diff --git a/neuralnetworks/1.1/vts/functional/Android.bp b/neuralnetworks/1.1/vts/functional/Android.bp
index 947ca2c..f755c20 100644
--- a/neuralnetworks/1.1/vts/functional/Android.bp
+++ b/neuralnetworks/1.1/vts/functional/Android.bp
@@ -17,9 +17,12 @@
 cc_test {
     name: "VtsHalNeuralnetworksV1_1TargetTest",
     srcs: [
-        "VtsHalNeuralnetworksV1_1.cpp",
-        "VtsHalNeuralnetworksV1_1BasicTest.cpp",
-        "VtsHalNeuralnetworksV1_1GeneratedTest.cpp",
+        "BasicTests.cpp",
+        "GeneratedTests.cpp",
+        "ValidateModel.cpp",
+        "ValidateRequest.cpp",
+        "ValidationTests.cpp",
+        "VtsHalNeuralnetworks.cpp",
     ],
     defaults: ["VtsHalTargetTestDefaults"],
     static_libs: [
diff --git a/neuralnetworks/1.1/vts/functional/BasicTests.cpp b/neuralnetworks/1.1/vts/functional/BasicTests.cpp
new file mode 100644
index 0000000..ed59a2d
--- /dev/null
+++ b/neuralnetworks/1.1/vts/functional/BasicTests.cpp
@@ -0,0 +1,58 @@
+/*
+ * Copyright (C) 2018 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#define LOG_TAG "neuralnetworks_hidl_hal_test"
+
+#include "VtsHalNeuralnetworks.h"
+
+namespace android {
+namespace hardware {
+namespace neuralnetworks {
+namespace V1_1 {
+namespace vts {
+namespace functional {
+
+// create device test
+TEST_F(NeuralnetworksHidlTest, CreateDevice) {}
+
+// status test
+TEST_F(NeuralnetworksHidlTest, StatusTest) {
+    Return<DeviceStatus> status = device->getStatus();
+    ASSERT_TRUE(status.isOk());
+    EXPECT_EQ(DeviceStatus::AVAILABLE, static_cast<DeviceStatus>(status));
+}
+
+// initialization
+TEST_F(NeuralnetworksHidlTest, GetCapabilitiesTest) {
+    Return<void> ret =
+        device->getCapabilities_1_1([](ErrorStatus status, const Capabilities& capabilities) {
+            EXPECT_EQ(ErrorStatus::NONE, status);
+            EXPECT_LT(0.0f, capabilities.float32Performance.execTime);
+            EXPECT_LT(0.0f, capabilities.float32Performance.powerUsage);
+            EXPECT_LT(0.0f, capabilities.quantized8Performance.execTime);
+            EXPECT_LT(0.0f, capabilities.quantized8Performance.powerUsage);
+            EXPECT_LT(0.0f, capabilities.relaxedFloat32toFloat16Performance.execTime);
+            EXPECT_LT(0.0f, capabilities.relaxedFloat32toFloat16Performance.powerUsage);
+        });
+    EXPECT_TRUE(ret.isOk());
+}
+
+}  // namespace functional
+}  // namespace vts
+}  // namespace V1_1
+}  // namespace neuralnetworks
+}  // namespace hardware
+}  // namespace android
diff --git a/neuralnetworks/1.1/vts/functional/GeneratedTests.cpp b/neuralnetworks/1.1/vts/functional/GeneratedTests.cpp
new file mode 100644
index 0000000..1f1cc7a
--- /dev/null
+++ b/neuralnetworks/1.1/vts/functional/GeneratedTests.cpp
@@ -0,0 +1,59 @@
+/*
+ * Copyright (C) 2018 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#define LOG_TAG "neuralnetworks_hidl_hal_test"
+
+#include "VtsHalNeuralnetworks.h"
+
+#include "Callbacks.h"
+#include "TestHarness.h"
+#include "Utils.h"
+
+#include <android-base/logging.h>
+#include <android/hidl/memory/1.0/IMemory.h>
+#include <hidlmemory/mapping.h>
+
+namespace android {
+namespace hardware {
+namespace neuralnetworks {
+
+namespace generated_tests {
+using ::generated_tests::MixedTypedExampleType;
+extern void Execute(const sp<V1_1::IDevice>&, std::function<V1_1::Model(void)>,
+                    std::function<bool(int)>, const std::vector<MixedTypedExampleType>&);
+}  // namespace generated_tests
+
+namespace V1_1 {
+namespace vts {
+namespace functional {
+
+using ::android::hardware::neuralnetworks::V1_0::implementation::ExecutionCallback;
+using ::android::hardware::neuralnetworks::V1_0::implementation::PreparedModelCallback;
+using ::android::nn::allocateSharedMemory;
+
+// Mixed-typed examples
+typedef generated_tests::MixedTypedExampleType MixedTypedExample;
+
+// in frameworks/ml/nn/runtime/tests/generated/
+#include "all_generated_V1_0_vts_tests.cpp"
+#include "all_generated_V1_1_vts_tests.cpp"
+
+}  // namespace functional
+}  // namespace vts
+}  // namespace V1_1
+}  // namespace neuralnetworks
+}  // namespace hardware
+}  // namespace android
diff --git a/neuralnetworks/1.1/vts/functional/Models.h b/neuralnetworks/1.1/vts/functional/Models.h
new file mode 100644
index 0000000..c3cadb5
--- /dev/null
+++ b/neuralnetworks/1.1/vts/functional/Models.h
@@ -0,0 +1,323 @@
+/*
+ * Copyright (C) 2018 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef VTS_HAL_NEURALNETWORKS_V1_1_VTS_FUNCTIONAL_MODELS_H
+#define VTS_HAL_NEURALNETWORKS_V1_1_VTS_FUNCTIONAL_MODELS_H
+
+#define LOG_TAG "neuralnetworks_hidl_hal_test"
+
+#include "TestHarness.h"
+
+#include <android/hardware/neuralnetworks/1.0/types.h>
+#include <android/hardware/neuralnetworks/1.1/types.h>
+
+namespace android {
+namespace hardware {
+namespace neuralnetworks {
+namespace V1_1 {
+namespace vts {
+namespace functional {
+
+using MixedTypedExample = generated_tests::MixedTypedExampleType;
+
+#define FOR_EACH_TEST_MODEL(FN)                                \
+    FN(add)                                                    \
+    FN(add_broadcast_quant8)                                   \
+    FN(add_quant8)                                             \
+    FN(add_relaxed)                                            \
+    FN(avg_pool_float_1)                                       \
+    FN(avg_pool_float_1_relaxed)                               \
+    FN(avg_pool_float_2)                                       \
+    FN(avg_pool_float_2_relaxed)                               \
+    FN(avg_pool_float_3)                                       \
+    FN(avg_pool_float_3_relaxed)                               \
+    FN(avg_pool_float_4)                                       \
+    FN(avg_pool_float_4_relaxed)                               \
+    FN(avg_pool_float_5)                                       \
+    FN(avg_pool_quant8_1)                                      \
+    FN(avg_pool_quant8_2)                                      \
+    FN(avg_pool_quant8_3)                                      \
+    FN(avg_pool_quant8_4)                                      \
+    FN(avg_pool_quant8_5)                                      \
+    FN(batch_to_space)                                         \
+    FN(batch_to_space_float_1)                                 \
+    FN(batch_to_space_quant8_1)                                \
+    FN(concat_float_1)                                         \
+    FN(concat_float_1_relaxed)                                 \
+    FN(concat_float_2)                                         \
+    FN(concat_float_2_relaxed)                                 \
+    FN(concat_float_3)                                         \
+    FN(concat_float_3_relaxed)                                 \
+    FN(concat_quant8_1)                                        \
+    FN(concat_quant8_2)                                        \
+    FN(concat_quant8_3)                                        \
+    FN(conv_1_h3_w2_SAME)                                      \
+    FN(conv_1_h3_w2_SAME_relaxed)                              \
+    FN(conv_1_h3_w2_VALID)                                     \
+    FN(conv_1_h3_w2_VALID_relaxed)                             \
+    FN(conv_3_h3_w2_SAME)                                      \
+    FN(conv_3_h3_w2_SAME_relaxed)                              \
+    FN(conv_3_h3_w2_VALID)                                     \
+    FN(conv_3_h3_w2_VALID_relaxed)                             \
+    FN(conv_float)                                             \
+    FN(conv_float_2)                                           \
+    FN(conv_float_channels)                                    \
+    FN(conv_float_channels_relaxed)                            \
+    FN(conv_float_channels_weights_as_inputs)                  \
+    FN(conv_float_channels_weights_as_inputs_relaxed)          \
+    FN(conv_float_large)                                       \
+    FN(conv_float_large_relaxed)                               \
+    FN(conv_float_large_weights_as_inputs)                     \
+    FN(conv_float_large_weights_as_inputs_relaxed)             \
+    FN(conv_float_relaxed)                                     \
+    FN(conv_float_weights_as_inputs)                           \
+    FN(conv_float_weights_as_inputs_relaxed)                   \
+    FN(conv_quant8)                                            \
+    FN(conv_quant8_2)                                          \
+    FN(conv_quant8_channels)                                   \
+    FN(conv_quant8_channels_weights_as_inputs)                 \
+    FN(conv_quant8_large)                                      \
+    FN(conv_quant8_large_weights_as_inputs)                    \
+    FN(conv_quant8_overflow)                                   \
+    FN(conv_quant8_overflow_weights_as_inputs)                 \
+    FN(conv_quant8_weights_as_inputs)                          \
+    FN(depth_to_space_float_1)                                 \
+    FN(depth_to_space_float_1_relaxed)                         \
+    FN(depth_to_space_float_2)                                 \
+    FN(depth_to_space_float_2_relaxed)                         \
+    FN(depth_to_space_float_3)                                 \
+    FN(depth_to_space_float_3_relaxed)                         \
+    FN(depth_to_space_quant8_1)                                \
+    FN(depth_to_space_quant8_2)                                \
+    FN(depthwise_conv)                                         \
+    FN(depthwise_conv2d_float)                                 \
+    FN(depthwise_conv2d_float_2)                               \
+    FN(depthwise_conv2d_float_large)                           \
+    FN(depthwise_conv2d_float_large_2)                         \
+    FN(depthwise_conv2d_float_large_2_weights_as_inputs)       \
+    FN(depthwise_conv2d_float_large_relaxed)                   \
+    FN(depthwise_conv2d_float_large_weights_as_inputs)         \
+    FN(depthwise_conv2d_float_large_weights_as_inputs_relaxed) \
+    FN(depthwise_conv2d_float_weights_as_inputs)               \
+    FN(depthwise_conv2d_quant8)                                \
+    FN(depthwise_conv2d_quant8_2)                              \
+    FN(depthwise_conv2d_quant8_large)                          \
+    FN(depthwise_conv2d_quant8_large_weights_as_inputs)        \
+    FN(depthwise_conv2d_quant8_weights_as_inputs)              \
+    FN(depthwise_conv_relaxed)                                 \
+    FN(dequantize)                                             \
+    FN(div)                                                    \
+    FN(embedding_lookup)                                       \
+    FN(embedding_lookup_relaxed)                               \
+    FN(floor)                                                  \
+    FN(floor_relaxed)                                          \
+    FN(fully_connected_float)                                  \
+    FN(fully_connected_float_2)                                \
+    FN(fully_connected_float_large)                            \
+    FN(fully_connected_float_large_weights_as_inputs)          \
+    FN(fully_connected_float_relaxed)                          \
+    FN(fully_connected_float_weights_as_inputs)                \
+    FN(fully_connected_float_weights_as_inputs_relaxed)        \
+    FN(fully_connected_quant8)                                 \
+    FN(fully_connected_quant8_2)                               \
