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authorGravatar Mehdi Goli <mehdi.goli@codeplay.com>2017-02-01 15:29:53 +0000
committerGravatar Mehdi Goli <mehdi.goli@codeplay.com>2017-02-01 15:29:53 +0000
commitbab29936a1cf0a68ffe4ccb1fd9b4807a3ec87ae (patch)
treec750b36227a31ddb2a1e0d5fd11f0036fda775db /unsupported/test/cxx11_tensor_builtins_sycl.cpp
parent48a20b7d956433713a39e04d39cba443b7a763de (diff)
Reducing warnings in Sycl backend.
Diffstat (limited to 'unsupported/test/cxx11_tensor_builtins_sycl.cpp')
-rw-r--r--unsupported/test/cxx11_tensor_builtins_sycl.cpp169
1 files changed, 86 insertions, 83 deletions
diff --git a/unsupported/test/cxx11_tensor_builtins_sycl.cpp b/unsupported/test/cxx11_tensor_builtins_sycl.cpp
index d5193d1ea..400a31d09 100644
--- a/unsupported/test/cxx11_tensor_builtins_sycl.cpp
+++ b/unsupported/test/cxx11_tensor_builtins_sycl.cpp
@@ -14,7 +14,7 @@
#define EIGEN_TEST_NO_LONGDOUBLE
#define EIGEN_TEST_NO_COMPLEX
#define EIGEN_TEST_FUNC cxx11_tensor_builtins_sycl
-#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
#define EIGEN_USE_SYCL
#include "main.h"
@@ -32,20 +32,20 @@ template <typename T> T cube(T x) { return x * x * x; }
template <typename T> T inverse(T x) { return 1 / x; }
}
-#define TEST_UNARY_BUILTINS_FOR_SCALAR(FUNC, SCALAR, OPERATOR) \
+#define TEST_UNARY_BUILTINS_FOR_SCALAR(FUNC, SCALAR, OPERATOR, Layout) \
{ \
/* out OPERATOR in.FUNC() */ \
- Tensor<SCALAR, 3> in(tensorRange); \
- Tensor<SCALAR, 3> out(tensorRange); \
+ Tensor<SCALAR, 3, Layout, int64_t> in(tensorRange); \
+ Tensor<SCALAR, 3, Layout, int64_t> out(tensorRange); \
in = in.random() + static_cast<SCALAR>(0.01); \
out = out.random() + static_cast<SCALAR>(0.01); \
- Tensor<SCALAR, 3> reference(out); \
+ Tensor<SCALAR, 3, Layout, int64_t> reference(out); \
SCALAR *gpu_data = static_cast<SCALAR *>( \
sycl_device.allocate(in.size() * sizeof(SCALAR))); \
SCALAR *gpu_data_out = static_cast<SCALAR *>( \
sycl_device.allocate(out.size() * sizeof(SCALAR))); \
- TensorMap<Tensor<SCALAR, 3>> gpu(gpu_data, tensorRange); \
- TensorMap<Tensor<SCALAR, 3>> gpu_out(gpu_data_out, tensorRange); \
+ TensorMap<Tensor<SCALAR, 3, Layout, int64_t>> gpu(gpu_data, tensorRange); \
+ TensorMap<Tensor<SCALAR, 3, Layout, int64_t>> gpu_out(gpu_data_out, tensorRange); \
sycl_device.memcpyHostToDevice(gpu_data, in.data(), \
(in.size()) * sizeof(SCALAR)); \
sycl_device.memcpyHostToDevice(gpu_data_out, out.data(), \
@@ -53,7 +53,7 @@ template <typename T> T inverse(T x) { return 1 / x; }
gpu_out.device(sycl_device) OPERATOR gpu.FUNC(); \
sycl_device.memcpyDeviceToHost(out.data(), gpu_data_out, \
