diff --git a/current.txt b/current.txt
index c2c8eaf..e164a3f 100644
--- a/current.txt
+++ b/current.txt
@@ -585,6 +585,8 @@
 fd65298e1e09e0e3c781ab18305920d757dbe55a3b459ce17814ec5cf6dfee99 android.hardware.wifi@1.0::IWifiP2pIface
 
 # HALs released in Android R
+34515afa2bb792d3c6d8495a5f5d907d179c8507ca5e55c10050d02ae1d516ef android.hardware.neuralnetworks@1.3::IDevice
+e2d20d4eb24f40b44a3766d05f77052581cb3f4df35fb48c0cc5d9cdcf5c872e android.hardware.neuralnetworks@1.3::types
 04395b26be33db17747c3d3b0e8066d323f891ff4f9f3b3ddb490b2f3f844a18 android.hardware.wifi@1.4::IWifi
 270f0eb670dfd9bc5cd718e09711f2534fa8425f54d06c1a46523ca156b509e2 android.hardware.wifi.supplicant@1.3::ISupplicant
 dd4b7cfbb6e1c6ff011c33920762ad89dd02240c63a4d3a3d5037f154eae3e3b android.hardware.wifi.supplicant@1.3::ISupplicantStaIface
diff --git a/neuralnetworks/1.2/vts/functional/Android.bp b/neuralnetworks/1.2/vts/functional/Android.bp
index 3ba8879..fc727b7 100644
--- a/neuralnetworks/1.2/vts/functional/Android.bp
+++ b/neuralnetworks/1.2/vts/functional/Android.bp
@@ -14,12 +14,28 @@
 // limitations under the License.
 //
 
+cc_library_static {
+    name: "VtsHalNeuralNetworksV1_2Callbacks",
+    defaults: ["VtsHalTargetTestDefaults"],
+    export_include_dirs: ["include"],
+    srcs: [
+        "Callbacks.cpp",
+    ],
+    static_libs: [
+        "android.hardware.neuralnetworks@1.0",
+        "android.hardware.neuralnetworks@1.1",
+        "android.hardware.neuralnetworks@1.2",
+    ],
+    header_libs: [
+        "libbase_headers",
+    ]
+}
+
 cc_test {
     name: "VtsHalNeuralnetworksV1_2TargetTest",
     defaults: ["VtsHalTargetTestDefaults"],
     srcs: [
         "BasicTests.cpp",
-        "Callbacks.cpp",
         "CompilationCachingTests.cpp",
         "GeneratedTestHarness.cpp",
         "TestAssertions.cpp",
@@ -37,6 +53,7 @@
         "android.hardware.neuralnetworks@1.0",
         "android.hardware.neuralnetworks@1.1",
         "android.hardware.neuralnetworks@1.2",
+        "android.hardware.neuralnetworks@1.3",
         "android.hidl.allocator@1.0",
         "android.hidl.memory@1.0",
         "libgmock",
@@ -44,6 +61,7 @@
         "libneuralnetworks_generated_test_harness",
         "libneuralnetworks_utils",
         "VtsHalNeuralNetworksV1_0_utils",
+        "VtsHalNeuralNetworksV1_2Callbacks",
     ],
     whole_static_libs: [
         "neuralnetworks_generated_V1_0_example",
diff --git a/neuralnetworks/1.3/Android.bp b/neuralnetworks/1.3/Android.bp
new file mode 100644
index 0000000..0615ec6
--- /dev/null
+++ b/neuralnetworks/1.3/Android.bp
@@ -0,0 +1,21 @@
+// This file is autogenerated by hidl-gen -Landroidbp.
+
+hidl_interface {
+    name: "android.hardware.neuralnetworks@1.3",
+    root: "android.hardware",
+    vndk: {
+        enabled: true,
+    },
+    srcs: [
+        "types.hal",
+        "IDevice.hal",
+    ],
+    interfaces: [
+        "android.hardware.neuralnetworks@1.0",
+        "android.hardware.neuralnetworks@1.1",
+        "android.hardware.neuralnetworks@1.2",
+        "android.hidl.base@1.0",
+        "android.hidl.safe_union@1.0",
+    ],
+    gen_java: false,
+}
diff --git a/neuralnetworks/1.3/IDevice.hal b/neuralnetworks/1.3/IDevice.hal
new file mode 100644
index 0000000..ee36fb4
--- /dev/null
+++ b/neuralnetworks/1.3/IDevice.hal
@@ -0,0 +1,171 @@
+/*
+ * Copyright (C) 2019 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.
+ */
+
+package android.hardware.neuralnetworks@1.3;
+
+import @1.0::ErrorStatus;
+import @1.1::ExecutionPreference;
+import @1.2::Constant;
+import @1.2::DeviceType;
+import @1.2::Extension;
+import @1.2::IDevice;
+import @1.2::IPreparedModelCallback;
+
+/**
+ * This interface represents a device driver.
+ */
+interface IDevice extends @1.2::IDevice {
+    /**
+     * Gets the capabilities of a driver.
+     *
+     * @return status Error status of the call, must be:
+     *                - NONE if successful
+     *                - DEVICE_UNAVAILABLE if driver is offline or busy
+     *                - GENERAL_FAILURE if there is an unspecified error
+     * @return capabilities Capabilities of the driver.
+     */
+    getCapabilities_1_3() generates (ErrorStatus status, Capabilities capabilities);
+
+    /**
+     * Gets the supported operations in a model.
+     *
+     * getSupportedOperations indicates which operations of a model are fully
+     * supported by the vendor driver. If an operation may not be supported for
+     * any reason, getSupportedOperations must return false for that operation.
+     *
+     * @param model A model whose operations--and their corresponding operands--
+     *     are to be verified by the driver.
+     * @return status Error status of the call, must be:
+     *     - NONE if successful
+     *     - DEVICE_UNAVAILABLE if driver is offline or busy
+     *     - GENERAL_FAILURE if there is an unspecified error
+     *     - INVALID_ARGUMENT if provided model is invalid
+     * @return supportedOperations A list of supported operations, where true
+     *     indicates the operation is supported and false indicates the
+     *     operation is not supported. The index of "supported" corresponds with
+     *     the index of the operation it is describing.
+     */
+    getSupportedOperations_1_3(Model model)
+        generates (ErrorStatus status, vec<bool> supportedOperations);
+
+    /**
+     * Asynchronously creates a prepared model for execution and optionally
+     * saves it into cache files.
+     *
+     * prepareModel is used to make any necessary transformations to or
+     * alternative representations to a model for execution, possibly including
+     * transformations on the constant data, optimization on the model's graph,
+     * or compilation into the device's native binary format. The model itself
+     * is not changed.
+     *
+     * Optionally, caching information may be provided for the driver to save
+     * the prepared model to cache files for faster model compilation time when
+     * the same model preparation is requested in the future. There are two
+     * types of cache file handles provided to the driver: model cache and data
+     * cache. For more information on the two types of cache handles, refer to
+     * getNumberOfCacheFilesNeeded.
+     *
+     * The file descriptors must be opened with read and write permission. A
+     * file may have any size, and the corresponding file descriptor may have
+     * any offset. The driver must truncate a file to zero size before writing
+     * to that file. The file descriptors may be closed by the client once the
+     * asynchronous preparation has finished. The driver must dup a file
+     * descriptor if it wants to get access to the cache file later.
+     *
+     * The model is prepared asynchronously with respect to the caller. The
+     * prepareModel function must verify the inputs to the preparedModel
+     * function related to preparing the model (as opposed to saving the
+     * prepared model to cache) are correct. If there is an error, prepareModel
+     * must immediately invoke the callback with the appropriate ErrorStatus
+     * value and nullptr for the IPreparedModel, then return with the same
+     * ErrorStatus. If the inputs to the prepareModel function that are related
+     * to preparing the model are valid and there is no error, prepareModel must
+     * launch an asynchronous task to prepare the model in the background, and
+     * immediately return from prepareModel with ErrorStatus::NONE. If the
+     * asynchronous task fails to launch, prepareModel must immediately invoke
+     * the callback with ErrorStatus::GENERAL_FAILURE and nullptr for the
+     * IPreparedModel, then return with ErrorStatus::GENERAL_FAILURE.
+     *
+     * When the asynchronous task has finished preparing the model, it must
+     * immediately invoke the callback function provided as an input to
+     * prepareModel. If the model was prepared successfully, the callback object
+     * must be invoked with an error status of ErrorStatus::NONE and the
+     * produced IPreparedModel object. If an error occurred preparing the model,
+     * the callback object must be invoked with the appropriate ErrorStatus
+     * value and nullptr for the IPreparedModel.
+     *
+     * Optionally, the driver may save the prepared model to cache during the
+     * asynchronous preparation. Any error that occurs when saving to cache must
+     * not affect the status of preparing the model. Even if the input arguments
+     * related to the cache may be invalid, or the driver may fail to save to
+     * cache, the prepareModel function must finish preparing the model. The
+     * driver may choose not to save to cache even if the caching information is
+     * provided and valid.
+     *
+     * The only information that may be unknown to the model at this stage is
+     * the shape of the tensors, which may only be known at execution time. As
+     * such, some driver services may return partially prepared models, where
+     * the prepared model may only be finished when it is paired with a set of
+     * inputs to the model. Note that the same prepared model object may be used
+     * with different shapes of inputs on different (possibly concurrent)
+     * executions.
+     *
+     * Multiple threads may call prepareModel on the same model concurrently.
+     *
+     * @param model The model to be prepared for execution.
+     * @param preference Indicates the intended execution behavior of a prepared
+     *     model.
+     * @param modelCache A vector of handles with each entry holding exactly one
+     *     cache file descriptor for the security-sensitive cache. The length of
+     *     the vector must either be 0 indicating that caching information is
+     *     not provided, or match the numModelCache returned from
+     *     getNumberOfCacheFilesNeeded. The cache handles will be provided in
+     *     the same order when retrieving the preparedModel from cache files
+     *     with prepareModelFromCache.
+     * @param dataCache A vector of handles with each entry holding exactly one
+     *     cache file descriptor for the constants' cache. The length of the
+     *     vector must either be 0 indicating that caching information is not
+     *     provided, or match the numDataCache returned from
+     *     getNumberOfCacheFilesNeeded. The cache handles will be provided in
+     *     the same order when retrieving the preparedModel from cache files
+     *     with prepareModelFromCache.
+     * @param token A caching token of length Constant::BYTE_SIZE_OF_CACHE_TOKEN
+     *     identifying the prepared model. The same token will be provided when
+     *     retrieving the prepared model from the cache files with
+     *     prepareModelFromCache.  Tokens should be chosen to have a low rate of
+     *     collision for a particular application. The driver cannot detect a
+     *     collision; a collision will result in a failed execution or in a
+     *     successful execution that produces incorrect output values. If both
+     *     modelCache and dataCache are empty indicating that caching
+     *     information is not provided, this token must be ignored.
+     * @param callback A callback object used to return the error status of
+     *     preparing the model for execution and the prepared model if
+     *     successful, nullptr otherwise. The callback object's notify function
+     *     must be called exactly once, even if the model could not be prepared.
+     * @return status Error status of launching a task which prepares the model
+     *     in the background; must be:
+     *     - NONE if preparation task is successfully launched
+     *     - DEVICE_UNAVAILABLE if driver is offline or busy
+     *     - GENERAL_FAILURE if there is an unspecified error
+     *     - INVALID_ARGUMENT if one of the input arguments related to preparing
+     *       the model is invalid
+     */
+    prepareModel_1_3(Model model, ExecutionPreference preference,
+                     vec<handle> modelCache, vec<handle> dataCache,
+                     uint8_t[Constant:BYTE_SIZE_OF_CACHE_TOKEN] token,
+                     IPreparedModelCallback callback)
+        generates (ErrorStatus status);
+};
diff --git a/neuralnetworks/1.3/types.hal b/neuralnetworks/1.3/types.hal
new file mode 100644
index 0000000..db5dd51
--- /dev/null
+++ b/neuralnetworks/1.3/types.hal
@@ -0,0 +1,361 @@
+/*
+ * Copyright (C) 2019 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.
+ */
+
+package android.hardware.neuralnetworks@1.3;
+
+import @1.0::DataLocation;
+import @1.0::OperandLifeTime;
+import @1.0::PerformanceInfo;
+import @1.2::OperandType;
+import @1.2::OperationType;
+import @1.2::SymmPerChannelQuantParams;
+
+import android.hidl.safe_union@1.0::Monostate;
+
+/**
+ * NOTE: Since NNAPI 1.2, OEM operation and data type are deprecated. Extensions
+ * are the preferred alternative.
+ *
+ * NOTE: Adding a new fundamental type requires updating the value of
+ * OperandTypeRange::FUNDAMENTAL_MAX.
+ */
+enum OperandType : @1.2::OperandType {
+    /**
+     * A tensor of 8 bit signed integers that represent real numbers.
+     *
+     * Attached to this tensor are two numbers that can be used to convert the
+     * 8 bit integer to the real value and vice versa. These two numbers are:
+     * - scale: a 32 bit floating point value greater than zero.
+     * - zeroPoint: a 32 bit integer, in range [-128, 127].
+     *
+     * The formula is:
+     * real_value = (integer_value - zeroPoint) * scale.
+     *
+     * Available since API level 30.
+     */
+    TENSOR_QUANT8_ASYMM_SIGNED = 14,
+};
+
+/**
+ * The range of operand values in the OperandType enum.
+ */
+enum OperandTypeRange : uint32_t {
+    BASE_MIN        = 0,
+    FUNDAMENTAL_MIN = 0,
+    FUNDAMENTAL_MAX = 14,
+    OEM_MIN         = 10000,
+    OEM_MAX         = 10001,
+    BASE_MAX        = 0xFFFF,
+};
+
+
+/**
+ * The capabilities of a driver.
+ *
+ * Performance of an operation comes from the type of its first operand.
+ * This represents performance for non extension operand types.
+ */
+struct Capabilities {
+    /**
+     * Driver performance when operating on float32 data but performing
+     * calculations with range and/or precision as low as that of the IEEE
+     * 754 16-bit floating-point format.
+     */
+    PerformanceInfo relaxedFloat32toFloat16PerformanceScalar;
+    PerformanceInfo relaxedFloat32toFloat16PerformanceTensor;
+
+    /**
+     * Driver performance when operating on a particular data type.
+     * In the case of float32 data, this is used when the calculations
+     * are not relaxed.
+     */
+    struct OperandPerformance {
+        OperandType type;
+        PerformanceInfo info;
+    };
+
+    /**
+     * Performance by operand type. Must be sorted by OperandType.
+     * If a particular OperandType is not present in operandPerformance,
+     * its performance is treated as
+     * { .execTime = FLT_MAX, .powerUsage = FLT_MAX }.
+     */
+    vec<OperandPerformance> operandPerformance;
+};
+
+/**
+ * Describes one operand of the model's graph.
+ */
+struct Operand {
+    /**
+     * The data type.
+     *
+     * Besides the values listed in {@link OperandType}, any value above
+     * {@link OperandTypeRange::BASE_MAX} is possible and should be interpreted
+     * as an extension type according to {@link Model::extensionNameToPrefix}.
+     */
+    OperandType type;
+
+    /**
+     * Dimensions of the operand.
+     *
+     * For a scalar operand, dimensions.size() must be 0.
+     *
+     * A tensor operand with all dimensions specified has "fully
+     * specified" dimensions. Whenever possible (i.e., whenever the
+     * dimensions are known at model construction time), a tensor
+     * operand should have (but is not required to have) fully
+     * specified dimensions, in order to enable the best possible
+     * performance.
+     *
+     * If a tensor operand's dimensions are not fully specified, the
+     * dimensions of the operand are deduced from the operand
+     * dimensions and values of the operation for which that operand
+     * is an output.
+     *
+     * In the following situations, a tensor operand's dimensions must
+     * be fully specified:
+     *
+     *     . The operand has lifetime CONSTANT_COPY or
+     *       CONSTANT_REFERENCE.
+     *
+     *     . The operand has lifetime MODEL_INPUT. Fully
+     *       specified dimensions must either be present in the
+     *       Operand or they must be provided in the corresponding
+     *       RequestArgument.
+     *       EXCEPTION: If the input is optional and omitted
+     *       (by setting the hasNoValue field of the corresponding
+     *       RequestArgument to true) then it need not have fully
+     *       specified dimensions.
+     *
+     * A tensor operand with some number of unspecified dimensions is
+     * represented by setting each unspecified dimension to 0.
+     *
+     * A tensor operand with unspecified rank is represented by providing
+     * an empty dimensions vector.
+     */
+    vec<uint32_t> dimensions;
+
+    /**
+     * The number of times this operand appears as an operation input.
+     *
+     * (For example, if this operand appears once in one operation's
+     * input list, and three times in another operation's input list,
+     * then numberOfConsumers = 4.)
+     */
+    uint32_t numberOfConsumers;
+
+    /**
+     * Quantized scale of the operand.
+     *
+     * Only applicable if the operand is of type TENSOR_QUANT8_ASYMM or
+     * TENSOR_INT32.
+     */
+    float scale;
+
+    /**
+     * Quantized zero-point offset of the operand.
+     *
+     * Only applicable if the operand is of type TENSOR_QUANT8_ASYMM.
+     */
+    int32_t zeroPoint;
+
+    /**
+     * How the operand is used.
+     */
+    OperandLifeTime lifetime;
+
+    /**
+     * Where to find the data for this operand.
+     * If the lifetime is TEMPORARY_VARIABLE, MODEL_INPUT, MODEL_OUTPUT, or
+     * NO_VALUE:
+     * - All the fields must be 0.
+     * If the lifetime is CONSTANT_COPY:
+     * - location.poolIndex is 0.
+     * - location.offset is the offset in bytes into Model.operandValues.
+     * - location.length is set.
+     * If the lifetime is CONSTANT_REFERENCE:
+     * - location.poolIndex is set.
+     * - location.offset is the offset in bytes into the specified pool.
+     * - location.length is set.
+     */
+    DataLocation location;
+
+    /**
+     * Additional parameters specific to a particular operand type.
+     */
+    safe_union ExtraParams {
+       /**
+        * No additional parameters.
+        */
+       Monostate none;
+
+       /**
+        * Symmetric per-channel quantization parameters.
+        *
+        * Only applicable to operands of type TENSOR_QUANT8_SYMM_PER_CHANNEL.
+        */
+       SymmPerChannelQuantParams channelQuant;
+
+       /**
+        * Extension operand parameters.
+        *
+        * The framework treats this as an opaque data blob.
+        * The format is up to individual extensions.
+        */
+       vec<uint8_t> extension;
+    } extraParams;
+};
+
+/**
+ * Describes one operation of the model's graph.
+ */
+struct Operation {
+    /**
+     * The operation type.
+     */
+    OperationType type;
+
+    /**
+     * Describes the table that contains the indexes of the inputs of the
+     * operation. The offset is the index in the operandIndexes table.
+     */
+    vec<uint32_t> inputs;
+
+    /**
+     * Describes the table that contains the indexes of the outputs of the
+     * operation. The offset is the index in the operandIndexes table.
+     */
+    vec<uint32_t> outputs;
+};
+
+/**
+ * A Neural Network Model.
+ *
+ * This includes not only the execution graph, but also constant data such as
+ * weights or scalars added at construction time. The only information that
+ * may not be known is the shape of the input tensors.
+ */
+struct Model {
+    /**
+     * All operands included in the model.
+     */
+    vec<Operand> operands;
+
+    /**
+     * All operations included in the model.
+     *
+     * The operations are sorted into execution order. Every operand
+     * with lifetime MODEL_OUTPUT or TEMPORARY_VARIABLE must be
+     * written before it is read.
+     */
+    vec<Operation> operations;
+
+    /**
+     * Input indexes of the model. There must be at least one.
+     *
+     * Each value corresponds to the index of the operand in "operands".
+     */
+    vec<uint32_t> inputIndexes;
+
+    /**
+     * Output indexes of the model. There must be at least one.
+     *
+     * Each value corresponds to the index of the operand in "operands".
+     */
+    vec<uint32_t> outputIndexes;
+
+    /**
+     * A byte buffer containing operand data that were copied into the model.
+     *
+     * An operand's value must be located here if and only if Operand::lifetime
+     * equals OperandLifeTime::CONSTANT_COPY.
+     */
+    vec<uint8_t> operandValues;
+
+    /**
+     * A collection of shared memory pools containing operand values.