+    FN(fully_connected_quant8_large)                           \
+    FN(fully_connected_quant8_large_weights_as_inputs)         \
+    FN(fully_connected_quant8_weights_as_inputs)               \
+    FN(hashtable_lookup_float)                                 \
+    FN(hashtable_lookup_float_relaxed)                         \
+    FN(hashtable_lookup_quant8)                                \
+    FN(l2_normalization)                                       \
+    FN(l2_normalization_2)                                     \
+    FN(l2_normalization_large)                                 \
+    FN(l2_normalization_large_relaxed)                         \
+    FN(l2_normalization_relaxed)                               \
+    FN(l2_pool_float)                                          \
+    FN(l2_pool_float_2)                                        \
+    FN(l2_pool_float_large)                                    \
+    FN(l2_pool_float_relaxed)                                  \
+    FN(local_response_norm_float_1)                            \
+    FN(local_response_norm_float_1_relaxed)                    \
+    FN(local_response_norm_float_2)                            \
+    FN(local_response_norm_float_2_relaxed)                    \
+    FN(local_response_norm_float_3)                            \
+    FN(local_response_norm_float_3_relaxed)                    \
+    FN(local_response_norm_float_4)                            \
+    FN(local_response_norm_float_4_relaxed)                    \
+    FN(logistic_float_1)                                       \
+    FN(logistic_float_1_relaxed)                               \
+    FN(logistic_float_2)                                       \
+    FN(logistic_float_2_relaxed)                               \
+    FN(logistic_quant8_1)                                      \
+    FN(logistic_quant8_2)                                      \
+    FN(lsh_projection)                                         \
+    FN(lsh_projection_2)                                       \
+    FN(lsh_projection_2_relaxed)                               \
+    FN(lsh_projection_relaxed)                                 \
+    FN(lsh_projection_weights_as_inputs)                       \
+    FN(lsh_projection_weights_as_inputs_relaxed)               \
+    FN(lstm)                                                   \
+    FN(lstm2)                                                  \
+    FN(lstm2_relaxed)                                          \
+    FN(lstm2_state)                                            \
+    FN(lstm2_state2)                                           \
+    FN(lstm2_state2_relaxed)                                   \
+    FN(lstm2_state_relaxed)                                    \
+    FN(lstm3)                                                  \
+    FN(lstm3_relaxed)                                          \
+    FN(lstm3_state)                                            \
+    FN(lstm3_state2)                                           \
+    FN(lstm3_state2_relaxed)                                   \
+    FN(lstm3_state3)                                           \
+    FN(lstm3_state3_relaxed)                                   \
+    FN(lstm3_state_relaxed)                                    \
+    FN(lstm_relaxed)                                           \
+    FN(lstm_state)                                             \
+    FN(lstm_state2)                                            \
+    FN(lstm_state2_relaxed)                                    \
+    FN(lstm_state_relaxed)                                     \
+    FN(max_pool_float_1)                                       \
+    FN(max_pool_float_1_relaxed)                               \
+    FN(max_pool_float_2)                                       \
+    FN(max_pool_float_2_relaxed)                               \
+    FN(max_pool_float_3)                                       \
+    FN(max_pool_float_3_relaxed)                               \
+    FN(max_pool_float_4)                                       \
+    FN(max_pool_quant8_1)                                      \
+    FN(max_pool_quant8_2)                                      \
+    FN(max_pool_quant8_3)                                      \
+    FN(max_pool_quant8_4)                                      \
+    FN(mean)                                                   \
+    FN(mean_float_1)                                           \
+    FN(mean_float_2)                                           \
+    FN(mean_quant8_1)                                          \
+    FN(mean_quant8_2)                                          \
+    FN(mobilenet_224_gender_basic_fixed)                       \
+    FN(mobilenet_224_gender_basic_fixed_relaxed)               \
+    FN(mobilenet_quantized)                                    \
+    FN(mul)                                                    \
+    FN(mul_broadcast_quant8)                                   \
+    FN(mul_quant8)                                             \
+    FN(mul_relaxed)                                            \
+    FN(mul_relu)                                               \
+    FN(mul_relu_relaxed)                                       \
+    FN(pad)                                                    \
+    FN(pad_float_1)                                            \
+    FN(relu1_float_1)                                          \
+    FN(relu1_float_1_relaxed)                                  \
+    FN(relu1_float_2)                                          \
+    FN(relu1_float_2_relaxed)                                  \
+    FN(relu1_quant8_1)                                         \
+    FN(relu1_quant8_2)                                         \
+    FN(relu6_float_1)                                          \
+    FN(relu6_float_1_relaxed)                                  \
+    FN(relu6_float_2)                                          \
+    FN(relu6_float_2_relaxed)                                  \
+    FN(relu6_quant8_1)                                         \
+    FN(relu6_quant8_2)                                         \
+    FN(relu_float_1)                                           \
+    FN(relu_float_1_relaxed)                                   \
+    FN(relu_float_2)                                           \
+    FN(relu_quant8_1)                                          \
+    FN(relu_quant8_2)                                          \
+    FN(reshape)                                                \
+    FN(reshape_quant8)                                         \
+    FN(reshape_quant8_weights_as_inputs)                       \
+    FN(reshape_relaxed)                                        \
+    FN(reshape_weights_as_inputs)                              \
+    FN(reshape_weights_as_inputs_relaxed)                      \
+    FN(resize_bilinear)                                        \
+    FN(resize_bilinear_2)                                      \
+    FN(resize_bilinear_relaxed)                                \
+    FN(rnn)                                                    \
+    FN(rnn_relaxed)                                            \
+    FN(rnn_state)                                              \
+    FN(rnn_state_relaxed)                                      \
+    FN(softmax_float_1)                                        \
+    FN(softmax_float_1_relaxed)                                \
+    FN(softmax_float_2)                                        \
+    FN(softmax_float_2_relaxed)                                \
+    FN(softmax_quant8_1)                                       \
+    FN(softmax_quant8_2)                                       \
+    FN(space_to_batch)                                         \
+    FN(space_to_batch_float_1)                                 \
+    FN(space_to_batch_float_2)                                 \
+    FN(space_to_batch_float_3)                                 \
+    FN(space_to_batch_quant8_1)                                \
+    FN(space_to_batch_quant8_2)                                \
+    FN(space_to_batch_quant8_3)                                \
+    FN(space_to_depth_float_1)                                 \
+    FN(space_to_depth_float_1_relaxed)                         \
+    FN(space_to_depth_float_2)                                 \
+    FN(space_to_depth_float_2_relaxed)                         \
+    FN(space_to_depth_float_3)                                 \
+    FN(space_to_depth_float_3_relaxed)                         \
+    FN(space_to_depth_quant8_1)                                \
+    FN(space_to_depth_quant8_2)                                \
+    FN(squeeze)                                                \
+    FN(squeeze_float_1)                                        \
+    FN(squeeze_quant8_1)                                       \
+    FN(strided_slice)                                          \
+    FN(strided_slice_float_1)                                  \
+    FN(strided_slice_float_10)                                 \
+    FN(strided_slice_float_2)                                  \
+    FN(strided_slice_float_3)                                  \
+    FN(strided_slice_float_4)                                  \
+    FN(strided_slice_float_5)                                  \
+    FN(strided_slice_float_6)                                  \
+    FN(strided_slice_float_7)                                  \
+    FN(strided_slice_float_8)                                  \
+    FN(strided_slice_float_9)                                  \
+    FN(strided_slice_qaunt8_10)                                \
+    FN(strided_slice_quant8_1)                                 \
+    FN(strided_slice_quant8_2)                                 \
+    FN(strided_slice_quant8_3)                                 \
+    FN(strided_slice_quant8_4)                                 \
+    FN(strided_slice_quant8_5)                                 \
+    FN(strided_slice_quant8_6)                                 \
+    FN(strided_slice_quant8_7)                                 \
+    FN(strided_slice_quant8_8)                                 \
+    FN(strided_slice_quant8_9)                                 \
+    FN(sub)                                                    \
+    FN(svdf)                                                   \
+    FN(svdf2)                                                  \
+    FN(svdf2_relaxed)                                          \
+    FN(svdf_relaxed)                                           \
+    FN(svdf_state)                                             \
+    FN(svdf_state_relaxed)                                     \
+    FN(tanh)                                                   \
+    FN(tanh_relaxed)                                           \
+    FN(transpose)                                              \
+    FN(transpose_float_1)                                      \
+    FN(transpose_quant8_1)
+
+#define FORWARD_DECLARE_GENERATED_OBJECTS(function) \
+    namespace function {                            \
+    extern std::vector<MixedTypedExample> examples; \
+    Model createTestModel();                        \
+    }
+
+FOR_EACH_TEST_MODEL(FORWARD_DECLARE_GENERATED_OBJECTS)
+
+#undef FORWARD_DECLARE_GENERATED_OBJECTS
+
+}  // namespace functional
+}  // namespace vts
+}  // namespace V1_1
+}  // namespace neuralnetworks
+}  // namespace hardware
+}  // namespace android
+
