- Add multiple A and B support to multiple D implementation (gridwise level)
- Add multi_abd GEMM (device level)
- Add instances (xdl parity)
- Add tests (both xdl and wmma)
- Add examples
- Add ckProfiler support (both xdl and wmma)
1. Port NCHW support from ConvFwd (#2375) to conv bwd data
2. Add new instance device_grouped_conv_bwd_data_xdl_f16_nchw_instances for nchw
Co-authored-by: azhuang <anzhong.huang@amd.com>
* add template for fp16 atomic add
* add template for unsigned short atomic add
* use atomicCAS in atomic add for fp16 and unsigned short
* revrt back to atomic add using casting
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.
* 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.
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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.
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Co-authored-by: illsilin <Illia.Silin@amd.com>
