clang format

src/targets/gpu/device/include/migraphx/gpu/device/reduce.hpp

@@ -167,7 +167,7 @@ __device__ inline void dpp_reduce(float& x, sum)
 }
 
 template <std::size_t N, class Op, class T, class F>
-__device__ auto  block_reduce(index idx, Op op, T init, std::size_t n, F f) 
+__device__ auto block_reduce(index idx, Op op, T init, std::size_t n, F f)
 {
     using type = decltype(f(idx.local));
     MIGRAPHX_DEVICE_SHARED type buffer[N / 64];

src/targets/gpu/device/logsoftmax.cpp

@@ -23,26 +23,30 @@ 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__ {
-                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__ {
-                data_idx[axis] = j;
-                auto val = input[data_idx] - batch_max;
-                return ::exp(to_hip_type(val));
-            });
+            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__ {
+                    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__ {
+                    data_idx[axis] = j;
+                    auto val       = input[data_idx] - batch_max;
+                    return ::exp(to_hip_type(val));
+                });
 
             auto log_batch_sum = ::log(to_hip_type(batch_sum)) + batch_max;
 
-            idx.local_stride(batch_item_num, [&](auto j) { 
-                data_idx[axis] = j;
+            idx.local_stride(batch_item_num, [&](auto j) {
+                data_idx[axis]   = j;
                 output[data_idx] = input[data_idx] - log_batch_sum;
             });
         });

src/targets/gpu/device/softmax.cpp

@@ -24,25 +24,29 @@ 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();
+            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__ {
-                data_idx[axis] = j;
-                return input[data_idx];
-            });
+            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__ {
-                data_idx[axis] = j;
-                auto val = input[data_idx] - batch_max;
-                return ::exp(to_hip_type(val));
-            });
+            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));
+                });
 
-            idx.local_stride(batch_item_num, [&](auto j) { 
-                data_idx[axis] = j;
-                auto val = input[data_idx] - batch_max;
+            idx.local_stride(batch_item_num, [&](auto j) {
+                data_idx[axis]   = j;
+                auto val         = input[data_idx] - batch_max;
                 output[data_idx] = ::exp(to_hip_type(val)) / batch_sum;
             });
         });