Merge "Use Widely Supported GSM Channels for NetworkScan"
diff --git a/atrace/1.0/default/AtraceDevice.cpp b/atrace/1.0/default/AtraceDevice.cpp
index 35d11e9..4e82b0a 100644
--- a/atrace/1.0/default/AtraceDevice.cpp
+++ b/atrace/1.0/default/AtraceDevice.cpp
@@ -94,7 +94,7 @@
for (auto& c : kTracingMap) {
for (auto& p : c.second.paths) {
if (!android::base::WriteStringToFile("0", p.first)) {
- LOG(ERROR) << "Failed to enable tracing on: " << p.first;
+ LOG(ERROR) << "Failed to disable tracing on: " << p.first;
if (p.second) {
ret = Status::ERROR_TRACING_POINT;
}
diff --git a/current.txt b/current.txt
index cea2bed..e717b7a 100644
--- a/current.txt
+++ b/current.txt
@@ -399,8 +399,8 @@
65a021fa89085b62fc96b2b6d3bef2f9103cf4d63379c68bc154fd9eef672852 android.hardware.health@1.0::types
b7ecf29927055ec422ec44bf776223f07d79ad9f92ccf9becf167e62c2607e7a android.hardware.keymaster@4.0::IKeymasterDevice
574e8f1499436fb4075894dcae0b36682427956ecb114f17f1fe22d116a83c6b android.hardware.neuralnetworks@1.0::IPreparedModel
-567de4ebb3a224721eabae40c4484fad2cd1608eb0e66ec9214eb88e9b15d3c9 android.hardware.neuralnetworks@1.0::types
-d51937a3567a50f239589e40300264c4b57f2c3582c6fc6df082f45eb74d90e3 android.hardware.neuralnetworks@1.1::types
+e75759b40a1c5f97b463b30aab91954012c9ea9e454dde308db853a56796e5a6 android.hardware.neuralnetworks@1.0::types
+eb754b58c93e5591613208b4c972811288b0fa16a82430d602f107c91a908b22 android.hardware.neuralnetworks@1.1::types
1d4a5776614c08b5d794a5ec5ab04697260cbd4b3441d5935cd53ee71d19da02 android.hardware.radio@1.0::IRadioResponse
ed9da80ec0c96991fd03f0a46107815d0e50f764656e49dba4980fa5c31d5bc3 android.hardware.radio@1.0::types
1d19720d4fd38b1095f0f555a4bd92b3b12c9b1d0f560b0e9a474cd6dcc20db6 android.hardware.radio@1.2::IRadio
@@ -451,7 +451,7 @@
92714960d1a53fc2ec557302b41c7cc93d2636d8364a44bd0f85be0c92927ff8 android.hardware.neuralnetworks@1.2::IExecutionCallback
36e1064c869965dee533c537cefbe87e54db8bd8cd45be7e0e93e00e8a43863a android.hardware.neuralnetworks@1.2::IPreparedModel
e1c734d1545e1a4ae749ff1dd9704a8e594c59aea7c8363159dc258e93e0df3b android.hardware.neuralnetworks@1.2::IPreparedModelCallback
-51725cd8541bf459190d8a911f9aae9a0825678256b88fc39fac81d69feb04af android.hardware.neuralnetworks@1.2::types
+e3b6176e3bf235c4e0e4e451b0166e396c7ee176cfe167c9147c3d46d7b34f0c 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
diff --git a/keymaster/4.0/vts/functional/VerificationTokenTest.cpp b/keymaster/4.0/vts/functional/VerificationTokenTest.cpp
index 3876b16..de28683 100644
--- a/keymaster/4.0/vts/functional/VerificationTokenTest.cpp
+++ b/keymaster/4.0/vts/functional/VerificationTokenTest.cpp
@@ -124,6 +124,65 @@
// report if times aren't nearly always <1ms apart.
EXPECT_LE(host_time_delta, km_time_delta + 2);
EXPECT_LE(km_time_delta, host_time_delta + 2);
+ ASSERT_EQ(result1.token.mac.size(), result2.token.mac.size());
+ ASSERT_NE(0,
+ memcmp(result1.token.mac.data(), result2.token.mac.data(), result1.token.mac.size()));
+}
+
+/*
+ * Test that the mac changes when the time stamp changes. This is does not guarantee that the time
+ * stamp is included in the mac but on failure we know that it is not. Other than in the test
+ * case above we call verifyAuthorization with the exact same set of parameters.
+ */
+TEST_F(VerificationTokenTest, MacChangesOnChangingTimestamp) {
+ auto result1 =
+ verifyAuthorization(0 /* operation handle */,
+ AuthorizationSet() /* paramtersToVerify */, HardwareAuthToken());
+ ASSERT_TRUE(result1.callSuccessful);
+ auto result1_time = getTime();
+
+ if (SecLevel() == SecurityLevel::STRONGBOX) {
+ // StrongBox should not implement verifyAuthorization.
+ EXPECT_EQ(ErrorCode::UNIMPLEMENTED, result1.error);
+ return;
+ }
+
+ EXPECT_EQ(ErrorCode::OK, result1.error);
+ EXPECT_EQ(0U, result1.token.challenge);
+ EXPECT_EQ(SecLevel(), result1.token.securityLevel);
+ EXPECT_EQ(0U, result1.token.parametersVerified.size())
+ << "We didn't supply any parameters to verify";
+ EXPECT_GT(result1.token.timestamp, 0U);
+
+ constexpr uint32_t time_to_sleep = 200;
+ sleep_ms(time_to_sleep);
+
+ auto result2 =
+ verifyAuthorization(0 /* operation handle */,
+ AuthorizationSet() /* paramtersToVerify */, HardwareAuthToken());
+ ASSERT_TRUE(result2.callSuccessful);
+ auto result2_time = getTime();
+ EXPECT_EQ(ErrorCode::OK, result2.error);
+ EXPECT_EQ(0U, result2.token.challenge);
+ EXPECT_EQ(SecLevel(), result2.token.securityLevel);
+ EXPECT_EQ(0U, result2.token.parametersVerified.size())
+ << "We didn't supply any parameters to verify";
+
+ auto host_time_delta = result2_time - result1_time;
+
+ EXPECT_GE(host_time_delta, time_to_sleep)
+ << "We slept for " << time_to_sleep << " ms, the clock must have advanced by that much";
+ EXPECT_LE(host_time_delta, time_to_sleep + 20)
+ << "The verifyAuthorization call took " << (host_time_delta - time_to_sleep)
+ << " ms? That's awful!";
+
+ auto km_time_delta = result2.token.timestamp - result1.token.timestamp;
+
+ EXPECT_LE(host_time_delta, km_time_delta + 2);
+ EXPECT_LE(km_time_delta, host_time_delta + 2);
+ ASSERT_EQ(result1.token.mac.size(), result2.token.mac.size());
+ ASSERT_NE(0,
+ memcmp(result1.token.mac.data(), result2.token.mac.data(), result1.token.mac.size()));
}
} // namespace test
diff --git a/neuralnetworks/1.0/types.hal b/neuralnetworks/1.0/types.hal
index a358946..b0a1c1a 100644
--- a/neuralnetworks/1.0/types.hal
+++ b/neuralnetworks/1.0/types.hal
@@ -1225,9 +1225,9 @@
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
* the input.
* * 1: An {@link OperandType::INT32} scalar, specifying the output
- * height of the output tensor.
- * * 2: An {@link OperandType::INT32} scalar, specifying the output
* width of the output tensor.
+ * * 2: An {@link OperandType::INT32} scalar, specifying the output
+ * height of the output tensor.
*
* Outputs:
* * 0: The output 4-D tensor, of shape
diff --git a/neuralnetworks/1.0/vts/functional/ValidateRequest.cpp b/neuralnetworks/1.0/vts/functional/ValidateRequest.cpp
index 72a5007..f0c93b7 100644
--- a/neuralnetworks/1.0/vts/functional/ValidateRequest.cpp
+++ b/neuralnetworks/1.0/vts/functional/ValidateRequest.cpp
@@ -34,7 +34,6 @@
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 test_helper::for_all;
using test_helper::MixedTyped;
@@ -42,53 +41,6 @@
///////////////////////// UTILITY FUNCTIONS /////////////////////////
-static void createPreparedModel(const sp<IDevice>& device, const V1_0::Model& model,
- sp<IPreparedModel>* preparedModel) {
- ASSERT_NE(nullptr, preparedModel);
-
- // see if service can handle model
- bool fullySupportsModel = false;
- Return<void> supportedOpsLaunchStatus = device->getSupportedOperations(
- model, [&fullySupportsModel](ErrorStatus status, const hidl_vec<bool>& supported) {
- ASSERT_EQ(ErrorStatus::NONE, status);
- ASSERT_NE(0ul, supported.size());
- fullySupportsModel =
- std::all_of(supported.begin(), supported.end(), [](bool valid) { return valid; });
- });
- ASSERT_TRUE(supportedOpsLaunchStatus.isOk());
-
- // launch prepare model
- sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
- ASSERT_NE(nullptr, preparedModelCallback.get());
- Return<ErrorStatus> prepareLaunchStatus = device->prepareModel(model, preparedModelCallback);
- ASSERT_TRUE(prepareLaunchStatus.isOk());
- ASSERT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(prepareLaunchStatus));
-
- // retrieve prepared model
- preparedModelCallback->wait();
- ErrorStatus prepareReturnStatus = preparedModelCallback->getStatus();
- *preparedModel = preparedModelCallback->getPreparedModel();
-
- // The getSupportedOperations call returns a list of operations that are
- // guaranteed not to fail if prepareModel is called, and
- // 'fullySupportsModel' is true i.f.f. the entire model is guaranteed.
