Reducing the code by generalising sycl backend functions/structs.

5 files changed, 60 insertions, 62 deletions

diff --git a/unsupported/test/CMakeLists.txt b/unsupported/test/CMakeLists.txt
index 17073dfa7..aeaea162a 100644
--- a/unsupported/test/CMakeLists.txt
+++ b/unsupported/test/CMakeLists.txt
@@ -140,6 +140,12 @@ endif()
 endif()
 
 if(EIGEN_TEST_CXX11)
+  if(EIGEN_TEST_SYCL)
+    ei_add_test_sycl(cxx11_tensor_sycl "-std=c++11")
+    ei_add_test_sycl(cxx11_tensor_forced_eval_sycl "-std=c++11")
+    ei_add_test_sycl(cxx11_tensor_broadcast_sycl "-std=c++11")
+    ei_add_test_sycl(cxx11_tensor_device_sycl "-std=c++11")
+  endif(EIGEN_TEST_SYCL)
   # It should be safe to always run these tests as there is some fallback code for
   # older compiler that don't support cxx11.
   set(CMAKE_CXX_STANDARD 11)
@@ -248,11 +254,3 @@ if(CUDA_FOUND AND EIGEN_TEST_CUDA)
 
   unset(EIGEN_ADD_TEST_FILENAME_EXTENSION)
 endif()
-
-
-if(EIGEN_TEST_SYCL)
-  ei_add_test_sycl(cxx11_tensor_sycl "-std=c++11")
-  ei_add_test_sycl(cxx11_tensor_forced_eval_sycl "-std=c++11")
-  ei_add_test_sycl(cxx11_tensor_broadcast_sycl "-std=c++11")
-  ei_add_test_sycl(cxx11_tensor_device_sycl "-std=c++11")
-endif(EIGEN_TEST_SYCL)
\ No newline at end of file
diff --git a/unsupported/test/cxx11_tensor_broadcast_sycl.cpp b/unsupported/test/cxx11_tensor_broadcast_sycl.cpp
index 0179ab243..ecebf7d68 100644
--- a/unsupported/test/cxx11_tensor_broadcast_sycl.cpp
+++ b/unsupported/test/cxx11_tensor_broadcast_sycl.cpp
@@ -28,7 +28,7 @@ using Eigen::TensorMap;
 // Types used in tests:
 using TestTensor = Tensor<float, 3>;
 using TestTensorMap = TensorMap<Tensor<float, 3>>;
-static void test_sycl_broadcast(){
+static void test_broadcast_sycl(){
 
 	cl::sycl::gpu_selector s;
   cl::sycl::queue q(s, [=](cl::sycl::exception_list l) {
@@ -75,5 +75,5 @@ static void test_sycl_broadcast(){
 }
 
 void test_cxx11_tensor_broadcast_sycl() {
-  CALL_SUBTEST(test_sycl_broadcast());
+  CALL_SUBTEST(test_broadcast_sycl());
 }
diff --git a/unsupported/test/cxx11_tensor_device_sycl.cpp b/unsupported/test/cxx11_tensor_device_sycl.cpp
index b9368cb84..f54fc8786 100644
--- a/unsupported/test/cxx11_tensor_device_sycl.cpp
+++ b/unsupported/test/cxx11_tensor_device_sycl.cpp
@@ -20,7 +20,7 @@
 #include "main.h"
 #include <unsupported/Eigen/CXX11/Tensor>
 
-void test_sycl_device() {
+void test_device_sycl() {
   cl::sycl::gpu_selector s;
   cl::sycl::queue q(s, [=](cl::sycl::exception_list l) {
     for (const auto& e : l) {
@@ -31,9 +31,9 @@ void test_sycl_device() {
       }
     }
   });
-  SyclDevice sycl_device(q);
+  Eigen::SyclDevice sycl_device(q);
 	printf("Helo from ComputeCpp: Device Exists\n");
 }
 void test_cxx11_tensor_device_sycl() {
-  CALL_SUBTEST(test_sycl_device());
+  CALL_SUBTEST(test_device_sycl());
 }
diff --git a/unsupported/test/cxx11_tensor_forced_eval_sycl.cpp b/unsupported/test/cxx11_tensor_forced_eval_sycl.cpp
index 7c4446192..182ec7fa8 100644
--- a/unsupported/test/cxx11_tensor_forced_eval_sycl.cpp
+++ b/unsupported/test/cxx11_tensor_forced_eval_sycl.cpp
@@ -22,7 +22,7 @@
 
 using Eigen::Tensor;
 
