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add simple test, and mv permutation test, update README

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fanasina
2023-10-17 16:45:57 +02:00
parent 130c356d95
commit c91910a278
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permutation_test/isgood.cu
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#include <gtest/gtest.h>
#include <stdlib.h>
//#include "/home/fanasina/progr_/ptens0neD/src/tensor/tens0neD/tens0neD.h"
#include "src/tensor/tens0neD/tens0neD.h"
//#include "cudatensor.h"
//#include "/home/fanasina/progr_/ptens0neD/src/tensor/tensCuda/tensCuda.h"
#include "src/tensor/tensCuda/tensCuda.h"
/*TEST(LineraCoodTransform, check_print) {
int t3[] = { [0] = 2,[1] = 4,[2] = 3 };
struct dimension D0(3, t3);
int coor0[3] = { 1,3,2 };
int* coor1 = new int[3];
int l0 = D0.CoordToLinear(coor0);
D0.print();
D0.LinearToCoord(coor1, l0);
for (int i = 0; i < D0.rank; i++) {
EXPECT_EQ(coor0[i], coor1[i]) << " coor0: " << coor0[i] << " coor1: " << coor1[i] << " i: " << i;
}
}*/
TEST(subArray, concatArray) {
int t[] = { 1,5,6,2,3 };
int t0[] = { 1,5,6 };
int t1[] = { 2,3 };
int n = 5;
int s0[3];
int s1[2];
int s[n];
subArray(s0, t, 0, 3, 0);
subArray(s1, t, 0, 2, 3);
ASSERT_EQ(0, memcmp(t0, s0, sizeof(int) * 3));
ASSERT_EQ(0, memcmp(t1, s1, sizeof(int) * 2));
concatArray(s, s0, s1, 0, 0, 3, 0, 2);
ASSERT_EQ(0, memcmp(t, s, sizeof(int) * 5));
}
TEST(tensorProdpetit, floatTemp) {
/*int t3[] = { 2, 4, 3 };
int t4[] = { 2, 4, 3, 2 };*/
int t3[] = { 3, 6, 5 };
int t4[] = { 3, 5, 8, 4 };
struct dimension d3(3, t3), d4(4, t4), d;
struct Tensor<float> M3(d3), M4(d4), M;
M3.initVal(3.0f); // M3.print();
M4.initVal(2.0f); // M4.print();
tensorProd<float>(M, M3, M4);
//tensorProd(M, M4, M3);
int coord[M.Dim.rank];
int coord3[M3.Dim.rank];
int coord4[M4.Dim.rank];
int idx3[M3.Dim.rank];
int idx4[M4.Dim.rank];
int lin3, lin4, lin;
d = M.Dim;
for (idx3[0] = 0; idx3[0] < d3.dim[0];idx3[0]++)
for (idx3[1] = 0; idx3[1] < d3.dim[1];idx3[1]++)
for (idx3[2] = 0; idx3[2] < d3.dim[2]; idx3[2]++)
for (idx4[0] = 0; idx4[0] < d4.dim[0];idx4[0]++)
for (idx4[1] = 0; idx4[1] < d4.dim[1];idx4[1]++)
for (idx4[2] = 0; idx4[2] < d4.dim[2];idx4[2]++)
for (idx4[3] = 0; idx4[3] < d4.dim[3];idx4[3]++) {
for (int i = 0; i < d3.rank; i++) coord3[i] = idx3[i];
for (int i = 0; i < d4.rank; i++) coord4[i] = idx4[i];
concatArray(coord, coord3, coord4, 0, 0, d3.rank, 0, d4.rank);
lin3 = d3.CoordToLinear(coord3);
lin4 = d4.CoordToLinear(coord4);
lin = d.CoordToLinear(coord);
//ASSERT_FLOAT_EQ(M.elements[lin], M3.elements[lin3] * M4.elements[lin4]) << " lin: " << lin << " lin3: " << lin3 << " lin4: " << lin4;
