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Merge "Fix caching interface according to vendor feedback."

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This commit is contained in:
Xusong Wang
2019-03-22 02:31:24 +00:00
committed by Android (Google) Code Review
parent 9b3504c317 b61ba1ed0b
commit ad439a0424
9 changed files with 820 additions and 450 deletions
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6
current.txt
View File

@@ -508,11 +508,11 @@ b9422a9aca84df1ff9623dc12c0562abce97716e28d63a965f2bfb88f9ad9607 android.hardwar
4cb139f729c29d8d6f4ecdab149c4feb571dad8a06e56cd57fcb52e70208bab4 android.hardware.media.c2@1.0::types
4880af120fc1640225abdc2c60bda6d79617d73484d5124913c7278af3b11e2d android.hardware.neuralnetworks@1.2::IBurstCallback
19877e466ad8c6ed42b38050b77bd010cf7800ff365fdc8574f45bbfda03a758 android.hardware.neuralnetworks@1.2::IBurstContext
96249c852dabeefa3a9496ecdfc44681a071c665bfbf88527bf775c88bf1ab1b android.hardware.neuralnetworks@1.2::IDevice
363821d1b71147b896a08e2a570946db9b9d46f90d9f91b085bd8d3013a2b4d5 android.hardware.neuralnetworks@1.2::IDevice
92714960d1a53fc2ec557302b41c7cc93d2636d8364a44bd0f85be0c92927ff8 android.hardware.neuralnetworks@1.2::IExecutionCallback
83885d366f22ada42c00d8854f0b7e7ba4cf73ddf80bb0d8e168ce132cec57ea android.hardware.neuralnetworks@1.2::IPreparedModel
36e1064c869965dee533c537cefbe87e54db8bd8cd45be7e0e93e00e8a43863a android.hardware.neuralnetworks@1.2::IPreparedModel
e1c734d1545e1a4ae749ff1dd9704a8e594c59aea7c8363159dc258e93e0df3b android.hardware.neuralnetworks@1.2::IPreparedModelCallback
2ef1bab554ea484523b396e48033117dbbefc2f90269f9e7e3eb5a58ba50bfb9 android.hardware.neuralnetworks@1.2::types
39a6d7cf9bc7290bd90739e971ccad5f35f5cc0faea4a417b59f22c9ca9f1f2a android.hardware.neuralnetworks@1.2::types
cf7a4ba516a638f9b82a249c91fb603042c2d9ca43fd5aad9cf6c0401ed2a5d7 android.hardware.nfc@1.2::INfc
abf98c2ae08bf765db54edc8068e36d52eb558cff6706b6fd7c18c65a1f3fc18 android.hardware.nfc@1.2::types
4cb252dc6372a874aef666b92a6e9529915aa187521a700f0789065c3c702ead android.hardware.power.stats@1.0::IPowerStats

4
neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp
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@@ -52,6 +52,7 @@ using ::test_helper::for_each;
using ::test_helper::MixedTyped;
using ::test_helper::MixedTypedExample;
using ::test_helper::resize_accordingly;
using HidlToken = hidl_array<uint8_t, static_cast<uint32_t>(Constant::BYTE_SIZE_OF_CACHE_TOKEN)>;
template <typename T>
void copy_back_(std::map<int, std::vector<T>>* dst, const std::vector<RequestArgument>& ra,
@@ -540,7 +541,8 @@ void PrepareModel(const sp<V1_2::IDevice>& device, const V1_2::Model& model,
sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
ASSERT_NE(nullptr, preparedModelCallback.get());
Return<ErrorStatus> prepareLaunchStatus = device->prepareModel_1_2(
model, ExecutionPreference::FAST_SINGLE_ANSWER, preparedModelCallback);
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));

