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* 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>
16 lines
346 B
Plaintext
16 lines
346 B
Plaintext
// SPDX-License-Identifier: MIT
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// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
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#include "@INSTANCES@.inc"
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namespace ck::tensor_operation::device::instance {
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@EXTERN_TEMPLATE_STATEMENTS@;
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void add_@INSTANCES@(
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@INSTANCES@& instances) {
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@CALL_STATEMENTS@;
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}
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} // namespace ck::tensor_operation::device::instance
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