(out.size()) * sizeof(SCALAR)); \
- for (int i = 0; i < out.size(); ++i) { \
+ for (int64_t i = 0; i < out.size(); ++i) { \
SCALAR ver = reference(i); \
ver OPERATOR std::FUNC(in(i)); \
VERIFY_IS_APPROX(out(i), ver); \
@@ -63,18 +63,18 @@ template <typename T> T inverse(T x) { return 1 / x; }
} \
{ \
/* out OPERATOR out.FUNC() */ \
- Tensor<SCALAR, 3> out(tensorRange); \
+ Tensor<SCALAR, 3, Layout, int64_t> out(tensorRange); \
out = out.random() + static_cast<SCALAR>(0.01); \
- Tensor<SCALAR, 3> reference(out); \
+ Tensor<SCALAR, 3, Layout, int64_t> reference(out); \
SCALAR *gpu_data_out = static_cast<SCALAR *>( \
sycl_device.allocate(out.size() * sizeof(SCALAR))); \
- TensorMap<Tensor<SCALAR, 3>> gpu_out(gpu_data_out, tensorRange); \
+ TensorMap<Tensor<SCALAR, 3, Layout, int64_t>> gpu_out(gpu_data_out, tensorRange); \
sycl_device.memcpyHostToDevice(gpu_data_out, out.data(), \
(out.size()) * sizeof(SCALAR)); \
gpu_out.device(sycl_device) OPERATOR gpu_out.FUNC(); \
sycl_device.memcpyDeviceToHost(out.data(), gpu_data_out, \
(out.size()) * sizeof(SCALAR)); \
- for (int i = 0; i < out.size(); ++i) { \
+ for (int64_t i = 0; i < out.size(); ++i) { \
SCALAR ver = reference(i); \
ver OPERATOR std::FUNC(reference(i)); \
VERIFY_IS_APPROX(out(i), ver); \
@@ -82,61 +82,62 @@ template <typename T> T inverse(T x) { return 1 / x; }
sycl_device.deallocate(gpu_data_out); \
}
-#define TEST_UNARY_BUILTINS_OPERATOR(SCALAR, OPERATOR) \
- TEST_UNARY_BUILTINS_FOR_SCALAR(abs, SCALAR, OPERATOR) \
- TEST_UNARY_BUILTINS_FOR_SCALAR(sqrt, SCALAR, OPERATOR) \
- TEST_UNARY_BUILTINS_FOR_SCALAR(rsqrt, SCALAR, OPERATOR) \
- TEST_UNARY_BUILTINS_FOR_SCALAR(square, SCALAR, OPERATOR) \
- TEST_UNARY_BUILTINS_FOR_SCALAR(cube, SCALAR, OPERATOR) \
- TEST_UNARY_BUILTINS_FOR_SCALAR(inverse, SCALAR, OPERATOR) \
- TEST_UNARY_BUILTINS_FOR_SCALAR(tanh, SCALAR, OPERATOR) \
- TEST_UNARY_BUILTINS_FOR_SCALAR(exp, SCALAR, OPERATOR) \
- TEST_UNARY_BUILTINS_FOR_SCALAR(expm1, SCALAR, OPERATOR) \
- TEST_UNARY_BUILTINS_FOR_SCALAR(log, SCALAR, OPERATOR) \
- TEST_UNARY_BUILTINS_FOR_SCALAR(abs, SCALAR, OPERATOR) \
- TEST_UNARY_BUILTINS_FOR_SCALAR(ceil, SCALAR, OPERATOR) \
- TEST_UNARY_BUILTINS_FOR_SCALAR(floor, SCALAR, OPERATOR) \
- TEST_UNARY_BUILTINS_FOR_SCALAR(round, SCALAR, OPERATOR) \
- TEST_UNARY_BUILTINS_FOR_SCALAR(log1p, SCALAR, OPERATOR)
+#define TEST_UNARY_BUILTINS_OPERATOR(SCALAR, OPERATOR , Layout) \
+ TEST_UNARY_BUILTINS_FOR_SCALAR(abs, SCALAR, OPERATOR , Layout) \
+ TEST_UNARY_BUILTINS_FOR_SCALAR(sqrt, SCALAR, OPERATOR , Layout) \
+ TEST_UNARY_BUILTINS_FOR_SCALAR(rsqrt, SCALAR, OPERATOR , Layout) \