+     *
+     * An operand's value must be located here if and only if Operand::lifetime
+     * equals OperandLifeTime::CONSTANT_REFERENCE.
+     */
+    vec<memory> pools;
+
+    /**
+     * 'true' indicates TENSOR_FLOAT32 may be calculated with range and/or
+     * precision as low as that of the IEEE 754 16-bit floating-point format.
+     * 'false' indicates TENSOR_FLOAT32 must be calculated using at least the
+     * range and precision of the IEEE 754 32-bit floating-point format.
+     */
+    bool relaxComputationFloat32toFloat16;
+
+    /**
+     * The mapping between extension names and prefixes of operand and
+     * operation type values.
+     *
+     * An operand or operation whose numeric type value is above
+     * {@link OperandTypeRange::BASE_MAX} or
+     * {@link OperationTypeRange::BASE_MAX} respectively should be interpreted
+     * as an extension operand. The low
+     * {@link Model::ExtensionTypeEncoding::LOW_BITS_TYPE} bits of the value
+     * correspond to the type ID within the extension and the high
+     * {@link Model::ExtensionTypeEncoding::HIGH_BITS_PREFIX} bits encode
+     * the "prefix", which maps uniquely to the extension name.
+     *
+     * For example, if a model contains an operation whose value is
+     * 0xAAAABBBB and extensionNameToPrefix contains an entry with
+     * prefix=0xAAAA and name="vendor.test.test_extension", then
+     * the operation should be interpreted as the operation 0xBBBB
+     * of the extension named vendor.test.test_extension.
+     *
+     * This is a one-to-one correspondence. That is, there must be at most one
+     * prefix corresponding to each extension name and at most one extension
+     * name corresponding to each prefix.
+     */
+    vec<ExtensionNameAndPrefix> extensionNameToPrefix;
+
+    /**
+     * A correspondence between an extension name and a prefix of operand and
+     * operation type values.
+     */
+    struct ExtensionNameAndPrefix {
+        /**
+         * The extension name.
+         *
+         * See {@link Extension::name} for the format specification.
+         */
+        string name;
+
+        /**
+         * The unique extension identifier within the model.
+         *
+         * See {@link Model::extensionNameToPrefix}.
+         */
+        uint16_t prefix;
+    };
+
+    /**
+     * Numeric values of extension operand and operation types have the
+     * following structure:
+     * - 16 high bits represent the "prefix", which corresponds uniquely to the
+     *   extension name.
+     * - 16 low bits represent the type ID within the extension.
+     */
+    enum ExtensionTypeEncoding : uint8_t {
+        HIGH_BITS_PREFIX = 16,
+        LOW_BITS_TYPE = 16,
+    };
+};
diff --git a/neuralnetworks/1.3/vts/OWNERS b/neuralnetworks/1.3/vts/OWNERS
new file mode 100644
index 0000000..b5a8e1f
--- /dev/null
+++ b/neuralnetworks/1.3/vts/OWNERS
@@ -0,0 +1,16 @@
+# Neuralnetworks team
+butlermichael@google.com
+dgross@google.com
+jeanluc@google.com
+levp@google.com
+miaowang@google.com
+mikie@google.com
+mks@google.com
+pszczepaniak@google.com
+slavash@google.com
+vddang@google.com
+xusongw@google.com
+
+# VTS team
+yim@google.com
+yuexima@google.com
diff --git a/neuralnetworks/1.3/vts/functional/Android.bp b/neuralnetworks/1.3/vts/functional/Android.bp
new file mode 100644
index 0000000..90ce852
--- /dev/null
+++ b/neuralnetworks/1.3/vts/functional/Android.bp
@@ -0,0 +1,58 @@
+//
+// Copyright (C) 2019 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.
+//
+
+cc_test {
+    name: "VtsHalNeuralNetworksV1_3TargetTest",
+    defaults: ["VtsHalTargetTestDefaults"],
+    srcs: [
+        "BasicTests.cpp",
+        "CompilationCachingTests.cpp",
+        "GeneratedTestHarness.cpp",
+        "TestAssertions.cpp",
+        "ValidateBurst.cpp",
+        "ValidateModel.cpp",
+        "ValidateRequest.cpp",
+        "VtsHalNeuralnetworks.cpp",
+    ],
+    shared_libs: [
+        "libfmq",
+        "libnativewindow",
+    ],
+    static_libs: [
+        "android.hardware.neuralnetworks@1.0",
+        "android.hardware.neuralnetworks@1.1",
+        "android.hardware.neuralnetworks@1.2",
+        "android.hardware.neuralnetworks@1.3",
+        "android.hidl.allocator@1.0",
+        "android.hidl.memory@1.0",
+        "libgmock",
+        "libhidlmemory",
+        "libneuralnetworks_generated_test_harness",
+        "libneuralnetworks_utils",
+        "VtsHalNeuralNetworksV1_0_utils",
+        "VtsHalNeuralNetworksV1_2Callbacks",
+    ],
+    whole_static_libs: [
+        "neuralnetworks_generated_V1_0_example",
+        "neuralnetworks_generated_V1_1_example",
+        "neuralnetworks_generated_V1_2_example",
+        "neuralnetworks_generated_V1_3_example",
+    ],
+    header_libs: [
+        "libneuralnetworks_headers",
+    ],
+    test_suites: ["general-tests"],
+}
diff --git a/neuralnetworks/1.3/vts/functional/BasicTests.cpp b/neuralnetworks/1.3/vts/functional/BasicTests.cpp
new file mode 100644
index 0000000..b64dc2f
--- /dev/null
+++ b/neuralnetworks/1.3/vts/functional/BasicTests.cpp
@@ -0,0 +1,64 @@
+/*
+ * 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::hardware::neuralnetworks::V1_3::vts::functional {
+
+using V1_0::DeviceStatus;
+using V1_0::ErrorStatus;
+using V1_0::PerformanceInfo;
+using V1_2::Constant;
+using V1_2::DeviceType;
+using V1_2::Extension;
+
+// create device test
+TEST_P(NeuralnetworksHidlTest, CreateDevice) {}
+
+// status test
+TEST_P(NeuralnetworksHidlTest, StatusTest) {
+    Return<DeviceStatus> status = kDevice->getStatus();
+    ASSERT_TRUE(status.isOk());
+    EXPECT_EQ(DeviceStatus::AVAILABLE, static_cast<DeviceStatus>(status));
+}
+
+// initialization
+TEST_P(NeuralnetworksHidlTest, GetCapabilitiesTest) {
+    using OperandPerformance = Capabilities::OperandPerformance;
+    Return<void> ret = kDevice->getCapabilities_1_3([](ErrorStatus status,
+                                                       const Capabilities& capabilities) {
+        EXPECT_EQ(ErrorStatus::NONE, status);
+
+        auto isPositive = [](const PerformanceInfo& perf) {
+            return perf.execTime > 0.0f && perf.powerUsage > 0.0f;
+        };
+
+        EXPECT_TRUE(isPositive(capabilities.relaxedFloat32toFloat16PerformanceScalar));
+        EXPECT_TRUE(isPositive(capabilities.relaxedFloat32toFloat16PerformanceTensor));
+        const auto& opPerf = capabilities.operandPerformance;
+        EXPECT_TRUE(std::all_of(
+                opPerf.begin(), opPerf.end(),
+                [isPositive](const OperandPerformance& a) { return isPositive(a.info); }));
+        EXPECT_TRUE(std::is_sorted(opPerf.begin(), opPerf.end(),
+                                   [](const OperandPerformance& a, const OperandPerformance& b) {
+                                       return a.type < b.type;
+                                   }));
+    });
+    EXPECT_TRUE(ret.isOk());
+}
+}  // namespace android::hardware::neuralnetworks::V1_3::vts::functional
diff --git a/neuralnetworks/1.3/vts/functional/CompilationCachingTests.cpp b/neuralnetworks/1.3/vts/functional/CompilationCachingTests.cpp
new file mode 100644
index 0000000..0ac4738
--- /dev/null
+++ b/neuralnetworks/1.3/vts/functional/CompilationCachingTests.cpp
@@ -0,0 +1,1377 @@
+/*
+ * Copyright (C) 2019 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 <android-base/logging.h>
+#include <fcntl.h>
+#include <ftw.h>
+#include <gtest/gtest.h>
+#include <hidlmemory/mapping.h>
+#include <unistd.h>
+
+#include <cstdio>
+#include <cstdlib>
+#include <random>
+#include <thread>
+
+#include "1.2/Callbacks.h"
+#include "GeneratedTestHarness.h"
+#include "MemoryUtils.h"
+#include "TestHarness.h"
+#include "Utils.h"
+#include "VtsHalNeuralnetworks.h"
+
+// Forward declaration of the mobilenet generated test models in
+// frameworks/ml/nn/runtime/test/generated/.
+namespace generated_tests::mobilenet_224_gender_basic_fixed {
+const test_helper::TestModel& get_test_model();
+}  // namespace generated_tests::mobilenet_224_gender_basic_fixed
+
+namespace generated_tests::mobilenet_quantized {
+const test_helper::TestModel& get_test_model();
+}  // namespace generated_tests::mobilenet_quantized
+
+namespace android::hardware::neuralnetworks::V1_3::vts::functional {
+
+using namespace test_helper;
+using V1_0::ErrorStatus;
+using V1_1::ExecutionPreference;
+using V1_2::Constant;
+using V1_2::IPreparedModel;
+using V1_2::OperationType;
+using V1_2::implementation::PreparedModelCallback;
+
+namespace float32_model {
+
+constexpr auto get_test_model = generated_tests::mobilenet_224_gender_basic_fixed::get_test_model;
+
+}  // namespace float32_model
+
+namespace quant8_model {
+
+constexpr auto get_test_model = generated_tests::mobilenet_quantized::get_test_model;
+
+}  // namespace quant8_model
+
+namespace {
+
+enum class AccessMode { READ_WRITE, READ_ONLY, WRITE_ONLY };
+
+// Creates cache handles based on provided file groups.
+// The outer vector corresponds to handles and the inner vector is for fds held by each handle.
+void createCacheHandles(const std::vector<std::vector<std::string>>& fileGroups,
+                        const std::vector<AccessMode>& mode, hidl_vec<hidl_handle>* handles) {
+    handles->resize(fileGroups.size());
+    for (uint32_t i = 0; i < fileGroups.size(); i++) {
+        std::vector<int> fds;
+        for (const auto& file : fileGroups[i]) {
+            int fd;
+            if (mode[i] == AccessMode::READ_ONLY) {
+                fd = open(file.c_str(), O_RDONLY);
+            } else if (mode[i] == AccessMode::WRITE_ONLY) {
+                fd = open(file.c_str(), O_WRONLY | O_CREAT, S_IRUSR | S_IWUSR);
+            } else if (mode[i] == AccessMode::READ_WRITE) {
+                fd = open(file.c_str(), O_RDWR | O_CREAT, S_IRUSR | S_IWUSR);
+            } else {
+                FAIL();
+            }
+            ASSERT_GE(fd, 0);
+            fds.push_back(fd);
+        }
+        native_handle_t* cacheNativeHandle = native_handle_create(fds.size(), 0);
+        ASSERT_NE(cacheNativeHandle, nullptr);
+        std::copy(fds.begin(), fds.end(), &cacheNativeHandle->data[0]);
+        (*handles)[i].setTo(cacheNativeHandle, /*shouldOwn=*/true);
+    }
+}
+
+void createCacheHandles(const std::vector<std::vector<std::string>>& fileGroups, AccessMode mode,
+                        hidl_vec<hidl_handle>* handles) {
+    createCacheHandles(fileGroups, std::vector<AccessMode>(fileGroups.size(), mode), handles);
+}
+
+// Create a chain of broadcast operations. The second operand is always constant tensor [1].
+// For simplicity, activation scalar is shared. The second operand is not shared
+// in the model to let driver maintain a non-trivial size of constant data and the corresponding
+// data locations in cache.
+//
+//                --------- activation --------
+//                ↓      ↓      ↓             ↓
+// E.g. input -> ADD -> ADD -> ADD -> ... -> ADD -> output
+//                ↑      ↑      ↑             ↑
+//               [1]    [1]    [1]           [1]
+//
+// This function assumes the operation is either ADD or MUL.
+template <typename CppType, TestOperandType operandType>
+TestModel createLargeTestModelImpl(TestOperationType op, uint32_t len) {
+    EXPECT_TRUE(op == TestOperationType::ADD || op == TestOperationType::MUL);
+
+    // Model operations and operands.
+    std::vector<TestOperation> operations(len);
+    std::vector<TestOperand> operands(len * 2 + 2);
+
+    // The activation scalar, value = 0.
+    operands[0] = {
+            .type = TestOperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = len,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = TestOperandLifeTime::CONSTANT_COPY,
+            .data = TestBuffer::createFromVector<int32_t>({0}),
+    };
+
+    // The buffer value of the constant second operand. The logical value is always 1.0f.
+    CppType bufferValue;
+    // The scale of the first and second operand.
+    float scale1, scale2;
+    if (operandType == TestOperandType::TENSOR_FLOAT32) {
+        bufferValue = 1.0f;
+        scale1 = 0.0f;
+        scale2 = 0.0f;
+    } else if (op == TestOperationType::ADD) {
+        bufferValue = 1;
+        scale1 = 1.0f;
+        scale2 = 1.0f;
+    } else {
+        // To satisfy the constraint on quant8 MUL: input0.scale * input1.scale < output.scale,
+        // set input1 to have scale = 0.5f and bufferValue = 2, i.e. 1.0f in floating point.
+        bufferValue = 2;
+        scale1 = 1.0f;
+        scale2 = 0.5f;
+    }
+
+    for (uint32_t i = 0; i < len; i++) {
+        const uint32_t firstInputIndex = i * 2 + 1;
+        const uint32_t secondInputIndex = firstInputIndex + 1;
+        const uint32_t outputIndex = secondInputIndex + 1;
+
+        // The first operation input.
+        operands[firstInputIndex] = {
+                .type = operandType,
+                .dimensions = {1},
+                .numberOfConsumers = 1,
+                .scale = scale1,
+                .zeroPoint = 0,
+                .lifetime = (i == 0 ? TestOperandLifeTime::MODEL_INPUT
+                                    : TestOperandLifeTime::TEMPORARY_VARIABLE),
+                .data = (i == 0 ? TestBuffer::createFromVector<CppType>({1}) : TestBuffer()),
+        };
+
+        // The second operation input, value = 1.
+        operands[secondInputIndex] = {
+                .type = operandType,
+                .dimensions = {1},
+                .numberOfConsumers = 1,
+                .scale = scale2,
+                .zeroPoint = 0,
+                .lifetime = TestOperandLifeTime::CONSTANT_COPY,
+                .data = TestBuffer::createFromVector<CppType>({bufferValue}),
+        };
+
+        // The operation. All operations share the same activation scalar.
+        // The output operand is created as an input in the next iteration of the loop, in the case
+        // of all but the last member of the chain; and after the loop as a model output, in the
+        // case of the last member of the chain.
+        operations[i] = {
+                .type = op,
+                .inputs = {firstInputIndex, secondInputIndex, /*activation scalar*/ 0},
+                .outputs = {outputIndex},
+        };
+    }
+
+    // For TestOperationType::ADD, output = 1 + 1 * len = len + 1
+    // For TestOperationType::MUL, output = 1 * 1 ^ len = 1
+    CppType outputResult = static_cast<CppType>(op == TestOperationType::ADD ? len + 1u : 1u);
+
+    // The model output.
+    operands.back() = {
+            .type = operandType,
+            .dimensions = {1},
+            .numberOfConsumers = 0,
+            .scale = scale1,
+            .zeroPoint = 0,
+            .lifetime = TestOperandLifeTime::MODEL_OUTPUT,
+            .data = TestBuffer::createFromVector<CppType>({outputResult}),
+    };
+
+    return {
+            .operands = std::move(operands),
+            .operations = std::move(operations),
+            .inputIndexes = {1},
+            .outputIndexes = {len * 2 + 1},
+            .isRelaxed = false,
+    };
+}
+
+}  // namespace
+
+// Tag for the compilation caching tests.
+class CompilationCachingTestBase : public testing::Test {
+  protected:
+    CompilationCachingTestBase(sp<IDevice> device, OperandType type)
+        : kDevice(std::move(device)), kOperandType(type) {}
+
+    void SetUp() override {
+        testing::Test::SetUp();
+        ASSERT_NE(kDevice.get(), nullptr);
+
+        // Create cache directory. The cache directory and a temporary cache file is always created
+        // to test the behavior of prepareModelFromCache, even when caching is not supported.
+        char cacheDirTemp[] = "/data/local/tmp/TestCompilationCachingXXXXXX";
+        char* cacheDir = mkdtemp(cacheDirTemp);
+        ASSERT_NE(cacheDir, nullptr);
+        mCacheDir = cacheDir;
+        mCacheDir.push_back('/');
+
+        Return<void> ret = kDevice->getNumberOfCacheFilesNeeded(
+                [this](ErrorStatus status, uint32_t numModelCache, uint32_t numDataCache) {
+                    EXPECT_EQ(ErrorStatus::NONE, status);
+                    mNumModelCache = numModelCache;
+                    mNumDataCache = numDataCache;
+                });
+        EXPECT_TRUE(ret.isOk());
+        mIsCachingSupported = mNumModelCache > 0 || mNumDataCache > 0;
+
+        // Create empty cache files.
+        mTmpCache = mCacheDir + "tmp";
+        for (uint32_t i = 0; i < mNumModelCache; i++) {
+            mModelCache.push_back({mCacheDir + "model" + std::to_string(i)});
+        }
+        for (uint32_t i = 0; i < mNumDataCache; i++) {
+            mDataCache.push_back({mCacheDir + "data" + std::to_string(i)});
+        }
+        // Dummy handles, use AccessMode::WRITE_ONLY for createCacheHandles to create files.
+        hidl_vec<hidl_handle> modelHandle, dataHandle, tmpHandle;
+        createCacheHandles(mModelCache, AccessMode::WRITE_ONLY, &modelHandle);
+        createCacheHandles(mDataCache, AccessMode::WRITE_ONLY, &dataHandle);
+        createCacheHandles({{mTmpCache}}, AccessMode::WRITE_ONLY, &tmpHandle);
+
+        if (!mIsCachingSupported) {
+            LOG(INFO) << "NN VTS: Early termination of test because vendor service does not "
+                         "support compilation caching.";
+            std::cout << "[          ]   Early termination of test because vendor service does not "
+                         "support compilation caching."
+                      << std::endl;
+        }
+    }
+
+    void TearDown() override {
+        // If the test passes, remove the tmp directory.  Otherwise, keep it for debugging purposes.
+        if (!testing::Test::HasFailure()) {
+            // Recursively remove the cache directory specified by mCacheDir.
+            auto callback = [](const char* entry, const struct stat*, int, struct FTW*) {
+                return remove(entry);
+            };
+            nftw(mCacheDir.c_str(), callback, 128, FTW_DEPTH | FTW_MOUNT | FTW_PHYS);
+        }
+        testing::Test::TearDown();
+    }
+
+    // Model and examples creators. According to kOperandType, the following methods will return
+    // either float32 model/examples or the quant8 variant.
+    TestModel createTestModel() {
+        if (kOperandType == OperandType::TENSOR_FLOAT32) {
+            return float32_model::get_test_model();
+        } else {
+            return quant8_model::get_test_model();
+        }
+    }
+
+    TestModel createLargeTestModel(OperationType op, uint32_t len) {
+        if (kOperandType == OperandType::TENSOR_FLOAT32) {
+            return createLargeTestModelImpl<float, TestOperandType::TENSOR_FLOAT32>(
+                    static_cast<TestOperationType>(op), len);
+        } else {
+            return createLargeTestModelImpl<uint8_t, TestOperandType::TENSOR_QUANT8_ASYMM>(
+                    static_cast<TestOperationType>(op), len);
+        }
+    }
+
+    // See if the service can handle the model.
