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7795e73b47a34a25b48a14f3e4e0e6d681fcbde5
616 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
Wojciech Laskowski
|
7795e73b47 |
Added large tensor support for grouped conv fwd wmma (#3437)
* Padding not supported for when BDataType is pk_i4_t. Added fix for correct check and removed padding instances. * Fixed typos * Updated the set of tests for FP16 * Updated the set of tests for FP16 * Fix typo * Moved f16xi4 test under the correct data layout group * example for gemm_universal_bf16 * Adding examples for gemm_wmma instances * Added the missing parameters * Fixed review comments and added executable to cmakeLists * Fixing clang format * Fixing build erros * Fixed compilation failure. * Modified some code as per gemm_universal_examples * Fixed the gemm specialization error * Fixed the build errors. * Fix strides of a/b_thread_desc The descriptors are larger than needed (even though the compiler don't alloc registers for unused values). * Load in M/NRepeat dims with thread copy's slice instead of a loop * Clone BlockwiseGemmXdlops_pipeline_v1 for WMMA implementation * Implement Intrawave and Interwave variants of pipeline v1 * Add instances for Interwave and Intrawave v1 * Add instances with ABlockLdsExtraM and BBlockLdsExtraN = 0 * Remove instances that are too slow (mostly because of register spilling) * Add a workaround for fp8/bf8->f32 packed conversion issue * Add instances for Interwave and Intrawave v1 * Enable profiling of mixed precision with f8 and int4 on WMMA * Fix segfault in profiler when B is pk_i4_t b_device_buf's size in bytes is larger than b_k_n_permute so b_device_buf.ToDevice reads out-of-bounds. * Remove instances that are too slow (mostly because of register spilling) * Add missing add_device_gemm_wmma_universal_f8_f8_bf16 declarations * Add test case for bf16_i4 * Add missing Regular tests * Add test_gemm_universal_xdl/wmma_fp16 to REGRESSION_TESTS They take more than 30 seconds * Fix a bug that fp16_i4 validation passes only with PermuteB A permutation required by conversion from pk_i4_t to half_t does not depend on PermuteB, they can be used independently. * Use PermuteB with f16_i4 in most instances (as xdl) Some instances use PermuteB = false for checking correctness. See also the previous commit. * Fix cache flushing for pk_i4 * Add mixed precision examples * Disable all tests and instances with f8 on gfx11 Even though f8_f16 and f16_f8 don't require f8 WMMA instructions, gfx11 still lacks hardware instructions for fast f8->f32 conversion. * Add FP16 KM_NK and KM_KN test suites for XDL These tests were added to common .inc for better testing of WMMA instances * Support multiple D in GridwiseGemm_wmma_cshuffle_v3 DeviceGemm_Wmma_CShuffleV3 is changed for new template parameters. * Use ThreadGroupTensorSliceTransfer_v7r3 * Clone for device_gemm_wmma_cshuffle_v3.hpp for future Multiple D support * Clone example/65_gemm_multiply_multiply/gemm_add_add_xdl_fp16.cpp for wmma * Implement DeviceGemmMultipleD_Wmma_CShuffleV3 * Make gemm_add_add_wmma to work with DeviceGemmMultipleD_Wmma_CShuffleV3 * Prepare gemma_add tests for adding wmma * Add gemm_add_fastgelu instances and test * Add a special wrapper to use DeviceGemmMultipleD_Wmma_CShuffleV3 with old API ckProfiler uses DeviceGemmMultipleD (tests also call its functions), the wrapper allows to use DeviceGemmMultipleDSplitK instances there. * removed unnecessary ck parts from compilation * initial gemm_add_multiply instance implementations * fixed profiler help message for gemm_add_multiply * improved multiply_add profiler layout help * fixed template arguments for test instances * added test for gemm_add_multiply * Support multiple D in GridwiseGemm_wmma_cshuffle_v3 DeviceGemm_Wmma_CShuffleV3 is changed for new template parameters. * Use ThreadGroupTensorSliceTransfer_v7r3 * Clone for device_gemm_wmma_cshuffle_v3.hpp for future Multiple D support * Clone example/65_gemm_multiply_multiply/gemm_add_add_xdl_fp16.cpp for wmma * Implement DeviceGemmMultipleD_Wmma_CShuffleV3 * Make gemm_add_add_wmma to work with DeviceGemmMultipleD_Wmma_CShuffleV3 * Prepare gemma_add tests for adding wmma * Add gemm_add_fastgelu instances and test * Add a special wrapper to use DeviceGemmMultipleD_Wmma_CShuffleV3 with old API ckProfiler uses DeviceGemmMultipleD (tests also call its functions), the wrapper allows to use DeviceGemmMultipleDSplitK instances there. * switched to splitK interface * log print added to splitk benchmarks * revert main cmake comments * newline change reverted * added add_fastgelu instances * revert unintended change in xdl add_fastgelu * created gemm_add_add_fastgelu instances * created fastegelu instances * added tests for all splitk fastgelus * Added tests. * multiply_add instances created * updates to add_multiply splitk instances * splitk xdl test fixes * added wmma multiply_multiply instances * fixed ONLY_XDL_AND_WMMA_KERNELS tag * Added gemm_add examples for wmma v1 and v3 * fixed / workarounded i8 instances * Modified the v3 code to added one fp16 bxdl instance. * added bf16 xdl instance. * adding gemm_add wmma_cshuffle and other support (cherry picked from commit ec447e7f564095ea969eddc39ec77b843aa52976) Co-authored-by: Cenxuan <cenxuan@streamhpc.com> * add instances into camkelists (cherry picked from commit 23bf2d2771c939ea3ca7f493433c55255bffd08e) Co-authored-by: Cenxuan <cenxuan@streamhpc.com> * This is work in progress, edited the template parameters in order to build (cherry picked from commit b4fde8a3314cb44659c4bbda35f1a0133c63dc41) Co-authored-by: Cenxuan <cenxuan@streamhpc.com> * temp work saved, changed the BDataType to f16 or bf16 since wmma currently not support non-equal A and B datatype (cherry picked from commit 22fbd68f1db458ab50780a394ee2544c7a1484d1) Co-authored-by: Cenxuan <cenxuan@streamhpc.com> * added datatype and use clang-format-12 (cherry picked from commit ae4e853682ef1bb27784b2f965b4a66b3751ceec) Co-authored-by: Cenxuan <cenxuan@streamhpc.com> * Fixing build errors * Added instances for v3 * Adding instances and executables * Code update of template parameters modified. * Renamed file. * Added tests. * resolved error tests. * Fixing build errors * Updated comments * removed the changes as per the MR review comment. * Updated tests. * fp8 instances - not tested * Restored the Cmake file that was reverted by mistake during rebase. * fixed wmma_op test * Updated comments. * Updated the template parameter description * fixed rdna4 instances * fixed back compatibility on gfx11 * cleanups * fix ckProfiler * one more cmake fix * added fp8 instances * Updated tests to ad BF16 instances as per review comment * Added include file and cleaned up(as per review comment) * Updated and optimized the example code for all types. * Fixed clang format * Resolve "Implement `device_gemm_bilinear` for RDNA4" * test generalization to handle FP16 shuffle better * added missing changes * Added bf16 wmma instance for add_relu * Added f16 wmma instance and corrected bf16 instance errors. * Added instances to Cmake * Modified the template parameters to make the instances work. * Fixed typo in profiler * Added v3 instances for gemm_add_relu * addressed core review comments * Added test for gemm_add_relu wmma instance * Cleaned up the code. * Added examples for gemm_add_relu * Fixing typo to resolve build errors. * Fixes applied to fix the precision loss. * fix billinear test after merge * Removed the old wmma instances. * Added wrapper and renamed the wmma_v3 instances * Updated copyrights and added wrappers. * Fixes applied according to review comments * Apply 1 suggestion(s) to 1 file(s) Co-authored-by: Robin Voetter <robin@streamhpc.com> * Removed the old wmma instances. * Updated wrapper for the v3 instances * removed the old wmma examples * Renamed the v3 instances * Deleted the gtest file added by mistake. * Updated thge profiler with wrapper * Fixed test errors. * Fixed the review comments * Fixed the if condition MACROS. * REVERTED THE PROFILER CHANGES * Revert "REVERTED THE PROFILER CHANGES" This reverts commit |
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yadaish
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c0ee71d735 |
Dev/a8w4 and a8w8splitk (#3447)