+ TEST_UNARY_BUILTINS_FOR_SCALAR(square, SCALAR, OPERATOR , Layout) \
+ TEST_UNARY_BUILTINS_FOR_SCALAR(cube, SCALAR, OPERATOR , Layout) \
+ TEST_UNARY_BUILTINS_FOR_SCALAR(inverse, SCALAR, OPERATOR , Layout) \
+ TEST_UNARY_BUILTINS_FOR_SCALAR(tanh, SCALAR, OPERATOR , Layout) \
+ TEST_UNARY_BUILTINS_FOR_SCALAR(exp, SCALAR, OPERATOR , Layout) \
+ TEST_UNARY_BUILTINS_FOR_SCALAR(expm1, SCALAR, OPERATOR , Layout) \
+ TEST_UNARY_BUILTINS_FOR_SCALAR(log, SCALAR, OPERATOR , Layout) \
+ TEST_UNARY_BUILTINS_FOR_SCALAR(abs, SCALAR, OPERATOR , Layout) \
+ TEST_UNARY_BUILTINS_FOR_SCALAR(ceil, SCALAR, OPERATOR , Layout) \
+ TEST_UNARY_BUILTINS_FOR_SCALAR(floor, SCALAR, OPERATOR , Layout) \
+ TEST_UNARY_BUILTINS_FOR_SCALAR(round, SCALAR, OPERATOR , Layout) \
+ TEST_UNARY_BUILTINS_FOR_SCALAR(log1p, SCALAR, OPERATOR , Layout)
-#define TEST_IS_THAT_RETURNS_BOOL(SCALAR, FUNC) \
+#define TEST_IS_THAT_RETURNS_BOOL(SCALAR, FUNC, Layout) \
{ \
/* out = in.FUNC() */ \
- Tensor<SCALAR, 3> in(tensorRange); \
- Tensor<bool, 3> out(tensorRange); \
+ Tensor<SCALAR, 3, Layout, int64_t> in(tensorRange); \
+ Tensor<bool, 3, Layout, int64_t> out(tensorRange); \
in = in.random() + static_cast<SCALAR>(0.01); \
SCALAR *gpu_data = static_cast<SCALAR *>( \
sycl_device.allocate(in.size() * sizeof(SCALAR))); \
bool *gpu_data_out = \
static_cast<bool *>(sycl_device.allocate(out.size() * sizeof(bool))); \
- TensorMap<Tensor<SCALAR, 3>> gpu(gpu_data, tensorRange); \
- TensorMap<Tensor<bool, 3>> gpu_out(gpu_data_out, tensorRange); \
+ TensorMap<Tensor<SCALAR, 3, Layout, int64_t>> gpu(gpu_data, tensorRange); \
+ TensorMap<Tensor<bool, 3, Layout, int64_t>> gpu_out(gpu_data_out, tensorRange); \
sycl_device.memcpyHostToDevice(gpu_data, in.data(), \
(in.size()) * sizeof(SCALAR)); \
gpu_out.device(sycl_device) = gpu.FUNC(); \
sycl_device.memcpyDeviceToHost(out.data(), gpu_data_out, \
(out.size()) * sizeof(bool)); \
- for (int i = 0; i < out.size(); ++i) { \
+ for (int64_t i = 0; i < out.size(); ++i) { \
VERIFY_IS_EQUAL(out(i), std::FUNC(in(i))); \
} \
sycl_device.deallocate(gpu_data); \
sycl_device.deallocate(gpu_data_out); \
}
-#define TEST_UNARY_BUILTINS(SCALAR) \
- TEST_UNARY_BUILTINS_OPERATOR(SCALAR, +=) \
- TEST_UNARY_BUILTINS_OPERATOR(SCALAR, =) \
- TEST_IS_THAT_RETURNS_BOOL(SCALAR, isnan) \
- TEST_IS_THAT_RETURNS_BOOL(SCALAR, isfinite) \
- TEST_IS_THAT_RETURNS_BOOL(SCALAR, isinf)
+#define TEST_UNARY_BUILTINS(SCALAR, Layout) \
+ TEST_UNARY_BUILTINS_OPERATOR(SCALAR, +=, Layout) \
+ TEST_UNARY_BUILTINS_OPERATOR(SCALAR, =, Layout) \
+ TEST_IS_THAT_RETURNS_BOOL(SCALAR, isnan, Layout) \
+ TEST_IS_THAT_RETURNS_BOOL(SCALAR, isfinite, Layout) \
