mirror of
https://github.com/ROCm/composable_kernel.git
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7ce0127e8f
* 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.
[ROCm/composable_kernel commit: 9e049a32a1]
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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