mirror of
https://github.com/ROCm/composable_kernel.git
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Reorganize project folders (#6)
This commit is contained in:
Joseph Macaranas
4
example/19_binary_elementwise/CMakeLists.txt
Normal file
4
example/19_binary_elementwise/CMakeLists.txt
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@@ -0,0 +1,4 @@
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add_example_executable(example_broadcast_add_2d_amn_bn broadcast_add_2d_amn_bn.cpp)
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add_example_executable(example_broadcast_add_3d_am_bmnk broadcast_add_3d_am_bmnk.cpp)
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add_example_executable(example_elementwise_add_1d elementwise_add_1d.cpp)
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add_example_executable(example_elementwise_add_4d elementwise_add_4d.cpp)
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141
example/19_binary_elementwise/broadcast_add_2d_amn_bn.cpp
Normal file
141
example/19_binary_elementwise/broadcast_add_2d_amn_bn.cpp
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@@ -0,0 +1,141 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
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#include <iostream>
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#include <cstdlib>
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#include "ck/ck.hpp"
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#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
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#include "ck/tensor_operation/gpu/device/impl/device_elementwise_dynamic_vector_dims_impl.hpp"
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#include "ck/library/utility/check_err.hpp"
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#include "ck/library/utility/device_memory.hpp"
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#include "ck/library/utility/host_tensor.hpp"
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#include "ck/library/utility/host_tensor_generator.hpp"
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#include "ck/library/utility/literals.hpp"
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using F16 = ck::half_t;
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using F32 = float;
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using ABDataType = F16;
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using CDataType = F16;
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using Add = ck::tensor_operation::element_wise::Add;
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using DeviceElementwiseAddInstance =
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ck::tensor_operation::device::DeviceElementwiseImpl<ck::Tuple<ABDataType, ABDataType>,
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ck::Tuple<CDataType>,
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Add,
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2,
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64,
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64,
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64,
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8,
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8,
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ck::Sequence<1, 0>,
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ck::Sequence<8, 8>,
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ck::Sequence<8>>;
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template <typename HostTensorA,
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typename HostTensorB,
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typename HostTensorC,
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typename Functor,
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int broadcastDim>
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void host_broadcast2D(
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HostTensorC& C, const HostTensorA& A, const HostTensorB& B, int M, int N, Functor functor)
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{
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using ctype = ck::remove_reference_t<decltype(C(0, 0))>;
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for(int m = 0; m < M; ++m)
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{
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for(int n = 0; n < N; ++n)
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{
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auto Amn = A(m, n);
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ctype Cmn = 0;
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if constexpr(broadcastDim == 0)