* Ck moe bs splitk pr (#3440) * splitk kick-off. Compilation fail * splitk hack pass * fix scale offset calc. * clang-format for a8w8_moe_blk_gemm1 splitk change * fix testcase error --------- Co-authored-by: oscar <huaiguxu@amd.com> Co-authored-by: huaiguxu <145733371+huaiguxu@users.noreply.github.com> * Zan/moe a8w4 (#3441) * update * update * update ck moe a8w4 * update * update * update * compile pass * update * update * python3 op_tests/test_moe_2stage.py -t 16 -e 1 -k 1 -dim 256,256 ready * support new a8w4 kernel * update * update ck_tile * re format * update * update * fix conflict * fix build * update ck_tile moe * fix clang format * fix the problem * fix accruacy issue * fix --------- Co-authored-by: oscar <huaiguxu@amd.com> Co-authored-by: huaiguxu <145733371+huaiguxu@users.noreply.github.com> Co-authored-by: Zzz9990 <zanzhang@amd.com> Co-authored-by: felix <felix.li@amd.com> |
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Kiefer van Teutem
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2ea710e88b |
Grouped convolution forward device implementation and base flavors for RDNA3/4 (#2964)
* Fixed typos for padded instances * Added tests for fp16, KM_KN and KM_NK * Padding not supported for when BDataType is pk_i4_t. Added fix for correct check and removed padding instances. * Fixed typos * Updated the set of tests for FP16 * Updated the set of tests for FP16 * Fix typo * Moved f16xi4 test under the correct data layout group * example for gemm_universal_bf16 * Adding examples for gemm_wmma instances * Added the missing parameters * Fixed review comments and added executable to cmakeLists * Fixing clang format * Fixing build erros * Fixed compilation failure. * Modified some code as per gemm_universal_examples * Fixed the gemm specialization error * Fixed the build errors. * Fix strides of a/b_thread_desc The descriptors are larger than needed (even though the compiler don't alloc registers for unused values). * Load in M/NRepeat dims with thread copy's slice instead of a loop * Clone BlockwiseGemmXdlops_pipeline_v1 for WMMA implementation * Implement Intrawave and Interwave variants of pipeline v1 * Add instances for Interwave and Intrawave v1 * Add instances with ABlockLdsExtraM and BBlockLdsExtraN = 0 * Remove instances that are too slow (mostly because of register spilling) * Add a workaround for fp8/bf8->f32 packed conversion issue * Add instances for Interwave and Intrawave v1 * Enable profiling of mixed precision with f8 and int4 on WMMA * Fix segfault in profiler when B is pk_i4_t b_device_buf's size in bytes is larger than b_k_n_permute so b_device_buf.ToDevice reads out-of-bounds. * Remove instances that are too slow (mostly because of register spilling) * Add missing add_device_gemm_wmma_universal_f8_f8_bf16 declarations * Add test case for bf16_i4 * Add missing Regular tests * Add test_gemm_universal_xdl/wmma_fp16 to REGRESSION_TESTS They take more than 30 seconds * Fix a bug that fp16_i4 validation passes only with PermuteB A permutation required by conversion from pk_i4_t to half_t does not depend on PermuteB, they can be used independently. * Use PermuteB with f16_i4 in most instances (as xdl) Some instances use PermuteB = false for checking correctness. See also the previous commit. * Fix cache flushing for pk_i4 * Add mixed precision examples * Disable all tests and instances with f8 on gfx11 Even though f8_f16 and f16_f8 don't require f8 WMMA instructions, gfx11 still lacks hardware instructions for fast f8->f32 conversion. * Add FP16 KM_NK and KM_KN test suites for XDL These tests were added to common .inc for better testing of WMMA instances * Support multiple D in GridwiseGemm_wmma_cshuffle_v3 DeviceGemm_Wmma_CShuffleV3 is changed for new template parameters. * Use ThreadGroupTensorSliceTransfer_v7r3 * Clone for device_gemm_wmma_cshuffle_v3.hpp for future Multiple D support * Clone example/65_gemm_multiply_multiply/gemm_add_add_xdl_fp16.cpp for wmma * Implement DeviceGemmMultipleD_Wmma_CShuffleV3 * Make gemm_add_add_wmma to work with DeviceGemmMultipleD_Wmma_CShuffleV3 * Prepare gemma_add tests for adding wmma * Add gemm_add_fastgelu instances and test * Add a special wrapper to use DeviceGemmMultipleD_Wmma_CShuffleV3 with old API ckProfiler uses DeviceGemmMultipleD (tests also call its functions), the wrapper allows to use DeviceGemmMultipleDSplitK instances there. * removed unnecessary ck parts from compilation * initial gemm_add_multiply instance implementations * fixed profiler help message for gemm_add_multiply * improved multiply_add profiler layout help * fixed template arguments for test instances * added test for gemm_add_multiply * Support multiple D in GridwiseGemm_wmma_cshuffle_v3 DeviceGemm_Wmma_CShuffleV3 is changed for new template parameters. * Use ThreadGroupTensorSliceTransfer_v7r3 * Clone for device_gemm_wmma_cshuffle_v3.hpp for future Multiple D support * Clone example/65_gemm_multiply_multiply/gemm_add_add_xdl_fp16.cpp for wmma * Implement DeviceGemmMultipleD_Wmma_CShuffleV3 * Make gemm_add_add_wmma to work with DeviceGemmMultipleD_Wmma_CShuffleV3 * Prepare gemma_add tests for adding wmma * Add gemm_add_fastgelu instances and test * Add a special wrapper to use DeviceGemmMultipleD_Wmma_CShuffleV3 with old API ckProfiler uses DeviceGemmMultipleD (tests also call its functions), the wrapper allows to use DeviceGemmMultipleDSplitK instances there. * switched to splitK interface * log print added to splitk benchmarks * revert main cmake comments * newline change reverted * added add_fastgelu instances * revert unintended change in xdl add_fastgelu * created gemm_add_add_fastgelu instances * created fastegelu instances * added tests for all splitk fastgelus * Added tests. * multiply_add instances created * updates to add_multiply splitk instances * splitk xdl test fixes * added wmma multiply_multiply instances * fixed ONLY_XDL_AND_WMMA_KERNELS tag * Added gemm_add examples for wmma v1 and v3 * fixed / workarounded i8 instances * Modified the v3 code to added one fp16 bxdl instance. * added bf16 xdl instance. * adding gemm_add wmma_cshuffle and other support (cherry picked from commit ec447e7f564095ea969eddc39ec77b843aa52976) Co-authored-by: Cenxuan <cenxuan@streamhpc.com> * add instances into camkelists (cherry picked from commit 23bf2d2771c939ea3ca7f493433c55255bffd08e) Co-authored-by: Cenxuan <cenxuan@streamhpc.com> * This is work in progress, edited the template parameters in order to build (cherry picked from commit b4fde8a3314cb44659c4bbda35f1a0133c63dc41) Co-authored-by: Cenxuan <cenxuan@streamhpc.com> * temp work saved, changed the BDataType to f16 or bf16 since wmma currently not support non-equal A and B datatype (cherry picked from commit 22fbd68f1db458ab50780a394ee2544c7a1484d1) Co-authored-by: Cenxuan <cenxuan@streamhpc.com> * added datatype and use clang-format-12 (cherry picked from commit ae4e853682ef1bb27784b2f965b4a66b3751ceec) Co-authored-by: Cenxuan <cenxuan@streamhpc.com> * Fixing build errors * Added instances for v3 * Adding instances and executables * Code update of template parameters modified. * Renamed file. * Added tests. * resolved error tests. * Fixing build errors * Updated comments * removed the changes as per the MR review comment. * Updated tests. * fp8 instances - not tested * Restored the Cmake file that was reverted by mistake during rebase. * fixed wmma_op test * Updated comments. * Updated the template parameter description * fixed rdna4 instances * fixed back compatibility on gfx11 * cleanups * fix ckProfiler * one more cmake fix * added fp8 instances * Updated tests to ad BF16 instances as per review comment * Added include file and cleaned up(as per review comment) * Updated and optimized the example code for all types. * Fixed clang format * Resolve "Implement `device_gemm_bilinear` for RDNA4" * test generalization to handle FP16 shuffle better * added missing changes * Added bf16 wmma instance for add_relu * Added f16 wmma instance and corrected bf16 instance errors. * Added instances to Cmake * Modified the template parameters to make the instances work. * Fixed typo in profiler * Added v3 instances for gemm_add_relu * addressed core review comments * Added test for gemm_add_relu wmma instance * Cleaned up the code. * Added examples for gemm_add_relu * Fixing typo to resolve build errors. * Fixes applied to fix the precision loss. * fix billinear test after merge * Removed the old wmma instances. * Added wrapper and renamed the wmma_v3 instances * Updated copyrights and added wrappers. * Fixes applied according to review comments * Apply 1 suggestion(s) to 1 file(s) Co-authored-by: Robin Voetter <robin@streamhpc.com> * Removed the old wmma instances. * Updated wrapper for the v3 instances * removed the old wmma examples * Renamed the v3 instances * Deleted the gtest file added by mistake. * Updated thge profiler with wrapper * Fixed test errors. * Fixed the review comments * Fixed the if condition MACROS. * REVERTED THE PROFILER CHANGES * Revert "REVERTED THE PROFILER CHANGES" This reverts commit |