-void test_sycl_gpu() {
+void test_forced_eval_sycl() {
 	cl::sycl::gpu_selector s;
   cl::sycl::queue q(s, [=](cl::sycl::exception_list l) {
     for (const auto& e : l) {
@@ -65,6 +65,4 @@ void test_sycl_gpu() {
 	printf("(a+b)*b Test Passed\n");
 }
 
-void test_cxx11_tensor_forced_eval_sycl() {
- CALL_SUBTEST(test_sycl_gpu());
-}
+void test_cxx11_tensor_forced_eval_sycl() { CALL_SUBTEST(test_forced_eval_sycl()); }
diff --git a/unsupported/test/cxx11_tensor_sycl.cpp b/unsupported/test/cxx11_tensor_sycl.cpp
index a4dc9f9fa..0f66cd8f0 100644
--- a/unsupported/test/cxx11_tensor_sycl.cpp
+++ b/unsupported/test/cxx11_tensor_sycl.cpp
@@ -6,11 +6,13 @@
 // Ralph Potter  Codeplay Software Ltd.
 // Luke Iwanski  Codeplay Software Ltd.
 // Contact: <eigen@codeplay.com>
+// Benoit Steiner <benoit.steiner.goog@gmail.com>
 //
 // 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/.
 
+
 #define EIGEN_TEST_NO_LONGDOUBLE
 #define EIGEN_TEST_NO_COMPLEX
 #define EIGEN_TEST_FUNC cxx11_tensor_sycl
@@ -30,7 +32,7 @@ using TestTensor = Tensor<float, 3>;
 using TestTensorMap = TensorMap<Tensor<float, 3>>;
 
 void test_sycl_cpu() {
-	cl::sycl::gpu_selector s;
+  cl::sycl::gpu_selector s;
   cl::sycl::queue q(s, [=](cl::sycl::exception_list l) {
     for (const auto& e : l) {
       try {
@@ -40,7 +42,7 @@ void test_sycl_cpu() {
       }
     }
   });
-	SyclDevice sycl_device(q);
+  SyclDevice sycl_device(q);
 
   int sizeDim1 = 100;
   int sizeDim2 = 100;
@@ -53,14 +55,14 @@ void test_sycl_cpu() {
   in1 = in1.random();
   in2 = in2.random();
   in3 = in3.random();
-	TestTensorMap gpu_in1(in1.data(), tensorRange);
-	TestTensorMap gpu_in2(in2.data(), tensorRange);
-	TestTensorMap gpu_in3(in3.data(), tensorRange);
-	TestTensorMap gpu_out(out.data(), tensorRange);
-
-	/// a=1.2f
-	gpu_in1.device(sycl_device) = gpu_in1.constant(1.2f);
-	sycl_device.deallocate(in1.data());
+  TestTensorMap gpu_in1(in1.data(), tensorRange);
+  TestTensorMap gpu_in2(in2.data(), tensorRange);
+  TestTensorMap gpu_in3(in3.data(), tensorRange);
+  TestTensorMap gpu_out(out.data(), tensorRange);
+
+  /// a=1.2f
+  gpu_in1.device(sycl_device) = gpu_in1.constant(1.2f);
+  sycl_device.deallocate(in1.data());
   for (int i = 0; i < sizeDim1; ++i) {
     for (int j = 0; j < sizeDim2; ++j) {
       for (int k = 0; k < sizeDim3; ++k) {
@@ -68,11 +70,11 @@ void test_sycl_cpu() {
       }
     }
   }
-	printf("a=1.2f Test passed\n");
+  printf("a=1.2f Test passed\n");
 
-	/// a=b*1.2f
-	gpu_out.device(sycl_device) = gpu_in1 * 1.2f;
-	sycl_device.deallocate(out.data());
+  /// a=b*1.2f
+  gpu_out.device(sycl_device) = gpu_in1 * 1.2f;
+  sycl_device.deallocate(out.data());
   for (int i = 0; i < sizeDim1; ++i) {
     for (int j = 0; j < sizeDim2; ++j) {
       for (int k = 0; k < sizeDim3; ++k) {
@@ -81,11 +83,11 @@ void test_sycl_cpu() {
       }
     }
   }
-	printf("a=b*1.2f Test Passed\n");
+  printf("a=b*1.2f Test Passed\n");
 