153
neuralnetworks/1.2/IDevice.hal
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@@ -113,44 +113,83 @@ interface IDevice extends @1.1::IDevice {
generates (ErrorStatus status, vec<bool> supportedOperations);
/**
* Gets whether the driver supports compilation caching.
* Gets the caching requirements of the driver implementation.
*
* isCachingSupported indicates whether the driver supports compilation caching.
* Even if so, the driver may still choose not to cache certain compiled models.
* There are two types of cache file descriptors provided to the driver: model cache
* and data cache.
*
* If the device reports the caching is not supported, the user may avoid calling
* IDevice::prepareModelFromCache and IPreparedModel::saveToCache.
* The data cache is for caching constant data, possibly including preprocessed
* and transformed tensor buffers. Any modification to the data cache should
* have no worse effect than generating bad output values at execution time.
*
* The model cache is for caching security-sensitive data such as compiled
* executable machine code in the device's native binary format. A modification
* to the model cache may affect the driver's execution behavior, and a malicious
* client could make use of this to execute beyond the granted permission. Thus,
* the driver must always check whether the model cache is corrupted before
* preparing the model from cache.
*
* getNumberOfCacheFilesNeeded returns how many of each type of cache files the driver
* implementation needs to cache a single prepared model. Returning 0 for both types
* indicates compilation caching is not supported by this driver. The driver may
* still choose not to cache certain compiled models even if it reports that caching
* is supported.
*
* If the device reports that caching is not supported, the user may avoid calling
* IDevice::prepareModelFromCache or providing cache file descriptors to
* IDevice::prepareModel_1_2.
*
* @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 supported A boolean indicating whether the driver supports compilation
* caching. Even on returning true, the driver may still choose
* not to cache certain compiled models.
* @return numModelCache An unsigned integer indicating how many files for model cache
* the driver needs to cache a single prepared model. It must
* be less than or equal to Constant::MAX_NUMBER_OF_CACHE_FILES.
* @return numDataCache An unsigned integer indicating how many files for data cache
* the driver needs to cache a single prepared model. It must
* be less than or equal to Constant::MAX_NUMBER_OF_CACHE_FILES.
*/
isCachingSupported() generates (ErrorStatus status, bool supported);
getNumberOfCacheFilesNeeded()
generates (ErrorStatus status, uint32_t numModelCache, uint32_t numDataCache);
/**
* Creates a prepared model for execution.
* Asynchronously creates a prepared model for execution and optionally saves it
* into cache files.
*
* prepareModel is used to make any necessary transformations or alternative
* 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 prepareModel function
* are correct. If there is an error, prepareModel must immediately invoke
* 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 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.
* 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
@@ -160,6 +199,14 @@ interface IDevice extends @1.1::IDevice {
* 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
@@ -173,6 +220,26 @@ interface IDevice extends @1.1::IDevice {
* @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
@@ -182,9 +249,12 @@ interface IDevice extends @1.1::IDevice {
* - 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 is invalid
* - INVALID_ARGUMENT if one of the input arguments related to preparing the
* model is invalid
*/
prepareModel_1_2(Model model, ExecutionPreference preference,
vec<handle> modelCache, vec<handle> dataCache,
uint8_t[Constant:BYTE_SIZE_OF_CACHE_TOKEN] token,
IPreparedModelCallback callback)
generates (ErrorStatus status);
@@ -192,22 +262,17 @@ interface IDevice extends @1.1::IDevice {
* Creates a prepared model from cache files for execution.
*
* prepareModelFromCache is used to retrieve a prepared model directly from
* cache files to avoid slow model compilation time. There are exactly two
* cache file descriptors provided to the driver: modelCache and dataCache.
* cache files to avoid slow model compilation time. 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 dataCache is for caching constant data, possibly including preprocessed
* and transformed tensor buffers. Any modification to the dataCache should
* have no worse effect than generating bad output values at execution time.
*
* The modelCache is for caching security-sensitive data such as compiled
* executable machine code in the device's native binary format. A modification
* to the modelCache may affect the driver's execution behavior, and a malicious
* client could make use of this to execute beyond the granted permission. Thus,
* the driver must always check whether the modelCache is corrupted before preparing
* the model from cache.
*
* The two file descriptors may be closed by the client once the asynchronous
* preparation has finished. The driver has to copy all the data it needs.
* 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
* prepareModelFromCache function must verify the inputs to the
@@ -241,13 +306,17 @@ interface IDevice extends @1.1::IDevice {
* used with different shapes of inputs on different (possibly concurrent)
* executions.
*
* @param modelCache A handle holding exactly one cache file descriptor for the
* security-sensitive cache.
* @param dataCache A handle holding exactly one cache file descriptor for the
* constants' cache.
* @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 match the numModelCache returned from getNumberOfCacheFilesNeeded.
* The cache handles will be provided in the same order as with prepareModel_1_2.
* @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 match the numDataCache returned from getNumberOfCacheFilesNeeded.
* The cache handles will be provided in the same order as with prepareModel_1_2.
* @param token A caching token of length Constant::BYTE_SIZE_OF_CACHE_TOKEN
* identifying the prepared model. It is the same token provided when saving
* the cache files with IPreparedModel::saveToCache. Tokens should be chosen
* the cache files with prepareModel_1_2. 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.
@@ -263,7 +332,7 @@ interface IDevice extends @1.1::IDevice {
* unspecified error
* - INVALID_ARGUMENT if one of the input arguments is invalid
*/
prepareModelFromCache(handle modelCache, handle dataCache,
prepareModelFromCache(vec<handle> modelCache, vec<handle> dataCache,
uint8_t[Constant:BYTE_SIZE_OF_CACHE_TOKEN] token,
IPreparedModelCallback callback)
generates (ErrorStatus status);

