clang format

src/include/migraphx/operators.hpp

@@ -939,9 +939,10 @@ struct logsoftmax
     shape compute_shape(std::vector<shape> inputs) const
     {
         check_shapes{inputs}.has(1);
-        if (axis < 0 || axis >= inputs[0].lens().size())
+        if(axis < 0 || axis >= inputs[0].lens().size())
         {
-            MIGRAPHX_THROW("LogSoftMax: input axis value " + std::to_string(axis) + " is out of range");
+            MIGRAPHX_THROW("LogSoftMax: input axis value " + std::to_string(axis) +
+                           " is out of range");
         }
         return inputs.at(0);
     }

src/onnx/onnx.cpp

@@ -229,8 +229,9 @@ struct onnx_parser
         return prog.add_instruction(op::reshape{{long(dims[0]), long(dims[1])}}, s);
     }
 
-    instruction_ref
-    parse_logsoftmax(const std::string&, const attribute_map& attributes, std::vector<instruction_ref> args)
+    instruction_ref parse_logsoftmax(const std::string&,
+                                     const attribute_map& attributes,
+                                     std::vector<instruction_ref> args)
     {
         int axis = 1;
         if(contains(attributes, "axis"))

src/targets/cpu/lowering.cpp

@@ -619,7 +619,7 @@ struct cpu_logsoftmax
     std::string name() const { return "cpu::logsoftmax"; }
     shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }
 
-    template<typename T>
+    template <typename T>
     std::size_t compute_batch_index(const T& idx, shape& batch_shape, int axis) const
     {
         std::vector<std::size_t> batch_idx(idx.begin(), idx.begin() + axis);
@@ -636,14 +636,15 @@ struct cpu_logsoftmax
 
         visit_all(result, args[0])([&](auto output, auto input) {
             using value_type = typename decltype(input)::value_type;
-            std::vector<value_type> batch_max(batch_shape.elements(), std::numeric_limits<value_type>::lowest());
+            std::vector<value_type> batch_max(batch_shape.elements(),
+                                              std::numeric_limits<value_type>::lowest());
             shape_for_each(output_shape, [&](auto idx) {
-                auto index = compute_batch_index(idx, batch_shape, op.axis);
+                auto index       = compute_batch_index(idx, batch_shape, op.axis);
                 batch_max[index] = std::max(batch_max[index], input(idx.begin(), idx.end()));
             });
 
             shape_for_each(output_shape, [&](auto idx) {
-                auto index = compute_batch_index(idx, batch_shape, op.axis);
+                auto index                     = compute_batch_index(idx, batch_shape, op.axis);
                 output(idx.begin(), idx.end()) = input(idx.begin(), idx.end()) - batch_max[index];
             });
 
@@ -653,14 +654,15 @@ struct cpu_logsoftmax
                 batch_sum[index] += std::exp(output(idx.begin(), idx.end()));
             });
 
-            for (std::size_t i = 0; i < batch_sum.size(); ++i)
+            for(std::size_t i = 0; i < batch_sum.size(); ++i)
             {
                 batch_sum[i] = std::log(batch_sum[i]);
             }
 
             shape_for_each(output_shape, [&](auto idx) {
                 auto index = compute_batch_index(idx, batch_shape, op.axis);
-                output(idx.begin(), idx.end()) = input(idx.begin(), idx.end()) - batch_max[index] - batch_sum[index];
+                output(idx.begin(), idx.end()) =
+                    input(idx.begin(), idx.end()) - batch_max[index] - batch_sum[index];
             });
         });