Formatting

test/onnx/gen_onnx.py

@@ -190,12 +190,16 @@ def atanh_test():
 
 @onnx_test
 def attention_test():
-    input = helper.make_tensor_value_info('input', TensorProto.FLOAT, [2, 384, 768])
-    weights = helper.make_tensor_value_info('weights', TensorProto.FLOAT, [768, 2304])
+    input = helper.make_tensor_value_info('input', TensorProto.FLOAT,
+                                          [2, 384, 768])
+    weights = helper.make_tensor_value_info('weights', TensorProto.FLOAT,
+                                            [768, 2304])
     bias = helper.make_tensor_value_info('bias', TensorProto.FLOAT, [2304])
-    mask_index = helper.make_tensor_value_info('mask_index', TensorProto.INT64, [2, 384])
-    result = helper.make_tensor_value_info('result', TensorProto.FLOAT, [2, 384, 768])
-    
+    mask_index = helper.make_tensor_value_info('mask_index', TensorProto.INT64,
+                                               [2, 384])
+    result = helper.make_tensor_value_info('result', TensorProto.FLOAT,
+                                           [2, 384, 768])
+
     node = helper.make_node('Attention',
                             inputs=['input', 'weights', 'bias', 'mask_index'],
                             outputs=['result'],

test/onnx/verify_onnx.cpp

@@ -22,9 +22,9 @@ TEST_CASE(attention_test)
     std::vector<float> bias_v(2304, 1);
     std::vector<int64_t> mask_index_v(2 * 384, 1);
     migraphx::parameter_map pp;
-    pp["input"] = migraphx::argument(s_i, input_v.data());
-    pp["weights"] = migraphx::argument(s_w, weights_v.data());
-    pp["bias"] = migraphx::argument(s_b, bias_v.data());
+    pp["input"]      = migraphx::argument(s_i, input_v.data());
+    pp["weights"]    = migraphx::argument(s_w, weights_v.data());
+    pp["bias"]       = migraphx::argument(s_b, bias_v.data());
     pp["mask_index"] = migraphx::argument(s_m, mask_index_v.data());
 
     auto result = p.eval(pp).back();