58
neuralnetworks/1.2/IPreparedModel.hal
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@@ -157,62 +157,4 @@ interface IPreparedModel extends @1.0::IPreparedModel {
fmq_sync<FmqRequestDatum> requestChannel,
fmq_sync<FmqResultDatum> resultChannel)
generates (ErrorStatus status, IBurstContext context);
/*
* Saves the prepared model to cache files.
*
* saveToCache is used to save a prepared model to cache files for faster
* model compilation time when the same model preparation is requested in
* the future. There are exactly two cache file descriptors provided to the
* driver: modelCache and dataCache.
*
* The dataCache is for caching constant data, possibly including preprocessed
* and transformed tensor buffers. Any modification to the dataCache should
* have no worse effect than generating bad output values at execution time.
*
* The modelCache is for caching security-sensitive data such as compiled
* executable machine code in the device's native binary format. A modification
* to the modelCache may affect the driver's execution behavior, and a malicious
* client could make use of this to execute beyond the granted permission. Thus,
* the driver must always check whether the modelCache is corrupted before preparing
* the model from cache.
*
* The two file descriptors must point to two zero-length files with offset
* positioned at the beginning of the file. The file descriptors may be closed
* by the client once the method has returned.
*
* If the driver decides not to save the prepared model without looking at the
* input arguments to the saveToCache function, saveToCache must return with
* ErrorStatus::GENERAL_FAILURE. Otherwise, the saveToCache function must verify
* the input arguments to the saveToCache function are valid, and return with
* ErrorStatus::INVALID_ARGUMENT if not. If the inputs are valid but the driver
* could not save the prepared model, saveToCache must return with the appropriate
* ErrorStatus. Otherwise, it must write the cache files and return
* ErrorStatus::NONE. Unless saveToCache returns ErrorStatus::NONE, the contents
* of the cache files are undefined.
*
* @param modelCache A handle holding exactly one cache file descriptor for the
* security-sensitive cache.
* @param dataCache A handle holding exactly one cache file descriptor for the
* constants' cache.
* @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 cache files with
* IDevice::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.
* @return status Error status of saveToCache, must be:
* - NONE if saveToCache is performed successfully
* - DEVICE_UNAVAILABLE if driver is offline or busy
* - GENERAL_FAILURE if the driver could not save the
* prepared model or if there is an unspecified error
* - INVALID_ARGUMENT if one of the input arguments is invalid,
* unless the driver decides not to save the prepared model
* without looking at the input arguments
*/
saveToCache(handle modelCache, handle dataCache,
uint8_t[Constant:BYTE_SIZE_OF_CACHE_TOKEN] token)
generates (ErrorStatus status);
};

