clang format

src/include/migraphx/operators.hpp

@@ -839,7 +839,8 @@ struct dot
         // according to the specification of the numpy.matmul()
         // inputs with the shape dims more than 2 are acceptable
         // as long as dim values are the same in the two inputs
-        if(!std::equal(a.lens().rbegin() + 2, a.lens().rend(), b.lens().rbegin() + 2, b.lens().rend()))
+        if(!std::equal(
+               a.lens().rbegin() + 2, a.lens().rend(), b.lens().rbegin() + 2, b.lens().rend()))
         {
             MIGRAPHX_THROW("DOT: dim values mismatch");
         }
@@ -854,13 +855,12 @@ struct dot
 
         auto out_lens   = a.lens();
         out_lens[dim_1] = b.lens()[dim_1];
-        if (inputs.size() == 3 && out_lens != inputs.at(2).lens())
+        if(inputs.size() == 3 && out_lens != inputs.at(2).lens())
         {
             MIGRAPHX_THROW("DOT: dimension mismatch, operand C: {" + to_string_range(c_lens) +
-                            "}, cannot add to operand A * B: {" + to_string_range(out_lens) +
-                            "}");
+                           "}, cannot add to operand A * B: {" + to_string_range(out_lens) + "}");
         }
-        
+
         return {t, out_lens};
     }
 };

src/targets/cpu/gemm.cpp

@@ -61,7 +61,7 @@ void migemm_impl(tensor_view<T> cmat,
             if(alpha != 0.0)
             {
                 c = c + alpha * a * b;
-            }            
+            }
         });
     });
 }
@@ -101,7 +101,8 @@ void migemm_impl(
 {
     auto lens = amat.get_shape().lens();
     bool batch_mul =
-        std::accumulate(lens.rbegin() + 2, lens.rend(), std::size_t{1}, std::multiplies<std::size_t>()) == 1;
+        std::accumulate(
+            lens.rbegin() + 2, lens.rend(), std::size_t{1}, std::multiplies<std::size_t>()) == 1;
     if(batch_mul)
     {
         migemm_impl(cmat, amat, bmat, alpha, beta, is_fast_gemm_type<T>{});

src/targets/cpu/lowering.cpp

@@ -369,14 +369,14 @@ struct cpu_gemm
 {
     op::dot op;
     std::string name() const { return "cpu::dot"; }
-    shape compute_shape(const std::vector<shape>& inputs) const 
-    { 
+    shape compute_shape(const std::vector<shape>& inputs) const
+    {
         if(inputs.size() == 3)
         {
             auto c_shape = inputs.at(2);
             check_shapes{{c_shape}}.not_broadcasted();
         }
-        return op.compute_shape(inputs); 
+        return op.compute_shape(inputs);
     }
 
     argument compute(context&, const shape& output_shape, std::vector<argument> args) const