- // If a driver has any doubt that it can prepare an operation, it must
- // return false. So here, if a driver isn't sure if it can support an
- // operation, but reports that it successfully prepared the model, the test
- // can continue.
- if (!fullySupportsModel && prepareReturnStatus != ErrorStatus::NONE) {
- ASSERT_EQ(nullptr, preparedModel->get());
- LOG(INFO) << "NN VTS: Unable to test Request validation because vendor service cannot "
- "prepare model that it does not support.";
- std::cout << "[ ] Unable to test Request validation because vendor service "
- "cannot prepare model that it does not support."
- << std::endl;
- return;
- }
- ASSERT_EQ(ErrorStatus::NONE, prepareReturnStatus);
- ASSERT_NE(nullptr, preparedModel->get());
-}
-
// Primary validation function. This function will take a valid request, apply a
// mutation to it to invalidate the request, then pass it to interface calls
// that use the request. Note that the request here is passed by value, and any
@@ -237,15 +189,8 @@
return requests;
}
-void ValidationTest::validateRequests(const V1_0::Model& model,
+void ValidationTest::validateRequests(const sp<IPreparedModel>& preparedModel,
const std::vector<Request>& requests) {
- // create IPreparedModel
- sp<IPreparedModel> preparedModel;
- ASSERT_NO_FATAL_FAILURE(createPreparedModel(device, model, &preparedModel));
- if (preparedModel == nullptr) {
- return;
- }
-
// validate each request
for (const Request& request : requests) {
removeInputTest(preparedModel, request);
diff --git a/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworks.cpp b/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworks.cpp
index 8883057..aee2f85 100644
--- a/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworks.cpp
+++ b/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworks.cpp
@@ -18,6 +18,10 @@
#include "VtsHalNeuralnetworks.h"
+#include <android-base/logging.h>
+
+#include "Callbacks.h"
+
namespace android {
namespace hardware {
namespace neuralnetworks {
@@ -25,6 +29,55 @@
namespace vts {
namespace functional {
+using ::android::hardware::neuralnetworks::V1_2::implementation::PreparedModelCallback;
+
+static void createPreparedModel(const sp<IDevice>& device, const V1_0::Model& model,
+ sp<IPreparedModel>* preparedModel) {
+ ASSERT_NE(nullptr, preparedModel);
+
+ // see if service can handle model
+ bool fullySupportsModel = false;
+ Return<void> supportedOpsLaunchStatus = device->getSupportedOperations(
+ model, [&fullySupportsModel](ErrorStatus status, const hidl_vec<bool>& supported) {
+ ASSERT_EQ(ErrorStatus::NONE, status);
+ ASSERT_NE(0ul, supported.size());
+ fullySupportsModel = std::all_of(supported.begin(), supported.end(),
+ [](bool valid) { return valid; });
+ });
+ ASSERT_TRUE(supportedOpsLaunchStatus.isOk());
+
+ // launch prepare model
+ sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
+ ASSERT_NE(nullptr, preparedModelCallback.get());
+ Return<ErrorStatus> prepareLaunchStatus = device->prepareModel(model, preparedModelCallback);
+ ASSERT_TRUE(prepareLaunchStatus.isOk());
+ ASSERT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(prepareLaunchStatus));
+
+ // retrieve prepared model
+ preparedModelCallback->wait();
+ ErrorStatus prepareReturnStatus = preparedModelCallback->getStatus();
+ *preparedModel = preparedModelCallback->getPreparedModel();
+
+ // The getSupportedOperations call returns a list of operations that are
+ // guaranteed not to fail if prepareModel is called, and
+ // 'fullySupportsModel' is true i.f.f. the entire model is guaranteed.
+ // If a driver has any doubt that it can prepare an operation, it must
+ // return false. So here, if a driver isn't sure if it can support an
+ // operation, but reports that it successfully prepared the model, the test
+ // can continue.
+ if (!fullySupportsModel && prepareReturnStatus != ErrorStatus::NONE) {
+ ASSERT_EQ(nullptr, preparedModel->get());
+ LOG(INFO) << "NN VTS: Unable to test Request validation because vendor service cannot "
+ "prepare model that it does not support.";
+ std::cout << "[ ] Unable to test Request validation because vendor service "
+ "cannot prepare model that it does not support."
+ << std::endl;
+ return;
+ }
+ ASSERT_EQ(ErrorStatus::NONE, prepareReturnStatus);
+ ASSERT_NE(nullptr, preparedModel->get());
+}
+
// A class for test environment setup
NeuralnetworksHidlEnvironment::NeuralnetworksHidlEnvironment() {}
@@ -68,6 +121,19 @@
::testing::VtsHalHidlTargetTestBase::TearDown();
}
+void ValidationTest::validateEverything(const Model& model, const std::vector<Request>& requests) {
+ validateModel(model);
+
+ // create IPreparedModel
+ sp<IPreparedModel> preparedModel;
+ ASSERT_NO_FATAL_FAILURE(createPreparedModel(device, model, &preparedModel));
+ if (preparedModel == nullptr) {
+ return;
+ }
+
+ validateRequests(preparedModel, requests);
+}
+
} // namespace functional
} // namespace vts
} // namespace V1_0
diff --git a/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworks.h b/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworks.h
index d4c114d..22285be 100644
--- a/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworks.h
+++ b/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworks.h
@@ -63,8 +63,12 @@
// Tag for the validation tests
class ValidationTest : public NeuralnetworksHidlTest {
protected:
- void validateModel(const Model& model);
- void validateRequests(const Model& model, const std::vector<Request>& request);
+ void validateEverything(const Model& model, const std::vector<Request>& request);
+
+ private:
+ void validateModel(const Model& model);
+ void validateRequests(const sp<IPreparedModel>& preparedModel,
+ const std::vector<Request>& requests);
};
// Tag for the generated tests
diff --git a/neuralnetworks/1.1/types.hal b/neuralnetworks/1.1/types.hal
index 335b803..73705bb 100644
--- a/neuralnetworks/1.1/types.hal
+++ b/neuralnetworks/1.1/types.hal
@@ -138,7 +138,7 @@
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
- * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM} (the pad value is undefined)
*
* Supported tensor rank: up to 4
*
@@ -161,6 +161,9 @@
* output0.dimension[i] =
* padding[i, 0] + input0.dimension[i] + padding[i, 1]
*
+ * NOTE: The pad value for {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
+ * is undefined.
+ *
* Available since API level 28.
*/
PAD = 32,
diff --git a/neuralnetworks/1.1/vts/functional/ValidateRequest.cpp b/neuralnetworks/1.1/vts/functional/ValidateRequest.cpp
index 5225bf7..f4adbab 100644
--- a/neuralnetworks/1.1/vts/functional/ValidateRequest.cpp
+++ b/neuralnetworks/1.1/vts/functional/ValidateRequest.cpp
@@ -34,7 +34,6 @@
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 test_helper::for_all;
using test_helper::MixedTyped;
@@ -42,54 +41,6 @@
///////////////////////// UTILITY FUNCTIONS /////////////////////////
-static void createPreparedModel(const sp<IDevice>& device, const V1_1::Model& model,
- sp<IPreparedModel>* preparedModel) {
- ASSERT_NE(nullptr, preparedModel);
-
- // see if service can handle model
- bool fullySupportsModel = false;
- Return<void> supportedOpsLaunchStatus = device->getSupportedOperations_1_1(
- model, [&fullySupportsModel](ErrorStatus status, const hidl_vec<bool>& supported) {
- ASSERT_EQ(ErrorStatus::NONE, status);
- ASSERT_NE(0ul, supported.size());
- fullySupportsModel =
- std::all_of(supported.begin(), supported.end(), [](bool valid) { return valid; });
- });
- ASSERT_TRUE(supportedOpsLaunchStatus.isOk());
-
- // launch prepare model
- sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
- ASSERT_NE(nullptr, preparedModelCallback.get());
- Return<ErrorStatus> prepareLaunchStatus = device->prepareModel_1_1(
- model, ExecutionPreference::FAST_SINGLE_ANSWER, preparedModelCallback);
- ASSERT_TRUE(prepareLaunchStatus.isOk());
- ASSERT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(prepareLaunchStatus));
-
- // retrieve prepared model
- preparedModelCallback->wait();
- ErrorStatus prepareReturnStatus = preparedModelCallback->getStatus();
- *preparedModel = preparedModelCallback->getPreparedModel();
-
- // The getSupportedOperations_1_1 call returns a list of operations that are
- // guaranteed not to fail if prepareModel_1_1 is called, and
- // 'fullySupportsModel' is true i.f.f. the entire model is guaranteed.
