From bab29936a1cf0a68ffe4ccb1fd9b4807a3ec87ae Mon Sep 17 00:00:00 2001 From: Mehdi Goli Date: Wed, 1 Feb 2017 15:29:53 +0000 Subject: Reducing warnings in Sycl backend. --- unsupported/test/cxx11_tensor_morphing_sycl.cpp | 119 ++++++++++++------------ 1 file changed, 60 insertions(+), 59 deletions(-) (limited to 'unsupported/test/cxx11_tensor_morphing_sycl.cpp') diff --git a/unsupported/test/cxx11_tensor_morphing_sycl.cpp b/unsupported/test/cxx11_tensor_morphing_sycl.cpp index 91353b81a..9b521bc6b 100644 --- a/unsupported/test/cxx11_tensor_morphing_sycl.cpp +++ b/unsupported/test/cxx11_tensor_morphing_sycl.cpp @@ -16,7 +16,7 @@ #define EIGEN_TEST_NO_LONGDOUBLE #define EIGEN_TEST_NO_COMPLEX #define EIGEN_TEST_FUNC cxx11_tensor_morphing_sycl -#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int +#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t #define EIGEN_USE_SYCL @@ -28,18 +28,18 @@ using Eigen::SyclDevice; using Eigen::Tensor; using Eigen::TensorMap; -template +template static void test_simple_reshape(const Eigen::SyclDevice& sycl_device) { - typename Tensor::Dimensions dim1(2,3,1,7,1); - typename Tensor::Dimensions dim2(2,3,7); - typename Tensor::Dimensions dim3(6,7); - typename Tensor::Dimensions dim4(2,21); + typename Tensor::Dimensions dim1(2,3,1,7,1); + typename Tensor::Dimensions dim2(2,3,7); + typename Tensor::Dimensions dim3(6,7); + typename Tensor::Dimensions dim4(2,21); - Tensor tensor1(dim1); - Tensor tensor2(dim2); - Tensor tensor3(dim3); - Tensor tensor4(dim4); + Tensor tensor1(dim1); + Tensor tensor2(dim2); + Tensor tensor3(dim3); + Tensor tensor4(dim4); tensor1.setRandom(); @@ -48,10 +48,10 @@ static void test_simple_reshape(const Eigen::SyclDevice& sycl_device) DataType* gpu_data3 = static_cast(sycl_device.allocate(tensor3.size()*sizeof(DataType))); DataType* gpu_data4 = static_cast(sycl_device.allocate(tensor4.size()*sizeof(DataType))); - TensorMap> gpu1(gpu_data1, dim1); - TensorMap> gpu2(gpu_data2, dim2); - TensorMap> gpu3(gpu_data3, dim3); - TensorMap> gpu4(gpu_data4, dim4); + TensorMap> gpu1(gpu_data1, dim1); + TensorMap> gpu2(gpu_data2, dim2); + TensorMap> gpu3(gpu_data3, dim3); + TensorMap> gpu4(gpu_data4, dim4); sycl_device.memcpyHostToDevice(gpu_data1, tensor1.data(),(tensor1.size())*sizeof(DataType)); @@ -63,9 +63,9 @@ static void test_simple_reshape(const Eigen::SyclDevice& sycl_device) gpu4.device(sycl_device)=gpu1.reshape(dim2).reshape(dim4); sycl_device.memcpyDeviceToHost(tensor4.data(), gpu_data4,(tensor4.size())*sizeof(DataType)); - for (int i = 0; i < 2; ++i){ - for (int j = 0; j < 3; ++j){ - for (int k = 0; k < 7; ++k){ + for (IndexType i = 0; i < 2; ++i){ + for (IndexType j = 0; j < 3; ++j){ + for (IndexType k = 0; k < 7; ++k){ VERIFY_IS_EQUAL(tensor1(i,j,0,k,0), tensor2(i,j,k)); ///ColMajor if (static_cast(DataLayout) == static_cast(ColMajor)) { VERIFY_IS_EQUAL(tensor1(i,j,0,k,0), tensor3(i+2*j,k)); ///ColMajor @@ -86,15 +86,15 @@ static void test_simple_reshape(const Eigen::SyclDevice& sycl_device) } -template +template static void test_reshape_as_lvalue(const Eigen::SyclDevice& sycl_device) { - typename Tensor::Dimensions dim1(2,3,7); - typename Tensor::Dimensions dim2(6,7); - typename Tensor::Dimensions dim3(2,3,1,7,1); - Tensor tensor(dim1); - Tensor tensor2d(dim2); - Tensor tensor5d(dim3); + typename Tensor::Dimensions dim1(2,3,7); + typename Tensor::Dimensions dim2(6,7); + typename Tensor::Dimensions dim3(2,3,1,7,1); + Tensor tensor(dim1); + Tensor tensor2d(dim2); + Tensor tensor5d(dim3); tensor.setRandom(); @@ -102,9 +102,9 @@ static void test_reshape_as_lvalue(const Eigen::SyclDevice& sycl_device) DataType* gpu_data2 = static_cast(sycl_device.allocate(tensor2d.size()*sizeof(DataType))); DataType* gpu_data3 = static_cast(sycl_device.allocate(tensor5d.size()*sizeof(DataType))); - TensorMap< Tensor > gpu1(gpu_data1, dim1); - TensorMap< Tensor > gpu2(gpu_data2, dim2); - TensorMap< Tensor > gpu3(gpu_data3, dim3); + TensorMap< Tensor > gpu1(gpu_data1, dim1); + TensorMap< Tensor > gpu2(gpu_data2, dim2); + TensorMap< Tensor > gpu3(gpu_data3, dim3); sycl_device.memcpyHostToDevice(gpu_data1, tensor.data(),(tensor.size())*sizeof(DataType)); @@ -115,9 +115,9 @@ static void test_reshape_as_lvalue(const Eigen::SyclDevice& sycl_device) sycl_device.memcpyDeviceToHost(tensor5d.data(), gpu_data3,(tensor5d.size())*sizeof(DataType)); - for (int i = 0; i < 2; ++i){ - for (int j = 0; j < 3; ++j){ - for (int k = 0; k < 7; ++k){ + for (IndexType i = 0; i < 2; ++i){ + for (IndexType j = 0; j < 3; ++j){ + for (IndexType k = 0; k < 7; ++k){ VERIFY_IS_EQUAL(tensor5d(i,j,0,k,0), tensor(i,j,k)); if (static_cast(DataLayout) == static_cast(ColMajor)) { VERIFY_IS_EQUAL(tensor2d(i+2*j,k), tensor(i,j,k)); ///ColMajor @@ -134,43 +134,43 @@ static void test_reshape_as_lvalue(const Eigen::SyclDevice& sycl_device) } -template +template static void test_simple_slice(const Eigen::SyclDevice &sycl_device) { - int sizeDim1 = 2; - int sizeDim2 = 3; - int sizeDim3 = 5; - int sizeDim4 = 7; - int sizeDim5 = 11; - array tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4, sizeDim5}}; - Tensor tensor(tensorRange); + IndexType sizeDim1 = 2; + IndexType sizeDim2 = 3; + IndexType sizeDim3 = 5; + IndexType sizeDim4 = 7; + IndexType sizeDim5 = 11; + array tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4, sizeDim5}}; + Tensor tensor(tensorRange); tensor.setRandom(); - array slice1_range ={{1, 1, 1, 1, 1}}; - Tensor slice1(slice1_range); + array slice1_range ={{1, 1, 1, 1, 1}}; + Tensor slice1(slice1_range); DataType* gpu_data1 = static_cast(sycl_device.allocate(tensor.size()*sizeof(DataType))); DataType* gpu_data2 = static_cast(sycl_device.allocate(slice1.size()*sizeof(DataType))); - TensorMap> gpu1(gpu_data1, tensorRange); - TensorMap> gpu2(gpu_data2, slice1_range); - Eigen::DSizes indices(1,2,3,4,5); - Eigen::DSizes sizes(1,1,1,1,1); + TensorMap> gpu1(gpu_data1, tensorRange); + TensorMap> gpu2(gpu_data2, slice1_range); + Eigen::DSizes indices(1,2,3,4,5); + Eigen::DSizes sizes(1,1,1,1,1); sycl_device.memcpyHostToDevice(gpu_data1, tensor.data(),(tensor.size())*sizeof(DataType)); gpu2.device(sycl_device)=gpu1.slice(indices, sizes); sycl_device.memcpyDeviceToHost(slice1.data(), gpu_data2,(slice1.size())*sizeof(DataType)); VERIFY_IS_EQUAL(slice1(0,0,0,0,0), tensor(1,2,3,4,5)); - array slice2_range ={{1,1,2,2,3}}; - Tensor slice2(slice2_range); + array slice2_range ={{1,1,2,2,3}}; + Tensor slice2(slice2_range); DataType* gpu_data3 = static_cast(sycl_device.allocate(slice2.size()*sizeof(DataType))); - TensorMap> gpu3(gpu_data3, slice2_range); - Eigen::DSizes indices2(1,1,3,4,5); - Eigen::DSizes sizes2(1,1,2,2,3); + TensorMap> gpu3(gpu_data3, slice2_range); + Eigen::DSizes indices2(1,1,3,4,5); + Eigen::DSizes sizes2(1,1,2,2,3); gpu3.device(sycl_device)=gpu1.slice(indices2, sizes2); sycl_device.memcpyDeviceToHost(slice2.data(), gpu_data3,(slice2.size())*sizeof(DataType)); - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 2; ++j) { - for (int k = 0; k < 3; ++k) { + for (IndexType i = 0; i < 2; ++i) { + for (IndexType j = 0; j < 2; ++j) { + for (IndexType k = 0; k < 3; ++k) { VERIFY_IS_EQUAL(slice2(0,0,i,j,k), tensor(1,1,3+i,4+j,5+k)); } } @@ -219,7 +219,8 @@ static void test_strided_slice_write_sycl(const Eigen::SyclDevice& sycl_device) sycl_device.memcpyDeviceToHost(tensor.data(), gpu_data1,(tensor.size())*sizeof(DataType)); sycl_device.memcpyDeviceToHost(tensor2.data(), gpu_data2,(tensor2.size())*sizeof(DataType)); - for(int i=0;i void sycl_morphing_test_per_device(dev_Selector s){ QueueInterface queueInterface(s); auto sycl_device = Eigen::SyclDevice(&queueInterface); - test_simple_slice(sycl_device); - test_simple_slice(sycl_device); - test_simple_reshape(sycl_device); - test_simple_reshape(sycl_device); - test_reshape_as_lvalue(sycl_device); - test_reshape_as_lvalue(sycl_device); + test_simple_slice(sycl_device); + test_simple_slice(sycl_device); + test_simple_reshape(sycl_device); + test_simple_reshape(sycl_device); + test_reshape_as_lvalue(sycl_device); + test_reshape_as_lvalue(sycl_device); test_strided_slice_write_sycl(sycl_device); test_strided_slice_write_sycl(sycl_device); } -- cgit v1.2.3