+#endif  // VTS_HAL_NEURALNETWORKS_V1_1_VTS_FUNCTIONAL_MODELS_H
diff --git a/neuralnetworks/1.1/vts/functional/ValidateModel.cpp b/neuralnetworks/1.1/vts/functional/ValidateModel.cpp
new file mode 100644
index 0000000..7a20e26
--- /dev/null
+++ b/neuralnetworks/1.1/vts/functional/ValidateModel.cpp
@@ -0,0 +1,513 @@
+/*
+ * Copyright (C) 2018 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#define LOG_TAG "neuralnetworks_hidl_hal_test"
+
+#include "VtsHalNeuralnetworks.h"
+
+#include "Callbacks.h"
+
+namespace android {
+namespace hardware {
+namespace neuralnetworks {
+namespace V1_1 {
+
+using V1_0::IPreparedModel;
+using V1_0::Operand;
+using V1_0::OperandLifeTime;
+using V1_0::OperandType;
+
+namespace vts {
+namespace functional {
+
+using ::android::hardware::neuralnetworks::V1_0::implementation::ExecutionCallback;
+using ::android::hardware::neuralnetworks::V1_0::implementation::PreparedModelCallback;
+
+///////////////////////// UTILITY FUNCTIONS /////////////////////////
+
+static void validateGetSupportedOperations(const sp<IDevice>& device, const std::string& message,
+                                           const V1_1::Model& model) {
+    SCOPED_TRACE(message + " [getSupportedOperations_1_1]");
+
+    Return<void> ret =
+        device->getSupportedOperations_1_1(model, [&](ErrorStatus status, const hidl_vec<bool>&) {
+            EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, status);
+        });
+    EXPECT_TRUE(ret.isOk());
+}
+
+static void validatePrepareModel(const sp<IDevice>& device, const std::string& message,
+                                 const V1_1::Model& model) {
+    SCOPED_TRACE(message + " [prepareModel_1_1]");
+
+    sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
+    ASSERT_NE(nullptr, preparedModelCallback.get());
+    Return<ErrorStatus> prepareLaunchStatus =
+        device->prepareModel_1_1(model, preparedModelCallback);
+    ASSERT_TRUE(prepareLaunchStatus.isOk());
+    ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(prepareLaunchStatus));
+
+    preparedModelCallback->wait();
+    ErrorStatus prepareReturnStatus = preparedModelCallback->getStatus();
+    ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, prepareReturnStatus);
+    sp<IPreparedModel> preparedModel = preparedModelCallback->getPreparedModel();
+    ASSERT_EQ(nullptr, preparedModel.get());
+}
+
+// Primary validation function. This function will take a valid model, apply a
+// mutation to it to invalidate the model, then pass it to interface calls that
+// use the model. Note that the model here is passed by value, and any mutation
+// to the model does not leave this function.
+static void validate(const sp<IDevice>& device, const std::string& message, V1_1::Model model,
+                     const std::function<void(Model*)>& mutation) {
+    mutation(&model);
+    validateGetSupportedOperations(device, message, model);
+    validatePrepareModel(device, message, model);
+}
+
+// Delete element from hidl_vec. hidl_vec doesn't support a "remove" operation,
+// so this is efficiently accomplished by moving the element to the end and
+// resizing the hidl_vec to one less.
+template <typename Type>
+static void hidl_vec_removeAt(hidl_vec<Type>* vec, uint32_t index) {
+    if (vec) {
+        std::rotate(vec->begin() + index, vec->begin() + index + 1, vec->end());
+        vec->resize(vec->size() - 1);
+    }
+}
+
+template <typename Type>
+static uint32_t hidl_vec_push_back(hidl_vec<Type>* vec, const Type& value) {
+    // assume vec is valid
+    const uint32_t index = vec->size();
+    vec->resize(index + 1);
+    (*vec)[index] = value;
+    return index;
+}
+
+static uint32_t addOperand(Model* model) {
+    return hidl_vec_push_back(&model->operands,
+                              {
+                                  .type = OperandType::INT32,
+                                  .dimensions = {},
+                                  .numberOfConsumers = 0,
+                                  .scale = 0.0f,
+                                  .zeroPoint = 0,
+                                  .lifetime = OperandLifeTime::MODEL_INPUT,
+                                  .location = {.poolIndex = 0, .offset = 0, .length = 0},
+                              });
+}
+
+static uint32_t addOperand(Model* model, OperandLifeTime lifetime) {
+    uint32_t index = addOperand(model);
+    model->operands[index].numberOfConsumers = 1;
+    model->operands[index].lifetime = lifetime;
+    return index;
+}
+
+///////////////////////// VALIDATE MODEL OPERAND TYPE /////////////////////////
+
+static const int32_t invalidOperandTypes[] = {
+    static_cast<int32_t>(OperandType::FLOAT32) - 1,              // lower bound fundamental
+    static_cast<int32_t>(OperandType::TENSOR_QUANT8_ASYMM) + 1,  // upper bound fundamental
+    static_cast<int32_t>(OperandType::OEM) - 1,                  // lower bound OEM
+    static_cast<int32_t>(OperandType::TENSOR_OEM_BYTE) + 1,      // upper bound OEM
+};
+
+static void mutateOperandTypeTest(const sp<IDevice>& device, const V1_1::Model& model) {
+    for (size_t operand = 0; operand < model.operands.size(); ++operand) {
+        for (int32_t invalidOperandType : invalidOperandTypes) {
+            const std::string message = "mutateOperandTypeTest: operand " +
+                                        std::to_string(operand) + " set to value " +
+                                        std::to_string(invalidOperandType);
+            validate(device, message, model, [operand, invalidOperandType](Model* model) {
+                model->operands[operand].type = static_cast<OperandType>(invalidOperandType);
+            });
+        }
+    }
+}
+
+///////////////////////// VALIDATE OPERAND RANK /////////////////////////
+
+static uint32_t getInvalidRank(OperandType type) {
+    switch (type) {
+        case OperandType::FLOAT32:
+        case OperandType::INT32:
+        case OperandType::UINT32:
+            return 1;
+        case OperandType::TENSOR_FLOAT32:
+        case OperandType::TENSOR_INT32:
+        case OperandType::TENSOR_QUANT8_ASYMM:
+            return 0;
+        default:
+            return 0;
+    }
+}
+
+static void mutateOperandRankTest(const sp<IDevice>& device, const V1_1::Model& model) {
+    for (size_t operand = 0; operand < model.operands.size(); ++operand) {
+        const uint32_t invalidRank = getInvalidRank(model.operands[operand].type);
+        const std::string message = "mutateOperandRankTest: operand " + std::to_string(operand) +
+                                    " has rank of " + std::to_string(invalidRank);
+        validate(device, message, model, [operand, invalidRank](Model* model) {
+            model->operands[operand].dimensions = std::vector<uint32_t>(invalidRank, 0);
+        });
+    }
+}
+
+///////////////////////// VALIDATE OPERAND SCALE /////////////////////////
+
+static float getInvalidScale(OperandType type) {
+    switch (type) {
+        case OperandType::FLOAT32:
+        case OperandType::INT32:
+        case OperandType::UINT32:
+        case OperandType::TENSOR_FLOAT32:
+            return 1.0f;
+        case OperandType::TENSOR_INT32:
+            return -1.0f;
+        case OperandType::TENSOR_QUANT8_ASYMM:
+            return 0.0f;
+        default:
+            return 0.0f;
+    }
+}
+
+static void mutateOperandScaleTest(const sp<IDevice>& device, const V1_1::Model& model) {
+    for (size_t operand = 0; operand < model.operands.size(); ++operand) {
+        const float invalidScale = getInvalidScale(model.operands[operand].type);
+        const std::string message = "mutateOperandScaleTest: operand " + std::to_string(operand) +
+                                    " has scale of " + std::to_string(invalidScale);
+        validate(device, message, model, [operand, invalidScale](Model* model) {
+            model->operands[operand].scale = invalidScale;
+        });
+    }
+}
+
+///////////////////////// VALIDATE OPERAND ZERO POINT /////////////////////////
+
+static std::vector<int32_t> getInvalidZeroPoints(OperandType type) {
+    switch (type) {
+        case OperandType::FLOAT32:
+        case OperandType::INT32:
+        case OperandType::UINT32:
+        case OperandType::TENSOR_FLOAT32:
+        case OperandType::TENSOR_INT32:
+            return {1};
+        case OperandType::TENSOR_QUANT8_ASYMM:
+            return {-1, 256};
+        default:
+            return {};
+    }
+}
+
+static void mutateOperandZeroPointTest(const sp<IDevice>& device, const V1_1::Model& model) {
+    for (size_t operand = 0; operand < model.operands.size(); ++operand) {
+        const std::vector<int32_t> invalidZeroPoints =
+            getInvalidZeroPoints(model.operands[operand].type);
+        for (int32_t invalidZeroPoint : invalidZeroPoints) {
+            const std::string message = "mutateOperandZeroPointTest: operand " +
+                                        std::to_string(operand) + " has zero point of " +
+                                        std::to_string(invalidZeroPoint);
+            validate(device, message, model, [operand, invalidZeroPoint](Model* model) {
+                model->operands[operand].zeroPoint = invalidZeroPoint;
+            });
+        }
+    }
+}
+
+///////////////////////// VALIDATE EXTRA ??? /////////////////////////
+
+// TODO: Operand::lifetime
+// TODO: Operand::location
+
+///////////////////////// VALIDATE OPERATION OPERAND TYPE /////////////////////////
+
+static void mutateOperand(Operand* operand, OperandType type) {
+    Operand newOperand = *operand;
+    newOperand.type = type;
+    switch (type) {
+        case OperandType::FLOAT32:
+        case OperandType::INT32:
+        case OperandType::UINT32:
+            newOperand.dimensions = hidl_vec<uint32_t>();
+            newOperand.scale = 0.0f;
+            newOperand.zeroPoint = 0;
+            break;
+        case OperandType::TENSOR_FLOAT32:
+            newOperand.dimensions =
+                operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1});
+            newOperand.scale = 0.0f;
+            newOperand.zeroPoint = 0;
+            break;
+        case OperandType::TENSOR_INT32:
+            newOperand.dimensions =
+                operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1});
+            newOperand.zeroPoint = 0;
+            break;
+        case OperandType::TENSOR_QUANT8_ASYMM:
+            newOperand.dimensions =
+                operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1});
+            newOperand.scale = operand->scale != 0.0f ? operand->scale : 1.0f;
+            break;
+        case OperandType::OEM:
+        case OperandType::TENSOR_OEM_BYTE:
+        default:
+            break;
+    }
+    *operand = newOperand;
+}
+
+static bool mutateOperationOperandTypeSkip(size_t operand, const V1_1::Model& model) {
+    // LSH_PROJECTION's second argument is allowed to have any type. This is the
+    // only operation that currently has a type that can be anything independent
+    // from any other type. Changing the operand type to any other type will
+    // result in a valid model for LSH_PROJECTION. If this is the case, skip the
+    // test.
+    for (const Operation& operation : model.operations) {
+        if (operation.type == OperationType::LSH_PROJECTION && operand == operation.inputs[1]) {
+            return true;
+        }
+    }
+    return false;
+}
+
+static void mutateOperationOperandTypeTest(const sp<IDevice>& device, const V1_1::Model& model) {
+    for (size_t operand = 0; operand < model.operands.size(); ++operand) {