- // If a driver has any doubt that it can prepare an operation, it must
- // return false. So here, if a driver isn't sure if it can support an
- // operation, but reports that it successfully prepared the model, the test
- // can continue.
- if (!fullySupportsModel && prepareReturnStatus != ErrorStatus::NONE) {
- ASSERT_EQ(nullptr, preparedModel->get());
- LOG(INFO) << "NN VTS: Unable to test Request validation because vendor service cannot "
- "prepare model that it does not support.";
- std::cout << "[ ] Unable to test Request validation because vendor service "
- "cannot prepare model that it does not support."
- << std::endl;
- return;
- }
- ASSERT_EQ(ErrorStatus::NONE, prepareReturnStatus);
- ASSERT_NE(nullptr, preparedModel->get());
-}
-
// Primary validation function. This function will take a valid request, apply a
// mutation to it to invalidate the request, then pass it to interface calls
// that use the request. Note that the request here is passed by value, and any
@@ -238,15 +189,8 @@
return requests;
}
-void ValidationTest::validateRequests(const V1_1::Model& model,
+void ValidationTest::validateRequests(const sp<IPreparedModel>& preparedModel,
const std::vector<Request>& requests) {
- // create IPreparedModel
- sp<IPreparedModel> preparedModel;
- ASSERT_NO_FATAL_FAILURE(createPreparedModel(device, model, &preparedModel));
- if (preparedModel == nullptr) {
- return;
- }
-
// validate each request
for (const Request& request : requests) {
removeInputTest(preparedModel, request);
diff --git a/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworks.cpp b/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworks.cpp
index 224a51d..08069f2 100644
--- a/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworks.cpp
+++ b/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworks.cpp
@@ -18,6 +18,10 @@
#include "VtsHalNeuralnetworks.h"
+#include <android-base/logging.h>
+
+#include "Callbacks.h"
+
namespace android {
namespace hardware {
namespace neuralnetworks {
@@ -25,6 +29,56 @@
namespace vts {
namespace functional {
+using ::android::hardware::neuralnetworks::V1_2::implementation::PreparedModelCallback;
+
+static void createPreparedModel(const sp<IDevice>& device, const V1_1::Model& model,
+ sp<IPreparedModel>* preparedModel) {
+ ASSERT_NE(nullptr, preparedModel);
+
+ // see if service can handle model
+ bool fullySupportsModel = false;
+ Return<void> supportedOpsLaunchStatus = device->getSupportedOperations_1_1(
+ model, [&fullySupportsModel](ErrorStatus status, const hidl_vec<bool>& supported) {
+ ASSERT_EQ(ErrorStatus::NONE, status);
+ ASSERT_NE(0ul, supported.size());
+ fullySupportsModel = std::all_of(supported.begin(), supported.end(),
+ [](bool valid) { return valid; });
+ });
+ ASSERT_TRUE(supportedOpsLaunchStatus.isOk());
+
+ // launch prepare model
+ sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
+ ASSERT_NE(nullptr, preparedModelCallback.get());
+ Return<ErrorStatus> prepareLaunchStatus = device->prepareModel_1_1(
+ model, ExecutionPreference::FAST_SINGLE_ANSWER, preparedModelCallback);
+ ASSERT_TRUE(prepareLaunchStatus.isOk());
+ ASSERT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(prepareLaunchStatus));
+
+ // retrieve prepared model
+ preparedModelCallback->wait();
+ ErrorStatus prepareReturnStatus = preparedModelCallback->getStatus();
+ *preparedModel = preparedModelCallback->getPreparedModel();
+
+ // The getSupportedOperations_1_1 call returns a list of operations that are
+ // guaranteed not to fail if prepareModel_1_1 is called, and
+ // 'fullySupportsModel' is true i.f.f. the entire model is guaranteed.
+ // If a driver has any doubt that it can prepare an operation, it must
+ // return false. So here, if a driver isn't sure if it can support an
+ // operation, but reports that it successfully prepared the model, the test
+ // can continue.
+ if (!fullySupportsModel && prepareReturnStatus != ErrorStatus::NONE) {
+ ASSERT_EQ(nullptr, preparedModel->get());
+ LOG(INFO) << "NN VTS: Unable to test Request validation because vendor service cannot "
+ "prepare model that it does not support.";
+ std::cout << "[ ] Unable to test Request validation because vendor service "
+ "cannot prepare model that it does not support."
+ << std::endl;
+ return;
+ }
+ ASSERT_EQ(ErrorStatus::NONE, prepareReturnStatus);
+ ASSERT_NE(nullptr, preparedModel->get());
+}
+
// A class for test environment setup
NeuralnetworksHidlEnvironment::NeuralnetworksHidlEnvironment() {}
@@ -68,6 +122,19 @@
::testing::VtsHalHidlTargetTestBase::TearDown();
}
+void ValidationTest::validateEverything(const Model& model, const std::vector<Request>& requests) {
+ validateModel(model);
+
+ // create IPreparedModel
+ sp<IPreparedModel> preparedModel;
+ ASSERT_NO_FATAL_FAILURE(createPreparedModel(device, model, &preparedModel));
+ if (preparedModel == nullptr) {
+ return;
+ }
+
+ validateRequests(preparedModel, requests);
+}
+
} // namespace functional
} // namespace vts
} // namespace V1_1
diff --git a/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworks.h b/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworks.h
index 1c8c0e1..f3f587b 100644
--- a/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworks.h
+++ b/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworks.h
@@ -72,8 +72,12 @@
// Tag for the validation tests
class ValidationTest : public NeuralnetworksHidlTest {
protected:
- void validateModel(const Model& model);
- void validateRequests(const Model& model, const std::vector<Request>& request);
+ void validateEverything(const Model& model, const std::vector<Request>& request);
+
+ private:
+ void validateModel(const Model& model);
+ void validateRequests(const sp<IPreparedModel>& preparedModel,
+ const std::vector<Request>& requests);
};
// Tag for the generated tests
diff --git a/neuralnetworks/1.2/types.hal b/neuralnetworks/1.2/types.hal
index 31576fa..c2e8f22 100644
--- a/neuralnetworks/1.2/types.hal
+++ b/neuralnetworks/1.2/types.hal
@@ -1593,9 +1593,9 @@
* the input. Since API level 29, zero batches is supported for this
* tensor.
* * 1: An {@link OperandType::INT32} scalar, specifying the output
- * height of the output tensor.
- * * 2: An {@link OperandType::INT32} scalar, specifying the output
* width of the output tensor.
+ * * 2: An {@link OperandType::INT32} scalar, specifying the output
+ * height of the output tensor.
* * 3: An optional {@link OperandType::BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
* Available since API level 29.
@@ -1603,15 +1603,15 @@
* Inputs (resizing by scale, since API level 29):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
* the input. Zero batches is supported for this tensor.
- * * 1: A scalar, specifying height_scale, the scaling factor of the height
+ * * 1: A scalar, specifying width_scale, the scaling factor of the width
* dimension from the input tensor to the output tensor. The output
- * height is calculated as new_height = floor(height * height_scale).
+ * width is calculated as new_width = floor(width * width_scale).
* The scalar must be of {@link OperandType::FLOAT16} if input0 is
* of {@link OperandType::TENSOR_FLOAT16} and of
* {@link OperandType::FLOAT32} otherwise.
- * * 2: A scalar, specifying width_scale, the scaling factor of the width
+ * * 2: A scalar, specifying height_scale, the scaling factor of the height
* dimension from the input tensor to the output tensor. The output
- * width is calculated as new_width = floor(width * width_scale).
+ * height is calculated as new_height = floor(height * height_scale).
* The scalar must be of {@link OperandType::FLOAT16} if input0 is
* of {@link OperandType::TENSOR_FLOAT16} and of
* {@link OperandType::FLOAT32} otherwise.
@@ -1999,7 +1999,8 @@
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
* * {@link OperandType::TENSOR_FLOAT32}
- * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM} (full support since API
+ * level 29, see the output section)
*
* Supported tensor rank: up to 4
*
@@ -2022,6 +2023,10 @@
* output0.dimension[i] =
* padding[i, 0] + input0.dimension[i] + padding[i, 1]
*
+ * NOTE: Before API level 29, the pad value for
+ * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} is undefined.
+ * Since API level 29, the pad value is always the logical zero.
+ *
* Available since API level 28.
*/
PAD = @1.1::OperationType:PAD,
@@ -4663,24 +4668,24 @@
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
* the input. Zero batches is supported for this tensor.
* * 1: An {@link OperandType::INT32} scalar, specifying the output
- * height of the output tensor.