+ TEST_IS_THAT_RETURNS_BOOL(SCALAR, isinf, Layout)
static void test_builtin_unary_sycl(const Eigen::SyclDevice &sycl_device) {
- int sizeDim1 = 10;
- int sizeDim2 = 10;
- int sizeDim3 = 10;
- array<int, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
+ int64_t sizeDim1 = 10;
+ int64_t sizeDim2 = 10;
+ int64_t sizeDim3 = 10;
+ array<int64_t, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
- TEST_UNARY_BUILTINS(float)
+ TEST_UNARY_BUILTINS(float, RowMajor)
+ TEST_UNARY_BUILTINS(float, ColMajor)
}
namespace std {
@@ -144,24 +145,24 @@ template <typename T> T cwiseMax(T x, T y) { return std::max(x, y); }
template <typename T> T cwiseMin(T x, T y) { return std::min(x, y); }
}
-#define TEST_BINARY_BUILTINS_FUNC(SCALAR, FUNC) \
+#define TEST_BINARY_BUILTINS_FUNC(SCALAR, FUNC, Layout) \
{ \
/* out = in_1.FUNC(in_2) */ \
- Tensor<SCALAR, 3> in_1(tensorRange); \
- Tensor<SCALAR, 3> in_2(tensorRange); \
- Tensor<SCALAR, 3> out(tensorRange); \
+ Tensor<SCALAR, 3, Layout, int64_t> in_1(tensorRange); \
+ Tensor<SCALAR, 3, Layout, int64_t> in_2(tensorRange); \
+ Tensor<SCALAR, 3, Layout, int64_t> out(tensorRange); \
in_1 = in_1.random() + static_cast<SCALAR>(0.01); \
in_2 = in_2.random() + static_cast<SCALAR>(0.01); \
- Tensor<SCALAR, 3> reference(out); \
+ Tensor<SCALAR, 3, Layout, int64_t> reference(out); \
SCALAR *gpu_data_1 = static_cast<SCALAR *>( \
sycl_device.allocate(in_1.size() * sizeof(SCALAR))); \
SCALAR *gpu_data_2 = static_cast<SCALAR *>( \
sycl_device.allocate(in_2.size() * sizeof(SCALAR))); \
SCALAR *gpu_data_out = static_cast<SCALAR *>( \
sycl_device.allocate(out.size() * sizeof(SCALAR))); \
- TensorMap<Tensor<SCALAR, 3>> gpu_1(gpu_data_1, tensorRange); \
- TensorMap<Tensor<SCALAR, 3>> gpu_2(gpu_data_2, tensorRange); \
- TensorMap<Tensor<SCALAR, 3>> gpu_out(gpu_data_out, tensorRange); \
+ TensorMap<Tensor<SCALAR, 3, Layout, int64_t>> gpu_1(gpu_data_1, tensorRange); \
+ TensorMap<Tensor<SCALAR, 3, Layout, int64_t>> gpu_2(gpu_data_2, tensorRange); \
+ TensorMap<Tensor<SCALAR, 3, Layout, int64_t>> gpu_out(gpu_data_out, tensorRange); \
sycl_device.memcpyHostToDevice(gpu_data_1, in_1.data(), \
(in_1.size()) * sizeof(SCALAR)); \
sycl_device.memcpyHostToDevice(gpu_data_2, in_2.data(), \
@@ -169,7 +170,7 @@ template <typename T> T cwiseMin(T x, T y) { return std::min(x, y); }
gpu_out.device(sycl_device) = gpu_1.FUNC(gpu_2); \
sycl_device.memcpyDeviceToHost(out.data(), gpu_data_out, \
(out.size()) * sizeof(SCALAR)); \
- for (int i = 0; i < out.size(); ++i) { \
+ for (int64_t i = 0; i < out.size(); ++i) { \
SCALAR ver = reference(i); \
ver = std::FUNC(in_1(i), in_2(i)); \