ASSERT_FLOAT_EQ(M.elements[lin], M3.elements[lin3] * M4.elements[lin4]) << " lin: " << lin << " lin3: " << lin3 << " lin4: " << lin4;
//ASSERT_NEAR(M.elements[lin], M3.elements[lin3] * M4.elements[lin4], 0.0001) << " lin: " << lin << " lin3: " << lin3 << " lin4: " << lin4;
}
}
TEST(tensorProd, doubleTemp) {
int t3[] = { 2, 4, 3 };
int t4[] = { 4, 3, 2,3 };
struct dimension d3(3, t3), d4(4, t4), d;
struct Tensor<double> M3(d3), M4(d4), M;
M3.initVal(3.0f); // M3.print();
M4.initVal(2.0f); // M4.print();
tensorProd(M, M3, M4);
//tensorProd(M, M4, M3);
d = M.Dim;
int coord[M.Dim.rank];
int coord3[M3.Dim.rank];
int coord4[M4.Dim.rank];
int idx3[M3.Dim.rank];
int idx4[M4.Dim.rank];
int lin3, lin4, lin;
for (idx3[0] = 0; idx3[0] < d3.dim[0];idx3[0]++)
for (idx3[1] = 0; idx3[1] < d3.dim[1];idx3[1]++)
for (idx3[2] = 0; idx3[2] < d3.dim[2]; idx3[2]++)
for (idx4[0] = 0; idx4[0] < d4.dim[0];idx4[0]++)
for (idx4[1] = 0; idx4[1] < d4.dim[1];idx4[1]++)
for (idx4[2] = 0; idx4[2] < d4.dim[2];idx4[2]++)
for (idx4[3] = 0; idx4[3] < d4.dim[3];idx4[3]++) {
for (int i = 0; i < d3.rank; i++) coord3[i] = idx3[i];
for (int i = 0; i < d4.rank; i++) coord4[i] = idx4[i];
concatArray(coord, coord3, coord4, 0, 0, d3.rank, 0, d4.rank);
lin3 = d3.CoordToLinear(coord3);
lin4 = d4.CoordToLinear(coord4);
lin = d.CoordToLinear(coord);
//ASSERT_FLOAT_EQ(M.elements[lin], M3.elements[lin3] * M4.elements[lin4]);
ASSERT_DOUBLE_EQ(M.elements[lin], M3.elements[lin3] * M4.elements[lin4]);
//ASSERT_NEAR(M.elements[lin], M3.elements[lin3] * M4.elements[lin4], 0.001) << " lin: " << lin << " lin3: " << lin3 << " lin4: " << lin4;
}
}
void printArray(int* t, int sz) {
for (int i = 0; i < sz;i++) printf(" %d ", t[i]);
}
TEST(tensorContractnProd, floatTemp) {
int t3[] = { 2, 4, 3 };
int t4[] = { 4, 3, 2, 3 };
struct dimension d3(3, t3), d4(4, t4), d;
struct Tensor<float> M3(d3), M4(d4), M;
M3.initVal(3.0f); // M3.print();
M4.initVal(2.0f); // M4.print();
int dee = 2;
try {
//tensorContractnProd(M, M3, M4, dee);
tensorContractnProd(M, M3, M4, dee);
}
catch (const std::invalid_argument& e) {
printf("bye from test tensorContractnProd floatTemp invalid arg! deep:\n");
dimension dM;
extractDimNestingDepth(dM, d3, d4, dee);
dM.print();
ASSERT_TRUE(false);
}
int coord[M.Dim.rank];
int coord3[M3.Dim.rank];
int coord4[M4.Dim.rank];
int idx3[M3.Dim.rank];
int idx4[M4.Dim.rank];
int l0, l1;
l0 = M3.Dim.rank - dee;
l1 = M4.Dim.rank - dee;
int pcoord3[l0];
int pcoord4[l1];
int r[dee];
int lin3, lin4, lin;
d = M.Dim;
d.print();
Tensor<float> Msum(d);
//for (size_t idx = 0; idx < d.size; idx++) Msum.elements[idx] = 0.0f;
//Msum.print();
for (idx3[0] = 0; idx3[0] < d3.dim[0];idx3[0]++)