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{
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auto Bn = B(n);
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functor(Cmn, Amn, Bn);
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}
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else
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{
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auto Bm = B(m);
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functor(Cmn, Amn, Bm);
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}
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C(m, n) = Cmn;
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}
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}
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}
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int main()
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{
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bool do_verification = true;
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bool time_kernel = false;
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ck::index_t M = 1024;
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ck::index_t N = 1024;
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ck::index_t Stride = 1024;
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auto f_host_tensor_descriptor1d = [](std::size_t len, std::size_t stride) {
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return HostTensorDescriptor({len}, {stride});
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};
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auto f_host_tensor_descriptor2d = [](std::size_t row, std::size_t col, std::size_t stride) {
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using namespace ck::literals;
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return HostTensorDescriptor({row, col}, {stride, 1_uz});
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};
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Tensor<ABDataType> a_m_n(f_host_tensor_descriptor2d(M, N, Stride));
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Tensor<ABDataType> b_n(f_host_tensor_descriptor1d(N, 1));
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Tensor<CDataType> c_m_n(f_host_tensor_descriptor2d(M, N, Stride));
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a_m_n.GenerateTensorValue(GeneratorTensor_3<ABDataType>{0.0, 1.0});
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b_n.GenerateTensorValue(GeneratorTensor_3<ABDataType>{0.0, 1.0});
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DeviceMem a_m_n_device_buf(sizeof(ABDataType) * a_m_n.mDesc.GetElementSpaceSize());
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DeviceMem b_n_device_buf(sizeof(ABDataType) * b_n.mDesc.GetElementSpaceSize());
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DeviceMem c_m_n_device_buf(sizeof(CDataType) * c_m_n.mDesc.GetElementSpaceSize());
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a_m_n_device_buf.ToDevice(a_m_n.mData.data());
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b_n_device_buf.ToDevice(b_n.mData.data());
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std::array<const void*, 2> input = {a_m_n_device_buf.GetDeviceBuffer(),
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b_n_device_buf.GetDeviceBuffer()};
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std::array<void*, 1> output = {c_m_n_device_buf.GetDeviceBuffer()};
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std::array<ck::index_t, 2> abc_lengths = {M, N};
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std::array<ck::index_t, 2> a_strides = {Stride, 1};
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std::array<ck::index_t, 2> b_strides = {0, 1};
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std::array<ck::index_t, 2> c_strides = {Stride, 1};
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auto broadcastAdd = DeviceElementwiseAddInstance{};
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auto argument = broadcastAdd.MakeArgumentPointer(
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abc_lengths, {a_strides, b_strides}, {c_strides}, input, output, Add{});
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if(!broadcastAdd.IsSupportedArgument(argument.get()))
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{
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throw std::runtime_error(
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"The runtime parameters seems not supported by the device instance, exiting!");
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};
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auto broadcastAdd_invoker_ptr = broadcastAdd.MakeInvokerPointer();