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Johannes Graner
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bb8445dca8 |
[CK] Integrate GPU reference into ckProfiler for convolutions (#3379)
Refactor and integrate CK GPU references into ckProfiler. - All convolution layouts and groupings supported for all three directions - Unit tests verifying GPU and CPU reference is the same - Support added to profiler (do_verification = 2 enables GPU reference) - One profiler-based test per direction changed to GPU reference to demonstrate usag Closes AICK-427 |
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Enrico Degregori
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87dd073887 |
Wmma support for grouped convolution bwd weight (#2947)
* Convolution bwd weight device implementation
* Merge branch 'grouped_conv_bwd_weight_device_impl_wmma' into 'feature/conv_bwd_weight_wmma'
Convolution bwd weight device implementation
See merge request amd/ai/composable_kernel!38
* Fix bug and disable splitK=-1 tests for wmma
* Add generic instances for bf16 f32 bf16
* check gridwise level validity in device impl for 1 stage D0
* Fix bugs in device implementation:
- rdna3 compilation error
- gridwise layouts (need to be correct to ensure that CheckValidaity()
works correctly)
* Add padding in conv to gemm transformers for 1x1Stride1Pad0 specialization
* Remove workaround for 1x1Stride1Pad0 conv specialization
* Add instances for xdl parity (for pipeline v1)
* Add two stage instances (xdl parity)
* Add multiple Ds instances
* Add examples
* Uncomment scale instances
* Fix copyright
* Fix examples compilation
* Add atomic add float4
* Fix compilation error
* Fix instances
* Compute tolerances in examples instead of using default ones
* Compute tolerances instead of using default ones in bilinear and scale tests
* Merge branch 'grouped_conv_bwd_weight_instances_examples' into 'feature/conv_bwd_weight_wmma'
Grouped conv: Instances and example bwd weight
See merge request amd/ai/composable_kernel!47
* Device implementation of explicit gemm for grouped conv bwd weight
Based on batched gemm multiple D
* Add instances for pipeline v1 and v3
* Add support for occupancy-based splitk
* Fix ckProfiler dependencies
* Review fixes
* Merge branch 'explicit_bwd_weight' into 'feature/conv_bwd_weight_wmma'
Device implementation of explicit gemm for grouped conv bwd weight
See merge request amd/ai/composable_kernel!52
* Fix cmake file for tests
* fix clang format
* fix instance factory error
* Adapt all grouped conv bwd weight vanilla Xdl instances to 16x16. MRepeat doubled for all but 12 of them (some static assert failure). Also added custom reduced profiler target for building grouped conv bwd weight vanilla only profiler. Verified with gtest test.
* Revert "Adapt all grouped conv bwd weight vanilla Xdl instances to 16x16. MRepeat doubled for all but 12 of them (some static assert failure). Also added custom reduced profiler target for building grouped conv bwd weight vanilla only profiler. Verified with gtest test."
This reverts commit
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linqunAMD
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7e93eed878 |
[ck][gfx12] support contraction on gfx12 (#3421)
* support contraction on gfx12 * increase tolerance for gfx11 in example contraction the precsion of gfx11 wmma is less than others. |
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Johannes Graner
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3143a5a480 |
[CK Grouped Gemm] Disable split-k kernel for split-k > 1 with non-contiguous strides (#3405)
* Disable kernel for split-k > 1 with non-contiguous strides * Update device_grouped_gemm_xdl_splitk_cshuffle.hpp --------- AICK-441 (partial) Co-authored-by: Bartłomiej Kocot <barkocot@amd.com> Co-authored-by: Illia Silin <98187287+illsilin@users.noreply.github.com> |
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John Shumway
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9ac51aa0f4 |
Add describe() method to device ops for runtime introspection (#3375)
Introduces a polymorphic describe() method to BaseOperator that enables runtime introspection of kernel configurations through a unified interface. Key changes: * Add virtual describe() method to BaseOperator returning Description objects * Implement describe() in 6 device operation classes (conv fwd/bwd variants) * Create conv_describe.hpp with factory function for ConvDescription * Extract type definitions to conv_types.hpp to resolve circular dependencies * Add InstanceStringDescription for kernels without full ConvDescription support Other Improvements: * Update tests to use describe() instead of GetInstanceString() * Remove circular dependency include from conv_traits.hpp * Add ODD_C to ConvFwdSpecialization enum and fix OddC mapping * Replace silent fallback in conv_layout() with compile-time error This provides a foundation for runtime kernel introspection and better tooling support for analyzing and debugging kernel configurations. |
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Enrico Degregori
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b4a34371a6 | Fix compilation ab scale multi target (#3413) | ||
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Enrico Degregori
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ce99cab605 |
Wmma support for gemm_ab_scale (#3314)
* Support gemm_ab_scale: - Add tests - Integrate scaling implementation in multiple D - Generalize existing b_scale for ab_scale - Add instances - Generalize implementation for ScaleBlockM, ScaleBlockN, ScaleBlockK - Add support for all layouts supported by xdl - Fix splitk xdl * Fix copyright * Wmma support for gemm_blockscale_wp (#3315) * Support for preshuffle with ab scale - add support for b preshuffle in GridwiseGemm_wmma_cshuffle_v3_ab_scale - add support for AScaleLayout amnd BScaleLayout (can be different from ALayout and BLayout, respectively) - add Run method in v1 pipeline to support preshuffle + scaling - add support for preshuffle gemms in common invoker - Add splitk support * Fix copyright header |
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John Shumway
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f5b0af2272 |
Simplify includes for CK builder reflection (#3357)
We only want to import enums and types into the builder reflection code. But, some of the enums are included in much larger files or even big trees of include files. This leads to unintended mixing of code and very confusing interactions and symbol conflicts. We organize the includes and extract two new enum-only headers to help with decoupling in CK. This refactoring is critical if we want to include reflection in a device-operator "describe" method. * Remove a few unnecessary includes from headers in builder/reflect/. * Extract enums scheduler and pipeline to their own headers so they can be used without importing other code. * Order includes alphabetically for better organization. The immediate goal is to unblock reflection integration, and this type of cleanup helps the flexibility and robustness of the CK header library. |
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JH-Leon-KIM-AMD
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4baa4c9fae |
[CK, CK_TILE] Add GPU Reference Implementations for Grouped Convolution (#3216)
* LWPCK-4043: Add GPU reference implementations for CK Tile convolution
This commit implements GPU-based reference kernels for CK Tile convolution
operations to enable faster verification of optimized kernels, especially
for large tensors (>2GB).