- * * 2: An {@link OperandType::INT32} scalar, specifying the output
* width of the output tensor.
+ * * 2: An {@link OperandType::INT32} scalar, specifying the output
+ * height of the output tensor.
* * 3: An {@link OperandType::BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
*
* Inputs (resizing by scale):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
* the input. Zero batches is supported for this tensor.
- * * 1: A scalar, specifying height_scale, the scaling factor of the height
+ * * 1: A scalar, specifying width_scale, the scaling factor of the width
* dimension from the input tensor to the output tensor. The output
- * height is calculated as new_height = floor(height * height_scale).
+ * width is calculated as new_width = floor(width * width_scale).
* The scalar must be of {@link OperandType::FLOAT16} if input0 is
* of {@link OperandType::TENSOR_FLOAT16} and of
* {@link OperandType::FLOAT32} otherwise.
- * * 2: A scalar, specifying width_scale, the scaling factor of the width
+ * * 2: A scalar, specifying height_scale, the scaling factor of the height
* dimension from the input tensor to the output tensor. The output
- * width is calculated as new_width = floor(width * width_scale).
+ * height is calculated as new_height = floor(height * height_scale).
* The scalar must be of {@link OperandType::FLOAT16} if input0 is
* of {@link OperandType::TENSOR_FLOAT16} and of
* {@link OperandType::FLOAT32} otherwise.
diff --git a/neuralnetworks/1.2/vts/functional/Android.bp b/neuralnetworks/1.2/vts/functional/Android.bp
index 891b414..6c26820 100644
--- a/neuralnetworks/1.2/vts/functional/Android.bp
+++ b/neuralnetworks/1.2/vts/functional/Android.bp
@@ -20,6 +20,7 @@
defaults: ["VtsHalNeuralNetworksTargetTestDefaults"],
srcs: [
"GeneratedTestsV1_0.cpp",
+ "ValidateBurst.cpp",
],
cflags: [
"-DNN_TEST_DYNAMIC_OUTPUT_SHAPE"
@@ -32,6 +33,7 @@
defaults: ["VtsHalNeuralNetworksTargetTestDefaults"],
srcs: [
"GeneratedTestsV1_1.cpp",
+ "ValidateBurst.cpp",
],
cflags: [
"-DNN_TEST_DYNAMIC_OUTPUT_SHAPE"
@@ -46,6 +48,7 @@
"BasicTests.cpp",
"CompilationCachingTests.cpp",
"GeneratedTests.cpp",
+ "ValidateBurst.cpp",
],
cflags: [
"-DNN_TEST_DYNAMIC_OUTPUT_SHAPE"
@@ -58,6 +61,7 @@
srcs: [
"BasicTests.cpp",
"GeneratedTests.cpp",
+ "ValidateBurst.cpp",
],
cflags: [
"-DNN_TEST_DYNAMIC_OUTPUT_SHAPE",
diff --git a/neuralnetworks/1.2/vts/functional/CompilationCachingTests.cpp b/neuralnetworks/1.2/vts/functional/CompilationCachingTests.cpp
index 167fc09..4411b90 100644
--- a/neuralnetworks/1.2/vts/functional/CompilationCachingTests.cpp
+++ b/neuralnetworks/1.2/vts/functional/CompilationCachingTests.cpp
@@ -16,21 +16,22 @@
#define LOG_TAG "neuralnetworks_hidl_hal_test"
-#include "VtsHalNeuralnetworks.h"
+#include <android-base/logging.h>
+#include <android/hidl/memory/1.0/IMemory.h>
+#include <ftw.h>
+#include <gtest/gtest.h>
+#include <hidlmemory/mapping.h>
+#include <unistd.h>
+
+#include <cstdio>
+#include <cstdlib>
+#include <random>
#include "Callbacks.h"
#include "GeneratedTestHarness.h"
#include "TestHarness.h"
#include "Utils.h"
-
-#include <android-base/logging.h>
-#include <android/hidl/memory/1.0/IMemory.h>
-#include <hidlmemory/mapping.h>
-#include <cstdio>
-#include <cstdlib>
-#include <random>
-
-#include <gtest/gtest.h>
+#include "VtsHalNeuralnetworks.h"
namespace android {
namespace hardware {
@@ -44,9 +45,9 @@
using ::android::nn::allocateSharedMemory;
using ::test_helper::MixedTypedExample;
-namespace {
+namespace float32_model {
-// In frameworks/ml/nn/runtime/tests/generated/, creates a hidl model of mobilenet.
+// In frameworks/ml/nn/runtime/test/generated/, creates a hidl model of float32 mobilenet.
#include "examples/mobilenet_224_gender_basic_fixed.example.cpp"
#include "vts_models/mobilenet_224_gender_basic_fixed.model.cpp"
@@ -54,6 +55,44 @@
[[maybe_unused]] auto dummy_createTestModel = createTestModel_dynamic_output_shape;
[[maybe_unused]] auto dummy_get_examples = get_examples_dynamic_output_shape;
+// MixedTypedExample is defined in frameworks/ml/nn/tools/test_generator/include/TestHarness.h.
+// This function assumes the operation is always ADD.
+std::vector<MixedTypedExample> getLargeModelExamples(uint32_t len) {
+ float outputValue = 1.0f + static_cast<float>(len);
+ return {{.operands = {
+ // Input
+ {.operandDimensions = {{0, {1}}}, .float32Operands = {{0, {1.0f}}}},
+ // Output
+ {.operandDimensions = {{0, {1}}}, .float32Operands = {{0, {outputValue}}}}}}};
+}
+
+} // namespace float32_model
+
+namespace quant8_model {
+
+// In frameworks/ml/nn/runtime/test/generated/, creates a hidl model of quant8 mobilenet.
+#include "examples/mobilenet_quantized.example.cpp"
+#include "vts_models/mobilenet_quantized.model.cpp"
+
+// Prevent the compiler from complaining about an otherwise unused function.
+[[maybe_unused]] auto dummy_createTestModel = createTestModel_dynamic_output_shape;
+[[maybe_unused]] auto dummy_get_examples = get_examples_dynamic_output_shape;
+
+// MixedTypedExample is defined in frameworks/ml/nn/tools/test_generator/include/TestHarness.h.
+// This function assumes the operation is always ADD.
+std::vector<MixedTypedExample> getLargeModelExamples(uint32_t len) {
+ uint8_t outputValue = 1 + static_cast<uint8_t>(len);
+ return {{.operands = {// Input
+ {.operandDimensions = {{0, {1}}}, .quant8AsymmOperands = {{0, {1}}}},
+ // Output
+ {.operandDimensions = {{0, {1}}},
+ .quant8AsymmOperands = {{0, {outputValue}}}}}}};
+}
+
+} // namespace quant8_model
+
+namespace {
+
enum class AccessMode { READ_WRITE, READ_ONLY, WRITE_ONLY };
// Creates cache handles based on provided file groups.
@@ -89,11 +128,137 @@
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, OperandType operandType>
+Model createLargeTestModelImpl(OperationType op, uint32_t len) {
+ EXPECT_TRUE(op == OperationType::ADD || op == OperationType::MUL);
+
+ // Model operations and operands.
+ std::vector<Operation> operations(len);
+ std::vector<Operand> operands(len * 2 + 2);
+
+ // The constant buffer pool. This contains the activation scalar, followed by the
+ // per-operation constant operands.
+ std::vector<uint8_t> operandValues(sizeof(int32_t) + len * sizeof(CppType));
+
+ // The activation scalar, value = 0.
+ operands[0] = {
+ .type = OperandType::INT32,
+ .dimensions = {},
+ .numberOfConsumers = len,
+ .scale = 0.0f,
+ .zeroPoint = 0,
+ .lifetime = OperandLifeTime::CONSTANT_COPY,
+ .location = {.poolIndex = 0, .offset = 0, .length = sizeof(int32_t)},
+ };
+ memset(operandValues.data(), 0, sizeof(int32_t));
+
+ // 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 == OperandType::TENSOR_FLOAT32) {
+ bufferValue = 1.0f;
+ scale1 = 0.0f;
+ scale2 = 0.0f;
+ } else if (op == OperationType::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 ? OperandLifeTime::MODEL_INPUT
+ : OperandLifeTime::TEMPORARY_VARIABLE),
+ .location = {},
+ };
+
+ // The second operation input, value = 1.
+ operands[secondInputIndex] = {
+ .type = operandType,
+ .dimensions = {1},
+ .numberOfConsumers = 1,
+ .scale = scale2,
+ .zeroPoint = 0,
+ .lifetime = OperandLifeTime::CONSTANT_COPY,
+ .location = {.poolIndex = 0,
+ .offset = static_cast<uint32_t>(i * sizeof(CppType) + sizeof(int32_t)),
+ .length = sizeof(CppType)},
+ };
+ memcpy(operandValues.data() + sizeof(int32_t) + i * sizeof(CppType), &bufferValue,
+ sizeof(CppType));
+
+ // 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},
+ };
+ }
+
+ // The model output.