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float ave_time =
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broadcastAdd_invoker_ptr->Run(argument.get(), StreamConfig{nullptr, time_kernel});
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std::cout << "Perf: " << ave_time << " ms" << std::endl;
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bool pass = true;
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if(do_verification)
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{
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c_m_n_device_buf.FromDevice(c_m_n.mData.data());
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Tensor<CDataType> host_c_m_n(f_host_tensor_descriptor2d(M, N, Stride));
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host_broadcast2D<Tensor<ABDataType>, Tensor<ABDataType>, Tensor<CDataType>, Add, 0>(
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host_c_m_n, a_m_n, b_n, M, N, Add{});
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pass &= ck::utils::check_err(c_m_n, host_c_m_n, "Error: Incorrect results c", 1e-3, 1e-3);
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}
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return pass ? 0 : 1;
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}
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125
example/19_binary_elementwise/broadcast_add_3d_am_bmnk.cpp
Normal file
125
example/19_binary_elementwise/broadcast_add_3d_am_bmnk.cpp
Normal file
@@ -0,0 +1,125 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
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#include <iostream>
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#include <cstdlib>
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#include "ck/ck.hpp"
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#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
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#include "ck/tensor_operation/gpu/device/impl/device_elementwise_dynamic_vector_dims_impl.hpp"
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#include "ck/library/utility/algorithm.hpp"
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#include "ck/library/utility/check_err.hpp"
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#include "ck/library/utility/device_memory.hpp"
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#include "ck/library/utility/host_tensor.hpp"
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#include "ck/library/utility/host_tensor_generator.hpp"
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using F16 = ck::half_t;
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using F32 = float;
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using ABDataType = F16;
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using CDataType = F16;
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using Add = ck::tensor_operation::element_wise::Add;
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using DeviceElementwiseAddInstance =
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ck::tensor_operation::device::DeviceElementwiseImpl<ck::Tuple<ABDataType, ABDataType>,
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ck::Tuple<CDataType>,
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Add,
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3,
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64,
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16,
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16,
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2,
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2,
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ck::Sequence<1, 0>,
|
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ck::Sequence<1, 2>,
|
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ck::Sequence<2>>;
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template <typename HostTensorA, typename HostTensorB, typename HostTensorC, typename Functor>
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void host_broadcast3D_am_bmnk(HostTensorC& C,
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const HostTensorA& A,
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const HostTensorB& B,
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const std::vector<std::size_t>& shape,
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Functor functor)
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{
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using ctype = ck::remove_reference_t<decltype(C(0, 0))>;
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for(std::size_t m = 0; m < shape[0]; ++m)
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for(std::size_t n = 0; n < shape[1]; ++n)