+    bool isModelFullySupported(const Model& model) {
+        bool fullySupportsModel = false;
+        Return<void> supportedCall = kDevice->getSupportedOperations_1_3(
+                model,
+                [&fullySupportsModel, &model](ErrorStatus status, const hidl_vec<bool>& supported) {
+                    ASSERT_EQ(ErrorStatus::NONE, status);
+                    ASSERT_EQ(supported.size(), model.operations.size());
+                    fullySupportsModel = std::all_of(supported.begin(), supported.end(),
+                                                     [](bool valid) { return valid; });
+                });
+        EXPECT_TRUE(supportedCall.isOk());
+        return fullySupportsModel;
+    }
+
+    void saveModelToCache(const Model& model, const hidl_vec<hidl_handle>& modelCache,
+                          const hidl_vec<hidl_handle>& dataCache,
+                          sp<IPreparedModel>* preparedModel = nullptr) {
+        if (preparedModel != nullptr) *preparedModel = nullptr;
+
+        // Launch prepare model.
+        sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
+        hidl_array<uint8_t, sizeof(mToken)> cacheToken(mToken);
+        Return<ErrorStatus> prepareLaunchStatus =
+                kDevice->prepareModel_1_3(model, ExecutionPreference::FAST_SINGLE_ANSWER,
+                                          modelCache, dataCache, cacheToken, preparedModelCallback);
+        ASSERT_TRUE(prepareLaunchStatus.isOk());
+        ASSERT_EQ(static_cast<ErrorStatus>(prepareLaunchStatus), ErrorStatus::NONE);
+
+        // Retrieve prepared model.
+        preparedModelCallback->wait();
+        ASSERT_EQ(preparedModelCallback->getStatus(), ErrorStatus::NONE);
+        if (preparedModel != nullptr) {
+            *preparedModel = IPreparedModel::castFrom(preparedModelCallback->getPreparedModel())
+                                     .withDefault(nullptr);
+        }
+    }
+
+    bool checkEarlyTermination(ErrorStatus status) {
+        if (status == ErrorStatus::GENERAL_FAILURE) {
+            LOG(INFO) << "NN VTS: Early termination of test because vendor service cannot "
+                         "save the prepared model that it does not support.";
+            std::cout << "[          ]   Early termination of test because vendor service cannot "
+                         "save the prepared model that it does not support."
+                      << std::endl;
+            return true;
+        }
+        return false;
+    }
+
+    bool checkEarlyTermination(const Model& model) {
+        if (!isModelFullySupported(model)) {
+            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 true;
+        }
+        return false;
+    }
+
+    void prepareModelFromCache(const hidl_vec<hidl_handle>& modelCache,
+                               const hidl_vec<hidl_handle>& dataCache,
+                               sp<IPreparedModel>* preparedModel, ErrorStatus* status) {
+        // Launch prepare model from cache.
+        sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
+        hidl_array<uint8_t, sizeof(mToken)> cacheToken(mToken);
+        Return<ErrorStatus> prepareLaunchStatus = kDevice->prepareModelFromCache(
+                modelCache, dataCache, cacheToken, preparedModelCallback);
+        ASSERT_TRUE(prepareLaunchStatus.isOk());
+        if (static_cast<ErrorStatus>(prepareLaunchStatus) != ErrorStatus::NONE) {
+            *preparedModel = nullptr;
+            *status = static_cast<ErrorStatus>(prepareLaunchStatus);
+            return;
+        }
+
+        // Retrieve prepared model.
+        preparedModelCallback->wait();
+        *status = preparedModelCallback->getStatus();
+        *preparedModel = IPreparedModel::castFrom(preparedModelCallback->getPreparedModel())
+                                 .withDefault(nullptr);
+    }
+
+    // Absolute path to the temporary cache directory.
+    std::string mCacheDir;
+
+    // Groups of file paths for model and data cache in the tmp cache directory, initialized with
+    // outer_size = mNum{Model|Data}Cache, inner_size = 1. The outer vector corresponds to handles
+    // and the inner vector is for fds held by each handle.
+    std::vector<std::vector<std::string>> mModelCache;
+    std::vector<std::vector<std::string>> mDataCache;
+
+    // A separate temporary file path in the tmp cache directory.
+    std::string mTmpCache;
+
+    uint8_t mToken[static_cast<uint32_t>(Constant::BYTE_SIZE_OF_CACHE_TOKEN)] = {};
+    uint32_t mNumModelCache;
+    uint32_t mNumDataCache;
+    uint32_t mIsCachingSupported;
+
+    const sp<IDevice> kDevice;
+    // The primary data type of the testModel.
+    const OperandType kOperandType;
+};
+
+using CompilationCachingTestParam = std::tuple<NamedDevice, OperandType>;
+
+// A parameterized fixture of CompilationCachingTestBase. Every test will run twice, with the first
+// pass running with float32 models and the second pass running with quant8 models.
+class CompilationCachingTest : public CompilationCachingTestBase,
+                               public testing::WithParamInterface<CompilationCachingTestParam> {
+  protected:
+    CompilationCachingTest()
+        : CompilationCachingTestBase(getData(std::get<NamedDevice>(GetParam())),
+                                     std::get<OperandType>(GetParam())) {}
+};
+
+TEST_P(CompilationCachingTest, CacheSavingAndRetrieval) {
+    // Create test HIDL model and compile.
+    const TestModel& testModel = createTestModel();
+    const Model model = createModel(testModel);
+    if (checkEarlyTermination(model)) return;
+    sp<IPreparedModel> preparedModel = nullptr;
+
+    // Save the compilation to cache.
+    {
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        saveModelToCache(model, modelCache, dataCache);
+    }
+
+    // Retrieve preparedModel from cache.
+    {
+        preparedModel = nullptr;
+        ErrorStatus status;
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        if (!mIsCachingSupported) {
+            ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+            ASSERT_EQ(preparedModel, nullptr);
+            return;
+        } else if (checkEarlyTermination(status)) {
+            ASSERT_EQ(preparedModel, nullptr);
+            return;
+        } else {
+            ASSERT_EQ(status, ErrorStatus::NONE);
+            ASSERT_NE(preparedModel, nullptr);
+        }
+    }
+
+    // Execute and verify results.
+    EvaluatePreparedModel(preparedModel, testModel,
+                          /*testDynamicOutputShape=*/false);
+}
+
+TEST_P(CompilationCachingTest, CacheSavingAndRetrievalNonZeroOffset) {
+    // Create test HIDL model and compile.
+    const TestModel& testModel = createTestModel();
+    const Model model = createModel(testModel);
+    if (checkEarlyTermination(model)) return;
+    sp<IPreparedModel> preparedModel = nullptr;
+
+    // Save the compilation to cache.
+    {
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        uint8_t dummyBytes[] = {0, 0};
+        // Write a dummy integer to the cache.
+        // The driver should be able to handle non-empty cache and non-zero fd offset.
+        for (uint32_t i = 0; i < modelCache.size(); i++) {
+            ASSERT_EQ(write(modelCache[i].getNativeHandle()->data[0], &dummyBytes,
+                            sizeof(dummyBytes)),
+                      sizeof(dummyBytes));
+        }
+        for (uint32_t i = 0; i < dataCache.size(); i++) {
+            ASSERT_EQ(
+                    write(dataCache[i].getNativeHandle()->data[0], &dummyBytes, sizeof(dummyBytes)),
+                    sizeof(dummyBytes));
+        }
+        saveModelToCache(model, modelCache, dataCache);
+    }
+
+    // Retrieve preparedModel from cache.
+    {
+        preparedModel = nullptr;
+        ErrorStatus status;
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        uint8_t dummyByte = 0;
+        // Advance the offset of each handle by one byte.
+        // The driver should be able to handle non-zero fd offset.
+        for (uint32_t i = 0; i < modelCache.size(); i++) {
+            ASSERT_GE(read(modelCache[i].getNativeHandle()->data[0], &dummyByte, 1), 0);
+        }
+        for (uint32_t i = 0; i < dataCache.size(); i++) {
+            ASSERT_GE(read(dataCache[i].getNativeHandle()->data[0], &dummyByte, 1), 0);
+        }
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        if (!mIsCachingSupported) {
+            ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+            ASSERT_EQ(preparedModel, nullptr);
+            return;
+        } else if (checkEarlyTermination(status)) {
+            ASSERT_EQ(preparedModel, nullptr);
+            return;
+        } else {
+            ASSERT_EQ(status, ErrorStatus::NONE);
+            ASSERT_NE(preparedModel, nullptr);
+        }
+    }
+
+    // Execute and verify results.
+    EvaluatePreparedModel(preparedModel, testModel,
+                          /*testDynamicOutputShape=*/false);
+}
+
+TEST_P(CompilationCachingTest, SaveToCacheInvalidNumCache) {
+    // Create test HIDL model and compile.
+    const TestModel& testModel = createTestModel();
+    const Model model = createModel(testModel);
+    if (checkEarlyTermination(model)) return;
+
+    // Test with number of model cache files greater than mNumModelCache.
+    {
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        // Pass an additional cache file for model cache.
+        mModelCache.push_back({mTmpCache});
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        mModelCache.pop_back();
+        sp<IPreparedModel> preparedModel = nullptr;
+        saveModelToCache(model, modelCache, dataCache, &preparedModel);
+        ASSERT_NE(preparedModel, nullptr);
+        // Execute and verify results.
+        EvaluatePreparedModel(preparedModel, testModel,
+                              /*testDynamicOutputShape=*/false);
+        // Check if prepareModelFromCache fails.
+        preparedModel = nullptr;
+        ErrorStatus status;
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        if (status != ErrorStatus::INVALID_ARGUMENT) {
+            ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+        }
+        ASSERT_EQ(preparedModel, nullptr);
+    }
+
+    // Test with number of model cache files smaller than mNumModelCache.
+    if (mModelCache.size() > 0) {
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        // Pop out the last cache file.
+        auto tmp = mModelCache.back();
+        mModelCache.pop_back();
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        mModelCache.push_back(tmp);
+        sp<IPreparedModel> preparedModel = nullptr;
+        saveModelToCache(model, modelCache, dataCache, &preparedModel);
+        ASSERT_NE(preparedModel, nullptr);
+        // Execute and verify results.
+        EvaluatePreparedModel(preparedModel, testModel,
+                              /*testDynamicOutputShape=*/false);
+        // Check if prepareModelFromCache fails.
+        preparedModel = nullptr;
+        ErrorStatus status;
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        if (status != ErrorStatus::INVALID_ARGUMENT) {
+            ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+        }
+        ASSERT_EQ(preparedModel, nullptr);
+    }
+
+    // Test with number of data cache files greater than mNumDataCache.
+    {
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        // Pass an additional cache file for data cache.
+        mDataCache.push_back({mTmpCache});
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        mDataCache.pop_back();
+        sp<IPreparedModel> preparedModel = nullptr;
+        saveModelToCache(model, modelCache, dataCache, &preparedModel);
+        ASSERT_NE(preparedModel, nullptr);
+        // Execute and verify results.
+        EvaluatePreparedModel(preparedModel, testModel,
+                              /*testDynamicOutputShape=*/false);
+        // Check if prepareModelFromCache fails.
+        preparedModel = nullptr;
+        ErrorStatus status;
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        if (status != ErrorStatus::INVALID_ARGUMENT) {
+            ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+        }
+        ASSERT_EQ(preparedModel, nullptr);
+    }
+
+    // Test with number of data cache files smaller than mNumDataCache.
+    if (mDataCache.size() > 0) {
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        // Pop out the last cache file.
+        auto tmp = mDataCache.back();
+        mDataCache.pop_back();
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        mDataCache.push_back(tmp);
+        sp<IPreparedModel> preparedModel = nullptr;
+        saveModelToCache(model, modelCache, dataCache, &preparedModel);
+        ASSERT_NE(preparedModel, nullptr);
+        // Execute and verify results.
+        EvaluatePreparedModel(preparedModel, testModel,
+                              /*testDynamicOutputShape=*/false);
+        // Check if prepareModelFromCache fails.
+        preparedModel = nullptr;
+        ErrorStatus status;
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        if (status != ErrorStatus::INVALID_ARGUMENT) {
+            ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+        }
+        ASSERT_EQ(preparedModel, nullptr);
+    }
+}
+
+TEST_P(CompilationCachingTest, PrepareModelFromCacheInvalidNumCache) {
+    // Create test HIDL model and compile.
+    const TestModel& testModel = createTestModel();
+    const Model model = createModel(testModel);
+    if (checkEarlyTermination(model)) return;
+
+    // Save the compilation to cache.
+    {
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        saveModelToCache(model, modelCache, dataCache);
+    }
+
+    // Test with number of model cache files greater than mNumModelCache.
+    {
+        sp<IPreparedModel> preparedModel = nullptr;
+        ErrorStatus status;
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        mModelCache.push_back({mTmpCache});
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        mModelCache.pop_back();
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        if (status != ErrorStatus::GENERAL_FAILURE) {
+            ASSERT_EQ(status, ErrorStatus::INVALID_ARGUMENT);
+        }
+        ASSERT_EQ(preparedModel, nullptr);
+    }
+
+    // Test with number of model cache files smaller than mNumModelCache.
+    if (mModelCache.size() > 0) {
+        sp<IPreparedModel> preparedModel = nullptr;
+        ErrorStatus status;
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        auto tmp = mModelCache.back();
+        mModelCache.pop_back();
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        mModelCache.push_back(tmp);
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        if (status != ErrorStatus::GENERAL_FAILURE) {
+            ASSERT_EQ(status, ErrorStatus::INVALID_ARGUMENT);
+        }
+        ASSERT_EQ(preparedModel, nullptr);
+    }
+
+    // Test with number of data cache files greater than mNumDataCache.
+    {
+        sp<IPreparedModel> preparedModel = nullptr;
+        ErrorStatus status;
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        mDataCache.push_back({mTmpCache});
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        mDataCache.pop_back();
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        if (status != ErrorStatus::GENERAL_FAILURE) {
+            ASSERT_EQ(status, ErrorStatus::INVALID_ARGUMENT);
+        }
+        ASSERT_EQ(preparedModel, nullptr);
+    }
+
+    // Test with number of data cache files smaller than mNumDataCache.
+    if (mDataCache.size() > 0) {
+        sp<IPreparedModel> preparedModel = nullptr;
+        ErrorStatus status;
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        auto tmp = mDataCache.back();
+        mDataCache.pop_back();
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        mDataCache.push_back(tmp);
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        if (status != ErrorStatus::GENERAL_FAILURE) {
+            ASSERT_EQ(status, ErrorStatus::INVALID_ARGUMENT);
+        }
+        ASSERT_EQ(preparedModel, nullptr);
+    }
+}
+
+TEST_P(CompilationCachingTest, SaveToCacheInvalidNumFd) {
+    // Create test HIDL model and compile.
+    const TestModel& testModel = createTestModel();
+    const Model model = createModel(testModel);
+    if (checkEarlyTermination(model)) return;
+
+    // Go through each handle in model cache, test with NumFd greater than 1.
+    for (uint32_t i = 0; i < mNumModelCache; i++) {
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        // Pass an invalid number of fds for handle i.
+        mModelCache[i].push_back(mTmpCache);
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        mModelCache[i].pop_back();
+        sp<IPreparedModel> preparedModel = nullptr;
+        saveModelToCache(model, modelCache, dataCache, &preparedModel);
+        ASSERT_NE(preparedModel, nullptr);
+        // Execute and verify results.
+        EvaluatePreparedModel(preparedModel, testModel,
+                              /*testDynamicOutputShape=*/false);
+        // Check if prepareModelFromCache fails.
+        preparedModel = nullptr;
+        ErrorStatus status;
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        if (status != ErrorStatus::INVALID_ARGUMENT) {
+            ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+        }
+        ASSERT_EQ(preparedModel, nullptr);
+    }
+
+    // Go through each handle in model cache, test with NumFd equal to 0.
+    for (uint32_t i = 0; i < mNumModelCache; i++) {
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        // Pass an invalid number of fds for handle i.
+        auto tmp = mModelCache[i].back();
+        mModelCache[i].pop_back();
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        mModelCache[i].push_back(tmp);
+        sp<IPreparedModel> preparedModel = nullptr;
+        saveModelToCache(model, modelCache, dataCache, &preparedModel);
+        ASSERT_NE(preparedModel, nullptr);
+        // Execute and verify results.
+        EvaluatePreparedModel(preparedModel, testModel,
+                              /*testDynamicOutputShape=*/false);
+        // Check if prepareModelFromCache fails.
+        preparedModel = nullptr;
+        ErrorStatus status;
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        if (status != ErrorStatus::INVALID_ARGUMENT) {
+            ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+        }
+        ASSERT_EQ(preparedModel, nullptr);
+    }
+
+    // Go through each handle in data cache, test with NumFd greater than 1.
+    for (uint32_t i = 0; i < mNumDataCache; i++) {
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        // Pass an invalid number of fds for handle i.
+        mDataCache[i].push_back(mTmpCache);
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        mDataCache[i].pop_back();
+        sp<IPreparedModel> preparedModel = nullptr;
+        saveModelToCache(model, modelCache, dataCache, &preparedModel);
+        ASSERT_NE(preparedModel, nullptr);
+        // Execute and verify results.
+        EvaluatePreparedModel(preparedModel, testModel,
+                              /*testDynamicOutputShape=*/false);
+        // Check if prepareModelFromCache fails.
+        preparedModel = nullptr;
+        ErrorStatus status;
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        if (status != ErrorStatus::INVALID_ARGUMENT) {
+            ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+        }
+        ASSERT_EQ(preparedModel, nullptr);
+    }
+
+    // Go through each handle in data cache, test with NumFd equal to 0.
+    for (uint32_t i = 0; i < mNumDataCache; i++) {
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        // Pass an invalid number of fds for handle i.
+        auto tmp = mDataCache[i].back();
+        mDataCache[i].pop_back();
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        mDataCache[i].push_back(tmp);
+        sp<IPreparedModel> preparedModel = nullptr;
+        saveModelToCache(model, modelCache, dataCache, &preparedModel);
+        ASSERT_NE(preparedModel, nullptr);
+        // Execute and verify results.
+        EvaluatePreparedModel(preparedModel, testModel,
+                              /*testDynamicOutputShape=*/false);
+        // Check if prepareModelFromCache fails.
+        preparedModel = nullptr;
+        ErrorStatus status;
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        if (status != ErrorStatus::INVALID_ARGUMENT) {
+            ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+        }
+        ASSERT_EQ(preparedModel, nullptr);
+    }
+}
+
+TEST_P(CompilationCachingTest, PrepareModelFromCacheInvalidNumFd) {
+    // Create test HIDL model and compile.
+    const TestModel& testModel = createTestModel();
+    const Model model = createModel(testModel);
+    if (checkEarlyTermination(model)) return;
+
+    // Save the compilation to cache.
+    {
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        saveModelToCache(model, modelCache, dataCache);
+    }
+
+    // Go through each handle in model cache, test with NumFd greater than 1.
+    for (uint32_t i = 0; i < mNumModelCache; i++) {
+        sp<IPreparedModel> preparedModel = nullptr;
+        ErrorStatus status;
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        mModelCache[i].push_back(mTmpCache);
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        mModelCache[i].pop_back();
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        if (status != ErrorStatus::GENERAL_FAILURE) {
+            ASSERT_EQ(status, ErrorStatus::INVALID_ARGUMENT);
+        }
+        ASSERT_EQ(preparedModel, nullptr);
+    }
+
+    // Go through each handle in model cache, test with NumFd equal to 0.
+    for (uint32_t i = 0; i < mNumModelCache; i++) {
+        sp<IPreparedModel> preparedModel = nullptr;
+        ErrorStatus status;
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        auto tmp = mModelCache[i].back();
+        mModelCache[i].pop_back();
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        mModelCache[i].push_back(tmp);
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        if (status != ErrorStatus::GENERAL_FAILURE) {
+            ASSERT_EQ(status, ErrorStatus::INVALID_ARGUMENT);
+        }
+        ASSERT_EQ(preparedModel, nullptr);
+    }
+
+    // Go through each handle in data cache, test with NumFd greater than 1.
+    for (uint32_t i = 0; i < mNumDataCache; i++) {
+        sp<IPreparedModel> preparedModel = nullptr;
+        ErrorStatus status;
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        mDataCache[i].push_back(mTmpCache);
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        mDataCache[i].pop_back();
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        if (status != ErrorStatus::GENERAL_FAILURE) {
+            ASSERT_EQ(status, ErrorStatus::INVALID_ARGUMENT);
+        }
+        ASSERT_EQ(preparedModel, nullptr);
+    }
+
+    // Go through each handle in data cache, test with NumFd equal to 0.