+ operands.back() = {
+ .type = operandType,
+ .dimensions = {1},
+ .numberOfConsumers = 0,
+ .scale = scale1,
+ .zeroPoint = 0,
+ .lifetime = OperandLifeTime::MODEL_OUTPUT,
+ .location = {},
+ };
+
+ const std::vector<uint32_t> inputIndexes = {1};
+ const std::vector<uint32_t> outputIndexes = {len * 2 + 1};
+ const std::vector<hidl_memory> pools = {};
+
+ return {
+ .operands = operands,
+ .operations = operations,
+ .inputIndexes = inputIndexes,
+ .outputIndexes = outputIndexes,
+ .operandValues = operandValues,
+ .pools = pools,
+ };
+}
+
} // namespace
// Tag for the compilation caching tests.
-class CompilationCachingTest : public NeuralnetworksHidlTest {
+class CompilationCachingTestBase : public NeuralnetworksHidlTest {
protected:
+ CompilationCachingTestBase(OperandType type) : kOperandType(type) {}
+
void SetUp() override {
NeuralnetworksHidlTest::SetUp();
ASSERT_NE(device.get(), nullptr);
@@ -139,21 +304,53 @@
}
void TearDown() override {
- // The tmp directory is only removed when the driver reports caching not supported,
- // otherwise it is kept for debugging purpose.
- if (!mIsCachingSupported) {
- remove(mTmpCache.c_str());
- rmdir(mCacheDir.c_str());
+ // 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);
}
NeuralnetworksHidlTest::TearDown();
}
- void saveModelToCache(const V1_2::Model& model, const hidl_vec<hidl_handle>& modelCache,
- const hidl_vec<hidl_handle>& dataCache, bool* supported,
- sp<IPreparedModel>* preparedModel = nullptr) {
- if (preparedModel != nullptr) *preparedModel = nullptr;
+ // Model and examples creators. According to kOperandType, the following methods will return
+ // either float32 model/examples or the quant8 variant.
+ Model createTestModel() {
+ if (kOperandType == OperandType::TENSOR_FLOAT32) {
+ return float32_model::createTestModel();
+ } else {
+ return quant8_model::createTestModel();
+ }
+ }
- // See if service can handle model.
+ std::vector<MixedTypedExample> get_examples() {
+ if (kOperandType == OperandType::TENSOR_FLOAT32) {
+ return float32_model::get_examples();
+ } else {
+ return quant8_model::get_examples();
+ }
+ }
+
+ Model createLargeTestModel(OperationType op, uint32_t len) {
+ if (kOperandType == OperandType::TENSOR_FLOAT32) {
+ return createLargeTestModelImpl<float, OperandType::TENSOR_FLOAT32>(op, len);
+ } else {
+ return createLargeTestModelImpl<uint8_t, OperandType::TENSOR_QUANT8_ASYMM>(op, len);
+ }
+ }
+
+ std::vector<MixedTypedExample> getLargeModelExamples(uint32_t len) {
+ if (kOperandType == OperandType::TENSOR_FLOAT32) {
+ return float32_model::getLargeModelExamples(len);
+ } else {
+ return quant8_model::getLargeModelExamples(len);
+ }
+ }
+
+ // See if the service can handle the model.
+ bool isModelFullySupported(const V1_2::Model& model) {
bool fullySupportsModel = false;
Return<void> supportedCall = device->getSupportedOperations_1_2(
model,
@@ -163,9 +360,14 @@
fullySupportsModel = std::all_of(supported.begin(), supported.end(),
[](bool valid) { return valid; });
});
- ASSERT_TRUE(supportedCall.isOk());
- *supported = fullySupportsModel;
- if (!fullySupportsModel) return;
+ EXPECT_TRUE(supportedCall.isOk());
+ return fullySupportsModel;
+ }
+
+ void saveModelToCache(const V1_2::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();
@@ -199,8 +401,8 @@
return false;
}
- bool checkEarlyTermination(bool supported) {
- if (!supported) {
+ bool checkEarlyTermination(const V1_2::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 "
@@ -250,21 +452,31 @@
uint32_t mNumModelCache;
uint32_t mNumDataCache;
uint32_t mIsCachingSupported;
+
+ // The primary data type of the testModel.
+ const OperandType kOperandType;
};
-TEST_F(CompilationCachingTest, CacheSavingAndRetrieval) {
+// 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<OperandType> {
+ protected:
+ CompilationCachingTest() : CompilationCachingTestBase(GetParam()) {}
+};
+
+TEST_P(CompilationCachingTest, CacheSavingAndRetrieval) {
// Create test HIDL model and compile.
- Model testModel = createTestModel();
+ const Model testModel = createTestModel();
+ if (checkEarlyTermination(testModel)) return;
sp<IPreparedModel> preparedModel = nullptr;
// Save the compilation to cache.
{
- bool supported;
hidl_vec<hidl_handle> modelCache, dataCache;
createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
- saveModelToCache(testModel, modelCache, dataCache, &supported);
- if (checkEarlyTermination(supported)) return;
+ saveModelToCache(testModel, modelCache, dataCache);
}
// Retrieve preparedModel from cache.
@@ -294,14 +506,14 @@
/*testDynamicOutputShape=*/false);
}
-TEST_F(CompilationCachingTest, CacheSavingAndRetrievalNonZeroOffset) {
+TEST_P(CompilationCachingTest, CacheSavingAndRetrievalNonZeroOffset) {
// Create test HIDL model and compile.
- Model testModel = createTestModel();
+ const Model testModel = createTestModel();
+ if (checkEarlyTermination(testModel)) return;
sp<IPreparedModel> preparedModel = nullptr;
// Save the compilation to cache.
{
- bool supported;
hidl_vec<hidl_handle> modelCache, dataCache;
createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
@@ -318,8 +530,7 @@
write(dataCache[i].getNativeHandle()->data[0], &dummyBytes, sizeof(dummyBytes)),
sizeof(dummyBytes));
}
- saveModelToCache(testModel, modelCache, dataCache, &supported);
- if (checkEarlyTermination(supported)) return;
+ saveModelToCache(testModel, modelCache, dataCache);
}
// Retrieve preparedModel from cache.
@@ -358,13 +569,13 @@
/*testDynamicOutputShape=*/false);
}
-TEST_F(CompilationCachingTest, SaveToCacheInvalidNumCache) {
+TEST_P(CompilationCachingTest, SaveToCacheInvalidNumCache) {
// Create test HIDL model and compile.
- Model testModel = createTestModel();
+ const Model testModel = createTestModel();
+ if (checkEarlyTermination(testModel)) return;
// Test with number of model cache files greater than mNumModelCache.
{
- bool supported;
hidl_vec<hidl_handle> modelCache, dataCache;
// Pass an additional cache file for model cache.
mModelCache.push_back({mTmpCache});
@@ -372,8 +583,7 @@
createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
mModelCache.pop_back();
sp<IPreparedModel> preparedModel = nullptr;
- saveModelToCache(testModel, modelCache, dataCache, &supported, &preparedModel);
- if (checkEarlyTermination(supported)) return;
+ saveModelToCache(testModel, modelCache, dataCache, &preparedModel);
ASSERT_NE(preparedModel, nullptr);
// Execute and verify results.
generated_tests::EvaluatePreparedModel(preparedModel, [](int) { return false; },
@@ -392,7 +602,6 @@
// Test with number of model cache files smaller than mNumModelCache.
if (mModelCache.size() > 0) {
- bool supported;
hidl_vec<hidl_handle> modelCache, dataCache;
// Pop out the last cache file.
auto tmp = mModelCache.back();
@@ -401,8 +610,7 @@
createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
mModelCache.push_back(tmp);
sp<IPreparedModel> preparedModel = nullptr;
- saveModelToCache(testModel, modelCache, dataCache, &supported, &preparedModel);
- if (checkEarlyTermination(supported)) return;
+ saveModelToCache(testModel, modelCache, dataCache, &preparedModel);
ASSERT_NE(preparedModel, nullptr);
// Execute and verify results.
generated_tests::EvaluatePreparedModel(preparedModel, [](int) { return false; },
@@ -421,7 +629,6 @@
// Test with number of data cache files greater than mNumDataCache.
