* Readme for GEMM Multi D
* GEMM Multi D partial Progress
* GEMM Multi D partial Progress!
* CK Tile Engine GEMM Multi D : All Python files generated
* Partial Progress
* Partial Progress
* Partial Progress
* Partial Progress : Incorrect Result
* Partial Progress : Debugging
* Partial Progress : Correct Results
* Partial Progress - Incorrect Results
* Partial Progress - Commenting Passthrough bypass logic
* Changing Passthrough to MultiplyMultiply
* Correct Results!
* Fix and debug the pass through feature
* Sample commit
* Correct Results : MultiplyMultiply
* Code Cleanup
* Removing Failed Instances
* Working code before Unary element support
* Custom Elementwise Function support and working implementation for Mul and Add
* Updating README
* Working for Passthrough
* Review Comments : Minor Fixes
* Review Comments : Minor Fixes
* Readme Updated
* Partial Changes after Rebase
* Working Code : Changes after Rebase
* Updating Jenkins file
* Removing default value changed while testing
* Configuration changes in config files
* Tile Handler changes in GEMM Multi D Tile Engine
* Tile Handler changes in GEMM Multi D Example
* Change log for Gemm Multi D in CK Tile Engine
* Configuration changes in config files
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Co-authored-by: ThomasNing <thomasning@amd.com>
* Elementwise kernel implementation
Co-authored-by: Sami Aario <samaario@amd.com>
Co-authored-by: Mohsen Saffari <mohsen.saffari@amd.com>
Co-authored-by: yashagar <yashagar@amd.com>
* Elementwise with generalized nDims
* Adding the n-ary input tensor feature
* Generalize dimensions on top of inputs
* Add TFLOPS + remove std usage for tuples
* 1D basecase optimization
* Cleanup code + refactoring to a common interface
* Generalize to unary and add an example
* Cleanup, refactoring and commenting
* Suggestions for LWPCK-3170: elementwise kernel improvements
* Clang-format: remod.py
* Replace InputTensorType with XDataType as the type of input_tensors
* Add Tuple::apply and use it in ElementWiseKernel::operator to call operation with the exact number of arguments in xs
* Move examples to folder 19_elementwise
* Add missing copyright headers and fix some existing ones
* Replace an assert with throw std::runtime_error in elementwise example
* Avoid reading the output by using make_static_distributed_tensor for y_tile
* Removed two unused includes
* No need to move windows to the next block when each workgroup processes a single tile
* Only copy input tensors to the device
* Use get_warp_size to obtain warp size, and use ceiling division for grid size also for the unary example
* Adding output strides to the kernel, transposition example and update the other examples
* Changes made by remod.py
* Use default template parameter values for memory operation and coherence in a call to make_naive_tensor_view
* Move binary operations to include/ck_tile/ops/elementwise/binary_elementwise_operation.hpp
* Reuse generic reference binary/unary operation in examples + refactoring the transpose reference
* Fix comments in elementwise_example.cpp
- Refer to AMD terminology except when suggesting NVIDIA alternatives in parentheses
- ElementWiseTraits was renamed to ElementWiseShape
- Adopt suggestions made by Copilot when prompted to check for factual or typographical errors
* Simplify CMakeLists.txt and remove the unused variables this uncovers
* Rename a file and fix some copyright statements
* Changes made by script/clang-format-overwrite.sh
* Add basic unit test for ElementWiseKernel
* Remove left-over uninformative comment in apply unit test
* Changes made by clang-format-overwrite.sh
* fixup! Use default template parameter values for memory operation and coherence in a call to make_naive_tensor_view
* Clean up test_tuple_apply.cpp and test_elementwise_1d.cpp
* Use make_uniform_array_with_factory to define h_xs and d_xs_mems_owner as type std::array
* Use a DeviceMem constructor that calls get_element_space_size_in_bytes internally
* Move examples to folder 20_elementwise
* Reduced register pressure on the CK tile elementwise kernel + add 4d input example to be able benchmark against old CK
* Fix CLang formating
* Bump up the elementwise example folder number
* Elementwise: add padding + minor cleanup
* Add Vector Size inference + fix issue with wrong vectorization due to missing GuaranteedLastDimensionVectorStride setting in make_naive_tensor_view
* Add isSupportedArg to Elementwise kernel + addapt example and unit tests
* Fix clang-format on the unit test file
---------
Co-authored-by: Damien Lejeune <damien.lejeune@amd.com>
Co-authored-by: Sami Aario <samaario@amd.com>
Co-authored-by: Mohsen Saffari <mohsen.saffari@amd.com>
Co-authored-by: Aviral Goel <aviral.goel@amd.com>
* ck_tile kernel for gemm with groupwise quantized A or B tensor.
This change introduces new pipelines with Intrawave scheduler and block gemm primitives that loads the scale tensor to registers to perform dequantization post MFMA on C tensor in registers.
Scale tensor data, AQ/BQ is spliced across threads in registers and not stored in LDS.
Current support is for the following combinations, but it should be fairly straightforward to extend support to more formats.
1. fp8, fp8 -> f32
2. bf8, bf8 -> f32
3. i4, fp8 -> f32
4. i4, bf8 -> f32
Group size can go down to as low as K length of underlying WarpGemm primitive.
For Gemm problems with quantized B tensor, this change also introduces preliminary support for flatmm pipeline which loads B tensor directly into registers.
* [Block Scale Gemm] Only run gemm quant examples on __gfx94__
- Only run gemm quant examples on __gfx94__ for usage of
`v_cvt_pk_fp8_f32`
- Format the code
* [Block Scale Gemm] Remove Bquant Gemm BlockScale
This cleanup is in preparation for future development of bquant. By
isolating Aquant-related code, we can streamline the codebase and make
it easier to add and maintain bquant functionality in subsequent
updates.
* [Block Scale Gemm] Format code with clang-format-12
The latest clang-format (v19) in ROCm 7.0 generate different result than
clang-format-12 which is used in CK CI.
Format code with clang-format-12 for consistency.
* [Block Scale Gemm] Split the k direction loop
- Split the k direction loop in block_universal_gemm_as_quant_bs_cr.hpp
to make the logic clearer.
- Disable C transposition.
* [Block Scale Gemm] Move block scale gemm example to 38_block_scale_gemm
* [Block Scale Gemm] Update copyright
* test
* Add TailHandler
* Move TileDistributionEncodingPatternAQ
* Refactor
* refactor
* fix bug
* fix bug
* help solve the PR comment
* Format the code
* [Block Scale Gemm] Add unit tests
* [Block Scale Gemm] Add support to 16x16x32 MFMA
- Add support to 16x16x32 MFMA
- Fix a bug when exchange data crossing lanes
---------
Co-authored-by: Vijay Krishnamoorthy <vjkrish@meta.com>
Co-authored-by: Cong MA <congma13@ctr2-alola-ctrl-01.amd.com>
Co-authored-by: ThomasNing <thomas.ning@amd.com>
* Multiple d, initial commit
* Check Ds Layout
* Readme and clang format
* Update branch & conflicts
* Multiple D - fix clang-formatter
* Rename elemetwise_op
* Fix CI
* Code review part1
* Remove printf
* Remove unnecessary comment
* Add new tests with Col layout
* Review part 2
* Added support for Multiple D GEMM
* Update comment
* Remove maybe_unused
* Clang-format
* Review part 3
* Add comment to function
* Add comment to function: another
* Take number of params for a refrence function
* Remove additional d param for 0 tensor
* Change name of function
* Fix CI fails
