* add for async load builtin
* add async load api
* fix some compiling errors
* fix a compiling error
* fix some compiling errors
* add a pipeline which copies from v4
* add a new pipeline for async load
* fix some compiling errors
* add async load tests
* fix some issues in async load
* fix
* fix async inline assembly
* fix async inline assembly
* add ignore header file
* comment some not gfx950 codes
* comment some not gfx950 codes
* fix a error
* update async load apis
* fix lds descriptor
* fix a compiling error
* fix some compiling errors
* fix a descriptor issue
* update lds descriptor
* change async pipeline's tile distribution pattern from thread to warp
* fix clang format
* update async policy
* fix a CRTP issue
* fix a typo error
* change lds layout
* fix some sync issues
* improve codes
* delete the async test
* fix a commented format issue
* avoid compiling device functions when compile host
* make gemm run
* add the copy kernel support
* finish the feature
* Address comment
* add the support for buffer_builtin
* solved the merging problem
* Comment Addressed
---------
Co-authored-by: joye <joye@amd.com>
Co-authored-by: joyeamd <John.Ye@amd.com>
* add prefetching physical block id for pagedkv
* start add pagedkv prefill
* rename pipeline
* add kernel for pagedkv
* add an init version pagedkv prefill
* fix redefine issue
* add struct BlockFmhaFwdPagedKVPipelineProblem and fmha_fwd_pagedkv_args
* generate dispatch code
* add body generating code
* comipling pass
* remove dropout from pagedkv
* set lse to false in generating code
* start changing qr kernel to pagedkv
* init version of kernerl with pagedkv
* change names of file that are generated
* chang host validation for pagedkv prefill
* using iglp to change blockgemm
* add kernel files to op head file
* show parameters
* rewrite print parameter fun
* add fwd
* remove default parameter of GridSize
* format
* fix nhead issue and add seqlen_k_ptr to batch mode
* format code
* remove no-longer used code
* format
* fix some comments
---------
Co-authored-by: ltqin <letaoqin@amd.com>
Co-authored-by: Po Yen Chen <PoYen.Chen@amd.com>
* support slice cross p
* fix some bug in y_len
* more case
* fix a bug when R exist
* support -1 to hint end of current length
* format
* change commit
* fix(precommit_install): script now installs packages in virtual env
* fix(precommit_install): installs packages in virtual env
* feat(precommit): added ruff for python linting and formatting
* feat(precommit): added ruff for python linting and formatting
* feat(precommit): run ruff when py files are commited
* feat(precommit): remod.py is run when ck_tile modified
* add empty line at the end
* style(precommit.yaml): remove empty line
---------
Co-authored-by: Max Podkorytov <4273004+tenpercent@users.noreply.github.com>
* Testing assignment of param fix
* Removing redundant changes
* Adding back unit test runs
* Ensuring Jenkins changes work on develop - to be reverted
* Revert "Ensuring Jenkins changes work on develop - to be reverted"
This reverts commit cf1cab4a43.
1. When conv spec is 1x1 stride1 pad0, nchw is equal with matrix A + column major, we only need minor change in conv transformer to support it.
2. when out is NKHW, it is equal with matrix C with column major. we need swap A & B to get best performance.
3. Add new instance device_grouped_conv_fwd_xdl_f16_nchw_instances for nchw.
* updates to support int8 in 03_gemm example
* added comments, using aliases, helper functions
* test(gemm_universal): add test cases for int8 gemm pipeline
* fix(test_gemm): fix for failing test unit test for int8
* test(ck_tile): add int8 unit test for gemm universal
* refactor(gemm_universal): GPU reference verification for GEMM code improved
* style(gemm_universal): removed extra comments and did clang format
* merging recent changes to universal gemm to tile_engine
* ck tile engine integration work
* feat(tile_engine): add int8 support to tile engine ops/gemm
* feat(tile_engine): added 32 32 16 mfma instances to tile engine for int8
* style: Format code with clang-format-12
* refactor(tile_engine): address review comments
* style: removed unhelpful comments & unused variables.