+    for (uint32_t i = 0; i < mNumDataCache; i++) {
+        sp<IPreparedModel> preparedModel = nullptr;
+        ErrorStatus status;
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        auto tmp = mDataCache[i].back();
+        mDataCache[i].pop_back();
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        mDataCache[i].push_back(tmp);
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        if (status != ErrorStatus::GENERAL_FAILURE) {
+            ASSERT_EQ(status, ErrorStatus::INVALID_ARGUMENT);
+        }
+        ASSERT_EQ(preparedModel, nullptr);
+    }
+}
+
+TEST_P(CompilationCachingTest, SaveToCacheInvalidAccessMode) {
+    // Create test HIDL model and compile.
+    const TestModel& testModel = createTestModel();
+    const Model model = createModel(testModel);
+    if (checkEarlyTermination(model)) return;
+    std::vector<AccessMode> modelCacheMode(mNumModelCache, AccessMode::READ_WRITE);
+    std::vector<AccessMode> dataCacheMode(mNumDataCache, AccessMode::READ_WRITE);
+
+    // Go through each handle in model cache, test with invalid access mode.
+    for (uint32_t i = 0; i < mNumModelCache; i++) {
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        modelCacheMode[i] = AccessMode::READ_ONLY;
+        createCacheHandles(mModelCache, modelCacheMode, &modelCache);
+        createCacheHandles(mDataCache, dataCacheMode, &dataCache);
+        modelCacheMode[i] = AccessMode::READ_WRITE;
+        sp<IPreparedModel> preparedModel = nullptr;
+        saveModelToCache(model, modelCache, dataCache, &preparedModel);
+        ASSERT_NE(preparedModel, nullptr);
+        // Execute and verify results.
+        EvaluatePreparedModel(preparedModel, testModel,
+                              /*testDynamicOutputShape=*/false);
+        // Check if prepareModelFromCache fails.
+        preparedModel = nullptr;
+        ErrorStatus status;
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        if (status != ErrorStatus::INVALID_ARGUMENT) {
+            ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+        }
+        ASSERT_EQ(preparedModel, nullptr);
+    }
+
+    // Go through each handle in data cache, test with invalid access mode.
+    for (uint32_t i = 0; i < mNumDataCache; i++) {
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        dataCacheMode[i] = AccessMode::READ_ONLY;
+        createCacheHandles(mModelCache, modelCacheMode, &modelCache);
+        createCacheHandles(mDataCache, dataCacheMode, &dataCache);
+        dataCacheMode[i] = AccessMode::READ_WRITE;
+        sp<IPreparedModel> preparedModel = nullptr;
+        saveModelToCache(model, modelCache, dataCache, &preparedModel);
+        ASSERT_NE(preparedModel, nullptr);
+        // Execute and verify results.
+        EvaluatePreparedModel(preparedModel, testModel,
+                              /*testDynamicOutputShape=*/false);
+        // Check if prepareModelFromCache fails.
+        preparedModel = nullptr;
+        ErrorStatus status;
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        if (status != ErrorStatus::INVALID_ARGUMENT) {
+            ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+        }
+        ASSERT_EQ(preparedModel, nullptr);
+    }
+}
+
+TEST_P(CompilationCachingTest, PrepareModelFromCacheInvalidAccessMode) {
+    // Create test HIDL model and compile.
+    const TestModel& testModel = createTestModel();
+    const Model model = createModel(testModel);
+    if (checkEarlyTermination(model)) return;
+    std::vector<AccessMode> modelCacheMode(mNumModelCache, AccessMode::READ_WRITE);
+    std::vector<AccessMode> dataCacheMode(mNumDataCache, AccessMode::READ_WRITE);
+
+    // Save the compilation to cache.
+    {
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        saveModelToCache(model, modelCache, dataCache);
+    }
+
+    // Go through each handle in model cache, test with invalid access mode.
+    for (uint32_t i = 0; i < mNumModelCache; i++) {
+        sp<IPreparedModel> preparedModel = nullptr;
+        ErrorStatus status;
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        modelCacheMode[i] = AccessMode::WRITE_ONLY;
+        createCacheHandles(mModelCache, modelCacheMode, &modelCache);
+        createCacheHandles(mDataCache, dataCacheMode, &dataCache);
+        modelCacheMode[i] = AccessMode::READ_WRITE;
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+        ASSERT_EQ(preparedModel, nullptr);
+    }
+
+    // Go through each handle in data cache, test with invalid access mode.
+    for (uint32_t i = 0; i < mNumDataCache; i++) {
+        sp<IPreparedModel> preparedModel = nullptr;
+        ErrorStatus status;
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        dataCacheMode[i] = AccessMode::WRITE_ONLY;
+        createCacheHandles(mModelCache, modelCacheMode, &modelCache);
+        createCacheHandles(mDataCache, dataCacheMode, &dataCache);
+        dataCacheMode[i] = AccessMode::READ_WRITE;
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+        ASSERT_EQ(preparedModel, nullptr);
+    }
+}
+
+// Copy file contents between file groups.
+// The outer vector corresponds to handles and the inner vector is for fds held by each handle.
+// The outer vector sizes must match and the inner vectors must have size = 1.
+static void copyCacheFiles(const std::vector<std::vector<std::string>>& from,
+                           const std::vector<std::vector<std::string>>& to) {
+    constexpr size_t kBufferSize = 1000000;
+    uint8_t buffer[kBufferSize];
+
+    ASSERT_EQ(from.size(), to.size());
+    for (uint32_t i = 0; i < from.size(); i++) {
+        ASSERT_EQ(from[i].size(), 1u);
+        ASSERT_EQ(to[i].size(), 1u);
+        int fromFd = open(from[i][0].c_str(), O_RDONLY);
+        int toFd = open(to[i][0].c_str(), O_WRONLY | O_CREAT, S_IRUSR | S_IWUSR);
+        ASSERT_GE(fromFd, 0);
+        ASSERT_GE(toFd, 0);
+
+        ssize_t readBytes;
+        while ((readBytes = read(fromFd, &buffer, kBufferSize)) > 0) {
+            ASSERT_EQ(write(toFd, &buffer, readBytes), readBytes);
+        }
+        ASSERT_GE(readBytes, 0);
+
+        close(fromFd);
+        close(toFd);
+    }
+}
+
+// Number of operations in the large test model.
+constexpr uint32_t kLargeModelSize = 100;
+constexpr uint32_t kNumIterationsTOCTOU = 100;
+
+TEST_P(CompilationCachingTest, SaveToCache_TOCTOU) {
+    if (!mIsCachingSupported) return;
+
+    // Create test models and check if fully supported by the service.
+    const TestModel testModelMul = createLargeTestModel(OperationType::MUL, kLargeModelSize);
+    const Model modelMul = createModel(testModelMul);
+    if (checkEarlyTermination(modelMul)) return;
+    const TestModel testModelAdd = createLargeTestModel(OperationType::ADD, kLargeModelSize);
+    const Model modelAdd = createModel(testModelAdd);
+    if (checkEarlyTermination(modelAdd)) return;
+
+    // Save the modelMul compilation to cache.
+    auto modelCacheMul = mModelCache;
+    for (auto& cache : modelCacheMul) {
+        cache[0].append("_mul");
+    }
+    {
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        createCacheHandles(modelCacheMul, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        saveModelToCache(modelMul, modelCache, dataCache);
+    }
+
+    // Use a different token for modelAdd.
+    mToken[0]++;
+
+    // This test is probabilistic, so we run it multiple times.
+    for (uint32_t i = 0; i < kNumIterationsTOCTOU; i++) {
+        // Save the modelAdd compilation to cache.
+        {
+            hidl_vec<hidl_handle> modelCache, dataCache;
+            createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+            createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+
+            // Spawn a thread to copy the cache content concurrently while saving to cache.
+            std::thread thread(copyCacheFiles, std::cref(modelCacheMul), std::cref(mModelCache));
+            saveModelToCache(modelAdd, modelCache, dataCache);
+            thread.join();
+        }
+
+        // Retrieve preparedModel from cache.
+        {
+            sp<IPreparedModel> preparedModel = nullptr;
+            ErrorStatus status;
+            hidl_vec<hidl_handle> modelCache, dataCache;
+            createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+            createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+            prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+
+            // The preparation may fail or succeed, but must not crash. If the preparation succeeds,
+            // the prepared model must be executed with the correct result and not crash.
+            if (status != ErrorStatus::NONE) {
+                ASSERT_EQ(preparedModel, nullptr);
+            } else {
+                ASSERT_NE(preparedModel, nullptr);
+                EvaluatePreparedModel(preparedModel, testModelAdd,
+                                      /*testDynamicOutputShape=*/false);
+            }
+        }
+    }
+}
+
+TEST_P(CompilationCachingTest, PrepareFromCache_TOCTOU) {
+    if (!mIsCachingSupported) return;
+
+    // Create test models and check if fully supported by the service.
+    const TestModel testModelMul = createLargeTestModel(OperationType::MUL, kLargeModelSize);
+    const Model modelMul = createModel(testModelMul);
+    if (checkEarlyTermination(modelMul)) return;
+    const TestModel testModelAdd = createLargeTestModel(OperationType::ADD, kLargeModelSize);
+    const Model modelAdd = createModel(testModelAdd);
+    if (checkEarlyTermination(modelAdd)) return;
+
+    // Save the modelMul compilation to cache.
+    auto modelCacheMul = mModelCache;
+    for (auto& cache : modelCacheMul) {
+        cache[0].append("_mul");
+    }
+    {
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        createCacheHandles(modelCacheMul, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        saveModelToCache(modelMul, modelCache, dataCache);
+    }
+
+    // Use a different token for modelAdd.
+    mToken[0]++;
+
+    // This test is probabilistic, so we run it multiple times.
+    for (uint32_t i = 0; i < kNumIterationsTOCTOU; i++) {
+        // Save the modelAdd compilation to cache.
+        {
+            hidl_vec<hidl_handle> modelCache, dataCache;
+            createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+            createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+            saveModelToCache(modelAdd, modelCache, dataCache);
+        }
+
+        // Retrieve preparedModel from cache.
+        {
+            sp<IPreparedModel> preparedModel = nullptr;
+            ErrorStatus status;
+            hidl_vec<hidl_handle> modelCache, dataCache;
+            createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+            createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+
+            // Spawn a thread to copy the cache content concurrently while preparing from cache.
+            std::thread thread(copyCacheFiles, std::cref(modelCacheMul), std::cref(mModelCache));
+            prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+            thread.join();
+
+            // The preparation may fail or succeed, but must not crash. If the preparation succeeds,
+            // the prepared model must be executed with the correct result and not crash.
+            if (status != ErrorStatus::NONE) {
+                ASSERT_EQ(preparedModel, nullptr);
+            } else {
+                ASSERT_NE(preparedModel, nullptr);
+                EvaluatePreparedModel(preparedModel, testModelAdd,
+                                      /*testDynamicOutputShape=*/false);
+            }
+        }
+    }
+}
+
+TEST_P(CompilationCachingTest, ReplaceSecuritySensitiveCache) {
+    if (!mIsCachingSupported) return;
+
+    // Create test models and check if fully supported by the service.
+    const TestModel testModelMul = createLargeTestModel(OperationType::MUL, kLargeModelSize);
+    const Model modelMul = createModel(testModelMul);
+    if (checkEarlyTermination(modelMul)) return;
+    const TestModel testModelAdd = createLargeTestModel(OperationType::ADD, kLargeModelSize);
+    const Model modelAdd = createModel(testModelAdd);
+    if (checkEarlyTermination(modelAdd)) return;
+
+    // Save the modelMul compilation to cache.
+    auto modelCacheMul = mModelCache;
+    for (auto& cache : modelCacheMul) {
+        cache[0].append("_mul");
+    }
+    {
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        createCacheHandles(modelCacheMul, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        saveModelToCache(modelMul, modelCache, dataCache);
+    }
+
+    // Use a different token for modelAdd.
+    mToken[0]++;
+
+    // Save the modelAdd compilation to cache.
+    {
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        saveModelToCache(modelAdd, modelCache, dataCache);
+    }
+
+    // Replace the model cache of modelAdd with modelMul.
+    copyCacheFiles(modelCacheMul, mModelCache);
+
+    // Retrieve the preparedModel from cache, expect failure.
+    {
+        sp<IPreparedModel> preparedModel = nullptr;
+        ErrorStatus status;
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+        ASSERT_EQ(preparedModel, nullptr);
+    }
+}
+
+static const auto kNamedDeviceChoices = testing::ValuesIn(getNamedDevices());
+static const auto kOperandTypeChoices =
+        testing::Values(OperandType::TENSOR_FLOAT32, OperandType::TENSOR_QUANT8_ASYMM);
+
+std::string printCompilationCachingTest(
+        const testing::TestParamInfo<CompilationCachingTestParam>& info) {
+    const auto& [namedDevice, operandType] = info.param;
+    const std::string type = (operandType == OperandType::TENSOR_FLOAT32 ? "float32" : "quant8");
+    return gtestCompliantName(getName(namedDevice) + "_" + type);
+}
+
+INSTANTIATE_TEST_CASE_P(TestCompilationCaching, CompilationCachingTest,
+                        testing::Combine(kNamedDeviceChoices, kOperandTypeChoices),
+                        printCompilationCachingTest);
+
+using CompilationCachingSecurityTestParam = std::tuple<NamedDevice, OperandType, uint32_t>;
+
+class CompilationCachingSecurityTest
+    : public CompilationCachingTestBase,
+      public testing::WithParamInterface<CompilationCachingSecurityTestParam> {
+  protected:
+    CompilationCachingSecurityTest()
+        : CompilationCachingTestBase(getData(std::get<NamedDevice>(GetParam())),
+                                     std::get<OperandType>(GetParam())) {}
+
+    void SetUp() {
+        CompilationCachingTestBase::SetUp();
+        generator.seed(kSeed);
+    }
+
+    // Get a random integer within a closed range [lower, upper].
+    template <typename T>
+    T getRandomInt(T lower, T upper) {
+        std::uniform_int_distribution<T> dis(lower, upper);
+        return dis(generator);
+    }
+
+    // Randomly flip one single bit of the cache entry.
+    void flipOneBitOfCache(const std::string& filename, bool* skip) {
+        FILE* pFile = fopen(filename.c_str(), "r+");
+        ASSERT_EQ(fseek(pFile, 0, SEEK_END), 0);
+        long int fileSize = ftell(pFile);
+        if (fileSize == 0) {
+            fclose(pFile);
+            *skip = true;
+            return;
+        }
+        ASSERT_EQ(fseek(pFile, getRandomInt(0l, fileSize - 1), SEEK_SET), 0);
+        int readByte = fgetc(pFile);
+        ASSERT_NE(readByte, EOF);
+        ASSERT_EQ(fseek(pFile, -1, SEEK_CUR), 0);
+        ASSERT_NE(fputc(static_cast<uint8_t>(readByte) ^ (1U << getRandomInt(0, 7)), pFile), EOF);
+        fclose(pFile);
+        *skip = false;
+    }
+
+    // Randomly append bytes to the cache entry.
+    void appendBytesToCache(const std::string& filename, bool* skip) {
+        FILE* pFile = fopen(filename.c_str(), "a");
+        uint32_t appendLength = getRandomInt(1, 256);
+        for (uint32_t i = 0; i < appendLength; i++) {
+            ASSERT_NE(fputc(getRandomInt<uint8_t>(0, 255), pFile), EOF);
+        }
+        fclose(pFile);
+        *skip = false;
+    }
+
+    enum class ExpectedResult { GENERAL_FAILURE, NOT_CRASH };
+
+    // Test if the driver behaves as expected when given corrupted cache or token.
+    // The modifier will be invoked after save to cache but before prepare from cache.
+    // The modifier accepts one pointer argument "skip" as the returning value, indicating
+    // whether the test should be skipped or not.
+    void testCorruptedCache(ExpectedResult expected, std::function<void(bool*)> modifier) {
+        const TestModel& testModel = createTestModel();
+        const Model model = createModel(testModel);
+        if (checkEarlyTermination(model)) return;
+
+        // Save the compilation to cache.
+        {
+            hidl_vec<hidl_handle> modelCache, dataCache;
+            createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+            createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+            saveModelToCache(model, modelCache, dataCache);
+        }
+
+        bool skip = false;
+        modifier(&skip);
+        if (skip) return;
+
+        // Retrieve preparedModel from cache.
+        {
+            sp<IPreparedModel> preparedModel = nullptr;
+            ErrorStatus status;
+            hidl_vec<hidl_handle> modelCache, dataCache;
+            createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+            createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+            prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+
+            switch (expected) {
+                case ExpectedResult::GENERAL_FAILURE:
+                    ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+                    ASSERT_EQ(preparedModel, nullptr);
+                    break;
+                case ExpectedResult::NOT_CRASH:
+                    ASSERT_EQ(preparedModel == nullptr, status != ErrorStatus::NONE);
+                    break;
+                default:
+                    FAIL();
+            }
+        }
+    }
+
+    const uint32_t kSeed = std::get<uint32_t>(GetParam());
+    std::mt19937 generator;
+};
+
+TEST_P(CompilationCachingSecurityTest, CorruptedModelCache) {
+    if (!mIsCachingSupported) return;
+    for (uint32_t i = 0; i < mNumModelCache; i++) {
+        testCorruptedCache(ExpectedResult::GENERAL_FAILURE,
+                           [this, i](bool* skip) { flipOneBitOfCache(mModelCache[i][0], skip); });
+    }
+}
+
+TEST_P(CompilationCachingSecurityTest, WrongLengthModelCache) {
+    if (!mIsCachingSupported) return;
+    for (uint32_t i = 0; i < mNumModelCache; i++) {
+        testCorruptedCache(ExpectedResult::GENERAL_FAILURE,
+                           [this, i](bool* skip) { appendBytesToCache(mModelCache[i][0], skip); });
+    }
+}
+
+TEST_P(CompilationCachingSecurityTest, CorruptedDataCache) {
+    if (!mIsCachingSupported) return;
+    for (uint32_t i = 0; i < mNumDataCache; i++) {
+        testCorruptedCache(ExpectedResult::NOT_CRASH,
+                           [this, i](bool* skip) { flipOneBitOfCache(mDataCache[i][0], skip); });
+    }
+}
+
+TEST_P(CompilationCachingSecurityTest, WrongLengthDataCache) {
+    if (!mIsCachingSupported) return;
+    for (uint32_t i = 0; i < mNumDataCache; i++) {
+        testCorruptedCache(ExpectedResult::NOT_CRASH,
+                           [this, i](bool* skip) { appendBytesToCache(mDataCache[i][0], skip); });
+    }
+}
+
+TEST_P(CompilationCachingSecurityTest, WrongToken) {
+    if (!mIsCachingSupported) return;
+    testCorruptedCache(ExpectedResult::GENERAL_FAILURE, [this](bool* skip) {
+        // Randomly flip one single bit in mToken.
+        uint32_t ind =
+                getRandomInt(0u, static_cast<uint32_t>(Constant::BYTE_SIZE_OF_CACHE_TOKEN) - 1);
+        mToken[ind] ^= (1U << getRandomInt(0, 7));
+        *skip = false;
+    });
+}
+
+std::string printCompilationCachingSecurityTest(
+        const testing::TestParamInfo<CompilationCachingSecurityTestParam>& info) {
+    const auto& [namedDevice, operandType, seed] = info.param;
+    const std::string type = (operandType == OperandType::TENSOR_FLOAT32 ? "float32" : "quant8");
+    return gtestCompliantName(getName(namedDevice) + "_" + type + "_" + std::to_string(seed));
+}
+
+INSTANTIATE_TEST_CASE_P(TestCompilationCaching, CompilationCachingSecurityTest,
+                        testing::Combine(kNamedDeviceChoices, kOperandTypeChoices,
+                                         testing::Range(0U, 10U)),
+                        printCompilationCachingSecurityTest);
+
+}  // namespace android::hardware::neuralnetworks::V1_3::vts::functional
diff --git a/neuralnetworks/1.3/vts/functional/GeneratedTestHarness.cpp b/neuralnetworks/1.3/vts/functional/GeneratedTestHarness.cpp
new file mode 100644
index 0000000..16a7d70
--- /dev/null
+++ b/neuralnetworks/1.3/vts/functional/GeneratedTestHarness.cpp
@@ -0,0 +1,418 @@
+/*
+ * Copyright (C) 2019 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.