{
- bool supported;
hidl_vec<hidl_handle> modelCache, dataCache;
// Pass an additional cache file for data cache.
mDataCache.push_back({mTmpCache});
@@ -429,8 +636,7 @@
createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
mDataCache.pop_back();
sp<IPreparedModel> preparedModel = nullptr;
- saveModelToCache(testModel, modelCache, dataCache, &supported, &preparedModel);
- if (checkEarlyTermination(supported)) return;
+ saveModelToCache(testModel, modelCache, dataCache, &preparedModel);
ASSERT_NE(preparedModel, nullptr);
// Execute and verify results.
generated_tests::EvaluatePreparedModel(preparedModel, [](int) { return false; },
@@ -449,7 +655,6 @@
// Test with number of data cache files smaller than mNumDataCache.
if (mDataCache.size() > 0) {
- bool supported;
hidl_vec<hidl_handle> modelCache, dataCache;
// Pop out the last cache file.
auto tmp = mDataCache.back();
@@ -458,8 +663,7 @@
createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
mDataCache.push_back(tmp);
sp<IPreparedModel> preparedModel = nullptr;
- saveModelToCache(testModel, modelCache, dataCache, &supported, &preparedModel);
- if (checkEarlyTermination(supported)) return;
+ saveModelToCache(testModel, modelCache, dataCache, &preparedModel);
ASSERT_NE(preparedModel, nullptr);
// Execute and verify results.
generated_tests::EvaluatePreparedModel(preparedModel, [](int) { return false; },
@@ -477,18 +681,17 @@
}
}
-TEST_F(CompilationCachingTest, PrepareModelFromCacheInvalidNumCache) {
+TEST_P(CompilationCachingTest, PrepareModelFromCacheInvalidNumCache) {
// Create test HIDL model and compile.
- Model testModel = createTestModel();
+ const Model testModel = createTestModel();
+ if (checkEarlyTermination(testModel)) return;
// Save the compilation to cache.
{
- bool supported;
hidl_vec<hidl_handle> modelCache, dataCache;
createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
- saveModelToCache(testModel, modelCache, dataCache, &supported);
- if (checkEarlyTermination(supported)) return;
+ saveModelToCache(testModel, modelCache, dataCache);
}
// Test with number of model cache files greater than mNumModelCache.
@@ -558,13 +761,13 @@
}
}
-TEST_F(CompilationCachingTest, SaveToCacheInvalidNumFd) {
+TEST_P(CompilationCachingTest, SaveToCacheInvalidNumFd) {
// Create test HIDL model and compile.
- Model testModel = createTestModel();
+ const Model testModel = createTestModel();
+ if (checkEarlyTermination(testModel)) return;
// Go through each handle in model cache, test with NumFd greater than 1.
for (uint32_t i = 0; i < mNumModelCache; i++) {
- bool supported;
hidl_vec<hidl_handle> modelCache, dataCache;
// Pass an invalid number of fds for handle i.
mModelCache[i].push_back(mTmpCache);
@@ -572,8 +775,7 @@
createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
mModelCache[i].pop_back();
sp<IPreparedModel> preparedModel = nullptr;
- saveModelToCache(testModel, modelCache, dataCache, &supported, &preparedModel);
- if (checkEarlyTermination(supported)) return;
+ saveModelToCache(testModel, modelCache, dataCache, &preparedModel);
ASSERT_NE(preparedModel, nullptr);
// Execute and verify results.
generated_tests::EvaluatePreparedModel(preparedModel, [](int) { return false; },
@@ -592,7 +794,6 @@
// Go through each handle in model cache, test with NumFd equal to 0.
for (uint32_t i = 0; i < mNumModelCache; i++) {
- bool supported;
hidl_vec<hidl_handle> modelCache, dataCache;
// Pass an invalid number of fds for handle i.
auto tmp = mModelCache[i].back();
@@ -601,8 +802,7 @@
createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
mModelCache[i].push_back(tmp);
sp<IPreparedModel> preparedModel = nullptr;
- saveModelToCache(testModel, modelCache, dataCache, &supported, &preparedModel);
- if (checkEarlyTermination(supported)) return;
+ saveModelToCache(testModel, modelCache, dataCache, &preparedModel);
ASSERT_NE(preparedModel, nullptr);
// Execute and verify results.
generated_tests::EvaluatePreparedModel(preparedModel, [](int) { return false; },
@@ -621,7 +821,6 @@
// Go through each handle in data cache, test with NumFd greater than 1.
for (uint32_t i = 0; i < mNumDataCache; i++) {
- bool supported;
hidl_vec<hidl_handle> modelCache, dataCache;
// Pass an invalid number of fds for handle i.
mDataCache[i].push_back(mTmpCache);
@@ -629,8 +828,7 @@
createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
mDataCache[i].pop_back();
sp<IPreparedModel> preparedModel = nullptr;
- saveModelToCache(testModel, modelCache, dataCache, &supported, &preparedModel);
- if (checkEarlyTermination(supported)) return;
+ saveModelToCache(testModel, modelCache, dataCache, &preparedModel);
ASSERT_NE(preparedModel, nullptr);
// Execute and verify results.
generated_tests::EvaluatePreparedModel(preparedModel, [](int) { return false; },
@@ -649,7 +847,6 @@
// Go through each handle in data cache, test with NumFd equal to 0.
for (uint32_t i = 0; i < mNumDataCache; i++) {
- bool supported;
hidl_vec<hidl_handle> modelCache, dataCache;
// Pass an invalid number of fds for handle i.
auto tmp = mDataCache[i].back();
@@ -658,8 +855,7 @@
createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
mDataCache[i].push_back(tmp);
sp<IPreparedModel> preparedModel = nullptr;
- saveModelToCache(testModel, modelCache, dataCache, &supported, &preparedModel);
- if (checkEarlyTermination(supported)) return;
+ saveModelToCache(testModel, modelCache, dataCache, &preparedModel);
ASSERT_NE(preparedModel, nullptr);
// Execute and verify results.
generated_tests::EvaluatePreparedModel(preparedModel, [](int) { return false; },
@@ -677,18 +873,17 @@
}
}
-TEST_F(CompilationCachingTest, PrepareModelFromCacheInvalidNumFd) {
+TEST_P(CompilationCachingTest, PrepareModelFromCacheInvalidNumFd) {
// Create test HIDL model and compile.
- Model testModel = createTestModel();
+ const Model testModel = createTestModel();
+ if (checkEarlyTermination(testModel)) return;
// Save the compilation to cache.
{
- bool supported;
hidl_vec<hidl_handle> modelCache, dataCache;
createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
- saveModelToCache(testModel, modelCache, dataCache, &supported);
- if (checkEarlyTermination(supported)) return;
+ saveModelToCache(testModel, modelCache, dataCache);
}
// Go through each handle in model cache, test with NumFd greater than 1.
@@ -758,23 +953,22 @@
}
}
-TEST_F(CompilationCachingTest, SaveToCacheInvalidAccessMode) {
+TEST_P(CompilationCachingTest, SaveToCacheInvalidAccessMode) {
// Create test HIDL model and compile.
- Model testModel = createTestModel();
+ const Model testModel = createTestModel();
+ if (checkEarlyTermination(testModel)) 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++) {
- bool supported;
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(testModel, modelCache, dataCache, &supported, &preparedModel);
- if (checkEarlyTermination(supported)) return;
+ saveModelToCache(testModel, modelCache, dataCache, &preparedModel);
ASSERT_NE(preparedModel, nullptr);
// Execute and verify results.
generated_tests::EvaluatePreparedModel(preparedModel, [](int) { return false; },
@@ -793,15 +987,13 @@
// Go through each handle in data cache, test with invalid access mode.
for (uint32_t i = 0; i < mNumDataCache; i++) {
- bool supported;
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(testModel, modelCache, dataCache, &supported, &preparedModel);
- if (checkEarlyTermination(supported)) return;
+ saveModelToCache(testModel, modelCache, dataCache, &preparedModel);
ASSERT_NE(preparedModel, nullptr);
// Execute and verify results.
generated_tests::EvaluatePreparedModel(preparedModel, [](int) { return false; },
@@ -819,20 +1011,19 @@
}
}
-TEST_F(CompilationCachingTest, PrepareModelFromCacheInvalidAccessMode) {
+TEST_P(CompilationCachingTest, PrepareModelFromCacheInvalidAccessMode) {
// Create test HIDL model and compile.
- Model testModel = createTestModel();
+ const Model testModel = createTestModel();
+ if (checkEarlyTermination(testModel)) return;
std::vector<AccessMode> modelCacheMode(mNumModelCache, AccessMode::READ_WRITE);
std::vector<AccessMode> dataCacheMode(mNumDataCache, AccessMode::READ_WRITE);
// Save the compilation to cache.
{
- bool supported;
hidl_vec<hidl_handle> modelCache, dataCache;
createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
- saveModelToCache(testModel, modelCache, dataCache, &supported);
- if (checkEarlyTermination(supported)) return;
+ saveModelToCache(testModel, modelCache, dataCache);
}
// Go through each handle in model cache, test with invalid access mode.
@@ -864,129 +1055,198 @@
}
}
-class CompilationCachingSecurityTest : public CompilationCachingTest,
- public ::testing::WithParamInterface<uint32_t> {
- protected:
- void SetUp() {
- CompilationCachingTest::SetUp();
- generator.seed(kSeed);
- }
+// 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];
- // 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);
- }
+ 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);
- const uint32_t kSeed = GetParam();
- std::mt19937 generator;
-};
-
-TEST_P(CompilationCachingSecurityTest, CorruptedSecuritySensitiveCache) {
- if (!mIsCachingSupported) return;
-
- // Create test HIDL model and compile.
