clang format

src/targets/gpu/device/logsoftmax.cpp

@@ -23,17 +23,21 @@ void logsoftmax(hipStream_t stream, const argument& result, const argument& arg,
     hip_visit_all(result, arg, batch_shape)([&](auto output, auto input, auto batch) {
         const std::size_t max_block_size = 256;
         const std::size_t block_size     = compute_block_size(batch_item_num, max_block_size);
-        gs_launch(stream, batch_shape.elements() * block_size, block_size)([=](auto i, auto idx) __device__ {
+        gs_launch(stream,
+                  batch_shape.elements() * block_size,
+                  block_size)([=](auto i, auto idx) __device__ {
             auto data_idx = batch.multi(i / block_size);
             using type    = device_type<std::remove_cv_t<typename decltype(input)::value_type>>;
             type init     = lowest();
 
-            auto batch_max = block_reduce<max_block_size>(idx, max{}, init, batch_item_num, [&](auto j) __device__ {
+            auto batch_max = block_reduce<max_block_size>(
+                idx, max{}, init, batch_item_num, [&](auto j) __device__ {
                     data_idx[axis] = j;
                     return input[data_idx];
                 });
 
-            auto batch_sum = block_reduce<max_block_size>(idx, sum{}, 0, batch_item_num, [&](auto j) __device__ {
+            auto batch_sum =
+                block_reduce<max_block_size>(idx, sum{}, 0, batch_item_num, [&](auto j) __device__ {
                     data_idx[axis] = j;
                     auto val       = input[data_idx] - batch_max;
                     return ::exp(to_hip_type(val));

src/targets/gpu/device/softmax.cpp

@@ -24,17 +24,21 @@ void softmax(hipStream_t stream, const argument& result, const argument& arg, in
     hip_visit_all(result, arg, batch_shape)([&](auto output, auto input, auto batch) {
         const std::size_t max_block_size = 256;
         const std::size_t block_size     = compute_block_size(batch_item_num, max_block_size);
-        gs_launch(stream, batch_shape.elements() * block_size, block_size)([=](auto i, auto idx) __device__ {
+        gs_launch(stream,
+                  batch_shape.elements() * block_size,
+                  block_size)([=](auto i, auto idx) __device__ {
             auto data_idx = batch.multi(i / block_size);
             using type    = device_type<std::remove_cv_t<typename decltype(input)::value_type>>;
             type init     = lowest();
 
-            auto batch_max = block_reduce<max_block_size>(idx, max{}, init, batch_item_num, [&](auto j) __device__ {
+            auto batch_max = block_reduce<max_block_size>(
+                idx, max{}, init, batch_item_num, [&](auto j) __device__ {
                     data_idx[axis] = j;
                     return input[data_idx];
                 });
 
-            auto batch_sum = block_reduce<max_block_size>(idx, sum{}, 0, batch_item_num, [&](auto j) __device__ {
+            auto batch_sum =
+                block_reduce<max_block_size>(idx, sum{}, 0, batch_item_num, [&](auto j) __device__ {
                     data_idx[axis] = j;
                     auto val       = input[data_idx] - batch_max;
                     return ::exp(to_hip_type(val));