+ */
+
+#include "GeneratedTestHarness.h"
+
+#include <android-base/logging.h>
+#include <android/hardware/neuralnetworks/1.0/IDevice.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/1.2/IDevice.h>
+#include <android/hardware/neuralnetworks/1.2/IExecutionCallback.h>
+#include <android/hardware/neuralnetworks/1.2/IPreparedModel.h>
+#include <android/hardware/neuralnetworks/1.2/IPreparedModelCallback.h>
+#include <android/hardware/neuralnetworks/1.2/types.h>
+#include <android/hardware/neuralnetworks/1.3/IDevice.h>
+#include <android/hardware/neuralnetworks/1.3/types.h>
+#include <android/hidl/allocator/1.0/IAllocator.h>
+#include <android/hidl/memory/1.0/IMemory.h>
+#include <hidlmemory/mapping.h>
+
+#include <gtest/gtest.h>
+#include <algorithm>
+#include <iostream>
+#include <numeric>
+
+#include "1.0/Utils.h"
+#include "1.2/Callbacks.h"
+#include "ExecutionBurstController.h"
+#include "MemoryUtils.h"
+#include "TestHarness.h"
+#include "Utils.h"
+#include "VtsHalNeuralnetworks.h"
+
+namespace android::hardware::neuralnetworks::V1_3::vts::functional {
+
+using namespace test_helper;
+using hidl::memory::V1_0::IMemory;
+using V1_0::DataLocation;
+using V1_0::ErrorStatus;
+using V1_0::OperandLifeTime;
+using V1_0::Request;
+using V1_1::ExecutionPreference;
+using V1_2::Constant;
+using V1_2::IPreparedModel;
+using V1_2::MeasureTiming;
+using V1_2::OperationType;
+using V1_2::OutputShape;
+using V1_2::SymmPerChannelQuantParams;
+using V1_2::Timing;
+using V1_2::implementation::ExecutionCallback;
+using V1_2::implementation::PreparedModelCallback;
+using HidlToken = hidl_array<uint8_t, static_cast<uint32_t>(Constant::BYTE_SIZE_OF_CACHE_TOKEN)>;
+
+enum class OutputType { FULLY_SPECIFIED, UNSPECIFIED, INSUFFICIENT };
+
+Model createModel(const TestModel& testModel) {
+    // Model operands.
+    hidl_vec<Operand> operands(testModel.operands.size());
+    size_t constCopySize = 0, constRefSize = 0;
+    for (uint32_t i = 0; i < testModel.operands.size(); i++) {
+        const auto& op = testModel.operands[i];
+
+        DataLocation loc = {};
+        if (op.lifetime == TestOperandLifeTime::CONSTANT_COPY) {
+            loc = {.poolIndex = 0,
+                   .offset = static_cast<uint32_t>(constCopySize),
+                   .length = static_cast<uint32_t>(op.data.size())};
+            constCopySize += op.data.alignedSize();
+        } else if (op.lifetime == TestOperandLifeTime::CONSTANT_REFERENCE) {
+            loc = {.poolIndex = 0,
+                   .offset = static_cast<uint32_t>(constRefSize),
+                   .length = static_cast<uint32_t>(op.data.size())};
+            constRefSize += op.data.alignedSize();
+        }
+
+        Operand::ExtraParams extraParams;
+        if (op.type == TestOperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL) {
+            extraParams.channelQuant(SymmPerChannelQuantParams{
+                    .scales = op.channelQuant.scales, .channelDim = op.channelQuant.channelDim});
+        }
+
+        operands[i] = {.type = static_cast<OperandType>(op.type),
+                       .dimensions = op.dimensions,
+                       .numberOfConsumers = op.numberOfConsumers,
+                       .scale = op.scale,
+                       .zeroPoint = op.zeroPoint,
+                       .lifetime = static_cast<OperandLifeTime>(op.lifetime),
+                       .location = loc,
+                       .extraParams = std::move(extraParams)};
+    }
+
+    // Model operations.
+    hidl_vec<Operation> operations(testModel.operations.size());
+    std::transform(testModel.operations.begin(), testModel.operations.end(), operations.begin(),
+                   [](const TestOperation& op) -> Operation {
+                       return {.type = static_cast<OperationType>(op.type),
+                               .inputs = op.inputs,
+                               .outputs = op.outputs};
+                   });
+
+    // Constant copies.
+    hidl_vec<uint8_t> operandValues(constCopySize);
+    for (uint32_t i = 0; i < testModel.operands.size(); i++) {
+        const auto& op = testModel.operands[i];
+        if (op.lifetime == TestOperandLifeTime::CONSTANT_COPY) {
+            const uint8_t* begin = op.data.get<uint8_t>();
+            const uint8_t* end = begin + op.data.size();
+            std::copy(begin, end, operandValues.data() + operands[i].location.offset);
+        }
+    }
+
+    // Shared memory.
+    hidl_vec<hidl_memory> pools = {};
+    if (constRefSize > 0) {
+        hidl_vec_push_back(&pools, nn::allocateSharedMemory(constRefSize));
+        CHECK_NE(pools[0].size(), 0u);
+
+        // load data
+        sp<IMemory> mappedMemory = mapMemory(pools[0]);
+        CHECK(mappedMemory.get() != nullptr);
+        uint8_t* mappedPtr =
+                reinterpret_cast<uint8_t*>(static_cast<void*>(mappedMemory->getPointer()));
+        CHECK(mappedPtr != nullptr);
+
+        for (uint32_t i = 0; i < testModel.operands.size(); i++) {
+            const auto& op = testModel.operands[i];
+            if (op.lifetime == TestOperandLifeTime::CONSTANT_REFERENCE) {
+                const uint8_t* begin = op.data.get<uint8_t>();
+                const uint8_t* end = begin + op.data.size();
+                std::copy(begin, end, mappedPtr + operands[i].location.offset);
+            }
+        }
+    }
+
+    return {.operands = std::move(operands),
+            .operations = std::move(operations),
+            .inputIndexes = testModel.inputIndexes,
+            .outputIndexes = testModel.outputIndexes,
+            .operandValues = std::move(operandValues),
+            .pools = std::move(pools),
+            .relaxComputationFloat32toFloat16 = testModel.isRelaxed};
+}
+
+static bool isOutputSizeGreaterThanOne(const TestModel& testModel, uint32_t index) {
+    const auto byteSize = testModel.operands[testModel.outputIndexes[index]].data.size();
+    return byteSize > 1u;
+}
+
+static void makeOutputInsufficientSize(uint32_t outputIndex, Request* request) {
+    auto& length = request->outputs[outputIndex].location.length;
+    ASSERT_GT(length, 1u);
+    length -= 1u;
+}
+
+static void makeOutputDimensionsUnspecified(Model* model) {
+    for (auto i : model->outputIndexes) {
+        auto& dims = model->operands[i].dimensions;
+        std::fill(dims.begin(), dims.end(), 0);
+    }
+}
+
+static Return<ErrorStatus> ExecutePreparedModel(const sp<IPreparedModel>& preparedModel,
+                                                const Request& request, MeasureTiming measure,
+                                                sp<ExecutionCallback>& callback) {
+    return preparedModel->execute_1_2(request, measure, callback);
+}
+static Return<ErrorStatus> ExecutePreparedModel(const sp<IPreparedModel>& preparedModel,
+                                                const Request& request, MeasureTiming measure,
+                                                hidl_vec<OutputShape>* outputShapes,
+                                                Timing* timing) {
+    ErrorStatus result;
+    Return<void> ret = preparedModel->executeSynchronously(
+            request, measure,
+            [&result, outputShapes, timing](ErrorStatus error, const hidl_vec<OutputShape>& shapes,
+                                            const Timing& time) {
+                result = error;
+                *outputShapes = shapes;
+                *timing = time;
+            });
+    if (!ret.isOk()) {
+        return ErrorStatus::GENERAL_FAILURE;
+    }
+    return result;
+}
+static std::shared_ptr<::android::nn::ExecutionBurstController> CreateBurst(
+        const sp<IPreparedModel>& preparedModel) {
+    return android::nn::ExecutionBurstController::create(preparedModel, /*blocking=*/true);
+}
+enum class Executor { ASYNC, SYNC, BURST };
+
+void EvaluatePreparedModel(const sp<IPreparedModel>& preparedModel, const TestModel& testModel,
+                           Executor executor, MeasureTiming measure, OutputType outputType) {
+    // If output0 does not have size larger than one byte, we can not test with insufficient buffer.
+    if (outputType == OutputType::INSUFFICIENT && !isOutputSizeGreaterThanOne(testModel, 0)) {
+        return;
+    }
+
+    Request request = createRequest(testModel);
+    if (outputType == OutputType::INSUFFICIENT) {
+        makeOutputInsufficientSize(/*outputIndex=*/0, &request);
+    }
+
+    ErrorStatus executionStatus;
+    hidl_vec<OutputShape> outputShapes;
+    Timing timing;
+    switch (executor) {
+        case Executor::ASYNC: {
+            SCOPED_TRACE("asynchronous");
+
+            // launch execution
+            sp<ExecutionCallback> executionCallback = new ExecutionCallback();
+            Return<ErrorStatus> executionLaunchStatus =
+                    ExecutePreparedModel(preparedModel, request, measure, executionCallback);
+            ASSERT_TRUE(executionLaunchStatus.isOk());
+            EXPECT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(executionLaunchStatus));
+
+            // retrieve execution status
+            executionCallback->wait();
+            executionStatus = executionCallback->getStatus();
+            outputShapes = executionCallback->getOutputShapes();
+            timing = executionCallback->getTiming();
+
+            break;
+        }
+        case Executor::SYNC: {
+            SCOPED_TRACE("synchronous");
+
+            // execute
+            Return<ErrorStatus> executionReturnStatus =
+                    ExecutePreparedModel(preparedModel, request, measure, &outputShapes, &timing);
+            ASSERT_TRUE(executionReturnStatus.isOk());
+            executionStatus = static_cast<ErrorStatus>(executionReturnStatus);
+
+            break;
+        }
+        case Executor::BURST: {
+            SCOPED_TRACE("burst");
+
+            // create burst
+            const std::shared_ptr<::android::nn::ExecutionBurstController> controller =
+                    CreateBurst(preparedModel);
+            ASSERT_NE(nullptr, controller.get());
+
+            // create memory keys
+            std::vector<intptr_t> keys(request.pools.size());
+            for (size_t i = 0; i < keys.size(); ++i) {
+                keys[i] = reinterpret_cast<intptr_t>(&request.pools[i]);
+            }
+
+            // execute burst
+            std::tie(executionStatus, outputShapes, timing) =
+                    controller->compute(request, measure, keys);
+
+            break;
+        }
+    }
+
+    if (outputType != OutputType::FULLY_SPECIFIED &&
+        executionStatus == ErrorStatus::GENERAL_FAILURE) {
+        LOG(INFO) << "NN VTS: Early termination of test because vendor service cannot "
+                     "execute model that it does not support.";
+        std::cout << "[          ]   Early termination of test because vendor service cannot "
+                     "execute model that it does not support."
+                  << std::endl;
+        GTEST_SKIP();
+    }
+    if (measure == MeasureTiming::NO) {
+        EXPECT_EQ(UINT64_MAX, timing.timeOnDevice);
+        EXPECT_EQ(UINT64_MAX, timing.timeInDriver);
+    } else {
+        if (timing.timeOnDevice != UINT64_MAX && timing.timeInDriver != UINT64_MAX) {
+            EXPECT_LE(timing.timeOnDevice, timing.timeInDriver);
+        }
+    }
+
+    switch (outputType) {
+        case OutputType::FULLY_SPECIFIED:
+            // If the model output operands are fully specified, outputShapes must be either
+            // either empty, or have the same number of elements as the number of outputs.
+            ASSERT_EQ(ErrorStatus::NONE, executionStatus);
+            ASSERT_TRUE(outputShapes.size() == 0 ||
+                        outputShapes.size() == testModel.outputIndexes.size());
+            break;
+        case OutputType::UNSPECIFIED:
+            // If the model output operands are not fully specified, outputShapes must have
+            // the same number of elements as the number of outputs.
+            ASSERT_EQ(ErrorStatus::NONE, executionStatus);
+            ASSERT_EQ(outputShapes.size(), testModel.outputIndexes.size());
+            break;
+        case OutputType::INSUFFICIENT:
+            ASSERT_EQ(ErrorStatus::OUTPUT_INSUFFICIENT_SIZE, executionStatus);
+            ASSERT_EQ(outputShapes.size(), testModel.outputIndexes.size());
+            ASSERT_FALSE(outputShapes[0].isSufficient);
+            return;
+    }
+
+    // Go through all outputs, check returned output shapes.
+    for (uint32_t i = 0; i < outputShapes.size(); i++) {
+        EXPECT_TRUE(outputShapes[i].isSufficient);
+        const auto& expect = testModel.operands[testModel.outputIndexes[i]].dimensions;
+        const std::vector<uint32_t> actual = outputShapes[i].dimensions;
+        EXPECT_EQ(expect, actual);
+    }
+
+    // Retrieve execution results.
+    const std::vector<TestBuffer> outputs = getOutputBuffers(request);
+
+    // We want "close-enough" results.
+    checkResults(testModel, outputs);
+}
+
+void EvaluatePreparedModel(const sp<IPreparedModel>& preparedModel, const TestModel& testModel,
+                           bool testDynamicOutputShape) {
+    if (testDynamicOutputShape) {
+        EvaluatePreparedModel(preparedModel, testModel, Executor::ASYNC, MeasureTiming::NO,
+                              OutputType::UNSPECIFIED);
+        EvaluatePreparedModel(preparedModel, testModel, Executor::SYNC, MeasureTiming::NO,
+                              OutputType::UNSPECIFIED);
+        EvaluatePreparedModel(preparedModel, testModel, Executor::BURST, MeasureTiming::NO,
+                              OutputType::UNSPECIFIED);
+        EvaluatePreparedModel(preparedModel, testModel, Executor::ASYNC, MeasureTiming::YES,
+                              OutputType::UNSPECIFIED);
+        EvaluatePreparedModel(preparedModel, testModel, Executor::SYNC, MeasureTiming::YES,
+                              OutputType::UNSPECIFIED);
+        EvaluatePreparedModel(preparedModel, testModel, Executor::BURST, MeasureTiming::YES,
+                              OutputType::UNSPECIFIED);
+        EvaluatePreparedModel(preparedModel, testModel, Executor::ASYNC, MeasureTiming::NO,
+                              OutputType::INSUFFICIENT);
+        EvaluatePreparedModel(preparedModel, testModel, Executor::SYNC, MeasureTiming::NO,
+                              OutputType::INSUFFICIENT);
+        EvaluatePreparedModel(preparedModel, testModel, Executor::BURST, MeasureTiming::NO,
+                              OutputType::INSUFFICIENT);
+        EvaluatePreparedModel(preparedModel, testModel, Executor::ASYNC, MeasureTiming::YES,
+                              OutputType::INSUFFICIENT);
+        EvaluatePreparedModel(preparedModel, testModel, Executor::SYNC, MeasureTiming::YES,
+                              OutputType::INSUFFICIENT);
+        EvaluatePreparedModel(preparedModel, testModel, Executor::BURST, MeasureTiming::YES,
+                              OutputType::INSUFFICIENT);
+    } else {
+        EvaluatePreparedModel(preparedModel, testModel, Executor::ASYNC, MeasureTiming::NO,
+                              OutputType::FULLY_SPECIFIED);
+        EvaluatePreparedModel(preparedModel, testModel, Executor::SYNC, MeasureTiming::NO,
+                              OutputType::FULLY_SPECIFIED);
+        EvaluatePreparedModel(preparedModel, testModel, Executor::BURST, MeasureTiming::NO,
+                              OutputType::FULLY_SPECIFIED);
+        EvaluatePreparedModel(preparedModel, testModel, Executor::ASYNC, MeasureTiming::YES,
+                              OutputType::FULLY_SPECIFIED);
+        EvaluatePreparedModel(preparedModel, testModel, Executor::SYNC, MeasureTiming::YES,
+                              OutputType::FULLY_SPECIFIED);
+        EvaluatePreparedModel(preparedModel, testModel, Executor::BURST, MeasureTiming::YES,
+                              OutputType::FULLY_SPECIFIED);
+    }
+}
+
+void Execute(const sp<IDevice>& device, const TestModel& testModel, bool testDynamicOutputShape) {
+    Model model = createModel(testModel);
+    if (testDynamicOutputShape) {
+        makeOutputDimensionsUnspecified(&model);
+    }
+
+    sp<IPreparedModel> preparedModel;
+    createPreparedModel(device, model, &preparedModel);
+    if (preparedModel == nullptr) return;
+
+    EvaluatePreparedModel(preparedModel, testModel, testDynamicOutputShape);
+}
+
+void GeneratedTestBase::SetUp() {
+    testing::TestWithParam<GeneratedTestParam>::SetUp();
+    ASSERT_NE(kDevice, nullptr);
+}
+
+std::vector<NamedModel> getNamedModels(const FilterFn& filter) {
+    return TestModelManager::get().getTestModels(filter);
+}
+
+std::string printGeneratedTest(const testing::TestParamInfo<GeneratedTestParam>& info) {
+    const auto& [namedDevice, namedModel] = info.param;
+    return gtestCompliantName(getName(namedDevice) + "_" + getName(namedModel));
+}
+
+// Tag for the generated tests
+class GeneratedTest : public GeneratedTestBase {};
+
+// Tag for the dynamic output shape tests
+class DynamicOutputShapeTest : public GeneratedTest {};
+
+TEST_P(GeneratedTest, Test) {
+    Execute(kDevice, kTestModel, /*testDynamicOutputShape=*/false);
+}
+
+TEST_P(DynamicOutputShapeTest, Test) {
+    Execute(kDevice, kTestModel, /*testDynamicOutputShape=*/true);
+}
+
+INSTANTIATE_GENERATED_TEST(GeneratedTest,
+                           [](const TestModel& testModel) { return !testModel.expectFailure; });
+
+INSTANTIATE_GENERATED_TEST(DynamicOutputShapeTest,
+                           [](const TestModel& testModel) { return !testModel.expectFailure; });
+
+}  // namespace android::hardware::neuralnetworks::V1_3::vts::functional
diff --git a/neuralnetworks/1.3/vts/functional/GeneratedTestHarness.h b/neuralnetworks/1.3/vts/functional/GeneratedTestHarness.h
new file mode 100644
index 0000000..b9277cf
--- /dev/null
+++ b/neuralnetworks/1.3/vts/functional/GeneratedTestHarness.h
@@ -0,0 +1,66 @@
+/*
+ * Copyright (C) 2019 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 ANDROID_HARDWARE_NEURALNETWORKS_V1_3_GENERATED_TEST_HARNESS_H
+#define ANDROID_HARDWARE_NEURALNETWORKS_V1_3_GENERATED_TEST_HARNESS_H
+
+#include <android/hardware/neuralnetworks/1.2/IPreparedModel.h>
+#include <android/hardware/neuralnetworks/1.3/IDevice.h>
+#include <android/hardware/neuralnetworks/1.3/types.h>
+#include <functional>
+#include <vector>
+#include "1.0/Utils.h"
+#include "TestHarness.h"
+#include "VtsHalNeuralnetworks.h"
+
+namespace android::hardware::neuralnetworks::V1_3::vts::functional {
+
+using NamedModel = Named<const test_helper::TestModel*>;
+using GeneratedTestParam = std::tuple<NamedDevice, NamedModel>;
+
+class GeneratedTestBase : public testing::TestWithParam<GeneratedTestParam> {
+  protected:
+    void SetUp() override;
+    const sp<IDevice> kDevice = getData(std::get<NamedDevice>(GetParam()));
+    const test_helper::TestModel& kTestModel = *getData(std::get<NamedModel>(GetParam()));
+};
+
+using FilterFn = std::function<bool(const test_helper::TestModel&)>;
+std::vector<NamedModel> getNamedModels(const FilterFn& filter);
+
+std::string printGeneratedTest(const testing::TestParamInfo<GeneratedTestParam>& info);
+
+#define INSTANTIATE_GENERATED_TEST(TestSuite, filter)                                     \
+    INSTANTIATE_TEST_SUITE_P(TestGenerated, TestSuite,                                    \
+                             testing::Combine(testing::ValuesIn(getNamedDevices()),       \
+                                              testing::ValuesIn(getNamedModels(filter))), \
+                             printGeneratedTest)
+
+// Tag for the validation tests, instantiated in VtsHalNeuralnetworks.cpp.
