparison
* Optimized Builder kernel execution works
- MakeArgument pattern implemented
- Builder-generated kernel executes successfully
- Tests passing (451ms execution)
Next: Add comparison
* VALIDATION COMPLETE: Builder == Reference
Builder-generated kernel output matches GPU reference!
Test: Validate_Optimized_vs_Reference_Forward_2D_FP16
Result: PASS ✓
This proves CK Builder generates correct code!
* Update to new Builder API
All tests passing
* Rename test file for clarity
test_builder_kernel_execution -> test_builder_kernel_validation
* Add all 3 directions support
- Forward, Backward Data, Backward Weight
- All reference factories working
- Dispatcher wired for all directions
- 9 tests passing
Tests:
- test_reference_execution: 3 tests (all directions)
- test_optimized_execution: 3 tests (all directions)
- test_builder_kernel_validation: 3 tests (fwd validated, bwd placeholders)
* Add backward direction support
- Backward data and weight dispatcher wiring
- Fix factories for new API
- All 3 directions tested
- 9 tests passing
* Refactor: Change IsReferenceAlgorithm from concept to consteval function
Address review feedback: Use consteval function in dispatcher instead of
concept, matching the pattern for other algorithms (Tile, XDL, WMMA, DL).
- Remove IsReferenceAlgorithm concept from conv_algorithm_concepts.hpp
- Add IsReferenceAlgorithm() consteval function to conv_dispatcher.hpp
- Update dispatcher to use function call: IsReferenceAlgorithm<T>()
- Remove redundant algorithm checks from reference factory requires clauses
All tests passing (9/9).
* Move Tile algorithm check outside direction block to support all directions
* Implement MakeInvokerPointer interface and add random input validation
- Implement full Argument/Invoker structs for old CK interface (not just nullptr)
- Refactor with reference_common.hpp to reduce code duplication
- Add random input validation tests: Builder vs direct GPU reference (all directions)
- Fix layout: GNHWC -> NHWGC to match reference kernel expectations
- All 12 tests pass with IDENTICAL results on random input
* Move ConvAlgorithm_Reference to test/impl/conv_algorithm_types.hpp
Keep types.hpp for data types only (enums), move algorithm descriptors
to conv_algorithm_types.hpp as suggested by review.
* Add static_assert to ensure reference factories only accept PassThrough operations
Reference implementation doesn't support fused elementwise operations.
Add compile-time validation to fail early with clear error message if
non-PassThrough operations are specified on input, weight, or output.
* Add InstanceTraits support for reference kernels
- Store SIGNATURE/ALGORITHM/VERSION in Instance for reflection
- Create shared ReferenceCommonTraits base for common properties
- Add 3 direction-specific InstanceTraits specializations in one file
- Include data type and layouts in instance_string output
* Remove optimized kernel validation tests from reference-only branch
* Use existing layout helper and organize reference tests
Use LayoutToCK from conv_tensor_layout.hpp and move reference InstanceTraits
test to validation folder.
* Merge develop branch
Fix DataType switch for new mixed precision types.
* Fix comment spacing for CI
* Convert IsReferenceAlgorithm from function to concept
* Add reference tests to CI smoke tests
* Consolidate 3 reference factories into single unified factory
---------
Co-authored-by: Ville Pietilä <188998872+vpietila-amd@users.noreply.github.com>
* Replace grouped convolution bwd weight wmma v3 bilinear and scale bf16f32bf16 support with bf16bf16bf16 support. Update tests.
* Tentative fix for bwd weight bilinear bf16bf16bf16, seems like the bilinear elementwise overload for this case (bf16, f32 accu, bf16) was wrong.
* feat: add RRR, CRR, CCR layouts for a/b quant grouped gemm tests and examples. Refactor example setup to improve compile time
* chore: split out bquant preshuffle test, and reduce tile size to 128 to temporarily solve slow compile times
* chore: set m/n warp tile to 16 as configurations with 32 seem to have some support problems
* fix: missing check for transposed load in bquant pipeline
* chore: lower unit test tensors dimensions a bit for faster tests
* chore: set grouped gemm example M/N warp tile to 16
---------
Co-authored-by: Thomas Ning <Thomas.Ning@amd.com>
* fix for splitk if splitk < grid
* add different splitk implementation
* minor bugfix for streamk gemm
* Add test
---------
Co-authored-by: Bartlomiej Kocot <barkocot@amd.com>
* reinstate conv_signature_utils.hpp
* added tests for elementwise operation getters
* add tests for getDataType functions
* added test for no data type specified
---------
Co-authored-by: Kevin Abraham <kevin.abraham@streamhpc.com>
Replace `decltype(TailHandler<>(...)){}` with direct function call
to fix compilation error when return type is void.
Co-authored-by: Yi DING <yi.ding@amd.com>
* [CK-TILE] Guard against compiler lexer diagnostic
A recent change to Clang added a lexer-level diagnostic about that C2y
language feature. Since that is lexer level, the `__extension__`
compiler built-in does not work as it is only respected *after* the
lexer when parsing.
This change adds guarding pragmas to disable the diagnostic in the
lexer and not lead to warnings being treated as errors.
* Fixing still existing build issue
Once the one warning was removed, another one poppoed up. Both are
related to the same c2y feature. Thus, ignoring both.
* clang-format handling
---------
Co-authored-by: Illia Silin <98187287+illsilin@users.noreply.github.com>
Refactors the way the number of XDL (matrix multiply-accumulate) instructions per wave is calculated and used in the grouped convolution forward implementations, especially to better support WMMA (Wave Matrix Multiply-Accumulate) instructions and 16x16 tiles.
The changes use MXdlPerWave instead of NXdlPerWave to increase number of waves per M dim.