VERIFY_IS_APPROX(out(i), ver); \
@@ -179,24 +180,24 @@ template <typename T> T cwiseMin(T x, T y) { return std::min(x, y); }
sycl_device.deallocate(gpu_data_out); \
}
-#define TEST_BINARY_BUILTINS_OPERATORS(SCALAR, OPERATOR) \
+#define TEST_BINARY_BUILTINS_OPERATORS(SCALAR, OPERATOR, Layout) \
{ \
/* out = in_1 OPERATOR in_2 */ \
- Tensor<SCALAR, 3> in_1(tensorRange); \
- Tensor<SCALAR, 3> in_2(tensorRange); \
- Tensor<SCALAR, 3> out(tensorRange); \
+ Tensor<SCALAR, 3, Layout, int64_t> in_1(tensorRange); \
+ Tensor<SCALAR, 3, Layout, int64_t> in_2(tensorRange); \
+ Tensor<SCALAR, 3, Layout, int64_t> out(tensorRange); \
in_1 = in_1.random() + static_cast<SCALAR>(0.01); \
in_2 = in_2.random() + static_cast<SCALAR>(0.01); \
- Tensor<SCALAR, 3> reference(out); \
+ Tensor<SCALAR, 3, Layout, int64_t> reference(out); \
SCALAR *gpu_data_1 = static_cast<SCALAR *>( \
sycl_device.allocate(in_1.size() * sizeof(SCALAR))); \
SCALAR *gpu_data_2 = static_cast<SCALAR *>( \
sycl_device.allocate(in_2.size() * sizeof(SCALAR))); \
SCALAR *gpu_data_out = static_cast<SCALAR *>( \
sycl_device.allocate(out.size() * sizeof(SCALAR))); \
- TensorMap<Tensor<SCALAR, 3>> gpu_1(gpu_data_1, tensorRange); \
- TensorMap<Tensor<SCALAR, 3>> gpu_2(gpu_data_2, tensorRange); \
- TensorMap<Tensor<SCALAR, 3>> gpu_out(gpu_data_out, tensorRange); \
+ TensorMap<Tensor<SCALAR, 3, Layout, int64_t>> gpu_1(gpu_data_1, tensorRange); \
+ TensorMap<Tensor<SCALAR, 3, Layout, int64_t>> gpu_2(gpu_data_2, tensorRange); \
+ TensorMap<Tensor<SCALAR, 3, Layout, int64_t>> gpu_out(gpu_data_out, tensorRange); \
sycl_device.memcpyHostToDevice(gpu_data_1, in_1.data(), \
(in_1.size()) * sizeof(SCALAR)); \
sycl_device.memcpyHostToDevice(gpu_data_2, in_2.data(), \
@@ -204,7 +205,7 @@ template <typename T> T cwiseMin(T x, T y) { return std::min(x, y); }
gpu_out.device(sycl_device) = gpu_1 OPERATOR gpu_2; \
sycl_device.memcpyDeviceToHost(out.data(), gpu_data_out, \
(out.size()) * sizeof(SCALAR)); \
- for (int i = 0; i < out.size(); ++i) { \
+ for (int64_t i = 0; i < out.size(); ++i) { \
VERIFY_IS_APPROX(out(i), in_1(i) OPERATOR in_2(i)); \
} \
sycl_device.deallocate(gpu_data_1); \
@@ -212,46 +213,48 @@ template <typename T> T cwiseMin(T x, T y) { return std::min(x, y); }
sycl_device.deallocate(gpu_data_out); \
}
-#define TEST_BINARY_BUILTINS_OPERATORS_THAT_TAKES_SCALAR(SCALAR, OPERATOR) \
+#define TEST_BINARY_BUILTINS_OPERATORS_THAT_TAKES_SCALAR(SCALAR, OPERATOR, Layout) \
{ \
/* out = in_1 OPERATOR 2 */ \
- Tensor<SCALAR, 3> in_1(tensorRange); \
- Tensor<SCALAR, 3> out(tensorRange); \
+ Tensor<SCALAR, 3, Layout, int64_t> in_1(tensorRange); \