- Model testModel = createTestModel();
-
- for (uint32_t i = 0; i < mNumModelCache; i++) {
- // Save the compilation to cache.
- {
- bool supported;
- hidl_vec<hidl_handle> modelCache, dataCache;
- createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
- createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
- saveModelToCache(testModel, modelCache, dataCache, &supported);
- if (checkEarlyTermination(supported)) return;
+ ssize_t readBytes;
+ while ((readBytes = read(fromFd, &buffer, kBufferSize)) > 0) {
+ ASSERT_EQ(write(toFd, &buffer, readBytes), readBytes);
}
+ ASSERT_GE(readBytes, 0);
- // Randomly flip one single bit of the cache entry.
- FILE* pFile = fopen(mModelCache[i][0].c_str(), "r+");
- ASSERT_EQ(fseek(pFile, 0, SEEK_END), 0);
- long int fileSize = ftell(pFile);
- if (fileSize == 0) {
- fclose(pFile);
- continue;
- }
- 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);
-
- // Retrieve 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);
- }
+ close(fromFd);
+ close(toFd);
}
}
-TEST_P(CompilationCachingSecurityTest, WrongLengthSecuritySensitiveCache) {
+// 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 HIDL model and compile.
- Model testModel = createTestModel();
+ // Create test models and check if fully supported by the service.
+ const Model testModelMul = createLargeTestModel(OperationType::MUL, kLargeModelSize);
+ if (checkEarlyTermination(testModelMul)) return;
+ const Model testModelAdd = createLargeTestModel(OperationType::ADD, kLargeModelSize);
+ if (checkEarlyTermination(testModelAdd)) return;
- for (uint32_t i = 0; i < mNumModelCache; i++) {
- // Save the compilation to cache.
- {
- bool supported;
- hidl_vec<hidl_handle> modelCache, dataCache;
- createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
- createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
- saveModelToCache(testModel, modelCache, dataCache, &supported);
- if (checkEarlyTermination(supported)) return;
- }
-
- // Randomly append bytes to the cache entry.
- FILE* pFile = fopen(mModelCache[i][0].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);
-
- // Retrieve 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);
- }
+ // Save the testModelMul compilation to cache.
+ auto modelCacheMul = mModelCache;
+ for (auto& cache : modelCacheMul) {
+ cache[0].append("_mul");
}
-}
-
-TEST_P(CompilationCachingSecurityTest, WrongToken) {
- if (!mIsCachingSupported) return;
-
- // Create test HIDL model and compile.
- Model testModel = createTestModel();
-
- // Save the compilation to cache.
{
- bool supported;
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ createCacheHandles(modelCacheMul, AccessMode::READ_WRITE, &modelCache);
+ createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ saveModelToCache(testModelMul, modelCache, dataCache);
+ }
+
+ // Use a different token for testModelAdd.
+ mToken[0]++;
+
+ // This test is probabilistic, so we run it multiple times.
+ for (uint32_t i = 0; i < kNumIterationsTOCTOU; i++) {
+ // Save the testModelAdd 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(testModelAdd, 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);
+ generated_tests::EvaluatePreparedModel(
+ preparedModel, [](int) { return false; },
+ getLargeModelExamples(kLargeModelSize),
+ testModelAdd.relaxComputationFloat32toFloat16,
+ /*testDynamicOutputShape=*/false);
+ }
+ }
+ }
+}
+
+TEST_P(CompilationCachingTest, PrepareFromCache_TOCTOU) {
+ if (!mIsCachingSupported) return;
+
+ // Create test models and check if fully supported by the service.
+ const Model testModelMul = createLargeTestModel(OperationType::MUL, kLargeModelSize);
+ if (checkEarlyTermination(testModelMul)) return;
+ const Model testModelAdd = createLargeTestModel(OperationType::ADD, kLargeModelSize);
+ if (checkEarlyTermination(testModelAdd)) return;
+
+ // Save the testModelMul 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(testModelMul, modelCache, dataCache);
+ }
+
+ // Use a different token for testModelAdd.
+ mToken[0]++;
+
+ // This test is probabilistic, so we run it multiple times.
+ for (uint32_t i = 0; i < kNumIterationsTOCTOU; i++) {
+ // Save the testModelAdd compilation to cache.
+ {
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ saveModelToCache(testModelAdd, 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);
+ generated_tests::EvaluatePreparedModel(
+ preparedModel, [](int) { return false; },
+ getLargeModelExamples(kLargeModelSize),
+ testModelAdd.relaxComputationFloat32toFloat16,
+ /*testDynamicOutputShape=*/false);
+ }
+ }
+ }
+}
+
+TEST_P(CompilationCachingTest, ReplaceSecuritySensitiveCache) {
+ if (!mIsCachingSupported) return;
+
+ // Create test models and check if fully supported by the service.
+ const Model testModelMul = createLargeTestModel(OperationType::MUL, kLargeModelSize);
+ if (checkEarlyTermination(testModelMul)) return;
+ const Model testModelAdd = createLargeTestModel(OperationType::ADD, kLargeModelSize);
+ if (checkEarlyTermination(testModelAdd)) return;
+
+ // Save the testModelMul 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(testModelMul, modelCache, dataCache);
+ }
+
+ // Use a different token for testModelAdd.
+ mToken[0]++;
+
+ // Save the testModelAdd compilation to cache.
+ {
hidl_vec<hidl_handle> modelCache, dataCache;
createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
- saveModelToCache(testModel, modelCache, dataCache, &supported);
- if (checkEarlyTermination(supported)) return;
+ saveModelToCache(testModelAdd, modelCache, dataCache);
}
- // 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));
+ // Replace the model cache of testModelAdd with testModelMul.
+ copyCacheFiles(modelCacheMul, mModelCache);
// Retrieve the preparedModel from cache, expect failure.
{
@@ -1001,8 +1261,153 @@
}
}
+static const auto kOperandTypeChoices =
+ ::testing::Values(OperandType::TENSOR_FLOAT32, OperandType::TENSOR_QUANT8_ASYMM);
+
+INSTANTIATE_TEST_CASE_P(TestCompilationCaching, CompilationCachingTest, kOperandTypeChoices);
+
+class CompilationCachingSecurityTest
+ : public CompilationCachingTestBase,
+ public ::testing::WithParamInterface<std::tuple<OperandType, uint32_t>> {
+ protected:
+ CompilationCachingSecurityTest() : CompilationCachingTestBase(std::get<0>(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 Model testModel = createTestModel();
+ if (checkEarlyTermination(testModel)) 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(testModel, 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<1>(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;
+ });
+}
+
INSTANTIATE_TEST_CASE_P(TestCompilationCaching, CompilationCachingSecurityTest,
- ::testing::Range(0U, 10U));
+ ::testing::Combine(kOperandTypeChoices, ::testing::Range(0U, 10U)));
} // namespace functional
} // namespace vts
diff --git a/neuralnetworks/1.2/vts/functional/ValidateBurst.cpp b/neuralnetworks/1.2/vts/functional/ValidateBurst.cpp
new file mode 100644
index 0000000..43bd400
--- /dev/null
+++ b/neuralnetworks/1.2/vts/functional/ValidateBurst.cpp
@@ -0,0 +1,337 @@
+/*
+ * 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 "Callbacks.h"
+#include "ExecutionBurstController.h"
+#include "ExecutionBurstServer.h"
+#include "TestHarness.h"
+#include "Utils.h"
+
+#include <android-base/logging.h>
+
+namespace android {
+namespace hardware {
+namespace neuralnetworks {
+namespace V1_2 {
+namespace vts {
+namespace functional {
+
+using ::android::nn::ExecutionBurstController;
+using ::android::nn::RequestChannelSender;
+using ::android::nn::ResultChannelReceiver;
+using ExecutionBurstCallback = ::android::nn::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 std::vector<Request>& requests) {
+ // 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());
+
+ // validate each request
+ for (const Request& request : requests) {
+ // 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 std::vector<Request>& requests) {
+ // 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());
+
+ // validate each request
+ for (const Request& request : requests) {
+ // 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) {
+ continue;
+ }
+
+ // 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));
+ }
+}
+
+///////////////////////////// ENTRY POINT //////////////////////////////////
+
+void ValidationTest::validateBurst(const sp<IPreparedModel>& preparedModel,
+ const std::vector<Request>& requests) {
+ ASSERT_NO_FATAL_FAILURE(validateBurstSerialization(preparedModel, requests));
+ ASSERT_NO_FATAL_FAILURE(validateBurstFmqLength(preparedModel, requests));
+}
+
+} // namespace functional
+} // namespace vts
+} // namespace V1_2
+} // namespace neuralnetworks
+} // namespace hardware
+} // namespace android
diff --git a/neuralnetworks/1.2/vts/functional/ValidateRequest.cpp b/neuralnetworks/1.2/vts/functional/ValidateRequest.cpp
index 870d017..9703c2d 100644
--- a/neuralnetworks/1.2/vts/functional/ValidateRequest.cpp
+++ b/neuralnetworks/1.2/vts/functional/ValidateRequest.cpp
@@ -35,9 +35,7 @@
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;
@@ -48,55 +46,6 @@
return timing.timeOnDevice == UINT64_MAX && timing.timeInDriver == UINT64_MAX;
}
-static void createPreparedModel(const sp<IDevice>& device, const Model& model,
- sp<IPreparedModel>* preparedModel) {
- ASSERT_NE(nullptr, preparedModel);
-
- // see if service can handle model
- bool fullySupportsModel = false;
- Return<void> supportedOpsLaunchStatus = device->getSupportedOperations_1_2(
- model, [&fullySupportsModel](ErrorStatus status, const hidl_vec<bool>& supported) {
- ASSERT_EQ(ErrorStatus::NONE, status);
- ASSERT_NE(0ul, supported.size());
- fullySupportsModel =
- std::all_of(supported.begin(), supported.end(), [](bool valid) { return valid; });
- });
- ASSERT_TRUE(supportedOpsLaunchStatus.isOk());
-
- // launch prepare model
- sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
- ASSERT_NE(nullptr, preparedModelCallback.get());
- Return<ErrorStatus> prepareLaunchStatus = device->prepareModel_1_2(
- 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();
- ErrorStatus prepareReturnStatus = preparedModelCallback->getStatus();
- *preparedModel = getPreparedModel_1_2(preparedModelCallback);
-
- // The getSupportedOperations_1_2 call returns a list of operations that are
- // guaranteed not to fail if prepareModel_1_2 is called, and
- // 'fullySupportsModel' is true i.f.f. the entire model is guaranteed.
