update unit tests (#2444)

doc/examples/abc/abc.cpp

@@ -55,7 +55,8 @@ int main()
     t_c->write(&v_c, 0, sizeof(v_c));
 
     // Invoke the function
-    backend->call(f, {t_result}, {t_a, t_b, t_c});
+    auto exec = backend->compile(f);
+    exec->call({t_result}, {t_a, t_b, t_c});
 
     // Get the result
     float r[2][3];

doc/examples/abc_operator/abc_operator.cpp

@@ -54,7 +54,8 @@ int main()
     t_c->write(&v_c, 0, sizeof(v_c));
 
     // Invoke the function
-    backend->call(f, {t_result}, {t_a, t_b, t_c});
+    auto exec = backend->compile(f);
+    exec->call({t_result}, {t_a, t_b, t_c});
 
     // Get the result
     float r[2][3];

doc/examples/mnist_mlp/dist_mnist_mlp.cpp

@@ -73,8 +73,7 @@ size_t accuracy_count(const std::shared_ptr<runtime::Tensor>& t_softmax,
 }
 
 float test_accuracy(MNistDataLoader& loader,
-                    std::shared_ptr<runtime::Backend> backend,
-                    std::shared_ptr<Function> function,
+                    std::shared_ptr<runtime::Executable> exec,
                     const std::shared_ptr<runtime::Tensor>& t_X,
                     const std::shared_ptr<runtime::Tensor>& t_Y,
                     const std::shared_ptr<runtime::Tensor>& t_softmax,
@@ -96,8 +95,7 @@ float test_accuracy(MNistDataLoader& loader,
         t_Y->write(loader.get_label_floats(),
                    0,
                    loader.get_label_batch_size() * sizeof(float));
-        backend->call(
-            function, {t_softmax}, {t_X, t_W0, t_b0, t_W1, t_b1});
+        exec->call({t_softmax}, {t_X, t_W0, t_b0, t_W1, t_b1});
         size_t acc = accuracy_count(t_softmax, t_Y);
         acc_count += acc;
         sample_count += batch_size;
@@ -236,6 +234,7 @@ int main(int argc, char* argv[])
             NodeVector{loss, softmax, W0_next, b0_next, W1_next, b1_next},
             ParameterVector{X, Y, N, learning_rate, W0, b0, W1, b1}),
         train_node_map);
+    auto train_exec = backend->compile(train_function);
 
     // Plain inference
     // X, W0, b0, W1, b1 -> softmax
@@ -243,6 +242,7 @@ int main(int argc, char* argv[])
     auto inference_function = clone_function(
         Function(NodeVector{softmax}, ParameterVector{X, W0, b0, W1, b1}),
         inference_node_map);
+    auto inference_exec = backend->compile(inference_function);
 
     set_scalar(t_learning_rate, .03f);
 
@@ -256,8 +256,7 @@ int main(int argc, char* argv[])
         t_Y->write(train_loader.get_label_floats(),
                    0,
                    train_loader.get_label_batch_size() * sizeof(float));
-        backend->call(
-            train_function,
+        train_exec->call(
             {t_loss,
              t_softmax,
              t_W0_next,
@@ -274,17 +273,15 @@ int main(int argc, char* argv[])
         if (train_loader.get_epoch() != last_epoch)
         {
             last_epoch = train_loader.get_epoch();
-            std::cout << "Test accuracy: "
-                      << test_accuracy(test_loader,
-                                       backend,
-                                       inference_function,
-                                       t_X,
-                                       t_Y,
-                                       t_softmax,
-                                       t_W0,
-                                       t_b0,
-                                       t_W1,
-                                       t_b1)
+            std::cout << "Test accuracy: " << test_accuracy(test_loader,
+                                                            inference_exec,
+                                                            t_X,
+                                                            t_Y,
+                                                            t_softmax,
+                                                            t_W0,
+                                                            t_b0,
+                                                            t_W1,
+                                                            t_b1)
                       << std::endl;
         }
     }

doc/examples/mnist_mlp/mnist_mlp.cpp

@@ -72,8 +72,7 @@ size_t accuracy_count(const std::shared_ptr<runtime::Tensor>& t_softmax,
 }
 
 float test_accuracy(MNistDataLoader& loader,
-                    std::shared_ptr<runtime::Backend> backend,
-                    std::shared_ptr<Function> function,
+                    std::shared_ptr<runtime::Executable> exec,
                     const std::shared_ptr<runtime::Tensor>& t_X,
                     const std::shared_ptr<runtime::Tensor>& t_Y,
                     const std::shared_ptr<runtime::Tensor>& t_softmax,
@@ -95,8 +94,7 @@ float test_accuracy(MNistDataLoader& loader,
         t_Y->write(loader.get_label_floats(),
                    0,
                    loader.get_label_batch_size() * sizeof(float));
-        backend->call(
-            function, {t_softmax}, {t_X, t_W0, t_b0, t_W1, t_b1});
+        exec->call({t_softmax}, {t_X, t_W0, t_b0, t_W1, t_b1});
         size_t acc = accuracy_count(t_softmax, t_Y);
         acc_count += acc;
         sample_count += batch_size;
@@ -223,6 +221,7 @@ int main(int argc, const char* argv[])
             NodeVector{loss, softmax, W0_next, b0_next, W1_next, b1_next},
             ParameterVector{X, Y, N, learning_rate, W0, b0, W1, b1}),
         train_node_map);
+    auto train_exec = backend->compile(train_function);
 
     // Plain inference
     // X, W0, b0, W1, b1 -> softmax
@@ -230,6 +229,7 @@ int main(int argc, const char* argv[])
     auto inference_function = clone_function(
         Function(NodeVector{softmax}, ParameterVector{X, W0, b0, W1, b1}),
         inference_node_map);
+    auto inference_exe = backend->compile(inference_function);
 
     set_scalar(t_learning_rate, .03f);
 
@@ -243,8 +243,7 @@ int main(int argc, const char* argv[])
         t_Y->write(train_loader.get_label_floats(),
                    0,
                    train_loader.get_label_batch_size() * sizeof(float));
-        backend->call(
-            train_function,
+        train_exec->call(
             {t_loss,
              t_softmax,
              t_W0_next,
@@ -261,17 +260,15 @@ int main(int argc, const char* argv[])
         if (train_loader.get_epoch() != last_epoch)
         {
             last_epoch = train_loader.get_epoch();
-            std::cout << "Test accuracy: "
-                      << test_accuracy(test_loader,
-                                       backend,
-                                       inference_function,
-                                       t_X,
-                                       t_Y,
-                                       t_softmax,
-                                       t_W0,
-                                       t_b0,
-                                       t_W1,
-                                       t_b1)
+            std::cout << "Test accuracy: " << test_accuracy(test_loader,
+                                                            exec,
+                                                            t_X,
+                                                            t_Y,
+                                                            t_softmax,
+                                                            t_W0,
+                                                            t_b0,
+                                                            t_W1,
+                                                            t_b1)
                       << std::endl;
         }
     }