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https://github.com/ROCm/composable_kernel.git
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094e5bad50
* 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>
[ROCm/composable_kernel commit: 606b0cc947]
48 lines
1.8 KiB
C++
48 lines
1.8 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include "ck_tile/core.hpp"
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#include "ck_tile/host/host_tensor.hpp"
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#include <thread>
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namespace ck_tile {
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template <typename ADataType, typename BDataType, typename ComputeDataType, typename ElementOp>
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CK_TILE_HOST void reference_unary_elementwise(const HostTensor<ADataType>& a,
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HostTensor<BDataType>& b,
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ElementOp element_op)
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{
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// TODO: imeplement gpu version reference function
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auto f = [&](auto i) {
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auto v_a = type_convert<ComputeDataType>(a.mData[i]);
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auto v_b = element_op(v_a);
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b.mData[i] = ck_tile::type_convert<BDataType>(v_b);
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};
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make_ParallelTensorFunctor(f, b.get_element_space_size())(std::thread::hardware_concurrency());
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}
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template <typename ADataType,
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typename BDataType,
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typename CDataType,
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typename ComputeDataType,
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typename ElementOp>
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CK_TILE_HOST void reference_binary_elementwise(const HostTensor<ADataType>& a,
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const HostTensor<BDataType>& b,
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HostTensor<CDataType>& c,
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ElementOp element_op)
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{
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// TODO: imeplement gpu version reference function
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auto f = [&](auto i) {
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auto v_a = type_convert<ComputeDataType>(a.mData[i]);
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auto v_b = type_convert<ComputeDataType>(b.mData[i]);
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auto v_c = element_op(v_a, v_b);
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c.mData[i] = ck_tile::type_convert<CDataType>(v_c);
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};
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make_ParallelTensorFunctor(f, c.get_element_space_size())(std::thread::hardware_concurrency());
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}
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} // namespace ck_tile
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