edit dropout

lite/kernels/mlu/bridges/dropout_op.cc

@@ -38,11 +38,12 @@ int DropoutConverter(void* ctx, OpLite* op, KernelBase* kernel) {
   auto output_tensor = graph->AddNode(
       out_var_name, output_dims, CNML_TENSOR, CNML_NCHW, graph->FPType());
 
+  // is_test is true by default
   // if(op_info->HasAttr("is_test")){
   //   auto is_test = op_info->GetAttr<bool>("is_test");
-  //     CHECK(is_test != true);   // The dropout op has no training
-  //     implementation, only inference implementation
+  //   CHECK(is_test != true);
   // }
+
   auto dropout_implementation =
       op_info->GetAttr<std::string>("dropout_implementation");
   auto dropout_prob = op_info->GetAttr<float>("dropout_prob");
@@ -53,12 +54,12 @@ int DropoutConverter(void* ctx, OpLite* op, KernelBase* kernel) {
   float beta = 0.;
 
   std::vector<int64_t> shape = {1, 1, 1, 1};
-
-  std::string prefix = string_format("_%p", op);
+  std::string alpha_var_name = string_format("dropout_alpha_%p", op);
+  std::string beta_var_name = string_format("dropout_beta_%p", op);
   auto alpha_tensor = graph->AddNode(
-      "Alpha" + prefix, shape, CNML_CONST, CNML_NHWC, graph->FPType());
+      alpha_var_name, shape, CNML_CONST, CNML_NHWC, graph->FPType());
   auto beta_tensor = graph->AddNode(
-      "Beta" + prefix, shape, CNML_CONST, CNML_NHWC, graph->FPType());
+      beta_var_name, shape, CNML_CONST, CNML_NHWC, graph->FPType());
 
   graph->BindConstRawData("Alpha" + prefix, &alpha, 1);
   graph->BindConstRawData("Beta" + prefix, &beta, 1);