+        if (mutateOperationOperandTypeSkip(operand, model)) {
+            continue;
+        }
+        for (OperandType invalidOperandType : hidl_enum_iterator<OperandType>{}) {
+            // Do not test OEM types
+            if (invalidOperandType == model.operands[operand].type ||
+                invalidOperandType == OperandType::OEM ||
+                invalidOperandType == OperandType::TENSOR_OEM_BYTE) {
+                continue;
+            }
+            const std::string message = "mutateOperationOperandTypeTest: operand " +
+                                        std::to_string(operand) + " set to type " +
+                                        toString(invalidOperandType);
+            validate(device, message, model, [operand, invalidOperandType](Model* model) {
+                mutateOperand(&model->operands[operand], invalidOperandType);
+            });
+        }
+    }
+}
+
+///////////////////////// VALIDATE MODEL OPERATION TYPE /////////////////////////
+
+static const int32_t invalidOperationTypes[] = {
+    static_cast<int32_t>(OperationType::ADD) - 1,            // lower bound fundamental
+    static_cast<int32_t>(OperationType::TRANSPOSE) + 1,      // upper bound fundamental
+    static_cast<int32_t>(OperationType::OEM_OPERATION) - 1,  // lower bound OEM
+    static_cast<int32_t>(OperationType::OEM_OPERATION) + 1,  // upper bound OEM
+};
+
+static void mutateOperationTypeTest(const sp<IDevice>& device, const V1_1::Model& model) {
+    for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+        for (int32_t invalidOperationType : invalidOperationTypes) {
+            const std::string message = "mutateOperationTypeTest: operation " +
+                                        std::to_string(operation) + " set to value " +
+                                        std::to_string(invalidOperationType);
+            validate(device, message, model, [operation, invalidOperationType](Model* model) {
+                model->operations[operation].type =
+                    static_cast<OperationType>(invalidOperationType);
+            });
+        }
+    }
+}
+
+///////////////////////// VALIDATE MODEL OPERATION INPUT OPERAND INDEX /////////////////////////
+
+static void mutateOperationInputOperandIndexTest(const sp<IDevice>& device,
+                                                 const V1_1::Model& model) {
+    for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+        const uint32_t invalidOperand = model.operands.size();
+        for (size_t input = 0; input < model.operations[operation].inputs.size(); ++input) {
+            const std::string message = "mutateOperationInputOperandIndexTest: operation " +
+                                        std::to_string(operation) + " input " +
+                                        std::to_string(input);
+            validate(device, message, model, [operation, input, invalidOperand](Model* model) {
+                model->operations[operation].inputs[input] = invalidOperand;
+            });
+        }
+    }
+}
+
+///////////////////////// VALIDATE MODEL OPERATION OUTPUT OPERAND INDEX /////////////////////////
+
+static void mutateOperationOutputOperandIndexTest(const sp<IDevice>& device,
+                                                  const V1_1::Model& model) {
+    for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+        const uint32_t invalidOperand = model.operands.size();
+        for (size_t output = 0; output < model.operations[operation].outputs.size(); ++output) {
+            const std::string message = "mutateOperationOutputOperandIndexTest: operation " +
+                                        std::to_string(operation) + " output " +
+                                        std::to_string(output);
+            validate(device, message, model, [operation, output, invalidOperand](Model* model) {
+                model->operations[operation].outputs[output] = invalidOperand;
+            });
+        }
+    }
+}
+
+///////////////////////// REMOVE OPERAND FROM EVERYTHING /////////////////////////
+
+static void removeValueAndDecrementGreaterValues(hidl_vec<uint32_t>* vec, uint32_t value) {
+    if (vec) {
+        // remove elements matching "value"
+        auto last = std::remove(vec->begin(), vec->end(), value);
+        vec->resize(std::distance(vec->begin(), last));
+
+        // decrement elements exceeding "value"
+        std::transform(vec->begin(), vec->end(), vec->begin(),
+                       [value](uint32_t v) { return v > value ? v-- : v; });
+    }
+}
+
+static void removeOperand(Model* model, uint32_t index) {
+    hidl_vec_removeAt(&model->operands, index);
+    for (Operation& operation : model->operations) {
+        removeValueAndDecrementGreaterValues(&operation.inputs, index);
+        removeValueAndDecrementGreaterValues(&operation.outputs, index);
+    }
+    removeValueAndDecrementGreaterValues(&model->inputIndexes, index);
+    removeValueAndDecrementGreaterValues(&model->outputIndexes, index);
+}
+
+static void removeOperandTest(const sp<IDevice>& device, const V1_1::Model& model) {
+    for (size_t operand = 0; operand < model.operands.size(); ++operand) {
+        const std::string message = "removeOperandTest: operand " + std::to_string(operand);
+        validate(device, message, model,
+                 [operand](Model* model) { removeOperand(model, operand); });
+    }
+}
+
+///////////////////////// REMOVE OPERATION /////////////////////////
+
+static void removeOperation(Model* model, uint32_t index) {
+    for (uint32_t operand : model->operations[index].inputs) {
+        model->operands[operand].numberOfConsumers--;
+    }
+    hidl_vec_removeAt(&model->operations, index);
+}
+
+static void removeOperationTest(const sp<IDevice>& device, const V1_1::Model& model) {
+    for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+        const std::string message = "removeOperationTest: operation " + std::to_string(operation);
+        validate(device, message, model,
+                 [operation](Model* model) { removeOperation(model, operation); });
+    }
+}
+
+///////////////////////// REMOVE OPERATION INPUT /////////////////////////
+
+static void removeOperationInputTest(const sp<IDevice>& device, const V1_1::Model& model) {
+    for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+        for (size_t input = 0; input < model.operations[operation].inputs.size(); ++input) {
+            const V1_1::Operation& op = model.operations[operation];
+            // CONCATENATION has at least 2 inputs, with the last element being
+            // INT32. Skip this test if removing one of CONCATENATION's
+            // inputs still produces a valid model.
+            if (op.type == V1_1::OperationType::CONCATENATION && op.inputs.size() > 2 &&
+                input != op.inputs.size() - 1) {
+                continue;
+            }
+            const std::string message = "removeOperationInputTest: operation " +
+                                        std::to_string(operation) + ", input " +
+                                        std::to_string(input);
+            validate(device, message, model, [operation, input](Model* model) {
+                uint32_t operand = model->operations[operation].inputs[input];
+                model->operands[operand].numberOfConsumers--;
+                hidl_vec_removeAt(&model->operations[operation].inputs, input);
+            });
+        }
+    }
+}
+
+///////////////////////// REMOVE OPERATION OUTPUT /////////////////////////
+
+static void removeOperationOutputTest(const sp<IDevice>& device, const V1_1::Model& model) {
+    for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+        for (size_t output = 0; output < model.operations[operation].outputs.size(); ++output) {
+            const std::string message = "removeOperationOutputTest: operation " +
+                                        std::to_string(operation) + ", output " +
+                                        std::to_string(output);
+            validate(device, message, model, [operation, output](Model* model) {
+                hidl_vec_removeAt(&model->operations[operation].outputs, output);
+            });
+        }
+    }
+}
+
+///////////////////////// MODEL VALIDATION /////////////////////////
+
+// TODO: remove model input
+// TODO: remove model output
+// TODO: add unused operation
+
+///////////////////////// ADD OPERATION INPUT /////////////////////////
+
+static void addOperationInputTest(const sp<IDevice>& device, const V1_1::Model& model) {
+    for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+        const std::string message = "addOperationInputTest: operation " + std::to_string(operation);
+        validate(device, message, model, [operation](Model* model) {
+            uint32_t index = addOperand(model, OperandLifeTime::MODEL_INPUT);
+            hidl_vec_push_back(&model->operations[operation].inputs, index);
+            hidl_vec_push_back(&model->inputIndexes, index);
+        });
+    }
+}
+
+///////////////////////// ADD OPERATION OUTPUT /////////////////////////
+
+static void addOperationOutputTest(const sp<IDevice>& device, const V1_1::Model& model) {
+    for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+        const std::string message =
+            "addOperationOutputTest: operation " + std::to_string(operation);
+        validate(device, message, model, [operation](Model* model) {
+            uint32_t index = addOperand(model, OperandLifeTime::MODEL_OUTPUT);
+            hidl_vec_push_back(&model->operations[operation].outputs, index);
+            hidl_vec_push_back(&model->outputIndexes, index);
+        });
+    }
+}
+
+////////////////////////// ENTRY POINT //////////////////////////////
+
+void ValidationTest::validateModel(const V1_1::Model& model) {
+    mutateOperandTypeTest(device, model);
+    mutateOperandRankTest(device, model);
+    mutateOperandScaleTest(device, model);
+    mutateOperandZeroPointTest(device, model);
+    mutateOperationOperandTypeTest(device, model);
+    mutateOperationTypeTest(device, model);
+    mutateOperationInputOperandIndexTest(device, model);
+    mutateOperationOutputOperandIndexTest(device, model);
+    removeOperandTest(device, model);
+    removeOperationTest(device, model);
+    removeOperationInputTest(device, model);
+    removeOperationOutputTest(device, model);
+    addOperationInputTest(device, model);
+    addOperationOutputTest(device, model);
+}
+
+}  // namespace functional
+}  // namespace vts
+}  // namespace V1_1
+}  // namespace neuralnetworks
+}  // namespace hardware
+}  // namespace android
diff --git a/neuralnetworks/1.1/vts/functional/ValidateRequest.cpp b/neuralnetworks/1.1/vts/functional/ValidateRequest.cpp
new file mode 100644
index 0000000..bd96614
--- /dev/null
+++ b/neuralnetworks/1.1/vts/functional/ValidateRequest.cpp
@@ -0,0 +1,262 @@
+/*
+ * Copyright (C) 2018 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#define LOG_TAG "neuralnetworks_hidl_hal_test"
+
+#include "VtsHalNeuralnetworks.h"
+
+#include "Callbacks.h"
+#include "TestHarness.h"
+#include "Utils.h"
+
+#include <android-base/logging.h>
+#include <android/hidl/memory/1.0/IMemory.h>
+#include <hidlmemory/mapping.h>
+
+namespace android {
+namespace hardware {
+namespace neuralnetworks {
+namespace V1_1 {
+namespace vts {
+namespace functional {
+
+using ::android::hardware::neuralnetworks::V1_0::implementation::ExecutionCallback;
+using ::android::hardware::neuralnetworks::V1_0::implementation::PreparedModelCallback;