* build: tile engine uses default config
* feat: add int8 support for CK_TILE GEMM
* style: added trailing commas to codegen_utils.py
* refactor: tile engine
* refactor: formatting and code review
* refactor: code formatting for python files
* fix: suppress build warning
* add support for gfx950
* refactor:KWarpTile size in gemms util
* Fix the branch and wrap up the k warp tile
* Add bf8 integration
* refactor: clang format and rebase
---------
Co-authored-by: zjli2013 <leezhengjiang@gmail.com>
Co-authored-by: AviralGoelAMD <aviral.goel@amd.com>
Co-authored-by: Khushbu Agarwal <khuagarw@amd.com>
* [CK_TILE] Refine fp8 in flatmm
1. Replace USING_MFMA_16x16x32 & USING_MFMA_16x16x32 with constexpr
2. Add an additional const check to avoid build error in HotLoopScheduler
3. Refine shuffleb to support both tile 32x32 and 16x16
4. Support command option -init
5. Move Gemm warp defintion to a separate struct
* fix clang format
* fix clang format
* keep default bhavior unchanged (warp tile = 16x16)
* fix tile engine build error
* fix a typo in codegen_utils.py
* address review comments
* address review comments
---------
Co-authored-by: Thomas Ning <Thomas.Ning@amd.com>
* Fix amd_ck_fp8.hpp macro definitions
1. Define CK_USE_FNUZ_FP8 and CK_USE_OCP_FP8 definitions only if they were not defined before.
2. Prefix __assert_fnuz_support and __assert_ocp_support with namespace
fp8_impl to avoid redefined error when building with rocm 6.4+
(rocm/6.4.0/include/hip/amd_detail/amd_hip_fp8.h)
Co-authored-by: Andriy Roshchenko <andriy.roshchenko@amd.com>
* Avoid passing indices (std::vector) by value to host tensor's operator()
Each access requires 2 allocations and copies of the vector.
* Remove 1 unneeded vector copy from the slowest part of fmha_bwd's verification
* Compute ds_hp_host_ref in parallel
This sequntial ForEach is the slowest part of validation and it benefits
from parallel computation.
* Do not use ForEach for simple copy and conversion of large tensors
These tensors all have the same shape {nhead, real_seqlen_q, real_seqlen_k} and
can be copied/converted without complex computations of linear indices.
* Some prep work for adding batched_gemm_wmma_universal. Moved batched_gemm in general to gfx11 and gfx12 categories, and split existing batched_gemm test into xdl and wmma versions. Updated profiler and instance factory. For now only adding f16-row-row-row-GemmDefault. For now actual device instance list is empty.
* Add DeviceBatchedGemm_Wmma_CShuffleV3 based on DeviceGemm_Wmma_CShuffleV3 and make sure it's used in the instance factory and tests. Currently the new batched device level struct cannot actually handle batching, but it does pass tests with a trivial batch size of 1, meaning that the overall structure is good.
* Add custom kernel and Argument type to DeviceBatchedGemm_Wmma_CShuffleV3. Batching arguments not passed to kernel yet.
* Implement kernel-level batching logic for DeviceBatchedGemm_Wmma_CShuffleV3. In principle the whole thing works now, just need to add other data types and perhaps do some cleanup.
* Add other layouts for batched gemm wmma chufflev3 f16 f16 f16. Now matching XDL (for f16).
* Add bf16 bf16 bf16 support for batched gemm wmma cshuffle v3 for all layouts.
* Fixup comments and TODOs
* Expand test cases for batched gemm wmma cshuffle v3 with more unusual shapes. Some of the original test cases for batched gemm do not work based on cshuffle v3 because the dimensions are too small.
* Fix argument order for calls to profile_batched_gemm_impl() ONLY in wmma tests.
* Take batching into account when using rotating memory or clearing the C tensor.
* Implement small refactors / comments etc. from review.
* Port recent gemm wmma updates to batched gemm wmma: V1 pipeline, non-main-k-block-loop, check compute type, packed buffer size calc. Ported new instance lists.
* Add MNKPadding instances to batched gemm wmma cshuffle v3, remove incompatible test problems.
* Put clearing the C matrix in a pre-process lambda for the non-flush case + small fixups.
* Once again switch order of strides and batch strides in calls to profile_batched_gemm_impl() from test_batched_gemm_wmma to match latest definition of that function.
---------
Co-authored-by: kiefer <kiefer.van.teutem@streamhpc.com>
* 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>
