From 622805a0c5d216141eca3090e80d58c159e175ee Mon Sep 17 00:00:00 2001
From: Mehdi Goli <mehdi.goli@codeplay.com>
Date: Fri, 18 Nov 2016 16:20:42 +0000
Subject: Modifying TensorDeviceSycl.h to always create buffer of type uint8_t
 and convert them to the actual type at the execution on the device; adding
 the queue interface class to separate the lifespan of sycl queue and
 buffers,created for that queue, from Eigen::SyclDevice; modifying sycl tests
 to support the evaluation of the results for both row major and column major
 data layout on all different devices that are supported by Sycl{CPU; GPU; and
 Host}.

---
 unsupported/test/cxx11_tensor_forced_eval_sycl.cpp | 47 +++++++++++++---------
 1 file changed, 29 insertions(+), 18 deletions(-)

(limited to 'unsupported/test/cxx11_tensor_forced_eval_sycl.cpp')

diff --git a/unsupported/test/cxx11_tensor_forced_eval_sycl.cpp b/unsupported/test/cxx11_tensor_forced_eval_sycl.cpp
index 5690da723..70b182558 100644
--- a/unsupported/test/cxx11_tensor_forced_eval_sycl.cpp
+++ b/unsupported/test/cxx11_tensor_forced_eval_sycl.cpp
@@ -21,33 +21,33 @@
 #include <unsupported/Eigen/CXX11/Tensor>
 
 using Eigen::Tensor;
-
+template <typename DataType, int DataLayout>
 void test_forced_eval_sycl(const Eigen::SyclDevice &sycl_device) {
 
   int sizeDim1 = 100;
-  int sizeDim2 = 200;
-  int sizeDim3 = 200;
+  int sizeDim2 = 20;
+  int sizeDim3 = 20;
   Eigen::array<int, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
-  Eigen::Tensor<float, 3> in1(tensorRange);
-  Eigen::Tensor<float, 3> in2(tensorRange);
-  Eigen::Tensor<float, 3> out(tensorRange);
+  Eigen::Tensor<DataType, 3, DataLayout> in1(tensorRange);
+  Eigen::Tensor<DataType, 3, DataLayout> in2(tensorRange);
+  Eigen::Tensor<DataType, 3, DataLayout> out(tensorRange);
 
-  float * gpu_in1_data  = static_cast<float*>(sycl_device.allocate(in1.dimensions().TotalSize()*sizeof(float)));
-  float * gpu_in2_data  = static_cast<float*>(sycl_device.allocate(in2.dimensions().TotalSize()*sizeof(float)));
-  float * gpu_out_data =  static_cast<float*>(sycl_device.allocate(out.dimensions().TotalSize()*sizeof(float)));
+  DataType * gpu_in1_data  = static_cast<DataType*>(sycl_device.allocate(in1.dimensions().TotalSize()*sizeof(DataType)));
+  DataType * gpu_in2_data  = static_cast<DataType*>(sycl_device.allocate(in2.dimensions().TotalSize()*sizeof(DataType)));
+  DataType * gpu_out_data =  static_cast<DataType*>(sycl_device.allocate(out.dimensions().TotalSize()*sizeof(DataType)));
 
   in1 = in1.random() + in1.constant(10.0f);
   in2 = in2.random() + in2.constant(10.0f);
 
   // creating TensorMap from tensor
-  Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_in1(gpu_in1_data, tensorRange);
-  Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_in2(gpu_in2_data, tensorRange);
-  Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_out(gpu_out_data, tensorRange);
-  sycl_device.memcpyHostToDevice(gpu_in1_data, in1.data(),(in1.dimensions().TotalSize())*sizeof(float));
-  sycl_device.memcpyHostToDevice(gpu_in2_data, in2.data(),(in1.dimensions().TotalSize())*sizeof(float));
+  Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout>> gpu_in1(gpu_in1_data, tensorRange);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout>> gpu_in2(gpu_in2_data, tensorRange);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout>> gpu_out(gpu_out_data, tensorRange);
+  sycl_device.memcpyHostToDevice(gpu_in1_data, in1.data(),(in1.dimensions().TotalSize())*sizeof(DataType));
+  sycl_device.memcpyHostToDevice(gpu_in2_data, in2.data(),(in1.dimensions().TotalSize())*sizeof(DataType));
   /// c=(a+b)*b
   gpu_out.device(sycl_device) =(gpu_in1 + gpu_in2).eval() * gpu_in2;
-  sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(float));
+  sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(DataType));
   for (int i = 0; i < sizeDim1; ++i) {
     for (int j = 0; j < sizeDim2; ++j) {
       for (int k = 0; k < sizeDim3; ++k) {
@@ -63,8 +63,19 @@ void test_forced_eval_sycl(const Eigen::SyclDevice &sycl_device) {
 
 }
 
+template <typename DataType, typename Dev_selector> void tensorForced_evalperDevice(Dev_selector s){
+  QueueInterface queueInterface(s);
+  auto sycl_device = Eigen::SyclDevice(&queueInterface);
+  test_forced_eval_sycl<DataType, RowMajor>(sycl_device);
+  test_forced_eval_sycl<DataType, ColMajor>(sycl_device);
+}
 void test_cxx11_tensor_forced_eval_sycl() {
-  cl::sycl::gpu_selector s;
-  Eigen::SyclDevice sycl_device(s);
-  CALL_SUBTEST(test_forced_eval_sycl(sycl_device));
+
+    printf("Test on GPU: OpenCL\n");
+    CALL_SUBTEST(tensorForced_evalperDevice<float>((cl::sycl::gpu_selector())));
+    printf("repeating the test on CPU: OpenCL\n");
+    CALL_SUBTEST(tensorForced_evalperDevice<float>((cl::sycl::cpu_selector())));
+    printf("repeating the test on CPU: HOST\n");
+    CALL_SUBTEST(tensorForced_evalperDevice<float>((cl::sycl::host_selector())));
+    printf("Test Passed******************\n" );
 }
-- 
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