Partial OpenCL support via SYCL compatible with ComputeCpp CE.

1 files changed, 157 insertions, 0 deletions

diff --git a/unsupported/test/cxx11_tensor_sycl.cpp b/unsupported/test/cxx11_tensor_sycl.cpp
new file mode 100644
index 000000000..1ec9b1883
--- /dev/null
+++ b/unsupported/test/cxx11_tensor_sycl.cpp
@@ -0,0 +1,157 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 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
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
+#define EIGEN_USE_SYCL
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::array;
+using Eigen::SyclDevice;
+using Eigen::Tensor;
+using Eigen::TensorMap;
+
+// Types used in tests:
+using TestTensor = Tensor<float, 3>;
+using TestTensorMap = TensorMap<Tensor<float, 3>>;
+
+void test_sycl_cpu() {
+	cl::sycl::gpu_selector s;
+  cl::sycl::queue q(s, [=](cl::sycl::exception_list l) {
+    for (const auto& e : l) {
+      try {
+        std::rethrow_exception(e);
+      } catch (cl::sycl::exception e) {
+        std::cout << e.what() << std::endl;
+      }
+    }
+  });
+	SyclDevice sycl_device(q);
+
+  int sizeDim1 = 100;
+  int sizeDim2 = 100;
+  int sizeDim3 = 100;
+  array<int, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
+  TestTensor in1(tensorRange);
+  TestTensor in2(tensorRange);
+  TestTensor in3(tensorRange);
+  TestTensor out(tensorRange);
+  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());
+  for (int i = 0; i < sizeDim1; ++i) {
+    for (int j = 0; j < sizeDim2; ++j) {
+      for (int k = 0; k < sizeDim3; ++k) {
+        VERIFY_IS_APPROX(in1(i,j,k), 1.2f);
+      }
+    }
+  }
+	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());
+  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) * 1.2f);
+      }
+    }
+  }
+	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());
+  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) *
+                             in2(i,j,k));
+      }
+    }
+  }
+	printf("c=a*b Test Passed\n");
+
+	/// 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) {
+        VERIFY_IS_APPROX(out(i,j,k),
+                         in1(i,j,k) +
+                             in2(i,j,k));
+      }
+    }
+  }
+	printf("c=a+b Test Passed\n");
+
+	/// 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) {
+        VERIFY_IS_APPROX(out(i,j,k),
+                         in1(i,j,k) *
+                             in1(i,j,k));
+      }
+    }
+  }
+
+	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());
+  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) * 3.14f
+                       + in2(i,j,k) * 2.7f);
+      }
+    }
+  }
+	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() {
+  CALL_SUBTEST(test_sycl_cpu());
+}