Changes:
- Add naive_grouped_conv_fwd.hpp: GPU reference for forward convolution
- Add naive_grouped_conv_bwd_data.hpp: GPU reference for backward data
- Add naive_grouped_conv_bwd_weight.hpp: GPU reference for backward weight
- Integrate GPU references with test infrastructure (replace -v=2 error)
- Support for 1D, 2D, and 3D convolutions
- Generic data type support (FP16, BF16, FP32)
- Grid-stride loop pattern for scalability
The GPU references use a simple, readable implementation that prioritizes
correctness over performance. They accumulate in float32 and handle
padding, stride, and dilation correctly.
* update gpu reference for ck tile grouped conv
* correct c++ 18 format
* Add GPU Reference Implementations for Old CK Convolution
This commit implements GPU-based reference kernels for Old CK convolution
operations to enable faster verification of optimized kernels.
Changes:
- Fixed old CK forward GPU reference (naive_conv_fwd.hpp)
* Fixed BF16 NaN issue (use type_convert instead of static_cast)
* Fixed FP8/BF8 arithmetic (accumulate in float)
* Fixed uninitialized variables
* All 9 data types now working (FP16/32/64, BF16, INT8, FP8, BF8, mixed)
- Created backward data GPU reference (naive_conv_bwd_data.hpp)
* Implements input gradient computation
* Verified equal to CPU reference
* Handles 1D, 2D, 3D convolutions
- Created backward weight GPU reference (naive_conv_bwd_weight.hpp)
* Implements weight gradient computation
* Verified equal to CPU reference
* Handles 1D, 2D, 3D convolutions
- Integrated with old CK examples
* Forward: 10 XDL examples now support do_verification=2
* Backward data: Integrated with example/17_convnd_bwd_data/
* Backward weight: Integrated with example/20_grouped_conv_bwd_weight/ (G=1 only)
* Updated parameter from boolean to int (0=no, 1=CPU, 2=GPU)
Testing:
- 50 comprehensive tests created
- 42/42 tests passing (100% success rate)
- CPU and GPU verification produce identical results
- Verified across multiple dimensions, sizes, and data types
Limitations:
- GPU references support standard convolution only (G=1)
- Fused operations (DL variants) not supported
- Some tests blocked by optimized kernel size constraints
Result: Old CK GPU references can replace CPU references for verification
with 50-100x performance improvement for large tensors.
* Apply clang-format to old CK GPU reference files
* Fix C++17 compatibility: use brace initialization for aggregate types
* add get_rtol, get_atl and consistency cout message
* Use triple bracket syntax for kernel launch per review feedback
Changed hipLaunchKernelGGL to <<<...>>> syntax as suggested by @aosewski.
This is more idiomatic HIP/CUDA style and equally correct.
All tests still passing after this change.
* Address review feedback: Use HIP_CHECK_ERROR and add v=3 mode
- Replace manual error checking with HIP_CHECK_ERROR macro
- Add v=3 verification mode (GPU ref vs CPU ref direct comparison)
- Consistent output format across all examples
- All tests passing (7/7 v=3 tests pass for FP16)
* Use ConvDims structure to simplify GPU reference kernels
Replace 24 individual parameters with ConvDims structure per review feedback.
- Add conv_common.hpp with ConvDims and helper function
- Update kernel signatures: 24 params → 1 structure
- Remove duplicate extraction code from host files
* Use get_block_id() and get_thread_id() helpers in CK Tile
Replace manual blockIdx.x/threadIdx.x arithmetic with helper functions.
Updated 3 CK Tile GPU reference kernels per review feedback.
* Use std::array for spatial parameters in CK Tile GPU references
Replace raw pointers with std::array for type safety per review feedback.
- Add conv_common.hpp with vector-to-array helper functions
- Update kernel signatures: pointers → std::array references
- Remove DeviceMem allocations for spatial parameters
* Use NDimSpatial+3 for stride array sizes
Replace hardcoded [10] with [NDimSpatial+3] per review feedback.
Array sizes now correctly reflect actual dimensions needed.
* Use #pragma once instead of include guards
Replace traditional include guards with #pragma once per review feedback.
Updated 3 Old CK GPU reference headers.
* Fix element-wise operation output in Old CK GPU references
Write transformed value (out_val/in_val/wei_val) instead of untransformed
result per Copilot feedback.
This ensures element-wise operations are correctly applied to output.
* Initialize element-wise operation variables
Initialize in_val, wei_val, out_val to avoid undefined behavior
per Copilot feedback.
Updated backward data and backward weight kernels.
* Use explicit zero initialization for element-wise variables
Change TIn{} to TIn{0} for consistency per Copilot feedback.
All 3 kernels now use consistent zero initialization.
* Fix copyright headers to match existing style
- Old CK: Use standard format without year
- CK Tile: Add 2018- prefix to year range
Addresses consistency feedback.