-	/// c=a*b
-	gpu_out.device(sycl_device) = gpu_in1 * gpu_in2;
-	sycl_device.deallocate(out.data());
+  /// c=a*b
+  gpu_out.device(sycl_device) = gpu_in1 * gpu_in2;
+  sycl_device.deallocate(out.data());
   for (int i = 0; i < sizeDim1; ++i) {
     for (int j = 0; j < sizeDim2; ++j) {
       for (int k = 0; k < sizeDim3; ++k) {
@@ -95,11 +97,11 @@ void test_sycl_cpu() {
       }
     }
   }
-	printf("c=a*b Test Passed\n");
+  printf("c=a*b Test Passed\n");
 
-	/// c=a+b
-	gpu_out.device(sycl_device) = gpu_in1 + gpu_in2;
-	sycl_device.deallocate(out.data());
+  /// c=a+b
+  gpu_out.device(sycl_device) = gpu_in1 + gpu_in2;
+  sycl_device.deallocate(out.data());
   for (int i = 0; i < sizeDim1; ++i) {
     for (int j = 0; j < sizeDim2; ++j) {
       for (int k = 0; k < sizeDim3; ++k) {
@@ -109,11 +111,11 @@ void test_sycl_cpu() {
       }
     }
   }
-	printf("c=a+b Test Passed\n");
+  printf("c=a+b Test Passed\n");
 
-	/// c=a*a
-	gpu_out.device(sycl_device) = gpu_in1 * gpu_in1;
-	sycl_device.deallocate(out.data());
+  /// c=a*a
+  gpu_out.device(sycl_device) = gpu_in1 * gpu_in1;
+  sycl_device.deallocate(out.data());
   for (int i = 0; i < sizeDim1; ++i) {
     for (int j = 0; j < sizeDim2; ++j) {
       for (int k = 0; k < sizeDim3; ++k) {
@@ -124,11 +126,11 @@ void test_sycl_cpu() {
     }
   }
 
-	printf("c= a*a Test Passed\n");
+  printf("c= a*a Test Passed\n");
 
-	//a*3.14f + b*2.7f
-	gpu_out.device(sycl_device) =  gpu_in1 * gpu_in1.constant(3.14f) + gpu_in2 * gpu_in2.constant(2.7f);
-	sycl_device.deallocate(out.data());
+  //a*3.14f + b*2.7f
+  gpu_out.device(sycl_device) =  gpu_in1 * gpu_in1.constant(3.14f) + gpu_in2 * gpu_in2.constant(2.7f);
+  sycl_device.deallocate(out.data());
   for (int i = 0; i < sizeDim1; ++i) {
     for (int j = 0; j < sizeDim2; ++j) {
       for (int k = 0; k < sizeDim3; ++k) {
@@ -138,21 +140,21 @@ void test_sycl_cpu() {
       }
     }
   }
-	printf("a*3.14f + b*2.7f Test Passed\n");
-
-	///d= (a>0.5? b:c)
-	gpu_out.device(sycl_device) =(gpu_in1 > gpu_in1.constant(0.5f)).select(gpu_in2, gpu_in3);
-	sycl_device.deallocate(out.data());
-	for (int i = 0; i < sizeDim1; ++i) {
-	  for (int j = 0; j < sizeDim2; ++j) {
-	    for (int k = 0; k < sizeDim3; ++k) {
-	      VERIFY_IS_APPROX(out(i, j, k), (in1(i, j, k) > 0.5f)
-	                                              ? in2(i, j, k)
-	                                              : in3(i, j, k));
-	    }
-	  }
-	}
-	printf("d= (a>0.5? b:c) Test Passed\n");
+  printf("a*3.14f + b*2.7f Test Passed\n");
+
+  ///d= (a>0.5? b:c)
+  gpu_out.device(sycl_device) =(gpu_in1 > gpu_in1.constant(0.5f)).select(gpu_in2, gpu_in3);
+  sycl_device.deallocate(out.data());
+  for (int i = 0; i < sizeDim1; ++i) {
+    for (int j = 0; j < sizeDim2; ++j) {
+      for (int k = 0; k < sizeDim3; ++k) {
+        VERIFY_IS_APPROX(out(i, j, k), (in1(i, j, k) > 0.5f)
+                                                ? in2(i, j, k)
+                                                : in3(i, j, k));
+      }
+    }
+  }
+  printf("d= (a>0.5? b:c) Test Passed\n");
 
 }
 void test_cxx11_tensor_sycl() {