for (idx4[2] = 0; idx4[2] < d4.dim[2];idx4[2]++)
for (idx4[3] = 0; idx4[3] < d4.dim[3];idx4[3]++) {
for (int i = 0; i < l0; i++) pcoord3[i] = idx3[i];
for (int i = 0; i < l1; i++) pcoord4[i] = idx4[i + dee];
concatArray(coord, pcoord3, pcoord4, 0, 0, l0, 0, l1);
lin = d.CoordToLinear(coord);
Msum.elements[lin] = 0.0f;
//for (idx3[1] = 0; idx3[1] < d3.dim[1];idx3[1]++)
//for (idx3[2] = 0; idx3[2] < d3.dim[2]; idx3[2]++)
for (idx4[0] = 0; idx4[0] < d4.dim[0];idx4[0]++)
for (idx4[1] = 0; idx4[1] < d4.dim[1];idx4[1]++)
{
for (int i = 0; i < dee; i++) r[i] = idx4[i];
concatArray(coord3, pcoord3, r, 0, 0, l0, 0, dee);
concatArray(coord4, r, pcoord4, 0, 0, dee, 0, l1);
//printf("[");printArray(coord3, M3.Dim.rank); printf("]["); printArray(coord4, M4.Dim.rank);printf("] =*= ("); printArray(coord, Msum.Dim.rank); printf(") |||");
lin3 = d3.CoordToLinear(coord3);
lin4 = d4.CoordToLinear(coord4);
Msum.elements[lin] += (M3.elements[lin3] * M4.elements[lin4]);
//printf("lin:%d lin3:%d lin4:%d el+:%f\n", lin, lin3, lin4, Msum.elements[lin]);
}
ASSERT_FLOAT_EQ(Msum.elements[lin], M.elements[lin]);
}
}
TEST(tensorContractnProdD, doubleTemp) {
int t3[] = { 2, 3, 4 };
int t4[] = { 3, 4, 2, 3 };
struct dimension d3(3, t3), d4(4, t4), d;
struct Tensor<double> M3(d3), M4(d4), M;
M3.initVal(3.0f); // M3.print();
M4.initVal(2.0f); // M4.print();
int dee = 2;
try {
//tensorContractnProd(M, M3, M4, dee);
tensorContractnProd(M, M3, M4, dee);
}
catch (const std::invalid_argument& e) {
printf("bye from test tensorContractnProd floatTemp invalid arg! deep:\n");
dimension dM;
extractDimNestingDepth(dM, d3, d4, dee);
dM.print();
ASSERT_TRUE(false);
}
int coord[M.Dim.rank];
int coord3[M3.Dim.rank];
int coord4[M4.Dim.rank];
int idx3[M3.Dim.rank];
int idx4[M4.Dim.rank];
int l0, l1;
l0 = M3.Dim.rank - dee;
l1 = M4.Dim.rank - dee;
int pcoord3[l0];
int pcoord4[l1];
int r[dee];
int lin3, lin4, lin;
d = M.Dim;
d.print();
Tensor<double> Msum(d);
//for (size_t idx = 0; idx < d.size; idx++) Msum.elements[idx] = 0.0f;
//Msum.print();
for (idx3[0] = 0; idx3[0] < d3.dim[0];idx3[0]++)
for (idx4[2] = 0; idx4[2] < d4.dim[2];idx4[2]++)
for (idx4[3] = 0; idx4[3] < d4.dim[3];idx4[3]++) {
for (int i = 0; i < l0; i++) pcoord3[i] = idx3[i];
for (int i = 0; i < l1; i++) pcoord4[i] = idx4[i + dee];
concatArray(coord, pcoord3, pcoord4, 0, 0, l0, 0, l1);
lin = d.CoordToLinear(coord);
Msum.elements[lin] = 0.0f;
//for (idx3[1] = 0; idx3[1] < d3.dim[1];idx3[1]++)
//for (idx3[2] = 0; idx3[2] < d3.dim[2]; idx3[2]++)
for (idx4[0] = 0; idx4[0] < d4.dim[0];idx4[0]++)
for (idx4[1] = 0; idx4[1] < d4.dim[1];idx4[1]++)
{
for (int i = 0; i < dee; i++) r[i] = idx4[i];
