mirror of
https://github.com/ROCm/composable_kernel.git
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* WIP POC of dispatcher * Dispatcher python workflow setup. * Dispatcher cleanup and updates. Further dispatcher cleanup and updates. Build fixes Improvements and python to CK example Improvements to readme * Fixes to python paths * Cleaning up code * Improving dispatcher support for different arch Fixing typos * Fix formatting errors * Cleaning up examples * Improving codegeneration * Improving and fixing C++ examples * Adding conv functionality (fwd,bwd,bwdw) and examples. * Fixes based on feedback. * Further fixes based on feedback. * Adding stress test for autogeneration and autocorrection, and fixing preshuffle bug. * Another round of improvements based on feedback. * Trimming out unnecessary code. * Fixing the multi-D implementation. * Using gpu verification for gemms and fixing convolutions tflops calculation. * Fix counter usage issue and arch filtering per ops. * Adding changelog and other fixes. * Improve examples and resolve critical bugs. * Reduce build time for python examples. * Fixing minor bug. * Fix compilation error. * Improve installation instructions for dispatcher. * Add docker based installation instructions for dispatcher. * Fixing arch-based filtering to match tile engine. * Remove dead code and fix arch filtering. * Minor bugfix. * Updates after rebase. * Trimming code. * Fix copyright headers. * Consolidate examples, cut down code. * Minor fixes. * Improving python examples. * Update readmes. * Remove conv functionality. * Cleanup following conv removable.
28 lines
571 B
JSON
28 lines
571 B
JSON
{
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"tile_config": {
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"tile_m": [128, 256],
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"tile_n": [128, 256],
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"tile_k": [32, 64],
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"warp_m": [2, 4],
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"warp_n": [2, 4],
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"warp_k": [1],
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"warp_tile_m": [16, 32],
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"warp_tile_n": [16, 32],
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"warp_tile_k": [16]
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},
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"trait_config": {
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"pipeline": ["compv4"],
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"epilogue": ["cshuffle"],
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"scheduler": ["intrawave"],
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"pad_m": [false],
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"pad_n": [false],
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"pad_k": [false],
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"persistent": [false, true]
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},
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"multi_d_config": {
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"elementwise_ops": ["MultiDAdd", "Relu", "Gelu"],
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"num_d_tensors": [1, 2]
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}
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}
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