+// TODO: Clean up the hierarchy for ValidationTest.
+class ValidationTest : public GeneratedTestBase {};
+
+Model createModel(const test_helper::TestModel& testModel);
+
+void PrepareModel(const sp<IDevice>& device, const Model& model,
+                  sp<V1_2::IPreparedModel>* preparedModel);
+
+void EvaluatePreparedModel(const sp<V1_2::IPreparedModel>& preparedModel,
+                           const test_helper::TestModel& testModel, bool testDynamicOutputShape);
+
+}  // namespace android::hardware::neuralnetworks::V1_3::vts::functional
+
+#endif  // ANDROID_HARDWARE_NEURALNETWORKS_V1_3_GENERATED_TEST_HARNESS_H
diff --git a/neuralnetworks/1.3/vts/functional/TestAssertions.cpp b/neuralnetworks/1.3/vts/functional/TestAssertions.cpp
new file mode 100644
index 0000000..7361078
--- /dev/null
+++ b/neuralnetworks/1.3/vts/functional/TestAssertions.cpp
@@ -0,0 +1,144 @@
+/*
+ * Copyright (C) 2019 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.
+ */
+
+#include <android/hardware/neuralnetworks/1.3/types.h>
+#include "TestHarness.h"
+
+namespace android::hardware::neuralnetworks::V1_3 {
+
+// Make sure that the HIDL enums are compatible with the values defined in
+// frameworks/ml/nn/tools/test_generator/test_harness/include/TestHarness.h.
+using namespace test_helper;
+#define CHECK_TEST_ENUM(EnumType, enumValue) \
+    static_assert(static_cast<EnumType>(Test##EnumType::enumValue) == EnumType::enumValue)
+
+using V1_2::OperationType;
+
+CHECK_TEST_ENUM(OperandType, FLOAT32);
+CHECK_TEST_ENUM(OperandType, INT32);
+CHECK_TEST_ENUM(OperandType, UINT32);
+CHECK_TEST_ENUM(OperandType, TENSOR_FLOAT32);
+CHECK_TEST_ENUM(OperandType, TENSOR_INT32);
+CHECK_TEST_ENUM(OperandType, TENSOR_QUANT8_ASYMM);
+CHECK_TEST_ENUM(OperandType, BOOL);
+CHECK_TEST_ENUM(OperandType, TENSOR_QUANT16_SYMM);
+CHECK_TEST_ENUM(OperandType, TENSOR_FLOAT16);
+CHECK_TEST_ENUM(OperandType, TENSOR_BOOL8);
+CHECK_TEST_ENUM(OperandType, FLOAT16);
+CHECK_TEST_ENUM(OperandType, TENSOR_QUANT8_SYMM_PER_CHANNEL);
+CHECK_TEST_ENUM(OperandType, TENSOR_QUANT16_ASYMM);
+CHECK_TEST_ENUM(OperandType, TENSOR_QUANT8_SYMM);
+CHECK_TEST_ENUM(OperandType, TENSOR_QUANT8_ASYMM_SIGNED);
+
+CHECK_TEST_ENUM(OperationType, ADD);
+CHECK_TEST_ENUM(OperationType, AVERAGE_POOL_2D);
+CHECK_TEST_ENUM(OperationType, CONCATENATION);
+CHECK_TEST_ENUM(OperationType, CONV_2D);
+CHECK_TEST_ENUM(OperationType, DEPTHWISE_CONV_2D);
+CHECK_TEST_ENUM(OperationType, DEPTH_TO_SPACE);
+CHECK_TEST_ENUM(OperationType, DEQUANTIZE);
+CHECK_TEST_ENUM(OperationType, EMBEDDING_LOOKUP);
+CHECK_TEST_ENUM(OperationType, FLOOR);
+CHECK_TEST_ENUM(OperationType, FULLY_CONNECTED);
+CHECK_TEST_ENUM(OperationType, HASHTABLE_LOOKUP);
+CHECK_TEST_ENUM(OperationType, L2_NORMALIZATION);
+CHECK_TEST_ENUM(OperationType, L2_POOL_2D);
+CHECK_TEST_ENUM(OperationType, LOCAL_RESPONSE_NORMALIZATION);
+CHECK_TEST_ENUM(OperationType, LOGISTIC);
+CHECK_TEST_ENUM(OperationType, LSH_PROJECTION);
+CHECK_TEST_ENUM(OperationType, LSTM);
+CHECK_TEST_ENUM(OperationType, MAX_POOL_2D);
+CHECK_TEST_ENUM(OperationType, MUL);
+CHECK_TEST_ENUM(OperationType, RELU);
+CHECK_TEST_ENUM(OperationType, RELU1);
+CHECK_TEST_ENUM(OperationType, RELU6);
+CHECK_TEST_ENUM(OperationType, RESHAPE);
+CHECK_TEST_ENUM(OperationType, RESIZE_BILINEAR);
+CHECK_TEST_ENUM(OperationType, RNN);
+CHECK_TEST_ENUM(OperationType, SOFTMAX);
+CHECK_TEST_ENUM(OperationType, SPACE_TO_DEPTH);
+CHECK_TEST_ENUM(OperationType, SVDF);
+CHECK_TEST_ENUM(OperationType, TANH);
+CHECK_TEST_ENUM(OperationType, BATCH_TO_SPACE_ND);
+CHECK_TEST_ENUM(OperationType, DIV);
+CHECK_TEST_ENUM(OperationType, MEAN);
+CHECK_TEST_ENUM(OperationType, PAD);
+CHECK_TEST_ENUM(OperationType, SPACE_TO_BATCH_ND);
+CHECK_TEST_ENUM(OperationType, SQUEEZE);
+CHECK_TEST_ENUM(OperationType, STRIDED_SLICE);
+CHECK_TEST_ENUM(OperationType, SUB);
+CHECK_TEST_ENUM(OperationType, TRANSPOSE);
+CHECK_TEST_ENUM(OperationType, ABS);
+CHECK_TEST_ENUM(OperationType, ARGMAX);
+CHECK_TEST_ENUM(OperationType, ARGMIN);
+CHECK_TEST_ENUM(OperationType, AXIS_ALIGNED_BBOX_TRANSFORM);
+CHECK_TEST_ENUM(OperationType, BIDIRECTIONAL_SEQUENCE_LSTM);
+CHECK_TEST_ENUM(OperationType, BIDIRECTIONAL_SEQUENCE_RNN);
+CHECK_TEST_ENUM(OperationType, BOX_WITH_NMS_LIMIT);
+CHECK_TEST_ENUM(OperationType, CAST);
+CHECK_TEST_ENUM(OperationType, CHANNEL_SHUFFLE);
+CHECK_TEST_ENUM(OperationType, DETECTION_POSTPROCESSING);
+CHECK_TEST_ENUM(OperationType, EQUAL);
+CHECK_TEST_ENUM(OperationType, EXP);
+CHECK_TEST_ENUM(OperationType, EXPAND_DIMS);
+CHECK_TEST_ENUM(OperationType, GATHER);
+CHECK_TEST_ENUM(OperationType, GENERATE_PROPOSALS);
+CHECK_TEST_ENUM(OperationType, GREATER);
+CHECK_TEST_ENUM(OperationType, GREATER_EQUAL);
+CHECK_TEST_ENUM(OperationType, GROUPED_CONV_2D);
+CHECK_TEST_ENUM(OperationType, HEATMAP_MAX_KEYPOINT);
+CHECK_TEST_ENUM(OperationType, INSTANCE_NORMALIZATION);
+CHECK_TEST_ENUM(OperationType, LESS);
+CHECK_TEST_ENUM(OperationType, LESS_EQUAL);
+CHECK_TEST_ENUM(OperationType, LOG);
+CHECK_TEST_ENUM(OperationType, LOGICAL_AND);
+CHECK_TEST_ENUM(OperationType, LOGICAL_NOT);
+CHECK_TEST_ENUM(OperationType, LOGICAL_OR);
+CHECK_TEST_ENUM(OperationType, LOG_SOFTMAX);
+CHECK_TEST_ENUM(OperationType, MAXIMUM);
+CHECK_TEST_ENUM(OperationType, MINIMUM);
+CHECK_TEST_ENUM(OperationType, NEG);
+CHECK_TEST_ENUM(OperationType, NOT_EQUAL);
+CHECK_TEST_ENUM(OperationType, PAD_V2);
+CHECK_TEST_ENUM(OperationType, POW);
+CHECK_TEST_ENUM(OperationType, PRELU);
+CHECK_TEST_ENUM(OperationType, QUANTIZE);
+CHECK_TEST_ENUM(OperationType, QUANTIZED_16BIT_LSTM);
+CHECK_TEST_ENUM(OperationType, RANDOM_MULTINOMIAL);
+CHECK_TEST_ENUM(OperationType, REDUCE_ALL);
+CHECK_TEST_ENUM(OperationType, REDUCE_ANY);
+CHECK_TEST_ENUM(OperationType, REDUCE_MAX);
+CHECK_TEST_ENUM(OperationType, REDUCE_MIN);
+CHECK_TEST_ENUM(OperationType, REDUCE_PROD);
+CHECK_TEST_ENUM(OperationType, REDUCE_SUM);
+CHECK_TEST_ENUM(OperationType, ROI_ALIGN);
+CHECK_TEST_ENUM(OperationType, ROI_POOLING);
+CHECK_TEST_ENUM(OperationType, RSQRT);
+CHECK_TEST_ENUM(OperationType, SELECT);
+CHECK_TEST_ENUM(OperationType, SIN);
+CHECK_TEST_ENUM(OperationType, SLICE);
+CHECK_TEST_ENUM(OperationType, SPLIT);
+CHECK_TEST_ENUM(OperationType, SQRT);
+CHECK_TEST_ENUM(OperationType, TILE);
+CHECK_TEST_ENUM(OperationType, TOPK_V2);
+CHECK_TEST_ENUM(OperationType, TRANSPOSE_CONV_2D);
+CHECK_TEST_ENUM(OperationType, UNIDIRECTIONAL_SEQUENCE_LSTM);
+CHECK_TEST_ENUM(OperationType, UNIDIRECTIONAL_SEQUENCE_RNN);
+CHECK_TEST_ENUM(OperationType, RESIZE_NEAREST_NEIGHBOR);
+
+#undef CHECK_TEST_ENUM
+
+}  // namespace android::hardware::neuralnetworks::V1_3
diff --git a/neuralnetworks/1.3/vts/functional/ValidateBurst.cpp b/neuralnetworks/1.3/vts/functional/ValidateBurst.cpp
new file mode 100644
index 0000000..95f9f42
--- /dev/null
+++ b/neuralnetworks/1.3/vts/functional/ValidateBurst.cpp
@@ -0,0 +1,407 @@
+/*
+ * Copyright (C) 2019 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 "1.2/Callbacks.h"
+#include "ExecutionBurstController.h"
+#include "ExecutionBurstServer.h"
+#include "GeneratedTestHarness.h"
+#include "TestHarness.h"
+#include "Utils.h"
+
+#include <android-base/logging.h>
+#include <cstring>
+
+namespace android::hardware::neuralnetworks::V1_3::vts::functional {
+
+using nn::ExecutionBurstController;
+using nn::RequestChannelSender;
+using nn::ResultChannelReceiver;
+using V1_0::ErrorStatus;
+using V1_0::Request;
+using V1_2::FmqRequestDatum;
+using V1_2::FmqResultDatum;
+using V1_2::IBurstCallback;
+using V1_2::IBurstContext;
+using V1_2::IPreparedModel;
+using V1_2::MeasureTiming;
+using V1_2::Timing;
+using ExecutionBurstCallback = ExecutionBurstController::ExecutionBurstCallback;
+
+// This constant value represents the length of an FMQ that is large enough to
+// return a result from a burst execution for all of the generated test cases.
+constexpr size_t kExecutionBurstChannelLength = 1024;
+
+// This constant value represents a length of an FMQ that is not large enough
+// to return a result from a burst execution for some of the generated test
+// cases.
+constexpr size_t kExecutionBurstChannelSmallLength = 8;
+
+///////////////////////// UTILITY FUNCTIONS /////////////////////////
+
+static bool badTiming(Timing timing) {
+    return timing.timeOnDevice == UINT64_MAX && timing.timeInDriver == UINT64_MAX;
+}
+
+static void createBurst(const sp<IPreparedModel>& preparedModel, const sp<IBurstCallback>& callback,
+                        std::unique_ptr<RequestChannelSender>* sender,
+                        std::unique_ptr<ResultChannelReceiver>* receiver,
+                        sp<IBurstContext>* context,
+                        size_t resultChannelLength = kExecutionBurstChannelLength) {
+    ASSERT_NE(nullptr, preparedModel.get());
+    ASSERT_NE(nullptr, sender);
+    ASSERT_NE(nullptr, receiver);
+    ASSERT_NE(nullptr, context);
+
+    // create FMQ objects
+    auto [fmqRequestChannel, fmqRequestDescriptor] =
+            RequestChannelSender::create(kExecutionBurstChannelLength, /*blocking=*/true);
+    auto [fmqResultChannel, fmqResultDescriptor] =
+            ResultChannelReceiver::create(resultChannelLength, /*blocking=*/true);
+    ASSERT_NE(nullptr, fmqRequestChannel.get());
+    ASSERT_NE(nullptr, fmqResultChannel.get());
+    ASSERT_NE(nullptr, fmqRequestDescriptor);
+    ASSERT_NE(nullptr, fmqResultDescriptor);
+
+    // configure burst
+    ErrorStatus errorStatus;
+    sp<IBurstContext> burstContext;
+    const Return<void> ret = preparedModel->configureExecutionBurst(
+            callback, *fmqRequestDescriptor, *fmqResultDescriptor,
+            [&errorStatus, &burstContext](ErrorStatus status, const sp<IBurstContext>& context) {
+                errorStatus = status;
+                burstContext = context;
+            });
+    ASSERT_TRUE(ret.isOk());
+    ASSERT_EQ(ErrorStatus::NONE, errorStatus);
+    ASSERT_NE(nullptr, burstContext.get());
+
+    // return values
+    *sender = std::move(fmqRequestChannel);
+    *receiver = std::move(fmqResultChannel);
+    *context = burstContext;
+}
+
+static void createBurstWithResultChannelLength(
+        const sp<IPreparedModel>& preparedModel, size_t resultChannelLength,
+        std::shared_ptr<ExecutionBurstController>* controller) {
+    ASSERT_NE(nullptr, preparedModel.get());
+    ASSERT_NE(nullptr, controller);
+
+    // create FMQ objects
+    std::unique_ptr<RequestChannelSender> sender;
+    std::unique_ptr<ResultChannelReceiver> receiver;
+    sp<ExecutionBurstCallback> callback = new ExecutionBurstCallback();
+    sp<IBurstContext> context;
+    ASSERT_NO_FATAL_FAILURE(createBurst(preparedModel, callback, &sender, &receiver, &context,
+                                        resultChannelLength));
+    ASSERT_NE(nullptr, sender.get());
+    ASSERT_NE(nullptr, receiver.get());
+    ASSERT_NE(nullptr, context.get());
+
+    // return values
+    *controller = std::make_shared<ExecutionBurstController>(std::move(sender), std::move(receiver),
+                                                             context, callback);
+}
+
+// Primary validation function. This function will take a valid serialized
+// request, apply a mutation to it to invalidate the serialized request, then
+// pass it to interface calls that use the serialized request. Note that the
+// serialized request here is passed by value, and any mutation to the
+// serialized request does not leave this function.
+static void validate(RequestChannelSender* sender, ResultChannelReceiver* receiver,
+                     const std::string& message, std::vector<FmqRequestDatum> serialized,
+                     const std::function<void(std::vector<FmqRequestDatum>*)>& mutation) {
+    mutation(&serialized);
+
+    // skip if packet is too large to send
+    if (serialized.size() > kExecutionBurstChannelLength) {
+        return;
+    }
+
+    SCOPED_TRACE(message);
+
+    // send invalid packet
+    ASSERT_TRUE(sender->sendPacket(serialized));
+
+    // receive error
+    auto results = receiver->getBlocking();
+    ASSERT_TRUE(results.has_value());
+    const auto [status, outputShapes, timing] = std::move(*results);
+    EXPECT_NE(ErrorStatus::NONE, status);
+    EXPECT_EQ(0u, outputShapes.size());
+    EXPECT_TRUE(badTiming(timing));
+}
+
+// For validation, valid packet entries are mutated to invalid packet entries,
+// or invalid packet entries are inserted into valid packets. This function
+// creates pre-set invalid packet entries for convenience.
+static std::vector<FmqRequestDatum> createBadRequestPacketEntries() {
+    const FmqRequestDatum::PacketInformation packetInformation = {
+            /*.packetSize=*/10, /*.numberOfInputOperands=*/10, /*.numberOfOutputOperands=*/10,
+            /*.numberOfPools=*/10};
+    const FmqRequestDatum::OperandInformation operandInformation = {
+            /*.hasNoValue=*/false, /*.location=*/{}, /*.numberOfDimensions=*/10};
+    const int32_t invalidPoolIdentifier = std::numeric_limits<int32_t>::max();
+    std::vector<FmqRequestDatum> bad(7);
+    bad[0].packetInformation(packetInformation);
+    bad[1].inputOperandInformation(operandInformation);
+    bad[2].inputOperandDimensionValue(0);
+    bad[3].outputOperandInformation(operandInformation);
+    bad[4].outputOperandDimensionValue(0);
+    bad[5].poolIdentifier(invalidPoolIdentifier);
+    bad[6].measureTiming(MeasureTiming::YES);
+    return bad;
+}
+
+// For validation, valid packet entries are mutated to invalid packet entries,
+// or invalid packet entries are inserted into valid packets. This function
+// retrieves pre-set invalid packet entries for convenience. This function
+// caches these data so they can be reused on subsequent validation checks.