concatArray(coord3, pcoord3, r, 0, 0, l0, 0, dee);
concatArray(coord4, r, pcoord4, 0, 0, dee, 0, l1);
//printf("[");printArray(coord3, M3.Dim.rank); printf("]["); printArray(coord4, M4.Dim.rank);printf("] =*= ("); printArray(coord, Msum.Dim.rank); printf(") |||");
lin3 = d3.CoordToLinear(coord3);
lin4 = d4.CoordToLinear(coord4);
Msum.elements[lin] += (M3.elements[lin3] * M4.elements[lin4]);
//printf("lin:%d lin3:%d lin4:%d el+:%f\n", lin, lin3, lin4, Msum.elements[lin]);
}
ASSERT_DOUBLE_EQ(Msum.elements[lin], M.elements[lin]);
}
}
TEST(reverseArray, innt) {
int n = 6;
int t4[6] = { 3, 4, 2, 3 ,5, 1 };
int revt4[6] = { 1,5,3,2, 4, 3 };
reverseArray(t4, n);
for (int i = 0; i < n; i++) {
ASSERT_EQ(t4[i], revt4[i]);
}
}
TEST(tensorContractnReverseProd, floatTemp) {
int t3[] = { 4, 4, 3 };
int t4[] = { 3, 4, 7, 2 };
struct dimension d3(3, t3), d4(4, t4), d;
struct Tensor<float> M3(d3), M4(d4), M;
M3.initVal(3.0f); // M3.print();
M4.initVal(2.0f); // M4.print();
int dee = 2;
try {
//tensorContractnProd(M, M3, M4, dee);
tensorContractnReverseProd(M, M3, M4, dee);
}
catch (const std::invalid_argument& e) {
printf("bye from test tensorContractnProd floatTemp invalid arg! deep:\n");
dimension dM;
extractDimNestingDepth(dM, d3, d4, dee);
dM.print();
ASSERT_TRUE(false);
}
int coord[M.Dim.rank];
int coord3[M3.Dim.rank];
int coord4[M4.Dim.rank];
int idx3[M3.Dim.rank];
int idx4[M4.Dim.rank];
int l0, l1;
l0 = M3.Dim.rank - dee;
l1 = M4.Dim.rank - dee;
int pcoord3[l0];
int pcoord4[l1];
int r[dee];
int rev[dee];
int lin3, lin4, lin;
d = M.Dim;
d.print();
Tensor<float> Msum(d);
for (idx3[0] = 0; idx3[0] < d3.dim[0];idx3[0]++)
for (idx4[2] = 0; idx4[2] < d4.dim[2];idx4[2]++)
for (idx4[3] = 0; idx4[3] < d4.dim[3];idx4[3]++) {
for (int i = 0; i < l0; i++) pcoord3[i] = idx3[i];
for (int i = 0; i < l1; i++) pcoord4[i] = idx4[i + dee];
concatArray(coord, pcoord3, pcoord4, 0, 0, l0, 0, l1);
lin = d.CoordToLinear(coord);
Msum.elements[lin] = 0.0f;
//for (idx3[1] = 0; idx3[1] < d3.dim[1];idx3[1]++)
//for (idx3[2] = 0; idx3[2] < d3.dim[2]; idx3[2]++)
for (idx4[0] = 0; idx4[0] < d4.dim[0];idx4[0]++)
for (idx4[1] = 0; idx4[1] < d4.dim[1];idx4[1]++)
{
for (int i = 0; i < dee; i++) {
r[i] = idx4[i];
rev[i] = idx4[dee - 1 - i];
}
concatArray(coord3, pcoord3, rev, 0, 0, l0, 0, dee);
concatArray(coord4, r, pcoord4, 0, 0, dee, 0, l1);
//printf("[");printArray(coord3, M3.Dim.rank); printf("]["); printArray(coord4, M4.Dim.rank);printf("] =*= ("); printArray(coord, Msum.Dim.rank); printf(") |||");
lin3 = d3.CoordToLinear(coord3);
lin4 = d4.CoordToLinear(coord4);
Msum.elements[lin] += (M3.elements[lin3] * M4.elements[lin4]);
