mirror of
https://github.com/ROCm/composable_kernel.git
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a1b2441f8d
* File renaming and class renaming for device element-wise operation * Add batchnorm-infer instances, external API and client example * Add batchnorm-infer profiler module and gtests * Remove file device_elementwise_extension.hpp and move NormalizeInInfer operation to element_wise_operation.hpp * Remove the using of class aliasing for DeviceElementwiseForBatchNormInfer * Rename class and file due to conflict from device_elementwise_2d.hpp * Fix namespace in batcnnorm_infer_nhwc client example
112 lines
4.0 KiB
C++
112 lines
4.0 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2022, 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/device/impl/device_elementwise_impl.hpp"
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#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.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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1,
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8,
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ck::Sequence<8, 8>,
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ck::Sequence<8>>;
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template <typename HostTensorA, typename HostTensorB, typename HostTensorC, 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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auto Am = A(m);
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auto Bm = B(m);
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ctype Cm = 0;
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functor(Cm, Am, Bm);
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C(m) = 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({len}, {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<CDataType> 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.GetElementSpaceSize());
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DeviceMem b_m_device_buf(sizeof(ABDataType) * b_m.mDesc.GetElementSpaceSize());
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DeviceMem c_m_device_buf(sizeof(CDataType) * c_m.mDesc.GetElementSpaceSize());
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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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std::array<const void*, 2> input = {a_m_device_buf.GetDeviceBuffer(),
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b_m_device_buf.GetDeviceBuffer()};
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std::array<void*, 1> output = {c_m_device_buf.GetDeviceBuffer()};
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std::array<ck::index_t, 1> abc_lengths = {M};
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std::array<ck::index_t, 1> a_strides = {1};
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std::array<ck::index_t, 1> b_strides = {1};
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std::array<ck::index_t, 1> c_strides = {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_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>, Tensor<ABDataType>, Tensor<CDataType>, Add>(
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host_c_m, a_m, b_m, M, Add{});
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pass &= ck::utils::check_err(c_m, host_c_m, "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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