mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-03-29 19:47:39 +00:00
aafc3ac27a
* Add elementwise operation kernel and example * Add comment * Add template argument of dim . Prepare to support multiple dimension * Rename example * Support 1 dimension * Add static assert * Add comment * Extract pad * Remove redundant argument * Support any dimension for elementwise operation * Remove line * Let it be the multiple number of CU * Move thread per block to the parameter of constructor * rename threadPerBlock with blockSize * Support double * rename kernel function name * remove redundant include header * Refine type * Need to the final dimension * Refine variable name * Refine type * Use index_t instead of int in API Co-authored-by: rocking <chunylai@amd.com>
111 lines
3.9 KiB
C++
111 lines
3.9 KiB
C++
#include <iostream>
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#include <cstdlib>
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#include "check_err.hpp"
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#include "config.hpp"
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#include "device.hpp"
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#include "host_tensor.hpp"
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#include "host_tensor_generator.hpp"
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#include "device_tensor.hpp"
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#include "binary_element_wise_operation.hpp"
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#include "device_binary_elementwise.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 EltwiseComputeDataType = F32;
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using Add = ck::tensor_operation::binary_element_wise::Add;
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using DeviceElementwiseAddInstance = ck::tensor_operation::device::
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DeviceBinaryElementwise<ABDataType, ABDataType, CDataType, EltwiseComputeDataType, Add, 1, 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 ComputeDataType,
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typename Functor>
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void host_elementwise1D(
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HostTensorC& C, const HostTensorA& A, const HostTensorB& B, int M, Functor functor)
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{
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using ctype = ck::remove_reference_t<decltype(C(0))>;
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for(int m = 0; m < M; ++m)
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{
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ComputeDataType Am = static_cast<ComputeDataType>(A(m));
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ComputeDataType Bm = static_cast<ComputeDataType>(B(m));
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ComputeDataType Cm = 0;
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functor(Cm, Am, Bm);
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C(m) = static_cast<ctype>(Cm);
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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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auto f_host_tensor_descriptor1d = [](std::size_t len, std::size_t stride) {
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return HostTensorDescriptor(std::vector<std::size_t>({len}),
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std::vector<std::size_t>({stride}));
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};
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Tensor<ABDataType> a_m(f_host_tensor_descriptor1d(M, 1));
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Tensor<ABDataType> b_m(f_host_tensor_descriptor1d(M, 1));
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Tensor<ABDataType> c_m(f_host_tensor_descriptor1d(M, 1));
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a_m.GenerateTensorValue(GeneratorTensor_3<ABDataType>{0.0, 1.0});
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b_m.GenerateTensorValue(GeneratorTensor_3<ABDataType>{0.0, 1.0});
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DeviceMem a_m_device_buf(sizeof(ABDataType) * a_m.mDesc.GetElementSpace());
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DeviceMem b_m_device_buf(sizeof(ABDataType) * b_m.mDesc.GetElementSpace());
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DeviceMem c_m_device_buf(sizeof(CDataType) * c_m.mDesc.GetElementSpace());
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a_m_device_buf.ToDevice(a_m.mData.data());
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b_m_device_buf.ToDevice(b_m.mData.data());
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auto broadcastAdd = DeviceElementwiseAddInstance{};
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auto argument = broadcastAdd.MakeArgumentPointer(a_m_device_buf.GetDeviceBuffer(),
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b_m_device_buf.GetDeviceBuffer(),
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c_m_device_buf.GetDeviceBuffer(),
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{M},
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{1},
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{1},
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{1},
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Add{});
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if(!broadcastAdd.IsSupportedArgument(argument.get()))
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{
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throw std::runtime_error("The runtime parameters seems not supported by the "
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"DeviceBinaryElementwise_2D 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_device_buf.FromDevice(c_m.mData.data());
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Tensor<CDataType> host_c_m(f_host_tensor_descriptor1d(M, 1));
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host_elementwise1D<Tensor<ABDataType>,
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Tensor<ABDataType>,
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Tensor<CDataType>,
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EltwiseComputeDataType,
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Add>(host_c_m, a_m, b_m, M, Add{});
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pass &= ck::utils::check_err(
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c_m.mData, host_c_m.mData, "Error: Incorrect results d1", 1e-3, 1e-3);
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}
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return pass ? 0 : 1;
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}
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