//printf("lin:%d lin3:%d lin4:%d el+:%f\n", lin, lin3, lin4, Msum.elements[lin]);
}
ASSERT_FLOAT_EQ(Msum.elements[lin], M.elements[lin]);
}
}
TEST(cudaTensorProd, floatTemp) {
int t3[] = { 15, 6, 24 };
int t4[] = { 23, 15, 6, 10 };
struct dimension d3(3, t3), d4(4, t4), d;
struct Tensor<float> M3(d3), M4(d4), M;
M3.initVal(1.0f); // M3.print();
M4.initVal(0.5f); // M4.print();
cudaTensorProd(M, M3, M4);
//tensorProd(M, M4, M3);
int coord[M.Dim.rank];
int coord3[M3.Dim.rank];
int coord4[M4.Dim.rank];
int idx3[M3.Dim.rank];
int idx4[M4.Dim.rank];
int lin3, lin4, lin;
d = M.Dim;
d.print();
for (idx3[0] = 0; idx3[0] < d3.dim[0];idx3[0]++)
for (idx3[1] = 0; idx3[1] < d3.dim[1];idx3[1]++)
for (idx3[2] = 0; idx3[2] < d3.dim[2]; idx3[2]++)
for (idx4[0] = 0; idx4[0] < d4.dim[0];idx4[0]++)
for (idx4[1] = 0; idx4[1] < d4.dim[1];idx4[1]++)
for (idx4[2] = 0; idx4[2] < d4.dim[2];idx4[2]++)
for (idx4[3] = 0; idx4[3] < d4.dim[3];idx4[3]++) {
for (int i = 0; i < d3.rank; i++) coord3[i] = idx3[i];
for (int i = 0; i < d4.rank; i++) coord4[i] = idx4[i];
concatArray(coord, coord3, coord4, 0, 0, d3.rank, 0, d4.rank);
lin3 = d3.CoordToLinear(coord3);
lin4 = d4.CoordToLinear(coord4);
lin = d.CoordToLinear(coord);
//ASSERT_FLOAT_EQ(M.elements[lin], M3.elements[lin3] * M4.elements[lin4]) << " lin: " << lin << " lin3: " << lin3 << " lin4: " << lin4;
//ASSERT_FLOAT_EQ(M.elements[lin], M3.elements[lin3] * M4.elements[lin4]) << " lin: " << lin << " lin3: " << lin3 << " lin4: " << lin4;
ASSERT_FLOAT_EQ(M.elements[lin], M3.elements[lin3] * M4.elements[lin4]) << " M " << M.elements[lin] << " lin: " << lin << " M3: " << M3.elements[lin3] << " lin3:" << lin3 << " lin4: " << lin4 << " M4 " << M4.elements[lin4] << std::endl;
//std::cout << " M " << M.elements[lin] << " lin: " << lin << " M3: " << M3.elements[lin3] << " lin3:" << lin3 << " lin4: " << lin4 << " M4 " << M4.elements[lin4] << std::endl;
//ASSERT_NEAR(M.elements[lin], M3.elements[lin3] * M4.elements[lin4], 0.0001) << " lin: " << lin << " lin3: " << lin3 << " lin4: " << lin4;
}
}
TEST(permuteTensor, float) {
int t4[] = { 3, 8, 2, 4 };
struct dimension d4(4, t4);
struct Tensor<float> M4(d4), M;
M4.initVal(1.0f);
permutation p(4, true);
int n = 5;
//for (int n = 0; n < 24;n++) {
PlaceToTab(p.perm, n, p.size);
printf(" %*d : ", 2, n);
for (int i = 0; i < p.size; i++)printf("(%d)%d ", i, p.perm[i]);printf("\n");
permuteTensor(M, M4, p);
//permuteTensorDef(M, M4, p);
int ind[4];
int coor[4];
size_t cM, cM4;
for (ind[0] = 0; ind[0] < M4.Dim.dim[0]; ind[0]++)
for (ind[1] = 0; ind[1] < M4.Dim.dim[1]; ind[1]++)
for (ind[2] = 0; ind[2] < M4.Dim.dim[2]; ind[2]++)
for (ind[3] = 0; ind[3] < M4.Dim.dim[3]; ind[3]++) {
p.permute(coor, ind);