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for(std::size_t k = 0; k < shape[2]; ++k)
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{
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auto a_val = A(m);
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auto b_val = B(m, n, k);
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ctype c_val = 0;
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functor(c_val, a_val, b_val);
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C(m, n, k) = c_val;
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}
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}
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int main()
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{
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bool do_verification = true;
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bool time_kernel = false;
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std::vector<std::size_t> mnk = {4, 16, 32};
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ck::index_t M = mnk[0];
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Tensor<ABDataType> a_m({M});
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Tensor<ABDataType> b_m_n_k(mnk);
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Tensor<CDataType> c_m_n_k(mnk);
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a_m.GenerateTensorValue(GeneratorTensor_3<ABDataType>{0.0, 1.0});
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b_m_n_k.GenerateTensorValue(GeneratorTensor_3<ABDataType>{0.0, 1.0});
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DeviceMem a_m_device_buf(sizeof(ABDataType) * a_m.mDesc.GetElementSpaceSize());
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DeviceMem b_m_n_k_device_buf(sizeof(ABDataType) * b_m_n_k.mDesc.GetElementSpaceSize());
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DeviceMem c_m_n_k_device_buf(sizeof(CDataType) * c_m_n_k.mDesc.GetElementSpaceSize());
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a_m_device_buf.ToDevice(a_m.mData.data());
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b_m_n_k_device_buf.ToDevice(b_m_n_k.mData.data());
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std::array<const void*, 2> input = {a_m_device_buf.GetDeviceBuffer(),
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b_m_n_k_device_buf.GetDeviceBuffer()};
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std::array<void*, 1> output = {c_m_n_k_device_buf.GetDeviceBuffer()};
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std::array<ck::index_t, 3> abc_lengths;
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std::array<ck::index_t, 3> a_strides = {1, 0, 0};
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std::array<ck::index_t, 3> b_strides;
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std::array<ck::index_t, 3> c_strides;
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ck::ranges::copy(mnk, abc_lengths.begin());
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ck::ranges::copy(b_m_n_k.mDesc.GetStrides(), b_strides.begin());
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ck::ranges::copy(c_m_n_k.mDesc.GetStrides(), c_strides.begin());
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auto broadcastAdd = DeviceElementwiseAddInstance{};
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auto argument = broadcastAdd.MakeArgumentPointer(
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abc_lengths, {a_strides, b_strides}, {c_strides}, input, output, Add{});
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|
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if(!broadcastAdd.IsSupportedArgument(argument.get()))
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{
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throw std::runtime_error(
|
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"The runtime parameters seems not supported by the device instance, exiting!");
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};
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auto broadcastAdd_invoker_ptr = broadcastAdd.MakeInvokerPointer();
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float ave_time =
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broadcastAdd_invoker_ptr->Run(argument.get(), StreamConfig{nullptr, time_kernel});
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std::cout << "Perf: " << ave_time << " ms" << std::endl;
|
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|
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bool pass = true;
|
||||
if(do_verification)
|
||||
{
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c_m_n_k_device_buf.FromDevice(c_m_n_k.mData.data());