+using ::android::hidl::memory::V1_0::IMemory;
+using generated_tests::MixedTyped;
+using generated_tests::MixedTypedExampleType;
+using generated_tests::for_all;
+
+///////////////////////// UTILITY FUNCTIONS /////////////////////////
+
+static void createPreparedModel(const sp<IDevice>& device, const V1_1::Model& model,
+                                sp<IPreparedModel>* preparedModel) {
+    ASSERT_NE(nullptr, preparedModel);
+
+    // see if service can handle model
+    bool fullySupportsModel = false;
+    Return<void> supportedOpsLaunchStatus = device->getSupportedOperations_1_1(
+        model, [&fullySupportsModel](ErrorStatus status, const hidl_vec<bool>& supported) {
+            ASSERT_EQ(ErrorStatus::NONE, status);
+            ASSERT_NE(0ul, supported.size());
+            fullySupportsModel =
+                std::all_of(supported.begin(), supported.end(), [](bool valid) { return valid; });
+        });
+    ASSERT_TRUE(supportedOpsLaunchStatus.isOk());
+
+    // launch prepare model
+    sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
+    ASSERT_NE(nullptr, preparedModelCallback.get());
+    Return<ErrorStatus> prepareLaunchStatus =
+        device->prepareModel_1_1(model, preparedModelCallback);
+    ASSERT_TRUE(prepareLaunchStatus.isOk());
+    ASSERT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(prepareLaunchStatus));
+
+    // retrieve prepared model
+    preparedModelCallback->wait();
+    ErrorStatus prepareReturnStatus = preparedModelCallback->getStatus();
+    *preparedModel = preparedModelCallback->getPreparedModel();
+
+    // The getSupportedOperations_1_1 call returns a list of operations that are
+    // guaranteed not to fail if prepareModel_1_1 is called, and
+    // 'fullySupportsModel' is true i.f.f. the entire model is guaranteed.
+    // If a driver has any doubt that it can prepare an operation, it must
+    // return false. So here, if a driver isn't sure if it can support an
+    // operation, but reports that it successfully prepared the model, the test
+    // can continue.
+    if (!fullySupportsModel && prepareReturnStatus != ErrorStatus::NONE) {
+        ASSERT_EQ(nullptr, preparedModel->get());
+        LOG(INFO) << "NN VTS: Unable to test Request validation because vendor service cannot "
+                     "prepare model that it does not support.";
+        std::cout << "[          ]   Unable to test Request validation because vendor service "
+                     "cannot prepare model that it does not support."
+                  << std::endl;
+        return;
+    }
+    ASSERT_EQ(ErrorStatus::NONE, prepareReturnStatus);
+    ASSERT_NE(nullptr, preparedModel->get());
+}
+
+// Primary validation function. This function will take a valid request, apply a
+// mutation to it to invalidate the request, then pass it to interface calls
+// that use the request. Note that the request here is passed by value, and any
+// mutation to the request does not leave this function.
+static void validate(const sp<IPreparedModel>& preparedModel, const std::string& message,
+                     Request request, const std::function<void(Request*)>& mutation) {
+    mutation(&request);
+    SCOPED_TRACE(message + " [execute]");
+
+    sp<ExecutionCallback> executionCallback = new ExecutionCallback();
+    ASSERT_NE(nullptr, executionCallback.get());
+    Return<ErrorStatus> executeLaunchStatus = preparedModel->execute(request, executionCallback);
+    ASSERT_TRUE(executeLaunchStatus.isOk());
+    ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(executeLaunchStatus));
+
+    executionCallback->wait();
+    ErrorStatus executionReturnStatus = executionCallback->getStatus();
+    ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, executionReturnStatus);
+}
+
+// Delete element from hidl_vec. hidl_vec doesn't support a "remove" operation,
+// so this is efficiently accomplished by moving the element to the end and
+// resizing the hidl_vec to one less.
+template <typename Type>
+static void hidl_vec_removeAt(hidl_vec<Type>* vec, uint32_t index) {
+    if (vec) {
+        std::rotate(vec->begin() + index, vec->begin() + index + 1, vec->end());
+        vec->resize(vec->size() - 1);
+    }
+}
+
+template <typename Type>
+static uint32_t hidl_vec_push_back(hidl_vec<Type>* vec, const Type& value) {
+    // assume vec is valid
+    const uint32_t index = vec->size();
+    vec->resize(index + 1);
+    (*vec)[index] = value;
+    return index;
+}
+
+///////////////////////// REMOVE INPUT ////////////////////////////////////
+
+static void removeInputTest(const sp<IPreparedModel>& preparedModel, const Request& request) {
+    for (size_t input = 0; input < request.inputs.size(); ++input) {
+        const std::string message = "removeInput: removed input " + std::to_string(input);
+        validate(preparedModel, message, request,
+                 [input](Request* request) { hidl_vec_removeAt(&request->inputs, input); });
+    }
+}
+
+///////////////////////// REMOVE OUTPUT ////////////////////////////////////
+
+static void removeOutputTest(const sp<IPreparedModel>& preparedModel, const Request& request) {
+    for (size_t output = 0; output < request.outputs.size(); ++output) {
+        const std::string message = "removeOutput: removed Output " + std::to_string(output);
+        validate(preparedModel, message, request,
+                 [output](Request* request) { hidl_vec_removeAt(&request->outputs, output); });
+    }
+}
+
+///////////////////////////// ENTRY POINT //////////////////////////////////
+
+std::vector<Request> createRequests(const std::vector<MixedTypedExampleType>& examples) {
+    const uint32_t INPUT = 0;
+    const uint32_t OUTPUT = 1;
+
+    std::vector<Request> requests;
+
+    for (auto& example : examples) {
+        const MixedTyped& inputs = example.first;
+        const MixedTyped& outputs = example.second;
+
+        std::vector<RequestArgument> inputs_info, outputs_info;
+        uint32_t inputSize = 0, outputSize = 0;
+
+        // This function only partially specifies the metadata (vector of RequestArguments).
+        // The contents are copied over below.
+        for_all(inputs, [&inputs_info, &inputSize](int index, auto, auto s) {
+            if (inputs_info.size() <= static_cast<size_t>(index)) inputs_info.resize(index + 1);
+            RequestArgument arg = {
+                .location = {.poolIndex = INPUT, .offset = 0, .length = static_cast<uint32_t>(s)},
+                .dimensions = {},
+            };
+            RequestArgument arg_empty = {
+                .hasNoValue = true,
+            };
+            inputs_info[index] = s ? arg : arg_empty;
+            inputSize += s;
+        });
+        // Compute offset for inputs 1 and so on
+        {
+            size_t offset = 0;
+            for (auto& i : inputs_info) {
+                if (!i.hasNoValue) i.location.offset = offset;
+                offset += i.location.length;
+            }
+        }
+
+        // Go through all outputs, initialize RequestArgument descriptors
+        for_all(outputs, [&outputs_info, &outputSize](int index, auto, auto s) {
+            if (outputs_info.size() <= static_cast<size_t>(index)) outputs_info.resize(index + 1);
+            RequestArgument arg = {
+                .location = {.poolIndex = OUTPUT, .offset = 0, .length = static_cast<uint32_t>(s)},
+                .dimensions = {},
+            };
+            outputs_info[index] = arg;
+            outputSize += s;
+        });
+        // Compute offset for outputs 1 and so on
+        {
+            size_t offset = 0;
+            for (auto& i : outputs_info) {
+                i.location.offset = offset;
+                offset += i.location.length;
+            }
+        }
+        std::vector<hidl_memory> pools = {nn::allocateSharedMemory(inputSize),
+                                          nn::allocateSharedMemory(outputSize)};
+        if (pools[INPUT].size() == 0 || pools[OUTPUT].size() == 0) {
+            return {};
+        }
+
+        // map pool
+        sp<IMemory> inputMemory = mapMemory(pools[INPUT]);
+        if (inputMemory == nullptr) {
+            return {};
+        }
+        char* inputPtr = reinterpret_cast<char*>(static_cast<void*>(inputMemory->getPointer()));
+        if (inputPtr == nullptr) {
+            return {};
+        }
+
+        // initialize pool
+        inputMemory->update();
+        for_all(inputs, [&inputs_info, inputPtr](int index, auto p, auto s) {
+            char* begin = (char*)p;
+            char* end = begin + s;
+            // TODO: handle more than one input
+            std::copy(begin, end, inputPtr + inputs_info[index].location.offset);
+        });
+        inputMemory->commit();
+
+        requests.push_back({.inputs = inputs_info, .outputs = outputs_info, .pools = pools});
+    }
+
+    return requests;
+}
+
+void ValidationTest::validateRequests(const V1_1::Model& model,
+                                      const std::vector<Request>& requests) {
+    // create IPreparedModel
+    sp<IPreparedModel> preparedModel;
+    ASSERT_NO_FATAL_FAILURE(createPreparedModel(device, model, &preparedModel));
+    if (preparedModel == nullptr) {
+        return;
+    }
+
+    // validate each request
+    for (const Request& request : requests) {
+        removeInputTest(preparedModel, request);
+        removeOutputTest(preparedModel, request);
+    }
+}
+
+}  // namespace functional
+}  // namespace vts
+}  // namespace V1_1
+}  // namespace neuralnetworks
+}  // namespace hardware
+}  // namespace android
diff --git a/neuralnetworks/1.1/vts/functional/ValidationTests.cpp b/neuralnetworks/1.1/vts/functional/ValidationTests.cpp
new file mode 100644
index 0000000..1c35ba8
--- /dev/null
+++ b/neuralnetworks/1.1/vts/functional/ValidationTests.cpp
@@ -0,0 +1,50 @@
+/*
+ * Copyright (C) 2018 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#define LOG_TAG "neuralnetworks_hidl_hal_test"
+
+#include "Models.h"
+#include "VtsHalNeuralnetworks.h"
+
+namespace android {
+namespace hardware {
+namespace neuralnetworks {
+namespace V1_1 {
+namespace vts {
+namespace functional {
+
+// forward declarations
+std::vector<Request> createRequests(const std::vector<MixedTypedExample>& examples);
+
+// generate validation tests
+#define VTS_CURRENT_TEST_CASE(TestName)                                           \
+    TEST_F(ValidationTest, TestName) {                                            \
+        const Model model = TestName::createTestModel();                          \
+        const std::vector<Request> requests = createRequests(TestName::examples); \
+        validateModel(model);                                                     \
+        validateRequests(model, requests);                                        \
+    }
+
+FOR_EACH_TEST_MODEL(VTS_CURRENT_TEST_CASE)
+
+#undef VTS_CURRENT_TEST_CASE
+
+}  // namespace functional
+}  // namespace vts
+}  // namespace V1_1
+}  // namespace neuralnetworks
+}  // namespace hardware
+}  // namespace android
diff --git a/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworksV1_1.cpp b/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworks.cpp
similarity index 64%
rename from neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworksV1_1.cpp
rename to neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworks.cpp
index b1d3be7..62381e6 100644
--- a/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworksV1_1.cpp
+++ b/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworks.cpp
@@ -16,16 +16,7 @@
 