cM = M.Dim.CoordToLinear(coor);
cM4 = M4.Dim.CoordToLinear(ind);
//printf("M[%ld]=%f M4[%ld]=%f \n", cM, M.elements[cM], cM4, M4.elements[cM4]);
ASSERT_FLOAT_EQ(M.elements[cM], M4.elements[cM4]);
}
}
TEST(cudapermuteTensor, float) {
int t4[] = { 3, 8, 2, 4 };
struct dimension d4(4, t4);
struct Tensor<float> M4(d4), M;
M4.initVal(1.0f);
permutation p(4, true);
int n = 5;
//for (int n = 0; n < 24;n++) {
PlaceToTab(p.perm, n, p.size);
printf(" %*d : ", 2, n);
for (int i = 0; i < p.size; i++)printf("{%d}%d ", i, p.perm[i]);printf("\n");
cudapermuteTensor(M, M4, p);
//permuteTensor(M, M4, p);
//permuteTensorDef(M, M4, p);
int ind[4];
int coor[4];
size_t cM, cM4;
for (ind[0] = 0; ind[0] < M4.Dim.dim[0]; ind[0]++)
for (ind[1] = 0; ind[1] < M4.Dim.dim[1]; ind[1]++)
for (ind[2] = 0; ind[2] < M4.Dim.dim[2]; ind[2]++)
for (ind[3] = 0; ind[3] < M4.Dim.dim[3]; ind[3]++) {
p.permute(coor, ind);
cM = M.Dim.CoordToLinear(coor);
cM4 = M4.Dim.CoordToLinear(ind);
//printf("M[%ld]=%f M4[%ld]=%f \n", cM, M.elements[cM], cM4, M4.elements[cM4]);
ASSERT_FLOAT_EQ(M.elements[cM], M4.elements[cM4]);
}
}
TEST(scanPermuteMatchContractTensorfromSrcToDst1, permId) {
int t[] = { 3, 8, 2, 3, 4 };
//int tm[] = { 4, 2, 7, 3 };
int tm[] = { 2, 3,4,7 };
struct dimension d(5, t);
struct dimension dm(4, tm);
struct Tensor<float> M4(d), M(dm);
M4.initVal(1.0f);
M.initVal(1.0f);
int dee = 3;
//int result[4] = { 1,3,0,2 };
int result[4] = { 0,1,2,3 };
int perm[M.Dim.rank];
ASSERT_TRUE(scanPermuteMatchContractTensorfromSrcToDst(perm, M, M4, dee));
for (int i = 0; i < M.Dim.rank; i++) printf(" %d[%d] ", i, perm[i]); printf(" first perm \n");
ASSERT_EQ(0, memcmp(result, perm, sizeof(int) * M.Dim.rank));
Tensor<float> tM;
permutation p(M.Dim.rank, perm);
permuteTensor(tM, M, p);
ASSERT_FALSE(scanPermuteMatchContractTensorfromSrcToDst(perm, M, M4, 4));
for (int i = 0; i < M.Dim.rank; i++) printf(" %d[%d] ", i, perm[i]); printf(": last perm \n");
tM.Dim.print();
int resultDim[] = { 2,3,4,7 };
ASSERT_EQ(0, memcmp(resultDim, tM.Dim.dim, sizeof(int) * tM.Dim.rank));
}
TEST(scanPermuteMatchContractTensorfromSrcToDst2, floatest) {
int t[] = { 3, 8, 2, 3, 4 };
int tm[] = { 4, 2, 7, 3 };
//int tm[] = { 2, 3,4,7 };
struct dimension d(5, t);
struct dimension dm(4, tm);
struct Tensor<float> M4(d), M(dm);
M4.initVal(1.0f);
M.initVal(1.0f);
int dee = 3;
int result[4] = { 1,3,0,2 };
//int result[4] = { 0,1,2,3 };
int perm[M.Dim.rank];
ASSERT_TRUE(scanPermuteMatchContractTensorfromSrcToDst(perm, M, M4, dee));
for (int i = 0; i < M.Dim.rank; i++) printf(" %d[%d] ", i, perm[i]); printf(" first perm \n");
ASSERT_EQ(0, memcmp(result, perm, sizeof(int) * M.Dim.rank));
Tensor<float> tM;