- // If a driver has any doubt that it can prepare an operation, it must
- // return false. So here, if a driver isn't sure if it can support an
- // operation, but reports that it successfully prepared the model, the test
- // can continue.
- if (!fullySupportsModel && prepareReturnStatus != ErrorStatus::NONE) {
- ASSERT_EQ(nullptr, preparedModel->get());
- LOG(INFO) << "NN VTS: Unable to test Request validation because vendor service cannot "
- "prepare model that it does not support.";
- std::cout << "[ ] Unable to test Request validation because vendor service "
- "cannot prepare model that it does not support."
- << std::endl;
- return;
- }
- ASSERT_EQ(ErrorStatus::NONE, prepareReturnStatus);
- ASSERT_NE(nullptr, preparedModel->get());
-}
-
// Primary validation function. This function will take a valid request, apply a
// mutation to it to invalidate the request, then pass it to interface calls
// that use the request. Note that the request here is passed by value, and any
@@ -316,14 +265,8 @@
return requests;
}
-void ValidationTest::validateRequests(const Model& model, const std::vector<Request>& requests) {
- // create IPreparedModel
- sp<IPreparedModel> preparedModel;
- ASSERT_NO_FATAL_FAILURE(createPreparedModel(device, model, &preparedModel));
- if (preparedModel == nullptr) {
- return;
- }
-
+void ValidationTest::validateRequests(const sp<IPreparedModel>& preparedModel,
+ const std::vector<Request>& requests) {
// validate each request
for (const Request& request : requests) {
removeInputTest(preparedModel, request);
diff --git a/neuralnetworks/1.2/vts/functional/VtsHalNeuralnetworks.cpp b/neuralnetworks/1.2/vts/functional/VtsHalNeuralnetworks.cpp
index 4728c28..4ddefe8 100644
--- a/neuralnetworks/1.2/vts/functional/VtsHalNeuralnetworks.cpp
+++ b/neuralnetworks/1.2/vts/functional/VtsHalNeuralnetworks.cpp
@@ -18,6 +18,10 @@
#include "VtsHalNeuralnetworks.h"
+#include <android-base/logging.h>
+
+#include "Callbacks.h"
+
namespace android {
namespace hardware {
namespace neuralnetworks {
@@ -25,6 +29,60 @@
namespace vts {
namespace functional {
+using ::android::hardware::neuralnetworks::V1_2::implementation::PreparedModelCallback;
+using HidlToken = hidl_array<uint8_t, static_cast<uint32_t>(Constant::BYTE_SIZE_OF_CACHE_TOKEN)>;
+using V1_1::ExecutionPreference;
+
+// internal helper function
+static void createPreparedModel(const sp<IDevice>& device, const Model& model,
+ sp<IPreparedModel>* preparedModel) {
+ ASSERT_NE(nullptr, preparedModel);
+
+ // see if service can handle model
+ bool fullySupportsModel = false;
+ Return<void> supportedOpsLaunchStatus = device->getSupportedOperations_1_2(
+ model, [&fullySupportsModel](ErrorStatus status, const hidl_vec<bool>& supported) {
+ ASSERT_EQ(ErrorStatus::NONE, status);
+ ASSERT_NE(0ul, supported.size());
+ fullySupportsModel = std::all_of(supported.begin(), supported.end(),
+ [](bool valid) { return valid; });
+ });
+ ASSERT_TRUE(supportedOpsLaunchStatus.isOk());
+
+ // launch prepare model
+ sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
+ ASSERT_NE(nullptr, preparedModelCallback.get());
+ Return<ErrorStatus> prepareLaunchStatus = device->prepareModel_1_2(
+ 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();
+ ErrorStatus prepareReturnStatus = preparedModelCallback->getStatus();
+ *preparedModel = getPreparedModel_1_2(preparedModelCallback);
+
+ // The getSupportedOperations_1_2 call returns a list of operations that are
+ // guaranteed not to fail if prepareModel_1_2 is called, and
+ // 'fullySupportsModel' is true i.f.f. the entire model is guaranteed.
+ // If a driver has any doubt that it can prepare an operation, it must
+ // return false. So here, if a driver isn't sure if it can support an
+ // operation, but reports that it successfully prepared the model, the test
+ // can continue.
+ if (!fullySupportsModel && prepareReturnStatus != ErrorStatus::NONE) {
+ ASSERT_EQ(nullptr, preparedModel->get());
+ LOG(INFO) << "NN VTS: Unable to test Request validation because vendor service cannot "
+ "prepare model that it does not support.";
+ std::cout << "[ ] Unable to test Request validation because vendor service "
+ "cannot prepare model that it does not support."
+ << std::endl;
+ return;
+ }
+ ASSERT_EQ(ErrorStatus::NONE, prepareReturnStatus);
+ ASSERT_NE(nullptr, preparedModel->get());
+}
+
// A class for test environment setup
NeuralnetworksHidlEnvironment::NeuralnetworksHidlEnvironment() {}
@@ -68,6 +126,20 @@
::testing::VtsHalHidlTargetTestBase::TearDown();
}
+void ValidationTest::validateEverything(const Model& model, const std::vector<Request>& requests) {
+ validateModel(model);
+
+ // create IPreparedModel
+ sp<IPreparedModel> preparedModel;
+ ASSERT_NO_FATAL_FAILURE(createPreparedModel(device, model, &preparedModel));
+ if (preparedModel == nullptr) {
+ return;
+ }
+
+ validateRequests(preparedModel, requests);
+ validateBurst(preparedModel, requests);
+}
+
sp<IPreparedModel> getPreparedModel_1_2(
const sp<V1_2::implementation::PreparedModelCallback>& callback) {
sp<V1_0::IPreparedModel> preparedModelV1_0 = callback->getPreparedModel();
diff --git a/neuralnetworks/1.2/vts/functional/VtsHalNeuralnetworks.h b/neuralnetworks/1.2/vts/functional/VtsHalNeuralnetworks.h
index 404eec0..8d1acbe 100644
--- a/neuralnetworks/1.2/vts/functional/VtsHalNeuralnetworks.h
+++ b/neuralnetworks/1.2/vts/functional/VtsHalNeuralnetworks.h
@@ -72,8 +72,14 @@
// Tag for the validation tests
class ValidationTest : public NeuralnetworksHidlTest {
protected:
- void validateModel(const Model& model);
- void validateRequests(const Model& model, const std::vector<Request>& request);
+ void validateEverything(const Model& model, const std::vector<Request>& requests);
+
+ private:
+ void validateModel(const Model& model);
+ void validateRequests(const sp<IPreparedModel>& preparedModel,
+ const std::vector<Request>& requests);
+ void validateBurst(const sp<IPreparedModel>& preparedModel,
+ const std::vector<Request>& requests);
};
// Tag for the generated tests
diff --git a/wifi/1.2/default/wifi_legacy_hal.cpp b/wifi/1.2/default/wifi_legacy_hal.cpp
index 55ec96d..375204c 100644
--- a/wifi/1.2/default/wifi_legacy_hal.cpp
+++ b/wifi/1.2/default/wifi_legacy_hal.cpp
@@ -550,7 +550,6 @@
}
// Fall through if failed. Failure to retrieve cached scan
// results should trigger a background scan failure.
- [[fallthrough]];
case WIFI_SCAN_FAILED:
on_failure_user_callback(id);
on_gscan_event_internal_callback = nullptr;