+ Tensor<SCALAR, 3, Layout, int64_t> out(tensorRange); \
in_1 = in_1.random() + static_cast<SCALAR>(0.01); \
- Tensor<SCALAR, 3> reference(out); \
+ Tensor<SCALAR, 3, Layout, int64_t> reference(out); \
SCALAR *gpu_data_1 = static_cast<SCALAR *>( \
sycl_device.allocate(in_1.size() * sizeof(SCALAR))); \
SCALAR *gpu_data_out = static_cast<SCALAR *>( \
sycl_device.allocate(out.size() * sizeof(SCALAR))); \
- TensorMap<Tensor<SCALAR, 3>> gpu_1(gpu_data_1, tensorRange); \
- TensorMap<Tensor<SCALAR, 3>> gpu_out(gpu_data_out, tensorRange); \
+ TensorMap<Tensor<SCALAR, 3, Layout, int64_t>> gpu_1(gpu_data_1, tensorRange); \
+ TensorMap<Tensor<SCALAR, 3, Layout, int64_t>> gpu_out(gpu_data_out, tensorRange); \
sycl_device.memcpyHostToDevice(gpu_data_1, in_1.data(), \
(in_1.size()) * sizeof(SCALAR)); \
gpu_out.device(sycl_device) = gpu_1 OPERATOR 2; \
sycl_device.memcpyDeviceToHost(out.data(), gpu_data_out, \
(out.size()) * sizeof(SCALAR)); \
- for (int i = 0; i < out.size(); ++i) { \
+ for (int64_t i = 0; i < out.size(); ++i) { \
VERIFY_IS_APPROX(out(i), in_1(i) OPERATOR 2); \
} \
sycl_device.deallocate(gpu_data_1); \
sycl_device.deallocate(gpu_data_out); \
}
-#define TEST_BINARY_BUILTINS(SCALAR) \
- TEST_BINARY_BUILTINS_FUNC(SCALAR, cwiseMax) \
- TEST_BINARY_BUILTINS_FUNC(SCALAR, cwiseMin) \
- TEST_BINARY_BUILTINS_OPERATORS(SCALAR, +) \
- TEST_BINARY_BUILTINS_OPERATORS(SCALAR, -) \
- TEST_BINARY_BUILTINS_OPERATORS(SCALAR, *) \
- TEST_BINARY_BUILTINS_OPERATORS(SCALAR, /)
+#define TEST_BINARY_BUILTINS(SCALAR, Layout) \
+ TEST_BINARY_BUILTINS_FUNC(SCALAR, cwiseMax , Layout) \
+ TEST_BINARY_BUILTINS_FUNC(SCALAR, cwiseMin , Layout) \
+ TEST_BINARY_BUILTINS_OPERATORS(SCALAR, + , Layout) \
+ TEST_BINARY_BUILTINS_OPERATORS(SCALAR, - , Layout) \
+ TEST_BINARY_BUILTINS_OPERATORS(SCALAR, * , Layout) \
+ TEST_BINARY_BUILTINS_OPERATORS(SCALAR, / , Layout)
static void test_builtin_binary_sycl(const Eigen::SyclDevice &sycl_device) {
- int sizeDim1 = 10;
- int sizeDim2 = 10;
- int sizeDim3 = 10;
- array<int, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
- TEST_BINARY_BUILTINS(float)
- TEST_BINARY_BUILTINS_OPERATORS_THAT_TAKES_SCALAR(int, %)
+ int64_t sizeDim1 = 10;
+ int64_t sizeDim2 = 10;
+ int64_t sizeDim3 = 10;
+ array<int64_t, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
+ TEST_BINARY_BUILTINS(float, RowMajor)
+ TEST_BINARY_BUILTINS_OPERATORS_THAT_TAKES_SCALAR(int, %, RowMajor)
+ TEST_BINARY_BUILTINS(float, ColMajor)
+ TEST_BINARY_BUILTINS_OPERATORS_THAT_TAKES_SCALAR(int, %, ColMajor)
}
void test_cxx11_tensor_builtins_sycl() {
generated by cgit v1.2.3 (git 2.39.1) at 2024-05-15 16:54:58 +0000