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deleting hip specific files that are no longer required

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This commit is contained in:
Deven Desai 2018-07-11 09:28:44 -04:00
parent dec47a6493
commit 1fe0b74904
2 changed files with 0 additions and 275 deletions
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172
test/hip_basic.cu
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2015-2016 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
// workaround issue between gcc >= 4.7 and cuda 5.5
#if (defined __GNUC__) && (__GNUC__>4 || __GNUC_MINOR__>=7)
#undef _GLIBCXX_ATOMIC_BUILTINS
#undef _GLIBCXX_USE_INT128
#endif
#define EIGEN_TEST_NO_LONGDOUBLE
#define EIGEN_TEST_NO_COMPLEX
#define EIGEN_TEST_FUNC hip_basic
#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
#include <hip/hip_runtime.h>
#include "main.h"
#include "hip_common.h"
// Check that dense modules can be properly parsed by hipcc
#include <Eigen/Dense>
// struct Foo{
// EIGEN_DEVICE_FUNC
// void operator()(int i, const float* mats, float* vecs) const {
// using namespace Eigen;
// // Matrix3f M(data);
// // Vector3f x(data+9);
// // Map<Vector3f>(data+9) = M.inverse() * x;
// Matrix3f M(mats+i/16);
// Vector3f x(vecs+i*3);
// // using std::min;
// // using std::sqrt;
// Map<Vector3f>(vecs+i*3) << x.minCoeff(), 1, 2;// / x.dot(x);//(M.inverse() * x) / x.x();
// //x = x*2 + x.y() * x + x * x.maxCoeff() - x / x.sum();
// }
// };
template<typename T>
struct coeff_wise {
EIGEN_DEVICE_FUNC
void operator()(int i, const typename T::Scalar* in, typename T::Scalar* out) const
{
using namespace Eigen;
T x1(in+i);
T x2(in+i+1);
T x3(in+i+2);
Map<T> res(out+i*T::MaxSizeAtCompileTime);
res.array() += (in[0] * x1 + x2).array() * x3.array();
}
};
template<typename T>
struct replicate {
EIGEN_DEVICE_FUNC
void operator()(int i, const typename T::Scalar* in, typename T::Scalar* out) const
{
using namespace Eigen;
T x1(in+i);
int step = x1.size() * 4;
int stride = 3 * step;
typedef Map<Array<typename T::Scalar,Dynamic,Dynamic> > MapType;
MapType(out+i*stride+0*step, x1.rows()*2, x1.cols()*2) = x1.replicate(2,2);
MapType(out+i*stride+1*step, x1.rows()*3, x1.cols()) = in[i] * x1.colwise().replicate(3);
MapType(out+i*stride+2*step, x1.rows(), x1.cols()*3) = in[i] * x1.rowwise().replicate(3);
}
};
template<typename T>
struct redux {
EIGEN_DEVICE_FUNC
void operator()(int i, const typename T::Scalar* in, typename T::Scalar* out) const
{
using namespace Eigen;
int N = 10;
T x1(in+i);
out[i*N+0] = x1.minCoeff();
out[i*N+1] = x1.maxCoeff();
out[i*N+2] = x1.sum();
out[i*N+3] = x1.prod();
out[i*N+4] = x1.matrix().squaredNorm();
out[i*N+5] = x1.matrix().norm();
out[i*N+6] = x1.colwise().sum().maxCoeff();
out[i*N+7] = x1.rowwise().maxCoeff().sum();
out[i*N+8] = x1.matrix().colwise().squaredNorm().sum();
}
};
template<typename T1, typename T2>
struct prod_test {
EIGEN_DEVICE_FUNC
void operator()(int i, const typename T1::Scalar* in, typename T1::Scalar* out) const
{
using namespace Eigen;
typedef Matrix<typename T1::Scalar, T1::RowsAtCompileTime, T2::ColsAtCompileTime> T3;
T1 x1(in+i);
T2 x2(in+i+1);
Map<T3> res(out+i*T3::MaxSizeAtCompileTime);
res += in[i] * x1 * x2;
}
};
template<typename T1, typename T2>
struct diagonal {
EIGEN_DEVICE_FUNC
void operator()(int i, const typename T1::Scalar* in, typename T1::Scalar* out) const
{
using namespace Eigen;
T1 x1(in+i);
Map<T2> res(out+i*T2::MaxSizeAtCompileTime);
res += x1.diagonal();
}
};
template<typename T>
struct eigenvalues {
EIGEN_DEVICE_FUNC
void operator()(int i, const typename T::Scalar* in, typename T::Scalar* out) const
{
using namespace Eigen;
typedef Matrix<typename T::Scalar, T::RowsAtCompileTime, 1> Vec;
T M(in+i);
Map<Vec> res(out+i*Vec::MaxSizeAtCompileTime);
T A = M*M.adjoint();
SelfAdjointEigenSolver<T> eig;
eig.computeDirect(M);
res = eig.eigenvalues();
}
};
void test_hip_basic()
{
ei_test_init_hip();
int nthreads = 100;
Eigen::VectorXf in, out;
#ifndef __HIP_DEVICE_COMPILE__
int data_size = nthreads * 512;
in.setRandom(data_size);
out.setRandom(data_size);
#endif
CALL_SUBTEST( run_and_compare_to_hip(coeff_wise<Vector3f>(), nthreads, in, out) );
CALL_SUBTEST( run_and_compare_to_hip(coeff_wise<Array44f>(), nthreads, in, out) );
// FIXME compile fails when we uncomment the followig two tests
