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Add exp, mod, acos, acosd, conj, norm and unittest.

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sunwen
2023-11-28 15:29:55 +08:00
parent 7d879c17d4
commit f65542523d
4 changed files with 419 additions and 0 deletions
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153
test/Function1D_Cuda_Test.cpp
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@@ -647,3 +647,156 @@ TEST_F(Function1D_Cuda_Test, compareSet)
}
}
}
TEST_F(Function1D_Cuda_Test, exp)
{
Aurora::Matrix hostMatrix = Aurora::Matrix::fromRawData(new float[8]{1.1,2.2,3.3,4.4,5.5,6.6,7.7,8.8}, 2,4);
Aurora::CudaMatrix deviceMatrix = hostMatrix.toDeviceMatrix();
auto result1 = Aurora::exp(hostMatrix);
auto result2 = Aurora::exp(deviceMatrix).toHostMatrix();
EXPECT_EQ(result2.getDataSize(), result1.getDataSize());
EXPECT_EQ(result2.getValueType(), result1.getValueType());
for(size_t i=0; i<result1.getDataSize() * result1.getValueType(); ++i)
{
EXPECT_FLOAT_AE(result1[i], result2[i]);
}
hostMatrix = Aurora::Matrix::fromRawData(new float[12]{1.1,2.2,3.3,4.4,5.5,6.6,7.7,8.8,9.9,10,11,12}, 3, 2, 1,Aurora::Complex);
deviceMatrix = hostMatrix.toDeviceMatrix();
result1 = Aurora::exp(hostMatrix);
result2 = Aurora::exp(deviceMatrix).toHostMatrix();
EXPECT_EQ(result2.getDataSize(), result1.getDataSize());
EXPECT_EQ(result2.getValueType(), result1.getValueType());
for(size_t i=0; i<result1.getDataSize() * result1.getValueType(); ++i)
{
EXPECT_FLOAT_AE(result1[i], result2[i]);
}
}
TEST_F(Function1D_Cuda_Test, mod)
{
Aurora::Matrix hostMatrix = Aurora::Matrix::fromRawData(new float[8]{1.1,2.2,3.3,4.4,5.5,6.6,7.7,8.8}, 2,4);
Aurora::CudaMatrix deviceMatrix = hostMatrix.toDeviceMatrix();
auto result1 = Aurora::mod(hostMatrix, 2);
auto result2 = Aurora::mod(deviceMatrix, 2).toHostMatrix();
EXPECT_EQ(result2.getDataSize(), result1.getDataSize());
EXPECT_EQ(result2.getValueType(), result1.getValueType());
for(size_t i=0; i<result1.getDataSize() * result1.getValueType(); ++i)
{
EXPECT_FLOAT_AE(result1[i], result2[i]);
}
result1 = Aurora::mod(hostMatrix, 3);
result2 = Aurora::mod(deviceMatrix, 3).toHostMatrix();
EXPECT_EQ(result2.getDataSize(), result1.getDataSize());
EXPECT_EQ(result2.getValueType(), result1.getValueType());
for(size_t i=0; i<result1.getDataSize() * result1.getValueType(); ++i)
{
EXPECT_FLOAT_AE(result1[i], result2[i]);
}
}
TEST_F(Function1D_Cuda_Test, acos)
{
Aurora::Matrix hostMatrix = Aurora::Matrix::fromRawData(new float[8]{0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8}, 2,4);
Aurora::CudaMatrix deviceMatrix = hostMatrix.toDeviceMatrix();
auto result1 = Aurora::acos(hostMatrix);
auto result2 = Aurora::acos(deviceMatrix).toHostMatrix();
EXPECT_EQ(result2.getDataSize(), result1.getDataSize());
EXPECT_EQ(result2.getValueType(), result1.getValueType());
for(size_t i=0; i<result1.getDataSize() * result1.getValueType(); ++i)
{
EXPECT_FLOAT_AE(result1[i], result2[i]);
}
}
TEST_F(Function1D_Cuda_Test, acosd)
{
Aurora::Matrix hostMatrix = Aurora::Matrix::fromRawData(new float[8]{0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8}, 2,4);
Aurora::CudaMatrix deviceMatrix = hostMatrix.toDeviceMatrix();