 #define LOG_TAG "neuralnetworks_hidl_hal_test"
 
-#include "VtsHalNeuralnetworksV1_1.h"
-#include "Utils.h"
-
-#include <android-base/logging.h>
-#include <hidlmemory/mapping.h>
-
-using ::android::hardware::hidl_memory;
-using ::android::hidl::allocator::V1_0::IAllocator;
-using ::android::hidl::memory::V1_0::IMemory;
-using ::android::sp;
+#include "VtsHalNeuralnetworks.h"
 
 namespace android {
 namespace hardware {
@@ -34,11 +25,6 @@
 namespace vts {
 namespace functional {
 
-// allocator helper
-hidl_memory allocateSharedMemory(int64_t size) {
-    return nn::allocateSharedMemory(size);
-}
-
 // A class for test environment setup
 NeuralnetworksHidlEnvironment::NeuralnetworksHidlEnvironment() {}
 
@@ -52,23 +38,49 @@
 }
 
 void NeuralnetworksHidlEnvironment::registerTestServices() {
-    registerTestService<V1_1::IDevice>();
+    registerTestService<IDevice>();
 }
 
 // The main test class for NEURALNETWORK HIDL HAL.
+NeuralnetworksHidlTest::NeuralnetworksHidlTest() {}
+
 NeuralnetworksHidlTest::~NeuralnetworksHidlTest() {}
 
 void NeuralnetworksHidlTest::SetUp() {
-    device = ::testing::VtsHalHidlTargetTestBase::getService<V1_1::IDevice>(
+    ::testing::VtsHalHidlTargetTestBase::SetUp();
+    device = ::testing::VtsHalHidlTargetTestBase::getService<IDevice>(
         NeuralnetworksHidlEnvironment::getInstance());
     ASSERT_NE(nullptr, device.get());
 }
 
-void NeuralnetworksHidlTest::TearDown() {}
+void NeuralnetworksHidlTest::TearDown() {
+    device = nullptr;
+    ::testing::VtsHalHidlTargetTestBase::TearDown();
+}
 
 }  // namespace functional
 }  // namespace vts
+
+::std::ostream& operator<<(::std::ostream& os, ErrorStatus errorStatus) {
+    return os << toString(errorStatus);
+}
+
+::std::ostream& operator<<(::std::ostream& os, DeviceStatus deviceStatus) {
+    return os << toString(deviceStatus);
+}
+
 }  // namespace V1_1
 }  // namespace neuralnetworks
 }  // namespace hardware
 }  // namespace android
+
+using android::hardware::neuralnetworks::V1_1::vts::functional::NeuralnetworksHidlEnvironment;
+
+int main(int argc, char** argv) {
+    ::testing::AddGlobalTestEnvironment(NeuralnetworksHidlEnvironment::getInstance());
+    ::testing::InitGoogleTest(&argc, argv);
+    NeuralnetworksHidlEnvironment::getInstance()->init(&argc, argv);
+
+    int status = RUN_ALL_TESTS();
+    return status;
+}
diff --git a/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworksV1_1.h b/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworks.h
similarity index 60%
rename from neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworksV1_1.h
rename to neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworks.h
index 426246c..0050e52 100644
--- a/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworksV1_1.h
+++ b/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworks.h
@@ -17,65 +17,71 @@
 #ifndef VTS_HAL_NEURALNETWORKS_V1_1_H
 #define VTS_HAL_NEURALNETWORKS_V1_1_H
 
-#include <android/hardware/neuralnetworks/1.0/IExecutionCallback.h>
-#include <android/hardware/neuralnetworks/1.0/IPreparedModel.h>
-#include <android/hardware/neuralnetworks/1.0/IPreparedModelCallback.h>
+#include <android/hardware/neuralnetworks/1.0/types.h>
 #include <android/hardware/neuralnetworks/1.1/IDevice.h>
 #include <android/hardware/neuralnetworks/1.1/types.h>
-#include <android/hidl/allocator/1.0/IAllocator.h>
 
 #include <VtsHalHidlTargetTestBase.h>
 #include <VtsHalHidlTargetTestEnvBase.h>
+
+#include <android-base/macros.h>
 #include <gtest/gtest.h>
-#include <string>
+#include <iostream>
+#include <vector>
 
 namespace android {
 namespace hardware {
 namespace neuralnetworks {
 namespace V1_1 {
+
+using V1_0::Request;
+using V1_0::DeviceStatus;
+using V1_0::ErrorStatus;
+
 namespace vts {
 namespace functional {
-hidl_memory allocateSharedMemory(int64_t size);
 
 // A class for test environment setup
 class NeuralnetworksHidlEnvironment : public ::testing::VtsHalHidlTargetTestEnvBase {
+    DISALLOW_COPY_AND_ASSIGN(NeuralnetworksHidlEnvironment);
     NeuralnetworksHidlEnvironment();
-    NeuralnetworksHidlEnvironment(const NeuralnetworksHidlEnvironment&) = delete;
-    NeuralnetworksHidlEnvironment(NeuralnetworksHidlEnvironment&&) = delete;
-    NeuralnetworksHidlEnvironment& operator=(const NeuralnetworksHidlEnvironment&) = delete;
-    NeuralnetworksHidlEnvironment& operator=(NeuralnetworksHidlEnvironment&&) = delete;
+    ~NeuralnetworksHidlEnvironment() override;
 
    public:
-    ~NeuralnetworksHidlEnvironment() override;
     static NeuralnetworksHidlEnvironment* getInstance();
     void registerTestServices() override;
 };
 
 // The main test class for NEURALNETWORKS HIDL HAL.
 class NeuralnetworksHidlTest : public ::testing::VtsHalHidlTargetTestBase {
+    DISALLOW_COPY_AND_ASSIGN(NeuralnetworksHidlTest);
+
    public:
+    NeuralnetworksHidlTest();
     ~NeuralnetworksHidlTest() override;
     void SetUp() override;
     void TearDown() override;
 
-    sp<V1_1::IDevice> device;
+   protected:
+    sp<IDevice> device;
 };
+
+// Tag for the validation tests
+class ValidationTest : public NeuralnetworksHidlTest {
+   protected:
+    void validateModel(const Model& model);
+    void validateRequests(const Model& model, const std::vector<Request>& request);
+};
+
+// Tag for the generated tests
+class GeneratedTest : public NeuralnetworksHidlTest {};
+
 }  // namespace functional
 }  // namespace vts
 
 // pretty-print values for error messages
-
-template <typename CharT, typename Traits>
-::std::basic_ostream<CharT, Traits>& operator<<(::std::basic_ostream<CharT, Traits>& os,
-                                                V1_0::ErrorStatus errorStatus) {
-    return os << toString(errorStatus);
-}
-
-template <typename CharT, typename Traits>
-::std::basic_ostream<CharT, Traits>& operator<<(::std::basic_ostream<CharT, Traits>& os,
-                                                V1_0::DeviceStatus deviceStatus) {
-    return os << toString(deviceStatus);
-}
+::std::ostream& operator<<(::std::ostream& os, ErrorStatus errorStatus);
+::std::ostream& operator<<(::std::ostream& os, DeviceStatus deviceStatus);
 