// CALL_SUBTEST( run_and_compare_to_hip(replicate<Array4f>(), nthreads, in, out) );
// CALL_SUBTEST( run_and_compare_to_hip(replicate<Array33f>(), nthreads, in, out) );
CALL_SUBTEST( run_and_compare_to_hip(redux<Array4f>(), nthreads, in, out) );
CALL_SUBTEST( run_and_compare_to_hip(redux<Matrix3f>(), nthreads, in, out) );
CALL_SUBTEST( run_and_compare_to_hip(prod_test<Matrix3f,Matrix3f>(), nthreads, in, out) );
CALL_SUBTEST( run_and_compare_to_hip(prod_test<Matrix4f,Vector4f>(), nthreads, in, out) );
CALL_SUBTEST( run_and_compare_to_hip(diagonal<Matrix3f,Vector3f>(), nthreads, in, out) );
CALL_SUBTEST( run_and_compare_to_hip(diagonal<Matrix4f,Vector4f>(), nthreads, in, out) );
// FIXME : Runtime failure occurs when we uncomment the following two tests
// CALL_SUBTEST( run_and_compare_to_hip(eigenvalues<Matrix3f>(), nthreads, in, out) );
// CALL_SUBTEST( run_and_compare_to_hip(eigenvalues<Matrix2f>(), nthreads, in, out) );
}

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test/hip_common.h
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#ifndef EIGEN_TEST_HIP_COMMON_H
#define EIGEN_TEST_HIP_COMMON_H
#include "hip/hip_runtime.h"
#include "hip/hip_runtime_api.h"
#include <iostream>
#ifndef __HIPCC__
dim3 threadIdx, blockDim, blockIdx;
#endif
template<typename Kernel, typename Input, typename Output>
void run_on_cpu(const Kernel& ker, int n, const Input& in, Output& out)
{
for(int i=0; i<n; i++)
ker(i, in.data(), out.data());
}
template<typename Kernel, typename Input, typename Output>
__global__ __attribute__((used))
void run_on_hip_meta_kernel(const Kernel ker, int n, const Input* in, Output* out)
{
int i = hipThreadIdx_x + hipBlockIdx_x*hipBlockDim_x;
if(i<n) {
ker(i, in, out);
}
}
template<typename Kernel, typename Input, typename Output>
void run_on_hip(const Kernel& ker, int n, const Input& in, Output& out)
{
typename Input::Scalar* d_in;
typename Output::Scalar* d_out;
std::ptrdiff_t in_bytes = in.size() * sizeof(typename Input::Scalar);
std::ptrdiff_t out_bytes = out.size() * sizeof(typename Output::Scalar);
hipMalloc((void**)(&d_in), in_bytes);
hipMalloc((void**)(&d_out), out_bytes);
hipMemcpy(d_in, in.data(), in_bytes, hipMemcpyHostToDevice);
hipMemcpy(d_out, out.data(), out_bytes, hipMemcpyHostToDevice);
// Simple and non-optimal 1D mapping assuming n is not too large
// That's only for unit testing!
dim3 Blocks(128);
dim3 Grids( (n+int(Blocks.x)-1)/int(Blocks.x) );
hipDeviceSynchronize();
hipLaunchKernelGGL(HIP_KERNEL_NAME(run_on_hip_meta_kernel<Kernel,
typename std::decay<decltype(*d_in)>::type,
typename std::decay<decltype(*d_out)>::type>),
dim3(Grids), dim3(Blocks), 0, 0, ker, n, d_in, d_out);
hipDeviceSynchronize();
// check inputs have not been modified
hipMemcpy(const_cast<typename Input::Scalar*>(in.data()), d_in, in_bytes, hipMemcpyDeviceToHost);
hipMemcpy(out.data(), d_out, out_bytes, hipMemcpyDeviceToHost);
hipFree(d_in);
hipFree(d_out);
}
template<typename Kernel, typename Input, typename Output>
void run_and_compare_to_hip(const Kernel& ker, int n, const Input& in, Output& out)
{
Input in_ref, in_hip;
Output out_ref, out_hip;
#ifndef __HIP_DEVICE_COMPILE__
in_ref = in_hip = in;
out_ref = out_hip = out;
#endif
run_on_cpu (ker, n, in_ref, out_ref);
run_on_hip(ker, n, in_hip, out_hip);
#ifndef __HIP_DEVICE_COMPILE__
VERIFY_IS_APPROX(in_ref, in_hip);
VERIFY_IS_APPROX(out_ref, out_hip);
#endif
}
void ei_test_init_hip()
{
int device = 0;
hipDeviceProp_t deviceProp;
hipGetDeviceProperties(&deviceProp, device);
std::cout << "HIP device info:\n";
std::cout << " name: " << deviceProp.name << "\n";
std::cout << " capability: " << deviceProp.major << "." << deviceProp.minor << "\n";
std::cout << " multiProcessorCount: " << deviceProp.multiProcessorCount << "\n";
std::cout << " maxThreadsPerMultiProcessor: " << deviceProp.maxThreadsPerMultiProcessor << "\n";
std::cout << " warpSize: " << deviceProp.warpSize << "\n";
std::cout << " regsPerBlock: " << deviceProp.regsPerBlock << "\n";
std::cout << " concurrentKernels: " << deviceProp.concurrentKernels << "\n";
std::cout << " clockRate: " << deviceProp.clockRate << "\n";
std::cout << " canMapHostMemory: " << deviceProp.canMapHostMemory << "\n";
std::cout << " computeMode: " << deviceProp.computeMode << "\n";
}
#endif // EIGEN_TEST_HIP_COMMON_H