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Tensor<CDataType> host_c_m_n_k(mnk);
|
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|
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host_broadcast3D_am_bmnk<Tensor<ABDataType>, Tensor<ABDataType>, Tensor<CDataType>, Add>(
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host_c_m_n_k, a_m, b_m_n_k, mnk, Add{});
|
||||
|
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pass &=
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ck::utils::check_err(c_m_n_k, host_c_m_n_k, "Error: Incorrect results c", 1e-3, 1e-3);
|
||||
}
|
||||
|
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return pass ? 0 : 1;
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}
|
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116
example/19_binary_elementwise/elementwise_add_1d.cpp
Normal file
116
example/19_binary_elementwise/elementwise_add_1d.cpp
Normal file
@@ -0,0 +1,116 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#include <iostream>
|
||||
#include <cstdlib>
|
||||
|
||||
#include "ck/ck.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/impl/device_elementwise_dynamic_vector_dims_impl.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
|
||||
#include "ck/library/utility/check_err.hpp"
|
||||
#include "ck/library/utility/device_memory.hpp"
|
||||
#include "ck/library/utility/host_tensor.hpp"
|
||||
#include "ck/library/utility/host_tensor_generator.hpp"
|
||||
|
||||
using F16 = ck::half_t;
|
||||
using F32 = float;
|
||||
|
||||
using ABDataType = F16;
|
||||
using CDataType = F16;
|
||||
|
||||
using Add = ck::tensor_operation::element_wise::Add;
|
||||
|
||||
using DeviceElementwiseAddInstance =
|
||||
ck::tensor_operation::device::DeviceElementwiseImpl<ck::Tuple<ABDataType, ABDataType>,
|
||||
ck::Tuple<CDataType>,
|
||||
Add,
|
||||
1,
|
||||
64,
|
||||
16,
|
||||
16,
|
||||
2,
|
||||
2,
|
||||
ck::Sequence<1, 0>,
|
||||
ck::Sequence<2, 2>,
|
||||
ck::Sequence<2>>;
|
||||
|
||||
template <typename HostTensorA, typename HostTensorB, typename HostTensorC, typename Functor>
|
||||
void host_elementwise1D(
|
||||
HostTensorC& C, const HostTensorA& A, const HostTensorB& B, int M, Functor functor)
|
||||
{
|
||||
using ctype = ck::remove_reference_t<decltype(C(0))>;
|
||||
|
||||
for(int m = 0; m < M; ++m)
|
||||
{
|
||||
auto Am = A(m);
|
||||
auto Bm = B(m);
|
||||
ctype Cm = 0;
|
||||
functor(Cm, Am, Bm);
|
||||
C(m) = Cm;
|
||||
}
|
||||
}
|
||||
|
||||
int main()
|
||||
{
|
||||
bool do_verification = true;
|
||||
bool time_kernel = false;
|
||||
|
||||
ck::index_t M = 1024;
|
||||
|
||||
auto f_host_tensor_descriptor1d = [](std::size_t len, std::size_t stride) {
|
||||
return HostTensorDescriptor({len}, {stride});
|
||||
};
|
||||
|
||||
Tensor<ABDataType> a_m(f_host_tensor_descriptor1d(M, 1));
|
||||
Tensor<ABDataType> b_m(f_host_tensor_descriptor1d(M, 1));
|
||||
Tensor<CDataType> c_m(f_host_tensor_descriptor1d(M, 1));
|
||||
|
||||
a_m.GenerateTensorValue(GeneratorTensor_3<ABDataType>{0.0, 1.0});
|
||||
b_m.GenerateTensorValue(GeneratorTensor_3<ABDataType>{0.0, 1.0});
|
||||
|
||||
DeviceMem a_m_device_buf(sizeof(ABDataType) * a_m.mDesc.GetElementSpaceSize());
|
||||
DeviceMem b_m_device_buf(sizeof(ABDataType) * b_m.mDesc.GetElementSpaceSize());
|
||||
DeviceMem c_m_device_buf(sizeof(CDataType) * c_m.mDesc.GetElementSpaceSize());
|
||||
|
||||
a_m_device_buf.ToDevice(a_m.mData.data());
|
||||
b_m_device_buf.ToDevice(b_m.mData.data());
|
||||
|
||||
std::array<const void*, 2> input = {a_m_device_buf.GetDeviceBuffer(),
|
||||
b_m_device_buf.GetDeviceBuffer()};
|
||||
std::array<void*, 1> output = {c_m_device_buf.GetDeviceBuffer()};
|
||||
|
||||
std::array<ck::index_t, 1> abc_lengths = {M};
|
||||
std::array<ck::index_t, 1> a_strides = {1};
|
||||
std::array<ck::index_t, 1> b_strides = {1};
|
||||
std::array<ck::index_t, 1> c_strides = {1};
|
||||
|
||||
auto broadcastAdd = DeviceElementwiseAddInstance{};
|
||||
auto argument = broadcastAdd.MakeArgumentPointer(
|
||||
abc_lengths, {a_strides, b_strides}, {c_strides}, input, output, Add{});
|
||||
|
||||
if(!broadcastAdd.IsSupportedArgument(argument.get()))
|
||||
{
|
||||
throw std::runtime_error(
|
||||
"The runtime parameters seems not supported by the device instance, exiting!");
|
||||
};
|
||||
|
||||
auto broadcastAdd_invoker_ptr = broadcastAdd.MakeInvokerPointer();
|
||||
float ave_time =
|
||||
broadcastAdd_invoker_ptr->Run(argument.get(), StreamConfig{nullptr, time_kernel});
|
||||
|
||||
std::cout << "Perf: " << ave_time << " ms" << std::endl;
|
||||
|
||||
bool pass = true;
|
||||
if(do_verification)
|
||||
{
|
||||
c_m_device_buf.FromDevice(c_m.mData.data());
|
||||
Tensor<CDataType> host_c_m(f_host_tensor_descriptor1d(M, 1));
|
||||
|
||||