* Rename GPU reference files: add _gpu suffix
* Refactor index calculations: use std::array and extract to helper functions
* Remove v=3 option: redundant as v=1 and v=2 comparison validates equivalence
---------
Co-authored-by: Illia Silin <98187287+illsilin@users.noreply.github.com>
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Enrico Degregori
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161835533b |
Wmma support for gemm_multiply_multiply_wp (#3278)
* Initial implementation with splitK support * Add gfx11 support * Fix compilation error * Add instances * Add irregular instances * Fix GetBuffer arguments * Minor changes * Address review comments * Fix compilation errors * Fix copyright header |
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Erwin Terpstra
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46f1d740f0 |
Add grouped gemm instances for RDNA4 (#3237)
* wip: grouped_gemm implementation based on wmma kernel + example for fp16 * chore: clean up grouped_gem_wmma_splitk_fp16 example * chore: add cmake options to fully disable XDL or WMMA kernels * feat: add tests for grouped gemma wmma instances for f16 and bf16 (all layouts) * chore: add grouped gemm wmma bf16 example * refactor: reuse more code between instance factory functions * chore: turn test failure if not all batch sizes are supported into a warning * chore: made failing of test on unsupported instances conditional to not break old tests * chore: add log message to failure case where AK1/BK1/KBatch is too high for K value * fix: issue with new overloads of GridwiseGemm_wmma_cshuffle_v3::Run() * fix: stray comma after parameter list * fix: compilation issues on RDNA3 and tests failing due to unsupported problems still being ran * chore: update copyright in header comments * nit: minor feebdack * refactor: unified XDL / wma tests * fix: properly disable FP8 instances when ONLY targeting gfx11 * refactor: add v3 suffix to grouped_gemm device struct name * fix: small typos in example code * fix: fully exclude xdl/wmma instances when using the corresponding cmake flags * chore: remove unused destructor and added pipeline support checks to remove unnecessary paths * fix: make sure to not add instance library to group if library was skipped * fix: make sure xdl grouped gemm doesnt fail the new test * fix: explicitly exclude test if no xdl/wmma support, as pattern matching fails in this case * fix: examples not working since dependent types and functions were moved to ck namespace in develop * fix: tests failing when compiling for just gfx11 due to trying to run unsupported instances * chore: replace/add copyright headers with new format |
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Aviral Goel
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de6466481f | chore(copyright): update copyright header for include directory (#3293) | ||
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John Shumway
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10a782d846 |
Fix template parameter macros (#3305)
Some of the device implementation templates have macros like GridwiseGemmMultiABDTemplateParameters that can cause build errors if multiple files are included together. This error comes up with our builder code. To clean up the macros and make them safer, we follow these follow rules: * Use more specific names to avoid duplication. * Undefine the macro after it is used to avoid leaking out of the file scope. * Use a prefix CK_ on the macro to avoid conflicting with other libraries. * Use all caps with underscores for preprocessor macro names. |
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lalala-sh
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f58bd56e6b |
fix static assert (#3178)
Co-authored-by: Illia Silin <98187287+illsilin@users.noreply.github.com> |
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Gavin Zhao
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07314ac543 |
Add support for RDNA1 GPUs (#3220)
* Allow compilation for RDNA1 (__gfx101__) Signed-off-by: Gavin Zhao <git@gzgz.dev> * More RDNA1 changes Signed-off-by: Gavin Zhao <git@gzgz.dev> * Even more RDNA1 changes Signed-off-by: Gavin Zhao <git@gzgz.dev> * cmake: skip build quantization for unsupported arches * add gfx10-1-generic support as well * add gfx1013 and complete gfx10-1-generic * fix clang format * enable DL kernels on gfx101x --------- Signed-off-by: Gavin Zhao <git@gzgz.dev> Co-authored-by: illsilin_amdeng <Illia.Silin@amd.com> Co-authored-by: Illia Silin <98187287+illsilin@users.noreply.github.com> |
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Aviral Goel
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f5ac3ee359 |
chore(copyright): update copyright header for include directory (#3224)
* chore(copyright): update copyright header for tile_engine directory * chore(copyright): update copyright header for script directory * chore(copyright): update copyright header for test_data directory * chore(copyright): update copyright header for python directory * chore(copyright): update copyright header for profiler directory * chore(copyright): update copyright header for library directory * chore(copyright): update copyright header for include directory |
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jefyang1
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d30babbd00 | Add new gemm multiply multiply instances on gfx950 (#3213) | ||
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yinglu
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2a73eb3bc0 |
Simulate TF32 with BF16x3 (#3142)
* tf32:bf16x3:use bf16x3 emulate tf32 gemm * change blockwiseGemm to demo bf16x3 * temp push * self review * self review * fix multi-device compile error * bug fix * code refactor * limit to gfx950 * enhance gemm gfx942 threshold * lower change from blockwise to warpwise * refact codes * refact codes * error fix * change threshold * bug fix * fix threshold error * change host reference implement to same as device * bug fix * bug fix * code refact * fix clang-format fail * code refine |
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Enrico Degregori
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7414a0f4d4 |
Wmma support for gemm_reduce (#3145)
* Initial implementation GEMM+Reduce: - device struct - epilogue struct * Fix tests, improve profiler and add initial instances * Add instances * Fix compilation error * Address review comments * Fix logging --------- Co-authored-by: Illia Silin <98187287+illsilin@users.noreply.github.com> |
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Enrico Degregori
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1c544abf57 |
Extend support for ak1 / bk1 WMMA (#3073)
* Extend AK1 / BK1 support: - Add support for AK1 != BK1 - Add support for AK1, BK1 > 8 - Introduce KInner template parameter for pipelines when loading multiple tiles with one instruction * fix clang format |
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Gino Lu
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e31a7a4f29 | fix MX bpreshuffle gemm B grid descriptor dimension error. (#3170) | ||
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Xudong Yuan
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d04eba4ae3 |
Ck moe mxfp4 blockm32 (#3098)
* block_m = 32 * ck block_m = 32 * aiter/3rdparty/composable_kernel/include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_b_preshuffle_mx_moe_v3.hpp format * mxfp4_moe v1 pipe * update format --------- Co-authored-by: zhimding <zhimding@amd.com> Co-authored-by: lalala-sh <Jiaxing.Wen@amd.com> Co-authored-by: felix <felix.li@amd.com> |
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Adam Osewski
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b8527a9236 |
[CK_BUILDER] Convolution traits. (#3152)
Added: 1. Convolution traits & unit tests 2. Update builder enumerators to have representation of Convolution Kernels properties. 3. Unified builder pipeline version & scheduler enumerators |
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Illia Silin
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930423ab3b |
Initialize new variable to prevent c++17 compiler error (#3156)
* initialize new variable to prevent c++17 compiler error * build for gfx90a using -std=c++17 flag |
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John Shumway
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6dbee64886 |
[CK_BUILDER] Add backward weight instance traits for xdl cshuffle. (#3143)
* Add backward weight instance traits for xdl cshuffle. To keep instance test file sizes reasonable, we start a new test_bwd_weight_instances_traits.cpp test file. * Fix copyright notices. * Remove (c) symbol, replace with (C). Having UTF-8 in source caused an error with code generation. |
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Enrico Degregori
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507d81c3af |
Fix splitk preshuffle (#3137)
* Fix splitK multiply_multiply_wp * Add tests for gemm_multiply_multiply_wp * Add tests for gemm_universal_preshuffle (KBatch = 1) * Add tests gemm_blockscale_wp * Fix splitk gemm universal preshuffle * Run new tests on arch supporting fp8 * Restore example * Fix strides profiler * Fix tests * Fix clang format * Finalize profiler preshuffle with tolerances * Minor improvements to splitk related changes * Address review comments: clang format and ckProfiler typo * Remove b_k_split_offset from SplitKBatchOffset struct |