auto result1 = Aurora::acosd(hostMatrix);
auto result2 = Aurora::acosd(deviceMatrix).toHostMatrix();
EXPECT_EQ(result2.getDataSize(), result1.getDataSize());
EXPECT_EQ(result2.getValueType(), result1.getValueType());
for(size_t i=0; i<result1.getDataSize() * result1.getValueType(); ++i)
{
EXPECT_FLOAT_AE(result1[i], result2[i]);
}
}
TEST_F(Function1D_Cuda_Test, conj)
{
Aurora::Matrix hostMatrix = Aurora::Matrix::fromRawData(new float[8]{0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8}, 2,4);
Aurora::CudaMatrix deviceMatrix = hostMatrix.toDeviceMatrix();
auto result1 = Aurora::conj(hostMatrix);
auto result2 = Aurora::conj(deviceMatrix).toHostMatrix();
EXPECT_EQ(result2.getDataSize(), result1.getDataSize());
EXPECT_EQ(result2.getValueType(), result1.getValueType());
for(size_t i=0; i<result1.getDataSize() * result1.getValueType(); ++i)
{
EXPECT_FLOAT_AE(result1[i], result2[i]);
}
hostMatrix = Aurora::Matrix::fromRawData(new float[12]{1.1,2.2,3.3,4.4,5.5,6.6,7.7,8.8,9.9,10,11,12}, 3, 2, 1,Aurora::Complex);
deviceMatrix = hostMatrix.toDeviceMatrix();
result1 = Aurora::conj(hostMatrix);
result2 = Aurora::conj(deviceMatrix).toHostMatrix();
EXPECT_EQ(result2.getDataSize(), result1.getDataSize());
EXPECT_EQ(result2.getValueType(), result1.getValueType());
for(size_t i=0; i<result1.getDataSize() * result1.getValueType(); ++i)
{
EXPECT_FLOAT_AE(result1[i], result2[i]);
}
}
TEST_F(Function1D_Cuda_Test, norm) {
//1Dim
float *data = new float[3]{1,2,-3};
auto matrix = Aurora::Matrix::fromRawData(data, 3);
auto deviceMatrix = matrix.toDeviceMatrix();
auto result = Aurora::norm(matrix.toDeviceMatrix(), Aurora::NormMethod::Norm1);
EXPECT_FLOAT_AE(result,6);
result = Aurora::norm(deviceMatrix,Aurora::NormMethod::Norm2);
EXPECT_FLOAT_AE(result,3.74166);
result = Aurora::norm(deviceMatrix,Aurora::NormMethod::NormF);
EXPECT_FLOAT_AE(result,3.74166);
//2Dims
data = new float[8]{1,2,-3,6,7,9,22.3,-8.6};
matrix = Aurora::Matrix::fromRawData(data, 4,2);
deviceMatrix = matrix.toDeviceMatrix();
result = Aurora::norm(deviceMatrix,Aurora::NormMethod::Norm1);
EXPECT_FLOAT_AE(result,46.9);
result = Aurora::norm(deviceMatrix,Aurora::NormMethod::Norm2);
EXPECT_FLOAT_AE(result,26.7284);
result = Aurora::norm(deviceMatrix,Aurora::NormMethod::NormF);
EXPECT_FLOAT_AE(result,27.4089);
//1Dim Complex
data = new float[6]{1,2,-3,4,5,-6};
matrix = Aurora::Matrix::fromRawData(data, 3,1,1,Aurora::Complex);
deviceMatrix = matrix.toDeviceMatrix();
result = Aurora::norm(deviceMatrix,Aurora::NormMethod::Norm1);
EXPECT_FLOAT_AE(result,15.0463);
result = Aurora::norm(deviceMatrix,Aurora::NormMethod::Norm2);
EXPECT_FLOAT_AE(result,9.5394);
result = Aurora::norm(deviceMatrix,Aurora::NormMethod::NormF);
EXPECT_FLOAT_AE(result,9.5394);
//2Dims Complex
data = new float[12]{1,2,-3,4,5,-6,7,8,9,22,24,25};
matrix = Aurora::Matrix::fromRawData(data, 3,2,1,Aurora::Complex);
deviceMatrix = matrix.toDeviceMatrix();
result = Aurora::norm(deviceMatrix,Aurora::NormMethod::Norm1);
EXPECT_FLOAT_AE(result,69.0553);
//not support
//result = Aurora::norm(matrix,Aurora::NormMethod::Norm2);
//EXPECT_FLOAT_AE(result,43.5314);
result = Aurora::norm(deviceMatrix,Aurora::NormMethod::NormF);
EXPECT_FLOAT_AE(result,44.3847);
}