Use cute::bfloat16_t

examples/cute/tutorial/pvc_sycl/pvc_sycl.cpp

@@ -20,11 +20,11 @@
 
 using test_clock = std::chrono::high_resolution_clock;
 
-using sycl::ext::oneapi::bfloat16;
+using namespace cute;
 
-using dtype_a = bfloat16;
-using dtype_b = bfloat16;
-using dtype_c = bfloat16;
+using dtype_a = bfloat16_t;
+using dtype_b = bfloat16_t;
+using dtype_c = float;
 using dtype_acc = float;
 
 bool identityData = false;
@@ -50,18 +50,19 @@ std::string makeTestName(const std::string &func, int tM, int tN, int tK,
 template <typename T>
 static void fill_matrix(std::vector<T> &M, size_t numRows, size_t numCols) {
   if (identityData) {
-    std::generate(std::begin(M), std::end(M), [&] { return 1.0f; });
+    std::generate(std::begin(M), std::end(M), [&] { return 1.0_bf16; });
   } else if (fixedData) {
     for (size_t r = 0; r < numRows; r++) {
       for (size_t c = 0; c < numCols; c++) {
-        M[r * numCols + c] = static_cast<float>(r + c);
+        M[r * numCols + c] = bfloat16_t(float(r + c));
       }
     }
   } else {
     std::random_device dev;
     std::mt19937 rng(dev());
     std::uniform_real_distribution<float> dist(-1.0, 1.0);
-    std::generate(std::begin(M), std::end(M), [&] { return dist(rng); });
+    std::generate(std::begin(M), std::end(M),
+                  [&] { return bfloat16_t(dist(rng)); });
   }
 }
 
@@ -179,16 +180,17 @@ go_dpas_blockread_vnni_tiled(sycl::queue queue, std::vector<dtype_acc> &c_vec,
               auto B = accB.get_multi_ptr<sycl::access::decorated::yes>().get();
               auto C = accC.get_multi_ptr<sycl::access::decorated::yes>().get();
 
-              using namespace cute;
-
               Tensor tAr = make_tensor<ushort>(Shape<_8, Int<MM>>{});
               Tensor tBr = make_tensor<uint>(Shape<_8, Int<NN>>{});
               Tensor tCr =
                   make_tensor<dtype_acc>(Shape<_8, Int<MM>, Int<NN>>{});
 
-            auto A_copy = make_xe_2d_copy<XE_2D_LOAD>(make_tensor(make_gmem_ptr(A), make_shape(M, K)));
-    auto B_copy = make_xe_2d_copy<XE_2D_LOAD>(make_tensor(make_gmem_ptr(B), make_shape(K, N)));
-    auto C_copy = make_xe_2d_copy<XE_2D_SAVE>(make_tensor(make_gmem_ptr(C), make_shape(M, N)));
+              auto A_copy = make_xe_2d_copy<XE_2D_LOAD>(
+                  make_tensor(make_gmem_ptr(A), make_shape(M, K)));
+              auto B_copy = make_xe_2d_copy<XE_2D_LOAD>(
+                  make_tensor(make_gmem_ptr(B), make_shape(K, N)));
+              auto C_copy = make_xe_2d_copy<XE_2D_SAVE>(
+                  make_tensor(make_gmem_ptr(C), make_shape(M, N)));
               // TODO: - decide on how to deal with vector types
               //       - create layouts with tiling/partitioning
 

include/cutlass/bfloat16.h

@@ -118,6 +118,10 @@ struct alignas(2) bfloat16_t {
 
     asm("cvt.rn.bf16.f32 %0, %1;\n" : "=h"(storage) : "f"(x));
 
+    #elif defined(CUTLASS_ENABLE_SYCL)
+
+    storage = sycl::ext::oneapi::detail::bfloat16ToBits(sycl::ext::oneapi::bfloat16(x));
+
     #else
     uint32_t bits;