Converting dispatch macros in fused_adam_cuda_kernel.cu

csrc/fused_adam_cuda_kernel.cu

@@ -203,7 +203,7 @@ void fused_adam_cuda(
                         tsize,
                         (adamMode_t) mode,
                         decay);
-                )
+                );
       } else {
             using namespace at;
             DISPATCH_DOUBLE_AND_FLOAT(g.scalar_type(), 0, "adam_cuda_kernel",
@@ -261,14 +261,14 @@ void fused_adam_cuda_mt(
         AT_ASSERTM(tensor_lists[0][0].scalar_type() == at::ScalarType::Float, "expected parameter to be of float type");
 //dich is done on the gradient type
         if (tl_sz == 5) {
-            AT_DISPATCH_FLOATING_TYPES_AND_HALF(tensor_lists[3][0].type(), "adam_cuda_mt_kernel", ([&] {
-                using accscalar_t = at::acc_type<scalar_t, true>;
+            DISPATCH_FLOAT_AND_HALF(tensor_lists[3][0].scalar_type(), 0, "adam_cuda_mt_kernel",
+                using accscalar_t = at::acc_type<scalar_t_0, true>;
                 multi_tensor_apply<5>(
                     BLOCK_SIZE,
                     chunk_size,
                     noop_flag,
                     tensor_lists,
-                    AdamFunctor<5, accscalar_t, scalar_t>(),
+                    AdamFunctor<5, accscalar_t, scalar_t_0>(),
                     beta1,
                     beta2,
                     eps,
@@ -276,16 +276,16 @@ void fused_adam_cuda_mt(
                     step_size,
                     (adamMode_t) mode,
                     decay);
-            }));
+            );
         } else {
-            AT_DISPATCH_FLOATING_TYPES_AND_HALF(tensor_lists[3][0].type(), "adam_cuda_mt_kernel", ([&] {
-                using accscalar_t = at::acc_type<scalar_t, true>;
+            DISPATCH_FLOAT_AND_HALF(tensor_lists[3][0].scalar_type(), 0, "adam_cuda_mt_kernel",
+                using accscalar_t = at::acc_type<scalar_t_0, true>;
                 multi_tensor_apply<4>(
                     BLOCK_SIZE,
                     chunk_size,
                     noop_flag,
                     tensor_lists,
-                    AdamFunctor<4, accscalar_t, scalar_t>(),
+                    AdamFunctor<4, accscalar_t, scalar_t_0>(),
                     beta1,
                     beta2,
                     eps,
@@ -293,17 +293,17 @@ void fused_adam_cuda_mt(
                     step_size,
                     (adamMode_t) mode,
                     decay);
-            }));
+            );
         }
     } else {
         if (tl_sz == 5) {
-            AT_DISPATCH_FLOATING_TYPES(tensor_lists[3][0].type(), "adam_cuda_mt_kernel", ([&] {
+            DISPATCH_DOUBLE_AND_FLOAT(tensor_lists[3][0].scalar_type(), 0, "adam_cuda_mt_kernel",
                 multi_tensor_apply<5>(
                     BLOCK_SIZE,
                     chunk_size,
                     noop_flag,
                     tensor_lists,
-                    AdamFunctor<5, scalar_t, scalar_t>(),
+                    AdamFunctor<5, scalar_t_0, scalar_t_0>(),
                     beta1,
                     beta2,
                     eps,
@@ -311,15 +311,15 @@ void fused_adam_cuda_mt(
                     step_size,
                     (adamMode_t) mode,
                     decay);
-            }));
+            );
         } else {
-            AT_DISPATCH_FLOATING_TYPES(tensor_lists[3][0].type(), "adam_cuda_mt_kernel", ([&] {
+            DISPATCH_DOUBLE_AND_FLOAT(tensor_lists[3][0].scalar_type(), 0, "adam_cuda_mt_kernel",
                 multi_tensor_apply<4>(
                     BLOCK_SIZE,
                     chunk_size,
                     noop_flag,
                     tensor_lists,
-                    AdamFunctor<4, scalar_t, scalar_t>(),
+                    AdamFunctor<4, scalar_t_0, scalar_t_0>(),
                     beta1,
                     beta2,
                     eps,
@@ -327,7 +327,7 @@ void fused_adam_cuda_mt(
                     step_size,
                     (adamMode_t) mode,
                     decay);
-            }));
+            );
         }
     }
     THCudaCheck(cudaGetLastError());