+static const std::vector<FmqRequestDatum>& getBadRequestPacketEntries() {
+    static const std::vector<FmqRequestDatum> bad = createBadRequestPacketEntries();
+    return bad;
+}
+
+///////////////////////// REMOVE DATUM ////////////////////////////////////
+
+static void removeDatumTest(RequestChannelSender* sender, ResultChannelReceiver* receiver,
+                            const std::vector<FmqRequestDatum>& serialized) {
+    for (size_t index = 0; index < serialized.size(); ++index) {
+        const std::string message = "removeDatum: removed datum at index " + std::to_string(index);
+        validate(sender, receiver, message, serialized,
+                 [index](std::vector<FmqRequestDatum>* serialized) {
+                     serialized->erase(serialized->begin() + index);
+                 });
+    }
+}
+
+///////////////////////// ADD DATUM ////////////////////////////////////
+
+static void addDatumTest(RequestChannelSender* sender, ResultChannelReceiver* receiver,
+                         const std::vector<FmqRequestDatum>& serialized) {
+    const std::vector<FmqRequestDatum>& extra = getBadRequestPacketEntries();
+    for (size_t index = 0; index <= serialized.size(); ++index) {
+        for (size_t type = 0; type < extra.size(); ++type) {
+            const std::string message = "addDatum: added datum type " + std::to_string(type) +
+                                        " at index " + std::to_string(index);
+            validate(sender, receiver, message, serialized,
+                     [index, type, &extra](std::vector<FmqRequestDatum>* serialized) {
+                         serialized->insert(serialized->begin() + index, extra[type]);
+                     });
+        }
+    }
+}
+
+///////////////////////// MUTATE DATUM ////////////////////////////////////
+
+static bool interestingCase(const FmqRequestDatum& lhs, const FmqRequestDatum& rhs) {
+    using Discriminator = FmqRequestDatum::hidl_discriminator;
+
+    const bool differentValues = (lhs != rhs);
+    const bool sameDiscriminator = (lhs.getDiscriminator() == rhs.getDiscriminator());
+    const auto discriminator = rhs.getDiscriminator();
+    const bool isDimensionValue = (discriminator == Discriminator::inputOperandDimensionValue ||
+                                   discriminator == Discriminator::outputOperandDimensionValue);
+
+    return differentValues && !(sameDiscriminator && isDimensionValue);
+}
+
+static void mutateDatumTest(RequestChannelSender* sender, ResultChannelReceiver* receiver,
+                            const std::vector<FmqRequestDatum>& serialized) {
+    const std::vector<FmqRequestDatum>& change = getBadRequestPacketEntries();
+    for (size_t index = 0; index < serialized.size(); ++index) {
+        for (size_t type = 0; type < change.size(); ++type) {
+            if (interestingCase(serialized[index], change[type])) {
+                const std::string message = "mutateDatum: changed datum at index " +
+                                            std::to_string(index) + " to datum type " +
+                                            std::to_string(type);
+                validate(sender, receiver, message, serialized,
+                         [index, type, &change](std::vector<FmqRequestDatum>* serialized) {
+                             (*serialized)[index] = change[type];
+                         });
+            }
+        }
+    }
+}
+
+///////////////////////// BURST VALIATION TESTS ////////////////////////////////////
+
+static void validateBurstSerialization(const sp<IPreparedModel>& preparedModel,
+                                       const Request& request) {
+    // create burst
+    std::unique_ptr<RequestChannelSender> sender;
+    std::unique_ptr<ResultChannelReceiver> receiver;
+    sp<ExecutionBurstCallback> callback = new ExecutionBurstCallback();
+    sp<IBurstContext> context;
+    ASSERT_NO_FATAL_FAILURE(createBurst(preparedModel, callback, &sender, &receiver, &context));
+    ASSERT_NE(nullptr, sender.get());
+    ASSERT_NE(nullptr, receiver.get());
+    ASSERT_NE(nullptr, context.get());
+
+    // load memory into callback slots
+    std::vector<intptr_t> keys;
+    keys.reserve(request.pools.size());
+    std::transform(request.pools.begin(), request.pools.end(), std::back_inserter(keys),
+                   [](const auto& pool) { return reinterpret_cast<intptr_t>(&pool); });
+    const std::vector<int32_t> slots = callback->getSlots(request.pools, keys);
+
+    // ensure slot std::numeric_limits<int32_t>::max() doesn't exist (for
+    // subsequent slot validation testing)
+    ASSERT_TRUE(std::all_of(slots.begin(), slots.end(), [](int32_t slot) {
+        return slot != std::numeric_limits<int32_t>::max();
+    }));
+
+    // serialize the request
+    const auto serialized = android::nn::serialize(request, MeasureTiming::YES, slots);
+
+    // validations
+    removeDatumTest(sender.get(), receiver.get(), serialized);
+    addDatumTest(sender.get(), receiver.get(), serialized);
+    mutateDatumTest(sender.get(), receiver.get(), serialized);
+}
+
+// This test validates that when the Result message size exceeds length of the
+// result FMQ, the service instance gracefully fails and returns an error.
+static void validateBurstFmqLength(const sp<IPreparedModel>& preparedModel,
+                                   const Request& request) {
+    // create regular burst
+    std::shared_ptr<ExecutionBurstController> controllerRegular;
+    ASSERT_NO_FATAL_FAILURE(createBurstWithResultChannelLength(
+            preparedModel, kExecutionBurstChannelLength, &controllerRegular));
+    ASSERT_NE(nullptr, controllerRegular.get());
+
+    // create burst with small output channel
+    std::shared_ptr<ExecutionBurstController> controllerSmall;
+    ASSERT_NO_FATAL_FAILURE(createBurstWithResultChannelLength(
+            preparedModel, kExecutionBurstChannelSmallLength, &controllerSmall));
+    ASSERT_NE(nullptr, controllerSmall.get());
+
+    // load memory into callback slots
+    std::vector<intptr_t> keys(request.pools.size());
+    for (size_t i = 0; i < keys.size(); ++i) {
+        keys[i] = reinterpret_cast<intptr_t>(&request.pools[i]);
+    }
+
+    // collect serialized result by running regular burst
+    const auto [statusRegular, outputShapesRegular, timingRegular] =
+            controllerRegular->compute(request, MeasureTiming::NO, keys);
+
+    // skip test if regular burst output isn't useful for testing a failure
+    // caused by having too small of a length for the result FMQ
+    const std::vector<FmqResultDatum> serialized =
+            android::nn::serialize(statusRegular, outputShapesRegular, timingRegular);
+    if (statusRegular != ErrorStatus::NONE ||
+        serialized.size() <= kExecutionBurstChannelSmallLength) {
+        return;
+    }
+
+    // by this point, execution should fail because the result channel isn't
+    // large enough to return the serialized result
+    const auto [statusSmall, outputShapesSmall, timingSmall] =
+            controllerSmall->compute(request, MeasureTiming::NO, keys);
+    EXPECT_NE(ErrorStatus::NONE, statusSmall);
+    EXPECT_EQ(0u, outputShapesSmall.size());
+    EXPECT_TRUE(badTiming(timingSmall));
+}
+
+static bool isSanitized(const FmqResultDatum& datum) {
+    using Discriminator = FmqResultDatum::hidl_discriminator;
+
+    // check to ensure the padding values in the returned
+    // FmqResultDatum::OperandInformation are initialized to 0
+    if (datum.getDiscriminator() == Discriminator::operandInformation) {
+        static_assert(
+                offsetof(FmqResultDatum::OperandInformation, isSufficient) == 0,
+                "unexpected value for offset of FmqResultDatum::OperandInformation::isSufficient");
+        static_assert(
+                sizeof(FmqResultDatum::OperandInformation::isSufficient) == 1,
+                "unexpected value for size of FmqResultDatum::OperandInformation::isSufficient");
+        static_assert(offsetof(FmqResultDatum::OperandInformation, numberOfDimensions) == 4,
+                      "unexpected value for offset of "
+                      "FmqResultDatum::OperandInformation::numberOfDimensions");
+        static_assert(sizeof(FmqResultDatum::OperandInformation::numberOfDimensions) == 4,
+                      "unexpected value for size of "
+                      "FmqResultDatum::OperandInformation::numberOfDimensions");
+        static_assert(sizeof(FmqResultDatum::OperandInformation) == 8,
+                      "unexpected value for size of "
+                      "FmqResultDatum::OperandInformation");
+
+        constexpr size_t paddingOffset =
+                offsetof(FmqResultDatum::OperandInformation, isSufficient) +
+                sizeof(FmqResultDatum::OperandInformation::isSufficient);
+        constexpr size_t paddingSize =
+                offsetof(FmqResultDatum::OperandInformation, numberOfDimensions) - paddingOffset;
+
+        FmqResultDatum::OperandInformation initialized{};
+        std::memset(&initialized, 0, sizeof(initialized));
+
+        const char* initializedPaddingStart =
+                reinterpret_cast<const char*>(&initialized) + paddingOffset;
+        const char* datumPaddingStart =
+                reinterpret_cast<const char*>(&datum.operandInformation()) + paddingOffset;
+
+        return std::memcmp(datumPaddingStart, initializedPaddingStart, paddingSize) == 0;
+    }
+
+    // there are no other padding initialization checks required, so return true
+    // for any sum-type that isn't FmqResultDatum::OperandInformation
+    return true;
+}
+
+static void validateBurstSanitized(const sp<IPreparedModel>& preparedModel,
+                                   const Request& request) {
+    // create burst
+    std::unique_ptr<RequestChannelSender> sender;
+    std::unique_ptr<ResultChannelReceiver> receiver;
+    sp<ExecutionBurstCallback> callback = new ExecutionBurstCallback();
+    sp<IBurstContext> context;
+    ASSERT_NO_FATAL_FAILURE(createBurst(preparedModel, callback, &sender, &receiver, &context));
+    ASSERT_NE(nullptr, sender.get());
+    ASSERT_NE(nullptr, receiver.get());
+    ASSERT_NE(nullptr, context.get());
+
+    // load memory into callback slots
+    std::vector<intptr_t> keys;
+    keys.reserve(request.pools.size());
+    std::transform(request.pools.begin(), request.pools.end(), std::back_inserter(keys),
+                   [](const auto& pool) { return reinterpret_cast<intptr_t>(&pool); });
+    const std::vector<int32_t> slots = callback->getSlots(request.pools, keys);
+
+    // send valid request
+    ASSERT_TRUE(sender->send(request, MeasureTiming::YES, slots));
+
+    // receive valid result
+    auto serialized = receiver->getPacketBlocking();
+    ASSERT_TRUE(serialized.has_value());
+
+    // sanitize result
+    ASSERT_TRUE(std::all_of(serialized->begin(), serialized->end(), isSanitized))
+            << "The result serialized data is not properly sanitized";
+}
+
+///////////////////////////// ENTRY POINT //////////////////////////////////
+
+void validateBurst(const sp<IPreparedModel>& preparedModel, const Request& request) {
+    ASSERT_NO_FATAL_FAILURE(validateBurstSerialization(preparedModel, request));
+    ASSERT_NO_FATAL_FAILURE(validateBurstFmqLength(preparedModel, request));
+    ASSERT_NO_FATAL_FAILURE(validateBurstSanitized(preparedModel, request));
+}
+
+}  // namespace android::hardware::neuralnetworks::V1_3::vts::functional
diff --git a/neuralnetworks/1.3/vts/functional/ValidateModel.cpp b/neuralnetworks/1.3/vts/functional/ValidateModel.cpp
new file mode 100644
index 0000000..44b32a9
--- /dev/null
+++ b/neuralnetworks/1.3/vts/functional/ValidateModel.cpp
@@ -0,0 +1,718 @@
+/*
+ * 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 "1.0/Utils.h"
+#include "1.2/Callbacks.h"
+#include "GeneratedTestHarness.h"
+#include "VtsHalNeuralnetworks.h"
+
+namespace android::hardware::neuralnetworks::V1_3::vts::functional {
+
+using V1_0::ErrorStatus;
+using V1_0::OperandLifeTime;
+using V1_1::ExecutionPreference;
+using V1_2::IPreparedModel;
+using V1_2::OperationType;
+using V1_2::OperationTypeRange;
+using V1_2::SymmPerChannelQuantParams;
+using V1_2::implementation::PreparedModelCallback;
+using HidlToken =
+        hidl_array<uint8_t, static_cast<uint32_t>(V1_2::Constant::BYTE_SIZE_OF_CACHE_TOKEN)>;
+
+///////////////////////// UTILITY FUNCTIONS /////////////////////////
+
+static void validateGetSupportedOperations(const sp<IDevice>& device, const std::string& message,
+                                           const Model& model) {
+    SCOPED_TRACE(message + " [getSupportedOperations_1_3]");
+
+    Return<void> ret = device->getSupportedOperations_1_3(
+            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 Model& model, ExecutionPreference preference) {
+    SCOPED_TRACE(message + " [prepareModel_1_3]");
+
+    sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
+    Return<ErrorStatus> prepareLaunchStatus =
+            device->prepareModel_1_3(model, preference, hidl_vec<hidl_handle>(),
+                                     hidl_vec<hidl_handle>(), HidlToken(), 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 = getPreparedModel_1_2(preparedModelCallback);
+    ASSERT_EQ(nullptr, preparedModel.get());
+}
+
+static bool validExecutionPreference(ExecutionPreference preference) {
+    return preference == ExecutionPreference::LOW_POWER ||
+           preference == ExecutionPreference::FAST_SINGLE_ANSWER ||
+           preference == ExecutionPreference::SUSTAINED_SPEED;
+}
+
+// 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, Model model,
+                     const std::function<void(Model*)>& mutation,
+                     ExecutionPreference preference = ExecutionPreference::FAST_SINGLE_ANSWER) {
+    mutation(&model);
+    if (validExecutionPreference(preference)) {
+        validateGetSupportedOperations(device, message, model);
+    }
+    validatePrepareModel(device, message, model, preference);
+}
+
+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 uint32_t invalidOperandTypes[] = {
+        static_cast<uint32_t>(OperandTypeRange::FUNDAMENTAL_MIN) - 1,
+        static_cast<uint32_t>(OperandTypeRange::FUNDAMENTAL_MAX) + 1,
+        static_cast<uint32_t>(OperandTypeRange::OEM_MIN) - 1,
+        static_cast<uint32_t>(OperandTypeRange::OEM_MAX) + 1,
+};
+
+static void mutateOperandTypeTest(const sp<IDevice>& device, const Model& model) {
+    for (size_t operand = 0; operand < model.operands.size(); ++operand) {
+        for (uint32_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::FLOAT16:
+        case OperandType::FLOAT32:
+        case OperandType::INT32:
+        case OperandType::UINT32:
+        case OperandType::BOOL:
+            return 1;
+        case OperandType::TENSOR_BOOL8:
+        case OperandType::TENSOR_FLOAT16:
+        case OperandType::TENSOR_FLOAT32:
+        case OperandType::TENSOR_INT32:
+        case OperandType::TENSOR_QUANT8_ASYMM:
+        case OperandType::TENSOR_QUANT8_SYMM:
+        case OperandType::TENSOR_QUANT16_ASYMM:
+        case OperandType::TENSOR_QUANT16_SYMM:
+        case OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL:
+            return 0;
+        default:
+            return 0;
+    }
+}
+
+static void mutateOperandRankTest(const sp<IDevice>& device, const Model& model) {
+    for (size_t operand = 0; operand < model.operands.size(); ++operand) {
+        const uint32_t invalidRank = getInvalidRank(model.operands[operand].type);
+        if (invalidRank == 0) {
+            continue;
+        }
+        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::FLOAT16:
+        case OperandType::FLOAT32:
+        case OperandType::INT32:
+        case OperandType::UINT32:
+        case OperandType::BOOL:
+        case OperandType::TENSOR_BOOL8:
+        case OperandType::TENSOR_FLOAT16:
+        case OperandType::TENSOR_FLOAT32:
+        case OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL:
+            return 1.0f;
+        case OperandType::TENSOR_INT32:
+            return -1.0f;
+        case OperandType::TENSOR_QUANT8_SYMM:
+        case OperandType::TENSOR_QUANT8_ASYMM:
+        case OperandType::TENSOR_QUANT16_ASYMM:
+        case OperandType::TENSOR_QUANT16_SYMM:
+            return 0.0f;
+        default:
+            return 0.0f;
+    }
+}
+
+static void mutateOperandScaleTest(const sp<IDevice>& device, const 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::FLOAT16:
+        case OperandType::FLOAT32:
+        case OperandType::INT32:
+        case OperandType::UINT32:
+        case OperandType::BOOL:
+        case OperandType::TENSOR_BOOL8:
+        case OperandType::TENSOR_FLOAT16:
+        case OperandType::TENSOR_FLOAT32:
+        case OperandType::TENSOR_INT32:
+        case OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL:
+            return {1};
+        case OperandType::TENSOR_QUANT8_ASYMM:
+            return {-1, 256};
+        case OperandType::TENSOR_QUANT8_SYMM:
+            return {-129, -1, 1, 128};
+        case OperandType::TENSOR_QUANT16_ASYMM:
+            return {-1, 65536};
+        case OperandType::TENSOR_QUANT16_SYMM:
+            return {-32769, -1, 1, 32768};
+        default:
+            return {};
+    }
+}
+
+static void mutateOperandZeroPointTest(const sp<IDevice>& device, const 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::FLOAT16:
+        case OperandType::FLOAT32:
+        case OperandType::INT32:
+        case OperandType::UINT32:
+        case OperandType::BOOL:
+            newOperand.dimensions = hidl_vec<uint32_t>();
+            newOperand.scale = 0.0f;
+            newOperand.zeroPoint = 0;
+            break;
+        case OperandType::TENSOR_BOOL8:
+        case OperandType::TENSOR_FLOAT16:
+        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:
+        case OperandType::TENSOR_QUANT8_SYMM:
+        case OperandType::TENSOR_QUANT16_ASYMM:
+        case OperandType::TENSOR_QUANT16_SYMM:
+            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::TENSOR_QUANT8_SYMM_PER_CHANNEL: {
+            newOperand.dimensions =
+                    operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1});
+            newOperand.scale = 0.0f;
+            newOperand.zeroPoint = 0;
+
+            SymmPerChannelQuantParams channelQuant;
+            channelQuant.channelDim = 0;
+            channelQuant.scales = hidl_vec<float>(
+                    operand->dimensions.size() > 0 ? static_cast<size_t>(operand->dimensions[0])
+                                                   : 0);
+            for (size_t i = 0; i < channelQuant.scales.size(); ++i) {
+                channelQuant.scales[i] = 1.0f;
+            }
+            newOperand.extraParams.channelQuant(std::move(channelQuant));
+        } break;
+        case OperandType::OEM:
+        case OperandType::TENSOR_OEM_BYTE:
+        default:
+            break;
+    }
+    *operand = newOperand;
+}
+
+static bool mutateOperationOperandTypeSkip(size_t operand, OperandType type, const Model& model) {
+    // Do not test OEM types
+    if (type == model.operands[operand].type || type == OperandType::OEM ||
+        type == OperandType::TENSOR_OEM_BYTE) {
+        return true;
+    }
+    for (const Operation& operation : model.operations) {
+        // Skip mutateOperationOperandTypeTest for the following operations.
+        // - LSH_PROJECTION's second argument is allowed to have any type.
+        // - ARGMIN and ARGMAX's first argument can be any of
+        // TENSOR_(FLOAT16|FLOAT32|INT32|QUANT8_ASYMM).
+        // - CAST's argument can be any of TENSOR_(FLOAT16|FLOAT32|INT32|QUANT8_ASYMM).
+        // - RANDOM_MULTINOMIAL's argument can be either TENSOR_FLOAT16 or TENSOR_FLOAT32.
+        // - DEQUANTIZE input can be any of
+        // TENSOR_(QUANT8_ASYMM|QUANT8_SYMM|QUANT8_SYMM_PER_CHANNEL), output can
+        // be of either TENSOR_FLOAT16 or TENSOR_FLOAT32.
+        // - QUANTIZE input can be either TENSOR_FLOAT16 or TENSOR_FLOAT32
+        // - CONV_2D filter type (arg 1) can be QUANT8_ASYMM or QUANT8_SYMM_PER_CHANNEL
+        // - DEPTHWISE_CONV_2D filter type (arg 1) can be QUANT8_ASYMM or QUANT8_SYMM_PER_CHANNEL
+        // - GROUPED_CONV_2D filter type (arg 1) can be QUANT8_ASYMM or QUANT8_SYMM_PER_CHANNEL
+        // - TRANSPOSE_CONV_2D filter type (arg 1) can be QUANT8_ASYMM or QUANT8_SYMM_PER_CHANNEL
+        switch (operation.type) {
+            case OperationType::LSH_PROJECTION: {
+                if (operand == operation.inputs[1]) {
+                    return true;
+                }
+            } break;
+            case OperationType::CAST:
+            case OperationType::ARGMAX:
+            case OperationType::ARGMIN: {
+                if (type == OperandType::TENSOR_FLOAT16 || type == OperandType::TENSOR_FLOAT32 ||
+                    type == OperandType::TENSOR_INT32 || type == OperandType::TENSOR_QUANT8_ASYMM) {
+                    return true;
+                }
+            } break;
+            case OperationType::QUANTIZE:
+            case OperationType::RANDOM_MULTINOMIAL: {
+                if (operand == operation.inputs[0] &&
+                    (type == OperandType::TENSOR_FLOAT16 || type == OperandType::TENSOR_FLOAT32)) {
+                    return true;
+                }
+            } break;
+            case OperationType::DEQUANTIZE: {
+                if (operand == operation.inputs[0] &&
+                    (type == OperandType::TENSOR_QUANT8_ASYMM ||
+                     type == OperandType::TENSOR_QUANT8_SYMM ||
+                     type == OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL)) {
+                    return true;
+                }
+                if (operand == operation.outputs[0] &&
+                    (type == OperandType::TENSOR_FLOAT16 || type == OperandType::TENSOR_FLOAT32)) {
+                    return true;
+                }
+            } break;
+            case OperationType::TRANSPOSE_CONV_2D:
+            case OperationType::GROUPED_CONV_2D:
+            case OperationType::DEPTHWISE_CONV_2D:
+            case OperationType::CONV_2D: {
+                if (operand == operation.inputs[1] &&
+                    (type == OperandType::TENSOR_QUANT8_ASYMM ||
+                     type == OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL)) {
+                    return true;
+                }
+            } break;
+            default:
+                break;
+        }
+    }
+    return false;
+}
+
+static void mutateOperationOperandTypeTest(const sp<IDevice>& device, const Model& model) {
+    for (size_t operand = 0; operand < model.operands.size(); ++operand) {
+        for (OperandType invalidOperandType : hidl_enum_range<OperandType>{}) {
+            if (mutateOperationOperandTypeSkip(operand, invalidOperandType, model)) {
+                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 uint32_t invalidOperationTypes[] = {
+        static_cast<uint32_t>(OperationTypeRange::FUNDAMENTAL_MAX) + 1,
+        static_cast<uint32_t>(OperationTypeRange::OEM_MIN) - 1,
+        static_cast<uint32_t>(OperationTypeRange::OEM_MAX) + 1,
+};
+
+static void mutateOperationTypeTest(const sp<IDevice>& device, const Model& model) {
+    for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+        for (uint32_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 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 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 bool removeOperandSkip(size_t operand, const Model& model) {
+    for (const Operation& operation : model.operations) {
+        // Skip removeOperandTest for the following operations.