 }  // namespace V1_1
 }  // namespace neuralnetworks
diff --git a/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworksV1_1BasicTest.cpp b/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworksV1_1BasicTest.cpp
deleted file mode 100644
index 10591dc..0000000
--- a/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworksV1_1BasicTest.cpp
+++ /dev/null
@@ -1,468 +0,0 @@
-/*
- * Copyright (C) 2018 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#define LOG_TAG "neuralnetworks_hidl_hal_test"
-
-#include "VtsHalNeuralnetworksV1_1.h"
-
-#include "Callbacks.h"
-#include "Models.h"
-#include "TestHarness.h"
-
-#include <android-base/logging.h>
-#include <android/hardware/neuralnetworks/1.1/IDevice.h>
-#include <android/hardware/neuralnetworks/1.1/types.h>
-#include <android/hidl/memory/1.0/IMemory.h>
-#include <hidlmemory/mapping.h>
-
-using ::android::hardware::neuralnetworks::V1_0::IPreparedModel;
-using ::android::hardware::neuralnetworks::V1_0::DeviceStatus;
-using ::android::hardware::neuralnetworks::V1_0::ErrorStatus;
-using ::android::hardware::neuralnetworks::V1_0::FusedActivationFunc;
-using ::android::hardware::neuralnetworks::V1_0::Operand;
-using ::android::hardware::neuralnetworks::V1_0::OperandLifeTime;
-using ::android::hardware::neuralnetworks::V1_0::OperandType;
-using ::android::hardware::neuralnetworks::V1_0::Request;
-using ::android::hardware::neuralnetworks::V1_1::Capabilities;
-using ::android::hardware::neuralnetworks::V1_1::IDevice;
-using ::android::hardware::neuralnetworks::V1_1::Model;
-using ::android::hardware::neuralnetworks::V1_1::Operation;
-using ::android::hardware::neuralnetworks::V1_1::OperationType;
-using ::android::hardware::Return;
-using ::android::hardware::Void;
-using ::android::hardware::hidl_memory;
-using ::android::hardware::hidl_string;
-using ::android::hardware::hidl_vec;
-using ::android::hidl::allocator::V1_0::IAllocator;
-using ::android::hidl::memory::V1_0::IMemory;
-using ::android::sp;
-
-namespace android {
-namespace hardware {
-namespace neuralnetworks {
-namespace V1_1 {
-namespace vts {
-namespace functional {
-using ::android::hardware::neuralnetworks::V1_0::implementation::ExecutionCallback;
-using ::android::hardware::neuralnetworks::V1_0::implementation::PreparedModelCallback;
-
-static void doPrepareModelShortcut(const sp<IDevice>& device, sp<IPreparedModel>* preparedModel) {
-    ASSERT_NE(nullptr, preparedModel);
-    Model model = createValidTestModel_1_1();
-
-    // see if service can handle model
-    bool fullySupportsModel = false;
-    Return<void> supportedOpsLaunchStatus = device->getSupportedOperations_1_1(
-        model, [&fullySupportsModel](ErrorStatus status, const hidl_vec<bool>& supported) {
-            ASSERT_EQ(ErrorStatus::NONE, status);
-            ASSERT_NE(0ul, supported.size());
-            fullySupportsModel =
-                std::all_of(supported.begin(), supported.end(), [](bool valid) { return valid; });
-        });
-    ASSERT_TRUE(supportedOpsLaunchStatus.isOk());
-
-    // launch prepare model
-    sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
-    ASSERT_NE(nullptr, preparedModelCallback.get());
-    Return<ErrorStatus> prepareLaunchStatus =
-        device->prepareModel_1_1(model, preparedModelCallback);
-    ASSERT_TRUE(prepareLaunchStatus.isOk());
-    ASSERT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(prepareLaunchStatus));
-
-    // retrieve prepared model
-    preparedModelCallback->wait();
-    ErrorStatus prepareReturnStatus = preparedModelCallback->getStatus();
-    *preparedModel = preparedModelCallback->getPreparedModel();
-
-    // The getSupportedOperations call returns a list of operations that are
-    // guaranteed not to fail if prepareModel is called, and
-    // 'fullySupportsModel' is true i.f.f. the entire model is guaranteed.
-    // If a driver has any doubt that it can prepare an operation, it must
-    // return false. So here, if a driver isn't sure if it can support an
-    // operation, but reports that it successfully prepared the model, the test
-    // can continue.
-    if (!fullySupportsModel && prepareReturnStatus != ErrorStatus::NONE) {
-        ASSERT_EQ(nullptr, preparedModel->get());
-        LOG(INFO) << "NN VTS: Early termination of test because vendor service cannot "
-                     "prepare model that it does not support.";
-        std::cout << "[          ]   Early termination of test because vendor service cannot "
-                     "prepare model that it does not support."
-                  << std::endl;
-        return;
-    }
-    ASSERT_EQ(ErrorStatus::NONE, prepareReturnStatus);
-    ASSERT_NE(nullptr, preparedModel->get());
-}
-
-// create device test
-TEST_F(NeuralnetworksHidlTest, CreateDevice) {}
-
-// status test
-TEST_F(NeuralnetworksHidlTest, StatusTest) {
-    Return<DeviceStatus> status = device->getStatus();
-    ASSERT_TRUE(status.isOk());
-    EXPECT_EQ(DeviceStatus::AVAILABLE, static_cast<DeviceStatus>(status));
-}
-
-// initialization
-TEST_F(NeuralnetworksHidlTest, GetCapabilitiesTest) {
-    Return<void> ret =
-        device->getCapabilities_1_1([](ErrorStatus status, const Capabilities& capabilities) {
-            EXPECT_EQ(ErrorStatus::NONE, status);
-            EXPECT_LT(0.0f, capabilities.float32Performance.execTime);
-            EXPECT_LT(0.0f, capabilities.float32Performance.powerUsage);
-            EXPECT_LT(0.0f, capabilities.quantized8Performance.execTime);
-            EXPECT_LT(0.0f, capabilities.quantized8Performance.powerUsage);
-            EXPECT_LT(0.0f, capabilities.relaxedFloat32toFloat16Performance.execTime);
-            EXPECT_LT(0.0f, capabilities.relaxedFloat32toFloat16Performance.powerUsage);
-        });
-    EXPECT_TRUE(ret.isOk());
-}
-
-// supported operations positive test
-TEST_F(NeuralnetworksHidlTest, SupportedOperationsPositiveTest) {
-    Model model = createValidTestModel_1_1();
-    Return<void> ret = device->getSupportedOperations_1_1(
-        model, [&](ErrorStatus status, const hidl_vec<bool>& supported) {
-            EXPECT_EQ(ErrorStatus::NONE, status);
-            EXPECT_EQ(model.operations.size(), supported.size());
-        });
-    EXPECT_TRUE(ret.isOk());
-}
-
-// supported operations negative test 1
-TEST_F(NeuralnetworksHidlTest, SupportedOperationsNegativeTest1) {
-    Model model = createInvalidTestModel1_1_1();
-    Return<void> ret = device->getSupportedOperations_1_1(
-        model, [&](ErrorStatus status, const hidl_vec<bool>& supported) {
-            EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, status);
-            (void)supported;
-        });
-    EXPECT_TRUE(ret.isOk());
-}
-
-// supported operations negative test 2
-TEST_F(NeuralnetworksHidlTest, SupportedOperationsNegativeTest2) {
-    Model model = createInvalidTestModel2_1_1();
-    Return<void> ret = device->getSupportedOperations_1_1(
-        model, [&](ErrorStatus status, const hidl_vec<bool>& supported) {
-            EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, status);
-            (void)supported;
-        });
-    EXPECT_TRUE(ret.isOk());
-}
-
-// prepare simple model positive test
-TEST_F(NeuralnetworksHidlTest, SimplePrepareModelPositiveTest) {
-    sp<IPreparedModel> preparedModel;
-    doPrepareModelShortcut(device, &preparedModel);
-}
-
-// prepare simple model negative test 1
-TEST_F(NeuralnetworksHidlTest, SimplePrepareModelNegativeTest1) {
-    Model model = createInvalidTestModel1_1_1();
-    sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
-    ASSERT_NE(nullptr, preparedModelCallback.get());
-    Return<ErrorStatus> prepareLaunchStatus =
-        device->prepareModel_1_1(model, preparedModelCallback);
-    ASSERT_TRUE(prepareLaunchStatus.isOk());
-    EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(prepareLaunchStatus));
-
-    preparedModelCallback->wait();
-    ErrorStatus prepareReturnStatus = preparedModelCallback->getStatus();
-    EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, prepareReturnStatus);
-    sp<IPreparedModel> preparedModel = preparedModelCallback->getPreparedModel();
-    EXPECT_EQ(nullptr, preparedModel.get());
-}
-
-// prepare simple model negative test 2
-TEST_F(NeuralnetworksHidlTest, SimplePrepareModelNegativeTest2) {
-    Model model = createInvalidTestModel2_1_1();
-    sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
-    ASSERT_NE(nullptr, preparedModelCallback.get());
-    Return<ErrorStatus> prepareLaunchStatus =
-        device->prepareModel_1_1(model, preparedModelCallback);
-    ASSERT_TRUE(prepareLaunchStatus.isOk());
-    EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(prepareLaunchStatus));
-
-    preparedModelCallback->wait();
-    ErrorStatus prepareReturnStatus = preparedModelCallback->getStatus();
-    EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, prepareReturnStatus);
-    sp<IPreparedModel> preparedModel = preparedModelCallback->getPreparedModel();
-    EXPECT_EQ(nullptr, preparedModel.get());
-}
-
-// execute simple graph positive test
-TEST_F(NeuralnetworksHidlTest, SimpleExecuteGraphPositiveTest) {
-    std::vector<float> outputData = {-1.0f, -1.0f, -1.0f, -1.0f};
-    std::vector<float> expectedData = {6.0f, 8.0f, 10.0f, 12.0f};
-    const uint32_t OUTPUT = 1;
-
-    sp<IPreparedModel> preparedModel;
-    ASSERT_NO_FATAL_FAILURE(doPrepareModelShortcut(device, &preparedModel));
-    if (preparedModel == nullptr) {
-        return;
-    }
-    Request request = createValidTestRequest();
-
-    auto postWork = [&] {
-        sp<IMemory> outputMemory = mapMemory(request.pools[OUTPUT]);
-        if (outputMemory == nullptr) {
-            return false;
-        }
-        float* outputPtr = reinterpret_cast<float*>(static_cast<void*>(outputMemory->getPointer()));
-        if (outputPtr == nullptr) {
-            return false;
-        }
-        outputMemory->read();
-        std::copy(outputPtr, outputPtr + outputData.size(), outputData.begin());
-        outputMemory->commit();
-        return true;
-    };
-
-    sp<ExecutionCallback> executionCallback = new ExecutionCallback();
-    ASSERT_NE(nullptr, executionCallback.get());
-    executionCallback->on_finish(postWork);
-    Return<ErrorStatus> executeLaunchStatus = preparedModel->execute(request, executionCallback);
-    ASSERT_TRUE(executeLaunchStatus.isOk());
-    EXPECT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(executeLaunchStatus));
-
-    executionCallback->wait();
-    ErrorStatus executionReturnStatus = executionCallback->getStatus();
-    EXPECT_EQ(ErrorStatus::NONE, executionReturnStatus);
-    EXPECT_EQ(expectedData, outputData);
-}
-
-// execute simple graph negative test 1
-TEST_F(NeuralnetworksHidlTest, SimpleExecuteGraphNegativeTest1) {
-    sp<IPreparedModel> preparedModel;
-    ASSERT_NO_FATAL_FAILURE(doPrepareModelShortcut(device, &preparedModel));
-    if (preparedModel == nullptr) {
-        return;
-    }
-    Request request = createInvalidTestRequest1();
-
-    sp<ExecutionCallback> executionCallback = new ExecutionCallback();
-    ASSERT_NE(nullptr, executionCallback.get());
-    Return<ErrorStatus> executeLaunchStatus = preparedModel->execute(request, executionCallback);
-    ASSERT_TRUE(executeLaunchStatus.isOk());
-    EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(executeLaunchStatus));
-
-    executionCallback->wait();
-    ErrorStatus executionReturnStatus = executionCallback->getStatus();
-    EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, executionReturnStatus);
-}
-
-// execute simple graph negative test 2
