* Shard several of the most costly targets.
Introduces a filter_tuple_by_modulo to break up tuples.
Drops build time of target from 21 minutes to under 14 minutes with 64
build processes, or 11 minutes with 128 build processes.
time ninja -j 64 device_grouped_conv3d_fwd_instance
* fix clang format
* Fix build errors in instantiation code.
I wasn't sure how to test the header-only instantiation code on my
initial commit. From Jenkins CI test results, I see that there is a
test target that depends on these headers:
ninja -j 128 test_grouped_convnd_fwd
This allowed me to test the build locally. I found three mistakes I
made, mostly related to early experiments on I tried on the code.
This was hard to find earlier because this PR is really too large.
I also discovered that there are five 2D convolution targets that now
dominate the compilation time. I will likely address those in a later
PR, rather than adding even more changes to this PR.
* Fix link errors from mismatched declarations.
Our pattern for instantiating MIOpen templates uses duplicate
declarations (instead of headers). This is fragile, and I didn't
notice that my last commit had a bunch of link errors. I fixed these
mistakes, and the bin/test_grouped_conv_fwd test target binary now links
correctly.
* Migrate the design to a code-generation approach.
Use a CMake function with template files to generate the source files for the
intantiating the kerenels and to generate the calling function.
* Shard the longest 2D convolution builds
Now that we have automated the shard instantiation, we can shard the 2D
convolution targets that take the longest to build. The target
test_grouped_conv2d_fwd now compiles in 15 minutes.
* Use PROJECT_SOURCE_DIR for submodule compatibility
I used CMAKE_SOURCE_DIR to refer to the top-level source directory in
the ShardInstantiation.cmake file, but this can cause issues with
git submodules. Instead, we should use PROJECT_SOURCE_DIR to ensure
compatibility when this project is used as a submodule in another
project.
* Migrate the design to a code-generation approach.
Use a CMake function with template files to generate the source files for the
intantiating the kerenels and to generate the calling function.
* Migrate the design to a code-generation approach.
Use a CMake function with template files to generate the source files for the
intantiating the kerenels and to generate the calling function.
* Remove accidental copy of a file
* Remove accidental copies of template files.
---------
Co-authored-by: illsilin <Illia.Silin@amd.com>
* Selective test filter initial commit.
* Expanded folder paths for parsing ninja dependencies.
* Fixing default branch name in the test evaluation script.
* Fixing paths for robustness and adding ctest command to the launch script.
* change jenkins file and few tests to upgrade CI
* Setting ninja build path.
* Fixing typo in Jenkinsfile, and wrong paths.
* Fixing typo in launch script.
* add few more tests to check CI logic
* Fixing header for shell script.
* turn off performance test by default, add option to run all unit tests
* revert dummy changes in source code to trigger tests
* make sure develop branch runs all unit tests
---------
Co-authored-by: Vidyasagar Ananthan <vidyasagar.ananthan@amd.com>
* Stream-K Ckprofiler Update
* new grid list based on sm number
* clang
* update for review
* Update profile_gemm_universal_streamk.cpp
---------
Co-authored-by: root <root@ctr-ubbsmc16.amd.com>
* add transpose load; no real logic
* fix some compile errors
* fix some issues
* update transpose load logic
* add some fixes
* fix a distribution issue
* update some codes
* add some fix
* can pass; but no logic
* transpose load enable
* update tile transpose
* miss output tile distribution mapping
* hack for transpose 16x16
* update output tensor distribution
* delete unused variables
* fix transpose related codes
* update transpose load example
* exchange the iteration order
* fix 16x16 related dimension transpose
* fix a transpose index issue
* fix a transpose index issue
* fix clang format check
* update load tile transpose related codes
* fix compile errors and pass 16x16 tests
* fix a typo
* update logic
* check other data types
* add transpose load api
* update transpose load api
* fix clang format check
* change file name
* refactor codes
* update code name
* delete some unused codes
* delete the unused oob flag for transpose load
* update tensor view api for transpose load
* update for testing
* fix a typo error
* move transpose ops to example directory
* update transpose api
* update include file
* fix for pr review
* fix compile errors
* add transpose load; no real logic
* fix some compile errors
* fix some issues
* update transpose load logic
* add some fixes
* fix a distribution issue
* update some codes
* add some fix
* can pass; but no logic
* transpose load enable
* update tile transpose
* miss output tile distribution mapping
* hack for transpose 16x16
* update output tensor distribution
* delete unused variables
* fix transpose related codes
* update transpose load example
* exchange the iteration order
* fix 16x16 related dimension transpose
* fix a transpose index issue
* fix a transpose index issue
* fix clang format check
* update load tile transpose related codes
* fix compile errors and pass 16x16 tests
* fix a typo
* update logic
* check other data types
* add transpose load api
* update transpose load api
* fix clang format check
* change file name
* refactor codes
* update code name
* delete some unused codes
* delete the unused oob flag for transpose load
* update tensor view api for transpose load
* update for testing
* fix a typo error
* move transpose ops to example directory
* update transpose api
* update include file
* fix for pr review
* fix compile errors
* change directory name
* delete the duplicated directory
* update cmakelists file
* delete the unused codes
* update function names
* update transpose policy
* update code after remod.py
* update codes
* add some comment
* Polish the instr infrastructure
* build up the fixed instr
* redesign the transpose api, currently it has numerical error
* add the bf16 transpose
* fix some issues
* add some comments
* update document
* Finished the refactor of API and pass through the verification
* fix the merging issue
---------
Co-authored-by: ThomasNing <thomas.ning@amd.com>
* Fix argument order for calls to profile_batched_gemm_impl()
* Revert previous and swap the order of the profile_batched_gemm_impl() function arguments instead.