+        // - SPLIT's outputs are not checked during prepareModel.
+        if (operation.type == OperationType::SPLIT) {
+            for (const size_t outOprand : operation.outputs) {
+                if (operand == outOprand) {
+                    return true;
+                }
+            }
+        }
+        // BIDIRECTIONAL_SEQUENCE_LSTM and BIDIRECTIONAL_SEQUENCE_RNN can have either one or two
+        // outputs depending on their mergeOutputs parameter.
+        if (operation.type == OperationType::BIDIRECTIONAL_SEQUENCE_LSTM ||
+            operation.type == OperationType::BIDIRECTIONAL_SEQUENCE_RNN) {
+            for (const size_t outOprand : operation.outputs) {
+                if (operand == outOprand) {
+                    return true;
+                }
+            }
+        }
+    }
+    return false;
+}
+
+static void removeOperandTest(const sp<IDevice>& device, const Model& model) {
+    for (size_t operand = 0; operand < model.operands.size(); ++operand) {
+        if (removeOperandSkip(operand, model)) {
+            continue;
+        }
+        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 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 bool removeOperationInputSkip(const Operation& op, size_t input) {
+    // Skip removeOperationInputTest for the following operations.
+    // - CONCATENATION has at least 2 inputs, with the last element being INT32.
+    // - CONV_2D, DEPTHWISE_CONV_2D, MAX_POOL_2D, AVERAGE_POOL_2D, L2_POOL_2D, RESIZE_BILINEAR,
+    //   SPACE_TO_DEPTH, SPACE_TO_DEPTH, SPACE_TO_BATCH_ND, BATCH_TO_SPACE_ND can have an optional
+    //   layout parameter.
+    // - L2_NORMALIZATION, LOCAL_RESPONSE_NORMALIZATION, SOFTMAX can have an optional axis
+    //   parameter.
+    switch (op.type) {
+        case OperationType::CONCATENATION: {
+            if (op.inputs.size() > 2 && input != op.inputs.size() - 1) {
+                return true;
+            }
+        } break;
+        case OperationType::DEPTHWISE_CONV_2D: {
+            if ((op.inputs.size() == 12 && input == 11) || (op.inputs.size() == 9 && input == 8)) {
+                return true;
+            }
+        } break;
+        case OperationType::CONV_2D:
+        case OperationType::AVERAGE_POOL_2D:
+        case OperationType::MAX_POOL_2D:
+        case OperationType::L2_POOL_2D: {
+            if ((op.inputs.size() == 11 && input == 10) || (op.inputs.size() == 8 && input == 7)) {
+                return true;
+            }
+        } break;
+        case OperationType::RESIZE_BILINEAR: {
+            if (op.inputs.size() == 4 && input == 3) {
+                return true;
+            }
+        } break;
+        case OperationType::SPACE_TO_DEPTH:
+        case OperationType::DEPTH_TO_SPACE:
+        case OperationType::BATCH_TO_SPACE_ND: {
+            if (op.inputs.size() == 3 && input == 2) {
+                return true;
+            }
+        } break;
+        case OperationType::SPACE_TO_BATCH_ND: {
+            if (op.inputs.size() == 4 && input == 3) {
+                return true;
+            }
+        } break;
+        case OperationType::L2_NORMALIZATION: {
+            if (op.inputs.size() == 2 && input == 1) {
+                return true;
+            }
+        } break;
+        case OperationType::LOCAL_RESPONSE_NORMALIZATION: {
+            if (op.inputs.size() == 6 && input == 5) {
+                return true;
+            }
+        } break;
+        case OperationType::SOFTMAX: {
+            if (op.inputs.size() == 3 && input == 2) {
+                return true;
+            }
+        } break;
+        default:
+            break;
+    }
+    return false;
+}
+
+static void removeOperationInputTest(const sp<IDevice>& device, const 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 Operation& op = model.operations[operation];
+            if (removeOperationInputSkip(op, input)) {
+                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 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 bool addOperationInputSkip(const Operation& op) {
+    // Skip addOperationInputTest for the following operations.
+    // - L2_NORMALIZATION, LOCAL_RESPONSE_NORMALIZATION, SOFTMAX can have an optional INT32 axis
+    //   parameter.
+    if ((op.type == OperationType::L2_NORMALIZATION && op.inputs.size() == 1) ||
+        (op.type == OperationType::LOCAL_RESPONSE_NORMALIZATION && op.inputs.size() == 5) ||
+        (op.type == OperationType::SOFTMAX && op.inputs.size() == 2)) {
+        return true;
+    }
+    return false;
+}
+
+static void addOperationInputTest(const sp<IDevice>& device, const Model& model) {
+    for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+        if (addOperationInputSkip(model.operations[operation])) {
+            continue;
+        }
+        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 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);
+        });
+    }
+}
+
+///////////////////////// VALIDATE EXECUTION PREFERENCE /////////////////////////
+
+static const int32_t invalidExecutionPreferences[] = {
+        static_cast<int32_t>(ExecutionPreference::LOW_POWER) - 1,        // lower bound
+        static_cast<int32_t>(ExecutionPreference::SUSTAINED_SPEED) + 1,  // upper bound
+};
+
+static void mutateExecutionPreferenceTest(const sp<IDevice>& device, const Model& model) {
+    for (int32_t preference : invalidExecutionPreferences) {
+        const std::string message =
+                "mutateExecutionPreferenceTest: preference " + std::to_string(preference);
+        validate(
+                device, message, model, [](Model*) {},
+                static_cast<ExecutionPreference>(preference));
+    }
+}
+
+////////////////////////// ENTRY POINT //////////////////////////////
+
+void validateModel(const sp<IDevice>& device, const 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);
+    mutateExecutionPreferenceTest(device, model);
+}
+
+}  // namespace android::hardware::neuralnetworks::V1_3::vts::functional
diff --git a/neuralnetworks/1.3/vts/functional/ValidateRequest.cpp b/neuralnetworks/1.3/vts/functional/ValidateRequest.cpp
new file mode 100644
index 0000000..6122123
--- /dev/null
+++ b/neuralnetworks/1.3/vts/functional/ValidateRequest.cpp
@@ -0,0 +1,172 @@
+/*
+ * 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 "1.0/Utils.h"
+#include "1.2/Callbacks.h"
+#include "ExecutionBurstController.h"
+#include "GeneratedTestHarness.h"
+#include "TestHarness.h"
+#include "Utils.h"
+#include "VtsHalNeuralnetworks.h"
+
+namespace android::hardware::neuralnetworks::V1_3::vts::functional {
+
+using V1_0::ErrorStatus;
+using V1_0::Request;
+using V1_2::IPreparedModel;
+using V1_2::MeasureTiming;
+using V1_2::OutputShape;
+using V1_2::Timing;
+using V1_2::implementation::ExecutionCallback;
+
+///////////////////////// UTILITY FUNCTIONS /////////////////////////
+
+static bool badTiming(Timing timing) {
+    return timing.timeOnDevice == UINT64_MAX && timing.timeInDriver == UINT64_MAX;
+}
+
+// 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);
+
+    // We'd like to test both with timing requested and without timing
+    // requested. Rather than running each test both ways, we'll decide whether
+    // to request timing by hashing the message. We do not use std::hash because
+    // it is not guaranteed stable across executions.
+    char hash = 0;
+    for (auto c : message) {
+        hash ^= c;
+    };
+    MeasureTiming measure = (hash & 1) ? MeasureTiming::YES : MeasureTiming::NO;
+
+    // asynchronous
+    {
+        SCOPED_TRACE(message + " [execute_1_2]");
+
+        sp<ExecutionCallback> executionCallback = new ExecutionCallback();
+        Return<ErrorStatus> executeLaunchStatus =
+                preparedModel->execute_1_2(request, measure, executionCallback);
+        ASSERT_TRUE(executeLaunchStatus.isOk());
+        ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(executeLaunchStatus));
+
+        executionCallback->wait();
+        ErrorStatus executionReturnStatus = executionCallback->getStatus();
+        const auto& outputShapes = executionCallback->getOutputShapes();
+        Timing timing = executionCallback->getTiming();
+        ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, executionReturnStatus);
+        ASSERT_EQ(outputShapes.size(), 0);
+        ASSERT_TRUE(badTiming(timing));
+    }
+
+    // synchronous
+    {
+        SCOPED_TRACE(message + " [executeSynchronously]");
+
+        Return<void> executeStatus = preparedModel->executeSynchronously(
+                request, measure,
+                [](ErrorStatus error, const hidl_vec<OutputShape>& outputShapes,
+                   const Timing& timing) {
+                    ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, error);
+                    EXPECT_EQ(outputShapes.size(), 0);
+                    EXPECT_TRUE(badTiming(timing));
+                });
+        ASSERT_TRUE(executeStatus.isOk());
+    }
+
+    // burst
+    {
+        SCOPED_TRACE(message + " [burst]");
+
+        // create burst
+        std::shared_ptr<::android::nn::ExecutionBurstController> burst =
+                android::nn::ExecutionBurstController::create(preparedModel, /*blocking=*/true);
+        ASSERT_NE(nullptr, burst.get());
+
+        // create memory keys
+        std::vector<intptr_t> keys(request.pools.size());
+        for (size_t i = 0; i < keys.size(); ++i) {
+            keys[i] = reinterpret_cast<intptr_t>(&request.pools[i]);
+        }
+
+        // execute and verify
+        ErrorStatus error;
+        std::vector<OutputShape> outputShapes;
+        Timing timing;
+        std::tie(error, outputShapes, timing) = burst->compute(request, measure, keys);
+        EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, error);
+        EXPECT_EQ(outputShapes.size(), 0);
+        EXPECT_TRUE(badTiming(timing));
+
+        // additional burst testing
+        if (request.pools.size() > 0) {
+            // valid free
+            burst->freeMemory(keys.front());
+
+            // negative test: invalid free of unknown (blank) memory
+            burst->freeMemory(intptr_t{});
+
+            // negative test: double free of memory
+            burst->freeMemory(keys.front());
+        }
+    }
+}
+
+///////////////////////// 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 //////////////////////////////////
+
+void validateRequest(const sp<IPreparedModel>& preparedModel, const Request& request) {
+    removeInputTest(preparedModel, request);
+    removeOutputTest(preparedModel, request);
+}
+
+void validateRequestFailure(const sp<IPreparedModel>& preparedModel, const Request& request) {
+    SCOPED_TRACE("Expecting request to fail [executeSynchronously]");
+    Return<void> executeStatus = preparedModel->executeSynchronously(
+            request, MeasureTiming::NO,
+            [](ErrorStatus error, const hidl_vec<OutputShape>& outputShapes, const Timing& timing) {
+                ASSERT_NE(ErrorStatus::NONE, error);
+                EXPECT_EQ(outputShapes.size(), 0);
+                EXPECT_TRUE(badTiming(timing));
+            });
+    ASSERT_TRUE(executeStatus.isOk());
+}
+
+}  // namespace android::hardware::neuralnetworks::V1_3::vts::functional
diff --git a/neuralnetworks/1.3/vts/functional/VtsHalNeuralnetworks.cpp b/neuralnetworks/1.3/vts/functional/VtsHalNeuralnetworks.cpp
new file mode 100644
index 0000000..4f0e150
--- /dev/null
+++ b/neuralnetworks/1.3/vts/functional/VtsHalNeuralnetworks.cpp
@@ -0,0 +1,173 @@
+/*
+ * 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 <android-base/logging.h>
+#include <hidl/ServiceManagement.h>
+#include <string>
+#include <utility>
+#include "1.0/Callbacks.h"
+#include "1.0/Utils.h"
+#include "GeneratedTestHarness.h"
+#include "TestHarness.h"
+
+namespace android::hardware::neuralnetworks::V1_3::vts::functional {
+
+using HidlToken =
+        hidl_array<uint8_t, static_cast<uint32_t>(V1_2::Constant::BYTE_SIZE_OF_CACHE_TOKEN)>;
+using V1_0::ErrorStatus;
+using V1_0::Request;
+using V1_1::ExecutionPreference;
+using V1_2::IPreparedModel;
+using V1_2::implementation::PreparedModelCallback;
+
+// internal helper function
+void createPreparedModel(const sp<IDevice>& device, const Model& model,
+                         sp<IPreparedModel>* preparedModel) {
+    ASSERT_NE(nullptr, preparedModel);
+    *preparedModel = nullptr;
+
+    // see if service can handle model
+    bool fullySupportsModel = false;
+    const Return<void> supportedCall = device->getSupportedOperations_1_3(
+            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(supportedCall.isOk());
+
+    // launch prepare model
+    const sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
+    const Return<ErrorStatus> prepareLaunchStatus = device->prepareModel_1_3(
+            model, ExecutionPreference::FAST_SINGLE_ANSWER, hidl_vec<hidl_handle>(),
+            hidl_vec<hidl_handle>(), HidlToken(), preparedModelCallback);
+    ASSERT_TRUE(prepareLaunchStatus.isOk());
+    ASSERT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(prepareLaunchStatus));
+
+    // retrieve prepared model
+    preparedModelCallback->wait();
+    const ErrorStatus prepareReturnStatus = preparedModelCallback->getStatus();
+    *preparedModel = getPreparedModel_1_2(preparedModelCallback);
+
+    // The getSupportedOperations_1_3 call returns a list of operations that are
+    // guaranteed not to fail if prepareModel_1_3 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;
+        GTEST_SKIP();
+    }
+    ASSERT_EQ(ErrorStatus::NONE, prepareReturnStatus);
+    ASSERT_NE(nullptr, preparedModel->get());
+}
+
+void NeuralnetworksHidlTest::SetUp() {
+    testing::TestWithParam<NeuralnetworksHidlTestParam>::SetUp();
+    ASSERT_NE(kDevice, nullptr);
+}
+
+static NamedDevice makeNamedDevice(const std::string& name) {
+    return {name, IDevice::getService(name)};
+}
+
+static std::vector<NamedDevice> getNamedDevicesImpl() {
+    // Retrieves the name of all service instances that implement IDevice,
+    // including any Lazy HAL instances.
+    const std::vector<std::string> names = hardware::getAllHalInstanceNames(IDevice::descriptor);
+
+    // Get a handle to each device and pair it with its name.
+    std::vector<NamedDevice> namedDevices;
+    namedDevices.reserve(names.size());
+    std::transform(names.begin(), names.end(), std::back_inserter(namedDevices), makeNamedDevice);
+    return namedDevices;
+}
+
+const std::vector<NamedDevice>& getNamedDevices() {
+    const static std::vector<NamedDevice> devices = getNamedDevicesImpl();
+    return devices;
+}
+
+std::string printNeuralnetworksHidlTest(
+        const testing::TestParamInfo<NeuralnetworksHidlTestParam>& info) {
+    return gtestCompliantName(getName(info.param));
+}
+
+INSTANTIATE_DEVICE_TEST(NeuralnetworksHidlTest);
+
+// Forward declaration from ValidateModel.cpp
+void validateModel(const sp<IDevice>& device, const Model& model);
+// Forward declaration from ValidateRequest.cpp
+void validateRequest(const sp<IPreparedModel>& preparedModel, const V1_0::Request& request);
+// Forward declaration from ValidateRequest.cpp
+void validateRequestFailure(const sp<IPreparedModel>& preparedModel, const V1_0::Request& request);
+// Forward declaration from ValidateBurst.cpp
+void validateBurst(const sp<IPreparedModel>& preparedModel, const V1_0::Request& request);
+
+void validateEverything(const sp<IDevice>& device, const Model& model, const Request& request) {
+    validateModel(device, model);
+
+    // Create IPreparedModel.
+    sp<IPreparedModel> preparedModel;
+    createPreparedModel(device, model, &preparedModel);
+    if (preparedModel == nullptr) return;
+
+    validateRequest(preparedModel, request);
+    validateBurst(preparedModel, request);
+}
+
+void validateFailure(const sp<IDevice>& device, const Model& model, const Request& request) {
+    // TODO: Should this always succeed?
+    //       What if the invalid input is part of the model (i.e., a parameter).
+    validateModel(device, model);
+
+    // Create IPreparedModel.
+    sp<IPreparedModel> preparedModel;
+    createPreparedModel(device, model, &preparedModel);
+    if (preparedModel == nullptr) return;
+
+    validateRequestFailure(preparedModel, request);
+}
+
+TEST_P(ValidationTest, Test) {
+    const Model model = createModel(kTestModel);
+    const Request request = createRequest(kTestModel);
+    if (kTestModel.expectFailure) {
+        validateFailure(kDevice, model, request);
+    } else {
+        validateEverything(kDevice, model, request);
+    }
+}
+
+INSTANTIATE_GENERATED_TEST(ValidationTest, [](const test_helper::TestModel&) { return true; });
+
+sp<IPreparedModel> getPreparedModel_1_2(const sp<PreparedModelCallback>& callback) {
+    sp<V1_0::IPreparedModel> preparedModelV1_0 = callback->getPreparedModel();
+    return IPreparedModel::castFrom(preparedModelV1_0).withDefault(nullptr);
+}
+
+}  // namespace android::hardware::neuralnetworks::V1_3::vts::functional
diff --git a/neuralnetworks/1.3/vts/functional/VtsHalNeuralnetworks.h b/neuralnetworks/1.3/vts/functional/VtsHalNeuralnetworks.h
new file mode 100644
index 0000000..fc654ce
--- /dev/null
+++ b/neuralnetworks/1.3/vts/functional/VtsHalNeuralnetworks.h
@@ -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.
+ */
+
+#ifndef ANDROID_HARDWARE_NEURALNETWORKS_V1_3_VTS_HAL_NEURALNETWORKS_H
+#define ANDROID_HARDWARE_NEURALNETWORKS_V1_3_VTS_HAL_NEURALNETWORKS_H
+
+#include <android/hardware/neuralnetworks/1.2/IPreparedModel.h>
+#include <android/hardware/neuralnetworks/1.3/IDevice.h>
+#include <android/hardware/neuralnetworks/1.3/types.h>
+#include <gtest/gtest.h>
+#include "1.0/Utils.h"
+#include "1.2/Callbacks.h"
+
+namespace android::hardware::neuralnetworks::V1_3::vts::functional {
+
+using NamedDevice = Named<sp<IDevice>>;
+using NeuralnetworksHidlTestParam = NamedDevice;
+
+class NeuralnetworksHidlTest : public testing::TestWithParam<NeuralnetworksHidlTestParam> {
+  protected:
+    void SetUp() override;
+    const sp<IDevice> kDevice = getData(GetParam());
+};
+
+const std::vector<NamedDevice>& getNamedDevices();
+
+std::string printNeuralnetworksHidlTest(
+        const testing::TestParamInfo<NeuralnetworksHidlTestParam>& info);
+
+#define INSTANTIATE_DEVICE_TEST(TestSuite)                                                 \
+    INSTANTIATE_TEST_SUITE_P(PerInstance, TestSuite, testing::ValuesIn(getNamedDevices()), \
+                             printNeuralnetworksHidlTest)
+
+// Create an IPreparedModel object. If the model cannot be prepared,
+// "preparedModel" will be nullptr instead.
+void createPreparedModel(const sp<IDevice>& device, const Model& model,
+                         sp<V1_2::IPreparedModel>* preparedModel);
+
+// Utility function to get PreparedModel from callback and downcast to V1_2.
+sp<V1_2::IPreparedModel> getPreparedModel_1_2(
+        const sp<V1_2::implementation::PreparedModelCallback>& callback);
+
+}  // namespace android::hardware::neuralnetworks::V1_3::vts::functional
+
+#endif  // ANDROID_HARDWARE_NEURALNETWORKS_V1_3_VTS_HAL_NEURALNETWORKS_H