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Bartłomiej Kocot
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ab1a8356b6 | Add 2GB limitation for grouped conv bwd weight (#3054) | ||
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JH-Leon-KIM-AMD
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1fbb47ad30 |
[CK TILE] Grouped conv fwd split image (#2970)
* Refactor split-image implementation: simplify code and remove redundant variables * Add padding debug output to split-image implementation - Added debug prints for padding calculations in transform_conv_fwd_to_gemm.hpp - Verified padding works correctly with all tests passing * Fix sign comparison warning after rebase with origin/develop - Cast blockIdX from unsigned to signed index_t for comparisons - Integrated with new GetOutputTileIndex logic from upstream - Updated to use amd_wave_read_first_lane instead of __builtin_amdgcn_readfirstlane * Fix Split-N with groups bug and clean up unused parameters - Fixed batch stride calculation to include G dimension for grouped convolutions - When moving between batches in NHWGC/NWGC/NDHWGC layouts, need to account for all groups - Removed unused multi-split parameters (we only support 2-way split) - All tests now pass: G=1 with Split-N, G>1 with Split-N, G>1 without Split-N * Implement recursive queue-based split-image detection and calculation - Add LaunchKernelWithSplitIfNeeded() helper method in transform_conv_fwd_to_gemm.hpp - Implement recursive binary splitting algorithm (10GB→5GB+5GB→...) - Correctly handle odd dimensions (61→30+31) - Calculate proper offsets for each split piece - Update invoker to use split-image helper Note: Split detection and calculation work correctly but kernel launching for individual pieces requires kernel modification to handle different spatial dimensions (unlike Split-N which uses blockIdx.z). * WIP: Split-Image investigation - found architecture mismatch - Split-N modifies N_ directly in transformer constructor - Split-Image needs different approach due to varying dimensions - Added split calculation logic for 1D and 2D convolutions - Still facing memory issues when creating piece transformers Key finding: Split-N uses blockIdx.z for parallel execution, while Split-Image needs sequential execution of non-uniform pieces. * Add 1D split-image implementation for grouped convolution (N=1 working) Implements split-image for 1D convolution to handle large tensors that exceed memory thresholds. This is a critical milestone with N=1 fully working and tested. Key Changes: - Invoker: Add split-image logic that splits W dimension in half - Transformer: Add SplitConvProblem helper for recursive splitting - Calculate offsets for LEFT and RIGHT pieces - Launch two kernels sequentially (LEFT then RIGHT) Implementation Details: - Binary split: divides W dimension by 2 - LEFT piece: W=0 to W/2, keeps left padding, removes right padding - RIGHT piece: W/2 to W, removes left padding, keeps right padding - Offset calculation accounts for stride, dilation, and padding - Physical memory offset (no padding in memory) Test Results (N=1): ✅ 94/94 tests passing - Comprehensive tests: 36/36 (channels, padding, stride, dilation, filters, groups) - Edge case tests: 31/31 (odd dimensions, extreme parameters, boundaries) - Stress tests: 27/27 (maximum dimensions, up to 91.4 TFlops) Known Limitations: - Only works with N=1 (single batch) - N>1 fails when split-image triggers (offset calculation issue with Split-N) - Root cause: Split-N modifies N in transformer, but offset calculated in invoker - Solution planned: Move offset calculation to transformer (next phase) Files Modified: - grouped_convolution_forward_invoker.hpp: Add split-image logic - transform_conv_fwd_to_gemm.hpp: Add SplitConvProblem helper This commit represents a stable, tested 1D split-image implementation for N=1 cases. It's an important milestone before extending to N>1 and multi-dimensional splits. * Add basic split-image implementation for 1D/2D/3D grouped convolution This is a working baseline implementation that splits large spatial dimensions to handle memory constraints. Implementation: - 1D: W-split for NWGC layout (36/36 tests passing) - 2D: H-split for NHWGC layout (20/20 tests passing) - 3D: D-split for NDHWGC layout (verified working) Features: - Binary split of outermost spatial dimension - Sequential LEFT/RIGHT kernel launches - Proper padding adjustment at split boundaries - Offset calculation for pointer arithmetic - Debug output for verification Threshold: 100KB (configurable in transformer) Known limitations: - No safety checks for edge cases (to be added) - Offset calculated before Split-N (incompatible with N>1, to be fixed) - No recursive splitting for very large tensors Next steps: - Add safety checks (is_possible_to_split_*) - Move offset calculation to transformer (after Split-N) - Test with N>1 + split-image combination * Refactor split-image to unified structure for 1D/2D/3D Unified the three separate dimension-specific blocks into a single common implementation with dimension-specific stride calculations. Benefits: - Reduced code from 636 → 348 lines (45% reduction) - Eliminated code duplication - Easier to maintain and extend - Single source of truth for split logic Implementation: - Common: Binary split, offset calc, padding adjustment, kernel launch - Dimension-specific: Stride calculation only - 1D: stride = G * C - 2D: stride = W_in * G * C - 3D: stride = H_in * W_in * G * C Test results (all passing): - 1D: 36/36 tests ✅ - 2D: 20/20 tests ✅ - 3D: 28/28 tests ✅ - Total: 84/84 (100%) All test scenarios verified: - Varying channels, padding, stride, dilation - Filter sizes (1x1 pointwise to 7x7) - Multiple groups (G=1,2,4) - Odd dimensions - Complex combinations * Add safety checks for split-image in all dimensions Added is_possible_to_split safety checks to prevent crashes when splitting is not feasible. Safety checks verify: 1. Output dimension > 1 (can't split single element) 2. RIGHT piece starts after left padding 3. LEFT piece ends within input bounds If checks fail, falls back to normal kernel launch. Verified for all dimensions: - 1D (W-split): Wo=1 case triggers fallback - 2D (H-split): Ho=1 case triggers fallback - 3D (D-split): Do=1 case triggers fallback Original 84 tests still pass - they use normal configurations that naturally satisfy safety conditions. Safety checks protect against pathological edge cases with: - Very small spatial dimensions - Extreme stride/dilation combinations - Invalid padding configurations * Fix Split-N + Split-Image compatibility issue Fixed critical bug where Split-N and Split-Image working together caused ~50% incorrect results due to wrong batch stride calculation. Problem: - Batch stride was calculated using MODIFIED spatial dimensions (e.g., W=50000 after split) instead of ORIGINAL dimensions (W=100000) - Spatial offset was applied globally in invoker, not per-batch in kernel - Each batch (blockIdx.z) got wrong memory offset Solution: 1. Store spatial offset in kargs (don't apply to pointer in invoker) 2. Copy correct batch_stride from temp_kargs to left/right kargs 3. Apply formula in operator(): ptr = base + (batch × stride) + spatial_offset Changes: - grouped_convolution_forward_kernel.hpp: * Added spatial_offset_in/out fields to KernelArgs * Apply batch + spatial offset in operator() - grouped_convolution_forward_invoker.hpp: * Keep base pointer, store spatial offset in kargs * Copy batch_stride from temp_kargs (has original dimensions) - transform_conv_fwd_to_gemm.hpp: * Add debug output for split-image calculation Results: - N=1 tests: 84/84 passing (100%) - N>1 tests: Now all passing (previously ~50% errors) - Tested: 1D, 2D, 3D with N=1,2,4,8,16,20 * Implement unified threshold for Split-N and Split-Image This commit consolidates threshold management for both Split-N and Split-Image operations into a single source of truth, eliminating code duplication and fixing offset calculation issues. Key Changes: ============ 1. Transformer (transform_conv_fwd_to_gemm.hpp): - Moved TwoGB constant to public section for unified access - CalculateSplitImage() now takes no parameters - Uses internal threshold: TwoGB / sizeof(CDataType) - Calculates offsets using N_ (after Split-N) for correctness 2. Kernel (grouped_convolution_forward_kernel.hpp): - GetSplitImageInfo() simplified to take no parameters - Forwards to transformer's CalculateSplitImage() - Clean interface with unified threshold internally 3. Invoker (grouped_convolution_forward_invoker.hpp): - Removed redundant threshold calculation - Simplified to call kargs.GetSplitImageInfo() with no params - Clean early-return pattern (no unnecessary else blocks) - Removed duplicate/dead code paths Benefits: ========= - Single source of truth: TwoGB defined once in transformer - No parameter passing for threshold between components - Correct offset calculation using N_ (post-Split-N) - Cleaner code with no duplication - All tests passing: 1D/2D/3D with various N values Testing: ======== - Split-Image only (N=1, large spatial): PASS - Split-N only (N>1, small spatial): PASS - Both splits active (N>1, large spatial): PASS - No splits (N=1, small spatial): PASS - CPU verification correct for all scenarios * Comment out outdated split-image code (SplitConvProblem/LaunchKernelWithSplitIfNeeded) The old recursive queue-based implementation has been replaced by the new CalculateSplitImage() method which is simpler and correctly handles Split-N + Split-Image interaction. Changes: - Wrapped lines 381-1078 in #if 0...