* Revert copyright years for unchanged files.
* Remove test_batched_gemm from REGRESSION_TESTS since it no longer takes more than 30 seconds to run.
---------
Co-authored-by: Kiefer van Teutem <kiefer.van.teutem@streamhpc.com>
* [ck-tile] fix default epilogue in gemm universal
* argument validation needs vector size D
* operator() needs to specify dram windows
* copy/paste from cshuffle epilogue
* clang-format
* mark unused argument
---------
Co-authored-by: Thomas Ning <Thomas.Ning@amd.com>
* [CK_TILE] Support multi-config in tile_example_gemm_universal
Add GemmConfig in run_gemm_example to support multiple tile config.
- It is useful when use you need compare gemm perf with different tile/pipeline config
- we also can use it simplify the code for wmma support in the furture.
* [CK_TILE] Support multi-config in tile_example_gemm_universal
Address review comments
* rebase code and fix clang format.
* fix clang format
* support pipeline v5.
* fix merge conflict
* address review comment
* add missing file
* address review comment v2
* fix build error
* Do not use warpSize as compile time constant as it is removed
* Update tile_image_to_column_shape.hpp
update warpSize usage.
* clean-up all use of warpSize, make sure code builds
* fix
---------
Co-authored-by: Illia Silin <98187287+illsilin@users.noreply.github.com>
Co-authored-by: illsilin <Illia.Silin@amd.com>
Co-authored-by: Bartlomiej Kocot <barkocot@amd.com>
* Add support for specifying valid flag when fetching elements for tile_scatter_gather
Add constexpr for operator[] of TrueGenerator
* Use different path when valid is enabled
* Multiple d, initial commit
* Check Ds Layout
* Readme and clang format
* Update branch & conflicts
* Multiple D - fix clang-formatter
* Rename elemetwise_op
* Fix CI
* Code review part1
* Remove printf
* Remove unnecessary comment
* Add new tests with Col layout
* Review part 2
* Added support for Multiple D GEMM
* Update comment
* Remove maybe_unused
* Clang-format
* Review part 3
* Add comment to function
* Add comment to function: another
* Take number of params for a refrence function
* Remove additional d param for 0 tensor
* Change name of function
* Fix CI fails
* Shard several of the most costly targets.
Introduces a filter_tuple_by_modulo to break up tuples.
Drops build time of target from 21 minutes to under 14 minutes with 64
build processes, or 11 minutes with 128 build processes.
time ninja -j 64 device_grouped_conv3d_fwd_instance
* fix clang format
* Fix build errors in instantiation code.
I wasn't sure how to test the header-only instantiation code on my
initial commit. From Jenkins CI test results, I see that there is a
test target that depends on these headers:
ninja -j 128 test_grouped_convnd_fwd
This allowed me to test the build locally. I found three mistakes I
made, mostly related to early experiments on I tried on the code.
This was hard to find earlier because this PR is really too large.
I also discovered that there are five 2D convolution targets that now
dominate the compilation time. I will likely address those in a later
PR, rather than adding even more changes to this PR.
* Fix link errors from mismatched declarations.
Our pattern for instantiating MIOpen templates uses duplicate
declarations (instead of headers). This is fragile, and I didn't
notice that my last commit had a bunch of link errors. I fixed these
mistakes, and the bin/test_grouped_conv_fwd test target binary now links
correctly.
* Migrate the design to a code-generation approach.
Use a CMake function with template files to generate the source files for the
intantiating the kerenels and to generate the calling function.
* Shard the longest 2D convolution builds
Now that we have automated the shard instantiation, we can shard the 2D
convolution targets that take the longest to build. The target
test_grouped_conv2d_fwd now compiles in 15 minutes.
* Use PROJECT_SOURCE_DIR for submodule compatibility
I used CMAKE_SOURCE_DIR to refer to the top-level source directory in
the ShardInstantiation.cmake file, but this can cause issues with
git submodules. Instead, we should use PROJECT_SOURCE_DIR to ensure
compatibility when this project is used as a submodule in another
project.
---------
Co-authored-by: illsilin <Illia.Silin@amd.com>
