clang format

src/targets/gpu/gemm.cpp

@@ -265,12 +265,16 @@ argument miopen_gemm::batch_matmul(context& ctx,
 
     std::vector<std::size_t> a_batch_lens(a_lens.begin(), a_lens.begin() + an_dim - 2);
     std::vector<std::size_t> b_batch_lens(b_lens.begin(), b_lens.begin() + bn_dim - 2);
-    if (a_batch_lens == b_batch_lens || a_batch_lens.empty() || b_batch_lens.empty())
+    if(a_batch_lens == b_batch_lens || a_batch_lens.empty() || b_batch_lens.empty())
     {
-        std::size_t numa_matrices =
-            std::accumulate(a_batch_lens.begin(), a_batch_lens.end(), std::size_t{1}, std::multiplies<std::size_t>());
-        std::size_t numb_matrices =
-            std::accumulate(b_batch_lens.begin(), b_batch_lens.end(), std::size_t{1}, std::multiplies<std::size_t>());
+        std::size_t numa_matrices = std::accumulate(a_batch_lens.begin(),
+                                                    a_batch_lens.end(),
+                                                    std::size_t{1},
+                                                    std::multiplies<std::size_t>());
+        std::size_t numb_matrices = std::accumulate(b_batch_lens.begin(),
+                                                    b_batch_lens.end(),
+                                                    std::size_t{1},
+                                                    std::multiplies<std::size_t>());
         std::size_t num_matrices  = std::max(numa_matrices, numb_matrices);
         rocblas_int stride_a      = (numa_matrices == 1) ? 0 : m * k;
         rocblas_int stride_b      = (numb_matrices == 1) ? 0 : k * n;
@@ -278,7 +282,7 @@ argument miopen_gemm::batch_matmul(context& ctx,
         output_shape.visit_type([&](auto as) {
             auto alpha_r    = to_rocblas_type(as(op.alpha));
             auto beta_r     = to_rocblas_type(as(beta));
-            auto to_pointer = [&](auto&& arg) { return to_rocblas_type(as.from(arg.data()));};
+            auto to_pointer = [&](auto&& arg) { return to_rocblas_type(as.from(arg.data())); };
             generic_rocblas_batched_gemm(
                 as,
                 ctx.get_stream().get_rocblas(),
@@ -336,8 +340,7 @@ argument miopen_gemm::batch_matmul(context& ctx,
                 auto to_pointer = [&](auto&& arg, std::size_t offset = 0) {
                     return to_rocblas_type(as.from(arg.data() + offset));
                 };
-                generic_rocblas_gemm(
-                    as,
+                generic_rocblas_gemm(as,
                                      ctx.get_stream().get_rocblas(),
                                      transb ? rocblas_operation_transpose : rocblas_operation_none,
                                      transa ? rocblas_operation_transpose : rocblas_operation_none,
@@ -447,9 +450,7 @@ argument miopen_gemm::compute(context& ctx,
         output_shape.visit_type([&](auto as) {
             auto alpha_r    = to_rocblas_type(as(op.alpha));
             auto beta_r     = to_rocblas_type(as(beta));
-            auto to_pointer = [&](auto&& arg) {
-                return to_rocblas_type(as.from(arg.data()));
-            };
+            auto to_pointer = [&](auto&& arg) { return to_rocblas_type(as.from(arg.data())); };
 
             generic_rocblas_batched_gemm(
                 as,

src/targets/gpu/include/migraphx/gpu/gemm.hpp

@@ -22,7 +22,8 @@ struct miopen_gemm
     private:
     void fill_result(const shape& output_shape, const argument& result, const argument& c) const;
 
-    argument batch_matmul(context& ctx, const shape& output_shape, const std::vector<argument>& args) const;
+    argument
+    batch_matmul(context& ctx, const shape& output_shape, const std::vector<argument>& args) const;
 };
 
 } // namespace gpu