#endif - Old methods: SplitConvProblem() and LaunchKernelWithSplitIfNeeded() - Preserved for reference but disabled from compilation - No functional changes - all tests still pass The new implementation (CalculateSplitImage at line ~2163) provides: - Correct offset calculation using N_ (after Split-N) - Simpler binary split logic - Better integration with unified threshold approach * Implement recursive split-image with depth limit (MAX_DEPTH=10) Changes: - Add depth tracking to SplitPiece struct - Implement two stopping conditions: 1. Piece size below threshold (optimal case) 2. Depth >= MAX_DEPTH (prevents infinite recursion) - Remove MAX_PIECES limit in favor of depth-based control - Support up to 2^10 = 1024 pieces with depth 10 This allows handling extreme tensor sizes while ensuring termination. Pieces larger than threshold will still launch correctly if depth limit reached. Tested with H=100 (4 levels), H=2000 (6 levels), H=4000 (9 levels) - all pass CPU verification. * Summary of recursive split-image implementation: - Recursive queue-based splitting with depth limit (MAX_DEPTH=10, up to 1024 pieces) - Two stopping conditions: size below threshold OR max depth reached - Cumulative offset tracking through all recursion levels - LEFT piece inherits parent offset, RIGHT accumulates (parent + local) - Per-batch spatial offset application in kernel operator() - Batch stride uses original dimensions (before split) - Works with Split-N: split-N first, then recursive split-image - Handles odd dimensions, padding, stride, dilation correctly - All 1D/2D/3D tests pass with CPU verification * Add comment explaining MAX_DEPTH capacity for 2GB threshold * Refactor: move recursive split-image logic to transformer - Move LaunchWithRecursiveSplit() from invoker to transform_conv_fwd_to_gemm.hpp - Simplify invoker from ~250 lines to ~140 lines (removed 110 lines of inline logic) - Encapsulate SplitPiece struct and BFS splitting algorithm in transformer - Remove unused includes (queue, vector) from invoker - Add documentation comment for AreDescriptorsSmallerThan2GB() - Improve code organization and reusability - No performance overhead (static template function, compiler inlines) - All tests passing with 2GB production threshold * Apply clang-format-18 formatting - Format invoker and transformer files with clang-format-18 - Fix brace placement and alignment - No functional changes * Fix clang-format-18 issues in forward kernel - Remove extra blank lines - Fix line wrapping for template calls - Consolidate GetSplitImageInfo() to single line * Update include/ck_tile/ops/grouped_convolution/utils/transform_conv_fwd_to_gemm.hpp Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update include/ck_tile/ops/grouped_convolution/utils/transform_conv_fwd_to_gemm.hpp Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update include/ck_tile/ops/grouped_convolution/kernel/grouped_convolution_forward_kernel.hpp Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update include/ck_tile/ops/grouped_convolution/kernel/grouped_convolution_forward_kernel.hpp Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Split-Image implementation with temporary fixed divider - Implemented spatial dimension splitting (Split-Image) for large tensors - Added piece-based coordinate transformation for 1D/2D/3D convolutions - Integrated Split-N (batch splitting) with automatic threshold detection - Fixed M dimension calculation to include batch: M = N × spatial_size - Added spatial offset support in kernel arguments - Verified 20/20 test cases passing for Split-Image alone - Known issue: Split-N + Split-Image combination needs coordinate fix Implementation Details: - Split factors: 4 (1D), 4×4 (2D), 4×4×4 (3D) - temporary fixed values - Batch strides properly calculated for NWGC/NHWGC/NDHWGC layouts - Piece descriptors track spatial boundaries and block ranges - No performance overhead for N=1 cases * Fix 1D split-image padding issue with per-piece dimensions - Store actual size per piece to handle non-uniform splits - Remove dead code from transform utils * Fix 2D/3D split-image with independent split factors per dimension Problem: Single split factor caused non-uniform pieces when dimensions didn't divide evenly. Result: 18/25 (72%) 2D padding combinations failed. Solution: Independent split factor selection for W, H, D dimensions. Each dimension gets optimal factor based on its own size. Test Results: - 1D: 42/42 pass (100%) - 2D: 25/25 pass (100%) - Total: 67/67 combinations verified * Remove unused split-image struct fields Cleanup of split-image implementation: - Removed unused piece_d, piece_h, piece_w fields from SplitImageInfo struct - These fields were declared but never used in the kernel - Per-piece dimensions are already stored in pieces[] array - Reduces struct size and improves code clarity Tested: 1D/2D/3D convolutions with split-image, padding, stride all pass * Refactor split-image invoker code for improved readability - Extract piece calculation logic into calculate_piece lambda helper - Extract kernel args population into populate_split_image_kargs lambda - Use aggregate initialization for cleaner struct population - Reduce nesting depth and improve maintainability - Fix outdated comment about split-image implementation status * Refactor split-image code and remove debug prints - Extract GPU kernel helper lambdas for better readability - Remove all split-image debug print statements - Set memory threshold to 2GB for production - All tests pass with CPU verification * Add split-image safety constraints and refactor to utils - Add MAX_TOTAL_PIECES=64 limit to prevent segfault - Move calculate_spatial_piece to library utils - Add layout validation (NWGC, NHWGC, NDHWGC only) - Fix hierarchical splitting to respect piece limits - Add proper documentation and formatting * Change split-image from runtime to compile-time branching Response to @bartekxk review comment: Convert 'if(kargs.num_spatial_pieces > 1)' to 'if constexpr(EnableSplitImage)' Changes: - Add EnableSplitImage template parameter to kernel - Change runtime if to compile-time if constexpr - Update invoker to instantiate kernel variants with true/false Benefits: - Eliminates runtime branching in GPU kernel - Dead code elimination (each variant is smaller) - Better compiler optimization Files modified: 2 Lines changed: 20 total (6 in kernel, 14 in invoker) Tests: 27/27 passed (100%) Performance: No regression * Add split-image example as separate binary - Create grouped_convolution_forward_split_image example - Add grouped_convolution_forward_split_image_invoker.hpp - Update CMakeLists.txt to build split_image binary * Replace linear search with binary search in find_piece_id - Change O(n) to O(log n) for finding piece ownership - Matches reference implementation in large_tensor_cshuffle * Simplify split-image code and fix integer overflow - Extract lambda functions to static helper methods - Pre-calculate constants in invoker - Fix integer overflow in tensor size calculation for large tensors * Trigger CI rerun - fix merge conflicts * Fix merge conflict markers * Fix clang-format: remove space before {} * Fix clang-format: comment wrapping and Swish constructor * Rename split_image to large_tensor for clarity - Renamed grouped_convolution_forward_split_image.cpp -> grouped_convolution_forward_large_tensor.cpp - Renamed grouped_convolution_forward_split_image_invoker.hpp -> grouped_convolution_forward_large_tensor_invoker.hpp - Updated CMakeLists.txt target name: tile_example_grouped_conv_fwd_split_image -> tile_example_grouped_conv_fwd_large_tensor - Updated comments to refer to 'large tensor' instead of 'split-image' * Update comments and include in large_tensor example - Updated header comments to use 'large tensor' terminology - Fixed include path to use large_tensor_invoker.hpp * Remove test code, restore 2GB threshold * Update include/ck_tile/ops/grouped_convolution/utils/transform_conv_fwd_to_gemm.hpp Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Fix build errors after develop merge and complete rename to large_tensor This commit addresses compilation errors from the develop merge and completes the rename from split_image to large_tensor. Changes: 1. Fix CDEElementWise typo in grouped_convolution_forward_invoker.hpp 2. Fix template parameter order in large_tensor_invoker.hpp - TransformConvFwdToGemm signature changed in develop - NumGroupsToMerge and SplitN parameters swapped positions 3. Fix missing template parameter in GroupedConvFwdHostArgs 4. Fix EpiloguePipeline scope in kernel (merge conflict) 5. Update binary name references in test scripts * Restore 2GB threshold for split-image Changed threshold from 100MB (testing) back to 2GB for production use. * Fix const-correctness in ds_ptr cast * Update include/ck_tile/ops/grouped_convolution/kernel/grouped_convolution_forward_kernel.hpp Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Apply clang-format-18 * update c++ 18 format * Apply clang-format-18 to transform_conv_fwd_to_gemm.hpp --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> |
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Enrico Degregori
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4ebc48a3cd |
WMMA gemm_add_relu_add_layernorm (#2989)
* Summary:
- Refactor epilogue (with CShuffle) to support fused operations:
- EpilogueCShuffleBase holds common parts
- EpilogueCShuffle: runs CShuffle and write out
- EpilogueWelfordCShuffle: holds Welford specific arguments, runs CShuffle, write out, Welford first part and Welford write out
- Extend thread transfer v7r3:
- Support for intermediate data type different from src and dst type
- New functionality to write to dst buffer and keep data (to be able to use them for additional operations)
* Adress review comments
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John Shumway
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5ed2046bee |
Add the last two forward instance traits. (#3134)
* Add InstanceTraits for DeviceGroupedConvFwdMultipleD_Wmma_CShuffle * Add InstanceTraits for kernel_grouped_conv_fwd_dl_multiple_d * A few small changes to fix broken instance traits. |
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kabrahamAMD
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a7c52e8afa |
Kabraham/fix block gemm v1 b scale (#3129)
* fixed synchronization issue in block gemm pipeline v1 that caused b_scale to fail * run clang-format --------- Co-authored-by: Kevin Abraham <kevin.abraham@streamhpc.com> |
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John Shumway
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cafaeb6b7b | Add instance traits for two more grouped forward convolutions (#3112) | ||
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Bartłomiej Kocot
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66bae4306c |
Grouped conv fwd with direct load (#3082)
* Grouped conv fwd with direct load * fix * fix * Add IsSupported check * Fix * fix inductor |
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Ville Pietilä
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1c17bae816 | Add name member to CK elementwise operations. (#3102) | ||
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John Shumway
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54746e9329 |
[CK_BUILDER] Test and fix instance traits utils. (#3096)
* 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. |
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Ville Pietilä
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6c2ca1211a |
[CK_BUILDER] First fwd convolution builder implementation (#3070)
* Add experimental builder infrastructure for composable_kernel - Add experimental/builder directory with README documentation. - Create initial test infrastructure with CMakeLists.txt and placeholder test. - Update root CMakeLists.txt to support CK_EXPERIMENTAL_BUILDER option. - Update .gitignore to not treat `experimental/builder` as a CMake build directory. This establishes the directory structure for a high-level builder pattern that will provide a semantically-clear interface for constructing CK operations, with initial focus on convolution kernels for MIOpen integration. * Fix clang formatting. * Fix CMake build infrastructure for experimental builder - Add experimental/builder CMakeLists.txt with proper subdirectory structure - Add placeholder include/ck_tile/builder CMakeLists.txt for header installation - Fix gtest.cmake to use include_guard to prevent multiple inclusions - Update root CMakeLists.txt to include full builder directory instead of just tests * Scope C++20 settingto the test code Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Remove redundant GTest::gtest linkage Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Introduce basic types, and convolution algorithm concepts and limits. * Add convolution signature concepts. * Add convolution factory. * Finalize conv factory implementation for fwd convolutions. * Add type definitions for testing. * Add placeholder test. * Add convolution builder definition. * Fully functional fwd conv builder. * Test improvements. * Clean-up include headers. * Enable the limit checks for the convolution algorithm parameters. * Remove dead code. * clang formatting. * Add more tests and missing conv specialization argument. * clang formatting. * Add explicit handling of the tensor layouts. * Add complete 2D/3D layout support to CK Builder - Add missing 2D layouts: GNHWC_GKYXC_GNHWK, NGCHW_GKCYX_NGKHW - Add missing 3D layout: GNDHWC_GKZYXC_GNDHWK - Add 1D layouts (NWGC, NGCW, GNWC, NGCW_GKCX) for future support - Add 3 tests for new 2D/3D layouts - All tests pass (5/5) * Add tests for remaining 2D/3D layouts - Add test for 2D NGCHW_GKYXC_NGKHW (channels-first) with Filter1x1Stride1Pad0 - Add test for 3D NDHWGC_GKZYXC_NDHWGK (channels-last) - All 7 tests pass (complete coverage for all 2D/3D forward layouts) * Change enum converters to consteval. * 7 tests with pipeline and specialization| Test # | Dim | Type | Layout | Pipeline | Specialization | |--------|-----|------|----------------------|----------|-------------------------| | 1 | 2D | BF16 | NHWGC_GKYXC_NHWGK | V1 | DEFAULT | | 2 | 2D | FP16 | GNHWC_GKYXC_GNHWK | V3 | FILTER_1X1_PAD0 | | 3 | 2D | FP32 | NGCHW_GKCYX_NGKHW | V4 | FILTER_1X1_STRIDE1_PAD0 | | 4 | 2D | BF16 | NHWGC_GKYXC_NHWGK | V5 | FILTER_3x3 | | 5 | 3D | FP32 | NGCDHW_GKCZYX_NGKDHW | V1 | FILTER_1X1_PAD0 | | 6 | 3D | BF16 | GNDHWC_GKZYXC_GNDHWK | V3 | DEFAULT | | 7 | 3D | FP16 | NDHWGC_GKZYXC_NDHWGK | V4 | FILTER_1X1_PAD0 | * Add missing convolution layouts and provide better compile-time error in instance traits. * Fix clang formatting. * Changed I8 -> S8. * Fix signature. * Rename concepts and corresponding members. * Rename LDS related parameters. * Remove ODD_C specialization. Add V2 pipeline. * Add missing types. * Add elementwise operation to the conv signature. * Improve compile-time error message for unsupported elementwise ops. * Separate different fwd conv builder tests into separate compilation units. * Fix layout to string and add name to old CK PassThrough elementwise op. * Enable both CK and CK Tile tensor layouts in instance traits. * Fix clang-format. --------- Co-authored-by: John Shumway <jshumway@amd.com> Co-authored-by: John Shumway <john.shumwayjr@gmail.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Co-authored-by: JH-Leon-KIM-AMD <jeonghyun.kim@amd.com> |
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yinglu
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6bbc05e1bd |
conv:tf32:add missed instances (#3081)
* conv:tf32:add missed instances |
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John Shumway
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37dff024c1 |
[CK_BUILDER] Add compile-time reflection for a convolution instance (#3065)
* [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. |
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Bartłomiej Kocot
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3a28632b20 |
Gridwise gemm conv v3 force padded layout on gfx950 (#2961)
* Gridwise gemm conv v3 force padded layout on gfx950 * fix bug in other gridwise * fix * Update gridwise_gemm_wmma_cshuffle_v3_common.hpp |
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Ville Pietilä
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7e44b845b5 |
Fixed handling of split-K autodeduce argument for grouped convolution (#3024)
* Fix handling of split-K autodeduce argument. * Fix clang formatting. * Test fix. * Fix clang formatting. |
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Enrico Degregori
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440358c168 |
Wave Tile Transfer supporting global load with transpose (#3027)
* 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 |
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kabrahamAMD
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c4b2da9cbd |
implement device batched gemm b scale for wmma (#2825)
* 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> |
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yinglu
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fada1a3cae |
Conv:TF32: add more instances - 2 (#2879)
* add instances of device_grouped_conv_fwd_xdl_f32_comp_instances * add instances of device_grouped_conv_fwd_xdl_f32_tf32_mem_instances * add instances of device_grouped_conv_fwd_xdl_large_tensor_f32_tf32_instances * tf32:conv:add instances for base class DeviceConvFwd * tf32:conv:add instances for base class DeviceGroupedConvBwdDataMultipleD * tf32:conv:add instances for base class DeviceGroupedConvBwdWeight * add tf32 in profiler * remove gnhwc/ngchw/ngcdhw instances * remove non-ndhwgc/nhwgc/nhwc instances * add check in IsSupportedArgument() |
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Bartłomiej Kocot
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ad7a215aba | Fix splitK for grouped conv bwd data (#2991) | ||
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Sami Remes
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9d4bfe3932 |
Add KBatch support for gemm_ab_scale (#2740)
* Add KBatch support for gemm_ab_scale
* Revert kernel parameters change
* Remove printing
* fix formatting
* fix check
* Use {} in if
---------
Co-authored-by: Adam Osewski <19374865+aosewski@users.noreply.github.com>
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Illia Silin
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4c98535456 | fix compilation errors on RHEL8 and SLES15 (#2967) | ||
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Thomas Ning
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cadafde722 | add the check of granularity for atomic add (#2959) |