5
neuralnetworks/1.2/types.hal
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@@ -30,6 +30,11 @@ enum Constant : uint32_t {
* The byte size of the cache token.
*/
BYTE_SIZE_OF_CACHE_TOKEN = 32,
/**
* The maximum number of files for each type of cache in compilation caching.
*/
MAX_NUMBER_OF_CACHE_FILES = 32,
};
enum OperandType : @1.0::OperandType {

13
neuralnetworks/1.2/vts/functional/BasicTests.cpp
View File

@@ -77,10 +77,15 @@ TEST_F(NeuralnetworksHidlTest, GetDeviceSupportedExtensionsTest) {
EXPECT_TRUE(ret.isOk());
}
// isCachingSupported test
TEST_F(NeuralnetworksHidlTest, IsCachingSupported) {
Return<void> ret = device->isCachingSupported(
[](ErrorStatus status, bool) { EXPECT_EQ(ErrorStatus::NONE, status); });
// getNumberOfCacheFilesNeeded test
TEST_F(NeuralnetworksHidlTest, getNumberOfCacheFilesNeeded) {
Return<void> ret = device->getNumberOfCacheFilesNeeded(
[](ErrorStatus status, uint32_t numModelCache, uint32_t numDataCache) {
EXPECT_EQ(ErrorStatus::NONE, status);
EXPECT_LE(numModelCache,
static_cast<uint32_t>(Constant::MAX_NUMBER_OF_CACHE_FILES));
EXPECT_LE(numDataCache, static_cast<uint32_t>(Constant::MAX_NUMBER_OF_CACHE_FILES));
});
EXPECT_TRUE(ret.isOk());
}
} // namespace functional

1023
neuralnetworks/1.2/vts/functional/CompilationCachingTests.cpp
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File diff suppressed because it is too large Load Diff

4
neuralnetworks/1.2/vts/functional/ValidateModel.cpp
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@@ -33,6 +33,7 @@ namespace functional {
using ::android::hardware::neuralnetworks::V1_2::implementation::ExecutionCallback;
using ::android::hardware::neuralnetworks::V1_2::implementation::PreparedModelCallback;
using HidlToken = hidl_array<uint8_t, static_cast<uint32_t>(Constant::BYTE_SIZE_OF_CACHE_TOKEN)>;
///////////////////////// UTILITY FUNCTIONS /////////////////////////
@@ -54,7 +55,8 @@ static void validatePrepareModel(const sp<IDevice>& device, const std::string& m
sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
ASSERT_NE(nullptr, preparedModelCallback.get());
Return<ErrorStatus> prepareLaunchStatus =
device->prepareModel_1_2(model, preference, preparedModelCallback);
device->prepareModel_1_2(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));

4
neuralnetworks/1.2/vts/functional/ValidateRequest.cpp
View File

@@ -37,6 +37,7 @@ namespace functional {
using ::android::hardware::neuralnetworks::V1_2::implementation::ExecutionCallback;
using ::android::hardware::neuralnetworks::V1_2::implementation::PreparedModelCallback;
using ::android::hidl::memory::V1_0::IMemory;
using HidlToken = hidl_array<uint8_t, static_cast<uint32_t>(Constant::BYTE_SIZE_OF_CACHE_TOKEN)>;
using test_helper::for_all;
using test_helper::MixedTyped;
using test_helper::MixedTypedExample;
@@ -66,7 +67,8 @@ static void createPreparedModel(const sp<IDevice>& device, const Model& model,
sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
ASSERT_NE(nullptr, preparedModelCallback.get());
Return<ErrorStatus> prepareLaunchStatus = device->prepareModel_1_2(
model, ExecutionPreference::FAST_SINGLE_ANSWER, preparedModelCallback);
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));