-TEST_F(NeuralnetworksHidlTest, SimpleExecuteGraphNegativeTest2) {
-    sp<IPreparedModel> preparedModel;
-    ASSERT_NO_FATAL_FAILURE(doPrepareModelShortcut(device, &preparedModel));
-    if (preparedModel == nullptr) {
-        return;
-    }
-    Request request = createInvalidTestRequest2();
-
-    sp<ExecutionCallback> executionCallback = new ExecutionCallback();
-    ASSERT_NE(nullptr, executionCallback.get());
-    Return<ErrorStatus> executeLaunchStatus = preparedModel->execute(request, executionCallback);
-    ASSERT_TRUE(executeLaunchStatus.isOk());
-    EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(executeLaunchStatus));
-
-    executionCallback->wait();
-    ErrorStatus executionReturnStatus = executionCallback->getStatus();
-    EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, executionReturnStatus);
-}
-
-class NeuralnetworksInputsOutputsTest
-    : public NeuralnetworksHidlTest,
-      public ::testing::WithParamInterface<std::tuple<bool, bool>> {
-   protected:
-    virtual void SetUp() { NeuralnetworksHidlTest::SetUp(); }
-    virtual void TearDown() { NeuralnetworksHidlTest::TearDown(); }
-    V1_1::Model createModel(const std::vector<uint32_t>& inputs,
-                            const std::vector<uint32_t>& outputs) {
-        // We set up the operands as floating-point with no designated
-        // model inputs and outputs, and then patch type and lifetime
-        // later on in this function.
-
-        std::vector<Operand> operands = {
-            {
-                .type = OperandType::TENSOR_FLOAT32,
-                .dimensions = {1},
-                .numberOfConsumers = 1,
-                .scale = 0.0f,
-                .zeroPoint = 0,
-                .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
-                .location = {.poolIndex = 0, .offset = 0, .length = 0},
-            },
-            {
-                .type = OperandType::TENSOR_FLOAT32,
-                .dimensions = {1},
-                .numberOfConsumers = 1,
-                .scale = 0.0f,
-                .zeroPoint = 0,
-                .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
-                .location = {.poolIndex = 0, .offset = 0, .length = 0},
-            },
-            {
-                .type = OperandType::INT32,
-                .dimensions = {},
-                .numberOfConsumers = 1,
-                .scale = 0.0f,
-                .zeroPoint = 0,
-                .lifetime = OperandLifeTime::CONSTANT_COPY,
-                .location = {.poolIndex = 0, .offset = 0, .length = sizeof(int32_t)},
-            },
-            {
-                .type = OperandType::TENSOR_FLOAT32,
-                .dimensions = {1},
-                .numberOfConsumers = 0,
-                .scale = 0.0f,
-                .zeroPoint = 0,
-                .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
-                .location = {.poolIndex = 0, .offset = 0, .length = 0},
-            },
-        };
-
-        const std::vector<Operation> operations = {{
-            .type = OperationType::ADD, .inputs = {0, 1, 2}, .outputs = {3},
-        }};
-
-        std::vector<uint8_t> operandValues;
-        int32_t activation[1] = {static_cast<int32_t>(FusedActivationFunc::NONE)};
-        operandValues.insert(operandValues.end(), reinterpret_cast<const uint8_t*>(&activation[0]),
-                             reinterpret_cast<const uint8_t*>(&activation[1]));
-
-        if (kQuantized) {
-            for (auto& operand : operands) {
-                if (operand.type == OperandType::TENSOR_FLOAT32) {
-                    operand.type = OperandType::TENSOR_QUANT8_ASYMM;
-                    operand.scale = 1.0f;
-                    operand.zeroPoint = 0;
-                }
-            }
-        }
-
-        auto patchLifetime = [&operands](const std::vector<uint32_t>& operandIndexes,
-                                         OperandLifeTime lifetime) {
-            for (uint32_t index : operandIndexes) {
-                operands[index].lifetime = lifetime;
-            }
-        };
-        if (kInputHasPrecedence) {
-            patchLifetime(outputs, OperandLifeTime::MODEL_OUTPUT);
-            patchLifetime(inputs, OperandLifeTime::MODEL_INPUT);
-        } else {
-            patchLifetime(inputs, OperandLifeTime::MODEL_INPUT);
-            patchLifetime(outputs, OperandLifeTime::MODEL_OUTPUT);
-        }
-
-        return {
-            .operands = operands,
-            .operations = operations,
-            .inputIndexes = inputs,
-            .outputIndexes = outputs,
-            .operandValues = operandValues,
-            .pools = {},
-        };
-    }
-    void check(const std::string& name,
-               bool expectation,  // true = success
-               const std::vector<uint32_t>& inputs, const std::vector<uint32_t>& outputs) {
-        SCOPED_TRACE(name + " (HAL calls should " + (expectation ? "succeed" : "fail") + ", " +
-                     (kInputHasPrecedence ? "input" : "output") + " precedence, " +
-                     (kQuantized ? "quantized" : "float"));
-
-        V1_1::Model model = createModel(inputs, outputs);
-
-        // ensure that getSupportedOperations_1_1() checks model validity
-        ErrorStatus supportedOpsErrorStatus = ErrorStatus::GENERAL_FAILURE;
-        Return<void> supportedOpsReturn = device->getSupportedOperations_1_1(
-            model, [&model, &supportedOpsErrorStatus](ErrorStatus status,
-                                                      const hidl_vec<bool>& supported) {
-                supportedOpsErrorStatus = status;
-                if (status == ErrorStatus::NONE) {
-                    ASSERT_EQ(supported.size(), model.operations.size());
-                }
-            });
-        ASSERT_TRUE(supportedOpsReturn.isOk());
-        ASSERT_EQ(supportedOpsErrorStatus,
-                  (expectation ? ErrorStatus::NONE : ErrorStatus::INVALID_ARGUMENT));
-
-        // ensure that prepareModel_1_1() checks model validity
-        sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback;
-        ASSERT_NE(preparedModelCallback.get(), nullptr);
-        Return<ErrorStatus> prepareLaunchReturn =
-            device->prepareModel_1_1(model, preparedModelCallback);
-        ASSERT_TRUE(prepareLaunchReturn.isOk());
-        ASSERT_TRUE(prepareLaunchReturn == ErrorStatus::NONE ||
-                    prepareLaunchReturn == ErrorStatus::INVALID_ARGUMENT);
-        bool preparationOk = (prepareLaunchReturn == ErrorStatus::NONE);
-        if (preparationOk) {
-            preparedModelCallback->wait();
-            preparationOk = (preparedModelCallback->getStatus() == ErrorStatus::NONE);
-        }
-
-        if (preparationOk) {
-            ASSERT_TRUE(expectation);
-        } else {
-            // Preparation can fail for reasons other than an invalid model --
-            // for example, perhaps not all operations are supported, or perhaps
-            // the device hit some kind of capacity limit.
-            bool invalid = prepareLaunchReturn == ErrorStatus::INVALID_ARGUMENT ||
-                           preparedModelCallback->getStatus() == ErrorStatus::INVALID_ARGUMENT;
-            ASSERT_NE(expectation, invalid);
-        }
-    }
-
-    // Indicates whether an operand that appears in both the inputs
-    // and outputs vector should have lifetime appropriate for input
-    // rather than for output.
-    const bool kInputHasPrecedence = std::get<0>(GetParam());
-
-    // Indicates whether we should test TENSOR_QUANT8_ASYMM rather
-    // than TENSOR_FLOAT32.
-    const bool kQuantized = std::get<1>(GetParam());
-};
-
-TEST_P(NeuralnetworksInputsOutputsTest, Validate) {
-    check("Ok", true, {0, 1}, {3});
-    check("InputIsOutput", false, {0, 1}, {3, 0});
-    check("OutputIsInput", false, {0, 1, 3}, {3});
-    check("DuplicateInputs", false, {0, 1, 0}, {3});
-    check("DuplicateOutputs", false, {0, 1}, {3, 3});
-}
-
-INSTANTIATE_TEST_CASE_P(Flavor, NeuralnetworksInputsOutputsTest,
-                        ::testing::Combine(::testing::Bool(), ::testing::Bool()));
-
-}  // namespace functional
-}  // namespace vts
-}  // namespace V1_1
-}  // namespace neuralnetworks
-}  // namespace hardware
-}  // namespace android
-
-using android::hardware::neuralnetworks::V1_1::vts::functional::NeuralnetworksHidlEnvironment;
-
-int main(int argc, char** argv) {
-    ::testing::AddGlobalTestEnvironment(NeuralnetworksHidlEnvironment::getInstance());
-    ::testing::InitGoogleTest(&argc, argv);
-    NeuralnetworksHidlEnvironment::getInstance()->init(&argc, argv);
-
-    int status = RUN_ALL_TESTS();
-    return status;
-}
diff --git a/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworksV1_1GeneratedTest.cpp b/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworksV1_1GeneratedTest.cpp
deleted file mode 100644
index 025d9fe..0000000
--- a/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworksV1_1GeneratedTest.cpp
+++ /dev/null
@@ -1,80 +0,0 @@
-/*
- * Copyright (C) 2018 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#define LOG_TAG "neuralnetworks_hidl_hal_test"
-
-#include "VtsHalNeuralnetworksV1_1.h"
-
-#include "Callbacks.h"
-#include "TestHarness.h"
-
-#include <android-base/logging.h>
-#include <android/hardware/neuralnetworks/1.1/IDevice.h>
-#include <android/hardware/neuralnetworks/1.1/types.h>
-#include <android/hidl/memory/1.0/IMemory.h>
-#include <hidlmemory/mapping.h>
-
-using ::android::hardware::neuralnetworks::V1_0::IPreparedModel;
-using ::android::hardware::neuralnetworks::V1_0::Capabilities;
-using ::android::hardware::neuralnetworks::V1_0::DeviceStatus;
-using ::android::hardware::neuralnetworks::V1_0::ErrorStatus;
-using ::android::hardware::neuralnetworks::V1_0::FusedActivationFunc;
-using ::android::hardware::neuralnetworks::V1_0::Operand;
-using ::android::hardware::neuralnetworks::V1_0::OperandLifeTime;
-using ::android::hardware::neuralnetworks::V1_0::OperandType;
-using ::android::hardware::neuralnetworks::V1_0::Request;
-using ::android::hardware::neuralnetworks::V1_1::IDevice;
-using ::android::hardware::neuralnetworks::V1_1::Model;
-using ::android::hardware::neuralnetworks::V1_1::Operation;
-using ::android::hardware::neuralnetworks::V1_1::OperationType;
-using ::android::hardware::Return;
-using ::android::hardware::Void;
-using ::android::hardware::hidl_memory;
-using ::android::hardware::hidl_string;
-using ::android::hardware::hidl_vec;
-using ::android::hidl::allocator::V1_0::IAllocator;
-using ::android::hidl::memory::V1_0::IMemory;
-using ::android::sp;
-
-namespace android {
-namespace hardware {
-namespace neuralnetworks {
-
-namespace generated_tests {
-using ::generated_tests::MixedTypedExampleType;
-extern void Execute(sp<V1_1::IDevice>&, std::function<Model(void)>, std::function<bool(int)>,
-                    const std::vector<MixedTypedExampleType>&);
-}  // namespace generated_tests
-
-namespace V1_1 {
-namespace vts {
-namespace functional {
-using ::android::hardware::neuralnetworks::V1_0::implementation::ExecutionCallback;
-using ::android::hardware::neuralnetworks::V1_0::implementation::PreparedModelCallback;
-
-// Mixed-typed examples
-typedef generated_tests::MixedTypedExampleType MixedTypedExample;
-
-// in frameworks/ml/nn/runtime/tests/generated/
-#include "all_generated_V1_0_vts_tests.cpp"
-#include "all_generated_V1_1_vts_tests.cpp"
-
-}  // namespace functional
-}  // namespace vts
-}  // namespace V1_1
-}  // namespace neuralnetworks
-}  // namespace hardware
-}  // namespace android