host_elementwise1D<Tensor<ABDataType>, Tensor<ABDataType>, Tensor<CDataType>, Add>(
|
||||
host_c_m, a_m, b_m, M, Add{});
|
||||
|
||||
pass &= ck::utils::check_err(c_m, host_c_m, "Error: Incorrect results c", 1e-3, 1e-3);
|
||||
}
|
||||
|
||||
return pass ? 0 : 1;
|
||||
}
|
||||
125
example/19_binary_elementwise/elementwise_add_4d.cpp
Normal file
125
example/19_binary_elementwise/elementwise_add_4d.cpp
Normal file
@@ -0,0 +1,125 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#include <iostream>
|
||||
#include <cstdlib>
|
||||
|
||||
#include "ck/ck.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/impl/device_elementwise_dynamic_vector_dims_impl.hpp"
|
||||
|
||||
#include "ck/library/utility/algorithm.hpp"
|
||||
#include "ck/library/utility/check_err.hpp"
|
||||
#include "ck/library/utility/device_memory.hpp"
|
||||
#include "ck/library/utility/host_tensor.hpp"
|
||||
#include "ck/library/utility/host_tensor_generator.hpp"
|
||||
|
||||
using F16 = ck::half_t;
|
||||
using F32 = float;
|
||||
|
||||
using ABDataType = F16;
|
||||
using CDataType = F16;
|
||||
|
||||
using Add = ck::tensor_operation::element_wise::Add;
|
||||
|
||||
using DeviceElementwiseAddInstance =
|
||||
ck::tensor_operation::device::DeviceElementwiseImpl<ck::Tuple<ABDataType, ABDataType>,
|
||||
ck::Tuple<CDataType>,
|
||||
Add,
|
||||
4,
|
||||
64,
|
||||
2,
|
||||
128,
|
||||
2,
|
||||
2,
|
||||
ck::Sequence<1, 0>,
|
||||
ck::Sequence<2, 2>,
|
||||
ck::Sequence<2>>;
|
||||
|
||||
template <typename HostTensorA, typename HostTensorB, typename HostTensorC, typename Functor>
|
||||
void host_elementwise4D(HostTensorC& C,
|
||||
const HostTensorA& A,
|
||||
const HostTensorB& B,
|
||||
const std::vector<std::size_t>& shape,
|
||||
Functor functor)
|
||||
{
|
||||
using ctype = ck::remove_reference_t<decltype(C(0, 0, 0, 0))>;
|
||||
|
||||
for(std::size_t n = 0; n < shape[0]; ++n)
|
||||
for(std::size_t c = 0; c < shape[1]; ++c)
|
||||
for(std::size_t h = 0; h < shape[2]; ++h)
|
||||
for(std::size_t w = 0; w < shape[3]; ++w)
|
||||
{
|
||||
auto a_val = A(n, c, h, w);
|
||||
auto b_val = B(n, c, h, w);
|
||||
ctype c_val = 0;
|
||||
functor(c_val, a_val, b_val);
|
||||
C(n, c, h, w) = c_val;
|
||||
}
|
||||
}
|
||||
|
||||
int main()
|
||||
{
|
||||
bool do_verification = true;
|
||||
bool time_kernel = false;
|
||||
|
||||
std::vector<std::size_t> nchw = {4, 16, 32, 32};
|
||||
|
||||
Tensor<ABDataType> a(nchw);
|
||||
Tensor<ABDataType> b(nchw);
|
||||
Tensor<CDataType> c(nchw);
|
||||
|
||||
a.GenerateTensorValue(GeneratorTensor_3<ABDataType>{0.0, 1.0});
|
||||
b.GenerateTensorValue(GeneratorTensor_3<ABDataType>{0.0, 1.0});
|
||||
|
||||
DeviceMem a_device_buf(sizeof(ABDataType) * a.mDesc.GetElementSpaceSize());
|
||||
DeviceMem b_device_buf(sizeof(ABDataType) * b.mDesc.GetElementSpaceSize());
|
||||
DeviceMem c_device_buf(sizeof(CDataType) * c.mDesc.GetElementSpaceSize());
|
||||
|
||||
a_device_buf.ToDevice(a.mData.data());
|
||||
b_device_buf.ToDevice(b.mData.data());
|
||||
|
||||
std::array<const void*, 2> input = {a_device_buf.GetDeviceBuffer(),
|
||||
b_device_buf.GetDeviceBuffer()};
|
||||
std::array<void*, 1> output = {c_device_buf.GetDeviceBuffer()};
|
||||
|
||||
std::array<ck::index_t, 4> abc_lengths;
|
||||
std::array<ck::index_t, 4> a_strides;
|
||||
std::array<ck::index_t, 4> b_strides;
|
||||
std::array<ck::index_t, 4> c_strides;
|
||||
|
||||
ck::ranges::copy(nchw, abc_lengths.begin());
|
||||
ck::ranges::copy(a.mDesc.GetStrides(), a_strides.begin());
|
||||
ck::ranges::copy(b.mDesc.GetStrides(), b_strides.begin());
|
||||
ck::ranges::copy(c.mDesc.GetStrides(), c_strides.begin());
|
||||
|
||||
auto broadcastAdd = DeviceElementwiseAddInstance{};
|
||||
auto argument = broadcastAdd.MakeArgumentPointer(
|
||||
abc_lengths, {a_strides, b_strides}, {c_strides}, input, output, Add{});
|
||||
|
||||
if(!broadcastAdd.IsSupportedArgument(argument.get()))
|
||||
{
|
||||
throw std::runtime_error(
|
||||
"The runtime parameters seems not supported by the device instance, exiting!");
|
||||
};
|
||||
|
||||
auto broadcastAdd_invoker_ptr = broadcastAdd.MakeInvokerPointer();
|
||||
float ave_time =
|
||||
broadcastAdd_invoker_ptr->Run(argument.get(), StreamConfig{nullptr, time_kernel});
|
||||
|
||||
std::cout << "Perf: " << ave_time << " ms" << std::endl;
|
||||
|
||||
bool pass = true;
|
||||
if(do_verification)
|
||||
{
|
||||
c_device_buf.FromDevice(c.mData.data());
|
||||
Tensor<CDataType> host_c(nchw);
|
||||
|
||||
host_elementwise4D<Tensor<ABDataType>, Tensor<ABDataType>, Tensor<CDataType>, Add>(
|
||||
host_c, a, b, nchw, Add{});
|
||||
|
||||
pass &= ck::utils::check_err(c, host_c, "Error: Incorrect results c", 1e-3, 1e-3);
|
||||
}
|
||||
|
||||
return pass ? 0 : 1;
|
||||
}
|
||||
Reference in New Issue
Block a user