permutation p(M.Dim.rank, perm);
permuteTensor(tM, M, p);
ASSERT_FALSE(scanPermuteMatchContractTensorfromSrcToDst(perm, M, M4, 4));
for (int i = 0; i < M.Dim.rank; i++) printf(" %d[%d] ", i, perm[i]); printf(": last perm \n");
tM.Dim.print();
int resultDim[] = { 2,3,4,7 };
ASSERT_EQ(0, memcmp(resultDim, tM.Dim.dim, sizeof(int) * tM.Dim.rank));
}
TEST(cudaTensorContractNestProd, floatTemp) {
int t3[] = { 77, 8, 25 };
int t4[] = { 8, 25, 52, 144 };
struct dimension d3(3, t3), d4(4, t4), d;
struct Tensor<float> M3(d3), M4(d4), M;
M3.initVal(1.0f); // M3.print();
M4.initVal(0.0f); // M4.print();
int dee = 2;
M4.Dim.print();
try {
//tensorContractnProd(M, M3, M4, dee);
cudaTensorContractNestProd(M, M3, M4, dee);
}
catch (const std::invalid_argument& e) {
printf("bye from test tensorContractnProd floatTemp invalid arg! deep: \n");
dimension dM;
extractDimNestingDepth(dM, d3, d4, dee);
dM.print();
ASSERT_TRUE(false);
}
int coord[M.Dim.rank];
int coord3[M3.Dim.rank];
int coord4[M4.Dim.rank];
int idx3[M3.Dim.rank];
int idx4[M4.Dim.rank];
int l0, l1;
l0 = M3.Dim.rank - dee;
l1 = M4.Dim.rank - dee;
int pcoord3[l0];
int pcoord4[l1];
int r[dee];
//int rev[dee];
int lin3, lin4, lin;
d = M.Dim;
d.print();
Tensor<float> Msum(d);
for (idx3[0] = 0; idx3[0] < d3.dim[0];idx3[0]++)
for (idx4[2] = 0; idx4[2] < d4.dim[2];idx4[2]++)
for (idx4[3] = 0; idx4[3] < d4.dim[3];idx4[3]++) {
for (int i = 0; i < l0; i++) pcoord3[i] = idx3[i];
for (int i = 0; i < l1; i++) pcoord4[i] = idx4[i + dee];
concatArray(coord, pcoord3, pcoord4, 0, 0, l0, 0, l1);
lin = d.CoordToLinear(coord);
Msum.elements[lin] = 0.0f;
//for (idx3[1] = 0; idx3[1] < d3.dim[1];idx3[1]++)
//for (idx3[2] = 0; idx3[2] < d3.dim[2]; idx3[2]++)
for (idx4[0] = 0; idx4[0] < d4.dim[0];idx4[0]++)
for (idx4[1] = 0; idx4[1] < d4.dim[1];idx4[1]++)
{
for (int i = 0; i < dee; i++) {
r[i] = idx4[i];
//rev[i] = idx4[dee - 1 - i];
}
//concatArray(coord3, pcoord3, rev, 0, 0, l0, 0, dee);
concatArray(coord3, pcoord3, r, 0, 0, l0, 0, dee);
concatArray(coord4, r, pcoord4, 0, 0, dee, 0, l1);
//printf("[");printArray(coord3, M3.Dim.rank); printf("]["); printArray(coord4, M4.Dim.rank);printf("] =*= ("); printArray(coord, Msum.Dim.rank); printf(") |||");
lin3 = d3.CoordToLinear(coord3);
lin4 = d4.CoordToLinear(coord4);
Msum.elements[lin] += (M3.elements[lin3] * M4.elements[lin4]);
//printf("lin:%d lin3:%d lin4:%d el+:%f\n", lin, lin3, lin4, Msum.elements[lin]);
}
ASSERT_FLOAT_EQ(Msum.elements[lin], M.elements[lin]) << " lin: " << lin << " Msumelem: " << Msum.elements[lin] << " Melem: " << M.elements[lin];
}
}
int main(int argc, char** argv) {
testing::InitGoogleTest(&argc, argv);
return RUN_ALL_TESTS();
}